HRM test cattle

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Assessing SYBR® Green I-based high-resolution melting assays for accurate single
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nucleotide polymorphisms genotyping in cattle
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Paula Nicolini1*
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Corresponding author
E-mail: [email protected]
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Ana Inés Trujillo2
E-mail: [email protected]
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María Pia Grignola2
E-mail: [email protected]
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Mariana Carriquiry2
E-mail: [email protected]
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Ana Meikle1
E-mail: [email protected]
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República. Lasplaces 1550. C.P. 11600. Montevideo, Uruguay.
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de la República. Avda. Garzón 780. Montevideo, Uruguay.
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Laboratorio de Técnicas Nucleares. Facultad de Veterinaria. Universidad de la
Departamento de Producción Animal y Pasturas. Facultad de Agronomía. Universidad
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Abstract
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Background. Single nucleotide polymorphisms (SNPs) at candidate genes have gained
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widespread interest as markers for phenotype-genotype association studies in cattle.
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Traditional techniques for SNP genotyping are either labor intensive, expensive or have
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low sensitivity. High-resolution melting (HRM) analysis is an innovative technique for
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genotyping and has many advantages including cost-effectiveness, speed, and accuracy.
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The aim of the current study was to develop and validate real time PCR-HRM assays
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using the non-saturating dye SYBR® Green I for the genotyping of known Class 1 SNPs
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at the bovine candidate genes insulin-like growth factor 1 (IGF-1, T>C), leptin (LEP,
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C>T), neuropeptide Y (NPY, G>A), and insulin (INS, C>T) in samples of Holstein (n =
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40) and Aberdeen Angus (n = 40) breeds.
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Results. For each SNP, this method allowed the amplification of a short PCR product
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(range 90- to 142-bp) and the genotyping of the SNP of interest in a closed-tube system
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in a 1 h 45 min-single assay. Three different melting curves corresponding to the three
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expected genotypes per SNP were readily distinguished based on melting temperature
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(Tm) shifts or shape differences of the melting curves. Observed Tm differences between
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alternative homozygotes were consistent with predicted ones, and were in agreement
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with Tm differences expected for Class 1 SNPs (about 0.5 ºC). High genotyping
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accuracy was verified by 100% concordance of HRM genotypes with results from
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independent genotyping methods, demonstrating that SYBR® Green I is highly suitable
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for HRM-based SNP genotyping. These results respond to a series of elements
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considered in the present work, such as type of SNPs involved, the carefull protocol
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followed up during the development and analysis of the assays, and the use of a high
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sensitive instrument.
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Conclusions. Real time PCR with SYBR® Green I and HRM analysis provides a
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valuable alternative for rapid and accurate SNP genotyping, and can potentially be
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applied to SNPs other than those studied in the present work. Its application will
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facilitate high-throughput analysis and population-level studies in dairy and beef cattle.
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Key words: real time PCR-HRM analysis, SYBR® Green I, SNP genotyping, cattle
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Background
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Population-based association studies are of major importance as a means of identifying
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genetic variants that contribute to complex phenotypic expression [1]. Over the last
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decade, single nucleotide polymorphisms (SNPs) in candidate genes have gained
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widespread interest as markers for phenotype-genotype association studies in dairy and
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beef cattle [2-10].
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Insulin-like growth factor 1 (IGF-1), leptin (LEP), neuropeptide Y (NPY), and insulin
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(INS) genes are known to play an important role in metabolism, growth and
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reproduction in cattle [11-14]. Due to their physiological roles, polymorphisms within
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these genes have the potential to determine differences in economically important traits.
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Moreover, SNPs in the IGF-1, LEP and NPY genes have been shown to be associated
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with several productive and reproductive traits in beef and dairy cattle [2-6,8-10, 15,16].
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These SNPs have been genotyped by conventional PCR-based methods, such as: SSCP
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(single-strand conformational polymorphism [17]), RFLP (restriction fragment length
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polymorphism [2,18]), RSI (restriction site insertion)-RFLP [19], and allele specific
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PCR (PASA [19]). In the case of the INS gene, although several SNPs have been
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reported in cattle [20], to the authors concern no information is available to date neither
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about genotyping methods of the SNPs identified at this bovine gene, nor about the
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effect of any reported INS SNP on any trait of interest in cattle. All the above mentioned
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methods include a step after PCR that requires the removal of the PCR product, which
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often takes hours to perform and increases the risk of crossover contamination of PCR
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products [21]. In addition, although sequencing can be considered the gold standard
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genotyping technique, it is generally unsuitable for routine genotyping because it is
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expensive to run [22]. Finally, SNP-chip platforms are the most cost-effective
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methodologies for genome-wide association studies, the development of rapid, cost-
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effective, and accurate methods for the genotyping of individual SNPs is of major
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importance, as it is the single-marker assays that are most useful in the latter stages of
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research and for diagnosis applications [23].
