Microarray-based method for detection of unknown genetic modifications

<p>Abstract</p> <p>Background</p> <p>Due to the increased use of genetic modifications in crop improvement, there is a need to develop effective methods for the detection of both known and unknown transgene constructs in plants. We have developed a strategy for detection and characterization of unknown genetic modifications and we present a proof of concept for this method using <it>Arabidopsis thaliana </it>and <it>Oryza sativa </it>(rice). The approach relies on direct hybridization of total genomic DNA to high density microarrays designed to have probes tiled throughout a set of reference sequences.</p> <p>Results</p> <p>We show that by using arrays with 25 basepair probes covering both strands of a set of 235 vectors (2 million basepairs) we can detect transgene sequences in transformed lines of <it>A. thaliana </it>and rice without prior knowledge about the transformation vectors or the T-DNA constructs used to generate the studied plants.</p> <p>Conclusion</p> <p>The approach should allow the user to detect the presence of transgene sequences and get sufficient information for further characterization of unknown genetic constructs in plants. The only requirements are access to a small amount of pure transgene plant material, that the genetic construct in question is above a certain size (here ≥ 140 basepairs) and that parts of the construct shows some degree of sequence similarity with published genetic elements.</p

HUNT-One Health Enabling human-animal health studies

publishedVersio

Characterization of unknown genetic modifications using high throughput sequencing and computational subtraction

<p>Abstract</p> <p>Background</p> <p>When generating a genetically modified organism (GMO), the primary goal is to give a target organism one or several novel traits by using biotechnology techniques. A GMO will differ from its parental strain in that its pool of transcripts will be altered. Currently, there are no methods that are reliably able to determine if an organism has been genetically altered if the nature of the modification is unknown.</p> <p>Results</p> <p>We show that the concept of computational subtraction can be used to identify transgenic cDNA sequences from genetically modified plants. Our datasets include 454-type sequences from a transgenic line of <it>Arabidopsis thaliana </it>and published EST datasets from commercially relevant species (rice and papaya).</p> <p>Conclusion</p> <p>We believe that computational subtraction represents a powerful new strategy for determining if an organism has been genetically modified as well as to define the nature of the modification. Fewer assumptions have to be made compared to methods currently in use and this is an advantage particularly when working with unknown GMOs.</p

Overview and recommendations for the application of digital PCR

The digital Polymerase Chain Reaction (dPCR), for the detection and absolute quantification of DNA, is a relatively new technique but its application in analytical laboratories is steadily increasing. In contrast to quantitative real-time PCR, DNA (fragments) can be quantified without the need for standard curves. Using dPCR, the PCR mix containing the (target) DNA is partitioned – depending on the device used – currently into a maximum of 10,000,000 small compartments with a volume as low as a few picoliters. These can be either physically distinct compartments on a chip (referred to as chamber-based digital PCR [cdPCR]), or these compartments correspond to water-in-oil droplets (referred to as droplet digital [ddPCR]). Common to both approaches, once PCR has been carried out simultaneously in all compartments/droplets, the number of positive and negative signals for each partition is counted by fluorescence measurement. With this technique, an absolute quantification of DNA copy numbers can be performed with high precision and trueness, even for very low DNA copy numbers. Furthermore, dPCR is considered less susceptible than qPCR to PCR inhibitory substances that can be co-extracted during DNA extraction from different sources. Digital PCR has already been applied in various fields, for example for the detection and quantification of GMOs, species (animals, plants), human diseases, food viruses and bacteria including pathogens. When establishing dPCR in a laboratory, different aspects have to be considered. These include, but are not limited to, the adjustment of the type of the PCR master mix used, optimised primer and probe concentrations and signal separation of positive and negative compartments. This document addresses these and other aspects and provides recommendations for the transfer of existing real-time PCR methods into a dPCR format.JRC.F.5-Food and Feed Complianc

Overview on the detection, interpretation and reporting on the presence of unauthorised genetically modified materials

The ad hoc working group on UGM was established on the basis of a mandate adopted by the ENGL steering committee on January 31st 2007. The working group has been chaired by Arne Holst-Jensen, National Veterinary Institute (NVI), Oslo, Norway, and by Yves Bertheau, Institut National de la Recherche Agronomique (INRA), Versailles, France. The other members of the working group have been: Theo Alnutt, Central Science Laboratory (CSL), UKHermann Broll, Federal Institute for Risk Assessment (BfR), GermanyMarc de Loose, Institute for Agricultural and Fisheries Research (ILVO), BelgiumLutz Grohmann, Federal Office of Consumer Protection and Food Safety (BVL), GermanyChristine Henry, CSL, UKLotte Hougs, Danish Plant Directorate (PDir), DenmarkWilliam Moens, JRC, ItalyDany Morisset, National Institute of Biology (NIB), SloveniaJaroslava Ovesna, Research Institute of Crop Production (VURV), Czech RepublicSven Pecoraro, Bavarian Health and Food Safety Authority (LGL), GermanyMaria Pla, Consejo Superior de Investigaciones Científicas (CSIC), SpainTheo Prins, RIKILT Institute of Food Safety, NetherlandsDaniel Suter, Swiss Federal Office of Public Health (FOPH), SwitzerlandDavid Zhang, Groupe d\u27Intérêt Public – Groupe d\u27Etude et de contrôle des Variétés et des Semences (GIPGEVES), France and Marc Van den Bulcke, European Commission Joint Research Centre (JRC), Italy

Analytical DSS module : how to support decisions in the analytical lab

National audienc

Gene stacking in transgenic plants: towards compliance between definitions, terminology and detection within the EU regulatory framework

The combination or stacking of different traits or genes in plants is rapidly gaining popularity in biotech crop production. Here we review the existing terminology regarding gene stacking in plants, and its implications in relation to genetics, biosafety, detectability and European regulations. Different methods of production of stacked gene traits, as well as the status of their cultivation and approval, are reviewed. Related to the different techniques of transformation and production, including classical breeding, and to differences in global authorization and commercialization practices, there are many types, definitions, and perceptions of stacking. These include: (1) stacking of traits and (2) stacking of events, which are the most widely accepted perceptions of stacking, and (3) stacking of genes, which from the analytical and traceability point of view may be a more appropriate perception. These differences in perceptions and definitions are discussed, as are their implications for analytical detection and regulatory compliance according to (in particular) European Union (EU) regulations. A comprehensive terminology regarding gene stacking with regulatory relevance is proposed. The haploid genome equivalent is proposed as the prevailing unit of measurement at all stages throughout the chain, in order to ensure that terminology and definitions of gene stacks are adapted to analytical detection, traceability, and compliance with EU regulations