A proteomics-based approach to understanding female genital immunity and the relationship to the vaginal microbiome
Bibliographic record
Abstract
Vaginal dysbiosis, characterized by Lactobacillus depletion and an overgrowth of facultative anaerobes, can result in the clinical condition called bacterial vaginosis (BV). BV affects 30% of women globally, affects their quality of life, and associates with an increased risk of STI’s, including HIV, and reproductive complications. In some BV cases there is an increased immune response, however this is not observed in all women, indicating there may be individual factors or particular bacteria that may be responsible for negative responses. More research is needed to understand the molecular mechanisms of BV. The development of advanced tools to characterize host and microbiome information offers an opportunity to understand host-bacterial relationships important for disease pathogenesis or prevention. Here we developed a mass- spectrometry based proteomics workflow to simultaneous obtain host and functional microbial data in a large-scale analysis of cervicovaginal lavage in a human cohort. Bacterial proteome taxa profiles were used to sort women into bacterial groups, then differential host and bacterial functions were assessed. Non-Lactobacillus communities showed decreased epithelial barrier associated proteins and increased immune cell recruitment, particularly with the important innate leukocytes’ neutrophils. Bacterial function in non-Lactobacillus communities showed enriched pathogenic associated pathways, such as biofilm formation and cell motility, and different carbohydrate metabolism pathways compared to L. crispatus dominant communities. In vitro experiments were used to better understand host-bacterial interactions. Experiments using vaginal epithelial cell lysate identified decreased barrier proteins in Gardnerella, Mobiluncus and Prevotella supernatant exposed cells compared to L. crispatus supernatant exposed cells. Neutrophil experiments identified increased neutrophil migration and activity upon exposure to Mobiluncus and not Lactobacillus crispatus. This study included development of a workflow II that would allow for metaproteomic analysis in large cohorts that can be applied to future studies for characterization of host bacterial interactions at mucosal surfaces. This method was then used to identify barrier disruption and neutrophil signatures in BV associated communities and created a new hypothesis of neutrophil activation as an inducer for vaginal inflammation and barrier damage observed in some cases of bacterial vaginosis, and Mobiluncus as one of the key bacteria to induce neutrophil migration and activation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".