Epidermal Growth Factor Receptor Signaling Promotes Pancreatic β-Cell Proliferation in Response to Nutrient Excess in Rats Through mTOR and FOXM1
Bibliographic record
Abstract
The cellular and molecular mechanisms underpinning the compensatory increase in β-cell mass in response to insulin resistance are essentially unknown. We previously reported that a 72-h coinfusion of glucose and Intralipid (GLU+IL) induces insulin resistance and a marked increase in β-cell proliferation in 6-month-old, but not in 2-month-old, Wistar rats. The aim of the current study was to identify the mechanisms underlying nutrient-induced β-cell proliferation in this model. A transcriptomic analysis identified a central role for the forkhead transcription factor FOXM1 and its targets, and for heparin-binding epidermal growth factor (EGF)-like growth factor (HB-EGF), a ligand of the EGF receptor (EGFR), in nutrient-induced β-cell proliferation. Phosphorylation of ribosomal S6 kinase, a mammalian target of rapamycin (mTOR) target, was increased in islets from GLU+IL-infused 6-month-old rats. HB-EGF induced proliferation of insulin-secreting MIN6 cells and isolated rat islets, and this effect was blocked in MIN6 cells by the EGFR inhibitor AG1478 or the mTOR inhibitor rapamycin. Coinfusion of either AG1478 or rapamycin blocked the increase in FOXM1 signaling, β-cell proliferation, and β-cell mass and size in response to GLU+IL infusion in 6-month-old rats. We conclude that chronic nutrient excess promotes β-cell mass expansion via a pathway that involves EGFR signaling, mTOR activation, and FOXM1-mediated cell proliferation.
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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 | 0.000 |
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".