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Rapid, sensitive and inexpensive detection of SCN5A genetic variations by high resolution melting analysis
Available online at www.sciencedirect.com
Clinical Biochemistry 42 (2009) 491 – 499
Rapid, sensitive and inexpensive detection of SCN5A genetic variations by high resolution melting analysis Gilles Millat a,b,⁎, Valérie Chanavat a , Claire Rodriguez-Lafrasse a,b , Robert Rousson a,b a
Laboratoire de Cardiogénétique Moléculaire, Centre de Biologie et Pathologie Est, Hospices Civils de Lyon, Lyon, F-69677 BRON Cedex, France b Université de Lyon, Lyon, F-69003, France; Université Lyon 1, Lyon, F-69003, France Received 19 August 2008; received in revised form 6 October 2008; accepted 20 October 2008 Available online 6 November 2008
Abstract Objectives: SCN5A mutations lead to a wide spectrum of cardiovascular disorders. Due to large cohorts to investigate and the large gene size, mutational screening must be performed using an extremely sensitive and specific scanning method. Design and methods: High Resolution Melting (HRM) analysis was developed for SCN5A mutation detection using control DNAs and DNAs carrying previously identified gene variants. A cohort of 40 patients was further screened. To evaluate HRM sensitivity, this cohort was also screened using an optimized DHPLC methodology. Results: All gene variants detected by DHPLC were also readily identified as abnormal by HRM analysis. Mutations were identified for 5 patients. Complete molecular SCN5A investigation was completed two times faster and cheaper than using DHPLC strategy. Conclusions: HRM analysis represents an inexpensive, highly sensitive and high-throughput method to allow identification of SCN5A gene variants. Identification of more SCN5A mutations could provide new insights into the pathophysiology of SCN5A-linked diseases syndromes. © 2008 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved. Keywords: Mutations; Cardiovascular disorders; High resolution melting; DHPLC; Polymorphisms; SCN5A
Introduction Voltage-gated sodium (Na+) channels, encoded by the SCNxA family of genes, are transmembrane proteins responsible for the rising phase of the action potential in nerve and muscle cells [1]. Due to this central role in excitability, it is not surprising that inherited mutations in SCN5A [MIM#: 600163, Swiss-Prot: Q14524], the gene encoding the Na+ channel αsubunit expressed in the human heart, could lead to a wide spectrum of cardiovascular disorders. These include Long-QT syndrome [LQTS] which has a prevalence estimated at about 1:5000 persons, Brugada syndrome [BS], dilated cardiomyopathy [DCM], progressive cardiac conduction disease [PCCD], Abbreviations: LQTS, Long-QT syndrome; SIDS, Sudden Infant Death Syndrome; AF, Atrial Fibrillation; DCM, Dilated Cardiomyopathy; HRMA, High Resolution Melting Analysis; BS, Brugada Syndrome; DHPLC, Denaturing High-Performance Liquid Chromatography. ⁎ Corresponding author. Laboratoire de Cardiogénétique Moléculaire, Centre de Biologie et Pathologie Est, Hospices Civils de Lyon, Lyon, F-69677 BRON Cedex, France. Fax: +33 472357246. E-mail address: [email protected] (G. Millat).
sick sinus syndrome [SSS], idiopathic ventricular fibrillation, and more complex overlapping phenotypes representing combinations of LQTS, conduction system disease, and Brugada syndrome [2–9]. Mutations or rare variants in SCN5A may also predispose patients, with or without underlying heart disease, to atrial fibrillation [AF] which is the most common cardiac arrhythmia in clinical practice [10,11]. Finally, 2–5% of cases diagnosed as sudden infant death syndrome [SIDS], the leading cause of mortality in the first year of life in the postneonatal period, carry functionally significant SCN5A genetic variants [12–14]. Consequently, in medical pratice, mutational screening on SCN5A of patients with syndromes quoted above is crucial for proper management of patients and affected families. Molecular analysis of these patients is however challenging owing to the size of this gene, presence of a large spectrum of mutations and the occurrence of numerous polymorphisms (for more information on previously reported mutations, see http://pc4.fsm.it:81/ cardmoc/). To date, most of the mutational screening in patients was performed either by direct sequencing or by DHPLC/ sequencing [15–20]. These methods for large-scale detection of
0009-9120/$ - see front matter © 2008 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved. doi:10.1016/j.clinbiochem.2008.10.014
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Table 1 HRM conditions for mutation scanning of SCN5A gene Exon
1
Forward primer (5′ → 3′)
Reverse primer (5′ → 3′)
Size T°a a (bp) (°C)
MgCl2 HRM conditions (mM) Melting T° ranges
GCCGCTGAGCCTGCGCCCAGT
GGAAAGTTGGGCGGCGGCAG
172
70–60 2
2a
GAATCAGGCCCATTGTCTGT
TCTCCTGCAAGGTGGTTGA
205
70–60 3
2b
CTGGCAGCCATCGAGAAG
GTAGGCAGGGCTGGAGGT
244
70–60 2
3
AGTCCAAGGGCTCTGAGCCAA
GGTACTCAGCAGGTATTAACTGCAA 228
70–60 3
4
TGGCCTGGCAGTGATGACCCC
AAAGGAAGGGAGGGGGCCACGTG
231
70–60 3
5
TCACTCCACGTAAGGAACCTG
ATGTGGACTGCAGGGAGGAAGC
324
70–60 1.5
6
CCCCACCCCCTTTCCTCCTCTGA
AGGGTTGCCTTGGCTCCCAGGTG
251
70–60 2.5
7
CCACCAGTGGAGCACAGAG
GGTCTGCTGGTCTCACAAAG
344
70–60 3
8
CGAGTGCCCCTCACCAGCATG
GGAGACTCCCCTGGCAGGACAA
152
70–60 3
9
GGGAGACAAGTCCAGCCCAGCAA
AGCCCACACTTGCTGTCCCTTG
262
70–60 3
10
CCAGAAGGGGCCCCAGTGAGG
AGGCTCCTCGGTGGCACTGCTCA
274
70–65 2
11
AAACGTCCGTTCCTCCACTCT
AACCCACAGCTGGGATTACCATT
226
70–60 3
CCTGGGCACTGGTCCGGCGCA
280
60
TGTGGTGCCTGCATCTCG
294
65–55 2.5
2
12 12a GCCAGTGGCTCAAAAGACAGGCT 12b AGCGGGGGAGAGCGAGAG
3
13
CCCTTTTCCCCAGCTGACGCAAA
GTCTAAAGCAGGCCAAGACAAATG
283
70–60 2
14
TCCTGGAAGGTATTCCAGTTAC
CTTACCCATGAAGGCTGTGC
394
70–60 3.5
15
GCCCCTGCCACAGCAAGAGTCAA
GCCTTCCACACCCCCCACCAT
309
65–55 3.2
16 16a GAGCCAGAGACCTTCACAAGGTCCCCT GCCAAAGAGCTGCATGCCCACCA
195
70–60 2.5
16b GGCACTGGGGAACCTGACACTG 17 17a GGGACTGGATGGCTTGGCATGGT
GGATGGTGTGTGTGTGGCCCTTG CGGGGAGTAGGGGGTGGCAATG
307 306
70–60 2.5 70–65 2
CTGTATATGTAGGTGCCTTATACATG
283
70–60 2
17b GCCCAGGGCCAGCTGCCCAGCT 18
AGGGTCTAAACCCCCAGGGTCA
CCCAGCTGGCTTCAGGGACAAA
280
65–55 3.2
19
GCTGCTACTCAGCCCACACT
TCTGGGTGGAACTGAGGCTA
226
70–60 3
20
ACAGGCCCTGAGGTGGGCCTGA
TGACCTGACTTTCCAGCTGGAGA
287
65–55
21
ATCGGCAGTGGTCCAGGCTT
CTCCGCCTCAGCTCCTTCTC
297
65–55 3
22
GCCTACTGTCTGTCCCCAAC
CACTCCCTGGTGGGAAGG
225
70–60 3
GCAAGTCTCCCTCTGTCTGG
216
70–60 2
CCATTGGGAGGAAGGAAGTC
212
70–60 2
23 23a GGTCTTGAAAAGGGCATGTG 23b GGAAGTTTGGGAGGTGCAT
Leading range
Trailing range
86.3–87.7 84–86.3 84.2–85.1 85.9–86.3 87.1–87.5 82.4–84.8 85.6–86.3 88.2–88.6 81.2–82.7 77.7–80.7 83–83.7 82–84 83–84 80.8–83 75.9–77.1 76.1–78.5 82.9–84 82.8–84.8 80.5–81.5 77.8–80.2 86.8–87.5 82.1–84.1 83.3–84.2 84–84.6 82.8–83.9 79.4–81.4 80.5–85 87.7–88.7 87.4–89.3 88.6–89.7 84.4–85.13 84.6–85.1 88.5–88.9 84.8–85.8 83.6–85.2 82.1–83.1 84.6–85.5 86.6–87.3 83.2–83.8 81.6–83 82.8–83.7 81.9–82.9 85.2–85.8 88.3–88.8 85.6–86.6 84.8–86.4 87.5–89 87.9–88.7 85.7–86.5 84.1–85.6 86–87.1 87.3–87.9 83.7–84.6 81.9–83.9 84.3–85.2 80.9–82.8 82.6–84.4 83.8–94.5 80.1–81.1
