MiR-7, miR-9 and miR-375 contribute to effect of Exendin-4 on pancreatic β-cells in high-fat-diet-fed mice
Bibliographic record
Abstract
Purpose The purpose of this study was to test whether glucagon-like peptide-1 (GLP-1) receptor activation preserved pancreatic β-cells via the regulation of microRNAs and target genes in high-fat-diet-fed mice. Methods C57BL/6 male mice were simultaneously treated with high-fat-diet (HFD) and GLP-1 analogue, Exendin-4 (Ex-4) (3 μg/kg/day or 30 μg/kg/day), i.p. or vehicle, for consecutive 13 weeks. Fasting blood glucose, postprandial blood glucose, ΔI30/ΔG30, HOMA-IR and HOMA-% β were measured in each group. Pancreatic β-cell mass was assessed by immunohistochemistry. The expression of miRNAs and related downstream genes were investigated using quantitative real-time PCR. Results Thirteen weeks of Ex-4 treatment significantly reduced body weight and food intake in HFD-fed mice (P<0.05). Insulin sensitivity, HOMA-IR and HOMA-% β were markedly improved in Ex-4-treated groups (P<0.05). Histological examination revealed that β-cell mass was significantly increased in high dose Ex-4-treated mice (P<0.05). Interestingly, Ex-4-treated islets displayed significant down-regulation of the expression of miR-7, miR-9 and miR-375 and up-regulation of the levels of mammalian target of rapamycin (mTOR), one cut homeobox 2 (OC-2) and phosphoinositide-dependent protein kinase-1 (P<0.05). Conclusion MicroRNAs and genes targeted in response to GLP-1 receptor agonism were involved in preserving β-cell mass and function in HFD-induced mice; which suggest a mechanism involving the GLP-1 receptor as a therapeutic approach for the treatment of type 2 diabetes mellitus and obesity. This study also shows the potential for elucidating other important therapeutic targets for diabetes.
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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) | 0.000 | 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".