Biasing genome-editing events toward precise length deletions with an RNA-guided TevCas9 dual nuclease
Bibliographic record
Abstract
The CRISPR/Cas9 nuclease is commonly used to make gene knockouts. The blunt DNA ends generated by cleavage can be efficiently ligated by the classical nonhomologous end-joining repair pathway (c-NHEJ), regenerating the target site. This repair creates a cycle of cleavage, ligation, and target site regeneration that persists until sufficient modification of the DNA break by alternative NHEJ prevents further Cas9 cutting, generating a heterogeneous population of insertions and deletions typical of gene knockouts. Here, we develop a strategy to escape this cycle and bias events toward defined length deletions by creating an RNA-guided dual active site nuclease that generates two noncompatible DNA breaks at a target site, effectively deleting the majority of the target site such that it cannot be regenerated. The TevCas9 nuclease, a fusion of the I-TevI nuclease domain to Cas9, functions robustly in HEK293 cells and generates 33- to 36-bp deletions at frequencies up to 40%. Deep sequencing revealed minimal processing of TevCas9 products, consistent with protection of the DNA ends from exonucleolytic degradation and repair by the c-NHEJ pathway. Directed evolution experiments identified I-TevI variants with broadened targeting range, making TevCas9 an easy-to-use reagent. Our results highlight how the sequence-tolerant cleavage properties of the I-TevI homing endonuclease can be harnessed to enhance Cas9 applications, circumventing the cleavage and ligation cycle and biasing genome-editing events toward defined length deletions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".