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Development of a high resolution melting method for the detection of genetic variations in Long QT Syndrome
Clinica Chimica Acta 412 (2011) 203–207
Contents lists available at ScienceDirect
Clinica Chimica Acta j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / c l i n c h i m
Short communication
Development of a high resolution melting method for the detection of genetic variations in Long QT Syndrome Gilles Millat a,b,⁎, Valérie Chanavat a, Hervé Créhalet a,b, Robert Rousson a,b a b
Laboratoire de Cardiogénétique Moléculaire, Centre de Biologie et Pathologie Est, Hospices Civils de Lyon, Lyon, France Université de Lyon, Lyon, F-69003, France; Université Lyon 1, Lyon, F-69003, France
a r t i c l e
i n f o
Article history: Received 20 August 2010 Received in revised form 8 September 2010 Accepted 10 September 2010 Available online 17 September 2010 Keywords: Mutations Long QT Syndrome High resolution melting DHPLC Polymorphisms Molecular diagnosis
a b s t r a c t Background: Inherited Long QT Syndrome (LQTS) is a cardiac channelopathy associated with a high risk of sudden death. The prevalence has been estimated at close to 1:2000. Due to large cohorts to investigate, the size of the 3 prevalent mutated genes, and the presence of a large spectrum of private mutations, mutational screening requires an extremely sensitive and specific scanning method. Methods: Efficiency of high resolution melting (HRM) analysis was evaluated for the most prevalent LQTScausing genes (KCNQ1, KCNH2) using control DNAs and DNAs carrying previously identified gene variants. A cohort of 34 patients with a suspicion of LQTS was further blindly screened. To evaluate HRM sensitivity, this cohort was also screened using an optimized DHPLC strategy. Results: HRM analysis was successfully optimized for KCNQ1 but optimisation of KCNH2 was more laborious as only 3 KCNH2 exons could be finally optimized. Remaining KCNH2 exons were analysed by direct sequencing. This molecular approach, which combined HRM and direct sequencing, was applied on the cohort of 34 cases and 9 putative mutations were identified. Using this approach, molecular investigation was completed faster and cheaper than using DHPLC strategy. Conclusions: This HRM/sequencing procedure represents an inexpensive, highly sensitive and highthroughput method to allow identification of mutations in the coding sequences of prevalent LQTS genes. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Long QT Syndrome (LQTS) is a primary cardiac channelopathy typically characterized by a prolongation (N440 ms) of the QTc interval, syncope, ventricular arrhythmias and a high risk of sudden death [1]. The prevalence has recently been estimated at close to 1:2000 [2]. This disease is genetically heterogeneous. To date, 12 genes have been linked to LQTS, but 90% of genetically defined LQTS patients have a disease-causing mutation in LQT1 (KCNQ1, MIM# 607542), LQT2 (KCNH2, MIM# 152427) or LQT3 (SCN5A, MIM# 600163) [1]. The majority of reported mutations (approximately 80%) are single nucleotide substitutions causing missense, nonsense, or splice mutations. The remaining 20% are small in-frame insertions or deletions, or rare large deletions (for more information, see the Human Gene Mutation Database; www.hgmd.cf.ac.uk). Although some recurrent mutations have been reported, most affected families segregate a unique private mutation.
Abbreviations: LQTS, Long QT Syndrome; HRM, High resolution melting; DHPLC, Denaturing high-performance liquid chromatography. ⁎ Corresponding author. Laboratoire de Cardiogénétique Moléculaire, Centre de Biologie Est, F-69677 BRON Cedex, France. Tel.: +33 472129674; fax: +33 427855900. E-mail address: [email protected] (G. Millat). 0009-8981/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.cca.2010.09.013
In medical practice, mutational screening of LQTS patients is crucial for proper management of patients and affected families but also to determine optimal therapeutic strategies as LQTS patients often have gene-specific ECG abnormalities and gene-specific triggers for arrhythmias. Molecular analysis of LQTS patients is however challenging owing to the number of disease-causing genes, the size of the 3 prevalent mutated genes, and the presence of a large spectrum of private mutations. To date, mutational screening in most LQTS cohorts was performed either by direct sequencing or by using scanning methods such as DHPLC or SSCP [3–7]. These methods for large-scale detection of mutations are expensive and technically time-consuming. As previously shown for SCN5A, high resolution melting (HRM) analysis has been successful in overcoming many of these limitations and constitutes a simple, semi-automated, and cost-effective approach to identify singlebase substitutions and small insertions/deletions with a nearly 100% detection [8,9]. This scanning method does not require any processing, reagent additions or separations after PCR. The aim of this study was to evaluate the possibility to develop an optimized protocol for scanning the 2 other prevalent LQTS-causing genes (KCNQ1 and KCNH2) by HRM analysis using the Rotor-Gene 6000 (Corbett Life Science). A cohort of 34 suspected LQTS patients was further blindly screened using both HRM and DHPLC strategies in order to determine the most efficient technique in terms of sensibility, specificity, practicability and cost.
