Simulated bacterial infection induces different changes in DNA methylation between introduced and native house sparrows <i>Passer domesticus</i>
Bibliographic record
Abstract
DNA methylation, which can change within‐individuals over time and regulate gene expression, is important to many aspects of avian biology. It is particularly important in avian responses to various stressors associated with introductions, such as infection and environmental changes. However, it remains unclear whether native and introduced bird populations differ in their epigenetic responses to stressors, and how DNA methylation may contribute to the success of non‐native populations because of the limited availability of epigenetic studies. To address this knowledge gap, we used epiRADseq to investigate changes in DNA methylation within‐individual house sparrows Passer domesticus prior to and eight hours after a simulated bacterial infection. We compare wild‐caught house sparrows from introduced populations with those from native populations, assessing the number of genomic locations that exhibit changes in methylation, the magnitude of those changes, and the variance among individuals. Our results show that individuals from introduced populations experience more widespread changes in DNA methylation, with greater magnitude and higher variance, compared to their counterparts from native populations. These findings suggest that DNA methylation plays a significant role in an individual's response to infection. They also indicate that individuals from introduced populations may exhibit distinct epigenetic responses compared to their native counterparts, consistent with the concept of epigenetic buffering.
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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".