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In silico quantification of optimal lysine synthesis during growth of corynebacterium glutamicum on mixed substrates (Glucose and Lactate).

Introduction

Corynebacterium glutamicum is an industrially important organism used for lysine production (Tryfona and Bustard, 2005). Therefore, the phenotype of this organism is well characterized for growth on different carbon sources and on mixed carbon sources, while optimizing for high lysine yield (Cocaign, et al., 1993, Wendisch, et al., 2000, Kiefer, et al., 2002, Gerstmeir, et al., 2003, Paegle and Ruklisha, 2003, Kiefer, et al., 2004, Wittmann, et al., 2004). Further, several metabolic engineering strategies have been employed to enhance its productivity (Peterswendisch, et al., 1993, Marx, et al., 1996, Marx, 1999, Uy, et al., 1999, Gourdon, et al., 2000, Petersen, et al., 2000, Sahm, et al., 2000, Claes, et al., 2002, Gerstmeir, et al., 2003, Paegle and Ruklisha, 2003, Delaunay, et al., 2004, Wittmann, et al., 2004, Sauer and Eikmanns, 2005, Tryfona and Bustard, 2005, Shirai, et al., 2007, Becker, et al., 2008). Experimental observation suggests that both anaplerotic reactions and glyoxalate cycle play an important role for the production of tricarboxylic acid (TCA) cycle derived amino acids (aspartate which is a precursor of lysine and glutamate families) (Petersen, et al., 2001). The activity of anaplerotic reactions and glyoxylate shunt depends on the nutritional status as well as on the environmental perturbations (Petersen, et al., 2000). C. glutamicum co-metabolizes glucose with other organic acids such as acetate, lactate, pyruvate and propionate (Cocaign, et al., 1993, Wendisch, et al., 2000, Claes, et al., 2002). Different carbon sources affect the metabolic state by changing the fluxes through the anaplerotic reactions and glyoxalate shunt. Previously, it was believed that only phosphoenolpyruvate (PEP) carboxylase is activated for maintaining the oxaloacetate pool during growth on glucose alone (Mori and Shiio, 1985). However, recent studies ensure that the activity of pyruvate carboxylase is prominent over PEP carboxylase (PetersWendisch, et al., 1997). Interestingly, in vivo studies also demonstrated the simultaneous activities of carboxylation and decarboxylation reactions (Marx, et al., 1996, Marx, 1999). Activities of PEP carboxylase and pyruvate carboxylase with malic enzymes have been observed during co-metabolism of glucose and lactate (Petersen, et al., 2000). Studies also revealed that the genetic and transcriptional activities of malate synthase and isocitrate lyase genes (responsible for glyoxalate cycle) are not expressed in the presence of lactate in the growth medium (Wendisch, et al., 2000). However, under these conditions the metabolic consequences of the activity of anaplerotic reactions towards lysine production are not well understood. Metabolic network analysis in tandem with experimental observation can provide meaningful insights into the working of the complex metabolic network.

The metabolic network of this organism is highly complex, formed by hundreds of densely interconnected chemical reactions. Powerful computational tools are essential to characterize such a complex metabolic system (Nielsen, 1998, Schilling, et al., 2000, Famili, et al., 2003, Klamt and Stelling, 2003, Palsson, et al., 2003, Reed and Palsson, 2003, Wiback, et al., 2004). Structure oriented approaches using the topology of the network (Metabolic flux analysis, Flux balance analysis, Extreme pathways and Elementary mode analysis) have gained popularity among researchers as they only require information regarding the stoichiometry of the metabolic network.

The fundamental principle of metabolic flux analysis (MFA) and flux balance analysis, (FBA) is the conservation of mass (Nissen, et al., 1997, Pramanik and Keasling, 1997, Nielsen, 1998, Follstad, et al., 1999, Stephanopoulos, 1999, Cornish-Bowden and Cardenas, 2000, Covert, et al., 2001, Ramakrishna, et al., 2001, Edwards, et al., 2002, Stelling, et al., 2002, Mahadevan and Palsson, 2005, Oliveira, et al., 2005). Mathematically, MFA is applicable for a fully determined system (zero order of freedom) (Follstad, et al., 1999, Stephanopoulos, 1999, Cornish-Bowden and Cardenas, 2000). Typically, biological systems are under-determined and FBA is capable of handling this scenario by imposing a linear optimization constraint (Ramakrishna, et al., 2001, Edwards, et al., 2002, Wiback, et al., 2004). Recently, two other approaches (Extreme pathways and Elementary mode analysis) have become popular (Edwards, et al., 2001, Price, et al., 2002, Wiback and Palsson, 2002, Price, et al., 2003, Bell and Palsson, 2005, Gayen and Venkatesh, 2006, Kell, 2006, Gayen, et al., 2007); elementary mode analysis is the most promising as it offers several advantages over other approaches (Klamt and Stelling, 2003). For example, there is a possibility of overlooking important routes connecting extra cellular metabolites by extreme pathway analysis, while elementary mode analysis is capable of accounting for all possible routes (Klamt and Stelling, 2003). Another advantage is that the connecting routes between different extracellular metabolites can be traced out and the maximum theoretical yield can be computed easily from elementary mode analysis. A number of software packages are available for generating elementary modes; these include ScrumPy: http://mudshark.brookes.ac.uk/index.php/ Software/ScrumPy; YANA: http:// yana. bioapps.biozentrum.uni-wuerzburg.de/. Recently, elementary mode analysis has been applied to predict optimal growth and optimal phenotypic space for accumulation of a specific target metabolite. It has also been applied to analyze biochemical networks for growth in mixed substrates and in biomedical applications (Edwards, et al., 2001, Stelling, et al., 2002, Gayen and Venkatesh, 2006, Kell, 2006, Gayen, et al., 2007, Schwartz, et al., 2007). Several experimental and theoretical studies have been reported for the quantification of metabolic network of C. glutamicum. However, quantification of anaplerotic reactions and glyoxylate shunt on the growth of C. glutamicum on mixture of glucose and lactate needs to be address. In the present work, we focus on in silico quantification of anaplerotic reactions using elementary mode analysis during growth on mixed carbon sources (glucose + lactate) for lysine production. Further, we also present the optimal phenotypic space for biomass and lysine synthesis at various glucose and lactate uptake rates.