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Table 1 Experimental procedures used to identify KCNQ1 nucleotide variations. Exon
Primers
PCR conditions
HRM conditions
Forward primer (5′→3′)
Reverse primer (5′→3′)
Size (bp)
T°a (°C)#
PCR kit⁎
MgCl2 (mM)
Leading range (°C)
Trailing range (°C)
Mutation detection threshold
1 2 3
AGTGGCTGCCCGCACTG ATCTGTGGGATGGGCAGA CTGGGTTCAAACAGGTTGC
CAGCTCTCAGGAAGCACCTT GAGATGCCAGCTTCCAAGG GAAACCTGGGCGTGACCT
528 203 248
65–55 70–60 70–60
5 1 1
ND 3 3
4 5 6 7 8 9 10 11 12 13 14 15
GACGAGAGCAGGGTGTATGC TGAACAGCTGAGCCCAGCCT TCGCTGGGACTCGCTGCCTT GGGTTTGGGTTAGGCAGTTGG TGGCCTGTGTGGACGGGA GGGAACAGGGAGGGGGAGCT AGGGCCTGGCAGACGATGTC CTGGCAGGTTGGGTGGGAGG CAGGGGCAGTGAGGGGATGA CACTGTCACTGCCTGCACTT GTCAAGCTGTCTGTCCCACA CCCCCAGCCCTACCACCC
GGCTCAGGAGGGGTGCTC CTAGTGTGGGCTGCTCTGCT TGTCCTGCCCACTCCTCAGCC AGCCACCCCAGGACCCCAG CAGTGACCAAAATGACAGTGA GGCCTCCCCACCTGCTAGCA CACAGCCCAAGGGAGAAA CCTCCAGCCCAGCCCTTCAC GTGGCTTGGGGGCGGAGG GTCTCAGCCCCTCCCTCCT ATGGCCCATTCTGACATCAT GCAGGAGCTTCACGTTCACA
222 223 291 247 181 267 295 211 270 272 231 249
70–60 70–60 70–60 70–60 70–60 70–60 70–60 70–60 70–65 70–65 70–60 70–60
3 2 1 1 1 2 1 1 2 2 1 3
3 3.2 2.5 3 3 3.2 3 3 3.2 3.2 3 3
90.1–91.1 93–94 90.4–90.7 93–94 90.9–91.9 92.4–93.1 90.9–91.6 91.2–92.3 90.1–91 92.4–93.5 90.8–91.7 88.8–89.7 90.2–91.3 93–94.2 89.9–90.9 90.1–90.7 90.6–91.7
90 90 95 90 90 90 90 90 90 90 90 90 90 90 90 90 90
16
TTCCCACCACTGACTCTCT
ACTCTTGGCCTCCCCCTCT
328
70–60
4
1.5
Direct sequencing 84.7–85.6 85–86 85–86 89.2–89.6 85.6–86.7 87.7–88.8 85.6–86.7 85.6–86.6 84.9–86 86.9–88 85.5–86.4 83.6–84.6 84.5–85.5 86.4–87.3 84.6–85.7 85.7–86.6 84.2–85.5 Direct sequencing
# annealing temperature. * 1: LightCycler® 480 High Resolution Melting Master kit (Roche Applied Science); 2: LightCycler® 480 Probes Master kit (Roche Applied Science) and SYTO-9 dye (Invitrogen); 3: SYBR® GreenER™ qPCR SuperMix (Invitrogen); 4: Hot Goldstar DNA polymerase kit (Eurogentec); 5: FailSafe™ PCR System (Epicentre).
exons of KCNQ1 and KCNH2 were amplified using intronic primers and PCR conditions reported in Tables 1 and 2. All pre-PCR steps were performed using the CAS-1200 liquid handling system (QIAGEN, Courtaboeuf, France). Real-time PCR cycling of the genomic DNA samples was carried out on the Rotor-Gene 6000 analyser (QIAGEN). Except KCNH2 exons 1 and 4, all amplicons were obtained using touchdown thermal cycling programs (Tables 1 and 2). 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 Tables 1 and 2. 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 Tables 1 and 2 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.
2. Materials and methods 2.1. Subjects Genomic DNA samples from control patients and from LQTS patients with previously characterized genetic variants were used to determine the sensitivity of HRM analysis. Optimized HRM conditions were applied to further screen mutations in a panel of 34 additional unrelated patients with a suspicion of LQTS. The study was conducted in accordance with the principles of the Declaration of Helsinki. Informed consent was obtained for all cases. 2.2. PCR amplification Genomic DNA was extracted from whole blood using a Wizard Genomic DNA Purification kit (Promega, Madison, WI). The coding
Table 2 Experimental procedures used to identify KCNH2 nucleotide variations. Exon
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Primers
PCR conditions
HRM conditions
Forward primer (5′→3′)
Reverse primer (5′→3′)
Size (bp)
T°a (°C)#
PCR kit⁎
MgCl2 (mM)
Leading range (°C)
CCGGCCGGGCCACCCGAAGCCTAGT GGTCCCCGCTCACGCGCACTCT ACTGAGTGGGTGCCAAGG CTCCGGGGCTGCTCGCGATCC GCCTGGTCTCCACTCTCGAT CCCCCGAGGTCCCATGGCCTGCCTC TGCCCCATCAACGGAATGTGC CCTGGTGCGGGGCCTGAGC AGAGTGTGGGTTGGGGG GGTGCCTGCTGCCTGGAT GCGCCCAGCCCTACTTTTTC GCTTCCTGCCCAGTCCTC GCAGCGGTGGTGCGTCTACCC GAGGCTGTCACTGGTGTCCCC GCCCCTTTCCCTCCCCTTCCT
CCCCGCCGTCCCCTCGCCAAAGCCT TTGACCCCGCCCCTGGTCGT AGACCACGAACCCCTGAG AGGGCCCAGAATGCAGCAAGC CCCTCTCCAAGCTCCTCCAA CTCTCCTCTCCCTACACCACCTGC TTCCTCCAACTTTGGGTTCCT TTCCCCTCTGCCACCCCACTCTT GAGGGCATTTCCAGTCCAGT CACACAGCTGGAAGCAGGAG CGCCTTCCAGCTCCCAGC GGGTAGACGCACCACCGCTGC ACCAGACTCCAGGGCGTGCCC ACAAGCGGGTCACGGTACATC GCCCAGCAGCGCCTTGATCC
315 314 250 570 351 524 490 346 355 308 266 442 284 276 273
69.3 TD 70/60 TD 70/60 61 TD 70/60 TD 70/60 TD 70/60 TD 70/60 TD 70/60 TD 70/60 TD 70/60 TD 70/60 TD 70/60 TD 70/60 TD 70/65
4 3+DMSO 1 3+DMSO 3 3 3 3 3 1 5 4 3 3 2
ND 1.5 3 1.5 1.5 1.5 1.5 1 1.5 2.5 1.5 ND 1.5 1.5 3.2
Direct sequencing Direct sequencing 84.3–85.2 Direct sequencing Direct sequencing Direct sequencing Direct sequencing Direct sequencing Direct sequencing 84.3–85 Direct sequencing Direct sequencing Direct sequencing Direct sequencing 84.6–85.7
Trailing range (°C)
Mutation detection threshold
91–91.9
90
89.9–90.8
90
92.5–93.4
90
# annealing temperature. * 1: LightCycler® 480 High Resolution Melting Master kit (Roche Applied Science); 2: SYBR® GreenER™ qPCR SuperMix (Invitrogen); 3: Hot Goldstar DNA polymerase kit (Eurogentec); 4: FailSafe™ PCR System (Epicentre); 5: Taq CORE kit (Qbiogene).
