Early growth response‐1 negative feedback regulates skeletal muscle postprandial insulin sensitivity <i>via</i> activating <i>Ptp 1b</i> transcription
Bibliographic record
Abstract
Postprandial insulin desensitization plays a critical role in maintaining whole-body glucose homeostasis by avoiding the excessive absorption of blood glucose; however, the detailed mechanisms that underlie how the major player, skeletal muscle, desensitizes insulin action remain to be elucidated. Herein, we report that early growth response gene-1 ( Egr-1) is activated by insulin in skeletal muscle and provides feedback inhibition that regulates insulin sensitivity after a meal. The inhibition of the transcriptional activity of Egr-1 enhanced the phosphorylation of the insulin receptor (InsR) and Akt, thus increasing glucose uptake in L6 myotubes after insulin stimulation, whereas overexpression of Egr-1 decreased insulin sensitivity. Furthermore, deletion of Egr-1 in the skeletal muscle improved systemic insulin sensitivity and glucose tolerance, which resulted in lower blood glucose levels after refeeding. Mechanistic analysis demonstrated that EGR-1 inhibited InsR phosphorylation and glucose uptake in skeletal muscle by binding to the proximal promoter region of protein tyrosine phosphatase-1B (PTP1B) and directly activating transcription. PTP1B knockdown largely restored insulin sensitivity and enhanced glucose uptake, even under conditions of EGR-1 overexpression. Our results indicate that EGR-1/PTP1B signaling negatively regulates postprandial insulin sensitivity and suggest a potential therapeutic target for the prevention and treatment of excessive glucose absorption.-Wu, J., Tao, W.-W., Chong, D.-Y., Lai, S.-S., Wang, C., Liu, Q., Zhang, T.-Y., Xue, B., Li, C.-J. Early growth response-1 negative feedback regulates skeletal muscle postprandial insulin sensitivity via activating Ptp1b transcription.
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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".