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textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/02%3A_Unit_II-_Bioenergetics_and_Metabolism/21%3A_Lipid_Biosynthesis/21.03%3A_Biosynthesis_of_Membrane_Glycerolipids.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry by William (Bill) W. Christie and Henry Jakubowski. This section is an abbreviated and modified version of material from the Lipid Web, an introduction to the chemistry and biochemistry of individual lipid classes, written by William Christie. Introduction In this section will be explore the synthesis of the membrane glycerophospholipids and their metabolic derivatives. We will start with the simplest one, phosphatidic acids, and end with phosphatidylinositols. Phosphatidic Acids and Derivatives Phosphatidic acid or 1,2-diacyl-sn-glycero-3-phosphate is a key intermediate in the biosynthesis both of other glycerophospholipids and of triacylglycerols. It is structurally one of the simplest of the phospholipids and was long thought to be important only as a precursor of other lipids, where it is indeed a key molecule, but it is now known to have many other functions in animals, plants, and other organisms by its influence on membrane structure and dynamics, and by its interactions with various proteins. As a lipid mediator, it modulates various signaling and cellular processes, such as membrane tethering, conformational changes and enzymatic activities of specific proteins, and vesicular trafficking. Moreover, its metabolite lysophosphatidic acid is recognized as a key signaling molecule with a myriad of biological effects mediated through specific receptors. Phosphatidic Acid – Occurrence and Biosynthesis Phosphatidic acid is not an abundant lipid constituent of any living organism, seldom greater than picomolar concentrations in cells, but it is extremely important both as an intermediate in the biosynthesis of other glycerophospholipids and triacylglycerols and as a signaling molecule or a precursor of signaling molecules. Indeed, it is often over-estimated in tissues as it can arise by inadvertent enzymatic hydrolysis during inappropriate storage or extraction conditions during analysis. It is the simplest diacyl-glycerophospholipid, and the only one with a phosphomonoester as the head group. The molecule is acidic and carries a negative charge, i.e., it is an anionic lipid. The structure of phosphatidic acid is shown in Figure $1$. There are at least four important biosynthetic pathways for phosphatidic acid biosynthesis in different organelles under various stimuli, and possibly resulting in the formation of different molecular species. The main pathway involves sequential acylation of sn-glycerol-3-phosphate, derived from catabolism of glucose, by acyl-coA derivatives of fatty acids. First, one acyltransferases catalyses the acylation of position sn-1 to form lysophosphatidic acid (1‑acyl-sn-glycerol-3-phosphate), and then a second specific acyltransferase catalyses the acylation of position sn-2 to yield phosphatidic acid. The synthesis of phosphatidic acid from glycerol-3-phosphate is shown in Figure $2$. In mammals, the glycerol-3-phosphate acyltransferase that catalyses the first step exists in four isoforms, two in the mitochondrial outer membrane (designated GPAT1 and 2) and two in the endoplasmic reticulum (GPAT3 and 4); all are membrane-bound enzymes, which are believed to span the membranes. GPAT1 is highly expressed in the liver and adipose tissue, where it is responsive to changes in feeding status via the sterol regulatory element binding protein-1 (SREBP-1), a master transcriptional regulator of lipogenic enzymes. It is essential in directing fatty acyl-CoA esters towards glycerolipid synthesis as opposed to β-oxidation. GPAT3 is especially important for triacylglycerol storage in adipocytes, while GPAT4 is the main contributor to lysophosphatidic acid synthesis in liver and brown adipose tissue. For the second step in phosphatidic acid biosynthesis, five mammalian acyl-CoA:lysophosphatidic acid acyltransferases are known of which three are in the endoplasmic reticulum (LPAAT or LPAT or AGPAT1, 2 and 3), with a further two (LPAT4 and 5) on the outer mitochondrial membrane. While LPAT1 and 2 have strict specificity for lysophosphatidic acid as acyl acceptor, other isoforms can esterify other lysophospholipids. Human LPAT1 showed higher activity with 14:0-, 16:0- and 18:2‑CoAs, while LPAT2 prefers 20:4-CoA and LPAT3 produces phosphatidic acid containing docosahexaenoic acid (22:6(n-3)); the last is especially important in retina and testes. LPAT4 and 5 have a preference for oleoyl-CoA and polyunsaturated acyl-CoAs as the acyl donor, suggesting a dual role in glycerolipid synthesis and remodeling. The activity in the endoplasmic reticulum predominates in adipose tissue, but the mitochondrial forms are believed to be responsible for half the activity in liver. However, as there is traffic of phosphatidic acid between the mitochondria and endoplasmic reticulum for remodeling or for synthesis of other lipids, the relative contributions of the two can be difficult to assess. In plants, the sn-glycerol-3-phosphate pathway exists both in plastids and at the endoplasmic reticulum with multiple isoforms of the two acyltransferases as well as differences in the acyl substrates. In brief most plant lipid biosynthesis begins with fatty acid biosynthesis in the chloroplasts. In plastids, the acyltransferase ATS1 transfers 18:1 acyl groups from acyl-acyl carrier protein (acyl-ACP) to position sn-1 of glycerol 3-phosphate, before ATS2 transfers a palmitoyl group from ACP to position sn-2, producing phosphatidic acid at the inner leaflet of the chloroplast inner envelope membrane (IEM). Fatty acids intended for the endoplasmic reticulum are released from ACP in the chloroplast stroma by IEM-associated thioesterases, exported and then activated by acyl-CoA synthetases of the outer envelope membrane to produce species with C18 fatty acids in both positions. Thus, acyl-CoAs are used for phosphatidic acid biosynthesis in the endoplasmic reticulum with marked differences in the specificity of the acyl substrates. Subsequently, phosphatidic acid in the plastids is utilized for biosynthesis of galactosyldiacylglycerols, while that in the endoplasmic reticulum is used for synthesis of triacylglycerols and phospholipids. In bacteria, two families of enzymes are responsible for acylation of position sn-1 of glycerol-3-phosphate. One present in Escherichia coli, for example, utilizes the acyl-acyl carrier protein (acyl-ACP) products of fatty acid synthesis as acyl donors as well as acyl-CoA derived from exogenous fatty acids. In a second wider group of bacteria, including cyanobacteria, there are enzymes (PlsX and PlsY) that make use of the unique acyl donors, acyl-phosphates derived in part from acyl-ACP, to acylate position sn-1. Acylation of position sn-2 in this instance is performed by a further family of enzymes (PlsC) that uses acyl-ACP as the acyl donor, although some bacterial species may use acyl-CoA also. In animals, a second biosynthetic pathway utilizes dihydroxyacetone phosphate (DHAP) as the primary precursor for the peroxisomal enzyme, DHAP acyltransferase, which produces acyl-DHAP. This intermediate is converted to lysophosphatidic acid in a NADPH-dependent reaction catalyzed by acyl-DHAP reductase, and this is in turn acylated to form phosphatidic acid by the same LPAT as in the previous mechanism. This pathway is of particular importance in the biosynthesis of ether lipids. The synthesis of phosphatidic acid from dihydroxyacetone phosphate is shown in Figure $3$. A third important route to phosphatidic acid is via hydrolysis of other phospholipids, but especially phosphatidylcholine, by the enzyme phospholipase D (or by a family or related enzymes of this kind). The enzyme is readily available for study in plants, where the special functions of phosphatidic acid have long been known (see below), but it is now recognized that phospholipase D is present in bacteria, yeasts and most animal cells. In the last, it exists in two main isoforms with differing specificities and cellular locations; PLD1 is found mainly in the Golgi-lysosome continuum, while PLD2 is present mainly in the plasma membrane. They are phosphoproteins, the activity of which is regulated by kinases and phosphatases and by binding to phosphatidylinositol-4,5-bisphosphate. In mitochondria, a distinctive enzyme of this type utilizes cardiolipin as substrate. The mechanism involves the use of water as the nucleophile to catalyse the hydrolysis of phosphodiester bonds in phospholipids. Phospholipase D activity is dependent on and regulated by neurotransmitters, hormones, small monomeric GTPases and lipids. The hydrolysis of phosphatidylcholine to phosphatidic acids by phospholipase D is shown in Figure $4$. In addition to its function in generating phosphatidic acid mainly for signaling purposes but also for the maintenance of membrane composition, phospholipase D is involved in intracellular protein trafficking, cytoskeletal dynamics, cell migration, and cell proliferation, partly through protein-protein interactions; it is considered to be important in inflammation and in cancer growth and metastasis as a downstream transcriptional target of proteins involved in the pathophysiology of these diseases. It also has an unusual activity as a guanine nucleotide exchange factor. By a transphosphatidylation reaction with ethanol, it generates phosphatidylethanol, a useful biomarker for ethanol consumption in humans. Under some conditions, phosphatidic acid can be generated from 1,2-diacyl-sn-glycerols by the action of diacylglycerol kinases, for example those produced from other phospholipids by the action of phospholipase C. Such enzymes appear to be ubiquitous in nature, although those in bacteria and yeast are structurally different from the mammalian enzymes. Diacylglycerol kinases, of which at least ten isoforms (DGKα to DGKκ) exist with different sub-cellular locations and functions in animals, use ATP as the phosphate donor. While the epsilon isoform (DGKε) utilizes the 1-stearoyl-2-arachidonoyl species of diacyl-sn-glycerols preferentially to produce phosphatidic acid for the biosynthesis of phosphatidylinositol, other isoenzymes phosphorylate diverse diacylglycerol species. Aside from producing phosphatidic acid for phospholipid production or signaling , these enzymes may attenuate the signaling effects of diacylglycerols. For example, diacylglycerol kinases can contribute to cellular asymmetry and control the polarity of cells by regulating the gradients in diacylglycerol and phosphatidic acid concentrations. Figure $5$ shows the synthesis of phosphatidic acid via diacylglycerols and the reverse reaction. The reverse reaction, hydrolysis, is catalyzed by lipins. These enzymes are of importance in regulating the local concentrations of phosphatidic acid and thence its biological activity. A further possible route to phosphatidic acid production for signaling specifically is via acylation of lysophosphatidic acid, which can be produced independently for signaling purposes as discussed below. This pathway may be especially relevant in membranes, where the protein endophilin has LPAT activity and is believed to generate phosphatidic acid from lysophosphatidic acid in order to alter the curvature of the membrane bilayer. Phosphatidic Acid - Role as a Lipid Precursor In summary, phosphatidic acid generated via 1-acyl-sn-glycerol-3-phosphate is the primary precursor of other glycerolipids, although other pathways may be more important for generating the lipid for signaling functions. Whether separate pools of this lipid for specific purposes really exist is not certain since dynamic changes of intracellular distribution occur under various cellular conditions. These are attributed to inter-organelle transfer via vesicular transport or at membrane contact sites by lipid transfer proteins. Control of its concentration in membranes, especially in the endoplasmic reticulum, is therefore of great importance, and a transcriptional repressor 'Opi1', which binds specifically to phosphatidic acid in membranes, is a key regulatory factor. However, many other phosphatidic acid-binding proteins have been identified that influence how phosphatidic acid is used either as a biosynthetic precursor or for signaling purposes. The mechanisms for phosphatidic acid homeostasis differ among animals, plants, yeasts, and bacteria in response to the differing functional requirements in these organisms. Figure $6$ shows the pathways for biosynthesis of complex glycerolipids. In addition to dietary, hormonal and tissue-specific factors in animals, the extent to which fatty acids are channeling either into triacylglycerol synthesis for storage in lipid droplets and secretion in lipoproteins or into glycerophospholipids for membrane formation depends to a large extent upon the enzymes of glycerol-3-phosphate pathway, their isoform expression, activities and locations. On the other hand, phosphatidic acid is not only a biosynthetic precursor of other lipids but also a regulatory molecule in the transcriptional control of the genes for glycerolipid synthesis, and regulation of its concentration in cells for this purpose is similarly essential. For example, the local concentration of phosphatidic acid in the endoplasmic reticulum is an important factor in the biogenesis of lipid droplets. The subsequent steps in the utilization of phosphatidic acid in the biosynthesis of triacylglycerols and of the various glycerophospholipids are described in separate documents of this website. Thus, hydrolysis of phosphatidic acid by phosphatidate phosphatase enzymes (including lipins 1, 2, and 3) is the source of most other glycerolipids, e.g. sn‑1,2‑diacylglycerols (DG), which are the precursors for the biosynthesis of triacylglycerols (TAG), phosphatidylcholine (PC) and phosphatidylethanolamine (PE) via the so-called Kennedy pathway (also of monogalactosyldiacylglycerols in plants). Via reaction with cytidine triphosphate, phosphatidic acid is the precursor of cytidine diphosphate diacylglycerol, which is the key intermediate in the synthesis of phosphatidylglycerol (PG), and thence of cardiolipin (CL), and of phosphatidylinositol (PI), and in prokaryotes and yeast but not animals phosphatidylserine (PS). Depending on the organism and other factors, phosphatidylserine can be a precursor for phosphatidylethanolamine, while the latter can give rise to phosphatidylcholine by way of mono- and dimethyl-phosphatidylethanolamine intermediates. The cytidine diphosphate diacylglycerol synthase is another enzyme that consumes phosphatidic acid and is important for modulating the concentration of phosphatidic acid in cells and for regulating processes mediated by this lipid. While the fatty acid composition of phosphatidic acid can resemble that of the eventual products, the latter are generally much altered by re-modeling after synthesis via deacylation-reacylation reactions. Phosphatidic Acid - Biological Functions in Animals In addition to its role as an intermediate in lipid biosynthesis, phosphatidic acid and especially that generated by the action of phospholipase D and by diacylglycerol kinases may have signaling functions as a second messenger, although it is not certain whether all the activities suggested by studies in vitro operate in vivo. Nonetheless, phosphatidic acid has been implicated in many aspects of animal cell biochemistry and physiology. Some of the observed effects may be explained simply by the physical properties of phosphatidic acid, which has a propensity to form a hexagonal II phase, especially in the presence of calcium ions. Thus, hydrolysis of phosphatidylcholine, a cylindrical non-fusogenic lipid, converts it into cone-shaped phosphatidic acid, which promotes negative membrane curvature and fusion of membranes. It differs from other anionic phospholipids in that its small anionic phosphomonoester head group lies very close to the hydrophobic interior of the lipid bilayer. In model systems, phosphatidic acid can effect membrane fusion, probably because of its ability to form non-bilayer phases. For example, the phosphatidic acid biosynthesis is believed to favor intraluminal budding of endosomal membranes with the formation of exosomes, and in many cell types, vesicle trafficking, secretion and endocytosis may require phosphatidic acid derived by the action of phospholipase D. Also of relevance in this context is its overall negative charge, and it is not always clear whether some of the observed biological effects are specific to phosphatidic acid or simply to negatively charged phospholipids in general. In contrast to phosphoinositide-interacting proteins, which have defined structural folds, the binding motifs of effector proteins with phosphatidic acid are not highly conserved. However, it has been demonstrated that the positively charged lysine and arginine residues on proteins can bind with some specificity to phosphatidic acid through hydrogen bonding with the phosphate group thus distinguishing it from other phospholipids. An ‘electrostatic-hydrogen bond switch model’ has been proposed in which the head group of phosphatidic acid forms a hydrogen bond to amino acid residues leading to de-protonation of the head group, increasing its negative charge from -1 to -2 and thus enabling stronger interactions with basic residues and tight docking with the membrane interacting protein. In this way, phosphatidic acid can tether certain proteins to membranes, and it can simultaneously induce conformational changes, hinder ligand binding and/or oligomerize proteins to alter their catalytic activity, stability and interactions with other molecules. It functions as a cellular pH sensor in effect in that binding to proteins is dependent on intracellular pH and the protonation state of its phosphate head group. One key target of the lipid is mTOR, a serine/threonine protein kinase with a signaling cascade that regulates cell growth, proliferation, motility and survival, together with protein synthesis and transcription, by integrating both nutrient and growth factor signals. This forms two distinct complexes of accessory proteins that regulate downstream targets. Of these, mTORC1 interacts directly with phosphatidic acid and this interaction allosterically activates the enzyme complex to regulate protein synthesis, mitochondrial metabolism and the transcription of enzymes involved in lipid synthesis. In contrast, phosphatidic acid appears to inhibit mTORC2 activity, for example in relation to insulin signaling . Phosphatidic acid is believed to regulate membrane trafficking events, and it is involved in activation of the enzyme NADPH oxidase, which operates as part of the defence mechanism against infection and tissue damage during inflammation. By binding to targeted proteins, including protein kinases, protein phosphatases and G-proteins, it may increase or inhibit their activities. Effects on gene transcription have been observed that are linked to inhibition of peroxisome proliferator-activated receptor (PPAR) activity. In yeast, phosphatidic acid in the endoplasmic reticulum binds directly to a specific transcriptional repressor to keep it inactive outside the nucleus; when the lipid precursor inositol is added, this phosphatidic acid is rapidly depleted, releasing the transcriptional factor so that it can be translocated to the nucleus where it is able to repress target genes. The overall effect is a mechanism to control phospholipid synthesis. In addition, phosphatidic acid regulates many aspects of phosphoinositide function. For example, the murine phosphatidylinositol 4-phosphate 5-kinase, the main enzyme generating the lipid second messenger phosphatidylinositol-4,5-bisphosphate, does not appear to function unless phosphatidic acid is bound to it; this lipid, generated by the action of phospholipase D, recruits the enzyme to the membrane and induces a conformational change that regulates its activity. It may have a role in promoting phospholipase A2 activity, a key enzyme in eicosanoid production from phosphoinositide precursors. In relation to signaling activities, it should be noted that phosphatidic acid can be metabolized to sn-1,2-diacylglycerols or to lysophosphatidic acid (see next section), both of which have distinctive signaling functions in their own right. Conversely, both of these compounds can be in effect be de-activated by conversion back to phosphatidic acid. Phospholipase D isoforms and phosphatidic acid have been implicated in a variety of pathologies including neurodegenerative diseases, blood disorders, late-onset Alzheimer's disease and cancer, leading to attempts to develop specific inhibitors of the enzyme for therapeutic purposes. Similarly, the expression of LPAT isoforms can enhance the proliferation and chemoresistance of some cancer cells. Diacylglycerol kinase alpha (DGKα) is highly expressed in several refractory cancer cells, where it attenuates apoptosis, and promotes proliferation. In addition, DGKα is highly abundant in T cells and induces a nonresponsive state, which enables advanced cancers to escape immune action. Inhibition of this enzyme also is seen as a promising treatment strategy. Phosphatidic Acid - Biological Functions in Plants Phosphatidic acid is present at higher levels in roots of plants in comparison to leaves and is believed to have a function in root architecture. Similarly, its concentration is elevated in flowers and reproductive tissues, but the significance of this is not known. In addition to its role as one of the central molecules in lipid biosynthesis, it facilitates the transport of lipids across plant membranes, and it is also the key plant lipid second messenger, which is rapidly and transiently generated in response to many different biotic and abiotic stresses. In contrast to animal metabolism, the diacylglycerol signaling pathway is believed to be relatively insignificant in plants. The main source of phosphatidic acid for these purposes is the action of phospholipase D (PLD) on membrane phospholipids, such as phosphatidylcholine and phosphatidylethanolamine. Plants contain numerous related enzymes of this type, 12 in Arabidopsis and 17 in rice, in comparison with two in humans and one in yeast, and individual iso-enzymes may elicit specific responses. In the former, the isoforms are grouped into six classes, based on the genic architecture, sequence similarities, domain structures and biochemical properties. These depend mainly on their lipid-binding domains, with some homologous to the human and yeast enzymes and with most containing a characteristic ‘C2’ (calcium- and lipid-binding) domain. The most widespread of these is PLDα, which does not require binding to phosphatidylinositol 4,5-bisphosphate, in contrast to other PLD isoforms and the mammalian enzyme, but millimolar levels of Ca2+ are necessary. Studies with fluorescent biosensors suggest that phosphatidic acid accumulates in the subapical region of the cytosolic leaflet of the plasma membrane. Phosphatidic acid can also be produced by the sequential action of phospholipase C and diacylglycerol kinase on membrane inositol phospholipids, with diacylglycerols as an intermediate (there are 7 isoenzymes in A. thaliana). One difference from animal metabolism is that diacylglycerol pyrophosphate can be synthesized from phosphatidic acid in plants (see below). Phosphatidic acid is required to bind and allosteric activate the monogalactosyldiacylglycerol synthase (MGDG1), located in the inner envelope membrane of the chloroplast, and it may be a regulator of the biosynthesis of thylakoid membranes. Phospholipase D activity and the phosphatidic acid produced have long been recognized as of importance during germination and senescence, and they have an essential role in the response to stress damage and pathogen attack, both in higher plants and in green algae. A high content of phosphatidic acid induced by phospholipase D action during wounding or senescence brings about a loss of the membrane bilayer phase, because of the conical shape of this negatively charged phospholipid in comparison to the cylindrical shape of structural phospholipids. This change in ionization properties has crucial effects upon lipid-protein interactions, "the electrostatic-hydrogen bond switch model" described above. By promoting negative curvature at the plasma membrane and binding to clathrin proteins, it is believed to facilitate the process of endocytosis. Similar phenomena may explain why phosphatidic acid is important in the response to other forms of stress, including osmotic stress (salinity or drought), cold and oxidation. Although much remains to be learned of the mechanism by which it exerts its effects, it is believed to promote the response to the plant hormone abscisic acid. In addition, phosphatidic acid may interact with salicylic acid to mediate defence responses. In plants, phosphatidic acid is involved in many different cell responses induced by hormones, stress and developmental processes. In relation to cellular signaling, it often acts in concert with phosphatidylinositol 4,5-bisphosphate by binding to specific proteins rather than acting via a receptor. As in mammalian cells, targets for such signaling include protein kinases and phosphatases in addition to proteins involved in membrane trafficking and the organization of the cytoskeleton. It can both activate or inhibit enzymes. If the target protein is soluble, binding to phosphatidic acid can cause the protein to be sequestered into a membrane with effects upon downstream targets. For example, it is involved in promoting the growth of pollen-tubes and root hairs, decreasing peroxide-induced cell death, and mediating the signaling processes that lead to responses to ethylene and again to the hormone abscisic acid. Thus, in the 'model' plant Arabidopsis, phosphatidic acid interacts with a protein phosphatase to signal the closure of stomata promoted by abscisic acid; it interacts also with a further enzyme to mediate the inhibition of stomatal opening effected by abscisic acid. Together these reactions constitute a signaling pathway that regulates water loss from plants. It is noteworthy that phosphatidic acid production can be initiated by opposing stress factors, such as cold and heat, as well as by hormones that are considered to be antagonistic, such as abscisic acid and salicylic acid. It is possible that phosphatidic acid molecules synthesized by the two main pathways differ in composition and cellular distributions and so may produce different responses, but this is an open question. Certainly, during low temperature stress, phosphatidic acid is generated by the action of diacylglycerol kinase. It also seems likely that these differing activities are controlled by the cellular environment where the lipid is produced and by the availability of target proteins or other molecules with which it can act synergistically. Genes encoding enzymes involved in phosphatidic acid metabolism have been manipulated to explore their potential application for crop improvements, based on effects on plant growth, development, and stress responses. As in animals, phosphatidic acid is catabolized and its signaling functions are terminated by lipid phosphate phosphatases and phosphatidic acid hydrolases, and by acyl-hydrolases and lipoxygenases with the production of fatty acids and other small molecules, which are subsequently absorbed and recycled. Lysophosphatidic Acid Figure $7$ shows the structure of a lysophosphatidic acid (note the absence of an acyl group at C2). Lysophosphatidic acid (LPA) or 1-acyl-sn-glycerol-3-phosphate differs structurally from phosphatidic acid in having only one mole of fatty acid per mole of lipid. As such, it is one of the simplest possible glycerophospholipids. It exists in the form of many different molecular species, i.e., esterified to 16:0 to 22:6 fatty acids, and there is preliminary evidence that saturated and polyunsaturated species may differ in their biological properties in some circumstances. As the sn-1-acylated form is more stable thermodynamically, facile isomerization ensures that this tends to predominate. As it lacks one fatty acid in comparison to phosphatidic acid, it is a much more hydrophilic molecule, while the additional hydroxyl group strengthens hydrogen bonding within membranes, properties that may be important for its function in cells. Although lysophosphatidic acid is present at very low levels only in animal tissues, it is extremely important biologically, influencing many biochemical processes. It is a biosynthetic precursor of phosphatidic acid, but there is particular interest in its role as a lipid mediator with growth factor-like activities. For example, it is rapidly produced and released from activated platelets to influence target cells. Biosynthesis: In the circulation, the most important source of lysophosphatidic acid is the activity of an enzyme with lysophospholipase D-like activity and known as ‘autotaxin’ on lysophosphatidylcholine (200 μM in plasma) to yield LPA in an albumin-bound form mainly, although it is relatively soluble in aqueous media because of its polarity and small size. This lipid is more abundant in serum (1 to 5 μM) than in plasma (100 nM), because of the release of its main precursor, lysophosphatidylcholine, from activated platelets during coagulation. Autotaxin is a member of the nucleotide pyrophosphatase-phosphodiesterase family and is also present in cerebrospinal and seminal fluids and many other tissues including cancer cell lines from which it was first isolated and characterized. Indeed, the name derives from the finding that it promoted chemotaxis on melanoma cells in an autocrine fashion. It binds to target cells via integrin and heparan sulfate proteoglycans and this may assist the delivery of lysophosphatidic acid to its receptors. Genetic deletion of the enzyme in mice results in aberrant vascular and neuronal development and soon leads to death of the embryos. However, the overexpression of autotaxin causes physical defects also and is eventually lethal to embryos. Figure $8$ shows the pathways for synthesis of lysophosphatidic acid. While autotaxin is the primary source of extracellular lysophosphatidic acid, it is now established that it is produced intracellularly by a wide variety of cell types by various mechanisms often with phosphatidic acid, derived from other phospholipids by the action of phospholipase D, as the primary precursor. For example, hydrolysis of phosphatidic acid by a phospholipase A2 (PLA2) is the main mechanism in platelets, but other cellular enzymes involved include a phosphatidic acid-selective phospholipase A1 (PLA1) producing sn-2-acyl-lysophosphatidic acid, a monoacylglycerol kinase (utilizing monoacylglycerols produced by the action of lipid phosphate phosphatases) and glycerol-3-phosphate acyltransferase (the first step in phosphatidic acid biosynthesis). In particular, secretory PLA2-IIA (sPLA2-IIA) is able to induce the release of LPA from phosphatidic acid exposed on the surface of extracellular vesicles derived from platelets and Ca2+-loaded erythrocytes upon stimulation by pro-inflammatory cytokines. General function: Although lysophospholipids are relatively small molecules, they carry a high content of information through the nature of the phosphate head group, the positional distribution of the fatty acids on the glycerol moiety, the presence of ether or ester linkages to the glycerol backbone, and the chain-length and degree and position of saturation of the fatty acyl chains. Lysophosphatidic acid acts upon nearly all cell types, often as a proliferative and pro-survival signal, inducing cellular invasion, migration and differentiation, while stimulating smooth muscle and fibroblast contraction, cytoskeletal rearrangement, secretion of cytokines/chemokines and numerous other effects. Many of these activities are displayed also by the 1-O-alkyl- and alkenyl-ether forms, which can be derived from platelet activating factor. On the other hand, it is possible that much of the lysophosphatidic acid produced intracellularly is used for synthesis of other phospholipids rather than for signaling purposes. Receptors: The informational content of the lysophosphatidic acid molecule leads to selectivity in the functional relationship with cell receptors. As most mammalian cells express receptors for lysophosphatidic acid, this lipid may initiate signaling in the cells in which it is produced, as well as affecting neighboring cells. Characterization of cloned lysophosphatidic acid receptors in combination with strategies of molecular genetics has allowed determination of both signaling and biological effects that are dependent on receptor mechanisms. At least six G protein-coupled receptors that are specific for lysophosphatidic acid have now been identified in vertebrates, each found in particular organs and coupled to at least one or more of the four heterotrimeric Gα proteins and designated LPAR1 to LPAR6, of which LPAR1 is virtually ubiquitous in tissues. These vary appreciably amino acid sequences but are classified into two subgroups, the EDG (LPAR1-3) and P2Y (LPAR4-6) families, with differing tissue distributions. Most cell types express these receptors in different combinations. There is also some interaction with transient receptor potential cation channel V1 (TRPV1), peroxisome proliferator-activated receptor gamma (PPARγ) and other proteins. Plasma lysophosphatidic acid binds to its receptors while it is bound to albumin. Experimental activation of the LPAR receptors has shown that a range of downstream signaling cascades are mediated by lysophosphatidic acid signaling via these various receptors. These include activation of adenylyl cyclase, cAMP production, intracellular Ca2+ and K+ production (by activating ion channels), protein kinases, phospholipase C, phosphatidylinositol 3-kinase, small GTPases (Ras, Rho, Rac), release of arachidonic acid, and much more. In this way, lysophosphatidic acid regulates cell survival, proliferation, cytoskeleton re-arrangement, motility, cytokine secretion, cell differentiation and many other vital cellular processes. Sometimes, lysophosphatidic acid appears to function in contradictory ways, and there is evidence that it is involved in cell survival in some circumstances and in programmed cell death in others, for example. Signaling by lysophosphatidic acid has regulatory functions in the mammalian reproductive system, both male and female, facilitating oocyte maturation and spermatogenesis through the action of the receptors LPAR1 to LPAR3. During early gestation, it regulates vascular remodeling at the maternal-fetal interface. There is also evidence that the lipid is involved in brain development, through its activity in neural progenitor cells, neurons, and glia, and in vascular remodeling. In the central nervous system, these receptors are thought to play a central role in both triggering and maintaining neuropathic pain by mechanisms that may involve demyelination of damaged nerves. Lysophosphatidic acid has been found in saliva in significant amounts, and it has been suggested that it is involved in wound healing in the upper digestive organs such as the mouth, pharynx, and oesophagus. When applied topically to skin wounds, it has similar effects probably by stimulating proliferation of new cells to seal the wound. Receptor LPAR6 together with the phospholipase A1 is required for the development of hair follicles, and this receptor is also involved in the regulation of endothelial blood-brain barrier function. The proliferation and survival of stem cells and their progenitors is regulated by lysophosphatidic acid signaling, while in bone cells, acting via LPAR1, lysophosphatidic acid is important for bone mineralization and repair. Disease: There is particular interest in the activity of lysophosphatidic acid in various disease states and cancer especially, as increased expression of autotaxin and the subsequent increased levels of lysophosphatidic acid have been reported in several primary tumors. For example, a finding that lysophosphatidic acid is markedly elevated in the plasma and peritoneal fluid (ascites) of ovarian cancer patients compared to healthy controls may be especially significant. Also, elevated plasma levels were found in patients in the first stage of ovarian cancer, suggesting that it may represent a useful marker for the early detection of the disease. It is believed that the secretory form of phospholipase A2 acts preferentially on lipids from damaged membranes or microvesicles, such as those produced by malignant cells, and this eventually results in increased levels of this lipid. Lysophosphatidic acid has been shown to stimulate the expression of genes for many different enzymes that lead to the proliferation of ovarian and other cancer cells and may induce cell migration via receptors LPAR1 to LPAR3 and possibly LPAR6, while LPAR4 and LPAR5 have opposing effects. Autotaxin and LPARs have been implicated in resistance to chemotherapy and radiation treatment in cancer therapy. As lysophosphatidic acid has growth-factor-like activities for many cell types that induce cell proliferation and migration, changes in cellular shape and increasing of endothelial permeability, it is perhaps not surprising that it is relevant to tumor biology. Treatment of various cancer cell types with lysophosphatidic acid promotes the expression and release of interleukin 8 (IL-8), which is a potent angiogenic factor, and thus it has a critical role in the growth and spread of cancers by enhancing the availability of nutrients and oxygen. There is evidence that signaling by lysophosphatidic acid is causally linked to hyperactive lipogenesis in cancer. For example, it activates the sterol regulatory element-binding protein (SREBP) together with the fatty acid synthase and AMP-activated protein kinase–ACC lipogenic cascades leading to elevated synthesis of lipids de novo. Increased autotaxin expression has been demonstrated in many different cancer cell lines, and the expression of many of the surface receptors for lysophosphatidic acid in cancer cells is aberrant. Cancer cells must evade the immune system during metastasis, and lysophosphatidic acid facilitates this process by inhibiting the activation of T cells. Therefore, lysophosphatidic acid metabolism is a target of the pharmaceutical industry in the search for new drugs for cancer therapy, aided by a knowledge of the crystal structures of three of the receptors. Signaling by lysophosphatidic acid has been implicated in many aspects of chronic inflammation, which it promotes by affecting the endothelium in several ways, for example by stimulating endothelial cell migration, the secretion of chemokines-cytokines and regulating the integrity of the endothelial barrier. Problems with lysophosphatidic acid signaling together with changes in autotaxin expression are believed to be factors in such metabolic and inflammatory disorders as obesity, insulin resistance, non-alcoholic fatty liver disease, rheumatoid arthritis, multiple sclerosis and cardiovascular disease. Further, there is evidence it contributes to neurological disorders, such as Alzheimer's disease and neuropathic pain, and to asthma, fibrosis and bone malfunction. Drugs that interact with the lysophosphatidic acid receptors are reported to be effective in attenuating symptoms of several diseases in animal models, and three have passed phase I and II clinical trials for idiopathic pulmonary fibrosis and systemic sclerosis in human patients. Drugs that target autotaxin production and catabolism of lysophosphatidic acid are also in development, and the steroidal anti-inflammatory agent, dexamethasone, appears to be especially useful. Under certain conditions, lysophosphatidic acid can become athero- and thrombogenic and might aggravate cardiovascular disease. As oxidized low-density lipoproteins promote the production of lysophosphatidic acid, its content in atherosclerotic plaques is high, suggesting that it might serve as a biomarker for cardiovascular disease. Indeed, lysophosphatidic acid promotes pro-inflammatory events that lead to the development of atheroma as well encouraging progression of the disease. By mediating platelet aggregation, it could lead to arterial thrombus formation. Related lipids: The sphingolipid analogue, sphingosine-1-phosphate, shows a similar range of activities to lysophosphatidic acid and the two lipids are often discussed together in the same contexts, although they may sometimes have opposing effects. Acute leukemia cells produce methyl-lysophosphatidic acids (the polar head-group is methylated). As these act as antigens to which a specific group of human T cells react strongly, it is possible that they might be a target for the immunotherapy of hematological malignancies. Other lysophospholipids are known to have distinctive biological functions. Catabolism: Deactivation of lysophosphatidic acid is accomplished by dephosphorylation to produce monoacylglycerols by a family of three lipid phosphate phosphatases (LPP1, 2 and 3), which also de-phosphorylate sphingosine-1-phosphate, phosphatidic acid and ceramide 1-phosphate in a non-specific manner. These are integral membrane proteins with the active site in the plasma membrane facing the extracellular environment, enabling them to access and hydrolyse extracellular lysophosphatidic acid and other phospholipids. Mice with a constitutive LPP3 deficiency are not viable, but this is not true for LPP1 and LPP2 knockout mice. Lysophosphatidic acid can be converted back to phosphatidic acid by a membrane-bound O-acyltransferase (MBOAT2) specific for lysophosphatidic acid (and lysophosphatidylethanolamine) with a preference for oleoyl-CoA as substrate. Phosphatidylcholine and Related Lipids Phosphatidylcholine - Structure and Occurrence Phosphatidylcholine or 1,2-diacyl-sn-glycero-3-phosphocholine (once given the trivial name 'lecithin') is a neutral or zwitterionic phospholipid over a pH range from strongly acid to strongly alkaline. It is usually the most abundant phospholipid in animals and plants, often amounting to almost 50% of the total complex lipids, and as such it is obviously a key building block of membrane bilayers. In particular, it makes up a very high proportion of lipids of the outer leaflet of the plasma membrane in animals. Virtually all the phosphatidylcholine in human erythrocyte membranes is present in the outer leaflet, for example, while in the plasma membranes of nucleated cells, 80 to 90% of this lipid is located on the outer leaflet. Phosphatidylcholine is also the principal phospholipid circulating in plasma, where it is an integral component of the lipoproteins, especially the HDL. On the other hand, it is less often found in bacterial membranes, perhaps ~10% of species, but there is none in the 'model' organisms Escherichia coli and Bacillus subtilis. In animal tissues, some of its membrane functions appear to be shared with the structurally related sphingolipid, sphingomyelin, although the latter has many unique properties of its own. Figure $9$ shows the structure of phosphatidylcholine In animal tissues, phosphatidylcholine tends to exist in mainly in the diacyl form, but small proportions (in comparison to phosphatidylethanolamine and phosphatidylserine) of alkyl,acyl and alkenylacyl forms may also be present. As a generalization, animal phosphatidylcholine tends to contain lower proportions of arachidonic and docosahexaenoic acids and more of the C18 unsaturated fatty acids than the other zwitterionic phospholipid, phosphatidylethanolamine. Saturated fatty acids are most abundant in position sn-1, while polyunsaturated components are concentrated in position sn-2. Indeed, C20 and C22 polyenoic acids are exclusively in position sn-2, yet in brain and retina the unusual very-long-chain polyunsaturated fatty acids (C30 to C38) of the n-6 and n-3 families occur in position sn-1. Dietary factors obviously influence fatty acid compositions, but in comparing animal species, it would be expected that the structure of the phosphatidylcholine in the same metabolically active tissue would be somewhat similar in terms of the relative distributions of fatty acids between the two positions. Table $1$ lists some representative data. Table $1$. Positional distribution of fatty acids in the phosphatidylcholine of some animal tissues. Position Fatty acid 16:0 16:1 18:0 18:1 18:2 20:4 22:6 Rat liver [1] sn-1 23 1 65 7 1 trace sn-2 6 1 4 13 23 39 7 Rat heart [2] sn-1 30 2 47 9 11 - - sn-2 10 1 3 17 20 33 9 Rat lung [3] sn-1 72 4 15 7 3 - - sn-2 54 7 2 12 11 10 1 Human plasma [4] sn-1 59 2 24 7 4 trace - sn-2 3 1 1 26 32 18 5 Human erythrocytes [4] sn-1 66 1 22 7 2 - - sn-2 5 1 1 35 30 16 4 Bovine brain (gray matter) [5] sn-1 38 5 32 21 1 - - sn-2 33 4 trace 48 1 9 4 Chicken egg [6] sn-1 61 1 27 9 1 - - sn-2 2 1 trace 52 33 7 4 1, Wood, R. and Harlow, R.D. Arch. Biochem. Biophys., 131, 495-501 (1969); DOI. 2, Kuksis, A. et al. J. Lipid Res., 10, 25-32 (1969); DOI. 3, Kuksis, A. et al. Can. J. Physiol. Pharm., 46, 511-524 (1968); DOI. 4, Marai, L. and Kuksis, A. J. Lipid Res., 10, 141-152 (1969); DOI. 5, Yabuuchi, H. and O'Brien, J.S. J. Lipid Res., 9, 65-67 (1968); DOI. 6, Kuksis, A. and Marai, L. Lipids, 2, 217-224 (1967); DOI. There are some exceptions to the rule as the phosphatidylcholine in some tissues or organelles contains relatively high proportions of disaturated molecular species. For example, it is well known that lung phosphatidylcholine in most if not all animal species studied to date contains a high proportion (50% or more) of dipalmitoylphosphatidylcholine. The positional distributions of fatty acids in phosphatidylcholine in representative plants and yeast are listed in Table $2$. In the leaves of the model plant Arabidopsis thaliana, saturated fatty acids are concentrated in position sn-1, but monoenoic fatty acids are distributed approximately equally between the two positions, and there is a preponderance of di- and triunsaturated fatty acids in position sn-2; the same is true for soybean ‘lecithin’. In the yeast Lipomyces lipoferus, the pattern is somewhat similar except that much of the 16:1 is in position sn-1. Table $2$: Composition of fatty acids (mol %) in positions sn-1 and sn-2 in the phosphatidylcholine from plants and yeast. Position Fatty acid 16:0 16:1 18:0 18:1 18:2 18:3 Arabidopsis thaliana (leaves) [1] sn-1 42 4 5 23 26 sn-2 1 trace 5 47 47 Soybean 'lecithin' [2] sn-1 24 9 14 47 4 sn-2 5 1 13 75 6 Lipomyces lipoferus [3] sn-1 24 18 trace 37 16 4 sn-2 4 5 trace 39 31 19 1, Browse, J., Warwick, N., Somerville, C.R. and Slack, C.R. Biochem. J., 235, 25-31 (1986); DOI. 2, Blank, M.L., Nutter, L.J. and Privett, O.S. Lipids, 1, 132-135 (1966); DOI. 3, Haley, J.E. and Jack, R.C. Lipids, 9, 679-681 (1974); DOI. Phosphatidylcholine – Biosynthesis There are several mechanisms for the biosynthesis of phosphatidylcholine in animals, plants and micro-organisms. Choline itself is not synthesized as such by animal cells and is an essential nutrient, not only for phospholipid synthesis but also for cholinergic neurotransmission (acetylcholine synthesis) and as a source for methyl groups for numerous other metabolites. It must be obtained from dietary sources or by degradation of existing choline-containing lipids, for example those produced by the second pathway described below. Once taken across membranes and into cells by specific transporters, choline is immediately phosphorylated by a choline kinase (1) in the cytoplasm of the cell to produce phosphocholine, which is reacted with cytidine triphosphate (CTP) by the enzyme CTP:phosphocholine cytidylyltransferase (CCT) (2) to form cytidine diphosphocholine (CDP-choline). CTP + PC → CDP-choline + Pi The latter enzyme exists in two isoforms of which CCTα is the more important and is a soluble protein found first in the nucleoplasm, but then in the nucleoplasmic reticulum. This is considered to be the rate-limiting step in phosphatidylcholine biosynthesis, and the activity of the enzyme is regulated by signals from a sensor in the membrane that reports on the relative abundance of the final product. However, choline kinase (ChoKα) also has regulatory functions. Figure $10$ shows an interactive iCn3D model of the mammalian (rat) CTP: Phosphocholine cytidylyltransferase catalytic domain (3HL4). Figure $10$: Mammalian (rat) CTP-Phosphocholine cytidylyltransferase catalytic domain (3HL4). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...tBYRYk7vPKD7f6 The biologically active homodimer is shown. The B chain is colored by secondary structure and the A chain is shown in gray. Key active site residues (only shown in the A chain) are in CPK-colored sticks and labeled. A bound CDP-choline analog ((2-cytidylate-O'-phosphonyloxy)-ethyl-trimethylammonium) is shown in spacefill CPK colors. This enzyme (CCT) catalyzes the key regulatory and rate-limiting step in PC synthesis. The C-terminal domain binds membrane lipids and regulates the enzyme. The iCn3D model above is for the catalytic domain with the regulatory domain deleted. Two nonconserved active site side chains, His 168 and Tyr 173 interact with and position the phosphocholine. Other active site residues include Arg-196 in L6, Lys-122 in L2, and Asp-94. Figure $11$ shows a simplified mechanism for the reaction In plants, nematodes and certain parasites, most phosphocholine is synthesized by sequential methylation of phosphoethanolamine by phospho-base N‑methyltransferases, but phosphatidylethanolamine is only methylated in this way in a few plant species. This is also the main route to free choline and betaine in plants. The CDP-choline produced is acted upon by the membrane-bound enzyme CDP-choline:1,2-diacylglycerol choline/ethanolamine-phosphotransferase in the endoplasmic reticulum (CEPT1), and a related choline phosphotransferase 1 (CPT1) in the trans-Golgi, which catalyse the reaction with sn-1,2-diacylglycerols to form phosphatidylcholine. The first of these is responsible for most phosphatidylcholine biosynthesis but with a somewhat different molecular species composition from the second, which has a preference for 1-alkyl precursors. This is the main pathway for the synthesis of phosphatidylcholine in animals and plants, and it is analogous to that for a major route to phosphatidylethanolamine; it is also found in a few bacterial species (e.g. Sinorhizobium meliloti). Phosphatidylcholine in mitochondria is obtained by transfer from the endoplasmic reticulum. Figure $12$ shows the main pathways for PC synthesis in plants and animals. The discovery of the importance of this pathway depended a little on serendipity in that in experiments in the laboratory of Professor Eugene Kennedy, samples of adenosine triphosphate (ATP) contained some cytidine triphosphate (CTP) as an impurity. However, luck is of little value without receptive minds, and Kennedy and co-workers demonstrated that the impurity was an important metabolite that was essential for the formation of phosphatidylcholine. The above reaction, together with the biosynthetic mechanism for phosphatidylethanolamine, is significantly different from that for phosphatidylglycerol, phosphatidylinositol and cardiolipin. Both make use of nucleotides, but with the latter, the nucleotide is covalently linked directly to the lipid intermediate, i.e., cytidine diphosphate diacylglycerol. However, a comparable pathway to the latter for biosynthesis of phosphatidylcholine occurs in bacteria (see below). The source of the sn-1,2-diacylglycerol precursor, which is also a key intermediate in the formation of phosphatidylethanolamine and phosphatidylserine, and of triacylglycerols, is phosphatidic acid. In this instance, the important enzyme is phosphatidic acid phosphatase (also known as lipin or phosphatidate phosphatase’ or ‘lipid phosphate phosphatase’ or ‘phosphatidate phosphohydrolase’). Figure $13$ shows the biosynthesis of the diacyl precursor of PC This enzyme is also important for the production of diacylglycerols as essential intermediates in the biosynthesis of triacylglycerols and of phosphatidylethanolamine. Yeasts contain two such enzymes, one of which is Mg2+-dependent (PAP1) and the other Mg2+-independent (PAP2). In mammals, much of the phosphatidic acid phosphatase activity resides in three related cytoplasmic proteins, termed lipins-1, -2, and -3. Lipin-1 is found mainly in adipose tissue, while lipin-2 is present mainly in liver. They are unique among biosynthetic enzymes for glycerolipids in that they can transit among cellular membranes rather than remain tethered to membranes. Of these lipin-1 is most important and exists in three isoforms, lipin-1α, lipin-1β and lipin-1γ with lipin-1α located mainly in the nucleus and lipin-1β in the cytoplasm. Lipin-1γ is present primarily in brain. The second pathway for biosynthesis of phosphatidylcholine involves sequential methylation of phosphatidylethanolamine, with S-adenosylmethionine (SAM) as the source of methyl groups, with mono- and dimethylphosphatidylethanolamine as intermediates and catalyzed by the enzyme phosphatidylethanolamine N‑methyltransferase. A single enzyme (~20 KDa) in two isoforms catalyses all three reactions in hepatocytes; the main form is located in the endoplasmic reticulum (ER) where it spans the membrane, while the second is found in the mitochondria-associated ER membrane. At least two N-methyltransferases are present in yeasts. This is a major pathway in the liver, generating one third of the phosphatidylcholine in this organ, but not in other animal tissues or in general in higher organisms. It may be the main route to phosphatidylcholine in those bacterial species that produce this lipid and in yeasts, but it appears to operate in only a few species of higher plants. When choline is deficient in the diet, this liver pathway is especially important. Figure $14$ shows the synthesis of PC via methylation of PE. A by-product of the biosynthesis of phosphatidylcholine from phosphatidylethanolamine is the conversion of S‑adenosylmethionine to S‑adenosylhomocysteine, which is hydrolyzed in the liver to adenosine and homocysteine. An elevated level of the latter in plasma is a risk factor for cardiovascular disease and myocardial infarction. Phosphatidylcholine biosynthesis by both pathways in the liver is necessary for normal secretion of the plasma lipoproteins (VLDL and HDL), and it is relevant to a number of human physiological conditions. It should be noted that all of these pathways for the biosynthesis of diacylphosphatidylcholine are very different and are separated spatially from that producing alkyl, acyl- and alkenylacyl-phosphatidylcholines de novo. Also, synthesis of phosphatidylcholine does not occur uniformly throughout the endoplasmic reticulum but is located at membrane interfaces or where it meets other organelles, and especially where the membrane is expanding dynamically. The enzymes in the endoplasmic reticulum responsible for the synthesis of all phospholipids are orientated in such a manner that their active sites are exclusively facing the cytosol. Problems would arise if there were a rapid expansion of the cytosolic leaflet while the luminal leaflet did not change, but a phospholipid transporter known as a scramblase enables a rapid bidirectional flip-flop of phospholipids between leaflets of the bilayer in an energy-independent manner. Compositional asymmetry in first seen in the trans-Golgi and is completed before the plasma membrane is formed with phosphatidylcholine and sphingolipids present mainly in the exofacial (outer) leaflet while phosphatidylethanolamine and phosphatidylserine are enriched in the cytosolic leaflet. Dietary phosphatidylcholine is rapidly hydrolyzed in the proximal small intestine by pancreatic enzymes with formation of lysophosphatidylcholine (and free fatty acids). Further hydrolysis can occur in the jejuno-ileal brush-border by the action of the membrane phospholipases, with the release of glycerophosphocholine, but much of the lysophosphatidylcholine is reacylated by the lyso-PC-acyl-CoA-acyltransferase 3 for export in chylomicrons. In plant cells, phosphatidylcholine biosynthesis occurs mainly in the endoplasmic reticulum, and it is a major component of most membranes other than the internal membranes of plastids; it is absent from the thylakoids and the inner envelope membrane, but is the main glycerolipid of the outer monolayer of the outer envelope membrane. Further complications arise in plants in that turnover or partial synthesis via lysophosphatidylcholine occurs in different organelles from different fatty acid pools or with enzymes with differing specificities, and in addition, fatty acids esterified to phosphatidylcholine serve as substrates for desaturases. The result is that an appreciable pool of the diacylglycerols for the biosynthesis of triacylglycerols, galactosyldiacylglycerols, and other glycerolipids pass through phosphatidylcholine as an intermediate so that the fatty acid compositions in different membranes change after the initial synthetic process. This mechanism has obvious differences from the remodeling of molecular species in animal tissues discussed next, although a comparable exchange of acyl groups does occur in part catalyzed by acyl transferases (see next section). Some transfer of phosphatidylcholine per se from the endoplasmic reticulum to plastids may occur via contact points between the two membranes or may be facilitated by specific transport proteins. While phosphatidylcholine is a major lipid in yeasts, recent work suggests that it is not essential if suitable alternative growth substrates are available, unlike higher organisms where perturbation of phosphatidylcholine synthesis can lead to inhibition of growth or even cell death. Remodeling of Phosphatidylcholine - the Lands' cycle Whatever the mechanism of biosynthesis of phosphatidylcholine in animal tissues, it is apparent that the fatty acid compositions and positional distributions on the glycerol moiety are determined post synthesis by extensive re-modeling involving orchestrated reactions of hydrolysis (phospholipase A2 mainly) to lysophosphatidylcholine, acyl-CoA synthesis and re-acylation by lysophospholipid acyltransferases or transacylases, a series of reactions that is sometimes termed the 'Lands' Cycle' after its discoverer W.E.M. (Bill) Lands. Similar processes occur with all glycerophospholipid classes. The final composition of the lipid is achieved by a mixture of synthesis de novo and the remodeling pathway. There are at least fifteen different groups of enzymes in the phospholipase A2 super-family, which differ in calcium dependence, cellular location and structure. All hydrolyze the sn-2 ester bond of phospholipids specifically, generating a fatty acid and lysophospholipid, both of which have important functions in their own right in addition to their role in the Lands cycle. There is also a phospholipase A1 family of enzymes, which are esterases that are able to cleave the sn-1 ester bond but are less important in this context. Figure $15$ shows Land's cycle The re-acylation step is catalyzed by membrane-bound coenzyme A-dependent lysophosphatidylcholine acyltransferases such as LPCAT3 (also designated ‘MBOAT5’), which is located chiefly within the endoplasmic reticulum, though also in mitochondria and the plasma membrane in organs such as the liver, adipose tissue and pancreas. It maintains systemic lipid homeostasis by regulating lipid absorption and composition in the intestines, the secretion of lipoproteins, and lipogenesis de novo in liver, and is notable in that it incorporates linoleoyl and arachidonoyl chains specifically into lysophosphatidylcholine (as does a related enzyme LPCAT2). There is also a CoA-independent acyltransferase in inflammatory cells that transfers arachidonic acid from phosphatidylcholine to ethanolamine-containing phospholipids. While LPCAT3 prefers 1-acyl lysophosphatidylcholine as an acyl acceptor, LPCAT2 utilizes both 1-acyl and 1-alkyl precursors. LPCAT2 is highly expressed in inflammatory cells such as macrophages and neutrophils, which contain ether-phospholipids, where it contributes to the production of eicosanoid lipid mediators. The highly saturated molecular species of phosphatidylcholine found in lung surfactant are formed from species with a more conventional composition by remodeling by an acyltransferase with a high specificity for palmitoyl-CoA acid (LPCAT1). In other tissues, those species containing high proportions of polyunsaturated fatty acids depend more on synthesis de novo. These and further related enzymes are involved in remodeling of all other phospholipids. Over-expression of the genes for these enzymes is associated with the progression of many different cancers and may be involved in other pathological conditions. Phosphatidylcholine has a central role in glycerolipid metabolism in plants and remodeling occurs for reasons and by mechanisms that are rather different from those in animal cells as described briefly above. For example, there is extensive remodeling as a site of fatty acid desaturation and as the main entry point for acyl groups exported from the plastid into the endoplasmic reticulum. In addition, the remodeling of phosphatidylcholine provides fatty acids for triacylglycerol synthesis in developing seeds and diacylglycerols for the synthesis of thylakoid lipids such as galactosyldiacylglycerols. In Arabidopsis, two lysophosphatidylcholine acyltransferases, LPCAT1 and LPCAT2, are involved in remodeling in developing seeds and leaves, with some preference for position sn-2 using fatty acids exported from the plastid. In some plant species, there is a strong preference for C18‑unsaturated acyl chains over 16:0. However, the lipases that generate lysophosphatidylcholine from phosphatidylcholine for this purpose are not yet known. Some remodeling in plant membranes occurs in response to stress. The yeast Saccharomyces cerevisiae is able to reacylate glycerophosphocholine, generated endogenously by the action of phospholipase B (an enzyme with both phospholipase A1 and A2 activities) on phosphatidylcholine, with acyl-CoA in the microsomal membranes by means of a glycerophosphocholine acyltransferase (Gpc1) to produces lysophosphatidylcholine, which can be converted back to phosphatidylcholine by the lysophospholipid acyltransferase (Ale1) with appreciable changes in the molecular species composition. The process is regulated in coordination with the other main lipid pathways and affects yeast growth. The enzyme Gpc1 does not affect other phospholipids in yeasts. A similar mechanism appears to operate in some plant species. Figure $16$ shows variants of the Land's cycle. Catabolism Phosphatidylcholine (and most other glycerophospholipids) in membranes can be metabolized by lipolytic enzymes, especially phospholipases, some isoforms of which are specific for particular lipid classes in humans. For example, in addition to the action of phospholipase A (discussed above), phospholipase C (six families in mammals differing in expression and subcellular distribution) yields diacylglycerols together with phosphocholine by hydrolyzing glycerophospholipids at the phosphodiester bond, a process that is especially important in relation to phosphoinositide metabolism. The sphingomyelin synthases also have phospholipase C activity (in the absence of ceramide). Phospholipase D generates phosphatidic acid and choline, while phospholipase B removes both fatty acids to yield glycerophosphocholine. Figure $17$ shows the activities of phospholipases on phosphatidyl choline. On catabolism in this way, the lipid components are re-cycled or they may have signaling functions, while much of the choline is re-used for phosphatidylcholine biosynthesis, often after being returned to the liver (the CDP-choline cycle). Some choline is oxidized in the kidney and liver to betaine, which serves as a donor of methyl groups for S-adenosylmethionine production, and some is lost through excretion of phosphatidylcholine in bile. A proportion is used in nervous tissues for production of acetylcholine, a neurotransmitter of importance to learning, memory and sleep. Phosphatidylcholine in the high-density lipoproteins of plasma is taken up by the liver, and perhaps surprisingly a high proportion of this is eventually converted to triacylglycerols via diacylglycerol intermediates. Phosphatidylcholine – Biological Functions Because of the generally cylindrical shape of the molecule, phosphatidylcholine organizes spontaneously into bilayers, so it is ideally suited to serve as the bulk structural element of biological membranes, and as outlined above it is makes up a high proportion of the lipids in the outer leaflet of the plasma membrane. The unsaturated acyl chains are kinked and confer fluidity on the membrane. Such properties are essential to act as a balance to those lipids that do not form bilayers or that form specific micro-domains such as rafts. While phosphatidylcholine does not induce curvature of membranes, as may be required for membrane transport and fusion processes, it can be metabolized to form lipids that do. In contrast, dipalmitoyl phosphatidylcholine is the main surface-active component of human lung surfactant, although in other animals the lung surfactant can be enriched in some combination of short-chain disaturated and monounsaturated species, mainly palmitoylmyristoyl- and palmitoylpalmitoleoyl- in addition to the dipalmitoyl-lipid. This is believed to provide alveolar stability by decreasing the surface tension at the alveolar surface to a very low level during inspiration while preventing alveolar collapse at the end of expiration. Also, the internal lipids of the animal cell nucleus (after the external membrane has been removed) contain a high proportion of disaturated phosphatidylcholine. This is synthesized entirely within the nucleus, unlike phosphatidylinositol for example, and in contrast to other cellular lipids its composition cannot be changed by extreme dietary manipulation; it has been suggested that it may have a role in stabilizing or regulating the structure of the chromatin, as well as being a source of diacylglycerols with a signaling function. A further unique molecular species, 1-oleoyl-2-palmitoyl-phosphatidylcholine, is located specifically at the protrusion tips of neuronal cells and appears to be essential for their function, while 1-palmitoyl-2-arachidonoyl-phosphatidylcholine is important in the regulation of the progression of the cell cycle and cell proliferation, and this is independent of eicosanoid production. Phosphatidylcholine is present bound non-covalently in the crystal structures of a number of membrane proteins, including cytochrome c oxidase and yeast cytochrome bc1. The ADP/ATP carrier protein has two binding sites for phosphatidylcholine, one on each side. In addition, it is known that the enzyme 3‑hydroxybutyrate dehydrogenase must be bound to phosphatidylcholine before it can function optimally. Both the head group and the acyl chains may be involved in the interactions depending on the protein. As noted above, phosphatidylcholine is by far the most abundant phospholipid component in plasma and in all plasma lipoprotein classes. Although it is especially abundant in high density lipoproteins (HDL), it influences strongly the levels of all circulating lipoproteins and especially of the very-low-density lipoproteins (VLDL), which are surrounded by a phospholipid monolayer. Indeed, phosphatidylcholine with polyunsaturated fatty acids in position sn-2 is essential for the assembly and secretion of VLDLs and chylomicrons in liver and the intestines, and it must be synthesized de novo in the latter. Similarly, phosphatidylcholine synthesis is required to stabilize the surface of lipid droplets in tissues where triacylglycerols are stored. Some of the phosphatidylcholine synthesized in the liver is secreted into bile by a specific flippase together with bile acids where it assists in the emulsification of dietary triacylglycerols in the intestinal lumen to facilitate their hydrolysis and uptake. Eventually, it is absorbed across the intestinal brush border membrane after hydrolysis to lysophosphatidylcholine, which may then be involved in the initiation of chylomicron formation in the endoplasmic reticulum of enterocytes by activation of a protein kinase. In addition, phosphatidylcholine produced in enterocytes is secreted into the intestinal lumen and forms part of the hydrophobic mucus layer that protects the intestinal surface. Phosphatidic acid generated from phosphatidylcholine by the action of phospholipase D in plants has key signaling functions. Similarly, phosphatidic acid generated in this way from phosphatidylcholine in animals is involved in the metabolism and signaling function of phosphoinositides by activating phosphatidylinositol 4-phosphate 5-kinase, the main enzyme generating the lipid second messenger phosphatidylinositol-4,5-bisphosphate. The plasmalogen form of phosphatidylcholine may also have a signaling function, as thrombin treatment of endothelial cells activates a selective hydrolysis (phospholipase A2) of molecular species containing arachidonic acid in the sn-2 position, releasing this fatty acid for eicosanoid production, while the diacyl form of phosphatidylcholine may have a related function in signal transduction in other tissues. In addition, phosphatidylcholine may have a role in signaling via the generation of diacylglycerols by phospholipase C, especially in the nucleus. Although the pool of the precursor is so great in many tissues that turnover is not easily measured, the presence of phospholipases C and D that are specific for phosphatidylcholine and are activated by a number of agonists suggests such a function especially in the cell nucleus. Diacylglycerols formed in this way would be much more saturated than those derived from phosphatidylinositol, and would not be expected to be as active in some functions. Phosphatidylcholine is the biosynthetic precursor of sphingomyelin and as such must have some influence on the many metabolic pathways that constitute the sphingomyelin cycle. It is also a precursor for phosphatidic acid, lysophosphatidylcholine and platelet-activating factor, each with important signaling functions, and of phosphatidylserine. Because of the increased demand for membrane constituents, there is enhanced synthesis of phosphatidylcholine in cancer cells and solid tumours; the various biosynthetic and catabolic enzymes are seen as potential targets for the development of new therapeutic agents. Impaired phosphatidylcholine biosynthesis has been observed in a number of pathological conditions in the liver in humans, including the development of non-alcoholic fatty liver disease, liver failure and impaired liver regeneration. Similarly, a deficiency in phosphatidylcholine or an imbalance in the ratio of phosphatidylcholine to phosphatidylethanolamine has negative effects upon insulin sensitivity and glucose homeostasis in skeletal muscle. Plants and bacteria: In addition to its structural role in plant membranes, phosphatidylcholine levels at the shoot apex correlate with flowering time, and this lipid is believed to bind to the Flowering Locus T, a master regulator of flowering. Molecular species containing relatively low levels of α-linolenic acid are involved. Diacylglycerols formed by the action of a family of enzymes of the phospholipase C type on phosphatidylcholine, as opposed to phosphatidylinositol, may be more important in plants and especially during phosphate deprivation for the generation of precursors for galactolipid biosynthesis and perhaps for lipid re-modeling more generally. In prokaryotes, phosphatidylcholine is essential for certain symbiotic and pathogenic microbe-host interactions. For example, in human pathogens such as Brucella abortus and Legionella pneumophila, this lipid is necessary for full virulence, and the same is true for plant pathogens, such as Agrobacterium tumefaciens. Bacteria symbiotic with plants, e.g. the rhizobial bacterium Bradyrhizobium japonicum, require it to establish efficient symbiosis and root nodule formation. Lysophosphatidylcholine Figure $18$ shows the structure of lysophosphatidylcholine Lysophosphatidylcholine (LPC), with one mole of fatty acid per mole of lipid in position sn-1, is found in trace amounts in most animal tissues, although there are relatively high concentrations in plasma (150–500µM). It is produced by hydrolysis of dietary and biliary phosphatidylcholine and is absorbed as such in the intestines, but it is re-esterified before being exported in the lymph. In addition, it is formed in most tissues by hydrolysis of phosphatidylcholine by means of the superfamily of phospholipase A2 enzymes as part of the de-acylation/re-acylation cycle that controls the overall molecular species composition of the latter, as discussed above. Much of the LPC in the plasma of animal species is secreted by hepatocytes into plasma in a complex with albumin, but an appreciable amount is formed in plasma by the action of the enzyme lecithin:cholesterol acyltransferase (LCAT), which is secreted from the liver. This catalyses the transfer of fatty acids from position sn-2 of phosphatidylcholine to free cholesterol in plasma, with formation of cholesterol esters and of course of lysophosphatidylcholine, which consists of a mixture of molecular species with predominately saturated and mono- and dienoic fatty acid constituents. Some LPC is formed by the action of an endothelial lipase on phosphatidylcholine in HDL. At high concentrations, lysophosphatidylcholine can disrupt membranes, while some biological effects at low concentrations may be simply due to its ability to diffuse readily into membranes, altering their curvature and indirectly affecting the properties of membrane proteins. In plasma, it is bound to albumin and lipoproteins so that its effective concentration is reduced to a relatively safe level. Lysophosphatidylcholine is considered to be an important factor in cardiovascular and neurodegenerative diseases. It is usually considered to have pro-inflammatory properties and it is known to be a pathological component of oxidized lipoproteins (LDL) in plasma and of atherosclerotic lesions, when it is generated by over-expression or enhanced activity of phospholipase A2. Its concentration is elevated in joint fluids from patients with rheumatoid arthritis. In addition, it is a major component of platelet-derived microvesicles and activates a specific receptor in platelets that ultimately leads to vascular inflammation, increasing the instability of atherosclerotic plaques. The intracellular acyltransferase LPCAT cannot remove lysophosphatidylcholine directly from plasma or lipoproteins, nor do there appear to be any enzymes with lysophospholipase A1 activity in the circulation. Lysophosphatidylcholine blocks the formation of early hemifusion intermediates required for cell-cell fusions. Lysophosphatidylcholine in insect bites attracts inflammatory cells to the site, enhances parasite invasion, and inhibits the production of nitric oxide, for example in Chagas disease. Elevated levels of 26:0‑lysophosphatidylcholine in blood are reported to be characteristic of Zellweger spectrum disorders (the result of a defect in peroxisome biogenesis). Elevated levels of lysophosphatidylcholine have been identified in cervical cancer and may be diagnostic for the disease. On the other hand, reduced concentrations of lysophosphatidylcholine are observed in some malignant cancers, and it has protective effects in patients undergoing chemotherapy. Stearoyl-lysophosphatidylcholine has an anti-inflammatory role in that it is protective against lethal sepsis in experimental animals by various mechanisms, including stimulation of neutrophils to eliminate invading pathogens through a peroxide-dependent reaction. Similarly, there are reports that lysophosphatidylcholine may have beneficial effects in rheumatoid arthritis and a number of other diseases. However, there are suggestions that some experimental studies in vitro of the activity of lysophosphatidylcholines may be flawed because insufficient levels of carrier proteins were used. A further point for consideration is that lysophosphatidylcholine is the precursor of the key lipid mediator lysophosphatidic acid via the action of the enzyme autotaxin in plasma, and this may be the true source of some of the effects described for the former, especially on cell migration and survival. There is evidence to suggest that lysophosphatidylcholine containing docosahexaenoic (DHA) and eicosapentaenoic (EPA) acids, presumably in position sn-2, in plasma targets more of these fatty acids into the brain, via a specific receptor/transporter at the blood-brain barrier known as the sodium-dependent LPC symporter 1 (MFSD2A), than occurs from the corresponding fatty acids in unesterified form. Hepatic lipase is especially important for generation of these lipids. This finding is now being explored in relation to potential therapeutic applications for neurological diseases, cognitive decline and dementia. Similarly, at the maternal plasma/placental interface, phosphatidylcholine is taken up and hydrolyzed to sn‑2‑lysophosphatidylcholine, presumably by the endothelial lipase, to facilitate transfer of polyunsaturated fatty acids across the basal membrane into the fetal circulation with the aid of the same LPC transporter. Lysophosphatidylcholine has been found to have some functions in cell signaling , and specific receptors (coupled to G proteins) have been identified, i.e., GPR119, GPR40 and GPR55. It activates the specific phospholipase C that releases diacylglycerols and inositol triphosphate with resultant increases in intracellular Ca2+ and activation of protein kinase C. Increased glucose-stimulated insulin secretion has been observed in different cell systems. Lysophosphatidylcholine also activates the mitogen-activated protein kinase in certain cell types, and it promotes demyelination in the nervous system. By interacting with the TRPV4 ion channels of skin keratinocytes, it causes persistent itching. Identification of a highly specific phospholipase A2γ in peroxisomes that is unique in generating sn-2-arachidonoyl lysophosphatidylcholine suggests that this may be of relevance to eicosanoid generation and signaling . For example, there is reportedly an enrichment of 2-arachidonoyl-lysophosphatidylcholine in carotid atheroma plaque from type 2 diabetic patients. In vascular endothelial cells, it induces the important pro-inflammatory mediator cyclooxygenase-2 (COX-2), a key enzyme in prostaglandin synthesis. However, it has beneficial effects on the innate immune system as it is able to activate macrophages and increase their phagocytic activity in the presence of T lymphocytes. As lysophospholipids in general and lysophosphatidylcholine in particular are important signaling molecules within mammalian cells, their levels are closely regulated, mainly by the action of the lysophospholipases A1 and A2 (LYPLA1 and LYPLA2), depending on the position to which the fatty acid is esterified; these are cytosolic serine hydrolases with esterase and thioesterase activity. The glycerophosphocholine produced can enter the Lands' cycle or be further degraded. In relation to plants, amylose-rich starch granules of cereal grains contain lysophosphatidylcholine as virtually the only lipid in the form of inclusion complexes or lining channels in the starch macromolecules. Phosphatidylethanolamine and Related Lipids Phosphatidylethanolamine – Structure and Occurrence Phosphatidylethanolamine or 1,2-diacyl-sn-glycero-3-phosphoethanolamine (once given the trivial name 'cephalin') is usually the second most abundant phospholipid in animal and plant lipids, after phosphatidylcholine, and it is frequently the main lipid component of microbial membranes. It can amount to 20% of liver phospholipids and as much as 45% of those of brain; higher proportions are found in mitochondria than in other organelles. As such, it is obviously a key building block of membrane bilayers, and it is present exclusively in the inner leaflet of the plasma membrane in animal cells, for example. It is a neutral or zwitterionic phospholipid (at least in the pH range 2 to 7), with the structure shown (with one specific molecular species illustrated as an example). Figure $19$ shows the structure of phosphatidylethanolamine. In animal tissues, phosphatidylethanolamine tends to exist in diacyl, alkyl,acyl and alkenyl, acyl forms. As much as 70% of the phosphatidylethanolamine in some cell types (especially inflammatory cells, neurons and tumor cells) can have an ether linkage, but in liver, the plasmalogen form of phosphatidylethanolamine, i.e., with an O‑alk-1’-enyl linkage, accounts for only 0.8% of total phospholipids. Generally, there is a much higher proportion of phosphatidylethanolamine with ether linkages than of phosphatidylcholine. If biosynthesis of the plasmalogen form is inhibited by physiological conditions, it is replaced by the diacyl form so that the overall content of the phospholipid remains constant. In general, animal phosphatidylethanolamine tends to contain higher proportions of arachidonic and docosahexaenoic acids than the other zwitterionic phospholipid, phosphatidylcholine. These polyunsaturated components are concentrated in position sn-2 with saturated fatty acids most abundant in position sn-1, as illustrated for rat liver and chicken egg in Table $3$. In most other species, it would be expected that the structure of the phosphatidylethanolamine in the same metabolically active tissues would exhibit similar features. Table $3$: Positional distribution of fatty acids in phosphatidylethanolamine in animal tissues. Position Fatty acid 14:0 16:0 18:0 18:1 18:2 20:4 22:6 Rat liver [1] sn-1 25 65 8 sn-2 2 11 8 8 10 46 13 Chicken egg [2] sn-1 32 59 7 1 sn-2 1 1 25 22 29 12 1, Wood, R. and Harlow, R.D., Arch. Biochem. Biophys., 131, 495-501 (1969); 2, Holub, B.J. and Kuksis, A. Lipids, 4, 466-472 (1969); The O-alkyl and O-alkenyl chains at the sn-1 position of the analogous ether lipids generally consist of 16:0, 18:0 or 18:1 chains, whereas arachidonic and docosahexaenoic acids are the most abundant components at the sn-2 position. The positional distributions of fatty acids in phosphatidylethanolamine from the leaves of the model plant Arabidopsis thaliana are listed in Table $4$. Here also saturated fatty acids are concentrated in position sn-1, and there is a preponderance of di- and triunsaturated in position sn-2. The pattern is somewhat different for the yeast Lipomyces lipoferus, where the compositions of the two positions are relatively similar. Table $4$: Composition of fatty acids (mol %) in positions sn-1 and sn-2 in the phosphatidylethanolamine from leaves of Arabidopsis thaliana and from Lipoferus lipoferus . Position Fatty acid 16:0 16:1 18:0 18:1 18:2 18:3 A. thaliana [1] sn-1 58 trace 4 5 15 18 sn-2 trace trace trace 2 60 38 L. lipoferus [2] sn-1 29 18 4 28 13 6 sn-2 23 15 3 34 17 6 1, Browse, J., Warwick, N., Somerville, C.R. and Slack, C.R. Biochem. J., 235, 25-31 (1986); DOI. 2, Haley, J.E. and Jack, R.C. Lipids, 9, 679-681 (1974); DOI . Phosphatidylethanolamine – Biosynthesis The two main pathways employed by mammalian cells for the biosynthesis of phosphatidylethanolamine are the CDP-ethanolamine pathway, i.e., one of the general routes to phospholipid biosynthesis de novo in plants and animals, and the phosphatidylserine decarboxylase pathway, which occur in two spatially separated organelles - the endoplasmic reticulum and mitochondria, respectively. Ethanolamine is obtained by decarboxylation of serine in plants, and in animals most must come from dietary sources and requires facilitated transport into cells. A small amount of ethanolamine phosphate comes from catabolism of sphingosine-1-phosphate, and this is essential for the survival of the protozoon Trypanosoma brucei. The initial steps in phosphatidylethanolamine biosynthesis occur in the cytosol with first the phosphorylation of ethanolamine by two specific ethanolamine kinases to produce ethanolamine phosphate; the reverse reaction can occur by means of the enzyme ethanolamine phosphate phosphorylase and this may have a regulatory function in some tissues. The second step is rate-limiting, i.e., reaction of the product with cytidine triphosphate (CTP) to form cytidine diphosphoethanolamine catalyzed by CTP:phosphoethanolamine cytidylyltransferase. In the final step, a membrane-bound enzyme CDP-ethanolamine:diacylglycerol ethanolaminephosphotransferase catalyses the reaction of cytidine diphosphoethanolamine with diacylglycerol to form phosphatidylethanolamine. There are two such enzymes, ethanolamine phosphotransferase 1 (EPT1) in the Golgi and choline/EPT1 (CEPT1) in the endoplasmic reticulum, but EPT1 is more important for the biosynthesis of the plasmalogen form, 1-alkenyl-2-acyl-glycerophosphoethanolamine, and especially molecular species containing polyunsaturated fatty acids, while CEPT1 produced species with shorter-chain fatty acids. The diacylglycerol precursor is formed from phosphatidic acid via the action of the enzyme phosphatidic acid phosphohydrolase. Figure $20$ shows the synthesis of phosphatidyethanolamine in the ER/Golgi. The second major pathway is the conversion (decarboxylation) of phosphatidylserine to form phosphatidylethanolamine in mitochondria. Conservation of the this pathway from bacteria to humans suggests that it has been preserved to optimize mitochondrial performance. Mitochondria are not connected with the rest of the cell's membrane network by classical vesicular routes, but must receive and export small molecules through the nonvesicular transport at zones of close proximity with other organelles at membrane contact sites, such as a specific domain of the endoplasmic reticulum termed the mitochondria-associated membrane (MAM). Lipid transport of phosphatidylserine is then enabled by tethers that bridge two membranes, lipid transfer proteins and recruitment proteins. In this process, the lipid must traverse two aqueous compartments, the cytosol and the mitochondrial intermembrane space, to reach the inner mitochondrial membrane. The phosphatidylserine decarboxylase is located on the external aspect of the mitochondrial inner membrane, and most of the phosphatidylethanolamine in mitochondria is derived from this pathway. While various isoforms of the phosphatidylserine decarboxylation exist in prokaryotes, yeasts and mammals, the main forms designated 'PSD1' are found only in mitochondria and are related structurally. An isoform designated 'PSD2' is located in the endosomal membranes of yeasts, and the phosphatidylethanolamine formed is the preferred substrate for phosphatidylcholine biosynthesis. It is evident that cellular concentrations of phosphatidylethanolamine and phosphatidylserine are closely related and tightly regulated. Figure $21$ shows the synthesis of phosphatidyethanolamine in mitochondria. In prokaryotic cells, such as E. coli, in which phosphatidylethanolamine is the most abundant membrane phospholipid, all of it is derived from phosphatidylserine decarboxylation. In this instance, the enzyme undergoes auto-cleavage for activation and utilizes a pyruvoyl moiety to form a Schiff base intermediate with phosphatidylserine to facilitate decarboxylation. This pathway is also important in mammalian cells and yeasts, although the relative contributions of the two main pathways for phosphatidylethanolamine synthesis in mammalian cells appears to depend on the cell type. In cells in culture, more than 80% of the phosphatidylethanolamine is reported to be derived from the phosphatidylserine decarboxylase pathway, but in hamster heart and rat hepatocytes, only ~5% of phosphatidylethanolamine synthesis comes from this route and most is from the CDP-ethanolamine pathway. In yeasts, 70% of the phosphatidylethanolamine is generated by PSD1. The spatially distinct pools within the cell are functionally distinct, but both are essential to life. For example, disruption of the phosphatidylserine decarboxylase gene causes misshapen mitochondria and has lethal consequences in embryonic mice, although phosphatidylethanolamine synthesis continues for a time in other cellular regions. Similarly, elimination of the endoplasmic reticulum route is embryonically lethal. Three additional minor biosynthetic pathways are known. Phosphatidylethanolamine can be formed by the enzymatic exchange reaction of ethanolamine with phosphatidylserine, or by re-acylation of lysophosphatidylethanolamine. The second of these is associated with the mitochondria-associated membrane where the phosphatidylserine synthase II is located. Finally, the bacterial plant pathogen Xanthomonas campestris is able to synthesize phosphatidylethanolamine by condensation of cytidine diphosphate diacylglycerol with ethanolamine. Ether lipids: It should be noted that all of these pathways for the biosynthesis of diacyl-phosphatidylethanolamine are very different and are separated spatially from that producing alkyl,acyl- and alkenyl,acyl-phosphatidylethanolamines, suggesting that there may be functional differences. In the protozoon T. brucei, for example, it has been demonstrated that the diacyl and ether pools of phosphatidylethanolamine have separate functions and cannot substitute for each other. Lands’ cycle: The various mechanisms produce different pools of phosphatidylethanolamine species, which are often in different cellular compartments and have distinctive compositions. Studies with mammalian cell types in vitro suggest that the CDP-ethanolamine pathway produces molecular species with mono- or di-unsaturated fatty acids on the sn-2 position preferentially, while the phosphatidylserine decarboxylation reaction generates species with polyunsaturated fatty acids on the sn-2 position mainly. However, as with other phospholipids, the final fatty acid composition in animal tissues is attained by a process of remodeling known as the Lands’ cycle . The first step is hydrolysis by a phospholipase A2 to lysophosphatidylethanolamine, followed by reacylation by means of various acyl-CoA:lysophospholipid acyltransferases. The enzymes LPCAT1, 2 and 3, which are involved in phosphatidylcholine biosynthesis, are also active with phosphatidylethanolamine, while LPEAT1 utilizes lysophosphatidylethanolamine mainly and is specific for oleoyl-CoA. Some of these isoforms appear to be confined to particular tissues. There is also a CoA-independent acyltransferase in macrophages that transfers arachidonic acid from phosphatidylcholine to ethanolamine-containing phospholipids. Phosphatidylethanolamine – Biological Function Physical properties: Although phosphatidylethanolamine has sometimes been equated with phosphatidylcholine in biological systems, there are significant differences in the chemistry and physical properties of these lipids, and they have different functions in biochemical processes. Both are key components of membrane bilayers and the ratio of the two may be important to many cellular functions. However, phosphatidylethanolamine has a smaller head group, which gives the lipid a cone shape. On its own, it does not form bilayers but inverted hexagonal phases. With other lipids in a bilayer, it is believed to exert a lateral pressure that modulates membrane curvature and stabilizes membrane proteins in their optimum conformations. It can hydrogen bond to adjacent lipids and to proteins through its polar head group. For example, the phosphate can be stabilized at the binding site by interactions with lysine and arginine side chains, or with hydroxyls from tyrosine side chains. There can also be strong interactions with acyl chains of the phospholipid. In contrast to phosphatidylcholine, phosphatidylethanolamine is concentrated with phosphatidylserine in the inner leaflet of the plasma membrane. On the other hand, it is present in both the inner and outer membranes of mitochondria, but especially the former, where it is present at much higher levels than in other organelles and facilitates membrane fusion and protein movement across membranes in addition to many other essential mitochondrial functions, including oxidative phosphorylation via the electron transport chain. It is an important functional component of membrane contact sites between the endoplasmic reticulum and mitochondria. In eukaryotic cells, especially those of insects, studies suggest that it has a similar function to cholesterol in membranes in that it increases the rigidity of the bilayer to maintain membrane fluidity. In bacterial membranes, it appears that a primary role for phosphatidylethanolamine is simply to dilute the high negative charge density of the anionic phospholipids. Protein Interactions: Membrane proteins amount to 30% of the genome, and they carry out innumerable biochemical functions, including transport, energy production, biosynthesis, signaling and communication. Within a membrane, most integral proteins consist of hydrophobic α-helical trans-membrane domains that zigzag across it and are connected by hydrophilic loops. Of those parts of the proteins outwith the bilayer, positively charged residues are much more abundant on the cytoplasmic side of membrane proteins as compared to the trans side (the positive-inside rule). Phosphatidylethanolamine is believed to have a key function in that it inhibits location of negative amino acids on the cytoplasmic side, supporting the positive-inside rule, and it has an appropriate charge density to balance that of the membrane surface and the protein. However, it can also permit the presence of negatively charged residues on the cytosolic surface in some circumstances in support of protein function. Phosphatidylethanolamine is vital for mitochondrial functionality, as demonstrated by defects in the operation of oxidative phosphorylation in the absence of PSD1, and by the role of the lipid in the regulation of mitochondrial dynamics in general and the biogenesis of proteins in the outer mitochondrial membrane. Synthesis of phosphatidylethanolamine in the inner membrane of mitochondria is critical for the function of the cytochrome bc1 complex (III), where there is a conserved binding site for the lipid in a specific subunit. Phosphatidylethanolamine binds non-covalently to a superfamily of cytosolic proteins with multiple functions termed 'phosphatidylethanolamine-binding proteins'. While four members have been identified in mammals (PEBP1-4), more than 400 members of the family that are conserved during evolution are known from bacteria to higher eukaryotes. These have many different functions including lipid binding, neuronal development, serine protease inhibition, and the regulation of several signaling pathways; in plants, they control shoot growth and flowering. PEBP4 is of particular interest as it is highly expressed in many different cancers and can increase their resistance to therapy. In animal tissues, phosphatidylethanolamine is especially important in the sarcolemmal membranes of the heart during ischemia, it is involved in the secretion of the nascent very-low-density lipoproteins from the liver and it has functions in membrane fusion and fission. It has a functional role in the Ca2+-ATPase in that one molecule of phosphatidylethanolamine is bound in a cavity between two transmembrane helices, acting as a wedge to keep them apart. This is displaced when Ca2+ is bound to the enzyme. Many other important proteins bind non-covalently to phosphatidylethanolamine in a similar way, including rhodopsin and aquaporins. The content of phosphatidylethanolamine in newly secreted VLDL particles and in apoB-containing particles isolated from the lumen of the Golgi is much higher than that in circulating VLDLs, suggesting that this lipid is involved in VLDL assembly and/or secretion. However, it is rapidly and efficiently removed from the VLDL in the circulation. With lipid droplets in cells, phosphatidylethanolamine is believed to promote coalescence of smaller droplets into larger ones. Although the mechanism has yet to be fully elucidated, effects on protein conformation are believed to be behind a finding that phosphatidylethanolamine is the primary factor in the brain required for the propagation and infectivity of mammalian prions. Host defense peptides are antimicrobial agents produced by both prokaryotic and eukaryotic organisms, and many of these have a high affinity for phosphatidylethanolamine as a lipid receptor to modulate their activities. For example, the peptide antibiotics cinnamycin and duramycins from Streptomyces have a hydrophobic pocket that fits around phosphatidylethanolamine such that the binding is stabilized by ionic interaction between the ethanolamine group of the lipid and the carboxylate moiety of the peptide; this complex exhibits activity against other Gram-positive organisms, such as Bacillus species. Much of the evidence for the unique properties of phosphatidylethanolamine comes from studies of the biochemistry of the bacterium E. coli, where this lipid is a major component of the membranes. Gram-negative bacteria have two membrane bilayers in the cell wall, and as much as 90% of the phospholipid in the inner leaflet of the outer membrane is phosphatidylethanolamine, with a high proportion in the cytoplasmic leaflet of the inner membrane also. In particular, phosphatidylethanolamine has a specific involvement in supporting the active transport of lactose by the lactose permease, and other transport systems may require or be stimulated by it. There is evidence that phosphatidylethanolamine acts as a 'chaperone' during the assembly of this and other membrane proteins to guide the folding path for the proteins and to aid in the transition from the cytoplasmic to the membrane environment, although in contrast it inhibits the folding of some multi-helical proteins. In the absence of this lipid, the transport membranes may not have the correct tertiary structure and so will not function correctly. Whether the lipid is required once the protein is correctly assembled is not fully understood in all cases, but it may be needed to orient enzymes correctly in the inner membrane. Some studies suggest that life in this organism can be maintained without phosphatidylethanolamine, but that life processes are inhibited. Autophagy and ferroptosis: A covalent conjugate of phosphatidylethanolamine with a protein designated 'Atg8' is formed by the action of cysteine protease ATG4 (belonging to the caspase family) and various other proteins, and is involved in the process of autophagy (controlled degradation of cellular components) in yeast by promoting the formation of membrane vesicles containing the components to be degraded (phosphatidylinositol 3-phosphate is also essential to this process). Similarly, oxidatively modified phosphatidylethanolamine is an important factor in ferroptosis, a form of apoptosis in which disturbances to iron metabolism lead to an accumulation of hydroperoxides. Precursor of other lipids: Phosphatidylethanolamine is a precursor for the synthesis of N-acyl-phosphatidylethanolamine (see below) and thence of anandamide (N‑arachidonoylethanolamine), and it is the donor of ethanolamine phosphate during the synthesis of the glycosylphosphatidylinositol anchors that attach many signaling proteins to the surface of the plasma membrane. In bacteria, it functions similarly in the biosynthesis of lipid A and other lipopolysaccharides. It is also the substrate for the hepatic enzyme phosphatidylethanolamine N-methyltransferase, which provides about a third of the phosphatidylcholine in the liver. Miscellaneous other functions: Phosphatidylethanolamine is the precursor of an ethanolamine phosphoglycerol moiety bound to two conserved glutamate residues in eukaryotic elongation factor 1A, which is an essential component in protein synthesis. This unique modification appears to be of great importance for the resistance of plants to attack by pathogens. Francisella tularensis bacteria, the cause of tularemia, suppresses host inflammation and the immune response when infecting mouse cells. The effect is due to a distinctive phosphatidylethanolamine species containing 10:0 and 24:0 fatty acids, and the synthetic lipid produces the same effects in vitro in human cells infected with dengue fever virus. It is hoped that this lipid will prove to be a potent anti-inflammatory therapeutic agent. Plants: In the seeds of higher plants, a deficiency of phosphorylethanolamine cytidylyltransferase, a rate-limiting enzyme in the biosynthesis of phosphatidylethanolamine, has profound effects on the viability and maturation of embryos. Lysophosphatidylethanolamine Figure $22$ shows the the structure of lysophosphatidyethanolamine Lysophosphatidylethanolamine (LPE), with one mole of fatty acid per mole of lipid, is found in trace amounts only in animal tissues, other than plasma (10 to 50µM, or ~1% of total serum phospholipids). It is formed by hydrolysis of phosphatidylethanolamine by the enzyme phospholipase A2, as part of a de-acylation/re-acylation cycle that controls its overall molecular species composition as discussed above. A membrane-bound O-acyltransferase (MBOAT2) specific for LPE (and lysophosphatidic acid) has been characterized with a preference for oleoyl-CoA as substrate. There are reports of the involvement of LPE in cellular functions, such as differentiation and migration of certain neuronal cells, but also of various cancer cells. For example, oleoyl-LPE in the brain stimulates neurite outgrowth and protects against glutamate toxicity. In plants, lysophosphatidylethanolamine is a specific inhibitor of phospholipase D, a key enzyme in the degradation of membrane phospholipids during the early stages of plant senescence. Through this action, it retards the senescence of leaves, flowers, and post-harvest fruits. Indeed, it has a number of horticultural applications when applied externally, e.g., to stimulate ripening and extend the shelf-life of fruit, delay senescence, and increase the vase life of cut flowers. In bacteria, lysophosphatidylethanolamine displays chaperone-like properties, promoting the functional folding of citrate synthase and other enzymes. Some biological properties have been reported in animal tissues in vitro, but a specific receptor has yet to be identified. Lysophospholipids and especially lysophosphatidylethanolamines are produced in the envelope membranes of bacteria by many different endogenous and exogenous factors and must be transported back into the bacterial cell by flippases for conversion back to the diacyl forms by the action of a peripheral enzyme, acyl-ACP synthetase/LPL acyltransferase. Lysophosphatidylethanolamines produced by certain bacteria act synergistically with the sulfonolipid rosette-inducing factors (RIFs) to maximize the activity of the latter to induce choanoflagellates to move from a unicellular to a multicellular state. N-Acyl Phosphatidylethanolamine In N-Acyl phosphatidylethanolamine, the free amino group of phosphatidylethanolamine is acylated by a further fatty acid. This lipid has been detected in rather small amounts in several animal tissues (~0.01%), but especially brain, nervous tissues, and the epidermis, when the N-acyl chain is often palmitic or stearic acid (human plasma: N16:0-PE (40%), N18:1-PE (23.3%), N18:0-PE (19%), N18:2-PE (16.6%) and N20:4-PE (1.4%)). Under conditions of degenerative stress, it can accumulate in significant amounts, for example as the result of ischemic injury, infarction, or cancer. It is present in plasma after feeding a high-fat diet to rats, and then it can cross into the brain where it accumulates in the hypothalamus. Figure $23$ shows the structures of N-Acyl Phosphatidylethanolamine. In animals, N-Acyl phosphatidylethanolamine is of particular importance as the precursor of anandamide, and of other biologically important ethanolamides (e.g., N-oleoylethanolamide) in brain and other tissues, but especially the intestines. In brief, it is formed biosynthetically by the action of a transferase (cytosolic phospholipase A2ε) exchanging a fatty acid from the sn-1 position of a phospholipid (probably phosphatidylcholine) to the primary amine group of phosphatidylethanolamine (without a hydrolytic step). Both diacyl- and alkenylacyl-species of phosphatidylethanolamine can serve as acceptors. In addition, some transfer can also occur from phosphatidylethanolamine per se by an intramolecular reaction. However, it should be noted that some N-acyl phosphatidylethanolamine can be formed artefactually as a result of faulty extraction procedures during analysis. In plants, N-acyl phosphatidylethanolamine is a common constituent of cereal grains (e.g., wheat, barley and oats) and of some other seeds (1.9% of the phospholipids of cotton seeds, but 10-12% of oats). In other plant tissues, it is detected most often under conditions of physiological stress. In contrast to animals, synthesis involves direct acylation of phosphatidylethanolamine with a free fatty acid, catalyzed by a membrane-bound transferase in a reverse serine-hydrolase catalytic mechanism. Activation of N-acyl phosphatidylethanolamine metabolism in plants with the release of N-acylethanolamines and phosphatidic acid formation seems to be associated with cellular stresses, but research is at an early stage. However, both N-acyl lipid classes have been implicated in such physiological processes as the elongation of main and lateral roots, regulation of seed germination, seedling growth, and defense from attacks by pathogens. N-Acyl phosphatidylethanolamine has been found in a number of microbial species, while N-acetyl phosphatidylethanolamine was detected in a filamentous fungus, Absidia corymbifera, where it comprised 6% of the total membrane lipids. It was accompanied by an even more unusual lipid 1,2‑diacyl-sn-glycero-3-phospho(N-ethoxycarbonyl)-ethanolamine. Phosphatidylserine and Related Lipids Phosphatidylserine or 1,2-diacyl-sn-glycero-3-phospho-L-serine is an important anionic phospholipid, which brings essential physical properties to membranes in both eukaryotes and prokaryotes. Independently of this, it has many biological functions in cells, including effects on blood coagulation and apoptosis, and it is the biosynthetic precursor for phosphatidylethanolamine in prokaryotes and in eukaryote mitochondria. Its metabolite lysophosphatidylserine has signaling functions and operates through specific receptors. Also, there is increasing interest in a structurally related lipid phosphatidylthreonine, and other phospholipids linked to amino acids. Phosphatidylserine - Structure and Occurrence Although phosphatidylserine is distributed widely among animals, plants, and microorganisms, it is usually less than 10% of the total phospholipids, the greatest concentration being in myelin from brain tissue. For example, mouse brain and liver contain 14 and 3% phosphatidylserine, respectively. However, it may comprise 10 to 20 mol% of the total phospholipids in the plasma membrane, where under normal conditions it is concentrated in the inner leaflet, and in the endoplasmic reticulum of cells. In the yeast Saccharomyces cerevisiae, it is a minor component of most cellular organelles other than the plasma membrane, where surprisingly it can amount to more than 30% of the total lipids. In most bacteria, it is a minor membrane constituent, although it is important as an intermediate in phosphatidylethanolamine biosynthesis. The 1‑octadecanoyl-2-docosahexaenoyl molecular species, which is especially important in brain tissue, is illustrated here. Figure $24$ shows the structure of phosphatidylserine Phosphatidylserine is an acidic (anionic) phospholipid with three ionizable groups, i.e., the phosphate moiety, the amino group, and the carboxyl function. As with other acidic lipids, it exists in nature in salt form, but it has a high propensity to chelate to calcium via the charged oxygen atoms of both the carboxyl and phosphate moieties, modifying the conformation of the polar head group. This interaction may be of considerable relevance to the biological function of phosphatidylserine, especially during bone formation for example. In animal cells, the fatty acid composition of phosphatidylserine varies from tissue to tissue, but it does not appear to resemble the precursor phospholipids, either because of selective utilization of specific molecular species for biosynthesis or because of the re-modeling of the lipid via deacylation-reacylation reactions with lysophosphatidylserine as an intermediate (see below). In human plasma, 1-stearoyl-2-oleoyl and 1-stearoyl-2-arachidonoyl species predominate, but in the brain (especially grey matter), retina and many other tissues 1-stearoyl-2-docosahexaenoyl species are especially abundant and appear to be essential for normal functioning of the nervous system. Indeed, the ratio of n-3 to n-6 fatty acids in brain phosphatidylserine is much higher than in most other lipids. The positional distribution of fatty acids in phosphatidylserine from rat liver and bovine brain are listed in Table $5$. As with most phospholipids, saturated fatty acids are concentrated in position sn-1 and polyunsaturated in position sn-2. Table $5$: Positional distribution of fatty acids in phosphatidylserine from rat liver and bovine brain Position Fatty acid 16:0 18:0 18:1 18:2 20:4 22:6 Rat liver [1] sn-1 5 93 1 sn-2 6 29 8 4 32 19 Bovine brain [2] sn-1 3 81 13 sn-2 2 1 25 trace 1 60 1. Wood, R. and Harlow, R.D. Arch. Biochem. Biophys., 135, 272-281 (1969); DOI. 2. Yabuuchi, H. and O'Brien, J.S. J. Lipid Res., 9, 65-67 (1968); DOI. In leaves of Arabidopsis thaliana, used as a 'model' plant in many studies, the fatty acid composition of phosphatidylserine resembles that of phosphatidylethanolamine. There is an intriguing report that the chain lengths of the acyl groups increase with age and stress in phosphatidylserine quite specifically, and 22:0 and 24:0 fatty acids have been reported to occur in this lipid in the plasma membrane of some plant species. In marked contrast to phosphatidylethanolamine, phosphatidylserines with ether-linked moieties (alkyl and alkenyl) are not common in animal tissues, although they are reported to be relatively abundant in human retina and macrophages (they were first found in rat lung). As a generality, the concentration of phosphatidylserine is highest in plasma membranes and endosomes but is very low in mitochondria. As it is located entirely on the inner monolayer surface of the plasma membrane (and of other cellular membranes) and it is the most abundant anionic phospholipid, it may make the largest contribution to interfacial effects in membranes involving non-specific electrostatic interactions. This normal distribution is disturbed during platelet activation and cellular apoptosis. N-Acylphosphatidylserine is reportedly present in the frontal cortex of patients with schizophrenia, as a minor component of the lipids of sheep erythrocytes, bovine brain, and the central nervous system of freshwater fish, and Bryozoans amongst others. The N-arachidonoyl form may be the precursor of the endocannabinoid N-arachidonoylserine. Biosynthesis of Phosphatidylserine L-Serine is a non-essential amino acid that is actively synthesized by most organisms. In animals, it is produced in nearly all cell types, although in brain it is synthesized by astrocytes but not by neurons, which must be supplied with this amino acid for the biosynthesis of phosphatidylserine (and of sphingoid bases). In animal tissues, phosphatidylserine is synthesized solely by calcium-dependent base-exchange reactions in which the polar head-group of an existing phospholipid is exchanged for L-serine. There are two routes involving distinct enzymes (PS synthase I and II) with 30% homology and several membrane-spanning domains that can utilize different substrates. Phosphatidylserine is synthesized by both enzymes on the cytosolic face of the endoplasmic reticulum (ER) of the cell, but mainly in a specific domain of this termed the mitochondria-associated membrane ('MAM'), because it is tethered transiently to the mitochondrial outer membrane, presumably by a protein bridge. In yeast, a complex of integrated proteins ('ERMES') has been characterized with a similar function. The reaction involves the exchange of L-serine with either phosphatidylcholine or phosphatidylethanolamine, catalyzed by PS synthase I (although it was long thought that only phosphatidylcholine was a substrate for this enzyme), while PS synthase II catalyzes a similar exchange with diacyl-phosphatidylethanolamine and its the plasmalogen form. Both enzymes are subject to feedback regulation by their product phosphatidylserine, thereby maintaining the correct amounts of this lipid. Figure $25$ shows the synthesis and metabolism of phosphatidylserine in animals. Phosphatidylserine synthase I is expressed in all mouse tissues, but especially the kidney, liver, and brain, while phosphatidylserine synthase II is most active in the brain and testis and much less so in other tissues. The latter enzyme has a high specificity for molecular species containing docosahexaenoic acid. It is not known why such a complex series of coupled reactions is necessary, or why there should be two enzymes, but one virtue is that the free ethanolamine and choline formed are rapidly re-utilized for phospholipid synthesis. Thus, both phosphatidylserine and phosphatidylethanolamine are produced without a reduction in the amount of phosphatidylcholine. Elimination of both enzymes is embryonically lethal in knock-out mice, but each of them can be knocked out separately and the mice survive, even though they have substantially reduced levels of phosphatidylserine and phosphatidylethanolamine. As with other phospholipids, the final fatty acid composition in animal tissues is attained by a process of remodeling known as the Lands’ cycle. The first step is hydrolysis by a phospholipase A2 to lysophosphatidylserine, followed by the reacylation by various acyl-CoA:lysophospholipid acyltransferases. One membrane-bound O-acyltransferase (LPCAT4 or MBOAT2) with a preference for oleoyl-CoA has been characterized, while a second (LPCAT3 or MBOAT5) incorporates linoleoyl and arachidonoyl chains (and also utilizes lysophosphatidylcholine). Following synthesis, phosphatidylserine molecules can diffuse laterally in a concentration-dependent manner to different regions of the membrane to fulfill their physiological functions. In humans, cytosolic transport proteins transfer phosphatidylserine and other acidic phospholipids between membranes, and this can also occur by a vesicular transport mechanism. Some of the newly synthesized phosphatidylserine is transferred to the plasma membrane, while a proportion is transported to the mitochondria, probably again via transient membrane contact (MAM), where it is decarboxylated to produce phosphatidylethanolamine by a specific decarboxylase in the inner mitochondrial membrane. In yeast, there is a preference for molecular species containing two monoenoic fatty acids for transport and metabolism; this process occurs also at the Golgi/endosome membranes. All the phosphatidylethanolamine in mitochondria is formed in this way, but some can return to the endoplasmic reticulum where it may be converted back to phosphatidylserine by the action of the PS synthases. Mitochondrial production of phosphatidylethanolamine from phosphatidylserine is not fully complemented by the CDP-ethanolamine pathway, as mice lacking the enzyme do not survive for long. Evidently, cellular concentrations of these two lipids are intimately related and tightly regulated. Figure $26$ shows the mitochondrial metabolism of phosphatidylserine Much of the phosphatidylserine thus formed is decarboxylated to phosphatidylethanolamine, and this may be the major route to the latter in bacteria. As phosphatidylcholine in yeast is produced via methylation of phosphatidylethanolamine, phosphatidylserine is the primary precursor for this phospholipid in these organisms. Bacteria and plants: In bacteria and other prokaryotic organisms and in yeast, phosphatidylserine is synthesized by a mechanism comparable to that of most other phospholipids, i.e., by reaction of L-serine with CDP-diacylglycerol, and depends on Mg2+ or Mn2+. Phosphatidylserine synthases belong to two different families: type I (non-integral membrane form) in the phospholipase D-like family as in E. coli, and type II (integral membrane form) in the CDP-alcohol phosphotransferase family as in Bacillus sp. and the yeast S. cerevisiae, although the latter shows no homology with the bacterial enzymes. In many plants, including in the model plant Arabidopsis, much of the phosphatidylserine is produced by a calcium-dependent base-exchange reaction in which the head group of an existing phospholipid is exchanged for L-serine in the luminal leaflet of the endoplasmic reticulum (i.e., mechanistically similar to PS synthase I). It is transferred to the cytoplasmic membrane leaflet by flippases and thence to the post-Golgi compartments before eventually accumulating at the plasma membrane. However, some vesicular transport may occur or there may be direct transfer at membrane contact sites. A CDP-diacylglycerol (prokaryotic-like) biosynthetic pathway exists in some species, e.g. wheat. Let's explore the mechanism of the Methanocaldococcus jannaschii phosphatidylserine synthase (MjPSS). The organism is a hyperthermophilic methanogen. Figure $27$ shows substrate binding by MjPSS. The large binding pocket for CDP-DAG in MjPSS extends from the hydrophobic membrane core to the active site near the cytoplasmic surface in the center of the dimer. In the closed structures (left), both CDP-DAG alkyl chains adopt similar conformations within the binding pocket, whereas the positions of helix 7 and 8 in the open structures (right) allow one alkyl chain to reach the membrane via a different path. Serine molecules are only found in open structures (right). In one closed conformation, there is a citrate near the substrate-binding site, whereas in the other closed structures this position is empty. A chain of three chloride ions extends parallel to the dimer interface from the active site to the cytoplasmic interface with the N-terminal helix hH from the other protomer. Figure $28$ shows the reaction cycle of MjPSS during the synthesis of PS from CDP-DAG and serine in the presence of Mg2+ and Ca2+. The CDPDAG binding site in MjPSS of is formed and stabilized by the divalent cations Mg2+ and Ca2+ (a). In the absence of CDP-DAG, Ca2+ most likely is coordinated by water molecules, as shown in a, or by residues in nearby loops that would be flexible in the absence of CDP-DAG. The binding of CDP-DAG (b) is driven by the coordination of Ca2+ by the negatively charged phosphates. Serine binds to the binding pocket after CDP-DAG (c). For the nucleophilic attack, the serine molecule is positioned with its hydroxyl group near the β-phosphate of CDP-DAG. The serine molecule is activated by deprotonation, attacks the β-phosphate, and forms the penta-coordinated transition state (d). The proton of serine is probably removed by one of the water molecules located in the interaction network of Asp66, Arg101, and the nearby chloride ions. Hydrolysis of the CDP-DAG/serine complex from the transition state leads to the complex of MjPSS with the products PS and CMP (e). The next cycle starts after release of the products and binding of CDP-DAG. Structural data are available for the state with bound CDP-DAG (b), CDP-DAG, and serine (c), and for the transition state of the CDP-DAG/serine complex (d). Figure $29$ shows an interactive iCn3D model of the Methanocaldococcus jannaschii phosphatidyl serine synthase (PSS) in the open state with bound CDP-DAG and serine (7B1L). The enzyme is also named CDP-diacylglycerol--serine O-phosphatidyltransferase. Figure $29$: Bacterial phosphatidyl serine synthase (PSS) in the open state with bound CDP-DAG and serine (7B1L) (Note the actual PDB file title names state that this is the closed state which it is not.) Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...B3Rn3gVN4c4ge6 The A chain of the homodimer is shown in gray and the B in plum. The active site residues in the A chain in the above mechanism are shown in stick, CPK colors, and labeled. Hover over the large ligand (58A) in the gray subunit. Two free serines are shown near it but only one is probably the substrate. Phosphatidylserine – Biological Function Membrane location: Phosphatidylserine modulates membrane charge locally, enabling the recruitment of soluble cations and proteins, and so it contributes to the organization of processes within cell membranes. Its distribution within membranes is tightly controlled as it facilitates signaling within the various cellular compartments. Thus, it undergoes a transition from the lumenal leaflet of the endoplasmic reticulum to the cytosolic leaflet in the trans-Golgi network, probably by the activity of flippases and scramblases in the Golgi, and it is highly enriched on the inner leaflet of the plasma membrane. Transport to the plasma membrane against a concentration gradient is aided in part by proteins designated 'ORP5' and 'ORP8' in humans (Osh 6 and Osh7 in yeast) with a 'PH' binding domain for phosphatidylinositol 4,5-bisphosphate and an 'ORD' domain for phosphatidylserine. At a membrane contact site between the endoplasmic reticulum and plasma membrane, phosphatidylserine is exchanged for phosphatidylinositol 4-phosphate. Such transfer requires an input of energy, which can be supplied in the form of ATP or by phosphoinositides Although it does not take part in membrane raft formation, phosphatidylserine is present in caveolae, where it is believed to interact with caveolin-1. It is also present in appreciable amounts in the endosomal compartment. The asymmetric structure of the plasma membrane with high concentrations of anionic lipids such as phosphatidylserine in the cytosolic leaflet with zwitterionic lipids in the extracellular leaflet generates two surfaces with greatly different electrostatic potentials that influence the association of proteins with the membrane surface and the activities of integral membrane proteins. This distribution is maintained and can be altered, after specific activation, by various flippases (transfer back into the cytoplasmic leaflet), floppases (transfer out of the cytoplasmic leaflet), and scramblases (bidirectional transfer), including ATP-dependent translocases selective for phosphatidylserine. Phosphatidylserine is highly enriched in the cytosolic leaflet of the membranes of recycling endosomes, which replenish the lipids and proteins of the plasma membrane, and it is essential for their function. Enzyme activation: In addition to its function as a component of cellular membranes and as a precursor for other phospholipids, phosphatidylserine is an essential cofactor that binds to and activates a large number of proteins, especially those with signaling activities. The negative charge on the lipid facilitates the binding to proteins through electrostatic interactions or Ca2+ bridges. For example, the presence of appreciable amounts of phosphatidylserine on the cytosolic leaflet of endosomes and lysosomes enables these compartments to dock with proteins that possess specific phosphatidylserine-binding domains including several important signaling and fusogenic effectors. The cytoskeletal protein spectrin binds to phosphatidylserine in this way, and it is also required by enzymes such as the neutral sphingomyelinase and the Na+/K+ ATPase, where the 18:0/18:1 molecular species is especially important. It is believed that the fatty acyl components of this species in the inner leaflet of the plasma membrane (and potentially other intracellular membranes) may interact (interdigitation or "hand-shake") with the very-long chains of sphingolipids in the outer leaflet in raft microdomains, resulting in a high local concentration of the anionic phospholipid and an accumulation of negative surface charge to which specific poly-cationic proteins in the membranes can bind. This may then enable the transfer of signals across the membrane to the cytosol. Similarly, phosphatidylserine participates directly in key signaling pathways in the brain by binding to the cytosolic proteins involved in neuronal signaling and thereby activating them. At least three major pathways are affected, including those involving phosphatidylinositol 3-kinase and protein kinase C. For example, most enzymes of the protein kinase C family contain a 'C2' calcium-dependent cysteine-rich region that recognizes phosphatidylserine, and in coordination with the 'C1' domain that binds to diacylglycerols, is essential for activating and locating them to the plasma membrane of appropriately stimulated cells. Phosphatidylserine is not involved in cell signaling through the formation of metabolites, as is the case with phosphatidylinositol. Blood coagulation: Phosphatidylserine is an important element of the blood coagulation process in platelets, where it is transported from the inner to the outer surface of the plasma membrane in platelets activated by exposure to fibrin-binding receptors, for example. Here, the exposed phosphatidylserine enhances the activation of prothrombin to thrombin (the key molecule in the blood clotting cascade) by triggering a cascade of reactions and providing the negatively charged platform that enables calcium ions to form bridges with γ-carboxyglutamic acid-containing domains on the coagulation factors. Membrane vesicles with phosphatidylserine exposed on the surface can also be released from platelets and promote the coagulation process. Apolipoprotein A-1 in high-density lipoproteins has a controlling function in that it neutralizes these procoagulant properties by arranging the phospholipid in surface areas that are too small to accommodate the prothrombinase complex. Blood coagulation is beneficial when it prevents the loss of blood from the circulatory system, but it is detrimental when it causes thrombosis, and the action of phosphatidylserine is essential to the regulation of the process. Apoptosis: In addition in response to particular calcium-dependent stimuli, phosphatidylserine is known to have an important role in the regulation of apoptosis or programmed cell death, the natural process by which aged or damaged cells are removed from tissues before they can exert harmful effects. When cells are damaged, a mechanism is initiated in which the normal distribution of this lipid on the inner leaflet of the plasma membrane bilayer is disrupted by stimulation of scramblases, which are ATP-independent and can move the lipid across the membrane to the outer leaflet. This occurs together with the inhibition of aminophospholipid translocases, which return the lipid to the inner side of the membrane. In erythrocytes, phosphatidylserine is located in the inner leaflet of the membrane bilayer under low Ca2+ conditions when a phospholipid scramblase is suppressed by membrane cholesterol, but it is exposed to the outer leaflet under elevated Ca2+ concentrations which activate the scramblase. After the collapse of this asymmetry and transfer of phosphatidylserine to the outer leaflet of an effete cell, it is believed that it is recognized by a cohort of receptors, either directly or indirectly, through bridging ligands on the surface of macrophages and related scavenger cells. These activate a family of cysteine-dependent aspartate-specific proteases, the caspases, and other enzymes to facilitate the engulfment of the apoptotic cells and their potentially toxic or immunogenic contents in a non-inflammatory manner. It is noteworthy that the transition from a pro-inflammatory to an anti-inflammatory state is defined by phagocytosis of neutrophils by macrophages via this phosphatidylserine-dependent process. During apoptosis, the generation of reactive oxygen species occurs, mainly hydrogen peroxide, which together with the enzyme cytochrome c brings about rapid oxidation of the fatty acids in phosphatidylserine before this lipid is externalized. Indeed, it is now apparent that molecular species of phosphatidylserine with an oxidatively truncated sn-2 acyl group that incorporates terminal γ-hydroxy(or oxo)-α,β-unsaturated acyl moieties are especially potent signals for scavenger receptors in macrophages as a prerequisite for engulfment of apoptotic cells. This has been described as "a dominant and evolutionarily conserved immunosuppressive signal that promotes tolerance and prevents local and systemic immune activation" or more succinctly as an "eat-me signal" (externalized phosphatidylinositol 3,4,5-trisphosphate (PI(3,4,5)P) may have a similar function). The binding of phosphatidylserine to specific proteins, such as apolipoprotein H (β2-glycoprotein 1), enhances the recognition and clearance. This process is essential for the development of the lung and brain, and it is also relevant to clinical situations where apoptosis plays an important part, such as cancer, chronic autoimmunity, and infections. For example, phosphatidylserine is a necessary component of the TAM family of receptor tyrosine kinases and the receptor-ligand complex of particular importance in cancer cells, where phosphatidylserine-TAM signaling regulates many aspects of inflammation and immune resolution and is seen as a target for therapeutic intervention. Exposure of phosphatidylserine is increased substantially on the surface of tumor cells or tumor cell-derived microvesicles, which have innate immunosuppressive properties and facilitate tumor growth and metastasis. Targeting phosphatidylserine is considered to be a promising strategy in cancer immunotherapy. In relation to atherosclerosis, phosphatidylserine is believed to have anti-inflammatory and protective effects as a component of the high-density lipoproteins, probably mediated by the apoptosis mechanism. In contrast, as this mechanism is important for the turnover of erythrocytes, it is relevant to thrombus formation and the stabilization of blood clots. The innate immunosuppressive effect of externalized phosphatidylserine has been hijacked by numerous viruses and bacteria to facilitate infection. A similar apoptotic mechanism operates in retinal pigment epithelial cells to remove the large amounts of photoreceptor cell debris that are generated continuously. In addition, appreciable amounts of phosphatidylserine are translocated by an analogous mechanism to the surface of T lymphocytes that express low levels of the trans-membrane enzyme tyrosine phosphatase. This change in distribution acts then as a signaling mechanism to modulate the activities of several membrane proteins. The anti-coagulant protein annexin V binds with high specificity to phosphatidylserine and is used as a probe to detect apoptotic cells. It is noteworthy that phosphatidylserine is a major component of the membranes of microvesicles in animal cells, and translocation to the outer leaflet upon cellular activation is essential for their biogenesis. In addition, exposure of phosphatidylserine on the cell surface is reported to be a factor in non-apoptotic forms of regulated inflammatory cell death, such as necroptosis. Role in infections: Unfortunately, viruses such as Ebola and HIV viruses can hijack this apoptosis machinery by incorporating phosphatidylserine into their viral envelopes so conning cells into engulfing them; the viral glycoprotein/cellular receptor complex may then facilitate the entry of foreign organisms into other cells. Similarly, parasites ingested in this manner, including Leishmania and Trypanosoma species, utilize host phosphatidylserine to establish infections and facilitate disease progression as they do not then elicit the production of proinflammatory cytokines. This mechanism has been termed 'apoptotic mimicry' and is critical for the survival of parasites within the macrophage. Other activities: Phosphatidylserine is required for the transmembrane movement of excess cholesterol, derived initially from the lysosomal degradation of low-density lipoproteins, from the plasma membrane to the endoplasmic reticulum thereby maintaining membrane integrity and ensuring cell survival. It is therefore an important element in cholesterol homeostasis. The mechanism is believed to involve proteins known as GRAMD1s embedded in the endoplasmic reticulum membrane at sites in contact with the plasma membrane. These have two functional domains: the StART-like domain that binds cholesterol and the GRAM domain that binds anionic lipids, such as phosphatidylserine, and so forms a link between the two membranes that enables the transfer of cholesterol. A further unusual function of phosphatidylserine is that it is a key component of the lipid-calcium-phosphate complexes that act as nucleation centers for hydroxyapatite formation and initiate mineral deposition during the formation of bone. It has been established that phosphatidylserine and inorganic phosphate must be present, before calcium ions are introduced, when the high affinity of phosphatidylserine for calcium ions becomes important. Nucleation is facilitated by the protein annexin V. Similarly, during bone repair and maintenance, the fusion of osteoclasts requires the non-apoptotic exposure of phosphatidylserine at the surface of fusion-committed cells with the aid of a transmembrane protein (DC-STAMP) expressed in dendrocytes. This activity is relevant to cardiovascular disease and in particular to the phenomenon of "hardening of the arteries," where atherosclerotic plaques can undergo mineralization with the deposition of hydroxyapatite. Among many other functions of phosphatidylserine, it is believed to be an essential surface membrane component for the fusion of cell types other than osteoclasts, including during the formation of fibers in muscle cells, and the fusion of macrophages into inflammatory giant cells and myoblasts into myotubes. Such cell fusions require the non-apoptotic exposure of phosphatidylserine at the surface of fusing cells, where it interacts with phosphatidylserine-recognizing proteins to regulate the time and place of cell fusion. Phosphatidylserine provides stable membrane domains in spermatozoa that are essential for fertilization, and it is also an essential component of the plasma membrane microdomains known as caveolae, where it is required both for their formation and stability possibly through specific binding to the cavin proteins. The high concentrations of docosahexaenoic acid (DHA) in the brain and retinal phosphatidylserine are certainly important for the development and function of these tissues. Accumulation of phosphatidylserine in neuronal membranes is promoted by DHA, and this is important for the maintenance of neuronal survival. Phosphatidylserine may also be a reservoir of DHA for protectin formation in neuronal tissue. On the other hand, the Food and Drug Administration in the USA considers that there is little scientific evidence to support claims that dietary supplements of phosphatidylserine reduce the risk of dementia or cognitive dysfunction in the elderly, and other nutritional claims appear to be dubious also. Antibodies to phosphatidylserine are formed in some disease states, including thrombosis and recurrent spontaneous pregnancy loss. The rare genetic disease Lenz-Majewski syndrome is caused by a mutation in the phosphatidylserine synthase I gene that greatly increases the activity of the enzyme while preventing feedback inhibition, and abnormal metabolism of phosphatidylserine has been implicated in other diseases. In yeasts such as Candida albicans, phosphatidylserine and the enzyme phosphatidylserine decarboxylase, which generates phosphatidylethanolamine, are both essential for the virulence of the organism towards a host species. Lysophosphatidylserine Figure $30$ shows the structure of lysophosphatidylserine Lysophosphatidylserine, i.e., with a fatty acid in one position only, is known to be a mediator of a number of biological processes, especially in the context of the immune system in animal tissues. It has been found in the thymus, peripheral lymphoid tissues, central nervous system, and colon, but is barely detectable in plasma. Deacylation of the diacyl lipid by phospholipases is the primary source. For example, a secreted isoform that is phosphatidylserine-specific (PLA1A) removes the sn-1 acyl group of phosphatidylserine to generate sn‑2‑lysophosphatidylserine containing unsaturated fatty acids, and this is upregulated greatly by various inflammatory stimuli. This extracellular enzyme utilizes phosphatidylserine exposed on the cell membrane as a substrate, although other phospholipases may operate intracellularly and produce sn‑1‑lysophosphatidylserine. In addition, platelets in some species (not significantly in humans) secrete a phospholipase A2 group IIA (ABHD16A), which generates saturated sn‑1‑lysophosphatidylserine (and other lysophospholipids). Lysophosphatidylserine has been detected after injury to animal tissues (tumor growth, graft rejection, burns), and it may have a similar function to lysophosphatidic acid in cell signaling, for example in regulating calcium flux and stimulating immune cells through G protein-coupled receptors of which three (GPR34, P2Y10 and GPR174, LPS1-3) have been detected in mice and humans. For example, GPR174 mediates the suppression of T-cell proliferation induced in vitro by lysophosphatidylserine. When cells are damaged, lysophosphatidylserine can be generated by a reaction dependent on the activation of the NADPH oxidase. It can diffuse and transmit the information to other cells, especially mast cells, and it is produced to enhance the clearance of activated and dying neutrophils. It thus has a role in the resolution of inflammation. One specific molecular species, i.e., 1‑(11Z‑eicosenoyl)-glycero-3-phosphoserine, is reported to be a true agonist of the Toll-like receptor 2/6 heterodimer of importance to the immune response to pathogens; both its polar head group and the length of the acyl chain are required for this activity. On the other hand, sn-2-lysophosphatidylserine has proinflammatory reactions in that it augments mast cell degranulation and mast cell-dependent anaphylactic shock; most other lysophospholipids have no such activity. Deregulated lysophosphatidylserine metabolism has been linked to certain cancers, cardio-metabolic disorders, night blindness, and the human genetic neurological disorder PHARC. High serum levels of PLA1A are associated with such autoimmune disorders as Graves' disease and systemic lupus erythematosus, and there is increased expression of the enzyme in metastatic melanomas. It is necessary for the assembly of the hepatitis C virus, and it can play a role in the antivirus innate immune response. In Schistosome infections, lysophosphatidylserine from the parasite is believed to be a key activator molecule in the host. Negatively charged lysophosphatidylserine species tend to organize in non-bilayer structures and are believed to facilitate the folding of certain membrane proteins in situ better than bilayer-forming lipids. Phosphatidylinositol and Related Phosphoinositides Although it had long been recognized that phosphatidylinositol or 1,2-diacyl-sn-glycero-3-phospho-(1'-myo-inositol) was a key membrane constituent, it was initially something of a surprise when the manifold biological activities of this lipid, and then of the derived phosphatidylinositol phosphates and their hydrolysis products, were discovered in animals, plants, and microorganisms. Many years after the initial discoveries in the 1950s, these lipids continue to be a major focus for research efforts around the world with considerable relevance to human health. Phosphatidylinositol and its various metabolites and relevant enzymes can be located and function within different membrane regions in cells, and they form part of what have been termed phosphoinositide and phosphatidylinositol cycles, their versatility stemming from the inositol head group, a six-carbon the hexahydroxy ring, which can be reversibly phosphorylated on the 3, 4 and 5 positions. In addition to their structural role in membranes, these lipids are intimately involved in innumerable aspects of membrane trafficking and signaling in eukaryotic cells, functions that are essential to cell growth and metabolism. Only a brief overview of such a highly complex topic is possible here. Glycosyl-phosphatidylinositol (GPI) is a related lipid that serves as an anchor for proteins. Phosphatidylinositol Structure and Occurrence: Phosphatidylinositol is an important lipid, both as a membrane constituent and as a participant in essential metabolic processes in all plants and animals, both directly and via a number of its metabolites. It is an acidic (anionic) phospholipid that in essence consists of a phosphatidic acid backbone, linked via the phosphate group to inositol (hexahydroxycyclohexane). In most organisms, the stereochemical form of the last is myo-D-inositol (with one axial hydroxyl in position 2 with the remainder equatorial, i.e. a chair-like structure), although other forms (scyllo- and chiro-) have been found on occasion in plants. The 1‑stearoyl,2-arachidonoyl molecular species, which is of considerable biological importance in animals, is illustrated. Figure $31$ shows the structure of phosphatidylinositol. Phosphatidylinositol is especially abundant in brain tissue, where it can amount to 10% of the phospholipids, but it is present in all tissues, cell types, and membranes at relatively low levels in comparison to many other phospholipids. In rat liver, it amounts to 1.7 micromoles/g., i.e. less than phosphatidylcholine, phosphatidylethanolamine, and phosphatidylserine. Under normal conditions, it is present entirely in the inner leaflet of the erythrocyte membrane and of the plasma membrane in nucleated cells. Phosphatidylinositol per se is rarely found in prokaryotes other than the Actinomycetales, although the thermophilic α-proteobacterium Rhodothermus marinus contains dialkylether glycerophosphoinositides. The fatty acid composition of phosphatidylinositol is rather distinctive as shown in Table $6$. Thus, in almost all animal tissues, the characteristic feature is a high content of stearic and arachidonic acids. All the stearic acid is linked to position sn-1 and all the arachidonic acid to position sn-2, and as much as 78% of the total lipid may consist of the single molecular species sn-1-stearoyl-sn-2-arachidonoyl-glycerophosphorylinositol (see Table $7$ below). Although 1-alkyl- and alkenyl- forms of phosphatidylinositol are known, they tend to be much less abundant than the diacyl form. In plant phosphatidylinositol, e.g. Arabidopsis thaliana as listed, palmitic acid is the main saturated fatty acid in position sn-1, while linoleic and linolenic acids are the main unsaturated components in position sn-2. Similarly in yeast, palmitic acid is in position sn-1 with oleic and palmitoleic acids in position sn-2 predominantly; the Amoebozoa have a C16 alkyl group in position sn-1 and cis-vaccenic acid in position sn-2. Table $6$: Fatty acid composition of phosphatidylinositol (wt % of the total) in animal and plant tissues. Tissue Fatty acids 16:0 18:0 18:1 18:2 18:3 20:3 20:4 22:3 22:5 22:6 Bovine brain [1] 8 38 10 1 - 5 34 2 tr. 1 Bovine liver [2] 5 32 12 6 1 7 23 4 3 5 Rat liver [3] 5 49 2 2 4 35 1 A. thaliana [4] 48 3 2 24 24 [1] = Holub, B.J. et al.. J. Lipid Res.., 11, 558-564 (1970); DOI. [2] = Thompson, W. and MacDonald, G., J. Biol. Chem., 250, 6779-6785 (1975); DOI. [3] = Wood, R. and Harlow, R.D. Arch. Biochem. Biophys., 135, 272-281 (1969); DOI. [4] = Browse, J. et al. Biochem. J., 235, 25-31 (1986); DOI. Biosynthesis: The basic mechanism for the biosynthesis of phosphatidylinositol and phosphatidylglycerol is sometimes termed a branch point in phospholipid synthesis, as phosphatidylcholine and phosphatidylethanolamine are produced by a somewhat different route. Phosphatidylinositol is found in all eukaryotes, which are in general able to synthesize inositol de novo via glucose-6-phosphate. As with phosphatidylglycerol (and hence cardiolipin), phosphatidylinositol is formed biosynthetically from phosphatidic acid via the intermediate cytidine diphosphate diacylglycerol, which is produced by the action of a CDP-diacylglycerol synthase believed to be the rate-limiting enzyme in phosphatidylinositol biosynthesis. Then, the enzyme CDP-diacylglycerol inositol phosphatidyltransferase ('phosphatidylinositol synthase' or 'PIS') catalyzes a reaction with myo-inositol to produce phosphatidylinositol. Figure $32$ shows the synthesis of phosphatidylinositol in eukaryotes. Only isoform of PIS exists in mammals and it is located in the endoplasmic reticulum, in part in a subcompartment of this associated with mitochondria (mitochondria-associated membranes - MAM) and in mitochondria per se. Indeed, it is reported that PIS is present in a mobile ER-derived subcompartment that makes transient contacts with other organelles, including the plasma membrane, and facilitates the distribution of phosphatidylinositol to other subcellular compartments. The other product of the reaction is cytidine monophosphate (CMP). As PIS catalyzes the reverse reaction also, the rate of phosphatidylinositol synthesis is determined by the relative concentrations of the precursors and product, and the latter must be transported away from the site of synthesis for the reaction to continue. Much of the phosphatidylinositol is delivered to other membranes by vesicular transport, but a family of soluble phosphatidylinositol transfer proteins (PITPα, PITPβ and PITPNC1) provides phosphatidylinositol from the ER to kinases for phosphorylation (see below). Molecular species specificity: The phosphatidylinositol synthase per se does not exhibit the fatty acyl specificity observed in the final product, but earlier in the biosynthetic process 1-stearoyl-2-arachidonoyl species of diacyl-sn-glycerols are converted preferentially into phosphatidic acid by the epsilon isoform of diacylglycerol kinase (DGKε), anchored to the membrane via its N-terminal hydrophobic helix segment; ATP is the phosphate donor. In addition, one of the CDP-diacylglycerol synthases (CDS2) has similar specificity in the generation of the immediate precursor CDP-diacylglycerols from phosphatidic acid, while some specificity may be introduced via lysophosphatidylinositol, formed as a by-product of eicosanoid formation (see below) or as an intermediate as part of the normal cycle of deacylation-acylation of phosphatidylinositol in tissues in which the fatty acid composition is remodeled to give the final distinctive composition. A membrane-bound O-acyltransferase (MBOAT7 or LPIAT1) specific for position sn-2 of lysophosphatidylinositol with a marked preference for arachidonoyl-CoA is ubiquitously expressed in animal tissues, and this may be one means by which free arachidonic acid and eicosanoid levels are regulated. In macrophages subjected to inflammatory stimuli, phosphatidylinositol containing two molecules of arachidonate is produced by remodeling reactions, and there is evidence that it is a novel bioactive phospholipid regulating innate immune responses in these cells. Further specificity may be introduced by lysocardiolipin acyltransferase (LYCAT; also known as LCLAT1 or ALCAT1), which exhibits a preference for lysophosphatidylinositol and lysophosphatidylglycerol over other phospholipids in vitro, and incorporates 18:0 rather than shorter chain fatty acids into position sn-1 of phosphatidylinositol and other phosphoinositides, especially phosphatidylinositol-4,5-bisphosphate and phosphatidylinositol-3-phosphate; this enzyme may be located adjacent to the phosphatidylinositol synthase in the endoplasmic reticulum. Some of the phosphatidylinositol in membranes is derived from recycling of polyphosphoinositides via the phosphatidylinositol cycle, and this could influence the molecular species composition (see below). The highly specific distribution of fatty acids on the glycerol moiety of phosphatidylinositol breaks down in some cancer cells, especially those with a mutation on the transcription factor p53 gene, which is one of the most highly mutated genes in cancers. Plants and bacteria: In contrast to animals, plants have two phosphatidylinositol synthase isoforms, PIS1 and PIS2, which display specificities for particular species of the CDP-diacylglycerol substrate. PIS1 generates phosphatidylinositol with saturated or monounsaturated fatty acids preferentially, while PIS2 generates polyunsaturated species, the two forms possibly having different functions. In protozoan parasites, such as Trypanosoma brucei, the active site of phosphatidylinositol synthase may be the lumen of the endoplasmic reticulum and Golgi. There is evidence for two distinct pools of product in this organism, the bulk membrane form derived from inositol imported from the environment, and a second used for the synthesis of GPI anchors, which uses myo-inositol synthesized de novo. In yeasts, some biosynthesis may occur on the cytosolic side of the plasma membrane. The enzyme is a transmembrane protein.and use CDP-diacylglycerol as a donor and either inositol (eukaryotes) or inositol phosphate (prokaryotes) as the acceptor alcohol. The structure of a similar enzyme, phosphatidylinositol-phosphate synthase from Renibacterium salmoninarum, is shown in Figure $33$. Figure $33$: Structure and reaction of phosphatidylinositol-phosphate synthase from Renibacterium salmoninarum. Clarke, O., Tomasek, D., Jorge, C. et al. Structural basis for phosphatidylinositol-phosphate biosynthesis. Nat Commun 6, 8505 (2015). https://doi.org/10.1038/ncomms9505. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/ Panel A shows the reaction for PIP synthases which involves the transfer of a diacylglycerol-substituted phosphate group (purple/red) from the CDP-DAG donor to the inositol phosphate acceptor (green), generating PIP and CMP. Panel B shows the structure of the RsPIPS-Δ6N homodimer in ribbon representation viewed from two orthogonal orientations (in the plane of the membrane on the left; towards the cytosol down the dimer axis on the right). One protomer is colored grey, and the helices of the other are depicted in spectral coloring, from blue (JM1) to red (TM6). The Af2299 extramembrane domain used to facilitate crystallization is not shown here. Figure $34$ shows a large cavity that contains the active site of RsPIPS. Panel (a) shows the structure of RsPIPS-Δ6N is shown in ribbon representation, with one protomer colored grey and the other colored by the Kyte–Doolitle hydrophobicity scale, from −4.5 (most polar, light blue) to 4.5 (most hydrophobic, orange). Two orthogonal representations are shown, on the left is a view in the plane of the membrane, and on the right is a view from the cytosol along the dimer axis. A transparent purple surface delineates the borders of the interfacial cavity, which contains three subregions as follows: 1, the inositol phosphate acceptor-binding pocket; 2, the nucleotide-binding pocket between TM2 and TM3; and 3, a hydrophobic groove between TM2 and JM1. (b) Detail of the active site viewed in the plane of the membrane, with side chains that contact the bound Mg2+ and SO42- ions labeled and depicted in stick representation. A nucleotide-binding site formed from transmembrane segments 1, 2, and 3 contains 8 conserved residues (D1xxD2G1xxAR…G2xxxD3xxxD4). The first 3 aspartic acid side chains coordinate a metal ion while the 4th is likely a general base in catalysis. Figure $35$ shows an interactive iCn3D model of the phosphatidylinositolphosphate (PIP) synthase with bound CDP-DAG from Renibacterium salmoninarum (5D92). Figure $35$: Phosphatidylinositolphosphate (PIP) synthase with bound CDP-DAG from Renibacterium salmoninarum (5D92).. Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...i9nhSw9U32Man8 The two identical subunits of the homodimer are shown in gray and plum. The two CDP-DAGs are shown in spacefill and CPK colors Function: In addition to functioning as negatively charged building blocks of membranes, the inositol phospholipids (including the phosphatidylinositol phosphates or 'polyphosphoinositides' discussed below) have crucial roles in the interfacial binding of proteins and in the regulation of protein activity at the cell interface. As phosphoinositides are polyanionic, they can be very effective in non-specific electrostatic interactions with proteins. However, they are especially efficient in specific binding to so-called ‘PH’ domains of cellular proteins. At least three phosphatidylinositol molecules are present in the crystal structure of human erythrocyte glycophorin, for example, and they are believed to influence binding to other proteins via their head groups. The lipid is a structural component of yeast cytochrome bc1. In animal tissues, phosphatidylinositol is the primary source of the arachidonic acid required for the biosynthesis of eicosanoids, including prostaglandins, via the action of the enzyme phospholipase A2, which releases the fatty acids from position sn-2. The reverse reaction also occurs. Figure $36$s shows the generation of arachidonic acid and eicosanoids from PI by means of phospholipase A2 Similarly, phosphatidylinositol and the phosphatidylinositol phosphates are the main sources of diacylglycerols that serve as signaling molecules in animal and plant cells, via the action of a family of highly specific enzymes collectively known as phospholipase C. In brief, diacylglycerols regulate the activity of a group of at least a dozen related enzymes known as protein kinase C, which in turn control many key cellular functions, including differentiation, proliferation, metabolism, and apoptosis. Indeed, the biological actions of the various components released have been the subject of intensive study over many years. 2‑Arachidonoylglycerol, an endogenous cannabinoid receptor ligand, may also be a product of phosphatidylinositol catabolism. Phosphatidylinositol Phosphates (Polyphosphoinositides) in Animals Structure and Occurrence: The pioneering work of Mable and Lowell Hokin in the 1950s led to the discovery that phosphatidylinositol was converted to polyphosphoinositides with important signaling and other functional activities, including cell communication via signal transduction, cell survival and proliferation, membrane trafficking and modulation of gene expression. Phosphatidylinositol is now known to be phosphorylated by a number of substrate-selective kinases that place the phosphate moiety on positions 3, 4, and 5 of the inositol ring with the balance among them maintained by distinct phosphatases and phospholipases. Seven different isomers are known (mono-, bis-, and tris-phosphorylated), which are produced in a tightly coordinated manner, and all of these have characteristic biological activities. They each turn over much more rapidly than the parent phosphatidylinositol molecule. In addition, there can be an array of molecular species of each of these isomers that differ in the nature of the fatty acyl groups. Although the most significant in quantitative and possibly biological terms were long thought to be phosphatidylinositol 4-phosphate and phosphatidylinositol 4,5‑bisphosphate, it is now recognized that phosphatidylinositol 3-phosphate and its metabolites are as important biologically at least. Figure $37$ shows the structures of phosphatidylinositol phosphates These lipids are usually present at low levels only in tissues, typically at about 0.5 to 1% of the total lipids of the inner leaflet of the plasma membrane, so they are unlikely to have an appreciable structural role. On the other hand, static measurements of lipids that turn over very rapidly do not provide a meaningful assessment of their cellular functions. The positional distributions of fatty acids in the phosphatidylinositol, phosphatidylinositol 4-phosphate, and phosphatidylinositol 4,5-bisphosphate of ox brain are listed in Table $7$. In each the saturated fatty acids are concentrated in position sn-1 and polyunsaturated, especially arachidonate, in position sn-2; there are few differences among the three lipids in this instance. Table $7$: Distribution of fatty acids (mol % of the total) in positions sn‑1 and sn‑2 in phosphatidylinositol (PI) and the phosphatidylinositol mono- and diphosphates of ox brain. Fatty acids PI PI monophosphate PI diphosphate sn-1 sn-2 sn-1 sn-2 sn-1 sn-2 16:0 15 9 7 18:0 74 69 69 18:1 10 10 20 13 21 10 18:2 1 2 trace 1 1 1 20:3(n-9) 5 10 10 20:3(n-6) 5 11 12 20:4(n-6) 67 49 52 22:3 7 10 7 22:6(n-3) trace trace trace Data from Holub, B.J. et al., J. Lipid Res., 11, 558-564 (1970); DOI. Molecular species data, see Traynor-Kaplan, A. et al., Biochim. Biophys. Acta, 1862, 513-522 (2017); DOI. Biosynthesis: Phosphatidylinositol per se is the ultimate precursor of all phosphoinositides, the head groups of which have different charges and structures that impact directly on membrane properties and via metabolic interactions can function as chemical switches. The individual phosphoinositides are maintained at steady state levels in membranes by a continuous and sequential series of phosphorylation and dephosphorylation reactions by specific kinases, phosphatases, and phospholipase C enzymes, which are regulated and/or relocated through cell surface receptors for extracellular ligands, the phosphoinositide cycle. While this has been termed a ‘futile cycle’, which can consume a significant proportion of cellular ATP production, it is only part of a wider pattern of reactions - the phosphatidylinositol cycle (see below). Controlled synthesis of these different phosphoinositides occurs in different intracellular compartments for distinct and independently regulated functions with spacially distinct target enzymes or receptors. In mammals, the complexity is such that 18 phosphoinositide inter-conversion reactions have been identified to date, and these are mediated by at least 20 phosphoinositide kinases and 34 phosphoinositide phosphatases that span 8 and 10 classes, respectively; some have yet to be characterized. Most of these enzymes are conserved across all of the eukaryota, and each has distinct functions and specificities that cannot be replaced by the activity of related isoforms. As a generality, most mono-phosphorylations occur in endomembranes, such as the endosomes and the Golgi network, while second and third phosphorylations occur primarily at the plasma membrane, and this is reflected in the lipid composition of each membrane. While these enzymes are believed to work independently and sequentially to produce a specific product, there remains a possibility that some participate in protein complexes to coordinate their activities. Specific transporters, especially the 'Nir2' protein, facilitate the exchange of phosphoinositides between membranes. It should be noted that there are links to the metabolism of phosphatidylcholine, which can be hydrolyzed by phospholipase D to phosphatidic acid, an important activator of key kinases. Figure $38$ shows an overview of polyphosphoinositide metabolism in animal tissues. Thus as an example, phosphatidylinositol 4-phosphate (PI(4)P) is produced by the action of a phosphatidylinositol 4-kinase (PI4K) in the Golgi, and is in turn phosphorylated by a phosphatidylinositol phosphate 5-kinase (PIPK I) to form phosphatidylinositol 4,5-bisphosphate (PI(4,5)P) at the plasma membrane, although this can also be formed by phosphorylation of phosphatidylinositol 5-phosphate by a specific 4-kinase (PIPK II). Four isoforms of PI4K in two structural families are known that each operate in different subcellular membrane compartments to produce phosphatidylinositol 4-phosphate for particular signaling functions. Some selectivity in the formation of molecular species or remodeling may occur to further enrich the arachidonic acid content. Subsequently, it was discovered that phosphatidylinositol is also phosphorylated by a 3-kinase (PI3K III or the VPS 34 complex) to produce phosphatidylinositol 3-phosphate (PI(3)P) in the early endosomes. In fact, three phosphatidylinositol 3-kinases families (eight isoforms) have been described, each with distinct substrate specificities. A second phosphoinositide signaling pathway involves activation of two of these 3‑kinases, stimulated by growth factors and hormones, which phosphorylate phosphatidylinositol 4,5-bisphosphate (by PI3K I - four isoforms) and phosphatidylinositol 4‑phosphate (by PI3K II - three isoforms) to produce phosphatidylinositol 3,4,5-trisphosphate (PI(3,4,5)P) and phosphatidylinositol 3,4‑bisphosphate (PI(3,4)P), respectively. While phosphatidylinositol 3-phosphate and other 3‑phosphorylated metabolites amount to only about 0.5% of the total phosphoinositides in resting mammalian cells, they are now recognized to be of profound importance for cellular metabolism. In addition to the activity of kinases, the amounts of these various metabolites are regulated by the activities of specific phosphoinositide phosphatases, which are highly conserved in eukaryotes and dephosphorylate phosphoinositides at the 3, 4, and 5 positions of the inositol ring. For example, so-called ‘SHIP’ phosphatases convert phosphatidylinositol 4,5‑bisphosphate back to phosphatidylinositol 4-phosphate by hydrolysis of the 5-phosphate group. 3‑Phosphorylated phosphoinositides are only degraded by phosphatases, especially those of the PTEN family, and not by phospholipase C (see below). The various organelles in cells have membranes with distinct functions and molecular compositions. Yet, all the phosphatidylinositol precursor is formed primarily at the endoplasmic reticulum, and the different membrane lipids must be transported between membrane sites via specific trafficking processes/proteins. There is selective recruitment of effector proteins to particular membranes by binding only to a single type of phosphoinositide, and this is followed by interactions between the phosphoinositide-binding proteins and various enzymes to channel phosphoinositide production to the required biological outcomes and to regulate signaling. For example, much of the phosphatidylinositol 4‑phosphate and phosphatidylinositol 4,5-bisphosphate involved in signaling is believed to be formed at contact sites between the endoplasmic reticulum and plasma membrane. A concept has emerged in which each phosphoinositide has its own role – the ‘lipid code’ hypothesis, in which defined lipids act as labels for each cellular membrane to organize cells into dynamic and responsive membrane-bound compartments and maintain the orderly flow required for the complexities of membrane trafficking and spatio-temporal signaling reactions. Thus, phosphatidylinositol 4-phosphate, phosphatidylinositol 4,5‑bisphosphate, phosphatidylinositol 3-phosphate and phosphatidylinositol 3,5‑bisphosphate are found mainly on the Golgi, plasma membrane, early endosomes, and late endocytic organelles, respectively, where they are sometimes regarded as landmarks for these compartments. For example, phosphatidylinositol 4,5‑bisphosphate is present throughout the plasma membrane and is considered a general marker for this, while phosphatidylinositol 3,4,5-triphosphate, is a characteristic component of the basolateral region of this membrane in a polarized cell but is absent from the apical part. On the other hand, it should be noted that this map of phosphoinositides to specific organelles is derived from their steady state distributions, but the highly dynamic generation and consumption of different phosphoinositides in response to different stimuli in the various sub-cellular compartments in living cells by the action of kinases and phosphatases together with lipase reactions, may lead to the formation of transient pools of distinct molecular forms. There must be a continuous replenishment of the precursors by new synthesis. Function: The distinctive subcellular location of the different phosphoinositide species, together with the rapid and reversible nature of phosphorylation, gives them a central and general position in the fields of cell signaling cascades and intracellular membrane trafficking. The precise locations of particular phosphoinositides are factors that contribute a specific identity to each organelle and sometimes even to each face of an organelle, such as the cis and trans faces of the Golgi apparatus, and this enables directional transport of cellular constituents between organelles or membranes. Phosphoinositides are able to achieve signaling effects directly by binding to specific cytosolic domains of membrane proteins via their polar head groups, thereby triggering downstream signaling cascades, often in conjunction with an acidic phospholipid, such as phosphatidylserine or phosphatidic acid at an adjacent-binding site. The term 'lipidon' has been coined to describe the unique collection of co-located lipids that distinguish biological membrane nano-environments and which provide the context for PI recognition in vivo. In this way, they can regulate the function of innumerable proteins integral to membranes, for example by relocating a protein from one area of the cell to another, e.g., from the cytosol to the inner leaflet of the plasma membrane, or they can attract cytoskeletal and signaling components to the membrane. Amongst the proteins that bind to phosphoinositides in this way are phospholipases, protein kinases, regulators of membrane trafficking, and cytoskeletal, scaffold, and ion channel proteins. Dysregulation of phosphoinositide metabolism and signaling is a factor in a number of diseases, including cancer. Binding usually involves electrostatic interactions with the negative charges of the phosphate groups on the inositol ring with characteristic clusters of basic amino acid residues in proteins to recruit them to intracellular membranes, while often leading to specific folding and thence increased activity of unstructured peptides. At least 70 distinct types of binding sites for phosphoinositides have been identified in proteins. In particular, a binding region termed the pleckstrin homology (PH) domain, consisting of ~100 amino acids, is the most abundant lipid-binding domain with more than 225 examples identified, and this can exhibit great specificity for particular polyphosphoinositides, often binding simultaneously with other proteins. While the interaction is driven by non-specific electrostatic interactions initially, it is followed by specific binding to increase the membrane residence time. The phox homology (PX) domain family with 49 members in humans is unique in that it can recognize all seven phosphoinositide forms, while proteins with a FYVE domain, which is enriched in cysteine and is stabilized by two zinc atoms, bind specifically to phosphatidylinositol 3-phosphate (PI(3)P). The protein kinase C family have C1 or C2 domains which recognize phosphatidylinositol 4,5-bisphosphate and phosphatidylinositol 3,4,5-trisphosphate specifically (and sometimes other lipids). The distinctive phosphoinositide composition of membranes in different organelles adds strength and specificity to the interactions by cooperative binding with other membrane proteins. Phosphatidylinositol 3-phosphate and the other phosphatidylinositol monophosphates are present in cells at low levels only, although their levels do not appear to fluctuate greatly. PI(3)P has been implicated in membrane trafficking through its interactions with certain proteins in endosomes. In particular, it plays a pivotal role in the initiation of autophagy, i.e. the controlled internal degradation and turnover of cellular constituents, while PI(3,5)P2 is important in the autophagosome–lysosome fusion step and in the subsequent acidification of this organelle. After sorting of the lysosomal contents, components of the internalized cargo are recycled to the plasma membrane and PI(3)P is dephosphorylated to phosphatidylinositol by a specific phosphatase, and this is in turn phosphorylated to PI(4)P. Thus the processes of internalization, sorting, and trafficking of membrane proteins depend on the interconversion of phosphoinositide species by coordinated phosphorylation-dephosphorylation reactions. In general, PI(3)P controls cellular processes by recruiting effector proteins through low to moderate affinity interaction with specific PI(3)P binding domains. A protein designated Akt (protein kinase B) is recognized as a direct effector of the PI3K signaling cascade with receptor tyrosine kinases as the main upstream activators, for example, but it is now known that every phosphatidylinositol phosphate has a specific set of effector proteins that are recruited to target membranes or are allosterically regulated by the specific receptors; each function may require a different effector. A further function of PI(3)P is in the regulation of the final stage of cell division (cytokinesis), and the lipid is known to accumulate where cells divide. As the class I PI3K isoforms especially have been implicated in the etiology and maintenance of various diseases and metabolic disorders, including cancer, inflammation, and autoimmunity, drug companies are actively pursuing the development of inhibitors. In particular, they mediate insulin-independent glucose transport and many of the physiological actions of insulin. In relation to lung cancer especially, RAS proteins, which are key signaling switches essential for the control of proliferation, differentiation, and survival of eukaryotic cells, regulate the activity of type I phosphatidylinositol 3-kinase (PI3K); this is essential for tumor initiation and maintenance. Phosphatidylinositol 4-phosphate is the precursor for the 4,5-bisphosphate, but it binds to a protein on the cytoskeleton of the cell and has its own characteristic functions. It is the most widely distributed of the phosphoinositides, and in addition to the Golgi and the plasma membrane, it is present in late endosomes, lysosomes, secretory vesicles, and autophagosomes. As a part of protein-lipid complexes, it is believed to have a role in essential nuclear processes. In yeast, it has a function in the anterograde transport from the trans-Golgi and the retrograde transport from the Golgi to the endoplasmic reticulum; it is also necessary for the formation of secretory vesicles in the Golgi that are targeted to the plasma membrane. Some of that in the plasma membrane is exchanged for phosphatidylserine by the action of specific transport proteins at junctions with the endoplasmic reticulum. In addition, PI(4)P is essential for the structure and function of the late endosomes, where it is required for the recruitment of specific proteins that control cargo exit (following hydrolysis of PI(3)P). Some of these participate in vesicle formation, while others like the oxysterol binding protein (OSBP) are involved in lipid transfer. After initiation of the process by PI(3)P, PI(4)P, PI(4,5)P2 and their binding proteins are modulators of autophagy at most stages of the process. PI(4)P has been called the 'fuel' that drives cholesterol transport, as its hydrolysis provides the energy that enables the establishment of active sterol concentration gradients across membrane-bound compartments with the aid of OSBP, which is a key regulator of cholesterol, oxysterol, and PI(4)P concentrations in membranes. In the plasma membrane, PI(4)P can support the functions of ion channels, and it contributes to the anchoring of proteins with polybasic domains, although it is not utilized for the synthesis of PI(4,5)P2 in this membrane. On the other hand, PI(4)P derived from PI(4,5)P2 in the membrane of primary cilia in the retina is important for vision. PI(4)P has an important influence on the progression of many diseases, especially virus replication, cancer, and various inflammatory diseases, and inhibitors of PI4-kinase are under study for their therapeutic potential. While the biological properties of phosphatidylinositol 5-phosphate have taken longer to unravel, because of the difficulties of separation of this isomer, it is now apparent that it is involved in osmoregulation both in plants and animals. It also has signaling functions, and although it is the least abundant phosphatidylinositol monophosphate, it is involved in signaling at the nucleus and in the cytoplasm, modulating cellular responses to various stresses, hormones and growth factors. In the endosomes, it is a regulator of protein sorting. Although phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2) is found primarily in the inner leaflet of the plasma membrane, where it may define membrane identity in eukaryotic cells, it is also present in endosomes, the endoplasmic reticulum and nucleus. It is an essential precursor of lipid second messengers such as diacylglycerols with vital signaling functions that operate through plasma membrane G-protein coupled receptors, receptor tyrosine kinases, and immune receptors. Because of its large head group and multivalent negative charge, PI(4,5)P2 has been described as an "electrostatic beacon" that interacts in various ways with membrane proteins, other lipids and cellular cations. In consequence and in spite of its relatively low concentration, it is a key regulator of innumerable events at the plasma membrane, including cell adhesion and motility, vesicle endocytosis and exocytosis, and the function of ion channels, especially those for potassium, calcium, and sodium. With ion channels, for example, it appears to be an obligatory factor, increasing their activity by activating key proteins, while its hydrolysis by phospholipase C reduces such activity. PI(4,5)P2 interacts with cationic residues of a large array of proteins in concert with cholesterol to form localized membrane domains that are distinct from the sphingolipid-enriched rafts. Indeed, it has a much higher concentration than other phosphoinositide species in cells, although most of this is in effect sequestered by binding proteins. Also, phosphatidylinositol 4,5-bisphosphate and its diacylglycerol metabolites are important for vesicle formation in membranes. For example, a major pathway in cells for the internalization of cell surface proteins such as transferrin is the clathrin-coated vesicle pathway. PI(4,5)P2 is essential to this process in that it binds to the machinery involved in the membrane, increasing the number of clathrin-coated pits and permitting the internalization of proteins. It has a related function in caveolae, where it is concentrated at the rim. Through its attachment to the apical plasma membrane, phosphatidylinositol 4,5-bisphosphate is intimately involved in the development of the actin cytoskeleton and thereby controls cell shape, motility, and many other processes. In particular, it binds with high specificity to effectors such as vinculin, a membrane-cytoskeletal protein that is involved in the linkage of integrin adhesion molecules to the actin cytoskeleton. Dysregulation of this function has been implicated in the migration and metastasis of tumor cells. In yeasts, it appears that the presence of stearic acid in position sn-1 is essential for this function. In the cell nucleus, this lipid is believed to be involved in maintaining chromatin, the complex combination of DNA, RNA, and protein that makes up chromosomes in a transcriptionally active conformation, as well as being a precursor for further signaling molecules. It has a role in gene transcription, and RNA processing, especially in the modulation of RNA polymerase activity, and in other nuclear processes. Via its binding to specific proteins, the lipid is an essential component of the immune response of animal tissues to toxic bacterial lipopolysaccharides. It is also involved in the pathophysiology of the HIV virus via an interaction with the Tat protein secreted by infected cells. PI(4,5)P2 is the primary precursor of the endocannabinoid 2-arachidonoylglycerol in neurons, and it is also an essential cofactor for phospholipase D and so affects the cellular production of phosphatidic acid with its specific signaling functions. By binding specifically to ceramide kinase, the enzyme responsible for the synthesis of ceramide-1-phosphate, it has an influence on sphingolipid metabolism. Like ceramide-1-phosphate, it binds to and activates the Ca2+-dependent phospholipase A2, which generates the arachidonate for eicosanoid production. One molecule of phosphatidylinositol 4,5‑bisphosphate is bound to each subunit of the protein in the X-ray crystal structures of mammalian GIRK2 potassium channel, where it enables a conformational change that assists the transport function of the protein. Perhaps, the best characterized of the phosphoinositide signaling functions results from the hydrolysis of phosphatidylinositol phosphates by phospholipase C isoforms, in this instance to produce sn-1,2-diacylglycerols and inositol 3,4,5-trisphosphate (see below), which act as second messengers. Only those polyunsaturated diacylglycerol species derived from PI(4,5)P2 are able to bind and activate protein kinase C (α, ε, δ) isoforms both in vitro and in vivo. This lipid is doubly important as it binds strongly to these enzymes via a basic patch distal to a Ca2+ binding site, and this targets them selectively to the plasma membrane. Aberrant expression of phospholipase Cγ2 may be a factor in neurodegenerative diseases. Via the action of PI3 kinase, PI(4,5)P2 is the precursor of PI(3,4,5)P3 with its own distinctive signaling properties. Phosphatidylinositol 3,4-bisphosphate can be produced by two routes and regulates a variety of cellular processes with relevance to health and disease that include B cell activation and autoantibody production, insulin sensitivity, neuronal dynamics, endocytosis, and cell migration. It is known to bind selectively to a number of proteins, and it acts as a secondary messenger by recruiting the protein kinases Akt (protein kinase B) and so may influence the cell cycle, cell survival, angiogenesis, and glucose metabolism. During endocytosis in the endolysosomal system, it is produced from PI(4,5)P2 and controls the maturation of endocytic-coated pits. Its synthesis and turnover of are spatially segregated within the endocytic pathway. In epithelial cells, it is located on the apical membrane, i.e. facing the lumen, as opposed to the basolateral membranes, and it is believed to be is a determinant of the identity and function of the apical membrane. Phosphatidylinositol 3,5-bisphosphate is present at low levels only in cells (0.04-0.1% of the total phosphatidylinositides) unless stimulated by growth factors, but it is important in membrane and protein trafficking, especially in the late endosomes in eukaryotes and in yeast vacuoles. For example, the conversion of PI(3)P to PI(3,5)P2 promotes endosomal maturation and degradative sorting. It is involved in the mediation of signaling in response to stress and hormonal cues and in the control of ion transport in membranes, while genetic studies confirm that it is essential for healthy embryonic development, especially in the nervous system. Phosphatidylinositol 3,4,5-trisphosphate (PI(3,4,5)P3) is almost undetectable in quiescent cells, but its intracellular level rises very rapidly from synthesis at the plasma membrane in response to agonists such as extracellular growth factors and hormonal stimuli. By recruiting proteins with pleckstrin homology (PH) domains to the plasma membrane, it has been implicated in a variety of cellular functions that include growth, cell survival, proliferation, cytoskeletal rearrangement, intracellular vesicle trafficking, and cell metabolism. In particular, it is an important component of a signaling pathway in the cell nucleus. In epithelial cells, it is located on the basolateral membrane, i.e. facing adjacent cells, where it may be a determinant of the identity and function of this membrane. In contrast to phosphatidylinositol 3-phosphate, it opposes autophagy by binding to and activating the PH domain of Akt, so inducing cell proliferation. During feeding, various physiological responses lead to the secretion of insulin, which stimulates the phosphorylation of phosphatidylinositol 4,5-bisphosphate to phosphatidylinositol 3,4,5-trisphosphate and triggers a signaling cascade that leads to the suppression of autophagy. When this pathway is impaired it has deleterious effects on insulin resistance associated with various metabolic diseases including obesity and diabetes. It has been implicated in tumor cell migration and metastasis. PI(3,4,5)P3 is also present in the nucleus and nucleoli of cells where it is believed to have functions in RNA processing/splicing, cytokinesis, protein folding, and DNA repair. In complete contrast, like phosphatidylserine, it is reportedly transferred to the outer leaflet of the plasma membrane in aged or damaged cells as an 'eat‑me' signal for phagocytes and apoptosis. The human immune system utilizes neutrophils, which are highly mobile cells, to eliminate pathogens from infected tissue. The first step is to track and then pursue molecular signals, such as cytokines, emitted by pathogens. It has been established that two phospholipids operate in sequence to point the neutrophils in the correct direction. The first of these is phosphatidylinositol 3,4,5-trisphosphate, which binds to a specific protein DOCK2 and enables it to translocate to the plasma membrane. Then phosphatidic acid, generated by the action of phospholipase D on phosphatidylcholine, takes over and directs the DOCK2 to the leading edge of the plasma membrane. This causes the polymerization of actin within the cell and in effect reshapes the neutrophil and points it in the direction from which the pathogens signals are coming. On the other hand, Mycobacterium tuberculosis is able to subvert phosphoinositide signaling to arrest phagosome maturation by dephosphorylation of phosphatidylinositol 3-phosphate. Water-Soluble Inositol Phosphates As mentioned briefly above, hydrolysis of phosphatidylinositol phosphates by calcium-dependent phospholipase C (or 'phosphoinositidase C') leads to the generation of sn‑1,2‑diacylglycerols, which act as second messengers in animal cells and are of enormous metabolic importance. There are many different enzymes of this type, but the activity of the phosphoinositide-specific phospholipase C constitutes an essential step in the inositide signaling pathways. The enzyme exists in six families consisting of at least 13 isoenzymes, all of which have conserved regions such as the plekstrin homology (PH) binding domain. Each one has a distinctive role and can have a characteristic cell distribution that is linked to a specific function. The activity of these enzymes is stimulated by signaling molecules such as G-protein coupled receptors, receptor tyrosine kinases, Ras-like GTPases, and calcium ions, thus linking the hydrolysis of phosphatidylinositol phosphates to a wide range of other cellular signals. As phospholipase C is a soluble protein located mainly in the cytosol, translocation to the plasma membrane is a crucial step in signal transduction. Regulation of these isoenzymes and the form PLCγ1 in particular is vital for health as they are associated with the activation or inhibition of important pathophysiological processes, especially in relation to cancer. Some phosphatidic acid is synthesized from the diacylglycerols produced within the plasma membrane through the activity of diacylglycerol kinases, and this is transported back to the endoplasmic reticulum and ultimately can be re-utilized for phosphatidylinositol biosynthesis. The other products of the phospholipase C reaction that are of special relevance because of their many essential functions are water-soluble inositol phosphates. Up to 60 different compounds of this type are possible, and at least 37 of these have been found in nature at the last count, all of which are extremely important biologically. However, polyphosphoinositides with a phosphate in position 3 are not substrates for phospholipase C. Figure $39$ shows the generation of inositol phosphates by phospholipase C. For example, under the action of various physiological stimuli in animals, including sphingosine-1-phosphate, and acting via various G-protein-coupled receptors, phosphatidylinositol 4,5-bisphosphate in the plasma membrane is hydrolyzed to release inositol 1,4,5-trisphosphate, an important cellular messenger that diffuses into the cytosol and stimulates calcium release from an ATP-loaded store in the endoplasmic reticulum via ligand-gated calcium channels (the diacylglycerols remain in the membrane to recruit and activate members of the protein kinase C family). The increase in calcium concentration, together with the altered phosphorylation status, activates or de-activates many different protein targets, enabling cells to respond in an appropriate manner to the extracellular stimulus. To enable rapid replenishment of the phosphatidylinositol 4,5‑bisphosphate used in this way, a cycle of reactions - the phosphatidylinositol cycle - must occur (see below). On the other hand, a recent publication suggests that phosphatidylinositol 4-phosphate in the plasma membrane may be a more important source of diacylglycerols following stimulation of G protein–coupled receptors. All of the various inositol phosphates appear to be involved in the control of cellular events in very specific ways, but especially in the organization of key signaling pathways, the rearrangement of the actin cytoskeleton, or intracellular vesicle trafficking. They have been implicated in gene transcription, RNA editing, nuclear export, and protein phosphorylation. As these remarkable compounds can be rapidly synthesized and degraded in discrete membrane domains or even sub-nuclear structures, they are considered to be ideal regulators of dynamic cellular mechanisms. From structural studies of inositol polyphosphate-binding proteins, it is believed that the inositides may act in part at least by modifying protein function by acting as structural cofactors, ensuring that proteins adopt their optimum conformations. In addition, phosphoinositides and the inositol polyphosphates are key components of the nucleus of the cell, where they have many essential functions, including DNA repair, transcription regulation, and RNA dynamics. It is believed that they may be activity switches for the nuclear complexes responsible for such processes, with the phosphorylation state of the inositol ring being of primary importance. As different isomers appear to have specific functions at each level of gene expression, extracellular events must coordinate the production of these compounds in a highly synchronous manner. In organisms from plants to mammals, an extra tier of regulatory mechanisms is produced by kinases that generate energetic diphosphate (pyrophosphate)-containing molecules from inositol phosphates. Conversely, these can be dephosphorylated by polyphosphate phosphohydrolase enzymes to regenerate the original inositol phosphates. These inositol pyrophosphates and the enzymes involved in their metabolism are also involved in the regulation of cellular processes by modulating the activity of proteins by a variety of mechanisms. It should be noted that the phospholipase C isoenzymes regulate the concentration of phosphatidylinositol 4,5-bisphosphate and related lipids and thence their activities in addition to the generation of new biologically active metabolites. Phosphatidylinositides in Plants In plants as in animals, phosphatidylinositol and polyphosphoinositides have essential biological functions, exerting their regulatory effects by acting as ligands that bind to protein targets via specific lipid-binding domains and so alter the location of proteins and their enzymatic activities. However, it appears that polyphosphoinositide metabolism developed in different ways after the divergence of the animal and plant kingdoms so the details of the processes in each are very different, not least because the subcellular locations of phosphoinositides differ appreciably between plants and animals. Phosphatidylinositol per se is of course the precursor of the phosphorylated forms and determines their fatty acid compositions. It also has a role in inhibiting programmed cell death by acting as the biosynthetic precursor of the sphingolipid ceramide phosphoinositol and so reducing the levels of ceramide. As in animals, the various phosphoinositides (five in total) are produced and inter-converted rapidly by a series of kinases and phosphatases (in many isoforms) in different cellular membranes in response to environmental or developmental cues. For example, phosphatidylinositol is generated mainly in the endoplasmic reticulum, while PI 4-kinases and their product are located in the trans-Golgi network and nucleus, and PI4P 5-kinases and product are present in the plasma membrane. During the biosynthesis of polyphosphoinositides, the first phosphorylation occurs at the hydroxyl group at positions 3 or 4 of the inositol ring, catalyzed by the appropriate kinases, while the second phosphorylation then takes place at position 5; PI 5-phosphate is produced by the action of a phosphatase on PI 3,5‑bisphosphate. Most other metabolites are produced via phosphatidylinositol 3-phosphate, and reports that some phosphatidylinositol 3,4,5-trisphosphate may be produced from phosphatidylinositol 4,5‑bisphosphate require confirmation. In contrast to mammalian phosphatidylinositol 3-kinases, which accept both phosphatidylinositol and its monophosphates as substrates, the plant enzyme acts only on the former. Figure $40$ shows polyphosphoinositide metabolism in plants The reverse reaction in plants is accomplished by phosphoinositide phosphatases, which can be grouped into three main families, the phosphatase/tensin (PTEN) family, 5-phosphatases (5-PTases) and phosphatases containing Suppressor of Actin (SAC) domains, each with differing subcellular locations, substrate specificities and regulatory mechanisms. Although what might be considered normal levels of phosphatidylinositol 4-phosphate are present, the concentrations of phosphatidylinositol 4,5‑bisphosphate and other phosphoinositides are extremely low in plants (10 to 20-fold lower than in mammalian cells), although they still have vital functions. There are differences between cell types, but in Arabidopsis epidermal root cells, PI(4,5)P2 is present at the highest concentration in the plasma membrane (apex region) and nucleus, while PI4P slowly distributes between the plasma membrane and Golgi, with the highest concentration in the former. Multivesicular bodies/late endosomes accumulate both PI3P and PI(3,5)P2, and the tonoplast and autophagosomes contain PI3P. How the various metabolites are transported between membranes has yet to be determined, but non-vesicular transport is believed to occur at membrane contact sites and vesicular transport probably occurs also. Highly polarized distributions of phosphoinositides are found within membranes, generally oriented toward the cytosolic leaflet, and they are believed to be organized in nanoclusters together with other lipids and proteins. For example, phosphatidylinositol-4-phosphate is an important constituent of the plasma membrane in plant cells, where it controls the electrostatic state and is involved in cell division. It may control the location and function of many membrane proteins, including those required for development, reproduction, immunity, nutrition, and signaling. PI(4)P is the only phosphoinositide present at the cell plate, i.e. the membrane separating two daughter cells during cell division. In addition, PI(4)P may interact with salicylic acid in the plant immune response, and it is produced during salt stress. However, specific functions are now being discovered for each of the plant phosphoinositides, which are produced rapidly in response to osmotic and heat stress, and it has become evident that a continuous turnover is essential for cell growth and development. For example, they have marked effects on the growth of many cell types and on guard cell function. In the nucleus, proteins have been identified that bind to phosphoinositides via the acyl chains, leaving the head group exposed for enzymatic modifications and signal transduction. Phosphoinositides are of special importance in microdomains at the tip of growing tissues such as the shoot apical meristem, pollen tubes, and root hairs where phosphatidylinositol 4,5-bisphosphate functions in stem cell maintenance and organogenesis. In the plasma membrane, it is enriched in the detergent-resistant component commonly equated with 'rafts'. Although its concentration is low, PI(4,5)P2 has been shown to have signaling functions by binding to a number of different target proteins, which have characteristic binding domains. For example, together with phosphatidic acid, PI(4,5)P2 regulates the activity of a number of actin-binding proteins, which in turn control the activity of the actin cytoskeleton. This has a key role in plant growth, the movement of subcellular organelles, cell division and differentiation, and plant defense. In addition, this lipid exerts control over ion channels, ATPases, and phospholipase C-mediated lipid degradation and the production of further second messengers. It is an important factor in both clathrin-mediated endocytosis and exocytosis. The specificity of the interactions may be dependent on the fatty acid composition of the lipid and on the activity of phosphatidylinositol 4-phosphate 5-kinase. As in animals, phosphoinositides have a role in endosomal sorting but through the central vacuole, which is a plant-specific organelle with both lytic and storage functions. Phosphatidylinositol 3,5-bisphosphate is the least abundant of the phosphoinositides, but it is a crucial lipid for membrane trafficking systems. The PI to PI(3)P to PI(3,5)P2 cascade, the second step requiring a kinase designated FAB1, is required for endosomal sorting events leading to membrane protein degradation or retrieval, vacuolar morphogenesis and autophagy. PI(3,5)P2 is involved in stomatal closure and the growth of root hairs, and it is also induced in salt stress. A number of different enzymes of the phospholipase C type that are specific for polyphosphoinositides have been isolated from higher plants; they are activated by Ca2+ and unlike their mammalian counterparts, they are not regulated by G proteins. It is not certain whether phosphatidylinositol is itself a substrate for these enzymes in vivo. Less is known of the metabolism of the water-soluble inositol phosphates produced in comparison to animals, and plants appear to lack a receptor for inositol 1,4,5-trisphosphate (IP3), although it is the most abundant metabolite of this type and is reported to induce the release of calcium ions to trigger stomatal closure. However, there is increasing evidence for lipid signaling mediated by phospholipase C in abiotic stress tolerance and development in plants. There is a general if contested belief that inositol hexakisphosphate (phytic acid or IP6), produced at least in part by sequential phosphorylation of inositol 1,4,5-trisphosphate, is a more important cellular messenger in plants and mobilizes an endomembrane store of calcium ions. Inositol-1,2,4,5,6-pentakisphosphate (IP5) is a structural co-factor of the jasmonic acid receptor coronatine insensitive 1, linking phosphoinositide signaling with phytohormone-controlled pathways. In plants in contrast to animals, diacylglycerols, the other product of phospholipase C hydrolysis of phosphoinositides, are rapidly converted to phosphatidic acid by diacylglycerol kinases and have not been considered important in signal transduction. Plants lack protein kinase C but they do have proteins with related properties that appear to be influenced by diacylglycerols. Via the action of phospholipase D, inositol phospholipids are a source of phosphatidic acid with its well-characterized signaling functions in plants, especially in defence. Lyso-Phosphoinositides Figure $41$ shows the structure of lysophosphatidylinositol Lysophosphatidylinositols: Lysophosphatidylinositols (LPI), i.e. with a single fatty acid only linked to the glycerol moiety, are formed as intermediates in the remodeling of the fatty acid compositions of the lipids by the action of phospholipase A1 or phospholipase A2 (e.g. cPLA2α), and when arachidonic acid is released for eicosanoid biosynthesis (see above). In ovarian cancer, LPI is elevated appreciably to around 15µM in ascites, and it is also present at high levels in obese subjects. It has become apparent relatively recently that like other lysophospholipids, phosphatidylinositol, and polyphospho-analogues may have messenger functions. For example, it has long been known to stimulate the release of insulin from pancreatic cells, suggesting a role in glucose homeostasis. sn-2-Arachidonoyl-lysophosphatidylinositol, in particular, is an endogenous ligand for a G protein-coupled receptor GPR55, and thereby can induce rapid phosphorylation of certain enzymes, including a protein kinase, which promote cancer cell proliferation, migration, and metastasis. Indeed, lysophosphatidylinositol is a biomarker for poor prognosis in cancer patients, and its concentration is elevated significantly in highly proliferative cancer cells in vitro. GPR55 is expressed in many regions of the brain, the intestines, endocrine pancreas and islets (where it may stimulate insulin release). It has been implicated in macrophage activation and inflammation. In addition to its role in cancer, lysophosphatidylinositol has been implicated in a number of metabolic diseases. It is reported to be a precursor of the endocannabinoid 2‑arachidonoylglycerol by the action of human glycerophosphodiesterase 3 as a lysophospholipase C. This enzyme suppresses the receptor for lysophosphatidylinositol, and so acts as a switch between GPR55 and endocannabinoid (CB2) signaling. Glycerophosphoinositol: Sequential removal of both fatty acids from phosphatidylinositol by a specific phospholipase A2 (PLA2IVα) with both phospholipase A2 and lysophospholipase activities releases water-soluble glycerophosphoinositol. While this can be hydrolyzed by a glycerophosphodiester phosphodiesterase to inositol 1-phosphate, glycerophosphoinositol per se has distinctive biological activities and functions, as do related compounds derived from the phosphatidylinositol phosphates. In particular, glycerophosphoinositol has anti-inflammatory activity in that it inhibits the inflammatory and thrombotic responses induced by bacterial lipopolysaccharides (endotoxins). Figure $42$ shows the structure of glycerophosphoinositol The Phosphatidylinositol Cycle Phosphatidylinositol can be considered to be at the center of a cycle of reactions and intermediates that are involved in innumerable aspects of cellular signaling in animals (a similar cycle could be described for plants). These are discussed individually at length above, but it is useful to point out how each component forms part of a larger pattern. In brief as illustrated, the various synthetic and hydrolytic reactions involved in phosphoinositide metabolism can be considered to constitute a phosphatidylinositol cycle with enzymes located both in the endoplasmic reticulum and plasma membrane, so lipids have to be transferred across the cytosol in both directions between the two to complete the cycle, probably via adjacent membrane structures and facilitated by proteins of the phosphatidylinositol transfer protein membrane-associated family (PITPNM or nir2), which may channel phosphoinositide production to specific biological outcomes. Phospholipase C and phosphatidylinositol-4-phosphate 5-kinase (PI4P 5K) are located in the plasma membrane, while the cytidine diphosphate-diacylglycerol synthase (CDS2) and phosphatidylinositol synthase are in the endoplasmic reticulum. The epsilon isoform of diacylglycerol kinase (DGKε) is located at contact sites between the endoplasmic reticulum and plasma membrane, but there are nine further isoforms with differing cellular and subcellular locations that may be involved in the cycle. Each turn of the cycle uses a great deal of energy and consumes three moles of ATP, together with cytidine triphosphate and inositol. If it is assumed that the pyrophosphate is hydrolyzed by endogenous pyrophosphatases to inorganic phosphate, the cycle can proceed in one direction only. Figure $43$ shows the phosphatidylinositol cycle. Factors such as membrane curvature must be taken into account, and the diagram is of necessity a considerable over-simplification. In addition to participating in this cycle, many of the lipid intermediates can be precursors for other lipids, and for example, diacylglycerols are potential precursors for triacylglycerols, while phosphatidic acid is a precursor for phosphatidylcholine and phosphatidylethanolamine. Each lipid intermediate is subject to remodeling of the acyl chains via the Lands cycle, and polyunsaturated fatty acids released can be utilized for eicosanoid production. A further by-product of the cycle is inositol triphosphate, which contributes to the regulation of intracellular calcium levels. It has been suggested that the unique molecular species composition of phosphoinositides (18:0-20:4) could influence their selective recycling back into phosphatidylinositol as many of the enzymes involved have a preference for this substrate. A further proposal is that the phosphatidylinositol cycle could act to enrich this species through multiple passages around the cycle.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/02%3A_Unit_II-_Bioenergetics_and_Metabolism/21%3A_Lipid_Biosynthesis/21.04%3A_Biosynthesis_of_Membrane_Sphingolipids.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry by William (Bill) W. Christie and Henry Jakubowski. This section is an abbreviated and modified version of material from the Lipid Web, an introduction to the chemistry and biochemistry of individual lipid classes, written by William Christie. Sphingolipids Introduction: The sphingolipids comprise a wide range of complex lipids in which the defining component is a long-chain or sphingoid base, which in living tissues is usually linked to a fatty acid via an amide bond. J.L.W. Thudichum, a German chemist working in London, first coined the root term “sphingo-” in 1884 following his discovery of the first glycosphingolipids, because the enigmatic nature of the molecules reminded him of the riddle of the sphinx. Regretfully, the importance of his work was not recognized until 25 years after his death, and it was 1947 before the term “sphingolipide” was introduced by Herbert Carter and colleagues. While they are much less enigmatic than they once were, sphingolipids are extremely versatile molecules that continue to fascinate as new knowledge is gained of their functions in healthy (and diseased) animal and plant tissues. They are found in only a few bacterial genera, but they are present in Sphingomonas, Sphingobacterium and a few other species, and many pathogenic species utilize host sphingolipids to promote infections. Novel sphingolipid structures continue to be reported, and as an example at the last count, 188 of the complex sphingolipids classified as gangliosides, with variations in the complex carbohydrate component alone, had been characterized in vertebrates. Long-chain or sphingoid bases, of which sphingosine is typical, are the basic elements and are the simplest possible functional sphingolipids. They vary in chain length and in the presence of various functional groups including double bonds of both the cis- and trans-configuration at different locations in the aliphatic chain. Ceramides, which contain sphingoid bases linked to fatty acids by amide bonds, vary appreciably in the compositions of both aliphatic components, depending on their biological origins. The structure of sphingosine and ceramide, the sphingolipid building blocks, are shown in Figure $1$. Long-chain bases and ceramides have important biological properties in their own right, for example in relation to intra- and inter-cellular molecular signaling, especially in animal cells, while another relatively simple sphingolipid, sphingosine-1-phosphate, is now recognized as a key factor in countless aspects of animal metabolism. The concentrations of these bioactive lipids respond rapidly to the action of specific stimuli and then regulate downstream effectors and targets. Ceramides are the precursors of a multitude of sphingo-phospho- and sphingo-glycolipids with an immense range of functions in tissues. The properties and functions of these complex sphingolipids are quite distinct from those of the comparable glycerophospho- and glyceroglycolipids. For example in animals, sphingomyelin has structural similarities to phosphatidylcholine, but has very different physical and biological properties, while the complex oligoglycosylceramides and gangliosides(glycosphingolipids, of which glucosylceramide is the precursor, have no true parallels among the glyceroglycolipids. Figure $2$ shows the structure of the complex sphingolipids sphingomyelin and glucosylceramide. Complex sphingolipids are synthesized in the endoplasmic reticulum and Golgi, but are located mainly in the plasma membrane of most mammalian cells where they have a structural function and also serve as adhesion sites for proteins from the extracellular tissue. The glycosphingolipids are especially important for myelin formation in the brain. However, sphingolipids have intracellular functions in all cellular compartments, including the nucleus. The first five carbon atoms of the sphingoid base in sphingolipids have a highly specific stereochemistry and constitute a key feature that has been termed the ‘sphingoid motif’, which in comparison to other lipid species facilitates a relatively large number of noncovalent interactions with other membrane lipids, via hydrogen-bonding, ion-ion interactions and induced dipole-induced dipole interactions. A distinctive property of sphingolipids in membranes is that they spontaneously form transient nanodomains termed 'rafts', usually in conjunction with cholesterol, where such proteins as enzymes and receptors congregate to carry out their signaling and other functions. Thus, in addition to their direct effects on metabolism, sphingolipids affect innumerable aspects of biochemistry indirectly via their physical properties. While it may be obvious that a well-balanced sphingolipid metabolism is important for health in animals, increasing evidence has been acquired to demonstrate that impaired sphingolipid metabolism and function are involved in the pathophysiology of many of the more common human diseases. These include diabetes, various cancers, microbial infections, Alzheimer's disease and other neurological syndromes, and diseases of the cardiovascular and respiratory systems. In humans, a number of important genetic defects in sphingolipid metabolism or sphingolipidoses have been detected, especially storage diseases associated with the lysosomal compartment where sphingolipids are catabolized. Sphingolipids and their metabolism are therefore likely to prove of ever increasing interest to scientists. There are appreciable differences in sphingolipid compositions and metabolism between animal and plant cells, both with respect to the aliphatic components and especially the polar head groups, although there are also some important similarities. While sphingomyelin is the most abundant sphingolipid in animals, it does not occur in plants and fungi. Although less is known of the role they play in plants, it has become apparent that complex sphingolipids are much more abundant in plant membranes than was once believed, and it is now recognized that they are key components of the plasma membrane and endomembrane system. Some General Comments on Sphingolipid Metabolism The biosynthesis and catabolism of sphingolipids involves a large number of intermediate metabolites, all of which have distinctive biological activities of their own. In animals, the relationships between these metabolites have been rationalized in terms of a ‘sphingomyelin, sphingolipid or ceramide cycle’, as shown in Figure $3$. Many different enzymes (and their isoforms) are involved, and their activities depend on a number of factors, including intracellular locations and mechanisms of activation. Each of the various compounds in these pathways has characteristic metabolic properties. Thus, free sphingosine and other long-chain bases, which are the primary precursors of ceramides and thence of all the complex sphingolipids, function as mediators of many cellular events, for example by inhibiting the important enzyme protein kinase C. Ceramides are involved in cellular signaling, and especially in the regulation of apoptosis, and cell differentiation, transformation and proliferation, and most stress conditions. In contrast, sphingosine-1-phosphate and ceramide-1-phosphate promote cellular division (mitosis) as opposed to apoptosis, so that the balance between these lipids and ceramide and/or sphingosine levels in cells is critical and necessitates exquisite control in each cellular compartment. Similarly, the ‘structural’ sphingolipids, such as sphingomyelin, monoglycosylceramides, oligoglycosylceramides and gangliosides, all have unique and characteristic biological functions, some of which are due to their physical properties and location within rafts, nanodomains of membranes. Most of the reactions in the sphingomyelin cycle are reversible and the relevant enzymes are located in the endoplasmic reticulum, Golgi, plasma membrane, and mitochondria, but the more complex sphingolipids are catabolized in the lysosomal compartment. Sphingolipids are especially important in providing the permeability barrier in the skin, where they are characterized by the presence of ultra-long fatty acyl components as well as fatty acyl groups linked to a hydroxyl group at the terminal end of the N‑linked fatty acids (thereby generating a three‑chain rather than a two‑chain molecule). Metabolic pathways that are comparable to those of the sphingomyelin cycle are believed to occur in plants, as shown in Figure $4$, although they have not been studied as extensively as those in animals. However, sphingolipid metabolites such as sphingosine-1-phosphate (or analogues) have been linked to programmed cell death, signal transduction, membrane stability, host-pathogen interactions and stress responses, for example. Plants also have a unique range of complex sphingolipids in their membranes, such as ceramide phosphorylinositol and the phytoglycosphingolipids, and these are now known to constitute a higher proportion of the total lipids than had hitherto been supposed, although their functions have hardly been explored. While sphingolipids are produced by relatively few bacterial species, sulfono-analogues of long-chain bases and ceramides (capnoids) are produced by some specie. Fatty acid Components of Sphingolipids The fatty acids of sphingolipids are very different from those of glycerolipids, consisting of very-long-chain (up to C26) odd- and even-numbered saturated or monoenoic and related 2(R)-hydroxy components, while even longer fatty acids (C28 to C36) occur in spermatozoa and the epidermis. The dienoic acid 15,18‑tetracosadienoate (24:2(n‑6)), derived from elongation of linoleic acid, is found in the ceramides and other sphingolipids of a number of different tissues, but at relatively low levels. Polyunsaturated fatty acids are only rarely present, although sphingomyelins of testes and spermatozoa are exceptions in that they contain such fatty acids, which are even longer in chain-length (up to 34 carbon atoms) and include 28:4(n‑6) and 30:5(n‑6). Skin ceramides also contain unusual very-long-chain fatty acids, while yeast sphingolipids are distinctive in containing mainly C26 fatty acids. In plants and yeasts, a similar range of chain-lengths occur as in animals, but 2-hydroxy acids predominate sometimes accompanied by small amounts of 2,3‑dihydroxy acids; saturated fatty acids are most abundant, but monoenes are present in higher proportions in the Brassica family (including Arabidopsis) and a few other species. Some fungal species contain monoenoic fatty acids with a trans-3 double bond and/or a hydroxyl group. Figure $5$ shows typical sphingolipid fatty acids. Very-long-chain saturated and monoenoic fatty acids for sphingolipid biosynthesis are produced from medium-chain precursors by elongases (ELOVL) in the endoplasmic reticulum of cells in mammals, and there is increasing evidence that specific isoforms are involved in the biosynthesis of certain ceramides. For example, ELOVL1 has been linked to the production of ceramides with C24 fatty acids (saturated and unsaturated), while ELOVL4 is responsible for the ultra-long-chain fatty acids in skin. Yeasts possess three elongation enzymes: Elo1 (for medium to long-chain fatty acids), Elo2 (up to C22) and Elo3 (up to C26). The hydroxyl group is believed to add to the hydrogen-bonding capacity of the sphingolipids, and it helps to stabilize membrane structures and strengthen the interactions with membrane proteins. Hydroxylation is effected by a fatty acid 2-hydroxylase in mammals, i.e. an NAD(P)H-dependent monooxygenase, which is an integral membrane protein of the endoplasmic reticulum. It converts unesterified long-chain fatty acids to 2‑hydroxy acids in vitro and probably also in vivo. For example, experimental evidence has been obtained that is consistent with 2‑hydroxylation occurring at the fatty acid level prior to incorporation into ceramides in the brain of mice where the enzyme is expressed at high levels. A second enzyme of this kind is known to exist but has yet to be characterized, and it is possible that a proportion of the odd-chain fatty acids in brain are synthesized by Peroxisomal α-oxidation of the 2‑hydroxy acids. Similarly, in skin, 2‑hydroxy and non-hydroxy fatty acids as their CoA esters are used with equal facility for ceramide biosynthesis by ceramide synthases. As mutations in the fatty acid 2‑hydroxylase in humans and mice give rise to demyelination disorders, such as leukodystrophy, it is evident that sphingolipids containing 2‑hydroxy acids have unique functions in membranes that cannot be substituted by non-hydroxy analogues. In plants, it appears that 2‑hydroxyl groups are inserted into fatty acyl chains while they are linked to ceramide, as ceramide synthase does not accept hydroxy fatty acids in vitro at least. Two fatty acid 2‑hydroxylases (di-iron-oxo enzymes) have been found in Arabidopsis, with one specific for very-long-chain fatty acids and one for palmitic acid. In fungi, a hydroxyl group is inserted at C2 of the fatty acid in a dihydroceramide intermediate. Although the fatty acids are only occasionally considered in terms of the biological functions of sphingolipids, their influence is considerable, especially but not only in relation to their physical properties and function in membranes. For example, very-long-chain fatty acids may play a role in stabilizing highly curved membrane domains as is required during cell division. The hydrophobic nature of the fatty acyl groups (together with the long-chain bases) enables the hydrogen bonding that is essential for the formation of raft nanodomains in membranes. As a general rule, lipid bilayers containing sphingolipids with 2-hydroxy-fatty acyl or 4-hydroxy-sphingoid base moieties, tend to generate condensed and more stable gel phases with higher melting temperatures than their non-hydroxylated equivalents, because they have a more extended and strengthened intermolecular hydrogen bonding network. Changes in fatty acid composition are seen in some disease states, and for example increased concentrations of fatty acids >C24 are a feature of adrenoleukodystrophy, an X-linked genetic disorder. Removal of very-long-chain fatty acids from sphingolipids in mutants of the model plant Arabidopsis inhibits completely the development of seedlings. As example of a more specific interaction, it has been demonstrated that synthetic glycerolipids must contain very-long-chain fatty acids (C26) to allow growth in yeast mutants lacking sphingolipids, probably by stabilizing the proton-pumping enzyme H+-ATPase. Similarly, ceramides containing different fatty acids can be used in highly specific ways. Thus in fungi, C16 or C18 hydroxy acids are used exclusively for synthesis of glucosylceramide, while those containing very-long-chain C24 and C26 hydroxy acids are used only for synthesis of glycosyl inositol phosphorylceramide anchors for proteins. In plants, sphingolipids containing 2-hydroxy acids are protective against oxidative and other biotic stresses. Links between Glycerolipid and Sphingolipid Metabolism Sphingolipid metabolism and glycerolipid metabolism have been widely treated as separate sciences until relatively recently, partly for historical reasons and partly because the analysis of the two lipid groups required different approaches and skills. However, there are many areas where the two overlap, not least because phosphatidylcholine is the biosynthetic precursor of sphingomyelin in animal cells, while in plants and fungi, phosphatidylinositol is the biosynthetic precursor of ceramide phosphorylinositol. In contrast, ethanolamine phosphate derived from the catabolism of sphingolipids via sphingosine 1-phosphate is recycled for the biosynthesis of phosphatidylethanolamine, and this is essential for survival in the protozoan parasite Trypanosoma brucei. In studies in vitro, sphingosine 1-phosphate has been shown to be an activator of the phospholipase C involved in the hydrolysis of the lipid mediator phosphatidylinositol 4,5-bisphosphate with formation of diacylglycerols and inositol triphosphate. The location and functions of glycerophospholipids in membranes is influenced both positively and negatively by sphingolipid-rich domains or rafts in membranes. In addition, there are several examples of phosphoinositides and other complex lipids binding to enzymes of sphingolipid metabolism, either as part of a regulatory function that controls their activity or to facilitate their location to various membranes. Thus, sphingosine kinase 2, one of the enzymes responsible for the biosynthesis of sphingosine 1-phosphate, binds to phosphatidylinositol monophosphates, while the ceramide kinase responsible for the biosynthesis of ceramide 1-phosphate requires phosphatidylinositol 4,5-bisphosphate to function. Similarly, the CERT protein involved in ceramide transport has a binding site for phosphatidylinositol 4-phosphate. Sphingomyelin production at the trans-Golgi network triggers a signaling pathway leading to dephosphorylation of phosphatidylinositol 4-phosphate, interrupting transport of cholesterol and sphingomyelin. Again, the interactions are not solely in one direction as ceramide 1‑phosphate (with phosphatidylinositol 4,5-bisphosphate) binds to the specific phospholipase A2 (cPLA2α) responsible for the hydrolysis of phosphatidylinositol and thence the release arachidonic acid for eicosanoid production. Other than the phosphoinositides, phosphatidylserine activates the neutral sphingomyelinase in brain. Long-Chain (Sphingoid) Bases Long-chain/sphingoid bases are the characteristic and defining structural unit of the sphingolipids, which are important structural and signaling lipids of animals and plants and of a few bacterial species. These are long-chain aliphatic amines, containing two or three hydroxyl groups, and often a distinctive trans-double bond in position 4. To be more precise, they are 2-amino-1,3-dihydroxy-alkanes or alkenes with (2S,3R)‑erythro stereochemistry, often with various further structural modifications in the alkyl chain. They are important for the physical and biological properties of all of the more complex sphingolipids, but free sphingoid bases are also bioactive and interact with specific receptors and target molecules. As discussed below, the mechanisms for biosynthesis of sphingoid bases and of the N-acylated form (ceramides) are intimately linked. Structures and Occurrence In animal tissues, the most common or abundant of the sphingoid bases is sphingosine ((2S,3R,4E)-2-amino-4-octadecene-1,3-diol) or sphing-4E-enine, i.e., with a C18 aliphatic chain, hydroxyl groups in positions 1 and 3 and an amine group in position 2; the double bond in position 4 has the trans (or E) configuration. This was first characterized in 1947 by Professor Herbert Carter, who was also the first to propose the term “sphingolipides” for those lipids containing sphingosine. It is usually accompanied by the saturated analogue dihydrosphingosine (or sphinganine). Sphingoid bases are illustrated in Figure $6$. For shorthand purposes, a nomenclature similar to that for fatty acids can be used; the chain length and number of double bonds are denoted in the same manner with the prefix 'd' or 't' to designate di- and trihydroxy bases, respectively. Thus, sphingosine is denoted as d18:1 and phytosphingosine is t18:0. The position of the double bond may be indicated by a superscript, i.e., 4-sphingenine is d18:1Δ4t or 4E-d18:1. While alternative nomenclatures are occasionally seen in publications, they are not recommended. The number of different long-chain bases that has been found in animals, plants and microorganisms now amounts to over one hundred, and many of these may occur in a single tissue or organism, but almost always as part of a complex lipid with an N-acyl-linked fatty acid and often phosphate or carbohydrate functional groups, as opposed to in the free form. The aliphatic chains can contain from 14 to as many as 28 carbon atoms, and most often they are saturated, monounsaturated or diunsaturated, with double bonds of either the cis or trans configuration. For example, the main dienoic long-chain base (sphingadienine) in human plasma is D-erythro-1,3-dihydroxy-2-amino-4-trans,14-cis-octadecadiene, and this is especially abundant in kidney, with more in women than in men. It is not present in zebra fish, widely used as a model species. Forms with three double bonds, such as sphinga-4E,8E,10E-trienine, sometimes with a methyl group in position 9, have been found the sphingolipids of some marine invertebrates and in a dinoflagellate. In addition, long-chain bases can have branched chains with methyl substituents in the omega‑1 (iso), omega‑2 (anteiso) or other positions, hydroxyl groups in positions 4, 5 or 6, ethoxy groups in position 3, and even a cyclopropane ring in the aliphatic chain in some organisms. N-Methyl, N,N-dimethyl and N,N,N-trimethyl derivatives of sphingoid bases have been detected in mouse brain. The main C18 components of long-chain bases of sphingomyelins of some animal tissues are accompanied by small amounts of C16 to C19 dihydroxy bases, although the latter attain higher proportions in tissues of ruminant animals. In gangliosides from human brain and intestinal tissues, eicosasphingosine (2S,3R,4E-d20:1) occurs in appreciable concentrations with variable amounts in different regions and membranes. However, human skin contains an especially wide range of isomers, including saturated, monoenoic and 6-hydroxy bases and phytosphingosines from C16 to C28 in chain-length. Shorter-chain bases are found in many insect species, and in the fruit fly, Drosophila melanogaster, which is widely used as a model species in genetic and metabolic experiments, the main components are C14 bases. In contrast to higher animals, nematodes such as Caenorhabditis elegans produce C17 iso-methyl-branched sphingoid bases, which are essential for normal sphingolipid function in the organism. The long-chain base composition of individual lipids can vary markedly between species, tissues, organelles and even different membranes within a single organelle. For example, the data in Table $1$ is perhaps from an extreme example, but it illustrates that remarkable differences that can exist among lipids in one cellular component (rat liver mitochondria). Only part of the data from the paper cited is listed, but it illustrates that 3-keto-sphinganine, produced in the first step of sphingosine biosynthesis (see below) and normally a minor component of sphingolipids - often not detectable, can vary from 28 to 100% of the sphingoid bases depending on the lipid class and membrane within the organelle. Table $1$: Long chain base composition of some lipid components of mitochondria from rat liver. Type Base (%) d18:1 d18:0-3keto t21:1 (phyto) Unidentified Ceramidesa 18 28 53 - Glucosylceramidesa 3 95 - 3 Lactosylceramidesb 100 a whole mitochondria; b mitochondrial inner membrane Data from Ardail, D. et al. FEBS Letts, 488, 160-164 (2001). Phytosphingosine or 4D-hydroxy-sphinganine ((2S,3R,4R)-2-amino-octadecanetriol) is a common long-chain base of mainly plant origin. It is a saturated C18-trihydroxy compound, although unsaturated analogues, for example with a trans (or occasionally a cis (Z)) double bond in position 8, i.e., dehydrophytosphingosine or 4D‑hydroxy-8-sphingenine, tend to be much more abundant. In many plant species, there are lipid class preferences also, and dihydroxy long-chain bases are more enriched in glucosylceramides than in glycosylinositolphosphoceramides, for example. This is true in the model plant Arabidopsis thaliana, where the data listed for whole tissue is probably representative largely of the latter lipid, as shown in Table $2$ below. Table $2$: Sphingolipid long-chain base composition of whole tissue and glucosylceramides from Arabidopsis thaliana. Base (%) t18:1 (8Z) t18:1 (8E) t18:0 d18:1 (8Z) d18:1 (8E) d18:0 Whole tissue 12 70 13 4 1 Glucosylceramides 44 22 5 28 2 Data from Sperling, P. et al. Plant Physiol. Biochem., 43, 1032-1038 (2005) Other plant long-chain bases have double bonds in position 4, which can be of either the cis or trans configuration, although trans-isomers are by far the more common, while the base d18:2Δ4E,8Z/E is relatively abundant in most plant species. In A. thaliana and related species, Δ4 long-chain bases are found mainly in the flowers and pollen and then exclusively as a component of the glucosylceramides. In general outwith Brassica species, the composition is dependent on species, but typically it is composed of up to eight different C18-sphingoid bases, with variable geometry of the double bond in position 8, i.e., (E/Z)-sphing-8-enine (d18:1Δ8), (4E,8E/Z)-sphinga-4,8-dienine (d18:2Δ4,8) and (8E/Z)-4-hydroxy-8-sphingenine (t18:1Δ8); d18:1Δ4, d18:0 and t18:0 tend to be present in small amounts only. Phytosphingosine is not restricted to plants, but is found in significant amounts in intestinal cells and skin of animals, with much smaller relative proportions in kidney. Although non-mammalian sphingoid bases in general tend to be poorly absorbed from the intestines, a small proportion of the phytosphingosine and related sphingoid bases found in animal tissues may enter via the food chain. Yeasts and fungi tend to have distinctive and characteristic long-chain base compositions. For example, filamentous fungi have 9-methyl-4E,8E-sphingadienine as the main sphingoid base in the glucosylceramides, as shown in Figure $7$, but not in the ceramide phosphoinositol glycosides, while yeasts contain mainly the saturated C18 bases sphinganine and phytosphingosine, although some trans-4/8-unsaturated forms are usually present. Only a few bacterial species synthesize sphingolipids, but the family Bacteroidetes, which is abundant in the human gut is an important exception; they usually contain saturated (and branched) long-chain bases. Other pathogenic bacteria may utilize sphingolipids and sphingoid bases from their hosts. Sphingoid bases are surface-active amphiphiles with critical micellar concentrations of about 20 μM in aqueous solutions; they probably exist in the gel phase at physiological temperatures. In that they bear a small positive charge at neutral pH, they are unusual amongst lipids, although their pKa (9.1) is lower than in simple amines as a consequence of intra-molecular hydrogen bonding. Together with their relatively high solubility (> 1μM), this enables them to cross membranes or move between membranes with relative ease. In so doing, they increase the permeability of membranes to small solutes. In esterified form in complex lipids, they participate in the formation of ordered lipid domains in membranes such as rafts. In the complex sphingolipids, the sphingoid base is linked via the amine group to a fatty acid, including very-long-chain saturated or monoenoic and 2-hydroxy components, i.e., to form ceramides, which can be attached a polar head group, such as phosphate or a carbohydrate, via the primary hydroxyl moiety. An important exception is sphingosine-1-phosphate, which is not acylated and has signaling functions in cells akin to those of lysophospholipids. Biosynthesis and Metabolism Sphinganine biosynthesis The basic mechanism for the biosynthesis of sphinganine involves condensation of palmitoyl-coenzyme A with L-serine, catalyzed by the membrane-bound enzyme serine palmitoyltransferase, requiring pyridoxal 5’-phosphate as a cofactor, which binds to a specific lysine residue on the enzyme. The reaction occurs on the cytosolic side of the endoplasmic reticulum in animal, plant and yeast cells with formation of 3-keto-sphinganine as illustrated in Figure $8$. This is believed to be the key regulatory or rate-limiting step in sphingolipid biosynthesis and is conserved in all organisms studied to date. Elimination of this enzyme is embryonically fatal in mammals and fruit flies. In mammals, serine palmitoyltransferase is a heterotrimer composed of two main subunits, designated SPTLC1 with either SPTLC2 or SPTLC3 (sometimes termed SPTLC2a and SPTLC2b, respectively). SPTLC1 is essential for activity, and it is ubiquitously expressed as is SPTLC2, while SPTLC3 is present in a relatively limited range of tissues and is most abundant in skin and placental tissue. In addition, there are two small subunits ssSPTA and ssSPTB (again other nomenclatures exist), which differ in a single amino acid residue, and may have regulatory functions; the active site is at the interface between the two main subunits. ssSPTA is essential for serine palmitoyltransferase function during development and hematopoiesis. A possible mechanism for the 1st step in the pathway, catalyzed by serine palmitotyltransferase, is shown in Figure $9$. The addition of either of the two small subunits to the complexes changes the substrate preferences substantially and enables the synthesis of the wide range of homologs found in nature. In mammals, the SPTLC1-SPTLC2 complex forms C18 sphingoid bases specifically (with some C19, and C20), while the combination of SPTLC1 and SPTLC3 gives a broader product spectrum, including an anteiso-methylbranched-C18 isomer (from anteiso-methyl-palmitate as the precursor). Such branched bases are synthesized to a limited extent in human skin, but they are the main forms in lower invertebrates such as C. elegans. The activity of the serine palmitoyltransferase is governed by negative feedback and partly by orosomucoid (ORM-like or ORMDL) proteins, three in mammals (ORMDL1 to 3) and two in yeast (Orm1/2), which are ubiquitously expressed trans-membrane proteins located in the endoplasmic reticulum. The availability of serine is also an important factor. Figure $10$ shows an interactive iCn3D model of the human serine palmitoyltransferase complex (7K0M). Figure $10$: Human serine palmitoyltransferase complex (7K0M).. Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...tuGThTq9Vi4J58 • Gray and Plum (SPT1 A and E Chains • Light Blue and Blue (SPT2 B and F Chains) • Light Brown (small subunit A - ssA, C and G chains) • Yellow (ORM D and H) The second step in sphinganine biosynthesis is reduction of the keto group to a hydroxyl in an NADPH-dependent manner by a specific 3‑ketodihydrosphingosine reductase ('3KSR'), also on the cytosolic side of the endoplasmic reticulum, a step that must occur rapidly as the intermediate is rarely encountered in tissues. The enzymes are presumed to be in similar subcellular locations in plant cells. In plants, serine palmitoyltransferase is a heterodimer composed of LCB1 and LCB2 subunits with some homology to the mammalian enzymes, while in the yeast Saccharomyces cerevisiae, there are three subunits: Lcb1, Lcb2, and Tsc3. In the few bacteria that synthesize sphingoid bases, serine palmitoyltransferase is a water-soluble homodimer. The enzyme in the apicomplexan parasite Toxoplasma gondii is a homodimer also in contrast to other eukaryotes, but it is located in the endoplasmic reticulum. Free sphinganine formed in this way is rapidly N-acylated by acyl-coA to form dihydroceramides by dihydroceramide synthases, which in animals are located primarily on the endoplasmic reticulum, presumably on the cytoplasmic surface. Animals and plants have multiple isoforms of this enzyme, for which the abbreviated term ‘ceramide synthase’ is now widely applied as they utilize most other sphingoid bases, such as those produced by hydrolysis of sphingolipids, as substrates. They are unique gene products with each located on a different chromosome and with considerable variation in the expression of the enzymes in different cell types within each tissue. Each isoenzyme has distinct specificities for the chain-length of the fatty acyl-CoA moieties but to a limited extent only for the base, suggesting that ceramides containing different fatty acids have differing roles in cellular physiology. All of these enzymes have six membrane spanning regions, but the only substantial difference is in an 11-residue sequence in a loop between the last two putative transmembrane domains. Ceramides are central to all elements of sphingolipid biochemistry. These steps are illustrated in Figure $11$. Humans and mice have six ceramide synthases, which utilize subsets of acyl-CoAs and thus producing ceramides with specific acyl chain lengths. Of these, ceramide synthase 2 is most abundant and is specific for coA esters of very-long-chain fatty acids (C20 to C26); it is most active in lung, liver and kidney. Ceramide synthase 1 is specific for 18:0 and is located mainly in brain with lower levels in skeletal muscle and testes. Ceramide synthase 3 is responsible for the unusual ceramides of skin and testes and uses C26-CoA and higher including polyunsaturated-CoAs with the latter tissue, while ceramide synthase 4 (skin, liver, heart, adipose tissue and leukocytes) uses C18 to C22-CoAs. Ceramide synthases 5 (lung epithelia and brain gray and white matter) generates C16 (mainly) and C18 ceramides, and ceramide synthase 6 (intestine, kidney and lymph nodes) produces C14 and C16 ceramides. However, hydroxylation and the presence or otherwise of double bonds in the acyl-coAs do not appear to influence the specificity of the ceramide synthases. Also, the expression of mRNA expression for ceramide synthases does not always correlate with the fatty acid composition of sphingolipids in a particular tissue, suggesting that other factors are involved in determining which molecular species are formed. One such is acyl-coenzyme A-binding protein (ACBP), which facilitates the synthesis of ceramides containing very-long fatty acids and stimulates ceramide synthases 2 and 3 especially. Insertion of the trans-double bond in position 4 to produce sphingosine occurs only after the sphinganine has been esterified in this way to form a ceramide as illustrated in Figure $11$, with desaturation occurring at the cytosolic surface of the endoplasmic reticulum also. The desaturases were first characterized in plants, and this subsequently simplified the isolation of the appropriate enzymes in humans and other organisms. Two dihydroceramide desaturases have now been identified in animals and designated 'DEGS1 and DEGS2'. Both enzymes insert trans double bonds in position 4, but DEGS2 is a dual function enzyme that also acts as a hydroxylase to generate phytoceramides, i.e., to add a hydroxyl group on position 4. Distribution of the enzymes in tissues is very different, with DEGS1 expressed ubiquitously but highest in liver, Harderian gland, kidney and lung. DEGS2 expression is largely restricted to skin, intestine and kidney, where phytoceramides are more important. A considerable family of Δ4-sphingolipid desaturases has now been identified, and an early study by Stoffel and colleagues demonstrated that Δ4-desaturation involves first syn-removal of the C(4)- HR and then the C(5)-HS hydrogens. This appears to have been the first evidence that desaturases in general operate in this stepwise fashion. The enzyme responsible for the insertion of the cis-14 double bond into sphinga-4-trans,14-cis-dienine is the fatty acid desaturase 3 (FADS3), which utilizes ceramides containing sphingosine as the precursor. The only other known activity of this enzyme is to insert a cis-double bond in position 13 of the CoA ester of vaccenic acid (11t-18:1) to produce the conjugated diene 11t,13c-18:2. Synthesis of sphingoid bases de novo is essential in most organisms and inhibition of the biosynthetic pathways affects growth and viability. However, this can be tissue specific, as deletion of the liver-specific SPTLC2 in mice, was found to have no effect on liver function, while a comparable deletion of adipocyte-specific SPTLC1 caused major tissue defects. Presumably, the latter tissue is unable to take up enough sphingolipid from the circulation to remedy the problem. Deficiencies in SPTLC3 are related to dermal pathologies, and genetic variant of SPTLC3 are associated with dyslipidemia and atherosclerosis. The essentiality of sphingoid base synthesis in plants has been demonstrated in a similar manner in studies with mutants in which specific enzymes have been deleted. Phytosphingosine and plant ceramides: Phytosphingosine is formed from sphinganine, produced as above, by hydroxylation in position 4, possibly via the free base in plants, although it can be formed both from sphinganine and a ceramide substrate in yeasts. A single sphinganine C4‑hydroxylase is present in yeast, but Arabidopsis has two such enzymes (SBH1 and 2), which are critical for growth and viability. Much remains to be learned of the processes involved, but it is known that the enzyme responsible is closely related to a Δ4 desaturase. Indeed, it has been shown that there are bifunctional Δ4‑desaturase/Δ4-hydroxylases in Candida albicans and mammals, especially in keratinocytes (DEGS2 discussed above) with which either 4‑hydroxylation or Δ4‑desaturation is initiated by removal of the proR C-4 hydrogen. Sphinganine linked to ceramide is the substrate for 4-hydroxylation in intestinal cells. In Arabidopsis thaliana leaves, 90% of the sphingoid bases are phytosphingosine with a Δ8‑double bond. In plants in general, in addition to Δ4‑desaturation, two distinct types (20 gene products) of sphingoid Δ8-desaturase have been characterized that catalyse the introduction of a double bond at position 8,9 of phytosphingosine. These are evolutionarily distinct from the Δ4‑desaturases. One type produces the trans (E)-8 isomer mainly and the other mostly the cis (Z)-8 isomer, with overall the trans-isomer tending to predominate but dependent upon plant species. It appears that the trans isomer is formed when the hydrogen on carbon 8 is removed first, and the cis when carbon 9 is the point of attack. While the main group of Δ8-desaturases requires a 4‑hydroxysphinganine moiety as substrate, the second does not. In Arabidopsis, three different isoforms of ceramide synthase have been identified and denoted LOH1, LOH2 and LOH3. Phytosphingosine is used efficiently by LOH1 and LOH3 (class II synthases), but only LOH2 (class I synthase) uses sphinganine efficiently; LOH2 and 3 prefer unsaturated long-chain bases. Marked fatty acid specificity is also observed with LOH2 showing almost completely specific for palmitoyl-CoA and dihydroxy bases, while LOH1 shows greatest activity for 24:0- and 26:0-CoAs and trihydroxy bases; none utilize unsaturated acyl-CoA esters efficiently. In plants, fatty acid desaturases and hydroxylases are also closely related, and sphingolipid fatty acid α-hydroxylation is believed to occur on the ceramide, as opposed to the free acyl chain. It is believed that the Δ8‑desaturase utilizes ceramide as the substrate and the channels the products selectively into the synthesis of complex sphingolipids, while Δ4‑desaturation channels ceramides for synthesis of glucosylceramide. It has been established that long-chain bases with 4-hydroxyl groups are necessary for the viability of the filamentous fungus Aspergillus nidulans and for growth in plants such as A. thaliana. The presence of an 8E double bond confers aluminium tolerance to yeasts and plants, and it is important for chilling resistance in tomatoes. However, a trans-4 double bond in the sphingoid base does not appear to be essential for growth and development in Arabidopsis. Fungal sphingoid bases: Fungi produce trans Δ8-isomers only, but Δ4- and Δ8-desaturases do not occur in the widely studied yeast S. cerevisiae. In the biosynthesis of sphingoid bases in fungi, the double bonds in positions 4 and 8 and the methyl group in position 9 are inserted sequentially into the sphinganine portion of a ceramide, the last by means of an S-adenosylmethionine-dependent methyltransferase similar to plant and bacterial cyclopropane fatty acid synthases. In S. cerevisiae the ceramide synthase is a heteromeric protein complex, containing three subunits, Lag1, Lac1, and Lip1, of which the first two are homologous proteins that feature eight transmembrane domains. In the yeast Pichia pastoris, there is a distinct ceramide synthase, which utilizes dihydroxy sphingoid bases and C16/C18 acyl-coenzyme A as substrates to produce ceramides. The long-chain-base components of the ceramide are then desaturated in situ by a Δ4‑desaturase and the fatty acid components are hydroxylated in position 2. Further desaturation of the long-chain base component by a Δ8-(trans)- desaturase occurs before the methyl group in position 9 is introduced by an S-adenosylmethionine-dependent sphingolipid C-9 methyltransferase. As a final step a trans-double bond may be introduced into position 3 of the fatty acid component. These ceramides are used exclusively for the production of glucosylceramides, and it is believed that a separate ceramide synthase encoded by a different gene produces the ceramide precursors for ceramide phosphorylinositol mannosides. Viral sphingoid bases: The genome of an important marine virus (EhV) encodes for a novel serine palmitoyltransferase, which hijacks the metabolism of algal hosts to produce unusual hydroxylated C17 sphingoid bases; these accumulate in lytic cells of infected algae such as the important bloom-forming species Emiliania huxleyi. While this may seem a rather esoteric topic, viruses constitute a high proportion of the marine biome, and their control of the growth of algal blooms has global consequences. Unesterified sphingosine: A cycle of reactions occurs in tissues by which sphingoid bases are incorporated via ceramide intermediates into sphingolipids, which are utilized for innumerable functions, before being broken down again to their component parts. It is worth noting that all the free sphingosine in tissues must arise by this route, in particular by the action of ceramidases on ceramides. Five such ceramidases are known with differing pH optima and varying subcellular locations. The levels of free sphingoids and their capacities to function as lipid mediators, as shown in Figure $12$, are controlled mainly by enzymic re‑acylation to form ceramides, although some is acted upon by sphingosine kinases to produce sphingosine-1-phosphate. Free sphingoid bases are absorbed by enterocytes following digestion of dietary sphingolipids in animals (including some from gut microorganisms), and while some of this is converted to complex sphingolipids, much is catabolized with the eventual formation of palmitic acid. Catabolism of sphingosine and other long-chain bases occurs after conversion to sphingosine-1-phosphate and analogues. In yeasts, an alternative means of detoxification has been reported in which an excess of phytosphingosine is first acetylated and then converted to a vinyl ether prior to export from the cells. Biological Functions of Unesterified Sphingoid Bases The primary function of sphingoid bases is to serve as a basic component of ceramides and complex sphingolipids, where variations in their compositions can influence the physical and biological properties of these lipids. Independently of this in their free (unesterified) form, they are important mediators of many cellular events even though they are present at low levels only in tissues (typically 25 and 50 nM in plasma), with intracellular levels determined by hydrolysis by ceramidases or by the action of sphingosine kinases (sphingosine-1-phosphate production). In animal cells, they inhibit protein kinase C indirectly, possibly by a mechanism involving interference with the binding of activators of the enzyme, such as diacylglycerols or phorbol esters. In addition, sphingoid bases are known to be potent inhibitors of cell growth, although they stimulate cell proliferation and DNA synthesis. They are involved in the process of apoptosis in a manner distinct from that of ceramides by binding to specific proteins and regulating their phosphorylation. While sphingosine does not appear to participate in raft formation in membranes, it may rigidify pre-existing gel domains in mixed bilayers, although any such effects will be dependent on local concentrations and pH. It should be noted that some of the biological effects observed experimentally may be due to conversion to sphingosine-1-phosphate. Free sphingosine has been implicated in various pathological conditions, and for example, plasma sphingosine levels are increased in hyperthyroidism and in patients with type 2 diabetes. Lysosomal storage of the lipid is an initiating factor in Niemann Pick type C disease, a neurodegenerative disorder, where it causes a change in calcium release leading to a buildup of cholesterol and sphingolipids. In the human adrenal cortex, sphingosine produced in situ by the acid ceramidase has a function in steroid production by serving as a ligand for steroidogenic factor 1 at the cell nucleus, which controls the transcription of genes involved in the conversion of cholesterol to steroid hormones. Unesterified sphingoid bases may have a protective role against cancer of the colon in humans. Thus, N,N‑dimethylsphingosine and dihydrosphingosine, like the deoxysphingoid bases, are known to induce cell death in a variety of different types of malignant cells. There is evidence that sphingadienes of plant and animal origin inhibit colorectal cancer in mouse models by reducing sphingosine-1-phosphate levels. In consequence, synthetic analogues of long-chain bases are being tested for their pharmaceutical properties. Free sphingosine is believed to have a signaling role in plants by controlling pH gradients across membranes. In addition, free long chain bases (and the balance with the 1-phosphate derivatives) are essential for the regulation of apoptosis in plants. Ceramides Structure and Occurrence The structure of ceramide is shown again in Figure $13$. Figure $13$: Structure of ceramides (with varying fatty acids in ester link) Ceramides consist of a long-chain or sphingoid base linked to a fatty acid via an amide bond. They are essential intermediates in the biosynthesis and metabolism of all sphingolipids including the complex sphingolipids in which the terminal primary hydroxyl group is linked to carbohydrate, phosphate, and so forth (sphingomyelin, glycosphingolipids and gangliosides) as shown in Figure $14$. They are also the primary source of unesterified sphingoid bases and of the important biological mediators sphingosine-1-phosphate and ceramide-1-phosphate. At the last count, 33 different enzymes were known to participate in ceramide metabolism. While ceramides are rarely found as such at greater than trace amounts in tissues other than skin, they can exert important biological effects of their own at these low levels. They are present in membranes where they participate in the formation of raft domains. Each organism and indeed each tissue may synthesize ceramides in which there are a variety of di- and trihydroxy long-chain bases linked to fatty acids. As discussed previously, the fatty acids consisting mainly of longer-chain (up to C24 or greater) saturated and monoenoic (mainly (n-9)) components, sometimes with a hydroxyl group in position 2. Other than in certain testicular cells, polyunsaturated fatty acids do not occur. More than 200 structurally distinct molecular species of ceramides have been characterized from mammalian cells. In plants, 2-hydroxy acids predominate sometimes accompanied by small amounts of 2,3-dihydroxy acids. Although small amounts of free ceramides are produced in all tissues as required for the specific biological functions described below, most is converted rapidly to more complex sphingolipids, including sphingomyelin (in animals) and the various glycosylceramides. The ceramides in skin are a remarkable exception to this rule, and as such they are discussed separately below. A shorthand nomenclature simply combines those used conventionally for fatty acids and long-chain bases to denote molecular species of ceramides, including those as components of more complex lipids, e.g. N-palmitoyl-sphingosine is d18:1-16:0. Ceramides containing sphinganine are sometimes termed ‘dihydroceramides’. Ceramide Biosynthesis Ceramide production is complex and involves at least three pathways. Biosynthesis de novo takes place in the endoplasmic reticulum with palmitoyl-CoA and serine as the precursors for the long-chain base component, which is subsequently converted to ceramide. Biosynthesis of the very specific fatty acids in ceramides involving various chain elongases (ELOVL) requires consideration also. Alternative routes for ceramide production involve regeneration from complex sphingolipids. For example, in animals in the sphingomyelinase pathway, conversion of sphingomyelin into ceramides (and vice versa) occurs in the plasma membrane, Golgi and mitochondria. Finally, the polar moieties of complex glycosphingolipids can be removed by various hydrolytic enzymes in the lysosomal compartment to recover the ceramides (or their component parts) in a re-cycling/catabolic process. As these biosynthetic or metabolic pathways are located in different organelles, specific pools of ceramide and sphingolipids result with differing biological properties and functions. Ceramide synthesis de novo: The first of these pathways is described in mechanistic. In brief in animals, sphinganine is coupled to a long-chain fatty acid to form dihydroceramide by means of one of six ceramide synthases in the endoplasmic reticulum mainly, before the double bond is introduced into position 4 of the sphingoid base. Of these, ceramide synthase 2 is most abundant and is specific for CoA esters of very-long-chain fatty acids (C20 to C26); it is most active in the central nervous system. Ceramide synthase 1 is specific for 18:0 and is located exclusively in brain and skeletal muscle, ceramide synthases 5 and 6 generate 16:0-containing ceramides, and ceramide synthase 3 is responsible for the unusual ceramides of skin and testes. Figure $15$ shows again the synthesis of ceramide from sphinganine and palmitoyl-CoA (a repeat of Figure $11$ Each synthase has six membrane-spanning domains and contains a characteristic motif with the specific structures required for catalysis and substrate binding that are essential for its activity, and they have been shown to differ primarily in an 11-residue sequence in a loop between the last two putative transmembrane domains. In addition to separate transcriptional regulation of each of these enzymes, ceramide synthase activity is modulated by many different factors including reversible dimerization, while ceramide synthase 2 has a sphingosine-1-phosphate binding motif and this lipid may inhibits its activity. Acyl-coenzyme A-binding protein (ACBP) facilitates the synthesis of ceramides containing very-long fatty acids and stimulates ceramide synthases 2 and 3 especially. Most of the ceramides generated in this way are rapidly utilized for synthesis of complex sphingolipids, especially sphingomyelin and hexosylceramides, to ensure that cellular ceramide concentrations are regulated to control their biological activities. In mammalian cells, most complex glycerolipids are synthesized in the endoplasmic reticulum prior to their transport to their final subcellular locations, but the process is rather different for sphingolipids. Ceramide is synthesized on the cytoplasmic leaflet of the endoplasmic reticulum, but subsequent formation of complex sphingolipids occurs in the Golgi apparatus, and a key cytoplasmic protein, ceramide transporter or 'CERT' (CERamide Trafficking), mediates the transport of ceramide between these organelles in a non-vesicular manner. It has a number of distinct functional domains, including an N-terminal phosphatidylinositol-4-monophosphate (PI(4)P)-binding or Pleckstrin homology (PH) domain, which targets the Golgi apparatus, and a C-terminal ‘START’ domain, which can recognize ceramide species with the natural D-erythro stereochemistry, including dihydroceramide and phytoceramide (but not sphingosine), and holds them within in a long amphiphilic cavity by hydrogen bonding with all three polar atoms of the sphingoid motif. There is also a short peptide motif (FFAT) that recognizes a specific protein in the endoplasmic reticulum. There is sufficient flexibility in the body of the protein to enable transfer of ceramide from the endoplasmic reticulum to the Golgi without free movement through the cytosol. Very-long-chain ceramides containing 24:0 or 24:1 fatty acids turn over much more rapidly in animal cells than those containing 16:0 or 18:0 fatty acids, because of the more rapid conversion of the former into complex sphingolipids, where they may regulate the levels and perhaps the biological functions of the latter. In contrast, ceramides containing d16:1 and d18:1 sphingoid bases turnover at similar rates so do not affect the flux of ceramides through these pathways. The CERT protein is a major factor in this specificity, as it extracts ceramides from membrane bilayers with a preference for those required for synthesis of complex sphingolipids. Removal of ceramide by this process provides the gradient that enables the process to continue, and prevents an accumulation of ceramide in the endoplasmic reticulum that might otherwise be disruptive to the membrane and even cause cell death. While the transfer process itself is not dependent on ATP, the overall process requires ATP, possibly to keep PI(4)P in a phosphorylated form, and the multiple factors that control the biosynthesis of this lipid must also influence sphingolipid metabolism. As a neutral lipid, ceramide can flip readily across membrane leaflets, and this is also necessary for the synthesis of sphingomyelin, which occurs on the lumen of the Golgi. The pool of ceramide utilized for the synthesis of glycosylceramide is delivered to the Golgi by a separate transport mechanism that also does not require ATP. In addition, some ceramide synthesis occurs in mitochondria although this has the potential to lead to cell death. Regulation of ceramide and subsequent sphingolipid biosynthesis is crucial as an excess of sphingolipids can be toxic, while reduced synthesis can inhibit cell proliferation. Some ceramides are transported from the liver to other tissues in plasma lipoproteins, but especially subclasses HDL2 and HDL3, i.e. those containing apolipoprotein B. There is a suggestion that transport of ceramides via lipoproteins could be a paracrine mechanism to regulate the metabolism of other cells. Ceramides are also produced during the catabolism of other complex sphingolipids, and especially by the action of one or other of the sphingomyelinases or of phospholipase C on sphingomyelin in animal tissues as part of the 'sphingomyelin cycle' as shown in Figure $16$. Many agonists including chemotherapeutic agents, tumor necrosis factor-alpha, 1,25-dihydroxy-vitamin D3, endotoxin, gamma-interferon, interleukins, nerve growth factor, ionizing radiation and heat stimulate hydrolysis of sphingomyelin to produce ceramide. In addition, reversal of the sphingomyelin synthesis reaction may generate ceramide, and some may be produced by operation of the enzyme ceramidase in reverse (see next section). Such reactions are much more rapid than synthesis de novo, so they are of special relevance in relation to the signaling functions of ceramides, especially when they occur at the plasma membrane. For example, in this context, the acid sphingomyelinase may be especially important by generating the ceramides that initiate the train of events that leads to apoptosis (see below). Glycosphingolipids can be hydrolyzed by glycosidases to ceramides also in tissues, but the process tends to be less important in quantitative terms (other than in skin). The key enzymes of sphingolipid metabolism were first characterized from the yeast Saccharomyces cerevisiae, and these were found to be sufficiently similar to the corresponding enzymes in mammals to facilitate their study in the latter. As discussed above, there are specific ceramide synthases that utilize specific fatty acids for ceramide biosynthesis in animals, and knowledge is slowly being acquired of how these are compartmentalized and regulated within cells. Thus, the synthesis and subsequent catabolism of ceramides involves a complex web of at least 28 distinct enzymes, including six ceramide synthases and five sphingomyelinases, which are all products of different genes. Each of these enzymes may produce distinctive molecular species of ceramides with their own characteristic biological properties. It has been determined that ceramide species containing very-long-chain fatty acids (C24) turnover more rapidly than those containing C16/18 components. Ceramide Catabolism In animals, ceramide metabolism and function are controlled in part by the action of ceramidases, which cause hydrolysis forming sphingoid bases and free fatty acids, and indeed this is the only route to the formation of unesterified sphingosine. This is illustrated in Figure $17$. Five such enzymes are known in humans, classified according to their pH optima, i.e. acid (‘ASAH1’), neutral (‘ASAH2’, which differs between humans and animals), and alkaline (three enzymes - ‘ACER1 to ACER3’), with differing cellular locations and fatty acid specificities and with the potential to affect distinct signaling and metabolic events. The acid ceramidase is of particular importance, and aberrations in its synthesis or activity is involved in several human disease states, including the rare autosomal-recessive Farber disease where there is a deficiency in the enzyme so ceramide accumulates; ceramide containing 26:0 in the blood is considered to be a biomarker for diagnosis of the disease. ASAH1 is located in the lysosomes and hydrolyses ceramides with small to medium-chain fatty acid components (C6 to C18) most efficiently. The neutral ceramidase is located in the plasma membrane and Golgi, especially of intestinal epithelial cells and colorectal tissues, and prefers long-chain components (C16 to C18); it also catalyzes the reverse reaction, and this may be a means of ceramide synthesis in mitochondria. ACER1 and ACER2 are found in the endoplasmic reticulum and Golgi, respectively, and they prefer species with very-long-chain acyl groups. ACER3 is present in both the endoplasmic reticulum and Golgi; it has a marked specificity for ceramides, dihydroceramides, and phytoceramides linked to unsaturated long-chain fatty acids (18:1, 20:1 or 20:4) in vitro at least. Neutral/alkaline ceramidase activity has also been found in mitochondria and nuclei. In Arabidopsis, an alkaline ceramidase (AtACER) can hydrolyze phytosphingosine-containing ceramides, and a related enzyme from rice has a preference for d18:1Δ4-ceramide; the latter can function in reverse to increase the content of C26- and C28-phytoceramides. Several neutral ceramidases (AtNCERs) have been identified, but there does not appear to be an equivalent to the acid ceramidase in plants. Ceramidases are also present in lower organisms such as Pseudomonas aeruginosa and slime molds, where they are secreted proteins rather than integral membrane enzymes. A neutral ceramidase only is found in prokaryotes, including some pathogenic bacteria. Sphingoid bases released by the action of acid ceramidase can escape from the lysosomes and be re-utilized for ceramide biosynthesis through the action of a ceramide synthase. This has been termed the ‘salvage’ pathway and is important in both quantitative and biological terms. For example, it has been estimated that it contributes from 50 to 90% of sphingolipid biosynthesis. The biological functions of ceramides are discussed below, but there are reasons to believe that ceramides derived from the salvage pathway are spacially and thence functionally distinct from those synthesized de novo. In addition, sphingoid bases released in this way have their own biological functions, which includes utilization for the synthesis of the biologically important metabolite sphingosine-1-phosphate. Therefore, regulation of ceramidase action is central to innumerable biological processes in animals. Biological Functions of Ceramides The role of ceramides in the biosynthesis of complex glyco- and phospho-sphingolipids are discussed elsewhere in this text. Ceramides, like other lipid second messengers in signal transduction, are produced rapidly and transiently in response to specific stimuli in order to target specific proteins, for example to activate certain serine/threonine protein kinases or phosphatases. They may also regulate cellular processes by influencing membrane properties. While they can be produced by synthesis de novo for such functions, activation of one of the sphingomyelinases under physiological stress or other agents is a more rapid means of generation in animal tissues at least. In fact, ceramides appear to be formed under all conditions of cellular stress by a multiplicity of activators in eukaryotic organisms. However, it should be noted that ceramides with different fatty acid and long-chain base (molecular species) compositions are formed in different compartments or membranes of the cell by various mechanisms over different time scales and potentially with distinct functions. The biological functions of those ceramides containing medium-chain (up to C14), long-chain (C16 and C18), and very-long-chain (C20 and longer) fatty acids, in particular, may have to be considered separately. Physical properties: Unsaturation in the sphingoid backbone augments intramolecular hydrogen bonding in the polar region, which permits a close packing of ceramide molecules and a tight intramolecular interaction in membranes. A further important factor in this context is the length of the fatty acyl moiety, as shorter-chain ceramides tend to produce a positive curvature in a lipid monolayer, while long-chain molecules have the opposite effect and possess a marked intrinsic negative curvature that facilitates the formation of inverted hexagonal phases as well as increasing the order of the acyl chains in bilayers. By their interactions with ion channels, ceramides influence the permeability of membranes and render bilayers and cell membranes permeable to solutes that vary from small- up to protein-size molecules. While ceramides are minor components of membranes in general, their physical properties ensure that they are concentrated preferentially into lateral liquid-ordered microdomains (a distinct form of 'raft' termed ‘ceramide-rich platforms’), although these effects are again chain-length specific. These domains differ appreciably in composition from those rafts enriched in sphingomyelin and cholesterol, and ceramides containing C12 to C18 fatty acids can in fact displace cholesterol from rafts to modify their physical properties. Ceramides are generated within rafts by the action of acid sphingomyelinase, causing small rafts to merge into larger units and modifying the membrane structure in a manner that is believed to permit oligomerization of specific proteins such as cytokines and death receptors. Ceramides are also essential for the formation and/or secretion of exosomes by facilitating or inducing membrane curvature. In contrast, sphingosine, sphingosine-1-phosphate and ceramide-1-phosphate do not facilitate raft formation. Through the medium of these modified rafts, ceramides are able to function in signal transduction. Specific receptor molecules and signaling proteins are recruited and cluster within such domains, thereby excluding potential inhibitory signals, while initiating and greatly amplifying primary signals. It is believed that ceramide-rich platforms amplify both receptor- and stress-mediated signaling events and thence may influence various disease states. Ceramide-enriched membrane domains formed in response to sphingomyelinase activity are sites for endocytic uptake of pathogens because of a concentration of pathogen receptors and signaling complexes, and in particular these can enhance viral infections, including Norovirus, Japanese encephalitis virus, Ebola and possibly SARS-CoV-2. However, elevated levels of ceramide inhibit cellular uptake of the HIV virus. Although ceramides and diacylglycerols have structural similarities, their occurrence, location, and behavior in membranes are different. Ceramides cross synthetic lipid bilayers relatively quickly in vitro, but it is not clear whether they can flip across more complex biological membranes equally readily, especially in the ceramide-rich platforms. Restricted flipping could have important effects on the signaling role of ceramides in that those generated by different enzymes on each side of a membrane could have distinct functions. Enzyme activation: In general, ceramides tend to modify intracellular signaling pathways to slow anabolism and promote catabolism. Amongst a wide range of biological functions in relation to cellular signaling, ceramides are especially important in triggering apoptosis, and they have also been implicated in the activation of various protein kinase cascades, dependent on the site of generation. The mechanism of these interactions is the subject of intensive study at present, but in relation to the latter, two intracellular targets for ceramide action of special importance have been discovered – at least two protein phosphatases (ceramide-activated protein phosphatases) and a family of protein kinases (ceramide-activated protein kinases). For example, the phosphatase may be involved in the regulation of glycogen synthesis, insulin resistance, and response to apoptotic stimuli. Ceramides generated by the action of sphingomyelinase and by synthesis de novo are both important to the process, while ceramidases have contrasting effects in these and other biological effects of ceramides. Apoptosis: The role of ceramides in the regulation of apoptosis, and cell differentiation, transformation, and proliferation has received special attention. Apoptosis is a normal process, which occurs in response to oxidative stress in particular, in which a cell can be considered to actively ‘commit suicide’. It is essential for many aspects of normal development and is required for maintaining tissue homeostasis. There are two pathways - 'extrinsic' initiated in the plasma membrane by ligation of so-called 'death factors', such as the tumor necrosis factor-α (TNF-α), and 'intrinsic' induced by external actions in mitochondria, e.g. by DNA damage, oxidation or radiation injury. Although the mechanism of the ceramide interaction with these pathways is uncertain, it is clear that a cascade of reactions is initiated that culminates in the release of intracellular proteases of the caspase family to promote apoptosis. In dysfunctional mitochondria, one mechanism involves the formation of channels in the membrane that enable the release of specific mitochondrial proteins that include caspases. Ceramides with fatty acids of differing chain lengths are believed to function in different ways, and 16:0-ceramide generated by ceramide synthase 6 is especially pro-apoptotic, for example, while ceramides with very-long-chain fatty acids accumulate in necroptosis, a form of apoptosis. On the other hand, ceramides containing 2-hydroxy acids in keratinocytes appear to be protective against apoptosis. Ceramides induce the related process of cellular senescence also. Failure to properly regulate apoptosis can have catastrophic consequences, and many disease states, including cancer, diabetes, neuropathies, Alzheimer's disease, Parkinson's disease, and atherosclerosis, are thought to arise from the deregulation of apoptosis. For example, ceramides have been implicated in the actions of TNF-α and in the cytotoxic responses to amyloid Aβ peptide, which are involved in Alzheimer’s disease and neurodegeneration. In addition, ceramides appear to be involved in many aspects of the biology of aging and of male and female fertility. These effects may hold implications for diseases associated with obesity and insulin resistance, including again diabetes and cardiovascular disease. Similarly, ceramides are intimately involved in the induction of autophagy, the 'maintenance' process by which cellular proteins and excess or damaged organelles are removed from cells by engulfing them in a membrane-enclosed cellular compartment called the phagosome. In particular, maturing phagosomes are enriched in very-long-chain ceramides. While this process is beneficial in that it aids the recycling of cellular nutrients, the presence of excess ceramide can lead to unnecessary apoptosis. As animals and plants have multiple isoforms of ceramide synthase that are specific for the chain length of the base and fatty acid, it has been suggested that ceramides containing different fatty acids have distinct roles in cellular physiology. In particular, C16 ceramide appears to be especially important in apoptosis in non-neuronal tissues, while C18 ceramide has growth-arresting properties and may be involved in apoptosis in some carcinomas treated with chemotherapy agents. In addition, a transferase has been identified that transfers the acetyl group from platelet-activating factor to sphingosine with a high specificity. The product, N-acetylsphingosine - the simplest of all ceramide molecules, has signaling functions that are distinct from those of the parent lipids or of other ceramides; it does not enter the salvage pathway in cancer cells in vitro and is cytotoxic. In contrast, the ceramide metabolite, sphingosine-1-phosphate, has opposing effects on cell survival and proliferation. As ceramide and sphingosine-1-phosphate are inter-convertible via sphingosine as an intermediate, which also has pro-apoptopic activity, the balance between these lipids and with ceramide-1-phosphate is obviously of great metabolic importance. It has been termed the ‘sphingolipid-rheostat’, as illustrated in Figure $18$. Plants: Comparatively little information is available on the role of ceramides in cell signaling in plants, but there are suggestions that sphingolipid catabolic products may be linked to programmed cell death, signal transduction, membrane stability, host-pathogen interactions, and stress responses. For example, there is evidence that enhanced synthesis of ceramides with very-long-chain fatty acids and trihydroxy sphingoid bases by ceramide synthases LOH1 and LOH3 promotes cell division and growth, while in contrast, accumulation of the ceramide species C16 fatty acid with a dihydroxy sphingoid base, due to LOH2 overexpression, leads to plant dwarfing and programmed cell death. Ceramides aggregate in rafts in plant membranes, together with other sphingolipids and sterols, as in animal tissues. Similarly, in the yeast S. cerevisiae, widely used as a model organism, it has been reported that ceramide species with different N-acyl chains and sphingoid bases are involved in the regulation of different sets of functionally related genes. Skin Ceramides The mammalian skin forms the protective barrier between the internal tissues of the host and the hostile external environment, which can include chemicals, ultraviolet light, mechanical damage, and pathogenic microorganisms, while preventing the loss of water and electrolytes. It consists of stratified layers of increasingly differentiated cells or keratinocytes of which the basal layer is responsible for the renewal of the tissue but begins to migrate upwards and differentiate, while accumulating specific lipids and proteins that change the cellular architecture. Eventually, the keratinocytes lose their nucleus and become flattened structures of insoluble protein surrounded by lipids termed ‘corneocytes’ in the outermost impermeable layer or stratum corneum. By secreting peptides and proteins that possess antimicrobial activity, keratinocytes add to the defensive capability of skin against commensal microorganisms and opportunistic pathogens, and this is reinforced by lipid mediators such as free sphingoid bases and eicosanoids in the stratum corneum and free fatty acids in sebum. The stratum corneum contains high levels of ceramides (as much as 50% of the total lipids), including O-acylceramides, which exist both in the free form and linked by ester bonds to structural proteins. They are present mainly in the extracellular domains (interstices) and are accompanied by nearly equimolar amounts of cholesterol and free fatty acids, a ratio that is believed to be essential for the normal organization of the tissue into the membrane structures that are responsible for the functioning of the epidermal barrier. In contrast to other biological membranes, the lipid organization in the membranes of skin consists of two lamellar phases, which form crystalline lateral phases mainly, with repeat distances of approximately 6 and 13 nm. Small sub-domains of lipids in a liquid phase may also exist. Some of these skin ceramides have distinctive structures not seen in other tissues, and many different forms are commonly recognized. They can contain the normal range of longer-chain fatty acids (a), e.g. formula 1 in the figure, some with hydroxyl groups in position 2 (a), e.g. formula 2, linked both to dihydroxy bases with trans-double bonds in position 4 or to trihydroxy bases. This is illustrated in Figure $18$. In addition, there are O‑acyl ceramides in which a unique very-long-chain fatty acid component (typically C30 or C32) has a terminal hydroxyl group, and this may be in the free form or esterified with linoleate (c), e.g., formulae 3 and 4; the sphingoid base can be either di- (b) or trihydroxy (b), e.g., formula 4; the latter is not a common feature in sphingolipids of animal origin, and can include both phytosphingosine and the unique 6‑hydroxy-4-sphingenine in human epidermis. Ceramides of type 1 in which the 1-O-hydroxyl group of the sphingoid base is acylated by a very-long-chain fatty acid are also present (1‑O‑acylceramides - illustrated above); these comprise 5% of the total ceramides in the epidermis of mice and humans and comprise as much as 700 molecular species. In all, 15 classes of free ceramides and 3 classes of covalently bound ceramides with up to 1700 distinct molecular species have been identified. Such lipids were first studied in detail in the skin of the pig as a convenient experimental model, but they have been characterized in humans and rats. In addition, several molecular forms of glucosylceramide, based on similar ceramide structures, have been characterized in skin, and these are also essential for its proper function. Depending on the particular layer of the skin (keratinocytes, stratum corneum, etc.), the lipid composition can vary. These lipids have an obvious role in the barrier properties of the skin, limiting the loss of water and solutes and at the same time preventing the ingress of harmful substances. As the aliphatic chains in the ceramides and the fatty acids are mainly non-branched long-chain saturated compounds with a high melting point and a small polar head group, the lipid chains are mostly in a solid crystalline or gel state, which exhibits low lateral diffusional properties and low permeability at physiological temperatures. There is a report that the stratum corneum layer of the skin has a water permeability only one-thousandth that of other biomembranes, for example. Natural and synthetic ceramides are now commonly added to cosmetics and other skin care preparations. Most steps in the biosynthesis of ceramides linked to ω-O-acylated fatty acids occur in the endoplasmic reticulum of keratinocytes. First, fatty acid synthesis of very-long-chain (and ultra-long-chain, ≥C26) acyl-CoA de novo must take place, requiring the chain-elongation enzymes ELOVL1 and ELOVL4. Desaturation can occur, and importantly oxidation in the 2 (α) and terminal (ω) positions. The ω‑hydroxylation step requires an enzyme of the cytochrome P450 family, designated CYP4F22, of the kind involved in the synthesis of hydroxy-eicosatetraenoic acids (HETE). Mutations are a cause of lamellar ichthyosis, and knockout mice deficient in the equivalent enzyme were found to die within 8 hours of birth. Ceramides are first synthesized by ceramide synthase 3 (CERS3), which has a high specificity for very-long-chain fatty acids (>C26) with the incorporation of the ω‑hydroxy fatty acid. This is acylated with linoleate by the action of an unusual enzyme related to the phospholipase A family, PNPLA1, which catalyzes esterification by first releasing linoleate from triacylglycerols in the skin while acting as an acyltransferase to link the linoleate directly to the ω-hydroxyl moiety of the ultra-long chain fatty acid. PNPLA1 is unique among phospholipases in that it is involved in the metabolism of sphingolipids rather than glycerophospholipids and catalyzes transacylation rather than hydrolysis. In addition, some linoleate for this purpose is released from triacylglycerols by the action of the adipose tissue lipase aided by a protein ABHD5. This process is vital for proper skin barrier function and keratinocyte differentiation, as mice with defective triacylglycerol biosynthesis and metabolism, including a deficiency of the acyl-CoA synthase ACSL1, are unable to synthesis ω‑O‑acylceramides and have an impaired skin barrier. Mutations in the human PNPLA1 gene are believed to be the cause of autosomal recessive disease congenital ichthyosis. The resulting ceramides are converted to the complex sphingolipids sphingomyelin and especially glucosylceramide, which are transferred with the aid of ATP-binding cassette (ABC) transporters together with degradative enzymes into the stratum corneum via specific organelles termed 'lamellar bodies.' These organelles must fuse with the apical plasma membrane of the outermost cell layer of the epidermis in order that their contents can be secreted. It is only then that the final step of hydrolysis of the lipid precursors occurs in the extracellular spaces of the stratum corneum, i.e. ceramides are generated from sphingomyelin by the action of acid sphingomyelinase and from glucosylceramides by β-glucocerebrosidase. This mechanism ensures that ceramides, with their potentially harmful biological activities, never accumulate within nucleated cells. Eventually, ceramides with a terminal ω-hydroxyl group in the fatty acyl moiety are bound covalently to the proteins of the cornified envelope, especially to involucrin. This is illustrated in Figure $19$. Sphingomyelin and Related Sphingophospholipids Structure and Occurrence of Sphingomyelin Sphingomyelin or ceramide 1-phosphocholine consists of a ceramide unit with a phosphorylcholine moiety attached to position 1 of the sphingoid base component. It is thus the sphingolipid analog of phosphatidylcholine, and like that lipid it is zwitterionic. The d18:1/16:0 molecular species is illustrated as an example in Figure $20$. Sphingomyelin is primarily of animal origin and is a ubiquitous component of all animal cell membranes, from mammals to nematodes (and in a few protozoa), where it is by far the most abundant sphingolipid. Indeed, it can comprise as much as 50% or more of the lipids in certain tissues, though it is usually lower in concentration than phosphatidylcholine. For example, it makes up about 10% of the lipids of the brain, where it is a key constituent of myelin, but 70% of the phospholipids of the human lens. Like phosphatidylcholine, sphingomyelin tends to be in greatest concentration in the plasma membrane of cells (up to 20%), and in the endocytic recycling compartment and trans Golgi network. It is also abundant in the nucleus where it is the main phospholipid associated with chromatin, but there is very little in the endoplasmic reticulum (2 to 4%) and even less in mitochondria. All the sphingomyelin in human erythrocyte membranes is in the outer leaflet, and ~90% of that in the plasma membrane of nucleated cells is in the outer leaflet. All lipoprotein fractions in plasma contain appreciable amounts of sphingomyelin with a higher proportion in the VLDL/LDL. Sphingomyelin is the single most abundant lipid in erythrocytes of most ruminant animals, where it replaces phosphatidylcholine entirely. In this instance, there is known to be a highly active phospholipase A that breaks down the glycerophospholipids, but not sphingomyelin. Sphingomyelin is not synthesized in plants or fungi, which produce the sphingophospholipid ceramide phosphoinositol and related lipids instead, or in bacteria, and its evolutionary significance is a matter for speculation. However, a number of bacteria and viruses utilize sphingomyelin or its metabolism in their hosts for growth and viability. Sphingosine is usually the most abundant long-chain base constituent, together with sphinganine and C20 homologues, although other bases can be present, especially in ruminant animals. In contrast, sphinganine is the major sphingoid base in the sphingomyelin of human lens membranes, linked mainly to 16:0. Typically, the fatty acids are very-long-chain saturated and monounsaturated, including odd-numbered components. In comparison to the glycosphingolipids, 2‑hydroxy acids are only rarely detected and then in small amounts, but they are found in testes, spermatozoa, kidney and skin sphingomyelin, for example. The absolute proportions of each fatty acid and sphingoid base can vary markedly between tissues and species, and some of the variability in compositions can be seen from the data in Table $3$ and Table $4$ below. Table $3$: Fatty acid compositions of sphingomyelin (wt % of the total) in some animal tissues. Source Fatty acids 16:0 18:0 18:1 20:0 22:0 22:1 23:0 23:1 24:0 24:1 Egg 66 10 1 4 6 1 2 - 5 3 Bovine brain 3 42 - 6 7 3 3 3 6 27 Cow's milk 14 3 1 1 22 - 32 - 19 5 Adapted from Ramstedt, B. et al. Analysis of natural and synthetic sphingomyelins using high-performance thin-layer chromatography. Eur. J. Biochem., 266, 997-1002 (1999); DOI. Table $4$: Long-chain base compositions of sphingomyelin (wt % of the total) in some animal tissues. Source Sphingoid base d16:0* d17:0 d17:1 d17:1-methyl d18:0 d18:1 d19:0 Egg 7 93 Bovine brain 19 81 Cow's milk 9 15 8 11 10 44 3 Also from Ramstedt, B. et al. Eur. J. Biochem., 266, 997-1002 (1999); DOI. * d = dihydroxy base Palmitic acid (16:0) is the most common fatty acid component of sphingomyelin in peripheral cells of mammals, while stearic acid (18:0) is more abundant in neural tissue, but this only hints at the potential complexity as there can be variability within tissues. For example, about 60% of the fatty acids of the sphingomyelin of the grey matter of the human brain consist of stearic acid (18:0), while lignoceric (24:0) and nervonic (24:1) acids make up 60% of the corresponding lipid of white matter, although this is dependent on the stage of development. During the first two years of life, the 18:0 concentration in sphingomyelin of white matter decreases from 82% to 33%, while the proportions of 24:0 and 24:1 increase. This pronounced shift from long-chain to very-long-chain sphingomyelins is not observed in the cerebral cortex. Approximately 100 molecular species of sphingomyelin have been detected in human plasma. Although polyunsaturated fatty acids such as arachidonic acid are rarely present, they have sometimes been mistakenly identified in the literature. Exceptions are the sphingomyelins of testes and spermatozoa, which contain very-long-chain polyunsaturated fatty acids (up to 34 carbon atoms), the major components being 28:4(n-6) and 30:5(n-6) with a proportion having hydroxyl groups in position 2. Biosynthesis, Metabolism and Function of Sphingomyelin The biosynthesis of sphingomyelin is distinct from that of phosphatidylcholine and indeed depends upon it, as it involves the transfer of phosphorylcholine from phosphatidylcholine to ceramide, synthesized in the endoplasmic reticulum, with the liberation of 1,2-diacyl-sn-glycerols. as illustrated in Figure $21$. The reaction is catalyzed by a ceramide choline-phosphotransferase (sphingomyelin synthase or SMS) and takes place primarily on the luminal side of the trans-Golgi but also in the plasma membrane, with two related enzymes each with six transmembrane domains and their N- and C-termini facing the cytosol, i.e., SMS1 and SMS2. Both enzymes are present in the Golgi, but only SMS2 is in the plasma membrane (facing the extra-cellular space in this instance) and may be necessary for the formation of raft domains (see below). SMS2 is also present in the membranes of nuclei from rat liver cells. It is noteworthy that in the absence of ceramide, both SMS1 and 2 have phospholipase C activity, and so may regulate the steady-state levels of phosphatidylcholine and diacylglycerols as well as that of sphingomyelin. The reaction does not use free phosphorylcholine or CDP-choline as a donor. Figure $22$ shows an interactive iCn3D model of the AlphaFold model of human Golgi membrane phosphatidylcholine:ceramide cholinephosphotransferase 1, also called sphingomyelin synthase 1 (Q86VZ5). Figure $22$: . Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...v6s5wHRgPXKJC6 The gray helices are the transmembrane helices. A cytoplasmic protein:protein interaction domain called SAM (sterile alpha motif) is shown in magenta. The other cytoplasmic C-terminal domain is shown in cyan Obviously much of the cytoplasmic domain is disordered in this computational structure. Side chains involved in binding phosphatidylcholine are shown as sticks colored CPK. Note that two, D95 and S97 are located in a disordered section in this model but would close in the actual active site in the actual structure. It has been proposed that Asp101 (95 in the AlphaFold structure) deprotonates Arg220 (214 in the model), which then acts as a nucleophile which attacks the phosphate group of phosphatidylcholine. Given the very high pKa of arginine, this mechanism, if true, is somewhat unique. Phosphocholine is linked to ceramide to produce sphingomyelin. Three key and extremely conserved amino acids in the active site are Asp101, Arg220 and Asn358. A specific ceramide transport molecule (CERT) is important to the reaction with SMS1 in that it transfers ceramide from the cytosolic surface of the endoplasmic reticulum to the trans-Golgi in an ATP-dependent and non-vesicular manner. Much of the sphingomyelin produced in the Golgi is then delivered to the apical plasma membrane by a vesicular transport mechanism. Sphingomyelin synthesis is regulated in part by phosphatidylinositide metabolism and is connected to sterol homeostasis through the oxysterol binding protein (OSBP). SMS2 in the plasma membrane is not dependent on CERT-mediated ceramide delivery, but is believed to convert ceramide produced locally by a sphingomyelinase back to sphingomyelin; this may be an important protective mechanism for the cell. The location of the enzymes explains the enrichment of sphingomyelin in specific membranes and the sidedness, i.e., the luminal trans-Golgi and the outer leaflet of the plasma membrane, while ceramide reaching the cis-Golgi is utilized for the synthesis of glucosylceramide. As the nature of the molecular species of sphingomyelins produced differs appreciably from that of the ceramide precursors, the sphingomyelin synthases must have considerable substrate specificity. The reaction can be reversible, using sphingomyelin to generate ceramide for specific signaling functions. It is evident that sphingomyelin biosynthesis forms a link between the sphingolipid signaling pathway (pro-apoptotic - see below) and that of glycerolipids via the mitogenic diacylglycerol by‑products. Although the importance of this production relative to that via phosphatidylinositol is not known, it is possible that it is significant locally at the external leaflet of the plasma membrane. An alternative pathway of sphingomyelin synthesis has been demonstrated in the endoplasmic reticulum in which ceramide is first converted to ceramide phosphoethanolamine via transfer of the head group from phosphatidylethanolamine, followed by stepwise methylation of the ethanolamine moiety. However, the physiological significance of this pathway has yet to be established. It was long thought that the only function of sphingomyelin was to serve as a substitute for phosphatidylcholine as a building block of membranes, i.e., by forming a stable and chemically resistant outer leaflet of the plasma membrane lipid bilayer. For example, it may limit the ingress of oxygen and thence oxidation of adjacent unsaturated acyl chains. While this is certainly one of its functions, the apparent similarity between phosphatidylcholine and sphingomyelin is superficial, and there are great differences in the hydrogen bonding capacities and physical properties of the two lipids. For example, sphingomyelin has an amide bond at position 2 and a hydroxyl at position 3 of the sphingoid base, both of which can participate in hydrogen bonding, while the trans double bond also appears to assist intermolecular interactions in membranes. Indeed, the first five carbon atoms of the sphingoid base in sphingolipids constitute a key feature that has been termed the ‘sphingoid motif’, which facilitates a relatively large number of molecular interactions with other membrane lipids, via hydrogen-bonding, charge-pairing, hydrophobic and van der Waals forces. With phosphatidylcholine, in contrast, the two ester carbonyl groups can act only as hydrogen acceptors. The degree of unsaturation of the alkyl moieties in each lipid is very different, and this gives them dissimilar packing properties in membranes. It is now recognized that sphingomyelin and other sphingolipids have a strong tendency to interact with proteins and cholesterol, often via strong van der Waals interactions and hydrogen bonding, to form transient nano-domains in membranes known as 'rafts' and on the surface of lipoprotein particles. Initially, there was a view that saturated sphingomyelin formed a liquid-ordered phase with cholesterol or a gel phase with saturated ceramides to lead to lateral segregation within the membrane, and that sphingomyelin and cholesterol metabolism were closely integrated, even that the sphingomyelin concentration might control the distribution of cholesterol in cells. On the other hand, the understanding of the mechanism of raft formation in membranes has changed substantially in recent years, and while an interaction with cholesterol is certainly important, it may not be the major factor in vivo. Ceramide can displace cholesterol from its association with sphingomyelin, when formed in membranes by hydrolysis of the latter. Other functions: Sphingomyelin per se is generally considered to be a relatively inert molecule, although modern molecular biology methods are uncovering potential regulatory functions via interactions with particular proteins. For example, it has been shown to inhibit the activity of phospholipase A2α, a key enzyme in eicosanoid production. Sphingomyelin in the plasma membrane may be essential for the internalization of transferrin and thence of iron into cells, and it appears to be required for the activity of a number of membrane-bound proteins, including those of certain ion channels and receptors. As the most abundant sphingolipid in the nucleus, it is intimately involved in chromatin assembly and dynamics as well as being an integral component of the nuclear matrix. A single molecular species of sphingomyelin with a C18 acyl chain binds specifically to a coat protein designated 'p24' to enable it to form membrane vesicles. In addition, sphingomyelin is selectively recognized and acts as a receptor for the actinoporins, which are pore-forming toxins produced by sea anemones. There is a specific binding site for sphingomyelin on the amyloid beta-peptide (Aβ) in brain, and there is evidence from studies in vitro that this may promote the aggregation of these proteins in Alzheimer's disease. In turn, this leads to depletion of brain sphingomyelin by activation of acid sphingomyelinase with disruption of many protein–lipid interactions and thence of downstream signaling pathways. In contrast, the ganglioside GM1 may have a protective role towards Aβ aggregation As well as its role in membranes, it serves as a precursor for ceramides, long-chain bases, sphingosine-1-phosphate, and many other biologically important sphingolipids, as part of the 'sphingomyelin cycle' (also termed the ‘sphingolipid’ or ‘ceramide’ cycles depending on the context). Some of these metabolites are intra- and inter-cellular messengers, and others are essential membrane constituents. The sphingomyelin cycle extends to other sphingolipids via the action of sphingomyelinases and enzymes such as glycosylhydrolases and glycosyltransferases in cells to produce innumerable new oligoglycosylceramides. It can also give rise to sn-1,2-diacylglycerols, which are central to many metabolic and signaling pathways. These molecular relationships are illustrated only briefly in Figure $23$. In particular, sphingomyelin is a major source of ceramides in most cellular organelles, including the nucleus and even mitochondria, via the action of sphingomyelinases (see next section), and in addition to being a source of other sphingolipids these are required to trigger apoptosis and other metabolic changes. As ceramides do not mix well with glycerophospholipids and cholesterol, this conversion results in the formation of new membrane domains enriched in ceramide that exclude cholesterol and so differ in composition from other sphingolipid rafts. This has profound effects on membrane function, especially of the plasma membrane, in that different proteins may be recruited or excluded depending on their relative affinities for cholesterol and ceramides. It may also influence disease states such as cancer. Chlamydiae, widespread bacterial pathogens, acquire sphingomyelin from the Golgi apparatus and plasma membrane of their hosts and this is necessary for the viability and growth of the organisms. Other pathogenic bacteria, notably Pseudomonas aeruginosa and Neisseria gonorrhoeae, can hijack sphingomyelin catabolic enzymes with deleterious effects upon the host. Likewise, human immunodeficiency virus (HIV) and the hepatitis C virus utilize host sphingomyelin for their own nefarious purposes. Nutrition: Although there is no known nutritional requirement for sphingomyelin and other sphingolipids, they are a component of any diet containing egg, meat or dairy products. Thus, it has been estimated that per capita sphingolipid consumption in the United States, for example, is of the order of 0.3-0.4 g/d. As sphingolipids constitute an appreciable proportion of the polar lipid constituents of milk, they may be significant if minor nutrients for infants and beneficial effects upon their development have been claimed. From animal experiments, there is evidence that dietary sphingolipids can reduce the intestinal absorption of cholesterol and other lipids, leading to reductions in serum lipid concentrations. Feeding sphingolipids inhibits colon carcinogenesis and may alleviate some of the symptoms of inflammatory bowel disease. 2-Hydroxyoleic acid suppresses the growth and induces autophagy in cancer cells by stimulating the synthesis of sphingomyelin and increasing the amount of this lipid in the plasma membrane. On the other hand, plasma sphingomyelin levels are considered to be an independent risk factor for atherosclerosis, possibly as a result of its ability to retain cholesterol in cells and the arterial wall with consequent diminished reverse cholesterol transfer via HDL. Sphingomyelin Catabolism In contrast to the glycerolipids, dietary sphingolipids are not hydrolyzed by pancreatic enzymes only. Rather, most of the sphingomyelin in the diet is hydrolyzed in the brush border of the intestines by an alkaline sphingomyelinase (at a pH of 8.5–9 optimally) to ceramide and thence by a neutral ceramidase to free fatty acids and sphingosine. Some of this enzyme is also present in liver from which it is secreted in bile into the intestinal lumen where it can hydrolyze sphingomyelin and other phospholipids with the aid of bile salts. The sphingosine released at the brush border is absorbed, some is re-N-acylated to form ceramides, and the remainder is converted via sphingosine-1-phosphate to palmitic acid, which is esterified into the triacylglycerol component of chylomicrons. In the process, some of these sphingolipid intermediates may have signaling functions and anti-inflammatory properties in intestinal cells. The alkaline sphingomyelinase is unusual in that is very different in its structure and other properties from intracellular enzymes with a related function; it is part of the (ecto)nucleotidepyrophosphatase-phosphodiesterase protein family (NPP) that includes autotaxin. The enzyme is believed to have a role in the production of sphingolipid metabolites within the intestines and colon especially, which may influence a number of disease states. For example, it appears to inhibit colon cancer by generating ceramides. In addition, alkaline sphingomyelinase has phospholipase C activity towards the pro-inflammatory metabolite platelet-activating factor and towards lysophosphatidylcholine with potentially further beneficial effects. By reducing the level of endogenous sphingomyelin and increasing that of ceramides in the membranes of intestinal cells, it is believed to reduce the uptake of dietary cholesterol. Catabolism in other tissues: The key enzymes for the degradation of sphingomyelin to ceramides in most tissues are also sphingomyelinases (phosphodiesterases), as shown in Figure $24$. These are similar in function to phospholipase C and generate ceramides with their innumerable and important signaling properties as the main product. There are many such enzymes with different pH optima and metal ion requirements that operate in different regions of the cell with potentially distinct biochemical roles. Thus, there is an acid sphingomyelinase in the endo-lysosomes, and different neutral sphingomyelinases in the plasma membrane, endoplasmic reticulum, Golgi, and mitochondria in addition to the alkaline sphingomyelinase in the intestines. It should not be forgotten that the other product of the reaction is phosphocholine, which has importance as a nutrient. Bacterial sphingomyelinases are known to lyse red blood cells, although intriguingly, there is a sphingomyelinase in the bacterium Pseudomonas aeruginosa that can also act as a sphingomyelin synthase in vitro at least. The lysosomal acid sphingomyelinase (pH optimum ca. 5) is expressed ubiquitously and has a key housekeeping role in maintaining normal membrane turnover and remodeling of the sphingolipid constituents, especially those of lipoproteins. While other lysosomal sphingolipid hydrolases require a saposin activator protein for full activity, the acid sphingomyelinase incorporates a built-in N-terminal saposin domain so does not require an external activator. Under resting conditions, acid sphingomyelinase is stored inside lysosomes, but upon stimulation, it undergoes vesicular transport to the plasma membrane where it docks with a specific protein and is exposed on the outer leaflet. It then generates ceramide by hydrolysis of sphingomyelin and initiates the train of events that leads to apoptosis. There are reports that acid sphingomyelinase, by acting at the plasma membrane to produce ceramides, regulates the localization and trafficking of palmitoylated proteins from the Golgi, and it may also facilitate bacteria-host interactions. Experiments in vitro have demonstrated that the enzyme can be considered as a phospholipase C that is active against a wide range of phospholipids, including ceramide-1-phosphate and the unique lysosomal phospholipid bis(monoacylglycero)phosphate. There is a related secreted acid sphingomyelinase (Zn2+-dependent), which can be transported to the outer membrane of the cell and is especially important in endothelial cells of the human coronary artery. This enzyme is produced by the same gene but differs from the lysosomal enzyme as it requires Zn2+ ions for activation and has a different glycosylation pattern. It can also operate at neutral pH and has multiple functions in that it is involved in many aspects of cellular signaling as well as in membrane sphingomyelin turnover. By acting at the plasma membrane to produce ceramides, it is believed to regulate the trafficking of palmitoylated proteins from the Golgi to their new location. Neutral sphingomyelinases (pH optima 7.4), of which four quite distinct enzymes are known, are located in membranes of the endoplasmic reticulum, Golgi, and plasma membrane with one in mitochondria (MA-NSM), where they have signaling functions by generating ceramides and thence other biologically active sphingolipids. Human NSM-1 has 423 amino acid residues and a molecular weight of 47.6 kDa; it has two putative transmembrane domains in the C-terminus and resides mainly in the nucleus and endoplasmic reticulum. It has a broad specificity for choline phospholipids, but it is most active with sphingomyelin and may not have a significant role in cellular signaling. In contrast, NSM-2 which is located in the Golgi apparatus and plasma membrane is activated by phosphatidylserine and is important for ceramide signaling. It is especially important in brain and nervous tissue, where it is required for the secretion of hypothalamic-sssreleasing hormones, although it is relevant to many cellular functions and physiological processes in most other tissues. Dysregulation of NMS-2 is reported to be a factor in many inflammation-related pathologies. Neutral sphingomyelinases-3 is found mainly in the plasma membrane of bone and cartilage, where it is vital for the process of mineralization; it is also important in striated and cardiac muscle. Little seems to be known of the function of the mitochondrial enzyme. Losses, mutation, and poor expression of the gene encoding neutral sphingomyelinase have been observed in several cancers, but exposure to ionizing irradiation led to rapid hydrolysis of sphingomyelin to ceramide by this enzyme, and thence to cancer cell death. A diverse range of factors activates the enzymes, including chemotherapeutic agents, tumor necrosis factor-alpha, 1,25-dihydroxy-vitamin D3, endotoxin, gamma-interferon, interleukins, nerve growth factor, and most conditions known to induce cellular stress, especially in relation to inflammation. As they utilize by far the most abundant sphingolipid in animal tissues to generate ceramides and other sphingolipid metabolites that have important signaling functions, sphingomyelinases are believed to function as regulators of signaling mechanisms, especially in the nucleus of the cell. Thus, they have a much wider metabolic role than simply catabolism of sphingomyelin. The type A and B forms of Niemann-Pick disease are lysosomal lipid storage disorders that are a consequence of a deficiency of acid sphingomyelinase with a resulting accumulation of sphingomyelin and smaller amounts of other sphingolipids, including gangliosides, in cells and tissues and especially in the monocyte/macrophage system to form the so-called “foam cells” that characterize the disease. A consequent lack of ceramide production may be involved in the pathology of the disease. Increasing sphingomyelin levels in turn result in elevated cholesterol concentrations. It is noteworthy that membranes containing ceramides have a much lower binding capacity for cholesterol, so sphingomyelin degradation may play a part in cholesterol homeostasis. Type C Niemann-Pick disease differs from the A and B forms and is caused by defects in two distinct cholesterol-binding proteins (NPC1 and NPC2). Glucosyl- and Galactosylceramides (Cerebrosides) There are two natural monoglycosylceramides of special importance in animals, i.e., glucosylceramide and galactosylceramide. Both have biological functions in their own right, but especially as structural components of membranes, as in the brain, for example, where galactosylceramide is required for the maintenance of the structure and stability of myelin and the differentiation of oligodendrocytes. Glucosylceramide is a vital component of all cell types, and is most abundant in human skin; it is the key intermediate in the biosynthesis of lactosylceramide and thence of complex oligoglycosphingolipids, including gangliosides. This monoglycosylceramide is also a major component of the membranes of plants and fungi. Although the two lipids have very similar structures in that D-galactose is an epimer of D-glucose and they differ only in the configuration at C4, they have very different biological properties. A few other monoglycosylceramides are produced in nature, for example by some bacteria of the order Sphingomonadales of α‑proteobacteria. Structure and Occurrence β-D-Galactosylceramide (Galβ1-1'Cer) is the principal glycosphingolipid in brain tissue, hence the trivial name "cerebroside", which was first conferred on it in 1874, although it was much later before it was properly characterized. In fact, galactosylceramides are found in all nervous tissues and indeed at low levels in all organs, but in they brain they can amount to 2% of the dry weight of grey matter and 12% of white matter or 23% of myelin lipids, where they insulate the axons of neuronal cells and constitute a substantial component of the extended plasma membrane of oligodendrocytes. It is also present in some fungal species. While galactosylceramide can be sulfated to form a sulfatide or sialylated to form ganglioside GM4, only a small proportion is subjected to further galactosylation to form Gal2Cer as the precursor for the limited gala-series of oligoglycosphingolipids. β-D-Glucosylceramide (Glcβ1-1'Cer), with the trivial name "glucocerebroside", is a major constituent of skin lipids, where it is essential for the maintenance of the water permeability barrier of the skin. Otherwise, it is most abundant in animal tissues such as the spleen and erythrocytes as well as in nervous tissues, especially in the neurons if at low levels, and it is also found in plants. Higher than normal concentrations of this glycosphingolipid have been reported for the apical plasma membrane domain of epithelial cells from the intestines (especially the absorptive villous cells) and urinary bladder. The d18:1/16:0 molecular species of the two lipids are illustrated in Figure $25$. However, of equal or greater importance to the natural occurrence of glucosylceramide per se is its role as the biosynthetic precursor of lactosylceramide in animals, and thence of most of the complex neutral oligoglycolipids and gangliosides. In contrast, glucosylceramide is the end-product of the biosynthetic pathway in plants and fungi. Interestingly, the proportion of galactosylceramides relative to glucosylceramides in myelin glycolipids increases greatly in the ascending phylogenic tree, and the ratio of hydroxy- to nonhydroxy fatty acids in cerebrosides increases with the complexity of the central nervous system. There is also an intriguing sex difference in the kidney, where it has been shown that galactosylceramide rather than glucosylceramide occurs in male mice only (or androgen-treated adult females). Only glucosylceramide is present in the nerves of the most primitive animals (protostomes). In the brain, the galactosylceramides are enriched in very-long-chain fatty acids (C22–C26). The fatty acid and long-chain base compositions of cerebrosides from the intestines of the Japanese quail are listed in Table $5$ for illustrative purposes. The fatty acid components resemble those of other sphingolipids, although the percentage of 2-hydroxy acids is higher than that in sphingomyelin, for example. They are exclusively saturated in this instance, though a small proportion of monoenoic components may also be found in other tissues. Glucosylceramides tend to contain mainly non-hydroxylated fatty acids that are of relatively shorter chain length. The proportion of trihydroxy bases listed is perhaps higher than in other many other tissues or species studied, probably reflecting the diet. Usually, sphingosine is the main long-chain base in cerebrosides of animal tissues. Table $5$: Composition of fatty acids and long-chain bases (wt % of the total) in cerebrosides of intestines from the Japanese quail.* Long‑chain bases Fatty acids Non-hydroxy acids 2-Hydroxy acids Type % % % t18:0 43 16:0 5 6 d18:0 9 18:0 3 trace d18:1 27 20:0 2 4 t20:0 6 21:0 trace 2 d20:0 3 22:0 4 43 d20:1 11 23:0 1 13 24:0 3 12 * The cerebrosides comprised 81% galactosylceramide and 19% glucosylceramide. From Hirabayashi, Y. et al., Lipids, 21, 710-714 (1986); DOI. Plants: Glucosylceramide is the only glycosphingolipid common to plants, fungi, and animals. It has often been described incorrectly as the main sphingolipid in plants, but this has been because the more polar complex glycosylinositol phosphoceramides are not easily extracted and until relatively recently were missed in conventional analyses. Nonetheless, glucosylceramide is abundant in photosynthetic tissues and constitutes approximately a third of the total sphingolipids, where the main long-chain bases are C18 4,8‑diunsaturated (Z/Z and E/Z) (not sphingosine as illustrated above); it is a major component of the outer layer of the plasma membrane and is also enriched in the late endosomes and plant tonoplast. Small amounts of monoglycosylceramides containing a β‑D‑mannopyranosyl unit may be present in non-photosynthetic tissues, but galactosylceramides have not been found in plants. Glucosylceramide is a common component of the lipids of yeast and other fungi, including most fungal pathogens. However, it does not occur in the yeast Saccharomyces cerevisiae, which is widely used as an experimental model, although trace levels of galactosylceramide have been detected. The fatty acid and long-chain base compositions of glucosylceramides from two plant sources are listed in Table $6$. Perhaps surprisingly, the fatty acid components are not very different in nature from those in animal tissues, comprising mainly longer-chain saturated and monoenoic acids, with a high proportion being saturated and having a hydroxyl group in position 2. In the examples selected for the table here, both di- and tri-hydroxy long-chain bases were found, mainly diunsaturated (Z/Z and E/Z) and almost entirely C18 in chain length. Much higher concentrations of glucosylceramides are found in pollen than in leaves, with substantial compositional differences. For example, the long-chain bases in Arabidopsis leaves consist mainly of t18:1, with relatively little d18:1, t18:0 and d18:0 (with 16:0, 24:0 and 24:1 hydroxy fatty acids mainly), but no d18:2 base although this is 50% of those in pollen. While saturated 2-hydroxy acids predominate in most plants, some cereal glucosylceramides contain high proportions of mono-unsaturated very-long-chain fatty acids of the n-9 family. Glucosylceramides from algae tend to resemble those from higher plants, although some novel structures have been reported from microalgae. Table $6$: Composition of fatty acids and long-chain bases (wt % of the total) in glucosylceramides of seeds from scarlet runner beans and kidney beans. Fatty acidsa Long-chain basesb Type Runner beans Kidney beans Runner beans Kidney beans % % % % 16:0 4 5 t18:0 trace trace Other non-hydroxy 1 2 t18:1-8t 13 11 14:0-OH 1 1 t18:1-8c 10 9 15:0-OH 1 1 d18:0 trace trace 16:0-OH 58 58 d18:1-8c/t 1 3 18:0-OH trace trace d18:1-4t trace trace 20:0-OH trace trace d18:2-4t,8t 45 60 22:0-OH 7 6 d18:2-4t,8c 31 17 23:0-OH 2 1 24:0-OH 23 23 25:0-OH 1 1 26:0-OH 1 1 From Kojima, M. et al., J. Agric. Food. Chem., 39, 1709-1714 (1991); DOI, but see also Yamashita, S. et al. for further data: DOI a including 2-hydroxy acids; b di- and tri-hydroxy bases with cis or trans double bonds in the positions indicated. Biosynthesis Ceramides synthesized both de novo and by catabolism of sphingomyelin are used for the biosynthesis of monoglycosylceramides in animal tissues. The biosynthetic mechanism resembles that for glycosyldiacylglycerols, i.e., there is a direct transfer of the carbohydrate moiety from a sugar-nucleotide, e.g. uridine 5-diphosphate(UDP)-galactose, UDP-glucose, etc, to a ceramide unit synthesized in the endoplasmic reticulum. This is illustrated in Figure $26$. During the transfer, which is catalyzed by specific glycosyl-transferases, inversion of the glycosidic bond occurs from the alpha to beta configuration. Synthesis of β-D-galactosylceramide takes place on the lumenal surface of the endoplasmic reticulum, although it has free access to the cytosolic surface by an energy-independent flip-flop process. Expression of the UDP-galactose:ceramide galactosyl transferase (galactosylceramide synthase) is restricted to oligodendrocytes, Schwann cells, kidneys, and testes. Prior to sulfation, galactosylceramide is transported to the trans-Golgi compartment. In contrast, after the transfer of the precursor ceramides from the endoplasmic reticulum to the cytosolic side of the early Golgi membranes with the aid of the CERT protein, glucosylceramide is produced by a glucosylceramide synthase present in this membrane (with the possible exception of neuronal tissues). If it is to be converted to more complex oligoglycosylceramides, this must be translocated to the luminal leaflet of the trans-Golgi membranes, a process that occurs both by vesicular and by non-vesicular transport. The latter is mediated by a conserved clade of integral membrane proteins, i.e., phospholipid flippases (P4-ATPases) designated ATP10A and ATP10D, together with the four phosphate adapter protein-2 (FAPP2) and glycolipid transfer protein (GLTP) in humans with related enzymes in fungi, which utilize the energy from ATP catalysis to translocate lipids across cellular membranes. The human enzymes are entirely specific for glucosylceramide and not galactosylceramide. Indeed, the galactosyl- and glycosylceramide synthases have no significant sequence homology, indicating different evolutionary origins. For their functions in protein interactions and signaling, both galactosyl- and glucosylceramide must be transported to and then across the plasma membrane. Some glucosylceramide is carried by lipoproteins (VLDL, LDL, and HDL) in the circulation and presumably requires active transport for absorption and distribution across the membranes of target tissues. In plants, glucosylceramides are also formed by an evolutionarily conserved glucosylceramide synthase involving UDP-glucose in the endoplasmic reticulum, although an alternative mechanism has been described that utilizes sterol glucoside as the immediate glucose donor to ceramide. There is also evidence for a requirement for ceramides containing Δ4 trans-double bonds for synthesis of glucosylceramides but not other sphingolipids in some plant and fungal tissues. However, there is a distinct ceramide synthase in the yeast Pichia pastoris, which produces ceramides of defined composition exclusively for the production of glucosylceramides. A separate ceramide synthase with different specificities produces the ceramide precursors for ceramide phosphorylinositol, which contains only phytosphingosine as the long-chain base. In fungi, glucosylceramide synthases have been characterized, but a galactosylceramide synthase has yet to be identified. Enzymes responsible for the biosynthesis of glucuronosylceramide and α-galactosylceramide in some bacterial species have been characterized. Function Galactosylceramides: A remarkable property of cerebrosides is that their 'melting point' is well above physiological body temperature, so that glycolipids have a para-crystalline structure at this temperature. Each cerebroside molecule may form up to eight inter- or intramolecular hydrogen bonds by lateral interaction between the polar hydrogens of the sugar and the hydroxy and amide groups of the sphingosine base of the ceramide moiety, and this dense network of hydrogen bonds is believed to contribute to the high transition temperature and the compact alignment of cerebrosides in membranes. As with sphingomyelin, monoglycosylceramides tend to be concentrated in the outer leaflet of the plasma membrane together with cholesterol and thence in myelin in the specific membrane domains termed 'rafts'. Indeed, the latter appear to facilitate segregation to a greater extent than sphingomyelin via the combination of hydrogen bonds and hydrophobic interactions, and these forces are also of great importance for binding to the wide range of proteins, including enzymes and receptors, which are found in raft domains. Galactosylceramide is essential to myelin structure and function and it is involved in oligodendrocytes differentiation. While molecular species with 2’-hydroxy fatty acid constituents are not essential for myelin formation, they are critical for the long-term stability of myelin, presumably because increased hydrogen bonding with neighboring lipids in membranes stabilizes the phase structure. Galactosylceramide is important as a precursor of 3’-sulfo-galactosylceramide, which is also essential to brain development in addition to numerous functions in other tissues. By interacting with sulfatide located in the membrane of opposing layers in the myelin sheath by carbohydrate-carbohydrate interaction, it forms what is known as a glycosynapse, which provides a necessary contribution to the long-term stability of myelin. Glucosylceramides: Glucosylceramides have similar physical properties in membranes to the galactose analog, and they are also concentrated in raft domains in the outer leaflet of the plasma membrane. As mentioned briefly above, they are the primary precursor for most of the more complex oligoglycosphingolipids in animal tissues, especially in brain, where synthesis is vital for the production of most neuronal oligoglycosphingolipids, while glucosylceramide per se is essential for axonal growth. They are major constituents of skin lipids, where they are essential for lamellar body formation in the stratum corneum and to maintain the water permeability barrier of the skin. In addition, the epidermal glucosylceramides (together with sphingomyelin) are the source of the unusual complex ceramides that are found in the stratum corneum including those with terminal hydroxyl groups and estolide-linked fatty acids. Some of the glucosylceramide in the skin is linked covalently to proteins via terminal hydroxyl groups, presumably to strengthen the epidermal barrier. Much of the evidence for the function of glycosylceramides in animals has been derived from cell lines in which synthesis of the lipid has been suppressed by various means in vitro. It appears that glucosylceramide is not essential for the viability of certain cell lines in culture, but disruption of the global synthase gene in mice results in the death of embryos. It is essential for the survival of cancer cells, and deletion from other cell types can lead to abnormalities. In addition to being an intermediate in the biosynthesis of more complex glycosphingolipids and its role in the permeability barrier of the skin (discussed above), glucosylceramide is believed to be required for intracellular membrane transport, cell proliferation, and survival, and for various functions in the immune system. In contrast, there are indications that it may have adverse implications for various disease states. For example, over-expression of glucosylceramide synthase in cancer cells has been linked to tumour progression with a reduction in ceramide concentration, resulting in increased resistance to chemotherapy. The lipid has also been associated with drug resistance in a wider context. In the nematode Caenorhabditis elegans, glucosylceramide containing the fatty acid 22:0 is reported to be a longevity metabolite that functions through the membrane localization of clathrin, a protein that regulates membrane budding. In Arabidopsis, glucosylceramides are critical for cell differentiation and organogenesis, but not necessarily for the viability of cells. It has been proposed that glycosphingolipids could impose positive curvature to membranes, thereby facilitating vesicle fusion. There is evidence that glycosylceramides (but not glycosyldiacylglycerols) together with sterols are located in 'rafts' in plant membranes in an analogous manner to sphingolipids in animal tissues, and that they are associated with specific proteins. Correlative studies suggest that glucosylceramides help the plasma membrane in plants to withstand stresses brought about by cold and drought. For example, glycosylceramides containing 2-hydroxy monounsaturated very-long-chain fatty acids and long-chain bases with 4-cis double bonds appear to be present in higher concentrations in plants that are more tolerant of chilling and freezing. While fungal glucosylceramides with a 9-methyl group within the sphingosine backbone elicit defence responses in rice, cerebrosides with double bonds in positions 4 and/or 8 of the long-chain base appear to be involved in the defense of some plant species against fungal attack. Less is known of the function of glucosylceramides in fungi, although they are certainly major constituents of the plasma membrane and cell wall. They are believed to be involved in such processes as cell wall assembly, cell division and differentiation, and signaling. The presence of the methyl branch in the long-chain base is essential for cell division and alkali tolerance. In the case of fungal pathogens, glucosylceramides are recognized by the host immune system and regulate virulence, often after export into the external environment as extracellular vesicles. In contrast to animals, ceramide monohexosides are not precursors for oligoglycosylceramides in fungi. Some molecular species of this lipid from plants (a Δ8 double bond in the long-chain base is essential) show fruiting-inducing activity in the fungus Schizophyllum commune. α-D-Galactosylceramides: Cerebrosides linked to an α-D- rather than a β-D-galactosyl unit such as that found in the marine sponge Agelas mauritianus, in human gut microflora, and even in cow's milk are potent stimulators of mammalian immune systems by binding to the protein CD1d on the surface of antigen-presenting cells and activating invariant natural killer T cells. Indeed this was one of the first pieces of evidence to show that glycolipids, like glycoproteins, could invoke an immune response. Subsequently, it was demonstrated that α-galactosylceramide with a 24:1 fatty acid, though present in very small amounts, is loaded onto the CD1d or CD40 protein and is presented as the natural endogenous ligand for NKT cells in the thymus and the periphery. Once activated, NKT cells secrete a range of pro-and anti-inflammatory cytokines to modulate innate and adaptive immune responses. The α‑glucosyl and α‑psychosine analogs show similar activity. It is not certain whether α-galactosylceramide is synthesized in animal tissues, and it is likely that is derived primarily from members of the gut microbiome such as Bacteroides fragilis and related species (although in general, few bacterial species produce sphingolipids). Ceramide-galactosyltransferases responsible for the synthesis of this lipid in two species of bacteria from the intestinal microbiome have been identified. In mouse gut, the main molecular form consisted of a 2‑(R)‑hydroxylated hexadecanoyl chain linked to C18-sphinganine, while that in B. fragilis contained longer-chain components with iso-methyl-branches in the sphingoid base and often fatty acid moieties. The sphinganine chain branching is a critical determinant of NKT cell activation by the bacterial enzyme. A decrease in the production of this lipid was observed in mice exposed to stress conditions that alter the composition of the gut microbiota, including Western-type diet, colitis, and influenza A virus infection with potential consequences upon the systemic immune responses. Its concentration within animal tissues is controlled by catabolic enzymes in a two-step mechanism: removal of the acyl chain by an acid ceramidase followed by hydrolysis of the sugar residue by an α-glycosidase. Initial studies with animal models suggest that treatment with α‑D‑galactosylceramides is effective against lung and colorectal cancers, melanomas and leukemia, and pre-clinical trials of this lipid and synthetic analogs so far have shown that these are safe and effective as an anti-tumour immunotherapeutic agents and vaccine adjuvants. Indeed, a phase I trial with high-risk melanoma patients has given promising preliminary results. Catabolism of Glycosphingolipids In animal tissues, the main sites for the degradation of all glycosphingolipids, including the monoglycosylceramides, oligoglycosphingolipids and gangliosides, are the lysosomes. These are membrane-bound organelles that comprise a limiting external membrane and internal lysosomal vesicles, which contain soluble digestive enzymes that are active at the acidic pH of this organelle. All membrane components are actively transported to the lysosomes to be broken down into their various primary components. In the case of glycosphingolipids, this means to fatty acids, sphingoid bases, and monosaccharides, which can be recovered for re-use or further degraded. Thus, sections of the plasma membrane enter the cell by a process of endocytosis, and they are then transported through the endosomal compartment to the lysosomes. The compositional and physical arrangement of the lysosomal membranes is such that they are themselves resistant to digestion with bis(monoacylglycero)phosphate (lysobisphosphatidic acid) as a characteristic component of the inner membrane. A glycocalyx of highly N-glycosylated integral membrane proteins protects the perimeter membrane with the aid of the ganglioside GM3, which is resistant to degradation. This glycocalix forms an efficient hydrophilic barrier at the luminal surface of the lysosomal perimeter membrane to protect it from degradation by proteases and hydrolases, and to prevent lipids and their hydrolysis products from escaping from the lumen of the lysosome. Degradation of oligoglycosylceramides and gangliosides occurs by sequential removal of monosaccharide units via the action of specific exohydrolases from the non-reducing end until a monoglycosylceramide unit is reached when glucosylceramide β-glucosidases or an analogous β-galactosidase (one isoform) removes the final carbohydrate moiety. Several glucosylceramidases are known; GBA1 is a lysosomal hydrolase, GBA2 is a ubiquitous non-lysosomal enzyme and GBA3 is a cytosolic β-glucosidase. The last is found in the kidney, liver, spleen and a few other tissues of mammals, but its function is not clear. As glycolipids with fewer than four carbohydrate residues are embedded in intralysosomal membranes, while the degradative enzymes are soluble, the process requires the presence of negatively charged lipids and specific activator proteins, which are water-soluble glycoproteins of low molecular weight. These are not themselves active catalytically but are required as cofactors either by directing the enzyme to the substrate or by activating the enzyme by binding to it in some manner. Five such proteins are known, the GM2-activator protein (specific for gangliosides) and Sphingolipid Activator Proteins or saposins A, B, C and D, which perturb the membranes sufficiently to enable the degradative enzymes to reach the glycolipid substrates. The four saposins are derived by proteolytic processing from a single precursor protein, prosaposin, which is synthesized in the endoplasmic reticulum, transported to the Golgi for glycosylation, and then to the lysosomes. Of these, saposin C is essential for the degradation of galactosyl- and glucosylceramide, while saposin B is required for the hydrolysis of sulfatide, globotriaosylceramide, and digalactosylceramide. The products of the hydrolysis reaction with monoglycosylceramides are ceramides and monosaccharides with net retention of the stereochemistry of the latter in the process. This is illustrated in Figure $26$. The reactions are aided by the presence of anionic lipids such as bis(monoacylglycero)phosphate. In particular, this increases the ability of the GM2-activator to solubilize lipids and stimulates the hydrolysis of membrane-bound GM1, GM2, and some of the kidney sulfatides. Saposin D stimulatesthe degradation of lysosomal ceramide by acid ceramidase, and it is also involved in the solubilization of negatively charged lipids at an appropriate pH. Eventually, the ceramides can in turn be hydrolyzed by an acid ceramidase to fatty acids and sphingoid bases. β-Glucosylceramidase and saposin C are also required for the generation of the structural ceramides from glucosylceramide in the outer region of the skin, a process essential for optimal skin barrier function and survival. Some glucosylceramide is hydrolyzed by the enzyme GBA2 at the plasma membrane, where the ceramide formed is rapidly converted to sphingomyelin by the sphingomyelinase 2, which may be co-located with the glucosidase. In addition, it has been established that cellular β-glucosidases are able to transfer the glucose moiety from glucosylceramide to and from other lipids as in the formation of cholesterol glucoside. Small but significant amounts of glucosyl- and galactosylceramides are ingested as part of the human diet. They are not hydrolyzed by pancreatic enzymes but are degraded in the brush border of the intestines by the enzyme lactase-phlorizin hydrolase (which also hydrolyses the lactose in milk) to ceramides and thence to sphingosine. An Arabidopsis homolog of human glucosylceramidase (AtGCD3) preferentially hydrolyses glucosylceramides that contain long acyl chains, and three further isoforms may exist based on sequence homology. Genetic disorders and Disease Harmful quantities of glucosylceramide accumulate in the spleen, liver, lungs, bone marrow, and, in rare cases, the brain of patients with Gaucher disease, the most common of the inherited metabolic disorders (autosomal recessive) involving storage of excessive amounts of complex sphingolipids. Three clinical forms (phenotypes) of the disease are commonly recognized of which by far the most dangerous are those affecting the brain (Types 2 and 3). All of the patients exhibit a deficiency in the activity of the lysosomal glucosylceramide-β-glucosidase (GBA1), which catalyzes the first step in the catabolism of glucosylceramide. The enzyme may be present, but a mutation prevents it from forming its correct conformation, although other factors may be involved as patients with a defective saposin C, the lysosomal activator protein, develop similar symptoms. In the brain, glucosylceramide accumulates when complex lipids turn over during brain development and during the formation of the myelin sheath of nerves. Other than in the brain, the excess glucosylceramide arises mainly from the biodegradation of old red and white blood cells. The result is that the glucosylceramide remains stored within the lysosomes of macrophages, i.e., the specialized cells that remove worn-out cells by degrading them to simple molecules for recycling, thus preventing them from functioning normally and often leading to chronic inflammation. The enlarged macrophages containing undigested glucosylceramide are termed Gaucher cells. They over-express and secrete certain proteins into the circulation, and some of these are used as biomarkers. In addition, glucosylceramide is converted more rapidly to gangliosides in these cells, leading to an increase in ganglioside GM3 in the plasma and spleen of patients with Gaucher disease. Fortunately, there are now effective enzyme replacement therapies for patients with the milder (non-neurological or Type 1) form of Gaucher disease that successfully reverse most manifestations of the disorder, including decreasing liver and spleen size and reducing skeletal abnormalities. Two oral drugs that inhibit glucosylceramide synthesis have also been approved. Defective GBA1 enzyme activity in humans has been implicated in an increased risk of multiple myeloma and other cancers. Oligoglycosylceramides and gangliosides in particular are known to be involved in the pathology of a number of cancers, and glucosylceramide is an important precursor of these. Inhibition of glucosylceramide synthase, which is overexpressed in many human tumors lead to a marked arrest of cell growth in cancer cells in vitro, so this is believed to have the potential for the treatment of colorectal and other cancers. A deficiency in glucocerebrosidase activity may predispose individuals to more common disorders such as Parkinson's disease and Lewy body dementia. Excess glucosylceramide production and thence of more complex glycosphingolipids is a factor in polycystic kidney disease. It appears to be a general rule that the mere process of lysosomal substrate accumulation in all lysosomal storage disorders impairs lysosome integrity and results in more general disruptions to lipid metabolism and membrane structure and function. On the other hand, inhibition of glucosylceramidases may be of benefit in cystic fibrosis. Krabbe disease is discussed in the next section. Galactosylceramide is believed to function as an initial receptor for the human immunodeficiency virus (HIV) in mucosal epithelial cells and controls the early infection-independent phase of HIV transfer to T cells. Glucosylceramide levels regulate the uptake of viruses that rely upon the late endosomal compartment for fusion, including the influenza A and Ebola viruses. Gangliosides The name ganglioside was first applied by the German scientist Ernst Klenk in 1942 to a mixture of complex glycosphingolipids newly isolated from ganglion cells of brain. Subsequently, he demonstrated that as part of an oligosaccharide chain, they contained an acidic carbohydrate component, which he named "neuraminic acid" - later termed "sialic acid" from the Greek "sialon" for saliva, from which they were first isolated. However, it was not until 1963 that the first ganglioside species was fully characterized. Innumerable sphingolipids are now known that differ in the nature of both the glycan (glucose, galactose, N-acetylgalactosamine, and sialic acid residues) and ceramide structures. They are present throughout the animal kingdom, from echinoderms up to higher animals, but not in plants. Such highly polar, acidic and relatively hydrophilic molecules have distinctive physical properties, which are essential for the vital functions of gangliosides in the membranes of the central nervous system and other tissues. Sialic acids and Gangliosides Sialic acids: Gangliosides are oligoglycosylceramides derived as a first step from lactosylceramide, and they are defined by the presence of one to as many as five sialic acid residues, i.e. carbohydrate molecules with a nine-carbon backbone and a carboxylic acid group, a subclass of the superfamily of naturally occurring non‑2‑ulosonic acids. Of the many forms that have been characterized, only a few are relevant to gangliosides, and the most important of these is N-acetylneuraminic acid (‘NANA’ or ‘SA’ or 'Neu5Ac' or 'NeuAc'). Less often the sialic acid component is N-glycolylneuraminic acid (Neu5Gc), which differs by only one oxygen atom at the C-5 N-acetyl group, or it can be a Neu5Ac analogue in which the amide group is replaced by a hydroxyl group, i.e. 3-deoxy-D-glycero-D-galacto-nonulosonic acid (ketodeoxynonulosonic acid or ‘KDN’). The sialic acids are joined via α-glycosidic linkages to one or more of the monosaccharide units, e.g. via the hydroxyl group on position 2, or to another sialic acid residue. The polar head groups of the lipids carry a net-negative charge at pH 7.0 and they are acidic. Their structures are shown in Figure $27$. Humans lack Neu5Gc: Neu5Ac is the biosynthetic precursor of Neu5Gc, a component of gangliosides from most animal species, including mice, horse, sheep, and goats, via the action of the enzyme CMP–N-acetylneuraminic acid hydroxylase (CMAH). However, NeuGc is not synthesized in humans (or birds and New World monkeys), although it is present in other primates such as the great apes, and indeed as it is a xeno-antigen, anti-NeuGc antibodies are produced normally in healthy humans (and especially after injection of NeuGc-containing glycoconjugates). The absence or irreversible inactivation of a number of relevant genes, but especially a critical exon in the CMAH gene, both for sialolipids and peptides in humans suggests that this may have been a major biochemical branch-point in human evolution that occurred ~2 to 3 million years ago after the divergence of humans and chimpanzees from a common ancestor. It may even be a factor in the superior performance of the human brain as the overexpression of Neu5Gc in the brains of transgenic mice was found to result in abnormal development. It could also mean that there might have been a fertility barrier between us and other hominids during evolution. While these are speculations, there is some evidence that the loss of Neu5Gc in humans had complex effects on immunity, providing greater capabilities to clear sublethal bacterial challenges. Some NeuGc may be obtained from the diet in meat and milk, for example, and this may be incorporated into human gangliosides to a limited extent, especially in fetal tissues and some cancers. In the latter, preferential expression of dietary Neu5Gc has been ascribed to their higher metabolic rate. 2. Structure and Occurrence of Gangliosides Most of the common range of gangliosides are derived from the ganglio- and neolacto-series of neutral oligoglycosphingolipids (Table 1), and they should be named systematically in the same way with the position of the sialic acid residue(s) indicated as for branched structures. However, they are more conveniently defined by a short-hand nomenclature system proposed by Svennerholm in which M, D, T and Q refer to mono-, di-, tri- and tetrasialogangliosides, respectively, and the numbers 1, 2, 3, etc refer to the order of migration of the gangliosides on thin-layer chromatography. For example, the order of migration of monosialogangliosides is GM3 > GM2 > GM1 (sometimes defined by subscripts, e.g. GM1 or GM1). To indicate variations within the basic structures, further terms are added, e.g. GM1a, GD1b, etc. Although alternatives have been proposed that are more systematic in structural terms, the Svennerholm nomenclature is that approved by IUPAC-IUB. Ganglio-series glycosphingolipids having 0, 1, 2 and 3 sialic acid residues linked to the inner galactose unit are termed asialo- (or 0-), a-, b- and c-series gangliosides, respectively, while gangliosides having sialic acid residues linked to the inner N-galactosamine residue are classified as α-series gangliosides. The structures for these groups are illustrated in the section on ganglioside biosynthesis below, for reasons of practical convenience. As of 2020, more than 200 gangliosides with variations in the carbohydrate chain had been characterized in vertebrates alone. One of the most studied monosialo-gangliosides and the first to be fully characterized is ganglioside GM1a (Neu5Acα2-3(Galβ1-3GalNAcβ1-4)Galβ1-4Glcβ1Cer), a major brain ganglioside of mammals and the preferred ligand of cholera toxin, illustrated in Figure $28$. It can also be depicted using the abbreviated structure shown in Figure $29$. An alternative nomenclature, which is less used, is recommended by IUPAC-IUB and is based upon the ganglio (Gg) root structure; it employs Roman numerals to designate each hexose unit and the location of the Neu5Ac along the carbohydrate chain with Arabic superscripts to designate the hydroxyl group to which this is linked. By this system, GM1a is defined as II3-α-Neu5Ac-Gg4Cer. Brain gangliosides: Gangliosides can amount to 6% of the weight of lipids from the brain (20 to 500 times more than in other tissues), where they constitute 10 to 12% of the total lipid content (20-25% of the outer layer) of neuronal membranes, for example. Aside from this, they are synthesized and are present at low levels (1 to 2% of the total lipids) in all animal tissues, where like the neutral oligoglycosphingolipids they are concentrated in the outer leaflet of the plasma membrane in the nanodomains known as 'rafts' or in related structures. Mammalian neurons actively synthesize gangliosides of the ganglio-series primarily, but oligodendrocytes in the brain produce instead myelin-forming glycosphingolipids, such as galactosylceramide and sulfatide together with a minor amount of ganglioside GM4. The brain contains as much as 20 to 500 times more gangliosides than most non-neural tissues, with three times as much in grey as in white matter. As the brain develops, there is an increase in the content of gangliosides and in their degree of sialylation. There are large differences between species and tissues. For example, during embryogenesis and the postnatal period in the human central nervous system, the total amount of gangliosides increases approximately threefold, while that of GM1 and GD1a increases 12 to 15-fold. During the same period, the hemato-series gangliosides GM3, GD3, and 9-OAc-GD3, which lack a hexosamine residue, are the predominant ganglioside species, but they are present in much lower amounts in adults and then in some areas of the brain only. In the mouse brain, the total amount of gangliosides is almost 8-fold greater in adults than in embryos, with a similar shift in composition from simple (GM3 and GD3) to more complex gangliosides. It is evident that the ganglioside changes during brain maturation are correlated with many neuro-developmental milestones, and there is no doubt that gangliosides play a crucial role in neuronal function and brain development, especially during infancy when there is high nutrient demand as the brain undergoes rapid restructuring. The main gangliosides (~95%) of adult mammalian brain are ganglio series GM1, GD1a, GD1b, and GQ1b, while lactosyl series gangliosides such as GM3 (sialyllactosylceramide) are found mainly in the extra-neural tissues. The remaining ~5% consists of minor components in the brain include gangliosides GM4, GM3, GD3, GM2, GD2, Fuc-GM1, Fuc-GD1b, GT1a and GP1c, the proportions of which vary depending on species. On the other hand, modern mass spectrometric methodology (electrospray ionization ion mobility MS) has revealed a much higher degree of sialylation than was previously recognized, including a complete series of mono- to octasialylated gangliosides in fetal frontal lobe. Subsequently, many previously unknown acetylated gangliosides were found in fetal hippocampus by this methodology. The content and composition of gangliosides in the brain also change with aging, with a substantial fall in the content of lipid-bound sialic acid but an increase in the proportion of the more complex forms in terms of carbohydrate structures in the elderly. Gangliosides in other tissues and species: Among the extraneural tissues, lactosyl series gangliosides such as GM3 (sialyllactosylceramide) and monosialogangliosides, in general, tend to predominate. Relatively high concentrations of ganglioside GD1a are present in erythrocytes, bone marrow, testis, spleen, and liver, while GM4 is more abundant in kidney, GM2 in bone marrow, GM1 in erythrocytes and GM3 in intestine. In germ cells of mice, there is a switch between gangliosides of the a- and 0-series upon differentiation when they are crossing the blood-testis barrier. Skin fibroblasts and many cells of visceral organs generate gangliosides of the globo series mainly. Similarly, glob-o and lacto series gangliosides are characteristic components of the stage-specific embryonic antigens (SSEA), which underlie the development and differentiation of human embryonic stem cells. A sialyl-lactotetraosylceramide is present in the latter and in the brains of children under the age of two, but not in tissues of adult humans. Gangliosides can cross the placental barrier into the fetus and those in milk, derived from the apical plasma membrane of secretory cells of the mammary gland, may be of nutritional importance for the newborn. GD3 is the main ganglioside in human breast milk at an early stage of lactation, whereas GM3 is more abundant in the later stages (and in bovine milk). Unfortunately, gangliosides are poorly characterized and quantified in foods in general. A 5-N-deacetylated form of ganglioside GM3 has been detected in human melanoma tumors. In addition, O-acetylation or lactonization of the sialic acid residue adds to the potential complexity. Gangliosides containing O-acetylated sialic acids, such as 9-OAc-GD3, are expressed during embryonic development and in the retina and cerebellum of adult rats, but not other brain regions. They occur also in certain tumors and may protect them from apoptosis. It is possible that such gangliosides are even more widespread, but they are missed after treatment with mild alkali during the isolation procedure, a common analytical practice. A further complexity is the occurrence of gangliosides with sulfate groups, and these have been isolated from human, mouse, and monkey kidney cells. KDN-containing gangliosides are minor components of egg, ovarian fluid, sperm and testis of fish and of some mammalian tissues Gangliosides from marine invertebrates (echinoderms), such as starfish and sea cucumbers, are very different in structure from those in vertebrates and do not have a shorthand nomenclature. They include forms with distinctive ceramide compositions, untypical carbohydrate residues, sialic acids within the oligosaccharide chain, or with glycosyl inositol-phosphoceramide structures. The mollusc, Aplysia kurodai, lacks gangliosides but produces complex oligoglycosylceramides with 2-aminoethylphosphonic acids and/or phosphoethanolamine groups attached that may serve as ganglioside surrogates. Ceramide structures: In general, the ceramide structures of gangliosides tend to be relatively simple. Sphingosine is usually the main sphingoid base, accompanied by the C20 analog in gangliosides of the central nervous system. Stearic acid (18:0) can be 80 to 90% of the fatty acid constituents in the brain, accompanied by small amounts of 16:0, 20:0 and 22:0, but with little or no polyunsaturated or 2-hydroxy acids, other than in some exceptional circumstances (e.g. some carcinomas). Palmitic acid is more abundant in gangliosides of the intestines and liver, while 2-hydroxylated fatty acids are relatively abundant in the last and in the kidney. There are also differences in the composition of the base and fatty acid components in different cells or regions of the brain. During development, the nature and concentrations of these constituents change markedly, and for example, the ratio of C20/C18-sphingosine in ganglioside GD1a of cerebellum increases 16-fold from 8-day-old to 2-year-old rats. In gangliosides outwith the nervous system, C20-sphingosine is barely detectable, and there is often a much wider range of fatty acid constituents (C14 to C24). The nature of the ceramide component is relevant to the biological function of gangliosides, and changing the fatty acid component to α-linolenic acid by synthetic means alters the biological activity of gangliosides dramatically in vitro. However, it is the carbohydrate moiety that has the primary importance for most of their functions, and detailed discussion of these structures would take us into realms of chemistry best left to carbohydrate experts (see the reading list below). In any given cell type, the number of different gangliosides may be relatively small, but their nature and compositions may be characteristic and in some way related to the function of the cell. It is noteworthy that some terminal glycan structures of gangliosides are also present in glycoproteins of membranes. 3. Biosynthesis There is evidence that the pool of glucosylceramide and thence of lactosylceramide that is utilized for ganglioside biosynthesis is different from that for the other neutral oligoglycosylceramides. This may explain some of the differences between the two groups in the fatty acid and sphingoid base components, which will also be dependent upon cell type. It is an open question how the ganglioside precursors enter the Golgi and trans-Golgi network where synthesis occurs at the luminal leaflet, but it appears that the regulation of intracellular sphingolipid traffic may be as important as the control of enzyme expression and activity in determining the final compositions of the various glycosphingolipid types. In humans, sialic acid biosynthesis occurs by a series of reactions in the cytosol, but the Neu5Ac produced is transferred to the nucleus and activated by the cytosine 5'-monophosphate N-acetylneuraminic acid synthetase (CMAS) to form CMP-Neu5Ac, which is transported to the Golgi apparatus by a family of sialyltransferases specific for particular glycosidic linkages (α2,3, α2,6, α2,8, and α2,9). Thereafter, the pathways for the biosynthesis of the common series of gangliosides of the ganglio-series, for example, involve sequential activities of distinct membrane-spanning sialyltransferases and glycosyltransferases as illustrated in Figure $29$ for the four main 0-, a-, b- and c-series of gangliosides. The required enzymes are bound to the membranes of the Golgi apparatus in a sequence that corresponds to the order of addition of the various carbohydrate components. Thus, the sialyltransferase that catalyzes the synthesis of the relatively simple ganglioside GM3 is located in the cis-region of the Golgi, while those that catalyse the terminal steps of ganglioside synthesis are located in the distal or trans-Golgi region. The GM3 synthase in particular, which catalyzes the transfer of Neu5Ac from cytidine monophosphate (CMP)-Neu5Ac onto the terminal galactose residue of lactosylceramide, has a unique specificity. The simple ganglioside GM3 is synthesized by the addition of sialic acid to lactosylceramide by CMP:LacCer α2-3 sialyltransferase (or GM3 synthase), before GD3 and GT3 are produced in turn by the action of appropriate synthases. Subsequently, GM3, GD3 and GT3 serve as precursors of more complex gangliosides by the action of further glycosyl- and sialyl-transferases. An alternative theory with some supporting evidence proposes that a multiglycosyl-transferase complex is responsible for the synthesis of each individual ganglioside rather than a series of individual enzymes. Further sialylation of each of the a, b, and c series and in different positions in the carbohydrate chain can occur to give an increasingly complex and heterogeneous range of products, such as the α-series gangliosides with sialic acid residue(s) linked to the inner N-acetylgalactosamine residue (not illustrated). GM4 or NeuAcα2,3Gal-Ceramide, a minor component of the brain and present in a few other tissues at low levels, is an exception in that galactosylceramide is its precursor. Finally, the newly synthesized gangliosides are transferred to the external leaflet of the plasma membrane via the lumenal surface of transport vesicles. Gangliosides are also important constituents of nuclear membranes. The changes that occur in ganglioside compositions of brain and other tissues in the embryonic and post-natal stages are governed mainly by changes in the expression level and activity of the glycosyl- and sialyl-transferases, although the former can also be regulated by glycosylation and phosphorylation. The presence of distinctive sialidases that differ from the catabolic lysosomal enzymes (see below) in raft-like regions of the plasma membrane bring about further changes in the composition of the cell surface gangliosides that can be specific to particular cell types, causing a shift from poly-sialylated species involving a decrease of GM3 and formation of GM2 then GM1 by hydrolysis of terminal sialosyl residues linked either α2‑8 on another sialic acid or α2‑3 on galactose. As GM1 is resistant to most sialidases, it tends to increase in concentration relative to oligosialo species as developmental and other GM1-requiring processes come into play. This may have consequences for important cellular events, such as neuronal differentiation and apoptosis. Conversely, sialylation may occur in some neuronal membranes, increasing the proportions of poly-sialylated species. In particular, a CMP-NeuAc:GM3 sialyltransferase is able to sialylate GM3. Gangliosides GM1 and GD1a have been identified in both membranes of the nuclear envelope together with two neuraminidases. Ganglioside lactones, where the sialic acids are linked together with ester linkages, have been detected as minor components in brain tissues, where lactonization occurs at the plasma membrane. As the process of lactonization profoundly influences the shape and biological properties of the original ganglioside, it is possible that lactonization-delactonization in a membrane might be a trigger for specific cellular reactions. Similarly, GD3 ganglioside can undergo O-acetylation at C9 of the outer sialic acid with important metabolic implications. Gangliosides added to many types of cell preparations in vitro are rapidly taken up by the cells, while gangliosides injected into animals in vivo are rapidly internalized by tissues. They can cross the blood-brain barrier, and via the placenta, they can enter the fetus. Similarly, dietary gangliosides are absorbed intact by intestinal cells but are broken down to their lipid and carbohydrate constituents for re-use. The sialic acids released by an intestinal sialidase are transported in plasma to the brain and other tissues where they influence ganglioside expression. Indeed, there is some experimental evidence that dietary gangliosides may improve cognitive functions in animals and humans. Catabolism Degradation of gangliosides takes place at the surface of intralysosomal luminal vesicles, generated by an inward budding of the endosomal membrane, and these are reached by a process of endocytosis. In brief in relation to gangliosides, soluble sialidases (neuraminidases) and exoglycohydrolases remove individual sialic acid and sugar residues sequentially from the non-reducing terminal unit, as illustrated for ganglioside GM1, with the eventual formation of ceramide, which is then split into long-chain base and fatty acids by ceramidases. This degradation occurs through the endocytosis-endosome-lysosome pathway with a requirement for an acidic pH inside the organelle. In addition to the sialidases and exoglycohydrolases, the various reactions have an absolute requirement for effector molecules, termed 'sphingolipid activator proteins', including saposins (Sap), and the specific GM2-activator protein (GM2-AP). Ganglioside GM3 is a component of the lysosomal perimeter membrane, but is protected from degradation by a glycocalix of the membrane facing the lysosol. Anionic lipids and especially bis(monoacylglycero)phosphate in the membranes stimulate ganglioside degradation while cholesterol is inhibitory. The catabolic pathway is shown in Figure $30$. This process constitutes a salvage mechanism that is important to the overall cellular economy since a high proportion of the various hydrolysis products are recycled for glycolipid biosynthesis. By generating ceramide and sphingosine, it may also be relevant to the regulatory and signaling functions of these lipids. In addition, some partial hydrolysis of gangliosides occurs in the plasma membrane as part of a biosynthetic remodeling process discussed above. Defects in catabolism lead to the gangliosidoses discussed later. Ganglioside Function Cell surface effects: In their natural biological environment, gangliosides have a negative charge because of the presence of sialic acids, which also add to the hydrophilicity of the polysaccharide constituent. This is balanced somewhat by the hydrophobic character of the ceramide moiety, so that over all the molecules are amphiphilic in nature, but very different from the glycerophospholipids, which are essential for the formation of membrane bilayers. Indeed, a ganglioside such as GM1 is virtually soluble in water, where it can form large aggregates though hydrophilic effects. The nature of the ceramide unit with its capacity to form hydrogen bonds with glycerophospholipids is important in ensuring that gangliosides are inserted in a stable manner into the outer layer of the plasma membrane. Thus, gangliosides are anchored in membranes by their ceramide units with the double-tailed sialoglycan components extending out from the cell surface, where they can participate in intermolecular interactions by a network of hydrogen bonds and hydrophobic interactions. For example, the glucose-ceramide bond of GM1 is oriented in the outer leaflet of the plasma membrane such that the glycan extends perpendicularly to the plane of the lipid bilayer. All gangliosides, but especially the simplest GM3 or Neu5Acα2-3Galβ1-4Glcβ1Cer, have a structural role, and they a natural propensity to laterally segregate and to associate with each other and with other sphingolipids, phospholipids and cholesterol into raft nano-domains or in related structures, such as the caveolae, where the very large surface area occupied by the oligosaccharide chain imparts a strong positive curvature to the membrane. In this environment, gangliosides can interact with each other through side-by-side hydrogen bonds mediated by water molecules that act as bridges between the chains. Further, molecules of GM3 and other gangliosides self-aggregate into clusters on the surface of lymphocytes of human peripheral blood, and there is evidence that the density of these clusters in membranes governs their reactivity as antigens. In addition, it is believed that gangliosides and other oligoglycosylceramides can cluster together through hydrogen donor-acceptor (cis) interactions because of the presence of hydroxyl and acetamide groups to form glycosynaptic domains, which are related to but functionally distinct from raft signaling platforms (with lower cholesterol concentrations). Many of the biological functions of gangliosides are mediated through their location in these nanodomains, where they may have specialized functions in cell adhesion, growth, and motility through interactions with specific proteins and signal transduction pathways. However, not all gangliosides are present in such raft-like structures. Receptor/signaling functions: Gangliosides can bind to membrane proteins directly by carbohydrate-carbohydrate or carbohydrate-amino acid interactions, usually involving specific ganglioside head groups, resulting in changes to the location of proteins within membrane microdomains for recruitment of signaling partners, or to dimerization or other effects upon receptors. In rafts and caveolae especially, gangliosides can modulate cell signaling processes by their interactions with specific receptors, adhesion molecules, and ion channels. Cell–cell (trans) interactions occur by sialoglycans on one cell binding to complementary binding proteins (lectins) on adjacent cells, bringing about adhesion of cells and enabling regulation of intracellular signaling pathways, e.g. myelin-associated glycoprotein on myelin sheaths binds to gangliosides present on axonal membranes. In addition, gangliosides act as receptors of interferon, epidermal growth factor, nerve growth facto,r and insulin, and they may regulate cell signaling and control growth and differentiation of cells in this way. While intact gangliosides inhibit growth by rendering cells less sensitive to stimulation by epidermal growth factor, removal of the N-acetyl group of sialic acid enhances this reaction and stimulates growth. Gangliosides function as antigens or receptors by recognizing specific molecules (lectins), including bacterial toxins, at the cell surface and by modulating the charge density at the membrane surface (see the section on Gangliosides and Disease below). They also regulate the activities of proteins within the plasma membrane and especially receptor-type tyrosine kinases. For example, the phosphorylation state and activity of insulin receptors in caveolae and thence the insulin resistance of cells is controlled by the concentration of GM3, the main ganglioside in plasma and other extraneural tissues. GM3 interacts also with the epidermal growth factor receptor leading to cell growth inhibition. GM1 strongly influences specific neuronal functions by interacting with specific receptors such as the tropomyosin receptor kinase (Trk) A (TrkA) receptor by altering its conformation to enable interaction with the nerve growth factor (NGF) ligand. GM3 (SA-Gal-Glc-Cer) is a serum ganglioside that is highly enriched in a type of membrane microdomain termed a 'glycosynapse', and it forms complexes with co-localized cell signaling molecules. It has a function in the innate immune function of macrophages and it has been demonstrated that molecular species of GM3 with differing acyl-chain structures and modifications can operate as pro- and anti-inflammatory modulators of Toll-like receptor 4 (TLR4); very-long-chain and α-hydroxy GM3 species increase TLR4 activation, while long-chain and unsaturated GM3 species have the opposite effect. In addition, gangliosides have been shown to be cell-type specific antigens that have key functions in immune defense. For example, a major immunological function of gangliosides and sialic acids is to protect cells from attack by our own immune system and from autoimmunity. They recognize and protect host organs and tissues from complement attack by binding to the complement regulatory protein factor H, which has the potential to exert strong cytotoxic and inflammation-inducing activity. In particular, sialic acids protect against complement killing of autologous cells by binding to this protein via the α2–3 linked sialic acid glycans of the GD3 ganglioside. On the other hand, the breakdown of this system can lead to autoimmune diseases. Brain function: One of the first examples of a ganglioside influencing a signaling event to be studied in some detail concerns the simple ganglioside GD3, which has a central role in early neurogenesis. GD3 binds to the epidermal growth factor receptor (EGFR) via a protein-carbohydrate interaction involving its terminal N-acetylneuraminic acid and a lysine residue in the transmembrane domain of the receptor and also by a carbohydrate-carbohydrate interaction thereby maintaining the latter in its inactive monomeric state. EGFR then binds to the epidermal growth factor and stimulates the transition of the receptor from an inactive monomeric to an active homodimeric form, and this in turn triggers receptor auto-phosphorylation and activation of a signaling cascade that promotes cell proliferation. This has proven to be essential for the regulation of the stem cell self-renewal capacity in the brain. In contrast, the neutral oligoglycosphingolipid Gb4 exerts the opposite effect on EGFR by interacting directly with it to potentiate its auto-phosphorylation with activation of the downstream cascade. The techniques of molecular biology such as targeted gene deletion, which enable specific enzymes to be eliminated from experimental animals, are now leading to a better understanding of the function of each ganglioside. It is evident that they are essential to central myelination, to maintain the integrity of axons and myelin, and for the transmission of nervous impulses. These effects may be mediated by interactions of the negatively charged sialic acid residues of gangliosides with calcium ions, which are critical for neuronal responses. For example, a variant of GD3, 9-O-acetyl GD3, appears to be involved in glial-guided neuronal migration during brain development in the rat, while GM1 may have a similar function in humans; it determines which growth cone of unpolarized neurons becomes the axon. By stabilizing neuronal circuits, gangliosides have a function in memory, and conversely, disturbances in ganglioside synthesis can lead to neurodegenerative disorders (see below). Ganglioside GM3 in raft domains has been shown to have an indispensable role for the development, function, and viability of cochlear hair cells and thence it is essential for hearing. On the other hand, mice that express GM3 primarily and are devoid of the typical complex gangliosides of the brain suffer weight loss, progressive motor and sensory dysfunction, and deterioration in spatial learning and memory with aging. GD3 is important for retinal structure and visual function in mice. Changes in ganglioside composition can be induced by nerve stimulation, environmental factors, or drug treatments. The various interconvertible ganglioside types in the plasma membrane of neurons are particularly important for its development in that they regulate such processes as axonal determination and growth, signaling, and repair. In addition, gangliosides are believed to be functional ligands for the maintenance of myelin stability and the control of nerve regeneration by binding to a specific myelin-associated glycoprotein. The occurrence of gangliosides in cell nuclei suggests a possible involvement of gangliosides in the expression of genes relevant to neuronal function. For example, the monosialoganglioside GM1 has been shown to promote the differentiation of various neuronal cell lines in culture. It has protective effects on the neural system by encouraging neural stem cell survival and proliferation, while facilitating the stability and regeneration of axons, and by inhibiting neurodegeneration through autophagy, for example after ischemic stroke. Within membrane rafts, this ganglioside has key roles in several signaling systems through association with specific proteins that have glycolipid-binding domains, including those that modulate mechanisms such as ion transport, neuronal differentiation, G protein-coupled receptors (GPCRs), immune system reactivities and neuroprotection. It is important for Ca2+ and Na+ homeostasis in the nucleus and plasma membrane and in regulating the effects of platelet-derived growth factor. However, there have been unpleasant complications when GM1 has been administered for therapeutic purposes. GD1a is sometimes considered to be a reserve pool for GM1. After nerve injury, toll-like receptor 2 (TLR2) signaling is important for the induction of neuropathic pain; ganglioside GT1b functions as a TLR2 agonist to produce mechanical and thermal hypersensitivity. Other functions: The ganglioside GD3 is essential for the process of apoptosis by blocking the activation of specific transcription factors and thence disabling the induction of antiapoptotic genes. 9-O-Acetylation of the GD3 molecule prevents ganglioside oxidation and blocks its pro-apoptotic effects. Similarly, GD3 is a regulator of autophagy, i.e. the degradation and/or recycling of cellular components. Gangliosides are also important in reproduction, and in mice, GD1a has been shown to be important to oocyte maturation, monospermic fertilization, and embryonic development, while GM1 is important in sperm-oocyte interactions and sperm maturation processes. Deletion of the GM2/GD2 synthase leads to infertility in male mice and the production of a novel fucosylated ganglioside containing very-long-chain polyunsaturated fatty acids. Related studies with gene knockout mice have revealed that b-series gangliosides are important in leptin secretion from adipocytes, while a-series gangliosides interact with the leptin receptor in the hypothalamus to influence the balance of energy. Gangliosides and Disease Bacterial toxins and viruses: In relation to adaptive immunity, a-series and o-series gangliosides in the plasma membrane are involved in the function and stimulation of receptors on certain subsets of T cells by acting as pattern-recognition receptors for invading pathogens. In particular, certain gangliosides bind specifically to viruses and to various bacterial toxins, such as those from botulinum, tetanus and cholera, and to blood merozoites of the deadliest malaria parasite Plasmodium falciparum, and they mediate interactions between microbes and host cells during infections, with NeuAc as the main recognition module. The best known example is cholera toxin, which is an enterotoxin produced by Vibrio cholerae where the specific cell surface receptor is ganglioside GM1; the five B-chains of cholera toxin each bind one molecule of GM1. Interestingly, the subsequent metabolism of the ganglioside-toxin complex is dependent on the nature of the fatty acid components of the ganglioside. It is believed that toxins utilize the gangliosides to hijack an existing retrograde transport pathway from the plasma membrane to the endoplasmic reticulum. For example, the passage of the cholera toxin through the epithelial barrier of the intestine is mediated by GM1, possibly by endocytosis of the toxin-GM1 complex via caveolae into the apical endosome and thence into the Golgi/endoplasmic reticulum, where the complex dissociates. The consequence is persistent activation of adenylate cyclase by the toxin and continuous production of cAMP that leads to the severe fluid loss typical of cholera infections. As a further example, the botulinus toxin binds to a complex of a polysialoganglioside with the protein synaptotagmin, which together act as a high-affinity receptor complex to enable the neurotoxic effects. Similarly, ganglioside GM2 binds to a toxin secreted by Clostridium perfringens. Influenza viruses have two glycoproteins in their envelope membranes, hemagglutinins, which bind to cellular receptors such as gangliosides, and after entry into respiratory epithelial cells, the sialidase (neuraminidase) of the virus cleaves the sialic acid from the receptors to prevent entry of further viruses to the cell. Variations in the structure of these proteins force the development of new vaccines The carbohydrate moiety of gangliosides is essential for the initial binding of viruses, but the lipid moiety is believed to be important for controlling their intracellular transport. Some gangliosides and GD1a especially have anti-inflammatory properties in that they inhibit the effects of bacterial lipopolysaccharides by preventing the activation of tumor necrosis factor (TNF) and other cytokines. In contrast, GM2 may increase cytokine production in similar circumstances, while the heat-labile toxins of Escherichia coli bind to several gangliosides in macrophages, thus activating an inflammatory response. Gangliosidoses and other neurodegenerative diseases: It appears to be a general rule that the mere process of lysosomal substrate accumulation in all lysosomal storage disorders impairs lysosome integrity and results in more general disruptions to lipid metabolism and membrane structure and function, inevitably triggering pathologic mechanisms. Endogenous generation of antibodies to gangliosides is often a factor, and it has been argued that gangliosides and their sialic acids components are at the border of immune tolerance. As with the neutral oligoglycosylceramides and ceramide monohexosides, a number of unpleasant lipidoses have been identified that involve the storage of excessive amounts of gangliosides in tissues because of failures in the catabolic mechanism. The most important of these are the GM2 gangliosidoses, i.e. Tay-Sachs disease (and the similar Sandhoff disease), a fatal genetic disorder found mainly in Jewish populations in which harmful quantities of ganglioside GM2 accumulate in the nerve cells in the brain and other tissues. Lyso-GM2 (non-acylated) in plasma may serve as a marker. A modified GM2 derivative that contains taurine in amide linkage to the sialic acid carboxyl group has been identified in the brain of such patients. As infants with the most common form of the disease develop, the nerve cells become distended and a relentless deterioration of mental and physical abilities occurs. The condition is caused by insufficient activity of specific enzymes, i.e. β‑N‑acetylhexosaminidase, which catalyzes the degradation of gangliosides by removing the terminal N-acetylgalactosamine residue from GM2, or the GM2 activator protein. In addition, a generalized GM1 gangliosidosis (an autosomal recessive and neurodegenerative disease) has been characterized in which ganglioside GM1 accumulates in the nervous system leading to mental retardation and enlargement of the liver. The condition is a consequence of a deficiency of the lysosomal β-galactosidase enzyme, which hydrolyses the terminal β-galactosyl residues from GM1 ganglioside to produce GM2. It appears that storage of substantial amounts of unwanted lipids in the lysosomal system leads to a state of cellular starvation, so that essential elements such as iron are depleted in brain tissue. The presence of lyso-GM1 in plasma is now seen as a useful aid to diagnosis. Small amounts of some gangliosides accumulate as secondary storage compounds in Niemann–Pick disease. The Guillain–Barré syndrome is an acute inflammatory disorder, usually triggered by a severe infection, which affects the peripheral nervous system. Antibodies to gangliosides are produced by the immune system, leading to damage of the axons, which can result in paralysis of the patient. Huntington’s disease is believed to involve disruption of the metabolic pathways between glycosylceramides and gangliosides, and there is a human autosomal recessive infantile-onset epilepsy syndrome caused by a mutation to a sialyl transferase. Impaired ganglioside metabolism is also relevant to Alzheimer’s disease, because complexation with ganglioside GM1 may cause aggregation of the amyloid β-protein deposits that characteristically accumulate in the brain in this condition (this explanation does not appear to be universally accepted). In general, in ganglioside deficiencies, natural or induced, it appears that progressive inflammatory reactions take place, leading to neurodegeneration in part because of the deterioration of the architecture of lipid rafts. On the other hand at normal tissue concentrations, gangliosides such as GM1 are believed to have an anti-inflammatory and neuroprotective role in certain types of neuronal injury, Parkinsonism, and some related diseases. For example in relation to Parkinson's disease, GM1 binds to α-synuclein and inhibits or eliminates fibril formation. It may have a protective role by preventing sphingomyelin-induced aggregation, although as the overall level of GM1 decreases during aging, its beneficial effect decreases. For these reasons, the therapeutic properties of ganglioside GM1, the most accessible species, and derived molecules are under clinical investigation. However, there is no approved therapy for any gangliosidosis, although a number of different therapeutic strategies are being studied, including hematopoietic stem cell transplantation and gene therapy. For the moment, the blood-brain barrier remains a challenge. Cancer: Gangliosides have important functions in cancer, especially in the regulation of signal transduction induced by growth-factor receptors in a specific microdomain termed a 'glycosynapse' in the cancer cell membranes, and in interactions with glycan recognition molecules involved in cell adhesion and immune regulation. In particular, depending on tissue, certain distinctive gangliosides are expressed at much higher levels in tumors than in normal healthy tissues, mainly by aberrant expression of glycosyltransferases and glycohydrolases. This enables tumor cells to escape immune surveillance and retain their malignancy. GM3 is not expressed in melanocytes normally, but is detected in 60% of primary melanomas and in 75% of metastatic melanomas, for example. Gangliosides can be shed from the surface of tumor cells into the local environment where they can influence interactions between cancer cells, including the transition of tumors from a dormant to a malignant state (angiogenesis); when present in the circulation they can be useful diagnostic aids. For example, the ganglioside GM3 is elevated in the serum of patients with breast cancer and may be a biomarker for the disease, while disialylated gangliosides GD2 and GD3 (Figure $31$) are considered to be markers of neuroectoderm origin in tumors (neuroblastoma). Specific gangliosides can have either positive or negative effects upon the regulation of the malignant properties of cancer cells. As a generality, disialyl glycosphingolipids or tandem-repeated sialic acid-structures confer malignant properties in various cancer systems; they are not merely markers. For example, the disialo-gangliosides GD2 and GD3 are present in trace amounts only in normal tissues, but are found at much higher concentrations in cancer cells, especially melanomas and neuroblastomas, with GD2 especially elevated in triple-negative breast cancer. These b-series gangliosides play a substantial part in the malignant properties of gliomas by mediating cell proliferation, migration, invasion, adhesion, and angiogenesis, and in preventing immunosuppression. They are considered to be tumor-associated antigens, and the GD2 and GD3 synthases are seen as important drug targets. In contrast, monosialyl gangliosides, such as GM1, GM2 and GM3, may suppress the malignant properties of various cancer cells. The mechanism is believed to involve complex formation at the cell surface with membrane proteins, such as growth factor receptors and adhesion receptors like those of the integrin family, leading to the modification of cell signals mediated by these receptors. Metastatic melanoma cells have high levels of GD3 in comparison to poorly metastatic cells or the normal counterpart, suggesting that GD3 may promote metastasis possibly by suppressing the anti-tumor immune response. Ganglioside GM3(Neu5Gc), i.e. containing an abnormal sialic acid, is sometimes considered to be a tumor-specific antigen and a target for cancer immunotherapy. Aberrant sialylation is found in many malignant cancers, where the levels of neuraminidases are key factors for metastasis and survival of cancer cells, and there can be a significant accumulation of unusual gangliosides containing N-glycolyl sialic acid in some cancers. N-Glycolyl-GM3, normally absent from human tissues, is present in all stage II breast cancers, and it is accompanied by a number of other less common complex gangliosides. Similarly, the 5-N-deacetylated form of GM3 is expressed in metastatic melanomas, but not in healthy tissue or even in primary melanomas; it is considered to be a specific marker for the metastatic condition and a target for potential therapy. Increased synthesis of 9-O-acetyl-GD3, dependent on a sialyl-O-acetyltransferase - CAS1 Domain-Containing Protein 1, occurs in acute lymphoblastic leukemia and in malignant melanomas, and this appears to limit apoptosis, while O-acetylated GD2 (OAcGD2) is expressed in breast cancer and other tumors. A unique fucosyl-GM1 in which the terminal galactose is α-1,2-fucosylated at the non-reducing end is found circulating in the serum of patients with a number of cancers and especially with small-cell lung cancer but rarely in normal conditions, and it is also considered to be a potential indicator of cancer and a candidate for immunotherapy. Clinical trials with an antibody to GD2 have been carried out successfully against the rare childhood cancer neuroblastoma, and the USDA has approved the use of this in combination with other drugs to treat this often lethal cancer. However, this antibody can have painful side effects due to an interaction with GD2 on neurons, and modified antibodies, which may be safer, are now being tested in multiple clinical trials. A phase I clinical trial with an antibody to GD3 has shown promising results in patients with malignant melanoma. Similarly, antibodies to OAcGD2 and fucosyl-GM1 have shown anti-tumour effects in vitro, and studies with human patients are underway. Other diseases: Aberrant production of the ganglioside GM3 has been linked to pathophysiological changes associated with obesity, metabolic disorders, and type 2 diabetes mellitus through its effects on insulin receptors. It has a role in autoimmune disorders such as multiple sclerosis. In epilepsy, it is believed that a deficiency in the enzyme ceramide synthase 1, which produces 18:0 ceramides, leads to reduced ganglioside formation. By their presence in certain subsets of T cells, gangliosides influence allergic responses and auto-immune diseases. As gangliosides are present on the surface of vascular, vascular-associated, and inflammatory cells, they may have a role in atherosclerosis and in aging.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/02%3A_Unit_II-_Bioenergetics_and_Metabolism/21%3A_Lipid_Biosynthesis/21.05%3A_Biosynthesis_of_Cholesterol_and_Steroids.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry By William (Bill) W. Christie and Henry Jakubowski. This section is an abbreviated and modified version of material from the Lipid Web, an introduction to the chemistry and biochemistry of individual lipid classes, written by William Christie. Sterols: Cholesterol and Cholesterol Esters In animal tissues, cholesterol (cholest-5-en-3β-ol) is by far the most abundant member of a family of polycyclic compounds known as sterols. It can also be described as a polyisoprenoid or a triterpene from its biosynthetic origin. Cholesterol was first recognized as a component of gallstones as long ago as 1769, while the great French lipid chemist Chevreul isolated it from animal fats in 1815. However, it was well into the 20th century before the structure was fully defined by the German Chemist Heinrich Wieland, who received the Nobel Prize in Chemistry for his work in 1927, the first of thirteen so honored for research on cholesterol and its metabolism. Cholesterol plays a vital role in animal life, and it is essential for the normal functioning of cells both as a structural component of cell membranes and as a precursor of steroid hormones and other key metabolites including vitamin D and bile acids. It is also important for cell signaling, transport processes, nerve conduction and the regulation of gene transcription. Every cell in vertebrates is able both to synthesize cholesterol and to metabolize it, and there is evidence that synthesis de novo is essential whatever the dietary intake; this is vital in the brain. However, excess cholesterol can contribute to the pathology of various diseases, notably cardiovascular disease, so cholesterol levels must be balanced to ensure an adequate but not excessive supply. Cholesterol – Structure, Occurrence, and Function in Membranes The struture of cholesterol is shown below in Figure $1$. Cholesterol consists of a tetracyclic cyclopenta[a]phenanthrene structure with an iso-octyl side-chain at carbon 17. The four rings (A, B, C, D) have trans ring junctions, and the side chain and two methyl groups (C-18 and C-19) are at an angle to the rings above the plane with β stereochemistry (as for the hydroxyl group on C-3 also); there is a double bond between carbons 5 and 6. Thus, the molecule has a rigid planar four-ring nucleus with a flexible tail. Of the two recognized numbering systems in use, one originally described by Fieser and Fieser in 1959 and a second by IUPAC-IUB in 1989, the first appears to be preferred by most current authors. Most of the atoms in cholesterol can be placed on the diamond lattice, which is the structure showing the position of each carbon atom in the network solid diamond. In that structure, each carbon is connected to four other carbons using sp3-hybridized atomic orbitals and tetrahedral geometry. A representation of the diamond lattice is shown below. It consists of a series of interconnected boat conformations of cyclohexane propagating in the xyz direction. The structures of two different fused cyclohexanes, trans- and cis-decalins as well as the structure of three fused cyclohexanes, adamantane, are shown in Figure $2$. Superimposing sp3-connected atoms onto a diamond lattice allows improved visualization of the real and allowed structures of more complicated molecules. Figure $3$ shows a superposition of 5-α-cholestane, a reduced and non-hydroxylated form of cholesterol, onto the diamond lattice. The pink cyclopentane D ring of 5-α-cholestane, which is distorted from the 1090 bond angle for sp3-hybridized carbon atoms, does not fit exactly onto the lattice. Cholesterol, with its double bond, would also deviate from the ideal position on the diamond lattice given the two sp2-hybridized carbons in the double bond. Cholesterol is a ubiquitous component of all animal tissues (and of some fungi), produced by every nucleated animal cell, where much of it is located in the membranes, although it is not evenly distributed. The highest proportion of unesterified cholesterol is in the plasma membrane (roughly 30-50% of the lipid in the membrane or 60-80% of the cholesterol in the cell), while mitochondria and the endoplasmic reticulum have much less (~5% in the latter), and the Golgi contains an intermediate amount. Cholesterol is also enriched in early and recycling endosomes, but not in late endosomes. It may surprise some to learn that the brain contains more cholesterol than any other organ, where it comprises roughly a quarter of the total free cholesterol in the human body, 70-80% of which is in the myelin sheath. Of all the organic constituents of blood, only glucose is present in a higher molar concentration than cholesterol. In animal tissues, it occurs in the free form, esterified to long-chain fatty acids (cholesterol esters), and in other covalent and non-covalent linkages, including an association with the plasma lipoproteins. In plants, it tends to be a minor component only of a complex mixture of structurally related 'phytosterols', although there are exceptions but it is nevertheless importance as a precursor of some plant hormones. Animals in general synthesize a high proportion of their cholesterol requirement, but they can also ingest and absorb appreciable amounts from foods. On the other hand, many invertebrates, including insects, crustaceans and some molluscs cannot synthesize cholesterol and must receive it from the diet; for example, spiny lobsters must obtain exogenous cholesterol to produce essential sex hormones. Similarly, it must be supplied from exogenous sources to the primitive nematode Caenorhabditis elegans, where it does not appear to have a major role in membrane structure, other than perhaps in the function of ion channels, although it is essential the production of steroidal hormones required for larval development; its uptake is regulated by the novel lipid phosphoethanolamine glucosylceramide. Some species are able to convert dietary plant sterols such as β-sitosterol to cholesterol. Prokaryotes lack cholesterol entirely with the exception of some pathogens that acquire it from eukaryotic hosts to ensure their intracellular survival (e.g., Borrelia sp.); bacterial hopanoids are often considered to be sterol surrogates. Cholesterol has vital structural roles in membranes and in lipid metabolism in general with an extraordinary diversity of biological roles, including cell signaling, morphogenesis, lipid digestion and absorption in the intestines, reproduction, stress responses, sodium and water balance, and calcium and phosphorus metabolism, and we can only touch on a few of these functions in this web page. It is a biosynthetic precursor of bile acids, vitamin D, and steroid hormones (glucocorticoids, estrogens, progesterones, androgens, and aldosterone), and it is found in covalent linkage to specific membrane proteins or proteolipids ('hedgehog' proteins), which have vital functions in embryonic development. In addition, it contributes substantially to the development and working of the central nervous system. On the other hand, excess cholesterol in cells can be toxic, and a complex web of enzymes is essential to maintain the optimum concentrations. Because plasma cholesterol levels can be a major contributory factor to atherogenesis, media coverage has created what has been termed a ‘cholesterophobia’ in the population at large. One of the main functions of cholesterol is to modulate the fluidity of membranes by interacting with their complex lipid components, specifically the phospholipids such as phosphatidylcholine and sphingomyelin. As an amphiphilic molecule, cholesterol is able to intercalate between phospholipids in lipid bilayers to span about half a bilayer. In its three-dimensional structure, it is in essence a planar molecule that can interact on both sides. The tetracyclic ring structure is compact and very rigid. In addition, the location of the hydroxyl group facilitates the orientation of the molecule in a membrane bilayer, while the positions of the methyl groups appear to maximize interactions with other lipid constituents. The structure of cholesterol as it would appear on the diamond lattice is shown in Figure $4$. As the α-face of the cholesterol nucleus (facing down) is 'smooth', it can make good contact with the saturated fatty-acyl chains of phospholipids down to about their tenth methylene group; the β-face (facing up) is made 'rough' by the projection of methyl groups from carbons 10 and 13. The interaction is mainly via van der Waals and hydrophobic forces with a contribution from hydrogen bonding of the cholesterol hydroxyl group to the polar head group and interfacial regions of the phospholipids, especially sphingomyelin. Intercalated cholesterol may also disrupt electrostatic interactions between the ionic phosphocholine head groups of nearby membrane phospholipids, leading to increased mobility of the head groups. Indeed, there is evidence that cholesterol forms stoichiometric complexes with the saturated fatty acyl groups of sphingomyelin and to a lesser extent of phosphatidylcholine. Experiments with mutant cell lines and specific inhibitors of cholesterol biosynthesis suggest that an equatorial hydroxyl group at C-3 of sterols is essential for the growth of mammalian cells. The Δ5 double bond ensures that the molecule adopts a planar conformation, and this feature also appears to be essential for cell growth, as is the flexible iso-octyl side-chain. The C-18 methyl group is crucial for the proper orientation of the sterol. While plant sterols appear to be able to substitute for cholesterol in supporting many of the bulk properties of membranes in mammalian cells in vitro, cholesterol is essential for other purposes. In the absence of cholesterol, a membrane composed of unsaturated lipids is in a fluid state that is characterized by a substantial degree of lipid chain disorder, i.e., it constitutes a liquid-disordered phase. The function of cholesterol is to increase the degree of order (cohesion and packing) in membranes, leading to the formation of a liquid-ordered phase. In contrast, it renders bilayers composed of more saturated lipids, which would otherwise be in a solid gel state, more fluid. Thus, cholesterol is able to promote and stabilize a liquid-ordered phase over a substantial range of temperatures and sterol concentrations. Further, high cholesterol concentrations in membranes reduce their passive permeability to solutes. These effects enable membranes to bend or withstand mechanical stresses, and they permit the fine-tuning of membrane lipid composition and organization, and regulate critical cell functions. Simplistically, the higher cholesterol concentrations in the plasma membrane support its barrier function by increasing membrane thickness and reducing its permeability to small molecules. In contrast, the endoplasmic reticulum has increased membrane flexibility because of its lower cholesterol concentrations and thus enables the insertion and folding of proteins in its lipid bilayer. While mitochondrial membranes have a low cholesterol content in total, this may be concentrated in nanodomains at regions of high curvature in the inner mitochondrial membrane with links to nucleoprotein complexes (nucleoids). In comparison to other lipids, it has been reported that cholesterol can flip rapidly between the leaflets in a bilayer, although this does not appear to be accepted universally, leading to doubts as to the trans-bilayer distribution of cholesterol in some biological membranes. However, much recent evidence suggests that the concentration of cholesterol in the inner leaflet of the plasma membrane is much lower than that in the outer leaflet in a range of mammalian cells. This distribution is important in that cholesterol promotes negative curvature of membranes and may be a significant factor in bringing about membrane fusion in the process of exocytosis. It may also be relevant for the regulation of various cellular signaling processes at the plasma membrane. Cholesterol also has a key role in the lateral organization of membranes and their free volume distribution, factors permitting more intimate protein-cholesterol interactions that may regulate the activities of membrane proteins. Many membrane proteins bind strongly to cholesterol, including some that are involved in cellular cholesterol homeostasis or trafficking, and contain a conserved region termed the ‘sterol-sensing domain’. Some proteins bind to cholesterol deep within the hydrophobic core of the membrane via binding sites on the membrane-spanning surfaces or in cavities or pores in the proteins, driven by hydrogen bond formation. Cholesterol has an intimate interaction with G-protein-coupled receptors (GPCRs) to affect ligand binding and activation, either by direct high-affinity binding to the receptor, by changing their oligomerization state, or by inducing changes in the properties of the membrane. For example, it is essential for the stability and function of the β2-adrenergic, oxytocin and serotonin receptors by increasing the agonist affinities, while the inactive state of rhodopsin is stabilized both through indirect effects on plasma membrane curvature and by a direct interaction between lipid and protein. The GPCR neurotransmitter serotonin1A receptor has ten closely bound cholesterol molecules, and these control its organization and positioning; the receptor senses membrane cholesterol via a lysine residue in a so-called 'CRAC' motif in transmembrane helix 2. Ion pumps such as the (Na+-K+)-ATPase, which have specific binding sites for cholesterol molecules, are the single most important consumer of ATP in cells and are responsible for the ion gradients across membranes that are essential for many cellular functions; depletion of cholesterol in the plasma membrane deactivates these ion pumps. In the brain in addition to being essential for the structure of the myelin sheath, cholesterol is a major component of synaptic vesicles and controls their shape and functional properties. In the nucleus of cells, cholesterol is intimately involved in chromatin structure and function. The role of cholesterol together with sphingolipids in the formation of the transient membrane nano-domains known as rafts (see the specific web page for detailed discussion), is of crucial importance for the function of cells, while the interaction of cholesterol with ceramides is essential for the barrier function of the skin. Cholesterol Biosynthesis Cholesterol biosynthesis involves a highly complex series of at least thirty different enzymatic reactions, which were unraveled in large measure by Konrad Bloch and Fyodor Lynen, who received the Nobel Prize for their work on the topic in 1964. When the various regulatory, transport, and genetic studies of more recent years are taken into account, it is obvious that this is a subject that cannot be treated in depth here. The bare bones of mechanistic aspects are therefore delineated, which with the references listed below should serve as a guide to further study. In plants, cholesterol synthesis occurs by a somewhat different pathway with cycloartenol as the key intermediate. We'll explore the reaction mechanisms for several of the enzymes on this complicated pathway given its medical importance. Almost all nucleated cells are able to synthesize their full complement of cholesterol. The first steps involve the synthesis of the important intermediate mevalonic acid from acetyl-CoA and acetoacetyl-CoA, both of which are in fact derived from acetate, in two enzymatic steps. These precursors are in the cytosol as is the first enzyme, 3-hydroxy-3-methyl-glutaryl(HMG)-CoA synthase. The second enzyme HMG-CoA reductase is a particularly important control point, and is widely regarded as the rate-limiting step in the overall synthesis of sterols; its activity is regulated at the transcriptional level and by many more factors including a cycle of phosphorylation-dephosphorylation. This and subsequent enzymes are membrane-bound and are located in the endoplasmic reticulum. The enzyme HMG-CoA reductase is among the targets inhibited by the drugs known as ‘statins’ so that patients must then obtain much of their cholesterol from the diet. The first two reactions in the synthesis of cholesterol are shown in Figure $5$. HMG-CoA Synthase We saw this reaction in the synthesis of ketone bodies in Chapter 17. This enzyme catalyzes the condensation of acetoacetyl–CoA (AcAc–CoA) and acetyl–CoA (Ac-CoA) to form 3-hydroxy-3-methylglutaryl (HMG)–CoA, and requires the formation of a C-C bond. HMG-CoA synthase forms a C-C bond by activating the methyl group of acetyl-cysteine. The acetyl group comes from an acyl-CoA "donor". The enzyme catalyzes the first committed step in the formation of complex isoprenoids (like cholesterol) and ketone bodies. The product, 3-hydroxy-3-methylglutaryl HMG–CoA, can either be reduced by HMG-CoA reductase to form mevalonate, which leads to cholesterol synthesis, or cleaved by the enzyme HMG-CoA lyase, to produce acetoacetate, a ketone body. HMG-CoA synthase catalyzes a bisubstrate reaction that displays ping-pong kinetics, characteristic of a covalent enzyme intermediate. The first substrate binds to the enzyme and transfers an acetyl group to a nucleophilic Cys 111 in the active site to form an acetyl-Cys 111 intermediate. Free CoA departs. Next the second substrate, acetoacetyl-CoA binds, and condenses with the acetyl group donated by acetyl-Cys 111. This condensation involves an enolate. A plausible reaction mechanism is shown in Figure $6$. Figure $6$: Reaction mechanism for HMG-CoA synthase Hence there are three parts of the reaction: acetylation/deacetylation, condensation/cleavage with an enolate intermediate, and C-C formation and hydrolysis/dehydration. Figure $7$ shows an interactive iCn3D model of the Staphylococcus aureus HMG-COA Synthase with bound HMG-CoA and acetoacetyl-CoA (1XPK) Figure $7$: Staphylococcus aureus HMG-COA Synthase with bound HMG-CoA and acetoacetyl-CoA (1XPK). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...deiFs7JceE5H76 The biologically active unit (homodimer) is shown with each monomer shown in a different color. The A chain (light cyan) has bound HMG-CoA (HMG) while the B chain (light gold) has acetoacetyl-CoA (CAA) bound. The Glu 79, Cys 111, and His 233 in each subunit are shown in CPK sticks and labeled. Note that the Cys 111 is covalently modified in each subunit. Figure $8$ shows an interactive iCn3D model of the human 3-hydroxy-3-methylglutaryl CoA synthase I with bound CoASH (2P8U) Figure $8$: Human 3-hydroxy-3-methylglutaryl CoA synthase I (monomer) with bound CoASH (2P8U). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...xxEoUfecTk3eL6 The active site side chains are numbered differently (Glu 95, Cys 129, and His 264) compared to the S. aurus protein. Only the monomer is shown in this model. HMG-CoA Reductase In this key rate-limiting step that commits HMG to the sterol and isoprenoid synthetic pathways, HMG is converted to mevalonate. A reducing agent (NADPH) is required for this biosynthetic reaction. A plausible mechanism is shown in Figure $9$. Figure $10$ shows an interactive iCn3D model of the catalytic domain of human HMG-CoA reductase with bound HMG-CoA (1DQ9). Figure $10$: Catalytic domain of human HMG-CoA reductase with bound HMG-CoA (1DQ9). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...dZvWzTocKhUzt8 Two of the four identical monomers in the biological unit, the C (gray) and D (cyan) chains are shown. HMG-CoA is shown in spacefill and CPK colors. Three key catalytic residues, His 866 in the C chain (b chain in the mechanism above) and Lys 691 and Asp 767 in the D chain (A chain in the mechanism above) are shown in sticks, CPK colors and labeled. Synthesis of 5-isopentenyl and 2-isopentenyl pyrophosphate The next sequence of reactions involves first the phosphorylation of mevalonic acid by a mevalonate kinase to form the 5‑monophosphate ester, followed by further phosphorylation to yield an unstable pyrophosphate, which is rapidly decarboxylated to produce 5-isopentenyl pyrophosphoric acid, the universal isoprene unit. An isomerase converts part of the latter to 3,3-dimethylallyl pyrophosphoric acid. These reactions are shown in Figure $11$. Two phosphorylations are required, one by mevalonate kinase, which proceeds by an ordered sequential binding of mevalonate as the first reactant and its phosphomevalonate as the first product released. The enzyme is inhibited by two downstream products of the reaction pathway, farnesyl pyrophosphate, and geranyl pyrophosphate. A mechanism for mevalonate kinase is shown in Figure $12$. A reaction mechanism for the second kinase, phosphomevalonate kinase, showing progression through the transition state, is shown in Figure $13$. The reaction proceeds through direct phosphorylation through a dissociative mechanism. Mevalonate diphosphate decarboxylase This enzyme catalyzes the decarboxylation of 5-pyrophosphate mevalonate to 5-isopentenylpyrophosphate as shown in Figure $14$. The binding of the two substrates, pyrophosphatemevalonate (or mevalonate pyrophosphate - MVAPP) and ATP to the enzyme (A), and a reaction mechanism (B, are shown in Figure $15$. Panel (a) shows changes in the enzyme structure upon substrate binding. The apo-enzyme is shown on the left. The middle structure shows the enzyme after binding of the first substrate, MVAPP. A key loop (β10-α4) is shown as a cyan surface. The enzyme is then shown in an open conformation with the second substrate (ATP) also bound. An additional phosphate-biding loop is shown in magenta. At the far right, the enzyme is shown in a closed conformation after conformational changes in both loops which traps substrates in the active site. These changes enable catalysis. Product release follows. Panel (b) shows the dissociative phosphoryl transfer catalytic mechanism. At the top-left, D282 is shown interacting with the 3′-OH group of MVAPP (red). The top-right shows a dissociative phosphoryl (blue) transition state. In the bottom-left, the phosphate attaches to the 3′ oxygen (red) of MVAPP. The bottom-right shows the products after the dephosphorylation and decarboxylation to produce IPP, ADP, phosphate, and CO2. K187 from the β10-α4 loop and metal ions in the active site are involved in neutralizing the negatively charged environment and assists catalysis. Figure $16$ shows an interactive iCn3D model of mevalonate diphosphate decarboxylase with mevalonate-5-diphosphate, AMPPCP and Magnesium (6E2U). Figure $16$: Mevalonate diphosphate decarboxylase with mevalonate-5-diphosphate, AMPPCP and Magnesium (6E2U) . (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...SukmP1BTyNhH26 The correctly positioned substrates interact with two Mg 2+ ions in the initial steps of the reaction. The conserved lysine facilitates the phosphoryl transfer. Polymerization of isoprene Isoprene, a small branched alkadiene, which can polymerize into larger molecules containing isoprene monomer to form isoprenoids, often called terpenes. Instead of using isoprene as the polymerization monomer, either dimethylally pyrophosphate (DMAPP) or isopentenylpyrophosphate (IPP) are used biologically. Figure $\PageIndex{x}$ below shows how DMAPP and IPP (both containing 5Cs) are used in a polymerization reaction to form geranyl-pyrophosphate (C10), farnesyl pyrophosphate (C15) and geranyl-geranyl pyrophosphate (C20). Figure $17$: The condensation of IPP and DMAPP is a head-to-tail condensation reaction. Another IPP reacts with geranylpyrophosphate using the same enzyme to produce farnesylpyrophosphate. DMAPP is first formed by the isomerization of an IPP to DMAPP catalyzed by isopentenyl-diphosphate delta-isomerase. It catalyzes the 1,3-allylic rearrangement of the homoallylic substrate isopentenyl (IPP). 5-isopentenyl pyrophosphate is a nucleophile, but its isomer, DMAPP, is highly electrophilic, which promotes the condensation of the two molecules. A mechanism for the next reaction, the first condensation of DMAPP and IPP to form geranylpyrophosphate by farnesyl pyrophosphate (diphosphate) synthase reaction, is shown in Figure $18$. The reaction appears to proceed using a carbocation transition state, followed by the transfer of a hydrogen atom (not ion) from IPP to pyrophosphate. Figure $19$ shows an interactive iCn3D model of E. Coli farnesyl pyrophosphate synthase bound to isopentyl pyrophosphate and dimethylallyl S-thiolodiphosphate (1RQI). Figure $19$: Farnesyl pyrophosphate synthase Bound to isopentyl pyrophosphate (IPP) and dimethylallyl S-thiolodiphosphate (1RQI). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...kQBU5dNnKrCVt6 DST in the structure is dimethylallyl S-thiolodiphosphate, an analog of dimethylallyl diphosphate (DMAPP). Key conserved amino acids involved in substrate binding, transition state stabilization, and catalysis are shown as sticks, CPK colors. Thr203, Gln241, and Lys202 presumably stabilized the carbocation intermediate/transition state. Lys202 also forms a hydrogen bond with DMAPP. Two arginines (69, 116) form salt bridges with the pyrophosphates of IPP and DMAPP. Many isoprenoid lipids are made from farnesyl pyrophosphate. For membrane purposes, the most important of these is cholesterol. Figure $20$ shows an overview of the synthesis of cholesterol from two farnesyl pyrophosphates linking together in a "tail-to-tail" reaction to form squalene, a precursor of cholesterol. Each isoprene unit (5Cs) is shown in different colors to make it easier to see. Figure $20$: Synthesis of squalene from isoprene units Figure $21$ shows reactants (two farnesyl pyrophosphate), intermediate (presqualene diphosphate) and product (squalene) in the reaction catalyzed by squalene synthase. In the squalene synthase reaction, two molecules of farnesyl pyrophosphate condense to yield presqualene pyrophosphate. In turn, this is reduced by NADPH to produce the key intermediate squalene. The enzyme squalene synthase, which regulates the flow of metabolites into either the sterol or non-sterol pathways (with farnesyl pyrophosphate as the branch point), is considered to be the first committed enzyme in cholesterol biosynthesis. Given the importance of this reaction, we will explore the unique mechanism of squalene synthase in some detail. Part 1: Formation of the cyclopropyl presqualene intermediate Figure $22$ shows the mechanism for the first part of the reaction in which the cyclopropyl intermediate presqualene form. The reaction proceeds through a series of carbocation intermediates. Part 2: Conversion of cyclopropyl intermediate to squalene - reduction The next part of the reaction involves the reductive formation of squalene, as shown in Figure $23$. Figure $24$ shows an interactive iCn3D model of human squalene synthase with bound inhibitor (1EZF). Figure $24$: Human squalene synthase with bound inhibitor (1EZF). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...jLreHkvbftoFPA Tyr 171 acts as a general acid/base in the first half of the reaction. Arg 218 and 228 stabilize the diphosphate in the transition state as it leaves. Phe 288 stabilizes the reactive carbocation. The enzyme has a single domain that contains a large channel in one face of the enzyme which leads from a solvent-exposed to the hydrophobic interior. Two FPPs bind in the beginning of the channel where key side chains for the first reaction are located. The cyclopropyl intermediate then moves into the hydrophobic end of the channel where it reacts in the second half of the reaction without exposure to water. In the next important step, squalene is oxidized by a squalene monooxygenase to squalene 2,3-epoxide, a key control point in the cholesterol synthesis pathway. This introduces the oxygen atom to squalene that becomes the signature oxygen of the hydroxyl group in cholesterol. The epoxide then undergoes cyclization catalyzed by the enzyme squalene epoxide lanosterol-cyclase to form the first steroidal intermediate lanosterol (or cycloartenol en route to phytosterols in photosynthetic organisms). This is illustrated in Figure $26$. In this remarkable reaction, there is a series of concerted 1,2-methyl group and hydride shifts along the chain of the squalene molecule to bring about the formation of the four rings. No intermediate compounds have been found. This is believed to be one of the most complex single enzymatic reactions ever to have been identified, although the enzyme involved is only 90 kDa in size. Again, the reaction takes place in the endoplasmic reticulum, but a cytosolic protein, sterol carrier protein 1, is required to bind squalene in an appropriate orientation in the presence of the cofactors NADPH, flavin adenine dinucleotide (FAD) and O2; the reaction is promoted by the presence of phosphatidylserine. The ring closure reaction starting with the epoxide involves a concerted flow of electrons from a source to the epoxide oxygen atom electron sink. This brings to mind a reaction known to all students who have ever studied chemistry, the reaction of the pH indicator phenolphthalein with a base to produce a pink colored-solution. That reaction which produces a more conjugated molecule that absorbs at 553 nm (green) which causes a magenta solution color, is illustrated for comparison (and fun) in Figure $27$. In subsequent steps, lanosterol is converted to cholesterol by a series of demethylations, desaturations, isomerizations, and reductions, involving nineteen separate reactions as illustrated in Figure $28$. Thus, demethylation reactions produce zymosterol as an intermediate, and this is converted to cholesterol via a series of intermediates, all of which have been characterized, and by at least two pathways that utilize essentially the same enzymatic machinery but differ in the order of the various reactions, mainly at the point at which the Δ24 double bond is reduced. Desmosterol is the key intermediate in the so-called 'Bloch' pathway, while 7‑dehydrocholesterol is the immediate precursor in the 'Kandutsch-Russell' pathway. While some tissues, such as adrenal glands and testis, use the Bloch pathway mainly, the brain synthesizes much of its cholesterol by the 'Kandutsch-Russell' pathway. This may enable the production of a variety of other minor sterols for specific biological purposes in different cell types/locations. The energy cost of the synthesis of one cholesterol molecule is roughly one hundred ATP equivalents, and eleven oxygen molecules are required. Synthesis occurs mainly in the liver, although the brain (see below), peripheral nervous system, and skin synthesize their own considerable supplies. Cholesterol is exported from the liver and transported to other tissues in the form of low-density lipoproteins (LDL) for uptake via specific receptors. In animals, cells can obtain the cholesterol they require either from the diet via the circulating LDL, or they can synthesize it themselves as outlined above. Cholesterol biosynthesis is highly regulated with rates of synthesis varying over hundreds of fold depending on the availability of any external sources of cholesterol, and cholesterol homeostasis requires the actions of a complex web of enzymes, transport proteins, and membrane-bound transcription factors, as discussed below. Regulation of Cholesterol Homeostasis In humans, only about a third of the cholesterol is of dietary origin (mainly eggs and red meat), the remainder is produced by synthesis de novo in the endoplasmic reticulum. The latter must be tightly regulated as it is an energetically expensive process that requires appreciable amounts of acetyl-CoA, ATP, oxygen, and the reducing factors NADPH and NADH, especially since cholesterol cannot be catabolized for energy purposes (see below). Many factors are involved in maintaining the large differences in cholesterol concentrations among the various membranes and organelles in cells within precise limits. In order to explain how cholesterol in the plasma membrane, where it is most abundant, can regulate cholesterol biosynthesis and uptake through enzymes in the endoplasmic reticulum, where it is least abundant, it has been suggested that a key to the process is that there are three pools of cholesterol in the plasma membrane with distinct functional roles. The first of these is “accessible” to receptor proteins for transport to the endoplasmic reticulum, while the second pool is sequestered by sphingomyelin and can be released by the action of sphingomyelinase if required. The third residual pool of cholesterol is essential for plasma membrane integrity. These correspond to about 16, 15, and 12 mol % of total plasma membrane lipids, respectively, in cholesterol-replete cells. Simplistically, when cholesterol in the plasma membrane is in excess for any reason, e.g., after LDL uptake by receptor-mediated endocytosis, there is a rise in accessible cholesterol, which is then transported to the endoplasmic reticulum to switch off cholesterol biosynthesis and expression of the LDL receptor. This process requires a host of regulatory proteins and mechanisms that can involve either vesicle formation or non-vesicular pathways that utilize specific transport proteins, such as the ABC transporters. Ultimately, post-translational control of the many different enzymes involved provides a rapid means for modifying flux through the biosynthetic pathway in the endoplasmic reticulum; some are rapidly degraded in response to tissue levels of cholesterol and its intermediates, while others have their activity altered through phosphorylation or acetylation mechanisms. For example, the second rate-limiting enzyme in cholesterol biosynthesis is squalene monooxygenase, which undergoes cholesterol-dependent proteasomal degradation when cholesterol is in excess, guided by a 12-amino acid hydrophobic sequence on the enzyme that can serve as a degradation signal. When the cholesterol concentration in the endoplasmic reticulum is high, the degradation sequence detaches from the membrane and is exposed to provide the signal for the enzyme to be degraded. Similarly, HMG-CoA reductase is recognized as the key enzyme in the regulation of cholesterol biosynthesis, and this can be regulated by a feedback mechanism involving ubiquitin–proteasomal degradation. Further regulation of cholesterol biosynthesis is exerted by sterol intermediates in cholesterol biosynthesis, such as lanosterol and 24,25‑dehydrolanosterol (dimethyl-sterols) by accelerating degradation of the biosynthetic enzymes such as HMG-CoA reductase. It is noteworthy that ceramide down-regulates cholesterol synthesis – another link between cholesterol and sphingolipid metabolism. The regulatory element-binding proteins (mainly SREBP-1c and SREBP-2), which contain an N-terminal membrane domain and a C-terminal regulatory domain, are essential to the maintenance of cholesterol levels. Each is synthesized as an inactive precursor that is inserted into the endoplasmic reticulum where it can encounter an escort protein termed SREBP cleavage-activating protein (SCAP), which is the cellular cholesterol sensor. When the latter recognizes that cellular cholesterol levels are inadequate, it binds to the regulatory domain of SREBP. The SCAP-SREBP complex then moves to the Golgi, where two specific proteases (designated site-1 and site-2 proteases) cleave the SREBP enabling the C-terminal regulatory domain to enter the nucleus. There it activates transcription factors, such as the nuclear liver X receptor (LXR), which stimulate the expression of the genes coding for the LDL receptor in the plasma membrane and for the key enzyme in cholesterol biosynthesis, HMG-CoA reductase. This in turn stimulates the rate of cholesterol uptake and synthesis. Conversely, when cholesterol in the endoplasmic reticulum exceeds a threshold, it binds to SCAP in such a way that it prevents the SCAP-SREBP complex from leaving the membrane for the nucleus, cholesterol synthesis and uptake are thereby repressed, and cholesterol homeostasis is restored. In effect, cholesterol exerts feedback inhibition by suppressing its own production by preventing the proteolytic cleavage and maturation of SREBP-2. Oxysterols, especially 25-hydroxycholesterol, are also inhibitors of this process. Cholesterol in the endoplasmic reticulum is transferred to the Golgi and eventually to the plasma membrane by vesicular and non-vesicular transport mechanisms involving in part soluble sterol transport proteins, including the so-called 'START' domain proteins, and partly by binding to those proteins that are intimately involved in the transport and metabolism of polyphosphoinositides such as phosphatidylinositol 4-phosphate (PI(4)P). In the latter mechanism, cholesterol is transported by binding to the ORD domain of oxysterol binding protein (OSBP) or Osh4 in yeast, before OSBP binds to PI(4)P in the plasma membrane to transfer its cargo. The key to this process is that cholesterol and PI(4)P are synthesized at two different locations, i.e., the endoplasmic reticulum for sterols and the trans-Golgi network and plasma membrane for PI(4)P, so the two lipids do not compete but rather can be exchanged. OSBP carries cholesterol in the forward direction to the trans-Golgi network and plasma membrane and PI(4)P, which binds to a C-terminal PH domain in the protein, in the reverse direction. The subsequent hydrolysis of PI(4)P is the energy source for the reaction, and indeed PI(4)P has been termed "lipid ATP". As this reaction is irreversible, a gradient of cholesterol along organelles of the secretory pathway is established. OSBP is thus a lipid transfer protein that enables two organelles to exchange cholesterol rapidly between them at membrane contact sites in a cycle of reactions involving membrane tethering, cholesterol transport, PI(4)P counter transport, and PI(4)P hydrolysis. A similar mechanism is involved in the transport of phosphatidylserine from the endoplasmic reticulum to the inner leaflet of the plasma membrane. Subsequently, the ATP binding cassette (ABC) transporters ABCA1 and ABCG1 in the plasma membrane, which contains much of the cellular cholesterol, are activated to export the excess. Nuclear factor erythroid 2 related factor-1 or NRF1 in the endoplasmic reticulum binds directly to cholesterol and senses when its level is high to bring about a de-repression of genes involved in cholesterol removal, also with mediation by the liver X receptor. Also, side-chain oxysterols, especially 25-hydroxycholesterol, can suppress the activation of SREBP by binding to an oxysterol-sensing protein in the endoplasmic reticulum. Within cells, cholesterol derived initially from the lysosomal degradation of low-density lipoproteins is transferred first to the plasma membrane and thence to the endoplasmic reticulum, the latter step by a mechanism involving proteins known as GRAMD1s embedded in the endoplasmic reticulum membrane at sites in contact with the plasma membrane. These have two functional domains: the START-like domain that binds cholesterol and the GRAM domain that binds anionic lipids, such as phosphatidylserine, and so are able to form a link between the two membranes that enable the transfer of cholesterol. In peripheral tissues, excess cholesterol is exported to high-density lipoproteins (HDL) in the circulation and returned to the liver, a process known as reverse cholesterol transport. The liver is important for cholesterol synthesis, but it is essential for its elimination from the body in bile. Also, some lipoproteins with their content of cholesterol and cholesterol esters are delivered to lysosomes by endocytosis for degradation. The cholesterol is transported to the inner surface of the lysosomal membrane through the glycocalyx, via a transglycocalyx tunnel, with the aid of Niemann-Pick C1, C2, and other proteins, and thence via contact sites between membranes to other organelles. Cholesterol in cellular membranes in excess of the stoichiometric requirement can escape back into the cell, where it may serve as a feedback signal to down-regulate cholesterol accumulation, while some is converted to the relatively inert storage form, i.e., cholesterol esters, and some is used for steroidogenesis. The intestines play a major part in cholesterol homeostasis via the absorption of dietary cholesterol and fecal excretion of cholesterol and its metabolites. A specific transporter (Niemann-Pick C1-like 1 or NPC1L1)in the brush border membrane of enterocytes in the proximal jejunum of the small intestine is involved in the uptake of cholesterol from the intestinal contents, while the metabolism of sterols in the intestines is controlled mainly by an acetyl-CoA acetyltransferase (ACAT2), which facilitates intracellular cholesterol esterification, and the microsomal triglyceride transfer protein (MTTP), which is involved in the assembly of chylomicrons for export into lymph. Some cholesterol can be transferred in the opposite direction (trans-intestinal cholesterol excretion), but the quantitative importance of this process is not clear. There is evidence that dietary or synthesized cholesterol is necessary to maintain intestinal integrity, as cholesterol derived from circulating lipoproteins is not sufficient for the purpose. In the intestines and especially the colon, the intestinal microflora are able to hydrogenate cholesterol from bile, diet, and desquamated cells to form coprostanol with an efficiency that is dependent on the composition of microbial species. Coprastanol is not absorbed by the intestinal tissue to a significant extent, and it may inhibit the uptake of residual cholesterol. There are two mechanisms for this conversion in bacteria, one involving direct reduction and another via cholestenone and coprostanone as intermediates, and as the relevant genes have now been identified the therapeutic potential is under investigation. Brain: There are substantial differences in cholesterol synthesis and metabolism in brain in comparison to the liver and peripheral tissues. Trace amounts only of cholesterol are able to cross the blood-brain barrier via transport in low-density lipoproteins. Therefore, virtually all the cholesterol in the brain must be synthesized de novo, mainly in astrocytes (glial cells). During the perinatal and adolescent years especially, cholesterol is synthesized in large amounts to form the myelin that surrounds the axons, before this rate begins to decline to eventually reach about 10% of earlier values. Cholesterol is transported to neurons in the form of Apo E complexes in discoidal HDL-like particles, for which seven main receptors have been identified in brain cells that take up cholesterol from these lipoproteins. Apo E is synthesized in the brain, and its transcription is regulated by 24-hydroxy-cholesterol concentrations. Similarly, in the brain and central nervous system, cholesterol synthesis is regulated independently of that in peripheral tissues, mainly by forms of the liver X receptor (LXR). As cholesterol and oxysterols are involved in providing neuroprotective effects and lowering neuroinflammation, dysregulation of their concentrations has been noted in many neurodegenerative disorders. Most of the lipoproteins in cerebrospinal fluid differ from the nascent poorly-lipidated HDL secreted by astrocytes, suggesting that the latter are modified during maturation. Cholesterol Catabolism Cholesterol is not readily degraded in animal tissues so does not serve as a metabolic fuel to generate ATP. Only the liver possesses the enzymes to degrade significant amounts, and then via pathways that do not lead to energy production. Cholesterol and oxidized metabolites (oxysterols) are transferred back from peripheral tissues in lipoprotein complexes to the liver for catabolism by conversion to oxysterols and bile acids. The latter are exported into the intestines to aid in digestion, while leading to some loss that is essential for cholesterol homeostasis. Until recently, it was believed that approximately 90% of cholesterol elimination from the body occurred via bile acids in humans. However, experiments with animal models now suggest that a significant amount is secreted directly into the intestines by a process known as trans-intestinal cholesterol efflux. How this occurs and its relevance to humans are under active investigation. Gut bacteria reduce some of the cholesterol in the diet to highly insoluble 5β-cholestan-3β-ol (coprostanol), which is excreted and can be used as a biomarker for sewage in the environment. Certain bacterial species contain a 3β-hydroxysteroid:oxygen oxidoreductase (EC 1.1.3.6), commonly termed cholesterol oxidase, a flavoenzyme that catalyzes the oxidation of cholesterol to cholest-5-en-3-one which is then rapidly isomerized to cholest-4-en-3-one as the first essential step in the catabolism of sterols. The enzyme is widespread in organisms that degrade organic wastes, but it is also present in pathogenic organisms where it influences the virulence of infections (see below). In biotechnology, it has been used for the production of a number of steroids, and it is employed in a clinical procedure for the determination of cholesterol levels in serum. Cholesterol Esters Cholesterol esters, i.e., with long-chain fatty acids linked to the hydroxyl group, are much less polar than free cholesterol and appear to be the preferred form for transport in plasma and as a biologically inert storage or detoxification form to buffer an excess. They do not contribute to membrane structures but are packed into intracellular lipid droplets. Cholesterol esters are major constituents of the adrenal glands, and they accumulate in the fatty lesions of atherosclerotic plaques. Similarly, esters of steroidal hormones are also present in the adrenal glands, where they are concentrated in cytosolic lipid droplets adjacent to the endoplasmic reticulum; 17β-estradiol, the principal estrogen in fertile women, is transported in lipoproteins in the form of a fatty acid ester. Because of the mechanism of synthesis (see below), plasma cholesterol esters tend to contain relatively high proportions of the polyunsaturated components typical of phosphatidylcholine as shown in Table $1$ below. Arachidonic and “adrenic” (20:4(n-6)) acids can be especially abundant in cholesterol esters from the adrenal gland. Table $1$. Fatty acid composition of cholesterol esters (wt % of the total) from various tissues. Form Fatty acids 16:0 18:0 18:1 18:2 18:3 20:4 22:4 Human plasma 12 2 27 45 8 liver 23 10 28 22 6 Sheep plasma 10 2 27 35 7 - - liver 17 9 29 7 4 3 - adrenals 13 7 35 18 2 4 2 Data from - Christie, W.W. et al. Lipids, 10, 649-651 (1975); DOI. Nelson, G.J. Comp. Biochem. Physiol., 30, 715-725 (1969); Horgan, D.J. and Masters, C.J. Aust. J. Biol. Sci., 16, 905-915 (1963); Nestel, P.J. and Couzens, E.A. J. Clin. Invest., 45, 1234-1240 (1966); DOI. In plasma and in the high-density lipoproteins (HDL) in particular, cholesterol esters are synthesized largely by the transfer of fatty acids to cholesterol from position sn-2 of phosphatidylcholine (‘lecithin’) catalyzed by the enzyme lecithin:cholesterol acyl transferase (LCAT); the other product is 1-acyl lysophosphatidylcholine. This is illustrated in Figure $29$. In fact, the reaction occurs in several steps. First, apoprotein A1 in the HDL acts to concentrate the lipid substrates near LCAT and present it in the optimal conformation; at the same time, it opens a lid on the enzyme that activates it by opening up the site of transesterification. Then, cleavage of the sn-2 ester bond of phosphatidylcholine occurs via the phospholipase activity of LCAT with the release of a fatty acyl moiety. This is transacylated to the sulfur atom of a cysteine residue forming a thioester, and ultimately it is donated to the 3β-hydroxyl group of cholesterol to form the cholesterol ester. Some LCAT activity has also been detected in apolipoprotein B100-containing particles (β-LCAT activity as opposed to α-LCAT with HDL). It has been established that human LCAT is a relatively small glycoprotein with a polypeptide mass of 49 kDa, increased to about 60 kDa by four N-glycosylation and two O-glycosylation moieties. Most of the enzyme is produced in the liver and circulates in the bloodstream bound reversibly to HDL, where it is activated by the main protein component of HDL, apolipoprotein A1. As cholesterol esters accumulate in the lipoprotein core, cholesterol is removed from its surface thus promoting the flow of cholesterol from cell membranes into HDL. This in turn leads to morphological changes in HDL, which grow and become spherical. Subsequently, cholesterol esters are transferred to the other lipoprotein fractions LDL and VLDL, a reaction catalyzed by cholesterol ester transfer protein. This process promotes the efflux of cholesterol from peripheral tissues (‘reverse cholesterol transport’), especially from macrophages in the arterial wall, for subsequent delivery to the liver. LCAT is often stated to be the main driving force behind this process, and it is of great importance for cholesterol homeostasis and a suggested target for therapeutic intervention against cardiovascular disease. The stereospecificity of LCAT changes with molecular species of phosphatidylcholine containing arachidonic or docosahexaenoic acid, when 2-acyl lysophosphatidylcholines are formed. This reaction may be especially important for the supply of these essential fatty acids to the brain in that such lysophospholipids are believed to cross the blood-brain barrier more readily than the free acids. In other animal tissues, a further enzyme acyl-CoA:cholesterol acyltransferase (ACAT) synthesizes cholesterol esters from CoA esters of fatty acids and cholesterol. ACAT exists in two forms, both of which are intracellular enzymes found in the endoplasmic reticulum and are characterized by multiple transmembrane domains and a catalytic histidine residue in a hydrophobic domain; they are members of the O-acyltransferase (MBOAT) superfamily. ACAT1 is present in many tissues, but especially in macrophages and adrenal and sebaceous glands, which store cholesterol esters in the form of cytoplasmic lipid droplets; it is responsible for the synthesis of cholesterol esters in arterial foam cells in human atherosclerotic lesions. ACAT2 is found only in the liver and small intestine, and it is believed to be involved in the supply of cholesterol esters to the nascent lipoproteins. Analogous enzymes are found in yeast where ergosterol is the main sterol, but a very different process occurs in plants. Oxidized Cholesterol Esters: All lipid classes containing polyunsaturated fatty acids are susceptible to oxidation. Under normal circumstances, cholesterol esters are considered to be relatively inert. However, when they contain oxidized polyunsaturated fatty acids, their properties change and they acquire biological activity. Such oxidized cholesterol esters may be formed by a reaction with 15‑lipoxygenase, but they can be produced also through free radical-induced lipid peroxidation, and they have been detected in lipoproteins, LDL especially, in human blood and atherosclerotic lesions. Those oxidized cholesterol esters in plasma are trafficked into cells and metabolized by the same mechanisms as the corresponding unoxidized lipids. Such "minimally oxidized LDL" do not bind to CD36 but rather to CD14, a receptor that recognizes bacterial lipopolysaccharides. The result is stimulation of toll-like receptor 4 (TLR4), although the response differs from that of lipopolysaccharides. In addition, oxidized metabolites of cholesteryl arachidonate of this kind stimulate macrophages to express inflammatory cytokines of relevance to atherosclerosis among other effects. Oxidized cholesterol esters can be hydrolyzed to release their fatty acids, which can then be incorporated into phospholipids with a different repertoire of activities. Hydrolysis of cholesterol esters: Cholesterol ester hydrolases in animals liberate cholesterol and free fatty acids when required for membrane and lipoprotein formation, and they also provide cholesterol for hormone synthesis in adrenal cells. Many cholesterol ester hydrolases have been identified, including a carboxyl ester hydrolase, a lysosomal acid cholesterol ester lipase, hormone-sensitive lipase, and hepatic cytosolic cholesterol ester hydrolase. These are located in many different tissues and organelles and have multiple functions. A neutral cholesterol ester hydrolase has received special study, as it is involved in the removal of cholesterol esters from macrophages so reducing the formation of foam cells and thence the development of fatty streaks within the arterial wall, a key event in the progression of atherosclerosis. Other Animal Sterols Cholesterol will oxidize slowly in tissues or foods to form a range of different products with additional hydroperoxy, epoxy, hydroxy or keto groups, and these can enter tissues via the diet. There is increasing interest in these from the standpoint of human health and nutrition since the accumulation of oxo-sterols in plasma is associated with inhibition of the biosynthesis of cholesterol and bile acids and with other abnormalities in plasma lipid metabolism. A number of other sterols occur in small amounts in tissues, most of which are intermediates in the pathway from lanosterol to cholesterol, although some of them have distinct functions in their own right. Lanosterol, the first sterol intermediate in the biosynthesis of cholesterol, was first found in wool wax, both in free and esterified form, and this is still the main commercial source. It is found at low levels only in most other animal tissues (typically 0.1% of the cholesterol concentration). As oxygen is required, lanosterol cannot be produced by primitive organisms, hence its absence from prokaryotes, leading to some speculation on its evolutionary significance. When sterols became available to eukaryotes, much greater possibilities opened for their continuing evolution. The production of cholesterol from lanosterol is then seen as ‘molecular streamlining’ by evolution, removing protruding methyl groups that hinder the interaction between sterols and phospholipids in membranes. Desmosterol (5,24-cholestadien-3β-ol), the last intermediate in the biosynthesis of cholesterol by the Bloch pathway, may be involved in the process of myelination, as it is found in relative abundance in the brains of young animals but not in those of adults, other than astrocytes. It is also found in appreciable amounts in testes and spermatozoa together with another cholesterol intermediate, testis meiosis-activating sterol. In addition, there is evidence that desmosterol activates certain genes involved in lipid biosynthesis in macrophages, and may deactivate others associated with the inflammatory response. There is a rare genetic disorder in which there is an impairment in the conversion of desmosterol to cholesterol, desmosterolosis, with serious consequences in terms of mental capacity. These and related sterols appear to be essential for human reproduction. In human serum, the levels of lathosterol (5α-cholest-7-en-3β-ol) were found to be inversely related to the size of the bile acid pool, and in general, the concentration of serum lathosterol is strongly correlated with the cholesterol balance under most dietary conditions. The isomeric saturated sterols, cholestanol, and coprastanol, which differ in the stereochemistry of the hydrogen atom on carbon 5, are formed by microbial biohydrogenation of cholesterol in the intestines, and together with cholesterol are the main sterols in feces. Further examples of animal sterols include 7-dehydrocholesterol (cholesta-5,7-dien-3β-ol) in the skin, which on irradiation with UV light is converted to vitamin D3 (cholecalciferol). These sterols are shown in Figure $30$. Figure $30$: Other animal sterols Marine invertebrates produce a large number of novel sterols, with both unusual nuclei and unconventional sidechains, some derived from cholesterol and others from plant sterols or alternative biosynthetic intermediates. For example, at least 80 distinct sterols have been isolated from echinoderms and 100 from sponges. Cholesterol and Disease Elevated cholesterol and cholesterol ester levels are associated with the pathogenesis of cardiovascular disease (atherosclerotic plaques, myocardial infarctions, and strokes), as is well known, and this is considered briefly on this website together with the metabolism of the plasma lipoproteins. The rate-limiting enzyme in the synthesis of cholesterol HMG-CoA reductase is the target of statins, but drugs that target other steps in the biosynthetic pathway, especially the squalene monooxygenase and lanosterol synthase, are under investigation. Further discussion of such a complex nutritional and clinical topic is best left to others better qualified than myself. Cholelithiasis or the presence of 'stones' in the gallbladder or bile ducts, which consist largely of cholesterol (~85%), is one of the most prevalent and costly digestive diseases in developed countries. The primary cause is the excessive excretion of cholesterol from the liver. Excess accumulation of cholesterol associated with the metabolism of bis(monoacylglycero)phosphate and causing disturbances in glycosphingolipid trafficking in cell membranes is involved in the pathogenesis of Niemann-Pick C disease, a lysosomal storage disease in which endocytosed cholesterol becomes sequestered in late endosomes/lysosomes because of gene mutations affecting two binding proteins (NPC1 and NPC2) thereby causing neuronal and visceral atrophy. In addition, deficiencies in cholesterol transport and metabolism are associated with many forms of neurodegeneration, including Alzheimer’s disease, Huntington’s disease, and related conditions associated with old age. These proteins are also key virulence factors for several viral and bacterial pathogens. Several genetic disorders of cholesterol biosynthesis have been identified in recent years that can result in developmental malformations including neurologic defects. As there is limited cholesterol transport across the placenta, the human fetus is highly dependent upon endogenous synthesis. While the molecular basis for the altered developmental pathways is not fully understood, impaired synthesis of the hedgehog family of signaling proteins, which require covalently linked cholesterol to function in membranes, is believed to be involved in many cases. In others, there are confirmed enzyme defects. For example, the recessive Smith-Lemli-Opitz syndrome in infants born with a decreased concentration of the enzyme 7-dihydrocholesterol reductase, produces symptoms varying from mild autism to severe mental and often fatal physical problems. The effects are due to a lack of cholesterol and the accumulation of 7-dehydrocholesterol and its 27-hydroxy metabolite, as brain tissue cannot utilize dietary cholesterol or that produced peripherally. In fact, at least eight different inherited disorders of cholesterol biosynthesis lead to congenital abnormalities in those afflicted. In animal models, deficiencies in SREBP-2 and genes encoding sterol biosynthetic enzymes display embryonic lethality. Deficiencies in the enzymes responsible for the hydrolysis of cholesterol esters, such as the lysosomal acid lipase, occur in Wolman disease and cholesterol ester storage disease. Cholesterol and other sterols bind directly to several immune receptors, especially in macrophages and T cells, and dynamic changes in cholesterol biosynthesis impact directly upon innate and adaptive immune responses, such that functional coupling between sterol metabolism and immunity has implications for health and disease. For example, cholesterol binds directly to the αβ T cell antigen receptor (αβTCR) and has at least two opposing functions in its activation. By binding to the trans-membrane domain of this receptor, it is kept in an inactive, non-signaling conformation, but when required it can stimulate the formation of receptor nanoclusters to increase their avidity for the antigen. In cancer, there is a high demand for cholesterol in order to support the inherent nature of tumor cells to divide and proliferate, and perturbations of reverse cholesterol transport can have negative consequences. Drugs that lower cholesterol levels in cancer cells by inhibiting the mevalonate pathway are undergoing clinical trials. When increased levels of sterols other than cholesterol are found in plasma, they usually serve as markers for abnormalities in lipid metabolism associated with disease states. For example, premature atherosclerosis and xanthomatosis occur in two rare lipid storage diseases, cerebrotendinous xanthomatosis, and sitosterolemia. In the former, cholestanol is present in all tissues, while in the latter, the dietary plant sterols campesterol and sitosterol accumulate in plasma and red blood cells. Inhibition of cholesterol biosynthesis may be associated with the appearance of some of the precursor sterols in the plasma. In infections with Mycobacterium tuberculosis, the organism uses host cholesterol as the major carbon and energy source and thereby promotes persistent infection with appreciable effects on pathogenicity. Similarly, Chlamydia trachomatis, a gram-negative obligate intracellular bacterium and a major cause of sexually transmitted infections, requires host cholesterol for growth. Many viruses use cholesterol as part of their life cycle, and reduction in cellular cholesterol is sometimes seen as an anti-viral strategy, although this may not always be helpful. For example, an HIV protein has a binding site for cholesterol, which it utilizes to facilitate the fusion with raft regions in the membranes of the host cell. Sterols: 2. Oxysterols and Other Cholesterol Derivatives Oxysterols as defined and discussed here are oxygenated derivatives of cholesterol and its precursors, i.e., with additional hydroxyl, epoxyl, or keto groups, that are found in all animal tissues. Many of these have vital functions in animals, while others are important as short-lived intermediates or end products in the catabolism or excretion of cholesterol or in the biosynthesis of steroid hormones, bile acids, and 1,25‑dihydroxy-vitamin D3. They are normally present in biological membranes and lipoproteins at trace levels only, though they can exert profound biological effects at these concentrations. However, they are always accompanied by a great excess (as much as 106-fold) of cholesterol per se. A multiplicity of different oxysterols are synthesized in cells by sequential reactions with specific oxygenases. However, because of the presence of the double bond in the 5,6-position, oxysterols can also be formed rapidly by non-enzymatic oxidation (autoxidation) of cholesterol and cholesterol esters within tissues with the formation of many different oxygenated derivatives. Simplistically, non-enzymatic oxidation leads mainly to the generation of products in which the sterol ring system is oxidized, while enzymatic processes usually produce metabolites with an oxidized side chain (7-hydroxylation is an important exception). Oxidized cholesterol molecules can also be generated by the gut microflora and be taken up through the enterohepatic circulation. Once an oxygen function is introduced into cellular cholesterol, the product can act as a biologically active mediator by interacting with specific receptors before it is metabolized to bile acids or is degraded further, processes assisted by the fact that oxysterols are able to diffuse much more rapidly through membranes than is cholesterol itself. Cholesterol metabolites of this kind are especially important in the brain, which is a major site for cholesterol synthesis de novo, and they are crucial elements of cholesterol homeostasis. Enzymatic Oxidation of Cholesterol Within animal cells, the oxidation of sterols is mainly an enzymic process that is carried out by several enzymes that are primarily from the cytochrome P450 family of oxygenases (named for a characteristic absorption at 450 nm). These comprise a disparate group of proteins that contain a single heme group and have a similar structural fold, though the amino acid sequences can differ appreciably. They are all mono-oxygenases. Oxysterol biosynthesis can be considered in terms of different pathways that depend on the position of the initial oxidation, but these pathways tend to overlap and lead to a complex web of different oxysterols (and eventually to bile acid formation). As these enzymes, which include cytochrome P450, cholesterol hydroxylase, hydroxysteroid dehydrogenases, and squalene epoxidase, are specific to particular tissues and indeed animal species, there is considerable variation in oxysterol distributions between organs. A few examples only of the first steps in some of these pathways are illustrated in Figure $31$. As an example, a primary product is 7α‑hydroxycholesterol, which is an important intermediate in the biosynthesis of bile acids by the 'neutral' pathway and of many other oxysterols, and it is produced in the liver by the action of cholesterol 7α-hydroxylase (CYP7A1), an enzyme that has a critical role in cholesterol homeostasis. The reaction is under strict regulatory control, and the expression of CYP7A1 is controlled by the farnesoid X receptor (FXR) and is activated by cholic and chenodeoxycholic acids. Any circulating 7α‑hydroxycholesterol represents leakage from the liver. Further oxidation of 7α‑hydroxycholesterol can occur, and the action of CYP3A4 in humans generates 7α,25‑dihydroxycholesterol as an important metabolite, for example, while oxidation by CYP27A1 yields 7α,27‑dihydroxycholesterol; the latter is regarded as a key step in a further pathway to oxysterols and bile acids. On the other hand, the epimer 7β‑hydroxycholesterol is produced in the brain by the action of the toxic β-amyloid peptide and its precursor on cholesterol, but whether this is involved in the pathology of Alzheimer’s disease has yet to be determined. The hydroxysteroid 11-β-dehydrogenase 1 (HSD11B1) is responsible for the conversion of 7β-hydroxy-cholesterol to the important metabolite 7-keto-cholesterol, while HS11B2 catalyzes the reverse reaction; 7-keto-cholesterol is also formed by autoxidation (see below). HSD11B1 is better known as the oxidoreductase that converts inactive cortisone to the active stress hormone cortisol in glucocorticoid target tissues. An alternative ('acidic') pathway to bile acids starts with the synthesis of 27-hydroxycholesterol (or more systematically named (25R)26‑hydroxycholesterol), which is produced by the cytochrome P450 enzyme (CYP27A1) and introduces the hydroxyl group into the terminal methyl carbon (C27 or C26 - used interchangeably). While this enzyme is present in the liver, it is found in many extra-hepatic tissues and especially the lung, which provides a steady flux of 27‑oxygenated metabolites to the liver. As a multifunctional mitochondrial P450 enzyme in the liver, it generates both 27‑hydroxycholesterol and 3β‑hydroxy-5-cholestenoic acid, the bile acid precursor, which occurs in small but significant amounts in plasma. 