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      Chemical structure predicts the effect of plant‐derived low‐molecular weight compounds on soil microbiome structure and pathogen suppression

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          Abstract

          • Plant‐derived low‐molecular weight compounds play a crucial role in shaping soil microbiome functionality. While various compounds have been demonstrated to affect soil microbes, most data are case‐specific and do not provide generalizable predictions on their effects. Here we show that the chemical structural affiliation of low‐molecular weight compounds typically secreted by plant roots—sugars, amino acids, organic acids and phenolic acids—can predictably affect microbiome diversity, composition and functioning in terms of plant disease suppression.

          • We amended soil with single or mixtures of representative compounds, mimicking carbon deposition by plants. We then assessed how different classes of compounds, or their combinations, affected microbiome composition and the protection of tomato plants from the soil‐borne Ralstonia solanacearum bacterial pathogen.

          • We found that chemical class predicted well the changes in microbiome composition and diversity. Organic and amino acids generally decreased the microbiome diversity compared to sugars and phenolic acids. These changes were also linked to disease incidence, with amino acids and nitrogen‐containing compound mixtures inducing more severe disease symptoms connected with a reduction in bacterial community diversity.

          • Together, our results demonstrate that low‐molecular weight compounds can predictably steer rhizosphere microbiome functioning providing guidelines to engineer microbiomes based on root exudation patterns by specific plant cultivars or crop regimes.

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          Is Open Access

          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            lavaan: AnRPackage for Structural Equation Modeling

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              UPARSE: highly accurate OTU sequences from microbial amplicon reads.

              Amplified marker-gene sequences can be used to understand microbial community structure, but they suffer from a high level of sequencing and amplification artifacts. The UPARSE pipeline reports operational taxonomic unit (OTU) sequences with ≤1% incorrect bases in artificial microbial community tests, compared with >3% incorrect bases commonly reported by other methods. The improved accuracy results in far fewer OTUs, consistently closer to the expected number of species in a community.
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                Author and article information

                Contributors
                Journal
                Functional Ecology
                Functional Ecology
                Wiley
                0269-8463
                1365-2435
                October 2020
                July 31 2020
                October 2020
                : 34
                : 10
                : 2158-2169
                Affiliations
                [1 ] Key Laboratory of Plant Immunity, Jiangsu Key Laboratory for Organic Solid Waste Utilization National Engineering Research Center for Organic‐based Fertilizers Nanjing Agricultural University Nanjing PR China
                [2 ] Jiangsu Key Laboratory for Eco‐Agricultural Biotechnology around Hongze Lake Jiangsu Collaborative Innovation Center of Regional Modern Agriculture & Environmental Protection Huaiyin Normal University Huaian PR China
                [3 ] Department of Biology University of York York UK
                [4 ] Department of Terrestrial Ecology Netherlands Institute of Ecology (NIOO‐KNAW) Wageningen The Netherlands
                [5 ] Laboratory of Nematology Department of Plant Science Wageningen University Wageningen The Netherlands
                [6 ] Institute for Environmental Biology Utrecht University Utrecht The Netherlands
                Article
                10.1111/1365-2435.13624
                6ea1e0df-b89d-4836-96f8-be2bb9b76dfc
                © 2020

                http://onlinelibrary.wiley.com/termsAndConditions#vor

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