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 proteome of Mannheimia succiniciproducens, a capnophilic rumen bacterium

Mannheimia succiniciproducens MBEL55E isolated from bovine rumen is an industrially important bacterium as an efficient succinic acid producer. Recently, its full genome sequence was determined. In the present study, we analyzed the M. succiniciproducens proteome based on the genome information using 2-DE and MS. We established proteome reference map of M. succiniciproducens by analyzing whole cellular proteins, membrane proteins, and secreted proteins. More than 200 proteins were identified and characterized by MS/MS supported by various bioinformatic tools. The presence of proteins previously annotated as hypothetical proteins or proteins having putative functions were also confirmed. Based on the proteome reference map, cells in the different growth phases were analyzed at the proteome level. Comparative proteome profiling revealed valuable information to understand physiological changes during growth, and subsequently suggested target genes to be manipulated for the strain improvement.     

Genome-scale model for Clostridium acetobutylicum: Part I. Metabolic network resolution and analysis

A genome-scale metabolic network reconstruction for Clostridium acetobutylicum (ATCC 824) was carried out using a new semi-automated reverse engineering algorithm. The network consists of 422 intracellular metabolites involved in 552 reactions and includes 80 membrane transport reactions. The metabolic network illustrates the reliance of clostridia on the urea cycle, intracellular L-glutamate solute pools, and the acetylornithine transaminase for amino acid biosynthesis from the 2-oxoglutarate precursor. The semi-automated reverse engineering algorithm identified discrepancies in reaction network databases that are major obstacles for fully automated network-building algorithms. The proposed semi-automated approach allowed for the conservation of unique clostridial metabolic pathways, such as an incomplete TCA cycle. A thermodynamic analysis was used to determine the physiological conditions under which proposed pathways (e.g., reverse partial TCA cycle and reverse arginine biosynthesis pathway) are feasible. The reconstructed metabolic network was used to create a genome-scale model that correctly characterized the butyrate kinase knock-out and the asolventogenic M5 pSOL1 megaplasmid degenerate strains. Systematic gene knock-out simulations were performed to identify a set of genes encoding clostridial enzymes essential for growth in silico.     

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.     

Development of a markerless gene knock-out system for Mannheimia succiniciproducens using a temperature-sensitive plasmid

A temperature-sensitive derivative of the Mannheimia varigena plasmid pMVSCS1 was constructed by hydroxylamine treatment for use in the development of a markerless gene knock-out system for Mannheimia succiniciproducens. The temperature-sensitive plasmid pMVSCS1-ts was stably maintained at 30 degrees C, but failed to replicate at 42 degrees C. DNA sequencing of the replication origin revealed a single base substitution as being responsible for its temperature sensitivity. The region of replication origin was amplified by PCR to construct an Escherichia coli-M. succiniciproducens shuttle vector pME19-ts to further examine the thermosensitivity. To make markerless mutants of M. succiniciproducens, the Cre-lox system with the variant lox66 and lox71 sites was used to prevent the instability caused by multiple loxP sites in the genome. The transient cre expression was carried out using the temperature-sensitive plasmid pCRX5, which was consequently cured after the verification of the markerless mutant by growing cells at 42 degrees C. For the demonstration of the markerless deletion of multiple genes using this method, the ldhA gene and the oadGAB operon of M. succiniciproducens encoding lactate dehydrogenase and oxaloacetate decarboxylase, respectively, were successfully deleted sequentially. This markerless deletion method should be useful for further metabolic engineering of M. succiniciproducens, which is a promising industrial bacterium for succinic acid production from renewable resources.     

Effects of dissolved CO2 levels on the growth of Mannheimia succiniciproducens and succinic acid production

A capnophilic rumen bacterium Mannheimia succiniciproducens produces succinic acid as a major fermentation end product under CO(2)-rich anaerobic condition. Since succinic acid is produced by carboxylation of C3 compounds during the fermentation, intracellular CO(2) availability is important for efficient succinic acid formation. Here, we investigated the metabolic responses of M. succiniciproducens to the different dissolved CO(2) concentrations (0-260 mM). Cell growth was severely suppressed when the dissolved CO(2) concentration was below 8.74 mM. On the other hand, cell growth and succinic acid production increased proportionally as the dissolved CO(2) concentration increased from 8.74 to 141 mM. The yields of biomass and succinic acid on glucose obtained at the dissolved CO(2) concentration of 141 mM were 1.49 and 1.52 times higher, respectively, than those obtained at the dissolved CO(2) concentration of 8.74 mM. It was also found that the additional CO(2) source provided in the form of NaHCO(3), MgCO(3), or CaCO(3) had positive effects on cell growth and succinic acid production. However, growth inhibition was observed when excessive bicarbonate salts were added. By the comparison of the activities of key enzymes, it was found that PEP carboxylation by PEP carboxykinase (PckA) is the most important for succinic acid production as well as the growth of M. succiniciproducens by providing additional ATP.     

