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University of Groningen

Matters of the heart: genetic and molecular characterisation of cardiomyopathies Posafalvi, Anna

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Matters of the heart: genetic and molecular characterisation of cardiomyopathies

Pósafalvi Anna

The work described in this thesis was supported by the University Medical Center Groningen, the Jan Kornelis de Cock Foundation, the NutsOhra Foundation and the Netherlands Heart Foundation. Printing of this thesis was supported by the Graduate School of Medical Sciences and the University Library, University of Groningen, Groningen, the Netherlands.

Copyright: © 2015 by Anna Posafalvi All rights reserved. No parts of this book may be reproduced, stored in retrieval system, or transmitted in any form or by any means without prior written permission of the author and the publishers holding the copyrights of the published articles.

Cover photograpy: ©Anna Posafalvi Design: dreamed of by Anna Posafalvi, dreams made come true by Joanna Smolonska Layout and printing:

Lovebird Design & Printing Solutions

ISBN: 978-90-367-7767-4 (printed) 978-90-367-7766-7 (electronic)

Matters of the heart: genetic and molecular characterisation of cardiomyopathies

PhD thesis

to obtain the degree of PhD at the University of Groningen on the authority of the Rector Magnificus Prof. E. Sterken and in accordance with the decision by the College of Deans. This thesis will be defended in public on Monday 20 April 2015 at 16:15 hours

by

Anna Posafalvi born on 17 June 1986 in Debrecen, Hungary

Supervisor Prof. RJ Sinke Co-supervisor Dr. JDH Jongbloed Assessment committee Prof. MH Breuning Prof. RA de Boer Prof. RMW Hofstra

Drága Nagyapámnak... To my dearest grandfather...



“Wheresoever you go, go with all your heart.” (Confucius)

Paranymphs Ena Sokol Eva Teuling

TABLE OF CONTENTS Preface: …about cardiomyopathies in a nutshell

9

Outline of this thesis

15

Appendix 1 List of genes Frequently used abbreviations

16 18

Chapter 1: Introduction New clinical molecular diagnostic methods for congenital and inherited heart disease (Expert Opin Med Diagn 2011, review)

23

Chapter 2: Candidate gene screening 2.1: Mutational characterisation of RBM20 in dilated cardiomyopathy and other cardiomyopathy subtypes

53

2.2: Missense variants in the rod domain of plectin increase susceptibility to arrhythmogenic right ventricular cardiomyopathy

79

Chapter 3: Exome sequencing 3.1: Hunting for novel disease genes in autosomal dominant cardiomyopathies: elucidating a role for the sarcomeric pathway

109

3.2: Homozygous SOD2 mutation as a cause of lethal neonatal dilated cardiomyopathy

139

3.3: One family, two cardiomyopathy subtypes, three disease genes: an intriguing case

157

Chapter 4: Targeted sequencing 4.1: Gene-panel based Next Generation Sequencing (NGS) substantially improves clinical genetic diagnostics in inherited cardiomyopathies

173

4.2: Titin gene mutations are common in families with both peripartum cardiomyopathy and dilated cardiomyopathy (Eur Heart J 2014)

209

Chapter 5: Discussion and future perspectives

233

Summary

251

Appendix 2 List of authors and affiliations About the author

268 270

Acknowledgements

272

PREFACE

…about cardiomyopathies in a nutshell The disease Cardiomyopathy is an insidious disease of the heart muscle (myocardium) leading to decreased pumping capacity, and resulting in a wide range of symptoms. These range from mild (dizziness, fatigue, chest pain or oedema) to severe (heart failure, arrhythmia, embolism, or even sudden death).

Figure 1. Schematic cross-section of a healthy heart (a) and hearts with DCM (b), HCM (c) and ARVC (d) In dilated cardiomyopathy (DCM), the left ventricle becomes enlarged with a thin, weakened muscle wall, and is unable to generate enough pumping force during contractions; the myocardium is thickened in hypertrophic cardiomyopathy (HCM); in arrhythmogenic right ventricular cardiomyopathy (ARVC) fibrofatty infiltration of the myocardium leads to arrhythmia. Figure published by Wilde & Behr, Nature Reviews Cardiology, 2013; used with permission. For more information on cardiomyopathy types, see box 1.



THE DISEASE

9

Clinical diagnosis and treatment guidelines When the symptoms of cardiomyopathy appear, the diagnosis of the disease is most frequently made by electrocardiogram (ECG), and noninvasive imaging techniques such as an X-ray of the chest, echocardiography (imaging of the heart with ultrasound), or MRI. In addition, patients receive a general medical examination combined with a simple blood test (measuring, for instance, molecular markers of heart failure or kidney function). Less regularly, cardiac catheterization or coronary angiography is used. Both these methods are “minimally invasive”, only a thin tube is inserted in one of the biggest veins of the body and threaded to the heart, instead of an open surgery. These help physicians acquire either a myocardial biopsy for further experimental analysis or enough information to exclude potential blockage (stenosis) of the heart and the coronary blood vessels. The therapy for cardiomyopathy largely depends on the disease type and the severity of the symptoms. Therapy aims at slowing down the progression of the disease or at disease prevention in susceptible individuals through life style changes and medical treatment using different antihypertensive, antiarrhythmic, diuretic or anticoagulant drugs (e.g., ACE inhibitors or calcium antagonists). In serious cases of arrhythmia, the implementation of an ICD (a small, implantable defibrillator) or a pacemaker may be the solution. Heart transplantationisonlyconsideredasalastresortinpatientswithend-stageheartfailure. The genetic causes Even though there are several environmental factors that may trigger the onset of cardiomyopathy (viral infections, the use of certain drugs, alcoholism, and other cardiovascular conditions, as well as certain systemic disorders), we often see the disease running in families (30-50% of ARVC and DCM cases; see box 1 for definitions). Most of these familial cardiomyopathy cases can be explained by an autosomal dominant (AD) inheritance pattern. To date, about 76 genes are known to be involved in different types of cardiomyopathy, which often show considerable genetic overlap (figure 2), and the majority of these 76 genes show AD inheritance. Additionally, a few genes, such as DMD, EMD, GLA, LAMP2, or TAZ, are involved in the X-linked form of the disease. Exceptionally, autosomal recessive inheritance is also observed. These patients usually exhibit more severe symptoms, and the disease generally begins in infancy or early childhood (paediatric cardiomyopathies; the genes involved include ANO5, MYL2, PKP2, TNNI3).

10

CLINICAL DIAGNOSIS AND TREATMENT GUIDELINES

PREFACE

Although it is also known that abnormalities in mitochondrial DNA can contribute to the pathogenesis of different cardiomyopathies (e.g., mutations of MTTL1), this has not yet been extensively studied. The possible complex, oligogenic or multifactorial causes for cardiomyopathies have also not been investigated in detail, nor have the potential roles of risk alleles of lower effect size, copy number variations (such as those including the BAG3 or PRDM16 genes), or microRNAs. To date, a significant proportion of familial cardiomyopathies (about 30-40% of HCM, 40-50% of ARVC, and around 50% of DCM cases; see box 1 for definitions) remain genetically unexplained.

Figure 2. Cardiomyopathy disease genes and the genetic overlap between subtypes of the disease (updated from Jongbloed at el, EOMD 2011) Not only is there considerable phenotypic overlap between the subtypes of cardiomyopathy, many genes are also involved in multiple forms of the disease. The official full names of the abbreviated genes, according to OMIM, are listed in appendix 1.



THE GENETIC CAUSES

11

Types of cardiomyopathy There are various forms of cardiomyopathy, each with different underlying causes for the insufficient circulation. The cardiomyopathies investigated in this thesis include: 1. dilated cardiomyopathy (DCM): one or both of the ventricles (in most cases only the left one) become enlarged with a thin, weakened muscle wall unable to generate enough pumping force during contractions (figure 1) 2. arrhythmogenic right ventricular cardiomyopathy (ARVC): the replacement of the degenerating myocardium with scar (fibrofatty) tissue results in disturbed electrical signals and conduction in the heart (arrhythmia) 3. hypertrophic cardiomyopathy (HCM): a thickened myocardium due to abnormal growth and arrangement (hypertrophy and disarray) of muscle fibres results in smaller chamber volume and sometimes blocks the blood flow (obstruction) 4. restrictive cardiomyopathy (RCM): due to their stiffness, the ventricles do not get refilled with enough blood during relaxation, hence the heart cannot supply the organs with sufficient circulation during contraction 5. left-ventricular non-compaction cardiomyopathy (LVNC): the wall of the left ventricle is spongiform, characterized by a meshwork of muscle fibres 6. peripartum cardiomyopathy (PPCM): a special form of dilated cardiomyopathy that becomes manifest towards the end of pregnancy or within a few months following delivery 7. paediatric cardiomyopathy: this type of cardiomyopathy becomes manifest in infancy or early childhood, and is usually characterized by more severe symptoms and worse outcomes than when the disease manifests in adulthood (from a structural-functional point of view, most frequently it is DCM>HCM>RCM>ARVC)

Our methods Candidate gene screening • Sanger-sequencing: This method of DNA-sequencing allows us to detect single nucleotide changes and small indels of DNA fragments with an average size of 400-500 base pairs. It can be used for screening candidate genes in a large cohort of patients, as well as for segregation analysis of a variant within a family, or for confirmation of DNA-variations detected by high-throughput sequencing. Disease gene mapping • haplotype sharing test (HST): An ideal, SNP-genotyping-based method for small cardiomyopathy families, who are usually not suitable for classical linkage analysis. With this method, we aim to identify chromosomal regions shared among affected family members, hypothesizing that the highest chance of finding the mutation is in the largest shared region of the family. We use this method as a filtering step in exome sequencing data analysis – if a variant is located in the 2nd largest shared haplotype of 10 cM, it is more likely to be causative than a variant located in the 57th largest shared haplotype of only 0.1 cM. High-throughput sequencing • exome sequencing: Sequencing all coding parts (exons) of all genes (about 1% of the genome). Though costly and requiring intensive data analysis, this method is suitable for identifying private coding mutations of novel disease genes in families with an unknown genetic cause of cardiomyopathy. • gene-panel based (targeted) sequencing: High-throughput sequencing of a DNA sample previously enriched for the small set of genes we are interested in. Since this method results in very high coverage across the regions of interest and high data quality, it has recently been implemented in routine diagnostics.

12

THE GENETIC CAUSES

PREFACE

The challenges we face Identifying a novel disease gene carrying the heterozygous causal variant (heterozygous because of the dominant inheritance) is usually more challenging than working on a recessive disease, but there are also other complications to be considered in our research. Cardiomyopathy is, in general, a late onset disease. For example, DCM usually begins between 20 and 50 years of age, while most ARVC patients are diagnosed before 40 years of age. Thus, low penetrance of the disease at young age makes it difficult to make the genetic diagnosis in a family as the disease status of young relatives is uncertain (partly due to the variety in the nature and severity of the symptoms). Furthermore, phenocopies also occur, with family members having comparable symptoms due to an independent cause (e.g. developing disease on the basis of another, often non-genetic, cardiovascular event: coronary artery disease). In consequence, the medical diagnosis of cardiomyopathy is based on exclusion criteria and performing segregation analysis for a putative pathogenic variant in families without being absolutely sure of the healthy/affected status of the screened individuals can be complicated. Since cardiomyopathy can be so difficult to diagnose, and because the chances of a successful treatment rapidly decline with time, our aims are (1) to obtain an early (molecular) diagnosis of the inherited form of the disease before severe symptoms become manifest, and (2) to enable preventive treatment (including life-style changes as well as medical treatment if necessary) of the endangered individuals, combined with regular, thorough cardiological check-ups.



THE CHALLENGES WE FACE

13

Recommended literature website of the National Heart, Lung, and Blood Institute, health topic on cardiomyopathies: http://www. nhlbi.nih.gov/health/health-topics/topics/cm/ website of the Children’s Cardiomyopathy Foundation: http://www.childrenscardiomyopathy.org/site/ main_brochure.htm Herschberger RE, Lindenfeld J, Mestroni L et al: Genetic evaluation of cardiomyopathy – a Heart Failure Society of America guideline. J Cardiac Fail 2009;15:83-97 Wilde AA & Behr ER: Genetic testing for inherited cardiac disease. Nat Rev Cardiol 2013;10:571-83 Teekakirikul P, Kelly MA, Rehm HL et al: Inherited cardiomyopathies – Molecular genetics and clinical genetic testing in the postgenomic era. J Mol Diagn 2013;15:158-70 Jongbloed JD, Pósafalvi A, Kerstjens-Frederikse WS et al: New clinical molecular diagnostic methods for congenital and inherited heart disease. Expert Opin Med Diagn 2011;5:9-24 Posafalvi A, Herkert JC, Sinke RJ et al: Clinical utility gene card for: dilated cardiomyopathy (CMD). Eur J Hum Genet 2013;21 doi: 10.1038/ejhg.2012.276 Te Rijdt WP, Jongbloed JD, de Boer RA et al: Clinical utility gene card for: arrhythmogenic right ventricular cardiomyopathy (ARVC). Eur J Hum Genet 2014;22. doi: 10.1038/ejhg.2013.124 Udeoji DU, Philip KJ, Morrissey RP et al: Left ventricular noncompaction cardiomyopathy: updated review. Ther Adv Cardiovasc Dis 2013;7:260-73 Caleshu C, Sakhuja R, Nussbaum RL et al: Furthering the link between the sarcomere and primary cardiomyopathies: restrictive cardiomyopathy associated with multiple mutations in genes previously associated with hypertrophic or dilated cardiomyopathy. Am J Med Genet A 2011;155A:2229-35 Peled Y, Gramlich M, Yoskovitz G et al: Titin mutation in familial restrictive cardiomyopathy. Int J Cardiol 2014;171:24-30 Wooten EC, Hebl VB, Wolf MJ et al: Formin homology 2 domain containing 3 variants associated with hypertrophic cardiomyopathy. Circ Cardiovasc Genet 2013;6:10-8 Chang B, Nishizawa T, Furutani M et al: Identification of a novel TPM1 mutation in a family with left ventricular noncompaction and sudden death. Mol Genet Metab 2011;102:200-6 Luxán G, Casanova JC, Martínez-Poveda B et al: Mutations in the NOTCH pathway regulator MIB1 cause left ventricular noncompaction cardiomyopathy. Nat Med 2013;19:193-201 Purevjav E, Varela J, Morgado M et al: Nebulette mutations are associated with dilated cardiomyopathy and endocardial fibroelastosis. J Am Coll Cardiol 2010;56:1493-502 Arndt AK, Schafer S, Drenckhahn JD et al: Fine mapping of the 1p36 deletion syndrome identifies mutation of PRDM16 as a cause of cardiomyopathy. Am J Hum Genet 2013;93:67-77 Ohno S, Omura M, Kawamura M et al: Exon 3 deletion of RYR2 encoding cardiac ryanodine receptor is associated with left ventricular non-compaction. Europace 2014;16:1646-54 Pinto JR, Yang SW, Hitz MP et al: Fetal cardiac troponin isoforms rescue the increased Ca2+ sensitivity produced by a novel double deletion in cardiac troponin T linked to restrictive cardiomyopathy: a clinical, genetic, and functional approach. J Biol Chem 2011;286:20901-12 van Hengel J, Calore M, Bauce B et al: Mutations in the area composita protein αT-catenin are associated with arrhythmogenic right ventricular cardiomyopathy. Eur Heart J 2013;34:201-10 Pruszczyk P, Kostera-Pruszczyk A, Shatunov A et al: Restrictive cardiomyopathy with atrioventricular conduction block resulting from a desmin mutation. Int J Cardiol 2007;117:244-53

14

RECOMMENDED LITERATURE

OUTLINE OF THE THESIS

OUTLINE OF THIS THESIS The aims of this thesis are (1) to provide a better understanding of the genetic background and the molecular pathomechanism of familial cardiomyopathies, (2) to identify novel disease genes in unsolved families, and (3) to improve the existing methods of molecular diagnostic testing. Chapter 1 is a detailed introduction to the field of cardiogenetics. This chapter reviews congenital and late onset heart diseases (the latter referring to cardiomyopathies and arrhythmia syndromes), categorizes the genes involved in the different types of heritable heart diseases, and thoroughly describes the research methods with special attention paid to their potential future diagnostic applications in cardiovascular diseases. The subsequent chapters contain experimental data and are subdivided based on the research methods used. In chapter 2, we applied the classical candidate gene screening approach Sanger sequencing. We were interested if (and to what extent) the known DCM gene RBM20 contributes to the genetic background of the disease in Dutch patients (2.1). In addition, we hypothesized that the desmosomal PLEC gene may play a role in the development of ARVC. In an attempt to prove this, we studied the clustering of sequence variations in patients compared to that in a healthy control population (2.2). High-throughput sequencing is a recent technological development that is revolutionizing the science of genetics. We applied two different experimental designs of this method to elucidate genetic causes for cardiomyopathies. In chapter 3, we have described families where mutations in known cardiomyopathy genes had been excluded, and we successfully applied exome sequencing to identify novel disease genes in both autosomal dominant (3.1) and recessive (3.2) cardiomyopathies, while 3.3 is an interesting case report on a family suffering from both forms of the disease. In chapter 4, we applied targeted enrichment of DNA samples to a set of well-defined candidate disease genes. We address the applicability and the quantitative advantages of targeted sequencing in routine diagnostics for a cohort of 252 unselected cardiomyopathy patients in 4.1, while report our findings on targeted sequencing of PPCM/DCM families in 4.2. The work described in this thesis is then discussed in a broader context, and future perspectives for the use of high-throughput sequencing in research and diagnostic settings, as well as potential research directions in the field of cardiogenetics, are presented in chapter 5.

…ABOUT CARDIOMYOPATHIES IN A NUTSHELL

15

APPENDIX 1 List of cardiomyopathy genes: (official abbreviations and names of genes included in figure 2 of the preface) ABCC9 ACTC1 ACTN2 ANKRD1 ANO5 BAG3 CALR3 CAV3 CRYAB CSRP3 CTNNA3 DES DMD DOLK DSC2 DSG2 DSP DTNA EMD EYA4 FHL1 FHL2 FHOD3 FKRP FKTN FXN GATAD1 GLA ILK JPH2 JUP LAMA4 LAMP2 LDB3 LMNA MIB1 MT-TL1

16

APPENDIX 1

ATP-binding cassette, subfamily C (CFTR/MRP), actin, alpha, cardiac muscle 1 actinin, alpha 2 ankyrin repeat domain 1 (cardiac muscle) anoctamin 5 BCL2-associated athanogene 3 calreticulin 3 caveolin 3 crystalline, alpha B cysteine and glycine-rich protein 3 (cardiac LIM protein) catenin (cadherin-associated protein), alpha 3 desmin dystrophin dolichol kinase desmocollin 2 desmoglein 2 desmoplakin dystrobrevin, alpha emerin EYA transcriptional coactivator and phosphatase 4 four and a half LIM domains 1 four and a half LIM domains 2 formin homology 2 domain containing 3 fukutin related protein fukutin frataxin GATA zinc finger domain containing protein 1 galactosidase, alpha integrin-linked kinase junctophilin 2 junction plakoglobin laminin, alpha 4 lysosomal-associated membrane protein 2 LIM domain binding 3 lamin A/C mindbomb E3 ubiquitin protein ligase 1 mitochondrially encoded tRNA leucine 1 (UUA/G)

member

9

myosin-binding protein C, cardiac myosin, heavy chain 6, cardiac muscle, alpha myosin, heavy chain 7, cardiac muscle, beta myosin, light chain 2, regulatory, cardiac, slow myosin, light chain 3, alkali; ventricular, skeletal, slow myosin light chain kinase 2 myozenin 2 myopalladin nebulette nexilin (F actin binding protein) NK2 homeobox 5 mitochondrial DNA PDZ and LIM domain 3 plakophilin 2 phospholamban PR domain containing 16 protein kinase, AMP-activated, gamma 2 noncatalytic subunit presenilin 1 presenilin 2 protein tyrosine phosphatase, non-receptor type 11 Raf-1 proto-oncogene, serine/threonine kinase RNA binding motif protein 20 ryanodine receptor 2 (cardiac) sodium channel, voltage-gated, type V, alpha subunit succinate dehydrogenase complex, subunit A, flavoprotein (Fp) sarcoglycan, delta (35kDa dystrophin-associated glycoprotein) tafazzin T-box 20 titin-cap transforming growth factor, beta 3 transmembrane protein 43 thymopoietin troponin C type 1 (slow) troponin I type 3 (cardiac) troponin T type 2 (cardiac) tropomyosin 1 (alpha) titin transthyretin thioredoxin reductase 2 vinculin

LIST OF CARDIOMYOPATHY GENES

APPENDIX 1

MYBPC3 MYH6 MYH7 MYL2 MYL3 MYLK2 MYOZ2 MYPN NEBL NEXN NKX2-5 mtDNA PDLIM3 PKP2 PLN PRDM16 PRKAG2 PSEN1 PSEN2 PTPN11 RAF1 RBM20 RYR2 SCN5A SDHA SGCD TAZ TBX20 TCAP TGFB3 TMEM43 TMPO TNNC1 TNNI3 TNNT2 TPM1 TTN TTR TXNRD2 VCL

17

APPENDIX 1 Frequently used abbreviations:

18

ACE

angiotensin convertase enzyme

AD

autosomal dominant inheritance pattern

AGVGD

align Grantham variation Grantham distance prediction software for missense variants)

AR

autosomal recessive

ARVC

arrhythmogenic right ventricular cardiomyopathy

bp

base pair

CGH

comparative genomic hybridization

CHD

congenital heart disorders

cM

centimorgan

CNV

copy number variation

DCM

dilated cardiomyopathy

dbSNP

NCBI’s SNP database

DMEM

Dulbecco’s Modified Eagle Medium

DNA

deoxyribonucleic acid

EBS

epidermolysis bullosa simplex

ECG

electrocardiogram

ES

exome sequencing

(pathogenicity

ESP

exome sequencing project (variant database of the NHLBI)

E. coli

Escherichia coli

FBS

fetal bovine serum

GERP

genomic evolutionary rate profiling (a score indicating the evolutionary conservation of a nucleotide)

GoNL

Genome of the Netherlands (database of the genomes of 500 individuals, used as a frequency database of “the Dutch wild type”)

GWAS

genome-wide association study

HCM

hypertrophic cardiomyopathy

HEK

human embryonic kidney 293T cells

HF

heart failure

HiSeq

Illumina’s Next Generation Sequencer system

HLA

major histocompatibility complex genes

HST

haplotype-sharing test

H2 O

hydrogen oxide (water)

H2 O 2

hydrogen peroxide

ICD

implantable cardioverter-defibrillator

LDB3

LIM domain binding 3 gene

LSH

longest shared haplotype

APPENDIX 1

left ventricular non-compaction cardiomyopathy

MD

muscular dystrophy

MiSeq

Illumina’s “personal sequencer”, the “little sister” of the HiSeq system in benchtop size, with faster workflow, allowing the assembly of small genomes or target regions

MRI

magnetic resonance imaging

mRNA

messenger RNA

NCBI

National Center for Biotechnology Information

NGS

next generation sequencing

NHLBI

National Heart Lung and Blood Institue, a division of National Institutes of Health in the USA

OMIM

“Online Mendelian Inheritance in Man” – a comprehensive database of human genes and genetic phenotypes authored and edited by the Johns Hopkins University

PBS

phosphate buffered saline

PCR

polymerase chain reaction

PLEC

plectin

PolyPhen

Polymorphism phenotype (pathogenicity prediction software for missense variants)

PPCM

peripartum cardiomyopathy

RBM20

RNA binding motif protein 20

RCM

restrictive cardiomyopathy

ROS

reactive oxygen species

RNA

ribonucleic acid

RT

reverse transcription

SCD

sudden cardiac death

SIFT

sorting intolerant from tolerant (pathogenicity prediction software for missense variants)

SNP

single nucleotide polymorphism

SOD2

superoxide dismutase 2

TFC

task force criteria (diagnostic criteria of ARVC)

tRNA

transfer RNA

TTN

titin, the longest gene of the human genome

VOUS

variant of unknown significance

VUS

variant of unknown significance

1000G

1000 Genomes catalog of human genetic variation

FREQUENTLY USED ABBREVIATIONS

APPENDIX 1

LVNC

19

CHAPTER 1 INTRODUCTION

Chapter 1: Introduction

Novel clinical molecular diagnostic methods for congenital and inherited heart disease

Jan DH Jongbloed, Anna Posafalvi, Wilhelmina S Kerstjens-Frederikse Richard J Sinke, J Peter van Tintelen

Published in Expert Opinion on Medical Diagnostics, 2011

Importance of the field: For patients with inherited and congenital heart disorders, causative mutations are often not identified due to limitations of current screening techniques. Identifying the mutation is of major importance for genetic counseling of patients and families, facilitating the diagnosis in persons at-risk and directing clinical management. Next generation sequencing (NGS) provides unprecedented opportunities to maximize mutation yields and improve clinical management, genetic counseling and monitoring of patients. Areas covered in this review: We review recent NGS applications, focusing on methods relevant for molecular diagnostics in cardiogenetics. We discuss requirements for reliable implementation into clinical practice and challenges that clinicians, bioinfomaticians and molecular diagnosticians must deal with in analyzing resulting data. What the reader will gain: Readers will be introduced to recent developments, techniques and applications in NGS. They will learn about possibilities of using it in clinical diagnostics. They will become acquainted with difficulties and challenges in interpreting the data and considerations around communicating these issues to patients and the community. Take home message: Although several obstacles are to overcome and much still to learn, NGS will revolutionize clinical molecular diagnostics of inherited and congenital cardiac diseases, maximizing mutation yields and leading to optimized diagnostic and clinical care.

Keywords: cardiogenetics, molecular clinical diagnostics, next-generation sequencing, targeted enrichment, exome sequencing, inherited and congenital heart disease

Article highlights: 1. Novel clinical molecular diagnostic methods in cardiogenetic diagnostics are to be found in the field of Next generation sequencing (NGS) and novel applications that have recently become available with the launching of this technology will become part of daily diagnostic practice. 2. The main challenges of the implementation of NGS in daily diagnostic work are the assurance of good quality control and reliable data analysis and interpretation. 3. The most important consideration for clinical counseling will be the ascertainment of variants with uncertain clinical significance and the only reasonable way to deal with this problem is to pursue maximum data dissemination in the scientific community. 4. NGS provides unique solutions and will bring shorter reporting times, maximize mutation detection rates, and decrease costs if all the disease-related genes can be tested in parallel in a single experiment. 5. Despite the technological, bioinformatical and ethical problems, the use of NGS technology will lead to much improved and more effective diagnostic and preventive care for patients suffering from inherited and congenital heart disorders (CHD) and their relatives.

INTRODUCTION

NOVEL CLINICAL MOLECULAR DIAGNOSTIC METHODS

CHAPTER 1

What started in the 1950s with observations of cardiac diseases segregating in families and suggesting heritable disease [1][2], has led in the last 15 years to the identification of many disease-associated genes and mutations. Advances in cardiogenetics have exceeded the level of being scientifically interesting phenomena and have major implications in genetic counseling and in directing clinical therapy [3][4][5]. Not only the expanding possibilities in DNA analyses, but also the increased awareness among cardiologists, pediatric cardiologists, and general practitioners of the potential heritability of cardiac disease has led to growing numbers of patients being referred to departments of genetics and/or cardiogenetic outpatient clinics for genetic counseling and DNA diagnostics. Diseases for which patients attend the cardiogenetics outpatient clinic are primary arrhythmia syndromes [4], cardiomyopathies [6] or familial congenital heart disorders (CHD) [7][8]. Examples of arrhythmia syndromes are the congenital long QT syndrome (LQTS), Brugada syndrome or cathecholaminergic-induced polymorphic ventricular tachycardia (CPVT), which are all associated with sudden cardiac death (SCD) at relatively young age. Most patients with a cardiomyopathy present with hypertrophic (HCM) or dilated (DCM) cardiomyopathy. Arrhythmogenic right ventricular cardiomyopathy (ARVC), restrictive (RCM) and left ventricular noncompaction cardiomyopathies (LVNC) are encountered less frequently. Cardiomyopathies frequently present with output-failure leading to fatigue, however, arrhythmias and SCD may occur. Finally, examples of CHD that may be heritable include either valvular abnormalities, such as bicuspid aortic valve/aortic valve stenosis (BAV/AVS) or pulmonary valve stenosis (PVS), septal defects (like atrial or ventricular septal defects; ASD/VSD), endocardial cushion defects (atrio-ventricular septal defect: AVSD), vascular abnormalities, such as coarctation of the aorta (CoA) or persistent ductus arteriosus (PDA), and more complex abnormalities like hypoplastic left heart syndrome (HLHS), tetralogy of Fallot (TOF), or heterotaxy-related cardiac abnormalities like transposition of the great arteries. Notably, genetics of CHD’s becomes increasingly important, because due to the enormous development in surgical and cardiological care many of the 1 in 100 people born with a CHD survive to have offspring [9][10]. Interestingly, the boundaries between the different clinical entities are disappearing as overlapping clinical phenotypes are being recognized more frequently. For example, patients suffering from arrhythmia syndromes

25

have been reported to also developing a cardiomyopathy [4]. In addition, patients diagnosed with inherited CHD’s have been reported in which also a cardiomyopathy is identified [11][12]. As expected from this, both clinical and genetic heterogeneity is often being observed within these disorders (see also below). This review describes new developments in clinical molecular diagnostic methods for inherited and CHD’s, with a focus on the novel applications that have recently become available with the launching of NGS (NGS) technologies.

CURRENT CARDIOGENETIC DIAGNOSTICS In recent years, the relevance of genetic analyses in the genetic counseling and monitoring of patients and their family members having a cardiac disease with a proven, or at least suspected familial nature, has been increasingly recognized. Genetic analyses have therefore become an important part of the diagnostic activities to reach a clinical diagnosis in such patients. Since the first discovery of the MYH7 gene underlying HCM [13], a growing number of tests for heart-related disease have been introduced in DNA diagnostic laboratories worldwide, including array-CGH technology for the diagnostics of CHD’s. This is exemplified by the fact that at Orphanet, the European database for rare diseases and orphan drugs [14], and or GeneTests [15] websites, the mutation analyses for the majority of known genes related to inherited cardiomyopathies, arrhythmia syndromes and congenital structural cardiac disorders are being offered in at least one of the European laboratories (see also Table 1). Important to note however is that in most inherited cardiac diseases, the genetic cause has not identified yet. For example, in at most 50% of ARVC, 25% of DCM, 60% of HCM, 25% of LVNC and ~10% of RCM patients the underlying disease gene was found. The large number of genes related to these different groups of inherited cardiac diseases underscores the fact that the genetic causes of these disorders show a high level of heterogeneity. Moreover, some of the genes have proved to be mutated in different cardiac diseases. This concept was first recognized within specific disease entities. As a result, the fact that both HCM and DCM can be caused by mutations in genes encoding components of the sarcomere, the contractile machinery of cardiomyocytes, has been known since about the year 2000 [17]. However, the boundaries between the different cardiac diseases are also fading, as there is no longer a strict separation between cardiomyopathies and channelopathies due to recent observations that mutations in ion-channel

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and related proteins can also play a role in the pathogenesis of DCM [18][19]. Moreover, a genetic overlap between cardiomyopathies/channelopathies and inherited structural cardiac disorders has also been suggested. For example, several mutations in sarcomeric proteins have been described that resulted in congenital heart malformations (Table 1) [8]. In addition, mutations in the cardiac T-box factor gene TBX20 were shown to result in cardiomyopathies in both mice and human, among other cardiovascular abnormalities [11]. The phenomenon discussed above is exemplified in Figure 1 by showing the genetic heterogeneity and overlap in genes that underlie different types of cardiomyopathies (DCM, HCM, LVNC, ARVC, and RCM), including a few genes that are also known to be involved in channelopathies (RYR2, SCN5A and PRKAG2) or CHD’s (MYH7, MYBPC3). Finally, in addition to the significant heterogeneity of monogenic cardiac diseases, there is an emerging recognition that a significant proportion of patients carry two or more independent disease-causing gene mutations, which lead to more severe forms of clinical disease [20]. These might occur in the same gene (compound heterozygotes) or in different genes (bi- or multigenic). There may also be genetic modifiers present that are associated with a poorer prognosis. This concept and the fact that many genes might underlie a disease support the idea that large numbers of genes should be analyzed in parallel preferably within the same experiment in patients with inherited cardiac disorders to improve risk-assessment. Together, these observations imply that at least 110 genes are putative candidate disease genes in patients presenting at cardiogenetic outpatient clinics for a genetic diagnosis, since there are now ~60 cardiomyopathy [21], ~20 channelopathy [22], and ~30 CHD disease genes [8] known to be involved in the respective diseases (Table 1). Up to today, these genes have been analyzed at the nucleotide level on a gene-by-gene basis mainly. For this purpose, various pre-screening techniques like denaturing gradient gel electrophoresis (DGGE), denaturing high-performance liquid chromatography (dHPLC), single strand conformation polymorphisms analysis (SSCP), conformation-sensitive capillary electrophoresis (CSCE), or high-resolution melting analysis (HRM) are generally being used to screen for aberrant PCR-amplified DNA sequences. The abnormal PCR fragments are then subsequently analyzed by Sanger sequencing to identify the exact nucleotide substitutions [23]. However, in a considerable number of genetic laboratories, the preferred screening approach is direct dideoxy sequencing of all exonic and adjacent intronic sequences of genes of interest without using pre-screening methods.

