Functional Characterization of Rare Variants …circep.ahajournals.org/.../early/2015/06/30/CIRCEP.114.002519.full.pdfFunctional Characterization of Rare Variants Implicated in Susceptibility

DOI: 10.1161/CIRCEP.114.002519

1

Functional Characterization of Rare Variants Implicated in Susceptibility

to Lone Atrial Fibrillation

Running title: Hayashi et al. Functional characterization of lone AF variants

Kenshi Hayashi, MD, PhD1; Tetsuo Konno, MD, PhD1; Hayato Tada, MD, PhD1;

Satoyuki Tani, BS1; Li Liu, MD, PhD1; Noboru Fujino, MD, PhD1; Atsushi Nohara, MD, PhD1;

Akihiko Hodatsu, MD, PhD1; Toyonobu Tsuda, MD1; Yoshihiro Tanaka, MD1;

Masa-aki Kawashiri, MD, PhD1; Hidekazu Ino, MD, PhD2; Naomasa Makita, MD, PhD3;

Masakazu Yamagishi, MD, PhD1

1Division of Cardiovascular Medicine, Kanazawa University Graduate School of Medical Science, Kanazawa; 2Komatsu Municipal Hospital, Komatsu; 3Department of Molecular

Physiology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan

Correspondence:

Kenshi Hayashi, MD, PhD

Division of Cardiovascular Medicine

Kanazawa University Graduate School of Medical Science

13-1, Takara-machi, Kanazawa

Ishikawa 920-8640, Japan

Tel: +81-76-265-2254

Fax: +81-76-234-4251

E-mail: [email protected]

Journal Subject Codes: [132] Arrhythmias - basic studies, [89] Genetics of cardiovascular disease, [152] Ion channels/membrane transport

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Masakazu Yamagishi, MD, PhD1

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CCCorrespondddence:

by guest on July 10, 2018http://circep.ahajournals.org/

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DOI: 10.1161/CIRCEP.114.002519

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Abstract:

Background - Few rare variants in atrial fibrillation (AF)-associated genes have been

functionally characterized to identify a causal relationship between these variants and

development of AF. We here sought to determine the clinical impact of rare variants in

AF-associated genes in patients with lone AF and characterized these variants

electrophysiologically and bioinformatically.

Methods and Results - We screened all coding regions in 12 AF-associated genes in 90 patients

with lone AF, with an onset of 47 ± 11 years (66 men; mean age: 56 ± 13 years), by

high-resolution melting curve analysis and DNA sequencing. The potassium and sodium currents

were analyzed using whole-cell patch clamping. In addition to using four individual in silico

prediction tools, we extended those predictions to an integrated tool (Combined Annotation

Dependent Depletion, CADD). We identified seven rare variants in KCNA5, KCNQ1, KCNH2,

SCN5A, and SCN1B genes in eight patients: two of eight probands had a family history of AF.

Electrophysiological studies revealed two variants showed a loss-of-function, and four variants

showed a gain-of-function. Five of six variants with electrophysiological abnormalities were

predicted as pathogenic by CADD scores.

Conclusions - In our cohort of lone AF patients, seven rare variants in cardiac ion channels were

identified in eight probands. A combination of electrophysiological studies and in silico

predictions showed these variants could contribute to the development of lone AF, although

further in vivo study is necessary to confirm these results. More than half of AF-associated rare

variants showed gain-of-function behavior, which may be targeted using genotype-specific

pharmacological therapy.

Keywords: atrial fibrillation, genetic variation, genotype-phenotype correlation

p , p g (

Dependent Depletion, CADD). We identified seven rare variants in KCNA5, KCNQNQNQ111,,, KCKCKCNHNHNH222,

SCN5A, and SCN1B genes in eight patients: two of eight probands had a family history of AF.

Elleecectrtrtroophyhysisisiooologggicicical studies revealed two variantstss shhhowed a loss-ooof-f funcncctitition, and four variants

hhhowowwed a gain-oofof-fffunnnctcc iooon.nn. FFiviviveee ofoff ff sisisix vavaariiannntsss wittth eleecectttropopphhhysiioloo ooogicccaalal aaabnnnororormmamalililitttieieiess wewewerrere

prprrededdici ted as pppathhhogeniiic byb CCCADADADD scsccororresss.

Concclululusisisionons - InInI oourur cc hhohoort offf lllonoonee AF ppatatatiieientnts, sseevenen raraarere vavaaririanntststs iiin caca ddrdiaiai c ioiionnn chchannnenellsls wwereree

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prpr ddediiic ititions hhshow dded tthhhese variiia tnts co llulddd co tnt iiribbbutte tto hththe dddevelllopmentt ffof lllone AFAFAF, llal hththou hhgh


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DOI: 10.1161/CIRCEP.114.002519

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Atrial fibrillation (AF) is the most prevalent tachyarrhythmia, with a prevalence of 1–2% in the

general population.1 In most cases, AF occurs along with hypertension, mitral stenosis, ischemic

heart disease, cardiomyopathy, and/or hyperthyroidism.1 In addition to these underlying diseases,

age, obesity, smoking, and alcohol are clinical risk factors for AF.1 However, 10–20% of AF

patients present with AF in the absence of predisposing factors; these are categorized as having

lone AF.2 Previous studies have shown that at least 5% of all patients with AF and 15% of those

with lone AF had a positive family history.3 Another study has shown that the risk for lone AF

was 3.5-times higher in those with a family history of lone AF in parents or in siblings,

compared with the risk in individuals without such family history.4 Recent studies have shown

that people with certain genotypes have an increased risk for future AF.5, 6 These reports indicate

that the development of AF is influenced by genetic background.

Genetic linkage analysis and candidate gene analysis for familial AF in 1997 indicated

that a gene responsible for familial AF is located in the region of 10q22–q24, and, in 2003, a

gain-of-function mutation in KCNQ1 was implicated in a large Chinese kindred with autosomal

dominant AF.7 To date, many variants in genes encoding ion channel subunits, cardiac gap

junctions, and signaling molecules have been identified in monogenic AF families.8, 9 These

genetic variants predispose individuals to AF by reducing the atrial refractory period as a

substrate for re-entrant arrhythmias, by lengthening the atrial action potential duration, which

results in ectopic activity, or by causing impaired electrical cell-to-cell communication, which

creates conduction heterogeneity as a substrate for the maintenance of AF.10

Here, we performed candidate gene studies to identify rare variants, under the hypothesis

that these variants pose an AF risk in these probands. Additionally, to determine the functional

significance of these variants, we performed cellular electrophysiological studies and in silico

hat people with certain genotypes have an increased risk for future AF.5, 6 Thesee e reeepopoportrtrts s s ininindididicate

hat theh devele oppmem nt of AF is influenced by genetiicc background.

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hhhatat aa gene resespooonssibleee fffor famamamili ial AFAA iss locccatted innn tttheee rrreeegiooonnn off 1000q22–q–q–q24244,, aaand, innn 2220030303, a

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DOI: 10.1161/CIRCEP.114.002519

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prediction analysis.

Methods

Detailed methods are provided in the SUPPLEMENTAL MATERIAL.

Study subjects

The study subjects were recruited from multiple hospitals in Japan. Lone AF was defined as AF

occurring in individuals < 65 years of age, who did not present with hypertension, overt structural

heart disease, myocardial infarction, congestive heart failure, or thyroid dysfunction. Two hundred

and fifty healthy Japanese subjects with no history of the cardiovascular disease described above

were also included in this study. Data from the NHLBI Exome Sequencing Project (ESP) Exome

Variant Server (EVS)11 and the Exome Aggregation Consortium (ExAC) data and browser12 were

used as reference groups.

The study observed the principles outlined in the Declaration of Helsinki and was

approved by the Ethics Committee for Medical Research at our institution. All study patients

provided written informed consent before registration.

DNA isolation and mutation analysis and genotype phenotype relationships

Genomic DNA was extracted from peripheral blood leukocytes using standard methods.

High-resolution melting curve analysis (MCA) was employed to screen KCNA5, KCNQ1, KCNH2,

SCN5A, SCN1B, SCN2B, SCN3B, KCNE1, KCNE2, KCNJ5, GJA5, and NPPA, using a

LightScanner (BioFire Defense, Salt Lake City, UT). Samples in which the melting curve

deviated from the wild-type (WT) control were subjected to DNA sequencing using an ABI

PRISM 310 Genetic Analyzer. The relationship between the clinical phenotype (AF) and the

genotype was determined for probands and their relatives in whom a variant was identified.

were also included in this study. Data from the NHLBI Exome Sequencing Projeectctct (((ESESESP)P)P) EEExoxoxomemm

Variant Server (EVS)11 and the Exome Aggregation Consortium (ExAC) data and brb owser1212 were

usssededed as referererence grgg oupspp .

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Plasmid constructs and electrophysiology

Mutant cDNAs were constructed using an overlap extension strategy13 or by using the

QuikChange XL Site-Directed Mutagenesis Kit (Agilent Technologies, Santa Clara, CA).

CHO-K1 or HEK293 cells were transiently transfected with WT or mutant (MT) cDNA, using an

X-tremeGENE 9 DNA Transfection Reagent (Roche Applied Science, Penzberg, Germany).

Cells were cotransfected with the same amount of green fluorescent protein (GFP) as each ion

channel cDNA.

Cells displaying green fluorescence at 48–72 h after transfection were subjected to

electrophysiological analysis. Potassium or sodium currents were studied using the whole-cell

patch clamp technique with an amplifier, Axopatch-200B (Molecular Devices, Sunnyvale, CA),

at room temperature. The voltage clamp protocols are described in the Figures. Data were

acquired using pCLAMP software (v. 9; Molecular Devices, Sunnyvale, CA). Data acquisition

and analysis were performed using a Digidata 1321 A/D converter and pCLAMP8.2 software

(Molecular Devices, Sunnyvale, CA).

In silico prediction analysis

A total of five prediction tools were applied in order to predict the pathogenicity of lone

AF-associated variants: the PolyPhen algorithm,14 Grantham chemical scores,14 sorting intolerant

from tolerant (SIFT) analysis,14 the protein variation effect analyzer (PROVEAN),15 and

Combined Annotation Dependent Depletion (CADD).16

Statistical analysis

Pooled electrophysiological data were expressed as mean ± standard error. The minor allele

frequency (MAF) in the AF cohort was compared with the MAF from the Exome Variant Server

and the Exome Aggregation Consortium using Fisher's exact test in 2 × 2 tables. Two-tailed

patch clamp technique with an amplifier, Axopatch-200B (Molecular Devices, Suununnynyn vavavalelele, , , CACACA),

at room m tet mpmpperatatturu e. The voltage clamp protocols arare described in the FiFigugug res. Data were

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Moleculalalar r Deviviicecc s,, SSSunununnyyyvaleee,,, CA).

