Common and Rare Variants in SCN10A Modulate the Risk...

DOI: 10.1161/CIRCGENETICS.113.000442

1

Common and Rare Variants in SCN10A Modulate the Risk

of Atrial Fibrillation

Running title: Jabbari et al.; Atrial fibrillation and SCN10A

Javad Jabbari, MSc1,2*; Morten S. Olesen, MSc, PhD1,2*; Lei Yuan, MD1*; Jonas B. Nielsen,

MD1,2; Bo Liang, MD, PhD1; Vincenzo Macri, PhD4; Ingrid E. Christophersen, MD5; Nikolaj

Nielsen, MSc1; Ahmad Sajadieh, MD, DMSc6; Patrick T. Ellinor, MD, PhD4; Morten Grunnet,

PhD, DMSc1; Stig Haunsø, MD, DMSc1,2,3 on behalf of LuCamp7; Anders G. Holst, MD, PhD1,2;

Jesper H. Svendsen, MD, DMSc1,2,3; Thomas Jespersen, PhD, DMSc1

1The Danish National Research Foundation Centre for Cardiac Arrhythmia (DARC), 2Laboratory for Molecular Cardiology, Rigshospitalet, 3Department of Clinical Medicine, Faculty of Health and Medical

Sciences, University of Copenhagen, Copenhagen, Denmark; 4Cardiac Arrhythmia Service and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA; 5Department of Medical Research, Bærum Hospital, Vestre Viken Hospital Trust, Rud, Norway; 6Department of Cardiology, Copenhagen University Hospital of Bispebjerg, Bispebjerg; 7The Lundbeck Foundation Centre for Applied Medical Genomics in

Personalized Disease Prediction, Prevention and Care, Copenhagen, Denmark*contributed equally

Correspondence:

Thomas Jespersen, PhD, DMSC

Heart and Circulatory Research Section

Department of Biomedical Sciences

Faculty of Health and Medical Sciences

Blegdamsvej 3,

2200 Copenhagen N, Denmark

Tel + 45 2480 5190

Fax +45 3532 7555

E-mail: [email protected]

Journal Subject Codes: [132] Arrhythmias - basic studies, [106] Electrophysiology, [5] Arrhythmias, clinical electrophysiology, drugs

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Abstract:

Background – Genome-wide association studies (GWAS) have shown that the common single

nucleotide polymorphism (SNP) rs6800541 located in SCN10A, encoding the voltage-gated

Nav1.8 sodium channel, is associated with PR interval prolongation and atrial fibrillation (AF).

SNP rs6800541 is in high linkage disequilibrium with the non-synonymous variant in SCN10A,

rs6795970 (V1073A, r2=0.933). We aim to determine whether common and rare SCN10A

variants are associated with early onset lone AF.

Methods and Results – The SCN10A gene was sequenced in 225 lone AF patients. In an

association study of the common variant V1073A, we included 515 AF patients, and two control

cohorts of 730 individuals free of AF and 6,161 individuals randomly sampled. Functional

characterization of two common and two rare variants was performed by whole-cell patch-

clamping. In the lone AF cohort, nine rare missense variants and one splice site donor variant

were detected. Interestingly, AF patients were found to have lower minor allele frequency of the

V1073A variant than controls (odds ratio = 0.74 [0.65-0.86]; p=2.3x10-05). Functional

characterization revealed that both of the common variants, V1073A and L1092P, induced a

gain-of-channel function, while the rare missense variants, V94G and R1588Q, resulted in a loss-

of-channel function.

Conclusions – We report that the common variant V1073A is associated with decreased

susceptibility to AF. In functional studies the two common variants gave rise to a gain-of-

function. The rare variants found in lone AF patients showed loss-of-function, indicating that

these variants increase susceptibility to AF. Hence, our study suggests that SCN10A variations

are involved in the genesis of AF.

Key words: atrial fibrillation arrhythmia, genetic polymorphism, electrophysiology, genotyping, Genome Wide Association Study, lone atrial fibrillation, SCN10A, Voltage Gated Sodium Channel Alpha Subunit Nav1.8, rs6795970, functional characterization

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Introduction

Atrial fibrillation (AF), which is the most commonly sustained cardiac arrhythmia, is a global

health problem accounting for increasing morbidity, mortality, and healthcare costs.1–3

Identifying and understanding the genetic basis of AF and/or association of genomic regions

with AF will provide valuable insight into the pathogenesis of AF, and potentially improve the

risk stratification and therapeutic options.

Genome wide association studies (GWAS) have identified 10 loci in the human genome

that are associated with AF.4 Thus, common genetic variants play a role in the development of

this multifactorial disease.5 Several studies have shown PR-interval prolongation on an

electrocardiogram to be an independent risk factor for developing AF.6–8 Five independent

GWAS publications have shown that genetic variants in SCN10A influence the PR-interval

duration.9–13 Pfeufer et al. showed that five out of nine PR-associated loci from GWAS increased

the risk of AF.10 They found that the single nucleotide polymorphism (SNP) rs6800541, which is

located in an intron of SCN10A, had the strongest association with PR-interval duration and one

of the strongest associations with AF among nine other GWAS hits. This SNP is in high and

moderate linkage disequilibrium with two common nonsynonymous SNPs in SCN10A:

rs6795970 (V1073A) and rs12632942 (L1092P), respectively.10 The substantial arrhythmogenic

potential of genetic variants in SCN10A is underscored by the fact that another SNP

(rs10428132) in this gene was the top hit in a GWAS on Brugada syndrome; a condition strongly

associated with AF.14,15 Moreover, very recently, a phenome-wide study associated SCN10A,

through rs6795970, directly with AF.16 This, however, contradicts with the findings by Holm et

al. who did not report an association of rs6795970 with AF.11

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SCN10A encodes the voltage-gated sodium channel, Nav1.8. This channel is the

predominant tetrodotoxin-resistant sodium channel in primary sensory neurons, with particularly

high levels of expression in nociceptive neurons, where it plays a key role in peripheral pain

processing.17,18 Expression has also been shown in vagal, but not in sympathetic fibers.19,20

Recently, a number of studies have indicated that SCN10A mRNA is present in both

human and mouse heart and that this channel is involved in the cardiac INa current.9,21–23 Yang et

al. demonstrated higher expression of Nav1.8 transcripts in mouse atria compared to ventricle.21

Facer et al. detected Nav1.8 protein in both atrial myocytes and nerve fibers in the myocardium.24

Using genetic lineage tracing, others have shown that Nav1.8 is expressed in aortic bodies and

the nerves around blood vessels of the heart.20 Interestingly, Verkerk et al. found that Nav1.8 is

highly expressed in intracardiac neurons.22 In summary, these studies suggest that Nav1.8 is

expressed in both cardiac myocytes and intracardiac neurons.

The recent notion that Nav1.8 might be important for cardiac electrophysiological

properties raises the possibility that altered function of this gene may be coupled with cardiac

arrhythmias.25 Thus, in the present study, we investigated whether the common SCN10A variant

rs6795970 is associated with AF and thereby would be the variant carrying the effect of the

GWAS hit. In addition, we screened 225 lone AF patients for SCN10A variants, and

characterized the two rarest variants together with the two common variants functionally using

patch-clamp electrophysiology.

