Biased signalling and proteinase‐activated receptors (<scp>PAR</scp>s): targeting inflammatory disease
Bibliographic record
Abstract
Although it has been known since the 1960s that trypsin and chymotrypsin can mimic hormone action in tissues, it took until the 1990s to discover that serine proteinases can regulate cells by cleaving and activating a unique four-member family of GPCRs known as proteinase-activated receptors (PARs). PAR activation involves the proteolytic exposure of its N-terminal receptor sequence that folds back to function as a 'tethered' receptor-activating ligand (TL). A key N-terminal arginine in each of PARs 1 to 4 has been singled out as a target for cleavage by thrombin (PARs 1, 3 and 4), trypsin (PARs 2 and 4) or other proteases to unmask the TL that activates signalling via Gq , Gi or G12 /13 . Similarly, synthetic receptor-activating peptides, corresponding to the exposed 'TL sequences' (e.g. SFLLRN-, for PAR1 or SLIGRL- for PAR2) can, like proteinase activation, also drive signalling via Gq , Gi and G12 /13 , without requiring receptor cleavage. Recent data show, however, that distinct proteinase-revealed 'non-canonical' PAR tethered-ligand sequences and PAR-activating agonist and antagonist peptide analogues can induce 'biased' PAR signalling, for example, via G12 /13 -MAPKinase instead of Gq -calcium. This overview summarizes implications of this 'biased' signalling by PAR agonists and antagonists for the recognized roles the PARs play in inflammatory settings.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".