Glycolysis and Flux Control
TONY ROMEO1* AND JACKY L. SNOEP2,3
[SECTION EDITOR: AUGUST BÖCK]
Posted October 7, 2005
Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia 30322,1 Department of Biochemistry, University of Stellenbosch, Private Bag X1, Matieland 7602, South Africa,2 and Department of Molecular Cell Physiology, Vrije Universiteit, Amsterdam, The Netherlands3
*Corresponding author. E-mail:
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Central metabolism of carbohydrates uses the Embden-Meyerhof-Parnas (EMP), pentose phosphate (PP), and Entner-Doudoroff (ED) pathways. This chapter reviews the biological roles of the enzymes and genes of these three pathways. Glucose, pentoses, and gluconate will be primarily discussed as the initial substrates of the three pathways, respectively. Other EcoSal chapters (chapters Hexose/Pentose and Hexitol/Pentitol Metabolism, Catabolism of Hexuronides, Hexuronates, Aldonates, and Aldarates and Glycerol and Methylglyoxal Metabolism) review the catabolism of sugars and related carbon sources that utilize these trunk pathways. The genetic and allosteric regulatory mechanisms of glycolysis and the factors that affect metabolic flux through the pathways are considered. Application of systems approaches to the study of glycolytic flux is presented later in this chapter. This chapter is not comprehensive, and the reader is referred to excellent previous reviews of this broad area (58, 59) and of subtopics as they are mentioned below.
Uptake of glucose into the cell is primarily by the phosphoenolpyruvate:carbohydrate phosphotransferase system (PTS), a process that is intimately involved in catabolite repression and other regulatory mechanisms (reviewed in Chapter Heat and Protein Denaturation; 68). During transport by the PTS system, glucose is phosphorylated at the expense of phosphoenolpyruvate (PEP) to produce glucose-6-P. This is the first of the three regulatory steps of the EMP pathway, the others being the metabolite interconversions occurring at the phosphofructokinase and pyruvate kinase reactions. Suppressor mutants that transport glucose in the absence of PTS can be readily isolated (54). When glucose is generated, e.g., by hydrolysis of a glucose-containing disaccharide, glucose-6-P is produced by the glucokinase (Glk) reaction. Mutations in glk have little effect on the growth of otherwise wild-type strains, and growth on glucose-containing disaccharides is not compromised in a glk mutant (36). The absence of a phenotype could occur because the resulting monosaccharides are phosphorylated by PTS internally and/or may accumulate in the medium and become phosphorylated by PTS upon uptake (discussed in reference 117). However, mutants that lack both PTS and Glk are severely defective for growth on glucose (36). Studies conducted on glucose-limited chemostat cultures (i.e., in the presence of micromolar concentrations of glucose) revealed that under this condition free glucose, rather than glucose-6-P, preferentially enters the cell via an ABC-type transporter (Mgl), suggesting that glucokinase might be essential under conditions of severe nutrient limitation (51, 124).
Growth on galactose, maltose, and malto-oligosaccharides and catabolism of endogenous glycogen generate glucose-1-P, which is converted to glucose-6-P by phosphoglucomutase (Pgm) to undergo further catabolism. This reversible reaction also generates glucose-1-P for the biosynthesis of ADP-glucose, the precursor of glycogen, and UDP-glucose (see chapters in EcoSal Domain Synthesis of Complex Polymers (Murein, Capsules, LPS, Glycogen, Polyphosphate)).
Expression of a chromosomal glk-lacZ fusion is modestly repressed by the catabolite repressor/activator, Cra (FruR), and by growth on glucose, but is not affected by other global regulators that have been examined, CsrA, Crp, Lrp, and RpoS (117). A putative Cra-binding sequence is located upstream of the glk promoter (117), suggesting that Cra may directly repress glk expression. Phosphoglucomutase enzyme and mRNA levels are negatively regulated by CsrA (152; Q. Wu and T. Romeo, unpublished data).
Glucose-6-P can be converted to fructose-6-P via the phosphoglucose isomerase reaction of the EMP pathway and either enter the oxidative branch of the PP pathway (Fig. 1) or be converted by phosphoglucomutase (Pgm) to glucose-1-P for sugar nucleotide synthesis. A pgi knockout mutant grows at a reduced rate on glucose or other sugars that are metabolized via glucose-6-P, using the PP pathway to provide fructose-6-P (62). In part, this growth defect is due to excessive reduction of NADP in the oxidative branch of the PP pathway, since the growth rate of pgi mutants is partially restored by strain modifications that promote NADPH oxidation (17, 99). Growth on fructose or other compounds that require phosphoglucose isomerase to generate glucose-6-P still occurs in pgi mutants, indicating that the latter compound is not essential (62, 195).
Pgi enzyme levels are increased ~2-fold by the RNA-binding protein CsrA (152). Transcriptome analyses reveal that this involves changes in pgi mRNA levels (Q. Wu and T. Romeo, unpublished data).
The interconversion of fructose-6-P and fructose-1,6-P2 is a regulatory step for the EMP pathway, involving two unidirectional reactions that each are catalyzed by two different allosteric enzymes. Two phosphofructokinase isozymes are present in both Escherichia coli and Salmonella enterica. Pfk-1 (pfkA) comprises ~90% of the total activity (12, 78); the remaining activity is contributed by an enzyme that is unrelated to Pfk-1 in amino acid sequence, Pfk-2 (pfkB) (4, 72, 73). Only a pfkA mutation causes a growth defect on glucose or other sugars metabolized through fructose-6-P. The growth defect of a pfkA mutant on glucose can be suppressed by a promoter-up mutation in pfkB (4, 37). This defect is also partially suppressed by an iclRc mutation, which derepresses the glyoxylate shunt (194). In this case, suppression requires a functional pfkB gene and appears to involve replenishment of phosphoenolpyruvate, which restores glucose transport by PTS (144).
