HRM test cattle

1
Assessing SYBR® Green I-based high-resolution melting assays for accurate single
2
nucleotide polymorphisms genotyping in cattle
3
4
5
6
7
Paula Nicolini1*
*
Corresponding author
E-mail: paula.nicolini@gmail.com
8
9
10
Ana Inés Trujillo2
E-mail: anatrujister@gmail.com
11
12
13
María Pia Grignola2
E-mail: piagrignola@gmail.com
14
15
16
Mariana Carriquiry2
E-mail: mcarriquiry@fagro.edu.uy
17
18
19
Ana Meikle1
E-mail: anamei@adinet.com.uy
20
1
21
República. Lasplaces 1550. C.P. 11600. Montevideo, Uruguay.
22
2
23
de la República. Avda. Garzón 780. Montevideo, Uruguay.
24
25
26
Laboratorio de Técnicas Nucleares. Facultad de Veterinaria. Universidad de la
Departamento de Producción Animal y Pasturas. Facultad de Agronomía. Universidad
27
Abstract
28
Background. Single nucleotide polymorphisms (SNPs) at candidate genes have gained
29
widespread interest as markers for phenotype-genotype association studies in cattle.
30
Traditional techniques for SNP genotyping are either labor intensive, expensive or have
31
low sensitivity. High-resolution melting (HRM) analysis is an innovative technique for
32
genotyping and has many advantages including cost-effectiveness, speed, and accuracy.
33
The aim of the current study was to develop and validate real time PCR-HRM assays
34
using the non-saturating dye SYBR® Green I for the genotyping of known Class 1 SNPs
35
at the bovine candidate genes insulin-like growth factor 1 (IGF-1, T>C), leptin (LEP,
36
C>T), neuropeptide Y (NPY, G>A), and insulin (INS, C>T) in samples of Holstein (n =
37
40) and Aberdeen Angus (n = 40) breeds.
38
Results. For each SNP, this method allowed the amplification of a short PCR product
39
(range 90- to 142-bp) and the genotyping of the SNP of interest in a closed-tube system
40
in a 1 h 45 min-single assay. Three different melting curves corresponding to the three
41
expected genotypes per SNP were readily distinguished based on melting temperature
42
(Tm) shifts or shape differences of the melting curves. Observed Tm differences between
43
alternative homozygotes were consistent with predicted ones, and were in agreement
44
with Tm differences expected for Class 1 SNPs (about 0.5 ºC). High genotyping
45
accuracy was verified by 100% concordance of HRM genotypes with results from
46
independent genotyping methods, demonstrating that SYBR® Green I is highly suitable
47
for HRM-based SNP genotyping. These results respond to a series of elements
48
considered in the present work, such as type of SNPs involved, the carefull protocol
49
followed up during the development and analysis of the assays, and the use of a high
50
sensitive instrument.
51
Conclusions. Real time PCR with SYBR® Green I and HRM analysis provides a
52
valuable alternative for rapid and accurate SNP genotyping, and can potentially be
53
applied to SNPs other than those studied in the present work. Its application will
54
facilitate high-throughput analysis and population-level studies in dairy and beef cattle.
55
56
57
Key words: real time PCR-HRM analysis, SYBR® Green I, SNP genotyping, cattle
58
Background
59
Population-based association studies are of major importance as a means of identifying
60
genetic variants that contribute to complex phenotypic expression [1]. Over the last
61
decade, single nucleotide polymorphisms (SNPs) in candidate genes have gained
62
widespread interest as markers for phenotype-genotype association studies in dairy and
63
beef cattle [2-10].
64
Insulin-like growth factor 1 (IGF-1), leptin (LEP), neuropeptide Y (NPY), and insulin
65
(INS) genes are known to play an important role in metabolism, growth and
66
reproduction in cattle [11-14]. Due to their physiological roles, polymorphisms within
67
these genes have the potential to determine differences in economically important traits.
68
Moreover, SNPs in the IGF-1, LEP and NPY genes have been shown to be associated
69
with several productive and reproductive traits in beef and dairy cattle [2-6,8-10, 15,16].
70
These SNPs have been genotyped by conventional PCR-based methods, such as: SSCP
71
(single-strand conformational polymorphism [17]), RFLP (restriction fragment length
72
polymorphism [2,18]), RSI (restriction site insertion)-RFLP [19], and allele specific
73
PCR (PASA [19]). In the case of the INS gene, although several SNPs have been
74
reported in cattle [20], to the authors concern no information is available to date neither
75
about genotyping methods of the SNPs identified at this bovine gene, nor about the
76
effect of any reported INS SNP on any trait of interest in cattle. All the above mentioned
77
methods include a step after PCR that requires the removal of the PCR product, which
78
often takes hours to perform and increases the risk of crossover contamination of PCR
79
products [21]. In addition, although sequencing can be considered the gold standard
80
genotyping technique, it is generally unsuitable for routine genotyping because it is
81
expensive to run [22]. Finally, SNP-chip platforms are the most cost-effective
82
methodologies for genome-wide association studies, the development of rapid, cost-
83
effective, and accurate methods for the genotyping of individual SNPs is of major
84
importance, as it is the single-marker assays that are most useful in the latter stages of
85
research and for diagnosis applications [23].