27‑Hydroxycholesterol is the most abundant circulating oxysterol, and its concentration in plasma correlates with that of total cholesterol. It can be oxidized to 7α,27‑dihydroxycholesterol by the enzyme CYP7B1. 4β‑Hydroxycholesterol is also abundant in plasma and is relatively stable; it is produced in humans by the action of the cytochromes CYP3A4 and CY3A5. In humans, the specific cytochrome P450 that produces 24S-hydroxycholesterol (cholest-5-ene-3β,24-diol) is cholesterol 24S‑hydroxylase (CYP46A1) and is located almost entirely in the smooth endoplasmic reticulum of neurons in the brain, including those of the hippocampus and cortex, which are important for learning and memory. It is by far the most abundant oxysterol in the brain after parturition, but during development, many more many oxysterols are produced. 24S‑hydroxycholesterol is responsible for 98-99% of the turnover of cholesterol in the central nervous system, which is the source of most of this oxylipin found in plasma. A small amount of it is converted in the brain directly into to 7α,24S‑dihydroxycholesterol by the cytochrome CYP39A1 and thence via side-chain oxidation in peroxisomes to bile acids, such as cholestanoic acid. It is evident that the blood-brain barrier is crossed by constant passive fluxes of oxysterols, but not of cholesterol per se, as a result of their permissive chemical structures and following their concentration gradients. In plasma, it is transported via high-density lipoproteins, as discussed further below. In contrast to humans, CYP46A1 is present in the liver of rodents as well as the brain. 25-Hydroxycholesterol is a relatively minor but biologically important cholesterol metabolite, which is produced rapidly by immune cells during the inflammation resulting from bacterial or viral infections. The dioxygenase enzyme cholesterol 25‑hydroxylase (CH25H in humans), which utilizes a diiron cofactor to catalyze hydroxylation, is the most important route to this metabolite in vivo, although at least two cytochrome P450 enzymes, CYP27A1 and CYP3A4, can catalyze this conversion to a limited extent. Further oxidation by CYP7B1 is a second route to 7α,25‑dihydroxycholesterol, and hence to further oxysterols. 24(S),25-Epoxycholesterol is not produced by the pathways described above but is synthesized in a shunt of the mevalonate pathway using the same enzymes that produce cholesterol, specifically squalene mono-oxygenase and lanosterol synthase, by means of which a second epoxy group is introduced on the other end of squalene from the initial epoxidation. A further mechanism in the brain is the action of CYP46A1 on desmosterol, another intermediate in cholesterol biosynthesis. The oxysterols formed by both autoxidation and enzymatic routes can undergo further oxidation-reduction reactions, and they can be modified by many of the enzymes involved in the metabolism of cholesterol and steroidal hormones, such as esterification and sulfation of position 3, as illustrated for 7-keto-cholesterol as an example in Figure $32$. In most tissues, esterification of the 3β-hydroxyl group only occurs and requires the activity of sterol O-acyltransferases 1/2 (SOAT1/2 or ACAT1/2) with the participation of cytosolic phospholipase A2 (cPLA2α) to liberate the required fatty acids from phospholipids. In plasma, oxysterols can be esterified by the lecithin–cholesterol acyltransferase (LCAT) for transport in lipoproteins, but in this instance, a diester can be produced from 27‑hydroxycholesterol specifically. Whether such esters are an inert storage form for oxysterols to be liberated on demand by esterases remains to be determined. It is noteworthy that the important human pathogen, Mycobacterium tuberculosis, utilizes a cytochrome P450 enzyme (CYP125) to catalyze C26/C27 hydroxylation of cholesterol as an essential early step in its catabolism as part of the infective process. Catabolism: Because of their increased polarity relative to cholesterol, oxysterols produced by both enzymatic and non-enzymatic means can exit cells relatively easily. A proportion is oxidized further and converted to bile acids, and some are converted to sulfate esters (especially at the 3-hydroxyl group) or glucuronides (see below) for elimination via the kidneys. Non-Enzymatic Oxidation of Cholesterol In biological systems in which both cholesterol and fatty acids are present, it would be expected that autoxidation of polyunsaturated fatty acids by free radical mechanisms would be favored thermodynamically with the formation of isoprostanes from arachidonic acid in phospholipids. However, there are circumstances that can favor cholesterol oxidation in vivo, and, for example, the concentration of cholesterol in low-density lipoprotein particles (LDL) is about three times higher than that of phospholipids, and the rate of cholesterol-hydroperoxide formation can be higher than that of phospholipid hydroperoxides. The rate and specificity of the reaction can depend also on whether it is initiated by free radical species, such as those arising from the superoxide/hydrogen peroxide/hydroxyl radical system (Type I autoxidation), or whether it occurs by non-radical but highly reactive oxygen species such as singlet oxygen, HOCl or ozone (Type II autoxidation). As examples of the main types of products of non-enzymatic oxidation, the structures of a few of the more important of these oxysterols are illustrated in Figure $33$. Oxysterols produced by this means can vary in the type (hydroperoxy, hydroxy, keto, epoxy), number and position of the oxygenated functions introduced, and nature of their stereochemistry. Derivatives with the A and B rings and the iso-octyl side-chain oxidized are illustrated, but compounds with oxygen groups in position 15 (D ring) are also important biologically. Many are similar to those produced by enzymatic means, although the stereochemistry will usually differ. Like the enzymic products, they are named according to their relationship to cholesterol, rather than by the strict systematic terminology. Mechanisms of autoxidation have been studied intensively in terms of unsaturated fatty acids, and it appears that similar mechanisms operate with sterols. The first event in lipid peroxidation by a radical mechanism is an initiation reaction in which a carbon with a labile hydrogen undergoes hydrogen abstraction by reaction with a free radical, which can be a non-lipid species such as a transition metal or hydroxyl or peroxynitrile radical, and this is followed by oxygen capture. The resulting reactive species recruits further non-oxidized lipids and starts a chain reaction termed the propagation phase. Finally, the reaction is terminated by the conversion of hydroperoxy intermediates to more stable hydroxy products by reaction with endogenous antioxidants such as tocopherols. As an example, the reaction mechanism leading to the production of 7-oxygenated cholesterol derivatives is illustrated in Figure $34$. Figure $34$: Cholesterol non-enzymatic oxidation mechanisms In aqueous dispersions, oxidation is initiated by a radical attack from a reactive-oxygen species such as a hydroxyl radical with the abstraction of hydrogen from the C-7 position to form a delocalized three-carbon allylic radical, which reacts with oxygen to produce 7α‑hydroperoxycholesterol, which gradually isomerizes to the more thermodynamically stable 7β-hydroperoxycholesterol. Subsequent enzymic and non-enzymic reactions lead to the 7-hydroxy and 7-keto analogs, which tend to be the most abundant non-enzymatically generated oxysterols in tissues, often accompanied by epoxy-ene and ketodienoic secondary products. Reaction with singlet oxygen (1O2) produces 5α‑hydroperoxycholesterol mainly together with some 6α- or 6β-hydroperoxycholesterol. The reaction does not occur readily at the other allylic carbon 4, presumably because of steric hindrance. When cholesterol is in the solid state, the reaction occurs primarily at the tertiary carbon-25, though some products oxygenated at C-20 may also be produced. Cholesterol hydroperoxides can be converted to stable diols only by the phospholipid hydroperoxide glutathione peroxidase - type 4 (GPx4) and then relatively slowly, but not by the type 1 glutathione peroxidase (GPx1) when in a membrane-bound state. However, in mammalian cells, monomeric GPx4 (~20 kDa), although present in several cellular compartments, including mitochondria, is much less abundant than tetrameric GPx1. Phospholipid-hydroperoxides are reduced most rapidly followed by cholesterol 6β-OOH > 7α/β-OOH >> 5α-OOH. The result is that cholesterol hydroperoxides are expected to have a relatively long half-life and so can potentially be rather dangerous in biological systems. Of these, 5α-OOH with the lowest reduction rate is the most cytotoxic of the hydroperoxides, unfortunately. Epimeric 5,6-epoxy-cholesterols may be formed by a non-radical reaction involving the non-enzymatic interaction of a hydroperoxide with the double bond, a process that is believed to occur in macrophages especially and in low-density lipoproteins (LDL). In this instance, the initial peroxidation product is a polyunsaturated fatty acid; the hydroperoxide transfers an oxygen atom to cholesterol to produce the epoxide, and in so doing is reduced to a hydroxyl. Other non-radical oxidation processes include reaction with singlet oxygen, which is especially important in the presence of light and photosensitizers and can generate 5-hydroxy- as well as 6- and 7-hydroxy products. In addition, the reaction with ozone in the lung can generate a family of distinctive oxygenated cholesterol metabolites. Similarly, a diverse range of oxidation products are generated by peroxidation of the cholesterol and vitamin D precursor 7‑dehydrocholesterol, which has the highest propagation rate constant known for any lipid toward free-radical chain oxidation, and these metabolites have important biological properties. Oxysterols occur in tissues both in the free state and esterified with long-chain fatty acids. For example, in human atherosclerotic lesions, 80–95% of all oxysterols are esterified. Appreciable amounts of oxysterols can be present in foods, especially those rich in cholesterol such as meat, eggs, and dairy products, where they are most probably generated non-enzymically during cooking or processing when such factors as temperature, oxygen, light exposure, the associated lipid matrix, and the presence of antioxidants and water all play a part. Those present in foods can be absorbed from the intestines and transported into the circulation in chylomicrons, but the extent to which dietary sources contribute to tissue levels either of total oxysterols or of individual isomers is not known and is probably highly variable but relatively lower than of cholesterol per se. Oxysterols – Biological Activity General Functions: In tissues in vivo, the very low oxysterol:cholesterol ratio means that oxysterols have little impact on the primary role of cholesterol in cell membrane structure and function, although it has been claimed that oxysterols could cause packing defects and thence atheroma formation in vascular endothelial cells. It is often argued that there are few reliable measurements of cellular or subcellular oxysterol concentrations, because of the technical difficulties in the analysis of the very low concentrations of oxysterols in the presence of a vast excess of native cholesterol; the average levels of 26-, 24- and 7α-hydroxy-cholesterol in human plasma that are often quoted are 0.36, 0.16 and 0.14 μM, respectively. Autoxidation products of cholesterol, especially 7-keto- and 7-hydroxy-cholesterol, are cytotoxic and may be useful markers of oxidative stress or for monitoring of the progression of various diseases. However, experts in the field caution that it can be difficult to extrapolate from experiments in vitro to the situation in vivo, because of the rapidity with which cholesterol can autoxidize in experimental systems and because of the difficulty of carrying out experiments with physiological levels of oxysterols. Nonetheless, aside from their role as precursors of bile acids and some steroidal hormones, it is evident that oxysterols have a variety of roles in terms of maintaining cholesterol homeostasis and perhaps in signaling, where those formed enzymatically are most important. They can exert potent biological effects at physiologically relevant concentrations by binding to various receptors to elicit transcriptional programs, i.e., to regulate gene and hence protein expression. Among many cell membrane receptors for oxysterols to have been identified, nuclear receptors are especially important and include the liver X receptors (LXRs), retinoic acid receptor-related orphan receptors (RORs), estrogen receptors (ERs), and glucocorticoid receptors (GRs). In addition, N-methyl-D-aspartate receptors (NMDARs) are expressed in nerve cells and work over a short time scale to regulate excitatory synaptic function, while G protein-coupled receptors operate at cell membranes and are activated by molecules outside the cell to activate signaling pathways within the cell. As various isoforms of these receptors exist in different tissues, and these can interact with several oxysterols, only a brief summary of this complex topic is possible here. A family of oxysterol-binding proteins (OSBP) transports and regulates the metabolism of sterols and targets oxysterols to specific membranes and especially to contact sites between organelles with the transport of phosphatidylinositol 4-phosphate in the reverse direction (see our web page on the latter). In this way, they can enable oxysterols to regulate membrane composition and function and mediate intracellular lipid transport. As with cholesterol, oxysterols can be eliminated from cells by transporters such as the ATP-binding cassette proteins ABCA1 and ABCG1, and they are transported in the blood-stream within lipoproteins, especially in association with HDL and LDL and mainly in the esterified form. Cholesterol homeostasis: While cholesterol plays a key role in its own feedback regulation, there is some evidence that oxysterols are regulators of cholesterol concentration in cell membranes, and that 25‑hydroxycholesterol and 24(S),25‑epoxycholesterol may be especially effective, although the other side-chain oxysterols 22-, 24- and 27‑hydroxycholesterol have been implicated. Several mechanisms appear to be involved, and it is suggested that 24(S),25‑epoxycholesterol especially acts as a ligand for the liver X receptor, which forms a heterodimer with the retinoic X receptor, to inhibit the transcription of key genes in cholesterol biosynthesis, as well as directly inhibiting or accelerating the degradation of such important enzymes in the process as HMG-CoA reductase and squalene synthase. Similarly, both 26-hydroxylanosterol and 25-hydroxycholesterol inhibit HMG-CoA reductase. 25‑Hydroxycholesterol inhibits the transfer of the 'sterol regulatory element binding protein' (SREBP-2) to the Golgi for processing to its active form as a transcription factor for the genes of the cholesterol biosynthesis pathway, and it stimulates the enzyme acyl-CoA:cholesterol acyl transferase in the endoplasmic reticulum to esterify cholesterol. By such mechanisms, these oxysterols fine-tune cholesterol homeostasis and ensure smooth regulation rather than substantial fluctuations in tissue concentration. Oxysterols and the immune system: Oxysterols and especially are known to have vital and diverse roles in immunity by regulating both the adaptive and innate immune responses to infection. For example, infection with viruses leads to the production of type I interferon, and in macrophages, this induces synthesis of 25‑hydroxycholesterol, which in general is regarded as anti-inflammatory and exerts broad antiviral activity by activating integrated stress response genes and reprogramming protein translation again via its interaction with LXR receptors. It is a potent inhibitor of SARS-CoV-2 replication, for example, possibly by a mechanism involving the blocking of cholesterol export from the late endosome/lysosome compartment and depletion of membrane cholesterol levels. However, the formation of 25‑hydroxycholesterol may be harmful in the case of influenza infections, as it can lead to over production of inflammatory metabolites. Similarly, the biosynthesis of 25-hydroxycholesterol in macrophages is stimulated by the endotoxin Kdo2-lipid A, the active component of the lipopolysaccharide present on the outer membrane of Gram-negative bacteria, which acts as an agonist for Toll-like receptor 4 (TLR4). There is enhanced expression of the oxygenase CH25H in immune cells in response to bacterial and viral infection. Many oxysterol species are active in a range of immune cells subsets, mediated through the control of LXR and SREBP signaling, but also by acting as ligands for RORs, and for the cell surface receptors G protein-coupled receptor 183 (GPR183) or CXCR2. Activation of LXR tends to dampen the immune response. In response to various stimuli, they can operate through ion channels to effect rapid changes in intracellular ion concentrations, especially of Ca2+, to bring about changes in membrane potential, cell volume, cell death (apoptosis, autophagy, and necrosis), gene expression, secretion, endocytosis, or motility. For example, 27‑hydroxycholesterol in human milk is reported to be active against the pathogenic human rotavirus and rhinovirus of importance in pediatrics, and 7-Dehydrocholesterol has anti-viral properties also. While they can exert their immune functions within the cell in which they are generated, oxysterols can also operate in a paracrine fashion towards other immune cells. 25‑Hydroxycholesterol in particular can have either pro- or anti-inflammatory effects, depending upon the conditions, but the enzyme CH25H responsible for its biosynthesis is induced markedly in macrophages activated by inflammatory agents. It is reported to have a regulatory effect on the biosynthesis of sphingomyelin, which is required with cholesterol for the formation of raft sub-domains in membranes, where signaling molecules are concentrated, and together with other oxysterols, such as 24S,25-epoxycholesterol, to regulate the activities of the hedgehog proteins involved in embryonic development. Metabolites of 25‑hydroxycholesterol, such as 7α,25‑dihydroxycholesterol, and further oxidation products, are pro-inflammatory act as chemoattractants to lymphocytes; they have a role in the regulation of immunity in secondary lymphoid organs by interactions with the receptor GPR183. Oxysterols in brain: Oxysterols are especially important for cholesterol homeostasis in the brain, which contains 25% of the total body cholesterol, virtually all of it in unesterified form, in only about 2% of the body volume. Cholesterol is a major component of the plasma membrane especially, where it serves to control fluidity and permeability. This membrane is produced in large amounts in the brain and is the basis of the compacted myelin, which is essential for the conductance of electrical stimuli and contains about 70% of brain cholesterol. While this pool is relatively stable, the remaining 30% is present in the membranes of neurons and glial cells of gray matter and is more active metabolically. Even in the fetus and the newborn infant, all the cholesterol required for growth is produced by synthesis de novo in the brain, not by transfer from the circulation across the blood-brain barrier, which consists of tightly opposed endothelial cells lining the extensive vasculature of the tissue. The fact that this pool of cholesterol in the brain is independent of circulating levels must reflect a requirement for constancy in the content of this lipid in membranes and myelin. In adults, although there is a continuous turnover of the membrane, the cholesterol is efficiently re-cycled and has a remarkably high half-life (up to 5 years). The rate of cholesterol synthesis is a little greater than the actual requirement so net movement of cholesterol out of the central nervous system must occur. An important component of this system is apolipoprotein E (Apo E), a 39-kDa protein, which is highly expressed in the brain and functions in the cellular transport of cholesterol and in cholesterol homeostasis. Apo E complexes with cholesterol are required for transport from the site of synthesis in astrocytes to neurons. Hydroxylation by CYP46A1 of cholesterol to 24(S)‑hydroxycholesterol (cerebrosterol) is responsible for 50–60% of all cholesterol metabolism in the adult brain. If cholesterol itself cannot cross the blood-brain barrier, this metabolite is able to do so with relative ease. When the hydroxyl group is introduced into the side chain, this oxysterol causes a local re-ordering of membrane phospholipids such that it is more favorable energetically to expel it at a rate that is orders of magnitude greater than that of cholesterol per se, though still only 3-7 mg per day. There is a continuous flow of the metabolite from the brain into the circulation, much of it in the form of the inactive sterol ester, where it is transported by lipoprotein particles to the liver for further catabolism, i.e., it is hydroxylated in position 7 and then converted to bile acids. This is illustrated in Figure $35$. Both 24(S)-hydroxycholesterol and 24(S),25-epoxycholesterol are believed to be important in regulating cholesterol homeostasis in the brain. They interact with the specific nuclear receptors involved in the expression and synthesis of proteins involved in sterol transport, and for example, 24‑hydroxy-cholesterol regulates the transcription of Apo E. In particular, it is an agonist of the nuclear liver X receptors (LXRs), influencing the expression of those LXR target genes involved in cholesterol homeostasis and inflammatory responses. It is also a high-affinity ligand for the retinoic acid receptor-related orphan receptors α and γ (RORα and RORγ). In this way, it can act locally to affect the functioning of neurons, astrocytes, oligodendrocytes, and vascular cells. 24(S)-Hydroxycholesterol down-regulates the trafficking of the amyloid precursor protein and may be a factor in preventing neurodegenerative diseases. Especially high levels of 24(S)‑hydroxycholesterol are observed in the plasma of human infants and in patients with brain trauma, while reduced levels are found in the plasma of patients with neurodegenerative diseases, including Parkinson’s disease, multiple sclerosis, and Alzheimer's disease. In contrast, there are elevated levels in the brain and especially cerebrospinal fluid in patients with these conditions, where it may be a marker of neurodegeneration. Increased expression of cholesterol 24-hydroxylase (CYP46A1) is believed to improve cognition, while a reduction leads to poor cognitive performance, as occurs at advanced stages of the disease, probably reflecting a selective loss of neuronal cells, and it may be a factor in age-related macular degeneration. An excess of 24(S)‑hydroxycholesterol and especially of its ester form can lead to neuronal cell death, and elevated levels in plasma are reported to be a potential marker for Autism Spectrum Disorders in children. On the other hand, it may be protective against glioblastoma, the most common primary malignant brain tumour in adults via activation of LXRs. 27‑Hydroxy-cholesterol diffuses across the blood-brain barrier from the bloodstream into the brain (in the reverse direction to 24‑hydroxycholesterol), where it does not accumulate but is further oxidized and then exported as steroidal acids. This flux may regulate certain key enzymes within the brain, and there are suggestions that the balance between the levels of 24- and 27-hydroxy-cholesterol, especially excess of the latter, may be relevant to the generation of β-amyloid peptides in Alzheimer's disease by reducing insulin-mediated glucose uptake by neurons. While 7β-hydroxycholesterol is pro-apoptotic, any links with Alzheimer's disease are unproven although there is a school of thought that other oxidized cholesterol metabolites may be major factors behind the development of this disease. For example, seco-sterols such as 3β‑hydroxy-5-oxo-5,6-secocholestan-6-al and its stable aldolization product, the main ozonolysis metabolites derived from cholesterol, have been detected in brain samples of patients who have died from Alzheimer's disease and Lewy body dementia; they are also found in atherosclerotic lesions. Oxidation products of the cholesterol precursor 7‑dehydrocholesterol and especially 3β,5‑dihydroxycholest-7-ene-6-one are involved in the pathophysiology of the human disease Smith-Lemli-Opitz syndrome. Cell differentiation: Oxysterols can influence the differentiation of many cell types and this was first studied in the skin, where 22(R)- and 25(R)‑hydroxycholesterol were shown to induce human keratinocyte differentiation. Subsequently, by stimulating nuclear binding receptors, oxysterols were found to have similar effects on mesenchymal stem cells. There have been many reports of the involvement of oxysterols in disease processes, especially atherosclerosis and the formation of human atherosclerotic plaques, but also cytotoxicity, necrosis, inflammation, immuno-suppression, phospholipidosis and gallstone formation. They have been implicated in the development of cancers, especially those of the breast, prostate, colon, and bile duct. For example, 27‑hydroxycholesterol is an element in cholesterol elimination from macrophages and arterial endothelial cells, but it is also an endogenous ligand for the human nuclear estrogen receptor (ERα) and the liver X receptor, and it modulates their activities with effects upon various human disease states, including cardiovascular dysfunction and progression of cancer of the breast and prostate, as well as having an involvement in the regulation of bone mineralization (osteoporosis). It has been linked to cancer metastasis through effects on immune cells, and there is hope that pharmacological inhibition of CYP27A1 and thence the formation of 27‑hydroxycholesterol may be a useful strategy in the treatment of breast cancer; CYP7A1 gene polymorphism has been associated with colorectal cancer. In contrast, oxysterols can interfere in the proliferation of several types of cancer cell (glioblastoma, leukemia, colon, breast, and prostate cancer). Cholesterol 5,6-epoxide (with either 5α or 5β stereochemistry) is formed non-enzymatically in tissues, but it is also believed to be produced by an unidentified cytochrome P450 enzyme in the adrenal glands. While it was for some time believed to be a causative agent in cancer, it is now recognized that downstream metabolites are responsible. Thus, cholesterol epoxide hydrolase converts cholesterol 5,6-epoxide into cholestane-3β,5α,6β-triol, which is transformed by 11β‑hydroxysteroid-dehydrogenase-type-2 into the oncometabolite 3β,5α-dihydroxycholestan-6-one (oncosterone). By binding to the glucocorticoid receptor, this oncosterone stimulates the growth of breast cancer cells, and it also acts as a ligand to the LXR receptors, which may mediate its pro-invasive effects. In contrast, in normal breast tissue, cholesterol 5,6‑epoxide is metabolized to the tumor suppressor metabolite, a steroidal alkaloid designated dendrogenin A that is a conjugation product with histamine and controls a nuclear receptor to trigger lethal autophagy in cancers; its synthesis is greatly reduced in cancer cells. Tamoxifen, a drug that is widely used against breast cancer, binds to the cholesterol 5,6-epoxide hydrolase, which is also a microsomal anti-estrogen binding site (AEBS), to inhibit its activity. 7-Ketocholesterol is a major oxysterol produced during the oxidation of low-density lipoproteins, and is one of the most abundant in plasma and atherosclerotic lesions; it accumulates in erythrocytes of heart failure patients. It has a high pro-apoptotic potential and associates preferentially with membrane lipid raft domains. As it is not readily exported from macrophages, it impairs cholesterol efflux and promotes the foam cell phenotype. In cardiomyocytes, this accumulation can lead to cell hypertrophy and death, and it has been suggested that oxysterols are a major factor precipitating morbidity in atherosclerosis-induced cardiac diseases and inflammation-induced heart complications. Photoxidation in the retina via the action of free radicals or singlet oxygen generates unstable cholesterol hydroperoxides, which may be involved in age-related macular degeneration. For example, these compounds can quickly be converted to highly toxic 7α- and 7β‑hydroxycholesterols and 7‑ketocholesterol, depending on the status of tissue oxidases and reductases. Three separate enzymatic pathways have developed in the eye to neutralize their activities. These sterols are metabolized by novel branches of the acidic pathway of bile acid biosynthesis. Those oxysterols formed non-enzymatically can be most troublesome in relation to disease in general. For example, they are enriched in pathologic cells and tissues, such as macrophage foam cells, atherosclerotic lesions, and cataracts. They may regulate some of the metabolic effects of cholesterol, but as cautioned above, effects observed in vitro may not necessarily be of physiological importance in vivo. Various oxysterols have been implicated in the differentiation of mesenchymal stem cells and the signaling pathways involved in this process. High levels of 7‑hydroxycholesterol and cholestane-3β,5α,6β-triol are characteristic of the lysosomal storage diseases Niemann-Pick types B and C and of lysosomal acid lipase deficiency. Cholesterol hydroperoxides: With the aid of START domain proteins, cholesterol hydroperoxides can translocate from a membrane of origin to another membrane such as mitochondria. Such transfer of free radical-generated 7-hydroperoxycholesterol, for example, has adverse consequences in that there is impairment of cholesterol utilization in steroidogenic cells, and of anti-atherogenic reverse-cholesterol transport in vascular macrophages. The antioxidant activity of GPx4 may be crucial for the maintenance of mitochondrial integrity and functionality in these cells. Vitamin D Vitamin D encompasses two main sterol metabolites that are essential for the regulation of calcium and phosphorus levels and thence for bone formation in animals. However, these have many other functions, including induction of cell differentiation, inhibition of cell growth, immunomodulation, and control of other hormonal systems. Vitamin D (with calcium) deficiency is responsible for the disease rickets in children in which bones are weak and deformed, and it is associated with various cancers and autoimmune diseases. Ultraviolet light mediates cleavage of 7-dehydrocholesterol, an important intermediate in the biosynthesis of cholesterol, with the opening of the second (B) ring in the skin to produce pre-vitamin D, which rearranges spontaneously to form the secosteroid vitamin D3 or cholecalciferol. Its structure is shown in Figure $36$. The newly generated vitamin D3 is transported to the liver where it is subject to 25-hydroxylation and thence to the kidney for 1α-hydroxylation to produce the active form 1α,25-dihydroxyvitamin D3 (calcitriol); this is a true hormone and serves as a high-affinity ligand for the vitamin D receptor in distant tissues. For transportation in plasma, it is bound to a specific glycoprotein termed unsurprisingly, the 'vitamin D binding protein (BDP)'. Vitamin D2 or ergocalciferol is derived from ergosterol, which is obtained from plant and fungal sources in the diet. Vitamin D3 functions by activating a cellular receptor (vitamin D receptor or VDR), a transcription factor binding to sites in the DNA called vitamin D response elements. There are thousands of such binding sites, which together with co-modulators regulate innumerable genes in a cell-specific fashion. In this way, it enhances bone mineralization by promoting dietary calcium and phosphate absorption, as well as having direct effects on bone cells. In addition, it functions as a general development hormone in many different tissues, while together with Vitamin D2 it has profound effects on immune responses in the defense against microbes. Steroidal Hormones and their Esters Steroidal hormones cannot be discussed in depth here as their structures, biosynthesis, and functions comprise a rather substantial and specialized topic. In brief, animal tissues produce small amounts of vital steroidal hormones from cholesterol as the primary precursor with 22R-hydroxycholesterol, produced by hydroxylation by the cholesterol side-chain cleavage enzyme (P450scc), as the first of its metabolites in the pathway. This step involves the 'STAR' protein which enables the transport of cholesterol into mitochondria where conversion to pregnenolone is rate-limiting and involves first hydroxylation and then cleavage of the side-chain. After export from the mitochondria, this can be converted directly to progesterone or in several steps to testosterone. 17β-Estradiol, for example, is the most potent and important of the endogenous estrogens; it is made mainly in the follicles of the ovaries and regulates menstrual cycles and reproduction, but it is also present in testicles, adrenal glands, fat, liver, breasts, and brain. Testosterone is the primary male sex hormone and an anabolic steroid, and it is produced mainly in the testes; it has a key function in the development of male reproductive tissues such as testes and prostate, in addition to promoting secondary sexual characteristics. Pregnane neurosteroids are synthesized in the central nervous system. Cholesterol homeostasis is therefore vital to fertility and a host of bodily functions. The structures of key steroidal hormones are shown in Figure $37$. Steroidal esters accumulate in tissues such as the adrenal glands, which synthesize corticosteroids such as cortisol and aldosterone and are responsible for releasing hormones in response to stress and other factors. It is also apparent that fatty acyl esters of estradiols, such as dehydroepiandrosterone, accumulate in adipose tissue in post-menopausal women. Small amounts of estrogens acylated with fatty acids at the C-17 hydroxyl are present in the plasma lipoproteins. In each instance, they appear to be biologically inert storage or transport forms of the steroid. Eventually, esterified steroids in low-density lipoproteins (LDL) particles are taken up by cells via lipoprotein receptors, and then are hydrolyzed to release the active steroid. Pharmaceutical interest in oleoyl-estrone, a naturally occurring hormone in humans, which was found to induce a marked loss of body fat while preserving protein stores in laboratory animals, has declined as clinical trials with humans were not successful. Sterols 3. Sterols and their Conjugates from Plants and Lower Organisms Plant Sterols - Structures and Occurrence Plants, algae, and fungi contain a rather different range of sterols from those in animals. Like cholesterol, to which they are related structurally and biosynthetically, plant sterols form a group of triterpenes with a tetracyclic cyclopenta[a]phenanthrene structure and a side chain at carbon 17, sometimes termed the C30H50O triterpenome. The four rings (A, B, C, D) have trans ring junctions, and the side chain and two methyl groups (C-18 and C-19) are at an angle to the rings above the plane with β stereochemistry (as for a hydroxyl group commonly located on C-3 also). The basic sterol from which other sterol structures are defined is 5α-cholestan-3β-ol with the numbering scheme recommended by IUPAC as shown in Figure $38$. The phytosterols (as opposed to zoosterols) include campesterol, β-sitosterol, stigmasterol, and Δ5‑avenasterol, some of which are illustrated in Figure $39$. These more common plant sterols have a double bond in position 5, and a definitive feature – a one- or two-carbon substituent with variable stereochemistry in the side chain at C-24, which is preserved during subsequent metabolism. For example, campesterol is a 24-methylsterol, while β-sitosterol and stigmasterol are 24‑ethylsterols. Occasionally, there is a double bond in this chain that can be of the cis or trans configuration as in stigmasterol (at C22) or fucosterol (C24), the main sterol in green algae. Phytosterols can be further classified on a structural or biosynthetic basis as 4‑desmethyl sterols (i.e. with no substituent on carbon‑4), 4α‑monomethyl sterols and 4,4‑dimethyl sterols. The most abundant group is the 4‑desmethyl sterols, which may be subdivided into Δ5-sterols (illustrated above), Δ7‑sterols (e.g. α-spinasterol) and Δ5,7-sterols depending on the position of the double bonds in the B ring. As the name suggests, brassicasterols (24‑methyl-cholesta-5,22-dien-3β-ol and related sterols) are best known from the brassica family of plants, but they are also common constituents of marine algae (phytoplankton). Phytostanols (fully saturated) are normally present at trace levels only in plants, but they are relatively abundant in cereal grains. Many different sterols may be present in photosynthetic organisms, and the amounts and relative proportions are dependent on the species. Over 250 different phytosterols have been recorded with 60 in corn (maize) alone, for example. As a rough generality, a typical plant sterol mixture would be 70% sitosterol, 20% stigmasterol, and 5% campesterol (or >70% 24-ethyl-sterols and <30% 24-methyl-sterols), although this will vary with the stage of development and in response to stress. Table 1 contains data on the main components from some representative commercial seed oils. Table $2$. Sterol composition in some seed oils of commercial importance (mg/Kg). corn cottonseed olive palm rapeseed safflower soybean sunflower cholesterol - - - 26 - - - - campesterol 2691 170 28 358 1530 452 720 313 stigmasterol 702 42 14 204 - 313 720 313 β‑sitosterol 7722 3961 1310 1894 3549 1809 1908 2352 Δ5‑avenasterol 468 85 29 51 122 35 108 156 Δ7‑stigmasterol 117 - 58 25 306 696 108 588 Δ7‑avenasterol - - - - - 104 36 156 brassicasterol - - - - 612 - - - other - - - - - 69 - 39 Data adapted from Gunstone, F.D. et al. The Lipid Handbook (Second Edition) (Chapman & Hall, London) (1994). Cholesterol is usually a minor component only of plant sterols (<1%), but it is unwise to generalize too much as it can be the main sterol component of red algae and of some families of higher plants such as in the Solanaceae, Liliaceae and Scrophylariaceae. It can also be a significant constituent sterol of chloroplasts, shoots, pollen and leaf surface lipids in other plant families; wheat roots contain 10% and Arabidopsis cells 19% of the sterols as cholesterol. Yeasts and fungi tend to contain ergosterol as their main sterol (see below). Ecdysteroids (phytoecdysteroids) are polyhydroxylated plant sterols that can occur in appreciable amounts in some species. Sterols are also found in some bacterial groups but not in archaea, and hopanoids in bacteria are considered to be functional triterpenic counterparts. Sterols can occur in plants in the 'free' state, i.e. in which the sterol hydroxyl group is not linked to any other moiety, but they are usually present also as conjugates with the hydroxyl group covalently bound via an ester bond to a fatty acid, for example, i.e. as sterol esters, or via a glycosidic linkage to glucose (and occasionally other sugars), i.e. as steryl glycosides. Plant Sterols - Biosynthesis The biosynthetic route to plant sterols resembles that to cholesterol in many aspects in that it follows an isoprenoid biosynthetic pathway with isopentenyl pyrophosphate, derived primarily from mevalonate, as the key building block in the cytoplasm (but not plastids) at least. The main pathway for the biosynthesis of isopentenyl pyrophosphate and dimethylallyl pyrophosphate, the isoprene units, is described previously and so need not be repeated here. It is known as the 'mevalonic acid (MVA) pathway' and functions in the cytosol, endoplasmic reticulum and mitochondria. However, an alternative pathway that does not use mevalonic acid as a precursor was established first for bacterial hopanoids, but has since been found in plant chloroplasts, algae, cyanobacteria, eubacteria, and some parasites (but not in animals). This route is variously termed the ‘non-mevalonate’, ‘1‑deoxy-D-xylulose-5-phosphate’ (DOXP) or better the 2C-methyl-D-erythritol 4-phosphate (MEP) pathway as the last compound is presumed to be the first committed intermediate in sterol biosynthesis by this route. In the first step, pyruvate and glyceraldehyde phosphate are combined to form deoxyxylose phosphate, which is in turn converted to 2C-methyl-D-erythritol 4-phosphate. The pathway then proceeds via various erythritol intermediates until isopentenyl pyrophosphate and dimethylallyl pyrophosphate are formed. This pathway is illustrated in Figure $40$. There is evidence that some of the isoprene units are exchanged between the cytoplasm and plastids. In much of the plant kingdom, both the MVA and MEP pathways operate in parallel, but green algae use the MEP pathway only. Thereafter, sterol biosynthesis continues via squalene and (3S)-2,3-oxidosqualene. In photosynthetic organisms (as opposed to yeast and fungi), the subsequent steps in the biosynthesis of plant sterols differ from that for cholesterol in that the important intermediate in the route from squalene via 2,3-oxidosqualene is cycloartenol, rather than lanosterol, and this is produced by the action of a 2,3(S)‑oxidosqualene-cycloartenol cyclase (cycloartenol synthase). Then, the enzyme sterol methyltransferase 1 is of special importance in that it converts cycloartenol to 24-methylene cycloartenol, as the first step in introducing the methyl group onto C-24, while the enzyme cyclopropyl sterol isomerase is required to open the cyclopropane ring. Animals lack the sterol C24-methyltransferase gene. While this pathway is in essence linear up to the synthesis of 24-methylene lophenol, a bifurcation then occurs that results in two alternative pathways, one of which leads to the synthesis of sitosterol and stigmasterol and the other to that of campesterol. This pathway is shown in Figure $41$. In fact, there are more than thirty enzyme-catalyzed steps in the overall process of plant sterol biosynthesis, each associated with membranes, and detailed descriptions are available from the reading list below. The 4,4-dimethyl- and 4α-methylsterols are part of the biosynthetic pathway, but are only minor if ubiquitous sterol components of plants. New biosynthetic pathways are now being discovered by genome analysis that reveal the complexity of sterol biosynthesis in different plant species. Dinoflagellates produce a characteristic 4-methylsterol termed dinosterol and others like gorgosterol via lanosterol as precursor. Protozoans synthesize many different sterols related to those in plants. For example, some species of Acanthamoeba and Naegleria produce both lanosterol and cycloartenol, but only the latter is used for the synthesis of other sterols, especially ergosterol, but in other protozoan species, sterol biosynthesis occurs via lanosterol. The best studied bacterial pathway is that of the methylotroph Methylococcus capsulatus, which produces a number of unique Δ18(14)-sterols and is known to possess a squalene epoxidase and a lanosterol-14-demethylase. Cholesterol in plants is produced from cycloartenol as the key intermediate with the Sterol Side Chain Reductase 2 (SSR2) as the key enzyme. It is now established that the cholesterol biosynthetic pathway in tomato plants comprises 12 enzymes acting in 10 steps. Of these, half evolved through gene duplication and divergence from phytosterol biosynthetic enzymes, whereas others act reciprocally in both cholesterol and phytosterol metabolism. Algae can also produce cholesterol in a multi-step process from cycloartenol, and many more sterols via 24-methylene lophenol as the key intermediate. It is hoped that genetic manipulation of these enzymes will lead to plants that synthesize high-value steroidal products. Oxidation: Phytosterols can be subjected to non-enzymatic oxidation with the formation of oxysterols in a similar manner to that of cholesterol in animals, resulting in ring products such as hydroxy-, keto-, epoxy- and triol-derivatives, and further enzymic reactions can oxidize the side chain. However, photosensitized oxidation is more common in plants and is much faster (>1500 times); it starts with the ene-addition of singlet oxygen (1O2) on either side of the double bond in the B ring to generate 5α-/6α-/6β-hydroperoxysterols, of which 5α-OOH is the most abundant and rearranges to form the more stable 7α‑OOH isomer. This is the main reaction in foods stored under LED lighting in food retailers. Plant Sterols - Function Like cholesterol, plant sterols are amphiphilic and are vital constituents of all membranes, and especially of the plasma membrane, the mitochondrial outer membrane and the endoplasmic reticulum. The three-dimensional structure of the plant sterols is such that there are planar surfaces at both the top and the bottom of the molecules, which permit multiple hydrophobic interactions between the rigid sterol and the other components of membranes. Indeed, they must determine the physical properties of membranes to an appreciable extent. It is believed that campesterol, β-sitosterol, and 24-methylcholesterol (in this order) are able to regulate membrane fluidity and permeability in plant membranes by restricting the mobility of fatty acyl chains in a similar manner to cholesterol in mammalian cells, but stigmasterol has much less effect on lipid ordering and no effect on the permeability of membranes. In the plasma membrane, plant sterols associate with the glycosphingolipids such as glucosylceramide, and glycosylinositolphosphoceramides in raft-like sub-domains, analogous to those in animal cells, and these support the membrane location and activities of many proteins with important functions in plant cells. The sterol glycosides are especially important in this context (see below). Sterols (and their conjugates) are involved in plant membrane adaptations to changes in temperature and other biotic and abiotic stresses. For example, β‑sitosterol is a precursor of stigmasterol via the action of a C22-sterol desaturase, and the ratio of these two sterols is important to the resistance of A. thaliana plants to low and high temperatures. In addition, plant sterols can modulate the activity of membrane-bound enzymes. Thus, stigmasterol and cholesterol regulate the activity of the Na+/K+-ATPase in plant cells, probably in a manner analogous to that of cholesterol in animal cells. Stigmasterol may be required specifically for cell differentiation and proliferation. As well as being the precursor of plant steroidal hormones, campesterol, is a signaling molecule that regulates growth, development, and stress adaptation. Perhaps surprisingly, cholesterol is a precursor for the biosynthesis of some steroidal saponins and alkaloids in plants, for example, the well-known steroidal glycoalkaloid in potato (α-solanine), as well as of other steroids including the phytoecdysteroids (in some species they are derived from lathosterol). While the physiological roles of ecdysteroids in plants yet to be been confirmed, they are believed to enhance stress resistance by promoting health and vitality. Withanolides are complex oxysterols, which are believed to be defense compounds against insect herbivores. Steroidal Plant Hormones They have crucial importance for plant growth processes, including cell elongation, division, differentiation, immunity, and development of reproductive organs, and they are involved in the regulation of innumerable aspects of metabolism. Via signal transduction pathways, they interact with transcription factors through phosphorylation cascades to regulate the expression of target genes. Brassinosteroids are also signaling molecules in abiotic stress responses such as drought, salinity, high temperature, low temperature, and heavy metal stresses. Outwith plants, they may have biomedical applications as anticancer drugs for endocrine-responsive cancers to induce apoptosis and inhibit growth. Some plant species produce small amounts of steroid hormones that are often considered to be of animal origin only, including progesterone and testosterone, and these may have physiological roles in plants. Sterol Esters in Higher Plants Sterol esters are present in all plant tissues, but they are most abundant in tapetal cells of anthers, pollen grains, seeds, and senescent leaves. In general, they are minor components relative to the free sterols other than in waxes. Usually, the sterol components of sterol esters are similar to the free sterols, although there may be relatively less of stigmasterol. The fatty acid components tend to resemble those of the other plant tissue lipids, but there can be significant differences on occasion. Sterol esters are presumed to serve as inert storage forms of sterols, as they are often enriched in the intermediates of sterol biosynthesis and can accumulate in lipid droplets within the cells. However, they have been found in some membranes, especially in microsomes and mitochondrial preparations, although their function there is uncertain. They may also have a role in transport within cells and between tissues, as they can be present in the form of soluble lipoprotein complexes. Biosynthesis of sterol esters in A. thaliana is known to occur in the endoplasmic reticulum by the action of a phospholipid:sterol acyltransferase, which catalyzes the transfer of a fatty acyl group to the sterol from position sn-2 of phospholipids - mainly phosphatidylethanolamine; the enzyme is very different from those in animals and yeasts. However, an acyl CoA:sterol acyltransferase closer in structure to the animal enzyme has been characterized also; it prefers saturated fatty acyl-CoAs as acyl donors and cycloartenol as the acceptor molecule. The enzymes responsible for the hydrolysis of sterol esters in A. thaliana are not yet known. Certain distinctive phytosterol esters occur in the aleurone cells of cereal grains, including trans-hydroxycinnamate, ferulate (4-hydroxy-3-methoxycinnamate), and p-coumarate esters. Similarly, rice bran oil is a rich source of esters of ferulic acid and a mixture of sterols and triterpenols, termed 'γ-orizanol'’, and an example of one of these compounds is illustrated in Figure $43$. This is sold as a health food supplement, because of the claimed beneficial effects, including cholesterol-lowering and antioxidant activities, while enhancing muscle growth and sports performance. However, none of these effects have been confirmed by rigorous clinical testing. Sterol Glycosides Leaf and other tissue in plants contain a range of sterol glycosides and sterol acyl-glycosides in which the hydroxyl group at C3 on the sterol is linked to the sugar by a glycosidic bond. Other than in the genus Solanum, where they can represent up to 85% of sterol fraction in tomato fruit as an example, they tend to be minor components relative to other lipids. Typical examples (glucosides of β-sitosterol) are illustrated in Figure $44$. Most of the common plant sterols occur in this form, but Δ5 sterols are preferred (Δ7 in some genera). Glucose is the most common carbohydrate moiety but galactose, mannose, xylose, arabinose can also be present depending on plant species; occasionally, complex carbohydrates with up to five hexose units linked in a linear fashion are present. Algae also contains sterol glycosides with a wide range of sterol and carbohydrate components. Plant, animal, fungal, and most bacterial steryl glycosides have a β‑glycosidic linkage, but in a few bacterial species there is an α-linkage. Similarly, the nature of the fatty acid component in the acyl sterol glycosides can vary as well as the hydroxyl group to which they are linked, although it is usually position 6 of the glucose moiety. In potato tubers, for example, the 6'-palmitoyl-β-D-glucoside of β-sitosterol is the major species, while the corresponding linoleate derivative predominates in soybeans. Usually, the sterol acyl-glycosides are present at concentrations that are two- to tenfold greater than those of the non-acylated forms. They are known to be located in the plasma membrane, tonoplasts and endoplasmic reticulum. Biosynthesis involves the reaction of free sterols with a glucose unit catalyzed by a sterol glycosyltransferase, or by the reaction of the sterol with uridine diphosphoglucose (UDP-glucose) and UDP-glucose:sterol glucosyltransferase on the cytosolic side of the plasma membrane. The acyl donor for acyl sterol glycoside synthesis is not acyl-coenzyme A but is believed to be a glycerolipid. Steryl β-D-glycoside hydrolases have been characterized from plants that reverse this reaction, but no fatty acyl hydrolase activity for sterol acyl-glycosides is yet known. One route to the biosynthesis of glucosylceramides in plants involves the transfer of the glucose moiety of sterol glycosides to ceramide. The functions of sterol glycosides and sterol acyl-glycosides are slowly being revealed, and they are believed to be significant components of the plasma membrane that associate with sphingolipids in raft-like domains; the esterified form especially may be involved in the adaptation of plant membranes to low temperatures and other stresses. It is possible that they have a role in signal transmission through membranes, and they are reported to be beneficial in the response to pathogens. It seems probable that sterol glycosides are oriented with the sterol moiety buried in the hydrophobic core of the lipid bilayer with the sugar located in the plane of the polar head groups of the membrane, while with sterol acyl-glycosides both the sterol moiety and the fatty acid chain are embedded in the hydrophobic core of the membrane. Sitosterol-β-D-glucoside in the plasma membrane is believed to be the primer molecule for cellulose synthesis in plants, as in cotton (Gossypium arboreum) fiber, where it may be required for the initiation of glucan polymerization. The sterol is eventually removed from the polymer by a specific cellulase enzyme (the multimeric cellulase synthase is believed to be stabilized by sterols in the plasma membrane). Sterol glycosides appear to be essential for the pathogenicity of certain fungi and for some bacteria, and ergosterol glycosides especially are especially troublesome components of plant fungal pathogens. Sterol glycosides have only rarely been reported from organisms other than plants and fungi, although some bacteria, such as the gram-negative bacterium Helicobacter pylori and Borrelia burgdorferi, the causative agent of Lyme disease produce cholesterol glucoside from host cholesterol. On the other hand, cholesteryl glucoside has been found as a natural component of a few animal tissues, and through acting as immunoadjuvants, sterol glycosides are reported to be efficacious in protecting animal hosts against lethal Cryptococcal infections. In the human diet, sterol glycosides have potential benefits in that like free sterols they inhibit the absorption of cholesterol from the gut and reduce the plasma cholesterol levels. The fatty acids are removed from sterol acyl-glycosides by enzymes in the intestine. A number of species of monocotyledons contain complex steroidal saponins, which consist of an aglycone based on a triterpenoid furostanol or spirostanol skeleton (derived from cholesterol) and an oligosaccharide chain of two to five hexose or pentose moieties attached to the 3β-hydroxyl group of the sterol. These can interact with cholesterol in plant membranes to form insoluble complexes, which increase membrane permeability. Ergosterol and Other Sterols in Yeasts and Fungi Yeasts and fungi, together with microalgae and protozoa, can contain a wide range of different sterols. However, ergosterol ((22E)‑ergosta-5,7,22-trien-3β-ol) is the most common sterol in fungi and yeast, and is accompanied by other sterols not normally abundant in higher plants including cholesterol, 24-methyl cholesterol, 24-ethyl cholesterol, and brassicasterol, depending upon species. In Saccharomyces cerevisiae, which is widely studied as a model species of yeasts, ergosterol is the most abundant sterol (ca. 12% of all lipids), with the highest levels in the plasma membrane (up to 40% of the lipids or 90% of the total cell sterols). Its structure is shown in Figure $45$. Like cholesterol and in contrast to the plant sterols, it is synthesized in the endoplasmic reticulum via lanosterol as the key intermediate and then zymosterol, but the pathway diverges at this stage to produce fecosterol on the way to ergosterol (see the reading list below for further details). Ergosterol is transported to other organelles within the cell in a non-vesicular manner by two families of evolutionarily conserved sterol-binding proteins - 'Osh' and 'Lam', which are able to optimize the sterol composition of cell membranes rapidly under conditions of stress. As in humans, a Niemann-Pick protein NCR1 integrates sterols into the lysosomal membrane prior to further distribution as part of the mechanism of sterol homeostasis. Some antifungal drugs are targeted against ergosterol, either by binding to it to cause damaging cellular leakage, or to prevent its synthesis from lanosterol. Many mutants defective in ergosterol biosynthesis have been isolated, and these have yielded a great deal of information on the features of the sterol molecule required for its structural role in membranes of yeast and fungi. Ergosterol stabilizes the liquid-ordered phase in the same manner as cholesterol and also forms raft microdomains with sphingolipids in membranes, whereas lanosterol does not. It is also evident that ergosterol has a multiplicity of functions in the regulation of yeast growth. Under some conditions, especially those that retard growth, a high proportion of the sterols in yeasts can be in esterified form, where they are stored in lipid droplets. Ergosterol esters are synthesized in yeast by enzymes (ARE1 and ARE2), which are related to ACAT-1 and ACAT-2 that perform this function in animals, and both transfer an activated fatty acid to the hydroxyl group at the C3-position of a sterol molecule. In addition, specific sterol ester hydrolases that catalyze the reverse reaction have been characterized from yeasts, two in lipid droplets and one at the plasma membrane. Many fungal species and slime molds contain steryl glycosides (ergosteryl β-monoglucopyranosides in the former), but they are present at very low levels only in the widely studied yeast Saccharomyces cerevisiae. Most fungi conjugate the 3β-hydroxyl group of ergosterol with aspartate in an RNA-dependent reaction catalyzed by an ergosteryl-3β-O-L-aspartate synthase, with the reverse reaction using a dedicated hydrolase. A phylogenomic study has shown that this pathway is conserved across higher fungi (except S. cerevisiae), including pathogens, and it has been suggested that these reactions constitute a homeostasis system with a potential impact upon membrane remodeling, trafficking, antimicrobial resistance, and pathogenicity. Bacterial Sterols Hopanoids take the place of sterols in many species of bacteria, but it has long been recognized that some bacteria take up cholesterol and other sterols from host animals for use as membrane constituents. Indeed, an external source of sterols is required for growth in species of Mycoplasma. In addition, there have been a number of reports of the biosynthesis of sterols by various bacterial species, although a high proportion of these appears now to have been discounted because of fungal contamination. In particular, the possibility of sterol biosynthesis in cyanobacteria has been controversial, and molecular biology studies have yet to detect the presence of the required enzyme squalene epoxide cyclase. That said, there is good evidence that a few species of prokaryotes at least have the capacity to synthesize sterols de novo. Among the eubacteria, certain methylotrophs (Methylobacterium and Methylosphaera species) produce mono- and dimethyl sterols, including lanosterol. Similarly, some soil bacteria produce 4‑desmethylsterols. It has now been established from gene sequence studies that a few bacteria contain enzymes of the sterol biosynthesis pathway such as oxidosqualene cyclase, but as these have no obvious evolutionary link it seems probable that they were acquired via lateral transfer from eukaryotes. Plant Sterols in the Human Diet The absorption of dietary plant sterols and stanols in humans is low (0.02-3.5%) compared to cholesterol (35-70%), although there are similar amounts in an average Western diet. The explanation is believed to be that the Niemann-Pick C1-like protein 1 (NPC1L1), which is responsible for cholesterol absorption in enterocytes does not take up plant sterols efficiently, while two transporters (ABCG5 and ABCG8) redirect any that are absorbed back into the intestinal lumen. In some rare cases, increased levels of plant sterols in plasma serve as markers for an inherited lipid storage disease (phytosterolemia) caused by mutations in the enterocyte transporters. Among many symptoms, accelerated atherosclerosis is often reported although the reasons for this are not clear. There is evidence that while plant sterols can substitute for cholesterol in maintaining membrane function in mammalian cells, they can exert harmful effects by disrupting cholesterol homeostasis. This may be relevant to the brain especially, since phytosterols are able to cross the blood-brain barrier, although they cannot be oxidized enzymatically because of the alkyl moiety on C24. In contrast, dietary supplements of plant sterols have been reported to have anti-cancer effects. Substantial amounts of phytosterols are available as by-products of the refining of vegetable oils and of tall oil from the wood pulp industry. As it appears that they can inhibit the uptake of cholesterol from the diet and thereby reduce the levels of this in the plasma low-density lipoproteins, there is an increasing interest in such commercial sources of plant sterols to be added as "nutraceuticals" to margarines and other foods, Hydrogenated phytosterols or "stanols" are also used for this purpose, and studies suggest they are as effective as sterols in reducing LDL cholesterol. The consensus amongst experts in the field (including the FDA in the USA) is that such dietary supplements do indeed have the effects claimed and such claims can be used in advertising of commercial products, with the important caveat that there are no randomized, controlled clinical trial data that establish ensuing benefits to health, especially with respect to cardiovascular disease. Other pharmacological effects are under investigation, and there may be beneficial effects for the development of the human fetus and newborn, and for the treatment of non-alcoholic steatohepatitis, inflammatory bowel diseases ,and allergic asthma. It is not clear whether oxy-phytosterols are generated in animal tissues, but those produced by enzymatic or non-enzymatic means can enter the food chain, especially when they are produced during cooking. Although they are not efficiently absorbed, 7-keto-sitosterol and 7-keto-campesterol have been detected in human plasma and have the potential to exert a variety of biological effects. For example, they have pro-atherogenic and pro-inflammatory properties in animal models.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/02%3A_Unit_II-_Bioenergetics_and_Metabolism/21%3A_Lipid_Biosynthesis/21.06%3A_Biosynthesis_of_Isoprenoids.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry By William (Bill) W. Christie and Henry Jakubowski. This section is an abbreviated and modified version of material from the Lipid Web, an introduction to the chemistry and biochemistry of individual lipid classes, written by William Christie. Isoprenoids: 1. Tocopherols and Tocotrienols (Vitamin E) Tocopherols and tocotrienols constitute a series of related benzopyranols (or methyltocols) that are synthesized in plants and other photosynthetic organisms, where they have many important functions but especially as part of a complex web of antioxidants that protect plants from the activities of reactive oxygen species (ROS). First described in 1922 as a dietary factor essential to prevent fetal reabsorption in rats, it was soon understood that plants contained a fat-soluble vitamin (vitamin E) that is essential for innumerable aspects of animal development. Of these many related molecules, only one form, i.e., α‑tocopherol, is recognized as having vitamin E activity in humans in that it prevents a spectrum of human deficiency diseases termed 'ataxia', which is characterized by very low concentrations of α‑tocopherol in plasma. The progression of the disease can be prevented by the administration of α-tocopherol only, although the pathogenic mechanism appears to be uncertain. While all tocopherols are known to be powerful lipid-soluble antioxidants in vitro at least, α‑tocopherol has indirect roles in signal transduction and gene expression in animal tissues. On the other hand, specific functions (non-vitamin E) for other tocopherol forms and their metabolites in animal tissues are now being revealed. Vegetable oils are a major dietary source of vitamin E for humans. Structure and Occurrence In the tocopherols, the C16 side chain is saturated, and in the tocotrienols it contains three trans double bonds. Together, these two groups are termed the tocochromanols. In essence, the tocopherols have a 20-carbon phytyl tail (including the pyranol ring) and four chiral centers in total, with variable numbers of methyl groups attached to the benzene ring, while the tocotrienols have a 20-carbon geranylgeranyl tail with double bonds at the 3', 7' and 11' positions relative to the ring system. Tocopherols contain three chiral carbons, one at C2 in the chromanol ring and two in the side chain at C4′ and C8′ with R,R,R stereochemistry. The four main constituents of the two classes are termed - alpha (5,7,8-trimethyl), beta (5,8-dimethyl), gamma (7,8-dimethyl) and delta (8-methyl). In contrast to the tocopherols, the tocotrienols have only one chiral center. Plastochromanol-8 is an analog of γ-tocotrienol with a much longer side-chain. Their structures are shown in Figure $1$. The tocochromanols are only synthesized by plants and other oxygenic photosynthetic organisms, such as algae and some cyanobacteria, but they are essential components of the diet of animals. Of these, only natural R,R,R-α-tocopherol is now designated ‘vitamin E’, as explained below, although the other tocopherols are sometimes termed ‘vitamers’ (some claim incorrectly - nor should all forms be termed isomers strictly speaking). In the USA, the current recommended dietary allowance (RDA) is 15 mg α‑tocopherol daily for adults. In plants, there is a great range of tocochromanol contents and compositions, and photosynthetic plant tissues contain from 10 to 50 μg tocochromanols per g fresh weight. α‑Tocopherol only is present in photosynthetic membranes of plant leaves, while γ-tocopherol and other forms are found principally in fruits, seeds, and nuts. While tocopherols are present in all photosynthetic organisms, the tocotrienols are found only in certain plant families. Seed oils are a major source for the human diet and the compositions of tocopherols in some unrefined oils are listed in Table 1. Sunflower and olive oils are good sources of α‑tocopherol and palm oil of the tocotrienols. In general, tocotrienols tend to be abundant only in seeds and fruits, especially of monocots such as wheat, rice, and barley, though a major commercial source is palm oil. In leaf tissue, α-tocopherol is often the main form, while γ-tocopherol is the primary tocopherol of many seeds. Plastochromanol-8 was first found in leaves of the rubber tree (Hevea brasiliensis) but has since been found in many other plants including rapeseed and maize, but usually at lower levels than of the tocopherols. In addition, tocopherol esters of fatty acids occur in plant tissues, where they may be an inert storage form, but unesterified tocopherols are not released during digestion in animals so they may not make a contribution to vitamin E activity. Table $1$: Tocopherol and tocotrienol contents (mg/Kg) in some seed oils. α-T* β-T γ-T δ-T α-TT* β-TT γ-TT δ-TT palm 89 - 18 - 128 - 323 72 soybean 100 8 1021 421 - - - - maize 282 54 1034 54 49 8 161 6 sunflower 670 27 11 1 - - - - rapeseed 202 65 490 9 - - - - * Abbreviations: T, tocopherol; TT, tocotrienol Data from: Gunstone, F.D., Harwood, J.L. and Padley, F.B. The Lipid Handbook (Second Edition) (Chapman & Hall, London)(1994). An unusual tocopherol that has been termed marine-derived α-tocomonoenol is found together with α-tocopherol in a wide range of marine fish species, where it appears to be a more efficient scavenger of free radicals at low temperatures. A related isomer with a Δ11 double bond has been found in palm oil and kiwi fruit. While pumpkin seeds contain both α- and γ-tocomonoenols, other plant species contain β, γ- and δ-tocomonoenols with unsaturation in the terminal isoprene unit of the side chain. Tocochromenols or 3,4-dehydrotocopherols, i.e., with a double bond in the pyranol ring, are also known in addition to more complex tocopherol-like molecules. The structures of tocomonoenols are shown in Figure $2$. α-Tocopherol is a minor but ubiquitous component of the lipid constituents of animal cell membranes (non-raft domains), with estimates ranging from one molecule of tocopherol to from 100 to 1000 molecules of phospholipids, depending on the membrane. The hydrophobic tail lies within the membrane, as might be expected, and the polar head group is orientated towards the surface but below the level of the phosphate moieties of the phospholipids. There may be some limited hydrogen bonding between the hydroxyl groups and phosphate depending on the degree of hydration of the membrane. On the other hand, there is a strong affinity of α-tocopherol for polyunsaturated fatty acids, where the chromanol unit may interact with the double bonds, suggesting that tocopherol is located deep within the membrane. α-Tocopheryl phosphate has recently been detected at low levels in plasma, liver, and adipose tissue. Its structure is shown in Figure $3$. Together with catabolic tocopherol metabolites, it has important biological properties. During the refining of vegetable oils, much of the natural tocopherols is lost or destroyed. Most commercial vitamin E is therefore prepared by chemical synthesis with trimethylhydroquinone and phytyl bromide as the precursors. The resulting product is a mixture of eight stereoisomers (from R,R,R- to S,S,S-methyl groups) of α-tocopherol, with the various stereoisomers differing by a factor of two in biologic activity, as a consequence of the stereochemistry of position 2 in the chromanol ring (i.e., 2S-α- compared to 2R-α-tocopherol). It is usually administered as the acetate derivative in vivo. Tocopherols are not usually regarded as effective antioxidants in the polyunsaturated seed oils of commerce, and at higher concentrations can even act as pro-oxidants, although the reasons for this are not understood. Biosynthesis and Functions of Tocochromanols in Plants The mechanism of biosynthesis of tocopherols has been elucidated and involves the coupling of phytyl diphosphate with homogentisic acid (2,5‑dihydroxyphenylacetic acid), followed by cyclization and methylation reactions. The plant chloroplast is the site of biosynthesis, and most of the enzymes are located on the inner membrane of the chloroplast envelope, although there is increasing evidence that plastoglobules associated with the thylakoid membrane may be involved. The pathway for biosynthesis of tocopherols is shown in Figure $4$. The aromatic amino acid tyrosine can be considered the basic precursor, and this is oxidized to p-hydroxypyruvic acid, which in the first committed step is converted to homogentisic acid by the enzyme p-hydroxyphenylpyruvate dioxygenase. Homogentisic acid is condensed with phytyl diphosphate, derived from phytol obtained from hydrolysis of chlorophyll, in a reaction catalyzed by a prenyl transferase to yield 2-methyl-6-phytyl-plastoquinol, which is first methylated to form 2,3-dimethyl-5-phytyl-1,4-benzoquinol and then converted by the enzyme tocopherol cyclase to γ-tocopherol. A further methylation reaction produces α-tocopherol, while modifications to the pathway produce β- and δ-tocopherols, together with plastoquinones and thence plastochromanol-8. Tocotrienols and tocomonoenols result from a similar series of reactions but with geranylgeranyl diphosphate and tetrahydro-geranylgeraniol diphosphate, respectively, as substrates in the condensation step. The isoprenoid precursors are synthesized in the plastid also by the non-mevalonate or 'MEP' pathway. In plants, tocopherols are found mainly in the chloroplasts of green tissues, but they are also present in seeds, fruits, roots and tubers. They are especially important as antioxidant molecules, limiting the damage from photosynthesis-derived reactive oxygen species during conditions of oxidative stress, including high-intensity light stress, and the mechanisms for this antioxidant activity are discussed below. However, recent studies seem to suggest that they are just one of a number of different components that are involved in photoprotection. Certainly, any tocochromanol peroxy radicals formed must be converted back to the original compounds by the concerted action of other plant antioxidants, for example by ascorbate, glutathione, ubiquinol or lipoic acid, and antioxidant enzymes, including superoxide dismutase, catalase, and peroxidases. Tocopherols are essential for the control of non-enzymatic lipid peroxidation during seed dormancy and germination of seedlings. In their absence, elevated levels of malondialdehyde and phytoprostanes are formed, and there can be inappropriate activation of plant defense responses. There is evidence that tocopherols play a part in intracellular signaling in plants in that they regulate the amounts of jasmonic acid in leaves, via modulating the extent of lipid peroxidation and gene expression, and so influence plant development and stress responses. Thus, by controlling the degree of lipid peroxidation in chloroplasts (redox regulation), they limit the accumulation of lipid hydroperoxides required for the synthesis of jasmonic acid, which in turn regulates the expression of genes that affect a number of abiotic stress conditions, including drought, salinity and extremes of temperature. The translocation of enzymes to the plasma membrane is regulated by tocopherols, possibly by modulating protein-membrane, altering membrane microdomains (lipid rafts), or by competing for common binding sites within lipid transport proteins. In addition, tocopherols are required for the development of the cell walls in phloem transfer cells under cold conditions. It appears that α- and γ‑tocopherol and the tocotrienols may each have distinct functions. For example, γ-tocopherol is reportedly more potent than α-tocopherol in protecting plants from the harmful effects of osmotic stresses and is important for the longevity of seeds. Efforts are underway to increase the tocopherol levels in plants by selective breeding and genetic manipulation with the aim of producing crops with greater potential health benefits to consumers and perhaps for the plants per se. Tocopherols Metabolism in Animals In animals, the first step in the digestion of tocopherols is their dissolution with other lipids in mixed micelles in the intestines. All tocopherol forms are absorbed to a similar extent in the intestines by means of transporters in the enterocyte apical membrane that have a broad specificity for hydrophobic molecules, such as cholesterol, vitamin D, and carotenoids. These include scavenger receptor class B type I (SR-BI), the CD36 protein, and NPC1-like intracellular cholesterol transporter 1 (NPC1L1). However, some passive diffusion cannot be ruled out. Transport across the enterocyte may involve cytoplasmic transporters or clathrin-coated vesicles before the tocopherols are incorporated into chylomicrons in free form in the Golgi apparatus for release into the lymph. At the liver, α-tocopherol specifically is taken up from the chylomicrons by a receptor-mediated mechanism with the aid of a specific tocopherol-binding protein (the α-tocopherol transfer protein (α-TTP)), i.e., a 30,500 Da cytosolic protein that has a marked affinity for α-tocopherol and can enhance its transfer between membranes. This recognizes α-tocopherol by the three methyl groups and hydroxyl on the chromanol ring and by the structure and orientation of the phytyl side chain. It is the chief regulator of whole body α-tocopherol status and is expressed primarily in the cytosol of hepatocytes in the liver, but has been reported (in much lower concentrations) in other tissues, such as the placenta. α-TTP ultimately regulates the egress of α-tocopherol selectively from hepatocytes with the aid of the ATP-binding cassette proteins ABCA1 and ABCG for conveyance in the plasma lipoproteins, mainly in the very-low-density lipoproteins or VLDL (and thence to LDL) and HDL in humans, to the peripheral tissues (together with much smaller amounts of γ-tocopherol). Most of the other tocopherol forms are directed toward catabolism. Once in the circulation, tocopherol can exchange spontaneously between membranes and lipoproteins, and no specific transport protein for vitamin E in plasma has yet been described. Transfer of tocopherols from the VLDL to peripheral tissues occurs as triacylglycerols are hydrolyzed by the enzyme lipoprotein lipase, while that in LDL is processed via the LDL receptor-mediated uptake pathway. Within cells of peripheral tissues, including the central nervous system, α-TTP functions in transporting α-tocopherol to wherever it is required in membranes, a process that appears to be aided by phosphatidylinositol metabolites. In the brain, tocopherol is transported by apo-E rich lipoproteins. Concentrations of tocopherols can vary appreciably amongst tissues, with most in adipose tissue and adrenals, less in kidney, heart, and liver, and least in the erythrocytes. The "α-tocopherol salvage pathway" is partly due to this process and partly to selective oxidation (see below), and the result is a 20- to 30-fold enrichment of α‑tocopherol in plasma (average concentration 22-28 μM) relative to the other tocopherols. Thus, the process of conservation of one specific tocopherol appears to determine the relative vitamin E activities of the tocopherols and tocotrienols in vivo, rather than their individual potencies as antioxidants as measured in model systems in vitro. Only α‑tocopherol (including synthetic material) or natural mixtures containing this can be sold under the label 'Vitamin E'. γ‑Tocopherol is the second most abundant form in plasma, and it is present in relatively greater proportions in the skin, adipose tissue, and skeletal muscle, where it has some specific biological properties that are distinct from those of α-tocopherol. Although tocotrienols are more potent antioxidants in vitro, they are not usually detected in tissues, although they are believed to have some important functions. Catabolism: The unwanted surplus of tocochromanols other than α-tocopherol may be excreted in the urine and feces in the form of carboxy-chromanols, including the so-called 'Simon metabolites' - tocopheronic acids (carboxyethylhydroxychromans, CEHC) and tocopheronolactones, after oxidative cleavage of much of the phytyl tail, although these are normally detected in the form of conjugates as sulfate or glucuronidate esters, the forms in which they are excreted in feces and urine. For example for illustrative purposes in liver cells, the first step in the catabolism of γ-tocopherol is ω-hydroxylation by cytochrome P450 (CYP4F2) at the 13' carbon to form γ-13'-hydroxychromanol in the endoplasmic reticulum, followed by ω-oxidation in the peroxisomes to produce γ‑13'‑carboxychromanol, and finally by stepwise β‑oxidation in the mitochondria to cut off two or three carbon moieties from the phytyl chain in each cycle. These steps are shown in Figure $5$. Various carboxychromanol intermediates have been identified for all of the tocopherols together with forms in which the hydroxyl group is sulfated in human cell cultures in vitro; sulfated carboxychromanols are the main tocopherol metabolites in the plasma of rodents. As the vitamin E ω-hydroxylase has a high affinity for the tocopherols other than the α-form and does not attack that bound to the α-tocopherol transfer protein, this provides a further specific enhancement of the α‑tocopherol concentration in plasma relative to the others. Some of these catabolic metabolites may have some biological activity in their own right. For example, carboxyethylhydroxychromans derived from γ-tocopherol were reported to induce apoptosis in cancer cells and to have anti-inflammatory effects by inhibition of cyclooxygenases and 5-lipoxygenase (see below). Tocotrienols are catabolized in a similar manner, but with additional steps in which the double bonds are reduced prior to oxidation; the final carboxyethylchromanols are the same as for tocopherols. Tocopherols as Antioxidants Although the syndrome associated with a lack of vitamin E in the diet of animals has been known for decades, the mode of action and specific location of tocopherols in cell membranes are not clearly understood. Several theories have been proposed to explain the functions of vitamin E in animal cells. From studies in vitro, it has long been believed that a major task is to act as an antioxidant to inhibit, decrease, delay, or prevent oxidative damage to unsaturated lipids or other membrane constituents and thence to tissues by scavenging free radicals. For example, vitamin E administration can prevent lipid peroxidation and hepatotoxicity upon exposure to the free radical-generating agent carbon tetrachloride. Lipid peroxidation is also a cause of ferroptosis, an iron-dependent form of nonapoptotic cell death. However, tocopherols have functions other than as antioxidants. In non-biological systems such as foods, cosmetics, pharmaceutical preparations, etc., tocopherols are invaluable as antioxidant additives. Because of their lipophilic character, tocopherols are located in the membranes or with storage lipids where they may be available immediately to interact with lipid hydroperoxides, such as those described in more detail in our web pages on isoprostanes, reactive aldehydes, and oxidized phospholipids. In brief, Reactive Oxygen Species (ROS), of which innumerable forms, exist can be derived by enzymatic or non-enzymatic means and produce superoxide anions and other peroxyl radicals. Superoxide radicals (O2•-) ultimately generate highly toxic hydroxyl (OH) or alkoxyl radicals, which can abstract a hydrogen atom from bis-allylic methylene groups of polyunsaturated fatty acids under aerobic conditions in vivo in animals and plants to generate lipid peroxyl radicals (LOO) and hydroperoxy-fatty acids. Singlet oxygen (1O2 or O=O) is an especially important ROS (non-radical) in photosynthetic tissues of plants. As radical generation is not enzymatic, all methylene groups between two cis double bonds can potentially be involved in the reaction, although not necessarily to the same degree. Tocopherols react rapidly in a non-enzymic manner unlike many other cellular antioxidants, which are dependent on enzymes, to scavenge lipid peroxyl radicals, i.e., the chain-carrying species that propagate lipid peroxidation. In model systems in vitro, all the tocopherols (α > γ > β > δ) and tocotrienols are good antioxidants, with the tocotrienols being the most potent. In general, the oxidation of lipids is known to proceed by a chain process mediated by such free radicals, in which the lipid peroxyl radical serves as a chain carrier. In the initial step of chain propagation, a hydrogen atom is abstracted from the target lipid by the peroxyl radical as shown in Figure $6$. The main function of α-tocopherol is to scavenge the lipid peroxyl radical before it is able to react with the lipid substrate as shown in Figure $7$. The potency of an antioxidant is determined by the relative rates of reactions (1) and (2). When a tocopheroxyl radical is formed, it is stabilized by the delocalization of the unpaired electron about the fully substituted chromanol ring system rendering it relatively unreactive, thus preventing propagation of the chain reaction. This also explains the high first-order rate constant for hydrogen transfer from α-tocopherol to peroxyl radicals, as studies of the relative rates of chain propagation to chain inhibition by α-tocopherol in model systems have demonstrated that α-tocopherol is able to scavenge peroxyl radicals much more rapidly than the peroxyl radical can react with a lipid substrate. In biological systems, oxidant radicals can spring from a number of sources, including singlet oxygen, alkoxyl radicals, superoxide, peroxynitrite, nitrogen dioxide, and ozone. α-Tocopherol is most efficient at providing protection against peroxyl radicals in a membrane environment. The reaction of the tocopheroxyl radical with a lipid peroxyl radical, as illustrated, yields 8α-substituted tocopherones, which are readily hydrolyzed to 8α-hydroxy tocopherones that rearrange spontaneously to form α-tocopherol quinones. In an alternative pathway, the tocopheroxyl radical reacts with the lipid peroxyl radical to form epoxy-8α-hydroperoxytocopherones, which hydrolyze and rearrange to epoxyquinones. Tocopherol dimers and trimers may also be formed as minor products. These reactions are shown in Figure $8$. Free radical-mediated lipid peroxidation is the major pathway of lipid oxidation taking place in humans, and α-tocopherol is a major antioxidant, but it does not scavenge the nitrogen dioxide radical, carbonate anion radical, and hypochlorite efficiently. Vitamin E forms with an unsubstituted 5-position, such as γ-tocopherol, are an exception to the rule that the various tocopherols have similar antioxidant properties in that they are able to trap electrophiles, including Reactive Nitrogen Species (RNS), which are enhanced during inflammation. The enzyme nitric oxide synthase is capable of continuously producing a large amount of nitric oxide (NO), which can react with superoxide to produce peroxynitrite (ONOO-), a potent and versatile oxidant that can attack a wide range of biological targets. It induces lipid peroxidation and nitrates aromatic compounds and unsaturated fatty acids while isomerizing cis-double bonds in fatty acids to the trans-configuration. γ-Tocopherol is superior to α‑tocopherol in detoxifying the NO2 radical and peroxynitrite with formation of 5-nitro-γ-tocopherol, as shown in Figure $9$. Figure $\PageIndex{xx}$: This occurs in vivo, and the concentrations of 5-nitro-γ-tocopherol have been shown to be elevated in the plasma of subjects with coronary heart disease and in carotid-artery atherosclerotic plaque. In plant and animal tissues, tocopherols can be regenerated from the tocopheroxyl radicals in a redox cycle mediated by a number of endogenous antioxidants, including vitamins A and C and coenzyme Q, and this must greatly extend their biological potency. Vitamin C (ascorbic acid) may be especially important in aqueous systems, although it may also act at the surface of membranes, to regenerate α-tocopherol, while in turn being oxidized to dehydroascorbic acid. This can be regenerated to the reduced form by glutathione (GSH) with the production of glutathione disulfide (GSSG), which can subsequently be enzymatically reduced by glutathione reductase with NAD(P)H as a cofactor. In plants, an NAD(P)H-dependent quinone oxidoreductase is involved at an early stage of the regeneration process, while tocopherol cyclase, an enzyme involved in the biosynthesis of tocopherols, re-introduces the chromanol ring. These linked cycles (the antioxidant network) are shown in Figure $10$. Thus, tocopherols are only one component of a complex web of metabolites and enzymes in tissues that have antioxidant activities and act by various mechanisms, including the stimulation of genes involved in signaling responses to environmental stresses. One antioxidant mechanism involves the removal of free radicals and reactive species by enzymes such as superoxide dismutase, catalase, and glutathione peroxidase, while electron donors, such as glutathione, tocopherols, ascorbic acid, vitamin K, coenzyme Q, and thioredoxin, scavenge free radicals also. Metal-binding proteins such as transferrin, metallothionein, haptoglobin, and ceruloplasmin have antioxidant activity by sequestering pro-oxidant metal ions, such as iron and copper, although some metals such as selenium and zinc are in fact antioxidants. Other antioxidants, including flavonoids, carotenoids, and phenolic acids in addition to tocopherols, enter animal tissues via the food chain. Although the discussion here has been limited to the effects upon lipids, free radicals can cause damage to proteins, DNA, and indeed virtually any native substance in living organisms. Biological Functions of Tocochromanols in Animals Vitamin E deficiency has been detected in patients with fat malabsorption, cystic fibrosis, Crohn's disease, liver disease, and pancreatic insufficiency, and in premature infants. Impairment of the normal functions of the immune system has been demonstrated in animals and humans in vitamin E deficiency, and this can be corrected by vitamin E repletion. It also displays activity against nonalcoholic hepatosteatosis. Although there are various proposals for the pathogenic mechanism, none as yet appears to be generally accepted. After the discovery of the effects of vitamin E on fertility in studies with laboratory animals, its importance was documented for the development of tissues and organs such as brain and nerves, muscle and bones, skin, bone marrow, and blood, most of which are specific to α-tocopherol. However, there is no evidence for an effect of vitamin E on fertility in humans, as was originally found in the rat. The rare genetic disorder “Ataxia with Isolated Vitamin E Deficiency” or “AVED” is the result of mutations in the gene coding for α-TTP. It is caused by the death of cerebellar Purkinje cells, but administration of α-tocopherol prevents this and the subsequent development of clinical symptoms of the disease. There appears to be little doubt that tocopherols inhibit many of the enzymes associated with inflammation in vitro in animals, and may contribute to the amelioration and treatment of some chronic diseases. However, it has been argued that data on the effects of vitamin E on biomarkers of oxidative stress in vivo are inconsistent. Oxidized metabolites of vitamin E, i.e., that have reacted as antioxidants, are barely detectable in tissues, and vitamin E maintenance in vivo does not appear to have been clearly associated with its regeneration. There appear to be significant differences between results obtained in studies with laboratory animals in comparison to those in humans. Thus, suggestions that dietary supplements of vitamin E may reduce the rate of oxidation of lipids in low-density lipoproteins in humans and thence the incidence or severity of atherosclerosis have not been confirmed by clinical intervention studies, although benefits in some conditions have been claimed. Indeed, there are suggestions that excessive vitamin E supplementation may even be harmful. One study has suggested that relatively high doses of natural α-tocopherol over a long period are required to demonstrate a significant reduction in the levels in the urine of F2 isoprostanes, which are considered to be the most reliable marker for oxidative stress in vivo. While there are many fat-soluble antioxidants in the diet, only α-tocopherol is a vitamin. It has even been suggested that tocopherol may be protected from functioning as an antioxidant in some tissues in vivo through a network of cellular antioxidant defenses, such that tocopherols are utilized only when other antioxidants are exhausted, although there is no experimental proof of this hypothesis. At the cellular level, RRR-α-tocopherol has been shown to inhibit protein kinase C, and in the process, it inhibits the assembly and radical-producing activity of NADPH oxidase in monocytes. Similarly, vitamin E suppresses the expression of xanthine oxidase, a source of reactive oxygen species, in the liver. It is thus possible that α-tocopherol is able to diminish the levels of free radicals by preventing their production and not by scavenging them. Its physical presence in membranes adjacent to polyunsaturated fatty acids may thus limit autoxidation. With the discovery that the antioxidant effects of various tocopherols and tocotrienols have little relation to their vitamin E activities in vivo has come the realization that they have other functions in tissues, most of which are specific to α-tocopherol. Most current research is concerned with how vitamin E and its metabolites act in signaling and controversially in the regulation of gene activity. While it is certainly true that most other vitamins are essential cofactors for specific enzymes or transcription factors, no receptor that binds specifically to vitamin E has yet been discovered. By preventing the increase of peroxidized lipids that alter both metabolic pathways and gene expression profiles within tissues and cells, it may act indirectly as a regulator of genes connected with tocopherol catabolism, lipid uptake, collagen synthesis, cellular adhesion, inflammation, the immune response and cell signaling. Vitamin E affects a number of transcription factors in this manner, including peroxisome proliferator-activated receptor gamma (PPARγ), nuclear factor erythroid-derived 2 (NRF2), nuclear factor kappa B (NFκB), RAR-related orphan receptor alpha (RORα), estrogen receptor beta (ERβ), and the pregnane X receptor (PXR). α-Tocopherol and its metabolites are believed to modulate the activity of several enzymes involved in signal transduction, including protein kinases and phosphatases, lipid kinases and phosphatases, and other enzymes involved in lipid metabolism, but especially those with inflammatory properties such as lipoxygenases, cyclooxygenase-2, and phospholipase A2. While the credentials of tocopherols as antioxidants in vivo have been doubted, this does not preclude a role in the inhibition of oxidative enzymes, especially in relation to the function of the immune system. For example, vitamin E regulates T cell function directly by its effects upon T cell membrane integrity, signal transduction, and cell division, and it also functions indirectly by affecting eicosanoids and related inflammatory mediators generated from other immune cells. Various tocopherols and tocotrienols have been shown to suppress COX-2 involvement in prostaglandin (PGD2 and PGE2) synthesis in lipopolysaccharide-activated macrophages. In addition, it has been established that the 13'-carboxy metabolite of α-tocopherol (α-T-13'-COOH) and other tocopherol ω-carboxylates are potent allosteric inhibitors of 5-lipoxygenase, a key enzyme in the biosynthesis of the inflammatory leukotrienes. α-T-13'-COOH accumulates in immune cells and inflamed exudates both in vitro and in vivo in mice, and it has even been suggested that the immune regulatory and anti-inflammatory functions of α-tocopherol depend on this endogenous metabolite. The structure of α-T-13'-COOH is shown in Figure $11$. α-Tocopherol has a stimulatory effect on the dephosphorylation enzyme, protein phosphatase 2A, which cleaves phosphate groups from protein kinase C, leading to its deactivation. The mechanism may involve the binding of vitamin E directly to enzymes in order to compete with their substrates, or it may change their activities by redox regulation. It may also compete for common binding sites within lipid transport proteins, and so may alter the traffic of lipid mediators indirectly with effects upon their signaling functions and enzymatic metabolism. For example, it binds to albumin as well as to a specific α-tocopherol-associated protein (TAP), and in the latter form especially it inhibits the phosphoinositide 3-kinase. It has been suggested that vitamin E may have a secondary role in stabilizing the structure of membranes, or it may interact with enzymes in membranes to interfere with binding to specific membrane lipids, or it may affect membrane microdomains such as lipid rafts. Evidence suggests that the biological activities of β-, γ- and δ-tocopherols do not reflect their behavior as chemical antioxidants, but anti-inflammatory, antineoplastic, and natriuretic actions have been reported. Some non-antioxidant effects of γ-tocopherol in tissues in relation to reactive nitrogen oxide species have been observed, but the specificity of these in vivo is not yet certain. In addition, anti-inflammatory properties have been described that have been attributed to a chain-shortened metabolite. Beneficial effects against cancer cells in vitro have been observed that have been ascribed to scavenging of reactive nitrogen species, since such effects are not seen with α-tocopherol. On the other hand, vitamin E and its derivatives are believed to regulate tumor cells by activating the mitogen-activated protein kinase (MAPK) signaling pathway. Tocotrienols have been shown to have neuroprotective effects and to inhibit cholesterol synthesis. They reduce the growth of breast cancer cells in vitro, possibly by influencing gene expression by interaction with the estrogen receptor-β. When administered in combination with either standard antitumor agents as in chemotherapy or with natural compounds with anticancer activity, they are reported to exert a synergistic antitumor effect on cancer cells. γ-Tocotrienol is reported to be an inducer of apoptosis via endoplasmic reticulum stress, while α-tocotrienol may be neuroprotective by inhibition of lipoxygenase activity. Although anti-obesity and anti-diabetic effects have been observed in mice, clinical trials with humans appear to have given inconclusive results. These properties are largely distinct from those of the tocopherols, and the pharmaceutical potential of tocotrienols against cancer, bone resorption, diabetes, and skin, cardiovascular and neurological diseases are currently being studied. The biological functions of α-tocopheryl phosphate are slowly being revealed. In addition to being a possible storage or a transport (water-soluble) form of tocopherol, it is involved in cellular signaling and regulates a number of genes, including those involved in angiogenesis and vasculogenesis, in a different manner from α‑tocopherol per se. As it lacks the free hydroxyl group, it cannot act directly as an antioxidant, and some consider it to be the biologically active form of the vitamin. It is certainly more active in a number of biological systems in vitro than α-tocopherol, so these effects cannot be ascribed to the hydrolyzed molecule, and in some instances, it is antagonistic to α-tocopherol, for example in its activity towards phosphatidylinositol 3-kinase. On the other hand, activation requires a kinase, while a phosphatase is needed to make the system reversible, but neither has yet been identified. Synthetic phosphate derivatives of γ-tocopherol and α-tocopheryl succinate are known to have potent anti-cancer properties. Isoprenoids: 2. Retinoids (Vitamin A) That a dietary factor was involved in visual acuity was known to the ancient Egyptians and Greeks, but it was the 1930s before the importance of the carotenoids and their metabolites was recognized, and β-carotene and retinol were fully characterized. It is now recognized that vitamin A activity now resides in the metabolites retinol, retinal and retinoic acid, and in several provitamin A carotenoids, most notably β-carotene. A share in the Nobel Prize for Medicine in 1967 was awarded to George Wald, who over many years showed how retinol derivatives (named for their function in the retina) constituted the chemical basis of vision. Now, it is recognized that retinol, retinoic acid, and their many metabolites have innumerable other functions in human metabolism from embryogenesis to adulthood, including growth and development, reproduction, cancer, and resistance to infection. They are important natural antioxidants with benefits to health, although some potentially harmful properties have been reported. Carotenoids are a class of highly unsaturated terpenoids that occur in innumerable molecular forms (>1000). They are common colorful pigments of plants, fungi, and bacteria, of vital importance to photosynthesis, and as dietary constituents, they can add ornament to some animal species. Apart from acting as precursors of retinoids, carotenoids per se appear to have a relatively limited range of functions in animal tissues, but they are important to vision and as antioxidants, especially in the skin. They do of course have important functions in plants and lower organisms where they originate, but this topic can only be dealt with briefly here. Other fat-soluble vitamins tocopherols (vitamin E), vitamin K, and vitamin D are discussed elsewhere. Occurrence and Basic Metabolism of Carotenoids and Retinoids The term ‘vitamin A’ is used to denote retinol (or all-trans-retinol, sometimes termed 'vitamin A1'), together with a family of biologically active C20 retinoids derived from this ('vitamers'). The structure of retinol is shown in Figure $12$. These are only found in animal tissues, where they are essential to innumerable biochemical processes. However, they cannot be synthesized de novo in animals and their biosynthetic precursors are plant carotenoids with a β-ionone ring (provitamin A), C40 tetraterpenes of which β‑carotene is most the efficient; it is an orange-red pigment that occurs in the photosynthetic tissues of plants and in seed oils. In the human diet in the developed world, plant sources tend to be less important than those from dairy products, meat, fish oils, and margarines, which provide vitamin A per se, although carrots and spinach are good sources of the provitamin. In the U.K., for example, all vegetable spreads must be supplemented with the same level of vitamin A (synthetic retinol or β-carotene) as is found in butter. While most research effort has been focused on retinoids, there is increasing interest in the biological activities of intact carotenoids in animal tissues. The biosynthesis of carotenoids in plants via isopentenyl diphosphate and dimethylallyl diphosphate has much in common with that of the plant sterols, but this is too specialized a topic to be treated at length here. They have many important functions in plants, for example during photosynthesis or as precursors of plant hormones, and these are discussed below. Some crop plants with increased carotene levels are available with the aim of preventing vitamin A deficiency in the populations of developing countries, and further efforts are underway. Non-photosynthetic bacteria produce a different range of carotenoids, some with chain lengths other than C40 (C30 to C50). In animals (including humans), dietary carotenoids such as β-carotene are solubilized with other dietary lipids in mixed micelles with the aid of bile acids, and they are absorbed in the intestines in intact form by a process facilitated by specific receptor proteins. Dietary retinol and retinol esters are absorbed similarly in the intestines, but the latter are first hydrolyzed by pancreatic lipase. Conversion to retinoids leading ultimately to retinol esters occurs in the enterocytes, where dietary β-carotene is subjected to oxidative cleavage at its center, the first step of which is catalyzed by a cytosolic enzyme β-carotene-15,15'-oxygenase‑1 (BCO1), which is specific for carotenes with a β-ionone ring, to yield two molecules of all-trans-retinal, which is reversibly reduced by a retinol reductase to retinol. Xanthophyll carotenoids are absorbed without cleavage mainly. These reactions are shown in Figure $13$. Based on the incorporation of 18O into the products. it appears that the enzyme that introduces the oxygen atom, β-carotene-15,15'-oxygenase‑1 (BCO1), into the cleavage products is a dioxygenase as both atoms of oxygen in dioxygen are incorporated into products. This is in contrast to a possible mechanism in which only one oxygen atom from dioxygen is added, with the other coming from H2O if the enzyme acted as a monooxygenase. Figure $14$ shows how oxygen could be introduced in the reaction catalyzed by BCO1 through both possible mechanisms. The dioxygenase mechanism on the right best accounts for the incorporation of 18O isotope of oxygen in dioxygen. Figure $15$ shows an interactive iCn3D model of the apocarotenoid cleavage oxygenase from Synechocystis, a Retinal-Forming Carotenoid Oxygenase (2BIW) Figure $15$: Retinal-Forming Carotenoid Oxygenase from Synechocystis (2BIW). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov...dttF958wFPAUP7 The enzyme has a bound carotene analog, (3R)-3-hydroxy-8'-apocarotenol. It contains an active site Fe2+ ion at the end of a hydrophobic tunnel. The ion is ligated by 4 histidine side chains. On binding, three of the trans C=C bonds convert to a distorted cis-trans-cis conformation. The middle trans bond is proximal to the Fe2+ which is ligated by dioxygen, the source of the incorporated oxygen atoms on cleavage. Any unchanged β-carotene and newly formed retinol esters in the enterocytes are incorporated into chylomicrons and released into the lymphatic system and thence into the bloodstream, where some is taken up by peripheral tissues before most is absorbed by the liver. Some intact carotene and other carotenoids are transferred to lipoproteins (LDL and HDL) for transport in plasma, with assistance from specific binding and transport proteins, and for example, carotene can be absorbed at the placental barrier and transferred to the fetus for conversion to retinoids that are essential for development. Within the hepatocytes, retinol esters are hydrolyzed in the late endosomes with the release of free retinol into the cytosol, from which it can be released back into the circulation, converted to retinoids or transferred to hepatic stellate cells for storage in lipid droplets, the main body reservoir of vitamin A. In these specialized cells, retinol is esterified to form retinyl palmitate by the transfer of fatty acids from position sn-1 of phosphatidylcholine, mainly via the action of a membrane-bound lecithin:retinol acyltransferase (LRAT) in the endoplasmic reticulum. There are also lesser acyl-CoA dependent pathways, including an acyl CoA:retinol acyltransferase and even the enzyme diacylglycerol acyltransferase 1 (DGAT1); esterification is facilitated by binding to cellular retinol-binding protein type II (CRBP2). Both retinol and retinoic acid are precursors of a number of metabolites (retinoids), which are required for specific purposes in tissues, by enzymatic modification of the functional groups and geometrical isomerization of the polyene chains. In the liver, activation of the retinol pathway involves first mobilization of the ester, followed by hydrolysis by retinol ester hydrolases, which includes carboxylesterase ES-10. Then, the reversible oxidation of retinol to retinal is carried out by one of several enzymes that include dehydrogenases and various cytochrome P450s, before some retinal is oxidized irreversibly to retinoic acid by enzymes with retinal dehydrogenase activity. On-demand conversion of retinol to retinoic acid occurs by the same mechanisms in other tissues, although for vision, retinol esters serve directly as the substrate for the formation of the visual chromophore 11‑cis-retinal (see below). Retinyl-β-D-glucoside, retinyl-β-D-glucuronide, and retinoyl-β-D-glucuronide are naturally occurring and biologically active metabolites of vitamin A, which are found in fish and mammals. Indeed, the last has similar activity to all-trans-retinoic acid without any of the unwanted side effects in some circumstances. Cleavage of β-carotene at double bonds other than that in the center or of a wider range of other carotenoids occurs by the action of a related enzyme β‑carotene-9',10'-dioxygenase (β‑carotene-oxygenase‑2 or BCO2) in mitochondria, which leads to the formation of similar molecules, i.e. β-apocarotenals and β‑apocarotenones of variable chain-length. While these may exert distinctive biological activities in their own right, there is evidence that they can also be metabolized to form retinal. In the aqueous environment within cells, as well as in plasma, retinol, retinal and retinoic acid are bound to retinoid-binding proteins (RBP), which solubilize, protect and in effect detoxify them. These proteins also have a role in facilitating retinoid transport and metabolism; some are present only in certain tissues, and many are specific for particular retinoids and metabolic pathways. To prevent infiltration through the kidneys, retinol, and holo-RBP form an association in blood with a protein termed transthyretin (TTR), which also serves as a thyroid hormone carrier and is essential for secretion. Normally, vitamin A circulates in plasma as a retinol:RBP:TTR complex with a 1:1:1 molar ratio. Unesterified retinol is the main form of the vitamin that is exported from the liver upon demand, and it is transported in the blood in this bound form in VLDL, LDL, and HDL, with some directly from the diet in the chylomicrons and their remnants. Peripheral tissues have specific receptors to take up what they require, probably after hydrolysis of any esters to retinol by means of the enzyme lipoprotein lipase. Then, retinol dissociates from the protein as it forms a complex with a receptor (STRA6) at a target cell and diffuses through the plasma membrane, a process driven by retinol esterification. The RBP-TTR complex does not bind to retinal and retinoic acid, although these do bind to RBP on its own, and most of the low levels of retinoic acid transported in the blood are bound to albumin. Local levels of retinoic acid are the result of an interplay between enzymes of synthesis, binding, and catabolism. For example, within cells retinoic acid binding proteins (CRABP1 and CRABP2) bind to the newly synthesized retinoic acid, increase its rate of metabolism and protect cells from an excess. In skin, 3,4-dehydroretinoids are synthesized from all-trans retinoids by the desaturase Cytochrome P450 27C1 with the assistance of cellular retinol-binding proteins (CRBPs). 3,4-Dehydroretinol, which is sometimes termed vitamin A2, is shown in Figure $16$. Its derivative 3,4‑dehydroretinal is used as a visual chromophore in many cold-blooded vertebrates including lampreys, fish, amphibians, and some reptiles (see below). Geranylgeranoic acid has structural similarities to retinoic acid and has been termed an acyclic retinoid, although it has no vitamin A activity. It is synthesized in animal tissues from mevalonate, and together with its 2,3‑dihydro metabolite, has potent anticancer properties. Retinol esters: A relatively small proportion of the cellular retinoids is located in membranes in tissues. Rather, retinol esters, mainly retinyl palmitate, are the main storage form of vitamin A, and they occur in many different organs, including adipose tissue and testes, but chiefly in stellate cells of the liver and pancreas. How the retinol is directed specifically to these cells and enters them prior to esterification is not known. Although hepatic stellate cells are much smaller and less abundant than hepatocytes (only 5 to 8% of all liver cells), they are characterized by cytoplasmic lipid droplets that contain 90-95% of the hepatic retinoids (and up to 80% of the body pool) in addition to other non-retinoid lipids; the lecithin:retinol acyltransferase is the only retinol ester synthase in this instance. In addition, specialized cells in the eye store retinoids essential for vision in the form of lipid droplets. When the supply of retinol in the diet is limited, hepatic stores of retinol esters are mobilized as retinol ester hydrolases are activated to maintain constant circulating retinol levels; hormone-sensitive lipase is the most important of these enzymes, although the adipose tissue triacylglycerol lipase and the lysosomal acid lipase are also involved. Catabolism: All-trans-retinoic acid formation is irreversible, so its synthesis and degradation must be tightly regulated. As a first step in catabolism, the excess is cleared by conversion to more polar metabolites through oxidation by various enzymes of the cytochrome P450 family. Secondly, the water-soluble retinoic acid metabolites, including 4-hydroxy-, 4-oxo- and 18-hydroxy-retinoic acids, conjugate with glucuronic acid and then can be rapidly removed from circulation and eliminated from the body via the kidney. Retinoids and Vision The structure of 11-cis-retinal, which we discussed in Chapter 11, is shown in Figure $17$ Retinoids are essential for vision, and there is now a good appreciation of how this works at the molecular level. In the eye, uptake of retinol from the circulation is mediated by the transmembrane cell-surface STRA6 receptor of the retinal pigment epithelium, a pigmented monolayer of cells located between the photoreceptors and choroid that nourishes retinal visual cells and catalyzes the release of retinol from retinol-binding proteins and transports it to the cytosol. The process by which light is converted to a signal recognized by the brain, sometimes termed the 'retinoid (visual) cycle', requires a two-cell system beginning in the retinal pigment epithelium and continuing in photoreceptor cells, i.e. retinal rod and cone cells in the eye that contain membranous vesicles that serve as light receptors. Roughly half of the proteins in these vesicles consist of the protein conjugate, rhodopsin, which consists of a protein – opsin – with the retinoid 11-cis-retinal. Each step in the visual process requires specific binding or transport proteins, and especially the interphotoreceptor retinoid-binding protein (IRBP). All-trans-retinol is first converted to its ester by the enzyme lecithin:retinol acyltransferase as described above in the RPE, and the products coalesce into lipid droplets, i.e. dynamic organelles termed 'retinosomes'. The next step involves a dual-purpose enzyme (RPE65) in the endoplasmic reticulum, which cleaves the O-alkyl bond (not a conventional hydrolysis reaction) in the retinol ester and at the same time causes a change in the geometry of the double bond in position 11 of retinol from trans to cis. The 11-cis-retinol is then oxidized to 11-cis-retinal by 11-cis-retinal dehydrogenase (RDH5). The full cycle is shown in Figure \(18\ below. The final part of the cycle occurs in the photoreceptor, where first the 11-cis-retinal is reacted with opsin to produce the protein conjugate rhodopsin in a protonated form. When rhodopsin is activated by light, the cis-double bond in the retinoid component is isomerized non-enzymatically by the energy of a photon to the 11‑trans form with a change of conformation that in turn affects the permeability of the membrane and influences calcium transport. This results in further molecular changes that culminate in the release of opsin and all-trans-retinal, which is the trigger that sets off the nerve impulse so that the light is perceived by the brain. A second mechanism for 11-cis-retinal formation that may function to ensure continuous visual responsiveness in bright light involves the (RPE)-retinal G protein-coupled receptor (RGR), which can function as a retinaldehyde photoisomerase. As the enzyme RPE65 functions optimally under low light conditions, it is believed that RGR prevents the saturation of photoreceptors under high light levels, and in this way facilitates vision in daylight. The isomerase, RPE65, and the photoisomerase, RGR, operate together to provide a sustained supply of the visual chromophore under different levels of illumination. The all-trans-retinal is removed from the photoreceptor either by reduction to all-trans-retinol by all-trans-retinol dehydrogenase 8 expressed in the outer segments of photoreceptors or after transport by means of a specific transporter (ABCA4), which provides phosphatidylethanolamine (PE) for conversion to the Schiff-base adduct, i.e. N-retinylidene-phosphatidylethanolamine, as shown in Figure $19$. It flips from the lumen to the cytosolic leaflet of the disc membrane. This process prevents non-specific aldehyde activity with the effect of removing potentially toxic retinoid compounds from the photoreceptors. The adduct is a transient sink that dissociates so the retinal can be reduced back to all-trans-retinol by the cytoplasmic retinol dehydrogenase. All-trans-retinol exits the photoreceptor and enters the retinal pigment epithelium with the aid of binding to the retinoid-binding protein (IRBP) where it is converted back to a retinyl ester to complete the cycle and restore light sensitivity. As a side-reaction, some troublesome bis-retinoid adducts of PE (and further byproducts) may be produced by non-enzymatic mechanisms, and these can accumulate with age to affect vision. Lower organisms: Bacteriorhodopsin is the best studied of a family of opsins, found in archaea, eubacteria, fungi, and algae. It is a protein with seven transmembrane helices that acts as an opto-electrical transducer or light-gated active ion pump to capture photon energy via its covalently bound chromophore, all-trans-retinal, converting it to 13-cis-retinal, and moves protons against their electrochemical gradient from the cytoplasm to the extracellular space. In Archaea, it is known as the "purple membrane" and can occupy a high proportion of the surface area of the organism. Other Functions of Retinoids in Health and Disease In addition to their function in vision, it is now realized that retinoids have essential roles in growth and development, reproduction and resistance to infection. They are particularly important for the function of epithelial cells in the digestive tract, lungs, nervous system, immune system, skin, and bone at all stages of life. They are required for the regeneration of damaged tissues, including the heart, and they appear to have some potential as chemo-preventive agents for cancer and for the treatment of skin diseases such as acne. Under pathological conditions, stellate cells lose their retinoid content and transform into fibroblast-like cells, contributing to the fibrogenic response. Cirrhosis of the liver is accompanied by a massive loss of retinoids, but it is not clear whether this is a cause or a symptom, and there appears to be confusion as to when supplementation may be helpful in this and other diseases of the liver. Like retinol and retinoic acid, the metabolite 9-cis-retinoic acid also has valuable pharmaceutical properties. With such a large number of double bonds in conjugation, it is not surprising that carotenoids in general, and retinoids in particular are efficient quenchers of singlet oxygen and scavengers of other reactive oxygen species. However, any direct antioxidant properties are not believed to be important in terms of general health in vivo, and it is not clear how relevant the physical properties of retinoids are to specific biochemical processes in comparison to their effects on signaling and gene transcription. There is a caveat that retinoids may stimulate some antioxidant genes and so have an indirect antioxidant function. In fact, nutritional studies with dietary supplements of carotenoids have sometimes suggested pro-oxidant activity. One explanation for detrimental effects may be that regeneration of the parent carotenoid or retinoid from the corresponding radical cation may be limited when concentrations of reductants such as ascorbic acid are low. Many of the retinol metabolites function as ligands to activate specific transcription factors for particular receptors in the nucleus of the cell, and thus they control the expression of a large number of genes (>500), including those essential to the maintenance of normal cell proliferation and differentiation, embryogenesis, for a healthy immune system, and for male and female reproduction. In the innate immune system, vitamin A is required for the differentiation of cells such as macrophages, neutrophils and natural killer cells, while all-trans-retinoic acid is involved in differentiating the precursors of dendritic cells. Retinoic acid and its 9-cis-isomer are especially important in this context, and they are often considered the most important retinoids in terms of function other than in the eye. This structure of the cis-isomer is shown in Figure $20$. In essence, retinoic acid moves to the nucleus with the aid of small intracellular lipid-binding proteins (CRABP2 and FABP5), which channel it to specific nuclear receptors, the retinoic acid receptors (RAR) of which there are three, RAR-α, β and γ. These are ligand-dependent regulators of transcription and they function in vivo as heterodimers with retinoid X receptors (RXR) to process the retinoic acid signal by acting through polymorphic retinoic acid response elements (RAREs) within the promoter regions of responsive genes. Similarly, 9-cis-retinoic acid and 9-cis-13,14-dihydroretinoic acid are high-affinity ligands for RXR in mice. Together with retinoic acid, these are also ligands for the farnesoid X receptor (FXR), which forms a heterodimer with RXR. The latter receptor complex is involved primarily in bile acid homeostasis, and conversely, there are suggestions that bile acids may have regulatory effects on vitamin A homeostasis. In addition, other nuclear receptors, such as the peroxisome proliferator-activated receptor PPARγ forms a heterodimer with the retinoid X receptor and is activated by retinoic acid to recruit cofactors. This complex in turn binds to the peroxisome proliferator response element (PPRE) gene promoter, leading to regulation mainly of those genes involved in lipid and glucose metabolism, including some involved in inflammation and cancer. To add to the complexity, retinoic acid has extra-nuclear, non-transcriptional effects, such as the activation of protein kinases and other signaling pathways. It has also become evident that many of the functions of retinoids are mediated via the action of specific binding proteins (as discussed briefly above), which control their metabolism in vivo by reducing the effective or free retinoid concentrations, by protecting them from unwanted chemical attack, and by presenting them to enzyme systems in an appropriate conformation. With some tissues, retinol-bound RBP in the blood is recognized by the membrane protein STRA6, which transports retinol into cells where it binds to an intracellular retinol acceptor, cellular retinol-binding protein 1 (CRBP1), and is then able to activate a signaling cascade that targets specific genes. In addition, a specific retinol-binding protein secreted by adipose tissue (RPB4) is involved in the development of insulin resistance and type 2 diabetes, possibly by affecting glucose utilization by muscle tissue, with obvious application to controlling obesity. In the eye, the activity of retinoic acid during development is controlled by binding to apolipoprotein A1. All-trans-retinoic acid has been shown to be effective against many different types of human cancers, especially in model systems but also in some clinical trials, because of its specific effects on cell proliferation, differentiation, and apoptosis (where its relatively low toxicity at normal tissue levels is a virtue). For example, it induces complete remission in most cases of acute promyelocytic leukemia when administered in combination with other chemotherapy techniques. Similarly, 13-cis-retinoic acid has been used successfully in the treatment of children with high-risk neuroblastoma to reduce the risk of recurrence and increase long-term survival rates. However, the efficacy of similar treatments against other types of acute myeloid leukemia and solid tumors appears to be poor. It is hoped that current efforts to obtain a better understanding of the mechanism of the anti-cancer activities will lead to improved treatments. Synthetic analogs of retinoic acid, termed rexinoids, which activate retinoic X receptors, also hold promise as anti-cancer agents. Vitamin A deficiency in children and adult patients is usually accompanied by impairment of the immune system, leading to a greater susceptibility to infection and an increased mortality rate, often with growth retardation and congenital malformations. However, vitamin A deficiency in malnourished children is the major reason for childhood mortality in the underdeveloped world, causing over 650,000 early childhood deaths annually and pediatric blindness. This is doubly tragic in that it is so easily prevented. In adults, vitamin A deprivation affects the reproductive system, inhibiting spermatogenesis in males and ovulation in females. Unfortunately, it is not always easy to distinguish between the effects of vitamin A deficiency and primary defects of retinoid signaling. Functions of Xanthophylls and Other Carotenoids in Humans Xanthophylls are plant C40 tetraterpenes that differ from the carotenoids in having oxygen atoms in the ring structures (hydroxyl, oxo, or epoxyl). Lutein, zeaxanthin, and meso-zeaxanthin from dietary sources, such as green leafy vegetables and yellow and orange fruits and vegetables, are found specifically in the macula of the eye in humans and other primates, i.e. the functional center of the retina in a small central pit known as the macula lutea, where they enhance visual acuity and protect the eye from high-intensity, short-wavelength visible light. They are powerful antioxidants in a region vulnerable to light-induced oxidative stress. Binding proteins specific for lutein- and zeaxanthin mediate the highly selective uptake of these carotenoids into the retina, but meso-zeaxanthin is mainly a metabolite of dietary lutein. Macular xanthophylls decrease the risk of age-related macular degeneration. In the brain, they may stimulate and maintain cognitive function in the elderly, and assist with brain development in infants. Hydroxylated xanthophylls such as lutein, shown in Figure $21$, occur both in the free form and esterified to fatty acids; the latter are hydrolyzed in the intestines when consumed by animals. Many other carotenoids are absorbed from the diet, and are subject to oxidative cleavage or other catabolic processes, partly in the intestines and partly in other tissues after transport in the lipoproteins. Some carotenoids remain intact and are believed to act as antioxidants, and some may have specific anti-inflammatory actions. For example, carotenoids accumulate in the skin of mammals, where they may have an antioxidant and photo-protective role as well as effects on the moisture content, texture, and elasticity. Lycopene may have protective effects against atherogenesis, coronary heart disease, and prostate cancer. Functions of Carotenoids in Plants As carotenoids have a polyene chain of 9 to 11 double bonds in conjugation, they are able to absorb light in the gap of chlorophyll absorption, and so function as additional light-harvesting pigments in plants. Their distinctive arrangement of electronic levels gives them the capacity to transfer excitation energy from the carotenoid excited state to chlorophyll in the light-harvesting complex (photosystem II). Energy can also be transferred back from chlorophyll to carotenoids as a photoprotection mechanism. During photosynthesis, damaging species are produced by both light and oxygen with reactive oxygen species (ROS) of special concern. The energy is transferred from chlorophyll to the polyene tail of the carotenoid where electrons are moved between the carotenoid bonds until the most balanced or lowest energy state (state) is reached. While there is therefore appreciable potential for carotenoids to act as antioxidants in plants, it is uncertain how important this is from a practical functional standpoint. The length of the polyene tail of carotenoids determines which wavelengths of light will be absorbed by the plant, and those not absorbed are reflected and so determine coloration. F Carotenoids are precursors for two plant hormones and a diverse set of apocarotenoids. For example, abscisic acid, shown in Figure $21$, is a C15 isoprenoid plant hormone, which is synthesized in plastids from the C40 carotenoid zeaxanthin. A series of enzyme-catalyzed epoxidations and isomerizations is involved followed by cleavage of the intermediate product by a dioxygenation reaction and further oxidations to yield eventually abscisic acid. Functioning via signaling cascades, abscisic acid regulates innumerable biological effects in plants, especially in relation to developmental processes that include plant growth, seed and bud dormancy, embryo maturation and germination, cell division and elongation, floral growth and the control of stomatal closure. It is critical for the responses to environmental stresses that include drought, cold and heat stress, salinity, and tolerance of heavy metal ions. Similarly, strigolactones are C15 oxidation products of carotenoids that are involved in the regulation of symbiosis between plants and arbuscular mycorrhizal fungi and in interactions with plant parasites. Isoprenoids: 3. Other Membrane-Associated Isoprenoids Terpenes (isoprenoids) are one of the most varied and abundant natural products produced by animals, plants, and bacteria. They are generally defined on the basis of their biosynthetic derivation from isoprene units (C5H8), with 55,000 different types characterized to date according to a recent estimate. By most definitions, all isoprenoids should be classified as ‘lipids’, from simple monoterpenes such as geraniol, which is derived from two prenol units, to complex polymers such as natural rubber. Only those isoprenoids that have a functional role in cellular membranes will be discussed here. These include plastoquinone, ubiquinone (coenzyme Q), phylloquinone and menaquinone (vitamin K), dolichol and polyprenols, undecaprenyl phosphate and lipid II, and farnesyl pyrophosphate, together with some key biosynthetic precursors. The nature and function of tocopherols and tocotrienols (vitamin E) and retinoids (vitamin A) are relevant here, but have been discussed previously. Of course, sterols are also isoprenoids. There are two basic mechanisms for the biosynthesis of the isoprene units that are the precursors for the biosynthesis of isoprenoids, i.e., isopentenyl pyrophosphate and dimethylallyl pyrophosphate. These are the mevalonate pathway, which is located in the cytosol of the cell, and the non-mevalonate pathway, found mainly in the plastids of plants. These have been discussed previously. Phytol Phytol or (2E,7R,11R)-3,7,11,15-tetramethyl-2-hexadecen-1-ol, i.e., with 20 carbons in a 16-carbon chain and one double bond, is an acyclic diterpene alcohol, which is synthesized in large amounts in plants as an essential component of chlorophyll, the most important photosynthetic pigment in plants and algae. Geranylgeranyl-diphosphate synthesized in chloroplasts via the 4-methylerythritol-5-phosphate (non-mevalonate) pathway is the primary precursor of phytol following reduction of three double bonds by geranylgeranyl reductase, and this can occur before or after attachment to chlorophyll, depending upon species. Chlorophyll dephytylase (CLD1) is the enzyme in plants responsible for chlorophyll hydrolysis and the release of phytol. The reactions are shown in Figure $22$. Little free phytol is present in plant tissues, although some phytol esters of fatty acids may occur, especially when plants are stressed during nitrogen deprivation or in senescence, when chlorophyll is degraded, fatty acids are released from glycerolipids and a phytol ester synthase is induced as part of a detoxification and recycling process. Bell peppers and rocket salad are especially rich sources under normal conditions. In addition, phytol as its diphosphate is utilized for the synthesis of tocopherols (vitamin E) and phylloquinol (vitamin K - see below), and the precursor geranylgeraniol and its fatty acid ester occur in small amounts in some plant species. It is the biosynthetic precursor of tocotrienols and the highly unsaturated carotenoids (and hence of retinoids). Phytenal has been isolated as an intermediate in the catabolism of phytol in plants, but further steps are uncertain although phytanoyl-CoA has been detected in stressed plants. As phytenal is highly reactive and potentially toxic via its interaction with proteins, its accumulation must be kept at a low level by competing pathways. In ruminant animals, chlorophyll is hydrolyzed by rumen microorganisms with the release of free phytol. This does not occur in humans, but some phytol may be ingested with plant foods either in free form or as phytol esters and can be absorbed from the intestines. Within animal tissues, phytol is oxidized to phytanic acid. Phytol and/or its metabolites have been reported to activate the transcription factors PPARα and retinoid X receptor. In mice, oral phytol induces a substantial proliferation of peroxisomes in many organs. Plastoquinone A molecule that is related to the tocopherols, plastoquinone, is found in cyanobacteria and plant chloroplasts, and it is produced in plants by analogous biosynthetic pathways to those of tocopherols in the inner chloroplast envelope with solanesol diphosphate as the biosynthetic precursor of the side chain; there appears to be a somewhat different mechanism in cyanobacteria. The molecule is sometimes designated - 'plastoquinone-n' (or PQ-n), where 'n' is the number of isoprene units, which can vary from 6 to 9. It's structure is shown in Figure $23$. Plastoquinone has a key role in photosynthesis, by providing an electronic connection between photosystems I and II, generating an electrochemical proton gradient across the thylakoid membrane. This provides energy for the synthesis of adenosine triphosphate (ATP). The reduced dihydroplastoquinone (plastoquinol) that results in the transfers further electrons to the photosynthesis enzymes before being re-oxidized by a specific cytochrome complex; the redox state of the plastoquinone pool regulates the expression of many of the genes encoding photosystem proteins. X-Ray crystallography studies of photosystem II from cyanobacteria show two molecules of plastoquinone forming two membrane-spanning branches. In addition, plastoquinone has antioxidant activity comparable to that of the tocopherols, protecting especially against excess light energy and photooxidative damage. Similarly, in thylakoid membranes, plastoquinol is able to scavenge superoxide with the production of H2O2. Plastoquinone is a cofactor participating in desaturation of phytoene in carotenoid biosynthesis, and the biosynthetic precursor of plastochromanols. With these many different functions, plastoquinone connects photosynthesis in plants with metabolism, light acclimation, and stress tolerance. Plastoquinone-9, together with phylloquinone, tocopherol, and plastochromanol-8, is stored in plastoglobuli, lipoprotein-like micro-compartments, which enable exchange with the thylakoid membrane and are also involved in chlorophyll catabolism and recycling. It has been suggested that the redox state of the plastoquinone pool is the main redox sensor in chloroplasts that initiates many physiological responses to changes in the environment and in particular to those related to light intensity by regulating the expression of chloroplast genes. Ubiquinone (Coenzyme Q) The ubiquinones, which are also known as coenzyme Q (CoQ) or mitoquinones, have obvious biosynthetic and functional relationships to plastoquinone and they are found in all the domains of life (hence the name). They have a 2,3‑dimethoxy-5-methylbenzoquinone nucleus and a side chain of six to ten isoprenoid units; the human form illustrated below has ten such units (coenzyme Q10), i.e., it is 2,3-dimethoxy-5-methyl-6-decaprenyl-1,4-benzoquinone, while that of the rat has nine, Escherichia coli has eight and Saccharomyces cerevisiae has six. In plants, ubiquinones tend to have nine or ten isoprenoid units. Forms with a second chromanol ring, resembling the structures of tocopherols, are also produced (ubichromanols), but not in animal tissues. They are generated on an industrial scale for pharmaceutical purposes by yeast fermentation. Because of their hydrophobic properties, ubiquinones are located entirely in membrane bilayers in most eukaryote organelles, probably at the mid-plane. Ubiquinones are synthesized de novo in mitochondria in most cells in animal, plant, and bacterial tissues by a complex sequence of reactions from the essential amino acid phenylalanine and then tyrosine to generate p-hydroxybenzoic acid, which is the key precursor that is condensed with the polyprenyl unit (from the cholesterol synthesis pathway) via a specific transferase; this is followed by decarboxylation, hydroxylation, and methylation steps, depending on the specific organism, although some of the required enzymes have yet to be fully characterized. In Escherichia coli, biosynthesis does not occur in a membrane environment as had been thought. Rather, the seven proteins that catalyze the last six reactions of the biosynthetic pathway, following the attachment of the isoprenoid tail, form a stable complex or metabolon in the cytoplasm so enabling modification of the hydrophobic substrates in a hydrophilic environment. In mitochondria, coenzyme Q is present both as the oxidized (ubiquinone) and reduced (ubiquinol) forms as shown in Figure $24$. Figure $24$: Ubiquinone-ubiquinol interconversion Ubiquinones are essential components of the respiratory electron transport system, possibly as part of supramolecular complexes, taking part in the oxidation of succinate or NADH via the cytochrome system to generate the protonmotive force used by the mitochondrial ATPase to synthesize ATP. In this process, coenzyme Q transfers electrons from the various primary donors, including complex I, complex II, and the oxidation of fatty acids and branched-chain amino acids, to the oxidase system (complex III), while simultaneously transferring protons to the outside of the mitochondrial membrane with the result of a proton gradient across the membrane. As a consequence, it is reduced to ubiquinol. Thus, it is an essential component of the cycle that generates the proton motive force driving ATP production via oxidative phosphorylation. In yeast, one coenzyme Q binding protein (COQ10), and in humans two related proteins (COQ10A and COQ10B) may serve as chaperones or transporters during this process. Mitochondrial coenzyme Q is also implicated in the production of reactive oxygen species by a mechanism involving the formation of superoxide from ubisemiquinone radicals, and in this way is responsible for causing some of the oxidative damage behind many degenerative diseases. In this action, it is a pro-oxidant. It is most abundant in organs with a high metabolic rate such as the heart, kidneys, and liver. In complete contrast in its reduced form (ubiquinol) in non-mitochondrial cellular membranes and plasma lipoproteins, it acts as an endogenous antioxidant, the only lipid-soluble antioxidant to be synthesized endogenously. It inhibits lipid peroxidation in biological membranes and serum low-density lipoproteins, and it may also protect membrane proteins and DNA against oxidative damage. The ferroptosis suppressor protein 1 (FSP1) replenishes ubiquinol, and this acts protectively by combating the lipid peroxidation that drives ferroptosis. The mechanism involves the recruitment of FSP1 to the plasma membrane following myristoylation, where this functions as an oxidoreductase that reduces ubiquinone to ubiquinol, which acts as a lipophilic radical-trapping antioxidant that halts the propagation of lipid peroxides. In this manner, it regulates cellular redox status and cytosolic oxidative stress, and thereby is a controlling factor in apoptosis. Other NAD(P)H dehydrogenases with CoQ reductase activity include cytochrome b5 reductase and NQo1 (NAD(P)H:quinone oxidoreductase). Although ubiquinone has only about one-tenth of the antioxidant activity of vitamin E (α-tocopherol), it is able to stimulate the effects of the latter by regenerating it from its oxidized form back to its active fully reduced state (similarly with vitamin C). However, ubiquinones and tocopherols appear to exhibit both cooperative and competitive effects under different conditions. Similarly, in bacteria and other prokaryotes, ubiquinones participate a large number of redox reactions, notably in the respiratory electron transport system but also in other enzyme reactions that require electron donation, including the formation of disulfide bonds. Coenzyme Q has many other functions that are not related directly to its antioxidant function. Some coenzyme Q is used in mitochondria by enzymes that link the mitochondrial respiratory chain to other metabolic pathways, including fatty acid β-oxidation as an electron acceptor, nucleotide biosynthesis de novo, amino acid oxidation (glycine, proline, glyoxylate, and arginine), and detoxification of sulfide. Via these activities, coenzyme Q may modulate metabolic pathways located outside the mitochondria indirectly. There are also suggestions that coenzyme Q may be involved in redox control of cell signaling and gene expression, and in particular to repress the expression of inflammatory genes. In relation to its anti-inflammatory properties, clinical studies suggest that supplementation with coenzyme Q10 reduces the levels of the inflammatory mediators C-reactive protein, interleukin-6, and tumor necrosis factor alpha (TNFα) to a significant degree. In addition, it is a regulator of mitochondrial permeability, and in relation to pyrimidine nucleotide biosynthesis, it is required for DNA replication and repair. Dietary ubiquinone, i.e., that in food or dietary supplements, is absorbed by enterocytes via a process of “passive facilitated diffusion”, probably requiring a carrier molecule, before incorporation into chylomicrons for transport to the liver. This eventually leads to elevated levels of ubiquinol in blood, especially in the LDL and VLDL lipoproteins, presumably because of the reduction of the oxidized form in the lymphatic system. In consequence, there is reported to be enhanced protection against lipid peroxidation with beneficial effects on health, especially in relation to cardiac function, sperm motility, and neurodegenerative diseases. For example, CoQ levels in both plasma and the heart correlate with heart failure in patients, and clinical trials of dietary supplementation have shown promising results. This may be of particular importance in the elderly or in patients on statins, when endogenous synthesis declines. A CoQ10 deficiency syndrome is associated with inherited pathological diseases, defined by a decrease of the CoQ10 content in muscle and/or cultured skin fibroblasts. Early clinical trials with CoQ10 and a synthetic analog, idebenone, against various neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Huntington's disease, and others, are encouraging. Phylloquinone and Menaquinones (Vitamin K) Phylloquinone or 2-methyl-3-phytyl-1,4-naphthoquinone is synthesized in the inner chloroplast envelope of cyanobacteria, algae, and higher plants by a mechanism analogous to that of the tocopherols, i.e., from chorismate in the shikimate pathway with a prenyl side chain derived from phytyldiphosphate. In this membrane, it is a key component of the photosystem I complex where it receives an electron from the chlorophyll a acceptor molecule and then donates an electron to the membrane-associated iron-sulfur protein acceptor cluster in the complex. In an obvious parallel to the plastoquinones (above), two molecules of phylloquinone form two membrane-spanning branches, as demonstrated by X-ray crystallography studies of photosystem I from cyanobacteria. Plastoglobules associated with the thylakoid membrane are believed to function as a reservoir for excess phylloquinone, and may also function in its metabolism. Edward A. Doisy and Henrik Dam received the Nobel Prize in Physiology or Medicine in 1943 for their discovery of vitamin K and its chemical structure. The structure of vitamin K is shown in Figure $25$. The menaquinones are related bacterial products, which function in the respiratory and photosynthetic electron transport chains of bacteria. They have a variable number (4 to 10) of isoprenoid units in the tail, and they are sometimes designated ‘MK-4’ to ‘MK-10’. In contrast to phylloquinone, these are usually highly unsaturated. In some species, there are methyl or other groups attached to the naphthoquinone moiety. Remarkably high concentrations of menaquinones are present in membranes of some extremophiles such as the haloarchaea, where it has been suggested that they act as ion permeability barriers and as a powerful shield against oxidative stress in addition to their functions as electron and proton transporters. Phylloquinone is an essential component of the diet of animals and has been termed 'vitamin K1'. It must be supplied by green plant tissues, where it occurs in the range 400-700 μg/100 g, or seed oils. The menaquinones, the main source of which in the human diet is cheese and yogurt, also have vitamin K activity and are termed 'vitamin K2'. They account for about 10-25% of the vitamin K content of the Western diet. A synthetic saturated form of this, which is used in animal feeds, is known as 'vitamin K3 or menadione', though strictly speaking it is not a vitamin but a pro-vitamin in that it can be converted to the menaquinone MK-4 in animal tissues by addition of a phytyl unit; it is too toxic for human nutrition. Vitamin K forms are absorbed from the intestines and transported in plasma in the form of lipoproteins in a similar manner to the other fat-soluble vitamins. Different tissues have differ storage capacities and presumably requirements for the various forms of vitamin K. As a high proportion is excreted, there appears to be a requirement for a constant intake. Vitamin K1 is taken up rapidly by the liver, but vitamin K2 remains in the plasma for much longer and maybe the main source of the vitamin in peripheral tissues. In addition, it has been established that some dietary phylloquinone is converted to menaquinone-4 in animals, although the quantitative significance has still to be established; the mechanism involves conversion to menadione in the intestines followed by transport to tissues where a geranylgeranyl side-chain is attached by a specific prenyl transferase. The primary role of vitamin K in animal tissues is to act as a cofactor specific to the vitamin K-dependent enzyme γ-glutamyl carboxylase in the endoplasmic reticulum in the liver mainly. Its function is the post-translational carboxylation of glutamate residues to form γ-carboxyglutamic acid in proteins, such as prothrombin. In this way, prothrombin and three related proteins are activated to promote blood clotting. The γ-carboxyglutamic acid residues are located at the binding site for Ca2+, and are vital for the activity of the enzyme. Phylloquinone must first be converted to the reduced form, phylloquinol, which is the actual cofactor for the enzyme; molecular oxygen and carbon dioxide are both required also. Phylloquinol donates hydrogen to the glutamic acid residue and is oxidized in the process to 2,3-epoxyphylloquinone. A further enzyme, vitamin K epoxide reductase, regenerates phylloquinone by reduction of the epoxide in a dithiol-dependent reaction so that this can be re-utilized many times ("the vitamin K cycle", shown below in Figure $26$). Menaquinones undergo the same cycle of reaction. By interfering with the last step in the metabolic cycle, warfarin, the rodenticide, prevents blood clotting. In the same way, a deficiency in vitamin K results in the inhibition of blood clotting and can lead to brain hemorrhaging in malnourished newborn infants, though this is not seen in adult humans, presumably because intestinal bacteria produce sufficient for our needs. Vitamin K-dependent proteins are also known to have important functions in the central and peripheral nervous systems, and vitamin K influences sphingolipid biosynthesis in the brain. Unsaturated isoprene units rather than phytol are used for the biosynthesis of menaquinones, and they differ from phylloquinone with respect to their chemical structure and pharmacokinetics. It is now apparent that vitamin K2 (MK-4 especially) has a number of different actions, some with specificities for particular tissues. For example, osteocalcin is a γ-carboxyglutamic acid-containing protein, which forms a strong complex with the mineral hydroxyapatite (calcium phosphate) of bone; it must be carboxylated to function properly and vitamin K2 appears to be of particular importance in this instance. Vitamin K2 also regulates bone remodeling by osteoclasts to remove old or damaged bone and its replacement by new bone. In addition, vitamin K2 is involved in vascular calcification, cell growth, and apoptosis. Side effects of the use of anticoagulants that bind to vitamin K can be osteoporosis and increased risk of vascular calcification. Although careful control of the dosage is necessary, vitamin K2 may be a useful adjunct for the treatment of osteoporosis, and it may reduce morbidity and mortality in cardiovascular health by reducing vascular calcification. Excess vitamin K1 and the menaquinones are catabolized in the liver by a common degradative pathway in which the isoprenoid side chain is shortened to yield carboxylic acid aglycones such as menadiol, which can be excreted in bile and urine as glucuronides or sulfates. Dolichols and Polyprenols Polyisoprenoid alcohols, such as dolichols, are ubiquitous if minor components, relative to the glycerolipids, of membranes of most living organisms from bacteria to mammals. They are hydrophobic linear polymers, consisting of up to twenty isoprene residues or a hundred carbon atoms (or many more in plants especially), linked head-to-tail, with a hydroxy group at one end (α-residue) and a hydrogen atom at the other (ω-end). In dolichols (or dihydropolyprenols), the double bond in the α-residue is hydrogenated, and this distinguishes them from the polyprenols with a double bond in the α-residue. Their structures are shown in Figure $27$. Polyisoprenoid alcohols are further differentiated by the geometrical configuration of the double bonds into three subgroups, i.e., di-trans-poly-cis, tri-trans-poly-cis, and all-trans. For many years, it was assumed that polyprenols were only present in bacteria and plants, especially photosynthetic tissues, while dolichols were found in mammals or yeasts, but it is now known that dolichols can also occur at low levels in bacteria and plants, while polyprenols have been detected in animal cells. Solanesol is a related and distinctive plant product with trans double bonds only and is a precursor of plastoquinone. Within a given species, components of one chain length may predominate, but other homologs are usually present. The chain length of the main polyisoprenoid alcohols varies from 11 isoprene units in eubacteria, to 16 or 17 in Drosophila, 15 and 16 in yeasts, 19 in hamsters, and 20 in pigs and humans. In plants, the range is from 8 to 22 units, but some species of plant have an additional class of polyprenols with up to 40 units. In tissues, polyisoprenoid alcohols can be present in the free form, esterified with acetate or fatty acids, phosphorylated or monoglycosylated phosphorylated (various forms), depending on species and tissue. Polyisoprenoid alcohols per se do not form bilayers in aqueous solution, but rather a type of lamellar structure. However, they are found in most membranes, especially the plasma membrane of liver cells and the chloroplasts of plants. Dolichoic acids, i.e., related molecules with a terminal carboxyl group and containing 14–20 isoprene units, have been isolated from the substantia nigra of the human brain. However, they were barely detectable in the pig brain. Biosynthesis of the basic building block of dolichols, e.g. isopentenyl diphosphate, follows either the mevalonate pathway or a more recently described methylerythritol phosphate pathway discussed in relation to cholesterol biosynthesis previously. Farnesyl pyrophosphate is the primary precursor in the biosynthesis of polyprenols and is the branch point in sterol/isoprene biosynthesis, depending on the nature of the organism (see a further note below). Subsequent formation of the linear prenyl chain is accomplished by cis-prenyl transferases that catalyze the condensation of isopentenyl pyrophosphate and farnesyl pyrophosphate and then the growing the allylic prenyl diphosphate chain. The end products are polyprenyl pyrophosphates, which are dephosphorylated first to polyprenol phosphate and thence to the free alcohol. Finally, a specific reductase has been identified from human tissues that catalyzes the reduction of the double bond in position 2 to produce dolichols. There is a family of cis-prenyl transferases, present in both eukaryotes and bacteria, that in addition to the synthesis of dolichols can catalyze the formation of isoprenoid carbon skeletons from neryl pyrophosphate (C10) to natural rubber (C>10,000), including the polyisoprenoid phosphates involved in protein glycosylation as discussed in the next section. The reactions in the synthesis of dolichol are shown in Figure $28$. Although polyprenols and dolichols were first considered to be simply secondary metabolites, they are now known to have important biological functions. Glycosylation of asparagine residues is the main protein modification in all three domains of life, and phosphorylated polyisoprenoids, including dolichols, are essential to this process (next section). There is also a suggestion that free dolichol may have a beneficial antioxidant function in cell membranes. Polyisoprenoid Phosphates and Glycosylation of Proteins Glycosylated phosphopolyisoprenoid alcohols are the carriers of oligosaccharide units for transfer to proteins and as glycosyl donors, i.e., they are substrates for glycosyl transferases for the biosynthesis of glycans in a similar manner to the cytosolic sugar nucleotides. They differ from the latter in their intracellular location, with the lipid portion in the membrane of the endoplasmic reticulum and the oligosaccharide portion specifically located either on the cytosolic or lumenal face of the membrane. The degree of unsaturation and chain length of the product is important for recognition by the enzymes in the next stage of the pathway. Dolichol phosphates: In eukaryotes, N-glycosylation begins on the cytoplasmic side of the endoplasmic reticulum with the transfer of carbohydrate moieties from nucleotide-activated sugar donors, such as uridine diphosphate N-acetylglucosamine, onto dolichol phosphate. Then, N-acetylglucosamine phosphate is added to give dolichol-pyrophosphate linked to N-acetylglucosamine, to which a further N-acetylglucosamine unit is added followed by five mannose units, the last catalyzed by dolichol phosphate mannose synthase, which is also essential for GPI-anchor biosynthesis. The dolichol-PP glycan is shown in Figure $29$. The resulting dolichol-pyrophosphate heptasaccharide is then flipped across the endoplasmic reticulum membrane to the luminal face with the aid of a “flippase”. Four further mannose and three glucose residues are added to the oligosaccharide chain by means of glycosyltransferases, which utilize as donors dolichol-phospho-mannose and dolichol-phospho-glucose, which are also synthesized on the cytosolic face of the membrane and flipped across to the luminal face. In humans, the final lipid product is a C95-dolichol pyrophosphate-linked tetradecasaccharide, the oligosaccharide unit of which is transferred from the dolichol carrier onto specific asparagine residues on a developing polypeptide in the membrane. The carrier dolichol-pyrophosphate is dephosphorylated to dolichol-phosphate then diffuses or is flipped back across the endoplasmic reticulum to the cytoplasmic face. The Archaea use dolichol in their synthesis of lipid-linked oligosaccharide donors with both dolichol phosphate (Euryarchaeota) and pyrophosphate (Crenarchaeota) as carriers; these can have variable numbers of isoprene units many of which can be saturated. In the haloarchaeon Haloferax volcanii, for example, a series of C55 and C60 dolichol phosphates with saturated isoprene subunits at the α- and ω-positions is involved in the glycosylation reaction of target proteins, while similar lipid carriers of oligosaccharide units appear to be present in methanogens. Archaea of course use isoprenyl ethers linked to glycerol as major membrane lipid components in addition to unusual carotenoids such as the C50 bacterioruberins. In many of these species, isoprenoid biosynthesis is via the 'classical' mevalonate pathway, but in other species, some aspects of this pathway differ. Undecaprenylphosphate: Most other bacteria use undecaprenyl-diphosphate-oligosaccharide as a glycosylation agent in a similar way for the biosynthesis of peptidoglycan, the main component of most bacterial cell walls and a structure unique to bacteria, of many other cell-wall polysaccharides, including lipopolysaccharides, O-antigenic polysaccharides and capsular polysaccharides, and of N-linked protein glycosylation in both in Gram-negative and Gram-positive bacteria. Undecaprenyl phosphate (a C55 isoprenoid), also referred to as bactoprenol, is the essential lipid intermediate. Its structure is shown in Figure $30$. It differs from the dolichol phosphates mainly in that the terminal unit is unsaturated, and is synthesized by the addition of eight units of isopentenyl pyrophosphate to farnesyl pyrophosphate, a reaction catalyzed by undecaprenyl pyrophosphate synthase in the cytoplasm, followed by the removal of a phosphate group. Undecaprenyl phosphate is required for the synthesis and transport of glycans for external polymer formation. Thus, glycans are covalently transferred to the carrier lipid by membrane-embedded or membrane-associated enzymes using nucleotide-activated precursors. For example, the carrier lipid with GlcNAc-MurNAc-peptide monomers, i.e., as lipid II, is hydrophilic and is then transported across the cytoplasmic membrane to external sites for peptidoglycan formation. Similarly, it is required for the synthesis of lipoteichoic acids and lipopolysaccharide O-antigens. Lipid II: Undecaprenyl diphosphate-MurNAc-pentapeptide-GlcNAc, often simply termed lipid II, is the last significant lipid intermediate in the construction of the peptidoglycan cell wall in bacteria (Lipid I is the biosynthetic precursor lacking the N-acetylgluosamine residue). Its structure is shown below in Figure $31$. Figure $31$: Lipid II This molecule must be translocated from the cytosolic to the exterior membrane of the organism, and three different protein classes have been identified that can accomplish this of which ATP-binding cassette (ABC) transporters are best characterized. Once across the membrane, lipid II is cleaved to provide the MurNAc-pentapeptide-GlcNAc monomer, which undergoes polymerization and crosslinking to form the complex peptidoglycan polymer that provides strength and shape to bacteria. The undecaprenyl-pyrophosphate remaining is hydrolyzed to undecaprenyl phosphate by a membrane-integrated member of the type II phosphatidic acid phosphatase family and is recycled back to the interior of the membrane by an as yet unidentified transport mechanism. The turnover rate is very high so the lipid II cycle is considered to be the rate-limiting step in peptidoglycan biosynthesis. There is also some evidence that lipid II has a function on the inner leaflet of the cytoplasmic membrane in organizing the proteins of the cytoskeleton. Because of its highly conserved structure and accessibility on the surface membrane, the proteins involved in the synthesis and transport of lipid II are considered important targets for the development of novel antibiotics. Figure $\PageIndex{xx}$: In a few prokaryotes, the membrane intermediate has a polyprenyl-monophosphate-glycan structure instead of lipid II, and undecaprenyl-phosphate-L-4-amino-4-deoxyarabinose is involved in lipid A modification in Gram-negative bacteria, for example. There are obvious parallels with the involvement of glycosylated phosphopolyisoprenoid alcohols as carriers of oligosaccharide units for transfer to proteins and as glycosyl donors in higher organisms (see above). β‑D‑Mannosyl phosphomycoketide is an isoprenoid phosphoglycolipid found in the cell walls of Mycobacterium tuberculosis, the lipid component of which is a C32-mycoketide, consisting of a saturated oligoisoprenoid chain with five chiral methyl branches. It acts as a potent antigen to activate T-cells upon presentation by CD1c protein. Its structure is shown in Figure $32$. Farnesyl Pyrophosphate and Related Compounds Farnesyl pyrophosphate is a key intermediate in the biosynthesis of sterols such as cholesterol, and it is the donor of the farnesyl group in the biosynthesis of dolichols and polyprenols (see above) as well as for the isoprenylation of many proteins (see the web page on proteolipids). However, it is also known to mediate various biological reactions in its own right via interaction with a specific receptor. It is synthesized by two successive phosphorylation reactions of farnesol. Presqualene diphosphate is unique among the isoprenoid phosphates in that it contains a cyclopropylcarbinyl ring. In addition to being a biosynthetic precursor of squalene, and thence of cholesterol, it is a natural anti-inflammatory agent, which functions by inhibiting the activity of phospholipase D and the generation of superoxide anions in neutrophils. Their structures are shown in Figure $32$.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/02%3A_Unit_II-_Bioenergetics_and_Metabolism/22%3A_Biosynthesis_of_Amino_Acids_Nucleotides_and_Related_Molecules/22.01%3A_Overview_of_Nitrogen_Metabolism.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Introduction Organic chemistry is usually described as the chemistry of carbon-containing molecules. But isn't that definition a bit carbon centric, especially since the prevalence of oxygen-containing molecules is staggering? What about nitrogen? We live in a dinitrogen-rich atmosphere (80%), and all classes of biomolecules (lipids, carbohydrates, nucleic acids, and proteins) contain nitrogen. Dinitrogen is very stable, given its triple bond and nonpolarity. We rely on a few organisms to fix N2 from the atmosphere to form ammonium (NH4+), which through nitrification and denitrification can form nitrite (NO2-), nitrate (NO2-), nitric oxide (NO), and nitrous oxide (N2O), the latter being a potent greenhouse gas. We'll concentrate on the metabolic fate of amino groups in amino acids and proteins in the next section. Before exploring their fates, look at Figure $1$ which shows an overall view of the biological nitrogen cycle. The study of biochemistry should encompass more than homo sapiens and expand to the ecosystem in which we are such a small but damaging part. Figure $1$: Nitrogen Cycle Let's break down the diagram from a biochemical perspective. There are aerobic and anaerobic processes (conducted by bacteria). Nitrogen-containing substances include both inorganic (ammonium, nitrate, nitrite) and organic (amino acids, nucleotides, etc) molecules. The reactions shown are oxidative and reductive (note: the oxidation number of the nitrogen atoms in the molecules is shown in red). Most of the reactions are carried out underground by bacterial and Archaeal microorganisms. Here are some of the major reactions: • N2 fixation (a reduction): N2 from the air is converted by bacteria to ammonium (NH4+) by the enzyme nitrogenase of soil prokaryotes. The energetically disfavored reaction requires lots of ATPs. Ammonium once made can then be taken up by primary producers like plants and incorporated into biomolecules such as amino acids, which animals consume. For those who may still believe that people have marginal effects on our biosphere, consider this. We may soon fix more N2 to NH3 through the industrial Born-Haber reaction (used for fertilizer and explosive productions) that is all made by the biosphere. Much of the nitrogen in use comes from the Born-Haber reaction. The excess NH4+ (upwards of 50%) produced industrially and which enters the soil in fertilizers (mostly as NH4NO3) has overwhelmed nature's ability to balance the nitrogen cycle and is not taken up by plants. It is metabolized by microorganisms to nitrite and nitrate. • Nitrification: Ammonium is converted to nitrite by ammonia-oxidizing aerobic microorganisms and further to nitrate by a separate group of nitrite-oxidizing aerobic bacteria. Here are the reactions (Rx 1 and 2) to produce nitrate through a hydroxylamine intermediate, followed by the formation of nitrate (Rx 3). NH3 + O2 + 2e- → NH2OH + H2O Rx 1 NH2OH + H2O → NO2- + 5H+ + 4e- Rx 2 NO2- + 1/2 O2 → NO3- Rx 3 These added ions exceed soil capacity and end up runoff water, polluting our rivers and lakes. • Denitrification: This anaerobic reaction pathway reproduces N2 from nitrate Here is the net reaction: 2NO3- + 10e- + 12H+ → 2N2 + 6H2O • Anammox reaction: This more recently discovered bacterial anaerobic reaction pathway converts ammonium and nitrate to N2. Here is the net reaction NO2- + NH3+ → N2 + 2H2O • Ammonification (not to be confused with mummification) occurs when plants and animals decompose, which returns ammonium to the soil for reuse by plants and microbes. These reactions are shown in the abbreviated Nitrogen Cycle shown in Figure $2$. Figure $2$: Abbreviated Nitrogen Cycle Nitrogen metabolites are nutrients for plants and perhaps the most important nutrients in the regulation of plant growth (primary productivity) and in regulating life diversity in the biosphere. All living organisms require feedstocks to produce energy and as substrates for biosynthetic reactions. Which is used depends on the organism. Plants are primary producers so they use their synthesized carbohydrates for both energy production and biosynthesis. For carnivores, proteins and their derived amino acids are the source of energy (through oxidation) and serve as biosynthetic precursors. For omnivorous organisms, the source of energy depends on the "fed" state. With abundant food resources, carbohydrates, and lipids are the source of energy. Unlike carbohydrates and lipids, which can be stored as glycogen and triacylglycerols for future use, excess protein, and their associated amino acids can not be stored, so amino acids can be eliminated or used for oxidative energy. In the fed state, carbohydrates are the main source, while in the unfed state, lipids take a predominant role. Under starving conditions, the organism's own proteins are broken down and used for oxidative energy production and for any biosynthesis that remains. In diseased states like diabetes, which can be likened to a starving state in the presence of abundant carbohydrates, both lipids and amino acids become the sources of energy. How are amino acids in animals oxidatively metabolized? Many pathways could be used to do so but it would seem logical that NH4+ would be removed and the carbons in the remaining molecule would eventually enter glycolysis or the TCA cycle in the form of ketoacids. NH4+ is toxic in high concentrations. Ammonium is not oxidized to nitrite or nitrates in humans as occurs in the soil by microorganisms. It can be recycled back into nucleotides or amino acids, and excess amounts are eliminated from the organism. Both processes must be highly controlled. We will turn out attention to the oxidation of amino acids in the next section. Nitrogenase: An Introduction Beauty is in the eye of the beholder. As the domain of biochemistry covers the entire biological world, the extent of coverage of a given topic in textbooks can depend, in part, on the interest and experiences of the author(s) presenting the material. Is relevance a metric that should determine coverage? If so, books focused on human or medical biochemistry would surely omit photosynthesis. If topics are selected based on their importance for life, then photosynthesis must surely be covered. If so, then nitrogenase must also be included. If the degree of chemical difficulty for a chemical reaction and the amazing eloquence of the evolved biochemistry solution is considered, then both photosynthesis and nitrogen fixation must be presented. Even though nitrogen fixation is a reductive reaction, it shares strong similarities with the oxygen-evolving complex of photosynthesis. They catalyze enormously important redox reactions that involve an abundant atmospheric gas using a very complicated and unique inorganic metallic cofactor that evolution has selected as uniquely suited for the job. Every first-year student of chemistry can draw the Lewis structure of dinitrogen, N2, which contains a triple bond and a lone pair on each nitrogen. If Lewis structures speak to them, they should be able to state that the triple bond makes N2 extraordinarily stable, thus explaining why we can breathe an atmosphere containing 80% N2 and not die. If they have taken biology, they are also aware that very few biological organisms can utilize N2 as a substrate, as this requires breaking bonds between the nitrogen atoms, a chemical process reserved for nitrogen “fixing” bacteria found in rhizomes of certain plants. Lastly, they probably memorized that high pressure and temperature are needed in the Haber-Bosch process used to convert N2 and H2 to ammonia, NH3. As with any scientific advance, the Haber-Bosch process has brought both harm (it's used for explosive weapons) and good (fertilizers). This process now fixes enough N2 in the form of fertilizers to support half of the world’s population, with nitrogenase supporting the rest. Efforts are underway to genetically modify plants to make nitrogenase, eliminating the need for fertilizers but perhaps creating unforeseen problems of its own. You might be surprised to find out that at room temperature the equilibrium constant favors ammonia formation, hence ΔG0 < 0. The reaction is favored enthalpically as it is exothermic at room temperature. It is disfavored entropically as should be evident from the balanced equation: $\ce{N2(g) + 3H2(g) → 2 NH3(g)}. \nonumber$ The thermodynamic parameters for the reaction (per mol) are ΔH° = –46.2 kJ, ΔS° = –389 J K–1, and ΔG° = –16.4 kJ at 298 K The entropy is negative since the reaction proceeds from 4 molecules to two molecules. From an enthalpy perspective, if you raise the temperature of an exothermic reaction, you drive it in a reverse direction. If you increase the pressure, you shift the equilibrium to the side that has the fewest number of molecules. If the reaction is favored thermodynamically at room temperature, why doesn’t it proceed? This story sounds familiar as this same descriptor applies to the oxidation of organic molecules with dioxygen. There we showed using MO theory that the reaction is kinetically slow. Same with NH3 formation. A superficial way to see this is that we must break bonds in the stable N2 to start the reaction, leading to a high activation energy, and making the reaction kinetics sluggish. One could jump-start the reaction by raising the temperature, but that would slow an exothermic reaction. The Keq (or KD) and ΔG0 are functions of temperature and for this reaction, the reaction becomes disfavored at higher temperatures. The solution Haber found was high pressure, forcing the reaction to the side that has fewer molecules of gas, and high temperature to overcome the activation energy barrier and make the reaction kinetically feasible. A complex metal catalyst (magnetite - Fe3O4 -with metal oxides like CaO and Al2O3 which prevent reduction of the Fe with H2) provides an absorptive surface to bring reagents together and facilitate bond breaking in H2 and N2. In photosynthesis, the oxygen-evolving complex (OEC) with Mn, Fe, S, and Ca is used to oxidize another very stable and ubiquitous molecule, H2O. Now we explore the amazing mechanisms behind the nitrogenase complex which fixes N2 to form NH3 in a reductive fashion. What might be needed to drive this reaction biologically? You might surmise the list to include: • a source of energy, most likely ATP, to facilitate this complex reaction; • a source of electrons as the N atoms move from an oxidation state of 0 in elemental N to 3- in NH3; this source turns out to be a protein called flavodoxin or ferredoxin. Of course, these electrons also have interesting sources before they were in the electron carriers of these proteins; • some pretty amazing metal centers to accept and donate electrons in a controlled way; these centers are mostly FeS clusters with an additional cluster containing molybdenum (Mo). The clusters are named F, P, and M • a source of hydrogen; you might have guessed correctly that it’s not H2 gas (from where would that come?), but H+ ions which are pretty ubiquitously available. • a net reaction that is different that the Haber-Bausch process (N2 + 3H2 → 2 NH3). Here is the actual reaction catalyzed by nitrogenase: $\ce{N2 + 8e^{-} + 16ATP + 8H^{+} → 2NH3 + H2 + 16ADP + 16P_i}. \nonumber$ Let’s think a bit about the reaction. As electrons are added the attraction between the nitrogen atoms must decrease. Eventually, bonds between them must be broken. Protons could be easily added to maintain charge neutrality. A basic mechanism might involve intermediates as shown in Figure $3$. Figure $3$: Possible intermediates in the conversion of dinitrogen to ammonia by nitrogenase Nitrogenase can also other small molecules with triple bonds, including C=O: and H-C=C-H. The Structure of Nitrogenase Nitrogenase is a multiprotein complex in which the functional biological unit is built from two sets of the following dimeric structures: • a homodimer of subunits, E and F, which have binding sites for the mobile carrier of electrons (the protein ferredoxin or flavodoxin), ATP, and an FeS cofactor (4Fe-4S, called the F cluster) which accept electrons. These subunits are hence called the nitrogenase reductase subunits • a heterodimer of alpha and beta subunits. The a (alpha chain) binds the 8Fe-7S F cluster and the Fe-S-Mo M cluster. These subunits comprise the (di)nitrogenase catalytic subunits. The iron-molybdenum M cluster is in the α subunit and is where N2 reduction occurs. The P-cluster is between the α and β subunits and facilitates electron flow between the Fe-protein (F cluster) and FeMo-cofactor (M cluster) For clarity, one-half of the overall structure of the protein complex with bound ATP and metal centers is shown in Figure $4$. Figure $4$: Nitrogenase structure (4wzb) This half-structure consists of a homodimer of the reductase monomers and a heterodimer of nitrogenase subunits. Figure $5$ shows an interactive iCn3D model of the half structure of nitrogenase complex from Azotobacter vinelandii (4WZB) (long load). Figure $5$: Nitrogenase complex from Azotobacter vinelandii (4WZB). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...zPNjfPRwq8MSeA (long load)) The structure is color-coded in a fashion similar to Figure 4. The F cluster is labeled as SF4, the P cluster as CLF (FE(8)-S(7) cluster), and the M cluster as ICS (iron-sulfur-molybdenum cluster with interstitial carbon). The reductase subunits (called Av2 in Azotobacter vinelandii), accept electrons from ferredoxin and is where the ATP analog, ACP (phosphomethylphosphonic acid adenylate ester), and the 4Fe-4S F cluster is bound. The nitrogenase subunits (called Av1 in Azotobacter vinelandii) convert N2 to NH3 and is where the 8Fe-7S P cluster, FeMo (C Fe7 Mo S9) M Cluster is bound. An enhanced view of the bound cofactors and ATP is shown in the same spatial orientation in Figure $6$below. Figure $6$: Bound cofactor metal clusters and ATP in nitrogenase You can easily image the direction of the flow of electrons from the F cluster to the P cluster to the M cluster. The metal centers are shown in Figure $7$ in more detail in both line and space fill views. Figure $7$: Detailed structures of the metal cofactors in nitrogenase Mo is bound to 3 sulfur ions and oxygen from 3-hydroxy-3-carboxy-adipic acid as shown in Figure 7 above. The M cluster has an interstitial carbide ion that derives from -CH3 attached to the sulfur of S-adenosyl-methionine (SAM) allowing the carbide to be labeled with either 13C or 14C for mechanistic studies. These labeled carbides are not exchanged or used as a substrate when the enzyme undergoes catalytic turnover. Hence it seems that the carbide probably just stabilizes the M cluster. it won't be shown in the figures below showing more detailed mechanisms. We'll focus on two features of the reaction mechanism, ATP hydrolysis and the flow of electrons and protons to N2 as it is reduced to NH3. ATP hydrolysis ATP binds in the reductase subunit (AV2) where ferredoxin brings in electrons, and where the F metal cluster is bound. Much as GTP hydrolysis controls conformational change and subunit dissociation in the heterotrimeric Gαβγ in signal transductions, ATP hydrolysis in reductase (AV2) subunits, drives not only electron transfer but dissociation/reassociation of the reductase and nitrogenase catalytic subunits. It appears that 2 ATPs are hydrolyzed per electron transferred from the F cluster to the Mo M cluster. Since the oxidation numbers of N are 0 and -3 in N2 and NH3, respectively, sequential rounds of ATP hydrolysis and dissociation/reassociation occur. Note that the Fe-protein hydrolyzes ATP only when bound to the MoFe-protein. The overall reaction involves the reduction of N2 to two molecules of NH3 at the FeMo cofactor. It involves a reductive elimination of hydrides that are bridged by Fe ions (Fe-H-Fe) in a reaction that also makes H2 as a by-product. We'll describe this complex reaction next. Nitrogenase Reaction: Part 1 - Addition of Electrons and Protons The sequential path of electrons from the reductase subunit containing the F cluster to the P and M clusters in the nitrogenase subunit should be apparent from the figures above. We will concentrate on the binding of N2 and how it receives electrons from the M cluster. Figure $8$ shows the FeMo-cofactor and some adjacent amino acid residues. Mo is labeled but not shown in spacefill. HCA is a bound molecule of 3-hydroxy-3-carboxy-adipic acid, which interacts with Mo. The carbide is shown in the middle as the green sphere and labeled CX. Figure $8$: Side chains near the M cluster If Val 70 is mutated to Ile, a substrate appears not to access the cluster suggesting that N2 may interact with the top part of the structure with the residues shown acting as gatekeepers. His side chains are often found at enzyme active sites so you might expect His 195 to be a general acid/base. Mutations lead to drastic losses in the reduction of N2. His 195 is involved in hydrogen bonds to sulfur S2B and bridges Fe2 and Fe3 in Figure 8, where reduction of N2 likely occurs. If His 195 moves, it can form short H bonds between the imidazole N and an H bond to HFe2. If the ring is rotated 1800, no proton transfer occurs from the surface. It appears that His195 might be involved in the first N2 protonation event. The Lowe and Thorneley (LT) model has been proposed as a mechanism for dinitrogen reduction. In this model, an electron and proton are added to the oxidized form of the enzyme (Eo) to produce E1. This is repeated 3 more times to form sequentially, E2, E3, and E4. Only then does N2 bind and the reduction of N2 occurs. Two of the added electrons are accepted by H+ ions which form H2, which is liberated on N2 binding. Hence only 6 electrons are added to the actual N2 molecule, in agreement with the change in oxidation numbers discussed above. The Lowe and Throneley model is shown in Figure $9$. Figure $9$: Lowe and Throneley model for electron and proton additions in nitrogenase The crystal structure shows 2 ATP analogs bound to the reductase subunit. The stoichiometry of the reaction shows 16 ATP used. Simple math suggests that 2 ATP are cleaved to support the entry of one electron into the complex, assuming 8 transferred electrons (6 to N2 and 2 to 2 protons to form H2). Part 1 - E1-E4: A potential structure for the E4 intermediate is shown in Figure $10$. Figure $10$: The E4 Janus intermediate in the reduction of N2. Note the central carbide is not shown. This is often called the Janus intermediate as it is halfway through the catalytic cycle. It is named for Janus, the Roman god of beginnings and transitions, and has been ascribed to gates, doors, doorways, and passages. Janus is typically shown with two faces, one looking to the future and one to the past (image below: DOI:10.1590/2177-6709.21.1.018-023.oin. License CC BY 4.0 Creative Commons Attribution 4.0 International) The hydrides bridge 2 Fe ions so these are examples of three-center, two-electron bonds. The H+ ions in Figure 10 balance the charge from the hydrides. How does this reaction occur? We must look to organometallic chemistry to help us understand the mechanism of this and subsequent steps. Hydride equivalents have been added to the metals, associated with the oxidation of metal ions in the center. This particular reaction is called an oxidative addition. Presumably, the sulfur ions act as Lewis bases as they gain protons from a Lewis acid, probably His 195. Oxidative addition reactions Figure $11$ shows oxidative additions to metal centers for three different types of reactants. Figure $11$: Three different types of oxidative addition reactions (after Schaller, http://employees.csbsju.edu/cschaller/ROBI1.htm) In oxidative insertion, the oxidation state of the metal ion increases, hence the name oxidative. The hydrogens are now hydrides. Note the example for insertion of H2, an example similar to the proposed hydride additions to the M cluster. Oxidative reduction occurs most readily when the two oxidation states of the metal ion are stable. It is likewise favored for metal centers that are not sterically hindered (makes sense if A-B is to be added) and if A-B has a low bond dissociation energy. One way to study reaction intermediates is to trap them. If N2 can't access the binding site and the temperature is reduced, the accumulated hydrides (and for charge balance the H+s) in E4 might leave in the opposite reaction, reductive elimination, which we will discuss below, as the reaction goes back to E1. In the elimination, they could form H2 as metal gains back electrons in a reduction. The Val70Ile discussed above would allow an intermediate to be trapped. Nitrogenase Reaction: Part 2 - Reduction of N2 Step E4 to E5 seems a bit bizarre as H2 gas is released. This would seem to waste ATP but we should trust evolution has led to this mechanism for a reason. This mechanism, the reverse of oxidative addition, is another classic organometallic reaction, reductive elimination. Reductive elimination reactions In this reaction, a molecule is eliminated or expelled from the complex as the metal ion is reduced and adds two electrons. Figure $12$ shows reductive elimination. Reductive elimination occurs most readily in higher oxidation state metal centers which can be stabilized on reduction. It occurs most readily from electron-rich ligands and if the other surrounding ligands are bulky. The dissociating species must also be cis to each other in the transition metal complex so they can form a bond with each other when they leave. Figure $12$: Reductive Elimination reaction (after Schaller, ibid) Oxidative addition and reductive elimination (OA/RE) reactions at metal centers are often coupled together in organometallic catalytic cycles, in the same way as a histidine can act as a general acid and then accept a proton back as a general acid to complete the catalytic cycle. In the OA/RE reactions at metal centers, some rearrangements or other modifications can also occur. Think about it. The FeS clusters must return to their original oxidation state after the complete LT cycle. We will encounter another organometallic reaction after the addition of N2, migratory insertion, in the second half of the reaction. Another advantage of coupling OA/RE is that the positive charge or oxidation state on the transition metal complex does not get too high, which is unstable. Making a cation with a positive charge more positive becomes more difficult, much as removing a second proton from a polyprotic acid is more difficult than removing the first (as reflected in the higher pKa for removal of the second proton). Is H2(g) really released? To study this, investigators have used alternative substrates like acetylene, HC=CH (similar to N=N), in the presence of D2 and N2 in an aqueous system. It helps to see Figure $9$ again. Figure $9$: Lowe and Throneley model for electron and proton additions in nitrogenase The acetylene was reduced and formed C2H2D2 and C2H3D. Hence E4 must have had 2 Ds in it, and E2 probably 1. These results support the reversible reductive elimination mechanism for the E4 to E4:N2 reaction above. Previously it had been shown that H+ are reduced by D2 in the presence of D2 and N2 in an aqueous system, so these results are consistent. In additional support, deuterium from D2 is not incorporated into products (C2H2D2, C2H3D, or HD) in the absence of N2. Let's return for a moment to the bridging hydrides as shown again Figure $10$. Figure $10$: The E4 Janus intermediate in the reduction of N2 To summarize, it appears likely that the reductive elimination of the two proximal bridging hydrides is the mechanism for the formation of H2. The bridging hydrides, which are strong bases, are much less likely to be protonated than if the hydrides were terminal. A simple and competing protonation reaction could form H2 as well, and if that occurred the reducing equivalents of the bridging hydrides would be lost. Hence the bridging hydrides (share by two metal centers) are more stable and hence can "wait" for the incoming N2 reactants before their reducing equivalents are lost. They may convert to terminal hydrides eventually to facilitate substrate reduction (hydrogenation). The Janus intermediate E4 is now in a position to bind N2 and release H2. For each H- that binds to the M cluster, two H+s bind to the M cluster sulfides for electrostatic stabilization. Migratory Insertions We need to consider one last common type of reaction at a metal center, migratory insertions (MI). In a MI reaction, a group attached to a metal ion center is transferred to another group attached to the same metal. Figure $13$ shows four examples of MI reactions. Figure $13$: Migratory insertion reactions at metal centers Panel (a) shows a generic MI reaction. Panel (b) shows the interaction of carbon monoxide, :C=O: (isoelectronic and analogous to :N=N:), with a metal center. Panel (c) shows how a hydride could engage in 1:1 insertion as it shifts and covalently bonds to the first atom of another ligand bound to the metal center. Note that metal does not have to have a negative charge. This could theoretically be important for the reduction of N2 bound as a ligand through a coordinate covalent (dative) bond to the metal. Panel (d) shows the migration of an alkyl group. We will see that the MI reaction is involved in adding Hs to N2 starting not with N2 but with the N2H2 stage as the intermediates insert into an Fe-H bond. N2 is not reactive to the insertion of a hydride as is carbon monoxide, CO, which provides a positive oxygen to facilitate electron flow during the insertion. In addition, the oxygen become neutral after the reaction. This offers a great explanation for how Nature chose the FeMo cluster for nitrogen fixation. The interaction of two hydrides requires a 4 Fe face (coordinated with the carbon) that allows for the storage of reducing equivalents for the initial reduction of N2. The large M cluster is more stable and effectively held together by a central carbide anion. This also allows the metal centers to never change their oxidation state by more than 1 charge unit. The source of the protons to form N2H2 comes directly from the two H2 attached to the two sulfurs as they can't come from the hydrides which are released as H2. The seeming wasteful reductive elimination of H2 and energy is required to allow the kinetically unreactive N2 molecule to bind to the reduced and activate 4Fe face which is also electrostatically facilitated by the 2 bound protons in what has been called a push (reductive)-pull (protonation) reaction. This first step in N2 reduction to N2H2 is the hardest. Oxidation States of Nitrogenase Fe centers First Half: It would be difficult to assign specific oxidation states to each Fe ion in the M complex. Instead, we can assign relative changes in the oxidation states as the reaction proceeds from E0 to E4. In each step of the LT model, 1 electron is added. We will first assign this to an average Fe ion, M0, with an arbitrarily assigned oxidation state of 0. On the addition of 1 electron, the oxidation state would go from M0 to M-1 as the metal is reduced. The M-1 state is then oxidized as an electron is transferred to H+, and when 2 electrons are transferred, a single H- is made. The diagram in Figure $14$ shows the change in oxidation state in going from E0 to E4. Figure $14$: Change in oxidation state in going from E0 to E4 The red boxes highlight thermodynamic cycle-like steps which show how changes in the redox state of the Fe ions (M) could be visualized. Note that in going from E0 to E4, the actual oxidation state of M changes from 0 to +1 to 0 to +1 and back to 0. That is quite amazing given that 4 electrons have been added. Note also that in the red box going from step E0 to E1, M goes from -1 to +1 which corresponds to our description of an oxidative addition when the metal center loses two electrons. This mechanism shows that nitrogenase could be considered a "hydride storage device". Second Half (facing forward to production NH3): How does N2 initially interact with E4? It must depend on how the hydrides are released as H2, which evidence shows occurs by reductive elimination (re) and not hydride protonation (hp). On addition, the N2 very quickly is converted to diazene, HN=NH, with the departing H2 taking with it 2 H+s and 2 electrons (or reducing equivalents). These events could occur as shown in Figure $15$. Figure $15$: Reaction mechanism for the formation of N2H2 Now, with N2 bound as diazene (N2H2) and H2 released, the rest of the reaction could occur as shown below. One new step, a migratory insertion, is shown in Figure $16$. Figure $16$: Reaction mechanism for the conversion N2H2 to NH3. The two halves of the reaction are similar with bridging hydrides utilized - the E4 Janus intermediate links the two halves together.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/02%3A_Unit_II-_Bioenergetics_and_Metabolism/22%3A_Biosynthesis_of_Amino_Acids_Nucleotides_and_Related_Molecules/22.02%3A_Biosynthesis_of_Amino_Acids.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Introduction By the time many students get to the study of amino acid biosynthesis, they have seen so many pathways that learning new pathways for the amino acids seems daunting, even though they can be clustered into subpathways. Most know that from a nutrition perspective, amino acids can be divided into nonessential and essential (need external dietary supplementation) amino acids. These are shown for humans below. • Nonessential amino acids: Alanine, Asparagine, Aspartate, Cysteine, Glutamate, Glutamine, Glycine, Proline, Serine, Tyrosine • Essential amino acids: Arginine, Histidine, Isoleucine, Leucine, Lysine, Methionine, Phenylalanine*, Threonine, Tryptophan, Valine Three of the essential amino acids can be made in humans but need significant supplementation. Arginine is depleted in processing through the urea cycle. When cysteine is low, methionine is used to replace it so its levels fall. If tyrosine is low, phenylalanine is used to replace it. The amino acids can be synthesized from glycolytic and citric acid cycle intermediates as shown in Figure $1$ Figure $1$: Summary amino acid synthesis from glycolytic and TCA intermediates For this chapter subsection, we will provide only the basic synthetic pathways in abbreviated form without going into mechanistic or structural details Amino acid synthesis from glycolytic intermediates From Glucose-6-Phosphate: Histidine The synthesis of histidine from a phosphorylated form of ribose (derived from glucose-6-phosphate) is shown in Figure $2$. Figure $2$: Synthesis of histidine from a phosphorylated form of ribose From 3-phosphoglycerate: Serine, Glycine, and Cysteine The synthesis of serine, glycine, and cysteine from 3-phosphoglycerate is shown in Figure $3$. Figure $3$: The synthesis of serine, glycine, and cysteine from 3-phosphoglycerate From Phosphenol Pyruvate: The Aromatics - Trp, Phe, and Tyr The synthesis of the first of the biosynthetic pathways for the aromatic amino acids phenylalanine, tryptophan, and tyrosine from phosphoenolpyruvate up to chorismate is shown in Figure $4$. Figure $4$: Synthesis of the first of the biosynthetic pathways for the aromatic amino acids phenylalanine, tryptophan, and tyrosine from phosphoenolpyruvate up to chorismate Chorismate to tryptophan The synthesis of the second half of the biosynthetic pathway for tryptophan from chorismate is shown in Figure $5$ Figure (5\): Synthesis of the second half of the biosynthetic pathways for the aromatic amino acid tryptophan from chorismate Chorismate to Phe and Tyr The synthesis of the second half of the biosynthetic pathway for phenylalanine and tyrosine from chorismate is shown in Figure $6$ Figure $6$: Synthesis of the second half of the biosynthetic pathway for phenylalanine and tyrosine from chorismate From Pyruvate: Ala, Val, Leu, Ile Ala can easily be synthesized from the alpha-keto acid pyruvate by a transamination reaction, so we will focus our attention on the others, the branched-chain nonpolar amino acids Val, Leu, and Ile. The synthesis of valine, leucine, and isoleucine from pyruvate is shown in Figure $7$. Figure $7$: The synthesis of valine, leucine, and isoleucine from pyruvate TCA Intermediates From α-ketogluatarate: Glu, Gln, Pro, Arg Since amino acid metabolism is so complex, it's important to constantly review past learning. Figure $8$ from section 18.2 shows the relationship among Glu, Gln, and keto acids. Figure $8$: Glutamate and glutamine synthesis from α-ketoglutarate As is evident from the figure, glutamic acid can be made directly through the transamination of α-ketoglutarate by an ammonia donor, while glutamine can be made by the action of glutamine synthase on glutamic acid. Arginine is synthesized in the urea cycle as we have seen before. It can be made from α-ketoglutarate through the following sequential intermediates: N-acetylglutamate, N-acetylglutamate-phosphate, N-acetylglutamate-semialdehyde, N-acetylornithine to N-acetylcitruline. The is deacetylated and enters the urea cycle. The pathway for conversion of α-ketoglutarate to proline is shown in Figure $9$. Figure $9$: Conversion of α-ketoglutarate to proline From oxalacetate: Asp, Asn, Met, Thr, Lys OAA to Aspartatic Acid This is a simple transamination Aspartic Acid to Asparagine This is catalyzed by the enzyme Asparagine Synthase as shown in the reaction equation below: Aspartate + Glutamine + ATP + H2O → Asparagine + Glutamic Acids + AMP + PPi Aspartic Acid to Lysine There are two pathways. • The diaminopimelic acid (DAP) pathway uses aspartate and pyruvate and forms diaminopimelic acid as an intermediate. It's found in bacteria, some fungi, and archaea and in plants. • The aminoadipic acid (AAA) pathway uses α-ketoglutarate and acetyl-CoA and forms aminoadipic acid as an intermediate. It is used by fungi., Here we present just the synthesis of lysine from aspartate and pyruvate using the diaminopimelic acid DAP pathway. The pathway is shown in Figure $10$. Figure $10$: The synthesis of lysine from aspartic acid in the diaminopimelic acid DAP pathway . Aspartic acid to Threonine The conversion of aspartic acid to threonine is shown in Figure $11$. Figure $11$: The conversion of aspartic acid to threonine Aspartic acid to Methionine The conversion of aspartic acid to methionine is shown in Figure $12$. Figure $12$: The conversion of aspartic acid to methionine
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/02%3A_Unit_II-_Bioenergetics_and_Metabolism/22%3A_Biosynthesis_of_Amino_Acids_Nucleotides_and_Related_Molecules/22.03%3A_Molecules_Derived_from_Amino_Acids.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Introduction Once made or ingested, amino acids have many metabolic fates. Of course, they are used for the synthesis of proteins. Aspartate and glutamate (and indirectly glutamine) can be converted to oxaloacetate and α-ketoglutarate, respectively, and used in the citric acid cycle for energy production. They can also be used for gluconeogenesis using mitochondrial and cytoplasmic enzymes. Branched-chain amino acids can be converted to acetyl-CoA and used in energy production or fat synthesis. A review summary of the use of amino acids in energy and biosynthetic metabolic pathways is shown in Figure $1$. Figure $1$: Review summary of the use of amino acid in energy and biosynthetic metabolic pathways. Lieu, E.L., Nguyen, T., Rhyne, S., et al. Amino acids in cancer. Exp Mol Med 52, 15–30 (2020). https://doi.org/10.1038/s12276-020-0375-3. Creative Commons Attribution 4.0 International License, http://creativecommons.org/licenses/by/4.0/. Amino acids are shown in green and other metabolites are in red. Orange represents transporters. Yellow boxes signify enzymes. Lesser known abbreviations for species include SHMT1 serine hydroxymethyltransferase, cytosolic, BCAT branched-chain amino acid transaminase, mitochondrial, BCAA branched-chain amino acid (valine, leucine, isoleucine), BCKA branched-chain ketoacid, GOT1 aspartate transaminase, cytosolic (AST), GLS glutaminase, GS glutamine synthetase (cytosolic and mitochondrial), ASNS asparagine synthetase, PRODH pyrroline-5-carboxylate dehydrogenase, PYCR pyrroline-5-carboxylate reductase, P5C pyrroline-5-carboxylate, GSH glutathione, PRPP phosphoribosyl pyrophosphate, LAT1 large-neutral amino acid transporter 1, SLC25A44 solute carrier family 25 member 44, GLUT glucose transporter, Cancer cells from an increased need for fuels and biosynthetic intermediates. Both can come from amino acids as described previously. Glutamine is a key amino acid, especially if glucose is depleted as α-ketoglutarate (α-KG) and subsequently oxaloacetate (OAA generated from it powers the TCA cycle as fumarate, malate, and citrate are significantly increased. Hence it is both anaplerotic and a source of fuel. Similar increases in citrate occur in hypoxia. Aerobic glycolysis (Warburg effect) occurs in cancer cells, which show enhanced glucose uptake and conversion to lactate even in the presence of oxygen. This process can go so quickly that the amount of ATP produced in cancer cells from aerobic glycolysis can be similar to the from oxidative metabolism in the mitochondria, even though it is far less efficient. More information on cancer cell metabolism is found in Chapter 23. In this chapter, we will discuss the conversion of amino acids to other molecules not directly involved in those metabolic pathways. We will focus on their use for the synthesis of polyamines, heme, and neurotransmitters in this chapter section. We won't discuss detailed mechanisms or structures for the proteins and enzymes involved in these pathways. In the next chapter section (22.4), we will present amino acids as substrates in the synthesis of pyrimidine and purine bases for nucleotides and nucleic acids. Polyamine synthesis If a non-quaternary amine has a single positive charge when protonated, a polyamine can have multiple positive charges. Hence they would be expected to bind to almost any negatively charged biomolecule, but especially those with multiple negative charges. These would include the polyanions RNA and DNA as well as proteins and lipid bilayers. They would then have the potential ability to regulate many features of cell life, including DNA replication and transcription, RNA translation, and a multitude of binding interactions. The question arises if these interactions are nonspecific, or specific, in which case they can be considered key regulators of cellular activity. Polyamine response elements have been found that regulate the transcription of genes including c-Myc and c-Jun. Polyamines have been shown tumor growth and aggressiveness. The main biological polyamines include putrescine, spermine, and spermidine. Another is cadaverine. Given their names, you can surmise that they smell horrible. The synthesis of three polyamines from arginine and SAM is shown in Figure $2$. Figure $2$: Polyamine synthesis form arginine and SAM Glutathione synthesis and redox balance Glutathione, γ-glutamylcysteinylglycine (GSH), is a chief regulator of the oxidation state of a cell. As a disulfide bond can be cleaved and hence reduced by the excess concentration of a thiol (sulfhydryl) like b-mercaptoethanol (which gets oxidized in the process), the free thiol on glutathione can act as a reducing agent in the cell. The production of reactive oxygen species (ROS) in normal but especially tumor cells, which have increased O2 demand and use, is countered by the generation of an antioxidant defense state. This is characterized in part by increased levels of reductants such as NADPH but especially glutathione. It can react with H2O2 through the enzyme GSH peroxidase to form water and the oxidized disulfide form of GSH, GSSG. The GSSG is oxide back to GSH by glutathione reductase (GR) and NADPH. Figure $3$ shows the synthesis of glutathione from glutamate, cysteine, and glycine. Figure $3$: Synthesis of glutathione NADPH is generated in the cell by the phosphopentose pathway metabolism of glucose and by malic enzyme. It can also be generated from the Ser-Glycine One Carbon Cycle (SGOT) that we saw in Chapter 18.4, which is shown again in Figure $4$. Under appropriate conditions, this cycle can produce NADPH. Figure $4$: The Ser-Gly One Carbon (SGOC) Cycle Serine Hydroxymethyltransferases (SHMTs) 2 is upregulated by HIF1α and helps maintain the NADPH/NADP+ ratio. Given the connection between the SGOC and the methionine cycle through folate, a decrease in serine concentration leads to a decrease in GSH. Heme Biosynthesis This section is derived from Aminat S. Ogun; Neena V. Joy; Menogh Valentine. https://www.ncbi.nlm.nih.gov/books/NBK537329/. Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/) Heme is a macrocyclic tetrapyrrole ring structure containing two nonpolar vinyl groups on one edge and two charge propionates on the other. It is extensively conjugated with 26 π electrons (4n+2 = 4(6)+2) so it is aromatic. The molecule without Fe2+ is called protoporphyrin IX and with a centrally-coordinate Fe2+, it is called heme. The structures of both are shown in Figure $5$. Figure $5$: Structure of protoporphyrin IX and heme It is found in oxygen-binding proteins and as substrates and cofactors for enzymes involved in electron transport. It is synthesized in the bone marrow and liver. Alternative forms of heme include heme b (in hemoglobin), heme a (cytochrome a), and heme c (cytochrome c). Its synthesis, as expected given its macrocyclic structure, is complicated. The key enzyme in the pathway for regulation is 5'-Aminolevulinic acid synthase (ALA-S). Liver and bone express ALAS2 while ALAS1 is expressed in all tissues. The synthesis starts in the mitochondria and ends in the cytosol. The overall pathway for heme synthesis is shown in Figure $6$. Figure $6$: Heme biosynthetic pathway. Wikimedia Commonsile: Heme-Synthesis-Chemical-Details-Mirror.svg 5'-Aminolevulinic acid synthase (ALA-S), a pyridoxal phosphate-dependent enzyme, catalyzes the rate-limiting step in heme synthesis in the liver and erythroid cells. It is highly regulated There are two forms of ALA Synthase, ALAS1, and ALAS2. All cells express ALAS1 while only the liver and bone marrow expresses ALAS2. The gene for ALAS2 is on the X-chromosome. After the synthesis of ALA in the mitochondria, it moves into the cytoplasm for the remaining steps. Figure $7$ shows a likely mechanism for the first committed step, the production of ALA. This enzyme is used in the synthesis of all tetrapyrroles, including heme, chlorophyll, and cobalamin. Figure $7$: Mechanism for 5'-Aminolevulinic acid synthesis by ALAS (Wikipedia. https://en.Wikipedia.org/wiki/Aminol..._acid_synthase) The pathway shown in Figure 6 above is called the C4 pathway and is found in mammals, fungi, and purple nonsulfur bacteria. A C5 pathway is found in most bacteria, all archaea, and plants. The biosynthetic pathway for heme synthesis in E. Coli is shown in Figure $8$. Figure $8$: Heme pathway in E. coli. Zhang, J., Kang, Z., Chen, J. et al. Optimization of the heme biosynthesis pathway for the production of 5-aminolevulinic acid in Escherichia coli. Sci Rep 5, 8584 (2015). https://doi.org/10.1038/srep08584. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/ The pathway is divided into three modules (module I, module II, and module III in the dotted box). The arrows in green and red represent the enzymes that are positive and negative to ALA accumulation, respectively. Dotted red arrows represent the feedback inhibition. α-KG: α-ketoglutarate, GSA: glutamate-1-semialdehyde, ALA: 5-aminolevulinic acid, PBG: porphobilinogen, HMB: hydroxymethylbilane, GltX: glutamyl-tRNA synthetase, HemA: glutamyl-tRNA reductase, HemL: glutamate-1-semialdehyde aminotransferase, HemB: 5-aminolevulinic acid dehydratase, HemC: porphobilinogen deaminase, HemD: uroporphyrinogen III synthase, HemE: uroporphyrinogen decarboxylase, HemF: coproporphyrinogen III oxidase, HemG: protoporphyrin oxidase, HemH: ferrochelatase. In immature red blood cells (reticulocytes), heme increase globin protein synthesis. The hormone erythropoietin increases heme synthesis. In the liver, heme is part of cytochrome P450s. Increased concentration of drugs causes increases in ALAS1 to produce the cytochrome P450s to metabolize them. Also, low heme concentration increases ALAS1 transcription. Mutations in ALAS2 can lead to X-linked sideroblastic anemia from decreased heme production even as Fe2+ continues to enter the cell. Yeast ALAS is a homodimer with PLP covalently attached through a Schiff base link to lysine 337 of just one of the monomers. The structures of a noncovalent complex of PLP with ALAS (pdb 5TXR) and the covalently bound one (5TXT) show large changes in the protein conformation. PLP when covalently attached reorders the active. A C-terminal extension not found in bacteria wraps around the dimer and binds near the active site and is important for activity. Mutations in the tail can result in human diseases. Figure $9$ shows an interactive iCn3D model of the 5-aminolevulinic acid synthase with covalently attached PLP (5TXT). Figure $9$: 5-aminolevulinic acid synthase with covalently attached PLP (5TXT). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/icn3d/share.html?fTBbWuS3HPTP8uRm9 Lysine 337 (spacefill, CPK colors, labeled) in the A chain (magenta, no bound PLP) is shown. Lys 337 in the B chain (cyan) is covalently linked to PLP. The side chain of lysine 337 covalently attached to PLP is shown in spacefill, CPK colors, and labeled. The C-terminal extension (493–548) is shown as a red backbone chain. The very distal end of the extension is disordered and missing in the B chain (cyan). Figure $10$ shows an interactive iCn3D model of aligned 5-aminolevulinic acid synthase with free PLP (not covalently attached, 5TXR) and with covalently attached PLP (5TXT ). Figure $11$: Alignment of 5-aminolevulinic acid synthase with free PLP (5TXR) and with and with covalently attached PLP (5TXT ). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...aqN4Qpi2pSLkD7 The 5TXT structure contains two molecules of a stabilizing molecule shown in stick form, which you can ignore. The A chain is shown in magenta and the B chain is in cyan. Press "a" to toggle back and forth between the structures. The C-terminal extension is missing from the figure. Figure $11$ shows another view of heme synthesis which emphasizes the role of mitochondrial and cytoplasmic enzymes. Neurotransmitters The section below is modified from Manorama Patri. Synaptic Transmission and Amino Acid Neurotransmitters. DOI: 10.5772/intechopen.82121. https://www.intechopen.com/books/neu...rotransmitters. Creative Commons Attribution 3.0 License, There are three major categories of amino acids and their derivatives act as neurotransmitters are: 1. Amino acids: The neurotransmitters of this group are involved in fast synaptic transmission and are inhibitory and excitatory in action (primarily glutamic acid, GABA, aspartic acid, and glycine). 2. Amines: Amines are modified amino acids such as biogenic amines, e.g., catecholamines. The neurotransmitters of this group involve in slow synaptic transmission and are inhibitory and excitatory in action (noradrenaline, adrenaline, dopamine, serotonin, and histamine). 3. Others: The ones which do not fit in any of these categories (acetylcholine and nitric oxide). Amino acids are among the most abundant of all neurotransmitters present within the central nervous system (CNS). Amino acid transmitters provide the majority of excitatory and inhibitory neurotransmission in the nervous system. Amino acids used for synaptic transmission are compartmentalized (e.g., glutamate, compartmentalized from metabolic glutamate used for protein synthesis by packaging the transmitter into synaptic vesicles for subsequent Ca2+-dependent release). Amino acid neurotransmitters are all products of intermediary metabolism except GABA. Unlike all the other amino acid neurotransmitters, GABA is not used in protein synthesis and is produced by an enzyme (glutamic acid decarboxylase; GAD) uniquely located in neurons. Here is some more specific information: • Glutamate: Glutamate is used at the great majority of fast excitatory synapses in the brain and spinal cord. Glutamate binds to glutamate receptors of which there are many subtypes based on other molecules (some amino acid derivatives) that can bind to them. These other molecules include N-methyl-D-aspartate (NMDA), α-amino-3-hydroxy-5-methyl-4-isoxazolepropionate (AMPA) kainate, and quisqualate. • Aspartate: Aspartate is the most abundant excitatory neurotransmitter in the CNS. Like glycine, aspartate is primarily localized to the ventral spinal cord. Note that the two major excitatory neurotransmitters both have carboxylic acid side chains. • Gamma-aminobutyric acid (GABA): GABA, which is not one of the canonical amino acids used in protein biosynthesis, is the most ubiquitous inhibitory neurotransmitter in the brain. • Glycine: lycine receptors are ligand-gated ion channels that increase Cl influx and hence are generally inhibitory. Hydroxymethyl transferase converts the amino acid serine to glycine. Glycine has been found to play a role in the functional modulation of NMDA receptors The pathways for the synthesis of amino acid-derived bioactive amines and neurotransmitters are shown in Figure $12$. Figure $12$: Pathways for the synthesis of amino acid-derived bioactive amines and neurotransmitters Note the structural similarity of the psychotropic and hallucinogenic drug LSD to serotonin (5HT), amphetamines to norepinephrine and epinephrine, and melatonin (a substance some take as a sleeping aid and which forms in the dark at night in brains. The name catecholamines derive from the common name of the 1,2-dihydroxybenzene group (catechol). The first and rate-limiting step in catecholamine synthesis is catalyzed by tyrosine hydroxylase (TH). It has no heme but it has an Fe2+ and tetrahydrobiopterin as a cofactor used in the synthesis of dihydroxyphenylalanine (DOPA). Tyrosine hydroxylase is rate-limiting for the synthesis of all three transmitters. The enzyme is inhibited by catecholamines including dopamine, a downstream product, and is activated by phosphorylation on serine 40. The structures of TH in the absence of dopamine and the pSer40 state are known. The protein is a tetramer with a regulatory domain (dimer) and catalytic domain (also a dimer) separated by 15 Å. The mammalian TH is a member of the aromatic amino acid hydroxylases (AAAHs) which are mainly found as homotetramers. Each subunit has 3 domains: • The N-terminal regulatory domain (RD) that has an unstructured variable-length section followed by an ACT (aspartate kinase-chorismate mutase-TyrA) domain. The N-terminal tail contains serine 40 which on phosphorylation relieves the inhibition when dopamine is bound to the catalytic domain. • a central catalytic domain (CD) that binds Fe2+, aromatic amino acid substrates, and the tetrahydrobiopterin cofactor • C-terminal oligomerization domain (OD) which leads to dimer and tetramer formation. Figure $13$ shows a potential model that illustrates dopamine (DA)-mediated feedback inhibition and its regulation by serine 40 phosphorylation through the interaction of the N-terminal tail of the regulatory domain (RD) with the catalytic domain. All forms containing bound dopamine (yellow star) are inactive. Figure $13$: Cartoon model of DA-mediated feedback inhibition and its regulation by S40 phosphorylation. Bueno-Carrasco, M.T., Cuéllar, J., Flydal, M.I. et al. Structural mechanism for tyrosine hydroxylase inhibition by dopamine and reactivation by Ser40 phosphorylation. Nat Commun 13, 74 (2022). https://doi.org/10.1038/s41467-021-27657-y. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. In the active, apo, and non-phospho states, the 39−58 α-helix of the N-terminal regulatory domain of TH is detached from the main structure (I, apo-TH). The feedback inhibitor DA binds to the TH active site, most likely in the open conformation (I′, TH(DA)). DA-binding favors the interaction of the N-terminal α-helix with the same binding site, which blocks DA exit and contributes to the high-affinity binding and strong inhibition of TH activity (II, TH(DA)). Protein Kinase (PK) phosphorylation of S40 in TH(DA), leads to state III (THS40p(DA)), prompting the detachment of the α-helix from the TH active site (IV′), which opens up for DA-dissociation and activation (IV, THS40p). PKs and protein phosphatase(s) (PP) control the transition between THS40p and unphosphorylated TH for both DA bound (I′ ↔ IV′ and II ↔ III) and apo-TH (I ↔ IV). States I′ and III are expected to be only transiently populated during DA binding as states II and IV' will be more stable. Hence states I′ and III states are faded. S40 is also expected to be less accessible in state II than in state I, which is indicated by stippled lines for phosphorylation of TH in state II. The case for dephosphorylation is not known, but it could be expected that state III is a poorer substrate for PP than the open states IV′ and IV. The dephosphorylation reaction III → II is therefore also stippled. The states where we provide structural details in this work (I, II, and IV) are marked with circles. Figure $14$ shows a model of the TH active site changes on phosphorylation of serine 40 using structural and molecular dynamics approaches that led to the cartoon model above. Figure $14$: Modeling of the TH active site. Panel (a) shows models demonstrating the effect of serine 40 phosphorylation on the interaction of the N-terminal α-helix with bound dopamine (DA). Representative conformations from the last 50 ns of a 500 ns MD simulations for TH(DA) (grey ribbon) and pS40-TH(DA) (light blue ribbon) are shown. The resulting structures show a slight shift of the N-terminal α-helix upon phosphorylation, most probably due to electrostatic repulsion between the phosphate and E325, E375, and D424. Panel (b) shows a detailed view of the atomic model of the TH(DA) active site. (left) The N-terminal α-helix (orange), establishes connections with the adjacent helix D360-E375 and with residues of the 290–297 and 420–429 loops (blue, right). Pane (c) shows a cartoon depicting the interactions established between residues of the N-terminal α-helix that enters the active site, and residues of adjacent regions. Figure $\PageIndex{15$ below shows an interactive iCn3D model of the full-length tyrosine hydroxylase in complex with dopamine (residues 40-497) in which the regulatory domain (residues 40-165) has been included only with the backbone atoms (6zvp) Figure $15$: Tyrosine hydroxylase dopamine complex (6zvp). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...hu6vJn22eDkWw7 Finally, Figure $16$ shows an interactive iCn3D model of the active site of tyrosine hydroxylase in complex with dopamine (6zvp) Figure $16$: Active site of tyrosine hydroxylase dopamine complex (6zvp). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/icn3d/share.html?sx5obQnzojmKXuLS9 The side chains binding the active site Fe2+ and the interaction of Fe2+ with dopamine (LDP) are shown in sticks and labeled. The oxygen of serine 40 is shown as a red sphere. Dopamine is the molecule containing the 1,2-dihydroxybenzene. Figure $17$ shows a final summary presentation of the conversion of phenylalanine, tyrosine, and tryptophan to neurotransmitters. Figure $17$: Comparison of monoamine synthesis pathways. Adapted from Hochman, Shawn. (2015). Neural Regeneration Research. 10. 10.4103/1673-5374.169625. Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/02%3A_Unit_II-_Bioenergetics_and_Metabolism/22%3A_Biosynthesis_of_Amino_Acids_Nucleotides_and_Related_Molecules/22.04%3A_Biosynthesis_and_Degradation_of_Nucle.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Introduction We conclude our exploration of metabolic pathways with the biosynthesis and breakdown of nucleotides, the monomers that comprise nucleic acids. We can't also forget the important role of ATP as the universal carrier of biological free energy, as well as the nucleotides involved in signal transduction (GTP in heterotrimeric G proteins, small G proteins, and ATP as substrate in protein phosphorylations by kinases). As with the other later sections on metabolism, we won't focus much on detailed reaction mechanisms or enzyme structures, with one exception, the enzyme that converts nucleotides to deoxynucleotides. Nucleotide synthesis is often included in chapters on amino acid metabolism as almost every atom in the purine and pyrimidine ring derives from them as shown in Figure $1$. Figure $1$: Source of atoms in nucleotide bases. Lieu, E.L., Nguyen, T., Rhyne, S., et al. Amino acids in cancer. Exp Mol Med 52, 15–30 (2020). https://doi.org/10.1038/s12276-020-0375-3. Creative Commons Attribution 4.0 International License, http://creativecommons.org/licenses/by/4.0/. For purines, glutamine and aspartate provide the nitrogen for the nucleotide's rings. They also provide the NH3s for the ring substituents (glutamine for adenine and aspartate for guanine). Glycine and formate provide the carbon atoms for the rings. The one carbon molecule formate, which derives from glycine, is also added to purine rings. Glycine provides a one-carbon unit indirectly through the main carrier of activated one-carbon units, 5,10-meTHF, which is converted to formate through 10-formyl THF. Pyrimidines are much smaller and their synthetic pathway reflects that. Instead of being synthesized as nucleobases as in the case of purines, they are made as ribonucleotides as they are linked to phosphoribosyl pyrophosphate (PRPP). Glutamine and aspartate again provide the ring C and N atoms. The one-carbon unit derives from serine-to-glycine conversion. A methyl group from the activated 1C donor, 5,10-meTHF, is added to dUMP to make dTMP. Purine Synthesis The material below derives from De Vitto, H.; Arachchige, D.B.; Richardson, B.C.; French, J.B. The Intersection of Purine and Mitochondrial Metabolism in Cancer. Cells 2021, 10, 2603. https://doi.org/10.3390/cells10102603. Creative Commons Attribution License Mammals have two pathways for purine synthesis, a de novo pathway and a salvage pathway to recycle nucleotide bases. The salvage pathway is typically sufficient as purine bases come from nucleic acid breakdown. The resulting free bases (adenine, guanine, and hypoxanthine are connected to phosphoribosyl pyrophosphate (PRPP) to form nucleoside monophosphates (NMP) using either adenine phosphoribosyltransferase (APRT) to form AMP or hypoxanthine-guanine phosphoribosyltransferase (HGRT) to form IMP and GMP. PRPP is a substrate in both the salvage and de novo pathways. The overall de novo and salvage pathways for purine synthesis are described in detail in Figure $2$. Figure $2$: Purine metabolic pathways. De Vitto, H.; Arachchige, D.B.; Richardson, B.C.; French, J.B. The Intersection of Purine and Mitochondrial Metabolism in Cancer. Cells 2021, 10, 2603. https://doi.org/10.3390/cells10102603. Creative Commons Attribution License The conserved de novo biosynthesis pathway to generate IMP consists of 10 chemical steps catalyzed by 6 gene products in humans. These include the trifunctional enzyme TGART, composed of GAR synthetase (GARS), GAR transformylase (GARTfase), and AIR synthetase (AIRS) domains; the bifunctional enzymes PAICS, composed of CAIR synthetase/AIR carboxylase (CAIRS) and SAICAR synthetase (SAICARS), and ATIC, composed of AICAR transformylase (AICART) and IMP cyclohydrolase (IMPCH); and three monofunctional enzymes, phosphoribosyl amidotransferase (PPAT), formyl glycin amidine ribonucleotide synthetase (FGAMS), and adenylosuccinate lyase (ADSL). Downstream IMP is converted to (1) GMP through stepwise reactions of IMP dehydrogenase (IMPDH) followed by GMP synthetase (GMPS) and (2) AMP via adenylosuccinate synthetase (ADSS) followed by ADSL. The salvage pathway requires PRPP to generate IMP and GMP through one-step reactions mediated by hypoxanthine phosphoribosyltransferase (HPRT) utilizing hypoxanthine and guanine bases. AMP is generated by adenine phosphoribosyltransferase (APRT) utilizing adenine base and PRPP as substrates. Mitochondria supply precursors for purine de novo biosynthesis including glycine, N10-formyl THF, and aspartic acid through their one-carbon cycle (1C cycle) and tricarboxylic acid cycle (TCA). The de novo pathway kicks in when there is high demand for purines. Six enzymes are required for the 10-step pathway. Three of these are multifunctional enzymes catalyzing multiple steps in the pathway, comprising the two bifunctional enzymes phosphoribosylaminoimidazole carboxylase (PAICS) and AICAR transformylase/inosine monophosphate cyclohydrolase (ATIC) and the trifunctional enzyme glycinamide ribonucleotide transformylase (TGART). When active, the pathway is limited both by substrate availability and by the reaction rate of its initial step, the conversion of PRPP to phosphoribosylamine (PRA) by phosphoribosylpyrophosphate amidotransferase (PPAT). The final product of the de novo biosynthesis pathway, IMP is a substrate for the production of both AMP and GMP. 6 ATP are used to make 1 IMP from PRPP. None are required in the salvage pathway. PPAT is also called Glutamine phosphoribosylpyrophosphate amidotransferase or amidophosphoribosyltransferase. It catalyzes the rate limiting step, is tightly regulated. PPAT possesses two nucleotide-binding sites near the active site, allowing for feedback control by downstream purine nucleotides via allosteric inhibition. Figure $3$ shows an interactive iCn3D model of the Glutamine Phosphoribosylpyrophosphate Amidotransferase from Arabidopsis thaliana (6LBP) Figure $3$: Glutamine Phosphoribosylpyrophosphate Amidotransferase from Arabidopsis thaliana (6LBP). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...uWnf1bTq1odk88 The plant enzyme is a homotetramer, which each subunit having a Fe4S4 center (spacefill). The active site residues in each subunit (DDS 432-444 and DS 369-370) are shown as colored sticks and labeled. Regulation of IMP production also occurs through enzyme phosphorylation. For example, Thr 397 on PPAT is phosphorylated by protein kinase B (PKB). PRPP concentrations also affects the rate. Another regulation of the flux through the de novo pathway is through the condensation of the enzymes into the purinosome, which contains PPAT, TGART, formylglycinamidine ribonucleotide synthetase (FGAMS), PAICS, adenylosuccinate lyase (ADSL), and ATIC. The purinosome also interacts with the mitochondria which would allow high local concentrations of ATP. A cartoon view of the purinosome is shown in Figure $4$. Figure $4$: Cartoon showing the purinosome. Baresova et al (2018) PLoS ONE 13(7): e0201432. https://doi.org/10.1371/journal.pone.0201432. Creative Commons Attribution License, Figure $5$ shows another view of de novo IMP synthesis in which the origin of each atom in the purine ring is shown in color. Figure $5$: De novo IMP synthesis showing the origin of atoms in IMP. Figure $6$ shows an expanded view of the conversion of IMP to GTP and ATP. Figure $6$: Conversion of IMP to GTP and ATP Pyrimidine Synthesis As mentioned in the introduction, pyrimidines have a much simpler biosynthetic pathway. Instead of being synthesized as nucleobases as in the case for purines, they are made as ribonucleotides as they are linked to phosphoribosyl pyrophosphate (PRPP). Glutamine and aspartate again provide the ring C and N atoms. The one-carbon unit derives from serine-to-glycine conversion. A methyl group from the activated 1C donor, 5,10-meTHF, is added to dUMP to make dTMP. The first step in the pathway for pyrimidine synthesis is the condensation of aspartate and carbamoyl phosphate. We have seen the synthesis of carbamoyl phosphate in the urea cycle by the enzyme carbamoylphosphate synthase I (CPSI) in Chapter 18.3 but present the reaction again in Figure $7$. Figure $7$: Synthesis of carbamoyl phosphate A different cytosolic version of the enzyme, CPS II, is used to synthesize both arginine and pyrimidine nucleotides. It uses glutamine as a donor of NH3. The pathway for the synthesis of UTP and CTP are shown in Figure $7$. It does not explicitly show the synthesis of carbamoylphosphate, which is an intergyral part of the pathway and one of the rate-limiting steps in pyrimidine synthesis. Figure $8$: De novo synthesis of UDP, UTP and CTP UDP and CDP can be converted to dCDP and dUDP, then on to dCPT and dUTP, and to dTMP as shown in Figure $9$. Figure $9$: Synthesis of dTMP Some of the material below derives from Li et al. Int. J. Mol. Sci. 2021, 22(19), 10253; https://doi.org/10.3390/ijms221910253. Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/) In purine synthesis, we saw multifunctional enzymes that catalyze several steps, as well as the assembly of the enzymes in the de novo pathway into the purinosome. In an analogous fashion, three different enzyme activities that catalyze the first three combined rate limiting steps of pyrimidine synthesis, Carbamoyl-phosphate synthetase, Aspartate transcarbamoylase, and Dihydroorotase are found in a single, multifunction protein referred to as CAD. Its structure is a hexamer of a 243K monomer. It has 4 domains that include • glutamine amidotransferase (GATase) which "moves" HCO3, glutamine, and ATP to the CPSIIase domain • carbamoylphosphate synthetase II (CPSIIase): This has two parts, CPSaseA, and CPSase B. They combine functionally with GATase to form a glutamine-dependent carbamoylphosphate synthase (CPSase) • aspartate transcarbamylase (ATCase) acts as a homotrimer • dihydroorotase (DHOase) catalyzes the reversible cyclization reaction. The CAD protein is a 'fusion' protein encoding these four enzymatic activities of the pyrimidine pathway. Figure $10$ shows the domain structure of the CAD protein. Figure $10$: Domain structure of CAD The red represents glutamine amidotransferase and the blue the carbamoyl phosphate synthase ATP binding domain. More specifically, the following amino acid stretches comprise the different domains: GATase (2-365), CPSase A (395-933), CPSlase B (934-1455), DHOase (1456-1788), and ATCase (1918-2225) Figure $11$ shows an interactive iCn3D model of the AlphaFold predicted model of the CAD protein (P27708) Figure $11$: AlphaFold predicted model of the CAD protein (P27708). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...5V2XUmfsz1qjZ6 The GATase domain is magenta, the CPSase domains orange, the DHOase domain yellow, and the ATCase domain cyan. The structure of the disordered loops could not be modeled. Ribonucleotide reductases (RNRs) Ruskoski and. Boal. J. Biol. Chem. (2021) 297(4) 101137. DOI:https://doi.org/10.1016/j.jbc.2021.101137. CC-BY license (http://creativecommons.org/licenses/by/4.0/). Ribonucleotide reductases (RNRs), also called ribonucleoside-diphosphate reductase, catalyze the oxidation of the C2'-OH on the ribose ring to C2'-H through a free radical mechanism for the oxidation of for all NDPs including ADP, GDP, CDP and UDP (which converts to dUDP and in a different reaction to dTDP). The reaction is as follows: [thioredoxin]-dithiol + ribonucleoside 5'-diphosphate ↔ [thioredoxin]-disulfide + 2'-deoxyribonucleoside 5'-diphosphate + H2O To refresh your mind, thioredoxin is a small protein (12K) that is part of a complex with thioredoxin reductase and thioredoxin-interacting protein. It has two key sulfhydryls at the active site which act as reducing agents as they get converted to a disulfide as shown in Figure $12$. Figure $12$: Reduced thioredoxin and its oxidized form This single enzyme is so critical to cellular life that we will examine it more closely. There are several classes of these enzymes (Ia-Ie, II, and III). The class I enzymes generally use a di-transition metal complex as a cofactor while class II uses adenosylcobalamin. We will focus on class I enzymes, which have two subunits, called α and β or M1 and M2, respectively. The NDP binds in an α/M1 subunit active site which is developed only in the dimer. The β/M2 subunit is often referred to as the radical-generating subunit as it contains the transition metal complex that generates a free tyrosyl radical cation critical to the reaction. Almost all class I ribonucleotide reductases (RNRs) use transition metal ions located in the β/M2 subunit in the catalytic cycle for the dehydroxylation of the 2' OH on the ribose ring of the nucleotide. The metal ion complex a β/M2 subunit tyrosine to a tyrosine free radical cation which oxidizes an active site cysteine in the α/M1 subunit to form a thiol radical cation (Cys•+), called a thiyl radical. This abstracts a H• from the 3'C on the ribose of the substrate, forming a 3'C radical cation. This facilitates a dehydration reaction which leads to the dehydroxylation of the 2' OH, regenerating the thiyl radical. Reducing equivalents to restore the catalytic function of the enzyme come from the oxidation of a thioredoxin disulfide bound in the other subunit of the protein or a formate. Given the importance of these enzymes, they must be highly regulated. There are two regulatory sites: • a specificity site: determines nucleotide (NDP) specificity • an activity site: regulates catalytic activity The specificity and activity sites are in the α/M1 subunit where allosteric regulators dNTPs and ATP bind to different sites. When ATP is bound, the enzyme uses CDP and UDP as substrates. When dGTP is bound, ADP is the preferred substrate. Finally, when dTTP is bound, GDP is the preferred substrate. The enzyme is inhibited by dATP binding to the actual active site. Figure $13$s shows an abbreviated mechanism and cartoon showing the activities of the two subunits. Figure $13$: Abbreviated mechanism and a cartoon showing the activities of the two subunits. in class I RNR. A, universal mechanism for nucleotide reduction in RNRs. B, diagram of the steps involved in radical translocation in class I RNRs. Ruskoski and. Boal. J. Biol. Chem. (2021) 297(4) 101137. DOI:https://doi.org/10.1016/j.jbc.2021.101137. CC-BY license (http://creativecommons.org/licenses/by/4.0/) Radical formation starts at tyrosine 122 in the metal center site in the β/M2 subunit. Electron transfer then occurs across the two subunits from a very distant active site Cys 439 in the α subunit. which enables the formation of the thiyl radical cation (Cys•+). The structure of an E. Coli Type IA enzyme with bound ligands has been determined after much effort that involved trapping of a long-life intermediate. It required replacement of Tyr 122 in both β chains with 2,3,5-trifluorotyrosine, which allowed the structure to be determined by cyro-EM. Tyr 122 in the β chain forms starts the process of electron transfer as it becomes the Tyr 122.+ radical cation. A detailed mechanisms showing both electron transfer to Y122.+ and accompanying proton transfer is shown in Figure $14$. Figure $14$: Pathway of electron and proton transfers for the formation of the thiyl radical cation (Cys•+). after Kang et al. Science (2020). DOI: 10.1126/science.aba6794) The path for electron flow from the sulfur of C439 in the β subuit to regenerate Y122 is shown. That electron transfer occurs over a very long distance of 35 Å as it hops from the original sulfur donor to the acceptor. Figure $15$ shows an interactive iCn3D model of the holocomplex of E. coli class Ia ribonucleotide reductase with GDP and TTP (6W4X). Figure $15$: Holocomplex of E. coli class Ia ribonucleotide reductase with GDP and TTP (6W4X). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...71ubUDRovXURG8 The structure is a tetramer of two α chains (different shades of gray) and two β chains (different shades of cyan). The GDP and TTP in the α subunit and the Fe cluster in the β subunit are shown in CPK spacefill and labeled. The key side chains in the α and β chains that participate in electron transfer over the 35 Å distance are shown in CPK-colored sticks and labeled. The Fe2+ ions are in the form of a μ-oxo-diron complex (2 Fe ions coordinated by 1 oxide). Figure $16$ shows specificity and catalytic sites of the biologically active tetramer form for Class 1 RNR as well as the transition metal cofactors (Fe, Mn, or both) in various Class 1 RNRs. Figure $16$: Quaternary structure of the active holoenzyme complex in class I RNR (PDB accession code 6W4X). Insets show the location of the active site in the catalytic α subunit (middle top) and the metallo- or radical cofactor (middle bottom and far right) in the β subunit The tyrosyl free radical forms on binding of dioxygen to the transition metal ion center electron and subsequent loss of an electron from tyrosine, as shown in Figure $17$: Figure $17$: Cofactor assembly mechanisms for class I RNRs. Manganese-dependent enzymes are highlighted in purple. Superoxide-dependent RNRs are highlighted in hot pink. Subclasses that require a NrdI activase are indicated with a yellow box. Metal-centered Cys oxidants shown in green and Tyr-derived radical Cys oxidants shown in blue. The mechanisms of cofactor actions are not fully understood. O2 initially adds to the Fe2+/Fe2+ cluster which forms a peroxo-Fe3+/Fe3+ intermediate. Structural features of the active site for different class I RNRs along with redox-active transition metal ions are shown in Figure $18$. Figure $18$: Comparison of metal-binding sites in class I RNRs The PDB codes for each structure are as follows: A, (3N3A), B, 4M1I), C, (6CWP), and D (6EBO). Water molecules are shown as red spheres. Now let's look at the regulation of class IA RNRs in more detail. Let's consider perhaps the most important allosteric regulators which bind 15 Å from the active site which affect RNR enzymatic activity: • dATP: inhibits RNRs when it binds to the α subunit • ATP: reverses the inhibition by dATP dATP/ATP also affects RNR enzyme specificity as they tilt the preference of RNR towards pyrimidine substrates, where TTP and dGTP promote purine substrate binding. These same rules apply to human and E. coli RNRs, with the locations of the active sites also being the same. The allosteric regulators appear to affect the quaternary structure of the enzyme. In E. Coli, the binding of dADP converts the structure of the enzyme from an active α2β2 form to an inhibited α4β4 ring structure. When dATP is bound, a "cone" domain in α forms interactions with the β subunit, leading to the formation of a dimer of the α2β2 tetramer. ATP reverses this effect by displacing dATP and pushing the equilibrium towards the active α2β2 form. This is shown in Panel A of Figure $19$. Figure $19$: Comparison of mechanisms of allosteric regulation of activity for E. coli and human RNRs. Brignole et al. 3.3-Å resolution cryo-EM structure of human ribonucleotide reductase with substrate and allosteric regulators bound eLife 7:e31502. https://doi.org/10.7554/eLife.31502. Attribution 4.0 International (CC BY 4.0). Panel B above illustrates the regulation of the human enzyme, which appears to be quite different. In the absence of either dATP or ATP, RNRs exist just as α2 dimers. On binding either dATP or ATP, the α2 dimer is converted to an α6 (a trimer of dimers) structure. Both are inactive in the absence of β subunits (which provide the metal cofactor site) • When β2 is added to the dATP bound α6 hexamer, the hexamer becomes stabilized, but the resulting complex is inhibited. • When β2 is added to the ATP bound α6 hexamer, the hexamer becomes destabilized and into smaller structures which are active. Hence the ratio of cellular dATP/ATP changes the aggregation state of the RNR and hence its activity in both E. Coli and humans. Figure $20$ shows an interactive iCn3D model of the Human ribonucleotide reductase large subunit (alpha) with dATP and CDP (6AUI). Figure $20$: Human ribonucleotide reductase large subunit (alpha) with dATP and CDP (6AUI). (Copyright; author via source). Click the image for a popup or use this external link:https://structure.ncbi.nlm.nih.gov/i...w1sFwwYSAD4nG6 Each of the α subunits in the hexamer is shown in a different color. dATPs (allosteric inhibitors) are shown in spacefill, red. CDPs (substrates) are shown in spacefill yellow. The hole in the middle of the structure prevents β2 from binding in a catalytically productive fashion, so the structure is inactive. A specific loop, loop 2, assists in the determination of RNRs specificity. It is between the base of dATP and the base of the substrate CDP. The backbone of the loop interacts with the adenine base and orients Gln 288 in the active site to interact with the cytosine of the substrate CDP. In both systems, backbone atoms of this loop ‘read out’ the adenine base and position Gln288 into the active site to recognize the cytosine of CDP. Figure $20$ gives further details on the origin of substrate specificity. Figure $21$: Determinants of substrate specificity are conserved from E. coli to humans. Panel (A) shows residues of human α (blue) interacting with CDP (carbons in orange) in the active site and dATP (carbons in yellow) in the specificity site. Density for CDP in orange mesh and for dATP in the yellow mesh. Panel (B) zooms in on dATP in the specificity site. Water molecules and oxygen atoms are in red, nitrogen in blue, magnesium in green, and phosphate in gold. Panel (C) zooms in on CDP in the active site. Panel (D) shows an overlay of human α from the α6 EM structure in blue with E. coli α from the α4β4-CDP-dATP cocrystal structure in gray (PDB: 5CNS) shows a nearly identical loop 2 conformation positioning Gln288 and Arg293 (Gln294 and Arg298 in E. coli). Panel (E) shows an overlay of human α from the α6 EM structure in blue with the crystal structure of human α with N- and C-termini truncated (residues 77–742) cocrystallized with dATP in tan (PDB: 2WGH) shows similar positioning of dATP but an altered conformation of loop 2 in the absence of bound CDP. The CDP shown is from the α6 EM structure. Panel (F) shows an overlay of human α from the α6 EM structure in blue with equivalent residues of yeast α structure with CDP and AMPPNP in brown (PDB: 2CVU) and shows a conformation of loop 2 that is distinct from that seen in structures of E. coli and human α Transcriptional regulation of RNRs Given the importance of this key enzyme, it should come as no surprise that its levels are regulated at the transcription level. As the activity of RNR is determined by its polymeric quaternary state, the transcriptional activation of the genes for RNRs is controlled in bacteria by quaternary states of the RNR-specific transcriptional repressor NrdR. The transcription factors bind to a specific DNA sequence called an NrdR box, which precedes the start site of transcription for RNR, where RNA polymerase binds. The NrdR protein acts to repress the synthesis of RNR genes. Its aggregation state is controlled by dATP/ATP ratios. When dATP is high, the NrdR binds to DNA and represses the synthesis of the RNR gene. In contrast when ATP is high, the protein does not bind to DNA and hence it can not repress transcription. The association /dissociation of the repressor is controlled by the aggregation state of NrdR. When abundant, NrdR exists as a 12-mer complex with two molecules of ATP bound per monomer. This ATP-bound 12-mer can't bind DNA, so transcription of the gene for RNR is not repressed. As dATP increases, one ATP is displaced so each monomer has 1 dATP and 1 ATP bound. This causes the NrdR to covet to an 8-mer. A 4-mer (tetrameric) version of this protein binds to the NrdR box sequence at the start of the RNR gene, repressing its synthesis. Figure $22$ shows the dodecameric, octameric, and tetrameric structures of NrdR and their functions. Figure $22$: Mechanism of NrdR function involving dodecameric, octameric, and tetrameric structures. Rozman Grinberg, I., Martínez-Carranza, M., Bimai, O. et al. A nucleotide-sensing oligomerization mechanism that controls NrdR-dependent transcription of ribonucleotide reductases. Nat Commun 13, 2700 (2022). https://doi.org/10.1038/s41467-022-30328-1. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. Panel (a) shows a surface representation of the cryo-EM maps for the dodecameric, octameric, and DNA-bound tetrameric NrdR structures. Panel (b) shows a cartoon representation of the ATP-loaded NrdR tetramer (left) and the dATP/ATP-loaded tetramer (right). Chains A, B, C, and D are colored in beige, green, pink, and blue, respectively. Panel (c) shows the interface between the ATP cones in chain A (beige) and chain B (green) in the ATP-loaded dodecamer. Panel (d) shows the dATP/ATP-loaded tetramer. Panels c, and d were made from the same perspective, based on an alignment of the ATP-cones in chains A and B in both structures. Figure $23$ shows an interactive iCn3D model of the Streptomyces coelicolor dATP/ATP-loaded NrdR in complex with its cognate DNA (7P3F). Figure $23$: Streptomyces coelicolor dATP/ATP-loaded NrdR in complex with its cognate DNA (7P3F). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...2u3HMRPmJHe9X9 The monomers of the NrdR tetramer are shown in different colors. The yellow spheres are dATP and the orange ones are ATP. Pyrimidine Salvage Pathway There is also a salvage pathway as shown in Figure $24$. Figure $24$: Pyrimidine salvage pathway (after Wang et al. Frontiers in Oncology, 11 (2021). https://www.frontiersin.org/article/...nc.2021.684961. DOI=10.3389/fonc.2021.684961 Cells at rest use the salvage pathway reactants derived from nucleic acid degradation generic nucleoside pools. Nucleotide Degradation Purines The pathway for purine degradation is shown in Figure $25$. Figure $25$: Purine degradation Pyrimidine Degradation The pathway for pyrimidine degradation is shown in Figure $26$. Figure $26$: Pyrimidine degradation
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/03%3A_Unit_III-_Information_Pathway/23%3A_Chromosome_Structure/23.01%3A_Gene_Mapping_and_Chromosomal_Karyotypes.txt	princeton-nlp/TextbookChapters	• 23.1: Gene Mapping and Chromosomal Karyotypes Genes provide instructions to build living organisms and each specific gene maps to the same chromosome in every cell. This physical gene location within the organism's chromosomes is called the gene loci. If two genes are found on the same chromosome, especially when they are in close proximity to one another, they are said to be linked. • 23.2: DNA Transposable Elements Eukaryotic genomes contain an abundance of repeated DNA, and some repeated sequences are mobile. Transposable elements (TEs) are defined as DNA sequences that are able to move from one location to another in the genome. TEs have been identified in all organisms, prokaryotic and eukaryotic, and can occupy a high proportion of a species’ genome. • 23.3: Chromosome Packaging Most eukaryotic chromosomes include packaging proteins which, aided by chaperone proteins, bind to and condense the DNA molecule to prevent it from becoming an unmanageable tangle. Before typical cell division, these chromosomes are duplicated in the process of DNA replication, providing a complete set of chromosomes for each daughter cell. 23: Chromosome Structure Search Fundamentals of Biochemistry Introduction Genes provide instructions to build living organisms and each specific gene maps to the same chromosome in every cell. This physical gene location within the organism's chromosomes is called the gene loci. If two genes are found on the same chromosome, especially when they are near one another, they are said to be linked. Genetic linkage is the tendency of DNA sequences that are close together on a chromosome to be inherited together during the meiosis phase of sexual reproduction. Two genetic markers that are physically near to each other are unlikely to be separated into different chromatids during chromosomal crossover and are therefore said to be more linked than markers that are far apart. In other words, the nearer two genes are on a chromosome, the lower the chance of recombination between them, and the more likely they are to be inherited together. Markers on different chromosomes are perfectly unlinked. Genetic linkage is the most prominent exception to Gregor Mendel's Law of Independent Assortment. The first experiment to demonstrate linkage was carried out in 1905. At the time, the reason why certain traits tend to be inherited together was unknown. Later work revealed that genes are physical structures related by physical distance. The typical unit of genetic linkage is the centimorgan (cM). A distance of 1 cM between two markers means that the markers are separated into different gametes on average once per 100 meiotic products, thus once per 50 meioses. A linkage map (also known as a genetic map) is a table for a species or experimental population that shows the position of its known genes or genetic markers relative to each other in terms of recombination frequency, rather than a specific physical distance along each chromosome. Linkage maps were first developed by Alfred Sturtevant, a student of Thomas Hunt Morgan. Figure $1$ shows a gene linkage map with the relative positions of allelic characteristics on the second Drosophila chromosome. A linkage map is a map based on the frequencies of recombination between markers during the crossover of homologous chromosomes. The greater the frequency of recombination (segregation) between two genetic markers, the further apart they are assumed to be. Conversely, the lower the frequency of recombination between the markers, the smaller the physical distance between them. Historically, the markers originally used were detectable phenotypes (enzyme production, eye color) derived from coding DNA sequences; eventually, confirmed or assumed noncoding DNA sequences such as microsatellites or those generating restriction fragment length polymorphisms (RFLPs) have been used. Linkage maps help researchers to locate other markers, such as other genes by testing for genetic linkage of the already known markers. In the early stages of developing a linkage map, the data are used to assemble linkage groups, a set of genes that are known to be linked. As knowledge advances, more markers can be added to a group, until the group covers an entire chromosome. For well-studied organisms, the linkage groups correspond one-to-one with the chromosomes. Traditional studies used to physically map genes onto specific chromosomes were painstaking and involved using restriction enzymes to fragment the genome of an organism and then clone the fragments into YACs or BACs creating a DNA library. The library could then be screened with specific genetic probes to determine which fragment contained a gene of interest. The fragments would then need to be sequenced and reassembled using overlapping patterns. Today, the sequencing of entire genomes from nearly any organism is possible and relatively easy in comparison. Thus, a traditional genetic map can more readily be overlayed on the physical chromosomal map of an organism as shown in Figure $2$. This was one of the overarching goals of the human genome project. Karyotypes The entire chromosome set of a species is known as a karyotype, which can be thought of as a global map of the nuclear genome. Karyotyping is the process by which the condensed chromosomes of an organism are stained and photographed using light microscopy. Karyotyping can be used to determine the chromosome complement of an individual, including the number of chromosomes and any abnormalities. Karyotypes describe the chromosome count of an organism and what these chromosomes look like under a light microscope. Attention is paid to their length, the position of the centromeres, banding pattern, any differences between the sex chromosomes, and any other physical characteristics. The preparation and study of karyotypes are part of the larger field of cytogenetics. The field of cytogenetics involves the study of inheritance in relation to the structure and function of chromosomes. Thus, karyotyping is a fundamental process within this field. The study of whole sets of chromosomes is sometimes known as karyology. The chromosomes are depicted (by rearranging a photomicrograph) in a standard format known as a karyogram or idiogram: in pairs, ordered by size and position of centromere for chromosomes of the same size, as shown in Figure $3$. The basic number of chromosomes in the somatic cells of an individual or a species is called the somatic number and is designated 2n. In the germ line (the sex cells) the chromosome number is n (humans: n = 23). Thus, in humans 2n = 46. In normal diploid organisms, autosomal chromosomes are present in two copies. There may, or may not, be sex chromosomes. Polyploid cells have multiple copies of chromosomes and haploid cells, usually, gametes, have single copies. Karyotypes can be used for many purposes; such as to study chromosomal aberrations, cellular function, taxonomic relationships, medicine and to gather information about past evolutionary events (karyosystematics). During the chromosomal staining processes used to produce a karyotype, the staining intensity along the chromosome can vary due to localized sequence and structural differences. These banding patterns are an inherent characteristic of a chromosome and can be utilized as a diagnostic tool. Typically, karyotypes are prepared from cells that are actively undergoing mitosis. The mitotic progression is blocked in prometaphase or metaphase when chromosomes exist in their most condensed state. The cells are lysed, but the nuclei are retained intact and are subsequently treated with a chemical fixing agent. Once fixed, a number of different types of stains can be used to visualize the chromosomes. One of the first types of chromosomal staining procedures developed was known as Q-banding, which was developed in 1970 by Torbjorn Caspersson. This technique uses the DNA-alkylating dye, quinacrine, which forms a covalent link with the DNA. Researchers noted that the staining patterns resulting from this technique were consistent and repeatable, demonstrating that banding patterns can be used to identify and characterize individual chromosomes. Giemsa dye, as shown in Figure 24.3, is more commonly used today, as it can be used with bright field microscopy and produces high detail banding patterns. A specific technique, called G-banding uses Giemsa staining following the treatment of mitotic chromosomes with the protease, trypsin. Pre-treating the sample with trypsin before staining causes the partial breakdown of chromosomal proteins leading to chromosomal relaxation. This allows more thorough staining of the chromosomes when treated with Giemsa dye. When the chromosomal region is more tightly packed into heterochromatin, it tends to stain more darkly with the Giemsa dye, than the more lightly packaged euchromatic regions. Heterochromatic regions tend to have higher A-T content and don't contain as many gene regions as euchromatic regions. Euchromatic regions stain more lightly with G-banding. Other types of staining with Giemsa, include R-banding or Reverse-banding, which involves heating the DNA before staining. This is thought to cause the melting of A-T-rich regions, reducing the Giemsa staining, when compared to G-C-rich, gene-containing regions of the chromosomes. When visualizing a karyotype, the chromosomal images are aligned so that heterologous chromosomes are paired together and positioned such that the p-arm (short arm) is on top and the q-arm (long arm) points downward. Karyotypes can be used to quickly identify gross chromatic abnormalities that are larger than a few megabases in difference. This includes abnormalities such as aneuploidy (the addition or absence of an entire chromosome), or translocations (the transfer of part of a chromosome to a neighboring chromosome), as shown in Figure $4$ and Figure $5$. The telomeric regions of chromosomes can also be identified using fluorescent staining techniques, as shown in Figure $6$. The structure of telomeric chromosomal regions is described in section 24.3. More recently, techniques such as chromosome painting, use fluorescently labeled probes to hybridize with specific chromosomes or even specific gene regions of a chromosome. Karyotypes originating from this technique are called spectral karyotypes. This technique can be especially useful in identifying translocations that have occurred in human cells, as shown in Figure $7$.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/03%3A_Unit_III-_Information_Pathway/23%3A_Chromosome_Structure/23.02%3A_DNA_Transposable_Elements.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Introduction Eukaryotic genomes contain an abundance of repeated DNA, and some repeated sequences are mobile. Transposable elements (TEs) are defined as DNA sequences that can move from one location to another in the genome. TEs have been identified in all organisms, prokaryotic and eukaryotic, and can occupy a high proportion of a species’ genome. For example, transposable elements comprise approximately 10% of several fish species, 12 % of the C. elegans genome, 37% of the mouse genome, 45% of the human genome, and up to >80% of the genome of some plants like maize. From bacteria to humans, transposable elements have accumulated over time and continue to shape genomes through their mobilization. TEs were discovered by Barbara McClintock during experiments conducted in 1944 on maize. Since they appeared to influence phenotypic traits, she named them controlling elements. However, her discovery was met with less than enthusiastic reception by the genetic community. Her presentation at the 1951 Cold Spring Harbor Symposium was not understood and at least not very well received. She had no better luck with her follow-up publications and after several years of frustration decided not to publish on the subject for the next two decades. Not for the first time in the history of science, an unappreciated discovery was brought back to life after some other discovery has been made. In this case, it was the discovery of insertion sequences (IS) in bacteria by Szybalski group in the early 1970s. In the original paper, they wrote: “Genetic elements were found in higher organisms which appear to be readily transposed from one to another site in the genome. Such elements, identifiable by their controlling functions, were described by McClintock in maize. It is possible that they might be somehow analogous to the presently studied IS insertions”. The importance of McClintock’s original work was eventually appreciated by the genetic community with numerous awards, including 14 honorary doctoral degrees and a Nobel Prize in 1983 “for her discovery of mobile genetic elements”. Her picture is shown in Figure $1$. The mobilization of TEs is termed transposition or retrotransposition, depending on the nature of the intermediate used for mobilization. There are several ways in which the activity of TEs can positively and negatively impact a genome; for example, TE mobilization can promote gene inactivation, modulate gene expression or induce illegitimate recombination. Thus, TEs have played a significant role in genome evolution. For example, DNA transposons can inactivate or alter the expression of genes by insertion within introns, exons, or regulatory regions. In addition, TEs can participate in the reorganization of a genome by the mobilization of non-transposon DNA or by acting as recombination substrates. This recombination would occur by homology between two sequences of a transposon located in the same or different chromosomes, which could be the origin of several types of chromosome alterations. Indeed, TEs can participate in the loss of genomic DNA by internal deletions or other mechanisms. The reduction in fitness suffered by the host due to transposition ultimately affects the transposon, since host survival is critical to the perpetuation of the transposon. Therefore, strategies have been developed by host and transposable elements to minimize the deleterious impact of transposition, and to reach equilibrium. For example, some transposons tend to insert in nonessential regions in the genome, such as heterochromatic regions, where insertions will likely have a minimal deleterious impact. In addition, they might be active in the germ line or embryonic stage, where most deleterious mutations can be selected against during fecundation or development, allowing only non-deleterious or mildly deleterious insertions to pass to successive generations. New insertions may also occur within an existing genomic insertion to generate an inactive transposon or can undergo self-regulation by overproduction-inhibition. On the other hand, host organisms have developed different mechanisms of defense against high rates of transposon activity, including DNA-methylation to reduce TE expression, several RNA interference-mediated mechanisms, mainly in the germ line, or through the inactivation of transposon activity by the action of specific proteins. In some cases, transposable elements have been “domesticated” by the host to perform a specific function in the cell. A well-known example is RAG proteins, which participate in V(D)J recombination during antibody class switching, and exhibit a high similarity to DNA transposons, from which these proteins appear to be derived. Another example is the centromeric protein CENP-B, which seems to have originated from the pogo-like transposon. The analogous human mariner Himar1 element has been incorporated into the SETMAR gene, which consists of the histone H3 methylase gene and the Himar1 transposase domain. This gene is involved in the non-homologous end-joining pathway of DNA repair and has been shown to confer resistance to ionizing radiation. From a genome-wide view, it has been estimated that ~25% of human promoter regions and ~4% of human exons contain sequences derived from TEs. Thus, we are likely underestimating the rate of domestication events in mammalian genomes. The first TE classification system was proposed by Finnegan in 1989 and distinguished two classes of TEs characterized by their transposition intermediate: RNA (class I or retrotransposons) or DNA (class II or DNA transposons). The transposition mechanism of class I is commonly called “copy and paste” and that of class II, “cut and paste.” In 2007 Wicker et al. proposed a hierarchical classification based on TEs structural characteristics and mode of replication, as shown in Figure $2$. Class I: Mobile Elements As mentioned above, class I TEs transpose through an RNA intermediary. The RNA intermediate is transcribed from genomic DNA and then reverse-transcribed into DNA by a TE-encoded reverse transcriptase (RT), followed by reintegration into a genome. Each replication cycle produces one new copy, and as a result, class I elements are the major contributors to the repetitive fraction in large genomes. Retrotransposons are divided into five orders: LTR retrotransposons, DIRS-like elements, Penelope-like elements (PLEs), LINEs (long interspersed elements), and SINEs (short interspersed elements). This scheme is based on the mechanistic features, organization, and reverse transcriptase phylogeny of these retroelements. Accidentally, the retrotranscriptase coded by an autonomous TE can reverse-transcribe another RNA present in the cell, e.g., mRNA, and produce a retrocopy of it, which in most cases results in a pseudogene. The LTR retrotransposons are characterized by the presence of long terminal repeats (LTRs) ranging from several hundred to several thousand base pairs. Both exogenous retroviruses and LTR retrotransposons contain a gag gene that encodes a viral particle coat and a pol gene that encodes a reverse transcriptase, ribonuclease H, and an integrase, which provide the enzymatic machinery for reverse transcription and integration into the host genome. Reverse transcription occurs within the viral or viral-like particle (GAG) in the cytoplasm, and it is a multi-step process. Unlike LTR retrotransposons, exogenous retroviruses contain an env gene, which encodes an envelope that facilitates their migration to other cells. Some LTR retrotransposons may contain remnants of an env gene, but their insertion capabilities are limited to the originating genome. This would rather suggest that they originated in exogenous retroviruses by losing the env gene. However, there is evidence that suggests the contrary, given that LTR retrotransposons can acquire the env gene and become infectious entities. Presently, most of the LTR sequences (85%) in the human genome are found only as isolated LTRs, with the internal sequence being lost most likely due to homologous recombination between flanking LTRs. Interestingly, LTR retrotransposons target their reinsertion to specific genomic sites, often around genes, with putative important functional implications for a host gene. It is estimated that 450,000 LTR copies make up about 8% of our genome. LTR retrotransposons inhabiting large genomes, such as maize, wheat, or barley, can contain thousands of families. However, despite the diversity, very few families comprise most of the repetitive fraction in these large genomes. Notable examples are Angela (wheat), BARE1 (barley), Opie (maize), and Retrosor6 (sorghum). The DIRS order clusters structurally diverged groups of transposons that possess a tyrosine recombinase (YR) gene instead of an integrase (INT) and do not form target site duplications (TSDs). Their termini resemble either split direct repeats (SDR) or inverted repeats. Such features indicate a different integration mechanism than that of other class I mobile elements. DIRS were discovered in the slime mold (Dictyostelium discoideum) genome in the early 1980s, and they are resent in all major phylogenetic lineages including vertebrates. It has been shown that they are also common in hydrothermal vent organisms. Another order, termed Penelope-like elements (PLE), has wide, though patchy distribution from amoebae and fungi to vertebrates with copy numbers up to thousands per genome. Interestingly, no PLE sequences have been found in mammalian genomes, and apparently, they were lost from the genome of C. elegans. Although PLEs with an intact ORF have been found in several genomes, including Ciona and Danio, the only transcriptionally active representative, Penelope, is known from Drosophila virilis. It causes the hybrid dysgenesis syndrome characterized by the simultaneous mobilization of several unrelated TE families in the progeny of dysgenic crosses. It seems that Penelope invaded D. virilis quite recently, and its invasive potential was demonstrated in D. melanogaster. PLEs harbor a single ORF that codes for a protein containing reverse transcriptase (RT) and endonuclease (EN) domains. The PLE RT domain more closely resembles telomerase than the RT from LTRs or LINEs. The EN domain is related to GIY-YIG intron-encoded endonucleases. Some PLE members also have LTR-like sequences, which can be in a direct or an inverse orientation, and have a functional intron. LINEs do not have LTRs; however, they have a poly-A tail at the 3′ ends and are flanked by the TSDs. They comprise about 21% of the human genome and among them L1 with about 850,000 copies is the most abundant and best-described LINE family. L1 is the only LINE retroposon still active in the human genome. In the human genome, there are two other LINE-like repeats, L2 and L3, distantly related to L1. A contrasting situation has been noticed in the malaria mosquito Anopheles gambiae, where around 100 divergent LINE families compose only 3% of its genome. LINEs in plants, e.g., Cin4 in maize and Ta11 in Arabidopsis thaliana, seem rare as compared with LTR retrotransposons. A full copy of mammalian L1 is about 6 kb long and contains a PolII promoter and two ORFs. The ORF1 codes for a non-sequence-specific RNA binding protein that contains zinc finger, leucine zipper, and coiled-coil motifs. The ORF1p functions as a chaperone for the L1 mRNA. The second ORF encodes an endonuclease, which makes a single-stranded nick in the genomic DNA, and a reverse transcriptase, which uses the nicked DNA to prime reverse transcription of LINE RNA from the 3′ end. Reverse transcription is often unfinished, leaving behind fragmented copies of LINE elements; hence most of the L1-derived repeats are short, with an average size of 900 bp. LINEs are part of the CR1 clade, which has members in various metazoan species, including fruit flies, mosquito, zebrafish, pufferfish, turtles, and chicken. Because they encode their own retrotransposition machinery, LINE elements are regarded as autonomous retrotransposons. SINEs evolved from RNA genes, such as 7SL and tRNA genes. By definition, they are short, up to 1000 base pairs long. They do not encode their own retrotranscription machinery and are considered nonautonomous elements and in most cases are mobilized by the L1 machinery. The outstanding member of this class from the human genome is the Alu repeat, which contains a cleavage site for the AluI restriction enzyme that gave its name. With over a million copies in the human genome, Alu is probably the most successful transposon in the history of life. Primate-specific Alu and its rodent relative B1 have limited phylogenetic distribution suggesting their relatively recent origins. The mammalian-wide interspersed repeats (MIRs), by contrast, spread before eutherian radiation, and their copies can be found in different mammalian groups including marsupials and monotremes. SVA elements are unique primate elements due to their composite structure. They are named after their main components: SINE, VNTR (a variable number of tandem repeats), and Alu. Usually, they contain the hallmarks of the retroposition, i.e., they are flanked by TSDs and terminated by a poly(A) tail. It seems that SVA elements are nonautonomous retrotransposons mobilized by L1 machinery, and they are thought to be transcribed by RNA polymerase II. SVAs are transpositionally active and are responsible for some human diseases. They originated less than 25 million years ago, and they form the youngest retrotransposon family with about 3000 copies in the human genome. Retro(pseudo)genes are a special group of retroposed sequences, which are products of reverse transcription of a spliced (mature) mRNA. Hence, their characteristic features are an absence of promoter sequence and introns, the presence of flanking direct repeats, and a 3′-end polyadenosine tract. Processed pseudogenes, as sometimes retropseudogenes are called, have been generated in vitro at a low frequency in the human HeLa cells via mRNA from a reporter gene. The source of the reverse transcription machinery in humans and other vertebrates seems to be active L1 elements. However, not all retroposed messages have to end up as pseudogenes. About 20% of mammalian protein-encoding genes lack introns in their ORFs. It is conceivable that many genes lacking introns arose by retroposition. Some genes are known to be retroposed more often than others. For instance, in the human genome, there are over 2000 retropseudogenes of ribosomal proteins. A genome-wide study showed that the human genome harbors about 20,000 pseudogenes, 72% of which most likely arose through retroposition. Interestingly, the vast majority (92%) of them are quite recent transpositions that occurred after primate/rodent divergence. Some of the retroposed genes may undergo quite complicated evolutionary paths. An example could be the RNF13B retrogene, which replaced its own parental gene in the mammalian genomes. This retrocopy was duplicated in primates, and the evolution of this primate-specific copy was accompanied by the exaptation of two TEs, Alu and L1, and intron gain via changing a part of the coding sequence into an intron leading to the origin of a functional, primate-specific retrogene with two splicing variants. Class II: Mobile Elements Class II elements move by a conservative cut-and-paste mechanism; the excision of the donor element is followed by its reinsertion elsewhere in the genome. DNA transposons are abundant in bacteria, where they are called insertion sequences, but are also present in all phyla. Two subclasses of DNA transposons have been distinguished, based on the number of DNA strands that are cut during transposition. Classical “cut-and-paste” transposons belong to subclass I, and they are classified as the TIR order. They are characterized by terminal inverted repeats (TIR) and encode a transposase that binds near the inverted repeats and mediates mobility. This process is not usually a replicative one, unless the gap caused by excision is repaired using the sister chromatid. When inserted at a new location, the transposon is flanked by small gaps, which, when filled by host enzymes, cause duplication of the sequence at the target site. The length of these TSDs is characteristic of particular transposons. Nine superfamilies belong to the TIR order, including Tc1-Mariner, Merlin, Mutator, and PiggyBac. The second-order Crypton consists of a single superfamily of the same name. Originally thought to be limited to fungi, now it is clear that they have a wide distribution, including animals and heterokonts. A heterogeneous, small, nonautonomous group of elements MITEs also belong to the TIR order, which in some genomes amplified to thousands of copies, e.g., Stowaway in the rice genome, Tourist in most bamboo genomes, or Galluhop in the chicken genome. Subclass II includes two orders of TEs that, just as those from subclass I, do not form RNA intermediates. However, unlike “classical” DNA transposons, they replicate without double-strand cleavage. Helitrons replicate using a rolling-circle mechanism, and their insertion does not result in the target site duplication. They encode tyrosine recombinase along with some other proteins. Helitrons were first described in plants, but they are also present in other phyla, including fungi and mammals. Mavericks are large transposons that have been found in different eukaryotic lineages excluding plants. They encode various numbers of proteins that include DNA polymerase B and an integrase. Kapitonov and Jurka suggested that their life cycle includes a single-strand excision, followed by extrachromosomal replication and reintegration to a new location. TEs are not randomly distributed in the genome As seen in the previous section, TEs are highly diverse and in principle, every TE sequence in a genome can be affiliated to a (sub)family, superfamily, subclass, and class. This is summarized in Figure $3$. However, much like the taxonomy of species, the classification of TEs is in constant flux, perpetually subject to revision due to the discovery of completely novel TE types, the introduction of new levels of granularity in the classification, and the ongoing development of methods and criteria to detect and classify TEs. The genome may be viewed as an ecosystem inhabited by diverse communities of TEs, which seek to propagate and multiply through sophisticated interactions with each other and with other components of the cell. These interactions encompass processes familiar to ecologists, such as parasitism, cooperation, and competition. Thus, it is perhaps not surprising that TEs are rarely, if ever, randomly distributed in the genome, as shown in Figure $4$. TEs exhibit various levels of preference for insertion within certain features or compartments of the genome. These are often guided by opposite selective forces, a balancing act of facilitating future propagation while mitigating deleterious effects on host cell function. At the most extreme end of the site-selection spectrum, many elements have evolved mechanisms to target specific loci where their insertions are less detrimental to the host but favorable for their propagation. For instance, several retrotransposons in species as diverse as slime mold and budding and fission yeast have evolved independently, but convergently, the capacity to target the upstream regions of genes transcribed by RNA polymerase III, where they do not appear to affect host gene expression but retain the ability to be transcribed themselves. TEs are an extensive source of mutations and genetic polymorphisms TEs occupy a substantial portion of the genome of a species, including a large fraction of the DNA unique to that species. In maize, where Barbara McClintock did her seminal work, an astonishing 60 to 70% of the genome is comprised of LTR retrotransposons, many of which are unique to this species or its close wild relatives, but the less prevalent DNA transposons are currently the most active and mutagenic (Fig. 24.2.4). Similarly, the vast majority of TE insertions in Drosophila melanogaster are absent at the orthologous site in its closest relative D. simulans (and vice versa), and most are not fixed in the population. Many TE families are still actively transposing and the process is highly mutagenic; more than half of all known phenotypic mutants of D. melanogaster isolated in the laboratory are caused by spontaneous insertions of a wide variety of TEs. Transposition events are also common and mutagenic in laboratory mice, where the ongoing activity of several families of LTR elements are responsible for 10–15% of all inherited mutant phenotypes. This contribution of TEs to genetic diversity may be underestimated, as TEs can be more active when organisms are under stress, such as in their natural environment. Because TE insertions rarely provide an immediate fitness advantage to their host, those reaching fixation in the population do so largely by genetic drift and are subsequently eroded by point mutations that accumulate neutrally. Over time, these mutations result in TEs that can no longer encode transposition enzymes and produce new integration events. For instance, our (haploid) genome contains ~ 500,000 L1 copies, but more than 99.9% of these L1 copies are fixed and no longer mobile due to various forms of mutations and truncations. It is estimated that each person carries a set of ~ 100 active L1 elements, and most of these are young insertions still segregating within the human population. Thus, as for any other organism, the ‘reference’ human genome sequence does not represent a comprehensive inventory of TEs in humans. Thousands of ‘non-reference’, unfixed TE insertions have been cataloged through whole genome sequencing and other targeted approaches. On average, any two human haploid genomes differ by approximately a thousand TE insertions, primarily from the L1 or Alu families. The number of TE insertion polymorphisms in a species with much higher TE activity such as maize dwarfs the number in humans. If TEs bring no immediate benefit to their host and are largely decaying neutrally once inserted, how do they persist in evolution? One key to this conundrum is the ability of TEs not only to propagate vertically but also horizontally between individuals and species. There is now a large body of evidence supporting the idea that horizontal transposon transfer is a common phenomenon that affects virtually every major type of TE and all branches of the tree of life. While the cellular mechanisms underlying horizontal transposon transfer remain murky, it is increasingly apparent that the intrinsic mobility of TEs and ecological interactions between their host species, including those with pathogens and parasites, facilitate the transmission of elements between widely diverged taxa. TEs are associated with genome rearrangements and unique chromosome feature Transposition represents a potent mechanism of genome expansion that over time is counteracted by the removal of DNA via deletion. The balance between the two processes is a major driver in the evolution of genome size in eukaryotes. Several studies have demonstrated the impact and range of this shuffling and cycling of genomic content on the evolution of plant and animal genomes. Because the insertion and removal of TEs are often imprecise, these processes can indirectly affect surrounding host sequences. Some of these events occur at high enough frequency to result in vast amounts of duplication and reshuffling of host sequences, including genes and regulatory sequences. For example, a single group of DNA transposons (MULEs) has been responsible for the capture and reshuffling of ~ 1,000 gene fragments in the rice genome. Such studies have led to the conclusion that the rate at which TEs transpose, which is in part under host control, is an important driver of genome evolution. In addition to rearrangements induced as a byproduct of transposition, TEs can promote genomic structural variation long after they have lost the capacity to mobilize. In particular, recombination events can occur between the highly homologous regions dispersed by related TEs at distant genomic positions and result in large-scale deletions, duplications, and inversions (Fig. 24.2.4). TEs also provide regions of microhomology that predispose to template switching during repair of replication errors leading to another source of structural variants. These non-transposition-based mechanisms for TE-induced or TE-enabled structural variation have contributed substantially to genome evolution. These processes can also make the identification of actively transposing elements more difficult in population studies that infer the existence of active elements through the detection of non-reference insertions. TEs also contribute to specialized chromosome features. An intriguing example is in Drosophila, where LINE-like retrotransposons form and maintain the telomeres in replacement of the telomerase enzyme which has been lost during dipteran evolution. This domestication event could be viewed as a replay of what might have happened much earlier in eukaryotic evolution to solve the ‘end problem’ created by the linearization of chromosomes. Indeed, the reverse transcriptase component of telomerase is thought to have originated from an ancient lineage of retroelements. TE sequences and domesticated transposase genes also play structural roles at centromeres. There is an intricate balance between TE expression and repression To persist in evolution, TEs must strike a delicate balance between expression and repression (Fig. 24.2.4). Expression should be sufficient to promote amplification, but not so vigorous as to lead to a fitness disadvantage for the host that would offset the benefit to the TE of increased copy numbers. This balancing act may explain why TE-encoded enzymes are naturally suboptimal for transposition and why some TEs have evolved self-regulatory mechanisms controlling their own copy numbers. A variety of host factors are also employed to control TE expression, which includes a variety of small RNA, chromatin, and DNA modification pathways, as well as sequence-specific repressors such as the recently profiled KRAB zinc-finger proteins. However, many of these silencing mechanisms must be at least partially released to permit the developmental regulation of host gene expression programs, particularly during early embryonic development. For example, genome-wide loss of DNA methylation is necessary to reset imprinted genes in primordial germ cells. This affords TEs an opportunity, as reduced DNA methylation often promotes TE expression. Robust expression of a TE in the germ lineage (but not necessarily in the gametes themselves) is often its own downfall. In one example of a clever trick employed by the host, TE repression is relieved in a companion cell derived from the same meiotic product as flowering plant sperm. However, this companion cell does not contribute genetic material to the next generation. Thus, although TEs transpose in a meiotic product, the events are not inherited. Instead, TE activity in the companion cell may further dampen TE activity in sperm via the import of TE-derived small RNAs. Another important consequence of the intrinsic expression/repression balance is that the effects of TEs on a host can vary considerably among tissue types and stages of an organism’s life cycle. From the TE’s perspective, an ideal scenario is to be expressed and active in the germline, but not in the soma, where expression would gain the TE no advantage, only disadvantages. This is indeed observed among many species, with ciliates representing an extreme example of this division—TEs are actively deleted from the somatic macronucleus but retained in the micronucleus, or germline. Another example is the P-elements in Drosophila, which are differentially spliced in the germline versus soma. Many organisms, including plants, do not differentiate germ lineage cells early in development; rather, they are specified from somatic cells shortly before meiosis commences. Thus, TEs that transpose in somatic cells in plants have the potential to be inherited, which suggests that the interest of TEs and hosts are in conflict across many more cells and tissues than in animals with a segregated germline. TEs are insertional mutagens in both germline and soma Like other species, humans contend with a contingent of currently active TEs where the intrinsic balance between expression and repression is still at play. For us, this includes L1 and other mobile elements that depend on L1-encoded proteins for retrotransposition. These elements are responsible for new germline insertions that can cause genetic disease. More than 120 independent TE insertions have been associated with human disease. The rate of de novo germline transposition in humans is approximately one in 21 births for Alu and one in 95 births for L1. Historically, little attention has been given to transposition in somatic cells and its consequences, because somatic transposition may be viewed as an evolutionary dead-end for the TE with no long-term consequences for the host species. Yet, there is abundant evidence that TEs are active in somatic cells in many organisms (Fig. 24.2.4). In humans, L1 expression and transposition have been detected in a variety of somatic contexts, including early embryos and certain stem cells. There is also a great deal of interest in mobile element expression and activity in the mammalian brain, where L1 transposition has been proposed to diversify neuronal cell populations. One challenge for assessing somatic activity has rested with the development of reliable single-cell insertion site mapping strategies. Somatic activity has also been observed in human cancers, where tumors can acquire hundreds of new L1 insertions. Just like for human polymorphisms, somatic activity in human cancers is caused by small numbers of so-called ‘hot’ L1 loci. The activities of these master copies vary depending on the individual, tumor type, and timeframe in the clonal evolution of the tumor. Some of these de novo L1 insertions disrupt critical tumor suppressors and oncogenes and thus drive cancer formation, although the vast majority appear to be ‘passenger’ mutations. Host cells have evolved several mechanisms to keep TEs in check. However, as the force of natural selection begins to diminish with age and completely drops in post-reproductive life, TEs may become more active. TEs can be damaging in ways that do not involve transposition TEs are best known for their mobility, and their ability to transpose to new locations. While the breakage and insertion of DNA associated with transposition represent an obvious source of cell damage, this is not the only or perhaps even the most common mechanism by which TEs can be harmful to their host. Reactivated transposons harm the host in multiple ways. First, de-repression of transposon loci, including their own transcription, may interfere with transcription or processing of host mRNAs through a myriad of mechanisms. Genome-wide transcriptional de-repression of TEs has been documented during replicative senescence of human cells and several mouse tissues, including the liver, muscle, and brain. De-repression of LTR and L1 promoters can also cause oncogene activation in cancer. Second, TE-encoded proteins such as the endonuclease activity of L1 ORF2p can induce DNA breaks and genomic instability. Third, accumulation of RNA transcripts and extrachromosomal DNA copies derived from TEs may trigger an innate immune response leading to autoimmune diseases and sterile inflammation (Fig. 24.2.4). Activation of interferon response is now a well-documented property of transcripts derived from endogenous retroviruses and may give immunotherapies a boost in identifying and attacking cancer cells. The relative contribution of all the above mechanisms in organismal pathologies remains to be determined. Following transcription (and sometimes splicing) of TEs, the next step in the process involves the translation of the encoded proteins and, for retroelements, reverse transcription of the TEs into cDNA substrates suitable for transposition. Once engaged by a TE-encoded reverse transcriptase protein, the resulting cytosolic DNAs and RNA:DNA hybrids can alert inflammatory pathways. An example of this is seen in patients with Aicardi–Goutières syndrome, where the accumulation of TE-derived cytosolic DNA is due to mutations in pathways that normally block TE processing or degrade TE-derived DNA. Although not all TEs encode functional proteins, some do, including a few endogenous retroviruses capable of producing Gag, Pol, or envelope (Env) proteins. Overexpression of these Env proteins can be cytotoxic and has been linked to at least two neurodegenerative diseases, multiple sclerosis, and amytrophic lateral sclerosis. Small accessory proteins produced by the youngest human endogenous retrovirus (HERV) group, HERV-K (HML-2), may play a role in some cancers but the evidence remains circumstantial. Key coding and non-coding RNAs are derived from TEs Although usually detrimental, there is growing evidence that TE insertions can provide the raw material for the emergence of protein-coding genes and non-coding RNAs, which can take on important and, in some cases essential, cellular function (Fig. 24.2.4). The process of TE gene ‘domestication’ or exaptation over evolutionary time contributes to both deeply conserved functions and more recent, species-specific traits. Most often, the ancestral or a somewhat modified role of a TE-encoded gene is harnessed by the host and conserved, while the rest of the TE sequence, and hence its ability to autonomously transpose, has been lost. Spectacular examples of deeply conserved TE-derived genes are Rag1 and Rag2, that catalyze V(D)J somatic recombination in the vertebrate immune system. Both genes, and probably the DNA signals they recognize, were derived from an ancestral DNA transposon around 500 million years ago. Indeed, DNA transposases have been co-opted multiple times to form new cellular genes. The gag and env genes of LTR retrotransposons or endogenous retroviruses (ERVs) have also been domesticated numerous times to perform functions in placental development, contribute to host defense against exogenous retroviruses, act in brain development, and play other diverse roles. One of the most intriguing examples of TE domestication is the repeated, independent capture of ERV env genes, termed syncytins, which appear to function in placentation by facilitating cell–cell fusion and syncytiotrophoblast formation. Notably, one or more syncytin genes have been found in virtually every placental mammalian lineage where they have been sought, strongly suggesting that ERVs have played essential roles in the evolution and extreme phenotypic variability of the mammalian placenta. Another example of a viral-like activity re-purposed for host cell function is provided by the neuronal Arc gene, which arose from the gag gene from a LTR retrotransposon domesticated in the common ancestor of tetrapod vertebrates. Genetic and biochemical studies of murine Arc show that it is involved in memory and synaptic plasticity and has preserved most of the ancestral activities of Gag, including the packaging and intercellular trafficking of its own RNA. Remarkably, flies appear to have independently evolved a similar system of trans-synaptic RNA delivery involving a gag-like protein derived from a similar yet distinct lineage of LTR retrotransposons. Thus, the biochemical activities of TE-derived proteins have been repeatedly co-opted during evolution to foster the emergence of convergent cellular innovations in different organisms. TEs can donate their own genes to the host, but they can also add exons and rearrange and duplicate existing host genes. In humans, intronic Alu elements are particularly prone to be captured as alternative exons through cryptic splice sites residing within their sequences. L1 and SVA (SINE/VNTR/Alu) elements also contribute to exon shuffling through transduction events of adjacent host sequences during their mobilization. The reverse transcriptase activity of retroelements is also responsible for the trans-duplication of cellular mRNAs to create ‘processed’ retrogenes in a wide range of organisms. The L1 enzymatic machinery is thought to be involved in the generation of tens of thousands of retrogene copies in mammalian genomes, many of which remain transcribed and some of which have acquired new cellular functions. This is a process still actively shaping our genomes; it has been estimated that 1 in every 6000 humans carries a novel retrogene insertion. TEs also make substantial contributions to the non-protein coding functions of the cell. They are major components of thousands of long non-coding RNAs in human and mouse genomes, often transcriptionally driven by retroviral LTRs. Some of these TE-driven lncRNAs appear to play important roles in the maintenance of stem cell pluripotency and other developmental processes. Many studies have demonstrated that TE sequences embedded within lncRNAs and mRNAs can directly modulate RNA stability, processing, or localization with important regulatory consequences. Furthermore, TE-derived microRNAs and other small RNAs processed from TEs can also adopt regulatory roles serving host cell functions. The myriad of mechanisms by which TEs contribute to coding and non-coding RNAs illustrate the multi-faceted interactions between these elements and their host. TEs contribute cis-regulatory DNA elements and modify transcriptional networks Cis-regulatory networks coordinate the transcription of multiple genes that function in concert to orchestrate entire pathways and complex biological processes. In line with Barbara McClintock’s insightful predictions, there is now mounting evidence that TEs have been a rich source of material for the modulation of eukaryotic gene expression (Fig. 24.2.4). Indeed, TEs can disperse vast amounts of promoters and enhancers, transcription factor binding sites, insulator sequences, and repressive elements. The varying coat colors of agouti mice provide a striking example of a host gene controlling coat color whose expression can be altered by the methylation levels of a TE upstream of its promoter. In the oil palm, the methylation level of a TE that sits within a gene important for flowering ultimately controls whether or not the plants bear oil-rich fruit. As TE families typically populate a genome as a multitude of related copies, it has long been postulated that they have the potential to donate the same cis-regulatory module to ‘wire’ batteries of genes dispersed throughout the genome. An increasing number of studies support this model and suggest that TEs have provided the building blocks for the assembly and remodeling of cis-regulatory networks during evolution, including pathways underlying processes as diverse as pregnancy, stem cell pluripotency, neocortex development, innate immunity in mammals, or the response to abiotic stress in maize. Indeed, TE sequences harbor all the necessary features of a ‘classical’ gene regulatory network. They are bound by diverse sets of transcription factors that integrate multiple inputs (activation/repression), respond to signals in both cis and trans, and are capable of co-ordinately regulating gene expression. In this context, TEs are highly suitable agents to modify biological processes by creating novel cis-regulatory circuits and fine-tuning pre-existing networks. Outlook As potent insertional mutagens, TEs can have both positive and negative effects on host fitness, but it is likely that the majority of TE copies in any given species—and especially those such as humans with small effective population size—have reached fixation through genetic drift alone and are now largely neutral to their host. When can we say that TEs have been co-opted for cellular function? The publication of the initial ENCODE paper, which asserted ‘function for 80% of the genome’, was the subject of much debate and controversy. Technically speaking, ENCODE assigned only ‘biochemical’ activity to this large fraction of the genome. Yet critics objected to the grand proclamations in the popular press (The Washington Post Headline: “Junk DNA concept debunked by new analysis of the human genome”) and to the ENCODE consortium’s failure to prevent this misinterpretation. To these critics, ignoring evolutionary definitions of function was a major misstep. This debate can be easily extended to include TEs. TEs make up the vast majority of what is often referred to as ‘junk DNA’. Today, the term is mostly used (and abused) by the media, but it has deep roots in evolutionary biology. Regardless of the semantics, what evidence is needed to assign a TE with a function? Many TEs encode a wide range of biochemical activities that normally benefit their own propagation. For example, TEs often contain promoter or enhancer elements that highjack cellular RNA polymerases for transcription and autonomous elements encode proteins with various biochemical and enzymatic activities, all of which are necessary for the transposon to replicate. Do these activities make them functional? The vast differences in TEs between species make standard approaches to establishing their regulatory roles particularly challenging. For example, intriguing studies on the impact of HERVs, in particular HERV-H, in stem cells and pluripotency must be interpreted using novel paradigms that do not invoke deep evolutionary conservation to imply function, as these particular ERVs are absent outside of great apes. Evolutionary constraints can be measured at shorter time scales, including the population level, but this remains a statistically challenging task, especially for non-coding sequences. Natural loss-of-function alleles may exist in the human population and their effect on fitness can be studied if their impact is apparent, but these are quite rare and do not allow systematic studies. It is possible to engineer genetic knockouts of a particular human TE locus to test its regulatory role but those are restricted to in-vitro systems, especially when the orthologous TE does not exist in the model species. In this context, studying the impact of TEs in model species with powerful genome engineering tools and vast collections of mutants and other genetic resources, such as plants, fungi, and insects, will also continue to be extremely valuable. Finally, a growing consensus is urging more rigor when assigning cellular function to TEs, particularly for the fitness benefit of the host. Indeed, a TE displaying biochemical activity (such as those bound by transcription factors or lying within open chromatin regions) cannot be equated to a TE that shows evidence of purifying selection at the sequence level or, when genetically-altered, result in a deleterious or dysfunctional phenotype. Recent advances in editing and manipulating the genome and the epigenome en masse yet with precision, including repetitive elements, offer the promise for a systematic assessment of the functional significance of TEs.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/03%3A_Unit_III-_Information_Pathway/23%3A_Chromosome_Structure/23.03%3A_Chromosome_Packaging.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Some of the material in this chapter section comes from Chapter 8.4, Chromosomes and Chromatin, as it was important to describe it earlier in the structure/function unit. In addition, some biochemistry courses might not get to the material in a late chapter in a text. Repetition of some of the material is easier in an online textbook as well. Introduction Recall from Chapter 8, that within eukaryotic cells, DNA is organized into long linear structures called chromosomes, as shown in Figure $1$. A chromosome is a deoxyribonucleic acid (DNA) molecule with part or all of the genetic material (genome) of an organism. Most eukaryotic chromosomes include packaging proteins which, aided by chaperone proteins, bind to and condense the DNA molecule to prevent it from becoming an unmanageable tangle. Before typical cell division, these chromosomes are duplicated in the process of DNA replication, providing a complete set of chromosomes for each daughter cell. The replicated arms of a chromosome are called chromatids. Before being separated into the daughter cells during mitosis, replicated chromatids are held together by a chromosomal structure called the centromere. Eukaryotic organisms (animals, plants, fungi, and protists) store most of their DNA inside the cell nucleus as linear nuclear DNA, and some in the mitochondria as circular mitochondrial DNA or in chloroplasts as circular chloroplast DNA. In contrast, prokaryotes (bacteria and archaea) do not have organelle structures and thus, store their DNA only in a region of the cytoplasm known as the nucleoid region. Prokaryotic chromosomes consist of double–stranded circular DNA. The genome of a cell is often significantly larger than the cell itself. For example, if the DNA from a human cell containing 46 chromosomes were stretched out in a line, it would extend more than 6 feet (2 meters)! How is it possible that the genetic information not only fits into the cell but fits into the cell nucleus? Eukaryota solves this problem by a combination of supercoiling and packaging DNA around the histone family of proteins (described below). Prokaryotes do not contain histones (with a few exceptions). Prokaryotes tend to compress their DNA using nucleoid-associated-proteins (NAPs) and supercoiling (Figure 24.3.2). Supercoiling DNA supercoiling refers to the over- or under-winding of a DNA strand, and is an expression of the strain on that strand, as shown in Figure $2$. Supercoiling is important in many biological processes, such as compacting DNA, and regulating access to the genetic code. DNA supercoiling strongly affects DNA metabolism and possibly gene expression. Additionally, certain enzymes such as topoisomerases can change DNA topology to facilitate functions such as DNA replication or transcription. In a “relaxed” double-helical segment of B-DNA, the two strands twist around the helical axis once every 10.4–10.5 base pairs of sequence. Adding or subtracting twists, as some enzymes can do, impose strain. If a DNA segment under twist strain were closed into a circle by joining its two ends and then allowed to move freely, the circular DNA would contort into a new shape, such as a simple figure-eight (Figure $2$). Such a contortion is a supercoil. The noun form “supercoil” is often used in the context of DNA topology. Positively supercoiled (overwound) DNA is transiently generated during DNA replication and transcription, and, if not promptly relaxed, inhibits (regulates) these processes. The simple figure eight is the simplest supercoil and is the shape a circular DNA assumes to accommodate one too many or one too few helical twists. The two lobes of the figure- eight will appear rotated either clockwise or counterclockwise with respect to one another, depending on whether the helix is over- or underwound. For each additional helical twist being accommodated, the lobes will show one more rotation about their axis. As a general rule, the DNA of most organisms is negatively supercoiled. Lobal contortions of a circular DNA, such as the rotation of the figure-eight lobes above, are referred to as writhe. The above example illustrates that twist and writhes are interconvertible. Supercoiling can be represented mathematically by the sum of twist and writhe (Figure $2$. The twist is the number of helical turns in the DNA and the writhe is the number of times the double helix crosses over on itself (these are the supercoils). Extra helical twists are positive and lead to positive supercoiling, while subtractive twisting causes negative supercoiling. Many topoisomerase enzymes sense supercoiling and either generate or dissipate it as they change DNA topology. In addition to forming supercoiled structures, circular chromosomes from bacteria have been shown to undergo the processes of catenation and knotting upon the inhibition of topoisomerase enzymes. Catenation is the process by which two circular DNA strands are linked together like chain links, whereas DNA knotting is the interlooping structures occurring within a single circular DNA structure, as shown in Figure $3$. In vivo, the action of topoisomerase enzymes is critical to keep knots and catenoids from tangling the DNA structure. Catenanes are effectively topologically linked circular molecules In part, because chromosomes may be very large, segments in the middle may act as if their ends are anchored. As a result, they may be unable to distribute excess twist to the rest of the chromosome or to absorb twist to recover from underwinding—the segments may become supercoiled, in other words. In response to supercoiling, they will assume an amount of writhe, just as if their ends were joined. Supercoiled circular DNA forms two major structures; a plectoneme or a toroid, or a combination of both (Figure 24.3.2). A negatively supercoiled DNA molecule will produce either a one-start left-handed helix, the toroid, or a two-start right-handed helix with terminal loops, the plectoneme. Plectonemes are typically more common in nature, and this is the shape most bacterial plasmids will take (Figure 4.10). For larger molecules, it is common for hybrid structures to form – a loop on a toroid can extend into a plectoneme, as shown in Figure $4$. DNA supercoiling is important for DNA packaging within all cells, and seems to also play a role in gene expression. Topoisomerases Topoisomerase can change the tension in supercoiled DNA. Think of how you untie a knot. It takes a lot of work sometimes, and if it's too hard, you simply cut the impediment to unknotting. Topoisomerases work by making transient breaks in the DNA before unwinding and religation. There are two main types of topoisomerases, topo I and topo II. It's very hard to describe their activities with just words and static diagrams. View the video below and you will get a great sense of what the enzymes do and how they are different. With this background, we can now explore each enzyme in more detail. They are both targets of cancer drugs which makes them even more interesting. Topo I enzyme relaxes DNA by nicking one stand. The dsDNA then rotates around the non-nicked strand. It unwinds new DNA and allows the condensation of chromosomes. When both DNA and RNA polymerase makes new DNA and RNA strands, respectively, they increase the supercoiling of the nucleic acid. Topoisomerases relax them. They also play a role in the regulation of gene expression by affecting gene promoters where RNA polymerase binds, with negative supercoiling enhancing transcription and positive supercoiling inhibiting it. Figure $5$ shows the topology of DNA and an overview of the mechanisms of Topo I and II. : DNA topology and DNA topoisomerase mechanism Figure $5$: DNA topology and DNA topoisomerase mechanisms. Shannon J. McKie, Keir C. Neuman, and Anthony Maxwell. Bioessays (2021). https://doi.org/10.1002/bies.202000286. Attribution 4.0 International (CC BY 4.0) (A) Topological consequences of DNA metabolism. i) During DNA replication, strand separation leads to positive supercoiling ahead of the advancing protein machinery, and precatenane formation behind. Precatenanes form as the newly-synthesized duplexes wrap around one another, and, if not removed before completion of replication, catenated DNA molecules are formed. ii) During transcription, strand separation leads to positive supercoiling ahead of the advancing protein machinery, and negative supercoil formation behind. iii) Hemicatenanes are a possible end result of replication, in which the parental strands of the replicated duplexes remain base-paired. iv: DNA knotting can also occur as a result of DNA replication in which a DNA molecule is intramolecularly linked. (B) Summary of topo categories and mechanism. The topos are categorized based on whether they catalyze single- (type I) or double-stranded (type II) DNA breaks. The type I topos are further subdivided to type IA, IB, and IC. Type IA form a transient covalent bond to the 5ʹ DNA phosphate and function via a strand passage mechanism. Type IB and IC form a transient covalent bond to the 3ʹ DNA phosphate and function via a controlled-rotation mechanism. Type II topos are further subdivided into type IIA and IIB. Both form a transient covalent bond to the 5ʹ DNA phosphate of both strands of the duplex and function via a strand-passage mechanism. (C) Summary of the topological manipulations performed by DNA topoisomerases, namely relaxation of positive and negative supercoils and decatenation. Type IA topos are color-coded pink, type IB are orange, type IC are yellow, type IIA are green, and type IIB are blue. The requirement of ATP or ssDNA for activity is denoted using a red or blue circle, respectively Topoisomerase I (Topo I): Class I topoisomerases wrap around the DNA and cut one strand. Keeping that spot in place, the helix can spin to reduce strain caused by either over- or underwinding. After these geometric contortions, the single-stranded DNA nick is repaired and the tension is relieved. Type IA topoisomerase from E. Coli is shown in Figure $6$. Figure $6$: Structure of a type IA topoisomerase. E. coli topoisomerase III is shown to illustrate the overall structure of a type IA topoisomerase and the typical toroidal fold observed in all members of this type. (A) Diagram showing the structure of the apo-enzyme [PDB 1D6M(7)]. In the absence of DNA, the active site, found at the intersection of domains I and III (encircled), is buried. (B) Diagram showing the structure of a complex with single-stranded DNA [PDB 1I7D (23)]. Note the movement of domains that occurs to accommodate DNA. In both diagrams, the four major domains of the protein are colored red, blue, purple, and green for domains I, II, III, and IV, respectively. The single-stranded DNA binding groove, shown circled in black, extends from domain IV to the active site. The active site residues as well as the single-stranded DNA in the complex are shown in a ball and stick representation. Dasgupta T, Ferdous S, Tse-Dinh YC. Mechanism of Type IA Topoisomerases. Molecules. 2020 Oct 17;25(20):4769. doi: 10.3390/molecules25204769. PMID: 33080770; PMCID: PMC7587558. Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). A simplified cartoon mechanism for Topo I is shown in Figure $7$. Figure $7$: Diagram showing the proposed mechanism of DNA relaxation by type IA topoisomerases. The mechanism involves several transient conformational intermediates both of the protein and the DNA. The sequence of the steps and the intermediates are hypothetical and more states are likely to be involved in the cycle. Processivity by the enzyme requires that after one relaxation event, the protein continues to another relaxation cycle without releasing the DNA. In the diagram, the protein is shown in grey, and the DNA in red/blue. The orange dot represents the presence of the covalent protein/DNA complex. The single-stranded DNA binding groove is shown in red or yellow. Dasgupta, T et al. ibid. A more detailed view of the domain structure and mechanism for Topo I is shown in Figure $8$. Figure $8$: Type IA DNA topoisomerases. Dasgupta, T et al. ibid. (A) Protein domain organization of Escherichia coli DNA topoisomerase IA (topo IA) and DNA topoisomerase III (topo III). Black vertical lines represent the active site tyrosines. (B) Crystal structure of E. coli topo I bound to ssDNA (PDB: 4RUL).[20] (C) Strand-passage mechanism for type IA topos. (1) topo binds the G-segment ssDNA region, (2) the G-segment is cleaved. (3) The topo DNA gate is opened, (4) which allows T-segment transfer through the cleaved G-strand. (5) The DNA gate is closed, (6) and the G-strand is re-ligated, changing the linking number by 1. (7) The topo can then go through another round of relaxation or dissociate from the DNA. Type IA topo (domains 1–4) is in pink, the active site tyrosine is yellow and the DNA is grey. (D) Crystal structure of E. coli topo III bound to ssDNA (PDB: 2O54).[26] (E) Crystal structures of human topo IIIα (blue) bound to RMI1(orange) (PDB: 4CGY),[39] and human topo IIIβ (magenta) bound to TDRD3 (green) (PDB: 5GVE).[60] For panels A, B, and C, the topo I and III domains are color-coded as follows: D1 is red, D2 is pink, D3 is yellow, D4 is orange, D5 is marine blue, D6 is purple, D7 is green, D8 is teal, and D9 is light blue Figure $9$ shows an interactive iCn3D model of E.Coli topoisomerase I in complex with ssDNA (4RUL). Figure $9$: E.Coli topoisomerase I in complex with ssDNA (4RUL). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...JxcFvds2e8p2WA The topoisomerase is shown in gray and the single-stranded DNA is in cyan. 4 Zn2+ ions are shown. Three form Zn-finger motifs. Two of these are shown near the protein binding site for DNA. A fourth Zn is bound to a single His 566 in the structure. The active site amino acids of the enzyme, which includes residues D111, D113, Y319, and R321 from D1 and D3, are shown and labeled. Also shown are active site residues, E115, F139 and Y312. There are 4 Cys-Zn ribbon domains. The ones that interact with the ssDNA involve π-stacking, some of which are illustrated in the iCn3D model. Class II topoisomerases (Topo II) A series of enzymes are included in this class including DNA gyrase and Topo (IV) from prokaryotes and Topo II from eukaryotes. In eukaryotes, they help sister chromosomes separate if they get tangled during cell division. This enzyme works by: this enzyme makes a double-stranded cut, moves on the helix through the cut, and reseals the cut. • binding the gate segment (G -segment) ds-DNA at a DNA gate where a double-stranded break is made • binding the transport segment (T-segment) ds-DNA at the N-gate where the nucleotide ATP binds • The T-segment DNA moves through the break in the G-segment and released the C-gate • The G- and T-segments are reconnected. After this, the N gate reopens to allow the process to occur again. The domain structure of Topo IIs and the general mechanism of action are shown in Figure $10$. Figure $10$: Type II DNA topoisomerases: domain organization and mechanism. (A) Protein domain organization for the type IIA topos: E. coli DNA gyrase, E. coli DNA topoisomerase IV (topo IV), yeast DNA topoisomerase II (topo II), Methanosarcina mazei DNA topoisomerase VI (topo VI), Paenibacillus polymyxa DNA topoisomerase VIII (plasmid-borne), and Pseudomonas phage NP1 Mini-A. (B) type II topo strand passage mechanism. (1) G-segment is bound at the DNA gate and the T-segment is captured. (2) ATP binding stimulates dimerization of the N-gate, the G-segment is cleaved and the T-segment is passed through the break. (3) The G-segment is re-ligated and T-segment exits through the C-gate. For type IIB topos, there is no C-gate so once the T-segment passes through the G-segment, it is released from the enzyme. (4) Dissociation of ADP and Pi allows N-gate opening, a scenario where the enzyme either remains bound to the G-segment, ready to capture a consecutive T-segment, or (5) dissociates from the G-segment. Figure $11$ shows an interactive iCn3D model of Yeast Topoisomerase II-DNA-AMPPNP complex (4GFH) . Figure $11$: Yeast Topoisomerase II-DNA-AMPPNP complex (4GFH). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...b5EHLfMwde5Dw8 The two protein chains in the homodimer are shown in magenta and cyan. The double-stranded DNA is shown with a brown backbone and CPK-colored spheres. ANP (AMPPNP), a nonhydrolyzable ATP analog, and Mg2+ ions are shown in spacefill and labeled. The ATP binding and ATPase domain of one of the monomers (cyan for example) is adjacent to the nuclease-cutting domain of the other monomer (magenta). This requires some conformational gymnastics as the ATP binding and cleavage domain move around each other to allow the DNA strand to pass in the right direction and to reset the enzyme. Note the circular nature of chloroplast and mitochondrial DNA, suggesting a bacterial origin for both of these organelle structures. Sequence alignments further lend support for the endosymbiotic theory, which proposes that bacteria were engulfed by early eukaryotic organisms and subsequently became symbiotic to their eukaryotic counterpart, rather than being digested. A reminder about mitochondrial DNA In the cells of extant organisms, the vast majority of the proteins present in the mitochondria (numbering approximately 1500 different types in mammals) are coded for by nuclear DNA. However, sequencing of the human mitochondrial genome has revealed 16,569 base pairs encoding 13 proteins (Figure 24.3.5). Many of the mitochondrially produced proteins are required for electron transport during the production of ATP, as shown in Figure $12$. Histones and DNA packing Within eukaryotic chromosomes, chromatin proteins, known as histones, compact and organize DNA. These compacting structures guide the interactions between DNA and other proteins, helping control which parts of the DNA are transcribed. Histones are highly alkaline proteins found in eukaryotic cell nuclei that package and order the DNA into structural units called nucleosomes. They are the chief protein components of chromatin, acting as spools around which DNA winds, and playing a role in gene regulation. Without histones, the unwound DNA in chromosomes would be very long (a length-to-width ratio of more than 10 million to 1 in human DNA). For example, each human diploid cell (containing 23 pairs of chromosomes) has about 1.8 meters of DNA; wound on the histones, the diploid cell has about 90 micrometers (0.09 mm) of chromatin. Five major families of histones exist: H1/H5, H2A, H2B, H3, and H4. Histones H2A, H2B, H3, and H4 are known as the core histones, while histones H1/H5 are known as the linker histones. The core histones all exist as dimers, which are similar in that they all possess the histone fold domain: three alpha helices linked by two loops. It is this helical structure that allows for interaction between distinct dimers, particularly in a head-tail fashion (also called the handshake motif). The resulting four distinct dimers then come together to form one octameric nucleosome core, approximately 63 Angstroms in diameter. Around 146 base pairs (bp) of DNA wrap around this core particle 1.65 times in a left-handed super-helical turn to give a particle of around 100 Angstroms across, called a nucleosome, as shown in Figure $13$. The linker histone H1 binds the nucleosome at the entry and exit sites of the DNA, thus locking the DNA into place and allowing the formation of a higher order structure, as shown in Figure $14$. The most basic such formation is the 10 nm fiber or beads on a string conformation. This involves the wrapping of DNA around nucleosomes with approximately 50 base pairs of DNA separating each pair of nucleosomes (also referred to as linker DNA). The nucleosome contains over 120 direct protein-DNA interactions and several hundred water-mediated ones. Direct protein – DNA interactions are not spread evenly about the octamer surface but rather located at discrete sites. These are due to the formation of two types of DNA binding sites within the octamer; the α1α1 site, which uses the α1 helix from two adjacent histones, and the L1L2 site formed by the L1 and L2 loops. Salt links and hydrogen bonding between both side-chain basic and hydroxyl groups and main-chain amides with the DNA backbone phosphates form the bulk of interactions with the DNA. This is important, given that the ubiquitous distribution of nucleosomes along genomes requires it to be a non-sequence-specific DNA-binding factor. Although nucleosomes tend to prefer some DNA sequences over others, they are capable of binding practically to any sequence, which is thought to be due to the flexibility in the formation of these water-mediated interactions. In addition, non-polar interactions are made between protein side-chains and the deoxyribose groups, and an arginine side-chain intercalates into the DNA minor groove at all 14 sites where it faces the octamer surface. The distribution and strength of DNA-binding sites about the octamer surface distort the DNA within the nucleosome core. The DNA is non-uniformly bent and also contains twist defects. The twist of free B-form DNA in solution is 10.5 bp per turn. However, the overall twist of nucleosomal DNA is only 10.2 bp per turn, varying from a value of 9.4 to 10.9 bp per turn. The histone tail extensions constitute up to 30% by mass of the histones but are not visible in the crystal structures of nucleosomes due to their high intrinsic flexibility, and have been thought to be largely unstructured (Figure 4.14). The N-terminal tails of histones H3 and H2B pass through a channel formed by the minor grooves of the two DNA strands, protruding from the DNA every 20 bp. The N-terminal tail of histone H4, on the other hand, has a region of highly basic amino acids (16-25), which, in the crystal structure, forms an interaction with the highly acidic surface region of a H2A-H2B dimer of another nucleosome, being potentially relevant for the higher-order structure of nucleosomes. This interaction is thought to occur under physiological conditions also, and suggests that acetylation of the H4 tail distorts the higher-order structure of chromatin. Figure $15$ shows an interactive iCn3D model of the human nucleosome (3afa). One member of each pair of histones is shown in cartoon rendering, while the other member of the pair is shown in the same color but in spacefill rendering. The structure of a human nucleosome (3afa) is shown below (H2A is shown in cyan, H2B in blue, H3 in magenta, and H4 in purple). Each strand of DNA is shown in a different shade of gray. Figure $15$: Human nucleosome (3afa). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...B2SwQHYDLj4BJ6 The formation of the DNA double helix represents the first-order packaging of the chromosome structure. The formation of nucleosomes represents the second level of packaging for eukaryotic chromosomes. In vitro data suggests that nucleosomes are then arranged into either a solenoid structure which consists of 6 nucleosomes linked together by the Histone H1 linker proteins or a zigzag structure that is similar to the solenoid construct, as shown in Figure $16$. Both the solenoid and zigzag structures are approximately 30 nm in diameter. The solenoid and zigzag structures reported from in vitro data have not yet been confirmed to occur in vivo. During interphase, each chromosome occupies a spatially limited, roughly elliptical domain which is known as a chromosome territory (CT). Each chromosome territory is comprised of higher-order chromatin units of ~1 Mb each. These units are likely built up from smaller loop domains that contain the solenoid/zigzag structural motifs. On the other hand, 1Mb domains can themselves serve as smaller units in higher-order chromatin structures. Chromosome territories are known to be arranged radially around the nucleus. This arrangement is both cell and tissue-type specific and is also evolutionarily conserved. The radial organization of chromosome territories was shown to correlate with their gene density and size. In this case, the gene-rich chromosomes occupy interior positions, whereas larger, gene-poor chromosomes, tend to be located around the periphery. Chromosome territories are also dynamic structures, with genes able to relocate from the periphery towards the interior once they have been ‘switched on’. In other cases, genes may move in the opposite direction, or simply maintain their position. The eviction of genes from their chromosome territories into the interchromatin compartment or a neighboring chromosome territory is often accompanied by the formation of large decondensed chromatin loops. Models describing chromosome territory arrangement With the development of high-throughput biochemical techniques, such as 3C (chromosome conformation capture) and 4C (chromosome conformation capture-on-chip and circular chromosome conformation capture), numerous spatial interactions between neighboring chromatin territories have been described, as shown in Figure $17$. These descriptions have been supplemented with the construction of spatial proximity maps for the entire genome (e.g., for a human lymphoblastoid cell line). Together, these observations and physical simulations have led to the proposal of various models that aim to define the structural organization of chromosome territories: 1. The chromosome territory-interchromatin compartment (CT-IC) model describes two principal compartments: chromosome territories (CTs) and an interchromatin compartment (IC). In this model, chromosome territories build up an interconnected chromatin network that is associated with an adjacent 3D space called the interchromatin compartment. The latter can be observed using both light and electron microscopy. Within a single chromosome territory, the interphase chromosome is divided into defined regions based on the level of chromosome condensation. Here, the inner part of the interphase chromosome is comprised of more condensed chromatin domains or higher-order chromatin fibers, while a thin (<200 nm) layer of more decondensed chromatin, known as the perichromatin region, can be found around the chromosomal periphery. Functionally, the perichromatin region represents the major transcriptional compartment and is also the region where most co-transcriptional RNA splicing takes place. DNA replication and DNA repair are also predominately carried out within the perichromatin region. Finally, nascent RNA transcripts, referred to as perichromatin fibrils, are also generated in the perichromatin region. Perichromatin fibrils are then subjected to the splicing events by the factors, provided by the interchromatin compartment. The lattice model, proposed by Dehgani et al. is based on reports that transcription also occurs within the inner, more condensed chromosome territories and not only at the interface between the interchromatin compartment and the perichromatin region. Using ESI (electron spectroscopic imaging), Dehgani et al. showed that chromatin was organized as an array of deoxyribonucleoprotein fibers of 10–30 nm in diameter. In this study, the interchromatin compartments, which are described in the CT-IC model as large channels between chromosome territories, were not apparent. Instead, chromatin fibers created a loose meshwork of chromatin throughout the nucleus that intermingled at the periphery of chromosome territories. Thus, inter- and intra-chromosomal spaces within this meshwork are essentially contiguous and together form the intra-nuclear space. 2. The interchromatin network (ICN) model predicts that intermingling chromatin fibers/loops can make both cis- (within the same chromosome) and trans- (between different chromosomes) contacts. This intermingling is uniform and makes a distinction between the chromosome territory and interchromatin compartment functionally meaningless. The advantage of the ICN model is that it permits high chromatin dynamics and diffusion-like movements. The authors propose that ongoing transcription influences the degree of intermingling between specific chromosomes by stabilizing associations between particular loci. Such interactions are likely to depend on the transcriptional activity of the loci and are therefore cell-type specific. The cell type-specific organization of chromosome territories has been studied by measuring the volume and frequency of intermingling between heterologous chromosomes. By using 3C (chromosome conformation capture) and FISH (fluorescence in situ hybridization) to map the regions of chromosome intermingling, it was revealed that these regions contain a higher density of active genes and are enriched with markers of transcriptional activation and repression, such as activated RNAPII. By comparing the positions of the CTs in undifferentiated mouse embryonic stem (ES) cells, ES cells in early stages of differentiation, and terminally differentiated NIH3T3 cells, it was shown that fully differentiated cells had a higher enrichment of RNAPII, compared to undifferentiated or less-differentiated cells. The findings support the notion that the intermingling regions have functional significance in the nucleus and provide a basis for understanding how the radial and relative positions of chromosomal territories evolve during the process of differentiation, explaining their organization in a cell type-dependent manner. 3. The Fraser and Bickmore model emphasizes the functional importance of giant chromatin loops, which originate from chromosome territories and expand across the nuclear space to share transcription factories. In this case, both cis- and trans- oops of decondensed chromatin can be co-expressed and co-regulated by the same transcription factory. 4. The Chromatin polymer models assume a broad range of chromatin loop sizes and predict the observed distances between genomic loci and chromosome territories, as well as the probabilities of contacts being formed between given loci. These models apply physics-based approaches that highlight the importance of entropy for understanding nuclear organization. By proposing the existence of conformational chromatin ensembles with structures based on three possible homopolymer states, these models also provide alternative structures to the traditional 30 nm chromatin fiber, which has been brought into question following recent studies. With a lack of experimental evidence to support these described models, it must be remembered that they serve only to hypothesize the structural and chemical properties of intermediate chromatin structures and to highlight unanswered questions. For example, the mechanisms that exist to control the rate and the extent of chromatin movement remain to be defined At the ends of the linear chromosomes are specialized regions of DNA called telomeres, shown in Figure $18$. The main function of these regions is to allow the cell to replicate chromosome ends using the enzyme telomerase, as the enzymes that normally replicate DNA cannot copy the extreme 3′ ends of chromosomes. These specialized chromosome caps also help protect the DNA ends, and stop the DNA repair systems in the cell from treating them as damage to be corrected. In human cells, telomeres are usually lengths of single-stranded DNA containing several thousand repeats of a simple TTAGGG sequence. In human cells, telomeres contain 300-8000 repeats of a simple TTAGGG sequence. The repetitive TTAGGG sequences in telomeric DNA can form unique higher-order structures called quadruplexes. Figure $19$ shows an interactive iCn3D model of parallel quadruplexes from human telomeric DNA (1KF1). The structure contains a single DNA strand (5'-AGGGTTAGGGTTAGGGTTAGGG-3') which contains four TTAGGG repeats. Figure $19$: A buried phenylalanine in low molecular weight protein tyrosyl phosphatase (1xww) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...y5joFHDgWJQsQ6 Rotate the model to see 3 parallel layers of quadruplexes. In each layer, 4 noncontiguous guanine bases interact with a K+ ion. Hover over the guanine bases in one layer and you will find that one layer consists of guanines 4, 10, 16, and 22, which derive from the last G in each of the repeats in the sequence of the oligomer used (5'-AGGGTTAGGGTTAGGGTTAGGG-3'). These quadruplexes certainly serve as recognition and the binding site for telomerase proteins. The guanine-rich telomere sequences which can form quadruplex may also function to stabilize chromosome ends During DNA replication, the double-stranded DNA is unwound and DNA polymerase synthesizes new strands. However, as DNA polymerase moves in a unidirectional manner (from 5’ to 3’), only the leading strand can be replicated continuously. In the case of the lagging strand, DNA replication is discontinuous. In humans, small RNA primers attach to the lagging strand DNA, and the DNA is synthesized in small stretches of about 100-200 nucleotides, which are termed Okazaki fragments. The RNA primers are removed, and replaced with DNA and the Okazaki fragments are ligated together. At the end of the lagging strand, it is impossible to attach an RNA primer, meaning that there will be a small amount of DNA lost each time the cell divides. This ‘end replication problem’ has serious consequences for the cell as it means the DNA sequence cannot be replicated correctly, with the loss of genetic information. To prevent this, telomeres are repeated hundreds to thousands of times at the end of the chromosomes. Each time cell division occurs, a small section of telomeric sequences is lost to the end replication problem, thereby protecting the genetic information. At some point, the telomeres become critically short. This attrition leads to cell senescence, where the cell is unable to divide, or apoptotic cell death. Telomeres are the basis for the Hayflick limit, the number of times a cell can divide before reaching senescence. Telomeres can be restored by the enzyme telomerase, which extends telomeres length (Figure 24.3.10). Telomerase activity is found in cells that undergo regular division, such as stem cells and lymphocyte cells of the immune system. Telomeres can also be extended through the Alternative Lengthening of Telomeres (ALT) pathway. In this case, rather than being extended, telomeres are switched between chromosomes by homologous recombination. As a result of the telomere swap, one set of daughter cells will have shorter telomeres, and the other set will have longer telomeres. A downside to telomere extension is the potential for uncontrolled cell division and cancer. Abnormally high telomerase activity has been found in the majority of cancer cells, and non-telomerase tumors often exhibit ALT pathway activation. As well as the potential for losing genetic information, cells with short telomeres are at high risk for improper chromosome recombination, which can lead to genetic instability and aneuploidy (an abnormal number of chromosomes). These guanine-rich telomere sequences may also stabilize chromosome ends by forming structures of stacked sets of four-base units, rather than the usual base pairs found in other DNA molecules (Figure 24.3.10). Here, four guanine bases form a flat plate and these flat four-base units then stack on top of each other, to form a stable G-quadruplex structure. These structures are stabilized by hydrogen bonding between the edges of the bases and the chelation of a metal ion in the center of each four-base unit. Other structures can also be formed, with the central set of four bases coming from either a single strand folded around the bases or several different parallel strands, each contributing one base to the central structure. In addition to these stacked structures, telomeres also form large loop structures called telomere loops or T-loops. Here, the single-stranded DNA curls around in a long circle stabilized by telomere-binding proteins. At the very end of the T-loop, the single-stranded telomere DNA is held onto a region of double-stranded DNA by the telomere strand disrupting the double-helical DNA and base pairing to one of the two strands. This triple-stranded structure is called a displacement loop or D-loop.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/03%3A_Unit_III-_Information_Pathway/24%3A_DNA_Metabolism/24.01%3A_DNA_Replication.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Introduction The elucidation of the structure of the double helix by James Watson and Francis Crick in 1953 provided a hint as to how DNA is copied during the process of DNA replication. Separating the strands of the double helix would provide two templates for the synthesis of new complementary strands, but exactly how new DNA molecules were constructed was still unclear. In one model, semiconservative replication, the two strands of the double helix separate during DNA replication, and each strand serves as a template from which the new complementary strand is copied. After replication in this model, each double-stranded DNA includes one parental or “old” strand and one daughter or “new” strand. There were two competing models also suggested: conservative and dispersive, which are shown in Figure $1$. Matthew Meselson and Franklin Stahl devised an experiment in 1958 to test which of these models correctly represents DNA replication, as shown in Figure $2$. They grew the bacterium, Escherichia coli for several generations in a medium containing a “heavy” isotope of nitrogen (15N) that was incorporated into nitrogenous bases and, eventually, into the DNA. This labeled the parental DNA. The E. coli culture was then shifted into a medium containing 14N and allowed to grow for one generation. The cells were harvested and the DNA was isolated. The DNA was separated by ultracentrifugation, during which the DNA formed bands according to its density. DNA grown in 15N would be expected to form a band at a higher density position than that grown in 14N. Meselson and Stahl noted that after one generation of growth in 14N, the single band observed was intermediate in position in between DNA of cells grown exclusively in 15N or 14N. This suggested either a semiconservative or dispersive mode of replication. Some cells were allowed to grow for one more generation in 14N and spun again. The DNA harvested from cells grown for two generations in 14N formed two bands: one DNA band was at the intermediate position between 15N and 14N, and the other corresponded to the band of 14N DNA. These results could only be explained if DNA replicates in a semiconservative manner. Therefore, the other two models were ruled out. As a result of this experiment, we now know that during DNA replication, each of the two strands that make up the double helix serves as a template from which new strands are copied. The new strand will be complementary to the parental or “old” strand. The resulting DNA molecules have the same sequence and are divided equally into the two daughter cells. Think about It: What would have been the conclusion of the Meselson-Stahl experiment if, after the first generation, they had found two bands of DNA? To synthesize double-stranded DNA, the parental strands must separate so DNA polymerases can copy both strands. As all DNA polymerases synthesize new DNA in a 5' to 3' direction from a 3' to 5' template, different mechanisms are used to faithfully synthesize both parental strands. The general mechanism is shown in Figure $3$. Figure $3$: The replication fork. Leading-strand synthesis proceeds continuously in the 5' to 3' direction. Lagging-strand synthesis also occurs in the 5' to 3' direction, but in a discontinuous manner. An RNA/DNA primer (labeled in green) initiates leading-strand synthesis and every Okazaki fragment on the lagging strand. Small RNA primers are needed for the new strands. Short (1000-2000 NT) DNA (Okazaki) fragments are made on the 3'-5' parental strand. Ultimately the RNA primers are degraded and filled, and the Okazaki fragments ligated. We will discuss replication in detail for E. Coli, a model prokaryote, followed by replication in eukaryotes. Figure $4$ shows a general overview of a DNA "replication fork" from where DNA strand synthesis proceeds.. DNA Replication in E. Coli DNA replication has been well studied in bacteria primarily because of the small size of the genome and the mutants that are available. E. coli has 4.6 million base pairs (Mbp) in a single circular chromosome and all of it is replicated in approximately 42 minutes, starting from a single origin of replication and proceeding around the circle bidirectionally (i.e., in both directions), as shown in Figure $5$. This means that approximately 1000 nucleotides are added per second. The process is quite rapid and occurs with few errors. E. coli has a single origin of replication, called oriC, on its one chromosome. The origin of replication is approximately 245 base pairs long and is rich in adenine-thymine (AT) sequences. Replication Overview - E. Coli The open regions of DNA that are actively undergoing replication are called replication forks. All the proteins involved in DNA replication aggregate at the replication forks to form a replication complex called a replisome. The initial assembly of the complex that initiates primer synthesis is called the primosome. Table $1$ below show the components that assemble at the replication fork to form the E. Coli replisome. Table $1$: Enzymes involved in DNA Replication in the prokaryote, E. coli In E. coli, DNA replication is initiated at the single origin of replication, oriC. Binding of the initiator protein, DnaA, locally unfolds the DNA to form two template ssDNA, which bind DnaB helicase. A DnaB hexamer adds to each strand in a process promoted by DnaC, a helicase loader. The single-stranded DNA binding protein B (SSPB)binds to and protects the rest of the ssDNA, preventing further binding by DnaB. The primase, DnaG, is recruited to the site by the DnaB hexamer and synthesizes the RNA primers. DnaB also recruits DNA polymer III holoenzyme (PolIII HE) which binds through a β clamp. All of the bound proteins collectively form the replisome. An overview of E. Coli replisome is shown in Figure $6$. Figure $6$: The bacterial replisome. Ilic, S.; Cohen, S.; Singh, M.; Tam, B.; Dayan, A.; Akabayov, B. DnaG Primase—A Target for the Development of Novel Antibacterial Agents. Antibiotics 2018, 7, 72. https://doi.org/10.3390/antibiotics7030072 Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/) Once assembled, replisomes move in opposite directions from the single oriC in the E. Coli chromosome. They meet at the opposite ends at a termination site (ter) to which Tus proteins are bound that create ‘replication fork traps'. After completion of DNA replication, the newly synthesized genomes are separated and segregated to daughter cells. An alternative term, the primosomes, is used to describe a subcomplex of the replisome which starts replication of the E. Coli chromosome, as well as some phages and plasmids. It contains 6 proteins including helicases and primases, and catalyzes the movement of the replication form by unwinding and primer synthesis. The motor protein helicases, use ATP to move along the ds-DNA backbone, unraveling it as it proceeds. The human genome has genes for 64 RNA and 31 DNA helicases (about 1% of eukaryotic genes). Primase and Polymerase activities The synthesis of both RNA strands by the DnaG primase, and DNA strands by DNA polymerase III holoenzyme (pol III) occurs at each start site for an Okazaki fragment. Both enzymes bind to the conserved carboxy-terminal tail of the single-stranded DNA-binding protein (SSB). It turns out that they can both be bound simultaneously. The primase (DnaG) has three domains: • N-terminus that binds the template • RNA polymerase domain • C-terminus that binds helicase and the C-terminus of SSB. Primase is displaced by polIII after about 10 nucleotides have been added to the RNA primer so DNA synthesis can now occur at the 3' end of the primer. Figure $7$ shows how the primase to polymerase switch is made. Figure $7$: Schematic representation of the primase-to-polymerase switch during DNA replication in E. coli. Bogutzki, A., Naue, N., Litz, L. et al. Sci Rep 9, 14460 (2019). https://doi.org/10.1038/s41598-019-51031-0. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/ Panel (a): Two primase molecules cooperate in the synthesis of the RNA primer (red). Panel (b): For elongation of the primer, pol III enters the complex, whereupon primase and pol III are concurrently bound to the primed site, possibly via interactions with the C-termini of an adjacent SSB tetramer. Panel (c): During pol III-mediated elongation of the primer by several nucleotides, both enzymes stay bound to the template. Only after the primer has been elongated by more than 10 nucleotides, one of the primases is released in the G4ori system. It is presumably the displacement of SSB by pol III that causes the consequent dissociation of primase. Whereas in the G4ori system, the two primases are positioned by hairpin structures that prevent SSB from binding to this part of the origin. At the E. coli replication fork, primases are brought into contact via their interaction with the replicative helicase DnaB. What happens if the replication fork does not move to the termination site? If the DNA is damaged or if the replisome falls off of the chromosome, it can rebind and restart using Pri proteins. PriA is a DNA helicase that can bind to replication forks through DNA motifs and through interactions with SSBs. Other proteins involved include PriB, PriC, DnaT, DnaC, DnaB helicase, and DnaG primase as illustrated in Figure $8$. The primosome and replisome are complicated in structure and in their functional activity. Words go only so far in painting an image of how it works. To help we show a few different images of the replisome of E. Coli below. The first is shown in Figure $9$. In this diagram, the leading strand is shown in the upper right end of the diagram. The central bottom loop shows the lagging strand. The τ3δδ′ψχ is the clamp loader and the DnaB (red) is hexameric. The ssDNA in the lagging strand loop is bound by ssDNA binding proteins (SSB). Figure $10$ shows the rebind primosome which is mostly similar to the regular one. Figure $10$: Mechanisms of helicase loading leading to replisome assembly in E. coli. (A)Recognition and melting of the oriC locus during initiation by DnaA. (B)Recognition of abandoned fork structures during replisome reloading by PriA and PriC. All pathways converge on the loading of the replicative helicase DnaB, which acts as an assembly platform for the remaining replisome components. Finally, Figure $11$ shows models of DNA polymerase for lagging strand synthesis. Figure $11$: Usage of DNA polymerase during lagging strand synthesis. (A)S chematic of the E. coli replisome during the elongation step of an Okazaki fragment. (B )Lagging strand polymerase meets the RNA primer of the previous Okazaki fragment and stops synthesis. (C) Current model of events following completion of an Okazaki fragment. DNA polymerase is released from the β clamp (step 1) and the same molecule rebinds to a new β clamp to start the next Okazaki fragment (step 2). (D)An alternative model based on evidence from T4 and T7 replisomes. After completing the Okazaki fragment, the DNA polymerase detaches from the rest of the replisome (step 1). A new molecule of DNA polymerase is recruited to the replisome (step 2) and engages in the synthesis of a new Okazaki fragment. In this tentative model, a local pool of “spare” polymerases may facilitate their exchange and additional components may exchange along with the polymerase (not depicted) E. Coli DNA Polymerases E. Coli has 5 DNA polymerases. DNA polymerase I aids in lagging strand synthesis as it removes the RNA primers and incorporates DNA in its place. DNA polymerase II, may play an editing role following lagging strand synthesis by DNA polymerase I. DNA polymerases I and II also play a role in DNA repair, as do DNA polymerases IV and V. DNA polymerases are shaped like a right hand in overall shape with three domains named palm, fingers, and thumb. The bottom of the cleft formed by the three domains forms the polymerase active site in the Palm domain, The monomeric nucleotides to be added bind through the finger domain, while the thumb domains facilitates the dissociation of the newly synthesized DNA. These features are illustrated for a polymerase that requires host thioredoxin for a bacteriophage T7 DNA polymerase in Figure $12$. Figure $12$: Structure of T7 DNA replication complex. Melum 103 - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/inde...curid=38408627 DNA Polymerase III Pol III is a fascinating enzyme. It consists of an αεθ core with both 5'-3' polymerase and 3′−5′ proofreading activities, a β2 ring-shaped "sliding clamp" that keeps the enzyme on the DNA track (processive) without iteratively jumping off and rebinding (distributive), and a (τ/γ)3δδ′ψχ clamp loader. The SSB protein has a conserved amphiphilic C-terminus that binds both DnaG (primase) and the χ subunit of the clamp loader. A After primer addition by DnaG, the β2clamp of polIII is brought to the end of the primer terminus by the clamp loader, after which α and ε subunits bind the clamp. The holoenzyme can add ∼1000 Nt/s and over 150 kb without falling off. Hence it is a very processive enzyme. Figure $13$ shows an interactive iCn3D model of the the E. coli replicative DNA polymerase III (alpha, beta2, epsilon, tau complex) bound to DNA (5FKV) • N-terminal domains of α (αNTD, residues 1–963, are colored in salmon • OB (964–1072) on the C-term domain of α (αCTD) colored brown, • τ-binding domains (TBD, 1173–1160) on the C-term domain of α (αCTD) colored dark salmon, • ε in yellow • θ in orange (?) • β2 in aquamarine • τC in slate gray • DNA in spacefill, backbone magenta and purple, bases in CPK colors Overall, there are significant conformational changes in the DNA Polymerase III complex upon binding to the DNA that cause the tail of the polymerase to move from interacting with the clamp in the DNA-bound state to a position 35 Å away from the clamp in the DNA-free state. It has been hypothesized that this large conformational change may help the polymerase act as a switch to facilitate the lagging strand synthesis. On the lagging strand, the polymerase repositions to a newly primed site every ∼1000 bp. To do so, the polymerase needs to release both clamp and DNA. The switch-like movement of the polymerase tail may play a part in the release and consequent repositioning of the polymerase at the end of the Okazaki fragment. Video 25.1.1: DNA Binding Induces Large Conformational Changes in the DNA Polymerase III Complex(click link to view). The video shows the linear morphing of the DNA-free to the DNA-bound state showing the large conformation change between the two states. The green subunit is the β-clamp, The α-subunit is shown in orange with the active-site residues in magenta, the α-C-terminal domain (α-CTD shown in brown, the ε-subunit in yellow, and the τ-tail shown in blue. Video from: Fernandez-Liero, R., et al. (2015) eLife 4:e11134 The complex can also proofread the newly synthesized DNA. This requires some conformational changes in the polIII complex, including a rotation/tilt of the dsDNA against the β2 ring-shaped "sliding clamp". The thumb domain moves between the two DNA strands containing a mismatch and produced a distorted DNA. The epsilon subunit, a nuclease, can reach the mismatched nucleotide and clip it off. Figure $14$ shows an interactive iCn3D model of the E. coli replicative DNA polymerase III-clamp-exonuclease-theta complex bound to DNA in the editing mode (5M1S) • PolIII α brown, • PolIII ε in yellow • PolIII θ in orange • PolIII β2 in cyan • DNA in spacefill, backbone primer in magenta, the template in purple, and bases in CPK colors DNA Polymerase I DNA polymerase I, as does polIII, has a 5' to 3' polymerase activity. Also, both have a 3' to 5' exonuclease activity for proofreading as well as a 5'-3' exonuclease to remove RNA primers. It contains three domains, a 5'-3' exonuclease followed by a 3'-5' exonuclease, then the polymerase domain. Selective proteolysis between the first two domains produces the Klenow fragment. In contrast, the 5'-3' exonuclease of polIII is in the separate epsilon subunit. Figure $15$ shows an interactive iCn3D model of the predicted AlphaFold structure of E. Coli DNA Polymerase I (P00582) https://structure.ncbi.nlm.nih.gov/i...oNSC2GJAsMEUk6 • 5' to 3' exonuclease, 1-323, magenta • 3' to 5' exonuclease, 324-517, orange • 5' to 3' polymerase, 521-928, cyan • Val700-Arg713, Motif A, yellow • Klenow fragment: 324-928 Key aspartate and glutamates involved in the polymerase active site are shown in sicks and labeled. Motif A is conserved in prokaryotic DNA polymerases. Essential roles of motif A in catalysis include interaction with the incoming dNTP and coordination with two divalent metal ions that are required for the polymerization reaction. Note the distance between the 3' to 5' exonuclease and the 5'-3' polymerase. Other enzyme activities DNA Ligase DNA Ligase enzymes seal the breaks in the backbone of DNA that are caused during DNA replication, DNA damage, or during the DNA repair process. The biochemical activity of DNA ligases results in the sealing of breaks between 5′-phosphate and 3′-hydroxyl termini within a strand of DNA. DNA ligases have been differentiated as being ATP-dependent or NAD+-dependent depending on the co-factor (or co-substrate) that is used during their reaction. Typically, more than one type of DNA ligase is found within an organism. Figure $16$ shows the structure of E. coli LigA in complex with nicked adenylated DNA from PDB 2OWO, visualized by UCSF Chimera. The various domains are indicated by different colors and relate to Pfam domains indicated. Figure $16$: Structure of DNA ligase. Pergolizzi, G., Wagner, G.K, and Bowater, R.P. (2016) Biosci Rep 36(5) e00391 DNA ligase enzyme is covalently modified by the addition of the AMP moiety to a Lysine residue on the enzyme. The AMP derives from the ATP or NADH cofactor. The downstream 5'-phosphate at the site of the DNA nick is able to mediate a nucleophilic attack on the AMP-enzyme complex, causing the AMP to transfer to the 5'-phosphate position of the DNA. The AMP serves as a good leaving group for the nucleophilic attack of the upstream 3'-OH with the 5'-phosphate to seal the DNA backbone, and release the AMP. DNA ligase can use either adenosine triphosphate (ATP) or nicotinamide adenine dinucleotide (NAD+) as a cofactor. Figure $17$ shows a mechanism of the ligation reaction, which is powered by ATP hydrolysis. Figure $18$ shows an interactive iCn3D model of Human DNA Ligase I bound to 5'-adenylated, nicked DNA (1X9N) The protein has three domains: • DNA binding domain (DBD), shown in magenta. In contrast to most DNA binding proteins, Ligase I binds to the minor grove around the area of DNA damage. • Adenylation domain (AdD), shown in cyan, has covalently attached cofactor AMP and key catalytic residues shown as sticks and labeled. It ligates the broken DNA and forms a phosphodiester bond. • The OB-fold domain (OBD), shown in yellow, facilitates catalysis as it binds and unwinds over the short region. The DNA strands are as follows: • ss DNA terminated with dideoxy is shown in green • ss template DNA is shown in brown • ss 5'-phosphorylated DNA is shown in gray The AdD and OBD domains are similar in structure to other covalent nucleotidyltransferases involved in DNA and RNA ligation and capping of messenger RNA. Glu 566, Glu 621, and Arg 573 interact with AMP and probably help determine the specificity of the AMP cofactor (over GTP). Divalent cations are required for catalysis but are not present in the above structure. A E566K mutation leads to severe immunodeficiency. Lys 568 forms the covalent AMP adduct. The dideoxynucleoside in the structure is not optimally positioned for reaction with 5'P of AppDNA. Glu 720 and Glu 621 are highly conserved and presumably involved in metal ion binding. Zoom into the structure to observe the changes at the 3' end of the dideoxy DNA and 5' end of the phosphorylated DNA. These nucleotides are given the abbreviation X in iCn3D as they are modified. Topoisomerases We just studied these in a previous section but here is a review for this section. The unwinding of the double-stranded helix at the replication fork generates winding tension in the DNA in the form of positive supercoils upstream of the replication fork. Enzymes called topoisomerases counteract this by introducing negative supercoils into the DNA in order to relieve this stress in the helical molecule during replication. There are four known topoisomerase enzymes found in E. coli that fall into two major classes, Type I Topoisomerases and Type II Topoisomerases, as shown in Figure $19$. Topoisomerase I and III are Type I topoisomerases, whereas DNA gyrase and Topoisomerase IV are Type II topoisomerases. Goodsell, D.S. (2015) RCSD PDB-101 Molecule of the Month Type I Topoisimerase Type I Topoisomerases relieve tension caused during the winding and unwinding of DNA. One way that they can do this is by making a cut or nick in one strand of the DNA double helix as shown in Figure $19$. The 5'-phosphoryl side of the nicked DNA strand remains covalently bound to the enzyme at a tyrosine residue, while the 3'-end is held noncovalently by the enzyme. The Type I topoisomerases rotate or spin the 3'-end of the DNA around the intact DNA strand. This releases the overwinding in the DNA and effectively releases tension. The enzyme completes the reaction by resealing the phosphodiester backbone or ligating the broken strand back together. Overall, only one strand of the DNA is broken during the reaction mechanism and there is NO requirement of ATP during the reaction. The E. coli Topo I enzyme can only remove negative DNA supercoils, but not positive ones. Thus, this enzyme is not involved in relieving the positive supercoiling caused by the DNA helicase during replication. This is in contrast to eukaryotic Topo I that can relieve both positive and negative supercoiling. Although E. coli Topoisomerase I is not directly involved in relieving the tension caused by DNA replication, it is essential for E. coli viability. It is thought to help balance the actions of the Type II topoisomerases and help maintain optimal supercoiling density within the chromosomal DNA. Thus, Topo I is thought to help maintain the homeostatic balance of chromosome supercoiling within E. coli. Topo III, which is also a Type I Topoisomerase, appears to play a role in the decatenation of the daughter chromosomes during DNA replication, but does not play a role in the relaxation of supercoiling. Type II Topoisomerases have multiple functions within the cell. They can increase or decrease winding tension within the DNA or they can unknot or decatanate DNA that has become tangled with another strand as shown in Figure $20$. It does so by a more dangerous method than their Type I counterparts, by breaking both strands of the DNA during their reaction mechanism. The enzyme is covalently attached to both broken sides while the other DNA helix is passed through the break. The double-stranded break is then resealed. The proposed type II topoisomerase reaction cycle is exemplified by topoisomerase IV. Topoisomerase IV subunits are denoted in grey, cyan, and yellow. The gate or G-DNA is in green and the transported or T-DNA is in mauve. ATP bound to the ATPase domains is denoted by a red dot. In step 1, the G-DNA binds with the enzyme. ATP and the T-DNA segment associated with the enzyme in step 2. In step 3, the G-DNA is cleaved and the T-DNA is passed through the break. Drug-targetable domains within the type II topoisomerase complex are highlighted in subsections A, B, and C with examples on the right-hand side of the figure. Type II Topoisomerase - D DNA gyrase is the type II topoisomerase enzyme that is primarily involved in relieving positive supercoiling tension that results due to the helicase unwinding at the replication fork. Type II Topoisomerases, especially Topo IV, also address a key mechanistic challenge that faces the bacterial replisome during the termination of DNA replication. The circular nature of the bacterial chromosome dictates that a pair of replisomes that initiate from a single origin of replication will eventually converge on each other in a head-to-head orientation. Positive supercoiling accumulates between the the two replisomes as they converge, but the activity of DNA gyrase, which normally removes positive supercoils, becomes limited by the decreasing amount of template DNA available. Instead, supercoils may diffuse behind the replisomes, forming precatenanes between newly replicated DNA; in E.coli these must be resolved by Topo IV for chromosome segregation to occur. Termination of Replication If starting replication is critically important and obviously highly controlled as illustrated above, then termination of replication must be equally critical, otherwise genome instability would arise. There is one discrete origin of replication in E. Coli, oriC, with a defined sequence. In contrast, there are 10, 23 base-pair, nonpalindromic termination sites (Ter)of slightly different sequences. These bind the termination protein, Tus. The affinity of Tus for the Ter site depends on the Ter sequence and, in general,is tight with a KD in the picomolar range. There are two types of Ter-Tus complexes, one an open"permissive" conformation that allows replication to continue, and a locked, "nonpermissive" form that stops it. In the nonpermissive conformation, a key and conserved cytosine on the leading strand at a conserved GC base pair is flipped out into a cytosine binding pocket, which you can think of as a "stop sign" for replication. If you think of the E. Coli circular chromosome as a clock with the oriC at 12 o'clock, there are 5 Ter sequences as the replication fork move counter-clockwise at about 7 o'clock and another 5 as the fork move clockwise at around 5 o'clock. The sequences run in opposite polarity to prevent the left-side replication fork from entering the right-hand side as it moves around the chromosome and vice versa. The replisome displaces the Tus proteins at the permissive Ter sites but stops at the nonpermissive site, where DnaB helicase unwinds the DNA, flipping out the cytosine as the locked conformation forms. These processes are illustrated in Figure $21$. Panel A, E. coli contains a single circular chromosome, which replicates bidirectionally from a single origin (small oval). The direction of replisome travel from the origin is depicted by arrows. Chromosomal midpoint indicated by a straight line. The location of the ter sites on the E. coli chromosome is shown relative to oriC. Permissive orientation is displayed in light blue, nonpermissive orientation is displayed in dark blue. Panel B, the structure of Tus-ter (PDB ID: 2I06) illustrating the nonpermissive and permissive faces (left) and the “locked” conformation formed by DNA unwinding at the nonpermissive face (right). The cytosine base at position 6 of ter (C6), which flips into a specific binding site on the nonpermissive face of Tus to form the “lock,” is indicated. Replication can proceed at the Ter site if the replisome is moving from the light blue to dark blue sequences of the TER site. The TER site hence exhibits polarity. Panel A, E. coli contains a single circular chromosome, which replicates bidirectionally from a single origin (small oval). The direction of replisome travel from the origin is depicted by arrows. Chromosomal midpoint indicated by a straight line. The location of ter sites on the E. coli chromosome is shown relative to oriC. Permissive orientation is displayed in light blue, nonpermissive orientation is displayed in dark blue. Panel B, structure of Tus-ter (PDB ID: 2I06) illustrating the nonpermissive and permissive faces (left) and the “locked” conformation formed by DNA unwinding at the nonpermissive face (right). The cytosine base at position 6 of ter (C6), which flips into a specific binding site on the nonpermissive face of Tus to form the “lock,” is indicated. Figure $22$ Crystal structure (PDB code: 2I06) of the locked Tus–Ter complex shows the flipped C(6) base at the non-permissive face (5) Figure $22$: Crystal structure (PDB code: 2I06) of the locked Tus–Ter complex shows the flipped C(6) base at the non-permissive face (5). Pandey et al. 2015 Jul 13; 43(12): 5924–5935. doi: 10.1093/nar/gkv527. Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), Figure $23$ shows interactive iCn3D models of the Escherichia Coli Replication Terminator Protein (Tus) Complexed With TerA DNA in open (left) or locked form (right). Escherichia Coli Replication Terminator Protein (Tus) Complexed With TerA DNA (2I05) Escherichia Coli Replication Terminator Protein (Tus) Complexed With DNA- Locked form (2I06) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/icn3d/share.html?7mc7UTqLBbgRZP8p8 (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/icn3d/share.html?bScTvhNGAMD456vt9 In the figures, the C6 that is flipped out in the locked nonpermissive form (right) is shown in spacefill and labeled as C324. The Arg 198 alters its orientation to allow the C6 to flip out and form the locked conformational form. A summary of the process of DNA replication is shown in Video 25.1.2 Click Here to View Video Video 9.2 Overview of the DNA Replication Process DNA Replication of Extrachromosomal Elements: Plasmids and Viruses To copy their nucleic acids, plasmids and viruses frequently use variations on the pattern of DNA replication described for prokaryote genomes. We will focus here on one style known as the rolling circle method. Whereas many bacterial plasmids replicate by a process similar to that used to copy the bacterial chromosome, other plasmids, several bacteriophages, and some viruses of eukaryotes use rolling circle replication as shown in Figure $24$. Figure $24$: Rolling Circle Replication. The process of rolling circle replication is initiated by a single stranded nick in the DNA. Within prokaryotes, DNA polymerase III is utilized to generate the daughter strand. DNA ligase rejoins nicks in the backbone and enables the initiation of DNA synthesis of the second daughter strand. Figure by Parker, N., et.al. (2019) Openstax. The circular nature of plasmids and the circularization of some viral genomes on infection make this possible. Rolling circle replication begins with the enzymatic nicking of one strand of the double-stranded circular molecule at the double-stranded origin (dso) site. In bacteria, DNA polymerase III binds to the 3′-OH group of the nicked strand and begins to unidirectionally replicate the DNA using the un-nicked strand as a template, displacing the nicked strand as it does so. Completion of DNA replication at the site of the original nick results in the full displacement of the nicked strand, which may then recircularize into a single-stranded DNA molecule. RNA primase then synthesizes a primer to initiate DNA replication at the single-stranded origin (sso) site of the single-stranded DNA (ssDNA) molecule, resulting in a double-stranded DNA (dsDNA) molecule identical to the other circular DNA molecule. DNA Replication in Eukaryotes The Cell Cycle The cell cycle is an ordered series of events involving cell growth and cell division that produces two new daughter cells. Cells on the path to cell division proceed through a series of precisely timed and carefully regulated stages of growth, DNA replication, and division that produce two genetically identical cells. The cell cycle has two major phases, interphaseand the mitotic phase, as shown in Figure $25$. During interphase, the cell grows and DNA is replicated. During the mitotic phase, the replicated DNA and cytoplasmic contents are separated and the cell divides. Watch this video about the cell cycle: http://openstax.org/l/biocellcyc Figure $25$: Diagram of the Cell Cycle. Fowler, S., et.al. (2013) Openstax A cell moves through a series of phases in an orderly manner. During interphase, G1 involves cell growth and protein synthesis, the S phase involves DNA replication and the replication of the centrosome, and G2 involves further growth and protein synthesis. The mitotic phase follows interphase. Mitosis is nuclear division during which duplicated chromosomes are segregated and distributed into daughter nuclei. Usually, the cell will divide after mitosis in a process called cytokinesis in which the cytoplasm is divided and two daughter cells are formed. During interphase, the cell undergoes normal processes while also preparing for cell division. For a cell to move from interphase to the mitotic phase, many internal and external conditions must be met. The three stages of interphase are called G1, S, and G2. The first stage of interphase is called the G1 phase, or first gap, because little change is visible. However, during the G1 stage, the cell is quite active at the biochemical level. The cell is accumulating the building blocks of chromosomal DNA and the associated proteins, as well as accumulating enough energy reserves to complete the task of replicating each chromosome in the nucleus. Throughout interphase, nuclear DNA remains in a semi-condensed chromatin configuration. In the S phase (synthesis phase), DNA replication results in the formation of two identical copies of each chromosome—sister chromatids—that are firmly attached at the centromere region,as shown in Figure $26$. At this stage, each chromosome is made of two sister chromatids and is a duplicated chromosome. The centrosome is duplicated during the S phase. The two centrosomes will give rise to the mitotic spindle, the apparatus that orchestrates the movement of chromosomes during mitosis. In mammals, the centrosome consists of a pair of rod-like centrioles at right angles to each other. Centrioles help organize cell division. Centrioles are not present in the centrosomes of many eukaryotic species, such as plants and most fungi. Figure $26$: Human Chromosome Structure. Figure A from: The National Human Genome Research Institute, and Figure B from: The School of Biomedical Sciences Wiki (A) Shows a spectral karyogram of a normal human female. Humans have a total of 23 pairs of chromosomes for a total of 46. Each pair of chromosomes are referred to as homologous chromosomes as they contain copies of the same gene regions. Each of the homologous pairs of chromosomes is stained the same color. Chromosomes are shown in their condensed, unreplicated state. (B) Shows a schematic diagram of a single chromosome before (lower diagram) and after (upper diagram) replication. Upon replication, the identical copies of the chromosome are called sister chromatids and are linked together at the centromere structure. Figure A from: The National Human Genome Research Institute, and Figure B from: The School of Biomedical Sciences Wiki In the G2 phase, or second gap, the cell replenishes its energy stores and synthesizes the proteins necessary for chromosome manipulation. Some cell organelles are duplicated, and the cytoskeleton is dismantled to provide resources for the mitotic spindle. There may be additional cell growth during G2. The final preparations for the mitotic phase must be completed before the cell is able to enter the first stage of mitosis. To make two daughter cells, the contents of the nucleus and the cytoplasm must be divided. The mitotic phase is a multistep process during which the duplicated chromosomes are aligned, separated, and moved to opposite poles of the cell, and then the cell is divided into two new identical daughter cells. The first portion of the mitotic phase, mitosis, is composed of five stages, which accomplish nuclear division. The second portion of the mitotic phase, called cytokinesis, is the physical separation of the cytoplasmic components into two daughter cells. If cells are not traversing through one of the phases of interphase or mitosis, they are said to be in G0 or a resting state. If cells enter G0 permanently, they are said to have entered a stage of replicative senescence and will no longer be maintained for long-term viability within the organism. The progression of cells through the cell cycle requires the coordinated actions of specific protein kinases, known as cyclin-dependent kinases. Cyclin-dependent kinases are usually abbreviated as CDK or CDC proteins. CDK/CDC proteins require the binding of a regulatory cyclin protein to become activated, as shown in Figure $27$. The major cyclin proteins that drive the cell cycle in the forward direction, are expressed only at discrete times during the cell cycle. When activated by a cyclin counterpart, CDK/CDC enzymes phosphorylate downstream targets involved with cell cycle progression. For example, the primary cyclin-CDK complex involved in the initiation of DNA replication during S-phase is the CyclinE-CDK2 complex. CDK2 is activated by the expression and binding of Cyclin E during late G1 phase. This causes CDK2 to phosphorylate downstream targets, including the retinoblastoma tumor suppressor protein, pRb. pRB normally binds and inhibits the activity of transcription factors from the E2F family. Following the release of E2F transcription factors from pRb, E2Fs activate the transcription of genes involved in DNA replication and the progression of cells into S-phase. Panel (A) shows CDK-cyclin complexes with direct functions in regulating the cell cycle are shown. CDK3/cyclin C drives cell cycle entry from G0. CDK4/6/cyclin D complexes initiate phosphorylation of the retinoblastoma protein (pRb) and promote the activation of CDK2/cyclin E complex. In late G1, CDK2/cyclin E complex completes phosphorylation and inactivation of pRb, which releases the E2F transcription factors and G1/S transition takes place. DNA replication takes place in S phase. CDK2/cyclin A complex regulates progression through S phase and CDK1/cyclin A complex through G2 phase in preparation for mitosis (M). Mitosis is initiated by CDK1/cyclin B complex. Panel (B) Shows the cyclical nature of cyclin expression during cell cycle progression. Cyclin abundance is regulated by transcriptional expression and rapid protein degradation. Thus, their biological activity is targeted at very specific time points during the cell cycle progression. Replication Initiation Origin organization, specification, and activation in eukaryotes are more complex than in bacterial or archaeal kingdoms and significantly deviate from the paradigm established for prokaryotic replication initiation. The large genome sizes of eukaryotic cells, which range from 12 Mbp in S. cerevisiae to 3 Gbp in humans, necessitates that DNA replication starts at several hundred (in budding yeast) to tens of thousands (in humans) origins to complete DNA replication of all chromosomes during each cell cycle, as shown in Figure $27$. Figure $27$: Eukaryotic chromosomes are typically linear, and each contains multiple origins of replication. The top figure is a graphic representation of the eukaryotic origins of replication, while the bottom image is a Cryo-electron micrograph image. The figure on the top is from Parker, N. et al. and the figure on the bottom is from Fritensky, B. and Brien, N With the exception of S.cerevisiae and related Saccharomycotina species, eukaryotic origins do not contain consensus DNA sequence elements but their location is influenced by contextual cues such as local DNA topology, DNA structural features, and chromatin environment. Nonetheless, eukaryotic origin function still relies on a conserved initiator protein complex to load replicative helicases onto DNA during the late M and G1 phases of the cell cycle, a step known as origin licensing. In contrast to their bacterial counterparts, replicative helicases in eukaryotes are loaded onto origin duplex DNA in an inactive, double-hexameric form and only a subset of them (10–20% in mammalian cells) is activated during any given S phase, events that are referred to as origin firing. The location of active eukaryotic origins is therefore determined on at least two different levels, origin licensing to mark all potential origins, and origin firing to select a subset that permits assembly of the replication machinery and initiation of DNA synthesis. The extra licensed origins serve as backup and are activated only upon slowing or stalling of nearby replication forks, ensuring that DNA replication can be completed when cells encounter replication stress. Together, the excess of licensed origins and the tight cell cycle control of origin licensing and firing embody two important strategies to prevent under- and overreplication and to maintain the integrity of eukaryotic genomes. Human Primosome In humans,, the primosome contains primase and DNA polymerase α (Polα), and makes RNA-DNA primers to which deoxynucleotides are added by polymerases δ and ϵ. Hence there are two catalytic sites for addition or ribo- and deoxyribonucleotides. The structure of the human primosome and the C-terminal domain of the primase large subunit (p58C) with bound DNA/RNA duplex is presented below. p58C coordinates the catalytic activities. As with other polymerases, primase synthesis of RNA primers has the following steps: • initiation (rate limiting): primase binds to DNA and makes a dinucleotide RNA; • elongation, which is not as fast as DNA replication since it is less processive, adding only around 10 nucleotides. These short fragments are moved to Polα where deoxynucleotides are added with inactivation of the primase • termination. The structures of the enzymes are as follows: Human Polα consists of a : • large catalytic subunit (p180) with a C-terminal p180C domain with two Zn2+ binding modules. • smaller accessory subunit (p70) with an N-terminal (p70N), a phosphodiesterase, and oligonucleotide/oligosaccharide-binding (OB) domains. Human primase consist of • catalytic (p49) • regulatory (p58) subunits with two domains, the N-terminal (p58N), which interacts with p49 and which connects primase and Polα, and a C-terminal (p58C) which contains an iron-sulfur cluster involved in substrate binding and primase activity. The structures are shown in Figure $28$. Figure $28$: Structure of the human primosome hetero-tetramer complex. Baranovskiy et al. JBC, 291, 10006-10020 (2016). DOI: https://doi.org/10.1074/jbc.M116.717405. Creative Commons Attribution (CC BY 4.0) Panel A shows a schematic representation of the domain organization. The flexibly tethered domains are shown as separate parts. p58C coordinates the iron-sulfur cluster. Exo* is an exonuclease domain with no associated activity due to the evolutionary substitution of the catalytic amino acid residues; PDE, phosphodiesterase. Pane B shows the crystal structure of the primosome. Subunits are shown as schematics and colored as in A. The α-carbons of catalytic aspartates are shown as purple spheres. Figure $29$ shows an interactive iCn3D model of the Human primosome without nucleic acids (5EXR) DNA primase small: catalytic (p49) - dark green DNA primase large: regulatory (p58) subunits. p58 has two distinct domains, N-terminal (p58N light blue) and C-terminal (p58C gray/purple), connected with an 18-residue linker (253–270) . p58N interacts with p49 and connects primase with Polα ), and an iron-sulfur cluster containing p58C plays an important role in substrate binding and primase activity DNA polymerase alpha catalytic subunit: large catalytic subunit (p180). has p180core (orange) and linker 1251-1265 then the C-terminal domain (p180C - blue) connects to small subunit p70. (p180C) contains Zn1 and Zn2 bind site DNA polymerase alpha subunit B: smaller accessory subunit (p70) with 3 domains: p70N (light green) then linker 79-156 BOTH NOT SHOWN IN STRUCTURE), the P70 phosphodiesterase, and oligonucleotide/oligosaccharide-binding (OB) domains (combined magenta). Figure $30$ shows an interactive iCn3D model of the C-terminal domain of the human DNA primase large subunit with bound DNA template/RNA primer (5F0Q) The ss-DNA is shown with a pale green backbone while the RNA backbone is shown in magenta. The FeS cluster and a Mg2+ ion are shown in the catalytic subunit. The Mg2+ is shown interacting with a terminal GTP of the RNA. Figure $31$ shows an interactive iCn3D model of the catalytic core of human DNA polymerase alpha in a ternary complex with an RNA-primed DNA template and dCTP (4QCL) The ss-DNA is shown with a cyan backbone while the RNA primer backbone is shown in magenta. dCTP is shown in spacefill. Eukaryotic DNA polymerases Similar to DNA replication in prokaryotes, DNA replication in eukaryotes occurs in opposite directions between the two new strands at the replication fork. Within eukaryotes, two replicative polymerases synthesize DNA in opposing orientations, as shown in Figure $32$. Polymerase ε (epsilon) synthesizes DNA in a continuous fashion, as it is “pointed” in the same direction as DNA unwinding. Similar to bacterial replication, this strand is known as the leading strand. In contrast, polymerase δ (delta) synthesizes DNA on the opposite template strand in a fragmented, or discontinuous, manner and this strand is termed the lagging strand. The discontinuous stretches of DNA replication products on the lagging strand are known as Okazaki fragments and are about 100 to 200 bases in length at eukaryotic replication forks. Owing to the “lagging” nature, the lagging strand generally contains a longer stretch of ssDNA that is coated by single-stranded binding proteins, which stabilizes ssDNA templates by preventing secondary structure formation or other transactions at the exposed ssDNA. In eukaryotes, ssDNA stabilization is maintained by the heterotrimeric complex known as replication protein A (RPA) (Figure 9.19). Each Okazaki fragment is preceded by an RNA primer, which is displaced by the procession of the next Okazaki fragment during synthesis. In eukaryotic cells, a small amount of the DNA segment immediately upstream of the RNA primer is also displaced, creating a flap structure. This flap is then cleaved by endonucleases (such as Fen1, discussed later). At the replication fork, the gap in DNA after removal of the flap is sealed by DNA ligase I. Owing to the relatively short nature of the eukaryotic Okazaki fragment, DNA replication synthesis occurring discontinuously on the lagging strand is less efficient and more time consuming than leading-strand synthesis. Replication on the leading and lagging strands is performed by Pol ε and Pol δ, respectively. Many replisome factors (including the FPC [fork protection complex], Claspin, And1, and RFC [the replication factor C clamp loader]) are charged with regulating polymerase functions and coordinating DNA synthesis with the unwinding of the template strand by Cdc45-MCM [mini-chromosome maintenance]-GINS [go-ichi-ni-san]. The replisome also associates with checkpoint proteins as DNA replication and genome integrity surveillance mechanisms. Figure $33$ shows an interactive iCn3D model of the Core human replisome (7PFO). (long load time) The leading DNA strand backbone is shown in spacefill magenta while the lagging strand backbone is shown in cyan. The DNA bases are shown as CPK spheres. The ATP analog, phosphaminophosphonic acid-adenlate ester, is shown in spacefill with CPK colors and labeled. The C-alpha traces of the different protein subunits are all shown in different colored alpha-C traces, except the DNA polymerase epsilon catalytic subunit A which is shown in cartoon form and colored by secondary structure. (long load time) At the eukaryotic replication fork, three distinct replicative polymerase complexes contribute to canonical DNA replication: α, δ, and ε. These three polymerases are essential for the viability of the cell. Because DNA polymerases require a primer on which to begin DNA synthesis, first, polymerase α (Pol α) acts as a replicative primase. Pol α is associated with an RNA primase and this complex accomplishes the priming task by synthesizing a primer that contains a short ~10-nucleotide RNA stretch followed by 10 to 20 DNA bases. Importantly, this priming action occurs at replication initiation at origins to begin leading-strand synthesis and also at the 5' end of each Okazaki fragment on the lagging strand. However, Pol α is not able to continue DNA replication. From in vitro studies, it was observed that DNA replication must be “handed off” to another polymerase to continue synthesis. The polymerase switching requires clamp loaders. Initially, it was thought that Pol δ performed leading-strand replication and that Pol α completed each Okazaki fragment on the lagging strand. Using mutator polymerase variants and mapping nucleotide misincorporation events, Kunkel and colleagues found that Pol ε and Pol δ mutations lead to mismatched nucleotide incorporation only on the leading and lagging strands, respectively. Thus, normal DNA replication requires the coordinated actions of three DNA polymerases: Pol α for priming synthesis, Pol ε for leading-strand replication, and Pol δ for generating Okazaki fragments during lagging-strand synthesis. In eukaryotes, DNA polymerases are grouped into seven families (A, B, C, D, X, Y, and RT). Crystal structures of the three nuclear replicative DNA polymerases demonstrate that they belong to the B family (Figure 25.1.17). All three replicative DNA polymerases are multi-subunit enzymes as shown in Table $2$ below. Table 25.1.2 Subunits of the Major Eukaryotic Replicative DNA Polymerases Table $2$: Subunits of the Major Eukaryotic Replicative DNA Polymerases. Doublié, S. and Zahn, K.E. (2014) Front. Microbiol 5:444 All B family polymerases are composed of five subdomains, the fingers, thumb, and palm which constitute the core of the enzyme, as well as an exonuclease domain and an N-terminal domain (NTD). The palm, a highly conserved fold composed of four antiparallel β strands and two helices, harbors two strictly conserved catalytic aspartates, located in motif A, DXXLYPS and motif C, DTDS , as shown in Figure $34$. This fold is shared by a very large group of enzymes, including DNA and RNA polymerases, reverse transcriptases, CRISPR polymerase, and even reverse (3′–5′) transferases. In contrast, the thumb and the fingers subdomains exhibit substantially more structural diversity. The fingers undergo a conformational change upon binding DNA and the correct incoming nucleotide. This movement allows residues in the finger subdomain to come in contact with the nucleotide in the nascent base pair. The thumb holds the DNA duplex during replication and plays a part in processivity. The exonuclease domain carries a 3′–5′ proofreading activity, which removes misincorporated nucleotides. The NTD seems to be devoid of catalytic activity. In pol δ the NTD comprises three motifs, one has a topology resembling an OB fold, one a single-stranded DNA binding motif, and the another has a RNA-binding motif (RNA Recognition Motif or RRM). The NTD likely plays a role in polymerase stability and fidelity through its interactions with other domains. DNA polymerases require additional factors to support DNA replication in vivo. DNA polymerases have a semi-closed hand structure, which allows them to load onto DNA and translocate. This structure permits DNA polymerase to hold the single-stranded template, incorporate dNTPs at the active site, and release the newly formed double strand. However, the conformation of DNA polymerases does not allow for their stable interaction with the template DNA. To strengthen the interaction between template and polymerase, DNA sliding clamps have evolved, promoting the processivity of replicative polymerases. In eukaryotes, this sliding clamp is a homotrimer known as proliferating cell nuclear antigen (PCNA), which forms a ring structure. The PCNA ring has polarity with a surface that interacts with DNA polymerases and tethers them securely to DNA. PCNA-dependent stabilization of DNA polymerases has a significant effect on DNA replication because it enhances polymerase processivity up to 1,000-fold (Figure 25.1.19). The DNA helicases (MCM proteins) and polymerases must also remain in close contact at the replication fork (Figure 25.1.19). If unwinding occurs too far in advance of synthesis, large tracts of ssDNA are exposed. This can activate DNA damage signaling or induce aberrant DNA repair processes. To thwart these problems, the eukaryotic replisome contains specialized proteins that are designed to regulate the helicase activity ahead of the replication fork. These proteins also provide docking sites for physical interaction between helicases and polymerases, thereby ensuring that duplex unwinding is coupled with DNA synthesis. Control of Origin Firing Origin usage in eukaryotes can be dynamic, with origin firing at different sites depending on cell type and developmental stage. Nevertheless, the mechanism of replisome assembly and origin firing is highly conserved. During late mitosis and Gphase, cell cycle proteins, such as Cdc6, associate with Ori sites throughout the genome and recruit the helicase enzymes, MCMs 2-7 as shown in Figure $35$. At this time, double hexamers of the MCM2-7 complex are loaded at replication origins. This generates a pre-replication complex (pre-RC). Origins with an associated pre-RC are considered licensed for replication. Licensed replication origins can then be “fired,” when replication actually initiates at the Ori. Origin firing is brought about by multiple phosphorylation events carried out by the cyclin E-CDK2 complex at the onset of S phase and by other cyclin-dependent kinases (CDKs) prior to individual origin firing (Figure $35$). Cyclin-dependent kinases (CDKs) are the families of protein kinases first discovered for their role in regulating the cell cycle. They are also involved in regulating transcription, mRNA processing, and the differentiation of nerve cells. CDKs are activated through the binding of an associated cyclin regulatory protein. Without a cyclin, CDKs exhibit little kinase activity. Following the phosphorylation of the pre-RC, origin melting occurs and DNA unwinding by the helicase generates ssDNA, exposing a template for replication (Figure $35$). The replisome then begins to form with the localization of replisome factors such as Cdc45. DNA synthesis begins on the melted template, and the replication machinery translocates away from the origin in a bidirectional manner. Pane (A) shows the combined activities of Cdc6 and Cdt1 bring MCM complexes (shown as blue circles of varying shades) to replication origins. Panel (B) shows CDK/DDK-dependent phosphorylation of pre-RC components leads to replisome assembly and origin firing. Cdc6 and Cdt1 are no longer required and are removed from the nucleus or degraded Panel (C) shows MCMs and associated proteins (GINS and Cdc45 are shown) unwinding DNA to expose template DNA. At this point, replisome assembly can be completed and replication initiated. “P” indicates phosphorylation. Replication through Nucleosomes Eukaryotic genomes are substantially more complicated than the smaller and unadorned prokaryotic genomes. Eukaryotic cells have multiple noncontiguous chromosomes, each of which must be compacted to allow packaging within the confined space of a nucleus. As seen in chapter 4, chromosomes are packaged by wrapping ~147 nucleotides (at intervals averaging 200 nucleotides) around an octamer of histone proteins, forming the nucleosome. The histone octamer includes two copies each of histone H2A, H2B, H3, and H4. In chapter 8, it was highlighted that histone proteins are subject to a variety of post-translational modifications, including phosphorylation, acetylation, methylation, and ubiquitination that represent vital epigenetic marks. The tight association of histone proteins with DNA in nucleosomes suggests that eukaryotic cells possess proteins that are designed to remodel histones ahead of the replication fork, in order to allow smooth progression of the replisome. It is also essential to reassemble histones behind the fork to reestablish the nucleosome conformation. Furthermore, it is important to transmit the epigenetic information found on the parental nucleosomes to the daughter nucleosomes, in order to preserve the same chromatin state. In other words, the same histone modifications should be present on the daughter nucleosomes as on the parental nucleosomes. This must all be done while doubling the amount of chromatin, which requires the incorporation of newly synthesized histone proteins. This process is accomplished by histone chaperones and histone remodelers, which are discussed below and shown in Figure $36$. Histones are removed from chromatin ahead of the replication fork. FACT may facilitate this process. Asf1 recruits histone H3-H4 dimers to the replication fork. CAF-1 and Rtt106 load newly synthesized (light purple) histones to establish chromatin behind the fork. Previously loaded histones (dark purple) are also deposited on both daughter DNA strands. The histone chaperones involved in these processes are associated with replisome proteins: CAF-1/Rtt106 with PCNA and FACT/Asf1 with MCMs. Several histone chaperones are known to be involved in replication-coupled nucleosome assembly, including the FACT complex. The FACT complex components were originally identified as proteins that greatly stimulate transcription by RNA polymerase II. In budding yeast, FACT was found to interact with DNA Pol α-primase complex, and the FACT subunits were found to interact genetically with replication factors. More recently, studies showed that FACT facilitates DNA replication in vivo and is associated with the replisome in budding yeast and human cells. The FACT complex is a heterodimer that does not hydrolyze ATP, but facilitates the “loosening” of histones in nucleosomes Replication Fork Barriers and the Termination of Replication In prokaryotes, such as the E. coli, bidirectional replication initiates at a single replication origin on the circular chromosome and terminates at a site approximately opposed from the origin. This replication terminator region contains DNA sequences known as Ter sites, polar replication terminators that are bound by the Tus protein. The Ter-Tus complex counteracts helicase activity, resulting in replication termination. In this way, prokaryotic replication forks pause and terminate in a predictable manner during each round of DNA replication. In eukaryotes, the situation differs. Replication termination typically occurs by the collision of two replication forks anywhere between two active replication origins. The location of the collision can vary based on the replication rate of each of the forks and the timing of origin firing. Often, if a replication fork is stalled or collapsed at a specific site, replication of the site can be rescued when a replisome traveling in the opposite direction completes copying the region. However, there are numerous programmed replication fork barriers (RFBs) and replication “challenges” throughout the genome. To efficiently terminate or pause replication forks, some fork barriers are bound by RFB proteins in a manner analogous to E. coli Tus. In these circumstances, the replisome and the RFB proteins must specifically interact to stop replication fork progression. Telomeres and Replicative Senescence The End Replication Problem We have discussed the structure of telomers in the previous section. Let's look now at their activity/function. In humans, telomeres consist of hundreds to thousands of repetitive sequences of TTAGGG at chromosomal ends for maintaining genomic integrity. Because the DNA replication is asymmetric along double strands, RNA primer sequence at the 3′-hydroxyl end cannot be replaced by DNA polymerase I, as there is no 3'-OH primer group present for the polymerase to extend the DNA chain. This causes the loss of 30–200 nucleotides with each DNA replication and cell division and is known as the end replication problem. Telomeres provide a repetitive noncoding sequence of DNA at their 3′ en, to prevent the loss of critical genetically encoded information during replication. Moreover, telomeres are coated with a complex of six capping proteins, also known as shelterin proteins, which are packed into a compact T-loop structure that hides the ends of the chromosomes. This prevents the DNA repair machinery from mistaking chromosomal ends for double-stranded DNA breaks, as shown in Figure $37$. Therefore, telomeres have been proposed as a mitotic clock that measures how many times a cell has divided and in essence, gives a cell a defined lifetime. Pane (A): shows telomeres located at the end of chromosomes, where they help protect against the loss of DNA during replication. Panel (B) shows DNA quadruplex formed by telomere repeats. The looped conformation of the DNA backbone is very different from the typical DNA helix, this is known as T-loop formation. The green spheres in the center represent potassium ions. The human telomerase enzyme is responsible for maintaining and elongating telomeres and consists of an RNA component (TERC) and a reverse transcriptase (TERT), that serves as the catalytic component, as shown in Figure $38$. The TERT uses the TERC as a template to synthesize new telomeric DNA repeats at a single-stranded overhang to maintain telomere length (Figure 25.1.26). Some cells such as germ cells, stem cells, hematopoietic progenitor cells, activated lymphocytes, and most cancer cells constitutively express telomerase and maintain telomerase activity to overcome telomere shortening and cellular senescence. However, most other somatic cells generally have a low or undetectable level of telomerase activity and concomitantly limited longevity. Interestingly, overall telomerase activity decreases with age, but increases markedly in response to injury, suggesting a role for telomerase in cellular regeneration during wound healing. The telomere length and integrity are regulated through the interplay between the telomerase and shelterin proteins. The active site of the telomerase enzyme contains the RNA template, TERC (shown in red) and aligns with the last few telomeric bases at the end of the chromosome (shown in blue). This creates a single-stranded overhang that can be used as a template by the TERT reverse transcriptase to extend the telomere sequence. In vivo, shortened telomeres and damaged telomeres generally caused by reactive oxygen species (ROS) are usually assumed to be the main markers of cellular aging and are thought to be the main cause of replicative senescence. In vitro, telomeres lose approximately 50–200 bp at each division due to the end-replication problem. Approximately 100 mitoses are thought to be sufficient to reach the Hayflick limit, or the maximum number of mitotic events allowed prior to entering replicative senescence. Cells in continual renewal, such as blood cells, compensate for telomere erosion by expressing telomerase, the only enzyme able to polymerize telomeric sequences de novo at the extremity of telomeres. Knocking out telomerase components, such as the catalytic subunit (TERT) or the RNA template (TERC), induces several features of aging in mice. In humans, germline mutations in telomerase subunits are responsible for progeroïd syndromes, such as Dyskeratosis congenita, a rare genetic form of bone marrow failure. Furthermore, healthy lifespan in humans is positively correlated with longer telomere length and patients suffering from age-related diseases and premature aging have shorter telomeres compared with healthy individuals. An accumulation of unrepaired damage within telomeric regions has also been shown to accumulate in aging mice and non-human primates, suggesting that damage of telomeres with age may also be contributing to age-driven disease states and reduced health span. Thus, one could argue that the activation and expression of telomerase may be a way of reducing age-related diseases and increasing overall longevity. However, the constitutive expression of telomerase, unfortunately, is a characteristic of almost all cancer cells. It is therefore, no surprise that transgenic animals over-expressing the catalytic subunit of telomerase (mTERT), develop cancers earlier in life. However, over-expression of telomerase in mice that are highly resistant to cancers has shown large increases in median lifespan and significantly reduced age-associated disorders. Since humans are not highly resistant to cancer, this is not a feasible option for humans. However, additional studies in mice, where constitutive expression of telomerase is only introduced into a small percentage of host cells using adenovirus gene therapy techniques has yielded more promising results. Adenoviruses are a group of viruses that form an icosahedral protein capsid that houses a linear double-stranded DNA genome. Infections in humans typically cause symptoms of the common cold and are usually mild in nature. These are a good target for gene therapy, as the DNA that they carry can be mutated, so that they are deficient in their ability to replicate once they have infected the host. They can also be transformed to carry a gene of interest into the host, where that gene can then integrate into the host genome. Experiments in mice that were infected with an adenovirus carrying the mTERT gene showed that mTERT was delivered to a wide range of tissues within the body, and increased telomere length within those tissues. Furthermore, the mTERT expressing mice were healthier than their litter mates and displayed a reduction in disabling conditions associated with physiological aging such as osteoporosis and insulin resistance, as shown in Figure $38$. Cognitive skills and metabolic functions were also improved. Noticeably, mice treated with gene therapy did not have increased incidence in cancer rates, suggesting that in at least for the short-lived mouse species, gene therapy approach to increased telomerase activity is safe. Within these animals, the median lifespan was increased by 24% when animals were treated at 1 year of age, and by 13% if treated at 2 years of age. Replication and Repair of Telomere Sequences In addition to the end replication problem, telomeric DNA (telDNA) replication and repair is a real challenge due to the different structural features of telomeres. First, the nucleotide sequence itself consists of a hexanucleotide motif (TTAGGG) repeated over kilobases, with the 5′-3′ strand named the “G-strand” due to its high content in guanine. During the progression of the replication fork, the lagging strand, corresponding to the G-strand, forms G-quadruplex (G4) structures, which have to be resolved to allow fork progression and to complete replication, as shown in Figure $39$. Secondly, R-loops corresponding to highly stable RNA:DNA hybrids, involving the long non-coding telomeric transcript TERRA (telomeric repeat-containing RNA) also have to be dissociated. Thirdly, the extremity of telomeres adopts a specific loop structure, the T-loop, which has to be unraveled. This is the loop that hides the double-stranded end from the DNA damage sensors, and is locked by the hybridization of the 3′ single strand overhang extremity with the above 3′-5′ strand, thereby displacing the corresponding 5′-3′ strand to form a D-loop (displacement loop) structure (Figure $38$). Lastly, replication also has to deal with barriers encountered elsewhere in the genome, such as torsions and a condensed heterochromatic environment. Panel (a) shows the Telomeric sequence, with the G-strand in a solid red line and the C-strand in a solid green line, is depicted. The terminal D-loop structuring the much larger T-loop is stabilized by the shelterin complex. The replisome (PCNA, pol ε, etc) polymerizes a new G-strand (depicted in a dotted red line) and frees the parental G-strand, enabling the formation of G4 secondary structure. R-loops corresponding to TERRA hybridization (in dotted black lines) with the 3'-5' strand and torsions due to the fork progression are also shown. Panel (b) shows replication helpers, such as helicases, either helping in G4 unwinding or in D-loop unlocking are depicted. The DNAses (Top2a, DNA2) and RNAses (RNAse H1 and FEN1) help in resolving torsions and RNA:DNA heteroduplexes, while Timeless stimulates the replisome and POT1 competes with RPA1 for binding of the single-strand and helps in G4 dissolution. The shelterin components, POT1, TRF1, and TRF2 help in loading the helper proteins (fine green arrows) Since telomeres face a host of obstacles to completing the replication process, as discussed in Figure 25.1.28, the cell possesses a set of specialized machinery to fully achieve their replication, such as the RTEL1, TRF1, and TRF2 proteins, DNAses, RNAsses, and Timeless. The recruitment of these factors is orchestrated by the shelterin complex. At the molecular level, the GGG telomeric repeats are particularly sensitive to ROS, which produce stretches of 8-oxoguanine that are especially difficult to repair. Coupled with inefficient telomere repair, these ROS-induced lesions produce single and double-strand breaks, and/or generate replicative stress, ultimately resulting in telomere shortening. The presence of unrepaired single or tandem 8-oxoguanine drastically inhibits the binding of TRF1 and TRF2, and impairs the recruitment of telomerase, especially when ROS damage is localized in the 3′ overhang. This type of damage contributes to telomere deprotection and shortening. Strikingly, ROS (and other metabolic stresses) also induce the relocation of TERT to mitochondria, as observed (i) in primary neurons after oxidative stress; (ii) in neurons exposed to the tau protein; (iii) in Purkinje neurons subjected to excitotoxicity; and (iv) in cancer cell lines treated with a G4 ligand. Mitochondrial TERT increases the inner membrane potential, as well as the mtDNA copy number, and decreases ROS production with a protective effect on mtDNA. Mitochondria are also critical sensors of cellular damage and contribute to the processes of autophagy and apoptosis (programmed cell death). The relocalization of TERT following chromosomal damage in the nucleus, may indicate one mechanism the mitochondria utilizes to monitor cellular stress and damage. Replication of Mitochondrial DNA Mammalian mitochondria contain multiple copies of a circular, double-stranded DNA genome approximately 16.6 kb in length, as shown in Figure $40$. The two strands of mtDNA differ in their base composition, with one being rich in guanines, making it possible to separate a heavy (H) and a light (L) strand by density centrifugation. The mtDNA contains one longer noncoding region (NCR) also referred to as the control region. In the NCR, there are promoters for polycistronic transcription, one for each mtDNA strand; the light strand promoter (LSP) and the heavy strand promoter (HSP). The NCR also harbors the origin for H-strand DNA replication (OH). A second origin for L-strand DNA replication (OL) is located outside the NCR, within a tRNA cluster. Falkenberg, M. (2018) Essays Biochem 62(3):287-296 As shown in Figure $40$, the genome encodes for 13 mRNA (green), 22 tRNA (violet), and 2 rRNA (pale blue) molecules. There is also a major noncoding region (NCR), which is shown enlarged at the top in blue. The major NCR contains the heavy strand promoter (HSP), the light strand promoter (LSP), three conserved sequence boxes (CSB1-3, orange), the H-strand origin of replication (OH), and the termination-associated sequence (TAS, yellow). The triple-stranded displacement-loop (D-loop) structure is formed by a premature termination of nascent H-strand DNA synthesis at TAS. The short H-strand replication product formed in this manner is termed 7S DNA. A minor NCR, located approximately 11,000 bp downstream of OH, contains the L-strand origin of replication (OL). A dedicated DNA replication machinery is required for its maintenance. Mammalian mtDNA is replicated by proteins distinct from those used for nuclear DNA replication and many are related to replication factors identified in bacteriophages. DNA polymerase γ (POLγ) is the replicative polymerase in mitochondria. In human cells, POLγ is a heterotrimer with one catalytic subunit (POLγA) and two accessory subunits (POLγB). POLγA belongs to the A family of DNA polymerases and contains a 3′–5′ exonuclease domain that acts to proofread the newly synthesized DNA strand. POLγ is a highly accurate DNA polymerase with a frequency of misincorporation lower than 1 × 10−6. The accessory POLγB subunit enhances interactions with the DNA template and increases both the catalytic activity and the processivity of POLγA. The DNA helicase TWINKLE travels in front of POLγ, unwinding the double-stranded DNA template. TWINKLE forms a hexamer and requires a fork structure (a single-stranded 5′-DNA loading site and a short 3′-tail) to load and initiate unwinding. Mitochondrial single-stranded DNA-binding protein (mtSSB) binds to the formed ssDNA, protects it against nucleases, and prevents secondary structure formation The most accepted model of DNA replication in the mitochondria is the strand displacement model, as shown in Figure $41$. Within this model, DNA synthesis is continuous on both the H- and L-strand. There is a dedicated origin for each strand, OH and OL. First, replication is initiated at OH and DNA synthesis then proceeds to produce a new H-strand. During the initial phase, there is no simultaneous L-strand synthesis and mtSSB covers the displaced, parental H-strand. By binding to single-stranded DNA, mtSSB prevents the mitochondrial RNA polymerase (POLRMT) from initiating random RNA synthesis on the displaced strand. When the replication fork has progressed about two-thirds of the genome, it passes the second origin of replication, OL. When exposed in its single-stranded conformation, the parental H-strand at OL folds into a stem–loop structure. The stem efficiently blocks mtSSB from binding and a short stretch of single-stranded DNA in the loop region, therefore remains accessible, allowing POLRMT to initiate RNA synthesis. POLRMT is not processive on single-stranded DNA templates. After adding approximately 25 nucleotides, it is replaced by POLγ and L-strand DNA synthesis is initiated. From this point, H- and L-strand synthesis proceeds continuously until the two strands have reached full circle. Replication of the two strands is linked, since H-strand synthesis is required for the initiation of L-strand synthesis. DNA Ligase III is used to complete the ligation of the newly formed DNA strands. During DNA replication, the parental molecule remains intact, which poses a steric problem for the moving replication machinery. Topoisomerases belonging to the type 1 family can relieve torsional strain formed in this way, by allowing one of the strands to pass through the other. In mammalian mitochondria, TOP1MT a type IB enzyme can act as a DNA “swivel”, working together with the mitochondrial replisome. Furthermore, replication of circular DNA often causes the formation of catenanes, or interlocked circles that need to be separated from one another. The type 1A topoisomerase, topoisomerase 3α (Top3α), is required to resolve the hemicatenane structure that can form during mtDNA replication. Curiously, not all replication events initiated at OH continue to full circle. Instead, 95% are terminated after about the first 650 nucleotides at a sequence known as the termination associated sequences (TAS) (Figure 25.1.23). This creates a short DNA fragment known as the 7S DNA, that remains bound to the parental L-strand, while the parental H-strand is displaced (Figure 25.1.23). As a result, a triple-stranded displacement loop structure, a D-loop, is formed. The functional importance of the D-loop structure is unclear and how replication is terminated at TAS is also not known. Mastering the Content Which of the following is the enzyme that replaces the RNA nucleotides in a primer with DNA nucleotides? 1. DNA polymerase III 2. DNA polymerase I 3. primase 4. helicase [reveal-answer q=”628075″]Show Answer[/reveal-answer] [hidden-answer a=”628075″]Answer b. DNA polymerase I is the enzyme that replaces the RNA nucleotides in a primer with DNA nucleotides.[/hidden-answer] Which of the following is not involved in the initiation of replication? 1. ligase 2. DNA gyrase 3. single-stranded binding protein 4. primase [reveal-answer q=”820951″]Show Answer[/reveal-answer] [hidden-answer a=”820951″]Answer a. Ligase is not involved in the initiation of replication.[/hidden-answer] Which of the following enzymes involved in DNA replication is unique to eukaryotes? 1. helicase 2. DNA polymerase 3. ligase 4. telomerase [reveal-answer q=”650146″]Show Answer[/reveal-answer] [hidden-answer a=”650146″]Answer d. Telomerase is unique to eukaryotes.[/hidden-answer] Which of the following would be synthesized using 5′-CAGTTCGGA-3′ as a template? 1. 3′-AGGCTTGAC-4′ 2. 3′-TCCGAACTG-5′ 3. 3′-GTCAAGCCT-5′ 4. 3′-CAGTTCGGA-5′ [reveal-answer q=”429167″]Show Answer[/reveal-answer] [hidden-answer a=”429167″]Answer c. 3′-GTCAAGCCT-5′[/hidden-answer] The enzyme responsible for relaxing supercoiled DNA to allow for the initiation of replication is called ________. [reveal-answer q=”855893″]Show Answer[/reveal-answer] [hidden-answer a=”855893″]The enzyme responsible for relaxing supercoiled DNA to allow for the initiation of replication is called DNA gyrase or topoisomerase II.[/hidden-answer] Unidirectional replication of a circular DNA molecule like a plasmid that involves nicking one DNA strand and displacing it while synthesizing a new strand is called ________. [reveal-answer q=”378861″]Show Answer[/reveal-answer] [hidden-answer a=”378861″]Unidirectional replication of a circular DNA molecule like a plasmid that involves nicking one DNA strand and displacing it while synthesizing a new strand is calledrolling circle replication.[/hidden-answer] More primers are used in lagging strand synthesis than in leading strand synthesis. [reveal-answer q=”25479″]Show Answer[/reveal-answer] [hidden-answer a=”25479″]True[/hidden-answer] 1. Why is primase required for DNA replication? 2. What is the role of single-stranded binding protein in DNA replication? 3. Below is a DNA sequence. Envision that this is a section of a DNA molecule that has separated in preparation for replication, so you are only seeing one DNA strand. Construct the complementary DNA sequence (indicating 5′ and 3′ ends).DNA sequence: 3′-T A C T G A C T G A C G A T C-5′ 4. Review Figure 1 and Figure 2. Why was it important that Meselson and Stahl continue their experiment to at least two rounds of replication after isotopic labeling of the starting DNA with15N, instead of stopping the experiment after only one round of replication? 5. If deoxyribonucleotides that lack the 3′-OH groups are added during the replication process, what do you expect will occur?
