Protein Kinase C-δ (PKC-δ) Is Activated by Type I Interferons and Mediates Phosphorylation of Stat1 on Serine 727
Bibliographic record
Abstract
It is well established that engagement of the Type I interferon (IFN) receptor results in activation of JAKs (Janus kinases), which in turn regulate tyrosine phosphorylation of STAT proteins. Subsequently, the IFN-dependent tyrosine-phosphorylated/activated STATs translocate to the nucleus to regulate gene transcription. In addition to tyrosine phosphorylation, phosphorylation of Stat1 on serine 727 is essential for induction of its transcriptional activity, but the IFNalpha-dependent serine kinase that regulates such phosphorylation remains unknown. In the present study we provide evidence that PKC-delta, a member of the protein kinase C family of proteins, is activated during engagement of the Type I IFN receptor and associates with Stat1. Such an activation of PKC-delta appears to be critical for phosphorylation of Stat1 on serine 727, as inhibition of PKC-delta activation diminishes the IFNalpha- or IFNbeta-dependent serine phosphorylation of Stat1. In addition, treatment of cells with the PKC-delta inhibitor rottlerin or the expression of a dominant-negative PKC-delta mutant results in inhibition of IFNalpha- and IFNbeta-dependent gene transcription via ISRE or GAS elements. Interestingly, PKC-delta inhibition also blocks activation of the p38 MAP kinase, the function of which is required for IFNalpha-dependent transcriptional regulation, suggesting a dual mechanism by which this kinase participates in the generation of IFNalpha responses. Altogether, these findings indicate that PKC-delta functions as a serine kinase for Stat1 and an upstream regulator of the p38 MAP kinase and plays an important role in the induction of Type I IFN-biological responses.
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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) | 0.001 | 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) | 0.003 | 0.002 |
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".