An Upstream Open Reading Frame Regulates Translation of GADD34 during Cellular Stresses That Induce eIF2α Phosphorylation
Bibliographic record
Abstract
Cellular stress such as endoplasmic reticulum stress, hypoxia, and viral infection activates an integrated stress response, which includes the phosphorylation of the eukaryotic initiation factor 2alpha (eIF2alpha) to inhibit overall protein synthesis. Paradoxically, this leads to translation of a subset of mRNAs, like transcription factor ATF4, which in turn induces transcription of downstream stress-induced genes such as growth arrest DNA-inducible gene 34 (GADD34). GADD34 interacts with protein phosphatase 1 to dephosphorylate eIF2alpha, resulting in a negative feedback loop to recover protein synthesis and allow translation of stress-induced transcripts. Here, we show that GADD34 is not only transcriptionally induced but also translationally regulated to ensure maximal expression during eIF2alpha phosphorylation. GADD34 mRNAs are preferentially associated with polysomes during eIF2alpha phosphorylation, which is mediated by its 5'-untranslated region (5'UTR). The human GADD34 5'UTR contains two non-overlapping upstream open reading frames (uORFs), whereas the mouse version contains two overlapping and out of frame uORFs. Using 5'UTR GADD34 reporter constructs, we show that the downstream uORF mediates repression of basal translation and directs translation during eIF2alpha phosphorylation. Furthermore, we show that the upstream uORF is poorly translated and that a proportion of scanning ribosomes bypasses the upstream uORF to recognize the downstream uORF. These findings suggest that GADD34 translation is regulated by a unique 5'UTR uORF mechanism to ensure proper GADD34 expression during eIF2alpha phosphorylation. This mechanism may serve as a model for understanding how other 5'UTR uORF-containing mRNAs are regulated during cellular stress.
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 | 0.000 | 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".