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High-resolution melting (HRM) analysis using fluorescent DNA dyes coupled with real
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time PCR is a relatively new method for SNP genotyping [24]. Briefly, the fluorescence
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emitted by the release of the dye from the amplicon is monitored as DNA samples
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transition from double- to single-stranded state with increasing temperature. Samples
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are then characterized by their dissociation behaviour and melting temperature (Tm),
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depending on their GC content, length, and sequence [25]. As a closed-tube system, the
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major advantage of HRM analysis over gel electrophoresis is that it eliminates the post
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PCR handling, thereby minimizing contamination concerns and reducing costs, turn-
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around time, labour, and technical expertise [26]. Furthermore, HRM has proven to be
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highly accurate for mutation scanning and genotyping applications, being suitable for
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medium to high-throughput analysis, with results comparable to conventional
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genotyping techniques [27,28].
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Saturating dyes such as LC Green® [24] and SYTO9® [29] have become the preferred
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choice for HRM analysis since, used at high concentrations (which means greater
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saturation of the double-stranded DNA sample and higher fidelity of fluorescent
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signals), are not inhibitory to PCR and are presumed to reduce dye redistribution effects
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during strand dissociation [24], which affects HRM sensitivity [30]. On the other hand,
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the use of the non-saturating dye SYBR® Green I has been discouraged for HRM-based
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SNP genotyping [24,31]. due to dye-dependent PCR inhibition [29,32], dye
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redistribution [30], and selective detection of amplicons in multiplex PCR [33].
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Nevertheless, with the recent development of new generation HRM instruments,
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SYBR® Green I has been successfully used for SNP genotyping [34-38].
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The aim of the present study was to assess the capability of real time PCR-HRM
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analysis in the presence of SYBR® Green I to readily and accurately genotype known
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SNPs at the bovine candidate genes IGF-1, LEP, NPY, and INS.
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Results
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We developed small-amplicon real time PCR-HRM assays in the presence of the non-
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saturating dye SYBR® Green I using the Rotor-Gene™ 6000 instrument, for the
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genotyping of one T>C (SNPIGF-1), two C>T (SNPLEP and SNPINS) and one G>A
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(SNPNPY) Class 1 SNPs [25], described at the bovine genes IGF-1 [2], LEP [18], NPY
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[5], and INS [39], in samples of Holstein and Aberdeen Angus breeds. For each SNP,
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this method allowed the amplification of a short PCR product (range 90- to 142-bp) and
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the genotyping of the SNP of interest in a closed-tube system in a 1 h 45 min-single
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assay.
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Real time PCR optimization and predicted amplicon Tm
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Preliminary results from real time PCR with amplicon (low) melting analysis (“three
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step with melt” assay) and agarose gel electrophoresis indicated that both PCR
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conditions and selected primer sets were optimal for the specific amplification of PCR
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products including the SNPIGF-1, SNPLEP, SNPNPY or SNPINS variant, provided amplicon
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for each SNP produced a unique melting peak (of expected Tm) and a robust single
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product of the expected length, respectively (data not shown). Predicted Tm values for
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homoduplex PCR products of alternative homozygotes per SNP analyzed are listed in
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Table 1. The Tm for heterozygotes are not included because absolute Tm is irrelevant for
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heterozygote detection, which is based on changes in melting curve shape.
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Real time PCR-HRM assays: quality control, genotyping and validation
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On average, 8.3% (15/180, range 5 to 12.5%, 4 runs) of the PCR samples did not meet
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one or more of the QC criteria applied and thus were discarded from HRM analysis. In
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general, they were samples with either atypical amplification plots or poor amplification
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(i.e., reaching a low signal plateau due to insufficient amount of DNA template and/or
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inefficient amplification reaction). For those samples that amplified well (n = 165),
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results for each SNP concerning PCR parameters included in the quality control (QC)
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criteria are summarized in Table 2. When considering the four runs together, the
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average run efficiency, estimated from the four standard curves, was 1.04 ± 0.15. In
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addition, based on comparative quantitation analyses, consistent individual reaction
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efficiencies, Ct values, and initial DNA yield for all compared samples were obtained
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(Table 2), with overall average values for the four runs being 1.84 ± 0.03, 16.52 ± 0.09,
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and 50.91 ± 5.79 ng, respectively).
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HRM SNP genotyping and validation
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For the genotyping of SNPIGF-1, SNPLEP, SNPNPY and SNPINS variants, we interpreted
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normalized HRM data from each SNP by visualizing derivative plots of melting curves
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(Figure 1Ai−Di), HRM curves (Figure 1Aii−Dii) and subtractive difference plots (Figure
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1Aiii−Diii). Inspection of HRM data revealed that the three possible genotypes per SNP
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(two alternative homozygotes and one heterozygote) could be readily discriminated
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from each other at each of the visualization ways, with samples of the same genotype
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always grouping together. Figure 1 displays HRM results from three independent
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samples for each possible genotype of the 93-bp SNPIGF-1 T>C, 142-bp SNPLEP C>T,
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90-bp SNPNPY G>A, and 104-bp SNPINS C>T amplicon, respectively. In the case of
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SNPINS, only two samples for one of the alternative homozygous genotypes were
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observed.
