Food Analytical Methods

, Volume 2, Issue 4, pp 325–336 | Cite as

Real-Time PCR-Based Ready-to-Use Multi-Target Analytical System for GMO Detection

  • Maddalena Querci
  • Nicoletta Foti
  • Alessia Bogni
  • Linda Kluga
  • Hermann Broll
  • Guy Van den Eede
Article

Abstract

This paper describes the development, production, and testing of a high-throughput analytical system, i.e., a unique screening tool for the unequivocal simultaneous identification of all currently EU-approved and all unapproved genetically modified organisms (GMOs) known to the Community Reference Laboratory for GM Food and Feed (CRL-GMFF), established according to Regulation (EC) No 1829/2003. The rationale and comparative advantage of the strategy selected as well as the formulation, potentiality, and flexibility of the system are illustrated here. The approach, developed in response to the worldwide growing testing needs, allows the event-specific simultaneous detection of 39 single-insert GMOs and their derived stacked events. System performance (specificity, efficiency, etc) has been successfully confirmed by experimental testing conducted within the CRL-GMFF and in collaboration with European control laboratories. The limit of detection (LOD) has been determined to be at least 0.045% expressed in haploid genome copies, thus in full compliance with EU requirements for method LOD. The “real-time PCR-based ready-to-use multi-target analytical system for GMO detection” developed by the Joint Research Centre is the first analytical tool worldwide allowing the simultaneous detection of so many genetic modification events using event-specific targets.

Keywords

GMO Detection Traceability Food Control Real-Time PCR 

Abbreviations

GMO

Genetically Modified Organism

PCR

Polymerase Chain Reaction

Introduction

In response to the ever-growing testing needs in Europe and due to the constantly increasing number of GM events commercialized in various parts of the world (James 2008), a new approach for the detection of genetically modified organisms (GMOs) was developed.

Today, more than 130 genetically modified (GM) crops representing 22 species are on the market worldwide (Ramessar et al. 2008). Different levels of regulations concerning authorization of such products exist: some countries, including EU Member States, follow a mandatory authorization procedure and a labeling provision for GM food and feed products (European Commission 2003a, b), while others, like the USA, have only a voluntary labeling procedure (Carter and Gruère 2006). A third category of countries precludes any GMOs or products thereof from the market, and finally, some countries have no regulations in place, neither for marketing nor for labeling (Ramessar et al. 2008).

Since the introduction in 1997 of mandatory labeling requirements in the EU (European Commission 1997), different analytical approaches have been developed for GMO identification and quantification (Marmiroli et al. 2008). The most direct and widely used detection methods target the genetic modification itself, i.e., the modified DNA, and are based on the amplification of the specific DNA targets using the PCR technique (Miraglia et al. 2004; Rodríguez-Lázaro et al. 2007). Several PCR methods, either in simplex or multiplex format, have been reported for qualitative analysis (Padgette et al. 1995; Zimmermann et al. 1998; Matsuoka et al. 2000; Hernandez et al. 2005). Other PCR-derived approaches as competitive PCR (Van den Eede et al. 2000; Garcia-Cañas et al. 2004) or real-time PCR (RTi-PCR) have been developed to quantify the GMO content in a sample (Brodmann et al. 2002; Rønning et al. 2003; Windels et al. 2003; Hernandez et al. 2004).

Among all alternatives tested so far, RTi-PCR proved to be the most successful, accurate, and powerful technique, and accordingly, it is now the method of choice in the EU and worldwide for GMO quantification (Miraglia et al. 2004).

Whereas the wide spreading and adoption of the RTi-PCR approach is linked to its reliability for DNA quantification (Holst-Jensen et al. 2003; Miraglia et al. 2004), the technique is also more and more frequently used for end point analysis and for qualitative detection purposes (Reiting et al. 2007). This novel application of RTi-PCR is due to its increased intrinsic specificity and to the fact that it allows extrapolation of results directly from the instrument software avoiding analysis of PCR products by gel electrophoresis, a step that represents the main risk in terms of laboratory contamination.

For the analysis of a food/feed product, a series of sequential PCR tests needs to be performed on each sample to be analyzed. Screening tests, based on the detection of regulatory sequences globally used in constructing GMOs such as the 35S promoter and the NOS terminator, are usually applied in a first instance to detect the presence of a GMO, irrespective of modification type.

From a practical view point, screening methods are useful for the rapid and reliable reduction of test samples by direct identification of negative samples, which do not need to be further analyzed (Querci et al. 2007). Depending on the outcome of the screening, additional tests are performed for identification purposes. A sample positive to a screening test needs to be further characterized to confirm its GM origin and to reveal the identity and number of the GMO(s) present. GM-specific PCR-based tests can be grouped into categories according to their level of specificity that depends upon the target of the DNA fragment that is amplified in the PCR (Holst-Jensen et al. 2003). “Gene-specific,” “construct-specific,” and “event-specific” target the inserted gene, the specific construct, and the junction between the recipient genome and the inserted DNA at the integration locus, respectively. A key technical requirement for the authorization of a GMO within the EU (European Commission 2003a, 2004, 2005) is the provision of an event-specific detection method to allow the control and monitoring of the GM event in the distribution chain, i.e., a method that can unambiguously distinguish and quantify the GMO of interest (i.e., the event) from all other possible GMOs. Such a method must also be validated according to internationally accepted standards in order to be proven as fit for EU regulatory purposes (European Commission 2004).

