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

Transcriptomic studies of solventogenic clostridia, Clostridium acetobutylicum and Clostridium beijerinckii

Solventogenic clostridia are not a strictly defined group within the genus Clostridium but its representatives share some common features, i.e. they are anaerobic, non-pathogenic, non-toxinogenic and endospore forming bacteria. Their main metabolite is typically 1-butanol but depending on species and culture conditions, they can form other metabolites such as acetone, isopropanol, ethanol, butyric, lactic and acetic acids, and hydrogen. Although these organisms were previously used for the industrial production of solvents, they later fell into disuse, being replaced by more efficient chemical production. A return to a more biological production of solvents therefore requires a thorough understanding of clostridial metabolism. Transcriptome analysis, which reflects the involvement of individual genes in all cellular processes within a population, at any given (sampling) moment, is a valuable tool for gaining a deeper insight into clostridial life. In this review, we describe techniques to study transcription, summarize the evolution of these techniques and compare methods for data processing and visualization of solventogenic clostridia, particularly the species Clostridium acetobutylicum and Clostridium beijerinckii. Individual approaches for evaluating transcriptomic data are compared and their contributions to advancements in the field are assessed. Moreover, utilization of transcriptomic data for reconstruction of computational clostridial metabolic models is considered and particular models are described. Transcriptional changes in glucose transport, central carbon metabolism, the sporulation cycle, butanol and butyrate stress responses, the influence of lignocellulose-derived inhibitors on growth and solvent production, and other respective topics, are addressed and common trends are highlighted.

Modeling Growth Kinetics, Interspecies Cell Fusion, and Metabolism of a Clostridium acetobutylicum/Clostridium ljungdahlii Syntrophic Coculture

Clostridium acetobutylicum and Clostridium ljungdahlii grown in a syntrophic culture were recently shown to fuse membranes and exchange cytosolic contents, yielding hybrid cells with significant shifts in gene expression and growth phenotypes. Here, we introduce a dynamic genome-scale metabolic modeling framework to explore how cell fusion alters the growth phenotype and panel of metabolites produced by this binary community. Computational results indicate C. ljungdahlii persists in the coculture through proteome exchange during fusing events, which endow C. ljungdahlii cells with expanded substrate utilization, and access to additional reducing equivalents from C. acetobutylicum-evolved H₂ and through acquisition of C. acetobutylicum-native cofactor-reducing enzymes. Simulations predict maximum theoretical ethanol and isopropanol yields that are increased by 0.64 mmol and 0.39 mmol per mmol hexose sugar consumed, respectively, during exponential growth when cell fusion is active. This modeling effort provides a mechanistic explanation for the metabolic outcome of cellular fusion and altered homeostasis achieved in this syntrophic clostridial community.IMPORTANCE Widespread cell fusion and protein exchange between microbial organisms as observed in synthetic C. acetobutylicum/C. ljungdahlii culture is a novel observation that has not been explored in silico The mechanisms responsible for the observed cell fusion events in this culture are still unknown. In this work, we develop a modeling framework that captures the observed culture composition and metabolic phenotype, use it to offer a mechanistic explanation for how the culture achieves homeostasis, and identify C. ljungdahlii as primary beneficiary of fusion events. The implications for the events described in this study are far reaching, with potential to reshape our understanding of microbial community behavior synthetically and in nature.

Changes in efflux pump activity of Clostridium beijerinckii throughout ABE fermentation

Pumping toxic substances through a cytoplasmic membrane by protein transporters known as efflux pumps represents one bacterial mechanism involved in the stress response to the presence of toxic compounds. The active efflux might also take part in exporting low-molecular-weight alcohols produced by intrinsic cell metabolism; in the case of solventogenic clostridia, predominantly acetone, butanol and ethanol (ABE). However, little is known about this active efflux, even though some evidence exists that membrane pumps might be involved in solvent tolerance. In this study, we investigated changes in overall active efflux during ABE fermentation, employing a flow cytometric protocol adjusted for Clostridia and using ethidium bromide (EB) as a fluorescence marker for quantification of direct efflux. A fluctuation in efflux during the course of standard ABE fermentation was observed, with a maximum reached during late acidogenesis, a high efflux rate during early and mid-solventogenesis and an apparent decrease in EB efflux rate in late solventogenesis. The fluctuation in efflux activity was in accordance with transcriptomic data obtained for various membrane exporters in a former study. Surprisingly, under altered cultivation conditions, when solvent production was attenuated, and extended acidogenesis was promoted, stable low efflux activity was reached after an initial peak that appeared in the stage comparable to standard ABE fermentation. This study confirmed that efflux pump activity is not constant during ABE fermentation and suggests that undisturbed solvent production might be a trigger for activation of pumps involved in solvent efflux. KEY POINTS: • Flow cytometric assay for efflux quantification in Clostridia was established. • Efflux rate peaked in late acidogenesis and in early solventogenesis. • Impaired solventogenesis led to an overall decrease in efflux.

Pathway dissection, regulation, engineering and application: lessons learned from biobutanol production by solventogenic clostridia

The global energy crisis and limited supply of petroleum fuels have rekindled the interest in utilizing a sustainable biomass to produce biofuel. Butanol, an advanced biofuel, is a superior renewable resource as it has a high energy content and is less hygroscopic than other candidates. At present, the biobutanol route, employing acetone-butanol-ethanol (ABE) fermentation in Clostridium species, is not economically competitive due to the high cost of feedstocks, low butanol titer, and product inhibition. Based on an analysis of the physiological characteristics of solventogenic clostridia, current advances that enhance ABE fermentation from strain improvement to product separation were systematically reviewed, focusing on: (1) elucidating the metabolic pathway and regulation mechanism of butanol synthesis; (2) enhancing cellular performance and robustness through metabolic engineering, and (3) optimizing the process of ABE fermentation. Finally, perspectives on engineering and exploiting clostridia as cell factories to efficiently produce various chemicals and materials are also discussed.

