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Data-driven multiple-level analysis of gut-microbiome-immune-joint interactions in rheumatoid arthritis
BACKGROUND: Rheumatoid arthritis (RA) is the most common autoimmune disease and affects about 1% of the population. The cause of RA remains largely unknown and could result from a complex interaction between genes and environment factors. Recent studies suggested that gut microbiota and their collec...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6371598/ https://www.ncbi.nlm.nih.gov/pubmed/30744546 http://dx.doi.org/10.1186/s12864-019-5510-y |
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author | Wang, QuanQiu Xu, Rong |
author_facet | Wang, QuanQiu Xu, Rong |
author_sort | Wang, QuanQiu |
collection | PubMed |
description | BACKGROUND: Rheumatoid arthritis (RA) is the most common autoimmune disease and affects about 1% of the population. The cause of RA remains largely unknown and could result from a complex interaction between genes and environment factors. Recent studies suggested that gut microbiota and their collective metabolic outputs exert profound effects on the host immune system and are implicated in RA. However, which and how gut microbial metabolites interact with host genetics in contributing to RA pathogenesis remains unknown. In this study, we present a data-driven study to understand how gut microbial metabolites contribute to RA at the genetic, functional and phenotypic levels. RESULTS: We used publicly available disease genetics, chemical genetics, human metabolome, genetic signaling pathways, mouse genome-wide mutation phenotypes, and mouse phenotype ontology data. We identified RA-associated microbial metabolites and prioritized them based on their genetic, functional and phenotypic relevance to RA. We evaluated the prioritization methods using short-chain fatty acids (SCFAs), which were previously shown to be involved in RA etiology. We validate the algorithms by showing that SCFAs are highly associated with RA at genetic, functional and phenotypic levels: SCFAs ranked at top 3.52% based on shared genes with RA, top 5.69% based on shared genetic pathways, and top 16.94% based on shared phenotypes. Based on the genetic-level analysis, human gut microbial metabolites directly interact with many RA-associated genes (as many as 18.1% of all 166 RA genes). Based on the functional-level analysis, human gut microbial metabolites participate in many RA-associated genetic pathways (as many as 71.4% of 311 genetic pathways significantly enriched for RA), including immune system pathways. Based on the phenotypic-level analysis, gut microbial metabolites affect many RA-related phenotypes (as many as 51.3% of 978 phenotypes significantly enriched for RA), including many immune system phenotypes. CONCLUSIONS: Our study demonstrates strong gut-microbiome-immune-joint interactions in RA, which converged on both genetic, functional and phenotypic levels. |
format | Online Article Text |
id | pubmed-6371598 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-63715982019-02-25 Data-driven multiple-level analysis of gut-microbiome-immune-joint interactions in rheumatoid arthritis Wang, QuanQiu Xu, Rong BMC Genomics Research Article BACKGROUND: Rheumatoid arthritis (RA) is the most common autoimmune disease and affects about 1% of the population. The cause of RA remains largely unknown and could result from a complex interaction between genes and environment factors. Recent studies suggested that gut microbiota and their collective metabolic outputs exert profound effects on the host immune system and are implicated in RA. However, which and how gut microbial metabolites interact with host genetics in contributing to RA pathogenesis remains unknown. In this study, we present a data-driven study to understand how gut microbial metabolites contribute to RA at the genetic, functional and phenotypic levels. RESULTS: We used publicly available disease genetics, chemical genetics, human metabolome, genetic signaling pathways, mouse genome-wide mutation phenotypes, and mouse phenotype ontology data. We identified RA-associated microbial metabolites and prioritized them based on their genetic, functional and phenotypic relevance to RA. We evaluated the prioritization methods using short-chain fatty acids (SCFAs), which were previously shown to be involved in RA etiology. We validate the algorithms by showing that SCFAs are highly associated with RA at genetic, functional and phenotypic levels: SCFAs ranked at top 3.52% based on shared genes with RA, top 5.69% based on shared genetic pathways, and top 16.94% based on shared phenotypes. Based on the genetic-level analysis, human gut microbial metabolites directly interact with many RA-associated genes (as many as 18.1% of all 166 RA genes). Based on the functional-level analysis, human gut microbial metabolites participate in many RA-associated genetic pathways (as many as 71.4% of 311 genetic pathways significantly enriched for RA), including immune system pathways. Based on the phenotypic-level analysis, gut microbial metabolites affect many RA-related phenotypes (as many as 51.3% of 978 phenotypes significantly enriched for RA), including many immune system phenotypes. CONCLUSIONS: Our study demonstrates strong gut-microbiome-immune-joint interactions in RA, which converged on both genetic, functional and phenotypic levels. BioMed Central 2019-02-11 /pmc/articles/PMC6371598/ /pubmed/30744546 http://dx.doi.org/10.1186/s12864-019-5510-y Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Wang, QuanQiu Xu, Rong Data-driven multiple-level analysis of gut-microbiome-immune-joint interactions in rheumatoid arthritis |
title | Data-driven multiple-level analysis of gut-microbiome-immune-joint interactions in rheumatoid arthritis |
title_full | Data-driven multiple-level analysis of gut-microbiome-immune-joint interactions in rheumatoid arthritis |
title_fullStr | Data-driven multiple-level analysis of gut-microbiome-immune-joint interactions in rheumatoid arthritis |
title_full_unstemmed | Data-driven multiple-level analysis of gut-microbiome-immune-joint interactions in rheumatoid arthritis |
title_short | Data-driven multiple-level analysis of gut-microbiome-immune-joint interactions in rheumatoid arthritis |
title_sort | data-driven multiple-level analysis of gut-microbiome-immune-joint interactions in rheumatoid arthritis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6371598/ https://www.ncbi.nlm.nih.gov/pubmed/30744546 http://dx.doi.org/10.1186/s12864-019-5510-y |
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