The Catalytic Activity of the Eukaryotic Initiation Factor-2α Kinase PKR Is Required to Negatively Regulate Stat1 and Stat3 via Activation of the T-cell Protein-tyrosine Phosphatase
Bibliographic record
Abstract
Tyrosine phosphorylation of the transcription factors Stat1 and Stat3 is required for them to dimerize, translocate to the nucleus, and induce gene transcription. Nuclear Stat1 and Stat3 are dephosphorylated and deactivated by the T-cell protein-tyrosine phosphatase (TC-PTP), which facilitates the return of both proteins to the cytoplasm. The protein kinase PKR plays an important role in translational control through the modulation of eukaryotic initiation factor-2alpha phosphorylation. Previous data have implicated PKR in cell signaling via regulation of Stat1 and Stat3, but the molecular mechanisms underlying these events have remained elusive. Using PKR(-/-) mouse embryonic fibroblasts and a conditionally active form of human PKR, we demonstrate herein that tyrosine (but not serine) phosphorylation of either Stat1 or Stat3 is impaired in cells with activated kinase. This reduction in Stat1 and Stat3 tyrosine phosphorylation by active PKR proceeds through TC-PTP, which is a substrate of the eukaryotic initiation factor-2alpha kinase both in vitro and in vivo. TC-PTP phosphorylation alone is insufficient to increase its in vivo phosphatase activity unless accompanied by the inhibition of protein synthesis as a result of PKR activation. These data reveal a novel function of PKR as a negative regulator of Stat1 and Stat3 with important implications in cell signaling.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".