Testing for genetically modified organisms (GMOs): Past, present and future perspectives

Biotechnology Advances 27 (2009) 1071–1082

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Biotechnology Advances j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / b i o t e c h a d v

Research review paper

Testing for genetically modified organisms (GMOs): Past, present and future perspectives Arne Holst-Jensen ⁎ Department of Feed and Food Safety, National Veterinary Institute, Ullevaalsveien 68, P.O. Box 750 Sentrum, 0106 Oslo, Norway

a r t i c l e

i n f o

a b s t r a c t This paper presents an overview of GMO testing methodologies and how these have evolved and may evolve in the next decade. Challenges and limitations for the application of the test methods as well as to the interpretation of results produced with the methods are highlighted and discussed, bearing in mind the various interests and competences of the involved stakeholders. To better understand the suitability and limitations of detection methodologies the evolution of transformation processes for creation of GMOs is briefly reviewed. © 2009 Elsevier Inc. All rights reserved.

Available online 27 May 2009 Keywords: Transgenic crops Detection Identification Quantification Transformation Quality assurance High throughput Validation Unapproved GMO Intragenics

Contents 1. Introduction . . . . . . . . . . . . 2. Stakeholder roles and responsibilities 3. Development and evolution of GMOs 4. Method development and availability 5. Unauthorised GMOs . . . . . . . . 6. Future perspectives . . . . . . . . References . . . . . . . . . . . . . . .

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1. Introduction Gene technology as a technology is potentially a short way to improving domesticated plants and animals, mainly because it can bypass biological barriers for recombination and genetic exchange across unrelated species by creating transgenes. However, from a societal point of view this technology is seen by many as a derail rather than a short way, as many still do not feel that the safety of the technology is conclusively demonstrated. Like any other technology, the gene technology has a potential for hostile abuse or unsafe use. Because of its wide ranging potential, the public perception of the technology has also affected the regulation of the technology and the testing requirements; — to an extent more comparable to medical

⁎ Tel.: +47 2321 6243; fax: +47 2321 6202. E-mail address: [email protected]. 0734-9750/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.biotechadv.2009.05.025

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drugs than to any other food related production technology, at least in some parts of the world. The first commercial genetically modified (GM) plant (the FlavrSavr tomato) was authorised for marketing in 1994 (Food and Drug Administration, 1994). As will become evident in the following, it was more future oriented than the majority of the currently marketed first generation GM organisms (GMOs) in some ways. The term GMO may be used in a broad sense to include all life forms, but the most common application of the term is limited to confine GM plants and animals. In the following, GMOs will refer strictly to GM plants because GM animals are yet to be commersialised, except for ornamental fish and pets. The annual increase in commercial plantings of GMOs has risen with an average of approximately 10% over the last decade. In 2007 GMOs occupied more than 143 million ha in 23 countries, with soybean, cotton, maize and rapeseed (canola) as the dominant crops (James, 2008). Regulation of gene technology varies from country to country. A few issues are fairly common, however: assessments on a case-by-

