Activation of TBK1 and IKKε Kinases by Vesicular Stomatitis Virus Infection and the Role of Viral Ribonucleoprotein in the Development of Interferon Antiviral Immunity
Bibliographic record
Abstract
Mounting an immune response to a viral pathogen involves the initial recognition of viral antigens through Toll-like receptor-dependent and -independent pathways and the subsequent triggering of signal transduction cascades. Among the many cellular kinases stimulated in response to virus infection, the noncanonical IKK-related kinases TBK1 and IKKepsilon have been shown to phosphorylate and activate interferon regulatory factor 3 (IRF-3) and IRF-7, leading to the production of alpha/beta interferons and the development of a cellular antiviral state. In the present study, we examine the activation of TBK1 and IKKepsilon kinases by vesicular stomatitis virus (VSV) infection in human lung epithelial A549 cells. We demonstrate that replication-competent VSV is required to induce activation of the IKK-related kinases and provide evidence that ribonucleoprotein (RNP) complex of VSV generated intracellularly during virus replication can activate TBK1 and IKKepsilon activity. In TBK1-deficient cells, IRF-3 and IRF-7 activation is significantly reduced, although transcriptional upregulation of IKKepsilon following treatment with VSV, double-stranded RNA, or RNP partially compensates for the loss of TBK1. Biochemical analyses with purified TBK1 and IKKepsilon kinases in vitro demonstrate that the two kinases exhibit similar specificities with respect to IRF-3 and IRF-7 substrates and both kinases target serine residues that are important for full transcriptional activation of IRF-3 and IRF-7. These data suggest that intracellular RNP formation contributes to the early recognition of VSV infection, activates the catalytic activity of TBK1, and induces transcriptional upregulation of IKKepsilon in epithelial cells. Induction of IKKepsilon potentially functions as a component of the amplification mechanism involved in the establishment of the antiviral state.
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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 | 0.000 |
| 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".