27

Figure 1. Genetic heterogeneity and overlap in genes causing cardiomyopathies. Shown are genes underlying DCM, HCM, LVNC, ARVC, and RCM. Notably, some of these genes are also known to be involved in channelopathies and/or congenital heart malformation (based upon [16]). Genes also involved in congenital cardiac disease are indicated in bold. Genes also involved in channelopathies are underlined. The genes incorporated are: ABCC9 (ATP-sensitive potassium channel), ACTC1 (cardiac α-actin), ACTN2 (α-atinin-2), CALR3 (Calreticulin 3), CAV3 (caveolin 3), (CSRP-3 (muscle LIM protein), CRYAB (Alpha-B chrystallin) DES (desmin), DSG2 (desmoglein-2), DSC2 (desmocollin-2), DSP (desmoplakin), DTNA (dystobrevin), DMD (dystrophin), EMD (emerin), EYA4 (Eyes absent 4), GLA (α-galactosidase), ILK (Integrin-linked kinase), JPH2 (junctophilin) JUP (junctional plakoglobin), LAMA4 (laminin α4), LAMP2 (lysosome-associated membrane protein 2), LDB3 (cypher/ ZASP), LMNA (lamin A/C), mtDNA (mitochondrial DNA), MYBPC3 (myosin-binding protein C), MYH6 (α-myosin heavy chain), MYH7 (β-myosin heavy chain), MYL2 (regulatory myosin light chain), MYL3 (essential myosin light chain), MYPN (myopalladin), NEXN (nexilin), PDLIM (PDZ and LIM domain protein 3), PKP2 (plakophilin-2), PLN (phospholamban), PSEN1 (Presenilin-1), PSEN2 (Presenilin-2), PRKAG2 (AMPK-γ2 subunit), RBM20 (RNA binding motif protein 20), RyR2 (ryanodine receptor 2), SCN5A (cardiac sodium channel), TAZ (Tafazzin), TCAP (titincap/telethonin), TGFb3 (transforming growth factor β3), TMPO (thymopoietin), TNNC1 (cardiac troponin C), TNNI3 (cardiac troponin I), TNNT2 (cardiac troponin T), TPM1 (α -tropomyosin), TTN (titin), VCL (metavinculin).

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If available for the respective genes, multiplex ligation-dependent probe amplification is used to screen for the deletion and/or duplication of one or more exons, as these are not identified using PCR-based techniques [24]. Also in cardiogenetics, examples have been found in arrhythmia syndromes and cardiomyopathies [25][26]. However, since using these approaches is laborious, relatively expensive and time-consuming, DNA diagnostics is often limited to a maximum of ~10 putative disease genes, as health insurance companies are not prepared to reimburse many more gene tests, if at all. It is therefore often difficult to decide which genes should be screened in a specific patient. In general, the genes being analyzed are those for which considerable mutation yields are reported in the literature. If a genotype-phenotype relationship has been identified, gene selection will of course be guided by the phenotypes identified in the respective patients and their affected family members. For example, in a patient presenting with DCM and conduction disease, the LMNA, DES and SCN5a genes are among the first genes to analyze, while patients presenting with an inherited arrhythmia syndrome should first be screened for genes encoding the respective ion channel proteins. As already mentioned above, in general, genetic testing in current cardiogenetic diagnostics is often limited to and guided by knowledge on the most common causative genes (for an overview of genes: see Table 1). The best possibilities to come to a genetic diagnosis in cardiomyopathies are in HCM, as mutations in the MYH7 and MYBPC3 genes account for ~80% of the cases in which a genetic cause is identified [21]. In HCM, genetic testing is therefore often started with these genes and, in addition, in the TNNT2 gene. When no mutation is identified in these 3 genes, the most logical option would be to analyze the other sarcomeric genes (TNNI3, TNNC1, ACTC1, TPM1, MYL2, MYL3 and TTN. The latter is very rarely screened since it is the largest human gene known). Other genetic analyses, like that of genes encoding Z-disk proteins, are often not performed because reported mutation yields are A 10:112541217 T 2 (DCM; HCM) G309E c.926G>A 10:112541293 T 1 (HCM) V487M c.1459G>A 10:112544579 T 1 (HCM) V535L c.1603G>C 10:112557341 S 1 (DCM) V545I c.1633G>A 10:112557371 T 1 (DCM) R623Q c.1868G>A 10:112570208 T 1 (unsp) R632K c.1895G>A 10:112572050 T 1 (unsp) P633L c.1898C>T 10:112572053 T 1 (unsp) R634P c1901G>C 10:112572056 S/T 1 (DCM) R634W c1900C>T 10:112572055 S/T 3 (DCM) R636H c1907G>A 10:112572062 S 1 (PPCM/DCM) S637N c1910G>A 10:112572065 T 1 (PPCM/DCM) G672S c2014G>A 10:112572169 S/T 3 (2 DCM; 1 HCM) R673Q c.2018G>A 10:112572173 T 1 (DCM) S675T c.2023T>A 10:112572178 T 1 (DCM) Y681C c2042A>G 10:112572197 S/T 5 (4 DCM; 1 HCM) G758S c.2272G>A 10:112572427 T 1 (HCM) D786G c.2357A>G 10:112572512 T 1 (DCM) I921F c.2761A>T 10:112581138 T 2 (1 DCM; 1 HCM) V1102A c.3305T>C 10:112581682 T 1 (HCM) Y1193C c.3578A>G 10:112595630 T 2 (1 DCM; 1 HCM) A1208V c.3623C>T 10:112595675 T 1 (DCM)

Table 2. RBM20 variants identified by Sanger sequencing (S) and targeted sequencing (T) The table includes information on each mutation, the method by which the mutation was identified, the number of patients carrying the mutation and their particular cardiomyopathy subtype, putative carriership of other likely pathogenic or pathogenic mutations, the frequency in the ExAC population and the respective classification of the mutations. Evolutionary conservation, predicted pathogenicity (by AGVGD, SIFT, PolyPhen2 and MutationTaster softwares) and allele frequencies (in dbSNP, ExAC, and GoNL) were taken into consideration for classification.

cardiomyopathy (PPCM) was also diagnosed. We also identified the missense variant D888N, which was formerly considered to be pathogenic (Refaat et al). However, we anticipate that D888N is a rare polymorphism, as it was only identified in 6 of our 374 Sanger-sequenced DCM patients (0.16%), in 13 of our 1200 NGS-analyzed patients (0.11%), and in comparably low frequencies in control populations: 0.18% in Dutch controls (GoNL; 9 in 996 alleles, which means 9/498 healthy individuals) and 0.28% in the ExAC database, D888N is therefore not included in summary table 2. Cosegregation and haplotype analysis CHAPTER 2.1

In cases where DNA of affected family members was available, we studied the carriership status of those individuals (for details, see table 1). We were able to show co-segregation of mutations R634P and R636H with the disease phenotype in the respective families. We did not find co-segregation of Y681C in the one small family that was available for testing. We also identified unrelated index patients carrying the same variants: three patients carrying the pathogenic mutation R634W and five patients having the likely pathogenic mutation Y681C. Although no family relation between these patients was known, we hypothesized that these mutations were inherited from common ancestors (founders). Therefore, we performed haplotype analysis using markers within the approximately ±5cM region surrounding the respective mutations. These studies revealed that the patients carrying the R634W mutation share a relatively large haplotype (see table S2b; results shown for two of the three patients), suggesting that the mutation originated from a common founder. In contrast, no shared haplotype could be identified for the Y681C mutation (data not shown). Together with lack of cosegregation of this variant in the small family studied, the fact that it was also found in 1/996 alleles in the GoNL database, and that one of the Y681C patients is also carrying a certainly pathogenic PLN deletion, these results suggest that Y681C is less likely to be pathogenic. However, more data is needed to verify this conclusion.

Functional evaluation In order to evaluate our classification of variants L100F, V535L, R634P, R634W, R636H, G672S and Y681C using a functional approach, we designed a differential splicing assay. In addition, we included the likely benign variants W768S, W768L and the D888N variant, which was formerly reported

RBM20 IN DILATED CARDIOMYOPATHY

63

as pathogenic but that we now designate as benign. For this purpose, we transfected HEK293 cells with wild type and “mutated” human RBM20 cDNA expressing vectors. The RNA isolated from these cells was reverse transcribed, and we then studied potential differences in the composition of isoforms of putative splicing targets of RBM20 (Guo et al 2012, Maatz et al) or other spliceosomal proteins. For this purpose, primers were designed for the CAMK2D, CAMK2G, LDB3, SH3KBP1, SORBS1(1), SORBS1(2), TNNT2, TPM1 and TRDN targets, and differences in splicing patterns in presence of wild type, R634P, or endogenous RBM20 production were analyzed. Out of the nine potential targets, only LDB3 showed clear effects in this assay. We therefore decided to continue evaluating differential splicing of the known cardiomyopathy gene (Vatta et al) and RBM20 target (Guo et al 2012, Maatz et al) LDB3. We first sequenced the three different length PCR products of LDB3 detected on agarose gel, using the primers corresponding to 3’ sequences of exon 3 and 5’ sequences of exon 7, respectively. The two longer products we identified were shown to correspond to transcripts NM_001080114.1 and NM_001080116.1 (product of 472 nucleotides, including exon 3, 5, 6 and 7), and transcripts NM_0070782 and NM_001080115.1 (product of 614 nucleotides, including exon 3, 4 and 7). However, the shorter 245 nucleotide product did not correspond to the remaining isoforms NM_001171610.1 and NM_001171611.1 and only contained sequences of exon 3 and 7 (see also figure 1). Next, we analyzed the resulting LDB3 products in cDNA derived from HEK293 cells expressing the different RBM20 mutations/variants. A preliminary screening (a PCR under saturating conditions followed by gel electrophoresis) did not show an obvious presence/absence of certain LDB3 products when comparing wild type cells with cells expressing one of the hotspot mutations. However, as we observed subtle differences in LDB3 product intensities between the different samples (data not shown), subsequent analyses were aimed at quantifying the respective products under non-saturating conditions. Unfortunately, this did not lead to the identification of significant differences in product intensities between cells carrying the vector expressing wild type or certainly pathogenic mutations of RBM20 (figure 2). As shown in figure 2, we did observe some differences in the product intensity patterns between non-transfected, wild type transfected and mutation (R634P(1), R634P(2), R634W and R636H) transfected cells, although this was less apparent for the R636H transfected cells. The most prominent effect we saw was relatively high amounts of the 613bp product in non-

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CHAPTER 2.1 Figure 1. LDB3 splicing products identified in transfected HEK293 cells. PCR amplified and sequenced LDB3 transcripts expressed by the HEK293 cells are shown. Two fragments correspond to known LDB3 transcripts/isoforms (472bp and 613bp), while the exon composition of the shortest one (245bp) does not correspond to known transcripts. The known transcripts of LDB3 have the following NCBI IDs: transcript/isoform 1 – NM_007078.2, 2 – NM_001080114.1, 3 – NM_001080115.1, 4 – NM_001080116.1

transfected cells, while we observed high proportions (90-100%) of the 245 bp product in wild type transfected cells and slightly smaller proportions (T 1204C>T 1265G>A 1298C>T 1534C>T 1569G>C 1745G>A 1836C>G 1874A>G 1917C>G 3130G>A 4093G>A 4280T>C 4400G>C 4439C>T 4486G>A 4777C>T 4937G>A 4943C>T 5009C>T 5054G>A 5576C>T 5623C>T 5801G>A 5860C>G 5887G>A 5888G>A 5914C>G 5920G>A 5932G>A 6109A>G 6329G>A 6473G>A 6722G>A 6743A>C 6827G>A 6931G>A 7256G>A 7678C>T 8080G>A 8231C>G

protein R141C A7V D42H Q10* D23_N24insY R316Q V402M R422Q R433Q V512M S523R A582V Y612* Y625C Q639H R1044C R1365W K1427R T1467R R1480H R1496C V1593M R1646H R1648Q R1670Q A1685V T1859M R1875W R1934H R1954G R1963W R1963Q L1972V E1974K A1978T K2037E R2110Q A2158V A2241V V2248G R2276H A2311T R2419Q A2560T R2694W A2744G

Sanger ARVC 1x 1x 1x 1x 1x 3x 1x 1x 1x 2x 1x

1x 1x 1x 1x 1x 2x 1x 3x 1x 1x 1x 1x 1x 1x 1x 1x 1x 2x 2x

NGS data GoNL freq (%) 0.1071 0.1010 0.2020 1.3655 0.2045 0.2079 0.1006 0.1027 0.2114 1.0664 0.2049 0.1014 0.4373 0.1420 0.2833 1.1152 0.2924 0.1761 0.6383 0.7143 0.5071 0.4464 -

classification VOUS LB VOUS VOUS VOUS VOUS VOUS LB LB VOUS VOUS LP LP LP LB VOUS LB LB VOUS LP LP LB VOUS VOUS LB LP VOUS LP LP LP LP VOUS LP LP LP LP LP LP VOUS VOUS LP VOUS LP LB LB LB

8423C>T 8458C>T 8462C>T 8612G>A 8881C>T 8900T>C 8920C>T 8923C>T 9227C>T 9231G>C 9388C>T 9464C>T 9958C>T 10336G>A 10372C>T 10469G>C 10909G>A 11056C>A 11281C>T 11324G>A 11740C>T 12022C>T 12131G>A 12437C>T 12442C>T 12598C>T 12601C>T 13195G>A

R2808Q A2820T R2821Q A2871V V2961M Y2967C E2974K E2975K R3076Q D3077E D3130N R3155Q D3320N R3446C G3458R G3490A R3637C A3686S E3761K A3775V E3914K G4008S T4044M R4146H V4148I V4200M E4201K V4399I

1x 3x 1x

1x

1x

0.8989 0.1096 0.3275 0.2232 0.2105 0.8368 0.5133 0.1048 0.9395 0.1042 0.1025 0.2053 1.4085 1.2195 1.8634 0.1008 1.3131 0.1008 0.1008 0.3067 0.3268 1.0121 0.1006 0.1004 0.2020 0.2016 -

LB LB LB VOUS VOUS LB VOUS VOUS VOUS B LB LB VOUS VOUS LB LB VOUS LB LP LB LB VOUS LB LP LP LP LP LB

CHAPTER 2

144995977 144995942 144995938 144995788 144995519 144995500 144995480 144995477 144995173 144995169 144995012 144994936 144994442 144994064 144994028 144993931 144993491 144993344 144993119 144993076 144992660 144992378 144992269 144991963 144991958 144991802 144991799 144991205

Abbreviations B: benign; LB: likely benign; LP: likely pathogenic; P: pathogenic; VOUS: variant of unknown significance

Clustering of novel, likely pathogenic variants in the rod domain We have identified one large, potentially disease-associated cluster of novel, missense genetic variants in the rod domain of the PLEC gene in the Dutch patient cohort (variants T1859M-R2110Q, see table 1). Interestingly, another, significantly overlapping ARVC-associated region was found in the same domain in the UK cohort (variants R1688C-E2157A, see table 2). This cluster of variants was not only promising due to its presence in ARVC patients and absence in control populations, but also because all variants within the cluster were classified as ‘likely pathogenic’ or ‘variant of unknown significance’ (VOUS) (for details of the variant classification, see supplementary table 1). Therefore, on the basis of the clustering of variants in patients and their predicted pathogenicity, we considered this region as ‘potentially pathogenic’.

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Table 2. PLEC variants identified in ARVC patients from the United Kingdom. Overview of all novel and low frequency (C 266G>A 305C>T 421G>A 503G>A 743C>T 947C>T 1204C>T 1298C>T 1534C>T 1569G>C 1634C>T 1745G>A 1917C>G 2474G>T 2549C>G 2959C>T 2962C>T 3130G>A 3352G>A 3872C>T 4093G>A 4189G>C 4280T>C 4400G>C 4439C>T 4486G>A 4637A>C 4777C>T 4943C>T 4967C>T 5009C>T 5054G>A 5062G>A 5240G>A 5284C>T 5651G>A 5726G>A 5882G>A 5887G>A 5888G>A 6431G>A 6470T>G 6473G>A 6609C>A 6722G>A 6736G>A 6743A>C

SANGER SEQUENCING

protein D10E P89L R102H R141C P168L R248Q R316Q V402M R433Q V512M S523R C545Y A582V Q639H P825Q C850S A987T V988M R1044C R1118C R1291Q R1365W R1397G K1427R T1467R R1480H R1496C V1546G V1593M R1648Q S1656L R1670Q A1685V R1688C A1747V E1762K T1884M A1909V A1961V R1963W R1963Q A2144V E2157A A2158V Q2203H A2241V R2246W V2248G

NGS data TFC+ TFC1x 1x 1x 1x 1x 5x 1x

1x 1x 1x

8x 1x

1x 1x 1x 1x

1x

1x

1x 1x 4x

1x

2x 1x 2x 1x 1x 2x 1x 1x

1x 1x

GoNL freq (%) 0.1071 0.1010 0.2020 1.3655 0.2045 0.2079 0.1006 0.1027 0.2114 1.0664 0.2049 0.1014 0.4373 0.1420 0.2833 1.1152 0.2924 0.1761 0.6383 0.7143

classification LB VOUS VOUS VOUS LB VOUS VOUS VOUS LB VOUS VOUS LP LP LB LP LP B VOUS VOUS LB LB LB LB LB VOUS LP LP LP LB VOUS LB LB LP VOUS LP LP VOUS VOUS LP LP VOUS LP VOUS LP LB VOUS VOUS VOUS

6857C>T 6947C>T 7678C>T 8080G>A 8423C>T 8452C>T 8458C>T 8462C>T 8593T>C 8612G>A 8744T>C 8881C>T 8900T>C 8917C>T 8920C>T 8923C>T 8941C>T 9227C>T 9231G>C 9388C>T 9445C>T 9454T>C 9464C>T 9958C>T 10004C>T 10054T>C 10102G>A 10225G>A 10336G>A 10372C>T 10454C>T 10469G>C 10541C>T 10747A>G 10909G>A 11056C>A 11158A>G 11281C>T 11324G>A 11438G>A 11447G>A 11740C>T 11762T>A 12010C>G 12022C>T 12131G>A 12437C>T 12442C>T 12598C>T 12601C>T 12616G>A 12655C>T 13129C>T 13228G>A 13885C>T 13999G>A

R2286Q R2316Q A2560T R2694W R2808Q E2818K A2820T R2821Q I2865V A2871V K2915R V2961M Y2967C D2973N E2974K E2975K A2981T R3076Q D3077E D3130N E3149K T3152A R3155Q D3320N R3335Q K3352E R3368C R3409C R3446C G3458R R3485Q G3490A R3514Q S3583P R3637C A3686S S3720P E3761K A3775V A3813V A3816V E3914K Q3921L D4004H G4008S T4044M R4146H V4148I V4200M E4201K R4206C D4219N A4377T P4410S G4629S R4667C

1x 1x

1x 2x 1x 2x 1x 1x 1x 2x 1x

2x 1x

13x 2x 2x 1x 1x

2x

3x

1x

1x 1x 1x

1x 1x 1x 1x 1x 3x 6x 1x 1x 1x

1x 1x

1x

1x 2x

1x

2x

5x

1x 1x 1x

1x

1x

1x 1x 1x

0.5071 0.4464 0.8989 0.1096 0.3275 0.2232 0.2105 0.8368 0.5133 0.1048 0.9395 0.1042 0.1025 0.2053 1.4085 1.2195 0.1008 1.3131 0.1008 0.1008 0.3067 0.3268 1.0121 0.1006 0.1004 0.2020 0.2016 -

LP VOUS LB LB LB LP LB LB LB VOUS VOUS VOUS LB VOUS VOUS VOUS LB VOUS B LB VOUS LB LB VOUS LB LB VOUS VOUS VOUS LB LB LB LB LP VOUS LB LB LP LB LB LB LB LP B VOUS LB LP LP LP LP LP LP LB LP LP LP

CHAPTER 2

144997651 144997561 144996830 144996320 144995977 144995948 144995942 144995938 144995807 144995788 144995656 144995519 144995500 144995483 144995480 144995477 144995459 144995173 144995169 144995012 144994955 144994946 144994936 144994442 144994396 144994346 144994298 144994175 144994064 144994028 144993946 144993931 144993859 144993653 144993491 144993344 144993242 144993119 144993076 144992962 144992953 144992660 144992638 144992390 144992378 144992269 144991963 144991958 144991802 144991799 144991784 144991745 144991271 144991172 144990515 144990401

Abbreviations B: benign; LB: likely benign; LP: likely pathogenic; P: pathogenic; VOUS: variant of unknown significance

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Absence of frequent non-synonymous variations in the rod domain region in controls Next, we investigated whether frequent (MAF>2%) SNPs reside in the ‘potentially pathogenic’ region in the rod domain in the GoNL control population. For this purpose, all variants of PLEC between chromosomal positions 144998038 - 144999446 were extracted from the GoNL database then uploaded to SeattleSeq for annotation with protein coding features (table 3). Only a few synonymous variants were identified, with the exception of one additional missense variant (K2047E) reported with the frequency of 8%. This variant, however, had a very low quality score, which indicated that this was a sequencing artefact rather than a true variant. Hence, it seems that missense mutations in this region are not “tolerated” without phenotypic consequences.

Further interesting regions of plectin Based on the absence of low frequency variants in patients versus controls, we identified additional interesting, potentially ARVC-associated regions of PLEC. Notably, these were only found in the UK cohort (probably due to the larger number of patients involved in the study) and spanned much shorter regions of the gene than the one in the rod domain. One of these ARVCassociated regions was the spectrin repeat region (P825Q-V988M): though this region only contained two ‘likely pathogenic’ variants in our patients, it did not contain any non-synonymous variant in the GoNL control population (data not shown). The other two variants, despite being classified as VOUS, might also be more damaging, since this segment of the encoded plectin protein is known to be responsible for interactions with, for example, actin, nesprin, and costameric proteins. Likewise, the region of variants R3335QR3409C, partially residing in the intermediate filament-binding plectinrepeats of the protein, may also contribute to the development of ARVC. Though this latter region was also free of coding non-synonymous genetic variants in Dutch controls (GoNL), two of the four variants found in patients were classified as ‘likely benign’ (primarily because they were predicted to be harmless) (see classification in supplementary table 1). Additionally, the C-terminus of PLEC (R4206C-R4667C) could be potentially interesting, but one relatively frequent missense variant (T4539M, 2.381% in GoNL), which was also found in 2/107 Dutch and 18/358 British patients, resides in this otherwise likely ARVC-related plectin-repeat region.

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The potential role of other desmosomal mutations and/or external factors: is ARVC a multifactorial disease?

CHAPTER 2

Of the 30 patients who were carriers of PLEC missense variants in the potentially disease-associated region of the rod domain, the vast majority had a TFC+ cardiac phenotype (true ARVC). Of these, 14 patients were found to carry other ARVC-related potentially pathogenic mutations or VOUS in addition to their PLEC cluster variants (table 4, only likely pathogenic and pathogenic variants included), mostly in the PKP2 gene but also in DSC2, DSP, JUP, SCN5A or TMEM43 for some cases. Moreover, five patients had multiple low frequency or novel genetic variants in PLEC (four of which were additional variants in the same rod cluster). While exercise (Perrin et al, Saberniak et al) and certain viral infections (Grumbach et al) remain important contributors to the onset of an ARVC phenotype, our study indicates that a potential oligogenic inheritance might complicate the seemingly multifactorial disease background and cause variable penetrance of ARVC. Table 3. High frequency (>2%) PLEC control variants localized in the ARVCassociated ‘potentially pathogenic’ region of the rod domain. Variants of PLEC localized in the potentially pathogenic cluster of missense variants associated with ARVC. This region was found to be enriched for missense variants in both the Dutch and UK ARVC patient cohorts and lacking low frequency (C; p.K2047E variant (highlighted in black), which most likely is an artefact, were identified in the GoNL control population. Synonymous variants are indicated in gray. dbSNP

GoNL data

genomic

variant position cDNA

protein

rs number

quality

remark

144999417

5091C>T

A1697A

rs55836855 9320,24

47,1429

144998868

5640C>T

A1880A

-

222,26

8,1633

144998514

5994C>T

A1998A

-

43,31

4,1096

most likely artefact

144998369

6319T>C

K2047E

-

41,57

8,0645

most likely artefact

144998190

2106A>G

A2106A

rs2857829

7241,12

31,1037

144998169

6339C>T

A2113A

rs1140522

10024,93

35,9177

frequency (%)

PLEC IN ARRHY THMOGENIC CARDIOMYOPATHY

91

  92

SANGER SEQUENCING

TFC status TFCTFCTFCTFCTFCTFCTFC+ TFC+ TFC+ TFCTFC+ TFC+ TFCTFCTFCTFCTFC+ TFCTFCTFCTFCTFCTFC+ TFC+ TFCTFC+ TFC+ TFC+ TFCTFC-

PLEC “cluster” variants R1688C (VOUS) R1688C (VOUS) R1688C (VOUS) A1747V (LP) E1762K (LP) E1762K (LP) T1859M (LB), K2037E (LP) R1875W (LP) T1884M (VOUS) A1909V (VOUS) R1934H (LP) R1954G (LP) A1961V (LP) R1963W (LP) R1963W (LP) R1963W (LP) R1963W (LP) R1963Q (VOUS) R1963Q (VOUS) R1963Q (VOUS) R1963Q (VOUS) R1963Q (VOUS) R1963Q (VOUS) R1963Q (VOUS), K2099R (LP) L1972V (LP) E1974K (LP) A1978T (LP) R2110Q (LP) A2144V (LP) E2157A (VOUS) Q10* (VOUS) D42H (VOUS) R433Q (LB) A2242V (B)

-

-

R422Q (LB), A2744G (LB) -

other PLEC variant

other ARVC-related mutation 1 other ARVC-related mutation 2 DSP c.7994C>T; p.T2665M (VOUS) JUP c.902A>G; p.E301G (VOUS) SCN5A c.665G>A; p.R222Q (P) PKP2 c.2062T>C; S688P (LP) PKP2 c.1848C>A; Y616X (P) PKP2 c.1132C>T; p.Q378X (P) PKP2 del exon 1-4 (P) PKP2 c.397C>T; p.Q133X (P) TMEM c.934C>T; p.R312W (VOUS) DSP c.4775A>G; p.K1992R (VOUS) SCN5A c.3206C>T; p.T1069M (LP) PKP2 c.2386T>C; C796R (P) PKP2 c.1211dup; L404fs (P) DSP c.269G>A; p.Q90R (VOUS) PKP2 c.235C>T; R79X (P) PKP2 c.1597_1600delATCC; p.P533fsX561 (P) -

Abbreviations B: benign; LB: likely benign; LP: likely pathogenic; P: pathogenic; TFC – task force criteria (diagnostic criteria of ARVC); VOUS: variant of unknown significance

patient ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Table 4. Carriership of additional potentially pathogenic variants in patients with PLEC variants in the ARVC-associated cluster of the rod domain. Of the patients carrying plectin variants in the putatively ARVC-associated region identified in the rod domain, 17 of the 43 were found to be carriers of additional genetic mutations in desmosomal genes or other known ARVC genes. Only genetic variants classified as pathogenic or likely pathogenic are included.

DISCUSSION

PLEC IN ARRHY THMOGENIC CARDIOMYOPATHY

CHAPTER 2

We hypothesized that the cytolinker protein plectin, which supports the binding of intermediate filaments to the desmosomes, might carry genetic variants that contribute to the development of, or at least the susceptibility for ARVC, which is known as a ‘disease of the cardiac desmosome’. Our reasons were that (1) plectin is highly expressed in the myocardium and is physically connected to the cell junctions which are known to be involved in the pathomechanism of ARVC, (2) its knock down in various mouse models leads to cardiac pathology, and (3) late-onset cardiac symptoms have recently been reported for a couple of mutated plectin-carrying EBS patients. Our Sanger sequencing and NGS-based analysis of the PLEC gene resulted in the identification of 96 novel or low frequency (T, V512M

c.1569G>C, S523R c.1634C>T, C545Y

c.1745G>A, A582V c.1836C>G, Y612* c.1874A>G, Y625C

c.1917C>G, Q639H

weak

1,42

high (zebrafish) 2,87 high (region) n.a. high (Xenopus) 2.06

high (zebrafish) 0.37 high (zebrafish) 5,61

high (zebrafish) 4,08

weak (cow) 1.42 high (zebrafish) 5.37

n.a.

n.a.

weak (horse) weak (region)

98 64

c.503G>A, P168L c.20C>T, A7V (NM_201383.1) c.124G>C, D42H (NM_201383.1) c.28C>T, Q10* (NM_201384.1) c.69_70insTAC, D23_N24insY (NM_201384.1) c.743C>T, R248Q c.947C>T, R316Q c.1204C>T, V402M

weak (cow)

180

c.421G>A, R141C

weak (cow)

PREDICTED EFFECT

C0

C65 n.a. C55

C65 C65

C0

C0 C0

C0 C0 C0

n.a.

n.a.

C0

tolerated tolerated

disease causing polymorphism

deleterious disease causing

deleterious disease causing

-

-

-

-

MutationTaster splicing

deleterious polymorphism deleterious disease causing

SIFT

0/2184 0/2184

0/2184

0/2184

benign benign possibly damaging benign probably damaging possibly damaging benign probably damaging benign n.a. probably damaging benign

n.a.

n.a.

-

-

-

-

tolerated

polymorphism

0/2184

0/2184 0/2184 0/2184

0/2184 0/2184

0/2184

7/2184 13/2184

0/2184 0/2184 0/2184

0/2184

CHAPTER 2

minor effect

deleterious disease causing minor effect n.a. n.a. deleterious disease causing -

deleterious disease causing deleterious disease causing

deleterious disease causing

tolerated polymorphism deleterious disease causing

deleterious disease causing minor effect deleterious disease causing deleterious disease causing -

n.a.

