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 to changes in environmental conditions. In the context of range expansions, plasticity is especially important, as plastic changes in traits can lead to rapid adaptation. For these reasons, there has been growing interest in the role of molecular epigenetic processes in range expansions. 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 some invasions. Nevertheless, we know little about how methylation is regulated in wildlife, especially expression of the enzymes responsible for altering methyl marks on the genome. The most important forms of these enzymes in vertebrates are DNA methyltransferase 1, which largely maintains existing methyl marks, DNA methyltransferase 3, which creates most de novo methyl marks, and TET2, which is a major demethylator of CpG motifs, genomic regions where most methyl marks occur. In this study, we compared expression of these genes in three tissues (i.e., gut, liver, and spleen) of house sparrows (Passer domesticus) from 9 locations. Some sparrow populations derived from the native range (i.e., Israel, Netherlands, Norway, Spain, and Vietnam) whereas others were introduced <150 years ago (i.e., Australia, Canada, New Zealand, Senegal). Our hypothesis was that non-native birds and/or birds from sites with comparatively unpredictable climates would express more of all three genes. We found, however, that DNMT expression differences, while extensive, were reversed of predictions: all three genes were expressed more in sparrows from the native range and from areas with more predictable temperatures. Surprisingly, gene expression was also strongly correlated among populations and within-individuals. Our results reveal no simple role for these enzymes in range expansions, but the appreciable among and within-population variation in these enzymes warrants more detailed investigations.
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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".