The <i>foraging</i> gene affects alcohol sensitivity, metabolism and memory in <i>Drosophila</i>
Bibliographic record
Abstract
The genetic basis of alcohol use disorder (AUD) is complex. Understanding how natural genetic variation contributes to alcohol phenotypes can help identify mechanisms underlying the genetic contribution of AUD. Recently, a single nucleotide polymorphism in the human foraging ( for ) gene ortholog, Protein Kinase cGMP-Dependent 1 (PRKG1), was found to be associated with stress-induced risk for alcohol abuse. However, the mechanistic role that PRKG1 plays in AUD is not well understood. We use natural variation in the Drosophila for gene to describe how variation of cGMP-dependent protein kinase (PKG) activity modifies ethanol-induced phenotypes. We found that variation in for affects ethanol-induced increases in locomotion and memory of the appetitive properties of ethanol intoxication. Further, these differences may stem from the ability to metabolize ethanol. Together, this data suggests that natural variation in PKG modulates cue reactivity for alcohol, and thus could influence alcohol cravings by differentially modulating metabolic and behavioral sensitivities to alcohol.
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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) | 0.002 | 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".