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/03%3A_Unit_III-_Information_Pathway/24%3A_DNA_Metabolism/24.02%3A_DNA_Mutations_Damage_and_Repair.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry The integrity of the DNA structure for cell viability is underscored by the vast amounts of cellular machinery dedicated to ensuring its accurate replication, repair, and storage. Even still, mutations within the DNA are a fairly common event. DNA Mutations Mutations are random changes that occur within the sequence of bases in DNA. They can be large-scale, altering the structure of the chromosomes, or small scale where they only alter a few or even a single base or nucleotide. Mutations can occur for many reasons. For example, DNA mutations can be caused by mistakes made by the DNA polymerase during replication. As noted in chapter 9, DNA polymerases are highly processive enzymes that contain proofreading and editing functions. With these safeguards, their error rates are typically very low and range from one in a million bases to one in a billion bases. Even with such high fidelity, this error rate will lead to between 3 and 3,000 errors within the human genome for each cell undergoing DNA replication. DNA mutations can also result through the replication of DNA that has been damaged by endogenous or exogenous agents. The next section will highlight common types of DNA damage and their effects. If a DNA polymerase encounters a damaged DNA base in the template DNA during replication it may place a random nucleotide base across from the lesion. For example, an adenine-containing nucleotide will often be added across a lesion, regardless of what the correct match should be. This can lead to the formation of transition or transversion mutations. A transition mutation is a point mutation that changes a purine nucleotide to another purine (A ↔ G) or a pyrimidine nucleotide to another pyrimidine (C ↔ T). Transversion refers to the substitution of a purine for a pyrimidine or vice versa. Sometimes lesions may cause bases to be skipped during replication or cause extra nucleotides to be inserted into the backbone. DNA polymerases can also slip during the replication of regions of the DNA that have repeated sequences or large stretches repeating a single base. Larger lesions or cross-links in the DNA during replication can lead to more catastrophic DNA damage including DNA strand breaks. Mutations may also occur during the processes of mitosis and meiosis when sister chromatids and/or homologous chromosomes are separated from one another. In nature, mutagenesis, or the process of generating DNA mutations, can lead to changes that are harmful, beneficial, or have no effect. Harmful mutations can lead to cancer and various heritable diseases, but beneficial mutations are the driving force of evolution. In 1927, Hermann Muller first demonstrated the effects of mutations with observable changes in chromosomes. He induced mutagenesis by irradiating fruit flies with X-rays. When a mutation is caused by an environmental factor or a chemical agent, that agent is called a mutagen. Typical mutagens include chemicals, like those inhaled while smoking, and radiation, such as X-rays, ultraviolet light, and nuclear radiation. Different mutagens have different modes of damaging DNA and are discussed further in the next section. It is important to note that DNA damage, in and of itself, does not necessarily lead to the formation of a mutation in the DNA. There are elaborate DNA repair processes designed to recognize and repair different types of DNA lesions. Fewer than 1 in 1,000 DNA lesions will result in a DNA mutation. The processes of DNA damage recognition and repair are the focus of later sections within this chapter. Types of Mutations There are a variety of types of mutations. Two major categories of mutations are germline mutations and somatic mutations. • Germline mutations occur in gametes, the sex cells, such as eggs and sperm. These mutations are especially significant because they can be transmitted to offspring and every cell in the offspring will have the mutations. • Somatic mutations occur in other cells of the body. These mutations may have little effect on the organism because they are confined to just one cell and its daughter cells. Somatic mutations also cannot be passed on to offspring. Mutations also differ in the way that the genetic material is changed. Mutations may change an entire chromosome or just one or a few nucleotides. Chromosomal alterations are mutations that change chromosome structure or number. They occur when a section of a chromosome breaks off and rejoins incorrectly or does not rejoin at all. Possible ways these mutations can occur are illustrated in the figure below. Chromosomal alterations are very serious. They often result in the death of the cell or organism in which they occur. If the organism survives, it may be affected in multiple ways. An example of a human chromosomal alteration is the mutation that causes Down Syndrome. It is a duplication mutation that leads to developmental delays and other abnormalities. It occurs when the individual inherits an extra copy of chromosome 21. It is also called trisomy ("three-chromosome") 21. Thus, large-scale mutations in the chromosomal structure include (1) Amplifications (including gene duplications) where repetition of a chromosomal segment or presence of an extra piece of a chromosome broken piece of a chromosome may become attached to a homologous or non-homologous chromosome so that some of the genes are present in more than two doses leading to multiple copies of all chromosomal regions, increasing the dosage of the genes located within them, (2) Deletions of large chromosomal regions, leading to loss of the genes within those regions, and (3) Chromosomal Rearrangements such as translocations (which interchange of genetic parts from nonhomologous chromosomes), insertions (which insert segments of one chromosome into another nonhomologous chromosome), and inversions (which invert or flip a section of a chromosome into the opposite orientation), as shown in Figure $1$. There are also smaller mutations that can occur that only alter a single nucleotide or a small number of nucleotides within a localized region of the DNA. These are classified according to how the DNA molecule is altered. One type, a point mutation, affects a single base and most commonly occurs when one base is substituted or replaced by another. Mutations also result from the addition of one or more bases, known as an insertion, or the removal of one or more bases, known as a deletion. Point mutations (Table $1$ and Figure $2$) may have a wide range of effects on protein function. As a consequence of the degeneracy of the genetic code, a point mutation will commonly result in the same amino acid being incorporated into the resulting polypeptide despite the sequence change. This change would not affect the protein’s structure and is thus called a silent mutation. A missense mutation results in a different amino acid being incorporated into the resulting polypeptide. The effect of a missense mutation depends on how chemically different the new amino acid is from the wild-type amino acid. The location of the changed amino acid within the protein also is important. For example, if the changed amino acid is part of the enzyme’s active site or greatly affects the shape of the enzyme, then the effect of the missense mutation may be significant. Many missense mutations result in proteins that are still functional, at least to some degree. Sometimes the effects of missense mutations may be only apparent under certain environmental conditions; such missense mutations are called conditional mutations. Rarely, a missense mutation may be beneficial. Under the right environmental conditions, this type of mutation may give the organism that harbors it a selective advantage. Yet another type of point mutation called a nonsense mutation, converts a codon encoding an amino acid (a sense codon) into a stop codon (a nonsense codon). Nonsense mutations result in the synthesis of proteins that are shorter than the wild type and typically not functional. Table $1$: Types of Point Mutations Type Description Example Effect Silent mutated codon codes for the same amino acid CAA (glutamine) → CAG (glutamine) none Missense mutated codon codes for a different amino acid CAA (glutamine) → CCA (proline) variable Nonsense a mutated codon is a premature stop codon CAA (glutamine) → UAA (stop) usually serious Smaller scale deletions and insertions also cause various effects. Because codons are triplets of nucleotides, insertions or deletions in groups of three nucleotides may lead to the insertion or deletion of one or more amino acids and may not cause significant effects on the resulting protein’s functionality. However, frameshift mutations, caused by insertions or deletions of a number of nucleotides that are not a multiple of three are extremely problematic because a shift in the reading frame results, as shown in Figure $3$. Because ribosomes read the mRNA in triplet codons, frameshift mutations can change every amino acid after the point of the mutation. The new reading frame may also include a stop codon before the end of the coding sequence. Consequently, proteins made from genes containing frameshift mutations are nearly always nonfunctional. The majority of mutations have neither negative nor positive effects on the organism in which they occur. These mutations are called neutral mutations. Examples include silent point mutations, which are neutral because they do not change the amino acids in the proteins they encode. Some mutations have a positive effect on the organism in which they occur. They are referred to as beneficial mutations. If they occur in germline cells (eggs or sperm) these traits can be heritable and passed from one generation to the next. Beneficial mutations generally code for new versions of proteins that help organisms adapt to their environment. If they increase an organism’s chances of surviving or reproducing, the mutations are likely to become more common within a population over time. There are several well-known examples of beneficial mutations. Here are just two: 1. Mutations have occurred in bacteria that allow the bacteria to survive in the presence of antibiotic drugs. The mutations have led to the evolution of antibiotic-resistant strains of bacteria. 2. A unique mutation is found in people in a small town in Italy. The mutation protects them from developing atherosclerosis, which is the dangerous buildup of fatty materials in blood vessels. The individual in which the mutation first appeared has even been identified. Harmful mutations can also occur. Imagine making a random change in a complicated machine such as a car engine. The chance that the random change would improve the functioning of the car is very small. The change is far more likely to result in a car that does not run well or perhaps does not run at all. By the same token, any random change in a gene's DNA is more likely to result in the production of a protein that does not function normally or may not function at all, than in a mutation that improves the function. Such mutations are likely to be harmful. Harmful mutations may cause genetic disorders or cancer. • A genetic disorder is a disease, syndrome, or other abnormal condition caused by a mutation in one or more genes or by a chromosomal alteration. An example of a genetic disorder is cystic fibrosis. A mutation in a single gene causes the body to produce thick, sticky mucus that clogs the lungs and blocks ducts in digestive organs. Genetic disorders are usually caused by gene mutations that occur within germline cells and are heritable. • Illnesses caused by mutations that occur within an individual, but are not passed on to their offspring, are mutations that occur in somatic cells. Cancer is a disease caused by an accumulation of mutations within somatic cells. It results in cells that grow out of control and form abnormal masses of cells called tumors. It is generally caused by mutations in genes that regulate the cell cycle, DNA repair, angiogenesis, and other genes that favor cell growth and survival. Because of the mutations, cells with the mutated DNA have evolved to divide without restrictions, hide from the immune system, and develop drug resistance. Types of DNA Damage DNA damage, due to environmental factors and normal metabolic processes inside the cell, occurs at a rate of 1,000 to 1,000,000 molecular lesions per cell per day. While this constitutes only 0.000165% of the human genome's approximately 6 billion bases (3 billion base pairs), if left unrepaired can cause mutations in critical genes (such as tumor suppressor genes) can impede a cell's ability to carry out their function and appreciably increase the likelihood of tumor formation and disease states such as cancer. The vast majority of DNA damage affects the primary structure of the double helix; that is, the bases themselves are chemically modified. These modifications can, in turn, disrupt the molecules' regular helical structure by introducing non-native chemical bonds or bulky adducts that do not fit in the standard double helix. Unlike proteins and RNA, DNA usually lacks tertiary structure, and therefore damage or disturbance does not occur at that level. DNA is, however, supercoiled and wound around "packaging" proteins called histones (in eukaryotes), and both superstructures are vulnerable to the effects of DNA damage. Several types of DNA damage can occur due either to normal cellular processes or due to the environmental exposure of cells to DNA-damaging agents. DNA bases can be damaged by: (1) oxidative processes, (2) alkylation of bases, (3) base loss caused by the hydrolysis of bases, (4) bulky adduct formation, (5) DNA crosslinking, and (6) DNA strand breaks, including single and double-stranded breaks. An overview of these types of damage is described below. Oxidative Damage Reactive oxygen species (ROS) can cause significant cellular stress and damage including oxidative DNA damage. Hydroxyl radicals (OH) are one of the most reactive and electrophilic of the ROS and can be produced by ultraviolet and ionizing radiations or from other radicals arising from enzymatic reactions. The OH can cause the formation of 8-oxo-7,8-dihydroguanine (8-oxoG) from guanine residues, among other oxidative products, as shown in Figure $4$. Guanine is the most easily oxidized of the nucleic acid bases because it has the lowest ionization potential among the DNA bases. The 8-oxo-dG is one of the most abundant DNA lesions, and it is considered as a biomarker of oxidative stress. It has been estimated that up to 100,000 8-oxo-dG lesions can occur daily in DNA per cell. The reduction potential of 8-oxo-dG is even lower (0.74 V vs. NHE) than that of guanosine (1.29 V vs NHE). Therefore, it can be further oxidized creating a variety of secondary oxidation products. As mentioned previously, increased levels of 8-oxo-dG in tissue can serve as a biomarker of oxidative stress. Furthermore, increased levels of 8-oxo-dG are frequently found associated with carcinogenesis and other disease states, as shown in Figure $5$. During the replication of DNA that contains 8-oxo-dG, adenine is most often incorporated across from the lesion. Following replication, the 8-oxo-dG is excised during the repair process and thymine is incorporated in its place. Thus, 8-oxo-dG mutations typically result in a G to T transversion. Alkylation of Bases Alkylating agents are widespread in the environment and are also produced endogenously, as by-products of cellular metabolism. They introduce lesions into DNA or RNA bases that can be cytotoxic, mutagenic, or neutral to the cell. Figure $6$ depicts the major reactive sites on the DNA bases that are susceptible to alkylation. Cytotoxic lesions block replication, interrupt transcription, or signal the activation of apoptosis, whereas mutagenic ones are miscoding and cause mutations in newly synthesized DNA. The most common type of alkylation is methylation with the major products including N7-methylguanine (7meG), N3-methyladenine (3meA), and O6-methylguanine (O6meG). Smaller amounts of methylation also occurs on other DNA bases, and include the formation of N1-methyladenine (1meA), N3-methylcytosine (3meC), O4-methylthymine (O4meT), and methyl phosphotriesters (MPT). Alkylating agents can cause damage to all exocyclic nitrogens and oxygens in DNA and RNA, as well as at ring nitrogens (Figure 25.2.6A). However, the percentage of each base site modified depends on the alkylating agent, the position in DNA or RNA, and whether nucleic acids are single- or double-stranded. Interestingly, O-alkylations are more mutagenic and harmful than N-alkylations, which may be more cytotoxic, but not as mutagenic. As we will explore in Chapter 13, methylation of DNA also serves as an important mechanism regulating gene expression. Base Loss An AP site (apurinic/apyrimidinic site), also known as an abasic site, is a location in DNA (also in RNA but much less likely) that has neither a purine nor a pyrimidine base, either spontaneously or due to DNA damage, as shown in Figure $7$. It has been estimated that under physiological conditions 10,000 apurinic sites and 500 apyrimidinic may be generated in a cell daily. AP sites can be formed by spontaneous depurination, but also occur as intermediates in base excision repair, the repair process described in section 25.2.5. If left unrepaired, AP sites can lead to mutation during semiconservative replication. They can cause replication fork stalling and are often bypassed by translesion synthesis, which is discussed in greater detail in section 12.8. In E. coli, adenine is preferentially inserted across from AP sites, known as the "A rule". The situation is more complex in higher eukaryotes, with different nucleotides showing a preference depending on the organism and environmental conditions. Bulky Adduct Formation Some chemicals are biologically reactive and will form covalent linkages with biological molecules such as DNA and proteins creating large bulky adducts, or appendages, that branch off from the main molecule. We will use the mutagen/carcinogen, benzo[a]pyrene, as an example for this process. Benzo[a]pyrene is a polycyclic aromatic hydrocarbon that forms during the incomplete combustion of organic matter at temperatures between 300°C (572°F) and 600°C (1,112°F). The ubiquitous compound can be found in coal tar, tobacco smoke, and many foods, especially grilled meats. Benzo[a]pyrene is a procarcinogen that needs to be biologically activated by metabolism before it forms a reactive metabolite, as in Figure $8$. Normally, when the body is exposed to foreign molecules, it will start a metabolic process that makes the molecule more hydrophilic and easier to remove as a waste product. Unfortunately, in the case of benzo[a]pyrene, the resulting metabolite is a highly reactive epoxide that forms a bulky adduct preferentially with guanine residues in DNA. If left unrepaired, during DNA replication an adenine will usually be placed across from the lesion in the daughter molecule. Subsequent repair of the adduct will result in the replacement of the damaged guanine base with thymine, causing a G --> T transversion mutation. DNA Crosslinking Crosslinking of DNA occurs when various exogenous or endogenous agents react with two nucleotides of DNA, forming a covalent linkage between them. This crosslink can occur within the same strand (intrastrand) or between opposite strands of double-stranded DNA (interstrand), as shown in Figure $9$. These adducts interfere with cellular metabolism, such as DNA replication and transcription, triggering cell death. UV light can cause molecular crosslinks to form between two pyrimidine residues, commonly two thymine residues, that are positioned consecutively within a strand of DNA, as shown in Figure $10$. Two common UV products are cyclobutane pyrimidine dimers (CPDs) and 6–4 photoproducts. These premutagenic lesions alter the structure and possibly the base pairing. Up to 50–100 such reactions per second might occur in a skin cell during exposure to sunlight, but are usually corrected within seconds by photolyase reactivation or nucleotide excision repair. Uncorrected lesions can inhibit polymerases, cause misreading during transcription or replication, or lead to the arrest of replication. Pyrimidine dimers are the primary cause of melanomas in humans. DNA Strand Breaks Ionizing radiation such as that created by radioactive decay or in cosmic rays causes breaks in DNA strands (see Figure above). Low-level ionizing radiation may induce irreparable DNA damage (leading to replication and transcription errors needed for neoplasia or may trigger viral interactions) leading to premature aging and cancer. Chemical agents that form crosslinks within the DNA, especially interstrand crosslinks, can also lead to DNA strand breaks if the damaged DNA undergoes DNA replication. Crosslinked DNA can cause topoisomerase enzymes to stall in the transition state when the DNA backbone is cleaved. Instead of relieving supercoiling and resealing the backbone, the stalled topoisomerase remains covalently linked to the DNA in a process called abortive catalysis. This leads to the formation of a single-stranded break in the case of Top1 enzymes or double-stranded breaks in the case of Top2 enzymes. DNA double-strand breaks due to topoisomerase stalling can also occur during the transcription of DNA, as shown in Figure $11$. Abortive catalysis and the formation of DNA strand breaks during transcriptional events may serve as a damage sensor within the cell and help to instigate DNA damage response signaling pathways that initiate DNA repair processes. Panel (1) shows that in the uninduced state of transcription, Pol II is paused between +25 and +100 from the transcription start site. The pausing is attributed to different elements including pausing-stabilizing transcription factors, the +1 nucleosome, and DNA structure and torsion. Positive supercoiling ahead of Pol II may require the function of TOP2B. Panel (2) shows transcription activation induced by various stimuli activates TOP2B to resolve DNA torsion in the promoter and gene body. Panel (3) shows that in this process, double-strand breaks could be formed from abortive catalysis of TOP2B, which occurs frequently in some genes. This may be responsible for DNA damage response signaling that has been observed in a number of stimulus-inducible genes in humans. Figure from: Cellular Stress and DNA Damage Response Genetic damage produced by either exogenous or endogenous mechanisms represents an ongoing threat to the cell. To preserve genome integrity, eukaryotic cells have evolved repair mechanisms specific for different types of DNA Damage. However, regardless of the type of damage a sophisticated surveillance mechanism, that elicits DNA damage checkpoints, detects and signals its presence to the DNA repair machinery. DNA damage checkpoints have been functionally conserved throughout eukaryotic evolution, with most of the relevant players in the checkpoint response highly conserved from yeast to humans. Checkpoints are induced to delay cell cycle progression and to allow cells time to repair damaged DNA before DNA replication, as shown in Figure $12$. Once the damaged DNA is repaired, the checkpoint machinery triggers signals that will resume cell cycle progression. Within cells, multiple pathways contribute to DNA repair, but independent of the specific repair pathway involved, three phases of checkpoint activation are traditionally identified: (1) Sensing of damage, (2) Activating the signaling cascade, and (3) Switching on downstream effectors. The sensor phase recognizes the damage and activates the signal transduction phase to block cell cycle progression and select the appropriate repair pathway. In addition to blocking cell cycle progression, DNA damage sensors also activate DNA repair mechanisms that are specific for the type of damage present. For example, single-stranded DNA breaks are repaired primarily by Base Excision Repair, bulky DNA adducts, and crosslinks are repaired by Nucleotide Excision Repair, and smaller nucleotide mutations, such as alkylation are repaired by Mismatch Repair. Cells also have two major mechanisms for repairing Double-Strand-Breaks (DSBs). They include Non-Homologous End-Joining (NHEJ) and Homologous Recombination (HR). If damage is too extensive to be repaired, apoptotic pathways will be elicited. In the following sections, details about the major DNA repair pathways will be given. In multicellular organisms, the response to DNA damage can result in two major physiological consequences: (1) Cells can undergo cell cycle arrest, repair the damage, and re-enter the cell cycle, or (2) cells can be targeted for cell death (apoptosis) and removed from the population. The cell cycle process is highly conserved and precisely controlled to govern the genome duplication and separation into the daughter cells. The cell cycle consists of four distinct and ordered phases, termed G0/G1 (gap 1), S (DNA synthesis), G2 (gap 2), and M (mitosis). Multiple checkpoints exist within each stage of the cell cycle to ensure the faithful replication of DNA in the S phase and the precise separation of the chromosomes into daughter cells. The G1 and G2 phases are critical regulatory checkpoints, whereby the restriction point between the G1 and S phase determines whether the cells enter the S phase or exit the cell cycle to halt at the G0 phase. The cell cycle progression requires the activity of cyclin-dependent kinases (CDKs), a group of serine/threonine kinases. CDKs are activated when they form complexes with cyclin regulatory proteins that are expressed specifically at different stages of the cell cycle. Cyclins bind to and stabilize CDKs in their active conformation. The formation of cyclin/CDKs controls the cell-cycle progression via phosphorylation of the target genes, such as tumor suppressor protein retinoblastoma (Rb). During DNA damage, the cell cycle is arrested or blocked by the action of cyclin-dependent kinase inhibitors. As noted in Figure 12.12, this is a complicated signal transduction cascade that has many downstream effects. A primary function of cell cycle arrest is that CDK inhibition allows time for DNA repair before cell-cycle progression into the S-phase or mitosis. As shown in Figure 25.2.12, two major cell-cycle checkpoints respond to DNA damage; they occur pre- and post-DNA synthesis in the G1 and G2 phases, respectively, and impinge on the activity of specific CDK complexes. The checkpoint kinases phosphatidylinositol 3-kinase (PI3K)-like protein kinases (PI3KKs) ataxia telangiectasia and Rad3-related (ATR) or ataxia telangiectasia mutated (ATM) protein, and the transducer checkpoint kinases CHK1 (encoded by the CHEK1 gene) and CHK2 (encoded by the CHEK2 gene) are key regulators of DNA damage signaling. The DNA damage signaling is detected by ATM/ATR, which then phosphorylates and activates CHK2/CHK1, respectively. The activated CHK2 is involved in the activation of p53, leading to p53-dependent early phase G1 arrest to allow time for DNA repair. The activation of p53 induces the expression of the Cyclin-Dependent Kinase Inhibitor (CKI) p21CIP1 gene, leading to the inhibition of cyclin E/CDK2 complexes and subsequent upregulation of DNA repair machinery. If the DNA repair cannot be completed successfully or the cells cannot program to respond to the stresses of viable cell-cycle arrest, the cells face the fate of apoptosis induced by p53. The activated CHK1 mediates temporary S phase arrest through phosphorylation to inactivate CDC25A, causing ubiquitination and proteolysis. Moreover, the activated CHK1 phosphorylates and inactivates CDC25C, leading to cell-cycle arrest in the G2 phase. The active CHK1 also directly stimulates the phosphorylation of WEE1, resulting in enhancing the inhibitory Tyr15 phosphorylation of CDK2 and CDK1 and subsequent cell-cycle blocking in the G2 phase. The activity of WEE1 can also be stimulated by the low levels of CDK activity in the G2 cell-cycle phase. The SAC, also known as the mitotic checkpoint, functions as the monitor of the correct attachment of the chromosomes to the mitotic spindle in metaphase, which is regulated by the TTK protein kinase (TTK, also known as monopolar spindle 1 (MPS1)). The activation of SAC transiently induces cell-cycle arrest by inhibiting the activation of APC/C. To establish and maintain the mitotic checkpoint, the TTK recruits many checkpoint proteins to kinetochores during mitosis via phosphorylating its substrates to ensure adequate chromosome segregation and genomic integrity. In this way, the genomic instability from chromosome segregation defects is protected by SAC. Once the SAC is passed, the APC/C E3 ligase complex stimulates and tags cyclin B and securin for ubiquitin-mediated degradation, leading to the initiation of mitosis. In a word, the checkpoints offer a failsafe mechanism to ensure the genomic integrity from the parental cell to the daughter cell. The signal transduction cascade of checkpoint activation eventually converges to CDK inhibition, which indicates the CDK function as a key driver of cell-cycle progression. Mismatch Repair DNA mismatch repair (MMR) is a highly conserved DNA repair system that greatly contributes to maintaining genome stability through the correction of mismatched base pairs and small modifications of DNA bases, such as alkylation. The major source of mismatched base pairs is replication error, although it can arise also from other biological processes. Thus, the MMR machinery must have a mechanism for determining which strand of the DNA is the template strand and which strand has been newly synthesized. In E. coli, methylation of the DNA is a common post-replicative modification that occurs. Thus, in newly synthesized DNA, the unmethylated strand is recognized as the new strand, and the methylated strand is used as the template to repair mismatches. In E.coli, MMR increases the accuracy of DNA replication by 20–400-fold. Mutations and epigenetic silencing in MMR genes have been implicated in up to 90% of human hereditary nonpolyposis colon cancers, indicating the significance of this repair system in maintaining genomic stability. Post-replicative MMR is performed by the long-patch MMR mechanism in which multiple proteins are involved and a relatively long tract of the oligonucleotide is excised during the repair reaction. In contrast, particular kinds of mismatched base pairs are repaired through very short-patch MMR in which a short oligonucleotide tract is excised to remove the lesion. Table$2$ below shows mismatch repair enzymes in bacteria, yeast, and humans. MMR in eukaryotes and most bacteria directs the repair to the error-containing strand of the mismatched duplex by recognizing the strand discontinuities. On the other hand, E. coli MMR reads the absence of methylation as a strand discrimination signal. The MutS protein recognizes mismatches, In both MMR systems, strand discrimination is conducted by nicking endonucleases. MutL homologs from eukaryotes and most bacteria incise the discontinuous strand to introduce the entry or termination point for the excision reaction. In E. coli, MutH nicks the unmethylated strand of the duplex to generate the entry point of excision. Figure $13$ shows different MMR pathway models. Figure $13$: A schematic representation of MMR pathway models. Fukui, K. (2010) J. Nuc. Acids 260512. Creative Commons Attribution License Vertical panel (a): Eukaryotic MMR. A DNA mismatch is generated by the misincorporation of a base during DNA replication. MutSα recognizes base-base mismatches and MutLα nicks the 3-or5-side of the mismatched base on the discontinuous strand. The resulting DNA segment is excised by the EXO1 exonuclease, in cooperation with the single-stranded DNA-binding protein RPA. The DNA strand is resynthesized by DNA polymerase δ and DNA ligase 1. Vertical panel (b): MMR in mutH-less bacteria. Mismatched bases are recognized by MutS. After the incision of the discontinuous strand by MutL, the error-containing DNA strand is removed by the cooperative functions of DNA helicases, such as UvrD, the exonucleases RecJ and ExoI, and the single-stranded DNA-binding protein SSB. DNA polymerase III and DNA ligase fill the gap to complete the repair. Vertical panel (c): E. coli MMR. MutS recognizes mismatched bases, and MutL interacts with and stabilizes the complex. Then, MutH endonuclease is activated to incise the unmethylated GATC site to create an entry point for the excision reaction. DNA helicase, a single-stranded DNA-binding protein, and several exonucleases are involved in the excision reaction. PDB IDs of crystal structures in this figure are 2O8B (human MutSα), 1H7S (human MutLα), 1L1O (human RPA), 3IAY (human DNA polymerase δ), 1X9N (human DNA ligase 1), 1E3M (bacterial MutS), 1B63 (bacterial MutL), 2AZO (E. coli MutH), 2ISI (bacterial UvrD), 2ZXO (bacterial RecJ), 3C95 (bacterial ExoI), 2CWA (bacterial SSB), 2HQA (bacterial DNA polymerase III), and 2OWO (bacterial DNA ligase). Figure $14$ shows an interactive iCn3D model of the E. Coli DNA Mismatch Repair Protein Muts Binding to a G-T Mismatch (1E3M). The MutS monomers are colored gray and cyan. The mismatched G9-T22 base pair is labeled. ADP is shown in spacefill. Phe 36 from the gray monomer is shown in magenta. The conformation of the monomers is different, so the dimer displays pseudo symmetry. Both subunits contribute to DNA binding, but only one (gray) binds both ADP and the actual mismatched GT base pair, the llatterthrough minor grove interactions, which kinks the DNA. General major grove interaction clamps the DNA. Note how far away the ADP binds. Phenylalanine 36 in the gray subunit (which binds the mismatch) inserts adjacent to the mismatch. ATP is bound and hydrolyzed to ADP by the MutS protein on binding the mismatch. Next, a dimer of MutL binds in a process that also requires ATP. MutH, a nuclease, also binds to MutL. The bound DNA is scanned until a "signal" is detected. In E. Coli, the signal is a GATC sequence that is methylated on just one strand and nicked by the MutH on the unmethylated GATC. Helicase II binds and unwinds the DNA in the region of the mismatch. Exonucleases (3' to 5' or 5' to 3') remove the sequence on the mismatched. PolII and DNA ligase then repair the DNA. MutS is yet another fascinating enzyme as it must scan millions of DNA bases without initiating repair until it localizes a mismatch. A series of sequential conformation changes that lead to specific recognition of the mismatch must occur. Fernandez-Leiro et al have determined the structure of MutS in a variety of stages along the repair pathway. Figure $15$ shows interactive iCn3D models of the E. Coli MutS scanning form (EMD-11791, PDB 7AI5) and the more progressed MutS:MutL kink clamped form (EMD-11795, PDB 7AIC) E. Coli MutS scanning form (EMD-11791, PDB 7AI5) E. Coli MutS with MutL in kink clamped form (EMD-11795, PDB 7AIC) Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...pdHkXxzXsZnqu6 Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...hQ6PeKbQeCw916 Figure $15$: E. Coli MutS scanning form (EMD-11791, PDB 7AI5) (left) and the more progressed MutS:MutL kink clamped form (EMD-11795, PDB 7AIC) (right) In the scanning form, the 2 monomers have been color coded as follows: monomer 1, N-terminal the interacts with DNA magenta, with the rest of the protein in red; monomer 2, the N-terminal part reacts with the DNA cyan, and the rest of the chain blue. In the kink-clamped state (right) the N-terminal magenta and N-terminal cyan sections were not present in the resolved structure. ATP is shown in spacefill. These structures suggest that during scanning by the homoduplex of normal DNA, the conformational change necessary for MutS to morph to the kink-clamped state can not occur due to a steric block. Kinking of the DNA at the mismatch removes the steric block. Click the links to download videos to get animations showing the role of MutS in mismatch repair. (Fernandez-Leiro, R., Bhairosing-Kok, D., Kunetsky, V. et al. The selection process of licensing a DNA mismatch for repair. Nat Struct Mol Biol 28, 373–381 (2021). https://doi.org/10.1038/s41594-021-00577-7, with permission) Video 1: Molecular mechanism of DNA mismatch repair initiation. Front and side views of MutS passing through the first four stages of the repair cascade: DNA scanning, mismatch recognition, intermediate state, and MutL recruitment. The movements show a computational morphing between the four cryo-EM structures. MutS monomer A is shown in a pale-green color, monomer B in pale blue, DNA in dark gray, and MutLLN40 in yellow. Video 2: Mismatch repair licensing at a mismatch. Top and side views of MutS as it transforms from the DNA-scanning state to the mismatch-bound state. The initial part of the movie represents the movement of monomer B relative to monomer A during the scanning state, as derived from the principal component analysis of the multibody refinement. Note that the MutS dimer explores multiple conformations, attempting to distort the DNA, without crossing over the opposite monomer. When a mismatch is present in the DNA, it allows MutS to deform and kink the DNA and the two MutS monomers to cross over in a clockwise manner. Movements show a computational morphing between the different states. MutS monomer A is shown in a pale-green color, monomer B in pale blue, and DNA in gray. The DNA mismatch is highlighted in pink Video 3: Multiple conformational changes of mismatch and connector domains tracking DNA. Front and side views of MutS as it goes from the mismatch-bound state to the MutLLN40-bound clamp state via the intermediate state. MutS monomer A is shown in a pale green color, monomer B in pale blue, and DNA in dark gray. DNA mismatch is highlighted in pink. The mismatch domain is shown in dark green and the connector domain in light green. The ends of a central helix in the connector domain are colored in red and blue for clarity. Movements show a computational morphing between the different states. Base Excision Repair Most oxidized bases are removed from DNA by enzymes operating within the Base Excision Repair (BER) pathway. Single-stranded DNA breaks can also be repaired through this process. Removal of oxidized bases in DNA is fairly rapid. For example, 8-oxo-dG was increased 10-fold in the livers of mice subjected to ionizing radiation, but the excess 8-oxo-dG was removed with a half-life of 11 minutes. 8-oxoG is excised by 8-oxoguanine DNA glycosylase (OGG1) leaving an apurinic site (AP site), as shown in Figure $16$. AP sites are then processed further into single-strand breaks via backbone incision of AP-endonuclease 1 (APE1). In long patch base excision repair, the base and some additional nucleotides are replaced dependent on the activity of polymerase delta (Polδ) and epsilon (Polε) together with proliferating cell nuclear antigen (PCNA). The old strand is removed by Flap-endonuclease 1 (FEN1), before ligase I (LigI) ligates the backbone back together. Short patch base excision repair constitutes of polymerase beta (Polβ) replacing the single missing base, ligase III (LigIII) ligating the DNA backbone back together, and X-ray repair cross-complementing protein 1 (XRCC1) aiding the process and serving as a scaffold for additional factors. Base excision repair (BER) of 8-oxo-7,8-dihydroguanine (8-oxoG). Oxidative DNA damage is repaired via several repair intermediates by base excision repair (BER). Through the emoval of the oxidized base, a reactive apurinic site (AP site) is formed. Incision of the strand creates a single-strand break, and the damaged site is then repaired through either short or long patch BER. 25.2.6 Nucleotide Excision Repair Bulky DNA adducts and DNA crosslinks, such as those caused by UV light are repaired using Nucleotide Excision Repair (NER) pathways. In higher eukaryotic cells, NER excises 24-32 nucleotide DNA fragments containing the damaged lesion with extreme accuracy. Reparative synthesis using the undamaged strand as a template, followed by ligation of the single-strand break that emerged as a result of the damage, is the final stage of DNA repair. The process involves the coordinated action of approximately 30 proteins that successively form complexes with variable compositions on the DNA. NER consists of two pathways distinct in terms of initial damage recognition. Global genome nucleotide excision repair (GG-NER) detects and eliminates bulky damages in the entire genome, including the untranscribed regions and silent chromatin, while transcription-coupled nucleotide excision repair (TC -NER) operates when damage to a transcribed DNA strand limits transcription activity. TC-NER is activated by the stalling of RNA polymerase II at the damaged sites of a transcribed strand, while GG-NER is controlled by the protein, XPC, a specialized protein factor that reveals the damage. A schematic GG-NER process is presented in Figure (17\) below. Genetic mutations in NER pathway genes can result in UV-sensitive and high-carcinogenic pathologies, such as xeroderma pigmentosum (XP), Cockayne syndrome (CS), and trichothiodystrophy (TTD), as well as some neurodegenerative manifestations. Xeroderma pigmentosum has provided the names of some of the genes involved in NER. Mutation of XP genes and loss of proper NER function causes the symptoms associated with the disease. People with XP have an impaired ability to repair bulky DNA adducts and crosslinks, such as thymine dimers that are caused by UV-light exposure. People suffering from XP have extreme photosensitivity, skin atrophy, hyperpigmentation, and a high rate of sunlight-induced skin cancer. The risk of internal tumors in XP patients is also 1,000-fold higher. Moreover, the disease is often associated with neurologic disorders. Currently, there is no effective treatment for this disorder. The detection of bulky DNA lesions during NER is particularly challenging for a cell, which can be solved only through highly sensitive recognition that requires multiple protein components. In contrast to BER, where a damaged base is simultaneously recognized and eliminated by a single specialized glycosylase, specialized groups of proteins are responsible for the recognition of the lesion and the excision of the lesion in NER. In eukaryotic NER, universal sensor proteins perform the initial recognition of the total range of bulky damages. In the case of TC-NER, it occurs when the transcribing RNA polymerase II is stalled by damage; in GG-NER, these are complexes of the XPC factor and DDB1-DDB2 heterodimer (XPE factor) enhancing the repair of UV damage. In general, NER recognition of damage is a multistep process involving several proteins that form near damaged complexes of variable compositions. The process is completed by the formation of a preincision complex ready to eliminate a damaged DNA fragment by specialized NER endonucleases. In a eukaryotic cell after stable XPC/DNA complex formation during the initial recognition of the damage, NER is performed by a repairasome, which is a complex of variable composition and architecture consisting of a large number of subunits. Individual subunits of the complex have no sufficient affinity and selectivity to the substrate (DNA containing bulky damage). The situation changes when specific protein complexes are established at the damage site. A total of 18 polypeptides must be accurately positioned within two or three DNA turns when a stable structure ready for damage removal is formed and excision starts. The structure of NER-associated proteins provides the possibility of contact with the DNA substrate and of dynamic specific protein-protein interactions. The changes in interactions performed by the same protein are one of the mechanisms that regulate the repair process and fine-tune the complexes, providing high-precision nucleotide excision repair. 25.2.7 Repair of Double-Stranded DNA Breaks Cells have evolved two main pathways to repair double-strand breaks within the DNA: the non-homologous end-joining (NHEJ) pathway, which ensures direct resealing of DNA ends; and the homologous recombination (HR) pathway that relies on the presence of homologous DNA sequences for DSB repair, as shown in Figure (18\) below. NHEJ repair is the simplest and most widely utilized mechanism to repair DSB that occur in DNA. Repair by NHEJ involves direct resealing of the two broken ends independently of sequence homology. Although being active throughout the cell cycle, NHEJ is relatively more important during the G1 phase. Proteins required for NHEJ include but are not restricted to, the highly conserved Ku70/Ku80 heterodimeric complex, DNA-dependent protein kinase catalytic subunit (DNA-PKcs), and DNA Ligase IV (LIG4) in complex with XRCC4. By directly binding DNA ends, Ku70/Ku80 ensures protection against exonucleases and, as such, acts as an inhibitor of HR. Very short sequence homologies are likely to help DNA end alignment before NHEJ-dependent repair, however, they are not strictly required. NHEJ protects genetic integrity by rejoining broken strands of DNA that may otherwise be lost during DNA replication and cell regeneration. However, during the process of NHEJ, insertions or deletions within the joined regions may occur (Fig 25.2.17). Non-homologous end-joining (NHEJ) and homologous recombination (HR) pathways act competitively to repair DNA double-strand breaks (DSBs). Key players of NHEJ and HR are depicted. The MRE11/RAD50/XRS2 (MRX) complex is recruited very early at DNA ends and appears to play important roles for both NHEJ and HR. Ku70/Ku80 heterodimer is required for NHEJ and, through inhibition of DNA end resection (5′–3′ exo), acts as a repressor of HR. The fidelity of NHEJ-dependent DSB repair is low and, most of the time, associated with nucleotide deletions and/or insertions at repair junctions. The common early step of HR-dependent mechanisms is the formation of ssDNA which is then coated by replication protein A (RPA). Single-strand annealing (SSA) mechanism requires the presence of direct repeats (shown in orange) on both sides of the break. SSA does not imply any strand invasion process and is therefore not dependent on RAD51 protein. Strand invasion and D-loop formation are however common steps of synthesis-dependent strand annealing (SDSA) and double Holliday junction (HJ) dissolution mechanisms. In the latter case, double Holliday junctions are resolved with or without crossing over. In contrast to NHEJ, homologous recombination (HR) requires a homologous DNA sequence to serve as a template for DNA-synthesis-dependent repair and involves extensive DNA-end processing. As expected, HR is extremely accurate, as it leads to precise repair of the damaged locus using DNA sequences homologous to the broken ends. HR predominantly uses the sister chromatid as a template for DSB repair, rather than the homologous chromosome. Correspondingly, HR is largely inhibited while cells are in the G1 phase of the cell cycle when the sister chromatid has not yet been replicated, as shown in panel (A) of Figure (19\) below. HR repair mechanisms play a bigger role in DSB repair that occurs after S-phase DNA replication (S-phase, G2, and M). Repair through HR is not defined by a unique mechanism but operates through various mechanistically distinct DSB repair processes, including synthesis-dependent strand annealing (SDSA), double Holliday junction resolution, and single-strand annealing (SSA). The common step for HR-dependent DSB repair mechanisms is the initial formation of single-stranded DNA (ssDNA) for pairing with homologous DNA template sequences. For this to occur, the 5' DNA strand at the DSB is processed by multiple nucleases and accessory proteins to create a 3' ssDNA section that can be used as a template for recombination (see Figure 18 above). Panel B of Figure (19\) below provides a more detailed look at the HR process. During the highly regulated process of HR, three main phases can be distinguished. Firstly, 3′-single-stranded DNA (ssDNA) ends are generated by nucleolytic degradation of the 5′-strands. This first step is catalyzed by endonucleases, including the MRN complex (consisting of Mre11, Rad50, and Nbs1). In the second step, the ssDNA-ends are coated by replication protein A (RPA) filaments. In the third step, RPA is replaced by Rad51 in a BRCA1- and BRCA2-dependent process, to ultimately perform the recombinase reaction using a homologous DNA template. Importantly, HR is not only employed to repair DNA lesions induced by DNA-damaging agents but is also essential for proper chromosome segregation during meiosis. The relevance of HR in these physiological processes is illustrated by its strict requirement during development. Mice lacking key HR genes, such as Brca1, Brca2, or Rad51, display extensive genetic alterations which lead to early embryonic lethality. Whereas homozygous inactivation of HR genes is usually embryonic lethal, heterozygous inactivation of,BRCA1 and BRCA2, does not interfere with cellular viability but rather predisposes individuals to cancer, including breast and ovarian cancer. The tumors that develop in individuals with heterozygous BRCA1/2 mutations invariably lose their second BRCA1/2 allele, indicating that in certain cancers, the absence of BRCA1/2 is compatible with cellular proliferation. How exactly such tumors cope with their HR defect is currently not fully understood. Panel (A) shows the DNA DSBs repair pathways in the context of cell cycle regulation. Non-homologous end joining (NHEJ) can be performed throughout the cell cycle and is indicated with the red line. Homologous recombination (HR) can only be employed in S/G2 phases of the cell cycle and is indicated in green. Pane (B) shows the key steps in the HR repair pathway are indicated. After DSB recognition, 5′–3′ end resection is initiated by the MRN (Mre11, Rad50, Nbs1) complex and CtIP. Subsequently, further resection by the Exo1, DNA2, and Sgs1 proteins is conducted to ensure ‘maintained’ resection. Then, resected DNA ends are bound by replication protein A (RPA). The actual recombination step within HR repair, termed strand exchange, is executed by the recombinase Rad51. Rad51 replaces RPA to eventually assemble helical nucleoprotein filaments called ‘presynaptic filaments.’ This process is facilitated by other HR components, including BRCA1 and BRCA2. The final step of junction resolution is executed by helicases including Bloom syndrome, the RecQ helicase-like (BLM) helicase. Error-Prone Bypass and Translesion Synthesis If DNA is not repaired before DNA replication, the cell must employ another strategy to replicate the DNA, even in the presence of a DNA lesion. This is important to avoid causing double-stranded DNA breaks that can occur when a replisome stalls at the replication fork. Under these circumstances, another strategy that cells use to respond to DNA damage is to bypass lesions found during DNA replication and continue with the replicative process. DNA damage bypass can occur by recombination mechanisms or through a novel mechanism called translesion synthesis. Translesion synthesis employs an alternate DNA polymerase that can substitute for a DNA polymerase that has stalled at the replication fork due to DNA damage. Specialized DNA polymerases, that are active in regions with DNA damage, have active sites that can accommodate fluctuations in DNA topography that enable them to bypass the lesions and continue with the replicative process. The evolution of DNA polymerases that can tolerate the presence of distorted DNA lesions and continue with the replicative process can be seen at all levels of life, from prokaryotic, single-celled organisms through eukaryotic multicellular organisms, including humans. In fact, within vertebrates, there has been a large expansion of DNA polymerases that play a role in DNA damage bypass mechanisms and highlight the importance of these processes in damage tolerance and cell survival, as shown in Table (3\) below. Table (3\): DNA Polymerases involved in Error-Prone Bypass The activity of error-prone DNA polymerases is tightly regulated to avoid the rampant introduction of mutations within the DNA sequence. One of the main mechanisms that is employed within a replisome that is stalled at the replication fork due to DNA damage, involves the monoubiquitination of PCNA. Recall from Chapter 9, that PCNA is the sliding clamp that enables the DNA polymerase to bind tightly enough with the DNA during replication to mediate efficient DNA synthesis. Monoubiquitination of PCNA enables the recruitment of a translesion DNA polymerases and the bypass of the damaged lesions during DNA synthesis. During translesion synthesis, the polymerase must insert a dNTP opposite of the lesion. None of the dNTP bases will likely be able to form stable hydrogen bond interactions with the damaged lesion. Thus, the nucleotide that causes the least distortion or repulsion will usually be added across from the lesion. This can cause transition or transversion mutations to occur at the lesion location. Alternatively, translesion polymerases can be prone to slippage, and either causes an insertion or deletion mutation in the vicinity of the DNA lesion. These slippages can lead to frameshift mutations if they occur within gene coding regions. Thus, over a lifetime, translesion synthesis in multicellular organisms can lead to an accumulation of mutations within somatic cells and cause the formation of tumors and the disease of cancer. Evolution by natural selection is also possible due to random mutations that occur within germ cells. Occasionally, germline mutations may lead to a beneficial mutation that enhances the survival of an individual within a population. If this gene proves to enhance the survival of the population, it will be selected over time within the population and cause the evolution of that species. An example of a beneficial mutation is the case of a population of people that show resistance to HIV infection. Since the first case of infection with human immunodeficiency virus (HIV) was reported in 1981, nearly 40 million people have died from HIV infection, the virus that causes acquired immune deficiency syndrome (AIDS). The virus targets helper T cells that play a key role in bridging the innate and adaptive immune response, infecting and killing cells normally involved in the body’s response to infection. There is no cure for HIV infection, but many drugs have been developed to slow or block the progression of the virus. Although individuals around the world may be infected, the highest prevalence among people 15–49 years old is in sub-Saharan Africa, where nearly one person in 20 is infected, accounting for greater than 70% of the infections worldwide, as shown in Figure (20\) below. Unfortunately, this is also a part of the world where prevention strategies and drugs to treat the infection are the most lacking. In recent years, scientific interest has been piqued by the discovery of a few individuals from northern Europe who are resistant to HIV infection. In 1998, American geneticist Stephen J. O’Brien at the National Institutes of Health (NIH) and colleagues published the results of their genetic analysis of more than 4,000 individuals. These indicated that many individuals of Eurasian descent (up to 14% in some ethnic groups) have a deletion mutation, called CCR5-delta 32, in the gene encoding CCR5. CCR5 is a coreceptor found on the surface of T-cells that is necessary for many strains of the virus to enter the host cell. The mutation leads to the production of a receptor to which HIV cannot effectively bind and thus blocks viral entry. People homozygous for this mutation have greatly reduced susceptibility to HIV infection, and those who are heterozygous have some protection from infection as well. It is not clear why people of northern European descent, specifically, carry this mutation, but its prevalence seems to be highest in northern Europe and steadily decreases in populations as one moves south. Research indicates that the mutation has been present since before HIV appeared and may have been selected for in European populations as a result of exposure to the plague or smallpox. This mutation may protect individuals from the plague (caused by the bacterium Yersinia pestis) and smallpox (caused by the variola virus) because this receptor may also be involved in these diseases. The age of this mutation is a matter of debate, but estimates suggest it appeared between 1875 years to 225 years ago, and may have been spread from Northern Europe through Viking invasions. This exciting finding has led to new avenues in HIV research, including looking for drugs to block CCR5 binding to HIV in individuals who lack the mutation. Although DNA testing to determine which individuals carry the CCR5-delta 32 mutation is possible, there are documented cases of individuals homozygous for the mutation contracting HIV. For this reason, DNA testing for the mutation is not widely recommended by public health officials so as not to encourage risky behavior in those who carry the mutation. Nevertheless, inhibiting the binding of HIV to CCR5 continues to be a valid strategy for the development of drug therapies for those infected with HIV. Practice Problems Multiple Choice Which of the following is a change in the sequence that leads to the formation of a stop codon? 1. missense mutation 2. nonsense mutation 3. silent mutation 4. deletion mutation [reveal-answer q=”745512″]Show Answer[/reveal-answer] [hidden-answer a=”745512″]Answer b. A nonsense mutation is a change in the sequence that leads to formation of a stop codon.[/hidden-answer] The formation of pyrimidine dimers results from which of the following? 1. spontaneous errors by DNA polymerase 2. exposure to gamma radiation 3. exposure to ultraviolet radiation 4. exposure to intercalating agents [reveal-answer q=”709151″]Show Answer[/reveal-answer] [hidden-answer a=”709151″]Answer c. The formation of pyrimidine dimers results from exposure to ultraviolet radiation.[/hidden-answer] Which of the following is an example of a frameshift mutation? 1. a deletion of a codon 2. missense mutation 3. silent mutation 4. deletion of one nucleotide [reveal-answer q=”688366″]Show Answer[/reveal-answer] [hidden-answer a=”688366″]Answer a. The deletion of one nucleotide is an example of a frameshift mutation.[/hidden-answer] Which of the following is the type of DNA repair in which thymine dimers are directly broken down by the enzyme photolyase? 1. direct repair 2. nucleotide excision repair 3. mismatch repair 4. proofreading [reveal-answer q=”755583″]Show Answer[/reveal-answer] [hidden-answer a=”755583″]Answer a. In a direct repair, thymine dimers are directly broken down by the enzyme photolyase.[/hidden-answer] Which of the following regarding the Ames test is true? 1. It is used to identify newly formed auxotrophic mutants. 2. It is used to identify mutants with restored biosynthetic activity. 3. It is used to identify spontaneous mutants. 4. It is used to identify mutants lacking photoreactivation activity. [reveal-answer q=”770537″]Show Answer[/reveal-answer] [hidden-answer a=”770537″]Answer b. It is used to identify mutants with restored biosynthetic activity.[/hidden-answer] Fill in the Blank A chemical mutagen that is structurally similar to a nucleotide but has different base-pairing rules is called a ________. [reveal-answer q=”702924″]Show Answer[/reveal-answer] [hidden-answer a=”702924″]A chemical mutagen that is structurally similar to a nucleotide but has different base-pairing rules is called a nucleoside analog.[/hidden-answer] The enzyme used in light repair to split thymine dimers is called ________. [reveal-answer q=”939657″]Show Answer[/reveal-answer] [hidden-answer a=”939657″]The enzyme used in light repair to split thymine dimers is called photolyase.[/hidden-answer] The phenotype of an organism that is most commonly observed in nature is called the ________. [reveal-answer q=”640686″]Show Answer[/reveal-answer] [hidden-answer a=”640686″]The phenotype of an organism that is most commonly observed in nature is called the wild type.[/hidden-answer] True/False Carcinogens are typically mutagenic. [reveal-answer q=”166576″]Show Answer[/reveal-answer] [hidden-answer a=”166576″]True[/hidden-answer] Think about It Why is it more likely that insertions or deletions will be more detrimental to a cell than point mutations? Critical Thinking Below are several DNA sequences that are mutated compared with the wild-type sequence: 3′-T A C T G A C T G A C G A T C-5′. Envision that each is a section of a DNA molecule that has separated in preparation for transcription, so you are only seeing the template strand. Construct the complementary DNA sequences (indicating 5′ and 3′ ends) for each mutated DNA sequence, then transcribe (indicating 5′ and 3′ ends) the template strands, and translate the mRNA molecules using the genetic code, recording the resulting amino acid sequence (indicating the N and C termini). What type of mutation is each? Mutated DNA Template Strand #1: 3′-T A C T G T C T G A C G A T C-5′ Complementary DNA sequence: [practice-area rows=”1″][/practice-area] mRNA sequence transcribed from template: [practice-area rows=”1″][/practice-area] Amino acid sequence of peptide: [practice-area rows=”1″][/practice-area] Type of mutation: [practice-area rows=”1″][/practice-area] Mutated DNA Template Strand #2: 3′-T A C G G A C T G A C G A T C-5′ Complementary DNA sequence: [practice-area rows=”1″][/practice-area] mRNA sequence transcribed from template: [practice-area rows=”1″][/practice-area] Amino acid sequence of peptide: [practice-area rows=”1″][/practice-area] Type of mutation: [practice-area rows=”1″][/practice-area] Mutated DNA Template Strand #3: 3′-T A C T G A C T G A C T A T C-5′ Complementary DNA sequence: [practice-area rows=”1″][/practice-area] mRNA sequence transcribed from template: [practice-area rows=”1″][/practice-area] Amino acid sequence of peptide: [practice-area rows=”1″][/practice-area] Type of mutation: [practice-area rows=”1″][/practice-area] Mutated DNA Template Strand #4: 3′-T A C G A C T G A C T A T C-5′ Complementary DNA sequence: [practice-area rows=”1″][/practice-area] mRNA sequence transcribed from template: [practice-area rows=”1″][/practice-area] Amino acid sequence of peptide: [practice-area rows=”1″][/practice-area] Type of mutation: [practice-area rows=”1″][/practice-area] <h212references">25.2.10 References 1. <lifootnote-99-2">World Health Organization. " Global Health Observatory (GHO) Data, HIV/AIDS." http://www.who.int/gho/hiv/en/. Accessed August 5, 2016. 2. Parker, N., Schneegurt, M., Thi Tu, A-H., Lister, P., Forster, B.M. (2019) Microbiology. Openstax. Available at: https://opentextbc.ca/microbiologyopenstax/ 3. Wikipedia contributors. (2020, July 15). DNA oxidation. In Wikipedia, The Free Encyclopedia. Retrieved 03:44, July 16, 2020, from https://en.Wikipedia.org/w/index.php?title=DNA_oxidation&oldid=967811859 4. Wakim, S. and Grewal, M. (2020) Human Biology. Libretexts. Available at: https://bio.libretexts.org/Bookshelves/Human_Biology/Book%3A_Human_Biology_(Wakim_and_Grewal) 5. Ahmad, A., Nay, S.L. and O'Conner, T.R. (2015) Chapter 4 Direct Reversal Repair in Mammalian Cells. Published through INTECH. Available at: https://cdn.intechopen.com/pdfs/48191.pdf 6. Wikipedia contributors. (2020, April 20). AP site. In Wikipedia, The Free Encyclopedia. Retrieved 18:15, July 23, 2020, from https://en.Wikipedia.org/w/index.php?title=AP_site&oldid=952117602 7. Wikipedia contributors. (2020, July 4). Benzo(a)pyrene. In Wikipedia, The Free Encyclopedia. Retrieved 06:15, July 24, 2020, from https://en.Wikipedia.org/w/index.php?title=Benzo(a)pyrene&oldid=965990545 8. Wikipedia contributors. (2020, June 23). Pyrimidine dimer. In Wikipedia, The Free Encyclopedia. Retrieved 06:54, July 24, 2020, from https://en.Wikipedia.org/w/index.php?title=Pyrimidine_dimer&oldid=964108515 9. Morimoto, S., Tsuda, M., Bunch, H., Sasanuma, H., Ausin, C. and Takeda, S. (2019) Type II DNA Topoisomerases Cause Spontaneous Double-Strand Breaks in Genomic DNA. Genes 10:868. Available at: https://www.researchgate.net/publication/336916880_Type_II_DNA_topoisomerases_cause_spontaneous_double-strand_breaks_in_genomic_DNA/figures?lo=1 10. Ding, L., Cao, J., Lin, W., Chen, H., Xiong, X., Ao, H., Yu, M., Lin, J., Cui, Q. (2020) The Roles of the Cyclin-Dependent Kinases in Cell-Cycle Progression and Therapeutic Strategies in Human Breast Cancer. Int. J. Mol Sci 21(6):1960. Available at: https://www.mdpi.com/1422-0067/21/6/1960/htm 11. Verma, N., Franchitto, M., Zonfrilli, A., Cialfi, S., Palermo, R., and Talora, C. (2019) DNA Damage Stress: Cui Prodest? Int. J. Mol. Sci. 20(5):1073. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6429504/ 12. Fukui, K. (2010) DNA Mismatch Repair in Eukaryotes and Bacteria. J. Nuc. Acids 260512. Available at: https://www.hindawi.com/journals/jna/2010/260512/#copyright 13. Petruseva, I.O., Evdokimov, A.N., and Lavrik, O.I. (2014) Molecular Mechanism of Global Genome Nucleotide Excision Repair. Acta Naturae 6(1):23-34. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3999463/ 14. Decottingnies, A. (2013) Alternative end-joining mechanisms: A historical perspective. Frontiers in Genetics 4(48):48. Available at: https://www.researchgate.net/publication/236129718_Alternative_end-joining_mechanisms_A_historical_perspective 15. Vitor, A.C., Huertas, P., Legube, G., and de Almeida, S.F. (2020) Studying DNA Double-Strand Break Repair: An Every-Growing Toolbox. Front. Mol Biosci 7:24. Available at: https://www.frontiersin.org/articles/10.3389/fmolb.2020.00024/full 16. Krajewska, M., Fehrmann, R.S.N., de Vries, E.G.E., and van Vugt, A.A.T.M. (2015) Regulators of homologous recombination repair as novel targets for cancer treatment. Front. Genet. 6:96. Available at: https://www.frontiersin.org/articles/10.3389/fgene.2015.00096/full
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/03%3A_Unit_III-_Information_Pathway/24%3A_DNA_Metabolism/24.03%3A_DNA_Recombination.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Homologous Recombination Homologous recombination is a type of genetic recombination in which genetic information is exchanged between two similar or identical molecules of double-stranded or single-stranded nucleic acids (usually DNA as in cellular organisms but may be also RNA in viruses). As noted in section 25.2, This process is widely used by cells to accurately repair harmful breaks that occur on both strands of DNA, known as double-strand breaks (DSB), in a process called homologous recombinational repair (HRR). Homologous recombination also produces new combinations of DNA sequences during meiosis, the process by which eukaryotes make gamete cells, like sperm and egg cells in animals. These new combinations of DNA represent genetic variation in offspring, which in turn enables populations to adapt during evolution. Homologous recombination is also used in horizontal gene transfer to exchange genetic material between different strains and species of bacteria and viruses. Although homologous recombination varies widely among different organisms and cell types, for double-stranded DNA (dsDNA) most forms involve the same basic steps. After a double-strand break occurs, sections of DNA around the 5' ends of the break are cut away in a process called resection. In the strand invasion step that follows, an overhanging 3' end of the broken DNA molecule then "invades" a similar or identical DNA molecule that is not broken. After strand invasion, the further sequence of events may follow either of two main pathways discussed below (see Models); the DSBR (double-strand break repair) pathway or the SDSA (synthesis-dependent strand annealing) pathway. Homologous recombination that occurs during DNA repair tends to result in non-crossover products, in effect restoring the damaged DNA molecule as it existed before the double-strand break. There are several different ways to repair DSB as illustrated in Figure $1$. The broken (or resected) DNA with a double-stranded break must find and come together (synapse), invade (or intertwine) with the DNA of a donor, that is homologous to it. The repair can then ensue. In somatic cells that undergo mitosis and not meiosis, the preferred donor is the sister chromatid (the copy of one chromosome made during cell division) and not the homologous chromosome (from the diploid cell). Variants include synthesis-dependent strand annealing pathway (SDSA). Other variants include nonhomologous end-joining (NHEJ), microhomology-mediated end-joining (MMEJ), and double Holiday junction (dHJ). Figure $1$: Model for the repair of DNA double-strand breaks by homologous recombination in somatic cells. Wright et al. J. Biol. Chem. (2018) 293(27) 10524 –10535. Creative Commons Attribution (CC BY 4.0) When a DNA double-strand break (DSB) occurs in a DNA molecule, a repair can proceed by multiple pathways largely controlled by end resection. NHEJ is capable of repairing unresected or minimally resected DSBs in a template-independent fashion. MMEJ and single-strand annealing (SSA) rely on different extents of homology between the two DSB ends for repair independent of a donor molecule. Homologous recombination proceeds as shown in the figure using a homologous donor DNA. Most of the extended D-loops in somatic cells are disrupted and subsequently repaired by SDSA. The result of the repair by SDSA is always a noncrossover outcome, thus avoiding the loss of heterozygosity produced by somatic crossovers. SDSA occurs by disruption of the extended D-loop and annealing the newly synthesized DNA with the second end of the broken molecule. Alternatively, the newly synthesized strand may invade the second end. The extended D-loop can also undergo second-end capture or invasion to form a double Holliday junction (dHJ). This may either lead to a crossover or a noncrossover outcome. Invasion by the second break end makes dHJ formation and hence crossover outcome more likely for another model for crossover generation. dHJs can be dissolved into noncrossovers by the concerted action of the Sgs1–Top3–Rmi1 complex to migrate the two junctions toward each other and then decatenate the strands of the hemicatenane by the Top3 topoisomerase activity. Each colored line indicates a strand of DNA, and dotted lines represent DNA synthesis. In the process of homologous recombination, a key intermediate is the Holiday junction, named after Robin Holiday who discovered it. It consists of branched nucleic acid with four double-stranded arms joined. A holiday junction is seen as the crossing of red and blue strands in the middle of Figure 1 labeled Nascent D-loop. Also, one is seen in the Extended D-loop just below it. A double Holiday junction is seen in the middle of the right-hand section. Two views of Holiday junctions are shown in Figure $2$. By Донор - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/inde...curid=48470765 By Antony-22 - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/inde...curid=38557614 Figure $2$. Two views of holiday junctions. The left-hand panel shows the primary and secondary sequences and some tertiary (3D) aspects of base-stacking conformational isomers of the Holliday junction. The bases nearest to the junction point determine which stacked isomer dominates. Figure $3$ shows an interactive iCn3D model of the structure of the Holliday junction intermediate in Cre-loxP site-specific recombination (3CRX). The alpha carbon backbone of the four Cre recombinase monomers in the tetramer is shown in red. The DNA is nearly planar with a twofold-symmetric DNA intermediate that is similar to a square and stacked Holiday junction for the DNA in the unbound state. Homologous recombination is conserved across all three domains of life as well as DNA and RNA viruses, suggesting that it is a nearly universal biological mechanism. The discovery of genes for homologous recombination in protists—a diverse group of eukaryotic microorganisms—has been interpreted as evidence that meiosis emerged early in the evolution of eukaryotes. Since their dysfunction has been strongly associated with increased susceptibility to several types of cancer, the proteins that facilitate homologous recombination are topics of active research. Homologous recombination is also used in gene targeting, a technique for introducing genetic changes into target organisms. For their development of this technique, Mario Capecchi, Martin Evans, and Oliver Smithies were awarded the 2007 Nobel Prize for Physiology or Medicine; Capecchi[3] and Smithies[4] independently discovered applications to mouse embryonic stem cells, however, the highly conserved mechanisms underlying the DSB repair model, including uniform homologous integration of transformed DNA (gene therapy), were first shown in plasmid experiments by Orr-Weaver, Szostack, and Rothstein. Before the beginning of meiosis, the replication of the DNA is required to form sister chromatids, as shown in Step 1 in Figure $4$. Once replicated, the DNA will condense and begin the process of meiosis. As the cells enter metaphase of meiosis, heterologous chromosomes are paired together (Step 2). When the homologous chromosomes are paired, they can under genetic mixing or DNA recombination form new chromosomal arrangements that are unique from the parental chromosomes (Step 3). Once the process of recombination is finished, the heterologous chromosomes are separated into two different daughter cells (Step 4). This is called Meiosis I. At this stage the chromosomes have been reduced from the diploid to the haploid state, however, each chromosome set is still paired with its sister chromatid and needs to undergo a second round of cell division to produce the final set of four gametes (Step 5). This is called Meiosis II and results in the formation of four haploid gametes that are genetically unique. During the process of meiosis, cell division is used to create the gametes or reproductive cells of an organism (the egg and the sperm cells). Meiosis results in the reduction of the genome from the 2n or diploid state to the 1n or haploid state. As you can see in Figure 25.3.1, the process of meiotic division results in the generation of four genetically unique haploid cells and involves the pairing of heterologous chromosomes during metaphase of meiosis. In humans, the meiotic process results in four viable sperm cells in the male and a single viable egg in the female. The other three cells produced in the female during the meiotic division are termed polar bodies and are very small and do not contain enough cytoplasmic components to survive. They get reabsorbed into the body. In either case, the resulting egg or sperm cell each carries a single copy of the genome and is in the haploid state. It is during the process of meiosis that homologous recombination occurs in a controlled manner to introduce genetic variation into the resulting gametes. As a result, each egg and sperm cell has a unique genetic makeup that is a mixture of both parental copies of the genome. Proper segregation during meiosis requires that homologs be connected by the combination of crossovers and sister chromatid cohesion. To generate crossovers, numerous double-strand breaks (DSBs) are introduced throughout the genome by the conserved Spo11 endonuclease. DSB formation and its repair are then highly regulated to ensure that homologous chromosomes contain at least one crossover and that no DSBs remain before meiosis I segregation. The synaptonemal complex (SC) is a meiosis-specific structure formed between homologous chromosomes during prophase that promotes DSB formation and biases the repair of DSBs to homologous chromosomes rather than back to the sister chromatids, ensuring that genetic recombination occurs. Synapsis, the pairing of homologous chromosomes, occurs when a particular recombination pathway is successful in establishing stable interhomolog connections. Formation of the Synaptonemal Complex In the 1950s, electron microscopists discovered an evolutionarily conserved, meiosis-specific structure formed between homologous chromosomes, unique to prophase I, called the SC, as shown in Figure $5$. The SC physically connects homologs during prophase I and is removed before metaphase I, when homologs are connected instead by the combination of crossovers and sister chromatid cohesion. What is the function of the SC? Decades of research have shown that this elaborate chromosomal structure is critical for the regulation of recombination, the process by which crossovers are generated. Panel (A) shows the synaptonemal complex. When chromosomes form synapses, recombination intermediates contain double Holliday junctions (shown by intersecting loops). When cells exit the pachytene stage, the stage of meiotic prophase when chromosomes fully synapse, Holliday junctions are resolved to form crossovers, and the SC is disassembled. DSB formation is greatly reduced by synapsis but is not completely abolished until cells exit pachynema. Panel (B) shows the sequence diversity between homologous chromosomes largely inhibits recombination and synapsis, resulting in persistent DSB formation. Panel (C) shows that in the absence of the central element, the transverse filament is not assembled, resulting in chromosomes that lack the central region. Recombination intermediates containing double Holliday junctions are still formed, but DSB formation is not down-regulated. Image from: SC formation begins with the condensation of sister chromatids along meiosis-specific protein cores to make axial elements. Axial elements from homologous chromosomes are “zippered” together by the insertion of the central region. (Note that after synapsis, axial elements are called lateral elements (Panel A above). The central region is comprised of (1) transverse filaments located perpendicular to the lateral elements, and (2) the central element, which runs parallel to the lateral elements midway through the central region. Assembly of the SC is initiated in the early stage of meiotic prophase I, which is commonly divided into five substages (leptotene, zygotene, pachytene, diplotene, and diakinesis). For proper assembly of the SC followed by the correct pairing of the homologous chromosome, lateral elements (LEs), which are composed of two main proteins (SYCP2 and SYCP3) should be formed along each chromosome at the initial stage, during leptotene. Later the two LEs associate with the linker part, known as the transverse filaments (TFs). TFs are primarily composed of the protein SYCP1. The central element (CE), which is composed of SYCE1, SYCE2, SYCE3, and TEX12, then connects to the LEs through the TFs, as shown in Figure $6$. The lateral elements complete their pairing during the zygotene stage leading to the formation of the tripartite SC structure seen during the pachytene stage of the first meiotic prophase as shown in Figure $7$ and Figure $8$. This occurs in both males and females during gametogenesis. Panel (A) shows a model of the SC. Lateral elements (light blue rods) of homologous chromosomes align and synapse together via a meshwork of transverse filaments (black lines) and longitudinal filaments (dark blue rods). The longitudinal filaments are collectively referred to as the “central element” of the SC. Ellipsoidal structures called recombination nodules (gray ellipsoid) are constructed on the central region of the SC. As their name implies, recombination nodules are believed to be involved in facilitating meiotic recombination (crossing over). The chromatin (red loops) of each homolog is attached to its corresponding lateral element. Because there are two “sister chromatids” in each homolog, two loops are shown extending laterally from each point along a lateral element. Panel (B) Top shows a set of tomato SCs. Chromatin “sheaths” are visible around each SC showing two tomato SCs. The chromatin has been stripped from the SCs, allowing the details of the SC to be observed. Each SC has a kinetochore (“ball-like” structure) at its centromere. Recombination nodules, ellipsoidal structures found on the central regions of SCs, mark the sites of crossover events (see inset). Zygotene is the sub-stage where synapsis between homologous chromosomes begins. It is also known as zygonema. This synapse can form up and down the chromosomes allowing numerous points of contact called 'synaptonemal complex', this can be compared to a zipper structure, due to the coils of chromatin. The SC facilitates synapsis by holding the aligned chromosomes together. After the homologous pairs synapse they are either called tetrads or bivalents. Bivalent is more commonly used at an advanced level as it is a better choice due to similar names for similar states (a single homolog is a 'univalent', and three homologs are a 'trivalent'). Once the synapse is formed it is called a bivalent (where a chromatid of one pair is synapsed/attached to the chromatid in a homologous chromosome and crossing over can occur. Subsequently, the synapses snap completing the crossing over of the genetic information. As a result, the variation in genetic material has increased significantly, because up and down the chromosome there has been an exchange of the mother and father's genetic material. The two sister chromatids separate from each other, but the homologous chromosomes remain attached. This makes the complex look much thicker. The SC is complete, allowing chiasma to form. This is what allows the crossing over alleles to occur as this is a process that only happens over a small region of the chromosomes. The chiasma is a structure that forms between a pair of homologous chromosomes by crossover recombination and physically links the homologous chromosomes during meiosis as shown in Figure $9$. Chiasmata are essential for the attachment of the homologous chromosomes to opposite spindle poles (bipolar attachment) and their subsequent segregation to the opposite poles during meiosis I. Mechanism of Homologous Recombination Meiotic recombination is a tightly regulated process that is triggered by the programmed induction of DNA double-strand breaks (DSBs). Once formed, the ends of the DSBs are nucleolytically processed to generate 3′ single-stranded DNA (ssDNA) tails. Meiotic recombination factors then engage these ssDNA tails to form a nucleoprotein ensemble capable of locating DNA homology in the chromosome homolog and mediating invasion of the homolog to form a DNA joint called a displacement loop or D-loop. The 3′ end of the invading strand is extended by DNA synthesis, followed by the pairing of the non-invading 3′ single-stranded tail with the displaced ssDNA strand in the enlarged D-loop (second-end capture). After DNA synthesis and DNA ligation, a double Holliday Junction (dHJ) intermediate is formed. Resolution of the dHJ intermediate can result in crossover recombinants that harbor a reciprocal exchange of the arms of the homologous chromosomes. Genetic studies have revealed that meiotic DSBs arise via the action of a protein ensemble that harbors the Spo11 protein, which bears homologous to archaeal Topo VIA, the catalytic subunit of a type II topoisomerase. Indeed, studies in S. cerevisiae, S. pombe, and M. musculus have shown that Spo11 becomes covalently conjugated to the 5′ ends of DNA through a tyrosine residue proposed to be the catalytic center of topoisomerase function. Thus, mutations in the putative catalytic tyrosine residue of Spo11 engender the same phenotype as spo11 deletion in S. cerevisiae, S. pombe, A. thaliana, and M. musculus. All these observations suggest that Spo11 is directly involved in catalyzing DSB formation to trigger meiotic recombination. Figure $10$ provides an overview of this process. Panel (A) shows a schematic of the formation of haploid gametes from a diploid cell with a single pair of homologous chromosomes. DSB formation and recombination promote homolog pairing and lead to the exchange of chromosomal fragments (crossovers) in the context of synapsed chromosomes. Panel (B) shows meiotic recombination is initiated by Spo11-mediated DSB formation and leads to the formation of crossovers via a ZMM-dependent double Holliday Junction (dHJ) resolution pathway or non-crossovers by synthesis-dependent strand annealing. Panel (C) shows the relationships between meiotic recombination and higher-order chromosome structure. DSB formation happens in the context of the loop-axis structure. As recombination progresses, the SC polymerizes between the axes and is disassembled before chromosome segregation. Axis proteins Red1 (red ovals) and Hop1 (yellow ovals) are shown. Panel (D)shows that in metaphase I, homologs are held together through chiasmata and sister chromatid cohesion. Image from: Following break formation, Spo11 remains covalently attached to the 5′-strands at both DNA ends and is released by an endonucleolytic cleavage reaction mediated by MRX (Mre11, Rad50, and Xrs2) and Sae2, which liberates Spo11 attached to a short oligonucleotide (Fig. 25.3.7B). The 5′-strands are further resected by 5′-3′ exonucleases to produce long single-stranded tails, which are coated with the ssDNA-binding protein, RPA. RPA is then replaced by recombinases Rad51 and Dmc1 that form a nucleoprotein filament and search for sequence similarity preferentially located on the homologous chromosome, producing D-loop structures. Following DNA synthesis using the homolog as a repair template, the recombination structures experience one of two main outcomes (Fig. 25.3.7B). The invading strand can be ejected from the donor by the action of helicases, which provides an opportunity for the DNA ends to re-anneal. This process is referred to as synthesis-dependent strand annealing (SDSA) and produces non-crossovers, that is, products not associated with reciprocal exchanges of chromosome fragments, but with the local transfer of genetic information from the repair template to the broken molecule (gene conversion). Alternatively, recombination structures are stabilized by the “ZMM” family of proteins and channeled through a pathway that produces mostly crossovers. Here, both ends of the break engage the donor to form a double Holliday Junction intermediate, which is resolved through a crossover-specific pathway that involves MutLγ and Exo1. Every aspect of meiotic recombination is tied to the structural organization of the chromosomes (Fig. 25.3.7C). Early in the meiotic prophase, chromosomes organize as a series of DNA loops that are anchored along a nucleoprotein axis. DSB formation happens in the context of this loop-axis structure. As recombination progresses, polymerization of a proteinaceous structure called the synaptonemal complex (SC) initiates between the two axes and elongates along their entire length. Recombination proceeds within the SC, inside a nodule embedded between the axes. After recombination is completed, the SC disassembles and crossovers, now cytologically visible as chiasmata, provide physical connections between the homologs until their segregation at anaphase (Fig. 25.3.7D).
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/03%3A_Unit_III-_Information_Pathway/25%3A_RNA_Metabolism/25.01%3A_DNA-Dependent_Synthesis_of_RNA.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Types of RNA Structurally speaking, ribonucleic acid (RNA), is quite similar to DNA. However, whereas DNA molecules are typically long and double-stranded, RNA molecules are much shorter and are typically single-stranded. A ribonucleotide within the RNA chain contains ribose (the pentose sugar), one of the four nitrogenous bases (A, U, G, and C), and a phosphate group. The subtle structural difference between the sugars gives DNA added stability, making DNA more suitable for the storage of genetic information, whereas the relative instability of RNA makes it more suitable for its more short-term functions. The RNA-specific pyrimidine uracil forms a complementary base pair with adenine and is used instead of the thymine that is found in DNA. Even though RNA is single-stranded, most types of RNA molecules show extensive intramolecular base pairing between complementary sequences within the RNA strand, creating a predictable three-dimensional structure essential for their function, as shown in Figure $1$ and Figure $2$. RNA can largely be divided into two types, one that carries the code for making proteins or coding RNA, which is also called messenger RNA (mRNA), and non-coding RNA (ncRNA). The ncRNA can be subdivided into several different types, depending either on the length of the RNA or on the function. Size classification begins with the short ncRNAs (~20–30 nt), which include microRNAs (miRs), and small interfering (siRNAs); the small ncRNAs up to 200 nt, which include transfer RNA (tRNA), small nuclear RNA (snRNA), and small nucleolar RNA (snoRNA); and long ncRNAs ( > 200 nt), which include ribosomal RNA (rRNA), enhancer RNA (eRNA) and long intergenic ncRNAs (lincRNAs), among others. Cells access the information stored in DNA by creating RNA, through the process of transcription, which then directs the synthesis of proteins through the process of translation. The three main types of RNA directly involved in protein synthesis are messenger RNA (mRNA), ribosomal RNA (rRNA), and transfer RNA (tRNA). The mRNA carries the message from the DNA, which controls all of the cellular activities in a cell. If a cell requires a certain protein to be synthesized, the gene for this product is “turned on” and the mRNA is synthesized through the process of transcription. The mRNA then interacts with ribosomes and other cellular machinery to direct the synthesis of the protein it encodes during the process of translation. mRNA is relatively unstable and short-lived in the cell, especially in prokaryotic cells, ensuring that proteins are only made when needed. rRNA and tRNA are stable types of RNA. In prokaryotes and eukaryotes, tRNA and rRNA are encoded by the DNA, where they are transcribed into long RNA molecules that are subsequently cut to release smaller fragments containing the individual mature RNA species. In eukaryotes, synthesis, cutting, and assembly of rRNA into ribosomes takes place in the nucleolus region of the nucleus, but these activities occur in the cytoplasm of prokaryotes. Within the nucleolus region, ribosome assembly requires the activity of numerous snoRNAs. Ribosomes are composed of rRNA and protein. As its name suggests, rRNA is a major constituent of ribosomes, composing up to about 60% of the ribosome by mass and providing the location where the mRNA binds. The rRNA ensures the proper alignment of the mRNA, tRNA, and the ribosomes; the rRNA of the ribosome also has an enzymatic activity (peptidyl transferase) and catalyzes the formation of the peptide bonds between two aligned amino acids during protein synthesis (Figure 26.1.3). Although rRNA had long been thought to serve primarily a structural role, its catalytic role within the ribosome was shown in 2000. Scientists in the laboratories of Thomas Steitz (1940–) and Peter Moore (1939–) at Yale University were able to crystallize the ribosome structure from Haloarcula marismortui, a halophilic archaeon isolated from the Dead Sea. Because of the importance of this work, Steitz shared the 2009 Nobel Prize in Chemistry with other scientists who made significant contributions to the understanding of ribosome structure. The structure and function of ribosomes will be discussed in further detail in Chapter 27. Transfer RNA (tRNA) is the third prominent type of RNA involved in protein translation. tRNAs are usually only 70–90 nucleotides long. They carry the correct amino acid to the site of protein synthesis in the ribosome. It is the base pairing between the tRNA and mRNA that allows for the correct amino acid to be inserted in the polypeptide chain being synthesized, as shown in Figure $3$. Any mutations in the tRNA or rRNA can result in global problems for the cell because both are necessary for proper protein synthesis. As described previously, some RNA molecules have enzymatic properties and serve as ribozymes. Within this chapter, the activity of snRNAs during the process of intron removal from mRNA sequences function as ribozymes and will be described. Furthermore, a detailed description of the enzymatic features of the ribosome structure will be provided in Chapter 27. Other small ncRNAs and lncRNA molecules play a role in the regulation of transcriptional and translational processes. For example, the post-transcriptional expression levels of many genes can be controlled by RNA interference, in which miRNAs, specific short RNA molecules, pair with mRNA regions and target them for degradation, as shown in Figure $4$. This process is aided by protein chaperones called argonautes. This antisense-based process involves steps that first process the miRNA so that it can base-pair with a region of its target mRNAs. Once the base pairing occurs, other proteins direct the mRNA to be destroyed by nucleases. Fire and Mello were awarded the 2006 Nobel Prize in Physiology or Medicine for this discovery. At steady state, the vast majority of human cellular RNA consists of rRNA (∼90% of total RNA for most cells) as shown in Figure $5$. Although there is less tRNA by mass, their small size results in their molar level being higher than rRNA (Figure 26.1.5). Other abundant RNAs, such as mRNA, snRNA, and snoRNAs are present in aggregate at levels that are about 1–2 orders of magnitude lower than rRNA and tRNA (Figure 26.1.5). Certain small RNAs, such as miRNA and piRNAs can be present at very high levels; however, this appears to be cell type dependent. lncRNAs are present at levels that are two orders of magnitude less than total mRNA. Although the estimated number of different types of human lncRNAs may have a very restricted expression pattern and thus, accumulate to higher levels within specific cell types. For example, sequencing of mammalian transcriptomes has revealed more than 100,000 different lncRNA molecules can be produced, compared with the approximate 20,000 protein-coding genes. The diversity and functions of the transcriptome within biological processes are currently a highly active area of research. RNA Polymerases This chapter will focus on the synthesis of RNA by DNA-dependent RNA Polymerase Enzymes (RNAPs). These enzymes are required to carry out the process of transcription and are found in all cells ranging from bacteria to humans. All RNAPs are multi-subunit assemblies, with bacteria having five core subunits, α2ββ'ω, that have homologs in archaeal and eukaryotic RNAPs. Bacterial RNAPs are the simplest form of RNA polymerases and provide an excellent system to study how they control transcription. Prokaryotic RNA Polymerase Enzymes The RNAP catalytic core within bacteria contains five major subunits (α2ββ'ω) (see Figure $7$) below. To position this catalytic core onto the correct promoter requires the association of a sixth subunit called the sigma factor (σ). Within bacteria, there are multiple different sigma factors that can associate with the catalytic core of RNAP that help to direct the catalytic core to the correct DNA locations, where RNAP can then initiate transcription. For example, within E. coli σ70 is the housekeeping sigma factor that is responsible for transcribing most genes in growing cells. It keeps essential genes and pathways operating. Other sigma factors are activated during certain environmental situations, such as σ38 which is activated during starvation or when cells reach the stationary phase. When the sigma subunit associates with the RNAP catalytic core, the RNAP has then formed the holoenzyme. When bound to DNA, the holoenzyme conformation of RNAP can initiate transcription. Transcription takes place in several stages. To start with, the RNA polymerase holoenzyme locates and binds to promoter DNA. At this stage the RNAP holoenzyme is it the closed conformation (RPc), as shown in Figure $6$. Initial specific binding to the promoter by sigma factors of the holoenzyme sets in motion conformational changes in which the RNAP molecular machine bends and wraps the DNA with mobile regions of RNAP playing key roles, as shown in Figure $6$. Next, RNAP separates the two strands of DNA and exposes a portion of the template strand. At this point, the DNA and the holoenzyme are said to be in an ‘open promoter complex’ (RPo), and the section of promoter DNA that is within it is known as a ‘transcription bubble’. Intermediates(I1-3) between RPc and RPo occur. In bacterial systems, the sigma factor locates the transcriptional start site using key DNA sequence elements located at -35 nucleotides and -10 nucleotides from the transcriptional initiation site, as shown in Panel A of Figure $7$. This region is called the Pribnow box. For RNAP from Thermus aquaticus, the −35 element interacts exclusively with σ4. The duplex DNA just upstream of the −10 element (−17 to −13) interacts with β′, σ2, and σ3 (Panel B). Flipping of the A−11(nt) base from the duplex DNA into its recognition pocket in σ2 is thought to be the key event in the initiation of promoter melting and the formation of the transcription bubble (Panel C). Once the transcription bubble has formed and transcription initiates, the sigma subunits dissociate from the complex and the RNAP catalytic subunit continues elongation on its own. Panel (A) shows oligonucleotides used for the crystallization of the RNAP holoenzyme in the open conformation. The numbers above denote the DNA position with respect to the transcription start site (+1). The −35 and −10 (Pribnow box) elements are shaded yellow, and the extended −10 and discriminator elements are purple. The nontemplate-strand DNA (top strand) is colored dark grey; template-strand DNA (bottom strand), light grey; RNA transcript, red. Panel (B) shows the overall structure of RNAP holoenzyme in the open conformation bound with the DNA nucleotides. The nucleic acids are shown as CPK spheres and color-coded as in diagram A. Within RNAP, the αI, αII, ω, are shown in grey; β in light cyan; β′ in light pink; Δ1.1σA in light orange. The Taq EΔ1.1σA (Taq derives from Thermus aquaticus) is shown as a molecular surface and the forward portion of the RNAP holoenzyme is transparent to reveal the RNAP active site Mg2+ (yellow sphere) and the nucleic acids held inside the RNAP active site channel. Panel (C) Electron density and model for RNAP holoenzyme nucleic acids in the open conformation. Color coding matches diagram A. Figure $8$ shows an interactive iCn3D model of the T. aquaticus transcription initiation complex containing bubble promoter and RNA (4XLN). It is colored coded in fashion similar to that shown in Figure $7$. The rendering of the DNA is as follows: • The non-template (nt-strand) DNA is colored dark grey; spheres • template (t-strand) DNA, light grey; spheres • Pribnow box yellow • discriminator purple The proteins are shown as surfaces with transparent secondary structures underneath. The color is as follows: • (αI, αII, ω, light yellow; • β, light cyan; • β′, light pink; • Taq EΔ1.1σA, light orange), The RNA polymerase active site is located at the Mg2+ (black sphere) binding site. The nucleic acids are inside the RNAP active site channel. Eukaryotic RNA Polymerase Enzymes In eukaryotic cells, three RNAPs (I, II, and III) share the task of transcription, the first step in gene expression. RNA Polymerase I (Pol I) is responsible for the synthesis of the majority of rRNA transcripts, whereas RNA Polymerase III (Pol III) produces short, structured RNAs such as tRNAs and 5S rRNA. RNA Polymerase II (Pol II) produces all mRNAs and most regulatory and untranslated RNAs. The three eukaryotic RNA polymerases contain homologs to the five core subunits found in prokaryotic RNAPs. In addition, the eukaryotic Pol I, Pol II and Pol III have five additional subunits forming a catalytic core that contains 10 subunits, as shown in Figure $9$. The core has a characteristic crab-claw shape, which encloses a central cleft that harbors the DNA, and has two channels, one for the substrate NTPs and the other for the RNA product. Two ‘pinchers’, called the ‘clamp’ and ‘jaw’ stabilize the DNA at the downstream end and allow the opening and closing of the cleft. For transcription to occur, the enzyme has to maintain a transcription bubble with separated DNA strands, facilitate the addition of nucleotides, translocate along the template, stabilize the DNA:RNA hybrid, and finally allow the DNA strands to reanneal. This is achieved by a number of conserved elements in the active site, which include the fork loop(s), rudder, wall, trigger loop, and bridge helix. DNA (black) is melting into a transcription bubble that allows template-strand pairing with RNA (red) in a 9-10 base pair RNA-DNA hybrid. The bridge helix (cyan) and trigger loop/helices (yellow/orange) lie on the downstream side of the active site. The presumed path of the NTP entry is indicated by the straight arrow. Interconversion of the trigger loop and trigger helices is indicated by the curved arrow. The RNA polymerase subunits are shown as semi-transparent surfaces with the identities of orthologous subunits in bacteria (α, β, and β', gray, blue, and pink, respectively), archaea (D, L, B, and A), and eukaryotic RNA polymerase II (RPB3, 11, RPB2, RPB1) indicated. The active site Mg2+ ions are shown as yellow spheres, and α,β-methylene-ATP in green and red. Table $1$ shows RNA polymerase (RNA pol) subunit composition in bacteria, archaea, and, yeast and plants (both eukaryotes). Bacteria Archaea RNA pol I RNA pol II RNA pol III RNA pol IV (plants) RNA pol V (plants) β Rpo1 (RpoA) RPA190 RPB1 RPC160 NRPD1 NRPE1 β' Rpo2 (RpoB) RPBA135 RPB2 RPC128 NRPD/E2 NRPD/E2 α Rpo3 (RpoD) RPAC40 RPB3 RPAC40 RPB3 [1] RPB3 [1] α Rpo11 (RpoL) RPAC19 RPB11 RPAC19 RPB11 RPB11 ω Rpo6 (RpoK) RPB6 RPB6 RPB6 RPB6 [1] RPB6 Rpo5 (RpoH) RPB5 RPB5 RPB5 RPB5 [3] NRPES5 Rpb8 (RpoG)* RPB8 RPB8 RPB8 RPB8 [1] RPB8 [1] Rpo10 (RpoN) RPB10 RPB10 RPB10 RPB10 RPB10 Rpo12 (RpoP) RPB12 RPB12 RPB12 RPB12 RPB12 Rpo4 (RpoF) RPA14 RPB4 RPC17 NRPD/E4 NRPD/E4 Rpo7(RpoE) RPA43 RPB7 RPC25 NRPD7 [1] NRPE7 RPA12 RPB9 RPC11 NRPD9b RPB9 Rpo13* RPA49 RPC53 RPA34.5 RPC37 RPC82 RPC34 RPC31 Table $1$: RNA polymerase (RNA pol) subunit composition. Abel, C., Verónica, M., I., G. A. , & Francisco, N. (2017). Subunits Common to RNA Polymerases. In (Ed.), The Yeast Role in Medical Applications. IntechOpen. https://doi.org/10.5772/intechopen.70936. Creative Commons Attribution 3.0 License Schematic representations of the structure of the eukaryotic RNA pols I, II and III are shown in Figure $10$. Figure $10$: Schematic representation of the structure of the RNA pols I, II and III. Each RNA pol common subunit is indicated in grey. The numbers correspond to each subunit are indicated in Subunits Common to RNA Polymerases. Abel et al, ibid. RNA polymerases must bind to DNA, and to host of transcription factors (TF) necessary for specific and regulatable transcription. (Note: RNAP is not considered a transcription factor.) The comparative structures of RNAP I-III are shown in Figure $11$. The "stalk" is a structural feature found in eukaryotes but not in prokaryotes. The figure focuses mostly on a comparison of RNAP I and RNAP III. Figure $11$: Comparison of RNAPI, II and III structures and transcription factors. Turowski TW and Boguta M (2021) Specific Features of RNA Polymerases I and III: Structure and Assembly. Front. Mol. Biosci. 8:680090. doi: 10.3389/fmolb.2021.680090. Creative Commons Attribution License (CC BY). Panel (A) shows the general architecture of RNAPII, consisting of the catalytic core and stalk. RNAPII core consists of a DNA binding channel, catalytic center, and assembly platform. RNAPII binds multiple transcription factors (TFs). Some TFs are homologous to additional subunits of specialized RNAPs (i.e., TFIIF). Panel (B) shows the subunit composition of eukaryotic RNAPs. Human nomenclature is shown for comparison. Please note that the C-terminal region of Rpa49 subunit harbors a “tandem winged helix” which is predicted in TFIIE and that human RNAPIII RPC7 subunit is coded by two isoforms α and β. The question mark indicates the name is unconfirmed. Panel (C) shows the subunit composition of yeast RNAPI. Panel (D) shows a Model of the RNAPI pre-initiation complex, showing an early intermediate with visible Rrn3 and core factor (CF). TATA-binding protein (TBP) and upstream-associated factor (UAF) are added schematically. Panel (E) shows the subunit composition of yeast RNAPIII. Panle (F) shows atomic model of RNAPIII pre-initiation complex with TFIIIB. The Rpc82/34/31 heterotrimer is involved in initiation and marked in green as in E. TFIIIC is added schematically. PDB: 5C4X, 5FJ8, 4C3J, 6EU0, and 6TPS The stalks have two proteins that are not as homologous as the core subunits. These are highlighted in blue in Panel B of Figure $11$. RNAP I and III have additional subunits compared to RNAP II. Overall RNAP I-III have 14, 12, and 17 subunits, respectively. RNAP I and III appear to have integrated transcription-like factors into their core enzyme. In contrast, RNAP II (which transcribes DNA to form messenger RNA), binds to discrete and separate transcription factors to form a preinitiation complex (PIC)which we will discuss below. Transcription Factors and the Preinitiation Complex (PIC) Unlike prokaryotic systems which can initiate the recruitment of RNAP holoenzymes directly onto the DNA promoter regions and mediate the conversion of RNAP to the open conformation, eukaryotic RNA polymerases require a host of additional general transcription factors (GTFs), to enable this process. Here we will focus on the activation of RNA Polymerase II as an example of the complexity of eukaryotic transcription initiation. Class II gene transcription in eukaryotes is a tightly regulated, essential process controlled by a highly complex multicomponent machinery. A plethora of proteins, more than a hundred in humans, are organized in very large multiprotein assemblies that include a core of General Transcription Factors (GTFs). The GTFs include the factors TFIIA, TFIIB, TFIID, TFIIE, TFIIF, TFIIH, RNA polymerase (RNA pol II), as well as a large number of diverse complexes that act as co-activators, co-repressors, chromatin modifiers, and remodelers, as shown in Figure $12$. Class II gene transcription is regulated at various levels: while assembling on chromatin, before and during transcription initiation, throughout elongation and mRNA processing, and termination. A host of activators and repressors has been reported to regulate transcription, including a central multisubunit complex called the Mediator that helps in the recruitment of GTFs and the activation of RNA Pol II. Here we will focus on the formation of the GTFs that make up the core preinitiation complex (PIC) during transcriptional activation. Class II gene transcription in humans is brought about by over a hundred polypeptides assembling on the core promoter of protein-encoding genes, which then give rise to mRNA. A PIC on a core promoter is shown in a schematic representation. PIC contains, in addition to promoter DNA, the GTFs (TFIIA, B, D, E, F, and H), and RNA Pol II. PIC assembly is thought to occur in a highly regulated, stepwise fashion, as indicated. TFIID is among the first GTFs to bind the core promoter via its TATA-box Binding Protein (TBP) subunit. Nucleosomes at transcription start sites contribute to PIC assembly, mediated by signaling through epigenetic marks on histone tails. The Mediator (not shown) is a further central multiprotein complex identified as a global transcriptional regulator. TATA = TATA-box DNA; BREu = B recognition element upstream; BREd = B recognition element downstream; Inr = Initiator; DPE = Down-stream promoter element. Figure from: Transcription of RNA pol II-dependent genes is triggered by the regulated assembly of the Preinitiation Complex (PIC). PIC formation starts with the binding of TFIID to the core promoter. TFIID is a large megadalton-sized multiprotein complex with around 20 subunits made up of 14 different polypeptides: the TATA-box binding protein (TBP) and the TBP-associated factors (TAFs) (numbered 1–13), as shown in Figure $13$. Some of the TAF subunits are present in two copies. A key feature in TAFs is the histone fold domain (HFD), which is present in 9 out of 13 TAFs in TFIID. The HFD is a strong protein–protein interaction motif that mediates specific dimerization. The HFD-containing TAFs are organized in discrete heterodimers, with the exception of TAF10, which is capable of forming dimers with two different TFIID components, TAF3 and TAF8. HFDs and several other structural features of TBP and the TAFs are well conserved between species. TFIID is a large megadalton-sized multiprotein complex comprising about 20 subunits made up of 14 different polypeptides. The constituent proteins of TFIID, TBP and the TAFs, are shown in a schematic representation depicted as bars (inset, left). Structured domains are marked and annotated. The presumed stoichiometry of TAFs and TBP in the TFIID holo-complex is given (far left, gray underlaid). TAF10 (in italics) makes histone fold pair separately with both TAF3 and TAF8. TAFs present in a physiological TFIID core complex extracted from eukaryotic nuclei are labeled in bold. The architecture of the TFIID core complex (EMD-2230) determined by cryo-EM is shown (bottom left) in two views related by a 90° rotation (arrows). The holo–TFIID complex is characterized by remarkable structural plasticity. Two conformations, based on cryo-EM data (EMD-2284 and EMD-2287), are shown on the right, a canonical form (top) and a more recently observed rearranged form (bottom). In the rearranged conformation, lobe A (colored in red) migrates from one extreme end of the TFIID complex (attached to lobe C) all the way to the other extremity (attached to lobe B). TFIID was shown to adopt an asymmetric, horse-shoe shape with three almost equal-sized lobes (A, B, and C), exhibiting a considerable degree of conformational flexibility with at least two distinct conformations (open and closed), as shown in Figure $12$. The TBP component of TFIID binds with a specific DNA sequence called the TATA box. This DNA sequence is found around 30 base pairs upstream of the transcription start site in many eukaryotic gene promoters. When TBP binds to a TATA box within the DNA, it distorts the DNA by inserting amino acid side chains between base pairs, partially unwinding the helix, and doubly kinking it. The distortion is accomplished through a great amount of surface contact between the protein and DNA. TBP binds with the negatively charged phosphates in the DNA backbone through positively charged lysine and arginine amino acid residues. The sharp bend in the DNA is produced through the projection of four bulky phenylalanine residues into the minor groove. As the DNA bends, its contact with TBP increases, thus enhancing the DNA-protein interaction. The strain imposed on the DNA through this interaction initiates the melting, or separation, of the strands. Because this region of DNA is rich in adenine and thymine residues, which base-pair through only two hydrogen bonds, the DNA strands are more easily separated. The role of TAFs is complicated. Take for example TAF11 and TAF13. These act as competitive inhibitors of TBP to the TATA (Pribnow) box as well as TAF1 which somewhat mimics the structural features of the Pribnow box. TAF11/TAF13 binds to the DNA surface where TBP binds. suggesting a novel regulation of TFIID. These interactions are illustrated in Figure $14$. Figure $14$: Novel TFIID regulatory state comprising TAF11/TAF13/TBP. Kapil Gupta,et al. (2017) Architecture of TAF11/TAF13/TBP complex suggests novel regulation properties of general transcription factor TFIID eLife 6:e30395. https://doi.org/10.7554/eLife.30395. Creative Commons Attribution License Given its role in transcribing the DNA for thousands of messenger RNAs, let's focus on the preinitation complex for RNAP II. Structures are known for the closed and open promoters. A key component is TFH (see Figure $12$). TFIIH opens the DNA for transcription. As if we need to complicate the structure of the preinitiation complex even more, it turns out the TFIIH is not a single dark green cartoon as shown in Figure 12, but a rather large complex itself. Its structure is shown in Figure $15$. Figure $15$: Structure of the TFIIH core complex. Greber et al. (2019). The complete structure of the human TFIIH core complex. eLife 8:e44771. https://doi.org/10.7554/eLife.44771. Creative Commons Attribution License Panes (A, B, C) shows three views of the structure of the TFIIH core complex and MAT1. Subunits are color-coded and labeled (in color); individual domains are labeled (in black) and circled if needed for clarity. Panel (D) shows the domain-level protein-protein interaction network between the components of the TFIIH core complex and MAT1 derived from the interactions observed in our structure. Proteins are shown with the same colors as in A and major unmodeled regions are shown in grey. Abbreviations: CTD: C-terminal domain; DRD: DNA damage recognition domain; FeS: iron sulfur cluster domain; NTD: N-terminal domain; vWFA: von Willebrand Factor A. The largest subunits are DNA-dependent helicases/translocases/ATPases XPB and XPD, which are bridged by MAT1. It appears that XPB starts and propagates a twist in the DNA which propagates ot open the DNA 30 BP downstream of the TATA box. This mostly likely is followed by the dissociation of TFIIH and a stoppage in DNA twisting, which allows RNA transcription to start. Figure $16$ shows an interactive iCn3D model of the human/mouse/mastadenovirus C RNA polymerase II core pre-initiation complex with open promoter DNA (7NVU). The following color schemes are used: • MAT1 - orange • TF2A - yellow • TF2B - green • TF2E - magenta • TF2F- purple • ATP-dependent translocase (helicase) subunit XPD - dark slate gray • TBP (TATA box binding protein) - red • TF2H - Pink • 2H-XPB helicase - maroon • NT-DNA - cyan • template (T)-DNA - blue (Note: part of it is shown in a yellow cartoon in the interactive model) • the SF4 cofactor is shown in spacefill with CPK colors In summary, the binding of TFIID to the core promoter is followed by the recruitment of further GTFs and RNA pol II. Several lines of evidence suggest that this process occurs in a defined, stepwise order and undergoes significant restructuring. First, PIC adopts an inactive state, the “closed” complex, which is incompetent to initiate transcription. In addition to TFIID, TFIIH is also critical for the shift of RNA Pol II from the closed to the open conformation. TFIIH has an ATP-dependent translocase activity within one of its subunits, that opens up about 11 to 15 base pairs around the transcription start site by moving along one DNA strand inducing torsional strain, leading to conformational rearrangements and the positioning of single-stranded DNA to the active site of RNA pol II. In this “open” complex, RNA pol II can enter elongation to transcribe throughout a gene in a highly processive manner without dissociating from the DNA template or losing the nascent RNA. In most eukaryotes, after synthesizing about 20–100 bases, RNA pol II can pause (Promoter proximal pause) and then disconnect from promoter elements and other components of the transcription machinery, giving rise to a fully functional elongation complex in a process called promoter escape. The promoter-bound components of the PIC, in contrast, remain in place, and thus only TFIIB, TFIIF, and RNA pol II need to be recruited for re-initiation, significantly increasing the transcription rate in subsequent rounds of transcription. Promoter escape is preceded by an abortive transcription in many systems, where multiple short RNA products of 3 to 10 bases in length are synthesized. In addition to promoter elements within the DNA, enhancer elements are also important for the initiation of transcription. Promoters are defined as DNA elements that recruit transcription complexes for the synthesis of coding and non-coding RNA. Enhancers are defined as DNA elements that positively regulate transcription at promoters over long distances in a position- and orientation-independent manner. However, studies have revealed that many enhancers can recruit Pol II and initiate transcription of enhancer RNA (eRNA), thus blurring the functional distinction between enhancers and promoters (Figure 10.13). Enhancer transcription produces relatively short ncRNA. Furthermore, transcription at enhancers is unstable and often leads to the termination of elongation. In contrast, transcription initiation at most Pol II promoters is stable and produces long mRNAs. Topological studies revealed that enhancers come in close proximity to target gene promoters during transcription activation. According to current gene activation models, the Mediator complex forms a physical bridge between distant regulatory regions and promoters, thereby promoting looping. Transcription of at least a subset of genes regulated by enhancers occurs in bursts indicating a discontinuous process of transcription complex recruitment, assembly, and/or conversion to elongation-competent forms. The bursting phenomenon suggests that enhancer/promoter contacts may be transient and infrequent, as shown in Figure $17$. Depicted are the steps involved in the recruitment of Pol II to SEs, assembly into elongation-competent transcription complexes, transcription initiation, and elongation, abortion and termination, and transfer to target genes. Transcription factors recruit Mediator and other co-regulators to SEs. Mediator recruits Pol II and assembles a fraction into elongation competent transcription complexes. Transcription is initiated by phosphorylation of the CTD. Early abortion and transcription termination conferred by Integrator releases Pol II, which is dephosphorylated and transferred to target gene promoters. Super Enhancer Element (SE). Transcriptional Elongation and Termination Prokaryotic Transcriptional Elongation The rate of transcription elongation by E. coli RNAP is not uniform. RNA synthesis is characterized by pauses, some of which may be brief and resolved spontaneously, whereas others may lead to the transcription elongation complex (TEC) backtracking. Elongation rate and pausing are determined by template sequence and RNA structure (e.g., stem-loops) and involve at least two components of the RNAP catalytic center, the bridge helix (BH) and trigger loop (TL). Elongation is proposed to occur in three steps, as shown in Figure $18$. First, the TL folds in response to NTP binding. Mutational analyses indicate that this conformational change in the TL can be rate-limiting, and reflects the ability of the incoming NTP to bind to TEC. The second step is the incorporation of the NTP and the release of pyrophosphate. The third step involves the translocation of the RNAP down the DNA Template such that the next RNA nucleotide can be added to the nascent transcript. The trigger loop hinges, bridge helix hinges, and bridge helix bending models are based on molecular dynamics simulations. At the top of the figure, diagrams of the closed TEC, the closed product TEC (after chemistry), and the translocating TEC are shown. DNA is grey; RNA is red; the NTP substrate (or incorporated NMP and pyrophosphate) is blue; the trigger loop (TL) is purple; the bridge helix (BH) is yellow. Interpretations of simulations are shown schematically below. Simulations indicate trigger loop hinges H1 and H2, bridge helix hinges H3 and H4 and bridge helix bend modes B1 (straighter) and B2 (more sharply bent). Backtracking of TEC may take place after a brief pause in transcription, caused by the thermodynamic properties of nucleic acids sequences surrounding the elongation complex. In addition, misincorporation events render elongation complexes prone to backtracking by at least one bp. In this case, the rescue from backtracking through the cleavage of the 3' end of the erroneous transcript also may be seen as a proofreading reaction. Any backtracking event causes a pause or arrest of transcription elongation, which may limit its overall rate (the average speed of RNAP along the template) or the processivity (the fraction of RNAP molecules reaching the end of the gene). While the general structure of the elongation complex (the transcription bubble, the RNA-DNA hybrid) remains unchanged during backtracking, the extension of RNA becomes impossible in this conformation. However, such complexes can be resolved by the hydrolytic activity of RNAP, which cleaves the phosphodiester bond in the active center of the backtracked complex, producing a new RNA 3' end in the active center. For single base backups, the hydrolytic reaction is catalyzed by a flexible domain of RNAP located in the secondary channel called the Trigger Loop (TL) and the two metal ions of the active center. Longer sequences of backtracked TEC can restart when acted upon by GreA/B factors, which restore the 3'-end of the nascent transcript to the active center. GreA and GreB are transcript cleavage factors that act on backtracked elongation complexes. When Gre factors are bound in the secondary channel, Gre factors displace the TL from the active center, as shown in Figure $19$. The displacement switches off the relatively slow TL-dependent intrinsic transcript hydrolysis, and imposes the highly efficient Gre-assisted hydrolysis. This efficiency is thought to be due to the stabilization of the second catalytic Mg2+ ion and an attacking water molecule by the Gre factors. Panel (A) shows a ribbon diagram of the GreA and GreB proteins. Panel (B) shows the mode of functioning of Gre factors. The Gre factor is bound to the active elongation complex but does not impose hydrolytic activity on it. Upon backtracking or misincorporation, the Gre factor protrudes its coiled-coil domain through the secondary channel of RNAP (shown in the lefthand diagram), where it substitutes for the catalytic domain Trigger Loop (TL). This substitution switches off the slow TL-dependent phosphodiester bond hydrolysis and, and instead, facilitates highly efficient Gre-dependent hydrolysis. After the resolution of the backtracked complex through RNA cleavage, the elongation complex returns to the active conformation, and the Gre factor gives way to the TL, which can now continue the catalysis of RNA synthesis (shown in the right-hand diagram). The controlled switching between Gre and the TL eliminates possible interference of Gre with the RNA synthesis. Prokaryotic Transcriptional Termination Transcription termination determines the ends of transcriptional units by disassembling the transcription elongation complex (TEC), thereby releasing RNA polymerases and nascent transcripts from DNA templates. Failure in termination causes transcription readthrough, which yields wasteful and possibly harmful intergenic transcripts. It can also perturb the expression of downstream genes when the unterminated TEC sweeps transcription initiation complexes off their promoters or collides with RNA polymerases that transcribe opposite strands. Transcriptional termination in prokaryotes can be template-encoded and factor-independent (intrinsic termination), or require accessory factors, such as Rho, Mfd, and DksA. Intrinsic termination occurs at specific template sequences - an inverted repeat followed by a run of A residues. Termination is driven by the formation of a short stem-loop structure in the nascent RNA chain, as shown in Figure $20$. RNA synthesis arrests and TEC dissociates at the 7th and 8th U of the run. Formation of the stem-loop dissociates the weak rU:dA hybrid. Stem-loop formation is hindered by upstream complementary RNA sequences that compete with the downstream portion of the stem, as well as by RNA: protein interactions in the RNA exit channel. Intrinsic termination depends critically upon timing. Hairpin folding and transcription of the termination point must be coordinated, so that the complete hairpin is formed by the time RNAP transcribes the termination point. The size of the stem, the sequence of the stem, and the length of the loop all affect termination efficiency. The bridge α-helix in the β' subunit borders the active site and may have roles in both catalysis and translocation. Mutations in the YFI motif (β' 772-YFI-774) affect intrinsic termination as well as pausing, fidelity, and translocation of RNAP. One mutation, F773V, abolishes the activity of the λ tR2 intrinsic terminator, although neighboring mutations have little effect on termination. Modeling suggests that this unique phenotype reflects the ability of F773 to interact with the fork domain in the β subunit. Panel (A) shows the open conformation of the RNAP during transcriptional elongation. RNAP is shown in yellow, the DNA template in blue, and the nascent RNA in red. Key elements of the RNAP RNA exit channel are shown in grey and labeled as indicated. Panel (B) shows the extension of the nascent RNA through the RNAP exit channel and the potential for forming the RNA hairpin structure when enough length has been achieved. Panel (C) shows the clamp opening and disintegration of the TEC when the RNA hairpin structure is encountered at the transcriptional bubble. Figure $21$ shows an interactive iCn3D model of the T. thermophilus RNAP polymerase elongation complex with the NTP substrate analog (2O5J). (long load time) Transcriptional termination can also be dependent upon accessory factors, such as the Rho protein. Transcription termination factor Rho is an essential protein in E. coli first identified for its role in transcription termination at Rho-dependent terminators, and is estimated to terminate ~20% of E. coli transcripts. The rho gene is highly conserved and nearly ubiquitous in bacteria. Rho is an RNA-dependent ATPase with RNA:DNA helicase activity, and consists of a hexamer of six identical monomers arranged in an open circle, as shown in Figure $22$. Rho binds to single-stranded RNA in a complex multi-step pathway that involves two distinct sites on the hexamer. The primary binding site (PBS), distributed on the N-terminal domains around the hexamer (cyan), ensures initial anchoring of Rho to the transcript at a Rut (Rho utilization) site, a∼70 nucleotides (nt) long, cytidine-rich and poorly-structured RNA sequence. Each Rho monomer contains a subsite capable of binding specifically the base residues of a 5′-YC dimer (Y being a pyrimidine). Biochemical and structural data suggest that Rho initially binds to RNA in an open, ‘lock-washer’ conformation that closes into a planar ring as RNA transfers to the central cavity. There, the ssRNA contacts an asymmetric secondary binding site (SBS) (green), and this step, which presumably is rate-limiting for the overall reaction, leads to motor activation. Upon hydrolysis of ATP, the ssRNA is pulled upon conformational changes of the conserved Q and R loops of the SBS, leading to Rho translocation, and ultimately promoting RNA polymerase (RNAP) dissociation. The molecular mechanism of Rho translocation based on single-molecule fluorescence methods appears to be tethered tracking. The tethered tracking model postulates that Rho maintains its contacts between the PBS and the loading (Rut) site upon translocation (Panel B). This mechanism would allow Rho to maintain its high-affinity interaction with Rut, and implies the growth of an RNA loop between the PBS and the SBS upon translocation. Panel (A) shows the molecular structure of the Rho protein (PDB 1pv4) Panel (B)shows how Rho assembles as a homo-hexameric ring (red spheres or tetragons), with RNA (black/yellow curve) binding to the primary binding sites (PBS, cyan) and the secondary binding sites inside the ring (SBS, green), where ATP-coupled translocation takes place. The Rut-specific binding site is depicted in yellow. The tethered-tracking model proposed that Rho translocates RNA while maintaining interactions between PBS and Rut. This model requires the formation of a loop that would shorten the extension of RNA upon translocation. Figure modified from: Figure $23$ shows an interactive iCn3D model of the E. Coli Rho transcription termination factor in complex with ssRNA substrate and ANPPNP (1PVO). The six subunits of the hexamer are shown in alternating slate gray and light gray. The di-ribonucleotides (5'-R(PUPC)-3') are shown in spacefill and colored CPK. ANPPNP is shown in spacefill yellow. Figure $24$ shows an interactive iCn3D model of the closed ring structure of the E. Coli Rho transcription termination factor in a complex with nucleic acid in the motor domains (2HT1). The six subunits of the hexamer are again shown in alternating slate gray and light gray. The ssRNAs are shown in spacefill with the backbones in one color and the bases in CPK colors. Figure $25$ shows an interactive iCn3D model of the E. coli Rho-dependent Transcription Pre-termination Complex (6XAS) (long load). • The RNA polymerase subunits (α2ββω) are shown in light cyan • The six subunits of the Rho hexamer are again shown in alternating slate gray and light gray • NusA is shown in magenta • Both DNA strands are shown in spacefill with the backbone blue and the bases red • Only part of the RNA is shown (spacefill, backbone yellow, bases CPK), so it appears discontinuous Eukaryotic Transcriptional Termination In eukaryotes, termination of protein-coding gene transcription by RNA polymerase II (Pol II) usually requires a functional polyadenylation (pA) signal, typically a variation of the AAUAAA hexamer. Nascent pre-mRNA is cleaved and the 5′ fragment is polyadenylated at the pA site shortly downstream from the hexamer by cleavage and pA factors (CPFs). Two mechanisms have been suggested for pA-dependent transcription termination. In the allosteric model, the pA signal and/or other termination signals bind with the pA signal downstream region (PDR) and induce reorganization of the Pol II complex. This includes the association or dissociation of endonuclease components such as the CPFs. This causes conformational changes in Pol II and TEC disassembly ensues. In the kinetic model, also known as the “torpedo” model, cleavage at the pA site separates the pre-mRNA from the TEC, which continues synthesizing a downstream nascent transcript. This new transcript is a substrate of XRN2/Rat1p, a processive 5′-to-3′ exoribonuclease that catches up with, and disassembles, the TEC by an unknown mechanism. The two pA-dependent models are not mutually exclusive, and unified models have been proposed. Loosely conserved pA signal sequences downstream of protein-coding genes bind to components of the polyadenylation factor (CF1) complex leading to the assembly of the cleavage and polyadenylation machinery. Termination is coupled to cleavage in a manner that has not yet been completely resolved, however, one of the major factors involved in yeast pA termination is the endonuclease, Ysh1. For example, the depletion of Ysh1 blocks TEC dissociation, but does not cause substantial readthrough at the termination site (Fig. 26.1.18 A&B). These results suggest that Ysh1 does not directly cause the pausing that occurs in the allosteric termination pathway, but rather plays a role in the dissociation of the Pol II complex from the DNA template, as shown in Figure $26$. It should be noted that not all pA-dependent termination is dependent on Ysh1 and that other mechanisms of pA-mediated termination still remain to be elucidated. Panel (A) shows the elongating Pol II (green) terminates pA transcripts (A) after an allosteric change (red) that reduces processivity. Panel (B) shows the depletion of Ysh1 leads to minimally extended readthrough transcripts but does not block the allosteric change in Pol II. Panel (C) shows how Nrd1 and Nab3 binding recruits Sen1 for termination of non-pA transcripts. Panel (D) shows Pol II elongation complex lacking Nrd1 does not recognize termination sequences in the nascent transcript and thus does not facilitate the allosteric transition in Pol II. This leads to a processive readthrough. Panel(E) shows how Nrd1 and Nab3 recognize terminator sequences allowing the allosteric change in Pol II but depletion of Sen1 blocks the removal of Pol II from the template. Figure from: The mechanisms of termination of Pol II-mediated transcription differ for coding and non-coding transcripts. Coding transcripts and possibly some stable uncharacterized transcripts (SUTs) are nearly always processed at the 3′-end by the cleavage and polyadenylation (pA) machinery and are processed by the pA-dependent termination mechanisms described above. In contrast, ncRNAs are terminated and processed by an alternative pathway that, in yeast, requires the RNA-binding proteins Nrd1 and Nab3, as well as, the RNA helicase Sen1. Nrd1 and Nab3 recognize RNA sequence elements downstream of snoRNAs and CUTs and this leads to the association of a complex that contains the DNA/RNA helicase Sen1 and the nuclear exosome. The nuclear exosome is a complex of ribonucleases with 3' to 5' exonuclease and endonuclease activity. It functions to degrade unstable or incorrect RNA transcripts. Both Nrd1 and Sen1 depletion lead to readthrough transcription of ncRNAs, suggesting their importance in non-pA-dependent transcription termination (Fig 26.1.18 C & D). 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textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/03%3A_Unit_III-_Information_Pathway/25%3A_RNA_Metabolism/25.02%3A_RNA_Processing.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Post-transcriptional modifications of rRNA and tRNA will be topics of Chapter 27 as their structure and function in protein synthesis will be a focal point. Thus, this section will focus on post-transcriptional modifications of mRNA. We'll spend most of our time on eukaryotic RNA processing. Prokaryotic RNA Processing First, let's take a brief look at a fascinating feature of transcription in bacterial cells. Bacterial cells do not have extensive post-transcriptional modifications of mRNA primarily because transcription and translation are coupled processes. Bacterial cells lack the physical barrier of a nucleus, which allows transcription and translation machinery to function at the same time, enabling the concurrent translation of an mRNA while it is being transcribed (Fig 26.2.1). As mRNA is synthesized in prokaryotes, a ribosome binding motif called the Shine-Dalgarno sequence, located in the 5' untranslated region of the mRNA emerges early, allowing the ribosome to bind and translation to occur. In addition, the protein N-utilzation Substance, better known as NusG, plays a critical role. NusG has three separate domains and the functions of two of them are known. The NusG N-terminal domain (NusG-NTD) can bind to RNAP, whereas the C-terminal domain (NusG-CTD) can combine with the NusE (RpsJ) component of ribosomes. These two functions of NusG enable transcription to be coupled with translation. NusG CTD can also bind to Rho to terminate transcription, as shown in Figure $1$. Figure $1$: The roles of NusG in transcription/translation coupling. (a) Composition of an active RNAP complex. RNAP is shown in dark grey, DNA in blue and nascent RNA in red. The ribosome is shown in green with the nascent polypeptide chain in light grey; the bulge in the small subunit denotes the location of NusE (RpsJ). NusG is shown in orange: its shape denotes two functional sections. The larger section denotes the N-terminal domain, which binds to RNAP. The smaller section denotes the C-terminal domain, which interacts with NusE in situ. Rho is shown in purple. (b) After the translation is completed, NusG remains bound to RNAP and may also bind to Rho through the C-terminal domain leading to the termination of transcription. Figure from: Cortes, T., and Cox, R.A. (2015) Microbiology 161:719-728. Another protein, NusA, slows RNAP, in contrast to NusG which increases its processivity, as reflected by the length of the RNA made before RNAP falls off. Eukaryotic RNA Processing In multicellular organisms, almost every cell contains the same genome, yet complex spatial and temporal diversity is observed in gene transcripts. This is achieved through multiple levels of processing leading from gene to protein, of which RNA processing is an essential stage. Following the transcription of a gene by RNA polymerases to produce a primary mRNA transcript, further processing is required to produce a stable and functional mature RNA product. This involves various processing steps including RNA cleavage at specific sites, intron removal, called splicing, which substantially increases the transcript repertoire, and the addition of a 5'CAP. Another crucial feature of the RNA processing of most genes is the generation of 3′ ends through an initial endonucleolytic cleavage, followed in most cases by the addition of a poly(A) tail, a process termed 3′ end cleavage and polyadenylation (CPA). Cleavage and 3'-Polyadenylation (CPA) Polyadenylation is a required step for the correct termination of nearly all mRNA transcripts. Except for replication-dependent histone genes, metazoan protein-encoding mRNAs contain a uniform 3' end consisting of a stretch of adenosines. In addition to determining the correct transcript length at transcription termination, the poly(A) tail helps to ensure the translocation of the nascent RNA molecule from the nucleus to the cytoplasm, enhances translation efficiency, acts as a signal feature for RNA degradation, and thereby contributes to the production efficiency of a protein. Cleavage and polyadenylation (CPA) are carried out by the cleavage/polyadenylation apparatus (CPA), a multi-subunit 3′ end processing complex, which involves over 80 proteins, comprised of four core protein subcomplexes, as shown in Figure $2$. These consist of 1. cleavage and polyadenylation specificity factor (CPSF), comprised of proteins CPSF1-4, factor interacting with PAPOLA and CPSF1 (FIP1L1), and WD repeat domain 33 (WDR33) (shown in green below); 2. cleavage stimulation factor (CstF), a trimer of CSTF1-3 (shown in red below; 3. cleavage factor I (CFI), a tetramer of two small nudix hydrolase 21 (NUDT21) subunits, and two large subunits of CPSF7 and/or CPSF6 (shown in orange in Figure 26.2.2 A); Note: Nudix are named for nucleoside diphosphates hydroxylases. 4. cleavage factor II (CFII), composed of cleavage factor polyribonucleotide kinase subunit 1 (CLP1) and PCF11 cleavage and polyadenylation factor subunit (PCF11) (shown in yellow in Figure 26.2.2 A). Additional factors include symplekin, the poly(A) polymerase (PAP), and the nuclear poly(A) binding proteins such as poly(A) binding protein nuclear 1 (PABPN1). CPA is initiated by this complex recognizing specific sequences within the nascent pre-mRNA transcripts termed polyadenylation signals (PAS). The PAS sequence normally consists of either a canonical 6 base sequence, the AATAAA hexamer, or a close variant usually differing by a single nucleotide (e.g., ATTAAA, TATAAA). It is located 10 to 35 nucleotides upstream of the cleavage site (CS) usually consisting of a CA dinucleotide. The PAS is also determined by surrounding auxiliary elements, such as upstream U-rich elements (USE), or downstream U-rich and GU-rich elements and G-rich sequences (DSE). As soon as the nascent RNA molecule emerges from RNA polymerase II (RNA Pol II), the CPSF complex is recruited to the PAS AATAAA hexamer, through numerous interactions. Upon successful assembly of this macromolecular machinery, CPSF3 performs the endonucleolytic cleavage followed by a non-templated addition of approximately 50-100 A residues. Panel (A) shows the core 3′ end processing machinery consists of complexes composed of multiple trans-acting proteins interacting with RNA via multiple cis-elements (USE = upstream sequence element; PAS = poly(A) signal; CS = cleavage site; DSE = downstream sequence element; CTD = C-terminal domain). Upon co-transcriptional assembly of these complexes, RNA cleavage and polyadenylation occur to form the 3′ end of the nascent RNA molecule. Panel (B) shows more than 70% of all genes harbor more than one polyadenylation signal (PAS). This gives rise to transcript isoforms differing at the mRNA 3′ end. While alternative polyadenylation (APA) in 3′UTR changes the properties of the mRNA (stability, localization, translation), internal PAS usage (in introns or the coding sequence (CDS)) changes the C-termini of the encoded protein, resulting in different functional or regulatory properties. Alternative polyadenylation (APA) occurs when more than one PAS is present within a pre-mRNA and provides an additional level of complexity in CPA-mediated RNA processing (Figure 26.2.2 B). Early studies revealed a significant portion of genes undergo APA, and with the advent of next-generation RNA sequencing technologies, the large-scale regulation of genes has become apparent, with approximately 70% of the transcriptome exhibiting APA regulation. As APA determines 3′UTR content and thus the regulatory features available to the mRNA, changes in the APA profile of a gene can have enormous impacts on expression. For those trying to understand the structure and mechanism of the cleavage/polyadenylation apparatus (CPA), it is especially frustrating that different names are given to the constituents that comprise it, especially when comparing the proteins from different organisms. Hence it is useful to see multiple representations of the complex. Figure $3$ shows a different cartoon representation of the cleavage and polyadenylation reactions. Note again the number of colored subcomplexes within the CPA as well as the different abbreviations shown for the individual proteins. This cartoon diagram is useful in visualizing the different steps involved. Figure $3$: Cis-regulatory sequence elements and protein factors involved in cleavage and polyadenylation. Marsollier, A.-C.; Joubert, R.; Mariot, V.; Dumonceaux, J. Targeting the Polyadenylation Signal of Pre-mRNA: A New Gene Silencing Approach for Facioscapulohumeral Dystrophy. Int. J. Mol. Sci. 2018, 19, 1347. https://doi.org/10.3390/ijms19051347. Creative Commons Attribution License Panel (A) shows that the specificity and efficiency of 3′end processing are determined by the binding of more than 80 RNA-binding proteins to regulatory cis-acting RNA sequence elements including the polyadenylation signal (PAS) A[A/U]UAAA; the cleavage site (represented by NN) and the downstream sequence element (DSE). Auxiliary sequences can be found near the polyadenylation signal or the DSE. The core processing complex, which is sufficient for the cleavage and polyadenylation, is composed of approximately 20 proteins, distributed in 8 complexes: the cleavage and polyadenylation specificity factor (CPSF), the cleavage stimulation factor (CstF); the mammalian cleavage factors I (CFIm) and the mammalian cleavage factors II (CFIIm); the single protein poly(A) polymerase (PAP); the single protein poly(A)-binding protein nuclear 1 (PABPN1); the single protein RNA polymerase II large subunit (Pol II); and the symplekin. Subunits of the different factors are indicated. Panel (B) shows how CPSF and CstF are co-transcriptionally recruited to the poly(A) signal and the DSE respectively, causing an endonucleolytic cleavage of the pre-mRNA between the PAS and the DSE at the cleavage site. Two fragments are generated: one fragment with a free 5′phosphate group which is rapidly degraded by exoribonucleases and one fragment with a free 3′hydroxyl group on which 250 adenines will be added by PAP. The newly-synthetized poly(A) tail is covered by PAPBN1, allowing mRNA circularization and stabilization. Now let's look at the structure of some of the complexes of the cleavage/polyadenylation apparatus (CPA). In yeast, the 3' processing is carried out by the cleavage and polyadenylation factor (CPF) which is called the CPSF in humans. On endonuclease cleavage, the RNA bound to RNA polymerase II is in two pieces, as shown in the figure above. The main mRNA now has a 3'-OH which is the site of polyadenylation. The minor cleavage fragment has a 5'-phosphate which gets degraded by the exonuclease Rat1, which as it cleaves the minor product helps displaces RNA polymerase II and helps to stop transcription. In yeast, the CPF has 14 subunits with polymerase, nuclease, and phosphatase subcomplexes or "modules". The polymerase module, as the name implies, has the poly(A) polymerase, Pap1. Table $1$ below shows some components of the polymerase module of both yeast CPF and human CPSF. yeast Polymerase Module of CPF Human mammalian polyadenylation specificity factor (mPSF) or CPSF Cft1 CPSF160 Pfs2 WDR33 Yth1 - RNA binding subunit CPSF30 - RNA binding subunit Fip1 - Pap1 binding subunit FIP1 - Pap1 binding subunit Table $1$ Some components of the polymerase module of both yeast CPF and human CPSF. The nuclease module has an endonuclease (Ysh1) and a Mpe1 protein, which facilitate the cleavage site selection and polyadenylation. Table $2$ below shows some components of the polymerase module in yeast and humans yeast human endonuclease Ysh1 endonuclease CPSF73 pseudo-nuclease Cft2 pseudo-nuclease CPSF100 multidomain protein Mpe1 multidomain protein RBBP6 Table $2$: Some components of the polymerase module in yeast and humans Part of the Cft2 called the yeast polymerase module interacting motif (yPIM), as its name implies, interacts with the polymerase module, in part through the interaction of key and conserved aromatic residues in it (F537, Y549, and F558) with a hydrophobic binding site in Cft1 and Pfs1. These interactions are key in activating and regulating the endonuclease and polyadenylation activities and hence controlling the termination of transcription. Figure $4$ shows an interactive iCn3D model of the yeast cleavage and polyadenylation specificity factor (CPF) polymerase module in complex with Mpe1, the yPIM of Cft2 and the pre-cleaved CYC1 RNA (7ZGR) The different parts of the complex are colored as shown below. • Yth1 (mRNA 3' processing protein): magenta • CFT1: green • MPE1: orange • Polyadenylation subunit 2 (Pfs2): yellow • cleavage factor 2 protein (CF2P): cyan • precleaved RNA CYC1: gray spacefill (backbone) with CPK-colored bases 5'-CAP Formation In eukaryotes, the 5′ cap, found on the 5′ end of the eventual mRNA molecule, consists of a guanine nucleotide connected to the mRNA via an unusual 5′ to 5′ triphosphate linkage, as shown in Figure $5$. This guanosine is methylated on the 7 position directly after capping in vivo by a methyltransferase. It is referred to as the 7-methylguanylate cap, abbreviated m7G. In multicellular eukaryotes and some viruses, further modifications exist, including the methylation of the 2′ hydroxy-groups of the first 2 ribose sugars of the 5′ end of the mRNA. Cap-1 has a methylated 2′-hydroxy group on the first ribose sugar, while cap-2 has methylated 2′-hydroxy groups on the first two ribose sugars. The 5′ cap is chemically similar to the 3′ end of an RNA molecule (the 5′ carbon of the cap ribose is bonded, and the 3′-OH unbonded). This provides significant resistance to 5′ exonucleases. Small nuclear RNAs (snRNAs) contain unique 5′-caps. Sm-class snRNAs are found with 5′-trimethylguanosine caps, while Lsm-class snRNAs are found with 5′-monomethylphosphate caps. In bacteria, and potentially also in higher organisms, some RNAs are capped with NAD+, NADH, or 3′-dephospho-coenzyme A. In all organisms, mRNA molecules can be decapped in a process known as messenger RNA decapping. For capping with 7-methylguanylate, the capping enzyme complex (CEC) binds to RNA polymerase II before transcription starts. As soon as the 5′ end of the new transcript emerges from RNA polymerase II, the CEC carries out the capping process (this kind of mechanism ensures capping, as with polyadenylation). The enzymes for capping can only bind to RNA polymerase II that is engaging in mRNA transcription, ensuring the specificity of the m7G cap almost entirely to mRNA. The 5′ cap has four main functions: 1. Regulation of nuclear export 2. Prevention of degradation by exonucleases 3. Promotion of translation (see ribosome and translation) 4. Promotion of 5′ proximal intron excision In addition to the polyA tail, the nuclear export of RNA is regulated by the cap-binding complex (CBC), which binds to 7-methylguanylate-capped RNA, as shown in Figure $6$. The CBC is then recognized by the nuclear pore complex and the mRNA is exported. Once in the cytoplasm after the pioneer round of translation, the CBC is replaced by the translation factors eIF4E and eIF4G of the eIF4F complex. This complex is then recognized by other translation initiation machinery including the ribosome, aiding in translation efficiency. Panel (a) CBC is required for pre-mRNA processing. The co-transcriptional binding of CBC to 7mG prevents the decapping activities of pre-mRNA degradation complexes [DXO (decapping exoribonuclease) and Dcp (decapping mRNA) Xrn2 (5′–3′ exoribonuclease 2)] and promotes pre-mRNA processing. CBC recruits P-TEFb [Cdk9/Cyclin T1 (CycT1)] to transcription initiation sites of specific genes promoting phosphorylation of the RNA pol II CTD at Ser2 residues. This results in the recruitment of splicing factors including SRSF1, which regulates both constitutive and alternative splicing events. Furthermore, CBC interacts with splicing machinery components that result in the spliceosomal assembly. CBC interacts with NELF and promotes pre-mRNA processing of replication-dependent histone transcripts. Panel (b) CBC forms a complex with Ars2 and promotes miRNA biogenesis by mediating pri-miRNA processing. Panel (c) CBC/Ars2 promotes pre-mRNA processing of replication-dependent histone transcripts. Panel (d) CBC promotes the export of U snRNA. CBC interacts with PHAX, which recruits export factors including CRM1 and RAN·GTP. Panel (e) CBC promotes the export of mRNA. For the export of transcripts over 300 nucleotides, hnRNP C interacts with CBC and inhibits the interaction between CBC and PHAX, allowing the CBC to interact with TREX and the transcript to be translocated to the cytoplasm. CBC interacts with the PARN deadenylase and inhibits its activity, protecting mRNAs from degradation. Panel (f) CBC mediates the pioneer round of translation. Cbp80 interacts with CTIF, which recruits the 40S ribosomal subunit via eIF3 to the 5′ end of the mRNA for translation initiation. Upon binding of importin-β (Imp-β) to importin-α (Imp-α), mRNA is released from CBC and binds to eIF4E for the initiation of the standard mode of translation. CBC-bound mRNP components not found in eIF4E-bound mRNPs are CTIF, exon junction complex (EJC), and PABPN1. Panel (g) The standard mode of translation is mediated by eIF4E cap-binding protein. eIF4E is a component of the eIF4F complex which promotes translation initiation. Capping with 7-methylguanylate prevents 5′ degradation in two ways. First, degradation of the mRNA by 5′ exonucleases is prevented by functionally looking like a 3′ end. Second, the CBC and eIF4E/eIF4G block the access of decapping enzymes to the cap. This increases the half-life of the mRNA, essential in eukaryotes as the export and translation processes take significant time. The mechanism that promotes the 5′ proximal intron excision during splicing is not well understood, but the 7-methylguanylate cap appears to loop around and interact with the spliceosome, potentially playing a role in the splicing process. The decapping of a 7-methylguanylate-capped mRNA is catalyzed by the decapping complex made up of at least Dcp1 and Dcp2, which must compete with eIF4E to bind the cap. Thus the 7-methylguanylate cap is a marker of an actively translating mRNA and is used by cells to regulate mRNA half-lives in response to new stimuli. During the decay process, mRNAs may be sent to P-bodies. P-bodies are granular foci within the cytoplasm that contain high levels of exonuclease activity. Triphosphatase and guanylyltransferase In capping the new mRNA, three different enzymes act sequentially: • A phosphatase cleaves a terminal phosphate from the 5' end which has 3 phosphates at the start leaving 2 phosphates (the yeast triphosphatase is called Cet1); • a GMP is added to the remaining diphosphate on the 5'-end to form a triphosphate in a reverse direction as shown in Figure 5 above (in yeast the RNA guanylyltransferase is called Ceg1); • a methyl group is added to the N7 of the guanine base by a methylase also called a methyl transferase These three enzymes are localized to a part of RNA polymerase that is highly phosphorylated, positioning them at the correct location for Cap formation. In yeast, the Figure $7$ shows an interactive iCn3D model of the Saccharomyces cerevisiae Cet1 (the triphosphatase)-Ceg1 (the guanylyltransferase) mRNA Capping Apparatus (3KYH) The two enzymes exist as a heterotetramer of two homodimers. Two beta (Cet1-triphosphatase) subunits are shown in gray and the two alpha (Ceg1-guanylyltransferase) subunits are shown in cyan. The active site residues are shown in CPK-colored sticks and labeled. The two enzymes interact with yeast RNAP II mostly through Ceg1. Specifically, the Ceg1 oligonucleotide domain interacts with a motif, WAQKW (247-251), on Cet1. A conformational change in a flexible linker after that motif allows capping to ensue. Methyl transferase The next step is the methyl transfer to the N7 of guanine in the 5' end of the cap. Figure $8$ shows an interactive iCn3D model of the Structure of a bacterial mRNA cap (Guanine-n7) methyltransferase (1RI1) 7-Methyl-guanosine-5'-triphosphate-5'-guanosine (GTG) is shown in sticks. The N7 nitrogen of guanine is labeled. S-Adenoslyl-L-homocysteine, the leaving group after methylation by S-adenosyl-L-methionine (SAM), is shown in spacefill. Key amino acid side chains in the active site are labeled (in small letters). The structure is most consistent with an in-line methyl transfer from SAM to the attacking nucleophile, the N7 of guanine. Specificity in most capping methylases occurs through noncovalent interactions (mostly base stacking, with two amino acid aromatic groups (an example of π-π stacking), or one aromatic and one nonpolar side chain. In the bacterial example shown above, the interactions include hydrophobic and hydrogen bonding interactions using Y284, F24, P175, E225, H144, and Y145. Decapping "What goes up must come down!" If the mRNA is capped during synthesis, there must be an enzyme to decap it. Figure $9$ shows an interactive iCn3D model of the yeast mRNA decapping enzyme Dcp1-Dcp2 complex in the ATP bound closed conformation (2QKM) The rendering is colored as shown below: • a chain in cyan • b chain in gray • nudix motif in magenta • ATP in spacefill Dcp2p, as with many other enzymes, has an open (inactive) and closed (active) conformation suggesting that a conformational change between the two states regulates decapping. The ATP binding site demarcates the active site. the Dcp1 protein probably functions to stabilize the closed state. mRNA Splicing Eukaryotic genes that encode polypeptides are composed of coding sequences called exons (ex-on signifies that they are expressed) and intervening sequences called introns (int-ron denotes their intervening role). Transcribed RNA sequences corresponding to introns do not encode regions of the functional polypeptide and are removed from the pre-mRNA during processing. All of the intron-encoded RNA sequences must be completely and precisely removed from a pre-mRNA before protein synthesis so that the exon-encoded RNA sequences are properly joined together to code for a functional polypeptide. If the process errs by even a single nucleotide, the sequences of the rejoined exons would be shifted, and the resulting polypeptide would be nonfunctional. The process of removing intron-encoded RNA sequences and reconnecting those encoded by exons is called RNA splicing. Intron-encoded RNA sequences are removed from the pre-RNA while it is still in the nucleus. Although they are not translated, introns appear to have various functions, including gene regulation and mRNA transport. On completion of these modifications, the mature transcript, the mRNA that encodes a polypeptide, is transported out of the nucleus, destined for the cytoplasm for translation. Introns can be spliced out differently, resulting in various exons being included or excluded from the final mRNA product. This process is known as alternative splicing. The advantage of alternative splicing is that different types of mRNA transcripts can be generated, all derived from the same DNA sequence. In recent years, it has been shown that some archaea also can splice their pre-mRNA. The splicing reaction is catalyzed by the spliceosome, a macromolecular complex formed by five small nuclear ribonucleoproteins (snRNPs), termed U1, U2, U4, U5, and U6, and approximately 200 proteins, as shown in Figure $10$. Each of these snRNPs contains snRNAs that can interact with each other through intrastrand hydrogen bonding and hence which help localize the snRNPs to the complexes. The assembly of the spliceosome on pre-mRNA includes the binding of U1 snRNP, U2 snRNP, the pre-formed U4/U6-U5 triple snRNP, and the Prp19 complex. This assembly occurs through the recognition of several sequence elements on the pre-mRNA that define the exon/intron boundaries, which include the 5′ and 3′ splice sites (SS), the associated 3′ sequences for intron excision, the polypyrimidine (Py) tract, and the branch point sequence (BPS). The assembly of the spliceosome during the process is depicted in Figure $10$. In the first step of the splicing process, the 5′ splice site (GU, 5′ SS) is bound by the U1 snRNP, and the splicing factors SF1/BBP and U2AF cooperatively recognize the branch point sequence (BPS), the polypyrimidine (Py) tract, and the 3′ splice site (AG, 3′ SS) to assemble complex E. The binding of the U2 snRNP to the BPS results in the pre-spliceosomal complex A. Subsequent steps lead to the binding of the U4/U5–U6 tri-snRNP and the formation of complex B. Complex C is assembled after rearrangements that detach the U1 and U4 snRNPs to generate complex B. Complex C is responsible for the two transesterification reactions at the SS. Additional rearrangements result in the excision of the intron, which is removed as a lariat RNA, and ligation of the exons. The U2, U5, and U6 snRNPs are then released from the complex and recycled for subsequent rounds of splicing There are different ways that pre-spliceosomes convert to full spliceosomes. The interactions of the small ribonucleoproteins U1 and U2, which bind at the 5' and 3' end of the introns respectively, are key. When multiple introns exist, a few different pathways occur. The interactions can be upstream (cross-introns) and downstream (cross-exons), as shown in Figure $11$. Figure $11$: Implications of synergistic U2 recruitment for the mechanism of exon and intron recognition. Braun et al. (2018) . Synergistic assembly of human pre-spliceosomes across introns and exons. eLife 7:e37751. https://doi.org/10.7554/eLife.37751. Creative Commons Attribution License The left-hand side of the figure shows how the interaction of U1 and U2 can occur across a single intron, to form the mature version of the spliceosome showing the U1-U2 interaction. The colored arrow shows possible interactions that either speed up or slow down the U1-U2 mature interactions. The right-hand side shows the formation of an intermediate cross-exon U2-U1 pair in which the exons that flank two introns are first delineated, which then leads to the physical U1-U2 interaction in the mature spliceosome when cross-intron interactions occur. Now let's move to show the actual structures of two of the spliceosome structures shown in Figure 10 above. Figure $12$ shows an interactive iCn3D model of the human fully-assembled precatalytic spliceosome (pre-B complex) (6QX9) The full structure of the spliceosome is almost too complicated to understand even if displayed in a molecular model. It's better grasped in a way by the cartoon figure shown previously. To avoid unnecessary visual complexity, the protein subunits in the iCn3D model of the human spliceosome are shown in a gray cartoon, and only the RNA molecules in the Un small ribonucleoproteins are shown in color, as described below. The resolution is such that each nucleotide in the RNA polymers is shown just as a single-colored sphere not connected to the next nucleotide in the RNA. • U1 snRNA - magenta • U6 snRNA - orange • U5 snRNA - yellow • U2 snRNA - cyan • U4 snRNA - blue • AdML pre-mRNA - black Since it has U1, U2, U4, U5, and U6, it most closely resembles Complex B (or an immediate precursor pre-B of it) as shown in Figure 10. This form occurs before U1 snRNP dissociates. A helix from the 5'-single-stranded U1 snRNA inserts into a helicase in the complex between two RecA domains which bind ATP and unfold the nucleic acids. This allows the 5' single-stranded RNA to form interactions with an ACAGAGA sequence (mobile loop) in U6. These conformational changes allow the eventual separation of the U4 and U6 snRNA, freeing the snRNA in U6 is form the catalytic site. The B complex itself does not have a functioning active site. After the dissociation of U1 and U4 snRNPs, and the binding of another 20 or so proteins, the active spliceosome is formed. Now let's look at the final catalytic structure, Complex C, which contains only three of the snRibonucleoproteins, U2, U5, and U6. Figure $13$ shows an interactive iCn3D model of the Human C Complex Spliceosome (7A5P) The color coding is as follows: • protein - gray • U6 snRNA - orange • U5 snRNA - yellow • U2 snRNA - cyan • AdML pre-mRNA - magenta The interactions of the snRNAs are key for structure and catalysis in the spliceosome, and in the figures above the interactions are had to discern. Figure $14$ shows the interactions among U2, U5, and U6 snRNA in the human and yeast spliceosomes. Figure $14$: The RNA elements and the splicing active site of the human mature Bact complex. Zhang, X., Yan, C., Zhan, X. et al. Structure of the human-activated spliceosome in three conformational states. Cell Res 28, 307–322 (2018). https://doi.org/10.1038/cr.2018.14. Creative Commons Attribution 4.0 Unported License. http://creativecommons.org/licenses/by/4.0/ Panel (A) shows the structure of the RNA elements in the core of the human mature Bact complex. The color-coded RNA elements of the human Bact complex are shown in the left panel, and their superposition with those of the S. cerevisiae Bact complex is displayed in the right panel. All yeast RNA elements are colored grey. The helix II of the U2/U6 duplex in the human Bact complex is bent relative to that in the yeast complex. Panel (B) shows a structural overlay of the active site RNA elements between the human and S. cerevisiae Bact complexes. Panel (C) shows that the U6/intron duplex in the human Bact complex is considerably longer than that in the S. cerevisiae Bact complex. In mammals, the first catalytic step of the splicing reaction begins when the U1 snRNP binds the 5′ SS of the intron (defined by the consensus sequence AGGURAGU), and the splicing factors SF1 and U2AF cooperatively recognize the BPS, Py, and 3′ SS to assembled complex E or the commitment complex. Subsequently, U2 snRNP and additional proteins are recruited to the pre-mRNA BPS to form the pre-spliceosome or complex A. The binding of the U4/U6-U5 tri-snRNP forms the pre-catalytic spliceosome or complex B. After RNA-RNA and RNA-protein rearrangements at the heart of the spliceosome, U1 and U4 are released to form the activated complex B or complex B This complex is responsible for executing the first catalytic step, through which the phosphodiester bond at the 5′ SS of the intron is modified by the 2′-hydroxyl of adenosine of the BPS to form a free 5′ exon and a branched intron, as shown in Figure $15$. The reaction of the 2'-hydroxyl from the branch point adenosine nucleotide is known as a transesterification reaction. During this process, additional rearrangements occur to generate the catalytic spliceosome or complex C (see Figure 10 above), which is responsible for catalyzing the second transesterification reaction leading to intron excision and exon–exon ligation. The resulting intron structure is referred to as a lariat structure. After the second catalytic step, the U2, U5, and U6 snRNPs are released from the post-spliceosomal complex and recycled for additional rounds of splicing. Panel (A) shows a schematic diagram of the pre-mRNA with exons and introns indicated. Key sequences are required for splicing at the 5' and 3' intron locations, and for the recognition and positioning of the branch point Adenosine residue for the first transesterification reaction. ( Panel (B) shows a schematic of the two transesterification reactions required for intron removal. The branch point 2'-OH residue mediates attack on the 5'-phosphate of the intron guanosine residue located at the 5'-splice site. This releases the 3' hydroxyl of Exon 1 which subsequently mediates the attack of the 5' phosphate of the first guanosine residue in Exon 2. The 3' hydroxyl of the intron guanine residue is released forming the Lariat structure and Exon 1 is ligated to Exon 2. Alternative Splicing (AS) offers an additional mechanism for regulating protein production and function. AS options are determined by the expression of or exposure to trans elements present within unique cellular locations and environments. Additional sequence elements within the mRNA, known as exonic and intronic splicing silencers or enhancers (ESS, ISS, ESE, and ISE, respectively), participate in the regulation of AS. Specific RNA-binding proteins, including heterogeneous nuclear ribonucleoproteins (hnRNPs) and serine/arginine-rich (SR) proteins, recognize these sequences to positively or negatively regulate AS, as shown in Figure $16$. These regulators, together with an ever-increasing number of additional auxiliary factors, provide the basis for the specificity of this pre-mRNA processing event in different cellular locations within the body. The core cis sequence elements that define the exon/intron boundaries (5′ and 3′ splice sites (SS), GU-AG, polypyrimidine (Py) tract, and branch point sequence (BPS)) are poorly conserved. Additional enhancer and silencer elements in exons and introns (ESE: exonic splicing enhancers; ESI: exonic splicing silencers; ISE: intronic splicing enhancers; ISI: intronic splicing silencers) contribute to the specificity of AS regulation. Trans-acting splicing factors, such as SR family proteins and heterogeneous nuclear ribonucleoprotein particles (hnRNPs), bind to enhancers and silencers and interact with spliceosomal components. In general, SR proteins bound to enhancers facilitate exon definition, and hnRNPs inhibit this process. These trans-acting elements are expressed differentially within different locations or under different environmental stimuli to regulate AS. There are several different types of AS events, which can be classified into four main subgroups. The first type is exon skipping, which is the major AS event in higher eukaryotes. In this type of event, a cassette exon is removed from the pre-mRNA as shown in Figure $17$ (panel A). The second and third types are alternative 3′ and 5′ SS selections (panels b and c). These types of AS events occur when the spliceosome recognizes two or more splice sites at one end of an exon. The fourth type is intron retention (panel d), in which an intron remains in the mature mRNA transcript. This AS event is much more common in plants, fungi, and protozoa than in vertebrates. Other events that affect the transcript isoform outcome include mutually exclusive exons (panel e), alternative promoter usage (panel f), and alternative polyadenylation (panel g). Here is a detailed and incredible narrated animation of splicing and the spliceosome.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/03%3A_Unit_III-_Information_Pathway/25%3A_RNA_Metabolism/25.03%3A_RNA-Dependent_Synthesis_of_RNA_and_DNA.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry RNA Viruses Infections with RNA viruses place a constant burden on our healthcare systems and economy. Over the past century, this has been particularly true for infections with the Human immunodeficiency virus 1 (HIV-1), Influenza A virus (IAV), Rotavirus (RotaV), West Nile virus (WNV), Dengue virus (DV), Measles virus (MV), and the Porcine reproductive and respiratory syndrome virus (PRRSV). But also emergent RNA viruses can have considerable consequences, such as the Severe acute respiratory syndrome-related coronavirus (SARS-CoV) in 2003, the Middle East Respiratory Syndrome coronavirus (MERS-CoV), and most recently the Severe acute respiratory syndrome-related coronavirus-2 (SARS-CoV-2) in December of 2019. Eukaryotes and bacteria can be infected with a wide variety of RNA viruses. On average, these pathogens share little sequence similarity and use different replication and transcription strategies. RNA virus genomes can consist of double-stranded RNA (dsRNA) or single-stranded (ssRNA) as shown in Figure $1$ (Panel a). In turn, the ssRNA viruses can be classified into positive sense (+RNA) and negative sense (−RNA) viruses, depending on the translatability of their genetic material. As summarized for four model RNA pathogens in Panel b, all RNA viruses use dedicated replication and transcription strategies to amplify their genetic material. The common denominator of these strategies is a conserved RNA-dependent polymerase domain. Figure $1$ describes variations of the replication process in the different types of RNA viruses. Panel a shows a simplified taxonomy of the genome architecture of representative RNA viruses. Panel b (+RNA virus)shows infection with a +RNA virus—as exemplified here with a CoV-like virion—releases a single-stranded RNA genome into the cytoplasm (1). (2) Translation of the 5′-terminal open-reading frame of the genome produces the viral replicase. (3) This multi-enzyme complex includes RNA-dependent RNA polymerase (RdRp) activity (orange) and associates with intracellular membranes before −RNA synthesis commences. Newly synthesized −RNAs are subsequently used to produce new +RNAs (4), which are typically capped (yellow) and polyadenylated (polyA). (Retrovirus) HIV-1 genomes are packaged as ssRNA in virions. When the ssRNA is released (1) a cDNA copy is synthesized by the reverse transcriptase enzyme (RT) (2). The RNA is next degraded by the intrinsic RNase H activity in the RT (3) and the single-stranded cDNA is converted to dsDNA (4). The dsDNA is imported into the nucleus (5) for integration into the host’s genetic material. (−RNA virus) (1) As illustrated here with an IAV-like particle, infection with an −RNA virus releases a viral RNA genome that is associated with a viral polymerase (orange) and nucleoprotein (green). (2) In the case of non-segmented −RNA viruses, these complexes support transcription to produce viral mRNAs or cRNAs. (3) Viral mRNAs are next translated and new viral proteins complex with cRNAs to synthesize new vRNAs. (5) The vRNA-containing complexes of some segmented −RNA viruses are imported into the nucleus of the host cell, where (6) the RdRp produces mRNAs or cRNAs. (7) mRNAs are transported to the cytoplasm, while cRNAs are bound by new viral proteins to form cRNPs for −RNA synthesis. (dsRNA virus) Fully duplexed RNA genomes lack cap and polyA elements. (1) The RdRp (orange), therefore, transcribes the viral genome inside the capsid of the virion (blue and red), so viral mRNAs can be (2) released into the cytoplasm as illustrated here with a rotavirus-like virion. In the cytoplasm, the mRNA is translated (3) or replicated by newly synthesized viral RdRps. Viral RNA-dependent RNA Polymerases (RdRps) were discovered in the early 1960s from studies on mengovirus and polio viruses when it was observed that these viruses were not sensitive to actinomycin D, a drug that inhibits cellular DNA-directed RNA synthesis. This lack of sensitivity suggested that there is a virus-specific enzyme that could copy RNA from an RNA template and not from a DNA template. RdRps show some structural similarity to telomerase enzymes suggesting a potential ancestral relationship of telomerase with RdRps. The most famous example of RdRp is of the polio virus. The viral genome is composed of RNA, which enters the cell through receptor-mediated endocytosis. From there, the RNA can act as a template for complementary RNA synthesis, immediately. The complementary strand is then, itself, able to act as a template for the production of new viral genomes that are further packaged and released from the cell ready to infect more host cells. The advantage of this method of replication is that there is no DNA stage; replication is quick and easy. The disadvantage is that there is no 'backup' DNA copy (Fig 26.3.1b; + RNA virus). Reverse Transcriptase (RT) enzymes, on the other hand, that are common to retroviruses, convert the + RNA strand from the virus into a cDNA copy (Fig 26.3.1b; retroviruses). The RT enzyme then degrades the RNA and the single-stranded cDNA is converted to dsDNA. The dsDNA is then integrated into the host's genome where it can remain in a dormant state. Upon reactivation, + RNA will be manufactured, along with viral proteins used in the assembly of the infectious viral particles. Structure and Mechanism of RNA Viral Polymerases The RNA-dependent polymerase domain is a member of the superfamily of template-dependent nucleic acid polymerases and typically <400 amino acids in length. Its sequence is highly variable on average, with some regions showing less than ~10% conservation. Strong amino acid conservation can be observed, however, in regions that are directly involved in nucleotide selection or catalysis, such as the strictly conserved glycine and aspartates in the center of the domain. The prototypic RNA virus RNA polymerase domain harbors seven of such regions or motifs, which are arranged in the order G, F1–3, A, B, C, D, and E from amino- to carboxy-terminus. Each of the seven motifs in the RNA polymerase domain adopts a specific and conserved fold, as shown in Figure $2$. Panel a shows the structure of the FMDV RdRp. The motifs A, B, C, D, E, F, and G are color-coded yellow, gold, orange, red, light green, aquamarine, and blue. Overall the polymerase structure adopts a shape that resembles a cupped right hand. Herein, motifs A–E lie on the palm, while motifs F and G are part of the fingers. In the side-view of the enzyme, the location of the template and NTP channels is indicated. Panel b shows conserved structural elements of the FMDV RdRp. Homomorphic A–G were mapped and color-coded yellow, gold, orange, red, light green, aquamarine, and blue, respectively. Images A and B are based on PDB accession 2E9R. Figure $3$ shows an interactive iCn3D model of the Foot-and-mouth disease virus RNA-polymerase in complex with a template- primer RNA, PPi, and UTP (2E9Z) Active site region side chains are shown as sticks and labeled. The template stand is shown in yellow and the primer is in green. Note the conserved amino acids Tyr-336, the catalytic Asp-338, and Lys-387 in motifs C and E, respectively. The primer 3'OH forms an H bond with active site Asp 338 (on motif C). In some structures, Asp 338 is bound to a metal ion that interacts with the PPP of the incoming NTP. Two metal ions are involved in catalysis. Tyr 336 interacts with the primer nucleotide while Lys 387 and Arg 388 interact with the primer backbone. These three residues are highly conserved. The acceptor base of the template strands is adjacent to the NTP binding site. There is no proofreading activity in viral RNA replication. RNA-dependent RNA polymerases generally have a groove on top of the enzyme where RNA enters. It exits in the front. Nucleotides enter at a rear channel. This is illustrated in Figure $4$ (Panel b). Following the convention for cellular DNA-dependent DNA polymerases (DdDp), the seven motifs and homomorphs are grouped into three subdomains. These subdomains are called fingers, palm, and thumb in reference to the polymerase domain’s likeliness to a cupped right hand, as shown in Figure $4$ (Panel A). The finger subdomain loops that interconnect the fingers with the thumb in the RNA-dependent RNA polymerases (RdRps) of +RNA and dsRNA viruses create an overall “closed-hand” conformation that is unique to RNA-dependent RNA polymerases (RdRps) and generally not seen in crystal structures of DNA-dependent DNA polymerases (DdDps) or reverse transcriptases (RTs). The three subdomains of the RNA-dependent polymerases work together to facilitate the binding of RNA and nucleotides (NTPs). The thumb subdomain contains various residues that are involved in RNA binding and these generally pack into the minor groove of the template RNA. In some polymerases, the thumb also contains sequences that protrude into the template channel to help stabilize the initiating NTPs on the ssRNA template. Crucially, these protrusions are also able to undergo relatively large conformational rearrangements to facilitate the translocation of the template following the first condensation reaction. The other residues of the thumb subdomain contribute to the formation of the NTP tunnel, which sits at an ~110° angle with the template channel as shown in Figure $4$, Panel A. The cavity is lined with positively charged amino acids, though charge interactions are likely not sufficient to guide NTPs into the interior. Indeed, molecular dynamics (MD) simulations have shown that the residues of the NTP channel can also explore a relatively large amount of space, which may allow the RdRp to actively “pump” NTPs down the channel. Panel (A) shows a ribbon representation of a typical picornaviral RdRP (model from the cardiovirus EMCV 3Dpol, PDB id. 4NZ0). The seven conserved motifs are indicated in different colors: motif A, red; motif B, green; motif C, yellow; motif D, sand; motif E, cyan; motif F, blue; motif G, pink; Panel (B) shows a lateral view of a surface representation of the enzyme (grey) that has been cut to expose the three channels that are the entry and exit sites of the different substrates and reaction products. The structural elements that support motifs A–G are also shown as ribbons. This panel also shows the organization of the palm sub-domain with motif A shown in two alternative conformations: the standard conformation (PDB id. 4NZ0) found in the apo-form of most crystallized 3Dpol proteins and the altered conformation found in the tetragonal crystal form of the EMCV enzyme (PDB id. 4NYZ). The alterations affect mainly Asp240, the amino acid in charge of incoming ribonucleotide triphosphate (rNTP) selection, and the neighboring Phe239 that move ~10 Å away from its position in the enzyme catalytic cavity directed towards the entrance of the nucleotide channel, approaching to motif F; Panel (C) shows a close-up of the structural superimposition of the two alternative conformations of the EMCV motif A; Panel (D) shows the PV replication-elongation complexes. Sequential structures illustrating the movement of the different palm residues from a binary PV 3Dpol-RNA open complex (left) to an open 3Dpol-RNA-rNTP ternary complex (middle) where the incoming rNTP is positioned in the active site for catalysis and, a closed ternary complex (right) after nucleotide incorporation and pyrophosphate (PPi) release. The residues DA (involved in rNTP selection through an interaction with the 2′ hydroxyl group), DC (the catalytic aspartate of motif C), KD (the general acid residue of motif D that can coordinate the export of the PPi group), and NB (a conserved Asn of motif B, interacting with DA) have been highlighted as sticks. The different structures correspond to the 3Dpol-RNA (PDB id. 3OL6), 3Dpol-RNA-CTP open complex (PDB id. 3OLB) and 3Dpol-RNA-CTP closed complex (PDB id. 3OL7) structures of PV elongation complexes, respectively The finger subdomain residues mainly pack into the major groove of the RNA template. Furthermore, upon entry of the template, they can unstack the single strand at position +3, as shown in Figure $5$ (Panel A). The non-conserved structural elements of the fingers subdomain play a role in RNA binding as well. In particular, the fingertips of dsRNA and some +RNA virus RdRps allow the polymerase to “hold” the template without extensive conformational changes. The variations and extensions in the fingers subdomain has also been shown to play roles in protein–protein interactions, phosphorylation, oligomerization, and nuclear import. In contrast, the HIV-1 RT lacks such extensions and adopts a more “open-hand” or U-shaped binding cleft. As a consequence, the RT structure must rearrange its subdomains to coordinate the binding of the template, nascent strand, and incoming dNTPs. Panel a shows the structure of the PV active site as it moves from a native state or elongation complex (i) to an open complex (ii), and a closed complex (iii). The closed complex depicted here shows the active site after catalysis. Highlighted are the metal-binding aspartates of motifs A and C, and the lysine of motif D that acts as a general acid. Color coding by motif as in Fig. 26.3.2. Image based on PDB accessions 3Ol6, 3OLB, and 3OL7. Panel b shows a schematic presentation of the RdRp active site. The aspartates (Asp) of motif A (yellow) and C (orange) bind divalent metal ions (marked Mg and shaded grey), which are used to coordinate the formation of a new phosphodiester bond at the 3′-OH (red in panel ii) of the nascent strand (yellow). The general acid (red Lys/His in panel ii) is positioned near the β-phosphate of the incoming NTP to protonate the PPi leaving group. Panel c shows a simplified schematic of the kinetic steps of RNA polymerases. Asterisk indicates a closed complex The NTP and template entry channels meet at the palm subdomain - Figure $5$, Panel A. This is a structure that is comprised of a central, partially formed three-stranded β-sheet, which is also present in RNA-recognition motifs (RRMs). The relative movement of these strands within the RRM is vital to catalysis and dependent on NTP binding. Only when a correct NTP binds can motif A and motif C align and the RRM fold be completed. This catalytically competent conformation of the active site is often referred to as the closed complex (not to be confused with the “closed-hand”, which refers to the overall structure of the RdRp) - Figure $5$, Panel d. The polymerase reaction creates new phosphodiester bonds between NTPs using RNA as a template. The NTP substrates involved in this reaction are coordinated by two metal ions, which are bound by the conserved aspartates of motifs A and C - Figure $5$ - panel a, i. They also position the NTP’s triphosphate optimally for attack by the sugar moiety of the nascent strand once its 3′ carbon has lost a proton- Figure $5$, panel b. The N-terminal aspartate of motif C thus uses a metal ion to fix the α-phosphate of the incoming NTP and reduce the pKa of the nascent RNA’s 3′-OH to facilitate the attack - Figure $5$, panel b, ii. The amino-terminal carboxylate of motif A, on the other hand, stabilizes the β- and γ-phosphates with its divalent metal as well as the pentavalent (phosphorane) intermediate that forms during catalysis - Figure $5$, panel b, ii. Structural and biochemical analyses have shown that the formation of this transient structure is dependent on the attack of the NTP’s α-phosphate by the 3′-OH, which is often the rate-limiting catalytic step in NTP condensation - Figure $5$, panel b. Motif D’s lysine or histidine assists the N-terminal aspartate of motif A in coordinating the β-phosphate of the incoming NTP, analogous to the trigger loop in DdDps. However, when the positively charged side chain of motif D approaches the β-phosphate, it can protonate the PPi leaving group as well - Figure $5$, panel b, ii. This second protonation step in the active site is not essential for the polymerase reaction, since catalysis can still take place when motif D’s lysine has been mutated to a residue with a different pKa. The polymerase reaction rate will nevertheless be 50- to 1,000-fold higher when a lysine or histidine is present. Recent data even suggests that motif D can coordinate the export of the PPi group from the active site once catalysis has taken place, thereby triggering the translocation of the RNA. Overall, the RNA replication process can be summarized with this four-step mechanism: 1. Nucleoside triphosphate (NTP) binding - initially, the RdRp presents with a vacant active site in which an NTP binds, complementary to the corresponding nucleotide on the template strand. Correct NTP binding causes the RdRp to undergo a conformational change. 2. Active site closure - the conformational change, initiated by the correct NTP binding, results in the restriction of active site access and produces a catalytically competent state. 3. Phosphodiester bond formation - two Mg2+ ions are present in the catalytically active state and arrange themselves in such a way around the newly synthesized RNA chain that the substrate NTP can undergo a phosphatidyl transfer and form a phosphodiester bond with the newly synthesized chain. With the use of these Mg2+ ions, the active site is no longer catalytically stable, and the RdRp complex changes to an open conformation. 4. Translocation - once the active site is open, the RNA template strand can move by one position through the RdRp protein complex and continue chain elongation by binding a new NTP, unless otherwise specified by the template. Figure $6$ shows a more detailed representation of the elongation catalytic cycle. Figure $6$: Elongation catalytic cycle of RNA-dependent RNA polymerases RNA synthesis can be performed using a primer-independent (de novo) or a primer-dependent mechanism that utilizes a viral protein genome-linked (VPg) primer. The de novo initiation consists of the addition of a nucleoside triphosphate (NTP) to the 3'-OH of the first initiating NTP. During the following so-called elongation phase, this nucleotidyl transfer reaction is repeated with subsequent NTPs to generate the complementary RNA product. The termination of the nascent RNA chain produced by RdRp is not completely known, however, it has been shown that RdRp termination is sequence-independent. One feature of RNA-dependent RNA polymerase replication is the immense error rate during transcription. RdRps and RTs are known to have a lack of fidelity on the order of 104 nucleotides, which is thought to be a direct result of their insufficient proofreading abilities. This high rate of variation is favored in viral genomes as it allows for the pathogen to overcome defenses developed by hosts trying to avoid infection allowing for evolutionary growth. Let's look at another RdRp from the poliovirus. The virus has a single-stranded, positive-sense RNA genome, which makes it infectious in itself but much more infectious when replicated. It is translated into a polyprotein which is cleaved into about 12 separate proteins. One protein, 3Dpol, is an RNA-dependent RNA polymerase that transcribes the infecting +RNA strand into a -RNA strand, which then serves as a template for more +RNA strands. Figure $7$ shows an interactive iCn3D model of the Poliovirus polymerase elongation complex with 2',3'-dideoxy-CTP (3OLB). The model shows the palm, finger, and thumb domains. Color coding is as follows: • red: finger domain • pink: pinky finger part of the finger domain • blue: thumb domain • light blue: primer grip at the beginning of the thumb domain • cyan: RNA template • green: RNA product • gray: palm domain Structural studies suggest that the pinky finger is involved in the initiation, while nucleotide binding and catalysis used the palm domain. The thumb domain appears to affect the translocation step. In most nucleases, the finger binds and positions NTPs in the active site. After catalysis, the reverse motion opens the active site which allows translocation by effectively ratcheting the template by one base pair which is driven by PPi release. These conformational changes can't be made so easily in +RNA stranded viral RdRps.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/03%3A_Unit_III-_Information_Pathway/25%3A_RNA_Metabolism/25.04%3A_26.4_References.txt	princeton-nlp/TextbookChapters	<h210.6refs">26.4.6 References 1. Parker, N., Schneegurt, M., Thi Tu, A-H., Lister, P., Forster, B.M. (2019) Microbiology. Openstax. Available at: https://opentextbc.ca/microbiologyopenstax/ 2. Palazzo, A., and Lee, E.S. (2015) Non-coding RNA: what is function and what is junk? Frontiers in Genetics 6:2 Available at: file:///C:/Users/flatt/AppData/Local/Temp/fgene-06-00002.pdf 3. Wikipedia contributors. (2020, July 9). RNA. In Wikipedia, The Free Encyclopedia. Retrieved 15:30, August 6, 2020, from https://en.Wikipedia.org/w/index.php?title=RNA&oldid=966784317 4. Burenina, O.Y., Oretskaya, T.S., and Kubareva, E.A. (2017) Non-Coding RNAs As Transcriptional Regulators in Eukaryotes. Acta Naturae 9(4):13-25. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5762824/ 5. Khatter, H., Vorlander, M.K., and Muller C.W. (2017) RNA polymerase I and III: similar yet unique. Current Opinion in Structural Biology 47:88-94. Available at: https://www.sciencedirect.com/science/article/pii/S0959440X17300313 6. Wikipedia contributors. (2020, May 8). Sigma factor. In Wikipedia, The Free Encyclopedia. Retrieved 17:50, August 7, 2020, from https://en.Wikipedia.org/w/index.php?title=Sigma_factor&oldid=955570499 7. Bae, B., Felkistov, A., Lass-Napiokowska, A., Landick, R., and Darst, S.A. (2015) Structure of a bacterial RNA polymerase holoenzyme open protomer complex. eLife 4:e08504. Available at: https://elifesciences.org/articles/08504 8. Petrenko, N., Jin, Y., Dong, L., Wong, K.H., and Struhl, K. (2019) Requirements for RNA polymerase II preinitiation complex formation in vivo. eLife 8:e43654. Available at: https://elifesciences.org/articles/43654 9. Gupta, K., Sari-Ak, D., Haffke, M., Trowitzsch, S., and Berger, I. (2016) Zooming in on transcription preinitiation. J Mol Biol. 428(12):2581-2591. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4906157/ 10. Wikipedia contributors. (2020, April 17). TATA-binding protein. In Wikipedia, The Free Encyclopedia. Retrieved 14:54, August 8, 2020, from https://en.Wikipedia.org/w/index.php?title=TATA-binding_protein&oldid=951583592 11. Patel, A.B., Greber, B.J., and Nogales, E. (2020) Recent insights into the structure of TFIID, its assembly, and its binding to core promoter. Curr Op Struct Bio 61:17-24. Available at: https://www.sciencedirect.com/science/article/pii/S0959440X19301113#fig0010 12. Ruff, E.F., Record, Jr., M.T., Artsimovitch, I., (2015) Initial events in bacterial transcription initiation. Biomolecules 5(2):1035-1062. Available at: https://www.mdpi.com/2218-273X/5/2/1035/htm 13. Kireeva, M., Opron, K., Seibold, S., Domecq, C., Cukier, R.I., Coulombe, B., Kashlev, M., and Burton, Z. (2102) Molecular dynamics and mutational analysis of the catalytic and translocation cycle of RNA polymerase. BMC Biophysics 5(1):11. Available at: https://www.researchgate.net/publication/225281979_Molecular_dynamics_and_mutational_analysis_of_the_catalytic_and_translocation_cycle_of_RNA_polymerase/figures?lo=1 14. Washburn, R.S., and Gottesman, M.E. (2015) Regulation of transcription elongation and termination. Biomolecules 5(2):1063-1078. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4496710/pdf/biomolecules-05-01063.pdf 15. Zenkin, N., and Yuzenkova, Y. (2015) New insights into the functions of transcription factors that bind the RNA polymerase secondary channel. Biomolecules 5(3):1195-1209. Available at: https://www.mdpi.com/2218-273X/5/3/1195/htm 16. Gocheva, V., LeGall, A., Boudvillain, M., Margeat, E., and Nollmann, M. (2015) Direct observation of the translocation mechanism of transcription termination factor Rho. Nuc Acids Res 43(1):10.1093. Available at: https://www.researchgate.net/publication/272162172_Direct_observation_of_the_translocation_mechanism_of_transcription_termination_factor_Rho 17. Miki, T.S., Carl, S.H., and Groβhans, H. (2017) Two disctinct transcription termination modes dictated by promoters. Genes & Dev 31:1-10. Available at: https://www.researchgate.net/publication/320350041_Two_distinct_transcription_termination_modes_dictated_by_promoters 18. Gurumurthy, A., Shen, Y., Gunn, E.M., Bungert, J. (2018) Phase separation and transcription regulation: Are Super-Enhancers and Locus Control Regions primary sites of transcription complex assembly? BioEssays 1800164. Available at: https://www.researchgate.net/publication/329331157_Phase_Separation_and_Transcription_Regulation_Are_Super-Enhancers_and_Locus_Control_Regions_Primary_Sites_of_Transcription_Complex_Assembly 19. Suñé-Pou, M., Prieto-Sánchez, Boyero-Corral, S., Moreno-Castro, C., El Yousfi, Y., Suñé-Negre, J.M., Hernández-Munain, C., and Suñé, C. (2017) Targeting splicing in the treatment of human disease. Genes 8(3):87. Available at: https://www.mdpi.com/2073-4425/8/3/87/htm 20. Schaughency, P., Merran, J., and Corden J.L. (2014) Genome-wide mapping of yeast RNA polymerase II termination. PLOS Genetics 10(10):e1004632 Available at: https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1004632 21. Nourse, J., Spada, S., and Danckwardt, S. (2020) Emerging roles of RNA 3'-end cleavage and polyadenylation in pathogenesis, diagnosis, and therapy of human disorders. Biomolecules 10(6):915. Available at: https://www.mdpi.com/2218-273X/10/6/915/htm 22. Wikipedia contributors. (2020, July 30). Five-prime cap. In Wikipedia, The Free Encyclopedia. Retrieved 05:53, August 11, 2020, from https://en.Wikipedia.org/w/index.php?title=Five-prime_cap&oldid=970240533 23. Cortes, T. and Cox, R.A. (2015) Transcription and translation of the rpsJ, rplN and rRNA operons of the tubercle bacillus. Microbiology (2015) 161:719-728. Available at: https://www.microbiologyresearch.org/docserver/fulltext/micro/161/4/719_mic000037.pdf?expires=1597159574&id=id&accname=guest&checksum=6FFC9C066EF41C7799FAE843CE94C49F 24. Hein, P.P. and Landick, R. (2010) The bridge helix coordinates movements of modules in RNA polymerase. BMC Biology 8:141. Available at: https://bmcbiol.biomedcentral.com/articles/10.1186/1741-7007-8-141 25. Gonatopoulos-Pournatzis, T., and Cowling, V.H. (2014) Cap-binding complex (CBC). Biochem. J. 457:231-242. Available at: https://www.researchgate.net/publication/259392894_Cap-binding_complex_CBC
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/03%3A_Unit_III-_Information_Pathway/26%3A_Protein_Metabolism/26.01%3A_The_Genetic_Code.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Overview of Translation Within this chapter, we will cover the details of prokaryotic and eukaryotic translation. Translation is the process of converting the information housed in mRNA into the protein sequence. Essentially, you are translating the language of nucleotides into the language of amino acids. Recall that prokaryotic and eukaryotic transcription and translation systems differ in large part due to the compartmentalization of larger eukaryotic cells. Due to this compartmentalization, transcription and translation are separated spatially and temporally within the cell. Transcription occurs within the nucleus of eukaryotes and translation occurs within the cytoplasm. Prokaryotes do not have compartmentalization and have, thus, evolved a coupled transcription/translation system where both processes occur simultaneously. Both are illustrated in Figure $1$. Panel (a) shows that prokaryotes lack cellular compartmentalization and show coupled transcription-translation processing; Panel (b) shows that eukaryotes have a high degree of compartmentalization and separate the processes of transcription, which is in the nucleus of the cell, from the processes of translation, which is localized in the cytoplasm. Recall that peptide formation is a dehydration reaction that combines the carboxylic acid of the upstream amino acid with the amine functional group of the downstream amino acid to form an amide linkage as shown in Figure $2$. Water is the by-product. The ribosome (a large complex of peptides and rRNA molecules) serves as the enzyme that mediates this reaction. It requires a mature mRNA to serve as the template and directionally performs peptide bond synthesis from the N to the C-terminal of the growing peptide/protein. This is known as N- to C-synthesis. Note that the overall protonation state shown is very unlikely since under conditions when the carboxyl groups are protonated, so would the amines. This representation makes it easier to highlight the departing water To maintain proper protein function, the error rate of translation is approximately 10-4 or 1 error in every 10,000 amino acids encoded. The fidelity of protein synthesis is maintained by the ribosome's ability to match the code from the template mRNA stand with the appropriate amino acid. Template mRNA is read by the ribosome in groups of three nucleotides, called a codon, as shown in Figure $3$. The template is non-overlapping and reads in discrete groups of three. This is known as the reading frame of the mRNA, and it is always read from the 5′ to 3′ direction. Thus, for each mRNA, there are three potential reading frames (panel B). Only one reading frame will be the correct one for protein synthesis. The ribosome must recognize and align the correct reading frame of the mRNA such that the correct codon sequences can be read. Small distinct tRNA molecules are tethered with specific amino acids and contain specific anticodons that complement mRNA codon sequences. The tRNA molecules can cycle on and off of the ribosome structure to hybridize with the correct codon sequences and chaperone the correct amino acid for peptide bond formation. The ribosome then serves as a ribozyme and mediates the peptidyl transferase activity to form the peptide bond. The mRNA is then shifted to reveal the next codon within the sequence and the process is repeated until the entire protein has formed. Panel C shows the codon chart for all of the possible combinations of three nucleotides. 64 possible codon combinations are possible using the 4 nucleotide possibilities, but only 20 amino acids are encoded during protein synthesis. Each codon is specific for a single amino acid. There is very little ambiguity within the code. However, there is redundancy within the code; i.e. many amino acids have more than one codon that encodes for that specific amino acid. To account for this redundancy, many tRNA molecules can recognize more than one codon using a single anticodon. This is known as degeneracy. Degeneracy usually occurs at the third position of the codon and is known as the wobble base position. Degeneracy helps to minimize the effects of mutations within the coding sequence, as mutations in the wobble base position will often lead to silent mutation– ie the mutation will still encode for the same amino acid. In addition, if comparing the polarity of amino acids encoded by the different codons, neighboring codons typically encode for amino acids with similar polarity, as shown in Figure $4$. This also helps to minimize the effects of mutations, by converting one amino acid within the sequence to one that has similar polarity. This type of mutation is more likely to cause less disturbance to the 3-dimensional structure of the resulting protein and retain biological function. Degeneracy within the genetic code also allows for differential A/T & G/C concentrations within species. For example, the G/C content of bacteria can range from as low as 30% to as high as 70%. Organisms living at high temperatures or extreme environments often have higher G/C content. This effectively increases the hydrogen bond strength between the strands of the DNA (G/C pairs have 3 H-bonds, whereas A/T pairs only have 2) and causes an increase in the melting temperature of the chromosome. Thus, the DNA is stable at a higher temperature or under more extreme ionic conditions, such as high salt. A more detailed discussion of how a single tRNA can function to recognize more than one codon is the topic of the next section. The Genetic Code is universal for almost all species alive on the planet, providing support for a single origin of life. Most deviations in the code occur within the mitochondria of eukaryotic species, as shown in Figure $5$. Transfer RNA (tRNA) Structure Transfer RNAs (tRNAs) are central players in translation, functioning as adapter molecules between the informational level of nucleic acids and the functional level of proteins. Typically, tRNA molecules are between 76 – 90 nucleotides long and show a highly conserved secondary and tertiary structure. They also show the highest amount of nucleotide modification of all types of RNA with modifications concentrated in two hotspots—the anticodon loop and the tRNA core region, where the D- and T-loop interact with each other, stabilizing the overall structure of the molecule, as shown in Figure $6$. These modifications can cause large rearrangements as well as local fine-tuning in the 3D structure of a tRNA. The life of a transfer RNA (tRNA) molecule starts with a series of important maturation steps that can vary in their sequential order from case to case. Leader and trailer sequences are removed by a set of endo- and exonucleases, and in several tRNA precursors, splicing reactions excise intronic sequences. Furthermore, in many organisms, the sequence CCA, which represents the site of amino acid attachment, is not encoded but has to be added post-transcriptionally by CCA-adding enzymes. While all primary tRNA transcripts are composed of the four standard RNA bases A, C, G, and U, many of these nucleotides are modified, altering their properties in very different ways. Currently, 93 post-transcriptional modifications are known, and the variety of their functions is at least similarly diverse and not fully understood. The complexity of such modifications ranges from simple methylations at the bases or the ribose to rather complex and large base hypermodifications, whose synthesis often requires a whole cascade of enzymatic reactions. Modifications can alter a tRNA’s shape in subtle ways, but can also lead to massive structural rearrangements. In addition, they ensure efficient translation by maintaining the anticodon loop structure and promoting correct codon-anticodon interactions. After maturation, tRNAs have multiple interaction partners in their life cycle, ranging from aminoacyl-tRNA-synthetases that are responsible for amino acid attachment, to translation factors, ribosomes, and mRNAs. Apart from synthetases, these interaction partners do not specifically act on one individual tRNA transcript or isoacceptor, but on all tRNAs, similar to the above-mentioned CCA-adding enzyme. Thus, despite a high sequence variation, a cell’s tRNAs show a well-conserved cloverleaf-like secondary structure that was originally discovered in 1965. The similar structure of all tRNA molecules allows them to bind to common protein synthesis machinery, such as the ribosome and CCA-adding enzymes. The cloverleaf consists of five parts: the acceptor stem (containing the tRNA’s 5′- and 3′-ends), the D-arm, the anticodon arm, the variable loop, and the TΨC-arm (T-arm). At the 3′-terminus, the tRNA carries the CCA sequence, required for aminoacylation, tRNA positioning in the ribosome, and translation termination. In a conserved network of tertiary interactions, mostly between D- and T-loop, tRNAs fold into an L-shaped three-dimensional structure, which was first solved by Kim et al. in 1974, as shown in Figure $6$ (Panel B). The anticodon and the amino acid-accepting CCA-ends are separated by the longest possible distance from each other. This conserved structure of a tRNA is essential for its recognition by the ribosome, other RNAs, and proteins and, consequently, for its functionality. For example, the CCA-adding enzyme uses the acceptor domain for substrate recognition, whereas aminoacyl-tRNA-synthetases use several recognition elements like anticodon, acceptor stem, or the discriminator position. Panel (A) shows the canonical cloverleaf secondary structure of cytosolic tRNAPhe from S. cerevisiae is shown with acceptor stem (blue), D-arm (green), anticodon arm (red), variable loop (purple) and TΨC-arm (yellow). The anticodon is labeled in grey, the discriminator base in orange and post-transcriptional modifications in red. Grey dashed lines indicate tertiary interactions based on structural data and the length of the RNA is indicated in parenthesis; Panel (B) shows the L-shaped tertiary structure of the cytosolic tRNAPhe from S. cerevisiae. Protein Data Bank entry (PDB): 1EHZ. The acceptor domain is composed of a stacked T-arm and acceptor stem, whereas D- and anticodon arm form the anticodon domain. The region where both domains come together and interact with each other via tertiary base pairing is also called the elbow region; Panel(C) shows the secondary structure of human mitochondrial tRNASer1, which lacks the whole D-arm; Panel (D) shows the secondary structure of the mitochondrial tRNAArg from the nematode Romanomermis culicivorax, which lacks both D- and T-arm. Instead, we find a so-called replacement loop. It represents the shortest tRNA found in vivo. Surprisingly, not all tRNAs fold into the canonical cloverleaf structure. Especially many mitochondrial tRNAs are reduced in length and sometimes completely lack the D- or T-arm as shown in Figure $6$, Panel C. In the mitochondria of nematodes, this situation is carried to an extreme, as tRNAs lacking one or even both arms seem to be the rule (Panel D). Figure $7$ shows an interactive iCn3D model of the yeast phenylalanine tRNA (1EHZ). The coloring, which matches those in Figure 6 above, is shown below: • Acceptor Stem - blue • D Arm - green • Anticodon= magenta • Variable Loop - pink • Tω C - yellow Post-transcriptional enzyme-catalyzed modification of tRNA occurs at many base and sugar positions and inﬂuence specific anticodon–codon interactions and regulates translation, its efficiency, and fidelity. This phenomenon of nucleoside modification is most remarkable and results in a rich structural diversity of tRNA of which over 93 modifications have been characterized. The variety of post-transcriptional modifications can be classified into two groups according to their complexity. The first group comprises the majority of modified bases, which have simple methylations at the ribose or base moiety that are usually introduced by a single enzymatic reaction. Simple modifications can be found at almost every position of the tRNA molecule with a high density in the tRNA core region, where tertiary interactions between D- and T-arm stabilize the three-dimensional fold, as shown in Figure $8$. The second group includes complex modifications, whose synthesis requires the sequential activity of several enzymes. Most often these hypermodified nucleosides are found in the anticodon of tRNAs, where they play a direct role in codon recognition and create what is known as the wobble base or wobble position. Panel (A) shows the colored tRNA structure shows the modification frequency of each base. The modification data were taken from the tRNAmodviz database and plotted on the crystal structure of tRNAPhe from S. cerevisiae. Blue-colored bases are rarely modified; red-colored bases are modification hotspots. tRNAs possess two regions with high modification levels—the anticodon loop (especially positions 34 and 37) and the core or elbow region, where D- and T-loop bases interact with each other and stabilize the tertiary fold. For some important positions, the chemical structure of the most frequent modification at this position is shown; Panel (B) shows the three-dimensional structure of pseudouridine at position 55 of tRNAPhe from S. cerevisiae. The additional H-bond donor at N1 interacts with the 5′-adjacent phosphates via a coordinated water molecule. The hydrogen bound to N1 was not resolved in the crystal structure. The ribose shows a stabilizing C3′-endo conformation. PDB: 1EHZ- Panel (C) shows the three-dimensional structure of D16 in the D-arm of tRNAiMet from Schizosaccharomyces pombe. The C5-C6 bond of dihydrouridine is reduced, which leads to a non-planar structure of the base. The ribose takes the less stable C2′ -endo conformation. PDB: 2MN0. A wobble base pair is a pairing between two nucleotides in RNA molecules that does not follow Watson-Crick base pair rules. The four main wobble base pairs are guanine-uracil (G-U), hypoxanthine-uracil (I-U), hypoxanthine-adenine (I-A), and hypoxanthine-cytosine (I-C), as shown in Figure $9$. To maintain the consistency of nucleic acid nomenclature, “I” is used for hypoxanthine because hypoxanthine is the nucleobase of the inosine nucleotide; nomenclature otherwise follows the names of nucleobases and their corresponding nucleosides (e.g., “G” for both guanine and guanosine – as well as for deoxyguanosine). The thermodynamic stability of a wobble base pair is comparable to that of a Watson-Crick base pair. Wobble base pairs are fundamental in RNA secondary structure and are critical for the proper translation of the genetic code. The wobble base position is usually the first position of the anticodon (read in the 5′ – 3′ direction), which aligns with the 3rd position of the mRNA codon. This helps to explain the degeneracy found within the genetic code as shown in Figure 3 above and Figure $10$. Degeneracy means that a single tRNA can recognize multiple different codons within mRNA. Panel (A) shows the interaction of the anticodon bases (34–36) of a tRNA with the corresponding bases of the mRNA codons (3, 2, 1). A wobble interaction is possible between codon base 3 and anticodon base 34. The latter is frequently modified and directs the wobble interactions with the third codon base; Panel (B) shows the standard genetic code is illustrated as a simple decoding table, 2-fold degenerate codon boxes are colored yellow, and 4-fold degenerate boxes are blue. Start and stop codons are colored green and red, respectively; Panel (C) shows a stereo image of the well-structured anticodon loop of tRNALys from E. coli. Modifications mnm5s2U34 and t6A37 prevent wrong base pairing inside the 7-nucleotide loop and promote the formation of the conserved U-turn motif. The stacked anticodon bases are located on the same side of the loop. PDB: 1FL8; Panel (D) shows a stereo image of a collapsed and unmodified anticodon loop of tRNATyr from Bacillus subtilis. Here, bases 32 and 38 as well as 33 and 37 interact with each other and the U-turn motif is missing. The anticodon bases are not ordered and are on opposite sides of the loop. PDB: 2LAC. A prominent example is tRNAIle carrying the anticodon UAU. In principle, this anticodon can read codons AUA (for isoleucine) and AUG (for methionine). Yet, it was shown in some instances that tRNAIle with unmodified UAU anticodon exists, but has a strong preference for its cognate AUA codon, while it rarely misreads AUG. In most organisms, however, tRNAIle carries the anticodon CUA. To avoid misreading of the methionine codon by this tRNA, C34 (position 1 of the anticodon) is modified to lysidine (k2C34, with the chemical structure shown in Figure 27.1.10, which restricts codon recognition to only AUA and thereby changes the amino acid identity of the tRNA from methionine to isoleucine. In the archaeal species, Haloarcula marismortui, Methanococcus maripaludis, and Sulfolobus solfataricus, this tRNAIle carries a different modification at C34, fulfilling the same purpose of restricting the interaction to AUA codons. Here, the original cytosine is modified at the C2-oxo position, which is replaced by agmatine (decarboxy-arginine), resulting in agmatidine (C+ or agm2C), as shown in Figure $11$. A complimentary modification is that of N4-acetylcytosine (ac4C34, whose chemical structure is shown in Figure 27.1.8) in the elongator-tRNAMet of E. coli, which prevents the recognition of the AUA isoleucine codon. In non-plant mitochondria, however, both AUG and AUA codons are read as methionine. Hence, mitochondrial tRNAMet (carrying the anticodon CAU) has to recognize both codon forms. This is achieved by the introduction of 5-formylcytidine (f5C, Figure 11, at position 34, a modification that pairs with both A and U residues at the corresponding codon position 3. The upper part of the image illustrates the systematic abbreviation of RNA modifications with N2,N2,2′-O-trimethylguanosine (m22Gm) as an example and also shows the atom numbering in the purine and pyrimidine rings as well as in the ribose. An abbreviation in front of the base letter describes a base modification, whereas letters after the base stand for ribose alterations. Superscripted numbers specify the position at the base and subscripted numbers indicate the frequency of identical modification at the same position. Abbreviations are as follows: ac—acetyl, acp—aminocarboxypropyl, chm—carboxyhydroxymethyl, cmo—oxyacetic acid, cmnm—carboxymethylaminomethyl, f—formyl, g—glycinyl, gal—galactosyl, hn—hydroxynorvalylcarbamoyl, ho—hydroxy, i—isopentenyl, inm—isopentenylaminomethyl, io—cis-hydroxyisopentenyl, m—methyl, man—mannosyl, mchm—carboxyhydroxymethyl methyl ester, mcm—methoxycarbonylmethyl, mcmo—oxyacetic acid methyl ester, mnm—methylaminomethyl, mo—methoxy, ncm—carbamoylmethyl, nm—aminomethyl, r(p) —5-O-phosphono-b-d-ribofuranosyl, s—thio, se—seleno, t—threonylcarbamoyl, tm—taurinomethyl. The Venn diagram summarizes data collected from the RNA modification database and contains the 93 post-transcriptional modifications that are found in tRNAs. Some examples mentioned throughout the text are shown with their chemical structure. Organisms vary in the number of tRNA genes in their genome. For example, the nematode worm C. elegans, a commonly used model organism in genetics studies, has 29,647 genes in its nuclear genome, of which 620 code for tRNA.The budding yeast Saccharomyces cerevisiae has 275 tRNA genes in its genome. The human genome has approximately 20,848 protein-coding genes, of which there are 497 nuclear genes encoding cytoplasmic tRNA molecules, and 324 tRNA-derived pseudogenes (tRNA genes thought to be no longer functional). Regions in nuclear chromosomes, very similar in sequence to mitochondrial tRNA genes, have also been identified (tRNA-lookalikes). These tRNA-lookalikes are also considered part of the nuclear mitochondrial DNA (genes transferred from the mitochondria to the nucleus). As with all eukaryotes, there are 22 mitochondrial tRNA genes in humans. Mutations in some of these genes have been associated with severe diseases like the MELAS syndrome. Cytoplasmic tRNA genes can be grouped into 49 families according to their anticodon features. These genes are found on all chromosomes, except the 22 and the Y chromosomes. High clustering on 6p is observed (140 tRNA genes), as well as on chromosome 1. Currently, it is unclear why there is so much redundancy within the genome to decode 61 of the 64 possible codons (the other three are stop codons used to terminate translation). Aminoacyl tRNA Synthetases Aminoacyl-tRNA synthetases (aaRSs) are universally distributed enzymes that catalyze the esterification of a tRNA to its cognate amino acid (i.e., the amino acid corresponding to the anticodon triplet of the tRNA according to the genetic code). The product of this reaction, an aminoacyl-tRNA (aa-tRNA), is delivered by elongation factors to the ribosome to take part in protein synthesis. Aminoacyl-tRNA synthetases are named after the aminoacyl-tRNA product generated, as such, methionyl-tRNA synthetase (abbreviated as MetRS) charges tRNAMet with methionine. In eukaryotes, an alternative nomenclature is often employed using the one-letter code of the amino acid (MARS), and a number is added to refer to the cytosolic (MARS1) or the mitochondrial (MARS2) variants. A total of 23 aaRSs have been described so far, one for each of the 20 proteinogenic amino acids (except for lysine, for which there are two) plus pyrrolysyl-tRNA synthetase (PylRS) and phosphoseryl-tRNA synthetase (SepRS), enzymes with a more restricted distribution that are only found in some bacterial and archaeal genomes. It is also worth noting that in eukaryotes the protein synthesis machinery of mitochondria and chloroplasts generally utilize their own, bacterial-like sets of synthetases and tRNAs that are distinct from their cytosolic counterparts. The aminoacyl-tRNA synthetases catalyze a two-step reaction that leads to the esterification of an amino acid to the 3’ end of a tRNA along with the hydrolysis of one molecule of ATP, yielding aminoacyl-tRNA, AMP, and PPi. In the first step, termed amino acid activation, both the amino acid and ATP bind to the catalytic site of the enzyme, triggering a nucleophilic attack of the α-carboxylate oxygen of the amino acid to the α-phosphate group of the ATP, condensing into aminoacyl-adenylate (aa-AMP), which remains bound to the enzyme, and PPi, which is expelled from the active site, as shown in Figure $12$. Although tRNA is usually not required for this first step, certain synthetases do require the tRNA species for productive amino acid activation. In the second part of the reaction, either the 2′- or 3′-hydroxyl group of the terminal adenine nucleotide attacks the carbonyl carbon of the adenylate, forming aminoacyl-tRNA and AMP (Figure 27.1.12 B). While the two-step aminoacylation reaction is universally conserved, the aaRSs that catalyze it show extensive structural, and in some instances functional, diversity. The 23 known aaRSs can be divided into two major classes based on the architecture of their active sites (Class I and Class II). In class I synthetases, the catalytic domain bears a dinucleotide or Rossman fold (RF) featuring a five-stranded parallel β-sheet connected by α-helices and is usually located at or near the N-terminus of the protein. This RF contains the highly conserved motifs HIGH and KMSKS, separated by a connecting domain termed connective peptide 1 (CP1), as shown in Figure $13$ (panel A). Class II active site architecture is organized as seven-stranded β-sheets flanked by α-helices and features three motifs that show a lesser degree of conservation than those in class I (panel B). Both classes also exhibit pronounced differences in their modes of substrate binding. Class I aaRSs bind the minor groove of the tRNA acceptor stem (with the exceptions of TrpRS and TyrRS) and aminoacylate the 2’-OH of the ribose of the terminal adenosine, while class II approach tRNA from the major groove and transfer amino acid to the 3’-OH (except PheRS). The mode of ATP binding is also different between both classes, being bound in an extended configuration in class I, while class II binds a bent configuration with the γ-phosphate folding back over the adenine ring. The kinetics of the aminoacylation reaction can also be used as a distinctive mechanistic signature, as aminoacyl-tRNA release is the rate-limiting step for class I enzymes (except for IleRS and some GluRS) while for class II it is the amino acid activation rate instead. Panel (a) shows the E. coliCysRS:tRNACys complex. The CP domain (red) and Rossmann fold catalytic domain (green), stem contact fold (cyan), helical bundle domain (magenta), and anticodon binding domain (orange) of CysRS are shown in a ribbon diagram; Panel (b) A single monomer of the homodimeric E. coli ThrRS:tRNAThr complex. The two N-terminal domains (red), catalytic domain (green), linker (cyan), and anticodon binding domain (orange) of ThrRS are shown in a ribbon diagram. For both structures, the tRNAs are shown in a stick diagram (blue) with a trace of their backbone (yellow). Figure $14$ shows an interactive iCn3D model of the Class I and II aminoacyl-tRNA synthetases Class I E. Coli Cysteinyl-tRNA synthetase -tRNA(Cys) (1U0B) class II E. coli threonyl-tRNA synthase - tRNA(Thr) (1QF6) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...P4oRWxKvJHFDL6 (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...6aXwfqXGCpD7c6 The color coding is analogous to Figure $13$ with the tRNA shown in gray. For the Class II E. coli threonyl-tRNA synthase - tRNA(Thr), key conserved amino acids in the active site are shown in CPK-colored sticks and labeled. The anticodon in the tRNA is shown in colored sticks as well. Note the large conformational change in the anticodon loop. The E. coli threonyl-tRNA synthetase is interesting in that it represses the translation of its own mRNA. A Zn2+ ion is involved in binding specificity for the amino acid. It is coordinated by H385, H511, and a water molecule (not shown). R363 interacts with the alpha phosphate, while F379 and R520 align on both sides of the adenine rig. D383 interacts with the amine group of the substrate threonine. To ensure the faithful translation of the genetic message, synthetases must identify and pair particular tRNAs with their cognate amino acid which relies on the proper recognition of both substrates. This can prove extremely challenging for the synthetases as not only have they to discriminate the correct tRNA isoacceptor amongst a set of other tRNAs very similar in structure and chemical composition but also be able to select the cognate amino acid amidst an extremely large pool of similar amino acids, both proteinogenic and non-proteinogenic. The evolutionary pressure to maintain fidelity has driven aaRSs to develop an elevated specificity for their substrates, both the tRNA and the amino acid. In addition, some synthetases have evolved editing activities that specifically target and hydrolyze misactivated amino acids and/or misacylated tRNAs. To date, editing activity has been described in 10 out of the 23 aaRSs. In class I synthetases, this activity is located in the highly conserved CP1 domain, although in some enzymes such as MetRS and LysRS the editing activity resides in the catalytic site. In class II synthetases, however, the editing domains are more idiosyncratic. Editing mechanisms can be divided into two categories, pre- or post-transfer editing, with regard to the editing taking place before or after the transfer of the amino acid to the tRNA, as shown in Figure $15$. Pre-transfer editing has been described in both class I and class II aaRSs and takes place after aa-AMP synthesis but before the aminoacyl moiety is transferred to the tRNA. Although the tRNA does not participate in the reaction itself, it has been reported that tRNA binding promotes editing activity in some aaRSs and is a requirement in IleRS and LeuRS. Pre-transfer editing can follow two main pathways. The first one is the selective release of the aa-AMP to the cytosol, where the labile phosphoester bond is spontaneously hydrolyzed. The second route involves the enzymatic breakdown of the product and may happen either in the active site or in an independent editing site. Post-transfer editing takes place after the transfer of the amino acid to the tRNA and involves the hydrolysis of the ester bond, in a domain separated from the active site. The specific mechanism of editing is idiosyncratic to each synthetase but in general, once formed the aa-tRNA triggers a conformational change, and the 3’ terminus containing the aa is translocated from the active site to the editing site, sometimes traversing distances as large as 40 Å. As the core of the tRNA remains bound to the enzyme, this translocation often involves a rearrangement of the 3’ terminus to relocate to the editing site. Ribosome Structure The ribosome is a highly conserved molecular machine. In all organisms, it is composed of two unequal subunits, which consist of a distinct set of ribosomal RNA (rRNA) and ribosomal proteins (RPs) that combine to form a large nucleoprotein complex. The ribosome structures in all living organisms harbor three different tRNA binding sites: The A-site, where decoding occurs and the correct aminoacyl-tRNA (aa-tRNA) is selected based on the mRNA codon displayed, the P-site, which carries the peptidyl-tRNA, and the E-site, which binds exclusively deacetylated tRNAs that are exiting the ribosome. Thus, during translation the tRNA moves from the A-site through the P- and E-site, where it leaves the ribosome, as shown in Figure $16$. The mRNA (shown in purple) is assembled between the small subunit and the large subunit of the ribosome (shown in green). tRNA molecules (shown in red) that are loaded with their cognate amino acid (shown in pink) are transitioned through the A-P-E sites of the ribosome during the elongation phase of translation. Movement of the tRNA molecules also shifts the position of the mRNA causing the next three codon bases to line up in the A-site of the ribosome. The catalytic peptidyl transferase activity occurs when the tRNA molecules are bound in the A- and P-sites, transferring the nascent peptide to the incoming tRNA molecule (Fig. 27.1.15). Ribosomes are ribozymes because the catalytic peptidyl transferase activity that links amino acids together is performed by the rRNA.the complexity of the ribosome structure is reflected in the process of protein synthesis, which can be intersected into three major steps: initiation, elongation, and termination/recycling. Ribosomes are either free-floating in the cytoplasm or they can be associated with the intracellular membranes that make up the rough endoplasmic reticulum (rER). Proteins translated into the rER will often be transported out of the cell or embedded into the plasma membrane. These processes are illustrated in Figure $17$. Figure $17$: A ribosome translating a protein that is secreted into the endoplasmic reticulum. Figure from: Bensaccount Ribosomes from bacteria, archaea, and eukaryotes in the three-domain system resemble each other to a remarkable degree. They differ in their size, much of the rRNA sequence, and the ratio of protein to RNA. Figure $18$ shows the eukaryotic rRNA from the large subunit of the ribosome with highly conserved nucleotide elements (>90% sequence identity) within all of the domains of life, termed universal CNEs or uCNEs indicated. The differences in sequence and structure between eukaryotes and prokaryotes allow some antibiotics to kill bacteria by inhibiting their ribosomes while leaving human ribosomes unaffected. In all species, more than one ribosome may move along a single mRNA chain at one time (as a polysome), each “reading” its sequence and producing a corresponding protein molecule. In this way, many proteins can be translated from a single mRNA molecule. Within bacteria, translation is also coupled with transcription, as the two processes are not physically separated from one another. This is illustrated in Figure $19$. In eukaryotic organisms, polysomes form during translation. However, transcription and translation are not coupled, as the processes are separated into the nucleus and cytoplasm, respectively. The mitochondrial ribosomes of eukaryotic cells functionally resemble many features of those in bacteria, reflecting the likely evolutionary origin of mitochondria. Prokaryotic Ribosome Structure Prokaryotic ribosomes have a mass of about 2500 kDa and a size of 70S (or Svedberg units: A Svedberg unit is a measure of the sedimentation rate in a centrifuge and thus is representative of size). A complete ribosome (70S) can be dissociated into a large subunit (50S) and a small subunit (30S), as shown in Figure $20$. The small subunit is formed by the interactions of 21 different proteins and a 16S RNA molecule, whereas the large subunit contains 34 different proteins and two RNA molecules, a 23S, and a 5S species. The rate-limiting step in protein synthesis is the formation of the 70S initiation complex which will be discussed in detail in the next section. Figure $21$ shows an interactive iCn3D model of the E. Coli ribosome (7K00). The RNA is shown in a faint trace backbone. The proteins are shown as cartoons with different colors. Note the large number of Mg2+ ions. The structure was determined using cryo-EM at high resolution. Some ribosomal proteins have isopeptide bonds (using side chain amine and carboxyl groups) as well as some thioamide backbone replacements for the usual amide links. Eukaryotic Ribosome Structure Eukaryotic ribosomes are larger than their prokaryotic counterparts at approximately 80S (although there is some modest variation between eukaryotic species). Human cytosolic ribosomes are composed of a large subunit (60S) that contains the 28S, 5.8S, and 5S rRNAs and 47 ribosomal proteins (RPs), and a small subunit (40S) that contains the 18S rRNA and 33 RPs. The assembly of eukaryotic ribosomal subunits starts in the nucleolus, where RNA polymerase I transcribes the major rRNA precursor (a 45S pre-RNA), from which, after processing and removal of the external and internal transcribed spacers (ETS and ITS), the mature rRNAs are generated, as shown in Figure $22$. The pre-RNA is modified during transcription by small nucleolar ribonucleoproteins (snoRNPs), processed by RNA nucleases, and assembled with numerous RPs. After processing the rRNA precursor, the pre-40S and pre-60S subunits follow separate biogenesis routes. Here we will describe the assembly of the 60S subunit in more detail. Although the exact assembly of the 60S subunit is not currently known, a model has been postulated that suggests that in the nucleolus, after circularization of rRNA domains, early 60S assembly is carried out sequentially, as shown in Figure $23$. As the transcription of the pre-rRNA proceeds, the rRNA quickly develops a core secondary structure that promotes the interaction of key Assembly Factors (AFs) and RPs before transcriptional termination. Specifically, during this time, the 5.8S portion, ITS2, domains I and II, and partially domain VI are folded in the earliest observed intermediate (state A in Fig. 23). Thus, it appears that the solvent-exposed back side of the large subunit forms like an exoskeleton and construction proceeds by formation of the exit tunnel. This model agrees with a previously suggested model of hierarchical folding based on RP depletion phenotypes. The peptidyl transferase center (PET) is predicted to be one of the later folding steps in the process (state F in Fig. Fig 23) Although the very late-folding peptidyl transferase center is the evolutionarily oldest part of the ribosome, it is likely that folding the solvent side first brings the advantage of providing a stable scaffold for the developing 60S subunit. The folding and assembly of the 40S subunit follow a similar pattern. The two subunits remain unattached until activated in the cytoplasm through the binding of a mRNA transcript with the small subunit. This begins the formation of the initiation complex that will mark the start of the transcriptional process. Assembly of RPs and AFs to the nascent 35S rRNA precursor starts co-transcriptionally. Very early, the pre-rRNA is circularized as domain VI binds to domains I and II and the 5.8S portion of the precursor rRNA. The formation of the PET (displayed here as a black circle) starts with this circularization. Its maturation progresses as rRNA domains fold following this order: VI, V, III, and IV. Full assembly of the PET is only achieved when domain V is completely folded as observed in state F. After that, only a few additional steps need to occur before the particles are exported to the cytoplasm, where they undergo final maturation.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/03%3A_Unit_III-_Information_Pathway/26%3A_Protein_Metabolism/26.02%3A_Protein_Synthesis.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Prokaryotic Initiation The small subunit of the ribosome (the 30S) interprets the genetic information by selecting aminoacyl-tRNAs cognate to the mRNA codons in the decoding center. The large subunit (the 50S) carries the catalytic peptidyl transferase center where amino acids are polymerized into a protein. Small and large subunits unite together at the start codon of a gene to form the 70S ribosome and dissociate again at the stop codon upon completing the synthesis of the encoded protein. This process consists of three phases: initiation, elongation, and termination. In this section, we will focus on the initiation of translation. In bacteria, the initiation phase of protein synthesis involves a limited number of “actors”. Aside from the two ribosomal subunits, key roles are played by the initiator tRNAfmet, the translation initiation region (TIR) of the mRNA, and three protein factors – the initiation factors (IFs) IF1, IF2, and IF3 – that ensure speed and accuracy to the overall process. The initiator tRNAfmet contains a methionine residue that has been enzymatically modified to contain an N-terminal formyl group, as shown in Figure $1$. fMet is only used for the initiation of protein synthesis and is thus found only at the N-terminus of the protein. Unmodified methionine is used during the rest translation. Once protein synthesis is completed, the formyl group on methionine may be removed by peptide deformylase, and on occasion, the entire methionine residue can be further removed by the enzyme methionine aminopeptidase. The TIR sequence within the mRNA contains the start codon and usually an upstream untranslated region that interacts with the small subunit of the ribosome. The bacterial cell produces and expresses a plethora of different mRNAs with different TIR sequences and structures; the efficiency by which these individual transcripts are translated depends not only upon their abundance and stability but also upon the nature of TIR. Thus, unlike the other aforementioned actors that represent constants, the mRNA TIRs represent essentially the only variable in the process of mRNA initiation site selection and can affect translation efficiency. Although the triplet AUG is by far the most frequent initiation codon found in TIRs, other initiation triplets (i.e., GUG, UUG, AUU, AUC, and AUA) are found in bacteria and the central U is the only universally conserved base of the start codon. Among the aforementioned triplets, those having a 3′-G (i.e., AUG, GUG, and UUG) are recognized equivalently and most efficiently by IF3 during the initiation complex formation. Another important characteristic of a large number of bacterial mRNA TIRs is the presence of a Shine–Dalgarno (or SD) sequence that is complementary to the 3′ end sequence of 16S rRNA (the anti-SD sequence or aSD). The SD sequence, when present, is usually at an optimal distance of 4–9 nucleotides upstream of the initiation codon, as shown in Figure $2$. While the SD sequence plays an important role in the efficient translation of many mRNA transcripts, it is not essential. Many other mRNA sequences fully lack an SD sequence but are still efficiently transcribed. Thus, the SD sequence is only one example of TIR mechanisms that can play an important role in mRNA binding with the small subunit of the ribosome. Prokaryotic mRNA sequences often share a highly conserved sequence upstream of the start codon known as the Shine-Dalgarno sequence. This consensus sequence is complimentary to the 3′-end of the 16S rRNA sequence in the small subunit of the ribosome. It is an important feature for the binding and docking of many mRNA molecules with the small ribosomal subunit during transcription initiation. The three protein initiation factors, IF1, IF2, and IF3, determine the kinetics and fidelity of the overall initiation process. The three IFs are bound, one copy each, to specific sites of the 30S subunit where they assist with the formation of the initiation complex and assembly of the 70S ribosome. As noted above, the initiator tRNA is first aminoacylated with methionine whose α-NH2 group is eventually blocked by a specific formyl transferase (TMF) to produce a tRNAfmet molecule. This modification prevents interaction with the elongation factor EF-Tu (which we will see plays an important role in the elongation phase of translation, but not the initiation phase!) Blocking EF-Tu binding ensures instead the recognition and binding of tRNAfmet by initiation factor IF2, effectively docking it with the 30S subunit. Furthermore, tRNAfmet binds with high affinity to the ribosomal P-site, unlike all other aminoacyl-tRNAs that bind to the A-site in a ternary complex with EF-Tu and GTP (details will be presented in the next section). In the P-site, the initiator tRNA must be recognized as correct by the other initiation factors IF3 and IF1. To form the 30S initiation complex, IF3, and IF2 are the first factors to bind to the 30S subunit forming an unstable 30S-IF3-IF2 complex, as shown in Figure $3$ (panel A). The binding of IF1 causes a conformational change in the 30S subunit stabilizing the complex and allowing the recruitment of the tRNAfmet by IF2. Notably, IF1 binds in the A site of the 30S subunit, where it contacts ribosomal protein S12. Recruitment of the tRNAfmet can also stabilize the mRNA interactions with the 30S subunit through the formation of hydrogen bonds between the codon of the mRNA and the anticodon of the tRNAfmet. Note that the binding of mRNA to the 30S subunit is IF-independent and can take place at any time during the 30S assembly process. Two potential routes of mRNA association are shown Figure $3$, panel A, where the mRNA is assembled either prior to or after tRNAfmet recruitment. Step 1: a vacant 30S ribosomal subunit binds IF3 and IF2. Step 2: IF1 binds to the 30S subunit in the presence of both IF3 and IF2. Steps 3 and 3′: in the presence of all three factors tRNAfmet is recruited. Steps 4 and 4′:the mRNA is bound with different on and off rates depending on its TIR structure; mRNAs with strong secondary structures are bound more slowly than those having little or no secondary structure. Step 5: mRNAs containing secondary structures must be unfolded in a process that is facilitated by IF2 bound to GTP and antagonized by IF3. Step 6: the isomerization of the 30S pre-IC allows the P-site codon–anticodon interaction to yield a more stable 30SIC from which mRNA and fMet-tRNA are more stably bound. Step 7: a 30SIC, containing IF1, IF2·GTP, IF3 and mRNA whose initiation triplet is P-site decoded by fMet-tRNA, is docked by a 50S subunit. Step 8: upon contact with the 50S subunit, the GTPase function of IF2 is activated and GTP is rapidly hydrolyzed leaving GDP+Pi bound to IF2. Step 9: this reversible conformational transition represents the last kinetic checkpoint of translation initiation fidelity by IF3 and IF1, as IF3 and IF1 dissociate from the complex. Step 10: The first-order isomerization of the IF2-GDP structure causes a shift in the ribosome structure that represents the rate-limiting step in 70SIC formation. Step 11: Pi is dissociated from IF2·GDP. Step 12: IF2 leaves the ribosome (or moves away from the A-site) clearing the way for EF-Tu binding. Step 13: the EF-Tu·GTP·aminoacyl-tRNA complex binds to the 70SIC and through a number of steps (not represented here) delivers to the ribosomal A-site the aminoacyl-tRNA encoded by the second mRNA codon. Step 14:the tRNAfMet bound in the P-site of the peptidyl transferase center donates its formyl-methionine to the A-site-bound aminoacyl-tRNA to yield the initiation dipeptide fMet-aa. Initiation is then complete and the elongation phase can begin. Following the recruitment of the mRNA and the tRNAfmet to the 30S initiation complex loaded with the IF2, IF3 and IF1 initiation factors, the 50S subunit is very rapidly docked to yield an initially unstable 70S initiation complex (Fig 27.2.3 b). It should be noted that the IF2 protein is a GTP hydrolase enzyme and, as such, binds with the cofactor GTP prior to the recruitment of the 50S subunit. Contact between the IF2 GTPase activating center with the 50S subunit causes the rapid hydrolysis of GTP to GDP + Pi. The formation of the 70S complex causes the dissociation of the initiation factors. IF2 is the last factor to be dissociated, leaving the ribosome after having positioned tRNAfMet in the P-site of the 70S initiation complex. It must be placed in the correct orientation to facilitate peptide bond formation. GDP and Pi also dissociate from the complex with the removal of IF2. The elongation factor, EF-G is then free to chaperone the first tRNA into the A-site and the first peptide bond is formed (Step 13 of Fig. 27.2.3 b). This marks the beginning of the elongation phase of protein synthesis. Eukaryotic Initiation Eukaryotic translation initiation is more complex than prokaryotic systems and requires the actions of at least 11 eukaryotic initiation factors (eIFs), plus additional auxiliary factors (Table $1$). We will not cover the action of all these eIFs in detail here, but rather focus a few key steps as outlined in Figure $4$. Table $1$: Comparison of Prokaryotic and Eukaryotic Translation Initiation Factors First, the initiator tRNAi is recruited to the small ribosomal subunit (40S) to form a ternary complex with the GTP-bound eukaryotic initiation factor 2 (eIF2). Formation of this 43S pre-initiation complex is strongly enhanced by additional factors, such as eIF3. eIF3 also interacts with the eIF4F complex, which consists of three other initiation factors: eIF4A, eIF4E, and eIF4G. eIF4G is a scaffolding protein that directly associates with both eIF3 and the other two components. eIF4E is the 5′-cap-binding protein. Binding of the mRNA cap by eIF4E is often considered the rate-limiting step of cap-dependent initiation, and the concentration of eIF4E is a regulatory nexus of translational control. Certain viruses cleave a portion of eIF4G that binds eIF4E, thus preventing cap-dependent translation to hijack the host machinery in favor of the viral (cap-independent) messages. eIF4A is an ATP-dependent RNA helicase that aids the ribosome by resolving certain secondary structures formed along the mRNA transcript. The poly(A)-binding protein (PABP) also associates with the eIF4F complex via eIF4G, and binds the poly-A tail of most eukaryotic mRNA molecules. This protein has been implicated in playing a role in circularization of the mRNA during translation.The 43S preinitiation complex accompanied by the protein factors moves along the mRNA chain toward its 3′-end, in a process known as ‘scanning’, to reach the start codon (typically AUG). After recognition of the start codon, the large ribosomal subunit (60S) assembles to form the 80S initiation complex, which is ready for elongation. Alternatively, under distinct conditions or on certain transcripts internal initiation can occur in a cap-independent manner at so called internal ribosome entry sites (IRES). Eukaroytic translation initiation is shown in Figure $4$. This is a simplified diagram of eukaryotic translation initiation detailing some of the eIFs involved in the process. eIF2 is critical for recruiting the initiation tRNAi to the 40S subunit. eIF3 enhances the activity of eIF2 and also promotes the binding of the 43S pre-initiation complex with the mRNA. eIF3 binds with the mRNA through the interaction of the eIF4 factors and causes the scanning of the pre-initiation complex down the mRNA to locate the start codon (usually AUG). Poly A Binding Proteins (PABPs) bind with the polyA tail sequence of the mRNA and also interact with the eIF4 factors causing the circularization of the mRNA. As seen in prokaryotic systems with the favored Shine Dalgarno sequence upstream of the start codon within the mRNA sequence, there are also preferred nucleotide sequences within the local vicinity of the start codon in eukaryotic mRNAs, as well. In eukaryotic mRNA, this is known as the Kozak sequence (Figure $5$). The sequence was originally defined as 5′-`(gcc)gccRccAUGG-3` where: 1. The underlined nucleotides indicate the translation start codon, coding for Methionine. 2. upper-case letters indicate highly conserved bases, i.e. the ‘AUGG’ sequence is constant or rarely, if ever, changes. 3. ‘R’ indicates that a purine (adenine or guanine) is always observed at this position (with adenine being more frequent according to Kozak rules) 4. a lower-case letter denotes the most common base at a position where the base can nevertheless vary 5. the sequence in parentheses (gcc) is of uncertain significance. The AUG is the initiation codon encoding a methionine amino acid at the N-terminus of the protein. (Rarely, GUG is used as an initiation codon, but methionine is still the first amino acid as it is the met-tRNA in the initiation complex that binds to the mRNA). Variation within the Kozak sequence alters the “strength” of the translational start site. Kozak sequence strength refers to the favor ability of initiation, affecting how much protein is synthesized from a given mRNA. This is shown in Figure $5$. The Elongation Phase of Translation Both prokaryotic and eukaryotic elongation phases of transcription utilize similar elongation factors during the process. Table $2$ provides a summary of their functions. Table $2$: Comparison of Prokaryotic and Eukaryotic Translation Elongation Factors Prokaryotic Elongation The prokaryotic elongation phase of transcription requires the activity of three primary elongation factors (EFs), EF-Tu, EF-Ts, and EF-G. During elongation, aminoacyl-tRNAs are delivered to the ribosome in the form of a ternary complex: the tRNA, a translational GTPase (in bacteria: EF-Tu or SelB), and a GTP molecule, as shown in Figure $6$. The tRNA decodes the information on the mRNA by forming hydrogen bonds (H-bonds) between codon and anticodon nucleobases. Remarkably, the free-energy difference between correct (cognate) and incorrect (near-cognate, non-cognate) base pairing alone does not explain the very high fidelity of decoding. Rather, high fidelity is achieved by a two-step decoding process: initial selection leading to GTPase activation and proofreading. In addition to the free-energy difference, kinetic effects contribute to the discrimination. The GTP hydrolysis rate is increased and tRNA rejection rate is decreased by the recognition of the correct codon. Small-subunit nucleotides A1492 and A1493 adopt a flipped-out conformation in the presence of a tRNA and, in this conformation, the tRNA anticodon hydrogen bonds with the codon of the mRNA forming a mini-helix structure as shown in Figure $7$. The flipped out nucleotides A1492, A1493 along with G530 were found to shield the codon–anticodon base pairs from solvent. This shielding of near-cognate base pairs from the solvent is incomplete causing an increase in the free-energy difference between near-cognate and cognate base pairs and more flexibility within the docking region. This reduces the strength of hydrogen bonding between a non-cognate tRNA and causes the inappropriate tRNA to leave the A-site before peptide bond formation can occur. This increases the fidelity and discrimination of tRNA selection, such that only the correct cognate tRNA is incorporated into the A-site. Interestingly aminoglycosides, a class of antibiotics, bind to the decoding center and lock nucleotides A1492/A1493 in the flipped-out conformation as shown in Figure $8$. In this way aminoglycosides promote the accommodation of near-cognate, thus wrong, tRNAs into proteins during synthesis causing wide-spread mutagenesis. This is toxic to the bacteria and leads to bacterial cell death. After GTP hydrolysis, the GTPase EF-Tu dissociates from the tRNA. At this point, the EF-Tu is tightly bound with a molecule of GDP and cannot release GDP on its own to be recycled for a second round of tRNA chaperoning. The recharging of EF-Tu is executed by the Elongation Factor Thermo stable (EF-Ts), as shown in Figure $9$. The binding of EF-Ts with EF-Tu-GDP causes a conformational change in EF-Tu that allows the release of GDP. The binding of a new molecule of GTP with the EF-Tu protein causes the dissociation of EF-Ts and fully recharges EF-Tu. Dissociation of EF-Tu from the ribosome allows the tRNA to move into to the peptidyl transferase center (A-site) on the large subunit. At the core of ribosomal translation is the catalysis of peptide bond formation, as shown in Figure $10$. The current reaction models point to a substrate assisted mechanism. Simulations indicate that the transition state forms due to extensive hydrogen bonding with water molecules and the surrounding rRNA bases and that the C-O bond cleavage takes place after C-N bond formation. Peptide bond formation results in the transition of the amino acid docked on the P-site tRNA to the nascent growing peptide that is now held on the tRNA in the A-site. Note that this mechanism causes the nascent growing peptide to always grow in the N- to C- direction. Once the peptide bond is formed, the ribosome needs to translocate down the mRNA to make the next mRNA codon available within the A-site. This also requires the shifting of the tRNA molecules, such that the tRNA in the A-site (which is now tethered to the nascent peptide) shifts to the P-site. The P-site tRNA (which is now empty) shifts to the E-site, and if there was an empty tRNA in the E-site, it will shift to exit the ribosome. Shifting the tRNAs and mRNAs within the ribosome core requires the action of the EF-G elongation factor, as shown in Figure $11$. Figure $12$ shows an interactive iCn3D model of the eukaryotic 80S ribosome with bound mRNA and tRNAs (6GX3) . (Very long load time) color coding as follows: • gray: protein tube • coiled coils: RNA trace • black spheres: mRNA • dark blue spheres: ap/P-site tRNA • cyan spheres: pe/E-site-tRNA • green spheres: Mg2+ EF-G is a GTP hydrolase protein that binds to the A-site of the ribosome. The EF-G protein has high flexibility that enables it to act as a hinge. Folding of EF-G is dependent on GTP hydrolysis. Thus, when binding to the ribosome, the fast hydrolysis of GTP acts as a power stroke folding the EF-G protein and causing a conformation shift in the ribosome that enables the translocation of the tRNA residues and the mRNA. Translocation of tRNAs is accompanied by large-scale collective motions of the ribosome: relative rotation of ribosomal subunits and L1-stalk motion, as shown in Figure $13$. The L1 stalk, which is a flexible part of the large subunit, is in contact and moves along with the tRNA from the P to the E site. Once in the EF-G-GDP form, the factor quickly dissociates from the ribosome, opening up the A-site for the recruitment of the next aa-tRNA molecule. The elongation cycle will continue to be repeated until a termination codon is reached. Eukaryotic Elongation The elongation phase in eukaryotic translation is very similar to prokaryotic elongation. Essentially, the mRNA is decoded by the ribosome in a process that requires the selection of each aminoacyl-transfer RNA (aa-tRNA), which is dictated by the mRNA codon in the ribosome acceptor (A) site, peptide bond formation and movement of both tRNAs and the mRNA through the ribosome, as shown in Figure $14$. A new amino acid is incorporated into a nascent peptide at a rate of approximately one every sixth of a second. The first step of this process requires guanosine triphosphate (GTP)-bound eukaryotic elongation factor 1A (eEF1α) to recruit an aa-tRNA to the aminoacyl (A) site, which has an anticodon loop cognate to the codon sequence of the mRNA. The anticodon of this sampling tRNA does not initially base-pair with the A-site codon. Instead, the tRNA dynamically remodels to generate a codon-anticodon helix, which stabilizes the binding of the tRNA-eEF1α-GTP complex to the ribosome A site. This helical structure is energetically favorable for cognate or correct pairing, and so discriminates between the non-cognate or unpaired and single mismatched or near-cognate species. This is important for the accuracy of decoding since it provides a mechanism to reject a non-cognate tRNA that carries an inappropriate amino acid. The pairing of the tRNA and codon induces GTP hydrolysis by eEF1α, which is then evicted from the A site. In parallel with this process, the ribosome undergoes a conformational change that stimulates contact between the 3′ end of the aa-tRNA in the A site and the tRNA carrying the polypeptide chain in the peptidyl (P) site. The shift in position of the two tRNAs [A to the P site and P to the exit (E) site] results in ribosome-catalyzed peptide bond formation and the transfer of the polypeptide to the aa-tRNA, thus extending the polypeptide by one amino acid. The second stage of the elongation cycle requires a GTPase, eukaryotic elongation factor 2 (eEF2), which enters the A-site and, through the hydrolysis of GTP, induces a change in the ribosome conformation. This stimulates ribosome translocation to allow the next aa-tRNA to enter the A-site, thus starting a new cycle of elongation. This schematic represents the four basic steps of eukaryotic translation elongation. The ribosome contains three tRNA-binding sites: the aminoacyl (A), peptidyl (P) and exit (E) sites. In the first step of peptide elongation, the tRNA, which is in a complex with eIF1 and GTP and contains the cognate anticodon to the mRNA coding sequence, enters the A site. Recognition of the tRNA leads to the hydrolysis of GTP and eviction of eEF1 from the A site. In parallel, the deacylated tRNA in the E site is ejected. The A site and the P site tRNAs interact, which allows ribosome-catalyzed peptide bond formation to take place. This involves the transfer of the polypeptide to the aa-tRNA, thus extending the nascent polypeptide by one amino acid. eIF5A allosterically assists in the formation of certain peptide bonds, e.g. proline-proline. eEF2 then enters the A site and, through the hydrolysis of GTP, induces a change in the ribosome conformation and stimulates translocation. The ribosome is then in a correct conformation to accept the next aa-tRNA and commence another cycle of elongation. The Ribosome as a Ribozyme Protein synthesis from a mRNA template occurs on a ribosome, a nanomachine composed of proteins and ribosomal RNAs (rRNA). Peptide bond formation occurs when another tRNA-amino acid molecule binds to an adjacent codon on mRNA. The tRNA has a cloverleaf tertiary structure with some intrastranded H-bonded secondary structure. The last three nucleotides at the 3' end of the tRNA are CpCpA. The amino acid is esterified to the terminal 3'OH of the terminal A by a protein enzyme, aminoacyl-tRNA synthetase. Covalent amide bond formation between the second amino acid to the first, forming a dipeptide, occurs at the peptidyl transferase center, located on the larger ribosomal subunit (50S and 60S in bacteria and eukaryotes, respectively). The ribosome ratchets down the mRNA so the dipeptide-tRNA is now at the the P or Peptide site, awaiting a new tRNA-amino acid at the A or Amino site. Figure $15$: below shows a schematic of the ribosome with bound mRNA on the 30S subunit and tRNAs covalently attached to amino acid (or the growing peptide) at the A and P site, respectively. A likely mechanism (derived from crystal structures with bound substrates and transition state analogs) for the formation of the amide bond between a growing peptide on the P-site tRNA and the amino acid on the A-site tRNA is shown in Figure $16$. Catalysis does not involve any of the ribosomal proteins (not shown) since none is close enough to the peptidyl transferase center to provide amino acids that could participate in general acid/base catalysis, for example. Hence the rRNA must act as the enzyme (i.e. it is a ribozyme). Initially it was thought that a proximal adenosine with a perturbed pKa could, at physiological pH, be protonated/deprotonated and hence act as a general acid/base in the reaction. However, none was found. The most likely mechanism to stabilize the oxyanion transition state at the electrophilic carbon attack site is precisely located water, which is positioned at the oxyanion hole by H-bonds to uracil 2584 on the rRNA. The cleavage mechanism involves the concerted proton shuffle shown below. In this mechanism, the substrate (Peptide-tRNA) assists its own cleavage in that the 2'OH is in position to initiate the protein shuttle mechanism. (A similar mechanism might occur to facilitate hydrolysis of the fully elongated protein from the P-site tRNA.) Of course all of this requires perfect positioning of the substrates and isn't that what enzymes do best? The main mechanisms for the catalysis of peptide bond formation by the ribosome (as a ribozyme) are intramolecular catalysis and transition state stabilization by the appropriately positioned water molecule. Translation Termination Prokaryotic Termination Termination of bacterial protein synthesis occurs when a stop codon is presented in the ribosomal A-site and is recognized by a class I release factor, RF1 or RF2. These release factors (RFs) have different but overlapping specificities, where RF1 reads UAA and UAG and RF2 reads UAA and UGA, with strong discrimination against sense codons. The RFs are multi-domain proteins, where binding and stop codon recognition by domain 2 at the decoding site causes the universally conserved GGQ motif of domain 3 to insert into the A-site of the PTC, some 80 Å away from the decoding site. This event triggers hydrolysis of the peptidyl-tRNA bond in the P-site of the PTC, and the nascent peptide chain can then be released via the ribosomal exit tunnel, as shown in Figure $14$. After peptide release, RF1 and RF2 dissociate from the post-termination complex. The dissociation is accelerated by a class II release factor called RF3, which functions as a translational GTPase that binds and hydrolyses GTP in the course of termination. While RF3 increases the efficiency of peptide hydrolysis, it is not an essential protein for the process. In gene knockout studies, RF3 is dispensable for growth of Escherichia coli, and its expression is not conserved in all bacterial lineages. For example, RF3 is not present in the thermophilic model organisms of the Thermus and Thermatoga genera and in infectious Chlamydiales and Spirochaetae. This means that both RF1 and RF2 are capable of performing a complete round of termination independently of RF3 or that other GTPases from the elongation or initiation phases of translation can compensate for the action of RF3. The release factors RF1 and RF2 acquire an open conformation (Figure $17$ on the 70S ribosome, which is distinctly different from the closed conformation observed in crystal structures of free RFs. The conformational equilibrium of the free RFs in solution shows that this open conformation is dominating at about 80%. During peptide hydrolysis, the RF factors cause rotational and conformational changes within the ribosome that allow the binding of a ribosome recycling factor (RRF) and the EF-G GTPase, which leads to the dissociation of the large subunit from the small subunit and the release of the mRNA, as shown in Figure $18$. When a stop codon enters the A-site of the ribosome RF1 or RF2 enter the A-site and bind with the mRNA. This leads to the hydrolysis of the protein and release through the exit tunnel. Binding of RF3 and GTP hydrolysis causes the dissociation of the RF factors and conformational change of the ribosome structure. Subse Eukaryotic Termination In eukaryotes and archaea, on the other hand, a single omnipotent RF reads all three stop codons. Although the mechanism of translation termination is basically the same, there is neither sequence nor structural homology between the bacterial RFs and the eukaryotic eRF1, apart from the universally conserved GGQ motif which is required for peptide hydrolysis from the tRNA. the eRF3 GTPase coordinates the release of eRF1 following hydrolysis. In Archaea, there is no eRF3 homolog, instead the aEF1A protein mediates this function. The process of eukaryotic ribosomal disassembly and recycling is currently not well understood, but appears to involve an ABC type ATPase called ABCE1. Mitochondria have independent RFs that can recognize standard and non-standard stop codons, and are more homologous with bacterial systems of ribosomal recycling and disassembly. Summary of Translation An overall summary of prokaryotic translation is given in Figure $19$. Left panel: Structure of the bacterial ribosome in complex with EF-Tu (PDB 5AFI). Right Panel: Scheme of the bacterial translation cycle. 30S: small subunit; 50S: large subunit; IF1, IF2, IF3: initiation factors; fM-tRNA: N-formylmethionine tRNA; aa-tRNA: aminoacyl tRNA; EF-Tu, EF-G: elongation factors; RF1, RF2, RF3: release factors; RRF: ribosome recycling factor; green trace: nascent protein. The question mark stands for a stop codon recognition.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/03%3A_Unit_III-_Information_Pathway/26%3A_Protein_Metabolism/26.03%3A_Translational_Regulation_and_Protein_Degradation.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Regulation of Translation Heterogeneity of Ribosome Structure Over the years, many studies performed in eukaryotes presented evidence that ribosomes can vary in their protein and rRNA complement between different cell types and developmental states. These observations culminated in the postulation of the ‘ribosome filter hypothesis’ by Mauro and Edelman in the year 2002. The authors propose that the ribosome composition functions as a translation determination factor. Depending on the RPs and rRNA sequences represented in the respective ribosome, the complex acts like a filter that selects for specific mRNAs and hence modulates translation, as shown in Figure $1$. RP heterogeneity can arise from differential expression of paralogs/homologs of RP proteins within different cell types or occur due to differential post-translational modifications of RPs, such as phosphorylation. The protein-to-rRNA ratio may also slightly vary within ribosomal composition affecting translation efficiency and selectivity. RNA genes are also present in multiple copies throughout the genomes of organisms from all domains of life. For example, the bacteria Streptomyces coelicolor harbors six copies of divergent large subunit (LSU) rRNA genes that constitute at least five different LSU rRNA species in a cell. These genes were shown to be differentially transcribed during the morphological development of the organism. Similarly, B. subtilis harbors ten rRNA operons and their reduction to one copy increased the doubling time as well as the sporulation frequency and the motility of the resulting mutant. Modification of the rRNA also provides another avenue of ribosomal heterogeneity. Similar to tRNA, rRNA residues can be chemically modified and commonly have 2-OH methylation. The conversion of uridine to pseudouridine is also quite common. In eukaryotes, the modifications are facilitated by snoRNAs and their tissue-specific expression might be a source for ribosome specialization. In light of the increasing evidence, ribosome heterogeneity, though still far from being entirely understood, proves to be an integral mechanism to modulate and fine-tune protein synthesis in response to environmental signals in all organisms. Effects of Sequence and Secondary Structure in mRNA The amount of protein produced from any given mRNA (i.e., the translational output) is influenced by multiple factors specified by the primary nucleotide sequence. These factors include GC content, codon usage, codon pairs, and secondary structure. For example, 5’UTR sequences in the mRNA may interact with small miRNAs and lead to RNA interference. miRNA interactions may also target mRNA for degradation(Figure $2$). This process is aided by protein chaperones called argonautes. This antisense-based process involves steps that first process the miRNA so that it can base-pair with a region of its target mRNAs. Once the base pairing occurs, other proteins direct the mRNA to be destroyed by nucleases. Fire and Mello were awarded the 2006 Nobel Prize in Physiology or Medicine for this discovery. • Step (1) shows how Exportin-5 transports a hairpin primary micro RNA (pri-miRNA) out of the nucleus and into the cytoplasm. • Step (2) shows how Dicer (not shown) trims the pri-miRNA and removes the hairpin loop. A group of proteins, known as Argonautes, form a miRNA/protein complex. • Steps (3,4) show how miRNA/protein complex hydrogen bonds with mRNA based on complimentary sequence homology, and blocks translation. • Step (5) shows the miRNA/protein complex binding speeds up the breakdown of the polyA tail of the mRNA, causing the mRNA to be degraded sooner. Effects of the Nascent Peptide on Ribosome Efficiency Since the Peptidyl Transferase Center (PTC) is buried within the large subunit, during translation the nascent peptide chain (NC) exits through a 100 Å-long tunnel (Figure $3$). The exit tunnel plays an active role in protein synthesis. Certain peptide sequences specifically interact with tunnel walls and induce ribosome stalling. Furthermore, the exit tunnel is a binding site for a clinically important class of antibiotics known as the macrolides. When synthesizing proteins containing proline stretches (i.e. several prolines in a row), ribosomes become stalled. Stalling is alleviated by a specialized elongation factor, EF-P in bacteria. Recently, cryo-EM structures of a ribosome stalled by a proline stretch with and without EF-P were resolved. In simulations of the PTC region, elongation factor P (EF-P) was observed to stabilize the P-site tRNA in a conformation compatible with peptide bond formation, while in the absence of EF-P, the P-site tRNA moved away from the A-site tRNA. The exit tunnel can accommodate 30–60 AAs, depending on the level of NC compaction. The rate of translation of about 4–22 AA per second in bacteria provides the NC with sufficient time to explore its conformational space and to start folding when still bound to the ribosome-tRNA complex. Proteasome The 26S proteasome is the central element of proteostasis regulation in eukaryotic cells. It is required for the degradation of protein factors in multiple cellular pathways and it plays a fundamental role in cell stability. The 26S proteasome has a structural configuration that confines the proteolytic active sites in a location unreachable for native and functional proteins, thus preventing uncontrolled degradation. The proteolytic active sites are found in the interior of a barrel-shaped core particle (CP or 20S). The entrances of the tunnel, placed at the distal ends of the barrel, are commonly occupied by the regulatory particle (RP or 19S), a sophisticated protein assembly that acts as a substrate processing machine. The regulatory particle has the important role of receiving, deubiquitinating, unfolding, and translocating substrates to the CP and it adopts different configurations depending on the activity states they exhibit. This process typically requires ATP hydrolysis. Moreover, conformationally distinct proteasomes may show different subcellular distributions depending on functional requirements in each cell type and environmental situation. Proteasomes are distributed throughout the cell, detected in the cytoplasm and the nucleus, and they can localize to hotspots in distinct intracellular regions or specific sites with high protein metabolism or with specific protein degradation requirements. The core of the proteasome consists of a symmetrical cylinder-shaped structure composed of four stacked rings, each containing 7 different subunits and is called the 20S proteasome, as shown in Figure $4$. The two outer rings are each composed of seven α-subunits (α1-α7 or PSMA1-7). During proteasome assembly, the α-rings serve as the backbone for the incorporation of β-subunits, followed by the dimerization of two half proteasomes. In mature proteasomes, the α-rings regulate substrate entrance since the α-subunits have hydrophobic loops that close the 20S barrel to prevent the random entry of substrates. The 20S core of the proteasome consists of 4 stacked rings. The outer rings contain seven α-subunits (white) while the inner rings contain seven β-subunits (purple). The catalytic subunits, β1, β2, and β5, are depicted in shades of blue. Gate opening of the 20S core occurs via capping by proteasome activators such as the 19S cap or PA28. The 19S cap is the most abundant activator and it forms the 26S proteasome together with the 20S core. Different cells have different caps. For example, interferoIFN-γ stimulation induces de novo formation of immunoproteasomes, which contains the immune subunits β1i (LMP2), β5i (LMP7), and β2i (MECL-1) (shades of red), as well as proteasome activation by PA28αβ (shades of green). Proteasomes in neural tissue are discussed below. In general, protein entry can only be established after gate opening by proteasome activators (PAs) such as the 19S cap, after which substrates can enter the interior of the 20S core for degradation. The inner two rings of the 20S barrel consist of the subunits β1-β7 (PSMB1-7). Each β-ring contains 3 catalytic subunits; termed β1, β2, and β5. In mature 20S complexes, the pro-peptides of these catalytic β-subunits are auto-catalytically removed. Upon autocatalytic processing, the N-terminal threonine residues become exposed as the catalytically reactive residues, harboring both the nucleophile (the hydroxyl group) and the catalytic base (the N-terminal amine) involved in peptide bond cleavage. Each catalytic subunit has selectivity toward specific residues. β1 has caspase- or peptidyl-glutamyl peptidase-like activity, preferring cleavage at the C-terminus of acidic residues. β2 has trypsin-like activity and cleaves after basic residues, while β5 has chymotrypsin-like activity and prefers cleavage after hydrophobic residues. Figure $5$ shows an interactive iCn3D model of the Human 20S Proteasome (6RGQ). The alpha subunits are shown in light gray and the beta subunits are in light cyan. Active site residue, as defined by databases, are shown for one of the alpha chain (L37, Q53, K55, H68, V170) and one of the beta chains (T1, D17, R19, K33, S130, D167, S170, G171). A possible generic mechanism for the cleavage of peptide bonds by the beta chain is shown in Figure $6$. The mechanism shows how the newly exposed N-terminal threonine acts as both the nucleophile (the hydroxyl group) and the catalytic base. Proteins are normally targeted to the proteasome using ubiquitin labels attached covalently to a lysine residue, usually in a chained form that produces a polyubiquitinated protein. The process of protein polyubiquitination is carried out by a highly specialized and diverse enzymatic system, which includes families of ubiquitin-activating enzymes (E1), ubiquitin-conjugating enzymes (E2), and ubiquitin ligases (E3). The covalent attachment of ubiquitin to specific target proteins is mainly accomplished by stepwise enzymatic cascade reactions, and ubiquitin is attached to the substrates via the concerted action of ubiquitin-activating enzyme (E1), ubiquitin-conjugating enzyme (E2), and ubiquitin ligase (E3). The attachment of ubiquitin or ubiquitin chains to the substrate is a successive process, as shown in Figure $7$. First, the C-terminal carboxylic acid is activated by adenylation using a molecule of ATP forming an adenylate (AMP-) intermediate. The adenylate acts as a good leaving group during the next reaction where an E1-ubiquitin thioester bond is formed between the C-terminal Gly carboxyl group of ubiquitin and the active site Cys of the E1 enzyme. AMP leaves the active site at this point. Note that ATP is used in many reactions to activate carboxylic acid functional groups through the formation of an adenylate intermediate and that this will be seen as a theme in many different types of reactions throughout this textbook. Once the ubiquitin is docked as a thioester on E1, it can be transferred to a Cys residue of the E2 enzyme to form an E2-ubiquitin thioester-linked intermediate. This enzymatic reaction is known as a transesterification. Eventually, the E2 transfers the ubiquitin to the substrate protein by E3. Ubiquitin is conjugated to the target protein through an isopeptide bond between its C-terminal glycine (Gly76) and the ε-amino group of a lysine residue. There are three typical ways of linking the ubiquitin with the substrate, as shown in Figure $8$. The first is called mono-ubiquitination, which refers to the modification of one site of a substrate by a single ubiquitin molecule. The second is multi-mono-ubiquitination, which means adding several ubiquitin molecules repetitively to distinct sites (multi-mono-ubiquitination). The third is called polyubiquitination (including linear polyubiquitination and branched polyubiquitination), in which ubiquitin molecules are added to the same site (polyubiquitination, including linear polyubiquitination and branched polyubiquitination) of a substrate. In the second and third ways of linking, the previously attached ubiquitin serves as the “acceptor” of subsequently added ubiquitin. Of course, polyubiquitin chains linked by the same Lys are homogeneous, while those linked at different Lys are heterogeneous or mixed ones. The process of ubiquitination from activation to the attachment to the substrate is catalyzed by three major enzymes. The substrates labeled by ubiquitin are degraded by the 26S proteasome or play a non-degradative role in other processes. Abbreviations: APC, Anaphase-promoting complex; DUBs, Deubiquitinating enzymes; E1, Ubiquitin-activating enzyme; E2, Ubiquitin-conjugating enzyme; E3, Ubiquitin-ligase enzyme; Cul-based, Cullin-RING box1-Ligase; HECT, Homology to E6-AP C Terminus; Ub, Ubiqitin; SUMO, Small ubiquitin-related modifier; RBX1, RING-Box 1; RING, Really interesting new gene; RBR, RING1-IBR(cysteine/histidine-rich region)-RING2. Subsequently, the substrate complex tagged by the ubiquitin is either degraded by the 26S proteasome or executes nonproteolytic functions, such as the regulation of gene expression, cellular trafficking, or other biological function. In most cases, polyubiquitinated proteins are recognized and degraded by the 26S proteasome, and the ubiquitin or ubiquitin chain is hydrolyzed and freed by deubiquitinating enzymes (DUBs) for reuse in further conjugation cycles after being removed from the substrate protein (Figure 27.3.6). The family of E3 enzymes is large and diverse. It is estimated that there are 600-700 E3 enzymes in humans, representing approximately 5% of the human genome. Thus, E3 enzymes can be very substrate specific, leading to the specialized degradation of a small subset of proteins within the cell. E2 enzymes are the intersection between E1 and E3 enzymes and help to determine the ubiquitination of specific target proteins by interacting with different types of E3 enzymes. Figure $9$ shows an interactive iCn3D model of the Yeast proteasome in resting state (C1-a) (6J2X). (long load) It's too complex to discuss this in much detail. The chains are presented in different colors. You should able to find the alpha and beta chains of the core 20S particle. The regulatory cap proteins comprising the lid are on top of the alpha ring.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/03%3A_Unit_III-_Information_Pathway/27%3A_Regulation_of_Gene_Expression/27.01%3A_Regulation_of_Gene_Expression_in_Bacteria.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Introduction Each nucleated cell in a multicellular organism contains copies of the same DNA. Similarly, all cells in two pure bacterial cultures inoculated from the same starting colony contain the same DNA, except for changes that arise from spontaneous mutations. If each cell in a multicellular organism has the same DNA, then how is it that cells in different parts of the organism’s body exhibit different characteristics? Similarly, how is it that the same bacterial cells within two pure cultures exposed to different environmental conditions can exhibit different phenotypes? In both cases, each genetically identical cell does not turn on, or express, the same set of genes. Only a subset of proteins in a cell at a given time is expressed. Genomic DNA contains both structural genes, which encode products that serve as cellular structures or enzymes, and regulatory genes, which encode products that regulate gene expression. The expression of a gene is a highly regulated process. Whereas regulating gene expression in multicellular organisms allows for cellular differentiation, in single-celled organisms like prokaryotes, it primarily ensures that a cell’s resources are not wasted making proteins that the cell does not need at that time. Elucidating the mechanisms controlling gene expression is important to the understanding of human health. Malfunctions in this process in humans lead to the development of cancer and other diseases. Understanding the interaction between the gene expression of a pathogen and that of its human host is important for the understanding of a particular infectious disease. Gene regulation involves a complex web of interactions within a given cell among signals from the cell’s environment, signaling molecules within the cell, and the cell’s DNA. These interactions lead to the expression of some genes and the suppression of others, depending on circumstances. Prokaryotes and eukaryotes share some similarities in their mechanisms to regulate gene expression; however, gene expression in eukaryotes is more complicated because of the temporal and spatial separation between the processes of transcription and translation. Thus, although most regulation of gene expression occurs through transcriptional control in prokaryotes, regulation of gene expression in eukaryotes occurs at the transcriptional level and post-transcriptionally (after the primary transcript has been made). In bacteria and archaea, structural proteins with related functions are usually encoded together within the genome in a block called an operon and are transcribed together under the control of a single promoter, resulting in the formation of a polycistronic transcript, as shown in Figure $1$. In this way, regulation of the transcription of all of the structural genes encoding the enzymes that catalyze the many steps in a single biochemical pathway can be controlled simultaneously, because they will either all be needed at the same time, or none will be needed. For example, in E. coli, all of the structural genes that encode enzymes needed to use lactose as an energy source are encoded next to each other in the lactose (or lac) operon under the control of a single promoter, the lac promoter. French scientists François Jacob (1920–2013) and Jacques Monod at the Pasteur Institute were the first to show the organization of bacterial genes into operons, through their studies on the lac operon of E. coli. For this work, they won the Nobel Prize in Physiology or Medicine in 1965. Each operon includes DNA sequences that influence its own transcription; these are located in a region called the regulatory region. The regulatory region includes the promoter and the region surrounding the promoter, to which transcription factors, proteins encoded by regulatory genes, can bind. Transcription factors influence the binding of RNA polymerase to the promoter and allow its progression to transcribe structural genes. A repressor is a transcription factor that suppresses the transcription of a gene in response to an external stimulus by binding to a DNA sequence within the regulatory region called the operator, which is located between the RNA polymerase binding site of the promoter and the transcriptional start site of the first structural gene. Repressor binding physically blocks RNA polymerase from transcribing structural genes. Conversely, an activator is a transcription factor that increases the transcription of a gene in response to an external stimulus by facilitating RNA polymerase binding to the promoter. An inducer, a third type of regulatory molecule, is a small molecule that either activates or represses transcription by interacting with a repressor or an activator. In prokaryotes, there are examples of operons whose gene products are required rather consistently and whose expression, therefore, is unregulated. Such operons are constitutively expressed, meaning they are transcribed and translated continuously to provide the cell with constant intermediate levels of the protein products. Such genes encode enzymes involved in housekeeping functions required for cellular maintenance, including DNA replication, repair, and expression, as well as enzymes involved in core metabolism. In contrast, other prokaryotic operons are expressed only when needed and are regulated by repressors, activators, and inducers. Prokaryotic operons are commonly controlled by the binding of repressors to operator regions, thereby preventing the transcription of the structural genes. Such operons are classified as either repressible operons or inducible operons. Repressible operons, like the tryptophan (trp) operon, typically contain genes encoding enzymes required for a biosynthetic pathway. As long as the product of the pathway, like tryptophan, continues to be required by the cell, a repressible operon will continue to be expressed. However, when the product of the biosynthetic pathway begins to accumulate in the cell, removing the need for the cell to continue to make more, the expression of the operon is repressed. Conversely, inducible operons, like the lac operon of E. coli, often contain genes encoding enzymes in a pathway involved in the metabolism of a specific substrate like lactose. These enzymes are only required when that substrate is available, thus expression of the operons is typically induced only in the presence of the substrate. The trp Operon - A Repressible Operon E. coli can synthesize tryptophan using enzymes that are encoded by five structural genes located next to each other in the trp operon, as shown in Figure $2$. When environmental tryptophan is low, the operon is turned on. This means that transcription is initiated, the genes are expressed, and tryptophan is synthesized. However, if tryptophan is present in the environment, the trp operon is turned off. Transcription does not occur and tryptophan is not synthesized. When tryptophan is not present in the cell, the repressor by itself does not bind to the operator; therefore, the operon is active and tryptophan is synthesized. However, when tryptophan accumulates in the cell, two tryptophan molecules bind to the trp repressor molecule, which changes its shape, allowing it to bind to the trp operator. This binding of the active form of the trp repressor to the operator blocks RNA polymerase from transcribing the structural genes, stopping the expression of the operon. Thus, the actual product of the biosynthetic pathway controlled by the operon regulates the expression of the operon. The five structural genes needed to synthesize tryptophan in E. coli are located next to each other in the trp operon. When tryptophan is absent, the repressor protein does not bind to the operator, and the genes are transcribed. When tryptophan is plentiful, tryptophan binds the repressor protein at the operator sequence. This physically blocks the RNA polymerase from transcribing the tryptophan biosynthesis genes. Figure $3$ shows an interactive iCn3D model of the E. Coli Trp repressor - operator complex (1TRO). The Trp repressor is shown as a dimer with one subunit gray and the other gold. The backbone of the two DNA strands is shown in spacefill magenta and cyan, except where the bases on the major grove interact with the Trp repressor. The tryptophan in each of the proton monomers is shown in spacefill with CPK colors and labeled. Noncovalent interactions (hydrogen bonds and salt bridges) between the protein and DNA are shown with dotted lines. 6 water-mediated hydrogen bonds to phosphate are not shown. Note that there are few H bonds between the protein and base hydrogen bond donors and acceptors, suggesting that the repressor might bind specifically through geometric interactions with the backbone along with the water-mediated hydrogen bonds. The Lac Operon: An Inducible Operon The lac operon is an example of an inducible operon that is also subject to activation in the absence of glucose. The lac operon encodes three structural genes, lacZ, lacY, and lacA, necessary to acquire and process the disaccharide lactose from the environment, as shown in Figure $4$. Panel (A) shows a schematic representation of the lac operon in E. coli. The lac operon has three structural genes, lacZ, lacY, and lacA that encode for β-galactosidase, permease, and galactoside acetyltransferase, respectively. The promoter (p) and operator (o) sequences that control the expression of the operon are shown. Upstream of the lac operon is the lac repressor gene, lacI, controlled by the lacI promoter (p). Panel (B) shows the lac repressor inhibition of the lac operon gene expression in the absence of lactose. The lac repressor binds with the operator sequence of the operon and prevents the RNA polymerase enzyme which is bound to the promoter (p) from initiating transcription. Panel (C) shows that in the presence of lactose, some of the lactose is converted into allolactose, which binds and inhibits the activity of the lac repressor. The lac repressor-allolactose complex cannot bind with the operator region of the operon, freeing the RNA polymerase and causing the initiation of transcription. Expression of the lac operon genes enables the breakdown and utilization of lactose as a food source within the organism The lacZ gene encodes the β-galactosidase (β-gal) enzyme responsible for the hydrolysis of lactose into simple sugars glucose and galactose, as shown in Figure $5$. The β-gal enzyme can also mediate the breakdown of the alternate substrate 5-bromo-4-chloro-3-indolyl-β-D-galactopyranoside (Xgal) (panel B). The breakdown product, 5-bromo-4-chloro-3-hydroxyindole – 1, spontaneously dimerizes to form the intensely blue product, 5,5′-dibromo-4,4′-dichloro-indigo – 2. Thus, Xgal has been a valuable research tool, not only in the study of the enzymatic activity of β-gal but also in the development of the commonly used blue-white DNA cloning system that utilizes the β-gal enzyme as a marker in molecular cloning experiments. The lac operon contains two more genes, in addition to lacZ (Fig. 4). The lacY gene encodes a permease that increases the uptake of lactose into the cell and lacA encodes a galactoside acetyltransferase (GAT) enzyme. The exact function of GAT during lactose metabolism has not been conclusively elucidated but acetylation is thought to play a role in the transport of the modified sugars. For the lac operon to be expressed, lactose must be present. This makes sense for the cell because it would be energetically wasteful to create the enzymes to process lactose if lactose was not available. In the absence of lactose, the lacI gene is constitutively expressed, expressing the lac repressor protein (Fig. 28.2.3 B). The lac repressor binds with an operator region of the lac operon and physically prevents RNA polymerase from transcribing the structural genes (Fig. 28.2.3 B). However, when lactose is present, the lactose inside the cell is converted to allolactose. Allolactose serves as an inducer molecule, binding to the repressor and changing its shape so that it is no longer able to bind to the operator DNA (Fig. 28.2.3 C). Removal of the repressor in the presence of lactose allows RNA polymerase to move through the operator region and begin transcription of the lac structural genes. In addition to lactose, laboratory experiments have revealed that the non-natural compound Isopropyl β-D-1-thiogalactopyranoside (IPTG) can also bind with the lac repressor and cause the expression of lac operon (panel C). Similar to Xgal, this compound has also been used as a research tool for molecular cloning. Figure $6$ shows an interactive iCn3D model of the lactose operon repressor and its complexes with DNA (1LBG). The resolution of the structure above was insufficient to show the amino acid side chains. Figure $7$ shows an interactive iCn3D model of the NMR solution structure of the dimer of LAC repressor DNA-binding domain complexed to its natural operator O1 (2KEI). Note the presents of white spheres representing hydrogen atoms (these don't appear in crystal structure but do in NMR structures. Color coding is the same as above. Zoom in to see specific interactions between the protein and the exposed DNA base hydrogen bond donors and acceptors. The complex of O1 and O2 shows similar specific and nonspecific contacts, which makes sense given the lambda repressor has similar affinity for those two operator sites. In contrast, one side of the O3 complex shows a loss of protein: DNA interactions, consistent with its lower affinity of its operator O3. The Lac Operon: Activation by Catabolite Activator Protein Bacteria typically can use a variety of substrates as carbon sources. However, because glucose is usually preferable to other substrates, bacteria have mechanisms to ensure that alternative substrates are only used when glucose has been depleted. Additionally, bacteria have mechanisms to ensure that the genes encoding enzymes for using alternative substrates are expressed only when the alternative substrate is available. In the 1940s, Jacques Monod was the first to demonstrate a preference for certain substrates over others through his studies of E. coli’s growth when cultured in the presence of two different substrates simultaneously. Such studies generated diauxic growth curves, like the one shown in Figure $8$. Although the preferred substrate glucose is used first, E. coli grows quickly and the enzymes for lactose metabolism are absent. However, once glucose levels are depleted, growth rates slow, inducing the expression of the enzymes needed for the metabolism of the second substrate, lactose. Notice how the growth rate in lactose is slower, as indicated by the lower steepness of the growth curve. The ability to switch from glucose use to another substrate like lactose is a consequence of the activity of an enzyme called Enzyme IIA (EIIA). When glucose levels drop, cells produce less ATP from catabolism and EIIA becomes phosphorylated. Phosphorylated EIIA activates adenylyl cyclase, an enzyme that converts some of the remaining ATP to cyclic AMP (cAMP), a cyclic derivative of AMP and an important signaling molecule involved in glucose and energy metabolism in E. coli, as shown in Figure $9$. As a result, cAMP levels begin to rise in the cell. This is an indicator to the cell, that overall energy levels are low and that ATP is being depleted. The lac operon also plays a role in this switch from using glucose to using lactose. When glucose is scarce, the accumulating cAMP caused by increased adenylyl cyclase activity binds to catabolite activator protein (CAP), also known as cAMP receptor protein (CRP). The complex binds to the promoter region of the lac operon, as shown in Figure $10$. In the regulatory regions of these operons, a CAP binding site is located upstream of the RNA polymerase binding site in the promoter. The binding of the CAP-cAMP complex to this site increases the binding ability of RNA polymerase to the promoter region to initiate the transcription of the structural genes. Thus, in the case of the lac operon, for transcription to occur, lactose must be present (removing the lac repressor protein) and glucose levels must be depleted (allowing the binding of an activating protein). When glucose levels are high, there is catabolite repression of operons encoding enzymes for the metabolism of alternative substrates. Because of low cAMP levels under these conditions, there is an insufficient amount of the CAP-cAMP complex to activate the transcription of these operons. Figure $11$ shows an interactive iCn3D model of the Catabolite activator protein CAP-DNA complex with bound cAMP (2CGP). Global Responses of Prokaryotes In prokaryotes, several higher levels of gene regulation have the ability to control the transcription of many related operons simultaneously in response to an environmental signal. A group of operons all controlled simultaneously is called a regulon. Alarmones When sensing impending stress, prokaryotes alter the expression of a wide variety of operons to respond in coordination. They do this through the production of alarmones, which are small intracellular nucleotide derivatives, such as guanosine pentaphosphate (pppGpp), as shown in Figure $12$. Alarmones change which genes are expressed and stimulate the expression of specific stress-response genes. For example, pppGpp signaling is involved in the stringent response in bacteria, causing the inhibition of RNA synthesis when there is a shortage of amino acids present. This causes translation to decrease and the amino acids present are therefore conserved. Furthermore, pppGpp causes the up-regulation of many other genes involved in stress response such as the genes for amino acid uptake (from surrounding media) and biosynthesis. The use of alarmones to alter gene expression in response to stress appears to be important in pathogenic bacteria, as well. On encountering host defense mechanisms and other harsh conditions during infection, many operons encoding virulence genes are upregulated in response to alarmone signaling. Knowledge of these responses is key to being able to fully understand the infection process of many pathogens and to the development of therapies to counter this process. Quorum Sensing Quorum sensing (QS) is an intercellular communication mechanism of bacteria used to coordinate the activities of individual cells at the population level in response to surroundings through the production and perception of diffusible signal molecules such as Acyl Homoserine Lactones or small signaling peptides, as shown in Figure $13$. The signal synthase, signal receptor, and signal molecules are three essential elements of the basic QS circuit machinery. Genes encoding signal-generating proteins are also included among the QS target genes. This forms an autoinduction feedback loop to modulate the generation of signal molecules. Several bacterial behaviors including virulence factors expression, secondary metabolites production, biofilm formation, motility, and luminescence are regulated by QS. Through complex regulatory networks, bacteria are capable of expressing corresponding genes according to their population size and of behaving in a coordinated manner. The left panel shows the typical Gram-negative quorum sensing mechanism. Acyl homoserine lactone molecules, synthesized by LuxI, passively pass the bacterial cell membrane and when a sufficient concentration is reached (threshold level) activate the intracellular LuxR which subsequently activates target gene expression in a coordinated way. Note that a single cell is shown for simplicity. However, acyl homoserine lactones will commonly diffuse and target neighboring cells within the colony to mediate a communal or population response within the bacterial colony. The right panel shows that quorum-sensing peptides are synthesized by the bacterial ribosomes as pro-peptidic proteins and undergo posttranslational modifications during excretion by active transport. The quorum-sensing peptides bind membrane-associated receptors which get autophosphorylated and activate intracellular response regulators via phosphotransfer. These phosphorylated response regulators induce increased target gene expression. For example, some microbial species, such as Staphylococcus aureus, can encase their community within a self-produced matrix of hydrated extracellular polymeric substances that include polysaccharides, proteins, nucleic acids, and lipid molecules. These encasements are known as biofilms. The formation of the biofilm on solid surfaces is a step-wise process comprising several stages, as shown in Figure $14$. It starts with the conditioning of the surface through the coating with macromolecules from the aqueous surrounding, which enables the initial reversible adhesion of microorganisms. The next step is the formation of stronger, irreversible attachments to the surface, followed by the proliferation and aggregation of microorganisms into multicellular and multilayered clusters, which actively produce an extracellular matrix. Some cells in the mature biofilms continuously detach and separate from the aggregates, representing a continuous source of planktonic bacteria that can subsequently spread and form new microcolonies. Biofilms are a common cause of chronic, nosocomial (originating in a hospital), and medical device-related infections because they can develop either on vital or necrotic tissue as well as on the inert surfaces of different implanted materials. Moreover, biofilms are linked with high-level resistance to antimicrobials, frequent treatment failures, and increased morbidity and mortality. As a consequence, biofilm infections and accompanying diseases have become a major health concern and a serious challenge for both modern medicine and pharmacy. The rough estimation shows that more than 60% of hospital-associated infections are attributable to the biofilms formed on indwelling medical devices, which result in more than one million cases of infected patients annually and more than \$1 billion in hospitalization costs per year in the USA. Biofilm infections share some common characteristics: slow development in one or more hot spots, delayed clinical manifestation, and persistency for months or years, usually with interchanging periods of acute exacerbations and absence of clinical symptoms. Even though they are less aggressive than acute infections, their treatment is challenging to a greater extent. There is upto a 1000-fold decrease in the susceptibility of biofilms to antimicrobial agents and disinfectants as well as resistance to host immune response. Thus, ways to reduce or inhibit biofilm formation are highly sought. The majority of the proposed biofilm-control methods focus on: (i) prevention and minimization of biofilm formation by selection and surface modifications of anti-adhesive materials; (ii) debridement techniques including ultrasound and surgical procedures; (iii) disruption of biofilm QS-signaling system; or (iv) achieving proper drug penetration and delivery to formed biofilms by the use of an electromagnetic field, ultrasound waves, photodynamic activation or specific drug delivery systems. Alternate σ Factors Since the σ subunit of bacterial RNA polymerase confers specificity as to which promoters should be transcribed, altering the σ factor used is another way for bacteria to quickly and globally change what regulons are transcribed at a given time. The σ factor recognizes sequences within a bacterial promoter, so different σ factors will each recognize slightly different promoter sequences. In this way, when the cell senses specific environmental conditions, it may respond by changing which σ factor it expresses, degrading the old one and producing a new one to transcribe the operons encoding genes whose products will be useful under the new environmental condition. For example, in sporulating bacteria of the genera Bacillus and Clostridium (which include many pathogens), a group of σ factors controls the expression of the many genes needed for sporulation in response to sporulation-stimulating signals. Prokaryotic Attenuation and Riboswitches Although most gene expression is regulated at the level of transcription initiation in prokaryotes, there are also mechanisms to control both the completion of transcription, as well as translation, concurrently. Since their discovery, these mechanisms have been shown to control the completion of transcription and translation of many prokaryotic operons. Because these mechanisms link the regulation of transcription and translation directly, they are specific to prokaryotes, because these processes are physically separated in eukaryotes. One such regulatory system is attenuation, whereby secondary stem-loop structures formed within the 5’ end of an mRNA being transcribed determine if transcription to complete the synthesis of this mRNA will occur and if this mRNA will be used for translation. Beyond the transcriptional repression mechanism already discussed, attenuation also controls the expression of the trp operon in E. coli as shown in Figure $15$. The trp operon regulatory region contains a leader sequence called trpL between the operator and the first structural gene, which has four stretches of RNA that can base pair with each other in different combinations. When a terminator stem-loop forms, transcription terminates, releasing RNA polymerase from the mRNA. However, when an antiterminator stem-loop forms, this prevents the formation of the terminator stem-loop, so RNA polymerase can transcribe the structural genes. When tryptophan is plentiful, translation of the short leader peptide encoded by trpL proceeds, the terminator loop between regions 3 and 4 forms, and transcription terminates. When tryptophan levels are depleted, translation of the short leader peptide stalls at region 1, allowing regions 2 and 3 to form an antiterminator loop, and RNA polymerase can transcribe the structural genes of the trp operon. A related mechanism of concurrent regulation of transcription and translation in prokaryotes is the use of a riboswitch, a small region of noncoding RNA found within the 5’ end of some prokaryotic mRNA molecules, as shown in Figure $16$. A riboswitch may bind to a small intracellular molecule to stabilize certain secondary structures of the mRNA molecule. The binding of the small molecule determines which stem-loop structure forms, thus influencing the completion of mRNA synthesis and protein synthesis. Riboswitches found within prokaryotic mRNA molecules can bind to small intracellular molecules, stabilizing certain RNA structures, and influencing either the completion of the synthesis of the mRNA molecule itself (left) or the protein made using that mRNA (right). Figure $17$ shows interactive iCn3D models of a series of bacterial riboswitches. They are described in the legend below. Guanine-responsive riboswitch bound to metabolite hypoxanthine (4FE5) A. (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...Nh1Sv7oSSyC6Z6 Divalent cation-sensing regulatory RNA (2QBZ) B. (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...VbU9Vf6DUA4VE8 Cyclid-di-GMP RNA riboswitch (3IRW) C. (Copyright; author via source). Click the image for a popup or use this external link:https://structure.ncbi.nlm.nih.gov/i...wVntbdScP3VCF8 GlmS ribozyme bound to glucosamine-6-phosphate (2Z75) D. (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...w9T4K9ffK1NU27 A: Guanine-responsive riboswitch bound to metabolite hypoxanthine (4FE5) - Hypothanine, involved in purine metabolism, is shown bound to RNA representing the 5' untranslated region of the xanthine phosphoribosyltransferase (xbt)/ xanthine-specific purine permease (pbux) genes that lead to transcription termination. B: The M-box in mycobacterial genes regulating Mg2+ transport binds divalent cation. They are transcribed under low Mg2+ concentrations. Salt bridges (ion-ion interactions) are shown in cyan and pi-cation interactions in red dotted lines C: Bis-(3'-5')-cyclic dimeric guanosine monophosphate (c-di-GMP) is a second messenger in bacteria and regulates many cellular processes including the formation of biofilms. The riboswitch shown here is from Vibrio cholerae. The U1 small nuclear ribonucleoprotein A is shown in cyan. Figure $18$ shows a cartoon of the actual riboswitch in the 5' untranslated region of target genes D. This ribozyme is in the 5′ untranslated region of glucosamine-6-phosphate synthase mRNA. The protein enzyme, 2. This protein enzyme catalyzes the conversion of fructose 6-phosphate and glutamine to glucosamine 6-phosphate (GlcN6P) and glutamate. The glmS ribozyme in the 5'-untranslated region cleaves itself on binding GlcN6P. This self-cleavage is inhibited by glucose 6-phosphate (Glc6P). Hence high levels of the gene product for the synthase lead to cleavage of its own mRNA. The glmS ribozyme RNA is shown in gray and its substrate RNA in cyan.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/03%3A_Unit_III-_Information_Pathway/27%3A_Regulation_of_Gene_Expression/27.02%3A_Regulation_of_Gene_Expression_in_Eukaryotes.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry As seen in Chapter 26, the initiation of transcription requires the assembly of a multitude of transcription factors (TF) localized at the promoter region. Transcription can also utilize far-reaching interactions of enhancers, that bind at a distant DNA site and loop back around to stabilize the RNA polymerase at the promoter. Control of transcriptional initiation is dependent on TF factor activation, TF binding with specific DNA recognition sequences, and chromatin remodeling. Transcription Factor (TF) Activation Many TFs are expressed within cells and held in an inactive conformation until the right environmental stimulus is present within the cell. Cellular signaling pathways can cause post-translational protein modifications leading to TF activation or small molecules may physically bind and allosterically modify the protein structure to mediate activation. Here we will use examples from the cell cycle signaling cascade and steroid hormone receptor pathways to highlight some mechanisms of TF activation. A key element to take away from this section is that transcription factor activation is often highly pleiotropic and has many cellular effects. Depending on the cell type and the environmental conditions, different combinations of downstream target genes may be activated or inactivated. Teasing apart these intricacies and the physiological effects that they have within an organism is a major goal of ongoing research. Cell Cycle Regulation by p53 p53 is one of the most studied proteins in science. To date, over 68,000 papers appear in PubMed containing p53 or TP53 in the title and/or abstract. Originally described as an oncogene (since a mutated, functionally altered form of the protein was first characterized), p53 is now recognized as the most frequently inactivated tumor suppressor in human cancers. It is a transcription factor that controls the expression of genes and miRNAs affecting many important cellular processes including proliferation, DNA repair, programmed cell death (apoptosis), autophagy, metabolism, and cell migration, as shown in Figure $1$. Many of those processes are critical to a variety of human pathologies and conditions extending beyond cancer, including ischemia, neurodegenerative diseases, stem cell renewal, aging, and fertility. Notably, p53 also has non-transcriptional functions, ranging from intrinsic nuclease activity to activation of mitochondrial Bak (Bcl-2 homologous antagonist killer) and caspase-independent apoptosis. As a transcription factor, p53 responds to various genotoxic insults and cellular stresses (e.g., DNA damage or oncogene activation) by inducing or repressing the expression of over a hundred different genes. p53 transcriptional regulation plays a dominant role in causing the arrest of damaged cells, facilitating their repair and survival, or inducing cell death when DNA is damaged irreparably. p53 can also cause cells to become permanently growth arrested, and there is compelling in vivo evidence that these “senescent” cells secrete factors that enhance their clearance by the immune system, leading to tumor regression. Through these mechanisms, p53 helps maintain genomic stability within an organism, justifying its long-held nickname “guardian of the genome”. Other p53 gene targets are involved in inhibiting tumor cell angiogenesis, migration, metastasis, and other important processes (such as metabolic reprogramming) that normally promote tumor formation and progression Figure $2$ shows an interactive iCn3D model of the human p53 tetramer bound to the natural CDKN1A(p21) p53-response element (3TS8). Each monomer of the p53 tetramer is shown in a different color. The noncovalent interactions of the brown monomer with the DNA are shown, with key amino acids and nucleic acid bases shown in CPK-colored sticks. They interact with the Zn2+ ions, shown in the light green monomer. Normally, p53 levels are kept low by its major antagonist, Mdm2, an E3 ubiquitin ligase that is itself a transcriptional target of p53. Stress signals, such as DNA damage, oncogene activation, and hypoxia, promote p53 stability and activity by inducing post-translational modifications (PTMs) and tetramerization of p53. p53 functions as a transcription factor that binds to specific p53 response elements upstream of its target genes. p53 affects many important cellular processes linked to tumor suppression, including the induction (green) of senescence, apoptosis, and DNA repair as well as inhibition (red) of metabolism, angiogenesis, and cell migration. These functions are largely mediated through transcriptional regulation of its targets (examples given). p53 protein function is regulated post-translationally by coordinated interaction with signaling proteins including protein kinases, acetyltransferases, methyl-transferases, and ubiquitin-like modifying enzymes as shown in Figure $3$. The majority of the sites of covalent modification occur at intrinsically unstructured linear peptide docking motifs that flank the DNA-binding domain of p53 which plays a role in anchoring or in allosterically activating the enzymes that mediate covalent modification of p53. In undamaged cells, the p53 protein has a relatively short half-life and is degraded by a ubiquitin-proteasome-dependent pathway through the action of E3 ubiquitin ligases, such as MDM2 (Fig 28.3.1). Following stress, p53 is phosphorylated at multiple residues, thereby modifying its biochemical functions required for increased activity as a transcription factor. Post-translational modifications help to stabilize the tetramer formation of the protein and enhance the translocation of the protein from the cytoplasm into the nucleus. The tetrameric form of p53 is then functional to bind to DNA in a sequence-specific manner and either activate or repress transcription, depending on the target sequence. Some post-translational modifications, such as acetylation, are DNA-dependent and can play a role in chromatin remodeling and activation of p53 target gene expression. It should be noted that single-point mutations that modify the ability of the protein to be phosphorylated in one position, typically do not show a decrease in the stabilization or activation of the protein following a damage or stress event. Thus, multiple modifications likely allow for redundancy within this pathway and ensure the activation of the protein following a stress event. Furthermore, the environment within the cell can lead to different p53 phenotypes, such as the activation of growth arrest and DNA repair processes (ie if there is not a lot of damage) or it can lead to the activation of apoptosis or programmed cell death pathways (ie if the damage is too extensive to be repaired). Steroid Hormone Receptors Steroid hormone receptors (SHRs) belong to the superfamily of nuclear receptors (NRs), which are one of the essential classes of transcriptional factors. NRs play a critical role in all aspects of human development, metabolism. and physiology. Since they generally act as ligand-activated transcription factors, they are an essential component of cell signaling. NRs form an ancient and conserved family that arose early in the metazoan lineage. NR molecular evolution is characterized by major events of gene duplication and gene losses. Phylogenetic analysis revealed a distinct separation of NR ligand binding domains (LBDs) into 4 monophyletic branches, the steroid hormone receptor-like cluster, the thyroid hormone-like receptors cluster, the retinoid X-like and steroidogenic factor-like receptor cluster and the nerve growth factor-like/HNF4 receptor cluster, as shown in Figure $4$. Here we will focus on the Steroid Hormone-Like Receptors branch (SHRs). SHRs plays a key role in many important physiological processes like organ development, metabolite homeostasis, and response to external stimuli. The estrogen receptor comes in two major forms, ERα and ERβ. Other members of this subgroup include the cortisol-binding glucocorticoid receptor (GR), the aldosterone-binding mineralocorticoid receptor (MR), the progesterone receptor (PR), and the dihydrotestosterone (DHT) binding androgen receptor (AR), as shown in Figure \(5\ below. Panel A shows a phylogenetic tree of the Steroid Hormone Receptor (SHR) family showing the evolutionary interrelationships and distance between the various receptors. Based on alignments available at The NucleaRDB [Horn et al., 2001]. Panel B shows that all steroid receptors are composed of a variable N-terminal domain (A/B) containing the AF-1 transactivation region, a highly conserved DNA Binding Domain (DBD), a flexible hinge region (D), and a C-terminal Ligand Binding Domain (LBD, E) containing the AF-2 transactivation region. The estrogen receptor α is unique in that it contains an additional C-terminal F domain. Numbers represent the length of the receptor in amino acids. The members of the Steroid Hormone Receptor family share a similar, modular architecture, consisting of several independent functional domains (Fig. 5B above). Most conserved is the centrally located DNA binding domain (DBD) containing the characteristic zinc-finger motifs. The DBD is followed by a flexible hinge region and a moderately conserved Ligand Binding Domain (LBD), located at the carboxy-terminal end of the receptor. The estrogen receptor α is unique in that it contains an additional F domain of which the exact function is unclear. The LBD is composed of twelve α-helices (H1-H12) that together fold into a canonical α-helical sandwich. Besides its ligand binding capability, the LBD also plays an important role in nuclear translocation, chaperone binding, receptor dimerization, and coregulator recruitment through its potent ligand-dependent transactivation domain, referred to as AF-2. A second, ligand-independent, transactivation domain is located in the more variable N-terminal part of the receptor, designated as AF-1. To date, no crystal structure of a full-length SHR exists, though structures of the DBD and LBD regions of most SHRs are available. These have helped significantly in understanding the molecular aspects of DNA and ligand binding, but have to some extent also led to biased attention to these parts of the receptor only. For example, many coregulator interaction studies are still performed with the LBD only, while numerous studies have demonstrated that the AF-2 domain often tells only part of the story. With the help of biophysical techniques, however, it is feasible to study the full-length receptor in its native environment. Most SHRs remain in the cytoplasm of the cell until they are bound with the appropriate steroid as shown in Figure $6$. Steroid binding causes the dimerization of SHRs and localization to the cell nucleus, where the SHRs interact with the DNA at sequence-specific motifs known as Hormone Response Elements (HREs) (Step 5). Many SHRs can also interact with membrane-bound receptors and affect cellular signaling pathways, in addition to the activation of gene expression (step 6). Steroid hormones, such as estrogens, reach their target cells via the blood, where they are bound to carrier proteins. Naturally occurring estrogens including estradiol, estrone, estriol, differ primarily in structure on the presence of hydroxyl groups (Fig. 28.3.6). Estradiol is the predominant estrogen during reproductive years both in terms of absolute serum levels as well as in terms of estrogenic activity. During menopause, estrone is the predominant circulating estrogen, and during pregnancy, estriol is the predominant circulating estrogen in terms of serum levels. Another type of estrogen called estetrol (E4) is produced also produced predominantly during pregnancy as shown in Figure $7$. Estrogens function in many physiological processes, including the regulation of the menstrual cycle and reproduction, maintaining bone density, brain function, cholesterol mobilization, and maturation of reproductive organs during development, and they play a role in controlling inflammation. Because of their lipophilic nature, it is thought that steroid hormones, such as estrogen, pass the cell membrane by simple diffusion, although some evidence exists that they can also be actively taken up by the endocytosis of carrier protein-bound hormones. For a long time, it has been assumed that binding of the ligand resulted in a simple on/off switch of the receptor (Fig. 6, step 1). While this is likely the case for typical agonists like estrogen and progesterone, this is not always correct for receptor antagonists, used in drug therapy. These antagonists come in two kinds, so-called partial antagonists (for the estrogen receptors known as SERMs for Selective Estrogen Receptor Modulators) and full antagonists. The partial antagonist can, depending on cell type, act as a SHR agonist or antagonist. In contrast, full antagonists (for ER known as SERDs for Selective Estrogen Receptor Downregulators) always inhibit the receptor, independent of cell type, in part by targeting the receptor for degradation. Binding of either type of antagonist results in major conformational changes within the LBD and in the release from heat shock proteins that thus far had protected the unliganded receptor from unfolding and aggregation (Fig. 6 step 2). Figure $8$ shows an interactive iCn3D model of the Androgen Receptor DNA-Binding Domain Bound to a Direct Repeat Response Element (1R4I). Transcription Factor (TF) Recognition and Binding to DNA TF controls gene expression by binding to their target DNA site to recruit, or block, the transcription machinery onto the promoter region of the gene of interest. Their function relies on the ability to find their target site quickly and selectively. In living cells, TFs are present in nM concentrations and bind the target site with comparable affinity, but they also bind any DNA sequence (nonspecific binding), resulting in millions of low affinity (i.e., >10−6 M) competing sites. Nonspecific binding facilitates the search for the target site by three major mechanisms as shown in Figure $9$. The second scenario is a ‘hopping’ mechanism, in which a TF might hop from one site to another in 3D space by dissociating from its original site and subsequently binding to the new site. This may happen within the same chain and re-association occurs adjacent to the former dissociated site. A third search mechanism is described as ‘intersegmental transfer’. In this scenario, the protein moves between two sites via an intermediate ‘loop’ formed by the DNA and subsequently binds at two different DNA sites. This mechanism applies to TFs with two DNA-binding sites. Proteins with two DNA-binding sites can occasionally bind non-specifically to two locations situated far apart within the DNA strand, that are brought into close contact through the formation of these loops. Such TFs transfer across a point of close contact without dissociating from the DNA. Top: When the transcription factor (pink ring) moves from one site to another by sliding along the DNA and is transferred from one base pair to another without dissociating from the DNA, this mechanism is called sliding. Center: Hopping occurs when the transcription factor moves on the DNA by dissociating from one site and re-associating with another site. Bottom: Intersegmental transfer describes the mechanism by which the transcription factor gets transferred through DNA bending or the formation of a DNA loop, resulting in the protein being bound transiently to both sides and subsequently moving from one site to the other. One of the main scenarios involves a ‘sliding’ mechanism, in which the protein moves from its initial non-specific site to its actual target site by sliding along the DNA (also known as 1-dimensional (1D) sliding). When the TF starts to move and shift counterions from the phosphate backbone, the same number of counterions binds to the site left free by the protein. The sliding rate is also dependent on the hydrodynamic radius of the protein; the required rotational movement over the DNA backbone is greater for larger proteins, that tend to slide slowly. Recent Updates: 9/25/23 The sliding model that was proposed by Von Hippel and Berg suggests that DNA-binding protein exists in two interconverting conformations. One is a specific form (O) that can bind to a target DNA sequence, such as an operator in DNA, through specific hydrogen bonds (along with electrostatic interaction) characterized by a low KD. The other is a nonspecific form (D) that binds mainly with weaker affinity through electrostatic interactions and a high KD. Nonspecific binding brings the protein to the DNA surface. Dynamic conformational changes from the O to D allow sampling of hydrogen bonds between donor and acceptors in the protein and in the major grove of the protein. The protein can diffuse much more quickly along the DNA to find its target site since the search for the specific target site is now effectively 1D instead of 3D. There is no thermodynamic barrier to sliding since counterions that leave the DNA when bound to the protein rebind behind it as the protein slides. These processes are illustrated in Figure $10$ below. Figure $10$: Two-state model sliding model of DNA protein binding to its specific binding site (operator) There are a large number of overlapping nonspecific binding sites (let's say each is 6 base pairs in length), which also help drive the nonspecific binding of the protein to the DNA through entropy increases (i.e consider the probability of binding to 1 site on the DNA versus multitudes of overlapping sites). Experiments show that the on-rate for a DNA binding protein for finding its target site increases with increasing length of the DNA molecule the specific site is embedded in. The opposite would be expected given the diffusion rate of large molecules. In fact, the kon was found to be greater than diffusion-controlled limits, which can be explained by the reduced dimensionality of the search for the specific site when the protein is loosely bound through nonspecific electrostatic interactions that enable the 1D search. Each eukaryotic TF controls tens to hundreds of genes scattered throughout the genome, and expressing each gene needs various TFs simultaneously binding to their sites to form the transcription complex, an extremely rare event in probabilistic terms. As a result, the in vivo site occupancy patterns of eukaryotic TFs are more complex than predicted by their in vitro site-specific binding profiles and do not strongly correlate with the actual levels of gene expression. An interesting feature highlighted by genome analysis is an accumulation of potential TF binding sites in regions flanking eukaryotic genes. Such clusters of degenerate recognition sites are assumed to be key for transcription control and thus are generally classified as gene regulatory regions (RR). For example, the affinity of the Drosophila TF Engrailed to the RRs of its target genes is strongly amplified by long tracts of degenerate consensus repeats that are present in such regions. Role of Short Tandem Repeats (STRs) in the Genome As we have mentioned previously, only about 1.5% of the human genome encodes genes for actual proteins. Much of the genome is transcribed at low levels into RNAs, some of which have clearly defined functions (examples include rRNA, mRNAs, and regulatory RNAs). A large part is presumably involved in facilitating the 3D organization of the genome and its dynamic architecture, which determines its replicative and transcriptive access. One poorly understood feature of genomic DNA is short tandem repeats (STRs). These repeats stretch up to 100 nucleotides in length with each repetitive tandem repeat running from just 1 to 6 bases long. They comprise about 6% of the genome (compared to the 1.5% for protein-coding genes), and are found in abundance in chromatin that is transcribed mRNA for proteins. For example, a specific DNA sequence might be GTCACGTGAC while a small STR would be (CG)6C(CG)11 Specifically, STRs surround sites where classic transcription factors (TF) bind. As we described in this and the previous chapter sections, TFs bind through specific DNA binding motifs (like helix-loop-helix or Zn2+ fingers) to consensus sequences (such as response elements and enhancers) in the DNA as they function to control transcription. The binding of transcription factors or other proteins to target sequences occurs initially through nonspecific electrostatic interactions, followed by a dimensionally restricted diffusion along the DNA as the protein finds its specific sequence target. In contrast, the STRs offer little sequence uniqueness for high affinity, low KD binding sites for specific protein interactions, so the question remains as to how they express function, which their omnipresence suggests they have. Studies by Horton et al (Science 381, 1304 (2023) have shown that classic TFs do indeed bind STRs, albeit at lower affinity (higher KD) compared to their binding to classic TF-specific sequences. The bind with higher affinity than to nonspecific DNA sequences. Just as nonspecific interactions facilitate TF binding to promoter sites, so do the multiple STRs that straddle the DNA binding element. If a TF binds its isolated target DNA with a low effective KD (high affinity) and -ΔG0 value, a target DNA surrounded by multiple STRs would have an even lower effective KD (higher affinity) and even more -ΔG0 value. They do so by increasing the effective on rate (kon) for protein binding. (Remember that KD = koff/kon. ) The effective size of the target for the TF becomes greater when it is an “island” in the middle of a STR “sea”. The increased affinity stems in large part from the more favorable entropy of having the TF bind not to just 1 site but effectively to multiple overlapping sites. This also increases the localized concentration of TF near the specific site which drives binding. The koff is not expected to change. Another effect of the STRs on TF binding is that multiple DNA-binding proteins can bind to the same site through their interactions with STRs at the site, leading to new ways to regulate gene transcription. The group studies just two TFS but sequence analyses suggest that many TF would use a similar mechanism. Histone Modification and Chromatin Remodeling Regulation of transcription involves dynamic rearrangements of chromatin structure. Recall that eukaryotic DNA is complexed with histone octamers, which are composed of dimers of the core histones H2A, H2B, H3, and H4. 147 bp of DNA are wrapped 1.65 times around each octamer forming nucleosomes, the basic packaging units of chromatin. Nucleosomes, connected by linker DNA of variable length as “beads on a string”, generate the 11 nm linear structure. The linker histone H1 is positioned at the top of the core histone octamer and enables higher organized compaction of DNA into transcriptionally inactive 30 nm fibers. To understand the role of chromatin in the regulation of transcription it is important to know where nucleosomes are positioned and how the positioning is achieved. Basically, there are four groups of activities that change chromatin structure during transcription: (1) histone modifications, (2) eviction and repositioning of histones, (3) chromatin remodeling, and (4) histone variant exchange. Histone modifiers introduce post-translational, covalent modifications to histone tails and thereby change the contact between DNA and histones. These modifications govern access to regulatory factors. Histone chaperones aid in the eviction and positioning of histones. A third class of chromatin restructuring factors is ATP-dependent chromatin remodelers. These multi-subunit complexes utilize energy from ATP hydrolysis for various chromatin remodeling activities including nucleosome sliding, nucleosome displacement, and the incorporation and exchange of histone variants. Post-translational modifications (PTMs) of histone proteins are a primary mechanism that controls chromatin architecture. Over 20 distinct types of histone PTMs have been described, among which the most abundant ones are acetylation and methylation of lysine residues. Histone PTMs can be deposited on and removed from chromatin by different enzymes, known as histone PTM ‘writers’ and ‘erasers’. Histone PTMs exert their regulatory effects via two main mechanisms. First, histone PTMs serve as docking sites for various nuclear proteins––histone PTM ‘readers’––that specifically recognize modified histone residues through their modification-binding domains. Recruitment of these proteins at specific genomic loci promotes key chromatin processes, such as transcriptional regulation and DNA damage repair. Second, some histone PTMs, such as acetylation, directly affect chromatin's higher-order structure and compaction, thereby controlling chromatin accessibility to protein machinery such as those involved in transcription. Chromatin may adopt one of two major states interchangeably. These states are heterochromatin and euchromatin. Heterochromatin is a compact form that is resistant to the binding of various proteins, such as transcriptional machinery. In contrast, euchromatin is a relaxed form of chromatin that is open to modifications and transcriptional processes, as shown in Figure $10$. Histone methylation promotes the formation of Heterochromatin whereas, histone acetylation promotes euchromatin. The addition of methyl groups to the tails of histone core proteins leads to histone methylation, which in turn leads to the adoption of a condensed state of chromatin called ‘heterochromatin.’ Heterochromatin blocks transcription machinery from binding to DNA and results in transcriptional repression. The addition of acetyl groups to lysine residues in the N-terminal tails of histones causes histone acetylation, which leads to the adoption of a relaxed state of chromatin called ‘euchromatin.’ In this state, transcription factors and other proteins can bind to their DNA binding sites and proceed with active transcription. Chromatin remodeling can also be an ATP-dependent process and involve histone dimer ejection, full nucleosome ejection, nucleosome sliding, and histone variant exchange as shown in Figure $12$. ATP-dependent chromatin remodeling complexes bind to nucleosome cores and the surrounding DNA, and, using energy from ATP hydrolysis, they disrupt the DNA-histone interactions, slide or eject nucleosomes, alter nucleosome structures, and modulate the access of transcription factors to the DNA (Figure 28.3.9). In addition to modulating gene expression, some of the complexes are involved in nucleosome assembly and organization, following transcription at locations in which nucleosomes have been ejected, packing of DNA, following replication, and DNA repair. Panel (a) shows a subset of ISWI and CHD complexes is involved in nucleosome assembly, maturation, and spacing. Panel (b) shows SWI/SNF complexes are primarily involved in histone dimer ejection, nucleosome ejection, and nucleosome repositioning through sliding, thus modulating chromatin access. Panel (c) shows INO80 complexes are involved in histone exchange. It should be noted that the complexes might be involved in other chromatin remodeling functions. Figure $13$s shows the effects of Histone Variant H3.3 on C. elegans Lifespan Protein-DNA Interactions Proteins use a wide range of DNA-binding structural motifs, such as homeodomain (HD), helix-turn-helix (HTH), and high-mobility group box (HMG) to recognize DNA. HTH is the most common binding motif and can be found in several repressor and activator proteins, as shown in Figure $14$. Despite their structural diversity, these domains participate in a variety of functions that include acting as substrate interaction mediators, enzymes to operate DNA, and transcriptional regulators. Several proteins also contain flexible segments outside the DNA-binding domain to facilitate specific and non-specific interactions. For example, many HD proteins use N-terminal arms and a linker region to interact with DNA. The Encyclopedia of DNA Elements (ENCODE) data suggest that about 99.8% of putative binding motifs of TFs are not bound by their respective TFs in the genome. It is, therefore, clear that the presence of a single binding motif per TF is not adequate for TF binding. Figure $14$ shows interactive iCn3D models of the transcription factor binding domains as depicted in the figure above. (Copyright; author via source) POU protein:DNA complex HTH-HD domain (3l1p) Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...fHmWMvUCiJus77 Human Hsf1 with Satellite III repeat DNA - HTH Domain (5d5v) Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...qkHMwpH7LdDmr7 POU-HMG-DNA ternary complex - HTM-HMG domain (1gt0) Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...b9X7DEGSK2n2V7 Klf4 zinc finger DNA binding domain in complex with methylated DNA(4m9e) Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...T7ZXxMhQ66WNL9 Lactose operon repressor and its complexes with DNA and inducer (1lbg) Click the image for a popup or use this external link:https://structure.ncbi.nlm.nih.gov/i...6v3y24U7N2qYC8 Myc-Max and Mad-Max recognizing DNA(1nkp) Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...qcTBN4UwxPFH99 Most of the search mechanistic studies that try to determine how TFs find their binding sites are limited to naked DNA-protein complexes, which do not reflect the actual crowded environment of a cell. Studies with naked DNA and transcription factors have shown that many DNA-binding proteins travel a long distance by 1D diffusion. However, the search process for eukaryotes must occur in the presence of chromatin, which can hinder protein mobility. In this case, the protein must dissociate from the DNA, enter a 3D mode of diffusion state, and continue the target site searching process. The sliding and intersegmental transfer mechanisms can be explained through the example of the lac repressor. The lac repressor contains 4 identical monomers (a dimer of dimers) for its DNA binding. The binding sequence of these dimers is symmetric or pseudo-symmetric, and each half is identified by these identical monomers. The HTH domain of the lac repressor is the DNA-binding domain that facilitates the interaction with its target site on DNA as shown in Figure $15$. As a result of a rapid search (sliding) along the DNA molecule and intersegmental transfer between distant DNA sequences, the lactose repressor finds its target sites faster than the diffusion limit. The section comprised between residues 1–46 of the HTH protein domain, characterized by three α-helices, maintains its secondary structure through specific and non-specific binding. When the repressor binds to a non-specific site, the HTH domain interacts with the DNA backbone and maintains the interaction with its helix region in the major groove juxtaposition. This arrangement facilitates the interaction of the recognition helix with the edges of the DNA bases, enabling the repressor to walk or search for its specific site on the DNA. The C-terminal residues of the DNA-binding domain, residues 47–62, form the hinge region, and are normally disordered during non-specific recognition; however, during specific site recognition, residues 50–58 acquire an α-helix configuration (hinge helix) (Fig. 15 above). The disordered hinge region and the flexibility of the HTH domain allow the protein to move freely along the DNA to search for its target site. In specific binding complexes, the hinge helix of each monomer is located at the symmetrical center of the binding site, thereby causing the hinge helices to interact with each other (intersegmental transfer) to allow better stability. Moreover, DNA bends at the symmetrical center of the specific binding site (37° angle), thereby supporting monomer-monomer interactions. In addition to the helix-turn-helix structure, the zinc finger motif is also very common, especially in eukaryotic TFs, as shown in Figure $16$. Proteins that contain zinc fingers (zinc finger proteins) are classified into several different structural families. Unlike many other clearly defined supersecondary structures such as Greek keys or β hairpins, there are a number of types of zinc fingers, each with a unique three-dimensional architecture. A particular zinc finger protein’s class is determined by this three-dimensional structure, but it can also be recognized based on the primary structure of the protein or the identity of the ligands coordinating the zinc ion. Despite the large variety of these proteins, however, the vast majority typically function as interaction modules that bind DNA, RNA, proteins, or other small, useful molecules. Variations in structures serve primarily to alter the binding specificity of a particular protein. The most common type of zinc finger motif utilizes two Cys and two His residues (CCHH) coordinating the Zn(II) ion to adopt a ββα fold with three hydrophobic residues responsible for the formation of a small hydrophobic core which offers additional stabilization of the zinc finger domain. Panel (a) shows the alignment of the TFIIIA-like zinc finger domains from different organisms. The green color denotes residues that are responsible for the hydrophobic core formation in most CCHH zinc fingers (L17, F11, and L2). Yellow and blue indicate the coordinating Cys and His residues, respectively. Panel (b) shows the 3D NMR structure of 15-th ZF from zinc finger protein 478 [PDB: 2YRH] Figure $17$ shows an interactive iCn3D model of C2H2-type zinc finger domain (699-729) from zinc finger protein 473 (2YRH). Overall, zinc finger motifs display considerable versatility in binding modes, even between members of the same class (e.g., some bind DNA, others protein), suggesting that they are stable scaffolds that have evolved specialized functions. For example, zinc finger-containing proteins function in gene transcription, translation, mRNA trafficking, cytoskeleton organization, epithelial development, cell adhesion, protein folding, chromatin remodeling, and zinc sensing, to name but a few. Zinc-binding motifs are stable structures, and they rarely undergo conformational changes upon binding their target. The last binding domain that we will consider in detail here is the helix-loop-helix domain found in Leucine zipper-containing proteins. Specifically, bZIPs (Basic-region leucine zippers) are a class of eukaryotic transcription factors. The bZIP domain is 60 to 80 amino acids in length with a highly conserved DNA binding basic region and a more diversified leucine zipper dimerization region. The two regions form α-helical structures that are connected via a looped region. This forms a core helix-loop-helix (HLH) structure within each monomer of the protein. Two monomers then join through the formation of a leucine zipper junction forming a heterodimeric protein structure. The resulting heterodimer can bind with DNA in a sequence-specific manner through the basic α-helices as shown in Figure $18$. Specifically, basic residues, such as lysines and arginines, interact in the major groove of the DNA, forming sequence-specific interactions ). Most bZIP proteins show a high binding affinity for the ACGT motifs. The bZIP heterodimers exist in a variety of eukaryotes and are more common in organisms with higher evolution complexity. Figure $19$ shows an interactive iCn3D model of the GCN4 basic region leucine zipper binds DNA as a dimer of uninterrupted alpha helices (1YSA). Epigenetics and Transgenerational Inheritance Even though all somatic cells of a multicellular organism have the same genome, different cell types have different transcriptomes (sets of all expressed RNA molecules), different proteomes (sets of all proteins), and, hence, different functions. Cell differentiation during embryonic development requires the activation and repression of specific sets of genes by the action of cell lineage-defining transcription factors. Within a cell lineage, gene activity states are often maintained over several rounds of cell divisions (a phenomenon called “cellular memory” or “cellular inheritance”). Since the rediscovery of epigenetics some 30 years ago (it was originally proposed by Conrad Hal Waddington in the early 1940s), cellular inheritance has been attributed to gene regulatory feedback loops, chromatin modifications (DNA methylation and histone modifications) as well as long-lived non-coding RNA molecules, which collectively are called the “epigenome”. Among the different chromatin modifications, DNA methylation and polycomb-mediated silencing are probably the most stable ones and endow genomes with the ability to impose silencing of transcription of specific sequences even in the presence of all of the factors required for their expression. Defining Transgenerational Epigenetic Inheritance The metastability of the epigenome explains why development is both plastic and canalized, as originally proposed by Waddington. Although epigenetics deal only with the cellular inheritance of chromatin and gene expression states, it has been proposed that epigenetic features could also be transmitted through the germline and persist in subsequent generations. The widespread interest in “transgenerational epigenetic inheritance” is nourished by the hope that epigenetic mechanisms might provide a basis for the inheritance of acquired traits. Yes, Lamarck has never been dead and every so often raises his head, this time with the help of epigenetics. Although acquired traits concerning body or brain functions can be written down in the epigenome of a cell, they cannot easily be transmitted from one generation to the next. For this to occur, these epigenetic changes would have to manifest in the germ cells as well, which in mammals are separated from somatic cells by the so-called Weismann barrier. Further, the chromatin is extensively reshaped during germ cell differentiation as well as during the development of totipotent cells after fertilization, even though some loci appear to escape epigenetic reprogramming in the germline. Long-lived RNA molecules appear to be less affected by these barriers and therefore more likely to carry epigenetic information across generations, although the mechanisms are largely unsolved. Evidence for Transgenerational Epigenetic Inheritance In the past 10 years, numerous reports on transgenerational responses to environmental or metabolic factors in mice and rats have been published. The factors include endocrine disruptors, high-fat diet, obesity, diabetes, undernourishment as well as trauma. These studies investigated DNA methylation, sperm RNA, or both. For example, when male mice are made prediabetic by treatment with streptozotocin it affects the DNA methylation patterns in their resulting sperm, as well as the pancreatic islets of F1 and F2 of the resulting offspring. Furthermore, studies have shown that traumatic stress in early life altered behavioral and metabolic processes in the progeny and that injection of sperm RNAs from traumatized males into fertilized wild-type oocytes reproduced the alterations in the resulting offspring. In humans, epidemiological studies have linked food supply in the grandparental generation to health outcomes in the grandchildren. An indirect study based on DNA methylation and polymorphism analyses has suggested that sporadic imprinting defects in Prader–Willi syndrome are due to the inheritance of a grandmaternal methylation imprint through the male germline. Because of the uniqueness of these human cohorts, these findings still await independent replication. Most cases of segregation of abnormal DNA methylation patterns in families with rare diseases, however, turned out to be caused by an underlying genetic variant. Thus, studies of this nature must rule out the effects of traditional genetic inheritance as being a factor of the observed phenotypes. Genetic inheritance alone cannot fully explain why we resemble our parents. In addition to genes, we inherited from our parents the environment and culture, which in parts have been constructed by the previous generations as shown in Figure $20$. A specific form of the environment is our mother’s womb, to which we were exposed during the first 9 months of our life. The maternal environment can have long-lasting effects on our health. In the Dutch hunger winter, for example, severe undernourishment affected pregnant women, their unborn offspring, and the offspring’s fetal germ cells. The increased incidence of cardiovascular and metabolic disease observed in F1 adults is not due to the transmission of epigenetic information through the maternal germline, but a direct consequence of the exposure in utero, a phenomenon called “fetal programming” or—if fetal germ cells and F2 offspring are affected—“intergenerational inheritance”. Panel shows that offspring inherit from their parent's genes (black), the environment (green), and culture (blue). Genes and the environment affect the epigenome (magenta) and the phenotype. Culture also affects the phenotype, but at present, there is no evidence of a direct effect of culture on the epigenome (broken blue lines). It is a matter of debate, how much epigenetic information is inherited through the germline (broken magenta lines). G genetic variant, E epigenetic variant. Panel b shows that an epimutation (promoter methylation and silencing of gene B in this example) often results from aberrant read-through transcription from a mutant neighboring gene, either in sense orientation as shown here or in antisense orientation. The presence of such a secondary epimutation in several generations of a family mimics transgenerational epigenetic inheritance, although it represents genetic inheritance. Black arrow, transcription; black vertical bar, transcription termination signal; broken arrow, read-through transcription Roadmap to Proving Transgenerational Epigenetic Inheritance Here are some steps to show that inheritance is determined by epigenetics and not classical genetics. • Rule out genetic, ecological, and cultural inheritance. For studies in mice and rats, inbred strains and strictly controlled environments need to be used. When a pregnant female animal is exposed to a specific environmental stimulus, F3 offspring and subsequent generations must be studied to exclude a direct effect of the stimulus on the embryos’ somatic cells and germ cells. Even more desirable is the use of in vitro fertilization (IVF), embryo transfer, and foster mothers. When a male animal is exposed to an environmental stimulus, F2 offspring must be studied to exclude transient effects on germ cells. To ensure that any phenotype is exclusively transmitted via gametes, IVF must be used, controlling for possible artifacts relating to IVF. In contrast with laboratory animals, it is impossible to rule out ecological and cultural inheritance in humans, but genetic effects should and can be excluded. If an epimutation follows Mendelian inheritance patterns, be cautious: you are more likely looking at a secondary epimutation and genetic inheritance. Study the haplotype background of the epimutation: if in a given family it is always on the same haplotype, you are again most likely dealing with a secondary epimutation. Do whole genome sequencing to search for a genetic variant that might have caused the epimutation and be aware that this variant might be distantly located. Good spots to start looking at are the two neighboring genes, where a mutation might cause transcriptional read-through in sense or antisense orientation into the locus under investigation. Unfortunately, if you don’t find anything, you still cannot be 100% sure that a genetic variant does not exist. • Identify the responsible epigenetic factor in the germ cells. Admittedly, this is easier said than done, especially in female germ cells, which are scarce or unavailable. Be aware that germ cell preparations may be contaminated with somatic cells or somatic DNA. Use swim-up (sperm) or micromanipulation techniques to purify germ cells to the highest purity. Exclude the presence of somatic cells and somatic DNA by molecular testing, for example by methylation analysis of imprinted genes, which are fully methylated or fully unmethylated only in germ cells. • Demonstrate that the epigenetic factor in the germ cells is responsible for the phenotypic effect in the next generation. If possible, remove the factor from the affected germ cells and demonstrate that the effect is lost. Add the factor to control germ cells and demonstrate that the effect is gained. While RNA molecules can and have been extracted from the sperm of exposed animals and injected into control zygotes, DNA methylation, and histone modifications cannot easily be manipulated (although CRISPR/Cas9-based epigenome editors are being developed and used for this purpose), and all of these experiments can hardly be done in humans. In light of these problems, this might currently be too much to ask for to prove transgenerational epigenetic inheritance in humans, but should, nevertheless, be kept in mind and discussed.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Fundamentals_of_Biochemistry_Vol._V_-_Problems/Enzyme_Kinetics_Problems/Carbonic_Anydrase_Inhibition.txt	princeton-nlp/TextbookChapters	These questions derive from the Research Literature Module - Carbon Capture Using Carbonic Anhydrase Question $1$ Using the equation below, at what ratio of CO2/[HCO3-] would the rate for the forward reaction (CO2 sequestration) be cut in half? v_0=\frac{V_M S}{K_M\left(1+\frac{I}{K is}\right)+S} If you need some help, hover over - Give me a hint! Answer \begin{gathered} v_{-I}=\frac{V_M S}{K_M+S}=\frac{(1)(1)}{(1+1)}=0.5 \ v_{+I}=\frac{V_M S}{K_M\left(1+\frac{[I]}{K_{i s}}\right)+S}=\frac{(1)(1)}{1\left(1+\frac{[I]}{2}\right)}=0.25=\frac{1}{1+\frac{[I]}{2}} \ 0.25\left(1+\frac{[I]}{2}\right)=1 \ 1+\frac{[I]}{2}=4 \ \frac{[I]}{2}=3 \ {[I]=6} \end{gathered} Hence it doesn't take much HCO3- buildup to inhibit the "capture" of CO2! Carbonic Anhydrase - Mechanism These questions derive from the Research Literature Module - Carbon Capture Using Carbonic Anhydrase Carbonic Anhydrase - Structure and Mechanism 1 An active site Zn2+ appears to bind a water molecule and reduce its pKa such that the bound form is OH-. This is illustrated in the left panel of Figure $1$ below, which depicts the local environment of the bound Zn2+ (coordinated by histidine side chains and an OH-) in the absence (left) and presence (right) of CO2. Note that the back histidine is difficult to barely visible (but still evident) in both structures. To assist in viewing the structure, the right panel shows an interactive iCn3D model of Zn- human carbonic anhydrase II at pH 7.8 and 0 atm CO (6LUW). Figure $1$: Left panel: Coordination of OH- to Zn2+ in carbonic anhydrase in the absence (left) and presence (right) of substrate CO2. Right panel: Zn- human carbonic anhydrase II at pH 7.8 and 0 atm CO (6LUW) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...43DYyZFvHJpZn9 Question $\PageIndex1}$ What is the coordination geometry of the Zn ion? Answer tetrahedral Question $2$ Draw a simplified reaction mechanism showing bicarbonate formation from the two reactants, CO2 and OH-. Answer The enzyme is reversible and in humans is important in CO2 transport in respiration and maintaining intracellular pH, which is also its key role in most organisms. CO2, like the other atmospheric gases O2 and N2, are nonpolar and have limited solubility in water. The solubility of these gases in water at 20oC and 1 atm pressure, in g/L and mM, are shown in Table $1$ below. Gas (ref) solubility (aq) (g/L) solubility (mM) CO2 1.7 38 O2 0.044 1.3 N2 0.019 0.68 Question $3$ Offer reasons that explain the significantly higher (but still low) solubility of CO2 in water compared to O2 and N2. Answer CO2 is considered a nonpolar molecule since it has no net molecular dipole. However, it does have 2 bond dipoles (pointing in opposite directions), so the carbon atom is δ+ while the Os are δ-. This probably contributes to its greater solubility than N2 and O2 which don't have bond dipoles. CO2 would not orient itself in a dipole electric field, but it would to some extent in a quadrupole (4 poles) electric field where the positive potentials are oriented north and south and the negative potentials at east and west. CO2 has a quadrupole moment. In addition, the continued reaction of CO2 and water to form the weak acid carbonic acid would contribute to its higher apparent solubility. It is not clear to the authors if these contributions to solubility are accounted for in the experimental values of solubility. A quadruple and its associated magnetic field with oriented CO2. https://commons.wikimedia.org/wiki/F...quadrupole.svg Question $4$ How does the enzyme facilitate the transport of CO2 in blood? How does it maintain intracellular pH? Answer It converts the poorly soluble carbon unit in CO2 to the strongly soluble bicarbonate anion. HCO3-. A simple explanation for maintaining intracellular pH comes from the chemical equation below. CO2(aq) + H2O (l) ↔ H2CO3 (aq) + H2O(l) H3O+(aq) + HCO3-(aq). HCO3- is the conjugate base of the weak acid, H2CO3 so the system is a classic buffer. For a complete explanation of why the system can act as a buffer at neutral pH even though the pKa of the weak acid is 3.6, see Chapter 2.3 for review. Let's explore the structure of two different CAs, human carbonic anhydrase II and the carbonic anhydrase from Neisseria gonorrhea. Human carbonic anhydrase II The structure of native human carbonic anhydrase II and its catalytic mechanism is shown in Figure $5$ below. Figure $5$: Structure of native human carbonic anhydrase II (Zn-CA II) and its catalytic mechanism. Kim, J.K., Lee, C., Lim, S.W. et al.Nat Commun 11, 4557 (2020). https://doi.org/10.1038/s41467-020-18425-5. Creative Commons Attribution 4.0 International License, http://creativecommons.org/licenses/by/4.0/. Panel a shows the active site consists of the zinc binding site, hydrophobic/hydrophilic regions, and the entrance conduit (EC). Panel b shows the water networks in the active site that are responsible for the proton transfer (red) and substrate/product/water exchange (blue) during enzyme catalysis. Panel c shows the forward reaction mechanism of Zn-CA II. The active site itself lies at the bottom of a deep cavity (15 Å deep) in the protein, which is readily accessible to solvent An interactive iCn3D model of human carbonic anhydrase II with bound bicarbonate and CO2 (2VVB) is shown in Figure $6$ below. Figure $6$: Human carbonic anhydrase II with bound bicarbonate and CO2 (2VVB) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...ibgmgsm3UWhjX6 The active site residues shown in Figure 6 are labeled and shown as sticks. Bound CO2 and HCO3- are also shown as sticks. Question $5$ This question addresses the Biomolecular Visualization Framework theme(s) Molecular Interactions (MI), Atomic Geometry (AG) Using iCn3D to show the noncovalent interactions between bicarbonate, Zn, and the protein by using iCn3D. Measure the distance between the hydrogen-bonded atoms in HCO3- and Thr 199. iCn3D instructions Trackpad and Mouse Controls rotate: click and drag (mouse: left click and drag) zoom: pinch and spread (mouse: rotate the scroll wheel) translate: two-finger click and drag (mouse: right click and drag) Re-center: left click View from the top menu bar, then select “Center Selection” •Note: ctrl-click on a PC = command-click on Mac; alt-click on PC = option click on Mac 1. Open the external link: https://structure.ncbi.nlm.nih.gov/i...ibgmgsm3UWhjX6 2. From the top menu bar, choose Analysis, Interactions 3. In the new popup window select the following prompts, then click 4. 3D Display interactions 4. Close all but the main modeling window. 5. Zoom into the bicarbonate binding site. From the top menu bar, choose Analysis, Distance, distance between 2 atoms, and pick the two atoms (by holding down the Alt key or Option on a Mac) involved in the hydrogen bond between the bicarbonate and the amide 6. Rescale the label size by choosing Analysis, Label Scale, 0.4. 7. From the top menu bar choose Select, Toggle highlights to remove yellow and box highligting. 8. Save a PNG file by choosing 9. You can reload the PNG file directly into iCn3D by choosing File, Open File, iCn3D PNG image Answer The blue dotted lines are ion-ion interactions. Note the green hydrogen bond between T199 and the bound bicarbonate. Remember that hydrogen atoms are not shown in PDB files from x-ray structures. The H-bond distance is 3.1 angstroms. For some reason, the H-bond does not show unless the initial constraint distances are moved to 4.2 Angstroms. Question $6$ What is a likely function of Val, Leu and Trp cluster in CAII (shown in Figures 5 and 6)? Answer These side chains are all hydrophobic (as illustrated in gold in Figure 5) and provide a weak binding environment for nonpolar CO2. Question $7$ What ligand would likely replace OH- at low pH values? What would happen to the activity of the enzyme at lower pHs? Answer At low pH, ie. at pH values lower than the pKa of the Zn-bound water (which deprotonates to form the OH- ligand and nucleophile), the ligand and nucleophile would be H2O. The enzyme would display a lower activity given the weaker nucleophile. Question $8$ From the mechanism shown in Figure 5, does bicarbonate coordinate the Zn2+ ion in a monodentate or bidentate manner? Answer Monodentate as only 1 coordinate covalent bond forms on an electron pair donation from the bicarbonate to the Zn2+. Question $9$ Write a verbal description of the mechanism of CAII based on Figure 5 Answer CO2 binds to the active site through loose association with the cluster of hydrophobic side chains. The Zn2+ bound OH- acting as a nucleophile attacks the central carbon of the CO2 forming HCO3. The carbonate forms a monodentate interaction with Zn and also a hydrogen bond to Thr 199. The HCO3- is then displaced by an incoming water molecule. The other product of the reaction, H+, moves through a hydrogen bond network of water molecules (W1 and W2) to His 64 and eventually to bulk water. The interactions with substrate and products are weak allowing fast exchange. Question $10$ Why is a proton transfer path needed? Answer H+ is a product of the reaction: CO2 (g) + H2O ↔ H2CO3 (aq) ↔ HCO3- (aq) + H+ (aq). It must depart to prevent charge build-up, maintain charge balance, and keep the correct electrostatic environment of the active site. Question $11$ Thr 199 plays a key role in the mechanism. State a reason for its importance in the reversible reaction. Answer Thr 199 supplies two hydrogen bonds to the bicarbonate. It actually destabilizes bicarbonate bonding with respect to a T199A mutant. In the mutant, carbonate might bind Zn2+ in a bidentate fashion, leading to tighter binding and a slower dissociation rate of the product, HCO3-. Question $12$ The dissociation constant KD (or Kis) for bicarbonate binding to HCAII is about 77 mM. What kind of inhibitor might it be for the forward reaction? Answer Given that it binds in the active site and would prevent binding of the substrate for the forward reaction, CO2, it is a competitive inhibitor. Question $13$ Using the equation below, at what ratio of CO2/[HCO3-] would the rate for the forward reaction (CO2 sequestration) be cut in half? Assume the VM=1, KM forward reaction is 1, [S] = 1 and Kis is 2 (a wide range of values are reported in the Brenda Database. v_0=\frac{V_M S}{K_M\left(1+\frac{I}{K is}\right)+S} If you need some help, hover over - Give me a hint! Answer \begin{gathered} v_{-I}=\frac{V_M S}{K_M+S}=\frac{(1)(1)}{(1+1)}=0.5 \ v_{+I}=\frac{V_M S}{K_M\left(1+\frac{[I]}{K_{i s}}\right)+S}=\frac{(1)(1)}{1\left(1+\frac{[I]}{2}\right)}=0.25=\frac{1}{1+\frac{[I]}{2}} \ 0.25\left(1+\frac{[I]}{2}\right)=1 \ 1+\frac{[I]}{2}=4 \ \frac{[I]}{2}=3 \ {[I]=6} \end{gathered} Hence it doesn't take much HCO3- buildup to inhibit the "capture" of CO2! Question $14$ The rate-limiting step for human CA II is the dissociation of a proton from Zn2+-bound water and not the removal of the resulting proton from the enzyme. What does that imply about the rate of removal of the proton from the enzyme? Answer It must be very fast, that is at diffusion-controlled limits through the H-bond channel. Synthetic mimetics of the active site of CA have been made. These are heteromacrocycles (similar to the heme of hemoglobin) as shown in Figure $9$ below. Figure $9$: The macrocycle mimetic has three imidazole groups coordinating zinc. Question $15$ The macrocycle mimetic has three imidazole groups coordinating zinc. Is the bicarbonate coordinated to the Zn2+ in a monodentate or bidentate fashion? From the "denticities" of the interactions of bicarbonate and Zn2+ for human CAII and the mimetic, which catalyst, CAII or the macrocycle would you expect to have a lower KD for bicarbonate? How might this affect the rate-limiting step for the mimetic? Answer The mimetic is bidentate, is it should bind more tightly to bicarbonate, hindering its dissociation, and hence making "product" inhibition more likely. Question $16$ What is the utility of having both CO2 and HCO3- bind weakly to the enzyme Answer Weak binding "permits their rapid exchange. The hydrogen-bonding arrangement in the active site is such that the water or hydroxide ion donates a hydrogen bond to a proximal threonine (Thr199 in hCA II) because the hydroxyl group of this residue is forced to donate its hydrogen in a hydrogen bond to a negatively charged glutamate side chain (Glu106 in hCA II). Site-directed mutations have confirmed this model of the catalytic mechanism. The substrates/products carbon dioxide/bicarbonate are fairly weakly bound against a hydrophobic wall in the active site, which permits their rapid exchange."
