Microbe-binding antibodies in the female genital tract: associations with the vaginal microbiome and genital immunology
Bibliographic record
Abstract
Abstract Background Mucosal antibodies in the gut maintain homeostasis between the host and the local microbiome through the clearance of pathogenic bacteria and the development of immune tolerance to inflammatory bacteria. Whether similar bacteria-immunoglobulin interactions modulate cervicovaginal inflammation and/or bacterial colonization in the female genital tract (FGT) is not well understood. Here, we used a flow cytometry-based assay to quantify microbe-binding IgA and IgG in the cervicovaginal secretions of 200 HIV-uninfected women from Nairobi, Kenya that were enriched for bacterial vaginosis (BV) and evaluated the associations of cervicovaginal IgA and IgG with the vaginal microbiome composition and local soluble immune factors. Results Total IgA and IgG were abundant in cervicovaginal secretions and frequently demonstrated ex vivo binding to key vaginal bacteria species Gardnerella vaginalis, Prevotella bivia, Lactobacillus iners, and Lactobacillus crispatus. Microbe-binding antibodies were generally not associated with the presence/absence of the corresponding bacteria. Total and microbe-binding IgA and IgG were inversely correlated with total bacterial abundance and positively correlated with several pro-inflammatory cytokines (IL-6, TNF) and chemotactic chemokines (IP-10, MIG, MIP-1α, MIP-1β, MIP-3α, MCP-1, IL-8), independent of total bacterial abundance. Conclusions Flow cytometry-based quantification of microbe-binding antibodies provides a platform to investigate host-microbiota interactions in the FGT of human observational and interventional studies. In contrast to the gut, cervicovaginal microbe-binding IgA and IgG do not appear to be immunoregulatory but may indirectly mitigate bacteria-induced inflammation by reducing total bacterial abundance.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".