Importin 8 mediates m <sup>7</sup> G cap-sensitive nuclear import of the eukaryotic translation initiation factor eIF4E
Bibliographic record
Abstract
Regulation of nuclear-cytoplasmic trafficking of oncoproteins is critical for growth homeostasis. Dysregulated trafficking contributes to malignancy, whereas understanding the process can reveal unique therapeutic opportunities. Here, we focus on eukaryotic translation initiation factor 4E (eIF4E), a prooncogenic protein highly elevated in many cancers, including acute myeloid leukemia (AML). Typically, eIF4E is localized to both the nucleus and cytoplasm, where it acts in export and translation of specific methyl 7-guanosine (m(7)G)-capped mRNAs, respectively. Nuclear accumulation of eIF4E in patients who have AML is correlated with increased eIF4E-dependent export of transcripts encoding oncoproteins. The subcellular localization of eIF4E closely correlates with patients' responses. During clinical responses to the m(7)G-cap competitor ribavirin, eIF4E is mainly cytoplasmic. At relapse, eIF4E reaccumulates in the nucleus, leading to elevated eIF4E-dependent mRNA export. We have identified importin 8 as a factor that directly imports eIF4E into the nucleus. We found that importin 8 is highly elevated in untreated patients with AML, leading to eIF4E nuclear accumulation. Importin 8 only imports cap-free eIF4E. Cap-dependent changes to the structure of eIF4E underpin this selectivity. Indeed, m(7)G cap analogs or ribavirin prevents nuclear entry of eIF4E, which mirrors the trafficking phenotypes observed in patients with AML. Our studies also suggest that nuclear entry is important for the prooncogenic activity of eIF4E, at least in this context. These findings position nuclear trafficking of eIF4E as a critical step in its regulation and position the importin 8-eIF4E complex as a novel therapeutic target.
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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) | 0.002 | 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".