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case basis, with focus on safety, a distinction between contained use and release into the environment, and a distinction between growing and using (e.g. as raw material for food or feed incl. processing). The concept of substantial equivalence (to the non-GMO isogenic counterpart) is widely adopted as a basis for assessing a GMO (Schauzu, 2000). A GMO may become deregulated (e.g. USA), may become authorised for limited use, such as imports as food (e.g. the EU), or specific products derived from specific GMOs may become authorised (e.g. PR China). Tolerance thresholds (e.g. for non-authorised GMOs) or thresholds for labeling are in place in some but not all countries. Specific thresholds also vary and labeling thresholds may be voluntary or mandatory. As a consequence, the specific needs for detection, identification and quantitation vary significantly. Testing for (detection of) GMOs may serve several purposes. Qualitative testing may be used to discriminate between authorised and unauthorised material or use of material, to identify safe or potentially unsafe material, or for certification of purity of identity preserved material. Quantitative testing may be used to control for compliance with legal (e.g. for labeling) or contractually agreed thresholds (e.g. with respect to botanical impurity). Testing may also play a role in the safety assessment and risk management of GMOs by providing a means of tracing and if necessary retracting the GMO material, by providing data from characterisation of the GMO itself (see e.g. Collonnier et al., 2005; Hernandez et al., 2004; Taverniers et al., 2005; Windels et al., 2001, 2003) and from environmental samples (Aono et al., 2006; Messean et al., 2007; Ortiz-Garcia et al., 2005; Quist and Chapela, 2001; Saji et al., 2005). Testing may also provide tools to implement traceability of GMO derivatives in animals and humans that have consumed GM material (Chowdhury et al., 2003a,b; Nielsen et al., 2005). 2. Stakeholder roles and responsibilities The stakeholders (users of tests) and the analysts (providers of tests) play distinct roles and may have very diverse competence and interests in the GMO testing. Trust and reliability are keywords. Inaccurate test reports may be misleading, but reporting also has a translation component since the analytical competence of the stakeholders is often inferior and certainly differs from that of the analyst. Misperception and inaccuracy may lead to incorrect or suboptimal decisions by the stakeholders. The test report therefore must provide information not only about the test result but also about the uncertainties and limitations associated with the test result. This information must be presented in a form that is perceived and interpreted correctly by the stakeholder. The responsibilities of the analysts include: 1) appropriate choice of testing method, including method validation status; 2) identification of potential sources of error in reporting and translation of results; and 3) communication with the stakeholders a priori, explaining what the analyst can provide, and a posteriori, explaining what the results mean including relevant limitations. Most testing is not performed by the same people who sample the material that is subject to testing, and sampling is not covered in the present paper. Because the sampling error may be much larger than the analytical measurement uncertainty or error, the interested reader is referred to Allnutt et al., (2008); Brera et al., (2005); Bridges, (2007); Committe Europeen de Normalisation (CEN), (2006); Degrieck et al., (2005); Emslie et al., (2007); Kobilinsky and Bertheau, (2005); Paoletti et al., (2006); and Remund et al., (2001) for more information on sampling. Selecting the analytical method can be challenging when multiple optional methods are available. The best choice for one laboratory or situation is not necessarily the best for another. Minimizing cost and time are often prioritized, but this can easily be at the expense of reliability. The European Network of GMO Laboratories (ENGL) has prepared a guidance document for method acceptance (European Network of GMO Laboratories, 2008) that has been adopted by the

European Commission's Community Reference Laboratory for GM Food and Feed (CRL-GMFF). The document is biased towards quantitative real-time PCR methods. This and other related documents on the CRL-GMFF website http://gmo-crl.jrc.ec.europa.eu/guidancedocs.htm may be useful for analytical laboratories in selecting, comparing and validating methods. If a multilaboratory trial has been performed on a method, it is often easier to assess its reliability than if the validation of the method is limited to experiments performed by a single (developing) laboratory. Method harmonisation is often desirable because it facilitates transparency and comparison of results between laboratories. Norms or standards have been published or are under establishment at regional and national level in many countries (e.g. P.R. China, France, Germany, Korea, Japan, Switzerland) as well as at European and international level (Codex Alimentarius Commission, 2008; International Organization for Standardization, 2004, 2005a,b,c,d, 2006). International method harmonisation is reinforced by the publication of collaborative trial validated methods and validation reports by the CRL-GMFF (European Commission, 2009). However, there may also be reasons to choose non-harmonised methods. Cost, specificity, convenience, new information or availability of reference materials are examples. Recently, a GMO database (Dong et al., 2008) providing information on detection methods and including specific DNA sequences of inserted and flanking elements in many of the GMOs, was launched. This database, although still suffering from minor inaccuracies in sequence annotations and lacking considerable parts of inserts of several GMOs, will certainly be a useful tool for method developers and even more so for analytical laboratories and stakeholders who wish to interpret or verify test results. Contributing to update this database and possibly establish mirror versions in other countries, could be seen as a collective task for stakeholders involved in detection of GMOs. Uncertainty and error associated with a measurement result must be communicated in the test reports. The analyst must identify the potential sources of uncertainty and error, and quantify or at least establish a relative rank of the contribution by each source (Zel et al., 2007). Lack of correspondence between test results produced in different laboratories may be caused by differences in methods specificity, sensitivity or analyte recovery, resulting in bias. However, several biological factors are too often ignored by analysts and stakeholders (Holst-Jensen et al., 2006). The unit of measurement and expression of GMO content is often indicated as the mass:mass ratio of GMO:ingredient, partly because the first generation of certified GMO testing reference materials were mass based (Trapmann et al., 2002a,b). However, the analytical methods applied usually measure particular analytes such as the number of copies of DNA molecules or proteins (Holst-Jensen et al., 2003, 2006). In order to facilitate translation from DNA based to mass based concentration of GMO, it has been proposed that a conversion factor may be applied (Kuribara et al., 2002; Shindo et al., 2002). The factor may adjust for some of the most influential biological factors discussed by Holst-Jensen et al., 2006. However, the proposed conversion factor is always derived from the reference material used and does not necessarily reflect the nature of the sample material. So, if for example the reference material is derived from a hemizygous GMO while the sample is derived from a homozygous GMO, then the conversion factor will yield an estimated mass based concentration that is approx. twice the true mass based concentration. Cloned DNA was therefore early proposed as an attractive and fitfor-purpose alternative type of reference material (Block and Schwarz, 2003; Burns et al., 2006; Kuribara et al., 2002; Shindo et al., 2002; Taverniers et al., 2001, 2004). The Japanese GMO standards have applied cloned DNA reference materials since 2002 (Shindo et al., 2002). More recently, reference materials have been made available certified for their DNA analyte content (Charels et al., 2007a,b). For qualitative purposes, and when the reference material is particularly precious, it may be suitable to use whole genome amplified DNA from

