Aurora-A phosphorylates splicing factors and regulates alternative splicing
Bibliographic record
Abstract
ABSTRACT Aurora-A kinase is well known to regulate progression through mitosis. However, the kinase also performs additional functions that could explain the failure of its inhibitors to be effective in cancer treatments. To identify these functions, we applied a proteomics approach to search for interactors of Aurora-A. We found a large number of proteins involved in pre-mRNA splicing, strongly suggesting an important role for Aurora-A in this biological process. Consistently, we first report the subcellular localization of Aurora-A in nuclear speckles, the storehouse of splicing proteins. We also demonstrate direct interaction of Aurora-A with RRM domain-containing splicing factors such as hnRNP and SR proteins and their phosphorylation in vitro . Further, RNA-sequencing analysis following pharmacological inhibition of Aurora-A resulted in alternative splicing changes corresponding to 505 genes, including genes with functions regulated by Aurora-A kinase. Finally, we report enrichment of RNA motifs within the alternatively spliced regions affected by Aurora-A kinase inhibition which are bound by Aurora-A interacting splicing factors, suggesting that Aurora-A regulates alternative splicing by modulating the activity of these interacting splicing factors. Overall our work identified Aurora-A as a novel splicing kinase and for the first time, describes a broad role of Aurora-A in regulating 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) | 0.002 | 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".