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      Proteome specialization of anaerobic fungi during ruminal degradation of recalcitrant plant fiber

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          Abstract

          The rumen harbors a complex microbial mixture of archaea, bacteria, protozoa, and fungi that efficiently breakdown plant biomass and its complex dietary carbohydrates into soluble sugars that can be fermented and subsequently converted into metabolites and nutrients utilized by the host animal. While rumen bacterial populations have been well documented, only a fraction of the rumen eukarya are taxonomically and functionally characterized, despite the recognition that they contribute to the cellulolytic phenotype of the rumen microbiota. To investigate how anaerobic fungi actively engage in digestion of recalcitrant fiber that is resistant to degradation, we resolved genome-centric metaproteome and metatranscriptome datasets generated from switchgrass samples incubated for 48 h in nylon bags within the rumen of cannulated dairy cows. Across a gene catalog covering anaerobic rumen bacteria, fungi and viruses, a significant portion of the detected proteins originated from fungal populations. Intriguingly, the carbohydrate-active enzyme (CAZyme) profile suggested a domain-specific functional specialization, with bacterial populations primarily engaged in the degradation of hemicelluloses, whereas fungi were inferred to target recalcitrant cellulose structures via the detection of a number of endo- and exo-acting enzymes belonging to the glycoside hydrolase (GH) family 5, 6, 8, and 48. Notably, members of the GH48 family were amongst the highest abundant CAZymes and detected representatives from this family also included dockerin domains that are associated with fungal cellulosomes. A eukaryote-selected metatranscriptome further reinforced the contribution of uncultured fungi in the ruminal degradation of recalcitrant fibers. These findings elucidate the intricate networks of in situ recalcitrant fiber deconstruction, and importantly, suggest that the anaerobic rumen fungi contribute a specific set of CAZymes that complement the enzyme repertoire provided by the specialized plant cell wall degrading rumen bacteria.

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          MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

          The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.
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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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              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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                Author and article information

                Contributors
                live.hagen@nmbu.no
                Journal
                ISME J
                ISME J
                The ISME Journal
                Nature Publishing Group UK (London )
                1751-7362
                1751-7370
                14 September 2020
                14 September 2020
                February 2021
                : 15
                : 2
                : 421-434
                Affiliations
                [1 ]GRID grid.19477.3c, ISNI 0000 0004 0607 975X, Faculty of Biotechnology, Chemistry and Food Science, , Norwegian University of Life Sciences, ; Aas, Norway
                [2 ]GRID grid.27860.3b, ISNI 0000 0004 1936 9684, University of California, ; Davis, CA USA
                [3 ]GRID grid.451303.0, ISNI 0000 0001 2218 3491, Pacific Northwest National Laboratory, ; Richland, WA USA
                [4 ]GRID grid.30064.31, ISNI 0000 0001 2157 6568, Washington State University, ; Richland, WA USA
                [5 ]GRID grid.451303.0, ISNI 0000 0001 2218 3491, Environmental Molecular Sciences Laboratory, , Pacific Northwest National Laboratory, ; Richland, CA USA
                [6 ]GRID grid.184769.5, ISNI 0000 0001 2231 4551, U.S. Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, ; Berkeley, CA USA
                [7 ]GRID grid.463764.4, ISNI 0000 0004 1798 275X, CNRS, UMR 7257, Université Aix-Marseille, ; 13288 Marseille, France
                [8 ]GRID grid.463764.4, ISNI 0000 0004 1798 275X, Institut National de la Recherche Agronomique, USC 1408 Architecture et Fonction des Macromolécules Biologiques, ; 13288 Marseille, France
                [9 ]GRID grid.412125.1, ISNI 0000 0001 0619 1117, Department of Biological Sciences, , King Abdulaziz University, ; Jeddah, 21589 Saudi Arabia
                [10 ]GRID grid.133342.4, ISNI 0000 0004 1936 9676, Department of Chemical Engineering, , University of California, ; Santa Barbara, CA USA
                [11 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Department of Plant and Microbial Biology, , University of California, ; Berkeley, CA USA
                [12 ]Department of Animal Science, University of Illinois, Urbana-Champaign, IL USA
                [13 ]GRID grid.19477.3c, ISNI 0000 0004 0607 975X, Faculty of Biosciences, , Norwegian University of Life Sciences, ; Aas, Norway
                Author information
                http://orcid.org/0000-0003-2606-5331
                http://orcid.org/0000-0002-1803-727X
                http://orcid.org/0000-0002-4501-9587
                http://orcid.org/0000-0002-3971-5439
                http://orcid.org/0000-0002-3434-8588
                http://orcid.org/0000-0002-6065-8491
                http://orcid.org/0000-0002-3136-8903
                http://orcid.org/0000-0001-6479-8427
                http://orcid.org/0000-0001-9853-5457
                http://orcid.org/0000-0002-2067-4059
                http://orcid.org/0000-0003-0321-0380
                Article
                769
                10.1038/s41396-020-00769-x
                8026616
                32929206
                2934e196-a5b5-4e1d-a0b1-961cdb780b0a
                © 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
                : 20 January 2020
                : 21 August 2020
                : 2 September 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100005416, Norges Forskningsråd (Research Council of Norway);
                Award ID: 250479
                Award Recipient :
                Funded by: European Research Commission Starting Grant Fellowship; 336355 - MicroDE
                Categories
                Article
                Custom metadata
                © The Author(s), under exclusive licence to International Society for Microbial Ecology 2021

                Microbiology & Virology
                microbiome,fungi,microbial ecology
                Microbiology & Virology
                microbiome, fungi, microbial ecology

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