HB-EGF Signaling is Required for Glucose-Induced Pancreatic β-Cell Proliferation in Rats
Bibliographic record
Abstract
ABSTRACT The molecular mechanisms of β-cell compensation to metabolic stress are poorly understood. We previously observed that nutrient-induced β-cell proliferation in rats is dependent on Epidermal Growth Factor Receptor (EGFR) signaling. The aim of this study was to determine the role of the EGFR ligand Heparin-Binding EGF-like Growth Factor (HB-EGF) in the β-cell proliferative response to glucose, a β-cell mitogen and key regulator of β-cell mass in response to increased insulin demand. We show that exposure of isolated rat and human islets to HB-EGF stimulates β-cell proliferation. In rat islets, inhibition of EGFR or HB-EGF blocks the proliferative response not only to HB-EGF but also to glucose. Furthermore, knockdown of HB-EGF in rat islets blocks β-cell proliferation in response to glucose ex vivo and in vivo in transplanted glucose-infused rats. Mechanistically, we demonstrate that HB-EGF mRNA levels are increased in β cells in response to glucose in a Carbohydrate Response Element Binding Protein (ChREBP)-dependent manner. In addition, chromatin-immunoprecipitation studies identified ChREBP binding sites in proximity to the HB-EGF gene. Finally, inhibition of Src family kinases, known to be involved in HB-EGF processing, abrogated glucose-induced β-cell proliferation. Our findings identify a novel glucose/HB-EGF/EGFR axis implicated in β-cell compensation to increased metabolic demand.
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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".