Epigenetic regulation of genomic imprinting from germ line to preimplantation
Bibliographic record
Abstract
Genomic imprinting is an epigenetic process that distinguishes parental alleles, resulting in parent-specific expression of a gene or cluster of genes. Imprints are acquired during gametogenesis when genome-wide epigenetic remodeling occurs. These imprints must then be maintained during preimplantation development, when another wave of genome-wide epigenetic remodeling takes place. Thus, for imprints to persist as parent-specific epigenetic marks, coordinated factors and processes must be involved to both recognize an imprint and protect it from genome-wide remodeling. Parent-specific DNA methylation has long been recognized as a primary epigenetic mark demarcating a genomic imprint. Recent work has advanced our understanding of how and when parent-specific DNA methylation is erased and acquired in the germ line as well as maintained during preimplantation development. Epigenetic factors have also been identified that are recruited to imprinted regions to protect them from genome-wide DNA demethylation during preimplantation development. Intriguingly, asynchrony in epigenetic reprogramming appears to be a recurrent theme with asynchronous acquisition between male and female germ lines, between different imprinted genes, and between the two parental alleles of a gene. Here, we review recent advancements and discuss how they impact our current understanding of the epigenetic regulation of genomic imprinting.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".