Convergence of the NF‐κB and Interferon Signaling Pathways in the Regulation of Antiviral Defense and Apoptosis
Bibliographic record
Abstract
The ubiquitously expressed interferon regulatory factor 3 (IRF-3) is directly activated following virus infection and functions as a key activator of the immediate-early Type 1 interferon (IFN) genes. Using DNA microarray analysis (8,556 genes) in Jurkat T cells inducibly expressing constitutively active IRF-3, several target genes directly regulated by IRF-3 were identified. Among the genes upregulated by IRF-3 were transcripts for a subset of known IFN-stimulated genes (ISGs), including ISG56, which functions as an inhibitor of translation initiation. Phosphorylation of C-terminal Ser/Thr residues--(382)GGASSLENTVDLHISNSHPLSLTSDQY(408)-is required for IRF-3 activation. Using C-terminal point mutations and a novel phosphospecific antibody, Ser396 was characterized as the minimal phosphoacceptor site required in vivo for IRF-3 activation following Sendai virus (SeV) infection, expression of viral nucleocapsid, or double-stranded RNA (dsRNA) treatment. The identity of the virus-activated kinase (VAK) activity that targets and activates IRF-3 and IRF-7 has remained a critical missing link in the understanding of interferon signaling. We report that the IKK-related kinases-IKKepsilon/TBK-1-are components of VAK that mediate IRF-3 and IRF-7 phosphorylation and thus functionally link the NF-kappaB and IRF pathways in the development of the antiviral response.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".