Ca<sup>2+</sup>signals promote GLUT4 exocytosis and reduce its endocytosis in muscle cells
Bibliographic record
Abstract
Elevating cytosolic Ca(2+) stimulates glucose uptake in skeletal muscle, but how Ca(2+) affects intracellular traffic of GLUT4 is unknown. In tissue, changes in Ca(2+) leading to contraction preclude analysis of the impact of individual, Ca(2+)-derived signals. In L6 muscle cells stably expressing GLUT4myc, the Ca(2+) ionophore ionomycin raised cytosolic Ca(2+) and caused a gain in cell surface GLUT4myc. Extra- and intracellular Ca(2+) chelators (EGTA, BAPTA-AM) reversed this response. Ionomycin activated calcium calmodulin kinase II (CaMKII), AMPK, and PKCs, but not Akt. Silencing CaMKIIδ or AMPKα1/α2 partly reduced the ionomycin-induced gain in surface GLUT4myc, as did peptidic or small molecule inhibitors of CaMKII (CN21) and AMPK (Compound C). Compared with the conventional isoenzyme PKC inhibitor Gö6976, the conventional plus novel PKC inhibitor Gö6983 lowered the ionomycin-induced gain in cell surface GLUT4myc. Ionomycin stimulated GLUT4myc exocytosis and inhibited its endocytosis in live cells. siRNA-mediated knockdown of CaMKIIδ or AMPKα1/α2 partly reversed ionomycin-induced GLUT4myc exocytosis but did not prevent its reduced endocytosis. Compared with Gö6976, Gö6983 markedly reversed the slowing of GLUT4myc endocytosis triggered by ionomycin. In summary, rapid Ca(2+) influx into muscle cells accelerates GLUT4myc exocytosis while slowing GLUT4myc endocytosis. CaMKIIδ and AMPK stimulate GLUT4myc exocytosis, whereas novel PKCs reduce endocytosis. These results identify how Ca(2+)-activated signals selectively regulate GLUT4 exocytosis and endocytosis in muscle cells.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".