Roles of GSK-3 and β-Catenin in Antiviral Innate Immune Sensing of Nucleic Acids
Bibliographic record
Abstract
The rapid activation of the type I interferon (IFN) antiviral innate immune response relies on ubiquitously expressed RNA and DNA sensors. Once engaged, these nucleotide-sensing receptors use distinct signaling modules for the rapid and robust activation of mitogen-activated protein kinases (MAPKs), the IκB kinase (IKK) complex, and the IKK-related kinases IKKε and TANK-binding kinase 1 (TBK1), leading to the subsequent activation of the activator protein 1 (AP1), nuclear factor-kappa B (NF-κB), and IFN regulatory factor 3 (IRF3) transcription factors, respectively. They, in turn, induce immunomodulatory genes, allowing for a rapid antiviral cellular response. Unlike the MAPKs, the IKK complex and the IKK-related kinases, ubiquitously expressed glycogen synthase kinase 3 (GSK-3) α and β isoforms are active in unstimulated resting cells and are involved in the constitutive turnover of β-catenin, a transcriptional coactivator involved in cell proliferation, differentiation, and lineage commitment. Interestingly, studies have demonstrated the regulatory roles of both GSK-3 and β-catenin in type I IFN antiviral innate immune response, particularly affecting the activation of IRF3. In this review, we summarize current knowledge on the mechanisms by which GSK-3 and β-catenin control the antiviral innate immune response to RNA and DNA virus infections.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".