Altered sperm DNA methylation in overweight men associates transposable element regulation to paternal origins of disease risk in children
Bibliographic record
Abstract
Abstract The occurrence of childhood neurodevelopmental disorders has been steadily increasing for decades yet we have little understanding of modes of inheritance implicated in these diseases. Epidemiology studies have revealed an association between an elevated paternal BMI and an increased risk for autism in children, suggesting non-genetic modes of inheritance may be involved. Epigenetic marks, like DNA methylation at cytosines, are especially susceptible to environmental, diet and lifestyle changes. Yet, whether a man’s BMI influences the sperm methylome and potentially impacts offspring development remains unresolved. Using MethylC capture (MCC)-sequencing, we identified over 38 000 differentially methylated CpGs (DMCs) in the sperm of men with an elevated BMI, with many occurring in regions that were enriched for neural gene and disease ontologies. Differentially methylated regions (DMRs) were enriched at transposable elements that can act as active enhancers during human zygotic genome activation, and at gene promoters for early lineage specification. These results suggest that sperm methylome alterations that are linked to an elevated BMI may influence key embryonic transcriptional process, notably those associated with trophectoderm and placental 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) | 0.000 | 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) | 0.004 | 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".