A. Holst-Jensen / Biotechnology Advances 27 (2009) 1071–1082

genomic DNA samples (Roth et al., 2008). However, this type of reference material is clearly unfit for quantitation. The materials analysed in the laboratory may vary from low processed grains to highly processed composite foods or feeds. The quality and quantity of analyte extracted and purified from the samples may vary correspondingly (Cankar et al., 2006; Moreano et al., 2005). The laboratory should always report the limits of detection (LOD) and/or quantification (LOQ) together with the test result. It is, however, important to distinguish between an LOD/LOQ determined for the analytical method and the LOD/LOQ determined for the sample subject to analysis. A processed and composite product may contain very little of the target analyte compared to an unprocessed single ingredient product. Consequently, the LOD/LOQ of the sample may be 100-fold inferior to the LOD/LOQ of the method (Berdal et al., 2008; Berdal and Holst-Jensen, 2001; Holst-Jensen et al., 2003). In such cases, reporting only the method specific LOD/LOQ is misleading to the stakeholders reading the test report, and may easily give an unjustified positive impression of the reliability of a negative result. 3. Development and evolution of GMOs The first GMOs on the market were plants carrying single trait genes regulated by only a few common promoter (mainly the cauliflower mosaic virus 35S promoter) and terminator (mainly the Agrobacterium tumefaciens 3′ nos) elements (AgBios, 2008; Hemmer, 1997). The transformation vectors carried an additional marker gene construct, usually transcriptionally inactive in the plant. With few exceptions, the traits were agronomic, dominated by herbicide (glyphosate, gluphosinate and oxinyl) tolerance and insect resistance (various forms of Bacillus thuringiensis Cry proteins). For laboratories

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wishing to develop detection methods, availability of corresponding reference material was often a problem (Holst-Jensen et al., 2003). Consequently, the GMO testing methods developed were focused on detection of generic elements or features found in the GMOs such as the promoter and terminator elements (DNA based methods) or Cry proteins in their native form (Anklam et al., 2002; Hernandez et al., 2005a,b; Holst-Jensen et al., 2003; James et al., 2003; MacCormick and Griffin, 1998; Rodriguez-Lazaro et al., 2007). As the number of commercialised GMOs increased towards the turn of the millennium, it became evident that the generic (screening) methods were often unable to comply with stakeholder requirements such as authorisation and labelling regulations because identification and/or quantitation could not be achieved. With a few early exceptions such as the Flavr Savr tomato, which was ripening delayed giving enhanced shelf life, practically all commercialised GMOs express agronomic traits. Traits primarily beneficial to the consumers are still in the pipeline, despite optimistic announcements from developers over the last decade (Engel et al., 2002). Examples include rice enriched with vitamin A and altered fatty acid composition of oils from oleaceous grains (AgBios, 2008). A new trend began with the new millennium, where multiple agronomic traits were combined (gene stacking or pyramiding; see Taverniers et al., 2008 and references therein). This introduced a new challenge to the analysts. With exception for testing of single seeds or tissue derived from individual plants, none of the existing detection methods could discriminate between the combined presence of two or more single trait GMOs and stacked GMOs (Akiyama et al., 2005; Holst-Jensen et al., 2006; Taverniers et al., 2008). The number of traits that have been combined in stacked GMOs is rapidly growing, and the first 8-stack (SmartStax) is presently subject to regulatory review by

Fig. 1. Evolution of GMO detection methods and associated reference materials.

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Table 1 Examples of PCR based detection methods.a Species

Event

OECD unique ID

Tradename(s)