86
High-resolution melting (HRM) analysis using fluorescent DNA dyes coupled with real
87
time PCR is a relatively new method for SNP genotyping [24]. Briefly, the fluorescence
88
emitted by the release of the dye from the amplicon is monitored as DNA samples
89
transition from double- to single-stranded state with increasing temperature. Samples
90
are then characterized by their dissociation behaviour and melting temperature (Tm),
91
depending on their GC content, length, and sequence [25]. As a closed-tube system, the
92
major advantage of HRM analysis over gel electrophoresis is that it eliminates the post
93
PCR handling, thereby minimizing contamination concerns and reducing costs, turn-
94
around time, labour, and technical expertise [26]. Furthermore, HRM has proven to be
95
highly accurate for mutation scanning and genotyping applications, being suitable for
96
medium to high-throughput analysis, with results comparable to conventional
97
genotyping techniques [27,28].
98
Saturating dyes such as LC Green® [24] and SYTO9® [29] have become the preferred
99
choice for HRM analysis since, used at high concentrations (which means greater
100
saturation of the double-stranded DNA sample and higher fidelity of fluorescent
101
signals), are not inhibitory to PCR and are presumed to reduce dye redistribution effects
102
during strand dissociation [24], which affects HRM sensitivity [30]. On the other hand,
103
the use of the non-saturating dye SYBR® Green I has been discouraged for HRM-based
104
SNP genotyping [24,31]. due to dye-dependent PCR inhibition [29,32], dye
105
redistribution [30], and selective detection of amplicons in multiplex PCR [33].
106
Nevertheless, with the recent development of new generation HRM instruments,
107
SYBR® Green I has been successfully used for SNP genotyping [34-38].
108
The aim of the present study was to assess the capability of real time PCR-HRM
109
analysis in the presence of SYBR® Green I to readily and accurately genotype known
110
SNPs at the bovine candidate genes IGF-1, LEP, NPY, and INS.
111
112
113
Results
114
We developed small-amplicon real time PCR-HRM assays in the presence of the non-
115
saturating dye SYBR® Green I using the Rotor-Gene™ 6000 instrument, for the
116
genotyping of one T>C (SNPIGF-1), two C>T (SNPLEP and SNPINS) and one G>A
117
(SNPNPY) Class 1 SNPs [25], described at the bovine genes IGF-1 [2], LEP [18], NPY
118
[5], and INS [39], in samples of Holstein and Aberdeen Angus breeds. For each SNP,
119
this method allowed the amplification of a short PCR product (range 90- to 142-bp) and
120
the genotyping of the SNP of interest in a closed-tube system in a 1 h 45 min-single
121
assay.
122
123
Real time PCR optimization and predicted amplicon Tm
124
Preliminary results from real time PCR with amplicon (low) melting analysis (“three
125
step with melt” assay) and agarose gel electrophoresis indicated that both PCR
126
conditions and selected primer sets were optimal for the specific amplification of PCR
127
products including the SNPIGF-1, SNPLEP, SNPNPY or SNPINS variant, provided amplicon
128
for each SNP produced a unique melting peak (of expected Tm) and a robust single
129
product of the expected length, respectively (data not shown). Predicted Tm values for
130
homoduplex PCR products of alternative homozygotes per SNP analyzed are listed in
131
Table 1. The Tm for heterozygotes are not included because absolute Tm is irrelevant for
132
heterozygote detection, which is based on changes in melting curve shape.
133
134
Real time PCR-HRM assays: quality control, genotyping and validation
135
On average, 8.3% (15/180, range 5 to 12.5%, 4 runs) of the PCR samples did not meet
136
one or more of the QC criteria applied and thus were discarded from HRM analysis. In
137
general, they were samples with either atypical amplification plots or poor amplification
138
(i.e., reaching a low signal plateau due to insufficient amount of DNA template and/or
139
inefficient amplification reaction). For those samples that amplified well (n = 165),
140
results for each SNP concerning PCR parameters included in the quality control (QC)
141
criteria are summarized in Table 2. When considering the four runs together, the
142
average run efficiency, estimated from the four standard curves, was 1.04 ± 0.15. In
143
addition, based on comparative quantitation analyses, consistent individual reaction
144
efficiencies, Ct values, and initial DNA yield for all compared samples were obtained
145
(Table 2), with overall average values for the four runs being 1.84 ± 0.03, 16.52 ± 0.09,
146
and 50.91 ± 5.79 ng, respectively).
147
148
HRM SNP genotyping and validation
149
For the genotyping of SNPIGF-1, SNPLEP, SNPNPY and SNPINS variants, we interpreted
150
normalized HRM data from each SNP by visualizing derivative plots of melting curves
151
(Figure 1Ai−Di), HRM curves (Figure 1Aii−Dii) and subtractive difference plots (Figure
152
1Aiii−Diii). Inspection of HRM data revealed that the three possible genotypes per SNP
153
(two alternative homozygotes and one heterozygote) could be readily discriminated
154
from each other at each of the visualization ways, with samples of the same genotype
155
always grouping together. Figure 1 displays HRM results from three independent
156
samples for each possible genotype of the 93-bp SNPIGF-1 T>C, 142-bp SNPLEP C>T,
157
90-bp SNPNPY G>A, and 104-bp SNPINS C>T amplicon, respectively. In the case of
158
SNPINS, only two samples for one of the alternative homozygous genotypes were
159
observed.
