Histone methylation-mediated microRNA-32-5p down-regulation in sensory neurons regulates pain behaviors via targeting Cav3.2 channels
Bibliographic record
Abstract
microRNA (miRNA)–mediated gene regulation has been studied as a therapeutic approach, but its functional regulatory mechanism in neuropathic pain is not well understood. Here, we identify that miRNA-32-5p (miR-32-5p) is a functional RNA in regulating trigeminal-mediated neuropathic pain. High-throughput sequencing and qPCR analysis showed that miR-32-5p was the most down-regulated miRNA in the injured trigeminal ganglion (TG) of rats. Intra-TG injection of miR-32-5p agomir or overexpression of miR-32-5p by lentiviral delivery in neurons of the injured TG attenuated established trigeminal neuropathic pain. miR-32-5p overexpression did not affect acute physiological pain, while miR-32-5p down-regulation in intact rats was sufficient to cause pain-related behaviors. Nerve injury increased the methylated histone occupancy of binding sites for the transcription factor glucocorticoid receptor in the miR-32-5p promoter region. Inhibition of the enzymes that catalyze H3K9me2 and H3K27me3 restored the expression of miR-32-5p and markedly attenuated pain behaviors. Further, miR-32-5p–targeted Cav3.2 T-type Ca2+ channels and decreased miR-32-5p associated with neuropathic pain caused an increase in Cav3.2 protein expression and T-type channel currents. Conversely, miR-32-5p overexpression in injured TG suppressed the increased expression of Cav3.2 and reversed mechanical allodynia. Together, we conclude that histone methylation-mediated miR-32-5p down-regulation in TG neurons regulates trigeminal neuropathic pain by targeting Cav3.2 channels.
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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) | 0.001 | 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".