麻豆淫院

Researchers from the Belgian Federal Institute Sciensano, as part of the DARWIN project, have developed a proof-of-concept for next generation detection methods of genome-edited organisms.

The article "Genetic fingerprints derived from genome database mining allow accurate identification of genome-edited rice in the food chain via targeted high-throughput sequencing," in the journal Food Research International, represents a significant advancement in the unambiguous detection of genome-edited organisms modified by New Genomic Techniques (NGTs).

Results from this study demonstrate that it is possible to detect and identify a specific genome-edited line even at very low levels (0.9% and 0.1%).

This shows that genome-edited organisms produced through gene editing, including those with very subtle genetic modifications (NGT products), can in principle be uniquely identified. Provided that prior genomic information on the genome-edited organism is available, detection is technically feasible.

"While this approach holds great promise as a pioneering strategy, its broader application is not without challenges," explains Nancy Roosens, Head of Division, Transversal Activity in Applied Genomics at Sciensano, and author of the study.

"The method is expected to be applicable primarily to gene-edited lines with a well-characterized and fully sequenced genetic background, particularly when supported by publicly accessible databases covering the main diversity of species. This represents a significant scientific advancement, yet additional work is needed before it can be implemented as a routine tool."

In this study, the authors used a Nipponbare rice line containing a single CRISPR-Cas-induced single nucleotide variation (SNV). They explored different combinations of key genetic elements, including the on-target site, potential off-target mutations, as well as rice cultivar markers.

Whole-genome sequencing of this genome-edited line showed that no off-target mutations were present in this specific case and were therefore excluded from the key elements used to create the unique genetic fingerprint.

Using machine-learning computational methods to compare the genome-edited rice line to all publicly available rice genomes (more than 3000), scientists from the Belgian Federal Institute Sciensano were able to generate cultivar-specific barcodes using pairs of SNVs unique to a specific rice cultivar. Each barcode consists of a combination of just two SNVs.

In brief, researchers combined whole-genome sequencing, public genome databases, and machine learning to identify a minimal set of unique genetic markers to form a "genetic fingerprint" for that specific line. They then validated this approach using a "targeted high-throughput" sequencing method based on multiplex PCR enrichment targeting both the on-target site and a pair of cultivar-specific barcodes.

"In the context of ongoing EU discussions on gene-edited plants, the study indicates that detection can be technically feasible when sufficient genomic information is provided," says Roosens.

"However, routine application will still require overcoming key challenges. Sharing detailed information on specific modifications and the genetic background of genome-edited products would make the creation of unique fingerprints more efficient and cost-effective for competent authorities."

This advance demonstrates the potential for the unambiguous detection of organisms developed with new genomic techniques (NGTs), supporting evolving regulatory compliance at EU level, enhancing traceability and consumer trust, and providing scientific knowledge on innovative plant breeding technologies.

This proof-of-concept is also a pivotal step toward developing robust detection methods for NGTs in DARWIN, whose aim is to deliver reliable detection methods to ensure transparency across food systems.