Differential Contribution of Insulin Receptor Substrates 1 Versus 2 to Insulin Signaling and Glucose Uptake in L6 Myotubes
Bibliographic record
Abstract
Insulin receptor substrates-1 and 2 (IRS-1 and IRS-2) are pivotal in relaying insulin signaling in insulin-responsive tissues such as muscle. However, the precise contribution of IRS-1 vis-a-vis IRS-2 in insulin-mediated metabolic and mitogenic responses has not been compared directly in differentiated muscle cells. This study aimed to determine the relative contribution of IRS-1 versus IRS-2 in these responses, using small interfering RNA (siRNA)-mediated specific gene silencing. In L6 myotubes, transfection of siRNA targeted specifically against IRS-1 (siIRS-1) or IRS-2 (siIRS-2) reduced the cognate protein expression by 70-75%. Insulin-induced ERK phosphorylation was much more sensitive to IRS-2 than IRS-1 ablation, whereas p38MAPK phosphorylation was reduced by 43 or 62% in myotubes treated with siIRS-1 or siIRS-2, respectively. Insulin-induced Akt1 and Akt2 phosphorylation was reduced in myotubes treated with siIRS-1, but only Akt2 phosphorylation was reduced in myotubes treated with siIRS-2. In contrast, siIRS-1 treatment caused a marked reduction in insulin-induced actin remodeling, glucose uptake, and GLUT4 translocation, and siIRS-2 was without effect on these responses. Notably, combined siIRS-1 and siIRS-2, although reducing each IRS by around 75%, caused no further drop in glucose uptake than that achieved with siIRS-1 alone, but abolished p38MAPK phosphorylation. We conclude that insulin-stimulated Akt1 phosphorylation, actin remodeling, GLUT4 translocation, and glucose uptake are regulated mainly by IRS-1, whereas IRS-2 contributes selectively to ERK signaling, and Akt2 and p38MAPK lie downstream of both IRS 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 | 0.001 |
| 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".