Molecular Breeding4: 509–517, 1998.© 1998Kluwer Academic Publishers. Printed in the Netherlands.

509

Monitoring of fluorescence during DNA melting as a method fordiscrimination and detection of PCR products in variety identification

Mervyn Shepherd∗ and Robert HenryCentre for Plant Conservation Genetics, Southern Cross University, Lismore, NSW 2480, Australia (∗author forcorrespondence; email. mshepher@scu.edu.au)

Received 19 February 1998; accepted in revised form 25 June 1998

Key words: cereals, DNA melting curve, genotyping, identification, PCR

Abstract

DNA melting curves of genotype-specific PCR fragments were used to differentiate between species and amongstvarieties of cereals. Melting curves were generated by ramping the temperature of PCR fragments through theirdissociation temperature in the presence of a double-stranded DNA binding dye. Genotypes were discriminatedby differences in the position and shape of the melting curve which is a function of the fragment’s sequence,length and GC content. Amplification of 5S ribosomal RNA genes generated species-specific fragments for sixof the major cereal crops. Of the 15 possible pairwise comparisons, 13 distinctions could be reliably made usingmelting curve position data. Wheat varieties were identified by the melting profiles of PCR products generatedusing microsatellite primers. DNA melting curve analysis was conveniently coupled with capillary-PCR using aLightCycler instrument to provide a rapid method of genotyping in cereals.

Introduction

Genotyping by direct analysis of DNA is importantin the diagnosis and study of genetic disorders inhumans, in the breeding of plants and animals andin the field of forensics [9]. The development ofpolymerase chain reaction (PCR) [21] based geno-typing methods has been significant in reducing thetime and effort required to detect polymorphism. Thepolymerase chain reaction enabled loci or regions ofknown polymorphism to be specified for investiga-tion. Genomic DNA clones, cDNA or ‘reverse’ geneticapproaches have been used as a source of sequenceinformation for primer design of sequence tagged sites(STS) [17]. For example, the major histocompatibilityloci in humans or intergenic spacer regions between5S ribosomal RNA (Rrn5) genes in plants are regionsof known polymorphism which have been exploitedin genotyping [4, 7]. Analysis has also been directedto repetitive chromosomal regions of the eukaryoticgenome including interspersed repeat regions such asmicrosatellites [24]. Analysis of interspersed repeti-tive DNA has indicated that they evolve rapidly [5]

and are frequently highly variable [20, 24]. Targetingrepetitive DNA sequences has therefore been usefulfor studies of organisms with low genetic diversity orwhere polymorphism is not evident in unique or lowcopy sequence DNA.

Significant advances have also occurred in meth-ods for the detection and analysis of DNA. To alarge extent, the techniques discussed so far rely onelectrophoresis to separate DNA fragments for analy-sis. Improvements in the resolution of electrophoreticmethods have been achieved by incorporating temper-ature or denaturing gradients to alter the migrationof DNA fragments according to characteristics otherthan length [e.g. 4, 13, 16]. Other detection meth-ods are available where qualitative analysis is suffi-cient, such as DNA hybridisation or an enzyme-linkedimmunosorbent assay (ELISA). These methods, likeelectrophoresis are limited in that they still requireseveral hours to perform and are complex multi-stepprocesses.

Recently, a rapid approach to the detection andanalysis of PCR products using fluorescence froma double-stranded DNA (dsDNA) binding dye and

510

melting curve analysis has been reported [29]. Melt-ing curves were generated by monitoring fluorescenceas PCR product passed through its dissociation tem-perature (Tm). A rapid decay in fluorescence wasfound to accompany the change in DNA conforma-tion from double to single strands. DNA melting peaks(the negative derivative of fluorescence plotted againsttemperature) were used to compare heterologous PCRproducts, both purified and as a mixture [18].

In this paper we describe the use of DNA meltingcurves as a method for genotyping. As an example ofthis approach for identification at the species level, aPCR based assay for variability in intergenic spacersbetween Rrn5 genes was used to differentiate betweenthe major cereal crops. The application of this tech-nique to differentiate closely related genotypes, wasdemonstrated in an analysis of microsatellite regionsin wheat varieties.