Traditionally, event-specific methods are performed independently from each other. Accordingly, ascertaining the presence of possibly several GMOs in any food/feed sample may require first, huge resources, often exceeding laboratories' capabilities, and second, an extensive amount of time. Therefore, the “real-time PCR-based ready-to-use multi-target analytical system for the detection of GMOs” was conceived as a rapid and ready-to-use system for the simultaneous detection of multiple GM events in a single experiment, reducing laboratory handling steps to a minimum.

The approach here described is based on TaqMan® RTi-PCR, a technology already widely used for quantification of GM crops (Kuribara et al. 2002; Holst-Jensen et al. 2003) and adopted in all methods submitted by applicants to the Community Reference Laboratory for GM Food and Feed (CRL-GMFF; http://gmo-crl.jrc.ec.europa.eu/) for validation so far.

The system consists of 96-well prespotted plates containing lyophilized primers and probes for the individual detection of targets allowing the simultaneous identification of 39 GM events by the use of event-specific primers and probe combinations (http://gmo-crl.jrc.ec.europa.eu/statusofdoss.htm). The system also contains taxon-specific methods for maize, cotton, rice, oilseed rape, soybean, sugar beet, and potato. As detection of stacked GM events is based on the use of event-specific methods developed for the parental GM events composing the stack, this system also allows the detection of all stacks derived thereof, in the seven plant species.

The use of the 96-well RTi-PCR platform was chosen to facilitate the immediate use of the proposed approach, which can be easily integrated in the laboratories' working routine, without the need for new instrumentation or new procedures. Additionally, as it will be discussed later, the ready-to-use format allows the operators to perform the complete identification analysis in a rapid way requiring only few simple steps.

Materials and Methods

Preparation of Test Samples and DNA Extraction

Source materials for DNA extraction consisted of positive (100% GMO) and negative control samples that, according to EU legislation (European Commission 2004), have to be provided to the CRL-GMFF by applicants.

As defined in the Regulation (EC) 1829/2003 Art. 2 (11) (European Commission 2003a), control samples are the GMO or its genetic material (positive sample) and the parental organism that has been used for the purpose of the genetic modification or its genetic material (negative sample). Accordingly, control samples consisted either of seeds or grains or of genomic DNA (gDNA) preparations, already provided by applicants in purified format. Seeds and grains were grinded to fine powder using Grindomix GM 200 (Retsch GmbH, Haan, Germany) with the following three-step program: 10 s at 8,000 rpm, 2 × 15 s at 10,000 rpm.

Genomic DNA from each wild-type plant species was extracted with a modified CTAB method (Murray and Thompson 1980). Where not yet available in purified format, gDNA from each GM event was extracted using the Nucleospin® kit (Macherey-Nagel GmbH, Düren, Germany). The integrity and purity of gDNA preparations were determined by agarose gel electrophoresis and Eppendorf-biophotometer. The gDNA quantity was determined by Picogreen dsDNA quantification assay (Invitrogen: Molecular Probes, Eugene, Oregon, USA; and Bio-Rad VersaFluor™ Fluorometer reader; data not shown) according to the manufacturer's instruction. All gDNA extracts were diluted to 20 ng/μl with sterile distilled water and subjected to inhibition tests to assess DNA quality (Žel et al. 2008).

Working DNA solutions at 1%, 0.1%, 0.045%, and 0.022% GM content were prepared by mixing each GM event DNA preparation with the corresponding wild-type species DNA. In addition, a blended DNA solution containing each GM event at 0.25% was prepared for testing repeatability and reproducibility of the system.

Selection of Methods

The methods included in the systems are the ones that, according to EU legislation (European Commission 2003a), have been submitted by applicants to the CRL-GMFF (http://gmo-crl.jrc.ec.europa.eu/) for validation, as an integral part of the approval process.

Methods include: event-specific methods for maize Bt11, NK603, GA21 (2 methods), MON863, 1507, T25, 59122, MON810, MIR604, Bt176, MON88017, LY038, 3272, MON89034, Bt10; oilseed rape T45, Ms8, Rf3, GT73, Rf1, Rf2, Ms1, Topas 19/2; cotton MON1445, MON88913, LLCotton25, MON 531, MON15985, 281-24-236 × 3006-210-23; soybean A2704-12, 40-3-2, MON89788, DP-356043; rice LLRice62, LLRice601, Bt63; sugar beet H7-1; potato EH92-527-1 and a P35S::bar construct-specific method; plus target taxon-specific methods for the corresponding plant species (http://gmo-crl.jrc.ec.europa.eu/statusofdoss.htm). The RTi-PCR method for the unapproved maize event Bt10 was designed for this study based on primers (Table 1) validated by the CRL-GMFF only for qualitative purposes.
Table 1