Method

Elementary modes were generated using YANA software (Schwarz, et al., 2005) that support SBML format to import reaction sets form metabolic data bases. The accumulation rates of the extracellular metabolites can be expressed in terms of the stoichiometries of the elementary modes as reported by Gayen & Venkatesh (Gayen and Venkatesh, 2006, Gayen, et al., 2007). Mathematically, this can be represented as S.E = M

S represents the stoichiometric matrix of the elementary modes, E is the unknown vector of the elementary modes and M is the known vector of the accumulation rates of the extracellular metabolites. Linear optimization is essential where vector M contains lesser number of elements than E {[e.sub.1], [e.sub.2], [e.sub.3] ... [e.sub.n]} vector, and is represented as

Max ([M.sub.i])

Such that S * E * = M * for all 0 [less than or equal to] [e.sub.i] [less than or equal to] [infinity]

Where [M.sub.i] is not considered as constraint in S *, E * and M *. It should be noted that all the elementary modes operate irreversibly and the lower bound is set to zero. This methodology is applied to quantify the metabolic network of C. glutamicum on mixed substrates of glucose and lactate in this work.

Results and Discussion

Metabolic network of Corynebacterium glutamicum consists of the EMP pathway, TCA cycle, Pentose phosphate pathway and gluconeogenesis to represent the central metabolism (Fig. 1). Ammonia is assimilated through glutamate synthase and oxygen is utilized by oxidative phosphorylation. Anaplerotic reactions are responsible to maintain TCA pools which are utilized for lysine biosynthesis. PEP carboxylase and malic enzyme play a vital role for channeling pyruvate pool, while C. glutamicum grows on lactate (Petersen, et al., 2000). Further, in vivo studies suggested that PYR carboxylase is more active than PEP carboxylase although in vitro studies indicated the reverse. It should be noted that PEP carboxykinase accounts for the gluconeogenesis while the cells grow on lactate. Carbon balance of biomass was obtained by accounting for several metabolites which are supported from NMR spectra including nitrogen, which was balanced through glutamate, glutamine, alanine, valine, aspartate and lysine. NADPH and ATP pools were also considered for biomass synthesis.

Elementary mode analysis revealed that 1042 modes were possible during growth on both glucose and lactate. However, previous studies suggested that glyoxalate shunt is inactive, while this organism grows on glucose alone or lactate alone or both glucose and lactate simultaneously (Petersen, et al., 2000). Therefore, glyoxalate shunt is not included in the current metabolic network of C. glutamicum. Further, we excluded the activity of oxaloacetate decarboxylase and PYR to malate reaction (malic enzyme) irreversibly as pyruvate overload will suppress this reaction during growth on lactate (Cocaignbousquet and Lindley, 1995, Petersen, et al., 2000). These modifications of the current metabolic network reduce the elementary modes to 252 (see methodology and Table 1). One mode (1st mode) represented the futile cycle including the conversions PEP--PYR--oxaloacetate--PEP, which was observed experimentally by Petersen et. al (Petersen, et al., 2000). Out of 252 modes, 214 modes had glucose as a carbon substrate and 147 modes had lactate as one of the carbon substrate, while 110 modes had both glucose and lactate as the carbon substrates (Table 1). Interestingly, all the modes contained oxygen as the substrate (except the one referred to as the futile cycle), suggesting that the organism is a strict aerobe. Ammonia was consumed by 228 modes which yielded biomass or lysine; the remaining 24 modes maintained the oxidation state without including the uptake of nitrogen. Lysine was synthesized in 93 modes while biomass and trehalose were produced in 155 and 117 modes, respectively. Both biomass and lysine were synthesized simultaneously in 20 elementary modes, where 62 modes accounted for the synthesis of both biomass and trehalose and lysine. Further, trehalose were synthesized together in 45 modes. A maximum theoretical lysine yield of 37.5 moles per 100 moles of lactate was obtained via mode 7 and a biomass yield of 70 moles per 100 moles of lactate via mode 12 while growing on lactate (Fig. 2). Interestingly, in both the cases, PYR carboxylase and PEP carboxykinase were active to maintain the gluconeogenesis during growth on lactate. Further, 75 moles of lysine yield per 100 moles of glucose was observed in the two modes 47 and 48 (Fig. 3a and Fig. 3b). It should be noted that both PEP carboxylase and PYR carboxylase were active when the organism operated through mode 47 and only PYR carboxylase was active when the organism operated through mode 48. Similarly, maximum biomass yield (136 moles per 100 moles of glucose) could be obtained from two modes (mode 70 and mode 71) during growth on glucose (Fig. 3c and Fig. 3d). Activity of PEP carboxylase and PYR carboxylase differed in these elementary modes. It should be noted that gluconeogenesis pathway was active during growth on lactate and inactive during growth on glucose.