Materials and methods

Plant material, DNA extraction and PCR conditions

Rrn5 assaysGrain sample origins have been described previously[10]. Genomic DNA from one variety each of bar-ley, wheat, oat and maize was prepared using amodified CTAB method [6]. Genomic DNA wassimilarly prepared for 5 varieties each of rye andrice. The 5 varieties represent a random sample fromcommercial and breeding lines of Australian rele-vance. Primers (5′-TGGGAAGTCCTCGTGTTGCA-3′ and 3′-CGCTAGTATGGTCGTGATTT-5′) comple-mentary to conserved Rrn5 gene sequences were syn-thesised by Colorado University USA [11]. Amplifi-cation was performed on a LightCycler (Idaho Tech-nology, Idaho Falls, ID) using the following cyclingconditions: 15 s hold at 94◦C, 35 cycles of (94◦C for0 s, 56◦C for 0 s, 72◦C for 30 s) followed by a holdat 72◦C for 30 s. Each PCR reaction comprised a vol-ume of 10µl containing ca. 5 ng of template, 0.2 mMeach dNTP, 0.4 U ofTaq polymerase, 0.55µM eachprimer, 1:20 000 dilution of SYBR Green I (MolecularProbes, Eugene, OR) and reaction buffer containing2 mM MgCl2, 50 mM Tris-HCl pH 8.3 and 500µg/mlBSA (Idaho Technology). After melting curve analysis(described below), samples were recovered from thecuvettes and the entire 10µl loaded on 2% agarosegels for electrophoretic separation. Gels were stainedwith ethidium bromide (0.5µg/ml) and visualisedunder UV light.

Wheat microsatellite assaysDNA for microsatellite analysis was prepared fromseedling tissue of 5 common wheat varieties(Gamenya, Halberd, Cunningham, Molineux, Chi-nese Spring) and two Triticale (Tahara and Tiga)using the CTAB method as above. Primers basedon sequences flanking the wheat microsatellite regionWMS 44 [19] were synthesised with the following se-quences: 5′-GTTGAGCTTTTCAGTTCGGC-3′ and3′-GTCGAGTCACCTACGGTCA-5′. PCR was per-formed using a PE 9600 thermocycler (Perkin Elmer,Foster City, CA), under the following conditions: 35cycles of (96◦C for 1 min, 60◦C for 1 min and 72◦Cfor 2 min), after which samples were held at 72◦C for10 min. Each PCR reaction comprised a 25µl volumeincluding ca. 100 ng genomic template, 10 mM Tris-HCl pH 8.3, 1.5 mM MgCl2, 50 mM KCl, 200µMeach dNTP, 250 nM of each primer and 0.4 U ofTaqpolymerase.

Microsatellites were analysed by polyacrylamidegel electrophoresis (PAGE) using 10% non-denaturinggels. Gels were stained and visualised as describedabove. Capillary electrophoresis was performed usingan ABI 310 prism genetic analysis system (AppliedBiosystems Division of PE, Foster City, CA). The for-ward primer of the WMS 44 pair was synthesised witha 5′-FAM 6 label (Oligonucleotide Synthesis Service,Lismore, NSW, Australia). Samples were injected us-ing a 5 s duration onto a 20 cm capillary and separatedusing the running conditions as per the standard GSSTR POP4C software module. Fragments were sizedusing the local Southern sizing method provided withthe instrument which interpolated unknown sizes fromthe internal size standard Genescan 500 TAMRA.

Melting curve analysis

DNA melting profile analysis was carried out as de-scribed in the manual for the LightCycler apparatus.The LightCycler, is a microvolume fluorimeter in-tegrated with a thermocycler. Glass capillaries areused as reaction vessels and cuvettes allowing forrapid temperature cycling and homogeneous reactionconditions [29].

The reporter fluorophore, SYBR Green I, wasadded to microsatellite reactions after amplificationto give a final concentration of 1:25 000. No furtheraddition of SYBR Green I was required for Rrn 5samples as dye was added prior to amplification. Melt-ing curves were generated during a temperature rampbetween 60–95◦C using a transition rate of 0.1◦C/s

511

and a fluorescent signal acquisition duration of 20 ms.Data was analysed using the melting curve analysissoftware provided with the LightCycler. Fluorescence(F) versus temperature (T) and melting peak (−dF/dTvs. T) plots were derived for each sample. Each ex-periment was replicated three times. An estimate ofthe dissociation temperature (Tm) was derived fromthe major melting peak for each genotype. Where agenotype generated more than one melting peak in itsprofile, the peak which occurred at the highest tem-perature was used in further analysis. ANOVA andpost-hoc tests were performed using Statistica V4 soft-ware for Windows (Statsoft, Tulsa, OK). Preliminaryanalysis of melting curves from serial dilutions of tem-plate indicated that template could be varied 100-foldwithout significant effect upon the estimatedTm, peakbreadth or shape (data not shown).