0/~13000

0/~13000 0/~13000 0/~13000

9/8342; 0 0/~13000

2/8416; 0

LIKELY BENIGN VOUS

VOUS

VOUS

VOUS

0,10% LIKELY BENIGN

0,10% LIKELY PATHOGENIC 0.0% LIKELY PATHOGENIC 0.0% LIKELY PATHOGENIC

0.2% VOUS 0,00% LIKELY PATHOGENIC

0,20% VOUS

LIKELY BENIGN LIKELY BENIGN

0,00% VOUS 0,10% VOUS 0,20% VOUS

0.0%

0.0%

0.0%

0,00% LIKELY BENIGN 0.0% LIKELY BENIGN

0,10% VOUS

0,00% VOUS

0.0% 0.0%

GoNL

1/8410; 57/4134 0.0% 144/8466; 14/4188 1.3%

0/~13000 0/~13000 0; 1/4212

0/~13000

0/~13000

0/~13000

0; 6/3732 0/~13000

0/~13000

4/8296; 1/4116

ESP (EA; AA) 0/~13000 0/~13000

CONTROL DATABASE FREQUENCIES CLASSIFICATION 1000 Genomes 0/2184 0/2184

possibly deleterious disease causing strong effect 0/2184 damaging (but large exon) n.a. n.a. n.a. 0/2184

PhyloP AGVGD PolyPhen [-14.1;6.4] 0.53 C0 benign 5.21 C0 probably damaging 2.38 C0 probably damaging 4.24 C0 probably damaging 0.29 C0 benign 0.37 C0 benign

29

Grantham conservation distance (up to...) 45 weak (cow) 98 weak (cow)

c.305C>T, R102H

c.30G>C, D10E c.266G>A, P89L

coordinates

VARIANT

Abbreviations ESP (EA; AA): allele frequency in European Ancestry and African Ancestry individuals in the Exome Sequencing Project database; GoNL: allele counts in the GoNL database; n.a.: not applicable/available

Supplementary table 1: Classification of PLEC variants

  102

SANGER SEQUENCING

c.5623C>T, R1875W

c.5284C>T, E1762K c.5576C>T, T1859M

c.5240G>A, A1747V

c.5062G>A, R1688C

c.4943C>T, R1648Q c.4967C>T, S1656L c.5009C>T, R1670Q c.5054G>A, A1685V

c.4637T>G, V1546G c.4777C>T, V1593M c.4937G>A, R1646H

c.4486G>A, R1496C

c.4439C>T, R1480H

c.3352G>A, R1118C c.3872C>T, R1291Q c.4093G>A, R1365W c.4189G>C, R1397G c.4280T>C, K1427R c.4400G>C, T1467R

c.2959C>T, A987T c.2962C>T, V988M c.3130G>A, R1044C

c.2549C>G, C850S

c.2474G>T, P825Q

coordinates

VARIANT

Grantham conservation PhyloP AGVGD PolyPhen distance (up to...) [-14.1;6.4] 76 high (zebrafish) 5.13 C65 probably damaging 112 high (zebrafish) 5.13 C65 probably damaging 58 weak (cow) 0,53 C0 benign 21 high (zebrafish) 2.14 C15 benign 180 weak (cow) 2,22 C25 possibly damaging 180 weak (cow) 0.21 C25 benign 43 weak (cow) 2.63 C0 benign 101 weak 0.93 C15 benign 125 weak (cow) 1,01 C45 benign 26 high (zebrafish) 2.22 C25 benign 71 weak (cow) 5,61 C15 possibly damaging 29 high (zebrafish) 5,61 C25 possibly damaging 180 high (zebrafish) 0,69 C65 possibly damaging 109 high (zebrafish) 2,06 C65 benign 21 weak (cow) 0.85 C0 benign 29 moderately (only 2.30 C0 benign R or K) 43 high (zebrafish) 0,77 C35 benign 145 weak 1.74 C0 benign 43 moderate 2.06 C0 benign 64 high (zebrafish) 5,45 C65 probably damaging 180 weak (cow) 1,74 C45 possibly damaging 64 high (zebrafish) 5,53 C65 possibly damaging 56 high (zebrafish) 5,53 C65 benign 81 weak (cow) 3.19 C0 possibly damaging 101 high (zebrafish) 2.63 C65 possibly damaging

PREDICTED EFFECT

-

-

-

disease causing polymorphism polymorphism disease causing

deleterious disease causing

deleterious disease causing tolerated disease causing

deleterious disease causing

deleterious disease causing

deleterious tolerated deleterious deleterious

-

-

-

-

-

deleterious disease causing deleterious polymorphism minor effect deleterious disease causing minor effect

deleterious disease causing

-

polymorphism polymorphism polymorphism minor effect disease causing disease causing disease causing -

deleterious disease causing

tolerated tolerated deleterious deleterious deleterious deleterious

tolerated polymorphism deleterious disease causing deleterious disease causing

deleterious disease causing

-

MutationTaster splicing

deleterious disease causing

SIFT

0/2184

1/2184 0/2184

0/2184

10/2184

0/2184 6/2184 0/2184 0/2184

0/2184 6/2184 0/2184

0/2184

0/2184

0/2184 0/2184 9/2184 9/2184 1/2184 0/2184

77/2184 0/2184 0/2184

0/2184

0/~13000

0/~13000 1/6312; 22/2888

0/~13000

0/~13000

0/~13000 0 26/6166; 2/2752 0/~13000

0/~13000 38/7890; 4/3790 0

0/~13000

1/8548; 0

1/8494; 0 0/~13000 16/8098; 2/3868 0; 23/4302 14/8436; 44/4178 0/~13000

3/8370; 442/4402 0; 13/4186 3/8480; 0

0/~13000

1000 ESP Genomes (EA; AA) 0/2184 0/~13000

LIKELY PATHOGENIC

LIKELY PATHOGENIC

LIKELY BENIGN LIKELY BENIGN LIKELY BENIGN LIKELY BENIGN LIKELY BENIGN VOUS

VOUS LIKELY BENIGN LIKELY BENIGN LIKELY PATHOGENIC

0.0%

LIKELY PATHOGENIC

0,00% LIKELY PATHOGENIC 0.0% VOUS

0,00% LIKELY PATHOGENIC

0,00% VOUS

0,20% 0.0% 1.0% 0,20%

0,00% LIKELY PATHOGENIC 0.1% LIKELY BENIGN 0.0% VOUS

0,40% LIKELY PATHOGENIC

0,10% LIKELY PATHOGENIC

0.0% 0.0% 1.0% 0,00% 0.0% 0,20%

0,00% BENIGN 0.0% VOUS 0,20% VOUS

0.0%

0.0%

GoNL

CONTROL DATABASE FREQUENCIES CLASSIFICATION

PLEC IN ARRHY THMOGENIC CARDIOMYOPATHY

103

107

c.6470T>G, E2157A

58

43 64

c.6329G>A, R2110Q c.6431G>A, A2144V

c.6931G>A, A2311T

56

c.6109A>G, K2037E

43

58

c.5932G>A, A1978T

c.6857C>T, R2286Q

56

c.5920G>A, E1974K

101 109 29

32

c.5914C>G, L1972V

c.6736G>A, R2246W c.6743A>C, V2248G c.6827G>A, R2276H

43

c.5888G>A, R1963Q

64

101

c.5887G>A, R1963W

c.6722G>A, A2241V

64

c.5882G>A, A1961V

24

125

c.5860C>G, R1954G

c.6609C>A, Q2203H

29

c.5801G>A, R1934H

64

64

c.5726G>A, A1909V

c.6473G>A, A2158V

81

c.5651G>A, T1884M 1,82

0,93

0,45

high (zebrafish) 2.14

high (zebrafish) 5,77

weak (dog) 1,82 weak (cow) 2,3 high (zebrafish) 4.00

high (zebrafish) 3,03

weak (cow)

high (zebrafish) 5.21

high (zebrafish) 4,32

high (zebrafish) 1.90 high (zebrafish) 5,21

high (zebrafish) 1.82

high (zebrafish) 2.71

high (zebrafish) 5.37

high (zebrafish) 5.37

high (zebrafish) 2.06

high (zebrafish) 1.90

high (zebrafish) 2,71

high (zebrafish) 0.85

high (zebrafish) 1.82

weak (cow)

weak (cow)

C0

C35

C25 C0 C25

C0

C0

C0

C0

C35 C0

C55

C55

C55

C25

C35

C65

C65

C65

C25

C0

C0

-

-

-

-

-

-

-

-

-

minor effect

-

tolerated

disease causing

-

deleterious disease causing deleterious disease causing minor effect

deleterious disease causing

deleterious disease causing

deleterious disease causing

deleterious disease causing

deleterious disease causing

deleterious disease causing

deleterious disease causing

deleterious disease causing

deleterious disease causing

deleterious polymorphism

deleterious polymorphism

0/2184

0/2184 1/2184

0/2184

1/2184

0/2184

0/2184

19/218

0/2184

0/2184

0/2184

0/2184

0/2184

0/2184

benign benign probably damaging probably damaging benign

benign

-

deleterious disease causing

deleterious disease causing

-

-

0/2184

0/2184

0/2184 0/2184 0/2184

5/2184

CHAPTER 2

deleterious polymorphism minor effect deleterious disease causing deleterious disease causing -

deleterious disease causing

possibly deleterious disease causing strong effect 0/2184 damaging (but large exon) benign tolerated polymorphism 10/2184

probably damaging possibly damaging probably damaging probably damaging possibly damaging probably damaging possibly damaging probably damaging probably damaging possibly damaging probably damaging benign probably damaging benign

0

0/~13000

0/~13000 0/~13000 1/8102; 0

2/7676; 3/3563

0/7114; 13/3336

1/7880; 0

1/7912; 1/3826

0 0/~13000

0

1/6216; 6/2628

0/~13000

0

35/4580; 4/1900

0/~13000

0/~13000

0/~13000

0/~13000

0/~13000

0/~13000

LIKELY PATHOGENIC

LIKELY PATHOGENIC

LIKELY PATHOGENIC

LIKELY PATHOGENIC

LIKELY PATHOGENIC

LIKELY PATHOGENIC

VOUS

LIKELY PATHOGENIC

LIKELY PATHOGENIC

0.0%

VOUS

0,00% LIKELY PATHOGENIC

0,00% VOUS 0,70% VOUS 0.0% LIKELY PATHOGENIC

0,60% VOUS

0,00% LIKELY BENIGN

0.1%

0,00% VOUS

0.0% LIKELY PATHOGENIC 0,00% LIKELY PATHOGENIC

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0,00% LIKELY PATHOGENIC

0.0%

0.0%

0,00% VOUS

0,00% VOUS

  104

SANGER SEQUENCING

43

58 101 60 43 56 58 43 29 64

26 21 194 23 56

56 58 43 45 23 56 58 43 23 43 56 180 180

180

125

c.7678C>T, A2560T c.8080G>A, R2694W c.8231C>G, A2744G c.8423C>T, R2808Q c.8452C>T, E2818K c.8458C>T, A2820T c.8462C>T, R2821Q c.8539T>C, I2865V c.8612G>a, A2871V

c.8744T>C, K2915R c.8881C>T, V2961M c.8900T>C, Y2967C c.8917C>T, D2973N c.8920C>T, E2974K

c.8923C>T, E2975K c.8941C>T, A2981T c.9227C>T, R3076Q c.9231G>C, D3077E c.9388C>T, D3130N c.9445C>T, E3149K c.9454T>C, T3152A c.9464C>T, R3155Q c.9958C>T, D3320N c.10004C>T, R3335Q c.10054T>C, K3352E c.10102G>A, R3368C c.10225G>A, R3409C

c.10336G>A, R3446C

c.10372C>T, G3458R

4.16 -0.36 1,98 0,29 0,04 2,47 -0.76 -0.12 2,55 -0.12 -0.36 1,25 1,17

-1.09 -0.12 1.58 3.11 1.66

0.53 2,06 2.30 1,5 3.68 -0.04 0,12 0,12 5.45

weak

-1.41

high (zebrafish) 1.09

high (zebrafish) high (zebrafish) weak (cow) high (zebrafish) moderate (zebrafish) moderate weak weak (cow) high (zebrafish) weak (lemur) high (zebrafish) weak weak high (zebrafish) weak (mouse) weak (cow) weak (cow) high (zebrafish)

weak (cow) weak (mouse) weak (cow) weak (cow) high (zebrafish) weak (lemur) weak (lemur) weak (cow) high (zebrafish)

PREDICTED EFFECT

C0

C65

C15 C0 C0 C35 C0 C55 C0 C0 C0 C0 C0 C55 C0

C25 C0 C0 C15 C55

C0 C0 C0 C0 C55 C0 C0 C0 C65

C35

benign benign benign benign benign benign benign benign benign benign benign benign possibly damaging probably damaging benign

possibly damaging benign benign benign benign benign benign benign benign probably damaging benign benign benign benign benign

tolerated

-

polymorphism

7/2184

0/2184 0/2184 0/2184 117/2184 0/2184 4/2184 0/2184 1/2184 0/2184 0/2184 0/2184 0/2184 0/2184

0/2184 0/2184 9/2184 0/2184 0/2184

5/2184 0/2184 0/2184 23/2184 0/2184 0/2184 2/2184 0/2184 1/2184

0/2184

0.0% 0.0% 0,10% 0,90% 0,10% 0,00% 0.0% 0,10% 0,20% 0,00% 0,00% 0,00% 0,00%

0,00% 0,20% 0.8% 0.0% 0.5%

0.5% 0,40% 0.0% 0,90% 0.0% 0,10% 0,30% 0,00% 0.2%

0.0%

62/8338; 7/3998

1.0%

LIKELY BENIGN

VOUS

VOUS LIKELY BENIGN VOUS BENIGN LIKELY BENIGN VOUS LIKELY BENIGN LIKELY BENIGN VOUS LIKELY BENIGN LIKELY BENIGN VOUS VOUS

VOUS VOUS LIKELY BENIGN VOUS VOUS

LIKELY BENIGN LIKELY BENIGN LIKELY BENIGN LIKELY BENIGN LIKELY PATHOGENIC LIKELY BENIGN LIKELY BENIGN LIKELY BENIGN VOUS

LIKELY PATHOGENIC

0,00% VOUS

GoNL

75/8412; 12/4184 1.0%

4/8332; 0 0 1/8538; 1/4330 39/8532; 927/4330 1/8206; 0 13/8308; 2/4118 0 0/~13000 0/~13000 0/~13000 0/~13000 0/~13000 2/8540; 0

0/~13000 0/~13000 99/8272; 10/3984 20/8328; 3/4018 21/8334; 0

49/8568; 9/4378 4/8198; 1/4296 0 33/8494; 131/4296 3/8524; 0 0/~13000 14/8518; 4/4312 6/8380; 1/4152 14/8348; 3/4140

0

1000 ESP Genomes (EA; AA) 7/2184 6/8470; 1/4256

CONTROL DATABASE FREQUENCIES CLASSIFICATION

strong effect 11/2184 (but large exon)

-

disease causing polymorphism disease causing polymorphism polymorphism disease causing polymorphism polymorphism minor effect disease causing polymorphism polymorphism minor effect polymorphism disease causing -

polymorphism minor effect polymorphism disease causing disease causing polymorphism -

polymorphism polymorphism minor effect polymorphism disease causing disease causing polymorphism polymorphism polymorphism disease causing -

deleterious disease causing

deleterious tolerated deleterious deleterious tolerated deleterious tolerated tolerated tolerated tolerated tolerated deleterious tolerated

deleterious deleterious tolerated deleterious deleterious

tolerated deleterious tolerated deleterious deleterious tolerated tolerated tolerated deleterious

deleterious disease causing

PhyloP AGVGD PolyPhen SIFT MutationTaster splicing [-14.1;6.4] 3,43 C0 benign deleterious disease causing -

high (zebrafish) 4.08

Grantham conservation distance (up to...) 43 high (frog)

c.7256G>A, R2419Q

c.6947C>T, R2316Q

coordinates

VARIANT

PLEC IN ARRHY THMOGENIC CARDIOMYOPATHY

105

43 60

43 74

180

99 74 56

64 64 64 56 113

81 56 81

29

29

21

56

180

23

58 29 74

56 180

c.10454C>T, R3485Q c.10469G>C, G3490A

c.10541C>T, R3514Q c.10747A>G, S3583P

c.10909G>A, R3637C

c.11056C>A, A3686S c.11158A>G, S3720P c.11281C>T, E3761K

c.11324G>A, A3775V c.11438G>A, A3813V c.11447G>A, A3816V c.11740C>T, E3914K c.11762T>A, Q3921L

c.12010C>G, D4004H c.12022C>T, G4008S c.12131G>A, T4044M

c.12437C>T, R4146H

c.12442C>T, V4148I

c.12598C>T, V4200M

c.12601C>T, E4201K

c.12616G>A, R4206C

c.12655C>T, D4219N

c.13129C>T, A4377T c.13195G>A, V4399I c.13228G>A, P4410S

c.13885C>T, G4629S c.13999G>A, R4667C

0.93

0,77 0,53 0,61 3,51 4,24

high (zebrafish) 4.00 high (zebrafish) 2,79

weak (cow) 0,93 weak 0.69 high (zebrafish) 5.86

high (zebrafish) 5,86

high (zebrafish) 1,98

high (zebrafish) 5,86

high (zebrafish) 5,86

high (zebrafish) 5,86

high (zebrafish) 5,86

high (zebrafish) 1,74 high (zebrafish) 2,47 high (zebrafish) 2.79

weak (cow) weak weak (lemur) weak (cow) high (zebrafish)

weak (mouse) -0.04 weak (cow) 0,12 high (zebrafish) 5,29

weak (cow)

weak (cow) 2,55 high (zebrafish) 4,81

weak (cow) 0,53 high (zebrafish) 5.86

C55 C65

C0 CC0 C65

C15

C65

C15

C15

C25

C25

C0 C55 C65

C0 C0 C0 C0 C65

C0 C0 C55

C35

C0 C65

C0 C55

benign possibly damaging benign probably damaging possibly damaging benign benign probably damaging benign benign benign benign probably damaging benign benign possibly damaging probably damaging probably damaging probably damaging possibly damaging probably damaging probably damaging benign benign probably damaging benign probably damaging -

-

-

-

-

-

-

-

-

-

0/2184 0/2184

0/2184 12/2184 1/2184

0/2184

0/2184

0/2184

0/2184

0/2184

0/2184

82/2184 0/2184 18/2184

0/2184 0/2184 0/2184 0/2184 0/2184

0/2184 0/2184 0/2184

1/2184

0/2184 0/2184

0/2184 21/2184

CHAPTER 2

deleterious disease causing minor effect deleterious disease causing -

tolerated polymorphism tolerated polymorphism deleterious disease causing

deleterious disease causing

deleterious disease causing minor effect

deleterious disease causing

deleterious disease causing

deleterious disease causing

deleterious disease causing

-

polymorphism polymorphism polymorphism minor effect disease causing disease causing -

tolerated polymorphism deleterious disease causing deleterious disease causing

tolerated tolerated tolerated tolerated deleterious

tolerated polymorphism deleterious polymorphism deleterious disease causing

deleterious disease causing

tolerated disease causing deleterious disease causing

deleterious polymorphism deleterious disease causing

VOUS

0,10% 0,00% 0,00% 0,30% 0,00%

LIKELY BENIGN LIKELY BENIGN LIKELY BENIGN LIKELY BENIGN LIKELY PATHOGENIC

0,10% LIKELY BENIGN 0,00% LIKELY BENIGN 0,10% LIKELY PATHOGENIC

0.1%

0,00% LIKELY BENIGN 0,00% LIKELY PATHOGENIC

0,00% LIKELY BENIGN 1.8% LIKELY BENIGN

0/~13000 0/~13000

6/8446; 0 0/8330; 2/4024 0/8304; 13/3962

0/~13000

0/~13000

4/8368; 1/4073

0/8362; 1/4056

0/~13000

0/~13000

0,00% LIKELY PATHOGENIC 0,00% LIKELY PATHOGENIC

0,00% LIKELY BENIGN 0.0% LIKELY BENIGN 0.0% LIKELY PATHOGENIC

0,00% LIKELY PATHOGENIC

0,00% LIKELY PATHOGENIC

0,20% LIKELY PATHOGENIC

0,20% LIKELY PATHOGENIC

0,10% LIKELY PATHOGENIC

0,10% LIKELY PATHOGENIC

2/8026; 408/3760 0,00% BENIGN 7/7656; 3/3544 0,30% VOUS 90/8382; 3/4112 1.0% LIKELY BENIGN

0; 1/4360 2/8272; 0/3880 4/8290; 0 0/~13000 2/8376; 0

0/~13000 0/~13000 0/~13000

10/8372; 0

1/8291; 0 0/~13000

0/~13000 139/8390; 8/4172

CHAPTER 3 EXOME SEQUENCING

Chapter 3.1

Hunting for novel disease genes in autosomal dominant cardiomyopathies: elucidating a role for the sarcomeric pathway

Rowida Almomani*, Anna Posafalvi*, Paul A van der Zwaag, Carlo L Marcelis, Bert Baars, Johanna C Herkert, Rudolf A de Boer, Karin Y van Spaendonck-Zwarts, Maarten P van den Berg, Richard J Sinke, J Peter van Tintelen§, Jan DH Jongbloed§

* The first two authors contributed equally §

The last two authors contributed equally

ABSTRACT We performed exome sequencing and a haplotype sharing test on a group of twelve families with autosomal dominant cardiomyopathy and no previous genetic diagnosis in order to identify potentially novel disease genes. Our approach resulted in the identification of the genetic cause of disease in 6/12 families. We found truncating variants in TTN in two dilated cardiomyopathy families, a frame-shift mutation in FLNC and a double missense mutation in FHL2 in two arrhythmogenic cardiomyopathy families, and missense variants in the COBL and STARD13 genes in two dilated cardiomyopathy families, respectively, both of which are genes that have not been related to cardiac pathology before. Thorough data-mining suggests a possible role for all of these genes in the disease mechanism of late onset cardiomyopathies. By creating a co-expression network of the five genes using an expression-arraybased bioinformatics database and software created in the department, we show that 100 of the 166 proteins included in our network have been annotated with a potential function in cardiac development and physiology. Of these 100 proteins, 28 are known as disease genes in various types of cardiomyopathy, and a role in sarcomere assembly seems to be the common molecular pathway connecting a large proportion of these genes.

INTRODUCTION

AUTOSOMAL DOMINANT CARDIOMYOPATHIES

CHAPTER 3.1

Dilated cardiomyopathy (DCM) is a progressive heart disease mainly characterised by left ventricular dilatation and impaired cardiac contraction, while arrhythmogenic right ventricular cardiomyopathy (ARVC) is a common cause of sudden cardiac death because of its association with ventricular arrhythmias (Hershberger et al, Basso et al). Currently, there are more than 50 genes linked to the pathogenesis of familial DCM. In the pre-NGS era these genes could only explain up to 20% of Dutch DCM cases (25% in familial cases and 8% in sporadic cases) (van Spaendonck et al, 2013), while including screening of the titin (TTN) gene improved this to 45-50% (Wilde & Behr, Posafalvi et al, van Spaendonck et al 2014). Our gene-panel-based Next Generation Sequencing (NGS) method, which was recently implemented into routine DNA diagnostics, resulted in the identification of mutations and likely pathogenic variants in up to 55% of DCM index patients (see chapter 4.1). On the other hand, to date there are still ‘only’ 13 ARVC genes known (te Rijdt et al). One of these is the desmosomal plakophilin 2 gene (PKP2), and mutations in this gene are the most frequent cause of familial ARVC, occurring in up to 70% of the patients (van Tintelen et al). Until recently, the yield of attempts to identify genetic mutations in ARVC patient cohorts via traditional sequencing was only ~50% (Cox et al, Quarta et al). In this study our aim was to identify the disease gene in families currently considered “unsolved” (without a known genetic factor potentially explaining the phenotype). For this purpose, we used exome sequencing (ES), i.e. sequencing of all protein-coding regions of the genome, to identify (potentially novel) disease genes in inherited cardiomyopathy patients/families. Since the inheritance pattern in the families studied was most likely to be autosomal dominant, and ES is well known to result in a huge number of heterozygous variants (potential mutations as well as benign variants), the data analysis was much more challenging than identifying the cause of the disease in a recessive form of the disease (such as in the rare cases of consanguinity). Hence, it was of special importance to narrow down the search for causal variants into chromosomal regions of particular interest. For this purpose, we combined ES with a haplotype sharing test (HST). HST has previously been shown to be a crucial step for successfully identifying regions carrying causative genes in cardiomyopathy families that are too small for classical linkage analysis (van der Zwaag et al). We applied HST as a filtering method during data analysis, and this helped us to prioritize the long list of genes containing heterozygous variants.

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Using this combined approach of ES and HST, we succeeded in identifying the disease gene or putative disease gene in six out of our twelve families with autosomal dominant cardiomyopathies. We identified five potential disease genes, of which three were novel, one had occasionally been associated with cardiomyopathies, and one was the known cardiomyopathy gene TTN, which was not routinely screened for at that time due to its enormous size.

MATERIALS AND METHODS Patients Families were selected because multiple affected members were available for HST analysis and because, in all cases, previous Sanger sequencing approaches and, in most cases, gene-panel-based NGS had not resulted in the identification of a pathogenic mutation or likely pathogenic variant. Eleven families were recruited from the cardiomyopathy cohort of the University Medical Center Groningen, the Netherlands, and one family was recruited from the University Medical Center Nijmegen, the Netherlands. The DCM patients were diagnosed according to established clinical criteria (Mestroni et al). One family had ARVC fulfilling the task force criteria (TFC) (Marcus et al), and one family had five family members with suspected ARVC, but not yet fulfilling all of those criteria. Our approach included (1) for most families, pre-screening of patients using gene-panel-based NGS targeting 55 known cardiomyopathy genes, and subsequent selection of candidate patients/families (some families were analysed using our gene-panel-based approach during the course of this study); (2) HST of all available affected family members and subsequent data analysis; (3) ES of at least two family members who are as distantly related as possible; (4) identification of probable disease regions and genes; (5) confirmation and co-segregation analysis; (6) mutational screening of probable disease genes in additional patients; and (7) co-expression network analysis to obtain supportive evidence of pathogenicity.

Targeted sequencing DNA samples isolated from peripheral blood of patients were sequenced for 55 known cardiomyopathy disease genes as formerly described by SikkemaRaddatz et al and Posafalvi et al (manuscript in preparation, see also chapter 4.1). Data analysis was performed using the MiSeq reporter program (Illumina, San

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Diego, CA, USA), Next Gene software (v2.2.1, Softgenetics, State College, PA, USA) and Cartagenia software (Cartagenia, Leuven, Belgium), as described (Sikkema-Raddatz et al, chapter 4.1).

Haplotype sharing test

CHAPTER 3.1

To establish haplotypes and to identify possible shared haplotypes, single nucleotide polymorphism (SNP) genotyping of the DNA samples was performed using the Human 610-quad beadchip® 610K SNP array (Illumina) according to the manufacturer’s protocols. The data was analysed using Microsoft® Office Excel 2007 (Microsoft, Redmond, WA, USA) software as previously described by van der Zwaag et al. The longest shared haplotypes (LSH) identified were used for “ranking” candidate variants in the last step of the exome sequencing data analysis. In this step we assume that the longer a shared region is between affected family members, the higher the chance that it contains the mutual causative mutation. In the cases in which the mutation identified was not localised in the 1st LSH, we checked if those chromosomal regions which ranked better than the one carrying the mutation contained any cardiomyopathy candidate genes, and mutations in those genes were excluded. Additionally, the array data was also used for quality control purposes: we performed a concordance check between the genotyping and exome sequencing datasets to exclude potential sample-swaps during the experimental procedures.

Exome sequencing Exome sequencing was performed on Illumina HiSeq2000 sequencers in paired end mode and 100bp read lengths following sample preparations using SureSelect exome capture kit All Exon V4 or V5 (Agilent Technologies, Inc., Santa Clara, CA, USA) enrichment according to the manufacturer’s protocol. The raw Fastq files were aligned by using bwa-0.5.9 to the human reference genome (hg 19, NCBI build 37) (Li et al, 2009a), SAM/BAM files were manipulated by Samtools-0.1.10 and Picard-1.57 (Li et al, 2009b). Then the Genome Analysis Toolkit was used to perform base quality score recalibration, duplicate removal and INDEL realignment (McKenna et al). The output vcf files were annotated by our in-house bioinformatics pipeline and SeattleSeq (http://gvs.gs.washington.edu/).

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Data analysis, filtering and prioritization After quality filtering of the data and checking the concordance of SNP calls from the genotyping and sequencing platforms, we used various, generally accepted, data filters in our analysis. These included filtering data for a minimal read depth, checking the allele balance (and only keeping heterozygous variant calls), and using a population frequency filter. From the remaining list of variants, we focused on those novel or rare coding variants that were shared among affected family members. At this step, we implemented both negative and positive filters. For instance, it is well known that olfactory receptor genes exhibit unusually high genetic variability between individuals (Waszak et al), hence those variants do not seem relevant in DCM (negative filter). On the other hand, we looked carefully at variants in genes which had been previously associated with cardiomyopathy or heart-specific phenotypes. For this purpose, we not only focussed on known cardiomyopathy genes, but also included genes known to show cardiac expression or found to be important for abnormal cardiomyocyte proliferation, or associated with a thin myocardial wall or other cardiac phenotypes in a heart-specific protein network built purely upon functional data (such as mouse models, yeast-twohybrid screening or other sources of experimental proteomics data, Lage et al, 2010) (positive filter). In addition, we performed thorough data-mining taking into consideration everything known about those genes that remained at the end of the analysis: their known function, their potential cardiac expression, and the existence of any pseudogenes. In parallel with this last step, the remaining variants were ranked according to their localization into one of the shared haplotypes of considerable size within the family and their putative pathogenicity (for details on variant classification, see also chapter 4.1; for a decision tree during our exome sequencing data analysis, see figure 1).

Mutation screening Sanger sequencing was performed for validation of the ES results in the DNA of the index patients, for segregation analysis of the identified genetic variants (potential mutations) within families, as well as for screening in larger patient cohorts (where appropriate). Primer sequences are available upon request. In order to screen for additional mutation carriers of the COBL mutation c.998G>A; p.(Arg333Gln) identified in family 5, restriction digestion analysis

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SEQUENCING DATA read depth (exclude nucleotides of coverage T; p.(Arg27373*) and Family 2, DCM: c.75607delA; p.(Ser25203Valfs*29), (NM_001256850.1) We have identified a disease-causing variant, c.82117C>T; p.(Arg27373*), in the TTN in family 1. The variant was a novel nonsense mutation encoded in the 3rd longest shared haplotype (2q14.1q31.1), and was shown to co-segregate with the disease phenotype in all six affected family members for whom a DNA sample was available (this family is also described in chapter 4.2). Mutations of TPM1 (candidate gene localised in the 2nd LSH) were excluded by Sanger sequencing. Likewise, in family 2 another novel TTN mutation, c.75607delA; p.(Ser25203Valfs*29), was identified in the two family members who were analysed by ES and in this case the

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CHAPTER 3.1

TTN gene was located in the second largest haplotype (2p11.2q33.1). No additional affected family members were available for this analysis. TTN is known to play a role in sarcomere assembly and stabilization (Granzier & Wang) and has been associated with heart failure (Hein et al) and cardiomyopathy (Gerull et al) for decades, but has not been extensively sequenced in patients due to its huge genomic size (TTN is the largest gene of the human genome with a length of ~0.3 Mbp). Currently, TTN is suggested to be involved in dilated, restrictive, hypertrophic, and arrhythmogenic right ventricular cardiomyopathies (Gerull et al, Peled et al, Satoh et al, Taylor et al) and was recently reported to carry truncating mutations in up to 25% of familial DCM cases (Herman et al). Furthermore, our group has performed functional analysis of the TTN isoform composition combined with single cardiomyocyte passive force measurements on another truncating TTN variant (p.Lys15664Valfs*13) recently identified in a peripartum cardiomyopathy patient (van Spaendonck-Zwarts et al, 2014). What we showed based on these analyses is that the physiological function of the sarcomeres was affected by the presence of the TTN variant. Due to the technical advances made in parallel with our initial exome sequencing studies, we have implemented a gene-panel-based NGS approach in order to analyse 55 known cardiomyopathy genes in the genome diagnostics laboratory of our department during the course of this project. This method is currently used as a routine screening step before applying exome sequencing on gene-panel negative patients only (see also Sikkema-Raddatz et al, chapter 4.1 of this thesis, and figure 1 in chapter 5). A remarkable advantage of this technical improvement is that all 363 exons of the TTN gene have now also been included in our targeted diagnostic approach. This resulted in the identification of TTN truncating mutations in up to 15% of criteria-positive DCM cases (see also chapter 4.1). Family 3, ARVC: FHL2 c.698_699delinsAA; p.(Gly233Glu) (NM_201555.1) In this family, we identified a putative mutation in the four and a half LIM domains 2 gene (FHL2), which is known to be much more prominently expressed in the heart than in other organs (Chan et al). Even though FHL2 seems not to be required for the embryonic development of the heart and its full knock out in mice does not cause any cardiac phenotype up to 15 months of age (Chu et al), the stress of sustained β-adrenergic stimulation by soproterenol treatment lead to cardiac hypertrophy in these animals (Kong et al, 2001).

