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

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Abstract
We present a genome‐scale metabolic model for the archaeal methanogen Methanosarcina barkeri . We characterize the metabolic network and compare it to reconstructions from the prokaryotic, eukaryotic and archaeal domains. Using the model in conjunction with constraint‐based methods, we simulate the metabolic fluxes and resulting phenotypes induced by different environmental and genetic conditions. This represents the first large‐scale simulation of either a methanogen or an archaeal species. Model predictions are validated by comparison to experimental growth measurements and phenotypes of M. barkeri on different substrates. The predicted growth phenotypes for wild type and mutants of the methanogenic pathway have a high level of agreement with experimental findings. We further examine the efficiency of the energy‐conserving reactions in the methanogenic pathway, specifically the Ech hydrogenase reaction, and determine a stoichiometry for the nitrogenase reaction. This work demonstrates that a reconstructed metabolic network can serve as an analysis platform to predict cellular phenotypes, characterize methanogenic growth, improve the genome annotation and further uncover the metabolic characteristics of methanogenesis. ### 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][1]). 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][2]). 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. 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][3]). 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 . 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][4]) 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][5]). This analysis demonstrates the advantage of integrative modeling, in which the model can directly determine the network flux through the physiological...