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      Small changes in rhizosphere microbiome composition predict disease outcomes earlier than pathogen density variations

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          Abstract

          Even in homogeneous conditions, plants facing a soilborne pathogen tend to show a binary outcome with individuals either remaining fully healthy or developing severe to lethal disease symptoms. As the rhizosphere microbiome is a major determinant of plant health, we postulated that such a binary outcome may result from an early divergence in the rhizosphere microbiome assembly that may further cascade into varying disease suppression abilities. We tested this hypothesis by setting up a longitudinal study of tomato plants growing in a natural but homogenized soil infested with the soilborne bacterial pathogen Ralstonia solanacearum. Starting from an originally identical species pool, individual rhizosphere microbiome compositions rapidly diverged into multiple configurations during the plant vegetative growth. This variation in community composition was strongly associated with later disease development during the later fruiting state. Most interestingly, these patterns also significantly predicted disease outcomes 2 weeks before any difference in pathogen density became apparent between the healthy and diseased groups. In this system, a total of 135 bacterial OTUs were associated with persistent healthy plants. Five of these enriched OTUs ( Lysinibacillus, Pseudarthrobacter, Bordetella, Bacillus, and Chryseobacterium) were isolated and shown to reduce disease severity by 30.4–100% when co-introduced with the pathogen. Overall, our results demonstrated that an initially homogenized soil can rapidly diverge into rhizosphere microbiomes varying in their ability to promote plant protection. This suggests that early life interventions may have significant effects on later microbiome states, and highlights an exciting opportunity for microbiome diagnostics and plant disease prevention.

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          Most cited references61

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          Metagenomic biomarker discovery and explanation

          This study describes and validates a new method for metagenomic biomarker discovery by way of class comparison, tests of biological consistency and effect size estimation. This addresses the challenge of finding organisms, genes, or pathways that consistently explain the differences between two or more microbial communities, which is a central problem to the study of metagenomics. We extensively validate our method on several microbiomes and a convenient online interface for the method is provided at http://huttenhower.sph.harvard.edu/lefse/.
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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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              Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy.

              The Ribosomal Database Project (RDP) Classifier, a naïve Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes (2nd ed., release 5.0, Springer-Verlag, New York, NY, 2004). It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The majority of classifications (98%) were of high estimated confidence (> or = 95%) and high accuracy (98%). In addition to being tested with the corpus of 5,014 type strain sequences from Bergey's outline, the RDP Classifier was tested with a corpus of 23,095 rRNA sequences as assigned by the NCBI into their alternative higher-order taxonomy. The results from leave-one-out testing on both corpora show that the overall accuracies at all levels of confidence for near-full-length and 400-base segments were 89% or above down to the genus level, and the majority of the classification errors appear to be due to anomalies in the current taxonomies. For shorter rRNA segments, such as those that might be generated by pyrosequencing, the error rate varied greatly over the length of the 16S rRNA gene, with segments around the V2 and V4 variable regions giving the lowest error rates. The RDP Classifier is suitable both for the analysis of single rRNA sequences and for the analysis of libraries of thousands of sequences. Another related tool, RDP Library Compare, was developed to facilitate microbial-community comparison based on 16S rRNA gene sequence libraries. It combines the RDP Classifier with a statistical test to flag taxa differentially represented between samples. The RDP Classifier and RDP Library Compare are available online at http://rdp.cme.msu.edu/.
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                Author and article information

                Contributors
                weizhong@njau.edu.cn
                Journal
                ISME J
                ISME J
                The ISME Journal
                Nature Publishing Group UK (London )
                1751-7362
                1751-7370
                22 July 2022
                22 July 2022
                October 2022
                : 16
                : 10
                : 2448-2456
                Affiliations
                [1 ]GRID grid.27871.3b, ISNI 0000 0000 9750 7019, Joint International Research Laboratory of Soil Health, Jiangsu Provincial Key Lab for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, National Engineering Research Center for Organic-based Fertilizers, , Nanjing Agricultural University, ; Nanjing, PR China
                [2 ]GRID grid.412022.7, ISNI 0000 0000 9389 5210, College of Food Science and Light Industry, , Nanjing Tech University, ; Nanjing, PR China
                [3 ]GRID grid.261055.5, ISNI 0000 0001 2293 4611, Department of Microbiological Sciences, , North Dakota State University, ; Fargo, ND USA
                [4 ]GRID grid.29857.31, ISNI 0000 0001 2097 4281, Department of Plant Science, , The Pennsylvania State University, ; University Park, PA USA
                [5 ]GRID grid.29857.31, ISNI 0000 0001 2097 4281, Huck Institutes of the Life Sciences, , The Pennsylvania State University, ; University Park, PA USA
                Author information
                http://orcid.org/0000-0003-2740-5561
                http://orcid.org/0000-0002-5662-9620
                http://orcid.org/0000-0002-6805-2486
                http://orcid.org/0000-0002-7967-4897
                Article
                1290
                10.1038/s41396-022-01290-z
                9478146
                35869387
                6662dcd2-e297-46e1-9341-036555371da0
                © The Author(s) 2022

                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/.

                History
                : 18 January 2022
                : 4 July 2022
                : 6 July 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 42090060
                Award ID: 41922053
                Award Recipient :
                Categories
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                © International Society for Microbial Ecology 2022

                Microbiology & Virology
                microbial ecology
                Microbiology & Virology
                microbial ecology

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