The two Pfk isozymes exhibit distinct allosteric regulation and genetic regulation. Pfk-1 activity is sigmoidal with respect to fructose-6-P and is activated by MgADP (or MgGDP) and inhibited by PEP (12, 78). Pfk-1 thus is designed to respond to energy charge and carbon flux. In contrast, Pfk-2 exhibits hyperbolic kinetics and is allosterically inhibited by MgATP at low concentrations of its other substrate, fructose-6-phosphate concentrations (4, 72, 73). Pfk levels are regulated (directly or indirectly) by Cra (FruR) and CsrA in S. enterica and E. coli (23, 152). In the former case, Cra was found to repress the total Pfk enzyme activity, but isozyme levels were not determined. CsrA was found to activate Pfk-1 and repress Pfk-2, and thus was proposed to shift the allosteric regulation of this step of glycolysis. This pattern of regulation indicates that Pfk-1 is similar to several other glycolytic enzymes with respect to Cra and CsrA, while Pfk-2 responds to CsrA as if it were a gluconeogenic enzyme (discussed below). A mutation causing the loss of MgATP inhibition of Pfk-2 compromises the growth of the strain on gluconeogenic compounds (38), possibly because it causes a futile cycle (188). This mutation therefore defines a biological role for the allosteric regulation of Pfk-2.
The primary fructose-1,6-bisphosphatase (Fbp) is required for growth of E. coli on gluconeogenic compounds (61). It is allosterically inhibited by AMP (5). A mutant enzyme that is insensitive to AMP inhibition did not lead to an apparent growth defect on either glycolytic (glucose) or gluconeogenic (glycerol) compounds, except in when it was overexpressed, which decreased the rate of growth on glucose (167). A second enzyme that catalyzes the same reaction is encoded by the glpX gene, which is located within an operon involved in glycerol metabolism, glpFKX (40). While the wild-type glpX gene is not sufficient for growth on gluconeogenic compounds in the absence of fbp, suppressor mutations for fbp apparently can arise in glpX. The biological function of this enzyme remains to be determined. It is not required for growth on glycerol.
E. coli possesses two fructose-1,6-P2 aldolases that are capable of catalyzing the reversible aldol condensation of glyceraldehyde-3-P and dihydroxyacetone-P to form fructose-1,6-P2 (179). The major isozyme, FbaA, is a Class II, zinc-dependent protein. First isolated in 1978 (10), its catalytic mechanism has been extensively studied (e.g., references 135 and 204) and its α/β barrel structure has been solved by X-ray crystallography (14, 35). FbaA is a constitutive enzyme that comprises ~95% of the activity during glycolytic growth. It is required for growth on sugars and other compounds that generate fructose-1,6-P2, but it is not needed for growth on gluconeogenic substrates. Suppressor mutations that eliminate reactions leading to the formation of fructose-1,6-P2 are restorative (162). A gnd mutation permits the growth of an fbaA mutant on gluconate, since the ED pathway that remains operative does not produce fructose-1,6-P2. Similarly, gnd and pgi mutations restore growth on glucose, leading to the accumulation of gluconate and its metabolism through the ED pathway. The inhibitory effect of fructose-1,6-P2 accumulation in two fbaA mutants has been found to cause high levels of ppGpp to accumulate, by an unknown mechanism, which in turn inhibits rRNA synthesis and other processes required for growth (160).
The second fructose-1,6-P2 aldolase is a Class I, Schiff-base forming enzyme. The gene for this enzyme (fbaB) was originally assigned as dhnA, based on a putative function as a dehydrin (28), before it was determined to encode the Class I aldolase of E. coli (186). FbaB enzyme levels are elevated during growth on gluconeogenic compounds, e.g., lactate, glycerol, or pyruvate. Under such conditions, this enzyme comprises ~60% of the total fructose-1,6-P2 aldolase activity (9, 157). Weak expression of fbaB during growth on sugars may explain the requirement for fbaA under this condition. Because no fbaB mutants have been studied, it is not clear whether the Class II enzyme can support gluconeogenic growth. Likewise, the regulatory mechanism(s) responsible for the accumulation of the Class I enzyme under gluconeogenic conditions has not been established.
Interconversion of glyceraldehyde-3-P and dihydroxyacetone-P is catalyzed by triosephosphate isomerase. This enzyme has been the subject of much study, in part because of its high reaction rate, which is limited by diffusion (reviewed in reference 104). High-resolution crystal structures of triosephoshate isomerases from several species have been solved, including that of E. coli (123). While this enzyme is essential for gluconeogenic growth, tpi mutants are capable of growth on lactate, succinate, or other compounds below the defect if a small amount of glycerol or glycerol-P is present to provide dihydroxyacetone-P (2, 89). During growth on sugars, production of glyceraldehyde-3-P should be sufficient for growth, but in fact tpi mutants either fail to grow or grow very poorly on glucose. The explanation apparently rests in the fact that excess dihydroxyacetone-P is converted by the enzyme methylgloxal synthase to Pi and the toxic metabolite methylglyoxal.
Methylglyoxal synthase (Mgs) is activated by its substrate and allosterically inhibited by Pi (83). This led to the suggestion that it may serve to bypass the Pi-requiring step of glycolysis and thereby help to recycle phosphate under limiting conditions and prevent a buildup of intermediates at the level of glyceraldehyde-3-P (reviewed in reference 33). Studies of a mutant of mgs and overexpression of this gene in E. coli, as well as chemostat studies of Klebsiella aerogenes, are consistent with the idea that this enzyme might facilitate the transition from carbon starvation to carbon excess by helping to dissipate inhibitory phosphorylated intermediates (183, 189). The crystal structure of methylglyoxal synthase from E. coli has been solved (151).
E. coli expresses two glyoxalases that function in tandem to detoxify methylglyoxal, although they are not present in sufficient levels to restore a tpi mutation (34). The Ni2+-containing enzyme glyoxalase I (29) isomerizes the spontaneous adduct of methylglyoxal and glutathione to form an S-D-lactoylglutathione. This compound is hydrolyzed to D-lactatic acid and glutathione by glyoxalase II (192).
Four reversible reactions permit the interconversion of glyceraldehyde-3-P and PEP. Mutants in the genes for three of the required enzymes, gapA, pgk, and eno were isolated based on their inability to grow on compounds above (glycerol) or below (succinate) the genetic lesion and their ability to grow on media containing both of these compounds (79, 89, 90). Mutations in phosphoglycerate mutase were not isolated by this scheme, possibly because either of two isozymes, GpmA or GpmI (64), is sufficient for this reaction. In contrast, the gene for an enzyme that was described as minor isozyme of glyceraldehyde dehydrogenase, gapB (1) is expressed at a low level and the GapB protein exhibits weak glyceraldehyde-3-P dehydrogenase activity (168). Thus, in the absence of gapA, the gapB gene will not support glycolytic growth unless it is overexpressed from a plasmid. It appears that the true role of gapB is to encode erythrose-4-P dehydrogenase, and a name change to epd has been suggested (200).