92.5–94 92.5–94.9 90.2–91 88–88.4 89.4–89.9 92–95 88.8–89.2 91.2–91.8 86.7–88.2 86.3–90 87.7–88.3 90.7–93 88–90 90–92.2 87–88.2 87–89.4 90.8–91.9 91.6–93.4 87.2–88.2 87.7–90 90.2–90.8 92–94 87.1–88 86.3–86.7 86.9–88 87.5–89.4 91.2–96 90.6–93 93.2–94.9 91.5–91.8 92.2–93 88.7–89.1 91.4–91.9 89.7–90.7 90–91.6 90–91.2 86.9–87.6 89.2–90 87.9–88.6 88.6–90 88.8–89.7 93.6–94.5 88.4–89.1 92.3–92.9 92.7–93.7 92.6–94.2 92–93.5 90.9–91.7 90.9–91.8 90.4–91.9 91.5–92.6 90.8–91.5 88.6–89.6 89.2–91.1 89.7–90.6 90.6–92.6 88.5–90.3 87.8–88.5 86.4–87.2
Mutation threshold confidence percentage 90 90 95 95 95 94 94 94 95 95 95 95 90 90 90 90 93 94 90 90 90 90 90 95 90 90 94 97 95 95 90 95 90 90 90 90 95 90 90 92 93 90 95 90 90 90 95 95 90 90 92 92 90 90 90 90 90 90 92
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Table 1 (continued) Exon
Forward primer (5′ → 3′)
Reverse primer (5′ → 3′)
Size T°a a (bp) (°C)
MgCl2 HRM conditions (mM) Melting T° ranges
24
CCAGAGCCCTAAGAAGCTCAA
AGCCTCAGGTGCCTGACTT
171
70–60 3
25
GCCTGTCTGATCTCCCTGTGTGA
CACCCTACCCAGCCCAGT
215
70–60 3.2
26
ATCCTGGCATCCTCATCAAG
CACTCCCACAAAACCAGGAG
230
70–60 3
27
CCCAGCGAGCACTTTCCATTTG
GCTTCTCCGTCCAGCTGACTTGTA
338
70–60 3
GAAGAGGCACAGCATGCTGTTGG
369
70–65 2
28b AAGTGGGAGGCTGGCATCGAC
GTGCTCTCCTCCGTGGCCACGC
303
70–65 1.5
28c GAGCCCAGCCGTGGGCATCCT
GTCCCCACTCACCATGGGCAG
310
70–60 1.5
28d CCAACCAGATAAGCCTCATCAACA 28e TGCTGCAACGCTCTTTGAAGCAT
CCGCCTGCTGACGGAAGAGGA AAAGGCTGCTTTTCAGTGTGTCCT
309 347
70–60 2 70–60 3
28 28a TGCACAGTGATGCTGGCTGGAA
a
Leading range
Trailing range
80.4–82.2 77.4–78.4 77.6–78.3 81.3–81.9 78–79 74.5–76.9 81.4–82.7 82.5–83.1 81.6–82.4 80–81.7 82.1–83.8 84.1–85.6 85.6–86 80.5–81.6 81.2–81.9 86.8–87.6 81.2–82.2 82–82.4 84.5–84.9 84.1–85.9 87.4–88.2 90.1–90.5
86.3–88 86.5–87.6 81.5–82 86.2–86.8 82.3–83.3 84–86.4 89.6–91 88.6–89.4 86.8–87.7 87.1–88.8 88–90 85.9–86.4 87.7–88.2 91–91.9 85.1–85.8 90.7–91.3 87.6–88.6 84.6–85 86.4–86.8 89.3–91 92.1–93 91.2–91.7
Mutation threshold confidence percentage 92 95 93 95 95 94 92 92 93 95 95 95 95 90 90 90 90 93 93 90 90 90
Touchdown PCRs.
mutations are expensive and technically time-consuming. High Resolution Melting (HRM) analysis has been successful in overcoming many of these limitations and constitutes a detection method with a nearly 100% detection [21]. This scanning method does not require any processing, reagent additions or separations after PCR. Once amplicons obtained, melting curves are generated by monitoring the fluorescence of a saturating dye that does not inhibit PCR. When combined with real-time PCR, this approach allowed a simple, semi-automated, and cost-effective detection of single-base substitutions and small insertions/deletions. In this study, we report an optimized protocol for scanning the SCN5A gene by HRM analysis using the Rotor-Gene 6000 (Corbett Life Science). A cohort of 40 patients (24 LQTS cases, 8 BS cases, 4 DCM cases, and 4 SIDS cases) was blindly screened using both the HRMA and DHPLC strategies in order to determine the most efficient technique in terms of sensibility, specificity, practicability and cost. Methods Subjects Genomic DNA samples from control patients and from patients with previously characterized genetic variants were used to determine the sensitivity of HRM analysis. These variants were previously detected in our laboratory by DHPLC and sequencing analysis [20]. Optimized HRM conditions were applied to further screen mutations in a panel of 40 additional
unrelated patients with could potentially carry a SCN5A mutation: 24 LQTS cases, 8 BS cases, 4 DCM cases and 4 SIDS cases. The study was conducted in accordance with the principles of the Declaration of Helsinki. Informed consent was obtained for all cases. Amplification of SCN5A exons Genomic DNA was extracted from whole blood using a WIZARD Genomic DNA Purification kit (Promega, Madison, WI). The coding exons of SCN5A were amplified using intronic primers and PCR conditions reported in Table 1. All pre-PCR steps were performed using the CAS-1200 liquid handling system (Corbett Life Science, Cambridgeshire, UK). Amplicon lengths were kept relatively short (171–389 bp) to improve discrimination between genotypes. Real-time PCR cycling of the genomic DNA samples were carried out on the Rotor-Gene 6000 analyser (Corbett Life Science). Except exon 12a, all amplicons were obtained using touchdown thermal cycling programs (Table 1). Amplifications were performed in a final volume of 20 μL containing 10 ng of genomic DNA, 100 μM of each dNTPs, 0.25 μM of each primer, and MgCl2 at indicated concentrations in Table 1. The cycling profile was 10 min denaturation at 95 °C, followed by 40 cycles of denaturation at 95 °C for 10 s, annealing at the indicated temperature in Table 1 for 15 s, and extension at 72 °C for 15 s. The final PCR step was a denaturation-renaturation procedure with 1 min at 95 °C followed by a decrease of temperature to 40 °C at 1 °C/s.
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DHPLC analysis DHPLC analysis was performed using the WAVE DNA Fragment Analysis System (Transgenomic, Cheshire, UK). Heteroduplex detection was performed using conditions previously reported [20]. Sequence analysis PCR products showing divergent DHPLC or HRM profiles were purified by using MultiScreen-PCR Plates (Millipore, Bedford, MA) and directly sequenced on both strands using the BigDye® Terminator v.3.1 Cycle Sequencing Kit (Applied Biosystems, Forster City, CA). Sequencing products were purified with Montage SEQ96 Sequencing Reaction Clean Up kit (Millipore) and applied onto an ABI 3730 automatic sequencer. Results Optimization of HRMA conditions
Fig. 1. High-resolution melting curves (A) and difference plots (B) obtained for exon 13 using optimized HRMA conditions. Using HRMA conditions reported in Table 1, the 3 genetic variants (2 SNPs [p.G639G, p.P648P] and 1 LQT3 mutation [p.G639R]) previously identified using DHPLC strategy were also readily visualized using HRMA.
Except for exons 1, 15, 17b, 18, 19, 20, 25 and 28e, all coding sequences were amplified using LightCycler® 480 High Resolution Melting Master kit according to the manufacturer's instructions (Roche Applied Science, Meylan, Fra). Exons 15, 18, 20 and 25 were amplified using LightCycler® 480 Probes Master kit (Roche Applied Science) and SYTO-9 dye (Invitrogen Carlsbad, CA) at final concentration of 2.5 μM. Exons 19 and 28e were amplified using HotGoldstar DNA polymerase (Eurogentec, Seraing, Bel) and SYTO-9 dye at final concentration of 2.5 μM. Exons 1 and 17b were amplified using Platinum (Invitrogen) and SYTO-9 dye at final concentration of 2.5 μM. Amplification of exon 1 was successfully optimized with 10% DMSO. HRM analysis HRM analysis was also performed using the Rotor-Gene 6000 analyser (Corbett Life Science). Melting curves are generated by ramping between 70 and 99 °C at 0.1 °C/s. Melting curves are normalised between two temperature ranges, the leading range and the trailing range. Optimized melting temperature ranges used to analyse PCR amplicons are given in Table 1. Fluorescence data were visualized using normalization plotting, and then analyzed using the automated grouping functionality provided by the Rotor-Gene 6000 scanning software. Melt curves were normalized and subsequently analysed manually to discriminate “noise” from true variation.
