Association between SCN5A and sudden unexplained nocturnal death syndrome in Thai decedents: a case–control study

Abstract

Background

Sudden unexplained nocturnal death syndrome (SUNDS) is a genetic disorder that can cause sudden death in young and healthy adults during sleep at night. Cardiac arrhythmia has been proposed as the factor responsible for such deaths. This study was thus aimed to investigate the association between SCN5A, the gene that has been extensively studied and thought to be associated with cardiac conduction system abnormality causing sudden cardiac death, and SUNDS in Thai decedents.

Results

Genomic DNA was extracted using blood samples from 12 unrelated SUNDS subjects and 151 unrelated non-SUNDS control decedents. Subjects were divided into three subgroups as follows: subjects who resided in regions other than Northeastern Thailand (n = 99), age-matched subjects who resided in Northeastern Thailand (n = 28), and subjects older than 40 years who resided in Northeastern Thailand (n = 24). Genomic DNA from SUNDS cases was screened for genetic variations for the entire 28 coding exons of SCN5A. The identified variants were also genotyped in control subjects using high-resolution melting analysis and sequencing.

Conclusions

Twelve variants of SCN5A were identified, including six polymorphisms and another six variants previously reported to be related with cardiac conduction defects. Two identified variants (rs1805126 and rs7429945) deserved further study because of their strong odd ratios.

Background

Sudden death is a major concern in forensic medicine, especially when the decedent is young and healthy and the postmortem examination fails to reveal any significant findings. Sudden unexplained nocturnal death syndrome (SUNDS) is a genetic disorder that can cause sudden death in young, healthy adults during sleep at night. Autopsy findings in cases of SUNDS reveal no evident histopathological changes that explain the cause of death. The clinical and pathologic features of these cases of sudden death, especially the characteristic lack of overt structural heart disease, are similar among cases. SUNDS is highly prevalent in healthy young Southeast Asian males (Munger and Booton 1998; Gotoh 1976; Cheng et al. 2011; Srettabunjong 2019), and it may have devastating effects on families in many different ways.

SUNDS has been given different local names, including “bangungut” in the Philippines (Munger and Booton 1998), “pokkuri” in Japan (Gotoh 1976), “sudden manhood death syndrome” in China (Cheng et al. 2011), and “lai-tai” in Thailand (Srettabunjong 2019). The annual incidence of SUNDS has been reported to be as high as 43 per 100,000 people, all being men aged 20–40 years in the Philippines (Gervacio-Domingo et al. 2007); 26–38 per 100,000 people, all being men aged 20–49 years in Northeast Thailand (Tatsanavivat et al. 1992; Tungsanga and Sriboonlue 1993); and as low as 1–2 per 100,000 people predominantly in men aged 17–55 years in Southern China (Cheng et al. 2011; Zheng et al. 2015). However, the true incidence of SUNDS is unknown.

Cardiac arrhythmia has been proposed as the factor responsible for death in individuals who experience SUNDS (Nademanee et al. 1997). Cardiac arrhythmia has also been assumed to be associated with the pathogenesis of sudden cardiac death in the young, such as congenital long QT syndrome (LQTS), catecholaminergic polymorphic ventricular tachycardia, arrhythmogenic right ventricular cardiomyopathy, and Brugada syndrome (BrS) (Elger et al. 2010). Previous studies reported that these conditions are allelic disorders, and some sporadic SUNDS cases may be caused by inherited cardiac arrhythmia, such as LQTS and BrS (Cheng et al. 2011; Liu et al. 2013). Additionally, both SUNDS and BrS represent the same autosomal dominant familial disorder, and they can result from mutations in the cardiac sodium channel SCN5A, thus causing loss of channel function (Sangwatanaroj et al. 2002). Some previous studies also reported that some SUNDS cases are likely caused by the same allelic disorder that causes BrS (Vatta et al. 2002; Liu et al. 2014; Huang et al. 2016). However, mutations have only been identified in a small proportion of probands and the clinical markers that would identify victims at high risk for SUNDS have not yet been identified. Moreover, the etiology of SUNDS remains unknown.