[FIGURE 1 OMITTED]

Fig. 2c shows the flux distribution in the network when lactate is used as the sole carbon source (assuming a normalized consumption rate of lactate as 100) for the case of maximum lysine synthesis. In this case, the activity of PYR carboxylase was high (62.5), while the activity of malic enzyme was zero to yield a maximum lysine yield of 37.5 from 100 moles of lactate uptake. PEP carboxykinase was active in order to contribute in gluconeogenesis, which resulted in the production of NADPH in pentose phosphate pathway used for the synthesis of lysine. In this case, normalized ammonia consumption rate was 75, while oxygen consumption rate was half that of the ammonia consumption rate and carbon dioxide production rate was same as that of ammonia consumption rate. TCA cycle was not operating except in the reaction converting SUC to SUCCoA, which was involved in lysine synthesis. It should be noted that half of that flux of [alpha]-keto glutarate (akG) is compensated from OaA to Asp reaction and another half from AKP to MDAP reaction (see reaction set in supplementary material). The maximum achievable yield of biomass was 68 moles per 100 moles of lactate (Fig. 2d). Here also the activity of both PYR carboxylase and PEP carboxykinase were prominent, while activity of malic enzyme was absent. Normalized ammonia consumption rate was 48 via glutamate synthase. Moreover, the activity of pentose phosphate cycle was 2.5 fold less than the activity under maximum lysine synthesis, which suggests that the requirement of NADPH for lysine synthesis was 2.5 times higher. In this case TCA cycle is partly active according to the TCA metabolites pool needed for biomass synthesis. However, there is less preference of TCA cycle for NADPH as pentose phosphate pathway (PPP) is thermodynamically and energetically favorable for production of required NADPH.

[FIGURE 2 OMITTED]

The flux distribution for optimal lysine yield that is 75 moles from 100 moles of glucose as carbon source is shown in Fig. 3e. Here, the activity of the PEP carboxylase (25) was half of the PYR carboxylase, while the activity of malic enzyme was zero. It should be noted that the activity of malic enzyme is not energetically favorable as it requires NADPH for conversion of PYR to malate (see reaction set in supplementary material). PPP was highly active to produce NADPH for lysine synthesis. Further, normalized oxygen consumption rate was 75, while carbon dioxide production rate and ammonia consumption rate were double that of the oxygen consumption rate. The activity of the TCA was absent except for the SUC to SUCCoA conversion. Gayen and Venkatesh (Gayen and Venkatesh, 2006) reported a maximum lysine yield of 63.5 moles per 100 moles of glucose, which is less than our current result (75). Gayen and Venkatesh included only PEP carboxylase as only anaplerotic reaction omitting glucogeneosis from their network. Here, gluconeogenesis plays an important role, which allows PPP to be a more active balancing NADPH for lysine synthesis.

Maximum feasible yield of biomass was 136 moles per 100 moles of glucose as a carbon source (Fig. 3f). In this case, the activity of pentose phosphate cycle was 2.5 fold less than that for maximum lysine synthesis. Further, the activities of both PYR carboxylase and PEP carboxylase were less than as compared to maximum lysine synthesis case. Ammonia and oxygen consumption rates and carbon dioxide production rate were also less active in this case. Fig. 4a shows the flux distribution of the metabolic network while the organism consumes two carbon sources simultaneously and equally (100 moles) for maximization of lysine synthesis. Maximum lysine synthesis was 112.5 with equal oxygen consumption rate and double carbon dioxide evaluation rate. In this case, only the activity of the PYR carboxylase was observed. Also, the activities of both gluconeogenesis and PPP were prominent. However, the activity of PPP reduces to one-third, while the organism is maximizing biomass and no gluconeogenesis was observed (Fig. 4b). In this case, activity of PEP decarboxylase is observed

[FIGURE 3 OMITTED]

[FIGURE 4 OMITTED]

For the current metabolic network of C. glutamicum, we consider glucose, lactate, ammonia, oxygen as substrates; the products contain biomass, lysine, trehalose and C[O.sub.2]. It is relevant to raise the question of minimum requirement of the extracellular measurements for quantifying the network. Analysis suggested that the uptake rates of four substrates (glucose, lactate, ammonia and oxygen) and one of the products (say biomass) were sufficient to uniquely quantify the network. Predicted values of the remaining extracellular metabolites were independent of the objective function while these five measurements were considered as decision variables to linear programming optimizer. If any five measurements are considered as decision variables out of eight measurements similar results are provided. Thus, five measurements are essential to uniquely quantify the remaining extracellular accumulation rates using elementary modes. However, the network can be simulated using four substrates accumulation rates as decision variables (i.e. glucose, lactate, ammonia and oxygen) with different choices of objective function (maximization of biomass/lysine) to study the network capability of the organism at various nutrients uptake levels.

