Rev-erbα Knockout Reduces Ethanol Consumption and Preference in Male and Female Mice
Bibliographic record
Abstract
Alcohol use is a contributor in the premature deaths of approximately 3 million people annually. Among the risk factors for alcohol misuse is circadian rhythm disruption; however, this connection remains poorly understood. Inhibition of the circadian nuclear receptor REV-ERBα is known to disrupt molecular feedback loops integral to daily oscillations, and impact diurnal fluctuations in the expression of proteins required for reward-related neurotransmission. However, the role of REV-ERBα in alcohol and substance use-related phenotypes is unknown. Herein, we used a Rev-erbα knockout mouse line and ethanol two-bottle choice preference testing to show that disruption of Rev-erbα reduces ethanol preference in male and female mice. Rev-erbα null mice showed the lowest ethanol preference in a two-bottle choice test across all genotypes, whereas there were no ethanol preference differences between heterozygotes and wildtypes. In a separate experiment, alcohol-consuming wildtype C57Bl/6N mice were administered the REV-ERBα/β inhibitor SR8278 (25 mg/kg or 50 mg/kg) for 7 days and alcohol preference was evaluated daily. No differences in alcohol preference were observed between the treatment and vehicle groups. Our data provides evidence that genetic variation in REV-ERBα may contribute to differences in alcohol drinking.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".