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Record W2555000253 · doi:10.1038/srep37215

Kin17 facilitates multiple double-strand break repair pathways that govern B cell class switching

2016· article· en· W2555000253 on OpenAlexafffund
Michael Le, Dania Haddad, Alexanda K. Ling, Conglei Li, Clare C. So, Amit Chopra, Rui Hu, Jaime F. Angulo, Jason Moffat, Alberto Martín

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsCytidine deaminaseHomologous recombinationImmunoglobulin class switchingGeneDNADouble strandBiologyDNA repairGenomeGeneticsCell biologyComputational biologyB cellAntibody

Abstract

fetched live from OpenAlex

Class switch recombination (CSR) in B cells requires the timely repair of DNA double-stranded breaks (DSBs) that result from lesions produced by activation-induced cytidine deaminase (AID). Through a genome-wide RNAi screen, we identified Kin17 as a gene potentially involved in the maintenance of CSR in murine B cells. In this study, we confirm a critical role for Kin17 in CSR independent of AID activity. Furthermore, we make evident that DSBs generated by AID or ionizing radiation require Kin17 for efficient repair and resolution. Our report shows that reduced Kin17 results in an elevated deletion frequency following AID mutational activity in the switch region. In addition, deficiency in Kin17 affects the functionality of multiple DSB repair pathways, namely homologous recombination, non-homologous end-joining, and alternative end-joining. This report demonstrates the importance of Kin17 as a critical factor that acts prior to the repair phase of DSB repair and is of bona fide importance for CSR.

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 score0.002
Threshold uncertainty score0.006

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)0.0020.001

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 head0.218
Teacher spread0.201 · 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

Citations11
Published2016
Admission routes2
Has abstractyes

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