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Fundamentals_of_Biochemistry_Vol._V_-_Problems/Global_Challenges_-_Literature-Based_Guided_Assessments_(LGAs)/1.__Global_Challenges%3A_Literature-based_Guided.txt	princeton-nlp/TextbookChapters	Introduction Instead of presenting a litany of end-of-chapter or end-of-book questions that are not linked in content or concepts, we will present a number of problem-solving assessments linked to research literature that deal with key challenges that face the world today. We will call these research literature modules (RLM). Each will focus on a particular biological system (enzyme, pathways, etc) and contain a series of sequential and linked questions on a particular protein, for example, and its function. The problems are summative and hence require an understanding of structure, noncovalent interactions, binding, kinetics, and reaction mechanisms. The modules are guided and have elements of problem-solving and POGIL questions. Each Module will ultimately focus on the structure and properties of key biomolecules. The modules will: 1. Address ASBMB Core concepts and Learning Objectives (generalized below) through analysis and interpretation of research findings in one or more research publications. • Energy is required by and transformed in biological systems. • Macromolecular structure determines function and regulation. • Information storage and flow are dynamic and interactive. • Biochemical systems maintain a state of homeostasis, a steady stable state while continually adjusting to conditions, which requires energy input, organization, and control mechanisms. • Evolution plays a pervasive role in shaping the form and function of all biological molecules and organisms. 2. Link to critical problems facing the world (see the Table) which have clearly identified biochemical components. These critical problems include health care disparities, climate change, pandemics, addiction, childhood trauma, food insecurity, biodiversity loss, ecosystem (ocean, soil, forest) health, and misinformation/disinformation. These problems are often linked and not mutually exclusive. Most biochemistry textbooks focus on problems using biomedical examples. Expanding to study key world problems that are not directly biomedical and are underrepresented in textbooks, allows students to apply their acquired knowledge and understanding into different areas. 3. Follow general features found in problem-based learning and in case studies, which provide contextual applications for the detailed learning opportunities found in biochemistry books and courses. • A broad introduction (text, videos, personal narratives) describing the critical world problem and the relevancy of the selected biochemical system to the problem • A more detailed description of the selected biochemical system, including links to specific locations in Fundamentals of Biochemistry as well as external resources • Research literature results (graphs, tables, models, etc), taken from journals that allow derivatives and reuse by appropriate Creative Commons licensing (for example, CC BY 4.0), for interpretation 4. Focus on representative biomacromolecules (protein, nucleic acid, glycan, lipid and combination of them) relevant to the broader problem for which detailed structure/function questions can be explored 5. Explicitly address and link to appropriate BioMolViz framework themes, goals and objectives to the biomolecules key to the RLM. Relationship of RLMs to BioMolViz and Molecular CaseNet The completed RLMs will consist of a broad introduction and relevant biochemical research findings woven into a narrative that will include nested questions based on the literature with an ultimate focus on a key biomacromolecule. It will not take the form or detail of a full case study as found in Molecular CaseNet (headed by Shuchi Dutta and its Steering Committee, which includes Henry Jakubowski, who is also on the Steering Committee of BioMolViz ). As the RLMs in Fundamentals of Biochemistry and indeed the whole text, as well as the Molecular CaseNet are free online educational resources (OERs), both communities can freely share resources. Since the RLMs have some attributes of case studies, we hope that contributors to Molecular CaseNet will freely use the RLMs and convert them to more expansive case studies, housed within Molecular CaseNet. Likewise, the research literature-based questions in the RLMs that focus on biomacromolecule structures will be explicitly linked to the themes, goals and objectives of the BioMolViz literacy framework. However, the specific questions will not be included in the web repository created by BioMolViz. The repository questions have gone through many iterative cycles of construction, revision, external review by expert panels, and validation by actual classroom use. Instead, the questions in Fundamentals of Biochemistry RLMs that target specific biomolecular visualization framework objectives will help to expand knowledge and understanding of biomolecule visual literacy and BioMolViz objectives, which ultimately is the goal of BioMolViz. A full semester of biochemistry would be necessary to complete a full RLM, as the questions extend from structure, binding, kinetics, mechanism, metabolism, and signal transduction. Yet parts of a complete RLM could be completed after students complete the corresponding chapter in the book. Hence parts of a given RLM will be listed in Volume 5 under the corresponding topic (carbohydrate structure, for example). A link will be provided back to the home RLM from which the questions were derived World Challenges as the Bases for the Research Literature Modules Here are the world challenges we have selected that we serve as the bases for the RLMs. World Problems Research Literature Modules Health Disparities Type II Diabetes Orphan receptors Poverty and stress response: Poverty and epigenetics PM2.5s Pb pollution Pollution (air/water) Climate Change Thermal tolerance plants Carbon Capture Photosynthesis, CO2 sequestration Heat Stroke Biofuels Modeling climate change Pandemics Vaccine Development Ebola Malaria Emerging Diseases Evolution Addiction Natural/Synthetic opiates Alcohol abuse Trauma PTSD Food Insecurity photosynthesis fertilizers Loss of Biodiversity extinction Ecosystem Health Soil Oceans Forest Mis- Disinformation Western blots and image modifications
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Fundamentals_of_Biochemistry_Vol._V_-_Problems/Global_Challenges_-_Literature-Based_Guided_Assessments_(LGAs)/2.__Global_Challenges_-_Climate_Change%3A__Carbo.txt	princeton-nlp/TextbookChapters	Research Literature Module - Carbon Capture Using Carbonic Anhydrase Critical World Challenges Climate Change Key Words, Concepts: protein structure, structure/function relationships, enzyme kinetics, enzyme mechanisms, reaction mechanisms, Western blot analysis, site-directed mutagenesis, biomolecular visualization, computational modeling, graphic analysis The Problem Our climate is changing as planetary temperatures rise from increasing amounts of the greenhouse gas carbon dioxide released into the atmosphere (detailed in Chapter 31) on the burning of fossil fuels. The rate of release is unparalleled in geological history. Present levels (415 ppm) have not been seen for at least 3 million years. Human societies and cultures have had the opportunity to develop in relatively stable climatic conditions. Figure $1$ below shows the rise in atmospheric CO2 over the last 1000 years. Figure $1$: CO2 levels in the atmosphere over the last 1000 years. Our world in data. https://ourworldindata.org/ The steep rise around 1790 coincides with the start of the industrial revolution. The rise in atmospheric CO2 has led to a corresponding rise in the average global temperatures, as illustrated in Figure $2$ below. Figure $2$: Average global temperature changes over the last 1000 years. Our world in data. https://ourworldindata.org/ The per capita emissions of CO2 across the world derive from the use of coal, oil, and gas, as illustrated in Figure $3$ below. Figure $3$: Per capita emission of CO2 from fossil fuel type If we want to decrease emissions, we also need to know in which economic sectors fossil fuels are used. The main sources of global energy-related CO2 emissions by sector are shown in Figure $4$: Figure $4$: Global energy-related CO2 emissions by sector. Updated on 10/26/22. https://www.iea.org/data-and-statist...ions-by-sector. IEA. License: CC BY 4.0 Note the use of coal, gas, and oil for energy production (electricity) accounts for 40% of global CO2 emissions, with transportation (mostly through the use of gasoline and diesel fuel) and industry accounting for about 25% each. Simple chemistry tells us that there are two ways to decrease the amount of product (in this case CO2) in a chemical reaction: • decrease the concentration of reactants (i.e. reduce fossil fuel use) • remove the product, in this case, CO2 from the air. The latter process is called carbon capture or sequestration. It is a daunting process that nature has mastered (through photosynthesis), but it clearly can't keep up with the huge injection of CO2 in the atmosphere caused by burning fossil fuel. We simply can't stop using fossil fuels, which would result in huge economic and social unrest. Alternative green fuels (solar, wind, for example) are being rapidly expanded but can't replace fossil fuels for many years. One of the reasons is that fossil fuels are very energy-dense (MJ/kg) compared to other sources of energy. It's also fascinating to look at the energy transitions humans have made over time. Figure $5$ below shows the energy transition over a log-time scale (for presentation purposes) as well as the energy densities of individual sources. Figure $5$: Human-created energy transitions (log time scale) and energy densities of individual sources New technologies are needed to capture CO2. We have to move much faster in a new clean energy transition than we have in our entire history. A potentially ideal solution would be to capture CO2 from power plants before they reach the atmosphere. We will now look at research into an old enzyme, carbonic anhydrase, that is being repurposed for industrial-level carbon capture, carbonic anhydrase. Carbonic Anhydrase (CA) We have already encountered this enzyme before (Chapter 6.1). It catalyzes the hydration of CO2 (g) as shown below. CO2 (g) + H2O ↔ H2CO3 (aq) ↔ HCO3- (aq) + H+ (aq) It is among the fastest of all enzymes, with a kcat of 106 s-1 and a kcat/Km of 8.3 x 107 M-1s-1 (reference). It is diffusion controlled in that the rate of diffusion of reactants and products, not the chemical steps, determine the reaction rate. It can convert 106 molecules of CO2(g) to HCO3- each second. No wonder scientists and engineers are studying it to capture CO2. It's a big challenge though to capture CO2 released on combustion of coal or natural gas in a power plant. Here are two problems that must be overcome: • The enzyme must be thermostable at elevated temperatures to capture the CO2 found in high-temperature power plant emissions • The enzyme is reversible so it will be inhibited by the product HCO3- • The enzyme must be stable to somewhat alkaline conditions (pH of 0.1M NaHCO3 = 8.3) For carbon capture from fossil fuel emissions, CA is immobilized by surface adsorption, covalent attachment, encapsulation, and entanglement. Immobilized enzymes are typically more thermostable and can be used in flow-through as opposed to solution phase capture. The immobilized enzyme matrix must withstand high temperatures (up to 100°C, and alkaline solvents used to strip the matrix for reuse. The enzyme is found throughout life and typically has an active site Zn2+. There are 8 families, α, β, γ, δ, ζ η, θ, and ι, with the α family being the most abundant. The α forms are generally active as dimers, but can act as monomers and tetramers.. There are 15 isoforms of the α form in humans and have a prime role in pH regulation. They are found in bacteria, fungi, plants, and algae. β-CAs are found in some types of bacteria, archaea, fungi, some higher plants, and invertebrates. CA in chloroplasts (and mitochondria (algae) are involved in carbon fixation. We will focus our attention on engineering carbonic anhydrase to make them more thermostable, alkali insensitive, and less susceptible to product inhibition by bicarbonate. Natural enzymes can be isolated and selected for thermal and alkali stability. In addition, new versions selected for these properties can be engineered using directed evolution or site-directed mutagenesis. You wish to increase the thermal stability of a protein using mutagenesis. Essentially you wish to perturb the equilibrium between the folded (native) protein and the unfolded (denatured) protein so as to preferentially stabilized the native state. Question $1$ Using mutagenesis, what residues might you change in a native protein to make it more stable at higher temperatures? Answer A characteristic of the native state of the protein is its conformational stability compared to the conformational flexibility of the many possible denatured states. In addition, the protein must undergo conformational changes as it unfolds. Hence anything that restricts conformational flexibility might preferentially stabilize the native state. These would include changing single or pairs of side chains to allow the formation of more salt bridges and intrachain disulfide bonds, as well as hydrogen bonds. Loops with greater flexibility, as determined by B-factors in the crystal structure files, or by molecular dynamic simulations, could be changed to contain a disulfide, which would clearly stabilize a flexible loop. Question $2$ What measurements would you make to quantitate the change in thermal stability? Answer Measures a signal that changes with increasing temperature. The signal can be enzyme activity, or more easily a spectroscopic signal such as absorbance at 280 nm or fluorescence as a function of temperature. Alternatively, the stability at room temperature could be measured using urea as a perturbant. These are discussed in Chapter 4.12. The actual amino acid composition and more strangely specific dipeptide sequences within a sequence are associated with thermal stability of hyperthermophilic proteins. For example, proteins from two different types of archaea with different optimal growth temperatures show that the one with the higher growth temperature have significantly higher levels of VK, KI, YK, IK, KV, KY, and EV and decreased levels of DA, AD, TD, DD, DT, HD, DH, DR, and DG. Similar experiments have been done in bacterial cells. Using machine learning, the dipeptide sequences KH, KR, TF, PM, F∗∗N, V∗∗Y, MW, and WQ were important in themostability where the * denotes a gap in the residues. Structure and Mechanism An active site Zn2+ appears to bind a water molecule and reduce its pKa such that the bound form is OH-. This is illustrated in the left panel of Figure $6$ below, which depicts the local environment of the bound Zn2+ (coordinated byhistidine side chains and an OH-) in the absence (left) and presence (right) of CO2. Note that the back histidine is difficult to barely visible (but still evident) in both structures. To assist in viewing the structure, the right panel shows an interactive iCn3D model of Zn- human carbonic anhydrase II at pH 7.8 and 0 atm CO (6LUW). Figure $6$: Left panel: Coordination of OH- to Zn2+ in carbonic anhydrase in the absence (left) and presence (right) of substrate CO2. Right panel: Zn- human carbonic anhydrase II at pH 7.8 and 0 atm CO (6LUW) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...43DYyZFvHJpZn9 Question $3$ What is the coordination geometry of the Zn ion? Answer tetrahedal Question $4$ Draw a simplified reaction mechanism showing bicarbonate formation from the two reactants, CO2 and OH-. Answer The enzyme is reversible and in humans is important in CO2 transport in respiration and maintaining intracellular pH, which is also its key role in most organisms. CO2, like the other atmospheric gases O2 and N2, are nonpolar and have limited solubility in water. The solubility of these gases in water at 20oC and 1 atm pressure, in g/L and mM, are shown in Table $1$ below. Gas (ref) solubility (aq) (g/L) solubility (mM) CO2 1.7 38 O2 0.044 1.3 N2 0.019 0.68 Question $5$ Offer reasons that explain the significantly higher (but still low) solubility of CO2 in water compared to O2 and N2. Answer CO2 is considered a nonpolar molecule since it has no net molecular dipole. However, it does have 2 bond dipoles (pointing in opposite directions), so the carbon atom is δ+ while the Os are δ-. This probably contributes to its greater solubility than N2 and O2 which don't have bond dipoles. CO2 would not orient itself in a dipole electric field, but it would to some extent in a quadrupole (4 poles) electric field where the positive potentials are oriented north and south and the negative potentials at east and west. CO2 has a quadrupole moment. In addition, the continued reaction of CO2 and water to form the weak acid carbonic acid would contribute to its higher apparent solubility. It is not clear to the authors if these contributions to solubility are accounted for in the experimental values of solubility. A quadruple and its associated magnetic field with oriented CO2. https://commons.wikimedia.org/wiki/F...quadrupole.svg Question $6$ How does the enzyme facilitate the transport of CO2 in blood? How does it maintain intracellular pH? Answer It converts the poorly soluble carbon unit in CO2 to the strongly soluble bicarbonate anion. HCO3-. A simple explanation for maintaining intracellular pH comes from the chemical equation below. CO2(aq) + H2O (l) ↔ H2CO3 (aq) + H2O(l) H3O+(aq) + HCO3-(aq). HCO3- is the conjugate base of the weak acid, H2CO3 so the system is a classic buffer. For a complete explanation of why the system can act as a buffer at neutral pH even though the pKa of the weak acid is 3.6, see Chapter 2.3 for review. We'll explore the structure of two different CAs, human carbonic anhydrase II and the carbonic anhydrase from Neisseria gonorrhea in this guided problem-solving module. Human carbonic anhydrase II The structure of native human carbonic anhydrase II and its catalytic mechanism is shown in Figure $7$ below. Figure $7$: Structure of native human carbonic anhydrase II (Zn-CA II) and its catalytic mechanism. Kim, J.K., Lee, C., Lim, S.W. et al.Nat Commun 11, 4557 (2020). https://doi.org/10.1038/s41467-020-18425-5. Creative Commons Attribution 4.0 International License, http://creativecommons.org/licenses/by/4.0/. Panel a shows the active site consists of the zincbinding site, hydrophobic/hydrophilic regions, and the entrance conduit (EC). Panel b shows the water networks in the active site that are responsible for the proton transfer (red) and substrate/product/water exchange (blue) during enzyme catalysis. Panel c shows the forward reaction mechanism of Zn-CA II. The active site itself lies at the bottom of a deep cavity (15 Å deep) in the protein, which is readily accessible to solvent An interactive iCn3D model of human carbonic anhydrase II with bound bicarbonate and CO2 (2VVB) is shown in Figure $8$ below. Figure $8$: Human carbonic anhydrase II with bound bicarbonate and CO2 (2VVB) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...ibgmgsm3UWhjX6 The active site residues shown in Figure 8 are labeled and shown as sticks. Bound CO2 and HCO3- are also shown as sticks. Question $7$ This question addresses the Biomolecular Visualization Framework theme(s) Molecular Interactions (MI), Atomic Geometry (AG) Using iCn3D to show the noncovalent interactions between bicarbonate, Zn, and the protein by using iCn3D. Measure the distance between the hydrogen-bonded atoms in HCO3- and Thr 199. iCn3D instructions Trackpad and Mouse Controls rotate: click and drag (mouse: left click and drag) zoom: pinch and spread (mouse: rotate the scroll wheel) translate: two-finger click and drag (mouse: right click and drag) Re-center: left click View from the top menu bar, then select “Center Selection” •Note: ctrl-click on a PC = command-click on Mac; alt-click on PC = option click on Mac 1. Open the external link: https://structure.ncbi.nlm.nih.gov/i...ibgmgsm3UWhjX6 2. From the top menu bar, choose Analysis, Interactions 3. In the new popup window select the following prompts, then click 4. 3D Display interactions 4. Close all but the main modeling window. 5. Zoom into the bicarbonate binding site. From the top menu bar, choose Analysis, Distance, distance between 2 atoms, and pick the two atoms (by holding down the Alt key or Option on a Mac) involved in the hydrogen bond between the bicarbonate and the amide 6. Rescale the label size by choosing Analysis, Label Scale, 0.4. 7. From the top menu bar choose Select, Toggle highlights to remove yellow and box highligting. 8. Save a PNG file by choosing 9. You can reload the PNG file directly into iCn3D by choosing File, Open File, iCn3D PNG image Answer The blue dotted lines are ion-ion interactions. Note the green hydrogen bond between T199 and the bound bicarbonate. Remember that hydrogen atoms are not shown in PDB files from x-ray structures. The H-bond distance is 3.1 angstroms. For some reason, the H-bond does not show unless the initial constraint distances are moved to 4.2 Angstroms. Question $8$ What is a likely function of Val, Leu and Trp cluster in CAII (shown in Figures 7 and 8)? Answer These side chains are all hydrophobic (as illustrated in gold in Figure 7) and provide a weak binding environment for nonpolar CO2. Question $\PageIndex{x}$ What ligand would likely replace OH- at low pH values? What would happen to the activity of the enzyme at lower pHs? Answer At low pH, ie. at pH values lower than the pKa of the Zn-bound water (which deprotonates to form the OH- ligand and nucleophile), the ligand and nucleophile would be H2O. The enzyme would display a lower activity given the weaker nucleophile. Question $9$ From the mechanism shown in Figure 7, does bicarbonate coordinate the Zn2+ ion in a monodentate or bidentate manner? Answer Monodentate as only 1 coordinate covalent bond forms on an electron pair donation from the bicarbonate to the Zn2+. Question $10$ Write a verbal description of the mechanism of CAII based on Figure 7 Answer CO2 binds to the active site through loose association with the cluster of hydrophobic side chains. The Zn2+ bound OH- acting as a nucleophile attacks the central carbon of the CO2 forming HCO3. The carbonate forms a monodentate interaction with Zn and also a hydrogen bond to Thr 199. The HCO3- is then displaced by an incoming water molecule. The other product of the reaction, H+, moves through a hydrogen bond network of water molecules (W1 and W2) to His 64 and eventually to bulk water. The interactions with substrate and products are weak allowing fast exchange. Question $11$ Why is a proton transfer path needed? Answer H+ is a product of the reaction: CO2 (g) + H2O ↔ H2CO3 (aq) ↔ HCO3- (aq) + H+ (aq). It must depart to prevent charge build-up, maintain charge balance, and keep the correct electrostatic environment of the active site. Question $12$ Thr 199 plays a key role in the mechanism. State a reason for its importance in the reversible reaction. Answer Thr 199 supplies two hydrogen bonds to the bicarbonate. It actually destabilizes bicarbonate bonding with respect to a T199A mutant. In the mutant, carbonate might bind Zn2+ in a bidentate fashion, leading to tighter binding and a slower dissociation rate of the product, HCO3-. Question $13$ The dissociation constant KD (or Kis) for bicarbonate binding to HCAII is about 77 mM. What kind of inhibitor might it be for the forward reaction? Answer Given that it binds in the active site and would prevent binding of the substrate for the forward reaction, CO2, it is a competitive inhibitor. Question $14$ Using the equation below, at what ratio of CO2/[HCO3-] would the rate for the forward reaction (CO2 sequestration) be cut in half? Assume the VM=1, KM forward reaction is 1, [S] = 1 and Kis is 2 (a wide range of values are reported in the Brenda Database. v_0=\frac{V_M S}{K_M\left(1+\frac{I}{K is}\right)+S} If you need some help, hover over - Give me a hint! Answer \begin{gathered} v_{-I}=\frac{V_M S}{K_M+S}=\frac{(1)(1)}{(1+1)}=0.5 \ v_{+I}=\frac{V_M S}{K_M\left(1+\frac{[I]}{K_{i s}}\right)+S}=\frac{(1)(1)}{1\left(1+\frac{[I]}{2}\right)}=0.25=\frac{1}{1+\frac{[I]}{2}} \ 0.25\left(1+\frac{[I]}{2}\right)=1 \ 1+\frac{[I]}{2}=4 \ \frac{[I]}{2}=3 \ {[I]=6} \end{gathered} Hence it doesn't take much HCO3- buildup to inhibit the "capture" of CO2! Question $15$ The rate-limiting step for human CA II is the dissociation of a proton from Zn2+-bound water and not the removal of the resulting proton from the enzyme. What does that imply about the rate of removal of the proton from the enzyme? Answer It must be very fast, that is at diffusion-controlled limits through the H-bond channel. Synthetic mimetics of the active site of CA have been made. These are heteromacrocycles (similar to the heme of hemoglobin) as shown in Figure $9$ below. Figure $9$: The macrocycle mimetic has three imidazole groups coordinating zinc. Question $16$ The macrocycle mimetic has three imidazole groups coordinating zinc. Is the bicarbonate coordinated to the Zn2+ in a monodentate or bidentate fashion? From the "denticities" of the interactions of bicarbonate and Zn2+ for human CAII and the mimetic, which catalyst, CAII or the macrocycle would you expect to have a lower KD for bicarbonate? How might this affect the rate-limiting step for the mimetic? Answer The mimetic is bidentate, is it should bind more tightly to bicarbonate, hindering its dissociation, and hence making "product" inhibition more likely. Question $17$ What is the utility of having both CO2 and HCO3- bind weakly to the enzyme Answer "permits their rapid exchange. The hydrogen-bonding arrangement in the active site is such that the water or hydroxide ion donates a hydrogen bond to a proximal threonine (Thr199 in hCA II) because the hydroxyl group of this residue is forced to donate its hydrogen in a hydrogen bond to a negatively charged glutamate side chain (Glu106 in hCA II). Site-directed mutations have confirmed this model of the catalytic mechanism. The substrates/products carbon dioxide/bicarbonate are fairly weakly bound against a hydrophobic wall in the active site, which permits their rapid exchange." Carbonic anhydrase from Neisseria gonorrhea (ngCA) Data from: Jo, B., Park, T., Park, H. et al. Engineering de novo disulfide bond in bacterial α-type carbonic anhydrase for thermostable carbon sequestration. Sci Rep 6, 29322 (2016). https://doi.org/10.1038/srep29322. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/ Now that we understand the general chemistry, structure, and reaction mechanism of carbon anhydrase (at least the alpha human CAII form), let's explore efforts to engineer more thermostable variants. One example is the carbonic anhydrase from N. gonorrheas. This CA has been used as a target for mutagenesis to increase thermal stability of the enzyme, through the introduction of new disulfide bonds. Even though only about 35% of the amino acids are identical, the overall structures are similar. This is illustrated in Figure $10$ below. Figure $10$: Alignment of the carbonic anhydrase from Neisseria gonorrhea (NG-CA) magenta,1KOQ) and human CA II (cyan, 2VVB) The active site is mostly conserved compared to human CA II. The Zn2+ bound water has a pKa of around 6.5, compared to the value of 7.0 in human CA II. The hydrophobic patch (pocket) is similar, with Phe 93, Leu 153 and Tyr 72 in the NG-CA replacing Phe 95, Phe 176, Phe 70 in human CA II, respectively. The histidine ligands to Zn2+ are His92 (94), His94 (96), and His111 (119), where the numbers in parentheses represent Hu CA II. The proton removed from Zn2+ bound water is transferred to His 66 (64 in human CA II) and then to His 64. The single disulfide bond between 181 and C28 is shown in Figure $1$ below Figure $11$: Single disulfide bond between 181 and C28 in wild type Carbonic anhydrase from Neisseria gonorrhea Question $18$ This question addresses the Biomolecular Visualization Framework theme(s) Atomic Geometry (AG) Identify the correct torsion angles in Figure $\PageIndex{x}$ above. Verbal definitions of torsional angles in a peptide chain are listed below. The successive atoms after the Cα leading away from the backbone atoms are Xβ-Xγ-Xδ-Xε (in that order). C is the backbone carbonyl C and N is the backbone nitrogen atom. • phi (φ) is the angle of right-handed rotation around N-Cα bond. φ = 0 if the Cα-C bond is cis (eclipsed) to the C-N bond. Values range from -180 to 180 degrees. • psi (ψ) is the angle of right-handed rotation around Cα -C bond. ψ = 0 if the C-N bond is cis (eclipsed) to the N-Cα bond. Values range from -180 to 180 degrees. • chi11) is the rotation around N-Cα-Xβ-Xγ • chi22) is the rotation around Cα-Xβ-Xγ-Xδ • chi33) is the rotation around Xβ-Xγ-Xδ-Xε Answer phi (Φ) = b, psi (Ψ) = a, chi11) = c, chi22) = d, and chi33) = e Question $19$ This question addresses the Biomolecular Visualization Framework theme(s) Atomic Geometry (AG), Topology and Connectivity (TC) Figure $12$ below shows an interactive iCn3D model of the atoms within 4A of the disulfide bond in Carbonic anhydrase from Neisseria gonorrhea (1KOQ). Rotate the model to determine the approximate chi33) dihedral angle. Hint: site down the S-S bond. Figure $12$: Atoms within 4A of the disulfide bond in Carbonic anhydrase from Neisseria gonorrhea (1KOQ). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...nutjP6EL2PubRA Answer The visually estimated chi33) angle for rotation around the S-S bond is 90o. Here is the actual angle (image made with Pymol) We mentioned previously that variants with higher thermal stability are likely to be more rigid and less flexible. Flexibility can be determined through analysis of molecular dynamic simulations and also by examining of the B factor values in PDB file. This number is a measure of the displacement of an atom from a mean The numbers in the last column in the file are called the temperature factors or B-factor. The B-factor describes the mean-square displacement, a measure of the displacement of an atom from an average value. If the atoms are more flexible, the electron density determined in x-ray structures is lower than if the atoms are more fixed, which gives high electron density. To make stabilizing disulfide bonds, investigators found site chains close enough that when mutated to cysteines could potentially form disulfide bonds. In addition, they search for such residues in surface loops (without alpha and beta structure) which are inherently more flexible. Introducing disulfide bonds into the loop would stabilize it and make it more rigid. Table $2$ below shows a description of the double cysteine CA variants in the study. Variant designation Position Wild-type residues Loop length Sum of B-factors T133C/D197C 133, 197 Thr/Asp 63 87.60 P56C/P156C 56, 156 Pro/Pro 99 80.82 N63C/P145C 63, 145 Asn/Pro 81 77.17 Table $2$: Description of the double cysteine CA variants in ngCA The locations of the side chair targeted for mutations to cysteine pairs are shown in Figure $13$ below. Figure $13$: 3D structure of ngCA and location of residue pairs for disulfide engineering The zinc (not shown)-coordinating histidine residues in the catalytic active site are shown in green. The proton shuttle histidine residue is shown in magenta. The native disulfide bond is colored yellow. An interactive iCn3D model of carbonic anhydrase from Neisseria gonorrhea (1KOQ) highlighting the 3 pairs of sidechains for mutations is shown in Figure $14$ below. Figure $14$: Carbonic anhydrase (Neisseria gonorrhea) with 3 paired side chains for engineered disulfide (1KOQ) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...KRP2tFJ1pV1FF6 The mutations were made and the wild-type proteins and three mutants were subjected to SDS-polyacrylamide gel electrophoresis (SDS-PAGE). The stained gels are shown in Figure $15$ below. Figure $15$: Expression and purification of disulfide CA variants. Panel (a) shows the expression of the protein in transformed cells 25 °C after IPTG induction and fractionated into soluble and insoluble fractions. SHuffle strain, an engineered E. coli strain that promotes cytoplasmic disulfide bond formation, was used. Panel (b) shows purification results. Each lane was loaded with 4 μg of each purified CA variant. The proteins were visualized with Coomassie blue staining after SDS-PAGE. The arrow indicates the position of the bands corresponding to ngCA variants. Lane: M, molecular weight marker; S, soluble fraction; IS, insoluble fraction. Question $20$ a. Interpret the results of the PAGE gels in Figure $15$ above b. How pure were the proteins based on the PAGE gel result in Panel (b). Can you infer from the gel that the proteins folded correctly? Answer a. It appears to show that a small fraction of the wild type and each mutant, especially the N63C/P145C pair, was found in the insoluble fraction. This might result from improper folding of the proteins in E. Coli, leading to hydrophobic side chain surface exposure and aggregation into insoluble "inclusion bodies". This appears to be just a minor issue. b. All the proteins appear very pure with some very small levels of contamination in the N63C/P145C. The PAGE results show the protein all have the same molecular weight but whether they folded to a native state with activity can not be determined. Nor can it be determined if the disulfide pairs in the mutants were made are if they were, were correctly paired. The investigators next determined if the expressed and purified wild-type and mutant proteins had the correct number of disulfide bonds. They did this by reacting the proteins in the absence and presence of dithiothreitol with DTNB or 5,5'-dithiobis(2-nitrobenzoic acid), also called Ellman's Reagent. Both structures are shown in Figure $16$ below. Figure $16$: Structures of DTT and DTNB DTT is a reducing agent that cleaves disulfide. Question $21$ Draw a mechanism showing the reaction of a disulfide with DTT. Answer Only surface and not buried free cysteines will be labeled unless the protein is unfolded to expose all the cysteines. DTNB reacts with free sulfhydryls to form the 2-nitro-5-thiobenzoic acid anion leaving group that absorbs at 412 nm. Question $22$ Draw a mechanism showing the reaction of free sulfhydryl (like Cys) with Ellman's reagent Answer Only surface and not buried free cysteines will be labeled unless the protein is unfolded to expose all the cysteines. The results of the reaction of the proteins with Ellmans's agent, in the presence and absence of DTT, are shown in Table $3$ below. CA variant Free thiol/protein (mol/mol) a Deduced no. S-S bonds −DTT +DTT Wild-type 0.06 ± 0.02 1.80 ± 0.14 ? T133C/D197C 0.08 ± 0.02 3.79 ± 0.22 ? P56C/P156C 0.06 ± 0.03 3.89 ± 0.06 ? N63C/P145C 0.08 ± 0.03 3.75 ± 0.05 ? Table $3$: Analysis of disulfides in CA using Ellman's reagent. aNumbers are represented in mean ± SD. Question $23$ How many S-S would you deduce from the table are present in the wild-type and mutant enzymes? Did the correct disulfide bonds form? Explain your answers Answer DTT reduces the disulfide in protein. For each disulfide, two free Cys side chains are made. The molar ratio of CysSH/CA for the wild-type is 1.8 in the presence of DTT. The value is very close to the expected value of 2. For the mutants, the ratio is about 3.8 in the presence of DTT, suggesting 4 free Cys consistent with 2 disulfide bonds. Table $4$ below shows the catalytic activities of the disulfide CA variants at 25 °C. CA variant CO2 hydration activity Relative esterase activity a kcat × 10−4 (s−1) K M (mM) kcat /KM × 10−6(M−1 s−1) Wild-type 1.00 1.44 14.2 1.01 T133C/D197C 1.49 1.97 16.7 1.18 P56C/P156C 1.03 1.44 16.9 0.85 N63C/P145C 0.55 0.27 17.3 0.16 Table $4$: catalytic activities of the disulfide CA variants at 25 °C aThe specific activity of the wild-type corresponds to 0.22 U/μmol-enzyme. Question $24$ Why did the investigators conduct this experiment? Interpret the results Answer All of the previous results suggest that the mutant proteins were made and had the correct number of double bonds, but the experiments could not tell if the bond pairs were correct. For example, did the T133C/D197C contain a native (C28-C181) and mutant (C133-C197) bond and not another combination? Activity is an excellent predictor of structure. All but one mutant retained nominal activity, as evidenced by a comparison of the rat constants. The N63C/P145C had a 6x lower kcat, but even then it is close to diffusion-controlled. Now comes the big question: were the investigators able to engineer thermal stability into the carbonic anhydrase? Experimental results to show the thermostability of the disulfide CA mutants are shown in Figure $17$ below. Figure $17$: Thermostability of the disulfide CA variants Panel (a) shows short-term kinetic stability. The enzyme solutions (40 μM) were incubated for 30 min at different temperatures, and the residual activities were measured by esterase activity assay. Activities of 100% correspond to untreated samples. Panel (b) shows long-term kinetic stability at 70 °C. The half-lives (t1/2) of the CA variants were estimated by fitting the experimental data to an exponential decay curve. Each value represents the mean of at least three independent experiments, and the error bars represent the standard deviations. Question $25$ Analyze the results in Panels (a) an (b). Which protein was most thermostable over the short (30 minute) and long (hours) incubating time at elevated temperatures? Answer At 80 °C, all the mutants showed increases thermostability to short term (30 minute) heating, but one, N63C/P145C was exceptionally thermostable. Longer time courses for heating at 70°C showed that the N63C/P145C was again far more stable over time. Its t1/2 was 31.4 h, compared to the values between 4-6 h for the others. Panel (c) shows heat-induced denaturation of disulfide CA variants. Temperature-dependent changes of the circular dichroism ellipticity were recorded at 220 nm on CD spectrometer. The denaturation curves were normalized to the fraction of unfolded protein. The horizontal dashed line indicates the point at which the fraction of unfolded protein is 0.5. The vertical dashed lines point to TM values. Panel (d) shows the overall RMSD of disulfide variants from molecular dynamic simulations performed at 400 K for 20 ns. Question $26$ Analyze the results in Panel (c). What do changes in the CD helicity show? Which protein was most thermostable based on TM values? Is the decrease in enzyme activity in panels (a) and (b) result from the denaturation of the protein? Answer CD measurements can give a measure of the retention of secondary structure (alpha helices and beta structure) on denaturation. The CD spectrum for different secondary structures is shown below (From Chapter 3.5). The curves were normalized to fit on a 0-1 scale on the y axis, which then gives a measure of percent denaturation. The temperature half-way up is the TM, or "melting temperature, at which an equilibrium mixture would contain half native and half denatured protein (true for a small protein with no intermediates). The TM values were for the wild-type, T133C/D197C, P56C/P156C, and N63C/P145C mutants 73.6 °C 74.7 °C, 77.4 °C, and 81.4 °C. These parallel the t1/2 values for enzyme activities, and support the idea that denaturation led to inactivation of the enzyme. Question $27$ Analyze the molecular dynamics simulation results in Panel (d) Answer The molecular dynamic simulations for all the proteins soon reach equilibrium values as indicated in the plateaus of average room mean square deviation of the protein backbone. The overall molecular root-mean-square deviation (RMSD) of N63C/P145C was the lowest, indicating that it was most rigid. This is in accord with the idea that increased flexibility destabilizes a protein and engineering a disulfide into makes it more rigid and hence more stable to temperature increases. The results are in accordance with the other experiments that show the N63C/P145C was the most thermostable. " In addition, T133C/D197C showed the highest values in both the overall and the residual RMSD (Fig 3D). This may explain and correlate with the increased activity of T133C/D197C (Ta) and the increased ΔS of unfolding" You may remember from both introductory chemistry and from Chapter 4.12, that you can calculate the thermodynamic parameters, ΔHo and ΔSo for N ↔D at room temperature from thermal denaturation curves using the van 't Hoff equation. In this case, Keq values can be calculated from thermal denaturation curves by monitoring change in CD signal at 220 nm, and applying this equation (also from Chapter 3.12). K_{e q}=\frac{[D]_{e q}}{[N]_{e q}}=\frac{f_D}{f_N}=\frac{f_D}{1-f_D} From this, we can calculate ΔG0. \Delta \mathrm{G}^0=-\mathrm{R} \operatorname{Tln} \mathrm{K}_{\mathrm{eq}}=-\mathrm{R} \operatorname{Tln}\left[\frac{\mathrm{f}_{\mathrm{D}}}{1-\mathrm{f}_{\mathrm{D}}}\right] Knowing Keq, ΔH0, DS0 can be calculated as shown below. A semi-log plot of lnKeq vs 1/T is a straight line with a slope of - ΔH0R and a y-intercept of + ΔS0/R, where R is the ideal gas constant. \begin{gathered} \Delta \mathrm{G}^{0}=\Delta \mathrm{H}^{0}-\mathrm{T} \Delta \mathrm{S}^{0}=-\mathrm{RTln} \mathrm{K}_{\mathrm{eq}} \ \ln \mathrm{K}_{\mathrm{eq}}=-\frac{\Delta \mathrm{H}^{0}-\mathrm{T} \Delta \mathrm{S}^{0}}{\mathrm{RT}} \ \ln \mathrm{K}_{\mathrm{eq}}=-\frac{\Delta \mathrm{H}^{0}}{\mathrm{RT}}+\frac{\Delta \mathrm{S}^{0}}{\mathrm{R}} \end{gathered} The equation below shows that the derivative of equation (8) with respect to 1/T (i.e. the slope of equation 8 plotted as lnKeq vs 1/T) is indeed -ΔH0/R. Equation (9) is the van 't Hoff equation, and the calculated value of the enthalpy change is termed the van 't Hoff enthalpy, ΔH0vHoff. \frac{d \ln \mathrm{K}_{\mathrm{eq}}}{d(1 / \mathrm{T})}=-\frac{\Delta \mathrm{H}^{0}}{\mathrm{R}}=-\frac{\Delta \mathrm{H}_{\mathrm{vHoff}}^{0}}{\mathrm{R}} Using this method, the thermodynamic parameters for unfolding of the protein were calculated. The results are shown in Table $4$ below. CA variant Melting temperature, TM (°C) Enthalpy change of unfolding, ΔH (kcal mol−1) Entropy change of unfolding, ΔS (kcal mol−1 K−1) Wild-type 73.6 48.8 0.141 T133C/D197C 74.7 52.8 0.153 P56C/P156C 77.4 35.1 0.091 N63C/P145C 81.4 30.0 0.085 Table $4$: Thermodynamic parameters for protein unfolding for WT and mutant CAs Question $28$ Which effects, enthalpy or entropy of unfolding, were associated with the increased thermal stability of the mutants compared to the wild-type protein. Remember were are considering the denaturation reaction, N↔ D. Answer For the reaction N ↔ D, the ΔH0 values were all positive, indicating the enthalpy changes favored the native state, not the denatured state. In contrast, the other two mutants were enthalpically destabilized compared to the wild-type as their ΔH0 were less positive so compared to the wild-type. The prime stabilizer of the native state was the lower entropy (hence a less negative and favored -TΔS0 for the denaturation reaction. This makes sense in these mutants are more rigid and would experience less loss of "conformational entropy). P56C/P156C and N63C/P145C exhibited lower ΔH (destabilizing) and ΔS (stabilizing), showing that the decreased entropic change of unfolding (i.e., the loss of conformational entropy of the unfolded state) by the disulfide bridge was the primary factor for the thermostabilization. These results are not surprising because design strategies aiming ‘entropic stabilization’ such as disulfide engineering do not always result in engineered proteins ideally with lower ΔS and unchanged ΔH. These results are in accord with the observation that N63C/P145C was the most thermostable variant and that T133C/D197C showed the highest values in both the overall and the residual RMSD (Fig. 3d). This may explain and correlate with the increased activity of T133C/D197C and the increased ΔS of unfolding. Finally, the enzymatic activity of the wild-type and mutants CAs (using a small ester substrate) were studied as a function of temperature. The relative activity of the wild-type and all 3 disulfide mutants are plotted as a function of temperature in the histogram graphs shown in Figure $18$ below. Figure $18$: Effect of temperature on the activity of disulfide CA variants. Esterase activities of disulfide variants were measured at each temperature and normalized to the activity of each enzyme at 25 °C. Each value represents the mean of three independent experiments, and the error bars represent the standard deviations. If you plotted the data as curves, you would get bell-shaped graphs. Question $29$ Explain why the histogram plots (and line plots if they were drawn) are bell-shaped. Are the results in accordance with the previous results. Answer Yes. Most chemical reaction show an increase in rate with increasing temperatures until competing reactions take precedence. For an enzyme-catalyzed reaction, that competing reaction is denaturation, which decreases the rate. Yes the graphs are in accord with the previous results. The N63C/P145C certainly stands out as the best mutant. The authors write that "considering the shifted optimal temperature and the thermoactivation as well as the enhanced thermostability, the disulfide engineered α-type CA with Cys63-Cys145 can be a promising biocatalyst for efficient CO2 sequestration performed under high temperature conditions." Disulfide engineering Craig, D.B., Dombkowski, A.A. Disulfide by Design 2.0: a web-based tool for disulfide engineering in proteins. BMC Bioinformatics 14, 346 (2013). https://doi.org/10.1186/1471-2105-14-346. Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), Several computational programs have been developed to determine amino acid pairs that could be mutated to high-temperature stabilizing disulfide bonds. The stabilizing effects appear largest when the disulfide bond is made within the largest (and most flexible) loops (between 25-75 residues). These loops also had the highest residue B-factors. In selecting pairs to form engineered disulfide, not only proximity (distance) but also geometry (torsion angles) of the resulting disulfide bond are important. We saw this previously in the analysis of the energy of butane rotamers, as illustrated in Figure $19$ below. Figure $19$: Newman projections for butane Programs to determine amino acid pairs to mutate for disulfide bond formation test S-S bond torsional stability by determining the torsion angle χ3 for the S-S bond. Evaluation of a database of many native proteins shows the χ3 angle are centered in two major peaks at -87 and +97 degrees, as shown in Figure $20$ below Figure $30$: Distribution of χ 3 torsion angles observed in 1505 native disulfide bonds found in 331 PDB protein structures. Peaks occur at -87 and +97 degrees. Craig, D.B., Dombkowski, A.A. Disulfide by Design 2.0: a web-based tool for disulfide engineering in proteins. BMC Bioinformatics 14, 346 (2013). https://doi.org/10.1186/1471-2105-14-346. Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), Question $4$ Does your calculated value of χ3 for the native disulfide in carbonic anhydrase from Neisseria gonorrhea follow the observed angles? Answer At about 90o so yes it does. An empirical energy equation was developed to determine the E vs χ3 dihedral angle for disulfide bonds (Craig, D.B., Dombkowski, A.A., ibid). It is shown below. E\left(\chi_3\right)=4.0\left[1-\cos \left(1.957\left[\chi_3+87\right]\right)\right] A graph of the equation made with Excel is shown in Figure $21$ below Figure $21$: E vs χ3 dihedral angle for disulfide bonds from empirical equation Figure $22$ shows the distribution of energy values in the 1505 native disulfide bonds using our updated function. The study by Craig and Dombkowski showed that almost all (90%) of disulfides in native proteins in the PDB have an energy < 2.2 kcal/mol, so this metric could be used to determine possible disulfide bond pairs created by mutagenesis. Figure $22$: Distribution of the disulfide bond energy calculated for 1505 native disulfide bonds in our survey set using the DbD2 energy function. The mean value is 1.0 kcal/mol, and the 90th percentile is 2.2 kcal/mol. (Craig, D.B., Dombkowski, A.A., ibid) Question $31$ You calculated the approximate value for χ3 dihedral for the C28-C181 native disulfide bond form in Carbonic anhydrase from Neisseria gonorrhea using iCn3D in Question x above. Determine the approximate energy from the empirical function graph in Figure x above for that χ3 dihedral. What percent of native disulfide bonds have that particular calculated energy in the 1505 native disulfide surveyed? Answer The estimated χ3 dihedral was 90o. The energy for that χ3 angle would be approximately <0.1kcal/mol, which reflects the energies of about 150/1505 or 10% of the disulfides in the database. It is hence in the most stable range of disulfides, based only on the χ3 dihedral angle. Yet there are other important parameters as well that would affect the energy of the disulfide bond in the protein. Figure $23$ below shows a comparison of native residue B-factors in stabilizing and destabilizing engineered disulfide bonds Figure $23$: Comparison of native residue B-factors in stabilizing and destabilizing engineered disulfide bonds. The native structures associated with engineered disulfides previously reported as stabilizing (S) or destabilizing (D), based on experimental evidence, were analyzed with DbD2. The mean B-factor for residues involved in stabilizing disulfide bonds was 31.6 compared with 16.5 for those involved in destabilizing bonds, P = 0.066. (Craig, D.B., Dombkowski, A.A., ibid) Question $32$ Which proteins were more stabilized by engineered disulfide, those with higher or low B factors. Answer Proteins with engineered disulfides that increase stability have higher B-factors. This makes sense in that proteins with higher B-factors and hence mobility would be predicted to alter conformation and potentially denature more readily. Another Paper: Prediction of disulfide bond engineering sites using a machine learning method https://www.nature.com/articles/s41598-020-67230-z Gao, X., Dong, X., Li, X. et al. Prediction of disulfide bond engineering sites using a machine learning method. Sci Rep 10, 10330 (2020). https://doi.org/10.1038/s41598-020-67230-z. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/ The amount of data present in a single PDB file is very large, but it is nothing compared to the collective data in all PDB files. If only we could extract empirical rules from the collective PDB files that govern disulfide bond formation. It turns out we can with machine learning and artificial intelligence that can be used to develop and train predictive algorithms. Machine learning has been used to predict amino acid pairs for cysteine mutations to form engineered disulfide bonds. It recognizes 99% of natural disulfide bonds. residues. It uses these parameters: • distances between the alpha-carbons and the beta-carbons of the bonded cysteine residues • three torsion angles around the disulfide bonds (χ1ss1’). An example of one variable that helps define the stability of disulfide bonds is the distances between the Cα atoms for the disulfide-bonded cysteines, as shown in Figure $24$ below. Figure $24$: The histogram of distances between Cα atoms of disulfide-bonded cysteines. The distances range from 3.0 Å and 7.5 Å. Knowing this would constrain the number of choices for paired amino acid side chains for mutagenesis to produce disulfide. Machine learning can also be used to find other distance constraints to optimize mutagensis experiments. Figure $25$ below shows a graph of 10 different distances and their relative importance in determining disulfide bond stability. Figure $25$: The relevance of the distance features to the classification outcome. Out of the 45 unique distances, 20 distances have negligible influence on the classification performance. The distances between Cβ and main-chain atoms of the pairing residue are important features in disulfide bond classifications. An interactive iCn3D model of carbonic anhydrase from Neisseria gonorrhea (1KOQ) highlighting two pairs of amino acids identified by machine learning as candidates for mutations to disulfide-bonded cysteines is shown in Figure $26$ below. Figure $26$: Carbonic anhydrase from Neisseria gonorrhea (1KOQ) highlighting two pairs of amino acids identified by machine learning as candidates for mutations to disulfide-bonded cysteines (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...ZH6Tv1urwkKhz8 Question $33$ This question addresses the Biomolecular Visualization Framework theme(s) Atomic Geometry (AG), Macromolecular Building Blocks (MB) Using the data in Figure $25$ and measurements made using the iCn3D model above (Figure $25$) , determine which pair would be most likely engineered into a disulfide bond. Complete the table below with the distances you made using iCn3D. iCn3D instructions: Open the external link and follow these instructions Trackpad and Mouse Controls rotate: click and drag (mouse: left click and drag) zoom: pinch and spread (mouse: rotate the scroll wheel) translate: two-finger click and drag (mouse: right click and drag) Re-center: left click View from the top menu bar, then select “Center Selection” •Note: ctrl-click on a PC = command-click on Mac; alt-click on PC = option click on Mac Instructions 1. Zoom to clearly see the amino acid pair for distance measures 2. From the top menu bar, choose Analysis, Distance, distance between 2 atoms 3. Pick the appropriate 2 atoms for measure distance by holding down the Alt key and selecting both 4. Record the distances in the table below. Mutation Pair CB1-CA2 (Å) CB1-CB2 (Å) CA1-CB2 (Å) 1 (L137) - 2 (W141) 1 (Y54) - 1 (S160) Answer Mutation Pair CB1-CA2 (Å) CB1-CB2 (Å) CA1-CB2 (Å) 1 (L137) - 2 (W141) 6 5.2 5.3 1 (Y54) - 1 (S160) 4.1 4.1 5.0 Errors, Misformation and Disinformation Under construction An interactive iCn3D model of the anti-arsonate germline antibody 36-65 in complex with a phage display derived dodecapeptide KLASIPTHTSPL without added hydrogens (2A6I) Figure $5$: Anti-arsonate germline antibody 36-65 in complex with a phage display derived dodecapeptide KLASIPTHTSPL without added hydrogens (2A6I). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...hi5pLnMmq6MEJ6 Download this file and open in iCn3D (File, Open File, iCn3D PNG Image) to see a model with attached hydrogen atoms 2.5A resolution, disallowed regions 1% Link to interpret quality me, districs Figure $5$ is the Percentile scores (ranging between 0-100) for global validation metrics of the entry a Figure $5$: geometric issues observed across the polymeric chains and their fit to the electron density. https://www.rcsb.org/structure/2a6i Rfree is a measure of the quality of a model from X-ray crystallographic data The Figure $5$below summarizes the geometric issues observed across the polymeric chains and their fit to the electron density. The red, orange, yellow, and green segments on the lower bar indicate the fraction of residues that contain outliers for > 3, 2, 1 and 0 types of geometric quality criteria respectively. A grey segment represents the fraction of residues that are not modeled. The numeric value for each fraction is indicated below the corresponding segment, with a dot representing fractions <5% The upper red bar (where present) indicates the fraction of residues that have poor fitt to the electron density. The numeric value is given above the bar Figure $5$" https://www.rcsb.org/structure/2a6i KLASIPTHTSPL only 9 observable electron density (S4 to end) updated version: 5VGA Exercise $1$ Show van der Waals steric clashes in the protein using the program Jsmol available at this link • check the with hydrogens box on the right-hand side • click in the load mmCIF by PDB ID and input the 2a6i (small letters) • select the Clashes button on the left • rotate the image to display the greatest density of clashes to the right. • Select the PNG + Jmol button on the right-hand side to download an image showing the clashes • hover over the region with the greatest number of clashes to identify amino acids in this region. Which chain (A, B, P) is involved in the most clashes? Answer The right-hand side with the greatest density of clashes shows the source of most clashes is the bound peptide P.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Fundamentals_of_Biochemistry_Vol._V_-_Problems/Signal_Transduction_Problems/NMDA_Receptor_-_Under_Construction.txt	princeton-nlp/TextbookChapters	Under Construction Glutamatergic transmission has been implicated in the pathophysiology of PTSD, particularly in the effects of N-methyl-D-aspartate receptor (NMDAR) signaling on the synaptic plasticity underlying learning and memory [13]. NMDARs comprise two GluN1 subunits and two GluN2 (A-D) or GluN3 (A, B) subunits. In adult forebrain regions, GluN2A and GluN2B are the main subunits forming receptor complexes with GluN1 at excitatory synapses. GluN2B-containing NMDARs play a preferential role in inducing synaptic plasticity, which is critical for the extinction of fear memories [1415]. Systemic injection of GluN2B-specific NMDAR antagonists ((RS)-3-(2-carboxypiperazin-4-yl)-propyl-1-phosphonic acid, ifenprodil) can impair the retention of fear extinction learning. GluN2B-containing NMDARs in both the amygdala and medial prefrontal cortex (mPFC) are also involved in reducing fear during extinction, whereas GluN2A-containing NMDARs play a greater role in the initial formation and/or stabilization of learned fear [15]. Rodent studies demonstrate that GluN2B subunit-containing NMDARs play pivotal roles in fear extinction learning. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4263351/"tetrameric complexes mainly composed of NMDA receptor GluNR1 and GluNR2 subunits with the NR2 subunits modifying the activity of the receptor. " N-methyl-d-aspartate (NMDA) receptors are Hebbian-like coincidence detectors, requiring binding of glycine and glutamate in combination with the relief of voltage-dependent magnesium block to open an ion conductive pore across the membrane bilayer. NMDA receptors are Hebbian-like coincidence detectors, requiring the binding of glycine and glutamate to GluN1 and GluN2 subunits, respectively, combined with membrane depolarization to relieve magnesium block . Activation of the receptor opens a cation-selective, calcium permeable channel, thus causing further depolarization of the cell membrane and influx of calcium . NMDA receptors are obligatory heterotetrameric assemblies, typically composed of two glycine-binding GluN1 subunits and two glutamate-binding GluN2A-D subunits, with the GluN1/GluN2A/GluN2B complex the predominate receptor at hippocampal synapses. Glycine-and d-serine-binding GluN3 subunits are additional subunits, expressed throughout the nervous system but with roles less well defined in comparison to the GluN1/GluN2 assemblies. A hallmark of NMDA receptors, by contrast with AMPA and kainate receptors, is a wide spectrum of allosteric modulation, from nanomolar concentrations of zinc, to the small molecule ifenprodil, polyamines and protons and to voltage-dependent ion channel block by MK-801, ketamine and memantine. The coordinates and structure factors for the structure have been deposited in the Protein Data Bank under accession code 4TLL and 4TLM for Structure 1 and Structure 2, respectively. These receptors have two GluN1 subunits and two GluN2 (A-D) or GluN3 (A, B) subunits. In the forebrain, GluN2A and B form complex with GluN1 at synapses, with the B subunit playing a role in synaptic plasticity. Synaptic plasticity is necessary to remove "hard-wired" fear circuits. A goal of PTSD therapies is the extinction of the previously acquired feared memories through learning. Learning, and more specifically extinction, requires synaptic plasticity. If one goal of PTSD treatment is the extinction of fear memories, then drugs that target GluN2B are potentially useful. Antagonist (such asd ifenprodil) of GluN2B seem to decrease the ability to extinguish fear retention memories. In other words, the learning and synaptic plasticity need to attenuate the fear memories are inhibited by the antagonist. GluN2B in the NMDAR receptors in the amygdala and medial prefrontal cortex appear to be involved in reducing feat during extinction. IN contact, GluN2A seem to be an important role in forming and stabilizing the learned fear response. "Glutamatergic transmission has been implicated in the pathophysiology of PTSD, particularly in the effects of N-methyl-D-aspartate receptor (NMDAR) signaling on the synaptic plasticity underlying learning and memory [13]. NMDARs comprise two GluN1 subunits and two GluN2 (A-D) or GluN3 (A, B) subunits. In adult forebrain regions, GluN2A and GluN2B are the main subunits forming receptor complexes with GluN1 at excitatory synapses. GluN2B-containing NMDARs play a preferential role in inducing synaptic plasticity, which is critical for the extinction of fear memories [1415]. Systemic injection of GluN2B-specific NMDAR antagonists ((RS)-3-(2-carboxypiperazin-4-yl)-propyl-1-phosphonic acid, ifenprodil) can impair the retention of fear extinction learning. GluN2B-containing NMDARs in both the amygdala and medial prefrontal cortex (mPFC) are also involved in reducing fear during extinction, whereas GluN2A-containing NMDARs play a greater role in the initial formation and/or stabilization of learned fear [15]. Rodent studies demonstrate that GluN2B subunit-containing NMDARs play pivotal roles in fear extinction learning." Describe how the NMDA receptor functions, and how it implements the Hebbian model of learning at the synaptic level. http://charlesfrye.github.io/Foundat...roscience//29/ https://www.pnas.org/doi/full/10.1073/pnas.95.12.7145 https://pubmed.ncbi.nlm.nih.gov/15888440/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4263351/ Ras Questions 1. Where is Ras located in the cell? What causes Ras to be localized here? 2. How does Ras get activated? 3. What are the three dimensional differences between GDP and GTP bound Ras. Why is the GTP bound form “active?” 4. What does Ras bound to GTP bind to? What are the net effects of this in terms of signal transduction? 5. What reaction does Ras catalyze as an enzyme? 6. What type of reaction is a GAP catalyzing? GEF? a. Are they opposite reactions? What is different (enzymatic control) 7. GAP is an acronym for GTPase activating protein. What GTPase is being activated? 8. Would RAS “turn off” without a GAP present? Why is this critical? 9. What molecule (GTP or GDP) would be bound to RAS if there were no GAPs or GEFs present? Why? 10. Why does a GAP increase the enzymatic activity RAS? What does a GAP provide that aids in the chemistry of the reaction? Paper about targeting phosphorylation and cancer therapy: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8642438/ 1. Ras can be phosphorylated at Tyr 32 and Tyr64 by Src kinase, which leads to inhibition of binding to Raf and increased GTP hydrolysis. Provide a rationale for why this post-translational modification plays a role in the overall function of Ras. (Not sure what to ask here- there seems to be a number of kinases involved that do different things as explained in the paper above) 2. There are mutations in Ras, including Gly 12, Gly13, and Gln 61 which impair GTPase activity and GAP-mediated GTP hydrolysis. Predict what changes this would cause in the cell. 3. Think about designing a small molecule drug that affects the Ras signaling pathway and treats cancer. What proteins could you target? For each protein target, should the drug increase or decrease the activity of its target? Explain your answers. Fig 1 Fig 2 Fiog 3
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Fundamentals_of_Biochemistry_Vol._V_-_Problems/Structure%2F%2FFunction_-_Protein_Problems/Disulfide_Bonds.txt	princeton-nlp/TextbookChapters	Disulfide engineering The first set of questions below are based on this reference as noted: Craig, D.B., Dombkowski, A.A. Disulfide by Design 2.0: a web-based tool for disulfide engineering in proteins. BMC Bioinformatics 14, 346 (2013). https://doi.org/10.1186/1471-2105-14-346. Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0) Several computational programs have been developed to determine amino acid pairs that could be mutated to high-temperature stabilizing disulfide bonds. The stabilizing effects appear largest when the disulfide bond is made within the largest (and most flexible) loops (between 25-75 residues). These loops also had the highest residue B-factors. In selecting pairs to form engineered disulfide, not only proximity (distance) but also geometry (torsion angles) of the resulting disulfide bond are important. We saw this previously in the energy analysis of butane rotamers, as illustrated in Figure $1$ below. Figure $1$: Newman projections for butane Programs to determine amino acid pairs to mutate for disulfide bond formation test S-S bond torsional stability by determining the torsion angle χ3 for the S-S bond. Evaluation of a database of many native proteins shows the χ3 angle are centered in two major peaks at -87 and +97 degrees, as shown in Figure $2$ below Figure $2$: Distribution of χ 3 torsion angles observed in 1505 native disulfide bonds found in 331 PDB protein structures. Peaks occur at -87 and +97 degrees. Craig, D.B., Dombkowski, A.A. Disulfide by Design 2.0: a web-based tool for disulfide engineering in proteins. BMC Bioinformatics 14, 346 (2013). https://doi.org/10.1186/1471-2105-14-346. Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), Question $1$ Does your calculated value of χ3 for the native disulfide in carbonic anhydrase from Neisseria gonorrhea follow the observed angles? Answer At about 90o so yes it does. An empirical energy equation was developed to determine the E vs χ3 dihedral angle for disulfide bonds. The equation is shown below (Craig, D.B., Dombkowski, A.A. ibid.). E\left(\chi_3\right)=4.0\left[1-\cos \left(1.957\left[\chi_3+87\right]\right)\right] A graph of the equation made with Excel is shown in Figure $3$ below Figure $3$: E vs χ3 dihedral angle for disulfide bonds from empirical equation Figure $4$ below shows the distribution of energy values in the 1505 native disulfide bonds using our updated function. The study by Craig and Dombkowski showed that almost all (90%) of disulfides in native proteins in the PDB have an energy < 2.2 kcal/mol, so this metric could be used to determine possible disulfide bond pairs created by mutagenesis. Figure $4$: Distribution of the disulfide bond energy calculated for 1505 native disulfide bonds. The mean value is 1.0 kcal/mol, and the 90th percentile is 2.2 kcal/mol. Craig, D.B., Dombkowski, A.A. ibid. Question $2$ You calculated the approximate value for χ3 dihedral for the C28-C181 native disulfide bond form in Carbonic anhydrase from Neisseria gonorrhea using iCn3D in Question x above. Determine the approximate energy from the empirical function graph in Figure x above for that χ3 dihedral. What percent of native disulfide bonds have that particular calculated energy in the 1505 native disulfide surveyed? Answer The estimated χ3 dihedral was 90o. The energy for that χ3 angle would be approximately <0.1kcal/mol, which reflects the energies of about 150/1505 or 10% of the disulfides in the database. It is hence in the most stable range of disulfides, based only on the χ3 dihedral angle. Yet there are other important parameters as well that would affect the energy of the disulfide bond in the protein. Figure $5$ below shows a comparison of native residue B-factors in stabilizing and destabilizing engineered disulfide bonds Figure $5$: Comparison of native residue B-factors in stabilizing and destabilizing engineered disulfide bonds. Analysis of previously reported proteins with stabilizing (S) or destabilizing (D) engineered disulfide bonds. The mean B-factor for residues in proteins with stabilizing disulfide bonds was 31.6 compared with 16.5 for those involved in destabilizing bonds. Craig, D.B., Dombkowski, A.A. ibid. Question $3$ Which proteins were more stabilized by engineered disulfide, those with higher or low B factors. Answer Proteins with engineered disulfides that increase stability have higher B-factors. This makes sense in that proteins with higher B-factors and hence mobility would be predicted to alter conformation and potentially denature more readily. Prediction of disulfide bond engineering sites using a machine learning method This set of questions based on this reference: Gao, X., Dong, X., Li, X. et al. Prediction of disulfide bond engineering sites using a machine learning method. Sci Rep 10, 10330 (2020). https://doi.org/10.1038/s41598-020-67230-z. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/ The amount of data present in a single PDB file is very large, but it is nothing compared to the collective data in all PDB files. If only we could extract empirical rules from the collective PDB files that govern disulfide bond formation. It turns out we can with machine learning and artificial intelligence that can be used to develop and train predictive algorithms. Machine learning has been used to predict amino acid pairs for cysteine mutations to form engineered disulfide bonds. It recognizes 99% of natural disulfide bonds. residues. It uses these parameters: • distances between the alpha-carbons and the beta-carbons of the bonded cysteine residues • three torsion angles around the disulfide bonds (χ1ss1’). An example of one variable that helps define the stability of disulfide bonds is the distances between the Cα atoms for the disulfide-bonded cysteines, as shown in Figure $6$ below. Figure $6$: The histogram of distances between Cα atoms of disulfide-bonded cysteines. Gao, X., Dong, X., Li, X. et al. Prediction of disulfide bond engineering sites using a machine learning method. Sci Rep 10, 10330 (2020). https://doi.org/10.1038/s41598-020-67230-z. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/ The distances range from 3.0 Å and 7.5 Å. Knowing this would constrain the number of choices for paired amino acid side chains for mutagenesis to produce disulfide. Machine learning can also be used to find other distance constraints to optimize mutagensis experiments. Figure $7$ below shows a graph of 10 different distances and their relative importance in determining disulfide bond stability. An interactive iCn3D model of carbonic anhydrase from Neisseria gonorrhea (1KOQ) highlighting two pairs of amino acids identified by machine learning as candidates for mutations to disulfide-bonded cysteines is shown in Figure $8$ below. Figure $8$: Carbonic anhydrase from Neisseria gonorrhea (1KOQ) highlighting two pairs of amino acids identified by machine learning as candidates for mutations to disulfide-bonded cysteines (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...ZH6Tv1urwkKhz8 Question $4$ This question addresses the Biomolecular Visualization Framework theme(s) Atomic Geometry (AG), Macromolecular Building Blocks (MB) Using the data in Figure $25$ and measurements made using the iCn3D model above (Figure $25$) , determine which pair would be most likely engineered into a disulfide bond. Complete the table below with the distances you made using iCn3D. iCn3D instructions: Open the external link and follow these instructions Trackpad and Mouse Controls rotate: click and drag (mouse: left click and drag) zoom: pinch and spread (mouse: rotate the scroll wheel) translate: two-finger click and drag (mouse: right click and drag) Re-center: left click View from the top menu bar, then select “Center Selection” •Note: ctrl-click on a PC = command-click on Mac; alt-click on PC = option click on Mac Instructions 1. Zoom to clearly see the amino acid pair for distance measures 2. From the top menu bar, choose Analysis, Distance, distance between 2 atoms 3. Pick the appropriate 2 atoms for measure distance by holding down the Alt key and selecting both 4. Record the distances in the table below. Mutation Pair CB1-CA2 (Å) CB1-CB2 (Å) CA1-CB2 (Å) 1 (L137) - 2 (W141) 1 (Y54) - 1 (S160) Answer Mutation Pair CB1-CA2 (Å) CB1-CB2 (Å) CA1-CB2 (Å) 1 (L137) - 2 (W141) 6 5.2 5.3 1 (Y54) - 1 (S160) 4.1 4.1 5.0
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Fundamentals_of_Biochemistry_Vol._V_-_Problems/Structure%2F%2FFunction_-_Protein_Problems/LGA%3A_Voltage-Gated_Sodium_Channel_-_Students_082423.txt	princeton-nlp/TextbookChapters	Written by Henry Jakubowski, Emily Schmitt Lavin, Arthur Sikora, and Subhasish Chatterjee Introduction Eukaryotic voltage-gated sodium (NaV) channels generate and sustain action potentials in nerve and muscle cells by moving Na+ ions from the outside to the inside of the cell. This increases and makes positive the transmembrane potential of the cell, which at rest is approximately -70 mV (more negative inside). Once activated, the channel undergoes a fast inactivation (1-2 ms), without which the firing of nerves and muscles becomes dysregulated, a potentially lethal effect. Please view the information in Chapter 11.3 on the voltage-gated sodium channel before you do these guided assessment activities. The questions below are derived from a paper from Jiang et al. on the structure and properties of the α-scorpion toxin LqhIII (MW 7,000) bound to rat cardiac sodium channel NaV1.5 (MW 227,000) by. (Jiang, D., Tonggu, L., Gamal El-Din, T.M. et al. Structural basis for voltage-sensor trapping of the cardiac sodium channel by a deathstalker scorpion toxin. Nat Commun 12, 128 (2021). https://doi.org/10.1038/s41467-020-20078-3. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/) The Lqh toxin is made by Leiurus quinquestriatus hebraeus. It is found in North Africa, the Middle East, and Western India and is shown below. The study shows how the deathstalker scorpion (LqhIII) toxin inhibits the fast inactivation of cardiac sodium channels (Nav1.5). In other words, you will see how the toxin keeps the channel open longer than it would be open in its absence. • In the absence of toxin, the sodium channel NaV1.5 returns in 1-2 ms to an inactive state when an 'inactivation gate" moves to occlude the open pore. • The α-scorpion toxin LqhIII inhibits the return of the channel to the inactive state. Since the toxin inhibits the channel's fast inactivation of Na+ ion flow into the cell, the channel stays open longer. The toxin leads to the inhibition of the normal fast inhibition (inactivation) of the channel. Hence the channel stays open (activated) longer. This is analogous to the statement that the enemy of my enemy is my friend! Techniques to study the Na Channel For the experiments described in the paper, the rat sodium cardiac channel NaV1.5 was purified, its structure determined, and its functional properties (the regulated movement of Na+ to the inside of the cell - electrophysiology) in human epithelial cells measured. The protein is expressed in rat cardiac cells and is found in the cell membrane of the cell. The iCn3D image below shows the alpha chain of rat cardiac NaV1.5 (6UZ3) embedded in a simple bilayer (DMPC) to model how it might appear in a cardiac cell membrane bilayer. PDB coordinates based on S. Jo, T. Kim, V.G. Iyer, and W. Im (2008). CHARMM-GUI: A Web-based Graphical User Interface for CHARMM. J. Comput. Chem. 29:1859-1865. S. Jo, T. Kim, and W. Im (2007) Automated Builder and Database of Protein/Membrane Complexes for Molecular Dynamics Simulations. PLoS ONE 2(9):e880 Exercise $1$ Complete the flow chart below to show two different approaches that could be used to purify the protein and prepare it for structural and functional studies. On the left show steps you could use to directly purify the protein from rat hearts. On the right use the rat gene (Scn5a) for the channel as the starting point for purification. Here are some links for review if needed. Answer From heart tissue: From heart tissue DNA Here are some review links: Exercise $2$ Key components of the buffer solution used to purify the channel include HEPES and 1% (w/v) n-dodecyl-β-D-maltopyranoside. Their structures are shown below. Describe the role of each. Answer . Exercise $3$ The following iCn3D shows the interaction of the purified sodium channel with n-dodecyl-β-D-maltopyranoside (BDDM). Explain the differences between the iCn3D models showing the protein in a bilayer and interacting with the BDDM. Answer . Exercise $4$ The elution of the complex on a size exclusion column (Panel A) and the analyses for the eluted fractions by SDS-PAGE (Panel B) are shown in the figure below. The α-scorpion toxin LqhIII: rat cardiac sodium channel NaV1.5 complex elutes in the area of the first peak shown in blue a. How do molecules separate on size exclusion chromatography? b. Compare the molecular weights of the first peak to the second complex peak in Panel A. c. Which band(s) in Panel B likely represent the rat cardiac sodium channel NaV1.5 based on the intensity of the stained band? The toxin LqhIII is the lowest band. ( bands at 17.5K and 12.5 are FGF12b and calmodulin, respectively, which were added to stabilize the channel) d. Why do the channel and toxin elute together in the size exclusion column shown in Panel A, but are separate bands in PAGE gel in Panel B? e. How could you get the complex to separate as two peaks, the free NaV1.5 channel, and the free α-scorpion toxin LqhIII? f. To get information on the receptor, go to Uniprot and paste in rat cardiac sodium channel NaV1.5 into the search box. Go to Sequence and Isoform in the left panel and find the actual MW. Knowing this, what are the major bands at about 170K and 60K? Figure: Purification of the recombinant NaV1.5C/LqhIII complex. a. Representative size-exclusion chromatography profile of purified rNav1.5C/LqhIII. Peak fractions collected for cryo-EM grid preparation are shown in blue. b. SDS-PAGE of the size exclusion peak fractions stained by Coomassie blue. Answer . CryoEM was used to determine the structure of the sodium channel:toxin complex. Here is a short YouTube video that describes the technique. Also review the appropriate part of Chapter 3.3: Analyses and structural predictions of protein structure. Exercise $5$ Describe the temperature conditions for protein samples in cryoEM. What is the reported resolution of cryo EM structure? How does this compare to X-ray structures? What are some advantages of using cryoEM over X-ray crystallography and NMR to determine the structure of proteins? Answer . Molecular dynamics was also used to probe the conformational changes in the structure of the complex on the picosecond (10-12 s) to nanosecond (10-9 s) time scale. For a review of molecular dynamics, see Chapter 3.3: Analyses and structural predictions of protein structure. It can be used to probe dynamic changes in protein structure which cryoEM can't. Exercise $6$ Answers these multiple choice questions (created by AIPDF through ChatGPT4 -paid version using this prompt: Write 5 question for a biochemistry major about the use of molecular dynamics and the finding in the paper) 1. What was one of the primary uses of molecular dynamics in this research? - A) Predicting the behavior of NaV channels without toxins. - B) Analyzing hydration and Na+ permeation through the rNaV1.5C/LqhIII complex. - C) Studying the interaction between different toxins. - D) Predicting the behavior of potassium channels. 2. In the molecular dynamics simulation analysis, what was aligned to the initial position for each snapshot? - A) The α-toxin LqhIII. - B) The voltage-sensing domain IV. - C) The Cα atoms from pore transmembrane helices. - D) The fast inactivation gate. 3. Approximately how long were the unrestrained "production" simulations generated? - A) 10.35 ns. - B) 5000 steps. - C) 300 ns. - D) 2 fs. 4. Based on the molecular dynamics analyses, what was observed about the activation gate structure of the rNaV1.5C/LqhIII complex? - A) It was fully open for Na+ conductance. - B) It was functionally closed for Na+ conductance. - C) It was in a metastable state. - D) It showed no significant change from the rNaV1.5C structure. Answer . The authors used two types of electrophysiological techniques, patch clamp, and voltage clamp. Here is some brief background. In a whole-cell patch clamp experiment, a pipet is placed on a cell, and suction is applied until a tight seal, indicated by a sharp rise in electrical resistance (gigaohm level) is made. This is illustrated in the figure below. Patch Clamp Resistance. Formation of gigaseal. Holst. https://en.wikipedia.org/wiki/Automa..._Animation.gif. CC BY-SA 3.0 The cell can then be connected to a patch clamp chip in such a way that transmembrane potential or current can be measured on single-channel ion flow. This is illustrated in the figure below. Holst. Patch Clamp Chip. Batch clamp chip showing a gigaseal, whole-cell recording configuration, and the ion channel and whole cell current. https://en.wikipedia.org/wiki/Automated_patch_clamp#/media/File:Patch_Clamp_Chip.svg. CC BY-SA 3.0 In patch-clamp fluorometry, part of the cell membrane is sucked into the tip with the seal intact. Fluorescent ligands can be applied to one side of the membrane that contains an ion channel and current measurements were made as illustrated in the figure below. Alternatively, as in this paper, side chains in the S4 voltage sensor were labeled with a fluorophore, and changes in fluorescence were observed with changes in membrane potential. Patch-Clamp Fluorometry. https://www.uniklinikum-jena.de/phys...n/Methods.html Exercise $7$ Answers these general multiple-choice questions (created by AIPDF through ChatGPT4 -paid version using this prompt: Write five multiple-choice questions about the use of patch clamp techniques to measure sodium currents in cells) 1. What is the primary purpose of the patch-clamp technique in cellular electrophysiology? - A) To visualize cell structures. - B) To measure the concentration of sodium ions inside cells. - C) To record ion currents across cell membranes. - D) To stimulate cellular growth. 3. In a typical neuron at resting potential (-70 mV) and in this study (epithelial cells transformed with the rat channel, what is the direction of the sodium current when sodium channels open? - A) Inward, into the cell. - B) Outward, out of the cell. - C) There is no movement of sodium. - D) Both inward and outward simultaneously. 4. Which of the following factors can influence the magnitude and direction of sodium currents measured using patch-clamp techniques? - A) The concentration of potassium ions outside the cell. - B) The voltage across the cell membrane. - C) The pH of the cell cytoplasm. - D) The size of the cell. 5. Why might a researcher use drugs or toxins during a patch-clamp experiment measuring sodium currents? - A) To increase the size of the cell. - B) To modulate or block sodium channels and observe the effects. - C) To change the color of the cell. - D) To stimulate cell division. Answer . Exercise $8$ Answer these general multiple-choice questions about patch-clamp fluorometry. (created by AIPDF through ChatGPT4 -paid version using this prompt: write 5 multiple choice questions of patch clamp fluorometry in which key amino acids in a membrane protein are labeled with a fluorophore) 1. What is the primary advantage of combining patch-clamp with fluorometry in studying membrane proteins? - A) It allows simultaneous measurement of electrical activity and conformational changes. - B) It increases the fluorescence of all amino acids. - C) It enhances the electrical activity of the protein. - D) It allows visualization of the entire cell in detail. 2. Why are specific amino acids in a membrane protein labeled with a fluorophore in patch-clamp fluorometry? - A) To increase the size of the protein. - B) To change the electrical properties of the protein. - C) To detect specific conformational changes in the protein during activity. - D) To make the protein more soluble in water. 3. Which property of the fluorophore is crucial for patch-clamp fluorometry? - A) Its electrical charge. - B) Its sensitivity to changes in the local environment or protein conformation. - C) Its ability to increase protein activity. - D) Its color in visible light. 4. In which scenario would patch-clamp fluorometry be especially useful? - A) When studying the overall shape of a cell. - B) When investigating the relationship between ion channel gating and conformational changes. - C) When trying to increase the fluorescence of a solution. - D) When observing the movement of proteins inside the cell. 5. What is a critical consideration when choosing a fluorophore for labeling amino acids in patch-clamp fluorometry? - A) The taste of the fluorophore. - B) The electrical conductivity of the fluorophore. - C) The photostability and brightness of the fluorophore. - D) The size of the fluorophore molecule. Answer . Nonstructural Lab Studies of LqhIII Toxin Effects on Rat Sodium Channel NaV1.5 (rNaV1.5C) HEK293S GnTI (epithelial-like) cells were transformed with the rat cardiac sodium channel NaV1.5 (rNaV1.5C). The cells were then studied in the absence and presence of the toxin at varying times after toxin addition and at various concentrations of the toxin. The opening and closing of the channel were determined by measuring changes in the Na+ currents into the cell on channel opening. Exercise $9$ The resting potential of a cell is around -70 mV (more negative inside). When the transmembrane potential is depolarized by raising the transmembrane potential to around -55 mV or even more positive, the Na+ channels are activated, and an inward Na+ current (black line in a modified form of Figure 1a from the paper below) which goes downward by convention) through the channel occurs. This is followed by a quick inactivation of the channel and the return to the baseline flow of ions. In the experiment below, the potential was raised from -100 mV (channel closed) to 0 mV (channel open). What is happening to the NaV1.5 Na+ channel during these 10 ms? What is special about the current at 6 ms (indicated by the dashed vertical line) Answer Initially, the change in voltage opens the rNaV1.5C and allows an inward flow of Naions as evidenced by the vertical drop. As described in the introduction, conformational changes in the NaV1.5C (closing of the inactivation loop), follow which closes the channel and stops the current, so the current returns to baseline within about 6 ms. The channel is in the inactive state by around 6 ms. Exercise $10$ Figure 1a from the paper (modified) below shows a series of lines of different colored (black to red) representing Na+ currents obtained at 0 (black line) and increasing concentrations (gray through red) of the LqhIII scorpion toxin. Let's assume that the downward Ipeak =1. The values of I 6ms/Ipeak, calculated from the approximate values shown on the graph, are also shown on the vertical axis Does the toxin alter the immediate response of the cells after the channel was activated? What effect does increasing [toxin] have on the response of the cell? Offer a structural explanation of how the toxin affects the cell by suggesting changes in the toxin-bound structure. Answer The toxin at any dose does not substantially affect the initial opening of the channel since the size of the current does not change at first. Then the toxin inhibits the rapid inactivation of the channel within the first 6 ms, leading to a prolonged inward Na+ current. The inhibition of the inactivation is dose-dependent on the concentration of the toxin. The toxin does not completely block the quick inactivation of the channel as the lines don't return to the black baseline in the time interval measured. This suggests that the toxin binds to the channel and either partially occludes it or prevents the normal conformational change in the NaV1.5C that causes a quick deactivation of the channel. Since Na+ ions still flow through the channel but at a lower rate than the open state, the toxin-bound channel likely represents a 4th, or partially-opened state of the channel. Exercise $11$ Figure 1a (left) from the paper below shows the dependency of the inhibition of the quick inactivation of the channel on the log of the LqhIII concentration. When the transmembrane potential is set to 0 mV (as in this experiment), the channel should open and the current would be maximal. The data points in the graph are close to the ones estimated in the graph from the previous question. a. In the absence of the toxin, what should the current I be at 6 ms compared to the maximal Na+ current? That is, what would be the value of I6ms/Ipeak? b. Is the channel completely inactivated in the presence of the toxin? c. At 6 ms, what concentration of toxin (nM) causes 50% inhibition of the maximum effect of the inhibitor on the normal rapid inactivation of the channel? Answer . Exercise $12$ In the next experiment, cells were kept at -120 mV at one fixed concentration (100 nM) of toxin. The toxin was left to incubate with the cells for various times up to 20 min. After the incubation time, the transmembrane potential was changed to 0 mV to activate the channel, and inward Na+ currents were measured. The results are shown in the top inset graph in Figure 1b from the paper. (Note: It is unclear from the paper if the control was determined at 0 min with 100 nM toxin or no toxin.) a. Did the length of time cells were pre-incubated with the 100 nM toxin affect Na+ currents after depolarization of the cells? How did the effects on the cells depend on the preincubation time? b. Describe and explain these results Answer . Here is another interesting feature of toxin binding. The toxin binds to a site on the resting state of the NaV1.5C with high affinity. When the cell becomes depolarized (made more + inside the cell), the affinity for the toxin decreases so it starts to dissociate. The affinity of the toxin for NaV1.5C decreases with increasing + transmembrane potential. At very high positive potentials (+100 mV) it appears not to bind. Exercise $13$ What might account for the decreasing affinity of the bound toxin for the NaV1.5C with an increasing transmembrane potential? Answer . Exercise $14$ Time course experiments were conducted on the complex at 100 nM of LqhIII scorpion toxin. A three-pulse protocol can be applied to alter membrane potentials: • 1st: a pulse from −120 mV to +100 mV for the indicated times then • 2nd: a 50-ms hyperpolarizing pulse (make membrane potential very negative, perhaps around -100 mV) • 3rd: a pulse of 50 ms to 0 mV Note that steps 2 and 3 both occur with 0.1 s. What is the purpose of each pulse? Answer . Exercise $15$ Figure 1c from the paper below shows results for a set of 3-pulse designed to allow recovery of the fast inactivation which was blocked by the previous toxin binding. Note that the transmembrane potential for the first pulse was +100 mV a. Describe what happens to the channel and complex. b. Explain the results c. Summarize thermodynamic and kinetic features that make the toxin so effective. Answer . Structural Studies of LqhIII Toxin Effects on Rat Sodium Channel NaV1.5 (rNaV1.5C) - CryoEM Before we discuss in detail the structure of the sodium channel and its complex with the toxin, let's look at an important attribute of molecules that helps determine their function, the actual size of the species involved. Exercise $16$ The Na+ and the K+ voltage-gated ion channels must have an open pore when the channel protein is active (Naions move across the channel). Extracellular Na+ and the K+ ions don't exist as "naked" ions but they are hydrated by water in an aqueous extracellular environment. The figure below shows the relative sizes of these Group I cations and their hydrated forms, in comparison to the diameter of the open NaV1.5 pore in the channel. Answer the following questions. The red sphere (c) represents the calculated value of the diameter of water assuming its volume when it is bound to a protein is 25 Å3. 1. Which represents the "naked" (nonhydrated) size of the K+ ion? 2. Which represents the hydrated Na+ ion? 3. Based on the pore size alone, which of these species could diffuse through the pore? Answer . Exercise $17$ The approximate relative sizes of the hydrated Na+ ion, pore opening, toxin, and the NaV1.5 protein are shown in the figure below along with the relative width of the bilayer (BL). A cardiac epithelium cell is shown as a rectangle to the right. A red dot (not visible in the large figure) in the membrane surrounded by the red-dotted circle represents a single NaV1.5 channel. 1. Which likely represents the pore? 2. Which represents NaV1.5 channel protein? 3. Which represents the toxin? Answer . Exercise $18$ If you hadn't read the paper, where would you consider the most likely location for a toxin to bind to affect the function of Nav1.5? Circle the most likely toxin binding site in the schematic below. Based on the paper, where did it bind? Redraw the toxin in the correct location based on the paper. Answer . Exercise $19$ How is this toxin (LqIII) different in terms of its binding location from other toxins that interfere with the functioning of Nav1.5? What is the effect of the toxin on the channel and the symptoms of this venom? Answer . Exercise $20$ What amino acids should be present in the S4 segment of Nav1.4 and why? Answer . Exercise $21$ The figure below shows an interactive iCn3D model of the rat sodium channel NaV1.5 bound to the LqhIII toxin (7k18). a. To which domain does the LqhIII toxin bind? b. Is the binding site close to the pore-forming segments of the domain or the voltage-sensitive segments? c. Does the layer of red spheres represent the outer (extracellular) or inner (intracellular) leaflet of the membrane? d. Offer reasons that parts of the protein are missing from the structure. Answer . Exercise $22$ The IFM motif has been shown to be conserved across all voltage-gated sodium channels. a. What role does it play in these channels and in Nav1.5 and why? b. The cause of Paroxysomal Extreme Pain Disorder (PEPD), an extremely rare disease with only 15 known affected families, appears to be mutations in the IFM motif which leads to increased sensations of pain. What is a likely effect of the mutations in the IFM motif? https://en.wikipedia.org/wiki/Paroxy..._pain_disorder Answer . Exercise $23$ The figure below shows a different interactive iCn3D model of the rat sodium channel NaV1.5 bound to the LqhIII toxin (7k18) without a membrane representation for clarity. It shows the selectivity filter DEKA (spacefill, CPK colors), the inactivation gate IFM and the IFM "internal receptor" F1651, L1660, and N1662 (spacefill, CPK colors), and a ring of hydrophobic residues V413, L941, I1471, and I1773 (spacefill, black) that in the closed state completely seal off the cytoplasmic opening in the pore. Rat sodium channel NaV1.5 bound to the LqhIII toxin without a membrane representation (7k18). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...pPzakHu6Ew6WH9 After viewing the structure from all angles, do you think that the toxin:NaV1.5 complex looks closed, open, or inactivated form? Explain Answer . Structural Studies of LqhIII Toxin Effects on Rat Sodium Channel NaV1.5 (rNaV1.5C) - Molecular Dynamic Simulations Exercise $24$ Now let's look at some data to see if the pore is really open, partially open, or closed in the toxin:channel complex. One clue is if the structure shows water in the pore as the Na+ ions must be hydrated to pass through the pore (the opposite case is seen with K+ channels when K+ pass through stripped of water). Molecular dynamic (MD) simulations were done on the NaV1.5 with and without the toxin to simulate the environment in the channel opening. The results of the MD simulations are shown below in Figures 6 a and c from the paper. Molecular dynamics analysis of hydration and Na+ permeation through the rNaV1.5C/LqhIII complex. Panel a shows a side view of rNaV1.5C (orange ribbons; domains II and IV) from MD simulations highlighting Na+ ions (blue spheres), the water-occupied volume within a cylinder of radius 8.5 Å (red surface), and the protein-occupied volume within a cylinder of radius 12 Å (colorless surface). The cavity within the pore is outlined with a black rectangle. The region of the intracellular activation gate is shown as a purple band. Panel c shows molecular representations of the gate containing Nwater = 3 (left) or 15 (right) water molecules Based on these studies, do you believe the pore is closed, open, or partially open? Answer Detailed Structural Analyses of LqhIII Toxin Effects on Rat Sodium Channel NaV1.5 (rNaV1.5C) Exercise $25$ From the iCn3D model, write the sequence of the S4 segment that contains the Arg side chains and describe the properties of the amino acids in the sequence. Do this by scrolling along the sequence window in iCn3D (shown below) until you find the labeled Arg shown in the model. Answer . Exercise $26$ Is the helix amphiphilic? That is, are the Arg side chains all on one face of the helix and the nonpolar on the other? To find out, copy and paste the sequence of S4 (above) in this helical wheel predictor and run the program. Answer . Exercise $27$ Make a simplified view of the iCn3D model by hiding Domains 1-III to more readily see the contributions of S5 and S6 of Domain IV to the pore. • open iCn3D and load 7K18. • With your mouse or trackpad, choose Sequence and Annotation in the top menu bar • Choose the Details tab • Ctrl-Click the two sequences highlighted in yellow below for Domain IV and the toxin. • Choose View from the top menu bar and then View Selection • Choose Style, Background, Transparent . Noncovalent Interactions of LqhIII and Domain IV/VS Now let's look at the actual interaction of the toxin with the Domain IV/VS of the channel. A closeup showing the interaction site is shown in Figures 3 b and c from the paper below. Panel C next to it shows the NMR-solution structures of the toxin in the absence of the channel. Each structure determined is represented by a single color line color codes red at the C-terminus to blue at the N-terminus. Panel b: CryoEM structure of the rNaV1.5C Domain IV/VS and LqhIII complex; Panel c: NMR structure of free LqhIII Exercise $28$ Using this iCn3D model, describe the secondary structure of the bound toxin. How many pairs of cysteine residues are in the LqhIII toxin. Identify which cysteines are involved in the disulfide bonds. What effect do the disulfide bonds have on the beta sheet structure? Answer . Exercise $1$ What sections of the toxin in panel B make the closest interactions with the Domain IV/VS of the channel? Describe their conformation flexibility in the free toxin. Answer . The figure below shows an interactive iCn3D model of a surface rending of Domain IV of the rat sodium channel NaV1.5 bound to the LqhIII toxin (7k18). Surface rending of Domain IV of the rat sodium channel NaV1.5 bound to the LqhIII toxin (7k18). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...x5QRPEctT5zsW7. The molecular surface and underlying secondary structure of the LqhIII toxin are shown in magenta, with key residues H15, H43, and K64 shown as CPK-colored sticks and labeled. Domain IV/VS of the channel is shown in cyan, with key amino acid side chains T1608, D1612, and Q1615 shown as colored and labeled sticks. Exercise $29$ Comment on the shape and possible side chain interactions that contribute to high-affinity binding of the inhibitor to Domain IV/VS. Answer . Figure 3d from the paper below shows the detailed interactions between LqhIII and DIV-VS. Key residues shown in sticks were labeled. Interaction surfaces of the DIV-VS (blue) and the LqhIII (purple). Key residues for the interaction are shown in yellow shading and embedded stick The figure below shows an interactive iCn3D model of the rat sodium channel NaV1.5 Domain IV bound to the LqhIII toxin (7k18). The toxin is shown in magenta. The segments are colored as follows: S1 is red, S2 is orange, S3 is yellow, S4 is cyan, S5 is brown and S6 is violet. Key amino acid pairs involved in the binding of the toxin to Domain IV are shown in sticks and labeled. Exercise $30$ In summary, name and locate the amino acid residues that serve the following roles in the LhqIII toxin: DIV-VS interactions. 1. Which amino acids in the toxin interact with D1612 (the paper describes the interaction as pincers surrounding D1612). 2. The conserved negatively charged residue in the Nav1.5 channel 3. What position is Thr in and what is thought to be its role in the mechanism? Answer Comparison of Activated DomIV-Voltage sensor (VS) with Toxin-bound Partially activated DomIV-VS Now we'll try to understand Figure 4, Conformational Change of DIV-VS, from the paper and pay special attention to the section of the text, “An intermediate-activated state of DIV-VS trapped by LqhIII” - Let’s dissect Figure 4 A and B. Figure 4a/4b from the paper below shows the conformational change of Domain IV-Voltage sensor (DIV-VS) comparing the activated and partially activated state with the bound toxin. Panel a shows the activated domain IV with key Arg side chains in S4. Panel B shows the partially activated domain IV with the same key Arg side chains in S4. The bound LqhIII is shown as a purple chain. In panels a and b, the: 1. activated Nav1.5 DIV-VS (the voltage sensing domain) is in grey (fig 4a) 2. the intermediate-activated Nav1.5DIV-VS is in blue (Fig 4b) 3. side chains of gating charges of Arg are shown in grey and blue sticks in 4a and in shades of blue sticks in 4b. Side chains in the ENC are shown in red, in the HCS in yellow, and in the INC in red; 4. the shift of each gating charge was indicated by black dashed lines between the structures in panels a and b. The shift from the cytoplasmic to extracellular parts of the channel is shown in the region between the two panels. The black Rs in the activated DIV-VS(panel A) are further up in the diagram (towards the extracellular region) and further down in the partially activated DIV-VS bound to the toxin. Exercise $31$ Locate the 6 arginines? (R1-R6) with the blue indicating the N atoms in the positively charged Arg side chain of S4 in Domain IV in one of the iCn3D models above. What do the following abbreviations mean? ENC, HCS, and INC. Answer . Exercise $29$ From Figure 4a to 4b, explain from an electrostatic viewpoint how the movement of the Args towards the extracellular region would promote the movement of Na ions inward. Explain how the movement of Na+ ions would be diminished in the presence of the toxin. Answer . Comparison of Active and Intermediate-Activated, and Intermediate/Resting state Now consider Figures 4 C and D from the paper below: 4c: Superposition of NaV1.5 DIV-VS between the fully activated state and toxin-bound intermediate-activated state. Red arrows indicate the conformational changes. 4d: Superposition of the intermediate-activated NaV1.5 DIV-VS and resting-state NaVAb-VS Exercise $32$ a. Locate the region in Figure c above that shifts the most from the fully activated to intermediate-activated state of the DIV-VS. b. Describe the difference shown in Figure d between the intermediate-activated DIV-VS structure (blue) upon the resting state NaVAb-VS structure (orange) c. What do these differences imply about the conformational states of the apo and toxin-bound channel? Answer . Summary Exercise $33$ Why might the mode of action be specific for cardiac muscle cells as compared to other toxins that act on sodium channels in skeletal and nerve cells? Answer . After this guided research literature module, you can hopefully better understand the findings in the paper which are summarized in this abstract: "Voltage-gated sodium (NaV) channels initiate action potentials in excitable cells, and their function is altered by potent gating-modifier toxins. The α-toxin LqhIII from the deathstalker scorpion inhibits fast inactivation of cardiac NaV1.5 channels with IC50 = 11.4 nM. Here we reveal the structure of LqhIII bound to NaV1.5 at 3.3 Å resolution by cryo-EM. LqhIII anchors on top of voltage-sensing domain IV, wedged between the S1-S2 and S3-S4 linkers, which traps the gating charges of the S4 segment in a unique intermediate-activated state stabilized by four ion-pairs. This conformational change is propagated inward to weaken binding of the fast inactivation gate and favor opening the activation gate. However, these changes do not permit Na+ permeation, revealing why LqhIII slows inactivation of NaV channels but does not open them. Our results provide important insights into the structural basis for gating-modifier toxin binding, voltage-sensor trapping, and fast inactivation of NaV channels." Extensions 1. Interesting sidebar: https://www.sciencedirect.com/science/article/pii/S0021925819308300?via%3Dihub Chlorotoxin (Cltx) is a 36-amino acid peptide that was originally isolated from Leiurus quinquestriatus venom (14) and has been shown to inhibit small conductance Cl− channels in colonic epithelial cells (14, 15). Cltx also inhibits Cl− fluxes across glioma membranes (13, 16). Immunohistochemical studies show that Cltx specifically and selectively binds to glioma cells (17) and radiolabeled Cltx targets tumor cells in mice bearing xenografted glioma tumors. Glioma cell migration and invasion into fetal brain aggregates is significantly reduced by Cltx (13). A recent survey of over 200 tissue biopsies from patients with various malignancies suggests that Cltx binds to the surface of gliomas and other embryologically related tumors of neuroectodermal origin (18) but not to normal brain. 2. Deathstalker scorpion venom also contains chlorotoxin - This is a very interesting story. Note and remember LqhIII is an alpha toxin 4. How much is deathstalker venom worth? 5. Looks like a great review article: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7277529/ 6. Scorpion Venom: Detriments and Benefits 7. Lookfor possible therapeutic potential here https://www.venomdoc.com/ 8. Very cool venom graphic - https://www.venomdoc.com/new-page-2 9. The Toxicogenomic Multiverse: Convergent Recruitment of Proteins Into Animal Venoms: https://static1.squarespace.com/static/55a239e2e4b0b3a7ae106f25/t/59814ceae6f2e10bc7ada5f1/1501646072821/2009_Fry_Toxicogenomic_multiverse.pdf 10. The deathstalker scorpion venom alone has been found to have several different kinds of toxins including chlorotoxin (inhibit chloride channels), charybdotoxin (inhibit potassium channels), and agitoxins (affect sodium channels).. https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/leiurus-quinquestriatus. Chlorotoxin was found to selectively bind to glioma cells and serve as a marker for glioblastoma. https://www.acs.org/molecule-of-the-week/archive/c/chlorotoxin.html#:~:text=Strichartz%20at%20Harvard%20Medical%20School,diagnosing%20and%20treating%20some%20cancers. This feature of the scorpion venom was developed by J.M. Olson at Fred Hutchinson Cancer Center (Seattle) as a product called Tumor Paint https://www.fredhutch.org/en/news/center-news/2014/09/tumor-paint-US-trial.html
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Fundamentals_of_Biochemistry_Vol._V_-_Problems/Structure%2F%2FFunction_-_Protein_Problems/Literature-based_Guided_Assessments%3A__Protein_Stability_-_Carbonic.txt	princeton-nlp/TextbookChapters	Carbonic Anhydrase Engineered Stability We have already encountered this enzyme before (Chapter 6.1). It catalyzes the hydration of CO2 (g) as shown below. CO2 (g) + H2O ↔ H2CO3 (aq) ↔ HCO3- (aq) + H+ (aq) It is among the fastest of all enzymes, with a kcat of 106 s-1 and a kcat/Km of 8.3 x 107 M-1s-1 (reference). It is diffusion controlled in that the rate of diffusion of reactants and products, not the chemical steps, determine the reaction rate. It can convert 106 molecules of CO2(g) to HCO3- each second. No wonder scientists and engineers are studying it to capture CO2. It's a big challenge though to capture CO2 released on combustion of coal or natural gas in a power plant. Here are two problems that must be overcome: • The enzyme must be thermostable at elevated temperatures to capture the CO2 found in high-temperature power plant emissions • The enzyme is reversible so it will be inhibited by the product HCO3- • The enzyme must be stable to somewhat alkaline conditions (pH of 0.1M NaHCO3 = 8.3) For carbon capture from fossil fuel emissions, CA is immobilized by surface adsorption, covalent attachment, encapsulation, and entanglement. Immobilized enzymes are typically more thermostable and can be used in flow-through as opposed to solution phase capture. The immobilized enzyme matrix must withstand high temperatures (up to 100°C, and alkaline solvents used to strip the matrix for reuse. The enzyme is found throughout life and typically has an active site Zn2+. There are 8 families, α, β, γ, δ, ζ η, θ, and ι, with the α family being the most abundant. The α forms are generally active as dimers, but can act as monomers and tetramers.. There are 15 isoforms of the α form in humans and have a prime role in pH regulation. They are found in bacteria, fungi, plants, and algae. β-CAs are found in some types of bacteria, archaea, fungi, some higher plants, and invertebrates. CA in chloroplasts (and mitochondria (algae) are involved in carbon fixation. We will focus our attention on engineering carbonic anhydrase to make them more thermostable, alkali insensitive, and less susceptible to product inhibition by bicarbonate. Natural enzymes can be isolated and selected for thermal and alkali stability. In addition, new versions selected for these properties can be engineered using directed evolution or site-directed mutagenesis. You wish to increase the thermal stability of a protein using mutagenesis. Essentially you wish to perturb the equilibrium between the folded (native) protein and the unfolded (denatured) protein so as to preferentially stabilized the native state. Question $1$ Using mutagenesis, what residues might you change in a native protein to make it more stable at higher temperatures? Answer A characteristic of the native state of the protein is its conformational stability compared to the conformational flexibility of the many possible denatured states. In addition, the protein must undergo conformational changes as it unfolds. Hence anything that restricts conformational flexibility might preferentially stabilize the native state. These would include changing single or pairs of side chains to allow the formation of more salt bridges and intrachain disulfide bonds, as well as hydrogen bonds. Loops with greater flexibility, as determined by B-factors in the crystal structure files, or by molecular dynamic simulations, could be changed to contain a disulfide, which would clearly stabilize a flexible loop. Question $2$ What measurements would you make to quantitate the change in thermal stability? Answer Measures a signal that changes with increasing temperature. The signal can be enzyme activity, or more easily a spectroscopic signal such as absorbance at 280 nm or fluorescence as a function of temperature. Alternatively, the stability at room temperature could be measured using urea as a perturbant. These are discussed in Chapter 4.12. The actual amino acid composition and more strangely specific dipeptide sequences within a sequence are associated with thermal stability of hyperthermophilic proteins. For example, proteins from two different types of archaea with different optimal growth temperatures show that the one with the higher growth temperature have significantly higher levels of VK, KI, YK, IK, KV, KY, and EV and decreased levels of DA, AD, TD, DD, DT, HD, DH, DR, and DG. Similar experiments have been done in bacterial cells. Using machine learning, the dipeptide sequences KH, KR, TF, PM, F∗∗N, V∗∗Y, MW, and WQ were important in themostability where the * denotes a gap in the residues. Carbonic anhydrase from Neisseria gonorrhea (ngCA) Data from: Jo, B., Park, T., Park, H. et al. Engineering de novo disulfide bond in bacterial α-type carbonic anhydrase for thermostable carbon sequestration. Sci Rep 6, 29322 (2016). https://doi.org/10.1038/srep29322. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/ Now that we understand the general chemistry, structure, and reaction mechanism of carbon anhydrase (at least the alpha human CAII form), let's explore efforts to engineer more thermostable variants. One example is the carbonic anhydrase from N. gonorrheas. This CA has been used as a target for mutagenesis to increase thermal stability of the enzyme, through the introduction of new disulfide bonds. Even though only about 35% of the amino acids are identical, the overall structures are similar. This is illustrated in Figure $10$ below. Figure $10$: Alignment of the carbonic anhydrase from Neisseria gonorrhea (NG-CA) magenta,1KOQ) and human CA II (cyan, 2VVB) The active site is mostly conserved compared to human CA II. The Zn2+ bound water has a pKa of around 6.5, compared to the value of 7.0 in human CA II. The hydrophobic patch (pocket) is similar, with Phe 93, Leu 153 and Tyr 72 in the NG-CA replacing Phe 95, Phe 176, Phe 70 in human CA II, respectively. The histidine ligands to Zn2+ are His92 (94), His94 (96), and His111 (119), where the numbers in parentheses represent Hu CA II. The proton removed from Zn2+ bound water is transferred to His 66 (64 in human CA II) and then to His 64. The single disulfide bond between 181 and C28 is shown in Figure $1$ below Figure $11$: Single disulfide bond between 181 and C28 in wild type Carbonic anhydrase from Neisseria gonorrhea Question $18$ This question addresses the Biomolecular Visualization Framework theme(s) Atomic Geometry (AG) Identify the correct torsion angles in Figure $\PageIndex{x}$ above. Verbal definitions of torsional angles in a peptide chain are listed below. The successive atoms after the Cα leading away from the backbone atoms are Xβ-Xγ-Xδ-Xε (in that order). C is the backbone carbonyl C and N is the backbone nitrogen atom. • phi (φ) is the angle of right-handed rotation around N-Cα bond. φ = 0 if the Cα-C bond is cis (eclipsed) to the C-N bond. Values range from -180 to 180 degrees. • psi (ψ) is the angle of right-handed rotation around Cα -C bond. ψ = 0 if the C-N bond is cis (eclipsed) to the N-Cα bond. Values range from -180 to 180 degrees. • chi11) is the rotation around N-Cα-Xβ-Xγ • chi22) is the rotation around Cα-Xβ-Xγ-Xδ • chi33) is the rotation around Xβ-Xγ-Xδ-Xε Answer phi (Φ) = b, psi (Ψ) = a, chi11) = c, chi22) = d, and chi33) = e Question $19$ This question addresses the Biomolecular Visualization Framework theme(s) Atomic Geometry (AG), Topology and Connectivity (TC) Figure $12$ below shows an interactive iCn3D model of the atoms within 4A of the disulfide bond in Carbonic anhydrase from Neisseria gonorrhea (1KOQ). Rotate the model to determine the approximate chi33) dihedral angle. Hint: site down the S-S bond. Figure $12$: Atoms within 4A of the disulfide bond in Carbonic anhydrase from Neisseria gonorrhea (1KOQ). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...nutjP6EL2PubRA Answer The visually estimated chi33) angle for rotation around the S-S bond is 90o. Here is the actual angle (image made with Pymol) We mentioned previously that variants with higher thermal stability are likely to be more rigid and less flexible. Flexibility can be determined through analysis of molecular dynamic simulations and also by examining of the B factor values in PDB file. This number is a measure of the displacement of an atom from a mean The numbers in the last column in the file are called the temperature factors or B-factor. The B-factor describes the mean-square displacement, a measure of the displacement of an atom from an average value. If the atoms are more flexible, the electron density determined in x-ray structures is lower than if the atoms are more fixed, which gives high electron density. To make stabilizing disulfide bonds, investigators found site chains close enough that when mutated to cysteines could potentially form disulfide bonds. In addition, they search for such residues in surface loops (without alpha and beta structure) which are inherently more flexible. Introducing disulfide bonds into the loop would stabilize it and make it more rigid. Table $2$ below shows a description of the double cysteine CA variants in the study. Variant designation Position Wild-type residues Loop length Sum of B-factors T133C/D197C 133, 197 Thr/Asp 63 87.60 P56C/P156C 56, 156 Pro/Pro 99 80.82 N63C/P145C 63, 145 Asn/Pro 81 77.17 Table $2$: Description of the double cysteine CA variants in ngCA The locations of the side chair targeted for mutations to cysteine pairs are shown in Figure $13$ below. Figure $13$: 3D structure of ngCA and location of residue pairs for disulfide engineering The zinc (not shown)-coordinating histidine residues in the catalytic active site are shown in green. The proton shuttle histidine residue is shown in magenta. The native disulfide bond is colored yellow. An interactive iCn3D model of carbonic anhydrase from Neisseria gonorrhea (1KOQ) highlighting the 3 pairs of sidechains for mutations is shown in Figure $14$ below. Figure $14$: Carbonic anhydrase (Neisseria gonorrhea) with 3 paired side chains for engineered disulfide (1KOQ) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...KRP2tFJ1pV1FF6 The mutations were made and the wild-type proteins and three mutants were subjected to SDS-polyacrylamide gel electrophoresis (SDS-PAGE). The stained gels are shown in Figure $15$ below. Figure $15$: Expression and purification of disulfide CA variants. Panel (a) shows the expression of the protein in transformed cells 25 °C after IPTG induction and fractionated into soluble and insoluble fractions. SHuffle strain, an engineered E. coli strain that promotes cytoplasmic disulfide bond formation, was used. Panel (b) shows purification results. Each lane was loaded with 4 μg of each purified CA variant. The proteins were visualized with Coomassie blue staining after SDS-PAGE. The arrow indicates the position of the bands corresponding to ngCA variants. Lane: M, molecular weight marker; S, soluble fraction; IS, insoluble fraction. Question $20$ a. Interpret the results of the PAGE gels in Figure $15$ above b. How pure were the proteins based on the PAGE gel result in Panel (b). Can you infer from the gel that the proteins folded correctly? Answer a. It appears to show that a small fraction of the wild type and each mutant, especially the N63C/P145C pair, was found in the insoluble fraction. This might result from improper folding of the proteins in E. Coli, leading to hydrophobic side chain surface exposure and aggregation into insoluble "inclusion bodies". This appears to be just a minor issue. b. All the proteins appear very pure with some very small levels of contamination in the N63C/P145C. The PAGE results show the protein all have the same molecular weight but whether they folded to a native state with activity can not be determined. Nor can it be determined if the disulfide pairs in the mutants were made are if they were, were correctly paired. The investigators next determined if the expressed and purified wild-type and mutant proteins had the correct number of disulfide bonds. They did this by reacting the proteins in the absence and presence of dithiothreitol with DTNB or 5,5'-dithiobis(2-nitrobenzoic acid), also called Ellman's Reagent. Both structures are shown in Figure $16$ below. Figure $16$: Structures of DTT and DTNB DTT is a reducing agent that cleaves disulfide. Question $21$ Draw a mechanism showing the reaction of a disulfide with DTT. Answer Only surface and not buried free cysteines will be labeled unless the protein is unfolded to expose all the cysteines. DTNB reacts with free sulfhydryls to form the 2-nitro-5-thiobenzoic acid anion leaving group that absorbs at 412 nm. Question $22$ Draw a mechanism showing the reaction of free sulfhydryl (like Cys) with Ellman's reagent Answer Only surface and not buried free cysteines will be labeled unless the protein is unfolded to expose all the cysteines. The results of the reaction of the proteins with Ellmans's agent, in the presence and absence of DTT, are shown in Table $3$ below. CA variant Free thiol/protein (mol/mol) a Deduced no. S-S bonds −DTT +DTT Wild-type 0.06 ± 0.02 1.80 ± 0.14 ? T133C/D197C 0.08 ± 0.02 3.79 ± 0.22 ? P56C/P156C 0.06 ± 0.03 3.89 ± 0.06 ? N63C/P145C 0.08 ± 0.03 3.75 ± 0.05 ? Table $3$: Analysis of disulfides in CA using Ellman's reagent. aNumbers are represented in mean ± SD. Question $23$ How many S-S would you deduce from the table are present in the wild-type and mutant enzymes? Did the correct disulfide bonds form? Explain your answers Answer DTT reduces the disulfide in protein. For each disulfide, two free Cys side chains are made. The molar ratio of CysSH/CA for the wild-type is 1.8 in the presence of DTT. The value is very close to the expected value of 2. For the mutants, the ratio is about 3.8 in the presence of DTT, suggesting 4 free Cys consistent with 2 disulfide bonds. Table $4$ below shows the catalytic activities of the disulfide CA variants at 25 °C. CA variant CO2 hydration activity Relative esterase activity a kcat × 10−4 (s−1) K M (mM) kcat /KM × 10−6(M−1 s−1) Wild-type 1.00 1.44 14.2 1.01 T133C/D197C 1.49 1.97 16.7 1.18 P56C/P156C 1.03 1.44 16.9 0.85 N63C/P145C 0.55 0.27 17.3 0.16 Table $4$: catalytic activities of the disulfide CA variants at 25 °C aThe specific activity of the wild-type corresponds to 0.22 U/μmol-enzyme. Question $24$ Why did the investigators conduct this experiment? Interpret the results Answer All of the previous results suggest that the mutant proteins were made and had the correct number of double bonds, but the experiments could not tell if the bond pairs were correct. For example, did the T133C/D197C contain a native (C28-C181) and mutant (C133-C197) bond and not another combination? Activity is an excellent predictor of structure. All but one mutant retained nominal activity, as evidenced by a comparison of the rat constants. The N63C/P145C had a 6x lower kcat, but even then it is close to diffusion-controlled. Now comes the big question: were the investigators able to engineer thermal stability into the carbonic anhydrase? Experimental results to show the thermostability of the disulfide CA mutants are shown in Figure $17$ below. Figure $17$: Thermostability of the disulfide CA variants Panel (a) shows short-term kinetic stability. The enzyme solutions (40 μM) were incubated for 30 min at different temperatures, and the residual activities were measured by esterase activity assay. Activities of 100% correspond to untreated samples. Panel (b) shows long-term kinetic stability at 70 °C. The half-lives (t1/2) of the CA variants were estimated by fitting the experimental data to an exponential decay curve. Each value represents the mean of at least three independent experiments, and the error bars represent the standard deviations. Question $25$ Analyze the results in Panels (a) an (b). Which protein was most thermostable over the short (30 minute) and long (hours) incubating time at elevated temperatures? Answer At 80 °C, all the mutants showed increases thermostability to short term (30 minute) heating, but one, N63C/P145C was exceptionally thermostable. Longer time courses for heating at 70°C showed that the N63C/P145C was again far more stable over time. Its t1/2 was 31.4 h, compared to the values between 4-6 h for the others. Panel (c) shows heat-induced denaturation of disulfide CA variants. Temperature-dependent changes of the circular dichroism ellipticity were recorded at 220 nm on CD spectrometer. The denaturation curves were normalized to the fraction of unfolded protein. The horizontal dashed line indicates the point at which the fraction of unfolded protein is 0.5. The vertical dashed lines point to TM values. Panel (d) shows the overall RMSD of disulfide variants from molecular dynamic simulations performed at 400 K for 20 ns. Question $26$ Analyze the results in Panel (c). What do changes in the CD helicity show? Which protein was most thermostable based on TM values? Is the decrease in enzyme activity in panels (a) and (b) result from the denaturation of the protein? Answer CD measurements can give a measure of the retention of secondary structure (alpha helices and beta structure) on denaturation. The CD spectrum for different secondary structures is shown below (From Chapter 3.5). The curves were normalized to fit on a 0-1 scale on the y axis, which then gives a measure of percent denaturation. The temperature half-way up is the TM, or "melting temperature, at which an equilibrium mixture would contain half native and half denatured protein (true for a small protein with no intermediates). The TM values were for the wild-type, T133C/D197C, P56C/P156C, and N63C/P145C mutants 73.6 °C 74.7 °C, 77.4 °C, and 81.4 °C. These parallel the t1/2 values for enzyme activities, and support the idea that denaturation led to inactivation of the enzyme. Question $27$ Analyze the molecular dynamics simulation results in Panel (d) Answer The molecular dynamic simulations for all the proteins soon reach equilibrium values as indicated in the plateaus of average room mean square deviation of the protein backbone. The overall molecular root-mean-square deviation (RMSD) of N63C/P145C was the lowest, indicating that it was most rigid. This is in accord with the idea that increased flexibility destabilizes a protein and engineering a disulfide into makes it more rigid and hence more stable to temperature increases. The results are in accordance with the other experiments that show the N63C/P145C was the most thermostable. " In addition, T133C/D197C showed the highest values in both the overall and the residual RMSD (Fig 3D). This may explain and correlate with the increased activity of T133C/D197C (Ta) and the increased ΔS of unfolding" You may remember from both introductory chemistry and from Chapter 4.12, that you can calculate the thermodynamic parameters, ΔHo and ΔSo for N ↔D at room temperature from thermal denaturation curves using the van 't Hoff equation. In this case, Keq values can be calculated from thermal denaturation curves by monitoring change in CD signal at 220 nm, and applying this equation (also from Chapter 3.12). K_{e q}=\frac{[D]_{e q}}{[N]_{e q}}=\frac{f_D}{f_N}=\frac{f_D}{1-f_D} From this, we can calculate ΔG0. \Delta \mathrm{G}^0=-\mathrm{R} \operatorname{Tln} \mathrm{K}_{\mathrm{eq}}=-\mathrm{R} \operatorname{Tln}\left[\frac{\mathrm{f}_{\mathrm{D}}}{1-\mathrm{f}_{\mathrm{D}}}\right] Knowing Keq, ΔH0, DS0 can be calculated as shown below. A semi-log plot of lnKeq vs 1/T is a straight line with a slope of - ΔH0R and a y-intercept of + ΔS0/R, where R is the ideal gas constant. \begin{gathered} \Delta \mathrm{G}^{0}=\Delta \mathrm{H}^{0}-\mathrm{T} \Delta \mathrm{S}^{0}=-\mathrm{RTln} \mathrm{K}_{\mathrm{eq}} \ \ln \mathrm{K}_{\mathrm{eq}}=-\frac{\Delta \mathrm{H}^{0}-\mathrm{T} \Delta \mathrm{S}^{0}}{\mathrm{RT}} \ \ln \mathrm{K}_{\mathrm{eq}}=-\frac{\Delta \mathrm{H}^{0}}{\mathrm{RT}}+\frac{\Delta \mathrm{S}^{0}}{\mathrm{R}} \end{gathered} The equation below shows that the derivative of equation (8) with respect to 1/T (i.e. the slope of equation 8 plotted as lnKeq vs 1/T) is indeed -ΔH0/R. Equation (9) is the van 't Hoff equation, and the calculated value of the enthalpy change is termed the van 't Hoff enthalpy, ΔH0vHoff. \frac{d \ln \mathrm{K}_{\mathrm{eq}}}{d(1 / \mathrm{T})}=-\frac{\Delta \mathrm{H}^{0}}{\mathrm{R}}=-\frac{\Delta \mathrm{H}_{\mathrm{vHoff}}^{0}}{\mathrm{R}} Using this method, the thermodynamic parameters for unfolding of the protein were calculated. The results are shown in Table $4$ below. CA variant Melting temperature, TM (°C) Enthalpy change of unfolding, ΔH (kcal mol−1) Entropy change of unfolding, ΔS (kcal mol−1 K−1) Wild-type 73.6 48.8 0.141 T133C/D197C 74.7 52.8 0.153 P56C/P156C 77.4 35.1 0.091 N63C/P145C 81.4 30.0 0.085 Table $4$: Thermodynamic parameters for protein unfolding for WT and mutant CAs Question $28$ Which effects, enthalpy or entropy of unfolding, were associated with the increased thermal stability of the mutants compared to the wild-type protein. Remember were are considering the denaturation reaction, N↔ D. Answer For the reaction N ↔ D, the ΔH0 values were all positive, indicating the enthalpy changes favored the native state, not the denatured state. In contrast, the other two mutants were enthalpically destabilized compared to the wild-type as their ΔH0 were less positive so compared to the wild-type. The prime stabilizer of the native state was the lower entropy (hence a less negative and favored -TΔS0 for the denaturation reaction. This makes sense in these mutants are more rigid and would experience less loss of "conformational entropy). P56C/P156C and N63C/P145C exhibited lower ΔH (destabilizing) and ΔS (stabilizing), showing that the decreased entropic change of unfolding (i.e., the loss of conformational entropy of the unfolded state) by the disulfide bridge was the primary factor for the thermostabilization. These results are not surprising because design strategies aiming ‘entropic stabilization’ such as disulfide engineering do not always result in engineered proteins ideally with lower ΔS and unchanged ΔH. These results are in accord with the observation that N63C/P145C was the most thermostable variant and that T133C/D197C showed the highest values in both the overall and the residual RMSD (Fig. 3d). This may explain and correlate with the increased activity of T133C/D197C and the increased ΔS of unfolding. Finally, the enzymatic activity of the wild-type and mutants CAs (using a small ester substrate) were studied as a function of temperature. The relative activity of the wild-type and all 3 disulfide mutants are plotted as a function of temperature in the histogram graphs shown in Figure $18$ below. Figure $18$: Effect of temperature on the activity of disulfide CA variants. Esterase activities of disulfide variants were measured at each temperature and normalized to the activity of each enzyme at 25 °C. Each value represents the mean of three independent experiments, and the error bars represent the standard deviations. If you plotted the data as curves, you would get bell-shaped graphs. Question $29$ Explain why the histogram plots (and line plots if they were drawn) are bell-shaped. Are the results in accordance with the previous results. Answer Yes. Most chemical reaction show an increase in rate with increasing temperatures until competing reactions take precedence. For an enzyme-catalyzed reaction, that competing reaction is denaturation, which decreases the rate. Yes the graphs are in accord with the previous results. The N63C/P145C certainly stands out as the best mutant. The authors write that "considering the shifted optimal temperature and the thermoactivation as well as the enhanced thermostability, the disulfide engineered α-type CA with Cys63-Cys145 can be a promising biocatalyst for efficient CO2 sequestration performed under high temperature conditions."
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Fundamentals_of_Biochemistry_Vol._V_-_Problems/iCn3D_Molecular_Modeling_Tutorials/iCn3D_Basics%3A_File_Types.txt	princeton-nlp/TextbookChapters	File, Retrieve by ID MMDB • MMDB (Molecular Modeling Database) files from the NCBI • derived from PDB atomic coordinates but with … • Database information (quaternary struct, molecular interactions, SNPs, conserved domains, clinical variants – i.e related structure info, not just xyz coord PDB • xyz coordinates RCSB MMTF ID (fast) • Great for very big structures that otherwise too slow in loading; Few modeling options. AlphaFold Structure • Computationally determined structures • Uses Uniprot or RCSB ID. OPM PDB ID • Get structures on membrane proteins Other Membrane Protein Links •MemProtMD: A database of membrane proteins embedded in lipid bilayers with lipids obtained in Molecular Dynamics simulation iCn3D Basics: Saving Files File, Save File • In a new iCn3D window choose Open File, iCn3D PNG image and see the same file you started with. • Likewise, in a new iCn3D window choose Open File, State/Script File and see the same file you started with. • They can be sent to others to open as well iCn3D Basics: Analysis Menu Analysis Menu iCn3D Basics: Mouse Commands iCn3D modeling screen Mouse commands rotate: click and drag (mouse: left click and drag; keyboard: j, i, l, and m keys) zoom: pinch and spread (mouse: rotate the scroll wheel; keyboard: x and z keys) translate: two finger click and drag (mouse: right click and drag) Re-center: left click View from the top menu bar, then select “Center Selection” Note: ctrl click on a PC = command click on Mac alt click on PC = option click on Mac iCn3D Basics: Selecting and Viewing with a mouse Selecting with the mouse (left) and viewing selection (right) iCn3D Basics: Style and Color Style and Color iCn3D Intro Tutorial A: Modeling a Short Peptide in a Protein A. Modeling short sections of a protein chain Pick one of the small protein fragments below for modeling using iCn3D PDB Description of protein (all small fragments) 2YW8 Crystal structure of human RUN and FYVE domain-containing protein 6EEY human Scribble PDZ4 R1110G Mutant 2PA1 PDZ domain of human PDLIM2 bound to a C-terminal extension from human beta-tropomyosin 3A03 Hox11L1 homeodomain 3IWL cisplatin bound to a human copper chaperone (monomer) 5Z2S DUX4-HD2 domain 6L1C PHF20L1 Tudor1 Y24L mutant 3D2N MBNL1 tandem zinc finger 1 and 2 domain 3RD2 NIP45 SUMO-like Domain 2 7NZC SH3 domain of POSH (Plenty of SH3 Domains protein) 1I2T HUMAN HYPERPLASTIC DISCS PROTEIN: AN ORTHOLOG OF THE C-TERMINAL DOMAIN OF POLY(A)-BINDING PROTEIN 1NTE CRYSTAL STRUCTURE ANALYSIS OF THE SECOND PDZ DOMAIN OF SYNTENI 2Y9U Structural basis of p63a SAM domain mutants involved in AEC syndrome 2FMA Alzheimer's Amyloid Precursor Protein (APP) Copper Binding Domain in 'small unit cell' form, atomic resolution 4OU0 Crystal Structure of RPA32C 1ZT3 C-terminal domain of Insulin-like Growth Factor Binding Protein-1 isolated from human amniotic fluid 2E3H Crystal structure of the CLIP-170 CAP-Gly domain 2 1L9L GRANULYSIN FROM HUMAN CYTOLYTIC T LYMPHOCYTES 5EFM Beclin 1 Flexible-helical Domian (FHD) (141-171) 2BZX Atomic model of CrkL-SH3C monomer 1NHL SNAP-23N Structure 7UW7 Crystal structure of the Human TRIP12 WWE domain (isoform 2) in complex with ADP 4N7F 3rd WW domain of human Nedd4-1 2Q9V C890S mutant of the 4th PDZ domain of human membrane-associated guanylate kinase 6T9Q second, C-terminal repeat of the DNA-binding domain of human TImeless 1WVN domain 3 of human alpha polyC binding protein 3I8Z human chromobox homolog 4 (CBX4) 2F60 Dihydrolipoamide Dehydrogenase (E3)-Binding Domain of Human E3-Binding Protein 7FGN FAF1 UBL1 5UM3 V122L mutant of human UBR-box domain from UBR2 2FJZ Alzheimer's Amyloid Precursor Protein (APP) copper-binding domain (residues 133 to 189) in 'small unit cell' form, metal-free 2. Input in your assigned pdb code and select Load Biological Unit 3. Choose Analysis, Seq and Annotation 4. Choose Details tab and uncheck conserved domains 5. With your mouse, select, hold, and sweep between the first 5-10 amino acids (given in single letter code) as illustrated below. When you select them, they will turn yellow. 6. Choose View, View Selection (to limit view to what you want 7. Choose Style, Proteins, Sticks to see all the bonds 8. Change the background from black by choosing from top menu bar Style, Background, Transparent 9. Choose, Analysis, Label, Per residue/#; then Analysis, Label Scale, 2 10. Next, color your model as shown below in different ways as described in the table below. Then take a screen capture of the selection and replace the image in the table cell with your own Color Paste snip of renderings as shown below. Spectrum, Selection to better see each amino acids in selection Charge Gray if no charges (ignore yellow highlight) Hydrophobicity (if nonpolar like oil) Atom (red oxygen, blue nitrogen, yellow Sulfur
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Fundamentals_of_Biochemistry_Vol._V_-_Problems/iCn3D_Molecular_Modeling_Tutorials/iCn3D_Intro_Tutorial_B%3A_Rendering_a_Protein.txt	princeton-nlp/TextbookChapters	B. Rendering Full Proteins Pick one of the proteins below that has both alpha helices and beta sheets. You will then change the protein style (rendering) to see the same protein in different ways to illustrate different properties of the proteins. Monomeric proteins with alpha (helices) and beta (sheets) 1HDO Human biliverdin IX beta reductase: NADP complex 2HC2 Engineered protein tyrosine phosphatase beta catalytic domain 5ZUN Crystal structure of human monoacylglycerol lipase in complex with compound 3l 1X3S Crystal structure of human Rab18 in complex with Gppnhp 4IN0 Crystal Structure of human splicing factor dim2/TXNL4B 1KGD Crystal Structure of the Guanylate Kinase-like Domain of Human CASK 5KQL Co-crystal structure of LMW-PTP in complex with 2-oxo-1-phenyl-2-(phenylamino)ethanesulfonic acid 1QGV HUMAN SPLICEOSOMAL PROTEIN U5-15KD 1MF7 INTEGRIN ALPHA M I DOMAIN 4RQR Crystal Structure of Human Glutaredoxin with MESNA 4JKA Open and closed forms of R1865A human PRP8 RNase H-like domain with bound Co ion 5C4M RhoA GDP with novel switch II conformation 6P0J Crystal structure of GDP-bound human RalA 4MMM Human Pdrx5 complex with a ligand BP7 4M6IJ Crystal structure of human dihydrofolate reductase (DHFR) bound to NADPH 3M9J Crystal structure of human thioredoxin C69/73S double mutant, reduced form 1. Load ID 4LPK, Crystal Structure of K-Ras protein with a small molecule, GDP, bound 2. Choose Color, Secondary, Sheet in Yellow 3. Style, Background, White 4. Choose Style, Proteins and display as ribbon, cylinder and plate, C alpha trace, backbone, lines and sphere 5. Paste your results in the table below. 6. Results (replace image with yours) PDB ID, description: ribbon Cylinder and plate C alpha trace backbone line sphere 7. Model a protein dimer that has two subunits. Use the pdb code for 1LFD (CRYSTAL STRUCTURE OF THE ACTIVE RAS PROTEIN COMPLEXED WITH THE RAS-INTERACTING DOMAIN OF RALGDS). Paste your favorite image below. 8. Model a huge structure, the human rhinovirus 14 (causes colds, PDB: 4RHV). It so big you have to load it in a different way, as shown below. Paste your favorite image below. iCn3D Intro Tutorial C: Finding Pockets in Proteins Small molecules that bind to larger proteins must have shape AND charge complementarity with the binding pocket in the protein. You can put a small molecule into an appropriate-sized pocket in a protein. You can’t put a positively charged small molecule into a pocket lined with a positive charge. Let’s find the pockets in a small protein, LMWPTP, a phosphatase that cleaves a negatively charged phosphate group (PO3-2) proteins (pdb 1xww). It also binds the small sulfate ion (SO4-2) in the same pocket. Finding Pockets Let’s find the pocket where the ligand could bind using a free program called CavityPlus. 2022 1. Load http://www.pkumdl.cn:8000/cavityplus/computation.php#/ and select Start Computing 2. Input 1xww, then select Click to Search. Wait until the structure loads to continue. 3. Then simply choose Submit. (Make sure that Use Ligand Mode is not selected) bnbnbn 4. After the run, you will see a new window open on the left-hand-side with the protein and the top #1 Cavity highlighted. Site 1 is the presumptive location for the binding of SO42-. Use your mouse to rotate the protein to better see the cavity. To see a list of the amino acids lining the binding pocket surface, and the surface area and volume of the cavity, select under More. They will appear in the Residue row. 5. Copy and Paste into the table below the list of amino acids comprising the pocket into the table below. Then take a screen snip as shown in the image to the right (Note: if you can unzip the downloaded file, you could select the Download Results link and use other programs to view the results). Amino acids in pocket Image snip Viewing Small Molecule in a Binding Pocket Now let’s model the phosphate (PO3-2)/sulfate (SO4-2) binding site in the phosphatase using iCn3D. 1. load 1XWW in iCn3D 2. Render the proteins as follows: • Analyses, Sequences and annotation, Details Tab, uncheck conserved domain • Click 1XWW_A 1st, then in the top menu bar choose Select, Save Selection and name it phosphatase • Color, Charge • Style, Surface Opacity, Fast Transparency, 3 • Style, surface type, molecular Surface 3. Next render the SO4 as follows: • Choose SO4 (2) with the mouse, then Select, Save Selection and name it SO4 • Style, Chemicals, Sphere 4. Snip and paste an image of the protein with the surface display and the bound SO4-2 in spheres. 5. Optional: To see actual interactions between SO4-2 and the protein • Style, Remove surface • Analysis, Interactions, • In popup window, choose for 1st set – sulfate; choose for the 2nd set – phosphatase; click 3D Display Interactions; Snip table with types/colors on interactions • View Selection; Style, Sidechains, Stick; Color, by Atom, • Analysis, Label, Per Residue and number; Analysis, Label Scale, 2; • Style, background, white • Snip an image of the interaction legend and modeled interactions, and paste below.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Fundamentals_of_Biochemistry_Vol._V_-_Problems/iCn3D_Molecular_Modeling_Tutorials/iCn3D_Intro_Tutorial_D%3A__Modeling_Psychoactive_Drugs_in_Target_Proteins.txt	princeton-nlp/TextbookChapters	Use iCn3D to model the binding of one of the psychoactive/analgesic drug to their receptor (either the 5HT or cannabinoid receptor). Paste the final model in the space shown. Serotonin (5-hydroxytryptamine) receptors 5-HTreceptors are indirectly involved in the mechanism of action of antidepressant drugs. Most antidepressant drugs like Prozac increase the concentration of 5-HT in the extracellular brain synapse by inhibiting its reuptake into neurons. Prozac doesn’t bind to the 5HT receptor but to a membrane protein that removes 5HT from synaptic region. There are 7 types of 5HT receptors and each has different biochemical effects Different drugs target different 5HT receptors. Let’s focus drugs that target 5-HT2A act as antidepressants, but also lead to hallucination. Serotonin (5HT) 2A receptor: These molecules bind to it. • serotonin (5HT), the physiological agonist, 7WC4 (does not cause hallucinations) • psilocin, 7WC5 , hallucinogenic, a metabolite of psilocibin) • LSD, 7WC6, hallucinogenic • lisuride, 7WC7, non-hallucinogenic • IHCH-7086, nonhallucinogenic THC and CBD receptors These molecules bind to them. • Human CB1 in complex with agonist AM11542 (5XRA) • class A GPCR Cannabinoid Receptor-Gi Complex Structure with bound agonist (6KPF) - The agonist is AM12033, which is similar to AM11542 • CBD-bound full-length rat TRPV2 in nanodiscs (6U88) Your model: PDB ID: "DRUG” RECEPTOR: Rendered Image iCn3D Skill: Alternative Rendering and Saving Files Structure • PDB ID: 1xww • Protein: Low molecular weight protein tyrosine phosphatase • Activity: hydrolyzes Tyr-OPO32- phosphoester bond • Description: single chain, bound SO42- (competitive inhibitor), bound glycerol (nonspecific stabilizer) Alternative Rendering Load Structure and Mouse/Trackpad Controls • Open iCn3D - https://www.ncbi.nlm.nih.gov/Structure/icn3d/full.html • For a simple menu, use the dropdown: File > Customize Menus > Simple Menus. • In the Please input MMDB or PDB, enter 1xww. Press enter or click load biological unit. • Default render is ribbon (cartoon) with black background and small molecules shown as sticks. Hover over objects with the mouse to reveal their identity. 1. From the top menu bar, choose Style, Protein, then try some of the available choices: 1. For your favorite protein styles, select Color by left-clicking on the top menu, then pick available choices. Try: • Secondary, Sheets in Yellow • Charge • Hydrophobicity 1. Under Style, choose ProteinRibbon. Under Color, choose Secondary, Sheets in Yellow before the next step. 2. To view sidechains, Style, Side Chains, Stick (they will remain the same color as secondary structure for now) 3. Color for SO42- and bound glycerol will default to CPK coloring (key below) 4. Convert back to cartoon (Style, Side Chains, Hide) Saving Files 1. Style, Background, Transparent 2. Saving Files: There are several ways to save your work. The first option below saves a PNG image, the second creates a share link 1. File, Save Files, iCn3D PNG image, original size; Give it a name. Can be reloaded in iCn3D with File, Load, iCn3D PNG IMAGE 2. File, Share Link, Save Lifelong Short URL. Copy and paste this link to share your work. Pre-Rendered Model Link To check your work (or if you got stuck during any of the steps above) catch up using this link: https://structure.ncbi.nlm.nih.gov/icn3d/share.html?CgfEnF27TN7aYQpr6 iCn3D Skill: Displays surface of a protein - Superoxide Dismutase A. The molecular surface of superoxide dismutase PDB ID: 2sod • Description • superoxide dismutase • dimer with 2 Cu2+ ions bound in each subunit • catalyses the reaction of O2- + O2- + 2H+ → H2O2 + O2 Instructions 1. File, Retrieve by ID, MMDB 2. Style, Surface Opacity, Fast Transparency, 0.2 3. Select, Select on 3D, Chain. Alt Click on one chain 4. Color, Secondary, Sheets in Yellow 5. Style, Surface Type, Molecular (takes a while to render) 6. Now Alt Click on the other chain 7. Color, Wimley White Hydrophobicity(takes a while to render) 8. Style, Surface Type, Molecular (takes a while to render) 9. Style, Background, White 10. File, Share Link, Save short url B. The electrostatic surface potential of superoxide dismutase Background: Superoxide (O2-) is a toxic free radical and hence dangerous. To capture it as effectively as possible, the surface distribution of charged side changes is such that a positve electrostatic potential surrounds each active site. 1. File, Retrieve by ID, MMDB 2. Select, Select on 3D, Chain. Alt Click on one chain, Control-click on the second. Alternatively, choose Analysis, Sequence and Annotations, Details Tabs (uncheck conserved domains), and click one chain (blue font) and then control-click the second so both are highlighted. 3. In the Details tab of Analysis, Sequence and Annotations, sweep both Cu ions (with mouse) to select them, the Color, Yellow 4. Analysis, DelPhi Potential, DelPhi Potential 5. Choose the Surface with Potential Map tab. Chose the options below 6. Select Surface with Potential. Look a the blue (positive potential) surround the yellow Zn ions in both active sites. 7. Style, Surface Type, Molecular (takes a while to render) 8. Style, Background, White 9. File, Share Link, Save short url
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Fundamentals_of_Biochemistry_Vol._V_-_Problems/iCn3D_Molecular_Modeling_Tutorials/iCn3D_Skill%3A__Aligning_two_structures.txt	princeton-nlp/TextbookChapters	Superimpose and annotate active (phosphorylated) form of cyclin-dependent kinase 2 (1JST) and inactive (1FIN) Description 1JST (active) • PHOSPHORYLATED CYCLIN-DEPENDENT KINASE-2 BOUND TO CYCLIN A. Has bound Mn2+ and ATPγS. • pT160 is on the regulatory T-loop of CDK2. It is mostly buried and neutralized by 3 Arg side chains. Compared to unphosphorylated CDK2–CyclinA, the T-loop moves by as much as 7 Å, • T loop about 147-163 1FIN (inactive): • CYCLIN A-CYCLIN-DEPENDENT KINASE 2 COMPLEX. Has a bound ATP as well as Cyclin A (B and D chains) Instructions 1. File, Align, Structure to Structure 2. Input 1JST and 1FIN 3. Choose All Matching Molecules Superimposed 4. In the Select residues in aligned alignment window, click the blue font 1JST-C to select the entire chain. Color, Green, Dark Green (green for go = active) 5. Likewise, select 1FIN-A. Color, Yellow, Yellow (so we can color the T loop red) 6. Alt Click in the modeling window MN and ctrl Click ATP. Select, Save Selection, MN_ATP 7. Style, Chemicals, Sphere 8. In long sequences, capture with mouse approx T loop 147-163. Select, Save Selection, T loops; Color red 9. In the long sequences, hover over and then click to select in 1JST_C x160 (this is phospho-Thr160) and then in 1FIN_A T160 (i.e. Thr 160). Select, Save Selection, Thr160s 10. Style, Sidechains, Sticks 11. Color, Atom 12. In the Defined Sets, click then ctrl click all of the selections you made: MnATP, Thr160s, align_1FIN_A, and align_1JST_C. Then save as 000All. Using 000 at the start of the name put the combined selection at the top of the Defined Sets window. This will be important later. 13. Make sure 000All is selected, then View, View Selection 14. Style, Background, White 15. File, Share Link, Lifelong Short Link, 16. Paste the link into a new window, much as a student would. Analysis, Defined Set, then click 000All at the top to highlight the superimposed chains. Once all is highlighted, toggle between the two states by clicking the Alternative (Key “a”) menu button directly under the File button. 17. Now try View, Side by Side to see both (don’t toggle this form) iCn3D Skill: Analysis of Noncovalent Interactions Structure • PDB ID: 3K83 • Protein: Inhibitor (below, abbreviated F278458) bound to a humanized variant of Fatty Acid Amide Hydrolase (FAH) • Activity: FAH Catalyzes the hydrolysis of endogenous amidated lipids like the sleep-inducing lipid oleamide, the endocannabinoid anandamide, and other fatty amides, regulating the signaling functions of these molecules • Description: The functional unit is a dimer, each with an active site. A chloride binds between the two subunits In this activity you will see key noncovalent interactions between the inhibitor and (FAH). You will pick one inhibitor and view its noncovalent interactions with one subunit Modeling Instructions 1. Open a new iCn3D window, 3K83 at: https://www.ncbi.nlm.nih.gov/Structure/icn3d/full.html 2. Select, Select on 3D to ensure default is Residue; With mouse, zoom and center to Alt-Click the inhibitor (F278458) in the magenta subunit (option-click on a Mac). 3. Select, Save Selection, name it Drug 4. Analysis, Interactions. 5. Under 1. Choose interaction types and their thresholds, Check only the noncovalent interactions shown below. Make sure the Contacts/Interactions is unchecked as it shows all interactions between contacting Van der Waals surfaces including many hydrophobic interactions, so it is quite cluttered. 1. Select the Drug as the first set (under 2. Select the first set); choose 3K83 (under 3. Select the second set) 1. Click on the box: 4. 3D Display Interactions. This will display interacting side chains. Drag the HBonds/Interactions window to the bottom right away from the molecular display. Note the coloring of the dotted lines: Green - hydrogen bonds Red - π-cation Blue - π stacking 1. Select, Save Selection, name it Interactions 2. Style, Sidechains, Stick (so the next step will color side chains) 3. Color, atom (Note: this drug is covalently bound to Serine241) 4. In defined sets Ctrl click Drug and Interaction to highlight all 5. View, View Selection (to only see Drug and Interactions) 6. Select, Toggle Highlight (to remove highlights if necessary) Note: Once you run interactions, iCn3D adds many new additions to the Defined sets window. Explore these to learn different ways to examine the interactions. Optional (if you’d like to add labels, recolor the background, and obtain the share link as in the previous activity) 1. Analysis, Label, Per Residue & No 2. Analysis, Label Scale, pick a number that works for you 3. Style, Background, Transparent 4. File, Share Link, copy short link Pre-Rendered Model Link To check your work (or if you got stuck during any of the steps above) view the model using this link: https://structure.ncbi.nlm.nih.gov/icn3d/share.html?pDr5EBZmo3TyAbTP6 Note: You can alter interactions on the 3D model, or examine 2D displays of them. Try: ● Click 5. Reset at the bottom of the HBonds/Interactions window. Add Contacts/ Interactions (mostly nonpolar) by clicking on the box. Choose Drug as the first set. Choose 3K83 as the second set. Then click 4. 3D Display Interactions to show. ● Next, try 4. 2D Interaction Network. In the popup that opens, click a colored line in the 2D window to highlight a specific interaction. If using the Pre-Rendered model: From the top menu, select: AnalysisInteractions. In the popup choose 5. Reset, select 3K83 (full protein) as set 1 and drug as set 2, then 4. 2D Interaction Network. Click colored lines in the 2D window.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Fundamentals_of_Biochemistry_Vol._V_-_Problems/iCn3D_Molecular_Modeling_Tutorials/iCn3D_Skill%3A__Creating_and_Saving_Selections.txt	princeton-nlp/TextbookChapters	Structure • PDB ID: 3UBB • Protein: Rhomboid intramembrane serine protease GlpG (3UBB) with phosphonofluoridate inhibitor • Activity: Integral membrane serine protease • Description: Single chain transmembrane protease from E. coli bound to a phosphonofluoridate inhibitor, which is covalently bonded to the catalytic serine. Red and blue dots (“dummy” atoms) indicate extracellular and intracellular membranes, respectively. Uses a catalytic dyad composed of serine (S201) and histidine (H254). Load Structure 1. Open iCn3D - https://www.ncbi.nlm.nih.gov/Structure/icn3d/full.html 2. In the Please input MMDB or PDB, enter 3UBB. Press enter or click load biological unit. Selecting using: 1. The structure viewer window with the Mouse and 2. The sequences and annotations menu (Key Learning Objectives) This section will show how you can select residues, chains, etc in the Modeling window. 1. Select, Select on 3D to ensure default is Residue; With cursor, hover over the inhibitor (name 3UB) and Alt Click (option click on Mac) it. A yellow halo will appear around it. 2. Select, Save Selection, name it Inhibitor. 3. Now, use the top menu to open the sequences and annotations tab: Analysis, Seq. and Annotations 4. In the sequences and annotations window, uncheck “Conserved Domains,” and then click the Details tab. Click individually on the one letter code for S201 and H254. On selection, they will turn yellow. (Note: To see the scroll bar in sequences/annotations in a Mac, choose Systems preferences in your operating system [not the iCn3D settings], General, show scroll bars and check always; see iCn3D About notes). 1. Select, Save Selection, name CatDyad Rendering (Optional, can use the highlighted link below instead) 1. Select, defined sets (Note that “defined sets” brings up a complete list of objects, many selections are pre-built into iCn3D to get you started with a model.) 2. In Selected Sets, click 3UBB_A. 3. Color, Unicolor, Gray, Light Gray 4. In Selected Sets, click CatDyad 5. We want to show the side chain of the catalytic dyad, so use the top menu: Style, SideChains, Sticks 6. Recolor to CPK coloring: Color, Atom 7. Analysis, Label, Per Residue and Number 8. Analysis, Label Scale, pick number that works for you 9. Style, Background, Transparent 10. Remove any active selections (yellow glow) by Select, Toggle highlight Pre-Rendered Model Link https://structure.ncbi.nlm.nih.gov/icn3d/share.html?fhT3dwckYg8i5XJj8 The short URL above may be used to catch up for the next section of the tutorial Selecting sphere within around 5Å of the inhibitor (Key Learning Objective) Our goal is to find all atoms with 5Å from the inhibitor, this time without showing interactions. In the next step, we will designate 2 sets of objects. Set 1 will be the inhibitor. Set 2 will be nearby residues/bound molecules. In our case, Set 2 will be defined as the protein. 1. Select, by Distance 2. For the first set, select inhibitor; for Set 2 click 3UBB 3. For Set 2. Sphere with a radius to 5 Å 4. Click Display; Close the Select by distance window 5. Now save the highlighted groups 5Å from the inhibitor through Select, Save Selection, Name 5AfromInhib 6. Style, Side chains, Stick 7. Color, Atom 8. Select both the inhibitor and the surrounding residues: In Defined Sets, Click Inhibitor, and Ctrl+Click 5AfromInhib (Command+Click on a Mac) 9. Display only the active site for clarity: View, View Selection 10. Analysis, Label, Per Residue & Number 11. To see water, Style, Water, Sphere 12. File, Share Link, copy short link Pre-Rendered Model Link To check your work (or if you got stuck during any of the steps above) view the model using this link: https://structure.ncbi.nlm.nih.gov/icn3d/share.html?sqZf4Zvqn5zWK21d9 This link shows the membrane. Choose View, Toggle Membrane to hide it.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Fundamentals_of_Biochemistry_Vol._V_-_Problems/iCn3D_Molecular_Modeling_Tutorials/iCn3D_Skill%3A__Mutations.txt	princeton-nlp/TextbookChapters	Analysis: Mutation A simple way to change 1 amino acid to another PDB ID: 1xww Description: human B-form low molecular weight protein tyrosine phosphatase (has single domain) with bound sulfate (SO4) and glycerol (GOL) Instructions 1. File, Retrieve by ID, MMDB 2. Analysis, Mutation 3. Input the desired mutation C12S this way (1XWW_A_12_S) where A is the A chain, 12 is the aa # and S is the new mutated amino acid residue Ser. Note that C12 is the wild-type active site nucleophile in phosphatases and changing it to Ser will effectively abolish phosphatase activity. To view you could use 3 different methods. We'll use one. 4. 3D with scap: toggle back and forth between structures using the key “a” 5. Interaction: shows the mutation in 3D and change in interactions 6. PDB: shows structure and exports a PDB file within 10 A of the mutation 7. Choose Interactions and say wow! 8. Color, Atom 9. Style, Background, White 10. Toggle back and forth between the structures with the letter “a” 11. File, Share Link, Lifelong Short Link iCn3D Skill: Selection through Sequence and Annotations Structure • MMDB ID: 1xww • Protein: Low molecular weight protein tyrosine phosphatase • Activity: hydrolyzes Tyr-OPO32- phosphoester bond • Description: single chain, bound SO42- (competitive inhibitor), bound glycerol (nonspecific stabilizer) Load Structure and Mouse/Trackpad Controls • Open iCn3D - https://www.ncbi.nlm.nih.gov/Structure/icn3d/full.html • For a simple menu, use the dropdown: File > Customize Menus > Simple Menus. • In the Please input MMDB or PDB, enter 1xww. Press enter or click load biological unit. • Default render is ribbon (cartoon) with black background and small molecules shown as sticks. Hover over objects with the mouse to reveal their identity. Figure: The Sequences and Annotations Menu For this part, we will be using the model from Activity 1. To load the premade model from Part 1, use this link: https://structure.ncbi.nlm.nih.gov/icn3d/share.html?CgfEnF27TN7aYQpr6 From the literature, it is known that the active site is a nucleophilic cysteine (C12). It is part of the phosphate-binding loop (P-loop, AA 12-18: sequence CLGNICR). Let’s find, select, and render these amino acids. Modeling Instructions 1. Under Analysis (top menu bar), choose Sequence and Annotations 2. Choose Details tab, uncheck Conserved Domains Before we continue, look at the built-in choices you have for selection: 1. In the Sequences and Annotation window, click Protein 1XWW_A 2. Under Select (top menu bar), choose Toggle Highlights 3. Hover over C12 in the sequence (in Seq and Annot window), click and hold down the mouse key, and sweep over C12-C18 to select the P loop 4. Select, Save Selection, name it: Ploop 5. Within this highlighted selection, Style, Side Chains, Sticks 6. Color, Atom 7. Analysis, Label, Per Residue & Number 8. Analysis, Label Scale, pick number that works for you 9. Analysis, Label, Change Label Color (globally). Click in the text box and a Color box will pop up, choose from a palette, then Display. (Alternatively, pick a hex code). 10. In the Sequences and Annotation window, click SO4 11. Style, Chemicals, Sphere to change the sulfate to a space filling rendering 12. Files, Share Link, Copy Short URL Pre-Rendered Model Link To check your work (or if you got stuck during any of the steps above) catch up using this link: https://structure.ncbi.nlm.nih.gov/icn3d/share.html?QhtGuE8pkaJpGs1X9 Note: For some enzymes, iCn3D can automatically display key active site and binding residues. These can be seen as shown by selecting the items indicated in the left figure.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Fundamentals_of_Biochemistry_Vol._V_-_Problems/iCn3D_Molecular_Modeling_Tutorials/iCn3D_Skill%3A__Showing_a_Protein-Protein_Interface.txt	princeton-nlp/TextbookChapters	Protein:Protein Interface PDB ID: 3S9D • binary complex between interferon alpha 2 (IFNa2) and its recetor IFNAR2 • Cyan: IFN, blue IFN receptor Instructions 1. Retrieve by ID, MMDB 2. Analysis, Seq. & Annotationsm, Details Tab. 3. Select magenta IFN alpha chain: 3s9d_A 4. Select, Save Selections INF 5. Repeat with blue 3s9d-b. Save Select INFR 6. Select, INF; Select by Distance, within 5A. Set 1: selected; Set 2: nonselected; Display 7. Select, Save Selection: 5Ang from IFN 8. Select 5A from INF: Style, side chains stick, color atom 9. repeat for INFR 10. Select Select by Distance, within 5A. Set 1: IFNR; Set 2: nonselected; Display 11. Select, Save Selection: 5Ang from IFNR 12. Select, Side Chains, sticks; Colore atom 13. Select 5A from IFNR: Style, side chain sticks. Color, charge, Style, surface type, vanderWaals 14. Analysis, Interactions, Set 1: 5A from INF; Set 2: 5A from INFR 15. Select, Save, Selections, Inteactions 16. Style, Background, White 17. File, Share Link, Lifelong iCn3D Tutorial: Binding interactions of SARS-Cov-2 Spike receptor domain A. 1st Way PDB ID: 6M0J Description: SARS-CoV-2 spike receptor-binding domain bound with ACE2 • Angiotensin-converting enzyme 2, chain A, denoted 6M0J_A, pink • Spike Protein S1, chain B, denoted 6M0J_E, blue Instructions 1. File, Retrieve by ID, MMDB 2. Seq. & Annotations, Uncheck Conserved Domains 3. Analysis, Interactions, then select the choices in the image below. (Leave out Contact/Interactions to decrease clutter) 4. Click 3D Display Interactions 5. Without closing Interactions window, choose Select, Save Selection, name it BindingInterface 6. Click 2D interaction network to get the image below 7. In the Selected Sets window, choose BindingInterface. 8. View, View Selection 9. Select, Select Side Chain 10. Color, Atom 11. In the Selected Sets window, choose BindingInterface. 12. Analysis, Label, Per Residue & Number 13. Style, Background, White 14. File, Share Link, Lifelong Short Link B. 2nd Way Instructions 1. File, Retrieve by ID, MMDB 2. Seq. & Annotations, Uncheck Conserved Domains, Details Tab 3. Check Interactions 4. From right hand side window sequences: Under 6M0J_A, select the blue label Interact .E, Save as A_with_E 5. Under 6M0J_E, select the blue label Interact .A, Save as E_with_A 6. Analysis, Interactions. 7. Choose A_with_E and E_with_A 8. 3D Display Interactions 9. Select, Sidechains 10. Color, Atom 11. In H Bonds/ Interactions window, choose Highlight Interactions in Table (can toggle on/off individual interactions), 12. Try the 2D Interaction Map and the Buried Surface Area 13. Style, Background, White 14. File, Share Link, Lifelong Short Link iCn3D Tutorial: Overlay many ACE2 receptor binding domain analogous to SA Use BlastP to align receptor-binding domains PDB ID: 6M0J • SARS-CoV-2 spike receptor-binding domain bound with ACE2 • Angiotensin-converting enzyme 2, chain A, denoted 6M0J_A, pink • Spike Protein S1, chain B, denoted 6M0J_E, blue Instructions 1. File, Retrieve by ID, MMDB 2. Seq. & Annotations. In Summary tab check Custom and Conserved Domains 3. In Summary tab, click on 6M0J_E 4. View, View Selection 5. In Details tab, Add track. 6. Choose the FASTA Alignment Tab in the new window 7. Cut and paste the aligned FASTA sequences file you made using the instructions on the next page or to save time just copy/paste the same FASTA Alignment file below into the iCn3D Fasta window. 8. Enter 319 for the Position of the first residue in Sequences & Annotations window (# is right there); Check Color sequence by identity: 9. Choose Add Tracks. The RBD appears color coded with the darkest rd 100% identity and the darkest blue 0% (See F377, K387) 10. Style, Background, White 11. File, Share Link, Lifelong Short Link (this might not work if you choose too many proteins) Instructions to use Blast Use of BlastP for COV-2 ACE2 Receptor Binding Domain (RBD) Find sequences homologous to the structure of the RBSthrough BlastP: Instructions - use this image for help 1. Go to BlastP 2. Enter this Accession number (in this case PDB ID with chain: 6M0J_E for the RBD 3. Under Choose Search Set 4. Database: Choose UniProt 5. Organism: input Coronavirus and click it when it appears in the search 6. Add Organism and then input SARS-CoV-2 and click it when it appears in the search; Click Exclude (since we already know the structure of the RBD of CoV-2) 7. Choose Blastp and then BLAST 8. After a few minutes you will get the general output shown below . 9. Check just the ones with percent identity >30% and choose Download, Fasta Aligned Sequences 10. Copy the resulting text file and use in iCn3D as described in the previous page.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Fundamentals_of_Biochemistry_Vol._V_-_Problems/iCn3D_Molecular_Modeling_Tutorials/iCn3D_Tutorial%3A__Protein_Kinase_B_%28AKT%29.txt	princeton-nlp/TextbookChapters	Protein Kinase B (AKT) A more complicated protein with multiple domains and at least two major conformations with different PDB structures: Protein Kinase B (aka AKT) PDB files: • 3CQW (active) • 3O96 (inactive) • 1UNQ PH domain A. Exploration of AKT – Domain Structure • PDB ID: 1UNQ • Description: Pleckstrin Homology Domain Of Protein Kinase B/Akt Bound To Ins(1,3,4,5)-Tetrakisphophate 1. Open new iCn3D 2. File, Retrieve by ID, MMDB 3. Alt Click the ligand. Style, Chemicals, Sphere 4. Select, Select on 3D, check Chain, alt click the protein 5. Color, Secondary, Sheets in Yellow With the protein still selected: 1. Analysis, Interactions 2. For the second set choose chemicals 3. Check just the noncovalent interactions shown below 4. Choose 3D Display Interactions. This will display interacting side chains 5. Selections, Save Selections, name it Interactions 6. Style, Sidechains, Stick (so next step will just color side chains) 7. Color, atom 8. In defined sets choose Interactions. Analysis, Label, Per Residue & No. 9. Analysis, Label Scale, 3 10. Style, Background, White 11. File, Share Link, copy short link B. Exploration of AKT – Self-defined aa, sequences, Render PH-domain, N-lobe, C-Lobe, Catalytic Loop, Activation Loop PDB ID: 3O96 (inactive) Description • Crystal Structure of Human AKT1 with an Allosteric Inhibitor • RAC-alpha serine/threonine-protein kinase; Chain A • Allosteric inhibitor IQO Cartoon structure Instructions 1. File, Retrieve by ID, MMDB 2. Analysis, Seq. & Annotations, Uncheck Conserved Domains 3. Analysis, Defined Sets. Click 1st domain (PH = top 3O96_A_3d_domain) 4. Style, Protein, C-Alpha Trace. 5. Color , Unicolor, Yellow 6. Defined Sets. Choose 2st domain (N-Lobe). Color, unicolor, Cyan 7. Defined Sets. Choose 3st domain (C-Lobe). Color, unicolor, Grey, Light Gray 8. Seq. & Annotations, Details Tab, Uncheck Conserved Domains. 9. Select by hand the catalytic loop (table below) by using the horizontal scroll to find and select 271V-287H. Hover over each amino acids and click to select it or use your mouse to sweep out a rectangle over all the range of amino acids. 10. Select, Save Selection, CatLoop 11. Color, Unicolor, Red 12. Repeat for Activation Loop and save the selection. Color Unicolor, Blue 13. Style, Background, White 14. File, Share Link, Lifelong Short Link Numbering or generic kinase and AKT SITE Generic Kinase Akt (+108) N lobe K72 K180 N lobe E91 E199 C lobe, cat loop R165 R273 C lobe, cat loop D166 D274 C lobe, act loop D184 D292 C lobe, act loop T197 T305 (NO SEQ) Approx Cat Loop 163-179 271V—287H Approx Act loop Start DFG (292-294) to APE 184-200 292-319 308Tmiss toAPE end 319 C. Compare 3CQW (active) and 3O96 (inactive) – Superimpose two AKT structures PDB ID: 3CQW and 3O96 Description 3O96 (inactive): Crystal Structure of Human AKT1 with an Allosteric Inhibitor • RAC-alpha serine/threonine-protein kinase; Chain A • Allosteric inhibitor IQO 3CQW (active): • Crystal Structure of Akt-1 complexed with substrate peptide (hence active form) and inhibitor • Peptide: Glycogen synthase kinase-3 beta peptide Instructions 1. File, Align, Structure to Structure 2. Input 3CQW and 3O96 3. Choose All Matching Molecules Superimposed 4. In the top of the right hand alignment window, save the aligned state as presented (al_seq1). Just the aligned sequences will be highlighted. 5. View, View Selection 6. Style, Background, White 7. File, Share Link, Lifelong Short Link 8. Toggle between the two states by clicking the toggle menu button just underneath the File menu button iCn3D Tutorial Question: Intermediate Problem - Cyclooxygenase II Structure • PDB ID: 4PH9 • Protein: Ibuprofen (IBP) bound to cyclooxygenase-2 • Activity: COX-2 catalyzes the conversion of arachidonic acid(AA) to prostaglandin G2 (PGG2), and is a target of non-steroidal anti-inflammatory drugs (NSAIDs) and COX-2 selective inhibitors (coxibs). Arachidonic acid (AA), not shown in this structure, binds in a “L” shape Key amino acids • Arg-121 and Tyr-356 are close to the carboxylate of AA • Phe205, Phe209, Val228, Val344, Phe381, and Leu534 form a hydrophobic groove for the ω-end of AA. • Ser 530, which is above this, gets acetylated by aspirin • Tyr 385, near C13 in AA, forms a free radical which removes a single electron from C13 For more information on the mechanism of COX-2, scroll down to the end of the chapter section in Fundamentals of Biochemistry. Description: Biological dimer with heme, ibuprofen (IBP), and many other ligands bound. The initial display is messy! Your task is to clearly render a specific structural feature. Assessment: The enzyme cyclooxygenase-2 (COX-2) produces arachidonic acid from prostaglandin G2, as shown in the reaction below. Prostaglandin G2 is an important metabolite in inflammation, so inhibition of (COX-2) reduces inflammation. Potential visualization activities for students: 1. Identify the noncovalent interactions of COX-2 with a heme in 1 subunit 2. Model and describe the noncovalent interactions of COX-2 with ibuprofen 3. Model the interactions at the dimer interface of the protein; identify two amino acids on different subunits that are participating in a [hydrogen bond / ionic interaction] 4. Show the key active site residues of the enzyme Steps for Discussion and/or Modeling STEP 1: As a table, or small group at one table, choose one from A-D above. STEP 2: Load the model 4PH9 at https://www.ncbi.nlm.nih.gov/Structure/icn3d/full.html With your group, broadly discuss the steps students would need to perform to accomplish the activity you chose in STEP 1. Consider: • for your course, would it make sense for them to start with the model as it is loaded? • If you were to pre-render the model and provide them a shared link, which steps would you perform ahead of time? Which steps of modeling are important to their learning STEP 3: Open the sequences and annotations tab: • Analysis → Sequences & Annotations • Uncheck “conserved domains,” and then check “functional sites” STEP 4: Discuss how the built in iCn3D features may help you create a model quickly. Pre-Rendered Model Links 1. Identify the noncovalent interactions of COX-2 with a heme in 1 subunit 1. Model and describe the noncovalent interactions of COX-2 with ibuprofen 1. Model the interactions at the dimer interface of the protein; identify two amino acids on different subunits that are participating in a [hydrogen bond / ionic interaction] 1. Show the key active site residues of the enzyme
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Fundamentals_of_Biochemistry_Vol._V_-_Problems/iCn3D_for_Biomolecular_Visualization_Learning_Themes_and_Goals/BioMolViz_Framework.txt	princeton-nlp/TextbookChapters	BIOMOLVIZ Promoting Molecular Visualization Literacy The BMV framework is used with permission from BioMolViz.Org Copy the appropriate row when assigning a theme, goal, and objective to a designated iCn3D or other biomolecular visualization assessment Atomic Geometry (AG) Three‐atom and four‐atom (dihedral) angles, metal size and metal‐ligand geometries, steric clashes AG1. Students can describe the ideal geometry for a given atom within a molecule and deviations from the ideal geometry due to neighboring interactions. AG1.01 Students can identify atomic geometry/hybridization for a given atom. (Novice) AG1.02 Students can measure bond angles for a given atom. (Novice) AG1.03 Students can identify deviations from the ideal bond angles. (Amateur) AG1.04 Students can explain deviations from the ideal bond angles due to local effects. (Amateur, Expert) AG1.05 Students can predict the effect of deviations from ideal bond angles on the structure and function of a macromolecule. (Expert) AG1.06 Students can identify the geometric features of bonds (e.g., peptide bond, glycosidic, phosphoester). AG2. Students can compare and contrast different structural conformations with regard to energy, the addition of substituents, and the impact on the structure/function of a macromolecule. AG2.01 Students can describe different conformations that a structure can adopt using visualization tools. (Amateur) AG2.02 Students can describe different conformations of atoms about a bond using visualization tools. (Novice) AG2.03 Students can distinguish energetically favorable and unfavorable conformations that a structure can adopt. (Amateur) AG2.04 Students can predict the effect of a given substituent on the structure and function of a macromolecule (e.g., substituent on a carbohydrate/ligand, R groups/rotamers, phosphorylation, methylation of nucleic acids, post-translational modifications). (Expert) AG3. Students can describe dihedral/torsion angles in biomolecules. AG3.01 Students can identify a dihedral/torsion angle in a three-dimensional representation of a molecule. (Novice) AG3.02 Students can identify the planes between which a dihedral/torsion angle exists within a three-dimensional representation of a macromolecule. (Novice) AG3.03 Students can identify phi, psi, and omega torsion/dihedral angles in a three-dimensional representation of a protein. (Amateur) Alternate Renderings (AR) Rendering of a macromolecular structure such as a protein or nucleic acid structure in various ways from the simplest possible way (connections between alpha carbons) to illustration of secondary structure (ribbons) to surface rendering and space filling. AR1. Students can interpret or create molecular images that convey features such as secondary structure, CPK coloring, and active sites. AR1.01 Students can manipulate rendered structures to illustrate molecular properties. (Novice) AR1.02 REMOVED (integrated with SF2.02) AR1.03 Students can describe or label structural differences among multiple structures. (Amateur, Expert) AR1.04 Students can infer information from rendering a structure in different ways. (Novice, Amateur, Expert) AR1.05 Students can create renderings that distinguish secondary structural features. (Novice) AR1.06 Students can create an information rich rendering of a structure that depicts structural features found in the literature. (Amateur) AR1.07 Students can create an information rich rendering of a structure containing ligands, covalent modifications, and noncanonical amino acids or nucleotides. (Amateur, Expert) AR1.08 Students can use molecular visualization to tell a story about a macromolecular structure. (Expert) AR1.09 REMOVED (integrated with MI1.02) AR1.10 Students can convert textbook images of small molecules into 3D representations in a molecular visualization program. (Amateur) AR2. Students can choose the best rendering of a macromolecule to use in a given situation. AR2.01 Students can recognize that a cartoon rendering is a summary of the detail in a line rendering. (Novice, Amateur) AR2.02 Students can describe the atoms and their representations in different renderings (e.g., coloring, showing hydrogens/double bonds). (Novice) AR2.03 Students can identify or create a suitable rendering, or combination of renderings, for a specific purpose (e.g., a surface rendering overlaid with a cartoon to highlight the van der Waals surface alongside secondary structure, or active site sticks shown over a cartoon). (Novice, Amateur) AR2.04 Students can identify the limitations in various renderings of molecular structures. (Amateur) AR2.05 Students can understand the level of detail of different molecular representations. (Novice, Amateur, Expert) AR2.06 Students can transition comfortably between equivalent 2D and 3D renderings of biomolecules. (Novice, Amateur, Expert) AR2.07 Students can use and interpret color in the context of macromolecules to clarify and/or highlight features (e.g., coloring amino acids differently by property, different molecules uniquely in a complex, protein chains, secondary structure). (Novice) Construction and Annotation (CA) Ability to build macromolecular models, either physical or computerized, and, where possible, add commentary, either written or verbal, to tell a molecular story. CA1. Students can compose information‐rich renderings of macromolecule‐ligand interactions. CA1.01 Students can construct and annotate a model of a macromolecule bound to a ligand. (Amateur) CA1.02 Students can construct a model of a macromolecule bound to a ligand and identify the types of molecular interactions. (Amateur) CA1.03 Students can construct a model of a macromolecule bound to a ligand and assess the importance of molecular interactions. (Expert) CA1.04 Students can produce a model of a macromolecule based on a known structure of a related macromolecule. (Amateur, Expert) CA2. Students can compose a rendering to predict the cellular location of a protein (e.g., extracellular, membrane associated, or cytoplasmic) based on the properties and orientations of functional groups. CA2.01 Students can design a rendering that conveys properties such as polarity, charge, secondary structure, etc. to suggest the cellular location of a macromolecule. (Amateur) CA2.02 Students can create protein images with colored polar/nonpolar residues to determine whether they fold with a hydrophobic core. (Amateur) CA2.03 Students can create images to display polar/nonpolar residues and propose a role for the protein and/or how it interacts with its environment ‐ and that the predictions would be plausible based on the protein. (Amateur) CA2.04 Students can make accurate predictions of the location/function of the protein that incorporates additional protein features, such as transmembrane helices, apparent docking surfaces, etc. (Expert) Ligands and Modifications (LM) Metals and metal clusters, additions such as glycosylation, phosphorylation, lipid attachment, methylation etc. LM1. Students can identify ligands and modified building blocks (e.g., hydroxyproline, aminosaccharides, modified nucleobase) within a rendered structure. LM1.01 Students can use the annotation associated with a pdb file to identify and locate ligands and modified building blocks in a given biomolecule. (Amateur) LM1.02 Students can visually identify non‐protein chemical components in a given rendered structure. (Amateur) LM1.03 Students can distinguish between nucleic acid and ligands (e.g., metal ions) in a given nucleic acid superstructure. (Amateur) LM1.04 Students can explain how a ligand in a given rendered structure associates with the biomolecule (e.g., covalent interaction with residue X). (Amateur) LM1.05 Students can locate/identify ligands and modified building blocks in unannotated structures and describe their role. (Expert) LM2. Students can describe the impact of a ligand or modified building block on the structure/function of a macromolecule. LM2.01 Students can look at a given rendered structure and describe how the presence of a specific ligand or modified building block alters the structure of that biomolecule. (Amateur) LM2.02 Students can explain how the removal of a particular ligand or modified building block would alter the structure of a given biomolecule. (Expert) LM2.03 Students can use molecular visualization tools to predict how a specified ligand or modified building block contributes to the function of a given protein. (Amateur, Expert) LM2.04 Students can predict how a ligand or modified building block contributes to the function of a protein for which the structure has been newly solved. (Expert) Macromolecular Assemblies (MA) Polypeptides, oligosaccharides, and nucleic acid and lipid superstructures (e.g. protein–nucleic acid complexes, lipid membrane-associated proteins) MA1. Students can describe various macromolecular assemblies. MA1.01 Students can identify individual biomolecules in a macromolecular assembly. (Novice, Amateur, Expert) MA1.02 Students can describe functions of individual biomolecules within a macromolecular assembly. (Novice, Amateur, Expert) MA1.03 Students recognize the various lipid ultrastructures (e.g., micelles, bicelles, vesicles, and lipid bilayers) in a 3D structure. (Novice) MA2. Students can compose information‐rich renderings of macromolecular assemblies. MA2.01 Students can render a macromolecular assembly to highlight individual structures. (Amateur) MA2.02 Students can render a macromolecular assembly to illustrate structural features (e.g., binding interfaces, symmetry, tertiary structure, etc.). (Novice, Amateur, Expert) Macromolecular Building Blocks (MB) Recognition of native amino acids, nucleotides, sugars, and other biomonomer units/building blocks. Understanding of their physical and chemical properties, particularly regarding functional groups. MB1. Students can identify individual building blocks of biological polymers. MB1.01 Given a rendered structure of a biological polymer, students can identify the ends of a biological polymer. (Novice, Amateur, Expert) MB1.02 Given a rendered structure, students can divide the polymer into its individual building blocks. (Novice) MB1.03 Given a rendered structure, students can identify the individual building blocks. (Novice) MB2. Students can describe the contributions different individual building blocks make in determining the 3‐D shape of the polymer. MB2.01 Students can describe the physical/chemical properties of an individual building block/functional group in a rendered structure of a polymer. (Amateur) MB2.02 Students can describe the significance of the location of individual building blocks within the 3D structure of a polymer (protein, carbohydrate, or nucleic acid). (Novice, Amateur, Expert) MB2.03 Students can identify physical/chemical properties of individual building blocks/functional groups in different local environments. (Amateur) MB2.04 Using a visualized structure, students can identify stereochemistry (e.g., in carbohydrate, lipid, and protein structures). (Amateur) MB2.05 Students can modify/mutate a building block to change the 3D structure of a polymer (protein, carbohydrate, or nucleic acid). (Amateur, Expert) Molecular Dynamics (MD) Animated motion simulating conformational changes involved in ligand binding or catalysis, or other molecular motion/dynamics. MD1. Students can describe the impact of the dynamic motion of a biomolecule on its function. MD1.01 Students can recognize that biological molecules have different conformations. (Novice, Amateur) MD1.02 Students can correlate molecular movement with function. (Novice, Amateur, Expert) MD2. Students can predict limits to macromolecular movement. MD2.01 Students can locate potential regions of flexibility and inflexibility in the structure of a biomolecule. (Novice, Amateur) MD2.02 Students can recognize acceptable/unacceptable movement within a macromolecule by determining whether the movement is within allowable bond angles. (Expert) MD2.03 Students can recognize acceptable/unacceptable movement within a macromolecule by determining whether the movement results in steric hindrance. (Amateur) MD2.04 Students can recognize acceptable/unacceptable movement within a macromolecule by considering the atomic packing constraints. (Expert) Molecular Interactions (MI) Covalent and noncovalent bonding governing ligand binding and subunit‐subunit interactions. MI1. Students can predict the existence of an interaction using structural and environmental information (e.g. bond lengths, charges, pH, dielectric constant). MI1.01 Students can distinguish between covalent and noncovalent interactions. (Novice) MI1.02 Students can identify different noncovalent interactions (e.g., hydrogen bonds, ionic interactions, van der Waals contacts, induced dipole) given a 3D structure. (Amateur) MI1.03 Students can predict whether a functional group (region) would be a hydrogen bond donor or acceptor. (Amateur) MI1.04 Students can render the 3D structure of a biomolecule so as to demonstrate the ionic interactions and/or charge distribution of the different non‐covalent interactions. (Amateur) MI1.05 As it relates to a particular rendered structure, students can rank the relative strengths of covalent and noncovalent interactions. (Amateur) MI2. Students can evaluate the effect of the local environment on various molecular interactions. MI2.01 Students can identify regions of a biomolecule that are exposed to or shielded from solvent. (Novice) MI2.02 Students can identify other molecules in the local environment (e.g., solvent, salt ions, metals, detergents, other small molecules) that impact a molecular interaction of interest. (Novice) MI2.03 Students can predict the impact of other molecules in the local environment (e.g., solvent, salt ions, metals, detergents, other small molecules) on a molecular interaction of interest. (Amateur) MI2.04 Students can predict the pKa of an ionizable group based on the influence of its local three-dimensional environment. (Amateur) MI2.05 Students can propose a change to the local environment that would yield a desired change in a molecular interaction. (Expert) MI2.06 Using molecular visualization tools, students can determine which intermolecular force is most critical to stabilizing a given interaction. (Expert) Symmetry/ Asymmetry Recognition (SA) Recognition of symmetry elements within both single chain and multi-chain macromolecules. SA1. Students can identify symmetric or asymmetric features in rendered molecules. SA1.01 Students can identify symmetric features in a rendered molecule (shown in fixed orientation). (Novice) SA1.02 Students can rotate a single macromolecule, multi-chain macromolecules (e.g., homo- or heteromers), complexes of macromolecules, and supramolecular assemblies to identify axes of symmetry. (Amateur) SA1.03 Students can identify symmetric and asymmetric features in rendered molecules after coloring a given rendered molecule to reveal structural features (charge, hydrophobicity, etc.). (Amateur) SA2. Students can hypothesize the functional significance of symmetry or asymmetry in rendered molecules. SA2.01 Students can explain the functional significance of rotational axes of symmetry (or asymmetry) in a given rendered molecule. (Novice, Amateur, Expert) SA2.02 Students can predict functional significance of symmetry (or asymmetry) in a given rendered molecule. (Amateur, Expert) Structure‐Function Relationship (SF) Active/binding sites, microenvironments, nucleophiles, redox centers, etc. (please also see LM2.03) SF1. Students can evaluate biomolecular interaction sites using molecular visualization tools. SF1.01 Students can identify functionally relevant cofactors, ligands or substrates associated with a macromolecule and describe their role (e.g., an active site magnesium ion). (Amateur, Expert) SF1.02 Students recognize that the size and shape of the ligand must match the size and shape of the binding site. (Novice, Amateur) SF1.03 Students recognize that the polarity or electrostatic potential of a surface complements that of the ligand or substrate. (Novice, Amateur) SF1.04 Students recognize that the hydrophobicity of a surface complements that of the ligand or substrate. (Novice, Amateur) SF1.05 REMOVED (integrated with SF1.03) SF1.06 Students can use docking software to predict how the surface properties of a macromolecule guide and allow the binding of a ligand or substrate. (Amateur) SF2. Using molecular visualization, students can predict the function of biomolecules. SF2.01 Students can recognize structurally related molecules. (Novice) SF2.02 Students can superimpose structurally related molecules. (Novice, Amateur) SF2.03 Students can identify functionally relevant features of a macromolecule (e.g., an active site cysteine, a functional loop). (Amateur) SF2.04 Students can predict molecular function given a binding site. (Amateur, Expert) SF3. Using molecular visualization, students can predict the function of an altered macromolecule. SF3.01 Students can structurally alter a macromolecule. (Novice) SF3.02 Students can propose structural alterations to test interactions in a macromolecule. (Amateur) SF3.03 Students can predict the impact of a structural alteration on the function of a macromolecule. (Amateur, Expert) Structural Model Skepticism (SK) Recognition of the limitations of models to describe the structure of macromolecules. SK1. Students can critique the limitations of a structural model of a macromolecule. SK1.01 Students can explain that the pdb file is a model based on data and that, as a model, it has limitations. (Novice, Amateur) SK1.02 Students associate resolution with reliability of atom positions. (Amateur) SK1.03 Students can identify building blocks (for example, amino acid side chains) whose orientation in a biopolymer is uncertain. (Expert) SK1.04 Students can evaluate the flexibility/disorder of various regions of a macromolecular structure. (Novice, Amateur, Expert) SK1.05 Students can reconcile inconsistent numbering of individual building blocks among species and structure files. (Novice) SK1.06 Students can utilize a Ramachandran plot/steric clashes to interpret the validity of a structure. (Amateur, Expert) SK1.07 Students can describe the limitations of a macromolecule‐ligand docking simulation. (Amateur, Expert) SK2. Students can evaluate the quality of 3D models including features that are open to alternate interpretations based on molecular visualization and PDB flat files. SK2.01 Students can evaluate a crystal structure for crystal packing effects. (Novice, Amateur, Expert) SK2.02 Students can resolve differences between the asymmetric unit and the functional biological assembly. (Expert) SK2.03 Students can differentiate functional ligands (with biological/biochemistry role) from nonfunctional ligands (most solvents, salts, ions, and crystallization agents). (Novice, Amateur, Expert) SK3. Students can discuss the value of experimentally altering a biomolecule to facilitate structure determination. SK3.01 Students can identify non‐native structural features. (Amateur) SK3.02 Students can propose molecular modifications to facilitate structure determination. (Amateur, Expert) SK3.03 Students can propose a purpose for the introduction of non‐native structural features to facilitate structure determination. (Amateur, Expert) Topology and Connectivity (TC) Following the chain direction through the molecule, translating between 2D topology mapping and 3D rendering. TC1. Students can describe or illustrate the linkages between building blocks within a macromolecule. TC1.01 Students can trace the backbone of a macromolecule in three dimensions. (Novice, Amateur) TC1.02 Students can use appropriate terms to describe the linkages/bonds/interactions that join individual building blocks together in a macromolecule or macromolecular assembly. (Novice, Amateur) TC1.03 Given a virtual model of individual building blocks, students can predict the types of linkages/bonds/interactions that are possible or favorable. (Amateur) TC1.04 Given individual building blocks, students can appropriately connect them to create a biological polymer (e.g., drawing carbohydrate linkages, a small peptide). (Amateur, Expert) TC2. Students can describe the overall shape and common motifs within a 3D macromolecular structure. TC2.01 Using molecular visualization software, students can describe the three-dimensional structure of a macromolecule, including overall shape and common structural motifs. (Novice, Amateur, Expert) TC2.02 Students can identify common domains/motifs within a macromolecule. (Amateur, Expert) TC2.03 Students can identify connectivity features between domains or subunits in a macromolecular structure. (Amateur) TC2.04 Students can identify interactions between domains or subunits in a macromolecular structure. (Amateur, Expert) TC2.05 Students can describe how domains/motifs in a macromolecule work together to achieve a concerted function in the cell. (Amateur, Expert) TC2.06 Students can identify the levels of protein structure (e.g., parse a tertiary/quaternary structure into a series of secondary structures/motifs) and the ways in which they are connected from a three‐dimensional structure. (Novice, Amateur, Expert) TC3. Students can explain how any given biomolecular interaction site can be made by a variety of topologies. TC3.01 Students can recognize that the groups that comprise a functional site only require proper arrangement in three-dimensional space rather than a particular order or position in the linear sequence. (Amateur) TC3.02 Students can recognize similarities and differences in two similar ‐ but not identical ‐ three dimensional structures. (Amateur) TC3.03 Students can describe dissimilar portions of homologous proteins as arising from genetic insertions/deletions/rearrangements. (Amateur)
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Fundamentals_of_Biochemistry_Vol._V_-_Problems/iCn3D_for_Biomolecular_Visualization_Learning_Themes_and_Goals/BioMolViz_Theme%3A_Alternate_Renderings_%28AR%29.txt	princeton-nlp/TextbookChapters	It is not only important to visualize pre-rendered models of biomolecules, but it is also important to be able to create them to address key aspects of structure and function. These efforts should be guided by a clear set of learning goals and objectives that target student understanding of structure and function. So it is fortunate that clear learning themes, goals and objectives are articulated in a Biomolecular Visualization Framework created by BioMolViz, BioMolViz Theme: Atomic Geometry (AG) It is not only important to visualize pre-rendered models of biomolecules, but it is also important to be able to create them to address key aspects of structure and function. These efforts should be guided by a clear set of learning goals and objectives that target student understanding of structure and function. So it is fortunate that clear learning themes, goals and objectives are articulated in a Biomolecular Visualization Framework created by BioMolViz, BioMolViz Theme: Construction and Annotation % It is not only important to visualize pre-rendered models of biomolecules, but it is also important to be able to create them to address key aspects of structure and function. These efforts should be guided by a clear set of learning goals and objectives that target student understanding of structure and function. So it is fortunate that clear learning themes, goals and objectives are articulated in a Biomolecular Visualization Framework created by BioMolViz, BioMolViz Theme: Ligands and Modifications ( It is not only important to visualize pre-rendered models of biomolecules, but it is also important to be able to create them to address key aspects of structure and function. These efforts should be guided by a clear set of learning goals and objectives that target student understanding of structure and function. So it is fortunate that clear learning themes, goals and objectives are articulated in a Biomolecular Visualization Framework created by BioMolViz, BioMolViz Theme: Macromolecular Assemblies ( It is not only important to visualize pre-rendered models of biomolecules, but it is also important to be able to create them to address key aspects of structure and function. These efforts should be guided by a clear set of learning goals and objectives that target student understanding of structure and function. So it is fortunate that clear learning themes, goals and objectives are articulated in a Biomolecular Visualization Framework created by BioMolViz, BioMolViz Theme: Macromolecular Building Block It is not only important to visualize pre-rendered models of biomolecules, but it is also important to be able to create them to address key aspects of structure and function. These efforts should be guided by a clear set of learning goals and objectives that target student understanding of structure and function. So it is fortunate that clear learning themes, goals and objectives are articulated in a Biomolecular Visualization Framework created by BioMolViz, BioMolViz Theme: Molecular Dynamics (MD) It is not only important to visualize pre-rendered models of biomolecules, but it is also important to be able to create them to address key aspects of structure and function. These efforts should be guided by a clear set of learning goals and objectives that target student understanding of structure and function. So it is fortunate that clear learning themes, goals and objectives are articulated in a Biomolecular Visualization Framework created by BioMolViz, BioMolViz Theme: Molecular Interactions (MI% It is not only important to visualize pre-rendered models of biomolecules, but it is also important to be able to create them to address key aspects of structure and function. These efforts should be guided by a clear set of learning goals and objectives that target student understanding of structure and function. So it is fortunate that clear learning themes, goals and objectives are articulated in a Biomolecular Visualization Framework created by BioMolViz, BioMolViz Theme: Structural Model Skepticism % It is not only important to visualize pre-rendered models of biomolecules, but it is also important to be able to create them to address key aspects of structure and function. These efforts should be guided by a clear set of learning goals and objectives that target student understanding of structure and function. So it is fortunate that clear learning themes, goals and objectives are articulated in a Biomolecular Visualization Framework created by BioMolViz, BioMolViz Theme: Structure‐Function Re It is not only important to visualize pre-rendered models of biomolecules, but it is also important to be able to create them to address key aspects of structure and function. These efforts should be guided by a clear set of learning goals and objectives that target student understanding of structure and function. So it is fortunate that clear learning themes, goals and objectives are articulated in a Biomolecular Visualization Framework created by BioMolViz, Asymmetry Recognit It is not only important to visualize pre-rendered models of biomolecules, but it is also important to be able to create them to address key aspects of structure and function. These efforts should be guided by a clear set of learning goals and objectives that target student understanding of structure and function. So it is fortunate that clear learning themes, goals and objectives are articulated in a Biomolecular Visualization Framework created by BioMolViz, BioMolViz Theme: Topology and Connectivity ( It is not only important to visualize pre-rendered models of biomolecules, but it is also important to be able to create them to address key aspects of structure and function. These efforts should be guided by a clear set of learning goals and objectives that target student understanding of structure and function. So it is fortunate that clear learning themes, goals and objectives are articulated in a Biomolecular Visualization Framework created by BioMolViz, Biomolviz- Constructing iCn3D Models to Target B It is not only important to visualize pre-rendered models of biomolecules, but it is also important to be able to create them to address key aspects of structure and function. These efforts should be guided by a clear set of learning goals and objectives that target student understanding of structure and function. So it is fortunate that clear learning themes, goals and objectives are articulated in a Biomolecular Visualization Framework created by BioMolViz,
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.01%3A_General_Features_of_Signal_Transduction.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Introduction to Cell Signaling Cell signaling is at the heart of biology. A cell must know how to respond to chemical signals in its environment. These signals control every aspect of cell life and interactions. A cell must sense when to grow, divide and die. It must sense the presence of foreign and toxic molecules. It must defend itself. The membrane represents the divide between the outside and inside world. Signals must cross that divide and this most often happens without the signaling molecule entering the cell. Just the signal itself is transduced across the membrane. And it doesn't stop there. Internal signaling, also across internal membranes of organelles in eukaryotes, propagates spatially and temporally across the cytoplasm in all cells (prokaryotes, Archaea, and eukaryotes). It's impossible to describe the myriad of processes that occur in cell signaling in a single chapter, but we will do our best to present the common features used across cells. In addition, signals in complex organisms must be integrated within tissues and organs, between organs (brain and liver for example), and within the entire organisms. Mathematical modeling is critical in understanding the complexity of these interconnected interactions. Consider the flee, fight, or freeze responses. What if you were walking down the street and suddenly saw a tiger approaching you? Most would flee. In that process, the neural, muscular, and metabolic systems must be integrated. The resting state is followed by the activation of the flight/flee response, the maintenance of the fleeing state, and then the return to the resting state. An alternative is the freeze response, which in some situations would be adaptive. Perhaps the most complex signaling occurs in the great communication networks in the neural and immune systems. Think of it. You can experience a traumatic or emotionally-charged event once and remember it forever. Ordinary events leave less permanent traces. The biochemical changes accompanying long and short-term memory are fascinating Let's consider general principles for signal transduction across membranes of any cell that must respond to its environment. Typically the agent that signals a cell to respond is a molecular signal. Signaling can also be mediated by pressure (for touch and hearing) or light for vision. The chemical signal binds either to a cell surface receptor or to a cytoplasmic receptor if the signaling agent is hydrophobic and can transit the membrane bilayer. The logic of signaling To a first approximation, you could consider a cell as a black box, as shown in Figure $1$, with input (an external molecule for example) and resulting output signals within a cell, such as activation of gene transcription, trafficking of molecules within the cell, chemical reactions, or even export of molecules from the cell. There could be one or more signals, and one or more outputs. The inputs might arrive gradually and reach a threshold before triggering an output, or the input might be abrupt leading to an immediate output. This Figure $1$: Note the use of terminology taken from the world of electronics. Figure $2$ shows the similarity in the complexity of an electronic wiring diagram (left) to the interconnected metabolic pathways of a cell (right). The analogy is much greater than you might think. Cell signaling researchers have adopted the language and symbols of electronics as they consider how two inputs could lead to different output signals. Figure $3$ shows a truth table with different Boolean operator logic gates and symbols used by electrical engineers that also apply to signals to cells and their outputs. Two input signals, A and B, arrive at different logic gates named AND, NOR, OR, XOR AND, and NAND. 0 indicates no signal (either input or output) while 1 indicates a signal (either input or output). Each of these logic gates leads to different outputs: • AND gates require both A and B input signals for an output; • OR gates require either A or B or both for an output signal; • NOR gates require neither A or B for an output signal; • NAND gates normally have an output signal unless both inputs A and B are present; • XOR (Exclusionary OR) gates give a signal if the two inputs A and B differ. An example of an analogous biological AND gate is N-WASP protein [a homolog to the Wiskott-Aldrich syndrome protein (WASP)]. This protein regulates actin polymerization and binds multiple signals. Stimulation with two proteins, Cdc42 and phosphatidylinositol (4,5)-bisphosphate (PIP2), activates polymerization. Researchers are designing protein logic gates by creating a series of heterodimeric proteins. Consider the following heterodimers: A:A', B:B', and C:C', where the second member in each pair is different from the first and bound reversibly through noncovalent interactions (indicated by the colon :) Other versions could exist, such as A:C'. An AND gate for the formation of an A:C' dimer would be made by making the covalent, single molecules A'-B and B'-C. The A:C' dimer could form only in the presence of both A'-B and B'-C. This is illustrated in Figure $4$. Signals and their responses must occur at the right time under the right conditions and for the right duration. Under opposing sets of conditions (for example well feed and starving), opposing signaling pathways, mediated by different signaling molecules, must be mutually integrated and regulated so one is turned on and one shut off. Methods must be in place to terminate signal effects. Given the complexity of signaling pathways, it's hard to know how to present the material in a single chapter. Binding initiates and mediates almost all biological events. That binding must also be specific to avoid off-target effects. If you were to predict what biomolecule could confer specificity in the binding of a signal and allow changes from the unbound to bound state, you would certainly pick proteins. Protein:ligand binding is key to understanding biosignaling. Other types of biomolecules such as lipids and nucleic acid are involved and are of clear importance, but they are typically involved downstream from the initial protein:signal binding event. Hence we will focus mostly on signaling proteins and conformational/activity changes that occur on signal binding. To simplify protein involvement in signaling, we can assume signaling proteins have an active and inactive state. The states are interconvertible (reversible). Let's assume the signaling protein is an enzyme. The activity of the enzyme depends on many factors as described in Figure (5\) below. The green color represents the active enzyme, while red indicated inactive. Of course, proteins that are not enzymes (for example transcription factors) can be regulated similarly. In addition, the amount (and localization as well) of a signaling protein can regulate the activity of the protein, as illustrated in Figure (6\) below. Each signaling protein can be considered to be a node in a larger pathway consisting of interconnected nodes. This chapter on cell signaling hence can be viewed as a capstone to Unit 1, which explores the structure and function of biomolecules and as a prelude to the study of whole metabolic pathways. Second Messengers and Signal Translocation The chemical species that trigger signaling typically binds to a target protein transmembrane receptor on the surface of a cell but does not itself enter the cell. Just the signal enters the cell. If you were to hypothesize how that might happen, you would predict two mechanisms: • an integral transmembrane receptor undergoes a conformational change on binding that propagates to its cytoplasmic domain, which can then interact either with other cytoplasmic proteins or cytoplasmic domains of other membrane proteins, transmitting the symbol into the cytoplasm; • the bound conformation of the receptor has enzyme activity and can catalyze chemical reactions on the luminal side of the membrane. This could include the chemical modification of proteins or the synthesis of new small molecules from cell metabolites. These new small molecules are called second messengers. They could be formed in the membrane bilayer or the cytoplasm. There are several diverse types of second messengers. Two common ones are cyclic derivatives of small nucleotides, including cyclic AMP (cAMP) derived from ATP and cyclic GMP (cGMP) derived from GTP. Ca2+ ions are usually found in low concentrations in the cytoplasm as they are pumped into internal organelles such as the endoplasmic reticulum and mitochondrion. Signaling processes can release Ca2+ ions in waves within the cell. Membrane lipids are also processed to form second messengers. Membrane phospholipids can be cleaved by cell signaling-activated lipases to form free arachidonic acid, sphingosine, diacylglycerol, and inositol-trisphosphate, which can act as second messengers. Redox signaling in the cell can also occur through hydroperoxides acting as second messengers. What if the response of the cell requires gene transcription? Somehow the signal has to translocate from the cell membrane through the cytoplasm through the nuclear membrane into the nucleus. Hence a series of translocations of multiple downstream signaling events must occur. We have already seen how newly synthesized proteins have signal sequences that target them to specific cellular locations like the cell membrane, mitochondria, nucleus (nuclear localization sequences - NLS and the small GTP binding protein RAN), or for export. Proteins involved in signaling also can move throughout the cell as part of the signaling process. Likewise, we have seen how cytoplasmic proteins can be targeted to membranes by attachment of fatty acids or isoprenoids. Post-translational modification of signaling proteins Nature has chosen the post-translational modifications (PTM) of proteins as a ubiquitous way to alter the signaling states of proteins. As we have seen previously, PTMs can alter protein conformation. They can also present new binding interfaces that allow interaction with other signaling proteins. The main (but not the only) PTM used for signaling is the reversible phosphorylation of the OH-containing amino acid side chains (Tyr, Ser, and Thr) as well as histidine (mostly in prokaryotes), so we will focus on them. Enzymes that catalyse the phosphorylation of proteins are called protein kinases. Reversibility is important since if phosphorylation of a target protein is associated with a specific signaling change (either activation or inhibition), then dephosphorylation can easily reverse the signaling event. Enzymes that dephosphorylate phosphoproteins are called protein phosphatases. Figure $7$ shows the generic reaction of protein kinases and phosphatases. There appear to be 518 protein kinases and 199 protein phosphatases encoded in the human genome. Why so many? If just one protein kinase existed, one mutation in it would be disastrous. In addition, the large number of kinases and phosphatase allows for great control in the specificity of these enzymes for their target protein substrates. Kinases Kinases are a class of enzymes that use ATP to phosphorylate molecules within the cell. The names given to kinases show the substrate which is phosphorylated by the enzyme. For example: • hexokinase - an enzyme that uses ATP to phosphorylate hexoses. • protein kinase - enzymes that use ATP to phosphorylate proteins within the cell. (Note: Hexokinase is a protein, but is not a protein kinase). • phosphorylase kinase: an enzyme that uses ATP to phosphorylate the protein phosphorylase within the cell If a protein is phosphorylated by a kinase, the phosphate group must eventually be removed by a phosphatase through hydrolysis. If it wasn't, the phosphorylated protein would be in a constant state of either being activated or inhibited. Kinases and phosphatases regulate all aspects of cellular function. About 1-2% of the entire genome encodes kinases and phosphatases. Kinases can be classified in many ways. One is substrate specificity: Eukaryotes have different kinases that phosphorylate serine/threonine or tyrosine side chains. Prokaryotes also have His and Asp kinases, but these are unrelated structurally to the eukaryotic kinases. There are 11 structurally different families of eukaryotic kinases, which all fold to a similar active site with an activation loop and catalytic loop between which substrates (ATP and the OH-containing side chain) bind. Simple, single-cell eukaryotic cells (like yeast) have predominantly cytoplasmic Ser/Thr kinases, while more complex eukaryotic cells (like human cells) have many Tyr kinases. These include the membrane-receptor Tyr kinases and the cytoplasmic Src kinases. Manning et al. have analyzed the entire human genome (DNA and transcripts) and have identified 518 different protein kinases, which cluster into 7 main families as shown in Table $1$: below. Family membership was determined by sequence comparisons of catalytic domains. The entire repertoire of kinases in the genome is called the kinome. Alterations in 218 of these appear to be associated with human diseases. Name Description AGC Contain PKA, PKG, and PKC families CAMK Ca2+/CAM-dependent PK CKI Casein kinase 1 CMGC Contain CDK, MAPK,GSK3, CLK families STE homologs of yeast sterile 7, 11, 20 kinases; MAP Kinase PTK Protein tyrosine kinase PTKL Protein tyrosine kinase-like RGC Receptor guanylate kinase Table $1$: The human kinome Phosphatases There are three main families of phosphatases, the phospho-Tyr phosphatases (PTP), the phospho-Ser/Thr phosphatases, and those that cleave both. Of all phosphorylation sites, most (86%) are on Ser, 12% involve Thr, and about 2% on Tyr. They can also be categorized by their molecular sizes, inhibitors, divalent cation requirements, etc. In contrast to kinases which differ in the structure of their catalytic domains, many phosphatases (PPs below) gain specificity by binding protein cofactors which facilitate translocation and binding to specific phosphoproteins. The active phosphatase hence often consists of a complex of the phosphatase catalytic subunit and a regulatory subunit. Regulatory subunits for Tyr phosphatases may contain a SH2 domain allowing binding of the binary complex to autophosphorylated membrane receptor Tyr kinases. Given this background, we can now start to explore signal transduction in a methodical way. There are two major ways to organize our discussions: • start from the binding of the molecular signal at the cell membrane and trace the signaling events inward into the cell, potentially all the way to the nucleus and gene expression • describe recurring motifs found in most pathways. We will use a combination of both, but it makes sense to start with the signaling proteins at the cell membrane. Next, we will focus on second messengers. We'll follow that with detailed explorations of kinases and phosphatases and specific signaling pathways.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.02%3A_At_the_cell_membrane-_receptors_and_receptor_enzymes.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Introduction We are going to start a more detailed description of cell signaling where it begins, at the cell membrane, and move inward, toward intracellular organs, where we will end at the nucleus and changes in gene expression mediated by the signal. Of course, this end is somewhat arbitrary as the signal could propagate from the nucleus back to the membrane where newly synthesized membrane protein might be inserted or even exported, as in the case of secreted antibodies. It is difficult enough to keep track of all the molecular players, let alone add onto that their initial location in the cell and their final location if they translocate. Signaling can also be more daunting for those with a more chemistry focus, who find the details of cellular structure and trafficking a bit daunting. Figure $1$ shows a truncated view of the cell membrane, membrane proteins, and some of the organelles that we will visit throughout this chapter. To stay localized we will repeat the figure or variants of it several times in this section. Receptors at Cell Membrane Let's start at the location where signaling almost invariable begins, in the cell membrane, where signals (hormones, neurotransmitters, nutrients, other cells) bind to cell surface transmembrane proteins shown in the red box in Figure $2$. We have already discussed integral and peripheral membrane proteins in Chapter 11. When occupied by a ligand signal, the signaling event mediated by a transmembrane receptor might be a change in the membrane protein conformation, which propagates to its intracellular domain. Alternatively, the receptor might change its conformation and become a ligand-gated kinase (or possibly a phosphatase) or a ligand-gated channel or pore. We will discuss signal-gated ion channels in Chapter 28.9 - Neural Signaling. Ultimately, intracellular enzymes are activated in cells in response to the external molecular signal. This provides amplification of the initial signal since a single activated enzyme undergoes multiple rounds of catalysis before it would become inactivated. If the multiple products (a second messenger or phosphorylated proteins for example) that are formed activate multiple addition enzymes, the signal is further amplified. Receptors with no kinase or transport activity - G Protein-Coupled Receptors (GPCRs) One major type of signaling receptor is the G-protein coupled receptor (GPCR). Over 800 GPCRs are encoded in the human genome which represents almost 4-5% of the number of protein-coding genes. The proteins below to five major families: rhodopsin, secretin, glutamate, adhesion, and frizzled/taste2. They don’t express enzymatic activity but on binding, they can activate enzymes inside the cell by interacting through their cytoplasmic domains with G proteins (GTP binding proteins) in the cytoplasm. GPCRs have been called serpentine receptors as their single amino acid chain proteins have 7 transmembrane-spanning α- helices. All of the GPCRs have similar yet slightly different structures, allowing them to interact with specific ligands. Many of the GPCRs bind to unknown ligands and hence are called orphan receptors. We will explore a few GPCRs in more detail below. GPCRs that modulate the membrane enzyme adenylyl cyclase: β-adrenergic receptor The β-adrenergic receptor is a prototypical GPCR. Found in muscle, liver, and fat cells, it binds epinephrine and adrenaline, which leads to energy mobilization and muscle activation (i.e. flight or fight response). The mechanism of activation of a GPCR is illustrated using the beta-adrenergic receptor as an example. The unoccupied adrenergic receptor is associated with a heterotrimeric G protein, which contains an α, β, and γ subunits. GDP is usually bound to the α subunit. Figure $3$ shows a cartoon of the GPCR interacting with the heterotrimeric G protein. When the hormone is bound to the receptor, a conformation change is propagated to the cytoplasmic domain of the GPCR altering its interaction with Gαβγ. This causes a conformational change in the Gα subunit, which leads to an exchange of bound GDP with GTP in the Gα subunit, promoting the dissociation of the Gα-GTP from Gβγ. Gα-GTP then binds to and activates an adjacent membrane enzyme called adenylate cyclase, which produces a second messenger by converting ATP to cyclic AMP (cAMP). Figure $4$ shows steps in the generic GPCR activation cycle. Figure $4$: Activation cycle of G-proteins by G-protein-coupled receptors. https://commons.wikimedia.org/wiki/F...PCR-Zyklus.png Creative Commons Attribution-Share Alike 3.0 Unported The primary message binds to the GPCR (1 leading to state 2. The cytoplasmic domain conformational changes allow GTP exchange in the Gα subunit as state 3 goes to 4. In 5 the Gα-GTP complex dissociates from Gβγ, which remains in the membrane. The Gα-GTP subunit is held and localized to the membrane (not evident in the above figure) through a lipid anchor attached through a post-translational modification. Remember, the GPCR has no ligand-gated enzymatic activity. Yet it indirectly leads to the activation of a membrane enzyme, adenylate cyclase, when the dissociated Gα-GTP binds to the cyclase. As long as GTP remains bound to the Gα subunit, it will continue to modulate the activity of adenylate cyclase. A built-in regulatory mechanism does exist in the protein since the Gα subunit has GTPase activity. The GTP will eventually hydrolyze, and the GDP-Gα subunit will lose affinity for its bound partner (adenylate cyclase), and return to the heterotrimeric G protein associated with the unbound receptor. GPCRs bind the signaling ligand (primary message) in a binding cavity localized at the extracellular face and between four of the transmembrane helices. The activity and structure of GPCRs have been studied using natural ligands (hormones and neurotransmitters), as well as agonists, partial agonists, inverse agonists, and antagonists. As discussed previously, agonists bind to the natural ligand binding site and elicit the same or a partial response (partial agonist). Inverse agonists bind and lower the response of a constitutively active receptor, and antagonists bind and prevent the normal response of an agonist. About 35% of pharmaceutical drugs target GPCRs, but target only about 15% of the ∼800 human GPCRs. The orphan GPCRs are increasingly targets for drug development. Most hormones and neurotransmitters work through GPCRs. In addition, our primary senses of vision, smell (olfaction), and taste (gustation) work through GPCRs. Figure $5$ shows the structure of the beta 2-andrenergic:Gs complex with bound agonist. No membrane is shown. The GPCR is shown in cyan. The 7 transmembrane helices should be obvious. The ligand is bound between them. The Gα subunit is shown in magenta, the Gβ in dark blue, and the Gγ in orange/brown. The gray subunit is a Camelid antibody VHH fragment (a single-chain nanoantibody used to stabilize the conformation for crystallization. ) The biggest conformational change that occurs when the GPCR binds an agonist is an outward movement (14 Å) at the intracellular domain of transmembrane segment 6 (TM6) and an extension of the TM5 helix. This leads to a movement of the Gα's alpha-helical domain, enabling GTP exchange for GTP. Of course, multiple reactions determine the fraction of the Gα in the active GTP-bound state. These would include the relative cellular concentrations of free cytoplasmic GDP and GTP, their KD values for the Gα, the rate constant for the hydrolysis of bound GTP, and the rate constants for the GDP ↔ GTP exchange. Figure $6$ shows an interactive iCn3D model of the Crystal structure of the beta2 adrenergic receptor-Gs protein complex 3SN6 The GPCR is shown in green, the Gα in magenta, Gβ in blue, and the Gγ in brown. An agonist (spacefill) is shown in CPK colors near the outer leaflet. Some bacterial toxins work by inactivating the GTPase activity of the Gα subunit, keeping it in the "stuck" position. For example, cholera toxin, an enzyme released by Vibrio cholera, catalyzes the ADP ribosylation of an Arg in the Gα subunit by transferring everything but the nicotinamide from NAD+ to the Arg residue. Since the Gα subunit stimulated the activity of adenylyl cyclases, it is often named a stimulatory Gα protein or G. Figure $7$ shows an interactive iCn3D model of the adenylyl cyclase activator G with GTP-γ-S (1azt) Now we can explore how the occupied GPCR, which again has no enzymatic activity, activates the enzyme adenylyl cyclase. Figure $8$ shows a cartoon of the G subunit bound to adenylyl cyclase as the Gβγ heterodimer remains associated with the membrane Adenylate cyclase converts ATP into the second messenger cyclic AMP (cAMP) as shown in Figure $9$. The figure also shows how cAMP is broken down into AMP by the enzyme cAMP-specific 3',5'-cyclic phosphodiesterase (PDE). The latter step is necessary to control the lifetime of the second messenger cAMP. Why cAMP? You might ask why cAMP and not just AMP is nature's choice for GPCR signaling. AMP is a very important metabolic species. High concentrations of it signal an energy-depleted state. AMP is used in another signaling process to mobilize a response to adjust the energy state of a cell using a protein called AMP-Protein Kinase, which we will describe later. Many enzymes are also allosterically regulated by AMP. Figure $10$ shows an interactive iCn3D model of the membrane adenylyl cyclase bound to an activated stimulatory G protein 6R3Q determined by cryo-EM. The carboxyl-terminal cytoplasmic domain has the catalytic and allosteric sites. Cannabinoid Receptors In contrast to the beta-adrenergic receptor which mediates activation of adenylate cyclase through G, some Gα subunits inhibit adenylate cyclase when bound. These Gα subunits are called Gi/oα in contrast to the stimulatory subunits, G. Also, Gα subunits interact with many proteins other than adenylate cyclase. Examples include cannabinoid receptors. Cannabinoid receptors are named after the exogenous and psychoactive drug Δ9-tetrahydrocannabinol (THC) that binds to the receptor. THC is the major phytocannabinoid (from plants) found in the Cannabis sativa plant and the marijuana-derived from it. The other main cannabinoid in the plant is cannabidiol (CBD). Phytocannabinoids bind to two types of human cannabinoid (CB) receptors, CB1 and CB2. They have 44% amino acid and 68% homologies in the entire protein and the transmembrane domain, respectively. The phytocannabinoids exert their effect through binding to human CB1 and CB2, whose endogenous ligands are two fatty acids derivatives called anandamide (AEA) and 2-arachidonoyl glycerol (2-AG). The structure of THC, CBD, and the two major endogenous ligands are shown in Figure $11$. Figure $11$: Structure of agonist and antagonist for cannabinoid receptors The cannabinoids from Cannabis sativa have a monoterpene isoprenyl group (C10) and a pentyl side chain (C5). The ligands are largely hydrophobic and probably access their binding site in the receptor mainly by lateral movement in the membrane. The receptors differ most in the N-terminal extracellular loop which is also involved in ligand binding. Figure $12$ shows an interactive iCn3D model of the class A GPCR Cannabinoid Receptor-Gi Complex Structure with bound agonist (6KPF) The gray protein is a nanobody used to stabilize the protein during crystallization. The agonist is AM12033, which is similar to AM11542 in the figure above, but with a -C=N terminus instead of a bromide. THC and CBD Cannabis sativa contains the psychoactive drug, Δ9-tetrahydrocannabinol (THC), which is a partial agonist for CB1 (binds with reported Ki values of 10 or 53 nm)and CB2 (Ki = 40 nm). Its psychoactive effects on mental activity as well as pain and appetite are well known. In contrast, cannabidiol (CBD) is the main, non-psychoactive cannabinoid. It has a much lower affinity for the recombinant CB1 (Ki = 1.5 µM ) and CB2 (Ki = 3.7 µM). It appears to be a partial antagonist for CB1 and a weak inverse agonist for CB2. It has also been shown that CBD is a negative allosteric modulator of the agonistic effects of THC and 2AG. The actual psychotropic effects of combining THC and CBD are complicated and not understood well. Figure $13$ shows an interactive iCn3D model of human CB1 in complex with agonist AM11542 (5XRA) How much of a receptor is bound with a cannabinoid depends on the concentration of the cannabinoid ligand and the Ki for the drug. The amount of THC and CBD depends on the genetics of the plant, which has been engineered to greatly decrease THC production (in the hemp plant used for nonpharmacological commercial properties) or increase either THC or CBD production at the expense of the other. The synthesis of THC and CBD proceeds through a common precursor, CBGA (cannabigerol acid). Two key flavoproteins, Δ9-tetrahydrocannabinolic acid synthase (THCAS) and cannabidiolic acid synthase (CBDAS) convert this common precursor CBGA into two new precursors, Δ9-THCA and CBDA, respectively. This final synthetic step involves an oxidative cyclization reaction using O2 and produces H2O2. Spontaneous, non-catalyzed decarboxylation and rearrangements of Δ9-THCA and CBDA lead to the final products, THC and CBD. This last process occurs on exposure to heat, which occurs in smoking and baking, and at a slower rate during storage. Commercially used preparations of THC and CBD for medicinal purposes vary widely in concentrations. THC concentration ranges for pain management (<5-10%) are much lower than those for psychotropic effects (<15%), with values of 21% or often found in "recreational" cannabis. High-potency THC strains can contain up to 25-30% THC by dry weight. For strands modified for CBD production, the maximal amount is about 25%. Even though CBD appears to be a partial antagonist for CB1, it appears that the ratio of THC:CBD is important in modulating the "high" or intoxicating state of THC. A ratio of THC:CBD of just over 1:1 leads to synergism or enhancement of the acute effects of THC whereas ratios of THC:CBD of 1:2 to 1:6 seem to have the least intoxicating effects. However, CBD decreases psychotic symptoms of THC and also decreases memory changes associated with THC. CBD also is an allosteric modulator of the μ-opioid receptor. At present, there are no structures of CBD bound to its cannabinoid receptors. Figure $14$ shows an interactive iCn3D model of the CBD-bound full-length rat transient receptor potential vanilloid 2 (TRPV2) in nanodiscs (6U88) Now let's make it even more complicated. There are more than 20 different Gα-like proteins known in 4 major families. • Gs and Gi regulate adenylyl cyclase • Gq activates phospholipase Cβ (described below). There are 4 members given these strange names: Gq, G11, G14, and G15/16 • G12/13 activate small GTPase protein (described in Chapter 28.5) The Gα protein involved in light sensation is named transducin, while those involved in odorant detection and taste are called Golfactory and Ggustatory, respectively. As we add more variants of each signaling component, the origin of the complexity of signaling systems becomes evident. For example, the neurotransmitter serotonin binds to its receptor, a GPCR, and instead of gating the protein open to ion flow (as with other ligand-gated ion channels in the activation of neurons as we will see in Chapter 28.9), it interacts with two different alpha subunits, Gs, which leads to activation of adenylyl cyclase, and G12, which interacts with other small GTP binding proteins called GEFs (we will also see these later). GPCRs modulate the activity of the membrane enzyme phospholipase C. These receptors use the same mechanism for activation of the membrane enzyme adenylyl cyclase. When the primary signal is bound to the GPCR, a conformation change is propagated to the cytoplasmic domain of the GPCR, altering its interaction with Gαβγ in which the alpha subunit is a member of the Gα(q) family. This causes a conformational change in the Gα subunit which leads to an exchange of bound GDP with GTP in the Gα subunit, promoting the dissociation of the Gα-GTP. Gα-GTP then binds to and activates an adjacent membrane enzyme, phospholipase C (PLC), which cleaves membrane phospholipids to produce two, second messengers, diacylglycerol and inositol 1,4,5-trisphosphate (IP3). Their structures are shown in Figure $15$. There appear to be 13 kinds of mammalian phospholipase Cs divided into six isotypes (β, γ, δ, ε, ζ, η). Phospholipase C is also named 1-phosphatidylinositol 4,5-bisphosphate phosphodiesterase. Figure $16$ shows an interactive iCn3D model of the Gα(q)-phospholipase C-β 3 structure (4GNK). The magenta structure is the Gα(q) protein with bound GDP in spacefill. The cyan structure is the Pleckstrin Homology (PH) domain of the protein. This domain targets proteins to inositol phospholipids in the membrane but appears not to have this function in this protein. Note that in contrast to adenylyl cyclase, PLC is a peripheral, not an integral membrane protein. It is found in the cytoplasm as well as associated with the inner leaflet of the cell membrane where its main activities, regulating and cleaving PIP2, occur. PLC localizes to lipid rafts enriched in PIP2. Figure $17$ shows the domain structure of phospholipase C β3 The N-terminal PH_14 represents the Pleckstrin Homology (PH) domain, which is among the top 15 of all domains in the human genome. All PLCs except PLCζ have this domain. Note that in another example of complexity, PLC-β binding to the inner leaflet does not require PIP2. The iCn3D model above indeed shows binding to the membrane seems to depend on adjacent structures and not the PH domain (cyan) specifically. Tabel $1$ below characteristics of common signals that signal through GPCRs. signal vasopressin epinephrine light odorant odorant sweet tastant receptor VR β-adrenergic rhodopsin odorant receptor 1 odorant receptor 2 sweet receptor Ga-like subunit Gi Gs transducin Golfactory Golfactory Ggustatory coupled enzyme adenylate cyclase adenylate cyclase phosphodiesterase phospholipase C adenylate cyclase adenylate cyclase 2nd messenger decrease cAMP increase cAMP decrease cGMP increase IP3 increase cAMP increase cAMP protein affected decrease PrK-A increase PrK-A dec. Ca, Na perm. inc. Ca perm inc.Ca, Na perm dec. K perm Tabel $1$: Characteristics of common signals that work through GPCRs. Changeux and Edelstein reviewed the MHC model 40 years after its conception and support its application to signal transduction processes. They include in signaling molecules not only hemoglobin, but regulatory enzymes (aspartate transcarbamylase, phosphofructokinase, LDH, glycogen phosphorylase), membrane receptors (acetylcholine receptor, rhodopsin), and nuclear receptors (lac repressor, steroid hormone receptors). In all these signaling proteins, residue distant from the "active" site participates in binding to allosteric ligands. Often the allosteric site is on a separate domain that can be cleaved from the protein and still maintain allosteric ligand binding properties. The proteins also consist of multiple subunits easily related by distinct symmetry axes. Allosteric ligands often bind in cavities in subunit interfaces along symmetry axes. In general, crystal structure analyses show that low-affinity T and high-affinity R forms of the signaling proteins exist but are accompanied by minor tertiary structure changes in individual subunits (i.e. perfect symmetry in all subunits is not preserved on the binding of allosteric ligand). For neurotransmitter membrane receptors, these two states can be correlated with an open and closed state (for ion flux), and open conformations of these proteins can often be found in mutant forms. However, for many ligand-gated ion channels and G-protein coupled receptors (serpentine), kinetic analyses show more complicated forms than can be represented by a simple two-state (R and T) model. High-resolution microscopy shows evidence for nonsymmetrical quaternary structural changes. These changes can be observed in the absence of ligand, which gives support to the MWC concept that allosteric ligands select certain conformational states, leading to equilibrium shifts in the unliganded receptor to the more high-affinity state. More refined methods of structural analysis will presumably show more evidence of subtle tertiary changes in the proteins that are preludes to quaternary structural changes. Yet the simplicity of the MWC model for explaining many features of signaling proteins remains. Receptors with signal-gated kinase activity - Receptor Tyrosine Kinases (RTK) Why bother binding a primary message to a GPCR and going through multiple steps before the activation of a membrane enzyme like adenylate cyclase? Wouldn't it be easier and more efficient to have the membrane receptor a ligand-activated enzyme? Such is the case with special membrane receptors called Receptor Tyrosine Kinases (RTKs). There are about 90 tyrosine kinases in the human genome of which 58 are RTKs. Figure $18$ shows the family domain structure of the RTKs. Note that the insulin receptor (InsR) is a dimer of two monomeric insulin receptor chains. Figure $19$s shows in more detail the domain structure of the epidermal growth factor receptor (EGFR). Figure $19$: Domain structure of the EGFR domain. The domain (red/brown) immediately after the blue domain is the transmembrane domain. Furin is a cellular endoprotease. The green represents L domains which comprise the ligand binding site. Each L domain consists of a single-stranded right-hand beta-helix. Here is the cascade of events for signaling through EGFR: The transmembrane has ligand-dependent tyrosine kinase activity. Binding of the hormone EGF causes receptor dimerization bringing the intracellular kinase domains (yellow PK_Try_Ser_Thr) together activating them. When active, they can phosphorylate each other (autophosphorylation) or other proteins. When the receptor is autophosphorylated, other proteins can bind to the cytoplasmic domain of the receptor Tyr kinase where they are phosphorylated. The target substrates phosphorylated by the receptor Tyr kinases are proteins with a common 100 amino acid domain called SH for src homology, based on structural homology to another cytoplasmic protein, Src. Src is an intracellular Tyr kinase activated when it binds through 2 SH domains to an autophosphorylated receptor Tyr kinase. Specifically, the SH2 domain has been shown to bind tyrosine-phosphorylated peptides. These domains target proteins to the autophosphorylated receptor Tyr kinase. Many proteins involved in signal transduction have SH2 domains. Some of these proteins also have catalytic domains with kinase activity. Others have phosphatase, transcription factor. or scaffolding domains. Figure $20$ shows the hormone-depended dimerization of RTKs, their autophosphorylation, and the recruitment of proteins with SH2 domains. It is easier to envision how a GPCR is activated by binding its target hormone than for RTKs. GPCRs are single-chain proteins that pass through the membrane using 7-transmembrane helices. RTKs have a single transmembrane helix. The dimeric form of the RTK has some additional flexibility in the short region between the extracellular and transmembrane domains, allowing for the conformational changes necessary for the activation of the intracellular kinase domains. The crystal structure of the full EGFR is not known given the difficulties in crystallizing membrane proteins that span the membrane with a single alpha helix. However, separate structures of the dimeric extracellular domain and the intracellular kinase domains are known. Figure $21$ shows an interactive iCn3D model of the dimeric extracellular and transmembrane domains of the epidermal growth factor receptor (3NJP). The two EGFR are shown in blue and magenta. The two bound EGFs are shown in cyan. Figure $22$ shows an interactive iCn3D model of a dimer of the intracellular dimeric EGFR kinase domains in complex with an ATP analog-peptide conjugate (2GS6) The two EGFR kinase domains are shown in cyan and magenta. The ATP analogs in each domain (spacefill) are thiophosphoric acid O-((adenosyl-phospho)phospho_-S-acetamidyldiester. The peptide substrates (green stick) are 13mers with a tyrosine (sticks, labeled Y, minus the OHs) connected to the ATP analog. We've introduced the essential cell membrane players in signal transduction. • GPCRs which are not enzymes but which activate the bound heterotrimer G prortein Gα subunit, which then can activate or inhibit the integral membrane protein adenylate cylase or activate the membrane-bound enyzme protein kinase C. These enzymes produce second messengers cAMP (adenylate cyclase) and diacylglycerol (DAG)and IP3 (phospholipase C) • Receptor tyrosine kinases, ligand-activated receptor kinases, which can,on ligand-induced dimerization, autophosphorylate themselves or other target protein in the cell. In the next chapter section we will explore the next downstream effects in signaling, mediated by the second messengers cAMP, DAG and IP3 and the substrates phosphorylated by the ligand-active receptor tyrosine kinases.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.03%3A_The_Next_step_-_The_Kinome_and_Activation_of_Kinases_at_the_Cell_Membrane.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry In the last section, we studied how the cell surface GPCRs indirectly activate membrane-bound enzymes adenylyl cyclase and phospholipase C (the GPCRs) and how RTKs become active enzymes (kinases) themselves on ligand-induced dimerization. We will now explore what happens next in signaling by examining the products of these activated enzymes and how they continue the signaling process. The products of adenylyl cyclase (cAMP) and phospholipase C (diacylglycerol - DAG - and IP3) are second messengers. These activate key protein kinases in the cell. The products of the activated RTKs are phosphoproteins (including itself) that are phosphorylated on tyrosines by the RTK. Target proteins bind to the autophosphorylated RTKs through SH2 domains on the target protein. First, we will explore protein kinases and their mechanism in general. The Kinome There are 518 different protein kinases (about 1.7% of the human genome) and as a group, they are the key players in most eukaryotic signaling pathways. Collectively they are called the kinome. They help regulate every aspect of cell life including metabolism, cell growth, differentiation, and division, as well as programmed cell death. In humans, most (388) are Ser/Thr kinases. 90 are Tyr kinases and 40 are classified as atypical. In this section, we will focus on the AGC kinases (60 in total) which include Protein Kinase A (PKA), Protein Kinase C (PKC), and also Protein Kinase B (PKB, better known as AKT. We'll wait for another section to explore Protein Kinase G (PKG) which is activated by cGMP, not AMP. We'll also study the receptor Tyr kinases (RTKs) Luckily kinases can be grouped into families based on structure and mechanism. The ones directly activated by the second messengers cAMP (Protein Kinase A) and DAG (Protein Kinase C) are members of one family of kinases the AGC Protein Kinase family. The clustered families of protein kinases are shown in Figure $1$. Figure $1$: The family of protein kinases. https://peerj.com/articles/126/. Illustration reproduced courtesy of Cell Signaling Technology, Inc. (www.cellsignal.com) Luckily, most of the kinases, which catalyze the phosphorylation of the OH-containing side chains (Tyr, Ser, and Thr) of proteins have similar catalytic sites. Given that phosphorylation of proteins often acts as an on/off switch for protein activity and function, it makes great sense that the catalytic site of protein kinases is not available for substrate binding and/or catalysis in the off state. Protein kinases have catalytic and activation loops and conformation changes on activation of protein kinases usually involve movement of the activation loop and rearrangement of the catalytic residues. These conformational changes are often initiated by phosphorylation by another upstream active protein kinase of key groups in the activation loop! Figure $2$ shows another representation of the different families of protein kinase and their common structural features. Pane (A) shows the nine eukaryotic and one atypical protein kinase groups with their numbers of kinases, structures, and inhibitors (shown in black) as well as mutations (in red) and SNPs (in green). Here, the atypical protein kinases are defined as all non-eukaryotic protein kinases including also the nonprotein kinase-like kinases. The middle of Panel (B) shows the activation and catalytic loops and other common features found in the active sites of protein kinases. The left and right parts of Panel (B) show two different kinases. The protein kinase domain of epidermal growth factor receptor (EGFR) is shown on the left and the Ser/Thr protein kinase domain of mTOR (mammalian or mechanistic Target Of Rapamycin), a protein we will explore in a separate section, is shown on the right. AGC Kinases These serine/threonine kinases are activated by the second messengers cAMP (A Kinase), cGMP (G kinase, which we will see later), and by diacylglycerol (DAG) formed by activation of phospholipase C. Figure $3$ shows the generic structure of active kinase domains (including tyrosine kinases) A generic kinase has an N-terminal and C-terminal lobe between which the substrates ATP and the target protein containing the key serine, threonine, or tyrosine side chain bind. In the active conformation, Glu 91 (E91) in the αC-helix in the N-lobe forms a salt-bridge (ion-ion interaction) with Lys72 to position it so it can stabilize the binding of the ATP through ion-dipole and H-bond interactions. In the active state, Thr 197 (T197) in the activation loop has been phosphorylated (represented by pT197) by an upstream kinase allowing a less flexible conformation of the loop. This facilitates optimal repositioning of active site side chains in the catalytic loop allowing substrate access and catalysis. Asp 166 (D166) in the catalytic loop acts as a general base in abstracting a proton from the target Ser, Thr or Tyr. Arginine 165 (R165) in the catalytic loop forms a salt bridge to phosphotyrosine 197 (pT197), stabilizing the catalysis-competent conformation of the activation loop. Aspartic acid 184 (D184) plays a key role in stabilizing the Mg2+ that itself stabilized the negative charges on the phosphates of ATP and in the developing transition state. Most kinases are also classified as RD kinases since they have a key arginine (R)- aspartic acid (D) sequence in the catalytic loop. The normal protein substrate for an AGC kinase would be a protein with a Ser or Thr projecting into the active site for phosphorylation by bound ATP. The normal sequence for phosphorylation in protein targets of PKA is Arg-Arg-X-Ser/Thr-X (or more fully as XR(R/K)X(S/T)B where B is a hydrophobic amino acid). Again we will focus on three AGC kinases here, PKA, PKC, and AKT (PKB), and defer our discussion of PKG to another section. cAMP-dependent Protein Kinase A - PKA Before we study PKA, the formation of the second messenger cAMP is presented in review in Figure $4$. PKA contains a kinase domain with short N- and C-terminal extensions, making it a prototypical and simple model for study. In the absence of the second messenger cAMP, this prototypic AGC kinase exists as a holo-heterotetramer (or dimer of a heterodimer). It contains two catalytic kinase subunits (C) and two regulatory subunits (R). cAMP produced upon GPCRs activation of adenylyl cyclase binds to the regulatory subunits, causing them to dissociate from the heterotetramer, freeing the catalytic subunits for activity. This is illustrated in Figure $5$. The cAMP serves as an allosteric activator of protein kinase A. Figure $6$ shows the actual structures of the protein kinase A RIIb tetrameric holoenzyme (3TNP) The inhibited catalytic subunits are shown in red and the two regulatory subunits are shown in cyan. Figure $7$ shows only one catalytic subunit of protein kinase A as it goes from the more closed inactive holoenzyme (tetramer, 3tnp) containing the regulatory subunits (not shown), to the more open apo-form (monomer, 1J3HA), without the regulatory subunit. The conformational change opens the free catalytic subunit to the substrate (protein and ATP) binding. Side chains required for MgATP binding and phosphoryl transfer are pre-formed in the apo form but some changes still occur on the binding of substrate. Figure $8$ shows an interactive iCn3D model of the mouse catalytic subunit of cAMP-dependent protein kinase complexed with MnATP and a peptide inhibitor (1ATP) Zoom into the model to see each of the labeled residues. All of the features shown in Figure $3$ are displayed in this model. The activation loop is shown in dark blue and the catalytic loop is shown in red. The peptide inhibitor is shown in cyan. The normal target motif for phosphorylation by Protein Kinase A (Arg-Arg-X-Ser/Thr-X) has been replaced in this model by Arg-Arg-X-Ala-X. Why does this make the peptide an inhibitor instead of a substrate? Many primary signals activate adenylate cyclase through GPCR and produce cAMP as a second messenger. These include corticotrophin, dopamine, epinephrine (β-adrenergic), follicle-stimulating hormone, glucagon, many odorants, prostaglandins E1and E2, and many tastants. All of these would activate protein kinase A. Some enzymes regulated by cAMP-dependent phosphorylation by PKA are shown in Table $1$ below Enzyme Pathway Glycogen Synthase Glycogen synthesis Phosphorylase Kinase Glycogen breakdown Pyruvate Kinase Glycolysis Pyruvate Dehydrogenase Pyruvate to acetyl-CoA Hormone-sensitive Lipase Triacylglycerol breakdown Tyrosine Hydroxylase Synthesis of DOPA, dopamine, norepinephrine Histone H1 Nucleosome formation with DNA Histone H2B Nucleosome formation with DNA Protein phosphatase 1 Inhibitor 1 Regulation of protein dephosphorylation CREB cAMP regulation of gene expression PKA consensus sequence XR(R/K)X(S/T)B (B = hydrophobic amino acid) Table $1$: Proteins phosphorylated by Protein Kinase A. An example of how epinephrine (a flight/fight hormone) can lead to the breakdown of glycogen (your main carbohydrate reserves in muscle and liver) is shown in Figure $9$. A cascade of events starts with the binding of the hormone to its receptor, followed by the activation of adenylate cyclase and the formation of the second messenger cAMP. This activates PKA, which phosphorylates and activates the enzyme phosphorylase kinase. Following the naming convention we discussed earlier, phosphorylase kinase is a protein kinase whose target enzyme is another enzyme called glycogen phosphorylase, which is NOT a kinase. When active, glycogen phosphorylase uses inorganic phosphate (Pi) as a nucleophile in a phosphorolysis reaction to cleave glucose from the end of glycogen polymers forming glucose-1-phosphate. This is the first step in the mobilization of glycogen as an energy reserve. No wonder it is so tightly regulated by this complicated signaling pathway. The second messengers DAG and IP3 and their activation of Protein Kinase C The activation of protein kinase C (PKC, a Ser/Thr kinase member of the ACG protein kinase family) is very similar to that of protein kinase A. To start, an extracellular signal molecule binds to a GPCR receptor (again with no intrinsic enzyme activity), causing a conformational change in the receptor that propagates through the membrane to its intracellular domain. That then activates the exchange of GTP for GDP in the alpha subunit of the bound heterotrimeric G protein, which contains the special Gαq subunit. The Gαq subunit dissociates and binds to the membrane protein enzyme phospholipase C (not adenylyl cyclase). Once activated, it cleaves the phospho-head group from the membrane phosphatidyl inositol - 4,5-bisphosphate (PIP2) into two, second messengers - diacylglycerol and inositol trisphosphate (IP3). These products are shown in Figure $10$. The formation of the second messengers IP3 and DAG is presented n Figure $11$. Note that phospholipase C is a peripheral membrane protein bound to the inner leaflet of the cell membrane. Diacylglycerol binds to and activates protein kinase C (PKC). The IP3 binds to ligand-gated receptor/Ca2+ channels on internal membranes, leading to an influx of calcium ions into the cytoplasm. The released calcium ions also activate PKC. These steps are illustrated in Figure $12$ in more detail below. The activation of the peripheral membrane phospholipase C (PLC) is very similar to that of the integral membrane protein adenylyl cyclase, both of which produce second messengers. Figure $12$: GPCR activation of phospholipase C, generation of second messengers IP3, DAG, and Ca2+ ions, and downstream activation of Protein Kinase C You might also expect the regulation of the activation of protein kinase C (PKC) activity to be similar to the regulation of the activation of protein kinase A (PKA). It is, but with a major difference. In contrast to the structure of PKA, which cycles between an inactive R2C2 (R is the regulatory and C the catalytic subunit) and an active separated form, PKC is a single chain that has a regulatory, domain, and catalytic domain as shown in Figure $13$. Variants of PKC are also shown. Protein kinase C was named "C" because of its activation by Ca2+ ions, but you could also remember it because it requires the upstream activation of phospholipase C. There are more than 500 PKCs divided into 15 subgroups and their downstream functions lead to gene expression. They all have 4 common domains, C1-C4. C1 and C2 are the functional regulatory domain and C3 and C4 are in the overall catalytic domain. They have the following activities. • C1 binds diacylglycerol (DAG) and phorbol esters (commonly known activators or PKC) • C2 binds Ca2+, another second messenger, which activates the protein; Novel PKCs use DAG and phosphatidyl ethanolamine but not Ca2+ to activate the protein, while atypical ones use only DAG. • C3 binds ATP • C4 binds target proteins for phosphorylation. The structure of the phorbol 12-myristate 13-acetate, which activates PKC, is shown in Figure $14$. In a novel way to keep the protein inactive, it contains a small peptide sequence (a pseudosubstrate) that mimics the amino acids around the target phosphorylation site (with the target Ser/Thr). This binds in the active site in the inactive form of PKC like a competitive inhibitor and prevents PKC activity. PKCα is found in the cytoplasm, cell membrane, nucleus, and mitochondria. Its activation by DAG suggests it becomes localized to membrane surfaces. The protein is conformationally very flexible (note the hinge domain in Figure $13$) so it has been difficult to get detailed structures. Figure $15$ shows an interactive iCn3D model of the PKC (alpha)-C2 domain complexed with Ca2+ and PtdIns(4,5)P2 (IP3) (PDBID 3GPE). The hydrogen bonds and ion-ion interaction between the C2 domain and IP3 are detailed and labeled. Protein kinase C is targeted to the membrane (as shown in Figure $12$), but it lacks the Pleckstrin Homology (PH) which most phospholipase Cs use to interact with PIP2 in the membrane. Before activation of PKC by DAG and Ca2+, it must be phosphorylated sequentially. Before these phosphorylations, PKC loosely binds to the membrane with the activation loop open to phosphorylation by kinases. These are the subsequent steps: 1. phosphorylation of Thr500 (PKC βII)) in the activation loop of PKC by an upstream kinase PDK1 (a kinase which also phosphorylates other AGC kinases such as AKT discussed below). This kinase can bind to the exposed activation loop 2. autophosphorylation at the C-terminus of PKC, which leads to conformational positioning of side chains needed for catalysis and substrate binding, and access to the substrate binding site PKC engages in a very dynamic cycle. It starts as the inactive cytoplasmic form that is autoinhibited by its pseudosubstrate sequence. It then moves to the membrane where the autoinhibition is relieved. All of this requires flexibility which makes it difficult to determine its structure. Figure $16$ shows how the inactive cytoplasmic form of PKC becomes activated at the cell membrane. The regulatory domain, which contains the C1 (DAG binding) and C2 (Ca2+) domains binds to the membrane freeing and activating the kinase domain on the release of the bound internal pseudosubstrate. Figure $17$ the optimal consensus sequence flanking both sides of the phosphorylation site in target proteins (based on model peptides) and the sequence of the internal pseudosubstrate motif for several PKCs. Boxed amino acids show structural similarity between the target and internal pseudosubstrate. Position 0 indicates the serine that is phosphorylated in the target. Note that it is replaced with alanine in the pseudosubstrate. Also, note the abundance of positively charged side chains. Figure $16$ also shows that phosphorylation of key side chains facilitates the activation of the enzyme. The upstream kinase 3-phosphoinositide-dependent protein kinase 1 (PDPK1 or PDK1) phosphorylates the key Ser/Thr in the activation loop as we discussed above. PDK1 is a "master" Ser/Thr kinase which phosphorylates and activates many proteins, including PKA, PKC, and protein kinase B (which we will explore below). Figure $18$ shows an interactive iCn3D model of the Protein Kinase C beta II (3PFQ) The C2 domain (magenta)has two bound Ca2+ ions (gray spheres) and interacts with the bottom leaflet of the cell membrane. The C1 domain (purple) has two bound Zn2+ ions. The N lobe of the kinase domain is shown in green and the C lobe is shown in brown. A nonhydrolyzable ATP analog, AMPPNP (ANP), is shown in spacefill between the N and C lobes. There is an additional NFD helix preceding the C-terminal tail which can adopt two positions, one which confers low activity and the other high PKC activity. The Phe629 in this region is out of the active site in the low activity form and in it and interacting with the adenine of ATP in the high activity form. Some signals that activate phospholipase C and make IP3 and diacylglycerol include acetylcholine (a different class than the type located at the neuromuscular junction that we discussed in the last chapter section), angiotensin II, glutamate, histamine, oxytocin, platelet-derived growth factor, vasopressin, gonadotropin-releasing hormone, and thyrotropin-releasing hormone. In the inactive form of PKC, the arginine-rich basic autoinhibitory pseudosubstrate interacts with acidic side chains in the substrate binding site.An acidic patch in the substrate-binding site (Figure 6.2). When PKC is activated by phosphorylation of the regulatory domain, it phosphorylates arginine-rich sites in protein substrates. This also releases pseudosubstrates from some target inactive protein kinases, which allows them to become active kinases in turn. PKC physiological substrates include receptors, cytoskeleton proteins, protein kinases, proteases, and nuclear proteins The Ca2+ ions also act as second messengers. The calcium ions bind to the calcium-modulatory protein, calmodulin, which binds to and activates the calmodulin-dependent kinase (CAM-PK), which we will discuss later. Some kinases regulated by calcium and calmodulin include myosin light chain kinase, PI-3 kinase, and CAM-dependent kinases. Ca/CAM also regulates other proteins which include: adenylate cyclase (brain), Ca-dependent Na channel, cAMP phosphodiesterase, calcineurin (phosphoprotein phosphatase 2B), cAMP gated olfactory channels, NO synthase, and plasma membrane Ca/ATPase. Protein Phosphorylation by activated Receptor Tyrosine Kinases (RTKs) Figure $19$s shows the dimeric structure of RTKs driven by extracellular signal binding. Table $2$ below shows the classification of RTKs into classes and families. Table $2$:EGFR: epidermal growth factor receptor; InsR: insulin receptor; PDGFR: platelet-derived growth factor receptor; VEGFR: vascular endothelial growth factor receptor; FGFR:fibroblast growth factor receptor; CCK: colon carcinoma kinase; NGFR, nerve growth factor receptor; HGFR: hepatocyte growth factor receptor; EphR: ephrin receptor; Axl: from the Greek word anex-elekto, or uncontrolled, a Tyro3 protein tyrosine kinase; TIE:tyrosine kinase receptor in endothelial cells; RYK: receptor related to tyrosine kinases; DDR: discoidin domain receptor; Ret: rearranged during transfection; ROS: RPTK, expressed in some epithelial cell types; LTK: leukocyte tyrosine kinase; ROR: receptor orphan; MuSK: muscle-specific kinase; LMR: Lemur. Sareshma Sudhesh Dev et al. Front. Pharmacol., 15 November 2021 \| https://doi.org/10.3389/fphar.2021.772510. Creative Commons Attribution License (CC BY). We introduced the activation of RTKs in the previous section and indicated that ligand-induced dimerization led to their activation. Let's expand on that now. As in the case of PKC, the intracellular kinase domains of RTKs are inhibited by specific structures in their chains (cis-autoinhibited). These include the activation loop but in addition C-terminal sequences and the sequence region linking the C-terminal domain with the transmembrane domain. This is called the juxtamembrane region. All of these must be phosphorylated for the activation of kinase activity. The autoinhibition is released on ligand binding and dimerization. The kinase domains can also be allosterically activated by the kinase domain of the dimer. Each domain phosphorylates the cytoplasmic domain of the other, so it's called trans-phosphorylation (i.e. a kinase domain on one monomer does not autophosphorylate itself, which would be called cis-phosphorylation). The now active kinase domains recruit target proteins containing SH2 domains (which bind p-Tyr peptides/proteins) in a fashion that propagates signaling. These include proteins involved in other signaling pathways including MAPK and phosphoinositide-3-kinase (PI3K)/Akt pathways which we will discuss later. A particular phospholipase Cγ (PLC-γ) is also activated by an RTK. Cancers can arise if the RTK signaling pathways, which control cell growth and division, are overactive. Figure $20$ shows four different mechanisms for the expression and/or activation of RTKs that could lead to cancer. We will focus on the epidermal growth factor receptor (EGFR) for most of the remaining discussion. One of the most interesting questions is how ligand binding in the extracellular domain of the biotopic integral membrane proteins leads to an intracellular signal in the cytoplasmic domain. It's difficult to imagine such an activation propagating through a single transmembrane helix. We'll discuss how ligand-promoted dimerization of the receptor appears to be the main mechanism of activation of RTKs. Figure $21$ shows the domain structure of three different RTKs, including the insulin receptor (IR) family. The insulin receptor is also synthesized as a single chain but undergoes proteolysis and interchain disulfide bond formation to give the "dimeric" structure shown below. The EGFR is a member of the ErbB family which consists of 4 members: EGFR (also called Erb1 or HER1), ErbB2 (HER2), ErbB3 (HER3), and ErbB4 (HER4). The name ErbB arises from the avian ERythroBlastosis oncogene B). They are also called HER after the Human Epidermal growth factor Receptor). The HER2 gene is often dysregulated in breast cancer. Members of the ErbB family have unique numbers and positions of tyrosine in the C-terminal kinase domains. EGFR has 20, of which 12 are phosphorylated. The EGFR is also a bit unique in that it has only one tyrosine in the activation loop that is phosphorylated but the tyrosine itself is not required for kinase activity. Although we suggested earlier that RTKs are activated on dimerization, studies show that RTKs However, an increasing number of studies demonstrate that RTKs exist as inactive dimers in the absence of the ligand. As shown in Figure $21$, ErbB receptors have 4 extracellular domains, a transmembrane domain, the juxtamembrane region (about 40 residues), the cytoplasmic kinase domain and a C-terminal extension that gets autophosphorylated and which binds downstream target protein through their SH2 domains. Extracellular domains I ( L1) and III/L2 have β-helix solenoid secondary motifs that bind the ligand. Domains II/CR1 and IV/CR2 are cysteine-rich disulfide bonds. Some fraction (>80% through cross-linking studies) of the ErbB receptors exist as dimers at the cell surface in the absence of ligand, a finding in contrast to the older view that ligand binding is required for dimerization The structure of the extracellular domains of the free ErbB and ligand-bound EGFR receptors show conformational changes that are required for dimer formation. In the absence of the ligand, a "tether" arm, denoted by an open triangle in domain IV in Figure $22$, is close to a buried "dimerization" arm (asterisk ) in domain II of the extracellular regions of EGFR, ErbB3, and ErbB4, effectively inhibiting dimerization. When the ligand is bound, domains I and III interact, freeing the dimerization domains to interact (* in EGFR). These features are illustrated in Figure $22$. Figure $12$: Schematic representations of the structures of the extracellular regions of the ErbB family. EGFR, ErbB3, and ErbB4 adopt the tethered conformation in the absence of ligand, while ErbB2 adopts an extended, or untethered, conformation that resembles the ligand-activated, dimerization-competent EGFR protomer in the ligand-bound form of the EGFR dimer, shown at the right. The ‘dimerization arm’ and ‘tethering arm’ are shown by an asterisk * and an open triangle, respectively. Ligands are shown in red. Domains I–IV correspond to the domains shown in Figure $21$. Not drawn to scale. Ichiro N. Maruyama 2014 Jun; 3(2): 304–330. doi: 10.3390/cells3020304' (http://creativecommons.org/licenses/by/3.0/). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4092861/ In addition, the tether arms on both monomers now interact in the dimer. Note that the ligand binding region is not involved in dimer formation and is not in the dimer interface, as it is with other RTKs where the ligand is involved in direct contact in the dimer interface. Mutations in the II/IV domains that inhibit their interactions do not lead to receptor activation so ligand binding is still required. ErbB2 exists in an extended conformation (no ligand is known for it) so it is free to interact with another ErbB chain in a heterodimer. Structural studies now suggest that EGFR kinase dimer has a symmetrical inactive conformation in which the activation loop is packed and occluding the active site. In addition, it has an asymmetrical active one in which the activation loop is unpacked and the active site is open. How is this transmitted across the subunits? It appears that the C-lobe of the "activator/donor" kinase interacts with the N-lobe of the adjacent "receiver/acceptor" kinase which activates it through a conformational change. When the ligand binds, the inactive dimer dissociates, and the asymmetric active dimer results. Two models have been proposed for ligand-gated activation of the Erb dimers: the dimerization and rotation/twist models, as shown in Figure $23$. • A. Dimerization model: Ligand binding to the I and III extracellular domains of a monomer lead to dimerization by causing the release of the tether arm between I and III, allowing the dimerization arms to become free and interact with each other. This causes the kinases domains to adopt the active state • B. Rotation/Twist model: The receptor is already a dimer in the unliganded state with the extracellular region untethered and the intracellular kinase domains in an inactive symmetric state. Ligand binding causes the two dimerization arms to extend, causing a twist in the transmembrane segment. This causes the kinase domains to adopt the active asymmetric state with the activator kinase domain of one monomer activating the receiver kinase domain of the second monomer in the dimer, with each forming an extended conformation. Figure $24$ shows an interactive iCn3D model of the intracellular dimeric EGFR kinase domains in complex with an ATP analog-peptide conjugate (2GS6) The two EGFR kinase domains are shown in cyan and magenta. The ATP analogs in each domain (spacefill) are thiophosphoric acid O-((adenosyl-phospho)phospho_-S-acetamidyldiester. The peptide substrates (green stick) are 13-mers with a tyrosine (sticks, labeled Y, minus the OHs) connected to the ATP analog. Figure $25$ shows an interactive iCn3D model of a single EGFR kinase domain showing N and C terminal lobes in complex with an ATP analog-peptide conjugate (2GS6) The ATP part of the ATP-peptide conjugate is sandwiched between N-lobe (green) and the C-lobe (brown) just as in PKA. The ATP in the peptide conjugate is shown in spacefill while the peptide is shown in gray. The tyrosine (Y) linked to the peptide is labeled. Figure $26$ shows an interactive iCn3D model of showing the similarity in structure between the PKA kinase domain (1J3H A chain) and the EGFR kinase domain (2GS6) The gray structure by itself is the second kinase domain of the EGFR dimer. The superimposed chains are on the other side. Red indicated identical residue and blue nonconserved in the structural alignment. Zoom in on the aligned sequences in blue and red to show how similar the kinase domains are. In the next chapter section, we will explore the next downstream effects in signaling, mediated by the second messengers cAMP, DAG, and IP3 and the substrates phosphorylated by the ligand-active receptor tyrosine kinases. EGFR and HER2 in breast cancer The HER2 receptor can form homo or heterodimers with single chain ErbB1-ErbB4 (same as HER1-HER4). HER receptors exist as both monomers and dimers, either homo- or heterodimers. Rapid dimerization of HER1, HER3, or HER4 occurs if they form heterodimers with HER2. In addition, any ErbB dimer with HER2 leads to strong intracellular signaling compared to other HER heterodimers. Ligand binding to HERI, HER3, or HER4 induces rapid receptor dimerization, with a marked preference for HER2 as a dimer partner. Since noncancer cells have very little HER2, correspondingly few heterodimers of HER2 around found. If HER2 is overexpressed as in HER2+ breast cancer cells, more heterodimers are found, and anomalously-high levels of signaling occur. This resulted in a poorer prognosis for HER2+ breast cancers in the past. A revolution in breast cancer therapy has changed that situation. Humanized antibodies (human antibodies made in mice cells) to HER2, called trastuzumab (Herceptin) are now used in therapy. Since the antibodies are derived from human genes, they are not targeted as foreign by the immune system. In early-stage HER2+ breast cancers, the antibody trastuzumab (Herceptin) is administered intravenously periodically for one year. The antibody selectively binds to HER2 on breast cancer cells. Once bound, the Fc portion of the bound antibody signals the immune system, leading to the recruitment of immune cells and modulators to the tumor cell, leading to its destruction. If the cancers are in a later stage or if tumor cells are found in lymph nodes, a variant of the anti-HER2 antibody can be given in which a chemotherapeutic drug is covalently attached to the antibody. The structure of the drug Ado-trastuzumab emtansine (T-DM1), trade name Kaycyla, is shown in Figure $27$. When the antibody-drug complex binds to the receptor on cancer cells, it is taken up into the cell. The antibody is degraded and the toxic drug is released into the cell sparing all other types of cells (only ones with the receptor on it are targeted). The released drug binds to microtubules comprising part of the internal cytoskeleton of the cell and prevents changes necessary for cell division. HER2 has a very low level of expression in noncancer cells so side effects can occur. However, these are much less server than traditional chemotherapy, which targets all dividing cells. The antibody acts as a homing device bringing the attached chemotherapy predominantly to tumor cells. It can be likened to a smart bomb or a cruise missile guided to just one target. A better image of DMI and the linker connecting to the antibody is shown in Figure $28$. The toxic chemotherapy drug is chemically linked to the antibody through a non-reducible thioether linker, N-succinimidyl-4-(N-maleimidomethyl) cyclohexane-1-carboxylate (SMCC). Figure $29$ shows a mechanism for the actual cross-linking reaction. The reaction of the maytansine derivative through its free thiol and the human antibody (trastuzumab) through a free amine should be readily understandable base on the reactions present in Chapter 2. The final linker after the departure of the N-hydroxysuccinimide is designated MCC. The C-MET receptor, illustrated as an example in Figure 19, is an RTK involved in cell proliferation and survival, and as such, alterations in its expression can lead to cancer. Its mature form results from selective proteolysis by furin. Its physiological ligand is hepatocyte growth factor (HGF), a multisubunit protein in its mature form. It also binds a naturally occurring smaller splicing variant of HGF called NK1. The structure of C-MET bound to both HGF and NK1 has been solved. Binding of either lead to dimerization of the C-MET receptor, as illustrated in the cartoon representation of Figure $30$. The glycosaminoglycan heparin enhances c-MET activation by HGF. This is illustrated in Figure 30 above. Heparin binds between the N domain of HGF I and the IPT1 domain of c-MET II, facilitating the interactions between the two domains. In addition, a long enough heparin chain could bridge both HGF I and II, further strengthening the full complex. Structural representations of the c-MET:HGF asymmetric dimer are shown in cartoon form in Figure $31$. Panel (a) shows the domain structures of human c-MET and HGF. The proteolytic cleavage site of c-MET is located between Arg307 and Ser308. The proteolytic cleavage site of HGF is located between Arg494 and Val495. C-MET927-LZ and full-length HGF were used for structural determination in this study. The dash boxes indicate the domains that were unsolved in cryo-EM maps. Panel (b) shows the 3D reconstruction of the 2:2 c-MET/HGF holo-complex and the corresponding ribbon representation of this complex fitted into the cryo-EM map at 4.8 Å resolution, shown in two orthogonal views. Panel (c) shows the ribbon representation of the c-MET/HGF holo-complex shown in two orthogonal views Figure $32$: Shows the structure of the c-MET/NK1 symmetrical dimer. Nuclear RTKs What makes signal transduction so complicated yet interesting is the unexpected. It turns out that some RTKs (EGFR, VEGFR, FGFR, IR, and NGFR have been found in the nucleus. It's experimentally easy to localize proteins in cells using immunofluorescence microscopy. It's hard to determine their functions. ErbB-2 is one that is also found in the nucleus. Kinase inhibitors blocked the expression of ErbB-2 in the nucleus suggesting that its kinase activity is required for it to translocate to the nucleus. The structure of the membrane forms of ErbB-2, EGFR, and ErbB-3 appear to be the same as the structure of the nuclear forms. The carboxy-terminal ends of EGFR and ErbB-4 can activate gene transcription as measured by the expression of luciferase (a fluorescent protein) reporter proteins. Hence they appear to act as transcription factors that bind DNA to promote gene expression. For example, nuclear EGFR increases the expression of cyclin D1 which drives progression through the cell cycle. ErbB-2 appears to activate transcription from the promoter of the gene for cyclooxygenase 2 (COX-2). The protein COX-2 leads to the synthesis of inflammatory prostaglandins. It also increases blood vessel growth and is dysregulated in tumors. Since proteomic analysis of these growth factor receptors shows no DNA binding motifs or domains, they must promote gene transcription through binding to other protein transcription factors in the nucleus. Back to AGC Kinase - AKT (Protein Kinase B) We've just explored the: • activation of Protein Kinase A (an AGC Kinase) through GPCR signaling, activation of the integral membrane protein adenylyl cyclase, production of the second messenger cAMP, which binds to inactive PKA and leads to its activation • activation of Protein Kinase C, (an AGC Kinase) again through GPCR signaling, which leads to activation of the peripheral membrane protein phospholipase C, production of the second messengers DAG and IP3 from PIP2, and activation of PKC at the membrane. • activation of receptor tyrosine kinases (RTKs) leading to autophosphorylation of the cytoplasmic kinase domain, followed by recruitment of downstream signaling proteins through binding the phosphorylated RTKs through the downstream protein's SH2 domain Now let's explore another AGC kinase called AKT or protein kinase B (PKB) that links signaling through RTKs to phosphoinositol-related signaling in a fashion similar to the link between phospholipase C and Protein Kinase C activities. In the process, we will introduce in this section our first nonprotein kinase involved in signaling. It's a lipid kinase called phosphoinositide 3-kinase (P13K) and it's very important. Since Protein Kinase B is usually referred to as AKT, we will stick with that abbreviation. As with other AGC kinases, AKT is a Ser/Thr protein kinase. There are three variants, AKT1, AKT2, and AKT3. These are involved in metabolism, growth, and proliferation so they are key players in signaling. It is a key player in the uptake of glucose into cells as it regulates the translocation of the glucose transporter SLC2A4/GLUT4 to the cell surface in response to insulin. You would expect aberrant expression of these would lead to cancer. The abbreviation AKT appears to derive from "a serine/threonine protein kinase encoded by the oncogene in the transforming retrovirus isolated from the thymoma cell line AKT-8, which is derived from the Stock A Strain k AKR". As with Protein Kinase C as phospholipase C, AKT is recruited to the inner leaflet of membranes. Recruitment is mediated by its binding through its pleckstrin homology (PH) domain to phosphatidylinositol (3,4,5)-trisphosphate, a modified form of PIP2, abbreviated either as PtdIns(3,4,5)P3 or more simply as PIP3. Note that PIP2 has 3 phosphate groups while PIP3 has 4.) The mechanism of membrane recruitment is similar to that of phospholipase C, which also binds membrane PIP2 through its pleckstrin homology (PH). (This is in contrast to PKC which is recruited through its C1 and C2 domains.) PIP3 is generated in the membrane from PIP2 by the enzyme phosphoinositide 3-kinase (P13K), a lipid kinase, whose own activation occurs through stimulation of insulin and growth factors receptor tyrosine kinases (RTKs). Class 1 PI3K has a regulatory/adapter subunit (p85) and a 110 kDa catalytic subunit (p110). The regulatory subunit has SH2 domains which recruit it to autophosphorylated RTKs. Figure $33$ shows how membrane PIP2 can be converted to the second messengers DAG and IP3 by phospholipase C, or to PIP3 by the enzyme phosphoinositide 3-kinase (P13K), which is a lipid kinase. The binding of AKT (PKB) to inner leaflet PIP3 through its PH domain causes a conformational change that activates AKT for phosphorylation by phosphoinositide-dependent kinase 1 (PDK1) a membrane protein kinase. Once activated. AKT dissociates from the membrane and acts enzymatically in the cytosol and nucleus. The overall activation of AKT is shown in Figure $34$. . The blunt arrows in the figure above show inhibition by the enzymes indicated. These (PTEN, PP2A, and PHLPP 1/2) are phosphatases. • PTEN is lipid phosphatase (phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase), which removes a phosphate from the lipid PIP3. • PP2A and PHLPP 1/2 are protein phosphatase that removes phosphates added by the kinase PDK1 in the kinase domain and mTORC2 in the C-terminal domain. Figure $35$ shows an interactive iCn3D model of AKT bound to a novel allosteric inhibitor is shown below (3o96). The PH domain (at N-terminus, bind to PIP3) is shown in magenta, the N-lobe in green, and the C-lobe in brown. The activation loop is in dark blue and the catalytic loop is in red. The blue activation loop is shown in two parts as the interior part, which contains T305 (equivalent to T197 in AGC kinases without a PH domain) is too disordered to resolve. Table $1$ shows the numbering of key amino acids and features of generic AGC kinase and the corresponding numbers in AKT. They are different since AKT has an N-terminal PH domain. Table $1$ SITE Generic Akt (+108) N lobe K72 K180 N lobe E91 E199 C lobe, cat loop R165 R273 C lobe, cat loop D166 D274 C lobe, act loop D184 D292 C lobe, act loop T197 T308* missing in this structure) Approx Cat Loop 163-179 271V—287H Approx Act loop Start DFG (292-294) to APE 184-200 292-308 308Tmiss toAPE end 319 The allosteric inhibitor shown in the structure above is especially interesting in that it requires both the PH domain as well as the kinase domains for its effect. Figure $36$ shows an interactive iCn3D model of the structural overlap between the inactive form (shown above, 3o96 containing a bound allosteric inhibitor) with an active form of AKT(3cqw), which has a bound substrate (from glycogen synthase kinase-3 beta, yellow spacefill). The bound decapeptide substrate (GRPRTTSFAE) in the active form becomes phosphorylated on the Ser chain by active AKT. The N-lobe is shown in cyan, while the catalytic loop is in red and the activation loop is in blue. By pressing the "a" key you can toggle between the inactive 3o96 form and the active 3cqw forms. Note the large change in the blue activation loop. Figure $37$: below shows a series of coupled equilibria reactions that regulate the activity of AKT1. The top part of the figure shows the cytoplasmic, nonmembrane-bound form of the enzyme. The PH domain is shown in orange. The top-right figure shows the inactive N- and C-lobes of the kinase loosely interacting with an "out" (or away) conformation of the PH domain with respect to the kinase domains. In the presence of the allosteric inhibitor (green hexagon, green bound ligand), the kinase domains tightly interact with the PH domain in the "in" conformation. The bottom three structures show AKT bound to the membrane through the interaction of the PH domain with PIP3 (purple). The bottom right kinase domains are identical in representation to the two in the top part of the figure, showing that they are inactive. Only when bound to the membrane is AKT phosphorylated on Thr 308 (red in the bottom middle figure), which activates the enzyme. The bottom left and middle structures show the yellow kinase domains in a different conformation (3cqw), both of which are phosphorylated (red). The active form can bind ATP and protein substrates for phosphorylation. It can also bind ATP analogs which would competitively inhibit the active form of the enzyme by occupying the ATP binding site. The activation loop in the inhibited form is missing part of its sequence which reflects its disorder. In this state, the loops partially occludes substrate interactions. On phosphorylation of Ser 308 in the activation loop, the loop adopts a different conformation which allows less restricted access to the active. The loop itself on phosphorylation has more local ordering as it shifts away from the active site as seen in the iCn3D model above. Another change decreases inhibitory noncovalent interactions of activation loop amino acids with catalytic residues, which increases catalytic efficiency. In summary, these two types of changes in the activation loop lead to more access by substrates and enhanced catalysis of bound substrates. Additional regulation of the kinase occurs through the PH domain which adds additional conditions on Akt conformational changes and subsequent activity. The PH domain "appears to lock the kinase in an inactive conformation and the kinase domain disrupts the phospholipid binding site of the PH domain". Figure $38$ shows an interactive iCn3D model of the separate AKT Pleckstrin Homology (PH) domain bound to the inner member through just the head group of PIP3 (inositol (1,3,4,5)-tetrakisphosphate) (1unq). Waters H bonded to the ligands is not shown. Figure $38$: AKT Pleckstrin Homology (PH) domain bound to inositol (1,3,4,5)-tetrakisphosphate (1unq) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...h9LwZyV9WrJsN6
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.04%3A__The_next_step_-_Downsteam_intracellular_signaling.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Intracellular signaling from activated PKA and PKC We discussed the basics of the activation of the protein kinase A holoenzyme (R4C4) by cAMP binding to the regulatory subunit, which frees the catalytic subunit C for activity. Likewise, we discussed the activation of PKC at the cell membrane by DAG, Ca2+ ions, and phosphorylation of key Ser/Thr in the protein. Where in the cell are the downstream protein targets of activated PKA and PKC? This is a much simpler question for RTKs since downstream signaling proteins come to them through the interaction of their SH2 domains with the autophosphorylated RTKs. For activated PKA and PKC, it turns out that their location is controlled by scaffolding proteins, which bind them either before their activation or after. Let's discuss a particularly important scaffolding protein, the A-kinase-anchoring protein (AKAP). There are 13 classes of these containing 50 different members. These proteins bind PKA through its regulatory subunit, where cAMP can mediate the activation of the holoenzyme (R4C4). In addition, AKAPs can bind other signaling proteins including PKC and phosphatases, the latter of which in turn counter-regulate signaling by phosphoproteins. For example, the bound phosphatases can dephosphorylate PKC to deactivate it as well as other downstream phosphoproteins. AKAPs can also bind phosphodiesterase, the enzyme that converts cAMP to AMP, returning signaling to baseline levels. AKAPs localize key signaling enzymes to sites where biologically appropriate protein targets are localized. In addition, they decrease indiscriminate phosphorylation of other off-target proteins elsewhere in the cell. They may also allosterically regulate the activity of bound signaling proteins. There are at least 50 A-kinase anchoring proteins or A-kinase anchor proteins (AKAPs) that bind the regulatory subunit of protein kinase A (PKA) and localize PKA to specific sites in the cell. By binding multiple signaling enzymes at specific sites, they integrate signaling pathways mediated by cAMP (for example) with others mediated by PKC (again for example). Here are some examples of AKAPs in humans (from UniProt). Note that one (12) also binds PKC • 1, mitochondrial: Binds to type I and II regulatory subunits of protein kinase A and anchors them to the cytoplasmic face of the mitochondrial outer membrane; • 6: Binds to type II regulatory subunits of protein kinase A and anchors/targets them to the nuclear membrane or sarcoplasmic reticulum; • 7 isoforms alpha and beta: Targets the cAMP-dependent protein kinase (PKA) to the plasma membrane, and permits functional coupling to the L-type calcium channel; • 7 isoform gamma: targets cAMP-dependent protein kinase (PKA) to the cellular membrane or cytoskeletal structures; • 8: Acts as an anchor for a PKA-signaling complex onto mitotic chromosomes, which is required for the maintenance of chromosomes in a condensed form throughout mitosis; • 8-like: Required for cell cycle G2/M transition and histone deacetylation during mitosis and recruitment of signaling enzymes into the nucleus; • 9: assembles several protein kinases and phosphatases on the centrosome and Golgi apparatus; • 12: Anchoring protein that mediates the subcellular compartmentation of protein kinase A (PKA) and protein kinase C (PKC) • 17A: Splice factor regulating alternative splice site selection for certain mRNA precursors. Mediates the regulation of pre-mRNA splicing in a PKA-dependent manner. Figure $1$ illustrates the localization/binding of signaling enzyme (PKA, PKA substrates, PDE, other kinases) to AKAPs. Note that some AKAPs can also bind PKA substrates, facilitating their phosphorylation and minimizing the phosphorylation of the wrong targets. AKAPs use an amphiphilic helix to interact with the R2 regulatory dimer of the PKA. Some AKAPs bind to just one of the regulatory subunits. Note that some AKAPs can also bind PKA substrates, facilitating their phosphorylation and minimizing the phosphorylation of the wrong targets. Figure $2$ shows specific AKAP complexes in the heart that could be targeted for drug therapies. Panel (A): Disruption of the AKAP18γ/δ-PLB (another phospholipase involved in signaling) interaction prevents PLB phosphorylation on Ser16 and dislocation from SERCA2 (Sarcoplasmic/endoplasmic reticulum calcium ATPase 2). This inhibits SERCA2 activation and consequently Ca2+ uptake into the sarcoplasmic reticulum Panel (B): Disruption of the nesprin-1α /mAKAP interaction promotes AKAP/PKA complex dissociation from the perinuclear membrane and might be a strategy to reduce hypertrophy. Nesprin 1 is a protein that forms a linking network between organelles and the actin cytoskeleton to maintain the subcellular spatial organization. Panel (C): Disruption of the connexin 43-ezrin interaction could prevent PKA-mediated phosphorylation increasing inter-cardiomyocyte conductivity which could be cardioprotective following myocardial infarction damage. Connexin is a gap junction protein. Ezrin is involved in the connections of major cytoskeletal structures to the plasma membrane. To add to the complexity of PKA activation and signaling, there are different forms of the regulatory subunits of the holoenzyme PKA. These include RIalpha (RIA), RIbeta (RIB), RIIalpha (RIIA), and RIIbeta (RIIB). They have different affinities for cAMP, the catalytic subunits Cs, and different AKAPs. Figure $3$ shows an interactive iCn3D model of the amphiphilic anchoring peptide AKAP-IS for AKAP binding to the docking and dimerization (D/D) domain of the RIIalpha regulatory subunit of PKA (2IZX) The brown represents the RII dimer D/D domains of the regulatory subunit. The anchoring peptide AKAP-IS is shown in gray. In both, the side chains involved in binding of the peptide to the regulatory subunit domains are shown as sticks and colored coded based on the hydrophobicity of the side chains. Green indicates the most hydrophobic. Rotate the model carefully to differentiate the side chains and note that the hydrophobic face of the peptide is interacting with hydrophobic side chains projecting into a groove made by the two RII dimer D/D domains. Polar side chains in AKAP help target the correct isoform of the R subunit. In addition to binding to some AKAP scaffolds, PKC also binds to Receptors for Activated C-Kinases (RACKs), heat shock proteins (HSP), importing, and annexins (AnxA1, A2, A5, and A6). The interactions of activated PKC with RACK1 and downstream events are shown in Figure $4$. The insect protein BR-C (Broad Complex) has a DNA binding domain (two zinc fingers) domain for the activation of gene transcription and a BTB) domain that allows binding to RACK1. On binding the PKC:RACK1 complex, BR-C is phosphorylated at Ser373 and Thr406, after which it is translocated into the nucleus where it binds DNA and activates gene transcription The binding of PKC to RACK1 stabilizes PKC for the phosphorylation of targets. PKC binds to RACK through its C2 regulatory domains. Binding may be to specific forms of PKC including unphosphorylated, inactive, and activated phosphorylated forms, as well as to specific isozymes of PKC. RACK1 may also recruit PKC to the ribosome and it inhibits the activity of SRC kinases which we will discuss later. PKC activity occurs in many cellular locations, including the cell membrane, nucleus, Golgi apparatus, mitochondria, and cytosol. RACKS also bind and recruit other signaling proteins including PLCγ, Src, and integrins. In addition to interactions of PKC with RACK through the C2 domain, PKC can localize through its C1 domain. Structure of RACK1 RACK1 (317 amino acids) has a very interesting structure. It is a member of a family of over 100 proteins that have tryptophan-aspartate (WD) repeats that are 44-60 amino acids and ending in WD or a variant. It is homologous to the beta subunit of the heterodimeric Gαβγ signaling protein. RACK 1 interacts with proteins through a 7-bladed propeller structure that allow the binding of proteins with SH2 domains, plextrin homology (PH) domains, and C2 domains (PKCs). Figure $5$ shows the WD repeats in human RACK1. Note that the N-terminal end of the WD repeat often is glycine-histidine (GH). Figure $6$ a model for the interaction of RACK1 and PKC-βII. Figure $6$: Model for PKC-βII and RACK1 interaction. Adams et al. Cell Communication and Signaling 2011, 9:22 http://www.biosignaling.com/content/9/1/22. Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0) Panel (A) shows the resting state with no interaction between RACK1 and inactive PKCbII. Panel (B) shows how activation of PKC-βII leads to its interaction with RACK1. Substrate binding and phosphorylation follow. R is a receptor and L is its ligand. Figure $7$ shows how RACK1 can translate into the nucleus after ligand (L) gated activation of GPCRs (R) through adenylyl cyclase production of cAM and activation of Protein Kinase A. Figure $7$Model for cAMP/PKA-mediated nuclear translocation of RACK1. Adams et al. Cell Communication and Signaling 2011, 9:22 http://www.biosignaling.com/content/9/1/22. Creative Commons Attribution License. (http://creativecommons.org/licenses/by/2.0) Panel (A) shows the resting state of RACK, which forms homodimers and heterodimers with the homologous Gβ subunit of the Gαβγ complex. Panel (B) shows how activation of PKA leads to dissociation of RACK which then can translate into the nucleus, where it leads to increased transcription of brain-derived neurotrophic factor (BDNF). Figure $8$ shows an interactive iCn3D model of a the human Rack1 (4AOW) color coded as in Figure $5$. Downstream signaling from activated receptor tyrosine kinases To review once again, when receptor tyrosine kinases get activated by binding a primary messenger such as a growth factor, the receptors dimerize, activating their cytoplasmic kinase domains. The activated RTK then autophosphorylates itself. The phosphorylated intracellular domain provides a docking site for other cellular "adaptor" proteins that contain an SH2 domain. The bound adaptor protein binds other signaling molecules within the cell leading to downstream propagation of the signal. Figure $8$ shows some RTKs and downstream signaling events. We have already discussed the activation of the lipid kinase phosphoinositide 3-kinase (P13K) which leads to the activation of Akt (PKB). In the rest of this section, we will focus on the next step after the activation of RTKs. We focus on the epidermal growth factor receptor (EGFR, ErbB1) again. 1. Downstream signaling from the epidermal growth factor receptor. Figure $9$ shows events associated with the activation of EGFR. Once bound and activated by binding of growth factors protein signals, the intracellular domains of the now dimeric EGFR autophosphorylates themselves on selected tyrosine side chains. This then recruits a protein called Growth factor receptor-bound protein 2 (GRB2), which has an SH2 (Sarc Homology 2) domain that binds phosphotyrosine motifs in proteins. GRB2 acts as an adaptor protein in that in addition to the SH2 domain, it has two SH3 (Sarc Homology 3) domains that bind proline-rich domains on other signaling proteins, including the protein Son of sevenless homolog (SOS). GRB2 does not have enzymatic activity. The adaptor protein GRB2 binds through its SH3 domain to the protein SOS, which then interacts with the protein Ras. This last member in the recruited complex is named because it causes Rat Sarcomas. There are many variants of these proteins but three are key in humans, HRas, KRas, and NRas. Ras is perhaps the key member of the family of intracellular small G proteins that bind GDP/GTP and are activated on the exchange of GTP for GDP. The proteins also have intrinsic GTPase activity as is found in the Gα protein of the heterotrimeric Gαβγ protein. So what does SOS do? The SOS bound to RAS facilitates the exchange of GTP for GDP on Ras, maintaining it in an active state. SOS is a member of another fascinating class of small proteins that catalyze the exchange of GTP for GDP. The family of GTP/GDP exchange proteins is called Guanine nucleotide Exchange Factors (GEFs). We will explore this in the next section. The EGFR -GRB2-SOS-Ras complex in the figure above looks somewhat similar to the structure of a GPCR-heterotrimeric G protein GαGβGγ complex, where Gα is also a GTP/GDP exchange protein with intrinsic GTPase activity. When the ligand binds to the GPCR, a conformational change ensues which facilitates the exchange of GTP for GDP on the Gα protein, activating intracellular signaling. Once RAS is activated (bound to GTP), it binds and activates key kinases in the cell, including the lipid kinase PI3K and RAF, a kinase shown in Figure $8$. Control of RAS activity is critical in signaling. It is one of the most commonly mutated proteins in cancer cells. Mutations that inhibit the intrinsic GTPase activity keep the protein active, leading to unregulated cell growth, proliferation, and differentiation, hallmarks of cancer cells. The domain structures of GRB2 and SOS are shown in Figure $10$. The proline-rich domain (motif) is not shown in the figure. GRB2 domain structure (green SH3 domains) SOS1 domain structure Figure $10$: Domain structure of GRB2 and SOS1 (proline rich domain not shown in SOS) Note As we discussed in Chapter xx (domains), new genes encoding proteins with new functionalities can be created by duplicating and adding gene segments for different domains in a preexisting gene. As we have seen with the SH2 and SH3 domains, signaling proteins often share common domains. Table $1$ below, adapted from the excellent book Cell Signaling by Lim, Mayer, and Pawson, shows some common domains found in signaling proteins. Domains in Signaling Molecules Domain Binding Target Cellular Process Example protein Pdb file (examples) Find your own in the PDB Bromo Acetyl-Lys Chromatin reg. BRD4 2YYN C1 diacylglycerol Plasma membrane recruitment Raf-1 3OMV C2 Phospholipid (Ca-dependent) Membrane targeting, vesicle trafficking PRKCA 3IW4 CARD Homotypic Interactions apoptosis CRADD 3CRD Chromo Methyl-Lys Chromo reg, gene transcription CBX1 3F2U Death (DD) Homotypic inter. Apoptosis Fas 3EZQ DED Homotypic inter. Apoptosis Caspase 8 1F9E DEP Memb, GPCRs Sig trans, protein trafficking Dsh human disheveled 2 2REY GRIP Arf/Art G prot Golgi traffic Golgin-97 (Golga5) 1R4A PDZ C-term peptide motifs Diverse, scaffolding PSD-95 Or discs large homolog 4 1L6O PH Phospholipids Membrane recruirtment Akt 1O6L 3CQW PTB Phospho-Y Y kinase signaling Shc 1 SHC-transforming protein 1 1UEF 1irs RGS GTP binding pocket of Galpha Sig trans RGS4 1EZT SH2 Phospho-Y pY-signaling Src 4U5W SH3 Pro-rich sequence Diverse, cytoskeleton Src 2PTK TIR Homo/Heterotypic Cytokine and immune TLR4 3VQ2 TRAF TNF signaling Cell survival TRAF-1 3ZJB VHL hydroxyPro ubiquitinylation VHL 1VCB Figure $11$ shows an interactive iCn3D model of the GRB2-SH2 domain in complex with a high affinity phosphopeptide KPFpYVNVEF (1BMB) Grb2 exists in both a monomeric state which can bind SOS, and a dimeric state which can't. You would expect the equilibrium between the monomer and dimeric form would be highly regulated. When a phospho-tyrosine ligand is bound to Grb2 through its SH2 domain, the dimer dissociated. This also occurs on phosphorylation of tyrosine 160 (Y160) on Grb2, a post-translational modification found in human prostate, colon, and breast cancers. Figure $12$ shows an interactive iCn3D model of the GRB2 N-terminal SH3 domain complexed with a ten-residue proline-rich peptide (1135 Ac-VPPPVPPRRR-NH2) derived from SOS (1GBQ) We will explore interactive structures of the small G protein Ras with the guanine nucleotide exchange factor SOS in the next chapter section. After activation of Ras through GTP exchange for GDP, the GTP-Ras protein binds to and activates the kinase Raf. We will continue our exploration of that later in this section. 2. Downstream signaling from the insulin receptor. When insulin binds to the receptor tyrosine kinase (RTK), it phosphorylates itself, which then leads to the binding of other proteins to the activated receptor and their phosphorylation. These lead to more intracellular signaling and ultimately changes in gene transcription. We'll focus on a specific adaptor protein, the Insulin Receptor Substrate 1, IRS1, a "scaffolding protein", which leads to the movement of the glucose transport protein GLUT4 to the cell surface, allowing glucose uptake. These activities are shown schematically in Figure $13$. Human IRS1 has two domains, a PH domain for binding to membranes through phosphorylated IP2 derivatives and an IRS/PTB domain which binds phosphotyrosines on proteins. The PTB and the SH2 domains are the most common domains for binding phosphotyrosines on proteins. PTB binds the NPXY, where X is a pTyr. We show a more detailed view in Figure $14$, in part, to review many of the signaling proteins we have seen before, including PI3K and PDK1. Likewise, another review of PIP2 derivatives is warranted. After phosphorylation by the activated insulin receptor protein tyrosine kinase, IRS-1 binds phosphatidylinositol 3-kinase (PI3K) that causes phosphorylation of the 3'OH on phosphatidyl inositol (PI) in the inner leaflet of the membrane to form PI(3)P. PI3K is a member of a family of kinases that phosphorylates PIP2. The metabolic pathway centered on pI3K is one of the most mutated in human cancers. PI(3)P in turn recruits to the membrane other inactive kinases, phosphoinositide-dependent kinase 1, PDK1, and Akt, also known as PKB. Figure $15$ shows phosphorylated phosphatidylinositol derivatives. On binding of PI(3)P, PDK1 becomes an active kinase, which phosphorylates and activates Akt. Akt kinases are major Ser/Thr protein kinase that phosphorylates proteins involved in a host of cell activities, including regulation of glucose transport, cell proliferation, and death. In the insulin signaling pathway, active (phosphorylated) Akt leads to the movement of the GLUT4 protein from intracellular endosomal vesicles to the cell surface, which offers a quicker way to import glucose into the cell than if Akt activated GLUT 4 gene expression. PDK1 phosphorylation of Akt2-T309 is required for insulin-stimulated Glut4 translocation. If T309 is mutated to A309 or if PDK1 is inhibited, GLUT 4 is not translocated to the cell membrane. Figure $16$ shows an interactive iCn3D model of the activated insulin receptor tyrosine kinase in complex with peptide substrate and ATP analog (1IR3)) The dimeric form of the kinase is shown. ANP is shown in spacefill. The peptide substrate containing the interior tyrosine (stick) for phosphorylation is shown in blue. Three key tyrosines in the activation loop that are autophosphorylated (Tyr1158, Tyr1162, and Tyr1163) are shown and labeled in the right-hand monomer. As with a protein kinase, the activation loop of the insulin receptor kinase domain is phosphorylated and the resulting conformational change allows ATP and target protein access. The activation loop gets phosphorylated on Tyr1158, Tyr1162, and Tyr1163 with Y1163 being key. Downstream signaling from Cytokine activated Receptors- The JAK/STAT pathway. Now we explore two signaling pathways mediated by the adaptor protein JAK and STAT. These are abbreviations for the Janus Kinase (JAK) and the Signal Transducer and Activator of Transcription (STAT). These play key roles in embryonic development, stem cell maintenance, hematopoiesis (formation of blood cells), and immune cell signaling. . This pathway is stimulated by cytokines and interleukins, protein modulators released by immune cells, as well as growth factors. In general, there are five groups of cytokines: • tumor necrosis factor alpha (TNF-alpha) • Interleukin-1 family members (IL-1_ • Transforming growth factor-beta (TGF-bet) • those that signal through RTKs (such as macrophage colony-stimulating factor (M-CSF) • Chemokines • cytokines that signal through JAK/STAT In contrast to RTKs, which have kinase domains activated on receptor dimerization, cytokine receptors that work through JAK/STAT do NOT have kinase domains. On cytokine binding to their target cytokine receptor, the now-activated receptors activate the prebound inactive Janus kinase through conformational changes. The kinase domains autophosphorylate themselves in a trans fashion. The active kinase then phosphorylates the cytoplasmic tails of the cytokine receptors. This trigger further signal transduction reactions mediated by the binding of target signaling proteins to the cytoplasmic region of the phosphorylated cytokine receptor. Just to reiterate, the cytokine receptor is NOT a RTK with latent kinase activity. Instead, it becomes phosphorylated by the bound and cytokine-activated JAK. A portion of the pathway is illustrated in Figure $17$. Panel (A1) shows that the kinase JAK is bound constitutively in an inactive state to the cytokine receptor cytoplasmic region, not through its SH2 domain, but through its FERM domain (Panel B). The figure implies the cytokine receptor is dimeric in the absence of a ligand. On cytokine binding, conformational changes and repositioning of the bound JAK activates its kinase domain, which phosphorylated the C-terminal tails of the cytokine receptor. STAT monomers, through their SH2 domains, bind to the phosphorylated cytokine receptor where they get phosphorylated by the active JAK. The phospho-STAT monomers form a dimer, dissociate from the complex, and translocate to the nucleus where they act as transcription factors. the Janus kinase is named after Janus, the two-faced Roman god of beginnings, endings, and duality since JAK has two nearly identical JH kinase domains. One has kinase activity while the other inhibits the first. Panels (B) shows the domain structure of JAK. The FERM domain facilitates JAK:cytokine receptor binding. The JH2 pseudokinase domain regulates the kinase activity of the JH1 kinase domain. Ps show site on JAK necessary for activation Panel (C) shows the domain structure of STAT. The SH2 domain binds phosphorylated tyrosines. The carboxy terminus transactivation domain is required for full transcriptional activation. P marks the conserved tyrosine residue whose phosphorylation is essential for STAT activation. The pseudokinase domain of JAK interacts with the kinase domain on the same chain and prevents its activity in the inactive monomer and dimer. Cytokine binding to the cytokine receptor induces a conformational change that promotes the interactions of the pseudokinase domain on one JAK monomer with the same domain on another, promoting dimerization and freeing the kinase domains for activity. Figure $18$ shows the pseudokinase domain (orange) interactions in the active JAK dimer. Figure $18$: The pseudokinase domain (orange) interactions in the active JAK dimer. The kinase domain of each dimer is shown in red and the pseudokinase domain is shown in orange. The activation loop (cyan) and ADP (spacefill, CPK colors) are shown in the red kinase domains. The orange pseudokinase domain has a bound adenosine (not ADP) that is shown spacefill and CPK color. However, it lacks the DFG motif required for catalysis. The phenylalanine cluster (F635, 657, 574) are labeled. The structure is actually for a mutant that has the V657F mutation that promotes dimerization and JAK activity. Hence the V657F mutation is oncogenic. JAK activity can be inhibited by the protein suppressor of cytokine signaling (SOCS). Transcription of the protein is activated by STAT, and the SOCS protein in a feedback inhibition loop binds to JAKs and also to IFNGR1, which inhibit JAK activity. Figure $19$ shows an interactive iCn3D model of the structure of the inhibitory protein SOCS1 in complex with JAK1 kinase domain (6C7Y) The N-lobe of the JAK1 kinase domain is shown in cyan and the C-lobe in magenta. ADP (sticks) and Mg2+ (green) are shown in the interface between the lobes. SOCS1 is shown in gray except for the kinase inhibitor region which is shown as spheres and CPK colors. It binds in the substrate binding grove and prevents substrate access. Activated JAK activity and signaling do not stop simply with the activation of STAT. In addition to stimulating signaling through phosphorylated dimers of STAT, cytokines also activate other signaling pathways through the same receptors. Examples include the PI3K pathway described in an earlier section and also the MAPK pathways, described in detail later in this section. Both the PI3K and MAPK pathways are activated by binding the cytokine IL6 to its cytokine receptor. The mechanism for PI3K activation is a bit unclear. The MAPK pathway is activated by a phosphatase called SHP2 for Src homology region 2 domain‐containing phosphatase 1. This protein binds to pTyr759 on the IL-6 receptor. As binding of the cytokine activates the prebound JAK, it also activates SHP2, ultimately activating signaling through Ras (a small G protein), which activates RAF, a kinase. Figure $20$ shows these three combined signaling pathways for a cytokine receptor: STAT, Ras, and PI3K activations. The cytokine receptor induces activation of JAKs after cytokine stimulation following the phosphorylation of STATs. Furthermore, phosphorylated STATs undergo dimerization and translocate to the nucleus to activate target gene transcription. SOCS, suppressors of cytokine signaling; PI3K, phosphatidyl inositol 3 kinase; Akt, protein kinase B; FOXO, Forkhead box protein O; mTOR, mammalian Target Of Rapamycin. Figure $21$ shows an interactive iCn3D model of the Crystal structure of a tyrosine phosphorylated STAT-1 dimer bound to DNA (1BF5) The dimer chains are shown in brown and gold except for those colored-coded by secondary structure (helix red, sheet yellow). The backbone of the nucleotides is shown in spacefill cyan and gray. Zoom in to see noncovalent interactions between the bound DNA and protein monomers. Two phospho-tyrosines labeled pTR701 are shown as well. The DNA binding domain of the STAT dimer has an immunoglobulin fold and forms a "C-shaped clamp" around the DNA. The domains colored by secondary structure are SH2 domains, with each recognizing and binding to the phosphorylated Tyr 701 (labeled pTR701), a very interesting use of the SH2 domain. Figure $22$ shows an interactive iCn3D model of the active Janus Kinase (JAK) dimer complexed with the intracellular domains (spacefill) of the interferon lambda(a cytokine) receptor (7T6F). Figure $22$: Active Janus Kinase (JAK) dimer complexed with the intracellular domain of the interferon lambda(a cytokine) receptor (7T6F). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...9s3VBNMxsk7WM7 Domain/protein Coloring • Red: PTKc-JAK-rpt2 kinase catalytic domain • Orange: PTK-JAK1-rpt1 pseudokinase domain • Yellow: FERM F2 • Magenta: FERM C-JAK1 • Cyan: SH2 • Brown: FERM F1 • Navy Blue and Blue spacefill: cytoplasmic domains of the cytokine receptor interferon lambda receptor 1 dimer • The gold large sphere represents interferon bound to the extracellular domain of the interferon lambda receptor 1 dimer Figure $23$ shows a model combining the active Janus kinase (JAK) containing the intracellular domain of the interferon alpha receptor 1 dimer (navy and blue spacefill) with AlphaFold models of the extracellular and transmembrane domain of the interferon-alpha receptor ((Q8IU57)) Figure $23$: AlphaFold model of Interferon lambda receptor 1 Extracellular Domain and transmembrane domain (Q8IU57) with Active Janus Kinase (JAK) dimer complexed with cytokine receptor intracellular domain (7T6F) Again, to reiterate as we did above, the cytokine receptor (in the figure above the interferon lambda receptor) is NOT a RTK with latent kinase activity. Instead, it becomes phosphorylated by the bound and cytokine-activated JAK which is resident in the cytoplasm. Here is a link to a second iCn3D model which shows the phenylalanine cluster that promotes pseudokinase domain interactions in the active JAK dimer. Downstream Intracellular signaling through Src Family Kinases - Membrane-associated non-receptor tyrosine protein kinases Another family of intracellular protein kinases - the Src family - are often activated on upstream activation of many different types of receptors including GPCRs, RTKs, cytokine receptors, as well as integrins and adhesion receptors that we explore in a later chapter section. We have already touched on them when we discussed proteins containing the src homology domains SH2 and SH3. Src, an intracellular Tyr kinase (MW 60,000), is the founding member of this family of protein kinases. Src is activated when it binds through its own SH2 domain to a phosphorylated membrane receptor. Src has many names including Proto-oncogene tyrosine-protein kinase Src, proto-oncogene c-Src, pp60c-src, and p60-Sr. These membrane-associated, non-receptor tyrosine kinases regulate cell proliferation, differentiation, apoptosis, migration, metabolism, and cytoskeleton organization. They are one of the major classes of intracellular kinases which are activated after upstream activation of the membrane receptors mentioned above (GPCRs, RTKs, cytokine receptors, as well as integrins and adhesion receptors). They in turn activate further downstream protein kinases by phosphorylation. They even phosphorylate the upstream membranes which led to their activation. There are 10 members in the Src family: Src, Frk, Lck, Lyn, Blk, Hck, Fyn, Yrk, Fgr, and Yes. They all share the same expanded domain structure shown in Figure $24$. They have an SH4 N-terminal region that can be post-translationally modified with fatty acids (myristoylation and palmitoylation), which can anchor it to the membrane. The spacer SH2-SH3 spacer is intrinsically disordered and differs in sequence among members of the Src family. Two key phosphorylation sites (Tyr416 and Tyr527) are important. In inactive Src, Try 527 is phosphorylated, allowing it to bind to the SH2 domain. The SH2-kinase linker also binds to the SH3 domain. This occludes the active site region and prevents the phosphorylation of Try 416 in the "activation loop" of the SH1 kinase domain. When Try 527 is dephosphorylated, a conformation change ensues the opens the binding site allowing autophosphorylation of Tyr 416 and its activation. Hsu et al, Cancers 12(6):1361. DOI: 10.3390/cancers12061361. CC BY 4.0 Figure $25$ shows an interactive iCn3D model of the Human tyrosine-protein kinase C-Src in complex with AMP-PNP (2SRC) pY527 is shown in stick and labeled. It binds to the SH2 domain (151-248) shown in cyan. The SH3 domain is in magenta and the kinase domain is shown in gray. The activation loop in the kinase domain is shown in red with the pY416 needed for activation shown in stick and labeled. ANP is shown in spacefill. We have now seen the structure of many kinase domains. Figure $26$ shows an interactive iCn3D model of the alignment of human c-Src (452 amino acids) (2SRC) and Erk2 (362 amino acids) also called MAPK1 (2Y9Q), a protein kinase will be explored at the end of this section Red shown conserved sequences, blue is aligned (but not conserved), and gray is nonaligned. Use the "a" key to toggle between the states. After much discussion of the binding of p-Tyrosine side chains to the SH2 domain, we now present Figure $27$, which shows an interactive iCn3D model of a phosphotyrosine peptide bound to the SH2 domain of Fer tyrosine kinase (6KC4) The SH2 domain is shown in gray. The phosphopeptide (DEpYENVD) is shown in cyan with the labeled pY in sticks. The side chains on the SH2 domains interacting with the pY are shown in stick and labeled. We are about to explore the last but incredibly important downstream kinases activated in signal transduction cascades, the mitogen activate protein kinase (MAPK) cascade. It could also be called the Erk cascade. Before we do that, we present parts of three pathways mediated by activated RTKs to refresh your minds and also because the more you see key players in the pathway, the more you start to remember the names, structural features, and function of the signaling molecules. Figure $28$ offers a quick and abbreviated look at signaling through activated RTKs that proceed through the adaptor protein and the GEF SOS, leading to the activation of Ras, a key small G protein. Ras in turn activates a protein (MAPKKK) in the MAPK cascade. Figure $28$: Simplified cartoon showing the activation of the MAPK cascade protein MAPKKK. Cordover E, Minden A. Signaling pathways downstream to receptor tyrosine kinases: targets for cancer treatment. J Cancer Metastasis Treat 2020;6:45. http://dx.doi.org/10.20517/2394-4722.2020.101. Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), Figure $29$ shows an abbreviated version of the activation of the lipid kinase PI3K and through the activation of PDK1 and AKT, the activation of two major kinase complexes, mTORC1, and mTORC2, which we will explore in its a separate chapter section. Finally, Figure $30$ show the combined activation of both the MAPK (ERK) cascade pathway and the mTOR complex through GPCR signaling using the adaptor protein Grb2. Downstream signaling through the Mitogen-Activated Protein Kinase Cascade Active upstream kinases like PKA, PKC, and RTKs phosphorylate target proteins and in doing so change their activities. The usual protein targets are kinases, which become active on phosphorylation. They in turn activate other kinases, resulting in a complex cascade and amplification of the original signal. Often the end product of such cascade is a phosphorylated transcription factor that can alter gene expression. Perhaps the most described of these cascades is the Mitogen-Activated Protein Kinase (MAPK) pathway. Mitogens are chemical species that lead to mitosis (cell division). The MAPK system has three layers and a confusing nomenclature (until you are used to it). The end (downstream) product of the cascade is the enzyme mitogen-activated protein kinase, abbreviated MAPK. It can be phosphorylated several times to produce MAPKP or MAPKPP where the last Ps in the abbreviation signifies the number of added phosphate groups. The kinase immediately upstream that phosphorylates MAPK is abbreviated MAPKK (for mitogen-activated protein kinase kinase) or MAP2K. MAPKK (MAP2K) is activated by yet another upstream kinase called MAPKKK or MAP3K. If these are also targets of another upstream unrelated kinase, they would be abbreviated MAP3KPP for example. Now, of course, there are families of the MAPK cascade enzyme, each with its own name. Figure $31$ shows the names of four different mammalian MAP3Ks leading to the activation of 5 different MAP2Ks which lead to the activation of 4 different MAPKs. Some of these enzymes are so widely discussed in textbooks and journal articles that it is good to remember them specifically with their alternative names. These include the MAP3K enzymes Raf (Rapidly Accelerated Fibrosarcoma) and MEK, and the MAPK enzymes ERK (Extracellular Related Kinase) and JNK (c-Jun N-terminal Kinase) Figure $32$ shows another representation of the MAPK cascade with some different enzyme names and added phosphates shown in circles. Figure $32$: Another representation of the MAPK cascade. Journal of Cardiovascular Development and Disease 6(3):27. 2019. 10.3390/jcdd6030027. CC BY 4.0 One way to organize a seminar on a complex topic is to use these 3 steps: tell your audience what you will tell them, tell them, and then tell them what you told them. Following that advice, we present in Figure $33$ an integrated view of signaling, starting from the membrane and moving inward to three enzymes in the MAPK kinase cascade, RAF (a MAP3K), MEK (a MAP2K) and ERK (a MAPK). Upstream signaling to the MAPK cascade comes RTKs, GPCRs, and Ca2+ signaling, which we will discuss later. The sequence of events is: • binding of an external signal to membrane receptor and activation of receptor kinase • phosphorylation of receptor kinase and interaction with an activator GTP binding protein such as Ras • binding of activated G-protein to and activation of a mitogen-activated protein kinase kinase kinase (MAPKKK) • MKKK phosphorylates and activates another kinase, MAPKK • MKK phosphorylates and activates mitogen-activated protein kinase, MAPK • MAPK phosphorylates inactive transcription factors (or other proteins) and activates them. Unfortunately (from a naming point of view) when the activated proteins are themselves protein kinases, they are called mitogen-activated protein kinase activated protein kinases (MAPKAPK) There are seven types of MAPKs, four conventional and three atypical. Four typical ones are described in the table below. Activator GTP binding protein Ras:GTP MAPKKK or MAPK3 Raf-1A/B c-Mos MEKK1-4 DLK MLK2 MEKK1-4 DLK MLK2 MEKK2/3 Tpl-2 MAPKK or MAPK2 MEK1,2 MEK4,7 MEK3,6 MEK5 MAPK or MAK ERK1,2 JNK1-3 p38 ERK5 MAPKAPK RSK 1-4 MNK2 MSK 1,2 MK2,3 MSK1,2 MK2,3 RSK1-4 An eventual Protein Target c-Jun c-Jun MAP Kinase System from Cell Signaling Structural, these proteins are similar in size and domain structure as we have seen for other kinases. Figure $34$ shows an interactive iCn3D model of the alignment of MEK 1 (4U7Z) and ERK2 (5NHJ) We now present multiple visual images of the activation of the MAPK cascade. Figure $35$ shows two. Details: Figure $35$: Two visual representations of the activation of the MAPK cascade through to activation of gene transcription. As with protein kinase A and protein kinase C, signaling efficiency and specificity with minimal phosphorylation of wrong targets occurs when multiple signaling partners are scaffolded. This is also true of the MAPK cascade trio of the kinase. Figure $36$ shows the role of scaffolds KSR and Ste5 in MAPK signaling. Computational Analyses - MAPK Cascade You might think that the interconnected reactions of the MAPK cascade are complicated. However, as presented in the figures above, it only consists of 3 enzymes, MAPKKK (MAPK3 or MK3), MAPKK (MAPK2 or MK2), and MAPK. We added complexity by converting the actual enzymes from an inactive state to an active state by phosphorylation. In reality, this pathway is simple compared to the complete signal transduction pathways they are part of, and very simple compared to whole catabolic and anabolic pathways that we will see in Part Two of this book. We have discussed kinetics in earlier chapters and have shown how it can be used to more fully understand an enzyme and its regulation/control. We need to extend such kinetic analyses to whole pathways as well. We can do that using the VCell. Let's look at two different models of the MAPK cascade using Vcell. One particularly interesting feature is the regulation of the pathway. We will see in the next section on metabolism that pathways are often regulated by the end product of the pathway. This makes sense since if that end product is abundant, it would make biological sense for that product to inhibit the first enzyme in the pathway to avoid making more of the ultimate end product. Of course, that inhibition would be relieved as the concentration of the end product falls. Hence there is a temporal sense to the inhibition. Let's look run two Vcell models for the MAPK Cascade, one with no end product inhibition and one with inhibition of the first step. Since we dealing with linked kinases, the inhibition of the first enzyme (MAP3K=MKKK) and hence the first reaction (MKKK ↔ MKKK_P) is not mediated by a chemical product of the last enzyme (MAPK_PP) but by phosphorylation of the first enzyme (MKKK) by the last (MAPK_PP). MAPK Cascade - Model 1: No feedback inhibition of the MAPK cascade by inhibition of the first step (MKKK ↔MKKK_P) by the "end product" of the cascade (MAPK__PP) try quick MODEL MAPK Cascade - no feedback inhibition Initial Condition - See simulation results Select Load [model name] below Interactive Element Select Plot to change Y axis min/max, then Reset and Play \| Select Slider to change which constants are displayed. For this model, select Vm, Km, Ki and I \| Select About for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health. Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington. Funded by NIH/NIGMS (RO1-GM123032-04) The various concentration vs time curves in the output graph should make "intuitive" sense. There are no surprises! Now let's add a twist. What if the last active enzyme in the pathway, MAPK that is doubly phosphorylated (MAPK_PP), the "final product" of the cascade, can, in a feedback reaction, inhibit the very first reaction of the cascade, MKKK → MKKK_P through an inhibiting phosphorylation. Run the simulation in Vcell to find out! MODEL MAPK Cascade - With feedback inhibition Initial Conditions: See simulation results Select Load [model name] below Interactive Element Select Plot to change Y axis min/max, then Reset and Play \| Select Slider to change which constants are displayed. For this model, select Vm, Km, Ki and I \| Select About for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health. Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington. Funded by NIH/NIGMS (RO1-GM123032-04) The various concentration vs time curves in the output graph should make "intuitive" sense. There are no surprises! Now let's add a twist. What if the last active enzyme in the pathway, MAPK that is doubly phosphorylated (MAPK_PP), the "final product" of the cascade, can, in a feedback reaction, inhibit the very first reaction of the cascade, MKKK → MKKK_P through an inhibiting phosphorylation. Run the simulation in Vcell to find out! MAPK Cascade - Model 2: Feedback inhibition of the MAPK cascade by inhibition of the first step (MKKK ↔MKKK_P) by the "end product" of the cascade (MAPK__PP) Kholodenko2000 - Ultrasensitivity and negative feedback bring oscillations in the MAPK cascade. https://www.ebi.ac.uk/biomodels/BIOMD0000000010. Based on Kholodenko BN. Negative feedback and ultrasensitivity can bring about oscillations in the mitogen-activated protein kinase cascades. Eur J Biochem. 2000 Mar;267(6):1583-8. doi: 10.1046/j.1432-1327.2000.01197.x. PMID: 10712587. There is utterly no way to have predicted, using intuition or simple mathematical analyses, the oscillatory changes in the concentration of all the enzymes and their phosphorylated variants. Of course, the oscillating concentrations depend on the chosen initial concentrations and on the rate constants. When enzymes are linked sequentially in signal transduction pathways, their actions are amplified by the preceding enzyme. If the first kinase (MK3) activates a 1000 molecules of the second kinase (MK2), and each of these activates 1000 of the last kinase (MAPK), the net effect of activating the first kinase is a million-fold amplification of the last! This causes this three-kinase pathway to be ultrasensitive to small changes to the first enzyme. Add other activating and inhibiting agents and the net activities of the pathway components become even more complicated. Just the simple feedback inhibition by the last enzyme of the first enzyme in this cascade can bring about the oscillation shown in Vcell model 2. Depending on the concentrations and rate constants used in the model, the oscillations can last from minutes to hours. These oscillations can produce waves of phosphoproteins that propagate through the cytoplasm of the cell. Here is a simplified animation of the MAPK cascade that shows changes in MKKK (red dots) and MAPK_PP) blue dots with no feedback inhibition (left, Model 1) and feedback inhibition (right, Model 2) in the cascade. (Animations produced by Shraddha Nayak and Hui Lui.) Model 1: No feedback inhibition of the MAPK cascade Model 2: Feedback inhibition of the MAPK cascade You might expect similar oscillatory behavior in proteins (cyclins and cyclin-dependent protein kinases) controlling the movement of cells through the cell cycle. We will see that in a subsequent section.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.05%3A_Small_G_proteins_GAPs_and_GEFs.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry G proteins: Cellular Switch for Kinases In the preceding chapter sections, we discussed two types of small G proteins, Gα, part of the heterotrimeric Gαβγ complex linked to GPCR signaling, and Ras (H, K, and N). Both bind GTP and GDP and have GTPase activity. When bound to GTP they are active while the GDP bound form is inactive. What a perfect molecular switch to turn on signaling and with a built-in off switch (the GTPase activity). It turns out that his simple on/off switch is too simple. For example, a single mutation that inhibits the GTPase activity would leave the protein on continually which could (and does) lead to unregulated growth and tumor formation. Two new sets of proteins that regulate the on-off activity of small G proteins are found abundantly in cells: • GTPase activating protein or GAPs: As the name implies they enhance the GTPase activity of the small G proteins, which would decrease G protein signaling; • Guanine nucleotide exchange proteins or GEFs: These lead to the dissociation of bound GDP and its replacement with GTP, which would increase G protein signaling. Mammalian cells contain 3 variants of Ras: H, K, and N. They all bind GDP/GTP and have GTPase activity. Ras is targeted to the cell membrane through the post-translational addition of a hydrophobic farnesyl group. When activated by binding to GTP, it can bind to and activate a protein called Raf-1, which on binding becomes an active tyrosine kinase. Ras has intrinsic GTPase activity, so eventually, active Ras will deactivate itself. Ras is just one member of a large superfamily of small G proteins, which all have GTPase activity. However, they are poor GTPases, so they need help to autocatalytically cleave the GTP to GDP. GAPs and GEFs evolved to regulate their activity by modulating the balance of bound GTP (an active form of the protein) and GDP (an inactive form of the small G protein). Before describing these proteins, we need to have a better understanding of the family of small G proteins. Small G proteins Small G proteins in the superfamily have a common 20 K molecular weight catalytic (GTPase) domain with 5 alpha helices, 6 beta strands, and connecting loops. The small G proteins are "active" in the GTP-bound form. Hydrolysis of GTP to GDP causes the protein to become inactive. Figure $1$ shows the domain structures of small G proteins. G boxes of the G domain are highlighted with orange boxes. The hypervariable region, including a polybasic region and a CAAX motif, is highlighted with pink boxes. The P-loop, switch I, and switch II are shown as bars colored green, red, and blue, respectively. The bottom structures show H-Ras bound to GDP and GTP. The P-loop, switch I, and switch II are colored green, red, and blue, respectively. Figure $2$ shows an interactive iCn3D model of human KRAS G12C mutant covalently bound to AMG 510, a covalent inhibitor (6OIM). This mutation flips the Ras switch so it is permanently on. The coloring coding is the same as in the above Figure 1: • P-Loop (same as G1) is Green • Switch 1 is red • Swithch 2 is blue • Polybasic region is pink • The GAAX motif was not observed (low electron density in the crystal structure) GDP is shown in colored sticks. The covalent inhibitor, AMG 510, is shown in spacefill and labeled MOV. It is covalently attached to the Cys 12 in the mutated version. The G12C human KRAS mutation is found in about 13% or patients with non-small cell lung cancer (NSCLC) with is any type of epithelial lung cancer that is not small cell lung cancer. NSCLCs include squamous cell carcinoma, large cell carcinoma, and adenocarcinoma. The G12C mutation causes the protein to be "stuck" in the active conformation, which leads to continued activation of signaling pathways leading to cell proliferation, a trait of cancer cells. Before the discoveryof AMG 510, Ras was considered "undrugable" since it was devote of obvious pockets that could inhibit its activity. The G12C mutation replaces glycine with cysteine, a potent nucleophile that can react with the covalent inhibitor and inhibit the always-switched on G12C mutant in the absence of the inhibitor. A new generation of inhibitors attempts to affect different aspects of its activity, including its localization to the membrane, the binding of different effectors, and nucletodide exchange. Figure $3$ shows these key structural features of Ras (with different color coding). Important parts of Ras necessary for GTP binding include the phosphate-binding (P loop), residues 10 to 16 (dark blue trace below), switch regions I (30 to 37, light blue trace) and II (60 to 76, green trace), which are flexible loops which sandwich GTP. Figure $4$ is an animation showing structural differences between the GTP bound form (blue, pdb id 5p21) and GDP form (red, pdb id 4q21) of the H-Ras protein. One helix and nearby loops are perturbed. There are about 150 members of the human Ras superfamily as shown in Figure $5$ and Table 1 below. Table $1$ shows common Ras superfamily functions Ras regulation of gene expression, cell proliferation, survival, and differentiation Rho regulation of actin cytoskeleton, cell shape, and movement, cell interactions with the extracellular matrix Rab vesicle trafficking, endocytosis, secretion Arf vesicle trafficking, endocytosis, secretion, microtubule assembly Ran nuclear cytoplasm transport, mitotic spindle We have discussed Ran before as a mediator of protein movement across the nuclear membrane in Chapter 11.5. It's mainly in the GDP-bound form in the cytoplasm and the GTP-bound form in the nucleus. Switches between a cytoplasmic GDP- and a nuclear GTP-bound state by nucleotide exchange and GTP hydrolysis. Nuclear import receptors with bound cargo protein containing a nuclear import signal bind RAN-GTP in the nucleus, leading to the release of the importin and the cargo protein. In contrast, cargo proteins with a nuclear export signal bind exportins and RAN-GTP in the nucleus and move into the cytoplasm, where the RAN-bound GTP is hydrolyzed on binding a RAN-GAP. This cycle is illustrated in Figure $6$. Small G proteins are a fundamental form of molecular switch. They are simply too important to not be regulated. Probably the most common mutation in human cancer cells involved a single amino acid change in Ras (H, K, and N form). If the GTPase activity is inhibited with mutation, the protein may be constitutively active. Such a Ras mutation is found in almost 90% of pancreatic cancers. Hence researchers have been trying to design drugs that inhibit its GTPase activity. This has proven difficult since it has very few targetable pockets that could bind a drug. Regulation of small G proteins: GAPs and GEFs Given the critical importance of small G proteins, it makes biological sense that their on/off activity would be exquisitely regulated. Indeed, they are. Two families of protein have evolved to regulate them by determining whether GTP or GDP is bound to the protein (leading to an active, and inactive small G protein respectively). One family, GTPase activating proteins (GAPs) facilitate the hydrolysis of bound GTP, leading to the inhibition of the protein. The other proteins are GTP exchange proteins (GEFs), which facilitate the exchange of GTP for GDP, activating the protein. The activity of Ras GAPs and GEFs, as well as various proteins interacting with Ras, are depicted in Figure $7$. Figure $7$: The activity of Ras GAPs and GEFs, as well as various proteins interacting with Ras It may seem crazy but the number of GEFs and GAPs is greater than the number of G proteins with which they interact. As shown in Figure $4$, there are 20 Rho G proteins but about 80 GEFs and 70 GAPs for them. This number presumably allows greater control of the specificity of the reactions controlled by the Rho G protein. GAPs - GTPase Activating Proteins The hydrolysis of the gamma phosphate of GTP by water in Ras proceeds by a pentavalent transition state with two axial and three equatorial ligands to the P. Developing charge in the transition state would usually be stabilized by catalytic residues in the catalytic domain of Ras. However, Ras is a poor GTPase. That's where GAP comes in. In the Ras/GAP complex, GAP positions its Arg 789 on the GAP in a position to stabilize the transition state for Ras-bound GTP cleavage. This Arg 789 is almost in the same position as Arg 178 in the Galpha inhibitor subunit of a heterotrimeric G protein which inhibits GPCR signaling. Both of these arginines have similar catalytic functions. Figure $8$ shows an interactive iCn3D model of Ras-GAP complex (1WQ1) Ras is shown in secondary structure colors, while GAP is shown in gray. GDP-AlF3, a GTP analog, is shown in color spacefill. Arginine 789 in GAP is shown in spacefill with CPK colors and labeled R789. It is a clear position to stabilize the bound GTP in the complex and its cleavage transition state. GEFs - GTP Exchange Factor Once bound to Ras, GDP dissociates very slowly. Values of 10-5 sec-1 have been reported for the first order rate constant of the dissociation of GDP from a small G protein. Assuming that the diffusion is controlled on the rate constant for the complex, the KD for the G protein:GDP complex would be 0.1 pM and the half-life would be 0.8 days, similar to the lac repressor:DNA operator complex. Hence the protein, when bound to GDP, is essentially locked in the off position. What if it needs to be reactivated quickly? How can the rate at which GDP dissociates be increased so that GTP could replace it? If it were to dissociate, GTP could quickly replace it since from an equilibrium point of view, Ras and other small G proteins would favor GTP binding since its concentration in the cell is higher. One could envision several ways to change the rate at which GDP dissociates. In organic chemistry, a favorite student response to many questions is to evoke steric effects. In biochemistry, the analog is often conformational changes. How could you change the conformation of Ras such that it might favor GTP binding? That could occur by ligand binding or more likely by a post-translation modification such as phosphorylation as part of a signaling process. It turns out that for the case of small G proteins, another mechanism is evoked: the binding of another protein, a GTP Exchange Factor or GEF, which promotes GTP exchange for the bound GDP. If the Ras:GEF:GDP complex has a 10,000 increase in koff for GTP, the half-life of the bound GDP is 7 seconds. There are 80 GEFs in the human genome. If you think about it, in GPCR coupled signaling, the ligand-bound GPCR is a GEF for the Gαsubunit of the heterotrimeric Gαβγ protein. The crystal structure of a Ras GEF, SOS, in complex with Ras allows a detailed understanding of the mechanism. SOS, a cytoplasmic protein, is recruited to the cell membrane where active Ras is found, tethered to the membrane with a hydrophobic farnesyl attachment. Figure $9$ shows an interactive iCn3D model of Ras and SOS (a GEF) complex (1bkd). The actual biological unit (functional structure) is a hetero 8-mer (A4B4) with C4 symmetry. The iCn3D model shows just a heterodimer for clarity. Ras is shown in cyan and SOS in dark blue. SOS as a GEF affects nucleotide binding to SOS in two essential ways. An alpha helix from SOS displaces Switch 1 (amino acids 30-38, shown in red) in Ras, which opens the binding site for the guanine nucleotides open. Additional conformational changes in Switch II (59–72, green) in Ras and interference from the side chains form the SOS alpha helix interfere with the binding of the phosphates on the bound nucleotide. This promotes the dissociation of the bound nucleotide and Mg2+. Now GTP can preferentially rebind. How? Before we answer that question, let's explore the conformational differences just in the structure of Ras in the Ras:GDP complex (4q21) and Ras in the Ras:SOS (1bkd) complex. These differences are shown in Figure $10$. The green structure is the Ras:GDP. The cyan structure is Ras without bound GDP but bound to SOS. Note the large shift in Switch 1 in the Ras structure from the Ras:SOS complex. This leaves a "gaping" hole from which GDP can "escape". Now how do the opening of the active site and release of bound GDP facilitate GTP binding? Once bound just to Ras, GDP dissociates very slowly. Values of 10-5 sec-1 have been reported for the first-order rate constant of the dissociation of GDP from a small G protein. Assuming that the on-rate for complex formation is diffusion-controlled, the KD for the G protein:GDP complex would be 0.1 pM and the half-life would be 0.8 days, similar to the lac repressor:DNA operator complex. Hence the protein, when bound to GDP, is essentially locked in the off position. The conformational changes on RAS binding to SOS open up the active site, allowing GDP dissociation. GTP can now replace it since from an equilibrium point of view, Ras and other small G proteins favor GTP binding since the concentration of GTP in the cell is higher than that of GDP. In addition, some additional noncovalent interactions with the extra phosphate on GTP probably help. Figure $11$ shows a cartoon showing the changes in Ras on GEF binding as illustrated in these coupled chemical equilibria: GEFloose:Ras:GDPtight ↔ GEFtight:Ras:GDPloose ↔ GEFtight:Ras
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.06%3A_Phosphatases.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Introduction Protein kinases phosphorylate proteins in a process that can either activate or inhibit the target protein's activities. To control signaling processes, the activities altered by protein phosphorylation can be readily reversed by dephosphorylation of the Ser-, Thr- or Tyr-phosphoesters by simple hydrolysis. These reactions are catalyzed by protein phosphatases. Some of these phosphatases also cleave phosphates from lipids as well. There are three main families of phosphatases, phospho-Tyr phosphatases (PTP), the phospho-Ser/Thr phosphatases, and those that cleave both. Of all phosphorylation sites, most (86%) are on serine, 12% involve threonine and about 2% are on tyrosine. They can also be categorized by molecular sizes, inhibitors, divalent cation requirements, etc. In contrast to kinases which differ in the structure of their catalytic domains, many protein phosphatases (PPs, also abbreviated Ppp for Protein phosphatases) gain specificity by binding protein cofactors which facilitate translocation and binding to specific phosphoproteins. The active phosphatase hence often consists of a complex of the phosphatase catalytic subunit and a regulatory subunit. Regulatory subunits for Tyr phosphatases may contain a SH2 domain allowing binding of the binary complex to autophosphorylated membrane receptor Tyr kinases. We'll consider examples of all four families. They recognize target proteins through protein:protein interactions and specific binding site motifs. There are over 10,000 pSer and p-Thr sites on target proteins, so targeting specific sites must be quite nuanced. Serine/Threonine Phosphatases Important Ser/Thr phosphatases (PPs for Protein Phosphatases) include: • Protein phosphatase 1 (PP-1 or Ppp1) - This is the most abundant PPP in humans. Different regulatory subunits target this to the liver glycogen particles (GL subunit), striated muscle glycogen, and sarcoplasmic reticulum (GM subunit) or smooth muscle fibers (M subunit). It is also present in the nucleus where it is presumably involved in the regulation of transcription factors. It is also involved in RNA splicing and signaling at neural synapses. • Protein phosphatase 2A (PP-2A or Ppp2) - is a trimer with catalytic, regulatory, and scaffolding (also regulatory) structural subunits. It is found mainly in the cytoplasm and is involved in a myriad of cellular processes. • Protein phosphatase 2B (PP-2B or Ppp3) - also called calcineurin or Ca2+/Calmodulin dependent protein phosphatase - It consists of a catalytic subunit (calcineurin A) and a regulatory, calcium-binding subunit (calcineurin B). It is inhibited by the complex of the immunosuppressant cyclosporin and FK506 with immunophilins. PP2B regulates PKA and PKC • (PP2C) - The catalytic subunits of PP1, 2A, and 2B share a great deal of amino acid homology, and based on this homology, belong to one family. PP2C belongs to another family. PPs are often categorized into three other families including, phosphoprotein phosphatases (PPPs) and metal-dependent protein phosphatases (PPMs). There are about 30 catalytic PP subunits (many fold fewer than Ser/Thr Kinases). They gain specificity by binding numerous modulatory regulatory subunits. As with other proteins, the names given to the proteins when discovered often do not reflect an organization scheme that would name different members based on structural similarities. PP-1, 2A, and 2B are better named Ppp1, Ppp2, and Ppp3 which denote members of the Protein PP (PPP) family. PP-2C would be named Ppm1 as the first member of the PPN family. All PPPs have three short sequence motifs that bind divalent cations. In addition, older names for the given PPs referred to both the catalytic subunit and the dimer with the regulatory subunits. For clarity, the name of the catalytic protein phosphatase 1 is PP1c, and its regulatory subunits as RIPPOs, regulatory interactors of protein phosphatase one. Protein Phosphate-1 (PP-1): PP-1 is involved in many signaling pathways that control cell division, protein synthesis, etc. It catalyzes most serine–threonine dephosphorylation in cells. It is perhaps best known for its regulation of glycogen mobilization. Insulin signals the well-fed state in healthy people and promotes glucose uptake through the GPCR insulin receptor which we discussed in Chapter 12.4. Under these condition excess glucose is used to elongate glycogen, our main carbohydrate energy storage polymer. In contrast, the starving or low energy state is signaled by the hormone glucagon. You would expect that signaling pathways activated in the presence of insulin would promote glycogen synthesis and inhibit glycogen breakdown. PP-1 is a key factor in the regulation of both processes: • Insulin activation of glycogen synthesis - PP1 dephosphorylates glycogen synthase (the enzyme that synthesizes glycogen) and in the process ACTIVATES it. • Insulin inhibition of glycogen breakdown - PPI dephosphorylates two key enzymes involved in glycogen breakdown, phosphorylase kinase and glycogen phosphorylase a (with a pSer14), and in the process INHIBITS them. PP1c interacts with many different regulatory subunits (RIPPOs) forming unique heterodimers. The regulatory subunits also bind potential substrates for PP1c and help localize the enzyme. at sites. The regulatory subunit involved in the PP1c effect on glycogen is called the glycogen-targeting subunit, GM. There are 7 such regulatory subunits involved in glycogen metabolism. GM (RGL) is expressed in muscles and GL in the liver. All of the regulatory subunits (unfortunately called G-subunits) have a conserved RVxF amino acid sequence which interacts with specific sites on the catalytic subunit typically distal to the active phosphatase site. Binding through the RVxF sequence does not affect the active site of the PP-1c. The binding of the regulatory subunit to the PP-1c can also occur outside of the canonical RVxF sequence. The regulatory subunits also have starch binding domain (SBD), also called the carbohydrate-binding module (CBM21). The subunits are often highly disordered until they are bound. Figure $1$ shows an interactive iCn3D model of protein phosphatase 1 (PP1) bound to the muscle glycogen-targeting subunit (Gm) and microcystin (6DNO) and the toxin microcystin. PP-1 alpha catalytic chain is shown in gray. Its active site, where it binds Ser/Thr phosphorylated proteins is shown in magenta. Bound in that site is the toxin microcystin. The small chain shown in cyan is protein phosphatase 1 regulatory subunit 3A. PP-1 binds to its regulatory sequence to a 65RVxF68 sequence common on many regulatory PP-1 subunits. This subunit contains a serine 67 which is phosphorylation by protein kinase A. This inhibits the binding of the catalytic and regulatory subunits. (PKA) of the “x” residue in the GM RVxF motif, Ser67GM, inhibits PP1 binding (16). Val79 and Lys80 on GM also form a motif that binds a corresponding pocket in the catalytic subunit. Note the pi-cation (red) and pi-stack (blue) interactions from Phe 68 of the GM 65RVSF68 motif of the regulatory subunit to the PPI catalytic subunit. Microcystin is 7-mer peptide ring that has 5 noncanonical amino acids and 2 regular ones. A covalent bond from Cys273 of PP1 (labeled in the above iCn3D model) to the methyl-dehydroalanine (Mdha) of the toxin forms. Microcystins are produced by toxic cyanobacteria and are very toxic and lethal, especially to animals including humans that drink water contaminated with the cyanobacteria. They will pose a greater threat in a warming world from climate change. They also bind to PP-2A. Another example of a regulatory subunit for PP1 is the PP1 nuclear target subunit (PNUTS). The activity of the complex in the nucleus regulates the phosphorylation state o many proteins involved in the cell cycle including p53, a tumor suppressor in many tumors. It also regulated chromatin structure and RNA processing. As with the muscle glycogen-targeting subunit (Gm), PNUTS is intrinsically disordered when not bound to PP1 and is very extended when bound. The catalytic subunits of PP1c and PP2A have an acidic, hydrophobic, and C-terminal grove. PNUTS binds one of the substrate groves at an arginine subsite, but not the active site. So far we can say the specificity of the binding of PPP is determined to some degree by the regulatory subunits. We turn to the contributions of the catalytic specificity of PP-1c when we compare it to PP-2Ac below. Mechanism of PP1-A and PP-2A and B The catalytical subunits of PP-1 and PP-2 are very homologous with nearly identical key active site side chains. The site contains two Mn2+ ions very near each other as shown in Figure $2$. The figure crudely shows each Mn2+ is octahedrally coordinated to side chains and one water oxygen for each metal ion. Water 2 (or more likely OH-) and an aspartate D92 oxygen bridge the two ions. The phosphate of the target Ser-OPO32- or Thr-OPO32-, or an inhibitor such as tungstate, also bridges the metal ion. Figure $3$ shows an interactive iCn3D model of Human Protein Phosphatase 1 Active Site Residue (4mov) which shows the same active site residues The metals singly or in combination probably reduce the pKa of bound water to produce the deprotonated hydroxide, which engages in an SN2 attack on the phosphate. Hence the metal ions act as an electrostatic catalyst. The subtle differences in the active site and the three groups contribute to the specificity of the PPPs. Comparison of the catalytic subunits of PP1A the PP2A As mentioned above, substrate specificity is altered by subtle changes in the active site and three groves of PPPs. Figure $4$ shows the acidic groves for PP1c and PP2Ac. The color is the electrostatic surface potential with red indicating negative and blue (notably absent) positive. The acidic groove is stronger in PP1c. The asterixis * shows the catalytic cleft containing two Mn2+ ions. Hoermann et al. Nature Communication \| (2020) 11:3583 \| https://doi.org/10.1038/s41467-020-17334-x. Creative Commons Attribution. 4.0 International License, http://creativecommons.org/licenses/by/4.0/. The negative acidic grove is highly enriched in negatively charged side chains. Figure $5$ shows the actual amino acids contributing to the negative electrostatic potential in aligned PP1c and PP2Ac. The orange spheres show the location of acidic side chains. PP1c is shown in blue/black and PP2Ac in red/gray. The stronger acidic groove in PP1Ac gives it great preferences for pSer/pThr-protein targets with basic motifs than PP2Ac in a fashion that is independent of the bound regulator subunit. In contrast, PP2A needs to interact with regulatory subunits with more acidic composition to target basic motifs in protein targets. These features for PP1c and PP2Ac are compared in Figure $6$. Panel (d) shows that the holoenzyme for PP1 has a great preference for positive basic motifs than the holoenzyme for PP2A, which needs to associate with a negatively charged regulatory subunit for activity towards target proteins with basic motifs. Panel (e) also shows that the catalytic subunits of both PP1 and PP2A prefer p-Thr protein targets compared to p-Velocity vs substrate graphs show these effects as well. e Both, PP1 and PP2A holoenzymes have a preference for pT due to higher catalytic efficiency of their respective catalytic subunits towards pT over pS. Hoermann et al. ibid Figure $7$ shows an interactive iCn3D model of the protein phosphatase 2A catalytic subunit in complex with a larger regulatory subunit and bound to the phosphatase inhibitor and tumor promoter okadaic acid (2IE4). The toxin, found in sponges and shellfish, is produced by dinoflagellates. The regulatory subunit is shown in secondary structure color. This scaffolding protein is shaped like a horseshoe. The phosphatase inhibitor okadaic acids are shown in spacefill bound to the catalytic subunit shown in gray. The side chains of the catalytic subunit interacting with the 2 Mn2+ ions are shown in CPK-colored sticks (zoom in to see them). On binding the catalytic subunit, the scaffolding regulatory subunit is quite flexible and adaptable in interacting with other proteins. Protein Phosphate 2B: Calcineurin (CN) Calcineurin (CN), or PP2B, is depended on Ca2+. It is involved in the development, immune signaling, and heart function. It consists of a catalytic site (CNA) and a calcium-binding regulatory subunit CNB so it is another example of PPP heterodimers. CNA has a catalytic domain and domains that bind CNB (the regulatory subunit), calmodulin (CAM, a calcium-binding protein that we will explore more in the next chapter section), and an autoinhibitory domain that blocks the active site. On Ca2+ release from internal organelles, the ion binds to both CNB and also CAM. These events cause conformational changes that release the bound autoinhibitor. As with the other phosphatases, much effort has been made to determine how CN interacts with specific pSer- and p-Thr sites on targets. We'll focus on one, the integral membrane Na+/H exchanger 1 (NHE1). This protein is itself regulated by Ca2+ ions and by phosphorylation by kinases we have previously studied, the MAPK ERK1/2 and the JNK kinase. Erk2 phosphorylates NHE1 at 6 Ser/Thr side chains in the recognition sequences name [S/T]P11. Several different phosphatases, including CN, can regulate NHE1 activity through direct dephosphorylation. CN binds short linear motifs (SLiMs) named PxIxIT and the LxVP that are found in interacting partners including regulatory subunits as well as inhibitors and substrates. As we saw above, the regulatory subunits of PP1A and PP2A are highly disordered. Likewise, SLiMs are on intrinsically disordered regions as well as interacting proteins. • PxIxIT binds to the catalytic domain of CNA22. It also enables interaction between CN and NHE1. • LxVP binds to a cleft between the CNA and CNB, which is only available in the active form of the protein. CN doesn't dephosphorylate multiple nearby p-Ser side chains of NHE1 (pS363, pS723, and pS726) since they are close to the NHE1-PxIxIT interaction sit,e which sterically restricts their binding to the active site. However the 3 other phospo S/T sites on NHE1 (pS771, pS785, and the actual target site (pT779) are far enough away from the NHE1's PxIxIT site so they can interact with the CN active site. Making the T779S mutation shows that dephosphorylation of their phosphorylated version shows a faster rate with pT779 and a slower, yet reasonable rate for pS779. Therefore other specificity factors are in play. A newly discovered very short 4-amino acid site motif in NHE1 including pS779 appears to be a source of selectivity. This TxxP motif in NHE1 is 779TPAP782. Such short recognition motifs are different than the selection of substrates by PP1 which involved multiple domain binding interactions and steric restrictions imposed by them. Figure $8$ shows a model of the structure of the NHE1 exchanger (left panel a) and the calcineurin CNA/CNB complex. jd Figure $8$: Docking motifs mediate the interaction of NHE1ct with CN The motifs present in NHE1 (LxVP and PxIxIT) in the intrinsically disordered tail of NHE are indicated in the left panel and their corresponding binding sites in the CNA dimer are shown in corresponding colors. The calmodulin binding site is also shown. Erk2 phosphorylation sites in NHE1 are shown are listed in panel C along with the consensus motif sequences (PxIxIT in purple, LxVP in orange, and TRAP (uncolored)). Hendus Altenburger et al. Nature Communication (2019) 10:3489 \| https://doi.org/10.1038/s41467-019-11391-7Creative Commons Attribution 4.0 International License. http://creativecommons.org/ licenses/by/4.0/. Figure $9$ shows an interactive iCn3D model of calcineurin (PP2B) complex bound to a peptide from the Na+ /H+ -exchanger 1 (6NUC) Protein Phosphatase 2C The protein phosphatase 2C (PP2C) is a member of a family of metal-dependent protein phosphatases sometimes abbreviated as PPMs. (Of course, the other phosphatases we discussed above are also metal-dependent.) The required Mg2+ or Mn2+. PP2C is a monomeric enzyme with at least four isoforms in humans. In humans, there are at least 17 members. One is unfortunately named protein phosphatase 1D but also referred to as PPM1D, PP2Cδ, or Wip1). It is involved in heterochromatin silencing and the cycle. Mutations in its gene can accordingly give rise to tumors. PP2Cs in humans have a binuclear metal cluster which reduces the pKa of water, producing OH- for SN2 attack on the phosphorus in the pSer or pThr in target phosphorylated proteins. Figure $10$ describes binding interactions around the binuclear site in PP2Cα. Figure $10$: Binding interactions around the binuclear site in PP2Cα. after Pan et al. Sci Rep 5, 8560 (2015). https://doi.org/10.1038/srep08560htt...cles/srep08560 The metal binding site with many water molecules Interactions with the metals is very different than the PP1 and PP2 active site shown in Figure $2$. If Cd is bound at the M1 site, the activity of the enzyme is blocked so it is required for catalysis. Making the mutations affecting M2 (D38A and D38K) suggests that M2 is involved in binding the phosphate of the substrate, and also stabilizes the transition state and the leaving group in the reaction. H62 probably acts as a general acid catalyst. Not all PPCs require both metal ions. The plant hormone abscisic acid regulates stress responses in plants. When it binds to a particular receptor called PYL1 (alternative name PYR1-like protein 1), the receptor interacts with a PP-2C called ABI1 (also called Absisic acid-insensitive 1). Figure $11$ shows an interactive iCn3D model of the ternary complex of Abscisic acid, PYL1 and ABI1 (phospholipase 2C) (3KDJ) The catalytic subunit, which in contrast to PP1, PP2A, and PP2B, has only 1 Mn2+ ion, is shown in gray with amino acids side chains interacting with the metal ion labeled. The cyan subunit is the receptor of abscisic acid, which is shown in spacefill. Protein Tyrosine Phosphatases Protein Tyr phosphatases (PTPs) consist of receptor-like (transmembrane) and intracellular Tyr phosphatases. They more resemble tyrosine kinases in their complexity than the Ser/Thr phosphatases. There are about 100 PTPs in the genome, a number similar to the number of protein tyrosine kinases. PTPs have an active site Cys in a CX5R-(S/T) motif with an active site Cys nucleophile and an Arg in the phosphate binding (P) loop. Some examples we will discuss include: • Low molecular weight PTPase - These have roles in the metabolism and differentiation of cells. They have a molecular weight of 18,000 and have an active site CX5R-(S/T) motif, where the C (Cys) is an active site nucleophile. • PTP1B - dephosphorylates many cell surface receptors (insulin, EGF, PDGF) that have been phosphorylated on Tyr residues. Its main activity seems to dephosphorylate nascent receptors in the endoplasmic reticulum before they get to the final cell membrane destination. • Tyrosine phosphatase nonreceptor type 11, ptpn11, commonly called SHP2 Figure $12$ shows the protein tyrosine phosphatase (PTP) superfamily. In contrast to the active site of the Ser/Thr phosphatases like PP-1, PP-2A and PP-2B, the active sites of protein tyrosine phosphatases (PTPs) do not have a bimetal ion cluster in the active site. Rather they all have an active site cysteine that acts as a nucleophilic catalyst in the hydrolysis of the p-Try phosphoester bond. The active site PTP domain is found in all of the proteins, so all use similar catalytic mechanisms shown in Figure $13$. The phosphotyrosine side chain of the target phosphoprotein binds in the phosphate-binding P-loop (H/VCxxxxxRS/T), which contains the nucleophilic Cys 12 and Arg 18 that stabilizes the charge on the phosphate. The Asp in the WPD loop positioned across from the nucleophilic Cys 12 acts as a general acid. Since the active site is nearly identical in the PTPs, it has been hard to design drugs that bind to the active site but that are also selective for specific PTPs. Low molecular weight protein tyrosine phosphatase - LMW-PTP This protein tyrosine phosphatase is the simplest of all in structure. It has the phosphate-binding P-loop (12CxxxxxR18) with the nucleophilic Cys 12 and Arg 18 that stabilizes the charge on the phosphate. It does not have the conserved WPD loop but deploys Asp 129 across from Cys 12 as a general acid. This enzyme exists as two main isozymes, A and B. Figure $14$ shows an interactive iCn3D model of human low molecular weight protein tyrosine phosphatase bound to sulfate (1xww) The active site is deep as shown in Figure $15$. The sulfate, a mimetic for the phosphate on the p-Try protein target, is deeply buried. The CPK-colored surface (green red, blue) around the sulfate are the side chains of Tyrosine 131 and 132 as well as Trp 49. Y131 and Y132 are part of a loop containing Asp 129, the general acid. This loop is analogous to the WPD loop. The three aromatic amino acids on top of the pocket make it deep enough that pSer and pThr side chains can't reach the active site nucleophile Cys 12. Trp 49 is also in a variable loop (34 amino acids, shown as an orange surface) that differentiates two of the major isozymes, the A and B forms, and contributes to substrate binding specificity. Tyrosine-protein phosphatase non-receptor type 1, also known as PTPN1 or PTP1B PTP1B PTP-1B regulates the endoplasmic reticulum unfolded protein response and is involved in insulin JAK/STAT and HER2 (ErbB2) signaling. It has a full-length form (MW 50,000) and a C-terminal shortened form (37,000) Given the difficulty in targeting the active site which is common in all PTPs, efforts have concentrated on the development of allosteric inhibitors that bind to exosites removed from the active site. An example is trodusquemine (MSI-1436) used in the treatment of obesity and type 2 diabetes. It binds much more tightly to the full-length form. Figure $16$ shows an interactive iCn3D model of the human Protein Tyrosine Phosphatase 1B (1-301) in complex with the inhibitor OTA (5K9W). The P-loop is shown in magenta, the WPD loop in cyan, and the substrate binding loop (SBL), which allows entry of pTyr but not pSer and pThr, in blue. The key side chain in the P-loop (Cys 215 and Arg 221) as well as the catalytic general acid (Asp 181) are shown in sticks and labeled. The inhibitor is shown in spacefill, and CPK colors. The movement of the WPD loop is rate-limiting for the hydrolysis of P-Tyr esters. On binding, the WPD starts to close, and in the process Arg 221 moves to form salt bridges with the phosphate. Full closure of the WPD follows, which positions Asp 181 for general acid catalysis. Key interactions of PTP-1B phosphoprotein in insulin and cytokine signaling, are shown in Figure $17$.: Figure $17$: Protein tyrosine phosphatase 1B (PTP1B) and its effects on signaling. Maja Köhn ACS Cent. Sci. 2020, 6, 4, 467–477. Publication Date: March 13, 2020. https://doi.org/10.1021/acscentsci.9b00909. This is an open-access article published under an ACS Author Choice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes. Panel (A, left) shows how PTP1B dephosphorylates the insulin receptor and the insulin receptor substrate (IRS), which we have explored in a previous section. Panel (A, right) show its activity in the JAK/STAT pathway, which we have all seen previously. One cytokine receptor that it regulates is the leptin receptor. The hormone leptin, released from fat cells (adipocytes) is a key regulator of lipid metabolism. Pane B shows the structures of key inhibitors of PTP-1B. Protein tyrosine phosphatase nonreceptor type 11 (ptpn11) also known as SHP2 (SH2-domain containing phosphatase-2) This is an example of another phosphatase in which a mutation leads to cancer. It is downstream and activated by most receptor tyrosine kinases (RTKs) involved in the activation of the MAPK pathway with its ultimate links into the nucleus and activation of gene transcription. Figure $18$ shows an interactive iCn3D model of Non-receptor Protein Tyrosine Phosphatase SHP2 in Complex with Allosteric Inhibitor Pyrazolo-pyrimidinone 5 (6MDB) The phosphatase domain is shown in gray. The N- and C-terminal SH2 domains are shown in green and blue, respectively. The allosteric inhibitor is shown in spacefill and CPK colors. The P-loop in the catalytic domain is shown in red with the Cys 459 (active site nucleophile) and R465 (stabilizer of phosphate in the complex) shown in sticks, CPK colors, and labeled. The bound inhibitor is especially interesting as it binds at an allosteric site. As mentioned above, it is very difficult to design specific inhibitors that target just one PTP given their common active sites and mechanisms. Figure $19$ shows multiple features of SHP2. Figure $19$:. SH2-domain containing phosphatase-2 SHP2. Köhn ibid. Panel (A) shows how SHP2 recruited to phosphorylated RTKs activates the MAPK pathway. The dotted line indicates multiple steps. Upon receptor activation, SHP2 is recruited in different ways to activate the MAPK pathway. Panel (B) shows that in the inactive state, the N-terminal SH2 domain (green) blocks access to the active site. When the N- and C-terminal domains bind pY residues in a single pY-protein, two pY-proteins, or pY residues on its C-terminal tail, a conformational change ensues opening the active site (5EHR). Panel (C) shows how another allosteric inhibitor keeps the protein in a closed state (5EHR). Panel (D) shows how SHP2 can decrease T-cell responses through the MHC:Tcell receptor (TCR) complex. Tumor cells express a ligand called PD-L1, which binds to the PD1 receptor on the T cell surface. After binding, SHP2 is recruited to PD1, decreasing T cell activation. This is not a good thing since it inhibits the immune response to the cancer cell. Dual Specificity Phosphatases (DUSPs) Another important phosphatase is phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN (phosphatase and tensin homolog). Dual-specificity protein phosphatase hydrolyzes pTyr- as well as pSer- and pThr- phosphoesters in target proteins. They don't require divalent metal cations and are closer in structure to protein tyrosine phosphatases. They have an active site cysteine in a P-loop also containing arginine. In addition, they are lipid phosphatases, removing phosphate from the inositol ring from phosphatidyl inositol derivatives. These both impact many signaling pathways. Its activity as a lipid phosphatase makes it a tumor suppressor protein as it inhibits the PI3K-AKT/PKB signaling pathway by dephosphorylating phosphoinositides. Hence it modulates both AKT and mTor pathways. The domain structure of PTEN is shown in Figure $20$. The C2 domain enables phospholipid binding. Multiple post-translational modification sites are indicated. The PEST motif is sequence rich in proline (P), glutamic acid (E), serine (S), and threonine (T) and bounded by positively charged amino acids (Lys, Arg, or His) that act as signals for protein degradation. The PDZ domain, often found at the C-terminal of signaling proteins, acts as a scaffolding site for interaction with other signaling proteins. In the next chapter section, we will consider redox signaling, for which PTEN is a great example. Disulfide formation (in a more oxidizing environment) between the nucleophilic Cys 124 and a nearby Cys 71 (figure above) inhibits PTEN phosphatase activity. Figure $21$ shows an interactive iCn3D model of an AlphaFold computational model of full-length human PTEN (Uniprot P60484). The phosphatase (PTPase) domain is shown in blue and the C2 domain is in orange. The P-loop is in red with the active site Cys 124 and R130 in colored sticks and labeled. The backbone of the highly extended intrinsically disordered C-terminus region is shown in gray. It contains the clustered residues Ser 380, Thr 382, Thr 383, and Ser 385 (shown in colored sticks and labeled) that are sites for phosphorylation by activated kinases. iFigure $22$ shows key molecules dephosphorylated by PTEN, including the lipid PIP3, and Thr 308 and Ser 473 on AKT. When PTEN dephosphorylates Akt1, it inhibits AKT activity and effectively antagonizes the main PTEN-PIP3-PDK1-Akt pathway. PTEN is considered a tumor suppressor for this reason. Mutations in PTEN hence are associated with cancer.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.07%3A_Calcium_Signaling.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry The following is adapted directly and modified from Sharma et al. Biomedicines 2021, 9(9), 1077; https://doi.org/10.3390/biomedicines9091077. Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Introduction Ca2+ is central to numerous cellular processes and functions. Its chemical features, including a low hydration energy, high polarizability, relative flexibility of coordination sites and bond length, and large concentration gradient across cellular membranes (100 nM intracellular to 2 mM extracellular) due to low intracellular levels make it the ion of choice at the core of cellular signaling in prokaryotes and eukaryotes alike. In studying calcium ion signaling we will focus on four key areas: • Buffering of intracellular Ca2+ ion concentrations: Basal low levels must be maintained, which allows transient increases to act as signals. Ca2+ ions hence are no different from other second messengers like cAMP, for example. What matters is the rise from a basal level to a threshold concentration level that allows binding to signaling proteins and subsequent signal transmission. • Storage of intracellular Ca2+: Calcium ions are stored in organelles such as ER, mitochondria, and lysosome. The ions must be released in the presence of specific signals, then returned to the storage organelle to maintain basal Ca2+ levels. • Signaling pathways activated by Ca2+ ion: We have seen many pathways simulated by a rise in second messengers and by phosphorylation of lipid and protein molecules in interconnected pathways. We will return to several pathways we have previously studied to see how they integrate with Ca2+ in signaling processes. • Ca2+ binding proteins and their binding partners in signaling pathways: We will focus on one key Ca2+ binding protein, calmodulin (CAM), and the kinase that it activates, Ca2+/CAM protein kinases or CAMKs. The next two sections are adapted and modified from Sharma et al. Biomedicines 2021, 9(9), 1077; https://doi.org/10.3390/biomedicines9091077. Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Buffering of intracellular Ca2+ on concentrations Calcium ions, like the hydronium ion, must be buffered in cells otherwise its potential as a signaling agent would be compromised. The mechanisms adopted by cells for intracellular Ca2+ buffering involve sequestration by special proteins as shown in Figure $1$. Intracellular Ca2+ levels are managed through binding to special proteins or sequestration within different cellular compartments. The three main ways by which intracellular Ca2+ is buffered are depicted in Figure 1. These include soluble or unbound proteins that are found in the cytosol or inside organelles, membrane proteins (generally Ca2+ channels, ATP-driven pumps (SERCA or PMCA), and ion exchangers (NCX), inside organelles like endoplasmic reticulum (ER), mitochondria, acidic vesicles (mainly lysosomes and Golgi bodies) or organelle junctions (endoplasmic reticulum-plasma membrane (ER-PM), endoplasmic reticulum-mitochondria, or endoplasmic reticulum-lysosomes). The major players regulating inter-organellar Ca2+ transfer are IP3R (inositol-3,4,5-triphosphate receptor), NCX (sodium-Ca2+ exchanger), ORAI1/CRACM1 (Ca2+ release activated modulator 1), PMCA, (plasma-membrane Ca2+ ATPase), SERCA (sarco-endoplasmic reticulum Ca2+ ATPase), STIM1 (stromal interaction molecule 1), SOCE (store-operated Ca2+ entry), TPC1/2 (two-pore channel), TRP (transient receptor potential) and VDAC (voltage-dependent anion channel). We will discuss some below. These proteins are involved in sequestering cytosolic Ca2+ upon sensing an increase in its levels and participate in relaying the associated cellular messages. Other proteins that work as intracellular Ca2+ buffers exist in the lipid bilayers, plasma membranes, or organelle membranes, like pumps or transporters. Apart from these proteins, intracellular Ca2+ is regulated by inter-organellar transport and the influx of Ca2+ ions from extracellular space. Storage of intracellular Ca2+ - Proteins Soluble and Unbound Intracellular Proteins: Calmodulin, Calbindin, and Calretinin Nonmembrane-associated proteins inside a cell can act as both Ca2+ sensors and buffers. Most of these proteins have EF-hand motif(s) that allows Ca2+ ions to bind and trigger changes in protein folding, influencing downstream or linked cellular pathways. Calmodulin (CaM) is one of the best-studied and ubiquitously expressed Ca2+-sensing proteins known to play a key role in intracellular Ca2+ homeostasis. It is a prototype for intracellular Ca2+ sensors. It has a 148 amino acid structure with two Ca2+-binding sites in two separate lobes, each with two EF-hand motifs. The lobes are connected in the holo (Ca2+-bound form that binds other proteins. N- and C-termini alpha-helices with a Ca2+ coordination loop in between providing affinity for Ca2+ ion docking and sequestration. The ability of CaM to transmit a change in free intracellular Ca2+ levels into a signal depends on the conformational flexibility of the Ca2+-dependent (apo) form. CaM can exist in a Ca2+-free closed conformational state (Apo-CaM), a semi-open (Ca2-CaM), or an open state (Holo-CaM or Ca4-CaM) after Ca2+-binding as shown in Figure $2$. Figure $3$ shows an interactive iCn3D model of Holo-calmodulin with 4 bound Ca2+ ions (1CLL) Side chains interacting with one Ca2+ ion in an EF-hand is shown in the model. The central helix is colored based on hydrophobicity with green indicating more hydrophobic. This amphiphilic helix can bind target proteins in this region through nonpolar interactions. Figure $4$ shows the change in conformation going from the apo form (without Ca2+) to the holo-form with the fully-formed central helix connecting to the two lobes of the "dumbbell". Differential Ca2+ binding to the two lobes of CaM makes fast buffering of a wide range of free intracellular Ca2+ possible for this protein. The presence of methionine residues in its lobes and the plasticity of the central linker in its structure also provides CaM with properties to function as an adaptor protein in intracellular Ca2+ signaling. CaM can bind to several targets or effector molecules over a variable distance and in multiple orientations to mediate change in intracellular Ca2+ signaling. Some major effector proteins that are regulated by CaM binding and are relevant for Ca2+ homeostasis include EGFR, PI3K, and connexins. CaM is required for spatial and temporal regulation of [Ca2+]as evident by its role in modulation (activation or inactivation) of Ca2+ pumps (such as PMCA and SERCA) and Ca2+ channels (such as CaV1.3, TRPV5 and 6, ORAI). CaM also acts via serine/threonine kinases known as Calmodulin-activated Kinases (CaMKs) to influence cellular processes like proliferation (for example, centrosome duplication at G1/S or anaphase to metaphase transition via CaMKII). We will discuss those in detail below. Integral Membrane protein molecular buffers: SERCA, PMCA, NCX, and TRP Integral membrane protein Ca2+ buffers primarily translocate free Ca2+ between domains and organelles. These mainly comprise ion exchangers, channels, and ATP-driven pumps. SERCA, Sarcoendoplasmic Reticulum Ca2+ ATPase, is an ATP-dependent ion pump known to significantly maintain free cytosolic Ca2+ concentration via actively pumping the ion into the endoplasmic reticulum (or sarcoplasmic reticulum in muscle cell). They share a general structure that includes 10-pass transmembrane helices and three cytoplasmic domain lobes as shown in Figure $5$ P-type Ca2+-ATPases also exist within the plasma membrane and maintain cytosolic Ca2+ levels by transferring them into the extracellular space. The Plasma Membrane Ca2+ ATPases (PMCAs) can PMCAs transport one Ca2+ ion per ATP molecule which differs from the two Ca2+ ions per ATP molecule stoichiometry of SERCA. The general structure of such Ca2+ transporters comprises 10 transmembrane segments with large cytosolic loops TM 1–2 and TM 3–4, a cytosolic N- and C-termini tails are shown in Figure $6$, panel A below. The cytoplasmic region of PMCA (left) has three loop structures with binding sites for signaling molecules like CAM. They also have phosphorylation sites for additional regulation. The C-terminal tail contains additional CAM sites as well as a PKA site. It has a PDZ domain that can anchor the protein to cytoskeletal components. Differential RNA processing lead to variations in the amino acid sequence in this region and hence binding specificity. The binding of CaM reverses auto-inhibition of the pump due to conformational shifts which displace C-tail from cytosolic loops. Other means of autoinhibition reversal include phosphorylation of C-tail (Ser/Thr residues) by protein kinase A or C, proteolytic cleavage of C-tail, or dimerization via the C-terminus. Transient Receptor Potential (TRP) channels have a similar function in neurons, epithelial and immune cells. The Mammalian TRP channel superfamily is composed of 28 family members belonging to six subfamilies—TRPC (Canonical), TRPA (Ankyrin), TRPM (Melastatin), TRPV (Vanilloid), TRPP (Polycystin), and TRPML (Mucopilin)—that differ in their sensitivity to various sensory stimulations and affinity for cations (including Ca2+ ions) sequestration. Commonly, TRP family members share a structure with six transmembrane domains, intracellular N- and C-termini, and a pore-forming TM 5–6 loop. The cytoplasmic C-terminus of each subunit is a site for protein interaction and post-translational modification. The C-tail of these channels can have PDZ protein binding domains (TRPV and C), sites for interaction with G-proteins (Gq/11)/calmodulin/PLCβ, ADP-ribose binding (NUDIX; TRPM2), or PLC-interacting kinase (PLIK; TRPM6 and 7) domain. TRP channels act as activators, integrators, as well as downstream effectors of Ca2+ signaling at the plasma membrane and in intracellular compartments. Many members of the TRPC subfamily are activated by DAG (diacylglycerol) which is produced by PLC β- or γ-mediated cleavage of phosphatidylinositol 4,5-bisphosphate (PIP2) after the ligand binding at GPCRs or RTKs. TRPP1/2, TRPA1, TRPM8, and TRPV1-4 are all expressed on the ER membrane. At this site, PLC-independent activation of the TRP channels (such as TRPV1) is suggested to induce ER Ca2+ release via inositol triphosphate receptor (IP3R) which further triggers bulk entry of extracellular Ca2+ into the cell. On the flip side, cytosolic Ca2+ regulates the activity of TRP channels in response to physiological stimuli. This regulatory effect is usually through CaM binding (inhibition of TRPV5, TRPV6, and sensitization of TRPV3) and indirectly through CaM-binding kinase II (CaMKII). These activities are shown in Figure $7$. Storage of intracellular Ca2+ ions - organelles Endoplasmic Reticulum: STIM, ORAI, IP3Rs, and TRPC1 in SOCE and SOCIC Ca2+ Entry Models The ER serves as the largest and most dynamic organelle reservoir for intracellular Ca2+ and is therefore central to many signaling processes for protein synthesis, folding, and post-translational modifications. In contrast to the cytosol, ER Ca2+ ion levels can range from 100 uM to 1 mM based on the cell type. ER, and other intracellular organelles buffer excessive cytosolic Ca2+ by both housing Ca2+-binding proteins (example: calreticulin in ER) and via active transport (example: SERCA pumps in ER). • Depletion of Ca2+ from the ER lumen actuates an indirect mode of Ca2+ entry into the organelle which is termed Store-Operated Ca2+ Entry (SOCE) or Ca2+ Release Activated Ca2+ (CRAC) entry; it is activated when plasma membrane receptors like PLC-coupled GPCRs (but not voltage-gated channels) trigger Ca2+ ion release from the organelle. • Exhaustion of the intraluminal ER Ca2+ ion store following such prolonged release is then sensed by STIM (Stromal Interaction Molecule) tethered to the ER membrane and subsequently relayed to the CRAC channels on the plasma membrane. Figure $8$ shows the domain structures of STIM 1/2, ORAI 1-3, and IP3Rs (panel A) and the mechanism of Ca2+ influx into the cell (B). IP3 Receptors (IP3Rs), on stimulation by IP3, open and allow Ca2+ influx from the organelle lumen into the cytoplasm. After activation of IP3Rs on the ER membrane by ligand IP3 and cytosolic Ca2+ from activation of phospholipase C, STIM dimers are activated once the luminal Ca2+ concentration drops below basal levels. These receptors provide intracellular Ca2+ ions for downstream Ca2+ signaling including NFAT-mediated transcription. The red semi-circle in the ER represents high luminal Ca2+ levels, the pink semi-circle is for moderately low Ca2+ ion concentration, and the pale semi-circle indicates extremely low Ca2+ concentration. CC, coiled-coil; NFAT, nuclear factor of activated T-cells; SAM, sterile alpha motif; SOAR, STIM1 Orai1-activating region; TM, transmembrane. Mitochondria and Acidic Vesicles (Mainly Lysosomes) Mitochondria also play a critical role in maintaining Ca2+ ion levels in the cytosol and endoplasmic reticulum. They are found mostly aggregated around the nucleus and store similar levels of intracellular Ca2+ as the cytosol (0.1 μM). Electrochemical proton gradient or membrane potential (Ψmt = −150 to −180 mV) and close association to the ER are the two key factors responsible for Ca2+ uptake in mitochondria. The free movement of small molecules (less than 5 kDa) from the outer mitochondrial membrane (OMM) into the inner mitochondrial space and their impermeability across the latter generates a high electrochemical proton gradient for ATP synthesis. This gradient simultaneously draws Ca2+ ions from the cytosol. Transfer of Ca2+ ions from ER to mitochondria occurs at specialized microdomains or contact sites known as Mitochondrial Associated Membranes (MAMs). These are characterized by the ER and OMM apposed at 10–25 nm from each other and are strewn with a cluster of channels, transporters, exchangers, and tethering proteins for facilitating Ca2+ ion transfer. IP3Rs localized at the ER side of the MAMs release Ca2+ ions that gate voltage-dependent anion channels (VDACs) located on the OMM. VDACs (1, 2, and 3) are 30 kDa polypeptides having a 19-strand beta-barrel structure that regulates the flux of metabolites (polyvalent anions like ADP and ATP) across the outer mitochondria membranes. These channels transport cations including Ca2+ more readily than anions like chloride. Due to voltage-dependent electrostatic gating, the ion selectivity and flux across VDACs change between open and closed states. Figure $9$ shows couped mitochondrial and lysosomal effects on intracellular Ca2+ signaling. Primary components of Ca2+ signaling at the mitochondrial associated membranes (MAMs) include IP3R3 on the endoplasmic reticulum, VDAC1 on the outer mitochondrial membrane, and MCU complex on the inner mitochondrial membrane [151,154,156,161]. Transport of Ca2+ ions from ER to mitochondria plays a crucial role in cellular metabolism (autophagy), cell survival (during unfolded protein response and impinging cell death signals), lipid production, and distribution. F While IP3 acts as the dominant Ca2+-mobilizing messenger, cADPR (cyclic ADP-ribose) and NAADP (nicotinic acid adenine dinucleotide phosphate) are also known to modulate intracellular Ca2+ stores. cADPR evokes Ca2+ ion release from ER by acting on ryanodine receptors (RyR; counterpart of IP3R in myocytes and co-expressed in some other cell types). NAADP releases Ca2+ from acidic and/or secretory vesicles such as lysosomes and endosomes. In most mammalian cells, lysosomes comprise ~5 percent of the cell volume and store similar levels of intracellular Ca2+ (0.5 mM) as the ER. Due to their relatively smaller size than ER, lysosomes release nearly undetectable amounts of intracellular Ca2+ in response to NAADP trigger Signaling pathways activated by Ca2+ ion It is daunting to both readers and writers to introduce a myriad of new signaling pathways. Instead will show how Ca2+ signaling fits into other pathways we have already discussed. A summary showing how Ca2+ signaling integrates with other pathways is shown in Figure $10$. An Overview of Calcium Signaling Pathway Ca2+ signaling, as described above, requires ion buffer, organelle storage, and Ca2+ protein pumps and channels. The concentration (amplitude) and frequency of Ca2+ release affect signaling. Figure $10$ shows the importance of upstream signaling through GPCRs, phospholipase C, RTKs, and IP3/DAGs. Ca2+ also enters the nucleus vs IP3 receptors (IP3Rs) and ryanodine recetors (RYR). An important family of cytoplasmic transcription factors, the Nuclear factor of activated T-cells (NFAT), which are important in immune responses and development of muscle and nervous systems, are activated in calcium ion signaling. As we described above, Ca2+ release from the ER is sensed by integral ER membrane proteins called STIMs. These bind Ca2+ ions as a buffering system, but if most of the ER calcium is depleted, the STIM-bound Ca2+ ions are also released. This lead to their self-association and ultimate activation or ORAI1, part of the CRAC complex in the cell membranes which allow extracellular Ca2+ ions to flow into the cell in a process called store-operated calcium entry (SOCE). Sufficient calcium now accumulates in the cell to activate the transcription factor NFAT through dephosphoylation by calcineurin (PP2B), also abbreviated CaN in Figure $10$. NFAt then translocates into the nucleus and activates gene transcription. Also, it has been shown that nuclear calcium ions directly can activate the cAMP response element binding protein (CREB), a transcription factor that activates gene transcription. In addition the CAM:CAMKII complex can translocate into the nucleus. Calcium signals also activate ERK1/2-MAPK cascade. Ca2+ binding proteins and their binding partners in signaling pathways We have already described the key calcium-binding protein, calmodulin. On binding Ca2+, it undergoes a profound conformational change that allows it to interact with a family of key signaling kinases called CAM and Ca2+/CAM-Dependent Protein Kinases (CAMKs). Ca2+/calmodulin-dependent protein kinase is a key signaling protein activated by Ca2+ through the binding of calmodulin to CAMK. Activated CAMK is a Ser/Thr kinase. There are many types of CAMKs. We will focus on multifunctional CAMKs that can phosphorylate multiple target proteins. These are important in learning and memory, metabolism, and gene transcription. As with other kinases, they have catalytic and regulatory domains. Some like CAMK II have association domains that allow the formation of CAMKII multimers. In addition, they must have a CAM binding domain. As with all kinases, the CAMKs must be able to switch from an inactive to an active form. CAMKI has a catalytic, substrate-binding domain and an autoinhibitory domain that blocks the active site. CAMKI is activated by an upstream kinase CAMKK (a naming system similar to the MAPK cascade) on the binding of Ca2+ to CAM. It helps regulate transcription, the cell cycle, hormone production, cell differentiation, actin filament organization, and neurite outgrowth. It is found in the cytoplasm and nucleus. We will focus our attention on CAMKII, which has four isoforms (α, β, γ, and δ). It is activated by the binding of Ca2+/CAM which promotes autophosphorylation. After that, it is active in the absence of CAM. It is important in learning and memory and synapse formation in neurons and the regulation of sarcoplasmic reticulum Ca2+ transport in skeletal muscles. They have an N-terminal catalytic domain and a C-terminal association domain that facilitates multimer formation into large holoenzymes with 12 or 14 CAMK monomeric subunits (a homomer or heteromer). These two domains are separated by a linker/regulatory domain that has a CAM binding site, an autoinhibitory region, and key Ser and Thr side chains that are targets for phosphorylation. Figure $11$ shows the domain structure of the CAMKII monomer (a), the overall structure of a homododecamer (b), and the mechanism for activation of kinase activity (c). T253, T286, and T305/306 are targets of autophosphorylation. M281/282 are also sites for oxidative modification. The C-terminal association domain allows multimer formation. It has a variable region that differentiates CaMKII subtypes. Panel (b) shows the multimer that forms on interactions of multiple association domains on different CAMKIIs. Panel (c) shows binding of CAM promotes the phosphorylation of key residues including T286 (through autophosphorylation). In the absence of Ca2+/CAM T286 amino acid forms interactions with the catalytic domain to maintain the inactive conformation. The regulatory domain effectively autoinhibits the kinase domain. On the formation of the Ca2+/CAM/CAMKII complex, a conformation change ensues that frees the catalytic domain from autoinhibition and exposes the active site. Each subunit in the dodecamer is activated separately. T286 is now free to be "autophosphorylated" by an adjacent active subunit. Once phosphorylated, pT286 prevents the rebinding of the autoinhibitory region to the catalytic domain, even when CAM dissociates. At this point, CAMKII is active in the absence of Ca2+/CAM. Phosphorylation of T286 also regulates its binding to target proteins for their phosphorylation. In addition, CAMKII can autophosphorylate T254 and T306 with further effects on activity. T306 is only autophosphorylated after CAM dissociates and the enzyme is autonomously active. Dephosphorylation by PP1 and PP2A returns the enzyme to an inactive state. Figure $12$ shows an interactive iCn3D model of a single subunit of human Ca2+ Calmodulin- Dependent Kinase II Holoenzyme (3SOA) The catalytic domain is shown in orange, the association domain in green, and the linker domain in light cyan. The spacefill light cyan site in the linker domain is the CAM binding site. The side chains of Thr 253 and Thr 286, sites for phosphorylation, are shown in spacefill CPK colors and labeled. The side chains of Cys 280 and Met 281, the site for redox regulation, are shown in spacefill CPK colors and labeled. Bosutinib (shown in ball and stick) is a small molecule BCR-ABL and src tyrosine kinase inhibitor used for the treatment of chronic myelogenous leukemia. It is bound in the ATP binding site of the catalytic subunit. Finally, the activation loop is shown in red. Figure $13$ shows an interactive iCn3D model of Human Ca2+ Calmodulin- Dependent Kinase II Holoenzyme (3SOA) The response of most protein kinases we have studied depends on the concentration (amplitude) of a binding ligand (like Ca2+). The CAMKII dodecamer also responds to the frequency of Ca2+ waves or spikes as it is released from intracellular organelles. This is important in neurons where CAMKII phosphorylates ion channels that control and regulate neuron response. If the frequency of Ca2+ spikes reaches a certain threshold level, the enzyme no longer depends on Ca2+. Modeling of Ca2+ signaling Model for signal-induced Ca2+ oscillations and their frequency encoding through protein phosphorylation. HVJ: In response to hormones and neurotransmitters, cyclic intracellular oscillations/spikes (as a function of time) and spatial waves of cytoplasmic Ca2+ arise. The Ca2+ ions derive from the opening of cell membrane channels but also, more importantly, from the release and recapture of the ions from intracellular compartments such as the endoplasmic reticulum (ER). Signaling through membrane GPCRs can lead to the activation of phospholipase C and the formation of inositol 1,4,5-trisphosphate (IP3 or InsP3) as a key second messenger. IP3 can interact with the IP3 receptor on ER membranes, leading to the release of intracellular stores of Ca2+ into the cytoplasm in ways that leads to oscillations in its concentration. These processes are shown in Figure $14$ below. Figure $14$: Ca2+ oscillations in response to inositol trisphosphate (IP3) increase, with and without Ca2+ influx from extracellular space. Catacuzzeno L, Franciolini F. Role of KCa3.1 Channels in Modulating Ca2+ Oscillations during Glioblastoma Cell Migration and Invasion. Int J Mol Sci. 2018 Sep 29;19(10):2970. doi: 10.3390/ijms19102970. PMID: 30274242; PMCID: PMC6213908. Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Panel (A) bottom shows a drawing illustrating the hormone-based production of IP3 that activates the IP3 receptor to release Ca2+ from the endoplasmic reticulum (ER). The biphasic effects of cytosolic Ca2+ on IP3 receptor gating (the basic mechanism for Ca2+ oscillations), whereby Ca2+ modulates positively the receptor at low [Ca2+] but negatively at high [Ca2+], is also illustrated. Top, Ca2+ oscillations as produced from the schematics below. Note the decaying trend of Ca2+ spikes due to the absence of Ca2+ influx from extracellular space; Panel (B) shows Ca2+ influx apparatus from extracellular spaces through ER-depletion by activated Orai channels on the plasma membrane (bottom), which generates sustained Ca2+ oscillations (top). Ca2+ levels fall through Ca2+ pumps on the sarco/endoplasmic reticulum (SERCA), which moves the ion into the SR/ER or the plasma membrane (PMCA) that then moves it to the outside of the cell. The SERCA and PMCA Ca2+ pumps as well as the ER STIM protein are not shown in Figure 14 above. The STIM1 protein is involved in what is known as store-operated Ca2+ entry (SOCE). This allows the influx of Ca2+ influx after intracellular stores are depleted. The STIM1 protein has an EF-hand so it binds and acts as a sensor for Ca2+. When Ca2+ is depleted, the STIM1 protein moves from the ER to the cell membrane and activates ORA1, a subunit of the Ca2+ release-activated Ca2+ (CRAC) channel to promote Ca2+ into the cell. In a variety of cells, hormonal or neurotransmitter signals elicit a train of intracellular Ca2+ spikes. The analysis of a minimal model based on Ca2+ -induced Ca2+ release from intracellular stores shows how sustained oscillations of cytosolic Ca2+ may develop as a result of a rise in inositol 1,4,5-trisphosphate (InsP3) triggered by external stimulation. This rise elicits the release of a certain amount of Ca2+ from an InsP3-sensitive intracellular store. The subsequent rise in cytosolic Ca2+ in turn triggers the release of Ca2+ from a second store insensitive to InsP3. The model shows how signal-induced Ca2+ oscillations might be effectively encoded in terms of their frequency through the phosphorylation of a cellular substrate by a protein kinase activated by cytosolic Ca2+. The release of intracellular Ca2+ release by IP3 can be broken down into 3 steps: β • agonist binding to GPCRs to activate the Phospholipase C pathway to produce the second messengers IP3 and DAG; • IP3 induces some Ca2+ release from intracellular stores in the SR/ER. This can be called the Primer Step characterized by a rate V1 to produce cytosolic Ca2+ of concentration [Z] that prime the next step. This step has a rate of v1β. • released Ca2+induces more Ca2+ release, a self-amplification positive feedback step, which causes the Ca2+ spike. A simplified view of this model is shown in Figure $15$ below. Figure $15$: Schematic representation of the one-pool model based on CICR with Ca2+-stimulated degradation of IP3. Lloyd, C.M., Lawson, J.L., Hunter, P.J. and Nielsen, P.F. The CellML Model Repository. Bioinformatics. 2008 September;24(18):2122-2123 (accessed 4/19/23; 5:40 am EDT). Creative Commons Attribution 3.0 Unported License. A simple model can be constructed to account for cytosolic Ca2+ fluxes. The steps include • IP3 causes a triggering a constant flux of Ca2+ into the cytosol, v1β ,to produce cytosolic [Ca2+] = Z (the primer) • cytosolic Ca2+ flows into an IP3-insensitive sequestered pool (concentration Y) with rate v2 to keep low levels of cytosolic Ca2+ • Spikes arise when the sequestered pool Y releases Ca2+ back into the cytosol at a rate v3 in a process that is activated by cytosolic Ca2+ Goldbeter at al (Proc. Natl. Acad. Sci. 87, 1461-1465 (1990) constructed a mathematical model to account for the oscillations/spikes In addition to the parameters mentioned above, they included 3 more: • vo, describing the influx of extracellular Ca2+ into the cytosol and k describing the efflux of cytosolic Ca2+ from the cell. These are controlled by Ca2+ pumps SERCA, PMCA, et al as described above. • kf, which describes the passive leak of Y into Z. The Ca2+ oscillations then are based on a self-amplified release of Ca2+ from intracellular stores. Here are their equations: • $dZ/dt=v_{0}+v_{1}beta - v_{2}+v_{3}+k_{f}Y-kZ$ • $dY/dt=v_{2}-v_{3}+v_{3}-k_{f}Y$ • $v_{2}=V_{m2}\frac{Z^{n}}{K^{n}_{2}+Z^{n}}$ • $v_{3}=V_{m3}\frac{Y^{m}}{K^{m}_{R}+Y^{m}}.\frac{Z^{p}}{K^{p}_{A}+Z^{p}}$ We'll use Vcell to plot the following species: • Species Z: Concentration (uM) of cytosolic Ca2+ • Species Y: Concentration (uM) of Ca2+ stored in the InsP3-insensitive pool. The model is based on code from EBI-Biomodels: https://www.ebi.ac.uk/biomodels/BIOMD0000000098. MODEL Calcium ion oscillation in the cell without oscillations in IP3 Here are the adjustable parameters: • v0: influx of Ca2+ into the cell, uM/s. • v1: influx of Ca2into the cell from the InsP3 receptor, uM/s. • beta: saturation function of the receptor for InsP3 , unitless. • Vm2: maximum rate of Ca2+ pumping into the intracellular InsP3-insensitive store, uM/s. • Vm3: maximum rate of Ca2+ released from the intracellular store, uM/s; • K2: Threshold constant for Ca2+ pumping, uM. • n: Hill function cooperativity coefficient for Ca2+ pumping into the store, unitless. • m: Hill function cooperativity coefficient for Ca2+ pumping from the store, unitless. • Ka: threshold constant for activation, uM. • kf: efflux of cytosolic Ca2+ from the cell, 1/s. • k: influx of extracellular Ca2+ into the cytosol, 1/s. • p denotes the degree of cooperativity of the activation process, unitless. Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play \| Select Slider to change which constants are displayed \| Select About for software information. Move the sliders to change the constants and see changes in the displayed graph in real time. After loading the GoldBeter model, select the 'Start' button below to simulate the model. Adjust the parameter sliders below the plot to see how they affect Ca2+ concentrations (Z, Y). The simulator only displays twelve parameters at a time. To choose others, select the 'Slider' button on the side and chose up to twelve parameters to adjust. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health. Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington. Funded by NIH/NIGMS (RO1-GM123032-04) Adjust the parameter sliders below the plot to see how they affect Ca2+ concentrations (Z, Y). The simulator only displays twelve parameters at a time. To choose others, hit the 'Slider' button on the side and chose up to twelve parameters to adjust. Only two variables, Y and Z, and some intra-connections are required to generate the oscillations, which do NOT required oscillation in the second messenger, IP3. One can imagine that cytosolic Ca2+ oscillations might also elicit oscillatory activity of protein kinases activated by it. Questions: • Does v0 change the oscillation frequency? • What other parameters affect the frequency? • How does Vm2 affect the oscillations?
