Differential Requirement for TANK-binding Kinase-1 in Type I Interferon Responses to Toll-like Receptor Activation and Viral Infection
Bibliographic record
Abstract
TANK-binding kinase-1 (TBK1) and the inducible IkappaB kinase (IKK-i) have been shown recently to activate interferon (IFN) regulatory factor-3 (IRF3), the primary transcription factor regulating induction of type I IFNs. Here, we have compared the role and specificity of TBK1 in the type I IFN response to lipopolysaccharide (LPS), polyI:C, and viral challenge by examining IRF3 nuclear translocation, signal transducer and activator of transcription 1 phosphorylation, and induction of IFN-regulated genes. The LPS and polyI:C-induced IFN responses were abolished and delayed, respectively, in macrophages from mice with a targeted disruption of the TBK1 gene. When challenged with Sendai virus, the IFN response was normal in TBK1(-/-) macrophages, but defective in TBK1(-/-) embryonic fibroblasts. Although both TBK1 and IKK-i are expressed in macrophages, only TBK1 but not IKK-i was detected in embryonic fibroblasts by Northern blotting analysis. Furthermore, the IFN response in TBK1(-/-) embryonic fibroblasts can be restored by reconstitution with wild-type IKK-i but not a mutant IKK-i lacking kinase activity. Thus, our studies suggest that TBK1 plays an important role in the Toll-like receptor-mediated IFN response and is redundant with IKK-i in the response of certain cell types to viral infection.
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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) | 0.001 | 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".