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ABSTRACT: Nature AND Nurture: Enabling formate-dependent growth in Methanosarcina acetivorans

PROVIDER: PRJNA1085037 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR28249802_1.fastq.gz	Fastqsanger.gz
	SRR28249802_2.fastq.gz	Fastqsanger.gz
	SRR28249803_1.fastq.gz	Fastqsanger.gz
	SRR28249803_2.fastq.gz	Fastqsanger.gz
	SRR28249804_1.fastq.gz	Fastqsanger.gz

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Project description:Benedict2011 - Genome-scale metoblic network of Methanosarcina acetivorans (iMB745) This model is described in the article: Genome-scale metabolic reconstruction and hypothesis testing in the methanogenic archaeon Methanosarcina acetivorans C2A. Benedict MN, Gonnerman MC, Metcalf WW, Price ND. J. Bacteriol. 2012 Feb; 194(4): 855-865 Abstract: Methanosarcina acetivorans strain C2A is a marine methanogenic archaeon notable for its substrate utilization, genetic tractability, and novel energy conservation mechanisms. To help probe the phenotypic implications of this organism's unique metabolism, we have constructed and manually curated a genome-scale metabolic model of M. acetivorans, iMB745, which accounts for 745 of the 4,540 predicted protein-coding genes (16%) in the M. acetivorans genome. The reconstruction effort has identified key knowledge gaps and differences in peripheral and central metabolism between methanogenic species. Using flux balance analysis, the model quantitatively predicts wild-type phenotypes and is 96% accurate in knockout lethality predictions compared to currently available experimental data. The model was used to probe the mechanisms and energetics of by-product formation and growth on carbon monoxide, as well as the nature of the reaction catalyzed by the soluble heterodisulfide reductase HdrABC in M. acetivorans. The genome-scale model provides quantitative and qualitative hypotheses that can be used to help iteratively guide additional experiments to further the state of knowledge about methanogenesis. This model is hosted on BioModels Database and identified by: MODEL1507180040. 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.

Project description:Kumar2011 - Genome-scale metabolic network of Methanosarcina acetivorans (iVS941) This model is described in the article: Metabolic reconstruction of the archaeon methanogen Methanosarcina Acetivorans. Satish Kumar V, Ferry JG, Maranas CD. BMC Syst Biol 2011; 5: 28 Abstract: BACKGROUND: Methanogens are ancient organisms that are key players in the carbon cycle accounting for about one billion tones of biological methane produced annually. Methanosarcina acetivorans, with a genome size of ~5.7 mb, is the largest sequenced archaeon methanogen and unique amongst the methanogens in its biochemical characteristics. By following a systematic workflow we reconstruct a genome-scale metabolic model for M. acetivorans. This process relies on previously developed computational tools developed in our group to correct growth prediction inconsistencies with in vivo data sets and rectify topological inconsistencies in the model. RESULTS: The generated model iVS941 accounts for 941 genes, 705 reactions and 708 metabolites. The model achieves 93.3% prediction agreement with in vivo growth data across different substrates and multiple gene deletions. The model also correctly recapitulates metabolic pathway usage patterns of M. acetivorans such as the indispensability of flux through methanogenesis for growth on acetate and methanol and the unique biochemical characteristics under growth on carbon monoxide. CONCLUSIONS: Based on the size of the genome-scale metabolic reconstruction and extent of validated predictions this model represents the most comprehensive up-to-date effort to catalogue methanogenic metabolism. The reconstructed model is available in spreadsheet and SBML formats to enable dissemination. This model is hosted on BioModels Database and identified by: MODEL1507180035. 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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