Genetic, epigenetic and pharmacological influences modulating tissue specific regulation of the cannabinoid receptor-1 gene (CB <sub>1</sub> ); implications for cannabinoid pharmacogenetics
Bibliographic record
Abstract
Abstract Cannabinoid receptor-1 (CB 1 ) represents a potential drug target against conditions that include obesity and substance abuse. However, drug trials targeting CB 1 (encoded by the CNR1 gene) have been compromised by differences in patient response. Towards addressing the hypothesis that genetic and epigenetic changes within the regulatory regions controlling CNR1 expression contribute to these differences, we isolated the human CNR1 promotor (CNR1prom) and demonstrate its activity in primary cells and transgenic mice. We also provide evidence of CNR1prom in CB 1 autoregulation and its repression by DNA-methylation. We further characterised a conserved regulatory sequence (ECR1) in CNR1 intron 2 that contained a polymorphism in linkage disequilibrium with disease associated SNPs. Deletion of ECR1 from mice using CRISPR genome editing significantly reduced CNR1 expression in the hippocampus. These mice also displayed reduced ethanol intake and hypothermia response to CB 1 agonism. Moreover, human specific C-allele variants of ECR1 (ECR1(C)) drove higher levels of CNR1prom activity in hippocampal cells than did the ancestral T-allele. We further demonstrate a role for the AP-1 transcription factor in driving higher ECR1(C) activity. In the context of the known roles of CB 1 the current study suggests a mechanism through which ECR1(C) may be neuroprotective in the hippocampus against stress. The cell-specific approaches used in our study to determine the functional effects of genetic and epigenetic changes on the activity of tissue-specific regulatory elements at the CNR1 locus represent an important step in gaining a mechanistic understanding of cannabinoid pharmacogenetics.
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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) | 0.001 | 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".