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Student’s t-test was used for the single comparisons between the two groups. One-way ANOVA,

followed by a Bonferroni post hoc test, was used to analyze data with unequal variance among

three groups. Two-way repeated-measures ANOVA was used to adjust for multiple comparisons

across the different values of membrane potentials. A value of P < 0.05 was considered as

statistically significant. Statistical analysis was performed using JMP Pro 11.0.0 (SAS Institute

Inc., NC, USA) and Origin 9.0 (OriginLab, Northampton, MA).

Results

Clinical characteristics and molecular genetic analysis of the study cohort

Of the 90 patients with lone AF that were enrolled, 26 subjects (29%) had a family history of AF

in at least one first-degree relative (Table 1). The study subjects had a mean age of 47 ± 11 years

at the onset of AF (Table 1). Sixty-six subjects were men (73%), and 57 of the 90 patients had

paroxysmal or persistent AF (63%; Table 1). Echocardiographic data of the cohort as a whole

revealed a normal mean ejection fraction, with a mean left atrial dimension of 40 ± 7 mm (Table

1). The 250 control subjects had a mean age of 39 ± 19 years, and 157 of them were men (63%).

Screening for ion channel variants in genomic DNA in the study cohort of 90 individuals

with lone AF identified a total of seven different variants present in KCNA5 (H463R and T527M),

KCNQ1 (L492_E493 ins DL), KCNH2 (T436M and T895M), SCN5A (R986Q), and SCN1B

(T189M; Figure1 and Table 2). SCN1B T189M was detected in two probands (one homozygous

and one heterozygous carrier). The presence of each of these variants was assessed in 250

ethnically matched population control individuals; all variants were rare (minor allele frequency

[MAF] < 1%; Table 2). According to the ExAC data and browser, the MAFs of KCNA5 T527M,

KCNH2 T436M, and SCN5A R986Q were 0.0236%, 0.001627%, and 0.001951%, respectively,

which were significantly lower compared to 0.6% in our case population (Table 2).

Of the 90 patients with lone AF that were enrolled, 26 subjects (29%) had a famiilylyly hhhisisistototoryryry ooof f f AFAA

n at least one first-degree relative (Table 1). The study subjects had a mean age of f 447 ± 11 years

attt tthehehe onset ooofff AF (Table 1).)) Sixtyyy-six subjects werrre men (7(( 3%), , ananand 57 of the 90 pppatients had

papaparoooxysmal or peeerrsisteenentt AF (6663%; Tabbbleee 1). EEEchoocaaardiogogograraraphphphic dddaatta of tthhehe cccohohhort aasas aaa wwwhhhole

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Clinical characteristics and functional properties of KCNA5 variants

The proband who was heterozygous for KCNA5 H463R was a 62-year-old woman with onset of

paroxysmal AF at age 51 years and no family history of AF. She underwent radiofrequency

catheter ablation at age 61 years (Table 3).

To define the functional effect of the H463R variant, we transiently expressed WT, H463R,

and WT+ H463R in cultured mammalian HEK293 cells for whole-cell voltage clamp

measurements. Voltage clamp recording from cells expressing H463R alone did not exhibit any

functional channels, as compared to those expressing WT alone (Figure 2A and Table 4).

Co-expression of H463R with WT resulted in a significant current density, which was less than

one-third of the control current observed with expression of WT alone (Figure 2A).

The current voltage relationships for activating currents (Figure 2B) and tail currents

(Figure 2C) were recorded during depolarizing pulses. Two-way repeated-measures ANOVA

revealed that there was a significant difference in the activating currents and the tail currents

(Figures 2B and 2C) among these three channels. The amplitudes of the activating currents at 40

mV and the tail currents at 30 mV for the WT+H463R and the H463R were significantly smaller

than those for WT (Figures 2B and 2C, and Supplemental Table 1). No significant difference in

activation kinetics for the WT+H463R was observed compared to WT (Figure 2D and

Supplemental Table 1). In silico analysis predicted that H463R was pathogenic, according to

three algorithms (Table 5).

The proband who was heterozygous for KCNA5 T527M was a 51-year-old man, with

onset of paroxysmal AF at age 49 years (Table 3). The ECG showed a prolonged PR interval of

220 ms and a normal QTc interval. Monitoring ECG in an outpatient clinic showed a sinus pause

of 3.6 s following termination of AF. His son, who also carried the T527M variant in KCNA5 had

one-third of the control current observed with expression of WT alone (Figure 2A)A)A)...d

ThThThe e cucucurrrenenent voltage relationships for activavaatitit ng currents (Figuree 2B2B2 ) and tail currents

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never experienced AF and had a prolonged PR interval of 220 ms.

HEK293 cells expressing the T527M mutant showed a larger Kv1.5 current than did

those expressing WT (Figure 3A). Cellular electrophysiological studies showed that the

activating current density for T527M was significantly larger than that for WT (Figure 3B and

Supplemental Table 1). The T527M mutant displayed a negative voltage shift in the normalized

activation curve and a significantly decreased the potential when half of the channels were

activated,V1/2 (Figure 3D and Supplemental Table 1). In silico analysis using four algorithms

predicted that T527M was pathogenic (Table 5).

Clinical characteristics and functional properties of KCNQ1 variants

The proband who was heterozygous for KCNQ1 L492_E493insDL was a 42-year-old man who

was diagnosed with AF on an ECG upon routine examination (Table 3). Because he had no

symptoms, the age of onset of paroxysmal AF was unknown. The ECG showed a normal QTc

interval at rest as well as during the late recovery phase of exercise stress testing. His daughter

and sons carrying the L492_E493insDL variant in KCNQ1 had never experienced AF and had

normal ECGs.

Whole-cell patch clamp experiments were conducted on CHO-K1 cells transfected with

vectors expressing KV7.1 WT or KV7.1 L492_E493insDL. The activating and tail current

amplitudes of KV7.1 WT/KCNE1 were similar to those of the KV7.1 mutant/KCNE1

(Supplemental Figures 1A–C and Supplemental Table 1). The normalized tail current voltage

relationship for KV7.1 WT/KCNE1 channels was also similar to that for KV7.1 mutant/KCNE1

channels (Supplemental Figure 1D and Supplemental Table 1).

Clinical characteristics and functional properties of KCNH2 variants

The proband who was heterozygous for KCNH2 T436M was a 61-year-old man with onset of AF

The proband who was heterozygous for KCNQ1 L492_E493insDL was a 42-yeararr-oooldldl mmmananan wwwhhho

was diiagggnoseed wiwitht AF on an ECG upon routine exaxamination (Table 3). BeB cause he had no

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nononormrmrmalala EEECGCGCGs.s.s.


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at age 38 years. The ECG showed chronic AF and a normal QTc interval (Table 3). His brother

and sister had been affected with AF from a young age.

We transiently expressed WT and T436M in cultured mammalian CHO-K1 cells for

whole-cell voltage clamp measurements (Supplemental Figure 2). Electrophysiological studies

showed that the peak current density for T436M was significantly greater than that for the WT

(Supplemental Figure 2B and Supplemental Table 2). No significant difference in activation

kinetics or the steady-state inactivation kinetics was observed between the mutant and WT

channels (Supplemental Figures 2D and 2F and Supplemental Table 2). In contrast, slow and fast

time constants in the mutant channel were increased significantly compared with those of the

WT (Supplemental Figure 2E and Supplemental Table 2). Two-way repeated-measures ANOVA

revealed that the fast time constants were significantly increased in the T436M compared to the

WT (Supplemental Figure 2E). In silico prediction analysis indicated that T436M was potentially

benign, according to five algorithms (Table 5).

The proband who was heterozygous for KCNH2 T895M was a 58-year-old man with

onset of AF at age 40 years (Table 3). The ECG showed a normal QTc interval. He underwent

radiofrequency catheter ablation at age 59 years. His father, who also carried T895M in KCNH2,

had experienced paroxysmal palpitations since his early 50s and developed a transient ischemic

attack at age 81 years. The proband’s son, who carried the same variant, had also experienced

paroxysmal palpitations about once a week since his late 20s.

When we transiently expressed WT and T895M in cultured mammalian CHO-K1 cells for

whole-cell voltage clamp measurements, we observed a larger current for the mutant channel

than the WT channel (Figure 4). Electrophysiological studies showed that the peak and tail

current densities for T895M were significantly larger than those for the WT, respectively

WT (Supplemental Figure 2E and Supplemental Table 2TT ). TwoTT -way repeated-meeasasuruu eseses ANANANOVOVOVA

evealed that t thee faf st time constants were significaantntly increased in the T4T436M compared to the

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(Figures 4B and 4C, and Supplemental Table 2). No significant difference in activation kinetics

or the steady-state inactivation kinetics as compared to the WT was observed (Figures 4D and 4F,

and Supplemental Table 2). In contrast, both slow and fast deactivation time constants for the

mutant channel were increased significantly compared to those of the WT for voltages at -40 and

-30 mV (Figure 4E and Supplemental Table 2). In silico analysis predicted that T895M was

likely to be pathogenic, although only according to the CADD score (Table 5).

Clinical characteristics and functional properties of SCN5A and SCN1B variants

The proband who was heterozygous for SCN5A R986Q was a 64-year-old man with onset of

paroxysmal AF at age 58 years (Table 3). None of his family members had clinical signs of AF.

CHO-K1 cells were transiently transfected with vectors expressing WT or R986Q cDNA

and the human beta1 subunit cDNA in combination with a bicistronic plasmid encoding GFP.

Compared to the WT, R986Q significantly reduced the peak sodium current density

(Supplemental Figure 3B and Supplemental Table 3). No significant difference was observed in

the voltage-dependence of steady-state activation or the voltage-dependence of steady-state fast

inactivation (Supplemental Figures 3C and 3D, and Supplemental Table 3). In silico analysis

predicted that R986Q was pathogenic, according to two algorithms (Table 5).

SCN1B T189M was detected in two probands with lone AF (Table 3). One proband who

was homozygous for the variant was a 59-year-old woman who showed paroxysmal AF with

palpitations every few months. Her AF was always terminated soon after taking pilsicainide. Her

daughter, who was heterozygous for the variant, was a 33-year-old woman and had never

experienced paroxysmal palpitations. The second proband, who was heterozygous for the variant,

was a 55-year-old woman who showed paroxysmal AF that was controlled well by pilsicainide.