Primers/probes systems and references of all methods included in the system

Target

Primer F

Primer R

Probe

HMG Maize reference

TTGGACTAGAAATCTCGTGCTGA

GCTACATAGGGAGCCTTGTCCT

CAATCCACACAAACGCACGCGTA

http://gmo-crl.jrc.ec.europa.eu/summaries/TC1507-WEB-Protocol-Validation.pdf

Bt11 Maize

GCGGAACCCCTATTTGTTTA

TCCAAGAATCCCTCCATGAG

AAATACATTCAAATATGTATCCGCTCA

http://gmo-crl.jrc.ec.europa.eu/summaries/Bt11-protocol.pdf

NK603 Maize

ATGAATGACCTCGAGTAAGCTTGTTAA

AAGAGATAACAGGATCCACTCAAACACT

TGGTACCACGCGACACACTTCCACTC

http://gmo-crl.jrc.ec.europa.eu/summaries/NK603-WEB-Protocol%20Validation.pdf

GA21 Maize (method 1)

CTTATCGTTATGCTATTTGCAACTTTAGA

TGGCTCGCGATCCTCCT

CATATACTAACTCATATCTCTTTCTCAACAGCAGGTGGGT

http://gmo-crl.jrc.ec.europa.eu/summaries/GA21-WEB-Protocol%20Validation.pdf

MON863 Maize

GTAGGATCGGAAAGCTTGGTAC

TGTTACGGCCTAAATGCTGAACT

TGAACACCCATCCGAACAAGTAGGGTCA

http://gmo-crl.jrc.ec.europa.eu/summaries/MON863-WEB-Protocol-Validation.pdf

1507 Maize

TAGTCTTCGGCCAGAATGG

CTTTGCCAAGATCAAGCG

TAACTCAAGGCCCTCACTCCG

http://gmo-crl.jrc.ec.europa.eu/summaries/TC1507-WEB-Protocol-Validation.pdf

T25 Maize

ACAAGCGTGTCGTGCTCCAC

GACATGATACTCCTTCCACCG

TCATTGAGTCGTTCCGCCATTGTCG

http://gmo-crl.jrc.ec.europa.eu/summaries/T25-Protocol.pdf

59122 Maize

GGGATAAGCAAGTAAAAGCGCTC

CCTTAATTCTCCGCTCATGATCAG

TTTAAACTGAAGGCGGGAAACGACAA

http://gmo-crl.jrc.ec.europa.eu/summaries/59122-Protocol%20Validation.pdf

MON810 Maize

TCGAAGGACGAAGGACTCTAACGT

GCCACCTTCCTTTTCCACTATCTT

AACATCCTTTGCCATTGCCCAGC

http://gmo-crl.jrc.ec.europa.eu/summaries/Mon810_validation_report.pdf

MIR604 Maize

GCGCACGCAATTCAACAG

GGTCATAACGTGACTCCCTTAATTCT

AGGCGGGAAACGACAATCTGATCATG

http://gmo-crl.jrc.ec.europa.eu/summaries/MIR604_validated_Method.pdf

Bt176 Maize

Not published

GA21 Maize (method 2)

CGTTATGCTATTTGCAACTTTAGAACA

GCGATCCTCCTCGCGTT

TTTCTCAACAGCAGGTGGGTCCGGGT

http://gmo-crl.jrc.ec.europa.eu/summaries/GA21Syng_validated_Method.pdf

MON88017 Maize

GAGCAGGACCTGCAGAAGCT

TCCGGAGTTGACCATCCA

TCCCGCCTTCAGTTTAAACAGAGTCGGGT

http://gmo-crl.jrc.ec.europa.eu/summaries/MON88017_validated_Method.pdf

LY038 Maize

TGGGTTCAGTCTGCGAATGTT

AGGAATTCGATATCAAGCTTATCGA

CGAGCGGAGTTTATGGGTCGACGG

http://gmo-crl.jrc.ec.europa.eu/summaries/LY038_validated_Method.pdf

3272 Maize

TCATCAGACCAGATTCTCTTTTATGG

CGTTTCCCGCCTTCAGTTTA

ACTGCTGACGCGGCCAAACACTG

http://gmo-crl.jrc.ec.europa.eu/summaries/3272_validated_Method.pdf

Bt10 Maize

CACACAGGAGATTATTATAGGGTTACTCA

ACACGGAAATGTTGAATACTCATACTCT

AATAACCCTGATAAATGCTTCA

http://gmo-crl.jrc.ec.europa.eu/summaries/Bt10%20Detection%20Protocol%20version2.pdf

