Aberrant RNA splicing due to genetic incompatibilities in sunflower hybrids
Bibliographic record
Abstract
Genome-scale studies have revealed divergent mRNA splicing patterns between closely related species or populations. However, it is unclear whether splicing differentiation is a simple byproduct of population divergence, or whether it also acts as a mechanism for reproductive isolation. We examined mRNA splicing in wild domesticated sunflower hybrids and observed 45 novel splice forms that were not found in the wild or domesticated parents, in addition to 16 high-expression parental splice forms that were absent in one or more hybrids. We identify loci associated with variation in the levels of these splice forms, finding that many aberrant transcripts were regulated by multiple alleles with nonadditive interactions. We identified particular spliceosome components that were associated with 21 aberrant isoforms, more than half of which were located in or near regulatory QTL. These incompatibilities often resulted in alteration in the protein-coding regions of the novel transcripts in the form of frameshifts and truncations. By associating the splice variation in these genes with size and growth rate measurements, we found that the cumulative expression of all aberrant transcripts was correlated with a significant reduction in growth rate. Our results lead us to propose a model where divergent splicing regulatory loci could act as incompatibility loci that contribute to the evolution of reproductive isolation.
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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 | 0.001 |
| 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".