https://orcid.org/0000-0002-7859-9661
Correction
3 Dec 2024: Nagata Y, Watanabe R, Eichhorn C, Ohno S, Aiba T, et al. (2024) Correction: Targeted deep sequencing analyses of long QT syndrome in a Japanese population. PLOS ONE 19(12): e0315106. https://doi.org/10.1371/journal.pone.0315106 View correction
Figures
Abstract
Long QT syndrome (LQTS) is one of the most common inherited arrhythmias and multiple genes have been reported as causative. Presently, genetic diagnosis for LQTS patients is becoming widespread and contributing to implementation of therapies. However, causative genetic mutations cannot be detected in about 20% of patients. To elucidate additional genetic mutations in LQTS, we performed deep-sequencing of previously reported 15 causative and 85 candidate genes for this disorder in 556 Japanese LQTS patients. We performed in-silico filtering of the sequencing data and found 48 novel variants in 33 genes of 53 cases. These variants were predicted to be damaging to coding proteins or to alter the binding affinity of several transcription factors. Notably, we found that most of the LQTS-related variants in the RYR2 gene were in the large cytoplasmic domain of the N-terminus side. They might be useful for screening of LQTS patients who had no known genetic factors. In addition, when the mechanisms of these variants in the development of LQTS are revealed, it will be useful for early diagnosis, risk stratification, and selection of treatment.
Citation: Nagata Y, Watanabe R, Eichhorn C, Ohno S, Aiba T, Ishikawa T, et al. (2022) Targeted deep sequencing analyses of long QT syndrome in a Japanese population. PLoS ONE 17(12): e0277242. https://doi.org/10.1371/journal.pone.0277242
Editor: Tomohiko Ai, Ohio State University, UNITED STATES
Received: July 24, 2022; Accepted: October 22, 2022; Published: December 8, 2022
Copyright: © 2022 Nagata et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data underlying the results presented in the study are available from NBDC (National Bioscience Database Center, http://biosciencedbc.jp/, accession number JGAS000579).
Funding: This work was in part supported by a research grant from the Japan Agency for Medical Research and Development (AMED; https://www.amed.go.jp/) 17km045109h0005 awarded to NM, KY, AT, TM, MH, WS, AN, TA, HM, HW, TI, YN, TN, KH, YA, T. Tsunoda and T. Tanaka. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Long QT syndrome (LQTS) is an arrhythmia disorder characterized by QT interval prolongation in the electrocardiogram (ECG). The QT interval indicates the duration of contraction and relaxation of cardiac ventricles. A prolonged QT interval is caused by the delayed repolarization after contraction of the cardiac ventricles and leads to syncope, ventricular fibrillation, and sudden cardiac death [1]. One in 2500 people has LQTS and familial aggregation has been reported [1, 2].
Previously, 15 causative genes for LQTS have been detected, and recently, CALM3 and TRDN have been newly added [3]. About 75% of the cases carry mutations in either of three ion channel genes, KCNQ1, KCNH2, or SCN5A. Another 5% have known variants in genes encoding beta-subunits of ion channels, multichannel interacting factors, scaffold proteins, or anchoring proteins [4], while causative genetic variants cannot be detected in approximately 20% of LQTS patients [5]. Mutations in any of these causative genes may alter function of corresponding ion channels, which control the action potential of cardiomyocytes. Hyperactivity of sodium and calcium channels or hypoactivity of potassium channels cause prolongation of action potentials, resulting in a long QT interval [6]. The most frequently detected mutations in the LQTS -related genes, KCNQ1, KCNH2, and SCN5A show a strong genotype-phenotype relationship, with a high corresponding risk of symptoms, which is one of the reasons why effective therapeutic options for mutations in these genes have been thoroughly investigated [7]. Other than the three major LQTS causative genes, ten minor genes (AKAP9, CACNA1C, CALM1, CALM2, CAV3, KCNE1, KCNE2, KCNJ2, KCNJ5, SCN4B, SNTA1) and two atypical type of LQTS causative genes (ANK2 [8] and KCNJ2 [9]) have been discovered to date, all of which were tested for within this study [10, 11].
However, similar to other Mendelian diseases, LQTS patients within our cohort exhibited incomplete penetrance [12]. That is, not all carriers with these genetic mutations were equally symptomatic or showing similar phenotypes [13]. These variabilities are considered to be due contributions by environmental factors and/or additional modifying genetic factors with small effect sizes [14].