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At first, sample typing for each SNP was achieved by examining derivative plots of
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melting curves (Figure 1Ai−Di), and adscribing genotypes as homozygous or
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heterozygous by the presence of one or two specific fluorescence peaks (melting peaks),
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respectively. The SNPs analyzed in this study are Class 1 C>T (or T>C) and G>A
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transitions that produce C:G and T:A homoduplexes, and C:A and T:G heteroduplexes
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[25]. As the amplification of a homozygous genotype produces only one homoduplex
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species, homozygous samples are expected to have one specific melting peak, with Tm
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differences (∆Tm) between alternative homozygous. Melting peaks from samples
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containing the most stable homoduplex are expected to be shifted to the right (higher
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Tm), reflecting their greater thermal stability. In this study, the Tm peak representing
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each alternative homozygous genotype per SNP was well resolved and clearly
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interpreted (Figure 1Ai−Di). For each SNP, samples with higher Tm were assigned as
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homozygotes containing the most stable homoduplex (C/C genotype for SNPIGF-1,
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SNPLEP and SNPINS, and G/G genotype for SNPNPY, respectively); whereas samples with
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lower Tm peaks were interpreted as the corresponding alternative homozygote,
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containing the less stable homoduplex (T/T genotype for SNPIGF-1, SNPLEP and SNPINS,
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and A/A genotype for SNPNPY, respectively). On the other hand, amplification of
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heterozygous samples produces four duplex species: two low-Tm heteroduplexes and
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two high-Tm homoduplexes [40]. Thus, in heterozygous samples composite melting
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curves with two separate peaks on their derivative plots are expected. In our study,
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samples with two clearly defined peaks appeared for each SNP assayed (Figure
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1Ai−Di), and were assigned as heterozygotes. The lower temperature peak was always
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smaller than the higher temperature peak (Figure 1Ai−Di), reflecting the lower stability
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of heteroduplex products. Table 1 summarizes average Tm values obtained empirically
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from HRM analysis for homoduplexes at each alternative homozygous genotype per
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SNP. Although observed Tm values were higher (on average 2.96 °C ± 0.88, range 1.80
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to 4.20) than theoretical calculations, the order of homozygote stabilities per SNP
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agreed with those expected (i.e., Tm T:A < Tm C:G). Moreover, for all SNPs, observed
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∆Tm between alternative homozygotes were consistent with predicted ones (Table 1),
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with empirical ∆Tm being, on average, 0.1 °C ± 0.14 (range 0 to 0.3) off from predicted
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ones.
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Secondly, SNP typing was performed by visualizing samples as normalized HRM
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curves (Figure 1Aii−Dii). In this type of plots, homozygous samples are expected to
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show a single and sharp melting transition, provided they contain only homoduplex
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species, whereas melting curves of heterozygous samples are expected to produce more
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complex and broader transitions, because their melting curves are a composite of the
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individual melting curves of both heteroduplex and homoduplex components. In
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addition, alternative homozygous variants usually present the same HRM curve shape,
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being differentiated primarily by a temperature shift to the right of the most stable
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homoduplex. On the other hand, heterozygotes are discriminated by a change in melting
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curve shape. The contribution of the relatively unestable heteroduplexes changes the
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shape of the heterozygous melting curve, and, because they dissociate more readily,
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shift melting curve left to lower temperatures. As shown in Figure 1Aii−Dii, in this
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study, all homozygous samples per SNP melted in a single transition, with melting
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curves from homozygotes containing the most stable homoduplex shifted to the right, as
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expected according to both the order of homozygote stabilities (predicted from
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theoretical calulations and the observed on derivative plots) and predicted and empirical
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Tm differences between alternative homozygotes (Table 1). In addition, for all SNPs
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analyzed, melting curves from heterozygotes differed in shape from homozygous
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samples and presented a more gradual transition over a larger temperature range,
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crossing the melting curve of the less stable homozygote (Figure 1Aii−Dii). Thus,
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sample genotyping first adscribed by derivative plot analysis, were fully confirmed by
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visualization of normalized HRM curves.
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Lastly, different genotypes per SNP were discerned by visual inspection of subtractive
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difference plots (Figure 1Aiii−Diii). In this study, the A/A genotype for SNPNPY and T/T
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genotype for SNPIGF-1, SNPLEP, and SNPINS were selected as references, as it provided
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the easiest discrimination of genotypes. By the examination of difference plots we
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observed that for each SNP heterozygous samples had a distinct melting curve profile
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compared to the non-reference homozygote sample (Figure 1Aiii−Diii).
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For all visualization ways, when multiple samples per SNP were displayed at the same
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time, melting profiles clustered tightly by genotype, reflecting the minor Tm variation
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observed among samples with the same genotype (see Table 1 for average Tm SDs and
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sample Tm range per SNP).
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For the 35 samples validated, there was complete correspondence between the
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genotypes assigned by HRM analysis and those derived using conventional PCR-RFLP
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and/or sequencing assay (data not shown). There were no false positive or false negative
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results in a total of 35 samples, giving a sensitivity and specificity of 100%,
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respectively, for the HRM technique. In addition, sequence analysis confirmed the
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identity of amplicons for each SNP, being identical to the corresponding regions of
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published sequences for IGF-1, LEP, NPY and INS cattle genes (GenBank accession #
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AF017143, AF120500, AY491054 and EU518675, respectively), and did not revealed
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the presence of unexpected polymorphisms in any of the amplicons studied.