160
At first, sample typing for each SNP was achieved by examining derivative plots of
161
melting curves (Figure 1Ai−Di), and adscribing genotypes as homozygous or
162
heterozygous by the presence of one or two specific fluorescence peaks (melting peaks),
163
respectively. The SNPs analyzed in this study are Class 1 C>T (or T>C) and G>A
164
transitions that produce C:G and T:A homoduplexes, and C:A and T:G heteroduplexes
165
[25]. As the amplification of a homozygous genotype produces only one homoduplex
166
species, homozygous samples are expected to have one specific melting peak, with Tm
167
differences (∆Tm) between alternative homozygous. Melting peaks from samples
168
containing the most stable homoduplex are expected to be shifted to the right (higher
169
Tm), reflecting their greater thermal stability. In this study, the Tm peak representing
170
each alternative homozygous genotype per SNP was well resolved and clearly
171
interpreted (Figure 1Ai−Di). For each SNP, samples with higher Tm were assigned as
172
homozygotes containing the most stable homoduplex (C/C genotype for SNPIGF-1,
173
SNPLEP and SNPINS, and G/G genotype for SNPNPY, respectively); whereas samples with
174
lower Tm peaks were interpreted as the corresponding alternative homozygote,
175
containing the less stable homoduplex (T/T genotype for SNPIGF-1, SNPLEP and SNPINS,
176
and A/A genotype for SNPNPY, respectively). On the other hand, amplification of
177
heterozygous samples produces four duplex species: two low-Tm heteroduplexes and
178
two high-Tm homoduplexes [40]. Thus, in heterozygous samples composite melting
179
curves with two separate peaks on their derivative plots are expected. In our study,
180
samples with two clearly defined peaks appeared for each SNP assayed (Figure
181
1Ai−Di), and were assigned as heterozygotes. The lower temperature peak was always
182
smaller than the higher temperature peak (Figure 1Ai−Di), reflecting the lower stability
183
of heteroduplex products. Table 1 summarizes average Tm values obtained empirically
184
from HRM analysis for homoduplexes at each alternative homozygous genotype per
185
SNP. Although observed Tm values were higher (on average 2.96 °C ± 0.88, range 1.80
186
to 4.20) than theoretical calculations, the order of homozygote stabilities per SNP
187
agreed with those expected (i.e., Tm T:A < Tm C:G). Moreover, for all SNPs, observed
188
∆Tm between alternative homozygotes were consistent with predicted ones (Table 1),
189
with empirical ∆Tm being, on average, 0.1 °C ± 0.14 (range 0 to 0.3) off from predicted
190
ones.
191
Secondly, SNP typing was performed by visualizing samples as normalized HRM
192
curves (Figure 1Aii−Dii). In this type of plots, homozygous samples are expected to
193
show a single and sharp melting transition, provided they contain only homoduplex
194
species, whereas melting curves of heterozygous samples are expected to produce more
195
complex and broader transitions, because their melting curves are a composite of the
196
individual melting curves of both heteroduplex and homoduplex components. In
197
addition, alternative homozygous variants usually present the same HRM curve shape,
198
being differentiated primarily by a temperature shift to the right of the most stable
199
homoduplex. On the other hand, heterozygotes are discriminated by a change in melting
200
curve shape. The contribution of the relatively unestable heteroduplexes changes the
201
shape of the heterozygous melting curve, and, because they dissociate more readily,
202
shift melting curve left to lower temperatures. As shown in Figure 1Aii−Dii, in this
203
study, all homozygous samples per SNP melted in a single transition, with melting
204
curves from homozygotes containing the most stable homoduplex shifted to the right, as
205
expected according to both the order of homozygote stabilities (predicted from
206
theoretical calulations and the observed on derivative plots) and predicted and empirical
207
Tm differences between alternative homozygotes (Table 1). In addition, for all SNPs
208
analyzed, melting curves from heterozygotes differed in shape from homozygous
209
samples and presented a more gradual transition over a larger temperature range,
210
crossing the melting curve of the less stable homozygote (Figure 1Aii−Dii). Thus,
211
sample genotyping first adscribed by derivative plot analysis, were fully confirmed by
212
visualization of normalized HRM curves.
213
Lastly, different genotypes per SNP were discerned by visual inspection of subtractive
214
difference plots (Figure 1Aiii−Diii). In this study, the A/A genotype for SNPNPY and T/T
215
genotype for SNPIGF-1, SNPLEP, and SNPINS were selected as references, as it provided
216
the easiest discrimination of genotypes. By the examination of difference plots we
217
observed that for each SNP heterozygous samples had a distinct melting curve profile
218
compared to the non-reference homozygote sample (Figure 1Aiii−Diii).
219
For all visualization ways, when multiple samples per SNP were displayed at the same
220
time, melting profiles clustered tightly by genotype, reflecting the minor Tm variation
221
observed among samples with the same genotype (see Table 1 for average Tm SDs and
222
sample Tm range per SNP).
223
For the 35 samples validated, there was complete correspondence between the
224
genotypes assigned by HRM analysis and those derived using conventional PCR-RFLP
225
and/or sequencing assay (data not shown). There were no false positive or false negative
226
results in a total of 35 samples, giving a sensitivity and specificity of 100%,
227
respectively, for the HRM technique. In addition, sequence analysis confirmed the
228
identity of amplicons for each SNP, being identical to the corresponding regions of
229
published sequences for IGF-1, LEP, NPY and INS cattle genes (GenBank accession #
230
AF017143, AF120500, AY491054 and EU518675, respectively), and did not revealed
231
the presence of unexpected polymorphisms in any of the amplicons studied.
232
As the ultimate objective for the development of HRM assays for SNP genotyping in
233
the current work was its application to large population studies, we reinterpreted the
234
samples (n = 165), using the same runs previously performed, but now selecting some
235
of the validated samples for each of the three possible genotypes per SNP as positive
236
controls for the auto-calling of genotypes by the Rotor-Gene™6000 HRM software.
