The eukaryotic translation initiation factor eIF4E elevates steady-state m <sup>7</sup> G capping of coding and noncoding transcripts
Bibliographic record
Abstract
G caps. These methodological advances led to unanticipated revelations: 1) Many RNA populations are inefficiently capped at steady state (∼30 to 50%), and eIF4E overexpression increased this to ∼60 to 100%, depending on the RNA; 2) eIF4E physically associates with noncoding RNAs in the nucleus; and 3) approximately half of eIF4E-capping targets identified are noncoding RNAs. eIF4E's association with noncoding RNAs strongly positions it to act beyond translation. Coding and noncoding capping targets have activities that influence survival, cell morphology, and cell-to-cell interaction. Given that RNA export and translation machineries typically utilize capped RNA substrates, capping regulation provides means to titrate the protein-coding capacity of the transcriptome and, for noncoding RNAs, to regulate their activities. We also discovered a cap sensitivity element (CapSE) which conferred eIF4E-dependent capping sensitivity. Finally, we observed elevated capping for specific RNAs in high-eIF4E leukemia specimens, supporting a role for cap dysregulation in malignancy. In all, levels of capping RNAs can be regulated by eIF4E.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".