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Record W2022178223 · doi:10.1074/jbc.m109036200

Impaired Protein Kinase C Activation/Translocation in Epstein-Barr Virus-infected Monocytes

2002· article· en· W2022178223 on OpenAlexaff
Mélanie R. Tardif, Martin Savard, Louis Flamand, Jean Gosselin

Bibliographic record

VenueJournal of Biological Chemistry · 2002
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsUniversité LavalCentre hospitalier de l'Université Laval
Fundersnot available
KeywordsChromosomal translocationVirologyVirusProtein kinase RProtein kinase AKinaseBiologyMitogen-activated protein kinase kinaseCell biologyGeneGenetics

Abstract

fetched live from OpenAlex

Infection of human monocytes by Epstein-Barr virus (EBV) has been linked to a decrease in the production of proinflammatory mediators as well as an impairment of phagocytosis. Considering the key role of protein kinases C (PKCs) in many biological functions of monocytes, including phagocytosis, we investigated the effects of EBV on the PKC activity in infected monocytes. Our results indicate that infection of monocytes by EBV impairs both phorbol 12-myristate 13-acetate (PMA)-induced translocation of PKC isozymes alpha and beta from cytosol to membrane as well as the PKC enzymatic activity. Similarly, the subcellular distribution of the receptor for activated C kinase (RACK), an anchoring protein essential to PKC translocation, was also found to be reduced in EBV-infected monocytes. Transfection of 293T cells with an expression vector coding for the immediate-early protein ZEBRA of EBV resulted in impaired PMA-induced translocation and activity of PKC. Using co-immunoprecipitation assays, the ZEBRA protein was found to physically interact with the RACK1 protein. Thus interaction of ZEBRA with RACK likely results in the inhibition of PKC activity, which in turn affects functions of monocytes, such as phagocytosis.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.013
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.025
GPT teacher head0.233
Teacher spread0.209 · 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.

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

Citations38
Published2002
Admission routes1
Has abstractyes

Explore more

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