Fine-Tuning of the RIG-I-Like Receptor/Interferon Regulatory Factor 3-Dependent Antiviral Innate Immune Response by the Glycogen Synthase Kinase 3/β-Catenin Pathway
Bibliographic record
Abstract
Induction of an antiviral innate immune response relies on pattern recognition receptors, including retinoic acid-inducible gene 1-like receptors (RLR), to detect invading pathogens, resulting in the activation of multiple latent transcription factors, including interferon regulatory factor 3 (IRF3). Upon sensing of viral RNA and DNA, IRF3 is phosphorylated and recruits coactivators to induce type I interferons (IFNs) and selected sets of IRF3-regulated IFN-stimulated genes (ISGs) such as those for ISG54 (Ifit2), ISG56 (Ifit1), and viperin (Rsad2). Here, we used wild-type, glycogen synthase kinase 3α knockout (GSK-3α(-/-)), GSK-3β(-/-), and GSK-3α/β double-knockout (DKO) embryonic stem (ES) cells, as well as GSK-3β(-/-) mouse embryonic fibroblast cells in which GSK-3α was knocked down to demonstrate that both isoforms of GSK-3, GSK-3α and GSK-3β, are required for this antiviral immune response. Moreover, the use of two selective small-molecule GSK-3 inhibitors (CHIR99021 and BIO-acetoxime) or ES cells reconstituted with the catalytically inactive versions of GSK-3 isoforms showed that GSK-3 activity is required for optimal induction of antiviral innate immunity. Mechanistically, GSK-3 isoform activation following Sendai virus infection results in phosphorylation of β-catenin at S33/S37/T41, promoting IRF3 DNA binding and activation of IRF3-regulated ISGs. This study identifies the role of a GSK-3/β-catenin axis in antiviral innate immunity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".