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.08%3A_Receptor_Guanylyl_Cyclases_cGMP_and_Protein_Kinase_G.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Much of this material is derived from Friebe et al. cGMP: a unique 2nd messenger molecule – recent developments in cGMP research and development. Naunyn-Schmiedeberg's Archives of Pharmacology volume 393, pages 287–302 (2020). Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/ Introduction We have considered many signal transduction pathways, starting with an extracellular signal, a primary messenger, that initiates signaling when it binds to a receptor (GPCR, RTK, Cytokine Receptor, etc). It elicits a conformation change in the receptor, which is transmitted to an intracellular domain, from where it can propagate and transmits a signal intracellularly through secondary messengers and phosphorylation of signaling proteins. Some primary messengers, however, actually pass through the cell either passively or through membrane carriers, so there is no need to generate a second messenger. We will consider two signals that translocate through the cell membrane, gases such as nitric oxide (NO), and steroid hormones (which we will discuss in a future section). NO produced within a cell activates the formation of an intracellular second messenger, cyclic GMP (analogous to cAMP). cGMP in turn activates our final member of the AGC Ser/Thr protein kinase family, protein kinase G (PKG). Intracellular NO has an unexpected role on adjacent cells. Given its small size and its nonpolar nature, it can diffuse out of the cell where it is produced and into an adjacent cell, where it can also initiate signaling. Some call this retrograde signaling. Before we consider cGMP and PKG, let's see how nitric oxide (NO) is produced. NO formation NO is synthesized by the enzyme nitric oxide synthase (NOS). There are three isoforms in mammals, neuronal (NOS1 or nNOS), inducible (NOS2 or iNOS), and endothelial (NOS3 or eNO2). Each is a homodimer with a complex domain structure, including a • N-terminal oxidase (NO_synthase) domain that binds heme • calmodulin binding site between the N and C terminal domains • C-terminal reductase domain containing the FMN (Flavodoxin_1) subdomain (which contains an autoinhibitory helix) and FAD/NADPH subdomain. Ca2+ ion activates the enzyme through the binding of Ca2+/CAM. A more detailed description of the domain structures of human NOS is shown in Figure $1$. The dimeric molecular weight of the neuronal NOS1 (321K) is greater than for iNOS (206) and eNOS (266) as it has a N-terminal PDZ domain. neuronal NOS 1 (MW 321) purple NADPH binding inducible (MW 206K) endothelial (MW 266) yellow NADPH binding Figure $1$: Domain structure of human nitric oxide synthases NOS catalyzes the conversion of the free amino acid arginine to citrulline and NO, as shown in the chemical equation in Figure $2$. The structures of the three enzymes with or without bound CAM are similar. Linkers between the domains and subdomains allow flexibility. Figure $3$ shows the flow of electrons from NADPH into the reductase (NADPH/FAD subdomain to the FMN subdomain), and on to the NOS synthase domain containing the heme. Two monooxygenase reactions occur in NOS synthase (oxidative domain) in which electrons are funneled into the heme and bound dioxygen (O2) leading to the formation of water and the final product, NO. Electron transfer only occurs within the dimer when calmodulin is bound. However, iNOS is active even at basal Ca2+ concentrations. Figure $3$ shows an interactive iCn3D model of the structure of human neuronal nitric oxide synthase (with its PDZ domain) predicted by AlphaFold (P29475). In the iCn3D model, orient the protein as shown in the figure above. The dark blue (left) is the PDZ domain, and cyan is the oxidase (NOS synthase) domain that contains the heme (which is not shown in AlphaFold models). The spacefill CPK color shown in the cyan domain is active site residues interacting with the heme (not shown). The orange domain is the FMN (Flavodoxin_1) subdomain in the reductase domain. The magenta (far right) shows the FAD/NADPH subdomains of the reductase domain. The yellow spacefill shows the NAD binding pocket and the white spacefill the FAD binding pocket. The structures of amino acids 129-304 between the PDZ domain and the oxidase domain are not predicted with any certainty. Crystal structures are available for the oxidase domain alone. The spacefill CPK-colored helix (amino acid 730-754) represents a helical peptide region that binds to calmodulin. NO that is synthesized in the cells can signal there or diffuse to another cell and signal there. Figure $11$ shows how NO synthesized in vascular epithelial cells that line blood vessels can move into the nearby muscle cells and initiate signaling through soluble guanylyl cyclase there, leading to vasodilation and a lowering of blood pressure. Endothelial nitric oxide synthase (eNOS) located in the vascular endothelium forms NO from plasma arginine. Two substrates, O2 and NADPH, are required along with the cofactors tetrahydrobiopterin (BH4), FAD, and flavin mononucleotide (FMN). NO diffuses into smooth muscle cells and activates soluble guanylyl cyclase (sGC), increasing cGMP production. cGMP subsequently activates protein kinase G (PKG), resulting in decreased [Ca2+] by these mechanisms: • inhibition of voltage-dependent calcium channels (VDCC), reducing calcium influx; • activation of plasma membrane calcium ATPases (PMCA), increasing ATP-dependent calcium efflux; • inhibition of inositol triphosphate receptors (IP3R), reducing calcium release from the sarcoplasmic reticulum (SR) to the cytoplasm; • activation of sarcoplasmic calcium ATPases (SERCA), increasing the ATP-dependent sequestration of calcium from the cytoplasm to the SR. Decreased [Ca2+] mediates smooth muscle relaxation via the activation of myosin light chain kinase and the inhibition of myosin light chain phosphatase (not shown in the figure), resulting in vasodilation. Figure $13$ shows retrograde diffusion of NO from an activated post-synaptic neuron back to the presynaptic neuron that excited it on the release of the neurotransmitter glutamate. NO is synthesized by post-synaptic cell nNOS after it's activated by Ca2+ inflow and binding to CAM (not shown). NO with the post-synaptic neuron binds to guanylyl cyclase to produce the second messenger cGMP which can directly activate other channels, protein kinase G, or phosphodiesterases (PDEs) Synaptic glutamate release activates postsynaptic NMDA and AMPA receptors (NMDAR, AMPAR) leading to Ca2+-induced nNOS activation. NO will diffuse back to the presynaptic cell and activate sGC to produce cGMP, which has many signaling roles including affecting presynaptic neurotransmitter release. cGMP directly targets several ion channels in the post-synaptic cell. As we saw in the previous chapter, many ion channels are voltage-gated. However, ion channels can be regulated directly by ions (ex Ca2+, Na+) as well as by cyclic nucleotides such as cAMP and cGMP. The later channels are called cyclic nucleotide-gated (CNG) channels. NO in the post-synaptic cell also associates with CAPON, a nNOS binding protein, leading to downstream MAP kinase cascade. Carbon Monoxide Everyone knows that carbon monoxide (CO) in high doses is lethal as it binds to heme Fe2+ in hemoglobin and myoglobin with a higher affinity than O2. Hence it may come as a surprise to you that endogenous CO is a signaling molecule, which now in retrospect might make sense given its similarity in chemical structure to NO. CO is produced through heme oxygenase (HOs). CO can act as a signaling molecule in neural, cardiovascular, respiratory, gastrointestinal, immune, and reproductive systems. In contrast to the lethal effects of inhaling exogenous CO from incomplete combustion, endogenous CO has anti-inflammatory and antioxidant effects. It can also act to dilate the vasculature system. Other gases like H2S also are signaling agents. cGMP formation The second (or third) messenger cyclic guanosine monophosphate (cGMP) is synthesized after activation of the enzyme guanylyl cyclase (GC) by nitric oxide. cGMP has many signaling effects in cells, some of which were outlined above. The cytoplasmic soluble GC (sGC) is activated by NO. The membrane-associated "particulate" GC (pGC) form is activated on the binding of natriuretic peptides (NPs) to natriuretic peptide receptors, which are NP-activated integral membrane guanylyl cyclase. The peptide hormones (ANP secreted by the atria and BNP secreted by ventricles) decrease blood pressure. The membrane form does not require NO for activation Figure $14$ shows the conversion of GTP to cGMP. Synthesis of cGMP from soluble GC is activated by NO or molecules like nitrates that can be metabolized to NO. These molecules are called NO donors. Since NO causes vasodilation, they are used to treat angina and hypertension. A class of drugs called stimulators (for example riociguat), increases cGMP production from sGC in the absence and synergistically in the presence of NO. They are also used to treat hypertension. Another class of drugs called activators can activate sGC even if heme is oxidized or even missing without upstream NO signaling. They are effective even if the heme is oxidized or lost from the NOS catalytic domain. We saw that cAMP is cleaved to AMP by phosphodiesterase. Likewise, phosphodiesterases (PDEs) cleave cGMP to GMP to attenuate signaling through cGMP. Selective drugs targeting PDE are available. These include sildenafil for the treatment of pulmonary hypertension and erectile dysfunction and tadalafil for benign prostatic hyperplasia (BPH). Pathways for activation of guanylyl cyclase activity (sGC and pGC) are shown in Figure $15$. Soluble guanylyl cyclase (cGC) structure and function The soluble form of GC is a heterodimer of α and β subunits. The domain structure of guanylyl cyclase is shown in Figure $16$. It appears that when NO binds to the heme group, a twist in the coiled-coil domain leads to its extension, which leads to the activation of the catalytic domain. Simulators likely cause similar conformational changes initiated by their binding to the top part of the CC domain. Figure $17$ shows an interactive iCn3D model of the human soluble guanylate cyclase in the riociguat (stimulator) and NO-bound state (7D9R) The A chain is dark gray and the B chain is light gray. The guanylate cyclase domain contributed to by each monomer is shown in cyan. A phosphonate GTP analog (labeled G2P) is shown in spacefill CPK color bound in the guanylyl cylase domain (cyan). The heme (HEM) and the stimulator riociguat (GZO) are shown in the HNOBA (Heme NO Binding Associated) domain and shown in spacefill CPK colors. The conformation change of A chain of the bent inactive form of guanylyl cyclase (6JT1) to the active extended form (6JT2) is shown in Figure $18$. The guanylyl cyclase domain is at the top of the figure. The conformational change is somewhat reminiscent of the change in apo-calmodulin on the binding of Ca2+ ions, although the central helix is fully intact in the inactive and active forms of guanylyl cyclase. Figure $19$ shows how oxidation of the heme iron and S-nitrosation of the protein that occurs in the presence of reactive oxygen species (ROS) and reactive nitrogen species (RNS) leads to heme loss and inactivation of sGC. pGC structure and function The other source of cytosolic cGMP is particulate guanylyl cyclase (pGC). These are integral membrane protein receptors for natriuretic peptides (NPs), which when bound to the receptor activate the cytoplasmic guanylyl cyclase domain of the receptor. In effect, they are ligand (NPs)-gated receptor enzymes. The peptide hormones (ANP secreted by the atria and BNP secreted by ventricles) decrease blood pressure. There are seven variants of pGC (A-G) found in mammals. GC-A (also called NPR-A or NPR1) and GC-B (NPR-B or NPR2), are both receptors for natriuretic peptides. The domain structure of NPR-A is shown in Figure $20$. Figure $21$ shows an interactive iCn3D model of the human atrial natriuretic peptide receptor1 AlphaFold predicted model (P16066) Orient the model as shown in the figure above. The coloring coding in the model is as follows: • green: Ligand (ANP)-binding domain of the type A natriuretic peptide receptor (NPR-A); • magenta: PK-GC pseudokinase domain; • cyan: cyclase domain; • rainbow helix: HNOBA domain is found to be associated with the HNOB domain and pfam00211 in soluble cyclases and signaling proteins. The HNOB domain is predicted to function as a heme-dependent sensor for gaseous ligands rainbow; • gray helix: transmembrane segment amino acids 474-494. Note the protein is an integral membrane protein that passes through the membrane using a single alpha helix (474-494). The N-terminal domains above it and the C-terminal domains below it would orient like a typical single-pass membrane protein in the presence of a bilayer. Protein Kinase G (PKG) To briefly review, NO production leads to the production of cGMP. cGMP can directly bind to and regulate membrane ion channels. In alignment with the basic paradigm of signaling described throughout this chapter, we will now discuss how it activates Protein Kinase G (PKG) a member of the Ser/Thr Protein Kinase AGC family. The are two mammalian genes for PKG1 and PKG2. Both are homodimers. PKG1 acts in the cytoplasm while PKG2 becomes tethered to the membrane by N-terminal myristoylation. Figure $22$ shows the domain structure of PKG-I, which is similar to PKG2. Red indicates the two nonidentical cGMP binding domains. Green is the N-terminal coiled-coil dimerization domain, which inhibits kinase activity in the absence of cGMP. On binding of cGMP, autoinhibition of the catalytic domain by the N-terminal domain is relieved. The binding of cGMP to the regulatory domain induces a conformational change which stops the inhibition of the catalytic core by the N-terminus and allows the phosphorylation of self (autophosphorylation) and then substrate proteins. Whereas PKG-I is predominantly localized in the cytoplasm, PKG-II is anchored to the plasma membrane by N-terminal myristoylation. PKG1 is involved in modulating Ca2+ activity, platelet activation, smooth muscle contraction, gene expression as well as neural function. PKG2 helps regulate bone growth, intestinal secretion, and synaptic plasticity. It also regulates gene expression and activates the MAPK cascade in bone cells. Figure $23$ shows an interactive iCn3D model of the predicted structure of Human cGMP-dependent protein kinase 2 (AlphaFold, Q13237). The cyan domain is the STK (Ser/Th Kinase) domain. The magenta and purple domains are the CAP-Ed domains. The spacefill side chain structures within them represent the cGMP (or with lower affinity cAMP) binding sites. The CPK-colored sticks in the cyan kinase domain are the amino acid side chains in the active site where ATP binds. The orange backbone represents the least confident part of the predicted structure. The black spacefill is the N-terminal Gly (after removal of Met) which is myristoylated, allowing targeting of the modified PKG2 to the cell membrane.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.09%3A_Gated_Ion_Channels_-_Neural_Signaling.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Introduction to Neural Signaling Ion channels are membrane proteins that allow the flow of normally impermeant ions across the hydrophobic "sea" of cell or organelle membranes. The channel proteins can be constitutively open or can be "gated" open (or closed) by signals that affect the conformation of the protein. The signals can be ligands (in the form of hormones or neurotransmitters), post-translational modifications (mostly phosphorylation, dephosphorylation), or more difficult-to-understand events (change in transmembrane potential, pressure, or temperature). Gated channels are perhaps best studied through their role in neural signaling. Neurochemistry is one of the most explosive areas of biological research. Scientists are now starting to unravel the molecular bases for memory, cognition, emotion, and behavior. The next decades will bring a greater understanding of brain chemistry and along with it the potential to alter human mood, and memory, and to treat mental illnesses such as schizophrenia much more effectively. The human brain, with about 86 billion neurons. Compare this with some estimates for the number of stars in the Milky Way galaxy (about 100 billion, a number derived from luminosity and mass measurements). Now imagine that each neuron can form connections - synapses - with 1000 to 10,000 other neurons. Throw in another 86 billion brain glial cells and you have one of the most complex structures in the universe. This section will explore the biology and chemistry of neurons. We will discuss two kinds of neurons - those that interact with muscles at the neuromuscular junction and those that interact with other neurons in the central nervous system. Neurons consist of a single, nucleated cell body with multiple signal-receiving outgrowths (dendrites) and multiple-signal sending outgrowths (axons) which end in a terminal button. These interact through the synapse with dendrites on other neurons. These characteristics are shown in Figure $1$. A presynaptic neuron can stimulate an adjacent postsynaptic neuron by releasing a neurotransmitter into the synapse between the cells, which binds to a receptor in the membrane of the post-synaptic cell, stimulating the cell, as shown in Figure $2$. Figure $2$: The synapse. https://cnx.org/contents/[email protected]:fEI3C8Ot@10/Preface..Creative Commons Attribution 4.0 International license. We will discuss the events which cause the post-synaptic cell to "fire", but we will not discuss the immediate events which lead to the release of neurotransmitters by the presynaptic neuron. Neurons (as do all cells) have a transmembrane potential across the membrane. Transient changes in the membrane potential are associated with neuron activation or inhibition. This arises in part due to the imbalance of ions across the membrane which was established by the Na+-K+-ATPase in the membrane. This electrogenic antiporter transfers 3 Na ions out of the cytoplasm for every 2 K ions it transports in. Likewise Cl- has a much higher level outside the cell. Membrane potentials are determined not only by the size of the ion gradients across the membrane but also by the differential permeability of membranes to ions. As we saw previously, synthetic bilayer membranes are not very permeable to ions, as shown in Table $1$ below. Ion permeability of phosphatidyl serine vesicles ION PERMEABILITY (cm/s) sodium <1.6 x 10-13 (lowest) potassium <9 x 10-13 chloride <1.5 x 10-11 (highest) Table $1$: Ion permeability of phosphatidyl serine vesicles Sodium would be expected to have a lower permeability than potassium since it has a higher charge density. It also is the largest in effective size due to the larger hydration sphere around the ion arising from its higher charge density. Chloride would be expected to have the highest permeability since it has the lowest charge density (due to the repulsion of the electron cloud in the negative ion). Intracellular charged proteins (which are mostly negative) are not permeable and help in creating the negative charge imbalance across the membrane. Much work has been done on the giant axon of the squid, which has uniquely high intracellular potassium (400 mM compared to 20 mM outside) and high extracellular fluid sodium (440 mM vs, 50 mM inside) and chloride (560 mM vs 52 mM inside). Mammalian cell concentrations are much lower, but the relative size of the gradient is about the same. Typical ion concentrations and permeabilities for mammalian membranes are shown in Table $2$ below. T Ion Cell (mM) Blood (mM) Permeability (cm/s) potassium 140 5 5 x 10-7 sodium 5-15 145 5 x 10-9 chloride 4 110 1 x 10-8 X- (negative macromolecules) 138 9 0 Table $2$: Typical ion concentrations and permeabilities for mammalian membranes How can we account for the markedly greater permeabilities of ions (1000x to 1,000,000 x) in mammalian cell membranes compared to synthetic lipid vesicles? Previously, we showed that glucose has a greater permeability through red blood cell membranes than through synthetic liposomes because of a membrane receptor that allows facilitated diffusion across the membrane and down a concentration gradient. The same thing is true of ion permeabilities in intact biological membranes. These membranes have several types of selective ion channels (non-gated - always open, and gated - open only after specific conformational changes). The non-gated channels dramatically increase the permeability of membranes to ions, as the glucose transport protein increased the permeability to glucose. It turns out that this differential permeability contributes to the transmembrane potential. Ion channels in nerves and muscles can move ions across the membrane at a rate of up to 109/s, which is comparable to kcat for the best enzymes. If we envision channels as pores, how can we account for the selectivity of the channel to specific ions? A larger pore should admit any ion less than a maximal size for the pore, so it is hard to imagine the nature of the selectivity filter. Because of this, many people discounted the ideas of channels in favor of a transporter, which would bind the ion selectivity and then, through conformational changes, move the ion across (much like the Na/K ATPase we discussed in the previous guide). This model could not, however, account for the incredible rates of ion flux across the membrane. Selectivity can be accounted for by a channel that contains a narrow opening that acts as an ion sieve. The ion loses most of its hydration sphere and would form specific interactions with amino acid side chains in the pore region. Such an interaction would be transient and not too tight since the ion must pass through the membrane. As we will see later, these ion channels: • pass ions down a concentration gradient in a thermodynamically favorable process • are specific for certain ions (although a few are less selective and will pass Na, K, Ca, and Mg ions) • allow ion flow through either ungated or gated channels., • saturate with increasing ion concentration (even though as concentration increases, the ions have a greater thermodynamic drive to pass through the channel). This is consistent with the binding of the ion at a selectivity filter in the narrow part of the pore. The KD for the interaction is usually in the mM range and indicates weak binding with large dissociation rate constants (koff). Transmembrane Potentials Several questions arise about the distribution of ions and the magnitude of the transmembrane potential. 1. How are the ion gradients established? 2. How does the transmembrane ion distribution contribute to the membrane potential? 3. How can the resting electrochemical potential and the ion distribution be maintained? The answer to these questions will be illustrated using studies on two types of brain cells, glial cells (which function as protectors, scavengers, and feeders for brain neurons) and neurons. Both types of cells have transmembrane potentials. Glial Cells The transmembrane ion gradients for ions can be established by different mechanisms. One uses ion-specific ATPases (P-type ion transporters), such as we discussed with the Na/K ATPase. This transporter ejects 3 sodium ions from the inside of the cell for every 2 potassium ions it transports in, all against a concentration gradient. Since it is an electrogenic antiporter, it helps generate the potential. Additionally, specific ion channels also contribute (as described below) to the transmembrane gradients and potentials. The harder question is how the ion distribution contributes to the membrane potential. Two things must occur for a membrane potential to exist: First, there must be a concentration gradient of charged ions (for example, sodium, potassium, or chloride) across the membrane. Second, the membrane must be differentially permeable to different ions. If the membrane were completely impermeable to ions, then no movement of ions across the membrane could occur, and no membrane potential would arise. If, however, membranes are differentially permeable to the ions, an electrical potential across the membrane can arise. Remember, synthetic bilayers are quite impermeable to ions, given the hydrophobicity of the internal part of the bilayer. Likewise, it is quite impermeable to glucose. It turns out that glial cells appear to have only a non-gated potassium channel, which allows the outward flow of potassium ions down the concentration gradient. The inside will then have a net negative charge since impermeable anions remain. The chemical potential gradient causes this outward flow of potassium ions. As more ions leave, the inside gets more negative, and a transmembrane potential develops which resists further efflux of potassium. Eventually, they balance, and the net efflux of potassium stops. The resting transmembrane potential reaches around -75 mV which is exactly the value obtained from the equations we will derive below. Since glial cells appear to only express a non-gated (or leakage) potassium channel, their resting potential is equal to the potassium equilibrium potential. Figure $3$ shows hypothetical transmembrane potentials (Ψ), and the electrical and chemical potentials (ΔG) for K+ loaded vesicles with and without a non-gated K+ channel As we have shown previously, very little K+ efflux is required to develop a transmembrane potential. At equilibrium, the K+IN is simply shown as < 0.1 M while K+OUT is shown as > 0.1 M Here is a PhET simulation showing the differences between non-gated (leakage, always open) and gated channels (opened in response to stimuli like ligands, transmembrane potential, mechanical forces, etc). Neurotransmitter Activation of Neurons What happens when a neurotransmitter binds to a receptor on the post-synaptic cell? We will study two examples. The first is the simplest: binding of the neurotransmitter acetylcholine, released by a motor neuron, to its receptor on muscle. This region is called the neuromuscular junction. The binding of acetylcholine will lead to a transient depolarization of the muscle cell. Next, we will discuss the interaction of a neurotransmitter with a post-synaptic neuron in the central nervous system. This is a much more complex system. Their differences are described below: In neurons interacting with muscles: • Most muscle fibers are innervated by only one neuron - a motor neuron • Neurotransmitter release at the neuromuscular junction leads only to muscle excitation, not inhibition. • All fibers are excited by the same neurotransmitter - acetylcholine. In the central nervous system, life is more complicated: • Stimuli are received from hundreds to thousands of different neurons. • Nerves receive both excitatory and inhibitory stimuli from neurotransmitters • Different kinds of receptors are present to receive stimuli, which control the activity of different kinds of channels. • The ion channels in neurons are gated by a variety of mechanisms in addition to changes in membrane potential, including gating by heat, cold, stretch, or covalent modification. • Most nerve cells have a resting potential of about -65 mV compared to -90 mV for a muscle cell. What happens when a neurotransmitter binds to the receptor on the post-synaptic cell? A depolarization occurs (mediated by conformational changes in the transmitter-receptor complex), raising the membrane potential from the resting equilibrium level. What happens next depends on the identity of the post-synaptic cell. In the muscle cell, the rising potential caused by the binding of acetylcholine ultimately leads to muscle contraction by opening intracellular organelle membrane calcium channels. In a neuron, the rising potential triggers an action potential by opening voltage-gated sodium channels. The potential rises to about + 35 mV but does not reach the Na ion equilibrium potential, because the high positive potential opens a voltage-gated potassium channel. The potential then falls until it reaches the K ion equilibrium potential when the cells are hyperpolarized. It slowly then relaxes back to the resting potential of -60 mV. This wave of changes in potential sweeps down the post-synaptic cell membrane and is the basis for the "firing" of the neuron. A plot of transmembrane voltage changes vs time for a typical action potential is shown in Figure $4$. Figure $5$ shows the actual changes in ion permeability in various phases of the action potential (replace figure, seek permission) mine Figure $5$: Na+ and K+ permeability during the action potential Figure $6$ shows an animation of a neuron firing showing all the key players. (produced by PhET, University of Colorado, Boulder). Proteins of the Neural Synapse We must now account for the rise and fall of the membrane potential to a variety of neurotransmitters, including the cholinergic transmitters (ex. acetylcholine), catecholamines (dopamine, epinephrine, norepinephrine), amino acid derivatives (ex. Glu, Asp, N-methyl-D-Asp, Gly, gamma-amino-butyric acid -GABA), and peptides (endorphins, enkephalins). We will consider five membrane proteins as shown below in Figure $7$. Many of these we have explored in detail in Chapter 11.3: Diffusion Across a Membrane - Channels, so we will focus on just one new one, the chloride channel, in the following section. Na+-K+-ATPase: It transports both sodium and potassium ions against a concentration gradient using ATP as an energy source. The protein is a sodium-dependent ATPase. Without this protein, the membrane potential could not be maintained since the sodium and potassium gradient would collapse. It also contributes to the potential since it is an electrogenic antiporter. (In addition, we have seen that ungated potassium and sodium channels are also present.) Neurotransmitter receptor: The receptors we will consider here are typically ligand-gated ion channels. Once the ligand binds, a conformational change occurs in the protein, allowing a flow of ions down a concentration gradient. Depending on the nature of the ion, the channel either initiates depolarization (when Na+ enters from the outside and raises ΔΨ) or inhibits depolarization (when Cl- enters from the outside and lowers ΔΨ). When chloride channels open, they hyperpolarize the transmembrane potential. Stimulatory neurotransmitters (like glutamate) lead to depolarization of the membrane, while inhibitory neurotransmitters (like gamma-aminobutyric acid) lead to hyperpolarization of the membrane (making the potential more negative). Na+ channel (voltage-gated): When the ligand-gated channel depolarizes the membrane to some threshold value, sodium channels undergo a conformational change and open allowing Na+ ions to flood into the cell, raising the potential to a positive approx. 33 mV (a value lower than the equilibrium sodium potential). This membrane protein is a voltage-gated channel, not a ligand-gated one. Two potent neurotoxins, tetrodotoxin (from Pufferfish) and saxitoxin bind to the channel and act as antagonists (inhibit the activity of the receptor by blocking sodium influx). Their structures are shown in Figure $8$. The guanidino group of the tetrodotoxin appears to bind with high affinity to the entrance of the channel that interacts with the hydrated sodium ion. Affinity chromatography using tetrodotoxin beads has been used to purify the protein. Figure $9$ shows the relative sizes of ions used to probe size requirements for the channel. the hydrated K+ ion can not pass through. Depolarization of the membrane potential may result in an outward movement and rotation of the positively charged helixes containing Lys and Arg side chains which had presumably formed salt bridges (ion-ion interactions) with negatively charged side chains within the protein. Depolarization of the membrane results in breaking a few salt bridges, and effectively leads to the movement of 1 or 2 charges on the helix through the membrane. Work occurs when charges are moved through an electric field. Work is also related to the ΔG for the system, which is also dependent on the ratio of the open and closed form of the channel. Other voltage-gated ion channels (for potassium and chloride) have a similar membrane topology and an S4 voltage-sensor helix. K+ channel (voltage-gated): When the membrane potential reaches around +25 mV or so, the K+ channel, a voltage-gated membrane protein, alters its conformation, allowing K+ efflux from the cells, lowering the potential until it reaches the potassium equilibrium potential. It slowly relaxes back to the cell resting potential of about -60 mV. Cl- channel: If these channels (typically ligand-gated) are open, they will hyperpolarize the cell and make it more difficult to fire. The selectivity filter is composed of many stacked rings of oxygens that can interact with a dehydrated K ion but not with a dehydrated Na ion which can not approach close enough to form significant interactions. Surrounding the filter are twelve aromatic amino acids which constrain the size of the pore opening. The interactions of the filter O's with the K ion make up for the energetically disfavored dehydration of the ion. The filter contains two K ions which repel each other, assisting in the vectorial discharge of the ions through the membrane. These ions must form weak interactions with the selectivity filter. The actual pore is mostly hydrophobic, which facilitates the flow-through of the ions. The central cavity of the pore can hold some water molecules in addition to the K ions which helps stabilize the ion in the middle of the pore. Inhibitory Neurotransmitters The main inhibitory neurotransmitters are GABA (gamma-aminobutyric acid), which is made from glutamic acid through decarboxylation of the α-C group, and glycine. They bind to transmitter-gated chloride channels, which when open hyperpolarize (make more negative) the transmembrane potential. Benzodiazepines (like Valium and Librium - anti-anxiety and muscle-relaxing agents) and barbituates (like phenobarbital-hypnotics) bind at allosteric sites on the GABA receptor and potentiate the binding of each other and GABA. This receptor is also affected by alcohol and anesthetics. Let's focus on the GABAA channels (also called the GABA(A) receptor or GABA(A)R) since it binds so many interesting pharmaceutical drugs. Figure $10$ shows an interactive iCn3D model of the human full-length heteromeric α1-β3-γ2L GABA(A)R in complex with picrotoxin, GABA, and megabody Mb38 (6huj) derived from cryo-EM (loads slowly). This structure is a Type-A γ-aminobutyric (GABA A ) receptor. It consists of two alpha chains (orange), two beta chains (magenta), and one gamma chain (brown). Two GABAs (spacefill, CPK colors) are bound between the orange and magenta subunits (between the α and β chains). Picrotoxin is shown in spacefill CPK colors in the central pore (bottom), near the cytoplasmic end (blue bilayer dummy atoms). The extracellular domain (above the red) has glycans shown in SNFG cartoon form and shown in Figure $11$. The structure of the receptor bound to many pharmacological agents has been solved. The structure of GABA (the agonist), bicuculline (a competitive antagonist), the benzodiazepines alprazolam (Valium) and diazepam (Xanax), a channel blocker (picrotoxin), ethanol, and Ro-15-4513 (an ethanol antagonist) are shown in Figure $12$. (12\): Structures of GABA-R binding molecules -GABA (the agonist), bicuculline (a competitive antagonist), the benzodiazepines alprazolam (Valium) and diazepam (Xanax), a channel blocker (picrotoxin), ethanol, and Ro-15-4513 (an ethanol antagonist) The benzodiazepine diazepam (Xanax) binds at an allosteric site and promotes GABA binding, so it is considered an "indirect agonist”. It is also an anxiolytic (a drug used to reduce anxiety) and an anticonvulsant. Ethanol activates the inhibitory GABA channel. Too much ethanol leads to cells hyperpolarized cells so neural responses are greatly inhibited, possibly leading to death. Ethanol acts synergistically with benzodiazepine, which makes this combination so lethal. Figure $13$ shows an interactive iCn3D model of the human full-length heteromeric α1-β3-γ2L GABA(A)R in complex with diazepam (Valium), GABA (6HUP) (loads slowly). https://structure.ncbi.nlm.nih.gov/i...fTZMqePnpdwTy8 The structure shows two alpha chains (orange), two beta chains (magenta), and one gamma chain (gray). Two GABAs (spacefill, CPK colors) are bound between the orange and magenta subunits (between the α and β chains). Three valiums (spacefill, CPK) are shown in the structure. One is bound to an allosteric site between the orange (alpha) and gray (gamma) chains. Two others are bound within the bilayer, this time between the alpha (orange) and beta chains (magenta) chains. Different benzodiazepines appear to bind in slightly different sites, and binding elicits their specific effects. A drug, Ro-15-4513, was developed in the 1980s that antagonizes the effect of ethanol. It has a complicated pharmacology. It is considered a benzodiazepine “partial inverse agonist”. It has no effect by itself. It reverses the anticonvulsant effects of benzodiazepines, and blocks the Cl- effects of ethanol so it is an inverse agonist of GABA-mediated Cl- flux. Hence it antagonizes the effect of benzodiazepines and alcohol. If given to intoxicated mice, they act normally! Such drugs have not reached the market as their use poses significant ethical issues. The effect of pharmacological agents on the GABA receptor In the “voltmeters” below, draw an arrow indicating if the transmembrane potential becomes more negative or more positive for the conditions given. Answer Add texts here. Do not delete this text first. Metabotropic Neural Receptors Some signaling molecules, whose effects are regulated by kinases (β-adrenergic and some olfactory signals by PKA and acetylcholine by PKC for example), are neurotransmitters. In all the examples presented previously, the neurotransmitters gate the inactive ion channels directly. These types of membrane receptors are classified as ionotropic. Typical examples of neurotransmitter-gated ion channels are the acetylcholine receptor in neuromuscular junctions and the Glu, Gly, and GABA receptors in the central nervous system. These receptors are multimeric proteins. Receptors with direct gating of ion flow are fast, with activities that last milliseconds, and are used in eliciting behavioral responses. However, ion channels can also be gated indirectly when the neurotransmitter binds to its receptor and leads to downstream events which subsequently open an ion channel that is distinct from the receptor. In this case, the occupied receptor communicates to an ion channel indirectly through a G protein for example. Examples of this indirect gating of ion channels include the serotonin, adrenergic, and dopamine receptors in the brain. These receptors are classic single-protein serpentine GPR receptors with 7 transmembrane helices and intracellular domains that can interact with G proteins as described above to increase second messenger levels (cAMP, DAG) in the neuron. The receptors are classified as metabotropic since they must activate a series of metabolic steps before ion channels are open. The second messengers can either activate kinases in the cell, which phosphorylate ion channels to either open or close them, or can bind directly to the channel and modulate its activity through an allosteric conformational change. In some cases, the G protein directly acts on the ion channel. These different ways are described in Figure $14$. In contrast to direct gating, receptors that indirectly gate ion channels have activities that are slow and last seconds to minutes. These receptors are usually involved in modulating behavior by changing the excitability of neurons and the strength of neural connections, hence modulating learning and memory. These changes can occur in many ways, summarized below: Phosphorylating ion channels: Receptors that act through a second messenger system can change ion channel activity by activating kinases that phosphorylate the channels. This may: • open the channel normally closed at the resting potential and produce an effect like gating. • close a channel usually opens at the resting potential (such as non-gated K channels which when closed would depolarize the cell and make it more excitable). Gα interaction with ion channels: • the Ga subunit of the G protein interacts with K channels after stimulation of the CNS Acetylcholine receptor, opening the channel and hyperpolarizing the cell Second messenger interaction with ion channels: • cGMP opens cation channels in retinal cells after activation of the photoreceptor by photons • cAMP opens cation channels in olfactory cells after activation of the olfactory receptor by odorants. Second messenger effects on proteins other than ion channels (usually different receptors): • the β-adrenergic receptors are phosphorylated by PKA and PKC (activated by stimulation of a different neurotransmitter receptor linked through a G protein to produce increased levels of second messengers cAMP and diacylglycerol). When phosphorylated, the β-adrenergic receptor, itself activated through G protein) can't bind Gs. This attenuates the response of the β-adrenergic receptor to its neurotransmitter which leads to desensitization to that signal. Second messengers regulate gene expression: • cAMP-activated PKA can phosphorylate an inactive transcription factor in the cell, which then can bind to a section of DNA called the cAMP Response Element (CRE), which is upstream of certain genes, leading to the transcription of the genes. The transcription factor is called CREB for cAMP Response Element Binding protein. Example: tyrosine hydroxylase (a monooxygenase) is involved in the synthesis of epinephrine and norepinephrine. The activity of this protein is increased when it is phosphorylated by PKA. Hence its activity can be increased quickly by this modification of the already present protein. If an animal is subjected to severe or long-term stress (cold or immobilization), presynaptic cells with norepinephrine will be stimulated to release the neurotransmitter. This requires continual synthesis of the neurotransmitter by the presynaptic cell. The increase in the synthesis of this neurotransmitter is caused by the presynaptic cell being stimulated by another neuron, which leads to increased levels of cAMP, and ultimately activation of CREB which increases transcription of the hydroxylase gene. Caffeine Caffeine produces a state of arousal in the central nervous system. High levels appear to block the binding of an inhibitory neurotransmitter, adenosine, to the A2A adenosine receptor. In the absence of caffeine, adenosine levels rise during the day, which promotes interaction with its receptor, leading to increased sleepiness and lack of concentration. When adenosine binds normally to its receptors, it activates the adenylate cyclase cascade, which activates PKA, leading to changes in the phosphorylation state of many proteins inside the cell, including protein phosphatase (2A). These changes inhibit neural firing. Caffeine blocks these changes. Hallucinogenic drugs Illicit drugs like LSD, psilocybin, and ecstasy can produce hallucinations as they have profound effects on consciousness and perception of self and reality. Recent clinical studies have shown that under tightly controlled conditions and doses, these drugs might have significant therapeutic effects in the treatment of mental health issues such as depression and post-traumatic stress syndrome. Hence their mechanisms of action have been the source of many studies. All of these drugs bind to the human serotonin 2A receptor (5-HT2AR), a metabotropic GPCR receptor of serotonin (5-hydroxytryptamine). When serotonin binds, it leads to GPCR signaling, partly through beta-arrestins. These are adapter proteins that form complexes with ligand-bound and activated GPCRs and GPCR protein kinases. Structures show that LSD binds to the orthosteric binding site (i.e. the active site, not an allosteric site) for serotonin. The study also found that serotonin and psilocin binding extends into an adjacent site called the extended binding pocket (EBP). These differences suggest that it should be possible to design agonists that have therapeutic, but not hallucinogenic properties. Serotonin binding does not cause hallucinations. A drug, IHCH-7086, in mouse studies seems to not provoke hallucinations but appeared to have antidepressive effects (based on observations of mouse behaviors like twitching and freezing). The structures of serotonin (5HT) and psychotropic drugs that bind the 5HT2AR are shown in Figure $15$. Figure $15$: Structures of serotonin (5HT) and psychotropic drugs that bind the 5HT2AR Figure $16$ shows a model of the serotonin receptor (5HT-2A) with each of the bound drugs. Note the different occupancy of the orthosteric and extended binding pockets. The GPCRs have been aligned for each of the poses. The ligands are represented in spacefill in a single color, as listed below: • red: serotonin (5HT), the physiological agonist, 7WC4 • yellow: psilocin, 7WC5 LSD, 7WC6 • magenta: LSD, 7WC6 • cyan: IHCH-7086 An alternative way to explain the fact that serotonin does not cause hallucinations and why the other agonists do is that in addition to binding plasma membrane GPCRs, the agonist might have additional effects that are associated with hallucinations by binding intracellular membrane GPCRs. The cerebral cortex plays a key role in what it means to be human. It is involved in higher-level processing involving thought, learning, emotion, personality, language, and memory. As such its dysregulation is involved in mental health conditions such as depression et al. in which synaptic plasticity is decreased as evidenced by a reduction in dendritic structure require for synaptic connections. The serotonin (5HT) reuptake inhibitors (SSRIs) used to treat depression appear to increase neural plasticity and connections over time (so they don't give immediate responses). Hallucinogens that target the 5-HT2A receptor (5-HT2AR) also promote neuroplasticity. However, neuron growth in cortical cultures is not affected by serotonin. What is the mechanism that allows various combinations of neuron plasticity and hallucinations all from the same receptor? One factor that determines these differential effects is that serotonin (5HT) is more polar and cannot readily enter cells, while the HT2AR agonists that are hallucinogenic are less polar (more lipophilic) and could potentially enter cells where they might elicit hallucinogenic effects. Intracellular hallucinogens would bind internal 5-HT2AR receptors, which are found in cortical neurons on the Golgi and other organelles that are more acidic than the cytoplasm or extracellular environment. (In fact, in cortical neurons, the main location of the 5-HT2ARs is intracellular.) This might lead to more prolonged retention of intracellular hallucinogens and longer signaling (LSD has a profound hallucinogenic effect that lasts for 10 hours or more) leading to neuronal growth as well. If serotonin is given to wild-type mice, no hallucinations occur, as evidenced by the lack of a head twitch response (HTR). However, if given to mutant mice who expressed serotonin transporter (SERT) on cortical neurons, the HTR response was observed. The import of serotonin led to neuroplasticity. It could be that serotonin is not the physiological ligand for intracellular HT2ARs. It might also imply that as with the case with endocannabinoids, we have endogenous psychedelics. The subtle difference in the binding of serotonin and the hallucinogens in Figure 16 might have little to do with their tendency to produce hallucinations. Recent Updates: LSD binding to the BDNF receptor TrkB 06/09/23 Much is still not known about how antidepressants work and what causes delays in their therapeutic effects. Increased synaptic connections through expanded neuroplasticity appears to be required for their therapeutic effects. The major action of drugs like Prozac, a serotonin reuptake inhibitor, is through blocking the serotonin transporter (SERT), increasing extracellular levels of serotonin in the neural synapse. Tricyclic anti-depressants as well as monoamine oxidase inhibitors (MAOI), also increases monoamine neurotransmitters in the synapse with delayed therapeutic effects (the monomaines act quickly however). Perhaps these agents bind other receptors! In fact, they do. The binding of both typical and fast antidepressants also occurs in the transmembrane domain of tyrosine kinase receptor 2 (TRKB), the brain-derived neurotrophic factor (BDNF) receptor, which is linked to increases in neuroplasticity. The receptor also binds cholesterol which modulates its activity. The antidepressants binding site is formed on dimerization of their transmembrane domains. Mutations in the transmembrane region block the efficacy of the antidepressants. LSD and other psychedelics also produce fast and long-lasting antidepressant effects promoted by increases in neuroplasticity. Studies have shown that LSD and psilocin bind to slightly overlapping sites in the transmembrane domain of the BDNF receptor as antidepressants, but with a 1000-fold higher affinity. If the LSD binding site on the serotonin 2A receptor (5-HT2A) is blocked, LSD still has antidepressant and increased neuroplasticity effects. Figure $17$ shows the interaction of LSD and the metabolite of psilocybin, psilocin (PSI), with the TRKB receptor. Figure $17$: Characterization of the psychedelics binding site in the TrkB TMD. Moliner, R., Girych, M., Brunello, C.A. et al. Psychedelics promote plasticity by directly binding to . BDNF receptor TrkB. Nat Neurosci 26, 1032–1041 (2023). https://doi.org/10.1038/s41593-023-01316-5. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. Panel ac show representative MD snapshots showing the binding pocket for LSD (purple) (a) and PSI (green) (c) in the extracellular-facing crevice of the TrkB TMD dimer (gray). Side chains (yellow) of relevant binding site residues are displayed. A structural model of full-length TrkB dimer (gray) embedded in a lipid membrane is shown with bound BDNF (blue) and LSD (purple) (b). Panel d shows in silico binding free energy estimations for fluoxetine, LSD and PSI. Each free energy estimate (ΔG, circles) and its statistical error (SE, bars) were estimated from a separate set of FEP simulations (n = 1). Dissociation constants are given as a range with upper and lower bounds converted from ΔG − SE and ΔG + SE, respectively. Panel e,f show chemical structures of LSD (e) and PSI (f) with atom numbers annotated. Panel i shows the distributions of TMD dimer C-terminal distance and shows that LSD and PSI stabilize the cross-shaped conformation of TrkB favorable for receptor activation in a 40 mol% CHOL membrane. Lines represent the mean distribution, and bands represent the standard errors (n = 10 independent simulations). TMD conformations corresponding to indicated C-terminal distances and drug-bound states are shown in the inset. Figure $18$ shows the different binding modes of LSD and Fluoxetine (Prozac), a selective serotonin reuptake inhibitor (SSRI) for TrkB. Fluoxetine is used to treat depression, obsessive-compulsive disorder (OCD), bulimia nervosa, and panic disorder. Figure $18$: Different TrkB binding modes of LSD and fluoxetine. Moliner et al., ibid. Panels a,b, show representative snapshots of atomistic MD simulations showing the front (a) and back (b) views of the binding pockets for LSD (purple) and fluoxetine (yellow) in the extracellular-facing crevice of TrkB TMD dimers. Side chains of relevant binding site residues are displayed. Superimposed structures of TrkB optimally bound to LSD or fluoxetine reveal that, while some residues involved in binding are shared (Y433 and V437), the binding modes are different. Fluoxetine binds at a site deeper within the dimer, locking the TMD dimers in a more open cross-shaped conformation (distance between the center of mass L451–L453 Cα atoms of each monomer ~20 Å). In contrast, LSD binds closer to the N-terminus of the TrkB TMD and establishes more stable interactions with the dimer: a hydrogen bond between the oxygen atom of the diethylamide group of LSD and the Y433 residue of one monomer, and pi-stacking of the aromatic backbone of the drug with the Y433 residue of the second monomer, locking the TMD dimer in a tighter cross-shaped conformation (L451–L453 Cα distance ~17 Å) compared with fluoxetine. Drugs are shown in van der Waals representation. Oxygen, nitrogen, and hydrogen atoms are shown in red, blue, and white, respectively.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.10%3A_Integrins-_Bidirectional_Cell_Adhesion_Receptors.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry This section was derived almost completely, with some modifications and additions, from the following source: Mezu-Ndubuisi, O.J., Maheshwari, A. The role of integrins in inflammation and angiogenesis. Pediatr Res 89, 1619–1626 (2021). https://doi.org/10.1038/s41390-020-01177-9. https://doi.org/10.1038/s41390-020-01177-9. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. Consult the original article for specific references. Introduction Integrins link the extracellular matrix to the intracellular cytoskeleton through a single transmembrane alpha-helical segment. They work with growth factor receptors to regulate cell survival, cell migration, and cell division. They contain a very large extracellular domain comprising most of the protein and a very small intracellular domain. Integrins are heterodimeric transmembrane cell adhesion molecules made up of alpha (α) and beta (β) subunits arranged in numerous dimeric pairings. These complexes have varying affinities to extracellular ligands. Integrins regulate cellular growth, proliferation, migration, signaling, and cytokine activation and release and thereby play important roles in cell proliferation and migration, apoptosis, and tissue repair, as well as in all processes critical to inflammation, infection, and angiogenesis. Integrins are a family of ubiquitous αβ heterodimeric receptors that exist in multiple conformations and interact with a diverse group of ligands. These molecules mediate interactions between cells and these cells with the extracellular matrix (ECM) and thereby serve a critical role in signaling and homeostasis. By facilitating dynamic linkages between the intracellular actin cytoskeleton and the ECM, integrins also transduce both external and internal mechanochemical cues and bi-directional signaling across the plasma membrane. Integrins are involved in a diverse range of body processes, including cellular survival, inflammation, immunity, infection, thrombosis, angiogenesis, and malignancy. In this review, we highlight the structure and function of integrins; the mechanisms involved in integrin activation and signaling; their role in inflammation, infection, and angiogenesis; and discuss current advances in integrin-targeted therapies. Understanding the factors that regulate integrin structure, function, and signaling would enable the identification of new therapeutic targets. Structure of integrins In mammals, the family of integrins is comprised of 24 αβ pairs of heterodimeric transmembrane adhesion receptors and cell-surface proteins. These pairings are known to involve 18 α and 8 β subunits as shown in Figure $1$. With respect to ligand specificity, integrins are generally classified as collagen-binding integrins (α1β1, α2β1, α10β1, and α11β1), Arg-Gly-Asp (RGD)-recognizing integrins (α5β1, αVβ1, αVβ3, αVβ5, αVβ6, αVβ8, and αIIbβ3), laminin-binding integrins (α3β1, α6β1, α7β1, and α6β4), and leukocyte integrins (αLβ2, αMβ2, αXβ2, and αDβ2). The right-hand side of Figure $1$ shows that the β2 integrin subunit (CD18) can pair with one of the four α subunits (αL-CD11a, αM-CD11b, αX-CD11c, and αD-CD11d), forming leukocyte function-associated antigen-1, Mac1/CR3 (macrophage-1 antigen, complement receptor 3), 150.95/CR4 (complement receptor 4), and CD18/CD11d, respectively. The structure of the heterodimers and their non-covalent associations are shown in Figure $2$. Each subunit consists of one large multi-domain extracellular segment, one transmembrane helix, and a short cytoplasmic tail. The extracellular region interacts with extracellular matrix (ECM) ligands and is composed of about 1104 residues in the α subunit and 778 residues in the β subunits and shorter cytoplasmic domains with 30–50 residues. The short cytoplasmic tails are composed of 20–70 amino acids and mediate interactions with intracellular cytoskeletal and signaling proteins. In response to intracellular or extracellular stimuli, integrin activation occurs by ligand binding or by the changes in the cytoplasmic domains, resulting in elongation and separation of the legs. Integrins appear in a closed or “bent” conformation on resting cells and display a low binding affinity for ligands rendering them inactive to ligand binding or signal transduction; while once activated, the integrin shape extends to an open conformation leading to a high affinity. In a closed conformation, integrins show low ligand-binding affinity, partly due to the bend in the center of the α and β subunits, which brings the ligand-binding site within 5 nm of the cell surface. However, when the conformation is open, the two subunits straighten with increased integrin affinity for the ligand. The initial binding of extracellular ligands causes separation of the cytoplasmic domains, allowing interaction with signal transduction and cytoskeletal molecules during outside-in signaling, while separation of the cytoplasmic domains by talin and other activators activates the head to enable ligand binding during inside-out signaling. The αβ pairings of integrin subunits dictate the specificity of the integrin to a particular ligand, modulate the formation of intracellular adhesion complexes, and regulate downstream signaling. Six α (α1–6) and seven β (β1–7) subunits are known to form several unique αβ subunit associations, as shown in Figure $1$. Interestingly, the earliest discovered integrins, lymphocyte function-associated antigen 1 (integrin αLβ2) and macrophage antigen 1 (integrin αMβ2), derive their specificity from specific α subunits, but these share the same β subunit.5 Integrin α subunit family The integrin α subunits carry a 200 amino acid “inserted” domain, the I-domain (αI). When present on an integrin, the αI domain is an exclusive ligand-binding site. αI integrins have 13 extracellular domains in 2 subunits, which interact with a variety of ligands. The I-domains are seen in 6 out of the 15 integrin α subunits. Integrin alpha-1/beta-1 is a receptor for laminin and collagen. It recognizes the proline-hydroxylated sequence G-F-P-G-E-R in collagen. the human α1 subunit (1179 amino acids) has the domain structure shown in Figure $3$: The green represents the Van Willibrand Factor Type A domain. The middle pinkish domains are FG-GAP extracellular domains. They are repeated up to 7 times in alpha integrins. The reddish domain at the very C-terminus is the transmembrane helix domain (1142-1164). This membrane protein is very different than those we have seen before as it has just a 15 amino acid C-terminal tail exposed in the cytoplasm. Figure $4$ shows an interactive iCn3D model of the predicted structure of human Integrin alpha-1 (AlphaFold, P56199). The color coding is gray spacefill for the C-terminal transmembrane helix, yellow spacefill for the inhibitor binding pocket and magenta for the collagen-binding site Integrin β subunit family In humans, integrin β subunits have a cytoplasmic tail that has <75 amino acids in length, except the β4 tail which is about 1000 amino acids long (includes four fibronectin type III repeats). The integrin β tails have one or two NPxY/F motifs (x refers to any amino acid) that recognize protein modules, phosphotyrosine-binding domains, that are involved in several signaling and cytoskeletal proteins at the cytoplasmic face of the plasma membrane through phosphorylation of the tyrosine (Y) in the NPxY/F motif. The integrin β subunit family includes β1–7, which bind the α subunits in different combinations. The most frequently seen β subunit integrin heterodimers are β1. Although β2 integrins show functional overlap, the corresponding α subunit defines its individual functional properties. The β2/CD18 chain has also received attention because of its involvement in several inflammatory receptors such as αLβ2, lymphocyte function-associated antigen-1 (LFA-1), and the αMβ2, Mac-1, complement receptor 3 (CR3). In these β2 integrins, the α subunits bind specific ligands such as the intercellular adhesion molecules (ICAMs). The non-I-domain α subunits in other integrins, such as the laminin-binding α3, α6, and α7, and others that recognize the arginine (R), glycine (G), aspartic acid (D) (RGD) motif (αV, α8, α5, and αIIb), are also closely related to each other. • The α subunit of each integrin is the primary determinant of its extracellular ligand specificity. • The β chain binds acidic residues in ICAMs and cytoplasmic adapters such as paxillin, talins, and kindlins to facilitate cellular adhesions with the ECM. Integrins interact with the actin cytoskeleton through the talin- and kindlin-binding motifs present in the cytoplasmic domains of their β subunits. Characteristics of specific integrin heterodimers Integrin αβ heterodimers are divided into four classes (leukocyte, collagen-binding, Arg-Gly-Asp (RGD)-binding, and laminin-binding integrins (as shown in Figure $1$, based on evolutionary associations, ligand specificity, and restricted expression on white blood cells (β2 and β7 integrins). • Leucocyte integrins have a common β2 chain that is linked to CD-18 and binds receptors such as ICAM and plasma proteins such as complement components C3b and C4b. • Collagen-binding integrins have a common β1 chain that binds various α chains in integrins α1β1, α2β1, α10β1, and α11β1. The α2β1 integrin binds its primary ligand, collagen, and chondroadherin, a matrix protein. • The RGD-binding integrins have a common αV chain or β1 chain. The RGD peptide motif was first discovered in fibronectin but was later found in several other ECM proteins, such as fibronectin, osteopontin, vitronectin, von Willebrand factor (VWF), and laminin. Among the 24 human integrin subtypes known to date, eight integrin dimers recognize the tripeptide RGD motif within ECM proteins, namely: αVβ1, αVβ3, αVβ5, αVβ6, αVβ8, α5β1, α8β1, and αIIbβ3. Laminin-binding integrins (α3β1, α6β1, α7β1, and α6β4) mediate the adhesion of cells to basement membranes in various tissues. The α4β1, α9β1, and α4β7 integrin family binds fibronectin in an RGD-independent manner. Figure $5$ shows an interactive iCn3D model of the structure of α6β1 integrin in complex with laminin-511 (7CEC) The cyan chain is integrin α6 and the magenta chain is integrin β1. The laminin chains are α (brown), β (orange), and γ (red). Note that each of the laminin subunits interacts with the α6β1 integrin. Two carboxylates on the C-terminal region of the laminin γ chain interact with the metal ion-dependent binding sites on the integrin beta subunit and an Asp 189 in the alpha subunit. Integrin–ligand binding and consequent activation The structure and function of integrins are complex. Integrins bind numerous extracellular ligands, intracellular signaling molecules, and the cytoskeleton in a bivalent-cation-dependent manner with varying specificities. Integrins also have many states with multiple conformations and affinities. Mechanism of integrin-ligand binding and conformational states Integrins bind cell-surface ligands to promote cellular interactions with the ECM and with other cells in the transduction of complex signals that modulate many cellular processes, such as adhesion, migration, and differentiation. These soluble, ECM, or cell surface-bound ligands may include growth factors, structural constituents of the ECM, proteases, cytokines, plasma proteins, microbial pathogens, or receptors specific to immune cells. The affinity and avidity of a ligand may change actively by inside-out signaling in specific pathways. Ligand affinity may vary with the strength of interaction and dissociation of a monovalent protein and its ligand, where ligand avidity refers to its ability to form multiple combinations of bonds. Integrins exist primarily in three conformational states: bent–closed (inactive; the predominant state), extended–closed (active; low affinity or intermediate state), and extended–open (active; high affinity). The affinity of integrins to various inhibitory and stimulatory ligands is modulated by bivalent cations, which induce a range of conformational changes in integrins ranging from a folded, inactive, and low-affinity state to a high-affinity conformation as shown in Figure $2$. These conformational changes in the extracellular domains of integrins modulate both ligand binding and downstream cellular signaling. Integrin activation The activation of integrins increases the affinity of these molecules to extracellular ligands. Integrin tail domains play a critical role in these steps, and any genetic mutations in these parts of integrins can disrupt downstream intracellular signaling. Integrin-mediated signaling across cell membranes is typically bi-directional and termed “outside-in” and “inside-out” signaling. When integrins interact with ECM ligands, a conformational change allows adherence to downstream adaptor molecules in the cell-membrane plane. Once clustered, integrins can recruit and activate kinases such as Src family kinases, focal adhesion, and scaffold molecules such as the adaptor protein p130CRK-associated substrate/breast cancer anti-estrogen resistance 1 (p130CAS/BCAR1). These integrin-associated complexes include discrete active and inactive integrin organizations, which can activate unique signaling pathways. The extracellular domains of integrins are known to undergo a diverse range of conformational changes that alter the ligand-binding domains. In the cytoplasmic tails of integrins, α-helices are seen as heterodimers, and the β-strands often bind intracellular proteins, such as talin or filamin. The cytoplasmic tail may undergo several specific conformational changes to bind a range of other signal transducers. This section is derived from Mechanobiology. https://www.mechanobio.info/what-is-mechanosignaling/what-is-the-extracellular-matrix-and-the-basal-lamina/what-is-integrin/how-is-integrin-activated/. Creative Commons Attribution-NonCommercial 4.0 International License. Integrin can be activated from two directions, from the inside by the regulated binding of proteins to the cytoplasmic tails, and from the outside by multivalent ligand binding. In either case, talin binding to the integrin β tails is an essential and the final common step. Though the two processes are conceptually separate, they are mutually cooperative i.e one can lead to the other. Some structural studies done with force application to mimic ligand/intracellular protein suggested that the combined action of these two events favors the transition from the closed, low affinity to an open, high-affinity conformation of integrin. Activation leads to bidirectional signaling crucial in a variety of anchorage-dependent events such as adhesion, cell spreading, migration, polarity, and organization of the ECM leading to physiological changes. Figure $6$ shows the different states of the integrin dimer in inside-out and outside-in signaling. (A) shows integrin in a low affinity, inactive, bent, conformation. (B1) and (B2) show inside-out integrin activation by cytoplasmic proteins or outside-in integrin activation via extracellular matrix (ECM) ligands, both of which lead to the complete extension of the extracellular domains. (C) shows high affinity and active integrin characterized by separation of the cytoplasmic leg domains. Inside-out signaling Signals received by other receptors foster the binding of talin and kindlin to the cytoplasmic end of the integrin β subunit, at sites of actin polymerization. Substantial information on signaling pathway leading activation is available for integrin αIIbβ3. Talin binds to integrin β-tail via the F3 phospho-tyrosine binding (PTB) domain, a unique interaction with the membrane-proximal (MP) region of the integrin (NPxY motif). This permits competition between a conserved lysine on talin and an aspartic acid on integrin α essential for α/β salt bridge disruption and sufficient for integrin activation. Addition interactions through the basic patches in the FERM subdomain F2 help to orient the β-subunit to promote spatial separation of the cytoplasmic domains. Kindlin is also an essential co-activator of integrin and binds to a membrane distal NxxY motif on β-integrin via its FERM F3 subdomain. A preceding threonine patch on integrins β1 and β3 that gets phosphorylated and a tryptophan on kindlin F3 are also required for binding. However, kindlins are not known to activate integrins on their own but may render integrin-specific effects. Outside-in signaling Ligand binding to the external domain causes conformational changes that increase ligand affinity, modify protein-interaction sites in the cytoplasmic domains, and thence the resulting signals. Besides conformational changes that extend integrin dimers, multivalent ligand binding leads to the clustering of integrins, which in turn activates the Src family of kinases (SFKs) by autophosphorylation. SFKs phosphorylate tyrosines of the integrin cytoplasmic domain (NPxY motifs) and other proteins leading to 1. control of ligand binding strength 2. alteration of binding with signaling molecules (kinases, GTPases, and adaptors), that constitute dynamic adhesion structures such as focal adhesions and podosomes Nevertheless, whether clustering triggers outside-in signaling to facilitate integrin activation or occurs after integrin activation is uncertain. David G. Menter, Raymond N. DuBois, "Prostaglandins in Cancer Cell Adhesion, Migration, and Invasion", International Journal of Cell Biology, vol. 2012, Article ID 723419, 21 pages, 2012. https://doi.org/10.1155/2012/723419. Creative Commons Attribution License, Let's look at another more detailed representation of integrin states. Each αβ dimer recognizes a different intercellular adhesion molecule (ICAM), ligand, or protein substrate in the basement membrane or extracellular matrix. The α subunit dictates the ligand specificity by a seven-bladed β-propeller head domain connected to a leg support structure made of a "thigh", a "calf"-1, a "calf"-2, a transmembrane, and a cytoplasmic domain. The β subunit interacts with the cell cytoskeleton and contains an N-terminal plexin-semaphorin-integrin (PSI) domain, a hybrid domain, a βI domain, four cysteine-rich epidermal growth factor (EGF) repeats, a transmembrane, and a cytoplasmic domain. In many cases, the N-terminal β-I domain of a β subunit inserts into the 7-bladed β-propeller domain of an α subunit (α1, α2, α10, α11, αL, αM, αX, and αD) to form a bulbous-binding headpiece complex. The formation of integrin receptor complexes depends on divalent cation (i.e., Ca2+, Mn2+, Mg2+) that bind to metal-ion-dependent adhesion site (MIDAS) motifs in the α subunits and adjacent to MIDAS (ADMIDAS) motifs in β subunits found in the N-terminus of these receptors. Together they joined α and β subunit termini form an N-terminal headpiece. These detailed features of the integrin dimer structure are shown in Figure $7$. Three conformation states exist for α and β subunit complexes. (1) The unliganded conformation has a closed headpiece and a bent receptor structure with the EGF domains of the β-subunit touching the calf-1-calf-2 domains of the α-subunit. (2) The headpiece remains closed, but structural changes in the β-subunit EGF domains cause a separation from the calf-1-calf-2 domains of the α-subunits causing an extended structure. (3) Conformational changes in the β 6-α 7 loops expose the ligand-binding site along with a complete separation of the β-subunit from the calf-1-calf-2 domains in the α-subunit. These conformational changes engage the specific integrin headpiece with its ligand" Figure $8$ shows an interactive iCn3D model of the headpiece of integrin αIIbβ3 in the headpiece extended and open conformation (3FCU) The αIIb part of the headpiece is shown colored based on the secondary structure with the yellow sheets comprising the beta propellor secondary structure motif. The gray chain is the β3 chain. The side chains of the β3 chain forming the binding interface between the αIIb and β3 chains are displayed as colored sticks. The three close metal ions (2 Ca2+ and 1 Mg2+) are important for ligand binding with the Mg2+ involved in coordination to acidic side chains of integrin ligands. These metal ions are present before ligand binding. The RGD binding motif of some integrin ligands binds through their aspartate to the Mg2+. Without bound Mg2+, acidic side chains around the site would interfere with binding. It appears that lateral forces are most important in activating integrins. This is in contrast to tensile forces which act along the length of the receptor. Tensile forces appear to stabilize the closed, extended low-affinity form, while lateral forces at the beta subunit, a mimic for moving cytoskeletal filaments, stabilized the open, extended, high-affinity form. This links conformational allosteric changes to cytoskeletal changes. The mechanism for activation is hence mechanochemical. Similar to conventional cell surface signal transducing receptors, integrins bind ligands and transmit information in an “outside-in signaling” as shown in the top panel of Figure $9$. “Outside-in signaling” behavior typically involves the engagement of integrins with the extracellular matrix or ICAM surface receptors. When external factors bind to exposed ligand binding sites on integrins this results in conformational changes described in the previous section. Most ECM proteins exhibit multivalent or recurrent molecular patterns, which trigger integrin clustering. As cells engage the repetitive patterns in the ECM, these events occur simultaneously thereby activating intracellular signals. The myriad of different extracellular signals that cells encounter in their microenvironment mediates cell polarity, cytoskeletal structure, adhesion, migration, invasion, gene expression, cell survival, and proliferation. The “outside-in” binding of ECM ligands to cell surface integrins stimulates conformational changes that activate focal adhesion kinase (FAK). FAK then is autophosphorylated on Tyrosine 397 near the catalytic domain, which binds Src. FAK contains a central kinase domain bordered by FERM (protein 4.1, ezrin, radixin, and moesin homology) domain at the N-terminus and a focal adhesion targeting (FAT) sequence at the C-terminus. Activated Src interacts with human enhancer of filamentation1 (HEF1) and p130 CRK-associated substrate (p130CAS) scaffold proteins that help to positively regulate Src-FAK-Crk interactions with Rac. FAK also activates (PKL/Git2)-β-Pix complexes and β-pix then serves either as an exchange factor for Cdc42 or a scaffold protein to promote signaling via Rac and p21-activated protein kinases (PAK). FAK also interacts with actin-related proteins (ARP2 and ARP3) which are regulated by the Wiskott-Aldrich Syndrome Protein (WASP). ARP2/ARP3 initiates the polymerization of new actin filaments. FAK also influences actin contraction and polarization through another GTPase protein, Rho. The regulation of Rho GTPase hydrolysis of GTP (active) to GDP (inactive) form occurs through the opposing activities of guanine nucleotide exchange factor (GEFs). The GTPase regulator associated with FAK (GRAF) and p190RhoGAP blocks actin cytoskeleton changes. In contrast, PDZRhoGEF and p190RhoGEF both serve to activate Rho. “Outside-in signaling” transfers integrin-mediated external signals to the inside of cells.“Inside-out signaling” depends on talin and kindlin. Both talin and kindlin contain FERM (4.1/ezrin/radixin/moesin) domains and a highly conserved C-terminal F3 domains. Talins bind β integrin, actin through the C-terminus, and also vinculin. Kindlins bind integrins, the cell membrane, and various actin adaptor proteins like migfilin, or integrin-linked kinase (ILK). Talin activation occurs through G-protein-coupled receptors that increase cytoplasmic Ca2+ and diacylglycerol. This activates GEF function in conjunction with Ras-proximate-1/Ras-related-protein-1-(Rap1-) GTPase. Rap1 then binds to the Rap1-GTP-interacting adaptor molecule (RIAM). RIAM recruits talin to the membrane and the α and β integrin cytoplasmic domains. Kindlin interacts with β integrin cytoplasmic domain stabilizing the activated state of the integrin complex. “Inside-out signaling” strengthens adhesive contacts and the appropriate force necessary for integrin-mediated cell migration, invasion, ECM remodeling, and matrix assembly. In the case of “outside-in signaling” initiated by ECM proteins, a single ligand-binding event can trigger integrin activation, but repetitive regularly spaced molecular patterns provide a more effective stimulus [122, 123]. This type of mechanoreception has been explored using nanopatterned molecular printing techniques that form regular cRGDfK patch spacings on a polyethylene glycol background matrix [122–125]. These adhesion-dependent sensory mechanisms lead to signal transduction inside the cell by the activation of multiple pathways. Focal adhesions are often formed as a result of cell interactions with the ECM substrata, which initiate signal transduction via kinase cascades and other mechanisms. Integrins in inflammation and infection In the resting state, β2 integrins are expressed specifically on leucocyte receptors. During inflammation, the inflammatory cytokines activate these integrins and promote cellular adherence to the counter-receptors such as ICAMs and promote phagocytosis and cytotoxic killing. Integrin receptors on leukocytes, such as the macrophage-1 antigen (Mac-1, also known as CR3, αMβ2, CD11b/CD18) interact with platelet antigens such as the glycoprotein Ibα (GPIbα) during inflammation. Integrins bind to the pro-domain of transforming growth factor (TGF)-β1 to activate it and promote its secretion. The pro-TGF-βs are biosynthesized and stored in tissues in latent forms, and integrins αVβ6 and αVβ8 can uniquely bind and activate pro-TGF-β1 and pro-TGF-β3. The αVβ6 integrin is known to specifically bind the RGDLXXL/I motif in TGF-β1 and TGF-β3. β2 integrins promote the recruitment of leukocytes to the sites of inflammation by promoting the adhesion of circulating leukocytes to vascular endothelium, transendothelial migration, the formation of immunological synapses in leucocytes, and inflammatory signaling in involved cells. β integrins on the leukocyte surface are also involved in the tethering, rolling, and adhesion of leukocytes to activated endothelial cells. β2 integrins can also initiate intracellular signaling pathways in macrophages and neutrophils and stimulate cytokine secretion from these cells either directly or in synergy with Toll-like receptors (TLRs). Integrins may also integrate the impact of the epidermal growth factor receptor, platelet-derived growth factor receptor, insulin receptor, met receptor superfamily (hepatocyte growth factor receptor), and the vascular endothelial growth factor receptor (VEGFR) in inflammatory cells. β2 integrins are important regulators of adhesion, leukocyte recruitment, and immunological signaling. These integrins mediate adhesive interactions between myeloid cells, endothelial cells, antigen-presenting cells, T cells, and the ECM. L-selectin, the CCR7 chemokine receptor, interacts with specific carbohydrate epitopes on the endothelium and promotes leukocyte rolling and transmigration through the vascular endothelium. Leukocyte rolling induces a rapid, although a transient, increase in the affinity of the β1 and β2 integrins to the endothelial ligands. Conformational changes in the structure of the inserted (I) domain of the αL subunit of LFA-1 enhance firm leukocyte adhesion under shear flow.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.11%3A_Signaling_by_Steroid_Hormones.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Introduction We will now consider signaling by steroid hormones. These hormones are derivatives of cholesterol, which is found in membrane bilayers and lipoproteins. They are mostly nonpolar. Steroid hormones can affect signaling in two major ways: • through binding to membrane receptors, which when occupied affect signaling through the myriad of ways we discussed throughout this chapter. These effects would be rapid. • through binding to cytoplasmic receptors after they diffuse into the cell through passive and active means. This signaling is hence similar to retrograde signaling by nitric oxide, which can passively diffuse out of a cell and enter an adjacent cell to effect signaling in that cell. If the steroid primary messenger is in the cell, it most often can enter the nucleus and regulate gene transcription. Binding to cytoplasmic receptors account for most of the biological effects of steroid hormones. Since transcription is involved, the pathways elicit a slower response. We will briefly discuss the first type of signaling (binding to membrane receptors) mostly by presenting figures which describe their signaling pathways. Then, we will focus on steroid hormone activation of gene transcription. There are many classes of steroid hormones. These are illustrated in Figure $1$ along with their overall synthetic pathway. Figure $1$: Diagram of the pathways of human steroidogenesis. WikiJournal of Medicine 1 (1). DOI:10.15347/wjm/2014.005. ISSN 20018762. The overall families include progestogens and estrogens (female sex hormones), androgens (male sex hormones), glucocorticoids (like cortisol which affects immune and metabolic systems), and mineral corticoids (which affect salt/water balance). We will mostly use the estrogens in the section as a prototypical example because of their involvement in breast cancer and epidermal growth factor receptors. Steroid hormone signaling through binding to membrane receptors Most of this subsection is taken from and adapted from the following source: Masi et al. Cells 2021, 10(11), 2999; https://doi.org/10.3390/cells10112999. Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Most people think of steroid signaling, it is through their nuclear effects on gene transcription (the predominant signaling effect). Signaling at the cell membrane is an emerging area of interest. We will present steroid signaling at the cell nucleus in four figures with their associated captions. We present these lesser-known effects of steroid signaling first since they utilize many of the pathways we have already studied. The figures will also give you a short review of some canonical pathways, which is always good in a field so complicated as signal transduction. The figures focus on the signaling effects in cancer. Figure $2$ shows membrane signaling through androgen receptors. Figure $3$ shows membrane signaling through estrogen receptors A tamoxifen (a selective estrogen receptor modulator used in breast cancer therapy) binding site has been found in NavMs voltage-gated sodium channels. Figure $4$ show membrane signaling through membrane progesterone receptors Figure $5$ shows membrane-associated progesterone receptor effects from progesterone signaling. Steroid hormone signaling through binding to cytoplasmic receptors and activation of gene transcription Now we will explore the major effects of steroid hormones on signaling, in which steroids enter the cell, bind to cytoplasmic receptors, and then translocate into the nucleus where they regulate gene expression. The naming of steroid receptors can be confusing since it is important to differentiate steroid receptors that are resident in the cell membrane and those that move to the nucleus. An added complexity arises from the factor that some nuclear receptors can be covalently modified with a fatty acid (palmitoylated) and targeted to the cell membrane. Estrogen receptors targeted to the membrane can then act independently of their nuclear transcriptional activity. We will primarily focus on estrogen effects in breast cancer (most diagnosed cancer) that are mediated through the nuclear steroid receptors, which belong to the nuclear receptor superfamily. Tamoxifen, a commonly used drug in the treatment of breast cancer is an estrogen receptor antagonist (also called an estrogen receptor modulator—SERM). Molecular Function of Steroid Receptors—Common Features Steroid receptors (SR) consist of four main domains, the C-terminal ligand-binding domain (LBD), the DNA-binding domain (DBD), the hinge region, and amino-terminal domain (NTD). Each SR contains also two motifs called activation function 1 and 2 (AF1 and AF2) within NTD and LBD, respectively, and are crucial for the regulation of gene transcription. Two zinc fingers are located in the DBD. Figure $6$: The domains are often labeled A-F. The N-terminal domain (NTD) is also called the A/B domain. It can also bind DNA and can weakly activate transcription in the absence of hormones. The C domain is the DNA binding domain (DBD) containing Zn fingers, which bind to the steroid response element in promoters of key genes. The D domain is the hinge domain, and The E domain binds hormones (like estrogen) and protein regulators and when bound can activate gene transcription. The last domain (F) varies in length and its function is not completely clear. The specific DNA structure for the estrogen receptor from Pfam is shown in Figure $7$. The green is the N-terminal Oest_recep domain (NTD, A/B). The red zf-C4 is the DNA binding domain which has two Zn fingers that bind DNA (DBD, C). The blue Hormone_Receptor is the ligand (estrogen) binding domain (LBD, domain E). The yellow is the C-terminal domain (F). These two genes for the estrogen receptor encoding ERα and ERβ. The transcriptionally active form is a dimer that forms on the binding of estrogens. The dimer then translocates to the nucleus and activates transcription at ERE sites. The ERα dimer promotes estrogen-dependent growth while the ERβ dimer inhibits it. Heterodimers can form which seem to reduce the proliferative effects of ERα. Both ERα and ERβ can be expressed in Figure $8$ shows an interactive iCn3D model of the human estrogen receptor computational model (P03372, AlphaFold) • red spacefill: N-terminal Met • green backbone trace: N-terminal domain disordered, which can bind DNA • magenta backbone trace: DNA binding domain with Zn fingers. The Cys side chains of the Zn fingers are shown in sticks, CPK colors, and labeled. • blue: Hormone_Receptor is the ligand (estrogen) binding domain (LBD, domain E) • yellow: C-terminal tail (domain) • Cyan spacefill: C terminal Val There are no full-length crystal structures of ER dimer. Most available structures are for the estrogen-binding domain. It's really useful to see the full predicted structure to see how all the domains are connected, but perhaps more interestingly, the extended regions of disordered structures, which you should imagine adopting specific structures on interaction with key signaling partners. Figure $9$ shows an interactive iCn3D model of the ligand binding domain of human estrogen receptor ERα bound to the antagonist tamoxifen (3ERT). The ligand binding domains (one for each of the ERα monomers) are shown in magenta and cyan. The antagonist Tamoxifen, one bound in each of the ligand binding domains, is shown in sticks, CPK color. The amino acids comprising the binding site for tamoxifen, are shown in stick, and CPK colors and labeled in the magenta subunit. The CPK-colored spheres show the binding site on the ligand binding domain for other binding proteins called corepressors or coactivators (not shown, discussed below). Figure $10$ shows the structures of estrogens and selective estrogen receptor modulators (SERMs). The iCn3D model for the human estrogen receptor ERα bound to the antagonist tamoxifen (3ERT) showed binding sites for other proteins called coactivators or corepressors. The ER-estrogen complex, after binding to DNA, can also bind a protein coactivator, which activates transcription. Likewise binding of a corepressor to the DNA-bound complex inhibits transcriptional activity. Tamoxifen binding to the ERα monomer leads to dimerization and DNA binding. The DNA-bound dimer can then bind either a corepressor (the usual case for tamoxifen binding to ER in the breast), leading to inhibition of DNA transcription (i.e. tamoxifen antagonizes ER transcriptional function), or a coactivator which stimulates gene transcription. A cartoon diagram illustrating the role of ER coactivators and corepressors is shown in Figure $11$. When a SERM binds to the estrogen receptor, the receptor adopts a unique conformation that allows dimerization and interaction with estrogen response elements (EREs) of the target genes. The unique conformational change induced by the binding of the SERM may result in a distinct pattern of cofactor recruitment. Before steroid binding, most steroid receptors are found in the cytoplasm bound to heat shock proteins like Hsp90. Phosphorylation of the Hsp:SR complex leads to dissociation of the Hsp, followed by dimerization and translocation into the nucleus. In some cases, hormone binding occurs in the nucleus. Figure $12$ shows an interactive iCn3D model of the estrogen receptor DNA-binding domain bound to DNA (1HCQ). The backbones of the dsDNA are shown in spacefill cyan and magenta. The DNA bases are shown in CPK colors. The two chains of the DNA binding domain of the estrogen receptor are shown in gray and gold. Zn2+ ions are shown as brown spheres. The coordinating Cys side chains in the gray DNA binding domain are shown in sticks, CPK colors, and labeled "C". The amino acid side chains from the gold DNA binding domain that interact with DNA are shown in sticks CPK colors. There are two types of ways that steroid hormones activate gene transcription, a direct and an indirect method Direct (classical): The DNA binding domain (containing the Zn fingers binds) of the dimer bind to the target hormone response element (HRE) or for steroids the steroid response element (SRE) sequences in the promoter site of specific genes under steroid hormone control. As seen in the iCn3D above, one of the two Zn fingers on each of the hormone receptors binds to the target site in the major groove of DNA. The other Zn fingers are involved in the dimerization of the hormone receptor. The SRE contains two 6-base pair repeats separated by 3 base pairs. The DNA sequence shown in the iCn3D above is CCAPGGTCA. The general consensus sequence for steroid hormones is 5′-GGTACAnnnTGTTCT-3′. The ER binds to 5′-GGTCAnnnTGACC-3′. Note that the complementary strand sequence is 5'-GGTCAnnTGACC so the sequence is a palindrome (the complementary strand has the same sequence going in the opposite direction. After binding the hormone:receptor dimer to DNA, coregulators bind. These modify histones and remodel the DNA to facilitate or inhibit transcription. Indirect: In this method, the steroid receptors bridge other DNA-bound transcription factors without the steroid hormone binding to its response element. The direct and indirect methods for steroid hormone effects on transcription are shown in Figure $13$. (1) Translation of a SR and binding of Hsp70. (2) Hsp70 to Hsp90 transition. (3) Ligand binding, Hsp90 dissociation, and dimerization. (4) Nuclear translocation. (5) Transcriptional action: induction (5a, 5c) or inhibition (5b, 5d) of target gene expression, performed either in the classical mechanism involving SRE-binding (5a, 5b) or by tethering other TFs (5c, 5d). (6) Ligand dissociation followed by disassembly of the transcriptional complex and SR binding to a molecular chaperone. (7) Rebinding of the ligand. (8) Ubiquitination. (9) Proteasomal degradation. SR—steroid receptor, SH—steroid hormone, Hsp 70—heat shock protein 70, Hsp90—heat shock protein 90, SRE—steroid response element, CoA—coactivators, CoR—corepressors, HAT—histone acetyltransferase, HDAC—histone deacetylase, TF—transcription factor, TFRE—transcription factor response element, Ub—ubiquitin. Although histone acetyltransferases (HATs) and histone deacetylases (HDACs) are classified as coregulators, here they are shown separately to emphasize their role. Illustration created using elements from Servier Medical Art https://smart.servier.com/, reproduced under Creative Commons Attribution 3.0 Unported License https://creativecommons.org/licenses/by/3.0/. Hormone dissociation leads to steroid hormone dissociation from DNA by chaperone proteins. The Hsp90-SR complex can reenter the cycle. The steroid hormone receptor can be targeted by proteolysis by the proteasome by ubiquitination in either the nucleus or the cytoplasm. Modulation of ER function by phosphorylation No pathways stand in isolation, so it should be no surprise the estrogen receptor (again used as an example) is regulated by post-translational modification, especially by phosphorylation. Phosphorylation can be ligand dependent or independent. Multiple kinases are involved in the phosphorylation of the N-terminal region. An especially important one occurs at Ser 118 at a cyclin-dependent kinase (involved in cell cycling) when the receptor is bound to estradiol. Ser 118 also gets phosphorylation through epidermal growth factor signaling by MAPK. This may increase cell proliferation in breast cancers even in the absence of estrogen. Figure $14$ shows the activation of ER by phosphorylation through growth factor and cytokine signaling pathways. Figure $14$: Activation of ER by phosphorylation induced by growth factor and cytokine signaling pathways. Siersbæk et al , 2018 Sep 1; 32(17-18): 1141–1154. doi:10.1101/gad.316646.118. Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.12%3A_mTOR_and_Nutrient_Signaling.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Introduction: mTOR and AMPK Imagine when you were in high school, you weighed 135 pounds (61 kg). Let's say it's 50 years later and you still weighed 135 pounds. In the intervening years, think of how much food and liquid you consumed. In 2011, the USA Food and Drug Administration indicated that the average American consumed about 2000 pounds (907 kg) each year, including liquids other than drinking water. (Compare this to 365 kg for someone in Somalia!) So over 50 years you would have consumed 100,000 lbs (45,400 kg) of food or 740 times your body weight. These numbers are essentially unchanged even if you gained one pound a year for a total of 50 pounds. These figures suggest that we have an elaborate system that regulates how much we eat and how much weight we gain or lose. There are obvious times in our lives when we are growing and actively gaining body mass. Incoming food is not only processed into energy but also into net protein, lipid, carbohydrate, and nucleic acid synthesis. This system has become dysregulated in an ever-increasing number of people with type II diabetes and obesity throughout the world. Obvious candidates for regulators in general of net body weight and in specific of protein and lipid synthesis are the nutrients we consume and store. Many systemic hormones and neurotransmitters are involved in hunger, satiety, and eating behavior. This chapter will not focus on those but rather on mechanisms of nutrient signaling pathways in growth which requires new protein, lipid, and nucleic acid synthesis for cell growth and division. Likewise, it will not focus on nutrient signaling through hexosamines and UDP-GlcNAc. A key player in these signaling pathways is mTORC (mammalian or mechanistic Target Of Rapamycin Complex). A key protein in this multiprotein complex is mTOR, a Ser/Thr kinase that regulates cell growth, division, protein synthesis, RNA synthesis (transcription), and even autophagy (the major process whereby cells die and their contents are recycled for use). There are two physiologically relevant complexes of mTOR, mTORC1, and mTORC2. These two complexes have been called the master regulators of metabolic and growth processes. • mTORC1 activates protein, lipid, and nucleotide synthesis, all required for cell growth and division; it is inhibited by rapamycin. For activation, it needs two obvious conditions: energy and growth factors. In addition, it needs amino acids. • mTORC2 activates many processes through phosphorylation; it is not inhibited by rapamycin. What is Rapamycin? It sounds like an antibiotic but it is an antifungal agent produced by certain bacteria as a defense against fungal (eukaryotic) pathogens. It blocks cell division in fungi by stopping cell growth. The cell cycle consists of the following general sequential steps: (Go-G1)→S →G2→M→ G1. Gap 0 (Go) is a quiescent phase outside of the cycle. In G1, cells are growing and preparing for DNA synthesis which occurs in the S phase. After DNA replication/synthesis (S phase), cells grow again and prepare for mitotic cell division (M phase). Rapamycin traps fungal cells in the G1 phase. It also traps mammalian cells, and in particular immune lymphocytes in G1 as well, preventing lymphocytes from dividing. Hence rapamycin has been used to prevent rejection of transplanted tissue as it suppresses the immune system. Rapamycin inhibits mTORC1. That it inhibits mTOR is consistent with its immunosuppressive (antigrowth and antiproliferative) effects Another key player regulated by mTOR is the energy sensor AMP Protein Kinase (AMPK). We will discuss both below. mTOR Inhibition by Rapamycin The structure of the mTORC1 complex is complex itself, in part, since some of its components were studied and named before their roles in the mTORC1 complex were elucidated. Investigators were interested in the molecular target(s) of rapamycin. Yeast (a fungus) was an easy organism to study using genetic techniques. Three genes were found that when mutated inhibited the effect of the inhibitor rapamycin (i.e. so that rapamycin did not inhibit mTOR). Two of these were the Ser/Thr kinases mTOR1 and mTOR2. The other was an analog of a protein found in humans (FK Binding Protein 12 - FKBP12). The family of FK binding proteins act as protein chaperones and have Pro-X peptidyl-prolyl isomerase activity (PPI). FKBP12 specifically binds a drug (FK506 also called Tacrolimus) that is an immunosuppressant (do you see the general link with immune cell division and growth?). The binary complex of FK506 and FKBP12 binds a third protein, a phosphatase called calcineurin, and blocks its phosphatase activity and signal transduction required for T cell activation and proliferation. This activity of FKBP12 strangely does not require its PPI activity. Now back to mTORC1. Here are the known components of the core complex: • mTOR, a Ser/Thr kinase; • Raptor, a Regulatory-associated protein of mTOR; modulates the specificity of the kinase; • mLST8, the mammalian lethal with SEC13 protein 8 (also called Mammalian Lethal With SEC13 Protein 8 e In addition, other proteins associate with the core complex • PRAS40, a proline-rich Akt substrate of Akt • DEPTOR, DEP Domain Containing MTOR-Interacting Protein (where DEP is Disheveled, Egl-10 and Pleckstrin domain found in these 3 proteins and others involved in G-protein signaling) • FKBP, which binds rapamycin. mTORC2 is not sensitive to rapamycin. Instead of RAPTOR, it has a protein called RICTOR (rapamycin-insensitive companion of TOR). Figure $1$ shows an interactive iCn3D model of the human mTORC1 containing mTOR, Raptor and mLST8 bound to FK506 binding protein (FKBP)-rapamycin complex (5FLC) Figure $1$: Human mTORC1 containing mTOR, Raptor and mLST8 bound to FK506 binding protein (FKBP)-rapamycin complex (5FLC). Click the image for a popup or use this external link https://structure.ncbi.nlm.nih.gov/i...iSuYwaymutw867 The complex acts physiologically as a dimer with rotational C2 symmetry. The static image in Figure $1$ is color coded as below. The iCn3D image is similarly. • chains B ,F ,1-4: pieces of mTOR (mol ID 1-3) as a homodimer - Red; • chains A and E: Raptor (mol id 4) orange; • chains D and H: LST8 (mol id 5) green; • chains C and G: FKBP (mol id 6) light blue; • rapamycin in C and G, yellow spacefill. Raptor has been likened to tape as it interacts with the two mTOR subunits, holding them together into a larger, donut-like structure, and stabilizing the dimer. How does rapamycin inhibit mTOR? You have studied many kinds of inhibitors (competitive, uncompetitive, mixed, or noncompetitive inhibition) in which the inhibitor binds to either free E or the ES complex. The figure above suggests that rapamycin binds at the interface of the mTOR kinase (red) and FKBP. Presumably the RAP:FKBP complex binds to mTOR. Figure $2$ shows how rapamycin (shown in sticks) is sandwiched between mTOR (red) and FKBP (light blue). This offers clues as to how it inhibits mTOR. The rapamycin:FKBP complex is an allosteric inhibitor with its effect dependent on both substrate and post-translational phosphorylation. Right across from the light blue FKBP protein is the green LST8 protein (see above). In between these is a cleft which is the active site. One could imagine that RAP:FKBP binding to mTOR might interfere with substrate binding in mTORC1 but not in mTORC2. RAPTOR in mTORC1 probably helps recruit substrates and hence is involved in the determination of substrate specificity. RAPTOR interacts with a short section called the TOR signaling (TOS) motif in mTORC1 substrates. The part of RAPTOR that binds TOS in substrates is at the base of the mTORC1 active site, probably narrowing it further as it provides exquisite substrate selectivity. mTOR Protein Kinase Structure and Activity How does the structure of such an important kinase (mTOR) differ from other kinases? Remember that there are 388 S/T Kinases, 90 Y Kinases, and 40 atypical protein kinases in the human genome. The generic structure is shown below with ATP and substrate binding between N- and C-terminal lobes. The C lobe has a catalytic lobe which contains an Asp side chain acting as a general base in nucleophilic attack on the gamma P of ATP. A disordered activation loop in the C lobe often prevents substrate binding to the enzyme and keeps the kinase in an inactive state. On phosphorylation of the activation loop or elsewhere, or substrate binding, conformational changes lead to movement of the activation loop away from the active site, activating the kinase activity. The structure of generic kinases is shown in Figure $3$. Compared to generic protein kinases, mTOR has several insertions (about 200 amino acids) into the protein sequence and these must be involved in the determination of its specificity toward protein substrates. The C-terminal domain structure of the mTOR kinase is shown in Figure $4$. The FAT domain precedes the mTOR kinase domain. The FRB is inserted into the N lobe of the kinase domain whereas the LBE and FATC are inserted into the C lobe of the kinase domain. Figure $5$ shows an interactive iCn3D model of the mTOR with the LST8 protein bound through the kinase LBE domain and with bound AGS, a nonhydrolyzable ATP analog (4JSP). Key regions are: • FAT domain: 1385-2000 • N Lobe: 2003-2240 • C Lobe: 2241-2549 • FRB domain (magenta) inserted into N lobe: 2021-2118 • LBE domain (medium blue) inserted into C lobe: 2259 (ILL) to 2296 (TAG) • Catalytic Loop (red): 2337 (GDR) to 2344 (SNL) • Activation loop (dark blue): 2357 (DFG) to 2379 (FRL) • FATC domain (purple) inserted in C lobe: 2519 (LDV) to 2549 (PFW) Now imagine the FKBP:RAMP complex binding to the FRB domain in the figure above and you can easily imagine how RAMP could inhibit a large protein substrate from binding. A close-up of the active site of the kinase showing bound AGS, the catalytic loop (red) containing the general base Asp 2388, and the activation loop is shown in Figure $6$. The mTOR kinase seems to be always primed for catalysis since the Asp 2388 is in a position to act as a general base. The FRB domain seems to be involved in directing substrate access (such as S6K1) and hence in controlling substrate specificity. Binding of FKBP:RAMP to the FRB domain would prevent substrate binding. Important substrates for each complex are shown below. mTORC1: Eukaryotic Translation Initiation Factor 4E Binding Protein 1 (EIF4EBP1): This protein inhibits translation by binding eukaryotic translation initiation factor 4EIF (eIF4E). In the absence of active IF4EBP1, eIF4E is part of a complex that recruits 40S ribosomal subunits to the 5' end of mRNAs, which allows the initiation of translation. The binding protein inhibits complex assembly and represses translation. Active mTORC1 phosphorylates the binding protein in a variety of conditions (UV irradiation and insulin) which leads to dissociation of the binding protein which allows eIF4E to initiate translation. Ribosomal S6 kinase 1 (RPS6KB1 aka S6K1): This is a Ser/Thr kinase involved in proliferation, protein synthesis, cell growth, and cell proliferation. It phosphorylates eIF4B. In nutrient depletion (non-growth conditions), it forms a complex with the EIF3 translation initiation complex which inhibits translation. Under growth conditions, it is phosphorylated by mTORC1, causing its dissociation from the EIF3 complex and activation of translation. The active form then phosphorylates and activates several substrates in the pre-initiation complex, including the EIF2B complex and the cap-binding complex component EIF4B. In the presence of amino acids, both EIF4EBP1 and S6K1 are phosphorylated. If amino acids are depleted, they are dephosphorylated. Lipin 1 (LPIN1): This is a phosphatase that converts phosphatidic acid to diacylglycerol in triglyceride synthesis. Interestingly, it is also a transcriptional coactivator with PPARs (peroxisome proliferator-activated receptors) to modulate genes involved in lipid synthesis. A summary figure of mTORC1 signaling is shown in Figure $7$. mTORC2: Akt (also known as Protein Kinase B): This is a Ser/Thr kinase that is involved in the regulation of metabolism, proliferation, cell survival, growth, and angiogenesis. It has a notable role in the movement of the glucose GLUT4 transporter to the cell membrane in response to insulin signaling. Akt also interacts with mTORC1. Serum/Glucocorticoid Regulated Kinase 1 (SGK1): This serine/threonine protein kinase is involved in cellular stress response. It activates certain potassium, sodium, and chloride channels. It also activates membrane transporters, enzymes, and transcription factors. Its effects regulate neuronal activity, cell growth, proliferation, survival, migration, and apoptosis. Protein Kinase C alpha (PRKCA): This is a Ser/Thr kinase involved in cell adhesion, proliferation, differentiation, and migration. Rho and Rac: These are small G-protein involved in cytoskeletal structure and cell cycle. Figure $8$ shows a more complete pathway of activation, regulation, and activity of both mTORC1 and mTORC2. Figure $\PageIndex{x}$ below is used with courtesy of Cell Signaling Technologies (www.cellsignal.com). This chapter section will mostly focus on mTORC1. Regulation of mTORC1 by Leucine mTORC1 is a key regulator of protein synthesis but that begs the question as to how it determines that protein synthesis is required. How does it sense that? Rregulators of mTORC1 might be amino acids in cells, but who would have thought that the master regulator would be leucine, a simple branched chain hydrophobic amino acid. It would be nice if free leucine bound directly to mTORC1, but it's not that simple. Rather, it binds to a "leucine" receptor, sestrin 2 (SESN2). Figure $9$ shows the binding interactions between Leu (spacefill) and key side chains in sestrin 2 (5dj4). The Leu is rather buried, which suggests a conformational change ensues on binding to the protein. Saxton et al (2016) describe three types of sestrin2 side chains involved in the interaction: Lid: Thr374, Thr377, and Thr386 form H bonds with the Leu amine and carboxyl group. Leucine is represented as a stick model (orange). Latch: Tyr375 and His86 form hydrogen bonds to the Leu. Note that these residues are distal in the chain and are probably pulled together during the conformational changes which occur after binding to form a latch to sequester the bound Leu. Floor: F447 and W444 which interact with the nonpolar side chain of Leu. Figure $10$ shows an interactive iCn3D model of Leucine-bound Sestrin2 (5DJ4). What happens after leucine binds? It's a complicated but understandable process described below in words and images. But first a quick review. Kinases must be regulated to be turned on and off at the right time. They are often regulated by phosphorylation, as mTOR is. In addition, they can be regulated by binding proteins as mTOR is (by Raptor, FKBP, etc). They can also be regulated by small G proteins (like Ras) which are active when bound to GTP and inactive when bound to GDP. Of course, whether small G proteins have GTP bound depends in part if they interact with GAPs (GTPase activating protein which inactivates small G proteins) or GEFs (which facilitate the exchange of GDP for GTP and activate them). Such a master regulator of growth as mTORC1 is regulated by all of these, in addition to the presence of abundant leucine. In the absence of leucine, sestrin 2 is bound to a protein called GATOR2 (GTPase-activating protein - GAP - activity toward Rags 2). The binding of leucine to sestrin 2 causes the dissociation of GATOR2. This is shown in Figure $11$. Free GATOR2 is a GAP that regulated mTORC1. Specifically, it regulates the activity of a heterodimer of small GTP binding proteins, RagA/B:RagC/D (see pathway above) which are associated with the outer leaflet of the lysosome. There they interact with a membrane protein, SLC38A9, and a protein that regulates the Rag proteins, which of course is named Ragulator. Active RagA/B:RagC/D recruits mTORC1, presumably through the Raptor subunit) from the cytoplasm to the lysosome membrane. Small G proteins like Ras, when activated by exchanging bound GDP for GTP, can interact with and activate kinases (like the Raf kinase for Ras). When mTORC1 binds to active RagA/B:RagC/D, it becomes activated. We often think of activating a protein by ligand binding, which promotes a conformational change, or by post-translational modification, which can provide a binding interaction or conformational change to activate the protein. Another way is to inhibit an inhibitor of a protein, as shown in Figure $12$.: Y inhibits Z as denoted by the blunt blunt-ended. If X inhibits Y, then Y can't inhibit Z, which is now active. This is analogous to the quote that "the enemy of my enemy is my friend", which has been attributed to Kautilya (from India) in the 4th century BCE. Leucine binding to sestrin 2 leads to free GATOR, which activates mTORC1 by blocking downstream inhibitors. Figure $13$ (after Buel and Blenis, 2016) shows the interactions from an activation (arrow) or inhibition (blunt arrow) perspective. The figure above shows the involvement of multiple proteins in the lysosome membrane that are involved in mTORC1 activation. There is yet another way that the RagA/B and RagC/D proteins are regulated (other than by the GATOR GAP activity. The main one appears to be Ragulator, which is a GEF for the Rag proteins. Here is a summary of the components of this lysosomal membrane recruitment center for mTORC1. • Ragulator (what a great name) binds and recruits the small G proteins Rag to the lysosome membrane where Ragulator acts as a GEF for RagA/B • SLC38A9 is a weak amino acid transporter in the lysosome membrane, with a preference towards polar amino acids. More likely it is yet another sensor of amino acids, particularly of arginine, which has a high concentration in the lysosome. The protein has a high Km for the transport of Arg. It has a Ragulator binding domain and is hence part of the complex that recruits mTORC1 to the lysosome • vacuolar adenosine triphosphatase (v-ATPase): function unclear These interactions, which involve multiple activations and inhibitions, are difficult to follow even with a diagram. The actions of small G proteins can be especially difficult to understand since the G protein is biologically INACTIVE in its GDP-bound form towards its target binding protein. This occurs when the GTPase activity of the G protein is ACTIVE. The arrows and blunt end arrows in the figure above represent the activity of the protein toward its target protein. Here are two alternative ways to make sense of the interactions: - Stepping backward from Rag A/B, Gator 1 (a GAP) inhibits the ACTIVITY of the protein Rag A/B as it acts as a GAP to leave Rag A/B in the inactive GDP-bound state. Paradoxically this occurs as the inherent GTPase activity of the protein is activated as described above). Free Gator 2 (also a GAP) appears to inhibit the GAP activity of Gator 1 (through an unknown mechanism), thereby increasing the amount of GTP-bound Rag A/B, which then can activate mTORC1. Free Gator 2 does this only if Sestrin 2 is bound to Leu which allows the Gator 2 to dissociate from the inactive sestrin 2:Gator 2 complex. - The diagram above shows that in the absence of leucine, three blunt end (inhibition) arrows occur between Sestrin 2 and Rag A/B. One blunt arrow denotes inhibition, two activation (inhibition of inhibition), and hence three net inhibition Hence in the absence of Leu (when Sestrin is bound to Gator 2, Rag A/B is inhibited in its ability to activate mTORC1 as Rag A/B is in the GDP-bound state. However, free leucine unblocks the inhibitor action of sestrin 2 as Gator 2 is now free and active on its own. Amino acids (especially arginine, which is abundant) in the lumen of the lysosome activate, through the v-ATPase and SLC38A9, the GEF activity of Ragulator. When Rag A/B has sufficient GTP, some conformational changes must ensue to allow mTORC1 recruitment to the lysosomal membrane. Regulation of mTORC1 by Energy Availability - AMP Kinase Believe it or not, another small G protein with GTPase activity, Rheb (Ras homolog enriched in the brain), is involved in both mTORC1 recruitment to the lysosomal membrane and activation of mTOR. This interaction is also shown in the figure above. Mostly, Rheb is involved in the activation of the kinase activity of the mTORC1 complex and specifically the phosphorylation by mTOR of the substrates S6K1 and EIF4EBP1. In the presence of growth factors, Rheb is localized to the membrane by a lipid anchor (a farnesyl group). The mTORC1 kinase-activating activity of Rheb stands in contrast to the role of the Rag G proteins which appears to be chiefly recruitment. How is the small G protein Rheb regulated? Of course, by its interaction with yet another GAP, named the tuberous sclerosis complex (TSC). In the absence of growth factors, TSC binds to Rheb and, acting as a GAP, promotes GTP hydrolysis. This inactivates Rheb, inhibiting mTOR kinase activity. How then is Rheb regulated? One way is through phosphorylation by AMP Kinase (AMPK), an enzyme that is itself regulated by the energy level of the cells. AMPK phosphorylates and activates the TSC, which, acting as a GAP, inactivates the small G protein Rheb complex (TSC complex). Sestrins 1 and 2 may also regulate AMPK. Let's look at the energy sensor of the cell in more detail. AMP Kinase is one of the cell's major fuel sensors and also in mammals responds systemically to hormone and nutrient levels. The enzyme is a heterotrimeric protein consisting of an alpha (catalytic), beta (regulatory), and gamma (regulatory) subunit that binds AMP, ADP, and ATP. Cellular ATP levels are determined in part by the enzyme adenylate kinase which helps interconvert adenine nucleotide (AXPs) as shown in the following equilibrium: Adenylate Kinase: ADP + ADP ↔ ATP + AMP, Keq = 0.44 In red blood cells, the concentrations of ATP, ADP, and AMP are approximately 1850 uM, 145 uM, and 5 uM. Even in cells that use lots of ATP (muscle for example), ATP never falls by much. Using the values above and simple general chemistry, an 8% drop in ATP would lead, through the action of adenylate kinase, to an ATP concentration of about 1710 uM and an AMP concentration of 20 uM. This value for AMP is still much lower than ADP and ATP. However, this change represents a 4 fold increase in AMP which, even with the low actual concentration of AMP, leads to the activation of AMPK. Another "normalized" indicator of cell energy status (or "charge") is the Energy Charge, EC. It is defined by an equation that gives a value from 0-1 where 0 indicates that all AXPs are in the AMP form and 1 where only ATP is present. The numerator of the equation of EC below represents the number of moles of phosphoanhydride linkages in the AXP pool (two for each ATP and one for ADP) and the denominator is the number of moles of AXPs (mass balance). The 1/2 term allows the bracketed term to equal 1 when only ATP exists and 0 when only AMP exists. The EC values of cells are regulated to remain around 0.85. \mathrm{EC}=\frac{1}{2}\left[\frac{2 \mathrm{ATP}+\mathrm{ADP}}{\mathrm{ATP}+\mathrm{ADP}+\mathrm{AMP}}\right] Before we explore the mechanism of energy sensing by AMPK, let's look at the domain structure of the three subunits of AMPK. They are shown in Figure $14$. Figure $14$: Domain structure of the alpha, beta, and gamma subunits of AMPK. Kim et al. Experimental & Molecular Medicine (2016) 48, e224. https://www.nature.com/articles/emm201616.pdf. a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. http:// creativecommons.org/licenses/by-nc-sa/4.0/ The mammalian α1/α2 and β1/β2 isoforms are very similar, and their characteristic features are shown. AMPKα subunits: KD, kinase domain containing Thr-172 for the activation by upstream kinases; AID, autoinhibitory domain; two α-RIM, regulatory subunit interacting motifs triggering the conformational changes in response to AMP binding to the AMPKγ subunit; α-CTD, C-terminal domain binding to the β-subunit. AMPKβ subunit: CBM, carbohydrate-binding module, in which Ser108 is important for the action of some direct AMPK activators, such as thienopyridone (A-769662) and salicylate; β-CTD, C-terminal domain containing α-subunit-binding site and immediately followed by the domain for γ-subunit interaction. AMPKγ subunit: three γ-subunit isoforms have variable N-terminal domains (NTDs); four CBS, cystathione-β-synthase domain, which forms two Bateman domains that create four adenosine nucleotide-binding sites (Sites 1–4). Site 2 appears to be always empty and Site 4 has a tightly bound AMP, whereas Sites 1 and 3 represent the regulatory sites that bind AMP, ADP, or ATP in competition. How AMPK detects this exponential but still small molar increase in AMP is interesting, especially given the much higher concentrations of ADP and ATP. AMPK contains four binding sites that can bind AMP, ADP, and ATP (AXPs) in the regulatory subunit (gamma). This is in addition to the binding of ATP and ADP at the active site in the catalytic subunit (alpha). What binds depends on the Kd for binding of different AXPs as well as their concentrations. Bound AMP seems to have three effects on AMPK. When bound to the gamma subunit, AMP • increases phosphorylation of Thr 172 in an "activation" loop in the catalytic alpha subunit by an upstream kinase which increases the kinase activity of AMPK by 100-200 fold. Phosphorylation is essential for the activity of the enzyme; • inhibits dephosphorylation of Thr 172 which is perhaps the major way that AMP enhances the kinase activity of the catalytic subunit. ADP binding also inhibits dephosphorylation as shown by studies that show that the binding of ADP and the dephosphorylation of the phospho-AMPK have the same ADP concentration dependency; • allosterically activates ten-fold the kinase activity of the catalytic alpha subunit (a secondary effect). ADP has no such effect. These effects are altered by the markedly higher concentrations of ATP which counteracts all these effects, enhancing the Energy Charge sensor activity of this enzyme. The gamma regulatory subunit has 4 binding sites for AXP. Crystal structures show site 2 is empty, site 4 is always bound to AMP, and sites 1 and 3 can bind AMP, ADP, or ATP. Site 1, which mediates the allosteric effects on AMPK binds all AXPs with similar affinity. This appears paradoxical since given the high energy charge, one would expect ATP and ADP to out-compete AMP for binding. However, it was found that the Mg2+ -ATP complex has marked lower affinity for the site, allowing both AMP and ADP, which under cellular conditions are mostly not bound to Mg2+ while ATP is, to out-compete Mg2+ -ATP for binding. Site 3 binds AMP and ADP with a 30-fold lower affinity but on binding protects p-AMPK from dephosphorylation of Thr 172. Figure $15$ shows an interactive iCn3D model of human AMPK (a2b2g1) in complex with a small molecule activator SC4 (6B2E) SC4 or similar molecules may be important drugs to target AMPK and be useful in the treatment of insulin resistance and Type II diabetes. SC4 activates α2 complexes and glucose uptake into muscle cells. Some would call this type of drug an importagog, as it increases the uptake of important metabolites into cells. The alpha catalytic kinase subunit is shown in gray with key catalytic residues and phosphorylated Thr and Ser shown in sticks, CPK coloring, and labeled. The beta subunit is shown in cyan. It appears to be two chains since the connecting section is not ordered in the crystal structure. The gamma subunit with bound AMP (spacefill, CPK colors, labeled) is shown in magenta. What effect does activated AMPK have on the cell? Active AMPK has an amazing number of effects (see figure below). It activates liver glycolysis (by phosphorylating phosphofructokinase 2 which forms F2,6-BP, an activator of PFK) and inhibits by phosphorylation enzymes involved in fatty acid synthesis (acetyl-CoA carboxylase), glycogen synthesis (glycogen synthase) and cholesterol synthesis (HMG-CoA reductase). Yeast AMPK has recently been shown to be also controlled by acetylation of the equivalent beta subunit (Sip2). Acetylation increases its interaction with the alpha catalytic subunit (Snf1) which decreases its kinase activity. This decreases the phosphorylation of downstream kinases (including an analog of Akt1 called Sch9) which slows the growth and increases longevity. Normal aging is associated with decreased acetylation of Sip2. Figure $16$ shows how the many signaling pathways we have studied interact with AMPK Figure $17$ shows a more complete pathway of activation, regulation, and activity of AMPK. The illustration is used with courtesy of Cell Signaling Technologies (www.cellsignal.com). It also shows the effect of AMPK on the master regulator of protein, lipid, and nucleic acids synthesis, mTOR. Synthesis of these molecules is necessary for cell growth and proliferation, two activities that cells do not engage in when AMP levels are high, which signifies an energy-depleted state. Regulation of mTORC1 by Insulin and Growth Factors mTORC1 is regulated by local factors (amino acids, energy state) and systems factors (growth factors). This list is growing daily. The following have been shown to lead to mTORC1 activation including small molecules such as amino acids, ATP (through AMP kinase), oxygen, and glucose, and larger ones such as insulin, other growth factors, cytokines (immune growth factors and regulators), oncogenes (which promote cell proliferation) and some infectious agents. Other molecules or processes inhibit mTORC1, including tumor suppressors and stress. mTORC1 promotes mRNA and protein synthesis as described above but also nucleotide and lipid synthesis, which is not described in detail above. In addition, it promotes aerobic glycolysis (Warburg effect), to supply not energy but intermediates for biosynthesis, as well as the pentose pathway, which forms NADPH for reductive biosynthesis and ribose for nucleic acid synthesis. Let's look at 2 specific external hormones, insulin and epidermal growth factor (EGF), and how they affect mTORC1 activity; Insulin: Insulin binding to its receptor leads to the activation through phosphorylation of the kinase Akt (aka Protein Kinase B) after upstream phosphorylation of membrane phosphoinositides in the membrane and activation of phosphoinositide-dependent kinase 1, PDK1. Atk, as shown in the signaling figure for AMPK, phosphorylates TSC2, the GAP for Rheb. The arrows on the AMPK kinase figure above are not consistent with our previous use of arrows. In the figure from CST, arrows show that both AMPK and Akt phosphorylate TSC2. The phosphorylated TSC is shown to inhibit Rheb, the small G protein. This would not make physiological sense. Phosphorylation of TSC2 by AMPK (signaling energy depletion) activates the TSC2 GAP protein which would inhibit RheB (the G protein) and hence inhibit mTORC1. In contrast, phosphorylation of TSC2 by Akt (signaling the abundance of glucose) leads to the inhibition of the GAP activity of TSC2. That would keep Rheb in the active, GTP-bound form, which leads to the activation of the bound mTORC1. A more complete description of the pathway where insulin binds to its receptor (an insulin-gated receptor tyrosine kinase) and leads to activation of mTORC1 through Akt is shown in Figure $18$. MAPK Cascade: EGF binds its receptor, activating it as a receptor tyrosine kinase. Typical of other receptor kinases, it activates the mitogen activate protein kinase system. This process is mediated by Ras (a small G protein) activator of Raf (a mitogen-activated protein kinase kinase kinase or MAP3K). Active Raf phosphorylates and activates MEK (a MAPK2) which activates ERK (a MAPK). Erk phosphorylates mTORC1 directly, which activates it. It also phosphorylates TSC2/TSC1, which inhibits this GAP protein, leading indirectly to the activation of the small G protein Rheb, which also activates mTORC1. These steps are shown in Figure $19$. A summary showing the kinases that activate or inhibit TSC2/1 is shown in Figure $20$. We tend to concentrate on our favorite protein and confer it with special status as critically important in a pathway. One could pick the GAP protein TSC2 as especially important in regulating the activity of mTORC1. Figure $20$: kinases that activate or inhibit TSC2/1 The figure above shows two additional proteins. One is REDD1 (not a kinase), which activates the GAP protein TSC2, leading to the inhibition of the small G protein Rheb, and hence the inhibition of mTORC1. REDD1 (regulated in development and DNA damage responses 1) is also called DDIT4 (DNA-Damage-Inducible Transcript 4). It is a gene whose expression is activated during hypoxia by hypoxia-inducible factor-1 and also during DNA damage. Hypoxia alters metabolism very quickly. The protein is degraded by the proteasome after it is targeted for degradation by the post-translational addition of ubiquitin. This suggests yet another way to regulate the activity of mTORC1. The other is IKK beta, also known as IKBKB ( Inhibitor Of Kappa Light Polypeptide Gene Enhancer In B-Cells, Kinase Beta). It is a kinase that phosphorylates and inhibits TSC2 which inhibits Rheb, leading to the activation of mTORC1. This kinase is activated by many stimuli including inflammation (mediated by cytokines), bacterial or viral infections, and DNA damage. It phosphorylates a bound inhibitor of NF-kappa beta. This allows ubiquitinylation of the inhibitor, targeting it for proteasomal degradation. The free NFKB can then enter the nuclease and alter the transcription of genes involved in the immune response and hence promote proliferation. Under these conditions, one would expect the activation of mTORC1. A final note: Less is known about how lipids regulate mTORC1. Two possible lipid signaling molecules, phosphatidic acid, and phosphatidyl inositol -3-phosphate are probably involved. The enzyme that makes them, phospholipase D and phosphoinositide 3-kinase (from the PIK3C3 gene), also known as VPS34 (for vacuolar protein sorting from yeast), are found in phagosomal and lysosomal vesicles and are involved in their processing, seem to be involved in mTOR signaling. Obesity and people with high-fat diets have elevated mTOR activity.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.13%3A_Regulation_of_the_Cell_Cycle_by_Protein_Kinases.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry This section is an integration of materials as referenced, with significant modifications and additions. Aleem and Arceci. Targeting cell cycle regulators in hematologic malignancies. Article in Frontiers in Cell and Developmental Biology 2015. DOI: 10.3389/fcell.2015.00016. Creative Commons Attribution 4.0 International Introduction For a cell to undergo successful division, it has to perform four key tasks in a highly ordered fashion. First, there is a preparatory synthetic phase (G1) that results in increased cell size in anticipation of DNA replication (S phase). Cells then proceed through (G2-phase) to prepare to equally segregate duplicated DNA (M phase) and finally divide into two equal daughter cells. From G1 a cell can also exit the cell cycle and enter a state of quiescence (G0), undergo differentiation, or re-enter the cell cycle to proliferate in response to mitogenic signals. The core molecular machinery controlling the mammalian cell cycle consists of a family of serine/threonine protein kinases called cyclin-dependent kinases (CDKs). These are catalytic subunits, which are activated in most cases by association with cyclin regulatory subunits. The activity of CDK/cyclin complexes is further regulated by CDK inhibitors (CKIs), phosphorylation and dephosphorylation, ubiquitin-mediated degradation, transcriptional regulation, substrate recognition, and subcellular localization. The family of CDKs/cyclins/CKIs contains more than 30 members. They are implicated in essential cellular functions such as transcription, DNA damage repair, epigenetic regulation, metabolism, proteolytic degradation, stem cell self-renewal, neuronal functions, and spermatogenesis. Figure $1$ shows the cell cycle and the involvement of CDKs/cyclins at key points. CDK3/cyclin C drives cell cycle entry from G0. CDK4/6/cyclin D complexes initiate phosphorylation of the retinoblastoma protein (pRb) and they sequester p21Cip1 and p27kip1 (not shown), which are both inhibitors of CDK2, thus promoting the activation of CDK2/cyclin E complex. In late G1, the CDK2/cyclin E complex completes phosphorylation and inactivation of pRb, which releases the E2F transcription factors and the G1/S transition takes place. DNA replication takes place in the S phase. CDK2/cyclin A complex regulates progression through the S phase and CDK1/cyclin A complex through the G2 phase in preparation for mitosis (M). Mitosis is initiated by CDK1/cyclin B complex (which will model at the end of this section). The activity of CDK1/cyclin B is tightly regulated by activating phosphorylation by the CDK-activating kinase CAK (a heterodimer of cyclin H-CDK7-MAT1) and inhibitory phosphorylations by Wee1 and Myt1 on Tyr15 and Thr14 (not shown). Some specific CDK4/CDK6 pharmacological inhibitors are also shown CDKs with Direct Functions in Cell Cycle Regulation The classical CDKs that directly regulate the mammalian cell cycle in complexes with cyclin subunits include CDK3, CDK4, CDK6, CDK2, and CDK1. CDK3 promotes cell cycle entry from quiescence in association with cyclin C. CDK8 has also been suggested to play a role in cell cycle entry from G0 and in the G1/S transition. In its simplest model, the mammalian cell cycle proceeds as follows: • In early G1, CDK4/CDK6 in complex with cyclin D receive mitogenic signals that result in activation of cell cycle entry, as shown in Figure $1$. Key signaling events include the initiation of retinoblastoma protein (pRb) phosphorylation and the sequestration of p21Cip1 and p27kip1, which are both inhibitors of CDK2, thus promoting the activation of CDK2/ cyclin E complex. In late G1, CDK2 in complex with cyclin E completes the phosphorylation and hence inactivation of pRb, which in turn releases the E2F transcription factors. E2F promotes transcription of cyclin E which is necessary for the G1/S transition. • Progression through the S phase is mediated by CDK2/cyclin A complex. • Mitosis is then initiated by CDK1/cyclin B complexes. We will model the regulation of CDK1 later in this section. CDK1/cyclin A complexes contribute to the preparation for mitosis in the G2 phase. The activity of CDK1/cyclin B is tightly regulated by activating phosphorylation by the CDK-activating kinase (CAK) (a heterodimer of cyclin H and CDK7) and inhibitory phosphorylations by WEE-1 and Myt1 on Tyr15 and Thr14. Mitosis starts after WEE-1 is degraded and CDC25C phosphatase releases the inhibitory phosphorylation on CDK1/cyclin B. The cyclins are also expressed in a coordinated fashion throughout the cell cycle. The cyclin expression cycle is shown in Figure $2$. The timing of expression is consistent with the explanations above. A graph showing multiple progressions through the cell cycle is shown in Figure $3$. Figure $3$: Sustained oscillations of the CDK network in mammalian cells. Gérard and Goldbeter, Front. Physiol., 02 November 2012 Sec. Systems Biology Archive. https://doi.org/10.3389/fphys.2012.00413. Creative Commons Attribution License The time evolution of cyclin D/Cdk4–6 (in black), cyclin E/Cdk2 (in blue), cyclin A/Cdk2 (in green), and cyclin B/Cdk1 (in red) is shown in the presence of a suprathreshold level of growth factor. Cyclin D/Cdk4–6 is the total active form of the kinases, which is composed of cyclin D/Cdk4–6 and also the complex formed by cyclin D/Cdk4–6 and p21/p27. Note again the oscillatory nature of the cyclin B/Cdk1 complex, which will explore at the end of this section. CDKs with Transcriptional and Other Functions In addition to their direct role in the mitotic cell cycle regulation, some classical CDK/cyclin complexes have essential functions in meiosis, such as CDK2, in transcription and/or DNA repair. Other CDKs act by activating the classical CDKs, such as CDK7/cyclin H (CAK) and the related CDK20, also known as cell cycle-related kinase (CCRK). Some CDKs function mainly in influencing transcription by phosphorylating the carboxy-terminal domain (CTD) of ribonucleic acid (RNA) polymerase II (RNA pol II). This phosphorylation also serves as a platform for RNA processing and chromatin regulation. CDKs that have important transcriptional roles include CDK7/cyclin H/MAT1 complex, a component of the basal transcription factor, TFIIH, and facilitate transcriptional initiation. CDK8/cyclin C, in addition to its role in transcription, is also involved in the Wnt/β-catenin pathway and inhibition of lipogenesis. Cyclin C can recruit CDK8 or CDK19 to the CDK8 module of the Mediator complex, which can function as a positive or negative regulator of transcription by RNA pol II. CDK3/cyclin C also plays a role in NHEJ-mediated DNA damage repair. While CDK9 in complex with cyclin T forms the phospho-transcription elongation factor b (p-TEFb) and promotes transcriptional elongation, CDK9 also functions in the DNA damage response when complexed with cyclin K. CDK10/cyclin M phosphorylates the Ets2 transcription factor and positively controls its degradation by the proteasome. Ets2 plays a key role in cancer and development. CDK11/cyclin L controls the assembly of the RNA pol II mediator complex. CDK12 and CDK13 in complex with cyclin K control RNA pol II transcription, and CDK12/cyclin K controls DNA damage response. Structure of CDKs–cyclins Open Biology. Wood and Jane A. Endicott (2018) https://doi.org/10.1098/rsob.180112. Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, The structures of inactive cyclin-free kinases are very similar but vary at the N-terminal and C-terminal ends. Figure $4$ shows an interactive iCn3D model of the prototypical active human cyclin-dependent kinase 2 with a bound ATP (1HCK). The model shows that CDK2 has structural features shown in all the kinases we have studied previously: • a smaller N-terminal lobe (light cyan) and larger C-terminal lobe (light magenta) in between which ATP binds (along with Mg2+). • the C-helix (residues 45 – 55, purple), which contains a conserved Glu. It forms an interaction with and helps position a key Lys in the active, which facilitates ATP binding and transition state stabilization; • hinge (residues 80 – 84, yellow), • activation loop (residues 145– 172, red), which contains T160 (sticks, CPK colors, labeled) that becomes phosphorylated on activation by yet another kinase called CDK-activation kinase (CAK). When T160 is phosphorylated, the kinase binds to cyclin A. The loop starts and ends with the conserved residue DFG and APE, respectively. • Not highlighted in the model is a conserved conformationally flexible glycine-rich region (residue 12-16) with the motif GXGXXG In the inactive form, the N-terminal end of the activation loop has a short alpha helix that prevents the C-helix from adopting the correct position for catalysis. Activation requires movement of the C-helix allowing the Glu in the C-helix to position the active site Lys. CDK2–cyclin A activation The binding of cyclin A to CDK2 activates it through the repositioning of the C-helix and the activation loop. When CDK2 is phosphorylated and bound to cyclin A, there is a large shift in the C-helix allowing the interaction of the C-helix Glu with the active site Lys. First, let's look at the structure of cyclins. Each cyclin has a unique sequence and structural features that allow them to interact with specific CDKs and associated proteins. However they all have a conserved "cyclin box" structure containing about 100 amino acids. Figure $5$ shows an interactive iCn3D model of bovine cyclin A (1VIN). Cyclin A has two linked cyclin box folds, each containing around 100 amino acids and comprised of five helices. They interact with the more disordered parts of unphosphorylated CDK2, which result in some low levels of activity. Cyclin binding causes a large movement of the C-helix enabling the Glu -- Lys interaction. Phosphorylation of T160 leads to the repositioning of the activation loop. Figure $6$ shows an interactive iCn3D model of Phosphorylated cyclin-dependent kinase 2 bound to cyclin A (1JST) CDK2 is shown in cyan and cyclin A in gray. Here are some structural features represented in the model. • the C-helix (residues 45 – 55, purple), • activation loop (residues 145– 172, red), which contains pT160 (sticks, CPK colors, labeled) • the catalytic "triad" Lys33, Glu51, and Asp145 Figure $7$ shows an animation of structural changes in just CDK2 when "apo"-CDK2 (without bound cyclin A, pdbID 1HCK) binds cyclin A (1JST). Gray represents the structure of CDK2 in the absence of cyclin A. The structure of just CDK2 in the presence of cyclin A is shown in magenta. Note that large shifts in the C-helix (purple) and activation loop (red) on binding cyclin A. Cyclin partners of CDK1 and CDK2 CDK1 is the closest member of the CDK family to CDK2 and for which structures of the cyclin-free and authentic cyclin-bound forms can also be compared. Depending on cyclin availability and concentration, CDK2 can bind cyclin A, B (if CDK1 expression is knocked down), and E (see Figure $1$. The binding interface between CDK2 and the cyclins is quite large compared to the interface between CDK1 and cyclins. Three large aromatic side cyclin side chains (Y170, Y177, and Y258) are conserved in the binding interface. In cyclin E, the corresponding amino acids are smaller (N112, I119, and L202). The binding interface between CDK1 and the cyclins is smaller so it appears that it might preferentially interact with cyclins A and B to gain binding affinity through the more robust interactions with the aromatic groups in the interface in the CDK2: Cyclin A and CDK2:cyclin B complexes. A comparison of the CDK1–cyclin B and CDK2–cyclin A/B/E structures also highlights the potential for these closely related CDKs to be differentially regulated by reversible phosphorylation. The antagonistic activities of Wee1/Myt1 kinases and Cdc25 phosphatases regulate the phosphorylation status of the CDK glycine-rich loop (defined by the GXGXXG motif, residues 11–16 in CDK2). The structure of CDK2–cyclin A phosphorylated on Y15 illustrates how phosphorylation promotes a glycine loop structure that antagonizes both peptide substrate binding and the ATP conformation required for catalysis. The flexibility of the glycine-rich loop is compatible with a model in which the phosphorylated Y15 side chain is solvent exposed and accessible to both kinases and phosphatases. CDK1 is also regulated by active-site phosphorylation, and the conserved nature of the structure in this region suggests that the mechanism of inhibition is also conserved. CDK substrate recognition Local and distal sequence motifs must be used to confer specificity to the binding of specific cyclins and other proteins to specific CDKs. One interesting example is provided by examining the structure of a phospho-CDK2 Cyclin A in complex with a peptide substrate derived from the protein CDC6. Figure $8$ shows an interactive iCn3D model of Phospho-CDK2:Cyclin A complex with a peptide containing both the substrate and recruitment sites of CDC6 (2CCI) The color coding is the same as the models above: • CDK2 is shown in cyan and cyclin A in gray. • the C-helix (residues 45 – 55, purple), • activation loop (residues 145– 172, red), which contains pT160 (sticks, CPK colors, labeled) • the catalytic "triad" Lys33, Glu51, and Asp145 The 30 amino acids peptide (numbers 67-96) shown in gold is a substrate for phosphorylation by the CDK2:cyclin A complex. It derives from an actual biological substrate in the protein cell division control protein 6 homolog, also called CDC6-related protein. It is involved in a checkpoint control of the cell cycle that "checks" that DNA replication is completed before mitosis. It is discontinuous in the model since part of the bound peptide is intrinsically disordered and not observed in the crystal structure. • The 1st fragment of the CDC6 peptide (67-73) contains the binding motif sequence S/T)PX(K/R) (the CDC6 substrate has the sequence 70Ser-Pro-Arg-Lys). Ser 70 is the target amino for phosphorylation by CDK2:cyclin A. • The second fragment seen in the model (amino acid 85-96) contains another binding motif, RXL (in this peptide RRL), which acts to recruit cyclin A. This binds to the sequence MRAIL (210-214) in cyclin A. What is so interesting is that this second binding site on cyclin A for its target protein is so far away from the active site of the CDK2:cyclin A. These kinds of interactions work to determine the specificity for CDKs and the binding cyclin partners. CDKs 7, 9, 12, and 13 phosphorylate the RNA polymerase carboxy-terminal domain (CTD). The sequence of the CTD is unusual, composed of 52 heptad repeats in humans, with the consensus sequence YSPTSPS. Extracted from cells, CTD residues S2 and S5 are the most abundantly phosphorylated serine residues, while S7 is phosphorylated to a lesser extent. CDK7 has been shown to predominantly phosphorylate S5 and S7, CDK9 to have activity towards all three serines, and CDK12 and CDK13 to predominantly phosphorylate S2. CDKs form complexes not only with target protein substrates but other proteins which can serve as scaffolding anchors that bind both the CDK and the cyclin. Figure $9$ shows an interactive iCn3D model of human CDK-activating kinase (CAK), a complex composed of cyclin-dependent kinase (CDK) 7, cyclin H, and the scaffolding protein MAT1 (6xbz). The gray protein is CDK7, the cyan is cyclin H and the orange MAT1. The purple again represents the C-helix of the CDK, and the red is the activation loop. The catalytic triad side chains in the active site of the CDK are shown in CPK-colored sticks. Also shown is phospho-Ser in the activation loop. The CDK activating kinase (CAK) shown above phosphorylates the target S/T in the activation loop (which is also called the T-loop) in CDKs, activating the kinase. In addition, it regulates the initiation of transcription by phosphorylating the YSPTSPS repeats in the C-terminus of RNA polymerase II subunit RPB1. There are 15 consecutive repeats in the sequence as well as others dispersed in the C-terminal domain. We already mentioned the motif RXL found in cyclin binding proteins that recruit them to cyclins (through, for example, their interaction with MRAIL (210-214) in cyclin A. Likewise short motifs in cyclins are used to bind to proteins that increase CDK activity or decrease it. A number of cyclin-encoded protein-binding sites or short peptide motifs have been structurally characterized. A well-characterized example is the recycling of the cyclin RXL recruitment site that is exploited to either enhance or inhibit CDK activity. Alternatively, short motifs encoded within the cyclin sequence can be used both to dock cyclins to substrates to enhance CDK activity and alternatively to localize them to CDK regulators frequently resulting in a loss of CDK activity. Members of the p27KIP1/p21CIP1 cyclin-dependent kinase inhibitor (CKI) family share an RXL motif with RXL-containing substrates and compete with them for CDK–cyclin association. The INK (inhibitors of CDK) family of CKIs selectively inhibits CDK4 or CDK6 and, through an allosteric mechanism, disfavors CDK–cyclin binding [15]. Their tandem ankyrin repeat structures exemplified bCy CDK6–p19INK4d bind in the vicinity of the CDK hinge on the interface opposite to the surface remodeled upon cyclin association. Modeling the Cell Cycle - Oscillations Lastly, we will focus on mathematical models that show how the oscillatory behavior of CDK1/Cyclin B arises (remember than CDKs become active on binding to a cyclin). When CDK1 is activated, the cell is driven into mitosis. It is driven out of mitosis by the activation of the anaphase-promoting (APC) complex, which contains APC-Cdc20, an E3 ubiquityl ligase. Yeast Cdc20 is an activator protein that regulates the ubiquitin ligase activity of APC by binding at the right time in the cell cycle to B cyclins that contain a D box motif. This recruits the B cyclin:CDK1 to APC which ubiquitinates the cyclin B, leading to its degradation by the proteasome. Figure $10$ shows the levels of both cyclin B/CDK1 (red) and cyclin A/CDK2 (green) with time. Now in your mind, imagine another curve on the graph showing similar oscillations of activated APC, only frameshifted a bit in time so that the active APC trails that of active cyclin B/CDK1. When cyclin B/CDK1 is at iitsactive peak, active APC is already beginning its rise. Figure $10$: The switch to sustained oscillations of cyclin A/Cdk2 (in green) and cyclin B/Cdk1 (in red) is shown following the overexpression of AP1. Gérard and Goldbeter, ibid. The dissociation constant, KD, for CDK1 and cyclin B is about 28 nM, which represents high affinity binding. Ubiquitinylation of cyclin B and its degradation allows for the freeing of CDK1 and inhibition of its activity. The oscillatory behavior in activity occurs only on the overexpression of yet another protein, AP1. Let's explore in detail a model proposed by Ferrell et al that accounts for the 25 min oscillatory behavior of CDK1 in Xenopus (frog) eggs. The eggs are very large and perhaps because of their size have different constraints on their cell cle cycle. For example, cells can enter mitosis before the completion of DNA synthesis. The players which regulate its activity are shown below in Figure $11$. Figure $11$: Proteins that help regulate the activity of CDK1 in Xenopus eggs. • Cyclin: binds to and activates CDKs; active CDK1 drives cells into mitosis. • APC: anaphase-promoting complex; active APC drives cell out of mitosis with its E3 ubiquitin ligase activity, modifying cyclin and targeting it for proteolysis; • Wee1: nuclear Ser/Thr kinase • Cdc25: cell cycle division phosphatase that activates cyclin-CDK1. Our goal in this discussion is not only to model the actual oscillatory behavior of CDK1 but also to show you how models are built and tested. When modeling enzyme inhibition data, it is important to fit the data to many models (reversible competitive, uncompetitive, mixed, noncompetitive) to find out which best fits the data. Typically one starts with the simplest possible model and then advances to more complicated models until the data with the best statistical fit to the data is found. Other examples, which are more relevant here, involve fitting binding and kinetic data using equations that give hyperbolic (for saturation binding and simple Michaelis-Menten kinetics) and sigmoidal fits (for cooperative binding and allosteric enzymes). So let's start with the simplest model that might lead to oscillatory activity of CDK1. In this section, active CDK1 will be designed as CDK1. Model 1: One-step process with negative feedback - CDK1 inhibits its own activation (for example by activating APC and hence ubiquitinylation of cyclin) We saw in the Vcell models for the MAP kinase cascade that feedback phosphorylation of the first enzyme in the cascade (MAPKKK) by the last enzyme in the cascade, MAPKPP, leads to oscillations in enzyme activity. Could it explain the oscillations in the activity of CPK1 in Xenopus eggs? Let's make the following set of assumptions, which all be understandable from the material presented in previous chapters: 1. CDK1* is inhibited by APC* (active APC), and to make it simpler, APC* can be expressed as a simple function of CDK1* so there is just one species that vary in the flux equation; 2. CDK1 activation occurs on rapid high-affinity binding of cyclin, which is synthesized at a constant rate a1; 3. The rate of CDK1 activation to produce CDK1* is given by mass action = rate activation - rate inactivation; 4. APC is activated to APC "instantaneously" by CDK1* so APC* is a very sensitive “cooperative” function of CDK1* which can replace the APC. For this type of "instantaneous (or highly cooperative effect), we will use the Hill equation which gives a sharp, sensitive, cooperative rise in complex instead of a simple formation of a complex between CDK1 and APC. We explored this property of the Hill equation in Chapter section 5.3.5 on Mathematical Analysis of Cooperative Binding. Here is the Hill expression commonly use to empirically fit the fractional saturation of a species Y=\frac{L^n}{K_D^n+L^n} It's a bit different than the Hill equation we saw for modeling the cooperative binding of O2 to hemoglobin (Chapter 5.3.5) since the KD term is also raised to the power n (which is not in the actual Hill equation). However, we did see that for oxygen binding to hemoglobin, \mathrm{K}_{\mathrm{D}}=\mathrm{P}_{50}^{\mathrm{n}} Hence the empirical expression used in fitting Model 1 is completely in accord with the Hill treatment of cooperativity. Again we use the Hill equation when modeling binding and kinetic data that show significant sensitivity to conditions. It gives yet another parameter to help fit the data and to test models. Two different representations of a reaction diagram showing Model 1 are shown in Figure $12$. Figure $12$: Two different representations of a reaction diagram for Model 1 - Activity of CDK1. The representation on the right is from Vcell. The one on the left shows that CDK1 can inhibit itself. Model 1 and its associated assumptions lead to the following differential equation that can easily be solved numerically in Vcell. All of the VCell outputs shown below were obtained from Vcell models kindly provided by Leslie Loew. v=\frac{d C D K 1^}{d t}=a_1-b_1\left(C D K 1^\right)\left(A P C^\right)=a_1-b_1\left(C D K 1^\right)\left(\frac{C D K 1^{* n 1}}{K_1^{n 1}+C D K 1^{* n 1}}\right) This rate equation has two terms (assumption 3). The first is the rate that CDK1* forms (a constant a1 defined by the rate of cyclin synthesis) and the rate at which it is degraded by APC. The constant b1 in the second term can be thought of as a second-order rate constant for the interaction of CDK1 and APC, a process that inactivates CDK1. The [APC] in the middle equation is replaced with the Hill equation for the effective fractional saturation concentration of APC (see assumption 4 of Model 1 described above) in the right-hand side. \left(A P C^\right)=\left(\frac{C D K 1^{* n 1}}{K_1^{n 1}+C D K 1^{* n 1}}\right) We will define the activity of the system as the rate at which CDK1* forms. \text { Activity }=\frac{d C D K 1^}{d t} Now let's see if changing the Hill coefficient n1 can cause oscillations in CDK1. MODEL CDK - Model 1 Initial Conditions Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play \| Select Slider to change which constants are displayed \| Select About for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health. Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington. Funded by NIH/NIGMS (RO1-GM123032-04) Note that all the graphs plateau quickly at which point the CDK1* activity is constant. The graph (gray) of the curve with the highest value of the Hill coefficient (n1=24) is linear and then abruptly plateaus. The slope of the velocity curve over the entire linear part of the n1=24 graph curve is 0.1, which is the value set for the rate of activation of CDK1. Then suddenly at around 4 seconds, an "almost infinitely cooperative" shift to a constant rate of formation occurs arising from an abruptly reached rate of inactivation of CDK1* by the APC complex. These graphs do not show oscillations. Now let's see the graph with no feedback inhibition, much as we did with the MAPK cascade in Chapter 12.4. The easiest way to do that is to set b1, the "second" order rate constant for the interaction of CDK1* and APC* in the model to 0. The graph is shown in Figure $13$. Figure $13$: Activity of CDK1* in Model 1 in the absence of feedback inhibition. The activity of CPK1* continually increases. When feedback inhibition is added, the curve "bends" to a plateau, but it does not start to decrease and shows no signs of oscillations. Time to move on to a more complex model! Model 2: Two-species model with activation and inhibition- This model is more complicated and shows 2 species (CDK1 and APC) both of which are activated and inhibited. We need 2 mass action differential equations, one for each. Figure $14$: Figure $14$: Two different representations of a reaction diagram of Model 2 The equation for dCDK1/dt is the same as in Model 1, as is repeated below. v=\frac{d C D K 1^}{d t}=a_1-b_1\left(C D K 1^\right)\left(A P C^\right)=a_1-b_1\left(C D K 1^\right)\left(\frac{C D K 1^{ n 1}}{K_1^{n 1}+C D K 1^{* n 1}}\right) Likewise, the equation for dAPC/dt consists of two terms, one for its activation and one for its inhibition. v=\frac{d A P C^}{d t}=k_f A P C-k_r A P C^* Assume that kf, the rate constant of the activation of APC, is equal to a constant a2 times a Hill function of CDK1, and kr, the rate constant for the inactivation of APC, is simply b2. Then the equation becomes v=\frac{d A P C^}{d t}=a_2\left(\frac{C D K 1^{ n 2}}{K_2^{n 2}+C D K 1^{* n 2}}\right) A P C-b_2 A P C^* Let's look at the output graphs for the following initial condition: • CDK1* = 0 uM • APC = 1 uM • APC* = 0 uM MODEL CDK Model 2 See equations in text. Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play \| Select Slider to change which constants are displayed \| Select About for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health. Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington. Funded by NIH/NIGMS (RO1-GM123032-04) Figure $15$ shows statics graphs of just CDK1* activity vs time for n1 values of 1, 4, 8, and 24 for Model 2. Figure $15$: Graphs of just CDK1* activity vs time in Model 2 for n1 values of 1, 4, 8, and 24. Wow! By simply adding an additional species to the model and a second differential equation for it, we see the first signs of oscillatory behavior in the activity of CDK1. The output at higher n1 values is best described as damped oscillations. Now let's try a final third model. Model 3: Three species model with activation and inhibition- This model contains the enzyme Plk1 (Polo-like kinase 1, also called serine/threonine-protein kinase 10-A), along with APC and CDK1. These three species are all activated and inhibited. Assume that Plk1 is activated by CDK1 and that it also helps activate APC. Following the arrows in the left part of the figure below shows that it acts as an "intermediary" between CDK1 and APC. Two different representations of a reaction diagram of Model 3 are shown in Figure $16$. Figure $16$: Two different representations of a reaction diagram of Model 3 We have 3 species, so we need three differential equations, as shown below. v=\frac{d C D K 1^}{d t}=a_1-b_1\left(C D K 1^\right)\left(\frac{A P C^{ n 1}}{K_1^{n 1}+A P C^{* n 1}}\right) v=\frac{d P l k 1^}{d t}=a_2\left(1-P l k 1^\right)\left(\frac{C D K 1^{* n 2}}{K_2^{n 2}+C D K 1^{* n 2}}-b_2 C D K 1^\right. and v=\frac{d A P C^}{d t}=a_3\left(1-A P C^\right)\left(\frac{P l k 1^{ n 3}}{K_3^{n 3}+P l k 1^{* n 3}}-b_3 A P C^\right. The equation for activation of CDK1 is the same as in Models 1 and 2. The equation for the activation of APC is similar to Model 2 with kf modeled as a Hill function of Plk1star, which activates APC The equation for the activation of Plk1 is similar to Models 2 and 3 with kf modeled as a Hill function of CDK1star which activates Plk1 Although you probably can't write these differential equations by yourself, hopefully, you can see that they make sense. MODEL CDK Model 3 Initial values Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play \| Select Slider to change which constants are displayed \| Select About for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health. Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington. Funded by NIH/NIGMS (RO1-GM123032-04) Figure $17$ shows graphs of just CDK1 activity vs time for n1 values of 1, 4 and 8 for Model 3 Figure $17$: Graphs of CDK1* activity vs time for n1 values of 1, 4 and 8 Finally, we observe oscillatory behavior in the activity of CDK1*, but only for higher values of the Hill coefficient (n1 = 4 and 8). Other models can produce oscillation, but this one seems perhaps most comprehensible to students who have studied mass action equations along with Hill binding and kinetic equations. The three-component system described in Model 3 is of course embedded in a large pathway of inputs and outputs so other factors most likely affect the oscillatory behavior.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.14%3A_Programmed_Cell_Death.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Introduction We have discussed often how cell signaling might go awry and lead to cancer. However, there are signaling systems that lead to cell death. There are many ways in which cells can die. We'll discuss not "accidental" cell death but one, apoptosis, that is programmed into the genome and highly regulated. Figure $1$ shows how normal cell proliferation and growth can be modulated by two classes of genes, oncogenes that cause proliferation, and tumor suppressor genes that inhibit it. Apoptosis involves chromatin aggregation and cleavage, the concentration of cell material, and apoptosis body formation. Mutations to aberrantly activate oncogenes or inhibit the expression of tumor suppressor genes lead to cancer. These cells would ideally undergo programmed cell death or apoptosis. As with control of proliferation, some genes promote apoptosis as well as anti-apoptotic genes which inhibit programmed cell death. Dysregulation of these can also cause cancer. Apoptosis is an important mechanism to kill viral-infected cells. However, this can go too far. For example, T helper cells (TH) infected with the HIV virus die. However, the collapse of the population of these cells is in part attributed to apoptosis. As we learn more about programmed cell death, it is clear that apoptosis is not the only way the genome is programmed to cause cell death. These other ways include: autophagy - This is a catabolic pathway in which intracellular proteins, protein complexes, and organelles are collected into large autophagosomes in which incorporated lysosomes and their degradative enzymes reprocess damaged or unneeded cell material. It is a highly programmed process, which if dysregulated, could lead to cell death. necroptosis: Infections and toxins are known to cause necrosis, which is a "passive" form of cell death. In contrast, programmed necrosis is called necroptosis. Overview of apoptosis. Apoptosis consists of 4 steps: • the decision to activate the pathway; • the actual "suicide" of the cell; • engulfment of the cell remains by specialized immune cells called phagocytes; • degradation of the engulfed cell. The actual steps in cell death require: • condensing the cell nucleus and breaking it into pieces • condensing and fragmenting of cytoplasm into membrane-bound apoptotic bodies; and • breaking chromosomes into fragments containing multiple numbers of nucleosomes (a nucleosome ladder) To commit suicide must be an extremely important cellular decision. Hence you would expect this process to be regulated and highly complicated. When would it be advantageous to the organism to want a cell to kill itself (or be told to kill itself)? Cell death would be used to: • "sculpt" an organism during development such as during embryo development, metamorphosis, and tissue atrophy • regulate the total number of cells. • defend and remove unwanted or dangerous cells like tumor cells, virally infected cells, or immune cells that recognize self (which could lead to autoimmune disease). Unregulated apoptosis could exacerbate or cause diseases such as: • AIDS, in which T helper cell numbers plummet. Part of the dramatic decline in these cells might be caused by health T helper cells being tricked into committing suicide; • neurodegenerative diseases like Alzheimer's; • ischemic stroke, when restricted blood flow to certain regions of the brain can lead to neural death through increased apoptosis' • cancer, in which tumor cells lose their ability to undergo apoptosis; • autoimmune disease, in which self-reactive immune cells trick normal body cells to kill themselves; • viral disease; Apoptosis does not require new transcription or translation, suggesting that the molecular machinery required for cell death lay dormant in the cell, and just requires appropriate activation. What "signals" induce apoptosis? Signals can be extracellular: • a hormone (such as thyroxine that causes apoptosis in tadpole tails • a lack of a "survival" signal (which inhibits apoptosis) such as a growth factor • a cell:cell contact from an adjacent cell Signals can be intracellular: • ionizing radiation • virus infection • oxidative damage from free radicals Apoptosis Much of this section was derived directly from the following reference, with modifications and additions. Fox, J., MacFarlane, M. Targeting cell death signaling in cancer: minimizing ‘Collateral damage’. Br J Cancer 115, 5–11 (2016). https://doi.org/10.1038/bjc.2016.111. Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ There are two apoptotic pathways in cells: • The extrinsic pathway: extracellular apoptotic ligands bind to membrane death receptors, leading to the assembly of the death-inducing signaling complex (DISC). Similar to the inflammatory response we have already discussed in Chapter 5, two specific cysteine-aspartic proteases (caspases) are activated, caspases 8 and 10. These activate other caspases in an amplification of the process. • The intrinsic pathway: intracellular signals such as damaged DNA or proteins are sensed by Bcl-2 proteins on the outer membrane of mitochondria. The BcL-2 (B-cell lymphoma-2) family of proteins all have Bcl homology domains. Their functions are carried out at the outer mitochondrial membrane. Some members of this family are antiapoptotic (Bcl-2, Bcl-xL, Mcl-1, Bcl-w, A1/Bfl-1, and Bcl-B/Bcl2L10), while others are proapoptotic (Bid, Bim, Puma, Noxa, Bad, Bmf, Hrk, Bik Bax, Bak, and Bok/Mtd). Apoptosis leads to activation of Bax/Bak, which initiate mitochondria degradation, starting with damage to the outer membrane and release of pro-apoptotic proteins like the inner membrane space protein cytochrome C into the cytoplasm. This leads to the assembly of the apoptosome and activation of caspases 9 and 13. Again this is very similar to the formation and activity of the inflammasome which we saw in Chapter 5. An overview of the extrinsic and intrinsic apoptotic pathways is shown in Figure $2$. We will explore some of the proteins involved in the section below. The extrinsic death receptor pathway is activated by death receptor ligands, including FasL, TNF-α, DR3, DR4, and DR5 or TRAIL, etc. FasL is an integral membrane protein found in cells. In addition, there are soluble versions of it. The binding of FasL to Fas, an integral membrane protein, initiates the recruitment of FADD, TRADD, and caspase-8 to form the DISC complex, which in turn activates caspase-8 and downstream caspases. The binding of tissue necrosis factor alpha (TNF-α) to its receptor, TNFR1 (a Fas protein), initiates the recruitment of TRADD, RIP, TRAF2/5, and cIAP1/2 to form complex I, which activates NF-κB and JNK pathways and increases the transcription of pro-survival genes. However, the modification of RIP or degradation of cIAP1/2 can lead to the disassociation of complex I. TRADD and RIP then associate with FADD and caspase-8 to form complex II, the so-called death complex. The intrinsic death receptor pathway is initiated by the BH3-only protein, BCL-2 homology 3 (BH3-only), under intracellular stress such as DNA damage. The BH3-only proteins activate apoptosis by binding and neutralizing the pro-survival proteins, allowing Bax/Bak to homo-oligomerize and permeabilize the mitochondria. For example, BH3-only protein can inactivate Bcl-2 and prevent Bcl-2 from effectively neutralizing Bax and Bak, leading to the activation of Bax and Bak. The activated Bax and Bak on the mitochondrial membrane alter its permeability, depolarizes the membrane, and leads to the release of cytochrome c and Smac, normally found in the inner membrane space, from mitochondria. Figure $3$ shows how monomeric BAK can form an altered dimeric form in the presence of detergent. The extended left-hand helix on the right-hand side is color-coded to show nonpolar residue (orange) and the polar/charged amino acids in gray. That same section of the protein is shown in cyan in the monomeric protein to the right. One can easily imagine how the apparent amphiphilic helices of the BAK dimer could bind to the outer mitochondrial membrane and alter its structure. Cytoplasmic cytochrome c associates with Apaf-1 and caspase-9 to form the apoptosome, which activates caspase-9 and downstream executing caspases. Smac can regulate apoptosis by inhibiting the inhibitor of apoptosis proteins (IAPs)." Zhou and Li. Chapter 9, Apoptosis in Polycystic Kidney Disease: From Pathogenesis to Treatment. License: This open-access article is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) Another diagram of the extrinsic and intrinsic apoptotic pathways that show more detail on the domain structures of some key protein and the "executioner" caspases is shown in Figure $4$. Extrinsic pathway: The first step is an association of death receptors with their cognate ligands, which leads to the recruitment of adaptor molecules, including FAS-associated death domain protein (FADD), and then caspase 8. Caspase 8 cleaves and activate caspase 3 and caspase 7 and can proteolytically activate BH3-only protein BH3-interacting domain death agonist (BID). Proteolytically activated BID (tBID) promotes mitochondrial membrane permeabilization through the activation of the assembly of BAX-BAK channels and represents the main link between the extrinsic and intrinsic pathways. Now let's look more closely at the ligands that activate the extrinsic pathway, as shown in Figure $5$. Soluble Fas and soluble FasL bind to the respective ligands inhibiting activation of the pathway. FLIP inhibits the activation of caspase-8 and is thus a major anti-apoptotic protein. Volpe E et al. (2016) Fas–Fas Ligand: Checkpoint of T Cell Functions in Multiple Sclerosis. Front. Immunol. 7:382. doi: 10.3389/fimmu.2016.00382. Creative Commons Attribution License (CCBY). Now we are in a position to examine the actual structure of some key components of the extrinsic pathway. Active human apoptosome with procaspase-9 (5JUY) Figure $6$ shows an interactive iCn3D model of the active human apoptosome with procaspase-9 (5JUY) Each of the 7 different subunits of the apoptotic protease-activating factors (Apaf-1) is shown in a different color. The seven yellow subunits are cytochrome Cs. The 4 red subunits underneath the disk plane of the other subunits are the zymogen procaspase 9s. The small spacefill CPK color ligands are 2'-deoxyadenosine 5'-triphosphate. The Apaf-1:pc9 pairs, interacting through their CARD domains, form a spiral underneath the disk. Apaf-1 can be considered an adaptor protein with an N-terminal caspase activation and recruitment domain (CARD), followed by a nucleotide-binding and oligomerization domain (NOD, also known as NB-ARC). Figure $7$ shows the domain structure of caspase 9 and Apaf-1. Caspase 9 domain structure Apaf-1 Figure $7$: domain structure of caspase 9 and Apaf-1. The presence of CARD domains in both allows their mutual binding and the assembly of the full apoptosome. An AlphaFold model of the Cas 9 zymogen Figure $8$ shows an interactive iCn3D model of human Cas 9 AlphaFold model (P55211) The green is the CARD domain and the salmon is the caspase (peptidase_C14) domain. Procaspase 9 is cleaved at Asp 315 (sticks, CPK colors, labeled) into two chains for activation. The activated Cas 9 has two key active site residues, His 237 and the catalytic nucleophile C287 (sticks, CPK colors, labeled). Phosphorylation at Thr-125 by MAPK1/ERK2 blocks procaspase activation by proteolysis. to block caspase-9 processing Apaf-1 Oligomeric Apaf-1 mediates the cytochrome c-dependent autocatalytic activation of pro-caspase-9 (Apaf-3), leading to the activation of caspase-3 and apoptosis Figure $9$ shows an interactive iCn3D model of Human Apaf-1 AlphaFold model (O14727) Domain colors: • The green is the N-terminal CARD domain • light red NB-ARC (nucleotide-binding and oligomerization domain - NOD) • purple is Apaf • yellow is WD40, gold the C-terminal WD40. Again the model above does not show the actual structure since the intrinsically disordered regions are not more structured. The CARD domain of Cas 9 inhibits the catalytic domain of Cas 9. When the CARD domain of Cas 9 interacts with the CARD domain of Apaf-1, the autoinhibition is removed. In addition, the Apaf-1 stimulates the catalytic activity of the protease. Before assembly into the apoptosome, Apaf-1 is monomeric and in an inactive dATP or ATP conformation. When cytochrome C is released into the cytoplasm, it binds to the WD domains, facilitating a dATP/ATP-cleavage associated conformation change in the Apaf-1, which in the presence of heat shock protein 70 (Hsp) folds to form which leads to the assembly of the active apoptosome. Fas - Tumor necrosis factor receptor superfamily member 6 - P25445 Figure $10$ shows an interactive iCn3D model of Fas-Tumor necrosis factor receptor superfamily member 6 AlphaFold model (P25445) The green is the N-terminal TNFR/NGFR domain that is highly enriched in Cys (spacefill, color CPK) in disulfide bonds. The gray spheres are the transmembrane helix. The Red shows the Death Domain. The death domains are common protein:protein binding domains that serve as adaptors or scaffolds. They can form homo- or heterodimers with other proteins containing the domain which is a part of the CARD domain, DED (Death Effector Domain), and PYRIN. Human FasL and a soluble Fas Receptor DcR Figure $11$ shows an interactive iCn3D model of the complex of Human FasL and Its Decoy Fas Receptor DcR (4MSV) The three gray subunits are soluble decoy receptor (DcR) versions of the Fas TNFR/NGFR domain, which again is highly enriched in Cys. It is structurally very similar to its typical membrane receptor ligand Fas (tumor necrosis factor receptor superfamily member 6 - P25445) DcR is a secreted member of the TNF family and disrupts apoptosis, which can allow tumors to survive. Fas and FADD death domain interactions Figure $12$ shows an interactive iCn3D model of two Fas death domains bound to two FADD death domains (3EZQ) tetrameric arrangement of four FADD death domains bound to four Fas death domain Two Fas death domains are shown in a different shade of gray. The two FADD death domains are shown in a different shade of magenta. Each domain consists of six alpha-helical bundles. Interaction between the dark and light grays Fas death domains and between the light gray Fas and light magenta FADD death domains are shown with side chains in stick with CPK colors. The Fas-FADD-procaspase 8 complex is collectively called the Death Inducing Signaling Complex (DISC). The Fas-FADD interactions lead to the binding of capase 8 and the completion of the DISC. The actual disc appears to contain 4 FADD death domains bound to 4 Fas death domains. Conformational changes allow both FADD:Fas and Fas:Fas interactions, some of which are weak but when formed switch on the activity of the building complex. The need for 4 monomers probably prevents accidental assembly which would be deadly to the cell. Mechanism and regulation of apoptosis Caspases Characterization of apoptotic mechanisms and cellular players started with the study of C. elegans, a roundworm. The mature worm has about 1000 cells. During development, 131 cells die. Two mutations were found in which the 131 cells did not die. These mutations were called ced3 and ced4 (ced stands for cell death). The sequence of ced 3 was very homologous to a protein called interleukin converting enzyme (ICE) which is required for proteolytic activation of the precursor to interleukin 1, a protein hormone released by certain immune cells during activation and which can promote inflammation. This suggested that proteolysis was required for apoptosis. Subsequent studies show that a whole family of proteases (about 10 in humans) called caspases (ICE has been renamed caspase 1) are required for programmed cell death. These proteases are found in the cell in an inactive form which must undergo limited proteolysis for activation. These caspases form a cascade of proteases which are activated in this process. They are endoproteases that have an active site Cys (C) and cleave at the C-terminal side of Asp residues (asp) and hence are known as caspases - cys containing-asp specific proteases). ICE is not normally involved in apoptosis, but its artificial activation in cultured mammalian cells can lead to it. Each caspase had the same sequence as they are designed to cleave, so it became evident that they probably cleave each other in an activation cascade mechanism, similar to the coagulation protease cascade of activation of precursors (zymogens) of serine proteases which activate the next in the series. Two series of caspases seem to be involved. One set initiates the process of caspase activation. Just as in the clotting system, the question of what activates the first caspase appeared problematic until investigators found that the initiator caspase can be activated if they aggregate to a critical concentration. This could occur by binding a suicide signal molecule at the cell surface. Conformational changes in the receptor can lead to aggregation of surface receptor molecules with concomitant aggregation of intracellular caspases which interact with the aggregated receptors. Intracellular signals How might intracellular activators of apoptosis (like radiation or reactive oxygen species) work? Research indicated the involvement of mitochondria in the apoptotic pathway. Believe it or not, cytochrome C, the heme protein which acts as a water-soluble mobile carrier of electrons in mitochondrial oxidative phosphorylation, shuttling electrons through cytochrome C oxidase or complex IV, leaks out of the intermembrane space and binds to a cytoplasmic protein called Apaf-1 for apoptotic protease activating factor-1. This then activates an initiator caspase-9 in the cytoplasm. These proteins seem to leak out of mitochondria after a collapse of the electrochemical potential across the inner membrane. The potential collapses as a consequence of the opening of a channel called a nonspecific inner membrane permeability transition pore, composed of both an inner membrane protein (adenine nucleotide translocator - ant) and an outer membrane protein (porin, the voltage-gated anion channel - VDAC). These proteins act together, probably at sites where the inner and outer membranes are in contact. This channel passes anything smaller than molecular weight 1500. Collapsing the proton gradient uncouples oxidation and phosphorylation in the mitochondria. Changes in ionic strength cause a swelling of the matrix. Since the inner membrane is highly convoluted and has a much greater surface area than the outer membrane, swelling of the matrix leads to a rupture of the outer membrane, spilling the inner membrane space proteins (cytochrome C and Apaf-1) into the cytoplasm. What causes all these changes in the mitochondria? Several interrelated events appear to be involved: 1. disruption of ox-phos. and electron transport, caused by irradiation and certain second messengers such as ceramide. 2. changes in cell redox potential and generation of reactive oxygen species (ROS). 3. DNA damage (caused by radiation, ROS, etc). A protein called p53 is often expressed in cells with DNA damage. Expression of this protein results in inhibition of cell division, or apoptosis, both of which would keep the damaged cell from becoming a tumor cell. Hence the p53 gene is a tumor suppressor gene. It is inactivated by mutation in approximately 50% of all human tumor cells studied. p53 can induce gene expression. Of the 14 different genes whose expression is significantly altered by p53, many seem to be used by cells to generate or respond to oxidative stress. Cells undergo p53 apoptosis through oxidative damage. 4. increases in intracellular calcium ions through signal transduction. Caspase targets: Apoptosis involves: 1. condensing of the cell nucleus and breaking into pieces 2. condensing and fragmenting of cytoplasm into membrane-bound apoptotic bodies 3. breaking chromosomes into fragments containing multiple numbers of nucleosomes (a nucleosome ladder) How does caspase activation lead to these events? A protein has been uncovered that when cleaved by a caspase leads to a nuclear breakup. The target protein is usually bound to another protein, a DNA endonuclease. When the target protein is cleaved, the DNase is free to migrate to the nucleus and begin the execution. Membrane changes in apoptosis occur when caspase 3 cleaves gelsolin, a protein involved in maintaining cell morphology. The cleaved gelsolin cleaves actin filaments inside the cell. Another protein is necessary to form apoptotic bodies: a kinase named p21-activated kinase 2 (PAK-2). This kinase is activated by caspase-3 by limited proteolysis. Caspases also cleave beta-amyloid precursor protein which might generate more beta-amyloid protein, causing neural cell death in Alzheimer patients. Controlling Apoptosis It should be clear that cells keep tight control of the caspases. Two players which appear to inhibit apoptosis are the mitochondrial proteins Bcl-2 and Bcl-X, which can block the release of cytochrome C from the mitochondria. The Bcl family of proteins has a hydrophobic tail and binds to the outside surface of mitochondria and other organelles like the nucleus and endoplasmic reticulum. These proteins seem to be able to form ion channels in liposomes. So far 15 members of this family (related to ced-9 of C. elegans) have been discovered in humans. Bcl-2 can also bind to Apaf-1 (mentioned above) and inhibit its activation of initiator caspase-9. Bcl-2 is regulated by changes in the expression of the Bcl-2 gene, by post-translational phosphorylation by kinases, or by cleavage by caspases. Overexpression of Bcl-2 can cause a cell to become a tumor cell. Other members of the family, BAX and BAD bind to mitochondria and facilitate apoptosis by stimulating cytochrome C release. In addition, other proteins called IAPs (inhibitors of apoptosis) can inhibit caspase or other apoptotic proteins. Some viruses make the protease to keep their host cells viable. Cell Membrane Events Cells can be instructed to undergo apoptosis through cell surface interactions with other cells which are often immune cells. One of the jobs of the immune cell is to destroy an altered cell (for example a virally-infected cell or a tumor cell). Immune cells themselves must also die after they are activated in an immune response. Activated lymphocytes (like cytotoxic T cells or natural killer cells) can target and kill cells using several ways which can involve apoptosis. In one, an activated lymphocyte binds to a target cell (like a virally infected cell) and secretes perforin, a protein that assembles in the target cell membrane to form a transmembrane channel. Other proteins released by the activated lymphocyte can enter the target cell through the pore and initiate apoptosis. One such protein that enters, granzyme B is a protease that activates caspases in the target cell. Target cells that express a specific membrane protein called CD95 (also called Fas) are also targeted for apoptosis. This protein receptor, a member of the tumor necrosis factor receptor (TNFR) binds to a membrane protein-ligand on the surface of an activated lymphocyte called CD95 Ligand - CD95L- (also called the Fas ligand). On binding, the CD95 (Fas) receptors on the target membrane aggregate after conformation changes. An adapter protein in the cell, FADD (Fas-associated death domain) binds to the aggregated cytoplasmic domain (the death domain) of CD95 (Fas) and recruits inactive caspase-8 to the site, where their concentration increases. This leads to the activation of the caspases. This mechanism is used to get rid of activated lymphocytes after they have finished their work. Activated immune cells start expressing Fas a few days after activation, targeting them for elimination. Some cells which have been stressed express both Fas and Fas ligands and kill themselves. Various cells express CD95 (Fas), but CD95L (Fas-Ligand) is expressed predominately by activated T cells. Cell surface events also can inhibit apoptosis. The binding of "survival" factors (like growth factors) to cell surface receptors can shut off apoptotic pathways in the cells. Some survival factor receptors are coupled to PI-3-kinase (phosphoinositol-3-kinase) through the G protein ras (p21) which is targeted to the cell membrane by post-translational addition of a hydrophobic anchor. The activated kinase produces PI-3,4-P2 and PI-3,4,5-P3, which activates Akt, a Ser/Thr protein kinase. This activated kinase phosphorylates the proapoptotic-protein BAD, which then becomes inactive. In addition, active Akt phosphorylates procaspase, which in its phosphorylated form will not interact with cytochrome C, hence inhibiting apoptosis. The endpoint of apoptosis is the engulfment of the fragmented cell by a phagocytic cell (such as a macrophage). In a recent article (Nature, 405, pg 85, 2000), it was shown that the activity of phagocytes could be inhibited stereospecifically by the addition of phosphatidyl serine (PS) to the mixture, but not by other negative phospholipids. If you remember from our description of lipids, PS is found exclusively in the inner leaflet of red blood cells). The investigators cloned a gene from the phagocytic cell for a receptor that recognizes PS. When added to ordinary T and B lymphocytes (immune cells), these cells could also take up apoptotic cells. The gene is homologous to genes in Drosophila (fruit fly) and C. elegans (roundworm) suggesting that it is conserved in nature. The message: when cells undergo apoptosis, PS, normally found only in the inner leaflet, is exposed to the outside. It can then bind to receptors on phagocytic cells to complete the process of apoptosis. Therapeutics Figure $13$ shows points of therapeutic intervention in the intrinsic and extrinsic apoptotic signaling pathways. Intrinsic and extrinsic apoptotic signaling pathways and points of therapeutic intervention. Apoptosis can be initiated by signals originating from either the plasma membrane via death receptor ligation (extrinsic pathway) or at the mitochondria (intrinsic pathway). Stimulation of the extrinsic pathway by TRAIL results in TRAIL receptor (TRAIL-R) aggregation and formation of the DISC, in which pro-caspase 8 becomes activated and initiates apoptosis by direct cleavage of downstream effector caspases. The addition of either agonistic TRAIL-R1/R2 antibodies or recombinant human TRAIL (rhTRAIL) has been used to trigger the extrinsic pathway for therapy. The intrinsic pathway is regulated by the BCL-2 family of proteins, which regulate pore formation in the outer mitochondrial membrane and the release of apoptogenic factors such as cytochrome c or SMAC from the mitochondria. The release of cytochrome c into the cytosol triggers caspase 9 activations through the formation of the cytochrome c/Apaf-1/caspase 9-containing apoptosome complex. SMAC promotes caspase activation through the neutralizing the inhibitory effect of IAPs. The intrinsic pathway has been targeted for therapy either by blocking the inhibitory action of the pro-survival BCL-2 family proteins with BH3 mimetics or by inhibiting the anti-apoptotic action of IAPs with SMAC mimetics. The extrinsic and intrinsic pathways are interconnected, for example, by BID, a BH3 domain-containing protein of the BCL-2 family, which upon cleavage by caspase 8 triggers intrinsic apoptosis, thereby further amplifying the signal from the extrinsic pathway. The entire pathway Now we can present detailed pathways showing apoptosis in its complexity. Trace the interconnections in the different views. Figure $14$: View 1 Figure $15$ presents a second view
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.15%3A_Signaling_in_Microorganisms.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry The main organization of this section derives from Bacterial transmembrane signaling systems and their engineering for biosensing. Jung et al :25 April 2018https://doi.org/10.1098/rsob.180023. Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/. Significant content from the source has been integrated into the section. Introduction Bacteria constantly interact with their surroundings. They identify and actively acquire nutrient resources, sense and respond to environmental stresses, and exchange information with other cells, while commensals and pathogens adapt their lifestyles for survival in their hosts. The cytoplasmic (inner) membrane of bacterial cells separates the cytoplasm from the outer world. Therefore, all information from the outside must be transferred across this interface, which contains various sensors that carry out this function. Bacteria use three major types of signaling systems: membrane-integrated one-component systems (for example -ToxR-like receptors), two-component systems consisting of a receptor histidine kinase and a response regulator, and extracytoplasmic function (ECF) sigma factors. These are shown in Figure $1$. The one-component signaling family ToxR (named after the main regulator of virulence in Vibrio cholerae) is the simplest. They have a periplasmic sensor domain, a single transmembrane helix, and an intracellular winged helix-turn-helix DNA-binding domain. The family is named after the main regulator of virulence in Vibrio cholerae, ToxR. In two-component systems, the membrane-integrated histidine kinase generally acts as a sensor for various stimuli and is also responsible for information transfer across the membrane. This process usually results in the autophosphorylation of the protein and the phosphoryl group is subsequently transferred to a specific soluble response regulator which usually acts as a transcription factor (see Figure $1$). The number of histidine kinase/response regulator systems varies widely between bacterial species, ranging from 30 in Escherichia coli and 36 in Bacillus subtilis to 132 in Myxococcus xanthus. In chemotactic systems, a soluble histidine kinase perceives the signal(s) conveyed by membrane-integrated chemoreceptors and transduces this information via phosphorylation/protein–proteins interaction to the flagellar motor. The ECF sigma factors are small regulatory proteins that bind to RNA polymerase and stimulate the transcription of specific genes. Many bacteria, particularly those with more complex genomes, contain multiple ECF sigma factors, and these regulators often outnumber all other types of sigma factors. Little is known about the roles or the regulatory mechanisms employed by the majority of ECF sigma factors. Most of them are co-expressed with one or more negative regulators. Often, these regulators include a transmembrane protein that functions as an anti-sigma factor, which binds and inhibits the cognate sigma factor. Let's look at three examples. One-component system: pH sensor CadC pH in E. Coli is regulated by a series of Cad proteins. CadA is a cytoplasmic decarboxylase, which converts lysine to cadaverine, while CadB is a membrane-integrated lysine/cadaverine antiporter. CadC acts as a homodimeric one-component regulator. Together, their activities lead to an increase in both internal and external pH, which favors the survival of E. coli under moderate acid stress and helps to maintain pH homeostasis. Their activities are shown in Figure $2$. CadC is the regulator of the cadBA operon encoding the lysine decarboxylase CadA and the lysine/cadaverine antiporter CadB. Under non-inducing conditions, the lysine-specific transporter LysP inhibits CadC. When cells are exposed to low pH in the presence of lysine, the interaction between LysP and CadC is weakened, rendering CadC susceptible to protonation and transcriptional activation. The end-product of decarboxylation, cadaverine, binds to CadC and thereby inactivates this receptor. CadC is activated by two stimuli, low pH (less than 6.8) and the presence of external lysine (greater than 0.5 mM), which are perceived by different mechanisms. The periplasmic domain of CadC directly senses a decrease in pH. It has two distinct subdomains: the N-terminal subdomain comprises a mixture of β-sheets and α-helices, and the C-terminal subdomain consists of a bundle of 11 α-helices. A patch of acidic amino acids (D198, D200, E461, E468, D471) detect changes in the external pH through protonation changes, altering their charges and noncovalent interactions between the subdomains/monomers. This in turn leads to dimer formation of the periplasmic domain, triggering receptor activation. Figure $3$ shows an interactive iCn3D model of a Transcriptional activator CadC One Component Model - AlphaFold Model (P23890) The gold represents the N-terminal CadC DNA binding domain. The green (hydrophobic) is the transmembrane helix and the gray is the outside periplasmic domain containing the acidic side chains (D198, D200, E461, E468, D471). The N term Met is magenta spacefill and the C-terminal Ser is cyan spacefill. Again, the long intrinsically disordered region between the DNA binding domain and the transmembrane domain would have a more defined structure in the actual membrane-bound form and probably form additional interactions with other molecules. It also presumably leads to a conformation change in the DNA binding domain which leads to its interaction with the target DNA. CadC senses external lysine only in interaction with the lysine-specific permease LysP. In addition, the products of lysine decarboxylation, CO2, and cadaverine, act as feedback inhibitors on CadC. Cadaverine binds to the periplasmic domain of CadC, thereby switching off cadBA transcription. Two Component System Two-component Histine kinase systems have (in general) two main components • A HK sensor protein binds a ligand in a receptor binding domain leading to the transfer of a gamma-phosphate from ATP to a His by the kinase domain. This component is often called a transmitter. The conserved His located in the H box. • a separate response regulator (or effector) protein containing a reactive Asp which receives the phosphate from p-Histidine. The conserved Asp (D) is located in the D box. This activates the response regulator protein. This component is often called the receiver. It may also transfer it to another His in a phospho-relay system. Histidine kinase/response regulator systems are the most commons in bacterial signaling across the membrane. In contrast to the myriad of serine/threonine (S/T) and less abundant tyrosine (Y) kinases that dominate signaling in mammals, the histidine kinase predominates in bacterial signaling. Before we present more detail on the two-component system, let's looks at protein kinases in general and see what is different about histidine kinases. ATP is a donor of a gamma-phosphate in both S/T/Y and H kinases. However, their products are very different energetically. pS, pT, and pY of the O-phosphoproteome are all phosphoesters, which are not high energy compared to their hydrolysis products. (Remember, there is no such thing as a "high energy" bond.) In contrast, pHis, a member of the N-phosphoproteome (along with pLys and pArg), is not a phosphoester but more analogous to the mixed anhydride of a carboxylic acid as in the case of phosphorylated aspartic acid. In pHis and pAsp, there is an electronegative N (in pHis) and O (in pAsp) bridging two atoms which are each connected to another atom by double bonds. This type of structure, which allows for bridging resonance between the center N (in pHis) and O (in pAsp) is also high energy compared to its hydrolysis products. Hence the phosphorylation of His by ATP is not as energetically favored as the phosphorylation of Ser, Thr, or Tyr since it produces another high-energy molecule (with respect to its hydrolysis products. Since the pHis is also considered high energy compared to its hydrolysis products, it can act as a phosphate donor to another receiving group. That could be water in a simple hydrolysis reaction or, if sheltered from water in an active site of an enzyme or receptor, to a carboxylate receiver like Asp to form another high energy mixed anhydride which is isoenergetic with the pHis. This is the process that occurs in the two-component His kinase signaling pathways in bacteria. Figure $4$ compares Ser, Thr, Tyr, and His kinase reaction and their products. The more common mammalian S/T/Y kinases are shown at the top and the histidine kinase at the bottom. Note that phosphorylation of His can occur at either nitrogen to produce either τ- or π-pHis. In the two-component system, instead of water being the receiver of the phosphate from the pHis (hydrolysis), the receiver is an Asp or another His in the same His Kinase receptor or in another receiver protein (shown in gray and its phosphorylated blue form in Figure $9$. You can imagine the phosphate on the original pHis jumping to a receiver, which then donates it to another receiver in the signaling process in a relay process. Proteins like the receptor His-kinase proteins in two-component systems have multiple domains with different functions. It's really helpful to present domain structure diagrams to help in understanding the protein's structure and activities. At the same time, the domain structures determine by various bioinformatic programs vary. Nevertheless is it useful to see multiple representations of domain structure, especially if they are shown in conjunction with actual structures. The first component of the two-component signaling system is the receptor His-kinase which can be viewed as a stimulus-activated kinase (much like receptor tyrosine kinases - RTKs). The second component is the response regulator protein, which is typically a second protein. Each of these in turn has its own domain structure. For example, the periplasmic sensing domain regulates the kinase domain of the receptor His-kinase (component one). The phosphate from the p-His in the first component is transferred to an Asp in the second component. The domain structures and phospho-transfer are illustrated in Figure $\PageIndex{5$ below. Panel (A) shows a prototypical two-component pathway. The transmembrane sensor HK (component one) and a cytoplasmic response regulator (RR) protein (component two) are shown. (Note: the actual protein is a dimer in the membrane.) The transmembrane segments are labeled TM1 and TM2. N, G1, F, and G2 are conserved sequence motifs in the ATP-binding domain. HK catalyzes ATP-dependent autophosphorylation of a specific conserved His residue within the HK dimerization domain. The phosphoryl group (P) is then transferred to a specific aspartate residue (D) at the conserved RR domain (component two). Phosphorylation of this domain usually triggers an associated (or downstream) effector domain, which ultimately produces a specific cellular response. Panel(B) shows a multi-component phospho-relay system that often involves a variant of HK with an additional internal C-terminal RR domain. In these complex systems, at least two His–Asp phosphoryl transfer events occur, typically involving a His-containing phosphotransfer protein (HPT) operating as a His-phosphorylated intermediate. In most prokaryotic systems, the response is directly carried by the RRD which functions as a transcription factor. Two-component systems also exist in some eukaryotes. They often interact with other downstream signaling pathways such as the MAPK system. However, in eukaryotic systems, the TCS are placed at the start of the pathways and establish an interface with more conventional signaling strategies such as mitogen-activated protein (MAP) kinase and cyclic nucleotide cascades The domain structure shown in Figure $10$ doesn't show the actual orientation of the proteins in a membrane sytem. Orientation is important since the sensing domain of the HK receptor must be in the environment of the stimuli. Stimuli can be encountered in the periplasmic (equivalent to extracellular) region, in the transmembrane region and in the cytoplasm. Variants of the HisK receptors exist that recognize stimuli in each of these locations as shown in Figure $6$. Most histidine kinases sense extracellular signals (left-hand structure). All have their cytoplasmic transmitter domains which contain the pHis. As mentioned previously, the histidine kinases are dimeric, so when activate they phosphorylate a His on the other monomer (transphosphorylation). In addition to the H box with the reactive histidine, they also have N, G1, F, and G2 boxes. The H box is also involved in dimerization The transmitter domain can be further divided into two parts: the H-box is involved in dimerization and obviously in phosphotransfer. The figure also shows the CA domain (HK-type ATPase catalytic or HATPase_c), also known as the catalytic and ATP-binding (CA) domain (Figure 1.3-3) The histidine kinase senses a variety of stimuli in its sensory domain. The stimuli can be generally grouped into organic (e.g. dicarboxylates, citrate, etc), ions (e.g. Mg2+, H+, K+), gaseous ligands (e.g. O2, N2), and physical changes (osmolarity/turgor, light, and temperature). Stimuli are "sensed" by a variety of different characterized folds. Some common sensing domains are PAS (Per-ARNT-Sim), CHASE (cyclase/histidine kinase-associated sensing extracellular), four-helix bundle (4HB), and NIT (nitrate and nitrite-sensing) classes. We will focus on one particular His kinase system, histidine kinase KdpD. Histidine kinase KdpD and the regulation of K+ ion concentration It is often difficult to identify the primary stimulus for a receptor, as exemplified by the histidine kinase KdpD which, together with the response regulator KdpE, controls the expression of a high-affinity K+-uptake system in many bacteria. K+ is the most abundant cation in all living cells, especially in bacteria it is crucial for the regulation of cell turgor and intracellular pH and the activation of several enzymes. To ensure a sufficient supply of K+, most bacteria have more than one K+-uptake system. For example, E. coli has at least three such systems, the constitutively expressed systems Trk and Kup, and the inducible high-affinity K+-uptake system KdpFABC. The genes kdpF, kdpA, kdpB, and kdpC form an operon that codes for four inner membrane proteins. The kdp operon is induced when E. coli is grown under K+ limitation, lacks the major K+ transporter Trk or has an increased need for K+ when under hyperosmotic stress. Under all these conditions, the membrane-integrated histidine kinase KdpD autophosphorylates and transfers the phosphoryl group to the cytoplasmic transcriptional (response) regulator KdpE, resulting in the induction of the kdp operon, as shown in Figure $7$. What is amazing is that KdpD has not only kinase activity but also phosphatase activity towards phosphorylated KdpE, which switches the signaling cascade off. It is a bifunctional enzyme/receptor. A single substitution (T677A) in the C-terminal domain results in no phosphatase activity. Hence KdpD can be thought of as a bifunctional receptor acting as both kinase and phosphatase to regulate gene expression. The bifunctional receptor histidine kinase KdpD acts as both an autokinase (including phosphotransferase) and phosphatase for the response regulator KdpE. Phosphorylated KdpE activates the expression of the genes encoding the high-affinity K+ transporter KdpFABC. KdpD autokinase activity depends on the external K+ concentration, and the phosphatase activity is influenced by the internal K+ concentration. K+ ions don't move through a channel in KdpD but through the KdpFABC. The cartoon in Figure $7$, as with all cartoons, can be misleading with respect to scale. Figure $8$ shows an interactive iCn3D model of the actual membrane domain of E. coli histidine kinase receptor KdpD (2KSF) 4 helices space the membrane. Hence both the N-terminal and C-terminal domains are actually in the cytoplasm, not one in the periplasmic space and one in the cytoplasm as you would infer from Figure $7$. The actual periplasmic (outsithe de of cell" domain) consists of only about 6 amino acids. Hence it most clearly is represented by the middle model in Figure $6$. Mutations of key residues in the periplasmic loop region (P466A, T469A,, L470A and V472A) drastically affect K+ recognition. Actually how it "senses" periplasmic K+ ions is not clear. Domain representation We present three different domain diagrams for KdpD in Figure $9$, not to confuse readers, but to show the utility of mulrepresentationstation they are likely to encounter in reading the literature. Moscoso et al. Journal of Bacteriology. 198 (2016) http://dx.doi.org/10.1128 /JB.00480-15. Creative Commons Attribution 3.0 Unported license B. Pfam C. Dutta et al. JBC. 296, 100771 (2021). DOI:https://doi.org/10.1016/j.jbc.2021.100771. Creative Commons Attribution (CC BY 4.0) Figure $9$: Multiple representations of the domain structure of the histidine kinase KdpD In panel A, the 4 boxes btw 360 and 500 are 4 transmembrane helices, which would not be evident in a simple cartoon as in Figure $7$. In panel B, the domains are shown as follows: • Green: K+ channel His kinase sensor domain 21-230; • Red: 4 transmembrane helices 402-508; the helices are lumped together in the red domain representation; • Blue: GAF domain 527-644; • Yellow and Purple combo: HK domain; • Yellow: HisKinase A phosphoacceptor domain 663-730 which contains the His acceptor, in effect the substrate of the kinase domain; • Purple: His Kinase/HSP 90 is like ATPase 773-883. The domain structure in panel C is the most detailed and also has the domain structure of the receiver (response regulator). Again the periplasmic domain which senses K+ consists of only a few amino acids, 424-427 and 467-474. Neither representations A nor B show that the major N-terminal and C-terminal halves of the protein are in the cytoplasm. Panel C shows more information about the domains, their function, and their orientation in the intracellular environment. It turns out there is also a sensor for intracellular K+ions, which is depicted in Figure $7$. A central question is how histidine kinase KdpD responds to changes in K+ concentrations. Both the kinase and phosphatase activities are regulated by K+. • When periplasmic (extracellular) K+ is > 5 mM (high), the ion appears to bind to the small extracellular loops (see Figure $8$), which inhibits the autokinase activity. Under the same conditions the intracellular C-terminal tail senses K+ and activates the phosphatase activity, which cleaves its pHis. These combined effects inhibit high-affinity K+ transport. • When periplasmic K+ becomes low, kinase activity is activated and the protein is autophosphorylated, ultimately leading to the activation of the gene for the high-affinity K+ transporter KdpFABC. As long as intracellular K+ levels are high, the phosphatase is active. When intracellular K+ levels drop sufficiently, the phosphatase becomes inhibited, which further simulations the transcription of both high-affinity K+ transporter KdpFABC. Hence the histidine kinase KdpD system is regulated by both periplasmic and cytoplasmic K+ ions. Yet another signal regulates the KdpD His Kinase receptor two-component signal. What has been conspicuously absent from this discussion about signaling in bacteria is the involvement of second messengers like cAMP (which activates Protein Kinase A and some membrane proteins). There does appear to be one major second messenger in bacteria - cytoplasmic di-AMP (c-di-AMP), whose structure is shown in Figure $10$. It binds in the N-terminal region of KdpD His Kinase receptor protein "sensor" domain region to a specific domain called the Universal stress protein (USP) domain as shown in panels A and C of Figure $9$. Figure $11$ shows an interactive iCn3D model of the Staphylococcus aureus universal stress protein (USP) domain of KdpD histidine kinase in complex with second messenger cyclic diadenosine phosphate (c-di-AMP) (7JI4) "Dual sensing thus emerges as a highly optimized regulation strategy. The key advantage of this strategy is that it confers on cells the ability to directly sense changes in both the supply of and demand for the limiting resource. It is, in fact, analogous to strategies that are widely used in control engineering, e.g. modern heating systems work with both exterior and interior thermometers to ensure constant room temperature." Escherichia coli nitrate/nitrite sensor kinase NarQ Let's examine another TCS protein, the Escherichia coli nitrate/nitrite sensor kinase NarQ, to see how the binding of a ligand to the periplasmic domain might transmit a signal so far into the cell through the plasma membrane. We won't discuss the His Kinases that lack transmembrane regions (about 1/4). The sensor domain hence is mostly in the periplasm, followed by the transmembrane domain (see Figure $8$), followed often by a cytoplasmic HAMP domain, with a four-helical parallel coiled-coil. The HAMP domain transmits the signal to downstream signaling domains like Dhp in the protein. Nitrate/nitrite is sensed by two different two-component systems, sensing systems NarX-NarL and NarQ-NarP, which regulate anaerobic respiration. NarQ phosphorylates two different proteins, NarL and NarP in the presence of nitrate or nitrite and dephosphorylates both proteins in the absence of ligands. Both NarQ (and NarX) have seven domains: a four-helical periplasmic sensor domain, TM bundle, HAMP domain, so-called signaling helix, (S-helix), GAF-like domain, dimerization and histidine phosphotransfer domain, and, finally, catalytic kinase domain, as shown in Figure $12$. Pane (a) shows the architecture of NarQ. Note that the functional protein is homodimeric. Approximate domain boundaries, according to InterPro [17], are TM1, residues 14–34; sensor, 39–146; TM2, 147–167; HAMP, 172–227; S-helix, 228–246; GAF-like, 247–360; DHp, 361–425; CA, 424–560. Panel (b) shows the overall structure of the sensor-TM-HAMP fragment of the R50S mutant (which allowed crystallization). The position of Ser50 is highlighted with spheres. The backbone structure is identical to that of the WT protein. Panel (c) shows the structure of the ligand (nitrate) -binding site in the WT protein. Panel (d) shows the structure of the ligand-binding pocket in the R50S mutant. Asp133 is reoriented towards Ser50. 2Fo−Fc electron density maps are contoured at the level of 1.2 × r.m.s. Putative water molecules are shown as red spheres. Gushchin et al. Int. J. Mol. Sci. 2020, 21, 3110; doi:10.3390/ijms21093110. Creative Commons Attribution. (CC BY) license. (http://creativecommons.org/licenses/by/4.0/). Although the sequence identity between NarQ and NarX is ~28%, their ligand binding sites—membrane-proximal parts of the sensor domain’s helices H1 called P boxes—are very well conserved: 14 out of 15 amino acids (residues 42–56 in NarQ) are identical, and the differing ones, Ile 45 in NarQ and Lys49 in NarX, are responsible for the differentiation between nitrate and nitrite. It appears that the ligand-induced conformational change in the ligand-binding site is helical rotation, which results in diagonal scissoring of the sensor domain helices, leading to the change in the secondary structure of the sensor-TM linker and, eventually, piston-like shifts of the transmembrane α-helices. Nitrate causes changes in the transmembrane region when the apo (nitrate free) and holo (nitrate bound) state structures of NarQ are compared. On binding of nitrate, the induced conformation changes in NarQ have been described as a "combination of changes in the lateral arrangement of the TM helices and piston-like shifts of the helices in the direction perpendicular to the membrane plane." This results in either symmetric or asymmetric changes and scissoring of the transmembrane helices (based on two different crystal structures of the holo-form. A "piston-like" movement of the helices is observed on both holo-forms. Results show that the binding of ligand to NarQ causes a piston-like displacement of the TM helices, which is accompanied by extensive symmetric or asymmetric rearrangements and scissoring of the TM helices. The rearrangements are different in the two presented holo-state structures, but the piston-like displacement is perfectly conserved. Thus, the latter appears to be a more robust mechanism of TM signal transduction. Figure $13$ shows an interactive iCn3D model of a fragment of nitrate/nitrite sensor histidine kinase NarQ (mutant R50K) in the symmetric holo state (5IJI) A homodimer (cyan and magenta) is shown with the N-terminus and C-terminus on the cytoplasmic side. Each monomer passes through the membrane with an alpha-helical domain twice. Nitrate is shown in spacefill in the extracellular domain outside of the outer leaflet (red spheres) of the membrane. Figure $14$ shows the conformational transition going from the symmetric apo state (5JEQ) magenta to the symmetric holo state (IJI) (cyan) with bound nitrate (not shown). Conformational changes in the HAMP domain seem to amplify and convert the piston-like conformational changes in the transmembrane domain. Note the splaying out to the helices at the bottom (cytoplasmic end) in the apo form. Phototaxic Photoreceptors We have discussed two-component systems that have a His-Kinase receptor transmitter protein. There are two other major types of bacterial receptors, chemoreceptors (involved in chemotaxis) and photoreceptors, involved in phototaxis. These often have similar modular domain structures. Chemotaxis receptors are, like the His Kinase receptor, dimers with extracellular domains that bind the chemotactic signal. The photoreceptors appear to be active as a trimer of dimers. The basic dimeric structure contains the microbial light sensor rhodopsin which contains the chromophore opsin, and its transducer Htr. In the halobacteria N. pharaonis, (archaeal, not a bacterial cell) the proteins are sensory rhodopsin II (NpSRII) with its transducer (NpHtrII), mediates negative phototaxis in halobacteria N. pharaonis. Microbial rhodopsins are phototransducing proteins with a conjugated chromophore retinal, covalently attached to the protein opsin through a Schiff base (imine) linkage. The holoprotein (opsin with the attached retinal) is called rhodopsin. Retinal is derived from beta-carotene. The structures of animal and microbial retinals are shown in Figure $15$. When light of the correct wavelength is absorbed, an electron in a pi molecular orbital in retinal is promoted to a pi antibonding molecular orbital, breaking a 2 electron pi bond in the structure at a certain site in the isoprenoid chain, allowing rotation around the now single bond. The final result after the electrons return to the ground state is photoisomerization of the trans 13-14 and cis 11-12 bonds in microbial and animal retinal, respectively, to their respective cis 13-14 and trans 11-12 configuration. This conformational change in the bound retinal induces a conformational change in the protein opsin, leading to signaling. Figure $16$ shows an interactive iCn3D model of one monomeric of bacteria rhodopsin (1C3W) containing retinal attached through a Schiff base to Lys 216. The covalently attached retinal is shown in gray spacefill. The protein opsin is a membrane protein that spans it with seven helices. Hence it is very similar to a GPCR. In the phototaxic receptor, when light is absorbed by rhodopsin (NpSRII), the resulting conformational change in the protein causes conformational changes in the transducer protein NpHtrII associated with it, leading to signaling through a two-component signal system. (The chemotaxis response to chemical signals occurs through a similar but ligand-induced process.) Figure $17$ shows the structure of the Archaeal photoreceptor complex. A-G, TM1, and TM2 are the transmembrane helices. The cytoplasmic part of NpHtrII consists of two HAMP domains (HAMP1 and HAMP2) connected by an α-helical linker (Inter-HAMP) and the kinase control module. Primes denote symmetry mates of the complex. Ishchenko, A., Round, E., Borshchevskiy, V. et al. New Insights on Signal Propagation by Sensory Rhodopsin II/Transducer Complex. Sci Rep 7, 41811 (2017). https://doi.org/10.1038/srep41811. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/ Figure $17$ shows a more details representation of both the transmitter and receiver in the Archeal two-component phototransduction system and how it leads to phototaxis. Figure $17$: Signal transduction pathway in case of the two-component phototaxis system of Natronomonas pharaonis5 and domain architecture of membrane chemo- and photoreceptors of TCS. Ryzhykau, Y.L., Orekhov, P.S., Rulev, M.I. et al. Molecular model of a sensor of the two-component signaling system. Sci Rep 11, 10774 (2021). https://doi.org/10.1038/s41598-021-89613-6. Creative Commons Attribution 4.0 International License, http://creativecommons.org/licenses/by/4.0/. Pane (A) shows Light activated sensory rhodopsin II (NpSRII) induces conformational and/or dynamical changes in the transducer (NpHtrII), which are converted by two HAMP domains and conveyed along the 200 Å long transducer to the tip region. Activated by the transducer histidine kinase CheA (bound to the adapter protein CheW) undergoes auto-phosphorylation and further transfers the phosphate group to the response regulators CheY or CheB. CheY affects the rotational bias of the flagellar motor, while the methylesterase CheB along with the methyltransferase CheR controls the adaptation mechanism. Panel (B) shows cartoon representations of the chemoreceptor dimer (Tar and Tsr in complex with kinases) from E. coli and of the photosensor dimer of the complex of the sensory rhodopsin II with its cognate transducer NpHtrII and kinases from N. pharaonis.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.16%3A_Signaling_in_Plants.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Introduction Plants are obviously comprised of cells. Hence they must engage in cell signaling within and between cells. It is beyond the scope of this book to give a detailed description of cell signaling in plants. Instead, we focus on 5 key classic plant hormones, auxins, cytokinins, ethylene, gibberellins, and abscisic acid, which are produced by leaves, flowers, shoots, roots, or fruit, and see how they initiate signaling in plants. Finally, we would be remiss if we didn't include the profound signaling in plants initiated by light. Most of this section comes directly from a series of sources, with modifications and additions (mostly molecule models). Auxins (3-indolebutyric acids derivatives) are regulators of growth and development and are found in actively growing parts of the plant (root, shoot, leaves) but mostly in the cell stem. Auxins facilitate the bending of plants toward the light, for example. They work in conjunction with other hormones like cytokinins. When auxins are higher than cytokinins, roots will form, while the opposite produces shoots. Auxins facilitate the elongation of cells, while cytokinins promote cell division and growth as well as wound repair. Gibberellins also are plant growth regulators and facilitate cell elongation. They also help in germination, elongation of the stem, fruit ripening, and flowering. Abscisic acid affects seed development and maturation and helps plants tolerate environmental or biotic stresses. It also inhibits growth and metabolism. Ethylene affects fruit ripening, organ abscission, and growth by restricting cell elongation. We will focus on the hormones, their protein receptors, and how the hormone:receptor complex initiates some key events in the cell. Auxin Much of this section derives from Kou et al. Appl. Sci. 2022, 12(3), 1360; https://doi.org/10.3390/app12031360. Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Auxins, the first plant hormones discovered, regulate plant growth and development. The most common auxin is 3-indole acetic acid. Figure $1$ shows the structures of naturally occurring auxins. Figure $2$ shows an interactive iCn3D model of auxin bound to its receptor, TIR1 ubiquitin ligase (2P1Q) Figure $2$: Auxin bound to its receptor TIR1 ubiquitin ligase (2P1Q) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...6xSkEZ3aJA4gT6 Auxin (IAA) is shown in spacefill CPK colors along with an unexpected binding cofactor, inositol hexakisphosphate (IHP), shown in spacefill CPK colors. The peptide shown in light brown sticks is part of the protein Auxin-responsive protein IAA7, a member of a class of proteins called AUX/IAAs. These are short-lived transcriptional factors that function as repressors of early auxin response genes at low auxin concentrations. The magenta subunit, TIR1 (transport inhibitor response 1), is part of the larger TIR1 complex, the SCF(TIR1) E3 ubiquitin ligase, of which only TIR1 is shown. Its mere name suggests that it is involved in the ubiquitinylation of a key protein involved in auxin activity, which will be targeted for proteolysis. That protein is the repressor protein IAA7 (an AUX/IAA protein). Auxin binds in a hydrophobic pocket, which accounts for the binding of the other largely hydrophobic auxins shown in Figure $1$. Note however that Arg 401 that forms a salt bridge (ion-ion interaction) with the carboxylate of 3-indole acetic acid Figure $3$ shows an interactive iCn3D model of auxin bound in the hydrophobic pocket of its receptor, TIR1 ubiquitin ligase (2P1Q). The green color represents nonpolar side chains. The brown side chains, Trp and Pro, are from the auxin-responsive protein IAA7 peptide. To reiterate, the protein IAA7 is a member of a class of proteins called AUX/IAAs which repress auxin activity. The IAA7 peptide packs over the auxin in the binding pocket. Now we can see how auxin function to regulate gene transcription. First, we must introduce another protein family, the auxin response factors (ARFs). These are transcription factors that bind to a key DNA sequence, the auxin response element (AuxRE) in promoter sequences of auxin-activated genes. Once bound they can either activate or repress transcription from target genes. Auxin binds its receptor TIR1 enabling the binding of an AUX/IAAs (like IAA7) repressor and the binding of the complex to ARF. The TIR1 ubiquitin ligase activity of the complex ubiquitinylates the bound AUX/IAAs (like IAA7) repressor, targeting it for degradation, freeing the ARF to become active in the regulation of gene transcription in the nucleus. ARFs are structurally similar, with most members containing three regions: DBD (DNA-binding domain), MR (middle region), and PB1 (Phox and Bem 1). Figure $4$ shows a model of how auxin affects ARF transcriptional activity. At low concentrations of auxin, the AUX/IAA repressor binds to the ARF transcription factor through their PB1 domains. The PB1 (Phox and Bem1) domain is about 80 amino acids in length. It acts as a protein binding module allowing heterodimerization or homo-oligomerization with proteins have also contained the PB1 domain. The dimer of AUX/IAA and ARF recruits the co-repressor TPL (TOPLESS) to inhibit the ARF activity and the expression of auxin-responsive genes. When the concentration of auxin is increased, Aux/IAA binds to the SCF TIR1/AFB complex and is ubiquitinated and then degraded by 26S protease. The ARF transcription factors are released to activate the transcription of downstream genes. DBD, DNA-binding domain; MR, middle region; PB1, Phox, and Bem 1. • At low concentrations of auxin, the AUX/IAA repressor binds to the ARF transcription factor and forms a dimer that recruits the co-repressor TPL (TOPLESS) to inhibit the ARF activity and the expression of auxin-responsive genes; • When the concentration of auxin is increased, Aux/IAA binds to the auxin:SCF TIR1/AFB complex (remember that the auxin receptor is the TIR1 component of the complex) and is ubiquitinated by the TIR1, which is also a ubiquitin ligase; • The ubiquitinated AUX/IAA protein is degraded by proteolysis by the 26S protease, allowing the ARF to become an active transcription factors It appears that the MR domain of ARF determines whether it activates or inhibits transcription. If it is rich in proline, serine, and threonine, it acts as an inhibitor. If it is enriched in glutamine and leucine it acts as an activator. Some reports show that Aux/IAA and ARFs can form not only dimers but also larger complexes (oligomers), noting that oligomerization of Aux/IAA proteins may be essential for the inhibition of ARF proteins and only sufficient amounts of Aux/IAA proteins can exert the inhibitory effect of ARF proteins. Figure $5$ shows an interactive iCn3D model of the DNA binding domain of arabidopsis thaliana auxin response factor 1 (ARF1) in complex with auxin response element-like sequence ER7 (4LDX) The ARF protein must translocate to the nucleus to regulate gene transcription. ARF7 and ARF19 have been shown to form micron-sized aggregates in the cytoplasm. These have low responses to auxin. Aggregation occurs through PB1 domain interactions between ARFs as well as through intrinsically disordered regions. Mutation of a single lysine in the PB1 prevents aggregation and leads to morphological changes in the plant. This shows the importance of regulating not only transcription but also the translocation of proteins to the nucleus. Figure $6$ reviews the activation of ARFs and some of the genes affected by ARF. Auxin promotes the formation of the TIR1/AFB Auxin/Indole-3-acetic acid inducible (Aux/IAA) co-receptor to promote the ubiquitylation and subsequent degradation of the Aux/IAA repressor. Aux/IAA degradation relieves repression of auxin response factor (ARF) transcription factors, allowing for auxin-responsive gene expression. One of the transcript families upregulated by auxin is the SAUR family. The small auxin up RNA (SAUR) proteins encoded by these transcripts have been suggested to play roles in multiple processes, one of which is interaction with and inhibition of members of the PP2C.D family of phosphatases, which act to regulate H+-ATPase activity. Further, indole-3-butyric acid response 5 (IBR5) and mitogen-activated protein kinase 12 (MPK12) have been implicated in regulating auxin-responsive gene transcription; this regulation is not through destabilization of the Aux/IAA repressors, suggesting a yet-to-be-discovered mechanism of regulating auxin-responsive gene expression. F Cytokinins (CKs) and Ethylene (ET) Much of this material derives from Bidon et al. Cells 2020, 9, 2526; doi:10.3390/cells9112526 . Creative Commons Attribution (CC BY) license. (http://creativecommons.org/licenses/by/4.0/). Cytokinins (CKs) and ethylene (ET) are among the most ancient organic chemicals on Earth. The structure of a representative cytokinin (kinetin) and ethylene are shown in Figure $7$. A wide range of organisms including plants, algae, fungi, amoebae, and bacteria use these substances as signaling molecules to regulate cellular processes. Because of their ancestral origin and ubiquitous occurrence, CKs and ET are also considered to be ideal molecules for inter-kingdom communication. Their signal transduction pathways were first determined in plants and are related to the two-component systems of bacteria (which we explored in a previous section), using histidine kinases as primary sensors. CKs share a common structure of N6-substituted adenine (see Figure $7$), with biological activities defined by the N6-substituents (isoprenoids or aromatic groups). They were originally described as the major hormones regulating cell division but are also implicated in the control of morphogenesis and embryogenesis and inhibition of senescence. Conversely, ET is a simple gas, often referred to as the senescence hormone in plants, acting to stimulate the senescence of leaves and petals as well as the ripening of fruits. Both CK and ET are also well known to orchestrate plant responses to many types of biotic and abiotic stresses. Signaling pathways in plants are related to the two-component systems typically described in prokaryotes. CKs and ET are perceived by two types of membrane-bound histidine kinase receptors, CRE1 and ETR1 as shown in Figure $8$. Panel A shows the cytokinin signaling pathway. CKs in Arabidopsis primarily are recognized by dimerized receptors such as the CRE1 receptor via the cyclase/histidine kinase-associated sensing extracellular (CHASE) domain. CRE1 then auto-phosphorylates (histidine kinase (HK) activity) and immediately transfers its phosphate group to the conserved histidine of a protein belonging to the histidine-containing phosphotransfer (HPt) family. This small protein then acts as a cytoplasm-to-nucleus shuttle and in turn phosphorylates a type B response regulator, which, when activated, positively regulates the transcription of response genes to the CK signal. Panel (B) shows the ET signaling pathway. Ethylene molecules are detected by ethylene receptors (labeled ETR1) with ethylene binding to the three transmembrane helices (shown in sky blue). The binding of ET to the dimerized ETR1 receptor downregulates its activity. In the absence of ET, ETR1 activates the serine/threonine kinase CTR1. The CTR1 protein then phosphorylates the EIN2 protein located in the ER membrane, leading to the proteolysis of EIN2. In the presence of ET, ETR1 activity is reduced, leading to less CTR1 activity; this leads to lower phosphorylation and accumulation of EIN2 protein and subsequent activation of the EIN3 and related transcription factors. EIN3 then positively regulates the transcription of ET signal response genes. Panel (C) shows the domain structure of the Arabidopsis ET (ETR1) and CK (CRE1) receptors. Mechanistically, the two pathways use fundamentally different families of downstream modules. It is now known that bacteria also use CK and ET signaling, as described in Figure $9$. Let's look in more detail at CRE1, cytokinin response 1, the main cytokinin receptor in plants. Different computational programs often show different domain structures. Figure $10$ shows the domain structure determined by Pfam. Uniprot describes this domain structure, color-coded as in Figure $11$ • 131-149: transmembrane • 200-382: Green Chase (Cyclases/Histidine kinases Associated Sensory Extracellular) • 420-443 transmembrane • 472-537: Red His Kinase A Phosphoaccepter domain • 584-760 Blue HK kinase, DNAgyrase, HSP-like ATPase • 786-920: Yellow Reg REsp 1 • 946-1071: Yellow Reg Regulator receiver domain Figure $11$ shows an interactive iCn3D model of Histidine kinase 4 - Cytokinin receptor 1 (CRE) from Arabidopsis thaliana (AlphaFold model - Q9C5U0). The coloring matches the Pfam domains shown in Figure $10$. Figure $12$ shows an interactive iCn3D model of Histidine kinase 4 - Cytokinin receptor 1 (CRE) from Arabidopsis thaliana (AlphaFold model) - Domain organization (Q9C5U0) that clearly shows the extracellular and intracellular domains. The N-terminal methionine is in cyan spacefill and the C-terminal Ser is in spacefill. Two transmembrane helices are shown in dark gray spacefill (125-145) and light gray spacefill (430-450). These connect the extracellular domain (cyan, 146-429) and the two cytoplasmic domains (magenta 1-124, which is mostly disordered in the model, and 451-1080). This model does not reflect the relative disposition of the protein in the actual structure, but clearly shows the extracellular and cytoplasmic domains. The extracellular domain (cyan, 146-429) is the CHASE domain. Here are the step involved in cytokinin signaling through its receptor (shown in Figure $8$ : • the cytokinin binds to the CHASE domain • the receptor autophosphorylates a His in the HK domain • a phosphotransfer from the pHis to an Asp in the Yellow Reg (Regulator) Receiver domain • a phosphotransfer from pAsp to the His in the histidine-containing phosphotransfer protein (HPt) • a final transfer from pHIs to an Asp in a response regulator (RR) The MAPK cascade is activated in the cytokinin signaling pathway. Phosphorylated pRR can also regulate target gene transcription. Type-A RRs are negative regulators of cytokinin signaling. It also acts with phytochromes (discussed at the end of this section) to regulate red light signaling. Cytokinin receptors can bind synthetical chemicals that act as defoliants and herbicides. Intermolecular interactions in the cytokinin signaling pathway leading to transcriptional effects are illustrated in Figure $13$. Gibberellin Much of this material derives from Hedden, P., Sponsel, V. A Century of Gibberellin Research. J Plant Growth Regul 34, 740–760 (2015). https://doi.org/10.1007/s00344-015-9546-1. https://doi.org/10.1007/s00344-015-9546-1. Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/) Gibberellin controls growth and development pathways in plants and fungi. They act in plants by removing growth limitation by promoting the degradation of the growth-inhibiting DELLA proteins which contain the Asp-Glu-Leu-Leu-Ala (DELLA) motif. The name gibberellin derives from the fungus Gibberella fujikuroi. There are many types of gibberellins, which are all diterpenoids. The structures of the main bioactive GAs in plants, GA1 and GA4, are shown below in Figure $14$. Gibberellin initiates signaling by binding to the nuclear gibberellin receptor. One such receptor is the Gibberellin Insensitive Dwarf1 (GID1). When bound, it leads to the proteolysis of another protein bound to it called a DELLA protein (an example is GAI), a transcriptional regulator that inhibits growth. The control of transcriptional activity by gibberellins is hence reminiscent of that of auxins. Figure $15$ shows an interactive iCn3D model of the gibberellin(GA3)- active gibberellin receptor GID1L1 bound to the DELLA domain of GAI (2ZSH). The gibberellin receptor is gray and associated with the inner leaflet (blue) of the membrane. The plant hormone gibberellin A3 is bound to the receptor. Amino acid side chains in the receptor involved in the interactions with gibberellin A3 are shown in sticks, colored CPK. The DELLA protein GAI RAG protein is shown in cyan. The 3 amino acid motifs within it (DELLA, cyan spacefill; and VHYNP and LExLE, both magenta spacefill) are also shown. As with the auxin receptor, GID11A binds gibberellin in a deep pocket, which is covered by an N-terminal helix of the receptor. That helix recognizes and binds to the DELLA sequence in the DELLA transcription regulator protein. Figure $16$ shows the effects of a mutation that leads to deficiencies in gibberellin 1-3 (right-hand side). The mechanism by which GAs promote growth is shown in Figure $17$. Binding of bioactive GA results in a conformational change in the GID1 receptor that promotes interaction with DELLA proteins. Recruitment of an F-box protein initiates ubiquitination of DELLA by an SCF E3 ubiquitin ligase targeting the DELLA for proteasomal degradation. Loss of DELLA relieves growth repression and suppresses other DELLA-mediated responses Abscisic acid Much of this material derives from Hewage et al. (2020). Advanced Science. https://doi.org/10.1002/advs.202001265. This is an open-access article under the terms of the Creative Commons Attribution License. The phytohormone abscisic acid (ABA) is the best-known stress signaling molecule in plants As such it will be key as plants struggle to adapt to climate change. Its structure is shown in Figure $18$. ABA protects land plants from biotic and abiotic stresses. ABA receptors proteins (PYLs) contain a conserved pyrabactin resistance/pyrabactin resistance-like/regulatory domain (PYR/PYL/RCAR) that binds ABA and triggers a cascade of signaling events. ABA has significant roles throughout a plant's life cycle. From the single-celled zygotic stage to the mature multicellular plant, plant developmental stages involve ABA. ABA allows germination only under optimum conditions and inhibits growth under stress conditions. The adult plant as well as the seedling experience biotic and abiotic stressors that vary in severity and persistence. ABA allows that plant to survive by inducing both short-term and long-term stress responses, including rapid and reversible stomatal closure, long-term growth inhibition, dormancy, senescence, and abscission. ABA is therefore both a developmental and a stress-signaling molecule with diverse roles, as shown in Figure $19$. ABA signaling in drought Let's look at a specific example of ABA signaling in the presence of drought, stress that will expand as the world's climate changes due to the combustion of fossil fuels. Insufficient levels of soil water can result in an imbalance of water between the cells and the outer environment. A resulting change in the cellular electrolyte content affects metabolism, resulting in an osmotic imbalance or stress. The osmotic stress thus leads to the accumulation of ABA in cells and triggers ABA signaling. The cellular pool of ABA is dramatically increased during drought. Biosynthesis, catabolism, conjugation, and transportation of ABA are coordinated to increase ABA levels. ABA rapidly regulates plant water levels by controlling stomata. Stomata, which are microscopic pores controlled by two highly differentiated epidermal cells (guard cells), have the primary role in regulating gas exchange between the air and plant. Open stomata allow CO2 to diffuse into the leaf mesophyll and reach the sites of photosynthesis. They also allow water vapor to exit from the plant interior to the atmosphere. By allowing transpirational water loss, stomata allow the cooling of the plant and the managing of the interior water levels. Thus, stomata are essential regulators that connected the plant interior to the outside environment. Increases in osmotic pressure in guard cells lead to water uptake and then to cell expansion; as the cells expand, the pore opens because of differential thickenings of guard cell walls. Stomatal movements are regulated by numerous environmental signals such as light, plant growth regulators, pathogens, drought, cold, and nutrient status. Stomatal movement is the quickest response to ABA signaling. Therefore, the core ABA signaling is essential for guard cell function. The involvement of ABA signaling events in guard cell function is summarized in Figure $20$. In the absence of ABA, (left) ABA receptors (PYLs) are in ligand-free form. H+ATPase action pumps H+ ions outside of the plasma membrane. The SnRK2 protein kinases and the S-type anion channel SLAC1 are kept dephosphorylated by PP2Cs. The dephosphorylation state of SLAC1 prevents the nonspecific activation of S-type anion channels. In the presence of ABA (right) PYLs bind to and inhibit PP2Cs. ABA inhibits H+ATPase activity, blocking the H+ pumping outside. The Ca2+-independent protein kinases (SnRK2s) are released from PP2C inhibition and activated by auto-phosphorylation. Ca2+-permeable cation (ICa) channels are released from PP2C-mediated inhibition, causing increases of ABA-responsive Ca2+ in cytosol leading to activate CPKs. The activated SnRK2s and CPKs phosphorylate SLAC1 The SnRK2.6/OST1 protein kinase phosphorylates and activates the R-type anion channel ALMT12/QUAC1. The K+ ions are effluxed via voltage-dependent outward K+ (K+out) channel GORK, causing a guard cell turgor decrease leading to stomatal closure. PYLs: ABA receptors; ABA: abscisic acid; PP2C: protein phosphatase 2C proteins; OST1: open stomata 1/SnRK 2.6 protein kinase; Ca/CPK: Ca2+/calcium dependent protein kinases; ICa2+: plasma membrane nonselective cation channel permeable to Ca2+ SLAC1: slow anion channel-associated 1 (SLAC1); QUAC: aluminum-activated malate transporter 12/quickly activating anion channel 1 (ALMT12/QUAC1); GORK: guard cell outward rectifying K+ channel (GORK); KAT1: K+ activated 1 potassium ion channel; A−: anions; K+: potassium ions. The ABA receptor core complex There are three main phases of ABA signaling: ABA synthesis/metabolism, long-distance transport, and ABA binding to its receptor. Downstream signaling ensues. through transcriptional activators/repressors and plasma membrane-located channel proteins. The main components of the core ABA signaling pathway are shown in Figure $21$. In the absence of ABA (A above), SnRK2 kinases are dephosphorylated by protein phosphatase 2C (PP2Cs). In the presence of ABA (B above) PP2Cs are inhibited by the complexes PYLs-ABA. Thus, the SnRK2 kinases are released and make a cascade of downstream transcription factors, NADPH transporters, and ion channels phosphorylate the transcription factors that induce ABA-responsive gene transcription, and ion channels act on the guard cells to bring about transpirational control. ABA receptors (PYLs) bind ABA, PP2C, and protein kinases. The ABA:PYL complex binds PP2Cs, leading to conformational changes in the active sites of PP2Cs that inhibits the phosphatase. This in turn leads to the release of downstream protein kinases (SnRK2s) from PP2C-mediated inhibition. The SnRK2s undergo autophosphorylation to activate a series of ion channels, NADPH oxidases, and transcription factors via phosphorylation. This activates both short-term and long-term ABA responses such as stomatal closure and upregulation of ABA-dependent gene expression. MAPKKKs (MAPK3s) also activate SnRK2.6 by phosphorylating a specific site during salinity stress. ABA binding regulates a double-negative regulatory system, in which the ABA receptor (PYLs) act as ABA receptors, PP2Cs as negative-regulatory coreceptors, and SnRK2s as negative regulators. In addition to the regulation by SnRK2 and PP2Cs, several post-translational modifications also regulate ABA signaling. Phosphorylation, dephosphorylation, ubiquitination, farnesylation, and sumoylation have been found to modulate ABA signaling by targeting core components (PYLs or PP2Cs) or other interacting proteins downstream. ABA Receptors (PYLs) PYLs are soluble proteins, and among the 14 PYLs in Arabidopsis, 13 functions as ABA receptors. All PYLs are known to share a dominant helix-grip structure. This characteristic motif consists of a seven-stranded antiparallel β-pleated sheet, which is flanked by two α helices. The β-pleated sheets enfold a long carboxy-terminal α-helix of PYLs. The apo-PYLs contain a sufficiently large hydrophobic pocket of 543˚A between the C-terminal helix and β sheet. The size of this pocket is estimated to be 480˚A in the ABA-bound state. The 23 pocket residues are highly conserved and are more hydrophobic than the other parts of PYLs. The interactions of ABA and PYL2 are shown in Figure $22$. Figure $22$: The binding mode of ABA and PYL2 (PDBID: 3KDI) in A) 3D and B) 2D (redrawn).[211] In the 3D structure, the cartoon of PYL2 is colored in white (A). The important residues and ABA are shown in sticks with blue and yellow colors respectively. The H-bonds are marked with red dotted lines. Figure $23$ shows an interactive iCn3D model of Abscisic acid bound to the Abscisic Acid Receptor (PYL2) (pdbid: 3KDI) In the absence of ABA, the apo-PYL2 has a pocket surrounded by four surface loops. When ABA binds, one loop (CL2) closes onto the pocket, forming a PP2C binding site for the phosphatase ABI1 and ABI2. This blocks the active site of the phosphatase. Figure $24$ shows an interactive iCn3D model of ABA-bound PYL1 and the Protein Phosphatase 2C ABI1 (pdbid 3kdj) The PP2C phosphatase (ABI1) is shown in gray with its active site highlighted in green spacefill. The ABA receptor PYL1 is shown in cyan with ABA shown in sticks, colored CPK. The CL2 loop of the ABA-bound PYL1 receptor is shown in red spacefill. It projects into the PP2C active site, inhibiting its activity. Light Signaling through Phytochromes Much of this material derives from Liu, Y., Jafari, F. & Wang, H. Integration of light and hormone signaling pathways in the regulation of plant shade avoidance syndrome. aBIOTECH 2, 131–145 (2021). https://doi.org/10.1007/s42994-021-00038-1. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. get rid of this Plants deal with competing plants in regions of high plant density by sensing changes in the intensity and wavelengths of light. Signaling leaves to responses (stem elongation, reduced branching, early flowering, etc) called shade avoidance syndrome (SAS). A photosensory system initiates signaling that alters gene transcription. In the SAS in plants in a large canopy, the upper leaves used red and blue light for photosynthesis. Multiple photoreceptors are used. Some transcription factors are also sensitive to light. For example, PIF3, a transcription factor, binds to light-responsive genes only when it binds to another transcription factor called Pr. Pr is resident in the cytoplasm but moves to the nucleus after altering conformation on absorbing red light. Legris, M., Ince, Y.Ç. & Fankhauser, C. Molecular mechanisms underlying phytochrome-controlled morphogenesis in plants. Nat Commun 10, 5219 (2019). https://doi.org/10.1038/s41467-019-13045-0. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. do Phytochromes are present in bacteria, cyanobacteria, fungi, algae, and land plants. We will focus mostly on phytochromes in Arabidopsis. In land plants, phytochromes are red and far-red light receptors that exist in two forms. They are synthesized in the inactive Pr state, which upon light absorption converts to the active Pfr conformation. Pfr is inactivated upon far-red (FR) light absorption or through thermal relaxation, which depends on temperature. Phytochromes act as dimers, resulting in three possible phytochrome species: Pr–Pr, Pfr–Pr, and Pfr–Pfr. Pr and Pfr have different absorption maxima, but due to overlapping spectra both conformers are always present in the light while only prolonged darkness returns all phytochrome to Pr. Given that phytochrome responses depend on the proportion of Pfr conformers, signaling is influenced by a combination of light quantity, color, and temperature. These features of phytochromes are summarized in Figure $25$. Panel a shows factors that control phytochrome activity. Phytochromes exist in two conformations, Pr and Pfr, the latter being the active form. They exist as dimers so three species can be found. Each monomer can be activated by red light (R) and inactivated by far-red light (FR) or by thermal reversion, a process that depends on temperature (T). At least in the case of phyB, Pfr in heterodimers reverts much faster than that in homodimers, allowing phyB to perceive temperature both during the day and during the night. Panel b shows plant phytochrome absorption spectra of the Pr and Pfr conformations. In dark-adapted seedlings, phytochromes are in the Pr form. Upon a saturating R pulse, due to overlapping absorption spectra of Pr and Pfr, only 87% of Pfr is achieved. Panel c shows action spectra for phyA and phyB in the control of hypocotyl elongation. Fluence rate (number of particles passing per unit time) response curves are measured at different wavelengths and fluence rate that leads to 40% inhibition compared with dark control is determined. To specifically determine action spectra for phyA and phyB, for phyB the curve was performed with phyB-GFP/phyAphyB seedlings, and for phyA using phyB-5 seedlings. Values are relative to the response obtained at the most efficient wavelength in each case Plant phytochrome structure Plant phytochromes are dimeric, each monomer consisting of ~1150 amino acids. The chromophore, a linear tetrapyrrole named phytochromobilin (PΦB), whose structure is shown in Figure $26$, is attached to the protein. Figure $26$: Structure of phytochromobilin (PΦB) The domain structure of phytochromes is shown in Figure $27$. The apoprotein can be divided into the N-terminal PSM, which consists of the N-terminal extension (NTE), for which structural information remains scarce, and three structurally related domains Period/Arnt/SIM (PAS), cGMP phosphodiesterase/adenylyl cyclase/FhlA (GAF), and a phytochrome-specific domain (PHY) and a C-terminal module (CTM) comprising two PAS domains and a histidine kinase-related domain (HKRD). The chromophore is bound covalently to a conserved cysteine in the GAF domain, which has intrinsic chromophore lyase activity. Light perception triggers a Z to E isomerization around the C15–C16 double bond of PΦB, which leads to a cascade of structural modifications in the protein. Figure $28$ shows an interactive iCn3D model of phytochrome (Deinococcus) Pfr form in the Photoactivated State (5C5K) The protein is shown in its active dimeric state. One chain is shown in secondary structure colors and the other in cyan. The chromophore (heme derivatives) are shown in spacefill in both subunits. Side chains surrounding the chromophore are shown in colored sticks in the cyan chain. At first glance, the presence of the histidine kinase-related domain (HKRD) would seem to suggest that phytochromes transduce their signal through the C-terminal module (CTM). Although many bacterial and cyanobacterial phytochromes have a C-terminal histidine kinase domain and act as light-regulated histidine kinases, plant phytochromes are not histidine kinases, and their role as Ser/Thr kinases remains contentious. The photosensory module (PSM) fused to a nuclear localization signal and a dimerization sequence is sufficient to restore most phyB functions, pointing to key signaling functions of the PSM. Major The major of the plant phytochrome CTM are dimerization, nuclear import, and localization to sub-nuclear structures known as photobodies. However, it was recently shown that the C-terminal part of phyB also engages in light-regulated interactions and regulation of PIF activity. Moreover, the activity of the CTM is controlled by post-translational modification with SUMOylation limiting the ability of active phyB to interact with downstream signaling targets thereby limiting light responses. In addition, the CTM modulates active (Pfr) phytochrome levels with the HKRD inhibiting the Pr–Pfr photoconversion while the PAS–PAS promotes thermal reversion. Hence, while the division of plant phytochromes into PSM and a CTM helps describe the molecule, both parts of the photoreceptor contribute to the regulation of active Pfr levels and downstream signaling activities. Figure $29$ shows a simplified mechanism for phytochrome control of transcription factors in different light environments. Panel a shows the response below the soil surface during growth in partial or a complete absence of light (called etiolated growth). For simplicity, we consider that phytochromes remain inactive (Pr) below the soil surface, which results in the accumulation of transcription factors PIFs, EIN3, and ARFs and subsequent induction of etiolation and auxin response genes. The COP1/SPA ubiquitin E3 ligase accumulates in dark and leads to proteasome-mediated degradation of HY5, a transcription factor that suppresses the expression of genes required for etiolation and induces expression of genes required for de-etiolation. Pane b shows changes that occur when light intensity increases (de-etiolation). Light perception activates phytochromes (Pfr) which promote de-etiolation by directly inhibiting PIFs and EIN3, and indirectly inhibiting ARFs by stabilizing Aux/IAA proteins. The Pfr form of either phyA or phyB interacts with SPA proteins, resulting in the inhibition of COP1/SPA. This results in the stabilization of HY5 leading to the induction of de-etiolation-related gene expression and repression of etiolation genes. Pane c shows de-etiolated plant in response to shade (reduced R/FR). Low R/FR in shade reduces the fraction of active phytochrome (Pfr/Ptot). PIFs accumulate and induce growth-promoting gene expression. In addition, PIFs induce a negative feedback loop exemplified by HFR1 expression. HFR1 (and other HLH proteins) binds to PIFs forming non-DNA-binding heterodimers. COP1/SPA is also involved in this loop by leading HFR1 to proteasome-mediated degradation. Arrows indicate positive regulation, blunt-ended arrows indicate negative regulation and dotted-lined arrows indicate nucleo-cytoplasmatic relocalization Figure $30$ summarizes how phytochromes affect transcription. Panel a (top to bottom) shows sequential steps by which Pfr inhibits PIFs. Top: PfrA interacts with PIF1 and PIF3 while PfrB interacts with PIF1–PIF8. Middle left: for PIF1, 3, and 4 phytochrome inhibits DNA binding. Middle right: Interaction with Pfr leads to the phosphorylation of PIFs. Many kinases have been found to phosphorylate PIFs (see text) with PPKs phosphorylating PIFs in response to light. Bottom: after light-induced phosphorylation, PIF3 is degraded by LRBs and EBFs with phyB co-degradation occurring in the LRB-mediated process (left, center), phosphorylated PIF7 interacts with 14-3-3 proteins and remains in the cytoplasm (right). Panel b shows other mechanisms of transcriptional control by phytochromes. Left: PfrA and PfrB interact with SPA and inhibit the COP1/SPA complex. Center: PfrB interacts with EIN3 to promote ERF-mediated EIN3 degradation. Right: PfrA and PfrB interact with Aux/IAA to prevent their degradation by SCFTIR1/AFB. Panel c shows Patterns of PIF abundance depending on the developmental state and growth conditions. In etiolated seedlings PIFs accumulate to high levels, promoting etiolated growth. Upon light exposure, PIFs are rapidly degraded in a phytochrome-dependent manner, with half-lives of ~5 min for PIF1 and PIF5, and ~10 min for PIF3 and PIF4 (left). In contrast, in light-grown seedlings PIFs are under strong transcriptional control, allowing accumulation of the protein even in conditions when phytochrome activity is predicted to be high (right), SD (short days), LD (long days)
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.17%3A_Signal_Transduction_-_Vision_and_Olfaction.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry This chapter section is taken in entirety from Genovese et al Front. Cell. Neurosci., 08 October 2021 \| https://doi.org/10.3389/fncel.2021.761416. Creative Commons Attribution License (CC BY) Sensory Transduction in Photoreceptors and Olfactory Sensory Neurons Photoreceptors and olfactory sensory neurons (OSNs) have highly specialized structures that enable them to capture their respective stimuli of light and odorant ligands. Both photoreceptors and OSNs have evolved highly specific abilities to detect and discriminate light wavelengths or odors. They use intricate transduction mechanisms to convert sensory stimuli into electrical signals. Their transduction cascades not only can greatly amplify the signal but also to enhance the signal to noise, enabling these cells to detect and distinguish minute stimuli within very noisy background conditions. Such transduction mechanisms provide for modulation at multiple steps to adapt the sensory neurons to different background stimulation and optimize the capture of useful information about the surrounding world. In this review, we summarize some of the key structural and functional features of vertebrate rod and cone photoreceptors and of OSNs, and the molecular mechanisms that underlie their function. While describing the features of both cell types, we emphasize the similarities and differences between photoreceptors and OSNs and the unique features of each cell type that make them perfectly suited to perform their function. Signal Detection in Photoreceptors and Olfactory Sensory Neurons’ Specialized Cilia Vertebrate rod and cone photoreceptors as well as OSNs are ciliary neurons (Figure 1) with specialized cilia where the initial detection of the sensory stimulus takes place to activate a sensory transduction cascade. Rods and cones have a single cilium that has evolved to accommodate a stack of ~1,000 membrane disks where the visual pigment is expressed at a very high 3–5 mM concentration (Figure 1A; Palczewski, 2006). In the case of rods, the disks are enveloped by the plasma membrane, whereas in cones the disks are formed by invaginations of the plasma membrane. As light enters the eye and reaches the retina, it travels along the length of the rod and cone outer segments. The orientation of the elongated outer segments along the light path, together with the high density of visual pigment in their disks results in ~50% probability that an incident photon is absorbed by a visual pigment molecule (Bowmaker and Dartnall, 1980). In the case of OSNs (Figure 1B), odorant ligands are detected in the ~20 cilia protruding from each dendritic knob which are immersed in the mucus layer covering the olfactory epithelium. The olfactory cilia, which are motile in amphibians but not in rodents, are only about 0.1–0.2 μm thin but can reach up to 100 μm in length depending on the species (Kleene and Gesteland, 1981; Ukhanov et al., 2021). While this greatly increases the surface membrane area available to incorporate olfactory receptor (OR) proteins to detect odorants, it also greatly reduces the ciliary volume with potentially detrimental effects (see below). Figure 1. Photoreceptors and olfactory sensory neurons (OSNs). (A) Simplified schematic representation of a rod and a cone in the retina. Photoreceptors are polarized neurons with a specialized morphology optimized to detect light stimuli. The outer segments of both rods and cones are modified sensory cilia, containing membrane disks organized in a stack. In the case of rods, the outer segment has a slim rod-like structure in which the disks are enclosed by the plasma membrane. The outer segment of the cones has a stocky conical-shaped structure, in which the disks are constituted by invaginations of the plasma membrane. The outer segment does not contain any proteins of the cell translation machinery, which are mostly localized in the inner segment, including the endoplasmic reticulum, Golgi, and mitochondria. Outer and inner segments are connected by the connecting cilium, while distal to the inner segment is the cell body containing the nucleus, followed by the axon and synaptic termini that extend into the outer plexiform layer where they synapse with the second-order neurons. When the light enters the eye, after reaching the retina, it travels along the length of the rod and cone inner segment until finally reaching the outer segments. (B) Simplified schematic of an OSN in the olfactory epithelium. OSNs are ciliated bipolar neurons, their apical dendrites extend to the surface of the epithelium terminating with a spherical structure called a dendritic knob, from which the sensory cilia enter the mucus layer. The ciliary membrane contains the olfactory receptors (ORs) necessary to detect different odorants. Distal from the knob is the cell body of the OSN with its nucleus, followed by a long axon that projects to the olfactory bulb, where it synapses with the second-order neurons. Images created with BioRender.com. Electrophysiological Approaches to Record Light- and Odorant-Induced Responses The similar morphological structure of rods, cones, and OSNs, with a ciliary part able to detect the respective stimuli and an adjacent cell body, allows similar electrophysiological approaches to recording stimulus-induced responses in these cell types. The cell body of a photoreceptor or an OSN can be sucked into the tip of a recording pipette by using a loose-patch (or suction pipette) recording configuration (Baylor et al., 1979; Lowe and Gold, 1991). This leaves the outer segment of photoreceptors or the olfactory cilia exposed and accessible to bath solution changes, e.g., the application of pharmacological agents or odorants, in the case of OSNs. Suction pipette recordings can be performed from isolated sensory neurons, as shown in Figures 2A,B,D (respectively, a salamander rod, salamander cone, and salamander OSN) but also from dissected retina tissue, as in the case of the outer segment of a mouse rod drawn in the recording electrode from a piece of the retina (Figure 2C). This recording configuration measures the transduction current entering the photoreceptor outer segment or olfactory cilia, and leaving via the cell body. A fundamental difference between photoreceptors’ and OSNs’ responses to stimuli lies in their polarity. In the absence of light, rods, and cones are kept depolarized by a standing inward current of approximately 20–40 pA for amphibian cones and rods, and 7–15 pA for mouse photoreceptors. This depolarizing current is gradually suppressed upon light stimulation until, for sufficiently high light intensities, it is reduced to zero (Figures 3A, B, mouse rod, and cone responses, respectively), leading to photoreceptor hyperpolarization. Similar to rods, but differently from cones, the OSNs show comparatively little spontaneous activity in absence of stimuli (Reisert, 2010; Connelly et al., 2013). Different OSNs show varying levels of spontaneous basal activity determined by the constitutive activity of their ORs (Reisert, 2010; Connelly et al., 2013). In the presence of odorants, OSNs generate an inward receptor current which leads to depolarization, and the generation of action potentials (Firestein and Werblin, 1989; Kurahashi, 1989; Reisert and Matthews, 1999). This receptor current is odorant concentration-depend and increases progressively with increasing stimulation until it eventually saturates at high odorant concentrations. Responses recorded from OSNs expressing different olfactory receptors can generate fairly different response amplitudes when stimulated with their respective agonists (Figures 3D, E: responses recorded from mouse OSNs that express the mOR-EG or the M71 olfactory receptor, which are activated by the ligands eugenol and acetophenone, respectively). The hyperpolarization and signals carried by graded potentials in photoreceptors vs. depolarization and signals carried by action potentials in OSNs represent another fundamental difference between these two types of sensory neurons. These topics and the differences in synaptic structure and transmission between photoreceptors and OSNs go beyond the focus of this review and are discussed in an excellent recent review on this topic (Lankford et al., 2020). Sensitivity of Photoreceptors and Olfactory Sensory Neurons In part due to their unique structure, photoreceptors, and, to a lesser extent, OSNs have achieved exquisite sensitivity that optimizes the detection of stimuli within the respective sensory organs. In addition, both sensory receptors use a transduction cascade to amplify the signal (see below). As a result, rod photoreceptors can reliably detect single photons (Baylor et al., 1979), enabling humans to perceive light with as few as six photons detected by adjacent rods (Hecht et al., 1942). This renders rods perfectly suited for dim light vision, with a dynamic range spanning lights from a dark cloudy night to sunrise (Fain et al., 2010). Cones are ~100-fold less sensitive than rods, making them suited for daytime light conditions. Figure 3C compares the intensity-response function of mouse rods and cones, demonstrating the much lower sensitivity of cones compared to rods. Most OSNs respond to odor concentrations in the low micromolar range (Bozza et al., 2002; Grosmaitre et al., 2009; Saito et al., 2009; Lee et al., 2011; Dibattista and Reisert, 2016), but they can also reach exquisite sensitivity and are capable of detecting odors at the nanomolar concentration range. Picomolar sensitivity is reached by a subset of OSNs that express receptors specialized in detecting amines, the trace-amine-associated receptors (Zhang et al., 2013). In comparison to rods, OSNs do not reach such high sensitivity, and cannot be activated by a single odorant molecule but instead require around 30 odorant binding events to begin firing action potentials reliably (Bhandawat et al., 2010). The detection of odorants in the olfactory epithelium can be further enhanced by the expression of a wider number of different OR genes, more than 350 in humans and 1,000 in mice (Malnic, 2007), with overlapping response profiles to odorants. A larger number of OSNs, particularly in species relying heavily on their sense of smell, may enhance further the detection of odorants. For instance, the human olfactory epithelium covers ~3–4 cm2 and contains approximately 5–6 million OSNs while in the case of dogs, the area of the olfactory epithelium is 18–150 cm2 and contains 150–300 million OSNs (Lippi and Heaney, 2020). Detection of Stimuli In both photoreceptors and OSNs, the detection of stimuli is mediated by G protein-coupled receptors. In photoreceptors, this function is achieved by rod and cone visual pigments, which consist of a protein, opsin, covalently attached to the visual chromophore, typically 11-cis-retinal (Ebrey and Koutalos, 2001). The chromophore serves as a reverse agonist, keeping the receptor molecule in the inactive ground state (Crouch et al., 1996). Absorption of a photon by 11-cis-retinal triggers its conformational change to all-trans-retinal, which, in turn, results in the rearrangement of the opsin transmembrane helices and switch of the visual pigment molecule into its active state. We discussed bacterial rhodopsin in an earlier section. We present it here again to show and note its similarity to animal light-sensing proteins. Review: Retinal, Opsin and Light Transduction in Bacteria Microbial rhodopsins are phototransducing proteins with a conjugated chromophore retinal, covalently attached to the protein opsin through a Schiff base (imine) linkage. The holoprotein (opsin with the attached retinal) is called rhodopsin. Retinal is derived from beta-carotene. The structures of animal and microbial retinals are shown below. When light of the correct wavelength is absorbed, an electron in a pi molecular orbital in retinal is promoted to a pi antibonding molecular orbital, breaking a 2 electron pi bond in the structure at a certain site in the isoprenoid chain, allowing rotation around the now single bond. The final result after the electrons return to the ground state is photoisomerization of the trans 13-14 and cis 11-12 bonds in microbial and animal retinal, respectively, to their respective cis 13-14 and trans 11-12 configuration. This conformational change in the bound retinal induces a conformational change in the protein opsin, leading to signaling. The figure below shows an interactive iCn3D model of one monomeric of bacteria rhodopsin (1C3W) containing retinal attached through a Schiff base to Lys 216. Bacterial rhodopsin (1C3W). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...9HzvfdFvb9kcc6 The covalently attached retinal is shown in gray spacefill. The protein opsin is a membrane protein that spans it with seven helices. Hence it has very similar to a GPCR. The activated visual pigment then binds to a G protein, transducin, activating it. The activation of transducin triggers the transduction cascade that ultimately generates the cellular response (Pugh and Lamb, 1993). Eventually, the all-trans-retinal chromophore is released from opsin after the covalent Schiff base between them is hydrolyzed, leaving behind chromophore-free opsin (Saari, 2016). Notably, without chromophore, opsin has residual activity, and in sufficient quantities can produce steady activation of the photoreceptors, similar to steady background light, thus modulating the sensitivity of photoreceptors (Fain et al., 1996). This process is known as bleaching adaptation, indicating the production of free opsin after the photoactivation of the visual pigment and dissociation of the visual chromophore. Unlike in photoreceptors, where the ligand, a light-sensitive reverse agonist, is covalently attached to opsin, in olfaction, the ligands are dissolved in the mucus covering the surface of the olfactory epithelium and come into direct contact with the OR proteins expressed in the OSN ciliary membrane. This results in the activation of the receptor protein that, in turn, is transduced downstream to a G protein to trigger a transduction cascade resulting in the cellular response. The binding of the ligand to the receptor protein is noncovalent and rapidly reversible. ORs, like other G protein-coupled receptors, do display antagonism, inverse, and partial agonism, leading to suppressed responses to their agonists, a reduction in basal activity in the absence of stimulation or suppression of the maximal response (Firestein et al., 1993; Oka et al., 2004; Reisert, 2010). Discrimination Between Stimuli The spectral sensitivity of individual rod and cone photoreceptors is dictated by the absorption properties of their visual pigments. Typically, each photoreceptor type expresses only one type of opsin; in the case of the human retina, rods express rod opsin, whereas cones express long wavelength (LW, red), middle wavelength (MW, green), or short wavelength (SW, blue) opsin (Nathans, 1987). When bound to the chromophore, the amino acid structure of each opsin determines the optical properties of the resulting visual pigment and the spectral sensitivity of the photoreceptors expressing it. As a result, species existing in environments with characteristic light distribution, such as deep-sea fish, have visual pigments that have evolved to optimize their spectral sensitivity (Hope et al., 1997). A second factor controlling the optical properties of the visual pigment is the structure of the visual chromophore. Most species, including mice and humans, use 11-cis-retinal, a derivative of Vitamin A, also known as A1. However, some amphibians and fish also use 3,4-dehydro 11-cis retinal, also known as A2. This chromophore has an extra conjugated double bond in its structure, which shifts the absorption spectrum of A2 visual pigments to longer wavelengths compared to the A1 visual pigment embedded in the same opsin molecule (Corbo, 2021). Some aquatic and amphibian species use the A1/A2 chromophores to shift their spectral sensitivity from murky waters dominated by longer wavelengths of light to seawater and air, dominated by shorter wavelength lights (Bridges, 1964). One notable example includes the toad, where the retina is populated by A1 visual pigment in its ventral section, receiving light from above the surface of the water, and by A2 visual pigment in its dorsal section, receiving light from below the surface of the water (Reuter et al., 1971). A shift in the chromophore can also occur during the lifetime of the animal as its environment changes, such as the A2 to A1 shift in salamanders as they metamorphose from the larval (aquatic) to the adult (terrestrial) stage (Ala-Laurila et al., 2007), or the A2 to A1 shift in Atlantic salmon during migration from sea to freshwater (Beatty, 1966). Similar to photoreceptors, the ligand specificity of the OSNs is also dictated by the expression of OR genes in their cilia. As photoreceptors, each OSN expresses generally only one receptor gene so that its ligand specificity is determined by the structure of the OR expressed in that particular cell. However, photoreceptors typically use no more than five opsin genes to cover the visible spectrum, while OSNs can use hundreds, in the case of humans, to thousand and more, for rodents and dogs, OR genes to cover the odor space (Malnic, 2007). The same OR can be activated by multiple odorants with different sensitivities, and a given odorant can activate different ORs with different half-maximal concentrations (Buck, 1996; Ache, 2020). This generates a complex mosaic of ORs and odorants response pairs. Figure 3F compares the dose responses of OSNs expressing either the mOR-EG or the M71 OR to eugenol and acetophenone, respectively. In this case, mOR-EG OSNs display higher sensitivity to their agonist compared to M71 OSNs. However, this does not preclude the possibility that the M71 OR is more sensitive to another ligand resulting in a more left-shifted dose-response relation than the one seen with acetophenone. Conversely, the dose-response relation of M71 OSNs to benzaldehyde is approximately 10-fold right-shifted compared to acetophenone (Bozza et al., 2002). Determining the ligand specificity of ORs is an ongoing endeavor (Abaffy et al., 2006; Saito et al., 2009; Kurian et al., 2021). Due to the large number and diversity of OR genes, as well as the nearly endless number of odorant molecules, understanding the overall mechanisms that control their ligand binding affinity and specificity remains a challenge. Receptor modeling approaches to understand and predict OR–odorant molecule interactions can provide valuable insights but are somewhat hampered by the lack of a crystal structure of any vertebrate OR. The rhodopsin structure is often used as a guide and homology model to predict the structure of ORs (Katada et al., 2005; Bavan et al., 2014). Sensory Transduction Activation In both photoreceptors and OSNs, the detection of stimuli by their respective G protein-coupled receptors is converted into electrical signals via the activation of a G protein-coupled to a second messenger transduction cascade. The two pathways, though distinct, share an amazing level of similarity (Figure 4). Thus, in both cases, the second messenger is a cyclic nucleotide, cGMP in photoreceptors (Pugh and Lamb, 1990) and cAMP in OSNs (Sklar et al., 1987; Bakalyar and Reed, 1990). As a result, the activation of both transduction cascades results in a rapid shift in the equilibrium between synthesis and hydrolysis of the respective cyclic nucleotide, which is then sensed by the cyclic nucleotide-gated (CNG) transduction channels in the plasma membrane of the photoreceptor outer segment or olfactory cilium. Figure 4. Activation of the transduction cascade in rod photoreceptors and OSNs. (A) Schematic representation of phototransduction cascade in rods. Abbreviations: rhodopsin (Rh), Tα, β, and γ subunits of the retinal G protein, transducin (T), guanosine-5′-triphosphate (GTP), guanosine-5′-diphosphate (GDP), phosphodiesterase (PDE), guanosine monophosphate (GMP), and cyclic guanosine monophosphate (cGMP), and cyclic nucleotide-gated (CNG) channel. (B) Schematic representation of the olfactory transduction cascade in OSNs. Abbreviations: Olfactory receptor (OR), guanosine-5′-triphosphate (GTP), guanosine-5′-diphosphate (GDP), Gαolf, β, and γ, subunits of the olfactory G protein; adenylyl cyclase 3 (AC3), adenosine-5′-triphosphate (ATP), cyclic adenosine monophosphate (cAMP), cyclic nucleotide-gated (CNG) channel; Ca2+-activated Cl channel anoctamin 2 (ANO2). Images created with BioRender.com. In the case of photoreceptors, the photoactivated visual pigment binds to and activates the trimeric G protein transducin (T) (Figure 4A), causing the exchange of GDP for GTP on its α-subunit, which is part of the Gαt protein family. Following the subsequent dissociation of the α-subunit (Tα) from its β/γ complex (Tβγ), Tα then binds to the cGMP phosphodiesterase (PDE) complex, relieving the inhibition of its catalytic α- and β-subunits by its inhibitory γ-subunits (Ebrey and Koutalos, 2001; Burns and Arshavsky, 2005). All these transduction proteins are embedded in or tethered to the disc membranes inside rods or are contained in the cell membrane of cones. As a result of their activation, the hydrolysis of free cGMP in the outer segment is upregulated, causing its rapid decline, and partial or complete closure of the cGMP-gated channels expressed in the rod and cone cell membrane (Luo et al., 2008). The closure of the CNG channels leads to the reduction of the inward transduction current, followed by the hyperpolarization of the cells, and a reduction of neurotransmitter release to second-order neurons within the retina. Inversely, in the absence of light, the opening of CNG channels and the resulting inward transduction current is sustained by the continuous cGMP production by guanylyl cyclase (GC). Similarly, in OSNs (Figure 4B), the ligand-activated OR proteins bind to the G protein Golf, causing its dissociation into active Gαolf and olfactory β- and γ-subunit, Gβγolf. In contrast to transducin, however, Gαolf is part of the Gαs protein family and binds to adenylyl cyclase 3 (AC3), activating it. As a result, the synthesis of cAMP in the olfactory cilia is upregulated, causing its rapid increase and the opening of cAMP-gated channels (Kleene, 2008; Su et al., 2009; Boccaccio et al., 2021). While both photoreceptors and OSNs use CNG transduction channels, their respective channels have different subunit compositions (Bradley et al., 2005). Rods and cones express heterotetramers consisting of the main A1 and A3 and the modulatory B1a and B3 subunits in 3:1 and 2:2 stoichiometries respectively. The olfactory CNG channel is a heterotetramer consisting of two units of the main A2 subunits and one each of the modulatory A4 and B1b subunits. Interestingly, the rod and the olfactory CNG channels express different splice variants of the same B1 subunit. In OSNs, the initial inward Na+ and Ca2+ current generated by the opening of the CNG channel raises ciliary Ca2+ and opens a secondary ion channel, the Ca2+-activated Cl channel Anoctamin 2. A high intraciliary Cl maintained by the Na+/K+/2Cl cotransporter 1 ensures a Cl efflux which further depolarizes the OSNs (Dibattista et al., 2017; Boccaccio et al., 2021). This depolarization triggers the generation of action potentials which further propagate along the axons, inducing glutamate release at synapses with the second-order neurons in the olfactory bulb (Murphy et al., 2004). In photoreceptors, the transduction cascade upon stimulation does not ultimately generate action potentials in the receptor cell, but only a graded receptor potential that directly causes a change in neurotransmitter release. Amplification As for any other sensory modality, proper amplification of the signal is required to detect small stimuli and the resulting high sensory sensitivity is critical for the survival and propagation of the species. Nature has reached the highest physically possible sensitivity in the case of rod photoreceptors that can produce a detectable electrical response to the absorption of a single photon. This impressive feat is achieved by employing a transduction cascade that allows tremendous amplification of the signal. During the ~50 ms active lifetime, a single photoactivated rhodopsin molecule activates ~20 transducins, producing an immediate 20-fold amplification (Burns and Pugh, 2010). The following activation of PDE by transducin does not directly produce amplification as each transducin has to bind to a PDE molecule to activate it. However, once activated, each PDE enzyme can hydrolyze thousands of cGMP molecules. Lastly, as the binding of cGMP to the CNG transduction channels is cooperative, a slight change in cGMP levels can reduce the number of cGMP molecules bound to the channel from 3 to 2. This results in channel closure and a sharp reduction in the transduction current, further enhancing the detection of photostimulation. Despite the similarities in the transduction cascades of rods and cones, the amplification in cone photoreceptors is substantially lower as a result of fine-tuning at several of the phototransduction steps (Yau, 1994; Kawamura and Tachibanaki, 2008). Interestingly, even though rod and cone visual pigments activate transducin with similar efficiencies, the lower thermal stability of the cone visual pigment results in higher intrinsic activity in cones compared to rods in darkness (Kefalov et al., 2003), effectively desensitizing the cones and shifting their function towards brighter daytime light conditions (see Figure 3C). Curiously, the activation of Golf by the OR molecule does not result in amplification. Indeed, the dwell time of the odorant ligand on the OR appears to be very short and on a millisecond timescale (Bhandawat et al., 2005). As a consequence, on average, this results in the activation of less than one G protein per activated receptor. As such, in contrast to phototransduction, where the lifetime of the activated rhodopsin greatly influences the response size and kinetics, in OSNs the response depends more prominently on the coupling efficacy of downstream transduction components while the odorant presence keeps the OR activated. To compensate for the lack of initial amplification at the G protein level, OSNs employ a secondary amplification step on top of the cAMP transduction cascade. The activation of AC3 by Golf results in the synthesis of most likely hundreds of cAMP molecules, the opening of the CNG channels which is followed by a unique secondary amplification based on excitatory Ca2+-activated Cl channels in the cilia (Figure 4B). The Cl current carries up to 80% of the overall transduction current (Dibattista et al., 2017). Physiological experiments with pharmacological and genetic modulation of the Cl conductance indicates that the Cl channels serve to set the length of the action potential train generated in response to odorant stimulation (Pietra et al., 2016) and to promote recognition of novel odorants (Pietra et al., 2016; Neureither et al., 2017). A puzzling aspect of the secondary amplification step is why Cl is the charge-carrying ion and not Na+, which could be achieved easily by increasing the expression level and/or the ion permeation and conductance of the olfactory CNG channel. Recent theoretical work hinted at two main advantages of Cl, instead of Na+, as the charge carrier. As the external environment of cilia is the nasal mucus, currents will depend on the ion concentration in the mucus, which can be unstable. A current that depends on the intracellular ion concentration, as is the case for Cl but not for Na+, is much less dependent on the mucosal ion concentration. For instance, this could become an issue in the case of a cold with a runny nose or during swimming, when the mucus becomes diluted. The second advantage results from the “compromise” to increase the ciliary surface area, at the expense of having a very small ciliary volume, in the femtoliter range. In such small volumes, even small ionic currents can lead to large changes in ion concentration and osmotic pressures. If the main charge carrier was Na+ this would lead to a large increase (tens of mM). This would cause a large increase in osmotic pressure and also would prevent Ca2+ clearance via the olfactory Na+/Ca2+, K+ exchanger (see below) with greatly deleterious effects. In contrast, high intracellular Cl is maintained throughout the OSN so that its local depletion in the cilia upon ligand activation is rapidly reversed by diffusion from the cell soma. Both these issues do not exist for photoreceptors as they are embedded in the interstitial fluid of the eye and photoreceptors are sufficiently large and their transduction currents are sufficiently small that ion concentration changes due to changes in transduction currents are relatively small (Reisert and Reingruber, 2019). Nevertheless, rod photoreceptors undergo osmotically-driven length changes upon light activation, an effect that is mitigated by the translocation of G protein subunits into the cytosol (Zhang et al., 2017). Receptor and G Protein Inactivation Timely and effective transduction inactivation is critical for allowing sensory neurons to continue to detect stimuli with high temporal resolution. Equally important is to extract behaviorally relevant information from the presented stimuli. In both photoreceptors and OSNs, all active transduction components need to be turned off and the level of cyclic nucleotides within the cells needs to be restored to the resting level before the sensory cell can be reset to the inactive state and become ready for subsequent activation (Figure 5). In the case of photoreceptors, the identity of the step determining the overall kinetics of the photoresponse inactivation was the subject of intense research and debate over several decades. As the visual chromophore ligand is covalently attached to opsin, inactivation of the visual pigment could potentially be extremely slow. Indeed, if left on its own, the active state of rhodopsin decays with a time constant of ~50 s (Imai et al., 2007). Its inactivation in photoreceptors is a two-step process, involving phosphorylation of the rhodopsin C-terminus by rhodopsin kinase (GRK1) which partially quenches its activity, followed by the binding of arrestin1, which completely inactivates the visual pigment (Figure 5A). Though the decay of the active state of cone pigment is significantly faster at ~2 s (Fu et al., 2008), this is still clearly too slow to enable the timely termination and reset of phototransduction. Thus, in both rods and cones, the visual pigments are inactivated by phosphorylation by rhodopsin kinase and the subsequent binding of arrestin long before they would decay spontaneously (Makino et al., 2003). The effective time constant of rod visual pigment inactivation is ~50 ms (Krispel et al., 2006). The slowest step in the inactivation of rod phototransduction turned out to be the hydrolysis of GTP which shuts off Tα, a reaction driven by the transducin GTPase activity and enhanced by a GTPase (GTPase activating protein, GAP) complex consisting of Gβ5 and the membrane anchoring protein R9AP (Arshavsky and Wensel, 2013). Inactivation of transducin results in its release from PDE, allowing the two PDE γ inhibitory subunits to resume their inhibition on the two catalytic subunits (α and β) of this enzyme. The kinetics of this reaction determines the overall kinetics of response inactivation in rod photoreceptors. In contrast, work from amphibian cones suggests that in cones the photoresponse duration is Ca2+-dependent and involves the quenching of the cone visual pigment (Matthews and Sampath, 2010). In OSNs, the inactivation by phosphorylation and arrestin are potentially not needed for the timely shutoff of the olfactory transduction cascade, due to the extremely short lifetime of the active ligand-bound receptor molecule. Early biochemical experiments suggested that OR phosphorylation does control cAMP kinetics (Dawson et al., 1993; Schleicher et al., 1993; Peppel et al., 1997), but it seems to play little, if any, role in the control of odorant-response kinetics for one particular OR, mOR-EG (Kato et al., 2014). It remains to be established whether this applies to all ORs, or whether a subset of ORs is subject to phosphorylation and inactivation. β-arrestin interacts with ORs, mediating internalization during prolonged stimulation and altering adaptation to repetitive odor stimuli (Mashukova et al., 2006). Experiments on isolated human and rat OSNs suggested a role for protein kinases A (PKA) and C (PKC) in the termination of the olfactory response. Ca2+ imaging showed that the inhibition of PKA and PKC increases intracellular Ca2+ responses in the presence of odorant mixtures, and blocks their termination after odorant stimulation ceases. While the inhibition of both PKA and PKC modulated the odor-induced intracellular Ca2+ increase in the human OSNs, only PKC and not PKA affected the Ca2+ response to odorants in rat OSNs, suggesting differences among species in the termination of the olfactory response (Gomez et al., 2000). The control of the lifetime of the olfactory G protein seems to be more complex and less well-understood compared to phototransduction. Ric-8B (resistant to inhibitors of cholinesterase-8B) has been identified as a GTP exchange factor (GEF) expressed in OSNs, which facilitates the exchange of GDP for GTP on Gαolf and its activation. Unusually, Ric-8B not only interacts with the G protein α-subunit but also with γ13, the olfactory γ-subunit. In a heterologous system, Ric-8B co-expression with olfactory transduction components can greatly increase cAMP production, suggesting that it could indeed modulate olfactory transduction (Von Dannecker et al., 2005; Kerr et al., 2008). A knockout mouse for Ric-8B displays impaired olfactory behavior, and, surprisingly, greatly reduced odorant responses. Ric-8B is localized primarily in the cell body and the dendritic knob of OSNs. Ric-8B knockout OSNs are devoid of Gαolf (Machado et al., 2017), suggesting that this gene is needed for the stable expression of Gαolf, and excludes addressing its potential role as a GEF in the odorant response. The Ric-8B knockout mice also display higher OSN cell death. Regulators of G protein signaling (RGS) are GAPs that modulate the lifetime of an activated G protein as described above. RGS2, instead of functioning as a GAP, directly inhibits AC3 to control the size of the odorant response (Sinnarajah et al., 2001). However, inconsistent and contradictory data on RGS2 and RGS3 expression and their roles in OSNs suggest that more research is needed (Norlin and Berghard, 2001; Kanageswaran et al., 2015; Saraiva et al., 2015). Adaptation Adaptation plays a critical role in the capacity of our sensory neurons to remain able to detect stimuli above the background in a complex and rapidly changing environment. For instance, in constant light conditions, the dynamic range for both rods and cones is only 100-fold, spanning a range from threshold stimulation to saturation (Figure 3C). However, as a result of light adaptation, photoreceptors can shift their functional range over a very wide span of light conditions, ranging from cloudy night to sunrise for rods, and starry night to bright sunny day for cones (Weale, 1961). Thus, using the adaptation of individual photoreceptors, the visual system can remain responsive to stimuli over a wide range of light conditions. In contrast, the ability of OSNs to adapt is rather limited even at modest levels of background odorant (Reisert and Matthews, 1999). Nevertheless, increasing concentrations of the same odorant can recruit less sensitive ORs, and therefore less sensitive OSNs, preserving its perception at higher concentrations and ensuring to report the presence of that odorant to the brain. In both types of sensory neurons, adaptation is mediated by a change in Ca2+ upon stimulation. This change is sensed by several Ca2+-binding proteins that trigger a negative feedback on the vision and olfaction transduction cascades by modulating several of their steps. In the outer segments of rods and cones and olfactory cilia, Ca2+ levels are controlled by the balance of influx via the CNG channels, whose current is carried in part by Ca2+, and efflux via Na+/Ca2+, K+ exchangers (NCKXs) that use the electrochemical gradient for Na+ and K+ to extrude Ca2+ (Figure 5; Yau and Nakatani, 1984). In rods (Figure 5A), this task is accomplished by rod-specific NCKX1, whereas cones employ two separate exchangers, NCKX2 and NCKX4 (Vinberg et al., 2017). At rest, both in darkness and in steady-state light, the influx of Ca2+ is matched with its extrusion and, as a result, the level of free Ca2+ in the outer segments is maintained constant. Upon photostimulation, the transduction cascade is activated, resulting in the depletion of cGMP, closure of CNG channels, and reduction in the influx of Ca2+ into the outer segments. However, Ca2+ extrusion by the Na+/Ca2+, and K+ exchangers carry on for at least a while and, as a result, the level of Ca2+ in the outer segments declines. Direct Ca2+ measurements in amphibian photoreceptors indicate a dynamic range from 670 to 30 nM in rods (Sampath et al., 1998) and 400–5 nM in cones (Sampath et al., 1999), in darkness and bright light, respectively. The light-driven decline in Ca2+ causes its release from several Ca2+-binding proteins. The dominant Ca2+-dependent feedback mechanism in both rods and cones controls the synthesis of cGMP by membrane-bound GC via a pair of GC-activating proteins (GCAPs)—GCAP1 and GCAP2. When Ca2+ in the outer segments is high, Ca2+-bound GCAPs bind to and partially inhibit the activity of GC. Upon photoactivation and the decline in Ca2+, GCAPs become Ca2+-free and release from GC, resulting in the upregulation of cGMP synthesis which restores the dark current after photostimulation and modulates the activation of the transduction cascade in the presence of background light (Dizhoor, 2000; Sakurai et al., 2011). Another mechanism by which Ca2+ modulates phototransduction involves the Ca2+-binding protein recoverin. As GCAPs, recoverin is a member of the EF-hands protein family, and when bound to Ca2+ in darkness, it inhibits rhodopsin kinase, thus slowing down the inactivation of the visual pigment (Makino et al., 2004; Sakurai et al., 2015). When the photoreceptors are activated and Ca2+ declines, it is released from recoverin, which in turn dissociates from rhodopsin kinase and relieves its inhibition. This enhances the phosphorylation of visual pigments and accelerates their inactivation, effectively reducing the activation of the transduction cascade by the background light. Finally, direct modulation of the CNG channels has also been suggested. However, in the case of rods, such modulation appears to play a marginal, at best, role (Koutalos and Yau, 1996) and is not mediated by the Ca2+-binding protein calmodulin (Chen et al., 2010). In zebrafish cones, the modulation of the CNG channels appears to play a more substantial role and is mediated by the Ca2+-binding protein CNG-modulin (Korenbrot et al., 2013). It is still unclear whether the mammalian homolog of CNG modulin, EML1 plays a similar role in mammalian cones. Adaptation in OSNs is less well understood compared to phototransduction. Early data, mostly of biochemical nature or obtained from heterologously-expressed proteins of interest, suggested three main molecular targets for adaptation. All three of them are mediated by the Ca2+ influx during the odorant response: Ca2+/calmodulin-mediated desensitization of the olfactory CNG channel to close the channel even in the presence of high cAMP (Chen and Yau, 1994); phosphorylation via CaM-kinase 2 of AC3 to reduce the rate of cAMP production (Wei et al., 1996, 1998); and Ca2+-mediated upregulation of phosphodiesterase 1C, which is expressed in olfactory cilia, and is assumed to degrade cAMP to AMP to terminate the response (Borisy et al., 1992). Follow-up experiments using recordings from OSNs all seem to indicate that none of these mechanisms plays as prominently or as originally thought role in transduction (Reisert and Zhao, 2011). A mouse with a mutation in the CNGB1b channel subunit that entirely prevents desensitization by Ca2+ surprisingly displays normal olfactory adaptation but instead shows a delayed response termination, suggesting that Ca2+/calmodulin-mediated desensitization of the CNG channel speeds up response termination (Song et al., 2008). A mouse model that carries a mutation in AC3 that prevents phosphorylation does not show a discernable phenotype of the olfactory response (Cygnar et al., 2012), although it might be possible that other, unknown phosphorylation sites in AC3 might be important. Finally, a knockout mouse for PCE1C has no deficits in response termination but instead shows much-reduced response amplitudes for unclear reasons (Cygnar and Zhao, 2009). This begs the obvious question as to what the role of PDE1C might be and what might actual to cAMP that is generated during the odorant response. For the latter, an interesting option is that cAMP diffuses out of the cilia into the cell body as a means to reduce ciliary cAMP, allowing OSNs to recover from stimulation (Cygnar and Zhao, 2009). One aspect that is reasonably understood is NCKX4, the Ca2+ exchanger in OSNs that is required to lower intraciliary Ca2+ during and after odorant stimulation, allowing the transduction cascade to recover from adaptation (Reisert and Matthews, 1998; Stephan et al., 2012). Diseases Disorders affecting photoreceptors are among the leading causes of blindness in the human population. One of the prevalent visual disorders, called retinitis pigmentosa, is a complex disease caused by a wide range of mutations in photoreceptors. Many of these mutations affect the expression, structure, and function of the rod's visual pigment (Athanasiou et al., 2018). Because of the very high expression of opsin in the outer segments of rods, this protein plays not only a functional role but is also critical for the proper formation of the outer segment itself. As a result, mutations affecting the expression, folding, or targeting of opsin to the rod outer segments, cause gradual degeneration of the rods. Other genes implicated in rod dysfunction and degeneration include those for phosphodiesterase (e.g., rd1, rd10; Chang et al., 2002), the CNG channels A and B subunits (channelopathies; Michalakis et al., 2018), GC, and GCAPs (Olshevskaya et al., 2002). Another diverse set of visual disorders is caused by abnormal chromophore production or supply to photoreceptors, which limits the ability to detect light and can also lead to degeneration (Ku and Pennesi, 2020). Notably, the efficiency of the visual system to produce chromophore seems to decline with age, which may result in poor rod function in dim light even in normally aging adults. It is also an early indicator of age-related macular degeneration, a devastating blinding disorder that affects the function of cones in the central retina responsible for acute vision and color discrimination (Jackson et al., 2002). Interestingly, rods and cones seem to coexist synergistically in the retina, and diseases caused by rod-specific mutations that result in rod degeneration, eventually lead to the loss of cones and central vision as well. Thus, considerable efforts are currently focused on developing methods for preserving rods even when they are not functional, as a way of protecting daytime cone-driven vision. Because the eye is a relatively accessible organ, novel therapeutic approaches for vision protection and restoration have led the field, with successful examples of gene therapy and stem cell therapy in experimental and clinical trial phases (Ovando-Roche et al., 2017). Compared to vision, in olfactory transduction, very few mutations in transduction components are known that lead to deleterious effects. Several aspects might account for this. Mutations causing a partial reduction of olfaction might go unnoticed in the human population as very little systematic olfactory testing is done. OSNs regenerate throughout life and only have a lifespan of a few weeks. Hence any slow degeneration as those seen in photoreceptors might not manifest in that time window. In a screen of families with congenital anosmia, no potentially causative mutations were found in three main transduction proteins (Gαolf, CNGA2, AC3), with these genes also being under purifying selection (Feldmesser et al., 2007). An interesting exception is patients suffering from retinitis pigmentosa, which is caused by mutations in the gene encoding the CNGB1 subunit expressed in both rods and OSNs. Those patients, identified because of their visual function decline, were found to be hyposmic or anosmic when tested for their olfactory ability (Charbel Issa et al., 2018). If congenital anosmia is considered to be a relatively rare and little-understood condition, more known and frequently detected are specific anosmias, which manifest in the inability to detect certain odorants (Keller et al., 2007; Trimmer et al., 2019). Broadly speaking, this is the olfactory equivalent of color blindness and is caused by known OR mutations. Arguably, the most common causes of smell loss are events that lead to the destruction of the olfactory epithelium and/or the olfactory nerves connecting it to the central nervous system (CNS). These events include head or face trauma, inhalation of toxic chemicals, or viral infection (such as SARS-CoV2), and, neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease (Attems et al., 2015; Cooper et al., 2020). In the former, the origin of the smell disorder can be tracked down to the periphery, the olfactory epithelium. In the case of neurodegenerative diseases, it has been thought that olfactory dysfunction originates centrally in the CNS, but it is becoming clearer now that peripheral olfaction can be affected in these cases as well, although the respective mechanisms have not been fully elucidated.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.18%3A_Signal_Transduction_-_Taste_%28Gustation%29.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry This chapter section is taken in entirety from Ahmad and Dalziel. Front. Pharmacol., 30 November 2020 \| https://doi.org/10.3389/fphar.2020.587664. Creative Commons Attribution License (CC BY) G Protein-Coupled Receptors in Taste Physiology and Pharmacology Heterotrimeric G protein-coupled receptors (GPCRs) comprise the largest receptor family in mammals and are responsible for the regulation of most physiological functions. Besides mediating the sensory modalities of olfaction and vision, GPCRs also transduce signals for three basic taste qualities of sweet, umami (savory taste), and bitter, as well as the flavor sensation kokumi. Taste GPCRs reside in specialized taste receptor cells (TRCs) within taste buds. Type I taste GPCRs (TAS1R) form heterodimeric complexes that function as sweet (TAS1R2/TAS1R3) or umami (TAS1R1/TAS1R3) taste receptors, whereas Type II are monomeric bitter taste receptors or kokumi/calcium-sensing receptors. Sweet, umami, and kokumi receptors share structural similarities in containing multiple agonist binding sites with pronounced selectivity while most bitter receptors contain a single binding site that is broadly tuned to a diverse array of bitter ligands in a non-selective manner. Tastant binding to the receptor activates downstream secondary messenger pathways leading to depolarization and increased intracellular calcium in TRCs, that in turn innervates the gustatory cortex in the brain. Despite recent advances in our understanding of the relationship between agonist binding and the conformational changes required for receptor activation, several major challenges and questions remain in taste GPCR biology that is discussed in the present review. In recent years, intensive integrative approaches combining heterologous expression, mutagenesis, and homology modeling have together provided insight regarding agonist binding site locations and molecular mechanisms of orthosteric and allosteric modulation. In addition, studies based on transgenic mice, utilizing either global or conditional knock-out strategies have provided insights to taste receptor signal transduction mechanisms and their roles in physiology. However, the need for more functional studies in a physiological context is apparent and would be enhanced by a crystallized structure of taste receptors for a more complete picture of their pharmacological mechanisms. Introduction G protein-coupled receptors (GPCRs) are the largest and the most diverse group of membrane receptors in eukaryotes. They are activated by a wide variety of ligands in the form of light energy, lipids, sugars, peptides, and proteins (Billington and Penn, 2003; Schoneberg et al., 2004; Lundstrom, 2009) which convey information from the outside environment into the cell to mediate their corresponding functional responses. The conformational changes of GPCRs upon ligand binding initiate a series of biochemical reactions within the cell. These intracellular reactions regulate sensory functions of smell, taste, and vision, and a wide variety of physiological processes such as secretion, neurotransmission, metabolism, cellular differentiation, inflammation, and immune responses (Lagerström and Schiöth, 2008; Rosenbaum et al., 2009; Venkatakrishnan et al., 2013; Ahmad et al., 2015). Taste is one of the most important sensations in human life, enabling us to perceive different tastes from the diverse range of food available in nature, and is a major determinant of our ingestion decisions. The anatomical units of taste detection are taste receptor cells (TRCs) that are assembled into taste buds distributed across different papillae of the tongue and palate epithelium. Taste processing is first achieved at the level of TRCs that are activated by specific tastants. They transmit information via sensory afferent fibers to the gustatory cortex in the brain for taste perception (Figure 1). Three different morphologic subtypes of TRCs in taste buds sense the different tastes we perceive. Type I glial-like cells detect salty taste while type II cells expressing GPCRs detect sweet, umami, and bitter tastes. Type III cells sense sour stimuli (Janssen and Depoortere, 2013). FIGURE 1. A schematic diagram shows taste signal transmission between the tongue and brain. Taste buds present in different papillae in the tongue and palate contain taste receptor cells (TRC) which contain taste G protein-coupled receptors (GPCRs). The left side shows how afferent nerves transmit a signal to the gustatory cortex in the brain via cranial/glossopharyngeal nerves. The right side shows taste buds with taste TRCs and a simplified signal transduction pathway of taste receptors where taste GPCRs are activated by a tastant that in turn recruits a specific G protein that further induces intracellular calcium release (created with BioRender.com). Sweet and umami stimuli are transduced by Type 1 taste GPCRs while the bitter taste is sensed by Type 2 taste GPCRs (Figure 2; Table 1). The more recently described kokumi sensation is mediated by another GPCR, the calcium-sensing receptor (CaSR) (Figure 2; Table 1). Taste GPCRs are activated by specific taste ligands present in foods and recruit G proteins to activate downstream signaling effectors (Figure 3). TABLE 1. Taste GPCRs classification and their downstream signaling regulators. FIGURE 3. Schematic representation of signal transduction pathway of sweet, umami, bitter, and kokumi-calcium sensing receptors (CaSR) in taste receptor cells on the tongue. Ligand-induced stimulation of the sweet (TAS1R2/TAS1R3), umami (TAS1R1/TAS1R3), bitter receptors (TAS2Rs) and kokumi sensation expressed in type II taste cells within taste bud activate a trimeric G protein composed of α-gustducin (Gα-gust) in sweet, umami, bitter and Gα-q/11 in kokumi-receptor and a complex consisting of Gβγ proteins. The released Gβγ-complex activates phospholipase C isoform β2 (PLCβ2) which then induces the production of inositol 1,4,5-trisphosphate (IP3) and diacylglycerol (DAG); the second messenger IP3, in turn, activates the IP3 receptor (IP3R), an intracellular ion channel that allows Ca2+ release from the intracellular endoplasmic reticulum (ER store). An increase in intracellular Ca2+ then activates the complex of transient receptor potential cation channel subfamily M member 4 and 5 (TRPM4/5) that are plasma membrane-localized sodium-selective channels which leads to depolarization and subsequent activation of voltage-gated sodium channels (VGSC). The combined action of increased Ca2+ and membrane depolarization activates the complex of calcium homeostasis modulator 1 and 3(CALHM1/3) channel and pannexin1 channels, thus resulting in the release of the neurotransmitter ATP. Increased ATP, in turn, activates P2X ionotropic purinergic receptors 2 and 3 (P2X2/P2X3) on the afferent cranial nerve generating an action potential that subsequently signals to the gustatory cortex for sensory perception. Besides well-known taste GPCR pathways, connecting proteins semaphorin 7A (Sem 7A) and 3A (Sem 3A) are depicted in close contact with sweet and bitter receptors as they provide instructive signals that fine-tune to sweet or bitter ganglion neurons, respectively. VFT, venus flytrap domain; CRD, cystine-rich domain; ECD, extracellular domain. (created with BioRender.com). In this review, we will first explore the basic architecture of the gustatory sensory system and its peripheral signal transmission. Then we will discuss taste GPCR signal transduction mechanisms for the different taste modalities, their molecular structure, and the conformational changes that occur following orthosteric/allosteric binding of endogenous and food-derived ligands. Taste Buds and Neural Transmission In mammals, taste buds on the tongue comprise 50–100 elongated epithelial cells and a small number of proliferative basal cells (Sullivan et al., 2010). Ultrastructural studies and patterns of gene expression with cell function reveal three distinct anatomical types of TRCs within each taste bud: Type I, Type II and Type III (Murray, 1986) (refer to Figure 2; Table 2). TABLE 2. Summary of taste receptor cell characteristics. Type II TRCs express either sweet, umami, or bitter taste receptors at their cell surface. These receptors share some commonality with their signal transduction mechanisms that are intrinsic to TRCs. Taste GPCRs (sweet, umami, and bitter) couple to heterotrimeric G proteins that include Gα-gustducin, Gβ3, and Gγ13 (Huang et al., 1999) and initiate a series of signal transduction cascades involving activation of phospholipase C-β2 (PLCB2), production of inositol-1,4,5-triophosphate (IP3), and IP3-dependent Ca2+ release from the endoplasmic reticulum (ER) via the IP3 receptor (IP3R). The increased intracellular [Ca2+]i then activates the transient receptor potential cation channel subfamily M member 4 and 5 (TRPM4/5) in the basolateral plasma membrane, leading to membrane depolarization that triggers Na+ action potential firing, and depolarization-induced release of ATP. In turn, ATP acts as the primary neurotransmitter stimulating purinergic receptors 2 and 3 (P2X2 and P2X3) on afferent cranial nerves whose activation triggers an action potential that subsequently activates the gustatory cortex in the brain (McLaughlin et al., 1992; Wong et al., 1996; Margolskee, 2002). α-gustducin is a distinct G protein selectively expressed in ∼30% of type II TRCs and shares 80% identity with retinal protein α-transducin (McLaughlin et al., 1992) and is a key contributor to signal transduction for sweet and bitter taste receptors (McLaughlin et al., 1992; Wong et al., 1996; Margolskee, 2002). An important aspect of taste transduction is how ATP signaling is conducted. Recent studies have discovered that calcium homeostasis modulators 1 and 3 (CALHM1/3) are enriched in type II TRCs where they interact and form a functional complex. Their genetic deletion abolishes responses to sweet, bitter, and umami tastes, supporting the requirement of the CALHM1/3 complex as an ATP release channel for the GPCRs mediated tastes (Taruno et al., 2013; Ma et al., 2018). New information has provided insight into how specific taste qualities are fine-tuned to recognize their partner ganglionic neurons in the brain. Lee et al. (2017) discovered semaphorin proteins, 7A and 3A as the physical links between sweet and bitter TRCs, respectively, and their partner ganglion neurons in the brain. It remains to be determined what physical links exist between umami TRCs and their corresponding neurons in the brain. Delineating the underlying molecular basis for this interaction would provide a further understanding of purinergic transmission in the taste system. In addition, whether these mechanisms are relevant for kokumi sensation has not yet been investigated, despite CaSR having distinct expression in TRCs and significant functional synergy with other prominent taste qualities (sweet, umami, and salty). Moreover, there is still debate regarding the recognition of kokumi as a sixth taste entity, consequently, the calcium-sensing receptor (CaSR) is not yet included in the nomenclature for any subtypes of taste GPCRs, although it would best fit with Type 1 taste receptors. Type 1 Taste G Protein-Coupled Receptors (Sweet and Umami) The type 1 taste receptors (TAS1Rs) belong to the class C GPCRs, which possess a large N-terminal extracellular domain (ECD) fused to the heptahelical seven transmembrane domain (TMD). The ECD is further divided into two ligand-binding domains (LBD1 and LBD2), having a bi-lobed structure called a Venus flytrap domain (VFT) due to its resemblance to this shape (Hoon et al., 1999). Except for GABAB receptors, a cysteine-rich domain (CRD) connects the VFT to the TMD (Leach and Gregory, 2017). In contrast to other receptors from this class C of GPCRs, such as the metabotropic glutamate receptor (mGluR) or γ-aminobutyric acid type B receptors (GABABRs) which function as homo- or heterodimers, respectively (Jones et al., 1998; Kaupmann et al., 1998; White et al., 1998; Kunishima et al., 2000), the TAS1Rs function as obligatory heterodimers. The distinct expression pattern of TAS1R1 and TAS1R2 in different subsets of murine cells led to the idea that they could detect two different taste profiles. However, following the discovery of the TAS1R3 subtype, it was clear that when TAS1R1 heterodimerizes with TAS1R2, the receptor detects sweet taste substances (Nelson et al., 2001; Ohkuri et al., 2009; Kim et al., 2017). On the other hand, if heterodimerized with TAS1R3 (TAS1R1/TAS1R3), it is responsible for umami or amino acid taste detection (Li et al., 2002; Nelson et al., 2002). Please refer to figure 4A for the basic structure of sweet and umami receptors. Figure $\PageIndex{x}$ shows an interactive iCn3D model of Taste receptor type 1 member 2 (TAS1R2) AlphaFold model (uniprot Q8TE23) The gray is the predicted transmembrane helices. The cyan is the intracellular domain. The blue is the extracellular domain. The predicted model of the structure has high confidence except for the yellow/orange at the distal end of the extracellular domain. Key residues in the ligand binding domain are shown as sticks, CPK colors, and labeled. Sweet Taste Signal Transduction Mechanisms The TAS1R2/TAS1R3 receptor recognizes a wide variety of sweet substances including natural sugars, artificial sweeteners, amino acids, and proteins (Li et al., 2002; Xu et al., 2004; Jiang et al., 2005a; Jiang et al., 2005c) (Table 3). This was demonstrated in studies using heterologous expression systems as well as knockout mice for TAS1R2 and/or TAS1R3 subtypes that showed a blunted response to sugars, sweeteners, and D-amino acids, confirming the TAS1R2/TAS1R3 heterodimer as the main sweet taste receptor in vivo (Li et al., 2002; Zhao et al., 2003; Xu et al., 2004). TABLE 3 \| Agonists of sweet taste receptors along with their EC50 values. Agonists Nature Binding pocket EC50 (mM) References Sucrose Natural carbohydrate VFT (TAS1R2 and TAS1R3) 62 (Li et al., 2002; Servant et al., 2010; Zhang et al., 2010; Zhang et al., 2003) Aspertame Peptide VFT (TAS1R2) 0.75 (Li et al., 2002; Liu et al., 2011; Masuda et al., 2012) Neotame Peptide VFT (TAS1R2) 5 (Li et al., 2002; Masuda et al., 2012) Cyclamate Sulfamate TMD (TAS1R3) 3.1 (Xu et al., 2004; Jiang et al., 2005c) Brazzein Protein CRD (TAS1R3) 0.08 (Li et al., 2002; Jiang et al., 2004; Ide, et al., 2009; Masuda et al., 2012) Thaumatin Protein CRD (TAS1R3) 0.005 Masuda et al., 2012; Jiang et al., 2004 Monellin Protein VFT (TAS1R3), VFT (TAS1R2) 0.01 Koizumi et al., 2007; Jiang et al., 2004 Neoculin Protein VFT (TAS1R2) 0.001 (Jiang et al., 2004; Koizumi et al., 2007) Saccharin N sulfonyl amide VFT (TAS1R2) 0.19 (Li et al., 2002; Masuda et al., 2012; DuBois, 2016) Suosan, cyanosuasan Arylurea VFT (TAS1R2) ND (Tinti and Nofre, 1991; Du Bois, 2016) SC-45647 Guanidinoacetic acid VFT (TAS1R2) 0.3 (DuBois, 1995; Sanematsu et al., 2014) Sucralose Halogenated carbohydrate VFT (TAS1R2 and TAS1R3) 0.06 (Li et al., 2002; Masuda et al., 2012) Acesulfame K Sulfamate ester VFT (TAS1R2) 0.54 (Li et al., 2002; Masuda et al., 2012) Perillartine Oxime, ethoxyphenyl urea, alkoxyaryl urea, TMD (TAS1R2) 15 (Li et al., 2002; Servant et al., 2010) Dulcin Ethoxyphenyl urea TMD (TAS1R2) 0.01 (Servant et al., 2010) S819 Alkoxyaryl urea TMD (TAS1R2) 0.025 (Zhang et al., 2008) D-tryptophan Amino acid VFT (TAS1R2) 2.09 (Li et al., 2002; Masuda et al., 2012) Xylitol, sorbitol Polyols VFT (TAS1R2) ND (Mahalapbutr et al., 2019) Maltotriose, acarbose Oligosaccharide, pseudotetrasaccharide ND ND (Pullicin et al., 2017; Pullicin et al., 2019) Where VFT, venus ytrap domain; TMD, transmembrane domain; ND, not determined. TABLE 3. Agonists of sweet taste receptors along with their EC50 values. The sweet receptor couples to heterotrimeric Gα-gustducin which include Gβ3 and Gγ13, as mice lacking Gα-gustducin, showed a reduced response to sweet substances either natural or artificial (McLaughlin et al., 1992; Wong et al., 1996; Margolskee, 2002). Moreover, a point mutation in the C-terminal region of gustducin (G352P) (critical for its receptor interaction) results in the loss of its ability to activate taste GPCRs while keeping other functions intact. Further, G352P acts as a dominant negative to block heterotrimeric G protein interaction with taste receptors and disrupts the responses to sweet and bitter compounds in both wild-type (WT) and null mice (Ruiz-Avila et al., 2001). In addition, the G352 mutant further reduces any residual sweet/bitter taste responses of the null mice by acting as a “βγ sink” to bind all unbound βγ-subunits and remove them from the viable pool of G protein heterotrimers available to the receptor (Ruiz-Avila et al., 2001). These observations confirm the essential requirement of Gα-gustducin in sweet and bitter taste transduction. In addition to the Gα-gustducin pathway, sweet taste transduction occurs via two additional signaling pathways involving different secondary messengers. The first one involves cAMP and the second one involves IP3. Normally, sugars elevate the level of cAMP, while sweeteners stimulate IP3 production (Tonosaki and Funakoshi, 1988; Uchida and Sato, 1997). Sucrose or other sugars bind to either TAS1R2 or TAS1R3 and recruit Gαs protein that leads to increased cAMP levels which initiate the influx of cations through ion channels. Alternatively, cAMP activates protein kinase A that leads to TRC cell depolarization resulting in an influx of calcium ions and neurotransmitter release (Avenet et al., 1988; Tonosaki and Funakoshi, 1988; Margolskee, 2002). Sweetener binding to the TAS1R2/TAS1R3 heterodimer recruits Gα-gustducin proteins that stimulate PLCβ2 which in turn hydrolyzes phosphatidylinositol 4,5-bisphosphate (PIP2) to diacylglycerol (DAG) and IP3 (Margolskee, 2002; Chandrashekar et al., 2006). IP3R3 (Hisatsune et al., 2007) induced Ca2+ release from ER stores (Figure 3) activates TRPM5 (Zhao et al., 2003; Hisatsune et al., 2007; Dutta Banik et al., 2018) that leads to an action potential (Yoshida et al., 2005; Yoshida et al., 2006) and subsequent release of neurotransmitters. Interestingly, Dutta Banik et al. (2018) confirmed that TRPM4 also mediates taste signaling independent of TRPM5, and knocking out both channel proteins (TRPM4/5) abolishes the sweet, umami, and bitter taste response completely. This revealed another layer of complexity to sweet signal transmission. This in-depth mechanistic research has increased our understanding of sweet and bitter receptors and presents a challenge to dissect the taste signal transmission pathways for umami and kokumi as well. Structural, Molecular, and Conformational Changes of Sweet Receptor Since the sweet taste receptor has not yet been crystallized, determining the structure of the sweetener binding site and mechanism of activation has been a challenge. Based on homology with other class C GPCRs (mGluRs and GABABRs), multiple studies propose similar activation mechanisms for the sweet receptor (Kunishima et al., 2000; Tsuchiya et al., 2002; Jingami et al., 2003; Muto et al., 2007; Perez-Aguilar et al., 2019). The many different sweet agonists and their diverse binding sites across receptor domains (VFT, TMD, and CRD) (Table 3) may explain its complex yet broadly tuned nature. For example, a single residue in VFT (I60) of TAS1R3 of the TAS1R2/TAS1R3 heteromer is required for a saccharin preference in inbred mouse strains (Max et al., 2001; Reed et al., 2004). Several studies utilizing homology and computational modeling based on the crystal structure of mGluR and GABABRs have predicted structural and functional aspects of orthosteric and allosteric binding sites for the sweet receptor (Kim et al., 2017; Cheron et al., 2019; Park et al., 2019). They reported that both VFT regions undergo ligand-dependent conformational changes and intersubunit interactions between ECDs that further stabilize heterodimer formation for subsequent downstream signaling (Perez-Aguilar et al., 2019). The binding of orthosteric agonists to VFT of TAS1R2 leads to major conformational changes that form a TMD6/TMD6 interface between TMDs of TAS1R2 and TAS1R3, which is consistent with the activation process observed biophysically on the mGluR2 homodimer. The initial role of the bound agonist is to pull the bottom part of VFT3 (VFT of TAS1R3) toward the bottom part of VFT2 (VFT of TAS1R2) to transmit this movement from VFT2 (where agonists bind) through the VFT3 and the CRD3 (VFT and CRD of TAS1R3) to the TMD3 (TMD of TAS1R3). This facilitates G protein coupling and downstream signaling. The CRDs are crucial in this streamlined relay of structural changes where disulfide bonds provide rigidity to the CRD and amplify the mechanical constraints that help in attaining an active conformation (Cheron et al., 2019). This is empirically supported by a study in which a single mutation (A537P) in the CRD of TAS1R3 abolished the response to all sweeteners, indicating that the CRD3 must couple ligand binding in VFT2 to the conformational changes required in TMD3 for receptor activation. Trafficking and cell surface expression are also crucial factors for sweet taste transduction. Molecular modeling with mutagenesis scanning revealed specific regions consisting of hydrophobic residues in ECD (site II; at the tip of CRD) and TMD regions (site IV; includes TMD6 and the cytoplasmic base of TMD5) of the TAS1R2 subunit to be important for dimerization with TAS1R3. Moreover, the CRD region and ECL2 domain of the transmembrane region seems to be important for surface co-expression of the TAS1R2/TAS1R3 dimer. In particular, the cytosolic C-terminus portion of the CRD region of TAS1R2 needs to be properly folded for coexpression and trafficking (Park et al., 2019). This reflects the difficulty in expressing these receptors at consistent levels in mammalian cell lines (Li et al., 2002; Shimizu et al., 2014). Positive Allosteric Modulation of Sweet Receptor Class C GPCRs pose an ideal target for allosteric modulation either positive (PAM) or negative (NAM). PAMs show little or no agonist activity on their own but significantly enhance agonist activity. Sweet taste is a major target of the food industry globally and non-caloric sweeteners are highly sought to exploit a huge commercial market. In a first comprehensive high throughput screen by Servant et al. (2010), novel PAMs (SE1, SE2, SE3; Table 4) for the sweet heteromer were reported that were not sweet on their own but significantly enhanced the sweetness of sucralose or sucrose. Agonist binding to the VFT region of TAS1R2 facilitates a closed conformation which constitutes an active state of the sweet receptor, while its open conformation represents an inactive state. Molecular modeling and mutagenesis studies revealed that these PAMs follow a similar mode of binding as that reported for umami PAMs (IMP and GMP). They bind near the opening of the binding pocket of the VFT region adjacent to their agonists, through Van der Waals and hydrogen bonding interactions, and utilize several critical residues for their activity. Although these residues are not in direct contact with any receptor-bound sweetener, mutation of some of them (K65, Y103, L279, D307, and R383) diminishes the response to sweeteners suggesting that these residues normally stabilize the closed conformation. The initial closing of the VFT region by agonist binding and further stabilization of the closed conformation by subsequent binding of SE modulators occurs in two steps. First, by interacting with the ECD region of TAS1R2, and second, by strengthening the hydrophobic interactions between the two lobes of ECD and lowering the free energy needed for their closure (Zhang et al., 2010). TABLE 4 \| Sweet taste receptor’s positive allosteric regulators with concentration (used in cell-based assays in studies) and negative allosteric modulators with their IC50 values. Positive allosteric modulators (PAMs) Nature Binding pocket Conc. (mM) References SE1, SE2, SE3 Undisclosed VFT (TAS1R2) 0.05 (Servant et al., 2010; Zhang et al., 2010) Neohesperidin dihydrochalcone (NHDC) Flavonoid TMD (TAS1R3) 0.25 (Jiang et al., 2005c; Winnig et al., 2007) Unnatural tripeptides (several) Biaryl derivative tripeptides ND 2 – 20 Yamada et al., 2019 Sodium, cholesterol Cation, lipid TMD (TAS1R2) ND Perez-Aguilar et al., 2019 NAMs IC50 (mM) Lactisole Carboxylic acid salt TMD (TAS1R3) 0.041 (Jiang et al., 2005c) (2-(2,4-dichlorophenoxy)propionic acid) Carboxylic acid salt TMD (TAS1R3) 0.006 (Nakagita et al., 2019) Gymnemic acid Triterpenoid glycoside TMD (TAS1R3) 6.9 (Sanematsu et al., 2014) Clofibric acid Herbicide TMD (TAS1R3) 1.4 (Maillet et al., 2009; Kochem and Breslin, 2017) Amiloride Diuretic TMD (TAS1R2) 0.87 (Imada et al., 2010; Zhao et al., 2018) Umami compounds: MSG, Glu-Glu, Glu-Asp Peptides VFT (TAS1R2) ND (Shim et al., 2015) Where VFT, Venus ytrap domain; TMD, transmembrane domain; ND, not determined. TABLE 4. Sweet taste receptor’s positive allosteric regulators with concentration (used in cell-based assays in studies) and negative allosteric modulators with their IC50 values. Using a high throughput chemical screening approach and heterologous expression of the TAS1R2/TAS1R3 heteromer, several unnatural tripeptides with a novel core biaryl structure were found as potential sweet enhancers (Yamada et al., 2019). This study divided the potential molecule into three parts namely, “head and linker” which together are essential for its sweet enhancer activity, while the “tail” determines the level of activity. This approach provided some useful inputs toward the synthesis of potent PAMs. Firstly, an amine incorporated at the α-position of carbonyl moiety in the tail structure interacts with the TAS1R2 subunit thereby increasing allosteric activity. Secondly, additional hydrophobic substitutions in the tail structure provided an increased allosteric activity to the molecule. Lastly, the distance between the head and linker and the insertion of an amide bond is crucial for its synthesis. Although their binding characteristics and allosteric mechanisms are not yet known, these observations provide a starting point to identify and synthesize new sweet PAMs in the future. Small molecule PAMs can also bind to the transmembrane domain in class C GPCRs, in contrast to agonist which binds to the extracellular domain (Urwyler, 2011). For example, the flavonoid sweetener, neohesperidin dihydrochalcone (NHDC) binds to TMD regions to enhance the agonist-induced sweet response. It interacts with a receptor binding pocket in the TMD of TAS1R3 and requires seventeen critical residues in TMDs and extracellular loop 2 for its allosteric activity (Winnig et al., 2007). These residues also contribute to cyclamate and lactisole binding sites. Among seventeen residues, eight alter receptor activation by NHDC (Q6373.29, S6403.32, H6413.33, Y6994.60, W7756.48, F7786.51, L7826.55, and C8017.39) and influence lactisole mediated inhibition. Similarly, nine of the seventeen residues (Q6373.29, H6413.33, H721ex2, S7265.39, F7305.43, W7756.48, F7786.51, L7826.55, and C8017.39) mediate activation by cyclamate, while six (Q6373.29, H6413.33, W7756.48, F7786.51, L7826.55, and C8017.39) influence receptor inhibition by lactisole as well as receptor activation by cyclamate [superscript refers to the nomenclature suggested for class C GPCRs by Pin et al. (2003) where first number denotes TMD region and the second number denotes residue position from the most conserved residue]. Notably, three critical residues in TMD6 (W7756.48, F7786.51, L7826.55) and one in TMD7 (C8017.39) of TAS1R3 were found crucial for allosteric binding, as their mutation to alanine altered the receptor's sensitivity to NHDC and cyclamate, as well as to the inhibitor lactisole (Winnig et al., 2005). Therefore, TMD6 and TMD7α helices of TAS1R3 are integral to allosteric modulation of the sweet receptor, implicating them in TAS1R2 and TAS1R3 subunit interactions and indicating an important role for this structural region in the conformational changes involved in receptor activation. Furthermore, these residues are conserved across mammalian species (Cheron et al., 2019). Negative Allosteric Modulation of Sweet Receptor Like PAMs, negative allosteric modulators (NAM) such as lactisole and gymnemic acid bind to the TMD region of TAS1R3 and inhibit sweet substance-induced responses. Lactisole, an aralkyl carboxylic acid not only inhibits sweet but also the umami receptor response in humans and presents a rare opportunity to study the structural cross-talk between these two taste qualities. Using heterologous expression and mutagenesis, Jiang et al. (2005b) reported that lactisole's sweet inhibition might be mediated by its binding to TMD3, TMD5, and TMD6 of TAS1R3 and induce a conformation change which restricts the movement required to stabilize the active state. Residues A7335.46 in TMD5, L7987.36 in TMD7, and R790ex3 in extracellular loop 3 were found to be crucially important for sensitivity to lactisole in humans (Jiang et al., 2005b). These observations were confirmed in a recent study where 2-(2,4-dichlorophenoxy)propionic acid (2,4-DP) was found to be a more potent antagonist and utilize the same residues as well as four additional ones (H6413.37, H7345.43, F7786.53, and Q7947.32) in binding to TAS1R3. Moreover, the (S)- isomer of both compounds was found to be more strongly bound to the TMD of TAS1R3 and be a more effective inhibitor [lactisole; (S)-lactisole IC50, 20 µM while (R)- lactisole exerted no inhibition at this concentration.; 2,4-DP: (S)-isomer was 10-fold more effective than (R)-2,4DP]. The (S)- lactisole isomer interacts with the TMD via its carboxyl group and stabilizes in only one orientation in the binding pocket that does not allow for very strong binding. In contrast, (S)-2,4- DP binds through two moieties simultaneously, a carboxyl group and an aromatic ring with two Cl groups and stabilizes in several different orientations through hydrophobic interactions that allow stronger binding, resulting in stronger negative allosteric modulation (Nakagita et al., 2019). These observations provide information about the relevance of structural modification in NAM compounds that could affect their interaction with the receptor. Although TMDs of TAS1R3 are the most likely regions responsible for allosteric modulation, TMDs and VFT regions of TAS1R2 cannot be ruled out completely. For example, the diuretic amiloride binds to TAS1R2 (TMD3, TMD5, TMD7) and inhibits the sweet response in a species-dependent manner (Zhao et al., 2018). Further, the umami compound [monosodium glutamate (MSG)] and peptides (Glu-Asp, Glu-Glu) bind to the VFT region of TAS1R2 and inhibit the sweet-induced response (Shim et al., 2015). These observations suggest that both subunits are important for the allosteric activity of TAS1R2/TAS1R3 and further structural studies are required to design novel sweet allosteric modulators. Umami Taste Signal Transduction Mechanisms In contrast to four well-known basic human tastes (sweet, bitter, salty, and sour), umami or ‘savory taste’ is relatively recent and was introduced in early 2000 by Kikuna Ikeda (Ikeda, 2002) as a new seasoning element in food. The main stimulus for the umami taste is the amino acid, L-glutamate present in the diet mainly in the form of MSG (Roper, 2007). Glutamate was first extracted from konbu/kombu (dried kelp of Fucus vesiculosus) and described as having a “unique taste” and “very different from other tastes”. The terminology “umami” comes from the Japanese word “umai” meaning “delicious.” Moreover, the taste of umami is also produced by food such as mushrooms and soy sauce that contain amino acids (L-aspartate), peptides, and synthetic ingredients similar to glutamate and some organic acids (Roper, 2007; Kinnamon, 2009) (Table 5). TABLE 5 \| Umami receptor agonists with their EC50 values and other pharmacological properties. Agonist Nature EC50 (mM) Binding pocket References L-amino acids (glutamate, aspartate, alanine, serine, asparagine, arginine, histidine, threonine, glutamine) L-theanine Amino acids Amino acid (plant origin) 3 (glutamate), ND for others ND VFT (TAS1R1) VFT (TAS1R1) (Li et al., 2002; Nelson et al., 2002; Zhang et al., 2008; Toda et al., 2013) (Narukawa et al., 2014) VFT, venus ytrap domain; ND, not determined. TABLE 5. Umami receptor agonists with their EC50 values and other pharmacological properties. The umami receptor (TAS1R1/TAS1R3) is a heteromeric member of the class C GPCRs, whereas most other receptors of this class exist as homodimers (Nelson et al., 2002; Temussi, 2009; Leach and Gregory, 2017). TAS1R1/TAS1R3 is the predominant umami taste receptor (Zhao et al., 2003; Behrens and Meyerhof, 2011) and the TAS1R1 subtype is critical for sensing umami taste as its deletion abolished the response to umami taste stimuli (Mouritsen and Khandelia, 2012). However, TAS1R1/TAS1R3 is not the only receptor capable of detecting umami ligands (Chaudhari et al., 2000; Kunishima et al., 2000; Li et al., 2002; Nelson et al., 2002). Studies using heterologous expression, afferent nerve recordings, and behavioral experiments have confirmed that metabotropic glutamate receptors 1, and 4 (taste-mGluR1 and taste-mGluR4) also sense umami stimuli (Chaudhari et al., 2000; Kunishima et al., 2000; Li et al., 2002; Nelson et al., 2002). Notably, TAS1R3 knock-out mice show a strongly diminished response to glutamate and sweet stimuli (Damak et al., 2003) and taste cells isolated from these mice respond to IMP and glutamate which is abolished in presence of mGluR antagonists (Pal Chaudhry et al., 2016). TAS1R1/TAS1R3 is not only activated by glutamate but this activation is strongly enhanced in the presence of 5′-ribonucleotides, (inosine 5′ monophosphate; IMP) a response that is a hallmark of umami taste (Rifkin and Bartoshuk, 1980). The main transduction components following the activation of TAS1R1/TAS1R3 are similar to those for sweet taste (Zhang et al., 2003), i.e., α-gustducin (and γ13/β1 or β3), PLCβ2, IP3R, and TRPM4/5. Cyclic nucleotides may also contribute to the transduction of umami taste in TRCs. When taste tissue is stimulated with umami, its cyclic AMP level is decreased (Abaffy et al., 2003). However, the consequence of decreased cAMP in TRCs has not yet been fully elucidated. Both α-transducin and α-gustducin are involved in umami taste signal transduction, as mice lacking the gene for one of these proteins showed a reduced response to this taste (He et al., 2004; Leach and Gregory, 2017). In the taste palate fungiform papillae, α-gustducin and α-transducin activate PDE that reduces cAMP levels. Ligand binding to the TAS1R1/TAS1R3 heterodimer releases Gβγ subunits to stimulate PLCβ2, which hydrolyzes PIP2 to DAG and IP3 (Kinnamon, 2009). IP3 then activates IP3R3 which results in the release of calcium ions from intracellular compartments (Clapp et al., 2001; Leach and Gregory, 2017) (Figure 3). Calcium ions activate TRPM5 and TRPM4 channels, leading to an influx of sodium ions, subsequent cell membrane depolarization, and finally release of ATP, which activates ionotropic purinergic receptors located in sensory fibers (Perez et al., 2002; Sugita, 2006). This pathway was confirmed when mice devoid of TRPM5, TRPM4, PLCβ2, and IP3R3 showed a reduced response to umami taste perception following glutamate stimuli (Damak et al., 2006; Kinnamon, 2009; Eddy et al., 2012). Structural, Molecular, and Conformational Changes of Umami Receptor In the last decade, several in-depth modeling and mutagenesis approaches have improved structural and molecular understanding of the umami receptor. The VFT regions of both subunits of TAS1R1/TAS1R3 comprise orthosteric and allosteric ligand binding sites for umami stimuli. Mutagenesis and molecular modeling studies reveal that the cognate agonist glutamate binds in the VFT region of the TAS1R1 subunit of TAS1R1/TAS1R3 and stabilizes the closed active receptor conformation. Moreover, four residues in the TAS1R1 VFT region (S172, D192, Y220, and E301) showed no detectable response to glutamate when they were mutated to alanine suggesting that they are critical for glutamate binding. The glutamate binding and stabilization of the closed conformation of TAS1R1, activates the downstream signaling pathway, while TAS1R3 remains in an open (inactive) conformation. Therefore, closure of the VFT is the key event that sensitizes umami taste receptor signal transduction (Lopez Cascales et al., 2010). Apart from glutamate, other L amino acids were also found to elicit functional responses by binding to the corresponding VFT region of TAS1R1. Six residues that contributed to the acidic amino acid agonist (L-glutamate and L-alanine) responses have been identified (S148, R151, A170, E174, A302, and D435). Allosteric Modulation of Umami Receptor Because of significant advancements in understanding and food industry application of umami taste, its allosteric modulators are sought after. Several allosteric umami ligands have been discovered with varying potency, only a few of which have been characterized at the molecular level. The best-characterized umami PAMs, the 5′-ribonucleotides: inosine 5′-monophosphate (IMP) and guanosine 5′-monophosphate (GMP), interact with the VFT region of the TAS1R1 subunit to enhance the glutamate-induced response that is the hallmark of umami taste (Table 6). IMP and GMP binding sites in the VFT are adjacent to that for glutamate binding. The mutation of four residues (H71, R277, S306, and H308) abolished the IMP/GMP-induced glutamate response suggesting their involvement in the allosteric binding of these nucleotides. Structurally, IMP and GMP stabilize the closed form of the TAS1R1 VFT region through electrostatic interactions and coordinate the positively charged residues that act as pincers. The ability of IMP and GMP to interact with the VFT region (as opposed to the TMD region) represents a unique mechanism of positive allosteric regulation within class C GPCRs (Urwyler, 2011). TABLE 6 \| Umami receptor allosteric modulators with concentrations used in cell-based assays and other pharmacological properties. Allosteric modulators Nature Conc. (mM) Binding pocket References IMP/GMP Nucleotide 1 VFT (TAS1R1) (Li et al., 2002; Nelson et al., 2002; Zhang et al., 2008) Cyclamate Sodium 8 TMD (TAS1R3) (Xu et al., 2004) cyclohexylsulfamate Methional (3-methylsulfanylpropanal) 0.12 TMD (TAS1R3) (Toda et al., 2018) Lactisole (2-4-methoxyphenoxy propionic acid) Carboxylic acid salt 5 TMD (TAS1R3) (Xu et al., 2004) Clofibric acid (4- chlorophenoxy)-2-methylpropanoic Herbicide acid 4 TMD (TAS1R3) (Maillet et al., 2009; Kochem and Breslin, 2017) acid Where VFT, Venus ytrap domain; TMD, transmembrane domain. TABLE 6. Umami receptor allosteric modulators with concentrations used in cell-based assays and other pharmacological properties. In contrast to IMP and GMP which bind to the TAS1R1 extracellular domain, the well-known flavor compound methional and its analogs bind to the TMD region and allosterically regulate the umami receptor in a species-dependent manner (Toda et al., 2018). Importantly, methional utilizes several distinct residues in different TAS1R1 transmembrane domains (TMD2-7) to act as a PAM in the human umami receptor, yet it behaves as a NAM in the mouse counterpart. This unusual phenomenon provided an opportunity to study the mechanisms of both positive and negative modulation in TAS1R1 simultaneously (Toda et al., 2018). Construction of chimeric receptors between human (h) and mouse (m) and their functional analysis demonstrated that the TMD of TAS1R1 is the key domain for switching the PAM/NAM activities of methional. Point mutation substitutions between these species identified four residues (h/m; F768/L769, N769/H770, S799/T800, and S802/G803) that are collectively required to switch PAM/NAM activities. A similar mode of allosteric regulation and PAM/NAM mode switching has been reported for mGluR5 (Gregory et al., 2013) suggesting this is an unusual and distinct phenomenon of the class C GPCRs. Further, alanine scanning mutagenesis in TAS1R1 of the corresponding residues vital for the activity of other taste inhibitors (sweetener inhibitors; NHDC and cyclamate; sweet and umami taste inhibitor; lactisole) revealed three residues required for PAM (W6974.50 F7285.40 and F7325.44) and a single residue (F6423.40) for NAM. These results suggest that both the PAM and NAM activities of methional are conferred by residues that are distinct from those required for the PAM/NAM switch. Knowing that methional is an important part of food seasoning globally, these observations could help in maximizing its use in enhancing flavors along with amino acids and nucleotides. Despite PAMs being a central focus for umami allosteric modulation, there has also been considerable research on negative allosteric modulation where lactisole emerged as a prominent NAM of the umami receptor, TAS1R2/TAS1R3. Because umami and sweet receptors share the TAS1R3 subunit, findings from studies on sweet receptor lactisole binding are relevant. A comprehensive study on the sweet receptor identified critical residues within the TMD regions (S6403.32, H6413.33 in TMD3 and F7786.51, L7826.55 in TMD6) of TAS1R3 required for lactisole binding pocket and showed a large effect on sensitivity to lactisole (Xu et al., 2004; Jiang et al., 2005b). Because lactisole shares structural similarities with two other classes of compounds: fibrates and phenoxy-herbicides, researchers studied them to search for novel sweet/umami inhibitors (Maillet et al., 2009). The lipid-lowering drug, clofibric acid inhibits the TAS1R3 umami receptor-mediated response both in vitro and in vivo (Table 6). Like lactisole, clofibrate inhibits the umami taste from glutamate by binding with a similar affinity to TAS1R1/TAS1R3. However, its specificity against the umami receptor still needs to be validated alongside other umami taste receptors (mGluR1, mGluR4, or NMDA). TYPE 2 TASTE G PROTEIN-COUPLED RECEPTORS (BITTER RECEPTORS) Type 2 taste GPCRs are represented by bitter taste receptors that have a distinct subset of bitter sensing cells in type II TRCs and notably, 25 bitter taste receptors (TAS2Rs) are reported to be expressed in humans (Chandrashekar et al., 2000; Devillier et al., 2015; Behrens and Meyerhof, 2018). A significant amount of work has been done to explore the diversity among TAS2Rs and their agonists in taste biology (Adler et al., 2000; Behrens and Meyerhof, 2009; Behrens and Meyerhof, 2018). Some TAS2Rs (TAS2R3, TAS2R5, TAS2R13, TAS2R50) are narrowly tuned to structurally similar bitter compounds, whereas others are broadly tuned (TAS2R10, TAS214, TAS2R46), responding to several bitter compounds. Initially, it was believed that each bitter-sensitive type II TRC expressed every TAS2R isoform (Adler et al., 2000) but other studies suggest that TAS2Rs can be expressed differentially, allowing for possible discrimination among bitter compounds (Caicedo and Roper, 2001; Behrens and Meyerhof, 2009; Behrens et al., 2009). Please refer to figure 4B for the basic structure of the bitter receptor. Bitter Taste Signal Transduction Mechanisms The bitter taste is the most complex of all the five basic tastes and protects against the ingestion of toxic substances by eliciting an innate aversive response across species (Chandrashekar et al., 2006; Behrens and Meyerhof, 2018). The TAS2Rs that mediate bitter taste perception are among ∼50 TAS2Rs identified in mammals, and 25 are known to be expressed in humans (Adler et al., 2000; Devillier et al., 2015; Yoshida et al., 2018). The TAS2R family is the most diverse and binds to a wide range of agonists compared with the other taste GPCRs (Jaggupilli et al., 2016) (Supplementary Table 1). TAS2Rs are distinctive among class A GPCRs in that many of them bind agonists with low apparent affinity in the micromolar range, rather than the nanomolar range (Di Pizio et al., 2016). The activation of TAS2Rs by harmless, minute amounts of bitter compounds such as those contained in most vegetables would limit the availability of food resources appearing safe for consumption and therefore could negatively affect survival. Hence, the concentration ranges at which bitter taste receptors are activated are well-balanced to allow species to maintain a healthy diet yet avoid ingestion of spoiled food containing strongly bitter ligands. Hundreds of bitter compounds have been reported to evoke bitterness and activate human bitter receptors in different cell-based assays. These bitter agonists include plant-derived and synthetic compounds such as peptides, alkaloids, and many other substances (Supplementary Table 1). (Pronin et al., 2004; Meyerhof et al., 2010; Iwata et al., 2014). Some TAS2Rs are activated by a wide range of compounds, whereas others show strict specificity for a single bitter compound (Behrens et al., 2009; Sakurai et al., 2010a; Born et al., 2013). Interestingly, TAS2R31, TAS2R43, and TAS2R46 have around 85% sequence homology, but they bind to different agonists (Brockhoff et al., 2010; Jaggupilli et al., 2016), reinforcing the idea that each TAS2R might have a unique ligand-binding pocket. The canonical TAS2R signal transduction cascade signaling molecules shared among bittersweet and umami receptors (Wong et al., 1996; Huang et al., 1999; Mueller et al., 2005), includes the heterotrimeric G protein subunits (Gα-gustducin, Gβ3, and Gγ13), (Ishimaru, 2009; Shi and Zhang, 2009), a phospholipase C (PLCβ2), an inositol trisphosphate receptor (InsP3R), and the TRPM5 ion channel. Upon receptor activation by bitter ligands, the G protein α-gustducin dissociates from its βγ subunits. The latter activates PLCβ2, leading to a release of Ca2+ from IP3-sensitive Ca2+ stores, resulting in Na+ influx through TRPM5 channels. This Na+ influx depolarizes the cells and causes the release of neurotransmitter ATP through gap junction hemichannels or CALHM1 ion channels (Finger et al., 2005; Chaudhari and Roper, 2010; Taruno et al., 2013) (Figure 3). Structural, Molecular, and Conformational Changes of Bitter Receptors Classification of TAS2Rs has always been ambiguous because they were originally considered to be a distinct family (Horn et al., 2003) or grouped with the frizzled receptors (Fredriksson et al., 2003; Jaggupilli et al., 2016), but most recent analyses (Di Pizio et al., 2016) support their classification with Class A GPCRs. The ability of bitter taste receptors to interact with numerous structurally diverse substances compared to other GPCRs is remarkable and includes a wide range of drugs/antibiotics, polyphenols, bacterial metabolites, salts, and metal ions (Supplementary Table 1). Therefore, exploring the criteria for the identification of highly heterogeneous bitter compounds with pronounced selectivity has become a major research area. Some of these studies rely solely on in silico homology/computational modeling (Dai et al., 2011; Tan et al., 2012; Di Pizio et al., 2020; Dunkel et al., 2020) and others on in vitro genetic modification and functional assay systems (Pronin et al., 2004; Nowak et al., 2018; Jaggupilli et al., 2019). As a group of over ∼50 receptor subtypes, TAS2Rs recognize structurally diverse agonists where some are broadly tuned (TAS2R46, TAS2R14, TAS2R10, and TAS2R43) recognize diverse agonists, while others (TAS2R1, TAS2R4, TAS2R7) show strong selectivity and narrow tuning (Liu et al., 2018; Wang et al., 2019). The agonist binding cavity in most bitter GPCRs is located deep within their transmembrane domain (TMD), except TAS2R7 in which it resides on the extracellular surface (Liu et al., 2018). TAS2Rs are also distinct in containing highly conserved TMD regions, with thirteen key residues and two motifs (LXXXR in TMD2 and LXXSL in TMD5) that are absent in class A GPCRs, and may reflect their different activation mechanisms (Singh et al., 2011). LXXSL plays a structural role by stabilizing the helical conformation of TMD5 at the cytoplasmic end and a functional role by interacting with residues in intracellular loop 3 (ICL3) which is important for proper receptor folding and function (Singh et al., 2011). Moreover, mutation of the conserved residues in LXXSL and LXXXR motifs results in protein misfolding and poor surface expression (Singh et al., 2011; Pydi et al., 2014a). The initial study highlighting the structure–activity relationship of bitter taste receptors was performed with receptors belonging to a subfamily of closely related TAS2Rs (Pronin et al., 2004). By physically swapping the extracellular loop 1 (ECL1) between TAS2R43 and TAS2R31, chimeric TAS2R31/TAS2R43 (ECL) gained responsiveness to the compound n-isopropyl-2methyl-5-nitrobenzenesulfonamide (IMNB), whereas the reverse chimera TAS2R31 (ECL)/TAS2R43 lost responsiveness for IMNB. Although this report supports an important contribution of residues located within the transmembrane region of the investigated receptors, the extracellular loops appear to be of importance for agonist selectivity. This empirical finding contrasts with earlier computational studies which predicted the agonist binding site to lie within the helical bundle of TAS2Rs without particular contacts between extracellular loops and docked agonists (Floriano et al., 2006; Miguet et al., 2006). Bitter Receptor Ligand Binding Pocket The emergence of TAS2Rs as the most broadly tuned taste receptors might give the impression that their specific interaction with numerous agonists is because of several binding pockets that accommodate subgroups of bitter compounds. However, structure–function analysis of TAS2Rs (except for TAS2R7) has demonstrated the presence of only a single agonist binding pocket comprising the upper parts of TMD2, TMD3, TMD5, TMD6, and TMD7. The reason for their broad tuning and recognition of such a broad spectrum of agonists might most likely be attributed to the presence of an additional extracellular binding site called a “vestibular site,” in addition to the orthosteric selecting as reported for TAS2R46 (Sandal et al., 2015). This two-site architecture offers more ligand recognition points than a single one and thus might help in selecting the appropriate agonists. Moreover, the presence of the vestibular site may also help to discriminate among the wide spectrum of bitter ligands. Although broadly tuned receptors (TAS2R46, TAS2R31, and TAS2R43) have high homology in amino acid sequence, their agonist profiles only slightly overlap (Kuhn et al., 2004; Brockhoff et al., 2007; Di Pizio and Niv, 2015) which suggests the involvement of key residues at different positions in agonist specificity. Consequently, when strychnine interacting positions in TAS2R46 (residues differ at this position in TAS2R31, TAS2R43) were exchanged between these two receptors not only was the strychnine responsiveness transferred to the recipient receptor (TAS2R31, TAS2R43), but also sensitivity to additional TAS2R46 agonists (absinthin and denatonium). Sensitivity to activation by aristolochic acid was lost in the mutant receptors (Brockhoff et al., 2010). This experimental evidence supports the presence of a common agonist binding pocket and agrees with other studies on TAS2R16, TAS2R14, and TAS2R7 receptors (Sakurai et al., 2010a; Sakurai et al., 2010b; Thomas et al., 2017; Liu et al., 2018; Nowak et al., 2018). Recent studies used homology modeling and mutagenesis to elucidate the nature of the ligand-binding pocket in TAS2R7, TAS2R14, and TAS2R16 receptors (Thomas et al., 2017; Liu et al., 2018; Nowak et al., 2018). They reported that the binding pocket is flexible and wide open to accommodate molecules of diverse sizes and shapes, and thus permits chemical modifications among agonists as well (Thomas et al., 2017; Liu et al., 2018; Nowak et al., 2018). Although the molecular basis for the promiscuity of bitter receptors is attributed to their apparent flexible spacious binding site, future work elucidating the contact points between TAS2Rs binding site residues and its agonists in terms of additional binding locations is required. Bitter Receptors Ligand Binding Domain and Amino Acid Residues A majority of the TAS2R studies are based on molecular modeling, mutagenesis, and heterologous expression systems (Biarnes et al., 2010; Brockhoff et al., 2010; Tan et al., 2012; Nowak et al., 2018; Shaik et al., 2019) suggest that the ligand binding pocket is formed by several key residues in most TMDs (TMD1, TMD2, TMD3, TMD5, TMD6, and TMD7), except for TMD4. Studies show similarities as well as differences regarding residues and positions involved in agonist-receptor interactions. However, most of them agree that besides position N3.36 in TMD3 (superscript as per Ballestros-Weinstein nomenclature for class A GPCRs) (Ballesteros and Weinstein, 1995) and other residues (L3.32, L3.33, and E3.37) in its close proximity, play a role in agonist activation of several broadly tuned TAS2Rs (TAS2R1, TAS2R16, TAS2R30, TAS2R38, TAS2R46) (Pronin et al., 2004; Biarnes et al., 2010; Brockhoff et al., 2010; Sakurai et al., 2010b; Dai et al., 2011). In contrast, for the narrowly tuned TAS2R7, one position in TMD3 (H943.37) and another in TMD7 (E2647.32) were found crucial for metal ion binding (Wang et al., 2019). Mutagenesis and molecular modeling revealed that these two residues contribute to the metal ion binding pocket in TAS2R7. Moreover, metal ions bind distinctively to residues lining the binding pocket and interestingly, the presence of calcium in the assay solution appears to affect the TAS2R7 response to metal ions. It is not clear how calcium affects metal ion binding to TAS2R7, but it might work cooperatively with certain ions and not others. Future studies focusing on structural interactions between the receptor and metal ions will provide further insights into how they activate the receptor. In TMD2, two studies suggest that position N2.61 is critical for binding in TAS2R1 (Singh et al., 2011) and TAS2R46 (Brockhoff et al., 2010). Likewise, in TMD7, position 2657.39 is implicated in binding to TAS2R46 (E265) and TAS2R1 (I263) (Dai et al., 2011). In TMD5, position H5.43 is implicated in binding in TAS2R16 and E5.46 in TAS2R1 (Dai et al., 2011) while, in TMD7, position E7.32 was crucial for metal ion binding (Wang et al., 2019). These residues represent putative contact points for agonist interaction and form a pattern of being spaced one helical turn from each other. Recent mutagenesis studies (Nowak et al., 2018; Di Pizio et al., 2020) performed in broadly tuned TAS2R14 with agonists (aristolochic acid, picrotoxinin, thujone) found several residues in TMDs to be involved in agonist binding. However, in contrast to TAS2R10 (Born et al., 2013) and TAS2R46 (Brockhoff et al., 2007), mutation of TAS2R14 did not result in a complete loss of function for all agonists but a varied reduction in responsiveness or selectivity toward agonists. Among several mutants, only mutation of W89A resulted in a complete loss of responsiveness against picrotoxinin while others showed more subtle agonist selective changes. This indicates that TAS2R14 is not streamlined for the most sensitive detection of selected agonists, but rather tailored to detect numerous diverse agonists, with comparatively lower apparent affinity. The binding characteristics of bacterial acyl homoserine lactones (AHLs) on TAS2Rs (TAS2R4, TAS2R14, and TAS2R20) suggest the presence of a single orthosteric site situated close to the extracellular surface and reinforce the significant role of the extracellular loop structure (ECL2) in TAS2R ligand binding and activation (Jaggupilli et al., 2018). The crucial AHL binding residues in TAS2R4 and TAS2R14 are predominantly located in the ECL2, while in TAS2R20 they are present in TMD3 and TMD7 helices. The ECL2 residues, N165 in TAS2R4, and R160 and K163 in TAS2R14 were found crucial for lactone binding. In contrast, TAS2R20 residues W88 (TMD3) and Q265 (TMD7) are essential for agonist binding (Pydi et al., 2014c; Zhang et al., 2017; Jaggupilli et al., 2018). In addition, the hydrophobic amino acids in the three TAS2Rs are considered important in directing the orientation of the hydrophobic acyl chains of lactones that facilitate receptor activation. The transmembrane domain in GPCRs is composed mainly of hydrophobic amino acids accommodated in the plasma membrane. Therefore, hydrophobic properties of the receptor binding pocket are important for any membrane-accessible agonist. Hydrophobic residues in TMD3 and TMD7 of TAS2R16 are important in forming a wide ligand-binding pocket (Thomas et al., 2017) that accommodates larger ligands like the β-glycosides. By using salicin analogs as TAS2R16 novel agonists (differ structurally from salicin in β-glucoside core constituents), several critical residues were identified that are required for signaling. Interestingly, these were identical to the residues critical for salicin signaling, except for W261, which was not required for activation by the analog 4-NP-β-mannoside. Importantly, all these residues are in the TMD helices or intracellular face of the receptor, consistent with classical GPCR signal transduction. These results suggest that larger ligands bind to the wide binding pocket of TAS2R16 on the extracellular side, and then their signal is transduced via conserved residues on the intracellular side. This can account for the broad spectrum of ligand recognition conferred by TAS2R16. Unlike broadly tuned receptors, narrowly tuned ones like TAS2R7 show two different types of critical residue in ligand binding. The first type includes D86, W170, and S181 which are agonist independent and their mutation significantly reduces the ability of TAS2R7 to bind agonist, while a second group consisting of D65 and W89 are selective for quinine and enhance binding to a specific category of ligand (Liu et al., 2018). Despite the variation in the amino acid type and location important for agonist binding among receptors of the bitter family, for the most part, ligand binding pockets are present on the extracellular surface of TMDs or ECL2. The function of the residues at these binding pockets is dictated by multiple factors that include the type of ligand, the movements in TMDs, and the associated movement of ECL2 to accommodate the ligand. Structure–function studies have identified a conserved KLK/R motif in the intracellular carboxyl-terminal domain of 19 TAS2Rs that is critical for cell surface expression, trafficking, and receptor activation (Upadhyaya et al., 2015; Jaggupilli et al., 2016). Agonist, Antagonist Binding and Modulation of Bitter Receptors In simple pharmacological terms, an antagonist is a ligand that inhibits the biological response induced by an agonist and does not induce any response of its own, while a ligand that reduces the constitutive/basal activity of a GPCR is considered an inverse agonist. An antagonist acts as a competitive inhibitor to block receptor activity. Large numbers of agonists have been identified for bitter receptors, but few antagonists have been found so far (Table 7). Finding an antagonist/inhibitor for bitter taste would not only help in understanding the TAS2R mechanism of signal transduction but have potential use in foods to overcome unwanted bitterness in consumer products. Such bitter blockers have been proposed to increase the palatability of bitter-tasting food and beverages, increase compliance in taking bitter-tasting drugs, especially children’s formulations and reduce or prevent off-target drug effects in extra-oral tissues (Clark et al., 2012) TABLE 7 \| Bitter taste receptor inhibitors with their IC50 values and other pharmacological properties. Antagonist Mode of action Bitter receptors Tested agonists IC50 (µM) References GIV3727or 4-(2,2,3-trimethylcyclopentyl) butanoic acid Competitive orthosteric inhibitor 31 acesulfameK 6.4 (Slack et al., 2010) 43 Aristolochic acid 11.33 4 Colchicine 108 40 Cohumulone 6.24 Gamma-aminobutyric acid (GABA) Orthosteric inhibitor 4 Quinine 3.2 (Pydi et al., 2014b) 3β-hydroxydihydrocostunolide (3HDC) ND 46 Absinthin 14.1 (Slack et al., 2010; Brockhoff et al., 2011) Andrographolide 4.9 Denatonium 6.8 Picrotoxinin 4.7 Strychnine 15.3 3-hydroxypelenolide(3HP) ND Absinthin 57.8 (Brockhoff et al., 2011) Andrographolide 44.5 Denatonium 51.4 Picrotoxinin 22.9 Strychnine 84.9 Probenecid Allosteric inhibitor 16 Salicin 292 (Greene et al., 2011) Sakuranetin ND 31 Saccharin 5.5 (Fletcher et al., 2011) 6-Methoxysakuranetin ND 31 Saccharin 10.2 (Fletcher et al., 2011) Jaceosidin ND 31 Saccharin 11.7 (Fletcher et al., 2011) 6,3′-dimethoxyﬂavanone ND 39 Epicatechin gallate (ECG) 4075 (Roland et al., 2014) Denatonium 240 6-Methoxyﬂavanone ND 39 Epicatechin gallate (ECG) 479 (Roland et al., 2014 N,N-bis(carboxymethyl)-l-lysine(BCML) ND 4 Quinine 0.059 (Pydi et al., 2014b) (±) abscisic acid (ABA) ND 4 Quinine 34.4 (Pydi et al., 2015) ND, not determined. TABLE 7. Bitter taste receptor inhibitors with their IC50 values and other pharmacological properties. To date, ∼12 bitter inhibitors have been reported to interact with only 10 TAS2Rs subtypes (Table 5) by binding to transmembrane domains in a similar manner to agonists. GIV3727 (4-(2,2,3-trimethylcyclopentyl) butanoic acid) was the first TAS2R antagonist discovered and to be well characterized structurally (Slack et al., 2010) that acts as an orthosteric competitive antagonist for TAS2R31. It competes with the acesulfame K agonist both in vitro and in vivo. GIV3727 is moderately selective because it inhibits multiple bitter receptors including, TAS2R4, TAS2R40, and TAS2R43. Homology modeling revealed that the -COOH group in GIV3727 is important for ligand-receptor interactions as its replacement with an ester or the corresponding alcohol abolished its antagonist activity. Moreover, a mutagenesis study in TAS2R31 and TAS2R43 revealed residues K2657.39 and R2687.39 in TMD7 to be crucial for its antagonistic activity (Slack et al., 2010). Similarly, another non-selective inhibitor, probenecid (p-(dipropylsulfamoyl) benzoic acid) was found to act as NAM of TAS2R16 activity and inhibits TAS2R38 and TAS2R43 as well (Greene et al., 2011). Two point mutations, P44T and N96T in TMD3 of hTAS2R16 were found to significantly suppress the ability of probenecid to inhibit salicin activity. Hydrophobicity seems important for their pharmacological activity as observed for both probenecid and GIV3727. The sesquiterpene lactone, 3β-hydroxydihydrocostunolide (3HDC) is an interesting bitter blocker as it acts as a competitive antagonist of TAS2R46, TAS2R30, TAS2R40, yet activates TAS2R4, TAS2R10, TAS2R14, and TAS2R31 as an agonist (Brockhoff et al., 2011). Similarly, various flavanones were also noted as antagonists for TAS2R31, and TAS2R39 with varying efficacy. Taken together most of the currently known antagonists are non-selective and there is an urgent need for studies that focus on selective antagonists of major broadly tuned TAS2Rs (such as TAS2R10, TAS2R14, TAS2R16, and TAS2R46). To target bitterness in terms of food industry needs, potential peptide inhibitors from different protein sources such as hen protein hydrolysates (inhibits TAS2R4, TAS2R7, TAS2R14) and beef proteins (inhibits TAS2R4) (Zhang et al., 2018; Xu et al., 2019) are reported to be effective. Several umami glutamyl peptides isolated from soybeans have been found to act as non-competitive allosteric inhibitors of TAS2R16 against the salicin-induced response (Kim et al., 2015). Constitutive Activity of Bitter Receptors A phenomenon in GPCR activity is that of constitutive activity, essentially an active state occurring in the absence of an agonist which has been demonstrated in more than 60 GPCRs (Seifert and Wenzel-Seifert, 2002). It is the production of a second messenger or downstream signaling by a receptor in a ligand-independent manner. The constitutive activity provides another possibility for taste inhibitor discovery using inverse agonists. Inverse agonists can inhibit both agonist-dependent and agonist-independent activity, while antagonists can inhibit only agonist-dependent activity (Chalmers and Behan, 2002). Interestingly, some mutations in GPCRs can lead to constitutive activity and receptors with this characteristic (including constitutively active mutants or CAM) are important tools to investigate new bitter inhibitors. Although constitutive activity has not been observed naturally in TAS2Rs, when induced by mutation these receptors provide a useful means to investigate the relationship between an active receptor conformation and inverse agonist pharmacology. Molecular modeling and functional assays report five CAMs critical residues for TAS2Rs, one in TMD7 (S2857.47) and four others in intracellular loop 3 (H214A, Q216A, V234A, and M237A) (Pydi et al., 2014a; Pydi et al., 2014b). Of the five CAMs, only the TAS2R4 with H214A mutation shows a 10-fold increase in constitutive activity. This histidine residue is highly conserved in most TAS2Rs. Mutation of H214 (H214A) helped in finding two new inverse agonists (GABA and ABA; Table 7) (Pydi et al., 2015). Similar pharmacological approaches can be used to generate mutants of all TAS2Rs to screen for their inverse agonist/bitter taste blockers. However, for better characterization and interpretation of TAS2Rs, future in vivo studies should be performed to understand the functional relevance of these CAMs. At the same time, it is worth noting that the potential presence of endogenous agonists makes it difficult to determine the true constitutive activity of GPCRs including TAS2Rs (Devillier et al., 2015). Kokumi Sensation Signal Transduction In addition to the five basic tastes, sensations beyond these add another dimension to taste perception. One such example is “kokumi” which is distinct from the other five tastes in that it does not have a taste as such but rather induces a sensation of “mouthfulness,” depth, thickness, and aftertaste in the flavors. Although this flavor has been used historically and is well recognized in Japanese cuisine, it was first characterized by Ueda et al. (1990) who isolated a kokumi taste substance from water extracts of garlic and onion and identified, γ-glutamylcysteinylglycine or glutathione (GSH) as the main active ingredient of kokumi flavor (Ueda et al., 1990; Ueda et al., 1997; Dunkel et al., 2007). GSH is abundantly present in food-grade yeast extract and has been used to make foods more flavorsome. Kokumi signal transduction was unknown until CaSR expression was reported in a subpopulation of taste cells in mice and rats that suggested it could function as a taste receptor for calcium and amino acids (San Gabriel et al., 2009; Bystrova et al., 2010). However, its apparent role in kokumi stimuli detection was not confirmed. Ohsu et al. (2010) for the first time reported that kokumi peptides (GSH, γ-Glu-Val-Gly, and various γ-glutamyl peptides; Table 8) signal through CaSR and can synergize with sweet, salty, and umami taste qualities to impart an augmented kokumi sensation, i.e., increased depth of flavor which was further complemented by later studies (Maruyama et al., 2012; Kuroda and Miyamura, 2015). By using heterologous expression systems and human sensory analysis these studies demonstrated that kokumi peptides impart kokumi sensation to sweet, salty, and umami taste via CaSR as the kokumi component was specifically suppressed in the presence of the CaSR-specific NAM NPS-2143. To further validate this idea, Maruyama et al. (2012) identified a distinct population of taste cells expressing CaSR in mouse lingual tissue which did not express either sweet or umami receptors. Notably, these cells are specifically responsive to kokumi substances and elicit a Ca2+ response to focally applied kokumi stimuli in mouse lingual slices. Moreover, this response was inhibited in the presence of NPS-2143. These findings support the idea that CaSR mediates kokumi sensation effects in TRCs TABLE 8 \| Kokumi sensation receptor agonists, allosteric modulators with concentrations used in cell-based assays. Ca2+ Orthosteric agonist/cation 1a VFT Mg2+ Orthosteric agonist/cation 10a VFT Gd2+ Orthosteric agonist/cation 0.02a VFT Al2+ Orthosteric agonist/cation 0.5a VFT Sr2+ Orthosteric agonist/cation 0.5a VFT Mn2+ Orthosteric agonist/cation 0.5a VFT Ni2+ Orthosteric agonist/cation 0.5a VFT Ba2+ Orthosteric agonist/cation 0.2a VFT Ca2+ Orthosteric agonist/cation 1a VFT Ca2+ Spermidine Orthosteric agonist/polyamine 0.002a VFT (Nemeth et al., 2018) Neomycin Orthosteric agonist/aminoglycoside antibiotic 0.06a VFT (Katz et al., 1992) Gentamicin Orthosteric agonist/aminoglycoside antibiotic 0.15a VFT Katz et al., 1992) Kanamycin Orthosteric agonist/aminoglycoside antibiotic 0.1 VFT (Katz et al., 1992) Amyloid β-peptides Orthosteric agonist/Peptide 0.001–0.04 (Ye et al., 1997) Poly-Lysine Orthosteric agonist/peptide 0.03 µMa VFT (Brown et al., 1991; Nemeth et al., 2018) Poly L-arginine Orthosteric agonist/peptide 0.004 µMa VFT Brown et al., 1991; Nemeth et al., 2018) Lysozyme Agonist/protein 0.59a ND (Yamamoto et al., 2020) Thaumatin Agonist/protein 0.07a ND (Yamamoto et al., 2020) Aromatic L-amino acids (Trp, Phe, His, Ala, Ser) PAMs 10 VFT (Conigrave et al., 2000; Mun et al., 2004; Geng et al., 2016) Anions (SO42-) NAM 10 VFT (Geng et al., 2016) Cinacalcet PAM/phenylalkylamine 0.051 µMa TMD (Miedlich et al., 2002; Petrel et al., 2004; Nemeth et al., 2004) Calindol PAM/phenylalkylamine 0.31 µMa TMD Miedlich et al., 2002; Petrel et al., 2004) NPS R-568 PAM/phenylalkylamine 0.5 µMa TMD (Miedlich et al., 2002; Petrel et al., 2004) NPS R-467 PAM/phenylalkylamine 0.01 TMD (Miedlich et al., 2002; Petrel et al., 2004) γ-Glu-Val-Gly PAM/Peptide 0.041 µMa (Ohsu et al., 2010) γ-Glu-Cys-Gly (Glutathione) PAM/Peptide 76.5 µMa VFT (Ohsu et al., 2010; Wang et al., 2006 γ-Glu-Ala PAM/Peptide 3.65 µMa ND (Wang et al., 2006; Ohsu et al., 2010) γ -Glu-Val PAM/Peptide 1.34 µMa ND (Wang et al., 2006; Ohsu et al., 2010) γ -Glu-Cys PAM/Peptide 0.45 µMa VFT (Ohsu et al., 2010; Wang et al., 2006) γ -Glu-α-aminobutyryl-Gly (Opthalmic acid) PAM/Peptide 0.018 µMa ND (Ohsu et al., 2010) NPS2143 NAM 0.0003 (IC50) TMD (Gowen et al., 2000; Petrel et al., 2004) Calhex 231 Mixed PAM/NAM 0.1–1 µM (PAM); 3–10 µM (NAM) TMD (Petrel et al., 2003; Petrel et al., 2004; Gregory et al., 2018) Where VFT, venus flytrap domain; TMD, transmembrane domain; ND, not determined. a shows EC50 value. TABLE 8. Kokumi sensation receptor agonists, allosteric modulators with concentrations used in cell-based assays. More recently, kokumi peptides have been found to have an extraoral physiological role in the gastrointestinal tract where they stimulate the secretion of hormones (cholecystokinin and glucagon-like peptide1 by activating CaSR (Depoortere, 2014; Yang et al., 2019). However, future studies with tissue-specific deletion of CaSR in taste buds would help delineate its role in taste physiology. CaSR involvement in taste is a relatively recent discovery, but its central role in extracellular calcium homeostasis in mammals is well recognized (Brown et al., 1993; Brown, 2013). Diverse ligands activate CaSR, including cations (Ca2+ and Gd3+), peptides, polyamines (Brown and MacLeod, 2001), and amino acids (Conigrave et al., 2000; Conigrave and Hampson, 2006) (Table 8). Unlike other taste modalities (sweet, bitter, and umami), CaSR–ligand binding and recruitment of G protein results in the activation of an intricate, amplifying signaling network that initiates numerous intracellular functions. The functional diversity of CaSR results from its ability to activate multiple Gα proteins (Gq/11, Gi/o, G12/13, and Gs) (Magno et al., 2011; Conigrave and Ward, 2013) which subsequently affect multiple signaling pathways related to the pathophysiology of parathyroid hormone secretion, cancer, and metastasis (Kelly et al., 2007; Wettschureck et al., 2007; Mamillapalli et al., 2008). Kokumi substrates activate CaSR and transmit their signal through Gαq/11 proteins which further activate PLCβ that results in the release of intracellular Ca2+ stored through activation of IP3 receptor channels in the ER. Whether the kokumi pathway strictly relies on Gαq/11 protein or can also use Gα-gustducin, like other taste modalities for downstream signaling, is still unknown (Figure 3). The growing number of reports on kokumi flavor signal transduction are shedding light on its potential use as a flavor enhancer. Structural, Molecular, and Conformational Changes of Kokumi Receptor CaSR belongs to the class C GPCR. Within this class, CaSR and metabotropic glutamate receptors (mGluRs) are known to function as disulfide-linked homodimers (Bai et al., 1998; Ward et al., 1998; Pidasheva et al., 2006) (Figure 4A). Structurally, the human CaSR is similar to sweet and umami taste receptors but differs in being a homodimer instead of a heterodimer (Hendy et al., 2013). The ECD of CaSR not only senses nutrients (Ca2+, L-Phe, and polypeptides; Table 8) and allows ligands to modulate CaSR cooperatively, but is also required for its dimerization (Ray et al., 1999; Zhang et al., 2014). The binding of Ca2+ and other ligands to the ECD changes the conformation of the seven transmembrane domains, causing alterations in the intracellular loops and the intracellular domain (ICD), which further trigger downstream signaling pathways (Brown et al., 1975). The ICD is relatively diverse among species and participates in controlling CaSR signaling in multiple ways by modulating receptor expression, trafficking, and desensitization (Gama and Breitwieser, 1998; Ward, 2004; Huang et al., 2006). Homology modeling, mutagenesis, and heterologous expression revealed distinct and closely located binding sites for Ca2+ and aromatic L-amino acids, in VFT and the cleft of the VFT, respectively (Silve et al., Conigrave et al.,2000; Huang et al., 2009). Notably, four putative Ca2+ binding sites of varying affinity have been predicted in the VFT of the CaSR and in which the interaction between site 1 and the other three sites plays a central role in positive cooperativity in sensing Ca2+ (Zhang et al., 2014). Besides Ca2+, aromatic L amino acids (L-Trp, L-Phe) also activate the CaSR by binding adjacent to the VFT region through three serine and one threonine residue (S169/S170/S171/T145). Interestingly, the double mutation T145/S170 was found to selectively impair L amino acid (Phe, Trp, His) sensing of CaSR, while Ca2+ sensing remained intact (Mun et al., 2004; Mun et al., 2005). The recent crystal structure of the entire extracellular domain of CaSR (Geng et al., 2016) identified four novel Ca2+ binding sites in each protomer of the homodimer including one at the homodimer interface which does not correspond to any of the sites reported previously by Huang et al., (2007). It is unclear why these additional calcium-binding sites were not found in earlier studies. This might be due to the different expression systems used, crystallization conditions, and methods of analysis. The conditions of the more recent studies may have stabilized an active conformational state in which these calcium sites become available (Geng et al., 2016). Among these four Ca2+-binding sites, site 4 seems most relevant to receptor activation as it directly participates in the active CaSR conformation. Moreover, a previously reported natural mutation G557E (Hendy et al., 2009) reduced the potency of Ca2+ possibly by affecting backbone conformation, thereby weakening the affinity of Ca2+ for this site. This confirms that a Ca2+ ion at site 4 stabilizes the active conformation of the receptor by facilitating homodimer interactions between the membrane-proximal LBD2 region and CRD of CaSR. The most interesting aspect of Ca2+ and L-amino acid interplay was reported by Zhang et al. (2014) who studied L-Phe binding characteristics by monitoring intracellular [Ca2+]i oscillations in living cells and performing molecular dynamic simulations. Their findings supported a previous observation that the L-Phe binding pocket is adjacent to the Ca2+ binding site 1. Importantly, by binding to this site, L-Phe influences all Ca2+ binding sites in the VFT region and enhances CaSR functional cooperativity through positive heterotropic cooperativity to Ca2+. Moreover, the dynamic communication of L-Phe at its predicted binding site in the hinge region with the Ca2+ binding sites not only influences the adjacent Ca2+ binding site 1 but also globally enhances cooperative activation of the receptor in response to alterations in extracellular Ca2+. The crystal structures (Geng et al., 2016) of the entire ECD region of CaSR in the resting and active conformations have provided additional information about the dynamics between calcium and L-amino acid binding (Geng et al., 2016). Most importantly, by using L-Trp, the study provided direct evidence that L-amino acids are CaSR co-agonists, and they act concertedly with Ca2+ to achieve full receptor activation. Several lines of evidence support this contention: 1) L-Trp binds at the interdomain cleft of the VFT, which is a canonical agonist-binding site for class C GPCRs (Kunishima et al., 2000; Muto et al., 2007; Geng et al., 2016) and shares a common receptor-binding mode with the endogenous agonists (amino acids or their analogs) of mGluR and GABAB receptors, (Kunishima et al., 2000; Tsuchiya et al., 2002; Muto et al., 2007; Geng et al., 2016). 2) L-Trp interacts with both LBD1 and LBD2 in ECD to facilitate its closure, a crucial first step during CaSR activation. In contrast, no Ca2+ ion is found at the putative orthosteric agonist-binding site to induce domain closure. 3) Mutations of L-Trp-binding residues (S147A, S170A, Y218A, and E297K) severely reduced Ca2+ induced IP accumulation and intracellular Ca2+ mobilization (Zhang et al., 2002; Silve et al., 2005), indicating that L-Trp is required for a Ca2+ induced receptor response. Notably, the presence of extracellular Ca2+ above a threshold level is required for amino-acid-mediated CaSR activation, amino acids increase the sensitivity of the receptor toward Ca2+. Taken together, amino acids and Ca2+ ions act jointly to trigger CaSR activation. Knowing that aromatic L-amino acids (Trp, Phe, His) are important tastants in kokumi flavor, CaSR becomes more relevant for taste biology. Moreover, the kokumi tripeptide, glutathione (GSH), and glutamyl peptide are suggested to bind allosterically to CaSR at the same site as L-amino acids (Wang et al., 2006; Broadhead et al., 2011) and enhance its activity in the presence of 0.5–1 mM free calcium, thereby acting as a positive allosteric modulator. In addition, an ECD crystal structure might help to explain the structural and molecular details of the GSH binding pocket such as the nature of critical residues and their binding characteristics. Given recent reports of calcium emerging as a taste modifier, it would be worth investigating how GSH and Ca2+ operate in kokumi human perception. Allosteric Modulation of Calcium-Sensing Receptor Classically CaSR is known to be involved in the pathophysiology of parathyroid and renal-related diseases by sensing calcium ions in the extracellular fluid (Brown, 2007). Research on related therapeutic applications has identified several classes of PAMs and NAMs that modulate CaSR agonist sensitivity. More recently this has been applied to kokumi taste signal transduction. Endogenous Modulators (L-amino Acids, Anions, and Glutathione Analogs) Several studies based on molecular modeling and mutagenesis report L-amino acids (L-Phe, L-Tyr, L-His, and L -Trp) as PAMs because they enhance the Ca2+-induced response of CaSR. Aromatic L-amino acids bind in the VFT domain (Mun et al., 2004) and require a highly conserved five residue binding motif (S147, S170, D190, Y218, and E297) (Conigrave and Hampson, 2006; Geng et al., 2016). Among these residues, E297 was identified through the natural mutation E297K as essential for structural and functional activity (Table 8) (Pollak et al., 1993; Bai et al., 1998; Conigrave et al., 2000; Zhang et al., 2002; Mun et al., 2004). As recently identified NAMs, anions SO42 and PO43 are important modulators of the Ca2+-induced response. They bind in the VFT region and act as moderate NAMs for CaSR activity (Geng et al., 2016; Centeno et al., 2019). Based on anomalous difference maps, four anion-binding sites were identified in the inactive and active CaSR ECD structures. Sites 1 and 3 are located above the interdomain cleft in LBD1, while site 4 lies in the LBD2 region. Sites 1 and 3 appear to stabilize the inactive conformation while site 2, which is present in both active and inactive conformations appears important for receptor function as mutations in its residues (R66H, R69E, and S417L) abolished the Ca2+-induced response. In addition, each protomer structure contains one Ca2+ ion and three SO42 ions which together contribute to the structural integrity of the receptor (Geng et al., 2016). Taken together, anions along with Ca2+ and amino acids are involved in an intricate interplay for CaSR activation to maintain conformational equilibrium between inactive and active states. As positive allosteric modulators, γ glutamyl peptides including glutathione (γGlu-Cys-Gly) and its analogs (Table 8) are predicted to have overlapping binding sites with L-amino acids in the VFT region (Wang et al., 2006; Ohsu et al., 2010; Broadhead et al., 2011). Kokumi peptides that activate CaSR resemble amino acids in having free α-amino and free α-carboxylate groups because they contain both amide bond formation between the γ-carboxylate group of L-glutamate and the α-amino group of its neighboring Cys residue. However, compared to amino acids, glutathione analogs have much larger side chains and are more potent activators of CaSR (Wang et al., 2006). Nonetheless, free sulfhydryl is not required for CaSR activation (Ohsu et al., 2010; Maruyama et al., 2012). The crystal structure of ECD enables mapping of the GSH binding site and investigation into how GSH binding works in synergy with Ca2+ to modulate the kokumi sensation. NPS2143, the sole kokumi NAM identified to date has been reported to inhibit kokumi taste sensation to GSH and its analogs which provides an opportunity to screen for novel kokumi-enhancing molecules in a cell-based assay. Synthetic Drugs as Allosteric Ligands of Calcium-Sensing Receptor Because of its pathophysiological importance, various synthetic PAMs and NAMs of CaSR have been identified and are in clinical use. The allosteric modulation of CaSR by synthetic drugs has been recently reviewed (Hannan et al., 2016; Chavez-Abiega et al., 2020; Leach et al., 2020). Since the 1990’s the terms calcimimetics and calcilytics, have been used for drugs that mimic or antagonize the effect of extracellular Ca2+ on CaSR activity, respectively. Pharmacologically, a calcimimetic activates the CaSR and includes agonists (type I) and allosteric ligands (type II). Most type I calcimimetics are either inorganic or organic polycations (e.g., Mg2+, Gd3+, neomycin), whereas type II calcimimetics are small naturally occurring molecules (aromatic amino acids or GSH) or synthetic drugs and peptides (NPS R-568, cinacalcet). Type II calcimimetics (like aromatic amino acids) bind in the ECD while others (e.g., NPS R-568, NPS R-467) bind in the TMD of the CaSR. Calcilytics are thus small organic molecules that appear to act as NAMs and bind in the TMD of the receptor (Widler, 2011; Nemeth, 2013). Homology modeling and mutational studies show that both PAMs and NAMs have overlapping but non-identical binding sites in TMD and can partially allosterically modulate CaSR activity in the complete absence of the ECD, but their potencies vary among structurally different compounds (Collins et al., 1998; Ma et al., 2011) (Table 8). Several residues reportedly critical for allosteric modulation, W8186.48, F8216.51 (TMD6) and E8377.39, I8417.43 (TMD7), R6803.28, F6843.32, F6883.36 (TMD3) impair calcimimetic and calcilytic induced CaSR signaling (Miedlich et al., 2004; Petrel et al., 2004; Leach et al., 2016). Nevertheless, subtle differences in ligand–receptor interactions drive negative vs. positive modulation of CaSR signaling, by NPS2143 or cinacalcet and NPSR-568, respectively (Miedlich et al., 2004; Leach et al., 2016; Keller et al., 2018). The details of CaSR allosteric modulation by synthetic drugs is out of the scope of the current review, for a comprehensive explanation refers to these studies (Chaves-López et al., 2014; Hannan et al., 2016; Leach et al., 2020). Conclusion Taste GPCR research has advanced rapidly over the past two decades providing a more thorough understanding of receptor molecular pharmacology and signal transduction pathways. Except for the kokumi receptor ECD, high-resolution crystal structures for any taste receptor would be a major step toward designing novel and potent surrogate taste receptor ligands and selective antagonists. This has been a challenge due to low taste GPCR functional heterologous expression, appropriate post-translational modifications, high conformational flexibility, and low detergent stability. However, significant advancements in structural biology technologies of serial femtosecond crystallography using X-ray free-electron lasers and high-resolution cryo-electron microscopy provide promising tools for understanding conformational dynamics and visualizing the process of receptor activation with high spatial and temporal resolution. The physiological relevance of taste GPCRs will be further advanced through in vivo studies to help provide information on potential synergies in taste signal transduction mechanisms, particularly among bitter, umami, sweet, and kokumi receptors.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.19%3A_Signal_Transduction_-__Temperature.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry This chapter section is taken in entirety from: Ion Channels and Thermosensitivity: TRP, TREK, or Both? Lamas et al. Int. J. Mol. Sci. 2019, 20(10), 2371; https://doi.org/10.3390/ijms20102371. Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Introduction Mammals and other animals spend large amounts of energy in maintaining a nearly constant body temperature, irrespective of the temperature of the environment. The mechanisms controlling thermal regulation are complex and often rely on negative feedback, where it is first necessary to determine the body and ambient temperature. The temperature of the environment can be sensed by external receptor cells, mainly located in the skin, whereas body temperature is sensed by internal receptors expressed by cells located in several internal organs. Traditionally, only the skin and core thermoreceptors (spinal cord, hypothalamus) have attracted the attention of researchers, but more recently, some very interesting information has emerged regarding visceral thermal receptors, even in humans. Although a hypothesis conceived many years ago, the terminals of receptor neurons are thought to contain branches of nerve fibers without any apparent structural specialization. Indeed, only recently have we begun to understand the molecular basis of thermoreception by cells. Many biochemical processes like chemical reactions, and physical processes like conformational changes, are extraordinarily dependent on temperature, and although these processes generally occur faster at higher temperatures, the relationships can be very complex [3]. If we consider the nervous system (NS), the effects of temperature on the resting membrane potential (RMP) were the first to be studied, as were its effects on the kinetics and speed of compound and single action potentials, long before the existence of ion channels was demonstrated. All neurons and ion channels are affected by changes in temperature, not least because channel gating is generally a temperature-dependent process. However, only some neurons can be called thermoreceptors and very few ion channel types can be designated as thermosensors. In general, only channels with a temperature coefficient (Q10) ≥2–5 are considered temperature dependent. (Q10 is the ratio of a reaction at two different temperatures that differ by 10o C. See Chapter 32.11 for more details.) Thermoreceptors are sensitive to changes in temperature rather than to the value of the temperature itself, probably due to their characteristic strong adaptation. These receptors are classified into two groups depending on whether their discharge frequency increases when they are heated or cooled (Figure 1). Based on this classification, it is common to speak of four thermal sensations (cold −10 to 15 °C, cool 16–30 °C, warm 31–42 °C and hot 43–60 °C), whereby cold and hot are potentially noxious and/or painful [11,12]. The modulation of TWIK-related potassium (TREK) channels by temperature has been touched on in several reviews, yet very few have dealt exclusively with this exciting topic. Conversely, after transient receptor potential (TRP) channels sensitive to temperature were discovered, they were studied extensively to understand how thermal stimuli were transduced. Such interest led to the appearance of good reviews covering this issue. In this review, we will focus on the less well-known role of TREK channels in thermosensation, and we will compare the behavior of these channels to that of TRP channels. Other thermosensitive proteins have also been described, like the Na/K ATPase and ENaC channels, or P2X receptors, and while these should also receive attention, we consider this to fall beyond the scope of this review. Indeed, cell thermosensitivity seems to be governed by the interplay of a number of channel types, as reported in hypothalamic neurons. TREK Channels The TWIK-related potassium channel (TREK) subfamily belongs to the two-pore domain potassium channels family (K2P) and is comprised of three members: TREK1, TREK2, and TRAAK (TWIK-related arachidonic acid-activated potassium channel). These are background potassium channels characteristically modulated by several physical and chemical stimuli, such as membrane stretch, pH, unsaturated fatty acids, general anesthetics, and temperature. In general, TREK channels display very weak activity at room temperature and normal pressure, even when overexpressed in heterologous systems. However, their activity increases strongly when a number of different stimuli are applied, including an increase in temperature. From a physiological point of view, it is important to note that at 37 °C, all three members of the TREK subfamily respond to stimuli (pH, membrane stretch, or arachidonic acid), much like they do at room temperature. TREK channels may fulfill a dual role in the transmission of thermal pain. Thus, their strong activation by noxious heat results in an outward current that provokes membrane hyperpolarization and a reduction of thermoreceptor firing, provoking heat-pain relief. Conversely, inhibition of TREK channels by noxious cold should depolarize thermoreceptors and increase their excitability, cooperating in the transduction of noxious cold sensations (see Figure 2). TREK1 Soon after their discovery, it was shown that TREK1 channels are strongly and reversibly activated by an increase in temperature when expressed in heterologous systems (cell lines derived from kidney (COS) cells, oocytes). If we consider that these are mostly voltage-independent channels open at resting potentials, TREK1 channels should function as cold sensors because low temperatures would dampen their activity and depolarize these thermoreceptors (see Figure 2). Many authors have demonstrated that macroscopic TREK1 currents are strongly outwardly rectifying at room temperature. While the outward current is not evident at 12 °C, it is strongly enhanced at 37 °C, and the current progressively increases as the temperature gradually augments. Indeed, the current increases around 7-fold with an increase of 10 °C in the range of 14 to 42 degrees, and importantly, maximal sensitivity (0.9-fold per degree) was reached at nearly physiological temperatures, between 32 and 37 °C. The current induced by heating is also outwardly rectifying, and it reverses at potentials close to the equilibrium potential for potassium (EK). In heterologous systems, the activation of TREK1 by temperature may be reversibly inhibited by cAMP, and this inhibition is suppressed by mutation of the C-terminal region that harbors a phosphorylation site for protein kinase A (PKA). Moreover, chicken embryonic atrial myocytes express TREK-like currents, and they have a resting membrane potential of around −20 mV in culture, which increases to −70 mV when the temperature rises to 35 °C, a change that was ascribed to the activation of TREK1/2 channels. In voltage-clamp, the outward current recorded at +60 mV increased 9-fold. Both TREK1 and TWIK-related arachidonic acid-activated potassium (TRAAK) channels have been proposed to shut down the firing of hippocampal neurons when the temperature rises too high. Figure $\PageIndex{a}$ shows an interactive iCn3D model of the mouse temperature sensitive K2P2.1 (TREK-1) potassium channels (6W84) The red dots represent the outer leaflet, The gray spheres are potassium ions. The threshold for the activation of slowly conducting C-fibers by noxious heat (30–50 °C) recorded in a skin-nerve preparation decreases in TREK1 KO mice, and the range of activation of these fibers by heat corresponds closely to the range in which TREK1 is activated (30–45 °C). The number of action potentials in response to a heating ramp (30–50 °C) was higher in the KO mice, although the response of C-fibers to a cooling ramp (32–10 °C) was similar in native and KO mice. Indeed, TREK1 KO mice were hypersensitive to thermal pain up to 50 °C but not at higher temperatures (52–56 °C), indicating that TREK1 channels may be important for the perception of low-threshold but not high-threshold thermal stimuli, for which TRP channels may be more important. Accordingly, the proportion of small diameter, cultured, dorsal root ganglia (DRG) neurons that respond to noxious heat (34%) increases in TREK1 KO (64%) and TREK1/TRAAK KO (74%) mice, as does the proportion of heat-responsive C-fibers in nerve-skin preparations from the single and double KO. TREK1 and TRAAK channels may counteract the stimulatory effect of heat-activated TRP channels in pain-transducing fibers when temperatures increase, such that the overall response may reflect a balance of the activity of these two functionally contrasting channel types. The threshold of thermoreceptors should certainly increase in the presence of TREK channels as temperatures increase. Cooling of DRG neurons in culture from 32 to 20 °C induces a depolarization of about 10 mV and often the firing of action potentials, an effect shown to be due to the inhibition of a background potassium current. Accordingly, the inhibition of a native TREK1-like current may underlie the excitation (depolarization and firing) produced by cold in small, cultured, trigeminal ganglion (TG) neurons (see Figure 2). Interestingly, cold induces subthreshold oscillations in cold-sensitive DRG neurons. Transduction seems to be rather complex, involving the dampening of a hyperpolarization-activated cationic current and a permissive role for a slowly inactivating potassium current. Interestingly, the TREK1/TRAAK double KO mutant shows a consistent cool allodynia (pain due to a stimulus that does not normally provoke pain), and oxaliplatin, a cancer therapy that causes peripheral nerve neuropathy, exacerbates cold sensitivity in many patients and animals, inducing allodynia to cool temperatures. Neither the deletion of TREK1 nor TRAAK increases the fraction of small DRG neurons sensitive to noxious cold stimuli (below 20 °C and down to about 10 °C), although the TREK1/TRAAK KO and the triple TREK1/TREK2/TRAAK KO showed a significant increase in such neurons. Similar results were obtained when recording C-fibers in a skin-nerve preparation, in which case the double KO C-fibers fired more strongly than the single TRAAK KO and wild-type fibers. Oxaliplatin also induced hypersensitivity to noxious cold temperatures, while double and triple KO mice but not the TREK1 KO mice are hypersensitive to cold, which is not further affected by oxaliplatin. Hence, the deletion of two of the three TREK channels appears to be sufficient to reach maximal hypersensitization. The neuroprotective agent riluzole induces an analgesic effect against painful cold in normal animals, but also in oxaliplatin-pretreated TREK2 KO and TRAAK KO animals. However, riluzole did not affect pain sensitivity in TREK1 KO animals treated with oxaliplatin, in animals treated with the TREK1 inhibitor spadin, or in untreated TREK1 KO mice or triple KO animals. Similarly, a presumed TREK leak outward current recorded in DRG neurons was inhibited by riluzole and fluoxetine at 22 and 30 °C but not at 14 °C, probably because the current was already inhibited at low temperatures. Together, these experiments suggest that TREK1 channels fulfill an essential role in the perception of noxious cold and that TREK1 and TRAAK channels work together in sensing cold. Cell-attached patches demonstrated that the basal activity of expressed TREK1 channels is insignificant at room temperature, gradually increasing as the temperature rises (17-fold for an increase of 20 °C) and with a threshold around 25 °C. The current activated by temperature also displays outward rectification and reverses around the equilibrium potential for K+ [31], although the single-channel conductance remains unaffected TREK1-like channels naturally expressed in cardiac ventricular myocytes and DRGs and recorded in cell-attached patches, do not open at 24 °C, yet they are very active at 37 °C Surprisingly, temperature increases fail to modulate TREK1 activity in outside-out and inside-out patches, but under the same conditions, TREK1 is still strongly activated by arachidonic acid TREK1 channels are ideally positioned to act as thermosensors because they are expressed in structures related to thermosensitivity and thermoregulation such as DRGs, the TG, nodose ganglia (NG), or the anterior and preoptic hypothalamus . TREK2 Heterologously expressed TREK2 channels also produce strong outward rectification when recorded in whole-cell configuration at room temperature, which increases greatly at temperatures around 37 °C in several heterologous systems In COS cells, a small TREK2 current was observed at 0 mV that augmented progressively with a gradual rise in temperature to about 40 °C. Notwithstanding, the response of TREK2 to abrupt changes in temperature was rapid. Importantly, the IVs of the TREK2 current at different temperatures (24 and 37 °C) showed that the effect of temperature was not voltage-dependent: both inward and outward currents increased to the same degree. In this range of temperatures, the current increased 14-fold per 10 °C, indicating a very strong temperature dependence that was even bigger than that of TREK1. Much like TREK1, TREK2 responds to temperature changes around the physiological range, with current activated reasonably well at 37 °C and at resting membrane potential (RMP). Most experiments on TREK channels have been carried out at room temperature and at 0 mV. However, in the future these currents should be investigated using more physiological parameters, around a resting potential and 37 °C, providing a more precise idea of their role in the behavior of central neurons Figure $\PageIndex{b}$ shows an interactive iCn3D model of the human two-pore domain temperature-sensitive potassium ion channel TREK2 (K2P10.1)(4BW5) Cerebellar granule and DRG neurons expressed native TREK2-like channels with weak activity at 24 °C in cell-attached patches, yet when the temperature increased to 37 or 41 °C they became very active at all voltages (−80 to +80 mV). Moreover, cultured cortical astrocytes have TREK2-like whole-cell outward currents that are strongly enhanced in the temperature range of 23–40 °C. Interestingly, ischemia significantly augmented the outward current provoked by an increase in temperature in these astrocytes In addition, it was recently reported that TREK2 channels contribute about 10 mV to the RMP of DRG neurons at about 30 °C Furthermore, single TREK2 and triple TREK1/TREK2/TRAAK KO mice were more sensitive to warm temperatures (40–42 °C) when tested with the tail-flick reflex. Using a skin-nerve preparation, it was demonstrated that the proportion of heat-sensitive C-fibers and their activity (the number of action potentials) increased in the TREK2 and triple KO mice when temperatures rose to noxious heat levels (ramped from 30 to 50 °C), whereas the temperature threshold for firing decreased At high temperatures (between 40 and 50 °C), the triple but not the single KO fibers were more active than their wild-type counterparts, indicating that TREK2 regulates C-fiber responses at temperatures below 40 °C, while at higher temperatures other TREK channels participate in these responses. Both KOs suffered hyperalgesia at temperatures around 45 °C, but only the triple KO showed the same behavior above this temperature. The withdrawal latency in the tail immersion test was reduced in both the TREK2 KO and the triple KO mice when innocuous cooling temperatures were tested (20–25 °C). As such, the KOs show enhanced sensitivity to temperatures in the normal range, and similar results were obtained in a temperature preference test. The percentage of C-fibers responding to moderate cold (30–21 °C) was higher in single and triple KOs when compared to those recorded from the nerve-skin preparation of wild-type mice. Interestingly, the cold threshold for C-fiber firing (21 °C) was lower in the triple KO (24 °C) but not in the TREK2 KO (23 °C) mice [63]. Moreover, oxaliplatin induces mice to spend more time on a hot plate (30 °C) than on a cold plate (20–25 °C) when compared to untreated animals, indicating that neuropathic mice have enhanced sensitivity to moderate cold [63]. It has been suggested that TREK2 is implicated in the neuropathic hypersensitivity induced by this drug and indeed, oxaliplatin almost halved the TREK2 mRNA detected in DRG neurons. Generally, the data suggest that TREK2 channels may be essential for the control of the C-fiber response to cold at moderate temperatures. The tail immersion test showed that triple KO mice were hypersensitive to noxious cold temperatures (15–5 °C), while the single TREK2 KO mice behaved much like the wild-type mice. Moreover, very similar results were obtained in the nocifensive dynamic cold plate test. Accordingly, it was suggested that TREK2 may not be important in noxious cold sensitivity but that it might be essential for thermoreception at moderately cool temperatures (25–20 °C). A clear, fast, and reversible increase in activity was also reported for single TREK2 channels in cell-attached patches held at −40 mV when the temperature increased (24 to 37 °C), with a threshold for this increase at 25 °C (from 24 °C) and not affecting the conductance. It should be noted that in these circumstances, the activity of TREK2 single channels was very low at 24 °C. Significantly, neither TASK3 nor TRESK2 showed such dependence on temperature. However, like TREK1, the activity of TREK2 in inside-out patches was not modified by changes in temperature (24 to 42 °C). Finally, it is important to consider that TREK2 channels are expressed strongly in the DRG, TG, and hypothalamus, yet less than TREK1 in the NG. TRAAK Much like the other members of the family, TRAAK currents showed a strong open-channel outward rectification when recorded in whole-cell configuration, and these currents increase strongly when the temperature rises (24 to 42 °C). Moreover, the percentage of small-diameter DRG neurons responding to noxious heat, in culture, is increased in TRAAK and TRAAK/TREK1 KO mice. Consistently in skin-nerve preparations, the percentage of fibers responding to heating (30–50 °C) and the number of action potentials in response to a heating ramp also clearly increases, while the firing threshold is reduced. Notably, TRAAK and TRAAK/TREK1 KO mice suffer heat hyperalgesia when evaluated in the tail immersion test in the 46–50 °C range. Moreover, the double but not the single KO also shows hypersensitivity at higher temperatures (52–56 °C) in the hot plate test. Knock-out of TRAAK did not modify the percentage of DRG neurons in culture that respond to noxious (12 °C) cold. Moreover, in the cold plate assay, TRAAK KO mice behave like wild-type mice, whereas TREK1/TRAAK KO mice are more sensitive to cooling in the 10 to 20 °C range. The activity of single TRAAK channels heterologously expressed in COS cells and recorded in cell-attached patches at −40 mV was very low at 24 °C, yet it increased progressively as the temperature rose from 24 to 37 °C. The threshold for activity was around 30 °C, slightly higher than that reported for TREK1 and TREK2. However, the behavior of TRAAK channels in inside-out patches mimics that of TREK1 and TREK2 such that their activity was not affected by changes in temperature (from 24 to 42 °C). Native TRAAK-like channels in DRG neurons displayed little activity at room temperature, but there was clear activity in all cell-attached patches at 37 °C [36, 48]. Finally, TRAAK channels are clearly expressed in the hypothalamus, TG, and DRG , yet they are only weakly expressed in the NG. Molecular Origin of Thermosensitivity When first discovered, mouse TREK1 was reported to have four transmembrane segments, two pore domains, and a sequence of 370 aa [37]. The activation of heterologously expressed TREK1 currents by increasing temperature is unaffected by the deletion of the cytoplasmic N-terminal region. By contrast, partial deletion of the C-terminal region (Δ103) or replacement of this region with that of TASK1 strongly dampens the activation of TREK1 by heat. The sensitivity of the TREK1 channels to temperature can be eliminated by mutating helix 1 of the pore (G137I), suggesting that temperature affects the TREK1 and TREK2 channels by manipulating the C-type gate. It was suggested that functional coupling between the C-terminal domain and the C-type gate through the M4 segment is crucial for the heat sensitivity of the TREK1 channel. Thus, it is tempting to speculate that increasing the affinity of the C-terminal domain for phospholipids of the inner leaflet would increase the activity of TREK1 by heat, as proposed for other stimuli like stretch, PUFAs, phospholipids, or pH. Conversely, dissociation of this domain from the membrane would result in TREK1 inhibition. Surprisingly, the replacement of the C-terminus of TREK2 with that of TASK3 did not reduce the sensitivity of the channel to changes in temperature in the range of 24 to 37 °C under similar conditions, although it became insensitive to pH and arachidonic acid. Heat enhances the activity of TREK1, TREK2, and TRAAK in whole-cell and cell-attached recordings, yet not in outside- and inside-out patches, indicating that the integrity of the cell, and probably also a second messenger, are necessary for this modulation. The contribution of TREK channels to maintaining the RMP has often been questioned; however, this assertion is mostly based on experiments carried out at room temperature. Thus, new experiments should be performed at physiological temperatures to ascertain the role of these channels on both the RMP and neuronal excitability. TRP Channels Six transient receptor potential (TRP) channels are considered thermosensors, four of them responding to heat and two to cool. Temperature-sensitive TRP channels (Thermo-TRP) are extremely dependent on temperature, showing very high Q10 values (>20). Heat-Sensitive TRP Channels Four TRP subtypes are activated by an increase in temperature (Figure 1). Two of them respond to warm stimuli (TRPV4 Warm >27 °C and TRPV3 Warm >34 °C), and the other two to hot-painful stimuli (TRPV1 Hot >43 °C and TRPV2 Hot >52 °C). TRPV1s are voltage- and temperature-dependent channels that display outward rectification when expressed in human embryonic kidney (HEK) cells and that is strongly enhanced by heating (to 48 °C) and by capsaicin. Figure $\PageIndex{c}$ shows an interactive iCn3D model of human TRPV1 with capsaicin at 48 degrees Celsius in an open state (7LPE) At room temperature, the current passing through these channels is negligible below 0 mV, but at 42 °C the channel activates more or less between −100 and +50 mV. These cationic channels are ten times more permeable to Ca2+ than to Na+ (PCa/PNa ~10) and are thought to be sensors for noxious heat but not activated by innocuous heat. Indeed, the response to noxious heat in mice lacking TRPV1 (KO) or DRG neurons was clearly weaker, although other channels may also contribute to the perception of noxious thermal stimuli because heat still evokes receptor activation in several preparations. The NG sensory neurons that innervate the lungs produce an inward current in response to an elevation in temperature (from 23 to 41 °C, with a threshold around 35 °C and a Q10 of about 30 in the range of 35–41 °C) as well as membrane depolarization and action potential firing. This response was ascribed to the presence of TRPV1 channels, even though the participation of TRPV2-4 could not be ruled out. We obtained similar results with NG neurons in culture, although these were slightly more complex because a hyperpolarization was observed before the depolarization and firing (unpublished data). It is interesting to note that inflammatory mediators like ATP and bradykinin strongly reduce the threshold of TRPV1 activation (30 °C) such that warm temperatures become painful. TRPV1 is strongly expressed in small-diameter sensory neurons of the DRG, TG, and NG, but also in the hypothalamus, sites where they may exert an important role in thermoreception. TRPV2 is activated at extremely high temperatures (52 °C), although it is not affected by capsaicin and shows an outwardly rectifying IV curve and a PCa/PNa ~3 . This channel has a Q10 of around 100, and it is thought that the temperatures that activate TRPV2 are more harmful than those that activate TRPV1. These channels are strongly expressed by myelinated medium-large diameter DRG neurons (Aδ and Aβ), as well as in the hypothalamus and the NG. TRPV3 channels are activated at warm, close to hot, temperatures (around 34–39 °C, with a Q10 around 6), generating currents with pronounced outward rectification and a PCa/PNa ~12. They are capsaicin-insensitive channels but stimulated by camphor, and they are thought to be involved in thermosensation and thermal nociception. Indeed, it has been suggested that TRPV3 channels contribute more to the speed with which mice select a more comfortable temperature than to the choice of the value of the temperature itself. By contrast, TRPV4 channels are more likely to be involved in choosing the preferred temperature from a non-painful range. Interestingly, it was proposed that TRPV3 channels transmit thermal stimuli through skin keratinocytes, which in turn will transmit this information to sensory endings. TRPV3 channels are expressed in sensory DRG and NG neurons but also in the hypothalamus. Interestingly, they co-localize with TRPV1 in DRG neurons. TRPV4 are cationic (PCa/PNa ~6) channels activated at even lower warm temperatures (around 27 °C, with a Q10 of about 10), generating outwardly rectifying currents and responding dynamically to temperature changes in the physiological range. These channels were proposed to play a role in thermosensation and thermoregulation although some authors were unable to activate these channels by increasing the temperature. Similarly, some behavioral studies reported a reduced response to temperature changes in TRPV4 KO mice, a behavior that was less clear in other studies. Much like TRPV3, the expression of these channels in keratinocytes was proposed to play an important role in the transmission of thermal information, which probably contributed to the controversy generated. The sensitivity of this channel to temperature is lost in excised patches, suggesting that it requires a soluble intracellular factor TRPV4 channels are expressed in DRG, TG, NG, and preoptic/anterior hypothalamic neurons, although in the hypothalamus they seem to be expressed in terminals rather than in the soma, such that their role in body thermoregulation is unclear. TRPM2 (>35 °C), TRPM3 (>40 °C), TRPM4 (>15 °C), and TRPM5 (>15 °C) are channels that can also be activated by warming (Figure 1), yet they have received less attention, probably because it was initially thought that they were not expressed by somatosensory neurons or keratinocytes. TRPM2 is voltage-insensitive, shows a PCa/PNa ~1, activates at 35 °C, and has a Q10 of around 15. TRPM3 is expressed broadly, generating an outwardly rectifying current, having a PCa/PNa between 0.1 and 10, and activating at >40 °C with a Q10 of 7. It is important to say that TRPM3 has been described as part of a triad of TRPs, together with TRPV1 and TRPA1, involved in the transduction of acute noxious heat in mice. The combined ablation of these channels (triple KO) was necessary for the complete reduction of acute noxious sensing; single or double KO combinations resulted in deficits in heat responsiveness, but mice still conserved vigorous withdrawal responses to noxious heat. Heat activation of TRPM2 and TRPM5 was obtained in inside-out patches, suggesting a membrane-delimited mechanism. Interestingly, TRPM2 activation seems to result from the increase in the IV slope while that of TRPM4 and TRPM5 results from a shift of the activation curve to negative potentials. These last two channels are essentially not permeable to calcium. Cold-Sensitive TRP Channels Two TRP channels are activated by decreases in temperature (Figure 1), TRPM8 (<25 °C) activates in the cool range while TRPA1 (<18 °C) senses cold-painful temperatures. Similarly, cool fibers (Aδ and C) have activation thresholds at about 30 °C, and cold fibers (C) have activation thresholds <20 °C. Accordingly, two populations of TG neurons were described in terms of their activation threshold when temperatures decrease: 30 and 20 °C for a low and high threshold, respectively. In general, cold fibers fire continuously at normal skin temperatures and they increase their firing frequency when the skin is cooled down, or they shut down when the skin is warmed. In addition, cold fibers can adapt to small decreases in temperature. TRPM8 channels are voltage-dependent cationic channels that are permeable to Na+, K+, Cs-, and Ca2+ (PCa/PNa ~3). When expressed in HEK cells and recorded in the whole-cell configuration, they show a voltage-dependent outwardly rectifying current that strongly increases upon cooling from 30 to 15 °C or through the application of menthol. Figure $\PageIndex{d}$ shows an interactive iCn3D model of TRPM8 ion channel in complex with the menthol analog WS-12 and PI(4,5)P2 (6NR2) The methanol analog is shown in spacefill and CPK colors in the membrane bilayers. PI(4,5)P2 is shown in spacefill just below the lower cytoplasmic leaflet. The TRPM8 receptor is a Ca2+ cation channel. Cooling compounds like menthol and WS-12 depend on allosteric interactions and membrane phosphatidylinositol 4,5-bisphosphate (PIP2 ). Importantly, both basal and cold-stimulated currents reverse around 0 mV and were almost negligible below this potential. Cooling CHO cells expressing TRPM8 (in the range of 25 to 15 °C) also induces an increase in intracellular calcium, and the Q10 in the range of 25 to 18 °C is around 24. The effect of temperature is due to an increase in the open probability and a shift in the conductance–voltage relationships along the voltage axis [9]. Similar results were obtained in inside-out macropatch recordings, although the stimulation occurred at lower temperatures, suggesting that the integrity of the cell is important but not indispensable. The role of this channel as a detector of painful cold has been questioned in experiments on KO mice, but it is accepted that it is an important cold sensor in vagal, TG, and especially DRG afferents. It was predicted that cold transduction may require the activation and inhibition of several different ion channels (see Figure 3), such as TRP, TREK, and ENaC channels. If this were the case, TRP channels would probably be more important in the noxious-cold range, whereas TREK channels might participate more strongly in the cool range of temperatures (Figure 1). TRPM8 is expressed in small-diameter DRG and TG neurons, presumably thermoreceptors, yet it seems not to co-localize with TRPV1. TRPA1 is activated by lower temperatures than TRPM8 (<18 °C), and while it would be expected to be involved in cold nociception, this is not that clear. TRPA1 generates an outward rectifying cationic current, both in control conditions and when cold activated (about 10 °C), with similar permeability for Ca2+ and Na+ (PCa/PNa ~1). Cinnamaldehyde can selectively activate currents through this channel in native DRG neurons, as can bradykinin (when co-expressed with BK receptors), strongly suggesting a role in sensing nociceptive stimuli. However, TRPA1 KO mice do not seem to have difficulties in sensing cold stimuli through the skin, while the response of TRPM KO mice to cold is significantly dampened. By contrast, about 50% of NG neurons in culture were activated by cooling (<24 °C), mainly through TRPA1 channel activation (increase in [Ca]i, depolarization, and AP firing). Interestingly, about 10% of the NG neurons responded to cold through a TRPA1- and Ca-independent pathway. TRPA1 often co-localizes with TRPV1, and in fact, this could explain the paradoxical hot sensation experienced with an extremely cold stimulus. Interestingly, most NG neurons sensitive to cold are also sensitive to heat. TRPA1 is expressed in DRG and TGs, while TRPA1 and TRPM8 are not co-expressed in DRG neurons, but they are in TG neurons. In summary, the data available suggest that TRPA1 is the principal ion channel involved in cold sensation in visceral (NG) neurons, while TRPM8 would fulfill the same role in somatic neurons. Molecular Origin of Thermosensitivity The mechanism by which temperature modulates TRP channels is still unclear, yet several hypotheses have been proposed: (1) changes in temperature could produce a ligand that binds to a receptor and affects the channel; (2) changes in temperature could produce a structural change in the channel that provokes its opening; (3) temperature changes could affect the structure of the membrane, causing changes in tension that would, in turn, affect ion channels. Because capsaicin induces burning pain, it has been hypothesized that both capsaicin and heat may use a common mechanism to activate TRPV1 and produce pain. Both stimuli affect excised patches, and in general, it is accepted that TRPV1 is directly activated by noxious heat, so that it can be considered a true heat sensor. The fact that TRPM8 can be activated by cooling in inside-out patches suggests that the mechanism is membrane delimited, also arguing against the participation of a second messenger pathway. Notwithstanding, inhibition of phospholipase C strongly dampened the increase in calcium provoked by cold stimuli in TRPM8-expressing CHO cells. Cooling activates TRPM8-expressed channels by causing a shift in the voltage dependence of activation to negative values, and the same mechanism is responsible for the activation of TRPV1 by heatbut not the activation of TRPM2. It has been proposed that temperature induces large rearrangements of the protein and thus, the existence of a temperature-sensing domain or “temperature sensor” in the structure of TRPM8 channels. Much like for TRPM8, inhibition of phospholipase C strongly reduces the increase in calcium provoked by cold stimuli in TRPA1-expressing CHO cells. Conclusions There are several important differences between the two main types of thermosensor channels that have been reviewed in this article (see Figure 2). First, TREKs are potassium channels with negative reversal potentials, such that their activation would result in a reduction in thermoreceptor activity. By contrast, as the reversal potential of TRPs (cationic channels) is close to 0 mV, their activation will result in increased thermoreceptor excitability. Second, the three TREK channels appear to increase their open probability as temperatures increase, while there are two possible situations in the case of TRPs: one in which its activity increases by increasing the temperature; and another in which activity increases when the temperature decreases (Figure 2). Although the activation of these channels generates opposing effects on thermoreceptors (depolarization versus hyperpolarization), the information available to date regarding the participation of TRP and TREK channels in thermosensitivity strongly suggests that both types of channels collaborate and complement each other to generate the sensations of heat, cold, and thermal pain. In support of this hypothesis, TREK, and TRP channels are very often co-expressed in thermoreceptors and other sensory neurons TRP channels are generally accepted as the primary thermosensors; however, several lines of evidence indicate that other channels are necessary to explain the full plethora of mechanisms involved in thermosensation. TREK2 channels appear to be important in thermoreception at moderate temperatures and sensing innocuous cold but not aversive cold, while TREK1 and TRAAK acting together may be important in sensing painful cold.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.20%3A_Signal_Transduction_-__Pressure.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry This chapter section is taken in entirety from Fang, XZ., Zhou, T., Xu, JQ. et al. Structure, kinetic properties, and biological function of mechanosensitive Piezo channels. Cell Biosci 11, 13 (2021). https://doi.org/10.1186/s13578-020-00522-z Creative Commons Attribution 4.0 International License, http://creativecommons.org/licenses/by/4.0/. We added iCn3D molecular models. Introduction Mechanotransduction, the process by which mechanical stimuli are converted into electrochemical signals, is essential for various biological processes, including neuronal cell development, pain sensation, and red blood cell volume regulation. As pivotal mechanosensors of in the mechanotransduction process, mechanosensitive (MS) ion channels have been found in organisms from bacteria to mammals. Extensive studies have revealed a variety of ion channels in eukaryotic cells that can sense various forms of mechanical forces (Table 1). These ion channels include transient receptor potential (TRP) channels and voltage-gated Na+, K+, and Ca2+ channels, whose dysfunction may be associated with human genetic diseases. Notably, the MS candidates identified in invertebrates either have no homologs (e.g., TRPN) or no functional conservation (e.g., DEG/ENaC/ASIC) in mammals. Furthermore, most MS candidates (the TRP channel in particular) are activated not only by mechanical stimuli but also by chemicals, temperature, osmolarity, and heat (> 27–34 °C). Defining the molecular details of MS cation channels in mammals is therefore of paramount importance to understand the mechanotransduction process and find potentially novel therapeutic strategies for mechanosensitivity disorders. Channel family Channel isoforms Ref. TRP channels TRPA1 [6] TRPC1 [7] TRPC6 [8] TRPV1 [9] TRPV4 [10] TRPM4 [11] TRPM7 [12] TRPN [13] TRPP2 [14] K + channels Shaker (Kv1.1) [15] Ca2+-activated K+ (BK) [16] TREK1/2 [17] TRAAK [18] HCN2 [19] Na+ channels Nav1.5 [20] Ca2+ channels L-type [21] N-type [22] T-type [23] Cl channels CFTR [24] OSCA protein family ScCSC1, HsCSC1 [25] DEG/ENaC superfamily C.elegans MEC (MEC-4, MEC-10) [26] ASIC [27] Other channels TMC1/2 [28] In 2010, Coste et al. revealed a novel family of mechanically activated (MA) cation channels in eukaryotes consisting of Piezo1 and Piezo2 channels, which have been proposed as the long-sought-after MS ion channels in mammals. The Piezo1 channel is present in nonsensory tissues, with particularly high expression in the lung, bladder, and skin; by contrast, the Piezo2 channel is predominantly present in sensory tissues, such as dorsal root ganglia (DRG) sensory neurons and Merkel cells. Since their discovery, tremendous effort has been made to reveal the structures and biological functions of Piezo 1 and 2. The partial molecular structure of a Piezo channel was determined by cryo-electron microscopy (cryo-EM). Furthermore, Piezo channels have been linked to various pathological and physiological processes, including erythrocyte volume regulation, cell division, and innate immunity. Moreover, Piezo channel mutations are associated with multiple hereditary human diseases, such as autosomal recessive congenital lymphatic dysplasia, hereditary xerocytosis (a rare, autosomal dominant congenital hemolytic anemia characterized by macrocytic stomatocytosis, and decreased red cell osmotic fragility due to a defect in cation permeability), and an autosomal recessive syndrome of muscular atrophy with perinatal respiratory distress. Considerable progress has been made toward characterizing the structural features, physiological significance, and biophysical properties of Piezo proteins. Given the importance of Piezo channels in understanding mechanotransduction processes, this review focuses on their structural details, kinetic properties, and potential functions as mechanosensors. We also briefly review the hereditary diseases caused by mutations in the Piezo genes, which is key to understanding their functions. Structure of Piezo channels Piezo proteins have an uncommonly large predicted size of approximately 2500 amino acids and encompass numerous transmembrane (TM) regions. Subsequent research has revealed that the mouse Piezo1 (mPiezo1) channel is an evolutionarily conserved pore-forming ion channel directly gated by membrane stretch. Several published cryo-EM studies have revealed that mPiezo1 exhibits a three-bladed, propeller-shaped homotrimeric structure that includes a central cap, three peripheral blade-like structures on the extracellular side, three long beams on the intracellular side that bridge the blades to the cap, and a TM region between these features (Fig. 1). Figure 1: Cryo-EM structure of the mPiezo1 channel (adapted from Zhao et al.). a Multiple views of the sharpened map of the trimeric channel with the major domains labeled, with the three subunits colored red, green, and blue. b Cartoon model in which the three subunits are colored red, green, and blue. In the middle panel, the front subunit has been omitted to provide a better view of the curvature of the TMs Structure of the Piezo1 channel Unprecedented 38-TM topology Piezo channels are predicted to possess an unusually large number of TM regions, ranging from 10 to 40. Zhao et al. recently produced high-resolution structures of mouse Piezo1 (mPiezo1), revealing a unique 38-TM topology in each subunit (Fig. 2a, b). The two TM regions (TM37 and TM38) closest to the center of the protein are designated as the inner helix (IH) and outer helix (OH), respectively, and enclose the transmembrane pore of the central pore module. The other 36 TM regions (TM1-36) form a curved blade-like structure with nine repetitive folds containing 4 TM regions each, named transmembrane helical units (THUs) Figure 2: A 38-TM topology model and key functional sites in mPiezo1(adapted from Zhao et al.). a A model showing one subunit with individual THUs and featured structural components. Residues L1342 and L1345 in the beam are indicated by red spheres. b A 38-TM topology model color-coded to match the cartoon model in A Figure $\PageIndex{a}$ shows an interactive iCn3D model of the mouse mechanosensitive Piezo1 channel (5Z10) (long load time) Central cap Kamajaya and colleagues [48] employed topological prediction modeling and found that residues 2210 to 2457 in Piezo1 form an extracellular loop following the last TM region from the C-terminus, defined as the C-terminal extracellular domain (CED) (Fig. 1). The deletion of residues 2218 to 2453 from the Piezo1 protein abolished expression of the central cap, suggesting that this region trimerizes to form the central cap (Figs. 1 and 3). Further analysis revealed that the central cap consists of the CED in the form of a trimeric complex that encloses an extracellular vestibule (EV) with openings (Fig. 3). Anchor A hairpin structure, referred to as the anchor, connects the OH-IH pair to the C-terminal domain (CTD) plane, which moves the OH-CED-IH-containing region of one subunit into the neighboring subunit in a clockwise direction (Figs. 1 and 2). The anchor is made up of three helices (α1, α2, and α3). Helices α1 and α2 were found to organize into an inverted V-shaped structure, which maintains the integrity of the ion-conducting pore (Fig. 2b). In parallel with the membrane plane, the long α3 helix links to the OH via a lysine-rich anchor-OH linker that interacts with the polar residue-rich α2–3 turn in the anchor and the glutamate-rich region of the CTD. A few mutations in Piezo1 at locations including KKKK (2182-K2185), T2143, T2142 (T2127 in human Piezo1), R2514, E2523, and E2522, which are located in α3 in the anchor, have been reported to cause severe disease. Additionally, SERCA2, a Piezo-interacting protein, suppresses Piezo1 by acting on the anchor-OH linker. These findings support the structural and functional importance of the anchor region. The long intracellular beam On the intracellular surface, Piezo1 contains three beam-like structures 90 nm in length that are organized at a 30° angle relative to the membrane plane (Figs. 1 and 2). Residues H1300-S1362 form the beam structure. The large intracellular THU7-8 loop, which contains approximately 390 residues, might provide the beam with the structural basis for mechanical transmission. Functionally, the three long intracellular beams not only support the whole TM skeleton but also physically bridge the distal THUs to the central ion-conducting pore. When residues 1280 to 1360 (which form this beam structure) were deleted, the resulting mutant protein was absent, suggesting the structural importance of the beam. Highly curved blades The nine peripheral THUs in each subunit form blade-like structures, with each blade twisted clockwise (Fig. 1b). The proximal TM25–TM36 and peripheral TM13-24 interact at a 100° angle, as viewed from 90º relative to the plasma membrane plane, and a 140° angle, as viewed from a line parallel to the plasma membrane plane. Another important feature of the blades is the L-shaped helical structures formed by TM13, TM17, TM21, TM25, and TM29. Both identifiable structural features appear to be ideal not only for mechanosensation but also for the induction of local membrane curvature. Intriguingly, the peripheral TM13-24 appears to be within a highly curved membrane plane, indicating that the Piezo1 channel can curve the membrane in which it resides. This is consistent with past studies implying that Piezo1 ion channels can be regulated by cellular membrane curvature and tension. The ion-conducting pathway As pore-forming ion channels, Piezo proteins contain a trimeric ion-conducting channel made up of residues 2,189 to 2,547, which contain the last two TMs (Fig. 3). The continuous central channel consists of three parts, an EV within the cap region, a transmembrane vestibule (MV) within the membrane, and an intracellular vestibule (IV) underneath the membrane. Both the EV and IV possess an opening that connects to the MVs, which are positioned above and below the membrane. Importantly, DEEED (2393–2397), a patch of negatively charged residues residing in the opening of the extracellular “cap” structure consisting of the CED, is required to ensure efficient ion conduction and determine the selection of cations over anions. Additionally, two critical acidic residues, E2495 and E2496, located at the CTD-constituted IV, may be responsible for divalent calcium ion selectivity, unitary conductance, and pore blockage. Structure of the Piezo2 channel Similar to Piezo1 channels, Piezo2 channels are large membrane proteins consisting of over 2,800 residues. However, the Piezo2 channel and Piezo1 channel share approximately only 42% sequence homology. Recent studies have shown that the overall structure of the Piezo2 channel is very similar to that of Piezo1 in that it forms a three-bladed, propeller-like homotrimeric structure comprising a central ion-conducting pore module and three peripheral blades with 38 TMs. Figure $\PageIndex{b}$ shows an interactive iCn3D model of the mammalian tactile channel PIEZO2 (6KG7) (long load time) In the Piezo2 channel, the charged residues at the interface between the beam and the CTD are required to ensure the normal mechanosensitivity of the channel. Moreover, single-channel recordings indicated that a previously unrecognized intrinsically disordered domain adjacent to the beam acts as a cytosolic plug that limits ion permeation, possibly by clogging the inner vestibule in both Piezo1 and Piezo2. Furthermore, by structurally comparing the Piezo1 and Piezo2 channels, Wang et al. found that the Piezo2 channel has additional constriction sites at L2743, F2754, and E2757 that might serve as a transmembrane gate controlled by the cap domain. Lever-like mechanotransduction mechanism Based on the unique topological features of the mPiezo1 channel, a lever-like mechanotransduction mechanism to explain its extraordinary mechanosensitivity was proposed (Fig. 4). In the mPiezo1 channel, the curved blades composed of THUs can act as a mechanosensor, while the beam structure, with the residues Ll1342 and Ll1345 acting as a pivot, can act as a lever-like apparatus. Coupling the distal blades and the central pore through the lever-like apparatus converts mechanical force into a force used for cation conduction. Adapted from Ge et al. b A lever-like mechano-gating model in Piezo1. The blue and red dashed arrows indicate input and output forces, respectively Figure 4: Model of the lever-like mechanotransduction model. The curved blades can act as a mechanosensor, while the beam structure, with residues Ll1342 and Ll1345 acting as a pivot, can act as a lever-like apparatus. Coupling of the distal blades and the central pore through the lever-like apparatus converts mechanical force into cation conduction. a Proposed model of the force-induced gating of Piezo channels. The blue and orange models represent the channel in its closed and open states, respectively. Red dashed lines indicate possible ion-conduction pathways. Because the pivot of the lever is positioned closer to the central pore than to the distal blades, the input force is effectively amplified through the lever-like apparatus. Additionally, a large conformational change in the distal blades is converted into a relatively slight opening of the central pore, allowing cation-selective permeation. Kinetics properties of Piezo channels Activation mechanisms of Piezo channels Normal Piezo channel kinetics can be modeled with three states: open, closed, and inactivated; these states have emerged, collectively, as an important mechanism in the Piezo channel function. Studies have proposed that the Piezo1 channel is gated directly by bilayer tension that can be modified by cytoskeletal proteins and linkages to the extracellular matrix (ECM). For example, in overhydrated red blood cells (RBCs), Piezo1-mediated Ca2+ influx activates K+ efflux through the Gardos channel (KCa3.1), which in turn leads to water loss and RBC dehydration. Piezo1 and Piezo2 channels not only exhibit a three-bladed, propeller-shaped trimeric architecture but also can locally deform lipid membranes into a dome-like shape. In addition, changes in the projection area of Piezo channels from closed to open are essential for their mechanosensitivity; this was investigated by calculating the available free energy. Based on these findings, the membrane dome mechanism was proposed and experimentally proved to explain the activation mechanisms of Piezo channels (Fig. 5). Essentially, the dome shape created by Piezo channels in their closed conformation acts as a potential energy source for MS gating. Under tension, lateral membrane tension flattens the Piezo dome, which increases the energy of the membrane-channel system in proportion to the expansion of the projected area of the dome. Piezo channels then open due to the relative energy difference. This mechanism can account for the highly sensitive mechanical gating of Piezo channels with a cation-selective pore. Although the membrane dome mechanism explains the exquisite mechanosensitivity of Piezo channels, it does not consider the shape of the surrounding membrane. Haselwandter et al. [57] proposed the membrane footprint hypothesis, which states that the Piezo1 channel deforms the shape of the membrane outside the perimeter of the channel such that it exhibits a curved membrane footprint, which amplifies the sensitivity of Piezo1 to changes in the membrane tension. Nevertheless, further experiments are needed to test and refine these hypotheses. Inactivation kinetics of Piezo channels Various types of mechanical stimulation trigger Piezo channel activation and sequentially elicit an MA current with rapid decay, even in the presence of continued stimulation, due to rapid inactivation. Coste et al. first described detailed information about the voltage-dependent inactivation kinetics of Piezo channels, characterized as fast at rather negative membrane potentials and slow at rather positive membrane potentials. Additionally, Piezo1 channel inactivation is relatively slow compared with Piezo2 channel inactivation. Several point mutations in Piezo channels have been reported to slow down the inactivation process, which produces larger cation fluxes and results in various human diseases. Given its demonstrated key role in normal channel function, we next review what is known about the inactivation kinetics of Piezo channels with a focus on the inactivation mechanism. The available information regarding the structures (residues/domains) and human disease-related point mutations have helped to clarify the mechanisms of ion channel inactivation. Currently, six gain-of-function mutations associated with dehydrated hereditary xerocytosis (DHS) have been found to slow the inactivation rate of Piezo channels (Table 2), most of which are clustered at the central core region of the Piezo channel structure. This implies that the pore region, which contains an OH, an IH, an extracellular cap domain, and an intracellular CTD, determines the kinetics of inactivation. Further detailed links between structural domains and inactivation kinetics have been investigated. Wu et al. identified that the distinct inactivation kinetics of Piezo1 and Piezo2 channels and characteristic voltage-dependent inactivation appears to be determined by the C-terminal extracellular domains (cap domain). Two potential inactivation gates within the IH and CTD have been thought to be sufficient for the normal inactivation of the Piezo1 and Piezo2 channels. Recently, three small subdomains within the extracellular cap were shown to individually confer Piezo channel inactivation. These results support the idea that the ion-conducting pore region of Piezo channels is essential for their inactivation properties. Table 2 Mutations in Piezo1 and Piezo2 Associated with Human Diseases: Full-size table Interestingly, a slowly inactivating MS current in mouse embryonic stem cells (mESs) has been described that is dependent on the Piezo1 channel. However, heterologous expression of Piezo1 cDNA from mES cells displays fast inactivation kinetics, indicating that additional regulatory mechanisms other than the amino acid sequence determine the slow kinetics of the Piezo1 channel in mES cells [70]. Recently, sphingomyelinase activity has been revealed to be a crucial determinant of Piezo1 inactivation. Various modulators, such as pH, temperature, divalent ion concentrations, alternative splicing, osmotic swelling, membrane lipid composition, co-expression of other membrane proteins, and G-protein-coupled pathways have also been reported to regulate the Piezo channel kinetics; however, we still know very little about the relationships among these factors and pivotal structural domains. Pharmacological modulators of Piezo channels Despite the relatively recent discovery of Piezos, there has been progress regarding small-molecule modulators of Piezo1. Piezo1 chemical activators, including Yoda1 and Jedi1/2, were able to open Piezo1 ion channels in the absence of mechanical stimulation. Jedi1/2, a novel hydrophilic Piezo1 chemical activator, acts through the peripheral blades and utilizes a peripheral lever-like apparatus consisting of the blades and a beam to gate the central ion-conducting pore whereas Yoda1 acts as a molecular wedge, facilitating force-induced conformational changes, effectively lowering the channel’s mechanical threshold for activation However, the reason why Yoda1 does not efficiently activate the Piezo2 channel is unclear. Specific inhibitors of Piezo1 are in their infancy. As nonspecific inhibitors of the ion pore in stretch-activated ion channels, gadolinium, and ruthenium red have also been shown to block mouse Piezo1 channels with IC50 values of approximately 5 mM. The commonly used toxin inhibitor of mechanosensitive channels, GsMTx4, was also found to inhibit the Piezo1channel, but it might not bind Piezo1 directly, rather acting via modulating local membrane tension near the channel. Dooku1, an analog of Yoda1 without a stimulatory effect, antagonizes Yoda1-evoked activation of Piezo1 and aortic relaxation. The function of Piezo channels Piezo channels are expressed in a wide range of mechanically sensitive cells and allow Ca2+ influx in response to different types of external forces, such as fluid flow, pulling, and ultrasonic forces. The biological function of Piezo channels was recently investigated in a number of studies (Fig. 6). The results of these studies verified the pivotal roles of Piezo channels in mechanotransduction under physiological and pathophysiological conditions. Here, we focus on reviewing the biological function of Piezo channels in several different types of MS tissues and cells. Figure 6: Expression and function of Piezo channels Multiple tissues and cells express Piezo channels, and each of those shown is discussed in this review. a–e demonstrate the vital role of the Piezo1 channel in the CNS, blood vessels, erythrocytes, lungs, gastrointestinal tract, and urinary tract. f–h illustrate the expression of both the Piezo1 channel and Piezo2 channel in articular cartilage, trigeminal ganglia, and dorsal root ganglia. i shows that the Piezo2 channel is expressed in Merkel cells, which are involved in sensing light touch Consult the original article for details on the roles of Piezo channels on the systems above.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/29%3A_Integration_of_Mammalian_Metabolism_-_Capstone_Volume_II/29.01%3A__Overview_of_Metabolism.txt	princeton-nlp/TextbookChapters	An Overview of Metabolic Pathways - Catabolism Biological cells have a daunting task. They must carry out 1000s of different chemical reactions required to carry out cell function. These reactions can include opposing goals such as energy production and energy storage, macromolecule degradation and synthesis, and breakdown and synthesis of small molecules. All of these reactions are catalyzed by proteins and RNAs enzymes whose activities must be regulated, again through chemical reactions, to avoid a futile and energy wasting scenario of having opposing pathways functioning simultaneously in a cell. Metabolism can be divided into two main parts, catabolism, the degradation of molecules, usually to produce energy or small molecules useful for cell function, and anabolism, the synthesis of larger biomolecules from small precursors. CATBOLISM: Catabolic reactions involve the breakdown of carbohydrates, lipids, proteins, and nucleic acids to produce smaller molecules and biological energy in the form of heat or small thermodynamically reactive molecules like ATP whose further degradation can drive endergonic process such as biosynthesis. Our whole world is reliant on the oxidation of organic hydrocarbons to water and carbon dioxide to produce energy (at the expense of releasing a potent greenhouse gas, CO2). In the biological world, reduced molecules like fatty acids and partially oxidized molecules such as glucose polymers (glycogen, starch), as well as simple sugars, can be partially or fully oxidized to ultimately produce CO2 as well. Energy released from oxidative reactions is used to produce molecules like ATP as well as heat. Oxidative pathways include glycolysis, the tricarboxylic acid cycle (aka Kreb's cycle) and mitochondrial oxidative phosphorylation/electron transport. To fully oxidize carbon in glucose and fatty acids to carbon dioxide requires splitting C-C bonds and the availability of series of oxidizing agents that can perform controlled, step-wise oxidation reactions, analogous to the sequential oxidation of methane, CH4 to methanol (CH3OH), formaldehyde (CH2O) and carbon dixoxide. • Glycolysis: This most primitive of metabolic pathways is found in perhaps all organisms. In glycolysis, glucose (C6H12O6), a 6C molecule, is split (or lysed) into two, 3C carbon molecules, glyceraldehyde-3-phosphate, which are then partially oxidized under anaerobic conditions (without O2) to form two molecules of pyruvate (CH3COCO2-). Instead of the very strong oxidizing agent, O2, a weaker one, NAD+ is used, which is reduced in the process to form NADH. Since none of the carbon atoms is oxidized to the state of CO2, little energy is released compared to the complete oxidation to CO2. This pathway comes to a screeching halt if all cellular NAD+ is converted to NADH as NAD+ is not replenished by the simple act of breathing as is the case with O2 in aerobic oxidation. To prevent the depletion of NAD+ from inhibiting the cycle and to allow the cycle to continue under anaerobic conditions, excess NADH is reconverted to NAD+ when the other product of glycolysis, pyruvate is converted to lactate by the enzyme lactate dehydrogenase. Glycolysis occurs in the cytoplasm of the cell. • Tricarboxylic Acid (Kreb's) Cycle: The TCA cycle is an aerobic pathway which takes place in an intracellular organelle called the mitochondria. It takes pyruvate, the incompletely oxidized product from glycolysis, and finishes the job of oxidizing the 3C atoms all the way to CO2. First the pyruvate moves into the mitochondria where is is oxidized to the 2C molecule acetylCoA with the release of one CO2 by the enzyme pyruvate dehydrogenase. The acetyl-CoA then enters the TCA cycle where two more CO2 are released. As in glycolysis, C-C bonds are cleaved and C is oxidized by NAD+ and another related oxidizing agent, FAD. What is very different about this pathway is that instead of being a series of linear, sequential reactions with one reactant (glucose) and one product (two pryuvates), it is a cyclic pathway. This has significant consequences since if any of the reactants within the pathways becomes depleted, the whole cyclic pathway can slow down and stop. To see how this happens consider the molecule oxaloacetate (OAA) which condenses with acetyl-CoA to form citrate (see diagram below). In this reaction, one OAA is consumed. However, when the cycle returns, one malate is converted to OAA so there is no net loss of OAA, unless OAA is pulled out of the TCA cycle for other reactions, which happens. • Mitochondrial Oxidative Phosphorylation/Electron Transport: The TCA cycle accomplishes what glycolysis didn't, that is the cleavage of all C-C bonds in glucose (in the form of pyruvate and acetyl-CoA, and the complete oxidation of all C atoms to CO2. Yet two problem remains. The pool of oxidizing molecules, NAD+ and FAD get converted to their reduced forms, NADH and FADH2. Unless NAD+ and FAD are regenerated, as was the case in anaerobic conditions when pyruvate gets converted to lacate, the pathway would again come to a grinding halt. In addition, not much ATP is made in the cycle (in the form of a related molecule GTP). Both these problems are resolved as the resulting NADH and FADH2 formed are reoxidized by mitochondrial membrane enzyme complexes which pass electrons from the oxidized NADH and FADH2 to increasingly potent oxidizing agents until they are accepted by the powerful oxidant O2,which is converted reduced to water. The net oxidation of NADH and FADH2 by dioxygen is greatly exergonic, and the energy released by the process drives the synthesis of ATP from ADP and Pi by an mitochondrial enzyme complex, the F0F1ATPase. Feeder Pathways: Other catabolic pathways produce products that can enter glycolysis or the TCA cycle. Two examples are given below. • Complex carbohydrates: In mammals, the major carbohydrate storage molecule is glycogen, a polymer of glucose linked a1-4 with a1-6 branches. The terminal acetal linkages in this highly branched polymer is cleaved sequentially at the ends not through hydrolysis but through phosphorolysis to produce lots of glucose-1-phosphate which can enter glycolysis. • Lipids: Lipids are stored mostly as triacylglycerides in fat cells (adipocytes). When needed for energy, fatty acids are hydrolyzed from the glycerol backbone of the triacylglyceride, and send into cells where they broken down in an oxidative process to form acetyl-CoA with the concomitant production of lots of NADH and FADH2. These can then enter the mitochondrial oxidative phosphorylation/electrons transport system, which produces, under aerobic conditions, lots of ATP. • Proteins: When intracellular proteins get degraded, they from individual amino acids. The amine N is lost as it enters the urea cycle. The rest of some amino acid structures can be ultimately converted to acetyl-CoA or keto acids (like alpha-ketoglutarate- a-KG) that are TCA intermediate. These amino acids are called ketogenic. Alternatively, some amino acids, after deamination, are coveted to pyruvate which can either enter the TCA cycle or in the liver be used to synthesize glucose in an anabolic process. These amino acids are called glucogenic. Chemical reactions such as these can be used to replenish intermediates in the TCA cycle which can become depleted as they are withdraw for other reactions. Anabolic Reactions Anabolic reactions are those that lead to the synthesis of biomolecules. In contrast to the catabolic reactions just discussed (glycolysis, TCA cycle and electron transport/oxidative phosphorylation) which lead to the oxidative degradation of carbohydrates and fatty acids and energy release, anabolic reactions lead to the synthesis of more complex biomolecules including biopolymers (glycogen, proteins, nucleic acids) and complex lipids. Many biosynthetic reactions, including those for fatty acid synthesis, are reductive and hence require reducing agents. Reductive biosynthesis and complex polymer formation require energy input, usually in the form of ATP whose exergonic cleavage is coupled to endergonic biosynthesis. Cells have evolved interesting mechanism so as not to have oxidative degradation reactions (which release energy) proceed at the same time and in the same cell as reductive biosynthesis (which requires energy input). Consider this scenario. You dive into a liver cell and find palmitic acid, a 16C fatty acid. From where did it come? Was it just synthesized by the liver cell or did it just enter the cell from a distant location such as adipocytes (fat cells). Should it be oxidized, which should happen if there is a demand for energy production by the cell, or should the liver cell export it, perhaps to adipocytes, which might happen if there is an excess of energy storage molecules? Cells have devised many ways to distinguish these opposing needs. One is by using a slightly different pool of redox reagents for anabolic and catabolic reactions. Oxidative degradation reactions typically use the redox pair NAD+/NADH (or FAD/FADH2) while reductive biosynthesis often uses phosphorylated variants of NAD+, NADP+/NADPH. In addition, cells often carry out competing reactions in different cellular compartments. Fatty acid oxidation of our example molecule (palmitic acid) occurs in the mitochondrial matrix, while reductive fatty acid synthesis occurs in the cytoplasm of the cell. Fatty acids entering the cell destined for oxidative degradation are transported into the mitochondria by the carnitine transport system. This transport system is inhibited under conditions when fatty acid synthesis is favored. We will discuss the regulation of metabolic pathways in a subsequent section. One of the main methods, as we will see, is to activate or inhibit key enzymes in the pathways under a given set of cellular conditions. The key enzyme in fatty acid synthesis, acetyl-CoA carboxylase, is inhibited when cellular conditions require fatty acid oxidation. The following examples give short descriptions of anabolic pathways. Compare them to the catabolic pathways from the previous section. • Glucose synthesis, better known as Gluconeogenesis: In glycolysis, glucose (C6H12O6), a 6C molecule, is converted to two, 3C molecules (pyruvate) in an oxidative process that requires NAD+ and makes two net ATP molecules. In a few organs, most predominately in the liver, the reverse pathway can take place. The liver does this to provide glucose to the brain when the body is deficient in circulating glucose, for example, under fasting and starving conditions. (The liver under these conditions can get its energy from oxidation of fatty acids). The reactions in gluconeogenesis are the same reactions in glycolysis but run in reverse, with the exception of three glycolytic steps which are essentially irreversible. These three steps have bypass enzymes in the gluconeogenesis pathway. Although the synthesis of glucose is a reductive pathway, it uses NADH instead of NADPH as the redundant as the same enzyme used in glycolysis is simply run in reverse. Gluconeogenesis, which also occurs in the cortex of the kidney, is more than just a simple reversal of glycolysis, however. It can be thought of as the net synthesis of glucose from non-carbohydrate precursors. Pyruvate, as seen in the section on catabolism, can be formed from protein degradation to glucogenic amino acids which can be converted to pyruvate. It can also be formed from triacylglycerides from the 3C molecule glycerol formed and released from adipocytes after hydrolysis of three fatty acids from triacylglycerides. However, in humans, glucose can not be made in net fashion from fatty acids. Fatty acids can be converted to acetyl-CoA by fatty acid oxidation. The resulting acetyl-CoA can not form pyruvate since the enzyme that catalyzes the formation for acetyl-CoA from pyruvate, pyruvate dehydrogenase, is irreversible and there is no bypass reaction known. The acetyl-CoA can enter the TCA cycle but since the pathway is cyclic and proceeds in one direction, it can not form in net fashion oxaloacetate. Although oxaloacetate can be remove from the TCA cycle and be use to form phosphoenolpyuvate, a glycolytic intermediate, one acetyl-CoA condenses with one oxaloacetate to form citrate which leads back to one oxaloacetate. Hence fatty acids can not be converted to glucose and other sugars in a net fashion. • Pentose Phosphate Shunt: This two-part pathway doesn't appear to start as a reductive biosynthetic pathway as the first part is the oxidative conversion of a glycolytic intermediate, glucose-6-phosphate, to ribulose-5-phosphate. The next, nonoxidative branch leads to the formation of ribose-5-phosphate, a key biosynthetic intermediate in nucleic acid synthesis as well as erthyrose-4-phosphate used for biosynthesis of aromatic amino acids . The oxidative branch is important in reductive biosynthesis as it is a major source of the reductant NADPH used in biosynthetic reactions. • Fatty acid and isoprenoid/sterol biosynthesis: Acetyl-CoA is the source of carbon atoms for the synthesis of more complex lipids such as fatty acids, isoprenoids, and sterols. When energy needs in a cell are not high, citrate, the condensation product of oxaloacetate and acetyl-CoA in the TCA cycle, builds up in the mitochondrial matrix. It is then transported by the citrate transporter (an inner mitochondrial membrane protein) to the cytoplasm, where it is cleaved back to oxaloacetate and acetyl-CoA by the cytoplasmic enzyme citrate lyase. The oxaloacetate is returned to the mitochondria by conversion first to malate (reduction reaction using NADH), which can move back into the mitochondria through the malate transporter, or further conversion to pyruate, using the cytosolic malic enzyme, which uses NADP+ to oxidize malate to pyruvate which then enters the mitochondria. The acetyl-CoA formed in the cytoplasm can then be used in reductive biosynthesis using NADPH as the reductant to form fatty acids, isoprenoids, and sterols. The NADPH for the reduction comes from the oxidative branch of the pentose phosphate pathway and from the reaction catalyzed by malic enzyme. The liver cells can still run the glycolytic pathway as the NADH/NAD+ ratio is low in the cytoplasm while NADPH/NADP+ ratio is high. Regulation and Integration None of these pathways exist in isolation. They are connected in a complicated web of interactions. Instead of cursory summaries of how these pathways are all interconnected, we have chosen to present a series of published articles that are freely available for reuse through Creative Common's licenses. They describe organ- and systems-specific metabolisms and emerging understanding of metabolism in disease states. These include:
