Plasmin-mediated Activation of Platelets Occurs by Cleavage of Protease-activated Receptor 4
Bibliographic record
Abstract
The activation of plasmin from its circulating precursor plasminogen is the mechanism of several clot-busting drugs used to clinically treat patients who have suffered a stroke; however, plasmin thus generated has been shown to activate platelets directly. There has been speculation as to whether plasmin interacts with the protease-activated receptors (PARs) because of its similarity in amino acid specificity with the classic platelet activator thrombin. We have investigated whether plasmin activates platelets via PAR activation through multiple complementary approaches. At concentrations sufficient to induce human platelet aggregation, plasmin released very little calcium compared with that induced by thrombin, the PAR-1 agonist peptide SFLLRN, or the PAR-4 agonist peptide AYPGKF. Stimulation of platelets with plasmin initially failed to desensitize additional stimulation with SFLLRN or AYPGKF, but a prolonged incubation with plasmin desensitized platelets to further stimulation by thrombin. The desensitization of PAR-1 had no effect on plasmin-induced platelet aggregation and yielded an aggregation profile that was similar to plasmin in response to a low dose of thrombin. However, PAR-4 desensitization completely eliminated aggregation in response to plasmin. Inclusion of the PAR-1-specific antagonist BMS-200261 inhibited platelet aggregation induced by a low dose of thrombin but not by plasmin. Additionally, mouse platelets naturally devoid of PAR-1 showed a full aggregation response to plasmin in comparison to thrombin. Furthermore, human and mouse platelets treated with a PAR-4 antagonist, as well as platelets isolated from PAR-4 homozygous null mice, failed to aggregate in response to plasmin. Finally, a protease-resistant recombinant PAR-4 was refractory to activation by plasmin. We conclude that plasmin induces platelet aggregation primarily through slow cleavage of 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 | 0.001 |
| 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) | 0.003 | 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".