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      Crop Management Impacts the Soybean ( Glycine max) Microbiome

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          Abstract

          Soybean ( Glycine max) is an important leguminous crop that is grown throughout the United States and around the world. In 2016, soybean was valued at $41 billion USD in the United States alone. Increasingly, soybean farmers are adopting alternative management strategies to improve the sustainability and profitability of their crop. Various benefits have been demonstrated for alternative management systems, but their effects on soybean-associated microbial communities are not well-understood. In order to better understand the impact of crop management systems on the soybean-associated microbiome, we employed DNA amplicon sequencing of the Internal Transcribed Spacer (ITS) region and 16S rRNA genes to analyze fungal and prokaryotic communities associated with soil, roots, stems, and leaves. Soybean plants were sampled from replicated fields under long-term conventional, no-till, and organic management systems at three time points throughout the growing season. Results indicated that sample origin was the main driver of beta diversity in soybean-associated microbial communities, but management regime and plant growth stage were also significant factors. Similarly, differences in alpha diversity are driven by compartment and sample origin. Overall, the organic management system had lower fungal and bacterial Shannon diversity. In prokaryotic communities, aboveground tissues were dominated by Sphingomonas and Methylobacterium while belowground samples were dominated by Bradyrhizobium and Sphingomonas. Aboveground fungal communities were dominated by Davidiella across all management systems, while belowground samples were dominated by Fusarium and Mortierella. Specific taxa including potential plant beneficials such as Mortierella were indicator species of the conventional and organic management systems. No-till management increased the abundance of groups known to contain plant beneficial organisms such as Bradyrhizobium and Glomeromycotina. Network analyses show different highly connected hub taxa were present in each management system. Overall, this research demonstrates how specific long-term cropping management systems alter microbial communities and how those communities change throughout the growth of soybean.

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          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            Cutadapt removes adapter sequences from high-throughput sequencing reads

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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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                Author and article information

                Contributors
                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                03 June 2020
                2020
                : 11
                : 1116
                Affiliations
                [1] 1Department of Microbiology and Molecular Genetics, Michigan State University , East Lansing, MI, United States
                [2] 2Department of Plant, Soil and Microbial Sciences, Michigan State University , East Lansing, MI, United States
                [3] 3Genetics and Genomic Sciences Program, Michigan State University , East Lansing, MI, United States
                [4] 4Department of Plant Biology, Michigan State University , East Lansing, MI, United States
                Author notes

                Edited by: Paulo José Pereira Lima Teixeira, University of São Paulo, Brazil

                Reviewed by: Angel Valverde, University of the Free State, South Africa; Sur Herrera Paredes, Stanford University, United States

                *Correspondence: Gregory Bonito, bonito@ 123456msu.edu

                ORCID: Reid Longley, orcid.org/0000-0001-7355-0263 Zachary A. Noel, orcid.org/0000-0001-6375-8300 Gian Maria Niccolò Benucci, orcid.org/0000-0003-1589-947X Martin I. Chilvers, orcid.org/0000-0001-8832-1666 Frances Trail, orcid.org/0000-0002-7124-9087 Gregory Bonito, orcid.org/0000-0002-7262-8978

                This article was submitted to Plant Microbe Interactions, a section of the journal Frontiers in Microbiology

                Article
                10.3389/fmicb.2020.01116
                7283522
                32582080
                0b50b4bb-6cd2-48e8-845a-4842b928988c
                Copyright © 2020 Longley, Noel, Benucci, Chilvers, Trail and Bonito.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 31 January 2020
                : 04 May 2020
                Page count
                Figures: 7, Tables: 3, Equations: 0, References: 91, Pages: 20, Words: 0
                Funding
                Funded by: National Institute of Food and Agriculture 10.13039/100005825
                Funded by: U.S. Department of Agriculture 10.13039/100000199
                Funded by: National Institute of General Medical Sciences 10.13039/100000057
                Categories
                Microbiology
                Original Research

                Microbiology & Virology
                soybean,agricultural management,rdna,amplicon sequencing,plant-microbe interactions

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