Histone arginine methylation in cocaine action in the nucleus accumbens
Bibliographic record
Abstract
Repeated cocaine exposure regulates transcriptional regulation within the nucleus accumbens (NAc), and epigenetic mechanisms-such as histone acetylation and methylation on Lys residues-have been linked to these lasting actions of cocaine. In contrast to Lys methylation, the role of histone Arg (R) methylation remains underexplored in addiction models. Here we show that protein-R-methyltransferase-6 (PRMT6) and its associated histone mark, asymmetric dimethylation of R2 on histone H3 (H3R2me2a), are decreased in the NAc of mice and rats after repeated cocaine exposure, including self-administration, and in the NAc of cocaine-addicted humans. Such PRMT6 down-regulation occurs selectively in NAc medium spiny neurons (MSNs) expressing dopamine D2 receptors (D2-MSNs), with opposite regulation occurring in D1-MSNs, and serves to protect against cocaine-induced addictive-like behavioral abnormalities. Using ChIP-seq, we identified Src kinase signaling inhibitor 1 (Srcin1; also referred to as p140Cap) as a key gene target for reduced H3R2me2a binding, and found that consequent Srcin1 induction in the NAc decreases Src signaling, cocaine reward, and the motivation to self-administer cocaine. Taken together, these findings suggest that suppression of Src signaling in NAc D2-MSNs, via PRMT6 and H3R2me2a down-regulation, functions as a homeostatic brake to restrain cocaine action, and provide novel candidates for the development of treatments for cocaine addiction.
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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 | 0.000 |
| 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".