ALF: a strategy for identification of unauthorized GMOs in complex mixtures by a GW-NGS method and dedicated bioinformatics analysis.

The majority of feed products in industrialised countries contains materials derived from genetically modified organisms (GMOs). In parallel, the number of reports of unauthorised GMOs (UGMOs) is gradually increasing. There is a lack of specific detection methods for UGMOs, due to the absence of detailed sequence information and reference materials. In this research, an adapted genome walking approach was developed, called ALF: Amplification of Linearly-enriched Fragments. Coupling of ALF to NGS aims for simultaneous detection and identification of all GMOs, including UGMOs, in one sample, in a single analysis. The ALF approach was assessed on a mixture made of DNA extracts from four reference materials, in an uneven distribution, mimicking a real life situation. The complete insert and genomic flanking regions were known for three of the included GMO events, while for MON15985 only partial sequence information was available. Combined with a known organisation of elements, this GMO served as a model for a UGMO. We successfully identified sequences matching with this organisation of elements serving as proof of principle for ALF as new UGMO detection strategy. Additionally, this study provides a first outline of an automated, web-based analysis pipeline for identification of UGMOs containing known GM elements.

Comparison and transfer testing of multiplex ligation detection methods for GM plants.

BACKGROUND: With the increasing number of GMOs on the global market the maintenance of European GMO regulations is becoming more complex. For the analysis of a single food or feed sample it is necessary to assess the sample for the presence of many GMO-targets simultaneously at a sensitive level. Several methods have been published regarding DNA-based multidetection. Multiplex ligation detection methods have been described that use the same basic approach: i) hybridisation and ligation of specific probes, ii) amplification of the ligated probes and iii) detection and identification of the amplified products. Despite they all have this same basis, the published ligation methods differ radically. The present study investigated with real-time PCR whether these different ligation methods have any influence on the performance of the probes. Sensitivity and the specificity of the padlock probes (PLPs) with the ligation protocol with the best performance were also tested and the selected method was initially validated in a laboratory exchange study. RESULTS: Of the ligation protocols tested in this study, the best results were obtained with the PPLMD I and PPLMD II protocols and no consistent differences between these two protocols were observed. Both protocols are based on padlock probe ligation combined with microarray detection. Twenty PLPs were tested for specificity and the best probes were subjected to further evaluation. Up to 13 targets were detected specifically and simultaneously. During the interlaboratory exchange study similar results were achieved by the two participating institutes (NIB, Slovenia, and RIKILT, the Netherlands). CONCLUSIONS: From the comparison of ligation protocols it can be concluded that two protocols perform equally well on the basis of the selected set of PLPs. Using the most ideal parameters the multiplicity of one of the methods was tested and 13 targets were successfully and specifically detected. In the interlaboratory exchange study it was shown that the selected method meets the 0.1% sensitivity criterion. The present study thus shows that specific and sensitive multidetection of GMO targets is now feasible.

Gene expression profiling for food safety assessment: examples in potato and maize.

Since the mid 1990s, microarray analysis has become one of the few tools that can analyze the entire contents of a cell regarding a specific information type. Especially since the development of whole genome microarrays the technique can be considered truly holistic. Most DNA based microarrays are used for the analysis of the total of messenger RNAs (transcriptome) and provide a snap-shot of what's going on in a cell population at the time of sampling. Within the last few years also full genome plant microarrays have become available for several crop species. With these it has been shown that several growing conditions can be separated based on their transcriptome pattern, such as location, year of harvest and agricultural input system, but also different cultivars of the same crop species, including genetically modified ones. A database comprising expression levels of the transcriptome in many different circumstances with a history of safe use would be a good comparator for evaluation of new agricultural practices or cultivars, genetically modified or otherwise obtained. New techniques as next generation sequencing may overcome issues on throughput time and cost, standard operation procedures and array design for individual crops.