The methyl donor S-adenosyl methionine reverses the DNA methylation signature of chronic neuropathic pain in mouse frontal cortex
Bibliographic record
Abstract
Introduction: Chronic pain is associated with persistent but reversible structural and functional changes in the prefrontal cortex (PFC). This stable yet malleable plasticity implicates epigenetic mechanisms, including DNA methylation, as a potential mediator of chronic pain–induced cortical pathology. We previously demonstrated that chronic oral administration of the methyl donor S-adenosyl methionine (SAM) attenuates long-term peripheral neuropathic pain and alters global frontal cortical DNA methylation. However, the specific genes and pathways associated with the resolution of chronic pain by SAM remain unexplored. Objective: To determine the effect of long-term therapeutic exposure to SAM on the DNA methylation of individual genes and pathways in a mouse neuropathic pain model. Methods: Male CD-1 mice received spared nerve injury or sham surgery. Three months after injury, animals received SAM (20 mg/kg, oral, 3× a week) or vehicle for 16 weeks followed by epigenome-wide analysis of frontal cortex. Results: Peripheral neuropathic pain was associated with 4000 differentially methylated genomic regions that were enriched in intracellular signaling, cell motility and migration, cytoskeletal structure, and cell adhesion pathways. A third of these differentially methylated regions were reversed by SAM treatment (1415 regions representing 1013 genes). More than 100 genes with known pain-related function were differentially methylated after nerve injury; 29 of these were reversed by SAM treatment including Scn10a, Trpa1, Ntrk1, and Gfap. Conclusion: These results suggest a role for the epigenome in the maintenance of chronic pain and advance epigenetic modulators such as SAM as a novel approach to treat chronic pain.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 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".