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Metabolic modeling of a mutualistic microbial community.

ABSTRACT: The rate of production of methane in many environments depends upon mutualistic interactions between sulfate-reducing bacteria and methanogens. To enhance our understanding of these relationships, we took advantage of the fully sequenced genomes of Desulfovibrio vulgaris and Methanococcus maripaludis to produce and analyze the first multispecies stoichiometric metabolic model. Model results were compared to data on growth of the co-culture on lactate in the absence of sulfate. The model accurately predicted several ecologically relevant characteristics, including the flux of metabolites and the ratio of D. vulgaris to M. maripaludis cells during growth. In addition, the model and our data suggested that it was possible to eliminate formate as an interspecies electron shuttle, but hydrogen transfer was essential for syntrophic growth. Our work demonstrated that reconstructed metabolic networks and stoichiometric models can serve not only to predict metabolic fluxes and growth phenotypes of single organisms, but also to capture growth parameters and community composition of simple bacterial communities.

SUBMITTER: Stolyar S

PROVIDER: S-EPMC1847946 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

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Metabolic modeling of a mutualistic microbial community.

Stolyar Sergey S Van Dien Steve S Hillesland Kristina Linnea KL Pinel Nicolas N Lie Thomas J TJ Leigh John A JA Stahl David A DA

Molecular systems biology 20070313

The rate of production of methane in many environments depends upon mutualistic interactions between sulfate-reducing bacteria and methanogens. To enhance our understanding of these relationships, we took advantage of the fully sequenced genomes of Desulfovibrio vulgaris and Methanococcus maripaludis to produce and analyze the first multispecies stoichiometric metabolic model. Model results were compared to data on growth of the co-culture on lactate in the absence of sulfate. The model accurate ...[more]

PMID: 17353934

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Project description:BackgroundThe role of methane in global warming has become paramount to the environment and the human society, especially in the past few decades. Methane cycling microbial communities play an important role in the global methane cycle, which is why the characterization of these communities is critical to understand and manipulate their behavior. Methanotrophs are a major player in these communities and are able to oxidize methane as their primary carbon source.ResultsLake Washington is a freshwater lake characterized by a methane-oxygen countergradient that contains a methane cycling microbial community. Methanotrophs are a major part of this community involved in assimilating methane from lake water. Two significant methanotrophic species in this community are Methylobacter and Methylomonas. In this work, these methanotrophs are computationally studied via developing highly curated genome-scale metabolic models. Each model was then integrated to form a community model with a multi-level optimization framework. The competitive and mutualistic metabolic interactions among Methylobacter and Methylomonas were also characterized. The community model was next tested under carbon, oxygen, and nitrogen limited conditions in addition to a nutrient-rich condition to observe the systematic shifts in the internal metabolic pathways and extracellular metabolite exchanges. Each condition showed variations in the methane oxidation pathway, pyruvate metabolism, and the TCA cycle as well as the excretion of formaldehyde and carbon di-oxide in the community. Finally, the community model was simulated under fixed ratios of these two members to reflect the opposing behavior in the two-member synthetic community and in sediment-incubated communities. The community simulations predicted a noticeable switch in intracellular carbon metabolism and formaldehyde transfer between community members in sediment-incubated vs. synthetic condition.ConclusionIn this work, we attempted to predict the response of a simplified methane cycling microbial community from Lake Washington to varying environments and also provide an insight into the difference of dynamics in sediment-incubated microcosm community and synthetic co-cultures. Overall, this study lays the ground for in silico systems-level studies of freshwater lake ecosystems, which can drive future efforts of understanding, engineering, and modifying these communities for dealing with global warming issues.

Project description:AIMS:To identify putative mutualistic interactions driving community composition in polymicrobial chronic wound infections using metabolic modelling. METHODS AND RESULTS:We developed a 12 species metabolic model that covered 74% of 16S rDNA pyrosequencing reads of dominant genera from 2963 chronic wound patients. The community model was used to predict species abundances averaged across this large patient population. We found that substantially improved predictions were obtained when the model was constrained with genera prevalence data and predicted abundances were averaged over 5000 ensemble simulations with community participants randomly determined according to the experimentally determined prevalences. Staphylococcus and Pseudomonas were predicted to exhibit a strong mutualistic relationship that resulted in community growth rate and diversity simultaneously increasing, suggesting that these two common chronic wound pathogens establish dominance by cooperating with less harmful commensal species. In communities lacking one or both dominant pathogens, other mutualistic relationship including Staphylococcus/Acinetobacter, Pseudomonas/Serratia and Streptococcus/Enterococcus were predicted consistent with published experimental data. CONCLUSIONS:Mutualistic interactions were predicted to be driven by crossfeeding of organic acids, alcohols and amino acids that could potentially be disrupted to slow chronic wound disease progression. SIGNIFICANCE AND IMPACT OF THE STUDY:Approximately 2% of the US population suffers from nonhealing chronic wounds infected by a combination of commensal and pathogenic bacteria. These polymicrobial infections are often resilient to antibiotic treatment due to the nutrient-rich wound environment and species interactions that promote community stability and robustness. The simulation results from this study were used to identify putative mutualistic interactions between bacteria that could be targeted to enhance treatment efficacy.

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Metabolic modeling of a mutualistic microbial community.

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Metabolic modeling of a mutualistic microbial community.

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