Results

Species-specific genotyping

Identification of cereal species was accomplished us-ing PCR amplification of the Rrn5 gene family. Theuniformity of Rrn5 PCR profiles across cereal varietiesand within species has been demonstrated [11]. ‘DNAfingerprints’ generated from amplification of spacerregions for six cereals are shown in Figure 1. Ampli-fication from all species generated multiple products,those of rye being similar in size and ca. 420 bpin length. The size range of products from the otherspecies was greater, maize for example showed threemajor products of ca. 270, 310 and 710 bp.

To investigate whether the polymorphism in Rrn5spacer regions of different cereals was detectable inmelting curves, DNA melting profile analysis was per-formed. DNA melting curves (not shown) and meltingpeaks were generated for each of the six cereal species(Figure 2). The peaks represent the temperatures atwhich the maximal fluorescence decay occurs andmay be indicative of the dissociation temperature ofthe amplified product(s). Peaks were evident for eachspecies within the range 79 to 88◦C. Maize and ryeprofiles produced two maxima. PCR products fromoats, barley, rice and wheat templates melted in asingle transition giving rise to a single maximum.Reactions containing cereal genomic DNA could bedistinguished from controls without template DNA asno amplification products were observed in the con-trol under these conditions (Figure 2). Controls with

Figure 1. PCR-amplified product from six cereal species. Se-quences were amplified on a LightCycler using consensus primersto conserved regions of the 5S ribosomal RNA locus in cereal plants.10µl of each sample was subjected to 2% agarose gel electrophore-sis and visualised under UV light after EtBr staining. O, oat; R, rice;M, maize; W, wheat; Ry, rye; B, barley; Std., 50 bp molecular sizeladder; Neg., control with no template.

Temperature (C)

–dF

/dT

0

0.2

0.4

0.6

0.8

1

1.2

75 80 85 90

Oat

Rice

Maize

Wheat

Rye

Barley

Control (no template)

Figure 2. −dF/dT curves for Rrn5 amplification products from sixcereal species. Fluorescence data was acquired with a 20 ms du-ration fluorescence acquisition setting, an instrument loading ofeight samples during a temperature ramp from 60–95◦C at 0.1◦C/s.−dF/dT vs. T curves were derived from melting curves, points toaverage setting of 20 and no baseline subtraction.

template for each cereal but withoutTaq were alsoanalysed. A small broad peak was observed in the ricecontrol with a maximum at 82◦C (data not shown).This peak did not correspond with the peak due toPCR product. The peak evident in the rice control wasthought to be due to a slightly higher concentrationof genomic template in this sample than the controlsfor the other species. Controls using 100 ng templateper reaction, rather than 5 ng template per reaction re-vealed broad peaks of low magnitude within the range80–90◦C for all species. This indicated there was the

512

Temperature (C)

–dF

/dT

0

0.1

0.2

0.3

0.4

0.5

0.6

75 80 85 90

Wheat replicate 1

Wheat replicate 2

Wheat replicate 3

A

Temperature (C)

–dF

/dT

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

75 80 85 90

Rye replicate 1Rye replicate 2Rye replicate 3 B

Figure 3. −dF/dT curves for three replicates of wheat (A) and rye(B). Data was generated using the conditions described for Figure 2.

potential to confound peaks generated from PCR prod-uct with peaks due to genomic template where hightemplate concentrations were used.

Three replicates of each sample were analysed toassess reproducibility of curve generation. First deriv-ative curves for the most variable species, wheat andleast variable species, rye are shown in Figure 3A andB. The position of the peak maxima were reproduciblealthough the degree of error associated with the esti-mate of theTm varied between species. An averageTmestimate was derived for each species, based on thepeak maximum for three replicates. Where more thanone peak occurred in a melting profile, the peak whichoccurred at the highest temperature was chosen forfurther analysis. The greater variability inTm estimatefor wheat was thought to result from the broadnessof its curve leading to lower precision in determiningthe peak maximum. AverageTm estimates for eachspecies were compared (1-way ANOVA, 5 df, F=74.2,α < 0.01). Multiple comparison testing basedon averageTm indicated that of the 15 possible pair-wise comparisons, 13 could be reliably made usingcurve positional data (Table 1). Wheat could not bedistinguished from oats and maize could not be dis-

Table 1. Comparisons ofTm estimatesderived from melting curves for six cerealcrops.