Kinetic modeling of Stickland reactions-coupled methanogenesis for a methanogenic culture

Studying amino acid catabolism-coupled methanogenesis is the important standpoints to decipher the metabolic behavior of a methanogenic culture. l-Glycine and l-alanine are acted as sole carbon and nitrogen sources for acidogenic bacteria. One amino acid is oxidized and another one is reduced for acetate production via pyruvate by oxidative deamination process in the Stickland reactions. Herein, we have developed a kinetic model for the Stickland reactions-coupled methanogenesis (SRCM) and simulated objectively to maximize the rate of methane production. We collected the metabolic information from enzyme kinetic parameters for amino acid catabolism of Clostridium acetobutylicum ATCC 824 and methanogenesis of Methanosarcina acetivorans C2A. The SRCM model of this study consisted of 18 reactions and 61 metabolites with enzyme kinetic parameters derived experimental data. The internal or external metabolic flux rate of this system found to control the acidogenesis and methanogenesis in a methanogenic culture. Using the SRCM model, flux distributions were calculated for each reaction and metabolite in order to maximize the methane production rate from the glycine–alanine pair. Results of this study, we demonstrated the metabolic behavior, metabolite pairing while mutually interact, and advantages of syntrophic metabolism of amino acid-directed methane production in a methanogenic starter culture.

Amino acid catabolism-directed biofuel production in Clostridium sticklandii: An insight into model-driven systems engineering

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Model-driven systems engineering has been more fascinating process for microbial biofuel production.

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Clostridium sticklandii is a potential strain for the solventogenesis and acidogenesis.

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The present review provides an insight for the protein catabolism-directed biofuel production.

Model-driven systems engineering has been more fascinating process for the microbial production of biofuel and bio-refineries in chemical and pharmaceutical industries. Genome-scale modeling and simulations have been guided for metabolic engineering of Clostridium species for the production of organic solvents and organic acids. Among them, Clostridium sticklandii is one of the potential organisms to be exploited as a microbial cell factory for biofuel production. It is a hyper-ammonia producing bacterium and is able to catabolize amino acids as important carbon and energy sources via Stickland reactions and the development of the specific pathways. Current genomic and metabolic aspects of this bacterium are comprehensively reviewed herein, which provided information for learning about protein catabolism-directed biofuel production. It has a metabolic potential to drive energy and direct solventogenesis as well as acidogenesis from protein catabolism. It produces by-products such as ethanol, acetate, n-butanol, n-butyrate and hydrogen from amino acid catabolism. Model-driven systems engineering of this organism would improve the performance of the industrial sectors and enhance the industrial economy by using protein-based waste in environment-friendly ways.

Development of a core Clostridium thermocellum kinetic metabolic model consistent with multiple genetic perturbations

BACKGROUND

Clostridium thermocellum is a Gram-positive anaerobe with the ability to hydrolyze and metabolize cellulose into biofuels such as ethanol, making it an attractive candidate for consolidated bioprocessing (CBP). At present, metabolic engineering in C. thermocellum is hindered due to the incomplete description of its metabolic repertoire and regulation within a predictive metabolic model. Genome-scale metabolic (GSM) models augmented with kinetic models of metabolism have been shown to be effective at recapitulating perturbed metabolic phenotypes.

RESULTS

In this effort, we first update a second-generation genome-scale metabolic model (iCth446) for C. thermocellum by correcting cofactor dependencies, restoring elemental and charge balances, and updating GAM and NGAM values to improve phenotype predictions. The iCth446 model is next used as a scaffold to develop a core kinetic model (k-ctherm118) of the C. thermocellum central metabolism using the Ensemble Modeling (EM) paradigm. Model parameterization is carried out by simultaneously imposing fermentation yield data in lactate, malate, acetate, and hydrogen production pathways for 19 measured metabolites spanning a library of 19 distinct single and multiple gene knockout mutants along with 18 intracellular metabolite concentration data for a Δgldh mutant and ten experimentally measured Michaelis-Menten kinetic parameters.

CONCLUSIONS

The k-ctherm118 model captures significant metabolic changes caused by (1) nitrogen limitation leading to increased yields for lactate, pyruvate, and amino acids, and (2) ethanol stress causing an increase in intracellular sugar phosphate concentrations (~1.5-fold) due to upregulation of cofactor pools. Robustness analysis of k-ctherm118 alludes to the presence of a secondary activity of ketol-acid reductoisomerase and possible regulation by valine and/or leucine pool levels. In addition, cross-validation and robustness analysis allude to missing elements in k-ctherm118 and suggest additional experiments to improve kinetic model prediction fidelity. Overall, the study quantitatively assesses the advantages of EM-based kinetic modeling towards improved prediction of C. thermocellum metabolism and develops a predictive kinetic model which can be used to design biofuel-overproducing strains.

Arginine deiminase pathway provides ATP and boosts growth of the gas-fermenting acetogen Clostridium autoethanogenum

Acetogens are attractive organisms for the production of chemicals and fuels from inexpensive and non-food feedstocks such as syngas (CO, CO₂ and H₂). Expanding their product spectrum beyond native compounds is dictated by energetics, particularly ATP availability. Acetogens have evolved sophisticated strategies to conserve energy from reduction potential differences between major redox couples, however, this coupling is sensitive to small changes in thermodynamic equilibria. To accelerate the development of strains for energy-intensive products from gases, we used a genome-scale metabolic model (GEM) to explore alternative ATP-generating pathways in the gas-fermenting acetogen Clostridium autoethanogenum. Shadow price analysis revealed a preference of C. autoethanogenum for nine amino acids. This prediction was experimentally confirmed under heterotrophic conditions. Subsequent in silico simulations identified arginine (ARG) as a key enhancer for growth. Predictions were experimentally validated, and faster growth was measured in media containing ARG (t_D~4h) compared to growth on yeast extract (t_D~9h). The growth-boosting effect of ARG was confirmed during autotrophic growth. Metabolic modelling and experiments showed that acetate production is nearly abolished and fast growth is realised by a three-fold increase in ATP production through the arginine deiminase (ADI) pathway. The involvement of the ADI pathway was confirmed by metabolomics and RNA-sequencing which revealed a ~500-fold up-regulation of the ADI pathway with an unexpected down-regulation of the Wood-Ljungdahl pathway. The data presented here offer a potential route for supplying cells with ATP, while demonstrating the usefulness of metabolic modelling for the discovery of native pathways for stimulating growth or enhancing energy availability.