Complete SCN5A mutational screening required the investigation of 28 exons. Sensitivity and specificity of mutation detection by HRM analysis could be affected by the length the amplicon but also by the presence of many melting domains within an amplicon. Each exon was evaluated by a DNA melting simulation software for in silico diagnostic assay design (www.biophys.uni-duesseldorf.de/local/POLAND/poland. Table 2 List of SCN5A genetic variants previously identified by DHPLC strategy and detected by HRMA Exon
SCN5A genetic variant a
2 3 4 6
IVS1–4 c N t; p.G9V; p.A29A; p.R34C; p.D82D IVS2–24 c N t; IVS2–24/25 cg N ta ; p.H118H IVS4 + 16 g N c p.V210V; p.S216L; p.T220I; p.R222Q; p.R225Q; IVS6 + 45 c N t p.I239I IVS8-57 t N g ; p.Y339Y IVS9-20 c N t; IVS9-3 c N a; IVS9-2 g N a; p.T427T IVS11-25 a N g p.S519S; p.S524Y; p.I529I; p.H558R; p.L561L; p.S581S p.G639G; p.G639R; p.P648P p.Q692K; IVS14 + 31 g N a IVS14-18 t N c; p.I788I; IVS15 + 12 g N a; IVS15 + 16 a N g IVS15-13 c N t ; IVS15-5 c N a IVS16-6 t N c; p.R975W; p.V1019V; p.Q1027R ; p.E1061E p.S1103Y; p.A1121A IVS19 + 10 c N t; IVS19 + 11 g N a p.R1193Q IVS21 + 17 g N a; IVS21-20 g N a p.S1333Y; p.G1406G IVS24 + 27 c N t; IVS24 + 28 c N t IVS25-3 c N t; p.S1504S p.S1609W; p.F1616F; p.T1783T; p.E1784K; p.D1819D; p.D1819N; p.I1948I; p.F2004L
7 9 10 11 12 13 14 15 16 17 18 19 20 21 23 24 26 28
a Genetic variant indicated in bold correspond to mutations; others variants were polymorphisms.
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Table 3 List of SCN5A genetic variants identified by HRMA in a cohort 40 patients Case
Sex
Clinical diagnosis
Mutation Exon
Polymorphisms Nucleotide change
Effect on protein
Affected region
1
M
BS
2 3 4 5 6 7 8 9 10 11 12
M F M M M M M F F M F
BS BS BS BS BS BS BS DCM DCM DCM DCM
13
M
LQTS
No mutation identified
14
M
LQTS
No mutation identified
15 16
M F
LQTS LQTS
No mutation identified No mutation identified
17 18 19
F M F
LQTS LQTS LQTS
No mutation identified No mutation identified No mutation identified
20
M
LQTS
No mutation identified
21 22 23 24
F M F M
LQTS LQTS LQTS LQTS
No mutation identified No mutation identified No mutation identified No mutation identified
25 26 27 28 29
M F M F M
LQTS LQTS LQTS LQTS LQTS
30
M
LQTS
31
M
LQTS
32 33
M F
LQTS LQTS
No mutation identified No mutation identified
34
F
LQTS
No mutation identified
35
M
LQTS
No mutation identified
36 37 38 39 40
M M M F F
LQTS SIDS SIDS SIDS SIDS
References
No mutation identified
22
c.3940-3941 del CT
28
c.5129 C N T
10
c.1282 G N A
No mutation identified p.L1314fs DIII/S4-S5 No mutation identified p.S1710L DIV/S5-S6 No mutation identified No mutation identified No mutation identified No mutation identified No mutation identified No mutation identified 1No mutation identified
No mutation identified p.E428K DI-DII No mutation identified No mutation identified No mutation identified
Novel [22,23]
[11]
No mutation identified
28
17
c.4931 G N A
c.3157G N A
p.R1644H
DIV/S4
p.E1053K DII-DIII No mutation identified No mutation identified No mutation identified No mutation identified
html). Exons which were greater than 400 bp or exons with more than two melting domains were amplified in two overlapping segments (Table 1).
[24– 26]
[11,27,28]
IVS2-24 c N t ; c.1673 A N G (ex12), c.1681 C N T (ex12) ; IVS16-6 t N c ; c.3183 G N A (ex17) ; c.5457 C N T (ex 28) c.87 G N A (ex 2) ; c.5457 C N T (ex 28) c.3183 G N A (ex17) No polymorphism identified c.5175 G N C (ex 28); c.5457 C N T (ex 28) c.87 G N A (ex 2) c.3080 A N G (ex 17) IVS9-3 c N a ; c.1673 A N G (ex12) No polymorphism identified c.1587 T N C (ex12) c.87 G N A (ex 2) c.87 G N A (ex 2) ; c.1673 A N G (ex12) ; c.6010 T N C (ex28) IVS14-18 t N c ; c.3363 G N A (ex 18); c.4848 C N T (ex28), c.5457 C N T (ex 28) IVS9-3 c N a ; c.1673 A N G (ex12) ; c.5457 C N T (ex 28) c.87 G N A (ex 2) c.87 G N A (ex 2) ; IVS9-3 c N a ; c.1673 A N G (ex12) c.87 G N A (ex 2) ; IVS15+12 g N a No polymorphism identified IVS2+51 G N A ; IVS9-3 C N A ; c.1673 A N G (ex12) c.87 G N A (ex 2) ; c.717 C N T (ex 7); c.4218 G N A (ex 23) IVS15+12 g N a; c.5457 C N T (ex 28) IVS9-3 c N a ; c.1673 A N G (ex12) c.5457 C N T (ex 28) c.87 G N A (ex 2) ; IVS2-24 c N t ; c.1681 C N T (ex 12) IVS9-3 c N a ; c.1673 A N G (ex12) IVS9-3 c N a ; c.1673 A N G (ex12) c.87 G N A (ex 2) ; c.5457 C N T (ex 28) No polymorphism identified IVS9-3 c N a ; c.1673 A N G (ex12) ; c.5457 C N T (ex 28) c.87 G N A (ex 2) ; IVS9-3 c N a ; c.1673 A N G (ex12) ; c.3183 G N A (ex17) ; IVS24+28 c N t c.100 C N T (ex2); IVS9-3 c N a ; c.1673 A N G (ex12) c.1557 T N C (ex 12); c.5457 C N T (ex 28) IVS9-3 c N a ; c.1673 A N G (ex12) ; c.5457 C N T (ex 28) IVS9-3 c N a ; c.1673 A N G (ex12) ; c.4218 G N A (ex 23) ; c.5457 C N T (ex 28) IVS9-3 c N a ; c.1673 A N G (ex12) ; c.5457 C N T (ex 28) No polymorphism identified No polymorphism identified c.87 G N A (ex 2) No polymorphism identified c.87 G N A (ex 2) ; c.1673 A N G (ex12) ; IVS16-6 t N c ; c.3183 G N A (ex17)
HRM analysis was optimized using a small cohort of 6 control DNAs and, if available, DNAs carrying previously identified SCN5A gene variants. Exons and exon/intron boun-
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daries of control DNAs were directly sequenced on both strands in order to determine which exonic or intronic polymorphisms have to be detected by scanning methods. For exons 5, 8, 22, 25 and 27, HRM optimization was performed only with control DNAs, as no DNA carrying genetic variations was previously identified. All DNAs analysed were extracted using the same method. Our first results showed that PCR optimisation plays a crucial role in successful HRM analysis. A variable amplification may lead to an increased number of false positives. Initially, all exons were amplified with the LightCycler® 480 High Resolution Melting Master kit which includes a hot start PCR enzyme and the ResoLight saturating DNA dye. Using this procedure, 29 of 37 amplicons were successfully amplified. As illustrated for exon 13, melting curves generated from these amplicons allowed an easy identification of all SCN5A genetic variants previously identified by DHPLC (Fig. 1). The remaining eight amplicons were successfully analysed using the SYTO-9 fluorescent dye combined with other DNA polymerases (see Materials and methods). Using PCR and HRMA conditions reported in Table 1, the 66 SCN5A gene variants previously detected by DHPLC strategy in our laboratory were readily identified (Table 2). For some fragments, up to 3 melting temperature ranges were required due to the complexity of the melting domains. The position of a mutation within a fragment did not appear to have a significant effect on mutation detection. Unlike DHPLC, most of the polymorphisms present in a homozygous status were detected by HRMA. Differents variants did not always produce unique melting profiles. In the case of exons carrying both a common polymorphism and a disease-causing mutation, HRM profiles were different from the control samples but not different from those observed for exons carrying only the polymorphism. Consequently, each abnormal HRM profile was sequenced. Detection efficiency and reproductibility were evaluated and validated by performing 3 separate amplifications for each exon. Molecular investigation of 40 patients that could possibly carry SCN5A pathogenic mutations To validate our optimized HRMA conditions, a blinded study was performed using DNAs from 40 patients that may carry SCN5A pathogenic mutations: 24 patients with Long-QT syndrome, 8 patients with Brugada syndrome, 4 patients with dilated cardiomyopathy, and 4 SIDS cases (Table 3). This cohort was also evaluated by a DHPLC/sequencing strategy using conditions previously reported [20]. All genetic variants detected by DHPLC were also readily identified as abnormal by HRM analysis. Molecular analysis of this cohort allowed identification of four missense mutations and a 2-bp deletion (Table 3, Fig. 2). These missense mutations affect highly conserved amino acid residues, and were not found in 200 control chro-
497
mosomes of French origin. The p.L1314fs mutation, identified in BS case (case 3), was the only SCN5A mutation not previously reported in the literature. The p.E428K mutation identified in a LQTS case (case 26) has recently been reported in probands with early onset lone AF that had undergone atrioventricular node ablation and pacemaker implantation for the treatment of drug-refractory AF [11]. Case 5 with Brugada syndrome was heterozygous for the p.S1710L mutation. This genetic variant was previously reported in a symptomatic Japanese patient that did not exhibit the typical Brugada ECG [22]. Heterologously expressed S1710L channels showed marked acceleration in the current decay together with a large hyperpolarizing shift of steady-state inactivation and depolarizing shift of activation [22,23]. Case 31 with Long QT syndrome was heterozygous for the p.R1644H mutation. This previously reported mutation is located near the cytoplasmic end of the S4 segment of domain IV which has been implicated in channel gating as a putative voltage sensor [24,25]. This mutant could either alter the structure of this region and/or interfere with successful binding of the inactivation gate to its receptor site [24]. Case 36 was heterozygous for p.E1053K mutation which was previously reported in Brugada cases [27,28]. Previous studies suggested that this gene variant abolishes binding of Na (v)1.5 to ankyrin-G, and also prevents accumulation of Na(v) 1.5 at cell surface sites in ventricular cardiomyocytes [28]. Discussion Many methods for mutation scanning have been developed to screen for differences between the two copies of DNA within an individual. Examples of such methods are singlestrand conformational polymorphism analysis [SSCP], denaturing gradient gel electrophoresis [DGGE], denaturing highperformance liquid chromatography [DHPLC], temperature gradient capillary electrophoresis [TGCE], PCR-RFLP, and obviously sequencing that provides both genotyping and scanning at the same time. All of these methods require separation of the sample on a gel or other matrix. Many methods are manual and labor intensive, while others are complex and require specialized instrumentation. Many are based on detection of heteroduplexes formed after amplification of heterozygous DNA. Since 2002, High Resolution Melting method represents another mutation scanning method. Advantages of HRM are derived from the simplicity of the technique as no separations or processing of the samples is required. After PCR amplification, melting curves are generated by monitoring the fluorescence of a saturating dye that does not inhibit PCR. Furthermore, this method is a nonconsumed closed-tube genotyping approach. HRM melt analysis is a cost effective post-PCR technique, and can be used for high throughput mutation scanning on genes for which large cohorts of patients must be investigated.