To study the network capability, firstly we assume that the organism's objective depends on only two carbon sources (glucose and lactate). Therefore, optimal phenotypic spaces of extracellular metabolites were evaluated, while C. glutamicum grows on both carbon sources. Fig. 6 shows the accumulation/consumption rates of different metabolites (biomass, lysine, ammonia, oxygen) with varying consumption rates of two carbon sources. These results show that the extracellular metabolites (biomass, lysine, ammonia and oxygen) operate through a plane. Maximum biomass yield was 204 nM, while organism consumes 100 nM glucose co-metabolized with 100 nM lactate as carbon sources. In this case, consumption of ammonia and oxygen were of 150 nM of and 153 nM. Maximum 68 nM and 109 nM of biomass yields were observed from 100 nM of lactate and glucose, respectively. No lysine yield was observed using maximization of biomass as the objective function. Similar result was obtained from calculating the optimal lysine yield with varying uptake rates of the two carbon sources. In this case, a reverse trend of biomass and lysine productivity was observed (i.e. a maximum lysine yield of 112.5 nM with no biomass accumulation was obtained [see Supplementary material Fig. 1S]).

In the preceding analysis, it was assumed that metabolic function of the organism depends only on availability of carbon sources as constraint on oxygen and ammonia consumption rates were omitted. However, an organism can suffer lack of availability of oxygen and ammonia as principle nutrients to maintain metabolic function. Therefore, the network was simulated with different combination of normalized carbon sources with various uptake rates of ammonia and oxygen. Fig. 6 shows the optimal solution space for biomass synthesis rates by varying uptake rates of oxygen and ammonia for different sets of fixed uptake rates of glucose and lactate (i.e. glucose-100, lactate-0; glucose-100, lactate-10; glucose-100, lactate-50 and glucose-100, lactate-100). The choice of objective function was maximization of biomass. Feasible oxygen and ammonia uptake rate was within range of 24-600 and 0-150 relative to glucose uptake rate only (Fig. 6a; glucose-100 and Lactate-0). This result demonstrated that the organism is able to perform its metabolic function by producing trehalose while no ammonia is available. NADPH/NADP balance will be fulfilling from PP pathway and gluconeogenesis. The feasible ranges of oxygen and ammonia enhanced, while lactate is associated maintaining high biomass synthesis rate (i.e. feasible oxygen and ammonia ranges were 55-900 and 0-225 for relative uptake rates of glucose 100 and lactate 100; Fig. 6d). The optimal solution space for lysine synthesis followed the similar trend like biomass synthesis (Fig. 2S in Supplementary material).

Interestingly, the optimal solution space for biomass synthesis was nil while the network was simulated with the choice of objective function maximizing lysine (Fig. 3S in Supplementary material). However, the optimal solution space for lysine synthesis while maximizing lysine (Fig. 7) was different from optimal solution space for lysine synthesis with the objective function of maximizing biomass (Fig. 2S in Supplementary material). Fig. 8 shows the comparison of optimal solution space of biomass and lysine synthesis rates with the choice of objective function maximizing biomass. Glucose is considered as only carbon source with a fixed value of 100. Experimental data points as reported by Vallino (Vallino, 1991) are also plotted on the solution spaces. It was observed that all the experimental data points for both biomass and lysine are located within the solution space.

[FIGURE 5 OMITTED]

[FIGURE 6 OMITTED]

[FIGURE 7 OMITTED]

[FIGURE 8 OMITTED]

Conclusions

We have demonstrated the metabolic network of C. glutamicum to study the fluxes of the anaplerotic reactions while the organism grows on mixed substrates of glucose and lactate. Metabolic network was quantified using elementary mode analysis in conjunction with linear optimization. The analysis yielded 252 elementary modes by including specific extracellular compounds omitting some reactions which are not active in current situation. The elementary modes suggested that the organism is an aerobic organism with all the elementary modes involving oxygen. A total number of 37 modes accounted for growth on lactate as the sole carbon source yielding a maximum biomass of 68 moles for a lactate uptake rate of 100. Flux balance indicated that the flux through PYR carboxylase was more active than PEP carboxylase while the organism grows on lactate. In vivo studies have also suggested that PYR carboxylase is more active than PEP carboxylase, although in vitro studies demonstrated a counter conclusion (Petersen, et al., 2000). Further, our analysis shows that the activity of PYR carboxylase is double that of the activity of PEP carboxylase. It was observed that gluconeogenesis pathway was active during growth on lactate to balance NAD[P.sup.+] /NADPH through pentose phosphate pathway. The analysis indicated that C. glutamicum can grow on a medium containing both glucose and lactate by simultaneous consumption of both carbon sources. The maximum yield of biomass on mixed substrates was the sum of the maximum of individual substrates. Thus, the growth on medium containing glucose and lactate can yield higher amount of biomass and lysine. In this case, the gluconeogenesis was absent as glucose was already present in the medium. Further, observation was similar to the growth on the individual subatrates.