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figure A

family 1

Family 1 A)

family 1

B)

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figure B

figure A

figure B

family 2

B)

CHAPTER 3.1

family 2

Family 2 A)

AUTOSOMAL DOMINANT CARDIOMYOPATHIES

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figure A

family 3

Family 3

figure A

family 4

Family 4 A)

family 4

B)

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figure B

figure A

figure B

family 5

B)

CHAPTER 3.1

family 5

Family 5 A)

AUTOSOMAL DOMINANT CARDIOMYOPATHIES

121

figure A

family 6

Family 6 A)

figure B

family 6

B)

Figure 2. Pedigrees including results of co-segregation analyses (A) and HST (haplotype sharing test) results (B) of the six solved families

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CHAPTER 3.1

Recent studies have also shown that FHL2 is able to prevent pathological growth of the heart via the suppression of calcineurin activation that is induced by stress (Hojayev et al). Also, the overexpression of FHL2 might be the reason why ROCK2 conditional knock out mice were rescued from cardiac hypertrophy (Okamoto et al). Most importantly, a missense variant of FHL2 (p.Gly48Ser) found in a DCM patient has been reported to affect the binding of titin to the encoded protein (Arimura et al). Our putative FHL2 mutation was found in all three affected (and exome sequenced) siblings in this family. Unfortunately, the unaffected parents were not available for carriership analysis, nor were further affected family members available for co-segregation analysis, and HST was not performed in this family. Nonetheless, we classified this mutation as likely pathogenic because it is novel (i.e. not present in any control populations), the affected residue is localised in an evolutionarily highly conserved region of the 4th LIM zinc-binding domain, and the mutation is suggested to be deleterious by most protein effect prediction programs. Family 4, ARVC-like: FLNC c.6864_6867dup; p.(Val2290Argfs*23) (NM_001458.4) We identified a potentially causative mutation in a gene-panel negative family with several family members suspected of ARVC, yet none fulfilling TFC in the filamin C gene (FLNC), which encodes an actin-crosslinking phosphoprotein (van der Flier & Sonnenberg). FLNC is highly expressed in murine cardiac and skeletal muscle during embryonic development and regeneration (Goetsch et al) and localizes to the Z-disk of striated muscle and to the intercalated disks in the heart (van der Ven et al, 2000). It is expected to have an essential role in the maintenance of the structural integrity of the cell and to protect it against mechanical stress as was observed in mutant zebrafish that suffered from enlarged hearts (Fujita et al). Moreover, FLNC was also shown to have interactions with delta- and gamma-sarcoglycan, in particular in the muscles (Thompson et al). FLNC mutations are known to cause distal and myofibrillar myopathy and might also affect the heart (reviewed by Selcen & Carpén), but had not thus far been associated with cardiomyopathy (or ARVC in particular), although a FLNC mutation associated with arrhythmia and late onset myofibrillar myopathy has been reported (Avila-Smirnow et al). Moreover, it is known that FLNC, along with other sarcomere genes (MYH7, TNNI3, TNNT2), shows differential splicing in failing heart, DCM and aortic stenosis (Kong et al, 2010). The insertion identified in this family causes a frameshift in exon 41 (which encodes filamin repeat 20 and mediates interaction with XIRP1 according to

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the Uniprot database, www.uniprot.org) leading to a premature stop 23 codons after the affected codon, and, as a consequence, the loss of filamin repeats 21-24 (unless the full sequence is subject to nonsense-mediated decay). The mutation was absent from control populations. Moreover, in the 6500 exomes of the ESP project, no truncating mutations were identified except for two truncations in the last but one exon of the gene, which probably do not have a large effect on the protein level and might escape nonsense-mediated decay. All affected family members for whom material was available were shown to carry the mutation. The mutation is located in the 26th longest shared haplotype (7q31.32q35), which is still a shared haplotype of considerable size (29.73cM), although in this particular case HST was not used for variant prioritization. The shared mutation was identified after exome sequencing and data analysis of four affected siblings (II:2, II:5, II:6, II:8) and filtering using exome data of one unaffected sibling (II:3). Family 5, DCM: COBL c.998G>A; p.(Arg333Gln) (NM_015198.3) In this family suffering from an unusually mild and low penetrant form of DCM, we identified a putative missense mutation in the cordon bleu WH2 repeat protein gene (COBL) localized in the second longest shared haplotype (7p14.1q11.22). At the same time, no mutations were found in the FKTN gene located in the 1st LSH, nor in the cardiomyopathy gene-panel. Even though this mutation affects a highly conserved region of the protein, we classified it as a variant of unknown significance (VOUS) due to the contradictory pathogenicity predictions and the fact that the variant was found with an allele frequency of 0.12% in the ESP database and present in only one individual within the genome of the Netherlands project. This VOUS co-segregated with the mild and low penetrance, late-onset DCM phenotype in the family. The paediatric patient (V:1 in the pedigree, see figure 2) was not carrying the same VOUS, but her severe symptoms and early onset of disease might indicate an independent cause of disease, perhaps according to a recessive inheritance pattern. The COBL protein is known to be of key importance in cytoskeletal dynamics as a very potent actin nucleator promoting the construction of long, unbranched filaments by elongation at the barbed ends (Ahuja et al). The knock out of the COBL homologue in zebrafish was previously found to cause developmental problems of the nervous system due to the inhibition of motile cilia causing insufficient determination of the left-right asymmetry axis. Interestingly, zebrafish also exhibited problems in the embryonic

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AUTOSOMAL DOMINANT CARDIOMYOPATHIES

CHAPTER 3.1

development of the heart (the direction of heart looping was disturbed), which is not unexpected given that the heart, just like the nervous system, develops from a ciliated cell layer called Kupffer’s vesicle. Unfortunately, there is no information available about whether there were any microscopic changes in the ultrastructure of muscle filaments in the hearts of these knock out animals (Ravanelli & Klingensmith). Curiously, another actin nucleator that, similar to COBL, promotes the growth of actin filaments at the barbed ends (though with somewhat weaker activity; Ahuja et al) was shown to play a role in sarcomere assembly in cardiomyocytes (Taniguchi et al). Also, a recent study showed significant association between hypertrophic cardiomyopathy and a missense variant of this gene, FHOD3 (formin homology 2 domain containing 3), and demonstrated the importance of its Drosophila homologue in normal systolic contractions of the adult heart in a knock down model (Wooten et al). To date, COBL has not been connected to heart diseases, yet it is known to be highly expressed in the heart according to the GeneCards database (www. genecards.org), and its interaction with actin filaments makes it an interesting candidate disease gene for DCM. Though a recent study investigated the functional consequences of mutating certain amino acids of the first two actin monomer binding WH2 domains of COBL by electron microscopy (Jiao et al), the potential role of the evolutionarily highly conserved KRAP motifs of the protein have yet to be discovered; one such motif is affected by the missense variant identified in our patient. In addition to the identification of this missense VOUS in affected members of family 4, we have screened a further 183 DCM index patients for carriership of this variant, and have identified one more, unrelated, paediatric patient carrying the same putative mutation. Due to the severity of the symptoms in this child, and the very early onset of the disease (at age 1 year), we anticipated that compound heterozygosity could explain her phenotype, yet no additional coding COBL variant was identified by Sanger sequencing. However, gene-panel-based NGS for 55 cardiomyopathy-related genes and the subsequent stringent variant classification in this patient resulted in the identification of a likely pathogenic missense variant c.263A>C p.(Glu88Ala) of the myosin light chain 2 gene (MYL2, NM_000432.3). The patient was confirmed to carry both mutations and we expected to identify their paternal and maternal origin, respectively. However, co-segregation analysis proved the maternal origin of both COBL and MYL2 mutations, raising the question if

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further genetic or other external factors may be behind the early manifestation of symptoms in the child. Family 6, DCM: STARD13 c.3017C>T; p.(Pro1006Leu) (NM_178006.3) A genetic variant in the START domain containing 13 (STARD13) gene was found in this family (START is the abbreviation of StAR-related lipid transfer; StAR stands for steroidogenic acute regulatory protein). The encoded protein is expected to be responsible for the binding of negatively charged small lipids such as phosphatidylcholine and fatty acids (Thorsell et al). STARD13 has been previously linked to several phenotypes including intracranial aneurysm (Yasuno et al) and insulin resistance related to metabolic syndrome (Nock et al). Combined with the facts that (1) irregular myocardial lipid turnover is a known phenomenon in dilated cardiomyopathy (Feinendegen et al) and (2) perturbed lipid metabolism, myocardial lipid accumulation, and a shift to the use of fatty acids instead of glucose as the predominant source of energy is observed in (and prior to the onset of) cardiomyopathy in diabetic patients and model animals (reviewed by Bayeva et al), these associations suggest that the genetic variant in STARD13 reported in this study could be related to disease in this family. The variant was identified in the 2nd longest shared haplotype of the family (13p13q13.3) and was classified as likely pathogenic due to its novelty, the high evolutionary conservation of the affected amino acid and the respective lipid-binding START domain, and predicted pathogenicity according to all available software. Mutations of known candidate genes were excluded by gene-panel-based sequencing, and of those affected family members tested all were found to be carriers of the STARD13 mutation. Upon the identification of this novel candidate gene, the medical records of the family were re-checked for possible signs of the diabetes mellitus or metabolic syndrome potentially associated with this mutation, but no such symptoms have been observed thus far in the patients (aged 80, 74, 67 and 53 years).

Network of the identified genes In order to gain insight into possible cardiac functions of the five genes identified in our exome sequenced families (COBL, FHL2, FLNC, STARD13, TTN), their HGNC approved gene symbols were uploaded to the Gene Network website (http://genenetwork.nl:8080/GeneNetwork), which predicted that all of them except STARD13 might potentially be involved in different cardiomyopathies using data from the Kyoto Encyclopedia of Genes

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AUTOSOMAL DOMINANT CARDIOMYOPATHIES

CHAPTER 3.1

and Genomes pathways (data not shown). Subsequently, the co-expressional network of these five genes was visualized in Cytoscape (http://genenetwork. nl:8080/GeneNetwork cytoscape.html) (figure 3). The resulting network consists of 166 genes, of which 27 were already well known to be involved in the pathogenesis of various types of cardiomyopathy: ACTC1, ACTN2, ANKRD1, CAV3, CRYAB, CSRP3, FHL1, FHL2, LDB3, MYBPC3, MYH6, MYH7, MYL2, MYL3, MYOZ2, MYPN, NEXN, PDLIM3, PLN, SCN5A, TCAP, TNNC1, TNNI3, TNNT2, TPM1, TTN, and VCL. In addition to these well characterised disease genes, the novel neonatal DCM-associated ALPK3 (alpha-kinase 3) was also present in the network. The knock out model for the mouse homologue of this gene encoding a nuclear protein kinase is known to suffer from cardiomyopathy (van Sligtenhorst et al), and we have recently discovered a homozygous mutation of the gene in the DCM-affected child of a consanguineous family (manuscript submitted). More importantly, we applied an unprecedented approach to putting the genes in a functionally meaningful perspective. While searching for this list of co-expressed genes in the database of the Cardiovascular Gene Ontology Annotation Initiative (http://www.ebi.ac.uk/QuickGO/GProteinSet?id=BHFUCL), we discovered that about 60% of the genes (100/166) have previously been manually annotated with a potential role in the physiological and/or pathological mechanisms of the cardiovascular system (table 1) based on the literature, and this is underscored by previous functional studies, as will be discussed below. For instance, triadin (TRDN) and xin actin-binding repeat containing 1 (XIRP1) are both known to be subject to tissue-specific splicing in the heart via RNA-binding motif protein 20 (RBM20), a known dilated cardiomyopathy protein that is part of the spliceosomal complex in the heart (Guo et al). TRDN is known to stimulate the ryanodine receptor-2 (RYR2) that functions as a sarcoplasmic Ca2+ release channel with the help of calsequestrin (CASQ2, also featured in the co-expression network), and in this way play a role in excitation-contraction coupling in the heart (Morad et al, Terentyev et al, 2005; Terentyev et al, 2007). Mutations of TRDN have been identified in patients with catecholaminergic polymorphic ventricular tachycardia (Roux-Buisson et al). Furthermore, the XIRP1 gene, which was formerly known as “cardiomyopathy associated 1” (CMYA1), is connected in the expression network to FLNC, and the respective protein was also shown to bind with the FLNC protein and participate in the process of sarcomere assembly and actin dynamics in cardiomyocytes (van der Ven et al, 2006).

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Green circles indicate the genes co-expressed with those five identified genes. Only those genes having multiple connections within the network are indicated: the lighter green genes are co-expressed with two of the five candidates, while the darker green ones are expressed with three of them.

Red circles indicate the five genes identified by exome sequencing.

Figure 3. Co-expression network built upon the five genes (TTN, FHL2, FLNC, COBL, and STARD13) identified in the exome sequenced cardimyopathy families

AUTOSOMAL DOMINANT CARDIOMYOPATHIES

CHAPTER 3.1

Curiously, the FLNC frameshift mutation identified in family 4 affects filamin repeat 20, which is known to mediate the binding of XIRP1. Moreover, proline-rich regions of XIRP1 were recently discovered to bind the SH3 domains of nebulin (NEB) and nebulette (NEBL), the myofibrillar proteins involved in the pathomechanism of nemaline myopathy and cardiomyopathy, respectively (Eulitz et al, Lehtokari et al, Purevjav et al). It is rather remarkable that several genes in the network are shown to be important in sarcomere assembly, and this also applies to the proteins encoded by three of the five genes we identified: COBL also functions as an actin nucleator (Ahuja et al), TTN is a known structural component of the sarcomere (Horowits et al), and FLNC is also expected to play a role in the assembly process (van der Ven et al, 2000; Bönnemann et al, Fujita et al). Comparably, tropomodulin-1 (TMOD1) and leiomodin (LMOD) were shown to be involved in sarcomere assembly, as they have a role in fine-tuning the length of thin filaments in cardiomyocytes. TMOD1 caps the pointed end of actin filaments in the M-line of sarcomeres, while the competing LMOD2 is an actin nucleation factor that promotes sarcomere assembly in a tropomyosin-dependent way (Chereau et al, Skwarek-Maruszewska et al; Tsukada et al). In line with this, the gene encoding the cardiomyopathy-related tropomyosin (TPM1) also appears in the co-expression network of the five genes. This suggests that these genes are part of a putative common molecular pathway. The fact that the disease genes we identified by exome sequencing are connected within such a functionally meaningful co-expression network, and that it is enriched for known cardiomyopathy genes as well as genes expected to play an essential role in the heart, underscores the usefulness of such databases in interpreting highthroughput genetic findings. Furthermore, our finding that this network is enriched for the sarcomeric components is in line with the recent observation in a cohort of 639 DCM patients that 14% of the known pathogenic mutations were related to the sarcomeric structure, making this the most frequently mutated cellular compartment in the disease (Haas et al). One of the limitations of our study is that we have not yet found additional patients with the same mutations, or with other relevant genetic variants in some of the candidate genes. Although our approach of combining HST and ES did help deal with the relatively small size of families, the limited number of affected individuals available in this study might have influenced our findings. Segregation analysis supported putative pathogenicity of the identified variants in most families, yet it is always challenging to perform

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gene ABRA ACTA1 ACTC1 ACTN2 ADPRHL1 ADSSL1 ALPK3 AMOTL2 AMPD1 ANKRD1 ANKRD2 ANXA3 APOBEC2 ASB2 ASB5 ATP1A2 ATP2A1 ATP2A2 AXL BAG2 BDNF C10orf7 CA3 CACNA1S CACNB1 CACNG1 CALD1 CAND2 CAP2 CASQ1 CASQ2 CAV3 CFL2 CHRNA1 CHRNB1 CHRND CKB CKM CMYA5 CNN1 COBL CORO6 COX6A2 CRYAB CSRP3 DMPK DUSP13 DUSP27 EEF1A2 ENO3 FABP3 FHL1 FHL2 FLNC HFE2 HRC HSPB3 HSPB6 HSPB7 HSPB8 IP6K3 ITGB1BP2 ITGB1BP3 KBTBD5 KERA LDB3 LMOD1 LMOD3 LRRC2 MB MLIP MURC MUSK

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name of gene Actin, alpha skeletal muscle Actin, alpha cardiac muscle 1 Alpha-actinin-2

AMP deaminase 1 Ankyrin repeat domain-containing protein 2

Sodium/potassium-transporting ATPase subunit alpha-2 Sarcoplasmic/endoplasmic reticulum calcium ATPase 1 Sarcoplasmic/endoplasmic reticulum calcium ATPase 2 Brain-derived neurotrophic factor Voltage-dependent L-type calcium channel subunit alpha-1S Voltage-dependent L-type calcium channel subunit beta-1 Voltage-dependent calcium channel gamma-1 subunit Caldesmon Calsequestrin-1 Calsequestrin-2 Caveolin-3 Acetylcholine receptor subunit alpha Acetylcholine receptor subunit beta Acetylcholine receptor subunit delta Creatine kinase B-type Creatine kinase M-type Calponin-1 Protein cordon-bleu Cytochrome c oxidase subunit 6A2, mitochondrial Alpha-crystallin B chain Cysteine and glycine-rich protein 3 Myotonin-protein kinase many other DUSPs many other DUSPs Beta-enolase Fatty acid-binding protein, heart Four and a half LIM domains protein 1 Four and a half LIM domains protein 2 Filamin-C Sarcoplasmic reticulum histidine-rich calcium-binding protein Heat shock protein beta-7 Inositol hexakisphosphate kinase 3 Integrin beta-1-binding protein 2 Keratocan Leiomodin-1 Myoglobin Muscle, skeletal receptor tyrosine-protein kinase

EXOME SEQUENCING

found by exome sequencing known in cardiomyopathy listed in Cardiovascular Gene Ontology Annotation Initiative

Table 1. List of the five genes identified in the exome sequenced cardiomyopathy families and the genes of the additional 161 co-expressed proteins Genes in black have been previously studied in the context of cardiomyopathies and connected to the disease, or have been included in the Cardiovascular Gene Annotation Ontology Initiative database on the basis of data-mining and functional studies suggesting a putative role in cardiovascular physiology or pathophysiology. Genes in grey have not been previously connected to cardiomyopathies or annotated with a putative role in cardiovascular physiology or pathophysiology.

Myosin-binding protein C, slow-type Myosin-binding protein C, fast-type Myosin-binding protein C, cardiac-type Myosin-binding protein H Myogenic factor 6 Myosin-1 Myosin-11 Myosin-2 Myosin-3 Myosin-6 Myosin-7 Myosin-8 Myosin regulatory light chain 2, ventricular/cardiac muscle isoform Myosin light chain 3 Myosin light chain 4 Myosin regulatory light chain 2, atrial isoform Myosin light chain kinase 3 Myosin regulatory light chain 2, skeletal muscle isoform Myoblast determination protein 1 Myoferlin Myogenin Myomesin-1 Myomesin-2 Myotilin Myozenin-1 Myozenin-2 Nexilin Podocin Natriuretic peptides A Natriuretic peptides B

Cardiac phospholamban

CHAPTER 3.1

MYBPC1 MYBPC2 MYBPC3 MYBPH MYF6 MYH1 MYH11 MYH2 MYH3 MYH6 MYH7 MYH8 MYL2 MYL3 MYL4 MYL7 MYLK3 MYLPF MYO18B MYOD1 MYOF MYOG MYOM1 MYOM2 MYOT MYOZ1 MYOZ2 MYPN NEXN NPHS2 NPPA NPPB NRAP OBSCN PACSIN3 PDLIM3 PDLIM5 PFKM PKIA PLN POPDC2 PPP1R27 PPP2R3A PRKAA2 PYGM RAPSN RBFOX RBM24 RP11-59J5.1 RP11-766F14.2 RRAD RTN2 RYR1 SCN4A SCN5A SGCA SGCG SH3BGR SLN SMPX SMTNL1 SMTNL2 SOX10 SRL SRPK3 STAC3 STARD13 SYNPO2 SYNPO2L TAGLN TCAP TECRL TGFB1I1 TMOD1 TNFRSF12A TNNC1 TNNC2 TNNI1 TNNI2 TNNI3 TNNT1 TNNT2 TNNT3 TPM1 TPM2 TRDN TRIM63 TTN UNC45B VCL VGLL2 XIRP1 ZFP106

many other PPP1Rs many other PPP2Rs 5’-AMP-activated protein kinase catalytic subunit alpha-2

Ryanodine receptor 1 Sodium channel protein type 4 subunit alpha Sodium channel protein type 5 subunit alpha Alpha-sarcoglycan Gamma-sarcoglycan Sarcolipin Small muscular protein

SRSF protein kinase 3 StAR-related lipid transfer protein 13 Transgelin Telethonin Tropomodulin-1 Troponin C, slow skeletal and cardiac muscles Troponin C, skeletal muscle Troponin I, slow skeletal muscle Troponin I, fast skeletal muscle Troponin I, cardiac muscle Troponin T, slow skeletal muscle Troponin T, cardiac muscle Troponin T, fast skeletal muscle Tropomyosin alpha-1 chain Tropomyosin beta chain Triadin Titin Protein unc-45 homolog B Vinculin Transcription cofactor vestigial-like protein 2 Xin actin-binding repeat-containing protein 1 IN TOTAL: KNOWN IN CARDIOMYOPATHY: ANNOTATED WITH PUTATIVE CARDIOVASCULAR ROLE:

166 genes 28 genes (17.47%) 100 genes (60.24%)

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this for a late-onset disease such as cardiomyopathy because the healthy or affected status of family members is sometimes questionable. This fact, combined with the occasional presence of phenocopies, makes the accurate phenotyping of relatives sometimes difficult and might hamper the accurate analysis of ES and HST data. We cannot fully exclude the possibility that these two issues might have affected the outcome (especially the lack of any mutation being identified) in some of our families. On the other hand, having no genetic cause of disease identified in half of our families might also be due to other, technical problems, such as a lack of sufficient coverage of the respective mutation/disease gene in the available ES data. It has been anticipated that the revolutionary development of new genetics methods necessitate the application of appropriate bioinformatic tools and functional follow up to better interpret the respective results (Singleton, 2014). A very appealing, recent, example of combining exome sequencing with the creation of networks in neurodegeneration was published by Novarino et al. This group identified mutations of novel candidate disease genes in consanguineous families of hereditary spastic paraplegias (HSP), and validated their findings by discovering additional novel genes (and their mutations) selected from the protein interaction network of these novel candidate genes and already known HSP disease genes. Although there have been protein-protein interaction networks created for certain cardiac phenotypic traits (but not for cardiomyopathies) (Lage et al, 2010; Lage et al, 2012), this is the first example of combining exome sequencing and the use of a co-expression based network for the interpretation of the role of potential cardiovascular disease genes and pathways in inherited cardiomyopathy. Admittedly, the network of genes created in this study is based on shared mRNA expression patterns instead of interactions at the protein level. However, in comparison with protein interaction networks, it has the advantage of not creating a bias through exclusion of those genes from the network analysis that have not yet been functionally studied or otherwise shown to interact with heart-specific proteins.

CONCLUSIONS We have performed haplotype sharing tests and exome sequencing in twelve families suffering from DCM or ARVC with no identified genetic cause of the disorder. This resulted in the identification of potentially causative, heterozygous variants in six of the twelve families sequenced.

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CHAPTER 3.1

Their involvement in disease was supported by the fact that the mutations identified co-segregated with the disease; most genes were located in one of the longest shared haplotypes and were absent or present at very low frequency in control populations. Moreover, the fact that 2/3 of the genes co-expressed with these five genes (TTN, FHL2, FLNC, COBL and STARD13) are annotated with a potential function in the heart, and many are related to the process of sarcomere assembly and reorganization of the cytoskeleton, suggests that a potential common molecular pathway may connect them in cardiomyopathy. Since one of the genes discovered in two families is the well-known DCM gene TTN, it has become part of the routine in our department to first perform targeted sequencing for 55 cardiomyopathy genes (including TTN; chapter 4.1) and then to only perform exome sequencing after excluding mutations in all these known disease genes. In the future, it will be of great importance to investigate the cellular function of the COBL and STARD13 genes, as well as the molecular pathways they play a role in, and the potential involvement of the six identified mutations in the pathomechanism of DCM and ARVC. Moreover, we will try to identify underlying disease genes in the other six families by (1) reanalysing the data, (2) incorporating exome sequence data of additional affected and unaffected family members, (3) analysing the data for putative large deletions/duplications, and/or (4) applying other genomic techniques, such as RNA sequencing or whole genome sequencing.

ACKNOWLEDGEMENTS The authors would like to thank the families for participating in this study; Ludolf Boven and Elisabetta Lazzarini for technical assistance; members of the Genomics Coordination Centre, UMCG, for assistance in data analysis; Ellen Otten, Gerdien Bosman, Sandra Hermers, Rina Keupink, Jolien Klein-Wassink-Ruiter, Karin Nieuwhof, Wilma van der Roest and Marijke Wasielewski for counselling of families; and Jackie Senior and Kate Mc Intyre for editing this manuscript. Rowida Almomani was supported by the Netherlands Heart Foundation (grant 2010B164) and Anna Pósafalvi was supported by grants from the Jan Kornelis de Cock Foundation.

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Lage K, Møllgård K , Greenway S et al. Dissecting spatio-temporal protein networks driving human heart development and related disorders. Mol Syst Biol 2010;6:381 Lage K, Greenway SC, Rosenfeld JA et al. Genetic and environmental risk factors in congenital heart disease functionally converge in protein networks driving heart development. Proc Natl Acad Sci USA 2012;109(35):14035-40 Lehtokari VL, Kiiski K, Sandaradura SA et al. Mutation update: The spectra of nebulin variants and associated myopathies. Hum Mutat 2014; doi:10.1002/humu.22693 Li H & Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009;25:1754–60 Li H, Handsaker B, Wysoker A et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 2009;25:2078–9 Marcus FI, McKenna WJ, Sherrill D et al. Diagnosis of arrhythmogenic right ventricular cardiomyopathy/dysplasia: proposed modification of the task force criteria. Circulation 2010;121:1533-41 McKenna A, Hanna M, Banks E et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 2010;20:1297–303 Mestroni L, Maisch B, McKenna WJ et al. Guidelines for the study of familial dilated cardiomyopathy. Eur Heart J 1999;20:93-102 Morad M, Cleemann L, Knollmann BC. Triadin: the new player on excitation-contraction coupling block. Circ Res 2005;96(6):607-9 Nock NL, Wang X, Thompson CL et al. Defining genetic determinants of the Metabolic Syndrome in the Framingham Heart Study using association and structural equation modeling methods. BMC Proc 2009;3(Suppl 7): S50 Novarino G, Fenstermaker AG, Zaki MS et al. Exome sequencing links corticospinal motor neuron disease to common neurodegenerative disorders. Science 2014;343(6170):506-11 Okamoto R, Li Y, Noma K et al. FHL2 prevents cardiac hypertrophy in mice with cardiac-specific deletion of ROCK2. FASEB J 2013;27:1439-49 Peled Y, Gramlich M, Yoskovitz G et al. Titin mutation in familial restrictive cardiomyopathy. Int J Cardiol 2014;171(1):24-30 Posafalvi A, Herkert JC, Sinke RJ et al. Clinical utility genecard for: dilated cardiomyopathy (CMD). Eur J Hum Genet 2013;21. doi: 10.1038/ejhg.2012.276

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Chapter 3.2

Homozygous SOD2 mutation as a cause of lethal neonatal dilated cardiomyopathy

Rowida Almomani1,*, Anna Posafalvi1,*, Johanna C Herkert1, Jan G Post2, Paul A van der Zwaag1, Peter Willems 3, Cindy Weidijk1, Peter GJ Nikkels4, Richard J Rodenburg5, Richard J Sinke1, J Peter van Tintelen1, Jan DH Jongbloed1

*These authors contributed equally to these studies.

Manuscript in preparation

ABSTRACT Although cases are rare, neonatal and paediatric dilated cardiomyopathy (DCM) is a severe and often lethal disease, in which a genetic factor plays an important role in disease development. Identifying this genetic component is of major importance for parents as it enables prenatal diagnostics to be performed in their future pregnancies. Here, we report the results of homozygosity mapping followed by exome sequencing in a DCM-affected neonate in whom autosomal recessive inheritance was anticipated. This approach revealed a potentially pathogenic, homozygous missense mutation, c.542G>T, p.(Gly181Val), in the gene encoding Superoxide dismutase 2 (SOD2). SOD2 is a mitochondrial matrix protein that converts the reactive oxygen species (ROS) superoxide anion (O2–•) into H2O2, and is therefore important for preventing cellular damage due to oxidative stress. We measured the oxidation of hydroethidine and detected a significant increase in O2−• levels in the fibroblasts of the patient compared with controls. This indicates that the mutation affects the catalytic activity of SOD2, which could lead to a drastic increase in damaging oxygen radical levels in the neonatal heart and result in rapidly developing heart failure and death. In conclusion, we have identified a novel mitochondrial gene involved in severe neonatal cardiomyopathy, thus expanding the wide range of genetic factors involved in paediatric cardiomyopathies.

INTRODUCTION

CHAPTER 3.2

Dilated cardiomyopathy (DCM) is characterized by left ventricular enlargement and systolic dysfunction, which can lead to heart failure and sudden cardiac death (Fatkin et al). It is the most common type of cardiomyopathy and the major reason for heart transplantations in children. The incidence of DCM in children is estimated to be 0.57/100,000 per year, and is even higher in children below the age of one year (8.34/100,000) (Towbin et al). Approximately 25-50% of DCM cases are familial, and mutations in more than 50 genes have been reported to be associated with adult-onset familial DCM, some of which are observed in paediatric DCM as well (Somsen et al, Dellefave & McNally, Posafalvi et al). DCM-associated genes encode diverse groups of proteins including cytoskeletal, sarcomeric, ion transport, nuclear membrane and mitochondrial proteins (Somsen et al, Dellefave & McNally, Posafalvi et al). In contrast to adult DCM, knowledge about the underlying genetic causes of paediatric cases is still limited. In familial cases, mutations are regularly found in the known DCM genes (Rampersaud et al). However, these neither explain the majority of pediatric cases in which rare mutations in autosomal recessive inherited genes underlie disease, nor the cases of children whose DCM is part of a syndromic or metabolic disease (Kindel et al). Therefore, Burns et al recently concluded that approaches using gene-panel based applications targeting ‘adult’ DCM disease genes are less appropriate for the severe infantile forms of the disease, and they suggested that gene discovery is likely to proceed more rapidly when exome sequencing (ES) or genome sequencing are applied. Successful application of ES to identify the causal mutations in paediatric DCM has been recently demonstrated (Theis et al 2011, 2014; Louw et al). Here we have used homozygosity mapping followed by ES to identify the genetic cause of lethal DCM in a three-day-old Dutch girl. The homozygous mutation, c.542G>T, p.(Gly181Val), we found in the SOD2 gene (NM_000636.2) most likely affects the catalytic activity of the protein, leading to excess oxygen radical levels with strongly damaging effects in the neonatal heart.

METHODS Case report The female patient was born at 39+2 weeks gestation after a caesarean delivery due to breech presentation and meconium staining of the amniotic fluid. The pregnancy was complicated by maternal nephrotic syndrome

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at 19 weeks gestation and treated with prednisone. Her Apgar scores were 2-3 and 9, her birth weight was 2240 g (T mutation could have an effect on mRNA splicing, we performed RT-PCR with primers specific for SOD2 and designed to amplify the exon that was expected to be affected by the mutation and flanking sequences (primers are available upon request). Target regions were amplified by PCR and the PCR products were examined by 2% agarose gel and analysed by Sanger sequencing. To test for effects of nonsense-mediated decay, fibroblasts were treated with cycloheximide for 4.5 hr, followed by RNA analysis using the same procedures as those for RNA from untreated cells.

Measurement of superoxide substrate levels Fibroblasts, cultured to 70% confluence, were incubated in HEPES-Tris medium containing 10 μM hydroethidine (HEt) for 10 min at 37°C. Within the cell, HEt reacts with O2–• to form the fluorescent and positively charged product ethidium (Et) or oxyethidium. The reaction was stopped by thoroughly washing the cells with PBS to remove excess HEt. For quantitative analysis of Et emission signals, coverslips were mounted in an incubation chamber placed on the stage of an inverted microscope (Axiovert 200 M; Carl Zeiss, Jena, Germany) equipped with a Zeiss ×40/1.3 NA fluor lens objective. Et was excited at 490 nm using a monochromator (Polychrome IV; TILL Photonics, Gräfelfing, Germany). Fluorescence emission was directed using a 525DRLP dichroic mirror (Omega Optical, Brattleboro, VT) through a 565ALP emission filter (Omega Optical) onto a CoolSNAP HQ monochrome charge-coupled device camera (Roper Scientific, Vianen, the Netherlands). The imagecapturing time was 100 ms. Routinely, 10 fields of view per coverslip were analysed.

