eIF2α Kinase PKR Modulates the Hypoxic Response by Stat3-Dependent Transcriptional Suppression of HIF-1α
Bibliographic record
Abstract
Hypoxia within the tumor microenvironment promotes angiogenesis, metabolic reprogramming, and tumor progression. In addition to activating hypoxia-inducible factor-1α (HIF-1α), cells also respond to hypoxia by globally inhibiting protein synthesis via serine 51 phosphorylation of translation eukaryotic initiation factor 2α (eIF2α). In this study, we investigated potential roles for stress-activated eIF2α kinases in regulation of HIF-1α. Our investigations revealed that the double-stranded RNA-dependent protein kinase R (PKR) plays a significant role in suppressing HIF-1α expression, acting specifically at the level of transcription. HIF-1α transcriptional repression by PKR was sufficient to impair the hypoxia-induced accumulation of HIF-1α and transcriptional induction of HIF-1α-dependent target genes. Inhibition of HIF-1A transcription by PKR was independent of eIF2α phosphorylation but dependent on inhibition of the signal transducer and activator of transcription 3 (Stat3). Furthermore, HIF-1A repression required the T-cell protein tyrosine phosphatase, which acts downstream of PKR, to suppress Stat3. Our findings reveal a novel tumor suppressor function for PKR, which inhibits HIF-1α expression through Stat3 but is independent of eIF2α phosphorylation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".