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      Specialized metabolic functions of keystone taxa sustain soil microbiome stability

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          Abstract

          Background

          The relationship between biodiversity and soil microbiome stability remains poorly understood. Here, we investigated the impacts of bacterial phylogenetic diversity on the functional traits and the stability of the soil microbiome. Communities differing in phylogenetic diversity were generated by inoculating serially diluted soil suspensions into sterilized soil, and the stability of the microbiome was assessed by detecting community variations under various pH levels. The taxonomic features and potential functional traits were detected by DNA sequencing.

          Results

          We found that bacterial communities with higher phylogenetic diversity tended to be more stable, implying that microbiomes with higher biodiversity are more resistant to perturbation. Functional gene co-occurrence network and machine learning classification analyses identified specialized metabolic functions, especially “nitrogen metabolism” and “phosphonate and phosphinate metabolism,” as keystone functions. Further taxonomic annotation found that keystone functions are carried out by specific bacterial taxa, including Nitrospira and Gemmatimonas, among others.

          Conclusions

          This study provides new insights into our understanding of the relationships between soil microbiome biodiversity and ecosystem stability and highlights specialized metabolic functions embedded in keystone taxa that may be essential for soil microbiome stability.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s40168-020-00985-9.

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          Most cited references91

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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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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              Picante: R tools for integrating phylogenies and ecology.

              Picante is a software package that provides a comprehensive set of tools for analyzing the phylogenetic and trait diversity of ecological communities. The package calculates phylogenetic diversity metrics, performs trait comparative analyses, manipulates phenotypic and phylogenetic data, and performs tests for phylogenetic signal in trait distributions, community structure and species interactions. Picante is a package for the R statistical language and environment written in R and C, released under a GPL v2 open-source license, and freely available on the web (http://picante.r-forge.r-project.org) and from CRAN (http://cran.r-project.org).
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                Author and article information

                Contributors
                rfzhang@njau.edu.cn
                Journal
                Microbiome
                Microbiome
                Microbiome
                BioMed Central (London )
                2049-2618
                31 January 2021
                31 January 2021
                2021
                : 9
                : 35
                Affiliations
                [1 ]GRID grid.27871.3b, ISNI 0000 0000 9750 7019, Jiangsu Provincial Key Lab of Solid Organic Waste Utilization, Jiangsu Collaborative Innovation Center of Solid Organic Wastes, Educational Ministry Engineering Center of Resource-saving fertilizers, , Nanjing Agricultural University, ; Nanjing, 210095 Jiangsu People’s Republic of China
                [2 ]GRID grid.410727.7, ISNI 0000 0001 0526 1937, Key Laboratory of Microbial Resources Collection and Preservation, Ministry of Agriculture, Institute of Agricultural Resources and Regional Planning, , Chinese Academy of Agricultural Sciences, ; Beijing, 100081 People’s Republic of China
                Author information
                http://orcid.org/0000-0002-3334-4286
                Article
                985
                10.1186/s40168-020-00985-9
                7849160
                33517892
                4bbad422-e553-4c10-b5e6-0aedbe80eec3
                © 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
                : 19 July 2020
                : 16 December 2020
                Funding
                Funded by: the Young Elite Scientists Sponsorship Program by CAST
                Award ID: 2018QNRC001
                Award ID: 2018QNRC001
                Award Recipient :
                Funded by: the Agricultural Science and Technology Innovation Program of CAAS
                Award ID: CAAS-ZDRW202009
                Award Recipient :
                Funded by: the Agricultural Science and Technology Innovation Program of CAAS
                Award ID: CAAS-ZDRW202009
                Award Recipient :
                Funded by: the Fundamental Research Funds for the Central Universities
                Award ID: KYXK202004
                Award Recipient :
                Funded by: the National Natural Science Foundation of China
                Award ID: 32072675
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2021

                soil incubation,microbial diversity and stability,co-occurrence network,machine learning,keystone function

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