The Cell Motility Modulator Slit2 Is a Potent Inhibitor of Platelet Function
Bibliographic record
Abstract
BACKGROUND: Vascular injury and atherothrombosis involve vessel infiltration by inflammatory leukocytes, migration of medial vascular smooth muscle cells to the intimal layer, and ultimately acute thrombosis. A strategy to simultaneously target these pathological processes has yet to be identified. The secreted protein, Slit2, and its transmembrane receptor, Robo-1, repel neuronal migration in the developing central nervous system. More recently, it has been appreciated that Slit2 impairs chemotaxis of leukocytes and vascular smooth muscle cells toward diverse inflammatory attractants. The effects of Slit2 on platelet function and thrombus formation have never been explored. METHODS AND RESULTS: We detected Robo-1 expression in human and murine platelets and megakaryocytes and confirmed its presence via immunofluorescence microscopy and flow cytometry. In both static and shear microfluidic assays, Slit2 impaired platelet adhesion and spreading on diverse extracellular matrix substrates by suppressing activation of Akt. Slit2 also prevented platelet activation on exposure to ADP. In in vivo studies, Slit2 prolonged bleeding times in murine tail bleeding assays. Using intravital microscopy, we found that after mesenteric arteriolar and carotid artery injury, Slit2 delayed vessel occlusion time and prevented the stable formation of occlusive arteriolar thrombi. CONCLUSIONS: These data demonstrate that Slit2 is a powerful negative regulator of platelet function and thrombus formation. The ability to simultaneously block multiple events in vascular injury may allow Slit2 to effectively prevent and treat thrombotic disorders such as myocardial infarction and stroke.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".