Genome-scale analysis of Saccharomyces cerevisiae metabolism and ethanol production in fed-batch culture

A dynamic flux balance model based on a genome-scale metabolic network reconstruction is developed for in silico analysis of Saccharomyces cerevisiae metabolism and ethanol production in fed-batch culture. Metabolic engineering strategies previously identified for their enhanced steady-state biomass and/or ethanol yields are evaluated for fed-batch performance in glucose and glucose/xylose media. Dynamic analysis is shown to provide a single quantitative measure of fed-batch ethanol productivity that explicitly handles the possible tradeoff between the biomass and ethanol yields. Productivity optimization conducted to rank achievable fed-batch performance demonstrates that the genetic manipulation strategy and the fed-batch operating policy should be considered simultaneously. A library of candidate gene insertions is assembled and directly screened for their achievable ethanol productivity in fed-batch culture. A number of novel gene insertions with ethanol productivities identical to the best metabolic engineering strategies reported in previous studies are identified, thereby providing additional targets for experimental evaluation. The top performing gene insertions were substrate dependent, with the highest ranked insertions for glucose media yielding suboptimal performance in glucose/xylose media. The analysis results suggest that enhancements in biomass yield are most beneficial for the enhancement of fed-batch ethanol productivity by recombinant xylose utilizing yeast strains. We conclude that steady-state flux balance analysis is not sufficient to predict fed-batch performance and that the media, genetic manipulations, and fed-batch operating policy should be considered simultaneously to achieve optimal metabolite productivity.     

Stoichiometric network constraints on xylose metabolism by recombinant Saccharomyces cerevisiae

Metabolic pathway engineering is constrained by the thermodynamic and stoichiometric feasibility of enzymatic activities of introduced genes. Engineering of xylose metabolism in Saccharomyces cerevisiae has focused on introducing genes for the initial xylose assimilation steps from Pichia stipitis, a xylose-fermenting yeast, into S. cerevisiae, a yeast traditionally used in ethanol production from hexose. However, recombinant S. cerevisiae created in several laboratories have used xylose oxidatively rather than in the fermentative manner that this yeast metabolizes glucose. To understand the differences between glucose and engineered xylose metabolic networks, we performed a flux balance analysis (FBA) and calculated extreme pathways using a stoichiometric model that describes the biochemistry of yeast cell growth. FBA predicted that the ethanol yield from xylose exhibits a maximum under oxygen-limited conditions, and a fermentation experiment confirmed this finding. Fermentation results were largely consistent with in silico phenotypes based on calculated extreme pathways, which displayed several phases of metabolic phenotype with respect to oxygen availability from anaerobic to aerobic conditions. However, in contrast to the model prediction, xylitol production continued even after the optimum aeration level for ethanol production was attained. These results suggest that oxygen (or some other electron accepting system) is required to resolve the redox imbalance caused by cofactor difference between xylose reductase and xylitol dehydrogenase, and that other factors limit glycolytic flux when xylose is the sole carbon source.     

Metabolic analysis of the synthesis of high levels of intracellular human SOD in Saccharomyces cerevisiae rhSOD 2060 411 SGA122

The synthesis of human superoxide dismutase (SOD) in batch cultures of a Saccharomyces cerevisiae strain using a glucose-limited minimal medium was studied through metabolic flux analysis. A stoichiometric model was built, which included 78 reactions, according to metabolic pathways operative in these strains during respirofermentative and oxidative metabolism. It allowed calculation of the distribution of metabolic fluxes during diauxic growth on glucose and ethanol. Fermentation profiles and metabolic fluxes were analyzed at different phases of diauxic growth for the recombinant strain (P+) and for its wild type (P-). The synthesis of SOD by the strain P+ resulted in a decrease in specific growth rate of 34 and 54% (growth on glucose and ethanol respectively) in comparison to the wild type. Both strains exhibited similar flux of glucose consumption and ethanol synthesis but important differences in carbon distribution with biomass/substrate yields and ATP production 50% higher in P-. A higher contribution of fermentative metabolism, with 64% of the energy produced at the phosphorylation level, was observed during SOD production. The flux of precursors to amino acids and nucleotides was higher in the recombinant strain, in agreement with the higher total RNA and protein levels. Lower specific growth rates in strain P+ appear to be related to the decrease in the rate of synthesis of nonrecombinant protein, as well as a decrease in the activities of the pentose phosphate (PP) pathway and TCA cycle. A very different way of entry into the stationary phase was observed for each strain: in the wild-type strain most metabolic fluxes decreased and fluxes related to energy reserve synthesis increased, while in the P+ strain the flux of 22 reactions (including PP pathway and amino acids biosynthesis) related to SOD production increased their fluxes. Changes in SOD production rates at different physiological states appear to be related to the differences in building blocks availability between respirofermentative and oxidative metabolism. Using the present expression system, ideal conditions for SOD synthesis are represented by either active growth during respirofermentative metabolism or transition from a growing to a nongrowing state. An increase in SOD flux could be achieved using an expression system nonassociated to growth and potentially eliminating part of the metabolic burden.     