This variant was also detected in a control subject in his 20s.

CHO-K1 cells were transiently transfected with vectors expressing WT ororr RRR9898986Q6Q6Q cccDNDNDNA

and thhe humaan bebetat 1 subunit cDNA in combination n with a bicistronic plalasms id encoding GFP.

CCoCommmpared too ththhee e WTWTWT,,, R9R9R9868686QQ sisisigngngnififificici ananantltltly rereredudud ceddd ttthe pepepeakakak sssodddiuiuium m m cucucurrrrrrenenent dedeensnsnsititityyy

SSSupupupplpp ementaal FFFigggure 3BBB anddd SSuS ppleleemmmennntal Taaable 3TT ))). NNNoo o signgngnificcanannt diffffferrrenenncecee was oobbbseeervvved innn

he voltagagage-ee depepependnn enncecece oof steaadydydy-state activavaatit ononon ooor rr the voooltaggge-depepependenceee of stststeadyyy-state fast

nnacacactitit vavavatitit ononon (((SuSuSupppppplelelememementntntalala FFFigiggururureseses 333CC C ananandd d 3D3D3D,,, ananandd d SuSuSuppppppleleememementntntalalal TTTaaablblbleee 333aaa ).).). InInIn sssilililicicicooo anananalalalysysysisiss


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Threonine at 189 is located in the C terminus of the NaV subunit and is highly

conserved among the mammalian homologs of this protein. Thus, we performed

electrophysiological analysis of the mutant NaV protein. When we co-expressed NaV1.5 with

the mutant NaV subunit, the expressed current density was significantly larger than that

observed with the WT NaV subunit (Figure 5A). The maximum peak current density of NaV1.5

plus NaV T189M, measured at -30 mV, was -463 ± 100 pA/pF, which was significantly smaller

than the -240 ± 34 pA/pF for NaV1.5 plus NaV WT (Figure 5B and Supplemental Table 3). I–V

curves of normalized peak sodium current showed that the mutant channel resulted in a

significant negative voltage shift of steady-state activation as compared to the WT channel

(Figure 5C and Supplemental Table 3). No significant difference was observed in the voltage

dependence of steady-state fast inactivation (Figure 5D and Supplemental Table 3). In silico

analysis predicted that T189M was pathogenic, according to three algorithms (Table 5).

In silico prediction analysis of rare variants associated with lone AF

CADD is an integrated algorithm based on a total of 65 annotations; this approach predicted that

both KCNA5 variants (H463R and T527M), KCNH2 T895M, SCN5A R986Q, and SCN1B

T189M were deleterious (CADD-score > 10; Table 5). Seven variants were divided into three

categories on the basis of their cellular electrophysiological profile and CADD scores, i.e., 1)

cellular electrophysiologically abnormal and bioinformatically deleterious variants, including

KCNA5 H463R and T527M, KCNH2 T895M, SCN5A R986Q, and SCN1B T189M, 2) cellular

electrophysiologically abnormal and bioinformatically benign variant, including KCNH2 T436M,

and 3) cellular electrophysiologically normal but bioinformatically radical variant, including

KCNQ1 L492_E493 ins DL (Figure 6).

We also investigated how many rare (MAF < 1%) variants were predicted to be deleterious

Figure 5C and Supplemental TableTT 3). No significant difference was observed innn ttthehehe vvvolololtatatagegege

dependdence oof stteae dy-state fast inactivation (Figurre e 5D and Supplementatal l Table 3TT ). In silicoII

anananalllysis predididictcttededed thhatatat T1T1T1898989MM wawawas s s papapathththogoo enenenicicic, accococordininng g g tototo ththt reeeeee alalalgogogoriririthththmsm (((TaTaTablblblee e 5)5))..

InInn ssilili ico predid ctttiooon annnaaalysisss ooof f rareree varararianntss assoccciatttededed wwwittth lononone AFF

CADD isss anaa integegegrateed d d alaa gogg rithhhm mm based on aaa tttotttalalal ooof f 65 annnnnnotationsss;;; this apppppproaccch h prpp edicted that

boboboththt KCKCKCNANANA555 vvvararariaiaantntntss s (H(H( 4646463R3R3R aaandndd TTT5252527M7M7M),),), KCKCKCNHNHNH222 TTT8989895M5M5M,, , SCSCSCN5N5N5AAA RRR9898986Q6Q6Q,, , ananandd d SCSCSCN1N1N1BBB


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using an in silico prediction tool (CADD scores > 10), and how many such alleles were present in

the 12 AF-associated genes, in the largest exome sequencing study to date, using 61,486 unrelated

individuals (ExAC data and browser).12 We found as many as 2,255 variants (17,859 alleles) that

could potentially be (mis) judged as causative based only on these frequency/in silico prediction

tool strategies (Supplemental Figure 4).

Discussion

In the present study, we analyzed 90 patients with lone AF for rare genetic variants in 12 genes

and identified seven rare variants in eight patients. This indicates that 8.9% of lone AF patients

carry rare genetic variants. Of these seven variants, two variants were identified in the 250

control subjects (MAF = 0.2% each), one variant was reported in the EVS (MAF < 0.01%), and

three variants were reported in the ExAC (MAF < 0.05% each). More variants were reported in

the ExAC than the EVS, probably because the ExAC includes data of east and south Asia, while

the EVS comprises data of African American and European American individuals. Because the

MAFs of three variants (KCNA5 T527M, KCNH2 T436M, and SCN5A R986Q) in the ExAC

database were significantly smaller than those in our case population, these seem to be extremely

rare variants. Recently, Olesen et al. reported that 29 rare variants had been identified among 192

very early-onset lone AF patients (onset of disease before the age of 40 years).17 The frequency

of rare variants in our lone AF cohort (8.9%) was lower than that in Olesen's study (15.1%).

There are several reasons for the difference in frequency: (1) our cohort included individuals

with onset of lone AF at an older age, (2) Olesen et al. defined rare variants as variants with a

MAF of < 0.1% in the EVS, and (3) they performed screening of genomic DNA for 14 genes,

while 12 genes were investigated in our study.

More than 100 rare variants have been reported in patients with lone AF;7-9 however, the

carry rare genetic variants. Of these seven variants, two variants were identified ininin ttthehehe 222505050

control subjects (MAF = 0.2% each), one variant was reported in the EVS (MAF < 0.01%), and

hhhrrereeee variantststs were reported in the ExAC (MAF < 0..05% each). MMMooore variants were reported in

hhhe EExE AC than thhheee EVVVS,, probbbaaably becaaaussse thhehe ExAAACCC innclclcludududesss daatta a of eaaastt t annnd d d southhh AAAsiiia,,, whhhileee

he EVEVEVSSS ccocomppririisesess dadad ttata oof f Affririicacacan n Amererricicicanan andndd EEEururopopeaeean AmAmAmerricicicanan iiinddndiivividi uaualslsls. BBBecacaususee thththee

MAFs ooofff thththrereree ee vavavariririananantststs (((KCKCKCNANANA555 T5T5T5272727M,MM KCKCKCNHNHNH222 TTT4343436M6M6M, anananddd SCSCSCN5N5N5AAA RRR9898986Q6Q6Q))) ininin ttthehehe EEExAxAxAC


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functional effect of most variants has not been evaluated. In the present study, we carefully

characterized each of the rare variants and could find that they have novel and/or overt

abnormalities in channel function.

One of the two KCNA5 variants identified in this study, H463R, was a novel mutation;

the histidine at codon 463 is located in the S5-pore loop, in the vicinity of the pore of the KV1.5

subunit. Our patch clamp study showed that this mutation caused dominant-negative suppression

of KV1.5 WT function, and was considered to have a loss-of-function effect. The loss of function

mutation in the KCNA5 gene can lead to a decrease in IKur, action potential prolongation, and

early after-depolarization. The other KCNA5 variant, T527M, showed a gain-of-function effect

with an enhanced steady-state activation, which was revealed by electrophysiological analysis. A

previous study reported that three gain-of-function variants in KCNA5 displayed a negative

voltage shift in the steady-state activation curves.18 The T527 variant had previously been

identified in a Chinese family with AF.19

We identified two KCNH2 variants with gain-of-function effects in patients with lone AF,

although only a few KCNH2 variants have so far been reported in lone AF patients.20 KCNH2

T436M had previously been identified in a LQTS family with low disease penetrance.21 The

proband in our study was affected with AF from the age of 38 years and had a number of

AF-affected family members. Furthermore, KCNH2 T895M had previously been identified in a

patient with sudden infant death syndrome.22 In our study, the proband's father and son, who both

carried the T895M variant, suffered paroxysmal palpitations. Our cellular electrophysiological

studies showed that the deactivation time course in both these KV11.1 channels were

significantly slower than that in the WT channel, which resulted in a gain-of-function effect. A

previous study of T895M in Xenopus oocytes showed that the deactivation time constants in the

with an enhanced steady-state activation, which was revealed by electrophysiologoggicicicalal aaanananalylylysisisis. A

previoous studydy repepported that three gain-of-function n vav riants in KCNA5 ddisisplpp ayed a negative

vovovoltttaga e shift iniin ttthehehe steteteadadadyyy--stststatteee acacactititivavavatititiononon cccurururvevv s.118 TTTheee TTT5252527 77 vaaariririananant tt hahahaddd prpp evvvioioiousususlylyly beeeeeenn n

dddenenentitt fied in a a Chhhinneseee ffafamilyyy wwwith AFAFA .19

WeWeWe identttififi ied twtwtwo oo KCNHNHNH2 variants wwwititi h h h gagagaininin-of-fuuuncnn tion effffffecee ts in papapatiennnts with lone AF,

alala ththt ouououghghg ooonlnlyy y aa a fefeeww w KCKCKCNHNHNH222 vavavaririananantststs hhavavavee e sososo ffararar bbbeeeeeenn n rererepopoportrtrtededed iinn n loloonenene AAAFFF papapatitit enenentststs...202020 KCKCKCNHNHNH222


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T895M channel were increased significantly compared with those of the WT,22 which

corresponded with our results. The gain-of-function KCNH2 variants implicated in susceptibility

to AF may exist more frequently than indicated in previous reports.

Several KCNQ1 gain-of-function mutations have been identified in lone AF patients to

date.8 We found a KCNQ1 insertion variant (L492_E493insDL) in a lone AF proband with

normal QTc intervals. This is a so-called radical variant, which is likely to be pathogenic;

however, its channel properties were similar to those of the WT channel.