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As the ultimate objective for the development of HRM assays for SNP genotyping in
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the current work was its application to large population studies, we reinterpreted the
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samples (n = 165), using the same runs previously performed, but now selecting some
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of the validated samples for each of the three possible genotypes per SNP as positive
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controls for the auto-calling of genotypes by the Rotor-Gene™6000 HRM software.
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When these analyses were performed, samples for all SNPs were automatically
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genotyped with confidence thresholds (assigned by the software based on the similarity
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of melting curves to control samples for each genotype) superior to 95%. Complete
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concordance between visual and automatic genotyping was observed for all samples.
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Discussion
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The real time PCR-HRM analysis has been shown to be a simple, inexpensive, high-
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sensitive and specific mean of SNP genotyping, among other applications [31]. In the
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present study, we report the development and validation of small-amplicon real time
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PCR-HRM assays using SYBR® Green I for the accurate genotyping of known SNPs at
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the bovine candidate genes IGF-1, LEP, NPY, and INS.
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Our results showed that all three possible genotypes per SNP examined were readily
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distinguished using the HRM assays described in this study. The complete accordance
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among our results from HRM and conventional genotyping methods proved that HRM
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analysis provide accurate results with excellent sensitivity of heterozygote detection and
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specificity for genotyping of the analyzed variants. These findings are in line with those
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from prior reports where sensitivity and specificity of HRM analysis were estimated in
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the range of 80 to 100% [41-44]. It has been shown that different instruments vary
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widely in their ability to genotype homozygous variants and scan for heterozygotes by
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amplicon melting analysis, with instruments specifically designed for HRM displaying
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better genotyping accuracy and scanning sensitivity and specificity [45]. In our study,
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we used the Rotor-Gene™ 6000, for which the highest values for sensitivity (100%) and
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specificity (95%) have been reported, compared to other machines [27]. Some authors
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have discouraged the use of SYBR® Green I for HRM analysis [24,31] since it seemed
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to inhibit PCR at high concentrations [29,32], and to redistribute during melting phase
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from low- to high-Tm duplexes, which may determine the preferential detection of high-
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Tm products in detrimental of heteroduplexes, affecting the detection of heterozygotes
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genotypes [24,30]. However, it has been suggested that these results are likely to be due
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to the less sensitivity of the early equipment used in the mentioned studies [46]. In our
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experiment, we did not observe PCR inhibition due to SYBR® Green I, as we used a
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standard commercial master mix containing a non-saturating concentration (although
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undisclosed by manufacturer) of the dye, optimally formulated for regular real time
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amplification analysis. Moreover, the phenomenon of dye redistribution did not appear
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to affect the melting curve analysis in our study, as melt profiles from heterozygous
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samples were detected and easily interpreted for all the SNPs analyzed. One reason for
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this may be that short-length PCR products, as those amplified in the present study,
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might reduce the re-intercalation of SYBR® Green I providing sufficient discrimination
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between Tm peaks of hetero- and homoduplexes, as stated by Pornprasert et al. [38]. In
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addition, Gundry et al. [30] have reported that faster cooling and heating rates improve
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heteroduplex formation and detection sensitivity, respectively. In our case, we used the
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Rotor-Gene™ 6000 cycler at which cooling/heating is achieved at rapid rates (max.
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ramp rate: 10°C/s, air-based system, Rotor-Gene™ 6000 Operator Manual).
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The power of HRM analysis is not only dependent on the instrument resolution and the
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selected DNA dye. Additionally, due to the highly sensitive nature of this technique,
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several factors may affect its performance, with a proper assay design being critical for
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successful results. Particular attention should be given to DNA and amplicon
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quality/quantity, primer and amplicon design, and optimization of protocols (both PCR
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and HRM), as strong PCR will lead to clearer and more reliable HRM data [47].
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Low-quality DNA (due to degradation or buffer carryover contamination) may produce
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nonspecific PCR products or failed reactions. In addition, differences in extraction
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method chemistry can also influence inter-sample variation of melting curves by
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changing the ionic strength [48]. For best results, it is recommended that all samples to
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be analyzed per run are processed in the same way and end up in the same low-salt
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buffer (e.g., TE) [25]. In our study, all DNA samples were extracted using the same
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protocol and were resuspended/diluted in TE, thus minimizing ionic strength differences
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and salt carryover during PCR preparation. Moreover, as indicated by spectral analysis,
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our DNA samples presented a good purity level.
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Large differences in the starting amount of DNA may also affect HRM results [27].
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However, up to a 10-fold variation in the initial DNA yield (which ensures
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amplification plots are within 3 Ct of each other) is acceptable as optimized PCR tends
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to equalize product concentrations at plateau [30,47]. In order to provide a consistent
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testing condition, it is important to keep sample DNA concentrations as similar as
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possible and to use the same amount of template per reaction; additionally, it is
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recommended to ensure every reaction has amplified to the plateau phase [47]. In the
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present work, quantitation analysis showed a consistent initial DNA yield among
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samples. We did not find it necessary to quantify post-PCR product in our assays as by
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cycling 40 times we ensured reactions had amplified to the plateau phase, and, in
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addition, only samples with normal amplification plots within 3 Ct of each other were
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included in the HRM analysis.
