Trends in Food Science & Technology 21 (2010) 442e454

Review

* Corresponding author.**Corresponding author.

0924-2244/$ - see front matter � 2010 Elsevier Ltd. All rights reserved.doi:10.1016/j.tifs.2010.06.004

Advances in

detection of

genetically

engineered crops by

multiplex polymerase

chain reaction

methods

Hari K. Shrestha, Kae-Kang

Hwu** and Men-Chi Chang*

Department of Agronomy, National Taiwan

University, No. 1, Section 4, Roosevelt Road,

Taipei 106, Taiwan, ROC (Tel.: D886 2 3366 4771;fax: D886 2 3366 4761; e-mails: khwu@ntu.edu.tw;

menchi@ntu.edu.tw)

In the past, several multiplex PCR (mPCR) methods have been

developed and validated for precise and accurate monitoring,

tracing and regulation of genetically Engineered (GE) food,

feed and seed. However, a complete and updated review of

mPCR is still lacking. Recently, mPCR methods have been cou-

pled with different qualitative and/or quantitative methods,

such as real-time PCR, micro-array, multiplex ligase chain re-

action, biosensors, and microfluidic device to raise the detec-

tion limit of accuracy, sensitivity and reproducibility. In this

paper, we provide thorough information and discussion of

mPCR methods including their development, application and

challenges for GE crops analysis.

IntroductionThe innovation of genetic engineering techniques has

led to the development of many GE crops with improved

insect resistance, herbicide tolerance, quality parametersand production value (AGBIOS, 2009). More than 20 GMcrops have been approved and produced for various com-mercial purposes in the world (AGBIOS, 2009). The rapiddevelopment of GE crops has created public concern aboutpotential ecological contamination and the awareness ofconsumers’ rights. Thus, to meet the ever-increasing de-mand of GE crop detection, high-accuracy, low-cost,high-throughput and quick standardized assays are needed.Besides, more than 40 countries have started to adoptimportant legislative packages for food safety and qualitycontrol of GE crops (Dong et al., 2008; Nikolic, Taski-Ajdukovic, Jevtic, & Marinkovic, 2009). The labelingthreshold differs from country to country, for example theEU, Korea and Taiwan/Japan require mandatory labelingof GE food or products when GE content exceeds 0.9,3.0 and 5.0% respectively in tested sample(s) (Gruere &Rao, 2007). To resolve this issue, international harmoniza-tion and standardization of thresholds and measures for thedetection of GE crops is also needed.

GE crop analysis is usually carried out qualitatively and/or quantitatively according to the corresponding foreignDNA or protein. For the protein-based method, ELISA (en-zyme-linked immunosorbent assays) and LFT/PST (lateral-flow strip/protein strip tests) are commonly applied forGEO detection. On the other hand, for the DNA-basedmethod either specific transgene in the GE crops or specificDNA elements in its associated construct are monitored.Protein-based methods are easy and cost-effective, andbased on real-life samples (Brett, Chambers, Huang, &Morgan, 1999; Stave, 2002; Van de Bulcke et al., 2007).However, protein-based methods still have some disadvan-tages: (1) the expression levels of target proteins can be var-ied among different plant tissues, which hamper the properquantification, (2) these methods are better used for screen-ing purposes, and (3) protein degradation due to its thermo-stability is another drawback (Brett et al., 1999; Van deBulcke et al., 2007). Hence, for detection of low levels ofGE material, PCR remains the more sensitive method(Matsuoka et al., 2000; Van de Bulcke et al., 2007; Table 1).To date, the website of Genetically modified organism de-tection method database (GMDD, 2009), intended to man-age GE information about GEO detection, contains morethan 400 pairs of PCR primers for detection of variousGE lines, 30 protein-based methods, 43 endogenous refer-ence GE lines from 17 taxa (Dong et al., 2008). This

Table 1. Summary of recent advances of mPCR methods for the detection, identification and quantification of GE crops.

Species GE events PCR system Target gene PCR strategy Sensitivity (%) Endo gene Reference

Maize Bt176 rt-PCR (1and2 plex)

P-35S S (Ql/Qt) 0.8e1.20 ltp Alary et al., 2002Soybean RRS lecMaize Bt11, Bt176;

MON810, T25mrt-PCR P-35S S (Qt) 0.01/0.05e1.0 zein Hohne et al., 2002

Canola GT73, HCN92/28,MS8/RF3,Oxy235; RRS;Event176, Bt11,MON810, T14/25

mPCR tml, gox247, oxy,barnase, pat; beta-lactin, cp4-epsps,P-35S, T-NOS; cry1A(b), pat, bar

S, G (Ql) e cru James, Schmidt, Wall,Green, & Masri, 2003Soybean 0.1 lec

Maize e ivr

Maize MON810, Bt11,Bt176, T25, GA21,DBT418, CBH351,35S, Amp, Nos, IPC

MQDA- PCR(12plex)

