Genome‐wide DNA methylation signatures of infection status in Trinidadian guppies (<i>Poecilia reticulata</i>)
Bibliographic record
Abstract
Epigenetic modification, especially DNA methylation, can play an important role in mediating gene regulatory response to environmental stressors and may be a key process affecting phenotypic plasticity and adaptation. Parasites are potent stressors with profound physiological and ecological effects on their hosts, yet it remains unclear how parasites influence host methylation patterns. Here, we used a well-studied host-parasite system, the guppy Poecilia reticulata and its ectoparasitic monogenean Gyrodactylus turnbulli to gain mechanistic insight into the dynamics of DNA methylation in host-parasite interactions. To explore this, we quantitatively measured genome-wide DNA methylation in guppy skin tissue using reduced representation bisulphite sequencing and characterized differential methylation patterns in guppies during distinct phases of infection. We identified 365, 313, and 741 differentially methylated regions (DMRs) between infected and control fish in early infection, peak infection and recovery phases, respectively. The magnitude of the methylation difference was moderate in DMRs, with an average of 29% (early infection), 27% (peak infection) and 30% (recovery) differential methylation per DMR. Approximately 50% of DMRs overlapped with CpG islands, and over half of the DMRs overlapped with gene bodies, several of which encode proteins relevant to immune response. These findings provide the first evidence of an epigenetic signature of infection by ectoparasites and demonstrate the changing relationship between epigenetic variation and immune response in distinct phases of infection.
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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".