Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Emmenegger, Barbara, Massoni, Julien, Pestalozzi, Christine M., Bortfeld-Miller, Miriam, Maier, Benjamin A., Vorholt, Julia A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
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
_version_ 1785153195764350976
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
work_keys_str_mv AT emmeneggerbarbara identifyingmicrobiotacommunitypatternsimportantforplantprotectionusingsyntheticcommunitiesandmachinelearning
AT massonijulien identifyingmicrobiotacommunitypatternsimportantforplantprotectionusingsyntheticcommunitiesandmachinelearning
AT pestalozzichristinem identifyingmicrobiotacommunitypatternsimportantforplantprotectionusingsyntheticcommunitiesandmachinelearning
AT bortfeldmillermiriam identifyingmicrobiotacommunitypatternsimportantforplantprotectionusingsyntheticcommunitiesandmachinelearning
AT maierbenjamina identifyingmicrobiotacommunitypatternsimportantforplantprotectionusingsyntheticcommunitiesandmachinelearning
AT vorholtjuliaa identifyingmicrobiotacommunitypatternsimportantforplantprotectionusingsyntheticcommunitiesandmachinelearning