Pl/P-35S, P-35S/IVS2, PEPC/Cry1A(b), PAT/35S-T,m-epsps, Cry1A(c),cry9C

C-E (Qt) 0.1e2 Hmga (Zm) Rudi et al.. 2003

Maize MON810, Bt11,Bt176, GA21

mPCR P-35S/hsp70, IVS2/PAT, Cry1A(b), OTP/m-epsps; e35S/CTP4

S (Ql) 0.25/- zein Germini et al., 2004

Soybean RRS lecMaize MON810, NK603 mrt-PCR

(3plex)Pl/P-35S, pl/rac1-P E (Ql/Qt) 0.5/0.1 zein

ivrHuang & Pan, 2004

Maize MON810, GA21 mrt-PCR P-35S C (Ql/Qt) e e Akiyama et al., 2005MON810xGA21 mPCR

Maize Bt176, Bt11,MON810

mPCR (2 & 3plex) P-35S S (Ql) 0.1/�; 0.5/� zein Forte et al., 2005

Soybean RRS T-NOS lecMaize NK603, Event176,

T25, GA21,MON863,MON810, TC1507,Bt11

mPCR (9 plex) hsp70i/cp4-epsps,P-PEPC/Cry1A(b),pat/pUC18, OTP/m-epsps, cry3Bb/tahsp17, hsp70i/cry1A(b), cry1Fa2/30-polyA, adh1-1SiVS5/cry1A(b)

C (Ql) 0.25/� ssIIb Onishi et al., 2005

Soybean RRS mPCRþ LDR-UA Cp4-epsps/T-NOS C (Ql) 1.0/�; 0.4/� lec Peano et al., 2005Maize MON810, Bt176,

Bt11, GA21P-35S/hsp70, P-35S/bar, P-35S/IVS2,ract-P/OTP,

zein

Maize Bt176, Bt11; RRS mPCR þ OM 18SrRNA, Cry1A(b),bar, NOS; 18SrRNA,35S/CTP, 35S/pl,NOS, cp4-epsps,CP4.CTP, NOS/cp4-epsps

S, G, C,E e Ivr; Xu et al., 2005Soybean lec

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Maize Bt176, Bt11,MON810, GA21,CBH351, T25

mPCR þ OM P/T-35S, P/T-NOS, P-FMV35S, T-Nptll,bar, pat, CaMV-CP,epsps, Rbc1, Fbp,Gox, Barnase,Barstar, Cry1A(b, c),Cry9C, 18SrRNA

S, G (Ql) 1 zein Xu et al., 2006

Soybean RRS 0.5 lec

Canola OXY235, MS8xRF3,GT73; T45

mPCR bar pat S (Ql) 0.05/<0.1 FatA Demeke & Ratnayaka, 2008

Maize Event176, Bt11,TC1507, NK603,T25, MON863,MON810, GA21,NK603xMON810,NK603x-MON863x-MON810

mPCR (9 plexþ) Bar/pl, pat/30-J, T-NOS/pl, pat/pUC18 V, T-tahsp17/pl, Cry1A(b)/pl, m-epsps/P-ractin

E-C (Ql) 0.25/-(0.1-ISTA) zein Shrestha et al., 2008

Maize TC1507, MON863,MON810, T25,NK603, GA21, Bt11,Bt176

mPCR þ FCGE PHI89999a/IR-CRY1F, P-35S/PAT,Pl/P-35S, Pl/R-ACT,P-CDPK/Cry1A (b)

E-C (Ql/Qt) 0.1/- hmga Heide et al., 2008b

Maize Bt176, Bt11 mPCR P-35S S (Ql) 0.1/� zein Nikolic et al., 2008Soybean RRS (3 plex) T-NOS lecMaize; Soybean;Sugarbeet

Bt11; RRS; H7-1 mPCR (3 plex) P-35S; T-NOS S (Ql) 0.1/- lec Nikolic et al., 2009

Maize Bt11, Event176,GA21, MON810,MON863, NK603,T25 and TC1507

mPCR þ mLCR PACT, P-35S, T-NOS, NPTII,BAR,CP4-EPSPS,PAT, lel, ssIIb,18SrRNA1

S-G (Ql) 0.05e0.5 ssIIb; lel; 18SrRNA1 Mano et al., 2009

Soybean RRS (7 plex, and 3 plex)Cotton Maize Rice MON531,

MON15985,Widerstrike; Bt176;Bt rice, GM rice withferritin gene

mPCR (12 plex) aadA, bar, hpt, nptll,pat

G (Ql) e - Randhawa et al., 2009

Note: For PCR-based GE crops detection strategies, the capital letter S, G, C and E indicates screening, gene, construct and event-s ecific strategy respectively; QI, qualitative analysis; Qt,qualitative analysis of GE crops assay; mPCR, multiplex PCR; Rt-PCR: real-time PCR; mrt-PCR, multiplex real-time PCR; LDR-UA, lig tion detection reaction-universal array; OM, oligonucle-otide array; MQDA, multiplex quantitative DNA array based PCR method; FCGE, Fluorescence capillary gel electrophoresis.