Methods

Study Subjects

Patient records with the ICD-10 diagnose code I48.9 (atrial fibrillation and flutter) were collected

and read. Only 225 patients with “lone AF” and onset of disease before age of 40 years were

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recruited. Lone AF was defined as AF in absence of clinical or echocardiographic findings of

cardiovascular disease, hypertension requiring medical therapy, metabolic- or pulmonary

diseases. For the genotyping of the rs6795970 SNP, we recruited a cohort of 358 Scandinavian

lone AF patients with onset of AF before the age of 50 (83% male gender, median age of AF

onset 34.5 years [interquartile range 28-39 years]) and a cohort of 157 unselected AF patients

(68% male gender, median age of 66 years [interquartile range 32-86 years]) (Online Appendix).

Blood samples, ECG and clinical data were collected from all participating subjects. The study

conforms to the principles outlined in the Declaration of Helsinki and was approved by the local

scientific ethics committees, and all patients provided written informed consent.

Control Population

A total of 730 healthy subjects (52% males, median 66 years [interquartile range 52-76 years])

from two control cohorts (control groups I and II) were included in this study (Online

Appendix). Control group I (complete sequencing of SCN10A) consisted of 216 unrelated

healthy Danish blood donors with a normal ECG and without any cardiac symptoms. Control

group II comprised 514 ethnically matched, middle-aged men and women without a history of

AF or other manifestations of cardiovascular disease; however, with a high prevalence of risk

factors for AF. Control groups I and II were previously described in detail.26,27 These control

groups were used in the genotyping of rs6795970 using a Taqman assay. To increase the

statistical power of our association study, we also used a third control cohort (control group III),

comprising 6,161 individuals randomly selected from a Danish cohort study (Inter99,

LuCamp).28 Although this control cohort could only provide data on rs6795970, due to the

exome-chip which was used in the Inter99 study. This control group is assumed to represent the

general population.

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SCN10A Screening

The method is available in an Appendix.

SNP Genotyping

We genotyped rs6795970, encoding V1073A, in 515 AF patients of which 358 were lone AF

patients. For comparison, in addition to control groups I and II (nTotal=730), we also used the data

from a European-American population (n=4,300) from the Exome Sequencing Project (ESP).

Genotyping of control groups I and II was performed as previously described.26 Furthermore, we

also used the exome-chip data on the rs6795970 SNP from control group III.

Molecular Biology and In vitro Electrophysiology

Introduction of variants, cell culturing and patch-clamping of transiently transfected Neuro2A

cells were performed as previously described.29 A detailed description is available in Online

Appendix.

Bioinformatics and Statistical Analysis

All variants were reviewed in publicly available SNP databases (dbSNP, and ESP6500). We

used 4 in silico tools to predict whether the variants were disease causing. The MAF in the 2 case

cohorts were compared one by one with the 3 control cohorts using the Chi-square test.

Similarly, we performed a pooled analysis where MAF in the 2 case groups were compared with

a pooled MAF from our largest control population and ESP. Data are presented as

mean ± standard error of mean (SEM) unless otherwise noted. Kolmogorov-Smirnov test was

applied to confirm Gaussian distribution. Two-tailed Student’s t-test, one-way or two-way

ANOVA combined with a Bonferroni post hoc test, or Chi-square tests, were used as appropriate

to test for significant differences. A value of P <0.05 was considered statistically significant.

Further description is available at the Online Appendix.

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Results

Genetic screening

We included 225 unrelated Danish patients with onset of disease before the age of 40 years for

full genetic screening (clinical data listed in Online Appendix). The individual clinical

characteristics of the patients with the rare SCN10A variations are listed in Table 1. In Figure 1

the positions of rare and common missense variants found in SCN10A in lone AF are illustrated

in the Nav1.8 protein topology. We identified nine rare missense variants (R14L; V94G; Y158D;

R814H; E825D; I999L; R1268Q; C1523Y; R1588Q) and one splice site donor variant

(rs75991777) (in exon 4 at second position T>C, Table 2). These variants were neither present in

our in-house control group (n = 432 alleles), nor have any been previously reported in

conjunction with AF. However, except for I999L and rs75991777, all variants were identified in

the Exome Sequencing Project database (n=6,503) with minor allele frequency (MAF) less than

0.5% in the European-American population. rs75991777 has been reported in the dbSNP

database from the 1000 Genomes Project with a MAF of 0.1%. The amino acid residues altered

in the rare variants were found to be highly conserved across eukaryotic species, except for

R814H and E825D, which differed in rat and mouse (data not shown). In our co-segregation

analysis, we were able to screen the family members of the patients with I999L, C1523Y and

rs75991777 variants. None of the family members diagnosed with AF carried the variant

identified in the probands. Family members of remaining geno-positive patients were not

available. PolyPhen2 prediction software predicted 78% (7 out of 9) of the rare variants to have a

functional effect on protein function (Table 2).30 By using the Giudicessi et al

in silico tools on these rare variants, it was predicted that 60% of the variants were damaging

(Table 2).31

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Genotyping of V1073A

The result of the SNP genotyping is listed in Table 3. We were able to genotype the SNP

rs6795970 in SCN10A in 515 AF patients (358 lone AF and 157 unselected AF patients) with a

total call rate of 98.5%. The MAF of rs6795970 in all AF cases was 31.8% compared to 37.8%

in 6,161 Danish exomes (Odds Ratio (OR) = 0.78, 95% Confidence Interval (CI) [0.68-0.90];

p=3.9x10-04). The same result was found in a meta-analysis in which we added 4,300 European-

American exomes from the ESP6500 database to the 6,161 Danish exomes (OR = 0.74 [0.65-

0.86]; p=2.3x10-05, Table 3). When analyzing only the lone AF patients, the same strong

association was found. They were also less likely to carry the G allele compared to 6,161 Danish

exomes (OR = 0.79, 95% CI [0.66-0.93]; p=0.003) and to the meta-analysis group (6,161 Danes

+ 4,300 European-Americans from ESP) (OR = 0.75 [0.64-0.89]; p=4.6x10-4). These results

indicate a protective effect of rs6795970 against AF (Table 3).

Clinical Features

Nine out of 11 of the AF patients harboring an SCN10A variant had paroxysmal AF and several

of these patients also had other arrhythmias (Table 1). The R14L variant was identified in a

patient with paroxysmal AF with onset of AF at age 31 and AV-nodal re-entry tachycardia

(AVNRT). The V94G variant was found in two patients with paroxysmal and persistent AF with

an onset of disease at age 28 and 27, respectively. The missense variants Y158D and R814H

were identified in a patient with persistent AF with onset at age 31, who had several

radiofrequency ablation (RFA) procedures for AF. The paroxysmal AF patient with onset of

disease at age 35 had a splice-site donor variant at exon 4. This patient had normal coronary

angiography with atrial flutter, AVNRT, inducible ventricular tachycardia and implantable

cardioverter-defibrillator. Furthermore, this patient had a family history of SCD and AF. The

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missense variant E825D was identified in a paroxysmal AF patient with very early onset of

disease at age 18. This patient also had AVNRT and several RAF procedures performed. The

patient carrying the variant I999L had onset of paroxysmal AF at the age of 35 and also

presented incomplete right bundle branch block. This patient also carries the variant L10P in

SCN3B, as previously reported.32 The R1268Q variant were identified in two AF patients with

onset of disease at age 23 (also had atrial flutter type II) and 31 (also had Incomplete Right

Bundle Branch Block (IRBBB) and several DC conversions). The patient with the rare variant

C1523Y was diagnosed with paroxysmal AF at age 30, and in another paroxysmal AF patient

with onset of disease at age 28, we identified the variant R1588Q. This latter patient had an RFA

procedure for AVNRT. Interestingly, four of the rare variant carriers have AVNRT, in

addition to AF, suggesting that the AV-node, and perhaps the autonomic nerve system, could

play an important role in the genesis of AF in these patients.