Species 1Tm ( ◦C)± SD 2LSD

Barley 83.39±0.1

Rice 84.36±0.4

Wheat 85.47±0.6 a

Oat 85.70±0.2 a

Maize 86.92±0.1 b

Rye 87.08±0.0 b

1Average of three replicates2Least sig-nificance difference test; species sharingthe same letter are not significantly dif-ferent atα = 0.05.

tinguished from rye using positional information. Itwas possible, however, to distinguish these pairs bya visual comparison of their curves. The shape of thecurve and relative magnitudes of the peak maxima ofprofiles were useful in making these distinctions. Todistinguish maize from rye for example, the maximumat the lower temperature for maize was always greateror equal in magnitude to the maximum at the highertemperature. Rye’s higher temperature maximum wasalways greater than the low temperature maximum.The symmetry of the oat curve could be used todistinguish it from the negatively skewed wheat curve.

To investigate the variability in melting profilesacross varieties within a species, melting peaks weregenerated for five varieties of rye and rice (Figure 4Aand B). Each rice variety produced a single positivelyskewed peak. In contrast, the rye varieties melted intwo transitions giving rise to a minor peak at ca. 82◦Cand a major symmetrical peak at ca. 86◦C. Symmetryor skewness of melting curves was therefore a speciescharacteristic.Tm estimates were determined for themajor peaks in each species. The variability of theTmestimate for rice (average 84.1± 0.29◦C) was similarto that for rye (average 86.6± 0.35◦C).

Sub-species genotyping

The utility of DNA melting curves to differentiategenotypes of highly related individuals was examinedin four hexaploid wheat (Gamenya, Halberd, Mo-lineux and Cunningham) and two Triticale (wheat/ryehybrids) varieties (Tiga and Tahara). PCR productsgenerated from primers designed to amplify the wheatmicrosatellite 44 (WMS44) region were investigated[19]. Polyacrylamide gel electrophoresis indicatedmultiple fragments ranging in size from ca. 92 to

513

Temperature (C)

–dF

/dT

-0.1

0.1

0.3

0.5

0.7

0.9

1.1

75 80 85 90

CKS

ASI

CES

DWA

ERC A

Temperatue (C)

–dF

/dT

-0.05

0.05

0.15

0.25

0.35

0.45

0.55

75 80 85 90

CEN

HAR

YR73

M7

DAW B

Figure 4. −dF/dT curves for 5 varieties each of rye (A) and rice(B). Data was generated using conditions described for Figure 2except that melt curves were generated with instrument loadings ofseven. Rye varieties: CKS, CK South Australia; ASI, Asia MinorExpedition; CES, Ceske; DWA, Dwarf Petkus R3; ERC, EuropeanRye Collection. Rice varieties: CEN, Century Patna; HAR, Hara;YR73, YR73; M7, M7; DAW, Dawn.

445 bp in the six varieties tested (Figure 5). Multipleproduct generation is not unusual for primers designedto amplify microsatellite regions as there may be mul-tiple priming sites in complex hexaploid genomes likewheat [2, 19]. Capillary electrophoresis was used toaccurately size fragments for the six varieties and areference variety, Chinese Spring, from which themicrosatellite region was sequenced. A stutter peaktypical in the amplification of a microsatellite regionwas evident in the reference variety at the expectedsize of 182 bp [19]. Based on this locus, there ap-peared to be four genotypes distinguishable among theseven varieties, three fragments of different sizes anda null allele in the Triticale varieties (Table 2).

DNA melting profile analysis was performed onall varieties except Chinese Spring. A single DNAmelting peak for each genotype indicated the multi-ple products melted in a single transition (Figure 6).The peak for each variety could be distinguished fromcontrol reactions without template DNA. Amplifica-

Figure 5. Variability in PCR fragments amplified from WMS44 mi-crosatellites from five wheat cultivars and two wheat/rye hybrids.10µl of each PCR reaction was subjected to 10% PAGE and visu-alised under UV light after staining with EtBr. M, molecular sizestandards consisting of a 50 bp ladder, 250 bp and 500 bp standardsare indicated; Gam, Gamenya; Hal, Halberd; Cun, Cunningham;Tig, Tiga; Tah, Tahara; Mol, Molineux. Tig and Tah are wheat/ryehybrids.

