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Record W2796961959 · doi:10.1038/s41467-018-03927-0

Incorporation of bridged nucleic acids into CRISPR RNAs improves Cas9 endonuclease specificity

2018· article· en· W2796961959 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNature Communications · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaNational Research Foundation of KoreaNational Research FoundationUniversities Space Research Association
KeywordsCRISPRNucleic acidCas9Genome editingDNAEndonucleaseTrans-activating crRNAComputational biologyCleavage (geology)Guide RNANucleic acid structureGeneBiologyChemistryRNABiochemistry

Abstract

fetched live from OpenAlex

Abstract Off-target DNA cleavage is a paramount concern when applying CRISPR-Cas9 gene-editing technology to functional genetics and human therapeutic applications. Here, we show that incorporation of next-generation bridged nucleic acids (2′,4′-BNA NC [N-Me]) as well as locked nucleic acids (LNA) at specific locations in CRISPR-RNAs (crRNAs) broadly reduces off-target DNA cleavage by Cas9 in vitro and in cells by several orders of magnitude. Using single-molecule FRET experiments we show that BNA NC incorporation slows Cas9 kinetics and improves specificity by inducing a highly dynamic crRNA–DNA duplex for off-target sequences, which shortens dwell time in the cleavage-competent, “zipped” conformation. In addition to describing a robust technique for improving the precision of CRISPR/Cas9-based gene editing, this study illuminates an application of synthetic nucleic acids.

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.

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 score0.110
Threshold uncertainty score0.473

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 science0.0010.000
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 head0.011
GPT teacher head0.318
Teacher spread0.307 · 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