Gene regulation in Drosophila melanogaster in response to an acute dose of ethanol
Bibliographic record
Abstract
Alcohol intake causes gene expression changes resulting in cellular and molecular adaptations that could be associated with a predisposition to alcohol dependence. Expression profiling using high-throughput microarrays has recently been used to identify changes in gene expression that may be associated with alcohol dependence. To clarify the mechanisms and biology underlying alcohol dependence, bioinformatics, behavioural and genetics methodologies were employed to analyse obtained raw microarray data set that was previously generated from Drosophila exposed to an acute dose of ethanol. Classical linear statistical modeling coupled with clustering and functional enrichment analyses were implemented to evaluate whole-head time series microarray data from ethanol-treated and control samples, and implicated many genes or pathways affected by acute ethanol treatment in Drosophila head including those involved in stress signaling, inter and intra cellular signaling, ubiquitinmediated signaling, metabolic switches, and possible transcriptional regulatory components. Further analysis identified interaction networks and patterns of transcriptional regulation within the set of identified genes. Seven of these genes, ana, Axin, hiw, hop, hsp26, hsp83, and mbf1, were verified and linked with novel roles in ethanol behavioural responses using functional tests. Additional work on two of these genes namely, hiw and hsp26 also revealed a role for glia, mushroom bodies and ellipsoid body neurons as important regulators of acute ethanol response in Drosophila. Finally, these studies have demonstrated that microarray analysis is an efficient method for identifying candidate genes and pathways that may be fundamental to human alcohol dependence or abuse.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".