4.7 Article

Data-driven multiple-level analysis of gut-microbiome-immune-joint interactions in rheumatoid arthritis

期刊

BMC GENOMICS
卷 20, 期 -, 页码 -

出版社

BMC
DOI: 10.1186/s12864-019-5510-y

关键词

Gut microbiota; Metabolism; Rheumatoid arthritis; Computational analysis

资金

  1. Pfizer 2015 ASPIRE Rheumatology and Dermatology Research Award [WI206753]
  2. Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under the NIH Director's New Innovator Award [DP2HD084068]
  3. NIH National Institute of Aging [1 R01 AG057557-01, 1 R01 AG061388-01, 1 R56 AG062272-01]
  4. American Cancer Society Research Scholar Grant [RSG-16-049-01 - MPC]
  5. NIH Clinical and Translational Science Collaborative of Cleveland [1UL1TR002548-01]

向作者/读者索取更多资源

BackgroundRheumatoid 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.ResultsWe 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.ConclusionsOur study demonstrates strong gut-microbiome-immune-joint interactions in RA, which converged on both genetic, functional and phenotypic levels.

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