Compromised oocyte quality and assisted reproduction contribute to sex-specific effects on offspring outcomes and epigenetic patterning
Bibliographic record
Abstract
Clinical studies have revealed an increased incidence of growth and genomic imprinting disorders in children conceived using assisted reproductive technologies (ARTs), and aberrant DNA methylation has been implicated. We propose that compromised oocyte quality associated with female infertility may make embryos more susceptible to the induction of epigenetic defects by ART. DNA methylation patterns in the preimplantation embryo are dependent on the oocyte-specific DNA methyltransferase 1o (DNMT1o), levels of which are decreased in mature oocytes of aging females. Here, we assessed the effects of maternal deficiency in DNMT1o (Dnmt1Δ1o/+) in combination with superovulation and embryo transfer on offspring DNA methylation and development. We demonstrated a significant increase in the rates of morphological abnormalities in offspring collected from Dnmt1Δ1o/+ females only when combined with ART. Together, maternal oocyte DNMT1o deficiency and ART resulted in an accentuation of placental imprinting defects and the induction of genome-wide DNA methylation alterations, which were exacerbated in the placenta compared to the embryo. Significant sex-specific trends were also apparent, with a preponderance of DNA hypomethylation in females. Among genic regions affected, a significant enrichment for neurodevelopmental pathways was observed. Taken together, our results demonstrate that oocyte DNMT1o-deficiency exacerbates genome-wide DNA methylation abnormalities induced by ART in a sex-specific manner and plays a role in mediating poor embryonic outcome.
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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) | 0.002 | 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".