SOD2 protein’s 3D structure As the 3D-structure of the SOD2 protein is known, HOPE software was applied to predict the potential effect of the p.(Gly181Val) missense mutation on the 3D structure of the protein (Venselaar et al). Additionally, the Uniprot protein database (www.uniprot.org) was used to search for known functional features within the mitochondrial Superoxide dismutase [Mn] protein (accession number: P04179) in the region affected by the genetic variation.

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RESULTS Case report

CHAPTER 3.2

Genealogical analysis found a distant relationship between the parents 6 to 8 generations previously, suggesting an autosomal recessive inheritance (figure 1). Array-CGH showed no pathogenic copy number variations. Diagnostic Sanger sequencing results of mitochondrial DNA, isolated from fibroblasts, and of the POLG, MYL2, MYH7, LMNA, DES, SUCLA2 and RYR2 genes were normal. Respiratory chain complexes were found to function normally. Echocardiography revealed no abnormalities in the mother or father (aged 27 and 29, respectively) or in the patient’s younger brother (cardiologically evaluated aged 1 week).

Figure 1. Pedigree of a Dutch family with a child with severe, lethal DCM, in whom autosomal recessive inheritance was expected due to the pedigree composition. The patient is marked with a black symbol.

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Figure 2. Homozygosity mapping results show the second longest homozygous region (the longest autosomal homozygous region) on chromosome 6, where the SOD2 gene is located.

Homozygosity mapping Homozygosity mapping in the patient (figure 1; X:1) revealed the longest homozygous region was on the X chromosome (figure 2). The longest autosomal region of homozygosity was located on chromosome 6, between rs378512 and rs9458499 (159,949,340-162,713,427 bp; UCSC Genome Browser, build hg19), spanning 268 SNPs and 4.26 cM. This homozygous region contains 26 genes, including the SOD2 gene.

Exome sequencing ES was performed to target all exons and exon/intron junction sequences of the known genes in the human genome to identify potentially pathogenic, disease-causing mutations. Using the sequence analysis pipeline from GATK, we identified 41,621 different variants in the patient’s exome data. Data filtering was performed to exclude all known variants with a high frequency (> 1%) in the dbSNP129, the 1000 Genomes Project, GoNL, ESP6500 databases and in our in-house database. We then selected for coding variants in the remaining 325 variants and subsequently for nonsense, missense, splice site, and frame shift variants in concordance with autosomal recessive inheritance

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(i.e. homozygous or compound heterozygous variants in one gene). This resulted in the identification of a homozygous mutation, c.542G>T; p.(Gly181Val) (NM_000636.2), in the SOD2 gene located in the second longest homozygous region on chromosome 6 (figure 2). This mutation was absent from known control populations (ESP6500, GoNL, and 1000 Genomes). Our ES data was also analysed for potential causal mutations in known cardiomyopathy genes, relevant metabolic and syndromic genes, and nuclear encoded mitochondrial genes, but no putative pathogenic mutations were identified.

Sanger Sequencing, gene-panel-based resequencing and RT-PCR product analysis

CHAPTER 3.2

Using Sanger sequencing, the homozygous mutation c.542G>T; p.(Gly181Val) was confirmed in the affected child (figure 3) and in heterozygous form in her parents, but it was absent in her brother (data not shown). Furthermore, Sanger sequencing of the SOD2 gene in an additional DCM cohort of 27 different paediatric patients and 161 adult patients, and genepanel-based resequencing of the gene in more than 1,000 adult cardiomyopathy patients revealed no pathogenic SOD2 mutations. RT-PCR product analysis of RNA isolated from patient fibroblasts, and cultured both with and without cycloheximide, showed only a transcript of wild type size, indicating that this mutation has no effect on splicing.

Superoxide (O2−•) substrate levels For superoxide substrate level measurements, hydroethidine was used as an intracellular probe to measure the levels of superoxide (O2−•) in the patient fibroblasts. Notably, hydroethidine is not sensitive to H2O2. Hydroethidine is a cell-permeable compound that interacts with O2−• to form ethidium or oxyethidium. The oxidation levels of hydroethidine measured in our in vitro assay indicated a significant increase of superoxide (O2−•) levels in the fibroblasts of the patient comparable to the order of magnitude seen in complex I deficient fibroblasts (figure 4). What we could not directly determine from this data was whether the significant increase of O2−• levels resulted from a complex I deficiency or from abnormal SOD2 enzyme activity. However, mitochondrial respiratory chain enzyme activities (complexes I, II, III, IV, and V) were also measured and revealed no differences in the activity, suggesting SOD2 activity as the likely mechanism.

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control

patient Figure 3. Sanger sequencing confirmed the presence of the homozygous SOD2 variant c.542G>T, p.(Gly181Val) in the affected patient (bottom) compared to control (top) and in heterozygous form in her parents (not shown).

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Figure 4. The oxidation of hydroethidine analysis shows a significant increase of ROS (O2•−) level as measured in both the nuclear and mitochondrial fractions in the fibroblasts of the patient compared to control fibroblasts. CHAPTER 3.2

SOD2 3D structure: predicting the effect of the p.(Gly181Val) mutation Using the HOPE software we retrieved the 3D structure information of the SOD2 protein through the WHAT IF Web services, the Uniprot database and a series of DAS-servers, in order to predict the effect of the p.(Gly181Val) mutation on the protein structure. The Gly181 residue is part of a manganese/ iron superoxide dismutase domain, which is important for the main activity of the protein. The domain has a function in superoxide dismutase activity (oxidoreductase activity) and metal ion binding. According to the Uniprot database, four important amino acid residues are involved in the formation of the Mn-binding pocket that binds the manganese co-factor of the enzyme (accession number: P04179). These residues are His50, His98, Asp183 and His187. Interestingly, the aspartic acid residue of key importance (Asp183) is only two amino acids away from the Gly181 residue that was mutated in our patient. The increased size of the mutant residue is predicted to disturb the core structure of the manganese/iron superoxide dismutase domain and, as a consequence, the catalytic activity of the enzyme (figure 5).

DISCUSSION Using a combination of homozygosity mapping and ES in the patient, we detected a novel homozygous missense mutation, c.542G>T; p.(Gly181Val), in an evolutionarily highly conserved domain of the SOD2 gene located in the

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A B

Figure 5. 3D structure of the SOD2 protein: (A) Overview of the SOD2 protein in ribbonpresentation. (B) Magnification of the part of the manganese/iron superoxide dismutase domain where the mutated residue is located. The protein backbone (grey) and the side chains of both the wild-type (green) and the mutant residue (red) are shown. The mutant residue is bigger than the wild-type residue, which may disturb the core structure of this domain and affect the catalytic activity of the enzyme.

second longest homozygous region on chromosome 6. To our knowledge, this is the first report of a major role for mutated SOD2 in human disease. Two facts support the potential pathogenicity of this mutation. The first is that the mutation is located in the functionally important C-terminal manganese/ iron superoxide dismutase region of the respective protein. The second is that drastic differences between the size and the physico­chemical characteristics of the wild-type glycine (which is the smallest of all residues and its presence is known to often provide flexibility to protein structures) and the mutant valine residues are predicted to disturb the core structure in this crucially important domain. Furthermore, according to the Uniprot database, the mutation is localized only two amino acids away from one of the four Histidine/Aspartic acid residues that are involved in the binding of the manganese co-factor.

The role of the mutation Hydroethidine oxidation measurements indicated a significant increase in the levels of O2−• (one of the major ROS which are the physiological substrate of the SOD2 enzyme) in the fibroblasts of the patient; this substrate level was comparable in order of magnitude to the levels seen in complex-I-deficient fibroblasts. Since no deficiency in any of the mitochondrial respiratory chain

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complexes I-V was seen, this significant increase in O2−• could probably be explained by the pathogenic effect of the c.542G>T; p.(Gly181Val) SOD2 mutation on the function of the encoded enzyme, leading to malfunctioning and accumulation of damaging oxygen radicals in the cells and increased oxidative stress.

The role of superoxide dismutase in disease

CHAPTER 3.2

SOD2 belongs to the manganese/iron superoxide dismutase family which is one of the primary families of antioxidant enzymes in mammalian cells. These antioxidant enzymes protect cells from the damage caused by ROS. In eukaryotic cells, there are three SOD homologs: Cu/ZnSOD (SOD1), Mn/ FeSOD (manganese superoxide dismutase 2; SOD2) and extracellular SOD3. SOD2 is a mitochondrial matrix protein which converts superoxide anion (O2–•) to H2O2 which is then metabolized by glutathione peroxidase into H2O (Alscher et al). Oxidative stress is a deleterious process mediated by ROS, and it can lead to severe damage of cellular structures and their building blocks, including proteins, DNA and lipids (Valko et al). ROS are naturally formed during mitochondrial metabolism, and cells self-regulate their ROS levels by producing antioxidant enzymes (Starkov, 2008). Deficiency of one the antioxidant enzymes, such as SOD2, may affect any organ at any age, but most often affect organs with a high energy demand, such as the heart and brain, as is commonly observed in mitochondrial disorders (Meyers et al). Furthermore, it has been reported that oxidative stress and mutations in the SOD2 gene are involved in the pathogenesis of several diseases such as mitochondrial dysfunction, cancer, neurological disorders, diabetes, and many cardiovascular diseases including hypertension, atherosclerosis, and restenosis (Hedskog et al, Jenner, 2003, Louzao & van Hest, Cai & Harrison, Griendling & FitzGerald). There have also been reports of the involvement of other nuclear genes, such as TAZ (D’Adamo et al), TXNRD2 (Conrad et al, Sibbing et al), DNAJC19 (Davey et al, Ojala et al), and SDHA (Levitas et al), in mitochondrial cardiomyopathy, and this also seems applicable to the current case. Superoxide dismutase in cardiomyopathy Oxidative stress and disturbed mitochondrial respiratory function are known to play a substantial role in the development of heart failure (Huss & Kelly) and the role of the SOD2 protein in cardiomyopathy has previously been demonstrated in mice. Homozygous Sod2 knockout mice showed

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neonatal lethality due to neurodegeneration and cardiomyopathy (Li et al 1995). In addition, the intake of antioxidants improved their phenotypes of dilated cardiomyopathy and muscle fatigue and had beneficial effects on electrophysiological disturbances in heart and muscle (Koyama et al, Sunagawa et al). Interestingly, heterozygous SOD2+/- mice showed reduced SOD2 enzyme activity, yet did not exhibit any disease phenotype at 9 months of age (Li et al 1995). Likewise, the parents of the severely affected child described here, who are heterozygous carriers of the SOD2 mutation, did not show any cardiac abnormalities. Finally, chemotherapeutic (anthracyclin-induced) cardiomyopathy and heart failure is believed to be a side effect of superoxide radical accumulation leading to the induction of mitochondrial dysfunction in the heart (Thayer, 1988). In fact, this phenotype was successfully rescued in transgenic mice by the overexpression of SOD2 (Yen et al), underscoring the cardioprotective role of this enzyme in healthy individuals.

CONCLUSIONS Here we have reported the successful use of a combined approach using homozygosity mapping and exome sequencing to identify the causal mutation in the mitochondrial protein, SOD2, in a child with severe neonatal cardiomyopathy. Protein conformation predictions and functional evaluation support the role of SOD2 deficiency in the abnormally elevated levels of oxidative stress found in our patient. Oxidative stress itself is known to be involved in the development of various diseases, including cardiomyopathies. The result from our patient adds a novel, nuclear-encoded disease gene to the list of genes involved in severe mitochondrial cardiomyopathies.

ACKNOWLEDGEMENTS We thank the parents and sibling of the patient for participating in this study; Ludolf Boven and Sander Grefte for technical assistance; members of the Genomics Coordination Centre, UMCG, for assistance in data analysis; and Jackie Senior and Kate Mc Intyre for editing this manuscript. Rowida Almomani was supported by the Netherlands Heart Foundation (grant 2010B164).

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REFERENCES drial superoxide dismutase-deficient mice. Molecules. 2013 18:1383-93 Levitas A, Muhammad E, Harel G et al. Familial neonatal isolated cardiomyopathy caused by a mutation in the flavoprotein subunit of succinate dehydrogenase. Eur J Hum Genet. 2010;18:1160-5 Li H & Durbin R: Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 2009;25:1754–60 Li H, Handsaker B, Wysoker A et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25:2078–9 Li Y, Huang TT, Carlson EJ et al. Dilated cardiomyopathy and neonatal lethality in mutant mice lacking manganese superoxide dismutase. Nat Genet. 1995;11:376–81 Louw JJ, Corveleyn A, Jia Y et al. Homozygous lossof-function mutation in ALMS1 causes the lethal disorder mitogenic cardiomyopathy in two siblings. Eur J Med Genet 2014; pii: S17697212(14)00136-0. doi: 10.1016/j.ejmg.2014.06.004 Louzao I & van Hest JC: Permeability effects on the efficiency of antioxidant nanoreactors. Biomacromolecules. 2013;14:2364-72 McKenna A, Hanna M, Banks E et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20:1297–303 Meyers DE, Basha HI, Koenig MK: Mitochondrial cardiomyopathy: pathophysiology, diagnosis, and management. Tex Heart Inst J. 2013;40:385-94 Ojala T, Polinati P, Manninen T et al. New mutation of mitochondrial DNAJC19 causing dilated and noncompaction cardiomyopathy, anemia, ataxia, and male genital anomalies. Pediatr Res 2012;72:432-7 Posafalvi A, Herkert JC, Sinke RJ et al. Clinical utility gene card for: dilated cardiomyopathy (CMD). Eur J Hum Genet. 2013;21. doi: 10.1038/ ejhg.2012.276 Rampersaud E, Siegfried JD, Norton N et al. Rare variant mutations identified in pediatric patients with dilated cardiomyopathy. Prog Pediatr Cardiol. 2011;31(1):39-47 Sibbing D, Pfeufer A, Perisic T et al. Mutations in the mitochondrial thioredoxin reductase gene TXNRD2 cause dilated cardiomyopathy. Eur Heart J 2011;32:1121-33 Somsen G, Hovingh G, Tulevski I et al. Familial dilated cardiomyopathy. In: Cinical Cardioge-

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Alscher RG, Erturk N, Heath LS: Role of superoxide dismutases (SODs) in controlling oxidative stress in plants. J Exp Bot. 2002;53:1331–41 Burns KM, Byrne BJ, Gelb BD et al. New mechanistic and therapeutic targets for pediatric heart failure: report from a National Heart, Lung, and Blood Institute working group. Circulation. 2014;130:79-86 Cai H & Harrison DG: Endothelial dysfunction in cardiovascular diseases: the role of oxidant stress, Circ. Res. 2000;87:840–4 Conrad M, Jakupoglu C, Moreno SG et al. Essential role for mitochondrial thioredoxin reductase in hematopoiesis, heart development, and heart function. Mol Cell Biol. 2004;24:9414-23 D’Adamo P, Fassone L, Gedeon A et al. The X-linked gene G4.5 is responsible for different infantile dilated cardiomyopathies. Am J Hum Genet. 1997;61:862-7 Davey KM, Parboosingh JS, McLeod DR et al. Mutation of DNAJC19, a human homologue of yeast inner mitochondrial membrane co-chaperones, causes DCMA syndrome, a novel autosomal recessive Barth syndrome-like condition. J Med Genet. 2006;43:385-93 Dellefave L & McNally EM: The genetics of dilated cardiomyopathy. Curr Opin Cardiol. 2010;25:198-204 Fatkin D, Otway R, Richmond Z: Genetics of dilated cardiomyopathy. Heart Fail Clin. 2010;6:129–40 Griendling KK & FitzGerald GA: Oxidative stress and cardiovascular injury: part I: basic mechanisms and in vivo monitoring of ROS, Circulation 2003;108:1912–6 Hedskog L, Zhang S, Ankarcrona M: Strategic role for mitochondria in Alzheimer’s disease and cancer. Antioxid Redox Signal. 2012;16:1476-91 Huss JM & Kelly DP: Mitochondrial energy metabolism in heart failure: a question of balance. J Clin Invest. 2005;115:547-55 Jenner P: Oxidative stress in Parkinson’s disease. Ann. Neurol. 2003;53:S26−S38 Kindel SJ, Miller EM, Gupta R et al. Pediatric cardiomyopathy: importance of genetic and metabolic evaluation. J Card Fail. 2012 18(5):396-403 Kong A, Gudbjartsson DF, Sainz J et al. A high-resolution recombination map of the human genome. Nat Genet. 2002;31:241-7 Koyama H, Nojiri H, Kawakami S et al. Antioxidants improve the phenotypes of dilated cardiomyopathy and muscle fatigue in mitochon-

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netics. Baars H, Doevendans P, Smagt J, eds. Springer 2011, 63–77 Starkov AA: The role of mitochondria in reactive oxygen species metabolism and signaling. Ann N Y Acad Sci 2008;1147:37-52 Sunagawa T, Shimizu T, Matsumoto A et al. Cardiac electrophysiological alterations in heart/ muscle-specific manganese-superoxide dismutase-deficient mice: prevention by a dietary antioxidant polyphenol. Biomed Res Int 2014:704291. doi:10.1155/2014/704291 Thayer WS: Evaluation of tissue indicators of oxidative stress in rats treated chronically with adriamycin. Biochem Pharmacol 1988;37:2189-94 Theis JL, Sharpe KM, Matsumoto ME et al. Homozygosity mapping and exome sequencing reveal GATAD1 mutation in autosomal recessive dilated cardiomyopathy. Circ Cardiovasc Genet. 2011;4(6):585-94 Theis JL, Zimmermann MT, Larsen BT et al. TNNI3K mutation in familial syndrome of conduction system disease, atrial tachyarrhythmia and dilated cardiomyopathy. Hum Mol Genet 2014; pii:ddu297 Towbin JA, Lowe AM, Colan SD et al. Incidence, causes, and outcomes of dilated cardiomyopathy in children. JAMA 2006;296:1867-76 Valko M, Leibfritz D, Moncol J et al. Free radicals and antioxidants in normal physiological functions and human disease. Int J Biochem Cell Biol 2007;39:44-84 Venselaar H, Te Beek TA, Kuipers RK et al. Protein structure analysis of mutations causing inheritable diseases. An e-Science approach with life scientist friendly interfaces. BMC Bioinformatics 2010;11:548 Yen HC, Oberley TD, Gairola CG et al. Manganese superoxide dismutase protects mitochondrial complex I against adriamycin-induced cardiomyopathy in transgenic mice. Arch Biochem Biophys 1999;362:59-66

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Chapter 3.3

One family, two cardiomyopathy subtypes, three disease genes: an intriguing case

Anna Posafalvi, Nicole Corsten-Janssen, Paul A van der Zwaag, Jan G Post, Richard J Sinke, J Peter van Tintelen, Jan DH Jongbloed

ABSTRACT Pedigree information is often crucial in making decisions in clinical genetic counselling and diagnostics. Here we report on how pedigree information guided genetic analysis in a large, complex family with three affected individuals suffering from neonatal or late-onset dilated cardiomyopathy. Exome sequencing in combination with haplotype sharing tests led to causal mutations in the MYL2 (myosin light chain 2) and SOD2 (superoxide dismutase 2) genes in two deceased babies, respectively. Now targeted next generation sequencing based on a cardiomyopathy gene panel has revealed the possible role of another gene, JUP (junction plakoglobin), in one of the grandmothers affected with adult DCM. We present the 10-generation family pedigree that was constructed during the course of continuing genetic analyses and discuss aspects that directed diagnostic routing. We show the benefit of using pedigree data for the clinical genetic work on an intriguing familial cardiomyopathy case.

INTRODUCTION

CHAPTER 3.3

Idiopathic dilated cardiomyopathy (DCM) is a rare, progressive disease of the myocardium, usually exhibiting an autosomal dominant inheritance pattern and late onset of symptoms of heart failure (such as dyspnoea, syncope, and oedema), arrhythmias and thromboembolism. In some cases, however, cardiomyopathy may start at a very young age or just after birth, when it often proves to be lethal. This form of the disease (called neonatal or paediatric cardiomyopathy) is believed to be caused by autosomal recessive mutations. There are more than 50 genes known to be involved in cardiomyopathies, but since they only explain the disease in a relatively small proportion of patients, there must be novel genes to be discovered in many unsolved families (Teekakirikul et al, Almomani et al, see also chapter 3.2; Posafalvi et al). Pedigree information is often very important for genetic screening decisions in cardiomyopathy families. Here we report on how the family’s pedigree guided our genetic analyses in an unusual case of an extended consanguineous family that is affected by two types of dilated cardiomyopathy. The family shows a regular adult-onset disease (putatively autosomal dominant (AD)) and a severe neonatal form (putatively autosomal recessive (AR)), with three possible disease-causing genes underlying the condition.

MATERIALS AND METHODS Patients The family pedigree is shown in figure 1. Patient X:1 died at the age of 6 months from a severe neonatal form of dilated cardiomyopathy, and was later found to carry a homozygous mutation (c.403-1G>C) in an acceptor splice site of intron 6 of the known DCM gene, myosin regulatory light chain 2 (MYL2). This mutation leads to the activation of a cryptic splice site, causing a frameshift in the C-terminal EF-hand motif of the encoded protein. Functional followup experiments showed that the calcium-binding properties of the mutant molecule were perturbed (Weterman et al). Parents IX:1 and IX:2 have since had a second affected baby who died from the same disease at age 4 months. This child was also homozygous for the MYL2 mutation, which caused huge emotional distress to the family. There is one healthy older sibling (not shown in pedigree) and the mother (IX:2) had three miscarriages before patient X:1 was born. The grandmother of patient X:1, VIII:2, was diagnosed with heart failure due to dilated cardiomyopathy at the age of 54 years. Patient X:2 also

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Figure 1. The 10-generation pedigree of a family with both neonatal and lateonset cardiomyopathy. Square symbols (men), circles (women); black symbol (child affected by neonatal dilated cardiomyopathy), grey symbol (person affected by adultonset dilated cardiomyopathy), diagonal line through symbol (deceased). The pedigree is incomplete; it only indicates the degree of relationship between patients VIII:2, X:1 and X:2. The genealogical cross-links within the family were discovered by Eric Hennekam.

suffered from a lethal, neonatal form of DCM and died three days after birth (see also chapter 3.1). Her parents, IX:3 and IX:4, and her brother (not shown in pedigree) were found to be unaffected.

Homozygosity mapping SNP genotyping on a HumanCytoSNP-12 BeadChip® 300K SNP array (Illumina, San Diego, CA, USA) and data analysis by genomestudio® (Illumina)

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and Microsoft® Office Excel 2010 (Version 14.0; Microsoft, Redmond, WA, USA) software was performed as described earlier by van der Zwaag et al. We aimed to identify chromosomal regions which are homozygous in the patients or shared by the patients.

Targeted NGS

CHAPTER 3.3

Sample preparation and targeted enrichment of a panel of 55 cardiomyopathy-related genes were performed according to the manufacturer’s instructions (SureSelect XT Custom library, SureSelect Library prep kit, Agilent Technologies, Inc., Santa Clara, CA, USA), and as recently described in more detail by Sikkema-Raddatz et al. Sequencing was performed on a MiSeq sequencer (Illumina, San Diego, CA, USA) using 151 bp paired-end sequencing. Subsequent data analysis and variant filtering were performed with Next Gene (v2.2.1, Softgenetics, State College, PA) and Cartagenia (Cartagenia, Leuven, Belgium) software, as described in chapter 4.1.

Variant classification To classify the variants identified, we performed a comprehensive analysis using information on the type of variation, the evolutionary conservation of the affected residue and the residing protein-region, the frequency of the variant in numerous control populations (such as 1000 Genomes, GoNL, and ESP6500), and the pathogenicity predicted by Alamut software (version 2.3.6), PolyPhen2, AGVGD, SIFT and MutationTaster. In addition, literature and database searches for further information were implemented. Finally, we uploaded the list of variants to the Combined Annotation Dependent Depletion online variant prioritizer tool (CADD, http://cadd.gs.washington. edu/info) to obtain a list of top candidate variants that could be considered to be likely pathogenic.

RESULTS The role of the pedigree in making genetic analysis decisions The initial three-generation pedigree of this family (not shown) indicated several possible modes of inheritance for the disease, including autosomal recessive or di-/oligogenic inheritance in particular. Initially, we considered the involvement of the same mutation in homozygous form in the neonatal cases and in heterozygous form in the late-onset DCM that affected the

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grandmother. Since there were two cousins affected by the same, very rare, lethal (supposedly recessive) disease in the same family, we were curious if there was a more complex relation between the family members. After successfully extending the pedigree to 10 generations (figure 1), and discovering the multiple genealogical cross-links and distant consanguinity between the four parents of the affected babies, the inheritance model shifted towards a combination of the two forms mentioned above, i.e. the same gene, carrying an autosomal recessive inherited mutation, was anticipated to underlie disease in the neonatal cases X:1 and X:2, while the genetic cause of the disease would be independent and autosomal dominant in the grandmother (VIII:2). To find shared homozygous regions, homozygosity mapping was performed on DNA samples of the two neonatal patients. However, there was also still a possibility of finding homozygosity in X:2 and compound heterozygosity in X:1, carrying the same mutation as X:2 but in heterozygous form and combined with another, independent mutation, inherited from the grandmother (VIII:2) and causing the late-onset of her phenotype. Surprisingly, this mapping approach did not result in the identification of particularly large, shared homozygous regions (the longest such region was only 3.59 cM). Moreover, our analyses with the goal of identifying a homozygous region in X:2 that was heterozygously present in X:1 did not reveal any putative candidate gene regions either. These negative results were later supported by the identification of the homozygous MYL2 mutation in X:1 and the exclusion of this mutation in patient X:2. Subsequently, homozygosity mapping on the individual samples was performed to identify independent homozygous regions in the genome of X:2 that were not shared with X:1. When conducting this analysis for X:1, the MYL2 splice site mutation was found to be located in the 3rd largest homozygous chromosomal region of 5.89 cM (12q24.11-q24.13), which happened to be the second longest autosomal homozygous fragment in the patient. The search for independent homozygous regions in the genome of X:2 that were not shared with X:1 was combined with exome sequencing. This resulted in the discovery of a causative, recessively inherited variant in the nuclear encoded mitochondrial enzyme superoxide dismutase (SOD2). This is located in the longest autosomal homozygous region (6q25.3-q26; the 2nd longest such chromosomal region), spanning 4.26cM. Functional studies performed on the fibroblast samples of patient X:2 confirmed elevated levels of the substrate (oxygen free radicals), while the possible defect of any of the mitochondrial

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complexes was excluded. Together, these findings strongly support the expected pathogenic role of the recessive mutation, c.542G>T; p.(Gly181Val), in this novel DCM gene (Almomani et al, see also chapter 3.2). Finally, as the 10-generation pedigree indicated that the DCM in the grandmother (VIII:2) could not be genetically related to the disease in X:1 or X:2, targeted NGS was performed on her DNA. Nevertheless, we excluded her as a carrier of either the MYL2 or SOD2 variants.

Targeted sequencing identifies the third gene in VIII:2

1 FAMILY, 2 T YPES OF CARDIOMYOPATHY, 3 DISEASE GENES

CHAPTER 3.3

The grandmother of the MYL2 patient, VIII:2, was shown to not carry the MYL2 mutation, neither could she potentially carry the SOD2 mutation. Targeted sequencing using our cardiomyopathy gene-panel revealed 152 variants in total in the 55 genes covered by the panel, of which four variants remained after filtering the data with the standard parameters of our analysis pipeline. Our routine classification method pointed to RYR2 (ryanodine receptor 2), c.3517A>G, p.(Met1173Val) and/or JUP (junction plakoglobin), c.746C>T, p.(Thr249Met) as likely pathogenic variants (see details in table 1). In both cases, the respective amino acid residues are highly conserved (the Met1173 of RYR2 at least up to chicken, and the Thr249 residue of JUP up to Drosophila), and all the protein effect prediction programs we used supported the likely pathogenicity. The RYR2 variant was absent from well-known population databases, while the JUP variant was reported only once in 8,600 European American alleles in the ESP database. Subsequent application of the CADD tool strongly suggested a primary pathogenic role, as the scaled C-score for the JUP variant is an extremely high 29.8. Since CADD uses a logarithmic scale, this means a much higher predicted deleteriousness than that of the RYR2 missense variant, which scored only 15.15. Additionally, a score of ~30 indicates that the variant belongs to the top 0.1% most deleterious of all substitutions that might theoretically occur in the human genome (CADD website and Kircher et al). Hence, without any further functional confirmation of the effects of the four variants, it seems most probable, from the currently available data, that JUP is the cause of the DCM in patient VIII:2 (although a digenic background for the disease development and causative roles for both variants cannot be excluded).

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Grantham distance

21

58

155

81

gene

RYR2

SCN5A

PKP2

JUP

c.746C>T

chr17:39923794G>A p.(Thr249Met)

JUP

variant effect

c.419C>T

chr12:33031395G>A p.(Ser140Phe)

PKP2

TABLE B

c.3304G>A

chr3:38620911C>T p.(Ala1102Thr)

C0

C0

C0

C15

AGVGD

NM_002230.2

NM_004572.3

NM_198056.2

(0.007)

(0.000)

probably damaging (1.000)

benign

benign

possibly damaging (0.920)

PolyPhen2

high / high

weak /moderate

not conserved / weak

weak /high

SCN5A

NM_001035.2

c.3517A>G

chr1:237732538A>G p.(Met1173Val)

RYR2

transcript ID

nucleotide / amino acid

protein

genomic, cDNA coordinate

gene

and

conservation

variant

 TABLE A

5.41

2.38

4.30

6.17

deleterious

tolerated

tolerated

deleterious

SIFT

5.86

0.77

-2.14

1.42

splicing no effect no effect no effect no effect

disease causing (p-value: 0.998) polymorphism (p-value: 0.996) polymorphism (p-value: 0.93) disease causing (p-value: 0.999)

armadillo

-

sodium transport-associated

SPRY (SPIa/Ryanodine receptor)

conserved domain

Mutation Taster

PhyloP GERP [-14.1;6.4] [-12.36;6.18]

Table 1 (A-D). Interpretation of putative pathogenicity of variants found in the adult DCM patient VIII:2 by gene-panel-based NGS. Only four variants remained after filtering for the region of interest, read depth, known polymorphisms and artefacts, and variant frequency in various databases. According to our standardized classification system based on evolutionary conservation (table A), prediction software (table B) and allele frequencies (table C), the RYR2/JUP missense variants were classified as the most likely to be causative. According to the CADD tool, JUP p.(Thr249Met) has the highest pathogenicity rank. Table D shows our final classification of the four variants.