Electrophysiology

The electrophysiological properties of Nav1.8 wild type and four variants were investigated by

whole-cell patch-clamping of Neuro-2A cells (Figure 2, and 3). We chose to analyze the V94G

found in two unrelated patients and R1588Q variants based on the lowest variant frequency in

the background population (not present in 2,000 non-AF Danish exomes, data not shown),

thereby reducing the risk of investigating a random finding. At the time of variant selection for

functional studies, the two variants were not found in the Exome Sequencing database (n=

5,400), but later appeared in one exome for each variant when 1,100 additional subjects were

included in the database (n= 6,500). We also investigated the two non-synonymous common

variants V1073A and L1092P. Figure 2A illustrates representative whole-cell currents from the

Neuro-2A cells expressing wild type and variant Nav1.8 channels. Nav1.8 channels are activated

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by depolarizing potentials more positive than -15 mV, with a fast activating current peaking at

+15 mV (Figure 2B). A part of the current is rapidly inactivated, while a long lasting current

component of approximately 10 % of the peak current level persists (Figure 2A).

The V94G-Nav1.8 channel does not conduct any current. The R1588Q variant showed

peak current amplitude similar to wild type channels, however, it had a faster time to peak,

together with a more than 6 mV negative shift in the steady-state inactivation (V½,WT -68.0±1.8

mV, V½,R1588Q -74.4±2.5 mV, Figure 3B). As -74.4 mV is close to the resting membrane potential

of both atrial cardiomyocytes and neurons, this shift would be expected to play a major role in

the channel availability, reducing the number of available channels as compared to wild type

Nav1.8. The combined electrophysiological characterization of R1588Q would therefore be

expected to result in a loss-of-function phenotype.

Compared with wild type Nav1.8 (WT, -29.9±4.1 pA/pF), the two common variants

expressed larger peak currents (V1073A, -50.6±7.0 pA/pF; L1092P, -61.4±8.5 pA/pF). While the

steady-state inactivation properties for V1073A and L1092P were not altered, the steady-state

activation properties were shifted to more positive potentials for these two common variants

(V½,WT 1.6±1.3 mV, V½,V1073A 7.1±1.3 mV, V½,L1092P 6.4±1.5 mV)(Figure 3A). For V1073A, the

time-dependent recovery from inactivation was decelerated (Figure 3C) and the time-to-peak

accelerated (Table 4). Both common variants have a slower current decay at a number of

different potentials (Figure 3E and 3F). Interestingly, the absolute sustained current level was

increased for both V1073A and L1092P (WT, 2.4±0.3 pA/pF; V1073A, 3.9±0.5 pA/pF; L1092P,

4.2±0.7pA/pF) (Figure 3D). For both WT and the two common variants the sustained current

level is 7-8 % of the peak current at +15 mV. Hence, the increase in the absolute sustained

current level of the variants is probably due to the overall increased activity of these variant

d to plp ay a major rrrrolooo e

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arggger pppeak currents ((((V1V1V110707073A3A3A3A, ,, -55500.0 66±6 77.000 0 pApAAA/p//p/ F;;; L1LLL 0909090 2P2P2P,,, -61616161.444 88±88.5 pppA/pFpp ).) Wh

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11

channels. Together, we found a depolarized shift in voltage-dependence of steady-state

activation on V1073A and L1092P and decelerated recovery from inactivation on the V1073A

variant. However, these two common variants also induced dramatically larger peak-current

amplitude, a slower current decay phase from inactivation, and a pronounced larger persistent

current. Hence, the combined electrophysiological changes of these two common variants would

result in gain-of-function phenotypes.

Discussion

In the present study, we found 10 rare missense SCN10A variants in 225 lone AF patients in

which the minor allele frequencies were less than 0.5% in the ESP. Furthermore, we showed that

the common non-synonymous variant V1073A (rs6795970) decreased the risk of AF. Functional

characterization of the two rarest variants (with the lowest MAF) found in lone AF revealed

reduced activity of Nav1.8, while conversely, the common variant V1073A (rs6795970),

associated with a decreased risk of AF, was found to increase activity of the channel.

The GWAS-hit, rs6800541, which is in close proximity to SCN10A, has been associated

with PR-interval duration.10 A sub-analysis indicated that this variant also seems to decrease the

odds ratio for AF (OR=0.92, p=1.4x10-03).10 Others have tested the association of rs6795970,

which is in high linkage with rs6800541, with AF and showed the same trend, however; the

association was not statistically significant (OR=0.97, p=0.29).10,11 In the present study, we

found that rs6795970 protects against both lone AF (OR=0.75, p=4.6x10-04) and AF in a mixed

cohort of patients (OR=0.74, p=2.3x10-05, Table 3). Functional studies of V1073A revealed a

number of altered biophysical parameters, with the greatest one being increased peak and

sustained current as well as a slowing of fast inactivation. We therefore suggest that this variant

has a gain-of-function phenotype, which seems to be protective against AF.

25 loooonenenene AAAAF FF F papapapatitititienenenentststt iiiin

minor allele frequencies were less than 0 5% in the ESP Furthermore we showe

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with a decreased risk of AF, was y

minor alaallelel leee freqeqeqquencies were less than 0.55% % in the ESP. Fuuurrrthermore, we showe

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atioiooon n nn ofofofof ththththeeee twwwwoooo rararararerereeststsst vararara iaiaaantntntntss (wwwwitititth h h h thhthe e e e lloll weweweweststst MMMMAFAFAFAF))) fofofofounununu d d dd innin llllononooneeee AFAFAFAF rerererevevevevealalalede

tivity of Nav1.8,8,8,8 wwwwhihihih lelee cocococonvnvnvvererere seseseelylylyl ,,, ththththeeee ccccomomommommom n vavavav ririiianananantt V1V1V1V 0707070 3A3A3AA ((((rrsr 6795970),

wiwwithth aa ddececrereasasededd rrisisk kk ofoff AAF,F,F wwasas ffououndndd tttoo inincrcreaeasese acactitiviviitytty oofff ththhee chhchanannenel.l.


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In lone AF patients, we identified 9 rare variants in SCN10A with MAF less than 0.5% in

the ESP and one splice site donor variant in exon 4 (Table 1). We performed electrophysiological

patch-clamp studies on the two rarest variants, V94G and R1588Q, which initially were found to

be novel. Later, both variants were reported in ESP6500, but it should be noted that this is a

nonselective database, where AF patients are not excluded. Since voltage-gated sodium channels

are inactivated at voltage potentials close to the resting membrane potential, a negative shift in

the steady-state inactivation, as seen for R1588Q, will result in a decreased availability of the

channels. The V94G variant did not conduct any current. Hence, both tested variants found in

lone AF patients have a loss-of-function phenotype. Since the AF protective variant V1073A had

a gain-of-function phenotype, these data indicate that the loss-of function variants V94G and

R1588Q increase the susceptibility for AF. The I999L variant is a novel variant; however, since

the patient also has a SCNB3 variant suspected to be disease causing, we did not include

functional data of this variant.32

Currently, the role of Nav1.8 in cardiac electrophysiology remains unclear. If the primary

role of Nav1.8 channels is in the cardiomyocytes, a loss-of-function phenotype could give rise to

