Anti-FIM and Anti-FHA Antibodies Inhibit Bordetella pertussis Growth and Reduce Epithelial Cell Inflammation Through Bacterial Aggregation
Bibliographic record
Abstract
The pertussis vaccination is highly recommended for infants, children, and pregnant women. Despite a high coverage of vaccination, pertussis continues to be of public health concern as a re-emerging infectious disease. The mechanism by which vaccine-elicited anti-pertussis antibodies mediate direct bactericidal effects is poorly understood. In this study, we showed that the interaction ofB. pertussiswith A549 epithelial cells induce release of biological factors which enhance bacteria growth. Complement-depleted antisera from vaccine-immunized guinea pigs or monoclonal antibodies targeting FHA and FIM mediate bacteria aggregation and elicit bactericidal effects. Ourin vitroresults indicated that aggregation of bacteria through anti-FIM and anti-FHA specific antibodies is one of the major biological mechanisms to clear bacterial infections and restore epithelial cell survivalin vitro. Our data also indicates that the anti-pertussis antibodies reduce secretion of proinflammatory chemokines and cytokines by preventing interaction ofB. pertussiswith host cells. The results of this study not only demonstrate mechanism of action of anti-FIM and anti-FHA antibodies, but also opens translational applications for potential therapeutic approaches or development of analytical assays such asin vitropotency assays.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".