Molecular characterization of an unauthorized genetically modified Bacillus subtilis production strain identified in a vitamin B2 feed additive.

Many food and feed additives result from fermentation of genetically modified (GM) microorganisms. For vitamin B2 (riboflavin), GM Bacillus subtilis production strains have been developed and are often used. The presence of neither the GM strain nor its recombinant DNA is allowed for fermentation products placed on the EU market as food or feed additive. A vitamin B2 product (80% feed grade) imported from China was analysed. Viable B. subtilis cells were identified and DNAs of two bacterial isolates (LHL and LGL) were subjected to three whole genome sequencing (WGS) runs with different devices (MiSeq, 454 or HiSeq system). WGS data revealed the integration of a chloramphenicol resistance gene, the deletion of the endogenous riboflavin (rib) operon and presence of four putative plasmids harbouring rib operons. Event- and construct-specific real-time PCR methods for detection of the GM strain and its putative plasmids in food and feed products have been developed.

Optimization of digital droplet polymerase chain reaction for quantification of genetically modified organisms.

Digital PCR in droplets (ddPCR) is an emerging method for more and more applications in DNA (and RNA) analysis. Special requirements when establishing ddPCR for analysis of genetically modified organisms (GMO) in a laboratory include the choice between validated official qPCR methods and the optimization of these assays for a ddPCR format. Differentiation between droplets with positive reaction and negative droplets, that is setting of an appropriate threshold, can be crucial for a correct measurement. This holds true in particular when independent transgene and plant-specific reference gene copy numbers have to be combined to determine the content of GM material in a sample. Droplets which show fluorescent units ranging between those of explicit positive and negative droplets are called 'rain'. Signals of such droplets can hinder analysis and the correct setting of a threshold. In this manuscript, a computer-based algorithm has been carefully designed to evaluate assay performance and facilitate objective criteria for assay optimization. Optimized assays in return minimize the impact of rain on ddPCR analysis. We developed an Excel based 'experience matrix' that reflects the assay parameters of GMO ddPCR tests performed in our laboratory. Parameters considered include singleplex/duplex ddPCR, assay volume, thermal cycler, probe manufacturer, oligonucleotide concentration, annealing/elongation temperature, and a droplet separation evaluation. We additionally propose an objective droplet separation value which is based on both absolute fluorescence signal distance of positive and negative droplet populations and the variation within these droplet populations. The proposed performance classification in the experience matrix can be used for a rating of different assays for the same GMO target, thus enabling employment of the best suited assay parameters. Main optimization parameters include annealing/extension temperature and oligonucleotide concentrations. The droplet separation value allows for easy and reproducible assay performance evaluation. The combination of separation value with the experience matrix simplifies the choice of adequate assay parameters for a given GMO event.

A statistical approach to quantification of genetically modified organisms (GMO) using frequency distributions.

BACKGROUND: According to Regulation (EU) No 619/2011, trace amounts of non-authorised genetically modified organisms (GMO) in feed are tolerated within the EU if certain prerequisites are met. Tolerable traces must not exceed the so-called 'minimum required performance limit' (MRPL), which was defined according to the mentioned regulation to correspond to 0.1% mass fraction per ingredient. Therefore, not yet authorised GMO (and some GMO whose approvals have expired) have to be quantified at very low level following the qualitative detection in genomic DNA extracted from feed samples. As the results of quantitative analysis can imply severe legal and financial consequences for producers or distributors of feed, the quantification results need to be utterly reliable. RESULTS: We developed a statistical approach to investigate the experimental measurement variability within one 96-well PCR plate. This approach visualises the frequency distribution as zygosity-corrected relative content of genetically modified material resulting from different combinations of transgene and reference gene Cq values. One application of it is the simulation of the consequences of varying parameters on measurement results. Parameters could be for example replicate numbers or baseline and threshold settings, measurement results could be for example median (class) and relative standard deviation (RSD). All calculations can be done using the built-in functions of Excel without any need for programming. The developed Excel spreadsheets are available (see section 'Availability of supporting data' for details). In most cases, the combination of four PCR replicates for each of the two DNA isolations already resulted in a relative standard deviation of 15% or less. CONCLUSIONS: The aims of the study are scientifically based suggestions for minimisation of uncertainty of measurement especially in -but not limited to- the field of GMO quantification at low concentration levels. Four PCR replicates for each of the two DNA isolations seem to be a reasonable minimum number to narrow down the possible spread of results.

Detecting un-authorized genetically modified organisms (GMOs) and derived materials.

Genetically modified plants, in the following referred to as genetically modified organisms or GMOs, have been commercially grown for almost two decades. In 2010 approximately 10% of the total global crop acreage was planted with GMOs (James, 2011). More than 30 countries have been growing commercial GMOs, and many more have performed field trials. Although the majority of commercial GMOs both in terms of acreage and specific events belong to the four species: soybean, maize, cotton and rapeseed, there are another 20+ species where GMOs are commercialized or in the pipeline for commercialization. The number of GMOs cultivated in field trials or for commercial production has constantly increased during this time period. So have the number of species, the number of countries involved, the diversity of novel (added) genetic elements and the global trade. All of these factors contribute to the increasing complexity of detecting and correctly identifying GMO derived material. Many jurisdictions, including the European Union (EU), legally distinguish between authorized (and therefore legal) and un-authorized (and therefore illegal) GMOs. Information about the developments, field trials, authorizations, cultivation, trade and observations made in the official GMO control laboratories in different countries around the world is often limited, despite several attempts such as the OECD BioTrack for voluntary dissemination of data. This lack of information inevitably makes it challenging to detect and identify GMOs, especially the un-authorized GMOs. The present paper reviews the state of the art technologies and approaches in light of coverage, practicability, sensitivity and limitations. Emphasis is put on exemplifying practical detection of un-authorized GMOs. Although this paper has a European (EU) bias when examples are given, the contents have global relevance.