Genetic fingerprints derived from genome database mining allow accurate identification of genome-edited rice in the food chain via targeted high-throughput sequencing
2025
Fraiture, Marie-Alice | D'aes, Jolien | Gobbo, Andrea | Delvoye, Maud | Meunier, Anne Cécile | Frouin, Julien | Guiderdoni, Emmanuel | Deforce, Dieter | De Vogelaere, Charlotte | De Keersmaecker, Sigrid C. J. | Vanneste, Kevin | Roosens, Nancy H.C.
Genome-edited (GE) organisms are currently classified as GMOs according to European legislation, requiring traceability and labelling in the food and feed supply chain. However, unambiguous identification of a specific GE organism with one or more induced single nucleotide variations (SNVs) dispersed across the genome remains challenging. This study explored whole-genome sequencing-based characterization, public genome databases, and machine learning tools to select key genetic elements and create a unique fingerprint for distinguishing a specific GE line. As a case study, a GE Nipponbare rice line containing a single CRISPR-Cas-induced SNV was used. To experimentally assess the detection of this fingerprint, a targeted high-throughput sequencing approach, including multiplex PCR-based enrichment of key genetic elements, was developed and successfully tested. This promising proof-of-concept demonstrates the potential of combining a unique genetic fingerprint with targeted high-throughput sequencing to facilitate the accurate detection of GE organisms, thereby supporting food traceability and regulatory compliance for the development of new GE lines, as well as protecting associated intellectual property.
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