MétaCan
Menu
Back to cohort
Record W2801021823 · doi:10.1128/jvi.00262-18

A Screen for Epstein-Barr Virus Proteins That Inhibit the DNA Damage Response Reveals a Novel Histone Binding Protein

2018· article· en· W2801021823 on OpenAlexafffund
Ting‐Hin Ho, Justine Sitz, Qingtang Shen, Ariane Leblanc-Lacroix, Eric I. Campos, Ivan Borozan, Edyta Marcon, Jack Greenblatt, Amélie Fradet‐Turcotte, Dong‐Yan Jin, Lori Frappier

Bibliographic record

VenueJournal of Virology · 2018
Typearticle
Languageen
FieldMedicine
TopicViral-associated cancers and disorders
Canadian institutionsUniversité LavalOntario Institute for Cancer ResearchUniversity of Toronto
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsLytic cycleBiologyHistoneEpstein–Barr virusUbiquitin ligaseChromatinVirusDNA damageGammaherpesvirinaeNon-histone proteinUbiquitinMolecular biologyVirologyDNACancer researchHerpesviridaeGeneticsGene

Abstract

fetched live from OpenAlex

Epstein-Barr virus (EBV) infects most people worldwide and is causatively associated with several types of cancer, including ∼10% of gastric carcinomas. EBV encodes ∼80 proteins, many of which are believed to manipulate cellular regulatory pathways but are poorly characterized. The DNA damage response (DDR) is one such pathway that is critical for maintaining genome integrity and preventing cancer-associated mutations. In this study, a screen for EBV proteins that inhibit the DDR identified BKRF4 as a DDR inhibitor that binds histones and blocks their ubiquitylation at the DNA damage sites. We also present evidence that BKRF4 is expressed in both latent and lytic forms of EBV infection, where it downregulates the DDR, as well as in EBV-positive gastric tumors. The results suggest that BKRF4 could contribute to the development of gastric carcinoma through its ability to inhibit the DDR.

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 score0.005

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)0.0010.000

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 head0.033
GPT teacher head0.315
Teacher spread0.282 · 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

Citations41
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueJournal of VirologySame topicViral-associated cancers and disordersFrench-language works237,207