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database provides worthy and reliable information to foodproducers, retailers, consumers and the authorities(Nikolic et al., 2009; Rudi, Rud, & Holck, 2003).

In General, PCR-based transgene detection in GE cropscan be categorized by different strategies, including screen-ing, gene-, construct-, and event-specific detection asshown in Fig. 1. The screening test usually is associatedwith common DNA elements of the construct inserted,such as the cauliflower mosaic virus 35S-promoter or thenos-terminator of Agrobacterium tumefaciens and/or select-able antibiotic resistant genes (nptII and hyg) (Aarts, Rie, &Kok, 2002; Holst-Jensen, Roanning, Lovseth, & Berdal,2003). This method is suitable for the initial screening,but not for the following traceability of specific GE lines(Fig. 1; Table 1). The same DNA elements may be foundin different GE lines and this poses a significant risk ofyielding false results (Yang et al., 2007; Chaouachi et al.,2008). Gene- and construct- specific PCR tend to showhigher specificity but their application for tracing specifictransgenic event could be problematic (Aarts et al., 2002;Grohmann, Brunen-Nieweler, Nemeth, & Waiblienger,2009; Holst-Jensen et al., 2003; Fig. 1; Table 1). Most com-mercialized GE varieties usually share similar genetic ele-ments in constructs that are integrated in differentchromosome regions with variable copy numbers. For in-stance, the presence of the Bacillus thuringiensis endotoxinCryIA(b) gene may derive from Bt176, Bt11 or MON810 orother GE maize lines (AGBIOS, 2009). Currently, to ensurehigh level of transgene expression and confirmation of thedesired agronomical traits before GE crops are producedcommercially, it would be necessary to select for specifictransgenic lines or events. Therefore, a sequence-specificPCR primer is needed to monitor or trace the presence ofa specific transgenic event in a GE cultivar. The primer de-sign should include the border sequences between the junc-tions of the gene construct and the 50 or 30 flanking genomicDNA sequences. The uniqueness of these sequences is im-portant in providing event specificity and allows for the dis-crimination of different transgenic lines. The variant of

Fig. 1. Four main PCR strategies including screening (1), gene-specific (2), cotion. The construct map of GE crop includes left and right side of host DNA, wspecific characters, such as insect resistant, herbicide tolerance, etc. The arro

to event-specific stra

PCR method, multiplex PCR (mPCR) that is based on si-multaneous amplification of multiple event-specific se-quences with more than one pair of primer sets representsa reliable, effective, low-cost and time-saving standardizedassay for GE crop detection (Chamberlain, Gibbs, Ranier,Nguyen, & Caskey, 1988; Chaouachi et al., 2008; Forteet al., 2005; Hernandez et al., 2004; Nikolic et al., 2009;Shrestha, Hwu, Wang, Liu, & Chang, 2008). However,the mPCR methods developed based on this strategy maynot be good enough for different characterization of thesamples either from a gene-stacked transgenic plant or dif-ferent transgenic event of mixed cultivars (Akiyama et al.,2005; Demeke & Ratnayaka, 2008; Holst-Jensen et al.,2003; Shrestha et al., 2008; Yang et al., 2007; Table 1).

Recently, several mPCR related methods have been de-veloped and used in GE crops detection (Table 1). In thefollowing section, we try to review comprehensively theprogress of mPCR methods for GE crops detection andqualification/quantification, including their advantages,limitations, advances, applications and major challenges.

Advantages and limitations of mPCR methodsCompared to standard simplex PCR systems, mPCR has

several advantages:

1. With multiple sequence- or event-specific primer pairs,mPCR can increase sample number and analysis sam-ples in a cost- and time-effective way. (Chaouachiet al., 2008; Shrestha et al., 2008; Zhang, Becquet,Li, & Zhang, 2003; Zhang et al., 2008).

2. In mPCR, internal control is designed together with thecorresponding targeted gene to monitor the absence orverify the presence of target DNA (Shrestha et al.,2008).

3. In mPCR, the quality of DNA template may be evalu-ated more effectively than simplex PCR. In particular,weaker band intensities or signals for long bands thanfor short tend to appear in degraded templates.

nstruct-specific (3) and event-specific (4) that used in GE crops detec-here modified target gene construct is inserted randomly for achievingw indicates the increase of GE crops detection specificity as screeningtegy was used.

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4. As master mix, excluding primers could be preparedeasily to reduce the possibility of pipetting error(QIAGEN, 2008; Shrestha et al., 2008).

