Variable DNA methylation of transposable elements: The case study of mouse Early Transposons
Bibliographic record
Abstract
Phenotypic variation stems from both genetic and epigenetic differences between individuals. In order to elucidate how phenotypes are determined, it is necessary to understand the forces that generate variation in genome sequence as well as its epigenetic state. In both contexts, transposable elements (TEs) may play an important role. It is well established that TE activity is a major generator of genetic variation, but recent research also suggests that TEs contribute to epigenetic variation. Stochastic epigenetic silencing of some TE insertions in mice has been shown to cause phenotypic variability between individuals. However, the prevalence of this phenomenon has never been evaluated. Here, we use 18 insertions of a mouse Endogenous Retrovirus (ERV) family, the Early Transposons (ETns), to detect insertion-dependent determinants of DNA methylation levels and variability between both cells and individuals. We show that the structure and age of insertions influence methylation levels and variability, resulting in a subgroup of loci that displays unexpectedly high variability in methylation and suggesting stochastic events during methylation establishment. Despite variation in methylation according to the age and structure of each locus, homologous CpG sites show similar tendencies in methylation levels across loci, emphasizing the role of the insertion's sequence in methylation determination. Our results show that differences in methylation of ETns between individuals is not a sporadic phenomenon and support the hypothesis that ERVs contribute to phenotypic variability through their stochastic silencing.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".