MétaCan
Menu
Back to cohort
Record W3005252231 · doi:10.1038/s41467-020-14546-z

DNA repair by Rad52 liquid droplets

2020· article· en· W3005252231 on OpenAlexafffund
Roxanne Oshidari, Richard Huang, Maryam Medghalchi, Elizabeth Y.W. Tse, Nasser Ashgriz, Hyun O. Lee, Haley D.M. Wyatt, Karim Mekhail

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsCanada Research ChairsUniversity of New BrunswickUniversity of Toronto
FundersCanadian Institutes of Health ResearchOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and ScienceGovernment of Canada
KeywordsRAD52DNADNA repairDNA damageCell biologySaccharomyces cerevisiaeNuclear DNAChemistryBiophysicsBiologyYeastRAD51BiochemistryMitochondrial DNAGene

Abstract

fetched live from OpenAlex

Cellular processes are influenced by liquid phase separation, but its role in DNA repair is unclear. Here, we show that in Saccharomyces cerevisiae, liquid droplets made up of DNA repair proteins cooperate with different types of DNA damage-inducible intranuclear microtubule filaments (DIMs) to promote the clustering of DNA damage sites and maintain genome stability. Rad52 DNA repair proteins at different DNA damage sites assemble in liquid droplets that fuse into a repair centre droplet via the action of petite DIMs (pti-DIMs). This larger droplet concentrates tubulin and projects short aster-like DIMs (aster-DIMs), which tether the repair centre to longer DIMs mediating the mobilization of damaged DNA to the nuclear periphery for repair. Our findings indicate that cooperation between Rad52 liquid droplets and various types of nuclear filaments promotes the assembly and function of the DNA repair centre.

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 score0.003

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)0.0010.001

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 head0.017
GPT teacher head0.308
Teacher spread0.291 · 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

Citations166
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueNature CommunicationsSame topicRNA Research and SplicingFrench-language works237,207