Construct C, gene G or event E specificb

Reference

Cotton

281-24-236 3006-210-23 GBH614 GK12 GK19 LLCOTTON25

DAS-24236-5 DAS-21023-5 BCS-GH002-5

Widestrike Widestrike

Mon531

MON-00531-6

Mon1445

MON-01445-2

Mon15985

MON-15985-7

Mon88913

MON-88913-8

SGK321 3272 59122 Bt10 Bt11

SYN-E3272-5 DAS-59122-7 – SYN-BT011-1

E E E C C E ME (QL) C E E ME (QL) C E ME (QL) C ME (QL) C E E E (QL) C

Baeumler et al. (2006), European Commission (2009) Baeumler et al. (2006), European Commission (2009) European Commission (2009) Cheng et al. (2007) Yang et al. (2005a) European Commission (2009) Kim et al. (2008b) Yang et al. (2005a) European Commission (2009), Yang et al. (2005b) European Commission s(2009), Yang et al. (2005b) Kim et al. (2008b) Lee et al. (2007) European Commission (2009) Kim et al. (2008b) Lee et al. (2007) Kim et al. (2008b) Yang et al. (2005a) European Commission (2009) European Commission (2009) European Commission (2009), Watanabe et al. (2007) Brodmann et al. (2002), Matsuoka et al. (2002), Peano et al. (2005b) Germini et al. (2004), Hernandez et al. (2003b), Matsuoka et al. (2001a), Onishi et al. (2005), Peano et al. (2005a) Rudi et al. (2003) Zimmermann et al. (2000) European Commission (2009), Rønning et al. (2003), Taverniers et al. (2005) Hernandez et al. (2005a,b), Xu et al. (2007) Bordoni et al. (2004), Ehlers et al. (1997) Garcia-Canas et al. (2004a,b) Brodmann et al. (2002), Matsuoka et al. (2002), Peano et al. (2005b),Vaïtilingom et al. (1999) Germini et al. (2004), Hernandez et al. (2003b), Matsuoka et al. (2001a), Onishi et al. (2005), Peano et al. (2005a) Rudi et al. (2003) Taverniers et al. (2005) Xu et al. (2007) Rudi et al. (2003) Matsuoka et al. (2001b)s Windels et al. (2003) Rudi et al. (2003) Matsuoka et al. (2002) European Commission (2009) Rudi et al. (2003) Hernandez et al. (2004), Matsuoka et al. (2002), Peano et al. (2005b) Germini et al. (2004), Hernandez et al. (2005a,b), Hernandez et al. (2003b),Matsuoka et al. (2001a), Nadal et al. (2006), Onishi et al. (2005), Peano et al. (2005a) European Commission (2009), Oguchi et al. (2008), Taverniers et al. (2005) Xu et al. (2007)

Maize (corn)

ACS-GH001-3

BollGardII

MC (QL)

MC E (comp) E

Bt176

ME (QL) G G (comp) C

SYN-EV176-9

MC (QL)

CBH351

DBT418 DLL25 Event 32 GA21

Starlink

DAS-59132-8 MON-00021-9

MC E ME (QL) MG C E (QL) MG C E MG C MC (QL)

E

LY038

REN-00038-3

Mir604

SYN-IR604-5

Mon802 Mon810

MON-00810-6

MaisGard

ME (QL) C E C E C C C (comp) MC (QL)

MC E

European Commission (2009) European Commission (2009) Matsuoka et al. (2002) Brodmann et al. (2002), Matsuoka et al. (2002), Peano et al. (2005b) Zimmermann et al. (1998) Germini et al.(2004), Hernandez et al. (2003b), Matsuoka et al. (2001a), Onishi et al. (2005), Peano et al. (2005a) Rudi et al. (2003) European Commission (2009), Gasparic et al. (2008), Hernandez et al. (2003a),Holck et al. (2002), Huang and Pan (2004),La Paz et al. (2007),Moreano et al. (2006),Pang et al. (2007), Salvi et al. (2008)

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Table 1 (continued) Species

Event

OECD unique ID

Mon863

MON-00863-5

Mon88017 Mon89034 NK603

MON-88017-3 MON-89034-3 MON-00603-6

Tradename(s)

Papaya Pepper Potato

Rapeseed (canola)

Rice

Soybean

55-1 (63-1) Non-authorised herbicide tolerant Bt6 EH92-527-1 RBMT21-350 SEMT15-15

Reference

ME (QL)

C C

Hernandez et al. (2005a,b), Huang and Pan (2004),Nadal et al. (2006),Xu et al. (2007) Lee et al. (2006) Onishi et al. (2005) European Commission (2009), Pan et al. (2006), Yang et al. (2005d) Xu et al. (2007) European Commission (2009) European Commission (2009) Onishi et al. (2005) European Commission (2009), Huang and Pan (2004), Nielsen et al. (2004) Huang and Pan (2004), Nadal et al. (2006) Brodmann et al. (2002), Matsuoka et al. (2002) Matsuoka et al. (2001a), Onishi et al. (2005) Rudi et al. (2003) Collonnier et al. (2005), European Commission (2009), Papazova et al. (2006) Hernandez et al. (2005a,b), Xu et al. (2007) Onishi et al. (2005) European Commission (2009), La Paz et al. (2006) Lo et al. (2007), Wall et al. (2004), Yamaguchi et al. (2006) Song et al. (2007)