SCN5A is located on chromosome 3p21 and encodes the type 5 α-subunit of the cardiac voltage-gated sodium channel (Na_V1.5) (Chen et al. 1998). The Na_V1.5 protein, which is 80 kb in size and consists of 2016 amino acids, is expressed only in the heart (Daimi et al. 2015). It is a pore-forming transmembrane protein consisting of four internal homologous domains (DI–DIV). Each domain consists of six transmembrane segments (S1–S6). The fourth transmembrane segment (S4) of each domain acts as a “voltage sensor.” Na⁺ ions can pass through this channel according to their electrochemical gradient. As such, mutations that inhibit the normal functions of SCN5A gene can lead to abnormal heart rhythms that may result in sudden death. Previous studies identified more than 250 SCN5A mutations linked to type 1 BrS, most of which are missense mutations (Brunklaus et al. 2014). Functional studies of SCN5A mutations uncovered loss-of-function in the sodium channel (Watanabe and Minamino 2016). Molecular mechanisms that may have an influence on this syndrome have been proposed, such as decreased protein expression from trafficking defects or the expression of non-functional channels, and decreased sodium influx by changes in channel gating properties via the process of activation and/or inactivation (Brunklaus et al. 2014; Watanabe and Minamino 2016; Amin et al. 2010). Van der berg et al. (2002) described a large family presenting with an SCN5A-linked disorder with features of premature nocturnal sudden death, LQTS, BrS, and familial conduction system disease. Several variants of SCN5A have been implicated as the underlying cause of LQTS type 3, BrS, and progressive cardiac arrhythmias (Vatta et al. 2002; Chen et al. 1998; Benito et al. 2008; Antzelevitch 2007). Mutations in this gene have also been reported as the cause of SUNDS in approximately 6–20% of cases (Vatta et al. 2002; Liu et al. 2014).

Based on our review of the literature, no previous studies investigated the relationship between SCN5A and SUNDS in Thai decedents. The aim of this study was to identify genetic variants of SCN5A in Thai SUNDS decedents and investigate whether any identified variations are significantly associated with SUNDS. Any findings of SCN5A genetic variations may benefit the relatives of SUNDS victims that may also be at risk of experiencing SUNDS.

Methods

This case–control study evaluated blood samples obtained from femoral veins of unrelated Thai decedents within 24 h after death. All decedent subjects underwent postmortem examination under the jurisdiction of the Department of Forensic Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, which is located in Bangkok, Central Thailand. The study protocol was ethically approved by the Siriraj Institutional Review Board, Faculty of Medicine Siriraj Hospital, Mahidol University. The subjects were divided into SUNDS and control groups. SUNDS cases were unrelated subjects who died during sleep at night with previous healthy history and a negative comprehensive postmortem investigation including histological and toxicological examination that can explain the cause of death. Control cases were unrelated subjects who died of any cause of death excluding SUNDS. To minimize the effect of genetic backgrounds that could lead to false-positive or false-negative results (Hu and Ziv 2008), control subjects were divided into three subgroups: subjects who resided in regions other than Northeastern Thailand (DR group, 99 subjects), age-matched subjects who resided in Northeastern Thailand (NE group, 28 subjects), and subjects older than 40 years who resided in Northeastern Thailand (NE⁺ group, 24 subjects). All control subjects were healthy and aged 15 years or older with no history of cardiac arrhythmic diseases, radiotherapy, chemotherapy, bone marrow transplantation, blood transfusion within 2 years, other SCN5A-related conditions (e.g., certain cardiovascular diseases), and genetic diseases. Subjects who developed some degree of decomposition or who were embalmed were excluded from the study. Subject information, including age, race, medical history, family history, autopsy findings, and cause of death, was documented.

All blood samples were stored at 4 °C shortly after being drawn from the femoral veins of the decedents. Genomic DNA for each subject was extracted from blood samples within 72 h after collection and quantified using a Nanodrop® ND-1000 (Thermo Fisher Scientific, Inc., Waltham, MA, USA).

Genetic testing

Genetic screening of all coding regions of SCN5A was performed in all SUNDS cases using polymerase chain reaction (PCR) and direct Sanger sequencing. A total of 40 pairs of PCR primers spanning all 28 exons of SCN5A were designed and employed for genetic screening (primer sequences available on request). PCR reactions were performed in a PTC-100™ Programmable Thermal Controller (MJ Research, Inc., St. Bruno, Quebec, Canada). Sanger sequencing was performed in an ABI 3730XL DNA Analyzer (Applied Biosystems, Inc. Foster City, CA, USA), using BigDye® Terminator Cycle Sequencing Kit version 3.1 (Applied Biosystems). The same primers were used for direct sequencing and PCR. Sequence chromatograms were then analyzed using Sequencing Analysis 5.2 software (Applied Biosystems).

