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A meta-analysis study of the robustness and universality of gut microbiome-metabolome associations

BACKGROUND: Microbiome-metabolome studies of the human gut have been gaining popularity in recent years, mostly due to accumulating evidence of the interplay between gut microbes, metabolites, and host health. Statistical and machine learning-based methods have been widely applied to analyze such pa...

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Autores principales: Muller, Efrat, Algavi, Yadid M., Borenstein, Elhanan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8507343/
https://www.ncbi.nlm.nih.gov/pubmed/34641974
http://dx.doi.org/10.1186/s40168-021-01149-z
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author Muller, Efrat
Algavi, Yadid M.
Borenstein, Elhanan
author_facet Muller, Efrat
Algavi, Yadid M.
Borenstein, Elhanan
author_sort Muller, Efrat
collection PubMed
description BACKGROUND: Microbiome-metabolome studies of the human gut have been gaining popularity in recent years, mostly due to accumulating evidence of the interplay between gut microbes, metabolites, and host health. Statistical and machine learning-based methods have been widely applied to analyze such paired microbiome-metabolome data, in the hope of identifying metabolites that are governed by the composition of the microbiome. Such metabolites can be likely modulated by microbiome-based interventions, offering a route for promoting gut metabolic health. Yet, to date, it remains unclear whether findings of microbially associated metabolites in any single study carry over to other studies or cohorts, and how robust and universal are microbiome-metabolites links. RESULTS: In this study, we addressed this challenge by performing a comprehensive meta-analysis to identify human gut metabolites that can be predicted based on the composition of the gut microbiome across multiple studies. We term such metabolites “robustly well-predicted”. To this end, we processed data from 1733 samples from 10 independent human gut microbiome-metabolome studies, focusing initially on healthy subjects, and implemented a machine learning pipeline to predict metabolite levels in each dataset based on the composition of the microbiome. Comparing the predictability of each metabolite across datasets, we found 97 robustly well-predicted metabolites. These include metabolites involved in important microbial pathways such as bile acid transformations and polyamines metabolism. Importantly, however, other metabolites exhibited large variation in predictability across datasets, suggesting a cohort- or study-specific relationship between the microbiome and the metabolite. Comparing taxonomic contributors to different models, we found that some robustly well-predicted metabolites were predicted by markedly different sets of taxa across datasets, suggesting that some microbially associated metabolites may be governed by different members of the microbiome in different cohorts. We finally examined whether models trained on a control group of a given study successfully predicted the metabolite’s level in the disease group of the same study, identifying several metabolites where the model was not transferable, indicating a shift in microbial metabolism in disease-associated dysbiosis. CONCLUSIONS: Combined, our findings provide a better understanding of the link between the microbiome and metabolites and allow researchers to put identified microbially associated metabolites within the context of other studies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40168-021-01149-z.
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spelling pubmed-85073432021-10-20 A meta-analysis study of the robustness and universality of gut microbiome-metabolome associations Muller, Efrat Algavi, Yadid M. Borenstein, Elhanan Microbiome Research BACKGROUND: Microbiome-metabolome studies of the human gut have been gaining popularity in recent years, mostly due to accumulating evidence of the interplay between gut microbes, metabolites, and host health. Statistical and machine learning-based methods have been widely applied to analyze such paired microbiome-metabolome data, in the hope of identifying metabolites that are governed by the composition of the microbiome. Such metabolites can be likely modulated by microbiome-based interventions, offering a route for promoting gut metabolic health. Yet, to date, it remains unclear whether findings of microbially associated metabolites in any single study carry over to other studies or cohorts, and how robust and universal are microbiome-metabolites links. RESULTS: In this study, we addressed this challenge by performing a comprehensive meta-analysis to identify human gut metabolites that can be predicted based on the composition of the gut microbiome across multiple studies. We term such metabolites “robustly well-predicted”. To this end, we processed data from 1733 samples from 10 independent human gut microbiome-metabolome studies, focusing initially on healthy subjects, and implemented a machine learning pipeline to predict metabolite levels in each dataset based on the composition of the microbiome. Comparing the predictability of each metabolite across datasets, we found 97 robustly well-predicted metabolites. These include metabolites involved in important microbial pathways such as bile acid transformations and polyamines metabolism. Importantly, however, other metabolites exhibited large variation in predictability across datasets, suggesting a cohort- or study-specific relationship between the microbiome and the metabolite. Comparing taxonomic contributors to different models, we found that some robustly well-predicted metabolites were predicted by markedly different sets of taxa across datasets, suggesting that some microbially associated metabolites may be governed by different members of the microbiome in different cohorts. We finally examined whether models trained on a control group of a given study successfully predicted the metabolite’s level in the disease group of the same study, identifying several metabolites where the model was not transferable, indicating a shift in microbial metabolism in disease-associated dysbiosis. CONCLUSIONS: Combined, our findings provide a better understanding of the link between the microbiome and metabolites and allow researchers to put identified microbially associated metabolites within the context of other studies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40168-021-01149-z. BioMed Central 2021-10-12 /pmc/articles/PMC8507343/ /pubmed/34641974 http://dx.doi.org/10.1186/s40168-021-01149-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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.
spellingShingle Research
Muller, Efrat
Algavi, Yadid M.
Borenstein, Elhanan
A meta-analysis study of the robustness and universality of gut microbiome-metabolome associations
title A meta-analysis study of the robustness and universality of gut microbiome-metabolome associations
title_full A meta-analysis study of the robustness and universality of gut microbiome-metabolome associations
title_fullStr A meta-analysis study of the robustness and universality of gut microbiome-metabolome associations
title_full_unstemmed A meta-analysis study of the robustness and universality of gut microbiome-metabolome associations
title_short A meta-analysis study of the robustness and universality of gut microbiome-metabolome associations
title_sort meta-analysis study of the robustness and universality of gut microbiome-metabolome associations
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8507343/
https://www.ncbi.nlm.nih.gov/pubmed/34641974
http://dx.doi.org/10.1186/s40168-021-01149-z
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