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 the CLK1 pre-mRNA produces a CLK1 variant lacking the catalytic site. Here, we aimed to understand how various SR proteins integrate into the regulatory program that controls CLK1 exon 4 splicing. Previously, we observed that the depletion of SRSF10 promoted the inclusion of CLK1 exon 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 that CLK1 exon 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 of CLK1 , which uses CLK1-mediated phosphorylation of SR proteins to regulate the inclusion of catalytic exon 4 in CLK1 transcripts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".