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Exons screened by HRM analysis were amplified using either LightCycler® 480 High Resolution Melting Master kit (Roche Applied Science, Meylan, Fra), or LightCycler® 480 Probes Master kit (Roche Applied Science) with SYTO-9 dye (Invitrogen Carlsbad, CA) at final concentration of 2.5 μM, or SYBR® GreenER™ qPCR SuperMix kit (Invitrogen). These kits were used according to the manufacturer's instructions. Exons analysed by direct sequencing were amplified using either HotGoldstar DNA polymerase (Eurogentec, Seraing, Bel) or FailSafe PCR system (Epicentre, Madison, WI) according to the manufacturer's instructions. 2.3. HRM analysis HRM analyses were performed using the Rotor-Gene 6000 analyser (QIAGEN). Melting curves were generated by ramping between 70 and 99 °C at 0.1 °C/s. Melting curves were normalised between two temperature ranges, the leading range and the trailing range. Optimized melting temperature ranges used to analyse PCR amplicons are given in Tables 1 and 2. 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. As HRM could induce some false positives, each amplicon was tested against a mean of 4 wild-type samples in order to decrease significantly the number of false positive calls. 2.4. DHPLC analysis DHPLC analyses were performed using optimized elution profiles and melting temperatures described previously [6]. 2.5. 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 BigDye XTerminator® Purification Kit (Applied Biosystems) and applied onto an ABI 3730 automatic sequencer. Table 3 List of KCNQ1 genetic variants identified by HRM analysis (NM_000218.2).
205
Table 4 List of KCNH2 genetic variants identified by HRM analysis (NM_000238.2). Exon Genetic variant 3 10 15
c.308GNT [p.Gly103Val], c.340CNT [p.Pro114Ser], c.442CNT [p.Arg148Trp], c.453dup [p.Thr152fs] c.2454GNT [p.Ser818Ser], c.2508CNT [p.Asp836Asp], c.2514GNT [p.Leu838Leu], c.2588GNA [p.Arg863Gln] c.3331-9_3331-8del
Genetic variants indicated in bold correspond to mutations; others variants to SNPs.
3. Results 3.1. Optimisation of HRM conditions Mutational screening of KCNQ1 and KCNH2 coding sequences by HRM analysis required the investigation of 31 coding exons. PCR and HRM optimisations were performed using a small cohort of 6 control DNAs for which exons and exon/intron boundaries were directly sequenced on both strands in order to determine which exonic or intronic polymorphisms were present. To perform HRM analysis with a near 100% sensitivity, exons which were greater than 400 bp or with more than two melting domains (use of Poland's algorithm [10]) were tentatively amplified in two overlapping segments. To avoid a variable amplification that may lead to an increased number of false positives, control genomic DNAs used for PCR optimisation were extracted using the same method and, subsequently, adjusted to a concentration of 2.5 ng/μL. Initially, all exons were tentatively amplified with the LightCycler® 480 High Resolution Melting Master kit. Using this procedure, only 10 amplicons could be successfully amplified. Seven of the remaining amplicons analysed by HRM were successfully amplified using the SYTO-9 fluorescent dye combined with other DNA polymerases (Tables 1 and 2). Despite use of different primer pairs (carefully selected with Primer3Plus software) and different amplification protocols (modifications of primer concentrations, MgCl2 concentrations and/or annealing temperatures), PCR optimisation of 14 exons, mostly KCNH2 exons, did not allow to obtain a sufficient amplicon quality for subsequent HRM analysis. Most of them
Table 5 List of putative KCNQ1 and KCNH2 mutations identified in a cohort of 34 unrelated patients with a suspicion of LQTS. Mutation
Type mutation Comments or affected domain
KCNQ1 1 c.217CNA
p.Pro73Thr
N-ter
7
c.1032GNA
p.Ala344Ala
IVS9
c.1252p.Val418X or 3_1252delinsAAG splice mutation
Splice mutation PTC§
11
c.1513CNT
p.Gln505X
PTC§
13
c.1597CNT
p.Arg533Trp
C-ter
KCNH2 1 2 11 14
c.1ANT c.92TNC c.2635GNC c.3302CNT
p.Met1? p.Ile31Thr p.Gly879Arg p.Pro1101Leu
ND N-ter C-ter C-ter
Exon/ Nucleotide intron change
Exon Genetic variant 2 3
4 5
6 7
8 9 10 11 12 13 14 15
c.387-32GNA, c.387-17GNA, c.459GNA [p.Thr153Thr], c.477+5GNA, c.477+9CNT c.478-56TNG, c.478-10GNA, c.478-8CNT, c.524-534 dup [p.Gly179fs], c.536GNA [p.Gly179Asp], c.562delT [p.Trp188fs], c.575GNA [p.Arg192His], c.595TNG [p.Ser199Ala] c.605-28ANG, c.605-24CNT c.684-25CNT, c.691CNT [p.Arg231Cys], c.720CNT [p.His240His], c.728GNC [p.Arg243Pro], c.775CNT [p.Arg259Cys], c.776GNA [p.Arg259His], c.780+40GNA c.804CNT [p.Ile268Ile], c.817CNT [p.Leu273Phe], c.858CNT [p.Asp286Asp] c.940GNA [p.Gly314Ser], c.947GNA [p.Gly316Glu], c.954GNA [p.Lys318Lys], c.1017_1019del [p.Phe340del], c.1022CNT [p.Ala341Val], c.1031CNT [p.Ala344Val], c.1032GNA [p.Ala344Ala], c.1032+5GNA, c.1032+11CNT c.1110GNA [p.Ala370Ala] c.1179GNT [p.Lys393Asn], c.1202GNA [p.Arg401Gln], c.1222CNG [p.Pro408Ala] c.1343CNG [p.Pro448Arg], c.1393+9CNT, c.1393+29GNA, c.1393+31 GNA c.1394-14CNT, c.1429CNT [p.Pro477Ser] c.1515-31CNG, c.1590+14TNC c.1638GNA [p.Ser546Ser], c.1685+36ANG c.1686-49CNT, c.1732+43TNC, c.1732+53CNT c.1733-53TNC, c.1733-45CNG, c.1733-29CNA, c.1794+16GNA, c.1794+32GNT
Genetic variants indicated in bold correspond to mutations; others variants to SNPs.
Previously reported* Previously reported* Novel mutation, ongoing in vitro studies Found with a homozygous status. Associated with sensorineural deafness Previously reported*
Novel mutation Novel mutation Novel mutation Novel mutation
* For more information on previously reported mutations, see the genetic mutations and inherited arrhythmias database: http://www.fsm.it/cardmoc. § PTC: Premature Termination Codon.