Fig. 2. Normalised High-resolution melting curves (A) and difference plots (B) of SCN5A mutations identified in our cohort of 40 patients. Red curves correspond to SCN5A mutations, blue curves correspond to SCN5A polymorphisms, and green curves represent wild-type HRM profiles.
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This methodological approach is well adapted for screening the SCN5A gene as this gene is involved in many cardiovascular disorders. Good amplicon design is essential to obtain robust and reproducible HRM assays, thus several exons were amplified in two overlapping fragments (Table 1). Using control and mutant DNAs, HRMA conditions were optimized for all exons. These optimized conditions were subsequently tested on a cohort of 40 genomic DNAs that could carry SCN5A pathogenic mutations. This cohort was also screened by a DHPLC/sequencing strategy as previously reported [20]. All SCN5A genetic variants detected by DHPLC were readily identified as abnormal HRM profiles. Similar results were previously reported for other genes such as F8, NF2 or CFTR [29–31]. Among identified SCN5A gene variants, 5 mutations were described: p.E428K, p.E1053K and p.R1644H on LQTS patients, p.S1710L and p.L1314fs on patients with Brugada syndrome. Unlike DHPLC, it is noteworthy that HRM analysis allowed us to detect most of the SNPs present in a homozygous status. In our study, no false negatives were observed: all genetic variations identified either on mutant DNAs or on control DNAs that were previously completely sequenced were also identified by HRM analysis. On the other hand, HRMA could induce some false positives. Even with good genomic DNA quality samples that were extracted with the same method, some amplicons produce melt curves with subtle differences which may results in increased false positive rates. This number of false positive calls was significantly decreased by testing each amplicon against a mean of 4 wild-type samples. Using these HRMA conditions, complete mutation identification of the cohort was technically achieved in slightly more than 2 weeks whereas, using DHPLC strategy, this screening was technically achieved approximately in 4 weeks. Moreover, complete molecular SCN5A investigation was completed two times cheaper than using DHPLC strategy. HRM analysis is clearly faster, less laborious and appears as a high-capacity low-cost mutation detection method. Obviously, like all PCR-based analysis, HRM is not able to detect deletions encompassing the whole gene or an entire exon. The rapid, low-cost, and highly efficient HRM strategy fulfills all the conditions required for the systematic detection of SCN5A genetic variants. This molecular strategy allows the rapid mutation screening of SCN5A, a better understanding of the pathophysiology of disease which further leads to a better management of patients with SCN5A pathogenic mutations. Elucidation of the molecular basis of inherited channelopathies and other inherited arrhythmias provides new insights into the mechanisms responsible for more prevalent arrhythmias while also permitting the identification of therapeutic opportunities for treatment and prevention. Acknowledgments The full disclosure presents no conflict of interest. This work was supported by PHRC 97061 and by French Ministery of Research (Diagnosis Network on Neuromuscular Diseases).The authors thank Ms C. Bulle, E. Froidefond, R. Perraudin, O. Vial for expert technical assistance.
References [1] Viswanathan PC. SCN5A-linked disease syndrome: complex monogenic disorders of cardiac rhythm. Future Cardiol 2005;1:29–38. [2] Remme CA, Wilde AA, Bezzina CR. Cardiac sodium channel overlap syndromes: Different faces of SCN5A mutations. Trends Cardiovasc Med 2008;18:78–87. [3] Chen Q, Kirsch GE, Zhang D, et al. Genetic basis and molecular mechanism for idiopathic ventricular fibrillation. Nature 1998;392:293–6. [4] Nguyen TP, Wang DW, Rhodes TH, George Jr AL. Divergent biophysical defects caused by mutant sodium channels in dilated cardiomyopathy with arrhythmia. Circ Res 2008;102:364–71. [5] Hesse M, Kondo CS, Clark RB, et al. Dilated cardiomyopathy is associated with reduced expression of the cardiac sodium channel Scn5a. Cardiovasc Res 2007;75:498–509. [6] Schott JJ, Alshinawi C, Kyndt F, et al. Cardiac conduction defects associate with mutations in SCN5A. Nat Genet 1999;23:20–1. [7] Wang DW, Viswanathan PC, Balser JR, George Jr AL, Benson DW. Clinical, genetic, and biophysical characterization of SCN5A mutations associated with atrioventricular conduction block. Circulation 2002;105:341–6. [8] Bezzina C, Veldkamp MW, van Den Berg MP, et al. A single Na+ channel mutation causing both long-QT and Brugada syndromes. Circ Res 1999;85:1206–13. [9] Grant AO, Carboni MP, Neplioueva V, Starmer, et al. Long QT syndrome, Brugada syndrome, and conduction system disease are linked to a single sodium channel mutation. J Clin Invest 2002;110:1201–9. [10] Ellinor PT, Nam EG, Shea MA, Milan DJ, Ruskin JN, MacRae CA. Cardiac sodium channel mutation in atrial fibrillation. Heart Rhythm 2008; 5:99–105. [11] Darbar D, Kannankeril PJ, Donahue BS, et al. Cardiac Sodium Channel (SCN5A) variants associated with atrial fibrillation. Circulation 2008;117: 1927–35. [12] Ackerman MJ, Siu BL, Sturner WQ, et al. Postmortem molecular analysis of SCN5A defects in sudden infant death syndrome. JAMA 2001;286: 2264–9. [13] Wang DW, Desai RR, Crotti L, et al. Cardiac sodium channel dysfunction in sudden infant death syndrome. Circulation 2007;115:368–76. [14] Arnestad M, Crotti L, Rognum TO, et al. Prevalence of long-QTsyndrome gene variants in sudden infant death syndrome. Circulation 2007;115:361–7. [15] Ning L, Moss A, Zareba W, et al. Denaturing high-performance liquid chromatography quickly and reliably detects cardiac ion channel mutations in long QT syndrome. Genet Test 2003;7:249–53. [16] Napolitano C, Priori SG, Schwartz PJ, et al. Genetic testing in the long QT syndrome: development and validation of an efficient approach to genotyping in clinical practice. JAMA 2005;294:2975–80. [17] Lai LP, Su YN, Hsieh FJ, et al. Denaturing high-performance liquid chromatography screening of the long QT syndrome-related cardiac sodium and potassium channel genes and identification of novel mutations and single nucleotide polymorphisms. J Hum Genet 2005; 50:490–6. [18] Tester DJ, Cronk LB, Carr JL, et al. Allelic dropout in long QT syndrome genetic testing: a possible mechanism underlying false-negative results. Heart Rhythm 2006;3:815–21. [19] Fodstad H, Bendahhou S, Rougier JS, et al. Molecular characterization of two founder mutations causing long QT syndrome and identification of compound heterozygous patients. Ann Med 2006;38:294–304. [20] Millat G, Chevalier P, Restier-Miron L, et al. Spectrum of pathogenic mutations and associated polymorphisms in a cohort of 44 unrelated patients with long QT syndrome. Clin Genet 2006;70:214–27. [21] Reed GH, Kent JO, Wittwer CT. High-resolution DNA melting analysis for simple and efficient molecular diagnostics. Pharmacogenomics 2007;8:597–608. [22] Akai J, Makita N, Sakurada H, et al. A novel SCN5A mutation associated with idiopathic ventricular fibrillation without typical ECG findings of Brugada syndrome. FEBS Lett 2000;479:29–34. [23] Shirai N, Makita N, Sasaki K, et al. A mutant cardiac sodium channel with multiple biophysical defects associated with overlapping clinical features
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of Brugada syndrome and cardiac conduction disease. Cardiovasc Res 2002;53:348–54. Wang DW, Yazawa K, George Jr AL, Bennett PB. Characterization of human cardiac Na+ channel mutations in the congenital long QT syndrome. Proc Natl Acad Sci U S A 1996;93:13200–5. Wang Q, Shen J, Li Z, et al. Cardiac sodium channel mutations in patients with long QT syndrome, an inherited cardiac arrhythmia. Hum Mol Genet 1995;4:1603–7. Tester DJ, Will ML, Haglund CM, Ackerman MJ. Compendium of cardiac channel mutations in 541 consecutive unrelated patients referred for long QT syndrome genetic testing. Heart Rhythm 2005;2:507–17. Priori SG, Napolitano C, Gasparini M, et al. Clinical and genetic heterogeneity of right bundle branch block and ST-segment elevation syndrome: a prospective evaluation of 52 families. Circulation 2000;102:2509–15.