AF, analogous to what has been observed for Nav1.5 channels, where decreased NaV1.5 current

has been associated with AF via augmented propensity for conduction delay with a subsequent

increased risk of re-entrant arrhythmias.33,29 However, of note, both loss-of-function and gain-of-

function variations in SCN5A have been reported in several studies of AF.33,29,34,35 Another

possibility is that the Nav1.8 peak current does not have a major impact, as it is quantitatively

much smaller than the Nav1.5 peak current. But, since the sustained (late) Nav1.8 current is 20-50

fold higher than the corresponding Nav1.5 late current, this depolarizing current could have a

significant impact on the action potential duration and refractory period, and thereby protect

tested variants founununund

ective va iiriianttt t V1V1V1V10707070733

u n

c s

a

d

rentl the role of Na 1 8 in cardiac electroph siolog remains nclear If the pr

unctitititiononon pppheheheennnotytytype, these data indicate thhhhatatat the loss-of funcctititiion variants V94G an

crrrreaaaase the susceceeptiibi iiili ityyy y ffof r AFAFAFA . TThe I9999L9L varariaaantntnn is aa noveveel vavavariannnt;t;t;t howowowevvverr, s

also hhhasasasas aaa SCNCNCNCNB3B3B3B vavarir ant tt suusususpectettetedddd tototot bbbbee didididiseseassasase cacacausssinnining,g wee diiiidddd nnonot inini llclclududddee

data of this variant.3232323

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against AF.36

Given the sparse expression of Nav1.8 channels in cardiac myocytes, the association of

SCN10A variants to AF could be mediated through neuronal input. One study has suggested that

rs6795970 in SCN10A may modulate the ventricular heart rate response during AF through a

modulation of the AV-node.37 The AV-node is highly innervated by parasympathetic nerve

fibers where Nav1.8 is expressed.38–40 Recognition of disease mechanisms overlap between the

AF and AV-nodal re-entry tachycardia and the fact that AVNRT may be the triggering factor for

AF has been reported in several studies.41–44 Consistent with this notion, several of our

paroxysmal AF patients with the rare variants have AVNRT (Table 1).

In AF patients, there has been substantial evidence of sympathetic tone-dependent AF.45

Changes in autonomic tone, also known as sympathovagal imbalance, are important triggers in

some forms of paroxysmal AF and also in the generation and maintenance of persistent AF.40–42

With the expression of Nav1.8 channels in vagal fibers and their absence in sympathetic fibers, it

is possible that the observed effects on Nav1.8 function of the different variants alters the

sympathovagal balance.19,20,22

We were able to examine 3 families for co-segregation of SCN10A variants identified in

the respective probands (Table 1), but none of the family members diagnosed with AF carried

the variant of the proband. It is, however, not surprising that a monogenetic segregation pattern is

absent since only a few reports have shown familial co-segregation of rare variants in AF.46 In

line with our study, it has been suggested that variation in the SCN5A, is not the main cause of

familial AF.35 Whether a person with a rare variant develops AF probably depends on both the

genetic background and environmental factors. Thus, the rare loss-of-function variants found in

our study should most likely be regarded as important modifiers for the genesis of AF.

on, several of our

.

A A

r

s of paroxysmal AF and also in the generation and maintenance of persistent AF

x b

that the obser ed effectsf on Na 1 8 f nction of the different ariants alters the

AF papapatititiienenentststs, thhhererere has been substantial evivivivided nce of sympathhheeetic tone-dependent A

aaauttttonomic tonneee, aalsssso knnknnoowo n nn aas syympappathovovaggaala iiimbmm aalaance, aaareee iiiimporororo tatt nntnt triggggger

s of papaparorororoxxxysmm lalal AAAAF FF anand alalalsoososo in thhhheee gegeeeneneraratititiionon aaaandndnd mmmaiaiaintntntntenenanance oooof fff ppperssisisi ttetentntt AAAF

xprpp ession of Navv11.11 888 8 chhhannelllsl iiin vagagg llll fififiibebbb rs and thhehh iriii absbbb ence iiin syyympppathetic fib

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Perspective

In summary, this study reveals a correlation between variations in SCN10A and AF. The results

thereby support the notion of SCN10A being important in cardiophysiology as genetic variations

now have been found to be implicated in cardiac conduction, Brugada syndrome, and AF. The

fact that SCN10A variations could play a promoting role in lone AF, as well as other types of AF,

highlights the importance of further studies on the cellular and electrophysiological factors

involved in the development of AF. Hence, our results further contribute to understanding the

complexity of cardiac electrophysiology and suggest that SCN10A genotyping in the future could

improve risk prediction.

Acknowledgments: We wish to thank Lasse Skibsbye, Mina Ghasemilee and Anne Katrine Kastberg for technical assistance. We also wish to thank LuCamp, The Lundbeck Foundation Centre for Applied Medical Genomics in Personalized Disease Prediction, Prevention and Care () for providing Danish population controls, as well as Christian Theil Have for bioinformatics support. For the LuCamp partner list, see the supplementary materials.

Funding Sources: This work was supported by the Research Committee of Rigshospitalet (University of Copenhagen), The Danish Heart Foundation, The John and Birthe Meyer foundation, The Arvid Nilsson Foundation, The Novo Nordisk Foundation, Fondsbørsvekselerer Henry Hansen’s Legat, Director Ib Henriksens Foundation, NIH grants R01HL092577, R01HL104156, K24HL105780, and an AHA Established Investigator Award 13EIA14220013.

Conflict of Interest Disclosures: Javad Jabbari is employed at LEO Pharma A/S. Anders G. Holst and Bo Liang are employed at Novo Nordisk A/S, Morten Grunnet at Lundbeck A/S.

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c Reees.s.s 22220101010 1;1;1;8999:7:7:7786–793.

MSMSMS, Nielsen MWMM , HHaHaH unnnnssøs SSSS, Svvveendsseen JJHH. AAAtrtrrriaiaial fibrbbriillattiooon::: ththththe roooolelelel ooof f f f commmnetetticiicic variantntnts. EEEur JJJ r Huuummm Geeenen ttt. 20111133;222:2297979797––3– 06060 .

D, Kannankeriiiil l l l PJPJPJJ, DoDoDoDonananan huhuhuueee e BSBSBSBS,, KuKuKuKucececerarara GGGG,,, StSS ububububblblbllefefefefieieieldldldld TTT,,, HaHaHainininineese JL, et al. Caannel ((SCN5A)) variaiii nts associiiat dedd wiiti hhh h atriiii llal fibriiilllllatioii n. CiCiCiCircullllation.9927–193555.


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40. Quan KJ, Lee JH, Van Hare GF, Biblo LA, Mackall JA, Carlson MD. Identification and characterization of atrioventricular parasympathetic innervation in humans. J Cardiovasc Electrophysiol. 2002;13:735–739.

41. Palma EC, Ferrick KJ, Gross JN, Kim SG, Fisher JD. Transition From Atrioventricular Node Reentry Tachycardia to Atrial Fibrillation Begins in the Pulmonary Veins. Circulation.2000;102:937–937.

42. Hurwitz JL, German LD, Packer DL, Wharton JM, McCarthy EA, Wilkinson WE, et al. Occurrence of atrial fibrillation in patients with paroxysmal supraventricular tachycardia due to atrioventricular nodal reentry. Pacing Clin Electrophysiol. 1990;13:705–710.

43. Brugada J, Mont L, Matas M, Navarro-López F. Atrial fibrillation induced by atrioventricular nodal reentrant tachycardia. Am J Cardiol. 1997;79:681–682.