The optimal solution spaces for different phenotypes (biomass and lysine synthesis) with various combinations of glucose and lactate availability were also studied. Gayen & Venkatesh (Gayen and Venkatesh, 2006) evaluated the optimal solution space for the organism for varying oxygenation and ammonia uptake rates, while keeping the glucose uptake rate constant. Edwards et al. (Edwards, et al., 2002) have reported the phase plane analysis of E. coli on glucose and acetate at various oxygenation levels. The space represented by the optimal phenotypic surface by their analysis captures all the possible phenotypes for the organism. Thus, elementary mode analysis is a powerful tool to gather quantitative information regarding feasible phenotypic behavior of an organism. Elementary mode analysis provides all the possible routes in a given biochemical network. However, organism may operate through very few elementary modes according to the nutritional status and environmental conditions to yield specific phenotypes. The method reported here attempts to capture specific phenotypes by evaluating the feasible space of C. glutamicum on mixed substrates. Our in silico analysis is capable of predicting the experimental observations which had been obtained by rigorous laboratory experiments. Similar analysis can be performed for other biological systems where experiments are lacking.

Reference

[1] Tryfona, T., and Bustard, M. T. (2005) Fermentative production of lysine by Corynebacterium glutamicum: transmembrane transport and metabolic flux analysis, Process Biochemistry 40: 499-508.

[2] Cocaign, M., Monnet, C., and Lindley, N. D. (1993) BATCH KINETICS OF CORYNEBACTERIUM-GLUTAMICUM DURING GROWTH ON VARIOUS CARBON SUBSTRATES - USE OF SUBSTRATE MIXTURES TO LOCALIZE METABOLIC BOTTLENECKS, Applied Microbiology and Biotechnology 40: 526-530.

[3] Wendisch, V. F., De Graaf, A. A., Sahm, H., and Eikmanns, B. J. (2000) Quantitative determination of metabolic fluxes during coutilization of two carbon sources: Comparative analyses with corynebacterium glutamicum during growth on acetate and/or glucose, Journal of Bacteriology 182: 3088-3096.

[4] Kiefer, P., Heinzle, E., and Wittmann, C. (2002) Influence of glucose, fructose and sucrose as carbon sources on kinetics and stoichiometry of lysine production by Corynebacterium glutamicum, Journal of Industrial Microbiology & Biotechnology 28: 338-343.

[5] Gerstmeir, R., Wendisch, V. F., Schnicke, S., Ruan, H., Farwick, M., Reinscheid, D., and Eikmanns, B. J. (2003) Acetate metabolism and its regulation in Corynebacterium glutamicum, Journal of Biotechnology 104: 99122.

[6] Paegle, L., and Ruklisha, M. (2003) Lysine synthesis control in Corynebacterium glutamicum RC 115 in mixed substrate (glucose-acetate) medium, Journal of Biotechnology 104: 123-128.

[7] Kiefer, P., Heinzle, E., Zelder, O., and Wittmann, C. (2004) Comparative metabolic flux analysis of lysine-producing Corynebacterium glutamicum cultured on glucose, or fructose, Applied and Environmental Microbiology 70: 229-239.

[8] Wittmann, C., Kiefer, P., and Zelder, O. (2004) Metabolic fluxes in Corynebacterium glutamicum during lysine production with sucrose as carbon source, Applied and Environmental Microbiology 70: 7277-7287.

[9] Peterswendisch, P. G., Eikmanns, B. J., Thierbach, G., Bachmann, B., and Sahm, H. (1993) PHOSPHOENOLPYRUVATE CARBOXYLASE IN CORYNEBACTERIUM-GLUTAMICUM IS DISPENSABLE FOR GROWTH AND LYSINE PRODUCTION, Fems Microbiology Letters 112: 269-274.

[10] Marx, A., deGraaf, A. A., Wiechert, W., Eggeling, L., and Sahm, H. (1996) Determination of the fluxes in the central metabolism of Corynebacterium glutamicum by nuclear magnetic resonance spectroscopy combined with metabolite balancing, Biotechnology and Bioengineering 49: 111-129.

[11] Marx, A., Eikmanns, B. J., Sahm, H., de Graaf (1999) Response of the central metabolism in Corynebacterium glutamicum to the use of an NADH-dependent glutamate dehydrogenase, Metabolic Engineering: 35-48.

[12] Uy, D., Delaunay, S., Engasser, J. M., and Goergen, J. L. (1999) A method for the determination of pyruvate carboxylase activity during the glutamic acid fermentation with Corynebacterium glutamicum, Journal of Microbiological Methods 39: 91-96.

[13] Gourdon, P., Baucher, M. F., Lindley, N. D., and Guyonvarch, A. (2000) Cloning of the malic enzyme gene from Corynebacterium glutamicum and role of the enzyme in lactate metabolism, Applied and Environmental Microbiology 66: 2981-2987.

[14] Petersen, S., de Graaf, A. A., Eggeling, L., Mollney, M., Wiechert, W., and Sahm, H. (2000) In vivo quantification of parallel and bidirectional fluxes in the anaplerosis of Corynebacterium glutamicum, Journal of Biological Chemistry 275: 35932-35941.

[15] Sahm, H., Eggeling, L., and de Graaf, A. A. (2000) Pathway analysis and metabolic engineering in Corynebacterium glutamicum, Biological Chemistry 381: 899-910.

