The vaginal microbiome amplifies sex hormone‐associated cyclic changes in cervicovaginal inflammation and epithelial barrier disruption
Why this work is in the frame
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Bibliographic record
Abstract
PROBLEM: Susceptibility to HIV is associated with the menstrual cycle and vaginal microbiome, but their collective impact on vaginal inflammation remains unclear. Here, we characterized the cervicovaginal proteome, inflammation, and microbiome community structure and function during the menstrual cycle. METHOD OF STUDY: Cervicovaginal secretions were collected from regularly cycling women (n = 16) at median day 10, 16, and 24 of each menstrual cycle and analyzed by mass spectrometry, 16S rRNA gene sequencing, and a multiplex bead array immunoassay. Follicular, ovulatory, and luteal phases were defined by serum sex hormone levels. RESULTS: Ovulation showed the largest mucosal proteome changes, where 30% and 19% of the 406 human proteins identified differed compared to the luteal and follicular phases, respectively. Neutrophil/leukocyte migration pathways were lowest during ovulation and peaked in the luteal phase, while antimicrobial and epithelial barrier promoting proteins were highest during ovulation. Vaginal microbial community structure and function did not vary significantly during the menstrual cycle, with the majority consistently Lactobacillus-dominant (63%) or non-Lactobacillus-dominant (25%). Fluctuations in the epithelial barrier protein RPTN between the ovulatory and luteal phase were amplified in women with Gardnerella vaginalis and anaerobic bacteria and reduced when Lactobacillus was dominant. CONCLUSION: This small study demonstrates that sex hormones modulate neutrophil/leukocyte inflammation, barrier function, and antimicrobial pathways in the female genital tract with the strongest changes occurring during ovulation. The data further suggest a microbiome context for hormone-driven changes in vaginal immunity which may have implications for HIV susceptibility.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it