To explore additional LQTS related genetic factors, several genomic analyses have been performed [15–17]. In a previous study [18], we performed whole-exome sequencing analyses and identified 85 novel candidate genes. The study results suggested that in 37.1% of patients, who did not have mutations in known causative genes, LQTS may be explained by novel genetic factors. Also, protein-protein interaction network analysis (PPI) categorized 10 out of 85 novel candidate genes into a network and half of these were predicted to interact with calmodulin [18]. In addition, further investigation revealed that 87.5% of newly identified mutations in unrelated LQTS patients were in calmodulin-interacting genes [18]. Calmodulin is a multifunctional calcium-binding protein expressed ubiquitously in all eukaryotic cells. Calmodulin senses intracellular calcium levels and plays an important role in calcium signal transduction pathways [19]. In the myocardium, calmodulin interacts with a variety of proteins including those encoded by known causative genes of LQTS and is involved in the control of the cardiac contraction cycle [20]. Additionally, mutations in the calmodulin gene itself have previously been reported to be responsible for ventricular tachycardia and sudden cardiac death [21]. Although the remaining genes identified in our previous study were not classified into specific categories by PPI analysis, their function might have been involved in LQTS development. Therefore, it seems reasonable to think that there may be additional novel risk factors in the 85 candidate genes identified in our previous study. In this study, we performed deep sequencing on 15 known and 85 candidate LQTS genes in 556 LQTS patients to identify additional novel LQTS related variants.
Materials and methods
Study subjects
Japanese patients with negative genetic tests for known variants of LQT1, LQT2, and LQT3 [22] were recruited at 21 medical institutes. Based on the Schwartz score [22], we calculated the score for each patient as follows; 3 points: QTc ≥ 480 ms, 2 points: QTc ≥ 460 ms, 1 point: QTc ≥450 ms in males, Torsade de pointes observed, syncope observed, familial history of LQTS. We analyzed 556 patients with 2 points or more scores as definite or probable LQTS patients. This study was reviewed and approved by the ethics committees of Nagasaki University, Shiga University of Medical Science, Niigata University, Nippon University, Nippon Medical School, University of Tsukuba, Okayama University, Gunma University, Kyoto University, Kanazawa University, Hiroshima University, Keio University, Saiseikai Kawaguchi General Hospital, Tokyo Saiseikai Central Hospital, Saitama City Hospital, The Jikei University School of Medicine, National Hospital Organization Tokyo Medical Center, Dokkyo Medical University Saitama Medical Center, Matsudo City General Hospital, National Cerebral and Cardiovascular Center, and Tokyo Medical and Dental University (G2000-225). According to the standards of the Declaration of Helsinki, all participants have provided written informed consent prior to any clinical or genetic investigations. Peripheral blood was collected in EDTA test tubes from the patients whose informed consent was obtained and genomic DNA was extracted using a commercially available DNA extraction kit.
Customized next generation sequencing panel design
Target deep sequencing was performed using peripheral blood DNA preparations. Using the cloud-based software program Ion AmpliSeqTM Designer v2.2.1 (Thermo Fisher Scientific, Waltham, MA, USA), we designed a custom sequencing panel. This panel was designed to amplify 100 genetic loci (15 known and 85 candidate genes; Table 1) that have been established or have been found to be associated with congenital LQTS based on the data obtained in the previous study by Shigemizu et al. [18], including 1000 bp upstream sequence of the first exon and 50 bp intronic sequence before and after each exon. Each of the corresponding primers of the 100 target genes was synthesized by the manufacturer (Thermo Fisher Scientific, Waltham, MA, USA).
Targeted deep sequencing
Gene libraries of each sample were prepared using Ion AmpliSeq Library Kit for Chef DL8 (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer’s protocol. Library preparation, template generation, and chip loading were automatically completed by using the Ion Chef System (Thermo Fisher Scientific, Waltham, MA, USA). The prepared chip (Ion PI Chip v3, Thermo Fisher Scientific, Waltham, MA, USA) was attached to the sequencer and underwent sequencing using the Ion Torrent Proton Sequencer (Thermo Fisher Scientific, Waltham, MA, USA) and Ion PI HI-Q Chef Kit. The acquired sequencing data was transferred to a server using Ion Torrent Suite (Version 5.0.4; Thermo Fisher Scientific, Waltham, MA, USA).
