In Vivo Selection of Kinase-responsive RNA Elements Controlling Alternative Splicing
Bibliographic record
Abstract
Alternative pre-mRNA splicing is often controlled by cell signals, for example, those activating the cAMP-dependent protein kinase (PKA) or the Ca2+/calmodulin-dependent protein kinase IV (CaMKIV). We have shown that CaMKIV regulates alternative splicing through short CA repeats and hnRNP L. Here we use a splicing reporter that shows PKA/CaMKIV promotion of exon inclusion to select from exons containing random 13-nt sequences for RNA elements responsive to the kinases in cultured cells. This selection not only identified both PKA- and CaMKIV-responsive elements that are similar to the CaMKIV-responsive RNA element 1 (CaRRE1) or CA repeats, but also A-rich elements not previously known to respond to these kinases. Consistently, hnRNP L is identified as a factor binding the CA-rich elements. Analyses of the motifs in the highly responsive elements indicate that they are indeed critical for the kinase effect and are enriched in alternative exons. Interestingly, a CAAAAAA motif is sufficient for the PKA/CaMKIV-regulated splicing of the exon 16 of the CaMK kinase beta1 (CaMKK2) transcripts, implying a role of this motif in signaling cross-talk or feedback regulation between these kinases through alternative splicing. Therefore, these experiments identified a group of RNA elements responsive to PKA and CaMKIV from in vivo selection. This also provides an approach for selecting RNA elements similarly responsive to other cell signals controlling 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".