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/29%3A_Integration_of_Mammalian_Metabolism_-_Capstone_Volume_II/29.02%3A__Regulation_of_glucose_metabolism_from_a_liver-centric_perspe.txt	princeton-nlp/TextbookChapters	Han, HS., Kang, G., Kim, J. et al. Regulation of glucose metabolism from a liver-centric perspective. Exp Mol Med 48, e218 (2016). https://doi.org/10.1038/emm.2015.122 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ Experimental & Molecular Medicine volume 48, pagee218 (2016) Abstract Glucose homeostasis is tightly regulated to meet the energy requirements of the vital organs and maintain an individual’s health. The liver has a major role in the control of glucose homeostasis by controlling various pathways of glucose metabolism, including glycogenesis, glycogenolysis, glycolysis and gluconeogenesis. Both the acute and chronic regulation of the enzymes involved in the pathways are required for the proper functioning of these complex interwoven systems. Allosteric control by various metabolic intermediates, as well as post-translational modifications of these metabolic enzymes constitute the acute control of these pathways, and the controlled expression of the genes encoding these enzymes is critical in mediating the longer-term regulation of these metabolic pathways. Notably, several key transcription factors are shown to be involved in the control of glucose metabolism including glycolysis and gluconeogenesis in the liver. In this review, we would like to illustrate the current understanding of glucose metabolism, with an emphasis on the transcription factors and their regulators that are involved in the chronic control of glucose homeostasis. Overview of glucose metabolism in the liver Under feeding conditions, dietary carbohydrates are digested and processed by various glucosidases in the digestive tract, and the resultant monosaccharides, mainly hexose glucose, are transported into various tissues as a primary fuel for ATP generation.1 In most mammalian tissues, the catabolism of glucose into pyruvate, termed glycolysis, is preserved as a major pathway in eliciting ATP. In tissues with abundant mitochondria, cytosolic pyruvate is transported into the mitochondrial matrix, converted to acetyl-CoA by pyruvate dehydrogenase complex, and incorporated into the tricarboxylic acid cycle in conjunction with oxaloacetate. The cycle generates energy equivalent to ATP (that is, GTP) as well as both NADH and FADH2, which serve as important electron carriers for electron transport chain-oxidative phosphorylation, resulting in the generation of ATP. In some cases, such as red blood cells lacking mitochondria or cells under ischemic conditions, pyruvate is converted into lactate in the cytosol to regenerate the NAD+ that is necessary for the continued generation of ATP by substrate-level phosphorylation via anaerobic glycolysis. Excessive carbohydrates in the liver are first converted into glycogen, a storage form of glucose in animals, by glycogenesis. In addition, in a carbohydrate-rich diet, the excessive carbohydrates are also converted into fatty acids via lipogenesis using the acetyl-CoA generated from glycolysis-driven pyruvate, which is incorporated into very low density lipoproteins for transport to white adipose tissue for the storage.2 The regulation of glycogen metabolism is examined in detail in this section, and the transcriptional control of glycolysis and lipogenesis is delineated in the following section. Under fasting conditions, the liver has a major role in generating glucose as a fuel for other tissues, such as the brain, red blood cells and muscles. Initially, an increase in the pancreatic hormone glucagon initiates the cascade of kinase action (stated below in detail) that releases glucose from the stored glycogen via glycogenolysis.1 Normally, stored glycogen is critical for maintaining glucose homeostasis in mammals during an overnight fasting period. During a longer term fast or starvation, essentially all of the stored glycogen in the liver is depleted (after ~30 h of fasting), and de novo glucose synthesis or gluconeogenesis is responsible for the generation of glucose as a fuel for other tissues. Major non-carbohydrate precursors for gluconeogenesis are lactate, which is transported from peripheral tissues such as skeletal muscles or red blood cells, and glycerol, which is released from the adipose tissues via enhanced lipolysis during fasting. Most of the initial precursors for gluconeogenesis are generated in the mitochondria (except glycerol 3-phosphate via glycerol kinase activity), but the majority of the reaction occurs in the cytosolic part of the cell. The complex regulatory mechanism is delineated in detail in the following section, with an emphasis on the transcriptional control of key regulatory enzyme genes. Regulation of glycogen metabolism in the liver The accumulation of glycogen in the liver during feeding conditions provides a storage form of glucose that can be used in times of reduced food intake (Figure 1). Multiple layers of regulation are required for this process for both the activation of glycogen synthase, which is a key enzyme of glycogenesis (glycogen synthesis), and the inhibition of glycogen phosphorylase, which is a key enzyme of glycogenolysis (glycogen breakdown) in the liver. Glycogen synthase is a major enzyme that facilitates the elongation of glycogen chains by catalyzing the transfer of the glucose residue of UDP-glucose to the non-reducing end of a pre-existing glycogen branch to produce a new α1→4 glycosidic linkage. The regulation of glycogen synthase has been mostly studied using a muscle-specific isoform. In the muscle, glycogen synthase is inactivated via phosphorylation on multiple serine residues by various serine/threonine kinases such as casein kinase-1, protein kinase A (PKA), and glycogen synthase kinase-3 (GSK-3). Most notably, the phosphorylation of glycogen synthase by GSK-3 at serine residues 640, 644 and 648 has been linked to the most important inhibitory post-translational modification for its catalytic activity. Regulation of hepatic glycogen metabolism. Under fasting conditions, glucagon and epinephrine induce cAMP-dependent signaling cascades, leading to the activation of glycogen phosphorylase and glycogenolysis while inhibiting glycogenesis. Conversely, feeding enhances insulin-mediated signaling in the liver, leading to the activation of both PP1 and Akt, thus promoting glycogen synthesis in response to increased glucose uptake in the liver. See the main text for more specific regulatory pathways. cAMP, cyclic AMP. Under fasting conditions, dephosphorylated and active GSK-3 phosphorylate and inactivate glycogen synthase, leading to the inhibition of hepatic glycogen synthesis. On feeding, increased insulin signaling activates Akt in the cell, which in turn phosphorylates and inactivates GSK-3, thus resulting in the activation of glycogen synthase. In addition, increased concentrations of glucose 6-phosphate allosterically activate this enzyme, thus potentiating its catalytic activity under feeding conditions.3, 4 One recent publication argues against the role of GSK-3 in the regulation of the liver-specific isoform of glycogen synthase. In that study, Guinovart et al.5 mutated the corresponding serine residues that are shown to be regulated by GSK-3 in the liver-isoform of glycogen synthase. They found that the mutation of those residues did not affect the overall enzyme activity but that the mutation of serine 7 to alanine, a site that is recognized and regulated by PKA, led to the increased activity of this enzyme. Further study is necessary to determine whether these results can be verified in vivo using animal models such as liver-specific knock-in mice for S7A liver glycogen synthase. The protein phosphatase 1 (PP1) may be responsible for the dephosphorylation and activation of glycogen synthase. Accordingly, both glucose and insulin have been shown to activate PP1 activity, whereas glucagon and epinephrine have been linked to the inhibition of its activity. Glycogen phosphorylase is a major enzyme involved in glycogenolysis (Figure 1). This enzyme catalyzes the reaction of the removal of a glucose residue from the non-reducing end of a glycogen chain, leading to the generation of glucose 1-phosphate.6 Glucose 1-phosphate can be converted into glucose 6-phosphate by phosphoglucomutase, and glucose 6-phosphate can be incorporated into glycolysis or further converted into glucose by glucose 6-phosphatase, depending on the energy status of the organism. Glycogen phosphorylase is active when it is phosphorylated at its serine 14 residue. The phosphorylation of glycogen phosphorylase requires a cascade mechanism of epinephrine and glucagon in the liver. On the activation of Gαs by the binding of hormones to cell surface G protein-coupled receptors (beta adrenergic receptor or glucagon receptor), the intracellular cyclic AMP (cAMP) levels increase via adenylate cyclase, leading to the activation of PKA. PKA is then responsible for the phosphorylation and activation of glycogen phosphorylase kinase, which in turn phosphorylates and activates glycogen phosphorylase to enhance glycogen breakdown. Under feeding conditions, this kinase cascade is inactive due to the lack of secretion of catabolic hormones. In addition, insulin promotes the activation of PP1, which dephosphorylates and inactivates glycogen phosphorylase. In essence, the anabolic hormone insulin promotes glycogenesis and inhibits glycogenolysis via the activation of PP1, leading to the dephosphorylation of glycogen phosphorylase (inactivation) and glycogen synthase (activation), and via the activation of Akt, leading to the phosphorylation of GSK-3 (inactivation) that is unable to phosphorylate and inactivate glycogen synthase. Control of hepatic glycolysis As stated above, glycolysis is critical to the catabolism of glucose in most cells to generate energy. The key rate-limiting enzymes for this pathway include glucokinase (GK, also termed hexokinase IV), which converts glucose into glucose 6-phosphate; phosphofructokinase-1 (PFK-1), which converts fructose 6-bisphosphate into fructose 1,6-bisphosphate; and liver-type pyruvate kinase (L-PK), which converts phosphoenolpyruvate (PEP) into pyruvate in the liver. These enzymes are tightly regulated by allosteric mediators that generally promote the catabolism of glucose in the cell.2, 7, 8, 9 GK is a high Km hexokinase that is present in the liver and the pancreatic beta cells, thus functioning as a glucose sensor for each cell type. Unlike the other hexokinase isotypes, GK activity is not allosterically inhibited by its catalytic product, glucose 6-phosphate in the cell, thus enabling the liver to continuously utilize glucose for glycolysis during conditions of increased glucose availability, such as during feeding conditions. GK is regulated via its interaction with glucokinase regulatory protein (GKRP). In the low intracellular glucose concentration during fasting, the binding of GK and GKRP is enhanced by fructose 6-phosphate, leading to the nuclear localization of this protein complex. Higher concentrations of glucose during feeding compete with fructose 6-phosphate to bind this complex, which promotes the cytosolic localization of GK that is released from GKRP, thus causing the increased production of glucose 6-phosphate in this setting.10 PFK-1 catalyzes the metabolically irreversible step that essentially commits glucose to glycolysis. This enzyme activity is allosterically inhibited by ATP and citrate, which generally indicate a sign of energy abundance. Reciprocally, it is allosterically activated by ADP or AMP, making it more efficient to bring about glycolysis to produce more ATP in the cell. In addition, PFK-1 activity is allosterically activated by fructose 2,6-bisphosphate (F26BP), a non-glycolytic metabolite that is critical for the regulation of glucose metabolism in the liver. F26BP is generated from fructose 6-phosphate by the kinase portion of a bifunctional enzyme that contains both a kinase domain (phosphofructokinase-2, PFK-2) and a phosphatase domain (fructose 2,6-bisphosphatase, F-2,6-Pase). PFK-2 is activated by the insulin-dependent dephosphorylation of a bifunctional enzyme that activates PFK-2 activity and simultaneously inhibits F-2,6-Pase activity to promote the increased F26BP concentration. Glucagon-mediated activation of PKA is shown to be responsible for the phosphorylation and inactivation of the kinase portion of this enzyme.7 Unlike its muscle counterpart, L-PK is also a critical regulatory step in the control of glycolysis in the liver. As in the case of other glycolytic enzymes, L-PK activity is regulated by both allosteric mediators and post-translational modifications. L-PK activity is allosterically activated by fructose 1,6-bisphosphate, an indicator for the active glycolysis. By contrast, its activity is allosterically inhibited by ATP, acetyl-CoA, and long-chain fatty acids, all of which signal an abundant energy supply. Additionally, the amino acid alanine inhibits its activity, as it can be readily converted to pyruvate by a transamination reaction. L-PK is inhibited by PKA following a glucagon-mediated increase in intracellular cAMP during fasting and is activated by insulin-mediated dephosphorylation under feeding conditions.7 In addition to the acute regulation of key regulatory enzymes, glycolysis is regulated by a transcriptional mechanism that is activated during feeding conditions. Two major transcription factors, sterol regulatory element binding protein 1c (SREBP-1c) and carbohydrate response element binding protein (ChREBP), are responsible for the transcriptional activation of not only glycolytic enzyme genes but also the genes involved in fatty acid biosynthesis (such as fatty acid synthase (FAS), acetyl-CoA carboxylase (ACC), and stearoyl-CoA desaturase 1 (SCD1)) and triacylglycerol formation (such as glycerol 3-phosphate acyltransferase (GPAT) and diacylglycerol acyltransferase 2 (DGAT2)), a process that is normally activated by a carbohydrate-rich diet (Figure 2).11 Because these processes are often coordinately regulated, that is activated during feeding and inhibited by fasting, they are sometimes collectively called lipogenesis. Regulation of hepatic glycolysis. Under feeding conditions, increased glucose uptake in hepatocytes promotes glycolysis and lipogenesis to generate triglycerides as storage forms of fuel. This process is transcriptionally regulated by two major transcription factors in the liver, SREBP-1c and ChREBP-Mlx heterodimer, which mediate the insulin and glucose response, respectively. See the main text for more specific regulatory pathways. Full size image SREBPs are the major regulators of lipid metabolism in mammals. They are members of the basic helix-loop-helix leucine zipper (b/HLH/LZ) type transcription factor families comprising SREBP-1a, SREBP-1c, and SREBP-2. SREBP is translated as an endoplasmic reticulum (ER)-bound precursor form that contains the N-terminal transcription factor domain and the C-terminal regulatory domain linked with the central transmembrane domain.12 Within this family of transcription factors, SREBP-2 is linked to the control of cholesterol uptake or biosynthesis in the liver by the transcriptional activation of the genes involved in the pathway including low density lipoprotein receptor (LDLR), 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR), hydroxy-3-methylglutaryl-coenzyme A synthase 1 (HMGCS1), and farnesyl diphosphate synthase (FDPS). SREBP-1c, however, activates the genes encoding the enzymes for lipogenesis (FAS, ACC, SCD1, and DGAT2) as well as GK, which is a first enzyme in the commitment step of glucose utilization in the liver. Indeed, liver-specific SREBP-1c knockout mice showed an impaired activation of lipogenic genes in a high carbohydrate diet, thus confirming the importance of this transcription factor in the regulation of hepatic glycolysis and fatty acid biosynthesis.13 SREBP-1a is not highly expressed in the liver but was shown to be involved in the formation of inflammasomes in response to lipopolysaccharide (LPS) treatment in macrophages by transcriptional activation of Nlrp1.14 The regulation of SREBP-2 and SREBP-1c are quite distinct in the liver. The expression of SREBP-2 is not controlled by sterols, but its proteolytic processing is tightly regulated by intracellular concentrations of cholesterol. It is normally bound in the ER via the interaction of SREBP-cleavage-activating protein (SCAP) and insulin-induced gene protein (INSIG) in the presence of high intracellular cholesterol levels, and the reduction in the cholesterol concentration releases the interaction of SCAP and SREBP-2/INSIG complex, resulting in the translocation of the latter complex into the Golgi apparatus and the liberation of the active SREBP-2 factor by sequential proteolytic cleavages.15 Unlike SREBP-2, SREBP-1c is mainly regulated at the transcription level by insulin. The exact transcription factor that mediates this insulin-dependent signal is not yet clear, although SREBP-1c itself might be involved in the process as part of an auto-regulatory loop. Interestingly, the oxysterol-sensing transcription factor liver X receptor (LXR) is shown to control the transcription of SREBP-1c, suggesting that SREBP-1c and SREBP-2 could be regulated differently in response to cellular cholesterol levels.16 Recent studies have revealed the involvement of various kinases in the control of SREBP-1c activity. In HepG2 cells, PKA was shown to reduce the DNA binding ability of SREBP-1a by the phosphorylation of serine 338 (equivalent of serine 265 for SREBP-1c).17 A report by Bengoechea and Ericsson suggested that GSK-3, a kinase known to reduce glycogen synthesis by targeting glycogen synthase, downregulates SREBP-1 activity via the phosphorylation of the C-terminal residue that promotes the ubiquitin ligase Fbw7-dependent degradation of SREBP-1 proteins.18 In addition, both AMP activated protein kinase (AMPK) and its related kinase salt-inducible kinase (SIK) 1 are involved in the down-regulation of its activity through inhibitory phosphorylation (serine 372 for AMPK, which blocks proteolysis and nuclear localization of SREBP-1c, and serine 329 for SIK1, which directly reduces its transcriptional activity).19, 20 These data suggest that the fine-tuning of SREBP-1c activity is critical to the maintenance of glucose and lipid homeostasis in the liver. The other prominent transcription factor for controlling glycolysis and fatty acid biosynthesis in the liver is ChREBP. ChREBP was initially known as Williams-Beuren syndrome critical region 14 (WBSCR14) and was considered one of the potential genes that instigate Williams-Beuren syndrome. Later, by using a carbohydrate response element (ChoRE) from L-PK, ChREBP was isolated as a bona fide transcription factor for binding ChoRE of glycolytic promoters.21 Indeed, ChREBP is highly expressed in tissues that are active in lipogenesis such as the liver, brown adipocytes, white adipocytes, small intestine, and kidney. As in the case for SREBP, ChREBP belongs to the b/HLH/LZ transcription factor family and forms a heterodimer with another b/HLH/LZ transcription factor Max-like protein X (Mlx) on the glycolytic promoter.22 As in the case for the SREBP-1c, the expression of ChREBP is increased in the liver as a result of a high carbohydrate diet, and the effect was recapitulated in primary hepatocytes with high glucose treatment. A recent report indeed suggested a role for LXR in the transcriptional activation of ChREBP in response to glucose, although the study needs to be further verified because the transcriptional response is shown not only by the treatment of D-glucose, a natural form of glucose present in animals, but also by the treatment of unnatural L-glucose, a form of glucose that is not known to activate lipogenesis in the liver.23 Moreover, studies performed in LXR knockout mice revealed no changes in ChREBP expression in the liver, arguing against the role of LXR in the control of ChREBP.24 Glucose is also shown to regulate ChREBP activity by controlling its nuclear localization. There are three prominent serine/threonine residues that are targeted by serine/threonine kinases. PKA is shown to phosphorylate serine 196, which is critical for cellular localization, and threonine 666, which is critical for its DNA binding ability, whereas AMPK phosphorylate serine 568 dictates its DNA binding ability. All three sites are phosphorylated under fasting conditions by these kinases and are dephosphorylated under feeding by xylulose 5-phosphate (X5P)-mediated activity of protein phosphatase 2A (PP2A).25, 26 However, the current model needs to be further verified due to the contrasting data that have been published regarding the role of these phosphorylations on ChREBP activity. First, high glucose concentrations in primary hepatocytes do not result in decreased cAMP levels or PKA activity, suggesting that other signals might be necessary to mediate the high glucose-dependent nuclear translocation of ChREBP. In addition, a serine to alanine mutant of ChREBP still requires high glucose for its full activity, suggesting that additional actions are necessary to recapitulate the high glucose-mediated activation/nuclear localization of ChREBP in the liver.27, 28 The physiological role of ChREBP in liver glucose metabolism was verified by in vivo studies. ChREBP knockout mice were born in a Mendelian ratio and showed no developmental problems. The knockout animals showed reduced liver triacylglycerol levels together with a reduction in lipogenic gene expression, thus confirming the role of ChREBP in the control of hepatic glycolysis and fatty acid synthesis.29 Interestingly, the compensatory increase in glycogen was observed in the livers of these mice, suggesting that these mice adapted to store more glycogen as a storage form of fuel as opposed to triacylglycerol. In ob/ob mouse liver, increased accumulation of nuclear ChREBP was shown, suggesting that this phenomenon might be causal to the fatty liver phenotype in these mice. Indeed, knockdown of ChREBP in ob/ob mice reduced the rate of lipogenesis with decreased expression of most lipogenic genes.30 Furthermore, the depletion of hepatic ChREBP in ob/ob mice improved hyperglycemia, hyperlipidemia, and hyperinsulinemia, suggesting that regulation of ChREBP might be critical in the control of metabolic disorders in the presence of obesity and insulin resistance. Control of hepatic gluconeogenesis Prolonged fasting or starvation induces de novo glucose synthesis from non-carbohydrate precursors, termed hepatic gluconeogenesis. This process initiates from the conversion of pyruvate to oxaloacetate by pyruvate carboxylase (PC) in the mitochondria and eventually concludes in the conversion into glucose via several enzymatic processes in the cytosol.7, 8, 9 Among the substrates for gluconeogenesis are amino acids, which can be converted into either pyruvate or intermediates of the tricarboxylic acid cycle; lactate, which can be converted into pyruvate by lactate dehydrogenase; and glycerol (from increased lipolysis in the white adipocytes under fasting), which can be converted into dihydroxyacetone phosphate, a gluconeogenic intermediate (a two-step process that is catalyzed by glycerol kinase and glycerol 3-phosphate dehydrogenase). Key regulatory enzymes in that pathway, including glucose 6-phosphatase (G6Pase), fructose 1,6-bisphosphatase (Fbpase1), PC, and phosphoenolpyruvate carboxykinase (PEPCK), are activated under fasting conditions to enhance gluconeogenic flux in that setting. Mitochondrial acetyl-CoA (derived from the increased fatty acid oxidation under fasting) functions as a key allosteric activator of PC, leading to the increased production of oxaloacetate for the gluconeogenesis. In addition, F26BP, which is a key allosteric regulator for glycolysis by activating PFK-1, was shown to inhibit gluconeogenesis via the allosteric inhibition of Fbpase1, which helps reciprocally control gluconeogenesis and glycolysis under different dietary statuses. Because Fbpase2 is activated but PFK-2 is inhibited under fasting, the lack of F26BP enables the activation of Fbpase1 and the increased production of fructose 6-phosphate in gluconeogenesis. The chronic activation of gluconeogenesis is ultimately achieved via transcriptional mechanisms. Major transcriptional factors that are shown to induce gluconeogenic genes include CREB, FoxO1, and several nuclear receptors (Figure 3).31 Regulation of hepatic gluconeogenesis. Under fasting conditions, hepatic gluconeogenesis is enhanced via a decreased concentration of insulin and an increased concentration of insulin counterregulatory hormones such as glucagon. CREB/CRTC2, FoxO1, and a family of nuclear receptors are critical in coordinating the fasting-mediated activation of gluconeogenesis in the liver. See the main text for more specific regulatory pathways. FoxO1, forkhead box O 1 Full size image Under fasting conditions, glucagon and epinephrine can increase the cAMP concentration in the liver via the activation of adenylate cyclase, leading to the activation of PKA and the subsequent induction of CREB via its serine 133 phosphorylation. The phosphorylation event is crucial in the recruitment of histone acetyltransferases (HAT) CBP/p300, leading to the histone H3 and H4 acetylation and the transcriptional activation of target genes.32, 33 CREB-dependent transcription is further enhanced by association with additional transcriptional coactivators CREB regulated transcription coactivators (CRTCs), which are a target for CBP/p300-mediated acetylation, which in turn promotes a tighter association of CREB, CBP/p300, and CRTC on the promoter.34, 35, 36 The role of CREB in the control of hepatic gluconeogenesis has been confirmed by in vivo studies by utilizing albumin promoter-driven ACREB (CREB inhibitor) transgenic mice and siRNA-mediated CREB knockdown mouse models.37, 38 In both mouse models, the inactivation of CREB reduced blood glucose levels and reduced the expression of gluconeogenic genes in mice, showing that CREB is a bona fide physiological transcriptional regulator of hepatic gluconeogenesis in vivo. In contrast, the role for CBP in gluconeogenesis is still controversial. Disruption of CREB-CBP interaction does not appear to affect glucose homeostasis because mice exhibiting a stable expression of mutant CBP that was unable to bind CREB showed normal glycemia.36 Furthermore, mutant mice producing CH1 null products (ΔCH1-a domain that is critical for insulin-mediated depression of CBP activity) displayed normal fasting gluconeogenesis.39 Thus, further studies are required to describe the potential role of HATs in the transcriptional control of CREB activity in this setting. The CRTC family of transcriptional coactivators consists of CRTC1, CRTC2 and CRTC3, which were isolated by the expression library screening as activaters of CREB-dependent transcription.34 CRTC activity is regulated by cellular localization, and the AMPK family of serine/threonine kinases, such as AMPK, SIK1 or SIK2, was shown to be involved in the inhibitory phosphorylation of this factor (serine 171 for CRTC2).40 In addition, the phosphorylation status of CRTC is regulated by a pair of serine/threonine phosphatases (PP2B or PP4) in response to cAMP signaling or calcium concentration in the cell.41, 42 CRTC activity is also further enhanced by O-GlcNAcylation on serine 171 and arginine methylation by protein arginine methyltransferase (PRMT) 6.43, 44 Among the family members, CRTC2 is the prominent isoform in the liver. Recent studies have delineated the role of CRTC2 in the regulation of hepatic gluconeogenesis in vivo. Knockdown of CRTC2 in mice by RNAi reduced blood glucose levels and led to a concomitant repression of gluconeogenic gene expression.36 In addition, CRTC2 knockout mice displayed lower plasma glucose levels and improved glucose tolerance, indeed showing that CRTC2 is crucial in controlling hepatic glucose metabolism in vivo.45 A recent study indicated that CRTC2 could also coactivate other bZIP transcription factors that are implicated in the regulation of glucose homeostasis.46, 47 Further study is required to delineate the potential contributions from other bZIP factors in the control of hepatic gluconeogenesis by using tissue-specific knockout mouse models. The forkhead box O (FoxOs) belongs to a class of forkhead families of transcription, which recognize the AT-rich insulin response element on the promoter.48, 49 Of the four major isoforms in mammals (FoxO1, FoxO3, FoxO4, and FoxO6), FoxO1 is the predominant isoform in the liver. The activity of this protein is also regulated by phosphorylation-dependent subcellular localization, and three major serine and threonine residues (threonine 24, serine 253 and serine 316 for murine FoxO1) are targeted by the insulin/Akt pathway. Following phosphorylation, FoxO1 moves to the cytosol via an association with 14-3-3, where it is degraded by the ubiquitin/proteasome-dependent pathway.50, 51, 52 In addition to phosphorylation, FoxO1 was shown to be regulated by the HAT-dependent acetylation of specific lysine residues (lysine 242, 245 and 262 for murine FoxO1), which also inhibit its transcriptional activity.53 In the liver, FoxO1 regulates hepatic gluconeogenesis via the transcriptional regulation of key genes in the pathway such as PEPCK and G6Pase and is considered a major regulatory point for the insulin-mediated repression of hepatic gluconeogenesis.54 Indeed, mice with liver-specific knockout of FoxO1 showed lower plasma glucose levels that those associated with reduced hepatic glucose output, thus underscoring the physiological significance of this factor in the control of glucose homeostasis in vivo.54, 55 As in the case for CREB, FoxO1 requires transcriptional coactivators for optimal transcriptional activity. Peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PGC-1α), a known coactivator for nuclear receptors, functions as a key transcriptional coactivator for FoxO1 in hepatic gluconeogenesis.56 PGC-1α was originally isolated in brown adipocytes and was shown to control adaptive thermogenesis in response to cold shock in that setting.57 In the liver, the expression of PGC-1α is upregulated under fasting conditions via a CRTC2-CREB-dependent mechanism and is critical in maintaining prolonged gluconeogenesis under starvation by enhancing the transcriptional activity of FoxO1 as a coactivator.38, 57, 58 Indeed, the depletion of hepatic PGC-1α in mice results in lower fasting glucose levels with a concomitant reduction in hepatic gluconeogenesis, thus showing the physiological relevance of this coactivator in the control of glucose homeostasis.59, 60 As is the case for CRTC2, FoxO1 activity is enhanced by arginine methylation by PRMT. In this case, PRMT1 promotes the asymmetric dimethylation of arginine 248 and 250 in FoxO1, which blocks the binding of Akt and the subsequent Akt-mediated phosphorylation of the adjacent serine residue (serine 253), thus enhancing the nuclear localization of FoxO1.61 Consequently, the chromatin occupancy of FoxO1 onto the gluconeogenic promoter and gluconeogenesis itself are increased due to the PRMT1-dependent arginine methylation.62 Acute knockdown of hepatic PRMT1 in mice reduces FoxO1-mediated glucose production, confirming that PRMT1 is crucial in modulating FoxO1 activity and subsequent gluconeogenesis in the physiological context. Nuclear receptors belong to the superfamily of transcription factors that possess two Cys2-His2 type zinc finger motifs as a DNA binding domain as well as both ligand-independent and ligand-dependent transactivation domains.63 The latter activation domain also contains a ligand-binding domain. Nuclear receptors can be classified into one of three subgroups based on their dimer-forming potential. Homodimeric nuclear receptors are also called cytosolic receptors because they reside in the cytosol and associate with molecular chaperones such as heat-shock proteins. On binding to the ligand, they form homodimers and translocate to the nucleus to bind a specific response element termed the hormone response element to elicit the ligand-dependent transcriptional response. Most of the steroid hormone receptors, such as the glucocorticoid receptor (GR), estrogen receptor (ER), and progesterone receptor (PR), belong to this subfamily. By contrast, heterodimeric nuclear receptors reside in the nucleus and are bound to their cognate binding sites together with the universal binding partner retinoid X receptor (RXR). In the absence of the ligands, these factors repress the transcription of target genes in association with transcriptional corepressors such as histone deacetylase or nuclear receptor corepressor (NCoR)/silencing mediator of retinoid and thyroid hormone receptors (SMRT). Ligand binding initiates the conformational changes of these heterodimeric nuclear receptors, which promotes the dissociation of corepressors and the association of coactivators such as CBP/p300, p160 steroid receptor coactivator family, and PGC-1α. Examples of this class of nuclear receptors include members of peroxisome proliferator-activated receptors, LXRs, vitamin D receptors and thyroid hormone receptors. The final subclasses of nuclear receptors are types that function as monomers. They usually lack specific endogenous ligands and are often called orphan nuclear receptors. Some of them also lack DNA binding domain and thus function as transcriptional repressors of various transcription factors, including members of nuclear receptors. They are called atypical orphan nuclear receptors. Among the homodimeric nuclear receptors, the role of GR has been linked to the control of hepatic gluconeogenesis. GR is activated by cortisol, which is released from the adrenal cortex in response to chronic stresses such as prolonged fasting.64, 65 GR was shown to directly bind to the cognate binding sites found in the promoters of gluconeogenic genes such as PEPCK and G6Pase and to enhance transcription of these genes under fasting conditions. The same response elements were also shown to be recognized and regulated by hepatocyte nuclear factor 4 (HNF4), a member of heterodimeric nuclear receptors, which suggests that these nuclear receptors could coordinately function to control hepatic gluconeogenesis in response to fasting.58 In accordance with this idea, the activity of these nuclear receptors can be effectively integrated by the function of transcriptional co-activator PGC-1α. Recently, estrogen-related receptor gamma (ERRγ), a member of monomeric nuclear receptors, was shown to be involved in the regulation of hepatic gluconeogenesis.66, 67 In the liver, ERRγ expression is increased under fasting or by insulin resistance in a CRTC2-CREB-dependent manner. This factor regulates hepatic gluconeogenesis by binding to unique response elements that are distinct from the known nuclear receptor-binding sites in the promoters of PEPCK and G6Pase. Inhibition of ERRγ activity by injecting either RNAi or the inverse agonist GSK5182 effectively reduced hyperglycemia in diabetic mice, suggesting that the control of this factor might potentially be beneficial in the treatment of patients with metabolic diseases. As is the case for other nuclear receptors that control hepatic gluconeogenesis, ERRγ activity is further enhanced by interaction with the transcriptional coactivator PGC-1α, showing that this coactivator functions as a master regulator for the hepatic glucose metabolism. Three members of atypical orphan nuclear receptors, the small heterodimer partner (SHP, also known as NR0B2); the dosage-sensitive sex reversal, adrenal hypoplasia critical region, on chromosome X (DAX-1, also known as NR0B1); and the SHP-interacting leucine zipper protein (SMILE) are implicated in the transcriptional repression of hepatic gluconeogenesis.68, 69, 70 SHP is ubiquitously expressed in mammalian tissues, with the highest expression occurring in the liver. Interestingly, metformin directly activates the transcription of SHP via an AMPK-mediated pathway. SHP directly inhibits cAMP-dependent transcription by binding to CREB, resulting in the reduced association of CREB with CRTC2.71, 72 The adenovirus-mediated overexpression of SHP could effectively reduce blood glucose levels in diabetic mice, thus showing the importance of this pathway in the control of hepatic glucose metabolism. These results provide a dual mechanism for a metformin-AMPK dependent pathway to inhibit hepatic gluconeogenesis at the transcriptional level; an acute regulation of CRTC2 phosphorylation to inhibit the CRTC2-CREB-dependent transcriptional circuit; and a longer-term regulation of gluconeogenic transcription by enhanced SHP expression. Both DAX-1 and SMILE were shown to repress hepatic gluconeogenesis by inhibiting HNF4-dependent transcriptional events.73, 74 SIK1, a member of the AMPK-related kinases, was shown to enhance DAX-1 expression in the liver, whereas Akt was shown to activate the transcription of SMILE to target the HNF4 pathway under feeding conditions. Interestingly, SMILE was shown to directly replace PGC-1α from HNF4 and the gluconeogenic promoters, suggesting that this factor could potentially function as a major transcriptional repressor of hepatic gluconeogenesis in response to insulin signaling. Further study is necessary to fully understand the relative contribution of these nuclear receptors in the control of glucose homeostasis in both physiological conditions and pathological settings. Concluding remarks In this review, we attempted to describe the current understanding of the regulation of glucose metabolism in the mammalian liver. Under feeding conditions, glucose, a major hexose monomer of dietary carbohydrate, is taken up in the liver and oxidized via glycolysis. The excess glucose that is not utilized as an immediate fuel for energy is stored initially as glycogen and is later converted into triacylglycerols via lipogenesis. Glycogenesis is activated via the insulin-Akt-mediated inactivation of GSK-3, leading to the activation of glycogen synthase and the increased glycogen stores in the liver. Insulin is also critical in the activation of PP1, which functions to dephosphorylate and activate glycogen synthase. In addition, PP1 inhibits glycogenolysis via the dephosphorylation/inactivation of glycogen phosphorylase. Glycolysis is controlled by the regulation of three rate-limiting enzymes: GK, PFK-1 and L-PK. The activities of these enzymes are acutely regulated by allosteric regulators such as ATP, AMP, and F26BP but are also controlled at the transcription level. Two prominent transcription factors are SREBP-1c and ChREBP, which regulate not only the aforementioned glycolytic enzyme genes but also the genes encoding enzymes for fatty acid biosynthesis and triacylglycerol synthesis (collectively termed as lipogenesis). The importance of these transcription factors in the control of glycolysis and fatty acid biosynthesis has been verified by knockout mouse studies, as described in the main text. The liver also has a critical role in controlling glucose homeostasis under fasting conditions. Initially, insulin counterregulatory hormones such as glucagon and epinephrine are critical in activating the PKA-driven kinase cascades that promote glycogen phosphorylase and glycogenolysis in the liver, thus enabling this tissue to provide enough fuel for peripheral tissues such as the brain, red blood cells and muscles. Subsequently, these hormones together with adrenal cortisol are crucial in initiating the transcriptional activation of gluconeogenesis such as PC, PEPCK and G6Pase. The major transcription factors involved in the pathway include CREB, FoxO1 and members of nuclear receptors, with aid from transcriptional coactivators such as CRTC, PGC-1α and PRMTs. These adaptive responses are critical for maintaining glucose homeostasis in times of starvation in mammals. Further study is necessary by using liver-specific knockout mice for each regulator of hepatic glucose metabolism to provide better insights into the intricate control mechanisms of glucose homeostasis in mammals. Acknowledgements This work is supported by the National Research Foundation of Korea (grant nos.: NRF-2012M3A9B6055345, NRF-2015R1A5A1009024 and NRF- 2015R1A2A1A01006687), funded by the Ministry of Science, ICT & Future Planning, Republic of Korea, a grant of the Korean Health technology R&D Project (grant no: HI13C1886), Ministry of Health & Welfare, Republic of Korea and a grant from Korea University, Seoul, Republic of Korea. Author information Affiliations 1. Division of Life Sciences, College of Life Sciences & Biotechnology, Korea University, Seoul, 136-713, Korea Corresponding author Correspondence to Seung-Hoi Koo. Ethics declarations Competing interests The authors declare no conflict of interest. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/29%3A_Integration_of_Mammalian_Metabolism_-_Capstone_Volume_II/29.03%3A_Emerging_role_of_the_brain_in_the_homeostatic_regulation_of_e.txt	princeton-nlp/TextbookChapters	Roh, E., Song, D. & Kim, MS. Emerging role of the brain in the homeostatic regulation of energy and glucose metabolism. Exp Mol Med 48, e216 (2016). https://doi.org/10.1038/emm.2016.4 This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ Abstract Accumulated evidence from genetic animal models suggests that the brain, particularly the hypothalamus, has a key role in the homeostatic regulation of energy and glucose metabolism. The brain integrates multiple metabolic inputs from the periphery through nutrients, gut-derived satiety signals and adiposity-related hormones. The brain modulates various aspects of metabolism, such as food intake, energy expenditure, insulin secretion, hepatic glucose production and glucose/fatty acid metabolism in adipose tissue and skeletal muscle. Highly coordinated interactions between the brain and peripheral metabolic organs are critical for the maintenance of energy and glucose homeostasis. Defective crosstalk between the brain and peripheral organs contributes to the development of obesity and type 2 diabetes. Here we comprehensively review the above topics, discussing the main findings related to the role of the brain in the homeostatic regulation of energy and glucose metabolism. Central regulation of energy metabolism In normal individuals, food intake and energy expenditure are tightly regulated by homeostatic mechanisms to maintain energy balance. Substantial evidence indicates that the brain, particularly the hypothalamus, is primarily responsible for the regulation of energy homeostasis.1 The brain monitors changes in the body energy state by sensing alterations in the plasma levels of key metabolic hormones and nutrients. Specialized neuronal networks in the brain coordinate adaptive changes in food intake and energy expenditure in response to altered metabolic conditions (Figure 1).2, 3 Integration of peripheral metabolic signals andthe central nervous system maintains energy homeostasis. The brain integrates metabolic signals from peripheral tissues such as the liver, pancreas, adipose tissue, gut and muscle. Specialized neuronal networks in the brain coordinate adaptive changes in food intake and energy expenditure in response to altered metabolic conditions. Neuropeptide Y/agouti-related protein- and proopiomelanocortin-producing neurons in the hypothalamic arcuate nucleus primarily sense the body energy state. These neurons project to other hypothalamic nuclei and to the nucleus of the solitary tract in the brain stem to control multiple aspects of the homeostatic regulation of energy balance. ARC, arcuate nucleus; CCK, cholecystokinin; GLP-1, glucagon-like peptide-1; IL-6, interleukin-6; PP, pancreatic polypeptide; PVN, paraventricular nucleus; PYY, peptide YY. Full size image Brain regulation of food intake The hypothalamus is considered a key organ in the regulation of food intake. The hypothalamic arcuate nucleus (ARC) is adjacent to the median eminence, one of the circumventricular organs, and surrounds the third cerebroventricle. Thus, hormones and nutrients in the systemic circulation and the cerebrospinal fluid can easily access the ARC. Anatomically, the ARC is considered a hypothalamic area that primarily senses metabolic signals from the periphery via the systemic circulation.4 In the ARC, there are two distinct neuronal populations: one group of neurons produces the orexigenic neuropeptides neuropeptide Y (NPY) and agouti-related peptide (AgRP), whereas the other subset of neurons expresses the anorexigenic neuropeptides proopiomelanocortin (POMC), and cocaine- and amphetamine-regulated transcript. These neurons are the first-order neurons on which peripheral metabolic hormones, including leptin, insulin, ghrelin and nutrients, primarily act.5 The anorexigenic effect of monoamine serotonin is also mediated by the 5HT-2C receptor in POMC neurons.6 POMC neurons project axonal processes to second-order neurons in hypothalamic areas such as the paraventricular nucleus (PVN), ventromedial hypothalamus (VMH) and lateral hypothalamus (LH), and to autonomic preganglionic neurons in the brain stem and spinal cord.7 The anorexigenic neuropeptide α-melanocyte-stimulating hormone (α-MSH) is produced by posttranscriptional processing of POMC and is released from the presynaptic terminals of POMC neurons. Upon binding to the melanocortin-3 and -4 receptors (MC3R and MC4R) on second-order neurons, α-MSH activates catabolic pathways, leading to reduced food intake and increased energy expenditure. Targeted deletion of the MC4R in mice induces hyperphagia, reduces energy expenditure and leads to obesity.8 In humans, MC4R mutations account for ~6% of severe early-onset obesity cases,9 suggesting an important role for the central melanocortin system in the maintenance of normal body weight. The endogenous MC-3/4R antagonist AgRP is released from the terminals of NPY/AgRP-producing neurons to the synaptic space of second-order neurons where it competes with α-MSH for MC3Rs and MC4Rs and antagonizes its effects.10 Selective ablation of NPY/AgRP neurons in young mice results in a significant decrease in food intake and body weight,11 suggesting that these neurons are critical for promoting food intake and preventing weight loss. Administration of NPY stimulates food intake via Y1 or Y5 receptors.12 NPY is required for the rapid stimulation of feeding, whereas AgRP stimulates feeding over a prolonged period.13 PVN neurons synthesize and secrete neuropeptides that have a net catabolic action, including corticotrophin-releasing hormone, thyrotropin-releasing hormone, somatostatin, vasopressin and oxytocin. On the other hand, PVN neurons control sympathetic outflow to peripheral metabolic organs, resulting in increased fatty acid oxidation and lipolysis.14 Destruction of PVN and haploinsufficiency of Sim1, a critical transcriptional factor in the development of PVN, cause hyperphagia and obesity,15 implying a inhibitory role of the PVN in food intake and weight gain. The VMH mainly receives neuronal projections from the ARC and projects their axons to the ARC, dorsomedial nucleus (DMN), LH and brain stem regions. The VMH contains neurons that sense glucose and leptin.16, 17 Moreover, the anorexigenic neuropeptide brain-derived neurotrophic factor is produced in the VMH.18 Destruction of the VMH causes hyperphagia, obesity and hyperglycemia.19 Thus, the VMH is regarded a pivotal area in generating satiety and maintaining glucose homeostasis. The DMN contains a high level of NPY terminals and α-MSH terminals originating from the ARC.20 Destruction of the DMN also results in hyperphagia and obesity.21 In contrast to the PVN, VMH and DMN, destruction of the LH leads to hypophagia and weight loss. Therefore, LH is considered a feeding center. LH contains two neuronal populations producing the orexigenic neuropeptides melanin-concentrating hormone (MCH) and orexin, also called hypocretin. NPY/AgRP- and α-MSH-immunoreactive terminals from ARC neurons are in contact with MCH- and orexin-expressing neurons.22 Orexin-producing neurons are also involved in glucose sensing and the regulation of sleep–awake cycles.23 Alterations in the orexin receptor-2 and orexin genes produce narcolepsy in animal models and humans.24 On the other hand, depletion of MCH or the MCH-1 receptor in mice attenuates weight gain, suggesting that MCH is an endogenous orexigenic molecule.25 The brain stem is another key brain area involved in the regulation of food intake. Satiety signals from the gastrointestinal tract are relayed to the nucleus tractus solitaries (NTS) through the sensory vagus nerve, a major neuronal connection between the gut and brain. Transection of sensory vagal fibers decreases meal size and meal duration, confirming that vagal afferents transfer meal-related signals to the brain.26 Like the ARC, the NTS is anatomically close to the area postrema, another circumventricular organ.27 Therefore, the NTS is perfectly located for receiving both humoral and neural signals. Meanwhile, the NTS receives extensive neuronal projections from the PVN and vice versa,28 indicating that there are intimate communications between the hypothalamus and the brain stem. Like hypothalamic neurons, NTS neurons produce appetite-regulating glucagon-like peptide-1 (GLP-1), NPY and POMC, and sense peripheral metabolic signals.29 For instance, NTS POMC neurons show the signal transducer and activator of transcription 3 (STAT3) activation in response to exogenous leptin.30 Thus, circulating hormones and nutrients may relay metabolic signals to the brain by acting on both the hypothalamus and brain stem. On the other hand, the brain reward system is involved in the control of hedonic feeding, that is, intake of palatable foods. Like other addiction behaviors, the mesolimbic and mesocortical dopaminergic pathways are involved in hedonic feeding. Intake of palatable foods elicits dopamine release in the ventral tegmental area (VTA), which in turn activates the neural pathways from the VTA to the nucleus accumbens via the medial forebrain bundles. Interestingly, hedonic feeding is modulated by metabolic signals. Leptin acts on the dopaminergic neurons in the VTA to suppress feeding.31 Conversely, hedonic feeding can override satiety signals. Mice lacking the D2 receptor are more sensitive to leptin.32 Brain regulation of energy expenditure The brain modulates various processes that consume energy, such as locomotor activity, fatty acid oxidation in the skeletal muscle and thermogenesis.33 Tumor growth factor-α, produced in the suprachiasmatic nucleus in a circadian manner, strongly inhibits locomotor activity by acting on the epidermal growth factor receptors expressed in the hypothalamic subparaventricular zone.34 Orexin-A produced by LH neurons promotes locomotor activity and wakefulness through orexin-1 and orexin-2 receptors.35 A role for orexin in food-seeking behavior in food-deprived conditions has been suggested.36 Leptin stimulates locomotor activity via a mechanism that depends on hypothalamic POMC neurons37. Leptin also enhances fatty acid oxidation in skeletal muscle via both central and peripheral mechanisms.38 Thermogenesis is theprocess that dissipates energy as heat to maintain body temperature. Thermogenesis mainly occurs in brown adipose tissue (BAT).39 Brown fat-like adipocytes, so-called browning of white adipose tissue (WAT), are found in the subcutaneous inguinal WAT under certain circumstances. Cold exposure or intracerebroventricular (ICV) coinjection of insulin and leptin induces WAT browning.40 Induction of WAT browning results in increased energy expenditure and attenuation of diet-induced obesity in mice. Conversely, inhibition of WAT browning by depletion of Prdm16 leads to obesity.39 The brain regulates BAT thermogenesis through modulation of the sympathetic nervous system. Norepinephrine released from sympathetic nervous terminals acts on the β3-adrenergic receptors in adipocytes in the BAT and inguinal fat pads. Activation of adrenergic receptors triggers cyclic-adenosine monophosphate signaling, which in turn increases the expression of uncoupling protein-1 in the mitochondria. BAT thermogenesis is important for maintaining body temperature in response to cold exposure and dissipating excess energy after high-calorie intake. Because metabolic fuel substrates such as glucose and fatty acid are consumed during BAT thermogenesis, BAT thermogenesis can affect body weight and body fat mass.41 In the past, BAT was thought to be present only in human infants. However, 18F-fluorodeoxyglucose positron emission tomography revealed the presence of BAT in the adult humans. Human BAT depots are distributed in the supraclavicular area and in perivascular and periviscus areas (for example, around the heart, airway, gut, liver and adrenal gland) of the chest and abdomen.42 BAT activity, determined by 18F-fluorodeoxyglucose positron emission tomography, is affected by outdoor temperature, age, sex, body mass index and the coexistence of diabetes. Because the amount of BAT is inversely correlated with body mass index, especially in older subjects, a potential role of BAT in adult human metabolism has been suggested.43 In thermogenic regulation, the hypothalamus integrates the sensation of body temperature with efferent sympathetic outflow. Hypothalamic areas such as the prooptic area, VMH, DMN and ARC modulate thermogenic activity by influencing the sympathetic nervous system.44 The prooptic area is an important area in the control of body temperature.45 VMH was the first hypothalamic nucleus to be studied regarding the regulation of BAT activity. The DMN also contains sympathoexcitatory neurons,46 which regulate thermogenic activity.47, 48 BAT thermogenesis is also related to the ARC melanocortin system because α-MSH stimulates BAT activity.49 Hormonal- and nutrient-mediated metabolic signals can influence sympathetic outflow to the BAT. Central administration of leptin, MC3/4R agonist, glucagon and GLP-1 stimulates BAT activity.50, 51 Central administration of insulin either stimulates or inhibits BAT thermogenesis, depending on the insulin dose. Central administration of high doses of insulin increases sympathetic nerve activity in the BAT, whereas low doses of insulin decrease it.52, 53 Food consumption or dietary composition also affects BAT thermogenesis. Although the mechanism of postprandial thermogenesis is unclear, norepinephrine turnover in the BAT is increased after a meal.54 Glucose administration increases thermogenesis, whereas fasting or food restriction inhibits thermogenesis. Low-protein diet and high-fat diet increase BAT activity.55 Peripheral signals modulating energy metabolism Adiposity signals Adiposity signals refer to the peripheral signals that circulate in proportion to the total amount of stored fat and inform the brain about the stored energy state. They modulate energy balance through the regulation of food intake and energy expenditure.2, 56 Insulin is a hormone that was first identified as an adiposity signal.5, 57 Insulin is secreted by β-cells in response to energy flux. Plasma insulin concentrations increase in proportion to the amount of stored fat.58 When insulin is administered directly into the central nervous system, it induces a dose-dependent reduction in food intake and body weight.59, 60 Thus, insulin is thought to signal adiposity to the brain. In hypothalamic neurons, insulin activates the insulin receptor substrate-2 (IRS2)–phosphatidylinositol 3-kinase (PI3K) signaling pathway. Neuronal deletion of insulin receptor and IRS2 results in increased food intake and susceptibility to diet-induced obesity.61, 62 The adipose tissue-derived hormone leptin was discovered by positional cloning of the obesity locus (ob) in 1994.63 Leptin is now considered a representative adiposity signal.64 The receptors activated by leptin are highly expressed in several regions of brain, including the hypothalamus.65 Genetic deficiency in leptin or the long-form leptin receptor (LepRb) is associated with hyperphagia, hypoactivity and obesity.66 Of several brain regions, the ARC is an important area that mediates leptin actions. Injection of leptin directly into the ARC reduces food intake and body weight.67 Leptin also stimulates locomotion through signaling in POMC neurons.37 Consistently, ICV administration of leptin in leptin-deficient (ob/ob) mice attenuates obesity.66 In hypothalamic neurons, leptin provokes several signaling cascades such as the Janus kinase–STAT pathway, IRS–PI3K signaling, the mammalian target of rapamycin–S6 kinase signaling, AMP-activated protein kinase (AMPK) signaling and ERK signaling.68 Of those, STAT3 signaling represents hypothalamic leptin signaling and is frequently used as a marker of leptin signaling activity. Nutrient signals Nutrients such as glucose, fatty acids and amino acids provide information on nutrient availability to the brain. Glucose signals the presence of anenergy supply to the brain, whereas hypoglycemia signals an energy deficit.69 Thus, central administration of glucose and long-chain fatty acids decreases food intake.70 In contrast, ICV administration of the glucose anti-metabolite 2-deoxy-D-glucose increases food intake.71 The malonyl-CoA content in hypothalamic neurons has been suggested to be a fuel gauge.56, 72 Administration of the fatty acid synthase inhibitor C75 induces accumulation of malonyl-CoA in hypothalamic neurons, leading to decreased food intake and body weight.73 Long-chain fatty acyl-CoA (LCFA-CoA) content in hypothalamic neurons also acts as a cellular nutrient sensor. An increased hypothalamic LCFA-CoA level due to ICV long-chain fatty acid (LCFA) administration leads to decreased food intake.70 Hypothalamic inhibition of carnitine palmitoyltransferase-1 inhibits food intake by elevating LCFA-CoA content in hypothalamic neurons.74 Gastrointestinal signals Hormones secreted by the gut in response to a meal provide information on energy intake. Cholecystokinin, peptide YY and GLP-1 released from the gut induce satiety by acting on the vagus nerve or in the brain.75 For example, GLP-1 is secreted from intestinal L-cells after a meal. GLP-1 receptors are prevalent in vagus nerve terminals,76 as well as in the central nucleus of the amygdala, the PVN and ARC of the hypothalamus, and the caudal brain stem.77 Both central and peripheral administration of GLP-1 promotes satiety.78, 79 In contrast, ghrelin is secreted by the stomach during a fast and promotes food intake.80 Signals from other organs Interleukin-6 (IL-6) is synthesized and released from contracting skeletal muscle during exercise. The elevation in the plasma IL-6 concentration during exercise correlates with exercise intensity and duration and the muscle mass recruited.81 IL-6 enters the brain across the blood–brain barrier. IL-6 may mobilize fat from storage sites to provide energy to the muscle. ICV administration of IL-6 stimulates energy expenditure, and mice lacking IL-6 develop mature-onset obesity.82 Hormones secreted from the endocrine pancreas are also involved in energy homeostasis. Insulin and amylin are co-secreted by β-cells. Like insulin, amylin acts as a satiety signal and reduces food intake via amylin receptors in the area postrema. Other brain sites mediating amylin action include the NTS and the lateral parabrachial nucleus.83 Amylin also acts as an adiposity signal because amylin levels are well correlated with body fat content. Glucagon, a counter-regulatory hormone to insulin, is secreted from α-cells. Glucagon reduces meal size by acting on the vagus nerves and stimulates energy expenditure through central and peripheral mechanisms.84 Pancreatic polypeptide is also secreted from the endocrine pancreas. Pancreatic polypeptide regulates gastric motility, pancreatic exocrine secretion and food intake. Systemic administration of pancreatic polypeptide reduces food intake and weight gain.85 The anorectic effect of pancreatic polypeptide is mediated by Y4 receptors in the dorsal vagal complex.86 Brain regulation of glucose metabolism The earliest demonstration of the role of the brain in glucose homeostasis was provided by the physiologist Claude Bernard in 1854. Dr Bernard demonstrated that a puncture in the floor of the fourth ventricle of the rabbit brain resulted in glycosuria.87 In the past few decades, the concept of central regulation of glucose metabolism has been further established by the subsequent discovery of glucose-sensing neurons in the hypothalamus88, 89 and the demonstration of their roles in maintaining normal glucose levels.90 A specialized neuronal population in the brain senses hormones (insulin and leptin) and nutrients (glucose and fatty acids) to regulate glucose homeostasis. The major sites of convergence of these metabolic signals are the hypothalamus and brain stem (Figure 2). Brain regulation of glucose homeostasis. The brain senses peripheral metabolic signals through hormones (insulin, leptin and so on) and nutrients (glucose, free fatty acids and so on) to regulate glucose metabolism. The sites of the convergence of these metabolic signals are the hypothalamus and brain stem. The autonomic nervous system intervenes in the brain and peripheral metabolic organs to modulate pancreatic insulin/glucagon secretion, hepatic glucose production and skeletal muscle glucose uptake. AP, area postrema; ARC, arcuate nucleus; BLM, basolateral medulla; DMN, dorsomedial nucleus; DMNX, dorsal motor nucleus of the vagus; FFA, free fatty acids; LH, lateral hypothalamus; NTS, nucleus of the solitary tract; PNS, parasympathetic nervous system; PVN, paraventricular nucleus; SNS, sympathetic nervous system; VMH, ventromedial hypothalamus. Full size image Neuronal populations controlling glucose metabolism Brain regions related to the control of glucose metabolism contain neurons whose excitability changes with alterations in glucose concentrations in the extracellular fluid. These glucose-sensing neurons are found in the hypothalamic nuclei and brain stem, which are also important areas in the control of energy balance. Glucose-sensing neurons are subgrouped into two types. Glucose-excited neurons are excited when extracellular glucose levels increase. In contrast, glucose-inhibited neurons are activated by a fall in extracellular glucose concentrations.91 Glucose-excited neurons are mostly located in the VMH, the ARC and the PVN,92 whereas glucose-inhibited neurons are distributed in the LH, ARC and PVN.89, 91 Both types of neurons are also located in the dorsal vagal complex in the brain stem, which encompasses the NTS, area postrema and the dorsal motor nucleus of the vagus.93, 94, 95 Peripheral signals affecting brain regulation of glucose metabolism Insulin During the past decade, the brain has been recognized to be a site of insulin action with regard to glucose homeostasis. Obici et al.96 showed that insulin acts on the brain to modulate hepatic glucose metabolism. They showed, by injecting insulin receptor antisense oligonucleotides into the cerebroventricle, that inhibition of central insulin action impaired insulin-mediated suppression of hepatic glucose production (HGP) during hyperinsulinemic clamp studies in rats. They also demonstrated that infusion of insulin into the cerebroventricle suppressed HGP, irrespective of circulating insulin levels. Moreover, central administration of insulin antibodies or inhibitors of the downstream signaling of insulin diminished the ability of insulin to inhibit glucose production.97 The hypothalamic insulin signaling pathway was investigated in subsequent studies. Overexpression of the insulin signaling molecules IRS2 and Akt in the hypothalamus enhances the glucose-lowering effect of insulin in streptozotocin-induced diabetic rats.98 These data support a role for hypothalamic insulin actions in controlling glucose metabolism in peripheral organs. The ATP-sensitive potassium (KATP) channel mediates insulin actions in hypothalamic neurons.99 Activation of neuronal KATP channels by ICV injection of a KATP channel activator (diazoxide) lowers glucose production,100 whereas infusion of a KATP blocker (sulfonylurea) negates the glucose production-lowering effect of centrally and peripherally administered insulin.96, 100 Moreover, mice lacking the sulfonylurea receptor subunit SUR1 of the KATP channel show a diminished response to central insulin action.100 Vagal efferent fibers constitute the brain–liver axis of insulin actions because hepatic vagotomy blocks central insulin actions.100 Interestingly, ICV infusion of insulin increases hepatic IL-6 expression, which leads to the activation of hepatic STAT3 signaling.101 Activated STAT3 inhibits FoxO1 activity and gluconeogenic gene expression in the liver. Collectively, central insulin actions are mediated via neuronal KATP channel–vagus nerve–hepatic IL6/STAT3 signaling, although the detailed mechanisms involved remain to be determined. Leptin Leptin has an important role in the control of glucose metabolism. A lack of leptin (ob/ob mice) or its functional receptor (db/db mice) leads not only to obesity, but also metabolic derangement, including insulin resistance and diabetes.102 Leptin treatment of ob/ob mice improves glucose homeostasis.103, 104 Notably, acute leptin treatment via both systemic and central routes in ob/ob mice restores glucose metabolism independently of changes in food intake and adiposity.105, 106 Consistently, leptin-treated ob/ob mice display a marked reduction in serum glucose and insulin concentrations.107 Leptin treatment in lipodystrophy mice improves insulin resistance and hyperglycemia independently of food intake.108, 109 Thus, leptin regulates glucose homeostasis independently of its anorectic effects. The hypothalamus is a key site of action of leptin-mediated control of glucose metabolism. ICV administration of leptin in the lipodystrophy mice model corrects insulin resistance and improves impaired insulin signaling in the liver. In contrast, peripheral injection of the same dose of leptin did not have a similar effect.110 Acute ICV injection of leptin suppresses glycogenolysis and reduces hepatic insulin resistance induced by high-fat feeding.111 Restoration of leptin signaling in the unilateral ARC by viral gene therapy in leptin receptor-null mice markedly improves hyperinsulinemia and normalizes blood glucose levels, with a mild decrease in body weight and food intake. These data demonstrate that leptin signaling in the ARC is critical for the maintenance of glucose homeostasis.112 Leptin-mediated regulation of glucose metabolism is mediated by hypothalamic STAT3 and PI3K signaling pathways. As in db/db mice, s/s mice with a mutated leptin receptor, which are unable to activate STAT3, exhibit severe hepatic insulin resistance.113 Blockade of leptin-induced STAT3 activation in the hypothalamus abolishes the suppressive effect of leptin on HGP, confirming the importance of leptin-induced STAT3 signaling.113 Conversely, hypothalamic deletion of suppressor of cytokine signaling 3, a negative regulator of STAT3 signaling, enhances leptin sensitivity and improves glucose metabolism.114 On the other hand, reconstitution of leptin receptors in the ARC of leptin-receptor-deficient fak/fak rats improves insulin sensitivity, which is attenuated by ICV infusion of PI3K inhibitor. Consistently, ARC expression of constitutively active Aktin fak/fak rats mimics the effect of restored hypothalamic leptin signaling.115 These findings indicate that PI3K–Akt signaling mediates leptin actions on glucose homeostasis. Glucose Glucose sensing in the hypothalamus is important in glucose homeostasis. Injection of 2-deoxy-D-glucose into the VMH increases plasma glucose levels by elevating plasma glucagon and catecholamine levels.116 Conversely, intra-VMH glucose infusion suppresses counter-regulatory hormonal responses to hypoglycemia.90 The brain stem is also involved in glucoprivic feeding and counter-regulatory hormone secretion during hypoglycemia. Injection of another glucose anti-metabolite, 5-thio-D-glucose, into the NTS and the basolateral medulla, which contain A1/C1 catecholaminergic neurons projecting to the hypothalamic PVN and ARC, induces feeding and glucose responses, as seen in hypoglycemia.117 Similarly, destruction of hindbrain catecholaminergic neurons by immunotoxins blocks 2-deoxy-D-glucose-induced feeding and blood glucose responses.118 The glucose-sensing mechanisms in hypothalamic neurons are similar to those in pancreatic β-cells.119 Glucose signaling in glucose-excited neurons requires glucose uptake via the type 2 glucose transporter, which is followed by glucose phosphorylation by glucokinase, intramitochondrial glucose oxidation, and an increased cellular ATP/ADP ratio. This leads to the closure of ATP-sensitive KATP channels, depolarization of the membrane potential, and influx of Ca2+ through voltage-dependent calcium channels, which stimulate neuronal activity and neurotransmitter release.120 The role of hypothalamic type 2 glucose transporter, glucokinase and KATP channels in sensing hypoglycemia and counter-regulatory hormone responses has been demonstrated.121, 122, 123, 124, 125, 126 How glucose inhibits neuronal activity in glucose-inhibited neurons is unclear. One possibility is that glucose increases the ATP/ADP ratio, which stimulates the Na+/K+-ATPase pump and triggers hyperpolarizing currents.127 Alternatively, glucose-induced activation of ATP-dependent Cl channels may induce hyperpolarization of the plasma membrane.91, 128 AMPK functions as a ‘fuel gauge’ that monitors cellular energy status and provokes adaptive responses to maintain cellular energy levels129, 130. ICV administration of glucose suppresses feeding via inhibition of hypothalamic AMPK activity.131, 132 Hypothalamic AMPK activation is critical for feeding and counter-regulatory responses to hypoglycemia.131 Intra-VMH administration of AICAR (5-aminoimidazole-4-carboxamide ribonucleotide), a chemical AMPK activator, increases HGP without changing the plasma levels of counter-regulatory hormones.133 AMPK activation in the VMH restores reduced counter-regulatory responses induced by repeated hypoglycemia.134 Consistent with these findings, inhibition of hypothalamic AMPK activity attenuates the counter-regulatory response during hypoglycemia.135 Fatty acids LCFA signals nutrient availability to the brain and modulates peripheral glucose metabolism.70 ICV administration of oleic acid suppresses HGP during basal insulin clamping. ICV administration of KATP channel blocker attenuates the inhibitory effect of oleic acid on glucose production, indicating an involvement of brain KATP channels in this process.70 Increased LCFA-CoA levels in hypothalamic neurons suppresses endogenous glucose production.74 Pharmacological inhibition of hypothalamic esterification of fatty acids or surgical resection of the hepatic branch of the vagus nerve increases HGP.136 Therefore, hypothalamic lipid sensing regulates glucose homeostasis via a mechanism involving the esterification of LCFAs to LCFA-CoAs, intact KATP channels and vagal outflow to the liver. Effector pathways in the brain control of glucose metabolism To the liver In rodents, direct action of insulin on the liver is necessary, but is insufficient to inhibit HGP, unless the indirect brain pathway is not fully functional. Restoration of insulin receptor expression in either the liver or brain of insulin receptor-null mice does not completely restore the ability of insulin to inhibit HGP.137 In contrast, restoration of insulin receptor expression in both the brain and liver normalizes insulin actions on HGP.138 Whether neuronal control of HGP is unique to rodents remains uncertain. ICV insulin infusion in the dog augments hepatic glucose uptake and glycogen synthesis without altering HGP,139 indicating that the regulation of gluconeogenesis by brain insulin signaling may differ among species. The basal HGP rate per weight is almost 5–10 times higher in rodents than in dogs and humans.140 Dogs and humans maintain hepatic glycogen storage even after a 42-h fasting.141, 142 In contrast, hepatic glycogen content is significantly depleted in rodents after a relatively short fast, which may be due to higher metabolic rates.143 Therefore, the contribution of the gluconeogenic pathway to HGP may be greater in rodents than in animals with a larger body size.139 Thus, changes in gluconeogenesis may be more easily detected in rodents. To the skeletal muscle Electrical stimulation of VMH neurons and local injection of leptin into the VMH increases glucose uptake in the skeletal muscle of rats independently of circulating insulin levels.144, 145 These effects appears to be mediated by the sympathetic nervous system as they are abolished by blockade of the sympathetic nervous system.146, 147 Consistently, central infusion of leptin improves glucose tolerance and enhances insulin-stimulated Akt phosphorylation in skeletal muscle.148, 149 Activated Akt leads to translocation of the glucose transporter GLUT4 from its sequestered cytoplasmic location to the cell membrane, facilitating glucose uptake150. In the skeletal muscle, AMPK activation is induced by muscle contraction and adrenergic agonist and mediates insulin-independent glucose uptake.151 Leptin activates skeletal muscle AMPK through the hypothalamus and sympathetic nervous system.152 Therefore, leptin may promote glucose uptake to the skeletal muscle through the sympathetic nervous system–muscle AMPK signaling pathway. On the other hand, orexin-producing neurons in the LH are activated by sweet foods. Orexin regulates skeletal muscle glucose uptake through VMH neurons expressing orexin receptors and the sympathetic nervous system.153 To the pancreas The autonomic nervous system controls the secretion of insulin and glucagon in the pancreas. Sympathetic and parasympathetic nerve endings are foundin pancreatic islets.154 Moreover, α- and β-cells express neurotransmitter receptors.155 Both sympathetic and parasympathetic nerve branches can stimulate glucagon secretion. In contrast, parasympathetic branches stimulate insulin secretion, whereas sympathetic branches inhibit it.156 Neurons in the dorsal motor nucleus of the vagus project nerve terminals to the pancreatic ganglions via the vagus nerve, and thus vagus nerves connect the dorsal motor nucleus of the vagus and endocrine pancreas.157 Insulin regulates whole-body glucose metabolism by acting on the brain, and modulating insulin and glucagon secretion. ICV administration of insulin increases pancreatic insulin output, demonstrating that pancreatic β-cells are influenced by insulin-sensitive cells of the brain.158 Moreover, insulin injection into the VMH inhibits glucagon secretion by pancreatic α-cells, indicating that insulin controls glucagon secretion via brain-mediated mechanisms.159 Taken together, the brain, especially the hypothalamus and brain stem, modulates pancreatic insulin and glucagon secretion via the parasympathetic and sympathetic efferent nerves that innervate pancreatic α- and β-cells.160 Dysregulation of energy/glucose metabolism in obesity and diabetes In healthy conditions, energy intake matches energy expenditure to maintain normal body weight. Impaired ability of the brain to maintain energy homeostasis may underlie pathological weight gain and obesity (Figure 3). Several defects in the negative-feedback pathway in energy homeostatic mechanisms have been suggested.3 Defects in the secretion of key metabolic hormones such as insulin and leptin may predispose weight gain. Because leptin primarily acts on hypothalamic neurons to regulate the energy balance, leptin transfer to the brain may be critical for its action.5 Leptin concentrations in the plasma increase in proportion to body mass index, an indicator of fat mass. However, the increase in leptin concentrations in the cerebrospinal fluid of obese individuals is less than that of plasma leptin concentrations.161 Therefore, reduced leptin transport to the brain may be due to reduced action of leptin in obesity. Pathogenesis of obesity and type 2 diabetes due to defective central regulation of energy and glucose homeostasis. Reduced nutrient sensing and impaired insulin and leptin signaling in the hypothalamus may result in a positive energy balance and predispose weight gain, causing insulin resistance in peripheral metabolic organs. Obesity-associated insulin resistance may lead to type 2 diabetes when it is combined with β-cell dysfunction. IRS, insulin receptor substrate; PI3K, phosphatidylinositol 3 kinase; STAT3, signal transducer and activator of transcription 3. Full size image Defective hypothalamic sensing of these hormones favors a positive energy balance because loss of leptin receptors in the hypothalamus leads to obesity in mice.13 Rats with diet-induced obesity have reduced expressions of leptin receptors in the hypothalamus.162 Impaired postreceptor signaling in hypothalamic neurons may also result in pathological weight gain. Disruption of the hypothalamic IRS–PI3K signaling pathway causes resistance to peripheral metabolic signals and leads to obesity.163 Likewise, mice with disrupted neuronal STAT3 signaling develop hyperphagia and obesity.164 In rodents, long-term high-fat feeding reduces the anorectic response and hypothalamic STAT3 activation induced by leptin, which is called leptin resistance.165 Increased hypothalamic expression of suppressor of cytokine signaling 3 has been suggested to be a mechanism of hypothalamic leptin resistance. Ablation of suppressor of cytokine signaling 3 expression in neurons mitigates high-fat diet-induced weight gain and hyperleptinemia and improves glucose tolerance and insulin sensitivity.166 Protein-tyrosine phosphatase 1B, a well-known negative regulator of insulin and leptin signaling,167, 168 has also been suggested to cause leptin and insulin resistance in hypothalamic neurons. Neuronal Protein-tyrosine phosphatase 1B knockout mice are hypersensitive to exogenous leptin and insulin, and display improved glucose tolerance during chronic high-fat feeding.169 Increased IKKβ-NFκB and endoplasmic stress have been found in the hypothalamus of obese rodents and shown to disrupt hypothalamic leptin and insulin signaling.170, 171 However, a recent paper has shown, using a leptin receptor antagonist, that endogenous leptin signaling and actions in high-fat diet-fed obese mice treated are comparable to those of chow diet-fed mice, arguing against the concept of leptin resistance.172 Thus, further studies are needed to clarify the issue of leptin resistance in obese humans and animals. Diabetes mellitus is a metabolic disorder characterized by hyperglycemia that affects ~9% of adults worldwide.173 It results from deficits in pancreatic insulin secretion and insulin signaling/actions in insulin target organs. Experimental evidence suggests that defective metabolic sensing in hypothalamic neurons may lead to dysregulation of glucose homeostasis and diabetes (Figure 3). Hypothalamic insulin–PI3K signaling is markedly impaired in rats with streptozotocin-induced diabetes.98 Pharmacological inhibition of hypothalamic PI3K signaling attenuates the glucose-lowering effect of insulin. Conversely, enhanced hypothalamic PI3K signaling via adenoviral gene therapy potentiates insulin-induced glucose lowering.98 Notably, central insulin actions are blunted by short-term high-fat diet feeding.174 Thus, a fat-rich diet may contribute to the development of diabetes by disrupting insulin signaling in the hypothalamus.174 Concluding remarks This review highlights the role of the brain in the homeostatic regulation of energy and glucose metabolism. The brain detects energy intake by sensing gut hormones released in response to food intake and detecting nutrients in circulating blood. The brain also monitors body energy stores by sensing adiposity-related signals. Information on nutrient availability and stored fat is transferred to specialized neurons in the hypothalamus and brain stem. In the control of the energy balance, outflow pathways from the brain regulate food intake and energy expenditure (thermogenesis or locomotor activity). The brain also has an important role in the maintenance of glucose homeostasis, which is achieved by the modulation of insulin/glucagon secretion in the endocrine pancreas, HGP, and skeletal muscle glucose uptake. The autonomic nervous system constitutes the outflow pathways from the brain to peripheral metabolic organs. Defective crosstalk between the brain and peripheral metabolic organs observed in the obese condition may lead to type 2 diabetes development and obesity progression. Therefore, better understanding of neural mechanisms involved in the regulation of glucose/energy homeostasis will provide us with the opportunity to develop new therapeutics combating obesity and diabetes. Acknowledgements This work was supported by grants from the National Research Foundation (NRF-2014R1A6A3A01057664, NRF-2013M3C7A1056024 for M-SK) and the Asan Institute for Life Sciences (2013-326). Author information Author notes 1. Eun Roh and Do Kyeong Song: These authors contributed equally to this work. Affiliations 1. Appeptite Regulation Laboratory, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, Korea 2. Department of Medicine, University of Ulsan College of Medicine, Seoul, Korea 3. Division of Endocrinology and Metabolism, Asan Medical Center, Seoul, Korea Corresponding author Correspondence to Min-Seon Kim. Ethics declarations Competing interests The authors declare no conflict of interest. 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textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/29%3A_Integration_of_Mammalian_Metabolism_-_Capstone_Volume_II/29.04%3A_Skeletal_Muscle_Regulates_Metabolism.txt	princeton-nlp/TextbookChapters	Josep M. Argilés, Nefertiti Campos, José M. Lopez-Pedrosa, Ricardo Rueda, Leocadio Rodriguez-Mañas. Journal of the American Medical Directors Association, Volume 17, Issue 9, 2016, Pages 789-796,ISSN 1525-8610,https://doi.org/10.1016/j.jamda.2016.04.019. Under a Creative Commons license Abstract Skeletal muscle is recognized as vital to physical movement, posture, and breathing. In a less known but critically important role, muscle influences energy and protein metabolism throughout the body. Muscle is a primary site for glucose uptake and storage, and it is also a reservoir of amino acids stored as protein. Amino acids are released when supplies are needed elsewhere in the body. These conditions occur with acute and chronic diseases, which decrease dietary intake while increasing metabolic needs. Such metabolic shifts lead to the muscle loss associated with sarcopenia and cachexia, resulting in a variety of adverse health and economic consequences. With loss of skeletal muscle, protein and energy availability is lowered throughout the body. Muscle loss is associated with delayed recovery from illness, slowed wound healing, reduced resting metabolic rate, physical disability, poorer quality of life, and higher health care costs. These adverse effects can be combatted with exercise and nutrition. Studies suggest dietary protein and leucine or its metabolite β-hydroxy β-methylbutyrate (HMB) can improve muscle function, in turn improving functional performance. Considerable evidence shows that use of high-protein oral nutritional supplements (ONS) can help maintain and rebuild muscle mass and strength. We review muscle structure, function, and role in energy and protein balance. We discuss how disease- and age-related malnutrition hamper muscle accretion, ultimately causing whole-body deterioration. Finally, we describe how specialized nutrition and exercise can restore muscle mass, strength, and function, and ultimately reverse the negative health and economic outcomes associated with muscle loss. Keywords Muscle glucose amino acid sarcopenia HMB ONS Skeletal muscle is integral to physical movement, posture, and vital actions, such as chewing, swallowing, and breathing.1, 2 Skeletal muscle also serves as a regulator of interorgan crosstalk for energy and protein metabolism throughout the body, a less recognized but critically important role. As such, skeletal muscle is a key site for glucose uptake and storage.3 Skeletal muscle is likewise a reservoir of amino acids that can support protein synthesis or energy production elsewhere in the body when other sources are depleted.4 This review of muscle metabolism describes how amino acids stored as protein in muscle can be broken down through proteolysis for ultimate use in energy production. Such breakdown occurs when energy demands are high (as with stress-induced hypermetabolism), or when supplies are low (as in severe starvation or longer-term protein energy malnutrition). Both of these states can be hallmarks of many diseases, either directly as a result of disease-related dysregulation of metabolism (such as in the extreme case of cancer-cachexia) or, more subtly, as a result of the general illness-associated loss of appetite. Muscle is therefore crucially important during illness, both for its role in balancing the metabolic needs of other organs and for its reserves of protein for use in energy production. Yet, during illness, the maintenance of muscle mass through exercise and nutrition are often overlooked or difficult to address, and muscle atrophy develops. Even more subtle is aging-related muscle loss, which can dramatically increase morbidity and mortality of otherwise survivable illnesses in the aged. This review also illustrates the consequences of muscle atrophy in aging and illness and proposes steps to combat these challenges. Muscle Basics Muscle Structure and Classification Skeletal muscle comprises the fibrillar proteins myosin (a thick filament) and actin (a thin filament) that interact to cause muscle contraction, a process requiring energy in the form of adenosine triphosphate (ATP). Different muscle types have been classified according to histochemical features, structural protein composition, and major metabolic properties.5, 6 Most commonly, skeletal muscles are referred to as either “slow” or “fast” to reflect speeds of contraction, or the shortening of myosin heavy chain (MHC) protein.6 The velocity of this shortening is dependent on the MHC isoform present; “fast” fiber isoforms MHCIIa and IIb demonstrate a higher shortening velocity than their “slow” fiber MHCI counterparts.6, 7 Classic histochemical staining methods also classify muscle as type I (slow) and type II (fast) based on the myosin ATPase enzyme forms revealed. Recently, these types have been further distinguished based on histology (types I, IC, IIC, IIAC, IIA, IIAB, and IIB).6 Muscle Metabolism and Interorgan Crosstalk Glucose regulation is central to energy balance both within muscle fibers and throughout the body. In the cytoplasm of most cells, glucose undergoes glycolysis to produce the substrate for ATP generation. Muscle fibers are also characterized on the basis of the speed and manner in which they metabolize glucose. The terms “fast” and “slow” can indicate the type of glucose metabolism occurring within the fiber. Slow muscles, which use aerobic metabolism, contain a high density of capillaries and oxidative enzymes that allow a greater resistance to fatigue.7 Fast muscles, which depend on anaerobic metabolism, or glycolysis, can quickly generate ATP and therefore contract more readily. Fast muscles also fatigue sooner than slow fibers, as the conversion of glucose to pyruvate generates less ATP than can be generated by using the rest of central metabolism, ultimately generating CO2. Muscle has the ability to store glucose in the form of glycogen, which facilitates the rapid initiation of energy production for contraction even when glucose is not readily available from the diet. This unique capacity, shared also by the liver and kidneys, makes skeletal muscle an important metabolic organ that helps all organs have access to essential energy substrates during fasting. Furthermore, the amino acids stored in muscle as protein can be broken down as a last resort during times of starvation or extreme energy shortfalls.4 Patterns of glucose utilization throughout the body as a whole reflect feeding status (Figure 1; Table 1). Based on a classic study of the fed state (measurement within 3 hours of eating), researchers estimated that 25% to 35% of an ingested carbohydrate load was quickly extracted from circulation and stored by the liver.3 Of the remaining glucose, approximately 40% was disposed in the muscle and 10% in the kidney.3 The brain used 15% to 20% of post-meal glucose.3 Table 1. Glucose Metabolism in Fasted and Fed States Fed State Fasted State Diet-sourced glucose (exogenous glucose) is absorbed from the intestine to circulate in blood; glucose serves as an energy source in cells throughout the body. Cytoplasmic glucose undergoes glycolysis, in turn producing ATP. Little or no blood glucose from dietary sources; alternative energy sources are needed for function of tissues body-wide. Glucose is primarily taken up by muscle and liver, where it can be used for energy or stored as glycogen (Glycogen synthesis). Glycogen stored in liver, kidney, and muscle is broken down to provide glucose as energy source (Glycogenolysis). Muscle uses glycogen-sourced glucose internally; liver and kidney can supply glucose to circulation. Gluconeogenic substrates are stored in various organs (eg, pyruvate in liver, glycerol in fat, and amino acids in muscle). Endogenous generation of glucose from noncarbohydrate carbon substrates, such as pyruvate, lactate, glycerol, and glucogenic amino acids (Gluconeogenesis); occurs primarily in the liver and muscle, and to a lesser extent in the kidney. In the fasted state (after 14 to 16 h without eating), the liver provides approximately 80% of glucose that is released into circulation. About half of this glucose comes from the breakdown of stored glycogen, and the rest from the metabolism of sources other than carbohydrate or glycogen, including certain amino acids, through a process known as gluconeogenesis.8 Interactions between muscle and liver are largely responsible for regulating carbohydrate metabolism and for achieving energy balance in normal fed and fasted states; the kidneys play a role similar to that of the liver, but to a lesser extent.3, 8 In addition, muscle tissue stores amino acids as protein, and adipose tissue serves as a depot of glycerol and fatty acids. As needed, amino acids and fatty acids can be metabolized to form acetyl coenzyme A for the tricarboxylic acid (TCA) cycle. As glycogen stores become depleted, increasingly more glucose is produced by gluconeogenesis. Gluconeogenesis provides 70% of glucose released into the body 24 hours after eating, and 90% by 48 hours.8 As fasting is prolonged, the kidneys contribute increasingly higher amounts of glucose from gluconeogenesis. Ultimately, amino acids stored in skeletal muscle are metabolized when the need for gluconeogenesis substrate is greatest. Skeletal muscle houses nearly 75% of all protein in the body and constitutes an important contributor to gluconeogenesis in states of drastic depletion. Maintenance of muscle protein content depends on the balance between protein synthesis and degradation.5 Under normal conditions, muscle protein mass gains during the fed state balance losses during the fasted state.4 However, under severe metabolic stress generated by serious illness or injury, muscle protein can become depleted by catabolism, and this can lead to harmful functional limitations. Skeletal muscle proteolysis can provide amino acid substrates for glucose and glycogen formation, notably glutamine and alanine. Alanine is released into circulation and reaches the liver, where it serves as an excellent substrate for gluconeogenesis. Glutamine also has a beneficial role in this process: the carbon skeleton of glutamine is a gluconeogenic precursor that can regulate gluconeogenesis independently of the insulin/glucagon ratio. Therefore, glutamine supplementation may also enhance glycogen synthesis and increase muscle glycogen stores even when insulin levels are low or when insulin resistance is present.9 In summary, dietary glucose is supplied by meals, and glucose is stored as glycogen in liver, kidney, and muscle for metabolic energy functions, as needed (Table 1). At times when glucose supplies are not sufficient to meet energy needs, breakdown of glycogen (glycogenolysis) occurs. When stored glucose products are no longer available, energy is released by breakdown of substrates other than glucose. In this review of muscle metabolism, we emphasize that amino acids stored as protein in muscle can be broken down by way of gluconeogenesis, ultimately entering the TCA cycle for energy production. Such breakdown occurs when energy demands are high, as with stress-induced hypermetabolism of disease, or when supplies are low, as in severe starvation or disease-associated loss of appetite. Such use can become problematic in that it reduces skeletal muscle mass and produces waste nitrogen, which requires further energy to sequester and secrete. Prolonged reliance on these processes can accelerate existing health problems and must be addressed by the health care provider. Muscle Plasticity: Changes in Muscle Mass, Strength, and Function Skeletal muscle is remarkably plastic. It changes continuously in response to calorie and nutrient intake, illness, and physical stress. Changes in adult skeletal muscle also may occur as fiber-type switching, which is influenced by changes in physical activity, loading, nerve stimulation, or hormone and cytokine levels.7, 10, 11, 12 Mechanisms of Muscle Growth and Strength Increase Muscle adapts positively to demands placed on it, such as the increased contractile activity associated with endurance training or the increased loading attributable to strength training. This tremendous plasticity is evident as muscle tissue accretion, specific changes within muscle, and muscle tissue breakdown.1 Muscle growth, or hypertrophy, occurs when protein synthesis within the muscles outpaces protein degradation. This process can be positively regulated by mammalian target of rapamycin (mTOR) signaling induced by insulin after calorie ingestion, by hormones such as testosterone, and by exercise.10 Mechanisms of Muscle Loss in Aging, Inactivity, Sickness, and Frailty This ability of skeletal muscle to change dynamically in response to body conditions is also manifest as changes resulting from injury, illness, or aging. When the metabolic demands placed on muscle outweigh the protein synthesis that occurs from dietary intake and after exercise, muscle mass is lost, metabolic storage products are depleted, and muscle fiber balance changes. Aging may lead to a loss of muscle mass resulting from both the shrinking of muscle fibers (atrophy) and the elimination of fibers altogether (Figure 2).6 This condition is known as primary sarcopenia, the age-related loss of muscle mass and function. Although both fiber types I and II lose mass, aging causes preferential atrophy of type II fibers; the net change is thus from type II to type I fibers, or from fast to slow muscle fibers.6, 13 Because fast muscle fibers mobilize ATP and create tension more readily than slow fibers, this shift can leave older adults without the energy to perform daily tasks.14 This shift to type I slow fibers leads to a corresponding increase in their characteristic oxidative metabolism relative to the glycolytic metabolism that occurs in type II fast fibers. Exacerbating the problems caused by muscle degradation in aging, it is possible that type I oxidative fibers normally experience higher protein turnover (i. protein synthesis and degradation), are less able to grow in size, and have different responses to insufficient nutrient intake, although these fiber-type differences remain poorly understood.7, 15, 16 Beyond aging, muscle wasting is associated with many pathological states and chronic diseases, such as malnutrition, cancer, chronic kidney disease, chronic obstructive pulmonary disease, burns, muscular dystrophies, acquired immunodeficiency syndrome, sepsis, and immune disorders, and forced immobilization and bed rest are devastating to patients who are already challenged by these factors (Figure 2).14, 17 Most of these pathological conditions are associated with variable degrees of local and/or systemic chronic inflammation, which plays a crucial role in the onset of muscle atrophy. Loss of muscle mass is frequently associated with increased production of proinflammatory cytokines. Systemic inflammation is associated with reduced rates of protein synthesis paralleled by enhanced protein breakdown, both accounting for the loss of muscle mass. The effects exerted by proinflammatory cytokines on muscle mass are partially mediated by activating the transcription factor nuclear factor κB (NF-κB).18 The transcriptional activity is regulated by the phosphorylation and consequent degradation of the inhibitor Iκ-Bα, allowing the positive regulation of muscle RING-finger protein-1 (MuRF1) and other atrophy-related genes. Proinflammatory cytokines act on muscle protein metabolism not only by activating catabolic pathways, but also by downregulating the anabolic pathways.19 Elevated tumor necrosis factor-alpha (TNF-α) and interleukin-1 (IL-1) lead to inhibition of the Akt/mTOR signal transduction pathway and a subsequent reduction in protein synthesis. The inflammatory process that takes place during trauma or fractures is controlled and finely regulated. In the short term, it can facilitate complete and efficient reconstruction of muscle fibers through the stimulation of myogenesis. However, chronic inflammation can be deleterious, driving uncontrolled muscle atrophy and affecting contraction ability. Balance between pro- and anti-inflammatory cytokines is well known to be important in regulating physiological muscle protein turnover and myogenesis, and evidence suggesting that inflammation can impair force generation in muscles is also growing.20, 21 As inflammation accelerates muscle catabolism, resting energy expenditure increases and amino acids are released from muscles to serve as substrates for gluconeogenesis in liver and elsewhere in the body (Table 2).22 The efficiency of energy production is low when amino acids are used to generate energy, so muscle is at further risk for breakdown to meet needs.23 In addition, the liver changes metabolic priorities, using amino acids to produce acute phase reactant proteins instead of normal proteins, such as serum albumin, and to support gluconeogenesis. This process continues until the cause of stress has subsided. Thus, when the dietary proteins supplied are inadequate to meet needs, muscle protein is broken down to supply amino acids throughout the body. This reaction releases waste nitrogen, which requires further energy to convert to urea, thereby exacerbating the problem of the energy shortfall.24 Table 2. Major Molecular Pathways Influencing Muscle Accretion Effector Mediator Major Pathway(s) Consequence Mammalian target of rapamycin (mTOR) +Induced by BCAAs, HMB Interacts with protein translation machinery to facilitate initiation and elongation mTOR stimulation by a number of pathways increases protein synthesis Insulinlike growth factor (IGF1) +Stimulated by meal-induced insulin IGF1R → PI3K → AKT → mTOR Reduced IGF1 from decreased eating and/or exercise leads to reduced protein synthesis and to muscle wasting +Stimulated by exercise Myostatin/Activin +Produced by skeletal muscle Activin receptors (ACTRIIA/B) → Smad2/3 –I mTOR Myostatins negatively regulate protein synthesis –Inhibited by Follistatin ACTRIIA/B → FoxO → UPS Inflammatory cytokines (TNFα, IL-1) +Upregulated by illness, injury Cytokine receptors → NFKB, p38, JAK, Caspases, E3 ligases Inflammation leads to apoptosis or autophagy-mediated muscle cell loss –Inhibited by exercise FoxO transcription factors → MAFBX; MURF1 → UPS (ubiquitin-proteasome system) Vitamin D +Levels are increased by diet and sunlight Vitamin D receptor → gene expression or repression in myogenic cells Vitamin D positively influences muscle growth AKT, protein kinase B; BCAA, branched-chain amino acid; FoxO, forkhead box protein O; HMB, β-Hydroxy β-Methylbutyrate; IGF1R, insulinlike growth factor 1 receptor; JAK, janus kinase; MURF1, muscle RING-finger protein-1 p38, mitogen-activated protein kinase; PI3K, phosphatidylinositide 3-kinase; UPS, ubiquitin-proteasome system. Complications Associated With Loss of Muscle As aging and illness lead to muscle breakdown and atrophy, reduced muscle mass leaves patients without a crucial reservoir of amino acids and effector molecules, such as myokines, cytokines released by muscle, to help the body combat illness, infection, and wasting (Figure 3).23, 25, 26, 27 Therefore, muscle atrophy is associated with a wide range of harmful health effects that can be life changing, especially for older people.28, 29, 30, 31, 32, 33, 34, 35, 36 The most relevant condition associated with the presence of sarcopenia in this population is a clinical syndrome called frailty. The most accepted physiological framework explaining frailty and its consequences was proposed by Walston and Fried,37 who described a relationship between sarcopenia and energy imbalance called the “frailty cycle.” This cycle affects multiple systems, especially those susceptible to changes in hormones (mainly sexual hormones, IGF-1, and insulin) and the progressive development of a proinflammatory state.38, 39, 40 Additional biomarkers have recently been identified for roles in frailty, such as those related to endothelial dysfunction or micro RNAs central to the aging process.41, 42 Frailty can be defined as an age-associated biological syndrome characterized by a decreased biological reserve resulting from a decline in multiple physiological systems that leaves the individual at risk for developing poor outcomes (disability, death, and hospitalization) in the presence of stressors.43, 44 The prevalence of frailty in people older than 65 is approximately 10%, increases with age, and is greater in women.45 Frailty is now a recognized clinical medical syndrome that provides a biological framework for understanding vulnerabilities resulting from aging or chronic conditions.44, 46 It is clinically important to detect frailty in those at risk of developing disability. As aging progresses, frailty increases as the prognostic factor for death and incident disability.47, 48 Frailty and its underlying sarcopenia have been shown to predict risk of death, disability, and other adverse outcomes, including muscle mass atrophy, metabolic deterioration, slowed wound or postsurgical healing, and delayed recovery from illness.32, 34, 35 Frailty and the weakness that follows muscle loss lead to higher risk of falls, fractures,30 physical disability,29 need for institutional care,29 reduced quality of life,36 and heightened mortality.29, 33 Early identification of frailty risk provides the opportunity to provide interventions and avoid or delay disability as well as enhance recovery. Loss of muscle associated with disease, injury, disuse, or aging significantly increases the cost of health care.34, 49, 50 Results of a recent study showed that older adults (mean age=70 years) who were very frail spent (Euro) 1917 more on total health costs in an interval of 3 months than did those who were not frail.51 In the United States, the direct cost of cachexia/sarcopenia to health care was reported to be 1.5% of annual total health care expenses.50 Such costs arise from the increased rate of hospitalization, incidence of complications, lengths of stay, and likelihood of readmission.52, 53 In the face of an aging population, the importance of identifying, preventing, and treating muscle loss cannot be overstated. Detection and Treatment of Muscle Loss Who Is at Risk of Muscle Atrophy, and How Do We Identify It? Screening is crucial for predicting risk, and proper, timely intervention can reduce or eliminate the ensuing muscle mass and metabolic atrophy, substantially affecting morbidity, mortality, and cost. Special attention should be paid to the main risk categories: people who are malnourished or at risk of malnutrition for any reason33, 54; frail adults, especially the very old; people who become deconditioned and lose muscle due to age- and disability-related physical inactivity35; those with diseases or conditions with inflammatory components, such as chronic heart failure,55 chronic or acute kidney disease,56 cancer,57, 58, 59 severe infection and sepsis,60 insulin resistance/diabetes,61 intensive care unit–acquired weakness,25 and wound/surgical recovery.34 Reaching an accurate diagnosis of age- or disease-related muscle atrophy is difficult, and a number of criteria have been proposed but have not yet assessed in the clinical setting.14 Nonetheless, specific criteria and measures can be used to diagnose sarcopenia or cachexia.13, 27, 62, 63 Sarcopenia can be diagnosed when a patient has muscle mass that is at least 2 SDs below the relevant population mean and also presents with a low gait (walking) speed. In addition, low muscle strength and general physical performance may be taken into consideration.14 Cachexia can be diagnosed when at least 5% of body weight is lost within 12 months in the presence of underlying illness, and 3 of the following criteria are also met: decreased muscle strength, increased fatigue, anorexia, low fat-free mass index, abnormal biochemistry, increased inflammatory markers C-reactive protein (>5.0 mg/L) or IL-6 (>4.0 pg/mL), anemia (<12 g/dL), or low serum albumin (<3.2 g/dL). Recent research into the molecular adaptations associated with the development of or that result from muscle atrophy and metabolic depletion may lead to the identification of biomarkers and, therefore, improvements in early detection (Table 2). A variety of signaling pathways known to positively influence muscle growth (bone morphogenetic proteins, brain-derived neurotrophic factors, follistatin, and irisin), as well as those known to negatively regulate muscle growth (transforming growth factor β, myostatin, activins, and growth and differentiation factor-15) and factors associated with muscle function and dysfunction (C-terminal agrin fragment and skeletal muscle specific troponin T) may emerge as biomarkers for muscle atrophy in aging and disease.64 To date, there is no universally recognized biomarker for muscle atrophy, but recent research in the field suggests that the combination of several biomarkers may facilitate the adequate diagnosis of muscle atrophy. Identification of such biomarkers and their incorporation into validated testing instruments should allow early identification of muscle atrophy (improving prognosis, and likely reducing cost to health care systems), but may also provide exciting targets for the development of new medications. Nutritional Strategies for Maintaining and Rebuilding Muscle Treatment of patients at risk can prevent or delay onset of muscle atrophy, or even target rebuilding of muscle when muscle atrophy is already evident (Figure 4).65 As a first step, treatment must provide adequate energy so that muscle proteins and their constituent amino acids are spared as an energy source. In addition, high protein intake is vital to treatment of muscle atrophy or for delaying its onset.7, 66, 67, 68, 69 It should be noted that the range of protein needs can vary widely from patient to patient. Because muscle mass may decrease or remain the same (based largely on how much protein synthesis outpaces protein degradation), the most direct way to prevent muscle loss is to ensure that sufficient protein is ingested. Use of high-protein oral nutritional supplements (ONS; ≥20% of total calories as protein) may be beneficial to such patients.70 By definition, the essential amino acids (EAAs) play a central role in protein nutritional status. Some amino acids play roles that are distinct from the traditional one of protein building blocks; many of these have little or nothing to do with protein synthesis, and are thus not included here. However, of central importance to the current discussion are the branched-chain amino acids (BCAAs), especially leucine.65, 71, 72 BCAAs promote protein synthesis in the muscles through a number of pathways.66 In particular, they are now known to have a key role in altering tissue response to a meal, the post-prandial response, especially in muscle, where they signal a reduction in protein breakdown and an increase in protein synthesis, resulting in net accretion of protein in muscle and helping to regulate blood amino acid levels. However, aspects of this postprandial regulation are not as robust in aged muscle, and muscle in hypercatabolic conditions, such as cancer, is challenged and its normal system is overwhelmed. In these cases, a substantial body of research suggests that significantly more of these amino acids are required in the diet to overcome resistance to protein anabolism; very high doses, such as 10 to 15 g of BCAAs, or 3 g or more of leucine per meal, have been studied to combat muscle loss in the elderly,44 although this may be a result of improved protein synthesis that does not lead to muscle mass accretion.73, 74, 75 This resistance to the normal of BCAAs in muscle protein homeostasis has prompted studies into leucine's mechanism of action. These have identified the leucine metabolite β-hydroxy β-methylbutyrate (HMB) as a potent stimulator of protein synthesis as well as an inhibitor of protein breakdown in the extreme case of cachexia.65, 72, 76, 77, 78, 79, 80, 81, 82, 83, 84 A growing body of evidence suggests HMB may help slow, or even reverse, the muscle loss experienced in sarcopenia and improve measures of muscle strength.44, 65, 72, 76, 77, 78, 79, 80, 81, 82, 83, 84 However, dietary leucine does not provide a large amount of HMB: only a small portion, as little as 5%, of catabolized leucine is metabolized into HMB.85 Thus, although dietary leucine itself can lead to a modest stimulation of protein synthesis by producing a small amount of HMB, direct ingestion of HMB more potently affects such signaling, resulting in demonstrable muscle mass accretion.71, 80 Indeed, a vast number of studies have found that supplementation of HMB to the diet may reverse some of the muscle loss seen in sarcopenia and in hypercatabolic disease.65, 72, 83, 86, 87 The overall treatment of muscle atrophy should include dietary supplementation with HMB, although the optimal dosage for each condition is still under investigation.68 In addition to dietary protein, EAAs/BCAAs including leucine, the leucine metabolite HMB, a number of other dietary or supplemental components have been explored for their ability to positively influence muscle mass during sarcopenia. These include creatine monohydrate, a variety of antioxidants, ornithine α-ketoglutarate, omega-3 fatty acids, ursolic acid, and nitrates.68, 88, 89, 90 Given the length of this list, its growing nature, and the difficulty many aged individuals experience ingesting proper calories and nutrients, additional studies will be needed to determine which components are most beneficial to maintaining muscle mass, as well as their optimal doses and administration routes both in isolation and in combination. Physical Activity Is Also Key Nutrition is important and can counteract metabolic alterations induced during periods of significant stress and inflammation; however, sufficient exogenous provisions of protein and energy substrates alone cannot completely eliminate or reverse the deteriorations associated with aging or the deleterious impact inadequate control and regulation of inflammation have on muscle.23, 66, 69 Protein synthesis occurs in muscle fibers following their contraction,91 and physical activity has been shown to induce a number of anabolic signaling pathways.92 Physical activity can likewise reduce degradation of muscle protein.93, 94 Even more, a lack of physical activity increases the resistance of muscle to anabolism, particularly the synthesis of proteins from amino acids.95 An exercise component to muscle atrophy treatment is therefore highly recommended, and exercise also may prevent the onset of sarcopenia later in life, possibly by increasing the presence of type I fibers that are less susceptible to degradation during sarcopenia.7, 25, 66, 89 Although aerobic and other types of exercise are all preferable to a lack of physical activity, resistance exercise in particular has been shown repeatedly to improve rates of protein synthesis and reverse muscle loss.88, 89, 94 This may be attributable to differential effects on muscle fiber types.7 It is therefore recommended that patients with muscle atrophy or at risk of developing muscle atrophy engage a regular exercise program containing both aerobic and anaerobic components, and the importance of appropriate resistance training cannot be overstated. Although this must be tailored to the individual's current physical status, it should also periodically be reviewed and increased to maximize its impact. Although resistance exercise and general physical activity are important to the stimulation of protein synthesis from amino acids in the diet, some aged and ill individuals experiencing extensive muscle atrophy are likely to have difficulty engaging in physical activities because of low energy and other medical complications. Nutrition and some supplements can be used to bolster results of exercise, both preventively and during sarcopenia. For example, bioactive substances known as nutraceuticals that mimic the molecular effects of exercise can induce signaling pathways that are thought to support or even underlie exercise's effects on health and muscle mass accretion. Found in a variety of foods, including some common fruits, green tea, and even red wine, these compounds can be isolated and added to nutritional supplements used in the treatment of muscle atrophy.44 Summary and Conclusions The classic physical functions of skeletal muscle are well known, but skeletal muscle is increasingly recognized as a one of the key regulators of energy and protein metabolism by way of metabolic crosstalk between body organs. Skeletal muscle is the primary site for glucose uptake and storage, and it is likewise a reservoir of amino acids that sustain protein synthesis in all other body sites. When dietary glucose intake decreases or metabolic needs increase, stored glucose is mobilized from liver, while energy is released from fat depots. When these energy supplies are depleted, the muscle reservoir of amino acids stores is tapped, and muscle proteins are broken down to provide amino acids for gluconeogenesis, thereby supplying energy to other parts of the body. Undernutrition and resultant muscle loss (muscle atrophy), as associated with aging and disease, can lead to adverse health and economic consequences. Conditions and diseases that lower dietary intake and increase nutrient needs are associated with catabolism of skeletal muscle, which in turn limits availability of protein and energy throughout the body. Loss of muscle mass, strength, and function has adverse consequences: slowed wound healing and recovery from illness, physical disability (due both to overall reduction of muscle status), as well as selective losses in type I fibers, which are essential for balance recovery (and thus fall prevention), poorer quality of life, and higher health care costs. Nutrition and exercise are key to growth and maintenance of muscle promoting overall health, well-being, and recovery from disease. A wealth of research underscores the importance of a few key dietary components: protein (EAAs/BCAAs in particular), and the leucine metabolite HMB. Others will very likely be added to this list as our knowledge base grows. In addition, physical activity, especially resistance strength training, is essential to the treatment of muscle atrophy. Others will very likely be added to this list as our knowledge base grows. Considerable evidence shows that ONS and enteral feeding formulations can help maintain and rebuild muscle mass and strength. Further studies are needed to show support for functional outcomes, such as ability to perform activities of daily living and maintain or restore independence. Acknowledgments The authors thank Jeffrey H. Baxter and Abby Sauer from ANR&D for their critical review of this article, as well as Cecilia Hofmann, PhD, and Hilary North Scheler, PhD (C Hofmann & Associates, Western Springs, IL), for valuable assistance with efficient compilation of the medical literature and with editing this English-language review article.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/29%3A_Integration_of_Mammalian_Metabolism_-_Capstone_Volume_II/29.05%3A_Intestinal_Fructose_and_Glucose_Metabolism_in_Health_and_Dise.txt	princeton-nlp/TextbookChapters	Merino, B.; Fernández-Díaz, C.M.; Cózar-Castellano, I.; Perdomo, G. Intestinal Fructose and Glucose Metabolism in Health and Disease. Nutrients 2020, 12, 94. https://doi.org/10.3390/nu12010094 This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Abstract The worldwide epidemics of obesity and diabetes have been linked to increased sugar consumption in humans. Here, we review fructose and glucose metabolism, as well as potential molecular mechanisms by which excessive sugar consumption is associated to metabolic diseases and insulin resistance in humans. To this end, we focus on understanding molecular and cellular mechanisms of fructose and glucose transport and sensing in the intestine, the intracellular signaling effects of dietary sugar metabolism, and its impact on glucose homeostasis in health and disease. Finally, the peripheral and central effects of dietary sugars on the gut–brain axis will be reviewed. 1. Introduction According to the World Health Organization, obesity is the epidemic of the 21th century. About 13% of the world’s adult population is obese [1]. Worldwide, between 1975–2016, the global obesity rate was nearly triplicated, increasing from 1% up to 6%–8% among children and adolescents [1]. As a major public health issue, clinical interventions based on low-fat diets attracted significant interest. However, over decades, the consumption of sugars has risen significatively worldwide, and has been partially associated to the rapid increase in the prevalence of obesity [2]. From the Industrial Revolution, the consumption of sweeteners has increased dramatically, causing a dietary switch in the world population [3]. Most of this increase in sugar consumption is derived from refined or processed fructose, which is obtained from the conversion of glucose from sugar cane and corn through a chemical process developed in 1957 [4,5]. Fructose constitutes a significant portion of the caloric intake in many countries [3]. The average daily consumption of added sugars (13%–17% of daily energy intake), of which about half is fructose, is above the recommended limit of 10% in many countries [6]. Of note, 16% of total energy in children’s diets comes from added sugars [7]. The increase in total fructose intake parallels a decrease in the proportion of dietary fructose coming from fruits, but augmented proportion from fructose-based sweeteners [3]. In the past, fructose was considered sweeter, more soluble, and less gluconeogenic than glucose and sucrose, and was proposed as a substitute for these sugars [8]. Over time, this idea has been reconsidered in view of the impact of high-fructose consumption on whole-body metabolism, and because it is a risk factor for developing obesity and diabetes [9,10,11]. Although fructose and glucose share the same molecular formula (C6H12O6) and caloric value (4 kcal/g), fructose tastes sweeter than glucose (relative to sucrose, which by consensus agreement is equal to one; the sweetness of glucose is 0.75, and fructose is 1.7), and has a lower glycemic index than glucose (23 versus 100, respectively) [12]. In addition, fructose is less satiating than glucose, increasing food intake [13]. As reviewed in detail below, intestinal fructose and glucose absorption are also quite different, because glucose transport is an energy-requiring process mediated by the sodium-glucose co-transporter 1 (SLGT1), whereas fructose moves through a facilitated passive transport mediated by GLUT5 [14]. Furthermore, fructose metabolism has a negligible impact on circulating insulin levels compared to glucose metabolism, which is related to insufficient leptin (the satiety hormone) secretion, and suppression of ghrelin (the hunger-promoting hormone) [15]. 2. Intestinal Fructose Transport and Metabolism: Implications for Health and Disease Whole body fructose homeostasis results from two main processes: Intestinal absorption and clearance, the latter is commonly assumed to be mainly mediated by the liver (~55%–71%) and, to a lesser extent, by kidneys (<20%) [16]. Dietary fructose moves from the intestinal lumen to the circulation through a facilitated passive transport [3] across enterocyte membranes by members of the facilitative glucose transport (GLUT; Slc2a) family [14]. Upon its intestinal absorption, fructose reaches the liver through the hepatic portal vein and undergoes metabolization in hepatocytes [16]. Fructose transport and metabolism has been extensively reviewed (see refs. [3,14,17]). Exhaustive description of fructose hepatic metabolism is out of the scope of this review. Here, we briefly describe the regulation of intestinal fructose transport and transporters and its intracellular metabolism. We focus on revisiting the role of the liver and small intestine in fructose clearance, the relevance of endogenous fructose production in human diseases, and plant extract inhibitors of fructose transporters. 2.1. Intestinal Fructose Transport Fructose uptake into enterocytes is an insulin-independent process [18]. Among the members of the GLUT family able to transport fructose (GLUT5, GLUT8 and GLUT11), GLUT5 (Slc2a5) is primarily responsible for fructose uptake into the enterocyte at the apical side of the membrane, whereas GLUT2 (Slc2a2) moves most of fructose from the cytosol into blood vessels at the basolateral side of the enterocyte [14,19,20,21] (Figure 1). Although GLUT5 belongs to the GLUT family, it only transports fructose without the ability to transport glucose or galactose. Conversely, GLUT2 can transport glucose and galactose in addition to fructose, with an affinity (Km) for fructose more than five-fold higher than that of GLUT5 [22,23]. The main site of GLUT5 expression is the apical membrane of intestinal epithelial cells, although to a much lower extent is also expressed in kidneys, brain, fat, testes, and muscle [24]. However, the physiological relevance of GLUT5 expression in these extraintestinal human tissues is uncertain. On the other hand, GLUT2, in addition to the basolateral membranes of intestinal epithelial cells, is highly expressed in hepatocytes, pancreatic β-cells, and the basolateral membranes of kidney epithelial cells [22]. The Km of GLUT5 for fructose varies depending on the study model and the species used for its assessment. Thus, Burant et al. reported a Km of ~6 mM in Xenopus oocytes expressing the mammalian GLUT5 [19]. In contrast, Kane et al. reported a Km of 11–15 mM using the same expression system for human GLUT5 [25]. Similar values (Km of 11–13 mM) were found for mouse and rabbit GLUT5 transporter expressed in oocytes [26,27]. Finally, Mate et al. reported a Km of ~8–11 mM in ileal brush border membrane vesicles of normotensive Wistar-Kyoto rats and their spontaneously hypertensive rats [28]. Assuming a Km value ranging from 11–15 mM for GLUT5, this Km is similar to that reported for intestinal luminal fructose concentrations (26 mM) in rats fed dietary fructose [29]. On the other hand, the Km of GLUT2 for fructose is ~11–17 mM [22,23]. 2.2. Dietary Fructose Metabolism High concentrations of dietary fructose in foods and drinks lead to elevated intestinal luminal fructose concentrations that are needed for driving the facilitated fructose transport across the enterocyte membrane, and fluctuate around the Km of GLUT5 for fructose [3]. Unlike the high fructose concentration in luminal small intestine, fructose concentrations in systemic circulation are relatively low as a result of intestinal absorption and liver clearance rates. In humans, estimates of fasting systemic blood fructose concentrations are low (<0.05 mM), even in those healthy humans consuming high-fructose or sucrose diets (~0.2–0.5 mM) [30,31,32,33], which is still very low compared to fasting blood glucose levels (5.5 mM). Finally, type 1 and type 2 diabetic patients exhibited 0.016 mM and 0.009–0.013 mM fasting fructose concentrations, respectively [34,35]. The low fructose concentrations in peripheral blood support the notion that the liver and kidneys are much more sensitive to small changes in circulating fructose levels than the small intestine. Nonetheless, it is unclear how hepatocytes or nephrons reabsorb fructose from the sinusoidal capillaries or glomerular filtrates, respectively, containing very low fructose levels. Metabolization of dietary fructose in the small intestine is a process regulated at various steps. In the first step of the classical Hers pathway for fructose metabolism, fructose is mobilized from intestinal lumen into the cytosol of enterocytes by GLUT5, where it is rapidly phosphorylated by the ketohexokinase (KHK, Khk), also known as fructokinase, to fructose-1-phosphate using ATP as a phosphate donor [36]. The Khk gene encodes two isoforms of the enzyme as a result of alternative splicing of the adjacent exons 3A and 3C of the gene leading to the KHK-A and KHK-C isoforms, respectively [37,38]. Studies of expression analysis in several human and rat tissues indicated that only one mRNA variant is expressed in each tissue [38], but the pancreas is an exception to this pattern because, although KHK-C expression predominates, some KHK-A is also expressed. The KHK-C mRNA variant is expressed at high levels in the liver, kidneys, and duodenum, and is considered more physiological than the KHK-A variant because its Km for fructose is <1 mM [39,40]. On the other hand, KHK-A is expressed at a low level in a wide range of tissues including skeletal muscle and adipose tissue [39,40]. KHK-A Km for fructose is 8 mM, suggesting that it poorly phosphorylates fructose at physiological concentrations and that it may have a more important role when fructose intake is excessive [41]. In the second step of the fructose pathway, fructose-1-phosphate is split into glyceraldehyde and dihydroxyacetone phosphate by aldolase B (ALDOB; Aldob) [16]. In the third and final step of the pathway, the triokinase (TKFC; ATP:D-glyceraldehyde 3-phosphotransferase) catalyzes the phosphorylation of glyceraldehyde by ATP to form glyceraldehyde-3-phosphate [16,36]. Both ALDOB and TKFC are highly expressed in the liver, kidneys, and small intestine, relative to other organs [42]. Unlike glycolysis, the catabolism of fructose (fructolysis) bypasses major regulatory steps of glycolysis and gluconeogenesis (i.e., phosphofructokinase and fructose-1,6-bisphosphatase), and it is not regulated by feedback inhibition [16,43]. In addition, fructolysis bypasses the glucose-6-phosphate and fructose-6-phosphate production from the pentose phosphate pathway for de novo synthesis of nucleotides and nucleic acids [44]. Thus, it is plausible that in conditions of excessive fructose consumption, KHK-mediated fructolysis leads to increased glyceraldehyde, dihydroxy-acetone-phosphate, and glyceraldehyde-3-phosphate production, which are the source of gluconeogenic and lipogenic substrates (e.g., pyruvate, lactate, acetyl-CoA, and glycerol-3-phosphate), leading to elevated rates of gluconeogenesis, glycogenesis, and/or lipogenesis. Another consequence of the fructolysis is a rapid ATP and Pi intracellular depletion [45,46]. 2.3. Regulation of GLUT5 In addition to dietary fructose catabolism, metabolism of fructose comprises its biosynthesis from glucose through the polyol pathway [16]. This two-step pathway becomes active when intracellular glucose concentrations are elevated. In the first step, glucose undergoes reduction by NADPH to sorbitol (polyol) by the rate-limiting enzyme in the pathway, the aldose reductase (AR), followed by metabolization of sorbitol into fructose by sorbitol dehydrogenase (SDH) in the presence of NAD+ as a cofactor [16]. Intestinal fructose metabolism is not only important for the metabolic fate of fructose but for the up-regulation of GLUT5, KHK, ALDOB, TKFC, fructose-1,6-bisphosphatease, and glucose-6-phosphate (Figure 1) [47,48]. Thus, it has been extensively shown that chronic or acute fructose exposure increases GLUT5 levels and activity in rodents and human proximal intestine regions [3,17]. The response of GLUT5 to its substrate requires partial or total metabolization of fructose because the nonmetabolizable fructose analog 3-O-methylfructose has a modest effect on GLUT5 expression [49], and blocking intracellular fructose metabolism in the HKH-/- mouse model prevents fructose up-regulation of GLUT5 [47]. Furthermore, these effects of fructose on GLUT5 expression are very specific because fructose, glucose, and nonmetabolizable glucose analogs have similar changes on GLUT2 expression in intestinal cells [49]. The molecular mechanisms underlying fructose-mediated regulation of GLUT5 in enterocytes remain incompletely understood. In rats, fructose-induced cAMP stimulates fructose uptake without affecting transcriptional regulation of Slc2a5 [50], whereas in human Caco-2 cells, fructose increases Slc2a5 mRNA stability mediated by the cAMP pathway [51]. On the other hand, the use of inhibitors or activators of the phosphatidylinositol 3-kinase (PI3K) and/or protein kinase B (PKB) have demonstrated that this signaling pathway mediates the fructose-induced increase in fructose transport without affecting transcriptional regulation of GLUT5 [52]. How does the PI3K/AKT signaling pathway mediate the effects of fructose on GLUT5 upregulation? It is known that Class II PI3Ks control the endocytic trafficking of transporters through the production of phosphatidylinositol 3-phosphate (PtdIns3P). This second messenger is required for Rab11 activation, a small GTPase of the Rab family that coordinates endosome recycling to the plasma membrane [53]. Enterocyte-specific Rab11aΔIEC ablation (Rab11a-KO mouse model) blunted fructose-induced upregulation of GLUT5 in the small intestine, most likely by impairing endosomal trafficking of the fructose transporter towards the apical membrane of the enterocyte [47]. The expression of GLUT5 in the intestine can also be regulated by the carbohydrate response element-binding protein (ChREBP), a liver glucose-responsive basic helix-loop-helix-leucine zipper transcriptional factor [54]. High fructose diet feeding increases intestinal ChREBP protein levels, accompanied by increased fructose transport (GLUT5), fructolytic (fructokinase, ALDOB, TKFC, and lactate dehydrogenase) and gluconeogenic (glucose-6-phosphatae and fructose-1,6-bisphosphatase) gene expression in mice [55]. Conversely, genetic ablation of ChREBP (ChREBP-KO mice) leads to fructose intolerance due to insufficient induction of these genes involved in fructose transport and metabolism [55,56,57,58,59]. The molecular mechanism by which fructose mediates ChREBP-induction of Slc2a5 gene expression involves direct interaction of ChREBP with the promoter of Slc2a5 [55] in mice, whereas ectopic co-expression of ChREBP and its heterodimer partner Max-like protein X (MLX) binds to carbohydrate response elements (ChoREs) and activates Slc2a5 promoter in Caco-2BBE human cells [55]. Further work is required to confirm whether, similarly to glucose, fructose might regulate ChREBP activity by posttranslational modifications such as O-glycosylation, phosphorylation and conformational changes in intestinal cells [57]. Another identified regulatory protein of intestinal fructose transport is the thioredoxin-interacting protein (TXNIP, Txnip), an arrestin-like protein that can bind to thioredoxin protein that regulates cellular metabolism and redox state [60,61]. In response to glucose, the transcriptional complex ChREBP/MLX and MondoA/MLX binds to the ChoRE on the Txnip promoter to induce mRNA expression [62,63]. Glucose-induced TXNIP inhibits glucose transport through interaction with GLUT1 and inducing its internalization through clathrin-coated pits, as well as reducing the expression of GLUT1, whereas energy stress leads to TXNIP degradation through phosphorylation by AMP-dependent protein kinase (AMPK), resulting in increased GLUT1 function and mRNA expression [61,64]. Dotimas et al. demonstrated that TXNIP regulates fructose absorption in the small intestine [65]. Although the precise mechanisms remains elusive, TXNIP is upregulated in response to fructose consumption and co-immunoprecipitates with GLUT2 and GLUT5. It may be possible that the link between fructose transport and TXNIP may be mediated by phosphorylation of the protein mediated by AMPK, similar to what we described above for GLUT1 [65]. The expression of GLUT5 and its activity is also regulated by early development in the intestine of mammalians (i.e., rat, rabbit, and humans). In rats, under normal conditions (suckling and weaning), intestinal fructose transport and GLUT5 mRNA levels are very low due to the fact that maternal milk is fructose-free, unless there is a precocious exposure to luminal intestine fructose signal, which in turn stimulates GLUT5 expression and activity [17]. The mechanism by which fructose increases GLUT5 expression and activity during weaning is complex and involves systemic levels of glucocorticoids, but not thyroxine [17,66,67,68]. In addition, the diurnal rhythm regulates GLUT5 mRNA and protein expression in adult rats, but this regulation is not present in neonates [69]. Independently of fructose uptake, 3–4 h before the onset of peak feeding, GLUT5 levels increase by four-fold. This diurnal rhythm is also accompanied by upregulation of GLUT2 [8]. 2.4. Fructose Metabolism in Human Diseases Major pathways of fructose metabolism are conversion to glucose and lipids [16]. Therefore, excessive fructose intake would result in increased portal fructose concentrations that stimulates endogenous glucose production and lipid synthesis in the liver, which is associated with metabolic syndrome (MetS) [70,71,72], non-alcoholic fatty liver disease (NAFLD) [73,74,75,76,77,78,79,80,81], obesity, and type 2 diabetes mellitus (T2DM) [9,10,11,82,83,84,85,86,87,88]. Although there is mounting epidemiological and experimental evidence linking fructose consumption to metabolic diseases, the relative contribution of fructose to these human diseases remains controversial [87,89,90,91]. 2.5. Revisiting the Role of Liver and Small Intestine in Fructose Clearance Traditionally, the liver has been considered as the main organ that metabolizes fructose before entering systemic circulation [16]. This assumption is based on the following evidences: (1) Intestinal absorption of fructose is primarily driven to the liver through portal circulation; (2) peripheral tissues, such as skeletal muscle, have low capacity for fructose metabolism; and (3) the ketohexokinase isoform KHK-C is expressed at the highest level in the liver relative to extrahepatic tissues, leading to a high capacity for fructose phosphorylation and extraction from the blood. In this way, the liver would prevent high fructose doses to spill over peripheral tissues [16]. The current notion that the liver is the main site of dietary fructose metabolism and clearance has been recently challenged by Jang et al. [92]. They used sophisticated and elegant isotopic tracing techniques and arterio-venous blood sampling to demonstrate that most ingested fructose is metabolized by the small intestine in mice. At low-doses of fructose (<0.5 g kg−1), ~90% of fructose phosphorylation occurs in the jejunum, duodenum, or ileum. Most of this fructose is metabolized in the small intestine, appearing in the portal circulation as glucose and lactate (~60%), and the remaining as fructose (<20%). In contrast, high-doses of fructose (≥1 g kg−1) saturate the absorption and catabolism of fructose in the small intestine, leading to fructose spill-over into the liver (>30%) and the colonic microbiota in mice [92] (Figure 2). This work challenges our current knowledge about the role of the small intestine in dietary fructose metabolism and spurs the notion that the small intestine shields the liver from toxic fructose exposure. However, several questions arise from this work and remain to be fully addressed: (1) A limitation of the study is regarding the dose-response to fructose, which may vary between mice and humans. Humans may saturate the capacity for fructose metabolism in the small intestine at relatively lower doses than mice. It is necessary to understand the associated dose-response pattern in humans. (2) The role of the small intestine in fructose metabolism in mice and humans may have diverged across evolution. In fact, humans have a relative shorter gut and smaller intestinal area than rodents [93]. (3) The long standing view is that the liver and kidneys are the only gluconeogenic organs in humans, but not the small intestine because it does not express glucose-6-phosphatase (G-6-Pase) [16]. This critical issue is important to translate experimental evidences from mice to humans. In this line, one study have shown the expression of G-6-Pase in the small intestine of humans [94], and another one showed some evidence of the existence of a conversion of fructose to glucose in human jejunum [95]. 2.6. Relevance of Endogenous Fructose Production in Human Diseases In addition to exogenous fructose, fructose can be synthesized from glucose through the polyol pathway [16,96], which has drawn attention on the potential role of endogenous fructose production in metabolic diseases. The biosynthetic fructose pathway is constituted by two enzymes; the aldose reductase that converts glucose into sorbitol, and the sorbitol dehydrogenase that converts sorbitol into fructose [16]. Under physiological conditions, this pathway is mostly inactive in the majority of body tissues and organs, which has been associated to lower fasting and postprandial circulating fructose levels [97]. However, this pathway can be activated after ingestion of a drink containing glucose (~30 g) and fructose (~30 g) in healthy individuals. Tracer dilution analysis estimated endogenous fructose production ~ 55 mug kg−1·min−1. This work evidenced, for the first time, the capacity for endogenous fructose production in humans [97]. Further research demonstrated the presence of an active polyol pathway in tissues other than those involved in metabolizing dietary fructose, such as the human brain [98,99,100]. Numerous studies using animal models have linked the polyol pathway to metabolic alterations such as obesity, insulin resistance, diabetes, diabetic nephropathy, chronic kidney disease, acute kidney injury, blood pressure, and MetS [101,102,103,104]. Nonetheless, although the presence of an active polyol pathway has been described in humans, and mounting evidences obtained in animal models of the importance of this pathway in diseases, its significance in human metabolic diseases awaits further confirmation. 2.7. Plant Extracts Inhibitors of Fructose Transporters As described above, multiple studies in humans and animal models have linked fructose consumption with diseases, which has spurred the notion of the potential use of GLUT5 inhibitors for preventing fructose-induced diseases. So far, no potent and specific inhibitors of GLUT5 have been discovered, although phloretin and cytochalasin B are used to inhibit GLUT2 for assessing fructose transport in vitro, whereas GLUT5 is insensitive to both inhibitors [22,25]. In the last decade, plant extracts have been used to screen compounds with inhibitory effects on intestinal GLUT5 transporters. Thus, green tea catechins inhibited D-fructose transport in Xenopus laevis oocytes expressing the mammalian GLUT5. Inhibition of D-fructose transport via GLUT5 was more efficient by catechins containing a gallate group [apparent Ki values between ~113 and ~117 μM for (−)-epigallocatechin-gallate and (−)-epicatechin-gallate, respectively] than by catechins lacking this group [apparent Ki values >500 μM for (−)-epicatechin and (−)-epigallocatechin] [105]. In this line of evidence, it has been shown that chamomile tea and green tea [containing (−)-epigallocatechin gallate (240 mg/g extract), (−)-epigallocatechin (70 mg/g extract), (−)-epicatechin (40 mg/g extract), and (+)-catechin (17 mg/g extract)] effectively inhibited fructose transport through GLUT2 in differentiated Caco-2 cells [106]. In addition, chamomile also inhibits D-fructose transport via GLUT5 in Caco-2 cells and in Xenopus oocytes expressing the mammalian GLUT5 [106]. Likewise, Satsu et al. demonstrated that epicatechin gallate inhibited fructose uptake in Caco-2 cells. Interestingly, this reduction in fructose uptake was not related to changes in the affinity (Km) of GLUT5 for fructose, but with a decrease in the maximal velocity (Vmax) [107]. Furthermore, authors demonstrated that epicatechin gallate suppressed fructose permeation in Caco-2 cells, suggesting that this compound suppressed the transepithelial transport of fructose across epithelial cell monolayers, in addition to its effect on fructose uptake. Lastly, authors reported that similar effects on fructose uptake and permeation were observed with nobiletin, another phytochemical tested in this study [107]. An additional compound extracted from the Chinese blackberry tea (rubusoside) inhibited GLUT5-mediated fructose transport in liposomes reconstituted with human GLUT5 purified from insect cells transduced with baculoviruses [18]. Likewise, astragalin-6-glucoside (a glycosylated derivative of astragalin) inhibited GLUT5-mediated fructose transport in these proteoliposomes [18]. The same group performed a virtual screening (in silico) for potential GLUT5 inhibitors using a 3D inward-facing GLUT5 model against a library of >600,000 chemicals [108]. The ability of the top ranked compounds for inhibiting GLUT5-mediated fructose transport were tested in GLUT5 proteoliposomes, identifying the N-[4-(methylsulfonyl)-2-nitrophenyl]-1,3-benzodioxol-5-amine (MSNBA) as an specific inhibitor, which did not affect the fructose transport of human GLUT2 or the glucose transport of human GLUT1-4 [108]. Additionally, whole-cell systems for high-throughput screening of potential GLUT5 inhibitors and activators have been developed using a yeast strain deficient in fructose uptake [109]. The ability of culinary plant extracts containing phytochemicals to inhibit fructose transport has also been assessed in Caco-2 cells. Lee et al. found that demethoxycurcumin and curcumin from turmeric extracts inhibited fructose transport by GLUT2- and GLUT5-mediated fructose uptake, respectively [110]. Similarly, catechin from guava leaf (Psidium guajava) inhibited GLUT5-mediated fructose uptake, whereas quercetin inhibited both GLUT5- and GLUT2-mediated fructose transport [110]. In addition, the ability of guava leaf and guava fruit extracts to inhibit glucose transport have also been demonstrated by Müller et al. in Caco-2 cells and mice (C57BL/6N) [111]. The effect of these extracts on glucose uptake in Caco-2 cells were related to inhibition of GLUT2, although the effects on fructose uptake were not assessed [111]. More recently, König et al. demonstrated that fruit extracts prepared from guava inhibited intestinal glucose resorption in a clinical trial [112]. The effects of hesperidin, a flavonoid present in orange juice, on fructose uptake in Caco-2 cell monolayers was studied by Kerimi et al. [113]. They showed that hesperidin inhibited fructose uptake in these cells using fructose (130 mM) as the only source of sugars. Of note, the inhibitory effect of hesperidin on fructose uptake was abolished in the presence of other sugars, such as glucose and sucrose, at high concentrations (120 mM and 130 mM, respectively). Using Xenopus laevis oocytes expressing human GLUT2 or GLUT5, they gained insights into the molecular mechanisms by which hesperidin inhibited fructose transport. Thus, hesperidin inhibited the uptake of fructose by GLUT5 expressed in Xenopus oocytes. In addition to its effects on fructose uptake, hesperidin lowered glucose uptake in Caco-2 cells and inhibited GLUT2 and GLUT5 transporters when expressed in Xenopus oocytes. Lastly, in an attempt to reproduce in vivo these previously observed effects of hesperidin, authors conducted three separated human intervention studies on healthy volunteers using orange juice with different amounts of added hesperidin and a control drink containing equivalent amounts of glucose, fructose, and sucrose, and measured the postprandial glycemic response as biomarker for the effect of hesperidin. They observed that the biggest difference in postprandial blood glucose between orange juice and the control drink was when the juice was diluted [113]. The inhibitory effects of other flavonoids, such as apigenin, on fructose uptake have also been investigated by Gauer et al. in Xenopus oocytes. Apigenin, as well as (−)-epigallocatechin gallate, inhibited fructose uptake in oocytes expressing GLUT5 [114]. Finally, acarbose, an α-glucosidase inhibitor that improves insulin sensitivity and decreases postprandial hyperglycemia [115], does not inhibit fructose transport in human Caco-2 cells or in Xenopus oocytes expressing the mammalian GLUT2 and GLUT5 [106]. These results suggest that the effects of acarbose on fructose absorption would be mediated by its well-known effects on attenuating sucrose digestion [116], rather than direct effects on fructose transport across the intestinal epithelium. 3. Intestinal Glucose Transport and Metabolism: Implications for Health and Disease Glucose is the main catabolic and anabolic substrate for the great majority of complex organs that controls energy homeostasis in the body. Glucose homeostasis is the result of three physiological events: Intestinal glucose absorption in the post-prandial state, hepatic glucose production (which accounts for ~90% of endogenous glucose production and is the net balance between gluconeogenesis, glycogenolysis, glycogen synthesis, glycolysis, and other pathways), and extrahepatic glucose usage, mainly by the brain, the skeletal muscle, and the adipose tissue. Glucose controls hormonal secretion in endocrine pancreas (i.e., insulin, glucagon, and somatostatin) [117,118,119] and neuronal signaling involved in glucose homeostasis, feeding regulation, and energy expenditure [120]. 3.1. Intestinal Glucose Transport. Gastric emptying and intestinal glucose absorption determine the glucose appearance rate in the bloodstream after a meal. Intestinal enterocytes are polarized cells responsible for glucose uptake from the intestinal lumen to capillary blood vessels, which is the main mechanism of glucose entrance into the body. Enterocytes express two glucose transporters named sodium-glucose co-transporter 1 (SGLT1; expressed in the brush border membrane) and GLUT2 (localized in the basolateral membrane). SGLT1 couples the transport of one glucose molecule and two sodium ions, which provides the energy to drive glucose accumulation in the enterocyte against its concentration gradient due to the energy stored in the sodium electrochemical potential gradient across the brush border membrane generated by the sodium transport. Sodium is then transported out into the blood vessels by the Na+/K+-ATPase in the basolateral membrane, maintaining the driving force to transport glucose. As a result, glucose accumulates within the enterocyte and diffuse out of the cell through GLUT2 into the blood stream. This process is ATP-dependent [121,122] (Figure 3). Intestinal SGLT1 is a high-affinity (Km ~0.4 mM), low-capacity transporter able to transport glucose or galactose. It is a monomeric integral membrane protein embedded in the lipid bilayer composed by 664 amino acids with 14 transmembrane-spanning regions and it has one glucose binding-site and two sodium binding-sites in the center of the protein. In humans, SGLT1 is encoded by the Slc5a1 gene, and it is highly expressed in the duodenum and skeletal muscle [123,124]. SGLT1 activity varies diurnally to meet fluctuating availability of glucose. The maximal transport capacity occurs when food is anticipated, and it could be regulated by clock genes [125,126]. After energy-dependent glucose uptake via SGLT1, glucose exits the enterocyte passively through GLUT2 located in basolateral membrane. Intestinal GLUT2 is a facilitative glucose uniporter with low glucose affinity (Km ~17 mM), but high transport capacity, located in basolateral membrane of the enterocytes. GLUT2 can also transport galactose, mannose, and fructose (with low affinity), and glucosamine with high affinity (Km ~ 0.8 mM) [127]. 3.2. Regulation of SGLT1 and GLUT2 SGLT1 expression in the intestinal lumen is regulated by dietary carbohydrate content. Thus, luminal glucose, but not intravenous administration of glucose, increases intestinal SGLT1 expression. High-diet glucose feeding increases SGLT1 expression and activity (rat, mouse, and sheep), which is accompanied by increased glucose transport. Similarly, obese mice exhibit increased intestinal glucose transport mediated by augmented SGLT1 transporters, without increased activity [128,129,130,131,132]. In addition to glucose-mediated regulation of SGLT1, phosphorylation by protein kinase A (PKA) and protein kinase C (PKC) regulates its activity. In humans, SGLT1 contains one consensus site for regulation by PKA and five consensus sites for PKC. The number of consensus sites and conserved sequences varies between species (rat, rabbit, and humans) [133,134]. In Chinese hamster ovary (CHO) cells overexpressing human SGLT1, activation of PKA increased the amount of SGLT1 in the membrane [135]. In contrast, stimulation of human embryonic kidney cells expressing human SGLT1 with 8-Br-cAMP (a brominated derivative of cyclic adenosine monophosphate that activates cAMP-dependent protein kinase) significantly reduced glucose transport [136]. On the other hand, PKC activation in the absence of RS1 increases transport capacity of human SGLT1, while in the presence of RS1, glucose transport is decreased [137]. The adipocyte-derived hormone leptin also regulates SGLT1. Although leptin is not required for intestinal SGLT1 expression, hyperleptinemia or leptin administration drastically reduce intestinal SGLT1 expression. The intracellular signaling pathways by which leptin regulates intestinal SGLT1 remain incompletely understood, but may include PKA, PKC, and the leptin receptor isoform b [138,139]. Finally, as in the case of GLUT5, green tea catechins markedly inhibit SGLT1-mediated glucose transport in the small intestine, being more pronounced by catechins containing a gallate group [(−)-epigallocatechin-gallate and (−)-epicatechin-gallate] than by catechins lacking this group [140]. The classical view of intestinal glucose absorption is underlined by the evidence that SGLT1 is in the apical membrane of enterocytes, while GLUT2 is located exclusively in the basolateral membrane, leading to the transepithelial glucose transport from the lumen into the portal circulation. This classical theory explains glucose absorption at low luminal glucose concentrations (≤10 mM) but it fails to explain the marked increase at glucose concentrations that surpass SGLT1 (≥25 mM) transport capacity. GLUT2 levels are also regulated by glucose concentrations in enterocytes. As part of an adaptive physiological mechanism in response to increased luminal glucose concentrations, caloric demand, and glucagon-like peptide 2 (GLP-2); GLUT2 is rapidly and transiently recruited to the brush border membrane of the enterocyte, leading to a three-fold enhancement of glucose transport [141,142] (Figure 3). This adaptive mechanism that is known as the “GLUT2 translocation” theory, which in addition to other theories, such as the “solvent drag” theory, have been proposed to explain the marked increase in glucose absorption in response to high luminal glucose concentrations [143]. Conversely, it has also been demonstrated that in addition to high luminal glucose concentrations, insulin decreases GLUT2 membrane levels as a result of the internalization of GLUT2 from plasma membranes back into intracellular pools, leading to the inhibition of glucose transport [144]. The regulation of intestinal glucose absorption by insulin is probably another physiological mechanism at the enterocyte level by which the hormone limits sugar excursions in the blood circulation during a sugar-rich meal. This evidence raised the idea that insulin resistance may provoke a loss of insulin-mediated control of GLUT2 membrane trafficking, leading to unleash intestinal glucose absorption upon high-sugar diets consumption. Tobin et al. demonstrated that insulin resistance in mice provoked a loss of GLUT2 trafficking control, where GLUT2 levels remain permanently elevated in the brush border membrane and low in the basolateral membrane of the enterocyte [144]. Ait-Omar et al. investigated the relevance of these previously described mechanisms in the small intestine of morbidly obese insulin resistant humans and lean control subjects. They found that GLUT2 was accumulated in apical and/or endosomal membranes of enterocytes in obese subjects. Interpretation of these findings is complex, but authors proposed that permanent apical GLUT2 localization in obese subjects would mediate blood-to-lumen glucose flux during fasting hyperglycemia, leading to glucose secretion into the intestinal lumen. In contrast, after consumption of a sugar-rich meal, permanent apical GLUT2 localization would provide a large glucose uptake from the intestinal lumen to the portal circulation [145]. 3.3.1. Relevance of Glycemic Index and Glycemic Load for T2DM The glycemic response (GR) is the appearance of glucose in blood after a meal. It depends on the amount of glucose absorbed, the rate of glucose entry into circulation, the rate of disappearance due to tissue uptake from circulation, and the regulation of hepatic glucose production [146]. Blood glucose concentrations will rise and fall rapidly or slowly depending on the carbohydrate content of food. The glycemic index (GI) is a tool developed to compare the postprandial responses to constants amounts of different carbohydrate-containing food. It is a useful tool for people with diabetes, providing information on the GR that might be expected when a person consumes the quantity of a food containing a fixed amount of carbohydrates [147]. The glycemic load (GL) concept was introduced as a mean of predicting the GR, considering the GI and the amount of available carbohydrate in a portion of the food eaten [148]. Thus, foods have been classified by GI into low (GI ≤ 55), medium (GI 56–69), and high (GI ≥ 70) categories, and classified by GL as being low (GL ≤ 10), medium (GL 11–19), and high (GL ≥ 20). Since these concepts were introduced, numerous studies have been performed to ascertain how GI and GL relate to health and disease. Of note, the American Diabetes Association (ADA) indicated that current knowledge is insufficient to relate low–GL diet with a reduction on diabetes risk, and that it has not been demonstrated that one method of assessing the relationship between carbohydrate intake and blood glucose response is better than other methods [149]. To shed light into this issue, Livesey et al. performed a review meta-analysis of prospective cohort studies for a comprehensive examination of evidence on the dose-response that links GL to T2DM. The analysis concluded that a GL over a dose range of 100 g/2000 kcal, increases the risk of T2DM by 45%, supporting the notion that GL is an important and underestimated dietary characteristic that contributes to the incidence of T2DM [150]. Greenwood et al., in a systematic review and dose-response meta-analysis of prospective studies, showed that there is a protective effect of low dietary GI and GL and risk of T2DM [151]. In addition, two previous systematic reviews concluded that there is evidence of a positive association between both dietary GI and GL and risk of T2DM [152,153]. In summary, despite the fact that epidemiological studies of GI and GL in relation to diabetes risk have yielded inconsistent results, there is important research in support of significantly positive associations between dietary GI and GL and the risk of T2DM, thus reducing the intake of high-GI foods may bring benefits in diabetes prevention. 3.3.2. Regulation of SGLT1 in Diabetes Mellitus Several studies in rodent models of T2DM and type 1 diabetes mellitus (T1DM) have shown a link between intestinal SGLT1 expression and diabetes. Streptozotozin (STZ)-induced diabetes in mice and rats (STZ; a toxic drug that produces a destruction of pancreatic β-cells causing insulin deficiency and hyperglycemia [154]) produces increased SGLT1 intestinal expression [155]. Likewise, a rat model of T2DM (Otsuka Log-Evans Tokushima Fatty rats) exhibited increased intestinal mRNA expression of SGLT1 associated with impaired glucose tolerance and occurred before the onset of insulin resistance and hyperinsulinemia [156]. Similar results were confirmed in patients with noninsulin-dependent diabetes mellitus where mRNA and protein levels were increased three- to four-fold in brush border membranes of enterocytes in the small intestine [157]. Finally, in morbid obese non-diabetic patients, increased SGLT1 expression in the intestine was found and it correlated with accelerated intestinal absorption [158]. Taken together, these findings are consistent with the concept that SGLT1-mediated glucose absorption in the intestine underlies the rapid post-prandial rise in blood glucose levels observed in obesity and T2DM. This knowledge has prompted the concept that pharmacological inhibition of SGLT1 in the small intestine can lower hyperglycemia by inhibiting glucose absorption and increasing GLP-1. The pharmacological tools that have been used to determine the potential of SGLT1 inhibition include phlorizin (or phloridzin), canagliflozin, LX4211 (or sotagliflozin), LP-925219, KGA-2727, and GSK-1614235 [159]. The use of these inhibitors in rodent models of T2DM and in humans has lent support to this pharmacological approach in the treatment of T2DM, but more studies are needed on long-term safety of SGLT1 inhibition. 4. Peripheral and Central Effects of Dietary Sugars in the Gut–Brain Axis in Health and Disease 4.1. The Gut–Brain Axis In the early 20th century, Ivan Pavlov discovered the existence of a close interaction between the gut and the brain. Pavlov observed in dogs how a stimulus associated with feeding induced vagal-dependent gastric acid secretion [160]. Since then, this interaction has been widely described and is enclosed in the term of “gut–brain axis”, a complex bidirectional communication system that maintains constant crosstalk between the gastrointestinal system and the enteric and central nervous system. This intimate connection involves numerous endocrine, immune, and neuronal pathways [161]. Through this complex system, the gut can send modulating signals to the brain via visceral messages that influence emotional and cognitive brain centers producing different psychobehavioural responses [161]. In the other direction, the brain is able to send orders for proper maintenance of gastrointestinal homeostasis (such as by modulating intestinal motility and mucin production) and can also modulate the immune system (such as by modulating cytokine production by mucosal cells) [161]. The gut–brain axis uses mostly four major information carriers to communicate with each other: Neural messages via vagal and spinal afferent neurons, immune mediators carried by cytokines, endocrine signals carried by gut hormones, and microbiota-related factors that reach the brain directly from the blood stream [162,163]. The integration of all these signals allows the maintenance of a large number of vital functions such as the control of food intake and satiety, the repulsion of harmful foods, and the adaptation of our gastrointestinal system to the environment, giving rise in pathological conditions to the sensation of nausea, pain, or even may result in gastrointestinal dysfunction [164,165]. 4.2. Regulation of the Gut–Brain Axis by Enteroendocrine Cells and Sensing of Intestinal Sugars Enteroendocrine cells (EECs) form the largest endocrine organ in the body and play a key role in regulating nutrients intake and postprandial metabolism. Following a meal, EECs in the small intestine sense luminal and circulating levels of nutrients, and simultaneously are stimulated by prevailing nutrients through multiple nutrient transporters and G protein-coupled receptors (GPCRs), leading to activation of intracellular signaling pathways that produce secretion of peptides and hormones. These hormones enter circulation and act on multiple distant tissues such as the brain, gallbladder, and pancreas, as well as, on neighboring enteric neurons, endothelial cells, and the gastrointestinal epithelium. Thus, the physiological role of the enteroendocrine system in response to ingested glucose and fructose is to detect nutrients in the intestinal lumen, to monitor energy status of the body, and to elaborate an appropriate response, through the production of more than 30 different hormones and neurotransmitters to control postprandial whole-body metabolic homeostasis [166]. EECs are endoderm-derived epithelial cells widely distributed in the villi and crypts, where they are interspersed between non endocrine cells [166]. The intestinal epithelium is in a constant turnover that is replenished from pluripotent stem cells at the base of intestinal crypts and their progenies migrating up the crypt–villus axis [167]. The spatial distribution and differentiation of EECs is regulated by an interplay of the surface protein Notch and three basic helix-loop-helix transcriptional factors (Math1, Neurogenin 3, and NeuroD), among other factors [167,168]. EECs are classified depending on their morphology and position in the gastrointestinal mucosa into “open-type” with a bottle neck shape and an apical prolongation with microvilli facing towards the intestinal lumen or “closed type” that are located close to the basal membrane, do not reach the lumen of the gut, and lack microvilli [169,170,171]. The open-type cells are activated by luminal content through the microvilli, whereas the close type cells are activated by luminal content indirectly through neuronal or humoral pathways. In both cases, hormones and peptides accumulate into cytoplasmatic secretory granules that are released by exocytosis at the basolateral membrane upon chemical, mechanical, or neural stimulation [170,172]. 4.2.1. Fructose-Induced Hormonal Secretion in Intestinal Cells Using specialized organoid cultures enriched in a single intestinal cell type, primarily enterocytes, Paneth or goblet, but not intestinal stem cells, Kishida et al. demonstrated that fructose can be sensed by absorptive enterocytes and secretory goblet and Paneth cells, but not stem cells [173]. In response to fructose there was an increased expression of fructolytic genes without affecting non-fructolytic gene expression. Sensing was independent of Notch, Wnt, and glucose concentrations in the culture medium, but required fructose uptake and metabolism. Stronger responses were found in more mature enterocyte- and goblet-enriched organoids. Of note, the response to fructose in enterocyte organoids was retained upon forced dedifferentiation to reacquire stem cells characteristics [173]. Fructose increases secretion of human peptide tyrosine tyrosine (PYY), cholecystokinin (CCK), neurotensin, and serotonin (5-HT) in EECs subtypes L, I, N, and enterochromaffin cells (EC), respectively [174,175]. Likewise, fructose stimulates secretion of glucagon-like peptide 1 (GLP-1) from L-subtype EECs in humans, rats and mice, but not glucose-dependent insulinotropic polypeptide (GIP; glucose-dependent insulinotropic polypeptide or gastric inhibitory peptide) [174,176]. On the other hand, fructose induced the secretion of GIP from K-subtype EECs in mice [176] but is unaffected or reduced in rats and humans [174,177]. 4.2.2. Glucose-Induced Hormonal Secretion in Intestinal Cells Oral glucose, but not intravenous glucose, leads to a greater stimulation of insulin secretion and modulation of glucagon secretion in the pancreas. This physiological response to glucose is called the incretin effect, which is due to the release of incretin hormones (GIP and GLP-1) from specialized EECs [178,179]. Of the three signals originating in the gut (glucose, incretins, and neutral signals transmitted by the autonomic nervous system) that regulate pancreatic insulin secretion, the incretin effect makes a substantial contribution to maintenance of glucose homeostasis [178,179]. GIP is secreted in response to glucose by K-cells located in the duodenum and upper jejunum. GIP is synthesized as a precursor pro-peptide (pro-GIP), which is cleaved to GIP by posttranslational processing. GLP-1 and GLP-2 are secreted in response to glucose by L-cells in the small and large intestine, with a gradient from low density in the duodenum to high density in the ileum, but also in the colon and rectum [180,181]. The proglucagon gene is cleaved to GLP-1 and GLP-2 by posttranslational processing. The biological active forms of GLP-1 are GLP-1 [7-36 amide] (amidated GLP-1), and GLP-1 [7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37] (glycine-extended GLP-1), which are “truncated” forms in comparison to the originally proposed sequences GLP-1 [1-36 amide] by the N-terminal six amino acids [182,183,184]. In pancreatic α-cells, the same proglucagon gene is processed in a different manner, yielding glucagon and a “major proglucagon fragment that is not further processed to GLP-1 and GLP-2 [185]. Finally, in addition to its role in the regulation of pancreatic insulin secretion, GLP-1 and GLP-2 promotes nutrient absorption [180]. 5-HT is secreted in response to glucose by EC cells located throughout the gastrointestinal tract, and regulates intestinal motility and brain control of appetite [186,187]. 5-HT and GLP-1 activate 5-HT3 and GLP-1 receptors in the intestinal vagus nerve, respectively, leading to vagal reflexes, which in turn slow the subsequent emptying of carbohydrates from the stomach and induce satiation [188,189]. 4.2.3. Intestinal Sweet Sensing and Glycemic Control The gastrointestinal tract is a major determinant of metabolic homeostasis. Sensing of nutrients, and particularly glucose, in the EECs provides feedback signals from the intestine to slow the rate of gastric emptying, limit postprandial glycemic excursions, and induce satiation. Intestinal sweet sensing is regulated by the sweet taste receptor (STR), which has been described on K-cells, L-cells, and EECs in humans. Additionally, STRs have been described in metabolic tissues that sense and respond to carbohydrates, such as hypothalamic neurons, hepatocytes, adipocytes, and β-cells (for review see refs. [190,191,192,193]). STR senses hexose sugars, D-amino acids, sweet proteins, and low-calorie sweeteners. The receptor is comprised of a heterodimer of class C, G-protein coupled receptors T1R2 and T1R3. The mechanisms of sweet taste transduction have been mostly studied in lingual sweet taste cells (this topic is out of the scope of this manuscript, for review see refs. [194,195,196,197]). Briefly, the interaction of sweet tastings with STR initiates the dissociation of the gustducin (the G-protein) into Gα and Gβγ subunits and activation of phospholipase C. Then, intracellular Ca2+ is released from inositol 1,4,5-triphosphate (IP3)-sensitive stores, leading to opening of the melastatine type-5 transient receptor potential cation (TRPM5) channel allowing sodium influx. Increases in intracellular Na+ and Ca2+ levels lead to depolarization of the basolateral membrane, which via 5-HT and ATP-dependent pathways activate intermediary taste cells and nerves involved in lingual sweet taste that convey information centrally to the cortex. Numerous studies in rodents and human cells support the notion that intestinal STR is a glucose sensor on the gut luminal membrane responsible for the regulation of SGLT1 expression and GLP-1 secretion. First, it was demonstrated that T1R2, T1R3, and the α-subunit of gustducin were co-expressed in K- and L-endocrine cells in rodents and humans [198], and to a lesser extend in EC cells containing serotonin in pig intestine [199]. Second, Parker et al. proposed that secretion of GLP-1 by L-cells and GIP by K-cells was through uptake of glucose by SGLT1, suggesting that SGLT1 was likely the mediator of the direct responsiveness of K- and L-cells to luminal glucose [200]. Third, genetic deletion of T1R3 or gustducin in mice abolished the ability of mouse intestine to upregulate SGLT1 expression in response to increased dietary carbohydrate, providing convincing evidence for the involvement of the STR in intestinal sweet transduction [198]. Fourth, genetic deletion of T1R3 and gustducin exhibited deficiencies in secretion of GLP-1 [201]. Fifth, luminal glucose above a threshold results in secretion of GPL-1, GLP-2, and GIP through a signaling pathway involving STR in enteroendocrine cells [198]. These evidences beg an important question: How does glucose activation of STR in EECs cause increased expression of SGLT1 in enterocytes? The communication between EECs and neighboring enterocytes likely resides in the involvement of intermediaries such as GLP-1 and/or GLP-2 and enteric neurons. Thus, GLP-2 receptors are present on enteric neurons [202,203], while enterocytes respond to GLP-2 in an enteric neuron-dependent manner [203]. In addition, GLP-2 upregulates SGLT1 expression [204,205,206], and STR-dependent release of GLP-1 and GLP-2 is detected at higher concentrations in the portal and lymphatic circulation in rodents [204,207]. All these evidences have led to a model of intestinal dietary glucose sensing. Luminal glucose is sensed by STR expressed on the luminal membrane of enteroendocrine cells. Above a threshold level of luminal glucose, the hexose binds to and activates STR, initiated by dissociation of gustducin into Gα and Gβγ subunits, which leads to activation of phospholipase C β2. Then, IP3-sensitive stores release intracellular Ca2+ that opens the TRPM5 channel increasing sodium influx. Intracellular elevation of Ca2+ and Na+ depolarizes the basolateral membrane resulting in the release of GLP-2. GLP-2 binds to its receptor on enteric neurons evoking an action potential that triggers the release of an unknown neuropeptide to the vicinity of neighboring enterocytes. The neuropeptide binds to its receptor located on basolateral membranes of enterocytes leading to a rise in intracellular levels of cAMP, which increases stabilization of the 3′end of Slc5a1 mRNA and ultimately augmented SGLT1 translation and insertion into the apical brush border membrane of the enterocyte (Figure 4). 4.3. Central Effects of Glucose and Fructose Consumption Sugar overconsumption has been associated with detrimental metabolic effects, such as obesity, dyslipidemia, MetS, and impaired insulin sensitivity [71,208,209]. Therefore, it is necessary to understand the specific molecular mechanisms by which dietary sugars cause an addictive eating behavior and how sugar intake affects the gut–brain axis. Herein, we will review the effects of the two main dietary monosaccharides: Glucose and fructose, the latter of which is usually consumed in the form of sucrose disaccharide (50% glucose, 50% fructose) or in the form of high-fructose corn syrup (HFCS) (range 47%–65% fructose, and 53%–35% glucose) [210], the major component of sweetened soft drinks. Appetite control is a complex crosstalk between the periphery and the central nervous system that involves a large number of peptides and hormones [211]. Disturbances in food intake control will ultimately be responsible for large changes in energy balance and different metabolic effects. The appetite regulatory hormones are secreted from peripheral tissues such as the pancreas (e.g., insulin), adipose tissue (e.g., leptin), or the gastrointestinal tract [e.g., ghrelin, CCK, PYY, GLP-1 and GIP], and bind to receptors located in the arcuate nucleus of the hypothalamus, where they inhibit or stimulate appetite or satiety [212]. Many studies have demonstrated that circulating levels of satiety hormones are regulated by the type of sugar consumed. In response to glucose stimuli, a cascade of hormonal secretion is triggered. Thus, glucose produces a repression of the hunger hormone ghrelin (secreted by the stomach), whereas there is a stimulation of the secretion of satiety hormones such as leptin, insulin, GIP, GLP-1, and PYY. However, fructose produces lower repression of ghrelin and a decreased stimulation of satiety hormones (leptin, insulin, GIP, GLP-1, and PYY) than glucose [13,83,212,213,214,215,216,217]. These effects may be related to different explanations such as the lower ratio of intestinal fructose uptake, the lower intestinal levels of GLUT5 compared to the high levels of GLUT2, and also due to the low expression of GLUT5 in pancreatic β-cells leading to decreased insulin release [218,219,220]. Some of these hormones regulated differentially by fructose or glucose convey signals to brain structures. Specifically, there are two neuronal types in the arcuate nucleus that integrate signals from the periphery, acting as metabolic sensors: Neurons co-expressing agouti-related peptide (AgRP) and neuropeptide Y (NPY), whose activation triggers orexigenic effects; and neurons expressing pro-opiomelanocortin (POMC), whose activation triggers anorexigenic effects [221,222,223]. These different types of neurons are sensitive to changes in hormone levels promoting or suppressing food intake. Therefore, the differential effect of dietary sugars on hormonal levels affects neuronal stimulation causing both short-term and long-term central effects in the regulation of food intake and energy homeostasis [224,225]. The low stimulatory capacity of fructose on satiety hormones such as leptin and insulin will lead to low stimulation of POMC neurons and the maintenance of the signal on NPY/AgRP neurons, thus promoting less satiety than glucose, and therefore increased food intake. In the same way, the hypothalamic AMPK functions as a ‘fuel gauge’ to monitor cellular energy status, and its inhibition promotes anorexigenic effect [226]. AMPK activity is inhibited by leptin and insulin. Intracerebroventricular glucose administration in rodents inhibits hypothalamic AMPK activity and suppresses food intake, whereas fructose activates it, thus promoting an orexigenic effect [227,228,229,230] (Figure 5). With the use of new technological advances, it is possible to evaluate the brain activity produced by the intake of different nutrients. In humans, differences in cerebral blood flow have been reported between subjects undergoing glucose and fructose infusions [231], and compared to glucose, fructose causes poor satiety stimulation in specific appetite-regulating regions (e.g., hypothalamus) [13]. It has also been observed that fructose ingestion compared to glucose resulted in a significantly greater incentive value of food cues [232]. These findings suggest that fructose promotes effects on brain activity that affect appetite, probably promoting less satiety than other sugars in humans. In addition to the above-mentioned findings, it has been described that high-fructose intake may affect central appetite regulation by altering specific components of the endocannabinoid system in rats. Fructose consumption has been reported to significantly increase the mRNA expression of the cannabinoid 1 receptor (CB1) [233], and induces an increase in fatty acid amide hydrolase (FAAH) and diacylglycerol lipase (DAG) 1β, but a decrease in DAG1α mRNA [234]. These changes in the endocannabinoid system suggest that fructose consumption may lead to increased hedonic reward for food, thus leading to disturbances in the eating behavior pattern. The consumption of dietary sugars has not only been related to central effects that control appetite and satiety, but also to disturbances in cognitive functions. In rodents, studies have shown that fructose consumption reduced phosphorylation levels of the insulin receptor, leading to impaired brain insulin signaling [235,236], a harmful feature associated with cognitive impairment [237]. Moreover, diminished phosphorylation of cAMP-response element binding and synapsin I, and reduced synaptophysin levels have been observed after fructose intake [236]. Together, these findings indicate that excessive fructose consumption could lead not only to detrimental effects in eating behavior, but also can trigger impaired cognitive function. Further work is required to investigate these evidences. The Fructose Hypothesis In view of the association between fructose consumption in Western diets and MetS, fructose has been suggested as one of etiological factor of MetS. The “fructose hypothesis” proposed that a high amount of fructose consumption is a leading risk factor for the development and progression of MetS, covering obesity, insulin resistance, dyslipidemia, fatty liver, and cardiovascular disease [238,239,240]. Fructose may cause insulin resistance by accumulation of triglycerides in the liver. There are two metabolic pathways to increased hepatic lipid content, i.e., lipogenesis and/or reduced mitochondrial fatty acid oxidation. Hepatic fructolysis leads to increased gluconeogenic sources resulting in elevated rates of lipogenesis [16,45,46]. Hepatic accumulation of toxic intermediary lipid metabolites, such as diacylglycerol (DAG) results in PKCε activation that impairs hepatic insulin signaling through phosphorylation of serine residues on the insulin receptor substrate 1 and 2 (IRS1/2). When hepatic insulin signaling is impaired, gluconeogenesis and glycogenolysis are unleashed, contributing to hyperglycemia and hyperinsulinemia. Under these circumstances, hepatic lipid synthesis is enhanced due to hyperinsulinemia [241,242]. Likewise, reduced fatty acid oxidation leads to hepatic triglycerides accumulation. Of note, Ohashi et al. demonstrated that excessive amounts of fructose consumption lead to epigenetic modifications, such as DNA hypermethylation of promoter regions of peroxisome proliferator-activated receptor alpha (PPARα) and carnitine palmitoyl transferase 1A (CPT1A) that results in lower amounts of mRNA levels [243]. Hepatic triglyceride accumulation results in augmented secretion of very low-density lipoprotein (VLDL) leading to increased lipid uptake in skeletal muscle and peripheral tissues. Similarly to what happens in the liver, intramyocellular lipid accumulation (particularly DAG) activates the PKCθ isoform that phosphorylates and inactivates IRS1 resulting in impaired insulin-stimulated glucose uptake, contributing to hyperglycemia, increased delivery of glucose to the liver, and hyperinsulinemia [241,242]. Fructose-induced hyperuricemia has also been proposed as a causal agent in the etiology of insulin resistance [244,245,246]. This notion arises from the observation that lowering uric acid levels prevents the development of MetS induced by fructose [244,245,246], defective endothelial NO production in mice leads to development of MetS [247], and that uric acid inhibits endothelial NO in in vitro and in vivo [248]. Two mechanisms have been proposed: The first mechanism proposed is that uric acid inhibits endothelial nitric oxide (NO) release, and NO increases blood flow ensuing enhanced insulin delivery and glucose disposal in skeletal muscle and peripheral tissues [249]. The second mechanism states that uric acid promotes inflammation and oxidative stress within the adipocyte [250,251,252]. In addition, uric acid-mediated insulin resistance in the adipose tissue, via the classical mechanisms (i.e., low-grade chronic inflammation mediated by proinflammatory cytokines secreted by the adipocytes, increased lipolysis, and reduced lipogenesis), may result in MetS [241,242]. Additionally, persistent high fructose consumption leads to higher levels of leptin and leptin resistance, which in turn increases food and energy intake [253]. Potential molecular mechanisms underlying leptin resistance may be related to impaired leptin transport across the blood-brain barrier and/or reduced basal levels of phosphorylated signal transducer and activator of transcription 3 (STAT3; a downstream component of the leptin receptor signaling cascade), despite equivalent expression of leptin receptors, in the hypothalamus [253]. However, the fructose hypothesis is not universally accepted. It has been argued that fructose is rarely consumed in its pure form and many published studies have used fructose levels that far exceed dietary composition [254]. Likewise, many animal studies have used extremely high-fructose doses or unusual glucose to fructose ratio that are not representative of actual human diets, which makes it difficult to extrapolate this phenomenon to humans. Therefore, caution in interpreting studies of the effects of fructose on health should be taken into consideration [254]. Another proposed argument to refute the fructose hypothesis is that the causative role of fructose in increasing the risk for the development and progression of MetS is not fully demonstrated. Carefully designed studies should be performed to tease apart the contribution of each risk factor associated to MetS (e.g., obesity, diabetes, or insulin resistance) from fructose, per se [254]. Gut Microbiota, Lipid Metabolism, and Liver Disease The gut microbiota is a complex and dynamic population of microorganisms that, in addition to acting as an immune barrier and protecting against pathogens, plays a crucial role as a metabolic organ itself modulating intestinal permeability, and therefore the nutrient availability [255]. It is generally known that diet exerts a large effect on the gut microbiota, which may affect intestinal permeability and ultimately cause a great metabolic impact [255,256,257,258]. High-fructose or high-glucose diets have been described as an intestinal microbiota modulator that increases inflammation, gut permeability, and metabolic endotoxemia, causing metabolic disturbances such as hepatic lipid accumulation, liver damage, and insulin resistance [258,259]. Likewise, sugar overconsumption also affects lipid metabolism. In obese and overweight subjects, the consumption of glucose-sweetened beverages leads to a lower increase in plasma triglycerides, de novo lipogenesis, and visceral adipose tissue compared to those that consumed fructose-sweetened beverage [71]. However, in rodents, both high-glucose and -fructose diets stimulated similar hepatic lipogenic gene expression [260]. Liver is the principal metabolic organ within the human body and has a major role in regulating carbohydrate metabolism [261]. Many studies point out to the direct implication of high-sugar diets in the development of serious liver diseases, such as NAFLD, hepatic steatosis, liver fibrosis, and dysfunction [262,263,264]. Multiple studies showed that fructose more potently stimulates hepatic de novo lipogenesis than glucose [78,265,266], and the effect is much higher when both monosaccharides were consumed simultaneously [265]. These differences in de novo lipogenesis between both sugars can be explained by differences in their hepatic metabolism. Fructose is directly phosphorylated by fructokinase, bypassing the enzyme phosphofructokinase, a major rate-limiting step in glucose metabolism, providing a larger available substrate for de novo lipogenesis than glucose [261,267]. Regarding the effect of isocaloric diets with different sugar composition, various studies have observed no differences in liver fat content between high-fructose or high-glucose diets [268,269], nor between isocaloric diets with high-fructose corn syrup or sucrose [270]. However, when comparing different doses of fructose in the diet, liver fat content was increased in high-fructose diet, probably associated with increased de novo lipogenesis and reduced whole-body fatty acid oxidation [266,270]. In the same direction as the previous findings, when comparing hypercaloric diets enriched in either fructose or glucose, no significant changes are observed between both diets, suggesting that high-glucose and high-fructose diets provide the same risk for the development of NAFLD [269,271,272]. Taken together, these data would point to the detrimental effect of fructose compared to glucose in terms of hepatic and lipid metabolism. However, there is controversy between different studies, probably due to differences in the doses of sugars administered and their form of administration (oral, intraperitoneal injection, etc.). Many of the above-mentioned studies were performed using supra-doses of fructose in rodents. Since humans typically do not consume fructose as a single sugar, and it is frequently consumed in the form of HFCS, the direct relationship with the real effect of fructose human consumption is not entirely clear. Therefore, more detailed studies on the pattern of sugar consumption in humans should be carried out. 4.5. Impact of Excessive Dietary Sugars Consumption on Incretin Secretion There are many associations reported between high-sugar consumption and the development of pathologies such as diabetes, obesity, and MetS [10,273,274,275]. These associations are mainly due to the current consumption of sugar-sweetened beverages, whose main sweetener is the HFCS. HFCS represents >40% of caloric sweeteners and its consumption has been increased by >1000% between 1970 and 1990. This sugar overconsumption can lead to important changes in the secretion of gut hormones, and therefore, lead to central effects that affect appetite and satiety control. Many authors have focused their studies on the effect that different GIs and GLs have on incretin secretion. Runchey et al. observed that 28-days consumption of a high-GL diet in weight-maintained healthy individuals led to statistically significant increased post-prandial GIP and lower GLP-1 concentrations compared with low-GL diets [276]. However, other authors did not corroborate these findings clearly. One study performed in healthy sedentary women reported that GLP-1 concentrations did not differ significantly following high- or low-GI meals [277]. In the same way, another study in overweight subjects observed no differences in GLP-1 concentrations when comparing consumption of low- and high-GI beverages [278]. Other authors suggest that the rate of small intestinal glucose exposure (i.e., GL) is a major determinant of the magnitude of the incretin effect, since they observed that the incretin effect was stronger when they administered larger intraduodenal glucose load [279]. However, it is necessary to be cautious with this upregulation of incretins in response to high-sugar diets, because it has been described that in diabetic patients, who have increased levels of incretins, a reduced incretin effect is observed, which suggest the development of an “incretin resistance” process [280,281,282,283]. 5. Future Directions During human evolution, ancestral human diets contained low carbohydrate levels and most of the sugars were derived from fruits and honey. In the last century, changes in lifestyle, nutritional habits in the world population, and the abusive use of sweeteners by the food industry have dramatically increased dietary sugar consumption, particularly constituent monomers, such as glucose and fructose, and fructose-based sweeteners. International and national health organizations have called attention into this issue and recommend reductions in sugars consumption due to concerns in their potential role as risk factors for developing human diseases such as obesity and T2DM. In the last decades, the scientific community has made great efforts to understand intestinal sugar absorption, identifying molecular and physiological mechanisms of fructose and glucose sensing and transport. In the case of fructose metabolism, the current notion that fructose is mainly metabolized by the liver has been challenged, and the new paradigm proposes that the small intestine shields the liver from toxic fructose exposure. This provocative view of intestinal fructose metabolism is awaiting confirmation in humans. Similarly, the finding that humans can synthesize fructose by the polyol pathway leaves open the question about the significance of this pathway in human metabolic diseases. In addition, more meta-analysis studies should be performed to clearly demonstrate the causal role of dietary fructose and glucose in developing human metabolic diseases. On the other hand, the role of intestinal glucose metabolism on the etiology of hyperglycemia remains incompletely clarified. Despite studies relating chronic hyperglycemia with impaired glucose transport and metabolism in the small intestine, more studies in humans are required to reveal if chronic hyperglycemia is a cause or consequence of impaired glucose homeostasis in the small intestine. The identification of molecular mechanisms by which glucose and insulin regulate SGLT1 have set this transporter, and its potential role in the physiopathology of hyperglycemia and intestinal insulin resistance, in the spotlight. In this line of thinking, further research is required to demonstrate the efficacy of SGLT1 inhibitors in the treatment of T2DM and obesity. Likewise, it remains to be clarified whether the apical localization of GLUT2 in response to high glucose levels in obese and/or diabetic patients is an adaptive mechanism to protect the body from excessive glucose concentrations, or if it is a consequence of hyperglycemia and insulin resistance. Finally, the differentiated effects of glucose and fructose on eating behavior and impaired cognitive function observed in rodent models are difficult to extrapolate to humans due to the use of extremely high-fructose diets or unusual glucose to fructose ratio. To clarify the causality of fructose in human eating disorders leading to metabolic diseases, it is necessary to develop new research tools and experimental approaches in humans. Author Contributions I.C.-C. and G.P. conceptualized the manuscript. B.M., C.M.F.-D., I.C.-C., and G.P. drafted the manuscript. B.M. and C.M.F.-D. prepared the figures. I.C.-C. and G.P. revised and edited the manuscript. All authors have read and agreed to the published version of the manuscript. Funding This research was funded by the Spanish MINISTERIO DE ECONOMÍA, INDUSTRIA Y COMPETITIVIDAD, grant numbers SAF2016-77871-C2-1-R and SAF2016-77871-C2-2-R to I.C-C. and G.P. respectively; the EFSD European Research Programme on New Targets for Type 2 Diabetes supported by an educational research grant from MSD to I.C-C. and G.P.; the FUNDACIÓN LA-CAIXA Y FUNDACIÓN CAJA DE BURGOS, grant number CAIXA-UBU001 to G.P. Conflicts of Interest The authors declare no conflict of interest.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/29%3A_Integration_of_Mammalian_Metabolism_-_Capstone_Volume_II/29.06%3A_Metabolic_consequences_of_obesity_and_type_2_diabetes-_Balanc.txt	princeton-nlp/TextbookChapters	Metabolic consequences of obesity and type 2 diabetes: Balancing genes and environment for personalized care Nicolas J. Pillon, Ruth J.F. Loos, Sally M. Marshall, Juleen R. Zierath,. Metabolic consequences of obesity and type 2 diabetes: Balancing genes and environment for personalized care. Cell, Volume 184, Issue 6, 2021, Pages 1530-1544, ISSN 0092-8674, https://doi.org/10.1016/j.cell.2021.02.012. Under a Creative Commons license. Attribution 4.0 International (CC BY 4.0) Summary The prevalence of type 2 diabetes and obesity has risen dramatically for decades and is expected to rise further, secondary to the growing aging, sedentary population. The strain on global health care is projected to be colossal. This review explores the latest work and emerging ideas related to genetic and environmental factors influencing metabolism. Translational research and clinical applications, including the impact of the COVID-19 pandemic, are highlighted. Looking forward, strategies to personalize all aspects of prevention, management and care are necessary to improve health outcomes and reduce the impact of these metabolic diseases. Introduction The COVID-19 pandemic has brought the deleterious health consequences of obesity and type 2 diabetes into sharp focus. Individuals with type 2 diabetes and/or obesity are more likely to have severe disease and to die than are individuals without diabetes (Barron et al., 2020). Fasting glucose level at the time of hospital admission predicts 28-day mortality even in those without a previous diagnosis of diabetes (Wang et al., 2020a). Glycemic control and body mass index along with older age, male sex, socio-economic deprivation, non-white ethnicity, and pre-existing renal and cardiovascular disease all independently increase mortality (Holman et al., 2020). COVID-19 is also a timely reminder that diabetes is not merely a state of glucose dysregulation but a multi-faceted syndrome driven by many medical and social risk factors and associated with pathophysiological changes throughout the body. The World Health Organization estimates that worldwide, 422 million people have diabetes, the majority living in low- and middle-income countries, and most having type 2 diabetes (who.int/health-topics/diabetes). The prevalence has risen dramatically for decades, as the population ages and becomes less active and more overweight (GBD 2019 Risk Factors Collaborators, 2020). Early detection is vital, particularly as long-term complications, such as referable diabetic retinopathy, may be present at diagnosis of type 2 diabetes (Kohner et al., 1998). Many developed countries have systematic screening programs of individuals deemed to be at high risk (American Diabetes Association, 2020). However, there is disagreement as to how to define “high risk” and how to screen (oral glucose tolerance test, fasting glucose or glycated hemoglobin, HbA1c). Glucose-based tests and HbA1c each identify slightly different populations. We do not know if these differences in diagnoses lead to important clinically different outcomes or if they signal slightly different pathological metabolic forms of glucose dysregulation (American Diabetes Association, 2020). The WHO defines overweight and obesity as body mass indexes (BMI) 3 25 and 30 kg/m2, respectively and estimated that 1.9 billion adults were overweight and 650 million obese in 2016 (https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight). Obesity is now regarded as a chronic, progressive disease with remissions and relapses (Bray et al., 2017) and an important driver of the development of diabetes and many of its associated features (GBD 2019 Risk Factors Collaborators, 2020). The deleterious effects of obesity and type 2 diabetes are seen in most, if not all, tissues in the body, with consequences resulting in significantly increased premature morbidity and mortality (GBD 2019 Risk Factors Collaborators, 2020). Social and cultural factors are also extremely important in the development, management, and clinical outcomes of obesity and type 2 diabetes. Despite advances in diabetes care over the recent decades, there remain vast challenges: developing an improved understanding of the heterogeneity of obesity and diabetes, how best to assess risk, to screen, to select individualized treatments and vitally how to engage the relevant populations in these programs. This review explores the genetic and metabolic aspects of diabetes and obesity (Figure 1) and discusses some of the latest work and emerging ideas related to basic biological mechanisms, translational research, and clinical applications. Genetics and metabolism The current obesogenic environment, favoring high-calorie foods and physical inactivity, is a major driver of the growing obesity and diabetes epidemic. However, not everyone exposed to this environment gains weight or develops type 2 diabetes. The way people respond to environmental factors is, at least in part, determined by their genetic predisposition to obesity and type 2 diabetes. Traditionally, the genetic contribution has been quantified by the heritability, which is a population-level estimate of how much of the variation in disease susceptibility is attributable to genetic variation. For obesity and type 2 diabetes, the heritability has been estimated to be moderate-to-high, ranging between 30% and 70% (Elks et al., 2012; Willemsen et al., 2015). The search for contributing genes started in the 1990’s with early success largely confined to monogenic forms of obesity and diabetes. Mutations that segregate in families or occur de novo were found to cause major disruptions in the function of genes in which they are located, providing the first insights in the pathophysiology of body-weight regulation and glucose metabolism (Hattersley and Patel, 2017; van der Klaauw and Farooqi, 2015). The search for genetic variants that contribute to common forms of obesity and diabetes began slowly with candidate gene and genome-wide linkage studies. However, the advent of genome-wide association studies (GWASs) in the mid-2000’s accelerated the pace of gene discovery. GWASs have identified thousands of genetic loci that are robustly associated with complex diseases and traits, including 700 for obesity (Yengo et al., 2018) and at least 400 for type 2 diabetes (Mahajan et al., 2018). From the earliest GWAS, tissue enrichment and pathway analyses for BMI-associated loci have suggested that the central nervous system plays a key role in body weight regulation (Locke et al., 2015). Loci associated with type 2 diabetes act predominantly through the perturbation of insulin secretion, pointing to the importance of beta cell function or mass, whereas few loci affect insulin resistance through an effect on body weight or fat distribution (Barroso and McCarthy, 2019). Despite the success of GWASs, pinpointing the causal gene(s) and variant(s) within each locus remains an ongoing challenge. So far, about 20% of loci associated with type 2 diabetes and a handful of loci associated with obesity have been mapped to the most likely causal variant, whereas the underlying biology of hundreds of additional loci remain to be elucidated (Larder et al., 2017; Mahajan et al., 2018; Rathjen et al., 2017). However, with increasing availability of high-throughput genome-scale technologies for mapping regulatory elements, comprehensive multi-omics databases, advanced computational tools, and the latest genetic engineering and molecular phenotyping approaches, we are poised to accelerate the translation of GWAS loci into meaningful biology in the years ahead. With GWASs, genetic susceptibility to disease can be assessed using polygenic scores. A polygenic score represents an individual’s overall genetic susceptibility to disease and is calculated by summing the number of disease-increasing alleles that were inherited from either parent, weighted by each variant’s effect size observed in a GWAS. Even though each locus has a small effect on disease risk and explains only a fraction of the variation in disease susceptibility, when aggregated in a polygenic score, their contribution can be substantial. Polygenic scores are normally distributed, with most individuals having an average score, and thus an average genetic susceptibility, whereas individuals at the extremes of the distribution have a (very) high or low genetic risk of disease. For example, in the UK Biobank, the average BMI of individuals with a high polygenic score (top decile) is 2.9 kg/m2 (equivalent to 8 kg in body weight) higher and their odds of severe obesity (BMI 3 40 kg/m2) is 4.2-fold higher, compared to those with a lower polygenic score (bottom 9 deciles) (Khera et al., 2019). Similarly, individuals with a very high polygenic score (top 5%) for type 2 diabetes have a 2.75-fold increased risk of disease compared to the remainder of the population (Udler et al., 2019). These observations have fueled expectations that genotype information, including polygenic scores, can soon be used in clinical care for early diagnosis of high-risk individuals, to tailor prevention and treatments strategies, and to improve disease prognostics. In fact, many online direct-to-consumer genomic companies are already informing customers about their risks and predispositions for a range of common diseases and traits based solely on genetic profiling, including for obesity and type 2 diabetes (Figure 2). However, even though the genetic associations observed in GWASs are robust, their ability to predict who will be at a high risk of obesity or type 2 diabetes is still low-to-moderate, and not ready for use in clinical settings (Udler et al., 2019). For example, a recent polygenic score applied to individuals of European ancestry of the UK Biobank explains only 8%–9% of the variation in BMI and is a weak predictor of obesity, with an area under the receiver operating characteristic curve (AUCROC) of 0.64 (Khera et al., 2019). Findings are similar for polygenic scores for type 2 diabetes, with AUCROC of 0.64–0.66 (Udler et al., 2019). The predictive ability of polygenic scores are expected to improve as GWASs increase in sample size and the per-variant effect estimates become more precise, and as algorithms to aggregate millions of genetic variants across the genome improve. Nevertheless, given the importance of socio-demographic, lifestyle and clinical risk factors in the etiology of obesity and type 2 diabetes, it is unlikely that a polygenic score on its own will ever be able to accurately predict obesity or type 2 diabetes. More comprehensive approaches that include a broad spectrum of genetic, demographic, environmental, clinical, and possibly also molecular markers are needed to accurately predict who is at risk of gaining weight and/or developing type 2 diabetes. The vast amount of new genetic information generated by GWASs is being used in sub-typing disease at a population level. Obesity and type 2 diabetes are highly heterogeneous diseases, and the diagnosis of these metabolic diseases is unrefined, based on a single marker (BMI 3 30 kg/m2 and hyperglycemia, respectively). Consequently, individuals with the same diagnosis may differ considerably in disease pathogenesis, clinical presentation, disease course and response to treatments. Subtypes of obesity and type 2 diabetes have been typically based on phenotypic differences and similarities. As the number of GWAS-identified loci continues to increase, subtyping of obesity and type 2 diabetes based on genetic information has become possible. In a recent study, 141 variants previously identified for diabetes and diabetes-related traits were clustered in five groups, based on their association with more than 75 traits (Udler et al., 2018). Variants with a similar association profile cluster in the same group, and the group-specific association profile can inform about the mechanisms underlying a given subtype of type 2 diabetes. For example, two of the five groups identified for diabetes-related traits represent reduced beta-cell function, of which one cluster is characterized by high and the other by low proinsulin levels. The three other groups of variants show features of insulin resistance, of which one group represents obesity-mediated insulin resistance, a second group represents abnormal body fat distribution (“lipodystrophy-like”), and a third group represents disrupted liver lipid metabolism. Genetic risk scores based on variants in each cluster are associated with distinct clinical outcomes (Udler et al., 2018). Further for obesity, genotype information has been used to identify individuals who are predisposed to increased adiposity and, concomitantly, are protected from cardiometabolic outcomes (representing the so-called metabolically healthy obesity phenotype) (Ji et al., 2019). Subtyping of heterogenous diseases, like obesity and type 2 diabetes, is key to precision medicine. Indeed, these more homogeneous subgroups are characterized by distinct underlying biological mechanisms, such that diagnosis and prognosis will be more precise and optimization of treatment more efficient (Chung et al., 2020). As GWASs continue to identify more loci, additional and possibly better-defined clusters may be identified to more accurately represent the heterogenous group of individuals with obesity and type 2 diabetes. As more GWAS loci are being discovered, Mendelian Randomization (MR) becomes an increasingly powerful approach to determine causality between an exposure (e.g., health-related behaviors, biomarkers [e.g., lipid levels, metabolites]) and an outcome (e.g., obesity, type 2 diabetes). Genetic variants that are robustly associated with the exposure are used to randomize a population in individuals with high exposure (i.e., carriers of the risk alleles) and those with low exposure (i.e., carriers of non-risk alleles). If the same genetic variants also associate with the disease outcome, through their association with the exposure, then causality between exposure and disease is inferred. For example, a large-scale MR study examined the causal role of a wide range of possible risk factors for type 2 diabetes, mostly confirming established risk factors, but also revealing new ones (e.g., insomnia) (Yuan and Larsson, 2020). As more GWAS data becomes available for a range of multi-omics biomarkers, MR analyses may reveal novel disease-causing biomarkers, broadening insights in the pathogenesis of obesity and type 2 diabetes. Epigenetic impact on metabolism Beyond genetic risk, the genes we inherit and the environmental factors we are exposed to can interact synergistically to modify our physiology and risk for obesity and type 2 diabetes through epigenetic modifications (Figure 3). Epigenetic modifications are biochemical processes that influence gene activity and expression, and ultimately modify cellular and whole-body physiology, without altering the DNA sequence of an organism’s genome (Barrès and Zierath, 2016). Mechanistically, epigenetic modifications can arise from chemical alterations of nucleosides in the DNA molecule itself by methylation or hydroxymethylation, alterations in chromatin structure or post-translational modifications of histones (i.e., methylation, phosphorylation, acetylation, ubiquitylation, and sumoylation) or RNA-associated gene silencing (Bošković and Rando, 2018). Although epigenetic modifications are generally thought to be fixed during development and maintained over an organism’s lifetime, there is some degree of plasticity in the epigenome, which engenders organismal adaptation to rapid environmental changes. Alterations in nutritional status, food supply, physical activity/exercise, thermal stress, toxins, or other environmental insults can trigger epigenetic modifications and lead to genomic changes in somatic cells within an individual that directly disrupt metabolic homeostasis (Barrès and Zierath, 2016; Bošković and Rando, 2018). These same factors may also modify the physiology of an organism by transgenerational epigenetic inheritance, whereby paternal or maternal environmental exposure can influence metabolism and manifest obesity- or type 2 diabetes-related traits in the offspring. Prenatal undernutrition affects glucose tolerance and risk of diabetes in the offspring, as demonstrated by epidemiological studies of several famines over the past century (Li et al., 2010; Ravelli et al., 1998). In rodents, paternal and maternal diet and exercise influence metabolic and cardiovascular outcomes in offspring over several generations (de Castro Barbosa et al., 2015; Murashov et al., 2016; Stanford et al., 2015). Thus, nutritional status in utero during fetal development may affect the epigenome for several generations, but the molecular transducers remain to be clarified. Additionally, food restriction during childhood, at different growth phases around puberty, also leads to epigenetic changes that influence the risk of cardiovascular and metabolic disease of offspring over several generations (Kaati et al., 2002). Accordingly, epigenetic factors passed on by the gametes may contribute to the global increase in obesity and type 2 diabetes. Thus, an area of emerging interest is the influence of the environment on epigenetic mechanisms, and how this modifies metabolic disease risk. A variety of dietary agents, as well as micronutrients and metabolites synthesized de novo, can serve as substrates or co-factors to influence the epigenome and potentially affect metabolic disease risk in humans, in part by affecting genomic plasticity (Tiffon, 2018). One-carbon metabolism encompasses folate and methionine cycles, which transfer one-carbon moieties and methyl groups for nucleotide synthesis, methylation reactions and reductive metabolism (Newman and Maddocks, 2017). Metabolites including acetyl-coA, AMP, NAD+, and S-adenosylmethionine are required for histone modifications (acetylation, phosphorylation) and methylation of DNA and histones. The extent to which nutritional factors, metabolites, and other co-factors directly modify the epigenome within a generation remains to be fully substantiated in humans. While it is important to stress that type 2 diabetes and obesity are complex multi-factorial, progressive metabolic diseases with diverse etiology, and not simply “lifestyle disorders,” diet and exercise regimes can prevent or delay disease progression. Changes in the concentration of cellular metabolites, nucleotides, or calcium levels in skeletal muscle in response to acute exercise alter DNA methylation or histone modifications and influence gene expression through epigenetic mechanisms (Barrès and Zierath, 2016). In humans, acute exercise alters DNA methylation of the promoters of genes involved in metabolic regulation in skeletal muscle (Barrès et al., 2012; Nitert et al., 2012). Epigenetic modifications have also been observed in skeletal muscle and adipose tissue in humans with obesity and weight loss (Barres et al., 2013; Multhaup et al., 2015). Thus, the impact of environmental exposures and epigenetic influences on the risk for metabolic diseases throughout the lifespan is an important aspect of biology to unravel. Circadian control of metabolism An evolutionarily conserved mechanism by which environmental factors can impact whole-body physiology is through internal biological clocks and the control of circadian rhythms (Young, 2018). Circadian rhythms are driven by cell-autonomous intrinsic clocks that anticipate day/night cycles in order to optimize the physiology and behavior of organisms. Circadian programs are regulated at both the central and peripheral level with the master clock, located in the suprachiasmatic nucleus region of the hypothalamus, acting as conductor to synchronize and direct peripheral oscillators (Young, 2018). Synchronization of these intrinsic circadian clocks can be achieved in response to photic and non-photic zeitgebers (time-givers). The most powerful zeitgeber is light, which synchronizes the central clock. In addition to receiving cues from the central clock, peripheral clocks are synchronized by external zeitgebers, including food intake, temperature, energetic stressors, and drive the expression of a broad network of genes, many of which are involved in metabolic homeostasis (Gabriel and Zierath, 2019). The precise mechanism by which circadian clocks coordinate whole-body homeostatic processes is an area of emerging interest given the importance of external zeitgebers and the regulation of gene programs controlling metabolism and development. One mechanism by which the circadian machinery influences metabolism is through the diurnal patterns of hormone secretion (Gamble et al., 2014). Endocrine organs release a variety of hormones in response to diverse environmental factors including diurnal cycles of light/dark, fasting/feeding, and temperature changes. For example, there are diurnal or circadian patterns of secretion of cortisol, growth hormone, prolactin, thyroid hormone, gonadal steroids, and melatonin related to sleep/wake cycles, whereas metabolic hormones including insulin, leptin, ghrelin, and glucagon vary in response to nutritional cues related to fasting/feeding cycles (Gamble et al., 2014). Many of these hormones including insulin, insulin-like growth factor 1, and glucocorticoids can act as zeitgebers to reset or fine tune the clock (Balsalobre et al., 2000; Crosby et al., 2019). Thus, an intimate relationship between circadian clocks and endocrine systems exists. This relationship is clinically relevant since disruption of the circadian clock is linked to metabolic disease. In humans, long duration of shift work is associated with an increased risk of type 2 diabetes, which is only partly explained by lifestyle factors and BMI (Vimalananda et al., 2015). Epidemiological studies show that disruption of the sleep/wake cycle through extended periods of rotating night shift work is associated with obesity and increased risk of type 2 diabetes (Lin et al., 2009; Pan et al., 2011). Chronic jet lag in mouse models disrupts exergy homeostasis and leptin signaling and leads to circadian dysfunction-induced obesity (Kettner et al., 2015). Similarly, a population-based cohort study indicates that social jet lag, defined as the discrepancy between circadian and social clocks, is associated with increased risk of metabolic syndrome and diabetes/prediabetes (Koopman et al., 2017). Thus, chronobiology has implications for obesity and type 2 diabetes pathogenesis. A basic paradigm of circadian regulation of metabolism is that oscillations of gene expression generate daily rhythms in cellular metabolism (Kim and Lazar, 2020). At the molecular level, circadian rhythms are generated by a cell autonomous and self-sustained transcriptional auto-regulatory feedback loop that is composed of transcriptional activators and their target genes, which rhythmically accumulate and form a repressor complex to inhibit transcriptional activity (Figure 4). Energy, nutrient, and oxygen sensors interact with the circadian clock machinery to control metabolic outputs including mitochondrial function, substrate utilization, insulin sensitivity, and glycemic control (Lamia et al., 2009; Peek et al., 2017; Sato et al., 2019). These sensors monitor oxygen availability and energy stress via hypoxia-inducible factor-1 alpha (HIF1α) and AMP-activated protein kinase (AMPK), respectively. Cells also integrate signals from nutrients and growth factors via mammalian target of rapamycin (mTOR). These energetic sensors not only exhibit circadian rhythmicity, but also regulate components of the core clock machinery through epigenetic modifications, mainly involving histone modifications (Kim and Lazar, 2020). Thus, cross-talk exists between the circadian clock and epigenetic factors that influence the genomic plasticity of organs controlling metabolic homeostasis. In rodents, dysregulation of the intrinsic molecular clock in a variety of tissues leads to obesity, insulin resistance, and altered glucose homeostasis (Rudic et al., 2004; Turek et al., 2005). Nevertheless, the mechanisms underlying disrupted circadian rhythmicity in people with type 2 diabetes are unknown. There is potential to coordinate behavioral changes with the body’s daily rhythm to improve metabolic homeostasis. Timing of exercise training bouts or meals and distribution of calories throughout the day may lead to improved outcomes for people with obesity or type 2 diabetes (Lundell et al., 2020; Savikj et al., 2019). Impact of energetic stressors on the control of metabolism Obesity, diabetes, exercise, and food restriction are energetic stressors that represent major challenges to organismal homeostasis, triggering wide-ranging responses in numerous cells and tissues controlling glucose and energy metabolism. An essential component of an organism’s survival is the ability to sense energy availability and to adapt accordingly. Metabolic flexibility, the ability to shift between fat and glucose oxidation with fasting and feeding, is reduced in individuals with metabolic diseases and contributes to the overall insulin resistance phenotype (Kelley et al., 1992). Skeletal muscle exhibits metabolic flexibility in fuel preference, likely due to its crucial role in hunting and surviving predation, situations requiring movement even if nutrient availability is not optimal (Freese et al., 2017). A body of literature supports the idea that metabolic flexibility can be directly influenced by physical activity, independent of changes in energy balance (Rynders et al., 2018). Physical exercise enhances skeletal muscle insulin sensitivity and improves whole-body glucose metabolism in people with type 2 diabetes (Savikj and Zierath, 2020). However, recent findings, based on stable-isotope tracer and liquid chromatography tandem mass spectrometry, demonstrate that skeletal muscle mitochondrial substrate preference is not altered in insulin resistant rodents and humans, calling into question the central role of metabolic flexibility in the pathogenesis of metabolic diseases (Song et al., 2020). Nevertheless, there is growing appreciation that insulin resistance, obesity, and type 2 diabetes can be avoided or at least delayed by lifestyle intervention strategies, including diet and exercise, which initiate diverse homeostatic responses across multiple organs (Savikj and Zierath, 2020). The concept of “time-restricted feeding” has gained traction as a dietary means to restore metabolic homeostasis, enhance insulin sensitivity, and curb obesity. Time-restricted feeding refers to restricting daily food intake to a few hours, without caloric restriction (Chaix et al., 2014). In rodents, time-restricted feeding synchronizes the feeding/fasting cycle with the central clock, thereby promoting robust circadian and metabolic cycles, which mitigates obesity and metabolic dysfunction (Hatori et al., 2012). Thus, timing of food intake with the molecular circadian clock may fine-tune metabolism. In humans, time-restricted feeding paradigms improve cardiometabolic health in people with obesity or metabolic disease (Cienfuegos et al., 2020; Wilkinson et al., 2020). Short-term time-restricted feeding schedules in men with obesity modulate the diurnal rhythm of lipid and amino acid metabolism, without affecting core clock gene expression in skeletal muscle (Lundell et al., 2020). Furthermore, the timing and type of nutritional intake throughout a day influences carbohydrate metabolism and protein synthesis (Areta et al., 2013). Whether this is dependent upon the release of hormones, metabolites, or thermogenesis warrants further investigation. Moreover, the weight and cardiometabolic benefits achieved with time-restricted feeding schedules may be related to reductions in calorie intake, rather than meal timing. Concordantly, a prospective randomized clinical trial including 116 men and women with overweight or obesity found that modest reductions in weight loss and energy intake from time-restricted eating did not differ from the control group (Lowe et al., 2020), hinting at the possibility that benefits of time-restricted feeding programs are mainly due to reductions in calorie intake. Diet and exercise have a synergistic effect on insulin sensitivity, which may be influenced by altering the timing of the meal or an exercise bout throughout the day. In rodents, there is a time-of-day-dependent effect of acute exercise on the diurnal oscillations of skeletal muscle metabolites and transcripts, with a greater reliance on glycolytic metabolism when exercise is performed during the early active phase of the day (Sato et al., 2019). Moreover, in a preliminary clinical investigation comparing the time-of-day impact of high intensity exercise in men with type 2 diabetes, greater blood glucose control was achieved with afternoon versus morning exercise (Savikj et al., 2019). The oxygen-sensitive transcription factor HIF1α links time-of-day-specific effects of exercise on gene expression and carbohydrate metabolism in mice models (Peek et al., 2017; Sato et al., 2019). This finding has clinical relevance, since intense exercise acutely increases skeletal muscle protein abundance and DNA binding activity of HIF1α in humans (Ameln et al., 2005). Moreover, energetic stressors, such as exercise and hypoxia, increase skeletal muscle glucose uptake in healthy and insulin resistant humans and rodents (Ranheim et al., 1997; Ryder et al., 2000). Thus, perturbing energy, nutrient, and/or oxygen sensors may have a varied response on cellular metabolism depending on the time of day. Collectively, these studies provide evidence that the timing of exercise bouts throughout the day is clinically relevant for the diurnal control of glycemia or systemic metabolism. Adjusting the timing of external cues (i.e., meal/exercise timing) may sustain or amplify circadian clock signals to prevent or mitigate metabolic disease. Thermal tolerance Excess energy can be dissipated in the form of heat, a process that occurs in brown adipose tissue and is stimulated by food intake and cold exposure (Chouchani et al., 2019). Feedback loops involving temperature sensors, thermogenesis, sweating, and the control of blood circulation are tightly regulated to maintain body temperature in humans at ∼37°C. Alterations in ambient temperature trigger acute and chronic changes in whole-body physiology, making climate a major environmental stressor that affects all individuals on the planet. Acute exposure to cold triggers shivering in skeletal muscle, where ATP is used to generate movement and its associated production of heat. Chronic adaptation to cold involves different mechanisms, the main one being activation of brown adipose tissue thermogenesis (Chouchani et al., 2019). Uncoupling protein 1 dissipates the proton gradient in the mitochondria to generate heat instead of ATP. Consequently, oxidative phosphorylation increases to maintain mitochondrial membrane potential. Therefore, exposure to cold temperatures increases the metabolic rate during sleep cycles, as well as diet-induced thermogenesis, thereby increasing total energy expenditure (Chouchani et al., 2019). A rise in ambient temperature above thermoneutrality also increases metabolism by promoting heat dissipation (Chouchani et al., 2019). The processes involved in heat acclimation have been extensively studied in humans and involve an increase in total body water, increased sweat volume and decreased sweat concentration, as well as adaptations of heart rate and skin blood flow (Périard et al., 2015). Mechanisms involved in heat acclimation and associated cardiovascular events are related to increased central heat production and dehydration and the ensuing deleterious consequences on blood pressure and cardiovascular function (Meade et al., 2020). Acute exposure to extreme ambient temperatures, often referred to as “cold stress” or “heat stress,” is associated with an increased risk of cardio-pulmonary mortality (Achebak et al., 2019). In this context, age, weight, obesity, and type 2 diabetes are major risk factors (Hajat et al., 2017; Huang et al., 2012). The mechanisms for increased risk of cardiovascular events secondary to extreme temperatures in people with metabolic diseases are poorly understood. Reduced heat tolerance in obesity might be due to impairments in blood flow and sweat production (Vroman et al., 1983). The reduced sweating ability is possibly linked to a decreased body surface area-to-body mass ratio in a person with obesity as compared to a leaner person. Individuals with type 2 diabetes also exhibit reduced skin blood flow in response to local and whole-body heating, likely due to impaired endothelial function (Meade et al., 2020). However, chronic exposure to mild electrical stimulation with heat shock improves visceral adiposity, glucose homeostasis, and insulin sensitivity in people with type 2 diabetes (Kondo et al., 2014). This paradox suggests that increasing heat tolerance by repeated acute exposures to heat might mitigate heat-induced cardiovascular events in individuals with metabolic diseases. Heat stress from both exercise and environmental factors can increase thermal strain in unacclimated individuals (Figure 5). Acute exercise increases core body temperature and high-intensity exercise can lead to heat illness consisting of symptoms ranging from minor cramps and syncope to major heat stroke, even in highly trained athletes (Charlot et al., 2017). The capacity to dissipate an exercise-induced elevation in body temperature is reduced in people with type 2 diabetes, but this can be overcome by regular exercise training, which is associated with improved heat tolerance (Kenny et al., 2016). Regular exercise training also reduces cardiovascular mortality and improves glucose control in people with type 2 diabetes (Savikj and Zierath, 2020). At a molecular level, exercise training increases heat shock protein abundance, a process that could contribute to the beneficial effects of exercise to enhance insulin sensitivity (Archer et al., 2018). Individuals with obesity or type 2 diabetes exhibit decreased levels of heat shock proteins in skeletal muscle (Chung et al., 2008). This decrease is reversible, and induction of heat shock proteins by mild electrical stimulation with heat shock improves visceral adiposity as well as plasma glucose and insulin levels (Kondo et al., 2014). Regular exposure to thermal stressors, such as exercise or environmental temperature, may improve heat tolerance through overlapping adaptive mechanistic responses (sweat volume and composition, body water, heart rate), thereby improving metabolism and decreasing risk of cardio-pulmonary events in individuals exposed to extreme ambient temperatures. Presently, most of the human population lives under conditions of thermoneutrality, which is made possible due to appropriate clothing and heating systems in homes and workplaces. Reduced energy expenditure, due to the comforts of our modern society and the decline in our prolonged exposure to cold environments, may contribute to the worldwide rise in obesity, although this is difficult to firmly establish. However, there is a clear link between thermal regulation, metabolic diseases, and associated complications. Most of the temperature-related cardio-pulmonary events occur on moderately hot and moderately cold days (Gasparrini et al., 2015), suggesting that steady increases in the average global temperature has the potential to impact the numbers of these events worldwide. The combination of an epidemic of obesity and type 2 diabetes, juxtaposed with an aging population and climate change, may potentially lead to a dramatic increase in cardio-vascular morbimortality. Understanding the molecular basis of heat intolerance in people with obesity or type 2 diabetes could open novel preventative and therapeutic perspectives. Inflammatory responses The stress that temperature, obesity, diabetes, exercise, and food exert on organismal homeostasis triggers activation of the immune system and different states of metabolic inflammation. The immune system is composed of specialized cells present in every organ that protect against a wide variety of insults, including infections, mechanical injuries, and a variety of diseases. The immune response comes in waves, starting with a pro-inflammatory activation and finishing with a resolving anti-inflammatory phase (Feehan and Gilroy, 2019). When the immune system fails to recover after an insult, a chronic inflammatory state occurs, leading to long-term deleterious consequences. This typically happens in obesity, where immune cells infiltrate tissues and lead to chronic low-grade inflammation, associated with increased risk of cardiovascular complications. The association of inflammation with type 2 diabetes and obesity has been extensively studied, as evidenced by the rapid development of the field of “immunometabolism,” which includes the analysis of the complex interactions between metabolic and inflammatory pathways in immune and metabolic tissues (Lee et al., 2018). Obesity and type 2 diabetes are associated with an accumulation of immune cells in key tissues involved in metabolic homeostasis. A link between metabolic diseases and immunology emerged with the detection of macrophage infiltration in adipose tissue, followed by the discovery that lymphocytes, neutrophils, and other specific subtypes of immune cells accumulate not only in adipose tissue but also in skeletal muscle and liver (Hotamisligil, 2017). Even neuroinflammation is part of the systemic inflammatory syndrome in metabolic diseases (Cai, 2013). The accumulation of triglycerides in adipocytes increases adipocyte size (hypertrophy) and number (hyperplasia), resulting in the rapid expansion of adipose tissue, which triggers hypoxia and the production of soluble mediators likely responsible for the attraction of immune cells. The first immune cells reaching adipose tissue are likely attracted to support tissue remodeling in a beneficial manner, but the chronic increase in adipose tissue volume and the establishment of a new obese steady-state leads to increasing lipolysis and circulating free fatty acids, which activate immune cells toward a pro-inflammatory phenotype and promote the establishment of chronic inflammation (Lee et al., 2018). Immune cells also respond to metabolic changes and are susceptible to the deleterious effects of an excessive lipid or glucose accumulation (i.e., “lipotoxicity” or “glucotoxicity”), as well as other metabolism-related danger signals that are released by tissues during metabolic stress (Wang et al., 2020b). The composition and phenotype of circulating immune cells is altered in blood of individuals with obesity, with an increase in CD16+ monocytes, and immune cell activation in response to high concentrations of glucose or fatty acids (Pillon et al., 2016). These findings suggest that the immune system is profoundly affected by whole-body glucose and lipid homeostasis. The mechanisms by which non-adipose tissues establish a state of inflammation is unclear. However, lipotoxicity, including the excessive accumulation of toxic lipid mediators such as ceramides, diacylglycerol, or acylcarnitine, and increased levels of circulating free fatty acids likely play a role. In addition, activated immune cells primed to respond to metabolism-related danger signals can impair whole-body metabolism. There is ample evidence to suggest that inflammation is associated with the development of metabolic diseases and the ensuing complications, but pharmacological targeting of pathways controlling immunometabolism has shown limited benefits for the treatment of metabolic diseases (Pålsson-McDermott and O’Neill, 2020). Perhaps the key to successful clinical intervention will be to identify relevant patient groups early, before the manifestation of a chronic low-grade inflammatory state. Acute exercise, especially intense and/or eccentric exercise, triggers an acute inflammatory response, which is necessary for skeletal muscle repair and adaptations to exercise training. Exercise training has beneficial anti-inflammatory effects (Gleeson et al., 2011). Thus, repeated peaks of inflammation triggered by acute bouts of moderate intensity exercise may be beneficial to reduce long-term basal concentrations of pro-inflammatory mediators. In severely obese individuals, combining exercise and dietary interventions can reduce macrophage infiltration and pro-inflammatory polarization in adipose tissue (Bruun et al., 2006). The anti-inflammatory effects of exercise could be secondary to an increased capacity for fatty acid utilization, as exercise training in people with obesity or type 2 diabetes reduces the level of deleterious lipid species such as DAG, acetylcarnitines, and ceramides in skeletal muscle (Lancaster and Febbraio, 2014). However, exercise training in healthy individuals also improves insulin sensitivity without changes in these lipid species, making the role of intramyocellular lipids on insulin sensitivity ambiguous and perhaps more relevant in an obesity context (Reidy et al., 2020). Unsuspected causes Currently known genetic, lifestyle, and environmental risk factors only partly explain the development of obesity and diabetes. Other yet unknown factors must be at play. A recent example of potential novel causes of diabetes is the high prevalence of extreme hyperglycemia/ketoacidosis in patients not known to have diabetes admitted to hospital with COVID-19 (Rubino et al., 2020). This seems both more common and more severe than has been seen with other infections/serious illnesses, so it may not represent “stress hyperglycemia” or unmasking of pre-existing, undiagnosed diabetes. Instead, these observations may suggest a specific pathological entity. The SARS-CoV-2 spike protein penetrates cell membranes by binding to the angiotensin converting enzyme (ACE) 2 receptor. This receptor is present on pancreatic beta cells (Hamming et al., 2004). Infection may result in acute loss of insulin secretory capacity and/or beta cell destruction (Apicella et al., 2020). ACE2 receptor is also present on adipocytes so that SARS-CoV-2 may also exacerbate chronic inflammation in adipose tissue (Kassir, 2020). The mechanisms whereby widely accepted risk factors such as obesity result in disease may have novel aspects. It is generally assumed that individuals with type 2 diabetes who are not obese have a different pathophysiological cause unrelated to weight. However, this belief has been challenged recently. The concept of a “personal fat threshold” arose from observations that the median BMI in the UK Prospective Diabetes Study was only 28 kg/m2 (Taylor and Holman, 2015) and that reversal of type 2 diabetes by weight loss could be achieved equally successfully in individuals with higher and lower BMI (Lim et al., 2011). The underlying mechanism appears to be lipotoxicity, an individual’s propensity to accumulate liver and pancreas fat, and their susceptibility to the adverse effects of fat accumulation. At any given body weight or BMI, at-risk individuals will accumulate more liver fat and be more susceptible to developing hepatic insulin resistance at any given liver fat content. The subsequent increase in VLDL-TG export from the liver drives fat accumulation in the pancreas and declining insulin secretion, both also dependent on the individual’s susceptibility. Remission of type 2 diabetes by weight loss is accompanied by reduction in liver and pancreatic fat, decreased hepatic VLDL export, and increased insulin secretion (Al-Mrabeh et al., 2020). Conversely, weight re-gain leading to re-emergence of diabetes is associated with increased liver fat export and pancreatic fat, with recurrent pancreatic dysfunction. The importance of these observations underscores the usefulness of weight loss in the management of diabetic individuals even of normal weight. However, diabetes does not remit in everyone following substantial weight loss, so weight loss is not a universal panacea. Further work is needed to establish if this relates to longer duration diabetes, perhaps with irreversible beta cell damage, or to different pathological mechanisms of disease. Bending the curve Weight loss is clearly the key to reducing rates of obesity and type 2 diabetes, with considerable individual and societal benefits. There is a continuum of action required in prevention of obesity and diabetes, and management and care if they develop (Chan et al., 2020). Many intervention programs have demonstrated successful short-term weight loss and reversal of diabetes, but perhaps the bigger challenge is in preventing weight re-gain (Forouhi et al., 2018). There may be a weight “set-point,” at which compensatory hormonal, metabolic and neurochemical mechanisms prevent further weight loss and drive weight regain (Blüher, 2019). However, a significant proportion of individuals who lose a substantial amount of weight, whether by diet or bariatric surgery, do not regain weight over years and maintain the metabolic benefits of the initial weight loss. Thus, weight loss programs must have two parts: an initial phase of weight loss, followed by a weight maintenance program. Obviously, reduction in energy intake by some means is essential for weight loss. Exactly how this is achieved is probably less important than an individual’s ability to adhere to the program long term (Johnston et al., 2014). The benefits of one regimen over another have been debated (Forouhi et al., 2018), but no one size fits all, and many different approaches are needed. Understanding the influence of social and cultural aspects in the development and management of obesity and diabetes is also crucial (Blüher, 2019). Individuals from socially deprived backgrounds are more likely to be at high risk to develop obesity and type 2 diabetes, to have poorer glycemic control, to develop more complications, and to have a greater reduction in life expectancy (Chan et al., 2020). Identifying and overcoming barriers to participation in screening and prevention programs and in diabetes and obesity care are vital. Most programs do not reach individuals from ethnic minorities or low socioeconomic class, who are most at need (Timpel et al., 2019). Involving overweight and obese individuals from a wide diversity of backgrounds in the identification of barriers to adherence and then in the design of weight loss and diabetes prevention/reversal programs is essential to improve engagement. Personalized medicine Although we talk about type 2 diabetes as one disease, this “blanket” diagnosis covers important heterogeneity (Ahlqvist et al., 2018). Only rarely is the heterogeneity obvious and explainable: slim rather than obese, or young age at presentation with a striking family history in monogenic diabetes. On many occasions, individuals with apparently similar phenotypes have very different clinical courses and respond quite differently to glucose lowering agents. Dissecting out particular forms of “type 2 diabetes,” whether by genetic analyses and risk scores or by improving our understanding of the underlying pathophysiological bases to dysglycemia, is currently possible at the population level but remains extremely difficult at an individual level. As a result, the selection of glucose-lowering agents for individuals is a “best guess” approach, far removed from personalized medicine. Personalized medicine is defined simply as the right treatment for the right person at the right time. The recent American Diabetes Association/European Association for the Study of Diabetes consensus report describes the ambition to personalize all aspects of an individual’s diabetes, including precision diagnosis, lifestyle and pharmacological management, and prognosis (Chung et al., 2020). Currently, for a very small number of individuals (for example, those with congenital leptin deficiency [Montague et al., 1997] and GCK-MODY [MODY 2] [Froguel et al., 1992; Hattersley et al., 1992]), precision diagnosis is possible. However, there are major challenges in precision diagnosis for individuals with the polygenic common forms of obesity and type 2 diabetes. Likewise, there are only a small number of examples of precision therapeutics (for example, leptin for management of severe obesity in congenital leptin deficiency [Farooqi et al., 2002] and sulphonylureas rather than insulin for individuals with neonatal diabetes due to mutations in the genes encoding the potassium channel [KCNJ11 and ABCC8] [Pearson et al., 2006]). For many individuals with obesity and type 2 diabetes, we have extremely blunt “precision” tools. For example, analysis of data from participants in the RECORD and ADOPT trials demonstrated that individuals with insulin resistance have a greater sustained fall in HbA1c on thiazolidenediones compared to sulphonylureas (Dennis et al., 2018). Additionally, there are benefits of SGLT2 inhibitors in individuals with high cardiovascular risk and/or renal disease (Lo et al., 2020). Work is beginning to examine possibilities for personalization of lifestyle measures. Grand challenges Prevention of obesity is probably the most important factor in reducing the prevalence of obesity and related metabolic diseases. This will require action at an individual and societal level (Chan et al., 2020). Societal action is necessary in many areas, including changes to road, rail, and cycling transport plans to encourage increased physical activity, as well as negotiations with the food industry (Chan et al., 2020). Governments also need effective communication plans that reach all sections of society (Timpel et al., 2019). Moreover, different strategies are required for different life stages. The lifestyles of almost everyone must change radically, and this must be facilitated by appropriate action by governments and many branches of industry. The challenges to overcome the status quo and vested interests are considerable. There is abundant evidence that many individuals with obesity at high risk of metabolic disease can lose substantial amounts of weight, reversing pre-diabetes and diabetes. A significant proportion then maintain the weight loss and improved metabolic status for years. Weight loss programs are projected to be more effective per quality-adjusted life year and cost-saving over a lifetime compared to standard care in individuals with type 2 diabetes (Xin et al., 2020). The challenge then is to expand and adapt these successful programs so that all individuals can access them and be supported through them. We must work with individuals who find current programs inappropriate for them, identifying barriers to participation and working together to develop practical solutions. Most current weight loss programs center on improving basic diet, with or without advice on exercise (Forouhi et al., 2018). Further incremental benefit may well be obtained by incorporating additional “personalized” measures, perhaps based on genes, occupation, and inflammatory status, such as advice on specific micronutrients, timing of food intake and exercise, and light exposure. However, the challenge will be to ensure that the message does not become so complex that adherence falls. There is a particular challenge for young people (Chan et al., 2020). The WHO estimated in 2016 that world-wide, 340 million children and adolescents aged 5–19 years were overweight or obese and, in 2019, that 38 million children aged <5 years were overweight/obese (https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight). Associated with this, type 2 diabetes is increasingly diagnosed in children, adolescents, and young adults (IDF Diabetes Atlas 9th edition 2019, www.diabetesatlas.org). A recent meta-analysis has demonstrated the greater impact of type 2 diabetes presenting at younger age: each one-year increase in age at diabetes diagnosis was associated with a 4%, 3%, and 5% decreased risk of all-cause mortality, macrovascular, and microvascular disease respectively (Nanayakkara et al., 2020). These changes underscore the need to prevent obesity and/or manage it appropriately in young people. Paralleling the rise in obesity in younger people is the rise in the number of women with hyperglycemia during pregnancy. The IDF estimated that, in 2017, 16% of women with live births had some form of hyperglycemia during pregnancy, and that 86% of them had gestational diabetes (IDF Diabetes Atlas 9th edition 2019, www.diabetesatlas.org). In addition to the immediate maternal and fetal adverse effects of hyperglycemia during pregnancy, many of these women will develop type 2 diabetes in the subsequent 5-10 years. There are also longer-term consequences to the offspring of increased risk of obesity, type 2 diabetes, hypertension, and cardiovascular disease (Catalano and Shankar, 2017). Some of these adverse consequences are now being reported over several generations of offspring, implicating an epigenetic influence (Catalano and Shankar, 2017). Thus, in addition to the immediate management of the index pregnancy, it is extremely important that further studies of the index in women, their children, and potentially subsequent generations are conducted urgently. Low levels of fitness are a risk factor for hospitalizations and all-cause mortality, and predict morbidity after surgical interventions (West et al., 2016). During the COVID-19 pandemic, public health recommendations regarding confinement and closure of recreation areas decreased daily activity in the general population (Sánchez-Sánchez et al., 2020), aggravating already high levels of inactivity in most countries (https://www.who.int/news-room/fact-sheets/detail/physical-activity). In young adults who only developed mild symptoms, COVID-19 decreased the predicted maximal aerobic capacity (Crameri et al., 2020), while persons more severely infected with SARS-CoV-2 exhibited anorexia and skeletal muscle loss, aggravated by long hospital stays, raising the question of whether COVID-19 could be a major cause of cachexia and sarcopenia (Morley et al., 2020). As obesity and type 2 diabetes are risk factors for COVID-19 complications, the underlying inflammatory conditions in combination with impaired skeletal muscle function may contribute to worse outcomes after infection (Guisado-Vasco et al., 2020). Whether exercise can protect against viral infection or influence disease severity is unclear, but the benefits of physical activity to prevent skeletal muscle wasting are important factors for prevention and rehabilitation of people in risks groups. Understanding the molecular mechanisms underlying the beneficial effects of physical exercise as an inflammatory modulator could thus potentially prevent or mitigate complications due to unexpected infections (da Silveira et al., 2021; Krause et al., 2020). On the horizon How do we move forward? Progress will only come if we tackle the problems at both a population and an individual level. Putting into practice what we already know will benefit many individuals (Chan et al., 2020). Incorporating the newer evidence described above—for example, around the timing of eating and exercise—and light exposure, in ways that do not overwhelm people, will bring added benefits. Better personalization of all aspects of prevention, management, and care should help adherence. In-depth large-scale analysis of genetic and environmental factors may help clarify why people respond differently to the whole gamut of care, allow stratification into refined sub-groups with specific risk factors and genetic predispositions, and potentially thus optimize the efficacy of both lifestyle and pharmacological interventions. Ongoing initiatives like the Innovative Medicines Initiative (www.imi.europa.eu) have demonstrated that combining large databases from multiple public and private organizations is possible, generating power to tackle relevant genetic and biomarker questions. Such initiatives, bringing together diverse stakeholders with people with obesity or diabetes, are essential in our efforts to provide personalized, timely, affordable, and equitable access to high-quality health interventions, with the aim of improving health outcomes for all. Acknowledgments N.J.P. was supported by an Individual Fellowship from the Marie Skłodowska-Curie Actions (European Commission, 704978). J.R.Z. was supported from the Swedish Research Council (Vetenskapsrådet) (2015-00165), a Novo Nordisk Foundation Challenge Grant (NNF14OC0011493), and the Novo Nordisk Foundation Center for Basic Metabolic Research at the University of Copenhagen (NNF18CC0034900). R.J.F.L. was supported by the National Institutes of Health (R01DK110113; R01DK107786; R01HL142302; R01 DK124097) and the Novo Nordisk Foundation Center for Basic Metabolic Research at the University of Copenhagen (Alliance 190503). Author contributions N.J.P., R.L., S.M., and J.R.Z. wrote the manuscript. All authors read and approved the final version of the manuscript. Declaration of interests The authors declare no competing interests. J.R.Z. is member of Cell Advisory Board.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/29%3A_Integration_of_Mammalian_Metabolism_-_Capstone_Volume_II/29.07%3A__Lipid-Induced_Mechanisms_of_Metabolic_Syndrome.txt	princeton-nlp/TextbookChapters	Yulia K. Denisenko, Oxana Yu Kytikova,Tatyana P. Novgorodtseva, Marina V. Antonyuk, Tatyana A. Gvozdenko,and Tatyana A. Kantur. VJournal of Obesity, Volume 2020 \|Article ID 5762395 \| https://doi.org/10.1155/2020/5762395 Copyright © 2020 Yulia K. Denisenko et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Metabolic syndrome (MetS) has a worldwide tendency to increase and depends on many components, which explains the complexity of diagnosis, approaches to the prevention, and treatment of this pathology. Insulin resistance (IR) is the crucial cause of the MetS pathogenesis, which develops against the background of abdominal obesity. In light of recent evidence, it has been shown that lipids, especially fatty acids (FAs), are important signaling molecules that regulate the signaling pathways of insulin and inflammatory mediators. On the one hand, the lack of n-3 polyunsaturated fatty acids (PUFAs) in the body leads to impaired molecular mechanisms of glucose transport, the formation of unresolved inflammation. On the other hand, excessive formation of free fatty acids (FFAs) underlies the development of oxidative stress and mitochondrial dysfunction in MetS. Understanding the molecular mechanisms of the participation of FAs and their metabolites in the pathogenesis of MetS will contribute to the development of new diagnostic methods and targeted therapy for this disease. The purpose of this review is to highlight recent advances in the study of the effect of fatty acids as modulators of insulin response and inflammatory process in the pathogenesis and treatment for MetS. 1. Introduction Metabolic syndrome (MetS) is a complex of several disorders (abdominal obesity, hyperglycemia, hypertriglyceridemia, and hypertension), which together dramatically raise the risk of developing atherosclerotic cardiovascular disease, insulin resistance, and diabetes mellitus [1, 2]. Because the prevalence of obesity has doubly increased worldwide over the past 30 years, the prevalence of MetS has markedly boosted in tandem [2–5]. Currently, clinicians and researchers have not identified an optimal treatment for MetS, and consequently, it is critical to identify new ways of approaching this syndrome in order to identify efficacious methods of diagnosing, screening, and treating MetS. Most researchers believe that hyperinsulinemia and/or insulin resistance (IR) is the first link in the chain of clinical-metabolic disturbances of MetS [5–7]. The development of IR is the result of a long chain of pathological events. Lipids play the crucial role in the pathogenesis of IR and the subsequent development of MetS [8–18]. All lipids are no longer considered the same. It is well known that excessive consumption of saturated fats contributes to the development of obesity and related diseases [19]. It has now been shown that high plasma levels of free fatty acids (FFAs), particularly saturated fatty acids (SFAs), may be associated with insulin resistance in obese patients with type 2 diabetes mellitus [17]. The lack of polyunsaturated fatty acids (PUFAs), especially n-3 PUFAs, some phospholipids, and plasmalogens in the cell membrane, is the cause of changes in glucose-insulin homeostasis and the development of inflammation [10, 13, 20–23]. Conversely, multiple investigations have established a connection between inflammation and changes in lipids and their derivatives in the setting of MetS [24–29]. Alteration in the metabolism of fatty acids affects the synthesis of eicosanoids and pro-resolving lipid mediators responsible for immune-metabolic homeostasis [30–33]. Recent studies have further elucidated the role of these metabolites in the contribution to the chronic, low-grade inflammatory state in MetS [34–36]. A comprehensive understanding of the importance of lipids in the pathogenesis of MetS contributes to the development of preventive and targeted lipid-correcting therapy. The aim of the review is to analyse the modern views on the role of lipids, particularly PUFAs and FFAs, in the pathogenesis of MetS. In this review, we summarized the molecular mechanisms of the relationship between fatty acids and glucose transport, inflammatory response, mitochondrial dysfunction, and endoplasmic reticulum stress in the development of MetS. 2. Metabolic Syndrome: Definitions and Criteria Metabolic syndrome (MetS) has become a widely debated scientific, medical, and social problem worldwide. Indeed, the definition of metabolic syndrome is important for clinical practice and deserves serious scientific and medical research. MetS is characterized by the following clinical criteria: abdominal obesity, decreased peripheral tissue sensitivity to insulin, and hyperinsulinemia, which cause metabolic disorders of carbohydrates, lipids, and purines [1, 2]. This combination of metabolic disorders is often found in one person and, thus, significantly increases the risk of cardiovascular disease (CVD), type 2 diabetes mellitus (T2DM), arthritis, chronic kidney disease, schizophrenia, nonalcoholic fatty liver disease (NAFLD), and several types of cancer [37–42]. MetS is characterized by a steadily increasing prevalence [3, 4]. However, its prevalence rates vary depending on the criteria used to determine MetS, genetic component, gender, age, population and area of residence, education, level of physical activity, nutrition, and lifestyle [39]. Approximately one-fourth of world’s adult population have MetS [43, 44]. Urbanization and its associated sedentary lifestyle and surplus nutrition are the root cause of this global epidemic. The determination of MetS uses the criteria of the following medical communities: WHO (World Health Organization), NCEPATP III (National Cholesterol Education Program-Adult Treatment Panel III), AACE (American Association of Clinical Endocrinologists), IDF (International Diabetes Federation), EGIR (European Group for the Study of Insulin Resistance), The International Diabetes Federation (IDF), American Heart Association/National Heart, Lung and Blood Institute (AHA/NLHBI), World Heart Federation (WHF), International Atherosclerosis Society (IAS), and The International Association for the Study of Obesity (IASO) [3]. A guideline was made in 2009 to unify the criteria for the diagnosis of MеtS. According to this guide, three of the five criteria are necessary for diagnosing MetS: (1) waist circumference ≥102 cm for males and ≥88 cm for females (for Asians ≥90 cm for males and ≥80 cm for females); (2) systolic blood pressure ≥130 mmHg or diastolic blood pressure ≥85 mmHg or antihypertensive medication; (3) fasting plasma glucose ≥5.6 mmol/L or on medication for high blood glucose; (4) HDL cholesterol <1.03 mmol/L for males and <1.30 mmol/L for females or on medications for reduced HDL cholesterol; and (5) triglycerides ≥1.7 mmol/L or on medications for elevated triglycerides [3]. Although the exact etiology of the MetS is not clearly understood, insulin resistance (IR) is considered as the principal factor for the pathogenesis of this syndrome [6, 11, 18, 45]. As found by the insulin-modified, frequently sampled intravenous glucose tolerance assay, insulin sensitivity is significantly lower in patients with two or more components of the MetS compared to those with none of these components [2]. Dysregulation of lipid metabolism is considered as an important link in the overall development chain of IR. It is well known that lipids play a critical role in the regulation of energy metabolism, glucose transport, and immune process in many organs and tissues such as the liver, adipose tissue, muscle, heart, and gastrointestinal tract [46]. However, the molecular mechanisms of this regulation remain largely unexplored. The study of lipid metabolism disorders is a promising direction for the development of methods for the effective treatment of this pathology. 3. Polyunsaturated Fatty Acids and Metabolic Syndrome Risk Fatty acids (FAs) play multiple roles in humans and other organisms. Most importantly, FAs are a substantial part of lipids. Fatty acids are either saturated or unsaturated carboxylic acids with carbon chains varying between 2 and 36 carbon atoms. Polyunsaturated fatty acids with an acid end containing the functional carboxylic acid group and a methyl end are also known as omega end. In omega-3 (ω-3 or n-3) and omega-6 (ω-6 or n-6) fatty acids, the first site of desaturation is located after the third and the sixth carbon from the omega end, respectively. Our body cannot synthesize some PUFAs, such as alpha-linolenic acid (18:3n3) and linoleic acid (18:2n6). These essential PUFAs enter our bodies only through diet. The dietary sources of n-3 PUFAs include fish oils rich in eicosapentaenoic acid (20:5n3) and docosahexaenoic acid (22:6n3), whereas the n-6 PUFA linoleic acid is mostly found in plants and vegetable oils [47]. Nowadays, there is growing evidence showing that dietary n-3 PUFAs have a variety of healthy properties such as the reduction of plasma atherogenic lipids and inflammation [16, 21, 25, 41, 47–49]. The associations between n-3 PUFAs and metabolic syndrome risk demonstrate inconsistent results [50]. Several cross-sectional and case-control studies have indicated that plasma/serum n-3 PUFAs were significantly higher in healthy subjects compared with those in patients with MetS, while some studies have suggested opposite and null associations [51]. Meanwhile, Guo et al. showed that higher circulating n-3 PUFAs were significantly associated with decreased MetS risk [52]. A study by Kim et al. demonstrated that in healthy individuals the level of long-chain n-3 PUFAs is positively correlated with insulin sensitivity [53]. A decrease in the level of PUFAs has been established in patients with T2DM and diabetic retinopathy. It was found that the development of insulin resistance is preceded by a reduction of essential n-3 PUFAs in the cell membranes [6]. There are several reasons why n-3 PUFAs are important in the pathogenesis and prevention of MetS. PUFAs perform a structural function, being important components of the cell membrane and determining its physical and chemical properties [10, 16]. The efficiency of glucose transport and expression of many receptors depend on the composition and ratio of PUFAs in the cell membrane [17]. Also, PUFAs are the precursors for inflammatory and pro-resolving lipids mediators’ synthesis [29, 32]. The imbalance between the synthesis of inflammatory and pro-resolving lipids mediators determines the development of chronic inflammation in MetS. Furthermore, we will summarize the main molecular mechanisms underlying the ability of n-3 PUFAs to prevent and/or ameliorate insulin resistance and inflammation in MetS. 3.1. Polyunsaturated Fatty Acids and Glucose Transport The identification of a causal relationship between the composition of fatty acids of cell membranes and MetS pathogenesis significantly contributes to an understanding of the main pathophysiological mechanisms of the disease. Polyunsaturated fatty acids affect the fundamental properties of the cell membrane, including its fluidity, elasticity, receptor expression activity, the functionality of embedded proteins, and signal transmission through lipid rafts, which leads to changes in cell signaling and modification of gene expression [54, 55]. The length and degree of the FA chain unsaturation have a profound effect on the physical and chemical properties of cell membranes [48]. The ratio of polyunsaturated to saturated fatty acids determines the membrane flexibility, which affects the efficiency of glucose transport using insulin-independent glucose transporters (GLUTs) and insulin-dependent GLUT4 [54]. GLUT1 is a monomeric protein with 12 transmembrane helical segments [56]. One molecule GLUT1 covers an area of about 17 molecules of a phosphatidylcholine bilayer with saturated fatty acids (SFAs), which requires a high membrane flexibility for pore formation. GLUT4 is inserted into the membrane of intracellular vesicles, which demands the flexibility of the vesicular membrane. The GLUT4 containing vesicles take part in a fusion process with the cell membrane. The increased flexibility of the membrane provides a smooth bending of the cell membrane bilayer and the fused pores formation [54]. Thus, decreased membrane flexibility causes a reduction in all Class 1 glucose transporters which, in turn, reduces the glucose flux and increases the plasma glucose concentration. Therefore, high membrane flexibility is a crucial factor in glucose transport. Changes in the fatty acid composition of membranes will result in disturbance in the physicochemical properties of the bilayer, such as flexibility and fluidity. Tighter membrane packaging due to increased saturated fatty acids in it leads to a reduction in the capacity for GLUT4 glucose transport [54]. A number of other studies have also revealed that an increase in the level of saturated fatty acids in the cell membrane is associated with a growth in blood glucose level and the development of insulin resistance [13, 14]. The important role of PUFAs in maintaining glucose-insulin homeostasis is confirmed by many studies [13–15, 21, 25, 47, 49, 51, 53]. Comprehensive evidence shows that diet n-3 PUFAs can improve insulin signal transduction in adipocytes, affecting in turn the insulin-stimulated glucose uptake through the regulation of the expression or the translocation of the GLUT4 [11]. In vitro studies have found that adipocytes from n-3 PUFAs-depleted rats had lower basal and insulin-stimulated glucose incorporation, while cultured adipocytes supplemented with fish oil increased levels of GLUT4 and GLUT1 [41]. González-Périz et al. [11] reported that feeding with a marine n-3 PUFAs-enriched diet improved insulin resistance in association with an increased expression of Irs-1 and Glut4mRNA in the adipose tissue of genetically obese ob/ob mice. The above indicates the huge importance of n-3 PUFAs in the development and regulation of components in the MetS, such as insulin resistance and glucose tolerance. 3.2. Polyunsaturated Fatty Acids and Inflammation Numerous studies have suggested that MetS, like its downstream sequelae of atherosclerotic cardiovascular disease and T2DM, is largely an inflammatory disease [24]. A chronic, low-grade inflammatory state caused by obesity leads to metabolic alterations responsible for multiple organ damage [57, 58]. This metabolic dysfunction could determine clinical conditions such as hypertension, hypercholesterinemia, and insulin resistance [40]. The contribution of inflammation to insulin resistance has been widely studied, and immunological changes occurring in various tissues are thought to be etiological factors affecting the development of insulin resistance [58]. A characteristic of obese people is a chronic, low-grade inflammation state promoted by the release of many inflammatory mediators by the adipose tissue and, more importantly, by infiltrating macrophages. PUFAs and their oxidized metabolites are important participants of the inflammatory processes of MetS [13, 15, 27, 28]. Understanding the molecular mechanisms of the participation of PUFAs and their metabolites in the pathogenesis of MetS will contribute to the development of new diagnostic methods and targeted therapy for this disease. 3.2.1. Specialized Pro-Resolving Mediators Inflammation is a complex, multifactorial adaptive process with different periods of development. Inflammation is a natural reaction to harmful irritants, such as bacterial infections, virus infections, and tissue damage. This is a host’s defensive reaction in which immune and endothelial cells and proinflammatory mediators are attracted to eliminate inflammatory agents, clear damaged cells and tissues, and initiate tissue repair. This response, when properly functioning, is self-limiting and leads to the cessation of the inflammatory response and a return to homeostasis, a process called the resolution of inflammation [16, 48]. Resolution of inflammation is now known to be an active process involving the activation of negative feedback mechanisms, such as anti-inflammatory cytokine secretion, reduction in receptor expression, activation of regulatory cells, and production of pro-resolving lipid mediators [57]. However, when acute inflammation is intense or prolonged, the resolution process is not successful, which leads to excessive tissue damage and ultimately resulting in chronic inflammation [16]. Many studies have confirmed that unresolved inflammation is the main mechanism for the pathogenesis of MetS [15, 16, 24, 48, 57, 58]. PUFAs are a source of synthesis of inflammatory and pro-resolving lipid mediators. The major substrate for the synthesis of inflammatory lipid mediators is arachidonic acid (20:4n6) (see Figure 1). The high content of 20:4n6 provides a direct link with inflammation since 20:4n6 released from cell membrane phospholipids acts as a substrate for cyclooxygenase (COX), lipoxygenase (LOX), and cytochrome P450 enzymes [29]. Eicosanoids are important regulators and mediators of acute inflammatory processes and include prostaglandins (PGs), thromboxanes (TBs), and leukotrienes (LTs). Many anti-inflammatory therapies, such as nonsteroidal anti-inflammatory drugs and COX inhibitors, target arachidonic acid metabolism [29, 35, 59, 60]. PUFA pathway and role of lipid mediators in the development and resolution of inflammation. Eicosapentaenoic acid (20:5n3) and docosahexaenoic acid (22:6n3) from the n-3 PUFAs family are a source of synthesizing specialized pro-resolving mediators (SPMs): maresins, resolvins, and protectins (see Figure 1) [32–35]. SPMs are a class of cell compounds generated at a later stage of the inflammation and initiate the resolution of the inflammatory process [33, 34, 61]. SPMs actively facilitate the resolution stage of acute inflammation unlike eicosanoids, which mainly act during the first stage of inflammation. A balanced n-6 : n-3 PUFAs ratio (where 1 : 1 to 2 : 1 is optimal) is important for homeostasis and normal development throughout the lifespan. High n-6 PUFA intake in the Western diet increases the n-6 : n-3 ratio to a range from 10 : 1 to 20 : 1 and may play a role in the pathogenesis of MetS and related diseases [36]. The balance between n-3 PUFAs and n-6 PUFAs determines the path of inflammatory response. The prevalence of n-6 PUFAs and the shortage of n-3 PUFAs may contribute to impaired inflammation resolution [31]. Docosahexaenoic acid- and eicosapentaenoic acid-derived SPMs are identified in the adipose tissue. At the same time, the levels of certain SPMs are markedly reduced with obesity, suggesting adipose SPM deficiency, potentially resulting in unresolved inflammation [36]. Resolvins are synthesized spontaneously from eicosapentaenoic and docosahexaenoic acids during inflammation and thus are designated as E‐series (RvE) and D‐series (RvD), respectively [32]. The anti-inflammatory effect of RvE1 is due to interaction with peroxisome proliferator-activated receptors (PPARs), which are classified as nuclear transcription factors with anti-inflammatory activity. Leukotriene B4 receptor 1 (BLT1) and G protein-coupled receptor, Chemerin Receptor 23 (ChemR23), are receptors for RvE1. RvE2 has a similar biologic effect; it regulates neutrophil chemotaxis and activates phagocytosis and proinflammatory cytokines synthesis [31, 62]. Protectins (PD) are another class of pro‐resolving molecules produced from 22:6n3 during the resolution of inflammation. Protectins are synthesized by a number of cells including brain cells, monocytes, and CD4+ lymphocytes [33]. PD1, the key representative of the protectin family, demonstrates a strong anti-inflammatory and neuroprotective effect. This mediator functioning is based on PPARs interacting and NF-κB blocking [62]. An alternative process for docosahexaenoic acid oxygenation is found in human macrophages and platelets, leading to the synthesis of maresin 1 (MaR1). In addition, 13S, 14S‐epoxy‐maresin, which has important biological activities of its own, is the precursor for maresin 2 (MaR2) [36]. Lipoxins (LXs) are powerful anti-inflammatory bioregulators suppressing inflammation and activating resolution and recovery processes, in particular in MetS [34]. The substrate for LXs synthesis is arachidonic acids. Two members of the LXs family, LXA4 and LXB4, have been well studied [31]. In general, LXs are a branch of the leukotriene family. For example, their production by platelets is catalyzed by 12-LOX through converting LTA4 [32]. Unlike proinflammatory LTs, LXs act as powerful anti-inflammatory bioregulators, suppressing the inflammation and activating the processes of resolution and recovery. The result of their action is the inhibition of chemotaxis and migration of macrophages and neutrophils to the inflammatory focus, blocking of the lipid peroxidation, the activation of NF-κB, and the suppression of the synthesis of proinflammatory cytokines. In addition, LXs are actively involved in functioning of macrophages that are associated with homeostasis restoration processes [32]. There is a considerable amount of evidence regarding the contribution of n‐3 PUFAs to diseases with inflammatory conditions, such as metabolic syndrome [25, 34–36, 47, 49, 51, 53]. It was reported that SPM levels reduced in metabolic syndrome as well as sensitivity to SPM of the adipose tissue [36]. Obesity reduces the levels of PD1, intermediates in the synthesis of D-series resolvins and protectins (17-HDHA), and intermediates in the maresin biosynthesis (14-HDHA) for the adipose tissues from diet- and genetically-induced obese mice [36]. One of the mechanisms resulting in a decrease in the SPM level in obesity is a change in the enzyme activities involved in biosynthesis or conversion of certain SPMs. N‐3 PUFAs supplementation increased the level of SPM in the blood of individuals with obesity and MetS. The effects of n-3 PUFAs are mediated by their ability to interfere with arachidonic acid metabolism and promote the synthesis of SPMs. The supply of n‐3 PUFAs increases the levels of resolvins, enhances resolution, and improves insulin sensitivity in an experiment with fat‐1 mice. In addition, n‐3 PUFAs prevent macrophage increase, adipokine secretion, and insulin resistance induced by a high‐fat diet. Synthetic pro-resolving lipid mediators (17-hydroxy-DHA) or n-3 PUFAs added to the treatment contributed to higher levels of pro-resolving lipid mediators in the adipose tissue, reduced inflammation, and increased insulin sensitivity [31]. N-3 PUFAs increased RvЕ-series levels in patients with MetS but did not affect RvD-series, which requires further studies into the mechanism of n-3 PUFAs influence in MetS. For instance, intraperitoneal administration of 17‐HDHA or RvD1 significantly reduced adipose inflammation and improved the glucose tolerance in diet‐induced obese mice and in db/db mice [63]. Treatment with either RvD1 or RvD2 also reduced the secretion of proinflammatory cytokines including TNF‐α, IL‐1β, and IL‐12 in the adipose tissue [64]. The MaR1 treatment improved insulin sensitivity, determined with an insulin tolerance test. MaR1 also increased adiponectin gene expression and Akt phosphorylation in the adipose tissues and attenuated adipose tissue inflammation in both ob/ob and diet‐induced obese mice [36]. PD1 treatment acutely increased the adiponectin transcripts in adipose tissue explants isolated from ob/ob mice. A potent ability to induce adiponectin expression/secretion has been demonstrated with synthetical RvD1, RvD2, and PD1 and their biosynthetic intermediate, 17‐HDHA [63]. Therefore, one of the pathogenetic mechanisms of the development of MetS is a reduction of the processes of resolving inflammation and the development of chronic, low-grade inflammatory. A decrease in the synthesis of specialized pro-resolving lipid mediators is the basis of the above disorders [65]. Thus, the anti-inflammatory effect of n-3 PUFAs in MetS can be mediated through the regulation of the SPM synthesis. 3.2.2. Toll-Like Receptor 4 The inflammatory process observed in individuals with metabolic syndrome differs from the classical inflammatory response and this type of inflammation characterized by a chronic, low-intensity reaction [58]. The toll-like receptor 4 (TLR4) signaling pathway is acknowledged as one of the main triggers of the obesity-induced inflammatory response [57]. TLR4 plays a significant role in the pathogenesis of inflammation mediated by insulin resistance in MetS [57]. Toll-like receptors, including TLR4, are type 1 transmembrane proteins with three domains: (1) extracellular domain with leucine-rich repeats (LRRs) responsible for ligand recognition; (2) transmembrane domain; and (3) intracellular toll/interleukin-1 receptor (TIR) domain. These provide signal transmission from the cell surface to adapter proteins. TLR4 was the first TLR reported in humans; it is expressed in innate immune cells, including monocytes, macrophages, and dendritic cells, as well as in other cell types, such as adipocytes, enterocytes, and muscle cells. TLR4 is a membrane-associated receptor involved in lipid recognition [66]. TLRs are activated both by the influence of endogenous ligands and by the participation of lipids—cholesterol, SFAs, and oxidized forms of phospholipids [67]. Humans with type I diabetes exhibit greater expression of TLR4 in the cellular membrane in monocytes. Individuals with T2DM show increased cellular membrane levels of TLR4 in blood monocytes, as well as a higher concentration of IL-1, IL-6, IL-8, and TNF in serum. Similarly, TLR4 is more highly expressed in blood mononuclear cells and in the abdominal subcutaneous white adipose tissue of obese and diabetic individuals [57, 68]. Lipids from foods change the expression of TLRs by cells [69]. On the one hand, SFAs activate the TLR4 signaling pathway (see Figure 2). Among the SFAs, lauric acid (12 : 0) and palmitic acid (16 : 0) had the strongest activation capacity through TLR4 [69]. On the other hand, TLRs can be inhibited by PUFAs [70]. Consumption of n-3 PUFAs, particularly 22:6n3, is associated with anti-inflammatory and cardioprotective effects. It is believed that the use of n-3 PUFAs is associated with anti-inflammatory activity due to inhibition of arachidonic acid metabolism [71]. The molecular effect of n-3 PUFAs, especially 20:5n3 and 22:6n3, on inflammatory-response modulation are based on the ability of these PUFAs to inhibit the expression of inflammatory genes, such as COX-2, iNOS, and IL-1 in macrophages [72]. PUFAs of the n-3 family reduce the activation of the NF-κB transcription factor pathway that is induced by various agonists [70]. The role of TLRs and FAs in the signaling mechanisms of inflammation in the adipose tissue and insulin resistance. The saturated fatty acids (SFAs) act as nonmicrobial TLR4 agonists or indirectly promote the TLR4 activation, triggering its inflammatory response and inflammation of the adipose tissue. Inflammatory signaling caused by saturated fatty acids via TLR4/MD-2 inhibits the phosphorylation of the insulin receptor, leading to the development of insulin resistance. GPR120 activation induced by n-3 PUFA leads to a decrease in the activity of IKK-β/NF-κB and JNK/AP-1 signaling pathways, which reduces the expression of proinflammatory genes. The anti-inflammatory properties of PPARs are achieved by inhibiting nuclear factor-kappa B (NF-κB). N-3 PUFAs directly interact with PPARs and modulate the expression of proinflammatory genes. Other mechanisms modulate the inflammatory response by fatty acids based on binding G protein-coupled receptor 120 (GPR120) [66]. GPR120 is a free fatty acid 4 receptor (FFAR4), and GPR120 activation induced by n-3 PUFA leads to β-arrestin 2 recruitment to the plasma membrane where this protein binds to GPR120 (see Figure 2) The GPR120/β-arrestin 2 complex is internalized into the cytoplasmic compartment where this complex binds to the TAK1-binding protein (TAB1). This process impairs the association between TAB1 and the kinase activated by the growth factor beta (TAK1) and, consequently, results in reduced TAK1 activation and decreases the activity of the IKK-β/NF-κB and JNK/AP-1 signaling pathways. The mitigation of TAK-1 activation by n-3 PUFAs leads to the reduced expression of TNF-α and IL-6 genes with proinflammatory actions [17, 57]. One more important molecular mechanism that is associated with the n-3 PUFA effects concerns their capacities to bind to PPARs [62]. Three isoforms of PPARs are known: PPARα (NR1C1), PPARβ/δ (NR1C2), and PPARγ (NR1C3). PPARs are involved in the regulation of inflammatory reactions and lipid metabolism. The anti-inflammatory properties of PPARs are mainly achieved by inhibiting nuclear factor-kappa B (NF-κB) which, in turn, is the proinflammatory nuclear transcription factor [73]. The interactions between PPARs, NF-κB, and toll-like receptors (TLRs) are of great interest. Along with the anti-inflammatory mechanism of action of PPARs, the proinflammatory activity of some isoforms of PPARs is also being studied. For example, PPARγ is considered a mediator of interactions between dendritic and T cells in the development of type 2 (or T2) inflammation [73]. N-3 PUFAs directly interact with PPARs and, therefore, modulate the expression of genes that are involved in lipid metabolism and the inflammatory response [57]. Anti-inflammatory effects of 20:5n3 and 22:6n3 on this signaling pathway can occur due to diminished nicotinamide adenine dinucleotide phosphate (NADPH) oxidase activity, which leads to lower TLR4 recruitment for lipid rafts and TLR4 dimerization [16]. Also, another possible mechanism of action of the n-3 PUFA concerns the capacity of incorporating 22:6n3 into the plasma membrane, which can lead to reduced TLR4 translocation for lipid rafts formation [74, 75]. The variety of molecular mechanisms in lipids and TLR4 signaling pathway interaction indicates the complexity of the pathogenesis of MetS and associated diseases. 3.3. Polyunsaturated Fatty Acids and Plasmalogens Permanent exogenous use of PUFA is a necessary condition for maintaining immune-metabolic homeostasis. The profile of fatty acids that are present in the Western diet consists of a high level of saturated fatty acids and trans fatty acids. While the total consumption of marine and plant n-3 polyunsaturated fatty acids in contemporary society is significantly reduced [7, 76]. Another reason for PUFAs reduction is deterioration in the plasmalogen synthesis [77]. Plasmalogens are a subclass of phospholipids characterized by having a vinyl ether bond linking the fatty aldehyde to the glycerol molecule in the 1-position and a fatty acyl bond in the 2-position. The sn-1 position consists of palmitic acid (16 : 0), stearic acid (18 : 0), or oleic acid (18 : 1) carbon chains, and the head group is usually either ethanolamine or choline. Thus, there are two main types of plasmalogens: ethanolamine plasmalogens and choline plasmalogens. The sn-2 position is generally occupied by PUFAs, specifically arachidonic acid or docosahexaenoic acid [78, 79]. The highest concentrations of plasmalogens are found in the brain, red blood cells, skeletal muscle, and spermatozoa and can represent as much as 18–20% of the total phospholipids in cell membranes [78, 79]. Plasmalogens are either derived from dietary sources and/or are synthesized mainly in the liver and gastrointestinal epithelium. Plasmalogens are not only important structural phospholipids in the cell membranes but they are also reservoirs of secondary messages and mediators of membrane dynamics and involved in membrane fusion, ion transport, cholesterol efflux, membrane-bound enzyme activity, and diffusion of signal-transduction molecules [80]. Secondary deficiency of plasmalogens triggered by their synthesis reduction or their degradation growth is associated with metabolic and inflammatory disorders such as cardiac diseases and diabetes mellitus [77]. The specificity of choline plasmalogens as a sensitive biomarker of an atherogenic state was confirmed. On the one hand, positive correlations of the choline plasmalogen content with serum adiponectin concentration and high-density lipoproteins (HDL), and on the other hand, inverse relationships with waist circumference, including triacylglycerides and low-density lipoproteins (LDL) content, have been identified. Reduced levels of ethanolamine plasmalogens in plasma have been shown to be also closely associated with cardiovascular, metabolic, and cancer diseases [81]. The content of plasmalogens is relatively stable in all lipoprotein fractions. However, the correlation between the levels of choline plasmalogens and HDL is stronger than that between the levels of ethanolamine plasmalogens and HDL. In the study by Pietiläinen et al., a decrease in the level of plasmalogens in adipocyte membranes in obese twins was established compared with metabolically healthy twins. Conversely, plasmalogen levels increase in trained people and dietary patients [82]. At the same time, it was found that the level of plasmalogens increases in the liver of rats receiving a high-fat diet [83]. The adaptation of the phospholipid composition of cells to exogenous lipid changes has been verified [83]. The compensatory response to a decrease in the plasmalogen level is the regulation of the level of phosphatidylethanolamine [84]. However, with plasmalogen deficiency, the total amount of PUFAs in phosphatidylethanolamine remains constant in human fibroblasts and in the brains of mice. Plasmalogens have been noted to play an important role as neuroprotectors and modulators of the signaling mechanisms of cell membranes [85]. Plasmalogens also act as endogenous antioxidants, protecting lipids and lipoproteins from oxidative stress [86]. This can be attributed to the fact that the hydrogen atoms adjacent to the vinyl ether bond are more susceptible to oxidation, protecting PUFAs from it that are found in the sn-2 position of the glycerol residue. Plasmalogen oxidation products are not capable of further initiation of lipid peroxidation processes. Another important function of plasmalogens is their participation in cell metabolism and transmembrane transport of FAs. The presence of PUFAs in the side chains of plasmalogens preconfigures their significant depositing function [77]. Cholesterol esterification depends on the level of plasmalogens. So, for example, the cells characterized by plasmalogen deficiency demonstrated a lower level of esterified cholesterol and a higher level of free and total cholesterol [84]. Therefore, the important role of plasmalogens as modulators of signaling mechanisms in protecting cells from lipid peroxidation and participation in PUFA metabolism has been made clear. However, the exact biological functions of plasmalogens and the underlying molecular mechanisms still remain to be discovered [52, 55]. 4. Free Fatty Acids and Metabolic Syndrome Risk Free fatty acids (FFAs), or nonesterified fatty acids (NEFAs), in circulating plasma are derived from the ingestion of dietary fat or from the triglycerides stored in adipose tissue that are distributed to cells to serve as fuel for muscle contraction and systemic metabolism [87]. As FAs are insoluble in water, they are transported by binding to plasma albumin. FFAs can be taken up from circulating plasma by all mitochondria-containing cells, and they are metabolized by β-oxidation [17]. FFAs carry out many important biological functions in the body, and they are a source of energy, signal molecules, and structural components of cell membranes [17]. FFAs are involved in the pathogenesis of insulin resistance and subsequent development of metabolic syndrome [12, 17, 87]. Chronic energy imbalance can trigger adipocyte hypertrophy, endoplasmic reticulum stress, and mitochondrial dysfunction, which lead to the systemic release of FFAs [17, 88–90]. When plasma FFA levels rise, as occurs in obesity, a lipotoxicity state is induced, which induce activation of different cell responses: oxidative stress, apoptosis, and inflammation [17]. Consequently, FFAs play a highly important role in the association between obesity and insulin resistance. 4.1. FFAs and Mitochondria There is an interesting hypothesis that IR is associated with the development of mitochondrial dysfunction [87, 90]. Lipid degradation occurs in mitochondria. On top of that, the normal functioning of mitochondria provides glucose-stimulated insulin secretion from β-cells of the pancreas. Initially, the theories have suggested that impaired mitochondrial function leads to impaired β-oxidation of lipids, which is accompanied by the accumulation of FFAs in peripheral tissues (lipotoxicity theory) [91]. The accumulation of lipid metabolites brings about the activation of kinases involved in the disruption of insulin signaling at the level of insulin receptor substrate 1 (IRS-1). In skeletal muscles, insulin signaling pathway disorder is accompanied by a decrease in the production of GLUT4 and glucose uptake by cells. In this case, an improvement in insulin sensitivity can be achieved by enhancing the β-oxidation of lipids. This theory was supported by studies that proved an increase in the rate of β-oxidation of lipids to be followed by protection against the development of IR [17]. Nevertheless, the early stages of obesity and IR development are characterized by an increase in β-oxidation of lipids. Besides, an impairment of fat oxidation results in higher insulin production. Therefore, mitochondrial dysfunction in skeletal muscles cannot be the only reason for the development of IR [92]. An alternative explanation of the relationship between mitochondria and insulin resistance is focused on the production of a reactive oxygen species (ROS) by mitochondria as a result of excess accumulation of FFAs in them [92]. Oxidative stress is known to be a pathogenetic component of chronic inflammation development and IR [88]. An oxidized redox environment can induce insulin resistance by directly affecting the protein involved in glucose uptake [89]. On the other hand, changes in redox cell homeostasis have been argued to step up the activity of the serine-/threonine-sensitive stress kinases that inhibit the transmission of insulin signals, inducing the development of IR [93, 94]. Oxidative stress also can stimulate the activation of transcriptional factors, such as nuclear factor-kappa B (NF-κB), activator protein 1 (AP-1), and hypoxia-inducible factor 1 (HIF-1), which promote the synthesis of inflammatory cytokines (IL-1β, IL-6, and TNF-α) (see Figure 3). These inflammatory cytokines contribute to obesity-associated local inflammation and directly induce insulin resistance. Also, chronic prolonged FFAs excess is the cause of pancreatic β-cells dysfunction. In addition, FFAs inhibit insulin gene expression and induce apoptosis in these cells [17]. FFA-induced insulin resistance through endoplasmic reticulum stress and oxidative stress. A high level of FFA induces an increase in the production of ROS by mitochondria and the formation of oxidative stress. ROS stimulates NF-κB, which promotes the synthesis of L-1β, IL-6, and TNF-α. These inflammatory cytokines contribute to obesity-associated local inflammation and directly induce insulin resistance. In response to the enhanced level of FFAs and other nutrients in fats, adipose cells can develop signs of ER stress. A decrease in SERCA expression promotes the development of ER stress. UPR triggers the activation of IRE. Activation of IRE induces interaction with TRAF protein, which stimulations activation of IKKβ and JNK kinases. Its reaction can phosphorylate the IRS, thus blocking insulin signaling. JNK and IKKβ also lead to NF-κB activation and the development of inflammation. Although discussing the role of mitochondrial skeletal muscle dysfunction in the pathogenesis of IR and type 2 diabetes is still underway [93], it is generally accepted that a mitochondrial defect does occur in these diseases. The connection between IR and mitochondrial dysfunction of liver cells, visceral, and subcutaneous adipose tissue has been proved [95]. Moreover, in the mitochondria of individuals suffering from obesity and type 2 diabetes, ATP synthesis is reduced, which correlates with the accumulation of FFAs and inhibition of insulin-stimulated glucose utilization. 4.2. FFAs and Endoplasmic Reticulum Stress Results of numerous studies establish that dysregulation of the endoplasmic reticulum (ER) function contributes to the development of MetS [96, 97]. Mitochondria are known to be both functionally and structurally associated with ER [97]. Obviously, the changes of the structure and function of these organelles can serve as a trigger for the development of metabolic homeostasis disorders [96]. ER is involved in maintaining Ca2+ homeostasis and participates in maturation and expression of membrane and secretion proteins. Cell stress conditions that increase ER demand and entail an overload of its functional capacity cause a series of alterations known as “endoplasmic reticulum stress.” Under these conditions, the ER activates a compensatory mechanism called the “unfolding protein response” (UPR), which attempts to restore the homeostasis of ER functions. With the stressful effects lasting for a long time, ER stress results in cell death (apoptosis) [17, 98]. UPR triggers activation of inositol-requiring endoribonuclease enzyme (IRE) (see Figure 3). The activation of IRE induces interaction with TRAF protein, which stimulations activation of IKKβ and JNK kinases. Its reaction can phosphorylate IRS, thus blocking insulin signaling. In response to the enhanced level of FFAs and other nutrients in fats, adipose cells can develop signs of ER stress [17]. ER stress produces insulin resistance mainly through JNK activation. JNK activity has been detected to be elevated in animal models of obesity, and JNK isoforms 1 and 2 deletion protects mice from insulin resistance induced by a fat-rich diet. Experimental evidence indicates that, on the one hand, JNK phosphorylates serine IRS-1, and on the other, it phosphorylates IKKβ, which leads to NF-κB activation and to inflammation development [96]. Remarkably, that change in expression of sarco/endoplasmic reticulum Ca2+ ATPase (SERCA), which has calcium elimination from the cytosol and returns it to the ER as their function, is associated with ER stress and subsequently with insulin resistance. The treatment of people with diabetes mellitus by rosiglitazone, an antidiabetic drug, increased SERCA expression, thus restoring the pump expression reduction observed in diabetic patients with altered hyperglycemia [17]. This way, the decrease in SERCA expression promotes the development of ER stress, with JNK ensuing activation, which desensitizes the insulin signal, thus generating a state of insulin resistance and contributing to chronic metabolic deterioration. 4.3. FFAs as Ligands for FFAR FFAs serve not only as energy sources but also as natural ligands for a group of orphan G protein-coupled receptors (GPCRs) termed free fatty acid receptors (FFARs) [99]. The GPCR superfamily is the largest one in the human genome and encompasses some subfamilies (Gq, Gi, Gs, and G12/13) [100]. These receptors respond to various ligands and, therefore, are involved in the pathogenesis of many diseases, e.g., MetS, and are the target for more than half of pharmaceutical products [101–106]. There are four main members of FFAR family: FFAR1 (GPR40), FFAR2 (GPR43), FFAR3 (GPR41), and FFAR4 (GPR120 and GPR84) (see Table 1) [75]. Table 1 Family of FFARs and their ligands. FFAR1 expression was revealed in neurons and in pancreas β-cells [99]. FFAR2 and FFAR3 are common in leukocytes and adipose tissues. Besides, FFAR3 is also expressed by pancreas cells, in the sympathetic nervous system and vessel plain muscles [100]. FFAR4 is expressed in adipocytes, the intestinal tract, macrophages, and in the central nervous system [105]. There are other specific receptors for FFA: GPR119 and GRP84. GPR119 is expressed in intestinal endocrine cells and pancreatic β-cells and activates the synthesis of GLP-1 and insulin. GPR84 is expressed in the spleen, thymus, leukocytes, and macrophages [99]. Long- and medium-chain length fatty acids are endogenous ligands for FFAR1, FFAR4, and GPR84. FFAR2 and FFAR3 are activated by short-chain FAs. FFAR2 is capable of binding with Gq and Gi proteins, whereas FFAR3 binds only with Gi. FFAR4 is activated by n-3 or n-6 PUFAs [99]. Thus, each FFAR can act as an FFA sensor with selectivity for a particular FFA carbon chain length derived from food or food-derived metabolites. FFARs have been reported to have physiological functions such as facilitation of insulin and incretin hormone secretion, adipocyte differentiation, anti-inflammatory effects, neuronal responses, and taste preferences [106]. Dysfunction of FFARs underlies the pathogenesis of many metabolic diseases, such as MetS and diabetes mellitus. It has been found that FFAR4 acts as an anti-inflammatory receptor in proinflammatory macrophages and mature adipocytes. Signaling of FFAR4 activated by n-3 PUFAs inhibits TLR signaling and TNF-α-induced inflammatory responses. FFAR4 dysfunction leads to obesity and glucose intolerance in humans and mice [107]. Many results strongly support that FFAR4-mediated anti-inflammatory effects reduce the infiltration of proinflammatory macrophages into the adipose tissue and improve insulin sensitivity [102]. The activation of FFAR1 signaling enhances glucose-stimulated insulin secretion (GSIS) directly via stimulation of insulin secretion from pancreatic β cells and indirectly via the production of incretin hormones. Also, the activation of FFAR1 signaling reduces the expression of inflammatory cytokines such as TNF-α and IL-8. It has been shown that α-linolenic (18:3n3) and oleic (18:1n9) acids improve insulin resistance in obesity and type 2 diabetes [108]. There is some scientific evidence that short-chain fatty acids (SCFAs) are a substantial modulator of MetS inflammation [109]. SCFAs are the end products of metabolic fermentation of dietary fibers by gut microbiota. FFAR2 is a receptor for SCFAs and is expressed in enteroendocrine cells, adipose tissues, and pancreatic β-cells [99]. Dietary fiber intake reduces the risk of obesity, diabetes, inflammatory bowel disease, colon cancer, and cardiovascular disease. SCFAs supplementation with a high-fat diet improved insulin sensitivity and increased energy expenditure in a mouse model of diet-induced obesity [110, 111]. SCFAs are involved in intestinal immune homeostasis due to their role in regulating T cell polarization and differentiation. In human monocytes, SCFAs decrease the production of TNF-α and increase the production of PGE2 [109]. Activation of FFAR2 by SCFAs regulates metabolic disorders, increases energy expenditure, and preferentially enables fat consumption by inhibition of insulin signaling in adipose tissues. The expression of FFAR2 in neutrophils and mononuclear cells regulates intestinal homeostasis and inflammation. In light of this evidence, regulation FFAR2 expression and/or high fiber consumption may be a potential target for therapeutic intervention of MetS. FFAR3 is also a receptor for SCFAs. FFAR3 is widely expressed in enteroendocrine cells, adipose tissues, the peripheral nervous system, peripheral blood mononuclear cells, monocytes, and macrophages [99]. FFAR3 expression in intestinal epithelial cells enhances the synthesis of proinflammatory mediators through extracellular signal-regulated kinase 1/2 and p38 MAPK [100]. Since these pathways help to protect against bacterial infection, FFAR3 can stimulate acute inflammatory reactions in the intestine that have beneficial effects on host homeostasis [112]. Thus, FFAR 3 can exhibit proinflammatory properties. 5. Conclusion The wide phenotypic heterogeneity of MetS and its complex pathogenesis make it difficult to identify a therapeutic target. This syndrome is considered as a cluster of pathogenetically related conditions caused by metabolic disorders and the development of chronic, low-grade inflammation. In this review, we examined the molecular mechanisms of the development of MetS driven by impaired lipid metabolism. PUFAs and FFAs have been shown to play an important role in both the pathogenesis and treatment of MetS. Fatty acids perform structural, energy, signaling, and immunoregulatory functions. These FAs properties underlie the pathogenetic mechanisms of glucose transport disturbance, the development of IR and chronic inflammation, the formation of oxidative stress, and mitochondrial dysfunction in MetS. Correction in lifestyle and nutrition is considered as the main way to minimize complications caused by an imbalance in the body between saturated and polyunsaturated fatty acids. At the same time, there are controversial data about the therapeutic efficacy of dietary n-3 PUFAs in MetS [50]. SPMs have shown potent pro-resolving actions in different disease models, including MetS [61]. SPM-based therapeutics could be one of the most optimistic treatments for MetS. Further studies are needed to detail the mechanisms of FA participation and their oxidized metabolites in the development of inflammation and pathogenesis of MetS. Conflicts of Interest The authors declare that there are no conflicts of interest regarding the publication of this paper. Acknowledgments The study was funded by the Ministry of Education and Science of the Russian Federation. Copyright © 2020 Yulia K. Denisenko et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/29%3A_Integration_of_Mammalian_Metabolism_-_Capstone_Volume_II/29.08%3A_Fundamentals_of_cancer_metabolism.txt	princeton-nlp/TextbookChapters	Fundamentals of cancer metabolism 1. Ralph J. DeBerardinis1,* and 2. Navdeep S. Chandel2,* Science Advances 27 May 2016: Vol. 2, no. 5, e1600200. DOI: 10.1126/sciadv.1600200 This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited. Abstract Tumors reprogram pathways of nutrient acquisition and metabolism to meet the bioenergetic, biosynthetic, and redox demands of malignant cells. These reprogrammed activities are now recognized as hallmarks of cancer, and recent work has uncovered remarkable flexibility in the specific pathways activated by tumor cells to support these key functions. In this perspective, we provide a conceptual framework to understand how and why metabolic reprogramming occurs in tumor cells, and the mechanisms linking altered metabolism to tumorigenesis and metastasis. Understanding these concepts will progressively support the development of new strategies to treat human cancer. INTRODUCTION AND OVERARCHING PRINCIPLES Cancer metabolism is one of the oldest areas of research in cancer biology, predating the discovery of oncogenes and tumor suppressors by some 50 years. The field is based on the principle that metabolic activities are altered in cancer cells relative to normal cells, and that these alterations support the acquisition and maintenance of malignant properties. Because some altered metabolic features are observed quite generally across many types of cancer cells, reprogrammed metabolism is considered a hallmark of cancer (1, 2). Precisely how metabolism becomes reprogrammed in cancer cells, whose functions or malignant properties are enabled by these activities, and how to exploit metabolic changes for therapeutic benefit are among the key questions driving research in the field. This review covers several fundamental principles in cancer metabolism, with the goal of introducing non-experts to the concepts motivating ongoing research. With the explosion of research in cancer metabolism over the past decade, no single review can possibly cover it all. The sections below highlight some of the essential, recent papers supporting these core principles. An overarching theme in cancer metabolism is that reprogrammed activities improve cellular fitness to provide a selective advantage during tumorigenesis. Most of the classical examples of reprogrammed activities either support cell survival under stressful conditions or allow cells to grow and proliferate at pathologically elevated levels. Three of these—altered bioenergetics, enhanced biosynthesis, and redox balance—are discussed at length below. It logically flows that if these activities provide benefit to the malignant cell, then some of them might be suitable therapeutic targets. This rendering of cancer metabolism is supported by many examples in which inhibition of an enhanced metabolic activity results in impaired growth of experimental tumors (3, 4). In some cases, the particular metabolic liabilities of cancer cells have been translated into effective therapies in human cancer. Asparaginase, an enzyme that converts the amino acid asparagine to aspartic acid and ammonia, is an essential component of treatment for acute lymphoblastic leukemia (ALL) (5). Because of their high rates of protein synthesis and poor ability to synthesize asparagine de novo, ALL cells require a constant supply of asparagine from the plasma. This supply is essentially eliminated by systemic administration of asparaginase. Ultimately, effective metabolic therapy will require defining the stage of tumor progression in which each pathway provides its benefit to the cancer cell. Some activities first become essential very early in tumorigenesis as the nascent tumor begins to experience nutrient limitations (6). In other cases, altered pathways may be dispensable in primary tumors but essential for metastasis (7, 8). Because new therapeutic targets are nominated from simple experimental models like cultured cells, it will be essential to define their context-specific roles in biologically accurate models of tumor initiation and progression. METABOLIC REPROGRAMMING AND ONCOMETABOLITES IN CANCER Altered metabolic activity supports anabolic growth during nutrient-replete conditions, catabolism to support cell survival during nutrient limitation, and fortification of redox homeostatic systems to counteract the metabolic effects of oncogene activation, tumor suppressor loss, and other stresses (9). Discovery and characterization of reprogrammed activities may provide opportunities to image tumor tissue noninvasively, predict tumor behavior, and prevent tumor progression by blocking essential pathways. It is important to differentiate “metabolic reprogramming” from “oncometabolites,” two terms widely used in the recent cancer metabolism literature (10). We propose that the term metabolic reprogramming be used to describe conventional metabolic pathways whose activities are enhanced or suppressed in tumor cells relative to benign tissues as a consequence of tumorigenic mutations and/or other factors. Oncometabolite is a relatively new term that refers to metabolites whose abundance increases markedly in tumors. We suggest that this term be reserved for metabolites for which (i) there is a clear mechanism connecting a specific mutation in the tumor to accumulation of the metabolite, and (ii) there is compelling evidence for involvement of the metabolite in the development of malignancy. The classical example of a reprogrammed metabolic pathway in cancer is the Warburg effect or aerobic glycolysis (11). Glycolysis is a physiological response to hypoxia in normal tissues, but Otto Warburg in the 1920s observed that tumor slices and ascites cancer cells constitutively take up glucose and produce lactate regardless of oxygen availability, an observation that has been seen in many types of cancer cells and tumors (12). The increase in glycolytic flux allows glycolytic intermediates to supply subsidiary pathways to fulfill the metabolic demands of proliferating cells (11). Like glycolytic intermediates, tricarboxylic acid (TCA) cycle intermediates are also used as precursors for macromolecule synthesis (13). Their utilization in biosynthetic pathways requires that carbon be resupplied to the cycle so that intermediate pools are maintained; pathways that “refill” the cycle are termed anaplerotic pathways, and they generate TCA cycle intermediates that can enter the cycle at sites other than acetyl-CoA (coenzyme A) (14). Two activities that provide anaplerotic fluxes in cancer cells are glutaminolysis, which produces α-ketoglutarate from glutamine, and pyruvate carboxylation, which produces oxaloacetate from glucose/pyruvate. Oxidation of the branched-chain amino acids (BCAAs) isoleucine and valine also provides an anaplerotic flux in some tissues. Despite the incredible genetic and histological heterogeneity of tumors, malignancy seems to involve the common induction of a finite set of pathways to support core functions like anabolism, catabolism, and redox balance (15). The general induction of these pathways may reflect their regulation by signaling pathways that are commonly perturbed in cancer cells (Fig. 1). Normal cells, upon stimulation by growth factors, activate phosphatidylinositol 3-kinase (PI3K) and its downstream pathways AKT and mammalian target of rapamycin (mTOR), thereby promoting a robust anabolic program involving increased glycolytic flux and fatty acid synthesis through activation of hypoxia-inducible factor–1 (HIF-1) and sterol regulatory element–binding protein (SREBP), respectively (16). Tumor cells very frequently contain mutations that allow the PI3K-AKT-mTOR network to achieve high levels of signaling with minimal dependence on extrinsic stimulation by growth factors (17). Many of the best-characterized oncogenes and tumor suppressors reside in the PI3K-AKT-mTOR network, and aberrant activation of this pathway is among the most frequent alterations seen in a diverse set of cancers. Another commonly deregulated pathway in cancer is gain of function of MYC by chromosomal translocations, gene amplification, and single-nucleotide polymorphisms. MYC increases the expression of many genes that support anabolic growth, including transporters and enzymes involved in glycolysis, fatty acid synthesis, glutaminolysis, serine metabolism, and mitochondrial metabolism (18). Oncogenes like Kras, which is frequently mutated in lung, colon, and pancreatic cancers, co-opt the physiological functions of PI3K and MYC pathways to promote tumorigenicity. Aside from oncogenes, tumor suppressors such as the p53 transcription factor can also regulate metabolism (19). The p53 protein–encoding gene TP53 (tumor protein p53) is mutated or deleted in 50% of all human cancers. The tumor-suppressive functions of p53 have been ascribed to execution of DNA repair, cell cycle arrest, senescence, and apoptosis. However, recent studies indicate that p53 tumor-suppressive actions might be independent of these canonical p53 activities but rather dependent on the regulation of metabolism and oxidative stress (20, 21). Loss of p53 increases glycolytic flux to promote anabolism and redox balance, two key processes that promote tumorigenesis (19). A salient feature of many tumors is that they reside in a low-oxygen environment (hypoxia) ranging from 0 to 2% O2 because the tumor cell proliferation rate often exceeds the rate of new blood vessel formation (angiogenesis) (22). The metabolic adaptation to hypoxia is coordinated by HIF-1, which induces metabolic genes involved in increasing glycolytic flux (23). Some tumors display constitutive activation of HIF-1 under normoxic conditions through a variety of mechanisms, including (i) hyperactivation of mTORC1, (ii) loss of von Hippel–Lindau, (iii) accumulation of ROS, and (iv) accumulation of the TCA cycle metabolites succinate or fumarate due to cancer-specific mutations in succinate dehydrogenase (SDH) or fumarate hydratase (FH), respectively (24). The robust coordinated induction of metabolic pathways that support tumorigenesis by combination of deregulation of PI3K-AKT-mTOR signaling pathways, loss of tumor suppressors, and activation of oncogenes alleviates the necessity of having mutations or amplifications in metabolic enzymes per se. Thus, examples of metabolic enzyme deregulation through genetic mutation are rare. One example is the elevated expression of phosphoglycerate dehydrogenase (PHGDH) due to amplification in a fraction of breast cancer and melanoma (25, 26). PHGDH catalyzes the conversion of the glycolytic intermediate 3-phosphoglycerate to 3-phosphohydroxypyruvate in the first step of the serine biosynthesis pathway. Serine metabolism supplies methyl groups to the one-carbon and folate pools contributing to nucleotide synthesis, methylation reactions, and NADPH (reduced nicotinamide adenine dinucleotide phosphate) production (27). Inhibiting serine biosynthesis by silencing PHGDH in cells with high levels of this enzyme results in growth suppression, and the enzyme displays oncogenic properties in gain of function assays (25, 26). The other examples of mutational deregulation of metabolic enzymes are those that generate oncometabolites. The current list of true oncometabolites is short (28). The term is most commonly and appropriately applied to D-2-hydroxyglutarate (D2HG), a reduced form of the TCA cycle intermediate α-ketoglutarate. D2HG is scarce in normal tissues but rises to millimolar concentrations in tumors with mutations in isocitrate dehydrogenase 1 or 2 (IDH1 or IDH2). These mutations occur commonly in gliomas, acute myelogenous leukemias (AMLs), and other types of cancer (2931). D2HG and its relationship to mutant IDH1 and IDH2 have been reviewed extensively elsewhere (32). The most relevant point here is that D2HG production requires a neomorphic enzyme activity imparted to IDH1/IDH2 by specific active-site mutations (33, 34). High levels of D2HG interfere with the function of dioxygenases requiring α-ketoglutarate as a cosubstrate. These include prolyl hydroxylases, cytosine hydroxylases, and histone demethylases, whose inhibition influences gene expression in part via an altered epigenetic state characterized by a failure to express differentiation programs (3541). Thus, although D2HG arises from an alteration of the metabolic network, its role in cancer seems to depend on nonmetabolic effects. Currently, D2HG is being used as a biomarker for disease monitoring, and inhibitors specific to mutants IDH1/IDH2 are in clinical trials for AML and solid tumors. The metabolite 2HG also exists as the enantiomer L-2HG (L2HG), which is not produced by mutant forms of IDH1/IDH2. This metabolite arises from the noncanonical activity of various dehydrogenases, including malate dehydrogenase and lactate dehydrogenase, whose promiscuous behavior reduces α-ketoglutarate to L2HG (4244). L2HG may be oxidized back to α-ketoglutarate by a FAD-linked enzyme, L2HG dehydrogenase (L2HGDH). L2HGDH deficiency, also called L2HG aciduria, is a rare neurometabolic disease of childhood caused by the inheritance of biallelic mutations in the gene encoding L2HGDH (45). Affected children have seizures, mental retardation, white matter abnormalities of the brain, and systemically elevated levels of L2HG. Remarkably, a number of these children have developed malignant brain tumors (46), providing an early clue to the significance of D2HG in IDH1/IDH2-mutant gliomas and raising the question of whether L2HG is also an oncometabolite. L2HG and D2HG exhibit different effects on dioxygenase function (38), suggesting that the sensitivity of a particular tissue to the presence of either metabolite may depend on which dioxygenases are expressed. Recent work has demonstrated modest accumulation of L2HG in cells experiencing hypoxia or electron transport chain (ETC) dysfunction (42, 47) and in human renal cell carcinomas, which frequently display epigenetic silencing of L2HGDH (48). It is unknown whether reducing L2HG levels in these settings will promote cellular differentiation or suppress tumor progression. The principle that oncometabolites exert their effects outside of the conventional metabolic network also applies to the other two molecules that can reasonably be called oncometabolites: fumarate and succinate (28). Both are TCA cycle intermediates found throughout the body, but some tumors accumulate massive levels of fumarate and/or succinate as a consequence of loss-of-function mutations in FH or the SDH complex, respectively (4951). Although these mutations markedly reprogram metabolism by impairing TCA cycle flux, the extent to which fumarate and succinate participate in cancer development likely involves their nonmetabolic functions (28). Like D2HG, evidence indicates that succinate and fumarate interfere with dioxygenase activity, supporting the notion that a general property of oncometabolites is the ability to regulate epigenetics (52, 53). PHGDH overexpression and mutations in IDH1/IDH2, SDH, and FH all alter metabolite levels that control epigenetics (54). Several other metabolites, including acetyl-CoA, α-ketoglutarate, and S-adenosylmethionine also participate in epigenetic reprogramming, and time will tell whether genetically specific alterations of these metabolites in tumors promote tumorigenesis (54). Some metabolites, notably fumarate, covalently bind to sulfhydryl groups in glutathione, proteins, and peptides, altering their function and perhaps accounting for another mechanism by which oncometabolites promote or perpetuate malignant phenotypes (5558). BIOENERGETICS Otto Warburg’s hypothesis that cancer cells take up glucose and generate a substantial amount of lactate in the presence of ambient oxygen due to impaired mitochondrial function led to the widely held misconception that cancer cells rely on glycolysis as their major source of ATP (59, 60). Today, it is clear that cancer cells exhibit aerobic glycolysis due to activation of oncogenes, loss of tumor suppressors, and up-regulation of the PI3K pathway, and that one advantage of high glycolytic rates is the availability of precursors for anabolic pathways (2, 61). Warburg’s observation that tumors display a high rate of glucose consumption has been validated in many human cancers with fluorodeoxyglucose positron emission tomography, which uses a radioactive glucose analog to image glucose uptake in tumors and adjacent normal tissue. Nevertheless, many studies have demonstrated that the great majority of tumor cells have the capacity to produce energy through glucose oxidation (that is, the process by which glucose-derived carbons enter the TCA cycle and are oxidized to CO2, producing ATP through oxidative phosphorylation). Furthermore, limiting glycolytic ATP production by inhibiting the activity of pyruvate kinase fails to prevent tumorigenesis, suggesting that the major role of glycolysis is not to supply ATP (62). Moreover, mitochondrial metabolism is necessary for cancer cell proliferation and tumorigenesis (6365). Thus, despite their high glycolytic rates, most cancer cells generate the majority of ATP through mitochondrial function, with the likely exception of tumors bearing mutations in enzymes involved in mitochondrial respiration (for example, SDH and FH) (66). Nevertheless, cells harboring mutations in FH or SDH still rely on mitochondrial metabolism by metabolically rewiring themselves to provide the necessary TCA cycle intermediates and ROS for cell proliferation (55, 6770). In addition to pyruvate derived from glycolysis, fatty acids and amino acids can supply substrates to the TCA cycle to sustain mitochondrial ATP production in cancer cells (Fig. 2). The breakdown of fatty acids (β-oxidation) in the mitochondria generates acetyl-CoA and the reducing equivalents NADH and FADH2, which are used by the ETC to produce mitochondrial ATP. The amino acid glutamine can generate glutamate and subsequently α-ketoglutarate to fuel the TCA cycle through a series of biochemical reactions termed glutaminolysis (71). Furthermore, the BCAAs isoleucine, valine, and leucine, which are elevated in plasma of patients with pancreatic cancers, can be converted into acetyl-CoA and other organic molecules that also enter the TCA cycle (72). The metabolic flexibility afforded by multiple inputs into the TCA cycle allows cancer cells to adequately respond to the fuels available in the changing microenvironment during the evolution of the tumor (9). A combination of the local tumor microenvironment and oncogenic lesions is likely to dictate the fuel used by mitochondria to sustain tumor growth. Solid tumors contain significant heterogeneity of perfusion, such that many tumor cells reside in nutrient- and oxygen-poor environments. Cancer cells have therefore adapted multiple mechanisms to sustain mitochondrial function for survival. For example, the mitochondrial ETC can function optimally at oxygen levels as low as 0.5% (73). Moreover, hypoxic tumor cells (<2% O2) can continue to cycle and use glutamine as a fuel for oxidative ATP production (7476). Kras-driven pancreatic cancer cells in nutrient-depleted conditions use proteins scavenged from the extracellular space to produce glutamine and other amino acids to fuel the TCA cycle for cell survival and growth (Fig. 2) (77). Furthermore, if pyruvate oxidation to acetyl-CoA is compromised by hypoxia or ETC impairment, glutamine can provide acetyl-CoA as a biosynthetic precursor to sustain tumor growth (69, 78, 79). When tumor cells become nutrient-deprived, they adapt to the microenvironment by decreasing their demand for ATP. The resultant increase in ATP availability maintains an adequate ATP/ADP (adenosine 5´-diphosphate) ratio to drive unfavorable biological reactions. The anabolic kinase mTOR, discussed in greater detail below, drives the energetically demanding growth of tumor cells. This kinase is inhibited when amino acids and oxygen levels are diminished (80). Furthermore, decreased mTOR activity increases autophagic flux. In oncogenic Kras- or Braf-driven non–small-cell lung cancer (NSCLC) cells, autophagy provides an intracellular glutamine supply to sustain mitochondrial function (81, 82). To survive the hypoxic tumor microenvironment, cancer cells also diminish their demand for ATP by decreasing highly demanding ATP-dependent processes, such as running the Na/K-dependent adenosine triphosphatase. If diminishing ATP demand is not sufficient to maintain ATP/ADP ratio, the rise in ADP activates adenylate kinase, a phosphotransferase enzyme that buffers the fall in ATP levels by converting two ADP molecules into adenosine 5´-monophosphate (AMP) and ATP (83). The rise in AMP during nutrient deprivation triggers the activation of AMP kinase (AMPK), which activates catabolic pathways like fatty acid oxidation to stimulate ATP production (84). In conditions of metabolic stress, certain Ras-driven cancer cells scavenge lipids to support ATP production (85). Ovarian cancer cells use fatty acids from neighboring adipocytes to fuel mitochondrial ATP production (86). Thus, there are multiple mechanisms by which cancer cells maintain their ATP/ADP ratio to sustain viability in nutrient- and oxygen-poor environments. BIOSYNTHESIS OF MACROMOLECULES Biosynthetic or anabolic pathways are an essential aspect of cancer metabolism because they enable cells to produce the macromolecules required for replicative cell division and tumor growth. As a general theme, these pathways involve the acquisition of simple nutrients (sugars, essential amino acids, etc.) from the extracellular space, followed by their conversion into biosynthetic intermediates through core metabolic pathways like glycolysis, the PPP, the TCA cycle, and nonessential amino acid synthesis, and finally the assembly of larger and more complex molecules through ATP-dependent processes (Fig. 3). The three macromolecular classes most commonly studied in cancer metabolism are proteins, lipids, and nucleic acids, which comprise approximately 60, 15, and 5% of the dry mass of mammalian cells, respectively. Evidence indicates that biosynthesis of all three classes is under the control of the same signaling pathways that govern cell growth and are activated in cancer via tumorigenic mutations, particularly PI3K-mTOR signaling. Protein biosynthesis is highly regulated and requires access to a full complement of essential and nonessential amino acids. Cancer cells and other cells under the influence of growth factor signaling express surface transporters that allow them to acquire amino acids from the extracellular space (87). This not only provides cells with the raw materials needed for protein synthesis but also allows them to maintain activity of the mTOR signaling system, specifically mTORC1. mTORC1 is stimulated by the presence of amino acids and activates protein synthesis via its effects on translation and ribosome biogenesis (80). Most nonessential amino acids are produced through transamination reactions, which transfer the amino group from glutamate to a ketoacid. Proliferating cancer cells take up glutamine and convert it to glutamate through a variety of deamidation and transamidation reactions, most notably the mitochondrial amidohydrolase glutaminase (71). Together, these enzymes generate a large intracellular glutamate pool available for nonessential amino acid synthesis. Both glutamine uptake and glutaminase activity are stimulated by mTORC1, providing glutamate for transamination reactions and/or maintenance of the TCA cycle, which also contributes to amino acid synthesis. Furthermore, when the intracellular glutamine supply exceeds the cell’s demands, glutamine can be exported in exchange for essential amino acids to stimulate mTORC1 and protein synthesis (88). Thus, growth conditions in which glutamine and essential amino acids are abundant enable mTORC1-mediated activation of protein synthesis. When nutrients are scarce, cells have access to a number of catabolic pathways to degrade macromolecules and resupply key pools of intracellular metabolic intermediates. Protein degradation pathways have been characterized extensively as mechanisms to supply amino acids in cancer cells. Intracellular proteins and other macromolecules can be recycled through autophagy, a highly regulated process through which proteins and organelles are delivered to the lysosome and degraded (89). Autophagy is an essential survival pathway during nutrient or growth factor deprivation, and genetic studies demonstrate that it contributes to some forms of cancer in mice (90, 91). However, because autophagy supplies amino acids through protein degradation, it does not serve as a source of net protein synthesis. It is also potently suppressed by mTORC1. Macropinocytosis allows cells to internalize proteins and other components of the extracellular milieu and deliver them for degradation via the endocytic pathway. Under conditions of nutrient depletion, macropinocytosis supplies both nitrogen and carbon to central metabolic pathways (92). Evidence indicates that extracellular protein degradation, like autophagy, is suppressed by mTORC1 (93). Suppressing pathways of protein degradation may help maximize rates of net protein synthesis when extracellular amino acids are available and mTORC1 is active. Tumor cells rapidly produce fatty acids for membrane biosynthesis, lipidation reactions, and cellular signaling. Fatty acid synthesis requires sources of acetyl-CoA and reducing power in the form of cytosolic NADPH; effective fatty acid synthesis therefore requires integration with other pathways of carbon and redox metabolism. In most cultured cells, glucose is the most prominent acetyl-CoA source for fatty acid synthesis (94, 95). Glutamine and acetate have been demonstrated to provide alternative carbon sources when access to glucose-derived acetyl-CoA is impaired by hypoxia or mitochondrial dysfunction (69, 78, 79, 96). Leucine degradation can also supply acetyl-CoA in some cell lines (97). The relative importance of these nutrients for fatty acid synthesis in vivo is unknown, although early studies suggested that most fatty acyl carbon in experimental tumors is derived from glucose (98, 99). Isotopic tracing experiments designed to assess the cytosolic NADPH pool have recently suggested that most NADPH used for fatty acid synthesis arises from the PPP (100, 101). Transcription of genes involved in fatty acid synthesis is regulated by the SREBP-1 transcription factor (102). SREBP-1 regulates not only the enzymes needed to convert acetyl-CoA into fatty acids but also the enzymes of the PPP and pathways required to convert acetate and glutamine into acetyl-CoA (103). This transcription factor therefore regulates genes encoding proteins that catalyze or facilitate fatty acid synthesis. In lipid-replete conditions, SREBP-1’s transcriptional activity is suppressed by its sequestration in the endoplasmic reticulum. Under conditions of sterol depletion, proteolytic cleavage releases the transcriptionally active domain, which travels to the nucleus and binds to sterol response elements in the promoters of lipogenic genes (104). In cancer cells with constitutively high rates of fatty acid synthesis, additional mechanisms help keep SREBP-1 in a transcriptionally active state. mTORC1 signaling, via its effector S6 kinase (S6K), activates a transcriptional program that includes both SREBP-1 and the related protein SREBP-2, which regulates transcription of genes in sterol biosynthesis (103). Both SREBP-1 and SREBP-2 are required for mTORC1-mediated cell proliferation. The mechanism of SREBP activation by mTORC1 is incompletely understood but involves nuclear entry of the phosphatidic acid phosphatase Lipin-1, which enhances nuclear SREBP abundance and activity on the promoters of lipogenic genes (105). Both fatty acids and lipids can also be acquired from the extracellular space to supply membrane biosynthesis. PI3K signaling activates fatty acid uptake and suppresses fatty acid oxidation, thereby maximizing lipogenesis in proliferating cells under the control of growth factors (106). Lipid uptake may acquire further importance during conditions of metabolic stress, when the ability to meet oncogene-driven demands for biosynthesis is compromised. The ability to scavenge lysophospholipids (lipid intermediates containing a glycerophosphate backbone with one acyl chain) is required for maximal cancer cell growth during hypoxia, which suppresses de novo fatty acid synthesis from glucose (85). Furthermore, in cancer cells with constitutive mTORC1 signaling, hypoxia induces a state of dependence on access to extracellular desaturated fatty acids to maintain endoplasmic reticulum integrity in support of protein biosynthesis (107). Notably, SREBP-1 was first identified as the transcription factor responsible for expression of the low-density lipoprotein receptor (LDLR) (108), implying that enhanced de novo lipogenesis occurs concomitantly with enhanced import of lipids from the extracellular space. These parallel pathways appear to be essential in glioma, where oncogenic activation of epidermal growth factor receptor (EGFR) signaling stimulates SREBP-1 and expression of LDLR (109). These cancer cells are highly sensitive to inhibitors of fatty acid and cholesterol biosynthesis. Inhibition of the EGFR-PI3K signaling axis but not of mTORC1 suppresses nuclear translocation of SREBP-1 in glioma cells with oncogenic EGFR, suggesting an alternate, mTORC1-independent mode of SREBP-1 activation in glioma cells (109). This transcriptional program includes LDLR expression and induces reliance on cholesterol uptake to maintain the intracellular pool (110). Impairing intracellular cholesterol availability by activating liver X receptor induced cell death both in culture and in vivo, suggesting a pharmacological approach to silence lipogenic programs in glioma (110). Purine and pyrimidine nucleotides are required for synthesis of RNA and DNA. De novo biosynthesis of nucleotides is complex, requiring input from several pathways in a coordinated fashion. The phosphoribosylamine backbone of these molecules is produced from ribose-5-phosphate, an intermediate of the PPP, and an amide donation reaction using glutamine as a substrate (111). The purine and pyrimidine bases are constructed from various nonessential amino acids and methyl groups donated from the one-carbon/folate pool. The TCA cycle contributes oxaloacetate, which is transaminated to aspartate, an intermediate required to synthesize both purine and pyrimidine bases. Conversion of ribonucleotides to deoxynucleotides by ribonucleotide reductase requires a source of NADPH. Well-characterized mechanisms of feedback inhibition exist to prevent excessive accumulation of nucleotides, and mutations interrupting these mechanisms can produce pathological accumulation of nucleotide intermediates (for example, precipitation of uric acid crystals in gout). Clearly, nucleotide biosynthesis is a targetable vulnerability in cancer cells because nucleoside analogs and antifolates have been a mainstay of chemotherapeutic regimens for decades (112). However, relatively little is known about how oncogenic signaling interfaces with nucleotide biosynthesis. It is likely that the effects of numerous signaling pathways on glucose and amino acid metabolism influence the availability of precursors for purines and pyrimidines. In the case of mTORC1, evidence points to a distinct mechanism by which activation of the signaling pathway enables nucleotide biosynthesis. The mTORC1 effector ribosomal S6K1 phosphorylates the trifunctional enzyme CAD (carbamoyl-phosphate synthetase 2, aspartate transcarbamoylase, dihydroorotase), which catalyzes the first three steps of pyrimidine synthesis (113). Phosphorylation on CAD S1859 is required for mTORC1-dependent stimulation of pyrimidine biosynthesis (113). Additional work is needed to determine how other aspects of de novo nucleotide synthesis, purine and pyrimidine salvage pathways, and accessory activities like folate metabolism are regulated in cancer cells in support of cell proliferation. REDOX BALANCE ROS are intracellular chemical species that contain oxygen and include the superoxide anion (O2), hydrogen peroxide (H2O2), and the hydroxyl radical (OH·) (114). The mitochondria and cytosolic NADPH oxidases (NOXs) produce O2 from the one-electron reduction of oxygen (115). O2 is converted into H2O2 by the enzymatic activity of superoxide dismutase 1 or 2, which are localized to the cytosol or mitochondrial matrix, respectively. H2O2 is subsequently detoxified to water by the enzymatic activity of mitochondrial and cytosolic peroxiredoxins (PRXs), which, as a consequence, undergo H2O2-mediated oxidation of their active-site cysteines (116). Thioredoxin (TXN), thioredoxin reductase (TrxR), and the reducing equivalent NADPH reduce oxidized PRXs to complete the catalytic cycle (117). Glutathione peroxidases (GPXs) can also convert H2O2 to water in the mitochondrial matrix and cytosol through H2O2-mediated oxidation of reduced glutathione (GSH) (118). Glutathione reductase (GR) and NADPH reduce oxidized glutathione (GSSG) back to GSH. Additionally, catalase, an abundant antioxidant in peroxisomes, can detoxify H2O2 to water without any cofactors. However, in the presence of ferrous or cuprous ions, H2O2 can become OH· and quickly cause the oxidation of lipids, proteins, and DNA, resulting in cellular damage. NADPH is required to maintain multiple antioxidant defense systems. The cytosol has multiple sources of NADPH generation, including the oxidative PPP, malic enzyme 1, IDH1, and one-carbon metabolism. NADPH generation in the mitochondria, in part, is controlled by one-carbon metabolism and IDH2. Historically, ROS have been thought of as lethal metabolic by-products of cellular respiration and protein folding. However, studies over the past two decades have unveiled a previously underappreciated role of ROS in cellular signaling. Low levels of ROS, particularly H2O2, can reversibly oxidize the cysteine residues of proteins to positively regulate cell proliferation and cellular adaptation to metabolic stress (119). As H2O2 levels increase, however, cell death signaling pathways are initiated, and H2O2 is converted to OH·, which can directly damage DNA, proteins, and lipids. Cancer cells have an increased rate of spatially localized mitochondria- and NOX-dependent ROS production compared to normal cells. This allows for the proximal activation of signaling pathways [PI3K and mitogen-activated protein kinase/extracellular signal–regulated kinase (MAPK/ERK)] and transcription factors [HIF and nuclear factor κB (NF-κB)] necessary for tumorigenesis. The cancer cell–specific increased rate of spatially localized ROS production is due to a combination of oncogenic lesions and the tumor microenvironment. For example, the activation of oncogenes, PI3K signaling pathway induction, and hypoxia (low-oxygen levels) stimulate the increased rate of ROS production from the mitochondria and NOXs in cancer cells (120122). Thus, mitochondria-targeted antioxidants and NOX inhibitors can prevent cancer cell proliferation, hypoxic activation of HIF, tumorigenesis, and metastasis (64, 123125). The increased localized ROS in cancer cells, which activates signaling pathways and transcription factors to promote tumorigenesis, needs to be buffered from reaching levels of ROS that incur cellular damage by the increased expression of antioxidant proteins (126). Thus, cancer cells have higher levels of ROS scavenging enzymes than normal cells, preventing ROS-mediated activation of death-inducing pathways like c-Jun N-terminal kinase (JNK) and p38 MAPK and oxidation of lipids, proteins, and DNA, resulting in irreversible damage and cell death. One mechanism by which cancer cells increase their antioxidant capacity is by activating the transcription factor nuclear factor (erythroid-derived 2)–related factor-2 (NRF2) (127). Specifically, NRF2 is activated following disruption of the interaction of NRF2 with its binding partner Kelch-like ECH-associated protein 1 (KEAP1). Critical cysteine residues within KEAP1 can undergo oxidation, succination, and glutathionylation, thereby inhibiting the KEAP1-NRF2 interaction, leading to the proteasomal degradation of NRF2. Additionally, NRF2 activation can occur independently of KEAP1 (128). Once activated, NRF2 induces the transcription of many antioxidant proteins including GPXs and TXNs as well as enzymes involved in GSH synthesis and cysteine import through the cystine/glutamate antiporter. Furthermore, to maintain the antioxidant capacity of GPXs and TXNs, NADPH is required. NRF2 plays an important role in activating enzymes that increase cytosolic NADPH levels. NRF2 also regulates the serine biosynthesis pathway, generating NADPH in the mitochondria, which is critical for redox balance under hypoxic conditions (129, 130). Therefore, inactivating NRF2 or disabling antioxidant proteins in cancer cells would allow for the accumulation of excessive amounts of ROS to levels that initiate toxicity and reduce tumorigenesis (128, 131, 132). During tumorigenesis and metastasis, redox homeostasis is required (Fig. 4). An emerging model of redox balance is that as a tumor initiates, the metabolic activity of cancer cells is increased, resulting in an increase in ROS production and subsequent activation of signaling pathways that support cancer cell proliferation, survival, and metabolic adaptation (126). Accordingly, to prevent toxic levels of ROS, tumor cells increase their antioxidant capacity to allow for cancer progression (133). The harsh tumor microenvironment increases ROS levels due to hypoxia, and the low glucose levels limit flux through the cytosolic oxidative PPP, thus decreasing cytosolic NADPH levels. Cells in these nutrient-deprived conditions activate AMPK to increase NADPH levels by stimulating PPP-dependent NADPH and diminishing anabolic pathways, such as lipid synthesis, that require high levels of NADPH (134, 135). ROS-dependent signaling and increased mitochondrial respiration are also necessary for tumor metastasis (124, 136). However, when tumor cells detach from a matrix, they encounter high levels of ROS that incur cellular damage and require activation of adaptive ROS-mitigating pathways to survive and grow (137, 138). The ability to up-regulate antioxidant proteins and increase flux through NADPH-producing metabolic pathways enables distant metastasis to occur (8). These findings suggest that perhaps disabling antioxidant capacity in cancer cells to raise ROS levels might be beneficial in preventing metastasis. TARGETING METABOLISM FOR CANCER THERAPY There are a few things to consider when determining what makes a good metabolic target for cancer therapy. First, inhibition of some metabolic enzymes is likely to be systemically toxic because of their physiological functions in normal tissues (139). The feasibility of targeting these pathways therapeutically depends on whether systemic blockade of the pathway can be tolerated. Normal proliferating cells, such as immune cells and stem cells, also reprogram their metabolism in a manner similar to cancer cells (140, 141). Metabolic inhibitors should likely not interfere with the adaptive immune system. Nevertheless, there are excellent examples of pathways whose reprogramming does provide an adequate therapeutic window in cancer. Enhanced nucleotide and DNA synthesis in tumor cells is targeted by antifolates (methotrexate, pemetrexed, and others) (112). Although these drugs do produce toxicity in normal proliferative tissues like the intestinal epithelium and bone marrow, they are essential components of highly successful chemotherapeutic regimens. Thus, it is critical to elucidate in normal cells any toxic effects of metabolic enzyme inhibition. To circumvent this challenge, one approach is to target a metabolic enzyme in a deregulated pathway specific to cancer cells. To date, many of the genetic and pharmacologic interventions on metabolic enzymes have been conducted using human cancer cells subcutaneously injected into athymic mice. Therefore, it will be important for future experiments to not only use patient-derived xenograft (PDX) models but also make use of genetically engineered mouse cancer models and syngeneic mouse models that have intact immune systems, especially given the promising results from immunotherapy. An emerging theme is that cancer cells display metabolic plasticity and can alter their metabolic profile during the course of tumorigenesis and metastasis. Thus, it is conceivable that cancer cells could develop resistance to inhibition of a particular metabolic pathway by expressing alternate protein isoforms or up-regulating compensatory pathways. Therefore, a rational cancer therapeutic strategy should involve targeting multiple metabolic pathways simultaneously or targeting a particular metabolic pathway in combination with therapies against oncogenic or signaling pathways. Here, we highlight a few promising metabolic targets in glycolytic, one-carbon, mitochondrial, and redox metabolism. Glycolysis was an early attractive target for cancer therapy given the clinical observation that many tumors exhibit a significant increase in glucose uptake compared with adjacent normal tissue (112). LDH-A, a metabolic enzyme that converts pyruvate (the final product of glycolysis) to lactate, was identified as the first metabolic target of the oncogene MYC (142). Genetic or pharmacologic inhibition of LDH-A has been shown to diminish MYC-driven tumors in xenograft models (143, 144). Furthermore, recent studies indicate that inhibition of LDH-A leads to the regression of established tumors in genetically engineered mouse models of NSCLC without systemic toxicity (145). Genetic ablation of LDH-A also delays the progression of myeloid leukemia (146). Thus, the increased expression of LDH-A, specifically in MYC-mutant cancer cells, may prove to be an attractive target. Another potential therapeutic target is the glycolytic protein HK2. Many tumor cells overexpress HK2, and preclinical mouse models of genetically engineered NSCLC and breast cancer demonstrate that HK2 inhibition delays tumor progression (3). Furthermore, systemic HK2 deletion in mice does not cause adverse physiological consequences. However, the effect of LDH-A and HK2 on the adaptive immune system is currently unknown. Lactate has been shown to inhibit cytotoxic T cells; thus, LDH-A inhibition may cooperate with immune checkpoint inhibitors to unleash host inflammatory T cells that will specifically attack tumor cells (147). Lactate can also reprogram macrophages to promote tumorigenesis (148). Thus, it may be efficacious to target LDH-A or HK2 in highly glycolytic tumors that overexpress these proteins. Another potential glucose-dependent target is PHGDH, an enzyme in the de novo serine synthesis pathway. High levels of PHGDH have been found in a subset of human melanoma and breast cancers, and these cancer cells require PHGDH for their growth in vitro (25, 26). Serine starvation in mice diminishes tumorigenicity of p53-null cancers (149). De novo synthesis or exogenous uptake of serine can enter the mitochondria where SHMT2 converts it into glycine to generate folate intermediates (101, 150). In many cancer types, SHMT2 expression is elevated and correlates with a poor prognosis. Furthermore, the transcription factors MYC and HIF induce SHMT2 under hypoxia to promote survival (130, 151). Currently, it is not known whether targeting PHGDH, SHMT2, or other enzymes in the one-carbon metabolism pathway would be effective in delaying or regressing tumor progression in genetically engineered, PDX, or syngeneic mouse models of cancer without incurring systemic toxicity. However, given the importance of one-carbon metabolism in supporting the anabolic needs of cancer cells and its up-regulation in cancer cells, it is likely that this pathway is needed for in vivo tumor progression (152). Mitochondrial metabolism has also emerged as a key target for cancer therapy, in part, due to the revelation that the antidiabetic drug metformin is an anticancer agent (153). Numerous epidemiological studies first suggested that diabetic patients taking metformin, to control their blood glucose levels, were less likely to develop cancer and had an improved survival rate if cancer was already present (154). Laboratory-based studies have also provided evidence that metformin may serve as an anticancer agent (155157). Biochemists recognized that metformin reversibly inhibits mitochondrial complex I (158160). Recent studies indicate that metformin acts as an anticancer agent by inhibiting mitochondrial ETC complex I (161). Specifically, metformin inhibits mitochondrial ATP production, inducing cancer cell death when glycolytic ATP levels diminish as a result of limited glucose availability. Metformin also inhibits the biosynthetic capacity of the mitochondria to generate macromolecules (lipids, amino acids, and nucleotides) within cancer cells (162). The remarkable safety profile of metformin is due to its uptake by organic cation transporters (OCTs), which are only present in a few tissues, such as the liver and kidney (163). Certain tumor cells also express OCTs to allow the uptake of metformin (164). However, in the absence of OCTs, tumors would not accumulate metformin to inhibit mitochondrial complex I. Ongoing clinical trials using metformin as an anticancer agent should assess the expression levels of OCTs to identify the tumors with highest expression, which are likely to be susceptible to metformin. Moreover, it is not clear whether the current antidiabetic dosing of metformin used in clinical trials allows for metformin accumulation to levels necessary to inhibit mitochondrial complex I in tumors. Thus, it is possible that metformin at doses higher than those currently used for diabetes might be more efficacious without causing toxicity. Like metformin, the biguanide phenformin exhibits anticancer properties by inhibiting mitochondrial complex I (165). In contrast to metformin, phenformin is readily transported into tumor cells and has been withdrawn from human use in most parts of the world due to its clinical increase in the incidence of lactic acidosis. However, it is worth considering phenformin as a possible cancer therapy because lactic acidosis can be monitored. Biguanide sensitivity can be improved in mice starved for serine or in tumors that have lost p53 or LKB1 (155, 166, 167). Thus, biguanides, and possibly other mitochondrial complex I inhibitors, may be effective anticancer agents. Another potential therapeutic strategy to inhibit mitochondrial metabolism in certain tumors would be to use autophagy or glutaminase inhibitors. Autophagy provides amino acids, such as glutamine, that fuel the TCA cycle in NSCLC and pancreatic cancers, and short-term autophagy inhibition has been shown to decrease tumor progression without incurring systemic toxicity in mouse models of NSCLC (168, 169). Some tumors are addicted to using glutamine to support TCA cycle metabolism even in the absence of autophagy; thus, glutaminase inhibitors can reduce tumor burden in these models (4, 75, 170). An alternative approach is to target acetate metabolism. Although a major function of the mitochondria is to provide acetyl-CoA to the cell, cancer cells can also use acetate to support cell growth and survival during metabolic stress (hypoxia or nutrient deprivation) (96, 171). The cytosolic enzyme acetyl-CoA synthase 2 (ACCS2), which converts acetate to acetyl-CoA, is dispensable for normal development; thus, ACCS2 is a promising target of acetate metabolism. ACCS2 knockout mice do not display overt pathologies, but genetic loss of ACCS2 reduces tumor burden in models of hepatocellular carcinoma (171). Human glioblastomas can oxidize acetate and may be sensitive to inhibitors of this process (172). Thus, targeting metabolism with inhibitors of autophagy, acetate metabolism, and other pathways that supply key metabolic intermediates may be efficacious in some contexts. Because mitochondrial inhibitors are unlikely to be effective cancer therapies as single agents, combination therapy is likely the best approach. For example, the use of metformin with the current clinical PI3K inhibitors, which reduce glucose uptake and glycolysis (173), is one approach that would impair both sources of ATP within cells. Targeted therapies against oncogenes such as KRAS, BRAF, and NOTCH1 kill a large majority of cancer cells but ultimately yield resistant cells that exhibit an increased sensitivity to inhibitors that impair mitochondrial metabolism (174176). Cancer-initiating cells also have increased sensitivity to mitochondrial inhibitors, adding further evidence that inhibiting mitochondrial metabolism may suppress tumor recurrence (177, 178). Furthermore, to counterbalance the increased production of ROS encountered during tumorigenesis and metastasis, cancer cells increase their antioxidant capacity (126). Thus, an additional therapeutic approach is to target redox metabolism, that is, selectively disable the antioxidant capacity of cancer cells causing ROS levels to rise and induce cancer cell death (133). The reducing equivalent NADPH is required to maintain multiple antioxidant defense systems. The cytosol has multiple sources of NADPH generation, including the oxidative PPP, malic enzyme 1, IDH1, and one-carbon metabolism. By contrast, NADPH generation in the mitochondria is controlled in part by one-carbon metabolism and IDH2. Many of these NADPH-generating systems are critical for normal cell survival and function. Nevertheless, there are two NADPH-generating systems that may serve as potential therapeutic targets. It is estimated that 400 million people worldwide are deficient in G6PDH, an enzyme in the oxidative PPP that converts NADP+ to NADPH. However, certain tumors rely on this pathway as a major source of cytosolic NADPH; therefore, it may be therapeutic to disable this pathway and induce oxidative stress and diminish tumor growth. Moreover, RNA profiling of metabolic enzymes identified the mitochondrial one-carbon metabolism protein MTHFD2, which can generate NADPH, as being highly expressed in 19 different cancer types but not in normal adult proliferating cells (152). Loss of MTHFD2 in cancer cells increases ROS levels and sensitizes the cells to oxidant-induced cell death in vitro. An interesting approach to depleting NADPH levels and increasing ROS is to administer high doses of vitamin C (ascorbate). Vitamin C is imported into cells through sodium-dependent vitamin C transporters, whereas the oxidized form of vitamin C, dehydroascorbate (DHA), is imported into cells through glucose transporters such as GLUT1 (179). When the cell takes up DHA, it is reduced back to vitamin C by glutathione (GSH), which consequently becomes GSSG. Subsequently, GSSG is converted back to GSH by NADPH-dependent GR. Because the blood is an oxidizing environment, vitamin C becomes oxidized to DHA before being taken up by the cell. Thus, high doses of vitamin C diminish the tumorigenesis of colorectal tumors that harbor oncogenic KRAS mutations and express high levels of GLUT1 by depleting the NADPH and GSH pools and consequently increasing ROS levels to induce cancer cell death (179, 180). Vitamin C administered at high doses intravenously is safe in humans and, in conjunction with conventional paclitaxel-carboplatin therapy, demonstrated a benefit in a small number of patients (181). Additional strategies to diminish GSH include the administration of buthionine sulfoximine, an irreversible inhibitor of γ-glutamylcysteine synthetase, which can be safely administered to humans and is efficacious in preclinical tumor models (182). Moreover, glutathione is a tripeptide consisting of cysteine, glutamate, and glycine. Thus, decreasing glutamate levels using glutaminase inhibitors or diminishing cysteine levels by preventing extracellular cystine (two linked cysteine molecules) uptake can also raise ROS levels in cancer cells to induce cell death. An important consideration is that normal stem cells are sensitive to ROS levels; thus, it is important to stratify patients on the basis of their expression levels of a particular antioxidant protein or antioxidant pathway. It is critical to determine which antioxidant pathways are likely up-regulated as a result of the high rate of ROS production within cancer cells. Many cancer types use the NRF2 pathway to maintain redox balance; therefore, targeting this pathway may provide a viable therapeutic opportunity (128). Additionally, superoxide dismutase 1 (SOD1) is overexpressed in NSCLC, and its inhibition kills human NSCLC cells and decreases the tumor burden in mouse models of NSCLC (183). Because NRF2 and SOD1 knockout mice develop normally, short-term inhibition of these pathways might be an effective way to kill cancer cells. TECHNOLOGIES ENABLING DISCOVERY IN CANCER METABOLISM Many recent advances in our understanding of cancer metabolism have been propelled by advanced technologies to detect metabolites and metabolic activities (184). A key concept is that quantifying metabolites (that is, metabolomics) is a more distinct form of metabolic analysis than measuring the activities of metabolic pathways [that is, metabolic flux analysis (185)]. Although these two approaches can provide complementary types of information, they are not interchangeable. One cannot infer metabolic activity from changes in metabolite levels, and altered metabolic fluxes may or may not cause changes in metabolite levels (186). Both of these approaches have provided important recent insights into cancer metabolism, and using the two techniques together provides the most complete assessment of metabolic phenotypes. Metabolomics experiments seek to characterize and quantify the metabolites in a biological sample, usually by nuclear magnetic resonance (NMR) or, more commonly, mass spectrometry. Depending on the methods of extraction, separation, and detection, metabolomics experiments may focus on particular classes of metabolites or provide a comprehensive analysis of as many metabolites as possible. Targeted approaches typically detect a few dozen to a few hundred molecules, whereas untargeted analyses may detect more than 1000. Detecting alterations of metabolite levels in cancer can be extremely valuable. The massive accumulation of D2HG in IDH1-mutant gliomas was initially discovered through a metabolomics approach (33). Because altered metabolite levels can be detected noninvasively using 1H magnetic resonance spectroscopy (MRS), perturbed metabolite levels discovered through metabolomics can sometimes be translated into clinical diagnostic techniques. Elevated levels of lactate, choline, glycine, and other metabolites are detected by MRS in glioma. More recently, MRS techniques have been developed to monitor specific metabolic states programmed by tumor-specific mutations in metabolic enzymes. Applications include elevated 2HG in IDH1/IDH2-mutated gliomas (187) and elevated succinate in SDH-deficient paragangliomas (188). Metabolic flux studies use isotope tracers like 13C, 15N, and 2H to track flow through metabolic pathways. Typically, a nutrient of interest is labeled by an isotope (for example, 13C-glucose) and supplied to cancer cells in the culture medium. Metabolites extracted from the culture are analyzed for isotope enrichment using mass spectrometry or NMR. The extent and distribution of labeling within informative metabolites encode information about which pathways are active in the cells. Incorporating additional data (for example, definitive rates of nutrient consumption, waste secretion, and biomass production) allows quantitative fluxes to be determined across a metabolic network. Isotope tracing studies provide information about metabolic alterations in cancer cells that cannot be detected by metabolite levels alone. For example, hypoxia and mutations in the ETC induce a restructuring of the TCA cycle in which many of the intermediates are produced in the reverse order from the conventional form of the cycle. The key reaction in this pathway involves the reductive carboxylation of α-ketoglutarate to isocitrate in a NADPH-dependent carboxylation reaction catalyzed by IDH1 and/or IDH2. Although metabolomics experiments can detect altered levels of TCA cycle metabolites in cells using the reductive carboxylation pathway or in cells with deficiencies in pyruvate import into mitochondria, the marked restructuring of the cycle is apparent only through isotope tracing experiments, particularly experiments using 13C-glutamine as the tracer (69, 78, 79, 189191). An example of the use of isotope tracers to identify metabolic liabilities involves the surprising discovery that a significant fraction of cellular NADPH, particularly in the mitochondria, is produced through folate metabolism (100, 101). These studies involved a sophisticated combination of 13C and 2H tracers, coupled with quantitative measurements of metabolic flux. Several recent studies have begun to use stable isotopes to investigate metabolism in intact tumors. Because these isotopes do not undergo radioactive decay, they are safe for administration to animals and human subjects. Systemic administration of 13C-labeled nutrients through either boluses or continuous infusions has been shown to generate substantial labeling of glycolytic and TCA cycle intermediates in tumors. In mice bearing orthotopic transplants of high-grade human gliomas, continuous infusion of 13C-glucose was demonstrated to produce steady-state labeling of metabolites from the TCA cycle within the tumor, enabling the assessment of several metabolic pathways (192). Here, tumors with diverse oncogenotypes oxidized glucose-derived pyruvate in the mitochondria and synthesized glutamine from glucose carbon. In contrast to most cultured glioma cell lines, these tumors did not demonstrate significant levels of 13C-glutamine oxidation in vivo, and primary cell lines derived from the tumors did not require glutamine for survival or proliferation. In another study, metabolism of 13C-glucose and 13C-glutamine in autochthonous models of MYC- or MET-driven tumorigenesis revealed that metabolic phenotypes depend not only on the tumor’s genetic driver but also on the tissue or origin. MYC but not MET stimulated glutamine catabolism in liver tumors, whereas MYC-driven lung tumors expressed glutamine synthetase and accumulated glutamine (193). Thus, in vivo isotope tracing can detect metabolic activities of intact tumors and characterize some of the factors that specify the metabolic phenotype. Administration of 13C-labeled nutrients has also proven to be valuable in human cancer (172, 194197). Fan et al. (196) used 13C-glucose to demonstrate that human non–small cell lung tumors metabolize glucose through glycolysis and the TCA cycle concurrently, with metabolites from both pathways demonstrating higher levels of labeling in tumors relative to adjacent lung tissue. In a subsequent study, these investigators demonstrated that the anaplerotic enzyme pyruvate carboxylase (PC) was highly expressed in lung tumors and contributed to 13C labeling in TCA cycle intermediates (195). Enhanced glucose oxidation involving both PC and pyruvate dehydrogenase (PDH) was demonstrated in a separate cohort of non–small cell lung tumors, in which formal analysis of metabolic fluxes was used to complement measurements of 13C labeling (197). An important conclusion from these studies, and from a similar study in mice bearing KRAS-driven tumors (198), is that non–small cell lung tumors demonstrate higher levels of both glycolysis and glucose oxidation relative to adjacent, benign lung. This finding sharply contrasts with the frequently invoked “switch” from oxidative metabolism to glycolysis in malignant tissue, commonly used to explain the Warburg effect (Fig. 5A). Rather, the data support a model in which the amplitude of both pathways is increased simultaneously, perhaps through increased substrate delivery and enzyme expression in tumor cells (Fig. 5B). It is also significant that human tumors exhibit substantial heterogeneity of metabolic phenotypes, both between tumors and even within distinct regions of the same tumor (197). The extent of glucose-dependent labeling of TCA cycle intermediates is predicted by noninvasive assessment of tumor perfusion by magnetic resonance imaging, providing an approach to identify areas of regional metabolic heterogeneity in human cancer (197). Metabolomics and metabolic flux analysis can be integrated with functional genomics to identify and understand metabolic vulnerabilities in cancer cells. This approach has produced several good examples of screens that identified potential therapeutic targets while stimulating entirely new lines of investigation in cancer cell biology. For example, the serine biosynthetic enzyme PHGDH was first identified as a metabolic vulnerability in breast cancer cells through a large-scale in vivo short hairpin RNA screen targeting thousands of metabolic enzymes (25). PHGDH is frequently amplified at the genomic level in breast tumors and melanomas and exhibits oncogene-like features in cell culture (25, 26). Subsequent work on serine biosynthesis, much of it involving metabolomics and metabolic flux analysis, has uncovered novel functions and liabilities of this pathway in cancer cell growth and stress resistance (129, 150, 151). Combining functional screens with metabolic analysis can also identify context-specific vulnerabilities that may be therapeutically actionable. A CRISPR (clustered regularly interspaced short palindromic repeats)–based loss-of-function screen identified GOT1, the cytosolic aspartate aminotransferase, as conditionally essential for survival during treatment with the ETC inhibitor phenformin (199). Isotope labeling then demonstrated that ETC blockade caused the direction of this enzyme to reverse from aspartate consumption in untreated cells to aspartate synthesis during ETC blockade (200). In addition to the discovery of synthetic lethality between ETC and GOT1 inhibition, these studies led to the novel biological concept that a major function of the ETC in proliferating cells is to support the synthesis of aspartate for nucleotide and protein synthesis (199, 200). CONCLUSIONS AND CURRENT CHALLENGES Substantial progress has been made in the past decade toward understanding the mechanisms, biological consequences, and liabilities associated with metabolic reprogramming in cancer. Several common themes have emerged from this research (Box 1). First, metabolic reprogramming is essential for the biology of malignant cells, particularly their ability to survive and grow by using conventional metabolic pathways to produce energy, synthesize biosynthetic precursors, and maintain redox balance. Second, metabolic reprogramming is the result of mutations in oncogenes and tumor suppressors, leading to activation of PI3K and mTORC1 signaling pathways and transcriptional networks involving HIFs, MYC, and SREBP-1. Third, alterations in metabolite levels can affect cellular signaling, epigenetics, and gene expression through posttranslational modifications such as acetylation, methylation, and thiol oxidation. Fourth, taken together, studies on cultured cells have demonstrated a remarkable diversity of anabolic and catabolic pathways in cancer, with induction of autophagy and utilization of extracellular lipids and proteins complementing the classical pathways like glycolysis and glutaminolysis. We have exited the period when cancer metabolism could be considered synonymous with the Warburg effect. Box 1 Key Principles and Lessons Learned Reprogrammed metabolic pathways are essential for cancer cell survival and growth. Frequently reprogrammed activities include those that allow tumor cells to take up abundant nutrients and use them to produce ATP, generate biosynthetic precursors and macromolecules, and tolerate stresses associated with malignancy (for example, redox stress and hypoxia). An emerging class of reprogrammed pathways includes those allowing cancer cells to tolerate nutrient depletion by catabolizing macromolecules from inside or outside the cell (for example, autophagy, macropinocytosis, and lipid scavenging). Reprogramming may be regulated intrinsically by tumorigenic mutations in cancer cells or extrinsically by influences of the microenvironment. Oncometabolites (for example, 2HG) accumulate as a consequence of genetic changes within a tumor and contribute to the molecular process of malignant transformation. Many metabolites exert their biological effects outside of the classical metabolic network, affecting signal transduction, epigenetics, and other functions. New approaches to assess metabolism in living tumors in humans and mice may improve our ability to understand how metabolic reprogramming is regulated and which altered pathways hold opportunities to improve care of cancer patients. Several challenges will likely shape research over the next decade. First, the studies cited above were performed primarily in cancer cell lines rather than intact tumors. These straightforward experimental models have been highly informative about the molecular mechanisms of metabolic reprogramming, particularly those linking aberrant signaling to altered metabolic fluxes. But it is challenging (perhaps impossible) to model an accurate tumor microenvironment in culture. Direct analysis of metabolic fluxes in intact tumors should begin to play a more prominent role in the field and may prove essential in determining precisely how to deploy metabolic inhibitors in clinical trials. Along these lines, it is remarkable that some tumor cell metabolic vulnerabilities observed in vivo are absent from cultured cell models (198) and that metabolic phenotypes are inconsistent even across single solid tumors in patients (197). Developing rational therapeutic strategies will be aided by learning how to derive metabolic information efficiently and comprehensively from both preclinical and clinical models of intact tumor growth. A further challenge for these in vivo studies will be to develop analytical or computational approaches to deconvolute the distinct metabolic phenotypes of discrete cell types (cancer cells, cancer-associated fibroblasts, lymphocytes, and endothelial cells) within solid tumors. This may allow us to understand the metabolic cooperativity among populations of cells within a tumor and whether metabolic reprogramming of stromal cells provides therapeutic opportunities. Second, by far the best recent candidate for a targetable, tumor-specific metabolic activity is the neomorphic function of mutant IDH1/IDH2. This has stimulated intense interest in finding other metabolic alterations for which the therapeutic window may be wide enough for real clinical opportunities. Third, although we have learned a great deal about the metabolic pathways that support cancer cell proliferation, we know much less about the metabolism that supports survival of nonproliferating tumor cells, which constitute the bulk of the malignant cells in most solid tumors. Along these lines, the metabolism of tumor-initiating cells/cancer stem cells is just now beginning to be investigated, and it will be of major interest to devise strategies to target metabolism in these cells. Finally, we still know relatively little about metabolic interactions between tumor and host. This area has the potential for enormous impact on public health. It is clear that obesity and diabetes, both of which are reaching epidemic proportions in the developed world, increase cancer risk, but we lack insight into how to break these links. 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Pierce, 11. D. Y. Gui, 12. L. B. Sullivan, 13. T. M. Wasylenko, 14. L. Subbaraj, 15. C. R. Chin, 16. G. Stephanopolous, 17. B. T. Mott, 18. T. Jacks, 19. C. B. Clish, 20. M. G. Vander Heiden , Environment impacts the metabolic dependencies of ras-driven non-small cell lung cancer. Cell Metab. 23, 517–528 (2016). 1. K. Birsoy, 2. T. Wang, 3. W. W. Chen, 4. E. Freikman, 5. M. Abu-Remaileh, 6. D. M. Sabatini , An essential role of the mitochondrial electron transport chain in cell proliferation is to enable aspartate synthesis. Cell 162, 540–551 (2015). 