Stepping up regulatory mechanisms of GLUT4 traffic in L6 skeletal muscle cellss
Bibliographic record
Abstract
Insulin increases glucose uptake into muscle and fat by enhancing GLUT4 glucose transporter externalization; a process requiring input from Akt and actin. Downstream of phosphatidylinositol-3-kinase, insulin signaling bifurcates into Akt and actin activating arms. Akt-mediated phosphorylation of the Rab-GAP AS160 is required for gain in surface GLUT4 by insulin. However, little is known of the mechanism(s) by which AS160 and/or actin dynamics modulate GLUT4 traffic in muscle. We recently showed that GLUT4 arrival and/or fusion can be regulated by insulin signaling molecules and phospholipids. Using ‘rounded up’ L6 myoblasts stably expressing GLUT4myc, we find that transient expression of a non-phosphorylatable mutant of AS160 (AS160-4P) abrogates the surface fusion of GLUT4myc and partially reduces its sub-membranous accumulation. In contrast, tetanus toxin-mediated cleavage of VAMP2 inhibits GLUT4myc fusion but not arrival to the plasma membrane. Conversely, disrupting actin dynamics with Latrunculin B or silencing expression of a cytoskeletal protein a-actinin4 precludes the insulin-induced cortical build-up of GLUT4myc. These data suggest that AS160 and actin dynamics impinge on distinct stages of insulin-regulated GLUT4 traffic: AS160 may contribute to peripheral retention and is essential for GLUT4myc vesicle docking/fusion. It will be interesting to note which Rabs facilitate these AS160-dependent events. Actin dynamics instead may allow GLUT4 vesicle movement to the cell surface and/or its retention, presumably via cortical anchoring mechanisms involving a-actinin4, whilst VAMP2 has a major role in GLUT4 vesicle fusion. Indeed, defects in AS160 phosphorylation and actin dynamics are associated with insulin resistant states. Thus, discerning which steps of GLUT4 traffic are modulated by these inputs may help elucidate strategies to bypass insulin resistance.
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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".