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Record W2078894479 · doi:10.1128/jvi.00432-12

Identification of RNA-Protein Interaction Networks Involved in the Norovirus Life Cycle

2012· article· en· W2078894479 on OpenAlexfundno aff
Surender Vashist, Luis Ureña, Yasmin Chaudhry, Ian Goodfellow

Bibliographic record

VenueJournal of Virology · 2012
Typearticle
Languageen
FieldMedicine
TopicViral gastroenteritis research and epidemiology
Canadian institutionsnot available
FundersUniversity of GlasgowImperial College LondonNational Institute for Health and Care ResearchGénome QuébecWellcome Trust
KeywordsBiologyNorovirusMurine norovirusRNAViral replicationCaliciviridaeVirologyRNA-dependent RNA polymeraseViral life cycleTranslation (biology)GenomeGeneticsVirusGeneMessenger RNA

Abstract

fetched live from OpenAlex

Human noroviruses are one of the major causes of acute gastroenteritis in the developed world, yet our understanding of their molecular mechanisms of genome translation and replication lags behind that for many RNA viruses. Due to the nonculturable nature of human noroviruses, many related members of the Caliciviridae family of small RNA viruses are often used as model systems to dissect the finer details of the norovirus life cycle. Murine norovirus (MNV) has provided one such system with which to study the basic mechanisms of norovirus translation and replication in cell culture. In this report we describe the use of riboproteomics to identify host factors that interact with the extremities of the MNV genome. This network of RNA-protein interactions contains many well-characterized host factors, including PTB, La, and DDX3, which have been shown to play a role in the life cycle of other RNA viruses. By using RNA coimmunoprecipitation, we confirmed that a number of the factors identified using riboproteomics are associated with the viral RNA during virus replication in cell culture. We further demonstrated that RNA inhibition-mediated knockdown of the intracellular levels of a number of these factors inhibits or slows norovirus replication in cell culture, allowing identification of new intracellular targets for this important group of 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.638
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.001
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 head0.036
GPT teacher head0.345
Teacher spread0.309 · 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 designObservational
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

Citations80
Published2012
Admission routes1
Has abstractyes

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