The total number of primer pairs that can be used inmPCR is limited (Rompler et al., 2006). As the numberof primers increases, the interaction effects also increaseand may result in the formation of amplification artifacts,such as primer-dimers, truncated DNA fragments, non-spe-cific bands or an absence of bands (Chaouachi et al., 2008;Randhawa, Chhabra, & Singh, 2009; Shrestha et al., 2008).Moreover, it’s not easy to adjust the annealing temperatureof PCR to be appropriate for all primer sets. To overcomethis drawback, careful design of primer pairs and pre-opti-mizing the master mix (with Hot Start) may provide thebest reproducibility and robustness (Randhawa et al.,2009; Rudi et al., 2003). Also, it is worthy to mentionthat a high-throughput multiplex assay based on the exist-ing SNPlex method for GM crop detection was developed(Chaouachi et al., 2008). This assay avoids certain pitfallsof mPCR methods and demonstrated its high specificityand sensitivity.

Factors affecting mPCRMany factors can affect specificity and sensitivity of

mPCR, such as quality of DNA, sample processing, equip-ment and chemicals, mPCR methods (including effectiveconcentration of the corresponding primers, PCR bufferconcentration, ratio between MgCl2 and dNTPs concentra-tions, annealing temperatures, and amount of Taq DNApolymerase and template DNA, primer designing, optimiz-ing and validation (Lipp et al., 2005; Markoulatos,Siafakas, & Moncany, 2002; QIAGEN, 2008; Shresthaet al., 2008). Among these factors, it is especially importantto adjust the ratio between primer and template correctly.Once the primer-to-template ratio is too high, the formationof primer-dimers will appear. On the other hand, if theprimer-to-template ratio is too low, the target productwon’t be amplified exponentially (Henegariu, Heerema,Dlouhy, Vance, & Vogt, 1997).

Qualitative and quantitative analysis of GE crops us-ing mPCR or multiplex real-time PCR (mRT-PCR)method

For GE crop identification, a qualitative screening test isusually carried out first. To further verify the exact amountof GE crop in samples, the competitive or real-time PCRwill then be applied. Formerly, very few (2e3) targets thatwere located at screen-specific region (s) were used in qual-itative mPCR analysis, and then this system was adjusted todetect several GE samples (Table 1; Fig. 2). Amaximum of 8GE maize lines and one endogenous gene (T25, GA21,TC1507, MON863, MON810, NK603, Event176, Bt11 andendogenous gene) have been included in qualitative mPCRanalysis with various detection methods, such as construct-specific mPCR (Onishi et al. (2005) as well as event- and

construct-specific mPCR (Heide, Heir, & Holck, 2008a;Heide, Dromtrop, Rudi, Heir, & Holc, 2008b; Shresthaet al., 2008) (Fig. 3).

A real-time PCR reaction has been applied successfully inroutine analysis for the accurate quantification of GE foods,such as Roundup Ready (RR) soybean (Huang& Pan, 2005),maize flours, semolina, pet food, biscuit powders, corn flakesand starch (Hohne, Shantisi, & Meyer, 2002; Permingeat,Reggiardo, & Vallejos, 2002). The validity and quality ofthe mRT-PCR test depends on the specificity and sensitivityof the particular method and sequential use of GeneticallyEngineered Organisms (GEO) sampling test flow from fieldto laboratory (Figs. 1 and 3). This method has been adoptedto monitor several GE crops, including the GE Canola events(OXY235 and T45), wheat and barley (Demeke &Ratnayaka, 2008; Table 1). More recently, mRT-PCR hasalso been used as a sensitive and promising tool for stackedtrait GEdetection (Akiyama et al., 2005).However, the quan-titative analyses of GE crops by mRT-PCR were restrictedbecause of the limited number of potential dyes and the over-laps of fluorescence spectra.

mPCRs coupled with different methodsTo raise the accuracy, sensitivity, and reproducibility for

GE crops detection and for automatic and high through-put,mPCRs have been coupled with other methods.

mPCR coupled with capillary gel electrophoresis(mPCR-CGE)

A number of multiplex PCR assays have been describedfor GEO analysis using agarose gel electrophoresis andsubsequent analysis with UV or laser-induced fluorescence(LIF) detection (Garcia-Canas et al., 2004; Onishi et al.,2005; Shrestha et al., 2008, Virginia, Ramon, &Alejandro, 2002, Table 1; Fig. 4). By incorporating flores-cence dyes-labeled forward primers in its reaction, the sys-tem allows identification of amplicons differing in size byas few as 2 bp (Nadal, Coll, La Paz, Esteve, & Pla,2006). This method can be automated and displayed witha high through-put. As an example, the sensitivity level(LOD, limit of detection) of eight different GE maizes(Bt11 (30), GA21, Bt11 (internal), Bt11 (50), CBH351,Mon810 and NK603 and maize endogenous control) anal-ysis by mPCR-CGE was 0.1% in a pentaplex system(Nadal et al., 2006). Moreover, mPCR-CGE assay for de-tection of cotton species and 5 events of GE cotton (Boll-gard, Bollgard II, RR, 3006-210-23, and 281-24-236) washighly specific (<1.7% of false classification rate), withLOD values of 0.1% for each event (Nadal, Esteve, &Pla, 2009). The assay was even more economic throughcombination of two rounds of multiplex PCR to allow si-multaneous detection of 6 cotton and 5 maize targets(two endogenous genes and 9 GE events). Similarly,mPCR can also be used for detection of eight GE maizeevents TC1507, Bt11, MON863, MON810, T25, NK603,

Fig. 2. Stepwise procedure for the sampling and testing of GE crops and GE derived food, feed and seed materials.