[16] Claes, W. A., Puhler, A., and Kalinowski, J. (2002) Identification of two prpDBC gene clusters in Corynebacterium glutamicum and their involvement in propionate degradation via the 2-methylcitrate cycle, Journal of Bacteriology 184: 2728-2739.

[17] Delaunay, S., Daran-Lapujade, P., Engasser, J. M., and Goergen, J. L. (2004) Glutamate as an inhibitor of phosphoenolpyruvate carboxylase activity in Corynebacterium glutamicum, Journal of Industrial Microbiology & Biotechnology 31: 183-188.

[18] Sauer, U., and Eikmanns, B. J. (2005) The PEP-pyruvate-oxaloacetate node as the switch point for carbon flux distribution in bacteria, Fems Microbiology Reviews 29: 765-794.

[19] Shirai, T., Fujimura, K., Furusawa, C., Nagahisa, K., Shioya, S., and Shimizu, H. (2007) Study on roles of anaplerotic pathways in glutamate overproduction of Corynebacterium glutamicum by metabolic flux analysis, Microbial Cell Factories 6: 11.

[20] Becker, J., Klopprogge, C., and Wittmann, C. (2008) Metabolic responses to pyruvate kinase deletion in lysine producing Corynebacterium glutamicum, Microbial Cell Factories 7: 15.

[21] Petersen, S., Mack, C., de Graaf, A. A., Riedel, C., Eikmanns, B. J., and Sahm, H. (2001) Metabolic consequences of altered phosphoenolpyruvate carboxykinase activity in Corynebacterium glutamicum reveal anaplerotic regulation mechanisms in vivo, Metabolic Engineering 3: 344-361.

[22] Mori, M., and Shiio, I. (1985) PURIFICATION AND SOME PROPERTIES OF PHOSPHOENOLPYRUVATE CARBOXYLASE FROM BREVIBACTERIUM-FLAVUM AND ITS ASPARTATE OVERPRODUCING MUTANT, Journal of Biochemistry 97: 1119-1128.

[23] PetersWendisch, P. G., Wendisch, V. F., Paul, S., Eikmanns, B. J., and Sahm, H. (1997) Pyruvate carboxylase as an anaplerotic enzyme in Corynebacterium glutamicum, Microbiology-Uk 143: 1095-1103.

[24] Nielsen, J. (1998) Metabolic engineering: Techniques for analysis of targets for genetic manipulations, Biotechnology and Bioengineering 58: 125-132.

[25] Schilling, C. H., Edwards, J. S., Letscher, D., and Palsson, B. O. (2000) Combining pathway analysis with flux balance analysis for the comprehensive study of metabolic systems, Biotechnology and Bioengineering 71: 286-306.

[26] Famili, I., Forster, J., Nielson, J., and Palsson, B. O. (2003) Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network, Proceedings of the National Academy of Sciences of the United States of America 100: 13134-13139.

[27] Klamt, S., and Stelling, J. (2003) Two approaches for metabolic pathway analysis?, Trends in Biotechnology 21: 64-69.

[28] Palsson, B. O., Price, N. D., and Papin, J. A. (2003) Development of network-based pathway definitions: the need to analyze real metabolic networks, Trends in Biotechnology 21: 195-198.

[29] Reed, J. L., and Palsson, B. O. (2003) Thirteen years of building constraint-based in silico models of Escherichia coli, Journal of Bacteriology 185: 26922699.

[30] Wiback, S. J., Mahadevan, R., and Palsson, B. O. (2004) Using metabolic flux data to further constrain the metabolic solution space and predict internal flux patterns: The Escherichia coli spectrum, Biotechnology and Bioengineering 86: 317-331.

[31] Nissen, T. L., Schulze, U., Nielsen, J., and Villadsen, J. (1997) Flux distributions in anaerobic, glucose-limited continuous cultures of Saccharomyces cerevisiae, Microbiology-Uk 143: 203-218.

[32] Pramanik, J., and Keasling, J. D. (1997) Stoichiometric model of Escherichia coli metabolism: Incorporation of growth-rate dependent biomass composition and mechanistic energy requirements, Biotechnology and Bioengineering 56: 398-421.

[33] Follstad, B. D., Balcarcel, R. R., Stephanopoulos, G., and Wang, D. I. C. (1999) Metabolic flux analysis of hybridoma continuous culture steady state multiplicity, Biotechnology and Bioengineering 63: 675-683.

[34] Stephanopoulos, G. (1999) Metabolic Fluxes and Metabolic Engineering, Metabolic Engineering 1: 1-11.

[35] Cornish-Bowden, A., and Cardenas, M. L. (2000) From genome to cellular phenotype--a role for metabolic flux analysis?, Nature Biotechnology 18: 267268.

[36] Covert, M. W., Schilling, C. H., Famili, I., Edwards, J. S., Goryanin, II, Selkov, E., and Palsson, B. O. (2001) Metabolic modeling of microbial strains in silico, Trends in Biochemical Sciences 26: 179-186.

[37] Ramakrishna, R., Edwards, J. S., McCulloch, A., and Palsson, B. O. (2001) Flux-balance analysis of mitochondrial energy metabolism: consequences of systemic stoichiometric constraints, American Journal of Physiology-Regulatory Integrative and Comparative Physiology 280: R695-R704.

