Deciphering associations between gut microbiota and clinical factors using microbial modules

Wang, Ran1,2; Zheng, Xubin1,2; Song, Fangda3; Wong, Man Hon2; Leung, Kwong Sak2,4; Cheng, Lixin1,5,6

2023-05-04

10.1093/bioinformatics/btad213

BIOINFORMATICS   Impact Factor & Quartile

1367-4803

1367-4811

Motivation 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 between gut microbiota and disease states as well as other intrinsic or environmental factors. However, 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 intragroup relationships, indicating potential microbe-microbe interactions and influences of underlying factors. Associations between the modules and several clinical factors were investigated, especially disease states. The IBD-associated modules performed better in stratifying the subjects compared with 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 microbial 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.Availability and implementation

SCI

English

First ; Corresponding

Shenzhen Science and Technology Program[JCYJ20220530152409020]

Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics

Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability

WOS:000991892700005

OXFORD UNIV PRESS

BIOLOGY & BIOCHEMISTRY

Web of Science

1.Southern Univ Sci & Technol, Jinan Univ, Shenzhen Peoples Hosp, Affiliated Hosp 1,Clin Med Coll 2, Shenzhen 518020, Peoples R China
2.Chinese Univ Hong Kong, Dept Comp Sci & Engn, Shatin, Hong Kong, Peoples R China
3.Chinese Univ Hong Kong, Sch Data Sci, Shenzhen 518000, Peoples R China
4.Hong Kong Shue Yan Univ, Dept Appl Data Sci, Hong Kong, Peoples R China
5.Guangdong Prov Clin Res Ctr Geriatr, Shenzhen Clin Res Ctr Geriatr, Shenzhen 518020, Peoples R China
6.Shenzhen Peoples Hosp, Guangdong Prov Clin Res Ctr Geriatr, Shenzhen Clin Res Ctr Geriatr, Shenzhen 518020, Peoples R China

Wang, Ran,Zheng, Xubin,Song, Fangda,et al. Deciphering associations between gut microbiota and clinical factors using microbial modules[J]. BIOINFORMATICS,2023,39(5).

Wang, Ran,Zheng, Xubin,Song, Fangda,Wong, Man Hon,Leung, Kwong Sak,&Cheng, Lixin.(2023).Deciphering associations between gut microbiota and clinical factors using microbial modules.BIOINFORMATICS,39(5).

Wang, Ran,et al."Deciphering associations between gut microbiota and clinical factors using microbial modules".BIOINFORMATICS 39.5(2023).