Supraspinal melatonin MT<sub>2</sub> receptor agonism alleviates pain via a neural circuit that recruits mu opioid receptors
Bibliographic record
Abstract
Abstract Melatonin, through its G protein‐coupled receptor (GPCR) (MTNR1B gene) MT 2 , is implicated in analgesia, but the relationship between MT 2 receptors and the opioid system remains elusive. In a model of rodent neuropathic pain (spared nerve injured [SNI]), the selective melatonin MT 2 agonist UCM924 reversed the allodynia (a pain response to a non‐noxious stimulus), and this effect was nullified by the pharmacological blockade or genetic inactivation of the mu opioid receptor (MOR), but not the delta opioid receptor (DOR). Indeed, SNI MOR, but not DOR knockout mice, did not respond to the antiallodynic effects of the UCM924. Similarly, the nonselective opioid antagonist naloxone and the selective MOR antagonist D‐Phe‐Cys‐Tyr‐D‐Trp‐Orn‐Thr‐Pen‐Thr‐NH2 (CTOP) blocked the effects of UCM924 in SNI rats, but not the DOR antagonist naltrindole (NTI). Electrophysiological recordings in the rostral‐ventromedial medulla (RVM) revealed that the typical reduction of the firing activity of pronociceptive ON‐cells, and the enhancement of the firing of the antinociceptive OFF‐cells, induced by the microinjection of the MT 2 agonist UCM924 into the ventrolateral periaqueductal gray (vlPAG) were blocked by MOR, but not DOR, antagonism. Immunohistochemistry studies showed that MT 2 receptors are expressed in both excitatory (CaMKIIα + ) and inhibitory (GAD65 + ) neuronal cell bodies in the vlPAG (~2.16% total), but not RVM. Only 0.20% of vlPAG neurons coexpressed MOR and MT 2 receptors. Finally, UCM924 treatment induced an increase in the enkephalin precursor gene (PENK) in the PAG of SNI mice. Collectively, the melatonin MT 2 receptor agonism requires MORs to exert its antiallodynic effects, mostly through an interneuronal circuit involving MOR and MT 2 receptors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 teacher head, 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".