MON89034 Maize

TCCTCCATATGGACCATCATACTCATT

CGGTATCTATAATACCGTGGTTTTTAAA

ATCCCCGGAAATTATGTT

http://gmo-crl.jrc.ec.europa.eu/summaries/MON89034_validated_Method.pdf

SAH7 Cotton reference

AGTTTGTAGGTTTTGATGTTACATTGAG

GCATCTTTGAACCGCCTACTG

AAACATAAAATAATGGGAACAACCATGACATGT

http://gmo-crl.jrc.ec.europa.eu/summaries/3006-210-23_cotton_Protocol.pdf

LLCotton25 Cotton

CAGATTTTTGTGGGATTGGAATTC

CAAGGAACTATTCAACTGAG

CTTAACAGTACTCGGCCGTCGACCGC

http://gmo-crl.jrc.ec.europa.eu/summaries/LLCotton25_validated_Method.pdf

MON 531 Cotton

TCCCATTCGAGTTTCTCACGT

AACCAATGCCACCCCACTGA

TTGTCCCTCCACTTCTTCTC

http://gmo-crl.jrc.ec.europa.eu/summaries/MON531_validated_method.pdf

MON1445 Cotton

GGAGTAAGACGATTCAGATCAAACAC

ATCGACCTGCAGCCCAAGCT

ATCAGATTGTCGTTTCCCGCCTTCAGTTT

http://gmo-crl.jrc.ec.europa.eu/summaries/MON1445_validated_Method.pdf

MON15985 Cotton

GTTACTAGATCGGGGATATCC

AAGGTTGCTAAATGGATGGGA

CCGCTCTAGAACTAGTGGATCTGCACTGAA

http://gmo-crl.jrc.ec.europa.eu/summaries/MON15985_validated_Method.pdf

MON88913 Cotton

GGCTTTGGCTACCTTAAGAGAGTC

CAAATTACCCATTAAGTAGCCAAATTAC

AACTATCAGTGTTTGACTACAT

http://gmo-crl.jrc.ec.europa.eu/summaries/MON88913_validated%20Method.pdf

281-24-236 Cotton

CTCATTGCTGATCCATGTAGATTTC

GGACAATGCTGGGCTTTGTG

TTGGGTTAATAAAGTCAGATTAGAGGGAGACAA

http://gmo-crl.jrc.ec.europa.eu/summaries/281-24-36_cotton_Protocol.pdf

3006-210-23 Cotton

AAATATTAACAATGCATTGAGTATGATG

ACTCTTTCTTTTTCTCCATATTGACC

TACTCATTGCTGATCCATGTAGATTTCCCG

http://gmo-crl.jrc.ec.europa.eu/summaries/3006-210-23_cotton_Protocol.pdf

PLD Rice reference

TGGTGAGCGTTTTGCAGTCT

CTGATCCACTAGCAGGAGGTCC

TGTTGTGCTGCCAATGTGGCCTG

http://gmo-crl.jrc.ec.europa.eu/summaries/LLRICE62_validated_Protocol.pdf

LLRICE62 Rice

AGCTGGCGTAATAGCGAAGAGG

TGCTAACGGGTGCATCGTCTA

CGCACCGATTATTTATACTTTTAGTCCACCT

http://gmo-crl.jrc.ec.europa.eu/summaries/LLRICE62_validated_Protocol.pdf

Rice GM events P35S::bar

TATCCTTCGCAAGACCCTTCC

ATGTCGGCCGGGCGTCGTTCTG

TCTATATAAGGAAGTTCATTTCATT

http://gmo-crl.jrc.ec.europa.eu/doc/P35S-bar%20sqRT-PCR%20-%20PGS0494-476%20310806.pdf

LLRice601 Rice

TCTAGGATCCGAAGCAGATCGT

GGAGGGCGCGGAGTGT

CCACCTCCCAACAATAAAAGCGCCTG

http://gmo-crl.jrc.ec.europa.eu/doc/LLRICE601%20sqRT-PCR%20-%20PGS0505-0476%20300806.pdf

Bt63 Rice

GACTGCTGGAGTGATTATCGACAGA

AGCTCGGTACCTCGACTTATTCAG

TCGAGTTCATTCCAGTTACTGCAACACTCGAG

Mäde et al. (2006)

