Glucagon-Like Peptide-1 Receptor Agonists Increase Pancreatic Mass by Induction of Protein Synthesis
Bibliographic record
Abstract
Glucagon-like peptide-1 (GLP-1) controls glucose homeostasis by regulating secretion of insulin and glucagon through a single GLP-1 receptor (GLP-1R). GLP-1R agonists also increase pancreatic weight in some preclinical studies through poorly understood mechanisms. Here we demonstrate that the increase in pancreatic weight following activation of GLP-1R signaling in mice reflects an increase in acinar cell mass, without changes in ductal compartments or β-cell mass. GLP-1R agonists did not increase pancreatic DNA content or the number of Ki67(+) cells in the exocrine compartment; however, pancreatic protein content was increased in mice treated with exendin-4 or liraglutide. The increased pancreatic mass and protein content was independent of cholecystokinin receptors, associated with a rapid increase in S6 phosphorylation, and mediated through the GLP-1R. Rapamycin abrogated the GLP-1R-dependent increase in pancreatic mass but had no effect on the robust induction of Reg3α and Reg3β gene expression. Mass spectrometry analysis identified GLP-1R-dependent upregulation of Reg family members, as well as proteins important for translation and export, including Fam129a, eIF4a1, Wars, and Dmbt1. Hence, pharmacological GLP-1R activation induces protein synthesis, leading to increased pancreatic mass, independent of changes in DNA content or cell proliferation in mice.
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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 | 0.000 |
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".