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Record W2963701417 · doi:10.1128/jvi.00989-19

Identification of ARKL1 as a Negative Regulator of Epstein-Barr Virus Reactivation

2019· article· en· W2963701417 on OpenAlexafffund
Umama Z. Siddiqi, Anup Vaidya, Xinliu Li, Edyta Marcon, Sai Wah Tsao, Jack Greenblatt, Lori Frappier

Bibliographic record

VenueJournal of Virology · 2019
Typearticle
Languageen
FieldMedicine
TopicViral-associated cancers and disorders
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchResearch Grants Council, University Grants CommitteeGovernment of Canada
KeywordsBZLF1BiologyLytic cycleGene silencingEpstein–Barr virusDerepressionPsychological repressionActivator (genetics)Reporter geneMolecular biologyVirusGene expressionGeneVirologyGeneticsHerpesviridae

Abstract

fetched live from OpenAlex

Epstein-Barr virus (EBV) maintains a life-long infection due to the ability to alternate between latent and lytic modes of replication and is associated with several types of cancer. We have identified a cellular protein (ARKL1) that acts to repress the reactivation of EBV from the latent to the lytic cycle. We show that ARKL1 acts to repress transcription of the EBV lytic switch protein by inhibiting the activity of the cellular transcription factor c-Jun. This not only provides a new mechanism of regulating EBV reactivation but also identifies a novel cellular function of ARKL1 as an inhibitor of Jun-mediated transcription.

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 score0.001
Threshold uncertainty score0.004

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.274
Teacher spread0.266 · 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

Citations6
Published2019
Admission routes2
Has abstractyes

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