Resolving the TCA cycle and pentose-phosphate pathway of Clostridium acetobutylicum ATCC 824: Isotopomer analysis, in vitro activities and expression analysis

Hydrogen production pathways in Clostridia and their improvement by metabolic engineering

Biological production of hydrogen has a tremendous potential as an environmentally sustainable technology to generate a clean fuel. Among the different available methods to produce biohydrogen, dark fermentation features the highest productivity and can be used as a means to dispose of organic waste biomass. Within this approach, Clostridia have the highest theoretical H2 production yield. Nonetheless, most strains show actual yields far lower than the theoretical maximum: improving their efficiency becomes necessary for achieving cost-effective fermentation processes. This review aims at providing a survey of the metabolic network involved in H2 generation in Clostridia and strategies used to improve it through metabolic engineering. Together with current achievements, a number of future perspectives to implement these results will be illustrated.

Genomics and physiology of Catenibacillus, human gut bacteria capable of polyphenol C-deglycosylation and flavonoid degradation

The genus Catenibacillus (family Lachnospiraceae, phylum Bacillota) includes only one cultivated species so far, Catenibacillus scindens, isolated from human faeces and capable of deglycosylating dietary polyphenols and degrading flavonoid aglycones. Another human intestinal Catenibacillus strain not taxonomically resolved at that time was recently genome-sequenced. We analysed the genome of this novel isolate, designated Catenibacillus decagia, and showed its ability to deglycosylate C-coupled flavone and xanthone glucosides and O-coupled flavonoid glycosides. Most of the resulting aglycones were further degraded to the corresponding phenolic acids. Including the recently sequenced genome of C. scindens and ten faecal metagenome-assembled genomes assigned to the genus Catenibacillus, we performed a comparative genome analysis and searched for genes encoding potential C-glycosidases and other polyphenol-converting enzymes. According to genome data and physiological characterization, the core metabolism of Catenibacillus strains is based on a fermentative lifestyle with butyrate production and hydrogen evolution. Both C. scindens and C. decagia encode a flavonoid O-glycosidase, a flavone reductase, a flavanone/flavanonol-cleaving reductase and a phloretin hydrolase. Several gene clusters encode enzymes similar to those of the flavonoid C-deglycosylation system of Dorea strain PUE (DgpBC), while separately located genes encode putative polyphenol-glucoside oxidases (DgpA) required for C-deglycosylation. The diversity of dgpA and dgpBC gene clusters might explain the broad C-glycoside substrate spectrum of C. scindens and C. decagia. The other Catenibacillus genomes encode only a few potential flavonoid-converting enzymes. Our results indicate that several Catenibacillus species are well-equipped to deglycosylate and degrade dietary plant polyphenols and might inhabit a corresponding, specific niche in the gut.

The exceptional form and function of the giant bacterium Ca. Epulopiscium viviparus revolves around its sodium motive force

Epulopiscium spp. are the largest known heterotrophic bacteria; a large cigar-shaped individual is a million times the volume of Escherichia coli. To better understand the metabolic potential and relationship of Epulopiscium sp. type B with its host Naso tonganus, we generated a high-quality draft genome from a population of cells taken from a single fish. We propose the name Candidatus Epulopiscium viviparus to describe populations of this best-characterized Epulopiscium species. Metabolic reconstruction reveals more than 5% of the genome codes for carbohydrate active enzymes, which likely degrade recalcitrant host-diet algal polysaccharides into substrates that may be fermented to acetate, the most abundant short-chain fatty acid in the intestinal tract. Moreover, transcriptome analyses and the concentration of sodium ions in the host intestinal tract suggest that the use of a sodium motive force (SMF) to drive ATP synthesis and flagellar rotation is integral to symbiont metabolism and cellular biology. In natural populations, genes encoding both F-type and V-type ATPases and SMF generation via oxaloacetate decarboxylation are among the most highly expressed, suggesting that ATPases synthesize ATP and balance ion concentrations across the cell membrane. High expression of these and other integral membrane proteins may allow for the growth of its extensive intracellular membrane system. Further, complementary metabolism between microbe and host is implied with the potential provision of nitrogen and B vitamins to reinforce this nutritional symbiosis. The few features shared by all bacterial behemoths include extreme polyploidy, polyphosphate synthesis, and thus far, they have all resisted cultivation in the lab.

Isotopic Tracer for Absolute Quantification of Metabolites of the Pentose Phosphate Pathway in Bacteria

The pentose phosphate pathway (PPP) plays a key role in many metabolic functions, including the generation of NADPH, biosynthesis of nucleotides, and carbon homeostasis. In particular, the intermediates of PPP have been found to be significantly perturbed in bacterial metabolomic studies. Nonetheless, detailed analysis to gain mechanistic information of PPP metabolism remains limited as most studies are unable to report on the absolute levels of the metabolites. Absolute quantification of metabolites is a prerequisite to study the details of fluxes and its regulations. Isotope tracer or labeling studies are conducted in vivo and in vitro and have significantly improved the analysis and understanding of PPP. Due to the laborious procedure and limitations in the in vivo method, an in vitro approach known as Group Specific Internal Standard Technology (GSIST) has been successfully developed to measure the absolute levels of central carbon metabolism, including PPP. The technique adopts derivatization of an experimental sample and a corresponding internal standard with isotope-coded reagents to provide better precision for accurate identification and absolute quantification. In this review, we highlight bacterial studies that employed isotopic tracers as the tagging agents used for the absolute quantification analysis of PPP metabolites.

Co‑cultivation of anaerobic fungi with Clostridium acetobutylicum bolsters butyrate and butanol production from cellulose and lignocellulose

A system for co-cultivation of anaerobic fungi with anaerobic bacteria was established based on lactate cross-feeding to produce butyrate and butanol from plant biomass. Several co-culture formulations were assembled that consisted of anaerobic fungi (Anaeromyces robustus, Neocallimastix californiae, or Caecomyces churrovis) with the bacterium Clostridium acetobutylicum. Co-cultures were grown simultaneously (e.g., 'one pot'), and compared to cultures where bacteria were cultured in fungal hydrolysate sequentially. Fungal hydrolysis of lignocellulose resulted in 7-11 mM amounts of glucose and xylose, as well as acetate, formate, ethanol, and lactate to support clostridial growth. Under these conditions, one-stage simultaneous co-culture of anaerobic fungi with C. acetobutylicum promoted the production of butyrate up to 30 mM. Alternatively, two-stage growth slightly promoted solventogenesis and elevated butanol levels (∼4-9 mM). Transcriptional regulation in the two-stage growth condition indicated that this cultivation method may decrease the time required to reach solventogenesis and induce the expression of cellulose-degrading genes in C. acetobutylicum due to relieved carbon-catabolite repression. Overall, this study demonstrates a proof of concept for biobutanol and bio-butyrate production from lignocellulose using an anaerobic fungal-bacterial co-culture system.