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Appropriate primer and amplicon design, along with optimized PCR and HRM
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protocols are also important to the success of HRM assays. Samples contaminated with
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post-amplification artifacts such as primer-dimer or non-specific products, or samples
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that amplify late or fail to reach the plateau can result in inconclusive HRM data [27].
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The amplicon length has been reported to impact on the sensitivity and specificity of
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HRM results [25,30]. In general, for HRM-based SNP genotyping the amplicon should
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preferably be small (80 to 100 bp), as Tm differences between homozygous genotypes,
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as well as heterozygote-detection sensitivity are greater, allowing better genotype
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differentiation [28]. Furthermore, shorter amplicons generally require minimal
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optimization to produce robust, clean products that tend to have less complex melt
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profiles and therefore, HRM data is more easily analyzed [49]. It is also critical for high
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sensitivity that the melt profile contains no more than one or two melt domains (i.e., one
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or two SNPs) [28]. Additionally, when a choice is available, Class 1 SNPs (G>A or
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T>C) [48]. should be selected as they are predicted to give the largest Tm shifts (up to
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0.5 °C) in short amplicons [25]. In the present study, we carefully followed the general
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considerations for primer and amplicon design, and PCR-HRM assay set-up described
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in the CorProtocol™6000-1-Sept06. The PCR assays were performed under standard
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eaction conditions, requiring minimal optimization. Moreover, implementation of our
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PCR-HRM designs were quite straightforward; one PCR run with low resolution melt
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analysis per SNP was needed prior to the definitive PCR-HRM run to test amplification
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conditions that rendered a good genotyping assay with high and consistent amplification
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efficiencies among samples, in agreement with what was previously reported [28,49].
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The use of a commercial real time PCR master mix and a real time instrument with
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HRM capability also helped to achieve optimization of protocols more easily. An
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advantage of performing real time PCR and HRM analysis using a single instrument is
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that amplification results are available for HRM analysis immediately after the PCR
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run. This not only improves turnaround time, but also allows assessment of
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amplification data for all samples before HRM analysis as a quality-control measure
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[51]. In our experience, evaluation of real time amplification plots and assessment of
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quantitation data enabled us to easily identify insufficiently or atypically amplified
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samples and exclude them from downstream HRM analysis. The quality-control criteria
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considered in the present work surely contributed to the high sensitivity and specificity
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observed, as reported by other studies [27,31,41,52]. In addition, our HRM assays were
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performed on Class 1 SNPs in short amplicons, which also assisted in achieving high
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specificity and sensitivity values. For all the SNPs analyzed, observed Tm differences
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between alternative homozygotes were consistent with predicted ones, and were in
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agreement with Tm differences expected for Class 1 SNPs. However, observed Tm was
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higher than theoretical calculations. These differences may respond to limitations of the
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software algorithm used for Tm calculation, as well as salt and concentration variables.
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In addition, it has been shown that SYBR® Green I has a stabilizing effect on the DNA
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duplex, thereby increasing the Tm by 1 to 2 °C [53], which might have also contributed
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to rise the observed Tm in our work.
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This study also addressed the usefulness of plotting data in various ways. The
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specificity for genotyping of variants can be greatly improved by displaying any genetic
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variation as difference plots of melting curves, provided even slight differences in curve
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shape and Tm become more apparent when plotted this way [24,51]. In line to this, our
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results showed that difference plots allowed the clearest visualization of genotype
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clusters. Genotyping by visual clustering on difference plots appears simple and
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accurate when the HRM assay is performed for the first time for the SNP of interest, as
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in the current study, or when the number of samples is limited; however, in high-
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throughput assays automatic calling of genotypes becomes the preferred choice.
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As shown in the present study, when a few simple guidelines are taken into account to
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maximise the benefits of HRM technique, along with the use of proper instrumentation
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and data analysis software, SYBR® Green I is highly suitable for HRM-based SNP
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genotyping. Our findings support recent publications that have questioned the restriction
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of the HRM methodology to only saturating dyes [34-38].
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Conclusions
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This study demonstrates that highly sensitive and specific real time PCR-HRM
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genotyping of Class 1 SNPs at the bovine candidate genes IGF-1, LEP, NPY, and INS is
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possible in the presence of SYBR® Green I. This alternative approach can be applied for
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rapid and accurate genotyping of other polymorphisms, facilitating high-throughput
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analysis and population-level studies in dairy and beef cattle.
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Methods
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Blood sample collection and DNA isolation
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Experimental procedures were carried out in accordance with regulations set by the
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Honorary Animal Care and Experimentation Committee of Universidad de la
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República, Uruguay. Six mL of blood samples for genomic DNA extraction were
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collected from Holstein (n = 40) and Aberdeen Angus (n = 40) cows by coccygeal
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venipuncture using K2EDTA Vacutainer® tubes (Becton Dickinson, NJ, USA).
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Genomic DNA was isolated from whole blood using a high-salt procedure [54].