Mathematical modelling of clostridial acetone-butanol-ethanol fermentation

Clostridial acetone-butanol-ethanol (ABE) fermentation features a remarkable shift in the cellular metabolic activity from acid formation, acidogenesis, to the production of industrial-relevant solvents, solventogensis. In recent decades, mathematical models have been employed to elucidate the complex interlinked regulation and conditions that determine these two distinct metabolic states and govern the transition between them. In this review, we discuss these models with a focus on the mechanisms controlling intra- and extracellular changes between acidogenesis and solventogenesis. In particular, we critically evaluate underlying model assumptions and predictions in the light of current experimental knowledge. Towards this end, we briefly introduce key ideas and assumptions applied in the discussed modelling approaches, but waive a comprehensive mathematical presentation. We distinguish between structural and dynamical models, which will be discussed in their chronological order to illustrate how new biological information facilitates the ‘evolution’ of mathematical models. Mathematical models and their analysis have significantly contributed to our knowledge of ABE fermentation and the underlying regulatory network which spans all levels of biological organization. However, the ties between the different levels of cellular regulation are not well understood. Furthermore, contradictory experimental and theoretical results challenge our current notion of ABE metabolic network structure. Thus, clostridial ABE fermentation still poses theoretical as well as experimental challenges which are best approached in close collaboration between modellers and experimentalists.

System-level modeling of acetone-butanol-ethanol fermentation

Acetone-butanol-ethanol (ABE) fermentation is a metabolic process of clostridia that produces bio-based solvents including butanol. It is enabled by an underlying metabolic reaction network and modulated by cellular gene regulation and environmental cues. Mathematical modeling has served as a valuable strategy to facilitate the understanding, characterization and optimization of this process. In this review, we highlight recent advances in system-level, quantitative modeling of ABE fermentation. We begin with an overview of integrative processes underlying the fermentation. Next we survey modeling efforts including early simple models, models with a systematic metabolic description, and those incorporating metabolism through simple gene regulation. Particular focus is given to a recent system-level model that integrates the metabolic reactions, gene regulation and environmental cues. We conclude by discussing the remaining challenges and future directions towards predictive understanding of ABE fermentation.

In silico analysis of Clostridium acetobutylicum ATCC 824 metabolic response to an external electron supply

The biological production of butanol has become an important research field and thanks to genome sequencing and annotation; genome-scale metabolic reconstructions have been developed for several Clostridium species. This work makes use of the iCAC490 model of Clostridium acetobutylicum ATCC 824 to analyze its metabolic capabilities and response to an external electron supply through a constraint-based approach using the Constraint-Based Reconstruction Analysis Toolbox. Several analyses were conducted, which included sensitivity, production envelope, and phenotypic phase planes. The model showed that the use of an external electron supply, which acts as co-reducing agent along with glucose-derived reducing power (electrofermentation), results in an increase in the butanol-specific productivity. However, a proportional increase in the butyrate uptake flux is required. Besides, the uptake of external butyrate leads to the coupling of butanol production and growth, which coincides with results reported in literature. Phenotypic phase planes showed that the reducing capacity becomes more limiting for growth at high butyrate uptake fluxes. An electron uptake flux allows the metabolism to reach the growth optimality line. Although the maximum butanol flux does not coincide with the growth optimality line, a butyrate uptake combined with an electron uptake flux would result in an increased butanol volumetric productivity, being a potential strategy to optimize the production of butanol by C. acetobutylicum ATCC 824.

A Quantitative System-Scale Characterization of the Metabolism of Clostridium acetobutylicum

Engineering industrial microorganisms for ambitious applications, for example, the production of second-generation biofuels such as butanol, is impeded by a lack of knowledge of primary metabolism and its regulation. A quantitative system-scale analysis was applied to the biofuel-producing bacterium Clostridium acetobutylicum, a microorganism used for the industrial production of solvent. An improved genome-scale model, iCac967, was first developed based on thorough biochemical characterizations of 15 key metabolic enzymes and on extensive literature analysis to acquire accurate fluxomic data. In parallel, quantitative transcriptomic and proteomic analyses were performed to assess the number of mRNA molecules per cell for all genes under acidogenic, solventogenic, and alcohologenic steady-state conditions as well as the number of cytosolic protein molecules per cell for approximately 700 genes under at least one of the three steady-state conditions. A complete fluxomic, transcriptomic, and proteomic analysis applied to different metabolic states allowed us to better understand the regulation of primary metabolism. Moreover, this analysis enabled the functional characterization of numerous enzymes involved in primary metabolism, including (i) the enzymes involved in the two different butanol pathways and their cofactor specificities, (ii) the primary hydrogenase and its redox partner, (iii) the major butyryl coenzyme A (butyryl-CoA) dehydrogenase, and (iv) the major glyceraldehyde-3-phosphate dehydrogenase. This study provides important information for further metabolic engineering of C. acetobutylicum to develop a commercial process for the production of n-butanol.

Currently, there is a resurgence of interest in Clostridium acetobutylicum, the biocatalyst of the historical Weizmann process, to produce n-butanol for use both as a bulk chemical and as a renewable alternative transportation fuel. To develop a commercial process for the production of n-butanol via a metabolic engineering approach, it is necessary to better characterize both the primary metabolism of C. acetobutylicum and its regulation. Here, we apply a quantitative system-scale analysis to acidogenic, solventogenic, and alcohologenic steady-state C. acetobutylicum cells and report for the first time quantitative transcriptomic, proteomic, and fluxomic data. This approach allows for a better understanding of the regulation of primary metabolism and for the functional characterization of numerous enzymes involved in primary metabolism.