Table 2. Microsatellite amplificationproduct sizes in five wheat and twowheat/rye hybrids. Product sizedetermined by capillary electrophoresis.Tiga and Tahara are wheat/rye hybrids.

Cultivar Fragment size (bp)

Tiga absent

Tahara absent

Gamenya 172

Halberd 172

Cunningham 186

Molineux 186

Chinese Spring 182

tion products could also be distinguished from controlreactions which contained noTaq. Melting peaks ofcontrols containing noTaq were broad, spanning ca.10◦C, small in magnitude and with a maximum at85.4◦C (data not shown). The peaks evident in thesecontrols did not appear to contribute significantly tothe curves when PCR products were generated (Fig-ure 6).

Small differences in peak position resulting fromthe melting of PCR products were evident amongst

514

Temperature (C)

–dF

/dT

0

0.15

0.3

0.45

0.6

65 70 75 80 85 90 95

Gamenya

Cunningham

Halberd

Tiga

Tahara

Molineux

Control (no template)

Figure 6. −dF/dT vs. T curves for the PCR fragments amplifiedfrom WMS44 microsatellites from the four wheat cultivars and thetwo wheat/rye hybrids in Figure 5. Conditions used during meltingcurve analysis were as for Figure 4 except that the fluorescenceacquisition duration was 3 ms.

Table 3. Comparison ofTm estimates derivedfrom melting curves of WMS 44 primers forfour wheat and two Triticale varieties.

Cultivar 1Tm ( ◦C)± SD 2LSD

Tahara 82.4±0.5 a

Tiga 82.8±0.3 ab

Gamenya 83.3±0.4 bc

Halberd 83.5±0.1 bcd

Cunningham 83.8±0.5 cde

Molineux 84.3±0.7 e

1Average of three replicates.2Least signifi-cant difference test; varieties sharing the sameletter are not significantly different atα =0.05.

some wheat varieties. DNA melting profile analy-sis was replicated to investigate the reproducibilityof curve generation and whether reliable distinctionswere possible between the varieties (1-way ANOVA,5 df, α < 0.01). Post-hoc tests based on averageTmindicated that amongst the wheat, Molineux could bedistinguished from Gamenya and Halberd (Table 3).

Discussion

The application of molecular genetic approaches in thebreeding, production and commercialisation of cerealcrops is hampered by the complexity and longevityof analysis techniques. DNA melting curves were in-vestigated as a rapid, simple method for identificationof cereal species and varieties within the species. Foridentification purposes, DNA melting analysis wasperformed on fragment pools differing in their intrin-sic properties sufficiently to distinguish one genotype

from another. We have shown that DNA meltingprofile analysis of Rrn5 spacers and microsatellite se-quences can discriminate between cereal species andamongst varieties respectively.

In plants, the Rrn5 genes exist as a multigenefamily clustered in two to three groups on differ-ent chromosomes (reviewed in [1]). They consist ofwell-conserved transcribed gene sequences of 120nucleotides separated by non-transcribed spacer re-gions. The spacer may differ in length and in thenumber of copies of repeat units between and withinspecies [8, 11, 15]. Amplification of intergenic spac-ers using primers complementary to conserved genesequences produces characteristic and highly repro-ducible patterns [11, 12]. In some cereals, highlyuniform PCR profiles occur across varieties and maybe used for cereal identification [11]. In this report wehave demonstrated that DNA melting profiles of Rrn5spacer regions are also uniform across varieties forsome cereals. Furthermore, the position of the melt-ing peaks can be used to distinguish between cerealspecies, providing an objective and reproducible mea-sure. Where the position of the curve was insufficientto distinguish a species, the shape provided furtherinformation. It should be possible to mathematicallyanalyse melting curves to provide a more objectiveanalysis than the visual comparisons used in this study.One approach may be to identify approximately lin-ear segments in the melting curve and describe it bypiecewise linear regression.

The high levels of polymorphism of many mi-crosatellites regions in cereal crops make them valu-able genetic markers for varietal identification. Allelicvariation in microsatellites is typically assessed by dif-ferences in fragment length. We have demonstratedthat DNA melting profile analysis can be used todistinguish amongst wheat varieties based on the prod-ucts amplified from primers designed to amplify amicrosatellite region. Although fewer discriminationswere possible with melt curve analysis than elec-trophoresis, it should be possible to screen primerpairs to find informative primer, variety combina-tions. The increasing availability of microsatellites formany cereal crops may make this a useful strategy forvarietal identification where rapid analysis is required.