Debottlenecking the biological hydrogen production pathway of dark fermentation: insight into the impact of strain improvement

Confronted with the exhaustion of the earth’s fossil fuel reservoirs, bio-based process to produce renewable energy is receiving significant interest. Hydrogen is considered as an attractive energy carrier that can replace fossil fuels in the future mainly due to its high energy content, recyclability and environment-friendly nature. Biological hydrogen production from renewable biomass or waste materials by dark fermentation is a promising alternative to conventional routes since it is energy-saving and reduces environmental pollution. However, the current yield and evolution rate of fermentative hydrogen production are still low. Strain improvement of the microorganisms employed for hydrogen production is required to make the process competitive with traditional production methods. The present review summarizes recent progresses on the screening for highly efficient hydrogen-producing strains using various strategies. As the metabolic pathways for fermentative hydrogen production have been largely resolved, it is now possible to engineer the hydrogen-producing strains by rational design. The hydrogen yields and production rates by different genetically modified microorganisms are discussed. The key limitations and challenges faced in present studies are also proposed. We hope that this review can provide useful information for scientists in the field of fermentative hydrogen production.

Hydrogen can be generated by microorganisms.

Dark fermentation is efficient for biological hydrogen production.

Strain improvement is critical to enhancing hydrogen-producing ability.

Molecular characterization of the missing electron pathways for butanol synthesis in Clostridium acetobutylicum

Clostridium acetobutylicum is a promising biocatalyst for the renewable production of n-butanol. Several metabolic strategies have already been developed to increase butanol yields, most often based on carbon pathway redirection. However, it has previously demonstrated that the activities of both ferredoxin-NADP⁺ reductase and ferredoxin-NAD⁺ reductase, whose encoding genes remain unknown, are necessary to produce the NADPH and the extra NADH needed for butanol synthesis under solventogenic conditions. Here, we purify, identify and partially characterize the proteins responsible for both activities and demonstrate the involvement of the identified enzymes in butanol synthesis through a reverse genetic approach. We further demonstrate the yield of butanol formation is limited by the level of expression of CA_C0764, the ferredoxin-NADP⁺ reductase encoding gene and the bcd operon, encoding a ferredoxin-NAD⁺ reductase. The integration of these enzymes into metabolic engineering strategies introduces opportunities for developing a homobutanologenic C. acetobutylicum strain.

Ferredoxin-NAD(P) + oxidoreductases are important enzymes for redox balancing in n-butanol production by Clostridium acetobutylicum, but the encoding genes remain unknown. Here, the authors identify the long sought-after genes and increase n-butanol production by optimizing the levels of the two enzymes.

A new strategy for dynamic metabolic flux estimation by integrating transient metabolome data into genome-scale metabolic models

Metabolic flux analysis (MFA) is a powerful tool for studying microbial cell physiology. Isotope-based MFA is the accepted standard for studying metabolic fluxes under steady-state conditions. However, its application under dynamic extracellular conditions is limited due to lack of proper techniques, such as rapid sampling and quenching, high cost and laborious execution. Here, we propose a new strategy to tackle this through incorporating dynamic metabolite abundance data into genome-scale metabolic models (GEM). First, a dummy extracellular pool concept is proposed for each dynamically changing metabolite, which represents a "sink" or "source", with corresponding dummy reactions coded into the GEM model. The dynamic model (expressed as differential equations) is then transformed into a quasi-steady-state model (expressed as linear equations), which can be easily solved by constraining the GEM model with the dynamic metabolite quantification data. For this, common linear-programming optimization algorithms were utilized to estimate the dynamic fluxes. Dynamic high-accuracy metabolite abundance data were obtained through the Isotope Dilution Mass Spectrometry (IDMS) method and high-speed sampling-quenching, and it was demonstrated that the newly proposed strategy could be successfully applied to obtain intracellular dynamic fluxes of Aspergillus niger under regimes of single and periodic extracellular glucose pulses. The applicability of the new method was also tested on dynamic fluxes estimation in a glucose pulse-response study of Saccharomyces cerevisiae. The proposed method provides a powerful tool to investigate cell physiology under dynamic conditions, especially relevant for bioprocess scale-up to industrial-scale bioreactors.

Quantitative Bioreactor Monitoring of Intracellular Bacterial Metabolites in Clostridium autoethanogenum Using Liquid Chromatography-Isotope Dilution Mass Spectrometry

We report a liquid chromatography-isotope dilution mass spectrometry method for the simultaneous quantification of 131 intracellular bacterial metabolites of Clostridium autoethanogenum. A comprehensive mixture of uniformly ¹³C-labeled internal standards (U-¹³C IS) was biosynthesized from the closely related bacterium Clostridium pasteurianum using 4% ¹³C-glucose as a carbon source. The U-¹³C IS mixture combined with ¹²C authentic standards was used to validate the linearity, precision, accuracy, repeatability, limits of detection, and quantification for each metabolite. A robust-fitting algorithm was employed to reduce the weight of the outliers on the quantification data. The metabolite calibration curves were linear with R ² ≥ 0.99, limits of detection were ≤1.0 μM, limits of quantification were ≤10 μM, and precision/accuracy was within RSDs of 15% for all metabolites. The method was subsequently applied for the daily monitoring of the intracellular metabolites of C. autoethanogenum during a CO gas fermentation over 40 days as part of a study to optimize biofuel production. The concentrations of the metabolites were estimated at steady states of different pH levels using the robust-fitting mathematical approach, and we demonstrate improved accuracy of results compared to conventional regression. Metabolic pathway analysis showed that reactions of the incomplete (branched) tricarboxylic acid "cycle" were the most affected pathways associated with the pH shift in the bioreactor fermentation of C. autoethanogenum and the concomitant changes in ethanol production.

From Escherichia coli mutant 13C labeling data to a core kinetic model: A kinetic model parameterization pipeline

Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available 13C-labeling distributions under multiple genetic and environmental perturbations. The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using 13C-Metabolic Flux Analysis (13C-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same (i.e., k-ecoli74) core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of 13C-MFA estimated values. By performing both tasks using the same network, errors associated with lack of congruity between the two networks are avoided, allowing for seamless integration of data with model building. Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities. Using this workflow, the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated.

