The eIF2α kinase PKR is a negative regulator of Stat1 and Stat3
Bibliographic record
Abstract
Cancerous cells contain overactive proteins, including the transcription factor Stat1, which is hyperphosphorylated in various blood and head and neck cancers. Numerous mechanisms exist which regulate Stat1, including dephosphorylation in the nucleus by the tyrosine phosphatase TC‐PTP. Stat1 activity is further controlled by the eIF2α kinase PKR, which inhibits the formation of Stat1 transcriptional complexes. We set out to determine if an indirect pathway exists that controls Stat1 phosphorylation via PKR. Using an inducible PKR cell line, we demonstrated that tyrosine phosphorylation of Stat1, and a related family member Stat3, is reduced in the presence of active PKR. Targeted reduction of TC‐PTP by RNAi in the same cell line resulted in a partial rescue of Stat1 and Stat3 phosphorylation, transcriptional activity and nuclear localization. PKR exerts this regulation by phosphorylating both TC‐PTP and eIF2α, thus controlling translation. These results describe a previously unknown pathway regulating the activity of Stat1 and Stat3, and also identify a TC‐PTP as a novel substrate of PKR. Since both PKR and Stat1 have anti‐tumour activity, and Stat3 is a proto‐oncogene, further insight into the relationship between these proteins may allow us to comprehend their roles in cancer development and progression. This project was funded by a grant from the CIHR/CBCRA.
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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 | 0.000 | 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) | 0.000 | 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".