Impact of assisted reproduction, infertility, sex and paternal factors on the placental DNA methylome
Bibliographic record
Abstract
Children conceived using Assisted Reproductive Technologies (ART) have a higher incidence of growth and birth defects, attributable in part to epigenetic perturbations. Both ART and germline defects associated with parental infertility could interfere with epigenetic reprogramming events in germ cells or early embryos. Mouse models indicate that the placenta is more susceptible to the induction of epigenetic abnormalities than the embryo, and thus the placental methylome may provide a sensitive indicator of 'at risk' conceptuses. Our goal was to use genome-wide profiling to examine the extent of epigenetic abnormalities in matched placentas from an ART/infertility group and control singleton pregnancies (n = 44/group) from a human prospective longitudinal birth cohort, the Design, Develop, Discover (3D) Study. Principal component analysis revealed a group of ART outliers. The ART outlier group was enriched for females and a subset of placentas showing loss of methylation of several imprinted genes including GNAS, SGCE, KCNQT1OT1 and BLCAP/NNAT. Within the ART group, placentas from pregnancies conceived with in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) showed distinct epigenetic profiles as compared to those conceived with less invasive procedures (ovulation induction, intrauterine insemination). Male factor infertility and paternal age further differentiated the IVF/ICSI group, suggesting an interaction of infertility and techniques in perturbing the placental epigenome. Together, the results suggest that the human placenta is sensitive to the induction of epigenetic defects by ART and/or infertility, and we stress the importance of considering both sex and paternal factors and that some but not all ART conceptuses will be susceptible.
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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".