DDX3 regulates endoplasmic reticulum stress-induced ATF4 expression
Bibliographic record
Abstract
Accumulation of unfolded and potentially toxic proteins in the endoplasmic reticulum (ER) activates a cell stress adaptive response, which involves a reprogramming of general gene expression. ATF4 is a master stress-induced transcription factor that orchestrates gene expression in cells treated with various ER stress inducers including those used to treat cancers. ER stress-induced ATF4 expression occurs mainly at the translational level involving the activity of the phosphorylated (P) translation initiation factor (eIF) eIF2α. While it is well established that under ER stress PeIF2α drives ATF4 expression through a specialised mode of translation re-initiation, factors (e.g. RNA-binding proteins and specific eIFs) involved in PeIF2α-mediated ATF4 translation remain unknown. Here we identified the RNA-binding protein named DDX3 as a promotor of ATF4 expression in cancer cells treated with sorafenib, an ER stress inducer used as a chemotherapeutic. Depletion experiments showed that DDX3 is required for PeIF2α-mediated ATF4 expression. Luciferase and polyribosomes assays showed that DDX3 drives ER stress-induced ATF4 mRNA expression at the translational level. Protein-interaction assays showed that DDX3 binds the eIF4F complex, which we found to be required for ER stress-induced ATF4 expression. This study thus showed that PeIF2α-mediated ATF4 mRNA translation requires DDX3 as a part of the eIF4F complex.
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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".