The obtained sequencing data were then compared with the corresponding referenced cDNA sequence of SCN5A retrieved from GenBank® (NG_008934.1) using Mutation Surveyor® (Demo) (SoftGenetics, PA, USA). To identify the association between SCN5A variants and SUNDS, variants were selected by focusing on SNP information for CDX (Chinese Dai in Xishuangbanna, China), CHS (Southern Han Chinese, China), KHV (Kinh in Ho Chi Minh City, Vietnam), and JPT (Japanese in Tokyo, Japan) populations, including allele frequency, linkage disequilibrium (LD), and single-nucleotide polymorphism (SNP) locations. SNPs with a minor allele frequency of approximately 5% from the Exome Aggregation Consortium (ExAC), 1000 Genomes Project (http://www.1000genomes.org/ensembl-browser), and NHLBI GO Exome Sequencing Project (GO-ESP, http://snp.gs.washington.edu/EVS) databases were preferable.

These identified variants were also screened in the controls using high-resolution melting (HRM) analysis. Genetic screening was performed using a CFX96™ Real-Time PCR Detection System (Bio-Rad Laboratories, Inc., Hercules, CA, USA). The primers of each variant suitable for HRM were designed using Primer3 version 0.4.0 (http://bioinfo.ut.ee/primer3-0.4.0/) (primer sequences available on request). Melting files generated using Precision Melt Analysis™ Software (Bio-Rad Laboratories, Inc., Hercules, CA, USA) were then analyzed. To validate the variants obtained from additional HRM patterns, large amplicons containing additional patterns were subjected to further genetic screening by PCR and Sanger sequencing.

Genetic variant analysis

All identified variants were filtered for candidate mutations based on allele frequency in the 1000 Genomes Project database, functional mutation types, predictive software programs, and a review of the related published literature. To determine whether the identified genetic variants were novel mutations, the variants were also compared with published data, including Single Nucleotide Polymorphism Database (dbSNP) (http://www.ncbi.nlm.nih.gov/snp/), the 1000 Genomes Project database, the GO-ESP database, and the inherited arrhythmias database (http://www.fsm.it/cardmoc/). To predict the deleterious effects of identified variants, three web-based in silico prediction programs were used in this study, namely MutationTaster (http://www.mutationtaster.org/), PROVEAN (http://provean.jcvi.org/index.php), and Human Splicing Finder (http://www.umd.be/HSF3/HSF.html).

Any variant identified in both SUNDS cases and controls was designated as a polymorphism. Polymorphisms with a minor allele frequency (MAF) exceeding 0.01 among the controls were classified as common polymorphisms. If the MAF was less than 0.01 among the controls, the polymorphism was regarded as rare polymorphism. Any variants observed only in SUNDS cases were designated as mutations. All nonsense, frameshift, and splice site variants were considered to be candidate pathogenic mutations, unless identified as a polymorphism.

Statistical analysis

All statistical analyses (unless otherwise specified) were performed using PASW Statistics for Windows, Version 18 (SPSS, Inc., Chicago, IL, USA). Continuous variables are shown as the mean ± standard deviation. The allele frequencies for each SNP site and genotype frequency for each polymorphism were calculated using the web-based SNPStat program (http://bioinfo.iconcologia.net/snpstats/start.htm). The genotype frequencies of the detected coding region polymorphisms in the SUNDS and control groups were tested for deviation from Hardy–Weinberg equilibrium using Pearson’s chi-square goodness-of-fit test (de Finetti) and Fisher’s exact test (SNPStat), respectively. Differences in genotype and allele frequencies between the SUNDS and control groups were also analyzed using Pearson’s chi-square goodness-of-fit test (de Finetti) and Fisher’s exact test (SNPStat), respectively. The association between multiple detected variants and LD was calculated using online SNPStat. To identify the detected variants that were inherited together, SNPStat was used to construct and estimate the haplotype frequency. The association between haplotypes and SUNDS was analyzed via logistic regression. The association between the detected polymorphisms and SUNDS risk was estimated by calculating the odds ratio (OR) and 95% confidence interval (95% CI) using Pearson’s chi-square test analysis. p values < 0.05 were considered statistically significant.

