Contraction-related stimuli regulate GLUT4 traffic in C<sub>2</sub>C<sub>12</sub>-GLUT4<i>myc</i>skeletal muscle cells
Bibliographic record
Abstract
Muscle contraction stimulates glucose uptake acutely to increase energy supply, but suitable cellular models that faithfully reproduce this complex phenomenon are lacking. To this end, we have developed a cellular model of contracting C(2)C(12) myotubes overexpressing GLUT4 with an exofacial myc-epitope tag (GLUT4myc) and explored stimulation of GLUT4 traffic by physiologically relevant agents. Carbachol (an acetylcholine receptor agonist) induced a gain in cell surface GLUT4myc that was mediated by nicotinic acetylcholine receptors. Carbachol also activated AMPK, and this response was sensitive to the contractile myosin ATPase inhibitor N-benzyl-p-toluenesulfonamide. The gain in surface GLUT4myc elicited by carbachol or by the AMPK activator 5-amino-4-carboxamide-1 beta-ribose was sensitive to chemical inhibition of AMPK activity by compound C and partially reduced by siRNA-mediated knockdown of AMPK catalytic subunits or LKB1. In addition, the carbachol-induced gain in cell surface GLUT4myc was partially sensitive to chelation of intracellular calcium with BAPTA-AM. However, the carbachol-induced gain in cell surface GLUT4myc was not sensitive to the CaMKK inhibitor STO-609 despite expression of both isoforms of this enzyme and a rise in cytosolic calcium by carbachol. Therefore, separate AMPK- and calcium-dependent signals contribute to mobilizing GLUT4 in response to carbachol, providing an in vitro cell model that recapitulates the two major signals whereby acute contraction regulates glucose uptake in skeletal muscle. This system will be ideal to further analyze the underlying molecular events of contraction-regulated GLUT4 traffic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".