Melatonin‐mediated inhibition of Cav3.2 T‐type Ca<sup>2+</sup> channels induces sensory neuronal hypoexcitability through the novel protein kinase C‐eta isoform
Bibliographic record
Abstract
Abstract Recent studies implicate melatonin in the antinociceptive activity of sensory neurons. However, the underlying mechanisms are still largely unknown. Here, we identify a critical role of melatonin in functionally regulating Cav3.2 T‐type Ca 2+ channels (T‐type channel) in trigeminal ganglion (TG) neurons. Melatonin inhibited T‐type channels in small TG neurons via the melatonin receptor 2 (MT 2 receptor) and a pertussis toxin‐sensitive G‐protein pathway. Immunoprecipitation analyses revealed that the intracellular subunit of the MT 2 receptor coprecipitated with Gα o . Both shRNA‐mediated knockdown of Gα o and intracellular application of QEHA peptide abolished the inhibitory effects of melatonin. Protein kinase C (PKC) antagonists abolished the melatonin‐induced T‐type channel response, whereas inhibition of conventional PKC isoforms elicited no effect. Furthermore, application of melatonin increased membrane abundance of PKC‐eta (PKC η ) while antagonism of PKC η or shRNA targeting PKC η prevented the melatonin‐mediated effects. In a heterologous expression system, activation of MT 2 receptor strongly inhibited Cav3.2 T‐type channel currents but had no effect on Cav3.1 and Cav3.3 current amplitudes. The selective Cav3.2 response was PKC η dependent and was accompanied by a negative shift in the steady‐state inactivation curve. Furthermore, melatonin decreased the action potential firing rate of small TG neurons and attenuated the mechanical hypersensitivity in a mouse model of complete Freund's adjuvant‐induced inflammatory pain. These actions were inhibited by T‐type channel blockade. Together, our results demonstrated that melatonin inhibits Cav3.2 T‐type channel activity through the MT 2 receptor coupled to novel G βγ ‐mediated PKC η signaling, subsequently decreasing the membrane excitability of TG neurons and pain hypersensitivity in mice.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".