The shape and position of the DNA melting curveis a function of the base composition, sequence andfragment length [18]. Studies on a mixture of two het-erogeneous sequences from humans has also shownthat a melting curve is a composite of individualcurves where several products are melted simultane-

515

ously [18]. Empirical determination of the meltingmaximum for each purified sequence indicated theydiffered by ca. 2.5◦C, both maxima being distin-guishable in the composite curve. In this study, toprovide an objective method of comparing curves, aTm was estimated from the melting curve for eachgenotype. ThisTm was an average for the multi-ple fragments of three replicates. For long fragments(>300 bp), it may also represent an average for sev-eral melting domains.Tm estimates were highly re-producible and alone were sufficient to discriminatesome genotypes. The smallest distinguishable differ-ence usingTm estimates and replication was 0.8◦C,less than the 2◦C reported for unreplicated analysis[18]. Using the equation for the theoretical determi-nation of Tm for long DNA molecules (Tm= 81.5–16.6(log10[Na+])+0.41(%G+C)×(600/N) whereN ischain length) [22], it can be shown that 0.8◦C repre-sents an ca. 2% change in GC content for any givenfragment length and salt concentration. Therefore rel-atively small changes in GC content can potentially bedetected by this approach.

Multiple PCR fragments were amplified in bothof the PCR systems examined in this study. Ampli-fication of the Rrn5 spacer regions generated severalfragments in each of the species tested, the rangeof fragment sizes varying considerably between somespecies. The complexity of the melting profile in termsof resolvable transitions, however, did not correspondwith the range or number of fragments generated. Al-though all species generated multiple PCR products,only the melting profiles of rye and maize showed tworesolvable maxima. This suggested that some frag-ments from these species differed sufficiently in theirproperties to be resolved by melting curve analysis.Maize products probably shared greater sequence ho-mology and GC content than those of rye, as the peakmaxima for both species were similar. This was de-spite the much greater difference in fragment sizes formaize than rye. In contrast, the products from oat,rice, wheat and barley generated curves with a singlemaximum indicating the fragments shared substantialsequence homology and GC content. The asymmetryof the curves for wheat and rice suggests the productsfrom these species do not share the same degree ofhomology as those from barley and oats with sym-metrical curves. Hence the fragment properties of basecomposition and sequence appeared more important indetermining curve shape and position than length.

Amplification from microsatellite primers, likeRrn5 spacer regions, resulted in DNA profiles with

multiple fragments. The fragment pools generatedfrom these primers melted in a single transition. Thesesingle peaks, spanned a large temperature range com-pared with those reported for single purified products[18]. This suggested that although the fragments dif-fered in length (ca. 5-fold), they probably had a highdegree of sequence homology and were not resolvableby the analysis method. Therefore, as with Rrn5 frag-ment analysis, fragment length was not necessarilyimportant inTm determination.

Given the small differences in allele size of mi-crosatellite regions and the limited impact length poly-morphism had onTm, it was surprising that meltingcurve analysis could detect differences in theTm ofamplified microsatellite regions. One explanation forthis effect may be that melting curves represented apool of fragments, the varietal differences in meltingcurves perhaps arising due to differences in the poolcomposition. Fragment pools biased towards frag-ments with lowerTm perhaps causing a downwardshift in the melting peaks compared with fragmentpools with higherTm.

As an identification method, melting curve analy-sis utilised a pool of sequences that were characteristicfor a particular genotype. The melting profiles of sam-ples of unknown identity could be compared withprofiles of known samples analysed concurrently orto a library of stored profiles. Identification by DNAmelting profiling used a single set of reaction parame-ters and relied upon inherent DNA sequence polymor-phism to differentiate genotypes. This distinguishesthis demonstration from a previous report where twoPCR products, generated under different conditionswere differentiated by product melting curve analysis[18]. Varietal identification or even zygosity deter-mination may be possible by melting curve analysiswith PCR systems which amplify a single loci. Asuccessful assay would depend upon the availabilityof primers giving rise to fragment species that dif-fer sufficiently in Tm (0.8◦C using replicates). Theutility of microsatellite primers in this respect is thesubject of ongoing investigation. Heteroduplex analy-sis is one method which may be used in zygositydetermination but is restricted to analysis of a het-erozygote or requires a mixing of fragments frommultiple genotypes.

