Genome-wide DNA methylation predicts environmentally-driven life history variation in a marine fish
Bibliographic record
Abstract
Abstract The molecular mechanisms underlying intraspecific variation in life history strategies are still poorly understood, despite the importance of this question for understanding of organism’s responses to environmental variability. Theoretical work proposed that epigenetic mechanisms such as DNA methylation might regulate intraspecific variation in life history strategies, however this assumption has rarely been verified empirically in wild populations. We examined associations between genome-wide methylation changes and environmentally-driven life history variation in two lineages of a marine fish that diverged approximatively 2.5 Mya, the capelin ( Mallotus villosus ), from North America and Europe. In both lineages, capelin harbour two contrasted life history strategies: some are strictly semelparous, experience fast actuarial senescence, but benefit from high hatching success by spawning on demersal sites where water temperature is low and relatively stable. In contrast, others are facultative iteroparous, have slower actuarial senescence, and suffer from lower hatching success by breeding in the intertidal zone where temperature is warmer, thermohaline parameters are less stable, and egg desiccation risk is high. Performing whole genome and epigenome sequencing, we showed that these contrasted life history strategies are more likely governed by epigenetic changes than by differences in DNA sequence. While genetic differentiation between the capelin harbouring different life history strategies was negligible, we detected parallel genome-wide methylation changes across lineages. We identified 1,067 differentially methylated regions (DMRs) comprising 15,818 CpGs, with 22% of them located within 5-kb around genes comprising promotor regions. We found that all DMRs were hypermethylated in demersal-spawning individuals. This striking result suggests that lower water temperature at demersal sites leads to an overall hypermethylation of the genome determined during the epigenetic reprogramming occurring over embryonic development. Our study emphasizes that parallel epigenetics changes in lineages with divergent genetic background could have a functional role in the regulation of intraspecific life history variation.
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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 | 0.001 | 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".