TLR4 Mediates Vaccine-Induced Protective Cellular Immunity to <i>Bordetella pertussis</i> : Role of IL-17-Producing T Cells
Bibliographic record
Abstract
Whole cell pertussis vaccines (Pw) induce Th1 responses and protect against Bordetella pertussis infection, whereas pertussis acellular vaccines (Pa) induce Ab and Th2-biased responses and also protect against severe disease. In this study, we show that Pw failed to generate protective immunity in TLR4-defective C3H/HeJ mice. In contrast, protection induced with Pa was compromised, but not completely abrogated, in C3H/HeJ mice. Immunization with Pw, but not Pa, induced a population of IL-17-producing T cells (Th-17), as well as Th1 cells. Ag-specific IL-17 and IFN-gamma production was significantly lower in Pw-immunized TLR4-defective mice. Furthermore, treatment with neutralizing anti-IL-17 Ab immediately before and after B. pertussis challenge significantly reduced the protective efficacy of Pw. Stimulation of dendritic cells (DC) with Pw promoted IL-23, IL-12, IL-1beta, and TNF-alpha production, which was impaired in DC from TLR4-defective mice. B. pertussis LPS, which is present in high concentrations in Pw, induced IL-23 production by DC, which enhanced IL-17 secretion by T cells, but the induction of Th-17 cells was also dependent on IL-1. In addition, we identified a new effector function for IL-17, activating macrophage killing of B. pertussis, and this bactericidal activity was less efficient in macrophages from TLR4-defective mice. These data provide the first definitive evidence of a role for TLRs in protective immunity induced by a human vaccine. Our findings also demonstrate that activation of innate immune cells through TLR4 helps to direct the induction of Th1 and Th-17 cells, which mediate protective cellular immunity to B. pertussis.
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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) | 0.001 | 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 | 0.001 |
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".