237
When these analyses were performed, samples for all SNPs were automatically
238
genotyped with confidence thresholds (assigned by the software based on the similarity
239
of melting curves to control samples for each genotype) superior to 95%. Complete
240
concordance between visual and automatic genotyping was observed for all samples.
241
242
Discussion
243
The real time PCR-HRM analysis has been shown to be a simple, inexpensive, high-
244
sensitive and specific mean of SNP genotyping, among other applications [31]. In the
245
present study, we report the development and validation of small-amplicon real time
246
PCR-HRM assays using SYBR® Green I for the accurate genotyping of known SNPs at
247
the bovine candidate genes IGF-1, LEP, NPY, and INS.
248
Our results showed that all three possible genotypes per SNP examined were readily
249
distinguished using the HRM assays described in this study. The complete accordance
250
among our results from HRM and conventional genotyping methods proved that HRM
251
analysis provide accurate results with excellent sensitivity of heterozygote detection and
252
specificity for genotyping of the analyzed variants. These findings are in line with those
253
from prior reports where sensitivity and specificity of HRM analysis were estimated in
254
the range of 80 to 100% [41-44]. It has been shown that different instruments vary
255
widely in their ability to genotype homozygous variants and scan for heterozygotes by
256
amplicon melting analysis, with instruments specifically designed for HRM displaying
257
better genotyping accuracy and scanning sensitivity and specificity [45]. In our study,
258
we used the Rotor-Gene™ 6000, for which the highest values for sensitivity (100%) and
259
specificity (95%) have been reported, compared to other machines [27]. Some authors
260
have discouraged the use of SYBR® Green I for HRM analysis [24,31] since it seemed
261
to inhibit PCR at high concentrations [29,32], and to redistribute during melting phase
262
from low- to high-Tm duplexes, which may determine the preferential detection of high-
263
Tm products in detrimental of heteroduplexes, affecting the detection of heterozygotes
264
genotypes [24,30]. However, it has been suggested that these results are likely to be due
265
to the less sensitivity of the early equipment used in the mentioned studies [46]. In our
266
experiment, we did not observe PCR inhibition due to SYBR® Green I, as we used a
267
standard commercial master mix containing a non-saturating concentration (although
268
undisclosed by manufacturer) of the dye, optimally formulated for regular real time
269
amplification analysis. Moreover, the phenomenon of dye redistribution did not appear
270
to affect the melting curve analysis in our study, as melt profiles from heterozygous
271
samples were detected and easily interpreted for all the SNPs analyzed. One reason for
272
this may be that short-length PCR products, as those amplified in the present study,
273
might reduce the re-intercalation of SYBR® Green I providing sufficient discrimination
274
between Tm peaks of hetero- and homoduplexes, as stated by Pornprasert et al. [38]. In
275
addition, Gundry et al. [30] have reported that faster cooling and heating rates improve
276
heteroduplex formation and detection sensitivity, respectively. In our case, we used the
277
Rotor-Gene™ 6000 cycler at which cooling/heating is achieved at rapid rates (max.
278
ramp rate: 10°C/s, air-based system, Rotor-Gene™ 6000 Operator Manual).
279
The power of HRM analysis is not only dependent on the instrument resolution and the
280
selected DNA dye. Additionally, due to the highly sensitive nature of this technique,
281
several factors may affect its performance, with a proper assay design being critical for
282
successful results. Particular attention should be given to DNA and amplicon
283
quality/quantity, primer and amplicon design, and optimization of protocols (both PCR
284
and HRM), as strong PCR will lead to clearer and more reliable HRM data [47].
285
Low-quality DNA (due to degradation or buffer carryover contamination) may produce
286
nonspecific PCR products or failed reactions. In addition, differences in extraction
287
method chemistry can also influence inter-sample variation of melting curves by
288
changing the ionic strength [48]. For best results, it is recommended that all samples to
289
be analyzed per run are processed in the same way and end up in the same low-salt
290
buffer (e.g., TE) [25]. In our study, all DNA samples were extracted using the same
291
protocol and were resuspended/diluted in TE, thus minimizing ionic strength differences
292
and salt carryover during PCR preparation. Moreover, as indicated by spectral analysis,
293
our DNA samples presented a good purity level.
294
Large differences in the starting amount of DNA may also affect HRM results [27].
295
However, up to a 10-fold variation in the initial DNA yield (which ensures
296
amplification plots are within 3 Ct of each other) is acceptable as optimized PCR tends
297
to equalize product concentrations at plateau [30,47]. In order to provide a consistent
298
testing condition, it is important to keep sample DNA concentrations as similar as
299
possible and to use the same amount of template per reaction; additionally, it is
300
recommended to ensure every reaction has amplified to the plateau phase [47]. In the
301
present work, quantitation analysis showed a consistent initial DNA yield among
302
samples. We did not find it necessary to quantify post-PCR product in our assays as by
303
cycling 40 times we ensured reactions had amplified to the plateau phase, and, in
304
addition, only samples with normal amplification plots within 3 Ct of each other were
305
included in the HRM analysis.
306
Appropriate primer and amplicon design, along with optimized PCR and HRM
307
protocols are also important to the success of HRM assays. Samples contaminated with
308
post-amplification artifacts such as primer-dimer or non-specific products, or samples
309
that amplify late or fail to reach the plateau can result in inconclusive HRM data [27].