C E C C E G

Rho et al. (2004), Watanabe et al. (2004) Broothaerts et al. (2007), European Commission (2009) Rho et al. (2004), Watanabe et al. (2004) Rho et al. (2004), Watanabe et al. (2004) Watanabe et al. (2004) Pribylova et al. (2006)

C MC (QL) E

Block and Schwarz (2003) Kim et al. (2007) European Commission (2009), Kim et al. (2006a), Taverniers et al. (2005), Yang et al. (2007) Demeke and Ratnayaka (2008) Wu et al. (2007) Demeke and Ratnayaka (2008) Kim et al. (2007) European Commission (2009), Wu et al. (2008) Demeke and Ratnayaka (2008) Wu et al. (2007) Wu et al. (2007) Demeke and Ratnayaka (2008) Kim et al. (2007) European Commission (2009), Wu et al. (2008) Kim et al. (2007) European Commission (2009), Yang et al. (2006) Demeke and Ratnayaka (2008) Wu et al. (2009) European Commission (2009), Mäde et al. (2006) European Commission (2009) European Commission (2009) European Commission (2009) European Commission (2009) European Commission (2009) European Commission (2009) Dainese et al. (2004) Kim et al. (2004), Lerat et al. (2005) Liu et al. (2005),Pan and Shih (2003),Peano et al. (2005b), Tani et al. (2005), Vaïtilingom et al. (1999), Vollenhofer et al. (1999),Wang and Fang (2005), Zhang et al. (2007),Zhou et al. (2007) Foti et al. (2006) Germini et al. (2004), Hernandez et al. (2003b), Peano et al. (2005a) Berdal and Holst-Jensen (2001), Burns et al. (2003), European Commission (2009),Huang and Pan (2005), Moreano et al. (2006),Pang et al. (2007),Taverniers et al. (2001),Terry and Harris (2001) Xu et al. (2007) European Commission (2009) Wall et al. (2004) Wall et al. (2004)

C MC (QL) E ME (QL) E E MC (QL) E ME (QL) C MC (QL) MC E

T25

TC1507

Construct C, gene G or event E specificb

ME (QL) MC (QL) E

DAS-01507-1

New leaf BPS-25271-9 New leaf plus New leaf Y

Non-authorised fungal resistant “ac2” Falcon GS40/90 GT73

ACS-BN-Ø1Ø-4 MON-00073-7

Ms1 Ms8

ACS-BN004-7 ACS-BN005-8

OXY-235 Rf1 Rf2 Rf3

ACS-BN011-5 ACS-BN001-4 ACS-BN002-5 ACS-BN003-6

T45

ACS-BN008-2

Topas 19/2 Bt63 LL62 LL601 A2704-12 A5547-127 DP-305423-1 DP-356043-5 GTS40-3-2

ACS-BN007-1 – ACS-OS002-5 – ACS-GM005-3 ACS-GM006-4 DP-305423-1 DP-356043-5 MON-04032-6

Liberty link

ME (QL) E MG (QL) MC (QL) E MC (QL) E E MG (QL) MC (QL) E MC (QL) E ME (QL) E C E E (QL) E E E RoundupReady

MG (QL) C

DC MC E

Squash

MON89788 CZW-3 ZW-20

MON-89788-1

ME (QL) E C C

(continued on next page)

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Table 1 (continued) Species

Event

OECD unique ID

Sugarbeet Tomato

RUR H7 FlavrSavr Huafan no1. Non-authorised virus resistant Screening marker (gene) 3′ nos Cry1A(b) Cry1A(c) Cry1Ab (Bt176) Cry1Ba Cry9C P35S

KM-000H71-4 –

Watermelon Unspecific taxon

Target version

Pat/bar Pat/pat Vip3A/vip-s

Tradename(s)

Construct C, gene G or event E specificb

Reference

E C C G

European Commission (2009) Meyer (1995) Yang et al. (2005c) Kim et al. (2008a)

Originally applied to various GMOs GM maize GM cotton GM maize CBH351 Starlink maize Various GMOs Chinese cabbage T25 maize Cotton and tobacco