High-resolution 13C metabolic flux analysis

Precise quantification of metabolic pathway fluxes in biological systems is of major importance in guiding efforts in metabolic engineering, biotechnology, microbiology, human health, and cell culture. ¹³C metabolic flux analysis (¹³C-MFA) is the predominant technique used for determining intracellular fluxes. Here, we present a protocol for ¹³C-MFA that incorporates recent advances in parallel labeling experiments, isotopic labeling measurements, and statistical analysis, as well as best practices developed through decades of experience. Experimental design to ensure that fluxes are estimated with the highest precision is an integral part of the protocol. The protocol is based on growing microbes in two (or more) parallel cultures with ¹³C-labeled glucose tracers, followed by gas chromatography-mass spectrometry (GC-MS) measurements of isotopic labeling of protein-bound amino acids, glycogen-bound glucose, and RNA-bound ribose. Fluxes are then estimated using software for ¹³C-MFA, such as Metran, followed by comprehensive statistical analysis to determine the goodness of fit and calculate confidence intervals of fluxes. The presented protocol can be completed in 4 d and quantifies metabolic fluxes with a standard deviation of ≤2%, a substantial improvement over previous implementations. The presented protocol is exemplified using an Escherichia coli ΔtpiA case study with full supporting data, providing a hands-on opportunity to step through a complex troubleshooting scenario. Although applications to prokaryotic microbial systems are emphasized, this protocol can be easily adjusted for application to eukaryotic organisms.

Omics-based analyses revealed metabolic responses of Clostridium acetobutylicum to lignocellulose-derived inhibitors furfural, formic acid and phenol stress for butanol fermentation

BACKGROUND

Clostridium acetobutylicum is a model fermentative anaerobe for consolidated bioprocessing of lignocellulose hydrolysates into acetone-butanol-ethanol (ABE). However, the main inhibitors (acids, furans and phenols) ubiquitous in lignocellulose hydrolysates strictly limit the conversion efficiency. Thus, it is essential to understand the underlying mechanisms of lignocellulose hydrolysate inhibitors to identify key industrial bottlenecks that undermine efficient biofuel production. The recently developed omics strategy for intracellular metabolites and protein quantification now allow for the in-depth mapping of strain metabolism and thereby enable the resolution of the underlying mechanisms.

RESULTS

The toxicity of the main inhibitors in lignocellulose hydrolysates against C. acetobutylicum and ABE production was systematically investigated, and the changes in intracellular metabolism were analyzed by metabolomics and proteomics. The toxicity of the main lignocellulose hydrolysate inhibitors at the same dose was ranked as follows: formic acid > phenol > furfural. Metabolomic analysis based on weighted gene coexpression network analysis (WGCNA) revealed that the three inhibitors triggered the stringent response of C. acetobutylicum. Proteomic analysis based on peptide mass spectrometry (MS) supported the above results and provided more comprehensive conclusions. Under the stress of three inhibitors, the metabolites and key enzymes/proteins involved in glycolysis, reductive tricarboxylic acid (TCA) cycle, acetone-butanol synthesis and redox metabolism were lower than those in the control group. Moreover, proteins involved in gluconeogenesis, the oxidative TCA cycle, thiol peroxidase (TPX) for oxidative stress were significantly upregulated, indicating that inhibitor stress induced the stress response and metabolic regulation. In addition, the three inhibitors also showed stress specificity related to fatty acid synthesis, ATP synthesis, nucleic acid metabolism, nicotinic acid metabolism, cell wall synthesis, spore synthesis and flagellum synthesis and so on.

CONCLUSIONS

Integrated omics platforms provide insight into the cellular responses of C. acetobutylicum to cytotoxic inhibitors released during the deconstruction of lignocellulose. This insight allows us to fully improve the strain to adapt to a challenging culture environment, which will prove critical to the industrial efficacy of C. acetobutylicum.

Metabolism the Difficile Way: The Key to the Success of the Pathogen Clostridioides difficile

Strains of Clostridioides difficile cause detrimental diarrheas with thousands of deaths worldwide. The infection process by the Gram-positive, strictly anaerobic gut bacterium is directly related to its unique metabolism, using multiple Stickland-type amino acid fermentation reactions coupled to Rnf complex-mediated sodium/proton gradient formation for ATP generation. Major pathways utilize phenylalanine, leucine, glycine and proline with the formation of 3-phenylproprionate, isocaproate, butyrate, 5-methylcaproate, valerate and 5-aminovalerate. In parallel a versatile sugar catabolism including pyruvate formate-lyase as a central enzyme and an incomplete tricarboxylic acid cycle to prevent unnecessary NADH formation completes the picture. However, a complex gene regulatory network that carefully mediates the continuous adaptation of this metabolism to changing environmental conditions is only partially elucidated. It involves the pleiotropic regulators CodY and SigH, the known carbon metabolism regulator CcpA, the proline regulator PrdR, the iron regulator Fur, the small regulatory RNA CsrA and potentially the NADH-responsive regulator Rex. Here, we describe the current knowledge of the metabolic principles of energy generation by C. difficile and the underlying gene regulatory scenarios.

A guide to 13C metabolic flux analysis for the cancer biologist

Cancer metabolism is significantly altered from normal cellular metabolism allowing cancer cells to adapt to changing microenvironments and maintain high rates of proliferation. In the past decade, stable-isotope tracing and network analysis have become powerful tools for uncovering metabolic pathways that are differentially activated in cancer cells. In particular, ¹³C metabolic flux analysis (¹³C-MFA) has emerged as the primary technique for quantifying intracellular fluxes in cancer cells. In this review, we provide a practical guide for investigators interested in getting started with ¹³C-MFA. We describe best practices in ¹³C-MFA, highlight potential pitfalls and alternative approaches, and conclude with new developments that can further enhance our understanding of cancer metabolism.

Tracing tagged molecules can help researchers understand the altered metabolism of cancer cells. The abilities of cancer cells to multiply rapidly and invade new tissues are supported by metabolic alterations, which can be investigated by feeding tagged molecules to cells and tracing how they are metabolized. These techniques, such as ¹³C metabolic flux analysis (¹³C-MFA), have been perceived as difficult to use, but recent advances are making them more accessible. Maciek Antoniewicz, University of Delaware, Newark, USA, has published a practical guide for researchers wanting to use 13C-MFA. The review includes best practices, pitfalls, alternative approaches, and new developments, especially new user-friendly software that allows researchers without extensive training in mathematics, statistics, or coding to perform 13C-MFA. Broadening access to tools for investigating altered metabolic pathways may spur development of new cancer therapies targeting these pathways.

Understanding regulation in substrate dependent modulation of growth and production of alcohols in Clostridium sporogenes NCIM 2918 through metabolic network reconstruction and flux balance analysis

Flux Balance Analysis was performed for Clostridium sporogenes NCIM 2918 grown on sole glucose and glycerol or glucose-glycerol combinations at varied concentrations. During acidogenesis, glucose and glucose-glycerol combinations favored improved growth and butyric acid production. Glycerol fermentation was however marked by reduced growth and predominant ethanol synthesis. Further, with increase of glycerol fraction in glucose-glycerol blend, flux towards ethanol synthesis linearly increased with simultaneous decrease in butanol flux. Elevated ATP demand due to improved growth was satisfied by upregulated carbon flux towards butyric acid synthesis during both glucose and dual substrate fermentations. Possible repression of pyruvate carboxylase by glycerol resulting in downturn of carbon uptake flux towards TCA cycle through anaplerotic reaction may be responsible for reduced growth in glycerol fermentation. Ammonium acetate mediated induction of acetic acid utilization, during acidogenesis, led to excess acetyl-CoA generation and its subsequent metabolism to lesser reduced products, butyric acid or ethanol.