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present abnormal PCR characteristics such as high Ct values or presence of extra products. Presence of primer dimers or non specific products should be avoided because it may alter the melt curve characteristics causing the generation of false positive results. For the other, although PCR characteristics seems to be correct, a wide spread in HRM curves from control DNA samples were observed. Consequently, as no optimized PCR/HRM conditions could be defined, these 14 exons were analysed by direct sequencing of double-stranded PCR. For the 17 successfully optimized exons, HRM analysis was further applied on the 6 control DNAs but also on 65 genomic DNAs
A
containing previously identified genetic variants. Each abnormal HRM profile was further sequenced. Melting curves generated from these amplicons allowed an easy identification of all genetic variants previously identified either by direct sequencing or by our DHPLC/ sequencing strategy (Tables 3 and 4). 3.2. Molecular investigation of 34 unrelated patients with a suspicion of LQTS To validate experimental procedures detailed in Tables 1 and 2, a blinded study was performed using genomic DNAs from 34 new cases
B WT WT
p.Ala344Ala p.Ala344Ala
Exon 7
Exon 7
WT
WT c.1252-3_1252delinsAAG
c.1252- 3_1252delinsAAG
Exon 10
Exon 10
WT
WT
c.1394-14C>T
c.1394-14C>T
p.Gln505X
p.Gln505X Exon 11
Exon 11
WT p.Arg533Trp
c.1638G>A + c.1685+36A>G
WT c.1685+36A>G Exon 13
c.1685+36A>G p.Arg533Trp Exon 13
c.1638G>A + c.1685+36A>G
Fig. 1. Normalised high resolution melting curves (A) and difference plots (B) of KCNQ1 mutations identified in a cohort of 34 new cases with a suspicion of LQTS. Green curves represent wild-type HRM profiles. The KCNQ1-p.Gln505X was identified in a homozygous status.
G. Millat et al. / Clinica Chimica Acta 412 (2011) 203–207
with a suspicion of LQTS (Table 5). This cohort was also scanned using our previously reported DHPLC strategy [6]. Molecular analysis of this cohort allowed identification of 9 putative mutated alleles (Table 5, Fig. 1). These 9 genomic variants were detected as abnormal either by DHPLC or by our HRM/sequencing analysis. Among them, five were novel mutations which were not found in 300 control chromosomes of French adults of Caucasian origin. Except KCNH2-p.Gly879Arg, novel missense mutations affected highly conserved amino acid residues among vertebrates. The pathogenicity of these new mutations will be further assessed using segregation analysis. However, in silico studies based on the use of 3 different softwares (PolyPhen, SIFT, and Align GVGD) could suggest that these novel missense variants are most likely disease causing. 4. Discussion Since 2002, among mutation scanning methods, HRM analysis is gaining more and more attention. As recently reviewed, the list of genes analysed by HRM is increasing [11]. Advantages of this scanning method are derived from the simplicity of the technique as no separation or processing of the samples is required. Furthermore, this method is a non-consumed closed-tube genotyping approach that greatly reduces contamination risk. Consequently, HRM melt analysis, which is a cost-effective post-PCR technique, is thus well adapted for high-throughput mutation scanning on genes for which large cohorts of patients have to be investigated such as, for instance, patients with arrhythmia syndromes and more particularly LQTS. Collectively, KCNQ1, KCNH2 and SCN5A mutations represent approximately 90% of genetically defined LQTS cases. As HRM analysis has been successful for scanning SCN5A [8], our aim was to develop an optimized protocol for scanning the 2 other prevalent LQTS-causing genes: KCNQ1 and KCNH2. Using control and mutant DNAs, HRM analysis was successfully optimized for KCNQ1 as conditions could be defined for 14 of the 16 exons. All KCNQ1 genetic variants detected by DHPLC were readily identified as abnormal HRM profiles. Similar results were previously reported for other genes [8,12–15]. However, optimisation of KCNH2 was more laborious and time-consuming. Despite use of different primer pairs and different amplification protocols, only 3 of 15 KCNH2 exons were finally optimized for HRM analysis. The remaining KCNH2 exons could only be analysed either by direct sequencing of double-stranded PCR (Table 2) or by DHPLC as previously described [6]. Optimized conditions detailed in this study were subsequently applied on a cohort of 34 new cases with a suspicion of LQTS. This cohort was also screened using a DHPLC strategy [6]. All genomic variants were detected as abnormal both by DHPLC and by our HRM/ sequencing analysis. Among the identified genomic variants, 9 putative mutated alleles were described, including 6 novel ones: 5 in KCNQ1 and 4 in KCNH2.
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Although many KCNH2 exons were sequenced, this reported HRM/ sequencing method allowed a complete molecular investigation of the cohort faster and cheaper than using DHPLC strategy. The rapid, lowcost, and highly efficient HRM/sequencing strategy fulfils all the conditions required for the systematic detection of genomic variants in the three most prevalent genes involved in LQTS. This molecular strategy allows a fast molecular exploration of LQTS-causing genes, which further leads to a better management of LQTS patients and families. Acknowledgements 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). References [1] Bokil NJ, Baisden JM, Radford DJ, Summers KM. Molecular genetics of long QT syndrome. Mol Genet Metab 2010;101:1–8. [2] Schwartz PJ, Stramba-Badiale M, Crotti L, et al. Prevalence of the congenital longQT syndrome. Circulation 2009;120:1761–7. [3] Splawski I, Shen J, Timothy KW, et al. Spectrum of mutations in long-QT syndrome genes. KVLQT1, HERG, SCN5A, KCNE1, and KCNE2. Circulation 2000;102:1178–85. [4] Jongbloed R, Marcelis C, Velter C, Doevendans P, Geraedts J, Smeets H. DHPLC analysis of potassium ion channel genes in congenital long QT syndrome. Hum Mutat 2002;20:382–91. [5] 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. [6] 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. [7] Kapplinger JD, Tester DJ, Salisbury BA, et al. Spectrum and prevalence of mutations from the first 2,500 consecutive unrelated patients referred for the FAMILION long QT syndrome genetic test. Heart Rhythm 2009;6:1297–303. [8] Millat G, Chanavat V, Rodriguez-Lafrasse C, Rousson R. Rapid, sensitive and inexpensive detection of SCN5A genetic variations by high resolution melting analysis. Clin Biochem 2009;42:491–9. [9] Erali M, Wittwer CT. High resolution melting analysis for gene scanning. Methods 2010;50:250–61. [10] Poland D. Recursion relation generation of probability profiles for specificsequence macromolecules with long-range correlations. Biopolymers 1974;13: 1859–71. [11] Montgomery JL, Sanford LN, Wittwer CT. High-resolution DNA melting analysis in clinical research and diagnostics. Expert Rev Mol Diagn 2010;10:219–40. [12] Millat G, Chanavat V, Julia S, Crehalet H, Bouvagnet P, Rousson R. Validation of high-resolution DNA melting analysis for mutation scanning of the LMNA gene. Clin Biochem 2009;42:892–8. [13] 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. [14] Sestini R, Provenzano A, Bacci C, et al. NF2 mutation screening by denaturing highperformance liquid chromatography and high-resolution melting analysis. Genet Test 2008;12:311–8. [15] Audrezet MP, Dabricot A, Le Marechal C, Ferec C. Validation of high-resolution DNA melting analysis for mutation scanning of the cystic fibrosis transmembrane conductance regulator (CFTR) gene. J Mol Diagn 2008;10:424–34.