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[28] Mohler PJ, Rivolta I, Napolitano C, et al. Nav1.5 E1053K mutation causing Brugada syndrome blocks binding to ankyrin-G and expression of Nav1.5 on the surface of cardiomyocytes. Proc Natl Acad Sci U S A 2004;101:17533–8. [29] Lin SY, Su YN, Hung CC, et al. Mutation spectrum of 122 hemophilia A families from Taiwanese population by LD-PCR, DHPLC, multiplex PCR and evaluating the clinical application of HRM. BMC Med Genet 2008;9:53. [30] Sestini R, Provenzano A, Bacci C, et al. NF2 mutation screening by denaturing high-performance liquid chromatography and high-resolution melting analysis. Genet Test 2008;12:311–8. [31] Audrezet MP, Dabricot A, Le Marechal C, Ferec C. Validation of highresolution DNA melting analysis for mutation scanning of the cystic fibrosis transmembrane conductance regulator (CFTR) gene. J Mol Diagn 2008;10:424–34.
Clinical Biochemistry 42 (2009) 491 – 499
Rapid, sensitive and inexpensive detection of SCN5A genetic variations by high resolution melting analysis Gilles Millat a,b,⁎, Valérie Chanavat a , Claire Rodriguez-Lafrasse a,b , Robert Rousson a,b a
Laboratoire de Cardiogénétique Moléculaire, Centre de Biologie et Pathologie Est, Hospices Civils de Lyon, Lyon, F-69677 BRON Cedex, France b Université de Lyon, Lyon, F-69003, France; Université Lyon 1, Lyon, F-69003, France Received 19 August 2008; received in revised form 6 October 2008; accepted 20 October 2008 Available online 6 November 2008
Abstract Objectives: SCN5A mutations lead to a wide spectrum of cardiovascular disorders. Due to large cohorts to investigate and the large gene size, mutational screening must be performed using an extremely sensitive and specific scanning method. Design and methods: High Resolution Melting (HRM) analysis was developed for SCN5A mutation detection using control DNAs and DNAs carrying previously identified gene variants. A cohort of 40 patients was further screened. To evaluate HRM sensitivity, this cohort was also screened using an optimized DHPLC methodology. Results: All gene variants detected by DHPLC were also readily identified as abnormal by HRM analysis. Mutations were identified for 5 patients. Complete molecular SCN5A investigation was completed two times faster and cheaper than using DHPLC strategy. Conclusions: HRM analysis represents an inexpensive, highly sensitive and high-throughput method to allow identification of SCN5A gene variants. Identification of more SCN5A mutations could provide new insights into the pathophysiology of SCN5A-linked diseases syndromes. © 2008 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved. Keywords: Mutations; Cardiovascular disorders; High resolution melting; DHPLC; Polymorphisms; SCN5A
Introduction Voltage-gated sodium (Na+) channels, encoded by the SCNxA family of genes, are transmembrane proteins responsible for the rising phase of the action potential in nerve and muscle cells [1]. Due to this central role in excitability, it is not surprising that inherited mutations in SCN5A [MIM#: 600163, Swiss-Prot: Q14524], the gene encoding the Na+ channel αsubunit expressed in the human heart, could lead to a wide spectrum of cardiovascular disorders. These include Long-QT syndrome [LQTS] which has a prevalence estimated at about 1:5000 persons, Brugada syndrome [BS], dilated cardiomyopathy [DCM], progressive cardiac conduction disease [PCCD], Abbreviations: LQTS, Long-QT syndrome; SIDS, Sudden Infant Death Syndrome; AF, Atrial Fibrillation; DCM, Dilated Cardiomyopathy; HRMA, High Resolution Melting Analysis; BS, Brugada Syndrome; DHPLC, Denaturing High-Performance Liquid Chromatography. ⁎ Corresponding author. Laboratoire de Cardiogénétique Moléculaire, Centre de Biologie et Pathologie Est, Hospices Civils de Lyon, Lyon, F-69677 BRON Cedex, France. Fax: +33 472357246. E-mail address: [email protected] (G. Millat).
sick sinus syndrome [SSS], idiopathic ventricular fibrillation, and more complex overlapping phenotypes representing combinations of LQTS, conduction system disease, and Brugada syndrome [2–9]. Mutations or rare variants in SCN5A may also predispose patients, with or without underlying heart disease, to atrial fibrillation [AF] which is the most common cardiac arrhythmia in clinical practice [10,11]. Finally, 2–5% of cases diagnosed as sudden infant death syndrome [SIDS], the leading cause of mortality in the first year of life in the postneonatal period, carry functionally significant SCN5A genetic variants [12–14]. Consequently, in medical pratice, mutational screening on SCN5A of patients with syndromes quoted above is crucial for proper management of patients and affected families. Molecular analysis of these patients is however challenging owing to the size of this gene, presence of a large spectrum of mutations and the occurrence of numerous polymorphisms (for more information on previously reported mutations, see http://pc4.fsm.it:81/ cardmoc/). To date, most of the mutational screening in patients was performed either by direct sequencing or by DHPLC/ sequencing [15–20]. These methods for large-scale detection of
0009-9120/$ - see front matter © 2008 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved. doi:10.1016/j.clinbiochem.2008.10.014
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Table 1 HRM conditions for mutation scanning of SCN5A gene Exon
1
Forward primer (5′ → 3′)
Reverse primer (5′ → 3′)
Size T°a a (bp) (°C)
MgCl2 HRM conditions (mM) Melting T° ranges
GCCGCTGAGCCTGCGCCCAGT
GGAAAGTTGGGCGGCGGCAG
172
70–60 2
2a
GAATCAGGCCCATTGTCTGT
TCTCCTGCAAGGTGGTTGA
205
70–60 3
2b
CTGGCAGCCATCGAGAAG
GTAGGCAGGGCTGGAGGT
244
70–60 2
3
AGTCCAAGGGCTCTGAGCCAA
GGTACTCAGCAGGTATTAACTGCAA 228
70–60 3
4
TGGCCTGGCAGTGATGACCCC
AAAGGAAGGGAGGGGGCCACGTG
231
70–60 3
5
TCACTCCACGTAAGGAACCTG
ATGTGGACTGCAGGGAGGAAGC
324
70–60 1.5
6
CCCCACCCCCTTTCCTCCTCTGA
AGGGTTGCCTTGGCTCCCAGGTG
251
70–60 2.5
7
CCACCAGTGGAGCACAGAG
GGTCTGCTGGTCTCACAAAG
344
70–60 3
8
CGAGTGCCCCTCACCAGCATG
GGAGACTCCCCTGGCAGGACAA
152
70–60 3
9
GGGAGACAAGTCCAGCCCAGCAA
AGCCCACACTTGCTGTCCCTTG
262
70–60 3
10
CCAGAAGGGGCCCCAGTGAGG
AGGCTCCTCGGTGGCACTGCTCA
274
70–65 2
11
AAACGTCCGTTCCTCCACTCT
AACCCACAGCTGGGATTACCATT
226
70–60 3
CCTGGGCACTGGTCCGGCGCA
280
60
TGTGGTGCCTGCATCTCG
294
65–55 2.5
2
12 12a GCCAGTGGCTCAAAAGACAGGCT 12b AGCGGGGGAGAGCGAGAG
3
13
CCCTTTTCCCCAGCTGACGCAAA
