Proteinase-Activated Receptor-2 Promotes Allergic Sensitization to an Inhaled Antigen through a TNF-Mediated Pathway
Bibliographic record
Abstract
The reason why particular inhaled Ags induce allergic sensitization while others lead to immune tolerance is unclear. Along with a genetic predisposition to atopy, intrinsic characteristics of these Ags must be important. A common characteristic of many allergens is that they either possess proteinase activity or are inhaled in particles rich in proteinases. Many allergens, such as house dust mite and cockroach allergens, have the potential to activate the proteinase-activated receptor (PAR)-2. In this study, we report that PAR-2 activation in the airways at the same time as exposure to inhaled Ags induces allergic sensitization, whereas exposure to Ag alone induces tolerance. BALB/c mice were administered OVA with a PAR-2 activating peptide intranasally. Upon allergen re-exposure mice developed airway inflammation and airway hyperresponsiveness, as well as OVA-specific T cells with a Th2 cytokine profile when restimulated with OVA in vitro. Conversely, mice given OVA alone or OVA with a PAR-2 control peptide developed tolerance. These tolerant mice did not develop airway inflammation or airway hyperresponsiveness, and developed OVA-specific T cells that secreted high levels of IL-10 when restimulated with OVA in vitro. Furthermore, pulmonary dendritic cell trafficking was altered in mice following intranasal PAR-2 activation. Finally, we showed that PAR-2-mediated allergic sensitization was TNF-dependent. Thus, PAR-2 activation in the airways could be a critical factor in the development of allergic sensitization following mucosal exposure to allergens with serine proteinase activity. Interfering with this pathway may prove to be useful for the prevention or treatment of allergic diseases.
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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".