CruA Oilseed reference

GGCCAGGGTTTCCGTGAT

CCGTCGTTGTAGAACCATTGG

AGTCCTTATGTGCTCCACTTTCTGGTGCA

http://gmo-crl.jrc.ec.europa.eu/summaries/Ms8_validated_Method.pdf

T45 Oilseed rape

CAATGGACACATGAATTATGC

GACTCTGTATGAACTGTTCGC

TAGAGGACCTAACAGAACTCGCCGT

http://gmo-crl.jrc.ec.europa.eu/summaries/T45_validated_RTPCR_method.pdf

Ms8 Oilseed rape

GTTAGAAAAAGTAAACAATTAATATAGCCGG

GGAGGGTGTTTTTGGTTATC

AATATAATCGACGGATCCCCGGGAATTC

http://gmo-crl.jrc.ec.europa.eu/summaries/Ms8_validated_Method.pdf

Rf3 Oilseed rape

AGCATTTAGCATGTACCATCAGACA

CATAAAGGAAGATGGAGACTTGAG

CGCACGCTTATCGACCATAAGCCCA

http://gmo-crl.jrc.ec.europa.eu/summaries/Rf3_validated_Method.pdf

GT73 Oilseed rape

CCATATTGACCATCATACTCATTGCT

GCTTATACGAAGGCAAGAAAAGGA

TTCCCGGACATGAAGATCATCCTCCTT

http://gmo-crl.jrc.ec.europa.eu/summaries/RT73_validated_Method.pdf

Rf1 Oilseed rape

Not published

Rf2 Oilseed rape

Not published

Ms1 Oilseed rape

Not published

Topas 19/2 Oilseed rape

Not published

Lectin Soybean reference

CCAGCTTCGCCGCTTCCTTC

GAAGGCAAGCCCATCTGCAAGCC

CTTCACCTTCTATGCCCCTGACAC

http://gmo-crl.jrc.ec.europa.eu/summaries/40-3-2_validated_Method.pdf

A2704-12 Soybean

GCAAAAAAGCGGTTAGCTCCT

ATTCAGGCTGCGCAACTGTT

CGGTCCTCCGATCGCCCTTCC

http://gmo-crl.jrc.ec.europa.eu/summaries/A2704-12_soybean_validated_Method.pdf

40-3-2 Soybean

TTCATTCAAAATAAGATCATACATACAGGTT

GGCATTTGTAGGAGCCACCTT

CCTTTTCCATTTGGG

http://gmo-crl.jrc.ec.europa.eu/summaries/40-3-2_validated_Method.pdf

MON89788 Soybean

TCCCGCTCTAGCGCTTCAAT

TCGAGCAGGACCTGCAGAA

CTGAAGGCGGGAAACGACAATCTG

http://gmo-crl.jrc.ec.europa.eu/summaries/MON89788_validated_Method.pdf

DP-356043 Soybean

GTCGAATAGGCTAGGTTTACGAAAAA

TTTGATATTCTTGGAGTAGACGAGAGTGT

CTCTAGAGATCCGTCAACATGGTGGAGCAC

http://gmo-crl.jrc.ec.europa.eu/summaries/356043_validated_Method.pdf

GS Sugar beet reference

GACCTCCATATTACTGAAAGGAAG

GAGTAATTGCTCCATCCTGTTCA

CTACGAAGTTTAAAGTATGTGCCGCTC

http://gmo-crl.jrc.ec.europa.eu/summaries/H7-1-Protocol%20Validated.pdf

H7-1 Sugar beet

TGGGATCTGGGTGGCTCTAACT

AATGCTGCTAAATCCTGAG

AAGGCGGGAAACGACAATCT

http://gmo-crl.jrc.ec.europa.eu/summaries/H7-1-Protocol%20Validated.pdf

UGPase Potato reference

GGACATGTGAAGAGACGGAGC

CCTACCTCTACCCCTCCGC

CTACCACCATTACCTCGCACCTCCTCA

http://gmo-crl.jrc.ec.europa.eu/summaries/EH92-527-Validated_method.pdf

EH92-527-1 Potato

GTGTCAAAACACAATTTACAGCA

TCCCTTAATTCTCCGCTCATGA

AGATTGTCGTTTCCCGCCTTCAGTT

http://gmo-crl.jrc.ec.europa.eu/summaries/EH92-527-Validated_method.pdf

Primer and probe synthesis and automatic spotting were outsourced (Applied Biosystems, Foster City, CA, USA). All probes (except for event-specific methods for 40-3-2 soybean, MON89034 and Bt10 maize, and MON88913 cotton and for the P35S::bar method) were labeled with 6-carboxyfluorescein (FAM) and 6-carboxytetramethylrhodamin (TAMRA) at the 5′ and 3′ ends, respectively. Probes for 40-3-2 soybean, MON89034 and Bt10 maize, and MON88913 cotton event-specific methods and for the P35S::bar method were labeled with FAM at the 5′ end and with nonfluorescent quencher linked with minor groove binder (MGB) at the 3′ end.

Real-Time PCR Assay

Plate setup is row-based (Fig. 1a) and includes 48 different assays allowing the analysis of two samples per plate. PCRs were performed in a 7900HT Real-Time PCR System (Applied Biosystems) in 50 µL reactions containing 100 ng input DNA, 1× TaqMan® Universal PCR Master Mix (Applied Biosystems). As indicated above, primers and probes were already present in each well in lyophilised format at concentrations of 900 nM primers/250 nM probes. The thermal profile used was: 95°C for 10 min, followed by 45 cycles of 95°C for 15 s and 60°C for 60 s.
Fig. 1

a Plate setup includes 48 assays (event-specific methods for 39 GM events + a P35S::bar specific method + target taxon specific methods for maize, cotton, rice, oilseed rape, soybean, sugar beet, and potato) allowing the analysis of two samples per plate. b Amplification plot for the identification of 0.1% Bt10 maize. Curves above the threshold (green horizontal line) indicate positive reaction for maize reference gene (well E1) and for event Bt10 (well H12)

Results and Discussion

The system is based on TaqMan® RTi-PCR technology and consists of 96-well prespotted plates containing lyophilized primers and probes for the individual detection and the simultaneous identification of 39 GM events by the use of event-specific primers and probe combinations.

