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Abstract

Many countries regulate the presence of genetically modified organisms (GMOs) in food and feed products. A labelling threshold of 0.9% per ingredient has been implemented in the European Union (EU) for authorised GMOs and a zero tolerance is in use for unauthorised GMOs (UGMOs). Standard curve based quantitative real-time polymerase chain reaction (qPCR) is used as a golden standard for GMO detection and quantification. However, the rise in number of authorised GMOs renders qPCR laborious and expensive. Additionally, with the increased use of authorised GMOs in the food and feed industries the incidence of UGMOs is also rising. Within this doctoral thesis we aim to develop and characterise multi-target approaches for quantification of authorised GMOs and identification of UGMOs.
The most abundant GMO ingredients in terms of authorised GM lines in the EU are maize and soybean. Thus, we designed multi-target droplet digital PCR (ddPCR) assays for quantification of 12 EU-authorised maize and 11 EU-authorised soybean lines. Two assays were designed for each ingredient; a 4- and 10-plex for maize and 6-and 7-plex for soybean. In all cases the transgenes were labelled with one fluorescent reporter and the endogene with another, thus enabling the quantification per ingredient. There are four additional GM soybean lines in the process of authorisation, therefore we have extended the 7-plex assay into an 11-plex, hence eliminating the need for further method characterisation upon an authorisation of one or more of these four GM lines. All multi-target ddPCR assays produce specific results, and perform within the international recommendations set for GMO testing. Performance of soybean 6- and 7-plex assays was tested in an inter-laboratory assessment.
As accurate and precise nucleic acid quantification is important in GMO testing, an assessment of the critical factors for accurate copy number determination is needed. In ddPCR this factor is droplet volume variability. The results of an inter-laboratory comparison of different droplet generation platforms and different mastermixes demonstrated a clear discrepancy between the measured droplet volume and that assigned by the manufacturer, showing an underestimation of copy number when relying on calculations made by the corresponding software.
An adapted genome walking approach called amplification of linearly-enriched fragments (ALF) was developed for identification of UGMOs. By coupling ALF to next-generation sequencing (NGS) and designing a (semi)automated data analysis pipeline we aimed at simultaneous identification of all GMOs, including UGMOs. A DNA mixture of four reference materials in an uneven distribution was prepared, with one GM line for which only element order is known, serving as a model for a UGMO. By submitting the sequences gained after NGS to the (semi)automated data analysis pipeline, we successfully identified sequences matching element order of this UGMO serving as proof of principle for ALF as new UGMO detection strategy.
The presented methods are not relevant only for GMO community, they may also be introduced in fields such as medicine, where several targets can be quantified as a group or where only partial DNA sequence is known. Additionally, determination of factors influencing ddPCR droplet volume allows for a more accurate copy number determination.