Incorporation of bridged nucleic acids into CRISPR RNAs improves Cas9 endonuclease specificity
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".