Epigenetic Alterations in Sperm DNA Associated with Testicular Cancer Treatment
Bibliographic record
Abstract
DNA methylation, a key component of the epigenome involved in regulating gene expression, is initially acquired in the germ line at millions of sites across the genome. Altered sperm methylation patterns are associated with infertility and transgenerational effects in humans and rodents. Testicular cancer is the most common form of cancer among men of reproductive age and has a high cure rate associated with chemotherapy treatment with bleomycin, etoposide, and cis-platinum (BEP). Although these drugs result in improved survival, they also affect the number and quality of germ cells. Our goal was to assess germ cell methylation patterns in a rodent model emulating the BEP treatment regimens used in human testicular cancer treatment. Animals were treated with control, or 0.3× (low) or 0.6× (high) dose of BEP, where a 1× dose is equivalent to human treatment regimens. Both dose-dependent and germ cell-dependent DNA methylation alterations were found at numerous loci throughout the genome. Of about 3000 loci tested, 42 loci were affected by BEP at the round spermatid stage of germ cell development, whereas 101 loci were affected in spermatozoa; 15 loci were consistently altered in spermatozoa of all high dose-treated rats. Both hyper- and hypomethylation were detected, suggesting either an interference with normal methylation patterning or abnormal repair of damaged patterns during spermatogenesis. The results indicate that a combination chemotherapy regimen used for testicular cancer treatment can result in altered DNA methylation patterns in spermatozoa and that some loci are more susceptible to damage than others.
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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 | 0.000 |
| 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".