Proteinase-activated Receptors, Targets for Kallikrein Signaling
Bibliographic record
Abstract
Serine proteinases like thrombin can signal to cells by the cleavage/activation of proteinase-activated receptors (PARs). Although thrombin is a recognized physiological activator of PAR(1) and PAR(4), the endogenous enzymes responsible for activating PAR(2) in settings other than the gastrointestinal system, where trypsin can activate PAR(2), are unknown. We tested the hypothesis that the human tissue kallikrein (hK) family of proteinases regulates PAR signaling by using the following: 1) a high pressure liquid chromatography (HPLC)-mass spectral analysis of the cleavage products yielded upon incubation of hK5, -6, and -14 with synthetic PAR N-terminal peptide sequences representing the cleavage/activation motifs of PAR(1), PAR(2), and PAR(4); 2) PAR-dependent calcium signaling responses in cells expressing PAR(1), PAR(2), and PAR(4) and in human platelets; 3) a vascular ring vasorelaxation assay; and 4) a PAR(4)-dependent rat and human platelet aggregation assay. We found that hK5, -6, and -14 all yielded PAR peptide cleavage sequences consistent with either receptor activation or inactivation/disarming. Furthermore, hK14 was able to activate PAR(1), PAR(2), and PAR(4) and to disarm/inhibit PAR(1). Although hK5 and -6 were also able to activate PAR(2), they failed to cause PAR(4)-dependent aggregation of rat and human platelets, although hK14 did. Furthermore, the relative potencies and maximum effects of hK14 and -6 to activate PAR(2)-mediated calcium signaling differed. Our data indicate that in physiological settings, hKs may represent important endogenous regulators of the PARs and that different hKs can have differential actions on PAR(1), PAR(2), and PAR(4).
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".