Bioinformatics
Raw sequencing reads were analyzed and filtered according to their Phred quality score and the minimum coverage. Minimum Phred quality score and coverage were set for 15 and 5, respectively [23]. Variants were detected by the Ion Torrent Variant Caller program. ANNOVAR was used to perform the functional annotation of variants, using GRCh37 assembly as a reference [24]. To find patients’ specific variants in an exon, the following filtering was performed: 1) variants located in exonic regions were extracted, 2) synonymous variants were excluded, 3) variants which are not described in several public databases (ExAC: Exome Aggregation Consortium, ESP6500: NHLBI Go Exome Sequencing Project, HGVD: Human Genetics Variation Database, jMorp: Japanese Multi Omics Reference Panel 14KJPN) or the minor allele frequency was less than 0.001 were extracted, then 4) functional effects of the altered protein structure of variants were predicted using the scores of PolyPhen-2 (http://genetics.bwh.harvard.edu/pph2/) [25], Sorting Intolerant From Tolerant (SIFT; http://sift.jcvi.org/www/) [26], Combined Annotation Dependent Depletion (CADD; https://cadd.gs.washington.edu/) [27], Protein Variation Effect Analyzer (PROVEAN; http://provean.jcvi.org/index.php) [28, 29] and CONsensus DELeteriousness score (CONDEL; http://bbglab.irbbarcelona.org/fannsdb/home) [30]. According to the definition of each algorithm, cut off scores were set to <0.05 for SIFT, >0.5 for polyphen-2, >20 for CADD, <-2.5 for PROVEAN and >0.49 and label “D” for CONDEL. When the amino acid positions indicated by the algorithms did not match the public databases, we excluded these variants from further consideration. Variant information was obtained from the annotation file output by ANNOVAR, Ensembl (https://ensembl.org/index.html), and NCBI (https://www.ncbi.nlm.nih.gov/).
To find LQTS specific variants in the upstream region, the following filtering was performed: 1) variants located in upstream regions were extracted, 2) single nucleotide variants were extracted, and 3) variants predicted to have affinity change for transcription factors (TFs) by transcription factor affinity prediction (TRAP; http://trap.molgen.mpg.de/cgi-bin/home.cgi) [31]. Forty-one sequences, including target SNP and ± 20 bases were applied to sTRAP with the following settings: matrix file; transfac_2010.1 vertebrates, background model; human_upstreams, multiple test correction; Benjamini-Hochberg. A list of TFs with a reference or alternative P-value < 0.01 and in the top ten rankings were queried gene ontology terms of these TFs by Comparative Toxicogenomics Database (CTD; http://ctdbase.org/tools/batchQuery.go) [32]. Known variants on the 15 LQTS causative genes (Table 1) were obtained from dbSNP (https://www.ncbi.nlm.nih.gov/snp/) and known LQTS related variants were listed. These variants were compared to the variants of all sample files. If the patients had these variants, the pathogenesis information of these variants was checked in the dbSNP and ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/) and excluded from this study [33–48]
Experimental validation
To validate potentially disease-causing variants detected in silico, we performed Sanger sequencing. Primer sets were individually tested for the optimal annealing temperature of each primer using two separate control DNA samples and a DNA-free negative control. Ten nanograms of genomic DNA were amplified using Taq DNA polymerase according to the manufacturer’s manuscript (Genscript, Piscataway, NJ, USA). Two microliters of dimethyl sulfoxide (DMSO) were added if necessary (Sigma-Aldrich, St. Louis, MI, USA). Thermal cycling (Veriti, Applied Biosystems, Foster City, CA, USA) was performed at 94°C for 2 minutes, followed by 35 cycles of 94°C for 30 seconds, appropriate annealing temperature for 30 seconds, and 72°C for 40 seconds. Two microliters of PCR products were electrophoresed on 0.8% agarose gel contains 0.5 μg/ml of ethidium bromide along with 5 μl of 1 Kb of DNA ladder (Nippon Genetics Europe GmBH, Düren, NRW, Germany). PCR product bands were quantified by LAS4000 luminescent image analyzer (General Electric, Boston, MA, USA). To inactivate residual dNTPs and primers, 2.5 μl of PCR product and 1 μl of ExoSAP-IT reagent (Affymetrix, Santa Clara, CA, USA) were mixed and incubated at 37°C 15 minutes, followed by 80°C for 15 minutes. Each PCR product was diluted with 136.5 μl of PCR grade water (Millipore, Billerica, MA, USA) and 2 μl was provided for Sanger sequencing. Sanger sequencing was performed using BigDye Terminator v3.1 Cycle Sequencing kit (Applied Biosystems, Foster City, CA, USA) according to the manufacturer’s protocol. The primer list is shown in S1 and S2 Tables.
