Proteinase-activated receptors (PARs) – focus on receptor-receptor-interactions and their physiological and pathophysiological impact
Bibliographic record
Abstract
Proteinase-activated receptors (PARs) are a subfamily of G protein-coupled receptors (GPCRs) with four members, PAR1, PAR2, PAR3 and PAR4, playing critical functions in hemostasis, thrombosis, embryonic development, wound healing, inflammation and cancer progression. PARs are characterized by a unique activation mechanism involving receptor cleavage by different proteinases at specific sites within the extracellular amino-terminus and the exposure of amino-terminal "tethered ligand" domains that bind to and activate the cleaved receptors. After activation, the PAR family members are able to stimulate complex intracellular signalling networks via classical G protein-mediated pathways and beta-arrestin signalling. In addition, different receptor crosstalk mechanisms critically contribute to a high diversity of PAR signal transduction and receptor-trafficking processes that result in multiple physiological effects.In this review, we summarize current information about PAR-initiated physical and functional receptor interactions and their physiological and pathological roles. We focus especially on PAR homo- and heterodimerization, transactivation of receptor tyrosine kinases (RTKs) and receptor serine/threonine kinases (RSTKs), communication with other GPCRs, toll-like receptors and NOD-like receptors, ion channel receptors, and on PAR association with cargo receptors. In addition, we discuss the suitability of these receptor interaction mechanisms as targets for modulating PAR signalling in disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".