Melatonin MT1 and MT2 Receptors Exhibit Distinct Effects in the Modulation of Body Temperature across the Light/Dark Cycle
Bibliographic record
Abstract
Melatonin (MLT) is a neurohormone that regulates many physiological functions including sleep, pain, thermoregulation, and circadian rhythms. MLT acts mainly through two G-protein-coupled receptors named MT1 and MT2, but also through an MLT type-3 receptor (MT3). However, the role of MLT receptor subtypes in thermoregulation is still unknown. We have thus investigated the effects of selective and non-selective MLT receptor agonists/antagonists on body temperature (Tb) in rats across the 12/12-h light–dark cycle. Rectal temperature was measured every 15 min from 4:00 a.m. to 9:30 a.m. and from 4:00 p.m. to 9:30 p.m., following subcutaneous injection of each compound at either 5:00 a.m. or 5:00 p.m. MLT (40 mg/kg) had no effect when injected at 5 a.m., whereas it decreased Tb during the light phase only when injected at 5:00 p.m. This effect was blocked by the selective MT2 receptor antagonist 4P-PDOT and the non-selective MT1/MT2 receptor antagonist, luzindole, but not by the α1/MT3 receptors antagonist prazosin. However, unlike MLT, neither the selective MT1 receptor partial agonist UCM871 (14 mg/kg) nor the selective MT2 partial agonist UCM924 (40 mg/kg) altered Tb during the light phase. In contrast, UCM871 injected at 5:00 p.m. increased Tb at the beginning of the dark phase, whereas UCM924 injected at 5:00 a.m. decreased Tb at the end of the dark phase. These effects were blocked by luzindole and 4P-PDOT, respectively. The MT3 receptor agonist GR135531 (10 mg/kg) did not affect Tb. These data suggest that the simultaneous activation of both MT1 and MT2 receptors is necessary to regulate Tb during the light phase, whereas in a complex but yet unknown manner, they regulate Tb differently during the dark phase. Overall, MT1 and MT2 receptors display complementary but also distinct roles in modulating circadian fluctuations of Tb.
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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".