Bibliographic record
Abstract
Background: Transposable elements (TEs) and genomes have been at war for millions of years. On one hand, genomes developed epigenetic systems to inactivate TEs. On the other hand, it appears that TEs can take advantage of stress to evade the genome’s repressing systems and spread throughout the genome. However, until recently it was unclear how and why stress influences transposable elements’ movement. In this review, we explore the mechanisms involved in TE stress-induced activation. Methods: The first part of the review looks into epigenetic mechanisms, its 19 references were taken from Handbook of Epigenetics 2nd edition (1) and reviews (2) (3). The rest of the studies presented in this review were drawn from searches done on Web of Science with the terms: TS=((Transposable element* OR mobile genetic element*) AND (Stress or Evolution) AND (Activation)) with peer-reviewed articles and reviews written in English included only. The search yielded 401 results and 56 were estimated relevant and of sufficient quality to be selected. Summary: The main conclusion reached by this review is that protection mechanisms against TEs movement, which are mostly epigenetic, are compromised by the presence of stress. Additionally, TEs themselves evolved diverse tools to promote their activation under certain stress conditions allowing them to evade the repression imposed by the genome. These two mechanisms provide opportunities for TEs to move around the genome and create genetic diversity during stress episodes. As such, TEs stress induced mobility certainly played a major role in the rapid adaption of populations and its impact can be witnessed across genomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".