Preferential Epigenetic Suppression of the Autonomous MusD over the Nonautonomous ETn Mouse Retrotransposons
Bibliographic record
Abstract
Nonautonomous retrotransposon subfamilies are often amplified in preference to their coding-competent relatives. However, the mechanisms responsible for such replicative success are poorly understood. Here, we demonstrate that the autonomous MusD long terminal repeat (LTR) retrotransposons are subject to greater epigenetic silencing than their nonautonomous cousins, the early transposons (ETns), which are expressed at a 170-fold-higher level than MusD in mouse embryonic stem (ES) cells. We show that, in ES cells, 5' LTRs and the downstream region of MusD elements are more heavily methylated and are associated with less-activating and more-repressive histone modifications than the highly similar ETnII sequences. The internal region of MusD likely contributes to their silencing, as transgenes with MusD, compared to those with ETnII sequences, show reduced reporter gene expression and a higher level of repressive histone marks. Genomic distribution patterns of MusD and ETn elements are consistent with stronger selection against MusD elements within introns, suggesting that MusD-associated silencing marks can negatively impact genes. We propose a model in which nonautonomous retrotransposons may gain transcriptional and retrotranspositional advantages over their coding-competent counterparts by elimination of the CpG-rich retroviral sequence targeting the autonomous subfamilies for 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 | 0.000 |
| 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".