We found a rare SCN5A variant, R986Q, in patient with lone AF. In a heterologous

expression study, R986Q was categorized as a loss-of-function variant and can cause shortening

of the refractory period and slowing of conduction. We also identified an SCN1B T189M variant

in two probands with lone AF and in one of 250 control subjects in this study. Previously, several

SCN1B variants have been reported in AF patients, and had a loss-of-function effect.8, 23 Our

cellular electrophysiological study showed that T189M was a gain-of-function variant, which

was predicted to lower the threshold potential for cellular excitability. This is the first report on

an AF-associated SCN1B variant that leads to an increased peak sodium current density and that

changes the voltage dependence of the steady-state fast activation of the Na+ channel. Moreover,

our data indicated that the sodium channel blocker pilsicainide was effective for AF in probands

with gain-of-function variants in SCN1B.

Many prediction tools are available for predicting a pathogenic or benign status for rare

variants identified in patients with various genetic disorders.14, 24, 25 Giudicessi et al. tested

whether four prediction tools—conservation analysis, Grantham values, SIFT, and

PolyPhen2—have the potential to enhance the classification of rare non-synonymous

single-nucleotide variants in types 1 and 2 LQTS.14 Their findings supported the potential

of the refractory period and slowing of conduction. We also identified an SCN1BBB TTT18181 9M9M9M vvvararariiiant

n twoo ppprobandn s wiw th lone AF and in one of 250 conontrol subjects in this ststudy. Previously, several

SCSCSCNNN1B variaaanntntsss hahahaveee bbbeeeeeenn n rerr popoportrtrtededed iin nn AFAFAF pppatatatieiei ntss, aaandd hhhadadad aaa lossssss-ofofof-f-- unununctctction n efefeffefefectctct...ff 8, 23 OuOuOurr r

ceceelllluulular electror phhhyyysiolllogggical stttuduu y shshhowwweeed thahahatt T118999M M M wwwas a a gaiinn---of-fuuunccctiooon n variaanantt,t, wwwhhhichff

was predddicicicted tooo llowererer ttthehh thresesshohh ld pppotentialalal ffororor cccelee lular exee citabilitytyty. This iiis ss theee first reppport on ff

ananan AAAFFF-asasassososocicic atatatededed SCSCSCN1N1N1BBB vvvararariaiaantntnt ttthahaatt t leleeadadadss s tototo aaann n inincrcrcreaeaeasesesedd d pepepeakaka sssodododiuiuumm m cucucurrrrrrenenentt t dededensnsnsititity y y ananandd d ththt atatat


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synergistic utility of these tools to enhance the classification of rare variants; however, their use

in isolation in clinical application remains limited. CADD is an attractive prediction tool that

uses a total of 65 annotations, including gene models, conservation measures, and ENCODE data

summaries.16 In the present study, five of six variants exhibiting electrophysiological

abnormalities were predicted to be pathogenic according to their CADD scores, reinforcing

classification of these rare variants as pathogenic in patients with lone AF.

Several clinical applications can be derived from this study. First, at least 9% of lone AF

patients have a rare variant in cardiac ion channel genes. Four of seven rare variants implicated

in susceptibility to AF showed gain-of-function effects by electrophysiological study. Second,

rare ion-channel variants identified in patients with lone AF have therapeutic implications.10 In

particular, patients with gain-of-function variants are likely to benefit from a drug that enables

selective inhibition of mutant channel complexes. A previous study showed the enhanced

sensitivity of KCNQ1 gain-of-function mutations for the IKs selective blocker HMR-1556.26 In

our study, sodium channel blockers were shown to be effective in patients harboring SCN1B

T189M. Similarly, IKur channel blocking or IKr channel blocking may be effective for preventing

the shortening of APD in patients with KCNA5 T527M or KCNH2 T436M and T895M. Thus,

genetic testing of major ion channel genes for patients with lone AF is therefore significant. In

addition, the rare variants annotated as pathogenic by CADD should be evaluated to determine

whether they have loss-of-function or gain-of-function effects by electrophysiological studies.

Third, several asymptomatic family members with rare variants were identified in this study and

may have a risk of developing AF in future. These subjects should be advised to avoid acquired

risk factors for the development of AF and preventive care for reducing these risk factors should

be provided.

are ion-channel variants identified in patients with lone AF have therapeutic impppliliicacc ttitiononons.s.s.101010 IIIn

particulu ar,,, papap tienntsts with gain-of-function variants arare e likely to benefit fromom a drug that enables

eeeleeecctive inhihiibibibitititiononon oooff f mumumutatatantnn ccchahahannnnnnelell cccomomomplplplexexexes. A A prevevevioioioususus stuuudydydy ssshohohowewewed d thhhee e enenenhahahancnccededed

eeensnssiti ivity off KCKCCNNNQ1 gaaain-ooof-f-fuff nctiionoo mmmutatttiooons ffforrr thehehe IIIKs seeeleccctivvve bloocockekerrr HHHMR--1-155556..262 Innn

our study,y,y, ssodiuum mm chananannenn l bloccckekk rs were showowown n n tototo bbbe effeeectcc ive in pppatata ients hahaharbororrini g gg SCN1B

T1T1T1898989M.M.M. SiSiS mimilalaarlrly,y,y, IIIKuKuKurrr chchc ananannenenell blblb ococockkininggg ororor IIIKrKrKr ccchahaannnnnnelele bbbloloockckc iningg g mamamayy y bebebe eeeffffececectitit veveve ffororor ppprerereveveventntntiningg g


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Study limitations

This study has several limitations. First, 12 major genes were examined here for rare variants

associated with AF, and were only a small fraction of potential genes related to AF.

Whole-exome sequencing or targeted next-generation sequencing may reveal more variants

associated with AF in many genes. Second, the case population (n = 90) and the control

population (n = 250) used in this study were relatively small. Indeed, recent reports studied

192 307 patients with lone AF or 216 240 healthy subjects as a reference.18, 27 However, we

used data from the EVS11 and the ExAC data and browser12 as reference groups. Finally, only the

individual variants identified in the cases were evaluated in the control population. It is possible

that a series of rare variants may have been identified in the controls if the rest of the coding

region of each gene had been fully screened in the controls. In fact, 2255 rare and deleterious

variants (MAF < 1% and CADD scores > 10) in 12 AF-associated genes were found in the ExAC

data and browser,12 which comprises in excess of 60,000 unrelated individuals sequenced

(Supplemental Figure 4). This emphasizes the importance of both phenotype assessments and

functional analyses for determination of disease-causing variants even in this comprehensive

genotyping era.

Conclusions

In our cohort of lone AF patients, seven rare variants in cardiac ion channel genes were identified

in eight probands, with a prevalence of approximately 9%. These variants were extremely rare

and characterized as causing susceptibility to AF by either electrophysiological study or in silico

prediction analysis. More than half of AF-associated rare variants showed gain-of-function

behavior, which is likely to benefit from a drug that blocks particular ion channels.

hat a series of rare variants may have been identified in the controls if the rest offf thhhe cococodididingngng

egionnn oooff f eaeae chchch gggenenene had been fully screened in thee cccono trols. In fact, 225555 rrrare and deleterious

vavavariiiana ts (MAAAFF F <<< 1%1%1% aaandndnd CCCADADADDDD scscscorororeeesss >>> 101010))) ini 122 AAFAF---asasassososocicic attededed gggenene eseses wwwere e e fofofoununundd d innn ttthehehe ExExExACAA

dadaatataa aand browsww errr,112 whhhiccch comomomprpp isesess innn eexexceessss of 606060,0000000 unnnreeelateeed indivvviddduauaalsls sequeueenncnceeed

Supplemmmenene tatal l FiFiFigugug rere 444).).). TThihis s emememphphp asasizizeses tthehehe impmpmpororo tatancnce e e ofof bbototh h phphphenenototypypype e e asassesesessssmementnts s and

fufufuncncnctititionononalalal aaanananalylylysesesesss fofoforrr dededetetetermrmrminininatatatioioionnn ofofof dddisisiseeeasasaseee-cccauauausisisingngng vvvararariaiaiantntntsss evevevenenen iiinnn thththisisis cccomomomprprprehehehenenensisisiveveve


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Acknowledgments: The authors gratefully acknowledge Dr. Haruhiro Higashida and Dr.

Shigeru Yokoyama, Dr. Hideji Tanii and Dr. Rie Oka for statistical advice and helpful

discussions, Dr. Taketsugu Tsuchiya and Dr. Kotaro Oe for providing patient's phenotype data,

and Takako Obayashi, Masako Fukagawa, Hitomi Oikawa, and Saori Nakano for technical

assistance.

Funding Sources: This study was supported by grants from the Ministry of Health, Labor, and

Welfare of Japan for Clinical Research on Intractable Diseases (H26-040 and H24-033 to K.H.),

and Takeda Science Foundation (K.H.).

Conflict of Interest Disclosures: None

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14. Giiudicessis JR,R,, Kapplinger JD, Tester DJ, Alderrss M, Salisbury BA, WiWilde AA, Ackerman MJ.Phhhylylyloogogeneteteticicic andndd ppphysicochemical analyses enhannnceee the classificaatitit on oofff rrrare nonsynonymous iiinggglel nucleotototidddeee vavv riririananantststs iiin nn tyyypepepe 111 aaandndnd 222 lononong-g-g-QTTT syyyndrdrdromomome.e.e. CiCiirrrc CCCararardididiovovo asc c c GeGeGenenenett.

2202 1122;5:519-528..

15. ChChChoioioi YYY,, , SiSiSimsmsms GGGE,E,E, MMMurururphy yy S,S,S, MMMiliiller r r JRJRJR,, , ChChChananan AAAP.P.P PPPrereedididictctc inining g thththe e e fufufunncnctiiionono alalal eeeffffffecececttt ofofof amimiminnono acid substststitii utionsnsns andd iiindndn els. PLPLPLoS One. 2012122;777:e:e:e464646688.

16166.. . KiKircrcrcheheerr r M,M,M, WWWititittetetenn n DMDMDM,, , JaJaJainin PPP,,, O'OO RoRoRoakaka BBBJ,J,J, CCCoooooopepeperr r GMGMGM,, , ShShS enenendududurerere JJJ... AA A gegegenenenerararall frframamameweweworororkk fofoorrreestimating the relative pathogenicity of human genetic variants Nat Genet 2014;46:310 315


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21. Priori SG, Napolitano C, Schwartz PJ. Low penetrance in the long-QT syndrome: clinical impact. Circulation. 1999;99:529-533.

