Folic acid intervention during pregnancy alters DNA methylation, affecting neural target genes through two distinct mechanisms
Bibliographic record
Abstract
BACKGROUND: We previously showed that continued folic acid (FA) supplementation beyond the first trimester of pregnancy appears to have beneficial effects on neurocognitive performance in children followed for up to 11 years, but the biological mechanism for this effect has remained unclear. Using samples from our randomized controlled trial of folic acid supplementation in second and third trimester (FASSTT), where significant improvements in cognitive and psychosocial performance were demonstrated in children from mothers supplemented in pregnancy with 400 µg/day FA compared with placebo, we examined methylation patterns from cord blood (CB) using the EPIC array which covers approximately 850,000 cytosine-guanine (CG) sites across the genome. Genes showing significant differences were verified using pyrosequencing and mechanistic approaches used in vitro to determine effects on transcription. RESULTS: FA supplementation resulted in significant differences in methylation, particularly at brain-related genes. Further analysis showed these genes split into two groups. In one group, which included the CES1 gene, methylation changes at the promoters were important for regulating transcription. We also identified a second group which had a characteristic bimodal profile, with low promoter and high gene body (GB) methylation. In the latter, loss of methylation in the GB is linked to decreases in transcription: this group included the PRKAR1B/HEATR2 genes and the dopamine receptor regulator PDE4C. Overall, methylation in CB also showed good correlation with methylation profiles seen in a published data set of late gestation foetal brain samples. CONCLUSION: We show here clear alterations in DNA methylation at specific classes of neurodevelopmental genes in the same cohort of children, born to FA-supplemented mothers, who previously showed improved cognitive and psychosocial performance. Our results show measurable differences at neural genes which are important for transcriptional regulation and add to the supporting evidence for continued FA supplementation throughout later gestation. This trial was registered on 15 May 2013 at www.isrctn.com as ISRCTN19917787.
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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 | 0.001 |
| 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".