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Background Despite more than 60 years of research, the etiology of bacterial vaginosis (BV) remains controversial. In this pilot study, we used shotgun metagenomic sequencing to characterize vaginal microbial community changes before the development of incident BV (iBV). Methods A cohort of African American women with a baseline healthy vaginal microbiome (no Amsel criteria, Nugent score 0–3 with no Gardnerella vaginalis morphotypes) were followed for 90 days with daily self-collected vaginal specimens for iBV (≥2 consecutive days of a Nugent score of 7–10). Shotgun metagenomic sequencing was performed on select vaginal specimens from 4 women, every other day for 12 days before iBV diagnosis. Sequencing data were analyzed through Kraken2 and bioBakery 3 workflows, and specimens were classified into community state types. Quantitative polymerase chain reaction was performed to compare the correlation of read counts with bacterial abundance. Results Common BV-associated bacteria such as G. vaginalis , Prevotella bivia , and Fannyhessea vaginae were increasingly identified in the participants before iBV. Linear modeling indicated significant increases in G. vaginalis and F . vaginae relative abundance before iBV, whereas the relative abundance of Lactobacillus species declined over time. The Lactobacillus species decline correlated with the presence of Lactobacillus phages. We observed enrichment in bacterial adhesion factor genes on days before iBV. There were also significant correlations between bacterial read counts and abundances measured by quantitative polymerase chain reaction. Conclusions This pilot study characterizes vaginal community dynamics before iBV and identifies key bacterial taxa and mechanisms potentially involved in the pathogenesis of iBV.
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Genome-scale metabolic network reconstruction analysis identifies bacterial vaginosis-associated metabolic interactions
Abstract Bacterial vaginosis (BV) is the most prevalent vaginal condition among reproductive-age women presenting with vaginal complaints. Despite its significant impact on women’s health, limited knowledge exists regarding the microbial community composition and metabolic interactions associated with BV. In this study, we analyze 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 characterize symptomatic BV (and thus guide therapeutic intervention by identifying responders and non-responders to treatment) are based on genomic profiling, our in silico simulations reveal functional metabolic relatedness between species as quite distinct from genetic relatedness. We grow several of the most common co-occurring bacteria (Prevotella amnii, Prevotella buccalis, Hoylesella timonensis, Lactobacillus iners, Fannyhessea vaginae, andAerrococcus christenssii) on the spent media ofGardnerellaspecies and perform metabolomics to identify potential mechanisms of metabolic interaction. Through these analyses, we identify BV-associated bacteria that produce caffeate, a compound implicated in estrogen receptor binding, when grown in the spent media of other BV-associated bacteria. These findings underscore the complex and diverse nature of BV-associated bacterial community structures and several of these mechanisms are of potential significance in understanding host-microbiome relationships.
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- Award ID(s):
- 2021791
- PAR ID:
- 10592513
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 16
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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