Metabolic engineering of Mannheimia succiniciproducens for malic acid production using dimethylsulfoxide as an electron acceptor

Microbial production of various TCA intermediates and related chemicals through the reductive TCA cycle has been of great interest. However, rumen bacteria that naturally possess strong reductive TCA cycle have been rarely studied to produce these chemicals, except for succinic acid, due to their dependence on fumarate reduction to transport electrons for ATP synthesis. In this study, malic acid (MA), a dicarboxylic acid of industrial importance, was selected as a target chemical for mass production using Mannheimia succiniciproducens, a rumen bacterium possessing a strong reductive branch of the TCA cycle. The metabolic pathway was reconstructed by eliminating fumarase to prevent MA conversion to fumarate. The respiration system of M. succiniciproducens was reconstructed by introducing the Actinobacillus succinogenes dimethylsulfoxide (DMSO) reductase to improve cell growth using DMSO as an electron acceptor. Also, the cell membrane was engineered by employing Pseudomonas aeruginosa cis-trans isomerase to enhance MA tolerance. High inoculum fed-batch fermentation of the final engineered strain produced 61 g/L of MA with an overall productivity of 2.27 g/L/h, which is the highest MA productivity reported to date. The systems metabolic engineering strategies reported in this study will be useful for developing anaerobic bioprocesses for the production of various industrially important chemicals.     

Characterization of the Arc two-component signal transduction system of the capnophilic rumen bacterium Mannheimia succiniciproducens

The ArcB/A two-component signal transduction system of Escherichia coli modulates the expression of numerous operons in response to redox conditions of growth. We demonstrate that the putative arcA and arcB genes of Mannheimia succiniciproducens MBEL55E, a capnophilic (CO2-loving) rumen bacterium, encode functional proteins that specify a two-component system. The Arc proteins of the two bacterial species sufficiently resemble each other that they can participate in heterologous transphosphorylation in vitro, and the arcA and arcB genes of M. succiniciproducens confer toluidine blue resistance to E. coli arcA and arcB mutants. However, neither the quinone analogs (ubiquinone 0 and menadione) nor the cytosolic effectors (d-lactate, acetate, and pyruvate) affect the net phosphorylation of M. succiniciproducens ArcB. Our results indicate that different types of signaling molecules and distinct modes of kinase regulation are used by the ArcB proteins of E. coli and M. succiniciproducens.     

Combined in silico modeling and metabolomics analysis to characterize fed-batch CHO cell culture

The increasing demand for recombinant therapeutic proteins highlights the need to constantly improve the efficiency and yield of these biopharmaceutical products from mammalian cells, which is fully achievable only through proper understanding of cellular functioning. Towards this end, the current study exploited a combined metabolomics and in silico modeling approach to gain a deeper insight into the cellular mechanisms of Chinese hamster ovary (CHO) fed-batch cultures. Initially, extracellular and intracellular metabolite profiling analysis shortlisted key metabolites associated with cell growth limitation within the energy, glutathione, and glycerophospholipid pathways that have distinct changes at the exponential-stationary transition phase of the cultures. In addition, biomass compositional analysis newly revealed different amino acid content in the CHO cells from other mammalian cells, indicating the significance of accurate protein composition data in metabolite balancing across required nutrient assimilation, metabolic utilization, and cell growth. Subsequent in silico modeling of CHO cells characterized internal metabolic behaviors attaining physiological changes during growth and non-growth phases, thereby allowing us to explore relevant pathways to growth limitation and identify major growth-limiting factors including the oxidative stress and depletion of lipid metabolites. Such key information on growth-related mechanisms derived from the current approach can potentially guide the development of new strategies to enhance CHO culture performance.     