[38] Edwards, J. S., Covert, M., and Palsson, B. (2002) Metabolic modelling of microbes: the flux-balance approach, Environmental Microbiology 4: 133-140.

[39] Stelling, J., Klamt, S., Bettenbrock, K., Schuster, S., and Gilles, E. D. (2002) Metabolic network structure determines key aspects of functionality and regulation, Nature 420: 190-193.

[40] Mahadevan, R., and Palsson, B. O. (2005) Properties of metabolic networks: Structure versus function, Biophysical Journal 88: L7-L9.

[41] Oliveira, A. P., Nielsen, J., and Forster, J. (2005) Modeling Lactococcus lactis using a genome-scale flux model, Bmc Microbiology 5.

[42] Edwards, J. S., Ibarra, R. U., and Palsson, B. O. (2001) In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data, Nature Biotechnology 19: 125-130.

[43] Price, N. D., Famili, I., Beard, D. A., and Palsson, B. O. (2002) Extreme pathways and Kirchhoffs second law, Biophysical Journal 83: 2879-2882.

[44] Wiback, S. J., and Palsson, B. O. (2002) Extreme pathway analysis of human red blood cell metabolism, Biophysical Journal 83: 808-818.

[45] Price, N. D., Reed, J. L., Papin, J. A., Famili, I., and Palsson, B. O. (2003) Analysis of metabolic capabilities using singular value decomposition of extreme pathway matrices, Biophysical Journal 84: 794-804.

[46] Bell, S. L., and Palsson, B. O. (2005) expa: a program for calculating extreme pathways in biochemical reaction networks, Bioinformatics 21: 1739-1740. [47] Gayen, K., and Venkatesh, K. V. (2006) Analysis of optimal phenotypic space using elementary modes as applied to Corynebacterium glutamicum, Bmc Bioinformatics 7.

[48] Kell, D. B. (2006) Systems biology, metabolic modelling and metabolomics in drug discovery and development, Drug Discovery Today 11: 1085-1092.

[49] Gayen, K., Gupta, M., and Venkatesh, K. V. (2007) Elementary mode analysis to study the preculturing effect on the metabolic state of Lactobacillus rhamnosus during growth on mixed substrates, In Silico Biology 07: 0012.

[50] Gayen, K., and Venkatesh, K. V. (2006) Analysis of optimal phenotypic space using elementary modes as applied to Corynebacterium glutamicum, Bmc Bioinformatics 7: 13.

[51] Schwartz, J. M., Gaugain, C., Nacher, J. C., de Daruvar, A., and Kanehisa, M. (2007) Observing metabolic functions at the genome scale, Genome Biology 8: 17.

[52] Schwarz, R., Musch, P., von Kamp, A., Engels, B., Schirmer, H., Schuster, S., and Dandekar, T. (2005) YANA--a software tool for analyzing flux modes, gene-expression and enzyme activities, Bmc Bioinformatics 6: 12.

[53] Cocaignbousquet, M., and Lindley, N. D. (1995) PYRUVATE OVERFLOW AND CARBON FLUX WITHIN THE CENTRAL METABOLIC PATHWAYS OF CORYNEBACTERIUM-GLUTAMICUM DURING GROWTH ON LACTATE, Enzyme and Microbial Technology 17: 260-267.

[54] Vallino, J. (1991) Identification of branch point restrictions in microbial metabolism through metabolic flux balance analysis and local network perturbations, PhD Thesis MIT, USA.

[55] Edwards, J. S., Ramakrishna, R., and Palsson, B. O. (2002) Characterizing the metabolic phenotype: A phenotype phase plane analysis, Biotechnology and

Kalyan Gayen (1) *, Manish Kumar (1), Meghna Rajvanshi (2) and K.V. Venkatesh (2, 3)

(1) Department of Chemical Engineering, IIT Gandhinagar, VGEC Campus, Chandkheda, Ahmedabad, Gujarat--382424, India

(2) School of Biosciences & Bioengineering, Indian Institute of Technology, Bombay, Powai, Mumbai--400076, India

(3) Department of Chemical Engineering, Indian Institute of Technology, Bombay, Powai, Mumbai--400076, India

* Corresponding Author E-mail: gkalyan@iitgn.ac.in
Table 1: Metabolites associated with elementary modes for metabolic
network of Corynebacterium glutamicum. Substrates: Glucose, lactate,
ammonia and oxygen; Products: Biomass, lysine, trehalose and carbon
dioxide.

Metabolites Number of elementary modes
Glucose 214
Lactate 147
Ammonia 228
Oxygen 251
Carbon dioxide 251
Biomass 155
Lysine 93
Trehalose 117
Glucose + Lactate 110
Glucose + Ammonia 197
Lactate + Ammonia 136
Biomass + Trehalose 62
Biomass + Lysine 20
Lysine + Trehalose 45

Table 2: Optimal yield coefficient (nM/ 100 nM substrate) of
different metabolites at various availability of Carbon sources
(glucose and lactate).

