GLP-1(28–36) improves β-cell mass and glucose disposal in streptozotocin-induced diabetic mice and activates cAMP/PKA/β-catenin signaling in β-cells in vitro
Bibliographic record
Abstract
Recent studies have demonstrated that the COOH-terminal fragment of the incretin hormone glucagon-like peptide-1 (GLP-1), a nonapeptide GLP-1(28-36)amide, attenuates diabetes and hepatic steatosis in diet-induced obese mice. However, the effect of this nonapeptide in pancreatic β-cells remains largely unknown. Here, we show that in a streptozotocin-induced mouse diabetes model, GLP-1(28-36)amide improved glucose disposal and increased pancreatic β-cell mass and β-cell proliferation. An in vitro investigation revealed that GLP-1(28-36)amide stimulates β-catenin (β-cat) Ser(675) phosphorylation in both the clonal INS-1 cell line and rat primary pancreatic islet cells. In INS-1 cells, the stimulation was accompanied by increased nuclear β-cat content. GLP-1(28-36)amide was also shown to increase cellular cAMP levels, PKA enzymatic activity, and cAMP response element-binding protein (CREB) and cyclic AMP-dependent transcription factor-1 (ATF-1) phosphorylation. Furthermore, GLP-1(28-36)amide treatment enhanced islet insulin secretion and increased the growth of INS-1 cells, which was associated with increased cyclin D1 expression. Finally, PKA inhibition attenuated the effect of GLP-1(28-36)amide on β-cat Ser(675) phosphorylation and cyclin D1 expression in the INS-1 cell line. We have thus revealed the beneficial effect of GLP-1(28-36)amide in pancreatic β-cells in vitro and in vivo. Our observations suggest that GLP-1(28-36)amide may exert its effect through the PKA/β-catenin signaling pathway.
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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 | 0.000 |
| 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".