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Quantity and quality of DNA samples were evaluated in a NanoDrop™ ND-1000 UV-
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Vis spectrophotometer (NanoDrop Technologies, Inc., Wilmington, DE). Absorbance
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ratio 260 nm to 280 nm (A260/280) was used to asses DNA purity. The A260/280 ratios were
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consistently in the range of 1.8 to 2.0. Prior to use, work dilutions were made in TE
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buffer (10 mM Tris-HCl, 0.1 mM Na2EDTA, pH 8) to an average final concentration of
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25 ng/uL (range 20 to 30 ng/uL).
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SNP selection
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Polymorphisms investigated in this report are known bi-allelic SNPs, previously
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identified in the cattle candidate genes IGF-1 (SNPIGF-1) [2], LEP (SNPLEP) [18], NPY
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(SNPNPY) [5], and INS (SNPINS) [39].
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PCR optimization and calculation of theoretical Tm of amplicons
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Forward and reverse primers for the specific amplification of SNPIGF-1, SNPLEP, SNPNPY
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or SNPINS avoiding the presence of other sequence variations in the amplified region,
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were designed using Primer3 algorithm at Primer-BLAST [55]. To maximize
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differences in Tm between homozygous genotypes, small (<150 bp) amplicons were
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designed [30]. Primer pairs having on average 20 bases in length, Tm of 60 °C, GC
396
contents of 55%, and checked for the potential to form primer-dimers or non-specific
397
amplicons were selected. Primers were ordered from Operon (Operon Biotechnologies
398
Inc., Germany). Table 3 resumes information on the SNPs studied at IGF-1, LEP, NPY
399
and INS genes, primer pairs designed in this report, and expected amplicon lengths. The
400
theoretical Tm values for homoduplexes of each alternative homozygote per SNP were
401
estimated according to the Nearest-Neighbor thermodynamic model [56], as
402
implemented in the OligoCalc analyzer [57].
403
A 3-step real time PCR protocol with amplicon (low) melting curve analysis (“Three
404
step with melt” option, Rotor-Gene™ 6000, Corbett Research Ltd., Sydney, Australia)
405
was performed in three randomly selected DNA samples per SNP in order to: 1) ensure
406
primer specificities; 2) check for the presence of primer-dimer formation, and 3)
407
determine the empirical Tm of amplicons (and thus select the melting range for the
408
HRM assays). For all SNPs, PCR was performed in a final volume of 20 µL containing:
409
50 ng (on average, range 40 - 60 ng) of genomic DNA, 10 µL of SYBR® Green I Master
410
Mix (Quantimix Easy SYG Kit™, Biotools B&M Labs, Madrid, Spain), 1 uL primer
411
mix (200 nM each) and 7 µL of mQ nuclease-free water. Cycling conditions were the
412
same for each SNP: 95 °C for 10 min followed by 40 cycles of 15 s at 95 °C, 30 s at 60
413
°C and 20 s at 72 °C. Amplification products from each SNP were also evaluated by 2%
414
agarose gel electrophoresis to confirm amplification of the expected fragment size.
415
416
Real time PCR-HRM assays and genotyping validation
417
Genomic DNA samples from Holstein (n = 40) and Aberdeen Angus (n = 40) cows
418
were used to develop real time PCR-HRM SNP genotyping protocols for SNPIGF-1,
419
SNPLEP, SNPNPY and SNPINS. For each breed, animals were selected from four farms (n
420
= 10 per farm), members of the Holstein or Aberdeen Angus National Genetic
421
Evaluation System of Uruguay, respectively, and with major contribution to the genetic
422
pool of the corresponding breed in Uruguay. The inbreeding coefficient, calculated with
423
ENDOG program [58] using a complete three generations pedigree file, was ≤ 2%. Due
424
to different research objectives of the authors, SNPIGF-1 was analyzed in both cattle
425
breeds (Holstein n = 30; Aberdeen Angus n = 30), SNPLEP and SNPNPY were studied in
426
Aberdeen Angus (n = 40), and SNPINS was analyzed in Holstein (n = 40). Based on
427
previously reported allelic frequencies for SNPIGF-1 [2,8], SNPLEP [18], and SNPNPY [5],
428
the number of animals selected to be analyzed from each breed would permit to detect
429
the less frequent genotype at each SNP. In the case of SNPINS, no reports are available,
430
to our knowledge, concerning allelic frecuencies at this SNP. For each SNP, blinded
431
samples and no-DNA template controls (NTC) were analyzed by real time PCR-HRM
432
assays in the presence of SYBR® Green I using the Rotor-Gene™ 6000 (Corbett
433
Research Ltd., Sydney, Australia). A duplicate 5-point genomic DNA standard curve
434
(DNA content range from 120 to 7.5 ng) was included at each run for both evaluation of
435
amplification efficiency per run and sample quantitation. Real time PCRs were carried
436
out on a 72-well Rotor-Disc in 0.1 mL-tubes. For all SNPs, the preparation of PCR
437
reaction mix and cycling conditions were the same as described in the previous section.
