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      Plant compartment and genetic variation drive microbiome composition in switchgrass roots

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          Summary

          Switchgrass ( Panicum virgatum) is a promising biofuel crop native to the United States with genotypes that are adapted to a wide range of distinct ecosystems. Various plants have been shown to undergo symbioses with plant growth‐promoting bacteria and fungi, however, plant‐associated microbial communities of switchgrass have not been extensively studied to date. We present 16S ribosomal RNA gene and internal transcribed spacer (ITS) data of rhizosphere and root endosphere compartments of four switchgrass genotypes to test the hypothesis that host selection of its root microbiota prevails after transfer to non‐native soil. We show that differences in bacterial, archaeal and fungal community composition and diversity are strongly driven by plant compartment and switchgrass genotypes and ecotypes. Plant‐associated microbiota show an enrichment in Alphaproteobacteria and Actinobacteria as well as Sordariales and Pleosporales compared with the surrounding soil. Root associated compartments display low‐complexity communities dominated and enriched in Actinobacteria, in particular Streptomyces, in the lowland genotypes, and in Alphaproteobacteria, specifically Sphingobium, in the upland genotypes. Our comprehensive root analysis serves as a snapshot of host‐specific bacterial and fungal associations of switchgrass in the field and confirms that host‐selected microbiomes persist after transfer to non‐native soil.

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          FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments

          Background We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability. Methodology/Principal Findings Where FastTree 1 used nearest-neighbor interchanges (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs. FastTree 2 uses heuristics to restrict the search for better trees and estimates a rate of evolution for each site (the “CAT” approximation). Nevertheless, for both simulated and genuine alignments, FastTree 2 is slightly more accurate than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default settings). Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100–1,000 times faster. FastTree 2 inferred a topology and likelihood-based local support values for 237,882 distinct 16S ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory. Conclusions/Significance FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.
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            Integration of biological networks and gene expression data using Cytoscape.

            Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape.
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              The gut microbiome in health and in disease

              Recent technological advancements and expanded efforts have led to a tremendous growth in the collective knowledge of the human microbiome. This review will highlight some of the important recent findings in this area of research.
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                Author and article information

                Contributors
                esinger@lbl.gov
                tjuenger@austin.utexas.edu
                Journal
                Environ Microbiol Rep
                Environ Microbiol Rep
                10.1111/(ISSN)1758-2229
                EMI4
                Environmental Microbiology Reports
                John Wiley & Sons, Inc. (Hoboken, USA )
                1758-2229
                31 January 2019
                April 2019
                : 11
                : 2 ( doiID: 10.1111/emi4.2019.11.issue-2 )
                : 185-195
                Affiliations
                [ 1 ] Department of Energy Joint Genome Institute Walnut Creek CA USA
                [ 2 ] Department of Integrative Biology, University of Texas Austin Austin TX USA
                [ 3 ] School of Integrative Plant Science, Cornell University Ithaca NY USA
                Author notes
                [*] [* ]For correspondence. E‐mail esinger@ 123456lbl.gov ; Tel. +1‐925‐296‐5759; Fax 925‐927‐2554. E‐mail tjuenger@ 123456austin.utexas.edu ; Tel. +1‐512‐232‐5751; Fax 512‐232‐9529.
                Author information
                https://orcid.org/0000-0002-3126-2199
                Article
                EMI412727
                10.1111/1758-2229.12727
                6850097
                30537406
                1b1ec78d-c566-40ad-bc5a-39cb0f6edc2f
                © 2018 The Authors. Environmental Microbiology Reports published by Society for Applied Microbiology and John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 18 May 2018
                : 28 November 2018
                : 04 December 2018
                Page count
                Figures: 4, Tables: 2, Pages: 11, Words: 7754
                Funding
                Funded by: U.S. DOE Office of Science Biological and Environmental Research , open-funder-registry 10.13039/100006206;
                Award ID: DE‐SC0014156
                Funded by: U.S. DOE Office of Science , open-funder-registry 10.13039/100000015;
                Award ID: DE‐AC02‐05CH11231
                Categories
                Brief Report
                Brief Reports
                Custom metadata
                2.0
                April 2019
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.7.1 mode:remove_FC converted:12.11.2019

                Microbiology & Virology
                Microbiology & Virology

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