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Clinica Chimica Acta j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / c l i n c h i m
Short communication
Development of a high resolution melting method for the detection of genetic variations in Long QT Syndrome Gilles Millat a,b,⁎, Valérie Chanavat a, Hervé Créhalet a,b, Robert Rousson a,b a b
Laboratoire de Cardiogénétique Moléculaire, Centre de Biologie et Pathologie Est, Hospices Civils de Lyon, Lyon, France Université de Lyon, Lyon, F-69003, France; Université Lyon 1, Lyon, F-69003, France
a r t i c l e
i n f o
Article history: Received 20 August 2010 Received in revised form 8 September 2010 Accepted 10 September 2010 Available online 17 September 2010 Keywords: Mutations Long QT Syndrome High resolution melting DHPLC Polymorphisms Molecular diagnosis
a b s t r a c t Background: Inherited Long QT Syndrome (LQTS) is a cardiac channelopathy associated with a high risk of sudden death. The prevalence has been estimated at close to 1:2000. Due to large cohorts to investigate, the size of the 3 prevalent mutated genes, and the presence of a large spectrum of private mutations, mutational screening requires an extremely sensitive and specific scanning method. Methods: Efficiency of high resolution melting (HRM) analysis was evaluated for the most prevalent LQTScausing genes (KCNQ1, KCNH2) using control DNAs and DNAs carrying previously identified gene variants. A cohort of 34 patients with a suspicion of LQTS was further blindly screened. To evaluate HRM sensitivity, this cohort was also screened using an optimized DHPLC strategy. Results: HRM analysis was successfully optimized for KCNQ1 but optimisation of KCNH2 was more laborious as only 3 KCNH2 exons could be finally optimized. Remaining KCNH2 exons were analysed by direct sequencing. This molecular approach, which combined HRM and direct sequencing, was applied on the cohort of 34 cases and 9 putative mutations were identified. Using this approach, molecular investigation was completed faster and cheaper than using DHPLC strategy. Conclusions: This HRM/sequencing procedure represents an inexpensive, highly sensitive and highthroughput method to allow identification of mutations in the coding sequences of prevalent LQTS genes. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Long QT Syndrome (LQTS) is a primary cardiac channelopathy typically characterized by a prolongation (N440 ms) of the QTc interval, syncope, ventricular arrhythmias and a high risk of sudden death [1]. The prevalence has recently been estimated at close to 1:2000 [2]. This disease is genetically heterogeneous. To date, 12 genes have been linked to LQTS, but 90% of genetically defined LQTS patients have a disease-causing mutation in LQT1 (KCNQ1, MIM# 607542), LQT2 (KCNH2, MIM# 152427) or LQT3 (SCN5A, MIM# 600163) [1]. The majority of reported mutations (approximately 80%) are single nucleotide substitutions causing missense, nonsense, or splice mutations. The remaining 20% are small in-frame insertions or deletions, or rare large deletions (for more information, see the Human Gene Mutation Database; www.hgmd.cf.ac.uk). Although some recurrent mutations have been reported, most affected families segregate a unique private mutation.
Abbreviations: LQTS, Long QT Syndrome; HRM, High resolution melting; DHPLC, Denaturing high-performance liquid chromatography. ⁎ Corresponding author. Laboratoire de Cardiogénétique Moléculaire, Centre de Biologie Est, F-69677 BRON Cedex, France. Tel.: +33 472129674; fax: +33 427855900. E-mail address: [email protected] (G. Millat). 0009-8981/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.cca.2010.09.013
In medical practice, mutational screening of LQTS patients is crucial for proper management of patients and affected families but also to determine optimal therapeutic strategies as LQTS patients often have gene-specific ECG abnormalities and gene-specific triggers for arrhythmias. Molecular analysis of LQTS patients is however challenging owing to the number of disease-causing genes, the size of the 3 prevalent mutated genes, and the presence of a large spectrum of private mutations. To date, mutational screening in most LQTS cohorts was performed either by direct sequencing or by using scanning methods such as DHPLC or SSCP [3–7]. These methods for large-scale detection of mutations are expensive and technically time-consuming. As previously shown for SCN5A, high resolution melting (HRM) analysis has been successful in overcoming many of these limitations and constitutes a simple, semi-automated, and cost-effective approach to identify singlebase substitutions and small insertions/deletions with a nearly 100% detection [8,9]. This scanning method does not require any processing, reagent additions or separations after PCR. The aim of this study was to evaluate the possibility to develop an optimized protocol for scanning the 2 other prevalent LQTS-causing genes (KCNQ1 and KCNH2) by HRM analysis using the Rotor-Gene 6000 (Corbett Life Science). A cohort of 34 suspected LQTS patients was further blindly screened using both HRM and DHPLC strategies in order to determine the most efficient technique in terms of sensibility, specificity, practicability and cost.
