eIF3d and eIF3e mediate selective translational control of hypoxia that can be inhibited by small molecules
Bibliographic record
Abstract
Exposure to hypoxia is linked to increased cellular plasticity and enhanced metastasis, effects that are primarily attributed to the transcriptional activation of large gene programs downstream of hypoxia-inducible factors (HIFs). However, translational effects in hypoxia, which likely precede transcriptional effects, have remained largely unexplored. Using ribosome profiling, we uncovered a selective translational response in acute hypoxia that is eukaryotic initiation factor (eIF)3d/eIF3e dependent and controls downstream hypoxic responses, including HIF1α accumulation and cellular invasion. We further demonstrated that eIF3e copy number and eIF3e and eIF3d expression signatures are associated with worsened outcomes for patients with breast cancer. Finally, we identified a class of novel small molecules that target eIF3e specifically, reducing the translational response to hypoxia and to endoplasmic reticulum (ER) stress, another stressor that is dependent on eIF3d-/eIF3e-mediated translation. Our data uncover critical functions for eIF3d/eIF3e in the hypoxic response and identify a potential means to inhibit stress-induced translation, and potentially plasticity and metastasis, mediated by eIF3e.
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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".