Interferon Regulatory Factors and the Atypical IKK-Related Kinases TBK1 and IKK-ε: Essential Players in the Innate Immune Response to RNA Virus Infection
Bibliographic record
Abstract
This chapter opens with a brief overview of the toll-like receptor (TLR)-dependent and -independent pathways of activation. It then focuses on the activation of transcription factors interferon regulatory factors (IRFs) IRF3 and IRF7 by the atypical IκB kinases (IKKs), TANK-binding kinase 1 (TBK1), and IKK-ε and their role in the induction of type I interferons (IFNs). The majority of IRFs are involved in distinct aspects of the antiviral response, while two members—IRF4 and IRF8—function mainly as regulators of hematopoiesis in concert with the Ets transcription factor. Importantly, expression of the IKK-related kinases is essential to initiate IRF signaling in response to de novo Sendai virus (SeV), vesicular stomatitis virus (VSV), or measles virus infection, and treatment with RNA interference directed against either IKK-ε or TBK1 reduces VSV-inducible IRF3 phosphorylation and IRF-dependent gene expression in human cells. Inhibition of Hsp90 expression by small interfering RNA (siRNA) resulted in an impaired activation of IRF3 following SeV infection. This study proposes that Hsp90 participates in the formation of a complex containing TBK1 and IRF3. In addition to linking TBK1 to the exocyst pathway and Ras-induced transformation, the study demonstrated that the host defense response requires the RalB/Sec5/TBK1 complex following double-stranded RNA (dsRNA) treatment or SeV 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".