Proteomic study identifies Aurora-A–mediated regulation of alternative splicing through multiple splicing factors
Bibliographic record
Abstract
The cell cycle regulator Aurora-A kinase presents an attractive target for cancer therapies, though its inhibition is also associated with toxic side effects. To gain a more nuanced understanding of Aurora-A function, we applied shotgun proteomics to identify 407 specific protein partners, including several splicing factors. Supporting a role in alternative splicing, we found that Aurora-A localizes to nuclear speckles, the storehouse of splicing proteins. Aurora-A interacts with and phosphorylates splicing factors both in vitro and in vivo, suggesting that it regulates alternative splicing by modulating the activity of these splicing factors. Consistently, Aurora-A inhibition significantly impacts the alternative splicing of 505 genes, with RNA motif analysis revealing an enrichment for Aurora-A interacting splicing factors. Additionally, we observed a significant positive correlation between the splicing events regulated by Aurora-A and those modulated by its interacting splicing factors. An interesting example is represented by CLK1 exon 4, which appears to be regulated by Aurora-A through SRSF3. Collectively, our findings highlight a broad role of Aurora-A in the regulation of alternative splicing.
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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".