GTCTAAAGCAGGCCAAGACAAATG
283
70–60 2
14
TCCTGGAAGGTATTCCAGTTAC
CTTACCCATGAAGGCTGTGC
394
70–60 3.5
15
GCCCCTGCCACAGCAAGAGTCAA
GCCTTCCACACCCCCCACCAT
309
65–55 3.2
16 16a GAGCCAGAGACCTTCACAAGGTCCCCT GCCAAAGAGCTGCATGCCCACCA
195
70–60 2.5
16b GGCACTGGGGAACCTGACACTG 17 17a GGGACTGGATGGCTTGGCATGGT
GGATGGTGTGTGTGTGGCCCTTG CGGGGAGTAGGGGGTGGCAATG
307 306
70–60 2.5 70–65 2
CTGTATATGTAGGTGCCTTATACATG
283
70–60 2
17b GCCCAGGGCCAGCTGCCCAGCT 18
AGGGTCTAAACCCCCAGGGTCA
CCCAGCTGGCTTCAGGGACAAA
280
65–55 3.2
19
GCTGCTACTCAGCCCACACT
TCTGGGTGGAACTGAGGCTA
226
70–60 3
20
ACAGGCCCTGAGGTGGGCCTGA
TGACCTGACTTTCCAGCTGGAGA
287
65–55
21
ATCGGCAGTGGTCCAGGCTT
CTCCGCCTCAGCTCCTTCTC
297
65–55 3
22
GCCTACTGTCTGTCCCCAAC
CACTCCCTGGTGGGAAGG
225
70–60 3
GCAAGTCTCCCTCTGTCTGG
216
70–60 2
CCATTGGGAGGAAGGAAGTC
212
70–60 2
23 23a GGTCTTGAAAAGGGCATGTG 23b GGAAGTTTGGGAGGTGCAT
Leading range
Trailing range
86.3–87.7 84–86.3 84.2–85.1 85.9–86.3 87.1–87.5 82.4–84.8 85.6–86.3 88.2–88.6 81.2–82.7 77.7–80.7 83–83.7 82–84 83–84 80.8–83 75.9–77.1 76.1–78.5 82.9–84 82.8–84.8 80.5–81.5 77.8–80.2 86.8–87.5 82.1–84.1 83.3–84.2 84–84.6 82.8–83.9 79.4–81.4 80.5–85 87.7–88.7 87.4–89.3 88.6–89.7 84.4–85.13 84.6–85.1 88.5–88.9 84.8–85.8 83.6–85.2 82.1–83.1 84.6–85.5 86.6–87.3 83.2–83.8 81.6–83 82.8–83.7 81.9–82.9 85.2–85.8 88.3–88.8 85.6–86.6 84.8–86.4 87.5–89 87.9–88.7 85.7–86.5 84.1–85.6 86–87.1 87.3–87.9 83.7–84.6 81.9–83.9 84.3–85.2 80.9–82.8 82.6–84.4 83.8–94.5 80.1–81.1
92.5–94 92.5–94.9 90.2–91 88–88.4 89.4–89.9 92–95 88.8–89.2 91.2–91.8 86.7–88.2 86.3–90 87.7–88.3 90.7–93 88–90 90–92.2 87–88.2 87–89.4 90.8–91.9 91.6–93.4 87.2–88.2 87.7–90 90.2–90.8 92–94 87.1–88 86.3–86.7 86.9–88 87.5–89.4 91.2–96 90.6–93 93.2–94.9 91.5–91.8 92.2–93 88.7–89.1 91.4–91.9 89.7–90.7 90–91.6 90–91.2 86.9–87.6 89.2–90 87.9–88.6 88.6–90 88.8–89.7 93.6–94.5 88.4–89.1 92.3–92.9 92.7–93.7 92.6–94.2 92–93.5 90.9–91.7 90.9–91.8 90.4–91.9 91.5–92.6 90.8–91.5 88.6–89.6 89.2–91.1 89.7–90.6 90.6–92.6 88.5–90.3 87.8–88.5 86.4–87.2
Mutation threshold confidence percentage 90 90 95 95 95 94 94 94 95 95 95 95 90 90 90 90 93 94 90 90 90 90 90 95 90 90 94 97 95 95 90 95 90 90 90 90 95 90 90 92 93 90 95 90 90 90 95 95 90 90 92 92 90 90 90 90 90 90 92
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Table 1 (continued) Exon
Forward primer (5′ → 3′)
Reverse primer (5′ → 3′)
Size T°a a (bp) (°C)
MgCl2 HRM conditions (mM) Melting T° ranges
24
CCAGAGCCCTAAGAAGCTCAA
AGCCTCAGGTGCCTGACTT
171
70–60 3
25
GCCTGTCTGATCTCCCTGTGTGA
CACCCTACCCAGCCCAGT
215
70–60 3.2
26
ATCCTGGCATCCTCATCAAG
CACTCCCACAAAACCAGGAG
230
70–60 3
27
CCCAGCGAGCACTTTCCATTTG
GCTTCTCCGTCCAGCTGACTTGTA
338
70–60 3
GAAGAGGCACAGCATGCTGTTGG
369
70–65 2
28b AAGTGGGAGGCTGGCATCGAC
GTGCTCTCCTCCGTGGCCACGC
303
70–65 1.5
28c GAGCCCAGCCGTGGGCATCCT
GTCCCCACTCACCATGGGCAG
310
70–60 1.5
28d CCAACCAGATAAGCCTCATCAACA 28e TGCTGCAACGCTCTTTGAAGCAT
CCGCCTGCTGACGGAAGAGGA AAAGGCTGCTTTTCAGTGTGTCCT
309 347
70–60 2 70–60 3
28 28a TGCACAGTGATGCTGGCTGGAA
a
Leading range
Trailing range
80.4–82.2 77.4–78.4 77.6–78.3 81.3–81.9 78–79 74.5–76.9 81.4–82.7 82.5–83.1 81.6–82.4 80–81.7 82.1–83.8 84.1–85.6 85.6–86 80.5–81.6 81.2–81.9 86.8–87.6 81.2–82.2 82–82.4 84.5–84.9 84.1–85.9 87.4–88.2 90.1–90.5
86.3–88 86.5–87.6 81.5–82 86.2–86.8 82.3–83.3 84–86.4 89.6–91 88.6–89.4 86.8–87.7 87.1–88.8 88–90 85.9–86.4 87.7–88.2 91–91.9 85.1–85.8 90.7–91.3 87.6–88.6 84.6–85 86.4–86.8 89.3–91 92.1–93 91.2–91.7
Mutation threshold confidence percentage 92 95 93 95 95 94 92 92 93 95 95 95 95 90 90 90 90 93 93 90 90 90
Touchdown PCRs.
mutations are expensive and technically time-consuming. High Resolution Melting (HRM) analysis has been successful in overcoming many of these limitations and constitutes a detection method with a nearly 100% detection [21]. This scanning method does not require any processing, reagent additions or separations after PCR. Once amplicons obtained, melting curves are generated by monitoring the fluorescence of a saturating dye that does not inhibit PCR. When combined with real-time PCR, this approach allowed a simple, semi-automated, and cost-effective detection of single-base substitutions and small insertions/deletions. In this study, we report an optimized protocol for scanning the SCN5A gene by HRM analysis using the Rotor-Gene 6000 (Corbett Life Science). A cohort of 40 patients (24 LQTS cases, 8 BS cases, 4 DCM cases, and 4 SIDS cases) was blindly screened using both the HRMA and DHPLC strategies in order to determine the most efficient technique in terms of sensibility, specificity, practicability and cost. Methods Subjects Genomic DNA samples from control patients and from patients with previously characterized genetic variants were used to determine the sensitivity of HRM analysis. These variants were previously detected in our laboratory by DHPLC and sequencing analysis [20]. Optimized HRM conditions were applied to further screen mutations in a panel of 40 additional
unrelated patients with could potentially carry a SCN5A mutation: 24 LQTS cases, 8 BS cases, 4 DCM cases and 4 SIDS cases. The study was conducted in accordance with the principles of the Declaration of Helsinki. Informed consent was obtained for all cases. Amplification of SCN5A exons Genomic DNA was extracted from whole blood using a WIZARD Genomic DNA Purification kit (Promega, Madison, WI). The coding exons of SCN5A were amplified using intronic primers and PCR conditions reported in Table 1. All pre-PCR steps were performed using the CAS-1200 liquid handling system (Corbett Life Science, Cambridgeshire, UK). Amplicon lengths were kept relatively short (171–389 bp) to improve discrimination between genotypes. Real-time PCR cycling of the genomic DNA samples were carried out on the Rotor-Gene 6000 analyser (Corbett Life Science). Except exon 12a, all amplicons were obtained using touchdown thermal cycling programs (Table 1). Amplifications were performed in a final volume of 20 μL containing 10 ng of genomic DNA, 100 μM of each dNTPs, 0.25 μM of each primer, and MgCl2 at indicated concentrations in Table 1. The cycling profile was 10 min denaturation at 95 °C, followed by 40 cycles of denaturation at 95 °C for 10 s, annealing at the indicated temperature in Table 1 for 15 s, and extension at 72 °C for 15 s. The final PCR step was a denaturation-renaturation procedure with 1 min at 95 °C followed by a decrease of temperature to 40 °C at 1 °C/s.