Predicting internal cell fluxes at sub-optimal growth

Background

Flux Balance Analysis (FBA) is a widely used tool to model metabolic behavior and cellular function. Applications of FBA span a breadth of research from synthetic engineering of biofuels to understanding evolutionary adaptations. FBA predicts metabolic reaction fluxes that optimize a given objective. This objective is generally defined for unicellular organisms by a theoretical reaction which simulates biomass production. FBA has been extremely successful at predicting in E. coli growth rates under different media and gene essentiality, amongst other things. In order to improve predictions, additional constraints are coupled with optimization of the biomass function. Studies have suggested, however, that unicellular organisms - like multicellular organisms - do not grow at optimal rates. To further improve FBA predictions, particularly of internal cell fluxes, new techniques to explore the sub-optimal solution space need to be developed.

Results

We present an innovative FBA method called corsoFBA based on the optimization of protein cost at sub-optimal objective levels. Our method shows good agreement with experimental data of E. coli grown at different dilution rates. Maintaining the objective function close to its maximum value predicts metabolic states that closely resemble low dilution rates; while higher dilution rates can be mirrored by lowering the biomass production value. By using a modified version of Extreme Pathways, we are also able to quantify the energy production and overall protein cost for all possible pathways in the central carbon metabolism.

Conclusion

Metabolic flux distributions at the optimal objective can be substantially different from the near-optimal distributions. Importantly, the behavior of E. coli central carbon metabolism can be better predicted by exploring the sub-optimal FBA solution space. The corsoFBA method presented here is able to predict the behavior of PEP Carboxylase, the glyoxylate shunt and the Entner-Doudoroff pathway at different glucose levels, a behavior not predicted by the minimization of metabolic steps and FBA alone. This technique can be used to better predict internal cell fluxes under different conditions, and corsoFBA will be of great help for the study of cells from multicellular organisms using Flux Balance Analysis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0153-3) contains supplementary material, which is available to authorized users.

Computational approaches to metabolic engineering utilizing systems biology and synthetic biology

Metabolic engineering modifies cellular function to address various biochemical applications. Underlying metabolic engineering efforts are a host of tools and knowledge that are integrated to enable successful outcomes. Concurrent development of computational and experimental tools has enabled different approaches to metabolic engineering. One approach is to leverage knowledge and computational tools to prospectively predict designs to achieve the desired outcome. An alternative approach is to utilize combinatorial experimental tools to empirically explore the range of cellular function and to screen for desired traits. This mini-review focuses on computational systems biology and synthetic biology tools that can be used in combination for prospective in silico strain design.

Elementary mode analysis reveals that Clostridium acetobutylicum modulates its metabolic strategy under external stress

Clostridium acetobutylicumis a strict anaerobe which exhibits two distinct steps in its metabolic network.

Constraint-based genome-scale metabolic modeling of Clostridium acetobutylicum behavior in an immobilized column

In this study a step-wise optimization procedure was developed to predict solvent production using continuous ABE fermentation with immobilized cells. The modeling approach presented here utilizes previously published constraint-based metabolic model for Clostridium acetobutylicum without direct flux constraints. A recently developed flux ratio constraint method was adopted for the model. An experimental data set consisting of 25 experiments using different sugar mixtures as substrates and differing dilution rates was simulated successfully with the modeling approach. Converted to end product concentrations the mean absolute error for acetone was 0.31 g/l, for butanol 0.49 g/l, and for ethanol 0.17 g/l. The modeling approach was validated with another data set from similar experimental setup. The model errors for the validation data set was 0.24 g/l, 0.60 g/l, and 0.17 g/l for acetone, butanol, and ethanol, respectively.

Development of a gene knockout system using mobile group II introns (Targetron) and genetic disruption of acid production pathways in Clostridium beijerinckii

Clostridium beijerinckii is a well-known solvent-producing microorganism with great potential for biofuel and biochemical production. To better understand and improve the biochemical pathway to solvents, the development of genetic tools for engineering C. beijerinckii is highly desired. Based on mobile group II intron technology, a targetron gene knockout system was developed for C. beijerinckii in this study. This system was successfully employed to disrupt acid production pathways in C. beijerinckii, leading to pta (encoding phosphotransacetylase)- and buk (encoding butyrate kinase)-negative mutants. In addition to experimental characterization, the mutant phenotypes were analyzed in the context of our C. beijerinckii genome-scale model. Compared to those of the parental strain (C. beijerinckii 8052), acetate production in the pta mutant was substantially reduced and butyrate production was remarkably increased, while solvent production was dependent on the growth medium. The pta mutant also produced much higher levels of lactate, suggesting that disrupting pta influenced the energy generation and electron flow pathways. In contrast, acetate and butyrate production in the buk mutant was generally similar to that of the wild type, but solvent production was consistently 20 to 30% higher and glucose consumption was more rapid and complete. Our results suggest that the acid and solvent production of C. beijerinckii can be effectively altered by disrupting the acid production pathways. As the gene disruption method developed in this study does not leave any antibiotic marker in a disrupted allele, multiple and high-throughput gene disruption is feasible for elucidating genotype and phenotype relationships in C. beijerinckii.

The putative influence of the agr operon upon survival mechanisms used by Clostridium acetobutylicum

The bacterium Clostridium acetobutylicum produces acids as an energy-yielding process during exponential growth. An acidic environment, however, is toxic to the cells and two survival mechanisms are in place to prevent them from dying. Firstly, during a solventogenesis phase, the cells take up these acids and convert them to solvents, thus raising the environmental pH. Secondly, the cells undergo sporulation to form highly resistant spores capable of surviving extreme conditions. One possible regulatory mechanism for these processes is the accessory gene regulatory (agr) quorum-sensing system, which is thought to coordinate cell population density with cell phenotype. We model this system to monitor its putative effect upon solventogenesis and the sporulation-initiation network responsible for triggering spore formation. We demonstrate that a high population density should be able to induce both solventogenesis and sporulation, with variations to the parameter set allowing sporulation alone to be triggered; additional distinct signals are capable of restoring the solventogenic response. We compare the agr system of C. acetobutylicum with that of Staphylococcus aureus in order to investigate why the differences in feedback between the two systems may have evolved. Our findings indicate that, depending upon the mechanism of interaction between the agr system and the sporulation-initiation network, the clostridial agr circuitry may be in place either to moderate the number of spores that are formed (in order for this number to reflect the urgency of the situation), or simply as an energy-saving strategy.