Glyceraldehyde-3-P dehydrogenase (GapA) catalyzes the oxidative phosphorylation of glyceraldehyde-3-P to yield 1,3-diphosphoglycerate using NAD+ as a cofactor. In glycolysis, this important reaction conserves the energy derived from the oxidation of the aldehyde group of glyceraldehyde-3-P in the form of a high-energy phosphate bond that is used for the synthesis of ATP by phosphoglycerate kinase. The mechanism of GapA has been well studied, and its crystal structure in the presence or absence of its substrates has been elucidated (41, 202). Although glyceraldehyde-3-P dehydrogenase from certain gram-positive bacteria also serves as a surface receptor (118, 128), no such function has been found in E. coli or its relatives.
The two phosphoglycerate mutases (GpmA, GpmI) of E. coli are evolutionarily unrelated enzymes. They are distinguished based on whether they depend on 2,3 bis-phosphoglycerate as a cofactor, the presence or absence of magnesium, differential inhibition by vanadate, and different patterns of expression (64).
Enolase catalyzes the dehydration of 2-phosphoglycerate to form PEP, which contains a high-energy phosphate group that is used both for ATP synthesis and transport by PTS. Enolase levels are modulated by CsrA (152) and eno mRNA is destabilized by RNase G (101), an enzyme that specifically affects several glycolytic messages (110). Enolase itself is a subunit of the RNA degradosome (108, 141), which is involved in mRNA degradation and processing of many stable RNAs (Chapter Exoribonucleases and Endoribonucleases). Recently, the presence of enolase in the degradosome complex was shown to affect ptsG mRNA decay (120).
In glycolysis, PEP is converted to pyruvate by two distinct pyruvate kinases, PykF and PykA (66, 67, 115). Both enzymes are dispensable for growth on glucose unless the strain is also defective for PTS, in which case it cannot produce pyruvate and fails to grow (136). A pykF pykA mutant also fails to grow on non-PTS sugars. These two enzymes exhibit distinct allosteric and genetic regulation. PykF is activated by fructose-1,6-bisphosphate, while PykA is activated by AMP and sugars of the pentose phosphate pathway (196, 197). The DNA-binding protein FruR or Cra represses pykF expression under gluconeogenic conditions but does not affect pykA (13). DNA binding at the pykF promoter by Cra is blocked by fructose-1,6-P2 or fructose-1-P, a process referred to as catabolite activation. CsrA activates pykF expression but not that of pykA (152). Further metabolism of pyruvate is the subject of subsequent EcoSal chapters (Chapters Tricarboxylic Acid Cycle and Glyoxylate Bypass and Anaerobic Formate and Hydrogen Metabolism).
PEP is also metabolized by PEP carboxylase in a CO2-fixing, anaplerotic reaction that produces oxaloacetate and is required for growth on sugars, unless a dicarboxylate such as succinate is available (3, 185). This enzyme is allosterically activated by acetyl-CoA and fructose-1,6-P2 and inhibited by aspartate (119) or more likely, in vivo, by malate (199). The crystal structure of this tetrameric enzyme has been determined and used to develop models for its allosteric transitions (reviewed in reference 102).
During gluconeogenic growth, PEP is produced from oxaloacetate by PEP carboxykinase and from pyruvate by PEP synthetase. Growth on succinate or other four carbon dicarboxylates occurs if either of these two enzymes is present (69). During growth using Pps, NAD- or NADP-dependent malic enzyme (MaeA or MaeB, respectively) is needed to produce pyruvate from oxaloacetate (75, 76, 92). Growth on pyruvate, lactate, or alanine requires PEP synthetase (31, 172).
PEP carboxykinase is inhibited by PEP and activated by calcium (69, 107). However, this is a monomeric enzyme and neither effect is likely to reflect allosteric regulation (180). PEP synthetase is activated by high adenylate energy charge and inhibited by PEP, oxaloacetate, α-ketoglutarate, glyceraldehyde-3-P, and ADP-glucose (24, 25). The NAD-specific malic enzyme is inhibited by ATP and coenzyme A and activated by aspartate (154). Genetic expression of pckA is activated by cAMP, i.e., subject to classical catabolite repression (70), activated by Cra (14), and repressed by CsrA (147). Similar effects of CsrA and Cra on PEP synthetase were also observed (152, 153).
The PP pathway is a flexible pathway, capable of serving several metabolic functions, including catabolism of pentoses, glucose, and gluconate, synthesis of pentoses, and providing precursors used in the biosynthesis of lipopolysaccharide, nucleotides, and several amino acids and vitamins (reviewed in reference 177). It includes two distinct phases or branches, oxidative and nonoxidative (Fig. 1 and 2). In the oxidative branch, glucose-6-P (G6P) is converted sequentially to 6-phosphogluconolactone (6PGL), 6-phosphogluconate (6PG), and ribulose-5-phosphate (RuP) and CO2 by the action of G6P dehydrogenase (Zwf), a lactonase specific for 6PGL (Ggl) and 6PG dehydrogenase (Gnd), respectively. Two molecules of NADP are reduced in the dehydrogenase reactions, which can be used for reductive biosynthesis, maintenance of redox balance, and regeneration of oxidative damage (16, 111). Single mutants in any of the genes of the oxidative phase grow well on glucose unless combined with a mutation such as pgi. In this case, zwf and gnd mutants fail to grow and pgl mutants grow slowly, apparently depending on the spontaneous hydrolysis of 6PGL (56, 57, 109). Flux analyses confirm that carbon flow in a zwf mutant is rerouted through the EMP and nonoxidative PP pathways, while the PP pathway serves as the main route of carbon flow in a pgi mutant growing on glucose, with some contribution of the ED pathway (52, 85). The gnd gene is also needed for residual growth on gluconate minimal medium in an edd mutant, which is blocked in the ED pathway (56).
