Conservation of DNA Methylation Programming Between Mouse and Human Gametes and Preimplantation Embryos
Bibliographic record
Abstract
In mice, assisted reproductive technologies (ARTs) applied during gametogenesis and preimplantation development can result in disruption of genomic imprinting. In humans, these technologies and/or subfertility have been linked to perturbations in genomic imprinting. To understand how ARTs and infertility affect DNA methylation, it is important to understand DNA methylation dynamics and the role of regulatory factors at these critical stages. Recent genome studies performed using mouse and human gametes and preimplantation embryos have shed light onto these processes. Here, we comprehensively review the current state of knowledge regarding global and imprinted DNA methylation programming in the mouse and human. Available data highlight striking similarities in mouse and human DNA methylation dynamics during gamete and preimplantation development. Just as fascinating, these studies have revealed sex-, gene-, and allele-specific differences in DNA methylation programming, warranting future investigation to untangle the complex regulation of DNA methylation dynamics during gamete and preimplantation development.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".