The eukaryotic translation initiation factor eIF4E reprograms alternative splicing
Bibliographic record
Abstract
Aberrant splicing is typically attributed to splice-factor (SF) mutation and contributes to malignancies including acute myeloid leukemia (AML). Here, we discovered a mutation-independent means to extensively reprogram alternative splicing (AS). We showed that the dysregulated expression of eukaryotic translation initiation factor eIF4E elevated selective splice-factor production, thereby impacting multiple spliceosome complexes, including factors mutated in AML such as SF3B1 and U2AF1. These changes generated a splicing landscape that predominantly supported altered splice-site selection for ~800 transcripts in cell lines and ~4,600 transcripts in specimens from high-eIF4E AML patients otherwise harboring no known SF mutations. Nuclear RNA immunoprecipitations, export assays, polysome analyses, and mutational studies together revealed that eIF4E primarily increased SF production via its nuclear RNA export activity. By contrast, eIF4E dysregulation did not induce known SF mutations or alter spliceosome number. eIF4E interacted with the spliceosome and some pre-mRNAs, suggesting its direct involvement in specific splicing events. eIF4E induced simultaneous effects on numerous SF proteins, resulting in a much larger range of splicing alterations than in the case of mutation or dysregulation of individual SFs and providing a novel paradigm for splicing control and dysregulation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".