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Record W2903623092 · doi:10.1101/gad.315978.118

Localized protein biotinylation at DNA damage sites identifies ZPET, a repressor of homologous recombination

2018· article· en· W2903623092 on OpenAlexafffund
David Moquin, Marie‐Michelle Genois, Jiamin Zhang, Jian Ouyang, Tribhuwan Yadav, Rémi Buisson, Stephanie A. Yazinski, Jun Jie Tan, Myriam Boukhali, Jean‐Philippe Gagné, Guy G. Poirier, Li Lan, Wilhelm Haas, Lee Zou

Bibliographic record

VenueGenes & Development · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsUniversité LavalCentre hospitalier de l'Université LavalCentre hospitalier universitaire de Québec
FundersNational Institute of General Medical SciencesNational Institutes of HealthNational Cancer InstituteFonds de Recherche du Québec - SantéMassachusetts General Hospital
KeywordsReplication protein ABiologyHomologous recombinationMolecular biologyDNA repairBiotinylationDNA damageDNA ligaseChromatinNon-homologous end joiningDNADNA-binding proteinCell biologyDNA replicationGeneticsTranscription factorGene

Abstract

fetched live from OpenAlex

with RAD18 leads to localized protein biotinylation at DNA damage sites, allowing identification of ZPET (zinc finger protein proximal to RAD eighteen)/ZNF280C as a potential DNA damage response (DDR) protein. ZPET binds ssDNA and localizes to DNA double-strand breaks (DSBs) and stalled replication forks. In vitro, ZPET inhibits MRE11 binding to ssDNA. In cells, ZPET delays MRE11 binding to chromatin after DSB formation and slows DNA end resection through binding ssDNA. ZPET hinders resection independently of 53BP1 and HELB. Cells lacking ZPET displayed enhanced homologous recombination (HR), accelerated replication forks under stress, and increased resistance to DSBs and PARP inhibition. These results not only reveal ZPET as an HR repressor but also suggest that localized protein biotinylation at DNA damage sites is a useful strategy to identify DDR proteins.

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 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 score0.015
Threshold uncertainty score0.612

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 integrity0.0000.000
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 head0.012
GPT teacher head0.244
Teacher spread0.231 · 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

Citations24
Published2018
Admission routes2
Has abstractyes

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