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      A single bacterial genus maintains root growth in a complex microbiome

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          Abstract

          Plants grow within a complex web of species that interact with each other and with the plant 110 . These interactions are governed by a wide repertoire of chemical signals, and the resulting chemical landscape of the rhizosphere can strongly affect root health and development 79, 1118 . Here, to understand how interactions between microorganisms influence root growth in Arabidopsis, we established a model system for interactions between plants, microorganisms and the environment. We inoculated seedlings with a 185-member bacterial synthetic community, manipulated the abiotic environment and measured bacterial colonization of the plant. This enabled us to classify the synthetic community into four modules of co-occurring strains. We deconstructed the synthetic community on the basis of these modules, and identified interactions between microorganisms that determine root phenotype. These interactions primarily involve a single bacterial genus ( Variovorax), which completely reverses the severe inhibition of root growth that is induced by a wide diversity of bacterial strains as well as by the entire 185-member community. We demonstrate that Variovorax manipulates plant hormone levels to balance the effects of our ecologically realistic synthetic root community on root growth. We identify an auxin-degradation operon that is conserved in all available genomes of Variovorax and is necessary and sufficient for the reversion of root growth inhibition. Therefore, metabolic signal interference shapes bacteria–plant communication networks and is essential for maintaining the stereotypic developmental programme of the root. Optimizing the feedbacks that shape chemical interaction networks in the rhizosphere provides a promising ecological strategy for developing more resilient and productive crops.

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          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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            Trimmomatic: a flexible trimmer for Illumina sequence data

            Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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              Fiji: an open-source platform for biological-image analysis.

              Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                5 July 2023
                November 2020
                30 September 2020
                08 July 2023
                : 587
                : 7832
                : 103-108
                Affiliations
                [1 ]Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
                [2 ]Howard Hughes Medical Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
                [3 ]Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
                [4 ]Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
                [5 ]Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
                [6 ]Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
                [7 ]Curriculum in Genetics and Molecular Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
                [8 ]Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
                [9 ]Present address: Department of Plant and Environmental Sciences, Institute of Life Science, The Hebrew University of Jerusalem, Jerusalem, Israel.
                [10 ]Present address: Future Food Beacon of Excellence, School of Biosciences, University of Nottingham, Sutton Bonington, UK.
                [11 ]Present address: Department of Biology, ‘Luiz de Queiroz’ College of Agriculture (ESALQ), University of São Paulo (USP), Piracicaba, Brazil.
                [12 ]These authors contributed equally: Omri M. Finkel, Isai Salas-González, Gabriel Castrillo, Jonathan M. Conway.
                Author notes

                Author contributions J.L.D. supervised the project. O.M.F., I.S.-G., G.C. and J.L.D. conceptualized the project. O.M.F., I.S.-G., G.C., J.M.C. and J.L.D. designed experiments. O.M.F., I.S.-G., G.C. and T.F.L. designed and performed most in planta agar-based experiments. C.R.F., O.M.F. and I.S.-G. designed and performed soil experiments. O.M.F., I.S.-G., G.C. and T.F.L. processed and sequenced bacterial DNA samples. J.M.C. and E.D.W. designed and performed bacterial gain- and loss-of function experiments and bacterial RNA-seq experiments. O.M.F., T.F.L. and P.J.P.L.T. processed and sequenced plant RNA-seq samples. O.M.F. and G.C. performed plant morphometric measurements. I.S.-G., O.M.F., G.C. and J.M.C analysed data. I.S.-G. performed bioinformatics analyses, including bacterial 16S rRNA, plant and bacterial RNA-seq analysis and bacterial comparative genomics, with input from O.M.F., G.C., J.M.C. and J.L.D. I.S.-G. performed statistical analysis, with input from O.M.F., G.C., C.D.J. and J.L.D. I.S.-G. curated the data used in the manuscript with help from O.M.F. O.M.F., I.S.-G. and G.C. wrote the manuscript with input from all co-authors.

                [] Correspondence and requests for materials should be addressed to J.L.D. dangl@ 123456email.unc.edu
                Article
                HHMIMS1911321
                10.1038/s41586-020-2778-7
                10329457
                32999461
                bec11359-d724-41d7-8452-31b716b44d42

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