A protease activated receptor-2 (PAR-2) activating peptide, tc-LIGRLO-NH2, induces protease release from mast cells: role in TNF degradation
Bibliographic record
Abstract
BACKGROUND: Mast cell (MC)-derived serine proteases have been implicated in a variety of inflammatory processes. We have previously shown that rat peritoneal MC (PMC) express mRNA for protease activated receptor 2 (PAR-2), a G-coupled receptor activated by trypsin-like proteases. Recent evidence also suggests that MC-induced inflammation can be mediated through PAR. Therefore, we hypothesized that specific PAR-2 agonist peptides (PAR-2ap) induce protease release from PMC. RESULTS: Western blot analysis of PMC supernatants revealed that a PAR-2ap, tc-LIGRLO (10 microM), stimulated the release of rat MC protease (RMCP)-1, RMCP-5 and carboxypeptidase-A. The release was evident by 20 min but further increased up to 8 h. To study the biological effects of protease release we tested supernatants from tc-LIGRLO, tc-OLRGIL (inactive control peptide) and antigen-activated PMC for proteolytic activity by seeding with TNF (150 pg/ml), incubating for 8 h at 37 degrees C, and measuring TNF remaining in the supernatants. Supernatants from tc-LIGRLO-stimulated PMC degraded 44 % of seeded TNF (n = 5). Moreover, this TNF proteolysis was dependent on the concentration of tc-LIGRLO used to stimulate PMC, and was significantly inhibited (94 %) by soybean trypsin inhibitor. Antigen and tc-OLRGIL induced no significant release of such proteolytic activity. CONCLUSIONS: These data indicate that a PAR-2ap induces the release of proteases from mast cells, which may degrade extracellular cytokines and other substrates thus modulating the inflammatory response.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".