Transcription of the zwf gene (and G6P dehydrogenase levels) is activated by oxidative stress, salicylate, and increasing growth rate. The former responses are mediated by SoxRS and MarA, respectively, and are well understood at the molecular level (50, 71, 94). Although cis-acting mutations affecting growth rate regulation of zwf expression have been isolated and characterized, they create new promoters and have not provided a mechanism by which the wild-type zwf promoter responds to growth rate (60, 63, 148, 149).
The expression of the gnd gene, encoding 6PG dehydrogenase, is also activated by increasing growth rate. This involves both an increase in transcription (131) and an increase in translation due to relief from translational repression by an anti-Shine-Dalgarno sequence that is present in the coding region of this gene (21). The later appears to require a long-range base-pairing interaction that blocks translation. The way in which gnd mRNA secondary structure responds to growth rate has not been determined. The activity of 6PG dehydrogenase is inhibited by ATP and fructose-1,6-bisphosphate, although the in vivo significance of this observation is unclear (127).
The nonoxidative phase of the pathway comprises reversible reactions that permit the interconversion of the pentose phosphates ribulose-5-phosphate (Ru5P), ribose-5-phosphate (R5P), and xylulose-5-phosphate (X5P), and the transfer of either a glycoaldehyde group (transketolase) or a dihydroxyacetone group (transaldolase) among sugar phosphates (Fig. 2).
When the oxidative branch provides substrate, Ru5P is converted to X5P by ribulose-phosphate epimerase (Rpe) and to R5P by ribose-phosphate isomerase (RpiA and RpiB). Disruption of rpe leads to a complete loss of Ru5P epimerase activity and results in a requirement for both ribose and xylose on minimal media, and growth impairment in the presence of gluconate and glycolytic carbon sources, even in rich media (112, 113). Evidence indicates that the latter inhibitory effects on growth are most likely due to changes in sugar-phosphate levels that lead to the inhibition of key metabolic enzymes. Two R5P isomerases are involved in R5P catabolism and synthesis (39, 48, 170, 171). RpiA is a constitutive enzyme (84). Disruption of rpiA leads to a growth requirement for ribose, but the inducible RpiB enzyme can permit an rpiA mutant to grow on D-ribose (150). In addition, a mutant lacking rpiB is prototrophic for ribose, indicating that rpiA is both necessary and sufficient for R5P synthesis from Ru5P. Ribose auxotrophy caused by a defect in rpiA can be suppressed by a multicopy plasmid clone of either rpiA or rpiB, or by disruption of the gene encoding a repressor of rpiB transcription, rpiR, which is divergently transcribed with respect to rpiB (175). X-ray diffraction studies reveal distinctly different structures for RpiA and RpiB (142, 205).
Transketolase of E. coli is expressed by two genes, tktA and tktB, encoding major and minor forms, respectively (86, 97, 98, 176, 206). By virtue of its ability to transfer a glycoaldehyde (ketol) group, transketolase permits the interconversion of intermediates of the EMP and PP pathways (Fig. 2). It is required for the production of erythrose-4-phosphate (E4P), a precursor of pyridoxine and shikimate, itself a precursor of aromatic amino acids and vitamins (chapters Biosynthesis of the Aromatic Amino Acids, Biosynthesis of Histidine, and Coenzymes, Cofactors, and Prosthetic Groups). It is also used for synthesis of R5P from sedoheptulose-7-P (S7P) and glyceraldehyde-3-P (G3P), which is used to produce phosphoribosylpyrophosphate, a precursor of histidine, tryptophan, and nucleotides. Sedoheptulose-7-phosphate is also synthesized by transketolase and provides heptose, a precursor of lipopolysaccharide (43). Growth on pentoses requires transketolase. While different tktA mutants exhibit different levels of "leakiness" with respect to aromatic requirements (98, 206), tktA is essential for pentose catabolism, and tktB was cloned based on its ability to complement the growth defect of a tktA mutant on ribose (86).
Transaldolase catalyzes the reversible transfer of a dihydroxyacetone group from a donor, such as F6P, to an acceptor, such as E4P (Fig. 2). The gene encoding transaldolase (talB) has been cloned and sequenced (178, 205). Disruption of talB caused a complete loss of transaldolase enzyme activity and protein in immunoblots, indicating that it encodes the only transaldolase of E. coli (161). Nevertheless, the talB mutation only modestly affects growth on glucose or pentoses. Transaldolase crystal structure has been solved and its active-site residues have been studied (96, 161).
The ED pathway is a primary route of sugar catabolism in many oxidative bacteria and is found in Bacteria, Archaea, and Eucarya (30). This broad phylogenetic distribution suggests that it may have evolved prior to the EMP pathway (146). The ED pathway uses gluconate-6-P (6PG) dehydratase (Edd) to convert 6PG to 2-keto-3-deoxy-6-phosphogluconate (KDPG), which is cleaved by KDPG aldolase (Eda) to produce pyruvate and glyceraldehyde-3-P (Fig. 1). The two structural genes are encoded together in the edd-eda operon (42). In E. coli, this inducible pathway is used for the catabolism of gluconate and L-idonate, and KDPG aldolase is needed for catabolism of glucuronate, galacturonate, and several other sugar acids (11; Chapter Catabolism of Hexuronides, Hexuronates, Aldonates, and Aldarates). Mutants lacking the dehydratase use the PP pathway to permit growth on gluconate at a reduced rate (203). Mutants in eda are unable to grow on the uronic acids or gluconate, although the latter effect appears to be the result of toxicity due to KDPG accumulation, since the additional loss of eda restores growth (49, 55). Galactonate is metabolized by a pathway similar to ED (32). Sugar phosphate catabolism by these pathways appears to play a crucial role in the growth and nutrition of E. coli in the mammalian intestinal tract (133, 181, 182; chapter The Life of Commensal Escherichia coli in the Mammalian Intestine). While E. coli normally does not use the ED pathway for glucose metabolism, it synthesizes a periplasmic pyrroloquinoline quinone (PQQ)-dependent glucose oxidase (82). In the presence of PQQ, provided exogenously (27, 116), this enzyme is able to synthesize 6PG from glucose, which induces edd-eda expression and 6PG metabolism (53). Besides its role in the ED pathway, KDPG aldolase (also known as 2-keto-4-deoxy-glutarate aldolase) has been implicated in glyoxylate detoxification and in recovery of respiration following the SOS response (20, 74).
