The Evolutionary Complexities of DNA Methylation in Animals: From Plasticity to Genetic Evolution
Bibliographic record
Abstract
The importance of DNA methylation in plastic responses to environmental change and evolutionary dynamics is increasingly recognized. Here, we provide a Perspective piece on the diverse roles of DNA methylation on broad evolutionary timescales, including (i) short-term transient acclimation, (ii) stable phenotypic evolution, and (iii) genomic evolution. We show that epigenetic responses vary along a continuum, ranging from short-term acclimatory responses in variable environments within a generation to long-term modifications in populations and species. DNA methylation thus unlocks additional potential for organisms to rapidly acclimate to their environment over short timeframes. If these changes affect fitness, they can circumvent the need for adaptive changes at the genome level. However, methylation has a complex reciprocal relationship with genetic variation as it can be genetically controlled, yet it can also induce point mutations and contribute to genomic evolution. When habitats remain constant over many generations, or populations are separated across habitats, initially plastic phenotypes can become hardwired through epigenetically facilitated mutagenesis. It remains unclear under what circumstances plasticity contributes to evolutionary outcomes, and when plastic changes will become permanently encoded into genotype. We highlight how studies investigating the evolution of epigenetic plasticity need to carefully consider how plasticity in methylation state could evolve among different evolutionary scenarios, the possible phenotypic outcomes, its effects on genomic evolution, and the proximate energetic and ultimate fitness costs of methylation. We argue that accumulating evidence suggests that DNA methylation can contribute toward evolution on various timescales, spanning a continuum from acclimatory plasticity to genomic evolution.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".