Regulatory interplay between SR proteins governs<i>CLK1</i>kinase splice variants production
Bibliographic record
Abstract
The CLK1 kinase phosphorylates SR proteins to modulate their splicing regulatory activity. Skipping of alternative exon 4 on theCLK1pre-mRNA produces a CLK1 variant lacking the catalytic site. Here, we aimed to understand how various SR proteins integrate into the regulatory program that controlsCLK1exon 4 splicing. Previously, we observed that the depletion of SRSF10 promoted the inclusion ofCLK1exon 4. Using the expression of tagged proteins and CRISPR/Cas9-mediated knockouts in HCT116 cells, we now identify TRA2β, TRA2α, SRSF4, SRSF5, SRSF7, SRSF8, and SRSF9 as activators of exon 4 inclusion. In contrast, SRSF3, SRSF10, and SRSF12 elicit exon 4 skipping. Using CRISPR/dCas13Rx and RNA immunoprecipitation assays, we map an enhancer in exon 4 interacting with TRA2β. Notably, CLK1 kinase inhibitors antagonized the repressor activity of HA-SRSF10, HA-SRSF12, and HA-SRSF3. Our results suggest thatCLK1exon 4 inclusion is determined primarily by a balance between the activities of TRA2 proteins and CLK-phosphorylated SRSF3. CLK-phosphorylated SRSF10 and SRSF12 would interact with TRA2 proteins to prevent their enhancer activity, allowing SRSF3 to enforce exon 4 skipping more efficiently. Our study provides insight into the complex regulatory network controlling the alternative splicing ofCLK1, which uses CLK1-mediated phosphorylation of SR proteins to regulate the inclusion of catalytic exon 4 inCLK1transcripts.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".