Thyroid hormone links environmental signals to DNA methylation
Bibliographic record
Abstract
Environmental conditions experienced within and across generations can impact individual phenotypes via so-called 'epigenetic' processes. Here we suggest that endocrine signalling acts as a 'sensor' linking environmental inputs to epigenetic modifications. We focus on thyroid hormone signalling and DNA methylation, but other mechanisms are likely to act in a similar manner. DNA methylation is one of the most important epigenetic mechanisms, which alters gene expression patterns by methylating cytosine bases via DNA methyltransferase enzymes. Thyroid hormone is mechanistically linked to DNA methylation, at least partly by regulating the activity of DNA methyltransferase 3a, which is the principal enzyme that mediates epigenetic responses to environmental change. Thyroid signalling is sensitive to natural and anthropogenic environmental impacts (e.g. light, temperature, endocrine-disrupting pollution), and here we propose that thyroid hormone acts as an environmental sensor to mediate epigenetic modifications. The nexus between thyroid hormone signalling and DNA methylation can integrate multiple environmental signals to modify phenotypes, and coordinate phenotypic plasticity at different time scales, such as within and across generations. These dynamics can have wide-ranging effects on health and fitness of animals, because they influence the time course of phenotypic adjustments and potentially the range of environmental stimuli that can elicit epigenetic responses. This article is part of the theme issue 'Endocrine responses to environmental variation: conceptual approaches and recent developments'.
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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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".