22. Otagiri T, Kijima K, Osawa M, Ishii K, Makita N, Matoba R, Umetsu K, Hayasaka K. Cardiac ion channel gene mutations in sudden infant death syndrome. Pediatr Res.2008;64:482-487.

23. Watanabe H, Darbar D, Kaiser DW, Jiramongkolchai K, Chopra S, Donahue BS, Kannankeril PJ, Roden DM. Mutations in sodium channel beta1- and beta2-subunits associated with atrial fibrillation. Circ Arrhythm Electrophysiol. 2009;2:268-275.

24. Sudandiradoss C, Sethumadhavan R. In silico investigations on functional and haplotype tag SNPs associated with congenital long QT syndromes (LQTSs). Genomic Med. 2008;2:55-67.

25. Yang RQ, Jabbari J, Cheng XS, Jabbari R, Nielsen JB, Risgaard B, Chen X, Sajadieh A, Haunso S, Svendsen JH, Olesen MS, Tfelt-Hansen J. New population-based exome data question the pathogenicity of some genetic variants previously associated with Marfan syndrome. BMC Genet. 2014;15:74.

26. Campbell CM, Campbell JD, Thompson CH, Galimberti ES, Darbar D, Vanoye CG, George AL, Jr. Selective targeting of gain-of-function KCNQ1 mutations predisposing to atrial fibrillation. Circ Arrhythm Electrophysiol. 2013;6:960-966.

27. Mann SA, Otway R, Guo G, Soka M, Karlsdotter L, Trivedi G, Ohanian M, Zodgekar P, Smith RA, Wouters MA, Subbiah R, Walker B, Kuchar D, Sanders P, Griffiths L, Vandenberg JI, Fatkin D. Epistatic effects of potassium channel variation on cardiac repolarization and atrial fibrillation risk. J Am Coll Cardiol. 2012;59:1017-1025.

he pathogenicity of some genetic variants previously associated with Marfan syndrdrdromomo e.e.e BMBMC CGenet. 2014;15:74.

26. Caampppbelll CM,M, Campbell JD, Thompson CH, GGala imberti ES, Darbarr DD, Vanoye CG, George ALLL, JrJrJr. SeS lelelectctctivi e ee tatatargeting of gain-of-function KCCCNQNQNQ1 mutations prprp edissspopoposing to atrial fifiibrbrbriilillation. CiCC rcrcrc AAArrhyhyhythththmm m ElEE ececectrtrtropopophyhyhysisisiololo . 2020201311 ;6:::96660-999666666.. .

27277. MMaM nn SA,A,, OOtwwway RRR,,, Guooo GGG, Sokkka a M,M,M Kaaarllsdotttteeer LLL, TrTTrivvveeedi GG,G, Ohannniaanan MMM, Zodgdgdgeeekaarar P,Smmititith h h RARARA,,, WoWoWoutututerrrsss MAMAMA,,, SuSS bbbbbbiaiaiahh h R,R,R WWWalalalkekekerrr B,B,B, KKKucucuchahahar rr D,D,D, SSSananndeded rrsrs PPP, , GrGrGriffffififithththsss L,L,L, VVVaanandededenbnbberererg g g JIJII,,,Fatkin DD.. EpEpEpistaaatitit c efffefefectcc s of pppotototassium channnnnnelll vvvararariai tion ooon cardiaaccc repopp lariririzatiiionoo and atrialfibrillatiiiononon rrriisisk.kk. J J J AmAmAm CCColololl l l CaCaCardrdrdioioiolll... 20202012122;5;5;59:9:9:1110171717-1-1-10202025.5.5.


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Table 1: Clinical characteristics of the lone AF population

Total 90

Age at onset AF, y 47±11

Age at enrollment, y 56±13

Male gender (%) 66 (73)

Family history (%) 26 (29)

AF type

Paroxysmal or persistent (%) 57 (63)

Permanent (%) 33 (37)

Complication

Bradyarrhythmia (%) 10 (11)

Brugada syndrome (%) 3 (3)

Echocardiogram

Left atrial size, mm 40±7

Ejection fraction, % 65±8

Catheter ablation (%) 20 (22)

Permanent (%) 33 (37)

Complication

BBBradyarrrrhhhythmia (%%) ) 10 (1111)

BrBB ugada synddrrromeee (((%%) 333 (3)))

Echoca drdrdiiiogram

Lefttt aatrtrtriaiaial sisisizezeze,, mmmmmm 40400±7±7±7


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Table 2: Summary of ion channel rare variants

Gene Amino acidchange

AF probands (n=90) Controls (n=250) Exome Variant Server Exome Aggregation Consortium

Allele count

Allele number MAF Allele

count Allele

number MAF Allele count

Allele number MAF Allele

count Allele

number MAF

KCNA5 H463R 1 180 0.006 0 500 0 N/A N/A N/A N/A N/A N/A

T527M 1 180 0.006 1 500 0.002 N/A N/A N/A 29 122816 0.000236†

KCNQ1 L492_E493 ins DL 1 180 0.006 0 500 0 N/A N/A N/A N/A N/A N/A

KCNH2 T436M 1 180 0.006 0 500 0 N/A N/A N/A 2 122940 1.627e-05*

T895M 1 180 0.006 0 500 0 N/A N/A N/A N/A N/A N/A

SCN5A R986Q 1 180 0.006 0 500 0 1 12116 8.253e-05† 2 102516 1.951e-05*

SCN1B T189M 3 180 0.017 1 500 0.002 N/A N/A N/A N/A N/A N/A

MAF=Minor allele frequency; N/A, not available; *P<0.01 or †P<0.05 vs. MAF of AF probands.

count number count number count number count number

H463R 1 180 0.006 0 500 0 N/A N/A N/A N/AAA N/N/N/AAA N/N/N/

T5T55272727MM M 1 180 0.006 1 500 0.0.0 00002 N/A N/A N/N/N/AAA 29 122816 0.000

L44929 _E493 ins DL 1 181 00 000.006 0 50000 0 NN/N/A N/AAA NN/N/AAA N/A N///A NNN/

T4T44363636M M M 111 18181 0 0.00 00000666 0 50505000 0 N/N/N/AAA N/NN AAA NN/N/AAA 222 1222929294000 11.1.6222777

T895M 111 18188000 0.0.0 0000006 6 6 000 505050000 000 NN/N/AAA N/N/N/AAA N/N/N/AAA N/N/N AAA N/N/N/AAA N/

R986Q 1 180 0 006 0 500 0 1 12116 8 253e 05† 2 102516 1 951


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Table 3: Clinical characteristics of patients with rare variants

ProbandNo

Patient type

Age y Genotype Gender Family

history Phenotype Onset y

ECGHR/PR/QRS/QTc

EchocardiographyLAD (mm)/EF (%)

Catheter ablation

1 Proband 62 KCNA5 H463R F No Persistent 51 67 / 0.14 / 0.08 / 0.452 34 / 66 Yes

2 Proband 51 KCNA5 T527M M No Persistent 49 67 / 0.22 / 0.07 / 0.391 35 / 70 No

Son 29 KCNA5 T527M M No AF(-) 77 / 0.22 / 0.10 / 0.395 No

3 Proband 42 KCNQ1 L492_E493 ins DL M No Paroxysmal 42 68 / 0.19 / 0.08 / 0.392 36 / 53 No

Daughter 18 KCNQ1 L492_E493 ins DL F No AF(-) 58 / 0.144 / 0.09 / 0.387 No

Son 16 KCNQ1 L492_E493 ins DL M No AF(-) 54 / 0.16 / 0.09 / 0.426 No

Son 7 KCNQ1 L492_E493 ins DL M No AF(-) 81 / 0.146 / 0.07 / 0.437 No

4 Proband 61 KCNH2 T436M M Yes Chronic 38 73 / - / 0.08 / 0.371 49 / 73 No

5 Proband 58 KCNH2 T895M M Yes Persistent 40 64 / 0.15 / 0.10 / 0.405 42 / 63 Yes

Father 87 KCNH2 T895M M Yes Paroxysmal palpitation 50s 69 / 0.17 / 0.11 / 0.451 No

Son 37 KCNH2 T895M M Yes Paroxysmal palpitation 20s 59 / 0.16 / 0.11 / 0.386 No

6 Proband 64 SCN5A R986Q M No Paroxysmal 58 52 / 0.134 / 0.11 / 0.369 37 / 70 No

7 Proband 59 SCN1B T189M (Homozygous) F No Paroxysmal 59 50 / 0.151 / 0.09 / 0.378 30 / 76 No

Daughter 33 SCN1B T189M (Heterozygous) F No AF(-) 56 / 0.13 / 0.08 / 0.424 No

8 Proband 55 SCN1B T189M (Heterozygous) F No Paroxysmal 55 65 / 0.18 / 0.08 / 0.420 30 / 65 No

Son 29 KCNA5 T527M M No AF(-) 77 / 0.22 / 0.10 / 0.395

Proband 42 KCNQ1 L492_E493 ins DL M No Paroxysmal 42 68 / 0.19 / 0.08 / 0.392 363636 /// 555333

Daughter 18 KCNQ1 L492_E493 ins DL F No AF(-) 58 / 0.144 / 0.09 / 0.387

Soonnn 161616 KCCCNQNQNQ1 L492_E493 ins DL M No AFAFF(((-)- 54 / 0.16 / 000.09099 / 0.426

SSSonnn 7 KCNQNQQ1 L4922_E_E_ 494933 ins DLL MM NoNo AF(---) 818181 /// 0.1146 / 00.0.07 / 0.43377

Proooband 61 KCKCNHNHNH2 T4T433636MMM MMM Yes Chroooniccc 3888 7773 /// - / 0.08088 / 000.37111 49499 / 77733

PrP oboboband 58 KCNHNHNH2 T89995MMM M Yes Perssiisteeent 4000 6664 /// 0.15 / 000.1000 / 000.40005 4222 / 663

Faathththererer 878787 KCKCKCNHNHNH22 2 T8T8T899595MMM M YeYeYess PaPP rorooxysssmalalal paallplpititit tatatiioionn 50505 ss 696969 // 000.1.1177 // / 00.0.11111 /// 00.44515151

Son 373737 KCKCKCNHNHNH22 2 T8T8T8959595MMM MMM YeYeYesss PaPaParororoxyxyxysmsmsmalala papapalplppitititatatatioioionnn 202020ss 595959 /// 000.1.1.1666 / / / 0.0.0 111111 // 000.3.33868686

