Altered splicing and cytoplasmic levels of tRNA synthetases in SF3B1-mutant myelodysplastic syndromes as a therapeutic vulnerability
Bibliographic record
Abstract
Myelodysplastic syndromes (MDS) are haematopoietic malignancies that are characterised by a heterogeneous clinical course. In recent years, sequencing efforts have uncovered recurrent somatic mutations within RNA splicing factors, including SF3B1, SRSF2, U2AF1 and ZRSR2. The most frequently mutated gene is SF3B1, mutated in 17% of MDS patients. While SF3B1 mutations and their effects on splicing have been well characterised, much remains to be explored about their more far-reaching effects on cellular homeostasis. Given that mRNA splicing and nuclear export are coordinated processes, we hypothesised that SF3B1 mutation might also affect export of certain mRNAs and that this may represent a targetable pathway for the treatment of SF3B1-mutant MDS. We used CRISPR/Cas9-genome editing to create isogenic cellular models. Comprehensive transcriptome and proteome profiling of these cells identified alterations in the splicing and export of components of the translational machinery, primarily tRNA synthetases, in response to the SF3B1 K700E mutation. While steady-state protein synthesis was unaffected, SF3B1 mutant cells were more sensitive to the clinically-relevant purine analogue, 8-azaguanine. In this study, we also demonstrated that 8-azaguanine affects splicing. Our results suggest that the simultaneous targeting of RNA metabolism and splicing by 8-azaguanine represents a therapeutic opportunity for SF3B1-mutant myelodysplastic syndromes.
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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) | 0.001 | 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".