Results

A total of 12 SUNDS decedents (all males) were included in this study. Most subjects (83%) resided in Northeastern Thailand. The age of the subjects ranged 20–45 years (mean, 32.9 ± 6.4 years). All cases had a normal heart structure. Half of the cases died during the summer (April–June). Of the 12 SUNDS cases, only one case had a family history of SUNDS. A total of 151 unrelated healthy control cases were also included in the study, including 127 males and 24 females. The control cases ranged in age from 15 to 59 years (mean, 32.9 ± 11.4 years). Control subjects were classified into DR (80 males and 19 females; age range, 15–59 years; mean age, 31.9 ± 11.8 years), NE (25 males and 3 females; age range, 15–38 years; mean age, 28.3 ± 7.5 years), and NE⁺ (22 males and 2 females; age range, 41–59 years; mean age, 45.3 ± 3.8 years).

The sequencing analysis revealed a total of 12 variations (Table 1). All variants were polymorphisms, including one non-synonymous variant [rs1805124 (c.1673A>G, H558A) in exon 12], four synonymous variants [rs6599230 (c.87A>G, A29A) in exon 2, rs7430407 (c3183A>G, E1061E) in exon 17, and rs1805126 (c.5457 T>C, D1818D) and rs376826051 (c.5712G>A, S1903S) in exon 28], and seven variants in the3′UTR [rs7429945 (c.*123A>G), rs41310757 (c.*753C>T), rs4073797 (c.*962 T>A), rs4073796 (c.*963C>T), rs11414422 (c.*1453_*1454insA), rs41315485 (c.*1537 T>C), and rs45592631 (c.*2145_*2146insGAGAAGAGAGTGGAAAAAGAGGG)]. Two variants (rs7430407, rs45592631) displayed only homozygous variants, and they appeared to be major alleles found in various populations (Table 2).

Table 1 Characteristics of SUNDS cases and controls

Table 2 Allele frequency of each variant identified in this study, using 151 control subjects compared with frequency from various population

Of the seven variants in the3′UTR, rs45592631 had a MAF of less than 0.01 (0.0068). Another two variants (rs11414422, rs4592631) were unclassified and further evaluated for functional effects using three prediction programs, which predicted both variants to be polymorphisms. However, one synonymous variant in exon 28 (rs376826051) was unclassified, and the MAF was not available in 1000 genomes. The ExAC database revealed that MAF of this variant was less than 0.01 (0.0005879). Two prediction programs (MutationTaster and Human Splicing Finder 3.0) found this variant to be disease causing due to a splice site change, whereas PROVEAN/SIFT predicted it to be a neutral polymorphism (Table 3).

Table 3 Prediction results of 12 variants of SCN5A obtained from SUNDS by three online prediction programs (MutationTaster, PROVEAN/SIFT, Human Splicing Finder)

Based on the aforementioned results, only 10 variants were selected for the association study between SCN5A variants and Thai SUNDS decedents. The genotype and allele frequencies of these variants identified in SUNDS and control cases were analyzed as shown in Table 4.

Table 4 Comparison between odds ratios of allele frequency from controls

To evaluate the association between each SCN5A variant with SUNDS, the variants that were in Hardy–Weinberg equilibrium were evaluated for differences in allele and genotype frequencies between the SUNDS and control groups including subgroups. The results revealed that only three variants had some associations with SUNDS: rs6599230, rs1805126, and rs7429945.

rs6599230 was linked to a statistically significant difference between the SUNDS and control groups regarding genotype frequency for the co-dominant [A/G, OR = 0.20 (0.04–0.98); p = 0.037] and over-dominant modes [A/G, OR = 0.18 (0.04–0.84); p = 0.012]. This variant was also linked to a statistically significant difference between the SUNDS and DR groups concerning genotype frequency for the over-dominant mode [A/G, OR = 0.20 (0.04–0.94); p = 0.02] but not for allele frequency. Additionally, the variant was associated with a statistically significant difference between the SUNDS and NE groups regarding genotype frequency for the co-dominant [A/G, OR = 0.09 (0.01–0.56); p = 0.009], dominant [A/G-A/A, OR = 0.19 (0.05–0.84); p = 0.025], and over-dominant modes [G/G-A/A, OR = 0.09 (0.02–0.53); p = 0.002] but not for allele frequency.