438
Fluorescence acquisition on the green channel (460 nm excitation; 510 nm detection)
439
was performed during the extension step. At the completion of cycling, samples were
440
rapidly cooled to 50 ºC for 30 s in order to encourage heterodulpex formation and HRM
441
was run at a rate of 0.1 °C/2 s from 75 to 85 °C (SNPIGF-1), 79 to 89 °C (SNPLEP), 76 to
442
86 °C (SNPNPY) and 83 to 93 °C (SNPINS), with a pre-melt conditioning for 90 s on the
443
first step (i.e., the first temperature of the melting range). Fluorescent signal was
444
acquired on the HRM channel. Both real time PCR and HRM data analyses were
445
performed using the Rotor-Gene™ 6000 software v.1.7 (Build 75), and included:
446
removal of background fluorescence (auto-find threshold option); standard curve-based
447
quantitation analyses (estimation of run efficiency, cycle threshold (Ct), and amplicon
448
concentration per sample); comparative quantitation (estimation of reaction efficiency
449
per sample); NTC verification and elimination; and automatic normalization of
450
fluorescence (pre- and post-melt fluorescence signals of all samples were normalized to
451
relative values of 1 and 0, respectively). Careful examination of real time amplification
452
and quantitation data was performed to select PCR samples for further HRM
453
interpretation in order to ensure reliable and reproducible melt comparisons. The
454
following quality-control (QC) criteria were considered, according to the Corbett
455
Research HRM assay design and analysis protocol [47]: 1) normal amplification curve
456
shape (i.e., plots having a steep log-linear phase); 2) Ct < 30 (indicating an adequate
457
amount of starting template); 3) amplification plots within 3 Ct of each other (indicating
458
a consistent amount of template among samples; 4) similar post-amplification sample
459
concentrations (i.e., all samples should reach the same plateau); and 5) reaction
460
efficiency per sample superior to 70% (amplification value ≥ 1.7, being 2 the
461
amplification value for a 100% efficient reaction). Amplicons that did not meet these
462
criteria were identified as outliers and removed from further HRM analysis.
463
For SNP genotyping fluorescence-normalized melting data was displayed as derivative
464
plots (derivative of the fluorescence relative to that of the temperature (dF/dT) vs.
465
temperature), HRM curves (fluorescence vs. temperature), and subtractive difference
466
plots (each HRM curve is subtracted from a user-defined reference curve, which
467
subtracted from itself became zero across all temperatures; ∆ fluorescence (%) vs.
468
temperature). For each SNP, visual inspection of each type of plot and assignment of
469
presumptive genotypes was independently performed by two operators. Melting
470
temperature differences and melting curve shape were used to ascribe samples as
471
alternative homozygotes or heterozygotes, respectively.
472
Results from HRM were cross-validated through PCR-RFLP genotyping (SNPIGF-1,
473
SNPLEP) or/and direct bidirectional sequencing (SNPIGF-1, SNPNPY, SNPLEP, SNPINS;
474
sequencing service of Macrogen Inc., Korea) of three different randomly selected PCR
475
samples for each presumptive genotype per SNP (n = 35 different DNA samples; for
476
SNPINS only two samples for one homozygous genotype were available for sequencing).
477
Previous to validation, PCR-HRM assays of these 35 samples were repeated by a
478
different operator in order to confirm accuracy in the assigned genotypes through visual
479
inspection. Genotyping by PCR-RFLP of SNPIGF-1 and SNPLEP was performed
480
according to [2] and [5], respectively. Sequencing genotyping for SNPIGF-1, SNPNPY,
481
SNPLEP, SNPINS, using primer sets designed in this study, was performed from PCR
482
samples recovered from the Rotor-Gene™ 6000 after PCR-HRM assays.
483
484
Abbreviations
485
Ct: cycle threshold; HRM: high-resolution melting; IGF-1: insulin-like growth factor
486
1gene; INS: insulin gene; LEP: leptin gene; NPY: neuropeptide Y gene; NTC: no-DNA
487
template control; PASA: PCR amplification of specific alleles; PCR: polymerase chain
488
reaction; QC: quality control; RFLP: restriction fragment length polymorphism; RSI-
489
RFLP: restriction site insertion-RFLP; SNP: single nucleotide polymorphism; SSCP:
490
single-strand conformational polymorphism; Tm: melting temperature.
491
492
Competing interests
493
The authors declare that they have no financial or non-financial competing interests .
494
495
Auhors´contribution
496
PN participated in sample collection, DNA extraction, design and optimization of the
497
assays, data acquisition and genotyping, and drafted the manuscript. AIT contributed
498
with sample collection, data interpretation and revision of the manuscript. MPG
499
participated in sample collection, DNA extraction, design and optimization of the
500
assays, data acquisition and genotyping, and revision of the manuscript. MC assisted
501
with the design of the assays and critical reading of the manuscript. AM contributed
502
with data interpretation and critical review of the manuscript. All authors read and
503
approved the final version of the manuscript.
504
505
Acknowledgements
506
This research was supported by INIA (National Institute for Agrcultural Research),
507
ANII (National Agency for Inovation and Research), and Universidad de la Republica,
508
Uruguay. We also thank to farmers of Holstein and Aberdeen Angus Societies for
509
kindly allow our group to take animal blood samples.
