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      Crop host signatures reflected by co-association patterns of keystone Bacteria in the rhizosphere microbiota

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          Abstract

          Background

          The native crop bacterial microbiota of the rhizosphere is envisioned to be engineered for sustainable agriculture. This requires the identification of keystone rhizosphere Bacteria and an understanding on how these govern crop-specific microbiome assembly from soils. We identified the metabolically active bacterial microbiota (SSU RNA) inhabiting two compartments of the rhizosphere of wheat ( Triticum aestivum L.), barley ( Hordeum vulgare L.), rye ( Secale cereale), and oilseed rape ( Brassica napus L.) at different growth stages.

          Results

          Based on metabarcoding analysis the bacterial microbiota was shaped by the two rhizosphere compartments, i.e. close and distant. Thereby implying a different spatial extent of bacterial microbiota acquirement by the cereals species versus oilseed rape. We derived core microbiota of each crop species. Massilia (barley and wheat) and unclassified Chloroflexi of group ‘KD4-96’ (oilseed rape) were identified as keystone Bacteria by combining LEfSe biomarker and network analyses. Subsequently, differential associations between networks of each crop species’ core microbiota revealed host plant-specific interconnections for specific genera, such as the unclassified Tepidisphaeraceae ‘WD2101 soil group’.

          Conclusions

          Our results provide keystone rhizosphere Bacteria derived from for crop hosts and revealed that cohort subnetworks and differential associations elucidated host species effect that was not evident from differential abundance of single bacterial genera enriched or unique to a specific plant host. Thus, we underline the importance of co-occurrence patterns within the rhizosphere microbiota that emerge in crop-specific microbiomes, which will be essential to modify native crop microbiomes for future agriculture and to develop effective bio-fertilizers.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s40793-021-00387-w.

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          Most cited references53

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          DADA2: High resolution sample inference from Illumina amplicon data

          We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
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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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              Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

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                Author and article information

                Contributors
                kolb@zalf.de
                Journal
                Environ Microbiome
                Environ Microbiome
                Environmental Microbiome
                BioMed Central (London )
                2524-6372
                12 October 2021
                12 October 2021
                2021
                : 16
                : 18
                Affiliations
                [1 ]GRID grid.433014.1, Microbial Biogeochemistry, Research Area Landscape Functioning, , Leibniz Centre for Agricultural Landscape Research e.V. (ZALF), ; Müncheberg, Germany
                [2 ]GRID grid.7468.d, ISNI 0000 0001 2248 7639, Thaer Institute, Faculty of Life Sciences, , Humboldt University of Berlin, ; Berlin, Germany
                Author information
                http://orcid.org/0000-0002-5455-8662
                Article
                387
                10.1186/s40793-021-00387-w
                8513244
                34641981
                edc25699-ecc2-4f2e-a14d-9d757bf8428c
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.

                History
                : 10 August 2021
                : 28 September 2021
                Funding
                Funded by: Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF) e.V. (3493)
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2021

                16s rrna,amplicon sequencing,barley,co-occurrence network,lefse,oilseed rape,rna,rye,wheat

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