Integrative modelling of pH-dependent enzyme activity and transcriptomic regulation of the acetone-butanol-ethanol fermentation of Clostridium acetobutylicum in continuous culture

In a continuous culture under phosphate limitation the metabolism of Clostridium acetobutylicum depends on the external pH level. By comparing seven steady-state conditions between pH 5.7 and pH 4.5 we show that the switch from acidogenesis to solventogenesis occurs between pH 5.3 and pH 5.0 with an intermediate state at pH 5.1. Here, an integrative study is presented investigating how a changing external pH level affects the clostridial acetone–butanol–ethanol (ABE) fermentation pathway. This is of particular interest as the biotechnological production of n-butanol as biofuel has recently returned into the focus of industrial applications. One prerequisite is the furthering of the knowledge of the factors determining the solvent production and their integrative regulations. We have mathematically analysed the influence of pH-dependent specific enzyme activities of branch points of the metabolism on the product formation. This kinetic regulation was compared with transcriptomic regulation regarding gene transcription and the proteomic profile. Furthermore, both regulatory mechanisms were combined yielding a detailed projection of their individual and joint effects on the product formation. The resulting model represents an important platform for future developments of industrial butanol production based on C. acetobutylicum.

Resolving cell composition through simple measurements, genome-scale modeling, and a genetic algorithm

The biochemical composition of a cell is very complex and dynamic. It varies greatly among different organisms and environmental conditions. Inclusion of proper cell composition data is critical for accurate genome-scale metabolic flux modeling using flux balance analysis (FBA). However, determining cell composition experimentally is currently time-consuming and resource intensive. In this chapter, a method for predicting cell composition using a genome-scale model and "easy to measure" culture data (e.g., glucose uptake rate, and specific growth rate) is presented. The method makes use of a genetic algorithm for nonlinear optimization of a biomass equation (a mathematical description of cell composition). As a case study, the method was used to optimize a biomass equation for Escherichia coli MG1655 under multiple growth environments. The availability of experimentally determined (13)C flux data allowed a direct comparison with FBA predicted fluxes through the TCA cycle. Results showed dramatic improvement upon optimization of the biomass equation. In a second case study, biomass equation optimization was also applied to Clostridium acetobutylicum, an organism with less available biochemical cell composition data in the literature. The method produced a biomass equation highly similar to one determined experimentally for the closely related Gram-positive Bacillus subtilis.

Are we ready for genome-scale modeling in plants?

As it is becoming easier and faster to generate various types of high-throughput data, one would expect that by now we should have a comprehensive systems-level understanding of biology, biochemistry, and physiology at least in major prokaryotic and eukaryotic model systems. Despite the wealth of available data, we only get a glimpse of what is going on at the molecular level from the global perspective. The major reason is the high level of cellular complexity and our limited ability to identify all (or at least important) components and their interactions in virtually infinite number of internal and external conditions. Metabolism can be modeled mathematically by the use of genome-scale models (GEMs). GEMs are in silico metabolic flux models derived from available genome annotation. These models predict the combination of flux values of a defined metabolic network given the influence of internal and external signals. GEMs have been successfully implemented to model bacterial metabolism for over a decade. However, it was not until 2009 when the first GEM for Arabidopsis thaliana cell-suspension cultures was generated. Genome-scale modeling ("GEMing") in plants brings new challenges primarily due to the missing components and complexity of plant cells represented by the existence of: (i) photosynthesis; (ii) compartmentation; (iii) variety of cell and tissue types; and (iv) diverse metabolic responses to environmental and developmental cues as well as pathogens, insects, and competing weeds. This review presents a critical discussion of the advantages of existing plant GEMs, while identifies key targets for future improvements. Plant GEMs tend to be accurate in predicting qualitative changes in selected aspects of central carbon metabolism, while secondary metabolism is largely neglected mainly due to the missing (unknown) genes and metabolites. As such, these models are suitable for exploring metabolism in plants grown in favorable conditions, but not in field-grown plants that have to cope with environmental changes in complex ecosystems. AraGEM is the first GEM describing a photosynthetic and photorespiring plant cell (Arabidopsis thaliana). We demonstrate the use of AraGEM given the current (limited) knowledge of plant metabolism and reveal the unexpected robustness of AraGEM by a series of in silico simulations. The major focus of these simulations is on the assessment of the: (i) network connectivity; (ii) influence of CO₂ and photon uptake rates on cellular growth rates and production of individual biomass components; and (iii) stability of plant central carbon metabolism with internal pH changes.

Genome-scale modeling using flux ratio constraints to enable metabolic engineering of clostridial metabolism in silico

BACKGROUND

Genome-scale metabolic networks and flux models are an effective platform for linking an organism genotype to its phenotype. However, few modeling approaches offer predictive capabilities to evaluate potential metabolic engineering strategies in silico.

RESULTS

A new method called "flux balance analysis with flux ratios (FBrAtio)" was developed in this research and applied to a new genome-scale model of Clostridium acetobutylicum ATCC 824 (iCAC490) that contains 707 metabolites and 794 reactions. FBrAtio was used to model wild-type metabolism and metabolically engineered strains of C. acetobutylicum where only flux ratio constraints and thermodynamic reversibility of reactions were required. The FBrAtio approach allowed solutions to be found through standard linear programming. Five flux ratio constraints were required to achieve a qualitative picture of wild-type metabolism for C. acetobutylicum for the production of: (i) acetate, (ii) lactate, (iii) butyrate, (iv) acetone, (v) butanol, (vi) ethanol, (vii) CO2 and (viii) H2. Results of this simulation study coincide with published experimental results and show the knockdown of the acetoacetyl-CoA transferase increases butanol to acetone selectivity, while the simultaneous over-expression of the aldehyde/alcohol dehydrogenase greatly increases ethanol production.