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GA21, Bt176 and one endogenous gene hmga. The detec-tion limit was approximately 0.1% (Heide et al., 2008a).

mPCR coupled with micro-arrayTowards developing a high through-put detection system

for GEO, the multiplex-PCR method was combined with theextensive screening capability of micro-array to increase itssensitivity and feasibility. This technology is conducted by am-plification ofGEO-specificDNA sequences bymPCR, and theresulting template was labeled and hybridized on a predefinedmicro-array. The array chip contains multiple capture probes(oligonucleotidesor smallDNAfragments) for specific targets,including screen-specific gene, species-specific referencegene, gene-specific gene, construct-specific gene, event-spe-cific gene, internal and negative control genes. After washing,image capturing and data analysis, a pure GEO or putativeGEO mixed sample can be confirmed (Fig. 4). This approachinherits the sensitivity of PCR and takes advantage of micro-

array which allows detection of non-limited number ofGEOs. The mPCR-oligonucleotide or low-density DNA mi-cro-array has been used to monitor various target genes in dif-ferent GE crops, including soybean (GTS40-3-2), maize(MON810, NK603, GA21), canola (T45, MS1/RF1), andrice (SCK) (Peano et al., 2005; Rudi et al., 2003; Xu et al.,2006, 2005; Zhou, Zhang, You, & Wu, 2008). This method ishighly rapid, reliable, and efficient and reduces the chancesof false positive and negative results (Xu et al., 2005). For ex-ample, thismethod candetectmore than95%ofcurrently com-mercializedGEplantswith aLODof0.5%for soybean and1%for maize (Xu et al., 2006).

Multiplex ligase chain reaction coupled with mPCRmethod (mPCR-mLCR)

To avoid the shortcomings of mPCR in GEO detection,another promising approach is to combine a post-PCRtechnique, ligase chain reaction (LCR) with PCR

Fig. 3. Stepwise development of mPCR method for GE crops detection. The procedure is divided into 4 main parts, where the first shows the PCRdesigning, the second step shows the optimization of PCR, the third step explains about the method validation and the last step includes the testing

and determination of GE crops in real field samples for its quality control.

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Fig. 4. Schematic diagram of mPCR (PCR photo adjusted with hypothetical data) coupled with different technologies to increase sensitivity and spec-ificity of GE crops detection. These technologies include 1. laser-induced image capturing by slab or capillary gel electrophoresis, 2. micro-array, 3.multiple ligase chain reaction (LCR), 4. biosensor, 5. PCR-microfluidic device (continuous-flow method). In each method, “I, II, III, IV and V” denotes

experimental process, assay procedure, advantages, disadvantages, and source of references respectively.

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(Wiedmann et al., 1994). This ligation based detectionmethod was originally developed for allelic-specific dis-crimination of single base mutation in human genetic dis-ease gene. Using thermostable DNA ligase, likeAmpligase, two adjacent oligoprobes that match perfectlyto a complementary region of target gene can be enzy-matic-catalyzed to join together and amplified exponen-tially. The ligated amplicons can be either detecteddirectly by gel electrophoresis or served as probes for ar-ray hybridization (Fig. 4). Usually, for simultaneous detec-tion of recombinant DNA segments (e.g., promoters, traitgenes, and terminators) of GE crops, these target DNAregions were amplified by mPCR first and the productswere then subjected to mLCR as template DNAs. In thefollow-up mLCR stem, four LCR probes were designedin a region between the forward and reverse primer ofeach corresponding PCR amplicon. Among the fourprobes, one so called discriminating probe was labeledwith a fluorescent dye, 6-carboxyfluorescein (FAM) atthe 50 end, one normal (50-OH) and the other two commonprobes were with phosphate at the 50-end. One of the com-mon probes was designed to hybridize adjacent to the dis-criminating probe enabling ligation. This heptaplex andtriplex mPCR-mLCR experiments were carried out forscreening of GE maize and soybean (Mano et al., 2009).

