Intergenerational Transmission of Paternal Epigenetic Marks: Mechanisms Influencing Susceptibility to Post-Concussion Symptomology in a Rodent Model
Bibliographic record
Abstract
Epigenetic transmission of phenotypic variance has been linked to paternal experiences prior to conception and during perinatal development. Previous reports indicate that paternal experiences increase phenotypic heterogeneity and may contribute to offspring susceptibility to post-concussive symptomology. This study sought to determine if epigenetic tags, specifically DNA methylation of promoter regions, are transmitted from rodent fathers to their sons. Using MethyLight, promoter methylation of specific genes involved in recovery from concussion and brain plasticity were analyzed in sperm and brain tissue. Promoter methylation in sperm differed based on paternal experience. Differences in methylation were often identified in both the sperm and brain tissue obtained from their sons, demonstrating transmission of epigenetic tags. For certain genes, methylation in the sperm was altered following a concussion suggesting that a history of brain injury may influence paternal transmission of traits. As telomere length is paternally inherited and linked to neurological health, this study examined paternally derived differences in telomere length, in both sperm and brain. Telomere length was consistent between fathers and their sons, and between brain and sperm, with the exception of the older fathers. Older fathers exhibited increased sperm telomere length, which was not evident in sperm or brain of their sons.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".