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      Degradation of biological macromolecules supports uncultured microbial populations in Guaymas Basin hydrothermal sediments

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          Abstract

          Hydrothermal sediments contain large numbers of uncultured heterotrophic microbial lineages. Here, we amended Guaymas Basin sediments with proteins, polysaccharides, nucleic acids or lipids under different redox conditions and cultivated heterotrophic thermophiles with the genomic potential for macromolecule degradation. We reconstructed 20 metagenome-assembled genomes (MAGs) of uncultured lineages affiliating with known archaeal and bacterial phyla, including endospore-forming Bacilli and candidate phylum Marinisomatota. One Marinisomatota MAG had 35 different glycoside hydrolases often in multiple copies, seven extracellular CAZymes, six polysaccharide lyases, and multiple sugar transporters. This population has the potential to degrade a broad spectrum of polysaccharides including chitin, cellulose, pectin, alginate, chondroitin, and carrageenan. We also describe thermophiles affiliating with the genera Thermosyntropha, Thermovirga, and Kosmotoga with the capability to make a living on nucleic acids, lipids, or multiple macromolecule classes, respectively. Several populations seemed to lack extracellular enzyme machinery and thus likely scavenged oligo- or monomers (e.g., MAGs affiliating with Archaeoglobus) or metabolic products like hydrogen (e.g., MAGs affiliating with Thermodesulfobacterium or Desulforudaceae). The growth of methanogens or the production of methane was not observed in any condition, indicating that the tested macromolecules are not degraded into substrates for methanogenesis in hydrothermal sediments. We provide new insights into the niches, and genomes of microorganisms that actively degrade abundant necromass macromolecules under oxic, sulfate-reducing, and fermentative thermophilic conditions. These findings improve our understanding of the carbon flow across trophic levels and indicate how primary produced biomass sustains complex and productive ecosystems.

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          DADA2: High resolution sample inference from Illumina amplicon data

          We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
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            SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing.

            The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.
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              Salmon: fast and bias-aware quantification of transcript expression using dual-phase inference

              We introduce Salmon, a method for quantifying transcript abundance from RNA-seq reads that is accurate and fast. Salmon is the first transcriptome-wide quantifier to correct for fragment GC content bias, which we demonstrate substantially improves the accuracy of abundance estimates and the reliability of subsequent differential expression analysis. Salmon combines a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure.
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                Author and article information

                Contributors
                sperezcastro@mbl.edu
                eruff@mbl.edu
                Journal
                ISME J
                ISME J
                The ISME Journal
                Nature Publishing Group UK (London )
                1751-7362
                1751-7370
                10 June 2021
                10 June 2021
                December 2021
                : 15
                : 12
                : 3480-3497
                Affiliations
                [1 ]GRID grid.144532.5, ISNI 000000012169920X, Ecosystems Center and Bay Paul Center, , Marine Biological Laboratory, ; Woods Hole, MA USA
                [2 ]GRID grid.47894.36, ISNI 0000 0004 1936 8083, Soil and Crop Sciences, , Colorado State University, ; Fort Collins, CO USA
                [3 ]GRID grid.22072.35, ISNI 0000 0004 1936 7697, Department of Geosciences, , University of Calgary, ; Calgary, AB Canada
                [4 ]GRID grid.410711.2, ISNI 0000 0001 1034 1720, Department of Marine Sciences, , University of North Carolina, ; Chapel Hill, NC USA
                [5 ]Present Address: Barnstable County Department of Health and Environment, Sandwich, MA USA
                [6 ]GRID grid.419509.0, ISNI 0000 0004 0491 8257, Present Address: Multiphase Chemistry Department, , Max Planck Institute for Chemistry, ; Mainz, Germany
                Author information
                http://orcid.org/0000-0002-2257-0966
                http://orcid.org/0000-0001-9451-8908
                http://orcid.org/0000-0003-3669-5425
                http://orcid.org/0000-0001-9600-3828
                http://orcid.org/0000-0002-6872-6188
                Article
                1026
                10.1038/s41396-021-01026-5
                8630151
                34112968
                dfbb60dd-0c01-4a66-956e-14cb007bfebc
                © The Author(s) 2021

                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
                : 8 December 2020
                : 26 May 2021
                : 27 May 2021
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                Custom metadata
                © The Author(s), under exclusive licence to International Society for Microbial Ecology 2021

                Microbiology & Virology
                water microbiology,environmental sciences
                Microbiology & Virology
                water microbiology, environmental sciences

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