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mutation database

HGMD

not present

not present

present (as disease causing)

not present

our classification

 

LIKELY PATHOGENIC

VOUS

Likely Benign

LIKELY PATHOGENIC

TABLE C

gene

RYR2

SCN5A

PKP2

JUP

TABLE D

gene

RYR2

SCN5A

PKP2

JUP 5,033,031

1,673,660

-0.582387

2,746,820

RawScore

CADD results

29.8

11.56

1,385

15.15

PHRED

rs377612199

rs150821281

-

-

rs number

CHAPTER 3.3

db134, validated, clinical significance: probable non-pathogenic allele; MAF: 3/2184=0.001 db138, no validation, no frequency data (0/2184 in 1000genomes)

-

not present

dbSNP

control database frequencies

CAUSATIVE VARIANT

 

 

 

 

final conclusion

not present

EA: A=1/G=8599 (ALL: 1/6503=0)

not present

not present (in about 13000)

6/996=0.006

not present

not present (in about 13000)

EA: A=26/G=8575 (ALL: 26/6503=0.004)

GoNL

ESP

DISCUSSION Here we report on the unusual and rare example of a multigeneration, triple-consanguineous family that is affected by two distinct types of cardiomyopathy (namely, an adult onset form and a lethal neonatal form). Three different genes have been associated with the respective phenotypes in the three patients. The family’s large, ten-generation pedigree played an important role in guiding the genetic analyses to uncover the mutations causing the cardiomyopathy disease types observed in the family. Although it was at first unexpected that the two affected babies would have different genetic causes of their disease, the genealogical reconstruction of the pedigree clearly indicated that they could have independent causes. This observation was further substantiated by the homozygosity mapping on the neonates’ DNA samples, which did not result in the identification of any obvious candidate regions. Based on the pedigree composition, one can quickly appreciate that the founder of the SOD2 mutation and the founder of the MYL2 mutation are likely to be ancestors from different branches of the family. The MYL2 mutation was most likely inherited from I:2, while potentially I:2, I:3, III:5 or III:6 could have been the individuals with the initial SOD2 mutation. Although the cardiac symptoms of both recessive patients seemed to be comparable at first glance, a systematic evaluation revealed clear differences in their phenotypes. Both were suffering from lethal neonatal cardiomyopathy, but the baby with the homozygous MYL2 mutation had myopathy with fibre type disproportion type 1, while the baby with the homozygous SOD2 mutation suffered from subependymal cysts (which is a possible manifestation of mitochondrial disease affecting the central nervous system), and she did not have typical skeletal muscle problems. For both genes, homozygosity mapping on the individual DNA samples (as shown in figure 2) supported their involvement in disease: MYL2 and SOD2 are localized in one of the longest autosomal homozygous regions of the patients (SOD2 in the 1st, MYL2 in the 3rd longest region). In fact, this was a major determinant in the completion of the exome sequencing data analysis for X:2. The fact that both patients carried relatively small homozygous regions (including those “relatively large” ones harbouring the causal mutations) supports the idea that both mutations are quite old and have been inherited from a founder many generations ago. We have identified the variant p.(Thr249Met) of JUP as the most likely cause of disease in VIII:2. Upon stringent filtering of the respective gene-

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Figure A

CHAPTER 3.3

A)

Figure B

B)

Figure 2. SNP genotyping results for patients X:1 and X:2. Homozygous regions identified in patient X:1 (A) and patient X:2 (B) are shown. The genes identified as causative are marked in the 2nd and 3rd longest homozygous regions, respectively.

1 FAMILY, 2 T YPES OF CARDIOMYOPATHY, 3 DISEASE GENES

167

panel-based targeted NGS data, two of the remaining four variants (the above-mentioned JUP and a missense variant of RYR2) were predicted and classified as “likely pathogenic”. The same JUP variant was previously reported as an incidental finding in 1/1236 alleles of exome-sequenced individuals (non-selected for cardiomyopathy, arrhythmia, or sudden death, though some of them having an increased risk or a history of coronary artery disease), and was also classified as a variant of unknown clinical significance (Ng et al). This finding neither supports, nor excludes the putative role of the variant in cardiomyopathy development. However, the use of the new CADD online variant prioritizer tool pinpointed this JUP p.(Thr249Met) variant as an order of magnitude more likely to be disease-causing than the second best candidate, an RYR2 missense variant. According to the Uniprot database (accession number: P14923), the JUP variant is located in the third ARM repeat of the encoded junction plakoglobin protein, which spans amino acids 216-255. It is involved in the interaction with desmocollin and desmoglein (Witcher et al), the cadherins known to play a role in cell adhesion and desmosome formation (Garrod et al). It is important to note that heterozygous missense mutations of neither JUP nor RYR2 have been associated with DCM so far, although both have been associated with arrhythmogenic right ventricular cardiomyopathy (ARVC). At this stage, we cannot exclude the possibility that the combination of both variants were the trigger to the development of DCM, or that an unidentified gene was also involved in the disease. However, according to our current data, the role of the JUP variant seems the most probable, and this could be easily followed up by functional experiments investigating the potentially impaired binding of desmocollin and desmoglein in the presence of the variant. Additionally, a recent study on 639 DCM patients suggested that the genetic overlap between various types of cardiomyopathy is much more extensive than previously estimated; it reported that 31% of the truly pathogenic mutations of DCM patients are mutations of typical ARVC-related genes and have been previously associated with ARVC (Haas et al). This intriguing family nicely exemplifies the importance of extensive analysis of the family history by pedigree reconstruction in genetic counselling. These genealogical studies led to an easier interpretation of why the MYL2 mutation was not found in patients VIII:2 and X:2, as well as of the rare recessive phenotypes caused by the two different genes. The common ancestor carrying the founder MYL2 mutation can be identified nine generations ago, and the one carrying the SOD2 mutation either nine or seven generations ago.

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Our homozygosity mapping data supports the idea that both mutations are old and were transmitted through multiple generations. The improved understanding of the genetic background of the family has essential practical implications too. The parents of both X:1 and X:2 have been counselled that they have a 25% recurrence risk due to their carriership of an autosomal recessive disease. With the identification of the causative gene, they can now consider the reproductive options available (e.g. prenatal screening). This is of outmost importance - especially given that IX:1 and IX:2 have, in the meantime, had a second baby who was also affected by the same lethal disorder. Fortunately, prenatal diagnosis in a very recent pregnancy of IX:4 indicated that the foetus was not a homozygous carrier of the SOD2 mutation. Thus, with good genetic counselling and prenatal screening, this family should be able to avoid having any more seriously affected children.

CHAPTER 3.3

ACKNOWLEDGEMENTS We would like to acknowledge all those involved in counselling the distinct branches of this family. We thank Eric Hennekam, UMCU, for the pedigree construction; Jos Dijkhuis and his team in the Genome Diagnostics laboratory, Department of Genetics, UMCG, for technical support and performing the molecular genetic tests; and Jackie Senior and Kate Mc Intyre for editing this manuscript.

REFERENCES Almomani R, Posafalvi A, Herkert JC et al. Homozygous SOD2 mutation as a cause of severe neonatal dilated cardiomyopathy (manuscript in preparation, see also chapter 3.2) Garrod DR, Merritt AJ, Nie Z. Desmosomal cadherins. Curr Opin Cell Biol 2002;14:537-45 Haas J, Frese KS, Peil B et al. Atlas of the clinical genetics of human dilated cardiomyopathy. Eur Heart J 2014; pii: ehu301 Kircher M, Witten DM, Jain P et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet 2014;46:310-5 Ng D, Johnston JJ, Teer JK et al. Interpreting secondary cardiac disease variants in an exome cohort. Circ Cardiovasc Genet 2013;6:337-46 Posafalvi A, Herkert JC, Sinke RJ et al. Clinical utility gene card for: dilated cardiomyopathy (CMD). Eur J Hum Genet 2013;21(10). doi: 10.1038/ejhg.2012.276

Sikkema-Raddatz B, Johansson LF, de Boer EN et al. Targeted next-generation sequencing can replace Sanger sequencing in clinical diagnostics. Hum Mutat 2013;34:1035-42 Teekakirikul P, Kelly MA, Rehm HL et al. Inherited cardiomyopathies: molecular genetics and clinical genetic testing in the postgenomic era. J Mol Diagn 2013;15:158-70 van der Zwaag PA, van Tintelen JP, Gerbens F et al. Haplotype sharing test maps genes for familial cardiomyopathies. Clin Genet 2011;79:459-67 Weterman MA, Barth PG, van Spaendonck-Zwarts KY et al. Recessive MYL2 mutations cause infantile type I muscle fibre disease and cardiomyopathy. Brain 2013:136;282-93 Witcher LL, Collins R, Puttagunta S et al. Desmosomal cadherin binding domains of plakoglobin. J Biol Chem 1996;271:10904-9

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CHAPTER 4 TARGETED SEQUENCING

Chapter 4.1

Gene-panel-based Next Generation Sequencing (NGS) substantially improves clinical genetic diagnostics in inherited cardiomyopathies

Anna Posafalvi*, Jan DH Jongbloed*, Renee C Niessen, Paul A van der Zwaag, Yvonne Hoedemaekers, Birgit Sikkema-Raddatz, Jos Dijkhuis, Sebastiaan RD Piers, Katja Zeppenfeld, Rudolf A de Boer, Paul L van Haelst, Daniela QCM Barge-Schaapveld, Folkert W Asselbergs, Jasper J van der Smagt, Maarten P van den Berg, J Peter van Tintelen§, Richard J Sinke§

*The first two authors contributed equally § The last two authors contributed equally

Manuscript submitted

ABSTRACT Background: Targeted next generation sequencing (NGS) is an attractive approach for the screening of multiple genes underlying genetic heterogeneous diseases, such as cardiomyopathies. We implemented an enrichment kit targeting 55 cardiomyopathy-related genes in our routine diagnostics work. The aim of this study was to determine the diagnostic yield, to evaluate the contribution of mutations in genes that were previously only infrequently or never screened for, and to obtain more insight into the suggested bigenic or multigenic inheritance patterns in a subset of patients. Methods and Results: DNA samples of 252 cardiomyopathy patients were analysed and their clinical characteristics collected. Patients with one or more variants labelled as ‘likely pathogenic’ or ‘pathogenic’ were considered to be ‘resolved’. Retrospective phenotype evaluation showed that of these 252 patients, 125 fulfilled the formal clinical criteria for a cardiomyopathy disease, 44 were suspected of having cardiomyopathy, and 37 had an unconfirmed diagnosis. We excluded 46 from further analysis. We identified pathogenic or likely pathogenic mutations in 107/206 (52%) patients: in 56% (40/72) of dilated cardiomyopathy (DCM) patients fulfilling the clinical criteria, and in 52% (12/23) of DCM-like patients. Truncating mutations in TTN were found in 14% of DCM patients. The yield in hypertrophic cardiomyopathy (HCM) and HCM-like patients was 46% (21/46) and 36% (4/11), respectively. In >50% of all our cardiomyopathy cases, we identified mutations in genes that were previously rarely analysed, and in 15% of cases, we found two or more pathogenic or likely pathogenic mutations. Conclusions: Targeted sequencing of cardiomyopathy genes results in a diagnostic yield of over 50%. In particular, our yield for genetic testing of DCM patients was substantially increased (approx. 55% vs. 20-25% earlier). As this NGS method enables a large set of genes to be screened, including some infrequently studied genes, it opens up new avenues for exploring the role of ‘rare’ genes and/or multiple mutations underlying inherited cardiomyopathies. Key Words: Next Generation Sequencing, targeted enrichment, clinical diagnostics, diagnostic yield, cardiomyopathy, genetics

INTRODUCTION

DIAGNOSTIC YIELD OF CARDIOMYOPATHIES

CHAPTER 4.1

Next Generation Sequencing (NGS) is one of the most promising developments in clinical genetics, including cardiogenetics, of the past few years (Jongbloed et al). This technique enables clinicians to make a genetic diagnosis – within a short time frame – for diseases which potentially have multiple genes underlying the phenotype. To apply NGS in a clinical diagnostics setting, the currently preferred method appears to be dedicated and reliable targeted enrichment, which provides sufficient specificity and sensitivity to replace the gold standard of Sanger sequencing (Sikkema-Raddatz et al). The use of several targeted enrichment methods (putatively) applicable for clinical diagnostics have been reported recently, with most of them using array-based enrichment and targeting a relatively small subset of genes (Harakalova et al, Almomani et al, Mook et al). However, approaches applying in-solution enrichment methods are also becoming increasingly popular (Sikkema-Raddatz et al, Lopes et al): these require smaller amounts of input DNA, while providing higher efficiency and better reproducibility, and being easier to handle (Querfurth et al, Shearer et al). Cardiomyopathies are a group of genetically and sometimes phenotypically overlapping heterogeneous disorders. The major subforms, in which over 50 disease genes have been identified, include arrhythmogenic right ventricular (ARVC), dilated (DCM), hypertrophic (HCM), left-ventricular non-compaction (LVNC), and restrictive (RCM) cardiomyopathies. Many of these genes are involved in different types of the disease (Teekakirikul et al, van Tintelen et al). In the pre-NGS era, the yield of diagnostic screening in well-defined patient cohorts varied widely: 35-70% in HCM (Christiaans et al, Pinto et al, Wilde & Behr), 20-25% in DCM (Wilde & Behr, Posafalvi et al, van Spaendonck-Zwarts et al, 2013), approximately 50% in ARVC (Cox et al, Quarta et al, te Rijdt et al), 25-40% in LVNC (Teekakirikul et al, Hoedemaekers et al), and approximately 35% in RCM (Teekakirikul et al). Since the number of genes associated with cardiomyopathies is large and still growing, this disease is an ideal candidate for the implementation of the rapidly developing NGS-based diagnostic tools. Several studies have already reported the screening of multiple cardiomyopathy genes (range 5 to 84 genes) within one experiment using NGS (Voelkerding et al, Gowrisankar et al, Zimmerman et al, Meder et al, Mook et al, Lopes et al, Pugh et al, Haas et al). Some of these studies only focused on cohorts of one type of cardiomyopathy.

175

We have recently demonstrated that the sensitivity, specificity and robustness of targeted NGS for cardiomyopathies is equal to those of Sanger sequencing (SS) (Sikkema-Raddatz et al). Subsequently, we constructed an improved enrichment kit targeting 55 cardiomyopathy genes and implemented this into our routine clinical diagnostic work. Here we report on the outcome and yield when we used this gene panel in a large cohort of cardiomyopathy patients. Patients were diagnosed with various types of cardiomyopathies including DCM, ARVC, HCM, LVNC, RCM, or with phenotypic characteristics related to cardiomyopathy, but not yet classified as a specific subtype. Their DNA was screened for variants using our 55 gene-panel-based method and, after data analysis, variant filtering and prioritization, we classified the variants found with the help of a strategy developed in-house. Our hypothesis was that implementing this test into routine diagnostics would lead to: (1) higher diagnostic yield, (2) identification of mutations in genes that were previously infrequently or never screened, and (3) provide more insight into the suggested bigenic or multigenic inheritance in a subset of cardiomyopathy patients.

METHODS Patient material DNA was isolated according to standard operating procedures from peripheral blood samples obtained from 252 cardiomyopathy patients, who were referred to our laboratory for gene-panel-based genetic analysis. Informed consent to perform the diagnostic screening was obtained from all patients. They were referred to our department by four Dutch clinical genetics centres: Groningen, Leiden, Nijmegen and Utrecht.

Targeted sequencing DNA fragment libraries were prepared according to the manufacturer’s instructions (SureSelect XT Custom library, SureSelect Library prep kit, Agilent Technologies Inc., Santa Clara, CA, USA). The following experimental steps were performed: fragmentation of genomic DNA samples, end-repair, adapter ligation, size selection, and amplification of the purified product. Targeted enrichment was performed according to the manufacturer’s instructions (Sureselect XT Custom library, Agilent Target Enrichment kit & Agilent SureSelect MP Capture Library kit, Agilent Technologies Inc.).

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CHAPTER 4.1

Hybridization of the DNA fragment libraries with the capture probes for 55 selected genes was performed, followed by purification and barcoding of the captured fragments. Finally, equimolar pools of 12 samples were prepared. Sequencing was performed on a MiSeq sequencer (Illumina, San Diego, CA, USA) using 151 bp paired-end reads according to the manufacturer’s instructions. The sample preparation, targeted enrichment and sequencing method has been described in detail by Sikkema-Raddatz et al. Capture probes of the following 55 cardiomyopathy-related genes were included in the custom designed, targeted enrichment kit. Their respective OMIM IDs are given in brackets, and genes marked by # were recently added to the improved version of the 48-gene enrichment kit described by SikkemaRaddatz et al: ABCC9 (*601439), ACTC1 (*102540), ACTN2 (*102573), ANKRD1 (*609599), BAG3 (*603883), CALR3 (*611414), CAV3# (*601253), CRYAB (*123590), CSRP3 (*600824), DES (*125660), DMD (*300377), DSC2 (*125645), DSG2 (*125671), DSP (*125647), DTNA# (*601239), EMD (*300384), EYA4# (*603550), GATAD1# (*614518), GLA (*300644), JPH2 (*605267), JUP (*173325), LAMA4 (*600133), LAMP2 (*309060), LDB3 (*605906), LMNA (*150330), MYBPC3 (*600958), MYH6 (*160710), MYH7 (*160760), MYL2 (*160781), MYL3 (*160790), MYPN (*608517), MYOZ1 (*605603), MYOZ2 (*605602), NEXN# (*613121), PKP2 (*602861), PLN (*172405), PRKAG2 (*602743), PSEN1 (*104311), PSEN2 (*600759), RBM20 (*613171), RYR2 (*180902), SCN5A (*600163), SGCD (*601411), SOD2# (*147460), TAZ (*300394), TBX20 (*606061), TCAP (*604488), TMEM43 (*612048), TNNC1 (*191040), TNNI3 (*191044), TNNT2 (*191045), TPM1 (*191010), TTN (*188840), TXNRD2# (*606448), VCL (*193065).

Sequence annotation and variant calling Data analysis was performed using the MiSeq reporter program (Illumina, San Diego, CA, USA) to generate fastq.gz output files. These were uploaded to the NextGene software (v2.2.1, Softgenetics, State College, PA, USA) and upon quality filtering, aligned to the reference genome (Human_v37.2). SNPs and indels were called, and the respective variant list was converted into the *.vcf file format for further analysis.

Variant filtering, interpretation and prioritization The *.vcf files obtained from NextGene were uploaded into the Cartagenia software (Cartagenia, Leuven, Belgium), with which variant filtering and

DIAGNOSTIC YIELD OF CARDIOMYOPATHIES

177

classification was performed (as summarized in figure 1 and described in the supplementary methods). Remaining variants were evaluated for their potential pathogenicity using in silico prediction tools and data, available via the Cartagenia and Alamut programs (versions 2.3.2 and 2.3.6, respectively; Interactive Biosoftware, Rouen, France) and/or other resources (see table 1). We took various factors into account, such as the nature and location of the variants, the conservation of this area, the frequency of the variant in the general population (when it was available in any of the healthy or patient population databases), and the predicted pathogenicity of the variant according to multiple prediction programmes. Moreover, data on variants available from the scientific literature and from disease and variant databases (such as the Leiden Open Variant Databases (LOVDs) and the ARVD/C genetic variants

Figure 1: Flowchart of the Cartagenia filtering tree used to determine our final variant list for analysis. Variant filtering strategy as used by the Cartagenia software. The input variant list contained on average 168 (± 24) variants per patient. Details of the filtering steps and strategy are described in the Supplementary methods. After performing the filtering steps, an average of 8 (± 6) variants per patient remained on the final variant list. These were classified after data-mining using Cartagenia and Alamut. Variants which were classified as ‘benign’ or ‘likely benign’ are regularly being added to our in-house database of managed variants (grey feed-back loop). The respective population control cohorts were: GoNL Genome of the Netherlands; 1000G 1000 Genomes project; ESP6500 6500 exomes from the NHLBI Exome Sequencing Project (ESP); and dbSNP the dbSNP database of NCBI.

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Table 1. Criteria for variant classification

Likely benign (LB)

Mutation type any intronic or synonymous

missense

Variant of uncertain significance (VOUS)

any

Likely pathogenic (LP) intronic

missense or synonymous

Pathogenic (P)

Criteria MAF* >0.02 No predicted# significant changes in RNA splicing No predicted# significant changes in RNA splicing AND No, or only ¼ of prediction programs^ used suggest pathogenicity AND residue and surrounding residues not evolutionary conserved Variants which do not fit into any of the other categories, or for which the available information is contradictory Large effect on recognition of consensus splice site (±1 and 2) predicted# in gene for which association of such mutation with phenotype has not yet been established AND MAF* C; p.S1362P MYH6, c.4328C>A; p.A1443D^ no TTN, c.91152T>A; p.Y30384* TTN, c.58432+2T>C no no

1 0

1

2

MYH6, c.3010G>T; p.A1004S^ no

no

no no

NEXN, c.1453G>A; p.E485K

2

NEXN, c.1174C>T; p.R392* & MYH6, c.961G>C; p.V321L

MYBPC3, c.2373dupG; p.W792Vfs*41^

1 0 1 1 0

2

RYR2, c.9454C>T; p.R3152C & LAMA4, c.3335C>A; p.P1112H LDB3, c.608C>T; p.S203L no TTN, c.89426G>A; p.R29809Q (splice) ACTN2, c.2035A>C; p.K679Q no

0 0 0 0 0 0 0

number of mutations

no no no no no no no

likely pathogenic mutation(s): gene, cDNA; protein

TNNI3, c.527G>A; p.W191*

no

no no no no no

no

no no no no no no no

pathogenic mutation(s): gene, cDNA; protein

DIAGNOSTIC YIELD OF CARDIOMYOPATHIES

199

DCM/ARVC F

F M

M

F F M

62

F

M M F F

DCM/ARVC M

LVNC HCM DCM HCM

50 51 52 53

M

61

DCM

49

M

HCM DCM

HCM

48

M

59 60

DCM

47

M

HCM

HCM

46

F

58

HCM

45

M

DCM DCM HCM

HCM

44

M M M

CM

HCM DCM HCM

41 42 43

F

55 56 57

HCM

40

M

54

HCM

39

55

47

n.a. 54

27

73 67 51

n.a.

n.a. 50 38 n.a.

50

49

59

65

64

65

n.a. 60 44

46

62

pos

pos

n.a. n.a.

pos

pos? pos pos?

n.a.

neg neg Pos n.a.

pos

neg

neg

neg

pos

neg

n.a. neg pos

pos

neg

suspected

suspected

fulfilling criteria fulfilling criteria

fulfilling criteria

fulfilling criteria fulfilling criteria fulfilling criteria

unconfirmed

suspected suspected unconfirmed unconfirmed

fulfilling criteria

fulfilling criteria

suspected

fulfilling criteria

fulfilling criteria

fulfilling criteria

fulfilling criteria fulfilling criteria fulfilling criteria

fulfilling criteria

fulfilling criteria

CHAPTER 4.1

ARVClike

DCMlike

HCM DCM

HCM

DCM DCM HCM

CM

LVNClike HCMlike DCM HCM

DCM

HCM

DCMlike

DCM

HCM

HCM

HCM DCM HCM

HCM

HCM

DSP, c.2297+2T>A

no

no no

MYBPC3, c.2373dupG; p.W792Vfs*41^

no no no

no

1

2

NEXN, c.995A>C; p.E332A & DES, c.1193T>C; p.L398P no

1 0

1

1 0 0

2

1 0 1 1

2

1

1

1

0

1

GLA, c.1153A>G; p.T385A^× no

no

JUP, c.849G>T; p.K283N no MYBPC3, c.841C>A; p.= (splice) TNNI3 c.626A>C; p.Glu209Ala^ MYPN, c.2242C>T; p.R748C & DMD, c.2827C>T; p.R943C SCN5A, c.659C>T; p.T220I^ no no

DSP, c.1778A>G; p.N593S

no no no no

ABCC9, c.4516C>T; p.R1506C

no

MYBPC3, c.3776delA; p.Q1259Rfs*72^ PLN, c.40_42delAGA; p.R14del^

no

no

no

no MYBPC3, c.3776delA; p.Q1259Rfs*72^

no

MYBPC3, c.2373dupG; p.W792Vfs*41^

0 1 0

2

no LMNA, c.992G>A; p.R331Q^× no

0

no MYBPC3, c.3065G>C; p.R1022P^ & EMD, c.149C>A; p.P50H

no no no

no

no

  200

TARGE TED SEQUENCING

CM DCM

DCM

HCM DCM DCM HCM DCM HCM DCM DCM DCM/HCM HCM DCM DCM HCM DCM HCM DCM DCM HCM

69 70

71

72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89

F M M F M F M F M F M M M M M M M F

M

F M

F

HCM

68

70 42 43 4 44 54 n.a. 50 n.a. 59 n.a. n.a. n.a. 66 56 41 54 69

55

34 50

65

n.a. pos pos pos pos n.a. n.a. n.a. n.a. Neg n.a. n.a. n.a. neg pos pos? neg neg

n.a.

neg neg

n.a.

neg n.a. pos neg neg

F M M M F

HCM DCM DCM DCM DCM

63 64 65 66 67

45 n.a. 56 41 66

Family Gender Age diagnosis History

Dx at Patient referal

HCMlike DCM DCM HCM DCM HCM DCM DCM DCMlike HCM DCM DCM HCM DCM HCM DCM DCM HCM

DCMlike

DCMlike DCMlike

HCM

HCMlike DCM DCM DCM DCM

Dx after phenotype evaluation

suspected unconfirmed fulfilling criteria fulfilling criteria fulfilling criteria fulfilling criteria fulfilling criteria fulfilling criteria suspected unconfirmed unconfirmed unconfirmed unconfirmed fulfilling criteria fulfilling criteria fulfilling criteria fulfilling criteria fulfilling criteria

suspected

suspected suspected

fulfilling criteria

suspected unconfirmed fulfilling criteria fulfilling criteria fulfilling criteria

patient categorisation

no no no no no no no no no no TNNI3 c.292C>T; p.R98*^ no no no no no no no

no

no no

MYBPC3, c.2864_2865delCT; p.P955Rfs*95^

no no no no no

pathogenic mutation(s): gene, cDNA; protein

1 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0

1

TTN, c.75332_75335dupTAAG; p.M25113Kfs*16 CSRP3, c.131T>C; p.L44P^ no no MYH7, c.2890G>C; p.V964L^ VCL, c.2467C>T; p.R823W no no no no no no no no no no no no no

0 1

1

0 0 1 1 1

number of mutations

no DSP, c.1778A>G; p.N593S

no no MYL3, c.517A>G; p.M173V^ LAMA4, c.4624A>T; p.N1542Y DSP, c.273+5G>A^

likely pathogenic mutation(s): gene, cDNA; protein

DIAGNOSTIC YIELD OF CARDIOMYOPATHIES

201

DCM

HCM (D)CM DCM HCM DCM

ARVC/CM

ARVC HCM

DCM

HCM DCM

DCM

DCM HCM DCM DCM CM

ARVC/DCM M

DCM HCM DCM

93

94 95 96 97 98

99

100 101

102

103 104

105

106 107 108 109 110

111

112 113 114

M M F

F M M M F

F

M M

F

F M

M

M F F F M

F

M F

LVNC ARVC

91 92

M

HCM

90

72 8 49

22

35 59 35 23 60

46

55 40

68

63 n.a.

n.a.

n.a. 28 36 57 42

38

61 n.a.

n.a.

pos pos pos?

pos

pos pos neg neg pos

n.a.

pos pos

neg

pos n.a.

pos

n.a. pos pos neg? n.a.

pos

pos n.a.

neg

DCM HCM DCM

CM

fulfilling criteria fulfilling criteria fulfilling criteria

suspected

fulfilling criteria fulfilling criteria fulfilling criteria suspected suspected

fulfilling criteria

fulfilling criteria fulfilling criteria

fulfilling criteria

suspected unconfirmed

suspected

unconfirmed fulfilling criteria fulfilling criteria fulfilling criteria fulfilling criteria

suspected

fulfilling criteria suspected

fulfilling criteria

CHAPTER 4.1

DCM HCM DCM DCMlike DCMlike

DCM

HCM DCM

DCM

ARVClike HCM

ARVClike

HCM DCM DCM HCM DCM

DCMlike

LVNC ARVClike

HCM

no no no

no

no no no no no

no

ACTN2, c.690T>A; p.D230E no LMNA, c.437C>A; p.A146D & DMD, c.2827C>T; p.R943C & MYPN, c.59A>G; p.Y20C^ no no MYH7, c.4377G>T; p.K1459N^ no ABCC9, c.2324C>A; p.P775H DSP, c.4915G>A; p.V1639M & ANKRD1, c.599_600delAT; p.D200Gfs*8 TTN, c.12897dupA p.G4300Rfs*3 no no

no

SCN5A, c.2582_2583delTT; p.F861Wfs*90^ no no

no SCN5A, c.2423G>A; p.R808H×

no MYBPC3, c.654+1G>A

no

PLN, c.40_42delAGA; p.R14del^

1 0 0

2

0 0 1 0 1

3

1 0

1

0 2

1

0 1 1 0 1

2

PKP2, c.1288A>G; p.K430E & DSP, c.939+1G>A^ no TTN, c.86872dupA; p.S28958Kfs*10 TTN, c.45616G>T; p.E15206* no TTN, c.32887+1G>C

0 0

2

no no

ANKRD1, c.368C>T; p.T123M^ & TTN, c.25922-6T>G

no no no no no

no

no no

no

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TARGE TED SEQUENCING

HCM HCM DCM DCM HCM HCM DCM

DCM

HCM HCM DCM HCM

HCM

HCM DCM ARVC DCM HCM HCM HCM HCM HCM DCM

118 119 120 121 122 123 124

125

126 127 128 129

130

131 132 133 134 135 136 137 138 139 140

M M F M M M F M F M

F

F M F M

M

M F M M M M M

F

DCM

117

n.a. n.a. 57 42 56 n.a. 62 n.a. n.a. n.a.

n.a.

n.a. n.a. n.a. n.a.

n.a.

n.a. 60 50 17 56 n.a. n.a.

n.a.

n.a. n.a. n.a. pos neg n.a. pos n.a. n.a. n.a.

n.a.

n.a. n.a. n.a. n.a.

n.a.

n.a. pos n.a. neg n.a. n.a. n.a.

n.a.

n.a. n.a.

F M

HCM DCM

115 116

n.a. n.a.

Family Gender Age diagnosis History

Dx at Patient referal

HCM DCM ARVC DCM HCM HCM HCMlike HCM HCM DCM

HCM

HCM HCM DCM HCM

DCM

HCM HCM DCM DCM HCM HCM DCM

DCM

HCM DCM

Dx after phenotype evaluation

unconfirmed unconfirmed fulfilling criteria fulfilling criteria fulfilling criteria fulfilling criteria suspected unconfirmed unconfirmed unconfirmed

unconfirmed

fulfilling criteria unconfirmed unconfirmed unconfirmed

fulfilling criteria

unconfirmed fulfilling criteria fulfilling criteria fulfilling criteria fulfilling criteria unconfirmed unconfirmed

unconfirmed

fulfilling criteria unconfirmed

patient categorisation

no no no no no no no no no no

no

no no no no

no

1 0 0 1 0 1 0 0 0 0

3

1 1 1 1

2

MYH7, c.5773C>G: p.R1925G^ & MYBPC3, c.3392T>C; p.I1131T^ JPH2, c.723C>G; p.S241R MYH6, c.2354G>A; p.R785H TTN, c.41329+1G>T RYR2, c.9418T>G; p.L3140V TNNT2, c.821+2dupT & GLA, c.427G>A; p.A143T^× & RYR2, c.8162T>C; p.I2721T ANKRD1, c.222dupA; p.L75Tfs*8 no no TTN, c.54339delA; p.E18113Dfs*10 no DSP, c.944G>A; p.R315H no no no no

0 0 0 1 0 0 1

1

1 0

number of mutations

no no no RYR2, c.3152G>A; p.R1051H× no no TTN, c.98990-1G>T

no

PLN, c.40_42delAGA; p.R14del^ no no no no no no no

no no

likely pathogenic mutation(s): gene, cDNA; protein

TNNT2, c.814C>T; p.Q272* no

pathogenic mutation(s): gene, cDNA; protein

DIAGNOSTIC YIELD OF CARDIOMYOPATHIES

203

HCM

DCM LVNC DCM

LVNC

HCM

162

163 164 165

166

167

ARVC DCM

HCM DCM DCM HCM HCM

154 155 156 157 158

160 161

DCM

153

DCM

DCM DCM HCM ARVC HCM DCM DCM DCM HCM DCM DCM

142 143 144 145 146 147 148 149 150 151 152

159

DCM

141

M

F

F M F

F

M M

M

M M F M F

F

M M M F M F F F M M F

M

48

76

34 n.a. 25

n.a.

n.a. n.a.

57

56 20 43 59 66

n.a.

57 56 7 38 1 61 50 49 63 46 37

n.a.

neg

pos

pos n.a. pos

n.a.

n.a. n.a.

pos

pos neg neg pos n.a.

pos

neg pos neg n.a. pos pos pos pos neg pos n.a.

n.a.

