Shaping Plant Adaptability, Genome Structure and Gene Expression through Transposable Element Epigenetic Control: Focus on Methylation
Bibliographic record
Abstract
In plants, transposable elements (TEs) represent a large fraction of the genome, with potential to alter gene expression and produce genomic rearrangements. Epigenetic control of TEs is often used to stop unrestricted movement of TEs that would result in detrimental effects due to insertion in essential genes. The current review focuses on the effects of methylation on TEs and their genomic context, and how this type of epigenetic control affects plant adaptability when plants are faced with different stresses and changes. TEs mobilize in response to stress elicitors, including biotic and abiotic cues, but also developmental transitions and ‘genome shock’ events like polyploidization. These events transitionally lift TE repression, allowing TEs to move to new genomic locations. When TEs fall close to genes, silencing through methylation can spread to nearby genes, resulting in lower gene expression. The presence of TEs in gene promoter regions can also confer stress inducibility modulated through alternative methylation and demethylation of the TE. Bursts of transposition triggered by events of genomic shock can increase genome size and account for differences seen during polyploidization or species divergence. Finally, TEs have evolved several mechanisms to suppress their own repression, including the use of microRNAs to control genes that promote methylation. The interplay between silencing, transient TE activation, and purifying selection allows the genome to use TEs as a reservoir of potential beneficial modifications but also keeps TEs under control to stop uncontrolled detrimental transposition.
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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 | 0.001 | 0.001 |
| 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".