Mast cell tryptase stimulates human lung fibroblast proliferation via protease-activated receptor-2
Bibliographic record
Abstract
Mast cells play a potentially important role in fibroproliferative diseases, releasing mediators including tryptase that are capable of stimulating fibroblast proliferation and procollagen synthesis. The mechanism by which tryptase stimulates fibroblast proliferation is unclear, although recent studies suggest it can activate protease-activated receptor (PAR)-2. We therefore investigated the role of PAR-2 in tryptase-induced proliferation of human fetal lung and adult lung parenchymal and airway fibroblasts and, for comparative purposes, adult dermal fibroblasts. Tryptase (0.7-70 mU/ml) induced concentration-dependent increases in proliferation of all fibroblasts studied. Antipain, bis(5-amidino-2-benzimidazolyl)methane, and benzamidine inhibited tryptase-induced fibroblast proliferation, demonstrating that proteolytic activity is required for the proliferative effects of tryptase. RT-PCR demonstrated the presence of PAR-2 mRNA, and immunohistochemical staining localized PAR-2 to the cell surface of lung fibroblasts. In addition, specific PAR-2 activating peptides, SLIGKV and SLIGRL, mimicked the proliferative effects of tryptase. In contrast, human dermal fibroblasts only weakly stained with the PAR-2 antibody, PAR-2 mRNA was almost undetectable, and fibroblasts did not respond to PAR-2 activating peptides. These results suggest that tryptase induces lung, but not dermal, fibroblast proliferation via activation of PAR-2 and are consistent with the hypothesis that the release of tryptase from activated mast cells may play an important role in the fibroproliferative response observed in asthma, chronic obstructive pulmonary disease, and patients with pulmonary fibrosis.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".