Higher incidence of embryonic defects in mouse offspring conceived with assisted reproduction from fathers with sperm epimutations
Bibliographic record
Abstract
Assisted reproductive technologies (ART) account for 1-6% of births in developed countries. While most children conceived are healthy, increases in birth and genomic imprinting defects have been reported; such abnormal outcomes have been attributed to underlying parental infertility and/or the ART used. Here, we assessed whether paternal genetic and lifestyle factors, that are associated with male infertility and affect the sperm epigenome, can influence ART outcomes. We examined how paternal factors, haploinsufficiency for Dnmt3L, an important co-factor for DNA methylation reactions, and/or diet-induced obesity, in combination with ART (superovulation, in vitro fertilization, embryo culture and embryo transfer), could adversely influence embryo development and DNA methylation patterning in mice. While male mice fed high-fat diets (HFD) gained weight and showed perturbed metabolic health, their sperm DNA methylation was minimally affected by the diet. In contrast, Dnmt3L haploinsufficiency induced a marked loss of DNA methylation in sperm; notably, regions affected were associated with neurodevelopmental pathways and enriched in young retrotransposons, sequences that can have functional consequences in the next generation. Following ART, placental imprinted gene methylation and growth parameters were impacted by one or both paternal factors. For embryos conceived by natural conception, abnormality rates were similar for WT and Dnmt3L+/- fathers. In contrast, paternal Dnmt3L+/- genotype, as compared to WT fathers, resulted in a 3-fold increase in the incidence of morphological abnormalities in embryos generated by ART. Together, the results indicate that embryonic morphological and epigenetic defects associated with ART may be exacerbated in offspring conceived by fathers with sperm epimutations.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".