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      Cropping practices manipulate abundance patterns of root and soil microbiome members paving the way to smart farming

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          Abstract

          Background

          Harnessing beneficial microbes presents a promising strategy to optimize plant growth and agricultural sustainability. Little is known to which extent and how specifically soil and plant microbiomes can be manipulated through different cropping practices. Here, we investigated soil and wheat root microbial communities in a cropping system experiment consisting of conventional and organic managements, both with different tillage intensities.

          Results

          While microbial richness was marginally affected, we found pronounced cropping effects on community composition, which were specific for the respective microbiomes. Soil bacterial communities were primarily structured by tillage, whereas soil fungal communities responded mainly to management type with additional effects by tillage. In roots, management type was also the driving factor for bacteria but not for fungi, which were generally determined by changes in tillage intensity. To quantify an “effect size” for microbiota manipulation, we found that about 10% of variation in microbial communities was explained by the tested cropping practices. Cropping sensitive microbes were taxonomically diverse, and they responded in guilds of taxa to the specific practices. These microbes also included frequent community members or members co-occurring with many other microbes in the community, suggesting that cropping practices may allow manipulation of influential community members.

          Conclusions

          Understanding the abundance patterns of cropping sensitive microbes presents the basis towards developing microbiota management strategies for smart farming. For future targeted microbiota management—e.g., to foster certain microbes with specific agricultural practices—a next step will be to identify the functional traits of the cropping sensitive microbes.

          Electronic supplementary material

          The online version of this article (10.1186/s40168-017-0389-9) contains supplementary material, which is available to authorized users.

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          Most cited references95

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          edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

          Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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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
                kyle.hartman@uzh.ch
                marcel.vanderheijden@agroscope.admin.ch
                raphael.wittwer@agroscope.admin.ch
                samiran.banerjee@agroscope.admin.ch
                jean-claude.walser@env.ethz.ch
                0041 58 468 72 92 , klaus.schlaeppi@agroscope.admin.ch
                Journal
                Microbiome
                Microbiome
                Microbiome
                BioMed Central (London )
                2049-2618
                16 January 2018
                16 January 2018
                2018
                : 6
                : 14
                Affiliations
                [1 ]ISNI 0000 0004 4681 910X, GRID grid.417771.3, Plant-Soil Interactions, Department of Agroecology and Environment, Agroscope, ; Zurich, Switzerland
                [2 ]ISNI 0000 0004 1937 0650, GRID grid.7400.3, Institute for Evolutionary Biology and Environmental Studies, , University of Zurich, ; Zurich, Switzerland
                [3 ]ISNI 0000 0001 2156 2780, GRID grid.5801.c, Genetic Diversity Centre, , ETH Zurich, ; Zurich, Switzerland
                [4 ]ISNI 0000000120346234, GRID grid.5477.1, Plant-Microbe Interactions, Institute of Environmental Biology, Faculty of Science, , Utrecht University, ; Utrecht, The Netherlands
                Author information
                http://orcid.org/0000-0003-3620-0875
                Article
                389
                10.1186/s40168-017-0389-9
                5771023
                29338764
                2df3d563-dfa4-4ced-a9ee-d254276961b6
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 2 August 2017
                : 17 December 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001711, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung;
                Award ID: PDFMP3_137136
                Award ID: 31003A_165891
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2018

                soil and root microbiomes,microbial co-occurrence,network analysis,cropping practices,microbiota management,smart farming

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