No fewer than four transporters (encoded by gntT, gntU, gntP, and idnT) and two kinases (encoded by gntK and idnK) are involved in gluconate uptake and phosphorylation to provide 6PG for the ED pathway (11, 93, 103, 134, 137, 187). The main system, GntI, includes operons encoding a kinase and low-affinity transporter (gntKU), a gluconate-binding repressor of the Gnt regulon (GntR), and a high-affinity transporter (GntT) (93, 132, 187). In strains defective for GntI, suppressors in idn permit gluconate to be metabolized. For many years, the latter system was thought to encode components of a subsidiary pathway for gluconate catabolism, GntII (7, 91). However, the main role of the latter system appears to be in the catabolism of idonate, in which gluconate is an intermediate (11).
GntR repressor regulates gluconate-responsive transcription of the edd-eda operon (42). Furthermore, the gnt genes are under cAMP-mediated catabolite repression, although edd-eda is not (42, 137, 187). In fact, growth on gluconate itself leads to decreased cAMP levels and catabolite repression (47) by a mechanism that is independent of classical EIIAGLC effects (81). The paradox offered by the role of gluconate as both a repressor of cAMP accumulation and the inducer of the Gnt regulon may reflect the need to balance the expression of this system to avoid toxicity due to KDPG and/or methylglyoxal (7, 65, 132). In contrast to edd, the eda gene is constitutively expressed, apparently from promoter(s) within the distal coding region of edd (42). In addition, eda, but not edd, is regulated by KdgR, which represses genes for the catabolism of 2-keto-3-deoxygluconate and its uronic acid precursors (42, 138, 143). The edd-eda operon also responds to the master regulator of the flagellar cascade, FlhD2C2, which indirectly activates edd-eda transcription via the oxygen-sensing chemotactic protein Aer (140), nutrient starvation conditions (125, 191), and DNA damage and the SOS response (20). The molecular mechanisms and biological significance of these responses remain to be determined.
Our current view of metabolic pathways such as the EMP and the PP is strongly influenced by the way they were discovered; one reaction step after the other. Textbooks give a detailed description of all the reaction steps and the enzyme kinetics but usually put little emphasis on the pathway as a system. Even when overall regulation or the function of a pathway is discussed, it is in qualitative terms only. Despite the fact that a lot of information on each of the reaction steps has been accumulated over the years for E. coli, surprisingly little quantitative information has been integrated to analyze glycolysis as a system. Therefore, it is appropriate to follow the preceding section of this chapter on a detailed description of each of the catalytic steps now by a systemic approach. We will discuss both structural and kinetic aspects.
Structural analyses of cellular reaction networks have been useful in interpretation of experimental data to calculate steady-state flux distributions over metabolic pathways.
In such analyses the reaction network is translated into a set of ordinary differential equations and linear algebra methods are used to calculate the steady-state solution space. Typically such a solution will be the expression of all reaction rates as a function of a limited set of external independent reaction rates. After experimental determination of the latter all reaction rates in the network can be calculated. Such flux (balance) analyses (FBA) (15, 193) are extensions of the traditional redox and carbon balancing methods applied in microbial physiology (169). Although these methods are robust and fast and can be applied to very large systems, their applicability is limited by the fact that they are fully dependent on a correct reaction stoichiometry for the system and hence on assumptions with respect to redox and energy metabolism.
To illustrate the approach we will apply the method to a metabolic reaction network consisting of the EMP, PP, and ED pathways (Table 1, Appendix 1 [below]). We assume the three pathways to operate in isolation from the rest of metabolism, i.e., no anabolic reactions occur and NADH and ATP production and consumption must be balanced within the scheme. Furthermore, NADP(H) and all exernal metabolites are treated as constants. Given these assumptions the flux distribution over the three pathways can be calculated if four external fluxes can be measured. The method is worked out in Appendix 1, in which it is shown how all internal fluxes can be calculated from a set of four independent external fluxes, i.e., acetate, ethanol, lactate production, and glucose consumption.
Table 1Reactions used in the structural analysis of the EMP, PP, and ED pathways |
Two disadvantages of FBA should be mentioned here. First, it is not always possible to have a set of independent reactions that all are external. In that case one cannot solve the complete system from external measurements only. Second, the accuracy of the assesment of the flux distribution, sometimes may be severely reduced as a consequence of diminished sensitivity of the metabolite measurements. For instance, the flux through the EMP will be orders of magnitude higher than through the ED route when E. coli is grown on glucose. To calculate the contribution of the ED route using the method above would entail measuring the acetate and ethanol production rates and the difference between the two would equal the flux through the ED pathway (see Appendix 1; vEDD = vACK − vADH). In this case this would involve subtracting two large numbers, each with an experimental error, which could lead to an inaccurate estimation of the ED pathway flux.
These disadvantages of the flux-balancing method have been addressed in an extension of FBA where 13C-labeled substrates were used (122, 155, 198). The method is based on the principle that 13C label of the substrate can lead to a pathway-specific labeling position in the intracellular metabolites. For instance glucose labeled at the C-1 position leads to unlabeled pyruvate when all glucose is converted via the PP route, to 50% of the pyruvate labeled at the C-3 position if all glucose is converted via the EMP pathway, and to 50% of the pyruvate labeled at the C-1 position if all glucose is converted via the ED route (122). Using GC-MS or NMR techniques the position of the 13C can be determined and sophisticated computer algorithms have been developed to analyze the experimental data. This method is independent of the FBA method and together these methods form a very strong tool for the analysis of internal flux distributions, as has been recently shown in E. coli for the distribution of carbon flux over the EMP, PP, and ED pathways (52, 85) and for analysis of pyruvate kinase knockouts (46).
Instead of using the complete steady-state solution space for the network structure, structural analysis methods can also be used to calculate maximal conversion ratios between substrates and products. Three slightly different approaches have been formulated, each leading to its own set of "extreme currents" (26), "extreme pathways" (159), or "elementary modes" (164), which are unique and can be calculated directly from the network structure. These three methods are quite similar and here only the method using elementary modes will be used as an illustration. The reader is referred to review articles comparing the different methods (129, 166).
