Proteinase-Activated Receptor-2 and Human Lung Epithelial Cells
Bibliographic record
Abstract
Proteinase-activated receptor (PAR)-2 is cleaved within its aminoterminal extracellular domain by serine proteinases such as trypsin, unmasking a new aminoterminus starting with the sequence SLIGKV, which binds intramolecularly and activates the receptor. PAR-2 has been reported to be involved in inflammation within the lungs. We show that PAR-2 is expressed not only by human alveolar (A549), but also by bronchial (16HBE) epithelial cell lines, using RT-PCR and flow cytometry with a PAR-2 antibody whose epitope maps over the trypsin cleavage site. PAR-2 activation by trypsin and by the activating peptide SLIGKV-NH(2) leads to intracellular calcium mobilization in both lung epithelial cells. During lung inflammation, airspaces are burdened by neutrophils that release elastase and cathepsin G, two serine proteinases. We demonstrate that these proteinases do not activate PAR-2, but rather disarm the receptor, preventing activation by trypsin but not by SLIGKV-NH(2). Preincubation of a PAR-2-transfected cell line, as well as 16HBE and A549 cells, with either proteinase led to the disappearance of the cleavage/activation epitope recognized by the PAR-2 antibody. We hypothesize that elastase and cathepsin G disarm PAR-2 by proteolysis of the extracellular domain downstream from the trypsin cleavage/activation site, while leaving unmodified the SLIGKV-NH(2)-binding site. These findings suggest that the neutrophil serine proteinases may play a role in PAR-2-mediated lung inflammation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".