PrPrProbobobanananddd 646464 SCSCSCN5N5N5AAA R9R9R9868686QQQ MMM NoNoNo PaPaParororoxyxyxysmsmsmalalal 585858 525252 /// 000 1.1.1343434 /// 000 1.1.1111 // / 00.0.363636999 373737 /// 777000

SCN1B T189M


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Table 4: In vitro cellular electrophysiology

Gene Amino acid Change Current density Channel kinetics

KCNA5 H463R Absent IKur No change

T527M Increased peak and tail currents Faster activation

KCNQ1 L492_E493 ins DL No change No change

KCNH2 T436M Increased peak currents Slower deactivation

T895M Increased peak and tail currents Slower deactivation

SCN5A R986Q Decreased peak currents No change

SCN1B T189M Increased peak currents Negative shift in steady-state acrivation

T527M Increased peak and tail currents Faster activation

Q1 L492_E493 ins DL No change No change

H2 T4363 M Increased peak currents Slower deactivattioion

T895M Increased peak and taiil ccuurrents Slowerr dddeeeactivation

555AA R999866Q DeDeecrcrcreasseddd peakkk cccurrennnts NoNoo chaaanggge

1BBB T1118999M InIncrcrcreeeaseeed pepep akakk cuururrenttts NeNeegagag tiivvve shihiiftf innn stteadydydy-ststs ate aacacriririvatttionnn


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Table 5: In silico prediction analysis

Gene Amino acid Change Polyphen-2 Grantham SIFTScore

PROVEAN Score Score CADD

KCNA5 H463R 0.195 29 0.031Damaging

-3.49 Deleterious

14.25Deleterious

T527M 0.954Possibly damaging 81 0.007

Damaging-4.96

Deleterious19.35

Deleterious

KCNQ1 L492_E493 ins DL N/A N/A N/A N/A N/A

KCNH2 T436M N/A 78 0.138 -0.42 1.52

T895M N/A 78 0.178 -1.57 17.78Deleterious

SCN5A R986Q N/A 103 Radical 0.308 -0.62 11.17 Deleterious

SCN1B T189M N/A 113 Radical 0Damaging -0.08 15.27

Deleterious

SIFT, Sorting intolerant from tolerant; PROVEAN, Protein variation effect analyzer; CADD, Combined Annotation Dependent Depletion

T527M 0.954Possibly damaging 81 0.007

Damaging-4.96

Deleterious191919.3.3.355

Deeeleeteteteriiououousss

Q1 L492_E493 ins DL N/A N/A N/A N/A N/A

HH222 T436MMM N/A 78 0.138 -0.4222 1.52

T895M NNN/A 7888 00.0.17177888 -1.5777 1777.7.7888Deleteeeriooouss

5A R9R9R9868686QQQ N/N//AAA 10101033 RaRRadid cacalll 000.33030888 0-0.666222 111111 1.1.177DeDeDeleterious

1B T118989MM N//AA 1111333 Radidicall 000DaDaDamamamagigigingngng -0-0.0.0.0888 151515.22.2777

DeDeDeleleleteteteriririououousss


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Figure Legends:

Figure 1: Sequencing of KCNA5, KCNQ1, KCNH2, SCN5A, and SCN1B. DNA sequencing

electropherograms demonstrated seven genetic variants. The KCNQ1 insertion variant was

confirmed by sequencing the mutant allele in KCNQ1. Topology of the voltage-gated ion

channels shows the location of the detected variants.

Figure 2: Functional characterization of KV1.5 H463R in HEK293 cells. (A) Representative

currents generated in HEK293 cells that had been transfected with 0.2 μg of a vector expressing

wild-type (WT) KCNA5 alone (left), 0.2 μg of a vector expressing KCNA5 H463R alone

(middle), or 0.1 μg of each of the KCNA5 WT and KCNA5 H463R vectors (right). Pulse protocol

is shown in inset. (B) and (C) I–V relationships for peak currents (B) and tail currents (C) in

HEK293 cells expressing WT alone (closed circle, n = 20), H463R (closed triangle, n = 9), and

WT plus H463R (closed square, n = 15). (D) Mean amplitudes of normalized tail currents for

WT alone (closed circle, n = 17) and WT+ H463R (closed square, n = 8). *P < 0.001 or †P < 0.01

among indicated current voltage relationships by two-way repeated-measures ANOVA.

Figure 3: Functional characterization of KV1.5 T527M in HEK293 cells. (A) Representative

currents generated in HEK293 cells, transfected with 0.2 μg of a vector expressing KCNA5 WT

alone (left), or 0.2 μg of a vector expressing KCNA5 T527M alone (right). (B) and (C) I–V

relationships for peak currents (B) and tail currents (C) in HEK293 cells transfected with the WT

currents generated in HEK293 cells that had been transfected with 0.2 μg of a veeectctc ororo eeexpxpxprereressssssinining g g

wilddd---tytytypepepe (((WTWTW ) KCKCK NA5 alone (left), 0.2 μg of a vvvececectot r expressing KCNANANA555 H463R alone

mmmiidddle), or 0.111 μμggg ofofof eaacachhh ofofof ttthhhe KCKCKCNANANA555 WWWT T anddd KKCKCNANANA5 HHH4464 3RR vecctototorsss (riririghghght). PuPuPulslsl eee prprpr totocococolo

s ssshohohownwnwn iiinn n inininsesesettt. (((B)B)B) aaandndnd (C)C)) II–V–VV rrreleelatttioioionsnsnshihihipspsps ffooror pppeaeaeak k cucucurrrrrenenentsts (((B)B)B) aandndnd tttaiaia l l cucucurrrrr enenentststs (((CC)C) iin n

HEK29333 ccceelellslsls eexpxpxprereressssssinining g g WTWTWT aaalololonnene (((clclclosososededed ccciri clclcle,e,e, nnn === 2220)0)0),,, H4H4H4636363R R R (c(cclololosesesed d d trtrtriaiaiangngnglelele,, n n n === 9)9)9), and

WWT l H463R ( l d 15) (D) M lit d f li d t il t f


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vector alone (closed circle, n = 33), or the T527M vector (closed triangle, n = 47). (D) Mean

amplitudes of normalized tail currents for WT alone (closed circle, n = 33) and T527M alone

(closed triangle, n = 42). *P < 0.05 between the indicated current voltage relationships by

two-way repeated-measures ANOVA.

Figure 4: Functional characterization of KV11.1 T895M in CHO-K1 cells. (A) Representative

currents generated in CHO-K1 cells, transfected with 1 μg of a vector expressing KCNH2 WT

alone (left) or 1 μg of a vector expressing KCNH2 T895M alone (right). Pulse protocol is shown

in inset. (B) and (C) I V relationships for peak currents (B) and tail currents (C) in CHO K1

cells transfected with WT alone (closed circle, n = 18), and T895M (closed triangle, n = 18). (D)

Mean amplitudes of normalized tail currents for WT alone (closed circle, n = 10) and T895M

(closed triangle, n = 13). (E) Deactivation time constants of KV11.1 currents (protocol shown in

inset). The deactivation process was fitted to bi-exponential functions. Deactivation time

constants of WT (fast component: open circle, n = 13; slow component: closed circle, n = 13)

and T895M (fast component: open triangle, n = 15; slow component: closed triangle, n = 15) are

shown. (F) Normalized steady-state inactivation curves for WT (closed circle, n = 9) and T895M

(closed triangle, n = 11). Pulse protocol is shown in inset. The current amplitude at the test

potential was normalized and plotted against the prepulse potential. Curves represent the best fits

to a Boltzmann function. *P < 0.01 or †P < 0.05 between the indicated current voltage

relationships by two-way repeated-measures ANOVA. ‡P < 0.05 vs. WT by Student’s t-test.

n inset. (B) and (C) I V relationships for peak currents (B) and tail currents (C) inini CCCHOHOHO KKK111

cellls s s trtrtrananansfsfsfececectett d d d wiww th WT alone (closed circle, n === 1811 ), and T895M (closossededed triangle, n = 18). (D)

MeMMeaaan amplitudedd ss oofof nnoorormamamallilizezezeddd taailili cccurrrreeents fofoor WTWTWT aloloonnne (((clcllosedd ccirclclleee, nnn == 111000) aandndnd TT89898 5M5M5M

clololoseseseddd trtrtriaiaiangngnglelele,,, nn = 1133)3). . . (E(( ) DeDeDeacacactititivvvatiiiononon tttimimimeee cococonsnsnstatatanntnts ss ofofof KKKVVVKKKK 1111.1.11 cccurururrrerentntntss (p(p(prororotototocococoll shshshowowown n n ininn

nset). ThThTheee dededeacaca tititivavavatititiononon ppprororocececessssss wwwaaas fffitititteteted dd tototo bbbi---exexexpopoponenenentntntiaiaialll fufufuncncnctititiononons.s.s. DDDeaeaeactctctivivivatatatioioion nn tititimememe

t t f WT (f t t i l 13 l t l d i l 13)


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Figure 5: Functional characterization of NaV1.5 + NaV 1 T189M in CHO-K1 cells. (A)

Representative currents generated in CHO-K1 cells that had been transfected with 0.6 μg each of

a vector expressing the NaV1.5 channel and WT NaV

expressing the NaV1.5 channel and mutant NaV

inset. (B) I–V relationships for peak currents of NaV1.5 + NaV 16) or

NaV1.5 + NaV

steady-state activation and fast inactivation of Na V1.5 + NaV

NaV1.5 + NaV n = 16), measured using standard pulse protocols.

Figure 6: Classification of rare variants according to their electrophysiological and bioinformatic

properties. Seven rare variants detected in this study were divided into three groups: cellular

electrophysiologically abnormal and bioinformatically deleterious variants, cellular

electrophysiologically abnormal and bioinformatically benign variants, and cellular

electrophysiologically normal and bioinformatically radical variants.

Figugugurerere 6:6:6: ClClClasaa siiififificcac tion of rare variants accordinnng g g tototo their electrophp ysiooololologig cal and bioinformatic

pppropopperties. Sevennn rrrarere vaarariaiaiantntnts s s dddeteteectctcteddd innn thhisis studydydy wwererereee dididivvivideddd iiinto tthrhh eeee gggrroroupsss: cccelellulul lalalar

elececctrtrtropopophyhyhysisisiololologogogiciccalalallylyly aabnbnbnormmamalll aaandndnd bioioioinininfofoformrmrmatataticicicallllylyly dddeleleletetetererrioioioususs vvvararariaiaiannnts,s,s, ccelelellulululalalarrr

electroppphyhyhysisisiololologogogicicicalalallylyly aaabnbnbnororormamamall l ananand bibibioioioinfnfnfororormamamatititicacacalllllly y y bebebeninin gngngn vvvararariaiaiantntnts,s,s, aaandndnd cccelelellulululalalarr r

l t h i l i ll l d bi i f ti ll di l i t


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Makita and Masakazu YamagishiAkihiko Hodatsu, Toyonobu Tsuda, Yoshihiro Tanaka, Masa-aki Kawashiri, Hidekazu Ino, Naomasa Kenshi Hayashi, Tetsuo Konno, Hayato Tada, Satoyuki Tani, Li Liu, Noboru Fujino, Atsushi Nohara,

FibrillationFunctional Characterization of Rare Variants Implicated in Susceptibility to Lone Atrial

Print ISSN: 1941-3149. Online ISSN: 1941-3084 Copyright © 2015 American Heart Association, Inc. All rights reserved.