An elementary mode is a minimal subset of reactions that fulfills the steady-state criteria and is in itself not decomposable into smaller subunits. For the combined pathways of EMP and PP seven such elementary modes were found (165). When we add the ED route to these pathways a total of 10 elementary nodes are obtained for these three pathways as listed in Appendix 1. The strength of this method is that it gives a robust method to define pathways that can function as nondecomposable units. Many of these elementary modes coincide with modules that have been identified on a functional basis (e.g., homolactic fermentation [mode 2], mixed acid fermentation [mode 6], and a complete oxidative PP cyclic mode [mode 1]). In addition the modes can be used to calculate optimal yields since the maximal conversion yield will always coincide with an elementary mode (166). Thus, maximal NADPH production yield from glucose is obtained via mode 1, yielding 12 NADPH per glucose but not leading to any ATP or NADH production. Maximal ATP production yield per glucose is obtained via mode 6, yielding 3 ATP per glucose.
Structural analysis methods are important to predict effects of knockout mutations on possible steady-state solutions. With a given set of elementary modes one can simply eliminate all the modes in which the enzyme that will be deleted is present. None of the other modes will be affected and thus one can immediately see whether a valid steady state is still possible and what the maximal conversion yield will be in the new strain. At least as important for biotechnological applications is the ability to predict effects of addition of enzymes. We will limit ourselves to one succesfull example illustrating the use of structural analysis methods in engineering of central carbon metabolism of E. coli.
As can be seen from the elementary modes analysis of the glycolytic pathways in E. coli the maximal conversion yield of glucose to ethanol yields 1 ethanol per glucose (i.e., mode 6, mixed acid fermentation). Ingram’s group at the University of Florida cloned the genes for two enzymes of Zymomonas mobilis, pyruvate decarboxylase (PDC) and alcohol dehydrogenase, into the pyruvate formate lyase gene of E. coli, thereby disrupting this gene (87, 126). The PDC introduces a new reaction to the system and when analyzing the elementary modes for the glycolytic system including PDC the maximal obtainable conversion yield is doubled. In this case the system is simple enough to do such a structural analysis without the use of computer algorithms. However, it should be mentioned that we have considered the glycolytic pathways here to be active in isolation while in the living cell they are always embedded in a much larger reaction network. In such larger networks, the steady-state solution space becomes intractable for the human mind and robust analysis methods are essential.
In the preceding section we focused on structural aspects and the constraints these can put on steady-state flux relations. With these methods one can calculate steady-state flux relations that obey the steady-state constraints but these methods in themselves do not have a predictive power as to what enzymes will be active, because they do not contain kinetic information. Kinetic constraints such as reversibility or a maximal value can be included in structural analyses methods, such as applied by the Palsson group (139). However, structural models can predict neither metabolite concentrations nor the extent to which changes in enzyme activities will affect the system. Whereas the structure of a system can put constraints on flux relations, the kinetics of a system will eventually determine the rates at which reactions occur. Models that include kinetic information of the reaction steps will always contain the reaction stoichiometry and therefore follow the structural constraints, but in addition to these also kinetic rate laws must be fulfilled.
Solid theoretical frameworks that indeed include the kinetics of the individual steps of biochemical pathways, such as Metabolic Control Analysis (MCA) and Biochemical Systems Theory (BST), were established in the seventies and extended thereafter (77, 100, 156). Such analyses make it possible to quantify the extent to which each of the steps in a pathway contributes to its steady-state behavior and this has been done to elucidate the control distributions of glycolysis in S. cerevisiae and Zymomonas mobilis (158, 173). However, for E. coli these methods have not been applied extensively and only few of the enzymes of its glycolysis have been analyzed by using MCA.
A major contribution in this respect has been made by the group of Postma (145, 150, 190, 201) on glucose uptake via the PTS system. Using genetic constructs, expression levels of all components of the PTS system were varied and effects on steady-state glycolytic flux and specific growth rate of small perturbations around the wild-type level of expression were measured (150, 190). These perturbation studies are importantly different from analyses in which strains are used that are deleted in an enzyme. Whereas such a latter analysis shows whether an enzyme is involved in a pathway it does not show whether the enzyme has a control on the flux at wild-type levels of expression. Thus, analyses with deletion strains are important in structural analyses, i.e., to establish a metabolic network, but to determine the control of an enzyme, subtler perturbations of the enzyme activity are necessary. Such experiments, essential to understand quantitatively the importance of the enzymes in a system, require precise perturbation tools (e.g., controllable expression systems) and well-controlled cultivation conditions for steady-state analyses.
The example of the PTS illustrates the importance of the reference state perfectly. Whereas each of the components EI, HPr, IIAGlc, and IICBGlc gained high control on the glucose oxidation rate (and on growth rate) upon an extensive decrease in their activity, they did not do so at wild-type levels of expression. Thus, a strong dependency of the glycolytic flux for each of the components of the PTS was observed at low-expression levels but at wild-type levels of expression no further increase in flux is observed upon overexpression of the PTS enzymes. These studies were performed in batch cultures at saturating glucose concentrations and there are indications that the PTS enzymes might have control at low-glucose concentrations such as prevailing in glucose-limited chemostat cultures (201). These latter studies show again that the control distribution in a system is a dynamic property, dependent on the state of the system, internally (e.g., expression levels of the enzymes) as well as externally (e.g., substrate and product concentrations in addition to pH, temperature, ionic strength, etc.).
The PTS is a system within itself, and control distribution over the different components on the glucose uptake rate was determined using α-glucoside as a substrate. In these studies, glucose uptake was examined in separation from the rest of metabolism, and therefore all control must reside in the PTS system itself. It was concluded that of all components of the PT-system, IICBGlc exerts highest control on the glucose uptake rate both in E. coli and in Salmonella enterica serovar Typhimurium (150, 190). These experimental results were reproduced in a detailed kinetic model of the PTS built on the basis of in vitro enzyme kinetic measurements (145).
