Genome-scale model for Clostridium acetobutylicum: Part I. Metabolic network resolution and analysis.
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SUBMITTER: Senger RS
PROVIDER: S-EPMC2760220 | biostudies-literature | 2008 Dec
REPOSITORIES: biostudies-literature
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Genome-scale model for Clostridium acetobutylicum: Part I. Metabolic network resolution and analysis.
Senger Ryan S RS Papoutsakis Eleftherios T ET
Biotechnology and bioengineering 20081201 5
A genome-scale metabolic network reconstruction for Clostridium acetobutylicum (ATCC 824) was carried out using a new semi-automated reverse engineering algorithm. The network consists of 422 intracellular metabolites involved in 552 reactions and includes 80 membrane transport reactions. The metabolic network illustrates the reliance of clostridia on the urea cycle, intracellular L-glutamate solute pools, and the acetylornithine transaminase for amino acid biosynthesis from the 2-oxoglutarate p ...[more]
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Project description:A regulated genome-scale model for Clostridium acetobutylicum ATCC 824 was developed based on its metabolic network reconstruction. To aid model convergence and limit the number of flux-vector possible solutions (the size of the phenotypic solution space), modeling strategies were developed to impose a new type of constraint at the endo-exo-metabolome interface. This constraint is termed the specific proton flux state, and its use enabled accurate prediction of the extracellular medium pH during vegetative growth of batch cultures. The specific proton flux refers to the influx or efflux of free protons (per unit biomass) across the cell membrane. A specific proton flux state encompasses a defined range of specific proton fluxes and includes all metabolic flux distributions resulting in a specific proton flux within this range. Effective simulation of time-course batch fermentation required the use of independent flux balance solutions from an optimum set of specific proton flux states. Using a real-coded genetic algorithm to optimize temporal bounds of specific proton flux states, we show that six separate specific proton flux states are required to model vegetative-growth metabolism and accurately predict the extracellular medium pH. Further, we define the apparent proton flux stoichiometry per weak acids efflux and show that this value decreases from approximately 3.5 mol of protons secreted per mole of weak acids at the start of the culture to approximately 0 at the end of vegetative growth. Calculations revealed that when specific weak acids production is maximized in vegetative growth, the net proton exchange between the cell and environment occurs primarily through weak acids efflux (apparent proton flux stoichiometry is 1). However, proton efflux through cation channels during the early stages of acidogenesis was found to be significant. We have also developed the concept of numerically determined sub-systems of genome-scale metabolic networks here as a sub-network with a one-dimensional null space basis set. A numerically determined sub-system was constructed in the genome-scale metabolic network to study the flux magnitudes and directions of acetylornithine transaminase, alanine racemase, and D-alanine transaminase. These results were then used to establish additional constraints for the genome-scale model.
| S-EPMC2745297 | biostudies-literature
Project description:BackgroundSolventogenic 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.ResultsWe 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.ConclusionsMicrobial 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.
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Project description:Caenorhabditis elegans is a powerful model to study metabolism and how it relates to nutrition, gene expression, and life history traits. However, while numerous experimental techniques that enable perturbation of its diet and gene function are available, a high-quality metabolic network model has been lacking. Here, we reconstruct an initial version of the C. elegans metabolic network. This network model contains 1,273 genes, 623 enzymes, and 1,985 metabolic reactions and is referred to as iCEL1273. Using flux balance analysis, we show that iCEL1273 is capable of representing the conversion of bacterial biomass into C. elegans biomass during growth and enables the predictions of gene essentiality and other phenotypes. In addition, we demonstrate that gene expression data can be integrated with the model by comparing metabolic rewiring in dauer animals versus growing larvae. iCEL1273 is available at a dedicated website (wormflux.umassmed.edu) and will enable the unraveling of the mechanisms by which different macro- and micronutrients contribute to the animal's physiology.
| S-EPMC5387690 | biostudies-literature
Project description:BACKGROUND: Microorganisms possess diverse metabolic capabilities that can potentially be leveraged for efficient production of biofuels. Clostridium thermocellum (ATCC 27405) is a thermophilic anaerobe that is both cellulolytic and ethanologenic, meaning that it can directly use the plant sugar, cellulose, and biochemically convert it to ethanol. A major challenge in using microorganisms for chemical production is the need to modify the organism to increase production efficiency. The process of properly engineering an organism is typically arduous. RESULTS: Here we present a genome-scale model of C. thermocellum metabolism, iSR432, for the purpose of establishing a computational tool to study the metabolic network of C. thermocellum and facilitate efforts to engineer C. thermocellum for biofuel production. The model consists of 577 reactions involving 525 intracellular metabolites, 432 genes, and a proteomic-based representation of a cellulosome. The process of constructing this metabolic model led to suggested annotation refinements for 27 genes and identification of areas of metabolism requiring further study. The accuracy of the iSR432 model was tested using experimental growth and by-product secretion data for growth on cellobiose and fructose. Analysis using this model captures the relationship between the reduction-oxidation state of the cell and ethanol secretion and allowed for prediction of gene deletions and environmental conditions that would increase ethanol production. CONCLUSIONS: By incorporating genomic sequence data, network topology, and experimental measurements of enzyme activities and metabolite fluxes, we have generated a model that is reasonably accurate at predicting the cellular phenotype of C. thermocellum and establish a strong foundation for rational strain design. In addition, we are able to draw some important conclusions regarding the underlying metabolic mechanisms for observed behaviors of C. thermocellum and highlight remaining gaps in the existing genome annotations.