碳源对琥珀酸放线杆菌发酵产丁二酸的影响及其代谢流量分析

在厌氧条件下, Actinobacillus succinogenes能够利用单糖、双糖和糖醇等碳水化合物发酵生成丁二酸, 其中以山梨醇为碳源时丁二酸的产量最高。代谢流量分析结果表明: 与葡萄糖发酵相比较, 由于代谢系统中积累了更多的NADH, 使得代谢网络关键节点PYR和AcCoA处的代谢流量分配有了较大的变化, 导致更多的碳源流向丁二酸和乙醇, 而乙酸和甲酸的分泌相对减少。     

ACoM: A classification method for elementary flux modes based on motif finding

Elementary flux mode analysis is a powerful tool for the theoretical study of metabolic networks. However, when the networks are complex, the determination of elementary flux modes leads to combinatorial explosion of their number which prevents from drawing simple conclusions from their analysis. To deal with this problem we have developed a method based on the Agglomeration of Common Motifs (ACoM) for classifying elementary flux modes. We applied this algorithm to describe the decomposition into elementary flux modes of the central carbon metabolism in Bacillus subtilis and of the yeast mitochondrial energy metabolism. ACoM helps to give biological meaning to the different elementary flux modes and to the relatedness between reactions. ACoM, which can be viewed as a bi-clustering method, can be of general use for sets of vectors with values 0, +1 or −1.     

Flux analysis of recombinant Saccharomyces cerevisiae YPB-G utilizing starch for optimal ethanol production

Genetically modified Saccharomyces cerevisiae strain (YPB-G) which secretes a bifunctional fusion protein that contains both Bacillus subtilis -amylase and Aspergillus awamori glucoamylase activities was used for the direct conversion of starch into ethanol. Starch was either supplied initially to different nutrient media or added instantaneously to the reactor at various discrete time instants (pulse feeding). Stoichiometric modeling was used to investigate the effects of initial substrate concentration and growth rate of the recombinant yeast culture on ethanol production. Reaction stoichiometries describing both the anabolism and catabolism of the microorganism were used as an input to flux balance analysis (FBA), the preferred metabolic modeling approach since the constructed stoichiometric network was underdetermined. Experiments for batch and fed-batch systems at different substrate concentrations were analyzed theoretically in terms of flux distributions using ethanol production rate as the maximization criteria. Calculated ethanol rates were in agreement with experimental measurements, suggesting that this recombinant microorganism is sufficiently evolved to optimize its ethanol production. The function of the main pathways of yeast metabolism (PPP, EMP, TCA) are discussed together with the node analyses of glucose-6-P and pyruvate branch points. Theoretical node analysis revealed that if the split ratio in G6P branch point is changed by genetic manipulations, the ethanol yield would be affected considerably.     

Inferring Metabolic States in Uncharacterized Environments Using Gene-Expression Measurements

