Prenatal Alcohol Exposure: Profiling Developmental DNA Methylation Patterns in Central and Peripheral Tissues
Bibliographic record
Abstract
Prenatal alcohol exposure (PAE) alters the development of neurobiological systems, leading to lasting neuroendocrine, neuroimmune, and neurobehavioral deficits. Although the etiology of this reprogramming remains unknown, emerging evidence suggests DNA methylation as a potential mediator and biomarker for the effects of PAE due to its responsiveness to environmental cues and relative stability over time. Here, we utilized a rat model of PAE to examine the DNA methylation profiles of rat hypothalami and leukocytes at four time points during early development to assess the genome-wide impact of PAE on the epigenome and identify potential biomarkers of PAE. Hypothalami were analyzed on postnatal (P) days 1, 8, 15, 22 and leukocytes at P22 to compare central and peripheral markers. Genome-wide DNA methylation analysis was performed by methylated DNA immunoprecipitation followed by next-generation sequencing. PAE resulted in lasting changes to DNA methylation profiles across all four ages, and a large number of differentially methylated regions (DMRs) displayed the same direction of change in the hypothalamus and leukocytes of P22 pups. The majority of these DMRs were located in intergenic regions, which contained several computationally-predicted transcription factor binding sites. Differentially methylated genes were generally involved in immune function, epigenetic remodelling, metabolism, and hormonal signaling. Persistent DNA methylation changes in the hypothalamus may be associated with the long-term deficits observed in PAE. Furthermore, correlations between epigenetic alterations in peripheral tissues and those in the brain will provide a foundation for the development of biomarkers of fetal alcohol spectrum disorder (FASD).
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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".