Epigenetic patterning in male germ cells: importance of DNA methylation to progeny outcome
Bibliographic record
Abstract
Epigenetics refers to non-sequence based mechanisms that control gene expression and has been described as the next ‘frontier’ in genetics. This chapter focuses on DNA methylation, one of the best-characterized DNA modifications associated with the modulation of gene activity. In humans, DNA methylation abnormalities have been linked to infertility, imprinting disorders in children and cancer. Recent studies have suggested that assisted reproductive technologies (ARTs) may be associated with an increased incidence of epigenetic defects in children and it is unclear whether the etiology is related to infertility with an underlying epigenetic cause or the specific techniques used. Gametes may be particularly vulnerable to epigenetic defects since genome-wide epigenetic methylation patterns are first initiated in the male and female germ lines; the acquisition of gametic methylation patterns is subsequently essential for normal embryonic development. Here, recent progress in our understanding of when and how methylation patterns are established in the male germ line, as well as the enzymes involved in this process, will be discussed. We will also review the factors involved in modulating DNA methylation and the disorders associated with DNA methylation abnormalities in the germ line. Although we will emphasize human studies where they exist, mouse studies will be included since much of our understanding of DNA methylation pattern establishment and propagation comes from studies done in mice. Epigenetics and gene expression Epigenetic alterations lead to heritable changes in gene expression without altering the underlying DNA sequence.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".