Epigenetic rRgulation in the Placenta and its Role in Fetal Growth
Bibliographic record
Abstract
Fetal growth potential reflects a complex regulatory system delivered by genetic and environmental factors acting directly on the fetus or through the placenta. Compromise of this potential, as seen in intrauterine growth restriction (IUGR), is associated with increased perinatal mortality and short and long term morbidity. The expression of several genes has been shown to be disturbed in placentas of fetuses with growth restriction. However, the primary causes for these changes have not yet been elucidated.\nI proposed that epigenetic mechanisms, specifically DNA methylation, may be involved in placental development leading to modulation of the expression of specific genes, and that their altered regulation will impact fetal development and growth.\nMy primary objective was to identify DNA methylation variation in placenta, in association with variation of gene expression and with poor fetal growth.\nI used a global genomic screening approach, with 24 selected placental samples, from newborns considered IUGR or normal controls, to identify candidate target genomic regions carrying epigenetic alterations. Candidate regions were followed up, by expression analysis of corresponding regulated genes, for associations with altered expression and by targeted methylation analysis in an expanded cohort of 170 samples, for associations with birthweight percentile. I analyzed methylation variation at imprinting centers (IC), gene promoters and CpG islands.\nIn two genome-wide case control screening studies using distinct commercial microarray platforms I identified approximately 68 differentially methylated autosomal candidate genomic regions overlapping gene promoters. Hypomethylated CpGs mapping to gene promoters were found to be more abundant in placentas of growth restricted newborns than in controls. One of the most interesting candidates, WNT2, was analyzed in an extended sample cohort and showed an association of high promoter methylation to low expression as well as low birthweight percentile. This gene is involved in a pathway that diverts cells from programmed apoptosis. It is highly expressed in placenta, and in mice, targeted biallelic inactivation of Wnt2 has been shown to cause poor growth and perinatal death in 50% of the affected pups.\nThese findings support the hypothesis that dysregulation of epigenetic mechanisms are involved in abnormal placental development and can impact fetal growth.
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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 | 0.001 | 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".