Dallas, TX 75231is published by the American Heart Association, 7272 Greenville Avenue,Circulation: Arrhythmia and Electrophysiology

published online June 30, 2015;Circ Arrhythm Electrophysiol.

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1

SUPPLEMENTAL MATERIAL

Supplemental Methods

Study subjects

The study subjects were recruited from multiple hospitals in Japan. Lone AF was defined as AF

occurring in individuals < 65 years of age, who did not present with hypertension, overt

structural heart disease, myocardial infarction, congestive heart failure, or thyroid dysfunction.

Paroxysmal AF was defined as AF that lasted > 30 s and terminated spontaneously. Persistent

AF was defined as AF that lasted > 7 days and required either pharmacological therapy or

electrical cardioversion for termination. AF refractory to cardioversion or where cardioversion

was not attempted was classified as permanent. Two hundred and fifty healthy Japanese

subjects with no history of the cardiovascular disease described above were also included in

this study. Data from the NHLBI Exome Sequencing Project (ESP) Exome Variant Server

(EVS)1 and the Exome Aggregation Consortium (ExAC) data and browser2 were used as

reference groups.

The study observed the principles outlined in the Declaration of Helsinki and was

approved by the Ethics Committee for Medical Research at our institution. All study patients

provided written informed consent before registration.

DNA isolation and mutation analysis and genotype−phenotype relations

Genomic DNA was extracted from peripheral blood leukocytes using standard methods.

Genomic DNA was amplified using a standard polymerase chain reaction (PCR) method.

High-resolution melting curve analysis (MCA) was employed to screen KCNA5, KCNQ1,

KCNH2, SCN5A, SCN1B, SCN2B, SCN3B, KCNE1, KCNE2, KCNJ5, GJA5, and NPPA, using

a LightScanner (BioFire Defense, Salt Lake City, UT). Samples in which the melting curve

deviated from the wild-type (WT) control were subjected to DNA sequencing using an ABI

http://exac.broadinstitute.org/about

2

PRISM 310 Genetic Analyzer. For detection of deletion or insertion mutations, we performed

TA cloning, using the TOPO TA cloning kit (Life Technologies, Carlsbad, CA), and

sequenced the normal and mutant alleles separately.3 To test whether the mutations that serve

as the basis of this study were also present in control subjects, restriction enzyme analysis or

DNA sequencing analysis was performed. The relationship between the clinical phenotype

(AF) and the genotype was determined for probands and their relatives in whom a variant was

identified.

Plasmid constructs and transfection of mammalian cell lines

The KCNA5 cDNA in the mammalian expression vector pBK-CMV, the KCNQ1 cDNA in the

pIREShyg vector, the KCNE1 cDNA in the IRES2-EGFP vector, the hERG (KCNH2) cDNA

in the pSI vector, the SCN5A cDNA in the pCGI vector, and SCN1B cDNA in the IRGFP vector

were kindly provided by Dr. Alfred L. George Jr. and Dr. Sabina Kupershmidt (Vanderbilt

University, Nashville, TN). Mutant cDNAs were constructed using an overlap extension

strategy4 or using a QuikChange XL Site-Directed Mutagenesis Kit (Agilent Technologies,

Santa Clara, CA).

CHO-K1 or HEK293 cells were transiently transfected with wild-type (WT) cDNA or

mutant (MT) cDNA, using X-tremeGENE 9 DNA Transfection Reagent (Roche Applied

Science, Penzberg, Germany). Cells were cotransfected with the same amount of green

fluorescent protein (GFP) as each ion channel cDNA. Cells displaying green fluorescence at

48–72 h after transfection were subjected to electrophysiological analysis.

Electrophysiology and data analysis

http://app.roche-biochem.jp/index.html

http://app.roche-biochem.jp/index.html

3

Potassium or sodium currents were studied using the whole-cell patch clamp technique with

an amplifier, Axopatch-200B (Molecular Devices, Sunnyvale, CA), at room temperature

(23-25°C for potassium currents and 20–22°C for sodium currents). Electrode resistance

ranged from 2 to 4 MΩ for potassium channel recordings and from 0.8 to 1.5 MΩ for sodium

channel recordings. The voltage clamp protocols are described in the Figures. Data were

acquired using pCLAMP software (v. 9; Molecular Devices, Sunnyvale, CA). Data acquisition

and analysis were performed using a Digidata 1321 A/D converter and pCLAMP8.2 software

(Molecular Devices, Sunnyvale, CA).

While recording potassium currents, the pipette solution (intracellular solution)

contained 110 mM KCl, 5 mM K2ATP, 2 mM MgCl2, 10 mM HEPES, and 5 mM K4BAPTA

at pH 7.2, and the bath solution contained 140 mM NaCl, 5.4 KCl mM, 2 mM CaCl2, 1.0

mM MgCl2, 10 mM HEPES, and 10 mM glucose, adjusted to pH 7.4 with NaOH. While

recording sodium currents, the pipette solution (intracellular solution) contained 10 mM NaF,

110 mM CsF, 20 mM CsCl, 10 mM EGTA, and 10 mM HEPES at pH 7.35 with CsOH. Two

types of bath solutions were prepared, depending on the mutant sodium channels present. The

loss of function SCN5A variant, R986Q, was evaluated by using the standard Tyrode solution

(130 mM NaCl, 2 mM CaCl2, 5 mM CsCl, 1.2 mM MgCl2, 10 mM HEPES, and 5 mM

glucose, adjusted to pH 7.4 with CsOH). The gain-of-function SCN1B variant, T189M, was

evaluated using a 36 mM NaCl Tyrode solution, which contained 36 mM NaCl, 10.9 mM

N-methyl-D-glucamine, 4 mM KCl, 1.8 mM CaCl2, 1 mM MgCl2, and 10 mM HEPES,

4

adjusted to pH 7.35 with HCl.

The voltage dependence of potassium current activation was determined for each

cell by fitting peak values of tail current (Itail) versus test potential to a Boltzmann function in

the following form: Itail=Itail-max /｛1+ exp[(V1/2-Vt) / k]｝, where Itail-max is peak Itail, Vt is the

test potential, V1/2 is the voltage at which Itail is half of Itail-max, and k is the slope factor5.

Steady-state inactivation of the hERG current was analyzed as described previously6. Briefly,

the corrected steady-state inactivation curves were fitted with a Boltzmann function in the

following form: I / (Imax- Imin) =1 /｛1+ exp[(Vt - V1/2) / k]｝+ Imin, where I is the amplitude of

the inactivating current corrected for deactivation, Imax is the maximum of I, Imin is the

minimum of I, Vt is the prepulse of test potential, V1/2 is the voltage at which I is half of Imax,

and k is the slope factor. Rates of deactivation of hERG channels were measured using a

two-step voltage protocol and by fitting tail currents with two exponential functions5.

The parameters for voltage dependence of sodium current activation were estimated

from the current−voltage relationship based on the Boltzmann equation I = Gmax × (V – Vrev)

× (1 + exp[V – V1/2] / k)–1, where I is the peak Na current during the test pulse potential V.

The parameters estimated by the fitting are Gmax (maximum conductance), Vrev (reversal

potential), and k (slope factor)7. Steady-state availability for fast inactivation was measured

with a standard double-pulse protocol, and the data were fit with the Boltzmann equation

I/Imax = (1 + exp[(V – V1/2) / k])–1, where Imax is the maximum peak Na current, to determine

the membrane potential for V1/2 and k7.

5

In silico prediction analysis

A total of five prediction tools were applied in order to predict the pathogenicity of lone

AF-associated variants. The PolyPhen algorithm was used to analyze nonsynonymous

variants at the structural level.8-10 This program calculates position-specific independent

count (PSIC) scores for each of the two variant alleles and then computes the PSIC score

difference between them. A PSIC score difference of 1.5 and above is considered to be

damaging. Grantham chemical scores (range: 5–215) were calculated in order to assess the

physicochemical properties of variants, as previously described.9, 11 Nonsynonymous variants

were considered as radical (Grantham value ≥ 100) or conservative (Grantham value < 100).

Sorting intolerant from tolerant (SIFT) analysis was used to predict whether an amino acid

substitution in a protein would affect protein function.9, 10, 12, 13 The cutoff value in the SIFT

program is a tolerance index of ≥ 0.05. The higher the tolerance index, the less functional

impact a particular amino acid substitution is likely to have.10 The protein variation effect

analyzer (PROVEAN) was applied in order to predict the functional effects of protein

sequence variations, including single or multiple amino acid substitutions, and in-frame

insertions and deletions.14 If the PROVEAN score was smaller than or equal to a predefined

threshold of -2.5, the variation was predicted as being deleterious. In addition to the

above-mentioned individual prediction tools, Combined Annotation Dependent Depletion

(CADD) scores, which objectively integrate many diverse annotations into a single measure

6

(C score)15 were also assessed for each variant. A scaled C-score of ≥ 10 indicated that the

variant is predicted to be among the 10% most deleterious substitutions that can occur in the

human genome.

