Alternative splicing and parallel patterns of gene expression underlie population and life history divergence in an amphibian
Bibliographic record
Abstract
Populations distributed across anthropogenic disturbance gradients can express different phenotypes adapted to distinct habitats. Previous studies have focused on the role of gene expression linking phenotypic and genetic variation in wild populations. However, recent studies have revealed the critical role of post-transcriptional processes such as alternative splicing in generating phenotypic variation in response to modified environments. Knowledge of alternative splicing should provide more comprehensive insights into understanding population responses to environmental change. Here, we examined gene expression and alternative splicing patterns in the wood frog, an amphibian known for strong phenotypic divergence in response to road-adjacency and runoff pollution. Roadside populations show locally maladaptive traits as embryos and larvae but adaptive traits as adults, suggesting potential tradeoffs across life history in response to road effects. Here, we found strong differences in gene expression and alternative splicing patterns between hatchlings and adults, along with population specific patterns. However, only two differentially expressed genes (Hsp70 and GPSM2) showed repeated divergence between roadside populations and populations located away from roads. These results suggest that while local populations and life history stages can diverge substantially in both gene expression and alternative splicing, a low degree of transcriptomic parallelism underlies (mal)adaptation to roads and pollution.
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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".