Several approaches to genotyping based on PCRdo not require specific genome sequence informa-tion to detect polymorphism. The randomly ampli-fied polymorphic DNA (RAPD) technique generatesfragments from primers of arbitrary sequence which

516

anneal at multiple sites within a DNA template [27,28]. A second technique, amplified fragment lengthpolymorphism (AFLP) [26], like RAPD uses PCR toamplify selected pools of fragments generated fromrestriction enzyme digestion of a DNA template. Fre-quently, these methods give rise to a ‘DNA finger-print’, an electrophoretic pattern of DNA fragmentswhich characterise a particular genotype [3]. Althoughit has not been tested, RAPD or AFLP in conjunc-tion with melting curve analysis may provide a rapidscreening method in organisms with little or no se-quence knowledge. This approach is unlikely to revealas much polymorphism as screening by size separa-tion techniques, nonetheless, for some identificationpurposes, a sacrifice in information content may beacceptable for decreased assay time.

The effort required to develop DNA melting curveassays may also be significantly less than that re-quired for strategies based on fluorescently labelledoligonucleotides [e.g. 14, 25]. These techniques re-quire synthesis of labelled probes or primers for eachpolymorphism surveyed. The effort and cost to de-velop such oligonucleotides may be prohibitive insituations where the volume of screening is low. Afurther advantage of the melting curve approach togenotyping is the general substrate requirement. AnydsDNA which melts in a detectable temperature rangeand can be distinguished from artifactual amplificationshould be suitable.

The major attraction of DNA melting profile analy-sis for genotyping is the potential for rapid identifica-tion. Although melting curve analysis could be per-formed with a standard thermocycler and a fluorimeterwith temperature control, a LightCycler apparatusgave significant savings in time and sample handling.For example, the total time required for cycling anddetection for the Rrn5 assay was ca. 30 min, comparedwith four hours using a conventional thermocycler andgel electrophoresis. Rapid analysis techniques are par-ticularly important in process control of agricultureand food products. DNA melting profile analysis, inconjunction with Rrn5 spacer fingerprinting, may pro-vide a rapid method for determining the compositionof food or agricultural products which are an admix-ture of several species. Melting profile analysis, inconjunction with microsatellites may be useful in theconfirmation of variety or varietal purity in commer-cial transactions of cereal crops or in the collectionof endpoint royalties for plant breeders rights. Theattributes of rapid analysis time, simplicity, generalsubstrate requirement and minimal sample handling

suggest melting curve analysis will be useful in abroad range of agricultural genotyping applications.

Acknowledgements

The authors wish to acknowledge the Grains Researchand Development Corporation, Australian ResearchCouncil and Southern Cross University for financialsupport, Steve Garland and Anne McLauchlan for as-sistance during the course of these investigations. Wealso wish to thank Harpal Saini and two anonymousreviewers for helpful comments during the preparationof the manuscript.

References

1. Appels R, Honeycutt RL: rDNA:Evolution over a billionyears. In: Dutta SK (ed) DNA Systematics, pp. 81–136. CRCPress, Boca Raton, FL (1986).

2. Bryan GJ, Collins AJ, Stephenson P, Orry A, Smith JB,Gale MD: Isolation and characterisation of microsatellitesfrom hexaploid bread wheat. Theor Appl Genet 94: 557–563(1997).

3. Caetano-Anolles G, Bassam BJ, Gresshoff PM: DNA ampli-fication fingerprinting: A strategy for genome analysis. PlantMol Biol Rep. 9: 294–307 (1991).

4. Fischer SG, Lerman LS: DNA fragments differing by singlebase-pair substitutions are separated in denaturing gradientgels: correspondence with melting theory. Proc Natl Acad SciUSA 80: 1579–1583 (1983).

5. Flavell RB: Chromosomal DNA sequences and their organi-sation. In: Parthier B, Boulter D (eds) Encyclopaedia of PlantPhysiology, New Series, pp. 46–74. Springer-Verlag, Berlin(1982).

6. Graham GC, Mayers P, Henry RJ: A simplified method forthe preparation of fungal genomic DNA for PCR and RAPDanalysis. Biotechniques 16: 48–50 (1994).

7. Gyllensten UB, Erlich HA: Generation of single-strandedDNA by the polymerase chain reaction and its application todirect sequencing of the HLA-DQA locus. Proc Natl Acad SciUSA 85: 7652–7656 (1988).