Liu et al. (2007), Reiting et al. (2007), Sun et al. (2007) Danson et al. (2006) Cheng et al. (2007), Singh et al. (2007) Lutz et al. (2006) Danson et al. (2006) Orlandi et al. (2002), Quirasco et al. (2004) Cankar et al. (2008), Kunert et al. (2006),Liu et al. (2007),Xu et al. (2006a) Lim et al. (2007) Weighardt et al. (2004) Singh et al. (2008)

a

This table does not list multiplex methods where the majority of targets are typical screening targets. MC = multiplex method where this target is construct specific, MG = multiplex method where this target is gene specific, ME = multiplex method where this target is event specific. QL = method is qualitative but not quantitative. b

the US Environmental Protection Agency (EPA) and is expected to reach the market in 2010 (http://www.news.dow.com/dow_news/ corporate/2008/20080616a.htm). There is good reason to anticipate that this trend will continue. However, in stead of combining single agronomic trait genes, the future GMOs may be transformed with gene clusters for example encoding partial or complete synthetic pathways. Artificial and engineered chromosomes can be developed and allow for combining the desired genes on stable inheritable single chromosomes (Birchler et al., 2008). This can be achieved in maize by exploiting the short supernumerary B chromosome to engineer a minichromosome comprised of little more than a centromere and a recombination segment where the insert(s) can be added. This system potentially improves genetic control and traceability, and may facilitate gene dosage regulation compared to Agrobacterium mediated or biolistic transformation techniques. Some stakeholders have expressed concern about the safety and ethics of transgenics, viz. moving genes across species barriers (Nielsen, 2003; Russel and Sparrow, 2008). Gene technology may also be applied to intragenics and famigenics, i.e. to move genes within or between reproductively compatible species, essentially speeding up conventional breeding processes (Nielsen, 2003). This could raise fewer concerns, but the diversity of available and attractive trait genes would be drastically reduced. Interestingly, the first commercial GMOs were intragenics; the Flavr Savr tomato from Calgene and a similar tomato from Zeneca were modified by insertion of a truncated version of a tomato polygalacturonase gene (AgBios, 2008). Silencing of undesirable genes via intragenics is another option that may reduce safety and ethics concern (Weeks et al., 2008). Although there has been impressive progress towards the development of synthetic living organisms (SLOs) (Lartigue et al., 2007), there is still a gap between the transfer of a copied genome to a new host and introduction of a designed novel genome (Holt, 2008). This technology will undoubtedly raise a cascade of ethical and scientific debates. While contained use of SLO microorganisms may be considered a realistic scenario within 1 or 2 decades, extension to environmental release is unlikely to obtain societal acceptance in the foreseeable future. Advanced life forms similar to plants or animals are almost beyond imagination. 4. Method development and availability The tools applied for GMO testing (Fig. 1) are primarily bioassays, protein based (mainly immunological) assays and DNA based assays (mainly applying the polymerase chain reaction [PCR] technology).

However, also other technologies have been developed and these will briefly be reviewed and discussed in the following along with the dominant technologies. Testing for a single trait or GMO event may require only a simple method, whereas testing for presence of multiple events, possibly for identification and quantification may require use of combinations of methods (Christianson et al., 2008; Holst-Jensen, 2007; James et al., 2003; Waiblinger et al., 2008). Bioassays are based on the principle of exposing plant seedlings from a seed batch to e.g. a herbicide to which the GMO plants are tolerant whereas non-GMOs are susceptible. Counting the survivors and compare with the number of affected plants will give the relative GMO content in the seed lot. The advantages of bioassays are potentially low costs, few requirements for user competence and the ability of the assays to confirm the desired biological properties of the GMOs. Their drawback is that they can only be applied to certain biological properties, they usually require longer time to perform than protein and DNA based assays, and their specificity is limited. Proteins can be detected by application of immunological and physicochemical techniques. The most common protein based assays are immunoassays where the target proteins (the antigens) are detected by specific antibodies coupled to a colorimetric detection system. Application of immunoassays has evolved little (Fig. 1) compared to application of DNA based methods, and they are mainly applied as convenient and cost effective screening tools for the large scale farming industry working with single ingredient materials of low or unprocessed grade. Two main factors may partially account for the limited evolution: the costs of developing specific antibodies, and the fact that antibodies can not be described and synthesised in a simple way in contrast to oligonucleotides applied with DNA based methods. Significant improvements are mainly a shift from polyclonal to more specific monoclonal antibodies, and from laboratory based enzyme linked immunosorbent assays (ELISA) to portable lateral flow strips (LFS) that can be applied in the field, at on- and off-loading points and at storage and processing facilities. The potential for quantitation has also improved. Although multiplexing of immunological methods could be achieved using microarray formats (Ling et al., 2007), the most promising initiative towards multiplexing of immunoassays for GMO detection until now is one involving the application of coloured beads coated with the antibodies and analysed by flow cytometry (Fantozzi et al., 2007). Because the expression and translation of genes can be low, e.g. if regulated by a tissue specific promoter or affected by environmental factors, sensitivity and reliable quantifiability is often a problem with