| S-EPMC2852388 | biostudies-literature
Project description:Prokaryotic genes are frequently organized in multicistronic operons (or transcriptional units, TUs), and usually the regulatory motifs for the whole TU are located upstream of the first TU gene. Although the number of sequenced genomes has increased dramatically, experimental information on TU organization is extremely limited. Even for organisms as extensively studied as Escherichia coli and Bacillus subtilis, TU annotation is far from complete. It therefore becomes imperative to rely on computational approaches to complement experimental information. Here we present a TU map for the obligate anaerobe Clostridium acetobutylicum ATCC 824. This map is largely based on the distance between pairs of consecutive genes but enhanced and refined by predictions of several types of promoters (sigmaA, sigmaE and sigmaF/G) and rho-independent terminator structures. Based on the set of known C.acetobutylicum TUs, the presented TU map offers an 88% prediction accuracy.
| S-EPMC390361 | biostudies-literature
Project description:BackgroundGenome-scale metabolic network models (GEMs) provide an efficient platform for the comprehensive analysis the physical and biochemical functions of organisms due to their systematic perspective on the study of metabolic processes. Eriocheir sinensis is an important economic species cultivated on a large scale because it is delicious and nutritious and has a high economic value. Feed improvement is one of the important methods to improve the yield of E. sinensis and control water pollution caused by the inadequate absorption of feed.ResultsIn this study, a GEM of E. sinensis, icrab4665, was reconstructed based on the transcriptome sequencing, combined with KEGG database, literature and experimental data. The icrab4665 comprised 4665 unigenes, 2060 reactions and 1891 metabolites, which were distributed in 12 metabolic subsystems and 113 metabolic pathways. The model was used to predict the optimal nutrient requirements of E. sinensis in feed, and suggestions for feed improvement were put forward based on the simulation results. The simulation results showed that arginine, methionine, isoleucine and phenylalanine had more active metabolism in E. sinensis. It was suggested that the amount of these essential amino acids should be proportionally higher than that of other amino acids in the feed to ensure the amino acid metabolism of E. sinensis. On the basis of the simulation results, we further suggested increasing the amount of linoleic acid, EPA and DHA in the feed to ensure the intake of essential fatty acids for the growth of E. sinensis and promote the accumulation of cell substances. In addition, the amounts of zinc and selenium in the feed were also suggested to be properly increased to ensure the basic metabolism and growth demand of E. sinensis.ConclusionThe largest GEM of E. sinensis was reconstructed and suggestions were provide for the improvement of feed contents based on the model simulation. This study promoted the exploration of feed optimization for aquatic crustaceans from in vivo and in silico. The results provided guidance for improving the feed proportion for E. sinensis, which is of great significance to improve its yield and economic value.
| S-EPMC9238104 | biostudies-literature
Project description:The genome sequence of the solvent-producing bacterium Clostridium acetobutylicum ATCC 824 has been determined by the shotgun approach. The genome consists of a 3.94-Mb chromosome and a 192-kb megaplasmid that contains the majority of genes responsible for solvent production. Comparison of C. acetobutylicum to Bacillus subtilis reveals significant local conservation of gene order, which has not been seen in comparisons of other genomes with similar, or, in some cases closer, phylogenetic proximity. This conservation allows the prediction of many previously undetected operons in both bacteria. However, the C. acetobutylicum genome also contains a significant number of predicted operons that are shared with distantly related bacteria and archaea but not with B. subtilis. Phylogenetic analysis is compatible with the dissemination of such operons by horizontal transfer. The enzymes of the solventogenesis pathway and of the cellulosome of C. acetobutylicum comprise a new set of metabolic capacities not previously represented in the collection of complete genomes. These enzymes show a complex pattern of evolutionary affinities, emphasizing the role of lateral gene exchange in the evolution of the unique metabolic profile of the bacterium. Many of the sporulation genes identified in B. subtilis are missing in C. acetobutylicum, which suggests major differences in the sporulation process. Thus, comparative analysis reveals both significant conservation of the genome organization and pronounced differences in many systems that reflect unique adaptive strategies of the two gram-positive bacteria.
