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Identifying microbiota community patterns important for plant protection using synthetic communities and machine learning
Plant-associated microbiomes contribute to important ecosystem functions such as host resistance to biotic and abiotic stresses. The factors that determine such community outcomes are inherently difficult to identify under complex environmental conditions. In this study, we present an experimental a...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10693592/ https://www.ncbi.nlm.nih.gov/pubmed/38042924 http://dx.doi.org/10.1038/s41467-023-43793-z |
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author | Emmenegger, Barbara Massoni, Julien Pestalozzi, Christine M. Bortfeld-Miller, Miriam Maier, Benjamin A. Vorholt, Julia A. |
author_facet | Emmenegger, Barbara Massoni, Julien Pestalozzi, Christine M. Bortfeld-Miller, Miriam Maier, Benjamin A. Vorholt, Julia A. |
author_sort | Emmenegger, Barbara |
collection | PubMed |
description | Plant-associated microbiomes contribute to important ecosystem functions such as host resistance to biotic and abiotic stresses. The factors that determine such community outcomes are inherently difficult to identify under complex environmental conditions. In this study, we present an experimental and analytical approach to explore microbiota properties relevant for a microbiota-conferred host phenotype, here plant protection, in a reductionist system. We screened 136 randomly assembled synthetic communities (SynComs) of five bacterial strains each, followed by classification and regression analyses as well as empirical validation to test potential explanatory factors of community structure and composition, including evenness, total commensal colonization, phylogenetic diversity, and strain identity. We find strain identity to be the most important predictor of pathogen reduction, with machine learning algorithms improving performances compared to random classifications (94-100% versus 32% recall) and non-modelled predictions (0.79-1.06 versus 1.5 RMSE). Further experimental validation confirms three strains as the main drivers of pathogen reduction and two additional strains that confer protection in combination. Beyond the specific application presented in our study, we provide a framework that can be adapted to help determine features relevant for microbiota function in other biological systems. |
format | Online Article Text |
id | pubmed-10693592 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106935922023-12-04 Identifying microbiota community patterns important for plant protection using synthetic communities and machine learning Emmenegger, Barbara Massoni, Julien Pestalozzi, Christine M. Bortfeld-Miller, Miriam Maier, Benjamin A. Vorholt, Julia A. Nat Commun Article Plant-associated microbiomes contribute to important ecosystem functions such as host resistance to biotic and abiotic stresses. The factors that determine such community outcomes are inherently difficult to identify under complex environmental conditions. In this study, we present an experimental and analytical approach to explore microbiota properties relevant for a microbiota-conferred host phenotype, here plant protection, in a reductionist system. We screened 136 randomly assembled synthetic communities (SynComs) of five bacterial strains each, followed by classification and regression analyses as well as empirical validation to test potential explanatory factors of community structure and composition, including evenness, total commensal colonization, phylogenetic diversity, and strain identity. We find strain identity to be the most important predictor of pathogen reduction, with machine learning algorithms improving performances compared to random classifications (94-100% versus 32% recall) and non-modelled predictions (0.79-1.06 versus 1.5 RMSE). Further experimental validation confirms three strains as the main drivers of pathogen reduction and two additional strains that confer protection in combination. Beyond the specific application presented in our study, we provide a framework that can be adapted to help determine features relevant for microbiota function in other biological systems. Nature Publishing Group UK 2023-12-02 /pmc/articles/PMC10693592/ /pubmed/38042924 http://dx.doi.org/10.1038/s41467-023-43793-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Emmenegger, Barbara Massoni, Julien Pestalozzi, Christine M. Bortfeld-Miller, Miriam Maier, Benjamin A. Vorholt, Julia A. Identifying microbiota community patterns important for plant protection using synthetic communities and machine learning |
title | Identifying microbiota community patterns important for plant protection using synthetic communities and machine learning |
title_full | Identifying microbiota community patterns important for plant protection using synthetic communities and machine learning |
title_fullStr | Identifying microbiota community patterns important for plant protection using synthetic communities and machine learning |
title_full_unstemmed | Identifying microbiota community patterns important for plant protection using synthetic communities and machine learning |
title_short | Identifying microbiota community patterns important for plant protection using synthetic communities and machine learning |
title_sort | identifying microbiota community patterns important for plant protection using synthetic communities and machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10693592/ https://www.ncbi.nlm.nih.gov/pubmed/38042924 http://dx.doi.org/10.1038/s41467-023-43793-z |
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