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Non-prejudiced Detection and Characterization of Genetic Modifications
Authors:Torstein Tengs  Anja Bråthen Kristoffersen  Haibo Zhang  Knut G. Berdal  Marie Løvoll  Arne Holst-Jensen
Affiliation:(1) Section for Food Bacteriology and GMO, National Veterinary Institute, P.O. Box 750, Sentrum, 0106 Oslo, Norway;(2) Section for Epidemiology, National Veterinary Institute, P.O. Box 750, Sentrum, 0106 Oslo, Norway;(3) Department of Informatics, University of Oslo, P.O. Box 1080, Blindern, 0316 Oslo, Norway;(4) School of Life Science and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China;
Abstract:The application of gene technology is becoming widespread much thanks to the rapid increase in technology, resource, and knowledge availability. Consequently, the diversity and number of genetically modified organisms (GMOs) that may find their way into the food chain or the environment, intended or unintended, is rapidly growing. From a safety point of view the ability to detect and characterize in detail any GMO, independent of publicly available information, is fundamental. Pre-release risk assessments of GMOs are required in most jurisdictions and are usually based on application of technologies with limited ability to detect unexpected rearrangements and insertions. We present an array-based approach to address these problems and show with three examples (GTS 40-3-2 Roundup Ready and event A5547-127 soybean as well as T25 Liberty Link Maize) that the method can detect and characterize GMOs with high accuracy while making very few prior assumptions about the actual genetic modifications or constructs in question. Based on the array results, a simple polymerase chain reaction-scheme is also described that will enable the user to characterize the inserted sequences to DNA sequence level. The method may provide the biotechnology developers and risk regulators with a useful tool to improve pre-market risk assessments as well as seed producers and other food chain and environmental stakeholders with a platform to improve their ability to detect and characterize GMOs.
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