Even with a simulated DNA mixture of eight GE maizelines, the recombinant DNA segments can be detected atthe concentration of 0.05% (v/v) of each line. Recently,Holck, Dromtorp, and Heir (2009) also developed a quanti-tative and sensitive nine-plex ligation-dependent probeamplification method (GEO-MLPA) for event-specific de-tection of maize by gel electrophoresis. The detectionrange can be as low as 0e2% in food or feed. Besides,it is worthy to mention that to reduce cross hybridizationand the non-specific background of micro-array and en-hanced detection sensitivity and specificity, the mPCR-mLCR method was also adapted for array hybridization(Peano et al., 2005). In the universal array approach, thediscriminating probe was fluorescent labeled. However;the common probe was designed to have a 30-tag sequence(corresponding Zip code) that can complement with a spe-cific target gene (Zip code) on the array. Compared to thestudy of Xu et al. (2006), the LOD was 0.4% for tracingRound Ready soybean and Bt maize using mPCR com-bined with ligation detection reaction-universal array(mPCR-LDR-UA) technology (Peano et al., 2005).

DNA biosensor coupled with biotinlyted mPCRDNA biosensors are defined as analytical devices incor-

porating a single-stranded oligonucleotide (probe)

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intimately associated with or integrated within a physiolog-ical transducer or transducing microsystem which may bemade in optical, electrochemical, thermometric, piezoelec-tric, magnetic or micromechanical device (Xu, Huang, Ye,Ying, & Li, 2009a; Fig. 4). DNA biosensors are able toproduce either discrete or continuous measurable signals,which are proportional to the concentration of complemen-tary (target) DNA sequence. Recently, a DNA sensingmethod based on Surface Plasmon Resonance (SPR) trans-duction has been established. This method mainly dependsupon the specific hybridization between nucleic acid targetsequences in solution and the complementary probe immo-bilized on the sensing surface (Wang, Minunni, Tombelli,& Mascini, 2004). The targeted DNA will be denaturedfirst then incubate with small oligonucleotides to preventre-annealing. The optimization of the pre-treated targetDNA allows the successful detection of amplified DNAwith SPR biosensor. Biosensor-based methodologies andSPR have been used for characterization of GE crops bybio-specific interaction analysis (BIA). For example, thedetection of Roundup Ready soybean was performed usingthis method (Feriotto, Borgatti, Mischiati, Bianchi, &Gambari, 2002). In addition, an SPR-based BIA protocolwas designed and tested for quantitative determinationsof GE maize (Feriotto, Gardenchi, Bianchi, & Gambari,2003). The efficiency of this method in discriminating var-ious amounts of Bt-176 maize containing materials wascomparable with quantitative real-time PCR assays andmore reliable than southern blot analysis (Feriotto et al.,2003). Furthermore, this assay could be repeated severaltimes on the sensor chip after washing and regenerationof the flow cells. In general, DNA biosensors is an emerg-ing field with enormous potentiality, however, it is difficultto commercialize due to the issues of inherent instability(Deisingh & Badrie, 2005). For GE crops detection, thismethod offers an advantage in its specificity, speed, repro-ducibility and low cost (Wang et al., 2004). However, de-velopment of an ultra sensitive DNA detection method isnecessary since the DNA concentration of the biologicalsample is usually very low for the biosensor’s require-ments (Xu et al., 2009a).

Continuous-flow PCR reaction microfluidicsTo be fast, efficient, automatic and high through-put

screening of GEO samples, PCR amplification conductedby the conventional PCR machine with heating/coolingblock must overcome the “ramping time” problem. Also,to minimize the biological and chemical analytical deviceof PCR, the intervention of micro-electro-mechanical sys-tems (MEMS) technology would be a good choice(Zhang, Xu, Ma, & Zheng, 2006; Fig. 4). Recently, a com-pact continuous-flow PCR microfluidic device for rapidanalysis of Roundup Ready soybean has been developedin a singlet or duplex system (Li, Xing, & Zhang, 2009).This device consists of three pieces of copper and transpar-ent polytetrafluoreothylene capillary tube. By adjustment of

flow rate, the P35S and Tnos sequences could be success-fully amplified within 9 min. The minimal DNA contentin tested sample which could be accessed by this devicewas 0.005 ng mL�1 (Li et al., 2009). In addition, thismethod could be coupled with the intercalating dyeSYBR Green I and the melting curve analysis of the ampli-fied products, where limit of detection of the DNA samplewas observed to be 0.01 ng mL�1 (Li et al., 2009). This sys-tem represents a more interesting system in terms of costand time saving, if adopted in mPCR. However, it is stillnecessary to develop and validate methods using GE %for GE DNA analysis in a sample of material(s).

Future challenges in GE crops detection systemAs more types of GE cultivars are developed and the

number of GE foods on the market increases rapidly, thedetection of GE crops faces some increasing challenges.