Genome-scale model for Clostridium acetobutylicum: Part II. Development of specific proton flux states and numerically determined sub-systems

A regulated genome-scale model for Clostridium acetobutylicum ATCC 824 was developed based on its metabolic network reconstruction. To aid model convergence and limit the number of flux-vector possible solutions (the size of the phenotypic solution space), modeling strategies were developed to impose a new type of constraint at the endo-exo-metabolome interface. This constraint is termed the specific proton flux state, and its use enabled accurate prediction of the extracellular medium pH during vegetative growth of batch cultures. The specific proton flux refers to the influx or efflux of free protons (per unit biomass) across the cell membrane. A specific proton flux state encompasses a defined range of specific proton fluxes and includes all metabolic flux distributions resulting in a specific proton flux within this range. Effective simulation of time-course batch fermentation required the use of independent flux balance solutions from an optimum set of specific proton flux states. Using a real-coded genetic algorithm to optimize temporal bounds of specific proton flux states, we show that six separate specific proton flux states are required to model vegetative-growth metabolism and accurately predict the extracellular medium pH. Further, we define the apparent proton flux stoichiometry per weak acids efflux and show that this value decreases from approximately 3.5 mol of protons secreted per mole of weak acids at the start of the culture to approximately 0 at the end of vegetative growth. Calculations revealed that when specific weak acids production is maximized in vegetative growth, the net proton exchange between the cell and environment occurs primarily through weak acids efflux (apparent proton flux stoichiometry is 1). However, proton efflux through cation channels during the early stages of acidogenesis was found to be significant. We have also developed the concept of numerically determined sub-systems of genome-scale metabolic networks here as a sub-network with a one-dimensional null space basis set. A numerically determined sub-system was constructed in the genome-scale metabolic network to study the flux magnitudes and directions of acetylornithine transaminase, alanine racemase, and D-alanine transaminase. These results were then used to establish additional constraints for the genome-scale model.     

Elucidation of metabolism in hybridoma cells grown in fed‐batch culture by genome‐scale modeling

Genome‐scale modeling of mouse hybridoma cells producing monoclonal antibodies (mAb) was performed to elucidate their physiological and metabolic states during fed‐batch cell culture. Initially, feed media nutrients were monitored to identify key components among carbon sources and amino acids with significant impact on the desired outcome, for example, cell growth and antibody production. The monitored profiles indicated rapid assimilation of glucose and glutamine during the exponential growth phase. Significant increase in mAb concentration was also observed when glutamine concentration was controlled at 0.5 mM as a feeding strategy. Based on the reconstructed genome‐scale metabolic network of mouse hybridoma cells and fed‐batch profiles, flux analysis was then implemented to investigate the cellular behavior and changes in internal fluxes during the cell culture. The simulated profile of the cell growth was consistent with experimentally measured specific growth rate. The in silico simulation results indicated (i) predominant utilization of glycolytic pathway for ATP production, (ii) importance of pyruvate node in metabolic shifting, and (iii) characteristic pattern in lactate to glucose ratio during the exponential phase. In future, experimental and in silico analyses can serve as a promising approach to identifying optimal feeding strategies and potential cell engineering targets as well as facilitate media optimization for the enhanced production of mAb or recombinant proteins in mammalian cells. Biotechnol. Bioeng. 2009;102: 1494–1504. © 2008 Wiley Periodicals, Inc.     

Characterizing Escherichia coli DH5alpha growth and metabolism in a complex medium using genome-scale flux analysis

Genome-scale flux analysis of Escherichia coli DH5alpha growth in a complex medium was performed to investigate the relationship between the uptake of various nutrients and their metabolic outcomes. During the exponential growth phase, we observed a sequential consumption order of serine, aspartate and glutamate in the complex medium as well as the complete consumption of key carbohydrate nutrients, glucose and trehalose. Based on the consumption and production rates of the measured metabolites, constraints-based flux analysis of a genome-scale E. coli model was then conducted to elucidate their utilization in the metabolism. The in silico analysis revealed that the cell exploited biosynthetic precursors taken up directly from the complex medium, through growth-related anabolic pathways. This suggests that the cell could be functioning in an energetically more efficient manner by reducing the energy needed to produce amino acids. The in silico simulation also allowed us to explain the observed rapid consumption of serine: excessively consumed external serine from the complex medium was mainly converted into pyruvate and glycine, which in turn, led to the acetate accumulation. The present work demonstrates the application of an in silico modeling approach to characterizing microbial metabolism under complex medium condition. This work further illustrates the use of in silico genome-scale analysis for developing better strategies related to improving microbial growth and enhancing the productivity of desirable metabolites.     