44. Sauer WH, Alonso C, Zado E, Cooper JM, Lin D, Dixit S, et al. Atrioventricular Nodal Reentrant Tachycardia in Patients Referred for Atrial Fibrillation Ablation Response to Ablation That Incorporates Slow-Pathway Modification. Circulation. 2006;114:191–195.

45. Arora R. Recent insights into the role of the autonomic nervous system in the creation of substrate for atrial fibrillation: implications for therapies targeting the atrial autonomic nervous system. Circ Arrhythm Electrophysiol. 2012;5:850–859.

46. Darbar D, Herron KJ, Ballew JD, Jahangir A, Gersh BJ, Shen W-K, et al. Familial atrial fibrillation is a genetically heterogeneous disorder. J Am Coll Cardiol. 2003;41:2185–2192.

trtrtrtrioioioioveveveventntntntriririricucucuculalalalar rrr NoNoNoNodddad llllation ReRRR sponse to AbAbAbAb:191 111195959595

R. Recent insights into the role of the autonomic nervous system in the creation oor atrial fibrillation: implications for therapies targeting the atrial autonomic nervr

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Table 1: Clinical characteristics of the lone AF patients with rare variants (MAF < 0.5% in EA in ESP6500)

Patients Exon rs ID Pos. of variants

ECG description

P-wave (ms)

PR interval

(ms)

QRS interval

(ms)

QTc(ms)

HR (bpm) Type of AF Onset

of AF Clinical information rs6795970(V1073A)

rs12632942(L1092P)

1 E1 rs141207048 R14L SR 135 206 98 434 112 Paroxysmal 31 AVNRT V / A L / P

2 E2 rs202143516 V94G SR 119 144 102 414 57 Paroxysmal 28 Father and sister pacemaker V / A L

3 E2 rs202143516 V94G SR 94 132 84 396 64 Paroxysmal 31 Nonhemorragic stroke after AF diagnosis V / A L

4

E4 rs202192818 Y158D SR, SVPCs with

aberration131 186 86 418 103 Persistent 32 AF RFA times four V / A L / P

E15 rs139861061 R814H

5 E4 rs75991777 c.599+1 SR 135 162 116 421 63 Paroxysmal 35

Atrial flutter, AVNRT, inducible VT and ICD. Normal

coronary angiography.Family history of SCD and AF.

A L

6 E15 rs146028829 E825D SR 110 172 72 423 84 Paroxysmal 18 AF RFA, AVNRT V / A L / P

7 E16 unknown I999L SR, IRBBB 118 160 100 412 61 Paroxysmal 35 Mother with SVPCs V / A L

8 E21 rs138832868 R1268Q SR, IRBBB 90 152 94 394 59 Paroxysmal 30 Several DC cardioversions A L / P

9 E21 rs138832868 R1268Q AF - - 92 - 96 - 23 Atrial flutter type II A L / P

10 E26 rs142217269 C1523Y SR, J-wave in II, III, aVF 119 154 96 398 54 Paroxysmal 30 Family history of AF A L

11 E27 unknown R1588Q SR 110 156 96 419 55 Paroxysmal 28 RFA for AVNRT A L / P

Positions and clinical characteristics of the patients with rare variants with minor allele frequencies (MAF) less than 0.5% in the European-American (EA) in the Exome Sequencing Project (n=6503, ESP6500) server. SR; Sinus rhythm, SVPC; Supraventricular premature complexes, IRBBB; Incomplete right bundle branch block, AF; Atrial fibrillation, AVNRT; AV-nodal re-entry tachycardia, RFA; Radiofrequency ablation, ICD; Implantable cardioverter-defibrillator, VT; Ventricular Tachycardia

ysmal 31

h i

131 186 86 418 103 P i t t 32

Acc

h ionnnn

13131331111 186 86 4118888 103 Persisisisistettt nt 32

135 16161662222 1111111 666 42424 11 63636363 PaPaPaParorororoxyxyxx smsmsmalalalal 35

Ainduc

cFaFaFamimi


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Table 2: Genetic variations in SCN10A in lone AF patients

Exon rs ID GVS FunctionAmino Acid Pos.

cDNAPos.

ESP6500

PolyPhen-2 Prediction SIFT Grantham Conservation

EA Allele # AA Allele # All Allele #MAF (%)

(EA/AA/All)

1 rs141207048 missense R14L 41 A=26/C=8574 A=2/C=4404 A=28/C=12978 0.30/0.045/0.21 Probably-damaging Damaging 102 Conserved D

1 rs34314583 coding-synonymous R15R 45 A=92/G=8508 A=15/G=4391 A=107/G=12899 1.07/0.34/0.82 - - - Conserved -

2 rs202143516 missense V94G 281 C=1/A=8599 C=0/A=4406 C=1/A=13005 0.012/0.0/0.0077 Probably-damaging Damaging 109 Conserved D

4 rs202192818 missense-near-splice Y158D 472 C=5/A=8595 C=0/A=4406 C=5/A=13001 0.058/0.0/0.038 Probably-damaging Damaging 160 Conserved D

4 rs75991777 splice-5 None c.599+1 - - - - - - - Conserved -

5 rs74717885 missense I206M 618 C=137/T=8463 C=14/T=4392 C=151/T=12855 1.59/0.32/1.16 Benign Tolerated 10 Conserved B

9 rs62244070 coding-synonymous E428E 1284 T=2158/C=6442 T=474/C=3932 T=2632/C=10374 25.09/10.76/20.24 - - - Conserved -

11 rs7617919 coding-synonymous L492L 1476 A=2169/G=6429 A=471/G=3935 A=2640/G=10364 25.23/10.69/20.30 - - - Conserved -

11 rs7630989 missense S509P 1525 G=241/A=8359 G=925/A=3481 G=1166/A=11840 2.80/20.99/8.97 Benign Tolerated 74 Conserved B

15 rs139861061 missense R814H 2441 T=6/C=8594 T=1/C=4405 T=7/C=12999 0.07/0.023/0.054 Possibly-damaging Damaging 29 Not Conserved B

15 rs146028829 missense E825D 2475 G=19/T=8581 G=2/T=4404 G=21/T=12985 0.22/0.045/0.16 Benign Tolerated 45 Not Conserved B

16 rs7374804 coding-synonymous K950K 2850 C=557/T=8043 C=362/T=4044 C=919/T=12087 6.48/8.22/7.07 - - - Not Conserved -

16 rs57326399 missense I962V 2884 C=2253/T=6347 C=482/T=3924 C=2735/T=10271 26.20/10.94/21.03 Benign Tolerated 29 Not Conserved B

16 rs59468016 coding-synonymous G979G 2937 A=2248/G=6352 A=224/G=4182 A=2472/G=10534 26.14/5.08/19.01 - - - Not Conserved -

16 unknown missense I999L 2995 - - - - Benign Tolerated 5 Conserved B

//0.00.00.00.0/0./0./0./0.0070070070077 7 7 7 Probabbbbly-ly-ly-ly-damdamdamdamagigigiinng nn

PPP b bb bb blllC 5/A 8595 C 0/A 4406 C 5/A 13001 0 058/0 0/0 038 y

1

C=5C 5C 5C /A=/A/A 85985999555 C=0/A=4406 C=5/A=/A/A/A 13001 0.058/58/5858 0.0/0.038 ydamaging

1 - - - - -

C=137/T=846363363 C=1=1=1=14/T4/4/4/ =4392 C=151/55 T=12855555555 1.59/09/0.32.3.3.3 /1.16 Benign

