Role of p38α Map Kinase in Type I Interferon Signaling
Bibliographic record
Abstract
Multiple signaling pathways are activated during engagement of the Type I interferon (IFN) receptor to mediate biological responses, including the Jak-Stat and Rac1/p38 Map kinase signaling cascades. In the present study we sought to determine the functional relevance of the p38alpha isoform in IFN signaling, using cells from mouse embryos with targeted disruption of the p38alpha gene. Our data demonstrate that p38alpha activation is essential for Type I IFN-dependent transcriptional regulation via ISRE or GAS elements. On the other hand, the function of p38alpha is not required for IFN-dependent Ser727 or Tyr701 phosphorylation of Stat1 and does not impact on the formation of ISGF3 or SIF nuclear binding complexes. In efforts to identify downstream effectors of p38 that may mediate IFN-dependent transcriptional responses, we found that IFNalpha activates the kinase Msk1, a known regulator of histone phosphorylation and chromatin remodeling. In other studies, we demonstrate that Type I IFN-dependent activation of the kinases MapKapK-2 and MapKapK-3 is defective in the absence of p38alpha, while Type I IFN-dependent antiviral properties are decreased in cells with targeted disruption of the MapKapK-2 gene. Altogether, our data establish that the p38alpha Map kinase pathway regulates activation of downstream effectors that participate in the induction of IFN-dependent gene transcription, to mediate IFN-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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".