P6066AMPK-ACC signaling modulates platelet phospholipids content and potentiate platelet function and thrombus formation
Bibliographic record
Abstract
Introduction: AMP-activated protein kinase (AMPK) α1 is activated in platelets upon thrombin or collagen stimulation, and as a consequence phosphorylates and inhibits its bona-fide substrate, the acetyl-CoA carboxylase (ACC). Purpose: Since ACC is crucial for the synthesis of fatty acids, which are essential for platelet structure, energy storage and signaling, we hypothesized that this enzyme plays a central regulatory role in platelet function. Methods: We used a double knock-in (DKI) mouse model in which the AMPK phosphorylation sites Ser79 on ACC1 and Ser212 on ACC2 were mutated to prevent AMPK-signaling to ACC. In vitro, platelet adhesion and thrombus formation were measured using a flow chamber-based assay. In vivo, thrombosis was studied upon ferric chloride-induced carotid artery injury. Results: As expected, thrombin or collagen stimulation led to a rapid increase in ACC phosphorylation in control platelets. However, baseline and thrombin- or collagen induced ACC phosphorylation remained undetectable in DKI platelets. Suppression of ACC phosphorylation promoted injury-induced arterial thrombosis in vivo and enhanced thrombus growth ex vivo on collagen-coated surfaces under flow. After collagen stimulation, loss of AMPK-ACC signaling was associated with amplified thromboxane generation and dense granule secretion. Interestingly, lipidomic analysis revealed that ACC DKI platelets had increased arachidonic acid-containing phosphatidylethanolamine plasmalogen lipids, which are major contributors of arachidonic acid and thromboxane generation following platelet stimulation.
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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) | 0.003 | 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".