Functional inhibition of <scp>PAR</scp><sub>2</sub> alleviates allergen‐induced airway hyperresponsiveness and inflammation
Bibliographic record
Abstract
BACKGROUND: Proteinase-activated receptor 2 (PAR2 ) is a G protein-coupled receptor activated by trypsin-like serine proteinases. PAR2 activation has been associated with inflammation including allergic airway inflammation. We have also shown that PAR2 activation in the airways leads to allergic sensitization. The exact contribution of PAR2 in the development of eosinophilic inflammation and airway hyperresponsiveness (AHR) in sensitized individuals is not clear. OBJECTIVE: To investigate whether functional inhibition of PAR2 during allergen challenge of allergic mice would inhibit allergen-induced AHR and inflammation in mouse models of asthma. METHODS: Mice were sensitized and challenged with ovalbumin (OVA) or cockroach extract (CE). To investigate the role of PAR2 in the development of AHR and airway inflammation, we administered blocking anti-PAR2 antibodies, or a cell permeable peptide inhibitor of PAR2 signalling, pepducin, i.n. before allergen challenges and then assessed AHR and airway inflammation. RESULTS: Administration of anti-PAR2 antibodies significantly inhibited OVA- and CE-induced AHR and airway inflammation. In particular, two anti-PAR2 antibodies, the monoclonal SAM-11 and polyclonal B5, inhibited AHR, airway eosinophilia, the increase of cytokines in the lung tissue and antigen-specific T cell proliferation, but had no effect on antigen-specific IgG and IgE levels. Pepducin was also effective in inhibiting AHR and airway inflammation in an OVA model of allergic airway inflammation. CONCLUSIONS AND CLINICAL RELEVANCE: Functional blockade of PAR2 in the airways during allergen challenge improves allergen-induced AHR and inflammation in mice. Therefore, topical PAR2 blockade in the airways, through anti-PAR2 antibodies or molecules that interrupt PAR2 signalling, has the potential to be used as a therapeutic option in allergic asthma.
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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) | 0.002 | 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".