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      Acidobacteria are active and abundant members of diverse atmospheric H 2-oxidizing communities detected in temperate soils

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          Abstract

          Significant rates of atmospheric dihydrogen (H 2) consumption have been observed in temperate soils due to the activity of high-affinity enzymes, such as the group 1h [NiFe]-hydrogenase. We designed broadly inclusive primers targeting the large subunit gene ( hhyL) of group 1h [NiFe]-hydrogenases for long-read sequencing to explore its taxonomic distribution across soils. This approach revealed a diverse collection of microorganisms harboring hhyL, including previously unknown groups and taxonomically not assignable sequences. Acidobacterial group 1h [NiFe]-hydrogenase genes were abundant and expressed in temperate soils. To support the participation of acidobacteria in H 2 consumption, we studied two representative mesophilic soil acidobacteria, which expressed group 1h [NiFe]-hydrogenases and consumed atmospheric H 2 during carbon starvation. This is the first time mesophilic acidobacteria, which are abundant in ubiquitous temperate soils, have been shown to oxidize H 2 down to below atmospheric concentrations. As this physiology allows bacteria to survive periods of carbon starvation, it could explain the success of soil acidobacteria. With our long-read sequencing approach of group 1h [NiFe]-hydrogenase genes, we show that the ability to oxidize atmospheric levels of H 2 is more widely distributed among soil bacteria than previously recognized and could represent a common mechanism enabling bacteria to persist during periods of carbon deprivation.

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          Most cited references71

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          MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.

          We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.
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            RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

            Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.
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              MUSCLE: multiple sequence alignment with high accuracy and high throughput.

              We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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                Author and article information

                Contributors
                stephanie.eichorst@univie.ac.at
                Journal
                ISME J
                ISME J
                The ISME Journal
                Nature Publishing Group UK (London )
                1751-7362
                1751-7370
                6 October 2020
                6 October 2020
                February 2021
                : 15
                : 2
                : 363-376
                Affiliations
                [1 ]GRID grid.10420.37, ISNI 0000 0001 2286 1424, Division of Microbial Ecology, Department of Microbiology and Ecosystem Science, Centre for Microbiology and Environmental Systems Science, , University of Vienna, ; Vienna, Austria
                [2 ]GRID grid.5117.2, ISNI 0000 0001 0742 471X, Center for Microbial Communities, Department of Chemistry and Bioscience, , Aalborg University, ; Aalborg, Denmark
                [3 ]GRID grid.10420.37, ISNI 0000 0001 2286 1424, Division of Terrestrial Ecosystem Research, Department of Microbiology and Ecosystem Science, Centre for Microbiology and Environmental Systems Science, , University of Vienna, ; Vienna, Austria
                [4 ]GRID grid.1002.3, ISNI 0000 0004 1936 7857, Department of Microbiology, Biomedicine Discovery Institute, , Monash University, ; Clayton, VIC 3800 Australia
                [5 ]GRID grid.1002.3, ISNI 0000 0004 1936 7857, School of Biological Sciences, , Monash University, ; Clayton, VIC 3800 Australia
                Author information
                http://orcid.org/0000-0001-9891-7252
                http://orcid.org/0000-0002-9017-7461
                http://orcid.org/0000-0001-8286-9073
                http://orcid.org/0000-0003-3479-0197
                http://orcid.org/0000-0003-3282-4808
                http://orcid.org/0000-0001-7616-0594
                http://orcid.org/0000-0002-1314-9926
                Article
                750
                10.1038/s41396-020-00750-8
                8027828
                33024291
                ec28c799-08dc-4c6f-abc0-1206bdd8b50a
                © 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
                : 16 April 2020
                : 30 July 2020
                : 12 August 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100002428, Austrian Science Fund (Fonds zur Förderung der Wissenschaftlichen Forschung);
                Award ID: P26392-B20
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100010663, EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council);
                Award ID: 636928
                Award Recipient :
                Funded by: ARC DECRA Fellowship [DE170100310] NHMRC EL2 Fellowship [APP1178715]
                Categories
                Article
                Custom metadata
                © The Author(s), under exclusive licence to International Society for Microbial Ecology 2021

                Microbiology & Virology
                soil microbiology,biodiversity,bacterial physiology
                Microbiology & Virology
                soil microbiology, biodiversity, bacterial physiology

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