Use of next generation sequencing data to develop a qPCR method for specific detection of EU-unauthorized genetically modified Bacillus subtilis overproducing riboflavin

Abstract

Background: Recently, the presence of an unauthorized genetically modified (GM) Bacillus subtilis bacterium overproducing vitamin B2 in a feed additive was notified by the Rapid Alert System for Food and Feed (RASFF). This has demonstrated that a contamination by a GM micro-organism (GMM) may occur in feed additives and has confronted for the first time,the enforcement laboratories with this type of RASFF. As no sequence information of this GMM nor any specific detection or identification method was available, Next GenerationSequencing (NGS) was used to generate sequence information. However, NGS data analysis often requires appropriate tools, involving bioinformatics expertise which is not alwayspresent in the average enforcement laboratory. This hampers the use of this technology to rapidly obtain critical sequence information in order to be able to develop a specific qPCRdetection method. Methods: Data generated by NGS were exploited using a simple BLAST approach. A TaqMan® qPCR method was developed and tested on isolated bacterial strains and on the feed additive directly. Results: In this study, a very simple strategy based on the common BLAST tools that can be used by any enforcement lab without profound bioinformatics expertise, was successfully used toanalyse the B. subtilis data generated by NGS. The results were used to design and assess a new TaqMan® qPCR method, specifically detecting this GM vitamin B2 overproducing bacterium. The method complies with EU critical performance parameters for specificity, sensitivity, PCR efficiency and repeatability. The VitB2-UGM method also could detect the B. subtilis strain in genomic DNA extracted from the feed additive, without prior culturing step. Conclusions: The proposed method, provides a crucial tool for specifically and rapidly identifying this unauthorized GM bacterium in food and feed additives by enforcement laboratories. Moreover, this work can be seen as a case study to substantiate how the use of NGS data can offer an added value to easily gain access to sequence information needed to develop qPCR methods to detect unknown andunauthorized GMO in food and feed.