Mice Lacking PECAM-1 and Ceacam1 Have Enhanced Platelet Secretion and Thrombus Growth: Novel Link with PAR4
Bibliographic record
Abstract
Abstract The Ig-ITIM bearing receptors, PECAM-1 and CEACAM1, have been shown net negative regulators of platelet–collagen interactions and hemiITAM signaling pathways. In this study, a double knockout (DKO) mouse was developed with deleted PECAM-1 and CEACAM1 to study their combined contribution in platelet activation by glycoprotein VI, C-type lectin-like receptor 2, protease activated receptor (PAR4), ADP purinergic receptors, and thromboxane receptor (TP) A2 pathways. In addition, their collective contribution was examined in thrombus formation under high shear and microvascular thrombosis using in vivo models. DKO platelets responded normally to ADP purinergic receptors and the TP A2 pathway. However, DKO platelets released significantly higher amounts of P-selectin compared with hyper-responsive Pecam-1−/− or Ceacam1−/− versus wild-type (WT) upon stimulation with collagen-related peptide or rhodocytin. In contrast, DKO platelets showed increased amounts of P-selectin exposure upon stimulation with PAR4 agonist peptide or thrombin but not Pecam-1−/− , Ceacam1−/− , or WT platelets. Blockade of phospholipase C (PLC) or Rho A kinase revealed that DKO platelets enhanced α-granule release via PAR4/Gαq/PLC signaling without crosstalk with Src/Syk or G12/13 signaling pathways. Severely delayed clot retraction in vitro was observed in DKO phenotype. The DKO model revealed a significant increase in thrombus formation compared with the hyper-responsive Ceacam1−/− or Pecam-1−/− versus WT phenotype. DKO platelets have similar glycoprotein surface expression compared with Pecam-1−/− , Ceacam1−/− , and WT platelets. This study demonstrates that PECAM-1 and CEACAM1 work in concert to negatively regulate hemiITAM signaling, platelet–collagen interactions, and PAR4 Gαq protein- coupled signaling pathways. Both PECAM-1 and CEACAM1 are required for negative regulation of platelet activation and microvascular thrombosis in vivo.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".