310
The amplicon length has been reported to impact on the sensitivity and specificity of
311
HRM results [25,30]. In general, for HRM-based SNP genotyping the amplicon should
312
preferably be small (80 to 100 bp), as Tm differences between homozygous genotypes,
313
as well as heterozygote-detection sensitivity are greater, allowing better genotype
314
differentiation [28]. Furthermore, shorter amplicons generally require minimal
315
optimization to produce robust, clean products that tend to have less complex melt
316
profiles and therefore, HRM data is more easily analyzed [49]. It is also critical for high
317
sensitivity that the melt profile contains no more than one or two melt domains (i.e., one
318
or two SNPs) [28]. Additionally, when a choice is available, Class 1 SNPs (G>A or
319
T>C) [48]. should be selected as they are predicted to give the largest Tm shifts (up to
320
0.5 °C) in short amplicons [25]. In the present study, we carefully followed the general
321
considerations for primer and amplicon design, and PCR-HRM assay set-up described
322
in the CorProtocol™6000-1-Sept06. The PCR assays were performed under standard
323
eaction conditions, requiring minimal optimization. Moreover, implementation of our
324
PCR-HRM designs were quite straightforward; one PCR run with low resolution melt
325
analysis per SNP was needed prior to the definitive PCR-HRM run to test amplification
326
conditions that rendered a good genotyping assay with high and consistent amplification
327
efficiencies among samples, in agreement with what was previously reported [28,49].
328
The use of a commercial real time PCR master mix and a real time instrument with
329
HRM capability also helped to achieve optimization of protocols more easily. An
330
advantage of performing real time PCR and HRM analysis using a single instrument is
331
that amplification results are available for HRM analysis immediately after the PCR
332
run. This not only improves turnaround time, but also allows assessment of
333
amplification data for all samples before HRM analysis as a quality-control measure
334
[51]. In our experience, evaluation of real time amplification plots and assessment of
335
quantitation data enabled us to easily identify insufficiently or atypically amplified
336
samples and exclude them from downstream HRM analysis. The quality-control criteria
337
considered in the present work surely contributed to the high sensitivity and specificity
338
observed, as reported by other studies [27,31,41,52]. In addition, our HRM assays were
339
performed on Class 1 SNPs in short amplicons, which also assisted in achieving high
340
specificity and sensitivity values. For all the SNPs analyzed, observed Tm differences
341
between alternative homozygotes were consistent with predicted ones, and were in
342
agreement with Tm differences expected for Class 1 SNPs. However, observed Tm was
343
higher than theoretical calculations. These differences may respond to limitations of the
344
software algorithm used for Tm calculation, as well as salt and concentration variables.
345
In addition, it has been shown that SYBR® Green I has a stabilizing effect on the DNA
346
duplex, thereby increasing the Tm by 1 to 2 °C [53], which might have also contributed
347
to rise the observed Tm in our work.
348
This study also addressed the usefulness of plotting data in various ways. The
349
specificity for genotyping of variants can be greatly improved by displaying any genetic
350
variation as difference plots of melting curves, provided even slight differences in curve
351
shape and Tm become more apparent when plotted this way [24,51]. In line to this, our
352
results showed that difference plots allowed the clearest visualization of genotype
353
clusters. Genotyping by visual clustering on difference plots appears simple and
354
accurate when the HRM assay is performed for the first time for the SNP of interest, as
355
in the current study, or when the number of samples is limited; however, in high-
356
throughput assays automatic calling of genotypes becomes the preferred choice.
357
As shown in the present study, when a few simple guidelines are taken into account to
358
maximise the benefits of HRM technique, along with the use of proper instrumentation
359
and data analysis software, SYBR® Green I is highly suitable for HRM-based SNP
360
genotyping. Our findings support recent publications that have questioned the restriction
361
of the HRM methodology to only saturating dyes [34-38].
362
363
Conclusions
364
This study demonstrates that highly sensitive and specific real time PCR-HRM
365
genotyping of Class 1 SNPs at the bovine candidate genes IGF-1, LEP, NPY, and INS is
366
possible in the presence of SYBR® Green I. This alternative approach can be applied for
367
rapid and accurate genotyping of other polymorphisms, facilitating high-throughput
368
analysis and population-level studies in dairy and beef cattle.
369
370
Methods
371
Blood sample collection and DNA isolation
372
Experimental procedures were carried out in accordance with regulations set by the
373
Honorary Animal Care and Experimentation Committee of Universidad de la
374
República, Uruguay. Six mL of blood samples for genomic DNA extraction were
375
collected from Holstein (n = 40) and Aberdeen Angus (n = 40) cows by coccygeal
376
venipuncture using K2EDTA Vacutainer® tubes (Becton Dickinson, NJ, USA).
377
Genomic DNA was isolated from whole blood using a high-salt procedure [54].
378
Quantity and quality of DNA samples were evaluated in a NanoDrop™ ND-1000 UV-
379
Vis spectrophotometer (NanoDrop Technologies, Inc., Wilmington, DE). Absorbance
380
ratio 260 nm to 280 nm (A260/280) was used to asses DNA purity. The A260/280 ratios were
381
consistently in the range of 1.8 to 2.0. Prior to use, work dilutions were made in TE
382
buffer (10 mM Tris-HCl, 0.1 mM Na2EDTA, pH 8) to an average final concentration of
383
25 ng/uL (range 20 to 30 ng/uL).
384
385
SNP selection
386
Polymorphisms investigated in this report are known bi-allelic SNPs, previously
387
identified in the cattle candidate genes IGF-1 (SNPIGF-1) [2], LEP (SNPLEP) [18], NPY
388
(SNPNPY) [5], and INS (SNPINS) [39].