Results
We performed deep sequencing focusing on 15 causative [49] and 85 candidate genes [18] of LQTS in 556 Japanese LQTS patients (Tables 1 and 2). Extracted variants were validated by Sanger sequencing, and we found 48 novel variants that are possibly associated with LQTS in either the exon or upstream regions (Table 3 and 4).
We found nine novel variants on exon regions of six known causative genes. In addition to that, we also found 21 novel variants on exon regions of 15 candidate genes (Table 3). They were inferred to have functional damage to each of the respectively coded proteins by functional prediction algorithms. Interestingly, among the known causative genes, we found three novel variants in KCNQ1, two novel variants in CACNA1C, one novel variant in SCN5A, ANK2, KCNH2 and KCNJ5 (Table 3). Among candidate genes, multiple mutations were detected in RYR2, LRBA, and CELSR1 (Table 3).
In the upstream region, we searched the variants which predicted to change the binding affinity for TFs in these genes and are involved in cardiac function using TRAP algorithm and CTD, respectively. As a result, we found four and 18 variants in causative and candidate genes, respectively (Table 4).
Discussion
In this study, we performed target deep sequencing of Japanese LQTS patients and found novel variants predicted to alter the biological function of the affected genes. We found three novel possibly pathogenic variants in the coding region of KCNQ1 (Table 3). One of these variants, c.785T>C changes the amino acid located at the boundary point of the cytosolic site of the fifth transmembrane (p.L262P), while a variant that changes the leucine to valine (p.L262V) has been reported as an LQTS-related variant previously [43, 50]. Since the structure of proline is quite different from that of leucine or valine, the function of p.L262P is expected to be more damaging compared to p.L262V, indicating that this variant is likely a strong causative candidate for LQTS.
We found one novel variant in the exon region of SCN5A in two siblings (c.1111C>G, p.Q371E, Table 3). SCN5A codes for a voltage-gated sodium channel subunit and regulates the initial upstroke of the action potential [51]. The variant is located at the pore-forming domain between the fifth and sixth transmembrane, suggesting to have a great influence on the function of SCN5A as an ion channel.
We found one novel variant in KCNH2 (c.202T>A, p.F68I), which encodes one of the alpha-subunit of the potassium channel and regulates the final repolarization of the cardiac action potential [52]. At this position, a variant which changes phenylalanine to leucine (c.202T>C, p.F68L) was reported in an LQTS patient [53]. This variant is located at the Per-Arnt-Sim (PAS) domain of the N-terminus cytosolic region of KCNH2, and mutation on the PAS domain has been suggested to alter the channel activity [54].
Among the minor causative LQTS genes, we found two novel variants in the exons of CACNA1C. CACNA1C encodes a calcium ion channel playing an important role in excitation and contraction of the heart, contributing to normal heart development and regulation of heart rhythm. We found two variants in CACNA1C, one of which was located in the intracellular loop linking repeat III and IV (c.3575A>T; p.Q1192L). Several variants located in the cytosolic loop have been reported as LQTS-related ones, suggesting that gain-of-function mutations affect the function of CACNA1C [5, 55, 56].
We found a novel variant in ANK2, located between the 24th ANK repeat and ZU5 domain (c.2699G>C, p.R900P). As the ANK repeat has a critical role in the normal electric activity of the heart, the novel variant might alter the maintenance of ion channels and transporters and affect cardiac function [57, 58].
KCNJ5 encodes a subunit of the potassium channel controlled by G-proteins and allows potassium ions into cells. We found one novel variant (c.592G>A, p.E198K) in the C-terminus cytosolic domain of KCNJ5. Previously, only one variant (p.G387R) was detected in KCNJ5 in a Chinese family affected by LQTS [48]. This variant was also located in the C-terminus cytosolic domain and indicated a loss of function phenotype. The novel variant found in our present study might also contribute to the development of LQTS by a similar mechanism.
