The Wnt/β-catenin pathway is important for replication of SARS-CoV-2 and other pathogenic RNA viruses
Bibliographic record
Abstract
Understanding how viruses affect cellular pathways during infection may facilitate development of host cell-targeted therapeutics with broad-spectrum antiviral activity. The interferon (IFN) response is critical for reducing replication and pathogenesis of many viruses including Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19. Mounting evidence indicates that peroxisomes which are best known as metabolic organelles, function in the IFN response. Recently, we reported that the Wnt/β-catenin signaling pathway strongly suppresses peroxisome biogenesis. Here, we show that SARS-CoV-2 infection activates Wnt/β-catenin signaling and hypothesized that pharmacological inhibition of this pathway would result in increased peroxisome formation and enhanced IFN production. Indeed, Wnt/β-catenin signaling potently inhibits replication of SARS-CoV-2 and other pathogenic RNA viruses in vitro and reduces viral load, inflammation and clinical symptoms in a mouse model of COVID-19. As such, targeting this cellular pathway may have prophylactic and/or therapeutic value in reducing the disease burden caused by emerging viral pathogens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".