Project description:(iMTD22IC) This model is described in these articles: Casini, I., McCubbin, T., Esquivel-Elizondo, S., Luque, G.G., Evseeva, D., Fink, C., Beblawy, S., Youngblut, N.D., Aristilde, L., Huson, D., Drager, A., Ley, R., Marcellin, E., Angenent, L.T., Molitor, B. 2023. An integrated systems biology approach reveals differences in formate metabolism in the genus Methanothermobacter. Iscience, 26(10). Casini, I., McCubbin, T., Esquivel-Elizondo, S., Luque, G.G., Evseeva, D., Fink, C., Beblawy, S., Youngblut, N.D., Aristilde, L., Huson, D., Drager, A., Ley, R., Marcellin, E., Angenent, L.T., Molitor, B. 2022. An integrated systems-biology platform for power-to-gas technology. bioRxiv, 2022.12.30.522236. Methanogenesis allows methanogenic archaea (methanogens) to generate cellular energy for their growth while producing methane. Hydrogenotrophic methanogens thrive on carbon dioxide and molecular hydrogen as sole carbon and energy sources. Thermophilic and hydrogenotrophic Methanothermobacter spp. have been recognized as robust biocatalysts for a circular carbon economy and are now applied in power-to-gas technology. Here, we generated the first manually curated genome-scale metabolic reconstruction for three Methanothermobacter spp.. We investigated differences in growth performance of three wild-type strains and one genetically engineered strain in two independent chemostat bioreactor experiments, first, with molecular hydrogen and carbon dioxide, and second, with sodium formate. In the first experiment, we found the highest methane production rate for Methanothermobacter thermautotrophicus ΔH, while Methanothermobacter marburgensis Marburg reached the highest biomass growth rate. Transcriptomics and proteomics data sets from these steady-state bioreactors, in combination with the implementation of a pan-model that contains combined reactions from all three microbes, allowed us to perform an interspecies comparison. While the observed differences in the growth behavior cannot be fully explained, the comparison enabled us to identify crucial differences in growth-related pathways such as formate anabolism. In the second experiment, we found stable growth with a M. thermautotrophicus ΔH plasmid-carrying strain on formate with similar performance parameters compared to wild-type Methanothermobacter thermautotrophicus Z-245. The results of the two studies demonstrate the advantages of an integrative approach combining fermentation and omics data with genome-scale modeling to reveal knowledge gaps of archaeal metabolisms and the biotechnological potential of Methanothermobacter spp. as production platform hosts.

Project description:This model is described in these articles: Casini, I., McCubbin, T., Esquivel-Elizondo, S., Luque, G.G., Evseeva, D., Fink, C., Beblawy, S., Youngblut, N.D., Aristilde, L., Huson, D., Drager, A., Ley, R., Marcellin, E., Angenent, L.T., Molitor, B. 2023. An integrated systems biology approach reveals differences in formate metabolism in the genus Methanothermobacter. Iscience, 26(10). Casini, I., McCubbin, T., Esquivel-Elizondo, S., Luque, G.G., Evseeva, D., Fink, C., Beblawy, S., Youngblut, N.D., Aristilde, L., Huson, D., Drager, A., Ley, R., Marcellin, E., Angenent, L.T., Molitor, B. 2022. An integrated systems-biology platform for power-to-gas technology. bioRxiv, 2022.12.30.522236. Methanogenesis allows methanogenic archaea (methanogens) to generate cellular energy for their growth while producing methane. Hydrogenotrophic methanogens thrive on carbon dioxide and molecular hydrogen as sole carbon and energy sources. Thermophilic and hydrogenotrophic Methanothermobacter spp. have been recognized as robust biocatalysts for a circular carbon economy and are now applied in power-to-gas technology. Here, we generated the first manually curated genome-scale metabolic reconstruction for three Methanothermobacter spp.. We investigated differences in growth performance of three wild-type strains and one genetically engineered strain in two independent chemostat bioreactor experiments, first, with molecular hydrogen and carbon dioxide, and second, with sodium formate. In the first experiment, we found the highest methane production rate for Methanothermobacter thermautotrophicus ΔH, while Methanothermobacter marburgensis Marburg reached the highest biomass growth rate. Transcriptomics and proteomics data sets from these steady-state bioreactors, in combination with the implementation of a pan-model that contains combined reactions from all three microbes, allowed us to perform an interspecies comparison. While the observed differences in the growth behavior cannot be fully explained, the comparison enabled us to identify crucial differences in growth-related pathways such as formate anabolism. In the second experiment, we found stable growth with a M. thermautotrophicus ΔH plasmid-carrying strain on formate with similar performance parameters compared to wild-type Methanothermobacter thermautotrophicus Z-245. The results of the two studies demonstrate the advantages of an integrative approach combining fermentation and omics data with genome-scale modeling to reveal knowledge gaps of archaeal metabolisms and the biotechnological potential of Methanothermobacter spp. as production platform hosts.

Project description:Richards2016 - Genome-scale metabolic reconstruction of Methanococcus maripaludis (iMR539) This model is described in the article: Exploring Hydrogenotrophic Methanogenesis: a Genome Scale Metabolic Reconstruction of Methanococcus maripaludis. Richards MA, Lie TJ, Zhang J, Ragsdale SW, Leigh JA, Price ND. J. Bacteriol. 2016 Dec; 198(24): 3379-3390 Abstract: Hydrogenotrophic methanogenesis occurs in multiple environments, ranging from the intestinal tracts of animals to anaerobic sediments and hot springs. Energy conservation in hydrogenotrophic methanogens was long a mystery; only within the last decade was it reported that net energy conservation for growth depends on electron bifurcation. In this work, we focus on Methanococcus maripaludis, a well-studied hydrogenotrophic marine methanogen. To better understand hydrogenotrophic methanogenesis and compare it with methylotrophic methanogenesis that utilizes oxidative phosphorylation rather than electron bifurcation, we have built iMR539, a genome scale metabolic reconstruction that accounts for 539 of the 1,722 protein-coding genes of M. maripaludis strain S2. Our reconstructed metabolic network uses recent literature to not only represent the central electron bifurcation reaction but also incorporate vital biosynthesis and assimilation pathways, including unique cofactor and coenzyme syntheses. We show that our model accurately predicts experimental growth and gene knockout data, with 93% accuracy and a Matthews correlation coefficient of 0.78. Furthermore, we use our metabolic network reconstruction to probe the implications of electron bifurcation by showing its essentiality, as well as investigating the infeasibility of aceticlastic methanogenesis in the network. Additionally, we demonstrate a method of applying thermodynamic constraints to a metabolic model to quickly estimate overall free-energy changes between what comes in and out of the cell. Finally, we describe a novel reconstruction-specific computational toolbox we created to improve usability. Together, our results provide a computational network for exploring hydrogenotrophic methanogenesis and confirm the importance of electron bifurcation in this process.Understanding and applying hydrogenotrophic methanogenesis is a promising avenue for developing new bioenergy technologies around methane gas. Although a significant portion of biological methane is generated through this environmentally ubiquitous pathway, existing methanogen models portray the more traditional energy conservation mechanisms that are found in other methanogens. We have constructed a genome scale metabolic network of Methanococcus maripaludis that explicitly accounts for all major reactions involved in hydrogenotrophic methanogenesis. Our reconstruction demonstrates the importance of electron bifurcation in central metabolism, providing both a window into hydrogenotrophic methanogenesis and a hypothesis-generating platform to fuel metabolic engineering efforts. This model is hosted on BioModels Database and identified by: MODEL1607200000. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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