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Table 1 Experimental procedures used to identify KCNQ1 nucleotide variations. Exon
Primers
PCR conditions
HRM conditions
Forward primer (5′→3′)
Reverse primer (5′→3′)
Size (bp)
T°a (°C)#
PCR kit⁎
MgCl2 (mM)
Leading range (°C)
Trailing range (°C)
Mutation detection threshold
1 2 3
AGTGGCTGCCCGCACTG ATCTGTGGGATGGGCAGA CTGGGTTCAAACAGGTTGC
CAGCTCTCAGGAAGCACCTT GAGATGCCAGCTTCCAAGG GAAACCTGGGCGTGACCT
528 203 248
65–55 70–60 70–60
5 1 1
ND 3 3
4 5 6 7 8 9 10 11 12 13 14 15
GACGAGAGCAGGGTGTATGC TGAACAGCTGAGCCCAGCCT TCGCTGGGACTCGCTGCCTT GGGTTTGGGTTAGGCAGTTGG TGGCCTGTGTGGACGGGA GGGAACAGGGAGGGGGAGCT AGGGCCTGGCAGACGATGTC CTGGCAGGTTGGGTGGGAGG CAGGGGCAGTGAGGGGATGA CACTGTCACTGCCTGCACTT GTCAAGCTGTCTGTCCCACA CCCCCAGCCCTACCACCC
GGCTCAGGAGGGGTGCTC CTAGTGTGGGCTGCTCTGCT TGTCCTGCCCACTCCTCAGCC AGCCACCCCAGGACCCCAG CAGTGACCAAAATGACAGTGA GGCCTCCCCACCTGCTAGCA CACAGCCCAAGGGAGAAA CCTCCAGCCCAGCCCTTCAC GTGGCTTGGGGGCGGAGG GTCTCAGCCCCTCCCTCCT ATGGCCCATTCTGACATCAT GCAGGAGCTTCACGTTCACA
222 223 291 247 181 267 295 211 270 272 231 249
70–60 70–60 70–60 70–60 70–60 70–60 70–60 70–60 70–65 70–65 70–60 70–60
3 2 1 1 1 2 1 1 2 2 1 3
3 3.2 2.5 3 3 3.2 3 3 3.2 3.2 3 3
90.1–91.1 93–94 90.4–90.7 93–94 90.9–91.9 92.4–93.1 90.9–91.6 91.2–92.3 90.1–91 92.4–93.5 90.8–91.7 88.8–89.7 90.2–91.3 93–94.2 89.9–90.9 90.1–90.7 90.6–91.7
90 90 95 90 90 90 90 90 90 90 90 90 90 90 90 90 90
16
TTCCCACCACTGACTCTCT
ACTCTTGGCCTCCCCCTCT
328
70–60
4
1.5
Direct sequencing 84.7–85.6 85–86 85–86 89.2–89.6 85.6–86.7 87.7–88.8 85.6–86.7 85.6–86.6 84.9–86 86.9–88 85.5–86.4 83.6–84.6 84.5–85.5 86.4–87.3 84.6–85.7 85.7–86.6 84.2–85.5 Direct sequencing
# annealing temperature. * 1: LightCycler® 480 High Resolution Melting Master kit (Roche Applied Science); 2: LightCycler® 480 Probes Master kit (Roche Applied Science) and SYTO-9 dye (Invitrogen); 3: SYBR® GreenER™ qPCR SuperMix (Invitrogen); 4: Hot Goldstar DNA polymerase kit (Eurogentec); 5: FailSafe™ PCR System (Epicentre).
exons of KCNQ1 and KCNH2 were amplified using intronic primers and PCR conditions reported in Tables 1 and 2. All pre-PCR steps were performed using the CAS-1200 liquid handling system (QIAGEN, Courtaboeuf, France). Real-time PCR cycling of the genomic DNA samples was carried out on the Rotor-Gene 6000 analyser (QIAGEN). Except KCNH2 exons 1 and 4, all amplicons were obtained using touchdown thermal cycling programs (Tables 1 and 2). 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 Tables 1 and 2. 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 Tables 1 and 2 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.
2. Materials and methods 2.1. Subjects Genomic DNA samples from control patients and from LQTS patients with previously characterized genetic variants were used to determine the sensitivity of HRM analysis. Optimized HRM conditions were applied to further screen mutations in a panel of 34 additional unrelated patients with a suspicion of LQTS. The study was conducted in accordance with the principles of the Declaration of Helsinki. Informed consent was obtained for all cases. 2.2. PCR amplification Genomic DNA was extracted from whole blood using a Wizard Genomic DNA Purification kit (Promega, Madison, WI). The coding
Table 2 Experimental procedures used to identify KCNH2 nucleotide variations. Exon
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Primers
PCR conditions
HRM conditions
Forward primer (5′→3′)
Reverse primer (5′→3′)
Size (bp)
T°a (°C)#
PCR kit⁎
MgCl2 (mM)
Leading range (°C)
CCGGCCGGGCCACCCGAAGCCTAGT GGTCCCCGCTCACGCGCACTCT ACTGAGTGGGTGCCAAGG CTCCGGGGCTGCTCGCGATCC GCCTGGTCTCCACTCTCGAT CCCCCGAGGTCCCATGGCCTGCCTC TGCCCCATCAACGGAATGTGC CCTGGTGCGGGGCCTGAGC AGAGTGTGGGTTGGGGG GGTGCCTGCTGCCTGGAT GCGCCCAGCCCTACTTTTTC GCTTCCTGCCCAGTCCTC GCAGCGGTGGTGCGTCTACCC GAGGCTGTCACTGGTGTCCCC GCCCCTTTCCCTCCCCTTCCT
CCCCGCCGTCCCCTCGCCAAAGCCT TTGACCCCGCCCCTGGTCGT AGACCACGAACCCCTGAG AGGGCCCAGAATGCAGCAAGC CCCTCTCCAAGCTCCTCCAA CTCTCCTCTCCCTACACCACCTGC TTCCTCCAACTTTGGGTTCCT TTCCCCTCTGCCACCCCACTCTT GAGGGCATTTCCAGTCCAGT CACACAGCTGGAAGCAGGAG CGCCTTCCAGCTCCCAGC GGGTAGACGCACCACCGCTGC ACCAGACTCCAGGGCGTGCCC ACAAGCGGGTCACGGTACATC GCCCAGCAGCGCCTTGATCC
315 314 250 570 351 524 490 346 355 308 266 442 284 276 273
69.3 TD 70/60 TD 70/60 61 TD 70/60 TD 70/60 TD 70/60 TD 70/60 TD 70/60 TD 70/60 TD 70/60 TD 70/60 TD 70/60 TD 70/60 TD 70/65
4 3+DMSO 1 3+DMSO 3 3 3 3 3 1 5 4 3 3 2
ND 1.5 3 1.5 1.5 1.5 1.5 1 1.5 2.5 1.5 ND 1.5 1.5 3.2
Direct sequencing Direct sequencing 84.3–85.2 Direct sequencing Direct sequencing Direct sequencing Direct sequencing Direct sequencing Direct sequencing 84.3–85 Direct sequencing Direct sequencing Direct sequencing Direct sequencing 84.6–85.7
Trailing range (°C)
Mutation detection threshold
91–91.9
90
89.9–90.8
90
92.5–93.4
90
# annealing temperature. * 1: LightCycler® 480 High Resolution Melting Master kit (Roche Applied Science); 2: SYBR® GreenER™ qPCR SuperMix (Invitrogen); 3: Hot Goldstar DNA polymerase kit (Eurogentec); 4: FailSafe™ PCR System (Epicentre); 5: Taq CORE kit (Qbiogene).
