Nocturnal activation of melatonin receptor type 1 signaling modulates diurnal insulin sensitivity via regulation of <scp>PI</scp> 3K activity
Bibliographic record
Abstract
Abstract Recent genetic studies have highlighted the potential involvement of melatonin receptor 1 ( MT 1 ) and melatonin receptor 2 ( MT 2 ) in the pathogenesis of type 2 diabetes. Here, we report that mice lacking MT 1 ( MT 1 KO ) tend to accumulate more fat mass than WT mice and exhibit marked systemic insulin resistance. Additional experiments revealed that the main insulin signaling pathway affected by the loss of MT 1 was the activation of phosphatidylinositol‐3‐kinase ( PI 3K). Transcripts of both catalytic and regulatory subunits of PI 3K were strongly downregulated within MT 1 KO mice. Moreover, the suppression of nocturnal melatonin levels within WT mice, by exposing mice to constant light, resulted in impaired PI 3K activity and insulin resistance during the day, similar to what was observed in MT 1 KO mice. Inversely, administration of melatonin to WT mice exposed to constant light was sufficient and necessary to restore insulin‐mediated PI 3K activity and insulin sensitivity. Hence, our data demonstrate that the activation of MT 1 signaling at night modulates insulin sensitivity during the day via the regulation of the PI 3K transcription and activity. Lastly, we provide evidence that decreased expression of MTNR 1A ( MT 1 ) in the liver of diabetic individuals is associated with poorly controlled 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) | 0.000 | 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 | 0.000 |
| 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".