Monitoring or tracing GE crops with stacked traitsIf more than one gene from another organism has been

transferred, the created GE crops have stacked genes ortraits and are called a gene stacked event (for example:MON603 � MON810; and MON603 � MON810 �MON863). The gene stacked event in GE crops can bedone either by genetic engineering, such as transformationwith a multiple gene expression cassette, co-transformationwith various expression plasmids, retransformation of anexisting transgenic line or cross two or more GE transgeniclines by conventional breeding. During the last decadea considerable number of stacked traits have been pro-duced, and molecular methods have been developed andvalidated for the detection of GE crop impurities(Taverniers, Papazova, Bertheau, Loose, & Holst-Jensen,2008; AGBIOS, 2009). Not like event-specific GE lines,stacked GEOs usually do not have unique flanking se-quences. Thus, a single PCR could not tell the intrinsicstacked GE traits from the non-intended stacking with mix-ture of several independent GE lines. In past, mPCR ormRT-PCR has been developed for detection of stackedtraits by single seed-based DNA analysis or sub-samplingapproach (Akiyama et al., 2005; Demeke & Ratnayaka,2008; Shrestha et al., 2008; Table 1). However; to monitormultiplex-events of GE traits in each seed or sub-sample istime-consuming and labor-intensive even conventionalmPCR allows for simultaneous detection of many genesin single reaction with various specific primer sets. Re-cently, to overcome the aforementioned problems, a univer-sal primer multiplex PCR (UP-mPCR) was used for rapidscreening of simultaneous detection of five target se-quences (NOS, 35S, Bt11 event, GA21 event, and IVR)in maize Bt11�GA21 (Xu et al., 2009b). This approachtakes advantage of primer design that a common sequenceat 50-end was added to all primers for the amplification oftarget or endogenous gene. After short round of mPCR am-plification of recombinant DNA segments in stacked maize,the universal primers were added for further amplification

451H.K. Shrestha et al. / Trends in Food Science & Technology 21 (2010) 442e454

of previous PCR template. The detection limit of thismethod was 0.1% (approximates to 38 haploid genomecopies). However; to be efficient, cost-saving and automaticin monitoring and quantification of stacked trait in GEcrops remains as a challenge and complicated process com-pared with routine checks of GE crops.

Detecting unknown (new) or unexpected DNAsequence in GE crops

Access to correct DNA sequence information, validat-ing methods and reference materials is a fundamental pre-requisite for setting up an effective strategy of GE cropdetection (Querci, Van den Bulcke, �Zel, Van den Eede,& Broll, 2009). An increasing problem is the inability todetect modified or unknown DNA sequence in new GEcrops at very low level. The most appropriate sources ofinformation on the inserted DNA sequences are the notifi-cation dossier for authorization/regulation of the use of GEcrops. Therefore, the sequence information should beavailable for general users immediately after approval ofthe GE lines. Besides, for detecting unknown sequencesthere are approaches such as differential PCR, AnchorPCR, finger printing, DNA array (Tilling array) systems.However, simple, cost and time-effective methods are stillnecessary.

Standardization of detection procedures, includingavailability of reference materials

To compare detection results of different GE crops, theharmonization of procedures and the availability and choiceof appropriate reference materials are additional key factorsfor reliability and accuracy (Chaouachi et al., 2007;Trapmann, Corbisier, Schimmel, & Emons, 2009). Certifiedreference materials (CRMs) are not available for all GEcrops with necessary levels from 0 to 100%. Therefore, de-velopment of CRMs capable of a wide range of applica-tions for all GE crops is important. Moreover, for GEcrops analysis by mPCR, genomic reference materials(GRMs) or plasmid reference materials (PRMs) should bedeveloped and validated further.

Time consuming and costly lengthy validationprocesses

The regular verification of method performance is neces-sary for up-to-date GE crop monitoring. However; methodvalidation is a time consuming and costly process. The in-creasing number of GE crops represents a heavy demandfor the modification of existing methods and developmentof new one. With existing inter-laboratory method valida-tion, the cost of the validation method is high. Therefore,cost minimization is important.

Inhibition effect or unequal amplification of PCRproducts in GE crop detection

In an equal proportion of GE mixture, the multiple spe-cific-events of GE lines could be easily detected in a sample.

However, it may not be detected in an unequally propor-tioned sample, such as a real field sample and artificiallyprepared heterogeneous samples. There could be a chanceto inhibit the amplification of another less abundant targetby more concentrated GE event(s) in mPCR (Onishiet al., 2005). The inhibiting or unequal amplification canlead to false negative results in GE crop detection. There-fore, when optimized mPCR methods are used, it is neces-sary to confirm the presence of samples one by one, first ina uniplex PCR and compare the result with mPCR(Henegariu et al., 1997).