8. Hembleden V, Werts D: Sequence organisation and putativeregulatory elements in the 5S rRNA genes of two higherplants (Vigna radiataandMatthila incana). Gene 62: 165–169(1988).

9. Kirby LT: Genetic Principles. In: DNA Fingerprinting: AnIntroduction, pp. 1–5. Stockton Press, New York (1990).

10. Ko HL, Henry RJ: Specific 5S ribosomal RNA primers forplant species identification in admixtures. Plant Mol Biol Rep14: 33–46 (1996).

11. Ko HL, Henry RJ, Graham GC, Fox GP, Chadbone DA,Haak IC: Identification of cereals using the polymerase chainreaction. J Cereal Sci 19: 101–106 (1994).

12. Kolchinsky A, Kanazin V, Yakovleva E, Gazumyan A, Kole C,Ananiev E: 5S-RNA genes of barley are located on the secondchromosome. Theor Appl Genet 80: 333–336 (1990).

13. Lessa EP: Rapid surveying of DNA sequence variation innatural populations. Mol Biol Evol 9: 323–330 (1992).

517

14. Livak KJ, Flood SAJ, Marmaro J, Giusti W, Deetz K: Oligonu-cleotides with fluorescent dyes at opposite ends provide aquenched probe system useful for detecting PCR productsand nucleic acid hybridization. PCR Meth Appl 4: 357–362(1995).

15. Moran G, Smith D, Bell JC, Appels R: The 5S RNA genes inPinus radiataand the spacer region as a probe for relationshipsbetweenPinusspecies. Plant Syst Evol 183: 209–221 (1992).

16. Myers RM, Maniatis T, Lerman LS: Detection and local-ization of single base changes by denaturing gradient gelelectrophoresis. Meth Enzymol 155: 501–527 (1987).

17. Olson M, Hood L, Cantor C, Botstein D: A common languagefor physical mapping of the human genome. Science 245:1434–1435 (1989).

18. Ririe KM, Rasmussen RP, Wittwer CT: Product differentia-tion by analysis of DNA melting curves during the polymerasechain reaction. Anal Biochem 245: 154–160 (1997).

19. Roder MS, Plaschke J, Konig SU: Abundance, variation andchromosomal location of microsatellites in wheat. Mol GenGenet 246: 327–333 (1995).

20. Saghai-Maroof MA, Biyashev RM, Yang GP, Zhang Q, AllardRW: Extraordinary polymorphic microsatellite DNA in bar-ley: species diversity, chromosomal locations, and populationdynamics. Proc Natl Acad Sci USA 91: 5466–5470 (1994).

21. Saiki RK, Scharf S, Faloona F, Mullis KB, Horn GT, ErlichHA, Arnheim N: Enzymatic amplification of beta-globin ge-nomic sequences and restriction site analysis for diagnosis ofsickle cell anemia. Science 230: 1350–1354 (1985).

22. Sambrook J, Fritsch EF, Maniatis T: Molecular Cloning: ALaboratory Manual. Cold Spring Harbor Laboratory Press,Cold Spring Harbor, NY (1989).

23. Tautz D, Renz M: Simple sequences are ubiquitous repeti-tive components of eukaryotic genomes. Nucl Acids Res 12:4127–4137 (1984).

24. Tautz D, Trick M, Dover GA: Cryptic simplicity in DNAis a major source of genetic variation. Nature 322: 652–656(1986).

25. Tyagi S, Kramer FR: Molecular Beacons: Probes that flu-oresce upon hybridisation. Nature Biotechnol 14: 303–308(1996).

26. Vos P, Hogers R, Bleeker M, Reijans M, van de Lee T, HornesM, Frijters A, Pot J, Peleman J, Kuiper M, Zabeau M: AFLP:a new technique for DNA fingerprinting. Nucl Acids Res 23:4407–4414 (1995).

27. Welsh J, McClelland M: Fingerprinting genomes using PCRwith arbitrary primers. Nucl Acids Res 18: 7213–7218 (1990).

28. Williams JG, Kubelik AR, Livak KJ, Rafalski A, Tingey SV:DNA polymorphisms amplified by arbitrary primers are usefulas genetic markers. Nucl Acids Res 18: 6531–6535 (1990).

29. Wittwer CT, Ririe KM, Andrew RV, David DA, Gundry RA,Balis UJ: The LightCycler: a microvolume multisample flu-orimeter with rapid temperature control. Biotechniques 22:176–181 (1997).