{"doi":"10.1101/2023.08.16.553525","title":"Characteristics of vaginal microbes and classification of the vaginal microbiome","abstract":"Summary Background The vaginal microbiome (VMB) has been classified into several discrete community state types, some of which have been associated with adverse human health conditions. However, the roles of the many vaginal bacteria in modulating the VMB and health remain unclear. Methods The associations among the vaginal taxa and other vaginal taxa, the vaginal pH, and the host gene expression responses were determined by calculating the correlation among the relative abundance of the vaginal taxa, the association between the vaginal pH and the predominant taxon in the VMB, and the correlation between the relative abundance of the vaginal taxa and human gene expression at the transcriptional level, respectively. Using these associations, an alternative more informative method, the biological vagitype (BVT), is proposed to classify community state types of the VMB. Findings Most Lactobacillus spp., with the exception of Lactobacillus iners , show significant correlations with host gene expression profiles and negative associations with dysbiosis-associated vaginal taxa. Many non- Lactobacillus spp. exhibit varied correlations with Lactobacillus spp., the vaginal pH, and host gene expression. Compared to other dysbiotic taxa, including Candidatus Lachnocurva vaginae, Gardnerella vaginalis has a stronger positive correlation with vaginal pH and a stronger negative correlation with Lactobacillus spp. Most dysbiosis-associated taxa are associated with stress responses of the host at the transcriptional level, but the genus Mycoplasma has a uniquely strong positive correlation with host immune responses. The association between BVTs of the VMBs and host characteristics, e.g., race/ethnicity, microbial infection, smoking, antibiotics, high blood pressure, economic state, diet, and others, was examined. The BVT classification method improved overall performance in associating specific vaginal microbial populations with host characteristics and phenotypes. Interpretation This study sheds light on the biological characteristics of the vaginal microbiota, including some less abundant or still unculturable taxa. Since the BVT method was established based on these biological characteristics, the classification outcome of the VMB may have more clinical relevance. Because the BVT method performs better in associating specific vaginal community types with diseases, e.g., bacterial vaginosis and gonorrhea, it could be beneficial for the predictive modeling of adverse health. Funding This work was supported by grants [UH3AI083263, U54HD080784, and R01HD092415] from the National Institutes of Health; and support from the [GAPPS BMGF PPB] grant from the Global Alliance to Prevent Prematurity and Stillbirth. We would also like to thank the Office of Research on Women’s Health at NIH for their generous support. Research in context Evidence before this study The vaginal microbiome (VMB) refers to the community of microorganisms in the female lower reproductive tract. The VMB is often a simple ecosystem dominated by a single species. The most predominant bacteria in the VMB include several Lactobacillus species and two non- Lactobacillus species, i.e., Candidatus Lachnocurva vaginae and Gardnerella vaginalis. Lactobacillus species produce lactic acid to lower the vaginal pH and inhibit the growth of disease-associated bacteria. Thus, the predominance of protective Lactobacilli, i.e., L. crispatus, L. jensenii , and L. gasseri , in the VMB is associated with overall vaginal health. However, the role of L. iners in promoting a healthy vaginal ecosystem is less clear. Actually, the biological and health relevance of many bacteria in the female lower reproductive tract is largely unknown. Some bacteria have low relative abundances, e.g., Peptostreptococcus and Coriobacteriaceae spp.; and others are not yet culturable, e.g., Candidatus Lachnocurva vaginae and BVAB TM7. When abundance of a taxon is low, its association with a host characteristic is a ch","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2023,"id":393547,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9337,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1168093,"name":"Katherine M. Spaine","orcid":null,"position":1,"is_corresponding":false},{"id":670150,"name":"Laahirie Edupuganti","orcid":null,"position":2,"is_corresponding":false},{"id":1168094,"name":"Andrey Matveyev","orcid":null,"position":3,"is_corresponding":false},{"id":59345,"name":"Myrna G. Serrano","orcid":"0000-0003-4327-0193","position":4,"is_corresponding":false},{"id":29050,"name":"Gregory A. Buck","orcid":"0000-0003-4621-3987","position":5,"is_corresponding":false},{"id":379067,"name":"Bin Zhu","orcid":"0000-0003-2829-2925","position":0,"is_corresponding":true}],"reference_count":56,"raw_metadata":null,"created_at":"2026-07-19T01:19:05.913428Z","pmid":"37645743","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}