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Record W4391997451 · doi:10.1038/s44298-024-00018-4

The Wnt/β-catenin pathway is important for replication of SARS-CoV-2 and other pathogenic RNA viruses

2024· article· en· W4391997451 on OpenAlexafffund
Zaikun Xu, Mohamed Elaish, Cheung Pang Wong, Bardes B. Hassan, Joaquín López-Orozco, Alberto Felix-Lopez, Natacha S. Ogando, Les P. Nagata, Lara K. Mahal, Anil Kumar, Joyce A. Wilson, Ryan S. Noyce, Irv Mayers, Christopher Power, David H. Evans, Tom C. Hobman

Bibliographic record

Venuenpj Viruses · 2024
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversity of SaskatchewanUniversity of Alberta
FundersCanadian Institutes of Health ResearchCanada Excellence Research Chairs, Government of Canada
KeywordsWnt signaling pathwayViral replicationBiologyInterferonVirologyBiogenesisSignal transductionRNAVirusCell biologyGeneGenetics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.114
GPT teacher head0.401
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2024
Admission routes2
Has abstractyes

Explore more

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