Opposite Effect of JAK2 on Insulin-Dependent Activation of Mitogen-Activated Protein Kinases and Akt in Muscle Cells
Bibliographic record
Abstract
Many cytokines increase their receptor affinity for Janus kinases (JAKs). Activated JAK binds to signal transducers and activators of transcription, insulin receptor substrates (IRSs), and Shc. Intriguingly, insulin acting through its own receptor kinase also activates JAK2. However, the impact of such activation on insulin action remains unknown. To determine the contribution of JAK2 to insulin signaling, we transfected L6 myotubes with siRNA against JAK2 (siJAK2), reducing JAK2 protein expression by 75%. Insulin-dependent phosphorylation of IRS1/2 and Shc was not affected by siJAK2, but insulin-induced phosphorylation of the mitogen-activated protein kinases (MAPKs) extracellular signal-related kinase, p38, and Jun NH2-terminal kinase and their respective upstream kinases MKK1/2, MKK3/6, and MKK4/7 was significantly lowered when JAK2 was depleted, correlating with a significant drop in insulin-mediated cell proliferation. These effects were reproduced by the JAK2 inhibitor AG490. Conversely, insulin-stimulated Akt phosphorylation, glucose uptake, and GLUT4 translocation were not affected by siJAK2. Interestingly, in two insulin-resistant states, siJAK2 led to partial restoration of Akt phosphorylation and glucose uptake stimulation but not of the MAPK pathway. These results suggest that JAK2 may depress the Akt to glucose uptake signaling axis selectively in insulin-resistant states. Inhibition of JAK2 may be a useful strategy to relieve insulin resistance of metabolic outcomes.
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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) | 0.002 | 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".