The large size of metabolic networks entails an overwhelming multiplicity in the possible steady-state flux distributions that are compatible with stoichiometric constraints. This space of possibilities is largest in the frequent situation where the nutrients available to the cells are unknown. These two factors: network size and lack of knowledge of nutrient availability, challenge the identification of the actual metabolic state of living cells among the myriad possibilities. Here we address this challenge by developing a method that integrates gene-expression measurements with genome-scale models of metabolism as a means of inferring metabolic states. Our method explores the space of alternative flux distributions that maximize the agreement between gene expression and metabolic fluxes, and thereby identifies reactions that are likely to be active in the culture from which the gene-expression measurements were taken. These active reactions are used to build environment-specific metabolic models and to predict actual metabolic states. We applied our method to model the metabolic states of Saccharomyces cerevisiae growing in rich media supplemented with either glucose or ethanol as the main energy source. The resulting models comprise about 50% of the reactions in the original model, and predict environment-specific essential genes with high sensitivity. By minimizing the sum of fluxes while forcing our predicted active reactions to carry flux, we predicted the metabolic states of these yeast cultures that are in large agreement with what is known about yeast physiology. Most notably, our method predicts the Crabtree effect in yeast cells growing in excess glucose, a long-known phenomenon that could not have been predicted by traditional constraint-based modeling approaches. Our method is of immediate practical relevance for medical and industrial applications, such as the identification of novel drug targets, and the development of biotechnological processes that use complex, largely uncharacterized media, such as biofuel production.     

Quantification of metabolism in Saccharomyces cerevisiae under hyperosmotic conditions using elementary mode analysis

Yeast metabolism under hyperosmotic stress conditions was quantified using elementary mode analysis to obtain insights into the metabolic status of the cell. The fluxes of elementary modes were determined as solutions to a linear program that used the stoichiometry of the elementary modes as constraints. The analysis demonstrated that distinctly different sets of elementary modes operate under normal and hyperosmotic conditions. During the adaptation phase, elementary modes that only produce glycerol are active, while elementary modes that yield biomass, ethanol, and glycerol become active after the adaptive phase. The flux distribution in the metabolic network, calculated using the fluxes in the elementary modes, was employed to obtain the flux ratio at key nodes. At the glucose 6-phosphate (G6P) node, 25% of the carbon influx was diverted towards the pentose phosphate pathway under normal growth conditions, while only 0.3% of the carbon flux was diverted towards the pentose phosphate pathway during growth at 1?M NaCl, indicating that cell growth is arrested under hyperosmotic conditions. Further, objective functions were used in the linear program to obtain optimal solution spaces corresponding to the different accumulation rates. The analysis demonstrated that while biomass formation was optimal under normal growth conditions, glycerol synthesis was closer to optimal during adaptation to osmotic shock.     

Metabolic pathway analysis of enzyme-deficient human red blood cells

Five enzymopathies (G6PDH, TPI, PGI, DPGM and PGK deficiencies) in the human red blood cells are investigated using a stoichiometric modeling approach, i.e., metabolic pathway analysis. Elementary flux modes (EFMs) corresponding to each enzyme deficiency case are analyzed in terms of functional capabilities. When available, experimental findings reported in literature related to metabolic behavior of the human red blood cells are compared with the results of EFM analysis. Control-effective flux (CEF) calculation, a novel approach which allows quantification and interpretation of determined EFMs, is performed for further analysis of enzymopathies. Glutathione reductase reaction is found to be the most effective reaction in terms of its CEF value in all enzymopathies in parallel with its known essential role for red blood cells. Efficiency profiles of the enzymatic reactions upon the degree of enzyme deficiency are obtained by the help of the CEF approach, as a basis for future experimental studies. CEF analysis, which is found to be promising in the analysis of erythrocyte enzymopathies, has the potential to be used in modeling efforts of human metabolism.     

Effect of the size of yeast flocs and zinc supplementation on continuous ethanol fermentation performance and metabolic flux distribution under very high concentration conditions

Taking continuous ethanol fermentation with the self‐flocculating yeast SPSC01 under very high concentration conditions as an example, the fermentation performance of the yeast flocs and their metabolic flux distribution were investigated by controlling their average sizes at 100, 200, and 300 µm using the focused beam reflectance online measurement system. In addition, the impact of zinc supplementation was evaluated for the yeast flocs at the size of 300 µm grown in presence or absence of 0.05 g L^?1 zinc sulfate. Among the yeast flocs with different sizes, the group with the average size of 300 µm exhibited highest ethanol production (110.0 g L^?1) and glucose uptake rate (286.69 C mmol L^?1 h^?1), which are in accordance with the increased flux from pyruvate to ethanol and decreased flux to glycerol. And in the meantime, zinc supplementation further increased ethanol production and cell viability comparing with the control. Zinc addition enhanced the carbon fluxes to the biosynthesis of ergosterol (28.6%) and trehalose (43.3%), whereas the fluxes towards glycerol, protein biosynthesis, and tricarboxylic acid cycle significantly decreased by 37.7%, 19.5%, and 27.8%, respectively. This work presents the first report on the regulation of metabolic flux by the size of yeast flocs and zinc supplementation, which provides the potential for developing engineering strategy to optimize the fermentation system. Biotechnol. Bioeng. 2010;105: 935–944. © 2009 Wiley Periodicals, Inc.     