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DHPLC analysis DHPLC analysis was performed using the WAVE DNA Fragment Analysis System (Transgenomic, Cheshire, UK). Heteroduplex detection was performed using conditions previously reported [20]. Sequence analysis PCR products showing divergent DHPLC or HRM profiles were purified by using MultiScreen-PCR Plates (Millipore, Bedford, MA) and directly sequenced on both strands using the BigDye® Terminator v.3.1 Cycle Sequencing Kit (Applied Biosystems, Forster City, CA). Sequencing products were purified with Montage SEQ96 Sequencing Reaction Clean Up kit (Millipore) and applied onto an ABI 3730 automatic sequencer. Results Optimization of HRMA conditions
Fig. 1. High-resolution melting curves (A) and difference plots (B) obtained for exon 13 using optimized HRMA conditions. Using HRMA conditions reported in Table 1, the 3 genetic variants (2 SNPs [p.G639G, p.P648P] and 1 LQT3 mutation [p.G639R]) previously identified using DHPLC strategy were also readily visualized using HRMA.
Except for exons 1, 15, 17b, 18, 19, 20, 25 and 28e, all coding sequences were amplified using LightCycler® 480 High Resolution Melting Master kit according to the manufacturer's instructions (Roche Applied Science, Meylan, Fra). Exons 15, 18, 20 and 25 were amplified using LightCycler® 480 Probes Master kit (Roche Applied Science) and SYTO-9 dye (Invitrogen Carlsbad, CA) at final concentration of 2.5 μM. Exons 19 and 28e were amplified using HotGoldstar DNA polymerase (Eurogentec, Seraing, Bel) and SYTO-9 dye at final concentration of 2.5 μM. Exons 1 and 17b were amplified using Platinum (Invitrogen) and SYTO-9 dye at final concentration of 2.5 μM. Amplification of exon 1 was successfully optimized with 10% DMSO. HRM analysis HRM analysis was also performed using the Rotor-Gene 6000 analyser (Corbett Life Science). Melting curves are generated by ramping between 70 and 99 °C at 0.1 °C/s. Melting curves are normalised between two temperature ranges, the leading range and the trailing range. Optimized melting temperature ranges used to analyse PCR amplicons are given in Table 1. Fluorescence data were visualized using normalization plotting, and then analyzed using the automated grouping functionality provided by the Rotor-Gene 6000 scanning software. Melt curves were normalized and subsequently analysed manually to discriminate “noise” from true variation.
Complete SCN5A mutational screening required the investigation of 28 exons. Sensitivity and specificity of mutation detection by HRM analysis could be affected by the length the amplicon but also by the presence of many melting domains within an amplicon. Each exon was evaluated by a DNA melting simulation software for in silico diagnostic assay design (www.biophys.uni-duesseldorf.de/local/POLAND/poland. Table 2 List of SCN5A genetic variants previously identified by DHPLC strategy and detected by HRMA Exon
SCN5A genetic variant a
2 3 4 6
IVS1–4 c N t; p.G9V; p.A29A; p.R34C; p.D82D IVS2–24 c N t; IVS2–24/25 cg N ta ; p.H118H IVS4 + 16 g N c p.V210V; p.S216L; p.T220I; p.R222Q; p.R225Q; IVS6 + 45 c N t p.I239I IVS8-57 t N g ; p.Y339Y IVS9-20 c N t; IVS9-3 c N a; IVS9-2 g N a; p.T427T IVS11-25 a N g p.S519S; p.S524Y; p.I529I; p.H558R; p.L561L; p.S581S p.G639G; p.G639R; p.P648P p.Q692K; IVS14 + 31 g N a IVS14-18 t N c; p.I788I; IVS15 + 12 g N a; IVS15 + 16 a N g IVS15-13 c N t ; IVS15-5 c N a IVS16-6 t N c; p.R975W; p.V1019V; p.Q1027R ; p.E1061E p.S1103Y; p.A1121A IVS19 + 10 c N t; IVS19 + 11 g N a p.R1193Q IVS21 + 17 g N a; IVS21-20 g N a p.S1333Y; p.G1406G IVS24 + 27 c N t; IVS24 + 28 c N t IVS25-3 c N t; p.S1504S p.S1609W; p.F1616F; p.T1783T; p.E1784K; p.D1819D; p.D1819N; p.I1948I; p.F2004L
7 9 10 11 12 13 14 15 16 17 18 19 20 21 23 24 26 28
a Genetic variant indicated in bold correspond to mutations; others variants were polymorphisms.
G. Millat et al. / Clinical Biochemistry 42 (2009) 491–499
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Table 3 List of SCN5A genetic variants identified by HRMA in a cohort 40 patients Case
Sex
Clinical diagnosis
Mutation Exon
Polymorphisms Nucleotide change
Effect on protein
Affected region
1
M
BS
2 3 4 5 6 7 8 9 10 11 12
M F M M M M M F F M F
BS BS BS BS BS BS BS DCM DCM DCM DCM
13
M
LQTS
No mutation identified
14
M
LQTS
No mutation identified
15 16
M F
LQTS LQTS
No mutation identified No mutation identified
17 18 19
F M F
LQTS LQTS LQTS
No mutation identified No mutation identified No mutation identified
20
M
LQTS
No mutation identified
21 22 23 24
F M F M
LQTS LQTS LQTS LQTS
No mutation identified No mutation identified No mutation identified No mutation identified
25 26 27 28 29
M F M F M
LQTS LQTS LQTS LQTS LQTS
30
M
LQTS
31
M
LQTS
32 33
M F
LQTS LQTS
No mutation identified No mutation identified
34
F
LQTS
No mutation identified
35
M
LQTS
No mutation identified
36 37 38 39 40
M M M F F
LQTS SIDS SIDS SIDS SIDS
References
No mutation identified
22
c.3940-3941 del CT
28
c.5129 C N T
10
c.1282 G N A
No mutation identified p.L1314fs DIII/S4-S5 No mutation identified p.S1710L DIV/S5-S6 No mutation identified No mutation identified No mutation identified No mutation identified No mutation identified No mutation identified 1No mutation identified
No mutation identified p.E428K DI-DII No mutation identified No mutation identified No mutation identified
Novel [22,23]
[11]
No mutation identified
28
17
c.4931 G N A
c.3157G N A
p.R1644H
DIV/S4
p.E1053K DII-DIII No mutation identified No mutation identified No mutation identified No mutation identified
html). Exons which were greater than 400 bp or exons with more than two melting domains were amplified in two overlapping segments (Table 1).
