Glucose Transporter 3 Potentiates Degranulation and Is Required for Platelet Activation
Bibliographic record
Abstract
OBJECTIVE: On activation, platelets increase glucose uptake, glycolysis, and glucose oxidation and consume stored glycogen. This correlation between glucose metabolism and platelet function is not well understood and even less is known about the role of glucose metabolism on platelet function in vivo. For glucose to enter a cell, it must be transported through glucose transporters. Here we evaluate the contribution of GLUT3 (glucose transporter 3) to platelet function to better understand glucose metabolism in platelets. APPROACH AND RESULTS: Platelet-specific knockout of GLUT3 was generated by crossing mice harboring GLUT3 floxed allele to a PF4 (platelet factor 4)-driven Cre recombinase. In platelets, GLUT3 is localized primarily on α-granule membranes and under basal conditions facilitates glucose uptake into α-granules to be used for glycolysis. After activation, platelets degranulate and GLUT3 translocates to the plasma membrane, which is responsible for activation-mediated increased glucose uptake. In vivo, loss of GLUT3 in platelets increased survival in a collagen/epinephrine model of pulmonary embolism, and in a K/BxN model of autoimmune inflammatory disease, platelet-specific GLUT3 knockout mice display decreased disease progression. Mechanistically, loss of GLUT3 decreased platelet degranulation, spreading, and clot retraction. Decreased α-granule degranulation is due in part to an impaired ability of GLUT3 to potentiate exocytosis. CONCLUSIONS: GLUT3-mediated glucose utilization and glycogenolysis in platelets promotes α-granule release, platelet activation, and postactivation functions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".