An integrated strategy combining DNA walking and NGS to detect GMOs
Marie-Alice Fraiture1, Philippe Herman2, Nina Papazova2
1Scientific Institute of Public Health (WIV-ISP), Platform of Biotechnology and Molecular Biology (PBB) and Biosafety and Biotechnology Unit (SBB), J. Wytsmanstraat 14, 1050 Brussels, Belgium; Institute for Agricultural, Fisheries and Food Research (ILVO), Technology and Food Sciences Unit, Burg. Van Gansberghelaan 115 bus 1, 9820 Merelbeke, Belgium; Ghent University, Faculty of Pharmaceutical Sciences, Laboratory of Pharmaceutical Biotechnology, Ottergemsesteenweg 460, 9000 Ghent, Belgium.
Food Chemistry
|May 12, 2017
Summary
A new DNA walking system coupled with next-generation sequencing enhances GMO detection in food. This improved method accurately identifies multiple genetically modified organisms (GMOs) and their genetic elements in various food samples.
Area of Science:
- Agricultural Biotechnology
- Molecular Biology
- Food Science
Background:
- Genetically modified organisms (GMOs) require robust detection methods for food and feed safety.
- Existing DNA-based detection systems can be limited in throughput and sensitivity.
Purpose of the Study:
- To present an enhanced DNA walking system for improved GMO detection and characterization.
- To integrate Pacific Biosciences Next-Generation Sequencing (NGS) for higher throughput and sensitivity.
Main Methods:
- Coupling a DNA walking system with Pacific Biosciences NGS technology.
- Assessing performance on grains with varying GMO content and processed rice noodles.
- Analyzing GMO mixtures and real-life food samples.
Main Results:
- The enhanced system demonstrated high detection and identification capabilities for GMOs in grains.
- Food processing did not impede the detection of GMOs in rice noodle samples.
- Multiple GMOs were unambiguously identified by characterizing transgene flanking regions and construct elements.
Conclusions:
- The novel DNA walking system coupled with NGS offers improved throughput and sensitivity for GMO detection.
- This strategy is applicable for routine analysis and unambiguous identification of multiple GMOs in complex food matrices.
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