Low paternal dietary folate alters the mouse sperm epigenome and is associated with negative pregnancy outcomes
Bibliographic record
Abstract
Epidemiological studies suggest that a father’s diet can influence offspring health. A proposed mechanism for paternal transmission of environmental information is via the sperm epigenome. The epigenome includes heritable information such as DNA methylation. We hypothesize that the dietary supply of methyl donors will alter epigenetic reprogramming in sperm. Here we feed male mice either a folate-deficient or folate-sufficient diet throughout life. Paternal folate deficiency is associated with increased birth defects in the offspring, which include craniofacial and musculoskeletal malformations. Genome-wide DNA methylation analysis and the subsequent functional analysis identify differential methylation in sperm of genes implicated in development, chronic diseases such as cancer, diabetes, autism and schizophrenia. While >300 genes are differentially expressed in offspring placenta, only two correspond to genes with differential methylation in sperm. This model suggests epigenetic transmission may involve sperm histone H3 methylation or DNA methylation and that adequate paternal dietary folate is essential for offspring health. Paternal diet has been shown to influence the metabolism of offspring in rats and mice. Here Lambrot et al.report DNA and histone methylation changes in the sperm of male mice on a folate-deficient diet and observe developmental defects in their offspring.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".