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Exons screened by HRM analysis were amplified using either LightCycler® 480 High Resolution Melting Master kit (Roche Applied Science, Meylan, Fra), or LightCycler® 480 Probes Master kit (Roche Applied Science) with SYTO-9 dye (Invitrogen Carlsbad, CA) at final concentration of 2.5 μM, or SYBR® GreenER™ qPCR SuperMix kit (Invitrogen). These kits were used according to the manufacturer's instructions. Exons analysed by direct sequencing were amplified using either HotGoldstar DNA polymerase (Eurogentec, Seraing, Bel) or FailSafe PCR system (Epicentre, Madison, WI) according to the manufacturer's instructions. 2.3. HRM analysis HRM analyses were performed using the Rotor-Gene 6000 analyser (QIAGEN). Melting curves were generated by ramping between 70 and 99 °C at 0.1 °C/s. Melting curves were normalised between two temperature ranges, the leading range and the trailing range. Optimized melting temperature ranges used to analyse PCR amplicons are given in Tables 1 and 2. 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. As HRM could induce some false positives, each amplicon was tested against a mean of 4 wild-type samples in order to decrease significantly the number of false positive calls. 2.4. DHPLC analysis DHPLC analyses were performed using optimized elution profiles and melting temperatures described previously [6]. 2.5. 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 BigDye XTerminator® Purification Kit (Applied Biosystems) and applied onto an ABI 3730 automatic sequencer. Table 3 List of KCNQ1 genetic variants identified by HRM analysis (NM_000218.2).
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Table 4 List of KCNH2 genetic variants identified by HRM analysis (NM_000238.2). Exon Genetic variant 3 10 15
c.308GNT [p.Gly103Val], c.340CNT [p.Pro114Ser], c.442CNT [p.Arg148Trp], c.453dup [p.Thr152fs] c.2454GNT [p.Ser818Ser], c.2508CNT [p.Asp836Asp], c.2514GNT [p.Leu838Leu], c.2588GNA [p.Arg863Gln] c.3331-9_3331-8del
Genetic variants indicated in bold correspond to mutations; others variants to SNPs.
3. Results 3.1. Optimisation of HRM conditions Mutational screening of KCNQ1 and KCNH2 coding sequences by HRM analysis required the investigation of 31 coding exons. PCR and HRM optimisations were performed using a small cohort of 6 control DNAs for which exons and exon/intron boundaries were directly sequenced on both strands in order to determine which exonic or intronic polymorphisms were present. To perform HRM analysis with a near 100% sensitivity, exons which were greater than 400 bp or with more than two melting domains (use of Poland's algorithm [10]) were tentatively amplified in two overlapping segments. To avoid a variable amplification that may lead to an increased number of false positives, control genomic DNAs used for PCR optimisation were extracted using the same method and, subsequently, adjusted to a concentration of 2.5 ng/μL. Initially, all exons were tentatively amplified with the LightCycler® 480 High Resolution Melting Master kit. Using this procedure, only 10 amplicons could be successfully amplified. Seven of the remaining amplicons analysed by HRM were successfully amplified using the SYTO-9 fluorescent dye combined with other DNA polymerases (Tables 1 and 2). Despite use of different primer pairs (carefully selected with Primer3Plus software) and different amplification protocols (modifications of primer concentrations, MgCl2 concentrations and/or annealing temperatures), PCR optimisation of 14 exons, mostly KCNH2 exons, did not allow to obtain a sufficient amplicon quality for subsequent HRM analysis. Most of them
Table 5 List of putative KCNQ1 and KCNH2 mutations identified in a cohort of 34 unrelated patients with a suspicion of LQTS. Mutation
Type mutation Comments or affected domain
KCNQ1 1 c.217CNA
p.Pro73Thr
N-ter
7
c.1032GNA
p.Ala344Ala
IVS9
c.1252p.Val418X or 3_1252delinsAAG splice mutation
Splice mutation PTC§
11
c.1513CNT
p.Gln505X
PTC§
13
c.1597CNT
p.Arg533Trp
C-ter
KCNH2 1 2 11 14
c.1ANT c.92TNC c.2635GNC c.3302CNT
p.Met1? p.Ile31Thr p.Gly879Arg p.Pro1101Leu
ND N-ter C-ter C-ter
Exon/ Nucleotide intron change
Exon Genetic variant 2 3
4 5
6 7
8 9 10 11 12 13 14 15
c.387-32GNA, c.387-17GNA, c.459GNA [p.Thr153Thr], c.477+5GNA, c.477+9CNT c.478-56TNG, c.478-10GNA, c.478-8CNT, c.524-534 dup [p.Gly179fs], c.536GNA [p.Gly179Asp], c.562delT [p.Trp188fs], c.575GNA [p.Arg192His], c.595TNG [p.Ser199Ala] c.605-28ANG, c.605-24CNT c.684-25CNT, c.691CNT [p.Arg231Cys], c.720CNT [p.His240His], c.728GNC [p.Arg243Pro], c.775CNT [p.Arg259Cys], c.776GNA [p.Arg259His], c.780+40GNA c.804CNT [p.Ile268Ile], c.817CNT [p.Leu273Phe], c.858CNT [p.Asp286Asp] c.940GNA [p.Gly314Ser], c.947GNA [p.Gly316Glu], c.954GNA [p.Lys318Lys], c.1017_1019del [p.Phe340del], c.1022CNT [p.Ala341Val], c.1031CNT [p.Ala344Val], c.1032GNA [p.Ala344Ala], c.1032+5GNA, c.1032+11CNT c.1110GNA [p.Ala370Ala] c.1179GNT [p.Lys393Asn], c.1202GNA [p.Arg401Gln], c.1222CNG [p.Pro408Ala] c.1343CNG [p.Pro448Arg], c.1393+9CNT, c.1393+29GNA, c.1393+31 GNA c.1394-14CNT, c.1429CNT [p.Pro477Ser] c.1515-31CNG, c.1590+14TNC c.1638GNA [p.Ser546Ser], c.1685+36ANG c.1686-49CNT, c.1732+43TNC, c.1732+53CNT c.1733-53TNC, c.1733-45CNG, c.1733-29CNA, c.1794+16GNA, c.1794+32GNT
Genetic variants indicated in bold correspond to mutations; others variants to SNPs.
Previously reported* Previously reported* Novel mutation, ongoing in vitro studies Found with a homozygous status. Associated with sensorineural deafness Previously reported*
Novel mutation Novel mutation Novel mutation Novel mutation
* For more information on previously reported mutations, see the genetic mutations and inherited arrhythmias database: http://www.fsm.it/cardmoc. § PTC: Premature Termination Codon.
