Unveiling Mechanisms in Vaginal Microbiome Dynamics through Model-Driven Analysis

Bacterial vaginosis (BV) is the most prevalent vaginal condition among reproductive-age women experiencing vaginal complaints. Despite its significant impact on women's health, limited knowledge exists regarding the microbial community structure and metabolic interactions associated with BV. In this study, we analyzed metagenomic data obtained from human vaginal swabs to generate in silico predictions of BV-associated bacterial metabolic interactions via genome-scale metabolic network reconstructions (GENREs). While most efforts to type symptomatic BV (and thus guide therapeutic intervention by identifying responders and non-responders to treatment) is based on genomic profiling, our in silico simulations revealed functional metabolic relatedness between species as quite distinct from genetic relatedness.