Transgenerational epigenetic effect of kings’ aging on offspring’s caste fate mediated by sperm DNA methylation in termites
Bibliographic record
Abstract
The discovery of transgenerational epigenetic inheritance and the unraveling of its molecular mechanisms are currently solving previously puzzling challenges that Mendelian genetics based solely on DNA could not explain, leading to significant paradigm shifts across various fields of biology. There has been a long-standing controversy over the factors determining the caste fate of individuals in social insects. Increasing evidence supports heritable influences on division of labor. Here, we provide evidence that transgenerational epigenetic inheritance influences caste determination in a termite. We demonstrate that the age of the king influences the caste fate of offspring, with young kings’ progeny showing a higher tendency for reproductive differentiation compared to offspring from older kings (under controlled conditions). Then, we conducted a high-quality chromosome-level genome assembly for the Japanese subterranean termite Reticulitermes speratus . Genome-wide methylome analysis of kings’ sperm reveals a drastic change in DNA methylation patterns with aging. Among 39,399,411 CpG sites, 21,611 sites showed significant age differences in methylation levels. We identified 13 genes whose methylation levels are significantly different between young and old kings and suggestively correlated with the offspring’s differentiation into the reproductive pathway. Our results suggest that sperm DNA methylation, which changes with the age of kings, is a potential transgenerational epigenetic factor involved in offspring caste differentiation in a termite. These findings may have broad applicability to caste differentiation in social insects and to phenotypic plasticity more generally.
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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) | 0.001 | 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".