1. L. B. Sullivan, 2. D. Y. Gui, 3. A. M. Hosios, 4. L. N. Bush, 5. E. Freinkman, 6. M. G. Vander Hediden , Supporting aspartate biosynthesis is an essential function of respiration in proliferating cells. Cell 162, 552–563 (2015). Acknowledgments: We are grateful to J. Schaffer for illustrating the figures. Funding: This work was supported by NIH grants RO1 CA12306708 (to N.S.C.) and RO1 CA157996 to (R.J.D.). Author contributions: N.S.C. wrote the abstract, bioenergetics, redox, and targeting metabolism for cancer therapy sections. R.J.D. wrote the introduction, biosynthesis, technology, Box 1, and conclusion sections. N.S.C. and R.J.D. both wrote the metabolic reprogramming and oncometabolites sections. Competing interests: R.J.D. is on the Advisory Boards of Agios Pharmaceuticals and Peloton Therapeutics. N.S.C. declares no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/29%3A_Integration_of_Mammalian_Metabolism_-_Capstone_Volume_II/29.09%3A_Immunometabolism-__Cellular_Metabolism_Turns_Immune_Regulator.txt	princeton-nlp/TextbookChapters	and . Immunometabolism: Cellular Metabolism Turns Immune Regulator. Journal of Biological Chemistry. MINIREVIEWS\| VOLUME 291, ISSUE 1, P1-10, JANUARY 01, 2016. https://www.jbc.org/article/S0021-92...233-5/fulltext Abstract Immune cells are highly dynamic in terms of their growth, proliferation, and effector functions as they respond to immunological challenges. Different immune cells can adopt distinct metabolic configurations that allow the cell to balance its requirements for energy, molecular biosynthesis, and longevity. However, in addition to facilitating immune cell responses, it is now becoming clear that cellular metabolism has direct roles in regulating immune cell function. This review article describes the distinct metabolic signatures of key immune cells, explains how these metabolic setups facilitate immune function, and discusses the emerging evidence that intracellular metabolism has an integral role in controlling immune responses. Metabolic Challenges Facing Immune Cells During the course of an immune response, immune cells can traverse multiple tissues containing diverse conditions of nutrient and oxygen availability. Additionally, in response to activation, immune cells often dramatically change their functional activities; a lymphocyte transforms from a relatively inert cell to a cell engaging in robust growth and proliferation, often producing large amounts of effector molecules such as cytokines. These microenvironmental and functional alterations represent significant metabolic stresses that are efficiently managed by immune cells due their ability to dynamically reprogram their cellular metabolism. Inflammatory Microenvironments Most normal tissue is well vascularized and replete with nutrients and oxygen. However, during an immune response, conditions in the local immune microenvironment can often be significantly less accommodating due to competition for nutrients. For example, tumor cells have a prodigious appetite for glucose and other nutrients. As a result, the microenvironment within solid tumors can become depleted of glucose, resulting in decreased rates of glycolysis in tumor infiltrating lymphocytes (, , ). Bacterial infections can also compete for nutrients with immune cells. Infection with Staphylococcus aureus, a common human pathogen, can result in localized tissue hypoxia due to elevated levels of oxygen consumption by the invading bacteria. As glucose is a key fuel for this bacteria, the levels of glucose available to immune cells will also be reduced (). Viral infection can also result in a decrease in the amount of glucose that is available to infiltrating immune cells; viruses can reprogram infected cells to up-regulate glucose uptake and metabolism to facilitate viral replication (, , ). Additionally, various cells at sites of inflammation can release enzymes that consume nutrients in the local microenvironment, including arginase and indoleamine-2,3-dioxygenase, which deplete arginine and tryptophan, respectively (). Inflammatory sites can also become hypoxic due to the pronounced influx of inflammatory cells such as neutrophils and monocytes (). Dynamic Changes in Cellular Function Immune activation is accompanied by substantial changes in cellular activities, such as those accompanying T cell activation. Naïve T cells are long-lived, relatively inert, exhibit low levels of cellular biosynthesis, and primarily require ATP to meet cellular demands (Fig. 1A). Following activation, T cells undergo substantial changes in function and engage in robust cellular growth and rapid cellular proliferation (). Essential in supporting these cellular activities is the provision of sufficient biomolecules (amino acids, nucleotides, lipids) for the biosynthesis of new cellular components. Therefore, in activated T cells, the objectives of cellular metabolism have shifted from primarily generating ATP to the generation of sufficient ATP plus large amounts of biomolecules for the generation of biomass (). Therefore, immune cells adapt their cellular metabolism to accommodate altered functional outputs. FIGURE 1.Configuring metabolism to match immune cell function. A, ATP is the key molecule that provides energy for cellular processes. Maintaining cellular ATP levels is essential for bioenergetic homeostasis and cell survival. Glucose, a key fuel source for mammalian cells, can be metabolized via two integrated metabolic pathways, glycolysis and OxPhos, that efficiently convert this simple sugar glucose into ATP. Glycolysis converts glucose to pyruvate through a series of enzymatic steps that occur in the cytosol, generating two molecules of ATP. Following its transportation into the mitochondria, pyruvate is further metabolized to CO2 by the Krebs cycle, which drives OxPhos and the translocation of protons across the mitochondrial inner membrane. The resulting proton gradient drives the enzyme ATP synthase, converting ADP to ATP, generating up to 34 ATP per molecule of glucose. In addition to the breakdown of glucose via glycolysis, cells have the ability to metabolize alternative substrates, such as lipids and glutamine, which feed into the Krebs cycle and drive OxPhos. Fatty acid β-oxidation and glutaminolysis replenish the Krebs cycle intermediates acetyl-CoA and α-ketoglutarate, respectively, thereby fueling OxPhos and the efficient generation of cellular ATP. B, aerobic glycolysis supports biosynthetic processes of the cell as it allows the uptake of larger amounts of glucose and the maintenance of elevated glycolytic flux. Glycolytic intermediates are then diverted into various pathways for the synthesis of biomolecules that support biosynthetic processes. For instance, glucose 6-phosphate (G6P), generated by the first step in glycolysis, can feed into the pentose phosphate pathway (PPP) to support nucleotide synthesis. This pathway also generates NADPH, a cofactor that is essential for various biosynthetic processes including lipid synthesis. Glucose can also be converted into cytoplasmic acetyl-CoA via citrate in the Krebs cycle for the production of cholesterol and fatty acids for lipid synthesis. Other glycolytic intermediates can also be converted into biomolecules used for protein and lipid synthesis. During aerobic glycolysis, a significant proportion of pyruvate is also converted to lactate and secreted from the cell. Although aerobic glycolysis is an inefficient way to generate ATP (generating only two ATP molecules per glucose) due to the high rates of flux through the pathway, the rate of ATP production can be sufficient to maintain energy homeostasis even when mitochondrial ATP synthesis is impaired. Alternative fuels including glutamine feed into the Krebs cycle and can also supply biomolecules for biosynthetic processes under certain conditions. Configuring Metabolism for Biosynthesis, Inflammation, and Longevity Aerobic Glycolysis for Cellular Biosynthesis A common feature of pro-inflammatory immune cells is that they adopt a distinct metabolic signature termed “aerobic glycolysis” to support cellular biosynthetic processes: that is, glucose metabolized to lactate in the presence of abundant oxygen (Fig. 1B). Aerobic glycolysis is adopted by cells engaging in robust growth and proliferation because it provides the biosynthetic precursors that are essential for the synthesis of nucleotides, amino acids, and lipids (). Many intermediates of the glycolytic pathway act as a source of carbon that feeds into a range of biosynthetic pathways (Fig. 1B). Therefore, for cells engaged in aerobic glycolysis, the function of glucose is not just as a fuel to generate energy but also as a source of carbon that can be used for biosynthetic purposes (). Hence, aerobic glycolysis provides immune cells with the components needed to facilitate proliferation and the synthesis of inflammatory molecules. Metabolic reprogramming to aerobic glycolysis has advantages beyond enhanced biosynthetic capacity. This metabolic signature allows cells to adapt and survive as they encounter metabolically restrictive conditions, such as hypoxia. Although hypoxia prevents efficient ATP synthesis through OxPhos (Fig 2), high rates of glycolysis can generate enough ATP to maintain energy homeostasis. Glycolytic reprogramming involves increased expression of glucose transporters, especially Glut1, that facilitates elevated glucose uptake and enables immune cells to compete for glucose in nutrient restrictive environments (). Immune cells also have a degree of metabolic plasticity in response to limiting glucose availability. For instance, when glucose levels are low, effector T cells have the ability to adapt and increase glutamine uptake and glutaminolysis to support cellular metabolism (). Aerobic Glycolysis in Activated Lymphocytes Upon stimulation through antigen or cytokine receptors, lymphocytes increase the rates of both glycolysis and OxPhos (Fig. 2) (). Although glucose is an essential fuel during T cell activation, glutamine is also important, and effector T cell differentiation is impaired when the supply of glutamine is disrupted (, , ). T cells that differentiate into effector subsets maintain aerobic glycolysis in response to various cytokines (). In contrast, FoxP3+ regulatory T cells (Tregs) switch to low levels of glycolysis and preferentially use oxidative metabolism (). However, another type of regulatory T cell, FoxP3 regulatory T cells (Tr1), maintains elevated glycolysis similar to effector T cells (). Although many of the functions of Tr1 cells overlap with those of Tregs, others are unique to Tr1 cells including granzyme/perforin-mediated cytolysis of target cells. Therefore, perhaps the distinct metabolic characteristics of these regulatory cells reflect the different mechanisms through which they regulate T cell responses. Similarly, B lymphocytes and NK cells also increase rates of glycolysis and OxPhos in response to various stimuli (,, ,). However, as metabolic analyses of B lymphocytes have all been performed using in vitro stimulated splenic B cells, the metabolic profile of distinct B cell subsets is currently unknown. Similarly, the metabolic signatures of distinct NK subsets, or indeed other innate lymphoid cells, also remain to be characterized. FIGURE 2.Distinct metabolic configurations of different immune cell subsets. Blue panels represent cells with oxidative metabolism, and red panels represent cells with glycolytic metabolism. A, naïve T cells use glucose and glutamine and OxPhos. B, effector lymphocytes and Tr1 regulatory T cells have high rates of both glycolysis and OxPhos, metabolize glucose to lactate, and use metabolic intermediates to support biosynthetic processes. Nuc, nucleotides; FA, fatty acids; AA, amino acids; Lac, Lactate. C, memory T cells use glucose to generate mitochondrial citrate, which is exported into the cytosol to support lipid synthesis. These de novo synthesized fatty acids are used with imported glycerol to generate and store TAGs. OxPhos is fueled by acetyl-CoA generated following β-oxidation of these TAGs. FAO, fatty acid oxidation; FAS, fatty acid synthesis. D, FoxP3+ regulatory T cells use exogenously derived fatty acids metabolized by β-oxidation to support OxPhos. E, M1 macrophages and mature DC engage aerobic glycolysis for ATP synthesis and to support biosynthesis while also inactivating OxPhos. F, M2 macrophage metabolism is characterized by fatty acid β-oxidation and OxPhos. β-Oxidation is fueled by lipids that are scavenged from the external microenvironment and also by lipids generated by de novo fatty acid synthesis. G, neutrophils are highly glycolytic with few functional mitochondrial and very low rates of OxPhos. Although the exact molecular mechanisms controlling glycolytic metabolism are not universal for all lymphocyte subsets, it is clear that mammalian target of rapamycin (mTOR) has a fundamental role (, ). mTOR complex 1 (mTORC1) activity is essential for the initial induction of glycolysis in T cells and is also required to maintain aerobic glycolysis in effector T cells subsets (, , ). The data also suggest that mTORC1 has an important role for cytokine-induced glycolysis in NK cells (). A number of transcription factors are involved in glycolytic reprogramming of T cells including both hypoxia-inducible factor (HIF1α) and c-Myc (, , ). In B cells, c-Myc but not HIF1α is important for the glycolytic response (). HIF1α and c-Myc directly bind the promoters of an array of genes, notably those of glycolytic enzymes and glucose transporters. Aerobic Glycolysis in Myeloid Cells Unlike lymphocytes, mature myeloid cells tend to be non-proliferative and so have substantially different metabolic requirements. Activated M1 macrophages, dendritic cells (DC), and granulocytes are all highly glycolytic with little or no flux through OxPhos (,, , , ,). In activated M1 macrophages and DC, OxPhos is inactivated following inducible NOS-dependent nitric oxide production, which directly inhibits oxidative phosphorylation (, ). In these cells, the Krebs cycle is no longer cycling, which allows the repurposing of Krebs cycle enzymes to generate molecules that are important for proinflammatory functions (, ). M1 macrophages generate high levels of the Krebs cycle metabolite succinate, which can lead to increased HIF1α activity and sustained IL1β production (). Levels of citrate are also elevated and are used to generate the antimicrobial metabolite itaconic acid that inhibits the growth of bacteria such as Salmonella enterica and Mycobacterium tuberculosis (, ). The metabolic changes following DC activation occur in two phases and result in a metabolic switch from fatty acid β-oxidation and OxPhos to glycolysis (). An initial increase in glycolysis occurs within minutes of DC activation to support de novo lipid biosynthesis, facilitating the expansion of endoplasmic reticulum and Golgi apparatus and increasing the biosynthetic capacity that is essential for mature DC function (). Over the course of 18 h, activated DC sustain elevated glycolysis and inactivate OxPhos (). This metabolic shift is important in regulating DC-induced T cell responses, in part due to the fact that it impacts upon DC lifespan and thus the duration over which DC can activate T cells (, ). The metabolism of granulocytes is best characterized for neutrophils, which rely almost entirely on glycolysis and exhibit very low levels of OxPhos (, ,, ). Neutrophil effector functions, including the formation of neutrophil extracellular traps, require mTORC1/HIF1α signaling and glucose metabolism (, , , ,). Although the metabolism of other granulocytes such as basophils and eosinophils remains poorly characterized, there is some evidence that these cells are also glycolytic and rely upon metabolic regulators such as HIF1α to maintain glycolysis and normal function (). For instance, HIF1α accumulation upon basophil activation was shown to be required for VEGF and IL4 production (). Oxidative Cellular Metabolism in Naïve Lymphocytes and Memory T Cells As previously mentioned, naïve lymphocytes are relatively inert cells with limited biosynthetic demands, and so ATP alone is relatively sufficient to sustain these cells. Given that these cells reside in well oxygenated tissues, oxidative metabolism is a consistent and efficient way to meet cellular metabolic demands. Memory cells generated during the course of an immune response share many of the same characteristics of naïve lymphocytes; they are long-lived, relatively inert cells with limited biosynthetic demands. As nothing is known regarding the metabolism of memory B cells, only memory T cells will be considered here. The key distinction between naïve and memory T cells is the rapid recall responses characteristic of memory T cells when compared with primary T cell responses. Although both naïve and memory T cells adopt oxidative metabolism, there are key differences in the metabolic configurations of these cells that contribute to rapid memory T cell recall responses. Memory T cells predominantly use fatty acid β-oxidation to generate acetyl-CoA to fuel OxPhos () (Fig. 2). β-Oxidation is an efficient method for generating ATP with each fatty acid molecule generating significantly more ATP (about 106 ATP/molecule of palmitate) when compared with one molecule of glucose (about 36 ATP/molecule of glucose). Indeed, fatty acid oxidation is essential for rapid memory T cell responses (). Interestingly, these fatty acids are not taken up from the surrounding microenvironment, but rather memory T cells use glucose and glycolysis to generate citrate for de novo fatty acid synthesis and the generation and storage of triacylglycerides (TAGs) (, ). These endogenously derived TAGs are then broken down by β-oxidation in the mitochondria to generate acetyl-CoA to fuel OxPhos (). From a bioenergetics standpoint, this would seem like an inefficient mechanism to fuel OxPhos as fatty acid synthesis utilizes both ATP and NADPH. Nonetheless, this seemingly futile cycle of fatty acid synthesis and fatty acid oxidation is important for memory T cell survival (, ). This approach may be taken by memory T cells, for which long term survival is of utmost importance, as glucose levels are stringently controlled in the blood, making glucose a more dependable fuel source than fatty acids, whose levels can vary in different tissues. Another advantage of this cycle of fatty acid synthesis and oxidation may be that it allows the cell to concurrently engage both glycolysis and OxPhos, thus maintaining the machinery required for rapid induction of metabolic flux through these pathways upon antigen recognition and so facilitating rapid functional responses. Indeed, memory T cells can induce rates of glycolysis much more rapidly and robustly than naïve T cells (, ). Oxidative Cellular Metabolism in Cells with Significant Biosynthetic Output FoxP3+ Tregs also primarily engage in oxidative metabolism, but in contrast to naïve lymphocytes and memory T cells, FoxP3+ Tregs are not inert cells and are in fact producing relatively large quantities of biomolecules (, ). Tregs make immunosuppressive cytokines IL10 and TGFβ and can also engage in cellular proliferation in response to IL2. In this respect, M2 macrophages are similar to FoxP3+ Tregs; M2 macrophages engage in oxidative metabolism and yet have significant biosynthetic outputs. M2 macrophages have roles in tissue repair and secrete anti-inflammatory cytokines, growth factors, and factors involved in tissue remodeling (). Tregs and M2 macrophages oxidize both glucose and fatty acids in the mitochondria to sustain OxPhos (, ,, ,). In contrast to memory T cells, Tregs fuel β-oxidation and the Krebs cycle using exogenously derived fatty acids. Meanwhile, in M2 macrophages, there is evidence that both exogenously derived lipids scavenged from the microenvironment and de novo synthesized lipids fuel β-oxidation and OxPhos (). It is likely that Tregs and M2 macrophages use glutamine metabolites to sustain cellular biosynthetic processes (Fig. 1) (). Indeed, M2 macrophages have increased glutamine metabolism when compared with M1 macrophages (). Additionally, given that M2 macrophages are professional scavengers of apoptotic debris, it is tempting to speculate that M2 macrophages sustain cellular biosynthesis using biomolecules scavenged from the surrounding microenvironment (, ). Oxidative Metabolism Supports Immune Cell Longevity Controlling the longevity of immune cells is an important aspect of a healthy immune system. For example, a long lifespan (years) is essential for naïve and memory T cells to maintain functional primary and recall T cell responses. In contrast, it is crucial that upon resolution of a viral infection, the large population of CTL undergoes apoptosis as these effector T cells have the potential to cause significant immunopathology (). Therefore, CTL have a short lifespan of days to weeks. Similarly, differences in lifespan are apparent in different subsets of macrophages. M1 macrophages are short-lived and are a key component of the innate immune system that forms the first line of defense occurring within hours to days of an immunological challenge. In contrast, M2 macrophages are longer-lived as they have important roles within the resolution phase and in tissue repair and remodeling. Strikingly, the cellular metabolic signature of an immune cell corresponds to the longevity of the cell; aerobic glycolysis is characteristic of short-lived immune cells, whereas oxidative metabolism is characteristic of long-lived cells (Fig. 2). It is perhaps unsurprising that OxPhos is important for longevity in immune cells given the importance of mitochondrial membrane potential in controlling the induction of apoptosis. Certainly, in activated DC, preserving OxPhos results in an increased cellular lifespan (). Moreover, in macrophages, switching cellular metabolism from glycolysis to oxidative metabolism promotes a shift from short-lived M1 macrophages to longer-lived M2 macrophages (). In addition, manipulating glycolytic versus oxidative metabolism impacts upon the formation of long-lived memory T cells; inhibiting glycolysis promotes memory T cell formation, whereas inhibiting fatty acid oxidation-dependent OxPhos represses memory T cell formation (, ). These reports are consistent with a number of other studies that also support the notion that promoting oxidative phosphorylation enhances cell survival and lifespan (, ,). On the other hand, there are also numerous reports on a variety of cell types showing that manipulating glycolytic metabolism has profound impacts upon cellular viability (,, , ,). Growth factors that promote elevated levels of cellular glycolysis also have the consequence of making that cell highly dependent on continued growth factor signaling and glycolysis for survival (). This provides an elegant mechanism for terminating effector T cell responses. For instance, glycolytic metabolism in CD8+ CTL is sustained by IL2, and upon IL2 withdrawal, as will occur upon resolution of a viral infection, glycolytic metabolism is rapidly lost and the CTL will die (, ). Metabolic Control of Immune Cell Function Metabolic Enzymes or Regulators Controlling Immune Cell Function Cellular metabolism is crucial for facilitating immune cell functions, but in addition, there is emerging evidence that metabolic enzymes and regulators can also have a direct role in controlling immune cell functions. For instance, in CD4 T cells, GAPDH has been described to bind to the 3′-UTR of IFNγ and IL2 mRNA and inhibit translation (). This function of GAPDH is perhaps unsurprising due to the numerous reports describing RNA binding activities for GAPDH over the past two decades (, ). Indeed, in myeloid cells, GAPDH is a component of the IFNγ-activated inhibitor of translation (GAIT) complex that binds defined 3′-UTR elements within a family of inflammatory mRNAs and suppresses their translation (). Importantly, GAPDH functions in glycolysis and mRNA binding are likely to be mutually exclusive so that in glycolytic cells, GAPDH is preferentially engaged in glycolysis, and thus the translation of IFNγ and IL2 mRNA is unconstrained. This mechanism provides a direct link between rates of glycolysis and the expression of important immunological effector molecules. Intriguingly, it appears that many other metabolic enzymes can bind to mRNA molecules including numerous glycolytic enzymes, Krebs cycle enzymes, and enzymes involved in other metabolic pathways (). Although the specific mRNA transcripts that these metabolic enzymes bind to still have to be identified, this study highlights the abundant potential for cellular metabolism to directly impact upon cellular functions. Various metabolic regulators that evolved to control cellular metabolic pathways have since acquired roles in directly controlling immune cell function. The glycolytic regulator HIF1α also promotes the expression of IL1β in M1 macrophages and programmed death ligand-1 (PD-L1), a ligand for the immune checkpoint receptor PD-1, on various myeloid cells (, ). The aryl-hydrocarbon receptor (AhR), which together with HIF1α controls glycolytic metabolism in Tr1 regulatory T cells, also directly regulates T cell responses. AhR promotes Th17 differentiation, while inhibiting Treg differentiation, and is required for the production of the Th17 cytokines IL17 and IL22 (, , ). Additionally, AhR is important for Tr1 regulatory T cell differentiation, directly promoting the expression of IL10 and IL21 (, ). The transcription factor sterol regulatory element-binding protein (Srebp), a central regulator fatty acid and cholesterol synthesis, has dual roles in controlling T cell metabolism and directly controlling genes required for immune function. CD8+ T cells lacking Srebp activity fail to undergo metabolic reprogramming and blastogenesis and do not mount a functional T cell response (). In CD4+ T cells, the Srebp1c isoform is involved in Th17 differentiation and directly binds to the IL17 promotor to inhibit AhR-induced IL17 expression (). Moreover, the Srebp1a isoform is required for pro-inflammatory functions in myeloid cells, including IL1β production, as it promotes the expression of a key component of the inflammasome, Nlrp1 (). Therefore, there is growing evidence that multiple important regulators of cellular metabolism have additional functions in directly controlling immune responses. Metabolites Controlling Immune Cell Function Distinct metabolic configurations will result in different levels of metabolites that can directly impact upon cellular function. It has recently been shown that the glycolytic intermediate phosphoenolpyruvate is important in sustaining T cell receptor (TCR) signaling and T cell effector functions. Phosphoenolpyruvate inhibits Ca2+ re-uptake into the endoplasmic reticulum, thus sustaining nuclear factor of activated T-cells (NFAT) signaling (). Mitochondrial reactive oxygen species generated as a side product of OxPhos are also important for optimal TCR signal transduction. T cells that cannot produce mitochondrial reactive oxygen species fail to activate nuclear NFAT, produce IL2, or engage in proliferative expansion (). In M1 macrophages, the levels of Krebs cycle metabolites are substantially altered, leading to dramatically elevated levels of succinate, the stabilization of HIF1α, and prolonged production of IL1β (, ). Succinate can stabilize HIF1α by inhibiting the α-ketoglutarate-dependent prolyl-hydroxylases responsible for tagging HIF1α for proteasomal degradation (, , ). Indeed, succinate can inhibit other α-ketoglutarate-dependent enzymes that can impact upon immune cells due their roles in controlling cellular epigenetics, namely TET2 DNA hydroxylates and Jumonji C (JmjC) domain-containing histone demethylases (discussed further below) (, ). Succinate can act as a signaling molecule that acts through the receptor SUCNR1 and can also be used as a substrate for the post-translational modification of proteins (that is, succinylation) (). Succinate acting through SUCNR1 impacts upon DC functions and also induces DC chemotaxis to enhance DC-induced T cell responses (). Numerous metabolic enzymes are succinylated on lysine residues, but at present, it is not clear whether this modification impacts upon the regulation of immune responses (). Citrate levels are also elevated in M1 macrophages, and this metabolite is important for the production of various proinflammatory molecules: nitric oxide, reactive oxygen species, and prostaglandins (, ). Cellular metabolites are also important substrates for various enzymes involved in the epigenetic control of gene expression via covalent modification of DNA and histones. Given that the distinct metabolic configurations that characterize immune cells result in different levels of these cellular metabolites, it follows that the epigenetic control of gene expression will differ in parallel with differences in metabolism. For example, TET family enzymes, which oxidize methylcytosine, leading to DNA demethylation, and JmjC domain-containing histone demethylases both require α-ketoglutarate as a substrate and are both inhibited by succinate (Fig. 3). Indeed, TET2 has recently been shown to regulate the expression of IFNγ, IL17a, and IL10 in Th1 and Th17 cells (). Jmjd3 has been shown to be of particular importance in controlling gene expression in LPS-stimulated macrophages (). Acetylation of histones is another post-translational modification that impacts on DNA structure and gene expression. Acetylation of histones by histone acetyl transferases (HATs) requires acetyl-CoA, which is supplied via the export of mitochondrial citrate (Fig. 3). Indeed, there is evidence in yeast that the concentration of acetyl-CoA is important for histone acetylation (). Histone acetylation levels are also controlled by the rate of deacetylation. The activity of sirtuin histone deacetylases is linked to cellular metabolism as these deacetylases are sensitive to the ratio of oxidized NAD+ to reduced NADH, which is affected by the balance of glycolysis and OxPhos (). Oxidized NAD+ is an essential substrate for sirtuins, whereas reduced NADH acts to inhibit sirtuin activity (Fig. 3) (). In fact, sirtuins can also deacetylate targets other than histones, which are important in immune regulation. For example, Sirt1 deacetylates FoxP3 to inhibit Treg responses and RORγt to promote Th17 responses (,, ,). Additionally, sirtuins can also have a negative impact upon inflammatory responses, in part through inhibition of NFκB activity (, ). Although there are numerous studies suggesting that cellular metabolism impacts upon epigenetic programming of immune cells to affect immune cell fate and function, the best evidence of this comes from a study of trained immunity in macrophages. Cheng et al. () elegantly demonstrated that mTORC1/HIF1α-stimulated glycolysis is required for changes in the epigenome of human or murine myeloid cells that provides enhanced nonspecific protection from secondary infections. Therefore, it is clear that metabolites can impact directly on immune cell function, and it is likely that further examples of this will be revealed as the field of immunometabolism progresses. FIGURE 3.Links between cellular metabolism and epigenetic modifications. Histone deacetylation by sirtuin (SIRT) family members requires NAD+ as a substrate, and the activity of these enzymes is inhibited by NADH. The balance of oxidized NAD+ and reduced NADH is affected by levels of glycolysis and OxPhos. Methylation of DNA and histones is controlled by the rates of methylation and demethylation. The activities of JmjC domain-containing histone demethylases and the TET2 hydroxylase lead to histone and DNA demethylation, respectively, and can be regulated by Krebs cycle intermediates α-ketoglutarate (α-KG) and succinate (Succ). α-Ketoglutarate is a substrate for these enzymes, and succinate acts as an inhibitor. NAM, nicotinamide; HDM, histone demethylase; SAM, S-adenosylmethionine. Immune Metabolism Relays External Signals to Regulate Immune Cell Function The data now support an important role for cellular metabolism in controlling the function of immune cells. Given that metabolic regulators and pathways are acutely sensitive to external levels of nutrients, oxygen, and growth factors, cellular metabolism represents a means to relay information from the local microenvironment to modulate immune cell function accordingly. Nutrients such as glucose, glutamine, and fatty acids that directly supply metabolic pathways also regulate the activity of important regulators of immune metabolism and function including mTORC1, HIF1α, and Srebp. Other nutrients are important for providing the substrates for enzymes that impact upon immune cell function. For example, methionine, which is an essential amino acid and so must be imported into the cell, is used to generate S-adenosylmethionine for epigenetic methylation of DNA and histones. Although most studies have focused on how activating immune receptors affect cellular metabolism, it is now becoming apparent that ligation of inhibitory receptors also alters metabolic pathways. Recent research has demonstrated that ligation of the inhibitory receptors PD-1 and CTLA-4 expressed on human CD4 T cells has pronounced effects on cellular metabolism, inhibiting aerobic glycolysis, and in the case of PD-1, promoting fatty acid oxidation (). These data suggest that the inhibitory actions of these receptors may be mediated, at least in part, due to changes in cellular metabolism. Final Comments The emerging data now argue that metabolism has duel roles in immune cells to facilitate requirements for energy and biosynthesis and to directly regulate immune cell functions. There are likely to be numerous opportunities for novel therapeutic strategies that modulate this metabolic regulatory axis.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/29%3A_Integration_of_Mammalian_Metabolism_-_Capstone_Volume_II/29.10%3A_Ethanol_metabolism-_The_good_the_bad_and_the_ugly.txt	princeton-nlp/TextbookChapters	David F.WilsonFranz M.Matschinsky. Medical Hypotheses, Volume 140, July 2020, 109638 https://doi.org/10.1016/j.mehy.2020.109638. Under a Creative Commons license Abstract Throughout the world, ethanol is both an important commercial commodity and a source of major medical and social problems. Ethanol readily passes through biological membranes and distributes throughout the body. It is oxidized, first to acetaldehyde and then to acetate, and finally by the citric acid cycle in virtually all tissues. The oxidation of ethanol is irreversible and unregulated, making the rate dependent only on local concentration and enzyme activity. This unregulated input of reducing equivalents increases reduction of both cytoplasmic and intramitochondrial NAD and, through the latter, cellular energy state {[ATP]/([ADP][Pi])}. In brain, this increase in energy state stimulates dopaminergic neural activity signalling reward and a sense of well being, while suppressing glutamatergic neural activity signalling anxiety and unease. These positive responses to ethanol ingestion are important to social alcohol consumption. Importantly, decreased free [AMP] decreases AMP-dependent protein kinase (AMPK) activity, an important regulator of cellular energy metabolism. Oxidation of substrates used for energy metabolism in the absence of ethanol is down regulated to accommodate the input from ethanol. In liver, chronic ethanol metabolism results in fatty liver and general metabolic dysfunction. In brain, transport of other oxidizable metabolites through the blood-brain barrier and the enzymes for their oxidation are both down regulated. For exposures of short duration, ethanol induced regulatory changes are rapid and reversible, recovering completely when the concentrations of ethanol and acetate fall again. Longer periods of ethanol exposure and associated chronic suppression of AMPK activity activates regulatory mechanisms, including gene expression, that operate over longer time scales, both in onset and reversal. If chronic alcohol consumption is abruptly ended, metabolism is no longer able to respond rapidly enough to compensate. Glutamatergic neural activity adapts to chronic dysregulation of glutamate metabolism and suppression of glutamatergic neural activity by increasing excitatory and decreasing inhibitory amino acid receptors. A point is reached (ethanol dependence) where withdrawal of ethanol results in significant metabolic energy depletion in neurons and other brain cells as well as hyperexcitation of the glutamatergic system. The extent and regional specificity of energy depletion in the brain, combined with hyperactivity of the glutamatergic neuronal system, largely determines the severity of withdrawal symptoms. Overview: ethanol and human metabolism In nature, ethanol is rarely found in foodstuffs at levels where consumption can raise blood concentrations to the levels reached by humans during social drinking. When it does occur, such as when fruit ferments either on the tree or after falling to the ground, exposure to that food source is brief. As a result, the amount of ethanol consumed is a tiny fraction of that consumed by even modest social drinkers. Ethanol is a “rogue” nutrient for which there was no evolutionary pressure for its regulation. Ethanol dependence became a problem only after humans developed methods for preservation of food, one of which was fermentation. In order to provide a framework for understanding the importance of ethanol metabolism and its effect on metabolic homeostasis in humans, Table 1 summarizes some aspects of ethanol consumption important to understanding its impact. Dettling et al. [25] measured the rate of elimination of ethanol by humans following ingestion of 0.9 g/kg body weight over a period of 2 h, an amount that resulted in maximal blood alcohol levels near 0.08 g/dl (legal limit for driving in US). After consumption stopped, ethanol disappearance, measured by decrease in blood levels, occurred at a constant rate (zero order) of about 0.016 g/dl/h for men and 0.018 g/dl/h for women. Translated into kilocalories (cal), for an 80 kg man this is 73 cal/h while for 60 kg women it is 55 cal/h. A 70 kg (155 lb) person expends about 102 cal/h working on a computer, 120 cal/h sitting in meetings, 130 cal/h during desk work or sitting in class, 186 cal/h doing police work, bartending or waitressing (Table 2). Maintenance diets for average adults are 2000 cal/day for females (83 cal/h) and 2600 cal/day (108 cal/h) for males. As such, ethanol becomes responsible for more than 50% of each individual’s energy metabolism until it is eliminated. Elimination requires about 5hr after consumption stops (7h total) and throughout that time, ethanol oxidation is the largest carbon source for energy metabolism. Further increase in alcohol consumption, either acutely or chronically, extends the time before blood alcohol levels return to near zero but not the rate at which it is removed. For ethanol dependent individuals, blood alcohol levels remain significant throughout each day. This means ethanol contributes about 1700 cal/day (80 kg male) and 1300 cal/day (60 kg female), and many metabolic and nutrient deficiencies may develop [66], [69], [62], [63], [64]. Table 1. Ethanol metabolism and the human diet (social drinking). 0.9 g/kg (male) Blood max 0.084 g/dl Elimination 0.016 g/dl/h time 7 h#; 5 h* 0.84 g/kg (female) Blood max 0.082 g/dl Elimination 0.018 g/dl/h Time 6.6 h#; 4.6hr* 80 kg/male (176 lb) Total, 72 g (5drinks) 511 calories 73 calories/h 60 kg/female (132 lb) Total, 50 g (3.6 drinks) 355 calories 54 calories/h Measurements of the rate of elimination of ethanol from the body in human volunteers [25]. Ethanol ingestion followed a typical breakfast and consisted of consuming drinks of the subject’s choice. The number of drinks was calculated for 14 g of pure ethanol per drink. The amount consumed raised blood alcohol to near 0.08 mg/dl (17 mM, legal limit for driving in US) by the end of the 2 h period for consumption. After consumption stopped, ethanol disappeared from the blood at a nearly linear (zero order) rate of about 0.016 g/dl/h for men and 0.018 g/dl/h for women. The times required for blood alcohol to fall to near zero, h# and h*, were measured from beginning and end of consumption, respectively. Table 2. Metabolic energy utilization by a 70 kg (155 lb) person. Sitting (computer work) 102 calories/h Sitting in meetings 120 calories/h Desk work or sitting in class 130 calories/h Police work, bartending, waitressing 186 calories/h As noted in Table 1, for a 70 kg person ethanol contributes about 63 calories/h while the total hourly caloric expenditure is shown in the table. Assuming a drinker is awake and at activities similar to sitting in class both while and after drinking, they would be expected to expend about 130 calories/h. In that case, ethanol would provide about 50% of whole body energy metabolism until the blood alcohol fell below about 0.006 g/dl (below the KM for ADH1 of 1.4 mM). Oxidation of ethanol occurs through three enzymes, alcohol dehydrogenase (ADH), catalase, and P450 (CYP2E1), and they all produce acetaldehyde. In the present paper, focus is on the first two because they are responsible for oxidation of most of the consumed ethanol. Ethanol readily passes through most biological membranes, and the concentration in all water spaces, including the brain, approximates that in blood plasma. Thus, tissues and cells throughout the body are nearly equally exposed to ethanol and its rate of metabolism is determined by local enzyme content. The reactions of the three ethanol oxidizing systems and the KM of each for ethanol are: 1. Alcohol dehydrogenase; ADH1, KM = 1.4 mM [12]: (1)Ethanol + NAD+ → Acetaldehyde + NADH + H+ 2. Catalase; KM = 12 mM [103]: (2)Ethanol + H2O2 → Acetaldehyde + 2 H2O 3. Cytochrome P450 2E1 (CYP2E1); KM = 8–10 mM (12): (3)Ethanol + O2 + NADPH → NADP+ + acetaldehyde + acetate The equilibrium constant for the ADH reaction at pH 7.0 is approximately 10−4 [1], [6], [83]. In the cytoplasm, where the reaction occurs, the [NAD+]/[NADH] is greater than 100 and acetaldehyde concentrations remain less than 20 μM [83]. The [NAD+)/[NADH) is high enough that the ratio of the forward to reverse reactions of ADH is about 10 and the net reaction is strongly toward acetaldehyde (and NADH) formation. The reactions of catalase and CYP2E1 are irreversible under all physiological conditions. Acetaldehyde is chemically reactive and can react nonenzymatically with other cellular components to form products that are metabolically active and/or cytotoxic. The concentration of acetaldehyde is kept low through rapid removal by aldehyde dehydrogenase (ALDH) which is widely distributed in tissues [12], [127], [128]. ALDH oxidizes acetaldehyde to acetate, passing the reducing equivalents to NAD+:(4)Acetaldehyde + NAD+ → Acetate + NADH + H+ ALDH2 is localized in the mitochondrial matrix, has a high affinity for acetaldehyde (KM = 1.3 μM in brain cortex [40], and high specific activity. The equilibrium constant for this reaction at pH 7.0 is approximately 4 × 109, and it is irreversible under physiological conditions [1]. As a result of ALDH activity, systemic levels of acetaldehyde are kept low [40]. Acetaldehyde is a chemically reactive compound with significant toxicity and, for individuals who express an inactive form of ALDH2, ethanol consumption can have side effects ranging from unpleasant sensations to serious illness [12], [60]. Neither ethanol oxidation (ADH, catalase, CYP2E1) nor acetaldehyde oxidation (ALDH) is subject to significant regulation and increasing alcohol concentration reduces both cytoplasmic (by ADH) and mitochondrial (by ALDH2) NAD pools. Because ethanol oxidation is not regulated, local (individual cell) rates of oxidation are cell specific, determined by the activity and degree of saturation of the responsible enzyme(s) in each cell. Acetate, the major product of ethanol oxidation, has roles both in energy metabolism and in regulation of metabolism. Acetate is converted to acetylCoA by acetylCoA synthetase:(5)acetate + CoASH + ATP → acetylCoA + AMP + pyrophosphate In mitochondria, acetylCoA primarily enters the citric acid cycle and is oxidized, but at the cellular level is also used to acetylate proteins, altering the activity of individual enzymes and, through histone acetylation, gene expression [74]. The regulatory role of protein acetylation, although important, is outside the scope of this paper and will not be further discussed. In summary: Ethanol readily distributes throughout the body and can be a major fraction of caloric utilization. Oxidation of ethanol and its products, acetaldehyde and acetate, is irreversible and unregulated. This “pushes” energy metabolism and can increase reduction of both cytoplasmic and mitochondrial NAD, thereby increasing cellular energy state. The rate of ethanol oxidation, and its associated metabolic disturbance, is dependent on ethanol concentration and local (cellular) enzyme content. Ethanol oxidation in liver and its metabolic consequences Most of consumed alcohol is oxidized in liver by ADH1 to acetate [52], [67]. In the process, there are profound alterations in hepatic metabolism (Fig. 1, including: excessive reduction of cytoplasmic and mitochondrial NAD pools, inhibition of gluconeogenesis and fatty acid oxidation, increased [ATP]/([ADP]f[Pi]), and decrease in [AMP]f. The subscript f is used to emphasis that the concentrations of ADP and AMP relevant to metabolic regulation are free concentrations, not total amounts in the cell. Lundquist and coworkers [67] reported that, in healthy humans infused iv with ethanol at rates sufficient to result in blood concentrations near 26 mM (0.12 g/dL) oxidation of ethanol to acetate accounted for most of hepatic oxygen consumption. Although some of the acetate produced in liver was further oxidized through the citric acid cycle, most (>75%) was released into the blood. The rate of NADH production from ethanol oxidation to acetate is high enough that flux through the citric acid cycle (CAC), and therefore CO2 production by the liver, is substantially decreased [52]. The lactate/pyruvate ratio is increased, indicating reduction of cytosolic NAD, as is the β-hydroxybutyrate/acetoacetate ratio, indicating increased reduction of intramitochondrial NAD [29], [52], [67]. Increase in intramitochondrial NAD reduction with little or no change in oxygen concentration or the rate of oxygen consumption [97] is consistent with increase in cellular energy state {[ATP]/([ADP]f[Pi])} [115], [118]. In liver, increase in energy state suppresses the activity of pyruvate dehydrogenase (increased acetylCoA, [NADH]/[NAD+], and [ATP]/[ADP]f), activates pyruvate carboxylase (high acetylCoA), decreases [AMP]f, lowering AMP-dependent protein kinase (AMPK) activity [61], [98], [105], [124]. Decreased AMPK activity increases acetylCoA carboxylase activity [19], [30], [38], [39], [42], [124] and the concentration of malonylCoA [19]. MalonylCoA inhibits carnitine palmitoyl transferase 1 [30], [73] suppressing uptake and oxidation of long chain fatty acids by mitochondria. Fatty acid synthesis is activated and this, combined with excess fatty acids taken up by the liver, results in excess fatty acids being made into triglycerides. Interestingly, Galli and coworkers [32] reported that expression of ADH1 in HeLa cells was sufficient to result in ethanol induced fat accumulation, consistent with ethanol oxidation induced inhibition of AMPK being responsible for cellular lipid accumulation. Acetate, the primary product of ethanol oxidation by the liver, is a short (shortest) chain fatty acid and oxidation of short chain fatty acids is not subject to the regulation imposed on oxidation of long chain fatty acids. Acetate produced by the liver results in a large increase in acetate concentration in the blood, to about 1 mM [44], [78]. The rate at which acetate is taken up, and oxidized, by other tissues is dependent on concentration in the blood. In both humans and rats, if blood acetate concentrations are maintained near 1 mM or higher, this results in under consumption of long chain fatty acids and activation of fatty acid synthesis [19], [30], [62], [64], [95], [122]. Since ethanol and acetate are only two carbons long, their products cannot be used for net synthesis of glucose or for anaplerotic support of the citric acid cycle or net glucose synthesis. The lactate/pyruvate ratio is increased, in large part due to decrease in pyruvate [52], and this limits the activity of pyruvate carboxylase, suppressing gluconeogenesis [29], [52], [53]. Blood glucose concentrations decrease [53], but only slightly because the set point for glucose homeostasis in only slightly decreased [72] and glucose consumption by peripheral tissue and brain is decreased. AMP-dependent protein kinase and the metabolic responses to ethanol oxidation. Essentially all cells have adenylate kinase and the activity is sufficient to maintain the reaction:(1)ATP + AMP = 2 ADPnear equilibrium. The equilibrium constant is approximately 1.0, and [ATP] is maintained nearly constant, so [AMP]f decreases as the square of the decrease in free [ADP]f. This makes [AMP]f a very sensitive measure of the energy state [51], [115], [118]. As a result, energy metabolism has evolved with [AMP]f as a core regulatory parameter in setting metabolic homeostasis. It is notable that metabolic homeostasis in eukaryotes has an [AMP]f “set point” and cellular metabolism operates over a narrow range of [AMP]f [115], [118]. Central to maintaining metabolic homeostasis is AMP-dependent protein kinase (AMPK) [30], [38], [39], [42], [49], [118]. Although [AMP]f also contributes to the regulation of many other enzymes and regulatory pathways, the importance of AMPK is shown schematically in Fig. 2A. This includes modulation not only of complex cellular functions, such as protein synthesis, autophagy, mitophagy, and gene expression, but also of individual enzymes and regulatory proteins (Fig. 2B). Most discussions of metabolic regulation involving AMPK focus on the conditions where [AMP]f increases, as occurs in exercise, hypoxia, or inhibition of oxidative phosphorylation, and AMPK activity increases. Activation of AMPK is designed to increase ATP production by increasing catabolic ATP production and inhibiting anabolic ATP consumption. Ethanol ingestion, however, decreases [AMP]f and thereby AMPK activity, suppressing catabolic metabolism and enhancing anabolic metabolism. Indeed, treatment with 5-aminoimidazole-4-carboxamide-1-β-D-ribofuranoside (AICAR), which activates AMPK, has been reported to provide significant protection from alcohol induced fatty liver in rats [105]. Because oxidation of ethanol and acetate is cell specific and largely unregulated, the metabolic consequences differ among cell types and overall metabolic integration disrupted. Ethanol/acetate metabolism and its effects on muscle Although liver and brain are the primary tissues of concern in when considering the consequences of ethanol consumption, other tissues are also affected. This includes cardiac and skeletal muscle. Although neither has significant ADH activity, they have high acetylCoA synthetase and acetylCoA carboxylase and rely heavily on fatty acid oxidation for energy metabolism. This is particularly true when only moderately active (heart) or during rest and after endurance exercise (skeletal muscle) (Fig. 3). Chen et al. [16] reported that chronic ethanol feeding in rats suppresses expression of AMPK, myocyte enhancer factor 2, and glucose transporter 4 (Glut4) in myocardium. The effects of ethanol on muscle metabolism would be expected to be indirect, arising from elevated blood acetate. This is consistent with the report by Kiviluoma [48] that addition of acetate to the perfusate of isolated rat hearts increases incorporation of fatty acids into myocardial lipids. In addition, adding acetate at mM concentrations to the perfusate of isolated rat hearts increases reduction of intramitochondrial NAD and energy state relative to glucose perfusion [48], [99]. Similar observations have been reported for rat skeletal muscle in vivo by Bertocci et al. [11]. When lactate and acetate consumption were measured in resting skeletal muscle, acetate provided most of the acetylCoA (65%) oxidized by the citric acid cycle (CAC). In contracting muscle, the fraction provided by acetate decreased, but only to 43%. Thus, in heart under moderate work load and in resting skeletal muscle, acetate taken up from the blood can be oxidized at rates sufficient to maintain energy metabolism. Acetate displaces (down regulates) oxidation of the physiologically preferred substrate, long chain fatty acids. Putman et al. [82] measured metabolites and pyruvate dehydrogenase activity in muscle during acetate infusion in humans. The reported changes included increase in acetylCoA/CoASH and acetyl-carnitine, as well as decrease in active pyruvate dehydrogenase (PDHa). Uncontrolled production of acetylCoA from acetate in mitochondria and the resultant increase in acetylCoA/CoA, if sustained, would deplete intramitochondrial CoASH. CoASH is required for citric acid cycle. Synthesis of acetyl-carnitine by carnitine acetyl transferase allows export of excess active acetyl groups to the cytoplasm but at the expense of intramitochondrial carnitine. This process appears to be self limiting due to depletion of intramitochondrial CoASH for acetylCoA synthase combined with decreased oxalacetate for citrate synthase limits input of acetylCoA to the rate of oxidation through the citric acid cycle. Evidence supports the view that, following ethanol ingestion, elevated blood acetate becomes a major energy source for resting and moderately active muscle, consistent with associated suppression of AMPK activity [16], [48], [99]. Interestingly, tissues from alcohol dependent humans show dysfunctions in protein synthesis, mitochondrial content, and morphology, consistent with chronic suppression of AMPK activity (Fig. 2A). Ethanol metabolism in brain and its metabolic consequences: A. General considerations Brain is a complex organ with several types of specialized cells, each with its own metabolic requirements and supporting complement of enzymes and metabolite transporters. Ethanol produces major metabolic disturbances in brain metabolism (shown schematically in Fig. 4) including: decreased glucose uptake and metabolism, increased monocarboxylate uptake and metabolism, stimulation of dopaminergic neural activity, suppression of glutamatergic neural activity, and disruption of glutamate metabolism. Catalase, ADH, and P450 systems for oxidizing ethanol are all present in brain, and brain actively takes up acetate from the blood. Determining the role of ADH, however, is complicated by the presence of multiple different forms of alcohol dehydrogenase as well as heterogeneous distributions among cells and tissues [5], [12], [13], [47]. Initial reports by Raskin and Sokoloff [84] of low, but significant, rates of oxidation of ethanol by ADH in subfractions of brain tissue were largely discounted because of the low activity and concerns about the method of analysis [126], [127]. The enzyme properties reported, however, were consistent with the presence of small amounts of ADH1. Initial immunohistochemical data focused on ADH3, an alcohol dehydrogenase that is widely distributed in brain but has negligible activity for ethanol [27], [126], leading some to conclude that metabolism of ethanol by ADH in brain was not significant. Later measurements using antibodies grown to purified liver ADH indicated that while the total amount of enzyme are low, it is concentrated in individual neurons of cerebral cortex, hypothalamus, infundibular stalk of pituitary, and Purkinje cells of cerebellum. This distribution correlates with known sites of ethanol toxicity (13. 47). More recently, selective antibodies for ADH1 (KM = 1.4 mM) and ADH4 (KM = 37 mM) have been used [27]. Again the total amounts were low but concentrated in a small fraction of the brain cells. Martinez et al [70] examined tissue sections from several regions of adult rat brain by in situ hybridization to detect expression of genes encoding ADH1. ADH1 mRNA was found in granular and Purkinje cell layers of cerebellum, in pyramidal and granule cells of hippocampal formation, and in some types of cells in cerebral cortex. ADH4 expression was detected in Purkinje cells, pyramidal and granule cells of the hippocampal formation, and pyramidal cells of cerebral cortex. Substantial levels of both ADH1 and ADH4 mRNAs were detected cells of the CNS epithelial and vascular tissues: leptomeninges, choroidplexus, ependymocytes of ventricle walls, and endothelium. Thus, current evidence is consistent with oxidation of ethanol by ADH capable of significantly affecting brain function in subpopulations of cells in which ADH1 and/or ADH4 are expressed. Ethanol and acetate metabolism in brain: B. Rates and contributions to total energy production Ethanol, acetaldehyde, and acetate have been shown to be oxidized in brain and the combined rates are high enough to disrupt glucose and amino acid metabolism. Wang and coworkers [111] studied the rate of ethanol oxidation in rat brain cortex in vivo by 13C MRI. Using a combination of 2-13C labeled ethanol and 1,2-13C labeled acetate, they reported significant rates of incorporation of carbon from ethanol into glutamate and glutamine. The authors calculated ethanol oxidation accounted for 12% and 20% cortical oxidative metabolism in ethanol naïve and ethanol pre-treated rats. The measurements were specifically of ethanol oxidation and did not include oxidation of acetate taken up from the blood. Measurements of acetate metabolism in rat brain in vivo using 13C MRI [21], [81], [107] show it is readily transported through the blood-brain barrier and metabolized. As acetate concentration in blood was raised to 2–3 mM, the rate of metabolism by the brain saturated. At saturation, enrichment of glutamine and glutamate at the C4 position were consistent with most of the acetylCoA entering the citric acid cycle in astrocytes arising from acetate with the remaining coming from unlabeled glucose. The models used in the interpretation assumed, however, that acetate metabolism occurred almost entirely in astrocytes with negligible contribution by neurons. This may not be appropriate [87] particularly as the neurons contain substantial levels of acetylCoA synthase. As noted earlier, when ethanol is ingested, acetate is produced in the liver and released into the blood. Blood acetate levels rise rapidly to about 1 mM or more [44], [78], and ethanol oxidation in brain is augmented by oxidation of acetate taken up from the blood. When these are combined, ethanol becomes a major source of acetylCoA for production of ATP used for ATP production in many brain cells. Recent reports include evidence that in mice ethanol consumption leads to rapid increase in histone acetylation in the brain with the acetyl moiety arising from ethanol [74]. This acetylation is reported to alter gene expression in hippocampus and to affect cellular mechanisms related to learning. The level of acetylCoA and the acetylCoA/CoASH ratio are important regulators of protein acetylation and are coupled to cellular energy state and AMPK activity. Ethanol exposure Phase 1: Acute effects of ethanol ingestion on metabolism As noted above, alcohol ingestion results in rapid changes in the metabolism of brain and other tissues. Following ingestion, oxidation of ethanol takes precedence over other metabolites used for energy metabolism and the latter are down regulated to accommodate the input from ethanol. The effects are cell and tissue specific and depend on how rapidly ethanol is oxidized and/or acetate is taken up through the monocarboxylate transporter. Liver, due to its large size, immediate exposure to the ingested alcohol from the portal circulation, and high content of ADH1 dominates systemic ethanol disposal. Effects of acute ethanol ingestion on liver include rapid increase in metabolic energy state, production and export of acetate, suppression of long chain fatty acid oxidation and synthesis, increased β-hydroxybutyrate/acetoacetate ratios, and under production of glucose by gluconeogenesis. The response of other tissues to ethanol exposure is heterogeneous. In pancreas, increased energy state due to ethanol/acetate metabolism slightly lowers the glucose homeostasis set point [116], [117], [119]. In most other tissues, input of acetylCoA from ethanol and acetate largely replaces acetylCoA from long chain fatty acids and amino acids for energy metabolism [31], [107], [110]. The oxidation of ethanol and/or acetate induces an increase in energy state and associated decrease in [AMP]f and AMPK activity [19], [30], [31], [38], [39]. Increase in blood acetate in rats to 300 µM, for example, decreased AMPK phosphorylation in hypothalmus by about 50% [31]. Decreased AMPK activity suppresses GLUT1, GLUT3, and GLUT4, the dominant glucose transporters in brain [31], [37], [75], [119]. Handa et al. [37] isolated plasma membranes and low density microsomal fractions from rat brain 4 h after their treatment with ethanol (3 g/kg body weight) and reported immunoblot measured protein decreased by 17% and 71% for GLUT1 and 54% and 21% for GLUT3, respectively. In addition, immunofluorescence imaging showed decreased GLUT1 in choroid plexus and cortical microvessels. This is consistent with reports by Muneer and coworkers [75] that chronic ethanol treatment suppresses glucose transport and GLUT1 protein expression in microvessels of the blood-brain barrier of rats and by Frost et al. [31] that it decreased GLUT1 in the plasma membrane of brain cells in primary culture. Accompanying ethanol induced decrease in glucose transporters is increase in monocarboxylate transporter [65]. Pyruvate is needed not only for anaplerotic maintenance of citric acid cycle activity but also for de novo synthesis of glutamate in astrocytes through the activity of pyruvate carboxylase [125]. Increased uptake of lactate from the blood [65] would normally provide increased pyruvate through oxidation by cytoplasmic NAD+. For cells with ADH1 and/or ADH4, however, oxidation of ethanol in the cytoplasm results in increased reduction of the cytoplasmic NAD pool and an increase in the lactate/pyruvate ratio [52]. In these and other cells, acetaldehyde and acetate oxidation in the mitochondria also increases reduction of intramitochondrial NAD, suppressing aspartate-glutamate shuttle activity. Although intracellular lactate concentrations in brain increase during ethanol oxidation, this is due to elevated systemic lactate/pyruvate and pyruvate concentrations actually decrease. Intracellular pyruvate concentrations are normally less than the KM of pyruvate carboxylase, which is near 400 μM [92], making the rate of pyruvate carboxylation strongly dependent on intracellular pyruvate concentration [52]. Ethanol induced decrease in intracellular pyruvate suppresses de novo synthesis of oxalacetate, and thereby of glutamate, in astrocytes and this disrupts glutamate homeostasis in astrocytes and glutamatergic neurons. Ethanol exposure Phase 2: Metabolic consequences of alcohol ingestion in greater amounts and over intermediate periods of time (days, weeks) Ethanol dependence arises after significant periods of nearly continuous alcohol consumption, typically months to years. This is not an all or nothing phenomenon, however, and there is a transition period during which withdrawal of alcohol results in a progressively greater sense of unease, anxiety, and irritability that can be overcome by consuming alcohol. The shortest time of exposure that generates significant ethanol dependence may be binge drinking, in which substantial amounts of ethanol are consumed over periods of one or more days. A few hours after drinking stops, ethanol levels approach zero and there is a period that often includes severe headaches and other unpleasant after effects, referred to as a hangover. The colloquial antidote for a hangover is “hair of the dog that bit you”, i.e. drinking some of the same alcoholic beverage consumed during the binge in order to alleviate the hangover. This antidote acutely relieves many of the symptoms, consistent with hangovers arising in part through deficiency in oxidizable substrates for energy metabolism in brain. The extended time and extent of alcohol exposure during a binge allows participation of slower regulatory processes, including altered rates of protein turnover and gene expression. When the levels of alcohol and acetate fall too quickly for regulation to fully compensate by switching back to the usual (non-ethanol) energy sources, the deficiency can be overcome by providing some alcohol. This temporarily relieves the deficiency in energy metabolism, and thereby the hangover, as well as providing additional time for metabolic regulation to compensate for lack of ethanol/acetate. Ethanol exposure Phase 3: Ethanol dependence resulting from heavy alcohol consumption over periods of months and years Individuals with alcohol dependence consume a substantial fraction of their daily caloric intake as ethanol, typically near 50% of the total calories [5], [25]. This large intake, spread throughout the day and sustained for weeks to months, results significant (mM) levels of ethanol being in the blood most of the time. Diets of individuals with ethanol dependence are often deficient in essential nutrients, including vitamins, minerals, unsaturated fatty acids, and amino acids. In addition, ethanol is only 2 carbons long and can not be used for de novo synthesis of glucose, glutamate and other metabolites critical to long term metabolic homeostasis. In brain, chronic suppression of the oxidation of glucose and other (non-ethanol related) metabolites results in depletion of their associated transporters and enzymes. Conversely, the capacity for metabolism of ethanol and acetate increases [43], [65], [107]. If ethanol consumption ends abruptly, ethanol and acetate levels in the body fall to near zero within a few hours. The oxidizable metabolites used to support energy metabolism in non-ethanol dependent animals have been chronically down regulated and can no longer increase sufficiently to maintain the cellular energy state. As noted above, for individuals who are ethanol dependent, ethanol normally provides 50% or more of the daily caloric intake. Glucose consumption in brain is substantially decreased by ethanol consumption [107], [108], [110], [112]. Oxidation of one mole of glucose provides slightly more ATP (38 ATP/mole) than does oxidation of two moles of ethanol. Oxidation of alcohol to acetaldehyde by ADH and of acetaldehyde to acetate by ALDH each contributes one NADH for oxidative phosphorylation (6 ATP/ethanol) and oxidation of acetate through the citric acid cycle provides an additional 3 NADH and 1 FADH2 (total of 17 ATP/ethanol, 11 ATP/acetate). Even after correction for the ATP required for acetate activation (acetylCoA synthetase), oxidation of 2 mol of ethanol provides 30 mol of ATP, about 80% of that supplied by oxidation of 1 mol of glucose. Volkow et al. [108] measured brain glucose metabolism in twenty healthy control subjects using positron emission tomography (PET) and fluorodeoxyglucose after administration of placebo or either 0.25 g/kg, or 0.5 g/kg of ethanol over a 40 min period. Both doses of ethanol significantly decreased whole-brain glucose metabolism (10% and 23% respectively). Similar decreases have been reported by other authors, i.e. 26% for 0.75 g/kg [112], 25% for approximately 0.6 g/kg iv [91]. Volkow et al. [107] reported that the response to ethanol is regionally specific; whereas 0.25 g/kg predominantly reduced glucose metabolism in cortical regions, 0.5 g/kg reduced metabolism in cortical as well as subcortical regions (i.e. cerebellum, mesencephalon, basal ganglia and thalamus). As noted earlier, suppression of brain glucose consumption is in response to combination of oxidation of ethanol within the brain and of acetate taken up from the blood [107], [108], [110], [111]. Indeed, in heavy drinkers studied during sobriety, acetate metabolism in the occipital cortex is reported to be increased [43]. Persistent reliance on acetate for energy metabolism of alcoholics during early alcohol detoxification may explain why the decreased brain glucose consumption observed in alcoholics mostly disappears within the first 2 weeks of detoxification [109]. It can be inferred that for cells in brain for which ethanol (and/or acetate) oxidation is responsible for substantial fraction of their ATP production, sudden withdrawal of this important support of energy metabolism results in cellular malfunction and thereby abnormal brain activity. In ethanol dependent mice, withdrawal symptoms correlate with decrease in blood alcohol and acetate, symptoms maximizing as the concentrations approach zero [85]. Derr and coworkers [22], [23], [24] reported that, for ethanol dependent rats, providing acetate during withdrawal significantly alleviated, and providing a mixture of butyrate, lactate, and β-hydroxybutyrate fully suppressed [22],the tremulous portion of their withdrawal symptoms. It did not affect the handling induced convulsions. This is consistent with the tremulous, but not the convulsive, part of the withdrawal symptoms arising from a deficiency in energy metabolism needed to support neuronal activity in the brain. Neurological responses to ethanol consumption: a prolog Our paper focuses on metabolism of ethanol and the impact of ethanol consumption on metabolism as a whole. This is an integral and important part of understanding how alcohol influences human biochemistry, physiology, and social behavior. In brain, we focus on effects of ethanol on metabolism of neurons and astrocytes, where changes in energy state directly influences not only all aspects of neurotransmitter function (release, receptor sensitivity, transport, and metabolism) but also induces long term alterations in the underlying cellular machinery (through gene expression, etc). It is recognized, however, that connections between cellular metabolism and neuronal function, and from neural function to behavior, are very complex. Others have made great effort to understand how ethanol alters neuronal function; receptor affinities, ion channel conductance, synaptic connections etc, but many of the particulars remain poorly understood. There is general agreement that ethanol induces substantial changes in neural function but the relative contribution of metabolism and concentration remains under discussion. Due to the complexity of the neurological consequences of ethanol consumption we will address only those we consider to be primarily responses to ethanol and acetate metabolism. There is, for example, an extensive literature on the role of ethanol metabolism and of AMPK activity in modulation of neuronal function (see as examples, [3], [93], [94], [123]). We emphasize the role of energy metabolism and [AMP]f in order to illustrate not just their importance in the response to ethanol but also their central role in coordinating metabolic processes essential to biological existence as we know it. Neurological responses to ethanol consumption A: Early alterations in the dopaminergic system and potentiation of consumption Ethanol induces dopamine release and increased activity in the striatum as indicated by functional imaging [91]. Dopaminergic neurons that respond to nutrients, such as glucose, involve metabolism of that nutrient. Increase in nutrient concentration causes an increase in energy state and modulates ion channel (such as the KATP channel) conductance to initiate the dopaminergic signaling cascade. Ethanol, in contrast to other nutrients, has access to neurons behind the blood-brain barrier and this augments the usual nutrient reward circuitry. Dopaminergic projections go to the nucleus accumbens, which has a crucial role in the reward system of human brain. These dopaminergic effects have been attributed to inhibition of GABAergic interneurons of the ventral tegmental area (VTA) by alcohol. Ethanol-induced dopamine release in nucleus accumbens has been reported to be antagonized by administration of an opiate receptor antagonist [34], suggesting inhibition of GABAergic VTA interneurons are at least partly mediated by opioidergic afferents to these neurons. It is reasonable to suggest ethanol induced dopamine release is in response to increased energy state (nutrient sensing) in GABAergic and dopaminergic neurons. Considerable attention has been paid to acetaldehyde in relation to the dopaminergic response. Inhibition of catalase activity has been reported to prevent ethanol induced dopaminergic signaling in nucleus accumbens [46]. The authors attributed this effect to decrease in acetaldehyde concentration, but inhibiting catalase alters metabolism in many ways, of which acetaldehyde concentration is only one. Follow-up studies have reported that, in rats bred for ethanol preference, administration of a lentiviral vector coding for aldehyde dehydrogenase-2 (ALDH2) into the ventral tegmental area decreased long term ethanol consumption. This decrease was observed, however, only for ethanol naïve rats and not rats that had consumed ethanol on a 24-hour basis for 81 days [46]. Rivera-Meza et al. [86] reported that treating naïve UChB alcohol preferring rats with N-(1,3-benzodioxol-5-ylmethyl)-2,6-dichlorobenzamide, a drug that increased ALDH2 activity in brain by 3 fold, markedly lowered the amount of alcohol they consumed. The authors suggested that activation of ALDH2 in brain decreased acetaldehyde concentrations and this was responsible for inhibiting both acquisition and maintenance of chronic ethanol intake by alcohol-preferring rats. It has also been suggested that acetaldehyde produced from ethanol reacts with dopamine to form salsolinol and that salsolinol is an agonist for μ-opioid receptors, contributing to the reward response to alcohol [10]. There is, however, little evidence for a role for salsolinol in ethanol consumption and metabolism in humans [56], [57] and the contribution of acetaldehyde concentration remains to be established. In humans, expression of inactive aldehyde dehydrogenase 2 (ALDH2) is protective against alcohol use disorders [12], [60]. This decrease in ethanol consumption, however, has been attributed to the unpleasant side effects of systemic increased acetaldehyde. Lack of ALDH2 also suppresses oxidation of acetaldehyde in mitochondria, disrupting ethanol induced increase energy state, particularly in cells with ADH1 and the reward response to ethanol consumption. Studies in mice by Tabakoff et al. [101] indicated that ethanol tolerance and physical dependence involve different mechanisms. The authors used intraventricular injection of 6-OH dopamine to deplete dopamine and norepinephrine in the brain. Injected prior to chronic ethanol exposure, this prevented development of tolerance to the hypnotic and hypothermic effects of ethanol without significantly affecting development of physical dependence as measured by withdrawal symptoms (convulsions etc). Once tolerance was established, however, 6-OH dopamine injection did not alleviate that tolerance. It is reasonable to conclude that dopaminergic and noradrenergic systems contribute substantially to the positive physiological response to ethanol and to development of alcohol tolerance, but not to alcohol dependence or withdrawal symptoms. Neurological responses to ethanol consumption B: Early effects on glutamate and GABA metabolism Chronic ethanol intake in rodents, primates, and humans results in physical dependence and it is widely considered that dependence involves excitatory glutamate and inhibitory GABA neurons. Ethanol oxidation is particularly disruptive of glutamate and GABA metabolism and handling in brain. It is hypothesized that metabolism of astrocytes and neurons is coordinated through glutamate/glutamine cycling [90] and/or lactate transfer [9]. Our version of that metabolic coordination in the absence of ethanol is shown schematically in Fig. 5. Astrocytes have high glycolytic activity and glycogen storage as well as high pyruvate carboxylase activity whereas neurons are oxidative and have high pyruvate dehydrogenase activity. Pyruvate carboxylase in astrocytes is responsible for de novo synthesis of oxaloacetate (and thereby glutamate and glutamine) from pyruvate provided by glycolysis (anaplerosis). Pyruvate carboxylase provides α-ketoglutarate (α-KG) for synthesis of glutamate by aspartate/α-KG aminotransferase. Glutamate is then condensed with ammonia (glutamine synthetase) to form glutamine which is exported and taken up by neurons. In neurons, the glutamine is deaminated (glutaminase) and used as both a neurotransmitter and energy source. In order to be used for ATP production, glutamate is first deaminated by either glutamate/oxalacetate aminotransferase [41] or glutamate dehydrogenase to α-KG which enters the CAC. The CAC, however, does not carry out net oxidation of substrates with chain lengths of 4 or more carbons [50]. α-KG is oxidized to malate in the CAC and malate is exported to the cytoplasm where malic enzyme oxidatively decarboxylates malate to pyruvate. In neurons, pyruvate dehydrogenase is active and pyruvate from either malate or lactate is oxidized to acetylCoA and then to CO2 and water by CAC plus Ox-Phos. Glutamate released at the synapse during neural activity is taken up partly by synaptic glutamate transporters [26], [120] while glutamate that diffuses out of the synapse into the perisynaptic space is taken up by astrocytes. Glutamate consumed for energy metabolism in neurons is replaced by de novo synthesis from pyruvate in astrocytes. Metabolism of ethanol and/or its metabolites inhibits both glucose uptake and glycolytic production of pyruvate through increased energy state (decreased [AMP]f) and increased NADH. Ethanol induced increase in systemic lactate/pyruvate also suppresses intracellular pyruvate concentrations, despite an increase in intracellular lactate through increased monocarboxylate transport. Intracellular pyruvate concentrations are typically below the KM for pyruvate carboxylase (about 400 μM), and decrease would be expected to suppress astrocytic de novo glutamate synthesis. Extracellular glutamate levels in the synapse also decrease due to increase in energy state through its effect on the energy dependence of synaptosomal glutamate transport [26]. The net early effects of ethanol on glutamatergic neural activity include decreased glutamate release in response to the increased energy state in neurons and increased levels of inhibitory neurotransmitter GABA [33]. These contribute to the calming effects of ethanol through suppression of glutamatergic anxiety producing neural activity. Neurological responses to ethanol consumption: C. Long term alterations in glutamatergic neural system and alcohol withdrawal syndrome It has been proposed that neurophysiological and pathological effects of ethanol are mediated, to an important extent, by glutamatergic neurons [35], [36], [79], [96], [106]. Biochemical and electrophysiological studies have reported that chronic ethanol treatment increases the number of excitatory N-methyl-D-aspartate (NMDA) receptor-ionophore complexes in hippocampus, a brain area associated with ethanol withdrawal seizure activity [35], [36], [96]. Withdrawal symptoms vary, but in mice removal of ethanol from the diet induces withdrawal-associated tremors and handling-induced seizures [35], [36], [85]. The withdrawal symptoms are reported to correlate with decrease in blood alcohol and acetate levels, with the most severe seizure activity occurring when these concentrations fall to near zero. In mice, the NMDA receptor levels return to control in 24 h and the mice no longer have handling induced seizures. Treatment with NMDA during withdrawal exacerbated handling induced withdrawal seizures, while administration of MK-801, a noncompetitive NMDA receptor antagonist, decreased the occurrence and severity of seizures [35]. Treatment with MK-801 did not, in alcohol dependent mice, suppress the tremors which occur during withdrawal [35], [36]. Thus, following chronic ethanol treatment the glutamate receptors, particularly NMDA receptors, appear to mediate handling induced seizures, but not tremors, associated with ethanol withdrawal. Blocking NMDA receptors by memantine, a moderate affinity but clinically approved NMDA receptor antagonist, has been shown to reduce withdrawal induced-seizures and neurotoxicity in mice [100]. In clinical studies, memantine has been reported to attenuate cue-induced craving for alcohol and withdrawal symptoms associated with alcohol consumption [54], [55]. In rodents, exposure to alcohol has been reported to induce a significant increase in the expression and synaptic localization of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPA receptors) in brain regions with reward circuitry [15], [17]. AMPA receptors are ionotropic transmembrane receptors for glutamate that mediate fast synaptic transmission in the CNS. Infusion of an AMPA receptor inhibitor into the dorsomedial striatum exhibited promising results in reducing alcohol consumption in rats [113] and an AMPA/kainate receptor antagonist, topiramate, is reported to suppress withdrawal symptoms in humans [55]. Another drug, acamprosate, known to affect two neurotransmitter systems, GABA (as an agonist) and glutamate (as an NMDA receptor agonist and mGluR5 antagonist), has been reported to be effective in increasing complete abstinence rate as well as cumulative abstinence duration in several long-term placebo-controlled trials in alcohol-dependent patients [59], [80], [88]. In a large clinical trial involving 1383 patients in nine possible treatment groups, however, acamprosate neither alone nor with naltrexone or combined behavioral intervention showed statistically significant reduction in alcohol consumption over placebo [4]. Despite the inconsistencies in findings, overall advantageous pharmacological effects of acamprosate on alcohol consumption are such that it is approved for treatment of alcohol use disorders in Europe and USA. Understanding how glutamate signaling may contribute to addiction needs to account for multiple pools of intra- and extra-cellular glutamate. Glutamate concentrations within glutamatergic synapses are determined by balance of vesicular release and energy dependent glutamate reuptake whereas that in the perisynaptic space is determined by diffusion from the synapse and extrasynaptic release/uptake [28], [104]. Perisynaptic glutamate regulates neurotransmission by stimulating group II glutamate receptors (mGluRs) in the presynaptic area [7], [121]. These are presynaptic receptors capable of inhibiting vesicular release [8], [18], [89]. Thus, presynaptic receptors permit cross talk between the two pools and altered nonvesicular glutamate release may contribute to pathological glutamate signaling linked to addiction [110]. As indicated above, under normal physiological conditions, plasma acetate concentration is low but with ethanol ingestion it rapidly increases to about 1 mM. Acetate is readily taken up and metabolized by brain [20], [81], [114], largely by astrocytes. Calculated rates of utilization of acetate from the blood were reported to be 15–25% of total glucose consumption in non-stimulated tissue and to range from 28 to 115% of estimated rates of glucose oxidation in astrocytes [20], [114]. During intoxication, blood acetate concentrations are higher for alcoholics than for controls [78]. This would facilitate metabolism of acetate by glial cells, as shown for laboratory animals chronically exposed to alcohol [107], [111]. Madayag and coworkers [68] reported that repeated N-acetylcysteine administration alters plasticity-dependent effects of cocaine. The authors provided evidence that the ability of N-acetylcysteine to regulate drug-seeking behavior results from actions on cystine-glutamate exchanger (system xc) and is associated with suppression of the large release of glutamate that occurs in accumbens during drug seeking. In vitro measurements have been reported to show that cystine induced activation of cystine/glutamate exchange system xc results in release of glial glutamate and stimulation of mGlu2 receptors [45]. This inhibits neuronal glutamate release. In cocaine addiction, decrease in reinstated cocaine seeking elicited by activating xc with N-acetylcysteine is prevented by pretreatment with an mGlu2/3 antagonist [58]. Pharmacological treatment with N-acetylcysteine is reported to modulate responses to stress and depressive-like behaviors by increasing xc expression and to indirectly activate mGlu2 receptors [76]. The fact that pharmacological blockade of the xc system also blocked N-acetylcysteine effects on mGlu2 receptors further supports linkage of mGlu2 regulation in promoting resilience to stress. A consistent biomarker of mood-related behaviors associated with electrophysiological changes in the dentate gyrus is reduction in the mGlu2 receptors that regulate vesicular release of glutamate at synapses [77]. Ethanol metabolism and associated alcohol withdrawal syndrome: a synopsis Ethanol, through its role as a major but essentially unregulated metabolite for energy metabolism, causes a truly metabolic disease. An initial positive sense of well being is induced by “pushing” energy metabolism and increasing energy state to above its homeostatic set point. The positive sense of well being is part of the neurological signaling system that has evolved to guide adaptation to the environment. Positive reward feelings associated with adequate food, warmth etc. are enhanced and negative feelings associated with hunger, anxiety, etc are suppressed. This leads to a euphoric state with an enhanced sense of being well fed and unstressed. Short exposures are reversible and cause little long term injury, but as ethanol exposure continues there is progressive disruption of critical regulatory mechanisms that maintain metabolic homeostasis. The extent of disruption is dependent on individual tissues and their metabolic requirements with many other contributing parameters (including genetic differences, diet, etc). A key aspect of ethanol metabolism appears to be the decrease in [AMP]f and attendant decrease in AMPK activity. Both chronic decrease in AMPK activity and alcohol dependence are reported to cause decreased activity of oxidative phosphorylation, abnormally shaped mitochondria and other cellular morphological and enzymatic changes [14], [102]. In addition to the debilitating effects of prolonged ethanol consumption, it is necessary to deal with alcohol withdrawal syndrome. As described earlier, this is a complex response to stopping ethanol consumption and correlates with disappearance of ethanol and its metabolic products from the body. The symptoms can be severe and life threatening but do not arise from a single cause. Due to the multiplicity in causes, a single drug is unlikely to fully alleviate the withdrawal symptoms. Effective treatment is likely to require simultaneous treatment of multiple causes. Identifiable primary metabolic contributions to alcohol withdrawal symptoms and treatments that should substantially suppress the symptoms include: 1). Correct the energy deficiency in specific brain cells (neurons and astrocytes). Derr and coworkers [22] reported this can be addressed by providing a metabolite “cocktail” (butyrate, lactate, and β-hydroxybutyrate) for energy metabolism and anaplerotic support of the citric acid cycle. Derr and Derr [23] quantified the tremulous and rigidity symptoms of alcohol withdrawal in rats and concluded that feeding β-hydroxybutyrate suppressed the tremulous, but not the rigidity, symptoms. Other suitable nutrients include branched chain amino acids and branched short chain fatty acids, as these also rapidly cross the blood-brain barrier. Importantly, each can provide both acetylCoA for oxidative metabolism and pyruvate for anaplerotic synthesis of glutamate. Given the importance of carnitine in acetate metabolism, supplementing with carnitine and/or acetyl-L-carnitine may also be helpful [71]. 2A). Stabilize glutamate distribution and metabolism in the brain. Treatment with N-acetylcysteine has been reported to modulate responses to stress and depressive-like behaviors in several ways. These include increasing expression of glutamate-cystine antiporter (system xc), increasing the concentrations of cysteine and cystine, and indirectly by activating mGlu2 receptors [2], [76]. This helps to rebalance glutamate homeostasis and glutamatergic neuronal/astrocyte interactions. 2B). Pharmacological compensation for increased sensitivity of the glutamatergic neural system. As noted above, chronic ethanol consumption induced suppression of glutamatergic activity results in increase in excitatory (NMDA, AMPA) and decrease in inhibitory (mGluR) glutamate receptors. The contribution of enhanced glutamatergic sensitivity and neurotoxicity of excessive NMDA receptor activity to ethanol withdrawal symptoms can be suppressed by antagonists for excitatory glutamate receptors. Among the glutamate receptor antagonists currently clinically approved or in trials are acamprosate, memantine, and MK-801. Suppression of glutamatergic sensitivity through the critical withdrawal period has been reported to decrease the severity of symptoms [55]. Once energy and glutamate metabolism have normalized, receptor numbers should return to near normal. Alcohol dependence is not just a metabolic disease, and there are substantial genetic, psychological, and social contributions. Identifying and characterizing the metabolic consequences of ethanol ingestion and of alcohol dependence is important to understanding the disease and how to more effectively treat alcohol dependence. This is, however, just one piece of a complex medical and social problem. Conflict of interest statement Neither DFW nor FMM have any financial and personal relationships that could influence the work presented in this manuscript. Acknowledgement The authors are indebted to Dr. Joseph J. Higgins for insightful and memorable discussions concerning ethanol metabolism and its regulation.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/29%3A_Integration_of_Mammalian_Metabolism_-_Capstone_Volume_II/29.11%3A_Gut_microbiota-derived_metabolites_as_central_regulators_in_m.txt	princeton-nlp/TextbookChapters	This article is directly taken from: Agus A, Clément K, Sokol H. Gut microbiota-derived metabolites as central regulators in metabolic disorders. Gut 2021;70:1174-1182. https://gut.bmj.com/content/70/6/1174. Creative Commons Attribution Non Commercial (CC BY-NC 4.0) http://creativecommons.org/licenses/by-nc/4.0/. References can be found in the original article. Abstract Metabolic disorders represent a growing worldwide health challenge due to their dramatically increasing prevalence. The gut microbiota is a crucial actor that can interact with the host by the production of a diverse reservoir of metabolites, from exogenous dietary substrates or endogenous host compounds. Metabolic disorders are associated with alterations in the composition and function of the gut microbiota. Specific classes of microbiota-derived metabolites, notably bile acids, short-chain fatty acids, branched-chain amino acids, trimethylamine N-oxide, tryptophan and indole derivatives, have been implicated in the pathogenesis of metabolic disorders. This review aims to define the key classes of microbiota-derived metabolites that are altered in metabolic diseases and their role in pathogenesis. They represent potential biomarkers for early diagnosis and prognosis as well as promising targets for the development of novel therapeutic tools for metabolic disorders. Key messages • Metabolic disorders, a growing worldwide health challenge, are associated with alterations in the composition and function of the gut microbiota. • Microbial metabolites are key factors in host-microbiota cross-talk. • Specific classes of microbiota-derived metabolites, notably bile acids, short-chain fatty acids, branched-chain amino acids, trimethylamine N-oxide, tryptophan and indole derivatives, have been strongly implicated in the pathogenesis of metabolic disorders. • Gut microbiota-derived metabolites represent potential biomarkers for the early diagnosis and show promise for identifying targets for the development of novel therapeutic tools for metabolic disorders. Introduction The human intestine harbours a complex and diverse system of mutualistic microorganisms, consisting of bacteria, fungi, viruses, archaea and protozoa. This rich ecosystem contributes to a large number of physiological functions: fermentation of indigestible dietary components and vitamin synthesis, defenses against pathogens, host immune system maturation and maintenance of gut barrier function. Thus, this central regulator, sometimes qualified as the ‘second brain’, plays a significant role in maintaining host physiology and homeostasis. All the species interconnected in the gut produce an extremely diverse reservoir of metabolites from exogenous dietary components and/or endogenous compounds generated by microorganisms and the host. Notably, while food is generally examined for calories and macronutrients and micronutrients, microbial metabolism (and even human enzymes) recognises food molecules and transforms them into metabolites. These microbial metabolites are key actors in host-microbiota cross-talk. The beneficial or detrimental effect of specific microbiota-derived metabolites depends on the context and the host state, suggesting the primordial nature of the symbiotic microbiota in ensuring optimal health in humans. With the widespread westernisation of lifestyles, alteration of the gut microbiota composition and functions has become a worldwide phenomenon. Despite the difficulty to distinct a direct causal relationship and an association between dysbiosis and diseases, several lines of evidence demonstrate that the alteration of the gut microbiota is involved in the pathogenesis of multiple diseases affecting the GI tract, such as IBD or colorectal cancer, as well as many non-digestive systems. Metabolic disorders have been recognised to be massively impacted by gut microbiota. In the last two decades, increasing calorie intake and decreasing levels of physical activity have contributed to a progression in the prevalence of metabolic disorders. Metabolic disorders represent a group of disorders with the clustering of various inter-related pathological conditions combining obesity, non-alcoholic steatohepatitis (NASH), dyslipidaemia, glucose intolerance, insulin resistance, hypertension and diabetes that, when occurring together, strongly increase the incidence of cardiovascular diseases and mortality. Deciphering the mechanisms of host-intestinal microbiota interactions represents a major public health challenge in the development of new preventive or curative therapeutic strategies. In the present review, we will focus on the results from the most significant studies dealing with the role of microbiota-derived metabolites in metabolic disorders. Disrupted equilibrium of the gut microbiome-host interactions in metabolic disorders The gut microbiota plays a crucial role in maintaining the physiological functions of the host. A disruption of the fragile host-microbiota interaction equilibrium can play a role in the onset of several metabolic diseases. The gut microbiota can interact with the host by producing metabolites, which are small molecules (<1500 Da) representing intermediates or end-products of microbial metabolism. These metabolites can derive directly from bacteria or the transformation of dietary or host-derived substrates. Gut microbiota incrimination The implication of the gut microbiota in the regulation of host metabolic balance has been demonstrated in the last decade. Studies conducted both in animal models and humans revealed a significant role of the gut microbiota in the pathogenesis of metabolic disorders, strongly influenced by diet and lifestyle modifications. Evidence from animal experiments The gut microbiota modulates energy expenditure and homeostasis in several animal models, including germ-free mice (GF mice) and genetically induced mice with obesity (ob/ob mice). GF mice are protected against obesity in a Western diet setting. Independent of daily food intake, Bäckhed et al reported a 60% increase in body fat, hepatic triglycerides and insulin resistance in conventionalised adult GF mice compared with GF mice, notably due to better absorption of monosaccharides. Interestingly, the transfer of gut microbiota from ob/ob mice to GF mice results in a significant increase in body weight and fat mass compared with colonisation with a lean microbiota, showing a causal relationship. The gut microbiota composition is unique to each individual. Caecal microbiota transplantation, from two mice with different responses to high-fat diet (HFD), into GF mice leads to the transmission of the donor’s responder (RR) or non-responder (NR) phenotype. The gut microbiota of severely hyperglycaemic RR mice is enriched in Firmicutes, whereas NR is dominated by Bacteroidetes and Actinobacteria. Moreover, the transplantation of faecal microbiota from human twin pairs, discordant for obesity, into GF mice led to the acquisition of lean and obese phenotypes according to the donor. This phenotype transmission is strongly diet-dependent and notably favoured by a low-fat diet enriched in vegetables and fruits and thus enriched in fibre. The effect of the gut microbiota seems to occur even before birth, as the maternal gut microbiota, through short-chain fatty acid (SCFAs), triggers embryonic GPR41 and GPR43 and influences prenatal development of neural, enteroendocrine and pancreatic systems of the offspring to maintain postnatal energy homeostasis and eventually prevent metabolic disorder development. Overall, these animal studies demonstrate the tight interconnection between diet and the gut microbiome in the pathogenesis of metabolic disorders as well as in its vertical transmissibility. Evidence from human studies Alterations in the gut microbiome composition and functions are associated with various traits observed in metabolic disorders. Although there are some conflicting results, the obesity-associated gut microbiota has been characterised by a decline in Bacteroidetes and a compensatory expansion of the Firmicutes phylum and by a reduction in microbial diversity and richness. There is notably a negative correlation between the severity of metabolic markers and the richness of the gut microbiota. Individuals with low microbiota gene content present more adiposity, insulin resistance and dyslipidaemia than high bacterial richness populations. Even in severe obesity conditions, those with diminished gut microbiota richness have a more severe metabolic condition. In patients with diabetes, the higher proximity of the altered microbiota to epithelial cells could promote pro-inflammatory signals, contributing to the development of aggravated metabolic alterations. In humans, faecal microbiota transplantation (FMT) demonstrated some positive but moderate effects in patients with metabolic syndrome traits, proving the involvement of the gut microbiota in the pathogenesis and its potential therapeutic role.However, the efficiency of FMT in improving metabolic amelioration was dependent on the recipient gut microbiota profile, with low baseline richness promoting gut microbiota engraftment. Gut microbiota-derived metabolite implications in metabolic diseases The gut metabolome Metabolomics, which consists of the study of the small molecules present in any type of biological sample, has proven to be helpful in enriching the knowledge on microbiota-host interactions. Several hundred faecal or serum metabolites have been associated with clinical features associated with metabolic disorders. Moreover, a combination of metagenomics and metabolomics was used to elucidate the associations between gut microbiota imbalances and metabolic disturbances. This field is still in its infancy and, for some metabolites, it remains difficult to determine whether they are fully microbiota-derived or if other sources are involved, including diet or the host itself. Metagenome and metabolome studies led to the discovery of new associations between microbial-derived metabolites and metabolic syndrome, but additional arguments are needed to establish a potential causality link. Notably, the decreased abundance of Bacteroides thetaiotaomicron, a glutamate fermenting commensal, in subjects with obesity is inversely correlated with serum glutamate. Furthermore, positive correlations between insulin resistance and microbial functions are driven mainly by a few species, such as Prevotella copri and Bacteroides vulgatus, suggesting that they may directly impact host metabolism. Metabolomics studies in plasma, saliva or urine identified different biochemical classes of metabolites that may be altered in metabolic disorders in association with gut microbiota perturbations. Dysregulation of lipolysis, fatty acid oxidation and aminogenesis and ketogenesis, as well as changes in the levels of triglycerides, phospholipids and trimethylamine N-oxide (TMAO) are described in samples from humans with metabolic disorders, and more recently, imidazole propionate (IMP) was discovered as being involved in insulin resistance. Shotgun metagenomics data suggest that hepatic steatosis and metabolic alterations are associated with dysregulated aromatic and branched-chain amino acid (BCAA) metabolism. The dysregulation of SCFA and bile acid (BA) metabolism are also associated with metabolic diseases, including obesity, type 2 diabetes mellitus and non-alcoholic fatty liver diseases. Bile acids BAs are small molecules synthesised in hepatocytes from cholesterol. The primary BAs chenodeoxycholic acid (CDCA) and cholic acid (CA), conjugated to glycine or taurine, are essential for lipid/vitamin digestion and absorption. Ninety-five per cent of them are reabsorbed actively from the terminal ileum and are recycled in the liver (enterohepatic circulation). Primary BAs are also transformed into secondary BAs and deconjugated by gut microbiota. They can be either passively reabsorbed to reenter the circulating BA pool or excreted in the faeces as shown in Figure 1 below. Bile acid (BA) dysmetabolism in metabolic syndrome. BA metabolism is altered in patients with metabolic syndrome (MetS) and is associated with hepatic steatosis and glucose and lipid dysmetabolism. Dietary animal fat consumption promotes taurocholic acid (TCA) production, which favours the proliferation of sulfite-reducing bacteria, Bilophila wadsworthia, leading to an increase in intestinal permeability and inflammation (panel 1). Gut microbiota alterations induce an impairment in the ileal absorption of BAs, which occurs normally via the apical-sodium BA transporter (ASBT). This induces a decrease in the expression of nuclear Farnesoid-X receptor (FXR) and fibroblast growth factor 19 (FGF19) in intestinal epithelial cells and the abundance of colonic primary conjugated BAs (panel 2). Gut microbiota dysfunction leads to a decreased transformation of primary conjugated BAs to secondary BAs in the colon, leading to defective activation of Takeda-G-protein-receptor-5 (TGR5). The effect of TGR5 activation on the increase in glucagon-like peptide 1 (GLP-1) and white adipose tissue (WAT) browning was thus inhibited (panel 3). Gut microbiota alterations impair bile salt hydrolase (BSH) activity, leading to primary conjugated BA accumulation in the colon (panel 4). BMI, body mass index; HDL, high-density lipoprotein; LDL, low-density lipoprotein. Increased total circulating BA levels in individuals with obesity positively correlate with body mass index and serum triglycerides in patients with hyperlipidaemia. BAs regulate their synthesis through FGF19/FGF15, but they also have metabolic effects through their receptors Farnesoid-X receptor (FXR) and Takeda-G-protein-receptor-5 (TGR5). Activation of FXR and TGR5 (1) promotes glycogen synthesis and insulin sensitivity in the liver; (2) increases insulin secretion by the pancreas; (3) facilitates energy expenditure, especially in the liver, brown adipose tissue and muscles (browning); (4) favours thermogenesis, resulting in a decrease in body weight and (5) mediates satiety in the brain. BAs also impact lipid metabolism, especially by exerting profound effects on triacylglycerol. The perturbations of the intestinal microbiota composition in metabolic disorders strongly impact BA metabolism, especially characterised by a failure to metabolise primary BAs, thus leading to their accumulation. Indeed, an increase in primary CDCA levels induces a decrease in very low-density lipoprotein production and plasma triglyceride concentrations. Short-term antibiotic supplementation in mice induces a decrease in secondary BA-producing bacteria and a reduction in hepatic deoxycholic acid (DCA) and lithocholic acid concentrations as well as serum triglyceride levels, suggesting that secondary BAs can act as regulators to maintain metabolic host homeostasis. Moreover, this alteration in the primary to secondary BA pool in metabolic disorders might play a role in the observed low-grade intestinal inflammation, as conjugated primary BAs exhibit pro-inflammatory effects on intestinal epithelial cells. Conversely, secondary BAs have anti-inflammatory properties. In addition, Parséus et al showed that the promoting effect of the gut microbiome on obesity and hepatic steatosis is dependent on the FXR pathway.However, the FXR-dependent role of secondary BAs in the regulation of glucose and lipid metabolism is debated and might be context-dependent. The accumulation of hepatic lipids, triglycerides and cholesterol has been observed in FXR-deficient mice on a normal chow diet, while in HFD-fed mice or an obese background, FXR deficiency improves glucose homeostasis and decreases body weight, possibly a consequence of different basal gut microbiota. The effects of FXR in the pathogenesis of metabolic disorders are also likely to be different from one tissue to the other, as demonstrated by studies in conditional knockout mice. FXR induces the transcription of fibroblast growth factor 19 (FGF19) in intestinal epithelial cells, which reach the liver and inhibit BA synthesis in a feedback loop. Mice overexpressing FGF19 exhibit increased metabolic activity and energy expenditure by increasing brown adipose tissue and decreasing liver expression of acetyl coenzyme A carboxylase 2, thus leading to protection against HFD-induced metabolic injury. Gut microbiota perturbations induce impairment in the ileal absorption of BAs, which normally occurs via the apical-sodium bile acid transporter, resulting in decreased expression of FXR and FGF19 and an imbalance of BAs, notably characterised by an increase in colonic primary conjugated BAs. Transgenic mice overexpressing TGR5 exhibit improved glucose tolerance with increased secretion of glucagon-like peptide 1 (GLP-1) and insulin. This BA-TGR5 axis elicits beige remodelling in subcutaneous white adipose tissue and may contribute to improvement in whole-body energy homeostasis. The alteration of gut microbiota-dependent BA metabolism, through qualitative (primary vs secondary and conjugated vs deconjugated BAs) or quantitative modification of the BA pool, is likely to participate in the pathogenesis of metabolic disorders. Moreover, BAs have an important impact on intestinal epithelium function. Primary BAs, such as CA and CDCA, and some secondary deconjugated BAs, such as DCA, increase epithelial permeability through the phosphorylation of occludin in intestinal Caco-2 cells. Some correlations have been observed between BA levels and intestinal permeability in mouse models. The effect of the BA-microbiota dialogue is massively impacted by diet. High consumption of animal fat promotes taurocholic acid production, leading to a shift in microbiota composition with a bloom of sulfite-reducing microorganisms such as Bilophila wadsworthia and to increased susceptibility to colitis in IL-10−/− mice and more severe liver steatosis, barrier dysfunction and glucose metabolism alteration in HFD-fed mice. Moreover, bile salt hydrolase (BSH) activity, which is responsible for BA deconjugation in the normal gut microbiota, is impaired in metabolic disorders and likely plays a role in the accumulation of primary conjugated BAs in the colon of these patients. In mouse models, correcting BSH defects by the administration of BSH-overexpressing Escherichia coli improved lipid metabolism, homeostasis and circadian rhythm in the liver and GI tract, resulting in protection against metabolic disorders. Short-chain fatty acids SCFAs, such as butyrate, propionate and acetate, are end-products of microbial fermentation implicated in a multitude of physiological functions. SCFAs participate in the maintenance of intestinal mucosa integrity, improve glucose and lipid metabolism, control energy expenditure and regulate the immune system and inflammatory responses as shown in Figure 2 below. They act through different mechanisms, including specific G protein-coupled receptor family (GPCR) and epigenetic effects. Short-chain fatty acids (SCFAs), branched-chain amino acids (BCAAs) and Trimethylamine N-oxide (TMAO): relevant effects for metabolic syndrome on the host. Microbiota-derived metabolites mediate diverse effects on host metabolism. SCFAs (green frame): (i) increase satiety and browning of white adipose tissue (WAT); (ii) induce a decrease in lipogenesis and associated inflammation; (iii) increase the secretion of glucagon-like peptide 1 (GLP-1) and peptide YY (PYY) and (iv) participate in the maintenance of intestinal barrier integrity. BCAAs (yellow frame): (i) increase thermogenesis, protein synthesis and hepatocyte proliferation but (ii) are also associated with insulin resistance and visceral fat accumulation. TMAO (red frame): increases cardiovascular risks by inducing hyperlipidaemia, oxidative stress and pro-inflammatory cytokines. The amount of SCFA-producing bacteria and SCFAs is reduced in faecal samples of dysmetabolic mice and in humans with obesity and diabetes. In rodents with diabetes and obesity, supplementation with SCFAs improves the metabolic phenotype by increasing energy expenditure, glucose tolerance and homeostasis. Adding back fermentable fibres (inulin) to an HFD seems to be enough to protect against metabolic alterations. In humans, SCFA administration (inulin-propionate ester, acetate or propionate) stimulates the production of GLP-1 and PYY, leading to a reduction in weight gain. The protective effects of SCFAs on metabolic alterations might occur as early as in utero. In mice, high-fibre diet-induced propionate from the maternal microbiota crosses the placenta and confers resistance to obesity in offspring through the SCFA-GPCR axis. Branched-chain amino acids The most abundant BCAAs, valine, isoleucine and leucine, are essential amino acids synthesised by plants, fungi and bacteria, particularly by members of the gut microbiota. They play a critical role in maintaining homeostasis in mammals by regulating protein synthesis, glucose and lipid metabolism, insulin resistance, hepatocyte proliferation and immunity. BCAA catabolism is essential in brown adipose tissue (BAT) to control thermogenesis. It occurs in mitochondria via SLC25A44 transporters and contributes to an improvement in metabolic status. Moreover, supplementation of mice with a mixture of BCAAs promotes a healthy microbiota with an increase in Akkermansia and Bifidobacterium and a decrease in Enterobacteriaceae. However, the potential positive effects of BCAAs are controversial. Elevated systemic BCAA levels are associated with obesity and diabetes, probably a consequence of the 20% increased consumption of calories over the last 50 years. In genetically obese mice (ob/ob mice), BCAA accumulation induces insulin resistance. The gut microbiota is a modulator of BCAA levels, as it can both produce and use BCAAs. Prevotella copri and B. vulgatus are potent producers of BCAAs, and their amounts correlate positively with BCAA levels and insulin resistance. In parallel, a reduced abundance of bacteria able to take up BCAAs, such as Butyrivibrio crossotus and Eubacterium siraeum, occurs in patients with insulin resistance. Further studies are needed to more precisely elucidate the effects of BCAAs in the pathogenesis of metabolic disorders. Trimethylamine N-oxide The gut microbiota can metabolise choline and L-carnitine from dietary sources (eg, red meat, eggs and fish) to produce trimethylamine (TMA). This gut microbiota-derived TMA is then absorbed and reaches the liver where it is converted into TMAO through the enzymatic activity of hepatic flavin monooxygenases 3. In humans, the level of TMAO increases in patients with diabetes or at risk of diabetes and in obesity. Increasing evidence demonstrates that the gut microbiota-dependent metabolite TMAO is also associated with a higher risk of developing cardiovascular disease and kidney failure. In mice, dietary supplementation with TMAO, carnitine or choline alters the caecal microbial composition, leading to TMA/TMAO production that increases the atherosclerosis risk. This effect is dependent on the gut microbiota, as it is lost in antibiotic-treated mice. Moreover, transferring the gut microbiota of high-TMAO mice recapitulates atherosclerosis susceptibility in recipient low-TMAO mice. Importantly, the role of the gut microbiota in the production of TMAO from TMA has also been demonstrated in humans. Overall, in metabolic disorders, the altered microbiota associated with an increased intake of choline and L-carnitine from dietary sources leads to an increase in plasma levels of TMAO, which is directly involved in the pathogenesis of metabolic disease comorbidities and particularly cardiovascular disorders. However, detailed investigations are needed in populations from different countries to understand the interaction between food consumption patterns, TMAO production and cardiovascular risks. Tryptophan and indole-derivative metabolites Tryptophan is an essential aromatic amino acid acquired through common diet sources, including oats, poultry, fish, milk and cheese. In addition to its role in protein synthesis, tryptophan is a precursor for crucial metabolites. Dietary tryptophan can follow two main pathways in host cells, namely, the kynurenine and serotonin routes. The third pathway implicates gut microorganisms in the direct metabolism of tryptophan into several molecules, such as indole and its derivatives, with some of them acting as aryl hydrocarbon receptor (AhR) ligands, as shown in Figure 3 below. Tryptophan metabolism alterations in metabolic syndrome. Tryptophan dysmetabolism is associated with liver inflammation, steatosis and insulin resistance. In metabolic syndrome (MetS), the inflammatory state is associated with kynurenine (KYN) production through the activation of indoleamine 2,3-dioxygenase 1 (IDO1). This leads to an increase in kynurenine-derived metabolites, such as kynurenic acid (KYNA), xanthurenic acid (XA), 3-hydroxykynurenine (3-H-KYN), 3-hydroxyanthranilic acid (3-HAA) and quinolinic acid (QA). In parallel, the gut microbiota presents a defect in the production of aryl hydrocarbon receptor (AhR) ligands such as indole-3-propionic acid (IPA). The incretin hormone glucagon-like peptide 1 (GLP-1) secretion from intestinal enteroendocrine L cells and interleukin (IL)-22 production are decreased, altering gut permeability and promoting lipopolysaccharide (LPS) translocation. Serotonin (5-HT) biosynthesis from intestinal enterochromaffin cells is also reduced in the context of MetS due to a decrease in the production of microbiota-derived metabolites inducing the production of host 5-HT. We have identified in a previous study, in both preclinical and clinical settings, that metabolic disorders are characterised by a reduced capacity of the microbiota to metabolise tryptophan into AhR agonists. Defective activation of the AhR pathway leads to decreased production of GLP-1 and IL-22, which contribute to intestinal permeability and lipopolysaccharide (LPS) translocation, resulting in inflammation, insulin resistance and liver steatosis. In this context, treatment with AhR agonists or administration of Lactobacillus reuteri, which naturally produces AhR ligands, can reverse metabolic dysfunction. Similarly, indole prevents LPS-induced alterations of cholesterol metabolism and alleviates liver inflammation in mice. Moreover, exploring human jejunum samples from patients with severe obesity led to the observation that a low AhR tone correlated with a high inflammatory score. Interestingly, the use of the AhR ligand is able to prevent damage to barrier integrity and inflammation in Caco-2/TC7 cells. We and others also showed strong activation of the kynurenine pathway in metabolic diseases. Genetic or pharmacological approaches inhibiting the activity of indoleamine 2,3-dioxygenase (IDO), the rate-limiting enzyme of the kynurenine pathway, are protective against HFD-induced obesity and metabolic alterations. The mechanism is likely to be mediated by the microbiota and AhR. The increased amount of available tryptophan, due to the inactivation of IDO, can be converted by the microbiota in AhR agonists. Conversely, in obesity, the overactivation of IDO, associated with an increase in plasma levels of downstream metabolites such as kynurenic acid, xanthurenic acid, 3-hydroxykynurenine, 3-hydroxyanthranilic acid and quinolinic acid, decreases the tryptophan pool, which is less available for the production of AhR agonists by the microbiota. The third pathway of tryptophan metabolism, serotonin (5-HT), is also involved, as it affects feeding behaviour and satiety and is thus important for obesity development. The gut microbiota, and primarily indigenous spore-forming bacteria, represent an essential modulator of the intestinal production of 5-HT in enterochromaffin cells that represents >80% of the whole body 5-HT synthesis. These effects are notably mediated by SCFAs and BAs. Mice deficient for the production of peripheral serotonin are protected from HFD-induced obesity. Mechanistically, 5-HT inhibits brown adipose tissue thermogenesis, thus leading to fat accumulation. Human data support these results, as elevated plasma levels of 5-hydroxyindole-3-acetic acid, an end-product of serotonin metabolism, are increased in patients with metabolic disorders. Imidazole propionate Exploring the interaction between food intake, gut microbiota and derived metabolites might be of interest to discover metabolites impacting metabolic health. As such, it was recently shown that IMP, a metabolite produced by histidine utilisation of gut microbiota, was enhanced in type 2 diabetes and associated with insulin resistance. In the liver, IMP appeared to affect the insulin signalling pathway via mammalian target of rapamycin complex 1 (mTORC1). The examination of IMP in large human cohorts also links it with metabolic health and lifestyle. IMP was elevated in subjects with prediabetes and diabetes in the MetaCardis cohort and in subjects with low bacterial gene richness and Bacteroides 2 enterotype in this cohort. Associations between IMP levels and markers of low-grade inflammation were also identified. Importantly, relationships were observed between serum IMP levels and unhealthy diet measured by dietary quality scores emphasising the importance of nutrition in this context. Thus, this study confirms that in type 2 diabetes, the intestinal microbiota may is switched towards IMP production, which can impact host inflammation and metabolism. Therapeutic relevance The mechanistic links between gut microbiota-derived metabolites and metabolic disorders make these interactions a promising therapeutic target in these complex diseases. Lessons from faecal microbiota transplantation FMT is a drastic strategy to modify the gut microbiome. It is highly effective in the treatment of recurring Clostridioides difficile infections and has been evaluated in small trials in metabolic syndrome and obesity. The clinical efficacy of this strategy is so far mild, with mostly some positive effects on insulin sensitivity in subgroups of patients. However, these studies had several limitations, including small size and limited duration of intervention. Nevertheless, they provide relevant information to identify the critical molecules involved in biological effects. Following successful FMT, both the microbiota composition and metabolomics, such as BA and SCFA profiles, can be restored. In patients with obesity, FMT can induce engraftment of the butyrate-producing and bile-hydrolysing genus Faecalibacterium, leading to a restoration of the BA profile and microbiota BSH activity. FMT increases the relative abundance of SCFA-producing bacteria such as Roseburia intestinalis and the protective strain Akkermansia muciniphila, with a possible role in the improvement in insulin sensitivity through regulation of GLP-1. A. muciniphila supplementation alone improves metabolic parameters in overweight/obese insulin-resistant volunteers characterised by better insulin sensitivity and a reduction in plasma total cholesterol levels and fat mass. In mice, A. muciniphila promotes the production of SCFAs and the restoration of HFD-induced alterations in tryptophan metabolism. These data highlight the key family of microbiota-derived metabolites with potential therapeutic effects. Synthetic agonists of bile acid receptors Given their potential benefits in metabolic diseases, BAs and synthetic FXR and TGR5 agonists are currently under development in the metabolic field. Preclinical trials based on in vitro and in vivo studies identified potent synthetic FXR and TGR5 agonists, which are currently being investigated in phase II or III clinical trials. Due to the regulatory roles of FXR and TGR5 receptors on glucose and lipid metabolism, multiple specific agonists have been designed. Obeticholic acid (OCA), one of the best-characterised FXR agonists, protects the liver from damage in mice with a reduction in hepatic steatosis and inflammation and is currently being evaluated in a phase III trial in patients with NASH. The synthetic FXR agonist GW4064 improves hyperglycaemic and hyperlipidaemia in mice with diabetes and is able to correct BA dysmetabolism and alleviate liver toxicity in rodents with short bowel. The intestine-restricted FXR agonist fexaramine can also promote adipose tissue browning and GLP-1 secretion in wild type (WT) and leptin receptor-deficient diabetic mice. Finally, a TGR5 agonist ameliorated insulin resistance and glucose homeostasis in mice with diabetes by the cyclic AMP/protein kinase A pathway in skeletal muscles. Short-chain fatty acid and branched-chain amino acid treatment Dietary supplementation with fermentable fibres, such as inulin in HFD-fed mice or inulin-propionate ester in overweight humans, protects against metabolic disturbances by restoring the gut microbial composition and the action of the IL-22-mediated axis.Oral SCFA treatment in obese mice can modulate lipid synthesis and insulin receptors by upregulating peroxisome proliferator-activated receptor-γ. It also improves intestinal barrier functions with a lower serum LPS concentration. SCFAs exert their beneficial effects partly through specific G-protein-coupled receptors, and their activation by specific agonists is an attractive strategy in the treatment of MetS. GPR40/FFA1, GPR41/FFA3, GPR43/FFA2 and GPR120/FFA4 agonists induce protection against diet-induced obesity in mice through the improvement in insulin, GLP-1 and incretin secretion and anti-inflammatory effects. In addition, a link between dietary BCAAs and energy balance was noted in animals with obesity, and reducing the proportion of dietary BCAAs was associated with a restoration of metabolic health. Concluding remarks Gut microbiota-derived metabolites have a central role in the physiology and physiopathology of metabolic disorders. The microbial metabolites described above, specifically BAs, SCFAs, BCAAs, TMAO, tryptophan and indole derivatives, are implicated in the pathogenesis of these complex disorders and represent potential biomarkers for the early diagnosis and prognosis of these diseases. Moreover, microbiota-derived metabolites and their host receptors, possibly in combination with dietary intervention, represent promising targets for the development of novel therapeutic tools for metabolic disorders. Acknowledgments The authors would like to thank BioRender, for its revolutionised tool to create custom scientific figures (https://biorender.com/)
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/30%3A_Abiotic_Origins_of_Life/30.01%3A_Abiotic_Origins_of_Life.txt	princeton-nlp/TextbookChapters	The Start of Life This book began with the notion that understandings derived from the study of simple molecules can be applied to complex biological macromolecules and systems. We developed an understanding of the structural, thermodynamic, and kinetic properties of the "simplest" biomolecules, including single chain amphiphiles like fatty acids, and double chain ones like phospholipids, and how these properties could explain the propensity of these molecules to form complex lipid aggregates (micelles and bilayers). We extended these ideas to the process of protein folding and the assembly of biological complexity. Is there something intrinsic to the property of molecules such that their localization together in the right microenvironment could lead to a "living cell"? How did life originate? That is the topic of this last capstone chapter. Defining life is actually quite difficult. Here is a list of requirements that seem reasonable, but other have noted that this list would exclude the mule. Life can self-replicate, self-sustain, evolve, respond to environmental changes, and die. The earliest known fossils (stromatolites from cyanobacteria) are approximately 3.5 billion years old. We have just finished studying the complex interactions involved in cell signaling. How could they have evolved? Consider the central dogma of biology. In the present biological world, proteins (DNA polymerase, RNA polymerase, transcription factors) are necessary for DNA synthesis, replication and gene transcription. But you need DNA to encode the proteins. This "chicken vs egg" dilemma has been addressed when it was realized that RNA can both carry genetic information as well as enzymatic activity (even at the level of the ribosome used for protein synthesis). https://clockwise.software/blog/solv...d-egg-problem/ for new picture Abiotic Synthesis of Amino Acids and Peptides Much work has been done to determine if the building blocks for present biological molecules could have been synthesized early in Earth's history. Amino acids and fatty acids have been found in meteors suggesting the possibility. Earth's early atmosphere would have had little oxygen, so most components should have been reduced. It probably consisted of methane, ammonia, hydrogen and water similar to the atmospheres of other planets in our solar system. The composition of the early atmosphere is still contentious. In 1953 (the same year that Watson and Crick published the structure of double-stranded DNA), Stanley Miller showed that electric discharges (to simulate lightening) in a reducing atmosphere over a "simulated sea" produced many amino acids. Up to 11 different amino acids have been produced in this fashion along with purines and pyrimidines (these required concentrated reaction mixtures) necessary for nucleic acids. Adenine can be produced just through the reaction of hydrogen cyanide and ammonia in an aqueous solution. Other nucleic acid bases can be made with hydrogen cyanide, cyanogen (C2N2) and cyanoacetylene (HC3N). No complex polymers arise through these reactions. However, in 2004, Lehman, Orgel and Ghadiri were able to show that in the presence of carbon disulfide, a gas discharged from volcanoes, homo- and hetero-peptides were produced. Amphiphilic peptides can even catalyze their own formation from peptide fragments, if the fragments are activated. The fragments would bind to the larger "template" peptide through nonpolar actions of the side chains which are oriented along one face of the helical axes. If the fragments bind in a fashion in which the electrophilic end is adjacent to the nucleophilic end of the other peptide fragment, condensation of the two peptide fragments results. The larger template peptide acts as a template (effectively as an "enzyme") in orienting the two fragments for chemical reaction and effectively increasing their local concentration. The reaction of the bound fragments is essentially intramolecular. The reaction even proceeds with amplification of homochirality. Could the prebiotic amino acids have polymerized into a protein that could fold in a fashion similar to modern proteins? That question has recently been addressed by Longo et al (2013). They asked the question whether the amino acids found in Miller-type prebiotic synthesis mixture and in comets/meteors (Ala, Asp, Glu, Gly, Ile, Leu, Pro, Ser, Thr and Val), a restricted set (10) compared to the present 20 naturally-occurring amino acid, could form a polymer that could fold. Notice that this reduced ensemble of amino acids lacks aromatic and basic amino acids. These proteins would be acidic with a low pI and may have trouble, given the lack of nonpolar aromatic amino acids, in forming a buried hydrophobic core which stabilize proteins. Nevertheless Longo et al were able to synthesize a protein with a slightly expanded set of amino acids (12, including Asn and Gln, with 70% prebiotic amino acids). The structure of one of the proteins, PV2, is shown below. The protein was more stable in 2 M NaCl (compared to 0.1 M) in which it showed a cooperative thermal denaturation with a melting point near 650C using differential scanning calorimetry. The protein had properties similar to those from halophilic organisms that thrive in high salt. These properties include low pIs and high negative charge density, which allows cation-protein interactions in the high salt environment, and lower stability in low salt environments. Earlier oceans were saltier. Halophiles are an example of extremeophiles which are highly represented in archea. Although most halophiles are aerobic, some are anerobic. Perhaps life arose in high salt environments. Figure: Structure of PV2 protein comprised of a reduced alphabet of mainly prebiotic amino acids. Abiotic Synthesis of Sugars Sugars are required for present energy production but also as a part of the backbone (ribose, deoxyribose) of present genetic material. Many sugars can be synthesized in prebiotic conditions, using carbon based molecules with oxygen, such as glycoaldehyde and formaldehyde (both found in interstellar gases), as shown in the figure below. Glycoaldehyde was recently found in star forming regions of the Milky Way where planets are likely to form. The presence of borate, which stabilizes vicinal OHs on sugars, is required for the production of sugars instead of tars. RNA molecules containing sugars such as threose, aldopentopyraonses and hexopyranose can also form stable secondary structures like helices. (Remember, RNA probably preceded DNA as the genetic carrier of information given that is also has enzymatic activity). Is there something special about ribose that made it selected over other sugars for nucleic acids, especially since it is found in low abundancy in the products in synthesis reactions conducted under prebiotic conditions? One probable reason is it unusually high (compared to other sugars) permeability coefficient through vesicles made of phospholipids or single chain fatty acids, as shown below. In general, the greater the number of carbon atoms, the smaller the permeability. However, the table above clearly shows large differences in permeability for sugar isomers with the same number of C atoms, and the difference is not affected by the lipid composition. Ribose has markedly elevated permeability compared to other 5C sugars (as do erythritol and threitol among 4 C sugar alcohols). What is so unique about ribose? 20% of the sugar is in the furanose form. Rate constants for ring opening of furanoses are elevated, suggesting greater flexibility. The α-furanose anomer is amphiphilic in that one face is hydrophobic and the other hydrophilic. All of these may promote ribose permeability. Abiotic Synthesis of Nucleobases As mentioned above, nucleobases can be made under abiotic conditions with appropriate sources of carbon molecules with nitrogen. These include hydrogen cyanide, cyanoamide (NH2CN) (along with ammonia), hydrogen cyanide, cyanogen (C2N2) and cyanoacetylene (HC3N). Cyanoamide and cyanoacetylene can both react with water to form urea and cyanoacetaldehyde, respectively. The latter two can condense to form cytosine as shown below. Figure: Abiotic Synthesis of Cytosine A major problem that has plagued prebiotic researchers is how the product of the carbon-oxygen compound (sugar) links to the product of the carbon-nitrogen compound (nucleobase) to form the nucleoside. Recent research by Powner et al has offered an innovative solution. Instead of forming the sugar and base in separate reaction, and then linking them covalently, the combined molecule could be synthesized in a single set of linked reactions. Inorganic phosphate serves as both a general acid/base catalyst (HA/A- in the figure below) in these new reactions in the formation of an important intermediate, 2-amino-oxazole, and as a nucleophilic catalyst. Glyceraldehyde, 2-amino-oxazole, and inorganic phosphate can react to form a ribocytidine phosphate. Possible reaction mechanisms are shown below. Abiotic Synthesis of Genetic Polymers Abiological synthesis of polymer precursors is a long way from creating genetic polymers like RNA and DNA. These genetic polymers have one property that at first glance seems not conducive to a genetic molecule. Both are polyanions, which must be packed into a cell and folded onto itself to form the classic dsDNA helix and many different RNA structures. This problem is solved to some degree by the presence of counterions that help mask the charge on the negative backbone of the nucleic acids. The presence of phosphate in the phospodiester backbone linkage does confer an important advantage over other possible links (carboxylic acid esters, amides and anhydrides). The electrophilic phosphorous atom is hindered from nucleophilic attack by the negative O attached to the phosphorous. Also, the phosphorous is sp3 hybridized compared to the sp2 hybridization of the electrophilic carbon atom in anhydrides, esters, and amides, and hence is less accessible to nucleophilic attack. Most people now believe that RNA, which can act both as an enzyme and genetic template, preceded DNA as the genetic carrier. The evolution of DNA as the primary genetic carrier required an enzyme to convert ribose to deoxyribose. This would make the nucleic acid less likely to cleave at the phosphodiester bond with the replacement of a nucleophilic 2' OH with an H, and make the genetic molecule more stable. Other types of genetic carriers might have preceded the RNA world, especially if the monomer required could be more readily synthesized from abiological sources. One such alternative are threose nucleic acids (TNA). Synthetics ssTNA can base pair with either RNA, DNA, or itself to form duplexes. Other possible candidate include peptide nucleic acids (PNA). These can also form double stranded structures with DNA, RNA, or PNA single strands. They were initially designed to bind to dsDNA in the major grove forming a triple-stranded structure. Binding could alter DNA activity, possibly by inhibiting transcription, for example. The structure of a single-stranded PNA is shown. Note that the backbone, a polymer of N-(2-aminoethyl)glycine (AEG) which can be made in prebiotic soups, is not charged, making it easier to bind to dsDNA. AEG polymerizes at 100oC to form the backbone. In addition to changing the backbone, additional bases other than A, C, T, G, and U can be accommodated into dsDNA and ssRNA molecules (Brenner, 2004) In a recent extension, Pinheiro et al have shown that 6 different foreign backbone architectures can produce xeno-nucleic acids (XNAs) that can be replicated by engineered polymerases which make XNAs from a complementary DNA strand, and a polymerase that can make a complementary copy of DNA from an XNA. XNAs can also be evolved as aptamers to bind specific target molecules. The investigators replaced the deoxyribose and ribose backbone sugar with xenoanalogs (congeners) including 1,5-anhydrohexitol (HNAs), cyclohexene (CeNA), 2'-O,4'-C-methylene-b-D ribose (locked nucleic acids - LNA), L-arabinose (LNA), 2'-fluoro-L-arabinose (FANAs) and threose (TNAs) as shown in the figure below. Figure: Xeno-nucleic acid sugar congeners Polymers of these XNA can bind to complementary RNA and DNA and as such act as nuclease-resistant inhibitors of translation and transcription. Von Kiedrowski, in an experiment similar to the self-replication of peptides described above, has shown that a single stranded 14 mer DNA strand, when immobilized on a surface, can serve as a template for the binding of complementary 7 mers and their conversion to 14 mers. When released by base, this process can occur with exponential growth of the complementary 14 mers. (von Kiedrowski Nature, 396, Nov 1998). Ferris has shown that if the clay montmorillonite is added to an aqueous solution of diadensosine pyrophosphate, polymerization occurs to produce 10 mers which are 85% linked in a 5' to 3' direction. Polyanions as Carriers of Genetic Information There are other reasons why polyanions are useful genetic molecules, other than their resistance to nucleophilic attack. The biological form of DNA is a large double stranded polyanionic polymer, in contrast to RNA which is a single-stranded polyanion polymer and protein which are polymers with varying combination of anionic, cationic, and hydrophobic properties. Even with counterions, it would be difficult to fold DNA into complicated and compact 3D structures as occurs for proteins, given the large electrostatic repulsions among the charged phosphates. Rather it forms a elongated double stranded rod, not unlike the rod-like structure of proteins denatured with sodium dodecyl sulfate (used in SDS PAGE gels). The elonged rod-shaped structure of ds-DNA is critical for the molecule which is the main carrier of our genetic information since mutations in the bases (leading to a switch in base pairs) causes no change in the overall structure of dsDNA. This enables evolutionary changes in the genetic material to produce new functionalities. A single change an amino acid of a protein, however, can cause a large change in the structure of a whole protein, a feature unacceptable for a carrier of genetic information. RNA structure effectively lies between that of DNA and proteins. Since it has less charge density than dsDNA, it can actually form dsRNA helices, so it can carry genetic information, as well as form complex 3D shapes necessary for its activity as an ribozyme. Perhaps more importantly, steric interference prevents ribose in RNA from adopting the 2'endo conformation, and allows only the 3'endo form, precluding the occurrences of extended ds-B-RNA helices. The Lipid World Let's assume that abiological precursors would react to form polymer-like molecules that might be complex enough to fold to structures that would allow binding, catalysis, and rudimentary replication. All this would be worthless unless they could be sequestered in a small volume which would limit diffusion and increase their local concentration. What is required is a membrane structure. Amphiphilic molecules, like lipids, with which we started this book, would be prime candidates since they spontaneously assembly to form micelles and bilayers, as shown in the review diagram below. As mentioned in Chapter 1, alternative lipid phases are possible. Bilayers can also be formed from single chain amphiphiles, such as certain fatty acids, as illustrated in the equilibrium shown below. This occurs more readily at pH values close to the the pKa of the fatty acid, at which the fatty acids are not all deprotonated with full maximal negative charges. Single chain amphiphiles like fatty acids, which were more likely to formed in abiotic conditions, have been found in meteorites. Clay surfaces, which have been shown to facilitate the formation of nucleic acids polymers, can also promote the conversion of fatty acid micelles to bilayers (Szostak). One such clay surface, montmorillonite, whose structure is shown below, promotes bilayer formation. It has an empirical formula of Na0.2Ca0.1Al2Si4O10(OH)2(H2O)10. Chime Molecule Modeling: montmorillonite : montmorillonite The effect of montmorillonite on vesicle formation can be shown by simple measurements of turbidity with time. Microscopy of fluorophore-encapsulated vesicles also shows encapsulated montmorillonite. The fatty acids presumably absorb to the cation layer of the clay particles. Time studies using light scattering also indicate that the vesicles grow in the presence of fatty acid micelles. To differentiate between the formation of new vesicles and the increase in size of pre-existing vesicles (which couldn't be done by simple light scattering without separation of the vesicles), investigators used two different fluorescent molecules to label fatty acid vesicles. The two probes were selected such that if the two probes came in close contact, energy transfer from the excited state of one fluorophore to the other fluorophore could occur, an example of fluorescence resonance energy transfer (FRET). FRET is observed when emission of the second dye occurs after excitation of the first dye, at a wavelength outside of the excitation wavelength of the second dye. If unlabeled vesicles were added to either labeled vesicles, no changes in FRET were observed, suggesting that the dyes did not move between vesicles. If fatty acid micelles were added, a decrease in FRET was observed, suggesting that new fatty acids were transferred to the doubly-labeled vesicles, effectively diluting the dye concentrations in the bilayer and their relative proximity, both which would decrease FRET. Most of the new fatty acid was incorporated into pre-existing vesicles which grew. The vesicles could also divide if extruded through a small pore. Later we will see that the energy to grow the vesicles can derive in part from a transmembrane proton concentration collapse. Division of vesicles might be promoted by bilayer assymetries associated with addition of substances to the outer leaflet, causing membrane distortion. Protocells At some point, early genetic material must have been encapsulated in a membranous vesicles. Would new properties emerge from this mixture that might have a competitive (evolutionary) advantage over either component alone, and thus be a step on the way to the formation of a "living" cell? The answer appears to be yes. Chen et al. have incorporated RNA into fatty acid vesicles with interest effects. They asked the question as whether those vesicles could grow at the expense of vesicles without encapsulated RNA. RNA, with a high charge density and its associated counter ions would create osmotic stress on the vesicles membranes. To relieve that stress they could acquire fatty acids from other fatty acid vesicles (or fatty acid micelles), increasing their surface area, and concomitantly reducing tension in the membrane. Oleic acids vesicles were first placed under stress by encapsulating 1 M sucrose in the vesicle and then diluting it in hypotonic media. Water would enter and swell the vesicle (but without bursting and resealing, as evident from control experiments). Then they prepared stressed and unstressed oleic acid vesicles in the presence of two nonpolar flurophores, NBD-PE (excitation at 430 nm, emission at 530 nm) and Rh-DHPE (emission at 586 nm). These fluorophores were chosen for fluorescence resonance energy transfer measurements. If the membrane vesicles changed size, the FRET signal would change, based on the relative concentration and proximity of the dual fluorophores. If the separation between probe molecules increased, the FRET signal would decrease. Conversely, if the vesicle shrunk, the FRET signal would increase. The results showing the effect of adding unlabeled swollen vesicles to labeled normal vesicles, and labeled swollen vesicles to unlabeled normal are shown below. The surface area of normal labeled vesicles decreased by about 25% when unlabeled swollen vesicles were added, but not when unlabeled normal vesicles were added. Labeled swollen vesicles increased 25% in size only if mixed with unlabeled normal vesicles, not with unlabeled swollen vesicles. Hence swollen vesicles win the competition and "steal" lipid from normal vesicles. Now what about vesicles swollen with encapsulated RNA? RNA, with its associated charge and charged counter ions also placed an osmotic stress on the vesicles. FRET labels (the two fluorophores) were place in vesicles without RNA. Fatty acids were removed from isotonic labeled vesicles in the presence of unlabeled tRNA swollen vesicles (left panel below). Labeled vesicles swollen with glycerol took fatty acids from unswollen vesicles (without tRNA), but not from tRNA swollen vesicles, as both were swollen so no net drive to reduce swelling by lipid exchange was present. These results show the vesicles with encapsulated RNA have a competitive (evolutionary) advantage over normal vesicles. This data suggests that having a polyanion as the source of genetic material is actually advantageous to the protocell. In addition the move in modern membranes to phospholipids with esterified fatty acids (instead of free ones) may actually have stabilized membranes, given the movement of free fatty acids to different membranes. Energy Transduction in Protocells In addition to a genetic macromolecule and a semipermeable membrane, a source of energy to drive intracellular processes must be present. A common source of free energy used in many cells to drive unfavorable processes is a proton gradient, whose formation in modern cells can be coupled to energy input from oxidation, ATP cleavage, light, or the collapse of another gradient. Could a proton gradient be formed in protocells? It can, quite easily, when coupled to the growth of fatty acid vesicles. If a fatty acid vesicle is to grow, more fatty acid must be added to the outer leaflet. The protonated, uncharged form of the fatty acid would preferentially be added, since it would lead to less electrostatic repulsion between adjacent head groups. The protonated, uncharged form of the fatty acid would also be most likely to flip to the inner leaflet to minimize stress asymmetries in the leaflets. Once in the inner leaflet, it could deprotonate to form H+(aq) in the inside of the membrane, creating a transmembrane proton gradient and transmembrane potential. The energy released on growth of the membrane is partly captured in the formation of a proton gradient, as shown in the figure below. The proton gradient would soon inhibit its own formation since further movement of protons into the cell would be attenuated by the positive transmembrane potential unless metal ions inside moved outside. In addition, the gradient would collapse after growth stopped. The investigators made fatty acids vesicles in the presence of pH 8.5 buffers whose pH was adjusted with an alkali metal hydroxide. The external pH was reduced to 8.0, resulting in a 0.5 pH unit proton concentration gradient. (Changes in intravesicular pH were measured with pH-sensitive fluorophore, HPTS.) Inward movement of protons down a concentration gradient, as shown in the figure below, would occur with time, collapsing the imposed concentration gradient. With fatty acid vesicles, this artificial pH gradient collapsed quickly, suggesting the vesicle permeability to protons was high. The rate was too high for simple flip-flop diffusion. Inward movement of protons appeared to be facilitated by outward movement of the M+ ions. The rate of decay of the proton gradient was exponential, and the resulting first order rate constant was easily determined. A graph of the rate constant for pH gradient collapse vs unsolvated ionic radius of M+ decreased with increasing radius (i.e. kNa > kK > KRb > KCs, suggesting that the pH gradient would be more stable if large, impermeable or otherwise trapped cations were encapsulated. When vesicles were made with encapsulated Arg+, the imposed pH gradient did not collapse for hours. If oleic acid micelles were added to oleic acid vesicles with encapsulated Arg+, with no artificial pH gradient induced across the membrane, the vesicle grew with concomitant movement of protons into the vesicle, producing a pH gradient of 0.3 within seconds. These experiments show that membrane growth and energy storage could be coupled, and the right composition of encapsulated material could lead to a stable transmembrane pH gradient, a source of energy to drive biological processes. It even suggests that a charge polyanion would be beneficial as a genetic carrier. Hydrothermal Vents or Primoridal Soup The case for the origin of life in deep sea hydrothermal vents and not in a primordial "Campbell's" soup has been argued convincingly by Lane et al (2010). What's needed for life are reasonably complex molecules and an energy source to drive unfavorable reactions. It's the latter on which that Lane et al focus. In an early primordial world that was low in oxygen, exergonic oxidation reaction of organic molecules would provide little energy. This can be surmised from the low energy yield (compared to aerobic respiration) achieved in present day glycolytic (fermentative) pathways from all major domains of life, archaea, bacteria, and eukaryotes. Background: Based on rRNA sequences, a primordial cell evolved into two different types of cells, one that became bacteria, and another that split further into archaea (single cells, similar to bacteria) and eukaryotic cells (complex cells with internal organelles that eventually formed multicellular organisms). Bacterial and archaea are collected called prokaroytic cells. In addition, these pathways required the evolution of up to dozen different enzymes to produce their relatively meager energy yield which ultimately depends on the oxidation of an organic molecule by another organic molecule instead of by a powerful oxidant like dioxygen. An anisotropic arrangement of molecules in a concentrated soup could lead to transient chemical potential fluctuations but these would be inefficient and impermanent sources of energy. Effectively the primordial soup would be at equilibrium and hardly expected to provide the energy for synthesis of RNA enzymes and replicators. UV light leads to photo-damage and photolysis not replication of complex molecules. What is needed is a way to drive the synthesis of molecule with high chemical potential energy (like sulfur esters and phosphoanhydrides) compared to their lytic products. These could then provide an energy sources to drive ATP synthesis, for example. A detailed look a the bioworld shows that the earliest organisms used energy from the collapse of the proton gradient (chemisomotic principle elucidated by Peter Mitchell). All present autotrophs (organisms that can fix CO2 and form complex organic molecules) and many heterotrophs (use complex organic molecules of other organisms for fuel) use redox complexes in membranes coupled to membrane gradients. These complexes would take reduced molecules and pass electrons from them to oxidizing agents (electron acceptors), including O2, CO2, and Fe3+ to form H2O, CH4, and Fe2+. Fermentors also use ATPase membrane enzymes to transport nutrients. Yet genomic analysis of bacteria and archaea show that enzymes involved in fermentation differ significantly, suggesting that they evolved separately towards a convergent function. Structure in common include DNA, RNA, ribosomes and membrane ATPases, which Lane et al suggest were in a the Last Universal Common Cell (LUCA). All autotrophs produce their energy source by fixing CO2 using either H2 directly or indirectly using H2O and H2S. All of the are available in nonhydrothermal deep sea vents. Volcanic vents, however, are extremely hot (not optimal for organic molecule synthesis), very acidic, and lack hydrogen gas. A different type of nonvolcanic vent, an alkaline hydrothermal one, might produce more conducive as a site of the origin of life. In these vents, water chemically reacts with minerals in the crust (such as olivine) leading to their hydroxylation and subsequent fracture, with promotes more water entry into the crust. It has been reported that there is more water found as hydroxylated minerals in the crust, that there is liquid water in the oceans. These processes result in temperatures up to 200 degrees Celsius and release of hydrogen gas into a moderately alkaline vents into the sea water at temperatures more conducive (70 degrees C) to the origin of life. Figure: Alkaline Vent Fixing CO2 Of the five different pathways known to fix CO2, all require ATP except one. That one is present in both methanogen, which produce methane from CO2 and H2 and in the acetogens, which produce acetate (CH3CO2-) in the form of acetylCoA. The simpler reactions of forming acetic acid and methane are shown below: 2CO2 + 4H2 --> CH3CO2H + 2H2O. CO2 + 4H2 --> CH4 + 2H2O. The DG0 values for these reaction (calculated using DG0f for gas phase H2, CO2 and CH4 and liquid acetic acid and water are -75 and -131 kJ, respectively, at 250C, showing that they are thermodynamically favored. Making AcetylCoA, a "high" energy molecule compared to its hydrolysis products (as is ATP) from acetic acid and CoASH, a would require energy input. A proton gradient is the likely source. Some bacteria and Achaea cells (primordial or present) use the reductive acetylCoA pathway, also known as the Wood-Ljungdahl pathway, to form, in a noncyclic process, acetyl CoA from CO2 and at the same time makes ATP. This process is paid for by a proton gradient. This has been described by Shock as "a free lunch you get paid to eat". The energetics of the present acetyl CoA pathway based on the overall reaction below show an approximate DG0 value of -59 kJ/mol which can drive ATP synthesis. 2CO2 + 4H2 + CoASH --> CH3COSCoA + 3H2O. The concentration of carbon dioxide in the primordial ocean was 1000 times higher than now. Vents produced large amounts of methane and hydrogen gas. There was little oxygen and hence lots of Fe2+. The enzymes involved in this acetyl-CoA pathway of carbon fixation have FeS clusters. It has also been shown that bubbles (which are really membrane bound spaces) of FeS and NiS can be made in deep sea vents. These could not only encapsulate precursor molecules but also serve as catalysts. Vents also can catalyze the fixation of nitrogen (to ammonia) and laboratory studied show that FeS can catalyze the conversion of formate (found in vents) into pyrimidines and purines. The studies of present methanogens and vent chemistry suggest that the critical ingredients and conditions for development of the first biological cells probably occurred in the vents. To produce polymers, an energy source and monomers must exists. Concentration gradients found in simulations of vents produce million fold concentrated molecules. The transient heating and cooling of any double-stranded nucleic acids could lead to concentration amplification by a PCR like strand separation followed by reannealing. In addition, these vent regions possess a powerful, reoccurring energy source, a pH gradient, as the alkaline vented material entered the acidic oceans that exists with high CO2 concentrations, creating a gradient across an inorganic membrane. This is startlingly analogous to the pH gradient across membranes (acidic outside, alkaline inside) driven by the membrane complexes in the mitochondria and bacteria. Lane et al argue that the existence of membrane proton gradients as an energy source in all cells (eukaryotes, bacteria, and archaea) and in chloroplasts, mitochondria, corroborate their hypothesis. Bacteria and archaea share homologous ATPases and electron carriers (ferredoxins, quinones, and cytochromes). These similarities contrast to the differences in enzyme structures in fermentative pathways. Arguments that proton pumps evolved to pump proteins (and reduce pH gradients) can't explain their ubiquitous presence even in organisms not subjected to low pH. Hence the ubiquity of proton pumps supports the conjecture that they arose from the first protocells, possible comprised of inorganic walls and ultimately with amphiphilic molecules synthesized from precursors. Creationists would argue that it would be impossible to evolve a structure with the complexity of membrane ATPase (which serve to collapse a pH gradient as the power the synthesis of molecules with large negative DG0 of hydrolysis). Lane et al propose that the earliest cells evolved ATPase like molecules in alkaline vents where pH gradients analogous to those in cells today arose. They envision cell-like columns lined by FeS membrane like structure with alkaline conditions inside and acid conditions outside. Nonpolar or amphiphilic molecule would line the inside of the cells/columns. A ATPase-like system could then take advantage of the pH gradient which constantly replenishes itself. If structures as complicated as ribosomes evolved from a subsequent RNA world, surely ATPase-like molecules could also. Other chemistry might have evolved earlier to utilize the energy source provided by the pH gradient. If life originated in the vents, it would need an energy source to leave the vents. Presumably it would have evolved one to utilized pH gradient to replace the one it left in the alkaline vents. The substrate level phosphorylation of glycolysis that requires ATP input to make ATP would not provide the energy source needed. Cells that left would have had to produce their own proton gradient. Perhaps all the was needed initially was concerted conformational changes in proteins that upon exposure of a different pH changed their shape inducing pKa shifts in adjacent proton donors/acceptors leading to vectorial discharge of protons across a membrane. Perhaps the method described above in protocells was sufficient. Recent analyses by Poehlein et a show that CO2 reduction (fixation) can be coupled to the production of a sodium ion gradient, which could collapse to drive ATP synthesis. Analysis of the genome of a gram positive bacteria, Acetobacterium woodii, an acetogen, shows the it has an ancient pathway for production of acetyl-CoA that can, in an anabolic fashion form biomass or in a catabolic fashion be cleaved to acetate with the production of ATP. It does not require classic electron carriers like ubiquinone or cytochrome C linked to protein gradient formation to drive ATP synthesis. Rather it has only a ferredoxin:NAD+ oxioreductase which couples oxidation to the formation of a sodium ion gradient, which collapses through an sodium ion transporter/ATP synthase to drive ATP synthase. A plausible reaction scheme based on genomic analysis is shown below: Figure: Acetyl-CoA Synthase and Acetogenesis The Role of Fe/S Center Let's return to the chicken and egg dilemma one more time. What is needed for biological polymer formation are monomeric precursors, an energy source, and a way to compartmentalized them all. We discuss how monomeric precursors could form, but wouldn't it be far better if even the synthesis of precursors could be catalyzed? One source of catalysis mostly absent from the "bioorganic" abiotic chemistry in the above discussion is the transition metals. Transition metals can form complexes. Ligands containing lone pairs on O, N, and S atoms can donate them to transition metals ions, which can hold up to 18 electrons in s, p, and d orbitals. Hence as many as 9 lone pairs on ligand molecules (which are often multidentate) could be accommodated around the transition metal ion. Many present small molecule metabolites and their abiotic precursors (H2O, CO, CO2, NH3 and thiols) bind cations as mono- or polydentate donors of electrons. Hence transition metal ions would have a thermodynamic tendencies to be bound in complexes. Bound ligands that contain potentially ionizable hydrogens could become deprotonated and made better nucleophiles for reactions. Hence the transition state metal ion, acting with the complex, becomes a catalyst as it decreases the pKa of a bound ligand (such as water). In addition, since transition metals ions can have multiple charge and oxidation states, they can easily act as redox centers in the oxidation/reduction of bound ligands that were redox active. Given the relative anoxic conditions of the early oceans, Fe2+ would predominant. It could easily be oxidized to Fe3+ as it reduced a bound ligand. Highly charged transition states would withdraw electron density from bound ligands leading to their possible oxidation. Metals obviously still play a strong role in catalysis, both indirectly in promoting correct protein folding and directly in stabilizing charge in both the transition state and intermediates in chemical reaction pathways. FeS clusters are of significant importance. Their biosynthesis involves removal by an active site Cys in a desulfurase enzyme of a sulfur from a free amino acid Cys followed by its transfer to an Fe in a growing FeS cluster in a FeS scaffold protein, which then transfers the cluster to an acceptor protein where it acts as a cofactor. FeS clusters can adopt a variety of stoichiometries and shapes, as well as redox states for the participating Fe ions. The continuing importance of FeS clusters in all cells, their involvement in not only redox enzymes in which electron transfer is facilitated by delocalization of electrons over both Fe and S centers, but also in coupled electron/proton transport in mitochondrial electron transport, Fe storage (ferrodoxins), and in regulation of enzyme activity and gene expression, suggests that they were of primordial importance in the evolution of life. T hey are often found at substrate binding sites of FeS enzymes involved in both redox and nonredox catalysis. A ligand can bind to a particular Fe in the cluster, activating it for hydration or dehydrogenation reactions. Fe 4 of the FeS cluster in the TCA enzyme aconitase can have a coordination numbers of 4, 5, or 6 as it binds water, hydroxide or substrate. It acts to both decrease electron density in the transition state and to change the pKa of bound water as the enzyme catalyzes an isomerization of tricarboxylic acids (citric and isocitric acid) through an elimination/addition reaction with water. In another example it can bind S-adenosylmethionine through its amine and carboxylate groups, which activates the molecule for cleavage and radical formation. In some cases metals other than Fe (Ni for example) are incorporated into the cluster. FeS effects on transcription factors involves facilitation of optimal structure for DNA binding. FeS and FeNi centers in proteins are similar in structure tp FeS units in minerals like greigite and presumably to FeS structure formed when H2S and S2- react with Fe2+ (present in abundance in the early ocean) and other metals in vents Metal sulfides participate in reduction of both CO and CO2. For example the synthesis of CH3SH from CO2 and H2S is catalyzed by "inorganic" FeS. The Minimal Genome This question is being addressed by eliminating "unnecessary" gene from simple bacteria. Cells placed in a rich nutrient broth with essential lipids, vitamins, and amino acids would need fewer genes than those placed in a more nutrient-poor medium. Bacteria cells like Mycoplasma genetalium, that live within "nutrient rich" eukaryotic cell, have been genetically manipulated to delete unnecessary genes. Based on knockout studies, it may be possible for the cell to survive with only 300-350 genes. Bacillus subtilis has approximately 4100 genes. Estimates have been made that it could survive with as few as 271 genes. more to be added 30: Abiotic Origins of Life "Ugly giant bags of mostly water" - The crystal life form describing humans on Star Trek Next Generation, Home Soil Episode. The Start of Life This book began with the notion that understandings derived from the study of simple molecules can be applied to complex biological macromolecules and systems. We developed an understanding of the structural, thermodynamic, and kinetic properties of the "simplest" biomolecules, including single chain amphiphiles like fatty acids, and double chain ones like phospholipids, and how these properties could explain the propensity of these molecules to form complex lipid aggregates (micelles and bilayers). We extended these ideas to the process of protein folding and the assembly of biological complexity. Is there something intrinsic to the property of molecules such that their localization together in the right microenvironment could lead to a "living cell"? How did life originate? That is the topic of this last capstone chapter. Defining life is actually quite difficult. Here is a list of requirements that seem reasonable, but other have noted that this list would exclude the mule. Life can self-replicate, self-sustain, evolve, respond to environmental changes, and die. The earliest known fossils (stromatolites from cyanobacteria) are approximately 3.5 billion years old. We have just finished studying the complex interactions involved in cell signaling. How could they have evolved? Consider the central dogma of biology. In the present biological world, proteins (DNA polymerase, RNA polymerase, transcription factors) are necessary for DNA synthesis, replication and gene transcription. But you need DNA to encode the proteins. This "chicken vs egg" dilemma has been addressed when it was realized that RNA can both carry genetic information as well as enzymatic activity (even at the level of the ribosome used for protein synthesis). https://clockwise.software/blog/solv...d-egg-problem/ for new picture Abiotic Synthesis of Amino Acids and Peptides Much work has been done to determine if the building blocks for present biological molecules could have been synthesized early in Earth's history. Amino acids and fatty acids have been found in meteors suggesting the possibility. Earth's early atmosphere would have had little oxygen, so most components should have been reduced. It probably consisted of methane, ammonia, hydrogen and water similar to the atmospheres of other planets in our solar system. The composition of the early atmosphere is still contentious. In 1953 (the same year that Watson and Crick published the structure of double-stranded DNA), Stanley Miller showed that electric discharges (to simulate lightening) in a reducing atmosphere over a "simulated sea" produced many amino acids. Up to 11 different amino acids have been produced in this fashion along with purines and pyrimidines (these required concentrated reaction mixtures) necessary for nucleic acids. Adenine can be produced just through the reaction of hydrogen cyanide and ammonia in an aqueous solution. Other nucleic acid bases can be made with hydrogen cyanide, cyanogen (C2N2) and cyanoacetylene (HC3N). http://www.hencoup.com/Photo%20Stanley%20Miller.jpg No complex polymers arise through these reactions. However, in 2004, Lehman, Orgel and Ghadiri were able to show that in the presence of carbon disulfide, a gas discharged from volcanoes, homo- and hetero-peptides were produced. Amphiphilic peptides can even catalyze their own formation from peptide fragments, if the fragments are activated. The fragments would bind to the larger "template" peptide through nonpolar actions of the side chains which are oriented along one face of the helical axes. If the fragments bind in a fashion in which the electrophilic end is adjacent to the nucleophilic end of the other peptide fragment, condensation of the two peptide fragments results. The larger template peptide acts as a template (effectively as an "enzyme") in orienting the two fragments for chemical reaction and effectively increasing their local concentration. The reaction of the bound fragments is essentially intramolecular. The reaction even proceeds with amplification of homochirality. Could the prebiotic amino acids have polymerized into a protein that could fold in a fashion similar to modern proteins? That question has recently been addressed by Longo et al (2013). They asked the question whether the amino acids found in Miller-type prebiotic synthesis mixture and in comets/meteors (Ala, Asp, Glu, Gly, Ile, Leu, Pro, Ser, Thr and Val), a restricted set (10) compared to the present 20 naturally-occurring amino acid, could form a polymer that could fold. Notice that this reduced ensemble of amino acids lacks aromatic and basic amino acids. These proteins would be acidic with a low pI and may have trouble, given the lack of nonpolar aromatic amino acids, in forming a buried hydrophobic core which stabilize proteins. Nevertheless Longo et al were able to synthesize a protein with a slightly expanded set of amino acids (12, including Asn and Gln, with 70% prebiotic amino acids). The structure of one of the proteins, PV2, is shown below. The protein was more stable in 2 M NaCl (compared to 0.1 M) in which it showed a cooperative thermal denaturation with a melting point near 650C using differential scanning calorimetry. The protein had properties similar to those from halophilic organisms that thrive in high salt. These properties include low pIs and high negative charge density, which allows cation-protein interactions in the high salt environment, and lower stability in low salt environments. Earlier oceans were saltier. Halophiles are an example of extremeophiles which are highly represented in archea. Although most halophiles are aerobic, some are anerobic. Perhaps life arose in high salt environments. Figure: Structure of PV2 protein comprised of a reduced alphabet of mainly prebiotic amino acids. Abiotic Synthesis of Sugars Sugars are required for present energy production but also as a part of the backbone (ribose, deoxyribose) of present genetic material. Many sugars can be synthesized in prebiotic conditions, using carbon based molecules with oxygen, such as glycoaldehyde and formaldehyde (both found in interstellar gases), as shown in the figure below. Glycoaldehyde was recently found in star forming regions of the Milky Way where planets are likely to form. The presence of borate, which stabilizes vicinal OHs on sugars, is required for the production of sugars instead of tars. RNA molecules containing sugars such as threose, aldopentopyraonses and hexopyranose can also form stable secondary structures like helices. (Remember, RNA probably preceded DNA as the genetic carrier of information given that is also has enzymatic activity). Is there something special about ribose that made it selected over other sugars for nucleic acids, especially since it is found in low abundancy in the products in synthesis reactions conducted under prebiotic conditions? One probable reason is it unusually high (compared to other sugars) permeability coefficient through vesicles made of phospholipids or single chain fatty acids, as shown below. In general, the greater the number of carbon atoms, the smaller the permeability. However, the table above clearly shows large differences in permeability for sugar isomers with the same number of C atoms, and the difference is not affected by the lipid composition. Ribose has markedly elevated permeability compared to other 5C sugars (as do erythritol and threitol among 4 C sugar alcohols). What is so unique about ribose? 20% of the sugar is in the furanose form. Rate constants for ring opening of furanoses are elevated, suggesting greater flexibility. The α-furanose anomer is amphiphilic in that one face is hydrophobic and the other hydrophilic. All of these may promote ribose permeability. Abiotic Synthesis of Nucleobases As mentioned above, nucleobases can be made under abiotic conditions with appropriate sources of carbon molecules with nitrogen. These include hydrogen cyanide, cyanoamide (NH2CN) (along with ammonia), hydrogen cyanide, cyanogen (C2N2) and cyanoacetylene (HC3N). Cyanoamide and cyanoacetylene can both react with water to form urea and cyanoacetaldehyde, respectively. The latter two can condense to form cytosine as shown below. Figure: Abiotic Synthesis of Cytosine A major problem that has plagued prebiotic researchers is how the product of the carbon-oxygen compound (sugar) links to the product of the carbon-nitrogen compound (nucleobase) to form the nucleoside. Recent research by Powner et al has offered an innovative solution. Instead of forming the sugar and base in separate reaction, and then linking them covalently, the combined molecule could be synthesized in a single set of linked reactions. Inorganic phosphate serves as both a general acid/base catalyst (HA/A- in the figure below) in these new reactions in the formation of an important intermediate, 2-amino-oxazole, and as a nucleophilic catalyst. Glyceraldehyde, 2-amino-oxazole, and inorganic phosphate can react to form a ribocytidine phosphate. Possible reaction mechanisms are shown below. Abiotic Synthesis of Genetic Polymers Abiological synthesis of polymer precursors is a long way from creating genetic polymers like RNA and DNA. These genetic polymers have one property that at first glance seems not conducive to a genetic molecule. Both are polyanions, which must be packed into a cell and folded onto itself to form the classic dsDNA helix and many different RNA structures. This problem is solved to some degree by the presence of counterions that help mask the charge on the negative backbone of the nucleic acids. The presence of phosphate in the phospodiester backbone linkage does confer an important advantage over other possible links (carboxylic acid esters, amides and anhydrides). The electrophilic phosphorous atom is hindered from nucleophilic attack by the negative O attached to the phosphorous. Also, the phosphorous is sp3 hybridized compared to the sp2 hybridization of the electrophilic carbon atom in anhydrides, esters, and amides, and hence is less accessible to nucleophilic attack. Most people now believe that RNA, which can act both as an enzyme and genetic template, preceded DNA as the genetic carrier. The evolution of DNA as the primary genetic carrier required an enzyme to convert ribose to deoxyribose. This would make the nucleic acid less likely to cleave at the phosphodiester bond with the replacement of a nucleophilic 2' OH with an H, and make the genetic molecule more stable. Other types of genetic carriers might have preceded the RNA world, especially if the monomer required could be more readily synthesized from abiological sources. One such alternative are threose nucleic acids (TNA). Synthetics ssTNA can base pair with either RNA, DNA, or itself to form duplexes. Other possible candidate include peptide nucleic acids (PNA). These can also form double stranded structures with DNA, RNA, or PNA single strands. They were initially designed to bind to dsDNA in the major grove forming a triple-stranded structure. Binding could alter DNA activity, possibly by inhibiting transcription, for example. The structure of a single-stranded PNA is shown. Note that the backbone, a polymer of N-(2-aminoethyl)glycine (AEG) which can be made in prebiotic soups, is not charged, making it easier to bind to dsDNA. AEG polymerizes at 100oC to form the backbone. In addition to changing the backbone, additional bases other than A, C, T, G, and U can be accommodated into dsDNA and ssRNA molecules (Brenner, 2004) In a recent extension, Pinheiro et al have shown that 6 different foreign backbone architectures can produce xeno-nucleic acids (XNAs) that can be replicated by engineered polymerases which make XNAs from a complementary DNA strand, and a polymerase that can make a complementary copy of DNA from an XNA. XNAs can also be evolved as aptamers to bind specific target molecules. The investigators replaced the deoxyribose and ribose backbone sugar with xenoanalogs (congeners) including 1,5-anhydrohexitol (HNAs), cyclohexene (CeNA), 2'-O,4'-C-methylene-b-D ribose (locked nucleic acids - LNA), L-arabinose (LNA), 2'-fluoro-L-arabinose (FANAs) and threose (TNAs) as shown in the figure below. Figure: Xeno-nucleic acid sugar congeners Polymers of these XNA can bind to complementary RNA and DNA and as such act as nuclease-resistant inhibitors of translation and transcription. Von Kiedrowski, in an experiment similar to the self-replication of peptides described above, has shown that a single stranded 14 mer DNA strand, when immobilized on a surface, can serve as a template for the binding of complementary 7 mers and their conversion to 14 mers. When released by base, this process can occur with exponential growth of the complementary 14 mers. (von Kiedrowski Nature, 396, Nov 1998). Ferris has shown that if the clay montmorillonite is added to an aqueous solution of diadensosine pyrophosphate, polymerization occurs to produce 10 mers which are 85% linked in a 5' to 3' direction. Polyanions as Carriers of Genetic Information There are other reasons why polyanions are useful genetic molecules, other than their resistance to nucleophilic attack. The biological form of DNA is a large double stranded polyanionic polymer, in contrast to RNA which is a single-stranded polyanion polymer and protein which are polymers with varying combination of anionic, cationic, and hydrophobic properties. Even with counterions, it would be difficult to fold DNA into complicated and compact 3D structures as occurs for proteins, given the large electrostatic repulsions among the charged phosphates. Rather it forms a elongated double stranded rod, not unlike the rod-like structure of proteins denatured with sodium dodecyl sulfate (used in SDS PAGE gels). The elonged rod-shaped structure of ds-DNA is critical for the molecule which is the main carrier of our genetic information since mutations in the bases (leading to a switch in base pairs) causes no change in the overall structure of dsDNA. This enables evolutionary changes in the genetic material to produce new functionalities. A single change an amino acid of a protein, however, can cause a large change in the structure of a whole protein, a feature unacceptable for a carrier of genetic information. RNA structure effectively lies between that of DNA and proteins. Since it has less charge density than dsDNA, it can actually form dsRNA helices, so it can carry genetic information, as well as form complex 3D shapes necessary for its activity as an ribozyme. Perhaps more importantly, steric interference prevents ribose in RNA from adopting the 2'endo conformation, and allows only the 3'endo form, precluding the occurrences of extended ds-B-RNA helices. The Lipid World Let's assume that abiological precursors would react to form polymer-like molecules that might be complex enough to fold to structures that would allow binding, catalysis, and rudimentary replication. All this would be worthless unless they could be sequestered in a small volume which would limit diffusion and increase their local concentration. What is required is a membrane structure. Amphiphilic molecules, like lipids, with which we started this book, would be prime candidates since they spontaneously assembly to form micelles and bilayers, as shown in the review diagram below. As mentioned in Chapter 1, alternative lipid phases are possible. Bilayers can also be formed from single chain amphiphiles, such as certain fatty acids, as illustrated in the equilibrium shown below. This occurs more readily at pH values close to the the pKa of the fatty acid, at which the fatty acids are not all deprotonated with full maximal negative charges. Single chain amphiphiles like fatty acids, which were more likely to formed in abiotic conditions, have been found in meteorites. Clay surfaces, which have been shown to facilitate the formation of nucleic acids polymers, can also promote the conversion of fatty acid micelles to bilayers (Szostak). One such clay surface, montmorillonite, whose structure is shown below, promotes bilayer formation. It has an empirical formula of Na0.2Ca0.1Al2Si4O10(OH)2(H2O)10. Chime Molecule Modeling: montmorillonite : montmorillonite The effect of montmorillonite on vesicle formation can be shown by simple measurements of turbidity with time. Microscopy of fluorophore-encapsulated vesicles also shows encapsulated montmorillonite. The fatty acids presumably absorb to the cation layer of the clay particles. Time studies using light scattering also indicate that the vesicles grow in the presence of fatty acid micelles. To differentiate between the formation of new vesicles and the increase in size of pre-existing vesicles (which couldn't be done by simple light scattering without separation of the vesicles), investigators used two different fluorescent molecules to label fatty acid vesicles. The two probes were selected such that if the two probes came in close contact, energy transfer from the excited state of one fluorophore to the other fluorophore could occur, an example of fluorescence resonance energy transfer (FRET). FRET is observed when emission of the second dye occurs after excitation of the first dye, at a wavelength outside of the excitation wavelength of the second dye. If unlabeled vesicles were added to either labeled vesicles, no changes in FRET were observed, suggesting that the dyes did not move between vesicles. If fatty acid micelles were added, a decrease in FRET was observed, suggesting that new fatty acids were transferred to the doubly-labeled vesicles, effectively diluting the dye concentrations in the bilayer and their relative proximity, both which would decrease FRET. Most of the new fatty acid was incorporated into pre-existing vesicles which grew. The vesicles could also divide if extruded through a small pore. Later we will see that the energy to grow the vesicles can derive in part from a transmembrane proton concentration collapse. Division of vesicles might be promoted by bilayer assymetries associated with addition of substances to the outer leaflet, causing membrane distortion. Protocells At some point, early genetic material must have been encapsulated in a membranous vesicles. Would new properties emerge from this mixture that might have a competitive (evolutionary) advantage over either component alone, and thus be a step on the way to the formation of a "living" cell? The answer appears to be yes. Chen et al. have incorporated RNA into fatty acid vesicles with interest effects. They asked the question as whether those vesicles could grow at the expense of vesicles without encapsulated RNA. RNA, with a high charge density and its associated counter ions would create osmotic stress on the vesicles membranes. To relieve that stress they could acquire fatty acids from other fatty acid vesicles (or fatty acid micelles), increasing their surface area, and concomitantly reducing tension in the membrane. Oleic acids vesicles were first placed under stress by encapsulating 1 M sucrose in the vesicle and then diluting it in hypotonic media. Water would enter and swell the vesicle (but without bursting and resealing, as evident from control experiments). Then they prepared stressed and unstressed oleic acid vesicles in the presence of two nonpolar flurophores, NBD-PE (excitation at 430 nm, emission at 530 nm) and Rh-DHPE (emission at 586 nm). These fluorophores were chosen for fluorescence resonance energy transfer measurements. If the membrane vesicles changed size, the FRET signal would change, based on the relative concentration and proximity of the dual fluorophores. If the separation between probe molecules increased, the FRET signal would decrease. Conversely, if the vesicle shrunk, the FRET signal would increase. The results showing the effect of adding unlabeled swollen vesicles to labeled normal vesicles, and labeled swollen vesicles to unlabeled normal are shown below. The surface area of normal labeled vesicles decreased by about 25% when unlabeled swollen vesicles were added, but not when unlabeled normal vesicles were added. Labeled swollen vesicles increased 25% in size only if mixed with unlabeled normal vesicles, not with unlabeled swollen vesicles. Hence swollen vesicles win the competition and "steal" lipid from normal vesicles. Now what about vesicles swollen with encapsulated RNA? RNA, with its associated charge and charged counter ions also placed an osmotic stress on the vesicles. FRET labels (the two fluorophores) were place in vesicles without RNA. Fatty acids were removed from isotonic labeled vesicles in the presence of unlabeled tRNA swollen vesicles (left panel below). Labeled vesicles swollen with glycerol took fatty acids from unswollen vesicles (without tRNA), but not from tRNA swollen vesicles, as both were swollen so no net drive to reduce swelling by lipid exchange was present. These results show the vesicles with encapsulated RNA have a competitive (evolutionary) advantage over normal vesicles. This data suggests that having a polyanion as the source of genetic material is actually advantageous to the protocell. In addition the move in modern membranes to phospholipids with esterified fatty acids (instead of free ones) may actually have stabilized membranes, given the movement of free fatty acids to different membranes. Energy Transduction in Protocells In addition to a genetic macromolecule and a semipermeable membrane, a source of energy to drive intracellular processes must be present. A common source of free energy used in many cells to drive unfavorable processes is a proton gradient, whose formation in modern cells can be coupled to energy input from oxidation, ATP cleavage, light, or the collapse of another gradient. Could a proton gradient be formed in protocells? It can, quite easily, when coupled to the growth of fatty acid vesicles. If a fatty acid vesicle is to grow, more fatty acid must be added to the outer leaflet. The protonated, uncharged form of the fatty acid would preferentially be added, since it would lead to less electrostatic repulsion between adjacent head groups. The protonated, uncharged form of the fatty acid would also be most likely to flip to the inner leaflet to minimize stress asymmetries in the leaflets. Once in the inner leaflet, it could deprotonate to form H+(aq) in the inside of the membrane, creating a transmembrane proton gradient and transmembrane potential. The energy released on growth of the membrane is partly captured in the formation of a proton gradient, as shown in the figure below. The proton gradient would soon inhibit its own formation since further movement of protons into the cell would be attenuated by the positive transmembrane potential unless metal ions inside moved outside. In addition, the gradient would collapse after growth stopped. The investigators made fatty acids vesicles in the presence of pH 8.5 buffers whose pH was adjusted with an alkali metal hydroxide. The external pH was reduced to 8.0, resulting in a 0.5 pH unit proton concentration gradient. (Changes in intravesicular pH were measured with pH-sensitive fluorophore, HPTS.) Inward movement of protons down a concentration gradient, as shown in the figure below, would occur with time, collapsing the imposed concentration gradient. With fatty acid vesicles, this artificial pH gradient collapsed quickly, suggesting the vesicle permeability to protons was high. The rate was too high for simple flip-flop diffusion. Inward movement of protons appeared to be facilitated by outward movement of the M+ ions. The rate of decay of the proton gradient was exponential, and the resulting first order rate constant was easily determined. A graph of the rate constant for pH gradient collapse vs unsolvated ionic radius of M+ decreased with increasing radius (i.e. kNa > kK > KRb > KCs, suggesting that the pH gradient would be more stable if large, impermeable or otherwise trapped cations were encapsulated. When vesicles were made with encapsulated Arg+, the imposed pH gradient did not collapse for hours. If oleic acid micelles were added to oleic acid vesicles with encapsulated Arg+, with no artificial pH gradient induced across the membrane, the vesicle grew with concomitant movement of protons into the vesicle, producing a pH gradient of 0.3 within seconds. These experiments show that membrane growth and energy storage could be coupled, and the right composition of encapsulated material could lead to a stable transmembrane pH gradient, a source of energy to drive biological processes. It even suggests that a charge polyanion would be beneficial as a genetic carrier. Hydrothermal Vents or Primoridal Soup? The case for the origin of life in deep sea hydrothermal vents and not in a primordial "Campbell's" soup has been argued convincingly by Lane et al (2010). What's needed for life are reasonably complex molecules and an energy source to drive unfavorable reactions. It's the latter on which that Lane et al focus. In an early primordial world that was low in oxygen, exergonic oxidation reaction of organic molecules would provide little energy. This can be surmised from the low energy yield (compared to aerobic respiration) achieved in present day glycolytic (fermentative) pathways from all major domains of life, archaea, bacteria, and eukaryotes. Background: Based on rRNA sequences, a primordial cell evolved into two different types of cells, one that became bacteria, and another that split further into archaea (single cells, similar to bacteria) and eukaryotic cells (complex cells with internal organelles that eventually formed multicellular organisms). Bacterial and archaea are collected called prokaroytic cells. In addition, these pathways required the evolution of up to dozen different enzymes to produce their relatively meager energy yield which ultimately depends on the oxidation of an organic molecule by another organic molecule instead of by a powerful oxidant like dioxygen. An anisotropic arrangement of molecules in a concentrated soup could lead to transient chemical potential fluctuations but these would be inefficient and impermanent sources of energy. Effectively the primordial soup would be at equilibrium and hardly expected to provide the energy for synthesis of RNA enzymes and replicators. UV light leads to photo-damage and photolysis not replication of complex molecules. What is needed is a way to drive the synthesis of molecule with high chemical potential energy (like sulfur esters and phosphoanhydrides) compared to their lytic products. These could then provide an energy sources to drive ATP synthesis, for example. A detailed look a the bioworld shows that the earliest organisms used energy from the collapse of the proton gradient (chemisomotic principle elucidated by Peter Mitchell). All present autotrophs (organisms that can fix CO2 and form complex organic molecules) and many heterotrophs (use complex organic molecules of other organisms for fuel) use redox complexes in membranes coupled to membrane gradients. These complexes would take reduced molecules and pass electrons from them to oxidizing agents (electron acceptors), including O2, CO2, and Fe3+ to form H2O, CH4, and Fe2+. Fermentors also use ATPase membrane enzymes to transport nutrients. Yet genomic analysis of bacteria and archaea show that enzymes involved in fermentation differ significantly, suggesting that they evolved separately towards a convergent function. Structure in common include DNA, RNA, ribosomes and membrane ATPases, which Lane et al suggest were in a the Last Universal Common Cell (LUCA). All autotrophs produce their energy source by fixing CO2 using either H2 directly or indirectly using H2O and H2S. All of the are available in nonhydrothermal deep sea vents. Volcanic vents, however, are extremely hot (not optimal for organic molecule synthesis), very acidic, and lack hydrogen gas. A different type of nonvolcanic vent, an alkaline hydrothermal one, might produce more conducive as a site of the origin of life. In these vents, water chemically reacts with minerals in the crust (such as olivine) leading to their hydroxylation and subsequent fracture, with promotes more water entry into the crust. It has been reported that there is more water found as hydroxylated minerals in the crust, that there is liquid water in the oceans. These processes result in temperatures up to 200 degrees Celsius and release of hydrogen gas into a moderately alkaline vents into the sea water at temperatures more conducive (70 degrees C) to the origin of life. Figure: Alkaline Vent Fixing CO2 Of the five different pathways known to fix CO2, all require ATP except one. That one is present in both methanogens, which produce methane from CO2 and H2, and in the acetogens, which produce acetate (CH3CO2-) in the form of acetyl-CoA. The simpler reactions of forming acetic acid and methane are shown below: 2CO2 + 4H2 → CH3CO2H + 2H2O. CO2 + 4H2 → CH4 + 2H2O. The ΔG0 values for these reaction (calculated using ΔG0f for gas phase H2, CO2 and CH4 and liquid acetic acid and water are -75 and -131 kJ, respectively, at 250C, showing that they are thermodynamically favored. Making AcetylCoA, a "high" energy molecule compared to its hydrolysis products (as is ATP) from acetic acid and CoASH, requires an additional source of energy to drive the reaction. A proton gradient is the likely source. Some bacteria and Achaea cells (primordial or present) use the reductive acetyl-CoA pathway, also known as the Wood-Ljungdahl pathway, to form, in a noncyclic process, acetyl CoA from CO2 and at the same time makes ATP. This process is paid for by a proton gradient. This has been described by Shock as "a free lunch you get paid to eat". The energetics of the present acetyl CoA pathway based on the overall reaction below show an approximate ΔG0 value of -59 kJ/mol which can drive ATP synthesis. 2CO2 + 4H2 + CoASH → CH3COSCoA + 3H2O. The concentration of carbon dioxide in the primordial ocean was 1000 times higher than now. Vents produced large amounts of methane and hydrogen gas. There was little oxygen and hence lots of Fe2+. The enzymes involved in this acetyl-CoA pathway of carbon fixation have FeS clusters. It has also been shown that bubbles (which are really membrane-bound spaces) of FeS and NiS can be made in deep-sea vents. These could not only encapsulate precursor molecules but also serve as catalysts. Vents also can catalyze the fixation of nitrogen (to ammonia) and laboratory studies show that FeS can catalyze the conversion of formate (found in vents) into pyrimidines and purines. The studies of present methanogens and vent chemistry suggest that the critical ingredients and conditions for the development of the first biological cells probably occurred in the vents. To produce polymers, an energy source and monomers must exist. Concentration gradients found in simulations of vents produce million-fold concentrated molecules. The transient heating and cooling of any double-stranded nucleic acids could lead to concentration amplification by a PCR-like strand separation followed by reannealing. In addition, these vent regions possess a powerful, reoccurring energy source, a pH gradient, as the alkaline vented material entered the acidic oceans that exist with high CO2 concentrations, creating a gradient across an inorganic membrane. This is startlingly analogous to the pH gradient across membranes (acidic outside, alkaline inside) driven by the membrane complexes in the mitochondria and bacteria. Lane et al argue that the existence of membrane proton gradients as an energy source in all cells (eukaryotes, bacteria, and archaea) and in chloroplasts and mitochondria, corroborate their hypothesis. Bacteria and Archaea share homologous ATPases and electron carriers (ferredoxins, quinones, and cytochromes). These similarities contrast to the differences in enzyme structures in fermentative pathways. Arguments that proton pumps evolved to pump proteins (and reduce pH gradients) can't explain their ubiquitous presence even in organisms not subjected to low pH. Hence the ubiquity of proton pumps supports the conjecture that they arose from the first protocells, possibly comprised of inorganic walls and ultimately with amphiphilic molecules synthesized from precursors. Creationists would argue that it would be impossible to evolve a structure with the complexity of membrane ATPase (which collapse a pH gradient as the power the synthesis of molecules with large negative ΔG0 of hydrolysis). Lane et al propose that the earliest cells evolved ATPase-like molecules in alkaline vents where pH gradients analogous to those in cells today arose. They envision cell-like columns lined by FeS membrane-like structures with alkaline conditions inside and acid conditions outside. Nonpolar or amphiphilic molecules would line the inside of the cells/columns. An ATPase-like system could then take advantage of the pH gradient which constantly replenishes itself. If structures as complicated as ribosomes evolved from a subsequent RNA world, surely ATPase-like molecules could also. Other chemistry might have evolved earlier to utilize the energy source provided by the pH gradient. If life originated in the vents, it would need an energy source to leave the vents. Presumably it would have evolved one to utilized pH gradient to replace the one it left in the alkaline vents. The substrate level phosphorylation of glycolysis that requires ATP input to make ATP would not provide the energy source needed. Cells that left would have had to produce their own proton gradient. Perhaps all the was needed initially was concerted conformational changes in proteins that upon exposure of a different pH changed their shape inducing pKa shifts in adjacent proton donors/acceptors leading to vectorial discharge of protons across a membrane. Perhaps the method described above in protocells was sufficient. Recent analyses by Poehlein et a show that CO2 reduction (fixation) can be coupled to the production of a sodium ion gradient, which could collapse to drive ATP synthesis. Analysis of the genome of a gram positive bacteria, Acetobacterium woodii, an acetogen, shows the it has an ancient pathway for production of acetyl-CoA that can, in an anabolic fashion form biomass or in a catabolic fashion be cleaved to acetate with the production of ATP. It does not require classic electron carriers like ubiquinone or cytochrome C linked to protein gradient formation to drive ATP synthesis. Rather it has only a ferredoxin:NAD+ oxioreductase which couples oxidation to the formation of a sodium ion gradient, which collapses through an sodium ion transporter/ATP synthase to drive ATP synthase. A plausible reaction scheme based on genomic analysis is shown below: Figure: Acetyl-CoA Synthase and Acetogenesis The Role of Fe/S Centers Let's return to the chicken and egg dilemma one more time. What is needed for biological polymer formation are monomeric precursors, an energy source, and a way to compartmentalized them all. We discuss how monomeric precursors could form, but wouldn't it be far better if even the synthesis of precursors could be catalyzed? One source of catalysis mostly absent from the "bioorganic" abiotic chemistry in the above discussion is the transition metals. Transition metals can form complexes. Ligands containing lone pairs on O, N, and S atoms can donate them to transition metals ions, which can hold up to 18 electrons in s, p, and d orbitals. Hence as many as 9 lone pairs on ligand molecules (which are often multidentate) could be accommodated around the transition metal ion. Many present small molecule metabolites and their abiotic precursors (H2O, CO, CO2, NH3 and thiols) bind cations as mono- or polydentate donors of electrons. Hence transition metal ions would have a thermodynamic tendencies to be bound in complexes. Bound ligands that contain potentially ionizable hydrogens could become deprotonated and made better nucleophiles for reactions. Hence the transition state metal ion, acting with the complex, becomes a catalyst as it decreases the pKa of a bound ligand (such as water). In addition, since transition metals ions can have multiple charge and oxidation states, they can easily act as redox centers in the oxidation/reduction of bound ligands that were redox active. Given the relative anoxic conditions of the early oceans, Fe2+ would predominant. It could easily be oxidized to Fe3+ as it reduced a bound ligand. Highly charged transition states would withdraw electron density from bound ligands leading to their possible oxidation. Metals obviously still play a strong role in catalysis, both indirectly in promoting correct protein folding and directly in stabilizing charge in both the transition state and intermediates in chemical reaction pathways. FeS clusters are of significant importance. Their biosynthesis involves removal by an active site Cys in a desulfurase enzyme of a sulfur from a free amino acid Cys followed by its transfer to an Fe in a growing FeS cluster in a FeS scaffold protein, which then transfers the cluster to an acceptor protein where it acts as a cofactor. FeS clusters can adopt a variety of stoichiometries and shapes, as well as redox states for the participating Fe ions. The continuing importance of FeS clusters in all cells, their involvement in not only redox enzymes in which electron transfer is facilitated by delocalization of electrons over both Fe and S centers, but also in coupled electron/proton transport in mitochondrial electron transport, Fe storage (ferrodoxins), and in regulation of enzyme activity and gene expression, suggests that they were of primordial importance in the evolution of life. T hey are often found at substrate binding sites of FeS enzymes involved in both redox and nonredox catalysis. A ligand can bind to a particular Fe in the cluster, activating it for hydration or dehydrogenation reactions. Fe 4 of the FeS cluster in the TCA enzyme aconitase can have a coordination numbers of 4, 5, or 6 as it binds water, hydroxide or substrate. It acts to both decrease electron density in the transition state and to change the pKa of bound water as the enzyme catalyzes an isomerization of tricarboxylic acids (citric and isocitric acid) through an elimination/addition reaction with water. In another example it can bind S-adenosylmethionine through its amine and carboxylate groups, which activates the molecule for cleavage and radical formation. In some cases metals other than Fe (Ni for example) are incorporated into the cluster. FeS effects on transcription factors involves facilitation of optimal structure for DNA binding. FeS and FeNi centers in proteins are similar in structure tp FeS units in minerals like greigite and presumably to FeS structure formed when H2S and S2- react with Fe2+ (present in abundance in the early ocean) and other metals in vents Metal sulfides participate in reduction of both CO and CO2. For example the synthesis of CH3SH from CO2 and H2S is catalyzed by "inorganic" FeS. The Minimal Genome This question is being addressed by eliminating "unnecessary" gene from simple bacteria. Cells placed in a rich nutrient broth with essential lipids, vitamins, and amino acids would need fewer genes than those placed in a more nutrient-poor medium. Bacteria cells like Mycoplasma genetalium, that live within "nutrient rich" eukaryotic cell, have been genetically manipulated to delete unnecessary genes. Based on knockout studies, it may be possible for the cell to survive with only 300-350 genes. Bacillus subtilis has approximately 4100 genes. Estimates have been made that it could survive with as few as 271 genes. more to be added
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.01%3A__The_Basics_of_Climate_Change.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Learning Objectives • to demonstrate how climate has changed over geological time through the present • to explain mechanisms, using knowledge from biology, chemistry, and physics, for climate change • to show the central role of atmospheric CO2 as a causative agent of past and present climate change • to contrast the effects of anthropogenic burning of fossil fuels on climate change with causes of past climate changes • to address arguments made by climate change skeptics Introduction We've known for a very long time that burning fossil fuels and releasing CO2 into the atmosphere would warm our climate. Perhaps the first paper addressing this, Circumstances affecting the Heat of the Sun's Rays, was published in 1856, before the US Civil War, by a woman scientist, Eunice Foote. John Tyndall (of the Tyndall effect) published more comprehensively on greenhouse gases in 1859. Given the complexity of the biosphere's climate, it was not until the 1980s that climate models became sophisticated enough for scientists like James Hansen to become convinced and alarmed enough to discuss in Congressional hearings the role of anthropogenic (made by humans) CO2 released into the atmosphere as a cause of ever-worsening global warming. The knowledge of human-induced climate change has been politicized and subjected to an orchestrated campaign of misinformation and disinformation by fossil fuel companies and their political contributors. We have delayed global actions for so long that we must act immediately and aggressively to address climate change before we reach climatic conditions that are so austere for humans that parts of the world become uninhabitable. Homo sapiens evolved in a world dominated by repetitive glaciation and deglaciation. Hans Joachim Schellnhuber, an atmospheric physicist, climatologist, and founding director of the Potsdam Institute for Climate Impact Research, has stated that humans have so affected the world that we have eliminated the possibility of the next glaciation cycle. Many readers might not be familiar with the data and models supporting human-caused climate change and that climate scientists are almost unanimous in their support of the data and models. As in any field, however, you will find outliers who don't and whose ideas carry disproportionate weight among climate change skeptics. Hence, we provide the basic data to show the relationship between increasing atmospheric CO2 to global warming, both drought and flooding, ocean acidification, and loss of biodiversity. We also provide supportive information that would allow users to address questions from those who question the reality of present human-induced climate change. We don't shy away from using basic physics as well since most students studying biochemistry at the level found in this book have also studied physics as well as biology. In subsequent sections, we will then address the biochemistry of climate change and its mitigation. Green House Effect Before the advent of the industrial revolution, the earth's climate was fairly constant since the last ice age, which peaked about 22,000 years ago (YA) and ended about 12,000 YA. There have been short (on a geological time scale) periods of cooling since the end of the last ice age. Humans evolved around 200,000 years ago with modern civilizations arising about 4000 BCE so it could be said that humans are ice-age peoples (a distinctly Northern Hemisphere perspective). Humans have had the benefits of a fairly stable climate since then. The sun's energy warms the earth. If the earth did not radiate back into space an equivalent amount of energy, it would slowly and continually warm. The earth reflects energy back in the form of light. In addition, as the earth is heated by the sun, the earth releases heat in the form of infrared light (as do all warm objects). Earth's temperatures are stable when the sum of the energy emitted by the earth equals the energy it receives from the sun. This is illustrated in Figure $1$. Figure $1$ Our stable climate has been enabled by fairly constant levels of atmospheric CO2, a trace atmospheric gas, which has hovered around 280 parts per million (ppm) until the start of the industrial revolution in 1770. CO2 is a greenhouse gas, which as anyone who has run an IR spectra knows, absorbs in the infrared. CO2 in the atmosphere absorbs some of the infrared radiation released by the earth, allowing the earth to be warmer than in its absence. The CO2 effectively acts as an insulating blanket. In fact, without CO2 or other "greenhouse" gases, the earth would be completely covered by snow. Other greenhouse gases in the atmosphere include methane and nitrous oxide. The IR spectra of these gases are shown in Figure $2$. Students who have taken organic chemistry labs and obtained IR spectra of samples always blank the instrument to remove spectral signals from both CO2 and H2O. Figure $2$: IR spectra of some greenhouse gases. NIST (ex: https://webbook.nist.gov/cgi/cbook.c...ndex=1#IR-SPEC) Since the start of the industrial revolution, humans have been releasing into the atmosphere ever-increasing amounts of CO2 from the burning of fossil fuels and methane from agricultural practices and natural gas production. CO2 levels, as of November 2022 have reached 415 ppm, while methane has increased to 1900 part per billion (ppb) or 1.9 ppm. Increasing methane in the atmosphere contributes about 20% of the global warming effect of the more concentrated CO2, given methane's intense IR absorbance spectra. It has a short half-life in the atmosphere (about 20 years) compared to that of CO2 (hundreds of years). Nitrous oxide (N2O) is also a powerful greenhouse gas, which also depletes ozone in the stratosphere. Our increased use of synthetic fertilizers and manure is the primary anthropogenic source of N2O. Its emission is exacerbated from poorly drained farmlands. Global Warming Potential The global warming potential (GWP) is used to calculate the total contribution of all emitted greenhouse gases. It is expressed in units of CO2 equivalents. It adds the contribution of other greenhouse gases like CH4 and nitrous oxide (N2O), each of which has unique IR absorption spectra (as shown in Figure 2 above) and atmospheric half-lives. The IPCC uses a 100-year time frame for the calculation of the GWP, which is often abbreviated as GWP100, and uses this formula: \mathrm{CO}_2 \text { equivalent } \mathrm{kg}=\mathrm{CO}_2 \mathrm{~kg}+\left(\mathrm{CH}_4 \mathrm{~kg} \times 28\right)+\left(\mathrm{N}_2 \mathrm{O} k g \times 265\right) • CO2 has GWP of 1 by definition since it is the reference. Its time frame in the atmosphere (100s to 1000 years) doesn't matter since it is the reference. • CH4 has a GWP of around 27-30 over 100 years. It reflects its higher IR absorbance but lower life-time (around 12 years). • N2O has a GWP of around 265-273 over a 100-year timescale. N2O has a life-time of around 109 years. Water is also a greenhouse gas as you can attest to on humid days and how its lack in the atmosphere in deserts leads to a large temperature drop at night. It's very different than other greenhouse gases. Its concentration varies enormously (from 40 ppm to 40000 or more) based on humidity and precipitation events, which remove it from the atmosphere. The amount of water in the atmosphere increases with increasing global temperatures, which gives rise to more intense precipitation events and also to warmer temperatures in a positive feedback loop. Its concentration in the atmosphere hence changes enormously on the time scale of hours and days, so its half-life in the atmosphere is short. In contrast, the half-life of CO2 in the atmosphere is measured in decades to centuries. Climate changes over the last million years The Skeptic's Corner: Climate Change Misinformation Climate has always changed. Our present period is no different, so there is no need for action. Indeed, the earth has been subject to cycles of glaciation and deglaciation for hundreds of thousands of years. Luckily, we are able to determine atmospheric levels of COdating back to hundreds of thousands of years ago by measuring entrapped CO2 in ice cores from Antarctica and Greenland. In addition, we've been able to infer the temperature over this time frame using proxies for temperature (tree rings, fossils, and more as described in section 31.2). Figure $3$ shows how atmospheric CO2 and temperature have varied over the last 800,000 years using ice core data. Several key features of the graph should be apparent: • Both atmospheric CO2 and temperature change (ΔT) are periodic. So yes, it is obviously true that "climate changes" as climate change skeptics argue • Both CO2 and ΔT change in synchrony. An obvious question might be what changes first. Does ΔT drive CO2 changes or vice versa? More on that in a bit. • The CO2 levels in more modern times (right hand side of the graph) have soared in ways not seen in the last 800,000 years! This change is caused by CO2 emissions from the burning of fossil fuels. In those 800K years, the earth has experience cycles of glaciation/deglaciation with recurring ice ages. Figure (4\) below shows a depiction of the last ice age which peaked 21,000 years ago (left). At that time, the ice cap over New York City was about 1 mile high (right) as CO2 was at 185 ppm! By 5000 BCE, the glacier had retreated to more modern levels, leaving ice over the Arctic ocean, and over Greenland. CO2 levels were then around 260 ppm. A change of just 100 ppm in CO2 was sufficient to lead to the melting of the Northern Hemisphere glaciers. The image above is not "Northern Hemisphere-Centric" since the great glaciers were localized in the Northern Hemisphere in the ice ages. That's because glaciers grow over land and most of the land on the planet is in the Northern Hemisphere. (Our climate studies won't include the time when one continent - Pangea- existed.) The video below shows an animation of the Northern Hemisphere ice shield as it changed with time from 19,000 BCE to now to a projected future that assumes little action to change CO2 emissions.. Pay special attention to the graphs which show sea level changes as well. Best estimates by Tierney et al now show that during the last ice age, the average global temperature was 6 degrees Celsius (11 F) cooler than today, which in the 20th century is 14 C (57 F). The Arctic however was much colder (about 14 C or 25 F). The group also came up with an estimate of climate sensitivity, the increase in temperature with increasing CO2. That value is a rise of 3.4 C (6.1 F) for a doubling in CO2. In 1896, Arrhenius, recognizing that CO2 was a greenhouse gas, actually calculated that doubling atmospheric CO2 would cause a rise of 4-5 °C. No one can say we haven't known! What the science shows Climate, CO2 and temperature have always changed over geological time, but our present rise in anthropogenic CO2 in such a brief time is unprecedented and has led to CO2 levels that far exceed those during the warmer interglacial periods when Northern Hemisphere glaciers had retreated. The Ice Ages, CO2 and Temperature The Skeptic's Corner: Climate Change Misinformation It's not increasing CO2 that is causing any observed increases in temperature. CO2 is going up after temperature increases so we don't have to worry about CO2 levels. It's just a natural process and requires no action to reduce fossil fuel use. Why reduce it if it doesn't cause global warming? Data and models show that the global increase in temperature is driven mostly by increases in CO2 (and not increasing temperatures driving increasing CO2) as the predominant cause. That begs the question as to what starts the process of deglaciation. It turns out that cyclic increases in solar irradiance that increase temperatures, especially in the Northern Hemisphere, start deglaciation. A prime factor is the changes in the orbital dynamics of the earth with respect to the sun. As you know, the orientation of the earth's rotation axis remains generally fixed and pointed in the same orientation as the earth rotates around the sun. This fixed orientation leads to our annual spring, summer, fall, and winter cycles on earth. In the winter, the northern hemisphere is pointed away from the sun, leading to decreased solar irradiance per square meter in the Northern Hemisphere, causing winter there. When the earth is on the opposite side of the sun, the axis points in the same direction but tilts towards the sun, leading to summer in the northern hemisphere. However, the orbital dynamics of the earth do change in cyclic fashions over long periods of time. These long-term changes in the earth's orbital shape (eccentricity), tilt (obliquity), and wobble (precession) are called the Milankovitch cycle, and are illustrated in Figure $5$. These cycles cause small temperature increases that start deglaciation. Click on each image below to download and view very short videos illustrating these orbital changes. Change in eccentricity (orbital shape) - (100,000 yr cycle) Change in obliquity (tilt) (41,000 yr cycle) Axle precession (wobble) (26,000 yr cycle) Figure $5$: The Milankovitch cycle showing changes in the earth's orbital dynamics with respect to the sun. https://climate.nasa.gov/news/2948/m...arths-climate/ Based on these cycles, Milankovitch calculated that recurring ice ages should occur approximately every 41000 years. Ice ages did occur at this interval from about 3 million years ago (MYA) to 1 million years ago (MYA). About 800,000 YA they lengthened to about 100,000 years, which corresponds to the earth's eccentricity cycle. The increased duration of the cycle led to longer-lasting glaciers which moved further south in the Northern Hemisphere. One likely explanation for the increase in time between ice ages is that repeated glaciation/deglaciation eroded the bedrock in the Northern Hemisphere, converting it to regolith (rocks, soil, and dust). This allowed an increased velocity of movement of the glaciers to the south due to decreased frictional resistance, and thicker ice cap formation (more time to accrue ice), which required a longer time to melt. This also provided a positive feedback loop as the increased northern ice area would reflect more of the sun's energy back into space, cooling the planet. Punctuating these rhythmic orbital and ice age cycles are other events such as large volcanic eruptions, asteroid impacts, etc, that could produce minor to major changes in climate, and resulting mass extinctions. Figure $6$ shows how a combination of tilt angle, precession axis, and orbital shape at around 200 KYA (narrow rectangle across all the graphs) combined to lead to low glacial ice volume (bottom graph). Figure $6$: Milankovitch cycle contribution to ice volume over the past 1M years If orbital changes (or forcing) trigger deglaciation, what is the role of increasing levels of the greenhouse gas CO2, which clearly covary with temperature (see Fig 3)? Temperature increases derived from orbital and hence solar "forcing" seem to precede CO2 increases for just short periods of time (perhaps 100 - 200 years). After that, CO2 causes almost all of the global increase in temperatures during deglaciation, with CO2 and temperature going up together. A global increase of about 0.3 C due to the Milankovitch cycle leads to greater Northern Hemisphere irradiance. This causes localized and limited melting of the Northern ice shield, leading to increases in ocean temperatures in the northern oceans. These increases slow a major ocean current (the Atlantic Meridional Overturning Circulation - AMOC) which inhibited the burial and return of cold water in tropical and southern oceans. This in turn led to a warming in the south accompanied by the release of large amounts of CO2 stored in the oceans (see Carbon Cycle in 31.3). The release of this greenhouse gas was then responsible for most of the warming that lead to massive deglaciation. This "interhemispheric see-saw" transfer of heat from the north waters to the southern waters is key. For the far majority of the warming during glacial melting, CO2 and temperature change synchronously. Interpreting climate data is difficult. For example, it was found through measuring 15N/14N ratios that gases like N2 and by extension CO2 could rapidly diffuse through the compacting snow (firn, comprising the top 50-100 meters of the ice cap) until it became trapped in the solid ice beneath it. This would lead to the presence of "newer" CO2 in older ice samples, and the conclusion the temperature changes preceded changes in CO2. Corrections are made to the data to address the "apparent" time shift. The CO2 trapped in bubbles in the ice core samples from Antarctica reflects global CO2 levels given atmospheric circulation but the temperatures measured from the same core samples (see Chapter 31.2) represent local (Antarctic) temperatures. Ice core samples from Greenland and ocean sediment samples from around the world are used to measure temperature at different locations over time. All of these data are required to model climate. Combined they lead us to our present interpretation of the linkage of CO2 and temperature rise over time. What the science shows Increased solar irradiance on earth arising from cyclic changes in the Earth's orbit leads to short, small temperature increases in the North Hemisphere. These lead to the release of the greenhouse gas COfrom the oceans, which causes synchronous warming of the planet and subsequent deglaciation. So when skeptics say that temperature increases preceded CO2 increases, you can acknowledge they did but that the bulk of the warming is attributed to increasing COreleased from ocean stores which leads to synchronous temperature increases and deglaciation. Using the words of chemistry, small temperature increases from orbital forcing catalyzed the release of huge amounts of CO2 dissolved in the ocean. In Chapter 31.3 we will explore the carbon cycle in more detail and look at how it affects CO2 levels. Termination of the Ice Ages How did the ice ages terminate? Contributions from the orbital forcing derived from the Milankovitch cycle play a part. Another factor seems to be dust derived from regolith, itself made by glacier movement as we mentioned above. How can that hypothesis be tested? By using proxies for dust, namely iron and long-chain n-alkanes (derived from plant waxes) that have been deposited in sediments. First let's look at a graph of CO2 and temperature changes and superimpose those on iron and long-chain fatty acid levels, as shown in Figure $7$. A close examination of the two vertically aligned graphs from around 120 K to 130 KYA shows that the iron and n-alkane depositions are at a minimum at the same that CO2 and temperature are peaking! What explains this negative correlation? It depends on the intimate connection of the biosphere with the nonbiological world (an arbitrary distinction). Iron and n-alkanes are circulated and delivered in dust. The long-chain alkanes, highly abundant in waxes and enriched in odd carbon number chains, were presumably derived from leaf waxes which prevent water loss from plants, especially during higher temperatures. Dust deposits were first observed in geological time in the switch from the warmer Pliocene (5.3 to 2.6 MYA) to the Pleistocene (2.6 MYA to 11.7KYA, see Fig 8 below). During the warmer Pliocene, the difference in global and atmospheric temperatures was lower, and with this smaller temperature gradient, winds that could globally transfer dust would be diminished. Also, the warmer Plicoene (5.3 to 2.6 MYA) would have more rain, which would have removed dust from the global circulation. As temperatures cooled in the Pleistocene (2.6 MYA to 11.7KYA), cycles of glaciation would produce more dust-containing regolith (rocks, soil, and dust), which would be dispersed through stronger global winds from higher temperature gradients and and less rain. Dust contains carbon (for example long chain fatty acids) and perhaps more importantly iron, which is needed for oceanic phytoplankton growth. Without Fe, the uptake of CO2 by phytoplankton (primary production) would not occur, leading to increased CO2 in the atmosphere. Stronger regional atmospheric winds would lead to increased upwelling of nutrients as well as deep ocean CO2. The CO2 would enter the atmosphere more readily in the absence of dust deposition of iron. In summary: • High CO2 and high temperature (lower global temperature gradients, more rain) are associated with low dust, as measured with the proxies Fe and n-alkanes). Low dust leads to low deposition of Fe and n-alkanes in the ocean, which decreases phytoplankton primary production, the fixing of CO2 into biomass), leading to increased CO2 movement from the ocean to the atmosphere, increasing temperature. This is an example of a positive feedback loop (higher temperatures leading to higher temperatures. • Low CO2 and low temperature (higher global temperature gradients, stronger winds, less rain) are associated with high dust with Fe and n-alkanes deposition. This increases phytoplankton primary production and decreases CO2 movement from the ocean to the atmosphere, in a negative feedback loop. By the end of a glacial deposition cycle, dust, blown by stronger winds from higher temperature gradients, was increasingly deposited on the ice sheets. Along with leading to more heat absorption by the sheets, it would also decrease their reflectivity (albedo). Both effects would promote ice sheet melting. Also, a cooler planet during glacial maximum had less precipitation, which along with lower CO2, would lead to more plant and tree death, increasing soil erosion and desertification, both effects which would have increased dust production and its deposition on ice sheets. Then when CO2 rose to 280 ppm, plant life renewed itself, and dust levels dropped. Climate change from 66 million years ago to now Antarctic ice core data are now available for the past 2 M years. Ocean sediment data can be used to go back even further in time to 66 million years ago (MYA) just before the dinosaurs died after the massive asteroid impact forming the Chicxulub crater buried underneath the Yucatán Peninsula in Mexico. A brief review of geological eras, periods and epochs is shown below in Figure $8$ Figure $8$: Geological Era, Periods and Epochs CO2 levels and associated temperatures derived from ocean sediment cores going back to 66 MYA are shown in Figure $9$. Figure $9$ CO2 levels (red) and temperatures (blue) derived from ocean sediment cores going back to 66 MYA = 66,000 KYA . Data from Rae et al. Annual review of earth and planetary sciences, 49, 2021 Note again the parallel rise and fall of CO2 and temperature. Eventually, they fall further in the Pliocene (5.3 to 2.6 MYA) and Pleistocene (2.6 MYA to 11.7KYA) epochs with cyclic glacier/interglacial periods we've discussed above. It wasn't until the late Miocene (10 to 6 MYA) that Northern hemisphere glaciation started and both poles of the planet had glacial sheets. The time frame shown in Figure 9 encompasses the Cenozoic era (65 MYA when the dinosaurs died to about now). CO2 levels were much higher than today in the greenhouse Paleocene and Eocene eras but decreased to about 500 ppm in the Oligocene (34 MYA). An almost stepwise drop in CO2 and temperature occurred in the Eocene to Oligocene transition (EOT), about 33 MYA. Data shows the development of large ice sheets appearing on Antarctica at this time. Before the EOT (33 MYA), Antarctica was ice-free, as shown in the recreation in Figure $10$. Figure $10$: Reconstruction of the West Antarctic mid-Cretaceous temperate rainforest. Image credit: J. McKay / Alfred-Wegener-Institut / CC-BY 4.0. https://www.sci.news/othersciences/p...ing-09921.html Proxy data for temperatures show that the transition was most likely caused by a decrease in CO2 and some orbital forcing was probably involved. Present models still struggle to explain the EOT (33 MYA) transition, but it is clear that both CO2 and temperature decreased. Where did the CO2 go? Most assuredly into the oceans. To understand that, we have to understand a bit about the carbon cycle, which we will discuss more fully in the next chapter section. Let's briefly discuss the role of atmospheric CO2 and its interaction with the ocean. The main gases in the atmosphere, N2 and O2, are found in very low concentrations in the ocean since they are nonpolar and generally unreactive. CO2 is also a nonpolar trace gas, but in contrast, it can readily react with water to form HCO3- and CO3-2, which are found in great abundance in ocean reserves. Hence the ocean chemistry of CO2 determines in large part the levels of atmospheric CO2. The coupled reactions of CO2 are shown below. \mathrm{CO}_2(\mathrm{~g}, \mathrm{~atm}) \leftrightarrow \mathrm{CO}_2(\mathrm{aq}, \text { ocean) } \mathrm{CO}_2(\mathrm{aq} \text {, ocean })+\mathrm{H}_2 \mathrm{O}(\mathrm{I} \text {, ocean }) \leftrightarrow \mathrm{H}_3 \mathrm{O}^{+}(\mathrm{aq})+\mathrm{HCO}_3^{-}(\mathrm{aq}) \mathrm{H}_2 \mathrm{O}(\mathrm{I})+\mathrm{HCO}_3^{-}(\mathrm{aq}) \leftrightarrow \mathrm{H}_3 \mathrm{O}^{+}(\mathrm{aq})+\mathrm{CO}_3{ }^{2-}(\mathrm{aq} \text {, sparingly soluble }) This chemistry helps determine the pH of the ocean. Figure $11$ shows atmospheric levels of CO2 and ocean pH over the last 66 million years. Figure $11$: Atmospheric levels of CO2 and ocean pH over the last 66 million years Before the EOT at 34 MYA, atmospheric CO2 levels were higher and ocean pH levels lower (around 7.7). After the EOT (33 MYA), atmospheric CO2 is much lower and ocean pH is higher (more basic, 7.9 rising to 8.1). What happened to the CO2 is a bit unclear. Atmospheric CO2 decreased by moving into the oceans but wouldn't that have lowered the pH based on the chemical equations presented above? It would have but it turns out that the ocean alkalinity is determined not just by H3O+ produced by the equations above, but by the dissolved inorganic carbon ions, HCO3- (aq) and CO32- (aq), which are conjugate bases. Increased HCO3- (aq) and SiO4-2 (aq) from weathering solid carbonates and silicates that entered the oceans would raise the pH of the oceans. A little review of introductory chemistry helps here. Let's take bicarbonate, the weak conjugate base of the weak acid carbonic acid. HCO3- can act as both an acid and base. Rx 1: Acts as an acid: HCO3- (aq) + H2O (l) ↔ H3O+(aq) + CO32- (aq) K_{a 2}=\frac{\left[\mathrm{H}_3 \mathrm{O}^{+}\right]\left[\mathrm{CO}_3^{2-}\right]}{\left[\mathrm{HCO}_3^{-}\right]}=4.7 \times 10^{-11} Rx 2: Acts as a base: HCO3- (aq) + H2O (l) ↔ H2CO3 (aq) + OH- (aq) K_{b 2}=\frac{\left[\mathrm{H}_2 \mathrm{CO}_3\right]\left[\mathrm{OH}^{-}\right]}{\left[\mathrm{HCO}_3^{-}\right]}=2.2 \times 10^{-8} The equilibrium constant for the reaction of HCO3- as a base is much larger so bicarbonate is a stronger base than acid. Whatever the mechanism of the CO2 drawdown, it led to decreasing temperatures in the EOT transition. Increased alkalinity of the ocean would also consume H3O+, increasing ocean pH. A summary of planetary temperatures across geological time is shown in Figure $12$. Figure $12$: Temperature of earth over 500 million years. https://commons.wikimedia.org/wiki/F...alaeotemps.png. (Excel available). Creative Commons Attribution-Share Alike 3.0 Unported There are several key features to note. The last time CO2 was as high as today (415 ppm) was about 3 million years ago. Repetitive cycles of glaciation/deglaciation are obvious in the Pleistocene (2.6 MYA to 11.7KYA). In addition, at around 55 MYA, a spike in temperatures of about 50 F occurred over about a 100K year timeframe. This was accompanied by a dramatic spike in CO2 and a dramatic drop in ocean pH as measured by the loss of deep-sea CaCO3 (chalk). These latter changes are visually evident in geological deep-sea sediment records as shown in Figure $13$. This very short time frame is called the Paleocene/Eocene thermal maximum (PETM, 55.5 MYA), which shows very quick spikes (on the geological time scale) can and do occur. Approximately 1.5 petagrams (1015) of CO2 were released annually during the PETM. Now we are releasing about 25 petagram per year. Our present rate of warming is much greater than the rate of warming during the PETM (55.5 MYA). The best candidates for the source of CO2 release that caused the PETM are volcanoes, the oceans, and the permafrost. In addition, methane hydrates (a solid form of methane found in low- temperature high-pressure waters) might also be another factor. Figure $13$: Overview for the Paleocene–Eocene Thermal Maximum (PETM, 55.5 MYA) data from deep-sea records and the terrestrial Polecat Bench (PCB) drill core against age. Westerhold et al. Clim. Past, 14, 303–319, 2018. https://doi.org/10.5194/cp-14-303-2018. Creative Commons Attribution 3.0 License Sediment cores were taken at various sites (1262, 1267, 1266, 1265, 1263, and 690) that are aligned from left to right according to the water depth from deep to shallow. Note at 55.93 million years ago, at the start of the PETM, there was a sharp transition from light brown/gray which is enriched in chalk, to dark brown enriched in clay. Ocean acidification dissolved the chalk. It took over 100,000 years to recover. Back to the Present Let's return to more recent human history and anthroprogenic forcing of our climate. Figure $14$ shows an interactive graph of atmospheric CO2 over more recent times. Zoom into the steep rise in CO2 starts which around 1760 with the industrial revolution. Figure $14$: Interactive graph of atmospheric CO2 vs time over the last 1000 years. Historical CO2 record from the Law Dome DE08, DE08-2, and DSS ice cores. Credits: D.M. Etheridge, L.P. Steele, R.L. Langenfelds, R.J. Francey and the Division of Atmospheric Research, CSIRO, Aspendale, Victoria, Australia. 2 Degrees Institute. Orbital mechanics cannot explain the warming of the planet in the brief (in geological terms) times since the industrial revolution. Neither can volcanic activity, changes in solar activity, changes in land use (for example deforestation that slows down photosynthesis and CO2 removal from the atmosphere), or even aerosols released on burning fossil fuels (which would actually decrease global temperature due to increased reflection of sunlight). Click on Figure $15$ to see an animated explanation of which factors best explains the global temperature increase since 1880. The results are clear: It's us! Figure $15$: Factors contributing to global warming based on NASA data and NASA's Goddard Institute for Space Studies (GISS) climate models. Unfortunately, other greenhouse gases have risen as well since 1975, as shown in Figure $16$. Figure $16$: Rise in greenhouse gases since 1975. https://www.co2.earth/annual-ghg-index-aggi Take special note of the zig-zag nature of the CO2 curve. The curve dips a bit in the summer when CO2 is actively removed by plants in the Northern hemisphere. Increasing methane now accounts for up to 20% of the warming observed. Figure $17$ shows as interactive graph a very worrisome rise of atmospheric methane with time. Figure $17$: Interactive graph of atmospheric CH4 vs time over the last 1000 years. 2 Degrees Institute. https://www.2degreesinstitute.org/ Present-day warming unequivocally is caused by humans burning of fossil fuels. Past Climate Anomalies The Skeptic's Corner: Climate Change Misinformation In recorded human history there have been other times of climate change, so we shouldn't worry about the present time! Look at the Little Ice Ages! Several dramatic but short-lived (in geological time) climate changes have punctuated recorded human history. Let's look at two, mostly to equip you to address climate skeptics. They also show the sensitivity of our climate to subtle changes. The Little Ice Ages Actual and proxy temperature records show a mild period in Europe from around 950-1100 followed by colder weather, especially from 1450 to 1850. The latter period is called the "Little Ice Ages" although there was no significant expansion of the North Hemisphere ice shield. It was especially cold worldwide in 1816 when much of the world experienced a "year without summer". The effect in 1816 has a clear cause, the explosion of the volcano Mount Tambora in Indonesia on April 10, 1815. But in addition to this identifiable influence in 1816, there was a cool period reported for the northern hemisphere from about 1800 to 1820 that started earlier than the Tambora eruption. Also, a low period of the sun's irradiance, called the Dalton Minimum, occurs from 1790-1860. Proxies for solar activity in the 1600s also show small solar irradiance drops, as we will discuss below. The dip in global average temperatures following the Medieval warm period, is shown in Figure $18$. Figure $18$: Dip in global average temperatures following the Medieval warm period, By RCraig09 - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/inde...curid=87832845 Modern climate changes have been captured in literature and art. One example is a painting showing "Ice Fairs" on the Thames in London, shown in Figure $19$. Figure $19$:https://commons.wikimedia.org/wiki/F...enell.jpgdfdfd Many factors probably contributed to the Little Ice Ages including a drop in solar irradiance. A newer explanation has also been proposed. Marine records show that the water near Greenland and the Nordic seas were warmer, caused by a strengthening of the Atlantic Meridional Overturning Circulation (AMOC). This would have caused the loss of Arctic ice in the late 1300s and 1400s, cooling the water and diluting its salinity, since ice when it crystallized with a tetrahedral hydrogen-bonded coordination of water, excludes salt. This would have collapsed the AMOC and its transfer of heat to the northern waters, leading to rapid and prolonged cooling. An analogous strengthening of the AMOC was observed between 1960 and 1980, which was attributed to a long-duration high-pressure system over Greenland. A similar event might have occurred to kick-start the Little Ice Ages. Tree rings show evidence of higher solar irradiance before the Little Ice Ages, which may be associated with the initial strengthening of the AMOC. The Little Ice Ages also affected China and may account in part for a crop failure in 1644, the year in which the Ming Dynasty fell. There was also an Arctic hurricane in 1588 that helped destroy the Spanish Armada. The Great Fire in London in 1666 was preceded by a very dry summer that followed an exceptionally cold winter. Food production were severely disrupted, which might have led to significant social change in Europe and elsewhere, much as the Plague in Europe shattered societal and cultural norms. The explosion of Mount Tambora, in present-day Indonesia, in 1816 greatly exacerbated the effects of cooling. The ash and SO2 aerosols block solar irradiance, Droughts, floods, cholera epidemics, famine, and migration from Europe to the US and from East to West arose in part from this event. One of the worst times to be alive: 536 Historians report that in 536 AD, parts of Europe, the Middle East, and Asia experienced 24 hours of darkness for up to 18 months. Summer temperatures plummeted. Famines occurred for a few years after. It snowed in China in the summer. The worst effects were in the Northern Hemisphere but the effects were world-wide. It was probably the most pronounced cooling in the last 2000 years. To make matters worse, a pandemic erupted around 541 that spread from southern Asia to northern Europe. It had a huge effect on the Byzantine Empire and has been called the Justinian (bubonic) Plague after the Byzantine emperor. Crop failures, an expansion of trade, and an influx of rodents derived from the cold temperatures could have led to and also exacerbated the plague. This second and severe example of cooling was shorter-lived in a geological time frame. Temperatures fell in the summer about 1.5-2.50C. A "smoking gun" has been linked to this cooling, a volcanic explosion in Iceland. In addition, another eruption occurred in 540, which dropped the temperature another 1.5-2.50C, and in 547. The combined effects of climate change and the plague led to a significant economic fall in Europe. Signs of airborne lead in the ice in 640, arising from silver mining, suggest a recovery of economic growth. You should ask yourself how the modern world with cope with such an occurrence. What the science shows The Little Ice Ages and the climate changes preceding and after the Justinian plague had multiple causes, including volcanic eruptions, small changes in solar irradiance, and changes in the North Atlantic ocean currents and associated weather patterns. These short-term climate changes had disastrous effects on people's lives and the economic health of societies. Predicted future warming arising from CO2 emitted from fossil fuel use (and other greenhouse causes) would bring far worse immediate and potentially irreversible consequences. It is incumbent on us as people who know the causes of climate change to act with due diligence and speed to avert the worst climate futures. Solar Activity and Climate Change The Skeptic's Corner: Climate Change Misinformation It's not increasing CO2 that causes any observed increases in temperature. The sun's activity is changing. It always has and always will. There's nothing we can do about it. We have discussed how the orbital forcing of the climate kick-started each of the recurring ice ages in the Pleistocene. Some effects of the change in solar activity independent of the sun's orbit have been noted above. Specifically, we have shown that it cannot account for present warming. We present a series of graphs from the NOAA (National Oceanic and Atmospheric Administration) in the collective Figure $20$ below to show the actual change in solar activity over recent times. Comments are shown at the bottom of each graph. (Above) The maximal % spread from the lowest to the highest is very small. Such a small change shouldn't have such dramatic effects on climate unless it is sustained, as it was from around 1630-1700. Hence this decline in solar activity probably played some part in part of the Little Ice Ages. The regular rise and fall (spikes) are associated with the 11-year sunspot cycle activity. Note that the rise in average temperature since 1910 (shown in red) cannot be accounted for by change sin solar activity The above graph shows that the irradiance decreased by about 0.06% (although other values have been reported as high as 0.22%) during the Maunder Minimum, which occurred in the Little Ice Ages. The average decrease in terrestrial temperatures was 1.0-20C. The graph above shows yearly average temperatures in the Northern Hemisphere. The dark red line shows the average change. Note that the averages are clearly lower in the Little Ice Age with the lowest values and lowest spike temperatures close to and in the Maunder Minimum. (Above) The 11-year repeat of sunspot activity and resulting solar irradiance is clearly seen in the graphs. In 2020, a low in activity occurred, yet 2020 was the second warmest year on record since 1880. This graph does not show the effects of climate forcing due to orbitals changes. Rather it shows that solar activity has not changed significantly for the 10000 years prior to 0 CE. Figures $20$: Changing in solar activity in recent geologic time. This would be true if not for the massive amount of CO2, approximately 1.5 trillion tons, injected into the atmosphere since the industrial revolution from the use of fossil fuels. Not all of that is still in the atmosphere, of course, but enough to raise CO2 to levels not seen for 3 million years. Based on the relationship between CO2 and temperature across the ice ages, science can predict when conditions might exist to initiate and propagate the next ice age. The data arising from these models, illustrated in Figure $21$, show how much incoming solar radiation (insolation) must arrive at the earth (watts/m2) to trigger the next ice age. Figure $21$: Incoming solar radiation required to trigger the next ice age. As shown in the left side of the figure, if CO2 were 280 ppm (typical of peaks in past interglacial periods), it would take repetitive drops of insolation below the threshold of about 455 watts/m2 (red line) to start glaciation. As of November 2022, we are at 415 ppm and rising. If it rises to 450 ppm, as it assuredly will in the absence of carbon capture, it would require much less insolation, since the greenhouse effect of the higher CO2 would warm the atmosphere The right graph shows there is little chance of another ice age in the absence of large and sustained volcanic activity or asteroid impact that would lead to blocking of solar radiation. What science says Changes in solar irradiance (not changes in earth's orbital dynamics) cannot account for warming since the Industrial Revolution. They have contributed to short-term (on a geological time scale) cooling during the Little Ice Ages. Summary of Climate Change Causes and Effects Since 900 AC Figure $22$ shows a great summary of possible contributions to temperature change over the last 1000 years. Note again that present-day warming can only be attributed to greenhouse gases (GHG). One panel shows changes in land use. This has caused a temperature drop since 1800. That effect is caused by deforestation and other land cover changes, which leads to more reflection of incident solar radiation back into space. This effect is increased in the winter if the changed land is snow- covered. Deforestation would also decrease CO2 capture (photosynthesis) by plants, which would raise the temperature. That component has been added to the GHG panel. Figure $22$: Simulated northern hemisphere temperature changes, smoothed with an 11 year running mean, relative to the period AD 950–1250. Owens et al. J. Space Weather Space Clim. 2017, 7, https://doi.org/10.1051/swsc/2017034. Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0). The black line in the top panel shows the observed instrumental northern hemisphere temperature variations with their associated uncertainties (Morice et al., 2012), which match the simulations well. The bottom panel shows a simulation with no changes to the radiative forcings. This quantifies the magnitude of natural internal variability in the simulations in the absence of changes in forcings. Note periodic dip but short time frame dip in temperature due to volcanic activity. Clearly, warming since the Industrial Revolution is due to emissions from the use of fossil fuels. Climate Justice: The Emitters and the Affected The Skeptic's Corner: Climate Change Misinformation Why should we make changes to reduce fossil fuel emissions when China is the biggest emitter of CO2! We present a series of graphs in Figure $24$ below, taken from CO2 Emissions - Our World in Data to show which countries have emitted the most CO2 in the past and now. In a just world, those countries which have emitted the most should move swiftly and forcefully not only to decrease emissions but to aid other countries' transitions to clean fuels and to help them with climate change mitigation and adaptation. We don't wish to demonize the fossil fuel industry and those who work in it. The use of fossil fuels, which are high energy, high density, and cheap fuel (because of historically massive subsidies) has lifted millions if not billions out of poverty over time. We had no alternative to fossil fuels until recently. Most did not realize how significantly fossil fuel use would affect our present and future climate and the health of not only humans but the entire biosphere. (Yet there is evidence that.) We can't just stop the use of fossil fuels without inflicting great economic pain on those who can least afford it. In order to help those who are currently suffering and who will suffer most in the future, as well as to help ourselves, our children and out grandchildren, we must move away from the use of fossil fuels as soon as possible. Above: The dip in total world emissions in 2020 was due to the COVID pandemic. Unfortunately, the rise has resumed. Note that China is now the biggest net emitter but the US and EU emissiond are dropping. India is on the rise and if they follow a similar economic path as China, which they need to lift many out of poverty, it will come with a huge cost in CO2 emission unless they can jumpstart their conversion of clean fuels. The world needs to help. Above: Although China is the biggest net emitter, the US and Australia are the biggest emitters per person, although that is dropping The US still leads the world in the total amount of CO2 emitted since the industrial revolution. We also have the greatest GDP. Pakistan suffered tragical flooding, exacerbated by climate change, in 2022. Up to a 1/3 of the country was under water. In a just world, the biggest emitters would aid the rest of the world. Above: Inequality is clearly evident in this graph as the wealthiest people (high and upper-middle income) collectively contribute 86% of CO2 emissions Figure $24$: CO2 emissions by country and income since the industrial revolution. Creative Commons BY license What science says The United States has emitted the most COsince the beginning of the industrial revolution and the most per capita. China is not even close. Future Projections We know the science, and we know the consequences if we choose not to act or act in ways insufficient to meet the challenges of climate change. It is one of the most difficult challenges we have faced as a species. It requires sacrifice and united action for the common good. The benefits of our choice are mostly in the future and for future generations. The Intergovernmental Panel on Climate Change (IPCC), a body composed of leading climate scientists and experts, has defined several different Relative Concentration Pathways (RCPs) leading to different emissions and different climate futures. Where we end up depends on economic, social, and political choices. The IPCC initially designated four pathways, RCP 2.6, 4.5, 6, and 8.5, with higher numbers associated with higher temperatures and CO2 levels. Each assumes a starting value and estimated emissions (which depend on technology, politics, economics, etc). RCP 8.5 assumes extra radiative forces (heat energy/(m2s)) by 2100 equal to 8.5 J/(s m2) or 8.5 watts/m2. This worst-case scenario assumes business as usual with no interventions to reduce our emissions, a totally unlikely scenario given present actions (including the rapid rise of clean energy). The RCP 2.6 scenario assumes that the peak radiative forcing would be 3 watts/m2 which would decline through very strong governmental and economic actions to 2.5 by 2030-2040. Table $1$ below shows the four RCP scenarios with projected ending CO2 equivalents (which include other greenhouse gases) and temperature increases. RCP (W/m2) Timeframe CO2 atm equivalents (ppm) Temp increase (oC/oF) Description 8.5 in 2100 1370 4.9/8.8 rising 6.0 after 2100 850 3/5.4 stabilizing without overshoot 4.5 after 2100 650 2.4/4.3 stabilizing without overshoot 2.6 decline from 3 before 2100 490 1.5/2.7 peak and decline Translating the projected CO2 equivalents in the atmosphere into associated temperature increases requires a high-quality value for climate sensitivity (rise in temperate/rise in CO2). Figure $25$ shows the likely increase in temperatures for the four different scenarios. The scenarios in Figure 25 are labeled SSP#-## with the second number ## representing the RCP number. The IPCC 6th report issued in 2021 changed from using RCP scenarios to Shared Socioeconomic Pathways (SSPs) scenarios which are based on possible social and economic developments that would pose different challenges to reduce future temperature increases and hence different strategies for mitigation and adaptation. The SSP scenarios are consistent with the RCP scenarios but use a more enhanced socio-economic and political framework for their construction. The mitigation strategies are based on the RCP forcing levels. The SSP scenarios are described below. They start with SSP1, which leads to a world that has adapted well and moved away from fossil fuels, to SSP5, which assumes a continued and high reliance on fossil fuels. SSP1: Sustainability – Taking the Green Road (Low challenges to mitigation and adaptation) The world shifts gradually, but pervasively, toward a more sustainable path, emphasizing more inclusive development that respects perceived environmental boundaries. Management of the global commons slowly improves, educational and health investments accelerate the demographic transition, and the emphasis on economic growth shifts toward a broader emphasis on human well-being. Driven by an increasing commitment to achieving development goals, inequality is reduced both across and within countries. Consumption is oriented toward low material growth and lower resource and energy intensity. SSP2Middle of the Road (Medium challenges to mitigation and adaptation) The world follows a path in which social, economic, and technological trends do not shift markedly from historical patterns. Development and income growth proceeds unevenly, with some countries making relatively good progress while others fall short of expectations. Global and national institutions work toward but make slow progress in achieving sustainable development goals. Environmental systems experience degradation, although there are some improvements and overall the intensity of resource and energy use declines. Global population growth is moderate and levels off in the second half of the century. Income inequality persists or improves only slowly and challenges to reducing vulnerability to societal and environmental changes remain. SSP3: Regional Rivalry – A Rocky Road (High challenges to mitigation and adaptation) A resurgent nationalism, concerns about competitiveness and security, and regional conflicts push countries to increasingly focus on domestic or, at most, regional issues. Policies shift over time to become increasingly oriented toward national and regional security issues. Countries focus on achieving energy and food security goals within their own regions at the expense of broader-based development. Investments in education and technological development decline. Economic development is slow, consumption is material-intensive, and inequalities persist or worsen over time. Population growth is low in industrialized and high in developing countries. A low international priority for addressing environmental concerns leads to strong environmental degradation in some regions. SSP4: Inequality – A Road Divided (Low challenges to mitigation, high challenges to adaptation) Highly unequal investments in human capital, combined with increasing disparities in economic opportunity and political power, lead to increasing inequalities and stratification both across and within countries. Over time, a gap widens between an internationally-connected society that contributes to knowledge- and capital-intensive sectors of the global economy, and a fragmented collection of lower-income, poorly educated societies that work in a labor-intensive, low-tech economy. Social cohesion degrades and conflict and unrest become increasingly common. Technology development is high in the high-tech economy and sectors. The globally connected energy sector diversifies, with investments in both carbon-intensive fuels like coal and unconventional oil, but also low-carbon energy sources. Environmental policies focus on local issues around middle and high income areas. SSP5: Fossil-fueled Development – Taking the Highway (High challenges to mitigation, low challenges to adaptation) This world places increasing faith in competitive markets, innovation and participatory societies to produce rapid technological progress and development of human capital as the path to sustainable development. Global markets are increasingly integrated. There are also strong investments in health, education, and institutions to enhance human and social capital. At the same time, the push for economic and social development is coupled with the exploitation of abundant fossil fuel resources and the adoption of resource and energy-intensive lifestyles around the world. All these factors lead to rapid growth of the global economy, while global population peaks and declines in the 21st century. Local environmental problems like air pollution are successfully managed. There is faith in the ability to effectively manage social and ecological systems, including by geo-engineering if necessary. The projected increases in emitted CO2 (Gigatons/yr) and other greenhouse gases over the next 80 years for each SSP scenario are shown in Figure $26$. The second number in the SSP label is the RCP scenario number based on radiative forcing listed in Table 1 above. Figure $26$: Projected increases in greenhouse gases under different SSP (RCP) scenarios. Masson-Delmotte et al. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I. to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001. Note the welcome decline in SO2 which causes acid rain as well as aerosols. This shows that under all SSP scenarios, we are moving to clean up our air (in this case reducing SO2 from burning sulfur-enriched coal or capturing SO2 before it enters the atmosphere). Paradoxically and unfortunately, decreasing aerosols leads to increasing temperatures due to lower reflectance of incident solar irradiation. Our final figure, Figure $27$, shows how each greenhouse gas and SO2 are projected to change in 2081-2100, compared to 1850-1900 levels, for each of the SSP scenarios. Figure $26$: Projected changes in greenhouse gas es and SO2 in 2081-2100 compared to 1850-1900 levels for different SSP scenarios. Masson-Delmotte et al, ibid. As all the data presented in this chapter shows, our climate fate will depend on the choices we make individually and collectively as societies. Key Points - Beta version from Chat.openai 1. Climate change is the long-term change in the average weather patterns on Earth. 2. The primary cause of climate change is the burning of fossil fuels, which releases large amounts of greenhouse gases, such as carbon dioxide (CO2), into the atmosphere. 3. Greenhouse gases trap heat in the atmosphere, causing the Earth's temperature to rise. This is known as the greenhouse effect. 4. The most significant contributor to climate change is CO2, which is released when fossil fuels are burned. Other significant contributors include methane, nitrous oxide, and fluorinated gases. 5. Climate change has a wide range of impacts on the Earth's systems, including rising sea levels, changes in precipitation patterns, increased frequency and intensity of extreme weather events, and disruptions to ecosystems. 6. The global temperature has already risen by 1 degree Celsius (1.8 degrees Fahrenheit) since the pre-industrial era, with most of the warming occurring in the last few decades. 7. The Intergovernmental Panel on Climate Change (IPCC) has stated that limiting global warming to 1.5 degrees Celsius (2.7 degrees Fahrenheit) above pre-industrial levels could significantly reduce the risks and impacts of climate change. 8. Reducing greenhouse gas emissions is essential in order to slow or stop climate change. This can be achieved through a combination of actions, such as increasing the use of renewable energy sources, improving energy efficiency, and reducing deforestation.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.02%3A__Use_of_Isotope_Analysis_in_Measuring_Climate_Change.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry In this chapter section, we will explore how we are able to reconstruct CO2 and temperature values across millions of years time. It is truly a remarkable, if not awe-inspiring achievement that shows the intellectual rigor and endurance scientists employ to obtain, interpret, and model data. The graphs of CO2 vs temperature across such a vast swath of time shown in the previous chapter section were obtained through analyses of isotopes of oxygen and carbon found in chemical species in ice and ocean floor sediments. The interpretation of the isotope data requires an understanding of the link between individual and linked chemical and biochemical reactions. So we have to take a deep dive into isotopes and their use. Why study isotope effects • Isotopes and their effects, critical in understanding both structure and activity in biochemistry, are key in climate science. • Both equilibrium and nonequilibrium reactions and processes apply to isotope partitioning into water and biomolecules, in ways similar to linked biochemical reactions and pathways. • The study and application of isotope effects can integrate and expand learning from previous science courses In more global terms • The strength and quality of our data and models that support explanations in biochemistry and climate analyzes must be explored. • Scientific and ethical rigor in pursuit of knowledge, explanations, and solutions as we pursue complex fields like biochemistry and climate change is essential if we are to place trust in our experts and faith in their findings. Absolute and proxy measurements for CO2 and temperature It's simple to see how CO2 levels over the last 800,000 years have been determined from ice cores since ancient air is actually trapped in bubbles in them. The bubbles can be liberated by melting/shattering and analyzed. However, how can we infer temperature changes or actual temperature from ice core samples and both CO2 and temperatures from ocean sediment cores? Scientists use "proxies" to determine temperature as modern thermometers and temperature scales were invented only recently (early- and mid-1700s by Fahrenheit and Celsius). These proxies include tree rings, growth bands in coral, pollens found in core samples, and calcite shells from marine organisms found in lake and ocean sediments. The organisms include certain types of algae, phytoplankton like dinoflagellates and diatoms, and foraminferas (single-celled protozoans with shells). From a more chemical perspective, the analyses of the isotopic compositions of ice, using the isotope ratios of 18O/16O in water, and of marine sediments, using the ratio of 18O/16O and 13C/12C in carbonate-containing shells, have proved critical in determining temperatures back millions of years ago. Even molecules like components of leaf waxes (as we discussed in Chapter 31.1) can be used. Isotope analyses of diatoms (with silicate shells) in lake sediments are also useful. The analysis of proxies is quite complicated since many factors contribute to the measurement derived from proxy use. Take tree rings as an example. The width of a given ring depends not only on temperature but precipitation. Calibrations for each proxy must be made using alternative data such as rings from a variety of other trees. Proxy data from ice cores go back a few million years, while data from marine sediments back as far as 100 million years ago. Isotope analysis in rocks formed from marine or land sediments can go back billions of years. Proxy data (tree rings) taken from recent times can be compared to actual temperatures measurement from the same time. The calibration relationships can then be used for samples from the past. Alternatively, proxy data can be taken across many different places and temperatures to develop calibration constants, an approach useful for pollen analysis. Organisms could also be cultured in different temperatures, nutrient, and CO2 conditions to calibrate past data. From a statistical sense, it is best to combine multiple proxies to develop past temperature records. Proxy data sets are available for over 10,000 sites around the world. Figure $\PageIndex{x}$ below gives a link to sites compiled by Carbon Brief where databases with information about each study is available. Figure $1$: Map of over 10,000 proxy data studies and sites. Robert McSweeney and Zeke Hausfather and Tom Prater. https://interactive.carbonbrief.org/...-distant-past/ Most people might care little about climate change millions of years ago. The value in understanding climate change so far in the past is, in part, to build confidence in the data, methods of analysis, and climate model to better understand the relationship between CO2 and temperature. Some, particularly the PAGES 2k Consortium, focus their attention on the last 2000 years of the Common Era. Figure $2$ shows global mean temperatures obtained from proxy data (yrs 0 - 2000) and direct observations (through the use of thermometer and satellite measurements) since around 1850. Figure $2$: Global mean surface temperature reconstruction (yellow line) and uncertainties (yellow range) for the years 0-2000 period from the PAGES 2k Consortium along with observations from Cowtan and Way from 1850-2017. Data available in the NOAA Paleoclimate Archive. Note the overlap of proxy measurements and direct observations of temperatures since around 1850. Ocean Microorganisms Students of biochemistry come from many backgrounds and all do not have a strong biology background. To help with that, and to develop a sense of wonder about the microorganisms that inhabit the oceans and play such a key part in the biosphere, let's look at a few relevant to this chapter. For those with a "chemistry-centric" background, these descriptions are probably new. Plankton The word plankton derives from a Greek word meaning drifter or wanderer. There are two main types. One is zooplankton, which are not plants, but rather microscopic animals and protozoans. They are heterotrophs that don't synthesize their own food. Most have calcite shells. The other is phytoplankton, which are autotrophic plants that use photosynthesis for food production. Hence they are carbon "capturers", which are key players in the carbon cycle in maintaining atmospheric CO2 and in producing O2. The phytoplankton broadly include algae (protists), cyanobacteria (also known as blue-green algae), and dinoflagellates which also fit into other groups. Here are some examples. Also remember that protists are eukaryotic organisms that are not animals, plants, or fungi. Table $1$ below lists types and examples of plankton. Type of plankton Examples Zooplankton (heterotrophs) benthic foraminiferans, which live mostly at sea bottoms and in sediment and capture carbon indirectly through the carbon cycle though the use of CO3- in their shells. planktonic foraminifera, which live near the surface but are found buried in ocean sediments after their death dinoflagellates that don't photosynthesize are small animals including tiny fish and crustaceans such as krill and jellyfish. radiolarians, single-cell protozoans that have calcium silicate shells Phytoplankton (autotrophs, primary producers) diatoms photosynthesizing dinoflagellates blue-green algae which are prokaryotic bacteria green algae, a photosynthetic eukaryotic protist. some foraminifera that live near the surface watera and can photosynthesize Zooplankton Existing shells in sea sediments from foraminifera have been critical in dating studies and determining CO2 and temperatures over millions of years. There are two major types which include benthic foraminifera (which live at the sea bottom and in sediment) and a smaller group of planktonic foraminifera which live near the surface. They are heterotrophs, but some, on ingestion of small autotrophic phytoplankton, can retain and sequester their chloroplasts, which can engage in photosynthesis for a period of time. Figures $\PageIndex{3-5}$ below show examples of zooplankton that have been important in climate studies. Figures $3$ above: Benthic foraminifera: Live mostly at sea bottoms and in sediment); capture carbon indirectly through the carbon cycle through the incorporation of CO3- into their shells. Living benthic foraminifera in the Bohai Sea, showing normal specimens and abnormal individuals (indicated by arrows). Figures $4$ above: Planktonic foraminifera Live near the surface but are found buried in ocean sediments after their death. (ah) Nano-CT scan of planktonic foraminifera specimens with color map of test thickness, warm colors indicating areas of relatively thicker shell; (a,b) Globigerinoides ruber (Tara), (c) Globigerina bulloides (Tara), (d) Neogloboquadrina dutertrei (Tara), (e) G. ruber (Challenger), (f) Trilobatus trilobus (Challenger), (g) N. acostaensis (Challenger), (h) N. dutertrei (Challenger); (ip) SEM images of selected planktonic foraminifera specimens; (i) T. trilobus (Tara), (j) G. ruber (Tara), (k) G. ruber (Challenger), (l) G. bulloides (Challenger), (m,n) G. ruber test cracked to reveal wall texture (Tara), (o,p) G. ruber test cracked to reveal wall texture (Challenger). Figures $5$ Above: Radiolaria .single-cell protists that secrete silica Figures $\PageIndex{3-5}$: Examples of zooplankton. Benthic foraminifera: https://commons.wikimedia.org/wiki/F...raminifera.p; Planktonic foraminifera: Creative Commons Attribution 4.0 International License. Fox, L., Stukins, S., Hill, T. et al. Quantifying the Effect of Anthropogenic Climate Change on Calcifying Plankton. Sci Rep 10, 1620 (2020). https://doi.org/10.1038/s41598-020-58501-w. http://creativecommons.org/licenses/by/4.0/.; Radilaria: https://commons.wikimedia.org/wiki/F...ria-sp2_hg.jpg Phytoplankton Phytoplankton are microscopic plants, and as such, engage in photosynthesis, capture CO2, and produce O2. Hence they are primary autotrophs. We will consider three types, diatoms, photosynthetic dinoflagellates and coccolithophores. Diatoms and photosynthetic dinoflagellates are the major ones and are prey for the zooplankton. They are described in Figures $\PageIndex{6-8}$ below. Figures $6$ Above: diatoms Single-celled eukaryotic algae surrounded by a silica shell (test). These can reach 1 mm in diameter and can form an assortment of shapes. Some can form multicellular chains. They engage in high efficiency photosynthesis and resulting carbohydrate synthesis. They are found in coastal and cold waters with lots of nutrients. Figures $7$ Above: photosynthetic dinoflagellates Algae with a single shell. They are smaller than diatoms. Most have two flagella for motion. They have a cellulose shell, which degrade on death. Hence, they don't have shells that enter the sediment. Some are nonphotosynthetic and are considered zooplankton. Figures $8$ Above: coccolithophores Coccolithus pelagicus; coccosphere. These are very small single cell algae, which form interlinked calcium carbonate circulate plates that cover the surface. Figures $\PageIndex{6-8}$: Some phytoplankton. Diatoms: https://commons.wikimedia.org/wiki/C...le:Diatom2.jpg; photosynthetic dinoflagellates: https://commons.wikimedia.org/wiki/F...lagellates.jpg; coccolithophores: https://commons.wikimedia.org/wiki/F..._pelagicus.jpg Along the coast in summer, nutrient-rich upwelling of water occurs which can lead to explosive growth of dinoflagellates, causing the water to become red-gold (often called a red tide). Some species in these blooms produce neurotoxins such as saxitoxin (inhibitor of sodium channels), which can produce paralytic shellfish poisoning if shellfish from the bloom area are eaten, and brevitoxin (stimulate voltage-gated sodium channels in nerve and muscle). Ice cores from Antarctica and Greenland can extend to over 3.4 km (2.1 miles) in depth and yield direct information on CO2 and indirect measurements of temperature. The oldest continuous ice core records extend to 130,000 years in Greenland, and 800,000 years in Antarctica. Data going back 2 million years is available using discontinuous cores. Concentrations of trapped CO2 as a function of time, and the temperature of each layer can be determined. Figure $9$ shows a section of an ice core from the West Antarctic Ice Sheet Divide (WAIS Divide). Figure $9$: The dark band in this ice core from the West Antarctic Ice Sheet Divide (WAIS Divide) is a layer of volcanic ash that settled on the ice sheet approximately 21,000 years ago. Credit: Heidi Roop, NSFhttps://icecores.org/about-ice-cores The remains of plankton shells described above are found in cores from sea sediments. Analyses of shells, especially from foraminifera, have provided climate data going back tens of millions of years ago. For both ice and ocean sediment cores, isotope analyses have been the key to obtaining CO2 and temperature data. Isotope Analyses In analyzing ice and ocean sediment cores, three things are needed: the age of the layer, a direct or indirect measure of the atmospheric CO2 at the time the layer was deposited, and an indirect measurement of the temperature at the time of deposition. As shown in the figure above, ice core samples have rings, similar to trees, that can be used to count backward in time. The rings get harder to distinguish the further back you go. Figure 3 above shows a visible dark band deposited by volcanoes 21,000 years ago. Ultimately, isotopic analyses of H2O and CO2 in ice samples and of carbonates in minerals and deposited microfossils in ocean samples are critical in determining past CO2 and temperature values. Most readers are familiar with 14C radioisotope dating and 13C-NMR. Metabolic pathways have been elucidated using 2H (deuterium), 3H (tritium), 13C, and 14C to label specific atoms in substrates and follow their flow into products. These same isotopes have been used in kinetic experiments to determine enzyme reaction mechanisms. We will explore isotopes in some detail in this section. Use of unstable radioactive isotopes The use of 14C radioisotope dating is limited in climate analyses given its short half live (t1/2 = 5730 years). In contrast to most isotopes made in stellar nucleosynthesis or by the radioactive decay of a precursor radioactive elements to an isotope of another element, 14C is made on a continual basis in the atmosphere when high energy neutrons (n) from solar radiation react with atmospheric nitrogen (N). The neutron kicks out a proton to form 14C as shown in the nuclear reaction below. $n+{ }_7^{14} \mathrm{~N} \rightarrow{ }_6^{14} \mathrm{C}+p$ 14C becomes oxidized to form 14CO2 which can then enter the carbon cycle and enter the organic carbon pool through uptake by photosynthetic organisms and organisms that consume them. It can also form inorganic bicarbonates and carbonates, which could enter into shells. All living things take in 14C until their death, after which 14C decays through the conversion of a neutron to a proton, a beta particle (electron) and an antineutrino, forming stable 14N. Hence the amount of 14C in dead organisms or their remains diminishes with a t1/2 = 5730 years, in a process not affected by temperature or pressure. 14C dating can be used in samples dating back about 55,000 years, a time span representing 9.6 half-lives. Only 0.13% of the original 14C would be left. Data by this method give the age of death of the organism. Carbon-14 dating depends on the assumption that its amount in the environment is constant, but the burning of fossil fuels and detonation of nuclear weapons has altered its amount (see box below). Changes in solar activity and resulting changes in high-energy neutrons also affect the amount of carbon 14. Also given the relatively short time frame used in 14C dating, differences in CO2 based on its sequestration and circulation in the oceans are also factors. 61% of the Northern Hemisphere is covered by oceans compared to 81% of the Southern Hemisphere. Books of calibration factors can be used to control for these effects. The calibrations are based on tree rings, lake and ocean sediments, corals and stalagmites data which allows dating back to 55,000 years ago. Nuclear Weapons, Fossil Fuels and 14C dating In the 1950s up to 1962, nuclear weapons were tested in the air, doubling the amount of 14C in the air. This spike has been taken up into organisms and into the ocean. Also since then, the amount of CO2 from the burning of fossil fuels has gone up dramatically. This source does not contain 14C as it derives from fossils that long ago decayed. The effects canceled each other in 2021. Since 2021 a lot more CO2 from fossil fuels has been added so the net effect is now lower levels of 14C equivalent to preindustrial time. It will continue to lessen until well after we stop using fossil fuels. By 2050 the levels might be equivalent to those in the Middle Ages. This, and the human-made stoppage of the next ice age glaciation cycle is yet another warning to us about our effects on the entire biosphere. The decay of other "unstable" radioactive isotopes is used for dating samples and determining their age of burial: • 39Ar , an extremely rare isotope (t1/2 =269 yr), has been used to date ice cores from the Tibetan Plateau over the last 1,300 years. • 40K (t1/2 =1.25 billion yr) decays to 40Ar (stable), so their ratios can be used to determine how much time has passed since magma solidification into rock, based on rates of diffusion of the resulting stable 40Ar . • The ratio of 26Al/10Be in buried samples is used in dating analyses. The two isotopes are rare and produced in a fixed ratio (6.75/1) when they are formed in surface quartz by solar radiation (much like the formation of 14C). When buried through geological processes, there is no further production of the isotope, but fortunately (for those who measure age of burial), they decay with different half lives (t1/2 = 717,000 yr for 26Al and t1/2 = 1.39 million yr 10Be) • The ratios of 21Ne/26Al and 21Ne/10Be can be used. 21Ne is a stable isotope and these ratios are independent of the 26Al/10Be rate. • Uranium isotopes are widely used in age measures on the long time scale. 238U (t1/2 =4.45 billion yr) is converted to 206Pb (stable) and 235U (t1/2 =704 million yr) to 207Pb (stable) by parallel decay routes which allow for multiple types of dating measurement. Use of stable isotopes Most of the data and graphs of CO2 and temperature vs time (years ago) presented in Chapter 31.1 were determined by using stable isotopes that do not decay. Much of the data is based on either the ratio of the stable isotope pairs of oxygen (18O/16O) or carbon (13C/12C) in buried ice or ocean sediment cores. These isotopes have also been used to infer the temperature or temperature change when targets were buried in ice core or ocean sediments. Temperatures at the time of deposition of water in the ice layers are often inferred from 18O/16O ratios in the ice layers. Ice core 18O/16O analyses The oceans are huge and generally homogenous reservoirs that can give clues to long-term changes in climate. Short-term climate change would have limited effects on the oceans. The 18O/16O ratios in Greenland and Antarctic ice cores have allowed dating and temperature reconstruction over geological time since the ratio is determined by the 18O/16O in the liquid oceans at the time of ice formation. The % natural abundance of 18O (0.205 %) and 16O (99.757 %) gives a ratio of the two isotopes of 0.0021, which is so small that the exact ratio is inconvenient for routine use. Rather, a comparison of the ratios in a target sample vs a universal reference, the δ18O value, is determined using a mass spectrometer. The δ18O value is calculated by the following equation: $\delta^{18} O=\left[\frac{\left(\frac{18}{16} O\right)_{\text {sample }}}{\left(\frac{18}{16} \mathrm{O}\right)_{\text {reference }}}-1\right] * 1000$ Similar δ values are determined for D/H ratios (δ2H) and for 13C/12C (δ13C) • The reference for δ18O calculations is the Standard Mean Ocean Water (SMOW or V-SNOW) • The δ2D reference value is also based on SMOW or V-SNOW • The standard for the analogous δ13C value is the Cretaceous Peedee Belemnite (an extinct order of squid-like cephalopods with an internal cone skeleton) sample from the Peedee belemnite (PDB) formation in South Carolina, USA. This standard is no longer available so an alternative, NBS 19, a carbonate material, is used in a new V-PDB (Vienna-PDB) scale. Table $4$ shows the ratio of the isotope abundancies in nature and in the references. Element ratio Ratio of natural abundance Reference ratio 18O/16O 0.205/99.757 = 0.00205 0.0020052 (SMOW or V-SMOW) 13C/12C 1.1/98.9 =0.0111 0.011238 (PDB or V-PDB) 2H/1H (D/H) 0.0156/99.9844 = 0.000156 0.00015576 Note that the ratios of the standards, and likewise of the samples, are small. Also note that in the equation for δ18O, the bracketed term is multiplied by 1000. If multiplied by 100, the value for δ18O would be a percentage. Instead, it's multiplied by 1000 to convert it to permill (per mil or %o) or parts per thousand (just like percent is parts per 100). Hence 1%o is 1 part per 1000 or 0.1%. Equations can be just collections of letters with little intuitive meaning, or they can be deconstructed by the user to make intuitive sense. To help understand this equation, which most readers have likely never encountered, given its importance in climate studies, let's look at 3 sets of conditions. If ... • 18O is enriched in the sample compared to the reference, then (18O/16O)sample/(18O/16O)reference is >1, so subtracting 1 from it makes the bracketed term +, along with δ18O; • 18O in the sample is equal to that in the reference, then (18O/16O)sample/(18O/16O)reference =1, so the bracketed term = 0, and δ18O = 0; • 18O in depleted in the sample compared to the reference, then (18O/16O)sample/(18O/16O)reference <1, so the bracketed term is -, along with δ18O. In summary, a sample with a higher 18O/16O ratio (enriched in heavier isotope) than the SMOW reference will have a positive (+) δ value. If the 18O/16O ratio of the substance is lower (depleted in heavier isotope) than the SMOW reference, the δ value will be negative (-). The δ values of SMOW (O and H isotopes) and PDB (C isotopes) are zero as they are compared to themselves. Now let's apply this to the analysis of ice and ocean δ18O values and see how delta values are used as a proxy for temperature or change in temperature at deposition. The ice shields come from snow which comes from water evaporated from the oceans. Water can have multiple isotopic compositions, but from the % abundancies, the most likely ones are H216O and H218O, which is heavier than the light form, H216O. H216O evaporates more readily from the mid-latitudes of the oceans, and when it reaches the poles, condenses to form snow and eventually ice enriched in H216O. Urey showed that the vapor pressure of H218O is about 1% less that that of H216O between 46.35°C and 11.25°C. In addition, the H218O that evaporates at lower-latitudes is more likely to condense and be removed in rain, leaving the southern oceans enriched in H218O. This effect is actually quite significant as water, in the form of ice, in Greenland and Antarctica has about 5% less H218O than water at 200C from midlatitudes, making the δ18O for water a proxy for temperature but even better as measures of ice volume and from that sea levels. Now consider the Ice Ages, when oceans were enriched in H218O. On glacial melting of H216O-enriched ice, with the melting flowing into the oceans, the H218O would get diluted with H216O. At the same time, the salinity of the ocean would decrease since ice condenses without oceans salts. These differences in H218O/H216O values in ice core samples are converted to δ18O values, which can be positive or negative, as shown in Figure $10$. Figure $10$: Typical δ18O values (in permil). Andreas Schmittner. https://eng.libretexts.org/Bookshelv...A_Paleoclimate Surface ocean water has δ18O values of around zero. Due to fractionation during evaporation, less heavy isotopes make it into the air, which leads to negative delta values of around -10 ‰ for the evaporated water vapor. Condensation prefers the heavy isotopes, as described above. In this example, the first precipitation thus has a δ18O value of about -2 ‰,(more positive than the first vapor). The remaining water vapor will be further depleted in 18O relative to 16O and its δ18O value become morr negative (-20 ‰). Any subsequent precipitation event further depletes 18O. This process is known as Rayleigh distillation and leads to very low δ18O values of less than -30 ‰ for snow falling onto ice sheets. Thus, ice has very negative δ18O of between -30 and -55 ‰. Deep ocean values today are about +3 to +4 ‰. During the last glacial maximum, as more water was locked up in ice sheets, the remaining ocean water became heavier in δ18O by about 2 ‰. We know this, as we explain below, because foraminifera build their calcium carbonate (CaCO3) shells using the surrounding sea water. Thus they incorporate the oxygen isotopic composition of the water into their shells which are then preserved in the sediments and can be measured in the lab. Bralower and David Bice. https://www.e-education.psu.edu/earth103/node/5. Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License(link is external). In summary, in cold conditions, Greenland and Antarctic ice is enriched in 16O since H216O preferentially evaporates and then condenses and freezes into ice a the poles. In addition, as we will see below, deep sea Foraminifera shells contain more 18O in shells since water is enriched H218O in cold conditiond, since less evaporates. Hence δ18O becomes more positive. Ocean sediment core 18O/16O analyses In the last section, we looked at δ18O values in ice core layers and their use as proxies for land and ocean temperatures as more fundamentally as measures of ice volume., which affects sea levels as well as ocean salinity. It's amazing what we can surmise about past climate based on the fact that H216O evaporates more readily than H218O and that H218O that did evaporate condenses at low and mid-latitudes more readily than H216O. These two factors lead to the enrichment of H216O in polar ice and the enrichment of H218O in low and mid-latitude ocean water. Remember that evaporation and condensation are physical reactions involving a change in state. Yet ice cores go back only so far in geological time. To go back further, scientists analyze shells buried in ocean sediments. More specifically, they analyze the isotopic ratio of 18O/16O in calcium carbonate (calcite, CaCO3) in buried shells of organisms like Foraminifera. (Calcite is a stable anhydrous form of CaCO3, but under high pressure it can form different calcite phases.) Now we are dealing with a new atom, C, in the carbonates, so the interpretation of δ18O of buried carbonate depends on many factors compared to than δ18O values of solid and liquid water. It must include these factors • the chemical reactions of inorganic carbonate formation (precipitation) and dissolution (compared to the physical reactions of water evaporation and condensation) • the biological formation of CaCO3 in shells • an understanding of the carbon cycle • the temperature at which the CaCO3 was deposited • the salinity at deposition as the formation of CaCO3 from its ions as the overall ionic strength of the medium in which CaCO3 formed would influence the thermodynamics and kinetics of how the separate ions approach each to form the solid • equilibrium and kinetic controls of the precipitation reactions. To truly understand biochemistry, we must include both biological and chemical aspects. Biochemistry requires a synthesis of knowledge from many disciplines, including introductory chemistry (where precipitation reactions were likely covered for the first time) and analytic chemistry (which delves more deeply into precipitation reactions). Hence we don't apologize for bringing back your previous knowledge of precipitation reactions. At the same time, our description of the use of δ18O values in buried ocean sediments is very simplified. We need to consider two different reactions to understand δ18O values in calcite Foraminifera shells. The first describes how 18O gets into CO32- in the first place. The second describes seemingly simple reactions for the formation of CaCO3 from its ions. Enrichment of CaCO3 with 18O The incorporation or fractionation of 18O from H218O into calcite shells can be described most easily by the reaction below. Fractionation Reaction: (1/3) CaC16O3 + H218O → (1/3) CaC18O3 + H216O In this reaction, the source of 18O comes from the most abundant and likely sources, H218O. A simplified reaction mechanism is shown in Figure $11$. Figure $11$: Incorporation of 18O into carbonate from H218O More broadly, there would be an exchange of isotopes in the entire dissolved inorganic pool (DIC) = CO2(aq) + H2CO3 + HCO3 (bicarbonate) + CO32 (carbonate) with H2O. Formation of CaCO3 Two reactions in general describe calcite formation and its growth: $\mathrm{Ca}^{2+}(\mathrm{aq})+\mathrm{CO}_3^{2-}(\mathrm{aq}) \leftrightarrow \mathrm{CaCO}_3$ and $\mathrm{Ca}^{2+}(\mathrm{aq})+\mathrm{HCO}_3^{-}(\mathrm{aq}) \leftrightarrow \mathrm{CaCO}_3+\mathrm{H}^{+}$ These show the uptake of 18O into CaCO3 should also include a consideration of HCO3-. In a simple and environmentally-controlled in the lab, reaction 2 above can be considered in equilibrium and defined by a Ksp value (as you learned in introductory chemistry courses. In Chapter 4.12, we saw the relationships between Keq, ΔG0, ΔH0, DS0 and temperature. There is an inverse relationship between Keq and temperature. \begin{gathered} \Delta \mathrm{G}^{0}=\Delta \mathrm{H}^{0}-\mathrm{T} \Delta \mathrm{S}^{0}=-\mathrm{RTln} \mathrm{K}_{\mathrm{eq}} \ \ln \mathrm{K}_{\mathrm{eq}}=-\frac{\Delta \mathrm{H}^{0}-\mathrm{T} \Delta \mathrm{S}^{0}}{\mathrm{RT}} \ \ln \mathrm{K}_{\mathrm{eq}}=-\frac{\Delta \mathrm{H}^{0}}{\mathrm{RT}}+\frac{\Delta \mathrm{S}^{0}}{\mathrm{R}} \end{gathered} Assuming that the formation of CaCO3 is in equilibrium, then you would expect that CaCO3 would be enriched in 18O, and have a +δ18O value. Why? Urey showed that under equilibrium conditions, calcite is enriched in C18O32- probably because of lower vibrational energy of the heavier form of carbonate, which favors stabiity and formation of the solid. In addition, the incorporation of 18O is even more pronounced in climatically colder water, which as we have seen, has a +δ18O value. Hence in cold periods with large ice shields, δ18O values from shells of foraminifera living both in the illuminated upper ocean (planktic foraminifera, which engage in photosynthesis) and deep sea benthic foraminifera, are more positive. On ice shield melting, as the δ18O values of water become more negative, so do the values of δ18O values of the foraminifera. Benthic foraminifera give a global temperature estimate as deep waters are more homogeneous. Planktic foraminifera δ18O values are proxies for more local temperatures as they are in a more changing, less mixed environment, and are more affected by evaporation and precipitation. Yet the formation of CaCO3 in shells in many cases is not in equilibrium and is in part determined by the concentration of the reactants, the rate of diffusion of ions into and out of the growing calcite shell, which would also depend on salinity (affecting electrostatic attractions of the ions to the growing crystal), the pH (which affects the ratio of CO32 and HCO3) and biological effects (from the mechanism by which shells are formed which at some point may involve HCO3 transporters). It also depends on the of transfer of carbonate within the dissolved inorganic carbon pool (DIC). The reaction has been shown to be in equilibrium in some species of foraminifera but not in others. The term fractionation is often used in the isotope and climate literature. Using water as an example, it describes the ratio of heavy to light isotopes of O that partition into the liquid, solid, and gas phases of water. The fractionation factor determines δ18O value of water. Likewise, there is a fractionation process that determines the partitioning of 18O from water into CO32- and CaCO3 during the precipitation of calcite. The fractionation factor α shows how the ratio of the isotopes changes in either a physical (such as a phase transition) or chemical process. It is the factor by which the abundance ratio of two isotopes will change during a chemical reaction or a physical process. The formation of calcite from HCO3 in controlled studies shows that CaCO3 has different oxygen isotope concentrations depending on the initial concentrations of reactants. The size of a shell can also affect the δ18O of additionally deposited CaCO3. These ideas support the notion that the fractionation of isotopes in CaCO3 occurs through both equilibrium fractionation and kinetic fractionation. Several different theoretical "paleotemperature" equations have been developed to show how temperature T is related to δ18O in calcite during equilibrium conditions. One theoretical quadratic equation is shown below. \begin{aligned} T &=16.9-4.38\left(\delta_{\mathrm{c}}-A\right)+0.10\left(\delta_{\mathrm{c}}-A\right)^2 \ A &=\delta_{\mathrm{w}}-0.27 \% \end{aligned} where T is the temperature in oC, δc and δw are the δ18O of calcite and water, respectively. The 0.27% is just an adjustment factor to convert from the PDB to the VPDB reference values. The equation applies to sea water of normal salinity and freshwater. A more recent calibration equation for the formation of calcite from HCO3- (done with bubbling of the reaction mixture with N2 for a variety of temperatures) was derived by Kim and O'Neil in 1997. The equation was derived from carefully controlled laboratory studies that apply under equilibrium conditions and is shown below. The Kim and O’Neil equation shows the relationship between the fractionation factor alpha (α) of 180/160 between inorganically precipitated CaCO3 and H20 as a function of the temperature, and is shown below. $1000 \ln \alpha\left(\text { Calcite- } \mathrm{H}_2 \mathrm{O}\right)=18.03\left(10^3 T^{-1}\right)-32.42$ Alpha is the fractionation factor, and T is in Kelvin. Note: An update of this equation to conform to IUPAC conventions gives 103 ln α = 18.04 x 1000 / T - 32.18) The oxygen isotope fractionation factor alpha between two substances A and B is defined as $\alpha=\left({ }^{18} \mathrm{O} /{ }^{16} \mathrm{O}\right)_{\mathrm{A}} /\left({ }^{18} \mathrm{O} /{ }^{16} \mathrm{O}\right)_{\mathrm{B}}$ The left hand side of the equation (1000xlnα) is used for convenience and its relationship to δ18O values (which are expressed per %o), similar in a way to the use of pKa = -log[KA] instead of KA. Here is an alternative form of the Kim and O'Neil equation expressed in quadratic form. $T\left({ }^{\circ} \mathrm{C}\right)=16.1-4.64 \cdot\left(\delta^{18} \mathrm{O}_{\mathrm{f}}-\delta^{18} \mathrm{O}_{\mathrm{w}}\right)+0.09 \cdot\left(\delta^{18} \mathrm{O}_{\mathrm{f}}-\delta^{18} \mathrm{O}_{\mathrm{w}}\right)^2$ A controlled equilibrium study using the cultured foraminifera B. marginata of different sizes at different temperatures was used to develop an experimental equation to compare with the theoretical equations described above. Figure $12$ shows graphs of the empirically-determined equation (nonred lines) vs the theoretical Kim and O'Neil equation (red line). Figure $12$: Comparison of experimental calibration equation with the theoretical equation for equilibrium calcite of Kim and O’Neil (1997). Barras, Christine & Duplessy, J.-C & Geslin, Emmanuelle & Michel, Elisabeth & Jorissen, Frans. (2010). Calibration of δ 18O of cultured benthic foraminiferal calcite as a function of temperature. Biogeosciences. 7. 1349-1356. 10.5194/bg-7-1349-2010. CC Attribution 3.0 License The brown, blue and green lines represent the calibration equations of cultured B. marginata from < 150, 150–200 and 200–250 μm size fractions, respectively. The quadratic equation derived from Kim and O’Neil (1997) relationship is represented by the red line. A quick inspection of the empirical equation for different sizes of B. marginata shows the same relationships between T an dδ18O values as shown in Table $5$ below. Table $5$: Best fit linear plot of temperature T vs (δ18Of - δ18Ow) for foraminifera B. marginata vs size, where the subscript f is foraminifera and w is water. We take this opportunity to reshow the graph that reconstructs changes in planetary temperatures over the last 66+ million years (Figure $13$). The data between 66 MYA and 100,000 years ago (note the change in scale in the x-axis to allow fitting of a large time range in one graph) was obtained, in large part, from δ18O values from deep ocean sediments, while the data from around 100,000 YA to the advent of modern temperature recordings were obtained mostly from δ18O from ice core samples from Antarctica and Greenland. Of course, other temperature proxies, as described above, were important as well. Figure $13$: https://commons.wikimedia.org/wiki/F...alaeotemps.png. (Excel available). Creative Commons Attribution-Share Alike 3.0 Unported These detailed, but hopefully understandable explanations of the relationship of temperature with δ18O in foraminifera shells from ocean sediment cores were presented for reasons expressed in the beginning of Chapter 31.2: • Isotopes and their effects, critical in understanding both structure and activity in biochemistry, are key in climate science. • Both equilibrium and nonequilibrium reactions and processes apply to isotope partitioning into water and biomolecules, in ways similar to linked biochemical reactions and pathways. • The study and application of isotope effects can integrate and expand learning from previous science courses Astute readers will notice that we concentrated on δ18O values (in water and carbonates) and barely mentioned δ13C values for carbonate precipitations. We will discuss that in the next chapter section as we consider the carbon cycle. Key Points - Beta version from Chat.openai 1. Isotope analysis is a technique used to measure the isotopic composition of elements in order to understand the processes and interactions that have occurred in the past, present, and future. 2. Isotopes of carbon, oxygen, and hydrogen can be used to study the effects of climate change on different Earth systems, such as the atmosphere, oceans, and biosphere. 3. Carbon isotopes can be used to study the sources and sinks of CO2 in the atmosphere, and to understand the role of different types of vegetation in the carbon cycle. 4. Oxygen isotopes can be used to study the sources and sinks of water vapor in the atmosphere, and to understand the effects of climate change on precipitation patterns. 5. Hydrogen isotopes can be used to study the sources and sinks of water vapor in the atmosphere and to understand the effects of climate change on the water cycle. 6. Isotope analysis is an important tool for understanding the complex dynamics of the Earth's climate system and for developing effective strategies to mitigate the impacts of climate change. 7. Isotope analysis can provide important information about the Earth's climate and environment and can help scientists understand the causes and impacts of climate change.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.03%3A__Climate_Change_-_The_Carbon_Cycle_and_Carbon_Chemistry.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry The Carbon Cycle In the last chapter section, we used oxygen isotopes in ice and ocean sediment cores going back millions of years ago to address the history and mechanisms of climate change. We focused on 18O/16O ratios in H2O and in calcite shells (CaCO3 ), and their corresponding δ18O values in ice and sediment cores, to determine CO2 and temperature over climatic history. Now it's time to talk about the other key atom, C, the ratio of 13C/12C and corresponding δ13C values, not only in CaCO3 but also in CO2 and the organic molecules it transforms into through photosynthesis and the heterotrophic organisms that consume them. 13C partitions not only into inorganic carbon but also into organic molecules throughout life. Hence we need a more detailed understanding of the carbon cycle The carbon cycle is likely discussed in introductory chapters in biology textbooks, but probably never in chemistry texts. It is fundamental to an understanding of climate, its control and change, and human processes that alter it. Figure $1$ shows a representation of the carbon cycle. Calculated amounts of carbon found in the lithosphere (the solid part of the earth), the atmosphere (specifically the lower part, the troposphere), and the hydrosphere are shown. (The cryosphere, the frozen ice found in Greenland and Antarctica, is not shown). The biosphere includes part of each of these "spheres" that harbor life. Since life has been shown to exist 10 km down in the crust, we'll refer to the entire region in the diagrams as the biosphere. Figure 1 presents carbon stores in petagrams (1015 g) or gigatons of carbon (GtC), as 1 petagram equals 1 billion metric tons (or approximately 1.1 billion US tons). Figure $1$: The carbon cycle. https://commons.wikimedia.org/wiki/F...te_diagram.svg In addition to the total amount of carbon stored in each region, (GtC), the net changes in carbon per year as it moves into and out of reserves (GtC/yr) are shown in blue arrows with attached numbers. The exchanges of carbon in the cycle occur at different time scales. Geologically "fast" exchange, on a time scale up to 1000s of years, occurs among the oceans, atmosphere, and land, while a slow exchange (over hundreds of thousands to millions of years) occurs in deep soils, deep ocean sediments, and rocks. We will mostly consider exchanges among the atmosphere, land, and oceans. CO2 in the terrestrial biosphere is removed by photosynthesis and returned by respiration by autotrophs like plants, and heterotrophs like microbes that consume soil carbon and plant remains. CO2 in the atmosphere is also removed by ocean autotrophs like ocean phytoplankton and through partitioning into dissolved inorganic carbons (DIC) molecules like HCO3- and CO32- into the oceans. Before we probe some relevant reactions within it, let's look at the big picture and perhaps the most relevant to our climate crisis - the factors causing our increasing CO2 atm and global warming. To do that, we must put numbers on the cycle to quantify it. Quantitating the carbon cycle In Chapter 31.1, we used parts per million (ppm) as a unit for expressing the amount of CO2 in the atmosphere. Table $1$ below shows how to translate the percentage (parts per 100) for each component gas in the atmosphere (with which you are familiar) into ppm. Gas % (parts per 100) in atm part per million N2 78.09 780,900 O2 20.94 209,400 Ar 0.93 9300 CO2 0.0415 415 Table $1$: Unit conversion - % to ppm Climate scientists use ppm instead of concentration (in molecules/m3) since they wish to know the relative percent or ppm increase with time, which does not depend on temperature and pressure. In contrast, concentration does depend on T and P, as you will remember from the ideal gas law, PV=nRT or n/V=P/RT that you studied in introductory chemistry. It is important to use dimensional analyses to interconvert units as well. Table $2$ below shows conversion factors to switch between GtC, Gt CO2, and ppm. Convert from to conversion factor GtC (Gigatons of carbon) ppm CO2 divide by 2.124 GtC (Gigatons of carbon) PgC (Petgrams of carbon) multiply by 1 Gt CO2 (Gigatons of carbon) GtC (Gigatons of C) divide by 3.664 = 44.01/12.01) GtC (Gigatons of carbon) MtC multiply by 10000 Table $2$: Unit conversion - GtC and Gt CO2 To be more technical, atmospheric CO2 concentrations are expressed in mol fraction of CO2 in the dry air atmosphere. The ppm for CO2 is hence μmol CO2 per mole of dry air. We have to put numbers on the components of the carbon cycle to quantitatively analyze changes in its components, otherwise, we can't know what is presently happening nor will we be able to predict with some certainty the future. Stoichiometry and reaction kinetics are probably the least liked parts of chemistry for many, but they are critical in understanding climate change We have to apply them on a global scale. Two key terms are important: Stocks or reserves: How much carbon (mass in Gigatons or petagrams) is stored in given locations in the biosphere. This allows us to understand what % of all carbon stocks are in the ocean, for example. Stocks are usually reported as gigatons of carbon (GtC), not gigatons of carbon dioxide, since many stocks (like fossil fuels) consist of mostly C and H without oxygen. As in stoichiometry calculations in introductory chemistry courses, GtC in the atmosphere can be converted to gigatons of CO2 by using dimensional analysis. Fluxes (rates): How much carbon is transferred from one reserve to another per year (Gigatons/yr). Climate scientists are simply applying the Law of Mass Conservation that you learned in introductory chemistry, to the entire biosphere. Figure $2$ shows the reserves/stock (GtC) for reserves and decadal (2012-2021) average fluxes (large and small arrows, GtC/yr) for individual or aggregated stocks. Figure $2$: Schematic representation of the overall perturbation of the global carbon cycle caused by anthropogenic activities averaged globally for the decade 2012–2021. E represents emission and S "sink". Earth Syst. Sci. Data, 14, 4811–4900, 2022. https://doi.org/10.5194/essd-14-4811-2022. © Author(s) 2022. This work is distributed under the Creative Commons Attribution 4.0 License. The abbreviations used are: EFOS (emissions, fossil fuels), ELUC (emissions land use changes - mostly deforestation), SLAND (terrestrial CO2 sink), SOCEAN (ocean CO2 sink), GATM (Growth Rate CO2 atm), BIM (carbon budget imbalance). Uncertainties are also shown except for the atmospheric CO2 growth rate which is known precisely and accurately through modern measurements. (It's also the easiest to measure). Human (anthropogenic changes) occurs on top of the carbon cycle. The upward arrows indicate release into the atmosphere and the downward arrows the absorption in the oceans and land. The thickness of the arrows gives a relative measure of the size of emission or absorption. The thickest arrow and highest value (9.6 GtC/yr) is for the anthropogenic emission of carbon from our use of fossil fuels. Think about that! Humans are presently the biggest contributor to the carbon cycle. Before the industrial revolution, human contributions were minimal. It's worse than that! Figure x above shows that on average, 9.6 GtC/yr was released from fossil fuel use between 2012-2021. The actual flux of anthropogenic carbon release in 2021 was 9.9 GtC, equivalent to 36.4 Gt CO2. If you add the up arrows and subtract from that sum the down ones, you get +4.8 GtC/yr. This represents the net average increase in GtC in atmosphere CO2 per year for 2012-2021. That is close to the accurately, and precisely known value of +5.2 GtC/yr increase from the CO2 we pour into the atmosphere through our use of fossil fuels. Hence the figure above is a bit out of balance (about 0.3 GtC too low - the BIM carbon budget imbalance), but given the difficulty in calculating these values, it is remarkably close to "mass balance" as you learned in introductory chemistry classes. In general, before the industrial revolution, the sum of the fluxes leading to the addition of CO2 into the atmosphere was equal to the sum of the fluxes that removed it. That is, the system was in a steady state. That is no longer the case. Figure $3$ shows a breakdown of the factors contributing to annual (left) and cumulative (right) fluxes of carbon (GtC/yr), a metric for CO2 flux, over time since 1850. Figure $3$: Combined components of the global carbon budget as a function of time for fossil CO2 emissions (EFOS, including a small sink from cement carbonation; grey) and emissions from land-use change (ELUC; brown), as well as their partitioning among the atmosphere (GATM; cyan), ocean (SOCEAN; blue), and land (SLAND; green). Panel (a) shows annual estimates of each flux ( GtC yr−1, and panel (b) shows the cumulative flux (the sum of all prior annual fluxes) since the year 1850. Again, the graph shows GtC not Gt CO2. . © Author(s), ibid You might ask why the atmospheric growth in CO2 (shown in green) is negative. We'll answer that question below. Lastly, let's think about the total cumulative changes in GtC released and absorbed since 1850 (pre-US civil war and before the big release of CO2 in modern times). Those data are shown in a bar graph in panel A of Figure $4$. The bar graph in the right panel shows the mean decadal averages that are shown in Figure 2 above. Figure $4$: Total cumulative changes in GtC released and absorbed since 1850 (panel A) and mean decadal fluxes (panel B). EFOS (emissions, fossil fuels), ELUC (emissions land use changes - mostly deforestation), SLAND (terrestrial CO2 sink), SOCEAN (ocean CO2 sink), GATM (Growth Rate CO2 atm), BIM (carbon budget imbalance). © Author(s), ibid The positive emission and negative absorption contributions are easy to see in the bar graph. The blue bar represents the net emission of carbon from fossil fuels and fills the gap to complete mass balance as we discussed above. It also explains the negative blue region in Figure 3. Just keep in mind that the blue net flux from fossil fuels is positive. The cumulative contributions from fossil fuel emissions required to close the gap and fulfill mass balance is is +275 GtC, which when multiplied by the conversion factor (1ppm/2.124 GtC) translates into a 129.5 ppm increase in atmospheric CO2 over that time. This is very close to independent measurements of a rise of 129.3 ppm (14.7-284.7) over that time. The data from Figure $4$ has been entered in the first four columns of Table $3$ below. Source Subtype Stock reserves (GtC) J (Fluxes) GtC/yr (avg 2012/2021) + emission - absorption J=kapp[stock] Atmosphere - 875 Buried Fossil Fuels Coal 560 +9.6 J=+9.6=k[905] k=0.0106 Oil 230 Gas 115 Terrestrial Permafrost 1,400 +1.2 (Land use Δ) -3.1 (Land uptake) Juse=+1.2=kuse[3550]; kuse=0.000338 Jup=-3.1=kup[875]; kup=0.00354 Soil 1,700 Vegetation 450 Oceans Coasts 10-45 -2.9 J=-2.9=k[875] k=0.00331 Ocean Surface Sediments 1,750 Organic carbon 700 Marine Biota 3 Dissolve Inorganic Carbon (DIC) 37,000 We can use this data to develop our own crude computational model predicting future CO2 emissions using Vcell, the program we used to produce time course (concentration vs time) graphs for both simple and coupled signal transduction reaction pathways. Vcell can be used to calculate fluxes (J) in reaction pathways, where J is the change in concentrations of a species with time, given the initial concentration or amount of a reactant, and the rate constants affecting its production or removal. If we use the amount of carbon (GtC) in each reservoir in the biosphere and crust as a relative measure of "concentration" and the known fluxes (GtC/yr) for the transfer of carbon to and from the atmosphere as given in Table 3, we could calculate an "apparent rate constant for each flux using this equation: Jstock = kapp[stock] (where stock is given in GtC). \mathrm{J}_{\text {stock }}=\mathrm{k}_{\mathrm{app}}[\text { stock in } \mathrm{GtC}] These "apparent" rate constants are needed to run the Vcell simulation that can reproduce the actual fluxes shown in Figures 2 and 4). The simulation can be run over time A simple four-term model based on Figures 2 and 4 is shown below. Run the simulation and see how atmospheric CO2 changes with time. This model is offered only to show how climate models are made and used, and also for fun. The graphs are valid and sound based on the input parameters, but the outputs are based on many assumptions that vastly simplify the model. Global Carbon Budget 2022 Model: Initial Values: kf = 2, kr = 4, A t=0 = A0 = 10 Note: Y-axis values on the generated plots are scaled incorrectly in the Vcell plots. This will be fixed in a future update. However, the shapes of the curves are accurate. To get the correct Y-axis values, download the .csv file and scale all concentration values by normalizing them to the actual intended initial concentrations of the reactants. An update will occur when the newest version of Vcell is released. The simulation shows CO atm levels peaking at about 982 GtC in 51 years (2073) from its average decadal (2011-2021) value of 875. That is an increase of 107 GtC over now (50 ppm CO2 rise from the present 414 to 463 ppm). Over a 50-year period, this gives an average annual rise of 2.14 GtC/yr or about 1 ppm CO2/yr. A comparison of the predicted atmospheric CO2 (ppm) levels through 2100 for the IPCC SSP1-2.6 scenario (blue) and simple Vcell model (red) is shown in Figure $5$. Figure $5$: Predicted atmospheric CO(ppm) for SSP1-2.6 scenario (blue) and simple Vcell model (red) SSP1-2.6 data - History: Meinshausen et al. GMD 2017 (https://doi.org/10.5194/gmd-10-2057-2017); Future: Meinshausen et al., GMD, 2020 (https://doi.org/10.5194/gmd-2019-222). https://climateanalytics.org/media/g...-3571-2020.pdfhttps://gmd.copernicus.org/articles/13/3571/2020/ However imperfect the Vcell model is (incorrect assumptions, lack of complexity and feedback mechanisms, etc), the results shown above are remarkably close to the projected increases in carbon dioxide in ppm described in IPCC reports for the SSP1-2.6 socioeconomic pathways, shown in the right panel (dark blue line) of Figure $6$. This pathway predicts a rise of approximately 1.80 C in average global temperatures. Figure $6$: IPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Again, remember that the model is based on a ten-year average of CO2 emissions. Think of all the other assumptions in this model (other than the stock reserves and fluxes) that would give higher or lower values of future CO2 levels. One major one is that flux values are all held constant to allow calculations of the apparent rate constants for Vcell use. The model depletes much of the fossil fuel reserve. In addition, CO2 emissions in 2021 were actually 9.9 GtC/yr and going up! In addition, a change in one parameter can affect the others. For example, the net uptake of atmospheric CO2 into the land and oceans has increased from 1960-2010, which makes sense given increased CO2 in the air forcing additional uptake (think LaChatelier's Principle). The oceans have taken up nearly 40% of the CO2 from fossil fuel use since the industrial revolution. If the rate of uptake decreases (i.e. if we start to saturate the uptake into oceans), CO2 accumulation in the atmosphere would accelerate. Data also suggest that if we successfully decrease CO2 in the atmosphere, the oceans would respond by decreasing uptake as well, which would slow the progress in reducing temperatures. An interesting example relating atmospheric and ocean CO2 occurred from 1990-2000 when it has been shown that the ocean acted as a weaker sink. This occurred because of a decreasing gradient (the Δ or"effective concentration differences") between atmospheric CO2 and ocean "CO2", which decreased the ability of the ocean to act as a sink for CO2. You can decrease the Δ in two ways: • decreasing the rate of entry of CO2 into the atmosphere from fossil fuel use. There indeed was a temporary slowdown in this decade. • paradoxically, by briefly making the ocean in a shorter term a better sink. This happened in 1991 after the eruption of Mt. Pinatubo, which led to decreased air and ocean temperatures. CO2 is a nonpolar gas, which has higher solubility in water at lower temperatures (think about soda). This was a short-term and more minor effect than the decreased rate of fossil fuel emissions. More complex models with more terms for emissions and absorptions of CO2 can be made. One is shown in Figure $7$. This model adds CO2 release from the soil through respiration by microorganisms, as well as from plant respiration (CHO to CO2atm). Another term has been added for release by oceans. Figure $7$: More complicated Vcell climate model. Fortunately, we don't have to rely on these simple models to predict future trends in temperature and CO2. A complex dynamic model simulator that is in accord with many different climate models is available at your fingertips. Developed at MIT and Climate Interactive, and available for free from any web browser, the EN-ROADS program allows users to change sliders for key inputs and see future predicted temperature and CO2 levels. In accordance with RCP and SSP IPCC pathways that tie future emissions to socio-economic policies (discussed in Chapter 31.1), the program allows users to change variables such as carbon pricing and incentives to move to clean energy in transportation, building and energy supplies sectors. Access the program directly from this page by clicking the Close icon in the program window in Figure $8$ below. Figure $8$: EN-ROADS global climate simulator Here is also an external link to the En-Roads global climate simulator (Developed by Climate Interactive, the MIT Sloan Sustainability Initiative, and Ventana Systems) Move the interactive sliders and see the effect on greenhouse gas emissions and global temperatures. Here is a link to a one-page tutorial on its use. The Skeptic's Corner: Climate Change Misinformation We should all be skeptical of models, especially ones that predict changes 80 or more years into the future. We gain confidence in a model if it accurately fits data going back in time and into the future data as well. We mentioned in Chapter 31.1 that oil company scientists knew of the likely climatic effects arising from fossil fuel emissions, but the company executives did not act on their models. Their models were startlingly accurate as shown in Figure $9$ below, which shows their predictions for both CO2 levels and the associated increases in temperature caused by them. Figure $9$: Historically observed temperature change (red) and atmospheric carbon dioxide concentration (blue) over time, compared against global warming projections reported by ExxonMobil scientists. Supran, G., Rahmstorf, S., and Oreskes, N. Assessing ExxonMobil's global warming projections. Science (2023). https://www.science.org/doi/abs/10.1...cience.abk0063. Reprinted with permission from AAAS. Not for reuse. Panel (A) shows “Proprietary” 1982 Exxon-modeled projections. Panel (B) shows a summary of projections in seven internal company memos and five peer-reviewed publications between 1977 and 2003 (gray lines). Panel (C) shows a 1977 internally reported graph of the global warming “effect of CO2 on an interglacial scale.” (A) and (B) display averaged historical temperature observations, whereas the historical temperature record in (C) is a smoothed Earth system model simulation of the last 150,000 years. As these graphs clearly show, oil companies knew since the late 1970s, over 40 years ago, of the climatic effects of CO2 emissions. They could even predict the temperatures since the last ice age. In the 70s, solar and wind energy were much more expensive to produce and use than now, but if we had subsidized their development back then as we have done for decades for the fossil fuel industries, our climatic situation now would be much less precarious. Figure $10$ below shows worldwide fossil fuel subsidies in US $billion and in % global GDP from 2015 to 2020 and projections after that. Figure $10$: Worldwide subsidizes in US$billion and in % global GDP. Bar graphs are for US$biillons and the circles and triangles for % global GDP. IMF. ttps://www.imf.org/en/Publications/W...bsidies-466004 The subsidies are broken down into explicit subsidies (tax breaks or direct payments to help fossil fuel companies to fund their uncompensated costs) and implicit ones (undercharging for environmental costs of fossil fuel use that the oil companies don't pay). These latter "hidden" costs are passed down to countries, states, and individuals. In 2020, global subsidies were$5.9 trillion or 6.8% of the world's GDP. The explicit subsidies given to fossil fuel companies, about 8% of the total, amounted to \$472 billion just in 2020! "Company executives chose to publicly denigrate climate models, insist there was no scientific consensus on anthropogenic climate change, and claim the science was highly uncertain when their own scientists were telling them the opposite" (ref). They also propagated the myth that the global climate was actually cooling. This is a powerful and unsettling example of disinformation with enormous consequences. Now that we have seen the big picture, let's look at how carbon moves through various pools of carbon-containing molecules. We have already discussed photosynthesis in great detail in Chapter 20, so we fill focus our attention more on dissolved inorganic carbons (DIC) including species such as HCO3- and CO32-. Another view of the carbon cycle that includes weathering of rocks to produce silicates and bicarbonates, and the formation of shells in the ocean from HCO3-, CO32- and silicates, is shown in Figure $10$. Figure $10$: Let's focus on the oceans first. The reversible movement of CO2 from the atmosphere to the oceans, CO2 atm ↔ CO2 ocean, depends on the difference in the partial pressures of CO2 (ΔpCO2) in the atmosphere and surface waters. The reaction is also driven to the right by the removal of CO2 (aq) as it forms carbonic acid (H2CO3), which then forms bicarbonate (HCO3) and carbonate (CO32). These coupled reactions chemically buffer ocean water, thus regulating ocean pCO2 and pH. pCO2 is not homogenous in ocean surface waters and varies with different conditions of current and temperature. CO2 can be more readily released from upwellings of nutrient-rich and warm waters, especially in the tropics. In cold Northern waters and also in the Southern Ocean, where water sinks, it is taken up from the atmosphere (again CO2 is more soluble in cold water). As we discussed in Chapter 31.1, the ocean chemistry of CO2 determines in large part the levels of atmospheric CO2. The coupled reactions of CO2 in the oceans are shown below. \mathrm{CO}_2(\mathrm{~g}, \mathrm{~atm}) \leftrightarrow \mathrm{CO}_2(\mathrm{aq}, \text { ocean) } \mathrm{CO}_2(\mathrm{aq} \text {, ocean })+\mathrm{H}_2 \mathrm{O}(\mathrm{I} \text {, ocean }) \leftrightarrow \mathrm{H}_3 \mathrm{O}^{+}(\mathrm{aq})+\mathrm{HCO}_3^{-}(\mathrm{aq}) \mathrm{H}_2 \mathrm{O}(\mathrm{I})+\mathrm{HCO}_3^{-}(\mathrm{aq}) \leftrightarrow \mathrm{H}_3 \mathrm{O}^{+}(\mathrm{aq})+\mathrm{CO}_3{ }^{2-}(\mathrm{aq} \text {, sparingly soluble }) These reactions should be familiar to all chemistry students and were presented previously in Chapter 31.1 and in Chapter 2. A significant contributor to ocean bicarbonate is weathering of rocks like limestone. and marble, which are both forms of CaCO3. The relevant reactions are shown below. \begin{aligned} &\mathrm{CaCO}_3(\mathrm{~s})+\mathrm{H}_2 \mathrm{O} \leftrightarrow \mathrm{Ca}^{2+}(\mathrm{aq})+\mathrm{CO}_3{ }^{2-}(\mathrm{aq}) \ &\mathrm{CO}_3{ }^{2-}(\mathrm{aq})+\mathrm{H}_2 \mathrm{O} \leftrightarrow \mathrm{HCO}_3{ }^{-}(\mathrm{aq})+\mathrm{OH}^{-}(\mathrm{aq}) \end{aligned} CO2 is nonpolar and not very soluble in water. Either is CO32- in the presence of divalent cations like Ca2+. However HCO3- is and can be considered a "soluble" form of carbon. This soluble form from terrestrial weatherings ends up in rivers and eventually enters the ocean. It is also the form of carbonate that is transferred into cells by anion transporters for eventual shell formation. HCO3- is also a chief regulator of both blood and ocean pH. Weathering is slow compared to anthropogenic emissions of CO2 from fossil fuel use, but it is nevertheless a key player in the carbon cycle and the regulation of ocean pH. The same weathering reactions on silicate rocks lead to the transfer of silicate ions into rivers and then into the ocean, where they can be taken up by diatoms in the formation of CaSiO4 shells. As the oceans take up more CO2, they become more acidic, which leads to the equivalent of "weathering" of shells of living organisms, leading to their potential death. Silicon is directly underneath carbon in the periodic table so the following simplified reaction is analogous to those we seen with CO2 and its inorganic ions. \mathrm{H}_4 \mathrm{SiO}_4=\mathrm{SiO}_2+2 \mathrm{H}_2 \mathrm{O} H4SiO4 is silicic acid. 13C/12C ratios in ice core and ocean sediments We are now in the position to explore how isotopes of carbon can be used for more than radio- 14C dating, which is quite limited in climate studies. 13C, a stable isotope of carbon, however, is extremely useful because C-13C bond dynamics are influenced by it. Reaction rates are affected by the presence of 13C when C-C bonds are made or cleaved. This isotope effect leads to different 13C/12C ratios in reactants and products, and hence different δ13C values. Isotopes have a long history in the study of biochemical reactions. The kcat and kcat/KM values for enzyme-catalyzed reactions can be affected if the rate-limiting step involves cleavage or the creation of a C-13C, C-D (deuterium) or C-T (tritium) bond. Substrates labeled with the isotopes have similar transition state energies for the formation/cleavage of a bond involving an isotope, but the ground state vibrational energy for the isotope-substituted atom are proportionately lower, as illustrated in Figure $11$. Figure $11$: Kinetic Isotope Effects. This primary kinetic isotope effect leads to higher activation energy for the formation/cleavage of a bond with the isotope. For C-D and C-T bond cleavages that are rate-limiting, the rates are 7X and 16X slower than the cleavage of a C-H bond, respectively. Cleavage or formation of bonds to heavy isotopes of carbon, oxygen, nitrogen, sulfur, and bromine have much smaller isotope effects (between 1.02 and 1.10). The difference in the magnitude of the kinetic isotope effect is directly related to the percentage change in mass. Large effects are seen when hydrogen is replaced with deuterium because the percentage mass change is very large (mass is being doubled). . Hence the kinetic isotope effect is at play in carbon fixation in photosynthesis, for example. This is evidenced by the observation that the 13C/12C ratios are lower in plants than in the atmosphere, showing that 12CO2 is preferentially "fixed" in the ribulose bisphosphate carboxylase/oxygenase reaction in plants and other photosynthetic organisms. Also, 12CO2, a lighter molecule, has a faster rate of diffusion through the stromata, regulated pores in leaves that facilitate the passage of CO2, O2 and H2O. In Chapter 31.2, we discussed the use of δ18O values in ice core and ocean core sediments for measuring past CO2 and temperatures. δ18O values for ice core water samples werer easier to interpret than δ18O values for CaCO3 sampls, since the deposition of ice is a simple physical process compared to the complexity of the deposition of CaCO3 in ocean sediments, which depends on chemical reactions and nonequilbrium processes (as described in Chapter 31). Climate scientists can determine and use δ13C values. An analogous equation for it is shown below. \delta^{13} C=\left[\frac{\left(\frac{13}{12} CO\right)_{\text {sample }}}{\left(\frac{13}{12} \mathrm{C}\right)_{\text {reference }}}-1\right] * 1000 As for using δ18O in carbonate samples, using δ13C is more difficult as well. The shells of ocean sediment foraminifera were made from dissolved inorganic carbon (DIC) in the ocean at the time so their δ13C values reflect that. However, shell formation is not a simple equilibrium process since biological shells are formed rapidly so kinetic effects in carbonate and hence isotope fractionation are important. In addition, the biochemistry of shell formation is complicated. In the open ocean, planktic foraminifera are perhaps the most important marine organism that forms shells given that they produce and export into the ocean about 2.9 Gt CaCO3/yr. Their shells form in a process involving many metastable calcite phases. It starts with a soft template that contains Mg2+ and Na+ ions which play a key role in crystallization. Growth occurs by successive additions of "chambers" to the shell. An F-actin mesh, which forms microtubular structures, leads to the formation of protective envelopes for chamber formation. The layered templates sequester and help control the mineralization of shells and separate bulk sea water for a more intracellular vs extracellular process for biomineralization. Seawater containing minerals becomes vacuolized in a process which for some foraminfera excludes a competing cation, Mg2+. In addition, both Ca2+ and HCO3- transporters are required. This all combines to form an environment low in Mg2+ and supersaturated in Ca2+ and CO32for calcite formation. The kinetic fractionation of 13C isotopes into shells is also different than for 18O isotopes since the "pool" of oxygen in the oceans is much greater than carbon. Likewise, the δ13C is more location-dependent that the δ18O. Buried organic matter can also be studied. The δ13C value for buried organic matter depends on primary productivity on land and in the oceans. As mentioned above, autotrophs preferentially take up 12CO2. Heterotrophs that eat them also become enriched in 12C. Hence organisms have negative δ13C values, typically around −25‰, with the number depending on pathways of incorporation and metabolism. Methane in hydrates in the ocean can be either biogenic, made by methanogens, for example, at low temperatures, or thermogenic, made during high-temperature reactions. Biogenic methane has a δ13C of around - 60‰, while thermogenic methane has a value of around −40‰. Terrestrial plants have different δ13 values. δ13C in C4 plants range from -16 to −10‰ while for C3 plants they range from −33 to −24‰. Changes in δ13C in ice cores and ocean sediments are used in climate studies. Sometimes it's confusing to understand the cause and effect of the changes. This following explanation for changes in the already negative values of δ13C might offer help to those with a chemistry-centric view of biochemistry who struggle with mass balance outside of simple chemical equations. Under climatic conditions, when there is an abundance of terrestrial plants that lock in and sequester atmospheric 12CO2, the atmosphere becomes depleted in 12CO2 and correspondingly enriched in 13CO2. Hence primary production (fixing of carbon and anabolic metabolism) by photoplankton in the oceans, under robust growing conditions, would sequester more 13C, causing an increase in δ13C (i.e. more positive) values for buried organic and calcite sediments. During times of mass extinction, when terrestrial plant primary production drops precipitously, the δ13C becomes more negative with the decrease in primary production and release of plant carbon, leaving more 12CO2 in the atmosphere. This drop is called a negative δ13C excursion. When life is robustly favored and carbon is fixed by autotrophs, and the organic carbon resulting from them is eventually buried in sedimentary rocks, the rise in δ13C is called a positive δ13C excursion. Examples of climatic events accompanied by changes in δ13C. Late Devonian period Fossil evidence from the late Devonian, when large terrestrial plants evolved and expanded, is characterized by increases in δ13C. Paleocene/Eocene Thermal Maximum We saw in Chapter 31.1 that around 55 MYA, sediment records indicate a spike in temperatures of about 50 F occurring over about a 100K year timeframe. This was accompanied by a dramatic spike in CO2 and a dramatic drop in ocean pH as measured by the loss of deep sea CaCO3 (chalk). This very short time frame is called the Paleocene/Eocene thermal maximum (PETM), which shows very quick spikes (on the geological time scale) can and do occur. Sediment records for this time indicate a large negative δ13C excursion, consistent with a loss of plants with their preferential uptake of 12CO2, leading to an accompanying increase in 12CO2 in the atmosphere. 1500-1650 CE We examined δ18O values during the Little Ice Ages in Chapter 31.2. What about δ13C values? CO2 and δ13C values from 1000 to 1900 are shown in Figure $12$. Figure $12$: CO2 and δ13C values from 1000 to 1900. Koch et al. Quaternary Science Reviews, 207, 2019, 13-36. https://doi.org/10.1016/j.quascirev.2018.12.004. CC BY license (http://creativecommons.org/licenses/by/4.0/). Panel (A) shows the CO2 concentrations recorded in two Antarctic ice cores: Law Dome (grey, MacFarling Meure et al., 2006) and West Antarctic Ice Sheet (WAIS) Divide (blue, Ahn et al., 2012). Panel (B) shows the carbon isotopic ratios recorded in CO2 from the WAIS Divide ice core (black, Bauska et al., 2015) showing an increased terrestrial carbon uptake over the 16th century (B). The yellow box is the span of the major indigenous depopulation event (1520e1700 CE). Loess smoothed lines for visual aid. Koch et al have strong evidence to suggest that the cooling after 1510 (area in the yellow box in the above figure) was associated with a dip in CO2 caused by the reforestation of indigenous peoples' land in Meso and South American after epidemics of European disease killed upwards of 90% (around 55 million) of the indigenous peoples. The open and agricultural land reverted back to forests. The diseases included smallpox, measles, influenza, the bubonic plague, and eventually malaria, diphtheria, typhus and cholera. Domesticated farm animals brought from Europe to the Americans led to most of the disease. Along with the death of so many people was a concomitant return of cleared and agricultural lands (about 56 million hectares or 212,000 mi2) to forest and plant growth. This may have led to a 7-10 ppm drop in CO2 in the late 1500s and early 1600s, peaking in 1601 (middle of the yellow box). This decrease in temperature was associated with a small rise (small positive excursion) in the δ13C values, as 12CO2 was preferentially removed from the atmosphere. Global surface air temperatures decreased by around 0.15oC. This "Great Dying" of Indigenous peoples shows the power of humankind to globally alter climate in calamitous ways, even before the use of fossil fuels. The decrease in δ13C values before 1500 was unexplained. 1800-the present δ13C values can also be used to unequivocally prove that the increase in CO2 since the industrial revolution is from the burning of fossil fuels, which is of biogenic origin and hence have more negative δ13C values. Figure $13$ shows atmospheric CO2 levels in ppm plotted along with δ13C values. There is a perfect correlation between the rise in atmospheric CO2 starting with the industrial revolution with the decrease in the δ13C values over the same time. Figure $13$: COconcentration (black circles) and the δ13C (brown circles) from 1000 to 2010. Rubino et al. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1002/jgrd.50668. With permission (Copyright Clearance Center) Summary In the first three sections of Chapter 31 (31.1, 31.2, and this one), we have introduced the basics of climate change, and how climate scientists obtain, analyze and interpret climate data. We emphasized the scientific rigor by which they do that and offered a detailed analysis of the use of isotopes to document past and present changes in climate, Finally, we offered models to predict and mitigate future climate changes. After reading this material, you should be enabled to discuss climate change with others from a sound and valid position. More importantly for this book, you will have a better knowledge base and understanding for the rest of the chapter sections, which will address specific topics in "biochemistry and climate change". Key Points - Beta version from Chat.openai 1. The carbon cycle is the process by which carbon moves through the Earth's systems, including the atmosphere, oceans, and biosphere. 2. The carbon cycle is driven by the exchange of carbon between different reservoirs, such as the atmosphere, oceans, and living organisms. 3. The main processes involved in the carbon cycle include photosynthesis, respiration, and the formation and weathering of rocks. 4. Human activities, such as burning fossil fuels and deforestation, have significantly increased the amount of carbon dioxide (CO2) in the atmosphere, disrupting the natural balance of the carbon cycle. 5. The increase in atmospheric CO2 is the primary driver of climate change, as it causes the greenhouse effect, trapping heat in the atmosphere and warming the Earth's surface. 6. The ocean also plays a critical role in the carbon cycle, as it acts as a sink for CO2, absorbing about 25% of the CO2 emitted by human activities. 7. The acidification of the ocean caused by the uptake of CO2 is having a significant impact on marine ecosystems, altering the chemistry of seawater and making it more difficult for some organisms to build and maintain their shells and skeletons. 8. Understanding the carbon cycle and carbon chemistry is crucial for understanding the causes and impacts of climate change and for developing strategies to mitigate and adapt to its effects.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.04%3A__Biofuels_A_-_Corn_and_Sugar_Cane_Ethanol.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Introduction The world has a great need for energy. We have invested vast sums of money in finding and using fossil fuels. Fossil fuels seem to be an ideal energy source since they are highly reduced, easily stored, energy-dense, and highly abundant. Yet we now know the immense cost of their use: pollution that shortens lives and climate change. We have dramatically increased our bioethanol production from corn and sugar cane to remove our reliance on fossil fuels for transportation. Ethanol is partially oxidized as it has one oxygen atom in the two-carbon molecule. Hence, the energy released per gram is about 63% (by mass) and 70% (by volume) compared to gasoline. The energy values for various fuels are shown in Table $1$ below, where ΔHc° is the standard enthalpy of combustion. Table $1$: Energy values for a variety of fuelsData source: https://www.engineeringtoolbox.com/s...nt-d_1987.html Name Formula State -ΔHc° kJ/mol -ΔHc° kJ/g or MJ/kg -ΔHc° kcal/kg Ammonia NH3 gas 383 22.48 5369 Butane C4H10 gas 2878 49.50 11823 Carbon (graphite) C cry 394 32.81 7836 Carbon monoxide CO gas 283 10.10 2413 Ethanol C2H6O liq 1367 29.67 7086 Hydrogen H2 gas 286 141.58 33817 Methane CH4 gas 891 55.51 13259 Methanol CH4O liq 726 22.65 5410 Naphthalene C10H8 cry 5157 40.23 9609 Octane C8H18 liq 5470 47.87 11434 Propane C3H8 gas 2220 50.33 12021 wood (red oak) 14.8 3540 coal (lignite) 15 3590 coal (anthracite) 27 4060 methyl stearate (biodiesel) (CH3(CH2)16(CO)CH3 40 9560 Nevertheless, ethanol is readily made and is a valuable biofuel. A glance at the table suggests that H2 would be the best possible fuel, given that it has the highest energy release per gram and contains no carbon. At present, it can't be produced at the scale needed, and it isn't easy to store and transport. The critical infrastructure for its widespread use is lacking. Yet these factors could be solved. We'll explore biohydrogen in a separate chapter section. In theory production of ethanol from plants at first glance is carbon neutral since each carbon in the ethanol is fixed from atmospheric CO2 during photosynthesis. Combustion of ethanol then returns the CO2 to the atmosphere in a net zero emission fashion, as shown in the reaction below. 6CO2 (g) + 6H2O (l) C6H12O6 (s) + 6O2 (photosynthesis) C6H12O6 (s) → 2 CH3CH2OH (l) + 2CO2 (g) (anaerobic ethanol biosynthesis) 2CH3CH2OH (l) + 6O2 → 4CO2 (g) + 6H2O (g) (combustion of ethanol) Six CO2s in, six out! It seems simple but it's not. We'll explain more later. First, let's explore how ethanol is synthesized for its two major uses, drinking and use as a biofuel. Ethanol Production Overview The scale of worldwide ethanol production is quite staggering. Let's first consider the production of ethanol by yeast for alcoholic beverages. About 100 billion US gallons/yr (BGY) of beer, 7 BGY of wine, and 6 BGY of spirits are produced yearly. Assuming beer, wine and spirits are about 5%, 12%, and 40% percent ethanol by volume, respectively, the volume of actual ethanol/year made by yeast in these alcoholic beverages is about 5 BGY (beer), 0.85 BGY (wine) and 2.4 BGY (spirits). This sums to about 8.3 billion gallons of ethanol produced by these microorganisms. Compare this to fuel ethanol production each year, shown in Figure $1$. Figure $1$: US Fuel Ethanol Production. Data from U.S. Bioenergy Statistics Note that the y-axis is in units of 1000s gallons of ethanol. Peak US production was in 2018, when 16 billion gallons were produced, about 1/10 of the gasoline used yearly in the US. The year Renewable Fuel Standards (RFS) were introduced in the USA (2005) is also shown. This dip in 2020 is attributed to the COVID pandemic. Combined, the US and Brazil produce about 85% of fuel ethanol, as shown below in Figure $2$. Figure $2$: Fuel ethanol production (billions of gallons or BG) around the world per year. https://afdc.energy.gov/data/10331 Almost all US ethanol is made from corn, while Brazil's primary source is sugar cane Since the significant ramp-up of fuel ethanol around 2005, the world now produces 3x the amount of ethanol to drive our outsized vehicles than microorganisms have for our drinking. These statistics show that the world can quickly respond when it meets our needs. An overview of ethanol biosynthesis Whether ethanol is made for the beverage or biofuel industries, yeast play the major role, as we explored in Chapter 14.2: Fates of Pyruvate under Anaerobic Conditions- Fermentation. Yeast contains all the enzymes necessary to convert glucose (6C), made from various "feedstocks", to pyruvate (3C) through the glycolytic pathway. This is followed by the conversion of pyruvate to ethanol using two key yeast enzymes. First, pyruvate is decarboxylated to acetaldehyde by pyruvate decarboxylase, which uses TPP as a cofactor. Acetaldehyde is then reduced to ethanol by ethanol dehydrogenase, using NADH as a substrate, in a process that reforms NAD+, allowing glycolysis to continue. These combined anaerobic reactions, known as fermentation, are shown in Figure $3$. Figure $3$: Summary of Ethanol Fermentation in Yeast Yeast are facultative (not obligate) anaerobes in that they can produce energy by glycolysis and ethanol fermentation in the absence of oxygen. Of course, in the presence of oxygen, the pyruvate made from glycolysis in yeast is preferentially converted to acetyl-CoA, which enters the citric acid cycle and oxidative phosphorylation pathways to maximize ATP production. Yeast is abundant, so all that is needed is a large source of glucose. An abundant source of glucose for bioethanol production are plants that contain starch (for example corn) or abundant simple sugars (for example sucrose in sugar cane). Starch, an α (1,4) polymer of glucose with α (1,6) branches, can easily be broken down in an industrial setting with amylases to form glucose. A significant problem with this "first" generation source of glucose is that food crops (corn, and to a lesser degree sugar cane) are used for biofuel consumption instead of for food. "Second" generation sources of glucose are crop and wood waste products that contain cellulose, a β (1,4) polymer of glucose which is found with another carbohydrate polymer lignin. A significant problem with the use of cellulose is the high chemical stability of the β (1,4) glycosidic bond. Fungi and bacteria are sources of β-glycosidases to liberate free glucose from cellulose. "Third" generation sources of glucose use algae, which does not displace cropland for bioethanol production. A "fourth" generation source of glucose are genetically engineered organisms, which might become future sources of bioethanol. Figure $4$ summarizes the different generations of feedstock sources for bioethanol production. Figure $4$: Generation feedstock sources for bioethanol production. Tse, T.J.; Wiens, D.J.; Reaney, M.J.T. Fermentation 20217, 268. https://doi.org/10.3390/fermentation7040268. Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). We will discuss the use of first generation sources, corn and sugar cane, which are used to produce most of the world's bioethanol, in this chapter section, and the other two in subsequent sections. Corn Bioethanol Corn is a significant source of starch, an α (1,4) polymer of glucose with α (1,6) branches. Hence glucosidases are used to hydrolyze starch to glucose. First, the dry corn is ground in a mill, breaking the outer coat of the corn kernel and increasing access to the starch. Heated water is added to form a mash or slurry. Cooking at greater than 85o C helps hydrolyze some glycosidic bonds and lowers the viscosity of the slurry. In the process of liquification, the pH is adjusted to around 6.0. Different α-amylases (endoglycosidases) are added, which cleave the α (1,4) glycosidic bonds to produce shorter dextrins (containing branched glucose units not cleaved by alpha-amylases), and α (1,4) linked glucose oligosaccharides of lengths from 2 glucose units (called maltose) up to 7-8. β-amylase, an exoamylase, is also used, which successively cleaves maltose units, Glc α (1,4)Glc, from the nonreducing ends of the chains Alpha-amylases A mixed-rendered structure of the human pancreatic alpha-amylase is shown below in Figure $5$. Figure $5$: Surface representation of the active site of HPA (5TD4) https://pdb101.rcsb.org/global-healt...ha-glucosidase. CC-BY-4.0 license. Attribution: David S. Goodsell and the RCSB PDB. The surface view highlights the deep C-shaped groove into which the substrate, in this case, octaose, is bound. Consistent with substrate numbering for proteases, the starch substrate is numbered ..-2, -1, +1, +2, with cleavage occurring between the -1 and the +1 bound alpha-glucose residues. The protein has three domains (orange, blue, and pink). This particular structure had an active site mutant (Asp300Asn, D300N). The enzyme has bound calcium and chloride ions. Ca2+ maintains the necessary structure, while Cl-, bound in the C domain, is an allosteric activator. The octaose binding site is between the A and B domains. Asp197, Glu233, and Asp300 are critical catalytic residues, with Asp 197 acting as a nucleophile to produce a glycosylated intermediate, which is hydrolyzed in the next step. Asp197 and Glu233 act as general acids/bases. We will explore in depth similar mechanisms for the action of beta-amylase (below) and cellulase (next chapter section). Figure $6$ shows an interactive iCn3D model of starch binding sites on the Human pancreatic alpha-amylase D300N variant complexed with an octaose substrate (5TD4) Figure $6$: Starch binding sites on the Human pancreatic alpha-amylase D300N variant complexed with an octaose substrate (5TD4). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...W29jf4yAc1JEq9 The domains in the enzyme are colored-coded, as in Figure 4. Key active site residues for binding and catalysis are shown as sticks and labeled. Beta amylase β-amylase (also called β-1,4-maltosidase) is a key enzyme in the saccharification process, in which starch and cellulose are broken down into monosaccharides. β-amylase is abundant in crops (wheat, barley, soybeans, etc.) and other higher plants, as well as bacilli and fungi. It is used in making beer and caramel (malt syrup). As an exo-glycosidase, it cleaves Glc α(1,4) Glc (maltose) units from the nonreducing end of starch. It is called β-amylase since the hydrolysis proceeds with the inversion of configuration at the reducing end of the freed maltose. It can't cleave at α-1,6 branches, so if used alone, this enzyme produces free maltose and large β-limit dextrins. When fruits ripen, the enzyme cleaves starch to produce sweet maltose. It is also used in seed germination. Malting Plants have to sprout, which requires energy and free sugars. Maltose is produced on activation of β-amylase during seed germination and sprouting. Although maltose is less sweet than sucrose and fructose, it is used in hard candies, given its tolerance to the heat needed in candy production. Malting of grains is accomplished by adding water to sprout them, leading to maltose and other sugars forming. This is followed by drying, with the malted grains used as sweeteners in the food industry. Malted grains are used to produce beer, whisky, some baked goods, and drinks. Barley is the most commonly malted grain used in cereals. Huge amounts of amylases are needed for corn ethanol production, and they must withstand the conditions necessary for the industrial production of ethanol. Much effort has been devoted to finding and characterizing microbial β-amylases. We'll describe one, AmyBa, from B. aryabhattai. Figure $7$ shows sequence similarities among various bacterial β-amylases. Figure $7$: Sequence and structure analysis of AmyBa. . Duan, X., Zhu, Q., Zhang, X. et al. Expression, biochemical and structural characterization of high-specific-activity β-amylase from Bacillus aryabhattai GEL-09 for application in starch hydrolysis. Microb Cell Fact 20, 182 (2021). https://doi.org/10.1186/s12934-021-01649-5. Creative Commons Attribution 4.0 International License. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. Panel A shows multiple sequence alignments of β-amylases. The strictly conserved residues are displayed on a red background, and the highly conserved residues are shown on a yellow background. The secondary structure elements are shown for B. cereus β-amylase (PDB ID: 5BCA). The signal-peptide-cleavage site and two catalytic residues (E) are indicated by black triangles (black inverted triangles). Conservation of the flexible loop motif (HXCGGNVGD) is noted. β-amylase accession numbers are as follows: B. aryabhattai (WP_033580731.1), B. cereus (P36924.2), B. flexus (RIV10038.1), B. firmus (P96513.1), B. circulans (P06547.1), T. thermosulfurigenes (P19584.1). A comparison of the structures of B. aryabhattai β-amylase with soybean β-amylases is shown in Figure $8$. Figure $8$ B Three-dimensional molecular model of B. aryabhattai β-amylase (AmyBa). C Superimposition of AmyBa (Blue) and soybean β-amylases (PDB ID: 1Q6C) (gray) and D (PDB ID: 1Q6C) (gray). The C-terminal SBD in microbial β-amylases (box, purple) and the C-terminal loop in plants (box, red). Duan, X, et al., ibid. The AmyBa has an additional starch binding domain at the carboxy terminus (Panel B) compared to soybean β-amylases (panel D). Since no structures of (AmyBa are publically available, we present Figure $9$, which shows an interactive iCn3D model of beta-amylase from Bacillus cereus var. mycoides in complex with maltose (1J0Z) Figure $9$: Beta-amylase from Bacillus cereus var. mycoides in complex with maltose (1J0Z). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...S2aWbL3z3jPnW8 Similar to alpha-amylase, β-amylase has an N-terminal catalytic domain with a beta-barrel, a connecting second domain, and a third C-terminal domain which is primarily antiparallel β-sheets. Two key catalytic side chains, Glu 172 and Glu 367, are found in the beta-barrel. In Chapter 20.6, we discussed starch synthesis (not its hydrolysis) in detail. We showed that the reaction, which uses a NDP sugar as a glycan donor, could proceed either with retention or inversion of the anomeric carbon of the donor NDP-sugar. This is illustrated for the reaction of a C1 α-NDP donor monosaccharide with a monosaccharide acceptor to produce the α(1,4) link with retention of configuration or the β(1,4) link with inversion as shown in Figure $10$ below. Figure $10$: Reaction of a donor NDP-monosaccharide and an acceptor monosaccharide with retention or inversion of configuration at the anomeric carbon of the donor The same stereochemical outcomes are possible for the hydrolysis of acetal bonds by glycosyl hydrolases. Alpha-amylases cleave the α (1,4) glycosidic bonds to produce shorter dextrins (containing branched glucose units not cleaved by alpha-amylases), and α (1,4) linked glucose oligosaccharides of lengths from 2 glucose units (maltose) up to 7-8. This reaction hence proceeds with the retention of configuration. In contrast, beta-amylases cleave starch to produce maltose with an inversion of configuration at the anomeric-reducing end of the maltose. We explore the chemistry of retention and inversion more in the next section on cellulase, which cleaves the β (1,4) acetal link in cellulose, but in general, reactions that proceed with inversion react in an SN2 response, similar to the nucleophilic attack on alkyl halides. For the glycosyl transferases that proceed with inversion, the attacking nucleophile on the acceptor is made more nucleophilic by general base catalysis by a deprotonated glutamic or aspartic acid. Figure $11$ shows the results of in silicon docking studies of a small glycan, maltotetraose, to AmyBa. Figure $11$: Molecular docking of AmyBa with maltotetraose. The overall structure and substrate binding pocket analysis of AmyBa are shown. AmyBa displayed very high amylase activity compared to other microbial β-amylases, and its enzymatic activity was much closer to sweet potato β-amylase. Molecular dynamic and docking programs can be used to calculate binding energies for substrates. The binding energy and enzymatic activities for bacteria and sweet potato β-amylase were highly correlated, suggesting that the extensive interactions of AmyBa and maltotetraose help drive catalysis by using the energy released on binding to lower activation energies for the reaction. Saccharification To enter glycolysis and fermentation, maltose must be converted to the monosaccharide glucose. The conversion of a polysaccharide to its monomers is called saccharification. To complete the conversion of starch to glucose, another enzyme, glucoamylase (also called amyloglucosidase and γ-amylase), is added. It is an exoglucosidase that cleaves both α (1,4) in amylose, amylopectin and maltose and α (1,6) branches, to form free glucose. It is a member of the glycoside hydrolase family 15 in fungi, glycoside hydrolase family 31 of human maltase-glucoamylase, and glycoside hydrolase family 97 of bacterial forms. Fermentation Glucose (C6H12O6) can now enter the glycolytic pathway and continue to ethanol after conversion of pyruvate to acetaldehyde by pyruvate decarboxylase and final conversion of acetaldehyde to ethanol by alcohol dehydrogenase: C6H12O6 (s) → 2 CH3CH2OH (l) + 2CO2 (g) (anaerobic ethanol biosynthesis) The yeast Saccharomyces cerevisiae catalyzes this entire pathway. The final fermentation process yields a 12-15% ethanol solution, which is distilled to form a 95% ethanol/5% water azeotrope. The water is removed by adding zeolites (molecular sieves) which can adsorb water but not ethanol. Life Cycle Analysis of Bioethanol: Is it better than fossil fuels? We reiterate the promise of bioethanol to address global warming and climate change by presenting again the chemical equations that suggest that its use as a fuel is carbon neutral: 6CO2 (g) + 6H2O (l) C6H12O6 (s) + 6O2 (photosynthesis) C6H12O6 (s) → 2 CH3CH2OH (l) + 2CO2 (g) (anaerobic ethanol biosynthesis) 2CH3CH2OH (l) + 6O2 → 4CO2 (g) + 6H2O (g) (combustion of ethanol) If only these three equations, this simple model for production and use of corn bioethanol, were the only factors influencing net CO2 emission on bioethanol burning, there would be no controversy about its use. Yet the actual CO2 emissions depend on many more hidden from view by these simple equations. What is needed is a life cycle analysis (LCA) that determines the environmental impact (in this case, net CO2 emissions) of corn ethanol through every phase of its existence, from cradle to grave, starting with the planting of corn to the combustion of bioethanol for transportation. All models must be tested. It's easiest to start with the simplest model. If the data fit the model, great, you're done. If not, new, more expansive models must be developed and tested. Those vociferously supporting bioethanol use often use the simple stoichiometry evident in the three equations to state that bioethanol is carbon neutral. Most, however, would want a detailed life cycle analysis (LCA) before jumping to an immediate conclusion. LCAs are very challenging, and data on a global scale is required. Some measurements at the worldwide scale have significant uncertainties (that don't include CO2 in the atmosphere, however) and are estimates, at best. A recent study looked at the impact of a specific event, the adoption of the US Renewable Fuel Standards (RFS) that regulate biofuels in the US (which produces about half of all the world's biofuels), on CO2 emission from the significant increase in corn plant and corn ethanol the followed the adoption of the standard. . Using LCA based on a series of economic and environmental metrics, the model shows that bioethanol is not a panacea for CO2 emissions and may be more detrimental than fossil fuels use for vehicles. The study calculated the carbon intensity changes for corn ethanol that followed after the adoption of the standards. Scientists have used other events that led to immediate changes (9/11) and 1-2 year changes (Covid pandemic) on environmental parameters like CO2 emissions. Carbon intensity measures how much energy-related CO2 is emitted per dollar generated (GDP). Ideally, policies should be implemented that decrease carbon intensity. Green energy derived from both solar and wind is an example. A similar metric is energy intensity, the total energy production per GDP, and both are consumption-based values. Figure $12$ shows carbon intensity per GDP per country over the last 30 years (data from Our World in Data). Figure $12$: Consumption-based carbon intensity from 1990 to 2018. Our World in Data. Generally, the world is moving to more efficient energy production, but remember that our energy consumption is still dramatically increasing. The LCA model showed that the RFS led to these interrelated outcomes. It: • increased corn prices by 30% and the prices of other crops by 20% • increased US corn cultivation by 2.8 Mha (8.7%) and total cropland by 2.1 Mha (2.4%) in the years following policy enactment (2008 to 2016). (1 hectare is an area of a square with100 meters sides, equivalent to 10,000 m2 • increased annual nationwide fertilizer use by 3 to 8% • increased water quality degradation by 3 to 5% • increased emissions from domestic land use changes These all combined to lead to a carbon intensity of corn ethanol that was "no less than gasoline and likely at least 24% higher", according to the study. The changes in the metric are visually described in Figure $13$. Figure $13$: Changes due to the RFS. (A) Corn planted area. (B) Cropland area. (C) Carbon emissions. (D) Nitrogen applications. (E) Nitrous oxide emissions. (F) Nitrate leaching. (G) Phosphorus applications. (H) Soil erosion. (I) Phosphorus runoff. Positive numbers indicate an increase due to the RFS. Field-level results were aggregated at the county level for enumeration and visualization. Tyler J. Lark et al. PNAS. 119, 2022 (https://doi.org/10.1073/pnas.2101084119) Creative Commons Attribution License 4.0 (CC BY). Land use changes include farming land that was retired or designated for conservation programs. Tilling additional land releases carbon stored in the soil. The increased farming significantly increased fertilizer production, which leads to N2O emissions. In addition, more of the existing cropland was planted with corn. These finds contrast with a USDA study that shows that corn ethanol has a 39% lower corn ethanol intensity than gasoline which was stated to derive from carbon captures from the newly planted crops. However, that study did not account for emissions from land use changes. LCA can identify aspects of production that lead to the most negative consequences, which for the sake of this chapter is greenhouse gas emissions. For example, the LCA for corn ethanol might improve if the CO2 released on its production during anaerobic ethanol biosynthesis could be captured. Outcomes would also change if renewable energy sources were used for stages of production that require fossil fuel use. This rigorous LCA did not address the moral question of using land that could be used to feed people to produce bioethanol for use in our ever-bigger vehicles. In addition, opponents of solar energy installations suggest that solar installs would require so much land that it would remove land for agricultural purposes. What is missing from their argument is the vast amount of land used now for corn ethanol. Farmers planted 90 million acres of corn in 2022 in the US, a land area about 90% the size of the entire state of California. 44% of that corn went to biofuels, and only 12% went to human consumption. In addition, approximately 44% percent was used to feed animals for human consumption, an inefficient and unsustainable use of crop land and resources. Production of sucrose and bioethanol from sugarcane Like corn, sugar cane, a tropical, perennial grass, is used (mainly in Brazil) to produce ethanol. Sugar cane is a C4 plant with a high ability to fix carbon. The fact that it is a perennial and does not need replanting each year makes it a more ideal feedstock than corn for bioethanol production. In 2020, sugar cane was by far the most-produced crop or livestock product in the world (1.87 billion metric tons), followed by corn (1.16 billion metric tons). The production by country for both corn and sugar cane is shown in Figure $14$. Figure $14$: Corn and sugar cane production by country. Graphs from Our World in Data. https://ourworldindata.org/agricultural-production# That sugar cane production is so high compared to grain crops that provide nutrition (not just "sweet" calories) might come as a surprise, but it shouldn't, given our addiction to sweet foods. Sugar cane is often harvested manually in developing countries. It is then cut, milled, and mixed with water to extract the soluble sucrose (table sugar). The sugar cane components during extraction are shown below in Figure $15$. Figure $15$: Components of Sugar Cane (after Larissa Canilha et al. 2012; 2012: 989572. doi: 10.1155/2012/989572 Sucrose is a nonreducing disaccharide (O-α-D-glucopyranosyl-(1,2)-β-D-fructofuranoside). Its structure is shown in Figure $16$. Figure $16$: Structure of fructose Sucrose, Caramel and Molasses Sucrose decomposes at 186 °C (367 °F) instead of melting (a feared event for organic chemistry students who wish to record melting temperatures in the lab) to form caramel. Molasses is a very viscous liquid product from refining sugar cane or sugar beets. It is used as a sweetener with its own taste properties, and it's a component of brown sugar as well. On a sweetness scale, if sucrose is assigned a value of 100, fructose is 140, high fructose corn syrup is 120-160, and glucose is 70-80. For bioethanol production, sucrose is degraded by the enzyme invertase to form monomeric glucose and fructose. Invertases are activated on the milling and liquification of the sugar cane, so if sucrose is the desired commercial product, an additional clarification step (heat to 115°C and treat with lime and sulfuric acid) is necessary to prevent hydrolytic cleavage of sucrose. Bioethanol production from sugar cane sucrose Bioethanol can be made from either the fibrous lignocellulose remains of the sugar cane, called bagasse or from water-soluble sucrose. We will describe the production of cellulosic ethanol from field crop stalks, called stover, and leaves, straw, wood chips, and sawdust (all "waste" biomass), in Chapter 31.5. The same principles apply to bioethanol production from bagasse, the solid remains after the juice extraction from sugar cane. (Bagasse is often burned to provide energy for sugar cane processing). In addition to sugar cane, sugar beets and sweet sorghum, a C4 plant similar to sugar cane, are used to produce bioethanol. As a C4 plant, sweet sorghum is very efficient at producing biomass through photosynthesis. It grows in temperate and tropical climates, has a short growing period, and is resistant to drought and cold. Its stalks have free sugars as well as lignocellulose stocks. This chapter will focus on bioethanol production from sugar cane sucrose. Again, this is accomplished using yeast (Saccharomyces cerevisiae), which has the enzyme invertase 2 (beta-fructofuranosidase 2 or Saccharase) needed to convert sucrose into sucrose fructose and glucose, which can enter glycolytic and fermentation pathways. Invertase, shown in 1842 to invert the stereochemistry of sugars, was first isolated from yeast in 1860. It has a secreted glycosylated homooctameric form and an intracellular form, all products of the same gene. It's a member of Family 32 of the glycoside hydrolases. The structure of the Saccharomyces invertase (SInv) octamer structure is shown in Figure $17$ below. Figure $17$: Structure of octameric SInv. M.Angela Sainz-Polo et al. JBC, 288, 9755-9766 (2013). DOI:https://doi.org/10.1074/jbc.M112.446435Creative Commons Attribution (CC BY 4.0) Panel a shows a view of the SInv octamer in ribbon (left) and solvent-accessible surface (right) representations, showing each subunit in a different color. Panel b shows that the octamer is rotated 90°, illustrating that it can be best described as a tetramer of two different kinds of dimers, AB/CD and EF/HG, which are compared by superimposing subunit F on subunit B in c Even though all eight subunits in the octamer are identical (58.5K, 512 aa), the quaternary structure of the 8-mer can best be viewed as a tetramer of dimers (i.e. 4 dimers pack to form two packed tetramers giving the octamer). The AB and CD dimers pack in a "closed form" with a narrow active site pocket, allowing a glycan of 3-4 monomers. The EF and GH dimers pack in an "open form" with a wide active site pocket for longer glycans. Of course, our main interest here is in the binding of sucrose. Figure $18$ shows an interactive iCn3D model of Saccharomyces cerevisiae invertase (4EQV) Figure $18$: Saccharomyces cerevisiae invertase (4EQV). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...ysQW8YSrvPkus9 The color coding of the subunits is the same as shown in the right top image of Panel A, Figure 16 above. The GH32 enzymes, including invertase, have a catalytic domain consisting of a 5-bladed β-propeller, with each blade having four antiparallel beta-strands. The blades surround an active site enriched in carboxyl side chains. The "closed" form active site of the AB and CD dimers has at its base Phe 388 and Phe 296, which provide hydrophobic interactions. The "open" form active site of the EF and GH dimers also has a salt bridge between Asp 45 and Lys 385. These are shown in Figure $19$, along with a bound 1-kestose, which is a trisaccharide"sucrose analog" found in vegetables. It consists of a β-D-fructofuranose connected to β-D-fructofuranosyl and α-D-glucopyranosyl residue at the 1- and 2-positions. Figure $19$: Dimer interface at the active site. The octameric SInv active site interfaces are detailed, keeping the same color pattern as above with one subunit being shown in ribbon representation for clarity. Angela Sainz-Polo et al. JBC, 288, 9755-9766 (2013). DOI:https://doi.org/10.1074/jbc.M112.446435Creative Commons Attribution (CC BY 4.0) Panel A shows that the AB/CD dimers are tightly made by interactions among both their catalytic and β-sandwich domains. Hydrophobic interactions around found at the base of the catalytic pocket through Phe-388 and Phe-296. Panel b, by contrast, shows that the EF/GH dimers interact only through their β-sandwich domains. In addition, the catalytic pocket is also paved by a new salt bridge formed between Asp-45 and Lys-385 from the β-sandwich domain, which lines the cavity. A putative 1-kestose molecule is shown in a spherical representation. The hydrolysis of sucrose by invertase proceeds with the retention of configuration at the anomeric carbon. An active site Aspartate 22 acts as a nucleophile to form a glycosylated intermediate (fructose-Asp). This is followed by hydrolysis of the intermediate. An active site Glutamate 203 acts as a general acid/base. The fructose could also be transferred to another glycan in a transglycosylation reaction. The hydrophobic side chains Phe-388 and Phe-296 line the base of the active site pocket. Figure $20$ shows an interactive iCn3D model of the AB dimer of Saccharomyces cerevisiae invertase with key active site residues (4EQV) Figure $20$: AB dimer of Saccharomyces cerevisiae invertase with key active site residues (4EQV). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...hX9FbxLLip1f97 Figure $21$ shows an interactive iCn3D model of the EF dimer of Saccharomyces cerevisiae invertase with key active site residues (4EQV). It has an additional salt bridge between Asp-45 and Lys-385. Figure $21$: EF dimer of Saccharomyces cerevisiae invertase with key active site residues (4EQV). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...Dh33BtT5c8GXK6 Life Cycle Analysis of Sugar Cane Ethanol Does the production of bioethanol from sugar cane lead to lower net CO2 emissions than bioethanol produced from corn? The answer would depend on if sucrose (first generation) or lignocellulose (second generation) from bagasse is the feedstock. A recent LCA has been performed on the first-generation (feedstock is sucrose) production of bioethanol from sugar cane in Ecuador. There is a lower cost of production from this sugar-based feedstock, which requires just grinding and the addition of yeast for fermentation. It does not require a saccharification step. Figure $22$ shows the various stages and processes used to perform LCA on the bioethanol production from sugar cane sucrose. It's presented to show the complexity of such analyses, so look at the detail only if you are especially interested. Figure $22$: Anhydrous ethanol life cycle system boundaries and main product flows quantification for year 2018. Arcentales-Bastidas, D.; Silva, C.; Ramirez, A.D. The Environmental Profile of Ethanol Derived from Sugarcane in Ecuador: A Life Cycle Assessment Including the Effect of Cogeneration of Electricity in a Sugar Industrial Complex. Energies 202215, 5421. https://doi.org/10.3390/en15155421. Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/) The study analyzed four stages for analysis, agricultural, milling, distillation and electricity generation (presumably by burning the byproduct bagasse) for impacts. They defined two functional units: • 1 ton of sugarcane "at the farm gate” for the agricultural stage; • 1 L of ethanol "at the plant (factory) gate”. The key results are as follows: • The global warming potential (GWP) impact at the farm gate level was 53.6 kg of carbon dioxide equivalent (kg COequiv) per ton of sugarcane produced; This arose mostly from N2O (34%), a potent greenhouse gas released in the process, and diesel fuel used in agricultural machinery (24%). • The GWP for 1 L of ethanol produced at the plant gate was 0.60 kg COequiv, with the distillation phase contributing the most. Before proceeding further, let's explain the key term, global warming potential (GWP), which is widely used in LCA. It adds the contribution of other greenhouse gases like methane (CH4) and nitrous oxide (N2O), each of which has unique IR absorption spectra and atmospheric half-lives. The IPCC uses a 100-year time frame for the calculation of the GWP, and uses this formula: CO2 Equivalent kg = CO2 kg + (CH4 kg x 28) + (N2O kg x 265) \mathrm{CO}_2 \text { equivalent } \mathrm{kg}=\mathrm{CO}_2 \mathrm{~kg}+\left(\mathrm{CH}_4 \mathrm{~kg} \times 28\right)+\left(\mathrm{N}_2 \mathrm{O} ~k g \times 265\right) • CO2 has GWP of 1 by definition since it is the reference. Its time frame in the atmosphere (100s to 1000 years) doesn't matter since it is the reference. • CH4 has a GWP of around 27-30 over 100 years. It reflects its higher IR absorbance but lower lifetime (around 12 years). • N2O has a GWP of around 265-273 over a 100-year timescale. N2O has a lifetime of around 109 years. The equation can be amended by adding other greenhouse gases released in manufacturing and the use of refrigerants. These include Freon-12 (Dichlorodifluoromethane) (CFC-12, with a lifetime of 100 years and a GWP100 of 10,200, and SF6 (used in the electricity industry to keep networks running safely and reliably) with a lifetime of 3200 years and GWP00 of 23,500! Nitrous Oxide - a potent greenhouse gas but not a laughing matter N2O (laughing gas) is an overlooked source of greenhouse gases, but it leads to about 7% of the warming effect of the greenhouse gases with long life-times and a high GWP100. Agricultural practices lead to about 65% of its total emission. It is a component of the soil and atmosphere nitrogen cycle. In soil, its concentration depends on soil microbes that engage in nitrification and denitrification processes. These in turn depend on the amount of fixed nitrogen, oxygen levels and metabolically available carbon sources. The nitrification reaction, which occurs in aerated and moist soils, involves the oxidation of NH3↔NH4+ to NO2 and NO3-, with some N2O release. The major source of N2O occurs under anaerobic conditions. These general reactions are shown below. • Nitrification (aerobic, oxidation): N2 → (NH3 ↔ NH4+) → NO2 → NO3- • Denitrification (anaerobic, reduction): NO3 → NO2 → NO → N2O → N2 In anaerobic sites, NO is an electron receptor during microbial respiration. N2O is produced when there is excess nitrogen available (past the needs of plants and microorganisms), so excess use of fertilizers and manure increases its production. Nitrifying and denitrifying bacteria are most active in producing N2O in environments with abundant N relative to assimilatory demands by other microorganisms or plants (Firestone and Davidson, 1989), as is often the case following soil amendment of fertilizers, manure, or crop residues. Physical processing of the soil (such as tillage) also affects N2O emissions by introducing crop residues in the soil, changing soil particle size and surface area, and by changing the porosity of the soil. All of these affect soil substrate/product availability and their aqueous and gas diffusion rates. Let's use dimensional analysis from introductory chemistry to convert the GWP from the farm gate/agricultural stage (53.6 kg CO2 equiv/ton of sugar cane) to kg CO2 equiv/1L of ethanol (EtOH) so we can add it the GWP from the pant gate, which is expressed in kg CO2 equiv/L ethanol produced. The dimensional conversion is shown in Table $2$ below. 53.6 kg CO2 equiv 1 ton SC 1 L Juice = 0.1 kg CO2 equiv 1 ton SC 800 L juice 0.7 L EtOH 1 L EtOH produced Table $2$: Conversion of 53.6 Kg CO2 equiv/ton of sugar cane from the farm gate (left hand column) to 0.1 Kg CO2 equiv/1L of ethanol (EtOH). Now add this to the reported 0.60 kg CO2 equiv/1 L of ethanol from the plant (factor) gate and you get a total of 0.7 kg CO2 equiv /1 L ethanol produced. Now use dimensional analysis from introductory chemistry to calculate how much CO2 is actually produced on the combustion of ethanol. That value is calculated in Table $3$ below. 1 L EtOH 1000 mL EtOH 0.789 g EtOH 1 mol EtOH 4 mol CO2 44 g CO2 1 kg CO2 = 1.5 kg CO2 1L EtOH 1 mL EtOH 46 g EtOH 2 mol EtOH 1 mol CO2 1000 g CO2 1 L EtOH Table $3$: Total Kg CO2 produced on combustion of 1 L of ethanol (EtOH) The promise of bioethanol is that for every 1 C atom used to create it, 1 C atom would be released. We saw that the LCA analysis for corn ethanol in the US did not meet that expectation. In the Ecuadorian analysis, it appears that it did, since 0.7 kg CO2 equivalents is required to produce 1 L of bioethanol from sugar cane, but 1.5 Kg CO2 is released on its burning The LCA analysis described above reflects just the global warming potential for the use of sugar cane sucrose for bioethanol production. However, bioethanol production from sugar cane juice has other negative impacts as listed in Table $4$ below. Table $4$: Impact categories included in the LCA Impact Category Characterization Factor Reference Unit Climate change Climate change—GWP100 kg CO2eq. Freshwater eutrophication Freshwater eutrophication potential—FEP kg Peq. Marine eutrophication Marine eutrophication potential—MEP kg Neq. Abiotic depletion Metal depletion—MDP kg Feeq. Photo oxidant formation Photochemical oxidant formation potential—POFP kg NMVOCeq. Particulate matter emissions Particulate matter formation potential—PMFP kg PM10eq. Terrestrial acidification Terrestrial acidification potential—TAP100 kg SO2eq. Key Points - Beta version from Chat.openai 1. Biofuels are renewable energy sources derived from biomass, such as plant materials and waste. 2. Corn and sugar cane ethanol are two examples of biofuels that are produced by fermenting the sugars found in these crops. 3. Corn ethanol is typically produced by converting the starch in corn kernels into glucose, which is then fermented to produce ethanol. 4. Sugar cane ethanol is produced by crushing the cane to extract the juice, which is then fermented to produce ethanol. 5. Both corn and sugar cane ethanol are primarily used as a gasoline additive to increase octane and reduce emissions. 6. Corn ethanol is a controversial biofuel because of the high amount of energy used to produce it and the impact on food prices and the environment. 7. Sugar cane ethanol is considered to be a more sustainable biofuel because it is less energy-intensive to produce and can be produced on land not suitable for food crops. 8. However, large scale production of sugar cane ethanol has also been criticized for leading to deforestation, loss of biodiversity and displacement of local communities. 9. Cellulosic ethanol, made from non-food feedstocks like switchgrass or wood chips, is considered to be a more sustainable biofuel alternative to corn and sugar cane ethanol.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.05%3A__Biofuels_B_-_Cellulosic_Ethanol.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Introduction In the last section, we explored how ethanol can be made from corn starch, an α(1,4) polymer of glucose with α(1,6) branches. Its production comes at a cost, however. Recent life cycle studies have shown that compared to fossil fuels, corn ethanol release as much but probably more CO2 than from fossil fuels. In addition, is it ethically justifiable to remove so much land from food production to produce bioethanol that, at present, is worse than fossil fuels from a climatic perspective? To address these issues, much work has been done to produce ethanol from cellulose, a β(1,4) polymer of glucose and the most abundant biomolecule in the world. Cellulose from trees, switch grasses, and "waste biomass" are prime sources of cellulose for the production of bioethanol. Waste biomass includes stover (field crop stalks and leaves), straw, wood chips and sawdust. From one ton of corn stover, about 113 gallons of ethanol can be made, close to the 124 gallons produced from corn. Nature breaks down cellulose routinely using cellulases, enzymes found in bacteria, fungi, protozoans, plants and some animals. Ruminants and even termites obtain cellulases from microbes living within their guts. The fungal-mediated decay of dead trees requires microbial cellulases but think how slow that process is. This stems partly from the very strong β(1,4) glycosidic link connecting glucose monomers in the polymer, which in the absence of a catalyst and at neutral pH, has an estimated half-life of 5 million years. Fossilized plants have been found to have intact cellulose and chitin, a β(1,4) polymer of N-acetylglucosamine. The β(1,4) glycosidic link in cellulose is orders of magnitude more stable than the phosphodiester bond of nucleic acids and the amide link of proteins. They are, however, readily cleaved by glycosidases such as cellulases, which can increase the kcat/KM over the uncatalyzed rate by up to 1017 fold, even in the absence of active site metal ions to facilitate hydrolysis (reference). Another reason for the slow decay of dead trees is the complex structure of the cell well, and in particular, the presence of a polymer called lignin, which stabilizes the cell wall and adds considerable barriers to the access of cellulose by added glycosidases. . A final reason for cellulose's extreme stability is the "quaternary" structure of the β(1,4)-linked cellulose strands, which consists of densely packed and intertwined strands of cellulose, which limits solvent (in this case water) accessibility necessary for hydrolysis. In addition, some exposed surface planes of the packed cellulose strands are hydrophobic. This might seem startling, given the polar nature of the glucose subunits of the polymer. Let's review it here now since our goal is present climate change from a biochemical perspective! Some of this material has been presented in previous chapters, but we will reuse it here so this chapter section can stand alone. A review: The Plant Cell Wall (See Chapter 7.3 for more details.) There are about 35 different types of plant cells, and each may have a different cell wall depending on the local needs of a given cell. Cells synthesize thin cell wall that extends and stay thin as the cell grows. Figure $1$ shows the primary cell wall of plants. The primary cell wall contains cellulose microfibrils (no surprise) and two other polymers, pectin and hemicellulose. The middle lamella consisting of pectins, is somewhat analogous to the extracellular matrix. After cell growth, the cell often synthesizes a secondary cell wall thicker than the first for extra rigidity. Since the enzymatic machinery for its synthesis is in the cytoplasm and the cell membrane, it is deposited between the cell membrane and the primary cell wall, as shown in the animated image in Figure $2$. Figure $3$ shows a structural representation of both the primary and secondary cell wall. The middle lamella, which contains pectins, lignins and some proteins, helps "glue together" the primary cell walls of surrounding plants. Primary Cell Wall: The main component of the primary plant wall is the homopolymer cellulose (40% -60% mass) in which the glucose monomers are linked β(1→4)-linked into strands that collect into microfibrils through hydrogen bond interactions. Two other groups of polymers, hemicellulose, and pectin, make up the plant cell wall. Hemicellulose can make up to 20-40% by the mass These polymers have β(1,4) backbones of glucose, mannose, or xylose (called xyloglucans, xylans, mannans, galactomannans, glucomannans, and galactoglucomanannans along with some β(1,3 and 1,4)-glucans. The most abundant hemicellulose in higher plants higher plants are the xyloglucans and have a cellulose backbone linked at O6 to α-D-xylose. Pectin consists of linked galacturonic acids forming homogalacturonans, rhamnogalacturonans, and rhamnogalacturonans II (RGII) [12] [13]. Homogalacturonans (α1→4) linked D-GalA make up more than 50% of the pectin. Figure $4$ shows some of the structures. The are generally branched, shorter than cellulose chains, and can often crystallize. Figure $4$: Variant of the cell wall components of a plant. Costa and Plazanet. Advances in Biological Chemistry 06(03):70-105. DOI: 10.4236/abc.2016.63008License CC BY 4.0 Secondary Cell Wall The structure of the secondary cell wall depends on the function and environment of the cell. It contains cellulose fibers, hemicellulose, and a new polymer, lignin. It is abundant in xylem vessels and fiber cells of woody plants. It gives the plant extra stability and new functions, including the transport of fluids within the plant through channels. The proportion of cellulose in the secondary cell wall is higher than in the primary cell wall and is less hydrated than in the primary cell wall. Given the relative volume of the secondary and primary cell walls inferred from Fig 2, most of the tree-derived cellulose for bioethanol production comes from the secondary cell wall. Switch grasses, a perennial plant, are also valuable sources of cellulose (32–45% wt) and hemicellulose (21–31% wt) but also have significant amounts of lignin (12–28% wt). In summary, the secondary cell wall, formed after the cell stop growing, accounts for most of the carbohydrate biomass of plants. Glycosidases, mostly α- and β-amylases, are needed to convert corn-derived starch into glucose for fermentation and ethanol production. Likewise, cellulases are needed to degrade cellulose into glucose for cellulosic-ethanol production. However, it is a much more complex process since most of the cellulose is in the secondary cell wall. The lignin barrier in the walls protects cellulose from accessibility to cellulases, even after chemical and thermal pre-processing. In addition, xylans, which can make up 30% of the mass of the secondary cell wall, also inhibit cellulose degradation. A thermochemical process can convert cellulose to the synthetic gases CO and H2, which can be used as reactants to form ethanol. We'll discuss the biochemical process using pretreatment and enzymatic hydrolysis to make cellulosic ethanol. Lignin can be recovered and used to provide energy for the industrial-scale synthesis of cellulosic ethanol. Let's explore the barriers posed by lignin and how they can be surmounted to facilitate access to cellulose and the liberation of glucose for cellulosic ethanol production. Lignin Structure and reactivity Lignins, which can make up to 25% of the biomass weight of secondary walls, are made from phenylalanine derivatives but more directly from cinnamic acid. This derives from is made from phenylalanine which is hydroxylated and converted through other steps to hydroxycinnamic alcohols called monolignols, as shown in Figure $5$. Three typical monomers, p-coumaryl, coniferyl, and sinapyl alcohols, can polymerize into lignins, with their units in the polymer (P) named hydroxyphenyl, guaiacyl and syringly, respectively. Lignols are activated phenolic compounds, which form phenoxide free radicals (catalyzed by enzymes called peroxidases), which can attack a second lignol to form covalent dimers. Reaction mechanisms for the dimerization of the MS sinapyl alcohol free radical are shown as an example in Figure $6$. Now imagine this polymerization continuing through the formation of more phenolic free radicals and coupling at a myriad of sites to form a large covalent lignin polymer. Figure $7$ shows one example of a larger lignin. Lignin strengthens the cell wall and further stabilizes the already unreactive cellulose fibers. Let's look at a specific example - using corn stover (CS) as a cellulose source - of how pretreatment of the biomass source with a chemical treatment followed by the addition of a bacterial strain Pandoraea sp. B-6 (B-6) isolated from long, narrow strips of bamboo (slips). Bamboo is a type of woody grass that grows rapidly. These bacteria produce two extracellular lignin-degrading enzymes, manganese peroxidase (MnP) and laccase (Lac). Laccase (Lac) is a multi-copper oxidase that uses O2 as an oxidizing agent in the degradation of the syringyl, guaiacyl and p-hydroxyphenyl monomers in lignins. MnP has similar properties. These and other enzymes can lead to the depolymerization of lignin and degradation of lignin-derived aromatic compounds The adddition of the B-6 bacteria (a source of MnP and Lac) to milled corn stover (CS) did not increase the rate of lignin degradation unless the corn stover was preincubated with a tetrahydrofuran–water (THF–H2O) with 0.5 wt% sulfuric acid and heated to 150 oC. This led to the erosion of the corn stover, allowing access to the bacterial enzyme. The untreated and pre-treated CS surface, along with a diagram showing access of Lac and MnP to the lignin, is shown in Figure $8$. Figure $8$: Untreated and pre-treated CS surface, and Lac and MnP interaction with lignins. Zhuo, S., Yan, X., Liu, D. et al. Use of bacteria for improving the lignocellulose biorefinery process: importance of pre-erosion. Biotechnol Biofuels 11, 146 (2018). https://doi.org/10.1186/s13068-018-1...068-018-1146-4. Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/) In addition to restricting access of cellulase to cellulose, cellulase can also nonspecifically adsorb to lignin and its pretreated forms since the lignin derivatives present a more hydrophobic surface that promotes cellulase interactions. Some plant laccases are involved in lignin biosynthesis, whereas in bacteria and fungi, they may be involved in lignin degradation Of course, fungi, which are prime degraders of dead biomass in forests, are also sources of enzymes for lignin degradation. For example, species of white rot fungi produce manganese peroxidase (MnP), lignin peroxidase (LiP), versatile peroxidase (VP), and laccase (Lac). They work through forming reactive lignin-derived aromatic free radicals (similar to those produced in lignin synthesis), leading to breaking ether bonds, aromatic ring cleavage and removal of methoxy groups from the substrate in a process called delignification. Pretreatment of the biomass increases yields higher amounts of available cellulose. Fungi, however, grow slowly, and the rate of delignification is still low. In addition, they also have hydrolytic enzymes that decrease the yield of cellulose. That is why bacterial sources like B6 are sought for delignification. As this is a biochemistry textbook, let's explore the structure and function of fungal laccase. The enzyme can bind a large variety of hydroxylated- and methyoxy-aromatic compounds as substrates, so its active site must be adaptable and likely dynamic. Structural analyses, in-silico docking experiments, and molecular dynamics simulations have been performed with the laccase (TvL) from the fungus Trametes versicolor. The enzyme has four copper ions in a T1 Cu site and a tri-nuclear Cu cluster (T2 Cu, T3α Cu and T3β Cu) at a T2/T3 site. As the mechanism involves free radical intermediates with O2 as an oxidant and substrate, 4 electrons are passed in single electron steps to the T1 Cu, then to the other three coppers, and finally to O2 to form two water molecules as products. The amino acid side chain ligands for the four copper ions are shown for white rot fungi laccase from Trametes Versicolor in Figure $9$. Figure $9$: T1 Cu (top left) and the trinuclear Cu cluster (T2, T3α and T3β) and their ligands for Trametes Versicolor laccase (TvL, pdb: 1GYC) Fungal laccases are extracellular proteins with about 550 amino acids arranged in three cupredoxin-like, beta-barrel domains. The T1 Cu is close to the surface and is found in domain 3, while the other copper ions are buried at the interface to domains 1 and 3. Figure $10$ shows an interactive iCn3D model of Laccase from the Fungus Trametes Versicolor (1GYC) Figure $10$: Laccase from the Fungus Trametes Versicolor (1GYC). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...cSYir86P9nxSW6 Domain 1 is green, domain 2 magenta and domain 3, which contains the single T1 Cu, orange. The protein is glycosylated, as shown in the blue glycan cube cartoons representing N-acetylglucosmine. Key substrate binding and catalytic side chains are shown in sticks and labeled; Asp 206 is a critical residue involved in substrate binding. The binding interactions of TvL with a wide variety of aromatic substrates are shown schematically in Figure $11$. Figure $11$: Binding modes of representative compounds for TvL. Mehra, R., Muschiol, J., Meyer, A.S. et al. A structural-chemical explanation of fungal laccase activity. Sci Rep 8, 17285 (2018). https://doi.org/10.1038/s41598-018-35633-8. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/ Most substrates interact with the highly conserved His-458 (blue color, ligand for the Ti Cu)and Asp-206 (orange color) residues and form hydrogen bonds, salt bridges, or π-π stacking interactions with them. Asn-264 (blue) and Phe-265 (green) form important hydrogen bonding and π-π stacking interactions with substrates. The green color highlights nonpolar side chains. Ligands bind near the Ti Cu in domain 3 to initiate electron transfer. The active site of TvL must be dynamic to bind the various-sized ligands shown above. Molecular dynamics simulations, shown in Figure $12$, support this. Figure $12$: Display of molecular dynamics simulations showing the loop regions of TvL (magenta colored) and another laccase, CuL (yellow colored), and high levels of fluctuations. Mehra, R., ibid Breaking down Cellulose We just explained how the lignin barrier could be degraded so that cellulase can access cellulose. As we described above, that also poses a difficult challenge given the stability of inaccessibility of the glucosidic bonds in cellulose. The inaccessibility of "naked" cellulose fibers stems partly from the tight binding of cellulose strands into crystal lattices. Multiple crystal forms of cellulose, called polymorphs, can form. Plant cellulose has two predominant polymorphs, cellulose Iβ and Iα. Their structures are shown below in Figure $13$. Figure $13$: Natural and synthetic cellulose polymorphs. Christina M. Payne et al. Chem. Rev. 2015, 115, 3, 1308–1448 (2015), https://doi.org/10.1021/cr500351c. Open access through a Creative Commons public use license. They both form hydrogen bonds within a layer, with the main differences resulting from interlayer stacking. There are no hydrogen bonds between layers. You might find that surprising at first glance until you remember that all the OH groups in the lowest energy chair form of the glucose are equatorial, which allows intralayer hydrogen bonding. The interactions between layers predominantly arise from Van der Waals interactions, specifically induced dipole-induced dipole interactions. The hydrophobic planes, arising from axial H atoms projecting above and above each planar layer of the cellulose fibers, can be readily seen in Figure $14$. Figure $14$: Hydrophobic planes arising from axial H atoms projecting above and above each planar layer of the cellulose fibers. Akira Isogai et al. Progress in Polymer Science, 86 (2018), https://doi.org/10.1016/j.progpolymsci.2018.07.007. Creative Commons license Now we can explore the structure of cellulases and how they bind to and cleaves cellulose. Cellulases, which cleave β(1,4) glycosidic bonds in cellulose, are members of a family of enzymes that go by many names, including glycosidases, or more recently, glycoside hydrolases (GH). The Carbohydrate Active Enzymes (CAZypedia) has over 128 glycoside hydrolase (GH) family web pages with enzymes that form hemiacetals on the cleavage of glycosidic bonds. The fungal cellulases that work on cellulose are found in GH families 5, 6, 7, 12 and 45. There are many types of secreted or cell-surface cellulases, including endoglucanases, exoglucanases (example is cellobiohydrolases (CBHs), and β -glucosidase (BG) ). We will focus on cellobiohydrolases (CBHs), the most studied one, which cleaves a 2-glucose unit (cellobiose) from either end of cellulose as it proceeds (processes) along the chain. Fungal and bacterial CBHs can work on crystalline cellulose as well. The resulting cellobiose is further cleaved by β-glucosidases. Ruminants and even termites obtain cellulases from microbes living within their guts. The enzyme has a "tunnel" between two surface loops which interacts with and processively cleaves cellulose. As mentioned above, fungi are the major degraders of biomass, and are critical in the carbon cycle. Some fungi(brown-rot) use the Fenton reaction (Chapter 13.3) to produce the very reactive hydroxyl free radical (.OH) which causes biomass degradation. Filamentous fungi (like white and soft rot like T. reesei ) use enzymes. The T. reesei cellulase called cellobiose hydrolase 1 is more recently named TrCel7A as it is in the GH 7A family. Cellulase mechanism. We have previously described the mechanisms of polysaccharide synthesis (Chapter 20.3), so we will discuss in less detail the mechanism of the very similar reaction of cellulose degradation by cellulase. Two general mechanisms are possible, one leading to the retention of configuration at the resulting hemiacetal end of the cellobiose and another that inverts the configuration. These mechanisms are shown in Figure $15$ for glucose in alpha-linkage at the anomeric carbon (not the beta-linkage found in cellulose). Figure $15$: Two Primary Catalytic Mechanisms of GHs. After Payne et al. Chem. Rev. 2015, 115, 3, 1308–1448. https://doi.org/10.1021/cr500351c. Scheme (A) shows the reaction that inverts the configuration. Water acts as a nucleophile in a SN2 type of reaction, with catalytic assistance by two proximal carboxylic acid side chains acting as general acids and bases. This results in an inversion of the stereochemistry at the anomeric carbon. Scheme (B) proceeds with the retention of configuration as two different nucleophilic attacks occur. In the first, an active site carboxylate forms a covalent acetal intermediate with the anomeric carbon. The carboxylate hence acts as a nucleophilic catalyst. Water, acting as a nucleophile, then attacks to form the hemiacetal with the expulsion of the carboxylate leaving group. As we discussed in Chapter 20.3 (section on glycosyl transferases), other variants of these mechanisms would include a SN1 reaction or one with an oxocarbenium-like transition state. The CBH1 (family 7) has a long tunnel for binding cellulose. The CBH1 (TrCel7A) cellulose catalytic site spans at least 9 glucose monomers (n-7, n-6,...,n-1,n+1, n+2) with cleavage typically of a cellobiose from the reducing end (between n-1 and n+1). The structure of the TrCel7A glycoside hydrolase (cellobiose hydrolase) with a small bound cellulose is shown in Figure $16$. Figure $16$: Crystal structure of the first GH7 CBH and EG. Payne et al. Chem. Rev. 2015, 115, 3, 1308–1448. https://doi.org/10.1021/cr500351c Open access through Creative Commons public use license. The ligand from the TrCel7A Michaelis complex (PDB code 4C4C (441)) is shown in all panels. (A) CBH TrCel7A CD (PDB code 1CEL (172)) view from side, exhibiting the β sandwich structure that is characteristic of GH7 enzymes. TrCel7A was the first GH7 structure solved and is the best-characterized member of GH7. (B) TrCel7A view from bottom showing the more closed substrate binding "tunnel". (C) EG F. oxysporum Cel7B (PDB code 1OVW (174)) view from side. (D) FoCel7B view from the bottom showing the more open binding "groove". (E) TrCel7A Michaelis complex (PDB code 4C4C (441)) shows the standard numbering of the substrate binding sites (catalytic residues shown in green for reference). A cellulose chain enters from the −7 site. Hydrolysis occurs between the −1 and +1 sites. The +1/+2 sites are termed the "product sites". Active site carboxylates (E212, D214, and E217) are shown near the -1/+1 cleavage site in Figure $17$. Glu 217 is covalently attached to the -1 glucose, supporting the retaining mechanism illustrated in Fig 15 above. Figure $17$: Michaelis complex and glycosyl-enzyme intermediate of TrCel7A. Payne et al. ibid. Panel (A) shows the TrCel7A Michaelis complex (PDB code 4C4C (441)). Panel (B) shows a TrCel7A glycosyl-enzyme intermediate (PDB code 4C4D (441)) with a covalent bond between the nucleophile and the broken cellooligomer chain. There is an approximate 30° rotation of the E217 nucleophile during glycosylation. Figure $18$ shows a more detailed view of the first step (glycosylation f Glu 217) for the Hypocrea jecorina GH Family 7 cellobiohydrolase Cel7A Figure $18$: Figure 2. Glycosylation step for Hypocrea jecorina GH Family 7 cellobiohydrolase Cel7A. Knott, Brandon C. et al. - J. Am. Chem. Soc.329 (2013) https://doi.org/10.1021/ja410291u. Open access article published under an ACS AuthorChoice License Panel (a) shows a snapshot of the reactant the conformation from a representative AS trajectory (with the substrate in green and catalytic residues in yellow) for the glycosylation step. The proton resides on the acid residue, Glu217. Panel (b) shows a representative snapshot of the transition state. The −1 glucopyranose ring now adopts a different conformation. Panel (c) shows the product of the glycosylation reaction. Panel (d) shows a schematic view of the overall glycosylation reaction with the collective variables identified by LM colored at the transition state. The best three-component RC identified by LM includes the forming/breaking bonds involving the anomeric carbon, the breaking bond between Glu217 and its proton, and the orientation of the nucleophile Glu212. Figure $19$ shows the corresponding deglycosylation (of Glu 217) step. Figure $19$: Figure 4. Deglycosylation step results. Knott, Brandon C. et al, ibid Panel (a) shows a snapshot of the reactant conformation from a representative AS trajectory (with the substrate in green and catalytic residues in yellow) for the deglycosylation step. The covalent glycosyl–enzyme bond is intact, and the cellobiose product is in primed GEI mode. Panel (b) shows a representative snapshot of the transition state. Note the distorted conformation of the −1 sugar, as the nucleophilic water molecule is ripped apart. Panel (c) shows a snapshot of the product in which the glycosyl-enzyme bond has been broken, and the catalytic residues have been regenerated. Panel (d) shows a schematic view of the overall deglycosylation reaction with the collective variables identified by LM colored at the transition state. The best three-component RC identified by LM includes the forming/breaking bonds involving the anomeric carbon, the forming/breaking bonds involving the transferring proton, and the orientation of the C3 hydroxyl of the +1 sugar. Binding of cellulase to cellulose fibers and lignin Many glycoside hydrolases contain distinct carbohydrate binding domains/modules (CBD/CBM) and catalytic domains (CD). For example, TrCel7A can be cleaved by the protease papain into a 56K domain with catalytic activity on small substrates but not large cellulose one and a smaller 10K (C terminal) domain that itself is glycosylated and which binds to the hydrophobic surface of cellulose crystals. Many GHs, in addition, have linkers connecting the catalytic domain (CD) and the carbohydrate module (CBM), which add different functions to the enzymes. The linkers vary in size and amino acid sequence. Linkers in fungi tend to be long and N- and O-glycosylated, affecting binding/catalysis. The linkers can also be intrinsically disordered, which adds dynamic complexity to their effects. The actual cellulose binding site on cellulase has been determined by solution NMR using a synthetic 36 amino acids protein fragment from the C-terminal domain of Trichoderma reesei Cel7A (the "carbohydrate binding module or CBM"). The amino acids involved in the binding of cellohexaose (6-mer) were determined by perturbation of the 2D NMR structure on binding cellohexaose. As we mentioned above, cellulase also binds lignin, decreasing their catalytic efficiency towards cellulase. Results of NMR binding studies of the TrCel7A carbohydrate binding module with cellohexaose and lignins from Japanese cedar (C-MWL) and Eucalyptus globulus (E-MWL) are shown in Figure $20$. Figure $20$: Comparison of interaction property between cellohexaose and MWLs. Tokunaga, Y., Nagata, T., Suetomi, T. et al. NMR Analysis on Molecular Interaction of Lignin with Amino Acid Residues of Carbohydrate-Binding Module from Trichoderma reesei Cel7A. Sci Rep 9, 1977 (2019). https://doi.org/10.1038/s41598-018-38410-9. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. Panel (a) shows cellohexaose specifically bound to the flat plane surface and cleft. The flat plane surface is defined by a triplet tyrosine (Y5, Y31, Y32) and H4, G6, Q7, I11, L28, N29, Q34, L36. Panel (b) shows both MWLs bound to multiple binding sites, some of which are included in the flat plane surface and cleft even in low concentrations of titrant. These non-specific binding sites are labeled green. Figure $21$ shows an interactive iCn3D model of the C-terminal cellulose-binding module of cellobiohydrolase I from Trichoderma reesei (2CBH). Figure $21$: C-terminal cellulose-binding module of cellobiohydrolase I from Trichoderma reesei (2CBH). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...Yva8BVMQCGdUZ7 This synthetic carbohydrate binding module (CBM) from the C-terminal domain of cellobiohydrolase I consists of 36 residues. NMR was used to determine the structure of the CBM. 2D NMR was used to determine the amino acids interacting with cellulose. The interacting side chains are shown as sticks underneath the molecular surface (gray). The side chains are colored according to hydrophobicity, with green followed by yellow being most hydrophobic. The numbering system refers to the 36 amino acid synthetic peptide, not the native protein. This model clearly shows this domain binds to the hydrophobic face of the cellulose microcrystal. The TrCel7A CBM has serine and threonine side chains that are glycosylated and affect binding. The interaction of the CBM with the nonpolar cellulose surface is shown in Figure $22$. Figure $22$: Glycosylated TrCel7A CBM on the hydrophobic surface of cellulose. Payne et al., ibid. Note that the aromatic groups of the triplet tyrosines, Y5, Y31, and Y32 (not labeled), are coplanar with the cellulose surface. As mentioned above, linkers that connect the C-terminal carbohydrate binding module (CBM) and the catalytic domain (CD) can be of different lengths and sequences and are also N- and O-glycosylated. Figure $23$ shows the interactions of the glycosylated linkers with the cellulose fibers. Figure $23$: Molecular snapshots of TrCel7A and TrCel6A wherein the linker binds to the cellulose surface from microsecond-long MD simulations. Payne et al., ibid. These computational predictions of cellulose linkers enhancing binding of CBMs to the cellulose surface were corroborated experimentally via binding isotherm measurements. N-glycosylation and O-glycosylation are shown in blue and yellow. The glycans attached to the enzyme significantly enhance the binding of cellulase to the cellulose fibers. Payne et al. Chem. Rev. 2015, 115, 3, 1308–1448. https://doi.org/10.1021/cr500351c Open access through Creative Commons public use license A pictorial view of the hydrolytic cleavage of cellobiose from cellulose fibers is shown in Figure $24$. Figure $24$: Complete processive cycle of a GH7 CBH. TrCel7A is shown with its CD, linker, and CBM in gray "cartoon" representation. Payne et al., ibid. N-glycosylation and O-glycosylation are shown in blue and yellow, respectively. The cellulose surface is green, and the released cellobiose product magenta. Following the CBM and CD adsorption to the substrate and initial chain threading, TrCel7A processively cleaves cellobiose from a cellulose chain end. The "Processive Cycle" includes chain processivity, hydrolysis, and product expulsion (Figure 35). This processive cycle repeatedly occurs until the enzyme desorbs from the cellulose surface. Figure $25$ shows an interactive iCn3D model of cellulose bound to cellobiohydrolase I from Trichoderma reesei (7CEL) Figure $25$: Cellulose bound to cellobiohydrolase I from Trichoderma reesei (7CEL). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...hfJFWg6hmJAxg7 Key Points - Beta version from Chat.openai 1. Cellulosic ethanol is a biofuel that is produced from the cellulose, hemicellulose, and lignin in plant material. 2. Unlike first-generation biofuels like corn and sugar cane ethanol, which are produced from sugars and starches, cellulosic ethanol can be produced from a wide range of plant material, including agricultural waste, wood chips, and switchgrass. 3. Cellulosic ethanol is considered a "second-generation" biofuel because it addresses many of the limitations of first-generation biofuels, including the competition with food crops for land and resources. 4. Cellulosic ethanol production involves a two-step process: first, the plant material is broken down into sugars through a process known as pretreatment, and then the sugars are fermented to produce ethanol. 5. The most common pretreatment methods include acid hydrolysis, ammonia fiber expansion, and steam explosion. 6. Cellulosic ethanol has a higher energy balance than first-generation biofuels and lower greenhouse gas emissions, making it a more sustainable option for biofuel production. 7. However, the technology for cellulosic ethanol production is still in the early stages of development, and the cost of production remains high. 8. Research is ongoing to improve the efficiency and cost-effectiveness of cellulosic ethanol production and to find ways to make it a viable alternative to fossil fuels.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.06%3A__Algae_-_an_Introduction.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry In the previous chapter sections, we discussed bioethanol production from plant starches (first generation) and lignocellulosic plants (second generation) from biomass waste such as stover and sugar cane. Each had its challenges. Now we will consider the third generation production of bioethanol from algae which has significant potential for minimizing damage to the environment. Below in Figure $1$ is a summary of bioethanol production from each generation feedstock. Figure $\PageIndex{x}$: Figure 1. General flowchart of bioethanol production, comparing the pre-fermentation processing of feedstocks for the first three generations of bioethanol production. The blue highlighted area provides an example of a value-added process that can enhance the value of bioethanol production. Tse, T.J.; Wiens, D.J.; Reaney, M.J.T. Production of Bioethanol—A Review of Factors Affecting Ethanol Yield. Fermentation 20217, 268. https://doi.org/10.3390/fermentation7040268. Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/) There are so many types of algae that it can be daunting to read about them. Some are single cells, some can form filaments and colonies, and some are multicellular with different cell types. Some are prokaryotes; some are eukaryotes. The Algae Database suggests that they are best defined as "oxygenic photosynthesizers other than embryophyte land plants". Naming and classification are generally based on botanical names ending as follows: phyla = -phyta, classes = -phyceae, order = -ales, and family = -aceae. The main groups are blue-green algae (cyanobacteria), rhodophytes, phaeophytes, chlorophytes, euglenophytes, charophytes, diatoms, dinoflagellates, and cryptophytes. To make it more complicated, some animal species have been categorized as algae and have zoological name endings ( -zoa, -ea, -ida, -idae). We apologize in advance for any errors and inconsistencies in the description of algae in the chapter section and would ask that you contact us with corrections.. Before we discuss biofuel production from algae, we will review the different algae types (much as we did for zooplankton and phytoplankton). Broadly, algae can be divided into microalgae (seen with a microscope) and macroalgae (seen with the eye). There are almost 170,00 species of algae listed in the Algaebase. Here is a summary of their properties. They can • fix carbon and produce food by photosynthesis. As such, they are primary producers. • be tiny (microalgae) or large and visible (macroalgae, also known as seaweed). Kelp, which can form large underwater forests, is a macroalgae. • be unicellular, form colonies or filaments, or larger multicellular structures • attach to objects or float freely. Some who study algae (phycologists) often consider any organism with chlorophyll but without the stems, roots, leaves, flowers, and vessels of plants to be algae. The naming and classification of algae are confusing, so let's start with a broad review of their classification. Biological classifications - a review Life can be divided into domains, kingdoms, phyla, and additional subcategories. Carl Woese proposed three domains, Archaea, Bacteria, and Eukarya, in 1990, based on analyses of ribosomal RNA sequences. These domains are further classified into kingdoms - Archaebacteria, (Eu)bacteria, Protista, Fungi, Plantae, and Animalia. A seventh kingdom was added in 1981 by Thomas Cavalier-Smith, who divided Protista was two kingdoms, Protista (unicellular eukaryotes like some protozoa and some molds) and a new kingdom, Chromista (uni- or multicellular eukaryotes such as algae, diatoms, and some protozoans). Both Protista and Chromista have organisms with chlorophyll, and both also have heterotropic organisms. Newer classifications based on additional biochemical data may yet be proposed. Table $1$ below shows a summary of the domains and kingdoms of life. Domains and Kingdoms Domain Bacteria Archaea Eukarya Kingdom (Eu)bacteria Archaebacteria Plantae Animale Fungi Protista Chromista Table $1$: Domains and kingdoms of life. All life arose from the last universal common ancestor, LUCA, as shown below in Figure $2$. Figure $2$: Phylogenetic tree linking all major groups of living organisms to the LUCAhttps://upload.wikimedia.org/wikiped..._1990_LUCA.svg Their biological classification of algae (which illustrates how widely spread they are among different domains, kingdoms, and phyla, shown below in Figure $3$ Figure $3$: Distribution of algae among groups in the Tree of Life as recognized by the ITIS and Species 2000 (and ife.org) in 2011. The deep classification of algae is the subject of great debate, and even the higher clades have been discussed and revised recently. Adapted from Verdelho Vieira, V.; Cadoret, J.