In this study, four different LQTS-related variants in RYR2 were identified. RYR2 is a component of the calcium channel and supplies calcium to the myocardium in response to ryanodine concentration [59]. Although several LQTS-related variants on RYR2 have been reported [18, 60, 61], a considerable part of RYR2 variants are shared with other cardiac diseases, and it is particularly considered as a causative gene for catecholaminergic polymorphic ventricular tachycardia (CPVT). Miyata et al. divided the variants found in RYR2 of CPVT and LQTS patients into N-terminal (1–2177 a.a.), central (2178–4075 a.a.), and C-terminal (4076–4959 a.a.) regions and found no significant positional differences between these diseases [61]. They suggested that patients with RYR2 variants and bradycardia should be carefully diagnosed to avoid misdiagnosing CPVT patients as LQTS patients [61]. Hirose et al. revealed RYR2 loss-of-function mutations causing QT prolongation [62]. We found that most LQTS-related variants reported in previous studies [18, 42, 48, 63] and in our present study were located on the large cytosolic region of the N-terminal side (Fig 1). Therefore, we suppose that clarifying the location and the type of dysfunction in RYR2 in detail might help to distinguish between LQTS and CPVT. Further analysis may reveal which RYR2 variants are important for the development of LQTS.
Only those predicted to be damaging in silico are shown. The number in the parentheses indicates the reference number in the manuscript. Those with surrounding lines are the novel variants identified in this study. Gray boxes indicate the transmembrane region and “P” indicates the pore-forming region.
Among the other candidate genes, LRBA showed three novel variants in this study (Table 3). LRBA is known to be involved in trafficking endosomes containing ligand-activated receptors [64, 65]. Many LQTS-related ion channel proteins are transported and recruited to the plasma membrane by endosomal trafficking [66, 67]. Two novel variants in LRBA (c.7013C>T, p.R2338Q and c.6434C>T, p.R2145H) were located in the BEACH domain which plays an important role in endosomal vesicle trafficking [68]. Many LQTS-related ion channel proteins are transported and recruited to the plasma membrane by endosomal trafficking and the formation of functional complexes [66, 67]. Therefore, LRBA could also possibly contribute to the maintenance of LQTS-related membrane proteins, and variants that alter the function of LRBA might contribute to the pathogenesis of LQTS.
In the upstream region of the gene, we found variants predicted to alter the binding affinity for TFs (Table 4). These TFs were annotated to be involved in the pathway of heart development, morphogenesis, and cell proliferation in GO term name [32]. If these TFs truly regulate the downstream genes examined in this study, it will alter the expression level of each gene product which is related to the function of ion channels and/or calmodulin. Inadequate expression of these proteins might affect the proper function of cardiac cells and lead to development of LQTS [69].
It is worth noting that one variant in the NET1 gene (chr10: 5453591) was shared with five patients (Table 4). NET1 is known to control Rho signaling [70, 71]. Rho signaling play an important role in the cardiac system [72] suggesting the variant we found in this study might influence the development of LQTS.
Recently, Adler et al. advocated that the known causative variants for LQTS have not been well experimentally validated and reported that among 17 reported causative genes for LQTS, only three (KCNQ1, KCNH2, SCN5A) were definitive genes for typical LQTS [3]. In our study, we found novel variants in six out of 15 causative genes and half of them were in the definite LQTS causative genes. However, among three definite LQTS genes, KCNQ1 and KCNH2 are also reported as causative genes for the short-QT syndrome [73]. Depending on the locus of mutations in the cardiac channel genes, different phenotypes can be observed. For example, the loss-of-function mutation in KCNQ1 causes prolongation of QT interval [74], and the gain-of-function mutation in KCNQ1 causes shortening of QT interval [75]. Because all novel variants found in this study are predictive, functional analyses and their validation remain to be performed to prove that these are truly causative variants/genes for LQTS. Revealing the effect of each variant on the onset of the disease would be expected to screen the potential LQTS patients at an early stage. In addition, genetic diagnoses of LQTS can be used for screening patients’ family members and contributes to the implementation of therapies for high-risk relatives [11]. We believe the elucidation of novel variants related to LQTS will improve diagnosis, risk stratification, and early treatment of LQTS patients in the future, especially for those who had previously failed the genetic screening.
Supporting information
S1 Table. List of primer sequences used for SNP validation on exon region.
https://doi.org/10.1371/journal.pone.0277242.s001
(XLSX)
S2 Table. List of primer sequences used for SNP validation on upstream region.
https://doi.org/10.1371/journal.pone.0277242.s002
(XLSX)
Acknowledgments
We appreciate Makiko Matsuda, Takamasa Ichikawa and Saori Takeshima for their kind assistance in experiments and preparing this manuscript.
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