β-Arrestin-2 mediates the proinflammatory effects of proteinase-activated receptor-2 in the airway
Bibliographic record
Abstract
Proteinase-Activated receptor-2 (PAR(2)), a G-protein-coupled Receptor, activated by serine proteinases, is reported to have both protective and proinflammatory effects in the airway. Given these opposing actions, both inhibitors and activators of PAR(2) have been proposed for treating asthma. PAR(2) can signal through two independent pathways: a β-arrestin-dependent one that promotes leukocyte migration, and a G-protein/Ca(2+) one that is required for prostaglandin E(2) (PGE(2)) production and bronchiolar smooth muscle relaxation. We hypothesized that the proinflammatory responses to PAR(2) activation are mediated by β-arrestins, whereas the protective effects are not. Using a mouse ovalbumin model for PAR(2)-modulated airway inflammation, we observed decreased leukocyte recruitment, cytokine production, and mucin production in β-arrestin-2(-/-) mice. In contrast, PAR(2)-mediated PGE(2) production, smooth muscle relaxation, and decreased baseline airway resistance (measures of putative PAR(2) "protective" effects) were independent of β-arrestin-2. Flow cytometry and cytospins reveal that lung eosinophil and CD4 T-cell infiltration, and production of IL-4, IL-6, IL-13, and TNFα, were enhanced in wild-type but not β-arrestin-2(-/-) mice. Using the forced oscillation technique to measure airway resistance reveals that PAR(2) activation protects against airway hyperresponsiveness by an unknown mechanism, possibly involving smooth muscle relaxation. Our data suggest that the PAR(2)-enhanced inflammatory process is β-arrestin-2 dependent, whereas the protective anticonstrictor effect of bronchial epithelial PAR(2) may be β-arrestin independent.
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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".