rs1805126 was linked to a statistically significant difference between the SUNDS and NE groups regarding both allele [OR = 3.25 (1.20–8.77); p = 0.018] and genotype frequencies for the co-dominant [T/T, OR = 13.00 (1.11–152.36); p = 0.038] and dominant modes [T/C-T/T, OR = 9.53 (1.08–84.14); p = 0.012].

rs7429945 was associated with a statistically significant difference between the SUNDS and NE groups concerning genotype frequency for the co-dominant [A/A, OR = 13.0 (1.11–152.36); p = 0.038] and dominant modes [A/G-A/A, OR = 9.53 (1.08–84.14); p = 0.012] but not for allele frequency.

To evaluate the associations among multiple variants obtained from SUNDS subjects, LD and haplotype analyses were performed. A total of four variants (rs1805126, rs7429945, rs4073796, and rs4073797) were found to be significant in LD analysis, each having a haplotype frequency exceeding 1%.

Discussion

SUNDS is believed to be associated mainly with cardiac sodium channel diseases that involve SCN5A mutations. Previous studies demonstrated a linkage between SUNDS and SCN5A variations (Zheng et al. 2015; Vatta et al. 2002). More recent studies (Liu et al. 2013; Liu et al. 2014; Huang et al. 2014) also reported that the cause of some cases of SUNDS might be SCN5A mutations that can result in a reduction of I_Na, with some mutations exhibiting a dominant-negative effect on wild-type (WT) channels, leading to an even more prominent decrease in current amplitudes (Hoshi et al. 2014).

All SUNDS cases in this study involved young males, the majority of whom resided in Northeastern Thailand, the region with the highest incidence of SUNDS (Srettabunjong 2019; Tungsanga and Sriboonlue 1993). Although some studies reported that SUNDS can affect both genders, its prevalence in males is thought to be about 4–14-fold higher than that in females (Cheng et al. 2011; Vatta et al. 2002; Gervacio-Domingo et al. 2007; Chen et al. 2016). Most SUNDS cases in this study occurred during the summer season (April–June), consistent with previous studies (Cheng et al. 2011; Srettabunjong 2019; Tungsanga and Sriboonlue 1993), suggesting that the season may be a risk factor for SUNDS.

In this study, postmortem genetic analysis was performed on all 28 coding regions of SCN5A in all 12 Thai SUNDS cases with a total of 12 variations (all polymorphisms) of SCN5A. The first three variants detected in this study (A29A, H558A, E1061E) were the same as those detected in a recent study by Liu et al. (2014), who also discovered seven missense mutations (V95I, R121Q, R367H, R513H, D870H, V1764D, S1937F). By contrast, there were no matches in variations between our study and a study conducted by Vatta et al. (2002), who found only three mutations (R367H, A735V, R1193Q). The fourth variant detected in our study (D1819D) was the same variant as found in a previous study by Cheng et al. (2011) who found only two polymorphisms, D1819D and 3666+69G>C, in SCN5A, which have been suggested as SUNDS-causing variants in Southern Chinese populations. However, the detection of mutations in different populations may vary significantly depending on ethnic and regional differences between cohorts, as well as differences in mutation calling (Cheng et al. 2011).

Interestingly, some of the variants in the present study were previously described as pathologic variants of cardiac conduction defects, even if they produced heterogeneous effects. A synonymous variant, rs6599230 (c.87A>G, A29A), was reported to be associated with PR interval shortening in people of European ancestry (Magnani et al. 2014). Mohanty et al. (2016) reported that the G allele of this variant is a protective allele that reduces the risk of non-paroxysmal atrial fibrillation by 43%. This finding was consistent with that of a study performed by Qureshi et al. (2015) who analyzed SCN5A mutations in patients with LQTS. Although the rs6599230 polymorphism found in this study was linked to a statistically significant difference in genotype frequency between the SUNDS and control groups including the DR and NE subgroups, its ORs were extremely small.