CONCLUSIONS

FBrAtio is a promising new method for constraining genome-scale models using internal flux ratios. The method was effective for modeling wild-type and engineered strains of C. acetobutylicum.

Experimental and modeling analysis of Synechocystis sp. PCC 6803 growth

BACKGROUND/AIMS

The influence of different parameters such as temperature, irradiance, nitrate concentration, pH, and an external carbon source on Synechocystis PCC 6803 growth was evaluated.

METHODS

4.5-ml cuvettes containing 2 ml of culture, a high-throughput system equivalent to batch cultures, were used with gas exchange ensured by the use of a Parafilm™ cover. The effect of the different variables on maximum growth was assessed by a multi-way statistical analysis.

RESULTS

Temperature and pH were identified as the key factors. It was observed that Synechocystis cells have a strong influence on the external pH. The optimal growth temperature was 33°C while light-saturating conditions were reached at 40 µE·m⁻²·s⁻¹.

CONCLUSION

It was demonstrated that Synechocystis exhibits a marked difference in behavior between autotrophic and glucose-based mixotrophic conditions, and that nitrate concentrations did not have a significant influence, probably due to endogenous nitrogen reserves. Furthermore, a dynamic metabolic model of Synechocystis photosynthesis was developed to gain insights on the underlying mechanism enabling this cyanobacterium to control the levels of external pH. The model showed a coupled effect between the increase of the pH and ATP production which in turn allows a higher carbon fixation rate.

Ribulokinase and transcriptional regulation of arabinose metabolism in Clostridium acetobutylicum

The transcription factor AraR controls utilization of L-arabinose in Bacillus subtilis. In this study, we combined a comparative genomic reconstruction of AraR regulons in nine Clostridium species with detailed experimental characterization of AraR-mediated regulation in Clostridium acetobutylicum. Based on the reconstructed AraR regulons, a novel ribulokinase, AraK, present in all analyzed Clostridium species was identified, which was a nonorthologous replacement of previously characterized ribulokinases. The predicted function of the araK gene was confirmed by inactivation of the araK gene in C. acetobutylicum and biochemical assays using purified recombinant AraK. In addition to the genes involved in arabinose utilization and arabinoside degradation, extension of the AraR regulon to the pentose phosphate pathway genes in several Clostridium species was revealed. The predicted AraR-binding sites in the C. acetobutylicum genome and the negative effect of L-arabinose on DNA-regulator complex formation were verified by in vitro binding assays. The predicted AraR-controlled genes in C. acetobutylicum were experimentally validated by testing gene expression patterns in both wild-type and araR-inactivated mutant strains during growth in the absence or presence of L-arabinose.

Dynamic flux balance modeling of S. cerevisiae and E. coli co-cultures for efficient consumption of glucose/xylose mixtures

Current researches into the production of biochemicals from lignocellulosic feedstocks are focused on the identification and engineering of individual microbes that utilize complex sugar mixtures. Microbial consortia represent an alternative approach that has the potential to better exploit individual species capabilities for substrate uptake and biochemical production. In this work, we construct and experimentally validate a dynamic flux balance model of a Saccharomyces cerevisiae and Escherichia coli co-culture designed for efficient aerobic consumption of glucose/xylose mixtures. Each microbe is a substrate specialist, with wild-type S. cerevisiae consuming only glucose and engineered E. coli strain ZSC113 consuming only xylose, to avoid diauxic growth commonly observed in individual microbes. Following experimental identification of a common pH and temperature for optimal co-culture batch growth, we demonstrate that pure culture models developed for optimal growth conditions can be adapted to the suboptimal, common growth condition by adjustment of the non-growth associated ATP maintenance of each microbe. By comparing pure culture model predictions to co-culture experimental data, the inhibitory effect of ethanol produced by S. cerevisiae on E. coli growth was found to be the only interaction necessary to include in the co-culture model to generate accurate batch profile predictions. Co-culture model utility was demonstrated by predicting initial cell concentrations that yield simultaneous glucose and xylose exhaustion for different sugar mixtures. Successful experimental validation of the model predictions demonstrated that steady-state metabolic reconstructions developed for individual microbes can be adapted to develop dynamic flux balance models of microbial consortia for the production of renewable chemicals.

Engineering of microorganisms for the production of biofuels and perspectives based on systems metabolic engineering approaches

The increasing oil price and environmental concerns caused by the use of fossil fuel have renewed our interest in utilizing biomass as a sustainable resource for the production of biofuel. It is however essential to develop high performance microbes that are capable of producing biofuels with very high efficiency in order to compete with the fossil fuel. Recently, the strategies for developing microbial strains by systems metabolic engineering, which can be considered as metabolic engineering integrated with systems biology and synthetic biology, have been developed. Systems metabolic engineering allows successful development of microbes that are capable of producing several different biofuels including bioethanol, biobutanol, alkane, and biodiesel, and even hydrogen. In this review, the approaches employed to develop efficient biofuel producers by metabolic engineering and systems metabolic engineering approaches are reviewed with relevant example cases. It is expected that systems metabolic engineering will be employed as an essential strategy for the development of microbial strains for industrial applications.

Applications of systems biology towards microbial fuel production

Harnessing the immense natural diversity of biological functions for economical production of fuel has enormous potential benefits. Inevitably, however, the native capabilities for any given organism must be modified to increase the productivity or efficiency of a biofuel bioprocess. From a broad perspective, the challenge is to sufficiently understand the details of cellular functionality to be able to prospectively predict and modify the cellular function of a microorganism. Recent advances in experimental and computational systems biology approaches can be used to better understand cellular level function and guide future experiments. With pressure to quickly develop viable, renewable biofuel processes a balance must be maintained between obtaining depth of biological knowledge and applying that knowledge.