[24– 26]
[11,27,28]
IVS2-24 c N t ; c.1673 A N G (ex12), c.1681 C N T (ex12) ; IVS16-6 t N c ; c.3183 G N A (ex17) ; c.5457 C N T (ex 28) c.87 G N A (ex 2) ; c.5457 C N T (ex 28) c.3183 G N A (ex17) No polymorphism identified c.5175 G N C (ex 28); c.5457 C N T (ex 28) c.87 G N A (ex 2) c.3080 A N G (ex 17) IVS9-3 c N a ; c.1673 A N G (ex12) No polymorphism identified c.1587 T N C (ex12) c.87 G N A (ex 2) c.87 G N A (ex 2) ; c.1673 A N G (ex12) ; c.6010 T N C (ex28) IVS14-18 t N c ; c.3363 G N A (ex 18); c.4848 C N T (ex28), c.5457 C N T (ex 28) IVS9-3 c N a ; c.1673 A N G (ex12) ; c.5457 C N T (ex 28) c.87 G N A (ex 2) c.87 G N A (ex 2) ; IVS9-3 c N a ; c.1673 A N G (ex12) c.87 G N A (ex 2) ; IVS15+12 g N a No polymorphism identified IVS2+51 G N A ; IVS9-3 C N A ; c.1673 A N G (ex12) c.87 G N A (ex 2) ; c.717 C N T (ex 7); c.4218 G N A (ex 23) IVS15+12 g N a; c.5457 C N T (ex 28) IVS9-3 c N a ; c.1673 A N G (ex12) c.5457 C N T (ex 28) c.87 G N A (ex 2) ; IVS2-24 c N t ; c.1681 C N T (ex 12) IVS9-3 c N a ; c.1673 A N G (ex12) IVS9-3 c N a ; c.1673 A N G (ex12) c.87 G N A (ex 2) ; c.5457 C N T (ex 28) No polymorphism identified IVS9-3 c N a ; c.1673 A N G (ex12) ; c.5457 C N T (ex 28) c.87 G N A (ex 2) ; IVS9-3 c N a ; c.1673 A N G (ex12) ; c.3183 G N A (ex17) ; IVS24+28 c N t c.100 C N T (ex2); IVS9-3 c N a ; c.1673 A N G (ex12) c.1557 T N C (ex 12); c.5457 C N T (ex 28) IVS9-3 c N a ; c.1673 A N G (ex12) ; c.5457 C N T (ex 28) IVS9-3 c N a ; c.1673 A N G (ex12) ; c.4218 G N A (ex 23) ; c.5457 C N T (ex 28) IVS9-3 c N a ; c.1673 A N G (ex12) ; c.5457 C N T (ex 28) No polymorphism identified No polymorphism identified c.87 G N A (ex 2) No polymorphism identified c.87 G N A (ex 2) ; c.1673 A N G (ex12) ; IVS16-6 t N c ; c.3183 G N A (ex17)
HRM analysis was optimized using a small cohort of 6 control DNAs and, if available, DNAs carrying previously identified SCN5A gene variants. Exons and exon/intron boun-
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G. Millat et al. / Clinical Biochemistry 42 (2009) 491–499
daries of control DNAs were directly sequenced on both strands in order to determine which exonic or intronic polymorphisms have to be detected by scanning methods. For exons 5, 8, 22, 25 and 27, HRM optimization was performed only with control DNAs, as no DNA carrying genetic variations was previously identified. All DNAs analysed were extracted using the same method. Our first results showed that PCR optimisation plays a crucial role in successful HRM analysis. A variable amplification may lead to an increased number of false positives. Initially, all exons were amplified with the LightCycler® 480 High Resolution Melting Master kit which includes a hot start PCR enzyme and the ResoLight saturating DNA dye. Using this procedure, 29 of 37 amplicons were successfully amplified. As illustrated for exon 13, melting curves generated from these amplicons allowed an easy identification of all SCN5A genetic variants previously identified by DHPLC (Fig. 1). The remaining eight amplicons were successfully analysed using the SYTO-9 fluorescent dye combined with other DNA polymerases (see Materials and methods). Using PCR and HRMA conditions reported in Table 1, the 66 SCN5A gene variants previously detected by DHPLC strategy in our laboratory were readily identified (Table 2). For some fragments, up to 3 melting temperature ranges were required due to the complexity of the melting domains. The position of a mutation within a fragment did not appear to have a significant effect on mutation detection. Unlike DHPLC, most of the polymorphisms present in a homozygous status were detected by HRMA. Differents variants did not always produce unique melting profiles. In the case of exons carrying both a common polymorphism and a disease-causing mutation, HRM profiles were different from the control samples but not different from those observed for exons carrying only the polymorphism. Consequently, each abnormal HRM profile was sequenced. Detection efficiency and reproductibility were evaluated and validated by performing 3 separate amplifications for each exon. Molecular investigation of 40 patients that could possibly carry SCN5A pathogenic mutations To validate our optimized HRMA conditions, a blinded study was performed using DNAs from 40 patients that may carry SCN5A pathogenic mutations: 24 patients with Long-QT syndrome, 8 patients with Brugada syndrome, 4 patients with dilated cardiomyopathy, and 4 SIDS cases (Table 3). This cohort was also evaluated by a DHPLC/sequencing strategy using conditions previously reported [20]. All genetic variants detected by DHPLC were also readily identified as abnormal by HRM analysis. Molecular analysis of this cohort allowed identification of four missense mutations and a 2-bp deletion (Table 3, Fig. 2). These missense mutations affect highly conserved amino acid residues, and were not found in 200 control chro-
497
mosomes of French origin. The p.L1314fs mutation, identified in BS case (case 3), was the only SCN5A mutation not previously reported in the literature. The p.E428K mutation identified in a LQTS case (case 26) has recently been reported in probands with early onset lone AF that had undergone atrioventricular node ablation and pacemaker implantation for the treatment of drug-refractory AF [11]. Case 5 with Brugada syndrome was heterozygous for the p.S1710L mutation. This genetic variant was previously reported in a symptomatic Japanese patient that did not exhibit the typical Brugada ECG [22]. Heterologously expressed S1710L channels showed marked acceleration in the current decay together with a large hyperpolarizing shift of steady-state inactivation and depolarizing shift of activation [22,23]. Case 31 with Long QT syndrome was heterozygous for the p.R1644H mutation. This previously reported mutation is located near the cytoplasmic end of the S4 segment of domain IV which has been implicated in channel gating as a putative voltage sensor [24,25]. This mutant could either alter the structure of this region and/or interfere with successful binding of the inactivation gate to its receptor site [24]. Case 36 was heterozygous for p.E1053K mutation which was previously reported in Brugada cases [27,28]. Previous studies suggested that this gene variant abolishes binding of Na (v)1.5 to ankyrin-G, and also prevents accumulation of Na(v) 1.5 at cell surface sites in ventricular cardiomyocytes [28]. Discussion Many methods for mutation scanning have been developed to screen for differences between the two copies of DNA within an individual. Examples of such methods are singlestrand conformational polymorphism analysis [SSCP], denaturing gradient gel electrophoresis [DGGE], denaturing highperformance liquid chromatography [DHPLC], temperature gradient capillary electrophoresis [TGCE], PCR-RFLP, and obviously sequencing that provides both genotyping and scanning at the same time. All of these methods require separation of the sample on a gel or other matrix. Many methods are manual and labor intensive, while others are complex and require specialized instrumentation. Many are based on detection of heteroduplexes formed after amplification of heterozygous DNA. Since 2002, High Resolution Melting method represents another mutation scanning method. Advantages of HRM are derived from the simplicity of the technique as no separations or processing of the samples is required. After PCR amplification, melting curves are generated by monitoring the fluorescence of a saturating dye that does not inhibit PCR. Furthermore, this method is a nonconsumed closed-tube genotyping approach. HRM melt analysis is a cost effective post-PCR technique, and can be used for high throughput mutation scanning on genes for which large cohorts of patients must be investigated.
Fig. 2. Normalised High-resolution melting curves (A) and difference plots (B) of SCN5A mutations identified in our cohort of 40 patients. Red curves correspond to SCN5A mutations, blue curves correspond to SCN5A polymorphisms, and green curves represent wild-type HRM profiles.
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This methodological approach is well adapted for screening the SCN5A gene as this gene is involved in many cardiovascular disorders. Good amplicon design is essential to obtain robust and reproducible HRM assays, thus several exons were amplified in two overlapping fragments (Table 1). Using control and mutant DNAs, HRMA conditions were optimized for all exons. These optimized conditions were subsequently tested on a cohort of 40 genomic DNAs that could carry SCN5A pathogenic mutations. This cohort was also screened by a DHPLC/sequencing strategy as previously reported [20]. All SCN5A genetic variants detected by DHPLC were readily identified as abnormal HRM profiles. Similar results were previously reported for other genes such as F8, NF2 or CFTR [29–31]. Among identified SCN5A gene variants, 5 mutations were described: p.E428K, p.E1053K and p.R1644H on LQTS patients, p.S1710L and p.L1314fs on patients with Brugada syndrome. Unlike DHPLC, it is noteworthy that HRM analysis allowed us to detect most of the SNPs present in a homozygous status. In our study, no false negatives were observed: all genetic variations identified either on mutant DNAs or on control DNAs that were previously completely sequenced were also identified by HRM analysis. On the other hand, HRMA could induce some false positives. Even with good genomic DNA quality samples that were extracted with the same method, some amplicons produce melt curves with subtle differences which may results in increased false positive rates. This number of false positive calls was significantly decreased by testing each amplicon against a mean of 4 wild-type samples. Using these HRMA conditions, complete mutation identification of the cohort was technically achieved in slightly more than 2 weeks whereas, using DHPLC strategy, this screening was technically achieved approximately in 4 weeks. Moreover, complete molecular SCN5A investigation was completed two times cheaper than using DHPLC strategy. HRM analysis is clearly faster, less laborious and appears as a high-capacity low-cost mutation detection method. Obviously, like all PCR-based analysis, HRM is not able to detect deletions encompassing the whole gene or an entire exon. The rapid, low-cost, and highly efficient HRM strategy fulfills all the conditions required for the systematic detection of SCN5A genetic variants. This molecular strategy allows the rapid mutation screening of SCN5A, a better understanding of the pathophysiology of disease which further leads to a better management of patients with SCN5A pathogenic mutations. Elucidation of the molecular basis of inherited channelopathies and other inherited arrhythmias provides new insights into the mechanisms responsible for more prevalent arrhythmias while also permitting the identification of therapeutic opportunities for treatment and prevention. Acknowledgments The full disclosure presents no conflict of interest. This work was supported by PHRC 97061 and by French Ministery of Research (Diagnosis Network on Neuromuscular Diseases).The authors thank Ms C. Bulle, E. Froidefond, R. Perraudin, O. Vial for expert technical assistance.
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