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 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".