Overview of the Genetic Basis and Epigenetic Mechanisms that Contribute to FASD Pathobiology
Bibliographic record
Abstract
Prenatal alcohol (ethanol) exposure (PAE) is the underlying cause for a variety of birth defects and neurodevelopmental deficits referred to as "Fetal Alcohol Spectrum Disorders (FASD)". The more visible phenotypes caused by PAE include growth retardation, and characteristic craniofacial abnormalities associated with functional and structural damage to the central nervous system. Ethanol is a teratogenic agent itself; but it can also alter gene expression. These changes may contribute to the spectrum of effects and different phenotypes that are dependent on alcohol metabolism, as well as the timing and duration of alcohol exposure. Evidence from both human patients and animal models show that genetic factors and epigenetic mechanisms such as DNA methylation, histone post-translational modifications and noncoding RNAs, contribute to the gene expression changes caused by ethanol. Not all embryos that are exposed to alcohol during development exhibit FASD symptoms after birth. FASD patients may present severe birth defects, while others are normal in physical appearance but present a variety of cognitive and behavioral difficulties. It has been hypothesized that maternal and paternal genetic factors may contribute to the sensitivity, resistance or vulnerability of the fetus to alcohol. Moreover, the epigenome is highly sensitive to a multitude of environmental insults including PAE. Studies also show 'transgenerational' effects of alcohol. In such cases, maternal or paternal preconception alcohol consumption could lead to FASD-like phenotypes in the newborn. Thus, the phenotypes in FASD can be modified by interplay between maternal/paternal genetic factors and epigenetic mechanisms. This current review summarizes the contribution of genetic and epigenetic mechanisms in FASD pathobiology, and how this information could be utilized for prevention, early diagnosis and potentially treatment of the affected individuals.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".