T=2158/C=/C=/C=C=644644644442222 T=4T 4T 4T 474/74/4/74/C=3CC 3C 3932932932932 T=2T 2T 2263263263263 /C=/C=/C=/C 10310310310 74 747474 25.25255 09/09/09/9 10.10.10.10.76/76/76/76 20.020.20.24 2424 4 -


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17 rs73062575 missense P1045T 3133 T=273/G=8327 T=22/G=4384 T=295/G=12711 3.17/0.5/2.27 Benign Tolerated 38 Not Conserved B

17 rs6791171 coding-synonymous T1064T 3192 T=1216/C=7384 T=880/C=3526 T=2096/C=10910 14.14/19.97/16.12 - - - Not Conserved -

17 rs6795970 missense V1073A 3218 G=5176/A=3424 G=3961/A=445 G=9137/A=3869 39.81/10.01/29.74 Benign Tolerated 64 Not Conserved B

18 rs12632942 missense L1092P 3275 G=2236/A=6364 G=625/A=3781 G=2861/A=10145 26.0/14.18/21.99 Benign Tolerated 98 Not Conserved B

18 unknown coding-synonymous D1113D 3339 - - - - - - - Not Conserved -

19 rs6771157 coding-synonymous T1131T 3393 C=2238/G=6362 C=626/G=3780 C=2864/G=10142 26.02/14.21/22.02 - - - Not Conserved -

21 rs138832868 missense-near-splice R1268Q 3803 T=24/C=8576 T=3/C=4403 T=27/C=12979 0.279/0.068/0.20 Probably-damaging Damaging 43 Conserved D

22 rs11711062 missense S1337T 4009 T=56/A=8544 T=3/A=4403 T=59/A=12947 0.65/0.07/0.45 Benign Tolerated 58 Conserved B

25 rs6790627 coding-synonymous K1441K 4323 C=1230/T=7370 C=1500/T=2906 C=2730/T=10276 14.30/34.04/20.99 - - - Conserved -

26 rs142217269 missense C1523Y 4568 T=23/C=8577 T=1/C=4405 T=24/C=12982 0.27/0.02/0.18 Probably-damaging Damaging 194 Conserved D

27 rs78425180 coding-synonymous T1570T 4710 T=122/C=8478 T=16/C=4390 T=138/C=12868 1.41/0.36/1.06 - - - Conserved -

27 unknown missense R1588Q 4763 T=0/C=8600 T=1/C=4405 T=1/C=13005 0.0/0.023/0.008 Probably-damaging Damaging 43 Conserved D

27 rs6599242 coding-synonymous S1622S 4866 G=7870/A=730 G=4232/A=174 G=12102/A=904 8.49/3.95/6.95 - - - Conserved -

27 rs77804526 missense V1697I 5089 T=118/C=8482 T=13/C=4393 T=131/C=12875 1.37/0.3/1.01 Benign Tolerated 29 Conserved B

27 rs116353929 coding-synonymous D1739D 5217 A=265/G=8335 A=36/G=4370 A=301/G=12705 3.08/0.82/2.31 - - - Conserved -

Positions of the variants found in lone AF cohort. The frequency and MAF of the alleles are reported from ESP6500 exome server. PolyPhen-2 prediction reports the possible impact of an amino acid substitution on protein structure and function based on Polymorphism Phenotyping-2 (PolyPhen-2) program. D: Disease Causing; B: Benign; EA: European American; AA: African American; ESP 6500 exomes: Exome Variant Server (chromosomes 1-22, and X); MAF (%) (EA/AA/All): the minor-allele frequency in percent listed in the order of European American (EA), African American (AA) and all populations (All). (delimited by /)

/0.068/0.20 Probablyyyydamamamagiagiagiaging ng ng ng

/0.07/0.0.0..45454545 BenBenBenBenignignignig

CCC=1230/T=T=T=T 737777000 C=1C 1C 1C 500000/T=/T/T/T 2909066 C=2CC 730330/T=102020276 767676 1414.30///34.333 04/20.20.20.20.99 99999 -

T=2T=2T=2T= 3/C3/C3/C/C=85=85=85=857777 7 T=1111/C=/C=/C=/C 440444444 5 T=2T=2T=2T 4/C4/C4/C4/C=12=12=1212982982982982 0.20.20.20.27/07/07/07/0.02.02.02/0./0./0/0.18181818 Prrobaablly-damdamdamagiagiagingngng

T=1T 122/22/C=8C 8478478 T=1T 16/C6/C=43439090 T=1T 138/38/C=1C 128628688 1.41.41/01/0.36.36/1./1.0606 -


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Table 3: Association of rs6795970 frequencies with AF

Control cohortsMAF (%)

553(37.9)

4606(37.4)

8030(38.4)

Cases vs. Control I and II (n=730)

Cases vs. Control III (n=6161)

Cases vs. Control III and EA ESP (n=10.461)

AF cohorts MAF(%)

OR [95% CI] p-value OR

[95% CI] p-value OR [95% CI] p-value

All AF

Casesn = 508

324(31.8)

0.77[0.64-0.91] 0.002 0.78

[0.68-0.90] 3.9x10-04 0.74[0.65-0.86] 2.3x10-05

Lone AF

Casesn = 354

226(31.9)

0.77[0.63-0.93] 0.007 0.79

[0.66-0.93] 0.003 0.75[0.64-0.89] 4.6x10-04

The allele frequencies are presented with Odds ratios (OR) and the p-values for their association with AF. ESP: Exome Sequencing Project, EA: European Americans, AF: Atrial Fibrillation, MAF: Minor Allele Frequency. The Chi-squared tests were used.

pppp vavavvalu[95% CI] p [95% CI] p

[ 0

[95%5%5%5% CCCCI]I p [95% CCCCI]III p

0.77[0.64-0.91] 0.0.0.000000002222 0.0 787878

[[[[0.00 68686868 0-000.99990]0]0]0] 3.9x10


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Table 4: Electrophysiological characterization of SCN10A variants

WT V1073A L1092P R1588Q

(n=15) (n=14) (n=13) (n=13)

Peak current at 15 mV (pA/pF) -29.9±4.1 -50.6±7* -61.4±8.5** -32.0±6.5

Steady-state inactivation (n=16) (n=13) (n=14) (n=12)

V1/2 (mV) -68.0± 1.8 -67.6± 2.1 -65.9± 1.4 -74.4± 2.5*

K 9.8± 1.2 9.7± 1.6 9.6±0.5 11.6±0.8

Steady-state activation (n=19) (n=14) (n=12) (n=14)

V1/2 (mV) 1.6± 1.3 7.1±1.3* 6.4± 1.5* 5.5±2.4

K 8.1.± 0.4 8.0± 0.4 7.8± 0.4 9.2± 0.9

Time course of recovery from inactivation at -85mV (ms)

(n=12) (n=11) (n=13) (n=11)

33.6±5.6 73.6±14.5* 46.2±5.4 45.5±8.7

(n=18) (n=14) (n=12) (n=15)

Time to Peak Current at 15mV(ms) 1.8±0.2 1.3±0.1* 1.5±0.1 1.3±0.1*

(n=11) (n=12) (n=13) (n=11)

Late INa at 15 mV (pA/pF) -2.4± 0.3 -3.9± 0.5* -4.2±0.7* -3.3±0.8

Data are presented as mean ± SEM. Two-way ANOVA combined with Bonferroni post-test was used to test for significant differences of Peak current at 15 mV. The other parameters were tested by Two-tailed Student's t-test. *p<0.05 and **p<0.01 versus wild-type (WT). N: the numbers of cells measured; T: time constant; V1/2 : midpoint potential; k: slope factor.

