|Title||NGS-based amplicon sequencing approach; towards a new era in GMO screening and detection|
|Author(s)||Arulandhu, Alfred J.; Dijk, Jeroen van; Staats, Martijn; Hagelaar, Rico; Voorhuijzen, Marleen; Molenaar, Bonnie; Hoof, Richard van; Li, Rong; Yang, Litao; Shi, Jianxin; Scholtens, Ingrid; Kok, Esther|
|Source||Food Control 93 (2018). - ISSN 0956-7135 - p. 201 - 210.|
Food Quality and Design
RIKILT - BU Toxicology Bioassays & Novel Foods
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||Amplicon sequencing - Bioinformatics - Genetically modified products - Illumina HiSeq - NGS - qPCR - Unauthorized GMOs|
The development and commercialization of Genetically Modified Organisms (GMOs) and its related products have been increasing in the last two decades. This challenges the currently applied time-consuming and expensive qPCR screening procedure from a practical perspective, due to the necessity to develop and validate additional targets at a regular pace and the increasing number of targets included in a single screening. In this study we developed a next generation sequencing (NGS)-based GMO screening approach covering 96 GMO targets and compared it to the two-step qPCR GMO screening approach; the two approaches were evaluated with five feed samples known to contain GMOs. The amplicons obtained from the feed samples were analyzed using 150-bp Paired-End sequencing, Illumina HiSeq 4000 platform. A dedicated data analysis pipeline was developed, which allows automated identification of GMOs and associated genetic elements and constructs. The result of the NGS-based screening were compared with the qPCR approach, indicating that 92% of the targets were commonly identified between the qPCR and NGS-based screening. The remaining 8% of the targets had discrepancies in detection between the two methods. This was mainly observed for targets that were detected in qPCR with high Cq values (above 36), which could not be detected in NGS-based screening. Additionally, due to the more extensive screening in the NGS-based strategy, in total 43 additional GMOs and related targets were identified compared to the standard qPCR screening. From the commonly identified targets in both approaches, 8 targets could not be associated with the detected GMOs. These targets had late Cq values (above 36) and could indicate traces of unknown GMOs in the samples. The current study shows the applicability of NGS as a novel, broad and reliable screening strategy for GMOs and its potential to improve current screening methods.