Faculty Opinions recommendation of IFN-regulatory factor 3-dependent gene expression is defective in Tbk1-deficient mouse embryonic fibroblasts.
Bibliographic record
Abstract
Virus infection, double-stranded RNA, and lipopolysaccharide each induce the expression of genes encoding IFN-␣ and - and chemokines, such as RANTES (regulated on activation, normal T cell expressed and secreted) and IP-10 (IFN-␥ inducible protein 10).This induction requires the coordinate activation of several transcription factors, including IFN-regulatory factor 3 (IRF3).The signaling pathways leading to IRF3 activation are triggered by the binding of pathogen-specific products to Toll-like receptors and culminate in the phosphorylation of specific serine residues in the C terminus of IRF3.Recent studies of human cell lines in culture have implicated two noncanonical IB kinase (IKK)-related kinases, IKK- and Traf family member-associated NF-B activator (TANK)-binding kinase 1 (TBK1), in the phosphorylation of IRF3.Here, we show that purified recombinant IKK- and TBK1 directly phosphorylate the critical serine residues in IRF3.We have also examined the expression of IRF3-dependent genes in mouse embryonic fibroblasts (MEFs) derived from Tbk1 ؊/؊ mice, and we show that TBK1 is required for the activation and nuclear translocation of IRF3 in these cells.Moreover, Tbk1 ؊/؊ MEFs show marked defects in IFN-␣ and -, IP-10, and RANTES gene expression after infection with either Sendai or Newcastle disease viruses or after engagement of the Toll-like receptors 3 and 4 by double-stranded RNA and lipopolysaccharide, respectively.Finally, TRIF (TIR domain-containing adapter-inducing IFN-), fails to activate IRF3-dependent genes in Tbk1 ؊/؊ MEFs.We conclude that TBK1 is essential for IRF3-dependent antiviral gene expression.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.458 | 0.313 |
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".