Enhancing low-level genome-edited crop detection and identification in food mixtures using nanopore adaptive sampling: Rice-Soybean mixture as proof-of-concept

The European Union regulates genetically modified organisms (GMOs) in the food chain (Regulations (EC) N° 1829/2003 and N° 1830/2003) to ensure safety, traceability, and freedom of choice. Since 2018, genome-edited (GE) organisms fall under this legislation. However, their detection and unambiguous identification are more challenging, sometimes differing from their wild-type by only one or a few single nucleotide variations (SNVs). High-throughput sequencing with SNV-based genetic fingerprint detection helps overcome these limitations but has so far only been applied to pure samples. Sequencing complex food mixtures renders reliable SNV detection costly and technically challenging. This study, for the first time, explored using high-throughput sequencing with adaptive sampling (AS) to selectively enrich a target species in food mixtures, reducing matrix complexity and enabling the detection and identification of GE lines. As a proof-of-concept, mixtures of soybean -and trace levels of GE or wild-type rice were analyzed under three sequencing modes: standard, AS enriching rice, and AS depleting soybean. Sequencing data were analyzed to determine whether the rice line, GE or wild-type, was successfully enriched and identified using its respective genetic fingerprint. This promising proof-of-concept represents a first step toward facilitating the detection and identification of GE organisms in the food chain.