Y-box Binding Protein 1: Providing a New Angle on Translational Regulation
Bibliographic record
Abstract
Current models of translational regulation are mostly focused on how translational factors engage a messenger mRNA to the ribosome to initiate translation. Since the majority of mRNAs in eukaryotes are translated in a cap-dependent manner, the mRNA 5' cap-binding protein eIF4E was characterized as a key player responsible for the recruitment of mRNAs to the initiation complex. The availability of eIF4E is believed to be especially critical for translational activation of mRNAs with extensive secondary structures in their 5'UTRs, many of which code for labile regulatory proteins essential for cell growth or viability. Surprisingly, little attention is paid to the other side of translational control, e.g., to define mechanisms responsible for translational silencing and storage of the above messages. In this review, we discuss the possibility that eIF4E per se may not be sufficient to release mRNAs from translational block. We found that many growth- and stress-related mRNAs are associated with the translational repressor YB-1, which can compete with the eIF4E-driven translation initiation complex for binding to the capped 5' mRNA terminus. Moreover, the cap-dependent repressor activity of YB-1 appears to be negatively regulated via Akt-mediated phosphorylation of the Ser-102 residue of YB-1. Taken together with recent evidence suggesting that translational activation of growth-related messages is a primary cellular response to activation of Ras-Erk and PI3K-Akt signaling pathways, our data suggest that differential expression of specific mRNA subsets is regulated by the PI3K-Akt pathway and achieved via coordinated activation of the components of translational machinery and inactivation of general translational repressors such as YB-1.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".