Risks and etiology of bacterial vaginosis revealed by species dominance network analysis

BV (bacterial vaginosis) influences 20-40% of women but its etiology is still poorly understood. An open question about the BV is which of the hundreds of bacteria found in the human vaginal microbiome (HVM) are the causal pathogens of BV? Existing attempts to identify them have failed for at least two reasons: (i) a focus on species per se that ignores species interactions; (ii) a lack of systems-level understanding of the HVM. Here, we recast the question of microbial causality of BV by asking if there are any reliable 'signatures' of community composition (or poly-microbial 'cults') associated with it? We apply a new framework (species dominance network analysis by Ma & Ellison (2019: Ecological Monographs) to detect critical structures in HVM networks associated with BV risks and etiology. We reanalyzed the metagenomic datasets of a mixed-cohort of 25 BV patients and healthy women. In these datasets, we detected 15 trio-motifs that occurred exclusively in BV patients. We failed to find any of these 15 trio-motifs in three additional cohorts of 1535 healthy women. Most member-species of the 15 trio motifs are BV-associated anaerobic bacteria (BVAB), Ravel's community-state type indicators, or the most dominant species; virtually all species interactions in these trios are high-salience skeletons, suggesting that those trios are strongly connected 'cults'. The presence of trio motifs unique to BV may act as indicators for its personalized diagnosis and could help elucidate a more mechanistic interpretation of its risks and etiology.