molBV reveals immune landscape of bacterial vaginosis and predicts human papillomavirus infection natural history

Here, Burk et al. develop an algorithm to diagnose bacterial vaginosis (BV) using the 16S rRNA gene, called molBV, which they use to profile the inflammatory landscape of BV and predict progression of human papillomavirus infection to cervical pre-cancer. Bacterial vaginosis (BV) is a highly prevalent condition that is associated with adverse health outcomes. It has been proposed that BV's role as a pathogenic condition is mediated via bacteria-induced inflammation. However, the complex interplay between vaginal microbes and host immune factors has yet to be clearly elucidated. Here, we develop molBV, a 16 S rRNA gene amplicon-based classification pipeline that generates a molecular score and diagnoses BV with the same accuracy as the current gold standard method (i.e., Nugent score). Using 3 confirmatory cohorts we show that molBV is independent of the 16 S rRNA region and generalizable across populations. We use the score in a cohort without clinical BV states, but with measures of HPV infection history and immune markers, to reveal that BV-associated increases in the IL-1 beta/IP-10 cytokine ratio directly predicts clearance of incident high-risk HPV infection (HR = 1.86, 95% CI: 1.19-2.9). Furthermore, we identify an alternate inflammatory BV signature characterized by elevated TNF-alpha/MIP-1 beta ratio that is prospectively associated with progression of incident infections to CIN2 + (OR = 2.81, 95% CI: 1.62-5.42). Thus, BV is a heterogeneous condition that activates different arms of the immune response, which in turn are independent risk factors for HR-HPV clearance and progression. Clinical Trial registration number: The CVT trial has been registered under: NCT00128661.

Automated summary

Burk et al. developed an algorithm called molBV, which uses the 16S rRNA gene to diagnose bacterial vaginosis (BV) and predict the progression of human papillomavirus infection to cervical pre-cancer. They found that an increase in BV activity directly predicts the clearance of high-risk HPV infection, and also identified an alternative inflammatory BV signature associated with infection progression.