Combinatorial complexity of pathway analysis in metabolic networks

Elementary flux mode analysis is a promising approach for a pathway-oriented perspective of metabolic networks. However, in larger networks it is hampered by the combinatorial explosion of possible routes. In this work we give some estimations on the combinatorial complexity including theoretical upper bounds for the number of elementary flux modes in a network of a given size. In a case study, we computed the elementary modes in the central metabolism of Escherichia coli while utilizing four different substrates. Interestingly, although the number of modes occurring in this complex network can exceed half a million, it is still far below the upper bound. Hence, to a certain extent, pathway analysis of central catabolism is feasible to assess network properties such as flexibility and functionality.     

Continuous modeling of metabolic networks with gene regulation in yeast and in vivo determination of rate parameters

A continuous model of a metabolic network including gene regulation to simulate metabolic fluxes during batch cultivation of yeast Saccharomyces cerevisiae was developed. The metabolic network includes reactions of glycolysis, gluconeogenesis, glycerol and ethanol synthesis and consumption, the tricarboxylic acid cycle, and protein synthesis. Carbon sources considered were glucose and then ethanol synthesized during growth on glucose. The metabolic network has 39 fluxes, which represent the action of 50 enzymes and 64 genes and it is coupled with a gene regulation network which defines enzyme synthesis (activities) and incorporates regulation by glucose (enzyme induction and repression), modeled using ordinary differential equations. The model includes enzyme kinetics, equations that follow both mass-action law and transport as well as inducible, repressible, and constitutive enzymes of metabolism. The model was able to simulate a fermentation of S. cerevisiae during the exponential growth phase on glucose and the exponential growth phase on ethanol using only one set of kinetic parameters. All fluxes in the continuous model followed the behavior shown by the metabolic flux analysis (MFA) obtained from experimental results. The differences obtained between the fluxes given by the model and the fluxes determined by the MFA do not exceed 25% in 75% of the cases during exponential growth on glucose, and 20% in 90% of the cases during exponential growth on ethanol. Furthermore, the adjustment of the fermentation profiles of biomass, glucose, and ethanol were 95%, 95%, and 79%, respectively. With these results the simulation was considered successful. A comparison between the simulation of the continuous model and the experimental data of the diauxic yeast fermentation for glucose, biomass, and ethanol, shows an extremely good match using the parameters found. The small discrepancies between the fluxes obtained through MFA and those predicted by the differential equations, as well as the good match between the profiles of glucose, biomass, and ethanol, and our simulation, show that this simple model, that does not rely on complex kinetic expressions, is able to capture the global behavior of the experimental data. Also, the determination of parameters using a straightforward minimization technique using data at only two points in time was sufficient to produce a relatively accurate model. Thus, even with a small amount of experimental data (rates and not concentrations) it was possible to estimate the parameters minimizing a simple objective function. The method proposed allows the obtention of reasonable parameters and concentrations in a system with a much larger number of unknowns than equations. Hence a contribution of this study is to present a convenient way to find in vivo rate parameters to model metabolic and genetic networks under different conditions.     

Genome-scale modeling and in silico analysis of ethanologenic bacteria Zymomonas mobilis

Bioethanol has been recognized as a potential alternative energy source. Among various ethanol-producing microbes, Zymomonas mobilis has acquired special attention due to its higher ethanol yield and tolerance. However, cellular metabolism in Z. mobilis remains unclear, hindering its practical application for bioethanol production. To elucidate such physiological characteristics, we reconstructed and validated a genome-scale metabolic network (iZM363) of Z. mobilis ATCC31821 (ZM4) based on its annotated genome and biochemical information. The phenotypic behaviors and metabolic states predicted by our genome-scale model were highly consistent with the experimental observations of Z. mobilis ZM4 strain growing on glucose as well as NMR-measured intracellular fluxes of an engineered strain utilizing glucose, fructose, and xylose. Subsequent comparative analysis with Escherichia coli and Saccharomyces cerevisiae as well as gene essentiality and flux coupling analyses have also confirmed the functional role of pdc and adh genes in the ethanologenic activity of Z. mobilis, thus leading to better understanding of this natural ethanol producer. In future, the current model could be employed to identify potential cell engineering targets, thereby enhancing the productivity of ethanol in Z. mobilis.     