HCM

LVNC

DCM LVNC DCM

HCM

fulfilling criteria

fulfilling criteria

fulfilling criteria fulfilling criteria fulfilling criteria

unconfirmed

suspected unconfirmed

suspected

fulfilling criteria fulfilling criteria fulfilling criteria fulfilling criteria fulfilling criteria

fulfilling criteria

fulfilling criteria fulfilling criteria fulfilling criteria fulfilling criteria fulfilling criteria fulfilling criteria fulfilling criteria fulfilling criteria fulfilling criteria unconfirmed fulfilling criteria

unconfirmed

CHAPTER 4.1

ARVClike DCM

DCMlike

HCM DCM DCM HCM HCM

DCM

DCM DCM HCM ARVC HCM DCM DCM DCM HCM DCM DCM

DCM

no

MYBPC3, c.2373dupG; p.W792Vfs*41^

no no no

no

no no

no

no no no no no

no

no no no no no no no no no no no

no

no

DMD, c.7988C>G; p.T2663R TTN, c.85809delA; p.K28603Nfs*6 DSP, c.1714C>T; p.R572W & RYR2, c.8162T>C; p.I2721T RBM20, c.1910G>A; p.S637N no DMD, c.8255A>G; p.Y2752C

TTN, c.31514-3A>G & DSC2, c.9431G>A^

0

1

1 0 1

2

1 1

2

0 0 0 0 0

2

ACTN2, c.1426G>T; p.A476S & DMD, c.343A>G; p.I115V no no no no no

1 1 1 0 1 1 0 0 0 1 1

2

DSP, c.1778A>G p.N593S TTN, c.59926+1G>A MYH7, c.2156G>A; p.R719Q^× no MYH7, c.1816G>A; p.V606M^ TTN, c.80314_80315del; p.V26772* no no no TNNC1, c.439C>T; p.R147C^ DSP, c.8500C>T; p.R2834C

MYBPC3, c.2827C>T; p.R943*^ANKRD1, c.222dupA; p.L75Tfs*8

  204

TARGE TED SEQUENCING

HCM

DCM

HCM DCM ARVC DCM DCM

HCM

DCM

HCM

DCM

178

179

180 181 182 183 184

185

186

187

188

M

F

M

M

M M M F F

M

F

M

DCM

177

54

70

53

68

77 69 n.a. 31 48

36

64

58

neg?

pos?

n.a.

pos

pos? pos? n.a. pos pos

pos

n.a.

pos

n.a. pos pos pos neg pos? neg pos pos

F M F M F F M F M

HCM HCM DCM DCM DCM CM LVNC DCM DCM

168 169 170 171 172 173 174 175 176

50 61 51 14 70 49 n.a. 49 68

Family Gender Age diagnosis History

Dx at Patient referal

DCM

HCM

DCM

HCMlike

HCM DCMlike ARVClike DCMlike DCMlike

DCM

HCM

DCM

HCM HCM DCM DCM DCMlike DCMlike LVNC DCM DCM

Dx after phenotype evaluation

fulfilling criteria

fulfilling criteria

unconfirmed

suspected

fulfilling criteria suspected suspected suspected suspected

fulfilling criteria

fulfilling criteria

fulfilling criteria

fulfilling criteria fulfilling criteria fulfilling criteria fulfilling criteria suspected suspected unconfirmed fulfilling criteria fulfilling criteria

patient categorisation

no

no

no

no

no no no no no

no

no

no

no no no no no no no no no

pathogenic mutation(s): gene, cDNA; protein

1 0 0 1 0 2 3 2 2

DES, c.170C>T; p.S57L & DTNA, c.1778C>A; p.S593Y ACTN2, c.690T>A; p.D230E & TTN, c.59926+1G>A & MYH6, c.3010G>T; p.A1004S^ LAMA4, c.3286C>T; p.R1096C & MYPN, c.2951G>A; p.R984Q LMNA, c.992G>A; p.R331Q^× & MYH6, c.3809G>A; p.R1270H

3 MYH6, c.4264C>T; p.R1422W no no RYR2, c.1939C>T; p.R647C no

0 DSP, c.4608_4612delACGCC; p.R1537Efs*5 & MYPN, c.59A>G; p.Y20C^ & MYH7, c.5773C>G; p.R1925G^

2

NEXN c.1453G>A; p.E485K & MYBPC3 c.649A>G; p.S217G^ no

1 0 1 0 0 0 0 0 0

number of mutations

CSRP3, c.208G>T; p.G70W no DMD, c.2827C>T; p.R943C no no no no no no

likely pathogenic mutation(s): gene, cDNA; protein

DIAGNOSTIC YIELD OF CARDIOMYOPATHIES

205

HCM

HCM

DCM

HCM

DCM DCM DCM DCM DCM

DCM

DCM

DCM

DCM M HCM/DCM M

DCM

DCM

191

192

193

194

195 196 197 198 199

200

201

202

203 204

205

206

M

M

M

M

M

M M M M M

M

F

M

M

M

DCM

190

+M

HCM WPW

189

77

56

53 40

57

55

59

51 23 45 61 42

n.a.

n.a.

49

49

22

69

n.a.

n.a.

n.a. n.a.

n.a.

n.a.

n.a.

n.a. n.a. n.a. n.a. n.a.

n.a.

n.a.

neg?

neg

n.a.

neg?

suspected

suspected

fulfilling criteria suspected

fulfilling criteria

fulfilling criteria

fulfilling criteria

fulfilling criteria fulfilling criteria fulfilling criteria fulfilling criteria fulfilling criteria

unconfirmed

unconfirmed

suspected

suspected

fulfilling criteria

fulfilling criteria

CHAPTER 4.1

DCM like

DCM like

DCM CM

DCM

DCM

DCM

DCM DCM DCM DCM DCM

HCM

DCM

HCMlike

HCMlike

DCM

HCM

2

TTN, c.17823delA; p.I5941Mfs*8 & DSP, c.3294C>G p.D1098E

no

no

PLN, c.40_42delAGA; p.R14del^ no

no no

no

no no

CALR3, c.147dupT; p.R50*

0 142

1

0 0

1

1

1

TTN, c.54406_54409delCAGT; p.Q18136Mfs*8

no

1 1 0 0 0

TTN, c.3100G>A; p.V1034M (splice)^ DES, c.1193T>C; p.L398P no no no

2

2

RYR2 c.8162T>C; p.I2721T

0

no

2

TPM1, c.853T>C; p.*285Glnext*20 & JPH2, c.8G>A; p.G3E ANKRD1, c.222dupA; p.L75Tfs*8 & LAMA4, c.3335C>A; p.P1112H

0

no

MYH6, c.3607dupG; p.A1203Gfs*30

no

MYBPC3, c.2373dupG; p.W792Vfs*41^ no no no no no

no

no

no

no

no

Supplementary Table 3: Total diagnostic yield CM subtype

neg

P

LP

pos (P + LP)

total

ARVC DCM HCM LVNC RCM CM

6 (60%) 50 (45%) 39 (52%) 3 (60%) 0 1 (25%)

3 (30%) 9 (8%) 8 (11%) 1 (20%) 1 (100%) 0

1 (10%) 53 (47%) 27 (36%) 1 (20%) 0 3 (75%)

4 (40%) 62 (55%) 35 (47%) 2 (40%) 1 (100%) 3 (75%)

10 112 74 5 1 4

total

99 (48%)

22 (11%)

85 (41%)

107 (52%)

206

*Abbreviations: ARVC: arrhythmogenic right ventricular cardiomyopathy, CM: cardiomyopathy, DCM: dilated cardiomyopathy, HCM: hypertrophic cardiomyopathy, LVNC: left ventricular non-compaction, LP: likely pathogenic (sometimes together with one or more LP’s), neg: negative, P: pathogenic (sometimes together with one or more LPs), pos: positive, RCM: restrictive cardiomyopathy. Supplementary Table 4: Gender and genetic diagnosis. Fulfilling

 

Suspected

 

M*

F

p-value

Total

76

48

 

39 25 ≥1 mut (51%) (52%)

1

no mut

 

37

23

Unconfirmed

M

F

p-value

25

19

 

15 8 (60%) (41%) 10

11

F

p-value

M

F

p-value

25

13

 

126

80

 

12 9 (48%) (69%)

0.36  

Total

M

13

66 42 (52%) (53%)

0.31

4

 

40

38

1  

Comparison of the sex distribution in the three patient categories related to their mutation carrier status (no mut = no mutation identified; ≥1 mut = one or multiple mutations identified. *M = male; F = female). Supplementary Table 5: LVEF in DCM patients fulfilling criteria related to presence or absence of single and/or multiple mutations. Mutations Data available LVEF[%]±SD

P-value

0

1

>1

32/32

27/30

9/10

29.3±9.0

27.1±10.8

34.6±8.5

0vs≥1

0vs1

0vs>1

1vs>1

0.11

1.00

0.47

0.15

 

* Groups were compared using one-way ANOVA Supplementary Table 6: IVS in HCM patients fulfilling criteria related to presence or absence of single and/or multiple mutations. Mutations 0

1

>1

23/25

16/17

2/4

19.5±4.4

18.1±3.0

19.5±2.1

Data available IVS[mm]±SD

P-value

* Groups were compared using one-way ANOVA

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TARGE TED SEQUENCING

0vs≥1

0vs1

0vs>1

1vs>1

0.29

0.78

1.00

1.00

Chapter 4.2 Titin gene mutations are common in families with both peripartum cardiomyopathy and dilated cardiomyopathy

Karin Y van Spaendonck-Zwarts, Anna Posafalvi, Maarten P van den Berg, Denise Hilfiker-Kleiner, Ilse AE Bollen, Karen Sliwa, Mariëlle Alders, Rowida Almomani, Irene M van Langen, Peter van der Meer, Richard J Sinke, Jolanda van der Velden, Dirk J Van Veldhuisen, J Peter van Tintelen*, Jan DH Jongbloed*

*

The last two authors contributed equally

Published in European Heart Journal, 2014

ABSTRACT Aims: Peripartum cardiomyopathy (PPCM) can be an initial manifestation of familial dilated cardiomyopathy (DCM). We aimed to identify mutations in families that could underlie their PPCM and DCM. Methods and Results: We collected 18 families with PPCM and DCM cases from various countries. We studied the clinical characteristics of the PPCM patients and affected relatives, and applied a targeted next-generation sequencing (NGS) approach to detect mutations in 48 genes known to be involved in inherited cardiomyopathies. We identified 4 pathogenic mutations in 4/18 families (22%): 3 in TTN and 1 in BAG3. In addition, we identified 6 variants of unknown clinical significance that are likely to be pathogenic in 6 other families (33%): 4 in TTN, 1 in TNNC1, and 1 in MYH7. Measurements of passive force in single cardiomyocytes and titin isoform composition potentially support an upgrade of one of the variants of unknown clinical significance in TTN to a pathogenic mutation. Only 2/20 PPCM cases in these families showed recovery of left ventricular function. Conclusion: Targeted NGS shows that potentially causal mutations in cardiomyopathy-related genes are common in families with both PPCM and DCM. This supports the earlier finding that PPCM can be part of familial DCM. Our cohort is particularly characterised by a high proportion of TTN mutations and a low recovery rate in PPCM cases. Keywords: cardiomyopathy, peripartum cardiomyopathy, genetics, pregnancy, titin

INTRODUCTION

T TN IN PERIPAR TUM CARDIOMYOPATHY

CHAPTER 4.2

Peripartum cardiomyopathy (PPCM) is an idiopathic cardiomyopathy presenting with heart failure secondary to left ventricular systolic dysfunction towards the end of pregnancy or in the first months following delivery, where no other cause of heart failure is found. The left ventricle may not be dilated but the ejection fraction is nearly always reduced below 45%.1 According to this recent definition, the time frame is not strictly defined, in contrast to previous definitions.2-4 The severity of PPCM is highly variable, ranging from complete recovery to rapid progression to end-stage heart failure. PPCM affects 1:300 to approximately 1:3000 pregnancies, with geographic hot spots of high incidence such as in Haiti and Nigeria.4,5 The precise mechanisms that lead to PPCM are not fully known. Several risk factors and possible underlying pathological processes have received attention, such as abnormal autoimmune responses, apoptosis, and impaired cardiovascular microvasculature.5,6 Recent work into the pathogenesis of PPCM has shown involvement of a cascade with oxidative stress, the prolactin-cleaving protease cathepsin D, and the nursing hormone prolactin, which may lead to a target for a disease-specific therapy, namely pharmacological blockade of prolactin by bromocriptine.7-9 In addition, involvement of cardiac angiogenic imbalance may explain why PPCM is a disease seen in late pregnancy and why pre-eclampsia and multiple gestation are important risk factors.10 PPCM is probably caused by a complex interaction of more than one pathogenic mechanism. The large variation in incidence and clinical characteristics may reflect the involvement of specific mechanisms, or combinations thereof, in certain subgroups of PPCM. We and others recently reported that PPCM can be an initial manifestation of familial dilated cardiomyopathy (DCM),11,12 indicating that, at least in a subset of cases, genetic predisposition plays a role in the pathophysiology of pregnancy-associated heart failure. Accordingly, Haghikia et al. reported a positive family history for cardiomyopathy in 16.5% (19/115) of PPCM cases from a German PPCM cohort.13 So far, eight cases with underlying mutations in DCM-related genes have been published11,12,14,15 and several other cases with familial occurrences of PPCM and DCM, as well as familial clustering of PPCM, have been reported.16-24 Here, we describe our extensive genetic analysis using next-generation sequencing (NGS) technology to identify potentially causal mutations in families with both PPCM and DCM from various parts of the world.

211

METHODS Subjects and Clinical Evaluation We collected a cohort of families with cases of both PPCM and DCM from various parts of the world (the Netherlands, Germany, and South Africa) and studied their clinical characteristics by reviewing medical reports. The local institutional review committees approved the study, and all participants gave their informed consent. PPCM was diagnosed when a patient had an idiopathic cardiomyopathy presenting with heart failure secondary to left ventricular systolic dysfunction towards the end of pregnancy or in the first months following delivery, where no other cause of heart failure was found.1 DCM was diagnosed when a patient had both a reduced systolic function of the left ventricle (left ventricular systolic ejection fraction 117% of the predicted value corrected for body surface area and age) and only after other identifiable causes like severe hypertension, coronary artery disease, and systemic disease had been excluded.25 If only one of the two criteria was fulfilled, the patient was labeled with “mild DCM”. If the family history suggested DCM in a relative but there were no medical reports to confirm this, the relative was labeled as having “possible DCM”. Familial PPCM/ DCM was diagnosed when there were ≥2 affected family members, at least one with PPCM and one with DCM or sudden cardiac death (SCD) ≤35 years.

Targeted Next-Generation Sequencing of 48 CardiomyopathyRelated Genes Genomic deoxyribonucleic acid (DNA) was extracted from blood samples obtained from all the available PPCM patients and their affected relatives. Targeted NGS was performed in one or two affected relatives in the selected families (these individuals are marked with an arrow in Figures 1 and 2). We developed a kit based on Agilent Sure Select Target Enrichment for mutation detection in 48 genes (all exonic and ± 20 bp of exon-flanking intronic sequences) known to be involved in inherited cardiomyopathies (ABCC9, ACTC1, ACTN2, ANKRD1, BAG3, CALR3, CRYAB, CSRP3/MLP, DES, DMD, DSC2, DSG2, DSP, EMD, GLA, JPH2, JUP, LAMA4, LAMP2, LMNA, MYBPC3, MYH6, MYH7, MYL2, MYL3, MYPN, MYOZ1, MYOZ2, PKP2, PLN, PRKAG2, PSEN1, PSEN2, RBM20, RYR2, SCN5A, SGCD, TAZ, TBX20, TCAP, TMEM43, TNNC1, TNNI3, TNNT2, TPM1, TTN, VCL, ZASP/LDB3).26 Samples

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were prepared according to the manufacturer’s protocols and multiplexed to an amount still permitting a theoretical coverage of 100 reads per targeted sequence/per patient. All samples were sequenced using 151 bp paired-end reads on an Illumina MiSeq sequencer and analyzed using the MiSeq Reporter pipeline and Nextgene software.27 Eleven amplicons with low coverage were also analyzed by Sanger sequencing. Identified mutations were confirmed by Sanger sequencing. To study co-segregation, affected relatives were screened for carriership of the identified mutations by Sanger sequencing.

Sanger Sequencing STAT3 Gene The STAT3 gene (all coding exons and flanking intronic sequences) was analysed by Sanger sequencing in PPCM patients of the collected families.

Classification of Identified Mutations

CHAPTER 4.2

The criteria used to classify mutations were published recently.28 Briefly, we used a list of mutation-specific features based on in silico analysis using the mutation interpretation software Alamut (version 2.2.1). A score was given depending on the outcome of a prediction test for each feature (i.e. the PolyPhen-2 prediction tool). Then, depending on the total score and the presence/absence of the mutation in at least 300 ethnically matched control alleles (data obtained from the literature and/or available databases, e.g. http://evs.gs.washington.edu/EVS and http://www.nlgenome.nl, or from our own control alleles), we classified mutations as: pathogenic, not pathogenic, or as a variant of unknown clinical significance (VUS; VUS1, unlikely to be pathogenic; VUS2, uncertain; VUS3, likely to be pathogenic). Cosegregation data and/or functional analysis were needed to classify a mutation as pathogenic.

Functional Analysis of TTN mutation Passive force was measured in single membrane-permeabilized cardiomyocytes mechanically isolated from the heart tissue.29,30 Titin isoform composition was analysed as described previously.30

T TN IN PERIPAR TUM CARDIOMYOPATHY

213

Figure 1. Pedigrees of the Dutch families (NL1-11). Square symbols indicate men; circles, women; diamonds, unknown sex; and triangles, miscarriage. Blue symbols indicate a clinical diagnosis of PPCM; black symbols, (mild) DCM; grey symbols, possible DCM; orange symbols, sudden cardiac death (SCD). Diagonal lines through symbols indicate deceased; arrows indicate patients selected for targeted next-generation sequencing; and the number in a symbol indicates the number of individuals with this symbol (question mark if unknown).

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CHAPTER 4.2

Figure 2. Pedigrees of the South African (SA1) and German families (GER1-6). Square symbols indicate men; circles, women; diamonds, unknown sex. Blue symbols indicate clinical diagnosis of PPCM; black symbols, (mild) DCM; orange symbol, sudden cardiac death (SCD). Diagonal lines through symbols indicate deceased; arrows indicate patients selected for targeted next-generation sequencing; the number in a symbol indicates the number of individuals with this symbol (question mark if unknown); and SB indicates still birth.

RESULTS Clinical Characteristics: Low Rate of Full Recovery in PPCM Cases of Familial PPCM/DCM We collected 18 families with familial PPCM/DCM. These families originated from the Netherlands (n=11), Germany (n=6), and South Africa (n=1; black). Clinical data of the PPCM cases in these families are summarised in Table 1 and of all (likely) affected relatives in Supplemental Table S1. The pedigrees of all the families are shown in Figures 1 (NL1-11) and 2 (SA1 and GER1-6). In two families there were two cases of PPCM (NL1 and SA1). Eight families (NL1-7 and SA1) have been described previously.11,31 The median age at diagnosis in PPCM patients was 29 years (n=15; range 20-36 years), with mean parity 2 (n=13; range 1-4). PPCM diagnosis was postpartum in 12/14 patients. Only 2/20 PPCM patients showed a full recovery of left ventricular function, one of them even had an uneventful next

T TN IN PERIPAR TUM CARDIOMYOPATHY

215

  216

TARGE TED SEQUENCING

II:6

III:4

III:3

III:1

III:2

III:1

III:2

III:1

III:3

III:1

III:6

III:1

II:5

II:6

II:1

II:1

NL1

NL1

NL2

NL3

NL4

NL5

NL6

NL7

NL8

NL9

NL10

NL11

SA1

SA1

GER1

GER2

PPCM (36) 3 weeks after delivery

PPCM (20) Just after delivery

Chest pain, coughing

Dyspnea, tachycardia

PPCM

PPCM

PPCM (22)

PPCM (23) 1 month after delivery

PPCM (30) Just after delivery

HF, respiratory insufficiency

PPCM (23) Just after delivery

PPCM (29) 2 months after delivery

30%

SB 27 weeks

P1

P2

25%

20%

43%

22%

Poor

Dilated heart, myocyte hypertrophy, fibrosis

LBBB, D asthma cardiale (26)

6 months 55%, 3 years normal

8 years 10%

No recovery

No recovery

Tachycardia

ICD, HTX

with 6 months 37%, Full recovery 2nd pregnancy 2 years normal 2uneventful years later

24%

4 months 30-35%

Thrombus LV apex, TIA, VT (35), PM, HTX (37), normal LVEF (51)

ICD/CRT (31), LVAD, VF, D cardiogenic shock (34)

Suspicion of neurodermitis

New pregnancy, terminated (35)

Signs of acute myocarditis (EMB), suspicion of vasculitis

Myocyte hypertrophy

D MOF (27)

Cardiological remarks and outcome (age in yrs) Pathology and other remarks D (31)

3 months 33% 9 months no recovery 6 months 44%, Thrombus LV apex, 7 years 42% tachycardia AF PVCs, VTs (46), D 6 months 23% HF (30), (51)

6 months 55%, P2 CS 29th years 45%, 3 week, HELLP 20-30% 2years 50-55%

P1 CS, twin pregnancy

18%

25%

P1 CS 29th week, eclampsia P2

23%

23%

21%

25%

20%

LVEF at LVEF Diagnosis at Follow-Up

P3

PPCM (33) 35th week of pregnancy P1 AI CS

PPCM (30) 3 months after delivery

PPCM (35) 2 weeks after delivery

Screening, asymptomatic

P4

P1

P4

Pregnancy

PPCM (33) 37th week of pregnancy P2 CS

PPCM (26) Few days after delivery

PPCM (27) 3 days after delivery

Diagnosis (age in yrs) Timing at Diagnosis PPCM (29) Just after delivery

HF

HF

HF

HF

HF

HF

HF

HF Cardiogenic shock

Family Patient Referred for

Table 1. Clinical characteristics of confirmed PPCM cases

T TN IN PERIPAR TUM CARDIOMYOPATHY

217

II:1

II:1

II:1

GER4

GER5

GER6

 

PPCM (33) 3 months after delivery

PPCM

PPCM

PPCM

 

P1

1 year 47% 6 months no   recovery

Subsequent pregnancy with 30% LVEF , 6 months 30% entered VAD after 2nd pregnancy, no recovery

 

Graves’disease, nicotin and drug abuse

Tested patient   II:3 II:2 III:2 II:3 III:1 II:6 III:1 II:1 II:1 II:1

  TTN BAG3 TNNC1 TTN TTN TTN TTN TTN TTN MYH7

Gene

  p.Arg27373* p.Gln340* p.Gln50Arg p.Asn28726Lysfs*3 p.Arg17599* p.Arg23956Thrfs*9† p.Ser27317Lysfs*10 p.Trp18357* p.Lys15664Valfs*13 p.Arg1303Gly

Amino acid change   c.82117C>T c.1018C>T c.149T>C c.86171_86174dupAAAG c.52795C>T c.71867_71876delGAGTTCTGGA† c.81949dupA c.55070G>A c.46990_46993delAAGG c.3907C>G

Nucleotide change   Pathogenic Pathogenic VUS3 VUS3 Pathogenic Pathogenic VUS3 VUS3 VUS3 VUS3

Classification

Yes/Unknown Yes Yes Yes Yes Yes Yes Yes Unknown Unknown Yes

Co-segregation

Affected relatives carrier II:1, II:3, II:4, III:4, III:5, III:6 II:2, III:1 II:5, III:2, III:5, IV:1 II:1, II:3 III:1, III:5 II:1, II:2, II:6, III:2, III:5, III:6 II:1, III:1 No samples available No samples available I:1, II:1

 

CHAPTER 4.2

Nomenclature according to HGVS (Human Genome Variation Society) using the reference sequences: TTN (NM_001256850.1; Q8WZ42-1), BAG3 (NM_004281.3), TNNC1 (NM_003280.2), MYH7 (NM_000257.2). VUS indicates variant of unknown clinical significance (VUS3, likely to be pathogenic, VUS2, uncertain). † VUS2 p.Arg279Trp (c.835C>T) on same allele

  NL1 NL3 NL4 NL6 NL9 NL10 NL11 GER1 GER4 GER5

Family

Table 2. Potentially causal mutations identified in 10/18 families

AF indicates atrial fibrillation; AI, artificial insemination; AT, atrial tachycardia; (Bi)(L)VAD, (bi)(left) ventricular assist device; CRT, cardiac resynchronization therapy; CS, caesarean section; D, death; EMB, endomyocardial biopsy; HELPP, hemolysis, elevated liver enzymes, low platelet count; HF, heart failure; HTX, heart transplantation; ICD, implantable cardiac defibrillator; LBBB, left bundle branch block; LV, left ventricle; LVEF, left ventricular ejection fraction; MOF, multiple organ failure; P, pregnancy; PM, pacemaker; PPCM, peripartum cardiomyopathy; PVC, premature ventricular contraction; RV, right ventricle; SB, still birth; TIA, transient ischemic attack; VF, ventricular fibrillation; VT, ventricular tachycardia.

II:1

GER3

pregnancy (NL9 III:1, LVEF still normal 3 years after diagnosis; and GER1 II:1, full recovery with uneventful second pregnancy two years later). Another PPCM patient showed recovery of left ventricular function, but only under treatment with a beta-blocker and ACE inhibitor (NL10 III:6). In addition to 20 confirmed PPCM patients in these families, five relatives show clinical characteristics suggestive for PPCM (NL4 II:2, GER1 I:1, GER3 I:1, GER4 I:1, GER5 I:1; Table S1). PPCM could not be confirmed because clinical data of these relatives was lacking. In addition, two relatives with DCM showed a decline of left ventricular function after delivery (NL2 IV:8 and SA1 II:3; Table S1).

Targeted Next-Generation Sequencing: Potential Causal Mutations in Cardiomyopathy-Related Genes, in particular TTN, are Common in Familial PPCM/DCM Using our validated NGS approach,27 a mean coverage of 220x per individual patient was reached and, on average, 98.5% of all targeted nucleotides were covered at least 20x. In 4/18 families (22%) pathogenic mutations in cardiomyopathy-related genes were identified (3 in TTN and 1 in BAG3). In addition, in 6 other families (33%) VUS3s were identified (4 in TTN, 1 in TNNC1, and 1 in MYH7). An overview of these mutations and VUS3s and the respective co-segregation analyses are shown in Table 2. All 7 TTN mutations/VUS3s were located in the titin A-band, for which over-representation of mutations in DCM patients was reported previously.32 No potential mutations were identified in 8 families (NL2, NL5, NL7, NL8, SA1, GER2, GER3, and GER6). An overview of the 26 mutations that were not classified as potentially disease-causing (VUS1s and VUS2s) identified in the 18 families is shown in Supplemental Table S2.

No STAT3 Mutations in PPCM Cases No STAT3 mutations were identified in 15 PPCM cases (DNA was available from 15/20 cases).

Functional and Protein Analyses Support the Pathogenicity of a Likely Pathogenic TTN Mutation Heart tissue from PPCM patient GER4 II:1 with a VUS3 in TTN was available for functional and protein analyses. Passive force was measured in

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single cardiomyocytes (n=4) at sarcomere lengths of 1.8 to 2.2 μm (see Figure 3). Our functional measurements of passive stiffness, which is largely based on titin composition in the heart, revealed a very low passive force development (1.0±0.3 kN/m2) at a sarcomere length of 2.2 μm in the PPCM sample compared to previously reported values in control hearts (~2.5 kN/m2).29,30 Analysis of titin isoform composition showed a shift towards the more compliant N2BA isoform evident from a higher N2BA/N2B ratio (0.72±0.02; mean of triplo) in the PPCM heart compared to the previously reported ratio (0.39±0.05) in control hearts.30

DISCUSSION

CHAPTER 4.2

This is the first report of a comprehensive genetic analysis in a large series of cases with familial occurrences of PPCM and DCM. We identified pathogenic mutations in cardiomyopathy-related genes in 4/18 families (22%) and VUSs that are likely to be pathogenic in 6 other families (33%). These data support the earlier finding that PPCM can be part of familial DCM.11,12 Cascade genetic screening can identify relatives at risk in those families in which an underlying mutation has been identified. Our data also specifically show a low recovery rate in our cohort (only 10%) compared to reports in other groups not selected for familial cases (recovery rates of around 25 to 50%),33-36 indicating that the presence of an underlying mutation or positive family history for cardiomyopathy in a patient with PPCM may be a prognostic factor for a low recovery rate. The targeted NGS approach that we have developed provides highthroughput, rapid and affordable molecular analysis for cardiomyopathies.27 As accurate annotation of mutations in cardiomyopathies is of the utmost importance,37 we were extremely careful in classifying these.28 Our study has

Figure 3. Force measurements in heart tissue of GER4 II:1. Single cardiomyocyte of the PPCM heart sample (A). Passive force development was measured at sarcomere lengths of 1.8, 2.0 and 2.2 μm. (B)

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several advantages: one is the inclusion of some large families, where co-segregation analysis added value to the classification of mutations. Another was the large number of genes we tested, including the large TTN gene, for which mutation analyses on a large scale were impossible before NGS became available, because exclusion of pathogenic mutations in 47 other candidate genes makes it more likely that the identified VUS3s have a pathogenic nature. Accordingly, the previously reported TNNC1 mutation is still the only potential genetic cause in family NL4.11 And although the pathogenicity of truncating TTN mutations is still under debate due to these types of mutations being found in apparently healthy controls (up to 3%) and the general population,32,38 the pathogenicity of TTN VUS3s identified in our families also becomes more likely after excluding pathogenic mutations in 47 other cardiomyopathy-related genes. Possible exclusion of mutations in other genes in patients carrying truncating TTN mutations was not explicitly addressed by Herman et al.32 As expected, we identified several mutations in the majority of patients, however, we focused on the pathogenic mutations and VUS3s. Other identified mutations (VUS1s and VUS2s; see Table S2) might be benign genetic variations, but some may also contribute to the development of disease in these families. Some of these VUSs might even be independently pathogenic, but additional testing is needed to confirm this (this might be the case for two VUS2s in TTN (p.Arg1408Cys (c.4222C>T) in GER2, and p.Glu2076Gly (c.6227A>G) in GER6). Other possibilities are that these VUSs may act as modifiers, or that they are risk factors with a low penetrance. The great majority of pathogenic mutations and VUS3s (7/10) were in the TTN gene, which encodes the giant sarcomeric protein titin. It was recently reported that truncating mutations in TTN account for a significant portion (approximately 25%) of the genetic etiology in familial DCM.32 The high yield of pathogenic mutations and VUS3s in TTN in our cohort of familial PPCM/ DCM cases (39%; 7/18) suggests that TTN mutations are specifically related to PPCM. Changes in isoform expression and phosphorylation status of titin have been reported in acquired forms of heart failure (reviewed by Hildalgo and Granzier).39 We were able to measure functional properties and titin isoform composition in heart tissue from one of the PPCM patients with a VUS3 in TTN. The passive force was twice as low as the value previously reported in control groups, and was associated with a shift towards the more compliant N2BA titin isoform. The shift towards more compliant N2BA has been reported in human heart failure.30,40,41 Overall, our data from functional and protein analyses support the pathogenicity of this particular TTN mutation.

