Applying multi-omic data integration methods to identify metabolites associated with dysbiotic vaginal microbiotas

Details

Supervisors

Symul, Laura

Faculty

Faculté des sciences

Degree label

Master [120] en statistique, orientation biostatistiques, à finalité spécialisée

Abstract

Bioinformatics and biostatistics is key to analyzing biologically sourced data across various fields. Within biostatistics, -omics data analysis focuses on processing and investigating data types such as transcriptomic, genomic, proteomic, and metabolomic. These analyses have significantly advanced microbiology and the study of microorganisms in their environment, enhancing our understanding of microbial activity’s potential role in the health of the human body. In the specific context of the vaginal microbiota, research has revealed microbiota dominated by Lactobacillus spp. is considered healthy, while its replacement by undesirable non-Lactobacillus spp. has detrimental effects on the host. These negative impacts are suspected to be partially mediated by bacteria-produced metabolites. However, the relationships between metabolite abundances and microbiota composition have yet to be described on an "omic" scale considering the entire set of metabolites and bacteria found in the vagina. This work aims to address this gap by performing a joint analysis of microbiota composition and metabolite abundance quantified on the same samples from two cohorts of American women. To acheive this, metabolomic and metagenomic datasets were preprocessed and integrated. For the compositional data, the relative abundance of microbes was calculated, and dimension reduction was performed using topic models, which grouped bacterial species into subcommunities. Metabolite data preprocessing involved handling missing values, filtering, transformation, batch effect correction. Following preprocessing, an integrative analysis was conducted using Structuration de Tableaux à Trois Indices de la Statistique, Simultaneous Component Analysis, and Canonical Correlation Analysis to identify associations between metabolites and microbial subcommunities. A comparative study was then performed to validate these associations. The most robust findings indicated that lactate and hippurate are associated with lactobacilli-dominated subcommunities, while putrescine and tyramine are linked to non-lactobacillus dominated subcommunities.