A biochemical framework for eIF4E-dependent mRNA export and nuclear recycling of the export machinery
Bibliographic record
Abstract
The eukaryotic translation initiation factor eIF4E acts in the nuclear export and translation of a subset of mRNAs. Both of these functions contribute to its oncogenic potential. While the biochemical mechanisms that underlie translation are relatively well understood, the molecular basis for eIF4E's role in mRNA export remains largely unexplored. To date, over 3000 transcripts, many encoding oncoproteins, were identified as potential nuclear eIF4E export targets. These target RNAs typically contain a ∼50-nucleotide eIF4E sensitivity element (4ESE) in the 3' UTR and a 7-methylguanosine cap on the 5' end. While eIF4E associates with the cap, an unknown factor recognizes the 4ESE element. We previously identified cofactors that functionally interacted with eIF4E in mammalian cell nuclei including the leucine-rich pentatricopeptide repeat protein LRPPRC and the export receptor CRM1/XPO1. LRPPRC simultaneously interacts with both eIF4E bound to the 5' mRNA cap and the 4ESE element in the 3' UTR. In this way, LRPPRC serves as a specificity factor to recruit 4ESE-containing RNAs within the nucleus. Further, we show that CRM1 directly binds LRPPRC likely acting as the export receptor for the LRPPRC-eIF4E-4ESE RNA complex. We also found that Importin 8, the nuclear importer for cap-free eIF4E, imports RNA-free LRPPRC, potentially providing both coordinated nuclear recycling of the export machinery and an important surveillance mechanism to prevent futile export cycles. Our studies provide the first biochemical framework for the eIF4E-dependent mRNA export pathway.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".