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) MT2, is implicated in analgesia, but the relationship between MT2 receptors and the opioid system remains elusive. In a model of rodent neuropathic pain (spared nerve injured [SNI]), the selective melatonin MT2 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 MT2 agonist UCM924 into the ventrolateral periaqueductal gray (vlPAG) were blocked by MOR, but not DOR, antagonism. Immunohistochemistry studies showed that MT2 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 MT2 receptors. Finally, UCM924 treatment induced an increase in the enkephalin precursor gene (PENK) in the PAG of SNI mice. Collectively, the melatonin MT2 receptor agonism requires MORs to exert its antiallodynic effects, mostly through an interneuronal circuit involving MOR and MT2 receptors.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".