Early Intraplatelet Signaling Enhances the Release of Human Platelet PAR-1 and -4 Amino-Terminal Peptides in Response to Thrombin
Bibliographic record
Abstract
Activation of washed human platelets initiated with alpha-thrombin, SFLLRN, or AYPGKF invariably results in the generation of PAR-1-(1-41) and PAR-4-(1-47). PAR-1-(1-41) and PAR-4-(1-47) are amino-terminal peptides generated when PAR-1 and -4 are cleaved in their first extracellular domains after R(41) and R(47), respectively, to expose the tethered ligand domains of PAR-1 and -4. Since soybean trypsin inhibitor decreases generation of PAR-1-(1-41) and PAR-4-(1-47) and other platelet aggregation-related responses to these three agonists, but does not inactivate alpha-thrombin, a platelet trypsin-like proteinase apparently activates PAR-1 and -4 to propagate PAR-dependent platelet responses. This study identified the signaling pathways implicated in the generation of the platelet proteinase that in turn produces PAR-1-(1-41) and PAR-4-(1-47), to thereby drive the subsequent PAR-dependent platelet aggregation-related responses to alpha-thrombin, SFLLRN, or AYPGKF. Only inhibitors of signaling enzymes that prevented ATP release (forskolin, PGE(1), or BIMI-1) prevented or delayed the generation of PAR-1-(1-41) and PAR-4-(1-47) in response to all three agonists. SBTI prevented platelet aggregation initiated by alpha-thrombin, SFLLRN, or AYPGKF but did so less effectively when it was added 10 s after each agonist. Thus, the platelet-derived proteinase acts within 10 s of each agonist addition to generate PAR-1-(1-41) and PAR-4-(1-47). Furthermore, alpha-thrombin may not effectively catalyze PAR-1-(1-41) and PAR-4-(1-47) generation. We propose that unidentified ATP-dependent phosphorylation reactions catalyzed by PKC help to generate the platelet-derived proteinase that propagates human platelet PAR-1 and -4 activation by the three agonists.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".