Glucagon-Like Peptide 1 Inhibits the Sirtuin Deacetylase SirT1 to Stimulate Pancreatic β-Cell Mass Expansion
Bibliographic record
Abstract
OBJECTIVE: The glucoincretin hormone glucagon-like peptide 1 (GLP-1) enhances glucose-stimulated insulin secretion and stimulates pancreatic β-cell mass expansion. We have previously shown that the forkhead transcription factor FoxO1 is a prominent transcriptional effector of GLP-1 signaling in the β-cell. FoxO1 activity is subject to a complex regulation by Akt-dependent phosphorylation and SirT1-mediated deacetylation. In this study, we aimed at investigating the potential role of SirT1 in GLP-1 action. RESEARCH DESIGN AND METHODS: FoxO1 acetylation levels and binding to SirT1 were studied by Western immunoblot analysis in INS832/13 cells. SirT1 activity was evaluated using an in vitro deacetylation assay and correlated with the NAD(+)-to-NADH ratio. The implication of SirT1 in GLP-1-induced proliferation was investigated by BrdU incorporation assay. Furthermore, we determined β-cell replication and mass in wild-type and transgenic mice with SirT1 gain of function after daily administration of exendin-4 for 1 week. RESULTS: Our data show that GLP-1 increases FoxO1 acetylation, decreases the binding of SirT1 to FoxO1, and stunts SirT1 activity in β-INS832/13 cells. GLP-1 decreases both the NAD(+)-to-NADH ratio and SirT1 expression in INS cells and isolated islets, thereby providing possible mechanisms by which GLP-1 could modulate SirT1 activity. Finally, the action of GLP-1 on β-cell mass expansion is abolished in both transgenic mice and cultured β-cells with increased dosage of SirT1. CONCLUSIONS: Our study shows for the first time that the glucoincretin hormone GLP-1 modulates SirT1 activity and FoxO1 acetylation in β-cells. We also identify SirT1 as a negative regulator of β-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 | 0.001 |
| 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".