Signal transduction meets vesicle traffic: the software and hardware of GLUT4 translocation
Bibliographic record
Abstract
Skeletal muscle is the major tissue disposing of dietary glucose, a function regulated by insulin-elicited signals that impart mobilization of GLUT4 glucose transporters to the plasma membrane. This phenomenon, also central to adipocyte biology, has been the subject of intense and productive research for decades. We focus on muscle cell studies scrutinizing insulin signals and vesicle traffic in a spatiotemporal manner. Using the analogy of an integrated circuit to approach the intersection between signal transduction and vesicle mobilization, we identify signaling relays ("software") that engage structural/mechanical elements ("hardware") to enact the rapid mobilization and incorporation of GLUT4 into the cell surface. We emphasize how insulin signal transduction switches from tyrosine through lipid and serine phosphorylation down to activation of small G proteins of the Rab and Rho families, describe key negative regulation step of Rab GTPases through the GTPase-activating protein activity of the Akt substrate of 160 kDa (AS160), and focus on the mechanical effectors engaged by Rabs 8A and 10 (the molecular motor myosin Va), and the Rho GTPase Rac1 (actin filament branching and severing through Arp2/3 and cofilin). Finally, we illustrate how actin filaments interact with myosin 1c and α-Actinin4 to promote vesicle tethering as preamble to fusion with the membrane.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".