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Exploring microbial controls on methane cycling using MLTreeMap Song, Young Chang


Biological methane (CH₄) production, or methanogenesis, plays a crucial role in the global carbon cycle. Biologically generated CH₄ can be emitted into the atmosphere, where it acts as greenhouse gas twenty five times more potent than carbon dioxide (CO₂), stored as “methane ice” (clathrates or hydrates) in marine sediments along continental margins, or oxidized under aerobic or anaerobic conditions by microbial agents effectively limiting atmospheric flux. Methanogenesis is orchestrated by a group of obligate anaerobic archaea within the phylum Euryarchaeota, known as the methanogens that produce CH₄ as the end product of their energy metabolism. Three methanogenic pathways have been described including the hydrogenotrophic, methylotrophic and aceticlastic. Although differing in their use of electron donors, all three pathways converge on a terminal step catalyzed by the heterohexameric methyl-coenzyme M reductase (MCR). Over the last decade cultivation-independent studies have identified anaerobic methane-oxidizing archaea (ANME-1, 2 and 3) related methanogens that appear to run one or more canonical methanogenic pathways in reverse including the terminal step catalyzed by MCR. The three genes encoding MCR subunits, mcrA, mcrB and mcrG possess phylogenetic resolution similar to that of the small subunit ribosomal RNA gene making them useful functional markers for detection and differentiation of methanogenic and methane-oxidation pathways in natural and human engineered ecosystems. Here I introduce an automated and culture-independent method for monitoring the taxonomic structure and genomic potential of methane-cycling environments that leverages and improves upon an existing software package called MLTreeMap. MLTreeMap is a user-extensible software framework that automates maximum likelihood analysis to recover phylogenetic or functional marker genes from environmental sequence data. I first describe the taxonomic structure and pathway representation of methane-cycling environments on a global scale based on the identification of MCRA alleles. I then chart both the metabolic potential and gene expression of marine sediments supporting the anaerobic oxidation of CH₄ using a series of reference trees representing near complete methane-cycling pathways.

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