Neutrophil Elastase Acts as a Biased Agonist for Proteinase-activated Receptor-2 (PAR2)
Bibliographic record
Abstract
Human neutrophil proteinases (elastase, proteinase-3, and cathepsin-G) are released at sites of acute inflammation. We hypothesized that these inflammation-associated proteinases can affect cell signaling by targeting proteinase-activated receptor-2 (PAR(2)). The PAR family of G protein-coupled receptors is triggered by a unique mechanism involving the proteolytic unmasking of an N-terminal self-activating tethered ligand (TL). Proteinases can either activate PAR signaling by unmasking the TL sequence or disarm the receptor for subsequent enzyme activation by cleaving downstream from the TL sequence. We found that none of neutrophil elastase, cathepsin-G, and proteinase-3 can activate G(q)-coupled PAR(2) calcium signaling; but all of these proteinases can disarm PAR(2), releasing the N-terminal TL sequence, thereby preventing G(q)-coupled PAR(2) signaling by trypsin. Interestingly, elastase (but neither cathepsin-G nor proteinase-3) causes a TL-independent PAR(2)-mediated activation of MAPK that, unlike the canonical trypsin activation, does not involve either receptor internalization or recruitment of β-arrestin. Cleavage of synthetic peptides derived from the extracellular N terminus of PAR(2), downstream of the TL sequence, demonstrated distinct proteolytic sites for all three neutrophil-derived enzymes. We conclude that in inflammation, neutrophil proteinases can modulate PAR(2) signaling by preventing/disarming the G(q)/calcium signal pathway and, via elastase, can selectively activate the p44/42 MAPK pathway. Our data illustrate a new mode of PAR regulation that involves biased PAR(2) signaling by neutrophil elastase and a disarming/silencing effect of cathepsin-G and proteinase-3.
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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".