The specificity of each method (event-specific methods for 39 GM events + the P35S::bar specific method + the target taxon specific methods for maize, cotton, rice, oilseed rape, soybean, sugar beet, and potato) was assessed and confirmed by testing each wild-type plant species and each GM event, individually, against the whole set of methods using a matrix approach. As shown in Table 2, each GM event resulted in a positive signal only with the corresponding event-specific method and with the corresponding taxon-specific method. MON15985 cotton gave additional positive signal with event-specific method for MON531; similarly, the 281-24-236 × 3006-210-23 stacked cotton event gave positive signal with the methods specific for event 281-24-236 and event 3006-210-23.
Table 2

The specificity of the methods assessed by testing each wild-type plant species and each GM event, individually, against the whole set of methods

Crosses (+) indicate a positive signal above the threshold for the corresponding sample/method combination

MON15985 is an insect-resistant cotton GM event derived by molecular transformation of the DP50B parent variety, which contained MON531 event; as such, it contains the genetic construct originally integrated into MON531 and the genetic construct integrated during the second transformation phase leading to MON15985.

Cotton 281-24-236 × 3006-210-23 is a stacked event, developed by Dow AgroSciences through crossing two independent transgenic cotton events, 281-24-236 and 3006-210-23, and accordingly, it contains the inserted genetic constructs of both parental events (http://www.agbios.com/main.php).

From a detection point of view, there is no difference between the two methodologies indicated above; in both cases, the derived GM events contain two (or more, in case of triple stacks) physically unlinked inserted genetic constructs (Holst-Jensen et al. 2006), and their detection is based on the use of the event-specific methods developed for the parental GM events composing the stack.

In addition to the event-specific methods for the 39 GM events, the system also contains a P35S::bar construct-specific method; this method was originally developed by Bayer CropScience to detect LLrice62 and LLrice601 rice events containing the P35S::bar construct. As the P35S::bar construct is also present in Bt176 maize, a positive signal is generated also in the presence of this maize event.

The sensitivity of the system was tested by individually loading, in each well, 100 ng of the GM and wt DNA solutions, respectively. As shown in Table 3, the Ct values obtained for each event-specific method using 100 ng of the respective DNA at 0.045% GM content varies from 31.86 (LLrice601) to 38.56 (GA21, method 1).
Table 3

Results for each method expressed as average Ct ± standard deviationa

Target

Species

Single targets

Mix 39 GM events

0.045%

0.25% (r)

