Gardnerella subgroup dominant microbiomes are associated with divergent cervicovaginal immune responses in a longitudinal cohort of Kenyan women
Bibliographic record
Abstract
Most cervicovaginal microbiome-immunology studies to date have relied on 16S rDNA microbial profiling which does not resolve the molecular subgroups of Gardnerella, believed to be central to the pathogenesis of bacterial vaginosis (BV) and subsequent risk of HIV acquisition. Here we used the cpn60 universal target which in addition to other microbial taxa, resolves four Gardnerella subgroups, for cervicovaginal microbial profiling in a longitudinal cohort of Kenyan women to examine associations with cellular and soluble markers of inflammation and HIV susceptibility. Participants (N = 41) were sampled, contributing 362 samples for microbiome analysis. All non-Lactobacillus dominant microbial communities were associated with high pro-inflammatory cytokine levels. Divergent associations were observed among different Gardnerella subgroup dominated communities with respect to the chemokine IP-10. Specifically, Gardnerella subgroup A dominant and polymicrobial communities were associated with reduced concentrations of IP-10 in adjusted linear mixed models (p<0.0001), compared to microbial communities dominated by Lactobacillus (non-iners) species. However, these associations did not translate to significant differences in the proportion or absolute number of CCR5, HLA-DR and CD38 expressed on cervical CD4+ T- cells. These findings suggest that some associations between Gardnerella subgroup dominant microbiomes and mucosal immunity differ and are relevant for the study of BV-pathogenesis and understanding the mechanisms of BV-associated HIV risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".