| S-EPMC99537 | biostudies-literature
Project description:BackgroundLignocellulosic biomass is a promising resource of renewable biochemicals and biofuels. However, the presence of inhibitors existing in lignocellulosic hydrolysates (LCH) is a great challenge to acetone-butanol-ethanol (ABE) fermentation by Clostridium acetobutylicum. In particular, phenolic compounds (PCs) from LCH severely block ABE production even at low concentrations. Thus, it is urgent to gain insight into the intracellular metabolic disturbances caused by phenolic inhibitors and elucidate the underlying mechanisms to identify key industrial bottlenecks that undermine efficient ABE production.ResultsIn this study, a time-course of ABE fermentation by C. acetobutylicum in the presence of four typical PCs (syringaldehyde, vanillin, ferulic acid, and p-coumaric acid) was characterized, respectively. Addition of PCs caused different irreversible effects on ABE production. Specifically, syringaldehyde showed the greatest inhibition to butanol production, followed by vanillin, ferulic acid, and p-coumaric acid. Subsequently, a weighted gene co-expression network analysis (WGCNA) based on RNA-sequencing data was applied to identify metabolic perturbations caused by four LCH-derived PCs, and extract the gene modules associated with extracellular fermentation traits. The hub genes in each module were subjected to protein-protein interaction analysis and enrichment analysis. The results showed that functional modules were PC-dependent and shared some unique features. Specifically, p-coumaric acid caused the most extensive transcriptomic disturbances, particularly affecting the gene expressions of ribosome proteins and the assembly of flagella, DNA replication, repair, and recombination; the addition of syringaldehyde caused significant metabolic disturbances on the gene expressions of ribosome proteins, starch and sucrose metabolism; vanillin mainly disturbed purine metabolism, sporulation and signal transduction; and ferulic acid caused a metabolic disturbance on glycosyl transferase-related gene expressions.ConclusionThis study uncovers novel insights into the inhibitory mechanisms of PCs for the first time and provides guidance for future metabolic engineering efforts, which establishes a powerful foundation for the development of phenol-tolerant strains of C. acetobutylicum for economically sustainable ABE production with high productivity from lignocellulosic biomass.
| S-EPMC7520030 | biostudies-literature
Project description:UnlabelledEngineering 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.ImportanceCurrently, 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.
| S-EPMC4669385 | biostudies-literature
Project description:BACKGROUND: Fermentation of xylose, the major component in hemicellulose, is essential for economic conversion of lignocellulosic biomass to fuels and chemicals. The yeast Scheffersomyces stipitis (formerly known as Pichia stipitis) has the highest known native capacity for xylose fermentation and possesses several genes for lignocellulose bioconversion in its genome. Understanding the metabolism of this yeast at a global scale, by reconstructing the genome scale metabolic model, is essential for manipulating its metabolic capabilities and for successful transfer of its capabilities to other industrial microbes. RESULTS: We present a genome-scale metabolic model for Scheffersomyces stipitis, a native xylose utilizing yeast. The model was reconstructed based on genome sequence annotation, detailed experimental investigation and known yeast physiology. Macromolecular composition of Scheffersomyces stipitis biomass was estimated experimentally and its ability to grow on different carbon, nitrogen, sulphur and phosphorus sources was determined by phenotype microarrays. The compartmentalized model, developed based on an iterative procedure, accounted for 814 genes, 1371 reactions, and 971 metabolites. In silico computed growth rates were compared with high-throughput phenotyping data and the model could predict the qualitative outcomes in 74% of substrates investigated. Model simulations were used to identify the biosynthetic requirements for anaerobic growth of Scheffersomyces stipitis on glucose and the results were validated with published literature. The bottlenecks in Scheffersomyces stipitis metabolic network for xylose uptake and nucleotide cofactor recycling were identified by in silico flux variability analysis. The scope of the model in enhancing the mechanistic understanding of microbial metabolism is demonstrated by identifying a mechanism for mitochondrial respiration and oxidative phosphorylation. CONCLUSION: The genome-scale metabolic model developed for Scheffersomyces stipitis successfully predicted substrate utilization and anaerobic growth requirements. Useful insights were drawn on xylose metabolism, cofactor recycling and mechanism of mitochondrial respiration from model simulations. These insights can be applied for efficient xylose utilization and cofactor recycling in other industrial microorganisms. The developed model forms a basis for rational analysis and design of Scheffersomyces stipitis metabolic network for the production of fuels and chemicals from lignocellulosic biomass.
| S-EPMC3310799 | biostudies-other