Few studies have been published on effects of perturbation of enzyme activities in glycolysis of wild-type E. coli possibly because only very small effects were observed upon small manipulation in any of the steps. This would suggest that control is distributed evenly over the enzymes.For instance in non growing E. coli cells it has been shown that varying the expression of the gene for aldolase had little or no effect on the glycolytic flux (6). On the other hand, it has been shown now that during balanced growth, the majority of the glycolytic flux control does not reside in the pathway but lies in utilization of one of the products of glycolysis, ATP. This result illustrates nicely the importance of a systems biology approach to studying pathways such as glycolysis. Although the textbook view might be that glycolysis runs from glucose to pyruvate, a quick look will learn that such a pathway can never work in isolation from the rest of metabolism since there is a net production of the cofactors NADH and ATP, which, respectively, need to be oxidized and hydrolyzed for the system not to run out of NAD and ADP. These are precisely the type of constraints that were discussed before in the structural analysis.
Work involving activation of futile cycles had already indicated that ATP hydrolysis potentially could have a large control on glycolytic flux. By overexpression of the gene for phosphoenolpyruvate synthase, pps, a potential increase in futile cycling between PEP and pyruvate was facilitated (130) and resulted in a twofold increase in glycolytic flux. In addition, recently, control by the ATP-hydrolyzing reaction was convincingly shown in a study in which the F1 part of the H+-ATPase was overexpressed in the cytosol, thereby increasing ATP hydrolysis without affecting the rest of metabolism (106). The authors worked in the framework of Metabolic Control Analysis and showed that more than 75% of the control of the glycolytic flux resides in the ATP-consuming steps. Independently, but by use of a similar strategy, these results were confirmed by Causey et al. (19). Such a control distribution was predicted on basis of theoretical considerations in a supply-demand analysis of the system (80). The distribution of control over the supply or demand reactions appears to be dependent on the organism and the growth conditions (105).
E. coli is used extensively in metabolic engineering studies, such as for ethanol production, for which high glycolytic fluxes are important in the production processes. The Ingram group at the University of Florida made a major contribution in this field, first by the construction of ethanol-producing E. coli strains (87, 126) and subsequently by the continuous improvement of these strains and widening the spectrum of the products and substrates (18, 19, 88). Several groups have been using these ethanol-producing strains to study the control of the glycolytic flux. Under nongrowing conditions, Emmerling et al. (44, 45, 46) studied the effect of overexpressing pyruvate kinase and phosphofructokinase from E. coli or other bacterial strains. When E. coli was pregrown under aerobic conditions the glycolytic flux could be increased by overexpression of a pyruvate kinase that is not allosterically regulated, an effect that could be enhanced by co-overexpression of phosphofructokinase. The glycolytic flux in cells pregrown under anaerobic conditions could not be enhanced by overexpression of these enzymes, although this affected the ratio between lactate and ethanol production rates.
The kinetic information obtained on isolated enzymes (as discussed in the first part of this chapter) can be integrated using computer models to simulate behavior of the reaction network formed by these enzymes. Successful examples of such approaches are the modeling of glycolysis in S. cerevisiae (184), the parasite Trypanosoma brucei (8), and the red blood cell (95, 121, 163). Here, rate equations have been constructed for each of the enzymes in the system on the basis of kinetic information determined on the isolated enzymes. Important in such enzyme kinetic studies is that they are performed under physiological conditions (i.e., mimicking the cytosol as much as possible and also taking effects of products into account). On the basis of these rate equations the system can be described as a set of ordinary differential equations, which can be integrated using standard routines. Typically, such model simulations produce time courses of metabolite concentrations or steady-state results, both of which can be verified by experiment.
Despite the existence of a number of initiatives to model E. coli metabolism such as the E-cell project (http://www.e-cell.org) and the international E. coli alliance (IECA) (http://www.uni-giessen.de/~gx1052/IECA/ieca.html) to our knowledge only one glycolytic model for E. coli has been published (22). The kinetic parameters in this model were estimated from rapid sampling experiments upon addition of glucose to a glucose-limited chemostat culture using mechanistic rate equations. The authors implemented MCA in their model and concluded that the PTS system was the step with the highest flux control. This agrees very well with the work of Yap (201) that suggests a high flux control by the PTS under glucose-limited conditions whereas no such control was found under glucose excess (150, 190).
With the rapid developments in the field of systems biology one would expect more attempts at kinetic modeling of central metabolism in E. coli in the near future, certainly with advanced metabolic profiling methods (52) becoming available, which are essential for validation of the models.
This chapter presents an overview of the current knowledge on the glycolytic pathways (EMP, ED, and PP) of E. coli. Detailed information on each of the individual kinetic steps is combined with a systemic approach, and where possible, this information has been integrated to provide understanding of the complete pathway. Although factors that regulate the expression of at least some of the genes in these pathways are known, much remains to be learned, especially with respect to the influences of such factors on flux through the pathways. While many of the enzymes in the pathway have been characterized, often a good set of kinetic data determined under the relevant conditions is lacking, hampering the integration of the individual reaction steps to the glycolytic system. With the rapid developments in the field of Systems Biology many new methods have been and will be developed, both for experimental and theoretical approaches, and we expect that these will be applied to E. coli glycolysis in the near future. Some of these new methods are reviewed and applied to glycolysis. Most of the existing systemic research focuses on the pathway structure, and only a few studies have analyzed small perturbations in enzyme activities. Such perturbations, which are necessary for an MCA analysis, have been applied to the PTS system and to ATP hydrolysis. Strikingly, the latter study has shown that under balanced growth conditions, the majority of the flux control of the EMP pathway resides in removal of its product ATP, i.e., in ATP hydrolysis, outside of the pathway itself.
For the structural analysis of the combined EMP, PP, and ED pathways the set of reactions as listed in Table 1 are used.
This set of reactions can be translated in a stoichiometry matrix that links the metabolites to the respective enzyme reactions in which they are reactants. GLCo, Lac, For, Ac, CO2, EtOH, and NADP(H) are considered to be external metabolites, i.e., they are treated as parameters of the system. From this stoichiometry matrix a set of ordinary differential equations for all independent metabolites can be derived using linear algebra methods. In steady state all ordinary differential equations should be zero and the possible solutions can be described as a function of a set of independent fluxes, leading to the following flux relations (Table 2).
Table 2Expression of the steady-state fluxes as a function of a set of independent fluxes |
By multiplying the central matrix with the independent flux vector, one can calculate all steady-state flux values. Using these flux relations one can calculate all steady-state internal fluxes in the left vector from the multiplication of the flux relation matrix with the right-hand vector containing four independent external fluxes. For example, the flux through the ED pathway (i.e., vEDD or vEDA) can be calculated from the difference between the flux through the acetate kinase and the alcohol dehydrogenase (vACK-vADH).
