Alternative splicing: An overlooked mechanism contributing to local adaptation?
Bibliographic record
Abstract
Identifying the molecular mechanisms contributing to phenotypic variation in natural populations is a major goal of molecular ecology. However, the multiple regulatory steps between genotype and phenotype mean that many potential mechanisms can lead to trait divergence. To date, the role of transcriptional regulation in local adaptation has received much focus, as we can readily measure mRNA quantity and have a reasonable grasp of how variation in the expression of many protein-coding genes can influence phenotype. Thus, studying the evolution of protein-coding gene mRNA abundance in candidate tissues has led to successes in detecting the molecular mechanisms underlying local adaptation (reviewed by Hill et al., 2021). However, the contribution of differential splicing of precursor mRNA (pre-mRNA) to adaptive differentiation, as well as the loci controlling this variation, remains largely unexplored in wild populations. In their "From the Cover'" article in this issue of Molecular Ecology, Jacobs and Elmer (2021) reanalyse muscle RNA sequencing (RNA-seq) data to quantify the relative contributions of variation in mRNA quantity (differentially expressed "DE" genes) and splice variant identity (differentially spliced "DS" genes) to parallel divergence of wild "benthic" and "pelagic" ecotypes of a salmonid fish, the Arctic charr (Salvelinus alpinus). They found little overlap in the identity and biological functions of DE and DS genes, suggesting that these two regulatory mechanisms act on different cellular traits to complementarily alter organismal phenotype. Furthermore, many DE and DS genes could be mapped to cis-acting QTL, arguing that some of this regulatory divergence is genetically based. DE and DS genes were also more likely to be "hub genes" than their nondivergent counterparts, hinting that this regulatory variation may have a variety of phenotypic effects. The comparison of three independently evolved pairs of benthic and pelagic charr uncovered greater than expected parallelism in both expression and splicing between ecotypes across different lakes, supporting a role for these molecular phenotypes in adaptive divergence. Overall, the findings of Jacobs and Elmer (2021) highlight the importance of alternative splicing as a potential mechanism underlying local adaptation and provide a framework for others hoping to make the most of their RNA-seq data.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".