The Dynamic Nature of DNA Methylation: A Role in Response to Social and Seasonal Variation
Bibliographic record
Abstract
An organism's ability to adapt to its environment depends on its ability to regulate and maintain tissue specific, temporal patterns of gene transcription in response to specific environmental cues. Epigenetic mechanisms are responsible for many of the intricacies of a gene's regulation that alter expression patterns without affecting the genetic sequence. In particular, DNA methylation has been shown to have an important role in regulating early development and in some human diseases. Within these domains, DNA methylation has been extensively characterized over the past 60 years, but the discovery of its role in regulating behavioral outcomes has led to renewed interest in its potential roles in animal behavior and phenotypic plasticity. The conservation of DNA methylation across the animal kingdom suggests a possible role in the plasticity of genomic responses to environmental cues in natural environments. Here, we review the historical context for the study of DNA methylation, its function and mechanisms, and provide examples of gene/environment interactions in response to social and seasonal cues. Finally, we discuss useful tools to interrogate and dissect the function of DNA methylation in non-model organisms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".