-P.; Acien, F.G.; Benemann, J. Clarification of Most Relevant Concepts Related to the Microalgae Production Sector. Processes 202210, 175. https://doi.org/10.3390/pr10010175. Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Microalgae Microalgae are single-cell organisms that can form filaments and colonies. This group has one prokaryotic member, cyanobacteria, also known as blue-green algae. As a prokaryote, it does not have mitochondria or chloroplasts. The rest of the microalgae are eukaryotic and include the phyla Chlorophyta, Rhodophyta, Glaucophyta, Cryptophyta, Euglenozoa, Cercozoa, Heterokontophyta, Haptophyta, and Miozoa (Myzozoa). Another simpler organizational system divides microalgae into four categories: • cyanophyta (blue-green algae/cyanobacteria) • pyrrophyta (dinoflagellates and cryptomonads, and can be yellowish-green to golden-brown) • chrysophyta (diatoms, heterokonts and golden brown algae) • chlorophyta (microscopic green algae). This term also applies to macroalgae (see below). Green algae are really diverse and are now broken up into two phyla, chlorophyta and charophyta with a combined 17 classes. AlgaeBase dynamic species counts shows that there are about 4,500 species of Chlorophyta including those that live on land, in freshwater, and those that are considered macroalgae seaweeds. There are about 2500 species of Charophyta that are entirely freshwater. Within the microalgae are found green, brown, and red algae. (Unfortunately for students of algae, there are also green, brown, and red macroscopic algae as well). Microalgae are much more efficient at photosynthesis than land plants. The diatoms are an example of microalgae and are the precursor of the "fossil" fuel oil deposits. Spirogyra is a unicellular green alga that forms long filaments (colonies) up to 0.1 mm in length, so it loosk like a multicellular organism. One class of microalgae, green algae, arose when a microbe acquired a cyanobacterium, which allowed photosynthesis. Green algae eventually evolved into higher plants, and a similar process led to red algae. Brown algae and diatoms, dinoflagellates, and euglenoids, other types of algae, arose from the uptake of red or green algae into other eukaryotic host cells. In general, green microalgae, which absorb red wavelengths, are found on the surface. The red microalgae, which absorb green, and blue wavelengths, are at lower levels. The brown microalgae are generally found in between these water layers. Figure $\PageIndex{x}$ below shows the light penetration spectrum in water. Light in the water The energy of light photons is given by E=hν=hc/λ, where ν if the frequency of the light and λ is the wavelength. As highly energetic x-rays penetrate matter, visible light can penetrate water. Water absorbs incident radiation, with the lower energy, higher wavelength photons of red light absorbed more readily in the top layers while the blue light penetrates farther into the water. The depths of penetration of light in the open ocean (left) and coastal (right) waters are shown in Figure $4$. Figure $4$: The light penetration spectrum in water as a function of color. https://oceanexplorer.noaa.gov/edu/m...fact-sheet.pdf When you are underwater, everything seems blue since red light is preferentially absorbed, leaving photons enriched in blue light reaching our eyes. Blue-enriched light reaches our eyes as it reflects off of objects. Clear water in the open ocean also appears blue since there are fewer particles like phytoplankton from which light scatters back to our eyes. Rayleigh (or elastic light scattering) depends on 1/λ6, so low wavelength light scatters most from particles. Sunsets and sunrises appear red when light passes through more of the atmosphere and the blue light is scattered from atmospheric particles before reaching our eyes. Coastal waters have more sediment, algae, and microscopic organisms like plankton that can scatter light. These waters appear more green-blue, since red light is significantly absorbed by the microorganisms and blue light is more scattered. Microalgae exist as single cells or can form multicellular filaments and colonies. They proliferate in the presence of simple nutrients and can produce large amounts of polysaccharides for industrial bioethanol production or fatty acids/triacylglycerols for biodiesel production. They are, as mentioned above, the source of underground oil deposits. Both freshwater (like Chlorella and Haematococcus) and ocean microalgae (Dunaliella, Phaeodactylum, and Tetraselmis) can be used for the production of biofuels. The cyanobacteria Spirulina sp. is commonly used for commercial purposes. It and Synechococcus have large amounts of glycogen that could be used for bioethanol production. Their triacylglycerols are low, so they can't be used for biodiesel (which we will discuss in a future section of the chapter). Most are familiar with algae blooms in freshwater lakes (and even in saltwater environments). Cyanobacteria (blue-green algae) are the main culprit. Microcystin, a potentially lethal toxin that targets serine/threonine protein phosphatases, is released from some algae blooms. Figure $5$ shows a microalgae bloom in Lake Erie, a shallow freshwater lake polluted with agricultural runoff, making it an excellent site for cyanobacterial blooms. Figure $5$: Microalgae bloom in Lake Erie, October 2011. https://commons.wikimedia.org/wiki/F..._Lake_Erie.jpg Red tides are another type of toxic algae blooms that occurs in coastal water. Along US coast they are caused by dinoflagellates and one diatom, both phytoplankton and types of microalgae. Around the Gulf of Mexico, the main cause of red tides is the microalgae Karenia brevis. They release large amounts of brevotoxin, a polycyclic ether that binds to and activates voltage-gated Na+ channels in nerve and muscle. Hence it is a potentially deadly neuortoxin. The frequency of red tides is increasing around the world. Two images of red tides are shown in Figure $6$. Figure $6$: Red Tide. License from Shutterstock Microalgae are sources of glycan polymers that can be used for bioethanol production; some are excellent candidates for biodiesel fuel, which is made from triacylglycerol reserves. Table $2$ below shows a classification of microalgae and Cyanobacteria along with their characteristic pigments (that impart their distinctive colors) and their energy reserves that could be used for biofuel production. Phylum Class Pigments Reserve Habitat Cyanobacteria Cyanophyceae Chl a, β-carotene, flavacene, Echinenone isozea-, zea-, myxo-, oscillaxanthin APC, C-PC, C-PE Starch (granule) and glycogen Marine Freshwater Terrestrial Euglenophyta Euglenophyceae Some colorless Chl a, b, diadinoxanthin Paramylon Ergosterol Marine Freshwater Terrestrial Heterokontophyta/ Ochrophyta Xanthophyceae Eustigmatophyceae Chl a and c, β-carotene, heteroxanthin, diadinoxanthin (++) Oil Leucosin Ergosterol Marine Freshwater Terrestrial Miozoa Dinophyceae Chl a, c, β-carotene, diadinoxanthin, dinoxanthin, peridinins Starch Lipids Marine Freshwater Heterokontophyta/ Ochrophyta Chrysophycea Chl a, c, β-carotene, Fuco-, Diato-, diadinoxanthin Chrysolaminarin Fucosterol Porifasterol Marine Freshwater Haptophyta Coccolithophyceae Pavlovophyceae Rappephyceae Chl a, c, β-carotdnd, Fuco-, Diato-, diadinoxanthin Chrysolaminarin Fucosterol Porifasterol Marine Freshwater Bacillariophyta (Diatoms) Bacillariophyceae Chl a, c, β-carotdnd, Fuco-, Diato-, diadinoxanthin Chrysolaminarin Oil Marine Freshwater Terrestrial Cryptophyta Cryptophyceae Chl a, c, Biliproteins, α-carotene, Allo-, Croco-, Monado-xanthin Starch (granule) Oil Carbohydrates Marine Freshwater Table $2$: Classification of microalgae and Cyanobacteria. Hachicha, R.; Elleuch, F.; Ben Hlima, H.; Dubessay, P.; de Baynast, H.; Delattre, C.; Pierre, G.; Hachicha, R.; Abdelkafi, S.; Michaud, P.; Fendri, I. Biomolecules from Microalgae and Cyanobacteria: Applications and Market Survey. Appl. Sci. 202212, 1924. https://doi.org/10.3390/app12041924. Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Cyanobacteria and red microalgae store glycogen and floridean starch (a hybrid between starch and glycogen) respectively, while green microalgae accumulate amylopectin-like polysaccharides. Macroalgae As mentioned above, macroalgae, known as seaweeds, do not have roots, stems, leaves, or flowers. Kelp in underwater forests appear to have roots, stems and leaves, but they are not plants. They have analogous structures such as holdfast, stipes, and blades which serve the functions of roots, stems and leaves found in plants. As do the microalgae, variants of macroalgae include red (Rhodophyta), green (Chlorophyta, also a type of microalgae), and brown (Phaeophyta) algae. It grows up to 30 times as quickly as land-based groups. In addition, it does not have lignin. They grow much faster than terrestrial plants and can form lots of biomass for commercial processing in much less area than land groups. They can be grown at low cost in sea farms without adding nutrients or pesticides. Hence they are ideal for both food and biofuel production. Brown and red algae have many present commercial uses. They have lots of carbohydrates for potential bioethanol production and triacylglycerols for biodiesel. Their carbohydrate composition includes mannitols and cell wall constituents which could also be used for fermentation. In contrast to present petroleum sources, the biodiesel from macroalgae does not contain sulfur. Red macroalgae (some material below for red and green macroalgae from https://bio.libretexts.org/Bookshelv...nd_Green_Algae) The red algae are almost exclusively marine, and some are unicellular, but most are multicellular. They have true chloroplasts with two membranes (no remnant peptidoglycan) containing chlorophyll. Like the cyanobacteria, they use phycobilins as antenna pigments, phycoerythrin (which makes them red), and phycocyanin. Red pigment allows the red algae to photosynthesize at deeper depths than the green or brown algae, harnessing more of the blue light waves that penetrate deeper into the water column. Unlike green algae and plants, red algae store carbohydrates as Floridean starch, which has glucose in α(1,4) linkages and occasional α(1,6) linkages, similar to amylopectin. Agar, the base for culturing bacteria and other microorganisms, is extracted from a red alga. Multicellular forms can be filamentous, leafy, sheet-like, coralloid, or even crust-like. Some examples are shown in Figure $7$. Figure $7$: These images show multicellular red algae, which can range from filamentous (first image) to "leafy" (second image, left) to sheet-like (second image, right). The red color is due to an abundance of the red pigment phycoerythrin, which gives this group reddish chloroplasts. First image by Melissa Ha CC-BY-NC. Second image by Maria Morrow CC-BY-NC. Right image: https://commons.wikimedia.org/wiki/F...ed_algae_3.jpg Green macroalgae These algae exhibit great diversity of form and function. Similar to red algae, green algae can be unicellular or multicellular. Many unicellular species form colonies and some green algae exist as large, multinucleate, single cells. Green algae primarily inhabit freshwater and damp soil and are a common component of plankton. They have chloroplasts and the photosynthetic pigments chlorophyll a and b, carotene, and xanthophylls. Examples include Chlamydomonas, Chlorella, Pediastrum, Netrium, Hydrodictyon, Acetabularia, Ulva, and Spirogyra. Lichens are a symbiotic combination of fungi and green algae. The nature of the evolutionary relationships between green algae is still debatable. As of 2019, genetic data supports splitting the green algae into two major lineages: chlorophytes and streptophytes. The green algae exhibit similar features to the land plants, particularly in chloroplast structure. They have chlorophyll a and b, have lost phycobilins but gained carotenoids, and store carbohydrates as starch inside plastids. Green algae are an important source of food for many aquatic animals. Two types of green macroalgae are shown in Figure $8$. Figure $8$:Two types of green macroalgae left: Figure 5.3.3.125.3.3.12: Trentepohlia is a genus of green algae found in terrestrial environments. It forms fluffy orange colonies on trees and is a photobiont in many lichens. One might not know they were looking at a green algae, due to the orange pigmentation. However, green algae have carotenoids. These terrestrial green algae produce an abundance of carotenoids, perhaps for protection from sun damage. Photo by Scott Loarie, CC0. Right: fresh water green algae. https://upload.wikimedia.org/wikiped...reen_Algae.jpg Green macroalgal blooms (called green tides) can also occur (just as blooms from the microalgae cyanobacteria). Green tides in the Yellow Sea (between China and Korea) are the largest known. A particularly large one is shown in Figure $9$: Figure $9$: Green Seaweed in the Yellow Sea , June 2021. https://earthobservatory.nasa.gov/im...the-yellow-sea This particular green time was from nontoxic green macroalgae, Ulva prolifera. It is often called sea lettuce as it is edible. Blooms can affect the local ecosystem and lead to hypoxic zones as they decay. Brown macroalgae (also known as Kelp) (some material below for brown macroalgae from https://bio.libretexts.org/Bookshelv...3A_Brown_Algae) Macroscopic brown algae arose when a heterotrophic eukaryote merged with a unicellular photosynthetic eukaryotic red algae chloroplasts. The red alga degenerated into a chloroplast, this time with four membranes -- the engulfing membrane from the oomycete, the red alga's plasma membrane, and the two membranes of the original chloroplast within the red alga. The chloroplast has lost one of these membranes in many groups derived from secondary endosymbiosis. Figure $10$ shows this secondary endosymbiosis event. Brown algae are brown due to the large amounts of carotenoids they produce, primarily one called fucoxanthin. These organisms are exclusively multicellular and can get so large that they require special conductive cells to transport photosynthates from their blades down to the rest of their tissues. These conductive cells are called trumpet hyphae and have sieve plates and resemble sieve tubes found in flowering plants. Much like Saprolegnia, the body of an alga is called a thallus because it is not differentiated into specialized tissues. The general morphology of a brown alga includes a holdfast, stipe, gas bladder(s)and blade(s). Figure $111$ shows a diagram of kelp structure. Figure $12$ shows a beautiful image of a kelp forest. Figure $12$: Kelp forest. https://commons.wikimedia.org/wiki/F...1115735%29.jpg Figure $13$ shows another type of a brown macroalgae. Figure $12$: Underwater "roses" of a brown algae Padina pavonica, commonly known as the peacock's tail (Israel). https://commons.wikimedia.org/wiki/F...l_(Israel).jpg Now we are in a position to discuss algae as a source of biofuels and nutritive foods in the next chapter section. Key Points - Beta version from Chat.openai 1. Algae are a diverse group of aquatic organisms that range from simple, single-celled organisms to complex, multicellular forms. 2. Algae are photosynthetic organisms and they play a critical role in the Earth's carbon cycle by converting carbon dioxide into oxygen. 3. Algae are found in a wide variety of environments, including freshwater, marine, and terrestrial ecosystems. 4. Algae have been used for a long time as a source of food, medicine and as a fertilizer. 5. Algae are also of great interest as a source of biofuel due to their high growth rates, ability to grow in non-arable land and their ability to produce large amounts of lipids (oils) that can be converted into biofuels. 6. Algae are a valuable resource for the production of biofuels, as they are able to produce large amounts of lipids and carbohydrates that can be converted into biofuels. 7. Algae-based biofuels have the potential to be more sustainable than biofuels produced from terrestrial crops, as they can be grown in non-arable land and do not compete with food crops for resources. 8. Algae can be grown in different systems, including open ponds, closed photobioreactors, and hybrid systems. 9. Algae can be grown using a variety of inputs, such as sunlight, CO2, and nutrients, and can produce a wide range of biofuels, including bioethanol, biodiesel, and biomethane. 10. Algae-based biofuels are still in the early stages of development, and research is ongoing to improve the efficiency and cost-effectiveness of algae cultivation and biofuel production. 11. Algae also have other potential uses, such as for the production of food and feed, as well as for bioremediation and carbon sequestration.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.07%3A__Algae_-_Bioethanol_production.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Learning Objectives • Understand the role that algae play in the production of bioethanol. • Explore the process of converting algae into bioethanol. • Learn about the potential benefits and drawbacks of using algae for bioethanol production. • Evaluate the sustainability of algae-based bioethanol production. • Analyze the potential of algae as a renewable source of bioethanol and its potential impact on climate change. Now we are in a position to discuss algae as a source of biofuels and foods. Algae can be used to produce bioethanol from the fermentation of algal polysaccharides (starch, cellulose, and other unique polysaccharides found in them). In addition, triacylglycerols can be converted to biodiesel (which we will discuss in a different chapter). Since we explored bioethanol production from starch and cellulose in previous sections, we'll focus mainly on the unique carbohydrates found in algae and how they can be converted into monomers for glycolytic fermentation by yeast to ethanol. Both microalgae and macroalgae contain starch reserves (in the cytoplasm and, in some cases, in the chloroplast) and cellulose (in cell walls). They can be grown quickly in aquatic environments, and the key molecules are harvested and processed for use in yeast fermentation by yeast and also by bacteria such as Zymomonas mobilis to produce bioethanol. This enzyme can use glucose, fructose, and sucrose as substrates. Starch from microalgae Green microalgae have been used as a source of both starch and cellulose. Some species of microalgae have very high starch/glucose composition by mass. Examples include Chlamydomonas reinhaedtil (60%), Chlorococcum humicola (33%), and Chlorella vulgaris (50%). Pretreatment of the microalgae biomass includes liquefaction using alpha-amylases followed by the addition of amyloglucosidase to produce glucose monomers (saccharification) or hydrolysis of glucosidic bonds using acid (sulfuric acid) or base (sodium hydroxide) pretreatment at elevated temperatures. As discussed previously, starch consists of amylose, an unbranched glucose polymer with α(1,4) glucosidic links, and amylopectin, which contains α(1,6) branches. Algae starch (for example, in Chlorophyta, Cryptophyta, and Dinophyta) is found in the cytoplasm or chloroplast. A particular type of starch, Floridean (the primary energy storage molecule in the red algae Rhodophyceae), is also found in the cytoplasm. A generic structure of branched starches is shown below in Figure $1$. Figure $1$: Branched starches (https://en.wikipedia.org/wiki/Floridean_starch. Amylopectin has α(1,6) branches every 25-30 glucose units, while animal glycogen, another α(1,4) glucose polymer, has shorter branches every 8-12 glucose units. Floridean starch has branch lengths between these repeat values but is closer to amylopectin. β-amylases can cleave Floridean starch to mainly form glucose and maltose, while mild acid hydrolysis can lead to isomaltose formation. Other unique glycans are potential sources of glucose for bioethanol production. These are described below. Starch-like molecules in microalgae Given algae diversity, it should be no surprise that some have alternative, starch-like glycans for their energy reserves that could also be used for bioethanol production. Laminarin (or Laminaran) Laminaran is a linear polymer of glucose with β(1,3) glycosidic links with β(1,6)-branches at a ratio of 3:1. It is used for energy storage in brown algae. It can be hydrolyzed by laminarinase, which cleaves β(1,3) glucosidic bonds. It is a linear polysaccharide with a β(1→3):β(1→6) ratio of 3:1. Its structure is shown in Figure $2$ below. Figure $2$: Structure of laminarin. Left - https://commons.wikimedia.org/wiki/F...ructure_V1.svg; Right - https://biocyc.org/compound?orgid=ME...3602#tab=STRUC Chrysolaminarin This glycan is a linear polymer of glucose monomers linked through β(1,3) glycosidic bonds with some β(1,6) linkages. It is found in Haptophyceae, Bacillariophyceae, and Chrysophyceae, which include diatoms. Its generic structure is shown in Figure $3$ below. Figure $3$: Structure of Chrysolaminarin. https://metacyc.org/compound?orgid=M...arin#tab=STRUC The ratio of branching is 11/1, as indicated in the figure. Algae contain enzymes that can cleave the β(1,3) and β(1,4) links, which can be cleaved by acid hydrolysis. Paramylon Paramylon is a linear polymer of glucose monomers linked through β(1,3) glycosidic bonds. It is found in Euglenophyceae, Xanthophyceae, and Prymnesiophyta. A β-1,3 glucanase from Euglena gracilis (Euglenozoa) cleaves the β(1,3) link. Its structure is shown in Figure $4$ below. Figure $4$: Paramylon. https://biocyc.org/compound?orgid=ME...ylon#tab=STRUC Algae Cell Walls In a previous chapter, we discussed the use of lignocellulosic feedstocks from plant cell walls to produce bioethanol. Both microalgae and macroalgae (red, green, and brown) have cellulose, a β(1,4) glucose polymer, in their cell walls. It is found in Chlorophyta, Dinophyta, Phaeophyta, Prymnesiophyceae, Rhodophyceae, and Xanthophyceae. There is little or much less lignin in algae and apparently none in macroalgae. In addition, there is less hemicellulose. These characteristics make algae excellent candidates for feedstocks for third-generation bioethanol production. There are problems to overcome as well. For example, the cell wall of Glaucocystis nostochinearum is almost 90% crystalline microfibrils, which adds to its physical and chemical stability. In addition, there are other glycans in some algae's extracellular matrix/cell wall, which can make it difficult to extract and use cellulose. Extracted cellulose can also be used as a feedstock for the chemical industry as well for the synthesis of bioplastics, a topic we will consider in another chapter sections. Algae cellulose synthesis is performed by membrane-bound cellulose synthase terminal complexes (TCs), with the geometry of the cellulose microfibrils determined by the geometry of the TCs, as shown in Figure $5$. The TCs are arranged in a hexagon with C6 symmetry, and they can form hexameric macrofibrils. Figure $5$: Organization and morphology of cellulose synthesizing terminal complexes (TCs) in different organisms. Wahlström, N. et al. Cellulose 27, 3707–3725 (2020). https://doi.org/10.1007/s10570-020-03029-5. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. Table $1$ below shows the different algae taxa's major polymers in the cell wall and extracellular matrix. Algal taxa Crystalline polysaccharides Hemicelluloses Matrix polysaccharides Chlorophyta (green algae) Cellulose Xyloglucans, xylans, mannans, glucuronan, (1 → 3)-β-glucan, (1 → 3),(1 → 4)-β-glucan Ulvans, pectins Charophyceae (green algae) Cellulose Xyloglucans, xylans, mannans, (1 → 3)-β-glucan, (1 → 3),(1 → 4)-β-glucan Pectins Phaeophyceae (brown algae) Cellulose Sulfated xylofucoglucan, sulfated xylofucoglucuronan, (1 → 3)-β-glucan Alginates, fucoidans Rhodophyta (red algae) Cellulose, (1 → 4)-β-mannan, (1 → 4)-β-xylan, (1 → 3)-β-xylan Xylans, mannans, glucomannans, sulfated (1 → 3),(1 → 4)-β-glucan, (1 → 3),(1 → 4)-β-xylan Agars, carrageenans, porphyran Dinophyta Cellulose Table $1$: Major polymers found in the cell wall and extracellular matrix of different algae taxa. Enio Zanchetta et al., Algal Research, 56 (2021). https://doi.org/10.1016/j.algal.2021.102288. The percent composition of cellulose, hemicellulose and lignin for bioethanol production is important. Table $2$ shows the % composition of these polymers based on total dry weight. (Note: The values for Chlorophyta/Ulvophyceae are highlighted in yellow in both Tables 2 and 3 for convenience of comparision. Empty Cell Phylum/class Strain Cellulose [%] Hemicellulose [%] Lignin [%] Microalgae A mix of microalgae & cyanobacteria from the wastewater treatment plant 7.1 16.3 1.5 Chlorophyta/Trebouxiophyceae Chlorella vulgaris 10–47.5 n.d. n.d. Ochrophyta/Eustigmatophyceae Nannochloropsis gaditana 25 det. n.d. Macroalgae Chlorophyta/Ulvophyceae Cladophora glomerata 21.6 det. n.d. Chlorophyta/Ulvophyceae Ulva lactuca 12.4 6.0 16.6 n.d. 12.2 32.5 n.d. 9.8 1.5 Chlorophyta/Ulvophyceae Ulva prolifera 19.4 14.4 9.4 Chlorophyta/Ulvophyceae Ulva pertusa 6.7 16.8 n.d. Chlorophyta/Ulvophyceae Ulva sp. 40.7 7.1 7.9 Ochrophyta/Phaeophyceae Cystosphaera jacquinottii 4.6 6.1 19 Ochrophyta/Phaeophyceae Fucus vesiculosusLaminaria digitata 8 n.d. n.d. Rhodophyta/Florideophyceae Gelidium elegans 17.2 29.5 4.5 Tracheophyta/Monocots Posidonia oceanica 32.5 31.4–40.0 40.0 23.3 21.8–25.7 20.8 28.2 29.3–29.8 29.8 Tracheophyta/Monocots Posidonia australis 20.2 11.7 14.5 Table $2$: Cellulose, hemicellulose, and lignin content in total dry weight basis of algal feedstock. n.d.: not determined, det.: detected (either directly or indirectly). Zanchetta et al, ibid. Table $3$ below shows the % composition in the cell wall (instead of total biomass) for each of the three polymers. Empty Cell Phylum/class Strain Cellulose [%] Hemicellulose [%] Lignin [%] Microalgae Chlorophyta/Trebouxiophyceae Chlorella pyrenoidosa 15.4 31 n.d. Ochrophyta/Eustigmatophyceae Nannochloropsis gaditana 75 det. n.d. Charophyta/Zygnematophyceae Staurastrum sp. 72 4.0 1.2–5.6 Macroalgae Chlorophyta/Ulvophyceae Valonia ventricosa 75 det. abs. Chlorophyta/Ulvophyceae Cladophora rupestris 28.5 abs. n.d. Chlorophyta/Ulvophyceae Ulva lactuca 19 det. n.d. Chlorophyta/Ulvophyceae Chaetomorpha melagonium 41 det. n.d. Chlorophyta/Ulvophyceae Enteromorpha sp. 21 det. n.d. Ochrophyta/Phaeophyceae Fucus serratus 13.5 det. n.d. Ochrophyta/Phaeophyceae Laminaria digitata 20 det. n.d. Ochrophyta/Phaeophyceae Laminaria saccharina 18 det. n.d. Ochrophyta/Phaeophyceae Halidrys siliquosa 14 det. n.d. Ochrophyta/Phaeophyceae Himanthalia lorea 8 det. n.d. Rodophyta/Florideophyceae Ptilota plumosa 24 det. n.d. Rodophyta/Florideophyceae Rhodymenia palmata 7 det. n.d. Table $3$: Cellulose, hemicellulose, and lignin content in the cell wall of algal feedstock. n.d.: not determined, det.: detected (either directly or indirectly), abs.: absent. Zanchetta et al, ibid. Note the complete absence of lignin in the cell walls of macroalgae, but the previous table does show some presence in some macroalgae as a whole. Other cell wall-associated glycans for bioethanol production. Macroalgae offer an abundant source of other glycans, three of which we describe below. Each requires unique sets of enzymes to convert them to free glucose for fermentation and bioethanol production. Alginates These are among the most abundant biopolymers in the world and are the prime carbohydrate in brown seaweeds, where they can reach up to 40% of the dry mass. It is found in the cell walls of macroalgae. They are already used for food or other commercial uses. For example, they are used as thickening agents in food and are also used in the textile industries. The alginate (high in brown algae) is a linear polymer of 1,4-β-D-mannuronic acid (M) and 1,4 α-L-guluronic acid (G) monomers, with stretches (blocks) of pure G, pure M, and mixed MG. Representative structures are shown in Figure $6$ below. Figure $6$: Left - https://commons.wikimedia.org/wiki/F...e_skeletal.svg; Right - https://biocyc.org/META/NEW-IMAGE?ty...bject=ALGINATE Agar/Agarose Agar is abundant in red seaweed. Agar, used in labs, is a mixture of agarose and agaropectin. It acts as a support for the cell wall and detaches with boiling. Agarose is a linear polymer of a disaccharide repeating unit of D-galactose and 3,6-anhydro-L-galactopyranose linked by α(1,3) and β(1->4) glycosidic bonds. It has many uses in the laboratory (agarose for chromatography and electrophoresis while agar for cell culture). Its structure is shown in Figure $7$ below. Figure $7$: Agarose. Left - https://commons.wikimedia.org/wiki/F...e_polymere.svg; Right - https://biocyc.org/compound?orgid=ME...oses#tab=STRUC Carrageenans These are linear sulfated glycans found in the cell walls of red seaweeds (Rhodophyta ). They have widespread use in the food industry and are used for thickening. They are similar to glycosaminoglycans (GAGs). Their repeating motif is a disaccharide of an α(1,3)-linked D-galactose and a β(1,4)-linked D-galactose. As with GAGs, their degree of sulfation can vary from 15-40%. Different subtypes have a different number of sulfates in the repeating disaccharide unit. For example, the κ form has one, iota (ι) has two, and λ has 3. Representative structures are shown in Figure $8$ below. Figure $8$: Carrageenans. https://upload.wikimedia.org/wikiped...enan_types.svg Variants include the carrageenoses, in which the α(1,3)-linked galactose has a 3,6-anhydro bridge. The carrageenoses are often just called carrageenan. Their structure is shown in Figure $9$. Figure $9$: https://biocyc.org/compound?orgid=ME...enan#tab=STRUC The 6-anhydro-D-galactose is not fermentable, so microorganisms that can contain enzymes that degrade carrageenan must also be used. Bioethanol production Now we can put all of this together and make bioethanol from algae. Figure $10$ presents an overview of the entire process. Figure $10$: Overview of ethanol production from major algal carbohydrates. Qusai Al Abdallah et al., Front. Energy Res., 04 November 2016. https://doi.org/10.3389/fenrg.2016.00036. Creative Commons Attribution License (CC BY). Panel (A) shows algae store simple sugars in the form of simple and complex food reserves and as structural polysaccharides. Panel (B) shows how food reserves and structural polysaccharides are degraded into their basic monosaccharides and uronic acids. Panel (C) shows the final fermentation into ethanol using microbial wild-type strains or their genetically engineered counterpartsDEHU, 4-deoxy-l-erythro-5-hexoseulose uronic acid. Figure $11$ below shows a schematic diagram for converting the algae feedstocks to glucose using key glycan-cleaving enzymes. Note that the colors and shapes are not those recommended in the SNFG standard. Those standards show glucose and galactose as blue and yellow circles, respectively. Figure $11$: SCHEMATIC DIAGRAMS FOR THE ENZYMATIC HYDROLYSIS OF ALGAL POLYSACCHARIDES. (A) Starch, Floridean starch, and glycogen, (B) laminarin, chrysolaminarin, and paramylon, (C) cellulose, (D) agarose by β-agarases, (E) agarose by α-agarases, and (F) alginate. DP, degree of polymerization; NAB, neoagarobiose; AB, agarobiose; DEHU, 4-deoxy-l-erythro-5-hexoseulose uronic acid; KDG, 2-keto-3-deoxy-gluconate; M, β-d-mannuronate; G, α-l-guluronaten. Macroalgae offer enormous advantages for producing food, bioethanol, and intermediates for conversion into bioplastics. Ocean macroalgae don't require irrigation or pesticides, or fertilizers! Figure $12$ below shows rope and raft ocean cultivation of macroalgae. Figure $12$: Macroalgae cultivation systems. Godvin Sharmila V et al. Bioengineered, Volume 12, 2021. https://doi.org/10.1080/21655979.2021.1996019. Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) Figure $13$ below summarizes how macroalgae (as well as microalgae) can be used to produce a variety of biofuels in addition to bioethanol. The algae cell can be chemically processed to produce hydrogen, methane, and syngas for fuels or combusted (as in the use of wood and coal) to provide heat and electricity. Alternatively, the post-harvest algae can be fractionated to produce carbohydrates for alcohol production, straight vegetable oils (SVO - pure triacylglycerols) for fuel, and fatty acid esters (biodiesel). Of course, they can be used directly as food and for food products. Figure $13$: Macroalgal biofuel refinery. Godvin Sharmila V et al., ibid. Table $4$ below details the species and methods presently used to produce biofuel from macroalgae. Type of Biofuel Species Type Pretreatment methods or Conversion techniques Pretreatment or conversion technique conditions Biofuel Yield or production potential Biodiesel Ulva fasciata Catalytic transesterification Molar ratio of methanol: oil – 9:1; Time – 6 hours; Temperature – 80-100°C 88% Chaetomorpha antennina Transesterification Chloroform-ethanol solvent- 1:20 (w/v) 2.1 mL/10 gbiomass Gracilaria corticata Transesterification Hexane-ether solvent – 1:20 (w/v) 2 mL/10 gbiomass Ulva intestinalis Transesterification - 32.3 mg/g dw Enteromorpha compressa Base transesterification Base – 1% NaOH, Methanol–oil ratio – 9:1, Temperature – 60°CTime – 70 min 90.6% Bioethanol Chaetomorpha linum Wet oxidation method Temperature – 200°C 44 g ethanol/100 g glucan Saccharina japonica Low acid pretreatment Acid – 0.06% (w/w) sulfuric acidTemperature – 170°CTime – 15 min 6.65 g/L Saccharinajaponica Thermal acid hydrolysis Acid – 40 mM H2SO4Temperature – 121°CTime – 60 min 7.7 g/L Laminaria digitata Oven drying Temperature – 70°C,Time −72 h 13.6 ± 0.2 μL/g DS Ulva linza Mild acid hydrolysis Acid condition – 3% H2SO4 12.01% Biohydrogen Laminaria japonica Microwave Temperature – 160°C,Time – 30 min 15.8 mL/g TS Laminaria japonica Ultrasonic Sonication frequency – 20 kHz 23.56 ± 4.5 mL/g Laminaria digitate Hydrothermal Temperature – 140°CTime – 20 min 44.0 ± 1.2 mL/g VS Chaetomorpha antennina Surfactant-aided microwave pretreatment Microwave power – 0.36 kW Ammonium dodecyl sulfate – 0.0035 74.5 mL/g COD Ulva reticulate Microwave-H2O2 alkali pretreatment Microwave power – 0.36 kWH2O2 dosage – 24 mg H2O2/g biomasspH – 10 87.5 mL H2/g COD Biomethane Palmaria palmata Anaerobic digestion Semi-continuous anaerobic digestion 320 mL CH4/g VS Chaetomorpha antennina Ozone disperser pretreatment Disperser g force – 1,613 g, Treatment time – 30 min, Ozone dosage – 0.00049 g O3/g TS 0.20 g COD/g COD Chaetomorpha antennina Thermo-chemo disperser Disperser g-force of 1613 g, Temperature – 80°C, NaOH – 1 N, pH – 11 215 mL/g VS Laminaria digitata Heat Temperature – 104°C Dried biomass – 97.66 m3 CH4/t fresh biomass – 67.24 m3 CH4/t Laminaria digitata Oven drying Temperature – 70°C,Time −72 h 235.4 ± 14.1 mL/gVS Bio-oil Saccharina japonica Fixed bed reactor pyrolysis Temperature – 450°C 47% conversion Ulva lactuca Microwave pyrolysis Temperature – 500°C 18.4 wt.% Porphyra tenera Packed tube reactor pyrolysis Temperature – 500°C 47.4 wt.% Laminaria japonica Packed tube reactor pyrolysis Temperature – 500°C 45.8 wt.% Undaria pinnatifida Packed tube reactor pyrolysis Temperature – 500°C 37.5 wt.% Table $4$: Biofuel production from macroalgae. Godvin Sharmila V et al., ibid. We will discuss in another chapter the production of biodiesel from algae. Key Points - Beta version from Chat.openai 1. Algae can be used to produce bioethanol, a biofuel that is similar to traditional ethanol produced from corn or sugar cane. 2. Algae-based bioethanol production involves the cultivation of algae, followed by the extraction of sugars and the fermentation of these sugars to produce ethanol. 3. Algae have a high growth rate and high sugar content, making them a potential source of bioethanol. 4. The process of bioethanol production from algae is being commercialized, and research is ongoing to improve the efficiency and cost-effectiveness of the process. 5. Algae-based bioethanol production has several advantages over traditional bioethanol production methods, such as the ability to grow algae in non-arable land and the ability to produce bioethanol from CO2 and sunlight. 6. The algae-based bioethanol production process, is considered more sustainable and environmentally friendly than traditional bioethanol production methods, as it does not compete with food crops for resources and consumes CO2 during production.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.08%3ABiodiesel_Syngas_and_Bioaviation_Fuel.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Diesel fuel was used in 1900 in an engine designed by Rudolf Diesel. The fuel was peanut oil. That might have worked fine in 1900, but a century later, the demands for diesel fuel are met not by peanut oil, a legume, but by oil. Gasoline (petrol) consists of molecules containing 5-12 carbons compared to diesel at 12-20. Both are obtained through fractional distillation of oil (petroleum). Diesel has a higher boiling and melting point and releases more energy per liter (36.9 vs. 33.7 MJ) than gasoline. Regular gasoline contains about 17% n-alkanes, 32% branched alkanes, 5% cycloalkanes, 2% alkenes (olefins), and 30% aromatics. High octane gas can contain around n- and branched alkanes, with the rest from alkenes. Diesel fuel contains about 75% saturated hydrocarbons and 25% aromatics, including alkylbenzenes and napthalenes. In a diesel engine, ignition occurs on compression of the fuel and air mixture and doesn't require a spark. They use glow plugs which provide heat but not spark. Biodiesel Oils (triacylglycerol) produced from biomass can be converted to diesel fuel. In the following sections, we will discuss the synthesis of gases and liquid fuels from nonpetroleum sources such as biomass through the creation of the synthetic gases H2 and CO (collectively called syngas) and their condensation into liquid fuels using the Fischer-Tropsch reaction. This section will limit our discussion to using fats to create biodiesel. Triacylglycerols for biodiesel production can come from plants, animals, algae, and even waste oils from the food industry. Because biodiesel fuel is composed of carbon-based molecules (often fatty acid esters) with high melting and boiling point ranges which hamper its utility in cold climates, it is often blended with regular diesel (for example, a 20% blend called B20). Still, it can be used at 100% (B100). Biodiesel enriched in unsaturated fatty acids has lower melting points and fewer problems in cold weather. As will the production of bioethanol from lignocellulosic food stocks, biodiesel production has evolved through multiple generations, as shown in Figure $1$ below. Figure $1$: Palani Vignesh et al., Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles, 76 (2021) 6. DOI: https://doi.org/10.2516/ogst/2020088. Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0) Table $1$ below shows the advantages and disadvantages of each generation of biodiesel. Biodiesel generation Advantages Disadvantages 1st generation biodiesel 1. Low emission of greenhouse gas. 2. Easy and low-cost technology for conversion. 1. Yield is inadequate to meet the demand. 2. Causes food shortages. 3. High land footprint. 2nd generation biodiesel 1. Using food waste as a feedstock. 2. Use of non-agricultural land to grow a limited amount of crops. 1. Costly pre-treatment. 2. Sophisticated technology is used to transform biomass into fuel. 3rd generation biodiesel 1. Simple to grow algae. 2. No competition for the use of food crops; wastewater, and seawater can be used. 1. More resource usage for algae cultivation. 2. Low lipid level or biomass accumulation in algae. 4th generation biodiesel 1. High biomass and production yield. 2. More capability to eliminate CO2. 1. The cost of the bio-reactor is higher. 2. At the early stage of research, a high investment is needed. Table $1$: Advantages and disadvantages of various biodiesel generations. Palani Vignesh et al., ibid Triacylglycerol feedstocks for biodiesel are often converted to methyl or ethyl esters through an alcoholysis or transesterification reaction, as shown in Figure $2$ below. Figure $2$: Methanolysis/transestiferation of triacylglycerol to produce methyl-fatty acid ester for biofuels This reaction is simply a base-catalyzed cleavage of the ester bonds in the triacylglycerol. Alternatively, vegetable oil can be treated at high pressure and temperature in a hydrogenation reaction to produce a variant called "renewable diesel". Figure $3$ below shows feedstocks and processing for first-generation biodiesel production. Feedstock: Processing Method 1st Gen Processing Waste cooking oil: esterification/transesterification Food crops: extraction/transesterification Organic oils: hydrolysis, distillation Animal fat: hydrolysis, fermentation Bioethanol/butanol: chemical synthesis Figure $3$: Feedstocks and processing for first-generation biodiesel production. Palani Vignesh et al., ibid Figure $4$ below shows feedstocks and processing for second-generation biodiesel production Feedstock: Processing Method 2nd Gen Processing Cellulose: advanced fermentation Hemicellulose: hydrolysis Lignin: gasification Tannins: biological synthesis Vegetable oil/animal fats: hydrogenation Figure $4$ below shows feedstocks and processing for second-generation biodiesel production. Palani Vignesh et al., ibid Finally, Figure $5$ below shows the processing steps for 3rd and 4th generation biodiesel from algae. Figure $5$: Processing steps for 3rd and 4th generation biodiesel from algae. Palani Vignesh et al., ibid Let's consider 3rd and 4th generation biodiesel production using algae. First, algae can be used to produce many different products that can be used for biofuels and chemical feedstocks. These are reviewed in Figure $6$ below. Figure $6$: Macroalgal biofuel refinery; Godvin Sharmila V et al., Bioengineered. 2021 Dec;12(2):9216-9238. doi: 10.1080/21655979.2021.1996019. PMID: 34709971; PMCID: PMC8809944. Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) To maximize biodiesel production from algae (3rd and 4th generation), steps in the anabolic pathways for fatty acid and triacylglycerol synthesis could be genetically modified. Below is an overview of triacylglycerol synthesis in microalgae in Figure $7$. Figure $7$: Schematic illustration of TAG synthesis in microalgae. NADPH; Nicotinamide adenine dinucleotide phosphate; ATP, Adenosine Triphosphate; DHAP, dihydroxyacetone phosphate; G3P/G3pDH, Glyceraldehyde 3-phosphate / G3P dehydrogenase; GPAT, Glycerol 3-phosphate acyltransferase; PA/LPA/LPAAT/PAP, Phosphatidic acid/Lyso-PA/LPA acyltransferase/PA phosphatase; DAG/DGAT, di-Acylglycerol/ DAG acyltransferase; FAT, Fatty acyl-ACP thioesterase; ACP, Acyl-carrier protein; ER, Endoplasmic reticulum; PC, Phosphatidylcholine; PDAT, Phospholipid: DGAT; ACCase, Acetyl-CoA carboxylase; FAS, Fatty acid synthase; KAS, 3-ketoacyl-ACP synthase; FAD, Flavin adenine dinucleotide. Sharma PK, et al. Front. Mar. Sci. 5:382, 2018. doi: 10.3389/fmars.2018.00382. Creative Commons Attribution License (CC BY) Each step in the combined pathways are sites for optimization, as shown in Figure $8$ below. Figure $8$: Schematic illustration of different genetic engineering strategies applied in microalgae for biodiesel application. WT, Wild type cells; TR, Transgenic cells; TF, Transcription factor; TCA, Tricarboxylic acid cycle; NADH, Nicotinamide adenine dinucleotide; FA, Fatty acid; LD, Lipid droplet. Sharma PK, et al., ibid Life cycle analyses for 3rd generation biodiesel production indicate they would lead to a net decrease in CO2 emissions, but most appear incomplete in their analyses. Synthetic Gas (Syngas) As an alternative to using fossil fuels as an energy stock to power our vehicles and as feedstock for chemical production, what if biomass could produce "gasoline-like" fuel for these purposes? We have already discussed the production of bioethanol from 1st (plant starch), 2nd (lignocellulose) and 3rd (algal) generation feedstocks. It is routinely added to gasoline to upward of 15%. It is also found in E85 (or flex fuel), a gasoline blend containing 50% to 80% ethanol. Instead of producing ethanol through the fermentation of glucose, wood could be incompletely burned to create "synthetic gases" (CO and H2), called syngas, which could be further burned in vehicles to power them or converted through chemical processes (Fisher-Tropsh reaction) to liquid organic fuels. Indeed, when fossil fuels were lacking, wood was used to create syngas power vehicles. Up to a million cars were powered by wood gas in Europe during World War II. A bus powered by wood gas (syngas) generated by a gasifier on a trailer is shown in Figure $9$ below. Figure $9$: Bus power by wood gas c. 1943 in Leeds, England. By Ministry of Information Photo Division Photographer, Smith Norman? - http://media.iwm.org.uk/iwm/mediaLib.../large.jpgThis is photograph D 15675 from the collections of the Imperial War Museums., Public Domain, https://commons.wikimedia.org/w/inde...curid=24364067 The syngas emitted was cleaned up somewhat to remove tars and soot/ash particles by passing through charcoal before entering the vehicle through a tube. Tars with polycyclic aromatic hydrocarbons and methane could be lowered if wood or coals were first converted to char before use in a process called pyrolysis (heating to high temperatures in the relative absence of air). The gases, derived from the incomplete combustion of the wood, have CO and H2 in various proportions depending on the temperature of burning and the source (wood, coal). A general and very simplified reaction for the incomplete combustion reaction is: \text { Carbon feedstock }+\text { air } \rightarrow \mathrm{CO}+\mathrm{H}_2+\mathrm{CH}_4+\mathrm{CO}_2+\mathrm{H}_2 \mathrm{O}+\mathrm{N}_2 Of course, the reaction is not clean, and many organic side products are produced. The relative ratios of CO and H2 produced can be changed by the addition of water in a second reaction called the water gas shift (WGS) reaction (as water shifts the ratio of CO to H2): \mathrm{CO}+\mathrm{H}_2 \mathrm{O} \leftrightarrow \mathrm{CO}_2+\mathrm{H}_2 \quad(\Delta \mathrm{H}=-41.2 \mathrm{~kJ} / \mathrm{mol} .) If run in reverse (rWGS) and at high temperatures, the water shift reaction would be a way to capture carbon. The H2 could come from the electrolysis of water 2 \mathrm{H}_2 \mathrm{O}(\mathrm{I}) \rightarrow 2 \mathrm{H}_2(\mathrm{~g})+\mathrm{O}_2(\mathrm{~g}) \quad(\Delta \mathrm{H}=286 \mathrm{~kJ} / \mathrm{mol}) The electrocatalytic reduction of CO2 and H2O to produce syngas is shown in $10$. Figure $10$: Syngas Generation by electrocatalytic reduction of CO2 and H2O. (after Kang Cheng et al. Advances in Catalysis, 60 (2017). https://doi.org/10.1016/bs.acat.2017.09.003 The oxidation numbers of each element are shown in the diagram. The cathode acts as a catalyst for the reaction. The reaction requires a power source, so this process is greener if electricity derived from green energy sources (wind/solar) is used. Electrocatalytic Reduction of CO2 for small molecule fuel and chemical feedstocks Electrocatalytic CO2 reduction (ECR) offers the potential to capture CO2 before it is emitted into the air and convert it to small alkanes, alcohols, and acids for fuels (for example, methanol and ethanol) and chemical synthesis (for example, CO and formate). Again this would require a clean source of electricity to power these endergonic reactions. Table $2$ below shows the standard reduction potentials for a variety of half-reactions that could be coupled to form the main ECR products. Table $2$: Lei Fan et al. Science Advances. 21 Feb 2020, Vol 6, DOI: 10.1126/sciadv.aay3111Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). The reactions are complex and require CO2 to adsorb onto the electrocatalytic surface of the cathode. Here is a possible reaction pathway for the conversion of CO2 to methane: \mathrm{CO}_2 \rightarrow * \mathrm{COOH} \rightarrow * \mathrm{CO} \rightarrow * \mathrm{CHO} \rightarrow * \mathrm{CH}_2 \mathrm{O} \rightarrow * \mathrm{CH}_3 \mathrm{O} \rightarrow \mathrm{CH}_4+* \mathrm{O} \rightarrow \mathrm{CH}_4+* \mathrm{OH} \rightarrow \mathrm{CH}_4+\mathrm{H}_2 \mathrm{O} The catalysts employed are heterogeneous (i.e., not solution phase) and are typically organometallic transition metal structures. Possible reaction pathways to produce small CO2 electrochemical reduction products are shown in Figure $11$ below. Figure $11$: Possible reaction pathways to produce small CO2 electrochemical reduction products. Lei Fan et al., ibid This technology is early in development and will require the development of more robust catalysts and cells before it becomes commercially viable. The synthesis of syngas (typically described as a mixture of CO, CO2, and H2) is widespread now, is used in various processes, and is used to make many products, including hydrocarbons for fuel and oxygen-containing derivatives, including methanol and ethanol. CO and H2 react in the Fischer-Tropsch reaction (described below) to produce alkanes and alkenes. \mathrm{CO}+2 \mathrm{H}_2 \rightarrow\left(\mathrm{CH}_2\right)+\mathrm{H}_2 \mathrm{O} \quad \Delta \mathrm{H}=-165 \mathrm{~kJ} / \mathrm{mol} where CH2 is a methylene repeat in longer-chain alkanes. The CO2 produced in syngas can be somewhat selectively removed by adsorption onto a CaO catalyst, as shown in Figure $12$ below. Figure $12$: Adsorption configurations of CO2 on the surfaces of CaO-based catalysts at 650 °C: (a) CO2 adsorption on CaO (100) surface; (b) CO2 adsorption on 10 wt % Ni/CaO (100) surface. Green, red, gray, and purple balls represent Ca, O, C, and Ni atoms. Zhao, B. et al. Catalysts 20199, 757. https://doi.org/10.3390/catal9090757. Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/) The adsorption energies of CO, CH4, and H2 are so small that they have little effect on CO2 adsorption. At present, the easiest way to make syngas is to react natural gas (methane) and steam under very high temperatures (up to 1000oC) over a Ni catalyst (a process called steam reforming). This results in a high H2/CO ratio of about 3. It can be done with liquified natural gas using Ni-ZrO2-CeO2-La2O3 catalyst. This process inherently would do little to decrease CO2 emissions. A gasification method converts coal, lignocellulosic biomass, and waste to syngas, with an H2/CO ratio of <1 for coal and about 0.6-1 for biomass. The electrocatalytic method described above has an H2/CO ratio of 0-2, depending on the nature of the cathodic catalyst. Syngas can also be made by the partial oxidation of methane. For the subsequent reactions (Fisher-Tropsch), the optimal H2/CO is about 2. In the gasification of lignocellulosic biomass, the water gas shift (WGS) is used to increase the H2/CO ratio. This requires lots of water and also produces CO2 as a product. The water shift reaction use catalysts such as Co–Mo–Al2O3, Fe2O3–Cr2O3, and Cu–ZnO–Al2O3) for coal gasification. Syngas can be used to make small molecule energy and chemical feedstocks, such as ethanol (as well as liquid alkanes and alkenes). Given the many different types of products, it is often essential to selectively make and purify products for commercial use. One method for ethanol production is shown in Figure $13$ below. Figure $13$: Conversion of syngas to ethanol proceeds through a tandem mechanism via methanol and acetic acid intermediates using a variety of sequentially positioned catalysts. Kang, J., He, S., Zhou, W. et al. Single-pass transformation of syngas into ethanol with high selectivity by triple tandem catalysis. Nat Commun 11, 827 (2020). https://doi.org/10.1038/s41467-020-14672-8. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. H-MOR is a zeolite, which is a microporous, crystalline structure made of aluminosilicate. In a steam reforming reaction, bioethanol could be converted to CO and H2 as well, as shown in the equation below. \mathrm{C}_2 \mathrm{H}_5 \mathrm{OH}+\mathrm{H}_2 \mathrm{O}(+\text { heat }) \rightarrow 2 \mathrm{CO}+4 \mathrm{H}_2 Even with the ability to capture CO2 from the synthesis of syngas, a central issue of concern is whether the production of syngas and syngas-derived fuels and their use is associated with lower net CO2 emissions. A life cycle analysis would be necessary to determine that. Fischer-Tropsch Synthesis (FTS) of Fuels Fischer was head of the Kaiser-Wilhelm Institute for Coal Research in Germany at the start of World War I. Germany had abundant coal but needed oil for the war, so his efforts were redirected toward that end. Fischer and Tropsch developed the water shift reaction discussed above. They deployed new cobalt catalysts to produce oil which ultimately covered 25% of car fuel and 10% of the German military fuel needs in World War II. Large amounts were also made in South African during the Apartheid regime as well. The Fischer–Tropsch synthesis (FTS) is a polymerization-like reaction that is used to convert gas-to-liquids (GTL), coal-to-liquids (CTL), or biomass-to-liquids (BTL) fuels. It starts with syngas (H2 and CO) produced from the gasification of coal/biomass or steam reforming/partial oxidation of natural gas), with the ratios of H2/CO determined by the water-shift reaction. If coal or biomass is used, a cleanup of residual products with heteroatoms and metal ions is necessary. The clean syngas is then passed into a reactor containing the required catalyst to the FTS of fuels. These reaction systems are summarized in Figure $14$. Figure $14$: A simplified diagram of the Coal-to-Liquids (CTL), Gas-to-Liquids (GTL), and Biomass-to-Liquids (BTL) processes. Shafer, W.D.; Gnanamani, M.K.; Graham, U.M.; Yang, J.; Masuku, C.M.; Jacobs, G.; Davis, B.H. Fischer–Tropsch: Product Selectivity–The Fingerprint of Synthetic Fuels. Catalysts 20199, 259. https://doi.org/10.3390/catal9030259. Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). The FTS reaction is conducted at moderate temperatures and pressure to produce fuels (diesel and jet), lubricants, waxes, and chemical feedstocks. The main representative reactions are given by: N-alkane production: (2 \mathrm{n}+1) \mathrm{H}_2(\mathrm{~g})+\mathrm{nCO}(\mathrm{g}) \rightarrow \mathrm{C}_{\mathrm{n}} \mathrm{H}_{2 \mathrm{n}+2}+\mathrm{nH}_2 \mathrm{O} Alkene production: (2 \mathrm{n}) \mathrm{H}_2(\mathrm{~g})+\mathrm{nCO}(\mathrm{g}) \rightarrow \mathrm{C}_{\mathrm{n}} \mathrm{H}_{2 \mathrm{n}}+\mathrm{nH}_2 \mathrm{O} The products can have from one carbon (CH4) to over 70, depending on the catalyst, T, P, and H2/CO ratio. The FTS is a polymerization reaction, which starts with adsorption of the gas to the catalytic surface, followed by multiple cycles of free radical initiation, propagation, termination, desorption, and reabsorption. Since this book focuses on structure/function and reaction mechanisms, we would be remiss not to include at least a simplified mechanism for these complex reactions. Several mechanisms have been proposed. Since iron was/is used in the catalyst, and since iron can form iron carbides, Fisher proposed a carbide mechanism. A second enol mechanism has also been proposed. Both are shown in Figure $15$ below. Figure $15$: Proposed mechanisms for the Fischer-Tropsch reaction. Left: A proposed FTS route based on the carbide mechanism; Right: A proposed FTS route based on the enol mechanism. M is the metal surface. In the carbide mechanism, CO adsorbs on the metal catalyst and dissociates in C and O atoms that cover the surface. These are hydrogenated to form H2O and CH2 (methylene), and H2 adds as the reaction proceeds, as shown. In the enol mechanism, CO adsorbs without dissociation into atoms. It reacts on the surface to surface-bound H atoms (that arise from the dissociation of adsorbed H2) to form hydroxymethylene (M-CHOH). This enol grows on condensation with adjacent hydroxymethylenes. The rate-determining step is the hydrogenation of adsorbed CO. Another proposed mechanism, CO insertion, is shown in Figure $16$ Figure $16$: A proposed FTS route based on the CO insertion mechanism. CO in this model inserts into a bond from a hydrogen atom to a metal on the catalyst. The rate-limiting step here is the hydrogenation of CO to the CH2 methylene group. The assumed monomer for this mechanism is simply CO through its insertion into metal-carbon bonds. Generally, the FTS reaction catalyst has either cobalt or iron ions. The metal catalyst can also be doped with potassium and copper ions and bind silica and alumina. Iron is abundant and cheap and better promotes the water-gas-shift reaction, so it is best for FTS synthesis of fuels from coal and biomass since syngas derived from them have a lower H2/CO ratio. Bioaviation Fuel Now we can turn our attention to the production and analysis of bioaviation fuel, which is more like kerosene and diesel fuel than ethanol in its composition. As we saw for bioethanol productions, the feedstocks can be 1st, 2nd and 3rd generation, as shown in Table $3$ below. First-generation (1-G) Second-generation (2-G) Third-generation (3-G) Fourth-generation (4-G) • Oil-seed crops: camelina, oil palm, rapeseed, soybean, sunﬂower, salicornia • Sugar and starchy crops: corn, wheat, sugarcane, sugar beets • Oil-seed energy crops: jatropha, castor bean • Grass energy crops: switchgrass, miscanthus, Napier grass • Wood energy crops: poplar, willow, eucalyptus • Agricultural and forestry residues: corn stover, sugarcane bagasse, wood harvesting/processing residues • Food and municipal waste: used cooking oil, animal fats, biogenic fraction of municipal solid waste • Algae: microalgae • Genetically modiﬁed organisms • Non-biological feedstocks: CO2, renewable electricity, water Table $3$: Feedstocks for bio-aviation fuel production. Doliente SS, Narayan A, Tapia JFD, Samsatli NJ, Zhao Y and Samsatli S (2020) Bio-aviation Fuel: A Comprehensive Review and Analysis of the Supply Chain Components. Front. Energy Res. 8:110. doi: 10.3389/fenrg.2020.00110. Creative Commons Attribution License (CC BY). A typical jet aviation fuel (Jet A-1) contains n- and branched-alkanes (often called paraffins) and some alkenes (often called olefins) with 8-16 carbon atoms, cycloalkanes, and aromatics. A comparison of the components of Jet-A1 with a typical bioaviation fuel, Bio-Jet, is shown in Figure $17$ below. Figure $17$: Molecular-class compositions of (a) Jet A-1 and (b) bio-jet identified by the relative signal area percentage analysis of GC–MS. After Cheon Hyeon Cho, Hee Sun Han, Chae Hoon Sohn, and Jeong Sik Han. ACS Omega 2021 6 (40), 26646-26658. DOI: 10.1021/acsomega.1c04002. Bioaviation fuel can be made from feedstocks containing triacylglycerol or, most readily using feedstocks shown in Table 2 above to create syngas for use in the Fischer-Tropsch reaction. We've already discussed those reactions above. Instead, let's focus on whether bioaviation fuel, better termed sustainable aviation fuels (SAF), is good for our climate. This presupposes that battery-powered planes and jets are not scalable to our current environmental needs. Life Cycle Analysis - Jet Fuel from Grasses As expected, the US is the largest user of aviation fuel and causes 25% of aviation CO2 emissions, as much as all the greenhouse gases emitted through fuel use in Spain. It is estimated that the US will need around 30 billion gallons/per (BGY) of jet fuel in 2040. A biojet fuel industry based on cellulose as a feedstock could theoretically produce that amounts. If the industry costs are estimated at $123 billion, then the cost of the jet fuel would be$4.30/gal. About 60% of the costs would arise from the conversion of biomass to sustainable aviation fuel (SAF), which we described above. Those costs include the building of the biorefineries. Their cost would be distributed over their lifetimes of the plants. The fuel would be derived from syngas from the Fisher-Tropsch reaction, a reasonably mature technology. The costs would be much lower (closer to $1/gal) if the infrastructure costs were not included. These total costs are comparable to the price paid for regular jet fuel (around$2.2/gal in 2021). Consumer costs would not go up 2-fold since fuels are only part of the cost paid by passengers (15-25%) Consumers' current price for fossil fuel is much less than its actual cost. The present price/gal does not include external costs associated with the use of fossil fuels. These include climate change effects on infrastructure, agriculture, industry, etc., and on human health (mostly from negative health consequences and diseases exacerbated by fossil pollution). We all ultimately pay for these hidden costs resulting from a failed market for pricing fossil fuels. On top of this, the fossil fuel industry has been massively subsidized for decades. Different prices have been placed on carbon emissions from fossil fuels to resolve this error in the market. The carbon price is based on the number of tons of CO2 equivalent emitted ($US/ton CO2e). If a reasonable carbon price is added, biojet fuels made from lignocellulosic stocks through the Fisher-Tropsch reaction would be theoretically competitive with conventional jet fuels made from fossil fuels. The extra added cost for sustainable aviation fuel (SAF) compared to traditional aviation fuel at different prices placed on carbon placed on each are shown in Table $4$ below, assuming an average cost of$2.20/gal for traditional aviation fuel. Price on carbon ($US/t CO2e) SAF - Traditional Aviation Fuel ($/gal) 0 +$1.90/gal$50 +$0.60/gal$175 $0 If the cost of traditional jet fuel went to$3/gal, as it did in the US in March 2022, SAF and conventional jet fuel would cost the same if a price on carbon of $100/t CO2e were included for both. The actual "social" cost of carbon has been calculated (9/22) to be$175/ton CO2e. Other factors other than the cost of carbon should be included in these analyzes. These include the issue of sustainable land use to grow feedstocks for the SAF. A recent analysis shows that it would be possible to produce 30 billion gall/yr of cellulosic SAF by planting 23.2 Mha (Million hectares, about the size of the state of Wyoming) of marginal agricultural lands (about one-third of croplands and 2/3 noncrop lands) in the Midwest with miscanthus, with a net cost of $4.1/gal and assuming a carbon price of a$50/ t CO2e. Miscanthus is a rapidly growing tall perennial grass with a high yield that grows in moderate climates. The life cycle analysis included interactions among atmospheric, land surface, ecosystem, and economic systems. Miscanthus gigantheus is shown below in Figure $18$. Figure $17$:Miscanthus gigantheus. https://commons.wikimedia.org/wiki/F...us_Bestand.JPG. Creative Commons Attribution-Share Alike 3.0 Unported Figure $19$ shows some data from the study. Four different scenarios are offered, each represented by bar graphs. Figure $19$: Land availability and conversion by existing use. Excel data and graph from https://dataverse.harvard.edu/datase...910/DVN/VBFLI2CC0 1.0 Public Domain. Four scenarios (left to right) were used to produce 30 MG/yr of SAF using a carbon price of \$50/t CO2e. • M25: only 25% of the marginally useful land was used • M100-reg4: marginal land bases with the lowest hydrological and climatic risks • M100: all of the marginally included land was made available • Unrestricted Panel (a) shows that in each case, about 23.2 Mha of land was converted to growing miscanthus out of the available land. Pane (b) shows stacked bars showing the percentages of each type of land available and converted for each scenario. The last scenario shows that demand can be made with the lowest % conversion of the marginal lands now used for corn/soybeans and other crops. It would appear that the marginal croplands converted to SAF production would be the same lands diverted to bioethanol production. Nevertheless, it would appear that up to 76% of projected aviation fuel needs could be met by planting marginal cropland and noncrop land for cellulosic SAF production. The study found that using available lands in the Plains was not feasible. Key Points - Beta version from Chat.openai 1. Biodiesel, syngas and bioaviation fuel are alternative biofuels derived from biomass that can be used to reduce the dependence on fossil fuels. 2. Biodiesel is a liquid fuel that can be used in diesel engines, it is produced by chemically converting vegetable oils or animal fats into a fuel that can be used in place of diesel fuel. 3. Syngas (synthetic gas) is a mixture of hydrogen and carbon monoxide that can be produced from biomass through processes such as gasification. It can be used as a fuel for heat and power generation or further converted into chemicals and liquid fuels. 4. Bioaviation fuel is a form of biofuel that can be used in aviation, it is made from biomass such as algae or woody biomass, it can be blended with traditional jet fuel and can help reduce emissions from airplanes. 5. These biofuels have different advantages and limitations, and their production process is still in the research and development stage. 6. Biodiesel has the advantage of being able to be used in existing diesel engines with little or no modification, it reduces emissions and it is renewable. 7. Syngas has the advantage of being able to be converted into different chemicals and liquid fuels, it has a higher energy content than bioethanol and it can be used for power generation. 8. Bioaviation fuel has the advantage of reducing emissions from airplanes, it can reduce dependence on fossil fuels in the aviation industry and it can be renewable.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.09%3A__Biohydrogen_-_An_Introduction.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry H2 as a fuel Hydrogen gas would be ideal if it could be produced at scale, easily transported and stored, or produced at local sites on demand. The reaction for the "burning" of hydrogen shows that the only greenhouse gas emitted is H2O. 2 \mathrm{H}_2(\mathrm{~g})+\mathrm{O}_2(\mathrm{~g}) \rightarrow 2 \mathrm{H}_2 \mathrm{O}(\mathrm{g}) H2O comes and goes in our atmosphere in short timescales and does not continually build up, as does CO2 from burning fossil fuels. The standard heat of combustion (in kJ/g or kcal/kg) for H2 is far higher than any other fuel, as shown in Table $1$ below, making it an ideal fuel. Name Formula State -ΔHc° kJ/mol -ΔHc° kJ/g or MJ/kg -ΔHc° kcal/kg Ammonia NH3 gas 383 22.48 5369 Butane C4H10 gas 2878 49.50 11823 Carbon (graphite) C cry 394 32.81 7836 Ethanol C2H6O liq 1367 29.67 7086 Hydrogen H2 gas 286 141.58 33817 Methane CH4 gas 891 55.51 13259 methyl stearate (biodiesel) (CH3(CH2)16(CO)CH3 liq 1764 40 9560 Naphthalene C10H8 cry 5157 40.23 9609 Octane C8H18 liq 5470 47.87 11434 Propane C3H8 gas 2220 50.33 12021 wood (red oak) - solid - 14.8 3540 coal (lignite) - solid - 15 3590 coal (anthracite) - solid 27 4060 Table $1$: Energy values for various fuels. Data source: https://www.engineeringtoolbox.com/s...nt-d_1987.html We won't discuss large-scale H2 storage or transport, two fundamental engineering problems. Instead, we will focus on the production of "biohydrogen." Of course, the prefix "bio" can mean many things, including the production of H2 in syngas using cellulose as a feedstock, the electrolysis of water powered by solar/wind energy, and its production by hydrogenases, enzymes found in some microbes. The fuel industry uses different colors as descriptors of hydrogen based on how it is produced. They are shown in Table $2$. Color Method of production Green electrolysis of H2O using solar/wind to generate electricity (expensive at present) Blue steam reforming of natural gas (CH4) with the other product, CO2 captured and stored (CCS) Grey steam reforming of natural gas (CH4) without CO2 capture and storage Black (coal/oil) gasification to form syngas Pink (purple/red) electrolysis powered by nuclear energy, which does not emit CO2; heat emitted produces steam for blue/gray H2 production Turquoise methane pyrolysis (heat in the absence of O2) to form H2 and C Yellow electrolysis using solar power without conversion to electricity as the power source. White underground H2 released through fracking Table $2$: Different "colors" of hydrogen based on production methods Of course, H2 in syngas can be produced from biomass, as described in Chapter 32.8, but it is unclear if a hydrogen color has been assigned to it. At present, the important feedstocks for H2 production around the world are natural gas (48%), oil (30%), coal (18%), and electrolysis (4%) - mostly all fossil fuels. Methods of Production H2 production is also classified based on the chemical processes used to produce it. These processes include 1. Biological (use of live bacteria and algae cells) 2. Thermochemical: (gas and liquid fuel reforming, coal and biomass gasification), 3. Electrochemical (electrolytic): (photothermal, photoelectrolytic, and photobiological) We will organize this chapter section using these three processes. We will start with Biological (1), followed by Electrochemical/Electrolytic (3), and end with Thermochemical (2). They are summarized in Figure $1$. Figure $1$: The main pathways for H2 production based on biomass. M.G. Eloffy et al., Chemical Engineering Journal Advances, 12 (2022). https://doi.org/10.1016/j.ceja.2022.100410. Creative Commons license Biomass can be used as the feedstock for all of these methods, so the resulting product can be called biohydrogen. Of course, nonbiological sources of feedstocks are the predominant ones used in thermochemical and electrochemical methods as well, as we discussed in the previous chapter section. 2H+ ↔ H2: An Overview We will mostly discuss the production of H2 as a society energy source. For industry use, it can be used in fuel cells to power spacecraft and cars, as shown in the reaction below. \begin{aligned} & \mathrm{O}_2+4 \mathrm{H}^{+}+4 \mathrm{e}^{-} \longrightarrow 2 \mathrm{H}_2 \mathrm{O} \ & \mathrm{H}_2 \longrightarrow 4 \mathrm{H}^{+}+4 \mathrm{e}^{-} \end{aligned} In the next chapter section, we will discuss in great detail the hydrogenases that produce and use H2 in microbes so that this chapter will treat them very generally. However, we need to review the topic. Use of H2 as a source of electrons for reduction reactions. Each hydrogen in H2 has an oxidation number of 0. Each hydrogen can be oxidized to H+ (oxidation number +1) with the 2 electrons passed on to a substrate/cofactor or a sequential series of substrates with higher and higher standard reduction potentials (better oxidizing agents), leading to the formation of reduced products. H2 + (substrate)OX → 2H+ + (product)RED This general reaction is analogous to the mitochondrial electron transport chain, in which electrons are passed from a source (NADH) to oxidized forms of acceptors. The general reaction below shows each redox pair in the electron transport chain. NADH/NAD+ → FAD/FADH2 → UQ/UQH2 → Cyto COX/Cyto CRED → O2/H2 Some organisms have evolved to produce energy by the oxidation of H2. This reaction is analogous to those used by photosynthetic organisms to obtain energy through the oxidation of water. In photosystem II, oxygen in H2O (oxidation number -2) gets oxidized by the oxygen-evolving complex to produce O2 (oxidation number 0). Some redox pairs, starting with H2O/O2, are shown below for photosystem II. H2O/O2 → P680/P680* → (Plastoquione)OX/(Plastoquione)RED The first reaction is endergonic and requires an energy source photons. Use of H+ as a sink for electrons for oxidation reactions that produce H2. H+ has an oxidation number of +1. Hence it can be reduced to H2 (oxidation number of 0) as it gains electrons from substrates/cofactors, which get oxidized. This general reaction is shown below. 2H+ + (substrate/cofactor)RED → H2 + (substrate/cofactor)OX Many microorganisms can produce H2 through variants of photosynthesis or through fermentation, both of which provide the two electrons needed. E. Coi has four hydrogenases (Hyd 1, 2, 3, and 4). It forms H2 through two reactions catalyzed by: • formate (HCO2-) dehydrogenase (FDH): 2HCO2⇌ 2CO2 + 2H+ + 2e- • hydrogenase (H2ase): 2H+ + 2e- → H2 The C in formate has an oxidation number of +2 and is oxidized to CO2, in which the C has an oxidation number of +4. Nothing is simple: H2 is an indirect greenhouse gas. H2 itself is not a greenhouse gas as it doesn't have any bond vibrations that produce transient dipoles and hence does not absorb in the infrared region of the spectrum. Yet by affecting atmospheric levels of methane, a very potent greenhouse gas, as well as levels of ozone, it can lead to warming. It's not emission from the combustion of H2 but rather the leakage into the atmosphere of transported and stored H2 gas that is problematic. Most of the H2 that finds its way into the atmosphere diffuses into the soil and is taken up by bacteria. The rest reacts with hydroxy radicals (.OH) in the atmosphere, as shown in the reaction below. H2 + .OH → H2O + H. (atomic hydrogen) The reaction of .OH with H2 decreases the hydroxy radical's availability to react with the very potent greenhouse gas methane, CH4. That reaction is shown below. CH4 + .OH → .CH3 + H2O The methyl radical .CH3 reacts rapidly with oxygen to form the methylperoxy radical (CH3O2.). This eventually forms formaldehyde, a water-soluble molecule that is removed from the atmosphere on precipitation. Hence the reaction of H2 with .OH increases the half-live of CH4 in the atmosphere. .OH is a key molecule in the troposphere and is considered a methane "sink" that leads to the drawdown of methane. We discussed the extreme reactivity of .OH in Chapter section's 12.3 and 12.4. It's so reactive that its half-life is in the order of seconds. It is also at very low concentrations of less than 1 part per trillion. .OH is produced from ozone, O3, by the following reactions: O3 + hν (UV) → O2 + .O .O + H2O → 2 .OH The first reaction is a photolysis, and experiments during a solar eclipse have shown the production of .OH in the atmosphere shuts down! Dr. Paul Crutzen, Nobel Prize winner in Chemistry, described .OH as the "detergent of the atmosphere" since it can react with and oxidize many deleterious trace gases in the troposphere, making them more water-soluble, leading to their elimination from the atmosphere. A main reaction of .OH is carbon monoxide (CO). It also reacts with volatile organic compounds (VOCs) and NOx (NO + NO2), which are precursors of tropospheric ozone, a health hazard. Even though dioxygen, which comprises 20% of the atmosphere, is also an excellent oxidizing agent, it is kinetically slow to react. Very few gases are not oxidized by .OH. One set includes the refrigerant gases chlorofluorocarbons, which without oxidation by .OH enter the stratosphere, where they react with stratospheric ozone and reduce its protective effect against dangerous UV light. It does react with hydrochlorofluorocarbons (HCFCs). Figure $2$ below summarizes the adverse climatic effects of the oxidation of H2 in the atmosphere. Figure $2$: Effects of hydrogen oxidation on atmospheric greenhouse gas concentrations and warming. I. Ocko and Steven P. HamburgAtmos. Chem. Phys., 22, 9349–9368, 2022. https://doi.org/10.5194/acp-22-9349-2022. Creative Commons Attribution 4.0 License. Note in the central panel that H. (atomic hydrogen) can start a free radical change reaction to produce tropospheric ozone, O3, a pollutant that is not only a greenhouse gas but which also causes serious health consequences. The message is this: Care has to be taken to minimize methane and H2 leakage during their production and use as fuels. Biohydrogen from Microalgae We will focus most of our attention on the Biological (1) and Electrolytic (3) processes for producing biohydrogen from microalgae. The Biological processes (1) require hydrogenases for H2 production within cells. The Electrolytic (3) processes use microalgae as a feedstock to provide substrates that other microbes can ferment. These can be combined to increase production. Figure $3$ below summarizes the Biological (1) and Electrolytic (3) metabolic processes that can be used for microalgae H2 production. Figure $3$: Metabolic pathways of biohydrogen production by micro-algal biomass. modified from Ahmed SF et al. Front. Energy Res. 9:753878. doi: 10.3389/fenrg.2021.753878. Creative Commons Attribution License (CC BY). These are mainly classified into three categories: i) the photobiological process through which biohydrogen is produced via direct and indirect photolysis in the microalgae, ii) fermentation, and iii) the electrochemical process that comprises photoelectrochemical and electrolytic. BIOLOGICAL (1) - Biophotolysis (photosynthesis) This consists of two processes, Direct and Indirect Photolysis (photosynthesis). Both use light to drive the ultimate reduction of 2H+ to H2 using hydrogenase or nitrogenase. We will explore the details in the next chapter section. The biophotolysis process is divided into indirect (using electrons from substrates) and direct (using electrons from water). These processes are simplified in Figure $4$. Figure $4$: Schematic diagram for biological (biophotolysis) process. M.G. Eloffy et al. Direct Biophotolysis (photosynthesis) In direct biophotolysis (photosynthesis), water molecules are oxidized in Photosystem II, which contains the Oxygen Evolving Complex (OEC). This endergonic process is driven by light. The electrons lost from water are passed through Cytochrome b6f and Photosystem I to ferredoxin then NADP+, which gets reduced to NADPH (as discussed in Chapter 20). These reactions are illustrated in Figure $5$. Figure $5$: Light reaction of photosynthesis and associated standard reduction potentials In direct photolysis, electrons are passed directly from reduced ferredoxin to 2H+ in a reaction catalyzed by a hydrogenase, as shown in Figure $6$ below. Figure $6$: Metabolic hydrogen production pathways used by Chlamydomonas reinhartii.FDX: ferredoxin; H2ase: hydrogenase; NPQR: NADPH−plastoquinone oxidoreductase; PFR: pyruvate:ferredoxin oxidoreductase; PSI: photosystem I; PSII: photosystem II. Touloupakis, E.; Faraloni, C.; Silva Benavides, A.M.; Torzillo, G. Recent Achievements in Microalgal Photobiological Hydrogen Production. Energies 202114, 7170. https://doi.org/10.3390/en14217170. Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). The overall reaction is simplified in the equation below. 2 \mathrm{H}_2 \mathrm{O}+\text { Light } \rightarrow 2 \mathrm{H}_2+\mathrm{O}_2 One problem with direct photolysis is that O2 can damage hydrogenases. Again we will discuss the biochemistry of hydrogenases in great detail in the next chapter section. Indirect Biophotolysis This process bypasses the damaging effects of O2 on hydrogenase by being carried out in the absence of O2 using fermentation to provide electrons for the hydrogenase reduction of 2H+ to H2. Photosynthesis is required to make the carbohydrates necessary for fermentation. Glucose can then be oxidized anaerobically (in the dark to avoid O2 formation from photosynthesis) to form pyruvate through the glycolytic pathway. Pyruvate can then be oxidatively decarboxylated through the pyruvate:ferredoxin oxioreductase (PFR) as ferredoxin gets reduced. It then passes its electrons on through hydrogenase to produce H2. The pathway is illustrated in the top/right parts of the above figure and the reaction diagram in Figure $7$ below. Figure $7$: Model of fermentative pathways involved in dark anaerobic H2 production in C. reinhardtiiProteins are shown as ovals. Photosynthetic ferredoxin (PETF). Jens Noth et al., Journal of Biological Chemistry, 288 (2013). https://doi.org/10.1074/jbc.M112.429985. Creative Commons license. Glucose and some amino acids can be converted into pyruvate, a substrate for PFR1 in the single-cell algae C. reinhardtii. PFR1 converts pyruvate to acetyl-CoA and CO2 with the electrons used to reduce ferredoxin. The reduced FDX2 passes electrons through hydrogenase (HYDA1) to form H2 Another enzyme used to continue fermentation, pyruvate:formate lyase (PFL1), converts pyruvate to formate and acetyl-CoA, which can be metabolized further to acetate and ethanol. A shift to pyruvate oxidation to PFR1 occurs if PFL1 is mutated or long term anoxic conditions. The key enzyme, pyruvate:ferredoxin oxioreductase (PFR), uses thiamine pyrophosphate (TPP) as a cofactor for the oxidative decarboxylation of the α-keto acid pyruvate, as expected. Figure $8$ shows an interactive iCn3D model of the pyruvate ferredoxin oxidoreductase (PFOR) from Desulfocurvibacter africanus in anaerobic conditions (7PLM). Figure $8$: Pyruvate ferredoxin oxidoreductase (PFOR) from Desulfocurvibacter africanus in anaerobic conditions (7PLM). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...XRFMoVhaoRbW86 PFOR, abbreviated here, is a 267 kDa homodimer containing three [Fe4S4] clusters (spacefill) per monomer. Only one monomer is shown, and TPP is shown in sticks. Again indirect photolysis occurs in the absence of O2. Light illumination leads to only transient H2 synthesis. If sulfur is limited in the growth media of the algae, more sustained H2 production occurs, as the lack of sulfur reduces PSII activity. Hence H2 production can be maximized by depleting sulfur and minimizing O2 even in the presence of light. In the absence of O2, hydrogenase gene expression increases. Nutrient depletion also leads to the production of formate and acetyl-CoA through the enzyme pyruvate:formate lyase (PFL1). This is predominant in Chlamydomonas cells in the dark. The green microalgae C. reinhardtii makes most of its H(approximately 90%) using direct photolysis. Commercially, the production of H2 in indirect photolysis is carried out in a separate sealed bioreactor to avoid O2. Indirect photolysis is shown in the above figures. The reactions to this process are as follows : \begin{gathered} 12 \mathrm{H}_2 \mathrm{O}+6 \mathrm{CO}_2+\text { hν } \rightarrow \mathrm{C}_6 \mathrm{H}_{12} \mathrm{O}_6+6 \mathrm{O}_2 \ \mathrm{C}_6 \mathrm{H}_{12} \mathrm{O}_6+12 \mathrm{H}_2 \mathrm{O}+\text { hν } \rightarrow 12 \mathrm{H}_2+6 \mathrm{CO}_2 \end{gathered} BIOLOGICAL (1) - Fermentation We have just discussed fermentation processes within living microalgae cells. Now let's consider fermentation processes using nonliving biomass feedstocks supplied to microbes to produce H2. This offers a significant way to make biohydrogen. A schematic diagram for Biological (1) fermentation is shown below in Figure $9$: Figure $9$: Schematic diagram for biological (fermentation) process. M.G. Eloffy et al. Fermentation involves the decomposition of organic biomass to produce CO2 and H2. The fermentation process can be separated into photofermentation (light fermentation) and dark fermentation. Photofermentation Some photosynthetic bacteria and microalgae use Photofermentation to produce H2 from organic acids like acetic, butyric, lactic, and succinic acids. Oxidation of the acids produces CO2 as well as H+s and e- for H2 production. Electrons are transferred through photosystem I and eventually, believe it or not, nitrogenase. It is a fermentation process as the process is anoxic. Some photosynthetic bacteria, like the purple nonsulfur bacteria, a facultative anoxygenic phototroph, and some microalgae, can produce H2 using a simplified system that has only one photosystem and uses the enzyme nitrogenase to produce H2. The photosystem can not generate an oxidizing agent strong enough to oxidize H2O, but under anaerobic conditions, they can oxidize organic acids and even H2S to provide electrons from H2 production. These reactions are shown below in Figure $10$. Figure $10$: Photofermentative hydrogen production in PNSB. Deo, D., Ozgur, E., Eroglu, I., Gunduz, U., & Yucel, M. (2012). Photofermentative Hydrogen Production in Outdoor Conditions. Hydrogen Energy - Challenges and Perspectives. doi: 10.5772/50390. Creative Commons Attribution 3.0 License We studied nitrogenase in Chapter x.xx. The net reaction for the fixation of nitrogen is shown below. \mathrm{N}_2+8 \mathrm{H}^{+}+8 \mathrm{e}^{-}+16 \mathrm{ATP} \rightarrow 2 \mathrm{NH}_3+\mathrm{H}_2+16 \mathrm{ADP}+16 \mathrm{Pi} In this reaction, N2 is reduced as the N atoms go from a 0 oxidation state to +3 in NH3. The needed electrons are made from organic acids and fed into the system and eventually go to ferredoxin, which transfers them to protons. The ratio of N2 to H2 produced is 1:1, at the expense of 16ATPs per H2 produced. The ATP produced by the collapse of the produced proton gradient through FoF1ATPase powers the reaction. In the absence of N2, the net reaction becomes 2 \mathrm{H}^{+}+2 \mathrm{e}^{-}+4 \mathrm{ATP} \rightarrow \mathrm{H}_2+4 \mathrm{ADP}+4 \mathrm{Pi} The electrons are still fed into nitrogenase, but in the absence of the substrate N2, they are used to reduce 2H+ to H2. Note that only 4 ATPs are required per each H2 produced, a significant energy gain. ATP produced during photosynthesis would be used for anabolic biosynthesis contributing to biomass, so extra ATP is needed to support H2 synthesis past that needed for growth. As anabolism is a reductive process (compared to oxidative catabolism), adequate sources of electrons for reduction are required. Multiple pathways need electrons, including CO2 fixation, N2 fixation (with associated H2 production, and organic acids like polyhydroxbutyrate. The bacteria use photosynthesis and the Calvin cycle under photoautotrophic conditions to fix CO2. When external energy supplies from organic acids are present, the bacteria can become photoheterotropic. Under these conditions, the Calvin cycle is used to maintain redox balance. Dark Fermentation We studied this indirectly above section in our discussion of hydrogenases in microalgae. Hydrogenases are induced in dark conditions, and this pathway involved heterotrophic fermentation (anaerobic) in some bacteria and microalgae. Many microbial species are used. Industrial wastewater enriched in organic material can be used as a feedstock. Feedstock materials are hydrolyzed and subjected to fermentation, during which H2 can be produced. For example, pyruvate produced by glycolytic fermentation can be oxidatively decarboxylated to acetyl-CoA and CO2 by pyruvate:ferredoxin oxidoreductase with electrons passed on to ferredoxin and even through hydrogenase to form H2 (as we described above). Addition H2-produced steps after fermentation include acetogenesis and methanogenesis. These processes are illustrated in Figure $11$ below. Figure $11$: The steps involved in anaerobic digestion [9]. Rosa, P. R. F., & Silva, E. L. (2017). Review of Continuous Fermentative Hydrogen-Producing Bioreactors from Complex Wastewater. Frontiers in Bioenergy and Biofuels. doi: 10.5772/65548. Creative Commons Attribution 3.0 License Examples of acidogenic (formation of short carboxylic/fatty acid), acetogenic (formation of acetic acid), and methanogenic (formation of methane) reactions that produce (and a few that consume) H2 are shown in Table $3$ below. Acidogenic reactions C6H12O6 + 2H2O → 2CH3COOH + 2CO2 + 4H2 C6H12O6 + 2H2O→ CH3CH2CH2COOH + 2CO2 + 2H2 Acetogenic reactions CO2+ 4H2→ CH3COOH+ 2 H2O CH3CHOHCOOH + H2O → CH3COOH + CO2 + 2H2 CH3CH2OH + H2O →CH3COOH + 2H2 CH3CH2COOH + 2 H2O → CH3COOH + CO2 + 3 H2 CH3(CH2)2COOH + 2 H2O → 2 CH3COOH + 2H2 Methanogenic reactions 4 H2 + CO2→ CH4 + 2 H2O CH3COOH → CH4 + CO2 2CH3(CH2)2COOH + 2H2O + CO2→ 4CH3COOH + CH4 Table $3$: Example of acidogenic, acetogenic, and methanogenic reactions in dark fermentation. Adapted from Rosa, P. R. F., & Silva, E. L., ibid. We have described a few of the enzymes involved in acidogenic reactions above. Figure $12$ shows a summary of the steps in acidogenesis. Figure $12$: An overview of the metabolic pathways of acidogenesis. Dzulkarnain, E.L.N., Audu, J.O., Wan Dagang, W.R.Z. et al. Microbiomes of biohydrogen production from dark fermentation of industrial wastes: current trends, advanced tools and future outlook. Bioresour. Bioprocess. 9, 16 (2022). https://doi.org/10.1186/s40643-022-00504-8. http://creativecommons.org/licenses/by/4.0/. A more complex list and summary of dark fermentation reactions are shown in Figure $13$ below. Figure $13$: Key enzymes and dominant microbial taxa involved during anaerobic digestion of organic matter. Dzulkarnain, E.L.N.et al. Ibid. ELECTROCHEMICAL/ELECTROLYTIC (3) Two primary electrochemical/electrolytic methods for H2 production are photoelectrochemical and electrolytic, as shown in Figure $14$ below. djfkjdk Figure $14$: Schematic diagram for the electrochemical process. M.G. Eloffy et al. Electrolysis In a microbial electrolytic cell (MEC), microalgae/cyanobacteria use industrially- and metabolically-processed feedstocks to oxidize organic substrates (for example, acetic acid) to CO2. The released electrons move from the anode (where the oxidation occurs) to the cathode for H+ reduction to H2. An external voltage is applied to increase electron flow to the cathode to facilitate the process. This increases the production of H2 over and above that of just fermentation by microbes in the electrolytic cell. Cyanobacteria and a mix of green microalgae are used, as well as bacteria, which can use dark fermentation (i.e., combining the processes described above). Photoelectrochemical Microbial photoelectrochemical cells (PEC) use light-sensitive semiconductor electrodes for water electrolysis. A membrane separates the two electrodes so the protons can be reduced to form H2. for the separated by a membrane, 2. THERMOCHEMICAL from Biomass We have already explored thermochemical methods to produce syngas (H2 and CO) and further use in the Fishcer-Tropsch reaction to make small and large molecules for chemical feedstocks and fuels. We also discussed electrochemical methods to produce syngas and other small organic molecules like formate and ethanol from CO2. Figure $15$ shows a schematic diagram for thermochemical (gasification) processes to produce H2. Figure $15$: Schematic diagram for thermochemical (aqueous phase reforming) process. M.G. Eloffy et al. Key Points - Beta version from Chat.openai 1. Biohydrogen is a form of biofuel that is produced from biomass through a process known as biological hydrogen production. 2. Biological hydrogen production involves the use of microorganisms, such as bacteria and algae, to convert organic matter into hydrogen gas. 3. Biohydrogen has the potential to be a clean, renewable, and sustainable source of energy, as it produces only water when burned and does not produce greenhouse gases. 4. Photosynthetic microorganisms such as algae and cyanobacteria are the most promising organisms for biohydrogen production, they can convert water and CO2 into hydrogen and oxygen through the process of photosynthesis. 5. Fermentative microorganisms such as bacteria and fungi can also be used to produce biohydrogen, they can convert organic materials such as sugars and starches into hydrogen through the process of fermentation. 6. Biohydrogen can be produced through different processes, including dark fermentation, light-driven fermentation, and photo-biological hydrogen production. 7. Dark fermentation is the process of using microorganisms to ferment organic matter in the absence of light to produce hydrogen gas. 8. Light-driven fermentation is the process of using microorganisms to ferment organic matter in the presence of light to produce hydrogen gas. 9. Photo-biological hydrogen production is the process of using algae or photosynthetic bacteria to produce hydrogen gas through photosynthesis. 10. Biohydrogen production is still in the early stages of development and research is ongoing to improve the efficiency and cost-effectiveness of the process. 11. Biohydrogen production from algae is considered more sustainable and environmentally friendly than traditional hydrogen production methods, which are often based on fossil fuels. 12. Biohydrogen has the potential to significantly reduce carbon emissions and to help decarbonize various sectors, including transportation and energy.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.10%3A__Biohydrogen_-_Hydrogenases.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Introduction In the last section, we described different ways to produce H2 and the colors ascribed to them based on the environmental impacts. Many look to the production and use of H2 to provide energy without releasing CO2. H2 can be used in fuel cells to power spacecraft and cars, as shown in the reaction below. \begin{aligned} & \mathrm{O}_2+4 \mathrm{H}^{+}+4 \mathrm{e}^{-} \longrightarrow 2 \mathrm{H}_2 \mathrm{O} \ & \mathrm{H}_2 \longrightarrow 4 \mathrm{H}^{+}+4 \mathrm{e}^{-} \end{aligned} Cars are already available that run on H2 considered a zero-emission fuel. These fully electric cars use fuel cells powered by the oxidation of H2 to produce electrical energy. Figure $1$: https://afdc.energy.gov/vehicles/how...tric-cars-work Given the scale needed, most H2 is presently derived from the steam reformation of natural gas and the electrolysis of water. From a biochemical perspective, cells have evolved to make H2 and use H2 as an energy source. It's unlikely that direct microbial production of H2 would meet society's energy needs. The 2023 International Energy Agency (IEA) report, "Hydrogen patents for a clean energy future", doesn't mention direct microbial production. However, much can be learned by studying how hydrogenases (H2ases, Hyd), enzymes that make or use H2, work. (Don't confuse hydrogenases with dehydrogenases that directly use NAD+/NADH and FAD/FADH2) for redox reactions. Transition metal active site mimetics can be made as potential catalysts for more industrial-level production of H2. Although the reversible formation of H2 involves the most elemental particles in chemistry, H+ and e-, the biological reactions that produce and consume H2 are complex. Before we proceed, let's see how these reactions are similar to other biochemical reactions and pathways we have already discussed. Use of H2 as a source of electrons for reduction reactions. Each hydrogen in H2 has an oxidation number of 0. Each hydrogen can be oxidized to H+ (oxidation number +1) with the 2 electrons passed on to a substrate/cofactor or a sequential series of substrates with higher and higher standard reduction potentials (better oxidizing agents), leading to the formation of reduced products. H2 + (substrate)OX → 2H+ + (product)RED This general reaction is analogous to the mitochondrial electron transport chain, in which electrons are passed from a source (NADH) to oxidized forms of acceptors. The general reaction below shows each redox pair in the electron transport chain. NADH/NAD+ → FAD/FADH2 → UQ/UQH2 → Cyto COX/Cyto CRED → O2/H2 Some organisms have evolved to produce energy by the oxidation of H2. This is analogous to photosynthetic organisms obtaining energy through the oxidation of water. In photosystem II, oxygen in H2O (oxidation number -2) gets oxidized by the oxygen-evolving complex to produce O2 (oxidation number 0). Some redox pairs, starting with H2O/O2, are shown below for photosystem II. H2O/O2 → P680/P680* → (Plastoquione)OX/(Plastoquione)RED The first reaction is endergonic and requires as an energy source photons. Use of H+ as a sink for electrons for oxidation reactions that produce H2. H+ has an oxidation number of +1. Hence it can be reduced to H2 (oxidation number of 0) as it gains electrons from substrates/cofactors, which get oxidized. This general reaction is shown below. 2H+ + (substrate/cofactor)RED → H2 + (substrate/cofactor)OX Many microorganisms can produce H2 through variants of photosynthesis or through fermentation, both of which provide the two electrons needed. E. Coi has four hydrogenases (Hyd 1, 2, 3, and 4). It forms H2 through two reactions catalyzed by: • formate (HCO2-) dehydrogenase (FDH): 2HCO2⇌ 2CO2 + 2H+ + 2e- • hydrogenase (H2ase): 2H+ + 2e- → H2 The C in formate has an oxidation number of +2 and is oxidized to CO2, in which the C has an oxidation number of +4. The formate hydrogenlyase (FHL) complex contains both the formate dehydrogenase (FDH) and a hydrogenase (H2ase) and reversibly interconverts HCO2 and H2. The E. coli FHL-1 complex, which makes H2 using fermentation, is shown below in Figure $2$. The complex can be immobilized on a Macro-mesoporous inverse opal (IO) indium tin oxide (ITO) electrode (IO-IPO) or ITO nanoparticles (NP), which can relay electrons. Figure $2$: Katarzyna P. Sokol et al. J. Am. Chem. Soc. 2019, 141, 44, 17498–17502. https://doi.org/10.1021/jacs.9b09575. CC-BY license Panel (a) shows the biological E. coli FHL-1 complex. FdhF, [Mo]-FDH; B/F/G, Fe–S cluster-containing proteins; HycE, [NiFe]-H2ase; HycD/C, membrane proteins. (17) Panel (b) shows a IO-ITO\|FDH\|\|IO-ITO\|H2ase cell: IO-ITO\|FDH wired to IO-ITO\|H2ase electrode. Panel (c) shows a FDH–ITO–H2ase nanoparticle (NP) system with enzymes immobilized onto ITO NP in solution. Species size not drawn to scale. All you need to synthesize H2 are 2 protons and 2 electrons (potentially derived from photosynthesis). Let's take a deeper look at the hydrogenase that catalyzes H2 production. Hydrogenases (H2ases) Hydrogenases catalyze the reversible conversion of 2H+ → H2. A hydrogenase database, HydDB, a web tool for hydrogenase classification and analysis of sequence, shows their high diversity and metabolic roles. There are three classes of hydrogenases, the Ni-Fe (most abundant, primarily for H2 conversion to 2H+), the Fe-Fe (highest kcat for H2 production), and the single Fe hydrogenases, as shown in Table $2$ below. We won't discuss the single Fe hydrogenases. CLASSES AND SUBCLASSES OF HYDROGENASES [NiFe] Group 1: Respiratory H2-uptake [NiFe]-hydrogenases 1a Periplasmic Electron input for sulfate, metal and organohalide respiration. [NiFeSe] variants. 1b Prototypical Electron input for sulfate, fumarate, metal and nitrate respiration. 1c Hyb-type Electron input for fumarate, nitrate and sulfate respiration. Physiologically reversible. 1d Oxygen-tolerant Electron input for aerobic respiration and oxygen-tolerant anaerobic respiration. 1e Isp-type Electron input primarily for sulfur respiration. Physiologically reversible. 1f Oxygen-protecting Unresolved role. May liberate electrons to reduce reactive oxygen species. 1g Crenarchaeota-type Electron input primarily for sulfur respiration. 1h Actinobacteria-type Electron input for aerobic respiration. Scavenges electrons from atmospheric H2. 1i Coriobacteria-type (putative) Undetermined role. May liberate electrons for anaerobic respiration. 1j Archaeoglobin-type Electron input for sulfate respirationπ. 1k Methanophenazine-reducing Electron input for methanogenic heterodisulfide respiration. [NiFe] Group 2: Alternative and sensory uptake [NiFe]-hydrogenases 2a Cyanobacteria-type Electron input for aerobic respiration. Recycles H2 produced by other cellular processes. 2b Histidine kinase-linked H2 sensing. Activates two-component system controlling hydrogenase expression. 2c Diguanylate cyclase-linked (putative) Undetermined role. May sense H2 and regulate processes through cyclic di-GMP production. 2d Aquificae-type Unresolved role. May generate reductant for carbon fixation or have a regulatory role. 2e Metallosphaera-type (putative) Undetermined role. May liberate electrons primarily for aerobic respiration. [NiFe] Group 3: Cofactor-coupled bidirectional [NiFe]-hydrogenases 3a F420-coupled Couples oxidation of H2 to the reduction of F420 during methanogenesis. Physiologically reversible. [NiFeSe] variants. 3b NADP-coupled Couples oxidation of NADPH to the evolution of H2. Physiologically reversible. May have sulfhydrogenase activity. 3c Heterodisulfide reductase-linked Bifurcates electrons from H2 to heterodisulfide and Fdox in methanogens. [NiFeSe] variants. 3d NAD-coupled Interconverts electrons between H2 and NAD depending on cellular redox state. [NiFe] Group 4: Respiratory H2-evolving [NiFe]-hydrogenases 4a Formate hydrogenlyase Couples formate oxidation to fermentative H2 evolution. May be H+-translocating. 4b Formate-respiring Respires formate or carbon monoxide using H+ as electron acceptor. Na+-translocating via Mrp. 4c Carbon monoxide-respiring Respires carbon monoxide using H+ as electron acceptor. H+-translocating. 4d Ferredoxin-coupled, Mrp-linked Couples Fdred oxidation to H+ reduction. Na+-translocating via Mrp complex. 4e Ferredoxin-coupled, Ech-type Couples Fdred oxidation to H+ reduction. Physiologically reversible via H+/Na+ translocation. 4f Formate-coupled (putative) Undetermined role. May couple formate oxidation to H2 evolution and H+ translocation. 4g Ferredoxin-coupled (putative) Undetermined role. May couple Fdred oxidation to proton reduction and H+/Na+ translocation. 4h Ferredoxin-coupled, Eha-type Couples Fdred oxidation to H+ reduction in anaplerotic processes. H+/Na+-translocating. 4i Ferredoxin-coupled, Ehb-type Couples Fdred oxidation to H+ reduction in anabolic processes. H+/Na+-translocating. [FeFe] Hydrogenases A1 Prototypical Couples ferredoxin oxidation to fermentative or photobiological H2 evolution. A2 Glutamate synthase-linked (putative) Undetermined role. May couple H2 oxidation to NAD reduction, generating reductant for glutamate synthase. A3 Bifurcating Reversibly bifurcates electrons from H2 to NAD and Fdox in anaerobic bacteria. A4 Formate dehydrogenase-linked Couples formate oxidation to H2 evolution. Some bifurcate electrons from H2 to ferredoxin and NADP. B Colonic-type (putative) Undetermined role. May couple Fdred oxidation to fermentative H2 evolution. C1 Histidine kinase-linked (putative) Undetermined role. May sense H2 and regulate processes via histidine kinases. C2 Chemotactic (putative) Undetermined role. May sense H2 and regulate processes via methyl-accepting chemotaxis proteins. C3 Phosphatase-linked (putative) Undetermined role. May sense H2 and regulate processes via serine/threonine phosphatases. [Fe] Hydrogenases All Methenyl-H4MPT dehydrogenase Reversibly couples H2 oxidation to 5,10-methenyltetrahydromethanopterin reduction. Dan Søndergaard et al., Scientific Reports volume 6, Article number: 34212 (2016). Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. The three main types have different main functions in general. The Ni-Fe, Fe-Fe, and Fe H2ases generally oxidize H2, produce H2 and promote H- (hydride) transfer, respectively, as shown in Figure $3$ below. Figure $3$: The active site structures of [NiFe] H2ases that mainly catalyze Hoxidation reactions, [FeFe] H2ases that mainly catalyze H2 evolution reactions, and [Fe] H2ases that catalyze H− transfer to the substrate via heterolytic H2cleavage. X, possible H2 active sites; Y, methenyltetrahydromethanopterin; GMP, guanosine monophosphate. Seiji Ogo et al., Science Advances. (2020). DOI: 10.1126/sciadv.aaz81. Creative Commons Attribution-NonCommercial License 4.0 (CC BY-NC). Much effort has been devoted to making transition state analogs of the active site to act as catalysts for H2 production for fuel cells. Transition metal catalysts that mimic the structures and activities of the three hydrogenases have been made. Three specific ones are shown below in Figure $4$. Figure $4$: The differing reactivity of the three isomers. Y′, methylene blue [MB]+.Seiji Ogo et al., ibid. The ligand containing P and PH is bis(diphenylphosphino)ethane. First, we will explore the Ni-Fe hydrogenases. Ni-Fe H2ases (Hyd): We'll discuss two examples of Ni-Fe H2ases Group 1a periplasmic (membrane-bound) hydrogenases - MBH These are used in fuel cells and H2-producing devices since they can adhere to surfaces that can be useful heterogeneous (not in solution) catalysts. Some also are damaged by O2. The enzyme consists of a large subunit) found in the periplasm and small subunit, which anchors the protein in the plasma membrane of bacteria. . This enzyme oxidizes H2: H2 → 2H++2e. The electrons enter the bacterial respiratory chain through quinones. The transmembrane part of the small subunit binds cytochrome b, which is involved in electron transfer with the quinones, as we saw in Complex II of mitochondrial electron transport. Some soil bacteria (like Ralstonia eutropha,) can use H2 as their sole energy source. The orientation of a NiFe MBH within a bacterial cell is shown in Figure $5$ below. Figure $5$: The orientation of a NiFe MBH within a bacterial cell. Lindsey A. Flanagan* and Alison Parkin 2016 Feb 15; 44(1): 315–328 (2016). doi: 10.1042/BST20150201 Creative Commons Attribution Licence 3.0. Panel (A) shows a cartoon depiction of how a NiFe MBH is located within the cytoplasmic membrane, with white boxes representing the redox active metal centers and blue, orange and purple blocks indicating the large, small and cytochrome subunits, respectively. Panel (B) shows how the E. coli hydrogenase-1 large (blue ribbon), small (orange ribbon) and cytochrome (purple ribbon) subunits can interact. Figure $6$ shows an interactive iCn3D model of the O2-Tolerant Membrane-Bound Hydrogenase 1 from Escherichia coli in Complex with Its Cognate Cytochrome b (4GD3). The same color coding is used for the subunits as in the above figures. Figure $6$: O2-Tolerant Membrane-Bound Hydrogenase 1 from Escherichia coli in Complex with Its Cognate Cytochrome b (4GD3). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...9p4CSQRDPM8nL7 The large and small chains of hydrogenase are shown in blue and orange, respectively, while cytochrome b is sown in magenta. Cofactors and key metals are shown in spacefill. F4S is the Fe4-S3 cluster, SF4 is the iron-sulfur cluster, HEM is hemoglobin, FCO is carbonmonoxide-(dicyano) Fe, and Ni is nickel. The biological (functional) unit consists of two heterodimers. Under low O2 levels, one dimer can reactivate the other if exposed to O2. The enzyme is found in the highest concentration during anaerobic fermentation. Remember that E. Coli is a facultative anaerobe and can shift its metabolic pathways to fit conditions. Perhaps its primary role is to reduce O2 to water and protect enzymes sensitive to it. The function of Cytochrome b may be mostly to anchor the dimeric H2ase in the membrane. A bifurcating Ni-Fe H2ase These enzymes are more complicated. They oxidize H2 and move the two electrons through a complicated path that bifurcates electron flow to different substrates/cofactors. They move an electron to a low-potential (i.e. not a great oxidizing agent), high-energy species, which gets reduced in an endergonic process. The other electron simultaneously moves to a high-potential (i.e., great oxidizing agent), low-energy species that also gets reduced in an exergonic process. The overall electron transfer is thermodynamically favorable. An example might prove helpful. NADH (E0' = -280 mV, higher potential) can reduce the protein ferredoxin (E0' = -500 mV, lower potential), which can then pass its electrons in other reactions, including the formation of H2, CH4, and NH3. ATP is not required. Bifurcating enzymes - We've seen one before! Four classes of bifurcating enzymes that use FAD/FADH2 or FMN/FMNH2 are known. They are optimal since they can participate in either 1 or 2 electron transfers. We will see an example of a Fe-Fe H2ase further below. In electron transport, we encountered an electron bifurcating complex in the Q-cycle of Complex III. Reduced ubiquinone (UQH2, or ubiquinol) is oxidized and the two lost electrons bifurcated to cytochrome C in a high potential pathway and to UQ to reform UQH2, as shown in Figure $7$ below. Figure $7$: Electron bifurcation in Complex III When ubiquinone, UQH "Electron bifurcation was first described in the Q-cycle of the respiratory complex III where the two electrons originating from ubiquinol oxidation are bifurcated via a high-potential pathway to cytochrome c, and via a low-potential path to reduce ubiquinone to ubiquinol. One example of a bifurcating Ni-Fe H2ase is the NiFe-HydABCSL protein from the bacteria A. mobile. The general structure of the pentameric form of the functional decamer is shown in Figure $8$ below. Figure $8$: Structure of the A. mobile NiFe-HydABCSL pentamer. XIANG FENG et al. SCIENCE ADVANCES. 2022. DOI: 10.1126/sciadv.abm7546. Creative Commons Attribution License 4.0 (CC BY). https://creativecommons.org/licenses/by/4.0/ The five subunits are called HydA (Hyd = hydrogenase), HydB, HydC, HydL (large subunit), and HydS (small subunit). Pane (A) shows the domain structure of the five subunits. The NiFe-HydB NTD and CTDs are partially flexible, as indicated by dashed outlines. Panel (B) shows the subunit organization of the NiFe-Hyd complex and their associated cofactors. NiFe-HydABCSL hydrogenase can reversibly oxidize H2 with the two electrons reducing ferredoxin in an endergonic process and reducing NAD in an exergonic process. FMN is surrounded by an FeS cluster and appears to be the center of bifurcation. The reaction is as follows: • The HydL oxidizes H2 with two electrons passing through the FeS centers in HydA to HybB. • The electrons are passed to FMN, where the bound NAD gets reduced. Figure $9$ jdkfjdkjfdkjff Figure $9$: Proposed mechanism of electron bifurcation/confurcation in A. mobile NiFe-HydABCSL. (A) Overall electron transfer pathway, highlighting the three branches of the electron transfer path. The mid-potential path is a black dashed line, the exergonic path is a blue dashed line, and the endergonic path is a red dashed line. (B) Conformational changes in the HydBC bifurcation core from the electron bifurcation state (BR state) to the electron transduction state (PB state). Figure $10$ shows an interactive iCn3D model of the electron bifurcating Ni-Fe hydrogenase complex HydABCSL in FMN/NAD(H) bound state 7T30 Figure $10$: Electron bifurcating Ni-Fe hydrogenase complex HydABCSL in FMN-NAD(H) bound state (7T30). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...G9nKGwqvuuh6G7 Figure $11$ shows an interactive iCn3D model of the cofactors in the electron bifurcating Ni-Fe hydrogenase complex HydABCSL in FMN/NAD(H) bound state 7T30 Figure $11$: Cofactors in the electron bifurcating Ni-Fe hydrogenase complex HydABCSL in FMN-NAD(H) bound state (7T30). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...hHuzaWUDYCMnAA. Zoom into the Ni-Fe center catalytic site. The ligands that form coordinate covalent bonds to the Fe are called FCC, or carbon monoxide-(dicyano)-Fe Figure $12$ below. There are also bridging sulfurs between Fe and Ni. Figure $12$: Carbonmonoxo-dicyano-Fe and is shown in detail in Fe-Fe hydrogenases These enzymes catalyze a variety of reactions as illustrated in Figure $13$ below. Figure $13$: [FeFe]-hydrogenases phylogeny and known functions. Morra S. Front Microbiol. 2022 Mar 2;13:853626. doi: 10.3389/fmicb.2022.853626. PMID: 35308355; PMCID: PMC8924675. Creative Commons Attribution License (CC BY) A phylogenetic tree shows the phylogeny of [FeFe]-hydrogenase sequences from public databases, as previously proposed. Enzymes that have been experimentally characterized are indicated on the tree to show their relative position. The proposed physiological function of each enzyme is also presented, where known. Hyd, hydrogenase subunit; FdhF, formate dehydrogenase subunit; Fdrex/ox, reduced/oxidized ferredoxin; NADH/NAD+, reduced/oxidized nicotinamide adenine dinucleotide. They are found in prokaryotic and eukaryotic microorganisms, but not in Archaea. These are the most active for H2 production with a kcat around 10,000 s-1. They contain a Fe2S2 cluster with CO and CN ligands forming bonds to the iron with the iron ions bridged by a -SCH2-NH-CH2S- (aza-dithiolate). A cysteine links the Fe2S2 to a Fe4S4 cluster. These two are called the H-cluster (or [Fe]H. Within this class are a soluble, monomeric cytoplasmic form, a heterodimeric periplasmic form, and a soluble, monomeric chloroplastic form. This one has a ferredoxin, connecting it to the electron transport chain in photosynthesis. Some in this group using both NADH and ferredoxin are called bifurcating types, as they send two electrons from a donor in two different directions. More on this later. They contain multiple FeS clusters. The H-cluster consists of a Fe2S2 linked to a Fe4S4 cluster (cubane-like) by a cysteine. The Fe2S2 group has CO and CN ligands, and the two Fe ions of Fe2S2 unit are coordinated by an azadithiolato ligand, as shown below in Figure $14$. Figure $14$: Chemical structure of the H-cluster, which is the active site of the [FeFe] hydrogenase enzyme. Rakesh C. Puthenkalathil et al., Phys. Chem. Chem. Phys., 2020, 22, 10447. https://pubs.rsc.org/fa/content/arti.../cp/c9cp06770a. This article is licensed under a Creative Commons Attribution 3.0 Unported Licence The Fe in the [2Fe2S] cluster is linked to the cubane [4Fe-4S] and has six ligands, so it is saturated. The other Fe has an extra coordination site denoted by X, which can bind H+ or H2. The cluster is buried in a hydrophobic catalytic site which helps restrict O2 access. As we did for the Fe-Ni H2ases, we will study two examples of Fe-Fe H2ases. Fe-Fe hydrogenase (CpI) from Clostridium pasteurianum Figure $15$ shows an interactive iCn3D model of the H-Cluster (HC1) of Fe-Fe hydrogenase (CpI) from Clostridium pasteurianum (1FEH). Figure $15$: H-Cluster (HC1) of Fe-Fe hydrogenase (CpI) from Clostridium pasteurianum (1FEH). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...TksW8FGn7crh39 In this model, the CN ligands are all displayed as CO. The sulfurs are shown in green. Hover over the atoms/ions to identify them. (In iCn3D, choose, Select, Select on 3D, atom). The [4Fe-4S] subcluster forms coordinate covalent bonds with four cysteines (300, 355, 499, and 503) with one cysteine (503) forming a bridge to the [2Fe] cluster. The Fe ions in that cluster have an octahedral arrangement of ligands surrounding them. One of the ligands is water (no connecting C atom). Figure $16$ shows an interactive iCn3D model of the Fe-Fe hydrogenase (CpI) from Clostridium pasteurianum (1FEH) Figure $16$: Fe-Fe hydrogenase (CpI) from Clostridium pasteurianum (1FEH). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...QJsoZ8zbvkpDc7. As we mentioned above, the net reaction is: 2H+ + (substrate/cofactor)RED → H2 + (substrate/cofactor)OX Many microorganisms can produce H2 through variants of photosynthesis or through fermentation, both of which provide the two electrons needed. E. Coi has four different hydrogenases, (Hyd 1, 2, 3 and 4). It forms H2 through two reactions catalyzed by: • formate (HCO2-) dehydrogenase: 2HCO2⇌ 2CO2 + 2H+ + 2e- • hydrogenase 3: 2H+ + 2e- → H2 Figure $17$ below shows a reaction scheme for the production of H2 linked to photosystem I in the chloroplast of microalgae under anaerobic conditions. It starts with absorption of a photon by P700, which in the excited state transfers an electron to a 4Fe4S cluster in ferredoxin, which then passes an electron to the HC-cluster and then onto H+. Figure $17$: Schematic representation of electron flow from Photosystem I to an [FeFe]-hydrogenase via a ferredoxin redox mediator (Photosystem I). JuanAmaro-Gahete et al.,Coordination Chemistry Reviews. 448, December 2021. https://doi.org/10.1016/j.ccr.2021.214172. Creative Commons CC-BY A possible mechanism for the formation of H2 in the H clusteris shown below in Figure $18$ below. Figure $18$: Proposed mechanistic cycle for hydrogen evolution in the H cluster by [FeFe]-hydrogenase adapted from Lubitz et al. JuanAmaro-Gahete et al., ibid Start at the top left which shows the resting oxidized state. In the enzyme's most oxidized resting system (Hox), the [4Fe4S] cubane is in a 2 + oxidation state while the catalytic subcluster [2Fe] is a mixed-valence FeIFeII state. The first one-electron reduction results in the formation of the Hred state, where the [4Fe4S] subcluster is reduced to a 1 + oxidation state. Protonation of the N of aza-propane-1,3-dithiolate ligand (adt-N) triggers an intramolecular charge shift to form HredH+ in which the [4Fe4S] cubane is in the 2 + state and the [2Fe] subsite reduced to a homovalent FeIFeI state. Subsequent one-electron reduction of the subcluster [4Fe4S] gives rise to the “super-reduced” state HsredH+. In the next step of the catalytic cycle, an intermediate hydride state [Hhyd] is formed by an intramolecular proton shift from the adt-N to the distal iron Fd. This process is coupled to an electron rearrangement in the [2Fe] subsite, leading to a formal FeIIFeII oxidation state. Addition of a second proton coupled to another charge shift from the reduced [4Fe4S] to the [2Fe] subsite either in one or two discrete steps gives rise to [HhydH+] that is characterized by a formal FeIFeII oxidation state. At this point, there is an equilibrium between the HhydH+ and Hox[H2] in which the hydride and the proton are combined into a hydrogen molecule at the distal iron of the system. The catalytic cycle is closed by H2 release, returning to the initial Hox configuration. A bifurcating [Fe-Fe] hydrogenase from Thermotoga maritima (HydABC) This enzyme, functionally a heterododecamer, uses NADH as a source of electrons, which passes electrons to FMN, the bifurcation site, with an electron going to oxidized ferredoxin (Fdox) and another to H+s for reduction to FDRED and H2. The enzyme consists of a dimer of a trimer of subunits HydA, HydB, and HydC, with dimer (HydABC)2 interacting with another (HydABC)2 to form a heterododecamer, with both halves acting independently. The two trimers (HydABC) in the dimer (HydABC)2 are connected by a [4Fe–4S] cluster. A flexible loop in the B and A chain has a "closed" and "open" bridge conformation with a nearby Zn2+ important in the loop conformation. Figure $19$ below shows the cryo-EM structure of the HydABC tetramer and the arrangement of the redox cofactors. Figure $19$: Cryo-EM structure of the HydABC tetramer and arrangement of the redox cofactors. Chris Furlan et al. (2022) eLife 11:e79361. https://doi.org/10.7554/eLife.79361. Creative Commons Attribution License Panel (A) shows the unsharpened 2.3 Å map of Hyd(ABC)4 with D2 symmetry enforced, showing a tetramer of HydABC heterotrimers. All four copies of HydB and C are colored blue and green, respectively. The four HydA copies that make up the core of the complex are orange, yellow, pink, and red. The top and bottom halves of the complex are constituted by dimers of HydABC protomers (each HydABC unit is a protomer); the two protomers within the same dimer are strongly interacting, while a weaker interaction is present between the top and bottom dimers. Panel (B) shows the HydABC dimer highlighting the iron–sulfur clusters and flavin mononucleotide (FMN) constituting the electron transfer network. Panel (C) shows the arrangement of redox cofactors within the protein complex, showing two independent identical redox networks (dashed circles); each redox network is composed of iron–sulfur clusters belonging to a Hyd(ABC)2 unit consisting of two strongly interacting HydABC protomers. Panel (D) shows a schematic of the electron transfer network of one of the two identical Hyd(ABC)2 units showing edge-to-edge distances (in Å) between the various cofactors. Note that our structure is of apo-HydABC and contains only the [4Fe–4S]H subcluster of the H-cluster. The 2H+/H2 interconversion reaction in (B) illustrates the site at which this reaction occurs, but this will only occur in the fully assembled H-cluster, including [2Fe]H. Figure $20$ shows an interactive iCn3D model of the electron-bifurcating [FeFe] hydrogenase from Thermotoga maritima (HydABC) (7P5H), using the same colors as the figure above. Figure $20$: electron-bifurcating [FeFe] hydrogenase from Thermotoga maritima (HydABC) (7P5H). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...2hwCUnJ9BgwJi8. Only the dimer (HydABC)2 is shown. The A (gold), B (blue), and C (green) chains are colored as in the previous figure. The conformationally flexible loop at the C-terminal of a B chain in the closed state is shown in red. The gate also includes the C-terminal part of the A subunit near it. Figure $21$ shows the closed-bridge and open-bridge conformations of HydABC (the closed loop was shown in the model above). Figure $21$: closed-bridge and open-bridge conformations of HydABC from Thermotoga maritime. Panel (D) shows the HydB bridge domain in the open position and its fitted model. Panel (E) shows a Zn2+ hinge region and the two possible conformations of the HydB bridge domain, open (blue) and closed (light blue). The similarities in cofactor arrangement in the Thermotoga maritime Hyd A, B and C subunits compared to the Nqo1, Nqo2, and Nquo3 subunits in Complex I from Thermus thermophilus (discussed in Chapter 19.1) are shown in Figure $22$ below. Figure $22$: Comparison of the HydA, B and C subunits of the electron bifurcating [FeFe] hydrogenase from Thermotoga maritima with the Nqo3, 1 and 2 subunits from respiratory complex I from Thermus thermophilus. Panel (A) shows the subunits HydA (red), HydB (blues), and HydC (green) overlaid with, respectively, Nqo3, Nqo1, and Nqo2 (all yellow) of complex I from Tthermophilus (Gutiérrez-Fernández et al., 2020, PDB: 6ZIY). Panel (B) shows a comparison of the NADH-binding site of the Nqo1 subunit of complex I from T. thermophilus (light blue) with the flavin mononucleotide (FMN) site in HydB; the high similarity suggests NADH binds in the proximity of FMN in HydABC similar to complex I. Panel (C) shows an electron transfer network in HydABC compared to complex I from T. thermophilus with edge-to-edge distances indicated in bold. The red, blue, and green dotted lines indicate the cofactors present in the HydA (Nqo3), HydB (Nqo1), and HydC (Nqo2) subunits, respectively. Note that our structure is of the apo-HydABC and lacks the [2Fe]H subcluster of the H-cluster. The 2H+/H2 interconversion reaction in (C) illustrates the site at which this reaction occurs, but this will only happen in the fully assembled H-cluster, including [2Fe]H. Here is a link to a video showing the conformational change observed between the ‘Bridge closed forward’ (7P8N) and ‘Open bridge’ (7PN2) classes. In the video, the HydB C-terminal iron–sulfur cluster domain is colored blue, and the HydA C-terminal iron–sulfur cluster domain is colored orange. The zinc ion (gray sphere) and ligating residues (three cysteine ligands and one histidine) are also shown. The location of the HydA C-terminal domain when the bridge is open is unknown, so it is shown transparently in both states for reference. The geometric separation of catalytic sites and the bifurcation mechanism prevents these thermodynamically favored reactions from happening • H2 production from ferredoxin oxidation (in the absence of NADH oxidation) • NAD+ reduction by H2 (in the absence of ferredoxin reduction) • ferredoxin oxidation by NAD+ Oxygen Sensitivity of Fe-Fe H2ases We have alluded to the fact that Fe-Fe H2ases can be sensitive to O2. A possible mechanism involves the interaction of one the Fe ions (Fed, the distal Fe) with oxygen, leading to the formation of damaging free radicals. As CO binds more strongly than O2 to the iron in hemoglobin, its interaction with the H-center can help protect the H2ases. Sulfides can also afford protection. These mechanisms are illustrated in Figure $23$: Figure $23$: Oxygen tolerance strategies in [FeFe]-hydrogenases. Morra S, ibid. Schematic representation of the H-cluster in the oxidized active state Hox (centre). In the absence of any exogenous protectant, numerous [FeFe]-hydrogenases undergo irreversible inactivation due to H-cluster damage with loss of Fe atoms (red pathway); carbon monoxide acts as a protective agent due to its ability to form Hox-CO, by binding reversibly to the H-cluster at the same site as O2 (purple pathway); in DdH, a similar mechanism occurs when sulphide binds to the H-cluster forming Hinact, via the Htrans intermediate (orange pathway); in CbA5H, a conformational change in the protein structure allows for a conserved cysteine to directly bind to the H-cluster, forming Hinact (green pathway). Fep, proximal iron atom; Fed, distal iron atom; Cys, cysteine residue. Key Points - Beta version from Chat.openai 1. Hydrogenases are enzymes that catalyze the reversible conversion of hydrogen gas (H2) to protons and electrons. 2. There are two main types of hydrogenases: [NiFe]-hydrogenases and [FeFe]-hydrogenases. 3. [NiFe]-hydrogenases are found in a variety of microorganisms and mostly used to oxidize H2. 4. [FeFe]-hydrogenases are mainly used to produce H2. 5. Hydrogenases play a crucial role in the metabolism of microorganisms, allowing them to produce or consume hydrogen gas as needed. 6. The activity of hydrogenases is regulated by various factors, including the availability of hydrogen and the presence of inhibitors such as oxygen. 7. Research is ongoing to improve the efficiency and cost-effectiveness of hydrogenase-based biohydrogen production and to understand the mechanisms of hydrogenase enzymes to develop more efficient and sustainable ways of producing hydrogen. 8. Genetic engineering techniques can be used to improve hydrogenase activity in microorganisms, and also to increase the tolerance of microorganisms to the toxic effects of H2.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.11%3A__A_Warmer_World%3A_Temperature_Effects_On_Chemical_Reactions.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Inspiration for the chapter comes from Biochemical Adaptation by Hochachka and Somero. Organisms adapt to their environment, with one of the main drivers being temperature. This has occurred over geological time (think of arctic camels 3.4 million years ago!) and space with temperature gradients in terrestrial and aquatic environments. This is evident in the different species that thrive at different mountain heights and ocean depths. Species that can move have advantages in selecting an environment best suited to their thermal needs. Historically, homo sapiens have engaged in seasonal migration, and aquatic species in vertical migrations. Temperature effects are universal throughout life, and physiology and biochemistry adaptations are ubiquitous. Metabolically-active life can exist from around -15oC to about 121oC (thermal saline springs). Unless greenhouse gas emissions are significantly decreased from present levels, parts of the world will become increasingly uninhabitable due to high temperatures and sea level rise. Estimates for the number of climate refugees range up to 1 billion people by 2050. Two similar questions arise. Can organisms adapt to increasing temperatures as the climate changes, and are organisms living close to their maximal survivable temperatures? Before we study the effects of temperature on chemical/biochemical reactions, let's review the basics of thermoregulation. The following classification of organisms by types of thermoregulation is from BioLibre text. Types of Thermoregulation (Ectothermy vs. Endothermy) Thermoregulation in organisms runs along a spectrum from endothermy to ectothermy. Endotherms create most of their heat via metabolic processes, and are colloquially referred to as “warm-blooded.” Ectotherms use external sources of temperature to regulate their body temperatures. Ectotherms are colloquially referred to as “cold-blooded” even though their body temperatures often stay within the same temperature ranges as warm-blooded animals. Ectotherm An ectotherm, from the Greek (ektós) “outside” and (thermós) “hot,” is an organism in which internal physiological sources of heat are of relatively small or quite negligible importance in controlling body temperature. Since ectotherms rely on environmental heat sources, they can operate at economical metabolic rates. Ectotherms usually live in environments in which temperatures are constant, such as the tropics or ocean. Ectotherms have developed several behavioral thermoregulation mechanisms, such as basking in the sun to increase body temperature or seeking shade to decrease body temperature. The cCommon frog is an ecotherm and regulates its body based on the temperature of the external environment Endotherms In contrast to ectotherms, endotherms regulate their own body temperature through internal metabolic processes and usually maintain a narrow range of internal temperatures. Heat is usually generated from the animal’s normal metabolism, but under conditions of excessive cold or low activity, an endotherm generate additional heat by shivering. Many endotherms have a larger number of mitochondria per cell than ectotherms. These mitochondria enables them to generate heat by increasing the rate at which they metabolize fats and sugars. However, endothermic animals must sustain their higher metabolism by eating more food more often. For example, a mouse (endotherm) must consume food every day to sustain high its metabolism, while a snake (ectotherm) may only eat once a month because its metabolism is much lower. Homeothermy vs. Poikilothermy Two other descriptors are also used. A poikilotherm is an organism whose internal temperature varies considerably. It is the opposite of a homeotherm, an organism which maintains thermal homeostasis. Poikilotherm’s internal temperature usually varies with the ambient environmental temperature, and many terrestrial ectotherms are poikilothermic. Poikilothermic animals include many species of fish, amphibians, and reptiles, as well as birds and mammals that lower their metabolism and body temperature as part of hibernation or torpor. Some ectotherms can also be homeotherms. For example, some species of tropical fish inhabit coral reefs that have such stable ambient temperatures that their internal temperature remains constant. Figure $1$ below shows the energy output vs temperature for a homeotherm (mouse) and poikilotherm (lizard). Another term is also employed, heterothermy, in which the temperature of a homeotherm can vary in different regions of the body (spatially) and also at different times (daily or seasonally as in hibernation). The core body of a homeotherm is usually warmer than the extremities that allow cooling when needed. In hibernation (or sustained torpor), both the body temperature and metabolic rates are decreased. Means of Heat Transfer Heat can be exchanged between an animal and its environment through four mechanisms: radiation, evaporation, convection, and conduction. Radiation is the emission of electromagnetic “heat” waves. Heat radiates from the sun and from dry skin the same manner. When a mammal sweats, evaporation removes heat from a surface with a liquid. Convection currents of air remove heat from the surface of dry skin as the air passes over it. Heat can be conducted from one surface to another during direct contact with the surfaces, such as an animal resting on a warm rock. Key Points • In response to varying body temperatures, processes such as enzyme production can be modified to acclimate to changes in temperature. • Endotherms regulate their own internal body temperature, regardless of fluctuating external temperatures, while ectotherms rely on the external environment to regulate their internal body temperature. • Homeotherms maintain their body temperature within a narrow range, while poikilotherms can tolerate a wide variation in internal body temperature, usually because of environmental variation. • Heat can be exchanged between the environment and animals via radiation, evaporation, convection, or conduction processes. Key Terms • ectotherm: An animal that relies on the external environment to regulate its internal body temperature. • endotherm: An animal that regulates its own internal body temperature through metabolic processes. • homeotherm: An animal that maintains a constant internal body temperature, usually within a narrow range of temperatures. • poikilotherm: An animal that varies its internal body temperature within a wide range of temperatures, usually as a result of variation in the environmental temperature. These terms are diagramed in Figure $2$ below. Figure $2$: Thermoregulatory Term. Buffenstein et al., Biol. Rev. (2021), doi: 10.1111/brv.12791. Creative Commons Attribution License We have discussed in previous chapter sections how temperature can affect macromolecules such as proteins (Chapter 4), nucleic acids (Chapter 9.1) as well as supramolecular assemblies such as membranes (Chapter 10.3). Temperature effects on small molecules and ions (such as salts in the Hofmeister series and glycerol, Chapter 4.9) in the environment that regulate the function/activity of these larger molecules and assemblies are also important. Hence we'll review and discuss the effects of temperature on these key molecular species in the next chapter section. First, we'll delve deeper into the general impact of temperature on chemical and biochemical reactions. Temperature Effects on the Rates of Chemical Reactions To understand temperature effects on metabolic processes, let's first review temperature effects on ordinary chemical and biochemical reactions. You may remember the general rule that the rate of a chemical reaction approximately doubles when the temperature is increased 10o C (10 K). How does that arise? This is generally true in a specific temperature range, as we will see below. The rates of reactions, either endothermic or exothermic, depend on the activation energy (Ea). The activation energy is required to move from a reactant to the transition state, which then can go on to form product. The activation energy can be obtained from the Arrhenius equation (that you learned in introductory chemistry), which shows how the rate of an individual chemical reaction depends on temperature. k=A e^{-E_a / R T} where k is the rate constant, Ea is the activation energy, Ea/RT is the average kinetic energy, and A is a constant (the "preexponential" factor). By taking the natural log (ln) of each side and rearranging the equation, you get a "linearized" equation that is easier for most. \ln k=\ln A-\frac{E_a}{R T} A plot of ln k vs 1/T has a slope = Ea/R, from which the activation energy can be calculated. An alternative form can be derived: \ln \frac{k_2}{k_1}=\frac{E_a}{R}\left(\frac{1}{T_1}-\frac{1}{T_2}\right) A derivation Here it is! Derivation From \ln k_1=\ln (A)-E_a / R T_1 solve for lnA \ln (A)=\ln \left(k_1\right)+E_a / R T_1 Substitute into the equation for ln(k2) gives \ln \left(k_2\right)=\ln \left(k_1\right)+E_a / R T_1-E_a / R T_2 Rearrange to get \ln \left(k_2\right)-\ln \left(k_1\right)=E_a / R T_1-E_a / R T_2 Simplify to get the final equation! \ln \left(\frac{k_2}{k_1}\right)=\frac{E_a}{R}\left(\frac{1}{T_1}-\frac{1}{T_2}\right) Solving for Ea gives E_a=\frac{R \ln \frac{k_2}{k_1}}{\frac{1}{T_1}-\frac{1}{T_2}} Let's use this equation to calculate an Ea that will give a doubling of the reaction rate (k2/k1 = 2) going from T1 = 295 K (21.90 C, 71.3F) to T2 = 305 K (21.90 C, 89.3F), a 10oC temperature rise \begin{aligned} E_a & =\frac{(8.314)(\ln 2)}{\frac{1}{295}-\frac{1}{305}} \ & =\frac{\left(8.314 \mathrm{~J} \mathrm{~mol}^{-1} \mathrm{~K}^{-1}\right)(0.693)}{0.00339 \mathrm{~K}^{-1}-0.00328 \mathrm{~K}^{-1}} \ & =\frac{5.76 \mathrm{Jmol}^{-1} \mathrm{~K}^{-1}}{\left(0.00011 \mathrm{~K}^{-1}\right)} \ & =52,400 \mathrm{Jmol}^{-1}=52.4 \mathrm{~kJ} \mathrm{~mol}^{-1} \end{aligned} Hence if a reaction has an activation energy Ea of about 54 kJ/mol, increasing the temperature from 295 to 305oC (i.e, by 10oC) doubles the reaction rate. Assuming that the activation energy is constant, the rate constants increase with temperature since a larger fraction of the molecules have the energy (> Ea) necessary to react. This is illustrated in Figure $3$ below. Figure $3$: Plot of a Maxwell-Boltzmann distribution of speeds for different temperatures T=100K, T=1200K, T=5000K. Points along the curve show (1) most likely speed, (2) average speed, and (3) thermal speed (velocity that a particle in a system would have if its kinetic energy were equal to the average energy of all the particles of the system). https://commons.wikimedia.org/wiki/F...xis-labels.svg. Creative Commons Attribution-Share Alike 4.0 International license. Let's look a the brown vertical line around 950 m/s. If we take that as the activation energy, very few molecules in the blue distribution have the required kinetic energy > Eact. Ar progressively higher temperatures, great fractions (as measured by the area under the curve to the right of the dotted line at 950 m/s) have the required energy, hence the rates increase with temperature. When the temperature change is 10oC, the ratio of the rate constants (or rates), k2/k1 is often called Q10, the temperature coefficient (unitless). Q10 is not a constant, since it depends on the two temperatures that differ by 100 C (10 K). Hence the Q10 value for the 100 range from 273-283K is different than the Q10 value from 373-383K) . Q10 for many reaction is around 2 (doubling of the reaction rate) - 3 (tripling the reaction rate) at physiological temperature . Q10 =2 for a given Ea only at one set of temperatures that differ by 10oC. The variation in Q10 values is illustrated in Table $1$ below for a reaction in which Ea = 44.5 kJ/mol. Q10 decreases from 2 as the temperatures T1 and T2=T1+10oC increase. T1 in K (oC) T2 (K) (oC) k2/k1 (Q10) 273 (- 0.15 oC) 283 (9.85 oC)oC 2 373 (99.9 oC) 383 (110 oC) 1.45 473 (200 oC) 483 (210 oC) 1.26 Table $1$: Q10 = k2/k1 values at different temperatures T1 and T2 that differ by 10oC. We will see how this is important in biological settings in a bit. If Q10 = 1, the reaction is independent of temperature, and a Q10 <1 shows a reaction that is not functioning. An example might be an enzyme-catalyzed reaction in which the threshold is reached at a higher temperature T2 = T1+10, at which the enzymes lose an active conformation and starts to unfold. The same equation and the Q10 parameters apply to enzyme-catalyzed reactions. The activation energies (Ea) for four enzymes involved in the degradation of lignocellulose in the surface soil and subsoil are shown in Table $2$ below. The enzymes include two hydrolases, β-glucosidase (BG) and cellobiohydrolase (CB), which cleave cellulose, and two oxidases, peroxidase (PER) and phenol oxidase (POX), which help degrade lignin. The overall average Ea for these enzymes is about 44.7 kJ/mol, similar to the example used in Table 1 above. Soil Type Ea (kJ/mol) BG CB PER POX Arctic surface 35.4 39.4 12.7 81.8 Subarctic surface 36.5 38.6 21.1 45.7 subsoil 52.2 41.5 22.4 39.4 Temperate 1 surface 40.9 38 64.9 102 subsoil 49.4 21.2 28 94.8 Temperate 2 surface 31 43.4 25.4 49.5 subsoil 40.9 39.9 19.8 47.5 Temperate 3 surface 51.5 53.6 28.8 73.2 subsoil 58.8 46.7 54.2 29 Tropical 1 surface 47.8 50.5 26.5 47.7 subsoil 56.6 47 47.1 27.1 Tropical 2 surface 39.3 42.5 58.3 82.5 subsoil 42.8 43.3 22.8 45.5 Avg 44.9 42.0 33.2 58.9 Table $2$: Activation Energies (Ea, kJ mol−1) for extracellular soil enzymes involved in the degradation of lignocellulose. Adapted from Steinweg JM et al. (2013) PLOS ONE 8(3): e59943. https://doi.org/10.1371/journal.pone.0059943. Creative Commons CC0 public domain Q10 temperature coefficients are also used to describe biological processes like respiration, speed of neural signal propagation, metabolic rates, etc. Many biological processes are affected by temperature, especially for ectotherms that adjust temperatures to outside environments, including daily and seasonal temperature shifts. Mammals and birds alter metabolic rates with temperature. This is true for hibernating animals. The Q10 temperature coefficient can be considered to be the factor by which the reaction rates (k or R) increase (factor of 2, 3, 1.5, etc) for each 10-degree K or C temperature increase. It is given by the following equation: Q_{10}=\left(\frac{k_2}{k_1}\right)^{10^{\circ} \mathrm{C} /\left(T_2-T_1\right)} It is also called the van't Hoff's temperature coefficient. To help understand Q10, let's consider some examples. • If T2-T1=10o, then Q10 is simply k2/k1 for the specified temperature pairs separated by a 100 C range (T1 and T2=T1+10). Remember that Q10 is not a constant but depends on the temperature pairs and that it goes down with increasing temperature. • If the temperature range is > 100 C, the the measured ratio k2/k1 is a factor > 1 x Q10 • If the temperature range is < 100 C, the the measured ratio k2/k1 is a fraction of Q10 This equation can be converted to k_2=k_1 Q_{10}^{\left(T_2-T_1\right) / 10^{\circ} \mathrm{C}} where the rate constant k2 is related to a "base" rate k1 at a base temperature of T1. An interactive graph of the above equation is shown in Figure $4$ below. Figure $4$: Interactive graph of k2 (rate 2) vs. delta T at different base rates k1 Change the base rate constant, k1, at a base temperature of T1, and Q10 coefficient to see how they change k2. Note that if Q10 =1, k2 at T1+10 = k1 at T1, the rate is independent of the temperature. For most biological systems, the Q10 value is ~ 2 to 3 under physiological relevant conditions. The ratios of the rates (R2/R1) for different Q10 values are shown in Figure $5$ below. Figure $5$: Idealized graphs showing the dependence on temperature of the rates of chemical reactions and various biological processes for several different Q10 temperature coefficients. The dots on the graph show how the rate change with a temperature difference of 10oC. Wikipedia. https://en.wikipedia.org/wiki/Q10_(t...e_coefficient). CC BY-SA 4.0 Again this hypothetical graph is meant to show the general meaning of Q10 values. The "Q" model has been used to fit complex reaction systems, not just individual reactions. Figure $6$ below shows the daily mean soil respiration rate as a function of soil temperature. In these graphs, the x-axis is Temperature, not ΔT. Figure $6$: Relationships between daily mean soil respiration (Rs) and soil temperature (Ts). Jia X et al., PLoS ONE 8(2): e57858. https://doi.org/10.1371/journal.pone.0057858. Creative Commons Attribution License The soil temperature, Ts, was measured at a 10-cm depth. Open circles are from January to June; closed circles are from July to December. The solid lines use a Q10 model, in which the observed Rs vs Ts data are fit with an equation that optimizes the Q10 parameter The dashed lines are fitted by a logistic model, which we used in Chapter 5.7 for fitting ELISAs data. Rs is significantly different between the first and second half of the year. The soil respiration rate, Rs, at 10 cm depth was strongly affected by temperature, with an annual Q10 value of 2.76. Daily estimates of Q10 averaged 2.04 and decreased with increasing Ts. A study of seagrass showed the Q10 values are affected by plant tissue age and that Q10 varied significantly with the initial temperature and temperature ranges. The use of Q10 values from the Arrhenius equation is based on the assumption that the chemical/biochemical processes are exponential functions of temperature. For complex processes like the decay of organic matter, it would be better to model the whole system by looking at the individual enzymes involved. One problem with using Q10 values for very complex systems is the choice of the base temperature value for rate comparisons. The anaerobic decomposition of organic matter is generally a linear function of temperature between 5°C and 30°C, which shows that a Q10 modeling system is not ideal. A more complex systems biology approach using programs like Vcell and COPASI would be better and less likely to cause errors in predicted CH4 emissions from the decomposition process, for example. Getting Back to Proteins In Chapter 6.1 we explored the mechanisms used by enzymes to catalyze chemical reactions. These included general acid/base catalysis, metal ion (electrostatic) catalysis, covalent (nucleophilic) catalysis, and transition state stabilization. Some physical processes included intramolecular catalysis and strain/distortion. The rate-limiting step in enzyme-catalyzed reactions can include actual bond breaking in the substrate, dissociation of product, and conformational change required to facilitate binding, catalysis, and dissociation. A rate-limiting conformational change may occur not in the active site pocket but in nearby loops that modulate the accessibility of reactant to and dissociation of product from the active site. These all may be influenced by temperature, with localized conformational flexibility especially important. An interesting example of localized conformational changes affecting enzyme activity is RNase A. His 48, 18 Å from the enzyme active site, is involved in the rate-limiting enzymatic step involving product release. Figure $7$ shows an interactive iCn3D model of bovine pancreatic Ribonuclease A in complex with 3'-phosphothymidine (3'-5')-pyrophosphate adenosine 3'-phosphate (1U1B) Figure $7$: Bovine pancreatic Ribonuclease A in complex with 3'-phosphothymidine (3'-5')-pyrophosphate adenosine 3'-phosphate (1U1B). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...SnrGYXSVXcLCk6 The substrate is shown in spacefill. The active site side chains and the distal His 48 are shown as sticks and labeled. Two flexible loops, Loop 1 (magenta) near His 48, and Loop 4 (cyan) near the active site are highlighted. On ligand binding, the loops move a few angstroms to make the active site more closed, inhibiting product release. Product release is associated with mobile regions including Loops 1 (20 Å from the active site) and 2. Loop 4, near the active site, is involved in the specificity for purines 5' to the substrate cleavage site. His 48 is conserved in pancreatic RNase A. If mutated to alanine, the kcat decreases greater than 10X, indicating a change in the rate-determining conformational motion. The enzyme is still very active compared to the uncatalyzed reaction. His 48 appears to regulate coupled motions in the protein that are rate-limiting. Figure $8$ below shows the subtle shift in the conformation of apo-RNase A (magenta, no ligand, 1FS3) to the substrate-bound form (cyan, ligand in sticks), 1U1B). Note the small motion in His 48 shown in sticks at the bottom of the image. Figure $8$: Conformational changes apo-RNase A (magenta, no ligand, 1FS3) on conversion to the substrate-bound form (cyan, ligand in sticks, 1U1B). We will explore temperature effects on protein structure and function more in the next chapter section.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.12%3A__A_Warmer_World%3A_Temperature_Effects_On_Proteins.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Inspiration for the chapter comes from Biochemical Adaptation by Hochachka and Somero. Introduction In the previous chapter section, we discussed the generalized effects of temperature on chemical/biochemical reactions. The rate for chemical reactions, including enzyme-catalyzed ones, typically increases 2-3 fold (Q10 values) with a 100 C temperature increase over an organism's typical temperature range. Q10 values decrease at higher temperature pairs differing by 100 C. At too high a temperature, a protein enzyme destabilizes, and Q10 values can fall to less than 1, a sign of potential trouble for an organism subjected to that higher temperature. As we saw in Chapter 32.11 and will here, two competing processes affect protein enzymes as temperatures increase. They are increased rates for the catalyzed reaction and increased conformational dynamics, which leads to eventual denaturation at high enough temperatures. Hence evolution would presumably select for increased protein stability for organisms adapted to higher temperatures. For cold-adapted organisms, the rates of catalysis are expected to decrease. Hence evolution might lead to higher kcat values for cold-adapted organisms. However, we saw in Chapter 32.11 that the rate-limiting step for enzyme-catalyzed reactions often involved localized conformational changes, which would be disfavored at colder temperatures. Hence evolution would also select for enzymes that could maintain flexibility at low temperatures. In Chapter 4.9, we discussed low-temperature protein denaturation. Proteins can be destabilized at low temperatures. In this section, we will study how enzymes can adapt to higher temperatures. We won't discuss how proteins adapt to cold since this topic is less relevant for human-caused climate change. We will follow the approach used throughout this book - that structure mediates function. We will use a lot of enzyme kinetics since kinetic parameters can tell us much about how bound substrate goes to product at high substrate concentrations (kcat or VM) or low substrate concentrations (kcat/KM) at different temperatures and for different orthologs of enzymes from species that have adapted to grow at low (psychrophile), medium (mesophile) or high temperatures (thermophiles). We'll next look at the structure of enzymes and which features allow them to adapt to their optimal temperature for growth. Finally, we will look at entire pathways to discern clues as to how they adapt to increased temperatures. An Overview - Soil Enzymes Soil microbes play a key part in the carbon cycle as they can both store and release carbon. Soil temperatures influence this balance between uptake and release of CO2 into the atmosphere. Soil Organic Carbon - SOC The soil is a sink for carbon and stores about 1500 gigatons [Gt] = 1.5 Pt = 1500 Pg), more than the atmosphere and vegetation combined. SOC derives ultimately from photosynthetic organisms. When they die, their carbon is used by heterotrophs for energy and biosynthesis. Carbon can also be returned to the atmosphere by aerobic oxidation by microorganisms, but this requires O2, which diminishes rapidly with soil dept. Oxygen levels depend on soil porosity, relative amounts of sand and clay, and hydration. Tilling of soil increases O2 exposure and hence oxidative respiration of SOC, increasing atmospheric CO2. No-till farming hence can decrease CO2 release into the atmosphere. Inorganic carbon from CO2 (HCO3, and CO3-2) bind with cations in the soil (mineralization) or is released into the atmosphere as CO2. Carbon input into the soil is mostly determined by photosynthesis, which correlates with root mass, and decay, while export is determined by soil microbial (bacteria, fungi, protists, animals) respiration. Microorganisms play a key role in the balance of carbon input and release in the soil and hence are prime determinants of SOC. SOC is high in northern latitudes since colder temperatures promote lower respiration rates and accumulation of SOC over time. SOC is low in the lush tropics (even given the high photosynthetic rates) because of a high microbial respiration rate at higher temperatures. Deforestation of the lush Amazon Rain Forest will leave soil poor SOC with little to balance CO2 release from the decomposition of what's left by the abundant soil microorganisms. About 21 Gt of the 1500 Gt of SOC consists of microbial mass (12 Gt fungi, 7 Gt bacteria and 2 Gt from animals). Fungi hence are key players in soil metabolism. They are involved in the slow decomposition of decaying organic matter and promote the growth of slow-growing organisms like trees. In contrast, bacteria are fast metabolizers, and are found in abundance in grasslands. Northern attitudes have a higher soil microbial mass than in the tropics, but they are less active, allowing great SOC stores. We often think of enzymes working in an aqueous environment in a test tube or a cell (which is very crowded with other molecules). Figure $1$ below represents the microenvironment of soil enzymes involved in the decomposition of SOC, like cellulose. Figure $1$: Location of enzymes in soils and their importance for carbon and nutrient cycling. Fanin, N. et al. (2022). Soil enzymes in response to climate warming: Mechanisms and feedbacks. Functional Ecology, 36, 1378– 1395. https://doi.org/10.1111/1365-2435.14027. Permission from John Wiley and Sons and Copyright Clearance Center. Figure $2$ below give a review and an overview of the effects of increasing temperature on soil enzymes. Figure $2$: Effects of temperature at the enzyme scale. Fanin, N. et al., ibid. Permission from John Wiley and Sons and Copyright Clearance Center. Panel (a) shows the many steps involved in enzyme catalysis that can be affected by temperature changes. Step 1 shows the binding of substrates. The KM (units M) for the enzyme gives a "measure" of the strength of the interaction (but remember that KM = KD - the dissociation constant - only under rapid equilibrium conditions). Step 2 reflects kcat, the "net" rate constant for converting bound substrate to product. Panel (b) shows how key constants change with increasing temperature. The figure shows that kcat increases with temperature, consistent with the Arrhenius equation, as the temperature coefficient Q10 decreases (as discussed in Chapter 12.11). (Remember that Q10 is the factor by which the reaction rate, k or R, changes for each 10-degree K or C temperature increase) )The enzyme's thermal inactivation rate, kinact, also increases with temperature, leading to the bell-shaped VM curve. Km, a measure of the apparent KD of the substrate for the enzymes, increases, reflecting weaker binding. The catalytic power, Epower = kcat /kinact, also decreases with increasing temperature as the slope of kinact is generally greater than that of kcat. The values for the temperature axis would be different for microbes that grow best at cold temperatures (psychrophilic), moderate temperatures (mesophilic), and high temperatures (thermophilic). The graphs above represent temperature effects at the enzyme level. The gray rectangle represents the optimal growing conditions, which show that enzymes are poised near Vfor substrate conversion (assuming abundant substrate) but with low catalytic power. Increasing temperatures also have an effect at the microbial community level. These can affect SOC. For example: • After an increase in the decomposition of SOC at higher temperatures, subsequent decreases in SOC can occur due to the depletion of available substrates (as enzymes are running at VM) and changes in carbon use among the microbial communities; • Additional decreases in SOC due to increased oxidation and shifts in the composition of the microbial community occur; • The levels and types of substrates for enzymes likely change; • Increased temperatures can lead to increases in microbial community mass, which requires more substrate, but in the long-term metabolic shifts might lead to a decrease of extracellular enzymes and microbial biomass; • Soil conditions also change with increasing temperatures, which affects biomass by changing substrate availability; • Increased temperature lead to short-term increases in CO2 emission due to higher microorganism metabolic rates explained by the Arrhenius equation, but additional effects caused by accompanying changes in the microbial community occur. Complex mathematical modeling (as we saw using Vcell with metabolic and signal transduction pathways) would be needed to understand the effects of warming on SOC stores and their return to the atmosphere as CO2 Enzyme properties with altitude - Mount Kilimanjaro It is possible that the loss of SOC with climate change may be mitigated to some extent as the soil microbial community thermally adapts to a lower respiration rate/microbial biomass. As Figure 1 shows, both extracellular and intracellular enzymes must be considered. Extracellular enzymes break down polymers like cellulose into monomers, which are transported into the cell for intracellular respiration and the formation of CO2 by intracellular enzymes. The extracellular (lytic) and intracellular (oxidative) enzymes might respond differently to higher temperatures. Polymers that are hard to degrade have high activation energies, making soils with higher concentrations of these polymers more sensitive to climate warming (based on the Arrhenius equation). Changes in the microbial community might include shifts in the fungal/bacterial ratio, causing changes in degradation pathways and the rates of enzyme-catalyzed reactions. At higher temperatures, such changes increase conformational flexibility, which could increase kcat but also decrease the apparent affinity of the enzyme for the substrate, as reflected in increased KM values. These compensatory effects might leave catalysis unaffected by increasing temperatures. Studies have been conducted on individual degradative enzymes in soil samples from Mount Kilimanjaro. Enzyme kinetic analyses were done at two different temperatures differing by 10C (10C and 20C), so Q10 values could be evaluated. The soil samples were obtained from different heights on the mountain to allow for the comparison of the kinetic parameters of enzymes from microbes adapted to different heights. All organisms in soil from different heights would experience 10 °C, while those at the highest altitudes (3000 m) would encounter 20C only in the summer. The microbes presumably would have slightly different optimal temperatures for growth, and would likely use different adaptive mechanisms at low and high altitudes. Keep in mind in interpreting the results below that the Q10 value determines the sensitivity of a parameter (v0, KM, VM, etc.) to an increase in the temperature of 100 C. Extracellular enzyme activities in soil from one altitude The activity of three extracellular enzymes in soil samples were studied: β-1,4-glucosidase (degrades cellulose), N-acetylglucosaminidase (degrades chitin from fungi and peptidoglycans from bacteria), and acid phosphatase. The first two enzymes catalyze "recalcitrant" reactions with higher activation energies. Michaelis-Menten plots for the three enzymes in soil samples taken at one height, 2010 meters (m), are shown in Figure $3$ below. In addition, a plot of glucose conversion to CO2, an intracellular process, which the authors termed "glucose" mineralization" (probably because they trapped CO2 using OH- to form HCO3-), is also shown. Figure $3$: Rates of reactions mediated by hydrolytic enzymes (a–c) and rates of glucose oxidation to CO2 (d) as dependent on substrate concentration at 10 and 20 °C for the site located at 2010 m a.s.l. Symbols – experimental data, lines – approximation by Michaelis–Menten kinetics. The red lines indicate assays performed at 20C, while the green lines are for assays run at 10C. Blagodatskaya, Е. et al. Temperature sensitivity and enzymatic mechanisms of soil organic matter decomposition along an altitudinal gradient on Mount Kilimanjaro. Sci Rep 6, 22240 (2016). https://doi.org/10.1038/srep22240. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/ The mineralization rate was determined using trace amounts of 14C-labeled glucose, which if fully oxidized, is converted to 14CO2. Given the conditions of the reactions, the added glucose did not cause microbial proliferation. The authors used a fluorophore (4-methylumbelliferone or MUF)-labeled small substrate analogs for cellulose (MUF-β-D-glucopyranoside), chitin (MUF-N-acetyl-β-D-glucosaminide dehydrate) and for acid phosphatase (4-MUF-phosphate). Reactions were carried out in soil samples and valid initial velocities for the reaction were determined. The Km values for the glucose oxidation (mineralization) is not a valid KM value since CO2 would be produced from the combined actions of the enzymes in glycolysis, pyruvate dehydrogenase and the citric acid cycle. It could be better called an "operational KM". For the three enzymes, the KM at 20 °C was 25–42% larger than the VM = kcatET at 20 °C, causing Q10KM > Q10VM. These compensatory changes canceled any increases in enzyme activity at low substrate concentrations, but not at high ones when the enzyme was saturated. Hence Q10 for the catabolic depolymerization reactions increased with substrate concentration. Note, however, that the rate of intracellular glucose oxidation (mineralization) increased at all substrate concentrations going from 10 to 20 °C and the canceling effect was not detected, even at low substrate levels. The temperature response of monomer oxidation showed a strongly accelerated reaction rate instead of a canceling effect Extracellular Enzyme activities in soils from different altitudes Next, kinetic analyses were performed on soil samples from 2010 m (warm-adapted microorganisms) and 3020 m (cold-adapted) on the mountain. Plots of Q10 (not vas in Michaelis-Menten plots) vs. substrate for these studies are presented in Figure $4$ below to show the temperature adaption capacities of the enzymes. Figure $4$: The Q10 values for enzymatic activities (a–c) and glucose oxidation to CO2 (d) as dependent on substrate concentration at two altitudes. The blue and red rectangles show the concentration range at which no temperature effects occur (i.e < Scrit) with shading colors corresponding to different altitudes. The Q10 values derived from experimental data are shown as symbols. The model simulations based on experimentally obtained parameters of Michaelis–Menten kinetics are shown as curves (a–c). For glucose oxidation (d) at 3020 m elevation, a non-linear trend was very weakly expressed. Bars show standard deviations of the means (n = 3). Blagodatskaya, Е. et al. ibid. The graphs show that Q10 for polymer degradation increased with increasing substrate concentration. The authors defined a substrate concentration threshold (Scrit) below which KM and VM values canceled, so no increase in rate was seen with increasing temperature based. The width of the rectangles is based on the best-fit dashed blue and red lines, not the data points. The Scrit values were 35–42% larger for the 3020 m (blue rectangle) soil samples than at 2010 m (red rectangle), even though SOC was lower at that elevation. Q10 values were always lower at S > Scrit at higher altitudes. This demonstrates that the enzymes responded less to higher temperatures at higher altitudes (blue dots and dashed lower curves), implying that the enzymes at higher latitudes demonstrated significant compensatory changes useful for microorganisms that experience a greater temperature range with larger shifts at these higher altitudes. Figure $5$ below shows that the Q values generally decrease over a larger range of altitudes for both enzyme activity (panel A) and glucose oxidation (Panel B). Figure $5$: The Q10total values for hydrolytic enzyme activity at saturating substrate concentrations (A) and the increase in Vmax and Km induced by a temperature increase from 10 to 20 °C for 14C-glucose oxidation (B) depending on altitude. Symbols – experimentally derived values for Q10total (B), Q10Vmax and Q10Km (A). Lines are the trend lines obtained by the best fitting of power (A) and linear functions (B) at P values < 0.05, bars show standard deviations of the means (n = 3). Blagodatskaya, Е. et al. ibid. Panel B shows how Q10 values for KM apparent (green) and VM (red) for intracellular glucose oxidation/mineralization both decreased with increasing altitude. Again consider these KM and VM values to be operationally defined and apply not to an individual enzyme but in less rigorous way to all the enzymes involved in the intracellular oxidation of glucose to CO2. Investigators could determine these values only by fitting the kinetic data for CO2 vs [glucose] to the Michaelis-Menten equation. At altitudes < 2435 m, all showed Q10 KM values > 1.9, showing that the apparent collective KM values were very sensitive to temperature increase. This implies the "collective" set of enzymes responsible for intracellular glucose oxidation was more conformational flexible, and higher temperatures caused significant increases in apparent KM values. However, at high altitudes, the Q10 values for the apparent KM were about 1, suggesting no temperature effects on the generic structure and apparent KM values for the enzymes. The high-altitude enzymes were effectively temperature-stable with respect to KM values. VM values were more sensitive to increasing temperatures at all altitudes, but little change was seen going from 2435 to 3020 m. This again shows that the enzymes for microorganisms from high altitudes were more strongly adapted to temperature changes, especially at lower substrate concentrations. The next part can be a bit confusing. The KM for a given enzyme increases with increasing temperature, as shown in Figure 2. This suggests that the apparent affinity of the given enzyme for substrates decreases, which makes intuitive sense. Figure $6$ shows that the Q10 for KM (i.e. the sensitivity of KM to a temperature increase of 100 C) decreases with increasing elevation for each enzyme studied. Figure $6$: The values of Q10Km (a) and Q10Vmax (b) for hydrolytic reactions and for reactions of glucose oxidation at low and high altitudes. Bars show standard deviations of the means (n = 3). The figure shows that both Q10Km and Q10Vmax were lower at high altitudes. Hence the enzymes in organisms from higher altitudes did not respond as strongly to temperature changes. This again suggests that larger compensatory changes are found in enzymes at high latitudes, allowing them to better adapt to the greater temperature range they would experience. These data suggest that thermal adaptions in the intracellular enzyme are driven more by a large range of temperatures experienced by the organisms and not the mean temperature. Compensatory and canceling changes in KM and VM at low substrate concentration led to a higher Scrit in cold-adapted organisms. Structural Mechanisms for enzyme temperature adaptation Structure determines function. A detailed understanding of how proteins, and more directly enzymes, adapt to temperature changes must come from detailed structural analyses that can be correlated to functions such as enzyme catalytic activity. Two approaches have been used to study the structural bases of enzyme temperature adaptation. One involves structural analysis of a single enzyme in organisms adapted to different temperatures. The other involves large computational analyses of databases of protein structure. We'll discuss both. First, let's explore orthologs (in this case, a protein from the same gene in different species) of a single enzyme, ketosteroid isomerase (KSI), from mesophilic (grown in moderate temperatures) and thermophilic (grown in warm temperatures) bacteria. Figure $7$ below shows structural and functional features that document KSIs temperature adaptation through changes in activity and stability. Let's step through each of the panels in order. Figure $7$: Enzyme temperature adaptation through changes in activity and stability. Pinney et al., Science, 371. (2021). DOI: 10.1126/science.aay2784 Reprinted with permission of the American Association for the Advancement of Science and Copyright Clearance Center. Panel (A) shows that as the optimal growth temperature for an organism increases, the optimal temperature for selected enzyme activity also increases. Each dot represents a different species with the enzymes broadly chosen across all enzyme commission classes (i.e. the dots do not represent just KSI). There is a strong linear correlation. Starting with Panel (B), we look at KSIs. Panel B shows the mechanism of isomerization of the steroid substrate, 5(10)-estren-3,17-dione [5(10)EST] by KSI. KSI has one of the highest kcat values of enzymes. The reaction changes the position of the C=C double bonds and proceeds through an enolate/oxyanion intermediate (EI) formed after the abstraction of a proton by Asp 40. The transition state, which would have a developing negative charge on the O atom, is stabilized by proximal Tyr 16 and Asp 103 in a developing oxyanion hole. Panel (C) shows the KSI sequences from P. putida (a mesoKSI) and M. hassiacum (thermoKSI). The sequences are 33% identical, but some key resides (gray) are identical. Similar ones are shown in blue. The thermophilic KSI (thermKSI) has Ser 103 instead of the often conserved Asp 103 in mesophilic organisms (mesoKSI). D103 and S103 are shown in red. Now let's look at Panel (D), which shows the activity (v0/E0) vs substrate [5(10)EST], for the meso- and thermophilic enzymes. At a nominal temperature (250 C, top left panel), the thermoKSI shows little activity. At their optimal growth temperatures (300 C for the mesoKSI and 650 C for thermoKSI, bottom left), the thermoKSI has both a higher VM=kcatE0, and KM at 650 C. The higher KM is consistent with the idea that Km values are usually higher at higher temperatures. The derived kcat and kcat/KM values are shown in the adjacent histogram. Remember that kcat is a measure of how many bound substrate molecules are converted to product per sec (at saturating substrate concentrations). The parameter kcat/KM is a measure of the effective biomolecular rate constant for product formation at low substrate concentrations ([S] <<KM). From an evolutionary perspective, early ancestral enzymes probably arose in warmer environments. When the earth cooled, enzymes had to "solve" the problem easily apparent from the Arrhenius equation (the rate of reactions decreases with decreasing temperatures). Hence evolution would lead to structural changes that would facilitate either higher kcat values, lower KM values, or both, at lower temperatures. We've seen in the examples above that KM decreases with decreasing temperatures, so evolutionary pressures would more likely lead to increased kcat values. This is consistent with localized changes in protein dynamics as modulators of kcat. The evolutionary pressure to maintain stability would be low since proteins become more stable at lower temperatures. Now the question arises, how thermally stable are the meso- and thermKSI? Urea denaturation experiments were used to determine the ΔG0 H2OU for unfolding of the enzyme, as described in Chapter 4.12. Panel (E) shows urea denaturation curves for mesoKSI (black) and thermoKSI (red) monitored by changes in internal tryptophan fluorescence (top) and stabilities extrapolated to 0 M urea (ΔGH2OU) (bottom). The denaturation curve for thermoKSI is significantly shifted to the right. The calculated values for ΔG0 H2O unfolding are +18.5 kcal/mol (mesoKSI )and about +25.3 (thermoKSI), a +6.8 kcal/mol difference. These values would be - 18.5 kcal/mol (mesoKSI) and about -25.3 (thermoKSI) for the reverse folding reaction (unfolded ↔ folded transition). Figure $8$ shows an interactive iCn3D models of the bacterial ketosteroid isomerase from the mesophilic bacteria Pseudomonas Putida (left) and from the thermophilic bacteria Mycobacterium hassiacum (right). Figure $\PageIndex{8a}$: Mesophilic Ketosteroid Isomerase D40N mutant (monomer) from Pseudomonas Putida (pKSI) bound to 3,4-dinitrophenol (6C17). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...kH877KZKPxWsGA Figure $\PageIndex{8b}$: Thermophilic Ketosteroid Isomerase D38N mutant (monomer) from Mycobacterium hassiacum (mhKSI) bound to 3,4-dinitrophenol (6P44). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...YbGw5zr2dRpnK8 Both structures have a bound 3,4-dinitrophenolate, a stable oxyanion and transition state analog. The corresponding active site side chains are shown in color sticks. Just one monomer of the dimer for each protein is shown for clarity. The key active-site residues F86, V88, V101, and D103 in mesoKSI are replaced by W86, L88, I101, and S103 in thermoKSI (all very conservative except for D103S). A water molecule forms a bridging hydrogen bond from S103 to the oxyanion in the phenolate. Figure $9$ below shows the key change in Asp 103 (see the mechanism in Fig. 7B) in mesoKSI to Ser 103 + H2O in thermoKSI Figure $9$: Change from Asp 103 in mesoKSI to Ser 103 + H2O in thermoKSI Asp 103 is initially protonated with a pKa is >9, much higher than in an aqueous solution (3.7). The hydrogen bond from the mesoKSI D103 to the phenolate is stronger than the bridging one from Ser 103 in the thermoKSI. This arises from the increased polarity of the OH on a carboxylic acid (Asp) compared to an alcohol (Ser). In addition, the hydrogen bond distance from the bound water to the phenolate is longer than from Asp 103. Hence the Asp 103 in the thermoKSI improves enzymatic stability. In contrast, at lower temperatures, thermoKSI is less active, but at higher temperature it is more stable. The role of Ser 103 in stabilizing the folded state is shown in Figure $10$ below Figure $10$: Roles of Asp 103 and Ser 103 in the folding to unfolding transition of KSI When mesoKSI unfolds, Asp 103, which is protonated and not accessible to solvent in the native state, becomes solvent exposed on denaturation. Its pka drops, which leads to its deprotonation. This extra deprotonation step stabilizes the unfolded state (pulling the reaction to the right), making thermoKSI less thermally stable. In contrast, Ser 103, on solvent exposure, does not deprotonate, so the unfolded state is not additionally stabilized. Hence Ser 103 leads to greater stability of the folded thermoKSI. The D103/S103 change is found in many KSI from many bacteria. Here are some additional findings: • The structures of mesoKSI and thermoKSI are highly similar even though they have only 33% sequence identity. Figure $11$ shows the conformational changes in the monomeric ketosteroid isomerase (KSI) going from the mesophilic enzyme (6C17, cyan) to the thermophilic one (6P44, magenta) enzymes Figure $11$: Conformational changes in the monomeric KSI going from the mesophilic enzyme (6C17, cyan) to the thermophilic one (6P44, magenta) enzyme • The F86W change in the thermoKSI stabilizes the conformation of S103 to maximize its stabilization of the oxyanion in the oxyanion hole and allows high-temperature activity. • Other KSI orthologs and mutants show higher enzyme activity if they have D103. • Changing key residues at 86, 88, and 101 in mesoKSI to those found in the thermoKSI additionally increased the stability of the mesoKSI • Analysis of 1140 KSIs showed that the fraction containing D103 decreases with increasing growth temperature, and the fraction containing S103 increases. It appears that the thermal adaption of KSI occurs mostly through one amino acid change (D103S). The change reduces kcat for the mesoKSI 10x at low temperatures but greatly increases stability at high temperatures. This switch and compensatory changes in activity and stability suggest that protonated Glu and Asp side chains involved in activity might be changed to other amino acids that confer more stability but reduce activity. A conserved and protonated active site Glu is found in glycosidases from high-temperature orthologs. Likewise, a protonated Asp side chain is found in thioredoxin. As a control, a change from a protonated Glu in triosphosphate isomerase distal to the active site to a Gln shows no effect on catalysis and was not found in thermophiles. It should be noted that not all stabilizing mutations decrease activity since many examples are known that don't. 67 protonated Asp and Glu side chains were identified in the PDB, 14 of which were replaced in high-temperature orthologs. Conservation of chaining pairs of amino acids between mesophilic and thermophilic organisms. Are there other broadly found changes in amino acids (in addition to Asp/Glu) at each position in a mesophilic protein and its thermophilic ortholog? Computational analysis in 2194 enzyme families in 5582 bacterial species (for a total of 17 million amino acid pairs) were performed to explore this question. Half of all families had an amino acid at a given position which correlated with growth temperature, resulting in almost 160,000 key positions. The results of this study are broadly outlined in Figure $12$ below. Figure $12$: Examination of temperature-associated residues and their interactions. Pinney et al., ibid. Reprinted with permission of the American Association for the Advancement of Science and Copyright Clearance Center. Let's explain each part of this complex figure. Panel (A) - Preferences in the types of amino acids associated with high or low-temperature growth: Panel (A) shows the difference in frequency of association of given amino acids with high to low temperatures. Some amino acids were associated with low-temperature growth. These include alanine (A), glutamine (Q), and aspartate (D), as in KSI). Others were associated with high-temperature growth (E, I, Y and K). F Panel (B) - Identities and frequencies of site-specific residue changes across temperature growth conditions: Now let's see how a specific acid changes in going from mesophilic (low T) to thermophilic (high T), which show key temperature-dependent amino acid changes. Panel (B) shows the frequency of observed site-specific changes in temperature-associated residues. The “+” indicates that the frequency of a change (such as Leu to Ile) is significant in comparison to the opposite change (Ile to Leu). Indeed the darkest square is from a Leu (at low temperatures) to Ile (at high temperatures change. Other darker squares show these changes occur going from low to high temperature-adapted bacteria: V → I, R → L, and D → E. The specific one found for KSI, D → S, does not stand out, most likely because of the diverse types of enzymes included in the analysis. Panel (C) - Identities and frequencies of physically interacting temperature-associated residue pairs (in a single protein): If one amino acid changes in going from a low to a high-temperature ortholog, it is likely that the original and changed amino acids physically contact different nearby amino acids in their respective protein. Panel (C) shows the difference in frequency of association with high vs. low-temperature growth bacterial enzymes for "all possible physically interacting pairs of residues (made up of residues R1 and R2) that change concomitantly with the growth temperature". The darker blue squares show interacting residue pairs found more often in low-growth temperature enzymes, while darker maroon squares show contacting pairs in high-growth temperature proteins. The asterisk shows pairs are statistically significant. One of the darkest maroon squares (high-temperature enzymes) shows an Ile-Ile interaction pair. The vertical column above Ile (high growth) in Panel B shows that changes to Ile in high-temperature enzymes occur frequently from 6 different amino acids found in low-temperature enzymes. Ile might be favored over Val to maximize buried hydrophobic surface area. Also, compared to Leu, it has great conformation flexibility and could better pack empty spaces in protein interiors. A Lys to Glu (K to E) interaction is strongly associated with high-temperature enzymes, while an Arg to Asp (R to D) interaction is strongly associated with the low-temperature enzyme. This implies that simply increasing the number of salt bridges (ion-ion interactions) does not make a protein more thermally adaptable. Lys salt bridges would have greater conformational flexibility than those using Arg. The same applies to Glu compared to Asp. Lys also has the largest hydrophobic surface area, which could enhance hydrophobic packing. Since Arg has hydrogen bond donors requiring more adjacent hydrogen bond acceptors, Arg use might depend more on adjacent amino acids and not just a binary pair. In summary, these results are more nuanced than previous explanations for high-temperature stability: • increasing branched chain residues like Ile, Leu, and Val in the packed hydrophobic core. Indeed, as seen in Panel B, the most frequent amino acid change observed are from Leu/Val in low-temperature growth orthologs to Ile in high-temperature growth ones. These are all branched-chain amino acids. They occur 2-3x more frequently than the reverse, Ile in low-temperature growth to Leu/Val in high-temperature growth enzymes. In fact, Panel C shows that Ile preferentially interacts with another Ile in high temperature adapted enzyme. Hence stability is not just improved by substitution with any hydrophobic side chain. • increasing number of salt bridges (ion-ion interactions) and hydrogen-bonding interaction charged side chains. There are more charge side chains and salt bridged in thermophilic proteins. However, the above data shows a clear preference for Lys in thermophilic proteins as changes from Arg to Lys are common in that group as shown in Panel B. Likewise, Asp to Glu changes from low to high temperature-adapted proteins are 3 times more probably than the reverse. In addition, Panel C shows that interactions between Lys and Glu are most strongly associated with high temperature-adapted proteins, while Arg and asp interactions are most often in low temperature-adapted proteins. Allosterism and Thermal Changes As we mentioned before, increasing temperature can cause local effects in a protein, instead of large global changes that obviously lead to denaturation at high temperatures. Changes occurring in the active site can clearly affect kcat, KM, and kcat/KM. Allosteric effects also occur. The protein imidazole glycerol phosphate synthase (HisFH) is a heterodimer with two active sites, so it is considered a bienzyme. The H subunit is a glutaminase, which cleaves glutamine into glutamine acids and ammonia, which can diffuse through a channel to the active site of the F subunit, the cyclase. The active form of the enzyme occurs only when both substrates are bound resulting on long-range allosteric activations. In particular, the oxyanion hole in the H subunit is formed in the activated form which allows the stabilization of the tetrahedral transition intermediate in the hydrolysis of glutamine. Figure $13$ shows an interactive iCn3D models of the heterodimeric imidazole glycerol phosphate synthase complex (7AC8) Figure $13$: (Copyright; author via source). Heterodimeric imidazole glycerol phosphate synthase complex (7AC8). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...S5MbiiA9bDvDQ8. The HisH (glutaminase) subunit is shown in orange with a molecular surface and the bound substrate (spacefill), glutamine. The HisF subunit is shown in cyan with a bound substrate. An allosteric effector molecule can bind in the active site of His F and induce long-range conformational changes in this HisH active site which increases its activity 5000x. The structures involved are shown in Figure $14$ below. Figure $14$: Imidazole glycerol phosphate synthase (IGPS) from the thermophile Thermotoga maritima (T. maritime). Maschietto, F., Morzan, U.N., Tofoleanu, F. et al. Turning up the heat mimics allosteric signaling in imidazole-glycerol phosphate synthase. Nat Commun 14, 2239 (2023). https://doi.org/10.1038/s41467-023-37956-1. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. IThe HisF and HisH subunits are colored respectively in green-to-blue and red-to-yellow gradients, respectively, and separated by a dotted line which marks the interface between HisF and HisH. The labels (fα2, fα3, fβ2, loop1, hα1, Ω-loop) indicate secondary structure elements that are directly involved in the allosteric regulation. Molecular dynamics and NMR studies have shown that increases in the temperature lead to conformational changes resembling those that occur on binding of the allosteric effector at room temperature. As the temperature of the apoenzyme (no substrates or effects bound) increases from 30 °C to 50 °C, the dynamics and structure increasingly resemble the state induced by the effector. Increasing temperatures from climate change are likely to cause subtle conformational and dynamic changes in all proteins with some having negative consequences. Thermal determinants of yeast metabolism With this basic background, we can attempt to understand the thermal determinants for entire metabolic pathways. This has been attempted in the yeast Saccharomyces cerevisiae, a eukaryotic organism with optimal growth around ~30 °C, extremely limited growth at 40 °C, and no growth/death at 42 °C. Mathematical modeling all metabolic pathways in a cell is a daunting task. Accurate concentrations, rate constants, and dissociation constants are needed for all reactions. Genome-scale metabolic models (GEM) use a multitude of constants that are experimentally or computationally determined. There are usually significant uncertainties in the parameters used in the model. Bayesian statistics has been used to decrease these uncertainties. In Bayesian statistics, parameters and models are updated with the known values and information. It is similar to machine learning models, which uses data to train the model and refine it. A Bayesian model for S. cerevisiae was used as it is the most abundantly used organism in industry and has many GEMs. The GEM used was the enzyme-constrained GEM (ecGEM). It was then further developed into the enzyme and temperature-constrained GEM (etcGEM), which, in addition, incorporates the temperature dependence of both the concentration of the native enzyme (EN) and kcat for the enzyme. For each enzyme, the melting point (TM), the change in heat capacity (ΔCp) for the transition state, and the optimal temperature (Topt) were included. We discussed both (TM) and the change in heat capacity (ΔCp) for proteins in Chapter 4.9. In addition, another term for non-growth associated maintenance (NGAM) of the cells, which is also temperature-dependent, was included. Examples of non-growth associated maintenance include maintaining membrane potential, turgor pressure, normal protein refolding and DNA repair. The Bayesian models reproduced the datasets well. Using the models, key enzymes that control the flux through metabolic pathways were determined at each temperature. The most rate-limiting enzyme at superoptimal temperature in yeast was squalene epoxidase (ERG1), found in sterol metabolism pathways. Replacing the S. cerevisiae enzyme with one from a thermotolerant yeast strain led to better growth than the wild-type cells. Figure $15$ below the complexity and extent of the metabolic proteins and pathways of the enzyme-constrained GEM (ecGEM). Figure $15\: Metabolic proteins and pathways included in the enzyme-constrained GEM. Benjamín J Sánchez et al., Molecular Systems Biology (2017)13:935. https://doi.org/10.15252/msb.20167411. Creative Commons CC BY (add soon: ecYeast7 model (both constrained and unconstrained; each as.mat,.sbml and.txt files): GitHub (https://github.com/SysBioChalmers/GECKO/tree/v1.0/Models) Now, let's explore some of the results of the study. Figure \(16$ below shows how the temperature dependencies of proteins were incorporated into the enzyme-constrained GEMs. Figure $16$: Using Bayesian statistical learning to integrate temperature dependence in enzyme-constrained GEMs. Li, G et al., Nat Commun 12, 190 (2021). https://doi.org/10.1038/s41467-020-20338-2. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. Panel a shows the complexity of the metabolic network used to produce the ecYeast7.6, shown in greater detail in Fig. 13 above, and how EN and kcat depend on temperature. Panel b shows how a two-state denaturation model was used. [E]N is the enzyme concentration in the native state; Topt is the optimal temperature at which the specific activity is maximized; Tm and T90 are temperatures at which there is a 50 and 90% probability that an enzyme is in the denatured state, respectively. In Chapter 4.4 we indicated that for a reversible two-state transition, TM is the temperature at which half of the enzyme is native, and half denatured, giving a Keq for the N ↔ D equilibrium of 1. Panel c shows how kcat depends on temperature. The insert show how the heat capacity change from E+S to the E-transition state. We have previously seen that the ΔCP > 0 for protein unfolding, and this +ΔCP value is a signature of the hydrophobic and occurs when nonpolar groups become more solvent exposed. We have shown previously that enzymes bind the transition state more tightly than the substrate. This -ΔCP is more in line with the latter. Panel d shows the temperature dependence of enzyme's specific activity, r, as a function of temperature, which is determined by EN (panel B) and kcat (panel C). Now we can explore the outputs of the ecGEM run with the separate added effects of temperature on NGAM, kcat, and EN (denaturation). Finally, the combined etcGEM was run. The combined etcGEM was able to produce the observed outcomes in yeast growth. Given that the contributions of each of the three factors, NGAM, kcat, and protein denaturation, to whole-cell growth could be modeled. The outcomes from these models as a function of temperature are shown in Figure $17$. They support the notion that the growth rate of yeast is explained by temperature effects on its enzymes. Figure $17$: Fig. 3: Yeast growth rate is explained by temperature effects on its enzymes. Li, G et al., ibid This figure shows how the temperature dependence of different processes combines to affect the growth rate. EC is the prediction from the enzyme contained GEM, —predictions with the enzyme-constrained model; ec+NGAM includes temperature effects on nongrowth associated maintenance; ec+kcat(T) incorporates the temperature effects on enzyme kcat values; ec+denaturation(T) incorporates the temperature effects on enzyme denaturation. Finally, in the etc model, the enzyme and temperature-constrained model incorporates temperature effects on all three (NGAM, kcat and enzyme denaturation) into ec model. The solid lines indicate median values and shaded areas indicate regions between the 5th and 95th percentiles (n = 100). Here are some summarized results: • <29 °C, only temperature-dependent kcat affected the cell growth rate (green line under the orange etc line <29 °C; • at 29 °C <T < 35 °C, both kcat and NGAM (gray line) determined the growth rate; • at T > 35 °C, enzyme denaturation at major effects and by 40 °C was dominant, Figure $\PageIndex{18d-e}$ shows using images how the temperature dependencies of the factors EN, kcat and r (specific activity) on yeast growth. A phenomenal amount of data is displayed in these images. Figure $\PageIndex{18d-e}$: Fig. 3: Yeast growth rate is explained by temperature effects on its enzymes. Li, G et al., ibid Panel d shows the probability that a given enzyme is in the native state. Think of the y-axis as increasing 1 pixel at a time from bottom to top, with each new added pixel representing a different enzyme for a total of 764 enzymes along the y-axis. The x-axis shows with each pixel at a given y-axis if the enzyme is native that temperature The interface between bright (native) and black (denatured) on the right side of the image shows that some enzymes (top) become unfolded at 400C, while some don't unfold until close to 600C Panel e shows normalized kcat values of 764 enzymes at different temperatures. The brightest white pixels show the highest kcat values. The image clearly shows the brightest vertical band at around 300C, the optimal growth temperature of yeast. Panel f shows the normalized specific activities (r) of 764 enzymes at different temperatures. Again the highest specific activity is centered around 300C. Note in Fig. 15d that most enzymes also denature at temperatures < -10 0C, but cells were not viable under those conditions. The etcGEM was able to replicate a finding that above 37 °C, yeast cells switch from respiration to partial fermentation accompanied by a larger flux through glycolysis. This occurred because of a decrease in specific activities of enzymes with increasing temperature, which constrains metabolism. In addition, the total protein concentration reaches a limit and can't increase further, which could have increased enzyme activity. Along with an increase in glycolysis, mitochondrial ATP production decreases. Respiration produces more ATP per mole of glucose, but glycolysis/fermentation produces more ATP/protein mass so when protein concentration reaches a maximum, glycolysis is more efficient. Lastly, the etcGEM was used to find the enzymes whose flux changed most at superoptimal temperatures. (We introduced the flux control coefficient in Chapter 14.3). The results of this modeling are shown in Figure $19$ below. Figure $19$: Flux control coefficients at superoptimal model for yeast. One enzyme stands out in Panel a, ERG1, squalene epoxidase, an enzyme involved in sterol oxidation. If the wildt-type enzyme was replaced with a temperature-insensitive ERG1, the specific growth rate increased significantly (over 55%). What does this have to do with climate change? Life's Thermal Tolerance and Limits We've just explored the molecular and metabolic adaptations that allow organisms to thrive at higher temperatures. The question is how quickly present life can adapt to global warming caused by increasing greenhouse gases. Life on the planet will adapt, but the diversity of life forms in the biosphere will change. Five mass extinction have occurred over the last 450 million years up until the present, as shown in Figure $20$ below. Figure $20$: Five mass extinction over the last 450 million years. Hannah Ritchie. Our World in Data. https://ourworldindata.org/mass-extinctions. Creative Commons BY license Many factors, often interrelated, can contribute to mass extinction. These include volcanism, asteroid impact, climate change, ocean anoxia, and the release of methane from ocean hydrates and permafrost. All are correlated with increased temperatures. as shown in Figure $21$ below. It documents the correlation between temperature changes and the extinction rate for marine animals over the last 450 years. The gray bars highlight the extinction cycle. Figure $21$: Temperature change and extinction rate over the past 450 million years. Song, H., Kemp, D.B., Tian, L. et al. Nat Commun 12, 4694 (2021). https://doi.org/10.1038/s41467-021-25019-2. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. Pane a shows the largest magnitude of temperature change (ΔT, absolute value) in each time interval (bin) Panel b shows the highest rate (R, absolute value) of temperature change in each time bin, defined at the million-year (Myr) scale. Panel c shows the generic extinction rates of marine animals calculated using gap-filler methods using data from the Paleobiology Database. The Big Five extinctions occurred in the end-Ordovician (OS), Frasnian-Famennian transition (FF), Permian-Triassic transition (PT), Triassic-Jurassic transition (TJ), and Cretaceous-Paleogene transition (KPg). Vertical bars show mean ± 1 x standard deviation (see Methods). O Ordovician, S Silurian, D Devonian, C Carboniferous, P Permian, T Triassic, J Jurassic, K Cretaceous, Pg Paleogene, N Neogene. Dark cyan, blue, and red dots represent ΔTR, and extinction rate, respectively. The authors suggest that a temperature change of >5.2 °C and a rate of >10 °C/Myr would lead to a new extinction rate comparable to the "Big Five". A rise of 5.2 °C is in the upper range (but likely only with human inaction to prevent it) of IPCC projection. The present rate of temperature increase (almost 2 °C/200 years) is unprecedented in at least the last 3 million years. So it appears that we are headed to a 6th mass extinction, caused by a combination of environmental degradation, pollution and climate change created by humans. Rothamn has suggested that we can tip over into an "official" mass extinction depending on the magnitude and the rate of change in the carbon cycle. If long-scale changes occur too quickly and organisms can't adapt, extinction follows. If the changes occur on a short time scale (as it happening now), the size of the change is a key factor. His analysis suggests that a key factor is anthroprogenic-related increases in ocean CO2. A threshold of 310 Pg (Gt), which we could reach by 2100, could officially trigger the sixth mass extinction. However, many would argue that we are already in the 6th extinction cycle. For example, of about 30,000 terrestrial vertebrate species, about 1.7% (515 species) are on the verge of extinction (having <1000 individuals). 77 mammal and bird species have lost most of their populations. When it comes to how organisms will adapt to our present climate change, Hochahka and Somero ask 6 relevant questions: 1. What is the thermal optima and how much change leads to suboptimal or lethal conditions? 2. What mechanisms set upper limits? 3. How close do organisms live to the limits of their thermal tolerance? 4. To what extent can organisms acclimatize to temperature increase? 5. When are genetic changes necessary for survival? 6. Does the acquisition of heat tolerance reduce cold tolerance? More simply, we can ask if organisms can survive and thrive and at what cost. Humans most assuredly can survive a warming world, but if temperatures become too high, they will not thrive and parts of the world we become inhabitable to them without great economic, social, political, and cultural costs. Some organisms will become extinct. Mass migrations of all species will occur as they seek more habitable environments. In an evolutionary sense, species occupy environments in which they can survive and adapt. Our climate has been fairly constant since the end of the last ice age, about 12,000 years ago. Climatic changes that make an environment suboptimal depend on how sensitive an organism is and how close it lives to its thermal limits. We have already documented the thermal limits of individual enzymes as well as whole metabolic systems As you learned in introductory chemistry, multiple linked reactions can only go as fast as their slowest step. An analogous insight is that a chain is only as strong as its weakest link. It may be the membranes, not proteins, are the weakest link with respect to temperature adaptation as higher temperatures alter phases and subphases like rafts with lipid bilayers which in turn can disrupt the activity of membrane proteins. Membrane functions (permeability, endo- and exocytosis, and maintenance of transmembrane potentials) are also very important. Temperature effects on neural communication in synapses might be key. An interesting example is a particular Antarctic fish, which after long exposures to 4 0C, dies if temperatures are raised to around 90C. Synaptic transmission through the acetylcholine receptor is altered as the amount of acetylcholine in the synapse increases and the rate at which it is degraded by acetylcholinesterase decreases. The lethal temperature for an organism can depend on many factors, including the rate of temperature increase, the length of exposure, previous long-term acclimatization, and for humans, humidity. Humans can't dissipate body heat without sweating if the external temperature is >370C (average body temperature). If the humidity is high enough, sweating is ineffective in dissipating heat. Since metabolism produces heat, the body temperature can increase past 370C even if the external temperature is lower. A lethal limit for humans can occur as low as 290C (840C) at a relative humidity of 85%. Even now, about 30% of the human population is exposed to lethal thresholds at least 20 days/yr, and this will only grow as temperatures and humidity increase. Comparisons of similar species (congeneric) that live in intertidal regions (region above the water level at low tide and underwater at high tide) vs. subtidal regions (close to a shore but always submerged) show significant differences. Organisms living in intertidal regions will experience a higher range of temperatures. 19 species of congeneric porcelain crabs exist that inhabit different latitudes and vertical depths in intertidal and subtidal regions. Intertidal crabs that experience a highly variable temperature region are exposed to temperatures much closer to their lethal temperature limit, showing the Tlethal - Tmax habitat get smaller. Heart function and neural activity at higher temperatures appear to be weak links in survival. Heat shock protein function is also different in congeners of marine snails. Lethal temperatures appear to be those at which protein synthesis dramatically decreases. Repair to damaged protein is decreased, which limits vertical migration to lower temperatures and can make organisms more susceptible to predation. These effects can occur over a small increase in temperature (a few degrees Celsius). It becomes more important to heed the warnings by the IPCC to limit global average temperature increases to 1.5 to the more likely 2.50C range. Laboratory experiments in directed evolution show that an organism's thermal tolerance can be increased, but there are limits. E. Coli can live at a variety of temperatures and experiments to evolve them at 37 0C (human gut temperature), at 42 0C (close to their lethal limit), and in an environment with fluctuating temperatures (between 32 0C - 42 0C) have been conducted. Over 1000s of generation, the rate of evolution was highest in the 42 0C group. The group that evolved at fluctuating temperatures was better adapted to both higher and lower temperatures. Under no circumstance did E. Coli evolve into a thermophilic species, which would require novel gene function and not just a small set of mutations that allow for more optimal values of kcat and KM for enzyme catalysis.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.13%3A_Biochemistry_Climate_Change_and_Human_Health.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Introduction Climate change affects human health and of course the health of the biosphere to which we are inextricably linked. Many, including the US CDC, describes the concept of One Health as "a collaborative, multisectoral, and transdisciplinary approach — working at the local, regional, national, and global levels — with the goal of achieving optimal health outcomes recognizing the interconnection between people, animals, plants, and their shared environment." We all know that pollution from the use of fossil fuels also has severe health consequences independent of effects mediated more directly by climate change. A solution to both is to dramatically decrease the use of fossil fuels and mitigate pollution from their use. People most likely do not understand the extent that climate change and fossil fuel use are linked to human health. If they did, perhaps they would become advocates for climate change action. This section will cover how climate change and fossil fuel use affect aspects of human health and diseases. In this section, we will focus on heat-related illnesses and pulmonary/cardiovascular diseases. We'll tackle climate change, emerging diseases, and pandemics in the next chapter section. Fossil fuel use and climate change obviously affect other diseases as well, some by indirect means For instance, increases in cancer deaths will occur due to lower availability of health care arising from extreme weather disasters that impact health facilities and peoples' access to them. An increase in cancer deaths occurred during the Covid pandemic since people deferred preventive healthcare treatments as well as cancer surgeries during the pandemic. Allergic illness will increase as growing seasons lengthen and species that cause allergic reactions shift to new growth regions. Heat-Related Illness Heat illnesses include heat cramps, heat exhaustion, heat syncope (fainting), and heat stroke, the latter of which can quickly become fatal. Heat affects normal physiology and health. It's estimated that about 1% of all cardiovascular deaths are linked to extreme temperatures. We know that the number of warm days and the number of heat waves has increased with climate change. Figure $1$ below shows how heat waves have changed in the US in the decades from 960 to 2022 using data from 50 large metropolitan areas. The 2020s is not even a third over but already shows increases in heat waves over the previous decade. Figure $1$: Climate Change Indicators: Heat Waves. EPA. https://www.epa.gov/climate-indicato...ors-heat-waves. Data source: NOAA (2022) Many historical heat waves have occurred in recent times. A heat wave in Europe in 2003 caused 15 000 heat-exposure-related deaths in France and 70,000 throughout Europe. On June 28, 2019, France recorded a temperature of 45.9 °C (115 °F). In 2022 China experienced a two-month heat wave and drought, with a record temperature of 45 °C (113 °F) set in Chongqing. The Chicago Heat Wave of 1995, with a maximal temperature of 106 °F, caused by high temperatures and humidity, kill over 500 people. The worst might be the Russian Heat Wave of 2010 when temperatures were 5 °C (9 °F) higher than normal and reached 40 °C (104 °F). Around 55,000 people died. Figure $2$ shows temperature and excess mortality from the 2022 heat wave in Europe during which 60,000 people died from heat-related causes. Figure $2$: Weekly temperature and heat-related mortality numbers in Europe during the summer of 2022. Ballester, J., Quijal-Zamorano, M., Méndez Turrubiates, R.F. et al. Heat-related mortality in Europe during the summer of 2022. Nat Med (2023). https://doi.org/10.1038/s41591-023-02419-z. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/ Panel a shows the weekly baseline (gray line) and observed (black line) temperature (°C) averaged over Europe. Temperature anomalies are defined as the difference between observed and baseline temperatures (gray shading). Baseline temperatures were computed as the mean annual cycle of observed temperatures in the reference period 1991–2020. Panels b and c show weekly heat-related mortality (weekly deaths) aggregated over Europe for the overall population (black), women (red) and men (blue) (b) and people aged 0–64 (blue), 65–79 (red) and 80+ (black) years (c), together with their 95% CIs (shadings). The numbers for women and men in b do not include the United Kingdom; values for the age groups in c do not include Germany, Ireland and the United Kingdom. Figure $3$ below shows the steady increase in deaths as the average summer temperature in Europe has increased. The outlier in 2022 is shown as a red dot. Figure $3$: The summer of 2022 within the context of rising temperatures in Europe. Relationship between summer mean temperature (°C) and summer heat-related mortality (summer deaths) in the analyzed European countries. The straight line shows the linear fitting for the 2015–2022 period. Ballester et al., ibid. In exercise studies under controlled conditions, the heart rate increases and plateaus after a temperature increase, but with further temperatures increases, the heart rate increases with plateauing, which is a sign of cardiovascular strain. In humid condition, cardiovascular strain develops even on slow walking at 34 °C (93.2 °F). Under dry condition, the strain developed at around 41 °C (106 °F). The strain (as indicted by an increasing heart rate proceeds by about 20 minutes a rise in core temperature. With each temperature increase over pre-Industrial Revolution values, the annual probably of heat waves with apparent temperatures of 40 °C/104 °F (dangerous with a high incidence of heat cramps, heat exhaustion, and heat strokes) and 55 °C./131 °F (very dangerous with heat stoke very likely) increases, as shown in Figure $4$ below. Figure $4$: Annual probability of occurrence of heat waves with apparent temperature (with contributions from humidity) peaks greater than 40 °C and 55 °C. Russo, S., Sillmann, J. & Sterl, A. Humid heat waves at different warming levels. Sci Rep 7, 7477 (2017). https://doi.org/10.1038/s41598-017-07536-7. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. Panels (ac) show the probability of occurrence of heat waves with AT peak ≥ 40 (AT40C) calculated at each grid point for all model years with global mean temperature anomaly relative to 1861–1880 at 1.5, 2, and 4 degrees warming (see Fig. 2), respectively. Panels (df) are similar to panels (ac) but show the occurrence of heat waves with AT peak ≥ 55 (AT55C). Heat Exposure and Kidney Disease Cumulative exposure to high effective temperatures caused by sublethal combinations of heat and humidity leads to chronic kidney disease. This is happening with increasing frequency to workers in poor agricultural areas and others who work in such hot conditions in industry and outdoors. This type of kidney disease is not caused by diabetes, hypertension, and other known disease of the glomeruli. Chronic kidney disease in such workers was noted in El Salvador and elsewhere in Central America. The disease has a high mortality rate. Just in El Salvador, the death from kidney disease is ten times higher than in the US. Initially, the disease was called Mesoamerican nephropathy. Biochemical correlates of the diseases are yet unclear. Serum creatinine levels are increased, which might affect renal perfusion and lead to kidney damage. The effects might be generally cumulative or from repetitive episodes of exposure. Sugarcane field workers who report nausea, vomiting, headaches, muscle weakness, back pain, and fevers have high levels of creatinine. Kidney biopsies show inflammation and kidney fibrosis. Similar diseases in other parts of the world that seem to have the same presentation include Sri Lankan nephropathy and Uddanam (in the Indian state of Andhra Pradesh) nephropathy. Some have categorized it as CKDu or Chronic Kidney Disease of Unknown etiology/Uncertain cause or as chronic kidney disease of non-traditional origin (CKDnt). Figure $5$ below shows the distribution of kidney disease in the Western Hemisphere in 2019. Figure $5$: Burden of Kidney Disease. 2019. Men and Women. Pan American Health Organization/WHO. Other factors such as increased exposure to herbicides, heavy metals, and microbial agents might also cause or contribute to the disease. However, the disease is most prevalent in the hotter regions of affected countries, as the incidence is lower when workers work at high altitudes. Increased incidence of chronic kidney disease also appears to be occurring in workers in Florida and California. Biochemical Mechanism for Heat Stroke The actual biochemical mechanisms of heat stroke effects (circulatory failure, organ injury, uncontrolled clotting, death) are not fully understood. Certainly, cell death plays a major role, but not through the classical apoptotic pathway which depends on the activation of caspases (see Chapter 28.14). Rather, cell death occurs through necroptosis, a caspase-independent pathway. In necroptosis, an upstream protein kinase RIPK3 (receptor-interacting serine/threonine protein kinase 1) activates through phosphorylation the effector protein MLKL (mixed lineage kinase domain-like protein). Phosphorylated-MLKL then translocates to the cell membrane where it leads to calcium influx and plasma membrane damage in the final "execution" phase of cell necrosis. The activation of RIPK3 and MLKL through other receptors, including the toll-like receptors (TLR3 and TLR4), and tumor necrosis factor receptor 1 (TNF-R1), is shown in Figure $6$ below. Figure $6$: Activation of RIPK3 by multiple stimuli. Morgan MJ, Kim YS. Roles of RIPK3 in necroptosis, cell signaling, and disease. Exp Mol Med. 2022 Oct;54(10):1695-1704. doi: 10.1038/s12276-022-00868-z. Epub 2022 Oct 12. PMID: 36224345; PMCID: PMC9636380. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/ RIPK3 can be activated via various receptors when bound by their respective ligands. These are TNF receptor 1 (TNF-R1), CD95, death receptors (DR4/5), Toll-like receptors (TLR3/4), and Z-DNA-binding protein-1 (ZBP1)/DAI. In the first three of these pathways (but not TLR3/4 or ZBP1), RIPK1 is required and binds to RIPK3 through its receptor-interacting protein homotypic interaction motif (RHIM). In the case of ZBP1, RIPK3 is recruited directly via the ZBP1 RHIM domain, while in the case of TLR3/4, RIPK3 is recruited indirectly via the RHIM domain of TRIF. Once activated, RIPK3 autophosphorylates and then phosphorylates and activates MLKL to induce a conformational change and translocation to the membrane, where membrane permeabilization follows. During this process, post-translational modifications positively and negatively regulate the necroptosis pathway. Two E3 ligases, Pellino-1 (PELI1) and carboxy terminus of HSC70-interacting protein (CHIP), may control the basal threshold of necroptosis. Another E3 ubiquitin ligase, TRIM21, is proposed to be a regulator of necroptotic cell death in response to TRAIL. PPM1B suppresses necroptosis by dephosphorylating RIPK3. The domain structure and phosphorylation sites on human RIPK3 are shown in Figure $7$ below. Figure $7$: Meng, Y., Horne, C.R., Samson, A.L. et al. Human RIPK3 C-lobe phosphorylation is essential for necroptotic signaling. Cell Death Dis 13, 565 (2022). https://doi.org/10.1038/s41419-022-05009-y. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/ "Schematic of human RIPK3 domain architecture and the phosphorylation sites identified. Phosphorylation sites with proposed functions are shown at the top. pT224 and pS227 positively regulate necroptosis (green) by recruiting MLKL. pS164 and pT165 negatively regulate necroptosis by inhibiting RIPK3 kinase activity (red) [38]. Phosphorylation of T182 (grey) was proposed to promote RIPK3 kinase activity and to recruit PELI1 to mediate proteasomal degradation of RIPK3 [54]. Phosphorylation sites with unknown functions are shown on the bottom (white). Asterisks (*) denotes multiple serine/threonine on the same peptide, as such the exact site of phosphorylation could not be unambiguously identified." Figure x shows a complex, the necrosome, containing multiple activated RIPK3s along with RIPK1. Aggregation of RIPK3 occurs through the RHIM (RIP homotypic interaction motifs) domain through the formation of amyloid fibers. The necrosome then phosphorylates MLKL, which forms oligomers and traffics to the membrane. Figure x above also shows that an internal sensor protein for viral DNA can also activate RIPK3. That protein is ZBP1, or Z-DNA-Binding Protein 1, which also binds Z-RNA. Nuclear Z-RNA can derive from viruses like influenza A, leading to the activation of the same pathway. Cytokine expression then produces a systemic inflammatory response. In addition to apoptosis and necroptosis, another type of programmed cell death caused by inflammation is called pyroptosis. Usually occurring in bacterial-infected macrophages, pyroptosis leads to the activation of intracellular inflammasomes, which then activate inflammatory cytokines through selective proteolysis by caspases. In pyroptosis, proteins called gasdermins are cleaved by caspases and their N-terminals self-associate in the cell membrane to form pores, from which the inflammatory cytokines IL-1β, and IL-18 are released. A final programmed cell death pathway for virally-infected cells is called PANoptosis, which uses the PANoptosome complex with downstream results not explained by the other three programmed cell death pathways (pyroptosis, apoptosis, and necroptosis) ZBP-1 leads to the activation of RIPK3, caspase-8 (key in the apoptosis pathway) and the NLRP3 inflammasome. ZBP-1 seems to play a key role in heat stroke. Its concentration increases with heat stress mediated by the heat shock transcription factor 1 (HSF1., which itself is induced by cellular stress. HSF1 induces a heat shock response which causes increased transcription of chaperones and heat shock proteins (HSPs) such as ZBP-1. Deletion/inactivation of ZBP-1, RIPK3, or MLKL and caspase 8 decreases heat stroke. The main role of ZBP-1 in cell death from heat stroke arises from the RIPK3/MLKL pathway and to less extent through cross-talk with the classical apoptosis pathway through caspase 8. How does ZBP-1 activate cell death during heat stroke without binding to and activating dsDNA or RNA derived from a viral infection? Does ZBP-1 have an endogenous ligand other than viral Z-RNA or Z-DNA? Let's first explore the domain structures of some key proteins in the RIPK3 activation pathway. Figure 1 above shows three key proteins, RIPK1, TRIFF, and ZBP1 that interact with RIPK3. Each of these proteins and RIPK3 have a RHIM domain for protein-protein interactions. Figure $8$ shows the domain structure of our key protein, ZBP-1, the cytosolic Z-DNA/Z-RNA sensor. Figure $8$: Domain structure of ZBP-1 (http://www.ebi.ac.uk/interpro/protein/UniProt/Q9H171/ ) The green bars in the N-terminal part of the protein are the Z-DNA binding domain. These are also called Zα domains. These regions are the most ordered in the protein, as indicated by the blue in the AlphaFold confidence bar. Figure $9$ shows an interactive iCn3D models of the AlphaFold-predicted model of human Z-DNA-Binding Protein 1 (ZBP1), (Q9H171) Figure $9$: AlphaFold-predicted model of human Z-DNA-Binding Protein 1 (ZBP1), (Q9H171). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...FMCRhJwHFVb1X7 The spacefill atoms labeled M1 represent the N-terminal methionine of the protein. The two Z-DNA binding domains follow, are well-ordered, and are shown as blue cartoons. Much of the protein can't be predicted as it is most likely intrinsically disordered. Two fairly well-structured motifs, shown in magenta and cyan are the RHIM1 and RHIM2 protein interaction motifs, which can be shown self-associated through their amyloid-like structures. These motifs allow ZBP1 to bind to other proteins with RHIM motifs and on to cell death through necrosis. The C-terminal domain appears to be involved in signal transduction type I interferon-mediated by DNA. Figure $10$ shows an interactive iCn3D model of the second Z-DNA binding domain of human DAI (ZBP1) in complex with Z-DNA (3EYI) Figure $10$: Second Z-DNA binding domain of human DAI (ZBP1) in complex with Z-DNA (3EYI). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...SLBmo2xqV1WpS6 In the absence of viral DNA or RNA, the Zα domain can bind endogenous ligands. Moreover, it appears that a deficiency in RIPK1 or of the RHIM in RIPK1 also triggers ZBP1 to induce necroptosis and inflammation. and that its Zα domain is required. If nuclear export was stopped, ZBP1 activates nuclear RIPK3 and then necroptosis. This suggests that nuclear ZBP1 interacts with endogenous nuclear Z-nucleic acids, probably Z-RNA from retroelements to activate RIPK3-dependent necroptosis and could lead to some forms of chronic inflammation. Here are a series of finding on RIPK3-dependent cell death on heat stress in mouse fibroblasts that show that Z-nucleic acid binding to ZBP1 is not required for heat stress effects: • Heat (43°C for 2 hr) induces phosphorylation of RIPK3 and MLKL within 2 hours, and cleavage of pro-caspases and GSDME in 6 hours but none occurred if RIPK3 was deleted. • Deletion of ZBP1 but not RIPK1, TRIF, affect heat induce death so so heat stress acts through ZBP1 and RIPK3. • In mice without ZBP1, the effects of heat stress (clotting, inflammation, organ injury, and death) were prevented. • Mutations in the RHIM domain, but not the Zα domains (made to prevent Z-nucleic acid binding) or in the C-terminal signaling region (to stop signaling) prevented death from heat stress . Hence Z-nucleic acid binding is not required but may contribute to cell death from heat stress. • Heat stress caused the aggregation of a ZBP1-GFP (green fluorescent protein) fusion protein through the RHIM domains of ZBP-1 Hence ZBP1 is an innate pathogen sensor and also an initiator of heat-related death in the absence of pathogens. Heat Stroke-Induced Epigenetic Changes Short of death, heat stroke can also cause long-term health issues. Increasing global temperatures are forcing people to work at more dangerous temperatures and at night to reduce heat exposure. Data suggests that people who have had a heat-related illness are more susceptible to additional heat exposure health consequences. This has been noted in exertional heat illnesses. (such as in athletes). Additional long-term effects on immune regulation have been observed. Epigenetics may play a role in long-term effects such as greater vulnerability to additional heat challenges. Studies show that a single episode of exceptional heat stroke changes DNA methylation patterns in bone marrow-derived monocytes from mice. The monocytes become immunosuppressed allowing for increased microbial disease and reduced heat shock responses. The epigenetic changes are passed onto progeny monocytes which also shows compromised function. The epigenetic changes persist for 30 days or more and we clearly noted in inflammatory cell signaling pathways. This suggests a mechanism for the reduced tolerance to those with previous heat-related illnesses. Cardiovascular and Pulmonary Diseases Many factors can cause cancer and lead to mortality. For example, mortalities from cancer can increase due to lower availability of health care arising from extreme weather disasters that impact health facilities and access to them. Early detection of many cancers is key to survival. Instead of discussing climate change links to cancer we will focus on pollution and in particular small particles. PM2.5 particulate pollution effects on cardiovascular and pulmonary health Pollution from the combustion of fossil fuels contributes to many chronic diseases. Here we will focus on one type of pollutant, particulate matter which can be inhaled. These particles can be liquids, solids, or combinations of both. They are classified according to size with common categories including: • PM10: diameters < 10 uM = 10,000 nm; • PM2.5: fine particles with diameters < 2.5 um = 2500 nm • PM0.1: ultra-fine particles with diameters < 0.1 um = 100 nm (also called nanoscopic particulate matter or NPM) Figure $11$ below shows the relative sizes of PM10 and PM2.5 particles compared to other biological structures. Figure $11$: Relative sizes of PM particles compared to biological structures. Sotirios Papathanasiou. Particulate Matter (PM2.5) Mega Guide. With Permission. https://seetheair.org/2022/05/16/par...-5-mega-guide/ Figure $12$ below shows the relative sizes of PM0.1 particles compared to a PM2.5 particle. Figure $12$: Relative sizes of PM01 particles compared to a PM2.5 particle.Sotirios Papathanasiou, ibid. Composition of PM2.5 particles PM2.5 particles obtained by collection from polluted city air can actually be purchased from the National Institute of Standards and Technology (NIST, SRM1648a) and used for experimental studies on living cells. It is typically added to water and a suspension produced through sonication. PM2.5 particles are derived from human sources such as emissions from vehicles and industry, and both human-caused and natural processes such as the burning of biomass, and the release of dust from land. They can also include salts from land and ocean sources. They arise from the burning of fossil fuels and wear and tear of products such as automobiles (including tires). PM2.5 particles contain mainly black carbon, polycyclic aromatic hydrocarbons (PAH), aryl hydrocarbon, volatile organic compounds (VOCs) as well as minerals, ions (sulfate, nitrate, ammonium), and general biological materials. The metal composition includes Group 1A (K, Na, Fr), Group 2A (Ca, Mg), Group 3A (Al), transition metals (Al, As, Cr, Fr, Mn, Pb, Ti, Zn), and counter ions Br and Cl. They also contain silicon and silicates. Of course, particles in the air, including dust, also derive from non-anthroprogenic sources. Atmospheric dust is also produced from land by winds and also by volcanic eruptions. In homes, dust has an abundance of dead skin cells. along with pollens, hair, fur, and fibers from clothes and paper. Humans have evolved with particulates in the air, but large increases in their abundance caused by human activities in many parts of the world pose serious health consequences. In addition, the reaction of pollutants in the atmosphere produces "secondary" pollutants. One, the tropospheric gas ozone (O3) produced from hydrocarbons and nitrogen oxides, is a known health risk, and its levels are increased in cities on sunny, hot, and humid days. Secondary organic carbon (SOC) is also generated from primary organic carbon, typically volatile organic compounds (VOC) through oxidative photochemical reactions. These VOCs (like m-xylene and 1,2,4-trimethylbenzene) can produce aerosols (larger particles, called secondary organic aerosols by reacting with each other to produce larger structures. Terpenes containing isoprene units like α-pinene and limonene (a monoterpene found in large abundance in fruit peels) are reactants for the products of much larger structures. (See Chapter 10.1 and Chapter 21.6 for a review of isoprene and terpenes). Figure $13$ below shows generalized pathways for the formation of particulate SOCs from smaller terpenes. Figure $13$: Proposed mechanisms for the formation of C20H33N3O12, C20H32N4O14, and C30H48N4O16 in the simultaneous oxidation (MIX) experiment. Takeuchi, M., Berkemeier, T., Eris, G. et al. Non-linear effects of secondary organic aerosol formation and properties in multi-precursor systems. Nat Commun 13, 7883 (2022). https://doi.org/10.1038/s41467-022-35546-1. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. Gas-phase mechanism via cross-reactions of α-pinene and limonene peroxy radicals (RO2·), and particle-phase mechanism via hemiacetal formation involving α-pinene and limonene oxidation products. APN-RO2, APN(=O), and APN(-OH) represent α-pinene oxidation intermediate (i.e., RO2·), α-pinene oxidation products with carbonyls, and α-pinene oxidation products with hydroxyl functional groups, respectively. Figure $14$ below shows the morphology of PM2.5 particles. Figure $14$: Morphology of PM2.5 particles. The scale bars are 20 μm for image (a) 2 μm for image (b) 1 μm for image (c) 40 μm for image (d). Shi, Y., Ji, Y., Sun, H. et al. Nanoscale characterization of PM2.5 airborne pollutants reveals high adhesiveness and aggregation capability of soot particles. Sci Rep 5, 11232 (2015). https://doi.org/10.1038/srep11232. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. Panel (a) shows a large area image of as-collected PM2.5 on the filamentary filter. Panel (b) and (c) show SEM images of a particle with flat and rough top surface, respectively. Panel (d) shows a SEM image of the PM2.5 transferred on a Silicon substrate. Inset, zoom-in SEM image of an Iron-rich particle. Many of the PM2.5 particles have rough surfaces which can deform more easily interact with (i.e. stick to) other particles through noncovalent interactions to produce even larger particles. Figure $15$ below shows the elemental composition of rough, semi-rough, and flat PM2.5 particles. Figure $15$: EDAX Chemical composition histogram of the particles collected with SEM/EDAX classified by surface roughness; a larger surface roughness (and therefore, stickiness and deformation) are linked to a larger content of Carbon, while particles with a flat surface (low stickiness and viscosity) are richer in Oxygen and metals. Shi, Y. et al., ibid. Figure $16$ below shows electron micrographs of actual airborne particles. Most are PM2.5 particles with diameters < 2.5 uM = 2500 nm. Figure $16$: A collage of SEM images for airborne particulates. (A) General classification of airborne particulates; (B) particulates with seeds-coating composite morphology; (C) sulfate particulates with different morphologies. Clara Yuan Li et al, Journal of Environmental Protection, Vol.7 No.10, 2016. https://www.scirp.org/journal/paperi...?paperid=71021 Creative Commons Attribution 4.0 International License. Given their composition and their structures, it doesn't take much thought to realize that the particles must cause significant health effects. Would you want to breath in these particles routinely? Health Effects of PM2.5 Particles PM2.5 particles are associated with just about every type of illness, including cardiovascular and pulmonary diseases, including asthma, as well as cancer. Since they are small, they can easily be inhaled and deposited in lung alveoli from where they can actually enter the bloodstream and be deposited in tissue. Particles up to 240 nm (0.25 uM) can cross the placenta and black carbon particles have been found to cross the placenta. PM2.5 particles can cause inflammation, DNA damage, organelle dysfunction and can also generate free radicals which are most likely involved in these toxic health effects. The Great London Fog of 1952 Aerosol particles can even have acute and lethal effects. During The Great London Smog in London in December 1952, around 12,000 people died in two weeks from its effects. Figure $1$ people in the thick smog from that event. The smog consisted of acidified water droplets arising from SO2 and NO2 released on burning coal that contains sulfur. This gas can be oxidized to sulfate in gas-phase reactions probably through the .OH free radical or in aqueous phase reactions using O3, peroxides, and NO2 as reactants/catalysts. The reaction of SO2 with NO2 in aqueous droplets is shown below: \mathrm{SO}_2(\mathrm{~g})+2 \mathrm{NO}_2(\mathrm{~g})+2 \mathrm{H}_2 \mathrm{O}(\mathrm{aq}) \rightarrow 2 \mathrm{H}^{+}(\mathrm{aq})+\mathrm{SO}_4^{2-}(\mathrm{aq})+2 \mathrm{HONO}(\mathrm{g}) Given the stoichiometry of the reaction with NO2, the reaction proceeds significantly only in the presence of high NO2. The reaction is favored under high relative humidity. In large water drops in clouds, the droplets are not very acidic but as water evaporates from the drops, the sulfate concentration and acidity dramatically increase. However, as the acidity increases the rate of oxidation and the solubility decreases. Major cities in China, including Beijing and Xian, have experienced high levels of haze and particulate matter in the atmosphere until recently. Yet these smogs were not lethal (very acidic) as in London due to the addition of NH3 in the droplets, which neutralizes the acidic particles. Atmospheric ammonia is derived from large amounts of agricultural fertilizers that get aerosolized and also from vehicles, which produce NH3 in catalytic converters and from urea used in the catalytic reduction in diesel engines. The relevant production of sulfates in the presence of NH3 is shown in the equation below. \begin{aligned} 2 \mathrm{NH}_3(\mathrm{~g})+\mathrm{SO}_2(\mathrm{~g})+2 \mathrm{NO}_2(\mathrm{~g}) & +2 \mathrm{H}_2 \mathrm{O}(\mathrm{aq}) \rightarrow 2 \mathrm{NH}_4^{+}(\mathrm{aq}) + \mathrm{SO}_4^{2-}(\mathrm{aq})+2 \mathrm{HONO}(\mathrm{g}) \end{aligned} Figure $17$ below shows a ghostly image of pedestrians in London during the Great Fog. Figure $17$: Ghost-like pedestrians making their way through the smog. https://heritagecalling.com/2022/12/...f-london-1952/. Public Domain The prevailing weather conditions (cold temperatures) increased emissions from coal use. A stalled high pressure system and resulting low winds caused the buildup of stagnant air with increasingly acidic PMs. The appalling death toll led politicians to pass the Clean Air Act in 1954 which over many years led to huge improvements in air quality in London and dramatically reduced negative health effects and deaths. This was a prelude to the Clean Air Act in the US which dramatically improved air quality as well. High levels of PM2.5s still are prevalent in much of the world, although there have been dramatic decreases in the US. The notable exceptions occur during forest fires that are exacerbated by climate change. In the US, the Air Quaility Index (AQI) is used as an indicator of health risk. It measures the value of 5 pollutants, fine particles (PM2.5 and PM10), ground-level ozone, SO2, NO2 and CO. The value of AQI at a given time is determined by which pollutant is highest. In haze produced by smoke, the reported AQI represents PM2.5 particles. AQI values < 50 or below represent good air quality, while an AQI value over 300 represents hazardous air quality. Western forest fires in Oregon in September 2020 lead to an AQI of 611 in Madras, Oregon. Forest fires in Eastern Canada, along with slow-moving weather system, led to PM2.5 levels over 800 (mg/m3) in New York City on June 7, 2023. No place is immune from PM2.5 particles from wild fires and human-caused pollution. • AirNow - gives present pollution data, primarily PM2.5 levels, based on US zip codes The following interactive graphs show the changes in PM2.5 particles over time (from Hannah Ritchie and Max Roser (2019), OurWorldInData.org/outdoor-air-pollution • CC BY. Source: Brauer et al. (2017) via World Bank). Figure $18$ shows the share of the population exposed to PM2.5 levels higher than those suggested by the World Health Organization. Figure $18$: Share of the population exposed to PM2.5 levels higher than those suggested by the World Health Organization.OurWorldInData.org/outdoor-air-pollution • CC BY. Source: Brauer et al. (2017) via World Bank Figure $19$ below shows the share of the population in the US and in India exposed to PM2.5 levels higher than those suggested by the World Health Organization. Figure $19\: PM2.5 levels in the US and India.Our World in Data. ibid Finally, Figure \(20$ below shows the death rate from PM2.5/100,000 people in 2017 in countries around the world. Figure $20$: Death rate from PM2.5/100,000 people in 2017 in countries around the world. Mechanisms for PM2.5 Health Effects In Chapter 5.4, we discussed how solids such as silica, cholesterol crystals, uric acid crystals, and even aggregated proteins such as prions can be engulfed by monocytes/macrophages (much as they engulf bacteria as part of their immune function) in a process called phagocytosis. The particles are enveloped in plasma bilayer-derived membrane which buds off into the cell. This vesicle merges with a lysosome which gets damaged in the process. They then release ATP into the cytoplasm which acts as a damage signal to activate inflammation. PM2.5 particles can also be taken up by phagocytosis to produce intracelluar phagosomes. They can also be taken up by pinocytosis, and caveolin and clathrin-mediated endocytosis to form endosomes. These can fuse with lysosomes and mitochondria and induce damage. The heavy metals from PM2.5 particles that are released into the cell also contribute to damage. Smaller PM2.5 particles of diameter less than 0.1 uM (100 nM), sometimes called nanoscale particulate matter (NPM), offer large surface areas to which proteins can adhere. The adsorbed proteins form a "crown" called a protein corona. The corona is actually larger than the NPM. Proteins bounds to the particles include hemoglobin, albumin and fibrinogen. The corona also mediates cellular interactions and participates in the mechanisms that lead to inflammation and cellular dysfunction. As the extracellular matrix includes protein components such as collagen and fibrin, the interaction of PM2.5 with fibrin, has been studied as a model for how the particles might interact with cells. In particular, lung fibroblasts embedded in a 3D matrix of fibrin (i.e. a 3D organotypic culture) were exposed to NPM with protein coronas, and the effects of the NPM particles on cell proliferation, oxidative stress, etc. were monitored. Figure $21\ below characterizes the interaction of the NPMs with the fibroblast in the 3D culture. Figure \(21$: Physicochemical characterization of airborne particulate matter. Li, Y., Wang, P., Hu, C. et al. Protein corona of airborne nanoscale PM2.5 induces aberrant proliferation of human lung fibroblasts based on a 3D organotypic culture. Sci Rep 8, 1939 (2018). https://doi.org/10.1038/s41598-018-20445-7. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. Panel (A) shows SEM images of airborne PM2.5 (nanoscale PM2.5 is marked in red). Panel (B) shows a chemical element analysis of PM2.5 by EDX analysis. Panel (C) shows an atomic force microscopy (AFM) image of nanoscale PM2.5 (NPM) from air pollutant samples (scale 3.5 μm). Panel (D) AFM image of airborne NPM from air pollutant samples (scale 1 μm). Panel (E) shows an SEM image of airborne NPM from air pollutant samples. Panel (F) shows an SEM image of an NPM-protein corona. Panel (G) shows a schematic diagram of the biological interaction between NPM and protein. Panel (H) shows a FTIR spectra of NPM, serum, and NPM-protein corona. The NPM-protein corona particle leads to the proliferation of the 3D-cultured human lung fibroblast cells over and above stimulation of the cells with just NPMs or serum alone. The bigger the size of the corona, the greater the proliferative effect. This is consistent with the extensive fibrosis of the lungs seen after chronic exposure to PM2.5 particles. Reactive oxygen species also increased in the presence of NPM-corona particles. These data suggest that NPM-protein corona are important in PM2.5-induced lung fibrosis and pulmonary disease. Neural Effects of PMs PM particles, particularly the ultrafine PM0.1 particles (UFP), can enter the brain, and affect neural function. Exposure to PM2.5 over long periods of time is associated with increased incidence of dementia and Alzheimer's Disease (AD). Four particular components of PMs (SO42, NH4+, black carbon, and organic matter) were most associated with a higher risk of dementia and AD. In the US, the first admission to a hospital for Parkinson, AD, and other dementia is "significantly" associated with the average annual mean PM2.5 exposure. Even low levels of exposure pose risks. Transgenic mice containing mutant forms of human presenilin 1, amyloid precursor protein, and the tau protein, were exposed to subchronic, "real-world" levels of PM2.5 through inhalation for 3 months. Neuronal loss was observed in the cortex but no motor or cognitive impairment was noted. Increased levels of phosphorylated forms of Tau were observed and free radical formation, as evidenced by levels of malondialdehyde (a marker of oxidative stress), was seen in the hippocampus and olfactory centers, consistent with inhalation of PM2.5 through the noise. No abnormal amyloid plaques were observed in this short exposure time. PMs damage to neurons occur through the generation of reactive oxygen species increased inflammatory responses and organelle damage (all of which are interrelated). Even in skin cells (keratinocytes), exposure to PM2.5 particles led to increased levels of ROS and malondialdehyde, decreased levels of superoxide dismutase, and increased DNA damage. Inflammatory Caspase levels also increased. Cells have mechanisms to detect and eliminate aberrant species before they led to worse biological effects. One process is autophagy which degrades misfolded protein and damaged organelles, processes important for normal neural function. Aberrant autophagy is a key player in the pathogenesis of dementia. A second pathway is ferroptosis, a kind of apoptotic pathway, which kills cells that have accumulated large amounts of iron ions, which of course are free radicals themselves. Through the Fenton reaction and others, iron ions can lead to the generation of damaging reactive oxygen species and oxidation of lipids, proteins, and nucleic acids (see Chapter 12.3). Several key proteins are involved in antioxidant defense and autophagy: • NRF2 (Nuclear factor erythroid 2-related factor 2): This transcription factor binds to antioxidant response elements (ARE) in front of protective genes. • Keap1 (Kelch-like ECH-associated protein 1): An adapter program that targets NRF2 for ubiquitination and as such is a sensor for oxidative stress. Under those conditions, electrophilic metabolites lead to post-translational modification of reactive Cys side chains which leads to the inactivation of ubiquitin ligase activity. This increases NRF2 and subsequent transcription of antioxidant genes. • SQSTM1 aka p62 (Sequestosome-1): This protein bridges autophagosomes and polyubiquitinated proteins (cargo for degradation). Hence under low oxidative stress, NRF2 are low through its degradation mediated by KEAP1. If SQSTM1 (p62), increases, and reduced autophagy, p62 biding to the NRF2 sites on KEAP1, leading to the release of NRF2, its transfer to the nucleus, and activation of gene transcription of protective genes. These three proteins also protect cells from ferroptosis. In the long term, neural cell death occurs during dementia. Hence apoptosis, necroptosis, and pyroptosis (from activation of the inflammatory response mediators capspase, Gasdermin, and key cytokines like IL-1β and IL-18) do "win out" to eventually kill cells. Figure $22$ below shows the domain structure of SQSTM1 (panel A) and the signaling process described above. Figure $22$: Positive feedback-loop of Nrf2 activation by p62/SQSTM1. Vomund S, Schäfer A, Parnham MJ, Brüne B, von Knethen A. Nrf2, the Master Regulator of Anti-Oxidative Responses. Int J Mol Sci. 2017 Dec 20;18(12):2772. doi: 10.3390/ijms18122772. PMID: 29261130; PMCID: PMC5751370. Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Panel (A) shows the domain structure of p62/SQSTM1; Panel (B) shows p62/SQSTM1 as an important protein for selective autophagy, as binds to Keap1 and other long-lived proteins and forms polyubiquitinated protein aggregates. Furthermore, it binds to the autophagy marker LC3 within the autophagosome, thereby leading the aggregated proteins into the autophagosome. After fusion with a lysosome, proteins and organelles, such as mitochondria, are degraded within the autophagosome. By binding to Keap1, p62/SQSTM1 stabilizes Nrf2 and enhances its translocation into the nucleus, where Nrf2 activates its target genes (↑ = upregulation of Nrf2 target genes). One of these genes is p62/SQSTM1 PM2.5 particles intefer with the protective autophagy and ferroptosis pathways, leading to increased NRF2 activity and expression of antioxidant genes which are beneficial processes to rid cells of aberrant particles and kill damaged cells in normal conditions but not on exposure of neuronal cells to PM2.5 particles. Lysosomal membrane permeabilization (LMP) as well as mitochondrial and ER damage might be a likely mechanism to initiate ultimate neuronal death on long-term exposure to PM2.5 particles. PM2.5 particles inhibit lysosomal activity and increase their permeability and release to degradative enzymes into the cytoplasm. Ultimately increased or decreased activation of Nrf2 lead to disease states. PMs and Cancer Long-term exposure can also cause lung cancers. This is associated with a conversion of lung cells from normal epithelial to mesenchymal cells (called the EMT transition). Phenotypically, mesenchymal cells can migrate, invade other tissues and cause enhanced production of the extracellular matrix, all hallmarks of tumor cells. This EMT transition is associated with significant changes in cell signaling and the production of transcription factors. These changes are documented in Figure $23$ below and its caption. Figure $23$: Brief schema of the putative signaling transduction mechanisms underlying EMT. Xu et al., Front. Physiol., 29 November 2019. Sec. Renal Physiology and Pathophysiology. Volume 10 - 2019 \| https://doi.org/10.3389/fphys.2019.01404. Creative Commons Attribution License (CC BY) Activation of the Wnt/β-catenin, PI3K/Akt, Ras/ERK, TGF-β/SMAD2/3, BMP/SMAD1/5/8, JAK/STAT3, Shh, and Notch pathways is highly correlated with EMT. After ligand-receptor binding, intracellular secondary messengers are activated and initiated downstream transduction, which generally induce the nuclear translocation of signaling-specific TFs and the transcriptional regulation of EMT-related genes, such as CDH1 and CDH2, EMT TFs, and mesenchymal markers, accompanied by a series of alterations on cellular physiological or pathological activities (e.g., dysjunction of adherin junctions, cytoskeleton remodeling, and increase of cellular motility). Arrows represent the molecular interactions in which downstream messengers are activated; T shape arrows represent inhibitive molecular interactions. EMT, epithelial-mesenchymal transition; PI3K, phosphoinositide 3-kinase; ERK, extracellular signal-regulated protein kinase; TGF-β, transforming growth factor β; JAK, Janus kinase; Shh, sonic hedgehog; TFs, transcription factors. PM2.5 particles and their deleterious contents (heavy metal ions, PAHs, etc), as well as ROS generated by them are associated with the changes seen in the EMT transition. These include activation of transforming growth factor β (TGF-β)/SMADs, NF-κB, growth factor (GF)/extracellular signal-regulated protein kinase (ERK), GF/phosphatidylinositol 3-kinase (PI3K)/Akt, wingless/integrated (Wnt)/β-catenin, Notch, Hedgehog, high mobility group box B1 (HMGB1)-receptor for advanced glycation end-products (RAGE), and aryl hydrocarbon receptor (AHR) signaling cascades and to cytoskeleton rearrangement.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.14%3A_Climate_Change_Infectious_Disease_and_Pandemics.txt	princeton-nlp/TextbookChapters	Written by Henry Jakubowski Introduction Microorganisms can cause both chronic and acute diseases, both of which if left untreated can lead to death. Infections with the Human Papillomavirus (HPV) can cause cancer of the cervix, vagina, vulva, penis, anus, and throat. Modern vaccines against HPV can prevent over 90% of these cancers. The bacteria H. pylori can, in some people, cause stomach illness (such as severe chronic gastritis and ulcer) which can lead to stomach cancer. The Coxsackie virus, through binding to receptors on cardiac myocytes, can cause heart disease (acute myocarditis and cardiomyopathy), and ultimately death. Acute microbial diseases that occur immediately after infection can cause epidemics and pandemics (worldwide epidemics). Everyone has experienced the COVID-19 pandemic caused by the SARS-CoV-2 coronavirus. Johns Hopkins estimates as of 3/10/23 (end of their data collection), that there were about 677 million reported cases of Covid-19 and about 6.9 million deaths. The WHO estimates that just for the first two years of the pandemic (2020 and 2021), there were 14.83 million excess deaths globally, 2.74 times more than the number or reported death (5.42 million) from the virus. Machine learning models suggest that there have been closer to 20 million excess death through the end of March 2023, as shown in Figure $1$ below. Figure $1$: https://ourworldindata.org/excess-mortality-covid The data from Johns Hopkins suggest that the average mortality rate was about 1% (deaths/infections). If there have been 20 million cases (based on excess deaths) out of a world population of 8 billion, the mortality rate was close to about 0.25% for the entire world. Another indicator of the severity of pandemics is a decrease in life expectancy. Figure $2$ below offers an interactive graph that shows the general rise in life expectancies since 1750 punctuated by steep drops. Figure $2$: Life expectancies since 1750 Note the small drop in 2020 in the United State was caused by the Covid-19 pandemic with some contribution from opioid-associated deaths. The graph is dominated by a stunning decline in 1918 due to the 1918 Flu Pandemic (also historically and inaccurately named the Spanish flu). The large drop in life expectancy in Sweden in 1772-1773 was probably attributed to the Russian plague epidemic of 1770–1772, also known as the Plague of 1771. Figure $3$ below shows a history of pandemics back to the Antonine Plague of 165-180 CE. In viewing the figure, remember that the numbers of deaths are estimates at best, especially for the historically early pandemics. Figure $3$: Visualizing the History of Pandemics. Attribution Visual Capitalist. https://www.visualcapitalist.com/his...ics-deadliest/ The graphic misses another key plaque in world history, the Plaque of Athens, which hit the city from around 430 BCE - 427 BCE, during which up to 25% of the city's population died. Smallpox has emerged as a possible candidate for that outbreak. Figure $4$: Figure $4$: Visualizing the History of Pandemics. Attribution Visual Capitalist. https://www.visualcapitalist.com/his...ics-deadliest/ The Black Death (also called the Bubonic Plague) was caused by the bacterium, Yersinia pestis. Humans usually get the bubonic plague after being bitten by a rodent flea that is carrying the plague bacterium or by handling an animal infected with the plague (notice the bold letter B to help you remember Black, Bubonic, Bacterium, Bite). The Black Death/Bubonic Plagues derives its name from the fact that many had black tissue from gangrene. Large buboes, and inflammatory swellings of lymphatic glands, especially in the groin or armpit, were common. Another variant caused by Y. pestis is the Pneumonic Plague, caused by breathing particles containing Y. pestis into the lungs, which leads to death from pneumonia and its complications. That was more infectious since it could be spread from person to person. Modern antibiotics are used to treat the plague, which still occurs. Measles: A disease not shown in the figure is measles, which probably has killed upwards of 200 million people throughout time. It emerged from a viral infection, rinderpest, which infects cattle, deer, and buffalo. In 2021 there were 128,000 deaths out of 9 million cases worldwide, even though there is a highly effective vaccine. Vaccinations have decreased since the Covid-19 pandemic. Since it is one of the most infectious viruses known, and one contract leads to life-long immunity, a large population (250,00-500,000) is needed for it to self-sustain. The most recent analysis of historical sequences suggests that it emerge (jumped to humans) around the 6th century BCE, around the time when cities of high enough population formed to allow its emergence. Measles is caused by an RNA virus, and since RNA is much more labile than DNA, few historical traces of the measles virus are available. The oldest one is from 1911 and it was from this and newer viruses that a phylogenetic RNA tree using a molecular clock model was constructed that led to the 6th century BCE time of emergence. Cities with a critical number of people for sustaining an emergence existed about 300 BCE in North Africa, India, China, Europe, and the Near East. A disease similar to measles was mentioned by Rhazes (Persia, 10th century CE). Past pandemics of unclear etiology could be attributed to measles, but it's difficult to know for sure given the difficulty in differentiating measles from other diseases. Influenza: The genome of the influenza virus consists of 8 separate segments of ssRNA, much like the human genome resides on 23 different "segmented" chromosomes. Because its genome consists of RNA, past traces of it that point to its origin is lacking. The human influenza virus arose from swine (causing swine flu) and birds (avian flu). Hippocrates wrote of a disease with similar symptoms in 412 BCE. In 1357 an epidemic called “influenza di freddo,” or cold influence, swept Florence, Italy. The influenza RNA genome and transcribed proteins are shown in Figure $5$ below. Figure $5$: Influenza RNA genes and their protein products. Ahmed Mostafa, Elsayed M. Abdelwhab, Thomas C. Mettenleiter, and Stephan Pleschka - mdpi.com/1999-4915/10/9/497/htm, CC BY 4.0, https://commons.wikimedia.org/w/inde...curid=92987475 The hemagglutinin (H) membrane protein, responsible for viral binding to host cells, and neuraminidase (N), required for the exit of newly replicated viruses and hence viral propagation, are especially key in understanding past and future pandemics. There are 18 different subtypes of H and 11 subtypes of N comprising 4 different types of viruses (A-D) with A and B being the most common. The main types in circulation in 2022 were types A (H3N2) and B (H1N1). Since the genome consists of RNA replicated by a RNA polymerase, which does not have proofreading functions, mutations occur on viral replication. This leads to slow changes in viral protein sequence and structure, called antigen drift, and hence to viruses less recognized by the host immune system. This is why new influenza vaccines are formulated each year (through a process that requires growing the virus in eggs). Large-scale pandemics occur through antigen shifts. This occurs when an animal such as a pig gets infected with an avian virus, a not unlikely occurrence given the co-farming of these animals in many places in the world. Newly replicated pig viruses could then contain some avian viral segments, which when transmitted to humans could produce lethal disease since they have no immunological memory in the host to produce an immediate immune response. Analyzes show that the horrific 1918 flu pandemic was caused by an avian influenza virus. An ancestral virus from the late 1880s is related to the horse (equine) H7N7 and equine H3N8 as well as to birds, humans, and swine viruses, and was the likely precursor of the 1918 flu virus. This ancestral virus led to a global change in the avian influenza virus which contributed most of the RNA segments to the 1918 pandemic. Smaller pandemics in the last half of the 20th century were likely caused by quick replacements of H3N2 and H1N1 genes leading to evolutionary fitness and ease of transmission. We should be on guard as there is an ongoing, worldwide highly virulent avian flu (H5N1) pandemic in wild birds and domestic poultry that has jumped to some animals, including humans who handle infected birds. The hemagglutinin protein, homotrimer (3 identical protein subunits), MW 220,000, is the most abundant protein on the viral surface. Only three have adapted to humans in the 20th century, giving pandemic strains H1 (1918), H2 (1957), and H3 (1968). Three recent avian variants (H5, H7, and H9) can jump directly to humans but have low human-to-human transmissibility. The viral hemagglutinin binds to glycoprotein receptors on human and other animal cells. The receptor binding site on host cells contains a terminal sialic acid (Sia) covalently attached to a galactose. The sialic acid is usually connected through an α(2,3) or α(2,6) link to galactose on N-linked glycoproteins. The viral subtypes found in avian (and equine) influenza bind preferentially to host Sia α(2,3) Gal which predominates in the avian GI tract where viruses replicate. Human influenza binds preferentially to Sia α(2,6) Gal links on human cells. The swine influenza HA binds to both Sia α(2,6) Gal and Sia α(2,3) Gal. The structures of the Sia-Gal disaccharide are shown in Figure $6$ below. Sia α(2,6) Gal (Human and swine) Sia α(2,3) Gal (Avian and Swine) (made with Sweet, with an OH, not AcNH on sialic acid on C5) (made with Sweet, with an OH, not AcNH on sialic acid on C5) Figure $6$: Structures of Sia α(2,6) Gal (human) and Sia α(2,3) Gal Gal (avian/swine) The H5N1 avian virus is deadly but lacks human-to-human transmissibility. Why? One reason is that it appears to bind deep in the lungs and is not released easily on coughing or sneezing. It appears that cell surface glycoproteins deeper in the respiratory tract have Sia (α2,3) Gal linkages which account for this pathology. Small changes in the amino acids of the viral hemagglutinin (HA) could change the preference for binding between Sia α(2,6) Gal (predominant human form) and Sia α(2,3) Gal (predominate form in birds) on host cells, and could dramatically affect both human lethality and transmission. Even though it was mostly of avian origin, the predominant 1918 hemagglutinin bound to the human Sia α(2,6) Gal. Figure $7$ shows an interactive iCn3D model of a H1 1918 hemagglutinin with a human receptor (2WRG), in this case, just the Sia α(2,6) Gal disaccharide from a target N-linked glycoprotein. Figure $7$: H1 1918 hemagglutinin with the human receptor - the Sia α(2,6) Gal dissachharide (2WRG). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...svY14r7wmnM1r5 The HAs in each of the 20th-century influenza pandemics, 1918 (H1N1), 1957 (H2N2), and 1968 (H3N2), preferentially bound to the Sia α(2,6) Gal even though the 1918 viruses and presumably the other, arose from avian viruses with a Sia α(2,3) Gal preference. Figure $8$ shows an interactive iCn3D model of α-2,6-linked sialyl-galactosyl ligand binding to the H1 1918 hemagglutinin (2WRG). Figure $8$: α-2,6-linked sialyl-galactosyl ligand binding to H1 1918 hemagglutinin (2WRG). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...yNJHQaQQebSuT9 A variant of the 1918 virus, A/South Carolina/1/18 (18H1), also circulated at the time. It contained a single amino acid mutation, D225G. in the HA protein. That variant switched the HA binding specificity on its target from Sia α(2,6) Gal to both Sia α(2,6) Gal and Sia α(2,3) Gal. This change eliminated a salt bridge (ion-ion interaction) between K222 and D225 in the main variant (see the blue-dotted line in the above model). This in turn allowed another key residue, Q226, to bind to the host receptor. The viral HA in the 2009 human influenza pandemic had K222 and D225, giving it specificity for Sia α(2,6) Gal. Late in that pandemic (as occurred in the 1918 pandemic), a mutated version, D225G, that produced more severe symptoms was isolated. It had also gained dual specificity. Another mutant D225E did not as the salt bridge was maintained and the binding to Sia α(2,6) Gal was actually strengthened. Binding studies showed that the D225G mutants in the HA of both 18H1 and 09H1 viruses bound with higher affinity than the wild-type HAs which likely allowed binding to host glycoproteins deeper in the lung., The dissociation constant KD and the on rate, kon, and off rate, koff, for the 09H1 and 18H1 hemagglutinins and relevant mutants, were determined by surface plasmon resonance spectroscopy (see Chapter 5.2 for a review of SPR). Table $1$ below shows their values. Hemagluttinin Sia-Gal link KD (μM) kon (s-1) koff (M-1s-1) 09H1 α(2,6) 3.74 319 0.00119 09H1 α(2,3) nd nd nd 09H1 D225G α(2,6) 0.475 3650 0.00173 09H1 D225G α(2,3) 2.24 1460 0.00327 18H1 α(2,6) 13.7 125 0.0017 18H1 α(2,3) nd nd nd 18H1 D225G α(2,6) 8.35 531 0.00444 18H1 D225G α(2,3) 4.73 984 0.00466 Table $1$: Dissociation and are constants for the interaction of hemagglutinins (H) from the 2009 and 1918 pandemics with Sia-Gal ligands. Adapted from Zhang et al., J Virol. 2013 May;87(10):5949-58. doi: 10.1128/JVI.00545-13. Epub 2013 Mar 20. PMID: 23514882; PMCID: PMC3648181. It's remarkable how one amino change that can lead from no to strong binding can alter the specificity of a protein for its ligand and human history as well. Slower-acting but very lethal microbial diseases have taken a vast number of lives Malaria: Each year there are an estimated 300-500 million cases that result in about 2.7 million death. Most deaths are children under 5 in sub-Saharan Africa. The disease is caused mainly by the female Anopheles mosquito which transmits Plasmodium falciparum and the less lethal Plasmodium vivax. In the 100 years of the 20th century between 150-300 million deaths have been attributed to malaria (2-5% of all deaths). It was brought to the new world from Africa by the slave trade of over 7 million Africans, and from Portugal and Spain (the main colonial powers where malaria was endemic. The bacteria probably moved from gorillas to humans long ago in Africa. No effective vaccine has yet been developed to prevent this disease. Proteins associated with the virus have been found in Egyptian samples from 3200 BCE and there were descriptions of the cyclic fevers associated with malaria in China in 270 BCE. It was also described by Homer (750 BCE), Plato, and Hippocrates in ancient Greece. It probably was first found in Rome around 0-100 ACE. The virus persisted in Europe for 2000 years. Tuberculosis: This disease is caused by the Mycobacterium tuberculosis bacteria and is spread through the breath. Estimates are that up to 1 billion people have died of TB over history. The BCG vaccine is somewhat effective against TB but not often administered given its low prevalence and the availability of antibiotics. Tuberculosis (TB) was called “phthisis” in ancient Greece, and “tabes” in ancient Rome. The modern common ancestor of this bacteria arouse around 6000 years ago and is associated with disease in both the Old and New World.. Older strains were likely found in seals and sea lions. Genetic analysis showed that the modern strain was found in Peru before the arrival of Europeans to the New World. The disease in the Western Hemisphere probably derived from sea mammals which crossed the ocean. Vaccines against some of our worst infectious disease agents have saved millions of lives. Here are some examples. Figure $9$: Figure $9$: : https://ourworldindata.org/microbes-...ience-vaccines Mathematical models show that from 12/20 through 12/22, Covid vaccines prevented over 120 million infections, 18.5 million hospitalizations, and 3.2 million deaths just in the United States. In the first year of the pandemic (12/20-12/21), models show that 14.4 million deaths (and 19.4 million excess deaths) were prevented in the whole world. Epidemics that decimated Indigenous peoples in the New World Before Columbus came to the New World, there was no typhoid, flu, smallpox, or measles there. These diseases were present in Eurasia where people lived in increasingly populated areas in close quarters with domesticated animals. They would have developed some immunity over time. Their microbes likely derived from domestic animals before jumping species to humans, much as modern flu can be passed from swine to humans and less regularly but more lethally from birds to humans. Even with the buildup of some immunity, new pandemics were utterly devastating. Indigenous peoples in the new world were never exposed to these pathogens before the arrival of people from the Ols World. They only utilized llamas for work and not generally for food and milk. Deaths were staggering. It's estimated that 90% of indigenous people died, a far higher proportion than seen even with the Black Plague in Europe. Imagine the loss of culture and civilization that would accompany a decline in the population of central Mexico from 15 million to 1.5 million in the 100 years after 1519. Figure $10$: Sixteenth-century Aztec drawings of victims of smallpox. https://en.wikipedia.org/wiki/Native..._and_epidemics Social conditions after the initial collapse of the indigenous people in the Americas led to their continued decline, even though they would have gained some immunity. An example is offered by Ostler who describes the health consequences of the Indian Removal Act of 1830, which led to the forced relocation of Native people east of the Mississippi River into "Indian Territory" (Oklahoma and Kansas). As an example, 16,000 Cherokee were expelled and forced to live in camps with few resources, where up to 2000 died of measles, malaria, dysentery, and whooping cough. 1500 more died as they moved west. More died in Oklahoma, leading to a death toll of 25% of the original group. Cumulative death rates in the COVID-19 pandemic show that Indigenous peoples in the United State still have barriers to optimal health care. Figure $11$: Cumulative Deaths and Age-Adjusted Rates per 100,000 in the United States. Infectious, Emerging and Pandemic Diseases - Links to Climate Change Our understanding of infectious diseases clearly shows that the great epidemics and pandemics of the world have arisen when microbial pathogens make the jump from animals to humans who have not experienced them before. For example, HIV/AIDS arose when simian immunodeficiency viruses, to which non-human primates were adapted, jumped to humans in central Africa. The best available data suggest that the SARS-CoV-2 virus jumped from bats to animals (raccoon dogs or other animals from Wuhan China live animal markets) and then to humans, although some data suggest the possibility of a lab leak. A Zoonotic disease (zoonosis, plural zoonoses) is a microbial infectious disease transmitted reversibly between animals and humans. The major types of zoonoses are viral, bacterial, parasitic, mycotic/fungal, rickettsial (obligate intracellular Gram-negative bacteria found in ticks, lice, fleas, mites, chiggers, and mammals), Chlamydial (bacteria that cause STDs), Protozoal or unconventional (such as prions). The ones most prevalent in the US are influenza, Salmonellosis, West Nile virus, Plague, coronaviruses, rabies, Brucellosi, and Lyme disease. Vector-borne diseases are caused by bacteria, viruses, and parasites transmitted through bites of vectors such as infected arthropods like mosquitoes, ticks, sandflies, and blackflies. The range of arthropod vectors expands with global warming as they are cold-blooded. Several anthropogenic (human-caused) factors, including climate change, increase the chances of such jumps. These factors, many of which are interrelated include: • movement of humans into environments where contact with disease-carrying organisms would increase transmission • biodiversity loss which allows species and their microbes to move into new areas • land use change (deforestation, farming, etc) that allows the expansion of species and microbes into new areas • global warming, which encourages the movement of species and their microbes to new areas where human exposure is more likely • climate change-induced changes in plant life that allow altered distributions of animals and microbes • climate change-derived changes in precipitation patterns that affect the adaptation of species and their microbes. Humans affect all of these factors by causing climate change, and land use changes including the expansion of agriculture, urbanization, and the rapid global movement of people, commodities, and other animals. Studies have shown that 58% of human infectious diseases have already worsened with climate change. Another study used databases of mammalian viruses and their host to see which ones might share viruses, an occurrence made much more likely when the species live in the same geographic area. Machine learning was used to model how mammals might share viruses and change their living range in a warming world through 2070. The study found over 4000 viruses could move among 3000 species, greatly enhancing the changes for the exchange of single and multiple viruses among species. Bats (see below) are especially worrisome as they harbor many viruses capable of infecting humans. As bats move habitats due to climate warming, their chances of infecting new species that could then infect humans are greatly increased. 25 years of land use changes in Australia led to altered bat (flying foxes) behavior and to their more permanent presence in agricultural land. This has resulted in viral"spillover" (transmission of a pathogen from a non-human vertebrate to a human) that is driven by periodic food shortages, especially in winters following El Nino weather patterns (characterized by less rain, warmer temperatures, and greater temperature extremes). These changes in bat behavior led to the emergence of the Hendra virus, which infected domestic horses (an intermediate vector), and could pass the virus to humans. The virus does not cause disease in bats but leads to a high mortality rate in horses (75%) and humans (57% based on just four deaths). With climate and land use change, bats persistently spent winters in agricultural lands close to horses. Spillovers occurred more frequently during low food conditions following an El Nino summer. The Black Death (Second Great Pandemic, 1347-1351), occurred during the Little Ice Age in Europe (1300-1850), which also led to a great famine from 1315-1322. There is a link between the pandemic and climate change, but it's difficult to ascertain the strength of the association. An association exists between periodic warm springs and wet summers in Central Asia (using tree-ring data) and outbreaks of the Plague in Europe about 15 years later. This suggests a continual re-importation of Yersinia pestis in Asian rodents into Europe and could explain how long the Plague lasted in Europe. The presence of the plague in gerbils in Kazakhstan would increase with warm spring and wet summer which increase gerbil and flee populations. This Moran effect (time correlation of two populations of a species with change in environment) is well known in population ecology. When gerbil populations collapse, the flea density of the remaining gerbils increases which also leads them to seek different hosts. The spread across geographic distance would take time. In the case of periodic import of flees to Europe, it has been proposed that the 4000 km from west central Asia to the Black Sea took 10-12 years (around 350km/y) . Pathogens from the North Most attention has been given to pathogens moving northward from the south as warmer temperatures allow them to thrive in traditionally colder climates. There is growing parallel concern about pathogens moving south from the Arctic as it warms. In fact, the high northern latitudes have experienced the greatest increase in temperature as the planet warms. The Arctic is predicted to be soon ice-free in the summers. One major concern is that thawing of the permafrost (comprising almost 1/4 of the northern hemisphere that is "permanently" frozen) will allow the release of CO2 (a metabolic product of microbes in the presence of oxygen) and CH4 (a metabolic product of Archeal microbes in an anoxic environment) from organic molecules previously found in frozen soils. (A similar concern is the release of "frozen" methane clathrates from a warmer ocean). An emerging concern is the "activation" of microbes from the permafrost that have been sequestered and dormant for 500,000 years or more. In fact, species like tardigrades, rotifers, and nematodes, that can go dormant and enter a state called cryptobiosis in harsh conditions such as freezing and dehydration, can reactivate. Two species of nematodes (roundworms), dating back to 46,000 years ago (based on radioactive dating), the last years of the Pleistocence, were recovered and revived. Periodic reoccurences of anthrax from the release of the Gram-positive bacterium Bacillus anthracis spores from permafrost thawed in summers have been reported. In 2016, when average temperatures were significantly elevated (see Figure $12$ below), over 2000 reindeer died (close to a 90% mortality rate) and close to 100 people were hospitalized from an anthrax outbreak in Siberoia. On June 20, 2020, a record temperature of 38 °C (100 °F) in the Russian town of Verkhoyansk was recorded! Figure $12$: Color map showing land surface temperature anomalies from -12 °C (-21.6 °F) (darkest red) to + 12 °C (+21.6 °F) (darkest red) during the week of July 20-27, 1916. https://earthobservatory.nasa.gov/im...n-extreme-year We also have to worry about emerging viruses that are released from the thawing of the tundra. Just as for the SARS-Covid-2 virus, we would have no immunity to these viruses. Figure $13$ below shows EM pictures of new infectious viruses isolated from seven different ancient Siberian permafrost samples. Figure $13$: Morphological features guiding the preliminary identification of newly isolated viruses (negative staining, TEM). Alempic, J.-M.; Lartigue, A.; Goncharov, A.E.; Grosse, G.; Strauss, J.; Tikhonov, A.N.; Fedorov, A.N.; Poirot, O.; Legendre, M.; Santini, S.; et al. An Update on Eukaryotic Viruses Revived from Ancient Permafrost. Viruses 202315, 564. https://doi.org/10.3390/v15020564. Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Panel (A) shows large ovoid particle (1000 nm in length) of Pandoravirus yedoma (strain Y2) ( showing the apex ostiole (white arrowhead) and the thick tegument characteristic of the Pandoraviridae family. Panel (B) shows a mixture of Pandoravirus mammoth (strain Yana14) oblate particles and of Megavirus mammoth (strain Yana14) icosahedral particles exhibiting a “stargate” (white starfish-like structure crowning a vertex, white arrowhead) Panel (C) shows the elongated particle of Cedratvirus lena (strain DY0) (1500 nm in length) exhibits two apex cork-like structures (white arrowheads) Panel (D) shows the elongated particle of Pithovirus mammoth (1800 nm in length) exhibiting a single apex cork-like structure (white arrowhead). Panel (E) shows the large (770 nm in diameter) “hairy” icosahedral particle of Megavirus mammoth (strain Yana14), showing the “stargate” (white arrowhead) characteristic of the Megavirinae subfamily Panel (F) shows the smaller icosahedral particle (200 nm in diameter) of Pacmanvirus lupus (strain Tums2) typical of asfarviruses/pacmanviruses. Fungal Diseases We have concentrated solely on bacteria and viral epidemics/pandemics. We also have to consider fungal outbreaks that affect human health but also the foods that sustain us. We have few medicines that treat fungal infections and no vaccines, so any outbreaks could be quite serious. Here are some examples of changes in fungal pathogens that are likely exacerbated by climate change. Candida auris This was first found in 2009 in Japan as a cause of an ear infection. It is now found around the world. Batrachochytrium dendrobatidis (Bd): This affects amphibians and has caused a high loss in amphibian diversity on all continents. Cryptococcus deuterogattii:, This was typically found in more tropical/subtropical climates but now is also found in western Canada and the Pacific Northwest. It causes infections in people and animals. Puccinia striiformis: This causes wheat rust which devastates crops and is now moving into warmer areas. Fusarium graminearum: This causes diseases in wheat and other food crops, especially in warm and wet conditions. Coccidioides immitis: This fungus, which grows in desert soil, can also spread through severe dust storms that cause fungal spores to be blown over wide regions. An example is the dispersal of Coccidioides immitis from Bakersfield, where it was endemic, to Sacramento County, where it wasn't, in 1977. Another example is Apophysomyces trapeziformis, which caused disease in 2011 in Joplin, Missouri after a tornado. Bats, Viruses, and Climate Change We have seen that new infectious diseases arise from pathogen jumps to humans from other species. The more distant the species, the more unlikely humans have encountered the disease and the more likely it could cause severe illness and pandemics. A clear example is the avian flu that led to the 1918 flu pandemic. Yet we also have to worry about zoonoses from pathogen transfer from mammals, including rodents, bats, moles, shrews, monkeys, pigs, camels (a host of the deadly MERS virus), whales, cats, dogs, and seals (a likely source of the original TB virus). Bats are a key source of zoonotic disease, including Middle East respiratory syndrome (MERS), which had a death rate of around 35%. Bats are the source of the Covid virus MERS-CoV which causes MERS. The virus spreads to people from camels. Severe acute respiratory syndrome (SARS) is another coronaviral disease, caused by the called SARS-associated coronavirus (SARS-CoV) which emerged in China in 2003. It had a death rate of around 12% but it was much higher in older people. Neither of these became lengthy full-blown pandemic, as with the SARS-CoV2 virus, the cause of the COVID-19 pandemic. In addition, viruses from bats include rabies, Ebola, and Marburg viruses, as well as the Nipah and Hendra viruses. Bats are more likely to be infected with zoonotic viruses than rodents. Why are bat viruses so key in our worst zoonotic diseases? Two features are important. The same viruses that are so virulent to humans do not kill bats. A clue as to the special nature of bats is that they are the only flying mammal. What might protect bats from their own viruses is that their core temperatures are quite elevated during flight, which requires a high metabolic rate. These high temperatures likely prevent these viruses from harming bats but also make the viruses immune to the high-temperature fevers accompanying infection in humans. The average core temperature of flying bats derived from a variety of species was 39.6 0C or 103.3 0F. Many pathogens replicate optimally at temperatures less than normal body temperature. Fevers in humans are regulated by the hypothalamus, mainly through prostaglandin E2 (PGE2). This response is part of the innate immune response and is elicited by most pathogens. PGE2 binds to the E-prostanoid-3 receptor (EP3), a G protein-coupled receptor in the hypothamlus, which determines the "set point" for body temperature. Hypothalmic PGE2 is produced from the endocannabinoid 2-arachidonylglycerol by the action of monoacylglycerol lipase, at least in mice stimulated with a bacterial cell wall component (LPS), that stimulates fever production. Figure $14$ shows an interactive iCn3D model of the human prostaglandin E receptor EP3 bound to prostaglandin E2 (6AK3). Figure $14$: Human prostaglandin E receptor EP3 bound to prostaglandin E2 (6AK3).. (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...cCgTGEKu3KoGx9 The model shows a dimer of two, GPCRs each bound to 1 PGE2. Research suggests that bats have also evolved to have a lower inflammatory response, mediated by the inflammasome (discussed in detail in Chapter 5.4). Here is a short review of the inflammasome modified from that chapter section. It's needed to give readers a more biochemical explanation for immunosuppression in bat cells, a topic critical to understand the role of bats in present and future pandemics. The inflammasome, part of the innate immune system, is activated by a plethora of pathogens or damaged host molecules. Our innate system immune cells (dendritic cells, macrophages, eosinophils, etc) have receptors that recognize common pathogen-associated molecular patterns (PAMPs) such as lipopolysaccharides (LPS) on the surface of bacteria, mannose on bacteria, and yeast, flagellin from bacterial flagella, dsRNA (from viruses) and nonmethylated CpG motifs in bacterial DNA. These antigens are recognized by pattern recognition receptors (PRRs) - specifically the Toll-like Receptors (TLRs) 1-10. These include plasma membrane TLRs (TL4 for LPS, TL5 for flagellin, TLR 1, 2 and 6 for membrane and wall components of fungi and bacteria) and intracellular endosomal TLRs (TLR3 for dsRNA, TLR 7 and 8 for ssRNA and TLR9 for dsDNA). Figure $15$ shows the TLR family, their binding signals, and intracellular adapter proteins used to transmit signals into the cell. Inflammasomes are also activated by Damage-associated molecular patterns (DAMPs). These are typically found on molecules released from the cell or intracellular compartments on cellular damage (hence the name DAMP). Many are nuclear or cytoplasmic proteins released from the cells. These would now find themselves in a more oxidizing environment which would further change their properties. Common DAMP proteins include heat shock proteins, histones and high mobility group proteins (both nuclear), and cytoskeletal proteins. Here are some other common non-protein DAMPS that can be released on cellular damage: ATP, uric acid, heparin sulfate, DNA, and cholesterol crystals. In the wrong location, these can be considered danger signals. They are sometimes referred to as "sterile" signals. If TLRs recognize PAMPs, what recognizes DAMPs? They are recognized by another type of intracellular pattern recognition receptor (PRR) called NOD (Nucleotide-binding Oligomerization Domain (NOD)- Like Receptors or NLRs. NLRs also recognize PAMPs. The abbreviation NLR also comes from the Nucleotide-binding domain (NBD) and Leucine-Rich repeat (LRR)–containing proteins (NLR)s. This family of proteins participates in the formation of a large protein structure called the inflammasome. (Sorry about the multiple abbreviations and naming systems!) As both PAMPs and DAMPs pose dangers, it would make sense that once they recognize their cognate PRRs (TLRs and NLRs, respectively), pathways leading from the occupied receptors might converge in a common effector system for the release of inflammatory cytokines from immune cells. Given that uncontrolled immune effector release from cells in an inflammatory response might be dangerous, it would be sometimes helpful to require two signals to trigger cytokine release from the cell. Two such inflammatory cytokines are Interleukin 1-β (IL 1-β) and IL-18. Activation of TLRs by a PAMP leads to activation of a potent immune cell transcription factor, NF-kbeta, which leads to transcription of the gene for the precursor of the cytokine, pro-interleukin 1-β. Without a specific proteolytic cleavage, the active cytokine will not be released from the cell. The protease required for this cleavage is activated by a signal arising when a DAMP activates a NLR, which then through a sequence of interactions leads to the proteolytic activation of another inactive protease, procaspase 1, on the inflammasome. The activated inflammasome activates procaspase to produce the active protein caspase (a cysteine-aspartic protease). The convergence of the signals from the PAMP activation of a TLR and DAMP activation of a NLD at the inflammasome is shown in Figure $16$. The active cytokine interleukin 1-β helps recruit innate immune cells to the site of infection. It also affects the activity of immune cells in the adaptive immune response (T and B cels). Active IL-18 leads to the increase of another cytokine, interferon-gamma and it also increases the activity of T cells that kill other cells. Figure $17$ shows an interactive iCn3D model of the NLRP3 double-ring cage, 6-fold (12-mer) (7LFH) Figure $17$: NLRP3 double-ring cage, 6-fold (12-mer) (7LFH). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/icn3d/share.html?DEbdkUoBtqRQ9bu59 The full-length mouse NLRP3 consists of 12- to 16-mer organized in a double-ring cage. It is held together by interactions between the leucine-rich repeats (LRR) domains. The pyrin domains are shielded by the structure, so they will not be activated without appropriate signals. The complex is also localized to the membrane. NLRP3 inflammasomes seem to be activated by cellular distress as well as cell exposure to pathogens. It is one of the main responders to a variety of microbial infections. In summary, two signals are again needed: Signal 1 The first signals are the bacterial and viral (influenza virus, poliovirus, enterovirus, rhinovirus, human respiratory syncytial virus, etc) PAMPs, which bind to TLRs and lead to the activation of the NFkb transcription factor. This activates not only the transcription of pro-interleukin 1-β and interleukin 18, but also the transcription of the NLRP3 sensor itself. Signal 2 Signal 2 is delivered by PAMPs and DAMPs indirectly to the sensor NLRP3. This leads to the assembly of the inflammasome. These DAMPs appear to prime the activation of NLRP3 protein and subsequent formation of the active NLRP3 inflammasome. But what activates NLR3P3? After many studies, it became clear that the typical bacterial ligands that would activate TLRs and perhaps NLRs only prime NLRP3 for activation. They don't bind to it directly. Extracellular ATP is a major activator of NLRP3. Nanoparticles are known to release ATP as well. Most studies show that K+ efflux from the cell is an early signal. Back to Bats To survive the viruses they harbor, bats decrease their inflammatory response upstream at the level of PAMP and DAMP recognition, as well as downstream at the level of caspase-1 inhibition. In addition, additional cleavage sites in IL-1β cause its loss through proteolysis. These events decreased inflammasome signaling. That multiple steps are inhibited suggests that they have been selected through evolutionary pressures. The activation of the bat NLRP3 inflammasome by a "sterile" agent (ATP), as well as 3 RNA viruses is significantly decreased in bat cells compared to responses to these signals in humans and mice cells. The viruses include: • H1N1 influenza A virus (a negative-sense single-stranded RNA virus known to activate the NLRP3 inflammasome); • the Melaka virus (a bat-borne zoonotic double-stranded RNA virus); • MERS-CoV (a positive-sense (+) ssRNA zoonotic virus). Even though the secretion of interleukin-1β is inhibited in bat cells, viral loads remained high in virally-infected bat cells. The altered NLRP3 activation in bat cells occurs in part through decreasing RNA splicing and an altered LRR domain in NLRP3. It should be clear that if bat population increases or if they move to new habitats, both events which could be promoted by climate change, humans are at greater risks for bat-derived pandemics. Coronaviruses, with over 3000 species, make up about 1/3 of the bat's viral load. The region comprising southern Yunan (in China, Myanmar and Laos have very high populations of bats and it is from these areas that new bat zoonoses are likely to derive. Models of past climate (in the 1900s) and bat species richness compared to present climatic conditions, along with the knowledge of specific climate conditions necessary to support bat population and diversity, show that the greatest increase in bat species population in the 20th century occurred in SE Asia. Less important sites included regions in Central Africa and some pockets of Central and South America. The region in China, Myanmar, and Laos is likely the location for the origin of the SAR-CoV1 and SARS-CoV2 viruses.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.15%3A__Pandemic_Diseases_and_Drug_Discovery_-_Under_Construction.txt	princeton-nlp/TextbookChapters	Under construction - 9/26/23 Written by Valerie Doze (change title to what you want) by clicking pencil icon when hovering over the title. Headings: Example On the menu bar, select Normal, Heading 2 You can use Heading 3 for subheadings but adding sub-subheadings gets to look cluttered. For a sub-sub heading just use bold font for the title. Numbering figures: Example Figure $\PageIndex{x}$ below shows the reaction of one key carboxylase. x is the figure number you fill in to make them sequential in the document. Figure $\PageIndex{x}$: Carboxylase used in the CETCH pathways to fix CO2 iCn3D model: Example (to replace) Figure $\PageIndex{x}$ shows an interactive iCn3D model of ribulose 1,5-bisphosphate carboxylase/oxygenase from Synechococcus PCC6301 (1RBL). (image is a png screen capture) I am sure you will have several to many iCn3D models. I can make them or teach you how to do it. Short but real learning curve. I sometimes use Pymol for some images when I feel that iCn3D doesn't show well. Figure Types: • png, jpeg, animated gifs (I can make them for you). Best file type is svg which can be made from PPTX images, ChemDraw, or exporting from other image file types using software like Adobe Illustrator or Affinity Designer • for svg files, must drag them from a folder on your computer to desired spot in the new section. For other can drag them or use picture icon on menu bar to select and import them from your file. References: Incorporate as you like but essential for images et al directly used form a Creative Common CC BY document, for example. 32.16: Fixing Carbon Fixation Search Fundamentals of Biochemistry Carbon Fixation Nature has produced many enzymes that can fix atmospheric CO2, and, of course, everyone knows that photosynthesis is the source of most fixed CO2 in the biosphere. Plankton, cyanobacteria, algae, and plants are key in removing atmospheric CO2 through photosynthesis. Yet we can't plant enough trees to reduce CO2 in the atmosphere in the next decade to avoid some of the worst consequences of anthropogenic climate change. Old-growth trees (mostly gone or under significant stress) are best at removing CO2. New trees would take decades of growth before their effect on carbon drawdown would be consequential. We also need to fix carbon dioxide not only to decrease atmospheric CO2 but also to make more food! A lot of carbon (about 100 petagrams) is sequestered each year in net primary production (fixation of carbon into biomolecules). This is split almost equally between land and ocean organisms. The key enzyme in this process is Rubisco in the C3 (Chapter 20.4) and C4/CAM (Chapter 20.5) pathways. The enzyme is big containing many large subunits and an equivalent number of small ones. A chaperonin is required for folding. It is also a very slow enzyme with a kcat of around 2-10 CO2/sec. In addition, it can bind another substrate, O2, and engage in a competing reaction of photorespiration as described in Chapter 20.4. Figure $1$ shows an interactive iCn3D model of ribulose 1,5-bisphosphate carboxylase/oxygenase from Synechococcus PCC6301 (1RBL) This structure is a hetero 16-mer of 8 small chains and 8 large chains. The small chains are in gray and the large ones in differing colors. Each large subunit contains a bound reaction intermediate analog 2-carboxyarabinitol 1,5-bisphosphate. This chapter section will focus on improving and designing new ways to capture carbon dioxide as one way to reduce its concentration in the atmosphere. Don't lose sight of the fact that the best way to deal with anthropogenic climate change is to stop putting CO2 in the atmosphere from burning fossil fuels. Naturally occurring pathways to fix carbon (Much of this immediate section derives from the following reference: Natural carbon fixation. Sulamita Santos et al., Natural carbon fixation and advances in synthetic engineering for redesigning and creating new fixation pathways, Journal of Advanced Research, Volume 47, 2023, Pages 75-92, ISSN 2090-1232, https://doi.org/10.1016/j.jare. Creative Commons license There are six naturally occurring pathways that fix carbon. These are illustrated in Figure $2$ below. Figure $2$: Natural carbon fixation. Sulamita Santos et al.,ibid. Panel (A) shows the CBB cycle which we discussed in great detail in Chapter 20.4. Here are the enzymes: ribulose-1,5-bisphosphate carboxylase/oxygenase, 3-phosphoglycerate kinase, glyceraldehyde-3-phosphate dehydrogenase, ribulose-phosphate epimerase. Panel (B) shows the reverse (reductive)-TCA cycle which we discussed in Chapter 16.4 in the section on the α-ketoacid pathway - A primordial, prebiotic anabolic "TCA-like" pathway. The enzymes include ATP-citrate lyase, malate dehydrogenase, succinyl-CoA synthetase, ferredoxin (Fd)-dependent-2-oxoglutarate synthase, isocitrate dehydrogenase, PEP carboxylase. Panel (C) shows the Wood–Ljungdahl (or reductive Acetyl-CoA) cycle which we discussed in Chapter 30.1. At the top of the pathway are the acetogens Archaea and at the bottoms are the methanogens Archaea. The enzymes include MPT-methylene tetrahydromethopterin, MFR-methanofuran, THF, tetrahydrofolate. Panel (D) shows the 3-hydroxypropionate (3HP) cycle. The enzymes include acetyl-CoA carboxylase, propionyl-CoA carboxylase, methylmalonyl-CoA epimerase, succinyl-CoA:(S)-malate-CoA transferase, trifunctional (S)-malyl-CoA, -methylmalyl-CoA, mesaconyl-CoA transferase, mesaconyl-C4-CoA hydratase. Panel (E) shows the hydroxypropionate/4-hydroxybutyrate (HP/HB) and the dicarboxylate/4-hydroxybutyrate (DC/HB) cycles. The enzymes include pyruvate synthase, PEP-carboxylase, malate dehydrogenase, fumarate hydratase/reductase, acetyl-CoA/propionyl-CoA carboxylase, 3-hydroxypropionate-CoA ligase/dehydratase, methylmalonyl-CoA mutase, succinyl-CoA reductase, 4-hydroxybutyrate-CoA ligase, crotonyl-CoA hydratase, acetoacetyl-CoA-ketothiolase. Table $1$ below shows a comparative description of the natural and synthetic carbon fixation pathways. Pathway Organisms Energy Source Input Output Reductants Key Enzyme Calvin-Benson (N) Plants, Algae, Cyanobacteria, Aerobic Proteobacteria, Purple bacteria Light 3 CO2, 9 ATP,6 NAD(P)H Glyceraldehyde-3- phosphate NAD(P)H RuBisCO rTCA (N) * Green sulfur bacteria, Proteobacteria, Aquificae, Nitrospirae Light and Sulfur 2 CO2, 2 ATP,4 NAD(P)H Pyruvate NAD(P)H & ferredoxin 2-Oxoglutarate synthase, Isocitrate dehydrogenase Wood–Ljungdahl (N) * Acetogenic, Methanogenic Archaea, Planctomycetes, Sulfate. Archaeoglobales, Hydrogen 2 CO2, 1 ATP, 4 NAD(P)H Acetyl-CoA Ferredoxin NAD-independent formate dehydrogenase, Acetyl-CoA synthase-CO dehydrogenase 3-HP (N) Chloroflexaceae Light 3 HCO , 5 ATP, 5 NAD(P)H Pyruvate NAD(P)H Acetyl-CoA carboxylase, Propionyl-CoA carboxylase HP/HB (N) Aerobic Sulfolobales Hydrogen/sulfur 2 HCO , 4 ATP, 4NAD(P)H Acetyl-CoA NAD(P)H Acetyl-CoA-Propionyl-CoA carboxylase DC/HB (N) * Anaerobic Thermoproteale,Desulfurococcales Hydrogen/sulfur 1 CO2, 1 HCO , 3ATP, 4 NAD(P)H Acetyl-CoA NAD(PH &Ferredoxi Pyruvate synthase, PEP carboxylase RHP (CN) Methanospirillum hungatei Hydrogen CO2, 3 ATP, 2 NAD(P)H Gluconeogenesis and glycolysis NAD(P)H RuBisCO Natural Reductive Glycine (CD) Candidatus phosphitivorax, anaerolimiDesulfovibrio desulfuricans Phosphite CO2, ATP, NAD(P), H Formate/ Pyruvate NAD(P)H &Ferredoxi CO2-reducing formate dehydrogenase(fdhAB) Reverse Otca (CD) Desulfurella acetivorans Hydrogen CO2, ATP, NAD(P) H Acetyl-CoA Ferredoxin Citrate synthase CETCH (S) Theoretical 2 CO2, 2 ATP, 3 NAD(P)H Glyoxylate NAD(P)H CoA- dependent carboxylase Reductive Glycine (S) Demonstrated in E. coli as host CO2, NADH Pyruvate Ferredoxin Glycine cleavage system Synthetic malyl-CoA- glycerate (S) Demonstrated in E. coli and Synechococcus elongatus PCC7942 host CO2, 3 ATP, 3 NADH Acetyl-CoA NAD(P)H PEP-carboxylase, RuBisCO SACA Pathway (S) Demonstrated in E. coli as host CO2 Acetyl-CoA NAD-independent formate dehydrogenase Formolase pathway (S) Theoretical CO2, NADH, ATP Dihydroxyacetone- phosphate NADH NAD-independent formate dehydrogenase Sulamita Santos et al., Natural carbon fixation and advances in synthetic engineering for redesigning and creating new fixation pathways, Journal of Advanced Research, Volume 47, 2023, Pages 75-92, ISSN 2090-1232, https://doi.org/10.1016/j.jare. Creative Commons license Plants and microorganisms that are photoautotrophic fix CO2 by the Calvin cycle using NADPH and ATP and produce O2. Some anerobic photosynthetic bacteria don't produce O2. In chemolithoautotrophic microorganisms, energy sources (i.e. electron donors like H2, H2S, sulfur, Fe2+, nitrite and NH3 found in bedrock - the lithosphere) other than NADPH, ATP and light can be used to drive CO2 uptake. For chemoorganotropic microbes, reduced organic molecules such as sugars and amino acids serve as electron (donor) sources. Genetic engineering and synthetic biology are now employed to improve preexisting enzymes and to create whole new pathways for carbon capture. As a simple example, people are trying to engineer carbonic anhydrase, used to convert CO2 to HCO3- for transport to the lung where it is converted back to CO2 and released. It is a diffusion-controlled enzyme with a kcat/KM reported as high as 8 x 107 M-1s-1, so how can it be made better? One way is to engineer thermal stability into the enzyme. A critical problem for the forward reaction is that the enzyme is readily reversible so bicarbonate, HCO3-, is a competitive inhibitor of the forward reaction. In addition, the enzyme can be engineered to be more stable at higher pH to allow product (HCO3-) removal by the addition of OH- in a process of mineralization, as shown in the equation below, HCO3- + OH- → CO32- + H2O where the carbonate anion can precipitate in the presence of divalent cations like Ca2+, Mg2+ and Fe2+. Here is a link to a Literature-based Guided Assessment on thermoengineering of carbonic anhydrase. Now let's explore the use of new pathways created by synthetic biology to capture carbon. Some of these pathways are engineered to produce reactants (feedstocks) for biofuels, which we discussed in-depth in previous Chapter 32 sections, and chemical synthesis. We'll focus on three: the CETCH pathway, the reductive glyoxylate and pyruvate synthesis (rGPS) cycle, and the malyl-CoA-glycerate (MCG) pathway. CETCH (Crotonyl-CoA-EThylmalonyl-CoA-4Hydorxybutyl-CoA) pathway To engineer a new pathway, Swander et al identified efficient carboxylases from known ones (acetyl-CoA carboxylase, Rubisco, propionyl-CoA carboxylase, PEP carboxykinase, 2-oxoglutarate carboxylase, and pyruvate carboxylase), all of which we have discussed in previous chapters. They created new pathways, calculated free energy and ATP/NADPH requirements, and then optimized the pathways. They chose CoASH–dependent carboxylases and enoyl-CoA carboxylases/reductases. Figure $3$ below shows the reaction of one key carboxylase. Figure $3$: Carboxylase used in the CETCH pathways to fix CO2 The pathway was named CETCH (Crotonyl-CoA-EThylmalonyl-CoA-4Hydorxybutyl-CoA) which catalyzes this next reaction in cell lysates (in vitro): 2CO2 + 3NAD(P)H + 2ATP + FAD → glycolate + 3NAD(P) + 2ADP +2Pi + FADH2 The rate of COfixation by the CETCH pathways was similar to the rate of the Calvin cycle rates in cell lysates. In a more expansive approach, Gleizer used synthetic biology to change E. Coli from a heterotroph to an autotroph in which its biomass (carbon reservoir) came from CO2. Formate (HCO2-) was used as a source of reducing power (electrons) as it was oxidized by an added formate dehydrogenase to produce NADH for the autotropic fixation of CO2 through the addition of Calvin cycle enzymes. Using isotopically labeled 13CO2 to follow carbon flow, after 10 generations and evolution, the cells were completely autotrophic through fixation of CO2. To accomplish this, they knocked out genes for phosphofructokinase (glycolysis) and glucose-6-phosphate-dehydrogenase (pentose-phosphate pathway) to impair these main metabolic pathways, and added carbonic anhydrase (to interconvert CO2 and HCO3-) as well as Rubisco and phosphoribulokinase. As formate was ultimately converted to CO2, the net effect was not exactlycarbon neutral but could be if atmospheric CO2 was used to make formate (for a feedstock) by electrochemical reduction. Figure $4$ below shows the next reactions in the synthetic E. Coli autotrophs. Figure $4$: Schematic Representation of the Engineered Synthetic Chemo-autotrophic E. coli. Shmuel Gleizer et al., Conversion of Escherichia coli to Generate All Biomass Carbon from CO2, Cell, 179 (2019). https://doi.org/10.1016/j.cell.2019.11.009. Creative Commons license. CO2 (green) is the only carbon source for all the generated biomass. The fixation of CO2 occurs via an autotrophic carbon assimilation cycle. Formate is oxidized by a recombinant formate dehydrogenase (FDH) to produce CO2 (brown) and NADH. NADH provides the reducing power to drive carbon fixation and serves as the substrate for ATP generation via oxidative phosphorylation (OXPHOS in black). The formate oxidation arrow is thicker than the CO2 fixation arrow, indicating a net CO2 emission even under autotrophic conditions. Figure $5$ shows that almost 100% of carbon atoms after many generations are labeled with 13C (detected by mass spec analysis) derived from 13CO2. Figure $5$: Isotopic Labeling Experiments Using 13C Show that All Biomass Components Are Generated from CO2 as the Sole Carbon Source. Gleizer et al., ibid. (A) Values are based on LC-MS analysis of stable amino acids and sugar-phosphates. The fractional contribution of 13CO2 to various protein-bound amino acids and sugar-phosphates of evolved cells grown on 13CO2 and naturally labeled formate showed almost full 13C labeling of the biosynthesized amino acids. The numbers reported are the 13C fraction of each metabolite, taking into account the effective 13CO2 fraction out of the total inorganic carbon (which decreases due to unlabeled formate oxidation to CO2). The numbers in parentheses are the uncorrected measured values of the 13C fraction of the metabolites. Synthetic reductive glyoxylate and pyruvate synthesis (rGPS) cycle and the malyl-CoA-glycerate (MCG) pathways These pathways were created to synthesize acetyl-CoA, pyruvate, and malate from CO2 in cell-free systems to free the system from cell growth and regulation requirements, and to make it insensitive to oxygen. These molecules are also intermediates in the created cycle, which could operate continuously for hours at the same or greater rates of CO2 fixation compared to photosynthesis. The cycle is shown in Figure $6$ below. Figure $6$: The rGPS–MCG cycle with acetyl-CoA as the end product. Luo, S., Lin, P.P., Nieh, LY. et al. A cell-free self-replenishing CO2-fixing system. Nat Catal 5, 154–162 (2022). https://doi.org/10.1038/s41929-022-00746-x . Creative Commons Attribution 4.0 International License, http://creativecommons.org/licenses/by/4.0/ The rGPS cycle consists of the reductive glyoxylate sythesis (rGS) pathway (blue) and the reductive pyruvate synthesis (rPS) pathway (green). The malyl-CoA-glycerate (MCG) pathway (orange) consists of the rGS pathway and the glycerate pathway. The red arrow indicates the carboxylation reaction. Gcl, glyoxylate carboligase; Tsr, tartronate semialdehyde reductase; Gk, glycerate 2-kinase; Eno, enolase and 2PG, 2-phospho-d-glycerate Microbial electrosynthesis from CO2 We mentioned above that if the formate used in the CETCH pathway could be synthesized through electrochemistry from CO2, then the pathway would be truly carbon neutral. In fact, new microbial electrochemical methods are being designed to synthesize a variety of small molecules that could serve as feedstocks for chemical synthesis in industry. The carbon in CO2 has an oxidation number of +4 while the carbon in formic acid has an oxidation number of +2. Hence two electrons must be added by electrochemically to make the CETCH pathway truly carbon neutral. Bigger electrochemical reductants of CO2 require more electrons. Figure $7$ below shows how key feedstocks could be made electrochemically from CO2 and how they are usually made in industry. Figure $7$ below. : Overview of the Main Products Formed from Microbial Electrosynthesis (MES) From CO2, Along With the Main Industrial Methods to Manufacture These Products. Jourdin et al., Trends in Biotechnology, April 2021, Vol. 39, No. 4 https://doi.org/10.1016/j.tibtech.2020.10.014. Creative Commons Attribution (CC BY 4.0) These feedstocks could be made by reductive electrosynthesis using electrons from the oxidation of water though electrolysis. Released electrons (oxidation number of O in water is -2 and 0 in O2) move to a biocathode to reduce CO2, as illustrated in Figure $8$ below. Figure $8$: Reactor configurations for MES-based CO2 conversion: (a) H-type, (b) single chamber, (c) dual chamber, (d) continuous stirred tank, and (e) schematic of electron transfer mechanism. G. S. Lekshmi et al., Microbial electrosynthesis: carbonaceous electrode materials for CO2 conversion. Mater. Horiz., 2023, 10, 292-312. DOI: 10.1039/D2MH01178F. Creative Commons Attribution-Non Commercial 3.0 Unported Licence The biocathode consists of a biofilm of cells printed onto a graphene, graphite, or carbon nanotube-laden support (all carbonaceous).
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.17%3A__Fixing_Nitrogen_Fixation.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Nitrogen Fixation We spent most of Chapter 22.1 discussing the biochemistry of nitrogenase which fixes the stable molecule N2 to form NH3/NH4+. It's a very complicated reaction conducted by symbiotic microbes (prokaryotes) that fix N2 for plants. The world uses the Haber-Bosch process to produce over 100 million metric tons of nitrogen fertilizer that supports half of the world's population's food supply. Only about half of the ammonium added to soil is taken up by plants. The rest is released into waterways or used by microbes, which can produce from it the potent greenhouse gas nitrous oxide (N2O). It has a 300x greater effect than CO2 based on weight. The oxidation number of N in NH3 is -3 and in N2O it is +1 showing that the NH3 can be oxidatively metabolized for energy production by the microbes. Also, excess NH4+ goes into waterways and leads to eutrophication, the overproduction of algae and plankton, which depletes O2 from the waters and kills other organisms. N2O emissions have increased dramatically since 1850, as shown in the interactive graph from Our World in Data in Figure $9$ below. Figure $9$: Our World in Data. https://ourworldindata.org/grapher/n...ions?tab=chart So what can we do to "fix" nitrogen fixation to reduce our reliance on the Haber process and its collateral climate effects? Perhaps we could use mutagenesis to make nitrogenase a better and more efficient enzyme. That would prove very difficult given the complexity of both the enzyme and the mechanism of N2 conversion to NH3 that requires many metal ion cofactors. A better alternative would be to express nitrogenase in plants so they could synthesize their own nitrogen fertilizer! Genes of Nitrogen Fixation The catalytic nitrogenase enzyme complex is encoded by three genes: • nifH gene for the subunit Nitrogenase iron protein 1 (also called Nitrogenase component II, and Nitrogenase reductase). It is a homodimer that binds one [4Fe-4S] cluster per dimer. • nifD gene for the subunit Nitrogenase molybdenum-iron protein alpha chain (also called Dinitrogenase and Nitrogenase component I). It catalyzes the key enzymatic reactions along with its partner nifK as part of a heterodimer. It binds one [8Fe-7S] cluster per heterodimer with nifK and binds 1 [7Fe-Mo-9S-C-homocitryl] cluster per subunit. • nifK gene for the subunit Nitrogenase molybdenum-iron protein beta chain (also called Dinitrogenase and Nitrogenase component I). With its partner nifD, it catalyzes the key enzymatic reactions as part of a heterodimer with nifD. It binds one [8Fe-7S] cluster per heterodimer with nifD and binds 1 [7Fe-Mo-9S-C-homocitryl] cluster per subunit. The nitrogenase enzyme complex has regulatory proteins as well: • nifA - Nif-specific regulatory protein required for activation of most nif operons. It senses N2. If there is insufficient quantities of N2, the protein NtrC activates NifA expression which activates the rest of the genes. • nifB - FeMo cofactor biosynthesis protein NifB (also called FeMo-cofactor maturase NifB, Nitrogenase cofactor maturase NifB, and radical SAM assemblase NifB). • nifL - Nitrogen fixation regulatory protein required for the inhibition of NifA activity (i.e. nitrogenase formation) in response to oxygen and low level of fixed nitrogen. • nifE - Periplasmic [NiFe] hydrogenase small subunit. • NifM - a possible peptidyl prolyl cis‐trans isomerase (i.e. a protein chaperone) which helps in the folding of NifH. Figure $10$ below shows a summary of these gene products. Figure $10$: Minimum set of nif genes essential for nitrogen fixation with molybdenum-iron nitrogenase. EMILY M. BENNETT et al., BIODESIGN RESEARCH. 10 Jan 2023, Vol 5, https://spj.science.org/doi/10.34133/bdr.0005 . Creative Commons Attribution License 4.0 (CC BY 4.0).https://doi.org/10.34133/bdr.0005 The stoichiometry depicted has not been adjusted. NifB contains one catalytic cluster (shown in white) and 2 substrate [4Fe-4S] clusters that react to produce the NifB cofactor. NifEN matures the NifB cofactor producing the FeMo cofactor. The molybdenum-iron (MoFe) nitrogenase (NifHDK) contains the FeMo cofactor at its active site. Electron donors transfer single electrons to the [4Fe-4S] cluster at the interface of the NifH homodimer. Electrons are moved from the [4Fe-4S] cluster into the active site of nitrogenase using energy produced by ATP hydrolysis by NifH. A minimum of 8 electrons are used to reduce each molecule of N2. It would be especially important to express nitrogenase in the main cereal food crops (rice, corn, and wheat) which get their nitrogen from soil microbes (in contrast to legumes, which contain nitrogen-fixing bacteria in nodules in their roots). It's a daunting task given the complexity of the protein complex, its metal cofactors, their inhibition by O2, and the multiple genes required for nitrogenase regulation. Ideally, the relevant gene clusters could be moved into a chlorplast which is evolutionarily derived from bacteria so the gene regulation system might be more suitable. It also has low O2 levels at night. However, O2 is produced in chloroplasts, which is problematic given the sensitivity of nitrogenase to O2. Using synthetic biology, Saccharomyces cerevisiae has been engineered to express the NifDK nitrogenase tetrameric protein in their mitochondria (after post-translational import). Yeast is a model organism and tools have been developed for synthetic biology experiments using yeast, so much can be learned that could apply to other eukaryotic organisms like plants. Mitochondria have high O2 consumption (as opposed to production as in the chloroplast) and the ability to synthesize bacterial-type iron–sulfur clusters. The Nif gene clusters were engineered into the XV chromosome as shown in Figure $11$ below. Figure $11$: nif gene assembly in yeast. Buren et al., ACS Synth. Biol. 2017, 6, 6, 1043–1055. https://doi.org/10.1021/acssynbio.6b00371. CC-BY license Panel (a) shows the assembly strategy for transcription units, subclusters, and full clusters inserted by homologous recombination in the genome of S. cerevisiae. Panel (d) shows a disgram of nif gene organization in DSN14, a strain of S. cerevisiae. Nitrogenase activity has also been functionally expressed in transgenic rice containing the NifH with a [4Fe-4S] cluster from Hydrogenobacter thermophilus and NifM (a peptidyl prolyl cis‐trans isomerase from Azotobacter vinelandii) which helps NifH fold. They were correctly targeted to the mitochondria which again minimizes O2 oxidative damage to the metal ion cofactors. The purified protein was able to transfer electrons to the MoFe protein (NfiDK dimer) but did so poorly. It also assisted in the assembly of the FeMo cofactor. However, the [4Fe-4S] cluster occupancy in the protein was poor. However purified protein was also able to reduce acetylene, HC=CH (an alternative substrate similar to N=N) after the addition of purified NifDK. Many steps have to be optimized to create a functional nitrogenase in plants like rice, corn, and wheat. For example, mitochondrial-expressed NifD is readily cleaved by a mitochondria endoprotease. Some NifD subunits are more resistant to proteolysis and a single amino acid change (Y100Q) leads to enhanced stability on the protein. AI will likely be extremely useful in maximizing the expression of nitrogen in crop plants. Synthetic Biology to Express Nitrogenase in Bacteria Bacteria can express nitrogenase that can fix atmospheric N2 but they won't work with the critical cereal crops unless the bacteria can interact with roots in the "rhizosphere", the layers of dirt intimately in contact with roots. This life in this area is often called the holobiont, which consists of the plant host and all species interacting with it in a symbiotic relationship. The metabolism in the holobiont is complex. For example, plant carbohydrates are used by other organisms in the holobiont. It's similar in a way to the gut biome, which consists of an ecosystem of human and microbial cells. Bacteria have now been engineered that express nitrogenase AND interact with corn roots to fix N2. The cells are derivatives of γ-proteobacterium (KV137), found on corns roots and which can fix N2. They have been engineered to turn nitrogenase genes on when N2 fixation is needed. The engineered bacteria is added to liquid fertilizer, reducing the need for chemical fertilizer by 25 lb/acre) and at the same time increasing yields. This bacterial-based fertilizer does not wash into waterways with its concomitant negative environmental effects. Likewise, no N2O is produced on microbial metabolism of excess fertilizers, which decreases the release of this potent greenhouse gas. In 2021 it was used on 3 million acres of corn. One problem with the use of biological N2 microbes in agricultural settings is that high levels of chemical fertilizers can effectively inhibit microbial N2 fixation, which is regulated (as mentioned above) to shut down if nitrogen is bioavailable. Still, bacteria that fix N2 can produce up to 10% of the nitrogen requirement. Genetic engineering is needed to overcome this inhibition. Such bacteria are diazotrophs as they can fix N2 and grow without exogenous sources of N2. Rhizobia are one example. which can fix N2 in the nodules of legumes. The diazotroph isolated from corn roots and mentioned above, Kv137, was gene-edited to produce a modified strain (Kv137-1036) that fixes N2 without inhibition by applied nitrogen fertilizers. The Kv137 strain has nifA and nifL genes and proteins which, as described above, regulate the expression of nitrogenase based on the need for nitrogen. These two genes are on one operon under the control of a single promoter. nifL was replaced with another promoter which removes the down-regulation of nifA since no nifL was present. This allowed the expression and activity of nitrogen even in the presence of exogenous fertilizer. Figure $12$ below shows that Kv137-1036 strain (red dots) does colonize corn roots. Figure $12$: Commercial efficacy of strain Kv137-1036 - Colonization of corn roots by microbes (red) after germination. Wen et al., Enabling Biological Nitrogen Fixation for Cereal Crops in Fertilized Fields. ACS Synth. Biol. 2021, 10, 12, 3264–3277. December 2021. https://doi.org/10.1021/acssynbio.1c00049. Attribution 4.0 International (CC BY 4.0)
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.18%3A__Turning_Trees_into_Plexiglass_-_Synthetic_Biology_For_Production_of_Green_Products.txt	princeton-nlp/TextbookChapters	Search Fundamentals of Biochemistry Introduction Manufacturing of any kind is usually energy and environmentally damaging, and contributes to climate change through the release of CO2 and other pollutants. A manufactured item has a lifetime after which it must be disposed of in a fashion that often involves little recycling. A circular economy in which a used product is always recycled for further use if done well, would be highly beneficial for the environment. Synthetic biology as a field seeks to genetically alter and redesign organisms to produce traditional or novel products in more sustainable ways with less energy input and polluting output. Although it is a nascent field, well know products are binding produced through its use. We will explore several products made through synthetic biology as well as several in which novel cells themselves are the products. Products from cells Burgers by Impossible Foods Making plant-based foods that taste more like meat, if people would eat them, could have a large effect on greenhouse gas emissions and climate change. One example is the Impossible Burgers and other similar meats from Impossible Foods. They have soy leghemoglobin, a monomeric heme-binding protein found in root nodules in legumes, to give the appearance and taste of blood in meat. As a single-chain heme-binding protein, it has a high affinity of O2, similar to animal myoglobin. The high affinity derives from very high on-rates for binding O2 (almost diffusion-controlled at around 2x108 s-1, and an off rate of around 20 s-1. This high affinity keeps O2 bound which would otherwise inhibit nitrogenase and nitrogen fixation by root-associated microbes. The heme is important for positive tastes when we eat red meat. Plant-based burgers containing leghemoglobin require much less land and lead to far lower greenhouse gas emissions. Figure $1$ shows an interactive iCn3D model of the alignment of sperm whale myoglobin (1MBO) and soy leghemoglobin (1BIN). Figure $1$: Alignment of sperm whale myoglobin (1MBO, cyan) and soy leghemoglobin (1BIN, magenta). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...4pUi1S6qFtGn4A The leghemoglobin in the Impossible burgers is produced in yeast so it can be scaled up easily. To produce leghemoglobin in yeast, large amounts of heme are required, which is also produced in the engineered cells on the introduction of the appropriate genes. The heme synthesis pathways (described in Chapter 22.3) for C4 (humans, animals, fungi, and purple non-sulfur phototrophic bacteria top) and C5 (archaea, plants, and other bacteria) for heme synthesis are shown in Figure $2$ below. The succinyl-CoA is derived from the citric acid cycle. Figure $2$: Top - heme synthesis pathway for C4 (humans, animals, fungi, and purple non-sulfur phototrophic bacteria top). Bottom - heme synthesis pathway for C5 (archaea, plants, and other bacteria). Heme biosynthetic pathway. Wikimedia Commonsile: Heme-Synthesis-Chemical-Details-Mirror (top) and Heme pathway in E. coli. Zhang, J., Kang, Z., Chen, J. et al. Optimization of the heme biosynthesis pathway for the production of 5-aminolevulinic acid in Escherichia coliSci Rep 5, 8584 (2015). https://doi.org/10.1038/srep08584. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/ (bottom). Note that both succinyl-CoA (C4 pathway) and α-ketoglutarate (C5 pathway) are derived from the citric acid cycle. The key precursor 5-aminolevulinate, ALA needs to be elevated, through engineering of the C4 or C5 pathways with the C5 pathways generally producing more ALA in engineered E. Coli. Leghemoglobin from soy (species name Glycine max) can also be synthesized in the methylotrophic (uses methanol as a sole carbon source) yeast Pichia pastoris which is often used for recombinant protein expression. Three groups of enzymes are needed" • (group 1: porphobilinogen synthase (PBGS) • group 2: uroporphyrinogen III synthase (UROS), uroporphyrinogen III decarboxylase (UROD), coproporphyrinogen III oxidase (CPO) • group 3: Ala synthase (ALAS), protoporphyrinogen oxidase (PPO), and ferrochelatase (FECH) Transcription of these genes in P. pastoris can be controlled by the use of the methanol-induced alcohol oxidase (AOX1) promoter, which is often used to achieve high expression of recombinant proteins. Hence when the cells also contain two copies of leghemoglobin along with the rest of the genes, high levels of the protein were made. A more detailed representation of the heme synthesis pathway is shown in Figure $3$ below. Figure $3$: The biosynthetic pathway of heme. Su, H.; Chen, X.; Chen, S.; Guo, M.; Liu, H. Applications of the Whole-Cell System in the Efficient Biosynthesis of Heme. Int. J. Mol. Sci. 202324, 8384. https://doi.org/10.3390/ijms24098384. Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/) The two biosynthetic pathways of 5-aminolevulinic acid (bolded to indicate its importance) are shown in green (C4 pathway) and red (C5 pathway). The three downstream synthetic pathways of heme are marked with blue (CPD), indigo (SHD) and purple (PPD). Solid lines indicate single reactions, and dashed lines indicate more than two reactions. The names of genes encoding the individual enzymes are in italics and some reactions have alternative genes. The abbreviations of the corresponding enzymes are shown in the grey rectangle. See Table 1 for a list of names and abbreviations for heme synthesis enzymes Figure $4$ below complements this figure and shows the synthetic biology strategies to enhance heme production. Figure $4$: Synthetic biology strategies to enhance heme production. Green, orange, and red color blocks indicate genes that need to be up-regulated, down-regulated, and knocked out, respectively. Su, H. et al., ibid. Computational tools such as AI can help the design of new pathways and novel enzymes to enhance production. It is becoming easier to transfer large pathways into yeast as well. Other food products from microbes and sustainable plants Significant effort is being devoted to growing meat in cell culture in the lab. This is a nascent field and has to overcome many problems, including consumer resistance to eating lab-created meat. At present meat grown in tissue culture is very expensive. Three key steps in growing meat are finding the best cells to grow, finding the nutrient conditions to maximize their growth, and adjusting conditions to make the lab meat taste like meat. Muscle stem cells have been used as they can multiply many times, but these have growth limits. Alternatively, immortal cells, such as those derived from chicken fibroblasts, could be used. They can also be converted to fat cells. Yet they could accrue mutations with possible, but unlikely health consequences. Animal cells grown in culture often use fetal cow serum for its rich composition of growth factors and nutrients. However, it is very expensive and has ethical concerns as well since it's derived from animals. Synthetic growth medium can be used but it is also expensive. Whether lab-grown meat can overcome high costs and consumer resistance will determine its potential as a meat substitute. More simply, people can use more peas, soy, grains, and nuts in their diet (i.e. being a vegan or vegetarian is the best approach to reducing your carbon footprint). Soy products have an extensive history of use as a source of protein but contain potential allergens (especially important in babies who use soy formulas) and isoflavones, which mimic human estrogen derivatives. Pea-based protein infant formulas are an increasing-used substitute. Expressed recombinant proteins made in genetically modified bacteria and yeast are also becoming more popular. Examples (other than leghemogloblin) include the production in the fungus Trichoderma reesei of β-lactoglobulin, a cow whey protein, for dairy and animal-free milk products. The genetically-modified yeast Pichia pastoris has also been engineered to make milk casein proteins, egg-white proteins, muscle myoglobin, and human breast milk proteins. Enzymes used in the manufacture of cheese (derived from calves' stomachs) can be replaced by chymosin made in yeast. Production is linked to fermentation for many of these proteins. Filamentous proteins that have a texture similar to chicken fiber can be made through fermentation in the filamentous fungi Fusarium venenatumin. Macroalgae like seaweeds can provide high-protein food and have long been used in many cultures. Kelp farming can help not only provide protein but also capture carbon. Finally, insects, long eaten in many cultures, could become more climate-friendly source of protein. If humans are in search of nonanimal sources of protein to fight climate change, why not produce and eat the most abundant protein in the biosphere, Rubisco? New products derived from the duckweed plant (genus Lemna) are coming to market. Figure $5$: Figure $5$: Duckweed (and a frog). https://commons.wikimedia.org/wiki/F...7678481%29.jpg Duckweed is high in nutrients, fast-growing, and a great source of Rubisco. It can be grown in aquaculture and does not require farmable land. It contains up to 50% protein. After harvesting, the plants are filtered, milled, and dried, which are all very simple technologies. Proteins, the most abundant being Rubisco, are then extracted. It can be used in baked goods and as meat and dairy substitutes. It is equal to eggs and meat in supplying all the essential amino acids required by humans. Genetic Manufacturing of Industrial Feedstocks Let's look at one example in which synthetic biology and computational techniques are used to create products such as plexiglass from a biological source of acrylates. Acrylates are esters of acrylic acid (typically made from propylene) synthesized by reacting it with alcohols like methanol. Life cycle analyses show that almost 4000 kg CO2 are produced per metric ton of acrylic acid made. To reduce the climate effect, biological feedstocks like glycerol and 3-hydroxypropanoic acid can be used, but large-scale supplies are needed. Figure $6$ shows an overview of acrylate production fossil and biological feedstocks. Figure $6$: Production pathway of acrylates using fossil fuel and renewable resources. Souza, L.R.d.; Whitfield, B.; Al-Tabbaa, A. Biobased Acrylate Shells for Microcapsules Used in Self-Healing of Cementitious Materials. Sustainability 202214, 13556. https://doi.org/10.3390/su142013556. Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/) When the alcohol is methanol, the final product is methylacrylate (MA). The structure of the cyclic acrylate monomer feedstock used to polymerize plexiglass (lucite) is methylene-butyrolactone, (MBL)whose structure is shown in Figure $7$ below. Figure $7$: Structure of methyl methacrylate and its lactone (a cyclic ester) Methyl methacrylate can undergo a free radical polymerization in the presence of an initiator (In.), as shown below in Figure $8$. Figure $8$: Mechanism of free radical polymerization of MMA This reaction can form large polymers like plexiglass. The market for acrylic acid, the feedstock for plexiglass, is estimated to reach 12 million metric tons by 2030. MBL, the lactone of MMA, is made in tulips from pathways that are not completely elucidated. It can also be used as a feedstock for the polymerization of plexiglass. Figure $9$ shows the polymerization products from MMA and MBL. Figure $9$: Structure of poly-MMA and poly-MBL Using synthetic biology and advanced computational methods, plexiglass can now be made from biological sources instead of fossil fuels. To accomplish this, Azerda has designed synthetic pathways from millions of potential metabolic pathways (using a software package called Scylax™), and intelligently redesigned key enzymes to maximize their catalytic potential for the synthesis of MBL (using the software Archytas™). They used high-throughput DNA and protein analyses to maximize expression. Finally, they engineered expression strains and downstream purification processes to maximize the final output of MBL. In summary, the key steps in the process were: • identifying a pathway from millions of reactions in databases of pathways that could produce MBL from simple sugars through a fermentation process; • engineering pathway enzymes to greatly increase catalytic efficiency and decrease inhibition; • producing test quantities of the products in cell strains; • scaling up production to levels needed for purification and reactions of the MBL • purifying sufficient amounts of MBL from large fermentation broths • making the desired product (plexiglass, for example) from the feedstock. Strains of bacteria, yeast, and filamentous fungi were modified to meet the above criteria. The ultimate substrate for the process was a lignocellulosic hydrolysate, so in the end the process converts trees to plexiglass (incredible to think about)! Of course, it is also amazing that CO2 from the air, water, and minerals/ions from the soil can become a tree! Starting with just a detectable level of product, the process was continually improved and scaled to eventually yield 5 g/L of broth, which is getting close to the 20 g/L required for commercial viability. Figure $10$ below shows plexiglass created from the lignocellulosic stock! Table $10$: Plexiglass made from biosourced MBL. Table $1$ below compares the key physical properties of the polymers from Arzeda's PMBL compared to literature values for fossil-fuel-based PMBL and for PMMA. Property Measure Lit PMBL Arzeda PMBL PMMA Thermal Glass transition pt Tg (oC) 194-195 195 105 Mechanical Elasticity (mPa) 1999/3439 5972 2855 Tensile strength (mPa) 36.7/62.7 72.7 70 Elongation at break 1.3%.6.5% 1.3% 2.5 Optical Light transmission NA >88% 92% Solvent resistance toluene, 30 days, 20oC NA Pass Fail Table $1$: https://www.energy.gov/sites/default...-korkegian.pdf
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/Chapter_33%3A__Your_Contribution_-_Sandbox/Arthur_Sikora_Lipid_rafts.txt	princeton-nlp/TextbookChapters	Exercise $1$ What kinds of lipids are found in a eukaryotic cell membrane under normal conditions? Here is a Hint if you need one Answer Glycerophospholipids, sphingolipids, sterols and fatty acids. Authored by Arthur Sikora. Last update: 6/5/2023 Date of origin Introduction Lipid rafts are formed and dissipated over time. They are regions where specific lipids are enriched, frequently resulting in protein aggregation as well New Heading [ADD TEXT] [ADD IMAGE] (saved to your computer and uploaded with picture icon from top menu bar or drag image file to location (required from svg image) Use the following under your picture: Figure $\PageIndex{x}$: [Add caption] Center Picture and Caption together using top menu bar Lipids being removed and added to a membrane [ADD iCn3D Model] Figure $\PageIndex{x}$ is an interactive iCn3D model of [INSERT DESCRIPTIVE TEXT - often a modified version of the title from the actual PDB page 4Q7Q Figure $6$: [INSERT THE PDB TITLE as above (INSERT PDB CODE)]. Click the image for a popup or use this external link: [https://structure.ncbi.nlm.nih.gov/i...PGDeccDmTH3Nq8]. (Copyright; author via source). iCn3D model made by Arthur Sikora MODEL No inhibition (left) and Uncompetitive Inhibition (right) Note that the Vcell reaction diagram is the same as for competitive and uncompetive inhibition. It doesn't explicitly show that the mixed inhibitor binds to both free and substrate-bound enzymes. Those interactions are addressed in the mathematical equations for mixed inhibition. Initial values No Inhibition Initial values With Uncompetitive Inhibitor I is fixed for each simulation (as it is not converted to a product) but can be changed in the simulation below. Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play \| Select Slider to change which constants are displayed \| Select About for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health. Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington. Funded by NIH/NIGMS (RO1-GM123032-04) sample modle My modle your text and graph Figure $\PageIndex{x}$: Add caption [ADD VCELL/SBML SIMULATION - REUSE] your text before and after insert as needed New Heading Add what you want BioMolViz Framework BIOMOLVIZ Promoting Molecular Visualization Literacy The BMV framework is used with permission from BioMolViz.Org Copy the appropriate row when assigning a theme, goal, and objective to a designated iCn3D or other biomolecular visualization assessment Atomic Geometry (AG) Three‐atom and four‐atom (dihedral) angles, metal size and metal‐ligand geometries, steric clashes AG1. Students can describe the ideal geometry for a given atom within a molecule and deviations from the ideal geometry due to neighboring interactions. AG1.01 Students can identify atomic geometry/hybridization for a given atom. (Novice) AG1.02 Students can measure bond angles for a given atom. (Novice) AG1.03 Students can identify deviations from the ideal bond angles. (Amateur) AG1.04 Students can explain deviations from the ideal bond angles due to local effects. (Amateur, Expert) AG1.05 Students can predict the effect of deviations from ideal bond angles on the structure and function of a macromolecule. (Expert) AG1.06 Students can identify the geometric features of bonds (e.g., peptide bond, glycosidic, phosphoester). AG2. Students can compare and contrast different structural conformations with regard to energy, the addition of substituents, and the impact on the structure/function of a macromolecule. AG2.01 Students can describe different conformations that a structure can adopt using visualization tools. (Amateur) AG2.02 Students can describe different conformations of atoms about a bond using visualization tools. (Novice) AG2.03 Students can distinguish energetically favorable and unfavorable conformations that a structure can adopt. (Amateur) AG2.04 Students can predict the effect of a given substituent on the structure and function of a macromolecule (e.g., substituent on a carbohydrate/ligand, R groups/rotamers, phosphorylation, methylation of nucleic acids, post-translational modifications). (Expert) AG3. Students can describe dihedral/torsion angles in biomolecules. AG3.01 Students can identify a dihedral/torsion angle in a three-dimensional representation of a molecule. (Novice) AG3.02 Students can identify the planes between which a dihedral/torsion angle exists within a three-dimensional representation of a macromolecule. (Novice) AG3.03 Students can identify phi, psi, and omega torsion/dihedral angles in a three-dimensional representation of a protein. (Amateur) Alternate Renderings (AR) Rendering of a macromolecular structure such as a protein or nucleic acid structure in various ways from the simplest possible way (connections between alpha carbons) to illustration of secondary structure (ribbons) to surface rendering and space filling. AR1. Students can interpret or create molecular images that convey features such as secondary structure, CPK coloring, and active sites. AR1.01 Students can manipulate rendered structures to illustrate molecular properties. (Novice) AR1.02 REMOVED (integrated with SF2.02) AR1.03 Students can describe or label structural differences among multiple structures. (Amateur, Expert) AR1.04 Students can infer information from rendering a structure in different ways. (Novice, Amateur, Expert) AR1.05 Students can create renderings that distinguish secondary structural features. (Novice) AR1.06 Students can create an information rich rendering of a structure that depicts structural features found in the literature. (Amateur) AR1.07 Students can create an information rich rendering of a structure containing ligands, covalent modifications, and noncanonical amino acids or nucleotides. (Amateur, Expert) AR1.08 Students can use molecular visualization to tell a story about a macromolecular structure. (Expert) AR1.09 REMOVED (integrated with MI1.02) AR1.10 Students can convert textbook images of small molecules into 3D representations in a molecular visualization program. (Amateur) AR2. Students can choose the best rendering of a macromolecule to use in a given situation. AR2.01 Students can recognize that a cartoon rendering is a summary of the detail in a line rendering. (Novice, Amateur) AR2.02 Students can describe the atoms and their representations in different renderings (e.g., coloring, showing hydrogens/double bonds). (Novice) AR2.03 Students can identify or create a suitable rendering, or combination of renderings, for a specific purpose (e.g., a surface rendering overlaid with a cartoon to highlight the van der Waals surface alongside secondary structure, or active site sticks shown over a cartoon). (Novice, Amateur) AR2.04 Students can identify the limitations in various renderings of molecular structures. (Amateur) AR2.05 Students can understand the level of detail of different molecular representations. (Novice, Amateur, Expert) AR2.06 Students can transition comfortably between equivalent 2D and 3D renderings of biomolecules. (Novice, Amateur, Expert) AR2.07 Students can use and interpret color in the context of macromolecules to clarify and/or highlight features (e.g., coloring amino acids differently by property, different molecules uniquely in a complex, protein chains, secondary structure). (Novice) Construction and Annotation (CA) Ability to build macromolecular models, either physical or computerized, and, where possible, add commentary, either written or verbal, to tell a molecular story. CA1. Students can compose information‐rich renderings of macromolecule‐ligand interactions. CA1.01 Students can construct and annotate a model of a macromolecule bound to a ligand. (Amateur) CA1.02 Students can construct a model of a macromolecule bound to a ligand and identify the types of molecular interactions. (Amateur) CA1.03 Students can construct a model of a macromolecule bound to a ligand and assess the importance of molecular interactions. (Expert) CA1.04 Students can produce a model of a macromolecule based on a known structure of a related macromolecule. (Amateur, Expert) CA2. Students can compose a rendering to predict the cellular location of a protein (e.g., extracellular, membrane associated, or cytoplasmic) based on the properties and orientations of functional groups. CA2.01 Students can design a rendering that conveys properties such as polarity, charge, secondary structure, etc. to suggest the cellular location of a macromolecule. (Amateur) CA2.02 Students can create protein images with colored polar/nonpolar residues to determine whether they fold with a hydrophobic core. (Amateur) CA2.03 Students can create images to display polar/nonpolar residues and propose a role for the protein and/or how it interacts with its environment ‐ and that the predictions would be plausible based on the protein. (Amateur) CA2.04 Students can make accurate predictions of the location/function of the protein that incorporates additional protein features, such as transmembrane helices, apparent docking surfaces, etc. (Expert) Ligands and Modifications (LM) Metals and metal clusters, additions such as glycosylation, phosphorylation, lipid attachment, methylation etc. LM1. Students can identify ligands and modified building blocks (e.g., hydroxyproline, aminosaccharides, modified nucleobase) within a rendered structure. LM1.01 Students can use the annotation associated with a pdb file to identify and locate ligands and modified building blocks in a given biomolecule. (Amateur) LM1.02 Students can visually identify non‐protein chemical components in a given rendered structure. (Amateur) LM1.03 Students can distinguish between nucleic acid and ligands (e.g., metal ions) in a given nucleic acid superstructure. (Amateur) LM1.04 Students can explain how a ligand in a given rendered structure associates with the biomolecule (e.g., covalent interaction with residue X). (Amateur) LM1.05 Students can locate/identify ligands and modified building blocks in unannotated structures and describe their role. (Expert) LM2. Students can describe the impact of a ligand or modified building block on the structure/function of a macromolecule. LM2.01 Students can look at a given rendered structure and describe how the presence of a specific ligand or modified building block alters the structure of that biomolecule. (Amateur) LM2.02 Students can explain how the removal of a particular ligand or modified building block would alter the structure of a given biomolecule. (Expert) LM2.03 Students can use molecular visualization tools to predict how a specified ligand or modified building block contributes to the function of a given protein. (Amateur, Expert) LM2.04 Students can predict how a ligand or modified building block contributes to the function of a protein for which the structure has been newly solved. (Expert) Macromolecular Assemblies (MA) Polypeptides, oligosaccharides, and nucleic acid and lipid superstructures (e.g. protein–nucleic acid complexes, lipid membrane-associated proteins) MA1. Students can describe various macromolecular assemblies. MA1.01 Students can identify individual biomolecules in a macromolecular assembly. (Novice, Amateur, Expert) MA1.02 Students can describe functions of individual biomolecules within a macromolecular assembly. (Novice, Amateur, Expert) MA1.03 Students recognize the various lipid ultrastructures (e.g., micelles, bicelles, vesicles, and lipid bilayers) in a 3D structure. (Novice) MA2. Students can compose information‐rich renderings of macromolecular assemblies. MA2.01 Students can render a macromolecular assembly to highlight individual structures. (Amateur) MA2.02 Students can render a macromolecular assembly to illustrate structural features (e.g., binding interfaces, symmetry, tertiary structure, etc.). (Novice, Amateur, Expert) Macromolecular Building Blocks (MB) Recognition of native amino acids, nucleotides, sugars, and other biomonomer units/building blocks. Understanding of their physical and chemical properties, particularly regarding functional groups. MB1. Students can identify individual building blocks of biological polymers. MB1.01 Given a rendered structure of a biological polymer, students can identify the ends of a biological polymer. (Novice, Amateur, Expert) MB1.02 Given a rendered structure, students can divide the polymer into its individual building blocks. (Novice) MB1.03 Given a rendered structure, students can identify the individual building blocks. (Novice) MB2. Students can describe the contributions different individual building blocks make in determining the 3‐D shape of the polymer. MB2.01 Students can describe the physical/chemical properties of an individual building block/functional group in a rendered structure of a polymer. (Amateur) MB2.02 Students can describe the significance of the location of individual building blocks within the 3D structure of a polymer (protein, carbohydrate, or nucleic acid). (Novice, Amateur, Expert) MB2.03 Students can identify physical/chemical properties of individual building blocks/functional groups in different local environments. (Amateur) MB2.04 Using a visualized structure, students can identify stereochemistry (e.g., in carbohydrate, lipid, and protein structures). (Amateur) MB2.05 Students can modify/mutate a building block to change the 3D structure of a polymer (protein, carbohydrate, or nucleic acid). (Amateur, Expert) Molecular Dynamics (MD) Animated motion simulating conformational changes involved in ligand binding or catalysis, or other molecular motion/dynamics. MD1. Students can describe the impact of the dynamic motion of a biomolecule on its function. MD1.01 Students can recognize that biological molecules have different conformations. (Novice, Amateur) MD1.02 Students can correlate molecular movement with function. (Novice, Amateur, Expert) MD2. Students can predict limits to macromolecular movement. MD2.01 Students can locate potential regions of flexibility and inflexibility in the structure of a biomolecule. (Novice, Amateur) MD2.02 Students can recognize acceptable/unacceptable movement within a macromolecule by determining whether the movement is within allowable bond angles. (Expert) MD2.03 Students can recognize acceptable/unacceptable movement within a macromolecule by determining whether the movement results in steric hindrance. (Amateur) MD2.04 Students can recognize acceptable/unacceptable movement within a macromolecule by considering the atomic packing constraints. (Expert) Molecular Interactions (MI) Covalent and noncovalent bonding governing ligand binding and subunit‐subunit interactions. MI1. Students can predict the existence of an interaction using structural and environmental information (e.g. bond lengths, charges, pH, dielectric constant). MI1.01 Students can distinguish between covalent and noncovalent interactions. (Novice) MI1.02 Students can identify different noncovalent interactions (e.g., hydrogen bonds, ionic interactions, van der Waals contacts, induced dipole) given a 3D structure. (Amateur) MI1.03 Students can predict whether a functional group (region) would be a hydrogen bond donor or acceptor. (Amateur) MI1.04 Students can render the 3D structure of a biomolecule so as to demonstrate the ionic interactions and/or charge distribution of the different non‐covalent interactions. (Amateur) MI1.05 As it relates to a particular rendered structure, students can rank the relative strengths of covalent and noncovalent interactions. (Amateur) MI2. Students can evaluate the effect of the local environment on various molecular interactions. MI2.01 Students can identify regions of a biomolecule that are exposed to or shielded from solvent. (Novice) MI2.02 Students can identify other molecules in the local environment (e.g., solvent, salt ions, metals, detergents, other small molecules) that impact a molecular interaction of interest. (Novice) MI2.03 Students can predict the impact of other molecules in the local environment (e.g., solvent, salt ions, metals, detergents, other small molecules) on a molecular interaction of interest. (Amateur) MI2.04 Students can predict the pKa of an ionizable group based on the influence of its local three-dimensional environment. (Amateur) MI2.05 Students can propose a change to the local environment that would yield a desired change in a molecular interaction. (Expert) MI2.06 Using molecular visualization tools, students can determine which intermolecular force is most critical to stabilizing a given interaction. (Expert) Symmetry/ Asymmetry Recognition (SA) Recognition of symmetry elements within both single chain and multi-chain macromolecules. SA1. Students can identify symmetric or asymmetric features in rendered molecules. SA1.01 Students can identify symmetric features in a rendered molecule (shown in fixed orientation). (Novice) SA1.02 Students can rotate a single macromolecule, multi-chain macromolecules (e.g., homo- or heteromers), complexes of macromolecules, and supramolecular assemblies to identify axes of symmetry. (Amateur) SA1.03 Students can identify symmetric and asymmetric features in rendered molecules after coloring a given rendered molecule to reveal structural features (charge, hydrophobicity, etc.). (Amateur) SA2. Students can hypothesize the functional significance of symmetry or asymmetry in rendered molecules. SA2.01 Students can explain the functional significance of rotational axes of symmetry (or asymmetry) in a given rendered molecule. (Novice, Amateur, Expert) SA2.02 Students can predict functional significance of symmetry (or asymmetry) in a given rendered molecule. (Amateur, Expert) Structure‐Function Relationship (SF) Active/binding sites, microenvironments, nucleophiles, redox centers, etc. (please also see LM2.03) SF1. Students can evaluate biomolecular interaction sites using molecular visualization tools. SF1.01 Students can identify functionally relevant cofactors, ligands or substrates associated with a macromolecule and describe their role (e.g., an active site magnesium ion). (Amateur, Expert) SF1.02 Students recognize that the size and shape of the ligand must match the size and shape of the binding site. (Novice, Amateur) SF1.03 Students recognize that the polarity or electrostatic potential of a surface complements that of the ligand or substrate. (Novice, Amateur) SF1.04 Students recognize that the hydrophobicity of a surface complements that of the ligand or substrate. (Novice, Amateur) SF1.05 REMOVED (integrated with SF1.03) SF1.06 Students can use docking software to predict how the surface properties of a macromolecule guide and allow the binding of a ligand or substrate. (Amateur) SF2. Using molecular visualization, students can predict the function of biomolecules. SF2.01 Students can recognize structurally related molecules. (Novice) SF2.02 Students can superimpose structurally related molecules. (Novice, Amateur) SF2.03 Students can identify functionally relevant features of a macromolecule (e.g., an active site cysteine, a functional loop). (Amateur) SF2.04 Students can predict molecular function given a binding site. (Amateur, Expert) SF3. Using molecular visualization, students can predict the function of an altered macromolecule. SF3.01 Students can structurally alter a macromolecule. (Novice) SF3.02 Students can propose structural alterations to test interactions in a macromolecule. (Amateur) SF3.03 Students can predict the impact of a structural alteration on the function of a macromolecule. (Amateur, Expert) Structural Model Skepticism (SK) Recognition of the limitations of models to describe the structure of macromolecules. SK1. Students can critique the limitations of a structural model of a macromolecule. SK1.01 Students can explain that the pdb file is a model based on data and that, as a model, it has limitations. (Novice, Amateur) SK1.02 Students associate resolution with reliability of atom positions. (Amateur) SK1.03 Students can identify building blocks (for example, amino acid side chains) whose orientation in a biopolymer is uncertain. (Expert) SK1.04 Students can evaluate the flexibility/disorder of various regions of a macromolecular structure. (Novice, Amateur, Expert) SK1.05 Students can reconcile inconsistent numbering of individual building blocks among species and structure files. (Novice) SK1.06 Students can utilize a Ramachandran plot/steric clashes to interpret the validity of a structure. (Amateur, Expert) SK1.07 Students can describe the limitations of a macromolecule‐ligand docking simulation. (Amateur, Expert) SK2. Students can evaluate the quality of 3D models including features that are open to alternate interpretations based on molecular visualization and PDB flat files. SK2.01 Students can evaluate a crystal structure for crystal packing effects. (Novice, Amateur, Expert) SK2.02 Students can resolve differences between the asymmetric unit and the functional biological assembly. (Expert) SK2.03 Students can differentiate functional ligands (with biological/biochemistry role) from nonfunctional ligands (most solvents, salts, ions, and crystallization agents). (Novice, Amateur, Expert) SK3. Students can discuss the value of experimentally altering a biomolecule to facilitate structure determination. SK3.01 Students can identify non‐native structural features. (Amateur) SK3.02 Students can propose molecular modifications to facilitate structure determination. (Amateur, Expert) SK3.03 Students can propose a purpose for the introduction of non‐native structural features to facilitate structure determination. (Amateur, Expert) Topology and Connectivity (TC) Following the chain direction through the molecule, translating between 2D topology mapping and 3D rendering. TC1. Students can describe or illustrate the linkages between building blocks within a macromolecule. TC1.01 Students can trace the backbone of a macromolecule in three dimensions. (Novice, Amateur) TC1.02 Students can use appropriate terms to describe the linkages/bonds/interactions that join individual building blocks together in a macromolecule or macromolecular assembly. (Novice, Amateur) TC1.03 Given a virtual model of individual building blocks, students can predict the types of linkages/bonds/interactions that are possible or favorable. (Amateur) TC1.04 Given individual building blocks, students can appropriately connect them to create a biological polymer (e.g., drawing carbohydrate linkages, a small peptide). (Amateur, Expert) TC2. Students can describe the overall shape and common motifs within a 3D macromolecular structure. TC2.01 Using molecular visualization software, students can describe the three-dimensional structure of a macromolecule, including overall shape and common structural motifs. (Novice, Amateur, Expert) TC2.02 Students can identify common domains/motifs within a macromolecule. (Amateur, Expert) TC2.03 Students can identify connectivity features between domains or subunits in a macromolecular structure. (Amateur) TC2.04 Students can identify interactions between domains or subunits in a macromolecular structure. (Amateur, Expert) TC2.05 Students can describe how domains/motifs in a macromolecule work together to achieve a concerted function in the cell. (Amateur, Expert) TC2.06 Students can identify the levels of protein structure (e.g., parse a tertiary/quaternary structure into a series of secondary structures/motifs) and the ways in which they are connected from a three‐dimensional structure. (Novice, Amateur, Expert) TC3. Students can explain how any given biomolecular interaction site can be made by a variety of topologies. TC3.01 Students can recognize that the groups that comprise a functional site only require proper arrangement in three-dimensional space rather than a particular order or position in the linear sequence. (Amateur) TC3.02 Students can recognize similarities and differences in two similar ‐ but not identical ‐ three dimensional structures. (Amateur) TC3.03 Students can describe dissimilar portions of homologous proteins as arising from genetic insertions/deletions/rearrangements. (Amateur)
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/Chapter_33%3A__Your_Contribution_-_Sandbox/Chetna_IMF_Test.txt	princeton-nlp/TextbookChapters	Authored by [YOUR NAME]. Last update: [FILL IN DATE] Date of origin [ADD CONTENT] New Heading [ADD TEXT] [ADD IMAGE] (saved to your computer and uploaded with picture icon from top menu bar or drag image file to location (required from svg image) Use the following under your picture: Figure $\PageIndex{x}$: [Add caption] Center Picture and Caption together using top menu bar [ADD iCn3D Model] [ADD MATHEMATIC GRAPH - REUSE] d graph Figure $\PageIndex{x}$: Add caption [ADD VCELL/SBML SIMULATION - REUSE] your text before and after insert as needed New Heading Add what you want Edmund - Structural Basis of Allostery The Kinase Model Authored by [YOUR NAME]. Last update: [FILL IN DATE] Date of origin [ADD CONTENT] New Heading [ADD TEXT] [ADD IMAGE] (saved to your computer and uploaded with picture icon from top menu bar or drag image file to location (required from svg image) Use the following under your picture: Figure $\PageIndex{x}$: [Add caption] Center Picture and Caption together using top menu bar [ADD iCn3D Model] Figure x is an interactive iCn3D model of [INSERT DESCRIPTIVE TEXT - often a modified version of the title from the actual PDB page (INSERT PDB CODE)] Figure 66: [INSERT THE PDB TITLE as above (INSERT PDB CODE)]. Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...KbCWx2etVfC2WA. (Copyright; author via source). iCn3D model made by [YOUR NAME] [ADD MATHEMATIC GRAPH - REUSE] your text and graph Figure $\PageIndex{x}$: Add caption [ADD VCELL/SBML SIMULATION - REUSE] your text before and after insert as needed New Heading Exercise $1$ What mutations to specific amino acid residues could be introduced to strengthen contacts between the upper and lower C-spine? Answer Mutations to create a greater extent of interaction or a stronger class of interaction are necessary to strengthen intramolecular contacts. Example 1: Mutation of a small hydrophobic amino acid to a bulky hydrophobic or aromatic amino acid can be used to bridge the gap. Example 2: Mutation to introduce a new hydrogen bond, salt bridge, or disulfide bond can be used to create a new link between these sections of the hydrophobic core. Emily Schmitt Sepiapterin Reductase-Beery Twins Story Exercise $1$ Which mutation was contributed by the mother that affected the Sepiapterian reductase (SPR) in the Beery Twins situation? Answer a) early stop of the protein (Lys to termination) Lys251X b) Arg 150 Gly c) there was no mutation from the mother that caused the disease d) Lys 250 Glu Here is a hint if you need one Authored by [YOUR NAME]. Last update: [FILL IN DATE] Date of origin New Heading [ADD TEXT] Figure 1: The pathway above shows where the drugs given to the twins exist in the pathway that involves Sepiapterin Reductase (SPR). Note that the twins were prescribed 5-HTP and L-Dopa which are downstream of the "broken" SPR. In this way dopamine and serotonin can still be produced in the twins. Figure 2: iCN3D recreation of Alen et al., 2019 Figure 5- Crystal structure of inhibitor 3 bound to SPR (PDB code 6I6P) Figure $\PageIndex{x}$ is an interactive iCn3D model of [INSERT DESCRIPTIVE TEXT - often a modified version of the title from the actual PDB page (INSERT PDB CODE)] INSERT PNG (just a screen snip) OF YOUR iCn3D MODEL Figure $6$: [INSERT THE PDB TITLE as above (INSERT PDB CODE)]. Click the image for a popup or use this external link: [INSERT Lifelong short URL from File, Share Link in iCn3D)]. (Copyright; author via source). iCn3D model made by [YOUR NAME] [ADD MATHEMATIC GRAPH - REU Page path to reuse: ``` ``` SE] your text and graph Figure $\PageIndex{x}$: Add caption [ADD VCELL/SBML SIMULATION - REUSE] *your text before and after insert as needed
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/Chapter_33%3A__Your_Contribution_-_Sandbox/Helena-Test.txt	princeton-nlp/TextbookChapters	Exercise $1$ Degradation of amino acids yields compounds that are common intermediates in the major metabolic pathways. Explain the distinction between glucogenic and ketogenic amino acids in terms of their metabolic fates. Answer Glucogenic amino acids are those which can be catabolized into pyruvate, oxaloacetate, a-ketoglutarate , fumarate, or succinyl-CoA, and thus can serve as glucose precursors. Ketogenic amino acids are catabolized to Acetyl-CoA or acetoacetate, and thus can serve as precursors for fatty acids or ketone bodies. Here is a hint if you need it. Authored by Helena Prieto. Last update: 06.05.23 Date of origin 06.05.23 [ADD CONTENT] New Heading [ADD TEXT] [ADD IMAGE] (saved to your computer and uploaded with picture icon from top menu bar or drag image file to location (required from svg image) Use the following under your picture: Figure $\PageIndex{x}$: [Add caption] Center Picture and Caption together using top menu bar [ADD iCn3D Model] [ADD MATHEMATIC GRAPH - REUSE] your text and graph Reversible Competitive inhibition occurs when substrate (S) and inhibitor (I) both bind to the same site on the enzyme. In effect, they compete for the active site and bind in a mutually exclusive fashion. This is illustrated in the chemical equations and molecular cartoons shown in Figure $1$. v_0=\frac{V_M S}{K_M\left(1+\frac{I}{K is}\right)+S} There is another type of inhibition that would give the same kinetic data. If S and I bound to different sites, and S bound to E and produced a conformational change in E such that I could not bind (and vice versa), then the binding of S and I would be mutually exclusive. This is called allosteric competitive inhibition. Inhibition studies are usually done at several fixed and non-saturating concentrations of I and varying S concentrations. The key kinetic parameters to understand are VM and KM. Let us assume for ease of equation derivation that I binds reversibly, and with rapid equilibrium to E, with a dissociation constant KIS. The "s" in the subscript "is" indicates that the slope of the 1/v vs 1/S Lineweaver-Burk plot changes while the y-intercept stays constant. KIS is also named KIC where the subscript "c" stands for competitive inhibition constant. A look at the top mechanism shows that even in the presence of I, as S increases to infinity, all E is converted to ES. That is, there is no free E to which I could bind. Now, remember that VM= kcatE0. Under these conditions, ES = E0; hence v = VM. VM is not changed. However, the apparent KM, KMapp, will change. We can use LaChatelier's principle to understand this. If I binds to E alone and not ES, it will shift the equilibrium of E + S → ES to the left. This would increase the KMapp (i.e. it would appear that the affinity of E and S has decreased.). The double reciprocal plot (Lineweaver-Burk plot) offers a great way to visualize the inhibition as shown in Figure $2$. In the presence of I, VM does not change, but KM appears to increase. Therefore, 1/KM, the x-intercept on the plot will get smaller, and closer to 0. Therefore the plots will consist of a series of lines, with the same y-intercept (1/VM), and the x-intercepts (-1/KM) closer and closer to 0 as I increases. These intersecting plots are the hallmark of competitive inhibition. Here is an interactive graph showing v0 vs [S] for competitive inhibition with Vm and Km both set to 100. Change the sliders for [I] and Kis and see the effect on the graph. Here is the interactive graphs showing 1/v0 vs 1/[S] for competitive inhibition, with Vm and Km both set to 10. Note that in the first three inhibition models discussed in this section, the Lineweaver-Burk plots are linear in the presence and absence of an inhibitor. This suggests that plots of v vs S in each case would be hyperbolic and conform to the usual form of the Michaelis Menton equation, each with potentially different apparent VM and KM values. An equation for v0 in the presence of a competitive inhibitor is shown in the above figure. The only change compared to the equation for the initial velocity in the absence of the inhibitor is that the KM term is multiplied by the factor 1+I/Kis. Hence KMapp = KM(1+I/Kis). This shows that the apparent KM does increase as we predicted. KIS is the inhibitor dissociation constant in which the inhibitor affects the slope of the double reciprocal plot. If the data were plotted as v0 vs log S, the plots would be sigmoidal, as we saw for plots of ML vs log L in Chapter 5B. In the case of a competitive inhibitor, the plot of v0 vs log S in the presence of different fixed concentrations of inhibitor would consist of a series of sigmoidal curves, each with the same VM, but with different apparent KM values (where KMapp = KM(1+I/Kis), progressively shifted to the right. Enzyme kinetic data is rarely plotted this way. These plots are mostly used for simple binding data for the M + L ↔ ML equilibrium, in the presence of different inhibitor concentrations. Reconsider our discussion of the simple binding equilibrium, M + L ↔ ML. For fractional saturation Y vs a log L graphs, we considered three examples: 1. L = 0.01 KD (i.e. L << KD), which implies that KD = 100L. Then Y = L/[KD+L] = L/[100L + L] ≈1/100. This implies that irrespective of the actual [L], if L = 0.01 KD, then Y ≈0.01. 2. L = 100 KD (i.e. L >> KD), which implies that KD = L/100. Then Y = L/[KD+L] = L/[(L/100) + L] = 100L/101L ≈ 1. This implies that irrespective of the actual [L], if L = 100 Kd, then Y ≈1. 3. L = KD, then Y = 0.5 These scenarios show that if L varies over 4 orders of magnitude (0.01KD < KD < 100KD), or, in log terms, from -2 + log KD < log KD< 2 + log Kd), irrespective of the magnitude of the KD, that Y varies from approximately 0 - 1. In other words, Y varies from 0-1 when L varies from log KD by +2. Hence, plots of Y vs log L for a series of binding reactions of increasingly higher KD (lower affinity) would reveal a series of identical sigmoidal curves shifted progressively to the right, as shown below in Figure $3$. The same would be true of v0 vs S in the presence of different concentrations of a competitive inhibitor, for initial flux, Jo vs ligand outside, in the presence of a competitive inhibitor, or ML vs L (or Y vs L) in the presence of a competitive inhibitor. In many ways plots of v0 vs lnS are easier to visually interpret than plots of v0 vs S . As noted for simple binding plots, textbook illustrations of hyperbolas are often misdrawn, showing curves that level off too quickly as a function of [S] as compared to plots of v0 vs lnS, in which it is easy to see if saturation has been achieved. In addition, as the curves above show, multiple complete plots of v0 vs lnS at varying fixed inhibitor concentrations or for variant enzyme forms (different isoforms, site-specific mutants) over a broad range of lnS can be made which facilitates comparisons of the experimental kinetics under these different conditions. This is especially true if Km values differ widely. Now that you are more familiar with binding and enzyme kinetics curves, in the presence and absence of inhibitors, you should be able to apply the above analysis to inhibition curves where the binding or the initial velocity is plotted at varying competitive inhibitor concentrations at different fixed nonsaturating concentrations of ligand or substrate. Consider the activity of an enzyme. Let's say that at some reasonable concentration of substrate (not infinite), the enzyme is approximately 100% active. If a competitive inhibitor is added, the activity of the enzyme decreases until at saturating (infinite) I, no activity would remain. Graphs showing this are shown below in Figure $4$. Progress Curves for Competitive Inhibition In the previous section, we explored how important progress curve (Product vs time) analyses are in understanding both uncatalyzed and enzyme-catalyzed reactions. We are aware of no textbooks which cover progress curves for enzyme inhibition. Yet progress curves are what most investigators record and analyze to determine initial rates v0 and to calculate VM, KM and inhibition constants, as described above. We will use Vcell to produce progress curves for reversibly inhibited enzyme-catalyzed reactions. MODEL Competitive Inhibition with constant [I]: No inhibition (left) and competitive inhibition (right) Initial conditions for no inhibition Initial conditions for competitive inhibition I is fixed for each simulation (as it is not converted to a product) but can be changed in the simulation below. Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play \| Select Slider to change which constants are displayed. For this model, select Vm, Km, Ki and I \| Select About for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health. Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington. Funded by NIH/NIGMS (RO1-GM123032-04) The graphs from your initial run show the concentrations of S, P and I as a function of time for just the initial conditions shown above. In typical initial rate laboratory analyzes, of competitive inhibition, at least three sets of reactions are run with the same varying substrate concentrations and different fixed concentrations of inhibitor. In the analyses above, [I] is fixed at 5 uM. Conduct a series of run at different values of I. Vary the KI, the dissociation constant for the EI complex, as follows: • I << KI, the dissociation constant for the EI complex • I >> KI, the dissociation constant for the EI complex. Then download the data and determine the initial rate for each of the initial conditions. Figure $5$ shows an interactive iCn3D model of human low molecular weight phosphotyrosyl phosphatase bound to a competitive inhibitor (5PNT) Figure $5$: Human low molecular weight phosphotyrosyl phosphatase bound to a competitive inhibitor (5PNT). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...XsEacG2tixDDi9 The competitive inhibitor, the deprotonated form of 2-(N-morpholino)-ethanesulfonic acid (MES), is actually the conjugate base of the weak acid (pKa = 6.15) of a commonly used component of a buffered solution. It is shown in color sticks with the negatively charged sulfonate sitting at the bottom of the active site pocket. The amino acids comprising the active site binding pocket are shown as color sticks underneath the transparent colored surface of the binding pocket. The normal substrates for the enzyme are proteins phosphorylated on tyrosine side chains so the sulfonate is a mimic of the negatively charged phosphate group of the phosphoprotein target. Two specials cases of competition inhibition Product Inhibition Let's look at an enzyme that converts reactant S to product P. Since P arises from S, they may have structural similarities. For example, what if GTP was the reactant and GDP was a product? If so, then P might also bind in the active site and inhibit the conversion of S to P. This is called product inhibition. It probably occurs in most enzymes, and when it does occur it will start bending downward the beginning part of the progress curve for P formation. If the product binds very tightly, it might cause a significant underestimation of the initial velocity (v0) or flux (J0) of the enzyme. Let's use Vcell to explore product inhibition. The model will explore two reactions: • E + R ↔ ER → E + Q (no product inhibition) • E + S ↔ ES → E + P (with product inhibition) Note that the chemical equation above does not explicitly show the product P binding the enzyme to form an EP complex. An actual reaction diagram showing the inhibition of an enzyme by an inhibitor I and by the product P is shown in Figure $6$ below. Figure $6$: reaction diagram showing inhibition of an enzyme by an inhibitor I and by the product P Vcell uses much simpler diagrams since it is most often used for modeling whole pathways or even entire cells. In the simpler Vcell reaction diagrams, the inhibitor is typically not shown since the inhibition is built into the equation for the enzyme, represented by the node or yellow square in the figure above. Let's now explore product inhibition in Vcell. R and Q are the reactant and product, respectively, in the reaction without product inhibition. S and P are used for the reaction with product P inhibition. MODEL Irreversible MM Kinetics - Without (left rx 1) and With (right, rx 2) Product Inhibition Initial Conditions: No product inhibition Initial Conditions: With product inhibition Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play \| Select Slider to change which constants are displayed \| Select About for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health. Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington. Funded by NIH/NIGMS (RO1-GM123032-04) Inhibition by a competing substrate - the specificity constant In the previous chapter, the specificity constant was defined as kcat/KM which we also described as the second-order rate constant associated with the bimolecular reaction of E and S when S << KM. It also describes how good an enzyme is in differentiating between different substrates. If an enzyme encounters two different substrates, one can be considered to be a competitive inhibitor of the other. The following equation gives the ratio of initial velocities for two competing substrates at the same concentration is equal to the ratio of their kcat/KM values. \frac{\mathrm{v}_{\mathrm{A}}}{\mathrm{v}_{\mathrm{B}}}=\frac{\frac{\mathrm{k}_{\mathrm{catA}}}{\mathrm{K}_{\mathrm{A}}} \mathrm{A}}{\frac{\mathrm{k}_{\mathrm{cat} \mathrm{B}}}{\mathrm{K}_{\mathrm{B}}} \mathrm{B}} A derivation of the specificity constant for an enzyme with competing substrates Here it is! Derivation \mathrm{v}_{\mathrm{A}}=\frac{\mathrm{V}_{\mathrm{A}} \mathrm{A}}{\mathrm{K}_{\mathrm{A}}\left(1+\frac{\mathrm{B}}{\mathrm{K}_{\mathrm{B}}}\right)+\mathrm{A}} \quad \mathrm{v}_{\mathrm{B}}=\frac{\mathrm{V}_{\mathrm{B}} \mathrm{B}}{\mathrm{K}_{\mathrm{B}}\left(1+\frac{\mathrm{A}}{\mathrm{K}_{\mathrm{A}}}\right)+\mathrm{B}} \frac{\mathrm{v}_{\mathrm{A}}}{\mathrm{v}_{\mathrm{B}}}=\frac{\frac{\mathrm{V}_{\mathrm{A}} \mathrm{A}}{\mathrm{K}_{\mathrm{A}}\left(1+\frac{\mathrm{B}}{\mathrm{K}_{\mathrm{B}}}\right)+\mathrm{A}}}{\frac{\mathrm{V}_{\mathrm{B}} \mathrm{B}}{\mathrm{K}_{\mathrm{B}}\left(1+\frac{\mathrm{A}}{\mathrm{K}_{\mathrm{A}}}\right)+\mathrm{B}}}=\frac{\frac{\mathrm{V}_{\mathrm{A}} \mathrm{A}}{\mathrm{K}_{\mathrm{A}}+\frac{\mathrm{K}_{\mathrm{A}} \mathrm{B}}{\mathrm{K}_{\mathrm{B}}}+\mathrm{A}}}{\frac{\mathrm{V}_{\mathrm{B}} \mathrm{B}}{\mathrm{K}_{\mathrm{B}}+\frac{\mathrm{K}_{\mathrm{B}} \mathrm{A}}{\mathrm{K}_{\mathrm{A}}}+\mathrm{B}}} Now in the above equation: multiple the top half of the right-hand expression by \frac{\frac{1}{K_A}}{\frac{1}{K_A}} multiple the bottom half of the right-hand expression by \frac{\frac{1}{K_B}}{\frac{1}{K_B}} replace VA with kcatAE0 and VB with kcatBE0 This gives the following expression for vA/vB: \frac{\mathrm{v}_{\mathrm{A}}}{\mathrm{v}_{\mathrm{B}}}=\frac{\frac{\mathrm{k}_{\mathrm{catA}}}{\mathrm{K}_{\mathrm{A}}} \mathrm{A}}{\frac{\mathrm{k}_{\mathrm{cat} \mathrm{B}}}{\mathrm{K}_{\mathrm{B}}} \mathrm{B}} Figure $\PageIndex{x}$: Add caption [ADD VCELL/SBML SIMULATION - REUSE] your text before and after insert as needed New Heading Figure $\PageIndex{x}$ is an interactive iCn3D model of [2B4Z cytochrome c] INSERT PNG () OF YOUR iCn3D MODEL Figure $6$: [2B4Z]. Click the image for a popup or use this external link: . (Copyright; author via source). iCn3D model made by [Helena Prieto]
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/Chapter_33%3A__Your_Contribution_-_Sandbox/Insert_a_question_with_hidden_answer_and_floating_hint.txt	princeton-nlp/TextbookChapters	Example $1$ Which elements can form hydrogen bonds? Solution fluorine, oxygen, or nitrogen Exercise $1$ Can phosphorus form hydrogen bonds? Here is a hint if you need one! Answer No. Phosphorus and hydrogen have almost equal values of electronegativity. Box Question with floating Hint 1. On your Chapter 33 page, click Edit. 2. From the menu bar that appears, select Elements, Templates 3. From the dropdown menu select Box: Exercise. The following box will appear Example $1$ Add example text here. Which elements can form hydrogen bonds? Solution Add example text here. fluorine, oxygen, or nitrogen $1$ Add exercises text here. Can phosphorus form hydrogen bonds? Answer No. Phosphorus and hydrogen have equal values of electronegativity. 4. Edit the Box Exercise as shown below by clicking in the text areas and replacing the text with your own. You can add an image by scrolling to the bottom of this page and selecting Attach a file A question $1$ Which does NOT describe sodium dodecyl sulfate (SDS). SDS .... 1. readily forms bilayers 2. readily form micelles 3. is a single-chain amphiphile 4. has (a) unsaturated acyl chain(s) Here is a hint if you need one! Answer It does not readily form a bilayer since it is a single-chain amphiphile. Instead, it forms micelles. 5. The hint in this example is a file that has been uploaded (see the list of attached files below). Right-click on the SDS.png file below and choose Copy the link address. Then double-click the word "hint" in the above box to highlight it, choose the link icon from the top menu bar, and paste the link into the Link To box after replacing the default link in the box. Under link options, select Open in Contextual Help Overlay to get the hint to float above the page. Insert an Image into FOB Inserting an image into FOB 1. Do a Google search for an image with Creative Commons permission for reuse. The optimal permission category is CC BY. 2. Make a copy and save it to your computer. 3. Open your Chapter 33.x Libretext file and select Edit in the top menu bar 4. Navigate to ADD IMAGE, remove these words but leave the cursor there 5. From the Editing menu bar, select Elements, Templates, and from the drop-down Template:FigureCenterCenter. 6. Click on the Copy/Paste Placeholder image and delete it. 7. Recenter the cursor centered above the automatic figure legend, click the Image icon from the menu bar. 8. Select the Attach Files tab, followed by Choose File, and navigate to the image you wish to upload. 9. Select Save Image, and the image will appear centered above the caption. 10. Change the placeholder text in the caption to one of your choosing. Inserting a mathematical simulation (SBML) into a FOB 1. Navigate to this page in a new window 2. Select and view the graph of interest 3. Navigate to your Chapter 33 section and select Edit from the top menu bar 4. Move the cursor to the location you wish to insert the graph 5. From the top menu bar select Elements, Content Reuse 6. Navigate through the folder tree to get to the page with the interactive mathematical models: Home Learning Objects Visualization and Simulations Progress Curve Analysis SBML Computational Models 7. Click the file name for the model of interest to insert. 8. Save the page.
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/Chapter_33%3A__Your_Contribution_-_Sandbox/Inserting_an_iCn3D_model_into_a_FOB_Chapter_section.txt	princeton-nlp/TextbookChapters	(If you need a refresher, here is a link to an iCn3D workshop tutorial presented by BioMolViz at the BMB 2023 in Seattle, WA on March 25, 2023.) A. Creating an iCn3D model in FOB 1. Open iCn3D, input a PDB code, and do the following: • Color, Secondary, Sheets in Yellow • Style, Background, Transparent • File, Share Link • Copy the Original URL with commands 2. Follow these instructions but use your code (open a new Word document for your code). Here is a sample code for 1XWW: https://www.ncbi.nlm.nih.gov/Structu...0&command=load mmdb 1xww \| parameters &mmdbid=1xww&bu=1; set thickness \| linerad 0.1 \| coilrad 0.3 \| stickrad 0.4 \| crosslinkrad 0.4 \| tracerad 0.1 \| ribbonthick 0.2 \| proteinwidth 1.3 \| nucleotidewidth 0.8 \| ballscale 0.3; set background transparent; color secondary structure yellow\|\|\|{"factor":"1.000","mouseChange":{"x":"0.000","y":"0.000"},"quaternion":{"_x":"-0.006542","_y":"0.8578","_z":"-0.04771","_w":"0.5117"}} 2. Copy all of the code BEFORE the \|\|\| and paste it in a Word document. Then add the 3 small red codes in the example below, and add them in the corresponding positions in your sample code. It will be helpful to color the newly added code red to make sure it is correct. It will look like this but your code will replace the back code below. template('EmbediCn3D/iCn3D',{config: '{"fullURL": "https://www.ncbi.nlm.nih.gov/Structu...30430&v=3.25.0&closepopup=1&command=load mmdb 1xww \| parameters &mmdbid=1xww&bu=1; set thickness \| linerad 0.1 \| coilrad 0.3 \| stickrad 0.4 \| crosslinkrad 0.4 \| tracerad 0.1 \| ribbonthick 0.2 \| proteinwidth 1.3 \| nucleotidewidth 0.8 \| ballscale 0.3; set background transparent; color secondary structure yellow"}'}) 3. Now go to this page in a new window to create your own iCn3D page for FOB 4. Open the file Template for Your Own Separate iCn3D Page 5. You must make a copy of this file. From the top menu bar click Options, Copy, and give the file a name derived from the PDB web page. For example Human Low Molecular Weight Protein Phosphatase (1XWW). Don't overnight the original file with your new name. 6. Then from the top menu bar for your new file select Edit. Click the + button (green circle) on the DekiScript box. 7. Paste your entire modified URL code from your Word document (with the 3 inserted red codes) into the DekiScript box. 8. Choose the Save just above the DekiScript box and you should see a small version of the molecule in a workable iCn3D window. B. Inserting your iCn3D model into your Chapter 33 Section 1. Open your Chapter 33 section and click Edit 2. Place your cursor on a new line where you would like to place the model to appear. 3. Go to this page in a new window, select Edit in the menu bar, and copy the material between the two horizontal lines into the desired location in your Chapter 33 section. 4. Take a screen snip showing the molecule in the iCn3D modeling window. Save it to your computer. Then insert the image in you Chapter 33 section where the text reads: INSERT PNG (just a screen snip) OF YOUR iCn3D MODEL. Use the top menu bar picture icon to select the file or drag the file from your computer into your section page. 5. Complete the captions for the image and make sure to include the PDB ID in your text as well as the iCn3D short link. 6. To make a hovering iCn3D model with a full menu within your chapter section (and not just from the external iCn3D short link), right-click the image you inserted in Step 4, and select Create Link (see image below). 7. Delete the link shown (red arrow) and replace it with the URL from the LibreText iCn3D file you just made in Part A above. Make sure that you choose Open in contextual help overlay. 8. Save the link. Inserting an interactive mathematic graph into FOB 1. Navigate to this page in a new window 2. Select and view the graph of interest 3. Navigate to your Chapter 33 section and select Edit from the top menu bar 4. Move the cursor to the location you wish to insert the graph 5. From the top menu bar select Elements, Content Reuse 6. Navigate through the folder tree to get to the page with the interactive mathematical models: Home Learning Objects Visualization and Simulations Interactive Figures Interactive Biochemistry Graphs 7. Click the file name for the model of interest to insert. 8. Save the page. KP Procko Test Authored by KP. Last update: 5/4/23 Date of origin [add content] New Heading [ADD TEXT] your text your text and image [ADD iCn3D Model] Figure $\PageIndex{x}$ is an interactive iCn3D model of mammalian respirasome (5GPN) Figure $6$: [INSERT THE PDB TITLE as above (INSERT PDB CODE)]. Click the image for a popup or use this external link: [INSERT Lifelong short URL from File, Share Link in iCn3D)]. (Copyright; author via source). iCn3D model made by [YOUR NAME] your text and model [ADD MATHEMATIC GRAPH - REUSE] your text and graph Exercise $1$ What is the pKa of acetic acid? Answer 4.7 [ADD VCELL SIMULATION - REUSE] MODEL No inhibition (left) and Uncompetitive Inhibition (right) Note that the Vcell reaction diagram is the same as for competitive and uncompetive inhibition. It doesn't explicitly show that the mixed inhibitor binds to both free and substrate-bound enzymes. Those interactions are addressed in the mathematical equations for mixed inhibition. Initial values No Inhibition Initial values With Uncompetitive Inhibitor I is fixed for each simulation (as it is not converted to a product) but can be changed in the simulation below. Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play \| Select Slider to change which constants are displayed \| Select About for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health. Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington. Funded by NIH/NIGMS (RO1-GM123032-04) your tex New Heading Add what you want
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/Chapter_33%3A__Your_Contribution_-_Sandbox/Make_Your_Own_Customized_FOB%3A__Assemble_and_Remix_a_Custom_Book__%28Short_Version%29.txt	princeton-nlp/TextbookChapters	A. Introduction Remixes are texts created from existing OER content. Constructing Remixes on the LibreTexts platform is facilitated by the OER Remixer tool. The title of the Remix often starts with the campus acronym (e.g., the Chemistry 110A remix at the University of California is "UCD: Chem 110A Introductory Quantum Mechanics") If using the OER Remixer to build a large textbook, it is advisable to construct a Remixing Map in a spreadsheet including to organize the sections you'll include. This content will be an effective Table of Contents. For this exercise, we’ll make a remix with just 3 chapter sections from 3 different chapters so no spreadsheet is needed. We’ll use these chapter sections: • Vol 1 – Chapter 6.5: Enzymatic Reaction Mechanisms (written by Henry and KP) • Vol 2 – Chapter 14.5: Metabolism and Signaling: The Steady State, Adaptation and Homeostasis • Vol 4 – Chapter 32.12: A Warmer World – Temperature Effects on Proteins (Note: you could add content from any LibreText book as well.) Instructions 1. Go to the main FOB page 2. An author with permission can see a blue vertical icon bar on the left side of the page. 3. Select Tools and from the right side menu choose OER Remixer. 4. Complete the step indicated in the figure below • Step 1: Enter the name you want for your custom book (typically your name and the name of the class) in the box labeled "LibreText name" (Step 1 in Figure 7.3.57.3.5). Ex: Henry Jakubowski_TrialRemixFOB_1 • For Step 2 and Step 3: Accept the default 5. In the left "Library Panel": Click the + by the bookshelves and continue to see Vol I, II, and IV (see image right). Keep expanding the list until you see Chapter 6 (Vol 1), then Chapter 14 (Vol 2), and finally Chapter 32 (Vol 4) 6. Insert the entire Chapter 6 by selecting it and dragging and dropping it to the right panel. Position it by moving it to the correct position (you will see a little blue line indicating the position) 7. Repeat and move the entire Chapt 14 and Chapter 32. 8. Now expand the moved Chapters in the panel. Select the section you do NOT want and click the delete icon (recycle bin) in the menu bar in the right panel. Continue deleting until each Chapter has just the desired section. 9. Select the Publish button (see image below) 10. In the next window select Publish again and wait until the remix is complete. Click the “Your new LibreText is available here to see your custom book (which is your Sandbox). 11. Your sandbox can be seen by clicking in your name on any LibreText page when you have logged in as shown below B. Making a PDF You can easily make a PDF of a chapter. For example, navigate to Chapter 14 and select PDF then Chapter and follow prompts. Make your own Chapter 33.x Section Make Your Own Chapter 33.x Section 1. Navigate to this page in a new window 2. Open the file named Template to Copy 3. On the top menu bar select Options, Copy 4. Add a page title with this format: FirstName LastName Short Descriptive Title and then chose Copy Page. 5. Editing a webpage is generally intuitive. Once you click the Edit button on the top left-hand side of the page, editing icons will appear, similar to a word processing program. The icons on the top menu bar next to the indent icon allow you to: create a link, add a picture directly, add a table, and search (magnifying glass). Pallavi- Test Authored by [YOUR NAME]. Last update: [FILL IN DATE] Date of origin [ADD CONTENT] New Heading [ADD TEXT] [ADD IMAGE] (saved to your computer and uploaded with picture icon from top menu bar or drag image file to location (required from svg image) * Figure $\PageIndex{x}$: [Add caption] Center Picture and Caption together using top menu ba Figure $\PageIndex{x}$ is an interactive iCn3D model of [GLN3] INSERT PNG (just a screen snip) OF YOUR iCn3D MODEL Figure $6$: [INSERT THE PDB TITLE as above (INSERT PDB CODE)]. Click the image for a popup or use this external link: [INSERT Lifelong short URL from File, Share Link in iCn3D)]. (Copyright; author via source). iCn3D model made by [YOUR NAME] [ADD MATHEMATIC GRAPH - REUSE] your text and graph Figure $\PageIndex{x}$: Add caption [ADD VCELL/SBML SIMULATION - REUSE] MODEL Initial Parameters Reaction Parameter Value r0 kf0 kr0 r1 kf1 kf1 r2 kf21 kf21 kf22 kf22 r3 VM KM Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play \| Select Slider to change which constants are displayed \| Select About for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health. Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington. Funded by NIH/NIGMS (RO1-GM123032-04) New Heading Add what you want
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/Chapter_33%3A__Your_Contribution_-_Sandbox/Pam_Mertz_Chapter_33_Test.txt	princeton-nlp/TextbookChapters	Authored by [YOUR NAME]. Last update: [FILL IN DATE] Date of origin Introduction Perilipin is a protein associated with lipid droplets that plays a key role in the regulated breakdown of triacylglycerols. New Heading [ADD TEXT] your text [ADD IMAGE] (saved to your computer and uploaded with picture icon from top menu bar or drag image file to location (required from svg image) Use the following under your picture: Figure $\PageIndex{x}$: [Add caption] Center Picture and Caption together using top menu bar Figure 𝑥� is an interactive iCn3D model of Low Molecular Weight Phosphotyrosyl Phosphatase, 1xww Figure 66: [INSERT THE PDB TITLE as above (INSERT PDB CODE)]. Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...7&t=1XWW(MMDB) in iCn3D. (Copyright; author via source). iCn3D model made by Pam Mertz [ADD MATHEMATIC GRAPH - REUSE] Figure $\PageIndex{x}$: Add caption MODEL AerobicGlycolysis Quantitative determinants of aerobic glycolysis identify flux through the enzyme GAPDH as a limiting step. .Shestov AA, Liu X, Ser Z, Cluntun AA, Hung YP, Huang L, Kim D, Le A, Yellen G, Albeck JG, Locasale JW. eLife , 7/ 2014 , Volume 3 , PubMed ID: 25009227. Biomodel MODEL1504010000 Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play \| Select Slider to change which constants are displayed \| Select About for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health. Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington. Funded by NIH/NIGMS (RO1-GM123032-04) your text before and after insert as needed New Heading Add what you want Rebecca Roberts Chapter 33 testing Authored by Rebecca Roberts. Last update: 6/5/2023 Date of origin Introduction Sometimes proteins need to bind to something specifically, but sometimes a protein needs to recognize a variety of ligands. How does this happen? New Heading [ADD TEXT] Use the following under your picture: Figure $\PageIndex{x}$: Major Histocompatability Complex Class II (MHC-II). The figure Center Picture and Caption together using top menu bar Figure 3 is an interactive iCn3D model of 3DS8, a protein of unknown function Figure 66: [INSERT THE PDB TITLE as above (INSERT PDB CODE)]. Click the image for a popup or use this external link: [INSERT Lifelong short URL from File, Share Link in iCn3D)]. (Copyright; author via source). iCn3D model made by [YOUR NAME] [ADD MATHEMATIC GRAPH - REUSE] your text and graph Figure $\PageIndex{x}$: This is an interactive model for Michaelis-Menten kinetics [ADD VCELL/SBML SIMULATION - REUSE] MODEL Reversible reaction E + S ↔ ES ↔ EP ↔ E + P Initial values Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play \| Select Slider to change which constants are displayed \| Select About for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health. Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington. Funded by NIH/NIGMS (RO1-GM123032-04) *your text before and after insert as needed New Heading Add what you want
textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/Chapter_33%3A__Your_Contribution_-_Sandbox/Rico_Acevedo_Testy.txt	princeton-nlp/TextbookChapters	Authored by [YOUR NAME]. Last update: [FILL IN DATE] Date of origin [add content] New Heading [ADD TEXT] your text [ADD IMAGE] (saved to your computer and uploaded with picture icon from top menu bar or drag image file to location (required from svg image) Use the following under your picture: Figure $\PageIndex{x}$: [Add caption] Center Picture and Caption together using top menu bar [ADD iCn3D Model] [ADD MATHEMATIC GRAPH - REUSE] Figure $\PageIndex{x}$: Add caption MODEL Initial Parameters Reaction Parameter Value r0 kf0 kr0 r1 kf1 kf1 r2 kf21 kf21 kf22 kf22 r3 VM KM Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play \| Select Slider to change which constants are displayed \| Select About for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health. Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington. Funded by NIH/NIGMS (RO1-GM123032-04) [ADD VCELL/SBML SIMULATION - REUSE] your text before and after insert as needed Figure x� is an interactive iCn3D model of Low molecular weight 1XWW IL Figure 66: Low molecular weight 1XWW. Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...ssdBPpCB3pWSV7 (Copyright; author via source). iCn3D model made by [YOUR NAME] New Heading Question 1 $1$ Which does NOT describe sodium dodecyl sulfate (SDS). SDS .... 1. readily forms bilayers 2. readily form micelles 3. is a single-chain amphiphile 4. has (a) unsaturated acyl chain(s) Here is a hint if you need one! Answer 1. readily forms bilayers. Samantha Wilner Ch 33 Test Authored by Samantha Wilner. Last update: June 5, 2023 Date of origin [ADD CONTENT] New Heading [ADD TEXT] [ADD IMAGE] (saved to your computer and uploaded with picture icon from top menu bar or drag image file to location (required from svg image) Use the following under your picture: Figure $\PageIndex{x}$: [Add caption] Center Picture and Caption together using top menu bar Figure $1$ is an interactive iCn3D model of a hydrolase (3H04) Figure $1$: 3H04. Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...Eng4pm1AcyxZx8. (Copyright; author via source). iCn3D model made by Samantha Wilner Figure $2$: First and Second Order Reactions MODEL AerobicGlycolysis Quantitative determinants of aerobic glycolysis identify flux through the enzyme GAPDH as a limiting step. .Shestov AA, Liu X, Ser Z, Cluntun AA, Hung YP, Huang L, Kim D, Le A, Yellen G, Albeck JG, Locasale JW. eLife , 7/ 2014 , Volume 3 , PubMed ID: 25009227. Biomodel MODEL1504010000 Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play \| Select Slider to change which constants are displayed \| Select About for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health. Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington. Funded by NIH/NIGMS (RO1-GM123032-04) Figure $3$: Aerobic Glycolysis New Heading Add what you want Subhasish-TEST2 Authored by [YOUR NAME]. Last update: [FILL IN DATE] Date of origin [ADD CONTENT] New Heading [ADD TEXT] [ADD IMAGE] (saved to your computer and uploaded with picture icon from top menu bar or drag image file to location (required from svg image) Use the following under your picture: Figure $\PageIndex{x}$: [Add caption] Center Picture and Caption together using top menu bar [ADD iCn3D Model] [ADD MATHEMATIC GRAPH - REUSE] Figure $\PageIndex{x}$: Add caption [ADD VCELL/SBML SIMULATION - REUSE] your text before and after insert as needed New Heading Add what you want Subhasish-test Goal After completing this how-to you will have ... Begin by ... Second Step Then continue with ... What's Next This is what was achieved and what was omitted in this how-to.
textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/1%3A_DNA/1.1%3A_DNA_as_Genetic_Material.txt	princeton-nlp/TextbookChapters	In the early 1900's many people thought that protein must be the genetic material responsible for inherited characteristics. One of the reasons behind this belief was the knowledge that proteins were quite complex molecules and therefore, they must be specified by molecules of equal or greater complexity (i.e. other proteins). DNA was known to be a relatively simple molecule, in comparison to proteins, and therefore it was hard to understand how a complex molecule (a protein) could be determined by a simpler molecule (DNA). What were the key experiments which identified DNA as the primary genetic material? 1928 F. Griffith Background: Diplococcus pneumoniae, or pneumococcus, is a nasty little bacteria which, when injected into mice, will cause pneumonia and death in the mouse. The bacteria contains a capsular polysaccharide on its surface which protects the bacteria from host defenses. Occasionally, variants (mutants) of the bacteria arise which have a defect in the production of the capsular polysaccharide. The mutants have two characteristics: 1) They are avirulent, meaning that without proper capsular polysaccharide they are unable to mount an infection in the host (they are destroyed by the host defenses), and 2) Due to the lack of capsular polysaccharide the surface of the mutant bacteria appears rough under the microscope and can be distinguished from the wild type bacteria (whose surface appears smooth). Figure 1.1.1: Wild type vs. Mutant type pneumococcus The virulent smooth wild type pneumococcus can be heat treated and rendered avirulent (still appears smooth under the microscope however). Finally, there are several different subtypes of pneumococcus capsular polysaccharide (subtypes I, II and III). These subtypes are readily distinguishable from one another, and each can give rise to mutants lacking capsular polysaccharide (i.e. the avirulent rough type). The experiments: Controls: • w.t. (smooth) + mouse = dead mouse • mutant (rough) + mouse = live mouse • heat treated w.t. (smooth) + mouse = live mouse Combinations: • heat treated w.t. (smooth) + mutant (rough) + mouse = dead mouse In this case when the bacteria were recovered from the cold lifeless mouse they were smooth virulent pneumococcus (i.e. indistinguishable from wild type). A closer look at what is going on, by keeping using, and keeping track of, different subtypes • heat treated w.t. (smooth) type I + mutant(rough) type II + mouse = dead mouse In this case when the bacteria were isolated from the cold lifeless mouse they were smooth virulent type I pneumococcus. The overall conclusions from these experiments was that there was a "transforming agent" in the the heat treated type I bacteria which transfomed the live mutant (rough) type II bacteria to be able to produce type I capsule polysaccharide. Question Was the "transforming agent" protein or DNA, or what? 1944 O.T. Avery Background: The experiment of Griffith could not be taken further until methods were developed to separate and purify DNA and protein cellular components. Avery utilized methods to extract relatively pure DNA from pneumococcus to determine whether it was the "transforming agent" observed in Griffith's experiments. The experiment: • w.t. (smooth) type I -> extract the DNA component • mutant (rough) type II + type I DNA + mouse = dead mouse Isolation of bacteria from the dead mouse showed that they were type I w.t. (smooth) bacteria A more sophisticated experiment: Purified type I DNA was divided into two aliquots. One aliquot was treated with DNAse - an enzyme which non-specifically degrades DNA. The other aliquot was treated with Trypsin - a protease which (relatively) non-specfically degrades proteins. • Type I DNA + DNAse + mutant (rough) type II + mouse = live mouse • Type I DNA + Trypsin + mutant (rough) type II + mouse = dead mouse Conclusion: The work of Avery provided strong evidence that the "transforming agent" was in fact DNA (and not protein). However, not everyone was convinced. Some people felt that a residual amount of protein might remain in the purified DNA, even after Trypsin treatment, and could be the "transforming agent". 1952 A.D. Hershey and M. Chase Background: T2 is a virus which attacks the bacteria E. coli. The virus, or phage, looks like a tiny lunar landing module: Figure 1.1.2: T2 phage The viral particles adsorb to the surface of the E. coli cells. It was known that some material then leaves the phage and enters the cell. The "empty" phage particles on the surface cells can be physically removed by putting the cells into a blender and whipping them up. In any case, some 20 minutes after the phage adsorb to the surface of the bacteria the bacteria bursts open (lysis) and releases a multitude of progeny virus. If the media in which the bacteria grew (and were infected) included 32P labeled ATP, progeny phage could be recovered with this isotope incorporated into its DNA (normal proteins contain only hydrogen, nitrogen, carbon, oxygen, and sulfur atoms). Likewise, if the media contained 35S labeled methionine the resulting progeny phage could be recovered with this isotope present only in its protein components (normal DNA contains only hydrogen, nitrogen, carbon, oxygen, and phosphorous atoms). The experiment: Phage were grown in the presence of either 32P or 35S isotopic labels. 1) E. coli were infected with 35S labeled phage. After infection, but prior to cell lysis, the bacteria were whipped up in a blender and the phage particles were separated from the bacterial cells. The isolated bacterial cells were cultured further until lysis occurred. The released progeny phage were isolated. Where the 35S label went: • Adsorbed phage shells 85% • Infected cells (prior to lysis) 15% • Lysed cell debris 15% • Progeny phage <1% 2) E. coli were infected with 32P labeled phage. The same steps as in 1) above were performed. Where the 32P label went: • Adsorbed phage shells 30% • Infected cells (prior to lysis) 70% • Lysed cell debris 40% • Progeny phage 30% Conclusion: The material which was being transferred from the phage to the bacteria during infection appeared to be mainly DNA. Although the results were not entirely unambiguous they provided additional support for the view that DNA was the "stuff" of genetic inheritance.
textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/1%3A_DNA/1.2%3A_Structure_of_DNA_and_RNA.txt	princeton-nlp/TextbookChapters	The Double Helix DNA (deoxyribonucleic acid) and RNA (ribonucleic acid) are composed of two different classes of nitrogen-containing bases: the purines and pyrimidines. The most commonly occurring purines in DNA are adenine and guanine: Figure 1.2.1: Purines The most commonly occurring pyrimidines in DNA are cytosine and thymine: Figure 1.2.2: Pyramidines RNA contains the same bases as DNA with the exception of thymine. Instead, RNA contains the pyrimidine uracil: Figure 1.2.3: Thymine vs. Uracil Adenine, guanine, cytosine, thymine and uracil are usually abreviated using the single letter codes A, G, C, T and U, respectively. Purines and pyrimidines can form chemical linkages with pentose (5-carbon) sugars. The carbon atoms on the sugars are designated 1', 2', 3', 4' and 5'. It is the 1' carbon of the sugar that becomes bonded to the nitrogen atom at position N1 of a pyrimidine or N9 of a purine. DNA precursors contain the pentose deoxyribose. RNA precursors contain the pentose ribose (which contains an additional OH group at the 2' position): Figure 1.2.4: Nucleosides Before a nucleoside can become part of a DNA or RNA molecule it must become complexed with a phosphate group to form a nucleotide (either a deoxyribonucleotide or ribonucleotide). Nucleotides can posess 1, 2 or 3 phosphate groups, e.g. the nucleotides adenosine monophosphate (AMP), adenoside diphosphate (ADP) and adenosine triphosphate (ATP). The phosphate groups are attached to the 5' carbon of the ribose sugar moiety. Beginning with the phosphate group attached to the 5' ribose carbon, they are labeled a, b and g phosphate. It is the tri-phosphate nucleotide which is incorporated into DNA or RNA. Figure 1.2.5: Nucleotide DNA and RNA are simply long polymers of nucleotides called polynucleotides. Only the a phosphate is included in the polymer. It becomes chemically bonded to the 3' carbon of the sugar moiety of another nucleotide: Figure 1.2.6: Polynucleotide In other words, the polynucleotide is connected by a series of 5' to 3' phosphate linkages. Note the sequence of the bases in the above diagram. Polynucleotide sequences are referenced in the 5' to 3' direction. Typically, polynucleotides will contain a 5' phosphate and 3' hydroxyl terminal groups. The common representation of polynucleotides is as an arrow with the 5' end at the left and the 3' end at the right. Summary of terms: Base Nucleoside Nucleotide RNA (monophosphate) DNA (monophosphate) Code Adenine Adenosine (Adenylic acid) AMP dAMP A Guanine Guanosine (Guanylic acid) GMP dGMP G Cytosine Cytidine (Cytidylic acid) CMP dCMP C Thymine Thymidine (Thymidylic acid) dTMP T Uracil Uridine (Uridylic acid) UMP U What is the structure of DNA? How is the structure related to function? 1950's The primary chemical structure of polynucleotides was known (i.e. the 3'-5' phosphate linkage). 1951 E. Chargaff The experiment: Take DNA from a variety of species and hydrolyze it to yield individual pyrimidines and purines. Determine the relative concentrations of the A, T, C and G bases. Result: Although different species had uniquely different ratios of pyrimidines or purines, the relative concentrations of adenine always equaled that of thymine, and guanine equaled cytosine. Chargaff's Law: A=T, G=C 1950's R.E. Franklin X-ray diffraction studies of DNA fibers demonstrated that DNA adopted a highly ordered helical structure. Franklin concluded that two or more chains must coil around each other to form a helix. Some basic dimensions of the helix were calculated from the x-ray diffraction data. 1953 L. Pauling and R.B. Corey Propose a three chain helical structure for DNA with the phosphate backbone in the center and the bases on the outside. 1953 J.D. Watson and F.H.C. Crick Identified a hydrogen bonding arrangement between models of thymine and adenine bases, and between cytosine and guanine bases which fullfilled Chargaff's rule: Figure 1.2.7: Chargaff's Rule Bonding Note that the "TA" pair can overlay the "GC" pair with the bonds to the sugar groups in similar juxtaposition. In the "double helix" model of Watson and Crick the polynucleotide chains interact to form a double helix with the chains running in opposite directions. The bases are directed towards the center (and stack on top of one another) and the sugar backbones face the outside of the helix. The Watson and Crick model had the following physical dimensions: • 34 Å per helical repeat • 10 base pairs per repeat (i.e. per turn of the helix) • 3.4 Å inter-base stacking distance • 20 Å diameter for the helical width Physical characteristics of the model matched those determined by Rosalind Franklin's x-ray diffraction studies. Consequenses of the model for genetic information: The Watson and Crick paper was an exercise in brevity (1 page only in Nature). The structure was so rich with implication that quite a bit could be written. The authors, however, chose only to say "It has not escaped our notice that the specific pairing we have postulated immediately suggests a possible copying mechanism for the genetic material". 1. If G always paired with C, and T always paired with A, then either strand could be regenerated from the complementary information in the other strand. 2. The basis of the complementarity was hydrogen bonding, i.e. non-covalent interactions which could be easily broken and re-formed. 3. The information which DNA carried was within the unique base sequence of the DNA. 4. From the general interior location of the bases, it would appear that the double helix would have to dissociate in order to access the information. 5. The non-equitorial location of the sugar moieties (see above) suggested that the DNA helix would have a major groove and a minor groove. General notation of double stranded DNA:
textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/1%3A_DNA/1.3%3A_Bacterial_Restriction_Modification_system.txt	princeton-nlp/TextbookChapters	The restriction/modification system in bacteria is a small-scale immune system for protection from infection by foreign DNA. W. Arber and S. Linn (1969) Plating efficiencies of bacteriophage lambda (l phage) grown on E. coli strains C, K-12 and B, when plated on these bacteria: E. coli strain on which parental phage had been grown E. coli strain for plating phage C K-12 B C 1 <10-4 <10-4 K-12 1 1 <10-4 B 1 <10-4 1 • The DNA of phage which had been grown on strains K-12 and B were found to have chemically modified bases which were methylated. • Additional studies with other strains indicate that different strains had specific methylated bases. • Typical sites of methylation include the N6 position of adenine, the N4 position of cytosine, or the C5 position of cytosine. Figure 1.3.1: Methylation • In addition, only a fractional percentage of bases were methylated (i.e. not every adenine was methylated, for example) and these occurred at very specific sites in the DNA. • A characteristic feature of the sites of methylation, was that they involved palindromic DNA sequences. Figure 1.3.2: EcoR1 methylase specificity. Rubin and Modrich, 1977 • In addition to possessing a particular methylase, individual bacterial strains also contained accompanying specific endonuclease activities. • The endonucleases cleaved at or near the methylation recognition site. Figure 1.3.3: Cleavage at methylation sites • These specific nucleases, however, would not cleave at these specific palindromic sequences if the DNA was methylated. Thus, this combination of a specific methylase and endonuclease functioned as a type of immune system for individual bacterial strains, protecting them from infection by foreign DNA (e.g. viruses). • In the bacterial strain EcoR1, the sequence GAATTC will be methylated at the internal adenine base (by the EcoR1 methylase). • The EcoR1 endonuclease within the same bacteria will not cleave the methylated DNA. • Foreign viral DNA, which is not methylated at the sequence "GAATTC" will therefore be recognized as "foreign" DNA and will be cleaved by the EcoR1 endonuclease. • Cleavage of the viral DNA renders it non-functional. Such endonucleases are referred to as "restriction endonucleases" because they restrict the DNA within the cell to being "self". The combination of restriction endonuclease and methylase is termed the "restriction-modification" system. Of course, this type of protective system is beaten if the attacking phage was previously grown on the same strain as that which it is infecting. In this case the phage will have its DNA already methylated at the appropriate sequence, and will be recognized as "self" (see the table above). E. coli strain 'C' (above) is strain which has no known restriction-modification system. We will discuss DNA replication later, but it should be mentioned that: • replicating host DNA will initially have one strand (parental) methylated and the other (nascent strand) non-methylated. • This is recognized as "self" and is not cleaved by the restriction endonuclease. • It is subsequently methylated by the host methylase. Structural and biochemical studies have indicated that for the common R/M systems (so called type II), the methylase recognizes and methylates one strand of the DNA duplex, whereas the restriction endonuclease recognizes both strands of the DNA (i.e. both strands must be non-methylated for recognition). It is able to do this because it is a homo-dimer protein. Restriction endonucleases Since different bacterial strains and species have potentially different R/M systems, their characterization has made available over 200 endonucleases with different sequence specific cleavage sites. • They are one of the primary tools in modern molecular biology for the manipulation and identification of DNA sequences. • Restriction endonucleases are commonly named after the bacterium from which it was isolated. Examples of different restriction enzymes Name Source Recognition Sequence Comments Alu I Arthrobacter luteus ``` \| 5'… A G C T … 3' 3'… T C G A … 5' \| ``` "Four cutter". Leaves blunt ends to the DNA. Bfa I Bacteroides fragilis ``` \| 5'… C T A G … 3' 3'… G A T C … 5' \| ``` "Four cutter". Leaves 5' overhang. Nci I Neisseria cinerea ``` \| C 5'… C C G G G … 3' 3'… G G C C C … 5' G \| ``` "Five cutter". Middle base can be either cytosine or guanine. Leaves 5' overhang. Different recognition sites may have non-complementary sequences. Eco R1 Escherichia coli ``` \| 5'… G A A T T C … 3' 3'… C T T A A G … 5' \| ``` "Six cutter". Leaves 5' overhang. Behaves like a "four cutter" ('star' activity) in high salt buffer. \$44 for 10,000 units. Hae II Haemophilus aegyptius ``` \| 5'… Pu G C G C Py … 3' 3'… Py C G C G Pu … 5' \| ``` "Six cutter". Pu is any purine, Py is any pyrimidine. Leaves 3' overhang. EcoO109I Escherichia coli ``` \| 5'… Pu G G N C C Py … 3' 3'… Py C C N G G Pu … 5' \| ``` "Seven cutter". Pu is any purine, Py is any pyrimidine, N is any base. Leaves 5' overhang. Different recognition sites may have non-complementary sequences. Bgl I Bacillus globigii ``` \| 5'… GCCN NNNNGGC … 3' 3'… CGGNNNN NCCG … 5' \| ``` "Six cutter with interrupted palindrome". Leaves 5' overhang. Different recognition sites may have non-complementary sequences. Bsa HI Bacillus stearothermophilus ``` \| 5'… G Pu C G Py C … 3' 3'… C Py G C Pu G … 5' \| ``` "Six cutter". Different recognition sites will be complementary. Aat II Acetobacter aceti ``` \| 5'… G A C G T C … 3' 3'… C T G C A G … 5' \| ``` "Six cutter" with 3' overhang. Same recognition sequence as Bsa HI, but different cleavage position. Bpm I Bacillus pumilus ``` \| 5'… C T G G A G N16 … 3' 3'… G A C C T C N14 … 5' \| ``` Non-palindrome, distal cleavage. Leaves 3' overhang. \$50 for 50 units. Not I Nocardia otitidiscaviarum ``` \| 5'… G C G G C C G C … 3' 3'… C G C C G G C G … 5' \| ``` "Eight cutter". Leaves 5' overhang. Bsm I Bacillus stearothermophilus ``` \| 5'… G A A T G C N … 3' 3'… C T T A C G N … 5' \| ``` "weird". Leaves 3' overhang. • The utility of restriction endonucleases lies in their specificity and the frequency with which their recognition sites occur within any given DNA sample. • If there is a 25% probability for a specific base at any given site, then the frequency with which different restriction endonuclease sites will occur can be easily calculated (0.25n): Nucleotide Specificity Example Frequency of Occurrence Four Alu I 256 (0.25 Kb) Five Nci I 1024 (1.0 Kb) Six EcoR I 4096 (4.1 Kb) Seven EcoO109I 16384 (16.4 Kb) Eight Not I 65536 (65.5 Kb) Thus, on average, any given DNA will contain an Alu I site every 0.25 kilobases, whereas a Not I site occurs once about every 65.5 kilobases. • Not I is therefore a very useful enzyme for isolating large regions of DNA, typically in research involving genomic DNA manipulations. • Alu I would be expected to digest a DNA sample into lots of little pieces. The assortment of DNA fragments would represent a specific "fingerprint" of the particular DNA being digested. Different DNA would not yield the same collection of fragment sizes. Thus, DNA from different sources can be either matched or distinguished based on the assembly of fragments after restriction endonuclease treatment. These are termed "Restriction Fragment Length Polymorphisms", or RFLP's. This simple analysis is used in various aspects of molecular biology as well as a law enforcement and genealogy. For example, genetic variations which distingish individuals also may result in fewer or additional restriction endonuclease recognition sites. Restriction endonucleases are supplied in various concentrations with activities that are based upon cleavage rates of "standard" DNA samples. • One unit of activity is typically defined as the amount of enzyme required to digest (or "restrict") one microgram of reference DNA in one hour at 37 °C. • The reference DNA may actually have one or more recognition sites for the nuclease in question. DNA's used as "standard" samples may include phage l DNA, or the plasmid pBR322. • The endonuclease hydrolysis is a spontaneous reaction and does not, for example, require addition of ATP. Reaction buffers for restriction endonucleases usually contain a buffer component (typically 10 mM TRIS buffer around pH 8.0), magnesium salt (often 10 mM MgCl2), a reducing agent (usually 1mM dithiothreitol, or DTT), a protective carrier protein (typically 100 ug/ml bovine serum albumin, or BSA), and salt (sodium chloride). • The biggest determinant of enzyme activity is typically the ionic concentration (NaCl content) of the buffer. Although there are hundreds of different restriction endonucleases, the majority of them can exhibit between 30-100% activity using a simple system of three buffers, containing either low (20 mM), medium (100 mM) or high (250 mM) salt (NaCl) concentrations in the above described buffer. Enzyme digests are typically performed for 1-2 hours at 37 °C. However, quantitative digestion can sometimes only be achieved after extended incubation (i.e. overnight).

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