Muscle cells engage Rab8A and myosin Vb in insulin-dependent GLUT4 translocation
Bibliographic record
Abstract
Insulin causes translocation of glucose transporter 4 (GLUT4) to the membrane of muscle and fat cells, a process requiring Akt activation. Two Rab-GTPase-activating proteins (Rab-GAP), AS160 and TBC1D1, were identified as Akt substrates. AS160 phosphorylation is required for insulin-stimulated GLUT4 translocation, but the participation of TBC1D1 on muscle cell GLUT4 is unknown. Moreover, there is controversy as to the AS160/TBC1D1 target Rabs in fat and muscle cells, and Rab effectors are unknown. Here we examined the effect of knockdown of AS160, TBC1D1, and Rabs 8A, 8B, 10, and 14 (in vitro substrates of AS160 and TBC1D1 Rab-GAP activities) on insulin-induced GLUT4 translocation in L6 muscle cells. Silencing AS160 or TBC1D1 increased surface GLUT4 in unstimulated cells but did not prevent insulin-induced GLUT4 translocation. Knockdown of Rab8A and Rab14, but not of Rab8B or Rab10, inhibited insulin-induced GLUT4 translocation. Furthermore, silencing Rab8A or Rab14 but not Rab8B or Rab10 restored the basal-state intracellular retention of GLUT4 impaired by AS160 or TBC1D1 knockdown. Lastly, overexpression of a fragment of myosin Vb, a recently identified Rab8A-interacting protein, inhibited insulin-induced GLUT4 translocation and altered the subcellular distribution of GTP-loaded Rab8A. These results support a model whereby AS160, Rab8A, and myosin Vb are required for insulin-induced GLUT4 translocation in muscle cells, potentially as part of a linear signaling cascade.
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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) | 0.001 | 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".