Note that in this analysis we assume that the set of reactions as given in Table 1 functions in isolation of the rest of metabolism. In the cell there are many branch points, for instance, with anabolic reactions and this would complicate the method above considerably. A well-defined reaction network is essential for the analysis method.
By using the same set of reactions used in Table 1, the following elementary modes can be determined by using the metatool software package (134a):
1: -5 v[PGI] - v[ALD] - v[TPI] + 2 v[RPI] + 4 v[RPE] + 2 v[TktI] + 2 v[Tal] + 2 v[TktII] + 6 v[ZWF] - v[PFK] - v[PYK] + v[PTS] + 6 v[GND]
2: v[PGI] + v[ALD] + v[TPI] + 2 v[GAPDH] + 2 v[PGK] + 2 v[PGM] + 2 v[ENO] + v[PFK] + v[PYK] + v[PTS] + 2 v[LDH])
3: 2 v[ALD] + 2 v[TPI] + 5 v[GAPDH] + 5 v[PGK] + 5 v[PGM] + 5 v[ENO] + v[RPI] + 2 v[RPE] + v[TktI] + v[Tal] + v[TktII] + 3 v[ZWF] + 2 v[PFK] + 2 v[PYK] + 3 v[PTS] + 3 v[GND] + 5 v[LDH]
4: -2 v[PGI] + v[GAPDH] + v[PGK] + v[PGM] + v[ENO] + v[RPI] + 2 v[RPE] + v[TktI] + v[Tal] + v[TktII] + 3 v[ZWF] + v[PTS] + 3 v[GND] + v[LDH]
5: -v[PGI] - v[ALD] - v[TPI] + 2 v[ZWF] - v[PFK] -v[PYK] + v[PTS] + 2 v[PTA] + 2 v[ACK] + 2 v[PFL] + 2 v[EDD] + 2 v[EDA]
6: v[PGI] + v[ALD] + v[TPI] + 2 v[GAPDH] + 2 v[PGK] + 2 v[PGM] + 2 v[ENO] + v[ACdh] + v[PFK] + v[PYK] + v[PTS] + v[PTA] + v[ACK] + 2 v[PFL] + v[ADH]
7: 2 v[GAPDH] + 2 v[PGK] + 2 v[PGM] + 2 v[ENO] + v[ACdh] + 2 v[ZWF] + 2 v[PTS] + 3 v[PTA] + 3 v[ACK] + 4 v[PFL] + v[ADH] + 2 v[EDD] + 2 v[EDA]
8: 4 v[ALD] + 4 v[TPI] + 10 v[GAPDH] + 10 v[PGK] + 10 v[PGM] + 10 v[ENO] + 2 v[RPI] + 4 v[RPE] + 2 v[TktI] + 2 v[Tal] + 2 v[TktII] + 5 v[ACdh] + 6 v[ZWF] + 4 v[PFK] + 4 v[PYK] + 6 v[PTS] + 6 v[GND] + 5 v[PTA] + 5 v[ACK] + 10 v[PFL] + 5 v[ADH]
9: -4 v[PGI] + 2 v[GAPDH] + 2 v[PGK] + 2 v[PGM] + 2 v[ENO] + 2 v[RPI] + 4 v[RPE] + 2 v[TktI] + 2 v[Tal] + 2 v[TktII] + v[ACdh] + 6 v[ZWF] + 2 v[PTS] + 6 v[GND] + v[PTA] + v[ACK] + 2 v[PFL] + v[ADH]
10: v[GAPDH] + v[PGK] + v[PGM] + v[ENO] + v[ZWF] + v[PTS] + v[PTA] + v[ACK] v[LDH] + v[PFL] + v[EDD] + v[EDA]
with the following overall reactions:
1: GLCo + 12 NADP = 6 CO2 + 12 NADPH
2: GLCo + 2 ADP = 2 Lac + 2 ATP
3: 3 GLCo + 5 ADP + 6 NADP = 5 Lac + 3 CO2 + 5 ATP + 6 NADPH
4: GLCo + ADP + 6 NADP = Lac + 3 CO2 + ATP + 6 NADPH
5: GLCo + 2 ADP + 2 NADP = 2 Form + 2 Ac + 2 ATP + 2 NADPH
6: GLCo + 3 ADP = 2 Form + EtOH + Ac + 3 ATP
7: 2 GLCo + 5 ADP + 2 NADP = 4 Form + EtOH + 3 Ac + 5 ATP + 2 NADPH
8: 6 GLCo + 15 ADP + 12 NADP = 6 CO2 + 10 Form + 5 EtOH + 5 Ac + 15 ATP + 12 NADPH
9: 2 GLCo + 3 ADP + 12 NADP = 6 CO2 + 2 Form + EtOH + Ac + 3 ATP + 12 NADPH
10: GLCo + 2 ADP + NADP = Lac + Form + Ac + 2 ATP + NADPH
In the analysis it is important whether reactions are reversible or not and, in our analysis, the majority of the enzymes were chosen to be reversible. The following reactions were modeled as irreversible: v[PTS], v[GND], v[PTA], v[ACK], v[LDH], v[PFL], v[ADH], v[EDD], and v[EDA]. These include all the export and import reactions and reactions thought to be irreversible. v[PFK] and v[PYK] were modeled reversible, reflecting the reverse reaction of fructose-1,6-bisphosphatase and phosphoenolpyruvate synthase. External glucose (GLCo), lactate (Lac), CO2, formate (Form), ethanol (EtOH), acetate (Ac), ADP, ATP, AMP, NADP and NADPH were taken as external metabolites, i.e., they are considered to be parameters of the system.
T.R. gratefully acknowledges the contributions to his research program made over the years by dedicated graduate students, postdoctoral researchers, and collaborators, as well as the financial support by NSF, NIH, and USDA.
Glycolysis and its control has been the subject of many stimulating discussions for J.S. with Joost Teixeira de Mattos and Hans V. Westerhoff in Amsterdam and the other Triple-J fellows, Johann Rohwer and Jannie Hofmeyr in Stellenbosch. J.S. has received financial support from the NRF and NBN.
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Module3.5.1
