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Metabolome–Microbiome Crosstalk and Human Disease

In this review, we discuss the growing literature demonstrating robust and pervasive associations between the microbiome and metabolome. We focus on the gut microbiome, which harbors the taxonomically most diverse and the largest collection of microorganisms in the human body. Methods for integrativ...

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Autores principales: Lee-Sarwar, Kathleen A., Lasky-Su, Jessica, Kelly, Rachel S., Litonjua, Augusto A., Weiss, Scott T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7281736/
https://www.ncbi.nlm.nih.gov/pubmed/32369899
http://dx.doi.org/10.3390/metabo10050181
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author Lee-Sarwar, Kathleen A.
Lasky-Su, Jessica
Kelly, Rachel S.
Litonjua, Augusto A.
Weiss, Scott T.
author_facet Lee-Sarwar, Kathleen A.
Lasky-Su, Jessica
Kelly, Rachel S.
Litonjua, Augusto A.
Weiss, Scott T.
author_sort Lee-Sarwar, Kathleen A.
collection PubMed
description In this review, we discuss the growing literature demonstrating robust and pervasive associations between the microbiome and metabolome. We focus on the gut microbiome, which harbors the taxonomically most diverse and the largest collection of microorganisms in the human body. Methods for integrative analysis of these “omics” are under active investigation and we discuss the advances and challenges in the combined use of metabolomics and microbiome data. Findings from large consortia, including the Human Microbiome Project and Metagenomics of the Human Intestinal Tract (MetaHIT) and others demonstrate the impact of microbiome-metabolome interactions on human health. Mechanisms whereby the microbes residing in the human body interact with metabolites to impact disease risk are beginning to be elucidated, and discoveries in this area will likely be harnessed to develop preventive and treatment strategies for complex diseases.
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spelling pubmed-72817362020-06-15 Metabolome–Microbiome Crosstalk and Human Disease Lee-Sarwar, Kathleen A. Lasky-Su, Jessica Kelly, Rachel S. Litonjua, Augusto A. Weiss, Scott T. Metabolites Review In this review, we discuss the growing literature demonstrating robust and pervasive associations between the microbiome and metabolome. We focus on the gut microbiome, which harbors the taxonomically most diverse and the largest collection of microorganisms in the human body. Methods for integrative analysis of these “omics” are under active investigation and we discuss the advances and challenges in the combined use of metabolomics and microbiome data. Findings from large consortia, including the Human Microbiome Project and Metagenomics of the Human Intestinal Tract (MetaHIT) and others demonstrate the impact of microbiome-metabolome interactions on human health. Mechanisms whereby the microbes residing in the human body interact with metabolites to impact disease risk are beginning to be elucidated, and discoveries in this area will likely be harnessed to develop preventive and treatment strategies for complex diseases. MDPI 2020-05-01 /pmc/articles/PMC7281736/ /pubmed/32369899 http://dx.doi.org/10.3390/metabo10050181 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Lee-Sarwar, Kathleen A.
Lasky-Su, Jessica
Kelly, Rachel S.
Litonjua, Augusto A.
Weiss, Scott T.
Metabolome–Microbiome Crosstalk and Human Disease
title Metabolome–Microbiome Crosstalk and Human Disease
title_full Metabolome–Microbiome Crosstalk and Human Disease
title_fullStr Metabolome–Microbiome Crosstalk and Human Disease
title_full_unstemmed Metabolome–Microbiome Crosstalk and Human Disease
title_short Metabolome–Microbiome Crosstalk and Human Disease
title_sort metabolome–microbiome crosstalk and human disease
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7281736/
https://www.ncbi.nlm.nih.gov/pubmed/32369899
http://dx.doi.org/10.3390/metabo10050181
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