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Record W2955347924 · doi:10.1038/s42003-019-0498-z

Pre-existing H4K16ac levels in euchromatin drive DNA repair by homologous recombination in S-phase

2019· article· en· W2955347924 on OpenAlexaff
Nobuo Horikoshi, Dharmendra Sharma, Fransisca Leonard, Raj K. Pandita, Vijaya Charaka, Shashank Hambarde, Nobuko T. Horikoshi, Puja Gaur, Sharmistha Chakraborty, Jacques Côté, Biana Godin, Clayton R. Hunt, Tej K. Pandita

Bibliographic record

VenueCommunications Biology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsUniversité Laval
FundersNational Institute of General Medical SciencesNational Cancer InstituteHouston Methodist Research InstituteNational Institutes of Health
KeywordsEuchromatinBiologyDNA repairHomologous recombinationCell biologyHistoneNucleotide excision repairDNAHeterochromatinGeneticsChromatinMolecular biology

Abstract

fetched live from OpenAlex

The homologous recombination (HR) repair pathway maintains genetic integrity after DNA double-strand break (DSB) damage and is particularly crucial for maintaining fidelity of expressed genes. Histone H4 acetylation on lysine 16 (H4K16ac) is associated with transcription, but how pre-existing H4K16ac directly affects DSB repair is not known. To answer this question, we used CRISPR/Cas9 technology to introduce I-SceI sites, or repair pathway reporter cassettes, at defined locations within gene-rich (high H4K16ac/euchromatin) and gene-poor (low H4K16ac/heterochromatin) regions. The frequency of DSB repair by HR is higher in gene-rich regions. Interestingly, artificially targeting H4K16ac at specific locations using gRNA/dCas9-MOF increases HR frequency in euchromatin. Finally, inhibition/depletion of RNA polymerase II or Cockayne syndrome B protein leads to decreased recruitment of HR factors at DSBs. These results indicate that the pre-existing H4K16ac status at specific locations directly influences the repair of local DNA breaks, favoring HR in part through the transcription machinery.

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)0.000
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 score0.212
Threshold uncertainty score0.736

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.001
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 head0.027
GPT teacher head0.333
Teacher spread0.306 · 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

Citations51
Published2019
Admission routes1
Has abstractyes

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