VIBES: A consensus subtyping of the vaginal microbiota reveals novel classification criteria

• Published in: Computational and structural biotechnology journal 2024-12, Vol.23, p.148-156
• Main Authors: Fernández-Edreira, Diego, Liñares-Blanco, Jose, V.-del-Río, Patricia, Fernandez-Lozano, Carlos
• Format: Article
• Language: English
• Subjects: Bacterial vaginosis; Consensus clustering analysis; Machine learning; Microbiome; Treatment response
• ISSN: 2001-0370; 2001-0370
• DOI: 10.1016/j.csbj.2023.11.050

This study aimed to develop a robust classification scheme for stratifying patients based on vaginal microbiome. By employing consensus clustering analysis, we identified four distinct clusters using a cohort that includes individuals diagnosed with Bacterial Vaginosis (BV) as well as control participants, each characterized by unique patterns of microbiome species abundances. Notably, the consistent distribution of these clusters was observed across multiple external cohorts, such as SRA022855, SRA051298, PRJNA208535, PRJNA797778, and PRJNA302078 obtained from public repositories, demonstrating the generalizability of our findings. We further trained an elastic net model to predict these clusters, and its performance was evaluated in various external cohorts. Moreover, we developed VIBES, a user-friendly R package that encapsulates the model for convenient implementation and enables easy predictions on new data. Remarkably, we explored the applicability of this new classification scheme in providing valuable insights into disease progression, treatment response, and potential clinical outcomes in BV patients. Specifically, we demonstrated that the combined output of VIBES and VALENCIA scores could effectively predict the response to metronidazole antibiotic treatment in BV patients. Therefore, this study's outcomes contribute to our understanding of BV heterogeneity and lay the groundwork for personalized approaches to BV management and treatment selection. •VIBES predicts robust subtypes of the vaginal microbiome based on minimal set of species.•The study introduces VIBES, a tool designed to facilitate the prediction of new clusters in external cohorts.•VIBES offers valuable insights into the clinical implications of microbiome subtypes, such as BV diagnosis and treatment response.