Enhanced isopropanol-butanol-ethanol mixture production through manipulation of intracellular NAD(P)H level in the recombinant Clostridium acetobutylicum XY16

BACKGROUND

The formation of by-products, mainly acetone in acetone-butanol-ethanol (ABE) fermentation, significantly affects the solvent yield and downstream separation process. In this study, we genetically engineered Clostridium acetobutylicum XY16 isolated by our lab to eliminate acetone production and altered ABE to isopropanol-butanol-ethanol (IBE). Meanwhile, process optimization under pH control strategies and supplementation of calcium carbonate were adopted to investigate the interaction between the reducing force of the metabolic networks and IBE production.

RESULTS

After successful introduction of secondary alcohol dehydrogenase into C. acetobutylicum XY16, the recombinant XY16 harboring pSADH could completely eliminate acetone production and convert it into isopropanol, indicating great potential for large-scale production of IBE mixtures. Especially, pH could significantly improve final solvent titer through regulation of NADH and NADPH levels in vivo. Under the optimal pH level of 4.8, the total IBE production was significantly increased from 3.88 to 16.09 g/L with final 9.97, 4.98 and 1.14 g/L of butanol, isopropanol, and ethanol. Meanwhile, NADH and NADPH levels were maintained at optimal levels for IBE formation compared to the control one without pH adjustment. Furthermore, calcium carbonate could play dual roles as both buffering agency and activator for NAD kinase (NADK), and supplementation of 10 g/L calcium carbonate could finally improve the IBE production to 17.77 g/L with 10.51, 6.02, and 1.24 g/L of butanol, isopropanol, and ethanol.

CONCLUSION

The complete conversion of acetone into isopropanol in the recombinant C. acetobutylicum XY16 harboring pSADH could alter ABE to IBE. pH control strategies and supplementation of calcium carbonate were effective in obtaining high IBE titer with high isopropanol production. The analysis of redox cofactor perturbation indicates that the availability of NAD(P)H is the main driving force for the improvement of IBE production.

Enhancement of solvent production by overexpressing key genes of the acetone-butanol-ethanol fermentation pathway in Clostridium saccharoperbutylacetonicum N1-4

Clostridium saccharoperbutylacetonicum N1-4 is well known as a hyper-butanol producing strain. However, little information is available concerning its butanol production mechanism and the development of more robust strains. In this study, key biosynthetic genes (either endogenous or exogenous) including the sol operon (bld-ctfA-ctfB-adc), adhE1, adhE1^D485G, thl, thlA1^V5A, thlA^V5A and the expression cassette EC (thl-hbd-crt-bcd) were overexpressed in C. saccharoperbutylacetonicum N1-4 to evaluate their potential in enhancement of butanol production. The overexpression of sol operon increased ethanol production by 400%. The overexpression of adhE1 and adhE^D485G resulted in a 5.6- and 4.9-fold higher ethanol production, respectively, producing final acetone-butanol-ethanol (ABE) titers (30.6 and 30.1gL^-1) of among the highest as ever reported for solventogenic clostridia. The most significant increase of butanol production (by 13.7%) and selectivity (73.7%) was achieved by the overexpression of EC. These results provides a solid foundation and essential references for the further development of more robust strains.

Clostridioides difficile 630Δerm in silico and in vivo - quantitative growth and extensive polysaccharide secretion

Antibiotic-associated infections with Clostridioides difficile are a severe and often lethal risk for hospitalized patients, and can also affect populations without these classical risk factors. For a rational design of therapeutical concepts, a better knowledge of the metabolism of the pathogen is crucial. Metabolic modeling can provide a simulation of quantitative growth and usage of metabolic pathways, leading to a deeper understanding of the organism. Here, we present an elaborate genome-scale metabolic model of C. difficile 630Δerm. The model iHD992 includes experimentally determined product and substrate uptake rates and is able to simulate the energy metabolism and quantitative growth of C. difficile. Dynamic flux balance analysis was used for time-resolved simulations of the quantitative growth in two different media. The model predicts oxidative Stickland reactions and glucose degradation as main sources of energy, while the resulting reduction potential is mostly used for acetogenesis via the Wood-Ljungdahl pathway. Initial modeling experiments did not reproduce the observed growth behavior before the production of large quantities of a previously unknown polysaccharide was detected. Combined genome analysis and laboratory experiments indicated that the polysaccharide is an acetylated glucose polymer. Time-resolved simulations showed that polysaccharide secretion was coupled to growth even during unstable glucose uptake in minimal medium. This is accomplished by metabolic shifts between active glycolysis and gluconeogenesis which were also observed in laboratory experiments.

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.

Enhanced butanol fermentation using metabolically engineered Clostridium acetobutylicum with ex situ recovery of butanol

In this study, metabolic target reactions for strain engineering were searched via intracellular coenzyme A (CoA) metabolite analysis. The metabolic reactions catalyzed by thiolase (AtoB) and aldehyde-alcohol dehydrogenase (AdhE1) were considered potential rate-limiting steps. In addition, CoA transferase (CtfAB) was highlighted as being important for the assimilation of organic acids, in order to achieve high butanol production. Based on this quantitative analysis, the BEKW_E1AB-atoB strain was constructed by overexpressing the thl (atoB), adhE1, and ctfAB genes in Clostridium acetobutylicum strain BEKW, which has the phosphotransacetylase (pta) and butyrate kinase (buk) genes knocked out. After 100h of continuous fermentation coupled with adsorptive ex situ butanol recovery, the concentrations found after considering desorption, yield, and productivity for the BEKW_E1AB-atoB strain were 55.7g/L, 0.38g/g, and 2.64g/L/h, respectively. The level of butanol production achieved (2.64g/L/h) represents the highest reported value obtained after adsorptive, long-term fermentation.

Biomass, strain engineering, and fermentation processes for butanol production by solventogenic clostridia

Butanol is considered an attractive biofuel and a commercially important bulk chemical. However, economical production of butanol by solventogenic clostridia, e.g., via fermentative production of acetone-butanol-ethanol (ABE), is hampered by low fermentation performance, mainly as a result of toxicity of butanol to microorganisms and high substrate costs. Recently, sugars from marine macroalgae and syngas were recognized as potent carbon sources in biomass feedstocks that are abundant and do not compete for arable land with edible crops. With the aid of systems metabolic engineering, many researchers have developed clostridial strains with improved performance on fermentation of these substrates. Alternatively, fermentation strategies integrated with butanol recovery processes such as adsorption, gas stripping, liquid-liquid extraction, and pervaporation have been designed to increase the overall titer of butanol and volumetric productivity. Nevertheless, for economically feasible production of butanol, innovative strategies based on recent research should be implemented. This review describes and discusses recent advances in the development of biomass feedstocks, microbial strains, and fermentation processes for butanol production.