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present abnormal PCR characteristics such as high Ct values or presence of extra products. Presence of primer dimers or non specific products should be avoided because it may alter the melt curve characteristics causing the generation of false positive results. For the other, although PCR characteristics seems to be correct, a wide spread in HRM curves from control DNA samples were observed. Consequently, as no optimized PCR/HRM conditions could be defined, these 14 exons were analysed by direct sequencing of double-stranded PCR. For the 17 successfully optimized exons, HRM analysis was further applied on the 6 control DNAs but also on 65 genomic DNAs
A
containing previously identified genetic variants. Each abnormal HRM profile was further sequenced. Melting curves generated from these amplicons allowed an easy identification of all genetic variants previously identified either by direct sequencing or by our DHPLC/ sequencing strategy (Tables 3 and 4). 3.2. Molecular investigation of 34 unrelated patients with a suspicion of LQTS To validate experimental procedures detailed in Tables 1 and 2, a blinded study was performed using genomic DNAs from 34 new cases
B WT WT
p.Ala344Ala p.Ala344Ala
Exon 7
Exon 7
WT
WT c.1252-3_1252delinsAAG
c.1252- 3_1252delinsAAG
Exon 10
Exon 10
WT
WT
c.1394-14C>T
c.1394-14C>T
p.Gln505X
p.Gln505X Exon 11
Exon 11
WT p.Arg533Trp
c.1638G>A + c.1685+36A>G
WT c.1685+36A>G Exon 13
c.1685+36A>G p.Arg533Trp Exon 13
c.1638G>A + c.1685+36A>G
Fig. 1. Normalised high resolution melting curves (A) and difference plots (B) of KCNQ1 mutations identified in a cohort of 34 new cases with a suspicion of LQTS. Green curves represent wild-type HRM profiles. The KCNQ1-p.Gln505X was identified in a homozygous status.
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with a suspicion of LQTS (Table 5). This cohort was also scanned using our previously reported DHPLC strategy [6]. Molecular analysis of this cohort allowed identification of 9 putative mutated alleles (Table 5, Fig. 1). These 9 genomic variants were detected as abnormal either by DHPLC or by our HRM/sequencing analysis. Among them, five were novel mutations which were not found in 300 control chromosomes of French adults of Caucasian origin. Except KCNH2-p.Gly879Arg, novel missense mutations affected highly conserved amino acid residues among vertebrates. The pathogenicity of these new mutations will be further assessed using segregation analysis. However, in silico studies based on the use of 3 different softwares (PolyPhen, SIFT, and Align GVGD) could suggest that these novel missense variants are most likely disease causing. 4. Discussion Since 2002, among mutation scanning methods, HRM analysis is gaining more and more attention. As recently reviewed, the list of genes analysed by HRM is increasing [11]. Advantages of this scanning method are derived from the simplicity of the technique as no separation or processing of the samples is required. Furthermore, this method is a non-consumed closed-tube genotyping approach that greatly reduces contamination risk. Consequently, HRM melt analysis, which is a cost-effective post-PCR technique, is thus well adapted for high-throughput mutation scanning on genes for which large cohorts of patients have to be investigated such as, for instance, patients with arrhythmia syndromes and more particularly LQTS. Collectively, KCNQ1, KCNH2 and SCN5A mutations represent approximately 90% of genetically defined LQTS cases. As HRM analysis has been successful for scanning SCN5A [8], our aim was to develop an optimized protocol for scanning the 2 other prevalent LQTS-causing genes: KCNQ1 and KCNH2. Using control and mutant DNAs, HRM analysis was successfully optimized for KCNQ1 as conditions could be defined for 14 of the 16 exons. All KCNQ1 genetic variants detected by DHPLC were readily identified as abnormal HRM profiles. Similar results were previously reported for other genes [8,12–15]. However, optimisation of KCNH2 was more laborious and time-consuming. Despite use of different primer pairs and different amplification protocols, only 3 of 15 KCNH2 exons were finally optimized for HRM analysis. The remaining KCNH2 exons could only be analysed either by direct sequencing of double-stranded PCR (Table 2) or by DHPLC as previously described [6]. Optimized conditions detailed in this study were subsequently applied on a cohort of 34 new cases with a suspicion of LQTS. This cohort was also screened using a DHPLC strategy [6]. All genomic variants were detected as abnormal both by DHPLC and by our HRM/ sequencing analysis. Among the identified genomic variants, 9 putative mutated alleles were described, including 6 novel ones: 5 in KCNQ1 and 4 in KCNH2.
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Although many KCNH2 exons were sequenced, this reported HRM/ sequencing method allowed a complete molecular investigation of the cohort faster and cheaper than using DHPLC strategy. The rapid, lowcost, and highly efficient HRM/sequencing strategy fulfils all the conditions required for the systematic detection of genomic variants in the three most prevalent genes involved in LQTS. This molecular strategy allows a fast molecular exploration of LQTS-causing genes, which further leads to a better management of LQTS patients and families. Acknowledgements 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). References [1] Bokil NJ, Baisden JM, Radford DJ, Summers KM. Molecular genetics of long QT syndrome. Mol Genet Metab 2010;101:1–8. [2] Schwartz PJ, Stramba-Badiale M, Crotti L, et al. Prevalence of the congenital longQT syndrome. Circulation 2009;120:1761–7. [3] Splawski I, Shen J, Timothy KW, et al. Spectrum of mutations in long-QT syndrome genes. KVLQT1, HERG, SCN5A, KCNE1, and KCNE2. Circulation 2000;102:1178–85. [4] Jongbloed R, Marcelis C, Velter C, Doevendans P, Geraedts J, Smeets H. DHPLC analysis of potassium ion channel genes in congenital long QT syndrome. Hum Mutat 2002;20:382–91. [5] 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. [6] 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. [7] Kapplinger JD, Tester DJ, Salisbury BA, et al. Spectrum and prevalence of mutations from the first 2,500 consecutive unrelated patients referred for the FAMILION long QT syndrome genetic test. Heart Rhythm 2009;6:1297–303. [8] Millat G, Chanavat V, Rodriguez-Lafrasse C, Rousson R. Rapid, sensitive and inexpensive detection of SCN5A genetic variations by high resolution melting analysis. Clin Biochem 2009;42:491–9. [9] Erali M, Wittwer CT. High resolution melting analysis for gene scanning. Methods 2010;50:250–61. [10] Poland D. Recursion relation generation of probability profiles for specificsequence macromolecules with long-range correlations. Biopolymers 1974;13: 1859–71. [11] Montgomery JL, Sanford LN, Wittwer CT. High-resolution DNA melting analysis in clinical research and diagnostics. Expert Rev Mol Diagn 2010;10:219–40. [12] Millat G, Chanavat V, Julia S, Crehalet H, Bouvagnet P, Rousson R. Validation of high-resolution DNA melting analysis for mutation scanning of the LMNA gene. Clin Biochem 2009;42:892–8. [13] 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. [14] Sestini R, Provenzano A, Bacci C, et al. NF2 mutation screening by denaturing highperformance liquid chromatography and high-resolution melting analysis. Genet Test 2008;12:311–8. [15] Audrezet MP, Dabricot A, Le Marechal C, Ferec C. Validation of high-resolution DNA melting analysis for mutation scanning of the cystic fibrosis transmembrane conductance regulator (CFTR) gene. J Mol Diagn 2008;10:424–34.