Molecular characterization of vaginal microbiota using a new 22-species qRT-PCR test to achieve a relative-abundance and species-based diagnosis of bacterial vaginosis

Abstract

Background: Numerous bacteria are involved in the etiology of bacterial vaginosis (BV). Yet, current tests only focus on a select few. We therefore designed a new test targeting 22 BV-relevant species.

Methods: Using 946 stored vaginal samples, a new qPCR test that quantitatively identifies 22 bacterial species was designed. The distribution and relative abundance of each species, α- and β-diversities, correlation, and species co-existence were determined per sample. A diagnostic index was modeled from the data, trained, and tested to classify samples into BV-positive, BV-negative, or transitional BV.

Results: The qPCR test identified all 22 targeted species with 95 - 100% sensitivity and specificity within 8 hours (from sample reception). Across most samples, Lactobacillus iners, Lactobacillus crispatus, Lactobacillus jensenii, Gardnerella vaginalis, Fannyhessea (Atopobium) vaginae, Prevotella bivia, and Megasphaera sp. type 1 were relatively abundant. BVAB-1 was more abundant and distributed than BVAB-2 and BVAB-3. No Mycoplasma genitalium was found. The inter-sample similarity was very low, and correlations existed between key species, which were used to model, train, and test a diagnostic index: MDL-BV index. The MDL-BV index, using both species and relative abundance markers, classified samples into three vaginal microbiome states. Testing this index on our samples, 491 were BV-positive, 318 were BV-negative, and 137 were transitional BV. Although important differences in BV status were observed between different age groups, races, and pregnancy status, they were statistically insignificant.

Conclusion: Using a diverse and large number of vaginal samples from different races and age groups, including pregnant women, the new qRT-PCR test and MDL-BV index efficiently diagnosed BV within 8 hours (from sample reception), using 22 BV-associated species.

Keywords: BVAB; MDL-BV index; bacterial vaginosis (BV); machine learning; qRT-PCR; vaginal microbiome; vaginitis.