Modeling central metabolism and energy biosynthesis across microbial life

Background

Automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles.

Results

To overcome this challenge, we developed methods and tools (http://coremodels.mcs.anl.gov) to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of model organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80 %) of our models were found to have some type of aerobic ETC, whereas 5,100 (62 %) have an anaerobic ETC, and 1,279 (15 %) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70 %) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30 %) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis.

Conclusions

We predict accurate energy yields based on our improved annotations in energy biosynthesis pathways and the implementation of diverse ETC reactions across the microbial tree of life. We highlighted missing annotations that were essential to energy biosynthesis in our models. We examine the diversity of these pathways across all microbial life and enable the scientific community to explore the analyses generated from this large-scale analysis of over 8000 microbial genomes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2887-8) contains supplementary material, which is available to authorized users.

A quantitative metabolomics study of high sodium response in Clostridium acetobutylicum ATCC 824 acetone-butanol-ethanol (ABE) fermentation

Hemicellulose hydrolysates, sugar-rich feedstocks used in biobutanol refinery, are normally obtained by adding sodium hydroxide in the hydrolyze process. However, the resulting high sodium concentration in the hydrolysate inhibits ABE (acetone-butanol-ethanol) fermentation, and thus limits the use of these low-cost feedstocks. We have thus studied the effect of high sodium on the metabolic behavior of Clostridium acetobutyricum ATCC 824, with xylose as the carbon source. At a threshold sodium concentration of 200 mM, a decrease of the maximum cell dry weight (−19.50 ± 0.85%) and of ABE yield (−35.14 ± 3.50% acetone, −33.37 ± 0.74% butanol, −22.95 ± 1.81% ethanol) were observed compared to control culture. However, solvents specific productivities were not affected by supplementing sodium. The main effects of high sodium on cell metabolism were observed in acidogenesis, during which we observed the accumulation of ATP and NADH, and the inhibition of the pentose phosphate (PPP) and the glycolytic pathways with up to 80.73 ± 1.47% and 68.84 ± 3.42% decrease of the associated metabolic intermediates, respectively. However, the NADP⁺-to-NADPH ratio was constant for the whole culture duration, a phenomenon explaining the robustness of solvents specific productivities. Therefore, high sodium, which inhibited biomass growth through coordinated metabolic effects, interestingly triggered cell robustness on solvents specific productivity.

Deciphering Clostridium tyrobutyricum Metabolism Based on the Whole-Genome Sequence and Proteome Analyses

Clostridium tyrobutyricum is a Gram-positive anaerobic bacterium that efficiently produces butyric acid and is considered a promising host for anaerobic production of bulk chemicals. Due to limited knowledge on the genetic and metabolic characteristics of this strain, however, little progress has been made in metabolic engineering of this strain. Here we report the complete genome sequence of C. tyrobutyricum KCTC 5387 (ATCC 25755), which consists of a 3.07-Mbp chromosome and a 63-kbp plasmid. The results of genomic analyses suggested that C. tyrobutyricum produces butyrate from butyryl-coenzyme A (butyryl-CoA) through acetate reassimilation by CoA transferase, differently from Clostridium acetobutylicum, which uses the phosphotransbutyrylase-butyrate kinase pathway; this was validated by reverse transcription-PCR (RT-PCR) of related genes, protein expression levels, in vitro CoA transferase assay, and fed-batch fermentation. In addition, the changes in protein expression levels during the course of batch fermentations on glucose were examined by shotgun proteomics. Unlike C. acetobutylicum, the expression levels of proteins involved in glycolytic and fermentative pathways in C. tyrobutyricum did not decrease even at the stationary phase. Proteins related to energy conservation mechanisms, including Rnf complex, NfnAB, and pyruvate-phosphate dikinase that are absent in C. acetobutylicum, were identified. Such features explain why this organism can produce butyric acid to a much higher titer and better tolerate toxic metabolites. This study presenting the complete genome sequence, global protein expression profiles, and genome-based metabolic characteristics during the batch fermentation of C. tyrobutyricum will be valuable in designing strategies for metabolic engineering of this strain.

IMPORTANCE

Bio-based production of chemicals from renewable biomass has become increasingly important due to our concerns on climate change and other environmental problems. C. tyrobutyricum has been used for efficient butyric acid production. In order to further increase the performance and expand the capabilities of this strain toward production of other chemicals, metabolic engineering needs to be performed. For this, better understanding on the metabolic and physiological characteristics of this bacterium at the genome level is needed. This work reporting the results of complete genomic and proteomic analyses together with new insights on butyric acid biosynthetic pathway and energy conservation will allow development of strategies for metabolic engineering of C. tyrobutyricum for the bio-based production of various chemicals in addition to butyric acid.

Two pathways for glutamate biosynthesis in the syntrophic bacterium Syntrophus aciditrophicus

The anaerobic metabolism of crotonate, benzoate, and cyclohexane carboxylate by Syntrophus aciditrophicus grown syntrophically with Methanospirillum hungatei provides a model to study syntrophic cooperation. Recent studies revealed that S. aciditrophicus contains Re-citrate synthase but lacks the common Si-citrate synthase. To establish whether the Re-citrate synthase is involved in glutamate synthesis via the oxidative branch of the Krebs cycle, we have used [1-(13)C]acetate and [1-(14)C]acetate as well as [(13)C]bicarbonate as additional carbon sources during axenic growth of S. aciditrophicus on crotonate. Our analyses showed that labeled carbons were detected in at least 14 amino acids, indicating the global utilization of acetate and bicarbonate. The labeling patterns of alanine and aspartate verified that pyruvate and oxaloacetate were synthesized by consecutive carboxylations of acetyl coenzyme A (acetyl-CoA). The isotopomer profile and (13)C nuclear magnetic resonance (NMR) spectroscopy of the obtained [(13)C]glutamate, as well as decarboxylation of [(14)C]glutamate, revealed that this amino acid was synthesized by two pathways. Unexpectedly, only the minor route used Re-citrate synthase (30 to 40%), whereas the majority of glutamate was synthesized via the reductive carboxylation of succinate. This symmetrical intermediate could have been formed from two acetates via hydration of crotonyl-CoA to 4-hydroxybutyryl-CoA. 4-Hydroxybutyrate was detected in the medium of S. aciditrophicus when grown on crotonate, but an active hydratase could not be measured in cell extracts, and the annotated 4-hydroxybutyryl-CoA dehydratase (SYN_02445) lacks key amino acids needed to catalyze the hydration of crotonyl-CoA. Besides Clostridium kluyveri, this study reveals the second example of a microbial species to employ two pathways for glutamate synthesis.

