The eukaryotic translation initiation factor eIF4E wears a “cap” for many occasions
Bibliographic record
Abstract
The eukaryotic translation initiation factor eIF4E plays important roles in controlling the composition of the proteome. Indeed, dysregulation of eIF4E is associated with poor prognosis cancers. The traditional view has been that eIF4E acts solely in translation. However, over the last ∼25 years, eIF4E was found in the nucleus where it acts in mRNA export and in the last ∼10 years, eIF4E was found in cytoplasmic processing bodies (P-bodies) where it functions in mRNA sequestration and stability. The common biochemical thread for these activities is the ability of eIF4E to bind the 7-methylguanosine cap on the 5' end of mRNAs. Recently, the possibility that eIF4E directly binds some mRNA elements independently of the cap has also been raised. Importantly, the effects of eIF4E are not genome-wide with a subset of transcripts targeted depending on the presence of specific mRNA elements and context-dependent regulatory factors. Indeed, eIF4E governs RNA regulons through co-regulating the expression of groups of transcripts acting in the same biochemical pathways. In addition, studies over the past ∼15 years indicate that there are multiple strategies that regulatory factors employ to modulate eIF4E activities in context-dependent manners. This perspective focuses on these new findings and incorporates them into a broader model for eIF4E function.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".