389
390
PCR optimization and calculation of theoretical Tm of amplicons
391
Forward and reverse primers for the specific amplification of SNPIGF-1, SNPLEP, SNPNPY
392
or SNPINS avoiding the presence of other sequence variations in the amplified region,
393
were designed using Primer3 algorithm at Primer-BLAST [55]. To maximize
394
differences in Tm between homozygous genotypes, small (<150 bp) amplicons were
395
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
References
1. Risch N, Merikangas K: The future of genetic studies of complex human
diseases. Science 1996, 273(5281):1516-1517.
514
515
2. Ge W, Davis M, Hines H, Irvin K, Simmen R: Association of a genetic marker
516
with blood serum insulin-like growth factor-I concentration and growth traits
517
in Angus cattle. J Anim Sci 2001, 79:1757-1762.
518
519
3. Nkrumah JD, Li C, Basarab JA, Guercio S, Meng Y, Murdoch B, Hansen C,
520
Moore SS: Association of a single nucleotide polymorphism in the bovine
521
leptin gene with growth feed intake, feed efficiency, growth, feeding behavior
522
carcass quality and body composition. Can J Anim Sci 2004, 84:211-219.
523
524
4. Schenkel FS, Miller SP, Ye X, Moore SS, Nkrumah JD, Li C, Yu J, Mandell IB,
525
Wilton JW, Williams JL: Association of single nucleotide polymorphisms in the
526
leptin gene with carcass and meat quality traits of beef cattle. J Anim Sci 2005,
527
83: 2009-2020.
528
529
5. Sherman EL, Nkrumah JD, Murdoch BM, Li C., Wang Z, Fu A, Moore SS:
530
Polymorphisms and haplotypes in the bovine neuropeptide Y, growth
531
hormone receptor, ghrelin, insulin-like growth factor 2, and uncoupling
532
proteins 2 and 3 genes and their associations with measures of growth,
533
performance, feed efficiency, and carcass merit in beef cattle. J Anim Sci 2008,
534
86:1-16.
535
536
6. Ruprechter G, Carriquiry M, Ramos JM, Pereira I, Meikle A: Metabolic and
537
endocrine profiles and reproductive parameters in dairy cows under grazing
538
conditions: effect of polymorphisms in somatotropic axis genes. Acta Vet Scand
539
2011, 53: 35-44.
540
541
7. Mullen MP, Creevey CJ, Berry DP, McCabe MS, Magee DA, Howard DJ, Killeen
542
AP, Park SD, McGettigan PA, Lucy MC, MacHugh DE, Waters SM:
543
Polymorphism discovery and allele frequency estimation using high-
544
throughput DNA sequencing of target enriched pooled DNA samples. BMC
545
Genomics 2012, 13:16-27.
546
547
8. Nicolini P, Carriquiry M, Meikle A: A polymorphism in the insulin-like growth
548
factor 1 gene is associated with postpartum resumption of ovarian cyclicity in
549
Holstein-Friesian cows under grazing conditions. Acta Vet Scand 2013, 55 (1):
550
11-18.
551
552
9. Nicolini P, Chilibroste P, Laborde D, Meikle A: Effect of the IGF-I genotype on
553
production and metabolic endocrinology traits in transition dairy cows under
554
grazing conditions. Veterinaria (Montevideo) 2013, 49 (190): 16-27.
555
556
10. Trujillo I, Casal A, Peñagaricano F, Carriquiry M, Chilibroste P: Association of
557
SNP of neuropeptide Y, leptin and IGF-1 genes with residual feed intake in
558
confinement and under grazing condition in Angus cattle. J Anim Sci 2013,
559
91(9):4235-4244.
560
561
562
11. Lucy, MC: Regulation of ovarian follicular growth by somatotropin and
insulin-like growth factors in cattle. J Dairy Sci 2000, 83:1635-1647.
563
564
12. Williams GL, Amstaldena M, Garcia MR, Stanko RL, Nizielski SE, Morrison CD,
565
Keisler DH: Leptin and its role in the central regulation of reproduction in
566
cattle. Domest Anim Endocrinol 2002, 23 (1–2): 339-349.
567
568
13. Rhoads RP, Kim JW, Leury BJ, Baumgard LH, Segoale N, Frank SJ, Bauman DE,
569
Boisclair YR: Insulin increases the abundance of the growth hormone receptor
570
in liver and adipose tissue of periparturient dairy cows. J Nutr 2004, 134:1020-
571
1027.
572
573
574
575
14. Wynne K, Stanley S, McGowan B, Bloom S: Appetite control. J Endocrinol
2005, (184): 291-318.
576
15. Siadkowska E, Zwierzchowski L, Oprzadek J, Strzalkowska N, Bagnicka E,
577
Kryzewski J: Effect of polymorphism in IGF-1 gene on production traits in
578
Polish Holstein-Friesan cattle. Anim Sci Pap Rep 2006, 24: 225-237.
579
580
16. Mullen MP, Berry DP, Howard DJ, Diskin MG, Lynch CO, Giblin L, Kenny DA,
581
Magee DA, Meade KG, Waters SM: Single nucleotide polymorphisms in the
582
insulin-like growth factor 1 (IGF-1) gene are associated with performance in
583
Holstein-Friesian dairy cattle. Front Genet 2011, 2: 3.
584
585
586
17. Ge, W., M. E. Davis, and H. C. Hines: Two SSCP alleles identified in the 5′flanking region of the bovine IGF1 gene. Anim Genet 1997, 28:155-156.