13C metabolic flux analysis at a genome-scale

Metabolic models used in 13C metabolic flux analysis generally include a limited number of reactions primarily from central metabolism. They typically omit degradation pathways, complete cofactor balances, and atom transition contributions for reactions outside central metabolism. This study addresses the impact on prediction fidelity of scaling-up mapping models to a genome-scale. The core mapping model employed in this study accounts for (75 reactions and 65 metabolites) primarily from central metabolism. The genome-scale metabolic mapping model (GSMM) (697 reaction and 595 metabolites) is constructed using as a basis the iAF1260 model upon eliminating reactions guaranteed not to carry flux based on growth and fermentation data for a minimal glucose growth medium. Labeling data for 17 amino acid fragments obtained from cells fed with glucose labeled at the second carbon was used to obtain fluxes and ranges. Metabolic fluxes and confidence intervals are estimated, for both core and genome-scale mapping models, by minimizing the sum of square of differences between predicted and experimentally measured labeling patterns using the EMU decomposition algorithm. Overall, we find that both topology and estimated values of the metabolic fluxes remain largely consistent between core and GSM model. Stepping up to a genome-scale mapping model leads to wider flux inference ranges for 20 key reactions present in the core model. The glycolysis flux range doubles due to the possibility of active gluconeogenesis, the TCA flux range expanded by 80% due to the availability of a bypass through arginine consistent with labeling data, and the transhydrogenase reaction flux was essentially unresolved due to the presence of as many as five routes for the inter-conversion of NADPH to NADH afforded by the genome-scale model. By globally accounting for ATP demands in the GSMM model the unused ATP decreased drastically with the lower bound matching the maintenance ATP requirement. A non-zero flux for the arginine degradation pathway was identified to meet biomass precursor demands as detailed in the iAF1260 model. Inferred ranges for 81% of the reactions in the genome-scale metabolic (GSM) model varied less than one-tenth of the basis glucose uptake rate (95% confidence test). This is because as many as 411 reactions in the GSM are growth coupled meaning that the single measurement of biomass formation rate locks the reaction flux values. This implies that accurate biomass formation rate and composition are critical for resolving metabolic fluxes away from central metabolism and suggests the importance of biomass composition (re)assessment under different genetic and environmental backgrounds. In addition, the loss of information associated with mapping fluxes from MFA on a core model to a GSM model is quantified.

Parallel labeling experiments for pathway elucidation and (13)C metabolic flux analysis

Metabolic pathway models provide the foundation for quantitative studies of cellular physiology through the measurement of intracellular metabolic fluxes. For model organisms metabolic models are well established, with many manually curated genome-scale model reconstructions, gene knockout studies and stable-isotope tracing studies. However, for non-model organisms a similar level of knowledge is often lacking. Compartmentation of cellular metabolism in eukaryotic systems also presents significant challenges for quantitative (13)C-metabolic flux analysis ((13)C-MFA). Recently, innovative (13)C-MFA approaches have been developed based on parallel labeling experiments, the use of multiple isotopic tracers and integrated data analysis, that allow more rigorous validation of pathway models and improved quantification of metabolic fluxes. Applications of these approaches open new research directions in metabolic engineering, biotechnology and medicine.

A universal mariner transposon system for forward genetic studies in the genus Clostridium

DNA transposons represent an essential tool in the armoury of the molecular microbiologist. We previously developed a catP-based mini transposon system for Clostridium difficile in which the expression of the transposase gene was dependent on a sigma factor unique to C. difficile, TcdR. Here we have shown that the host range of the transposon is easily extended through the rapid chromosomal insertion of the tcdR gene at the pyrE locus of the intended clostridial target using Allele-Coupled Exchange (ACE). To increase the effectiveness of the system, a novel replicon conditional for plasmid maintenance was developed, which no longer supports the effective retention of the transposon delivery vehicle in the presence of the inducer isopropyl β-D-1-thiogalactopyranoside (IPTG). As a consequence, those thiamphenicol resistant colonies that arise in clostridial recipients, following plating on agar medium supplemented with IPTG, are almost exclusively due to insertion of the mini transposon into the genome. The system has been exemplified in both Clostridium acetobutylicum and Clostridium sporogenes, where transposon insertion has been shown to be entirely random. Moreover, appropriate screening of both libraries resulted in the isolation of auxotrophic mutants as well as cells deficient in spore formation/germination. This strategy is capable of being implemented in any Clostridium species.

Methods and advances in metabolic flux analysis: a mini-review

Metabolic flux analysis (MFA) is one of the pillars of metabolic engineering. Over the past three decades, it has been widely used to quantify intracellular metabolic fluxes in both native (wild type) and engineered biological systems. Through MFA, changes in metabolic pathway fluxes are quantified that result from genetic and/or environmental interventions. This information, in turn, provides insights into the regulation of metabolic pathways and may suggest new targets for further metabolic engineering of the strains. In this mini-review, we discuss and classify the various methods of MFA that have been developed, which include stoichiometric MFA, (13)C metabolic flux analysis, isotopic non-stationary (13)C metabolic flux analysis, dynamic metabolic flux analysis, and (13)C dynamic metabolic flux analysis. For each method, we discuss key advantages and limitations and conclude by highlighting important recent advances in flux analysis approaches.

OpenFLUX2: (13)C-MFA modeling software package adjusted for the comprehensive analysis of single and parallel labeling experiments

BACKGROUND

Steady-state (13)C-based metabolic flux analysis ((13)C-MFA) is the most powerful method available for the quantification of intracellular fluxes. These analyses include concertedly linked experimental and computational stages: (i) assuming the metabolic model and optimizing the experimental design; (ii) feeding the investigated organism using a chosen (13)C-labeled substrate (tracer); (iii) measuring the extracellular effluxes and detecting the (13)C-patterns of intracellular metabolites; and (iv) computing flux parameters that minimize the differences between observed and simulated measurements, followed by evaluating flux statistics. In its early stages, (13)C-MFA was performed on the basis of data obtained in a single labeling experiment (SLE) followed by exploiting the developed high-performance computational software. Recently, the advantages of parallel labeling experiments (PLEs), where several LEs are conducted under the conditions differing only by the tracer(s) choice, were demonstrated, particularly with regard to improving flux precision due to the synergy of complementary information. The availability of an open-source software adjusted for PLE-based (13)C-MFA is an important factor for PLE implementation.

RESULTS

The open-source software OpenFLUX, initially developed for the analysis of SLEs, was extended for the computation of PLE data. Using the OpenFLUX2, in silico simulation confirmed that flux precision is improved when (13)C-MFA is implemented by fitting PLE data to the common model compared with SLE-based analysis. Efficient flux resolution could be achieved in the PLE-mediated analysis when the choice of tracer was based on an experimental design computed to minimize the flux variances from different parts of the metabolic network. The analysis provided by OpenFLUX2 mainly includes (i) the optimization of the experimental design, (ii) the computation of the flux parameters from LEs data, (iii) goodness-of-fit testing of the model's adequacy, (iv) drawing conclusions concerning the identifiability of fluxes and construction of a contribution matrix reflecting the relative contribution of the measurement variances to the flux variances, and (v) precise determination of flux confidence intervals using a fine-tunable and convergence-controlled Monte Carlo-based method.