Growth conditions Max biomass Max Lysine Max Trehalose

Glucose 136 75 48
Lactate 68 37.5 20
Glucose + Lactate 204 112.5 68

Reactions involved in the metabolic network of C. glutamicum

Enzyme name reversible? Reaction equation

AS1 [check] GLUT + OAA = AKG + ASP
AS2 [] ASP + ATP + 2 NADPH + PYR = ADP +
 AKP + H2O + 2 NADP
AS3 [] AKP + GLUT + H2G + SUCCOA = AKG +
 COA + MDAP + SUC
AS4 [] MDAP = CO2 + LYSI
ATPdiss [] ATP = ADP
Acetateprod [check] ACCOA + ADP = AC +ATP + COA
Alaninesyn [] GLUT * PYR = AKG + ALA
Biomasssyn [] 332 ACCOA + 54 ALA + 80 ASP + 1500
 ATP + 7 FRUSP + 150 G3P + 13 GAP +
 21 GLCSP + 25 GLUM + 446 GLUT + 33
 LYSI + 100 NADPH + 52 PEP + 30 PYR
 + 126 RIB5P + 40 VAL = 1500 ADP +
 364 AKG + 1000 BIOMASS + 143 CO2 +
 332 COA +100 NADP
CR1 [check] CO2 + PEP = OAA
CR2 [] ATP + CO2+ PYR = ADP + OAA
CR4 [] CO2 + NADPH + PYR = MAL + NADP
EMP1 [check] GLC6P = FRU6P
EMP2 [check] ATP + FRU5P = ADP + 2 GAP
EMP3 [check] ADP + GAP + NAD = ATP + G3P + NADH
EMP4 [check] G3P = H2O + PEP
EMP5 [] ADP + PEP = ATP + PYR
EMP6 [check] NADH + PYR = LAC + NAD
Ex_Biomass [] BIOMASS=X BIOMASS
Ex_C02 [] CO2 = X_CO2
Ex H2O [] H2O = X_H2O
Ex_lysine [] LYSI=X_LYSI
Ex_trehalose [] TREHALOSES = X_TREHALOSE
GLUT1 [check] AKG + NADPH + NH3 = GLUT + H2O +
 NADP
GLUT2 [] ATP + GLUT + NH3 = ADP + GLUM
Glu_storage [check] ATP + 2 GLC6P = ADP + TREHALOSE
OX11 [] 4 ADP + 2 NADH + O2 = 4 ATP + 2 H2O
 + 2 NAD
OX12 [] 2 ADP + 2FADH + O2 = 2 ATP + 2 FAD
 + 2 H20
PP1 [] GLC5P + H20 + 2 NADP = CO2 + 2
 NADPH + RIBU5P
PP2 [check] RIBU5P = RIB5P
PP3 [check] RIBU5P = XYL5P
PP4 [check] RIB5P + XYL5P = GAP + SED7P
PP5 [check] GAP + SED7P = E4P + FRU5P
PP6 [check] E4P+XYL5P = FRU6P + GAP
TC1 [] COA + NAD + PYR = ACCOA + CO2 + NADH
TC2 [check] ACCOA + H2O + OAA = COA + ISOCIT
TC3 [check] ISOCIT + NADP = AKG + CO2 + NADPH
TC4 [] AKG + COA + NAD = CO2 + NADH +
 SUCCOA
TC5 [check] ADP + SUCCOA = ATP + COA + SUC
TC6 [check] FAD + H2O + SUC = FADH + MAL
TC7 [check] MAL + NAD = NADH + OAA
Uptake_Ammonia [] X_N H3 = NH3
Uptake_Glucose [] PEP + X GLC = GLC6P + PYR
Uptake_Oxygen [] X_O2 = O2
Uptake_lactate [] X_LAC = LAC
Valinesynthesis [] GLUT NADPH + 2 PYR = AKG + CO2 +
 H2O + NADP + VAL

Enzyme name Annotation

AS1 Asparate atlcl family
AS2 Asparatlc acid family

AS3 Asparatlc acid family

AS4 Asparatlc acid family
ATPdiss ATP dissipation
Acetateprod Acetate production
Alaninesyn Alanine synthesis
Biomasssyn Biomass synthesis

CR1 Carboxylation
CR2 Carboxilatlon reaction
CR4
EMP1 EMP1
EMP2 EMP2
EMP3 EMP3
EMP4 EMP4
EMP5 EMP5
EMP6
Ex_Biomass External Biomass
Ex_CO2 External CO2
Ex H2O External H2O
Ex_lysine Externa lysine
Ex_trehalose Externa trehalose
GLUT1 Glutamic acid

GLUT2
Glu_storage
OX11 Oxidative phosphorylation

OX12 Oxidative phosphorylation

PP1 PPP1

PP2 PPP2
PP3 PPP3
PP4 PPP4
PP5 PPP5
PP6 PPP5
TC1 TC1
TC2 TC2
TC3 TC3
TC4 TC4

TC5 TC5
TC6 TC5
TC7 TC7
Uptake_Ammonia Ammonia uptake
Uptake_Glucose Glucose uptake
Uptake_Oxygen Oxygen uptake
Uptake_lactate
Valinesynthesis Valine Synthesis
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Author:Gayen, Kalyan; Kumar, Manish; Rajvanshi, Meghna; Venkatesh, K.V.
Publication:International Journal of Biotechnology & Biochemistry
Date:Feb 1, 2011
Words:5876
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