Understanding Intraspecific Diversity of Gardnerella vaginalis in the Vaginal Microbiome

Project Summary
Gardnerella vaginalis has been associated with bacterial vaginosis (BV) and preterm birth (PTB) risk,
but with little success in being able to identify, treat, and prevent risk. Increasingly, G. vaginalis variants have
been differentially associated with these outcomes and associated pathogenic behaviors. These differences
have been linked to vast phylogenetic and phenotypic diversity across the named species, G. vaginalis, which
has been suggested to represent as many as 13 genomic species. In order to eventually understand, prevent,
and treat pathogenic outcomes, we must define a consistent and reliable method to identify G. vaginalis
variants. We have demonstrated the ability to identify the presence and abundance of six subspecies G.
vaginalis clades in shotgun metagenomic sequencing data, which is an important step in understanding how
these clades may shape their microbial communities. Preliminary evidence suggests potential differences in
community signatures associated with each clade. We hypothesize six subspecies G. vaginalis clades differ in
overall relative transcription rates, transcription of genes related to pathogenicity, and that fold increases of G.
vaginalis transcripts in preterm birth vary among the clades. We also hypothesize that G. vaginalis clades differ
in phenotypes that relate to the ability to shape the microbial community and host health. We will use paired
metagenomic and metatranscriptomic sequencing data to describe G. vaginalis clade transcription in
pregnancy and preterm birth. We will also use in vitro assays to assess epithelial adhesion, biofilm formation,
and competitive growth against Lactobacillus spp. of the clades. Successful completion of these aims will
demonstrate clinically relevant variation among six G. vaginalis clades. Therefore, these aims represent a
unique multidisciplinary approach to the systematic understanding of G. vaginalis diversity, which is necessary
for identifying health risks in conditions such as BV and PTB, and targeting successful intervention strategies.
These aims will also provide training in both computational and technical skills to foster an independent
researcher capable of bridging disciplines to solve complex problems in biomedicine.