Detection of genome edits in plants—from editing to seed

Abstract

Genome editing (also known as gene editing) employs a range of tools such as Meganucleases, Zinc Finger Nucleases, TALENs, and more recently CRISPR to make defined changes in genes, regulatory sequences, untranslated regions, or intergenic regions. It is increasingly being applied in plant science research and to improve plant varieties. The benefits of having effective detection tools begin with optimization of the genome editing process itself and continue with selection and characterization of tissue cultures and/or regenerated plants. Detection tools are also used throughout the breeding process, and for preparation of regulatory dossiers when required, as well as for seed production, and may be necessary for monitoring products in the marketplace. Detection and identification of genome edits employs a wide range of analytical approaches including PCR, digital PCR, and sequencing methods. This article examines the applicability of each category of detection or identification approach, from the optimization of genome editing processes, through creation of edits, selection and characterization, and breeding. The challenges surrounding the detection of genome edits present at low levels in large seed, plant, or grain populations and of differentiating directed genome edits from conventional mutations are also explained.

CRISPR/Cas- and Topical RNAi-Based Technologies for Crop Management and Improvement: Reviewing the Risk Assessment and Challenges Towards a More Sustainable Agriculture

“…Some in vitro and in vivo methods have been developed to detect these mutations, such as Digenome-seq ( Kim D. et al, 2015 ), GUIDE-seq ( Tsai et al, 2014 ), SITE-seq ( Cameron et al, 2017 ), CIRCLE-seq ( Tsai et al, 2017 ), and DISCOVER-seq ( Wienert et al, 2019 ). A gold standard recommendation would be performing genome-wide NGS for the identification of these potential off-target mutations, however, it seems not to be practical/feasible in most cases ( Hahn and Nekrasov, 2019 ; Shillito et al, 2021 ), especially for polyploid crops. Consequently, an underestimation of off-target mutation rates might be occurring, although not likely.…”