Modeling methanogenesis with a genome-scale metabolic reconstruction of Methanosarcina barkeri

Adam M Feist¹, Johannes C M Scholten², Bernhard Ø Palsson¹, Fred J Brockman² & Trey Ideker¹

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Synopsis

Methanogenesis is a unique way of life for a group of archaea (methanogens) that generate energy by converting simple substrates such as acetate, methanol or H₂/CO₂ to methane. Because of this, methanogens serve as a key component of the carbon cycle by degrading low carbon molecules in a number of anaerobic environments. The methane they produce contributes to the greenhouse effect and is a potential source of renewable energy (Garcia et al, 2000). In addition, some methanogens can form syntrophic relationships with other microorganisms, making them an interesting target for the study of interactions between different organisms. Although many pieces of methanogenic metabolism are understood, there are still many questions to be answered about the biochemistry of methanogenesis and how these pieces work together in the context of the whole organism. To address these questions, we reconstructed a genome-scale metabolic network for one of the most versatile methanogens, Methanosarcina barkeri, and analyzed the network to determine biochemical properties of key components and methanogenic metabolism as a whole.

The genome-scale metabolic model for M. barkeri was generated and refined using an iterative model building procedure (Figure 1). In this reconstruction process, we integrated data from primary literature, biochemical databases, the draft genomic sequence and other sources to generate a model which encompassed current biochemical and genetic information. In total, the model contains 692 potential metabolic genes associated with 509 reactions and 558 distinct metabolites, the largest number for an archaeal reconstruction to date. An additional 110 reactions were included because they have been reported in prior literature, or because they were required to fill a gap in the reconstructed network. In addition, to permit flux simulations we formulated a biomass objective function which specifies the properties of metabolic constituents of the cell.

Figure 1

The iterative model building procedure used to generate iAF692. The draft genome annotation was used as a scaffold, on which GPR assignments were made. The reactions added to the model were taken from both biochemical databases and published data. Once a reaction was found to be in the network, it was manually curated and either associated to a potential ORF or added with no gene assignment. A biomass objective function was formulated to perform model simulations based on cellular composition. Modeling simulations were run under steady-state conditions to determine the reaction flux distribution in the network. The results from the simulations were interpreted and compared to experimental data. From the comparison, physiological capabilities of the cell were confirmed or the network was further refined or updated.

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In the course of reconstructing the metabolic model, we generated new functional annotations for predicted open reading frames (ORFs) in the M. barkeri genome. In all, 55 of the genes associated with reactions in the model were linked to potential ORFs that were either uncharacterized (30 genes) or likely misannotated (25 genes) in the draft annotation. These functional predictions were made by combining weak or ambiguous sequence homology search results with metabolic interconnections in the network. The network assisted in filtering the lists of ambiguous homology matches by indicating which homologous genes fulfilled a metabolic requirement of the cell or bridged a gap between metabolites in the network.

Using the reconstructed model, we computationally determined the essential genes and reactions in the methanogenic pathway needed for growth of M. barkeri on different methanogenic substrates (Figure 5). In our modeling simulations, we removed each reaction in turn from the model, simulating a loss-of-function mutation of any single gene or group of genes associated with the reaction. Through interpretation of the computational results, it was possible to determine why certain mutant strains fail to grow whereas others are still viable. These results were compared to experimental measurements on M. barkeri mutants and were found to have a high level of agreement with observed phenotypes under the same environmental and genetic conditions. This high level of agreement between the model predictions and experimental findings shows promise in the use of the computational model as a high-throughput analysis tool for studying the growth of M. barkeri.

Figure 5

Essential reactions and genes in the methanogenic pathway of M. barkeri. Listed are the enzymes of the methanogenic pathway, the protein encoding genes needed to produce the functional enzymes and the abbreviation for the reaction they perform. Each of the reactions catalyzed by the enzyme listed was removed from the network and growth phenotypes were determined. For each computational prediction, green indicates methanogenic growth, blue indicates acetogenic growth, and red indicates no growth (0 net flux through the BOF). A plus symbol for a given condition indicates an agreement between model predictions and experimental characterization. A negative symbol indicates a disagreement. The colored enzyme and encoding gene sets (pink, yellow and green) indicate equal flux correlated reaction sets that possess the same reaction flux value under all growth conditions since they belong to a linear pathway. The simulation results can be used to determine the growth phenotypes of mutant strains and interpret the active pathways under each given condition.

Full figure and legend (154K)Figures & Tables index

Combining simulations with experimental data, allowed for the prediction of unknown aspects of methanogenic metabolism. One topic that we specifically examined was the proton translocation stoichiometry of the Ech hydrogenase reaction in M. barkeri, a currently unknown value. Using growth yields and substrate uptake rates as constraints on the model, we applied constraint-based analyses (Price et al, 2004) to determine a probable proton translocation efficiency for the Ech hydrogenase catalyzed reaction. Another unknown aspect of M. barkeri metabolism was the efficiency of the energy coupling between ATP and the nitrogenase catalyzed reaction. This efficiency was predicted by incorporating data in which the activity of the nitrogenase reaction in M. barkeri was isolated over two environmental conditions (Bomar et al, 1985). This analysis demonstrates the advantage of integrative modeling, in which the model can directly determine the network flux through the physiological reactions available to the cell, compared to classical 'best guess' equations for reaction flux values and yields. These two examples demonstrate the utility of the reconstructed model to predict unknown aspects of methanogenic growth when combined with even a limited amount of experimental data.

Perhaps most importantly, the genome-scale metabolic model of M. barkeri produces an analysis platform for the study of methanogenic growth in future work. Here, we have demonstrated how the model can be used to predict unknown metabolic aspects of methanogenesis and the physiological state of the cell under given conditions. In the future, the model will provide the structure for the incorporation of regulatory interactions and additional cellular processes such as transcription and translation. Furthermore, the model will serve as an integral piece of the computational framework for predictive modeling of more complex syntrophic communities. These types of analyses will provide a stepping stone for the study of increasingly complex intercellular interactions such as those within tissues.

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