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      Diversification of methanogens into hyperalkaline serpentinizing environments through adaptations to minimize oxidant limitation

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          Abstract

          Metagenome assembled genomes (MAGs) and single amplified genomes (SAGs) affiliated with two distinct Methanobacterium lineages were recovered from subsurface fracture waters of the Samail Ophiolite, Sultanate of Oman. Lineage Type I was abundant in waters with circumneutral pH, whereas lineage Type II was abundant in hydrogen rich, hyperalkaline waters. Type I encoded proteins to couple hydrogen oxidation to CO 2 reduction, typical of hydrogenotrophic methanogens. Surprisingly, Type II, which branched from the Type I lineage, lacked homologs of two key oxidative [NiFe]-hydrogenases. These functions were presumably replaced by formate dehydrogenases that oxidize formate to yield reductant and cytoplasmic CO 2 via a pathway that was unique among characterized Methanobacteria, allowing cells to overcome CO 2/oxidant limitation in high pH waters. This prediction was supported by microcosm-based radiotracer experiments that showed significant biological methane generation from formate, but not bicarbonate, in waters where the Type II lineage was detected in highest relative abundance. Phylogenetic analyses and variability in gene content suggested that recent and ongoing diversification of the Type II lineage was enabled by gene transfer, loss, and transposition. These data indicate that selection imposed by CO 2/oxidant availability drove recent methanogen diversification into hyperalkaline waters that are heavily impacted by serpentinization.

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          Basic local alignment search tool.

          A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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            KEGG: kyoto encyclopedia of genes and genomes.

            M Kanehisa (2000)
            KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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              IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

              Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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                Author and article information

                Contributors
                eboyd@montana.edu
                Journal
                ISME J
                ISME J
                The ISME Journal
                Nature Publishing Group UK (London )
                1751-7362
                1751-7370
                30 November 2020
                30 November 2020
                April 2021
                : 15
                : 4
                : 1121-1135
                Affiliations
                [1 ]GRID grid.41891.35, ISNI 0000 0001 2156 6108, Department of Microbiology and Immunology, , Montana State University, ; Bozeman, MT 59717 USA
                [2 ]GRID grid.254549.b, ISNI 0000 0004 1936 8155, Department of Civil and Environmental Engineering, , Colorado School of Mines, ; Golden, CO 80401 USA
                [3 ]GRID grid.296275.d, ISNI 0000 0000 9516 4913, Bigelow Laboratory for Ocean Sciences, ; East Boothbay, ME 04544 USA
                [4 ]GRID grid.266190.a, ISNI 0000000096214564, Department of Geological Sciences, , University of Colorado, ; Boulder, CO 80309 USA
                Author information
                http://orcid.org/0000-0003-4458-3108
                http://orcid.org/0000-0002-4664-7438
                http://orcid.org/0000-0003-4436-5856
                Article
                838
                10.1038/s41396-020-00838-1
                8115248
                33257813
                f7e38ec9-65d7-4f0a-bb79-7c372f7f4ca2
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 28 May 2020
                : 28 October 2020
                : 11 November 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/100012627, NASA | NASA Astrobiology Institute (NAI);
                Award ID: NNA15BB02A
                Award ID: NNA15BB02A
                Award Recipient :
                Funded by: National Science Foundation grants OCE-1335810 and OIA-1826734
                Categories
                Article
                Custom metadata
                © The Author(s), under exclusive licence to International Society for Microbial Ecology 2021

                Microbiology & Virology
                ecology,evolution
                Microbiology & Virology
                ecology, evolution

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