The Role of Alternative Splicing and Differential Gene Expression in Cichlid Adaptive Radiation
Bibliographic record
Abstract
Species diverge eco-morphologically through the continuous action of natural selection on functionally important structures, producing alternative adaptive morphologies. In cichlid fishes, the oral and pharyngeal jaws are such key structures. Adaptive variation in jaw morphology contributes to trophic specialization, which is hypothesized to fuel their rapid speciation in the East African Great Lakes. Much is known about the genes involved in cichlid jaw and craniofacial development. However, it is still unclear what salient sources of variation gave rise to trophic-niche specialization, facilitating adaptive radiation. Here, we explore two sources of transcriptional variation that may underlie species-specific disparities in jaw morphology. Using whole transcriptome RNA-sequencing, we analyze differences in gene expression and alternative splicing, at the end of postlarval development, in fully functional jaws of six species of cichlids from the Lake Tanganyika tribe Tropheini. Our data reveal a surprisingly high degree of alternative splicing events compared with gene expression differences among species and trophic types. This suggests that differential trophic adaptation of the jaw apparatus may have been shaped by transcriptional rewiring of splicing as well as gene expression variation during the rapid radiation of the Tropheini. Specifically, genes undergoing splicing across most species were found to be enriched for pharyngeal jaw gene ontology terms. Overall, jaw transcriptional patterns at postlarval developmental stage were highly dynamic and species-specific. In conclusion, this work indicates that shifts in alternative splicing could have played a more important role in cichlid adaptive radiation, and possibly adaptive radiation in general, than currently recognized.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".