The Vaginal Microbiome as a Tool to Predict rASRM Stage of Disease in Endometriosis: a Pilot Study

Endometriosis remains a challenge to understand and to diagnose. This is an observational cross-sectional pilot study to characterize the gut and vaginal microbiome profiles among endometriosis patients and control subjects without the disease and to explore their potential use as a less-invasive diagnostic tool for endometriosis. Overall, 59 women were included, n = 35 with endometriosis and n = 24 controls. Rectal and vaginal samples were collected in two different periods of the menstrual cycle from all subjects. Gut and vaginal microbiomes from patients with different rASRM (revised American Society for Reproductive Medicine) endometriosis stages and controls were analyzed. Illumina sequencing libraries were constructed using a two-step 16S rRNA gene PCR amplicon approach. Correlations of 16S rRNA gene amplicon data with clinical metadata were conducted using a random forest-based machine-learning classification analysis. Distribution of vaginal CSTs (community state types) significantly differed between follicular and menstrual phases of the menstrual cycle (p = 0.021, Fisher’s exact test). Vaginal and rectal microbiome profiles and their association to severity of endometriosis (according to rASRM stages) were evaluated. Classification models built with machine-learning methods on the microbiota composition during follicular and menstrual phases of the cycle were built, and it was possible to accurately predict rASRM stages 1–2 verses rASRM stages 3–4 endometriosis. The feature contributing the most to this prediction was an OTU (operational taxonomic unit) from the genus Anaerococcus. Gut and vaginal microbiomes of women with endometriosis have been investigated. Our findings suggest for the first time that vaginal microbiome may predict stage of disease when endometriosis is present. Similar content being viewed by others Data Availability The raw sequencing data of the vaginal and rectal samples used in this study as well as the associated metadata have been deposited in the NCBI Sequence Read Archive (SRA) under the BioProject PRJNA424567.

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The authors would like to acknowledge the support of Pawel Gajer and Jacque Ravel for providing access to speciateIT and McClassifier, the trained support vector machine learning model utilized for CST assignment in this study. Funding Library preparation and sequencing efforts through the MIT BioMicro Center were funded by the National Institute of Environmental Health Sciences of the NIH under award P30-ES002109. This study was funded by grants from the Brazilian Council for Scientific and Technological Development (CNPq). Specifically, the Massachusetts Institute of Technology (MIT)/Brazil Seed Fund (Process number: CNPq-MIT 457125/2012-8). Author information Authors and Affiliations Ethics declarations The Internal Review Boards of the University of Sao Paulo (CAPPesq #814.743) and the Massachusetts Institute of Technology (COUHES protocol # 1107004572) approved this protocol. Written informed consent was obtained from all participants. Additional information Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Electronic Supplementary Material ESM 1 (download DOCX ) (DOCX 19 kb) Rights and permissions About this article Cite this article Perrotta, A.R., Borrelli, G.M., Martins, C.O. et al. The Vaginal Microbiome as a Tool to Predict rASRM Stage of Disease in Endometriosis: a Pilot Study. Reprod. Sci. 27, 1064–1073 (2020). https://doi.org/10.1007/s43032-019-00113-5 Received: Accepted: Published: Version of record: Issue date: DOI: https://doi.org/10.1007/s43032-019-00113-5