CONCLUSIONS

The developed open-source OpenFLUX2 provides a friendly software environment that facilitates beginners and existing OpenFLUX users to implement LEs for steady-state (13)C-MFA including experimental design, quantitative evaluation of flux parameters and statistics.

CeCaFDB: a curated database for the documentation, visualization and comparative analysis of central carbon metabolic flux distributions explored by 13C-fluxomics

The Central Carbon Metabolic Flux Database (CeCaFDB, available at http://www.cecafdb.org) is a manually curated, multipurpose and open-access database for the documentation, visualization and comparative analysis of the quantitative flux results of central carbon metabolism among microbes and animal cells. It encompasses records for more than 500 flux distributions among 36 organisms and includes information regarding the genotype, culture medium, growth conditions and other specific information gathered from hundreds of journal articles. In addition to its comprehensive literature-derived data, the CeCaFDB supports a common text search function among the data and interactive visualization of the curated flux distributions with compartmentation information based on the Cytoscape Web API, which facilitates data interpretation. The CeCaFDB offers four modules to calculate a similarity score or to perform an alignment between the flux distributions. One of the modules was built using an inter programming algorithm for flux distribution alignment that was specifically designed for this study. Based on these modules, the CeCaFDB also supports an extensive flux distribution comparison function among the curated data. The CeCaFDB is strenuously designed to address the broad demands of biochemists, metabolic engineers, systems biologists and members of the -omics community.

Parallel labeling experiments validate Clostridium acetobutylicum metabolic network model for (13)C metabolic flux analysis

In this work, we provide new insights into the metabolism of Clostridium acetobutylicum ATCC 824 obtained using a systematic approach for quantifying fluxes based on parallel labeling experiments and (13)C-metabolic flux analysis ((13)C-MFA). Here, cells were grown in parallel cultures with [1-(13)C]glucose and [U-(13)C]glucose as tracers and (13)C-MFA was used to quantify intracellular metabolic fluxes. Several metabolic network models were compared: an initial model based on current knowledge, and extended network models that included additional reactions that improved the fits of experimental data. While the initial network model did not produce a statistically acceptable fit of (13)C-labeling data, an extended network model with five additional reactions was able to fit all data with 292 redundant measurements. The model was subsequently trimmed to produce a minimal network model of C. acetobutylicum for (13)C-MFA, which could still reproduce all of the experimental data. The flux results provided valuable new insights into the metabolism of C. acetobutylicum. First, we found that TCA cycle was effectively incomplete, as there was no measurable flux between α-ketoglutarate and succinyl-CoA, succinate and fumarate, and malate and oxaloacetate. Second, an active pathway was identified from pyruvate to fumarate via aspartate. Third, we found that isoleucine was produced exclusively through the citramalate synthase pathway in C. acetobutylicum and that CAC3174 was likely responsible for citramalate synthase activity. These model predictions were confirmed in several follow-up tracer experiments. The validated metabolic network model established in this study can be used in future investigations for unbiased (13)C-flux measurements in C. acetobutylicum.

Proteomic analyses of the phase transition from acidogenesis to solventogenesis using solventogenic and non-solventogenic Clostridium acetobutylicum strains

The fermentation carried out by the solvent-producing bacterium, Clostridium acetobutylicum, is characterized by two distinct phases: acidogenic and solventogenic phases. Understanding the cellular physiological changes occurring during the phase transition in clostridial fermentation is important for the enhanced production of solvents. To identify protein changes upon entry to stationary phase where solvents are typically produced, we herein analyzed the proteomic profiles of the parental wild type C. acetobutylicum strains, ATCC 824, the non-solventogenic strain, M5 that has lost the solventogenic megaplasmid pSOL1, and the synthetic simplified alcohol forming strain, M5 (pIMP1E1AB) expressing plasmid-based CoA-transferase (CtfAB) and aldehyde/alcohol dehydrogenase (AdhE1). A total of 68 protein spots, corresponding to 56 unique proteins, were unambiguously identified as being differentially present after the phase transitions in the three C. acetobutylicum strains. In addition to changes in proteins known to be involved in solventogenesis (AdhE1 and CtfB), we identified significant alterations in enzymes involved in sugar transport and metabolism, fermentative pathway, heat shock proteins, translation, and amino acid biosynthesis upon entry into the stationary phase. Of these, four increased proteins (AdhE1, CAC0233, CtfB and phosphocarrier protein HPr) and six decreased proteins (butyrate kinase, ferredoxin:pyruvate oxidoreductase, phenylalanyl-tRNA synthetase, adenylosuccinate synthase, pyruvate kinase and valyl-tRNA synthetase) showed similar patterns in the two strains capable of butanol formation. Interestingly, significant changes of several proteins by post-translational modifications were observed in the solventogenic phase. The proteomic data from this study will improve our understanding on how cell physiology is affected through protein levels patterns in clostridia.

Capturing the response of Clostridium acetobutylicum to chemical stressors using a regulated genome-scale metabolic model

BACKGROUND

Clostridia are anaerobic Gram-positive Firmicutes containing broad and flexible systems for substrate utilization, which have been used successfully to produce a range of industrial compounds. In particular, Clostridium acetobutylicum has been used to produce butanol on an industrial scale through acetone-butanol-ethanol (ABE) fermentation. A genome-scale metabolic (GSM) model is a powerful tool for understanding the metabolic capacities of an organism and developing metabolic engineering strategies for strain development. The integration of stress-related specific transcriptomics information with the GSM model provides opportunities for elucidating the focal points of regulation.

RESULTS

We describe here the construction and validation of a GSM model for C. acetobutylicum ATCC 824, iCac802. iCac802 spans 802 genes and includes 1,137 metabolites and 1,462 reactions, along with gene-protein-reaction associations. Both (13)C-MFA and gene deletion data in the ABE fermentation pathway were used to test the predicted flux ranges allowed by the model. We also describe the CoreReg method, introduced in this paper, to integrate transcriptomic data and identify core sets of reactions that, when their flux was selectively restricted, reproduced flux and biomass-formation ranges seen under all regulatory constraints. CoreReg was used in response to butanol and butyrate stress to tighten bounds for 50 reactions within the iCac802 model. These bounds affected the flux of tens of reactions in core metabolism. The model, incorporating the regulatory restrictions from CoreReg under chemical stress, exhibited an approximate 70% reduction in biomass yield for most stress conditions.

CONCLUSIONS

The regulation placed on the model for the two stresses using CoreReg identified differences in the respective responses, including distinct core sets and the restriction of biomass production similar to experimental observations. Given the core sets predicted by the CoreReg method, remedial actions can be taken to counteract the effect of stress on metabolism. For less well-known systems, plausible regulatory loops can be suggested around the affected metabolic reactions, and the hypotheses can be tested experimentally.

