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      Experimentally-validated correlation analysis reveals new anaerobic methane oxidation partnerships with consortium-level heterogeneity in diazotrophy

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          Abstract

          Archaeal anaerobic methanotrophs (“ANME”) and sulfate-reducing Deltaproteobacteria (“SRB”) form symbiotic multicellular consortia capable of anaerobic methane oxidation (AOM), and in so doing modulate methane flux from marine sediments. The specificity with which ANME associate with particular SRB partners in situ, however, is poorly understood. To characterize partnership specificity in ANME-SRB consortia, we applied the correlation inference technique SparCC to 310 16S rRNA amplicon libraries prepared from Costa Rica seep sediment samples, uncovering a strong positive correlation between ANME-2b and members of a clade of Deltaproteobacteria we termed SEEP-SRB1g. We confirmed this association by examining 16S rRNA diversity in individual ANME-SRB consortia sorted using flow cytometry and by imaging ANME-SRB consortia with fluorescence in situ hybridization (FISH) microscopy using newly-designed probes targeting the SEEP-SRB1g clade. Analysis of genome bins belonging to SEEP-SRB1g revealed the presence of a complete nifHDK operon required for diazotrophy, unusual in published genomes of ANME-associated SRB. Active expression of nifH in SEEP-SRB1g within ANME-2b—SEEP-SRB1g consortia was then demonstrated by microscopy using hybridization chain reaction (HCR-) FISH targeting nifH transcripts and diazotrophic activity was documented by FISH-nanoSIMS experiments. NanoSIMS analysis of ANME-2b—SEEP-SRB1g consortia incubated with a headspace containing CH 4 and 15N 2 revealed differences in cellular 15N-enrichment between the two partners that varied between individual consortia, with SEEP-SRB1g cells enriched in 15N relative to ANME-2b in one consortium and the opposite pattern observed in others, indicating both ANME-2b and SEEP-SRB1g are capable of nitrogen fixation, but with consortium-specific variation in whether the archaea or bacterial partner is the dominant diazotroph.

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          Most cited references94

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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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            The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

            SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
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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
                ksmetcalfe@gmail.com
                vorphan@gps.caltech.edu
                Journal
                ISME J
                ISME J
                The ISME Journal
                Nature Publishing Group UK (London )
                1751-7362
                1751-7370
                15 October 2020
                15 October 2020
                February 2021
                : 15
                : 2
                : 377-396
                Affiliations
                GRID grid.20861.3d, ISNI 0000000107068890, Division of Geological and Planetary Sciences, , California Institute of Technology, ; 1200 E California Blvd, Mail Code 170-25, Pasadena CA, 91125 USA
                Author information
                http://orcid.org/0000-0002-2963-765X
                http://orcid.org/0000-0002-6225-3279
                http://orcid.org/0000-0002-5374-6178
                Article
                757
                10.1038/s41396-020-00757-1
                8027057
                33060828
                3e2f63e6-9ce9-4a92-bdd1-f2eef98f5057
                © 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
                : 10 April 2020
                : 28 July 2020
                : 21 August 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000001, National Science Foundation (NSF);
                Award ID: Graduate Student Fellowship
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100012530, Columbia University | LDEO | U.S. Science Support Program, Lamont-Doherty Earth Observatory (USSSP, LDEO);
                Award ID: Schlanger Fellowship
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100006132, DOE | Office of Science (SC);
                Award ID: DE-SC0020373
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000141, NSF | GEO | Division of Ocean Sciences (OCE);
                Award ID: 1634002
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000936, Gordon and Betty Moore Foundation (Gordon E. and Betty I. Moore Foundation);
                Award ID: 3780
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s), under exclusive licence to International Society for Microbial Ecology 2021

                Microbiology & Virology
                stable isotope analysis,symbiosis,metagenomics,microbial ecology,water microbiology

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