Chemical Inhibition of Splicing-Related Protein Kinases Reveals Phosphorylation-Driven Regulation of RNA Alternative Splicing in <i>Arabidopsis</i> Seedlings
Bibliographic record
Abstract
ABSTRACT Serine / arginine (SR) protein kinases (SRPKs) are capable of transmitting external signals to the spliceosome by phosphorylating SR proteins. In plants, few studies have looked at the regulation of RNA splicing by post-translational modification (PTMs), despite the spliceosome and splicing factor proteins exhibiting extensive phosphorylation. Many of these PTM events have the potential to dramatically change the expression of various genes, such as those involved in abiotic stress. Here, we sought to explore the regulatory function of Arabidopsis thaliana SRPKs in the context of RNA alternative splicing. To do so, we utilized four well-known and well-characterized chemical inhibitors specifically designed to target and inhibit SRPK enzymatic activity. Our data found that SRPK chemical inhibitors SPHINX31 and SRPIN340 induced shorter root development phenotypes and an abolishment of root hair formation. Using a multi-omics approach combining transcriptomics and phosphoproteomics, we find extensive changes in the splicing of genes involved in root development, RNA splicing, cytoskeletal organization, and cell differentiation. We also reveal splicing factors exhibiting differential alternative splicing as well as a down-regulated in their phosphorylation status. Overall, our data indicate that AtSRPKs phosphorylate diverse splicing factors and influence the alternative splicing of genes involved in root development and a wide-range of related pathways.
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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 | 0.001 |
| 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".