Metabolic network reconstruction and genome-scale model of butanol-producing strain Clostridium beijerinckii NCIMB 8052

BACKGROUND

Solventogenic clostridia offer a sustainable alternative to petroleum-based production of butanol--an important chemical feedstock and potential fuel additive or replacement. C. beijerinckii is an attractive microorganism for strain design to improve butanol production because it (i) naturally produces the highest recorded butanol concentrations as a byproduct of fermentation; and (ii) can co-ferment pentose and hexose sugars (the primary products from lignocellulosic hydrolysis). Interrogating C. beijerinckii metabolism from a systems viewpoint using constraint-based modeling allows for simulation of the global effect of genetic modifications.

RESULTS

We present the first genome-scale metabolic model (iCM925) for C. beijerinckii, containing 925 genes, 938 reactions, and 881 metabolites. To build the model we employed a semi-automated procedure that integrated genome annotation information from KEGG, BioCyc, and The SEED, and utilized computational algorithms with manual curation to improve model completeness. Interestingly, we found only a 34% overlap in reactions collected from the three databases--highlighting the importance of evaluating the predictive accuracy of the resulting genome-scale model. To validate iCM925, we conducted fermentation experiments using the NCIMB 8052 strain, and evaluated the ability of the model to simulate measured substrate uptake and product production rates. Experimentally observed fermentation profiles were found to lie within the solution space of the model; however, under an optimal growth objective, additional constraints were needed to reproduce the observed profiles--suggesting the existence of selective pressures other than optimal growth. Notably, a significantly enriched fraction of actively utilized reactions in simulations--constrained to reflect experimental rates--originated from the set of reactions that overlapped between all three databases (P = 3.52 × 10-9, Fisher's exact test). Inhibition of the hydrogenase reaction was found to have a strong effect on butanol formation--as experimentally observed.

CONCLUSIONS

Microbial production of butanol by C. beijerinckii offers a promising, sustainable, method for generation of this important chemical and potential biofuel. iCM925 is a predictive model that can accurately reproduce physiological behavior and provide insight into the underlying mechanisms of microbial butanol production. As such, the model will be instrumental in efforts to better understand, and metabolically engineer, this microorganism for improved butanol production.

An improved kinetic model for the acetone-butanol-ethanol pathway of Clostridium acetobutylicum and model-based perturbation analysis

Background

Comprehensive kinetic models of microbial metabolism can enhance the understanding of system dynamics and regulatory mechanisms, which is helpful in optimizing microbial production of industrial chemicals. Clostridium acetobutylicum produces solvents (acetone-butanol–ethanol, ABE) through the ABE pathway. To systematically assess the potential of increased production of solvents, kinetic modeling has been applied to analyze the dynamics of this pathway and make predictive simulations. Up to date, only one kinetic model for C. acetobutylicum supported by experiment has been reported as far as we know. But this model did not integrate the metabolic regulatory effects of transcriptional control and other complex factors. It also left out the information of some key intermediates (e.g. butyryl-phosphate).

Results

We have developed an improved kinetic model featured with the incorporation of butyryl-phosphate, inclusion of net effects of complex metabolic regulations, and quantification of endogenous enzyme activity variations caused by these regulations. The simulation results of our model are more consistent with published experimental data than the previous model, especially in terms of reflecting the kinetics of butyryl-phosphate and butyrate. Through parameter perturbation analysis, it was found that butyrate kinase has large and positive influence on butanol production while CoA transferase has negative effect on butanol production, suggesting that butyrate kinase has more efficiency in converting butyrate to butanol than CoA transferase.

Conclusions

Our improved kinetic model of the ABE process has more capacity in approaching real circumstances, providing much more insight in the regulatory mechanisms and potential key points for optimization of solvent productions. Moreover, the modeling strategy can be extended to other biological processes.

Computational screening of novel thiamine-catalyzed decarboxylation reactions of 2-keto acids

A molecular modeling strategy to screen the capacity of known enzymes to catalyze the reactions of non-native substrates is presented. The binding of pyruvic acid and non-native ketoacids in the active site of pyruvate ferredoxin oxidoreductase was examined using docking analysis, and our results suggest that enzyme-non-native ketoacid-bound species are feasible. Quantum mechanics/molecular mechanics methods were then used to study the geometry of the covalent intermediate formed from the enzyme and the various ketoacids. Finally, quantum mechanical methods were used to study the decarboxylation reaction of 2-keto acids at the mechanistic level. This hierarchical screening ranked the substrates from those that cannot be accommodated by the enzyme (phenyl pyruvate) to those whose conversion rate would most closely approach that of the native substrate (2-ketobutanoic acid and 2-ketovaleric acid). Most notably, our investigation suggests that novel pathways generated using generalized enzyme actions may be screened using the hierarchical approach employed here.

Brønsted-Evans-Polanyi relationships for C-C bond forming and C-C bond breaking reactions in thiamine-catalyzed decarboxylation of 2-keto acids using density functional theory

The concept of generalized enzyme reactions suggests that a wide variety of substrates can undergo enzymatic transformations, including those whose biotransformation has not yet been realized. The use of quantum chemistry to evaluate kinetic feasibility is an attractive approach to identify enzymes for the proposed transformation. However, the sheer number of novel transformations that can be generated makes this impractical as a screening approach. Therefore, it is essential to develop structure/activity relationships based on quantities that are more efficient to calculate. In this work, we propose a structure/activity relationship based on the free energy of binding or reaction of non-native substrates to evaluate the catalysis relative to that of native substrates. While Brønsted-Evans-Polanyi (BEP) relationships such as that proposed here have found broad application in heterogeneous catalysis, their extension to enzymatic catalysis is limited. We report here on density functional theory (DFT) studies for C-C bond formation and C-C bond cleavage associated with the decarboxylation of six 2-keto acids by a thiamine-containing enzyme (EC 1.2.7.1) and demonstrate a linear relationship between the free energy of reaction and the activation barrier. We then applied this relationship to predict the activation barriers of 17 chemically similar novel reactions. These calculations reveal that there is a clear correlation between the free energy of formation of the transition state and the free energy of the reaction, suggesting that this method can be further extended to predict the kinetics of novel reactions through our computational framework for discovery of novel biochemical transformations.