7

Supplemental Tables

Supplemental Table 1. Channel properties of mutant KV1.5 and KV7.1 channels Activating current density (pA/pF) Tail current density (pA/pF) Activation

V1/2 (mV) k (mV) KCNA5 H463R WT 619 ± 131 at 40 mV (n=20) 87 ± 20 at 30 mV (n=20) -7.0 ± 1.1 (n=17) 4.4 ± 0.7 (n=17) H463R 49 ± 11 at 40 mV (n=9)* 8 ±2 at 30 mV (n=9)† NA NA WT+H463R 182 ± 38 at 40 mV (n=15)* 28 ± 12 at 30 mV (n=15)† -5.5 ± 1.3 (n=8) 3.0 ± 0.3 (n=8) KCNA5 T527M WT 587 ± 76 at 10 mV (n=43) 97 ± 14 at 0 mV (n=43) -6.8 ± 0.5 (n=33) 4.2 ± 0.2 (n=33) T527M 940 ± 151 at 10 mV (n=47)† 166 ± 31 at 0 mV (n=47)† -11.4 ± 0.8 (n=42)* 4.0 ± 0.4 (n=42) KCNQ1 L492_E493 ins DL WT 331 ± 47 at 60 mV (n=16) 78 ± 10 at 60 mV (n=16) 20.5 ± 3.1 (n=13) 15.1 ± 0.8 (n=13) L492_E493 ins DL 329 ± 62 at 60 mV (n=19) 77 ± 13 at 60 mV (n=19) 17.8 ± 4.9 (n=11) 12.7 ± 1.0 (n=11) *P<0.01 vs WT; †P<0.05 vs WT; NA, not applicable

8

Supplemental Table 2. Channel properties of mutant KV11.1 channels Activating current

density (pA/pF) Tail current

density (pA/pF) Activation Steady-state

inactivation Deactivation

V1/2 (mV) k (mV) V1/2 (mV) k (mV) τfast (ms) τslow (ms) KCNH2 T436M WT 13.0 ± 1.4

at 20 mV (n=35) 24.4 ± 3.5

at 60 mV (n=33) 1.3 ± 1.50

(n=35) 8.2 ± 0.4 (n=35)

-58.8 ± 2.8 (n=11)

19.3 ± 0.7 (n=11)

151 ± 12 at -70 mV (n=26)

601 ± 39 at -70 mV (n=23)

T436M 20.2 ± 3.1 at 20 mV (n=36)†

35.0 ± 5.1 at 60 mV (n=36)

2.7 ± 1.5 (n=36)

8.5 ± 0.2 (n=36)

-52.3 ± 4.1 (n=12)

21.5 ± 1.0 (n=12)

262 ± 359 at -70 mV (n=15)*

1104 ± 234 at -70 mV (n=15)*

KCNH2 T895M WT 15.9 ± 2.9

at 20 mV (n=18) 21.8 ± 4.4

at 30 mV (n=17) -2.6 ± 2.0

(n=10) 7.0 ± 0.3 (n=10)

-54.4 ± 2.4 (n=9)

15.5 ± 0.9 (n=9)

199 ± 23 at -40 mV (n=11)

1212 ± 111 at -40 mV (n=12)

T895M 31.5 ± 5.9 at 20 mV (n=18)†

45.4 ± 7.2 at 30 mV (n=18)*

-4.9 ± 3.1 (n=13)

7.1 ± 0.2 (n=13)

-56.6 ± 2.7 (n=11)

15.5 ± 0.6 (n=11)

304 ± 39 at -40 mV (n=10)†

1733 ± 197 at -40 mV (n=12)†

*P<0.01 vs WT; †P<0.05 vs WT.

9

Supplemental Table 3. Channel properties of mutant NaV1.5 channel and mutant NaVβ1 subunit Peak current density (pA/pF) Activation Steady-state inactivation

V1/2 (mV) k (mV) V1/2 (mV) k (mV) SCN5A R986Q WT -1158 ± 99 at -20 mV (n=17) -43.8 ± 1.7 (n=16) -5.2 ± 0.2 (n=16) -80.0 ± 0.6 (n=16) 5.4 ± 0.1 (n=16) R986Q -617 ± 97 at -20 mV (n=17)* -42.5 ± 1.7 (n=17) -5.1 ± 0.2 (n=17) -80.2 ± 1.0 (n=16) 5.5 ± 0.2 (n=16) SCN1B T189M WT -240 ± 34 at -30 mV (n=16) -31.8 ± 1.6 (n=16) -8.0 ± 0.4 (n=16) -74.9 ± 2.0 (n=16) 8.8 ± 0.3 (n=16) T189M -463 ± 100 at -30 mV (n=16)† -38.6 ± 2.5 (n=16)† -6.3 ± 0.6 (n=16)† -75.5 ± 2.2 (n=15) 9.0 ± 0.2 (n=15) *P<0.01 vs WT; †P<0.05 vs WT.

10

Supplemental Figures

11

12

13

14

Supplemental Figure Legends

Supplemental Figure 1. Functional characterization of KV7.1 L492_E493insDL in

CHO-K1 cells.

(A) Representative currents generated in CHO-K1 cells that had been cotransfected with 1 µg

of a vector encoding KCNQ1 WT and co-expressed 1 µg of a vector encoding KCNE1 (left),

or 1 µg of mutant KCNQ1 co-expressed with 1 µg of KCNE1 (right). Pulse protocol is shown

in the inset. (B) and (C) I–V relationships for peak currents and tail currents of Kv7.1 WT +

KCNE1 (closed circle, n = 14) and Kv7.1 mutant + KCNE1 (closed triangle, n = 14). (D)

Mean amplitudes of normalized tail currents for Kv7.1 WT + KCNE1 (closed circle, n = 13)

and Kv7.1 mutant + KCNE1 (closed triangle, n = 11).

Supplemental Figure 2. Functional characterization of KV11.1 T436M in CHO-K1 cells.

(A) Representative currents generated in CHO K1 cells that had been transfected with 1 µg of

KCNH2 WT alone (left) or 1 µg of KCNH2 T436M alone (right). (B) and (C) I–V

relationships for peak currents (B) and tail currents (C) in CHO-K1 cells transfected with WT

alone (closed circle, n = 35) and T436M (closed triangle, n = 36). (D) Mean amplitudes of

normalized tail currents for WT alone (closed circle, n = 35) and T436M (closed triangle, n =

35). (E) Deactivation time constants of KV11.1 currents. Deactivation time constants of WT

(fast component: open circle, n = 27; slow component: closed circle, n = 27) and T436M (fast

component: open triangle, n = 16; slow component: closed triangle, n = 16) are shown. (F)

Normalized steady-state inactivation curves of WT (closed circle, n = 11) and T436M (closed

triangle, n = 12). *P < 0.05 or †P < 0.0001 between WT and T436M by two-way repeated

measures ANOVA.

15

Supplemental Figure 3. Functional characterization of NaV1.5 R986Q + NaVβ1 in

CHO-K1 cells.

(A) Representative currents generated in CHO-K1 cells that had been transfected with 0.6 µg

each of vectors encoding the WT NaV1.5 channel and NaVβ1 subunit (left) or 0.6 µg each of

vectors encoding the mutant NaV1.5 channel and NaVβ1 subunit (right). (B) I–V

relationships for peak currents of NaV1.5 WT + NaVβ1 (closed circle, n = 17) or NaV1.5

R986Q + NaVβ1 (closed triangle, n = 17). (C) and (D) The voltage dependence of

steady-state activation and fast inactivation of NaV1.5 WT + NaVβ1 (closed circle, n = 17) or

NaV1.5 R986Q + NaVβ1 (closed triangle, n = 17), measured with standard pulse protocols.

*P < 0.001 between the indicated current−voltage relationships by two-way repeated

measures ANOVA.

Supplemental Figure 4. Rare and potentially deleterious variants of 12 AF-associated

genes in the global reference database.

Number of variants (A) and alternate alleles (B) with a MAF < 1% and CADD score > 10 are

illustrated for 12 AF-associated genes in 61,486 unrelated individuals in the ExAC data and

browser.

16

Supplemental References

1. NHLBI Exome Sequencing Project (ESP). Exome Variant Server Web site.

http://evs.gs.washington.edu/EVS/. Accessed 01-02-2015

2. ExAC Browser (Beta) | Exome Aggregation Consortium Web site.

http://exac.broadinstitute.org/. Accessed 01-02-2015.

3. Hayashi K, Fujino N, Uchiyama K, Ino H, Sakata K, Konno T, Masuta E, Funada A,

Sakamoto Y, Tsubokawa T, Nakashima K, Liu L, Higashida H, Hiramaru Y, Shimizu M,

Yamagishi M. Long QT syndrome and associated gene mutation carriers in Japanese

children: results from ECG screening examinations. Clin Sci (Lond). 2009;117:415-424.

4. Liu L, Hayashi K, Kaneda T, Ino H, Fujino N, Uchiyama K, Konno T, Tsuda T,

Kawashiri MA, Ueda K, Higashikata T, Shuai W, Kupershmidt S, Higashida H,

Yamagishi M. A novel mutation in the transmembrane nonpore region of the KCNH2

gene causes severe clinical manifestations of long QT syndrome. Heart Rhythm.

2013;10:61-67.

5. Hayashi K, Shimizu M, Ino H, Yamaguchi M, Mabuchi H, Hoshi N, Higashida H.

Characterization of a novel missense mutation E637K in the pore-S6 loop of HERG in a

patient with long QT syndrome. Cardiovasc Res. 2002;54:67-76.

6. Smith PL, Baukrowitz T, Yellen G. The inward rectification mechanism of the HERG

cardiac potassium channel. Nature. 1996;379:833-836.

7. Makita N, Behr E, Shimizu W, Horie M, Sunami A, Crotti L, Schulze-Bahr E, Fukuhara

S, Mochizuki N, Makiyama T, Itoh H, Christiansen M, McKeown P, Miyamoto K,

Kamakura S, Tsutsui H, Schwartz PJ, George AL, Jr., Roden DM. The E1784K mutation

in SCN5A is associated with mixed clinical phenotype of type 3 long QT syndrome. J

Clin Invest. 2008;118:2219-2229.

8. Ramensky V, Bork P, Sunyaev S. Human non-synonymous SNPs: server and survey.

Nucleic Acids Res. 2002;30:3894-3900.

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9. Giudicessi JR, Kapplinger JD, Tester DJ, Alders M, Salisbury BA, Wilde AA, Ackerman

MJ. Phylogenetic and physicochemical analyses enhance the classification of rare

nonsynonymous single nucleotide variants in type 1 and 2 long-QT syndrome. Circ

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10. Sudandiradoss C, Sethumadhavan R. In silico investigations on functional and haplotype

tag SNPs associated with congenital long QT syndromes (LQTSs). Genomic Med.

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11. Grantham R. Amino acid difference formula to help explain protein evolution. Science.

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12. Ng PC, Henikoff S. Accounting for human polymorphisms predicted to affect protein

function. Genome Res. 2002;12:436-446.

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on protein function using the SIFT algorithm. Nat Protoc. 2009;4:1073-1081.

14. Choi Y, Sims GE, Murphy S, Miller JR, Chan AP. Predicting the functional effect of

amino acid substitutions and indels. PLoS One. 2012;7:e46688.

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