Mechanism and regulation of GLUT-4 vesicle fusion in muscle and fat cells
Bibliographic record
Abstract
Twenty years ago it was shown that recruitment of glucose transporters from an internal membrane compartment to the plasma membrane led to increased glucose uptake into fat and muscle cells stimulated by insulin. The final step of this process is the fusion of glucose transporter 4 (GLUT-4)-containing vesicles with the plasma membrane. The identification of a neuronal soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) complex as a requirement for synaptic vesicle-plasma membrane fusion led to the search for homologous complexes outside the nervous system. Indeed, isoforms of the neuronal SNAREs were identified in muscle and fat cells and were shown to be required for GLUT-4 incorporation into the cell membrane. In addition, proteins that bind to nonneuronal SNAREs were cloned and proposed to regulate vesicle fusion. We have summarized the molecular mechanisms leading to membrane fusion in nonneuronal systems, focusing on the role of SNAREs and accessory proteins (Munc18c, synip, Rab4, and VAP-33) in incorporation of GLUT-4 into the plasma membrane. Potential modes of regulation of this process are discussed, including SNARE phosphorylation and interaction with the cytoskeleton.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".