A. Holst-Jensen / Biotechnology Advances 27 (2009) 1071–1082

immunoassays. Improved sensitivity may be achieved for example by combining the immunoassay with PCR (Allen et al., 2006). Genes regulated by constitutive promoters may also have relatively stable expression and translation levels. Quantitative application of immunoassays is an option under certain conditions but normally not in highly processed or composite products (Ermolli et al., 2006; Rodriguez-Nogales et al., 2008; Shim et al., 2007; Van den Bulcke et al., 2007; van Duijn et al., 2002). Alternative protein based methods described include use of immunomagnetic electrochemical sensors (Volpe et al., 2006), 2-dimensional gel electrophoresis (Kim et al., 2006b) and mass spectrometry (Ocana et al., 2007). DNA based assays are particularly applied by global traders, the food processing industry and law enforcement authorities. The advantages of DNA based assays are primarily specificity and sensitivity. Their drawbacks are primarily cost and competence requirements. Since the genetic modification by definition are modifications of DNA, it is evident that DNA based methods are at the highest level of metrological traceability, compared to methods that detect and measure transcriptional (RNA), translational (protein) or phenotypic (bioassays) derivatives of the modified DNA, respectively (listed in falling order of metrological traceability). Table 1 is an incomplete list of major GMOs and PCR based methods for their detection. The Table includes multiplex methods and the validation status for many of the methods is uncertain. It is strongly recommended to make further investigations to ensure that only validated methods are applied for diagnostic purposes. One of the most sensational and debated studies of the environmental impact of GMOs (Quist and Chapela, 2001) has been heavily criticized for possible use of insufficiently validated methods or methods unfit-forpurpose and lack of appropriate controls (Christou, 2002; Kaplinsky et al., 2002; Metz and Fütterer, 2002) with the consequence that the results are subject to reduced acceptance. A wide range of alternatives to conventional gel electrophoresis exist for detection and identification of the PCR amplified targets: capillary gel electrophoresis (Garcia-Canas et al., 2004b; Heide et al., 2008a,b; Nadal et al., 2006), hybridisation to labelled and coloured beads and flow cytometry (Fantozzi et al., 2008), array hybridisation (Germini et al., 2005; Hamels et al., 2009; Leimanis et al., 2006; Morisset et al., 2008a;

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Prins et al., 2008; Xu et al., 2007, 2006b), immunological detection with dipsticks (Kalogianni et al., 2006), surface plasmon resonance (Feriotto et al., 2003), various electrochemical sensors (Kumar and Kang, 2007; Sun et al., 2007, 2008; Xu et al., 2006a) and detection by liquid chromatography and mass spectrometry (Shanahan et al., 2006). While PCR is the dominating DNA based technology, alternatives such as isothermal amplification (Fukuta et al., 2004; Morisset et al., 2008a,b), direct detection of genomic DNA by electrochemical sensors (Stobiecka et al., 2007), cDNA analysis by microarray (Chen et al., 2004) and direct hybridisation of genomic DNA to microarrays (Nagarajan and De Boer, 2003; Tengs et al., 2007) have also been proposed. High throughput methods have recently been developed and are presently finding their way to routine laboratories. These tools become particularly attractive if they can be combined with automation technology. These high throughput methods are either based on combinations of one or several oligoplex PCRs followed by multiplex (pooled) identification of the amplified DNA (Hamels et al., 2009; Heide et al., 2008a,b; Leimanis et al., 2008; Mano et al., 2009; Nadal et al., 2006) or apply multiple simultaneous PCRs (Chaouachi et al., 2008; Mano et al., 2009). 5. Unauthorised GMOs One of the main challenges of today is the possible presence of unauthorised GMO or derived material in the food chain or the environment (Holst-Jensen, 2008; U.S. Department of Agriculture, 2008; United States Government Accountability Office, 2008; Vermij, 2006). There is currently a rapid increase in the number of GMO events authorised in some but not all countries (sometimes referred to as asynchronous authorisation) and the number of additional events subject to field trials potentially leading to pollen flow (Fox, 2003, 2001; Holst-Jensen, 2008; Krueger and Le Buanec, 2008; MacCormick and Griffin, 1998). Illegal intended or unintended releases for example from experiments in research laboratories may also take place, although with a very low probability (Holst-Jensen, 2008). These may pose significant risks to human and animal health and the environment. The immediate consequences are affecting international trade (European Commission, 2007; Krueger and Le Buanec, 2008; U.S. Department of Agriculture, 2008; United States Government

