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Record W1992336935 · doi:10.1074/jbc.m703988200

Identification of Human Kinases Involved in Hepatitis C Virus Replication by Small Interference RNA Library Screening

2007· article· en· W1992336935 on OpenAlexaff
Ľubica Supeková, František Supek, Jong‐Kook Lee, Shawn Chen, Nathanael S. Gray, John Paul Pezacki, Achim Schlapbach, Peter G. Schultz

Bibliographic record

VenueJournal of Biological Chemistry · 2007
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsSteacie Institute for Molecular Sciences
Fundersnot available
KeywordsKinaseVirologyIdentification (biology)RNA interferenceReplication (statistics)BiologyHepatitis C virusRNAInterference (communication)Computational biologyViral replicationCell biologyVirusGeneticsGeneComputer scienceComputer network

Abstract

fetched live from OpenAlex

The propagation of the hepatitis C virus (HCV) is a complex process that requires both host and viral proteins. To facilitate identification of host cell factors that are required for HCV replication, we screened a panel of small interference RNAs that preferentially target human protein kinases using an HCV replicon expressing the firefly luciferase gene as a genetic reporter. Small interference RNAs specific for three human kinases, Csk, Jak1, and Vrk1, were identified that reproducibly reduce viral RNA and viral protein levels in HCV replicon-bearing cells. Treatment of replicon cells with a small molecule inhibitor of Csk also resulted in a significant reduction in HCV RNA and proteins, further supporting a role for Csk in HCV replication. The effects of siRNAs targeting eight kinases known to be negatively regulated by Csk were then examined; knock down of one of these kinases, Fyn, resulted in up-regulation of the HCV replicon, suggesting that Csk mediates its effect on HCV replication through Fyn. This conclusion was further corroborated by demonstration that replicon cells treated with Csk inhibitor contained lower levels of the phosphorylated form of Fyn than control cells.

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)0.001
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 score0.119
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.080
GPT teacher head0.343
Teacher spread0.263 · 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

Citations104
Published2007
Admission routes1
Has abstractyes

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