Posttranscriptional activity of the eukaryotic translation initiation factor eIF4E contributes to HoxA9-driven leukemogenesis
Bibliographic record
Abstract
Abstract HoxA9, a homeodomain-containing transcription factor, is mis-expressed in over half of acute myeloid leukemia (AML) cases, and is associated with poor prognosis. Previous studies indicated that HoxA9 binds to the eukaryotic translation initiation factor eIF4E in primary specimens and that HoxA9 stimulated the RNA export and translation efficiency of selected RNAs via eIF4E. However, the relevance of this to its leukemogenic transformation capacity was unknown. Here, we used a double point mutation (HoxA9AA) to disrupt the physical and functional interaction between eIF4E and HoxA9 while retaining the HoxA9 transcriptional signature. Surprisingly, the mutation dramatically increased AML latency from a median of 90 to 280 days and resulted in incomplete penetrance. Re-transplantation of bone marrow cells from leukemic animals demonstrated even more pronounced differences in disease kinetics and penetrance with all animals succumbing to disease by day 60 in the wildtype group, while some HoxA9AA mice never developed leukemia. Collectively, these findings uncover a novel, transcription-independent mechanism of HoxA9-driven leukemogenesis through eIF4E and positions eIF4E as a potential therapeutic target AML patients expressing high levels of HoxA9. Key Points A double point mutation in HoxA9 disrupted the physical and functional interaction between eIF4E and HoxA9 while retaining the HoxA9 transcriptional signature. Eukaryotic translation initiation factor eIF4E contributes to HoxA9-driven leukemogenesis and is important for the maintenance of acute myeloid leukemia.
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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".