Alternative Splicing and Gene Expression Variation Underlie Population and Life History Differences in an Amphibian
Bibliographic record
Abstract
ABSTRACT Phenotypic variation is common across life history and among populations occupying different environments, yet the molecular mechanisms underlying these axes of divergence remain poorly understood. Much work has focused on gene expression as a link between genetic variation, environmental variation, and phenotypes, but post‐transcriptional processes such as alternative splicing—which affect how transcripts are assembled rather than how much of a transcript is produced—are increasingly recognized as additional modulators of plasticity and adaptation. Here, we examined gene expression and alternative splicing together in the wood frog ( Rana sylvatica ), an amphibian with a complex life cycle whose populations differ across replicated gradients of road adjacency and associated pollution. We found extensive transcriptomic differences between hatchlings and adults, with thousands of genes differentially expressed or spliced. Individuals clustered strongly by population for both expression and splicing. Differences at the habitat level were less extensive, but revealed two differentially expressed genes ( HSP70 and Gpsm2 ) and one differentially spliced gene ( Cd82 ) that consistently distinguished roadside and woodland populations. Overall, genetic differentiation between populations was low, suggesting that phenotypic and transcriptomic differences likely emerge in the presence of gene flow and reflect plastic responses. Together, these results highlight transcriptomic plasticity as an important mechanism shaping variation across both development and population differentiation.
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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".