A Manifold-Based Framework for Studying the Dynamics of the Vaginal Microbiome

The vaginal bacterial community plays a crucial role in preventing infections. The composition of this community can be classified into five main groups, termed community state types (CSTs). Four of these CSTs, which are primarily consisted of Lactobacillus species, are considered healthy, while the fifth, which is composed of non-Lactobacillus populations, is considered less protective. This latter CST is often considered to represent a state termed Bacterial vaginosis (BV) – a common disease condition associated with unpleasant symptoms and increased susceptibility to sexually transmitted diseases. However, the exact mechanisms underlying BV development are not yet fully understood, including specifically, the dynamics of the vaginal microbiome in BV, and the possible routes it may take from a healthy to a BV state. This study aims to identify the progression from healthy Lactobacillus-dominant populations to symptomatic BV by analyzing 8,026 vaginal samples and using a manifold-detection framework. This approach is inspired by single-cell analysis and aims to identify low-dimensional trajectories in the high-dimensional composition space. This framework further order samples along these trajectories and assign a score (pseudo-time) to each sample based on its proximity to the BV state. Our results reveal distinct routes of progression between healthy and BV state for each CST, with pseudo-time scores correlating with community diversity and quantifying the health state of each sample. BV indicators, including Nugent score, positive Amsel’s test, and several Amsel’s criteria, can also be successfully predicted based on pseudo-time scores. Additionally, Gardnerella vaginalis can be identified as a key taxon in BV development using this approach, with increased abundance in samples with high pseudo-time, indicating an unhealthier state across all BV-development routes on the manifold. Taken together, these findings demonstrate how manifold detection can be used to successfully characterizes the progression from healthy Lactobacillus-dominant populations to BV and to accurately quantify the health condition of new samples along the route of BV development.