Deciphering associations between gut microbiota and clinical factors using microbial modules
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AbstractMotivation: Human gut microbiota plays a vital role in maintaining body health. The dysbiosis of gut microbiota is associated with a variety of diseases. It is critical to uncover the associations be-tween gut microbiota and disease states as well as other intrinsic or environmental factors. Howev-er, inferring alterations of individual microbial taxa based on relative abundance data likely leads to false associations and conflicting discoveries in different studies. Moreover, the effects of underlying factors and microbe-microbe interactions could lead to the alteration of larger sets of taxa. It might be more robust to investigate gut microbiota using groups of related taxa instead of the composition of individual taxa.
Results: We proposed a novel method to identify underlying microbial modules, i.e., groups of taxa with similar abundance patterns affected by a common latent factor, from longitudinal gut microbiota and applied it to inflammatory bowel disease (IBD). The identified modules demonstrated closer intra-group relationships, indicating potential microbe-microbe interactions and influences of underly-ing factors. Associations between the modules and several clinical factors were investigated, espe-cially disease states. The IBD-associated modules performed better in stratifying the subjects com-pared to the relative abundance of individual taxa. The modules were further validated in external cohorts, demonstrating the efficacy of the proposed method in identifying general and robust micro-bial modules. The study reveals the benefit of considering the ecological effects in gut microbiota analysis and the great promise of linking clinical factors with underlying microbial modules.
Results: We proposed a novel method to identify underlying microbial modules, i.e., groups of taxa with similar abundance patterns affected by a common latent factor, from longitudinal gut microbiota and applied it to inflammatory bowel disease (IBD). The identified modules demonstrated closer intra-group relationships, indicating potential microbe-microbe interactions and influences of underly-ing factors. Associations between the modules and several clinical factors were investigated, espe-cially disease states. The IBD-associated modules performed better in stratifying the subjects com-pared to the relative abundance of individual taxa. The modules were further validated in external cohorts, demonstrating the efficacy of the proposed method in identifying general and robust micro-bial modules. The study reveals the benefit of considering the ecological effects in gut microbiota analysis and the great promise of linking clinical factors with underlying microbial modules.
Acceptance Date12/03/2023
All Author(s) ListRan Wang, Xubin Zheng, Fangda Song, Man Hon Wong, Kwong Sak Leung, Lixin Cheng
Journal nameBioinformatics
Year2023
Month5
Volume Number39
Issue Number5
Article numberbtad213
ISSN1367-4803
eISSN1460-2059
LanguagesEnglish-United States
