Genome-Wide Transcriptome Landscape of Embryonic Brain-Derived Neural Stem Cells Exposed to Alcohol with Strain-Specific Cross-Examination in BL6 and CD1 Mice
Bibliographic record
Abstract
We have previously reported the deregulatory impact of ethanol on global DNA methylation of brain-derived neural stem cells (NSC). Here, we conducted a genome-wide RNA-seq analysis in differentiating NSC exposed to different modes of ethanol exposure. RNA-seq results showed distinct gene expression patterns and canonical pathways induced by ethanol exposure and withdrawal. Short-term ethanol exposure caused abnormal up-regulation of synaptic pathways, while continuous ethanol treatment profoundly affected brain cells' morphology. Ethanol withdrawal restored the gene expression profile of differentiating NSC without rescuing impaired expression of epigenetics factors. Ingenuity Pathway Analysis (IPA) analysis predicated that ethanol may impact synaptic functions via GABA receptor signalling pathway and affects neural system and brain morphology. We identified Sptbn2, Dcc, and Scn3a as candidate genes which may link alcohol-induced neuronal morphology to brain structural abnormalities, predicted by IPA analysis. Cross-examination of Scn3a and As3mt in differentiated NSC from two different mouse strains (BL6 and CD1) showed a consistent pattern of induction and reduction, respectively. Collectively, our study identifies genetic networks, which may contribute to alcohol-mediated cellular and brain structural dysmorphology, contributing to our knowledge of alcohol-mediated damage to central nervous system, paving the path for better understanding of FASD pathobiology.
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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".