Predictive value of the composition of the vaginal microbiota in bacterial vaginosis, a dynamic study to identify recurrence-related flora

Bacterial vaginosis (BV) is a highly prevalent disease in women, and increases the risk of pelvic inflammatory disease. It has been given wide attention because of the high recurrence rate. Traditional diagnostic methods based on microscope providing limited information on the vaginal microbiota increase the difficulty in tracing the development of the disease in bacteria resistance condition. In this study, we used deep-sequencing technology to observe dynamic variation of the vaginal microbiota at three major time points during treatment, at D0 (before treatment), D7 (stop using the antibiotics) and D30 (the 30-day follow-up visit). Sixty-five patients with BV were enrolled (48 were cured and 17 were not cured), and their bacterial composition of the vaginal microbiota was compared. Interestingly, we identified 9 patients might be recurrence. We also introduced a new measurement point of D7, although its microbiota were significantly inhabited by antibiotic and hard to be observed by traditional method. The vaginal microbiota in deep-sequencing-view present a strong correlation to the final outcome. Thus, coupled with detailed individual bioinformatics analysis and deep-sequencing technology, we may illustrate a more accurate map of vaginal microbial to BV patients, which provide a new opportunity to reduce the rate of recurrence of BV.