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Record W3152730670 · doi:10.1128/jvi.00266-21

SARS-CoV-2 Nonstructural Protein 1 Inhibits the Interferon Response by Causing Depletion of Key Host Signaling Factors

2021· article· en· W3152730670 on OpenAlexafffund
Ray Ishida, Tania Strilets, Jamie Cole, Joaquín López-Orozco, Nawell Fayad, Alberto Felix-Lopez, Mohamed Elaish, Danyel Evseev, Katharine E. Magor, Lara K. Mahal, Les P. Nagata, David H. Evans, Tom C. Hobman

Bibliographic record

VenueJournal of Virology · 2021
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsDefence Research and Development CanadaUniversity of Alberta
FundersCanadian Institutes of Health ResearchLi Ka Shing Institute of Virology, University of AlbertaGovernment of Canada
KeywordsBiologySTAT2InterferonVirologyCoronavirusSTAT1Interferon type ISignal transductionIRF3ImmunologyInnate immune systemDiseaseCell biologyImmune systemstatCoronavirus disease 2019 (COVID-19)Medicine

Abstract

fetched live from OpenAlex

SARS-CoV-2 is the causative agent of COVID-19, a serious disease that can have a myriad of symptoms from loss of taste and smell to pneumonia and hypercoagulation. The rapid spread of SARS-CoV-2 can be attributed in part to asymptomatic transmission, where infected individuals shed large amounts of virus before the onset of disease. This is likely due to the ability of SARS-CoV-2 to effectively suppress the innate immune system, including the IFN response. Indeed, we show that the IFN response is efficiently blocked during SARS-CoV-2 infection, a process that is mediated in large part by nonstructural protein 1 and nucleocapsid. Our study provides new insights on how SARS-CoV-2 evades the IFN response to successfully establish infection. These findings should be considered for the development and administration of therapeutics against SARS-CoV-2.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.041
GPT teacher head0.341
Teacher spread0.299 · 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 source (direct Gemma or distilled Codex), 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

Citations115
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueJournal of VirologySame topicSARS-CoV-2 and COVID-19 ResearchFrench-language works237,207