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.

Experimental evidence and isotopomer analysis of mixotrophic glucose metabolism in the marine diatom Phaeodactylum tricornutum

BACKGROUND

Heterotrophic fermentation using simple sugars such as glucose is an established and cost-effective method for synthesizing bioproducts from bacteria, yeast and algae. Organisms incapable of metabolizing glucose have limited applications as cell factories, often despite many other advantageous characteristics. Therefore, there is a clear need to investigate glucose metabolism in potential cell factories. One such organism, with a unique metabolic network and a propensity to synthesize highly reduced compounds as a large fraction of its biomass, is the marine diatom Phaeodactylum tricornutum (Pt). Although Pt has been engineered to metabolize glucose, conflicting lines of evidence leave it unresolved whether Pt can natively consume glucose.

RESULTS

Isotope labeling experiments in which Pt was mixotrophically grown under light on 100% U-(13)C glucose and naturally abundant (~99% (12)C) dissolved inorganic carbon resulted in proteinogenic amino acids with an average 13C-enrichment of 88%, thus providing convincing evidence of glucose uptake and metabolism. The dissolved inorganic carbon was largely incorporated through anaplerotic rather than photosynthetic fixation. Furthermore, an isotope labeling experiment utilizing 1-(13)C glucose and subsequent metabolic pathway analysis indicated that (i) the alternative Entner-Doudoroff and Phosphoketolase glycolytic pathways are active during glucose metabolism, and (ii) during mixotrophic growth, serine and glycine are largely synthesized from glyoxylate through photorespiratory reactions rather than from 3-phosphoglycerate. We validated the latter result for mixotrophic growth on glycerol by performing a 2-(13)C glycerol isotope labeling experiment. Additionally, gene expression assays showed that known, native glucose transporters in Pt are largely insensitive to glucose or light, whereas the gene encoding cytosolic fructose bisphosphate aldolase 3, an important glycolytic enzyme, is overexpressed in light but insensitive to glucose.

CONCLUSION

We have shown that Pt can use glucose as a primary carbon source when grown in light, but cannot use glucose to sustain growth in the dark. We further analyzed the metabolic mechanisms underlying the mixotrophic metabolism of glucose and found isotopic evidence for unusual pathways active in Pt. These insights expand the envelope of Pt cultivation methods using organic substrates. We anticipate that they will guide further engineering of Pt towards sustainable production of fuels, pharmaceuticals, and platform chemicals.

Publishing 13C metabolic flux analysis studies: a review and future perspectives

(13)C-Metabolic flux analysis ((13)C-MFA) is a powerful model-based analysis technique for determining intracellular metabolic fluxes in living cells. It has become a standard tool in many labs for quantifying cell physiology, e.g., in metabolic engineering, systems biology, biotechnology, and biomedical research. With the increasing number of (13)C-MFA studies published each year, it is now ever more important to provide practical guidelines for performing and publishing (13)C-MFA studies so that quality is not sacrificed as the number of publications increases. The main purpose of this paper is to provide an overview of good practices in (13)C-MFA, which can eventually be used as minimum data standards for publishing (13)C-MFA studies. The motivation for this work is two-fold: (1) currently, there is no general consensus among researchers and journal editors as to what minimum data standards should be required for publishing (13)C-MFA studies; as a result, there are great discrepancies in terms of quality and consistency; and (2) there is a growing number of studies that cannot be reproduced or verified independently due to incomplete information provided in these publications. This creates confusion, e.g. when trying to reconcile conflicting results, and hinders progress in the field. Here, we review current status in the (13)C-MFA field and highlight some of the shortcomings with regards to (13)C-MFA publications. We then propose a checklist that encompasses good practices in (13)C-MFA. We hope that these guidelines will be a valuable resource for the community and allow (13)C-flux studies to be more easily reproduced and accessed by others in the future.

Metabolic engineering of Clostridium acetobutylicum for the enhanced production of isopropanol-butanol-ethanol fuel mixture

Butanol is considered as a superior biofuel, which is conventionally produced by clostridial acetone-butanol-ethanol (ABE) fermentation. Among ABE, only butanol and ethanol can be used as fuel alternatives. Coproduction of acetone thus causes lower yield of fuel alcohols. Thus, this study aimed at developing an improved Clostridium acetobutylicum strain possessing enhanced fuel alcohol production capability. For this, we previously developed a hyper ABE producing BKM19 strain was further engineered to convert acetone into isopropanol. The BKM19 strain was transformed with the plasmid pIPA100 containing the sadh (primary/secondary alcohol dehydrogenase) and hydG (putative electron transfer protein) genes from the Clostridium beijerinckii NRRL B593 cloned under the control of the thiolase promoter. The resulting BKM19 (pIPA100) strain produced 27.9 g/l isopropanol-butanol-ethanol (IBE) as a fuel alcohols with negligible amount of acetone (0.4 g/l) from 97.8 g/l glucose in lab-scale (2 l) batch fermentation. Thus, this metabolically engineered strain was able to produce 99% of total solvent produced as fuel alcohols. The scalability and stability of BKM19 (pIPA100) were evaluated at 200 l pilot-scale fermentation, which showed that the fuel alcohol yield could be improved to 0.37 g/g as compared to 0.29 g/g obtained at lab-scale fermentation, while attaining a similar titer. To the best of our knowledge, this is the highest titer of IBE achieved and the first report on the large scale fermentation of C. acetobutylicum for IBE production.

Metabolic engineering with plants for a sustainable biobased economy

Plants are bona fide sustainable organisms because they accumulate carbon and synthesize beneficial metabolites from photosynthesis. To meet the challenges to food security and health threatened by increasing population growth and depletion of nonrenewable natural resources, recent metabolic engineering efforts have shifted from single pathways to holistic approaches with multiple genes owing to integration of omics technologies. Successful engineering of plants results in the high yield of biomass components for primary food sources and biofuel feedstocks, pharmaceuticals, and platform chemicals through synthetic biology and systems biology strategies. Further discovery of undefined biosynthesis pathways in plants, integrative analysis of discrete omics data, and diversified process developments for production of platform chemicals are essential to overcome the hurdles for sustainable production of value-added biomolecules from plants.

From the first drop to the first truckload: commercialization of microbial processes for renewable chemicals

Fermentation of carbohydrate substrates by microorganisms represents an attractive route for the manufacture of industrial chemicals from renewable resources. The technology to manipulate metabolism of bacteria and yeast, including the introduction of heterologous chemical pathways, has accelerated research in this field. However, the public literature contains very few examples of strains achieving the production metrics required for commercialization. This article presents the challenges in reaching commercial titer, yield, and productivity targets, along with other necessary strain and process characteristics. It then reviews various methods in systems biology, synthetic biology, enzyme engineering, and fermentation engineering which can be applied to strain improvement, and presents a strategy for using these tools to overcome the major hurdles on the path to commercialization.