Transcriptional regulation by insulin: from the receptor to the geneThis paper is one of a selection of papers published in this Special issue, entitled Second Messengers and Phosphoproteins—12th International Conference.
Bibliographic record
Abstract
Insulin, after binding to its receptor, regulates many cellular processes and the expression of several genes. For a subset of genes, insulin exerts a negative effect on transcription; for others, the effect is positive. Insulin controls gene transcription by modifying the binding of transcription factors on insulin-response elements or by regulating their transcriptional activities. Different insulin-signaling cascades have been characterized as mediating the insulin effect on gene transcription. In this review, we analyze recent data on the molecular mechanisms, mostly in the liver, through which insulin exerts its effect. We first focus on the key transcription factors (viz. Foxo, sterol-response-element-binding protein family (SREBP), and Sp1) involved in the regulation of gene transcription by insulin. We then present current information on the way insulin downregulates and upregulates gene transcription, using as examples of downregulation phosphoenolpyruvate carboxykinase (PEPCK) and insulin-like growth factor binding protein 1 (IGFBP-1) genes and of upregulation the fatty acid synthase and malic enzyme genes. The last part of the paper focuses on the signaling cascades activated by insulin in the liver, leading to the modulation of gene 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".