DEAD-box protein family member DDX28 is a negative regulator of HIF-2α and eIF4E2-directed hypoxic translation
Bibliographic record
Abstract
ABSTRACT Hypoxia occurs when there is a deficiency in oxygen delivery to tissues and is connected to physiological and pathophysiological processes such as embryonic development, wound healing, heart disease and cancer. The master regulators of oxygen homeostasis in mammalian cells are the heterodimeric hypoxia-inducible transcription factors HIF-1 and HIF-2. The oxygen-labile HIF-2α subunit has not only been implicated in transcription, but also as a regulator of eIF4E2-directed hypoxic translation. Here, we have identified the DEAD-box protein family member DDX28 as a novel interactor and negative regulator of HIF-2α that suppresses its ability to activate eIF4E2-directed translation. We demonstrate that stable silencing of DDX28 via shRNA in hypoxic human U87MG glioblastoma cells caused an increase, relative to control, to: HIF-2α protein levels, the ability of eIF4E2 to bind the m 7 GTP cap structure, and the translation of select eIF4E2 target mRNAs. DDX28 depletion elevated both nuclear and cytoplasmic HIF-2α, but HIF-2α transcriptional activity did not increase possibly due to its already high nuclear abundance in hypoxic control cells. Depletion of DDX28 conferred a proliferative advantage to hypoxic, but not normoxic cells, which is likely a consequence of the translational upregulation of a subset of hypoxia-response mRNAs. DDX28 protein levels are reduced in several cancers, including glioma, relative to normal tissue. Therefore, we uncover a regulatory mechanism for this potential tumor suppressor in the repression of HIF-2α- and eIF4E2-mediated translation activation of oncogenic mRNAs.
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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 | 0.001 |
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".