587
588
18. Buchanan F, Fitzsimmons C, Van Kessel A, Thue T, Winkelman-Sim D, Schmutz
589
S: A missense mutation in the bovine leptin gene is correlated with carcass fat
590
content and leptin mRNA levels. Genet Sel Evol 2002, (34): 1-12.
591
592
19. De la Rosa Reyna XF, Montoya HM, Castrellón VV, Rincón AMS, Bracamonte
593
MP, Vera WA: Polymorphisms in the IGF1 gene and their effect on growth
594
traits in Mexican beef cattle. Genet Mol Res 2010, 9(2):875-883.
595
596
597
20. The
Single
Nucleotide
Polymorphism
database
(dbSNP)
[http://www.ncbi.nlm.nih.gov/snp/?term=bos+taurus+ins]
598
599
600
21. Abdolmohammadi A, Atashi H, Zamani C, Bottema C: High resolution melting
as
an
alternative
method
to
genotype
diacylglycerol
O-
601
acyltransferase1(DGAT1) K232A polymorphism in cattle. Czech J Anim Sci
602
2011, 56(8): 370-376.
603
604
22. Kyseľová J, Rychtářová J, Sztankóová Z, Czerneková V: Simultaneous
605
identification of CSN3 and LGB genotypes in cattle by high-resolution
606
melting curve analysis. Livest Sci 2012, 145 (1): 275-279.
607
608
23. Tabone T, Mather DE, Hayden MJ: Temperature Switch PCR (TSP): Robust
609
assay design for reliable amplification and genotyping of SNPs. BMC
610
Genomics 2009, 10:580.
611
612
24. Wittwer CT, Reed GH, Gundry CN, Vandersteen JG, Pryor RJ: High resolution
613
genotyping by amplicon melting analysis using LCGreen. Clin Chem 2003,
614
49:853-860.
615
616
25. Liew M, Pryor R, Palais R, Meadows C, Erali M, Lyon E, Wittwer C: Genotyping
617
of single-nucleotide polymorphisms by high-resolution melting of small
618
amplicons. Clin Chem 2004, 50:1156-1164.
619
620
26. Zhou Z-W, Yan J-B, Li H, Ren Z-R: Application of high-resolution melting for
621
genotyping bovine mitochondrial DNA. Biotech Prog 2011, 27 (2): 592-595.
622
623
27. White H, Potts G: Mutation scanning by high resolution melt analysis: Evaluation
624
of Rotor-Gene 6000 (CorbettLife Science), HR-1 and 384 well LightScanner
625
(Idaho Technology). National Genetics Reference Laboratory, (Wessex 2006).
626
[http://www.ngrl.org.uk/Wessex/downloads.htm]
627
628
28. Vossen RHAM, Emmelien A, Roos A, den Dunnen JT: High resolution melting
629
analysis (HRMA)—more than just sequence variant screening. Hum Mutat
630
2009, 30:860-866.
631
632
29. Monis PT, Giglio S, Saint CP: Comparison of SYTO9 and SYBR Green I for
633
real-time polymerase chain reaction and investigation of the effect of dye
634
concentration on amplification and DNA melting curve analysis. Anal Biochem
635
2005, 340:24-34.
636
637
30. Gundry CN, Vandersteen JG, Reed GH, Pryor RJ, Chen J, Wittwer CT: Amplicon
638
melting
analysis
with
labeled
primers:
a
closed-tube
method
for
639
differentiating homozygotes and heterozygotes. Clin Chem 2003, 49:396-406.
640
641
31. Reed GH, Kent JO, Wittwer CT: High-resolution DNA melting analysis for
642
simple and efficient molecular diagnostics. Pharmacogenomics 2007, 8:597-
643
608.
644
645
32. Wittwer CT, Herrmann MG, Moss AA, Rasmussen RP: Continuous fluorescence
646
monitoring of rapid cycle DNA amplification. Biotechniques 1997, 22:130-1.
647
648
33. Giglio S, Monis PT, Saint CP: Demonstration of preferential binding of SYBR
649
Green I to speciWc DNA fragments in real-time multiplex PCR. Nucleic Acids
650
Res 2003, 31(22):e136.
651
652
34. Liu JZ, Yan M, Wang ZY, Wang LR, Zhou Y, Xiao B: Molecular diagnosis of a-
653
thalassemia by combining real-time PCR with SYBR Green1 and dissociation
654
curve analysis. Transl Res 2006, 148(1): 6-12.
655
656
35. Kwok A, Bassam B, Reja V: Genotyping a Class 4 SNP by high resolution melt
657
(HRM) using SYBR Green I. Corbett Research report August 2007.
658
[http://www.gene-quantification.de/kwok-class4-hrm-sybr.pdf]
659
660
36. Price EP, Smith H, Huygens F, Giffard PM: High-resolution DNA melt curve
661
analysis of the clustered, regularly interspaced short-palindromic-repeat locus
662
of Campylobacter jejuni. Appl Environ Microbiol 2007, 73: 3431-3436.
663
664
37. Panyasai S, Sukunthamala K, Pornprasert S: Molecular confirmatory testing of
665
hemoglobin Constant Spring by real-time polymerase chain reaction SYBR
666
Green1 with high-resolution melting analysis. Eur J Haematol 2010, 84(6):550-
667
552.
668
669
38. Pornprasert
S,
Wiengkum
T,
Srithep
S,
Chainoi
I,
Singboottra
P,
670
Wongwiwatthananukit S: Detection of α-thalassemia-1 Southeast Asian and
671
Thai Type Deletions and β-thalassemia 3.5-kb Deletion by Single-tube
672
Multiplex Real-time PCR with SYBR Green1 and High-resolution Melting
673
Analysis. Korean J Lab Med 2011, 31:138-142.