Lack of up-to-date database and information of GEcrops or derived products

In view of the rapidly increasing number of GE cropsand their diversity in world markets, information and de-tection methods should be updated, upgraded, modifiedand/or invented regularly. For this issue, a reliable centraldatabase regarding GE crop development, detection,identification and quantification is necessary on a globalscale to provide comprehensive information to the largenumber of stakeholders. There are some websites withuseful information about GE crops, such as AGBIOS(2009), BATS (2003), GMDD (2009), NCBI (2009),GMO-Compass (2009), CRL (2009) who hold validationreports, national standards and international standards.However, it is always necessary to update usefulsequences of inserted genes. Without this information,the detection of GE crops will ultimately be hamperedby difficulties in PCR primers design and reactionoptimization.

ConclusionsA range of PCR methods have been applied in GE crop

detection, aimed at screen-, gene-, construct- and/or event-specific target regions. Among them, event-specificmethods based on amplification of the DNA sequence lo-cated at the junction between insert and genomic regionare more popular and specific for GE crop detection. Mul-tiplex-PCR is particularly attractive to GE crops analysts,stockholders and retailers as it can reduce cost and effec-tive analysis of more samples. More interestingly, to raiseits accuracy, sensitivity, reproducibility in detection limitof GE crops as well as processing speed, mPCR can becoupled with capillary gel electrophoresis, micro-array,multiplex ligase chain reaction, biotinalyted PCR, real-time PCR, and continuous-flow PCR microfluidics devices(Fig. 4). All these new designs have enabled mPCR to be-come more competent and given it the potential to analyzecomplicated gene stacked GE crops in the future. How-ever, the availability of mPCR methods for GE crop detec-tion still relies on an up-to-date of database for primersdesign, unknown sequences, and harmonization andstandardization of the related procedures and referencematerials.

452 H.K. Shrestha et al. / Trends in Food Science & Technology 21 (2010) 442e454

Abbreviations

18SrRNA consensus region of 18SrRNA gene in plantgenomes

BAR phosphinothricin-N ¼ acetyltransferase gene fromStreptomyces hygroscopicus

Cry9C delta endotoxin genes from B. thuringiensis; CryA(b), synthetic delta endotoxin gene derived fromBacillus thuringiensis

epsps gene encodes the enzyme 5-enolpyruvylshikimate-3-phosphate synthase isolated from Agrobacteriumtumefaciens strain CP4 (CP4 EPSPS), for herbi-cide tolerance

FatA fatty acyl-ACP thieosterase geneFCGE fluorescence capillary gel electrophoresisGE genetically engineeredhsp70-int. 1 the no. 1 intron sequence from maize hsp70

gene (heat-shock protein)intron no. 9 from maize phosphoenolpyruvate carboxylase

(PEPC) geneIVS2-int. the no. 2 intron sequence from alcohol dehydro-

genase (adh 1-I)IVS6-int. the no. 6 intron sequence from maize alcohol de-

hydrogenase 1 gene (adh 1-IS)Lel Lectin I gene of Glycine maxltp maize gene encoding lipid transfer proteinM-EPSPS point mutated epsps gene derived from maize

Multi-genes

potato Udp-Glucose Pyrophosphorylase, Ugpasetomato Lycopersicon esculentum) species-specific gene,

LAT52maize alcohol dehydrogenase, Adh1soybean sugar-binding proteins, lectinsugarbeet Glutamine Synthetase, Gln synthaserapeseed acetyl-coenzyme A carboxylase from Brassica

napus, BnACCg8cotton stearoyl-acyl carrier protein desaturase, Sadmedocago accbarley ( y-hordein), Rice (ppi-PPF)sunflower helwheat Wx-D1sPCR suppression PCRSNP single nucleotide polymorphismNPTII neomycin phosphotransferase II geneNptII nopaline synthase gene from A. tumefaciensOTP optimized transit peptide sequenceP-35S Cauliflower mosaic virus P-35SromoterP4-AS1 a promoter that contains four copies of the AS-1

element and a part from the cauliflower mosaic vi-rus (CaMV)

PACT promoter region of rice actin genePAT phosphinotricin-N-acetyltransferase gene derived

from Streptomyces viridochromogenes

P-CDPKpollen-specific calcium-dependent protein kinase(CDPK) promoter from maize

P-PEPC green tissue-specific phosphoenolpyruvate car-boxylase (PEPC) promoter from maize P-riceact1with OTP rice Actin promoter containing the no.1 intron optimized transit peptide sequence (OTP)

P-Zmubi promoter Zea mays ubiquitinSSIIb starch synthase Iib gene of Zea maysT-NOS terminator region of nopaline synthase derived

from Rhizobium radiobacterT-NOS transcription terminator from Agrobacterium tu-

mefaciens nopaline synthase geneT-ORF25 PolyA 30 polyadenylation signal from ORF25

(Agrobacteriu tumefaciens)UNG Amperase uracil N-glycosylase genew/v weight/volumewt. CAB 50-terminal untranslated region of wheat chloro-

phyll a/b binding the expression of target genescru storage protein in seeds of Brassica napus, the 12S

globulin cruciferin

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