The non-synonymous variant rs1805124 (c.1674A>G, H558R) is located in the intracellular domain I-II linker of SCN5A, and it results in the missense exchange of a histidine residue to an arginine residue at amino acid position 558. This variant is found in 20% of the population (Viswanathan et al. 2003), and it has been reported to modify the expression of an arrhythmia-causing mutation (Ye et al. 2003) and found to be associated with BrS (Chen et al. 2004). Shinlapawittayatorn et al. (2011) reported that this polymorphism could reverse the gating defects acquired from the SCN5A–P2006A mutation located in the C-terminal region of the sodium channel and restore normal function. They also suggested that this variant might play a crucial role in normalization from fast inactivation of this channel, and it might also be a disease-modifying gene, as previously described by other authors (Viswanathan et al. 2003; Ye et al. 2003; Poelzing et al. 2006). This result is suggestive of an interaction between sodium channel alpha subunits, and it is similar to the interaction with the V1951L mutation associated with sudden infant death syndrome, which is also located in the C-terminal region of the sodium channel. Although the sodium channel alpha subunit alone is sufficient to form a functional conducting pore, these findings were consistent with other reports (Aldrich et al. 1983; Undrovinas et al. 1992; Keller et al. 2005), suggesting that sodium alpha subunits may interact with each other. This variant also interacts with a BrS-associated mutation (SCN5A–R282H), resulting in restoration of the trafficking defect of the mutation (Poelzing et al. 2006). In addition, this variant and two synonymous variants (rs1805126, c.5457 T>C, D1819D and rs6599230, c.87A>G, A29A) were previously reported to be associated with cardiac conduction by affecting PR and QRS intervals (Magnani et al. 2014). However, this polymorphism was not associated with a statistically significant difference between the SUNDS and control groups including the DE, NE, and NE⁺ subgroups regarding either allele frequency or genotype frequency in this study. This finding was consistent with that of a recent study in a Southern Han Chinese population by Liu et al. (2014).

Gouas et al. (2005) reported that rs1805126 (c.5457 T>C, D1819D), a synonymous variant, was more frequently found in healthy subjects with shorter QTc intervals and that the C allele might be protective. However, the present study found that this variant was linked to significant differences between the SUNDS and NE groups regarding both allele and genotype frequencies with large ORs of approximately 3 and 10, respectively, suggesting that this variant might be associated with SUNDS in the Thai population. This finding was consistent with that of the previous study by Cheng et al. (2011). Thus, whether the rs1805126 variant is associated with SUNDS requires further investigation.

For untranslated variants located in the 3′UTR of SCN5A, recent studies reported that rs41310757, rs4073797, rs4073796, and rs41315485 affect gene expression by changing the binding site of miRNA (Zhao et al. 2015; Daimi et al. 2015).

The rs7429945 (c.*123A>G) variant was found in patients with BrS (Uzieblo-Zyczkowska et al. 2014), and no defects in splicing sites were demonstrated by bioinformatics analysis. However, this study found that this variant was associated with significant differences between the SUNDS and NE groups regarding genotype frequency with large ORs of approximately 10. As such, whether the rs7429945 variant is associated with SUNDS requires further investigation.

With respect to multiple variant association with SUNDS, this study revealed that rs1805126 and rs7429945 have strong linkage disequilibrium with each other, suggesting that they may co-segregate.

Conclusion

This study employed postmortem genetic testing to investigate the association between SCN5A and SUNDS in a Thai population. To our knowledge, this is the first report on SCN5A genetic screening in Thai SUNDS decedents. As a preliminary investigation, this study identified 12 variants of SCN5A. Although no new mutations were found in this study, two identified variants (rs1805126 and rs7429945) deserve further study. Furthermore, they have strong linkage disequilibrium with each other, suggesting that they may co-segregate. Given the preliminary nature of this study, further study may be needed with a larger number of SUNDS cases and controls to confirm these results and elucidate the association between these variants and SUNDS in the Thai population. Other investigation of the association between common polymorphisms in SCN5A and SUNDS, including electrophysiological investigations, may also be needed. Since the SUNDS cases evaluated in this study were not subjected to a scan of the entire SCN5A gene, the incidence of rare variants residing outside the coding exons (which might have had a putative role in the function of this gene) should also be investigated to ensure a comprehensive understanding of potential pathogenic variants. Information and conclusions based on these findings may be beneficial to the relatives of people who died of SUNDS, and who might also be at risk for this disorder.