Development of chemically defined medium for Mannheimia succiniciproducens based on its genome sequence

This study presents a novel methodology for the development of a chemically defined medium (CDM) using genome-scale metabolic network and flux balance analysis. The genome-based in silico analysis identified two amino acids and four vitamins as non-substitutable essential compounds to be supplemented to a minimal medium for the sustainable growth of Mannheimia succiniciproducens, while no substitutable essential compounds were identified. The in silico predictions were verified by cultivating the cells on a CDM containing the six non-substitutable essential compounds, and it was further demonstrated by observing no cell growth on the CDM lacking any one of the non-substitutable essentials. An optimal CDM for the enhancement of cell growth and succinic acid production, as a target product, was formulated with a single-addition technique. The fermentation on the optimal CDM increased the succinic acid productivity by 36%, the final succinic acid concentration by 17%, and the succinic acid yield on glucose by 15% compared to the cultivation using a complex medium. The optimal CDM also lowered the sum of the amounts of by-products (acetic, formic, and lactic acids) by 30%. The strategy reported in this paper should be generally applicable to the development of CDMs for other organisms, whose genome sequences are available.     

Metabolic engineering of Mannheimia succiniciproducens for succinic acid production based on elementary mode analysis with clustering

Mannheimia succiniciproducens, a capnophilic gram‐negative rumen bacterium, has been employed for the efficient production of succinic acid. Although M. succiniciproducens metabolism was previously studied using a genome‐scale metabolic model, more metabolic characteristics are to be understood. To this end, elementary mode analysis accompanied with clustering (‘EMC’ analysis) is used to gain further insights on metabolic characteristics of M. succiniciproducens allowing efficient succinic acid production. Elementary modes (EMs) generated from the central carbon metabolic network of M. succiniciproducens are clustered to systematically analyze succinic acid production routes. Based on the results of EMC analysis, zwf gene is identified as a novel overexpression target for the improved succinic acid production. This gene is overexpressed in a previously constructed succinic acid‐overproducing M. succiniciproducens LPK7 strain. Heterologous NADPH‐dependent mdh is later intuitively selected for overexpression to synergistically improve succinic acid production by utilizing abundant NADPH pool mediated by the overexpressed zwf. The LPK7 strains co‐expressing mdh alone and both zwf and mdh genes are subjected to fed‐batch fermentation to better examine their succinic acid production performances. Strategies of EMC analysis will be useful for further metabolic engineering of M. succiniciproducens and other microorganisms to improve production of succinic acid and other chemicals of interest.     

Selective extraction of acetic acid from the fermentation broth produced by Mannheimia succiniciproducens

Acetic acid is by-product from fermentation processes for producing succinic acid using Mannheimia succiniciproducens . To obtain pure succinic acid from the final fermentation broth, acetic acid was selectively removed based on the different extractability of succinic acid and acetic acid with pH using tri-n-octylamine (TOA) as extractant. When successive batch extractions were performed using 0.25 mol TOA kg(-1) dissolved in 1-octanol at pH 5, the mol ratio of succinic acid to acetic acid before extraction was 4.9 and the final ratio after the fourth batch was 9.4.     

A genome‐scale metabolic network reconstruction of tomato (Solanum lycopersicum L.) and its application to photorespiratory metabolism

Tomato (Solanum lycopersicum L.) has been studied extensively due to its high economic value in the market, and high content in health‐promoting antioxidant compounds. Tomato is also considered as an excellent model organism for studying the development and metabolism of fleshy fruits. However, the growth, yield and fruit quality of tomatoes can be affected by drought stress, a common abiotic stress for tomato. To investigate the potential metabolic response of tomato plants to drought, we reconstructed iHY3410, a genome‐scale metabolic model of tomato leaf, and used this metabolic network to simulate tomato leaf metabolism. The resulting model includes 3410 genes and 2143 biochemical and transport reactions distributed across five intracellular organelles including cytosol, plastid, mitochondrion, peroxisome and vacuole. The model successfully described the known metabolic behaviour of tomato leaf under heterotrophic and phototrophic conditions. The in silico investigation of the metabolic characteristics for photorespiration and other relevant metabolic processes under drought stress suggested that: (i) the flux distributions through the mevalonate (MVA) pathway under drought were distinct from that under normal conditions; and (ii) the changes in fluxes through core metabolic pathways with varying flux ratio of RubisCO carboxylase to oxygenase may contribute to the adaptive stress response of plants. In addition, we improved on previous studies of reaction essentiality analysis for leaf metabolism by including potential alternative routes for compensating reaction knockouts. Altogether, the genome‐scale model provides a sound framework for investigating tomato metabolism and gives valuable insights into the functional consequences of abiotic stresses.