Characterization of vaginal microbiota diversity by 16S rRNA high-throughput sequencing.

Authors

Menglu Chen, Jinling Li, Hetao Chen, Adilai Tuerxun, Mengfen Mao, Jiahui Song, Boya Li, Yaonan Mo, Xiandun Zhai

Published in

Frontiers in microbiology. Volume 17. Pages 1777216. Epub Apr 20, 2026.

Abstract

This study employed high-throughput sequencing to analyze the diversity of vaginal microbiota in healthy women of reproductive age, thereby establishing a foundational dataset that may inform future forensic applications such as individual identification.
A cohort of 100 healthy reproductive-age women meeting the inclusion criteria was enrolled. Vaginal secretion samples were collected, after which microbial DNA was extracted and assessed for quality using agarose gel electrophoresis. The V3-V4 region of the 16S rRNA gene was subsequently amplified with universal primers and subjected to Illumina high-throughput sequencing for comprehensive microbial analysis.
Sequencing analysis revealed that the five most predominant genera in the female vaginal microbiota were Lactobacillus, Gardnerella, Prevotella, Serratia, and Atopobium. Cluster analysis of the 100 samples classified them into five major clusters, further delineated into eight subtypes, ranging from absolute dominance of Lactobacillus to dominance of non-Lactobacillus bacteria. Analysis of influence factors showed that microbial diversity was positively associated with age, higher in urban compared to suburban participants, and lower in pregnant versus non-pregnant women. The 16S rRNA high-throughput sequencing of vaginal microbiota exhibited distinct inter-group clustering, with Lactobacillus identified as a core candidate marker for vaginal secretions.
The compositional profile of the vaginal microbiota exhibited variations associated with individual characteristics such as age range and geographic region. This study provides a foundational description of vaginal microbiota composition in this cohort. Such foundational data are a necessary first step toward exploring potential future applications in forensic science, though direct inference of individual characteristics is not supported by the current dataset.

PMID:
42088275
Bibliographic data and abstract were imported from PubMed on 14 Sep 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.