(n=12)2)2)2) (n(n(n(n=1=111

(n=18) (n=14) (n=12)

en

1 6± 1 3 7 1±1 3 6 4± 1 5 5 5±2

0

±

(n=1

1.6± 1.3 7.7.1±1.3 6.66 4± 1.5 5.5±2

888.8 1.1.1.1.±± 000.0 44 8.0±±± 000.444 7.8±±± 0000.4 9.999 222±2 0

e of recovery frooom mn at -85mV (ms))))

(n(n(n(n=1=== 2)))) (n(n(nn=1=1=11)1)1)) (n(n(n(n=11113)3)3)3) (n(n(n( ==1=

3333333 66.6±5±5±55.666 7373737 6.666±±1±± 4.5*5*5*5 46464646 22.2±5±5±5±5.444 45.5±

((n 1=18)8) ((n 1=14)4) ((n 1=12)2) ((n 1=1 by guest on July 16, 2018http://circgenetics.ahajournals.org/

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Figure Legends:

Figure 1. Nav1.8 topology. Positions of rare and common variants found in SCN10A in lone AF

are indicated on the Nav1.8 protein.

Figure 2. Current recordings of SCN10A variants. A) Representative whole-cell current traces of

Nav1.8 wild-type (WT) and mutant channels. Currents were recorded following a voltage step

protocol with 5 mV increments from -70 mV to +50 mV, preceded with a -100 mV step. B)

Current-voltage (I-V) relationship. The current is normalized to cell capacitance to provide a

measure of Na+ current density. C) Peak current density at 15mV, Two-way ANOVA with

Bonferroni post-tests was applied to test for significant differences. Mean ± SEM values are

presented in Table 4. B, C) n=13-15 for each group. Asterisks indicate the voltages at which the

parameters were statistically different versus WT *p<0.05, **p<0.01 and ***p<0.001.

Figure 3. Activation and inactivation properties of SCN10A variants. A) Steady-state activation

curves. Activation properties were determined from I-V relationships by normalizing peak INa to

driving force and maximal INa, and plotting normalized conductance vs. Vm. B) Steady-state

inactivation curves. Protocol is shown in insert. Boltzmann curves were fitted to both steady-

state activation and inactivation data. C) Time course of recovery from inactivation following a

pre-potential protocol (insert) was fitted to a one-exponential equation: I/Imax =y0+A x exp(-

constant, respectively. D) Late (sustained) sodium current was normalized to cell size. E,F,G)

Voltage dependence of inactivation time constants. The decaying phase of whole-cell current

th a -100 mV step.p.p.. BBBB)

apacititititance tttto prppp ovvvvidididide

h

r

n Table 4. B, C) n=13-15 for each group. Asterisks indicate the voltages at whic

Na+a+a+ ccurururrererentnn dddeeensity. C) Peak current deeeensnnsity at 15mV, Twowowo-way ANOVA with

ppposssst-tests wass aaappplieieieied d tototot testtt ffof rr signnniificanant dididiffffffeeree eencecces. MMeeeann n ±±± SEEEEMMM M vavavalueees ar

n Tababbableelele 4444. B, CCC) )) ) n=n 1331313-1555 fofofoorr eachchchh gggrooooupp. AAAAstttererreriiisisksksks indndndicicicicatattee thttt e vovvovoltltltltaggeses aat tt whwhhic

were statisticallllylyl ddddififififffef rent versus WTWTW ****p<pp 000.0 0050 , ,, ******* p<pp 0.00 01010101 a ddndd *********** p<pp 0.001.


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traces (as in Figure 2A) was fitted with 2 exponential equation: I/Imax =Af x exp(-

exp(-

values respectively, n=8-11 for each group. A-D) Averaged values and the numbers of cells

measured are presented in Table 4. Asterisks indicate the voltages at which the parameters were

statistically different versus WT (*p<0.05).


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Anders G. Holst, Jesper H. Svendsen and Thomas JespersenChristophersen, Nikolaj Nielsen, Ahmad Sajadieh, Patrick T. Ellinor, Morten Grunnet, Stig Haunsø, Javad Jabbari, Morten S. Olesen, Lei Yuan, Jonas B. Nielsen, Bo Liang, Vincenzo Macri, Ingrid E.

Modulate the Risk of Atrial FibrillationSCN10ACommon and Rare Variants in

Print ISSN: 1942-325X. Online ISSN: 1942-3268 Copyright © 2014 American Heart Association, Inc. All rights reserved.

TX 75231is published by the American Heart Association, 7272 Greenville Avenue, Dallas,Circulation: Cardiovascular Genetics

published online August 1, 2014;Circ Cardiovasc Genet.

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SUPPLEMENTAL MATERIAL

Methods

Study Subjects

From eight hospitals in the region of Copenhagen, patient records with the ICD-10 diagnose

code I48.9 (atrial fibrillation and flutter) were collected and read. Clinical characteristics of the

patients are shown in Table S1.

Table S1. Clinical data of patient and control populations

Lone AF AF

Control cohorts I & II

N 358 157 750

Median age of AF onset, y (IQR) 34.5 (28-39) - -

Median age, y (IQR) - 66.2 (32-86) 66 (52-76)

Male gender 297 (83%) 108 (68%) 385 (52%)

BMI 26 27.1 26

AF type

Paroxysmal 224 (62.6%) 19 (12%) -

Persistent 113 (31.6%) 93 (58.9%) -

Permanent 21 (5.9%) 46 (29.1%) -

First degree relatives with AF 127 (35.5%) - -

Hypertension -

70 (44.3%) 430 (58%)

Diabetes -

17 (10.8%)

30 (4%)

BMI is given as mean values. AF, atrial fibrillation; BMI, body mass index (calculated as weight [kg]/height2 [m2]; IQR, interquartile range.

SCN10A Screening

Genomic DNA was isolated from blood samples using the ReliaPrep™ Blood gDNA Miniprep

System (Promega, USA). Using intronic primers the entire coding sequence and splice junctions

of SCN10A [NM_006514.2] were amplified and analyzed using high-resolution melting curve

analysis (Light Scanner, Idaho Technology, Salt Lake City, UT, USA). The control cohorts and

publicly available databases (Exome sequencing project database (n=6,503, ESP6500 and

dbSNP) were screened for all rare variants identified in the patients. In probands with non-

synonymous variants, bidirectional sequencing of genes previously associated with AF was

performed.

All primers were designed with M13 tail sequences. Fragments with different melting curves

than wild-type DNA were directly sequenced using Big Dye chemistry (DNA analyzer 3730,

Applied Biosystems, Foster City, CA, USA). All identified non-synonymous variants were

validated by resequencing in an independent polymerase chain reaction.

In probands with non-synonymous variants bidirectional sequencing of SCN1-3B

(NM_001037.4, NM_004588, NM_018400.3), SCN5A (NM_00035), KCNQ1 (NM_000218.2),

KCNH2 (NM_000238), KCNN3 (NM_002249.5), KCNN2 (NM_021614.2), KCNA5

(NM_002234.2), KCNE1/2/3/5 (NM_001127668, NM_172201, NM_005472.4, NM_012282.2),

KCNJ2/3/5 (NM_000891.2, NM_002239.3, NM_000890.3), ANP (NM_006172.3), Cx40/43

(NM_005266.5, NM_000165.3) and LMNA (NM_005572) was performed.

