Immunization with whole cell but not acellular pertussis vaccines primes CD4 T <sub>RM</sub> cells that sustain protective immunity against nasal colonization with <i>Bordetella pertussis</i>
Bibliographic record
Abstract
Protective immunity wanes rapidly after immunization of children with acellular pertussis (aP) vaccines and these vaccines do not prevent nasal colonization or transmission of Bordetella pertussis in baboons. In this study, we examined the role of tissue-resident memory T (TRM) cells in persistent protective immunity induced by infection or immunization with aP and whole-cell pertussis (wP) vaccines in mice. Immunization of mice with a wP vaccine protected against lung and nasal colonization, whereas an aP vaccine failed to protect in the nose. IL-17 and IFN-γ-secreting CD69+CD4+ TRM cells were expanded in the lung and nasal tissue after B. pertussis challenge of mice immunized with wP, but not aP vaccines. However, previous infection induced the most persistent protection against nasal colonization and this correlated with potent induction of nasal tissue TRM cells, especially IL-17-secreting TRM cells. Blocking T cell migration to respiratory tissue during immunization with a wP vaccine impaired bacterial clearance, whereas transfer of TRM cells from convalescent or wP-immunized mice conferred protection to naïve mice. Our findings reveal that previous infection or wP vaccination are significantly more effective than aP vaccination in conferring persistent protective immunity against B. pertussis and that this is mediated by respiratory TRM cells.
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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".