Toll-Like Receptor 4-Mediated Innate IL-10 Activates Antigen-Specific Regulatory T Cells and Confers Resistance to <i>Bordetella pertussis</i> by Inhibiting Inflammatory Pathology
Bibliographic record
Abstract
Signaling through Toll-like receptors (TLR) activates dendritic cell (DC) maturation and IL-12 production, which directs the induction of Th1 cells. We found that the production of IL-10, in addition to inflammatory cytokines and chemokines, was significantly reduced in DCs from TLR4-defective C3H/HeJ mice in response to Bordetella pertussis. TLR4 was also required for B. pertussis LPS-induced maturation of DCs, but other B. pertussis components stimulated DC maturation independently of TLR4. The course of B. pertussis infection was more severe in C3H/HeJ than in C3H/HeN mice. Surprisingly, Ab- and Ag-specific IFN-gamma responses were enhanced at the peak of infection, whereas Ag-specific IL-10-producing T cells were significantly reduced in C3H/HeJ mice. This was associated with enhanced inflammatory cytokine production, cellular infiltration, and severe pathological changes in the lungs of TLR4-defective mice. Our findings suggest that TLR-4 signaling activates innate IL-10 production in response to B. pertussis, which both directly, and by promoting the induction of IL-10-secreting type 1 regulatory T cells, may inhibit Th1 responses and limit inflammatory pathology in the lungs during infection with 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".