Fig. 2. The matrix approach. An example of a stepwise application. 1) A relational matrix correlating the performance of specific analytical modules with known GMO events is first created. It is recommended to verify experimentally that the correlation is true. If a module is known to yield a positive signal when reference material of a particular GMO event is analysed, then a ‘+’ is tabulated in the matrix (upper left). If a module is known to yield a negative signal, then a ‘−’ is tabulated. 2) Samples are analysed using either a multiplex method or multiple simplex modules for screening targets A, B, C, D and E, and construct specific targets F, G and H. The results for each sample and target is tabulated (lower left). 3) The observed patterns for each sample are matched against the relational matrix. If all targets known to yield a positive signal with reference material of a particular event are positive when the sample is tested, then the match is “Perfect” for that particular GMO event in that particular sample (upper right). If the majority but not all of the targets known to yield a positive signal with reference material are positive, then the match is “Part_missing”. If only a minority of the targets known to yield a positive signal with reference material is positive, then the match is “Mostly_missing”. If none of the targets known to yield a positive signal with reference material are positive, then the match is “Negative”. 4) To check for possible presence of GMO events not included in the relational matrix, e.g. unauthorised GMO events, the match pattern is further examined, e.g. listing all observed targets that can be referred to one or more “Perfect” matches, followed by a slash and a list of the targets that cannot be referred to one or more “Perfect” matches (lower right). The latter list of targets may be used as a basis for assessment of presence of material from an unauthorised GMO event. Notably, the matrix approach may be applied in a number of different but related ways. Usually, a software is needed to support data interpretation. Verification may be done, e.g. using event specific analytical modules. When concentrations of targets are near or below the limit of detection, then the probability of false negatives is high, and this may affect the match scoring. Care should therefore always be taken when results are interpreted.

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Accountability Office, 2008). Other possible consequences include reduced consumer trust in the industry, technology and authorities. For the incidents of unauthorised events reported so far, no evidence of significant harm to human health has been provided. Yet, these repeated incidents challenge the present regulations in many countries that require authorisation based on thorough safety assessments prior to any release or marketing. To cope with the challenge, a number of detection approaches have been developed and additional approaches are under development. The simplest include application of event specific methods for unauthorised events (Akiyama et al., 2007; Babekova et al., 2009; Grohmann and Mäde, 2009; Mäde et al., 2006). Another alternative is application of combinations of screening methods and comparing the results with tabulated data on presence/absence in individual authorised events; where patterns that do not match are indicative of presence of unathorised GMO (James et al., 2003; Mano et al., 2009; Waiblinger et al., 2008). This approach is often referred to as the matrix approach (Chaouachi et al., 2008; Hamels et al., 2009; Remacle and Bertheau, 2001) (Fig. 2) and is favoured by many laboratories as part of their general GMO screening strategy e.g. prior to application of quantitative and confirmative event specific detection. One of the drawbacks of the matrix approach is that it does not provide conclusive evidence of the presence of unauthorised GMO. It is also hampered by the uncertain performance of the screening tests with individual GMOs, and may be challenged by simultaneous presence of multiple GMOs in a sample. One strategy that may at least partially improve the applicability of the matrix approach is the use of differential quantitative screening (Cankar et al., 2008). Combining screening targets with fingerprinting approaches such as anchor PCR (Theuns et al., 2002) followed by confirmatory sequencing of the suspected amplified fragment(s) may further facilitate the detection and identification of unauthorised GMOs (Taverniers et al., pers.commun.), as exemplified by the detection of unexpected inserts and rearranged DNA in Roundup Ready soybean by (Windels et al., 2001). However, this approach requires a comprehensive fragment profile reference database and may not be suited for samples with low level presence. Finally, advanced approaches based on screening of whole genomes for foreign DNA e.g. by microarray analysis have been proposed and partly demonstrated (Nesvold et al., 2005; Tengs et al., 2007). 6. Future perspectives Current testing methods are generally too expensive and unfit for in-field applications, or lack sufficient specificity and ability to quantify the GMOs. Future technologies are expected to be faster, cheaper and allow for both multiplexing and quantitation, should be portable and yet sufficiently specific and quantitative. Achieving all these goals may be difficult. In the meantime it is likely that laboratories will be increasingly dependent on efficient screening strategies based on both multiplex protein and DNA screening, applying a matrix approach to determine the need for more specific identification and quantification methods. Semiquantitative methods may find increased popularity when formal thresholds are in place, in order to sort samples into three categories: 1) compliant with specific quantitative threshold, 2) non-compliant with specific threshold, and 3) in need for further quantitative analysis. Low level presence of events that are not authorised but have been evaluated and considered safe in other countries may become more acceptable in many countries in order to minimise negative effects on global trade. However, on a global scale the requirements for GMO testing with particular focus on presence of unauthorised GMOs are likely to increase, see e.g. (U.S. Department of Agriculture, 2008; United States Government Accountability Office, 2008). Given the limitations of the matrix approach as currently implemented, it is evident that there will be a need for developments of new analytical tools to facilitate detection of non-tolerable unauthorised events.

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