SNP genotyping

The DNA was extracted from whole blood, using the ReliaPrep™ Blood gDNA Miniprep

System (Promega, USA). The SNP genotype for rs6795970 was determined using fluorescence-

based real-time polymerase chain reaction (PCR) (ABI PRISM 7900 Sequence Detection

System; Applied Biosystems, Foster City, CA) and a predeveloped TaqMan assay (Applied

Biosystems). An allelic discrimination run was performed allowing for discrimination between

the allele compositions of each sample. For genotype and allele frequencies please see Table S2.

Table S2. The Genotype and Allele Frequencies of rs6795970.

Genotype Frequency

(%) Allele Frequency

(%)

Minor Allele Homozygote Heterozygote Major Allele

Homozygote Allele Allele

rs6795970 AA AG GG A G

Total AF cases 58 208 243 324 694

n = 508 (11,4) (40,9) (47,7) (31,8) (68,2) Lone AF

cases 45 136 173 226 482

n = 354 (12,7) (38,4) (48,9) (31,9) (68,1) Controls

Holter 68 256 190 392 636

n=514 (13,2) (49,8) (37,0) (38,1) (61,9)

Controls 27 107 82 131 249

n=216 (12,5) (49,5) (38,0) (34,5) (65,5) Total

Controls 95 363 272 553 907

n=730 (13,0) (49,7) (37,3) (37,9) (62,1) Danish Control 899 2808 2454 4606 7716

n=6161 (14.6) (45.6) (39.8) (37.4) (62.6) Controls

ESP 676 2072 1552 3424 5176

n = 4300 (15.7) (48.2) (36.1) (39.8) (60.2) Controls

Danish Cohort+ESP

1575 4880 4006 8030 12892

(15.1) (46.6) (38.3) (38.4) (61.6)

The genotype distributions and allele frequencies of rs6795970 in different cohorts are presented

Bioinformatics and Statistical Analysis

We used 4 in silico tools to predict whether the variants were disease causing (PolyPhen-2, SIFT,

Grantham Prediction and Conservation). Co-segregation analyses of the variants within the

family members were also done. The variants were checked for whether they were located in

conserved genomic sequence of the family members of hSCN1A–hSCN10A. Species alignment

across eukaryotic species was performed.

Cloning of Sodium Channel Subunits

For the site directed mutagenesis, PCR with the variant selective primers was performed with the

following PCR parameters: 1) 96°C/2 min; 2) 96°C/30 sec; 3) 55°C/15 sec; 4) 72°C/7 min; 5)

72°C/10 min, with the total number of cycles (Step 2-4) being 20. The PCR reaction was

subsequently digested with DpnI (Fermentas, Denmark) and transformed into and amplified in

SURE cells (Agilent Technologies, Denmark).

Cell Culture and transfection

The neuroblastoma cell line, Neuro-2A cells (ATCC, USA), were maintained in 90% Dulbecco’s

Modified Eagles Medium supplemented with 10% fetal calf serum and 1%

penicillin/streptomycin in a 5% CO2 incubator at 37°C. Neuro-2A cells were trypsinized, diluted

in culture medium, and grown in 35-mm dishes. When grown to 30–50% confluence, cells were

transiently co-transfected with 1 μg wild-type (WT) or mutant pc-hSCN10A and 0.2 μg of

pcDNA3-EGFP as a reporter gene, using Lipofectamine 2000 (Invitrogen, USA) according to the

manufacturer’s instructions.

Molecular Cloning of Sodium Channel

The genetic variations in hSCN10A (GenBank Acc. No. [NM_006514.2]), were introduced into

the pCMV6-AC-GFP-hSCN10A plasmid (Origene, USA), via site-directed mutagenesis using a

full plasmid overlap PCR strategy (PfuTurbo DNA Polymerase, Stratagene, Denmark). The

variants V94G (GTG->GGG), V1073A (GTC->GCC), L1092P (CTA->CCA), and R1588Q

(CGA->CAA) were introduced by overlapping oligonucleotides designed in Vector NTI

Advanced 10 (Invitrogen, Denmark) and constructed by Eurofins MWG Operon (Germany). All

of the plasmid constructs were verified by complete DNA sequencing of the hSCN10A cDNA

and flanking regions (Macrogen Inc., Seoul, Republic of Korea).

In vitro Electrophysiology

Patch-clamp experiments were performed 2–3 days after transfection. Whole-cell currents were

measured at room temperature (20–22 °C). The internal pipette solution consisted of (in mM)

CsCl 60, Cesium aspartate 70, CaCl2 1, MgCl21, HEPES 10, EGTA 11, MgATP 5 (pH 7.2 with

CsOH); the external solution consisted of (in mM): NaCl 130, CsCl 5, CaCl2 2, MgCl2 1.2,

HEPES 10, glucose 5 (pH 7.4 with CsOH). Measurements were made with Pulse software and

using an EPC-9 amplifier (HEKA Elektronik, Germany). Borosilicate glass pipettes were pulled

on a DPZ-Universal puller (Zeit Instrument, Germany). The pipettes had a resistance of 1.5–2.5

MΩ when filled with intracellular solution. The series resistances recorded in the whole-cell

configuration were 2–5 MΩ and were compensated (80 %). As the Neuro-2A cells endogenously

express TTX sensitive sodium currents, cells where incubated with 300 nM TTX prior to

measurement. Recordings were performed between minute 5 and 10 after obtaining a whole cell

configuration. Electrophysiological data were analyzed using Igor Pro (Wavemetrics, USA) and

GraphPad Prism (GraphPad Software Inc., USA).

LuCamp Partner List

Professor, LuCamp Centre Director, Oluf B. Pedersen

1) Hagedorn Research Institute, Gentofte, Denmark

2) Marie Krogh Center for Metabolic Research, Section of Metabolic Genetics,

Faculty of Health Sciences, University of Copenhagen, Denmark

Research Manager, Daniel R. Witte, MD, PhD

Steno Diabetes Center, Gentofte, Denmark.

Professor Torben Hansen

1) Marie Krogh Center for Metabolic Research, Section of Metabolic Genetics,


2) Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark.

Gitte Andersen, PhD

Hagedorn Research Institute, Copenhagen, Denmark.

Professor Jun Wang

BGI-Shenzhen, Shenzhen, China

Professor Lars Bolund

Department of Human Genetics, University of Aarhus, Denmark

Professor Torsten Lauritzen

Faculty of Health Sciences, University of Aarhus, Denmark

Professor, Head of Department, Karsten Kristiansen

Department of Biology, University of Copenhagen, Denmark

Professor Torben Jørgensen

Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark.

Professor Arne Astrup

Department of Human Nutrition, Faculty of Life Sciences, University of Copenhagen,

Copenhagen, Denmark.

Professor Thue W. Schwartz

1) Marie Krogh Center for Metabolic Research, Metabolic Receptology and

Enteroendocrinology


2) Laboratory for Molecular Pharmacology, University of Copenhagen, Copenhagen, Denmark

Professor Rasmus Nielsen

1) Department of Biology, University of Copenhagen, Copenhagen, Denmark

2) Department of Statistics, University of California Berkeley, Berkeley, California, USA.

PhD Anders Albrechtsen

1) Department of Biology, University of Copenhagen, Copenhagen, Denmark

2) Department of Statistics, University of California Berkeley, Berkeley, California, USA.

Common and Rare Variants in SCN10A Modulate the Risk...

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