0.25% (R)b

HMG

Maize

24.27 ± 0.33

25.94 ± 0.20

25.94 ± 0.31

SAH7

Cotton

25.28 ± 0.38

27.49 ± 0.70

27.70 ± 0.50

PLD

Rice

21.63 ± 0.22

25.81 ± 0.22

25.94 ± 0.17

CruA

Oilseed rape

24.05 ± 0.11

26.95 ± 0.38

27.07 ± 0.42

Lectin

Soybean

23.25 ± 0.24

26.77 ± 0.39

27.26 ± 0.67

GS

Sugar beet

23.52 ± 0.43

28.54 ± 0.33

28.72 ± 0.38

UGPase

Potato

16.88 ± 0.35

22.83 ± 0.46

22.96 ± 0.90

Bt11

Maize

38.38 ± 2.00

39.06 ± 3.06

38.19 ± 3.38

NK603

Maize

36.86 ± 0.82

35.85 ± 0.45

35.61 ± 0.58

GA21 method 1

Maize

38.56 ± 1.79

35.81 ± 0.76

34.97 ± 0.81

MON863

Maize

36.28 ± 0.83

34.78 ± 0.38

34.75 ± 0.40

1507

Maize

37.60 ± 1.92

34.34 ± 0.50

34.40 ± 0.49

T25

Maize

35.48 ± 0.77

33.50 ± 0.47

33.69 ± 0.59

59122

Maize

36.58 ± 0.90

34.83 ± 0.37

35.10 ± 0.42

H7-1

Sugar beet

34.97 ± 0.38

33.58 ± 0.44

34.44 ± 1.08

MON810

Maize

36.76 ± 0.59

35.14 ± 0.29

35.03 ± 0.41

281-24-236

Cotton

35.97 ± 0.45

34.23 ± 0.27

34.64 ± 0.55

3006-210-23

Cotton

34.72 ± 1.30

33.97 ± 0.49

34.20 ± 0.60

LLRICE62

Rice

32.77 ± 0.37

31.74 ± 0.22

31.76 ± 0.65

T45

Oilseed rape

34.00 ± 0.89

32.54 ± 0.26

32.53 ± 0.51

EH-92-527-1

Potato

37.14 ± 0.30

34.61 ± 0.34

34.69 ± 0.37

Ms8

Oilseed rape

34.83 ± 0.92

33.50 ± 0.77

33.62 ± 0.37

Rf3

Oilseed rape

36.64 ± 0.19

34.80 ± 0.33

34.61 ± 0.45

GT73

Oilseed rape

35.25 ± 0.74

33.94 ± 0.82

34.24 ± 0.86

LLCotton25

Cotton

33.89 ± 0.34

32.33 ± 0.20

32.22 ± 0.47

MON531

Cotton

36.86 ± 0.74

33.10 ± 1.67

35.49 ± 2.11

A2704-12

Soybean

32.53 ± 0.35

30.84 ± 0.19

30.96 ± 0.24

MIR604

Maize

31.96 ± 0.13

29.50 ± 0.27

29.50 ± 0.27

Rf1

Oilseed rape

35.26 ± 0.41

32.95 ± 0.84

32.79 ± 0.22

Rf2

Oilseed rape

33.99 ± 0.22

31.80 ± 0.22

31.72 ± 0.29

Ms1

Oilseed rape

36.04 ± 0.98

34.52 ± 0.44

34.16 ± 0.54

Topas19/2

Oilseed rape

36.74 ± 0.43

35.21 ± 0.36

36.02 ± 0.63

MON1445

Cotton

35.60 ± 0.60

34.04 ± 0.31

34.10 ± 0.35

Bt176

Maize

35.24 ± 0.33

32.61 ± 0.41

32.51 ± 0.26

MON15985

Cotton

36.18 ± 1.15

33.21 ± 0.75

32.76 ± 0.40

40-3-2

Soybean

32.57 ± 0.04

32.15 ± 0.21

32.69 ± 0.38

GA21 method 2

Maize

34.92 ± 0.51

33.52 ± 0.27

33.17 ± 0.49

MON88017

Maize

36.52 ± 0.69

33.95 ± 0.35

34.13 ± 0.26

LY038

Maize

36.47 ± 0.57

34.31 ± 0.35

34.16 ± 0.32

3272

Maize

34.75 ± 0.47

32.60 ± 0.33

33.01 ± 0.34

MON89788

Soybean

34.72 ± 0.37

32.42 ± 0.28

32.15 ± 0.36

MON89034

Maize

35.66 ± 0.19

34.03 ± 0.28

33.80 ± 0.32

DP-356043

Soybean

34.36 ± 0.34

32.35 ± 0.26

31.95 ± 0.37

MON88913

Cotton

36.13 ± 0.20

34.37 ± 0.31

34.69 ± 0.43

P35S::BAR

Rice

34.97 ± 0.46

30.92 ± 0.25

30.87 ± 0.50

LLRice601

Rice

31.86 ± 0.55

30.97 ± 0.31

30.51 ± 0.65

Bt63

Rice

34.91 ± 0.42

32.55 ± 0.25

32.26 ± 0.41

Bt10

Maize

38.54 ± 0.52

36.52 ± 0.36

36.50 ± 0.70

In columns 1 and 2 are specific targets and corresponding plant species; column 3 contains results obtained by loading in each well 100 ng of wild-type or GM DNA solution at 0.045% (Ct = average of eight replicates). In columns 4 and 5 are results obtained with a blended DNA solution containing each event at 0.25% under repeatability conditions (column 4 (r), Ct = average of ten replicates) or under reproducibility conditions (column 5 (R), Ct = average of 11 replicates)

aFluorescence threshold set at 0.2

bData from laboratories using the 7900HT Real-Time PCR System.

These results are in line with the intrinsic performance characteristics of each individual method; indeed, as each method was originally developed and optimized independently by the respective applicants, the PCR protocols differ significantly. The sensitivity results obtained are also in agreement with the different genome sizes (Arumuganathan and Earle 1991) of the individual plant species under testing (Table 4).
Table 4

Copy number equivalents (C1 average values according to Arumuganathan and Earle 1991) for the seven plant species using 100 ng total DNA/reaction

Common name

C1 average value (pg)

Species copy numbers

GM copies (0.1%)

GM copies (0.045%)

GM copies (0.022%)

Maize

2.725

36.697

36

17

8

Cotton

2.33

42.918

42

19

9

Rice

0.45

222.222

222

100

50

Oilseed rape

1.15

86.956

86

39

20

Soybean

1.13

88.495

88

40

20

Sugar beet

1.25

80.000

80

36

18

Potato

1.8

55.555

55

25

12

GM copy numbers were calculated assuming homozygous status for all GM events

All GM event DNA solutions were successfully detected by the system at the lowest tested GM content (0.022%), and the corresponding lowest copy number detected for each species is indicated in Table 4. However, due to the differential efficiency of the individual methods, it was noted that the Ct values of some of them (e.g., Bt11, GA21 method 1, Bt10) at 0.022% GM content could be higher than 40. Due to the impracticability of defining the individual sensitivity for each method, it was decided to establish a unique sensitivity level for the system as a whole. Accordingly, sensitivity of the system for all methods was confirmed and defined to be at least 0.045%, in line with method specificities and in compliance with requirements for method limit of detection (LOD) laid down in the document on “Definition of minimal performance requirements for analytical methods of GMO testing” (http://gmo-crl.jrc.ec.europa.eu/guidancedocs.htm).

A blended DNA solution containing each GM event at 0.25% was tested under repeatability and reproducibility conditions (Table 3). For reproducibility testing, prespotted plates were distributed to 33 EU control laboratories together with the blended DNA solution and a negative sample consisting of a 20 ng/μL herring sperm DNA solution (Sigma-Aldrich, USA, cat n. 31149). Data returned by all laboratories running the system on different platforms (7900HT Real-Time PCR System, 7300/7500 Real-Time PCR Systems, ABI PRISM® 7000/7700 SDS [Applied Biosystems], iCycler iQ Real-Time PCR Detection System [Bio-Rad, Hercules, CA], MX3000 (Stratagene, La Jolla, CA) showed high levels of reproducibility, with 100% correct positive results for 47 methods, while the Bt11 maize method provided false-negative results in 45% of the cases. The Bt11 method was the only method unable to retain specificity and overall performance under the unique set of conditions defined for the system.