The protease inhibitor chagasin of Trypanosoma cruzi adopts an immunoglobulin-type fold and may have arisen by horizontal gene transfer

Methylglyoxal metabolism was studied during Saccharomyces cerevisiae grown with D-glucose as the sole carbon and energy source. Using for the first time a specific assay for methylglyoxal in yeast, metabolic fluxes of its formation and D-lactate production were determined. D-Glucose consumption and ethanol production were determined during growth. Metabolic fluxes were also determined in situ, at the glycolytic triose phosphate levels and glyoxalase pathway. Maximum fluxes of ethanol production and glucose consumption correspond to maxima of methylglyoxal and D-lactate formation fluxes during growth. Methylglyoxal formation is quantitatively related to glycolysis, representing 0.3% of the total glycolytic flux in S. cerevisiae.     

Metabolic-flux and network analysis in fourteen hemiascomycetous yeasts

In a quantitative comparative study, we elucidated the glucose metabolism in fourteen hemiascomycetous yeasts from the Genolevures project. The metabolic networks of these different species were first established by (13)C-labeling data and the inventory of the genomes. This information was subsequently used for metabolic-flux ratio analysis to quantify the intracellular carbon flux distributions in these yeast species. Firstly, we found that compartmentation of amino acid biosynthesis in most species was identical to that in Saccharomyces cerevisiae. Exceptions were the mitochondrial origin of aspartate biosynthesis in Yarrowia lipolytica and the cytosolic origin of alanine biosynthesis in S. kluyveri. Secondly, the control of flux through the TCA cycle was inversely correlated with the ethanol production rate, with S. cerevisiae being the yeast with the highest ethanol production capacity. The classification between respiratory and respiro-fermentative metabolism, however, was not qualitatively exclusive but quantitatively gradual. Thirdly, the flux through the pentose phosphate (PP) pathway was correlated to the yield of biomass, suggesting a balanced production and consumption of NADPH. Generally, this implies the lack of active transhydrogenase-like activities in hemiascomycetous yeasts under the tested growth condition, with Pichia angusta as the sole exception. In the latter case, about 40% of the NADPH was produced in the PP pathway in excess of the requirements for biomass production, which strongly suggests the operation of a yet unidentified mechanism for NADPH reoxidation in this species. In most yeasts, the PP pathway activity appears to be driven exclusively by the demand for NADPH.     

Metabolic pathway analysis of Scheffersomyces (Pichia) stipitis: effect of oxygen availability on ethanol synthesis and flux distributions

Elementary mode analysis (EMA) identifies all possible metabolic states of the cell metabolic network. Investigation of these states can provide a detailed insight into the underlying metabolism in the cell. In this study, the flux states of Scheffersomyces (Pichia) stipitis metabolism were examined. It was shown that increasing oxygen levels led to a decrease of ethanol synthesis. This trend was confirmed by experimental evaluation of S. stipitis in glucose-xylose fermentation. The oxygen transfer rate for an optimal ethanol production was 1.8 mmol/l/h, which gave the ethanol yield of 0.40 g/g and the ethanol productivity of 0.25 g/l/h. For a better understanding of the cell's regulatory mechanism in response to oxygenation levels, EMA was used to examine metabolic flux patterns under different oxygen levels. Up- and downregulation of enzymes in the network during the change of culturing condition from oxygen limitation to oxygen sufficiency were identified. The results indicated the flexibility of S. stipitis metabolism to cope with oxygen availability. In addition, relevant genetic targets towards improved ethanol-producing strains under all oxygenation levels were identified. These targeted genes limited the metabolic functionality of the cell to function according to the most efficient ethanol synthesis pathways. The presented approach is promising and can contribute to the development of culture optimization and strain engineers for improved lignocellulosic ethanol production by S. stipitis.