Temperature predictability and introduction history affect the expression of genes regulating DNA methylation in a globally distributed songbird
Bibliographic record
Abstract
Phenotypic plasticity is a major mechanism whereby organisms adjust their traits within‐generations to changes in environmental conditions. In the context of range expansions, plasticity is thought to be especially important, as plastic changes in traits can lead to rapid adaptation. One epigenetic process in particular, DNA methylation, enables organisms to adjust gene expression contingent on the environment, which suggests it may play a role in range expansions. At present, we know little about how methylation is regulated in wildlife, especially expression of the enzymes responsible for altering methyl marks on the genome. In this study, we compared expression of three epigenetic regulator genes (DNA methyltransferase 1, DNMT1; DNA methyltransferase 3, DNMT3; and one ten‐eleven translocation methylcytosine dioxygenase, TET2) in three tissues (gut, liver, and spleen) of house sparrows Passer domesticus from nine countries. Some countries are in the native range of the species (Israel, the Netherlands, Norway, Spain, and Vietnam) whereas others are sites the species has colonized in the last 150 years (i.e. Australia, Canada, New Zealand, and Senegal). In this exploratory study, we asked whether non‐native birds and/or birds from sites with comparatively unpredictable climates would express different levels of these genes. We found that all three genes were expressed more in sparrows from the native range and from areas with more stable temperatures. Expression of all three genes was also strongly correlated among‐locations and within‐individuals, but mean expression was quite different among tissues. Many factors (e.g. urbanization of the capture site, sex of the bird) did not significantly affect gene expression, but others surprisingly did (e.g. latitude). Our results suggest that these enzymes could be important in range expansions or geographic distribution generally, but more detailed investigations will be insightful.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".