Laboratories also reported 100% correct negative results for 42 methods, 97% correct negative results for four methods, and 94% correct negative results for one method.

The system provided correct detection results when composite and processed samples were tested. Among others, the test material GeM MU01 from FAPAS® GEMMA Proficiency Scheme (consisting of GM events in mixed flours: <0.72% RR soybean; 1.29% MON810 maize; and 1.33% NK603 maize) and the test material C4.4 from FAPAS Proficiency Scheme (consisting of GM events in maize flour: 0.4% MON810 maize; 1.5% GA21 maize; 0.8% Bt176 maize; 3.0% Bt11; 1.0% Herculex maize and 1.5% MON863 maize) provided positive signals for all and only the GM events reported to be present (http://www.fapas.com/) in the corresponding samples (data not shown). The above-mentioned experimental data indicate that this system is adequate for detecting several GM events in a single experiment [LOD of at least 0.045% expressed in haploid genome copies], thus in full compliance with EU requirements for method LOD. The methodology and the format allow immediate implementation into laboratories' working routine since RTi-PCR based on 96-well platforms is used worldwide and adopted by most EU control laboratories. The ready-to-use format requires only a few simple steps to be performed by the operator (Fig. 2): extraction of the DNA from the sample, addition of the DNA to the PCR master mix, loading of the mixture on the plate, and running the thermal cycling program. Results are then extrapolated directly using the ad hoc instrument software (Fig. 1b). In addition, the use of prespotted plates improves comparability of results; the automated prespotting leads to the production of homogeneous batches of plates which are tested prior to utilization to guarantee the absence of variability within the same batch.
Fig. 2

Workflow and approximate timing for GMO analysis using the ready-to-use multi-target analytical system

Finally, the approach presented is flexible and constitutes the starting point for a whole new set of applications currently under development: inclusion of new GMO targets as methods become available (e.g., substitution of the Bt11 with an improved method recently validated), formulation of crop-specific or screening prespotted plates or even 96-well prespotted plates for quantitative determination of GM content.

To our knowledge, the “real-time PCR based ready-to-use multi-target analytical system” developed by the Joint Research Centre is the first analytical tool allowing the simultaneous detection of so many GM events using event-specific targets.

Notes

Acknowledgments

The authors thank Marco Cappelletti for advice in setting up the system and Steven Price for editorial revision of the manuscript. The authors also thank all EU control laboratories who participated in the collaborative evaluation of the newly developed GMO detection tool (Austrian Agency for Health and Food Safety; Federal Environment Agency Austria; Scientific Institute of Public Health (IPH); Walloon Agricultural Research Centre (CRA-W) Belgium; Institute for Agricultural and Fisheries Research Belgium; National Center of Public Health Protection (NCPHP) Bulgaria; Swiss Federal Office of Public Health; Czech Agriculture and Food Inspection Authority; Crop Research Institute Czech Republic; Federal Office of Consumer Protection and Food Safety Germany; Bavarian Health and Food Safety Authority; Chemisches und Veterinäruntersuchungsamt Freiburg (CVUA) Germany; Institut für Hygiene und Umwelt der Hansestadt Hamburg Germany; National Institute of Chemical Physics and Biophysics Estonia; Laboratory Agroalimentary of the Ministry of Agriculture Spain; National Centre for Food, Spanish Food Safety Agency; Finnish Customs Laboratory; General Chemical State Laboratory (GCSL), Food Division- Athens Greece; Central Agricultural Office, Food and Feed Safety Directorate, Central Feed Investigation Laboratory - National Reference Laboratory Hungary; BIOMI Ltd Hungary; Central Agricultural Office, Food and Feed Safety Directorate, Laboratory for GMO food Hungary; Veterinary Public Health Institute for Lazio and Toscana Regions, National Reference Centre for GMO Analysis Italy; National Veterinary Laboratory Lithuania; National Diagnostic Centre of Food and Veterinary Service Latvia; RIKILT Institute of Food Safety The Netherlands; Food and Consumer Product Safety Authority The Netherlands; National Veterinary Institute Norway; Plant Breeding and Acclimatisation Institute Radzikow Poland; Institute for Diagnosis and Animal Health Romania; National Food Administration Sweden; National Institute of Biology Slovenia; Laboratory of Government Chemist Ltd United Kingdom; Danish Plant Directorate, Laboratory for Diagnostics in Plants, Seed, and Feed). The “real-time PCR based ready-to-use multi-target analytical system” was developed in the frame of the Project “Scientific and technical contribution to the development of an overall health strategy in the area of GMOs” (Contract CT 30249).

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Maddalena Querci
    • 1
  • Nicoletta Foti
    • 1
  • Alessia Bogni
    • 1
  • Linda Kluga
    • 1
  • Hermann Broll
    • 1
  • Guy Van den Eede
    • 1
  1. 1.Institute for Health and Consumer Protection (IHCP), Molecular Biology and Genomics UnitEuropean Commission-Joint Research CentreIspra (Va)Italy

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