Comparative genomic and transcriptomic analysis revealed genetic characteristics related to solvent formation and xylose utilization in Clostridium acetobutylicum EA 2018

Background

Clostridium acetobutylicum, a gram-positive and spore-forming anaerobe, is a major strain for the fermentative production of acetone, butanol and ethanol. But a previously isolated hyper-butanol producing strain C. acetobutylicum EA 2018 does not produce spores and has greater capability of solvent production, especially for butanol, than the type strain C. acetobutylicum ATCC 824.

Results

Complete genome of C. acetobutylicum EA 2018 was sequenced using Roche 454 pyrosequencing. Genomic comparison with ATCC 824 identified many variations which may contribute to the hyper-butanol producing characteristics in the EA 2018 strain, including a total of 46 deletion sites and 26 insertion sites. In addition, transcriptomic profiling of gene expression in EA 2018 relative to that of ATCC824 revealed expression-level changes of several key genes related to solvent formation. For example, spo0A and adhEII have higher expression level, and most of the acid formation related genes have lower expression level in EA 2018. Interestingly, the results also showed that the variation in CEA_G2622 (CAC2613 in ATCC 824), a putative transcriptional regulator involved in xylose utilization, might accelerate utilization of substrate xylose.

Conclusions

Comparative analysis of C. acetobutylicum hyper-butanol producing strain EA 2018 and type strain ATCC 824 at both genomic and transcriptomic levels, for the first time, provides molecular-level understanding of non-sporulation, higher solvent production and enhanced xylose utilization in the mutant EA 2018. The information could be valuable for further genetic modification of C. acetobutylicum for more effective butanol production.

A systems biology approach to investigate the effect of pH-induced gene regulation on solvent production by Clostridium acetobutylicum in continuous culture

BACKGROUND

Clostridium acetobutylicum is an anaerobic bacterium which is known for its solvent-producing capabilities, namely regarding the bulk chemicals acetone and butanol, the latter being a highly efficient biofuel. For butanol production by C. acetobutylicum to be optimized and exploited on an industrial scale, the effect of pH-induced gene regulation on solvent production by C. acetobutylicum in continuous culture must be understood as fully as possible.

RESULTS

We present an ordinary differential equation model combining the metabolic network governing solvent production with regulation at the genetic level of the enzymes required for this process. Parameterizing the model with experimental data from continuous culture, we demonstrate the influence of pH upon fermentation products: at high pH (pH 5.7) acids are the dominant product while at low pH (pH 4.5) this switches to solvents. Through steady-state analyses of the model we focus our investigations on how alteration in gene expression of C. acetobutylicum could be exploited to increase butanol yield in a continuous culture fermentation.

CONCLUSIONS

Incorporating gene regulation into the model of solvent production by C. acetobutylicum enables an accurate representation of the pH-induced switch to solvent production to be obtained and theoretical investigations of possible synthetic-biology approaches to be pursued. Steady-state analyses suggest that, to increase butanol yield, alterations in the expression of single solvent-associated genes are insufficient; a more complex approach targeting two or more genes is required.

Genome-scale metabolic modeling of a clostridial co-culture for consolidated bioprocessing

An alternative consolidated bioprocessing approach is the use of a co-culture containing cellulolytic and solventogenic clostridia. It has been demonstrated that the rate of cellulose utilization in the co-culture of Clostridium acetobutylicum and Clostridium cellulolyticum is improved compared to the mono-culture of C. cellulolyticum, suggesting the presence of syntrophy between these two species. However, the metabolic interactions in the co-culture are not well understood. To understand the metabolic interactions in the co-culture, we developed a genome-scale metabolic model of C. cellulolyticum comprising of 431 genes, 621 reactions, and 603 metabolites. The C. cellulolyticum model can successfully predict the chemostat growth and byproduct secretion with cellulose as the substrate. However, a growth arrest phenomenon, which occurs in batch cultures of C. cellulolyticum at cellulose concentrations higher than 6.7 g/L, cannot be predicted by dynamic flux balance analysis due to the lack of understanding of the underlying mechanism. These genome-scale metabolic models of the pure cultures have also been integrated using a community modeling framework to develop a dynamic model of metabolic interactions in the co-culture. Co-culture simulations suggest that cellobiose inhibition cannot be the main factor that is responsible for improved cellulose utilization relative to mono-culture of C. cellulolyticum.

Exploring biodiversity for cellulosic biofuel production

Industrial production of solvents such as EtOH and BuOH from cellulosic biomass has the potential to provide a sustainable energy source that is relatively cheap, abundant, and environmentally sound, but currently production costs are driven up by expensive enzymes, which are necessary to degrade cellulose into fermentable sugars. These costs could be significantly reduced if a microorganism could be engineered to efficiently and quickly convert cellulosic biomass directly to product in a one-step process. There is a large amount of biodiversity in the number of existing microorganisms that naturally possess the enzymes necessary to convert cellulose to usable sugars, and many of these microorganisms can directly ferment sugars to EtOH or other solvents. Currently, the vast majority of cellulolytic organisms are poorly understood and have complex metabolic networks. In this review, we survey the current state of knowledge on different cellulases and metabolic capabilities found in various cellulolytic microorganisms. We also propose that the use of large-scale metabolic models (and associated analyses) is potentially an ideal means for improving our understanding of basic metabolic network function and directing metabolic engineering efforts for cellulolytic microorganisms.

Network thermodynamics in the post-genomic era

Network models have been used to study the underlying processes and principles of biological systems for decades, providing many insights into the complexity of life. Biological systems require a constant flow of free energy to drive these processes that operate away from thermodynamic equilibrium. With the advent of high-throughput omics technologies, more and more thermodynamic knowledge about the biological components, processes and their interactions are surfacing that we can integrate using large-scale biological network models. This allows us to ask many fundamental questions about these networks, such as, how far away from equilibrium must the reactions in a network be displaced in order to allow growth, or what are the possible thermodynamic objectives of the cell.