bioRxivPlant Biology
Comparison of efficiency and specificity of CRISPR-associated (Cas) nucleases in plants: An expanded toolkit for precision genome engineering
Abstract
Molecular tools adapted from bacterial CRISPR (Clustered Regulatory Interspaced Short Palindromic Repeats) systems for adaptive immunity have become widely used for plant genome engineering, both to investigate gene functions and to engineer desirable traits. A number of different Cas (CRISPR-associated) nucleases are now used but, as most studies performed to date have engineered different targets using a variety of plant species and molecular tools, it has been difficult to draw conclusions about the comparative performance of different nucleases. Due to the time and effort required to regenerate engineered plants, efficiency is critical. In addition, there have been several reports of mutations at sequences with less than perfect identity to the target. While in some plant species it is possible to remove these so-called off-targets by backcrossing to a parental line, the specificity of genome engineering tools is important when targeting specific members of closely-related gene families, especially when recent paralogues are co-located in the genome and unlikely to segregate. Specificity is also important for species that take years to reach sexual maturity or that are clonally propagated. Here, we directly compare the efficiency and specificity of Cas nucleases from different bacterial species together with engineered variants of Cas9. We find that the nucleotide content correlates with efficiency and that Cas9 from Staphylococcus aureus is comparatively most efficient at inducing mutations. We also demonstrate that high-fidelity variants of Cas9 can reduce off-target mutations in plants. We present these molecular tools as standardised DNA parts to facilitate their re-use.
- organism: plant (other)
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- research focus: variant calling
- research focus: variant effect prediction
- research focus: benchmarking
- approach: genetic perturbation / CRISPR
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