Opposing Roles of GSK3α and GSK3β Phosphorylation in Platelet Function and Thrombosis
Bibliographic record
Abstract
One of the mechanisms by which PI3 kinase can regulate platelet function is through phosphorylation of downstream substrates, including glycogen synthase kinase-3 (GSK3)α and GSK3β. Platelet activation results in the phosphorylation of an N-terminal serine residue in GSK3α (Ser21) and GSK3β (Ser9), which competitively inhibits substrate phosphorylation. However, the role of phosphorylation of these paralogs is still largely unknown. Here, we employed GSK3α/β phosphorylation-resistant mouse models to explore the role of this inhibitory phosphorylation in regulating platelet activation. Expression of phosphorylation-resistant GSK3α/β reduced thrombin-mediated platelet aggregation, integrin αIIbβ3 activation, and α-granule secretion, whereas platelet responses to the GPVI agonist collagen-related peptide (CRP-XL) were significantly enhanced. GSK3 single knock-in lines revealed that this divergence is due to differential roles of GSK3α and GSK3β phosphorylation in regulating platelet function. Expression of phosphorylation-resistant GSK3α resulted in enhanced GPVI-mediated platelet activation, whereas expression of phosphorylation-resistant GSK3β resulted in a reduction in PAR-mediated platelet activation and impaired in vitro thrombus formation under flow. Interestingly, the latter was normalised in double GSK3α/β KI mice, indicating that GSK3α KI can compensate for the impairment in thrombosis caused by GSK3β KI. In conclusion, our data indicate that GSK3α and GSK3β have differential roles in regulating platelet function.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".