510
511
512
513
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Table 1.- Predicted and empirical Tm* values for homoduplex PCR products of alternative
homozygous genotypes for the SNPs studied at the bovine insulin-like growth factor 1 (IGF-1),
leptin (LEP), neuropeptide Y (NPY), and insulin (INS) genes
Target information
Tm (°C )
1
SNP
Genotype
Homoduplex
SNPIGF-1
T/T
C/C
T/T
C/C
A/A
G/G
T/T
C/C
T:A
C:G
T:A
C:G
A:T
G:C
T:A
C:G
SNPLEP
SNPNPY
SNPINS
762
763
764
765
766
767
768
769
*
Predicted
3
Tm
∆Tm
77.0
77.3
81.6
81.8
77.3
77.7
84.8
85.3
0.3
0.2
0.4
0.5
Average
SD
79.9
80.5
83.4
83.6
80.2
80.6
88.8
89.2
0.03
0.03
0.02
0.03
0.08
0.05
0.05
0.01
2
Empirical
Range
79.85−79.97
80.42−80.50
83.35−83.43
83.60−83.70
80.05−80.35
80.60−80.70
88.68−88.88
89.2−89.22
∆Tm
0.6
0.2
0.4
0.4
4
n
20
6
18
8
12
6
15
2
Temperature of melting.
Calculated according to the Nearest-Neighbour thermodynamic model [56].
2
Obtained from real time PCR-HRM assays (derivative plots data).
3
∆Tm: Tm difference between alternative homozygotes.
4
Number of samples used for empirical estimation of Tm from the PCR sample set selected per SNP for HRM data
interpretation.
1
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
Table 2.- Real time amplification data included in the quality control criteria for the selection of samples to be interpreted
by HRM analysis
2
1
1
SNP
n
SNPIGF-1
SNPLEP
SNPNPY
SNPINS
55
38
35
37
Average
16.54
16.57
16.59
16.38
Ct
SD
0.43
0.34
0.39
0.29
3
Range
15.92−17.77
15.87−17.12
15.92−17.47
15.83−16.92
DNA template yield (ng)
Average
SD
Range
48.05
1.30 22.39−70.62
47.74
1.29 31.64−80.60
59.59
1.30 32.72−93.88
48.25
1.26 31.35−73.93
4
Amplification value
Average
SD
Range
1.84
0.05 1.75−1.96
1.83
0.05 1.76−1.97
1.88
0.06 1.77−2.02
1.82
0.05 1.73−1.97
Run
5
efficiency
0.86
1.12
0.97
1.20
Number of samples selected for HRM data interpretation from the PCR sample set assayed in a single run for each SNP (n = 60
for SNPIGF-1, and n = 40 each for SNPLEP, SNPNPY, and SNPINS).
2
Ct: threshold cycle.
3
Estimated from quantitative analysis of real time data (Rotor-Gene™ 6000, Corbett Research Ltd., Sydney, Australia).
4
Indicates the individual reaction efficiency in a score out of 2 (2 = 100% efficiency); e.g., an amplification value of ~1.83 means
that the reaction has an efficiency of 0.83 (or 83%).
5
Estimated at each run from a duplicate 5-point genomic DNA standard curve (DNA content range 120 to 7.5 ng).
785
786
787
788
789
790
791
792
793
794
Figure 1. Real time PCR-HRM genotyping using SYBR® Green I of Class 1 SNPs at the bovine
genes insulin-like growth factor 1 (SNPIGF-1; T>C)(Ai-Aiii), leptin (SNPLEP; C>T) (Bi-Biii),
neuropeptide Y (SNPNPY; G>A)(Ci-Ciii), and insulin (SNPINS; C>T)(Di-Diii). Derivative plots of
melting curves (Ai-Di), normalized HRM curves (Aii-Dii), and difference plots (Aiii-Diii). Y-axis:
fluorescence; x-axis: temperature (ºC). All three possible genotypes per SNP (two alternative
homozygous and one heterozygous) are clearly discriminated from each other in all visualization ways.
Three samples of each genotype are displayed per SNP (except for INS, where only two samples with the
C/C genotype were detected).
795
796
797
Table 3.- General information on the SNPs studied at the bovine insulin-like growth factor 1 (IGF1), leptin (LEP), neuropeptide Y (NPY), and insulin (INS) genes
798
799
SNP gene location
and/or base
position
GenBank
Accession N°
or dbSNP rs#
cluster id
Class 1 T>C
Exon 2-512
AF017143
3
4
Class 1 C>T
Exon 2-198
AF120500
4
NPY
4
Class 1 G>A
Intron 2-666
AY491054
5
INS
29
Class 1 C>T
5' near gene
rs42194737
Gene
BTA
IGF-1
5
LEP
SNP type
1
BTA: Bos taurus chromosome, F: forward, R: reverse.
[50]; 2 designed in this report; 3 [2]; 4 [18]; 5 [5].
1
Primer sequence
(5‘- 3’)
2
F: AATAAAATTGCTCGCCCATCC
R: TAACTTTCTACCGGGCGTGA
F: GGACCCCTGTATGGATTCCT
R: TCCCTACCGTGTGTGAGATG
F: GCTGGGTCACCAAAGACATT
R: AAACACTGTACGGGGGAAA
F: CGGCTTTATAGCCCCTGAG
R: AGGGAAATGATCCGGAAACT
Amplicon
length
(bp)
93
142
90
104