Brief alcohol exposure alters transcription in astrocytes via the heat shock pathway
Bibliographic record
Abstract
Astrocytes are critical for maintaining homeostasis in the central nervous system (CNS), and also participate in the genomic response of the brain to drugs of abuse, including alcohol. In this study, we investigated ethanol regulation of gene expression in astrocytes. A microarray screen revealed that a brief exposure of cortical astrocytes to ethanol increased the expression of a large number of genes. Among the alcohol-responsive genes (ARGs) are glial-specific immune response genes, as well as genes involved in the regulation of transcription, cell proliferation, and differentiation, and genes of the cytoskeleton and extracellular matrix. Genes involved in metabolism were also upregulated by alcohol exposure, including genes associated with oxidoreductase activity, insulin-like growth factor signaling, acetyl-CoA, and lipid metabolism. Previous microarray studies performed on ethanol-treated hepatocyte cultures and mouse liver tissue revealed the induction of almost identical classes of genes to those identified in our microarray experiments, suggesting that alcohol induces similar signaling mechanisms in the brain and liver. We found that acute ethanol exposure activated heat shock factor 1 (HSF1) in astrocytes, as demonstrated by the translocation of this transcription factor to the nucleus and the induction of a family of known HSF1-dependent genes, the heat shock proteins (Hsps). Transfection of a constitutively transcriptionally active Hsf1 construct into astrocytes induced many of the ARGs identified in our microarray study supporting the hypothesis that HSF1 transcriptional activity, as part of the heat shock cascade, may mediate the ethanol induction of these genes. These data indicate that acute ethanol exposure alters gene expression in astrocytes, in part via the activation of HSF1 and the heat shock cascade.
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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".