674
675
676
39. The
Single
Nucleotide
Polymorphism
database
(dbSNP)
[http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=42194737]
677
678
40. Nataraj AJ, Olivos-Glander I, Kusukawa N, Highsmith WE Jr: Single-strand
679
conformation polymorphism and heteroduplex analysis for gel-based
680
mutation detection. Electrophoresis, 1999 20(6):1177-1185.
681
682
41. Reed GH, Wittwer CT: Sensitivity and specificity of single-nucleotide
683
polymorphism scanning by high-resolution melting analysis. Clin Chem 2004,
684
50:1748-1754.
685
686
42. Do H, Krypuy M, Mitchell P, Fox S, Dobrovic A: High resolution melting
687
analysis for rapid and sensitive EGFR and KRAS mutation detection in
688
formalin fixed paraffin embedded biopsies. BMC Cancer 2008, 8: 142.
689
690
43. Garritano S, Gemignani F, Voegele C, Nguyen-Dumont T, Le Calvez-Kelm F, De
691
Silva D, Lesueur F, Landi S, Tavtigian SV: Determining the effectiveness of
692
High Resolution Melting analysis for SNP genotyping and mutation scanning
693
at the TP53 locus. BMC Genetics 2009, 10:5.
694
695
44. Ye MH, Chen JL, Zhao GP, Zheng MQ, Wen J: Sensitivity and specificity of
696
high-resolution melting analysis in screening unknown SNPs and genotyping
697
a known mutation. Anim Sci Pap Rep 2010, 28 (2):161-170.
698
699
45. Herrmann MG, Durtschi JD, Wittwer CT, Voelkerding KV: Expanded
700
instrument comparison of amplicon DNA melting analysis for mutation
701
scanning and genotyping. Clin Chem 2007, 53:1544 -1548.
702
703
704
46. The
Gene
Quantification
page
[http://www.gene-quantification.de/hrm-
dyes.html#sybr]
705
706
707
47. CorProtocolTM 6000. HRM Assay Design and Analysis, pp.24. [http://
www.gene-quantification.de/hrm-protocol-cls.pdf]
708
709
48. Seipp MT, Durtschi JD, Liew MA,Williams J, Damjanovich K, Pont-Kingdon G,
710
Lyon E, Voelkerding KV, Wittwer CT: Unlabeled oligonucleotides as internal
711
temperature controls for genotyping by amplicon melting. J Mol Diagn 2007,
712
9:284-289.
713
714
49. Gundry CN, Dobrowolski SF, Martin YR, Robbins TC, Nay LM, Boyd N, Coyne
715
T, Wall MD, Wittwer CT, Teng DH: Base-pair neutral homozygotes can be
716
discriminated by calibrated high-resolution melting of small amplicons.
717
Nucleic Acids Res 2008, 36 (10): 3401-3408.
718
719
50. Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, Smith HO,
720
Yandell M, Evans CA, Holt RA, Gocayne JD, Amanatides P, Ballew RM, Huson
721
DH, Wortman JR, Zhang Q, Kodira CD, Zheng XH, Chen L, Skupski M,
722
Subramanian G, Thomas PD, Zhang J, Gabor Miklos GL, Nelson C, Broder S,
723
Clark AG, Nadeau J, McKusick VA, Zinder N, et al.: The sequence of the human
724
genome. Science 2001, 291:1304-1351.
725
726
51. Krypuy M, Newnham GM, Thomas DM, Conron M, Dobrovic A: High resolution
727
melting analysis for the rapid and sensitive detection of mutations in clinical
728
samples: KRAS codon 12 and 13 mutations in non-small cell lung cancer.
729
BMC Cancer 2006, 6:295.
730
731
52. Normabuena PA, Copeland JA, Krenkova P, Stambergova A, Macek M Jr.
732
Diagnostic method validation: High resolution melting (HRM) of small
733
amplicons genotyping for the most common variants in the MTHFR gene.
734
Clin Biochem 2009, 42(12):1308-1316.
735
736
53. von Ahsen N, Wittwer CT, Schutz E: Oligonucleotide melting temperatures
737
under
PCR
conditions:
nearest-neighbor
corrections
for
Mg2,
738
deoxynucleotide triphosphate, and dimethyl sulfoxide concentrations with
739
comparison to alternative empirical formulas. Clin Chem 2001, 47:1956-1961.
740
741
742
54. Montgomery GW, Sise A: Extraction of DNA from sheep white blood cells.
New Zeal J Agric Res 199, 33:437-441.
743
744
55. Primer-BLAST [www.ncbi.nlm.nih.gov/tools/primer-blast/]
745
746
56. Sugimoto N, Nakano S, Yoneyama M, Honda K: Improved thermodynamic
747
parameters and helix initiation factor to predict stability of DNA duplexes.
748
Nucleic Acids Res 1996, 24 (22):4501-4505.
749
750
57. Kibbe WA. OligoCalc: an online oligonucleotide properties calculator. Nucleic
751
Acids
752
[www.basic.northwestern.edu/biotools/oligocalc.html]
753
754
Res
2007,
35
(Suppl
2):
W43-W46.
58. Gutiérrez JP, Goyache F: A note on ENDOG: a computer program for
755
analyzing pedigree information. J Anim Breed Genet 2005, 122: 172-176.
756
757
758
759
760
761
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