The interplay between epigenomic and transcriptomic variation during ecotype divergence in stickleback
Bibliographic record
Abstract
BACKGROUND: Populations colonizing contrasting environments are likely to undergo adaptive divergence and evolve ecotypes with locally adapted phenotypes. While diverse molecular mechanisms underlying ecotype divergence have been identified, less is known about their interplay and degree of divergence. RESULTS: Here we integrated epigenomic and transcriptomic data to explore the interactions among gene expression, alternative splicing, DNA methylation, and microRNA expression to gauge the extent to which patterns of divergence at the four molecular levels are aligned in a case of postglacial divergence between marine and freshwater ecotypes of nine-spined sticklebacks (Pungitius pungitius). Despite significant genome-wide associations between epigenomic and transcriptomic variation, we found largely non-parallel patterns of ecotype divergence across epigenomic and transcriptomic levels, with predominantly nonoverlapping (ranging from 43.40 to 87.98%) sets of differentially expressed, spliced and methylated genes, and candidate genes targeted by differentially expressed miRNA between the ecotypes. Furthermore, we found significant variation in the extent of ecotype divergence across different molecular mechanisms, with differential methylation and differential splicing showing the highest and lowest extent of divergence between ecotypes, respectively. Finally, we found a significant enrichment of genes associated with ecotype divergence in differential methylation. CONCLUSIONS: Our results suggest a nuanced relationship between epigenomic and transcriptomic processes, with alignment at the genome-wide level masking relatively independent effects of different molecular mechanisms on ecotype divergence at the gene level.
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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".