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CHAPTER 4.2

We still classify this mutation as VUS3, however extended experience with these functional analyses might drive us to re-classify this VUS3 towards a pathogenic mutation. Recent studies indicated that titin phosphorylation is indirectly altered by increased oxidative stress42 and, as such, may represent a likely pathomechanism in PPCM. Future studies will need to reveal the functional deficits induced by mutations in the TTN gene in relation to high oxidative stress, as present in PPCM. There may be genetic factors specific for PPCM development, for example a factor tentatively underlying the geographical hotspot of incidence in Haiti, and a locus near the PTHLH gene reported by Horne et al.39 We only focused on the STAT3 gene as a possible specific genetic factor for PPCM. Because mice with cardiomyocyte-specific deletion of STAT3 develop PPCM,7 STAT3 might also be involved in human PPCM but there are no human genetic data supporting this yet. STAT3 mutations are so far only known to cause hyper-IgE syndrome.40 In contrast to the PPCM cases, some women in our PPCM/DCM families went through several pregnancies without developing PPCM. We therefore hypothesized that STAT3 mutations in the PPCM cases of these families contributed to the development of PPCM, in addition to an underlying cardiomyopathy-related mutation. However, we found no STAT3 pathogenic mutations or VUSs in these PPCM cases, which was consistent with previous findings.7 Exome sequencing of rare familial PPCM cases could lead to identifying novel genetic factors specific for PPCM. However, this approach is limited by the fact that familial PPCM cases with more than two affected relatives or with affected distant relatives are lacking. An alternative strategy could be to compare the data from exome sequencing on different PPCM cases in order to identify a shared genetic cause, but this might not lead to a result because the causal genetic factor may well be unique to each family. Limitations One limitation of our study is that it does not provide data on the frequency of familial disease in PPCM. Currently, we only have data from a German cohort reporting a positive family history for cardiomyopathy in 16.5% of PPCM cases,13 but we hope to gain more information via the Peripartum Cardiomyopathy Registry of EURObservational Research Programme (www.eorp.org). (unpublished data, 2013, manuscript submitted to European Journal of Heart Failure) Another limitation is that retrieving information on larger deletions/duplications from NGS data is not possible yet, although software to enable such analysis is being developed. We may therefore

T TN IN PERIPAR TUM CARDIOMYOPATHY

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have missed that type of mutation in our analyses. A further limitation is the difficulty of judging which TTN mutations are pathogenic, given the presence of truncating TTN mutations in the general population and reported truncating mutations that do not segregate with disease in DCM families.30,36,41 In contrast to the latter observation, we were able to show co-segregation of truncating TTN mutations/VUS3s in five of our families (NL1, NL6, NL9, NL10 and NL11; Table 2), and we have data from functional and protein analyses supporting the pathogenicity of one likely pathogenic TTN mutation (GER4 II:1). Additional functional studies on TTN mutations and collection of large families carrying these mutations are needed. Moreover, although our findings suggest a specific role for TTN mutations in families with PPCM and DCM, we do realise that the number of families studied is currently too small to definitely conclude this. Finally, we were lacking some clinical data, especially of cases that showed clinical characteristics suggestive of PPCM. Conclusions and Practical Implications Potentially causal mutations in cardiomyopathy-related genes are common in families with both PPCM and DCM, in particular TTN mutations. The targeted next-generation sequencing approach we applied has been shown to be suitable for identifying such mutations. Functional studies as performed in the present study may provide a future tool to confirm pathogenicity of TTN mutations. Our results provide more support for the earlier finding that PPCM can be a manifestation of familial DCM. Cascade genetic screening can identify relatives at risk in those families in which an underlying mutation has been identified. Moreover, the presence of an underlying mutation or a positive family history for cardiomyopathy in a PPCM patient may be a prognostic factor for low recovery rate.

ACKNOWLEDGEMENTS We thank all the patients who participated in this study; the Study Group on PPCM of the Heart Failure Association of the European Society of Cardiology; Birgit SikkemaRaddatz for her help in validating and implementing the targeted enrichment kit; Ludolf Boven, Eddy de Boer and Lennart Johansson for technical assistance; Wies Lommen for assistance with functional analyses; Nicolaas de Jonge, cardiologist, for cardiac evaluation of family NL10; Wilma van der Roest, genetic counselor, for counseling some of the Dutch families; and Jackie Senior for editing this manuscript. Rowida Almomani was supported by the Netherlands Heart Foundation (grant 2010B164).

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4

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7

8

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12

13

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II:1 II:3

II:4 II:5 II:6 III:4 III:5 III:6

III:8 I:2 II:2 II:3 III:2

III:3

IV:2 IV:4

IV:5 IV:6 IV:8

IV:9 I:2 I:3 II:2

III:1 II:1

II:2 II:4

NL1 NL1

NL1 NL1 NL1 NL1 NL1 NL1

NL1 NL2 NL2 NL2 NL2

NL2

NL2 NL2

NL2 NL2 NL2

NL2 NL3 NL3 NL3

NL3 NL4

NL4 NL4

F F

F F

F F M M

M F F

F M

F

M F F M F

M F F F M M

M F

DCM (20) DCM (28) DCM (35)

Mild DCM (25) DCM (22)

PPCM (26)

SIDS Possible DCM SCD (27) DCM DCM (41)

DCM (61) SCD (54) PPCM (29) PPCM (27) DCM (48) DCM (48)

Diagnosis (age in yrs) DCM (83) DCM (61)

10 weeks after P5, but 4 years earlier already abnormal contraction LV with preserved LVEF

Few days after delivery

Just after delivery 3 days after delivery

Timing at diagnosis

P4

P4 P1

Pregnancy

Died Screening

P2

CHAPTER 4.2

SCD (26) Just after delivery Mild DCM (63)

Screening Possible DCM (21) Died SCD (25) Died SCD (57) Screening, fatique, DCM (57) palpitations HF PPCM (33) 37th week of pregnancy P2 CS Died DCM (54)

Screening Screening, palpitations Screening Screening Screening

HF

HF

Died

Died

Screening Died HF Cardiogenic shock Dyspnea, chest pain Screening

Screening Dyspnea

Family Patient M/F Referred for

25%

40% 30-40% 43%

45-50% 37-49%

25%

20% 24% 40%

3 months 33%

5 years 35-40% 8 years 45%

6 years 53% 7 years 40-45%

9 years 18%

4 years 40%

LVEF at LVEF at Follow-Up Diagnosis 44% 23% 1 year 41%, 12 years 20-25% 32%

Pathology and other remarks

D (54)

PVCs

PVCs (15), VTs (25)

Dilated heart, myocyte hypertrophy, hyperchromatic nuclei Rheumatic disease

D (63) LBBB, VTs (41), ICD (51), intramyo- Myocyte hypertrophy, cardial stem cell implantation interstitial fibrosis (53), HTX, D DIC (54) LBBB, D asthma cardiale Dilated heart, myocyte (26) hypertrophy, fibrosis

D HF (60)

D (31) D MOF (27) Myocyte hypertrophy PVCs, VTs (48), ICD (58), D HF (59) PVCs, VTs (48), ICD (50), appropriate ICD shock (53)

PVCs, VTs (61), AF (70)

Cardiological remarks and outcome (age in yrs) PVCs, VTs (70) AVB1, PVCs, VTs, ICD (61)

Table S1. Clinical characteristics of PPCM cases and all affected (or likely affected) relatives

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II:5 III:2 III:5

IV:1

II:1

II:4 III:1

II:1 II:3 III:2

II:2

III:1

II:3 II:4

III:3

IV:1

II:3 II:5

III:1

III:5 II:1 II:2

II:6

III:2

NL4 NL4 NL4

NL4

NL5

NL5 NL5

NL6 NL6 NL6

NL7

NL7

NL8 NL8

NL8

NL8

NL9 NL9

NL9

NL9 NL10 NL10

NL10

NL10

F

M

F F M

F

F M

F

F

M M

F

M

M M F

F F

F

F

F F F

HF, respiratory insufficiency VF

Dyspnea Dyspnea

HF

Collapse

HF

Screening

AF Dyspnea HF

Screening HF

HF

Screening HF Screening, palpitations Heart murmur

Family Patient M/F Referred for

DCM

DCM (47)

DCM (25) DCM (58) DCM

PPCM (30)

DCM (42) DCM (42)

DCM (16)

PPCM (35)

DCM (50) Possible DCM (72)

PPCM (23)

Just after delivery

20%

23%

23%

21% 50%

T

VUS1

NL4

III:2

TTN

p.Ala9135Pro

c.27403G>C

VUS1

NL5

II:1

PKP2

p.Asp26Asn

c.76C>T

VUS1

NL6

II:3

DMD

p.Asn2713Ser

c.8138T>C

VUS2

NL6

II:3

RYR2

p.Ile2721Thr

c.8162T>C

VUS2

NL6

II:3

TTN

p.Glu21080Lys

c.63238G>A

VUS1

NL7

II:2, III:1‡

MYBPC3

p.Ala833Thr

c.2497G>A

VUS2

NL7

II:2, III:1‡

TMEM43

p.Arg312Trp

c.934C>T

VUS1

NL7

II:2, III:1‡

TTN

p.Glu10855dup

c.32562_32564dupAGA VUS1

NL8

III:3

RBM20

p.Ser637Asn

c.1910G>A

VUS2

NL10

II:6

TTN

p.Arg279Trp†

c.835C>T†

VUS2

NL10

II:6

TTN

p.Pro17045Ala

c.51133C>G

VUS2

NL11

III:1

TTN

p.Lys4401Glu

c.13201A>G

VUS1

SA1

II:5

MYPN

p.Ser774Tyr

c.2321C>A

VUS2

SA1

II:5

PKP2

p.Val842Ile

c.2524C>T

VUS1

SA1

II:5

TTN

p.Ser1400Thr

c.4199G>C

VUS1

SA1

II:5

TTN

p.Glu18378Lys

c.55132G>A

VUS1

SA1

II:5

TTN

p.Val32108Met

c.96322G>A

VUS2

SA1

II:5

TTN

p.Arg33402Cys

c.100204C>T

VUS2

GER2

II:1

PKP2

p.Ile531Ser

c.1592T>G

VUS1

GER2

II:1

TTN

p.Arg1408Cys

c.4222C>T

VUS2

GER5

II:1

TTN

p.Glu15076Asp

c.45228G>C

VUS1

GER5

II:1

TTN

p.Ile17461Thr

c.52382T>C

VUS2

GER6

II:1

TTN

p.Glu2076Gly

c.6227A>G

VUS2

Nomenclature according to HGVS (Human Genome Variation Society) using the reference sequences: TTN (NM_001256850.1; Q8WZ42-1), LAMA4 (NM_001105206.1), PRKAG2 (NM_016203.3), PKP2 (NM_004572.3), DMD (NM_004006.2), RYR2 (NM_001035.2, MYBPC3 (NM_000256.3), TMEM43 (NM_024334.2), RBM20 (NM_001134363.1), MYPN (NM_.032578.2). VUS indicates variant of unknown clinical significance (VUS1, unlikely to be pathogenic; VUS2, uncertain). ‡ II:2 and III:1 were both analyzed; only shared mutations were investigated further (analyzed in silico) † pathogenic mutation on same allele (p.Arg23956Thrfs*9 (c.71867_71876delGAGTTCTGGA))

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CHAPTER 5 DISCUSSION

Chapter 5: Discussion

Discussion and future perspectives

Anna Posafalvi



DISCUSSION AND FUTURE PERSPEC TIVES

CHAPTER 5

Cardiomyopathy is an insidious disease of the myocardium, which can manifest with a wide range of symptoms at various ages, but which usually presents in adulthood. Currently, there are 76 genes known to be involved in the familial form of this disease (for an overview of known disease genes, see the preface of this thesis). Cardiomyopathy has several subtypes in which impairment of various molecular pathways leads to insufficient circulation (as reviewed by Teekakirikul et al). Hypertrophic cardiomyopathy (HCM) was initially thought to be primarily a disease of sarcomeric proteins, while arrhythmogenic right ventricular cardiomyopathy (ARCV) was considered mostly a disease of the desmosomal complex. Restrictive cardiomyopathy has been frequently shown to be caused by desmin (and sometimes sarcomeric) mutations. In addition to these molecules, a large number of proteins responsible for the construction of the cytoskeleton and the nuclear envelope or having a role in calcium/sodium handling have been shown to be involved in dilated cardiomyopathy (DCM) (see review by Posafalvi et al). However, there is increasing evidence that it is not only the phenotypic characteristics of these cardiomyopathy subtypes that are entwined and overlapping, but that the same overlapping pattern is present in their genetic background, as mutations of known genes are increasingly discovered to underlie other subtypes of the disease (Teekakirikul et al). An example of this overlap is described in chapter 4.1 of this thesis: our diagnostic screening of 55 genes implicated in different types of cardiomyopathy led to the discovery of potentially pathogenic variants in genes that would not have been chosen for sequencing in the earlier Sanger-sequencing era. At that time, decisions about which genes to sequence were made based on the clinical phenotype of the patients, and screening was limited to a small number of genes per patient. In addition to the examples of genes now shown to be involved in previously unexpected cardiomyopathy subtypes (reported in chapter 4.1), the complicated genetic overlap among the types of disorder that were already known is visualized in figure 2 of the preface. Traditionally, Sanger-sequencing of a few disease genes was the standard method used in genetic diagnostics of cardiomyopathies, and the same method was also applied to the screening of novel candidate genes in a research setting. The recent development of whole genome, exome, and gene panel-based high-throughput sequencing technologies created revolutionary possibilities for both diagnostic and research-related applications. The work described in this thesis shows the recent impact of these technical developments on diagnostics and research in cardiogenetics.

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Due to the difficulty of defining a small and distinct set of candidate genes to be screened based purely on the specific phenotypic features that a familial cardiomyopathy patient exhibits, we currently apply the gene panelbased targeted-next-generation sequencing described in chapter 4 in the routine DNA diagnostics of cardiomyopathies. Using this technique we are able to sequence 55 well-established disease genes in one experiment, and are in some cases able to identify the genetic explanation of the disease in a gene which would not have been chosen for screening by classical Sangersequencing based on the patient’s phenotype (for examples, see chapter 4.1). In those individuals whose cardiac health problems could not be explained by genetic variation in the 55 known genes, exome sequencing combined with a haplotype sharing test (when appropriate and depending on the size of the family) seemed to be an effective way of searching for novel candidate disease genes (as shown in chapter 3) that can later be searched for in screens of larger cardiomyopathy cohorts by Sanger-sequencing (as shown in chapter 2). This approach requires phenotypically well-characterized, multi-generational families in which the affected/healthy disease status of individuals has been clearly determined. A flowchart of the cardiogenetics workflow as currently applied in our department is shown in figure 1. How can we know that we have found the true causative variant? When sequencing a set of 55 disease genes, there is a fair chance that we will identify likely causal genetic variants in at least one of them. For this reason, we need to take further steps to verify that the variant we are looking at is truly the cause of the patient’s disease. After checking the predicted pathogenicity of the variant using multiple software packages, the presence/absence and (if applicable) frequency of the variant in different population frequency databases (such as GoNL, and the slightly more critically handled dbSNP, 1000G or ESP, which may contain causative variants as well) and performing segregation analysis in the family (checking if all affected family members carry the putative causative variant), we might be able to finally classify the variant as ‘benign’, ‘likely benign’, ‘variant of unknown significance’, ‘likely pathogenic’ or ‘pathogenic’. In this thesis, we have used strict and robust criteria for variant classification (see chapters 2 and 4 for description and examples). Additionally, we may easily screen a patient cohort to search for an additional carrier of the same variant, and if we identify further (unrelated) patients carrying the same mutation, we would apply haplotype analysis in the hope of discovering a potential founder effect.

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DNA sample of cardiomyopathy index patient gene-panel based targeted NGS (currently for 55 cardiomyopathy genes)

DIAGNOSTICS

„solved cases”: in about 50% of patients we identify the (likely) pathogenic mutation(s) in one or more known cardiomyopathy gene(s) „unsolved cases”: hunting for a novel disease gene (HST and/or exome sequencing)

RESEARCH

„solved cases”: mutation identified in a novel cardiomyopathy gene

„unsolved cases”: missing heritability

candidate gene screening: searching for additional patients with mutations of the same gene in a large cohort functional follow-up on the novel gene

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Figure 1: Current cardiogenetics workflow Since gene panel-based targeted-sequencing is a straightforward approach that sequences all cardiomyopathy disease genes in one experiment, we now implement this as a routine diagnostic screening test. Unsolved cases might later be subject to haplotype sharing analysis, whole exome or genome sequencing, or other disease-gene hunting methods. Novel disease genes identified in these ways are then Sanger-sequenced in large patient cohorts (although we might expect some of these to be private mutations/ genes in the families examined), and may be further investigated functionally. In order to ensure up-to-date DNA diagnostics, newly discovered and well-established disease genes can periodically be added to the targeted enrichment kit used for gene panelbased sequencing.

In order to have a more precise idea of the potential pathogenicity level of genetic variants, and to be able to better prioritize variants in large datasets (e.g. as a result of exome/genome sequencing), it is crucial that more reliable and standardized prediction programs and software become available. For example, the novel Combined Annotation-Dependent Depletion tool seems to outperform existing software and sources in predicting deleteriousness via incorporating known databases and tools as well as results of the ENCODE project (Kircher et al). Other bioinformatics tools such as well-established annotation databases (a good example is the Cardiovascular Gene Ontology Annotation Initiative) and network tools (for instance the co-expression

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network Cytoscape, or protein interaction networks which contain functional information on the genes supported by the literature) have also proven to be of great utility. Chapter 3.1 is a straightforward demonstration of how to use such sources in interpreting high-throughput sequencing data. Ultimately, the best way to prove pathogenicity is to perform functional studies on the identified variants themselves, and examples of functional analyses via in vitro experiments are also described in this thesis. In chapter 2.1 we show how we tried to experimentally evaluate the expected pathogenicity of RBM20 variants and mutations using a splicing assay. In chapter 3.2 we measured the enzyme activity of superoxide dismutase in patient-derived fibroblasts in order to prove the pathogenicity of a missense variant of the Mn-binding pocket. Finally, in chapter 4.2 we analysed the titin isoform composition and passive force generation of single cardiomyocytes isolated from explanted tissue of a TTN frameshift mutation carrier. There are other ways of acquiring further evidence on the pathogenic nature of the detected genetic variant via functional analysis. A popular but time-intensive method is to set up a knock out/knock in gene in an animal model. Examples for the use of such models to gain more knowledge about the general function of a gene or protein related to the content of this thesis are • the RBM20 knock out rat, which has been used for the identification of target RNA molecules of the spliceosomal RBM20 via sequencing of RNA isolated from heart biopsies of mutant and wild type animals (Guo et al) • the null-mutant, tissue- and isoform-specific knock out PLEC mice, from which much has been learned about the function of plectin in the past decades (Winter et al) • the lethal mice and Drosophila SOD2 knock outs suggesting the essential role of this enzyme in the heart (Li et al, Kirby et al) • the zebrafish model showing the effect of COBL knock out on embryonal development of the neural tube and heart (Ravanelli et al). However, creating an animal model carrying the homolog of the investigated gene with an identical mutation to the one our patient carries is usually a complicated job, which only the recent development of novel gene targeting technologies (TALEN and CRISPR/cas) makes more feasible (Menke 2013). Alternatively, fibroblasts may be acquired from the patient (via a ’simple’ skin biopsy), reprogrammed through iPS cells then differentiated into specific cell types such as cardiomyocytes. These cells will be genetically (and also theoretically phenotypically) identical to those in the heart of the

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patient, yet are acquired in a less invasive way than a cardiac biopsy sample obtained via catheterization. The derived cells can be used to examine arrhythmogenic cardiac phenotypes and the underlying molecular pathways, as well as to investigate potential opportunities for personalized and/or regenerative therapy. However, this novel technique has been criticized for the low yield of cardiomyocytes produced, their tendency to dedifferentiate and the immature electrophysiological character of the derived cells. It is of crucial importance that the derived cardiomyocytes contain plenty of the genetic variants (mutations and polymorphisms alike) carried by the patient. Therefore, the combination of this technique with a rescue experiment is necessary to exclude other variants from the disease pathomechanism and to prove the sole pathogenicity of the candidate variant under investigation (Sinnecker et al, Knollmann et al).

Where is the “missing heritability” and what indications do we have of the mechanisms in cardiomyopathies? To date, a significant proportion of familial cardiomyopathies (about 30-40% of HCM, 40-50% of ARVC, and around 50% of DCM cases) remain genetically unexplained. Below we describe some of the possible underappreciated mechanisms that might be behind the “missing heritability” for cardio-myopathies on the DNA, RNA and protein levels, respectively. 1. On the DNA level

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In past decades, cardiomyopathy was primarily considered a monogenic disorder, most often exhibiting an autosomal dominant pattern. In the families that do not carry mutations of the 76 disease genes identified so far, we can search for novel candidate genes by exome or whole genome sequencing, when appropriate, in combination with the haplotype sharing test. It is important to keep in mind that some of these families might carry private mutations and no additional affected carriers will be identified in follow-up screening of large patient cohorts. Chapter 3 shows some examples of how we tried to identify novel disease genes in one autosomal recessive family and several autosomal dominant families, with the latter group being naturally much more challenging. The potential oligogenic background of late onset heart diseases is an increasingly popular concept, with a growing number of publications in HCM and ARVC supporting this idea. We also discuss it in chapters 2.2 (in which

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variant effect size

large

Mendelian/ monogenic di-/trigenic

medium

oligogenic

complex cases (series of risk factors)

small

very rare variant 10%

increasing environmental influence

variant frequency

solitary cases

Figure 2: Different disease models in cardiomyopathies Rare genetic variants of large effect size cause Mendelian, monogenic diseases, while variants of relatively high frequency, but small effect size, are the ones classically identified by GWAS, and associated with certain complex phenotypes. Familial cardiomyopathies are traditionally considered and investigated as monogenic disorders, while a smaller number of studies have tried to establish genetic associations in relatively “large” cohorts of not necessarily familial forms of the disease (between HLA genotypes and cardiac phenotype, for example). Recently there were a few reports of di- and oligogenic cardiomyopathy cases, which suggest the possibility that variants of relatively low frequency and medium effect size may increase an individual’s susceptibility to the disease and may also mediate the environmental influence on the disease onset and phenotypic variability. The degree of darkness of the clouds indicates how well studied those disease models are in cardiomyopathies.

we argue that genetic variants of PLEC are expected to be involved in passing the threshold needed for the manifestation of ARVC) and 4.1 (in which we show that 15% of our diagnostically screened patients carry more than one potentially pathogenic variant). Our results suggest that it is not only novel or very rare variants with large effect size that may be implicated in the disease,

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but that there are also several low frequency variants with slightly lower effect size that may increase the genetic susceptibility for cardiomyopathy in a non-monogenic disease model (see figure 2). Additionally, the association of cardiomyopathies with complex diseases such as diabetes or coeliac disease has been observed, supporting the idea that alleles of relatively high frequency and low effect size may be involved in a multifactorial background of the disease. For example, the increased risk for DCM in patients with coeliac disease (a disease famous of its complex genetics) was apparent, but statistically not significant in a large population-based study (Emilsson et al), while “diabetic cardiomyopathy” is a well-known disease entity that can be treated with targeted antioxidant therapy (reviewed by Huynh et al). There are also a couple of studies that support the idea of the complex genetics of cardiomyopathy by marking the connection between certain HLA genotypes (for example, the HLA-DQB1 0309 allele) and DCM (Pankuweit et al). Yet, due to the relatively low incidence of the disease and the low number of affected individuals, it is not easy to perform classical genome-wide association studies (GWAS) in cardiomyopathy while looking for risk or protective factors. It may be possible to compare frequencies of genetic variants of cardiomyopathy genes between patient and control cohorts upon DNA sequencing, but this will require the collection of material from patient cohorts from many different laboratories, while also taking into account the ethnic background of these patients. The role of mitochondrial processes in cardiomyopathies is evident, yet only a few genes related to these processes are shown to be the potential cause of the disease. In this thesis, we have described a mutation of the chromosomally encoded mitochondrial enzyme SOD2, which led to lethal cardiomyopathy with additional mitochondrial symptoms in a homozygote newborn (chapter 3.2). There are also mitochondrially encoded tRNA genes that have been reported to be causative, such as the mutation of the gene encoding tRNA glutamic acid that in nearly homoplasmic state proved fatal in an infant (Van Hove et al). In the case of inherited cardiomyopathies, there has not yet been enough attention paid to possible large indels and copy number variations (CNVs). There are only a few examples of CNVs identified to date: those of the BAG3 gene via array CGH (Norton et al), single large deletions of LMNA in DCM (Gupta et al) and PKP2 in ARVC (Li Mura et al), and a large duplication observed in MYBPC3 in HCM (Meyer T et al). The remaining unsolved affected families may also have duplications, large insertions, or deletions underlying their

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phenotypes. Hopefully, in the future, exome and whole genome sequencing methods will provide us with sufficient information on these types of genetic variants. Epigenetic regulations (the potential mediators of gene-environment interaction via chromatin modification) have never been associated with familial cardiomyopathies. Instead, the influence of the environment is ex-pected to trigger the onset of the phenotype in other ways. For example, pregnancy in individuals who are genetically susceptible for DCM causes earlier onset of the disease (see chapter 4.2), while stress and over-exercising probably contribute to individuals passing the thresholds for the development of an ARVC phenotype (Perrin et al). 2. On the RNA level The discovery of RBM20 mutations and the multiple RBM20-target molecules and their heart-specific splicing pattern meant the beginning of a new era in cardiogenetics, and this RNA-based pathway is also closely examined in this thesis (chapter 2.1). Yet we still do not know much about the potential role of miRNAs in cardiomyopathy, and the potential differential splicing effect of variants of known cardiomyopathy genes is also usually underestimated. These can easily be investigated by RNA sequencing. Perhaps the most exciting problem related to the role of RNA molecules in cardiomyopathy is that of the titin gene (TTN). The longest gene of the human genome, TTN has been connected to heart failure (Hein et al) and DCM (Gerull et al) for about two decades, but was never extensively screened due to its enormous size (~0.3Mb). Making things more complicated, TTN is not only large, but also has a highly complex pattern of post-translational modifications on the protein level. TTN also undergoes random changes on the RNA-level before translation: during its age-dependent splicing it randomly loses a gradually increasing part of the gene between exons 50 and 219 (Guo et al). TTN has been recently reported to harbour truncating variants in familial and sporadic DCM (Herman et al), and is nowadays often screened for due to the availability of easy-to-perform gene panel-based sequencing platforms (also shown in chapters 4.1 and 4.2). The inclusion of this gene in DNA-diagnostics resulted in the identification of truncating mutations in ~15% of DCM cases (chapter 4.1). Despite these advances in screening, a problem we continue to face is that we might be underestimating the importance of missense variants. It is possible that the transcribed mRNA

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molecules carrying truncating variants are subject to nonsense mediated decay leading to decreased protein production that only becomes a serious issue in homozygous state, while the right missense variant could disrupt a domain or binding site of key importance in the encoded protein and perturb its function in a heterozygous form. Yet despite the existence of some limited literature on functional evaluation of TTN missense variants (e.g. a missense mutation of the N2B domain specifically expressed in cardiac isoforms of titin caused a cardiomyopathy-like phenotype in zebrafish (Xu et al)), and further N2B mutations shown to affect the binding of various interacting proteins via yeast-two-hybrid assays by Matsumoto et al), we are biased towards the truncating mutations due to the recent finding of Herman et al that up to 25% of familial DCM is caused by them. In case we identify them localized in one of the exons that might get spliced out in some individuals (hence rescuing the onset of any sort of heart symptoms), it is quite difficult to correctly determine the pathogenicity level even for TTN truncations, let alone missense variants. In chapter 4.2, we have performed a functional experiment measuring passive force in single isolated patient cardiomyocytes, and our result supported the “pathogenic” labelling of that studied frameshift variant. 3. On the protein level

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Though it has not received much attention thus far, protein aggregation, a current focus in the field of neurodegeneration, may also be related to the pathomechanism of cardiomyopathies. There are examples in the literature showing that certain proteins do form aggregates and are therefore expected to lead to cardiovascular abnormalities. It has, for instance, been previously shown that PLEC knock out mouse models as well as skin biopsies of PLEC mutant patients with EBS-MD have large, desmin-positive protein aggregates accumulating in their cells (reviewed in Winter & Wiche and also mentioned in chapter 2.2). An exciting, translational potential of this mechanism was demonstrated by the recent discovery that protein aggregation could be inhibited and the phenotype improved in the muscles of plectin deficient conditional knock out mice by the chemical chaperon 4-phenylbutyrate (Winter et al 2014). Desmin aggregation is also a known phenomenon in heart failure (Sanbe et al) and in desminopathies (myopathies and cardiomyopathies related to abnormal desmin) caused by mutations of DES (desmin), CRYAB (alphaB-crystallin or small heat shock protein), MYOT (myotilin), BAG3 (BCL2-

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associated athanogene 3), LDB3 (LIM domain-binding 3), or FLNC (filamin C) (reviewed by Goldfarb et al). Interestingly, the expression of the BAG3 gene (for which a large deletion of about 8 kbp and point mutations have been reported in DCM, Norton et al) was shown to suppress the aggregation and cytotoxic effect of mutant CRYAB in cultured cells (Hishiya et al), a discovery that links the two genes to a mutual pathway. PSEN 1 and 2, the genes connected to Alzheimer disease as well as cardiomyopathy, are also known to be involved in the formation of amyloid plaques in the myocardium of DCM patients (Gianni et al). Deletions of the PLN gene were also recently shown to lead to perinuclear aggregates of the encoded protein in the hearts of deceased DCM and ARVC patients (manuscript submitted). Hopefully, a better understanding of protein aggregation in cardiomyopathies will open up novel possibilities of targeted therapy using various molecules with chaperone activity.

Further aspects and mechanisms While not yet extensively studied, an interesting observation is that there are some gender differences observed in the epidemiology, genetics, and clinical course of autosomal inherited cardiomyopathies (reviewed by Meyer S et al and Fairweather et al). Beyond the environmental influence of the cardiovascular challenges occurring during pregnancy that trigger peripartum cardiomyopathy (PPCM) or DCM at an earlier age in genetically susceptible women (see chapter 4.2), there are also hormone-related pathways involved in the pathomechanism. For example, male LMNA carriers are more severely affected than females, and this observation was associated with the nuclear accumulation of androgen receptors in LMNA mutant mice (Arimura et al). In contrast, a recent retrospective study found no worsening of symptoms in LMNA mutation-carrying women during pregnancy (Palojoki et al). The practical implications of gender differences for the diagnosis, management, and pharmacotherapy of cardiomyopathies were discussed in detail by Fairweather et al. Another question is how certain genes can be involved in the pathomechanisms of several diseases affecting multiple organs leading to a combined phenotype, while in other cases the same genes only cause the disease of one organ. There is a well-known correlation between ARVC, generalized myopathy and various skin diseases. For instance, truncating mutations of PLEC cause epidermolysis bullosa, yet missense mutations are observed in

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cardiomyopathy without the involvement of blistered skin (chapter 2.2). But there are many other desmosome-related genes also involved in dermatological diseases: for example, mutations in JUP cause palmoplantar keratoderma with woolly hair, while in DSP they may result in lethal acantholytic epidermolysis bullosa, or skin fragility with woolly hair. Systemic muscular involvement occurs quite frequently in cardiomyopathies: e.g. LMNA mutations were found in limb-girdle dystrophy and lipodystrophy besides DCM. PSEN1 and PSEN2 genes, when mutated, lead to neurodegeneration (Alzheimer’s disease), just as mutations in the potassium channel KCND3 are associated with another disease of the central nervous system, spinocerebellar ataxia, and with Brugada syndrome (characterized by lethal arrhythmia) (Duarri et al). Mutations in cardiomyopathy genes may affect the health of the sensory organs as well, for instance, in the case of EYA4 causing hearing loss with DCM and CRYAB causing cataract, and/or myofibrillar myopathy with DCM.

…about pharmacogenetics in a nutshell

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The basic principle of personalized medicine and pharmacogenetics was created some fifty years ago with the idea that serious side-effects could be prevented and the therapeutic response optimized, if only we were able to give the right medicine in the right dose to the right patient, making the right decision based on his/her individual genetic make-up. Even though genetic research has gone through unprecedented development in the past few decades, and pharmacovigilance databases provide excellent research material for such studies, the number of truly practical implications in patient stratification is still limited. We have some examples showing the efforts to stratify cardiomyopathy patients, yet these mostly resulted in treatment protocols based not on the genetic background but rather on the symptoms of the patients. For instance, patients suffering from DCM with asymptomatic systolic dysfunction are thought to benefit from pharmacological treatment (Colucci et al). Further examples include the recent observation that PPCM patients may have improved left ventricular ejection fraction when under bromocriptine treatment (Sliwa et al), or that ARVC patients carrying a PLN p.Arg14del mutation need the implantation of an ICD earlier than other ARVC patients (van der Zwaag et al). Based on the genetic background of a patient, classical pharmacodynamic or pharmacokinetic pharmacogenetics could be implemented. Pharmaco-

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dynamic pharmacogenetics of cardiomyopathies is not yet a rewarding research field, because cardiomyopathy is usually treated with widely used cardiovascular drugs (such as beta blockers, ACE inhibitors, or calcium channel blockers). These are not known to cause devastating adverse drug reactions (bizarre or type B ADRs) and, if not well tolerated by the patient, are easily replaced by a comparable drug targeting a different pathway. In contrast, pharmacokinetic pharmacogenetics, may be much more promising, because it is of utmost importance that these drugs are administered in the right dose, taking into account the patients’ metabolic abilities to achieve optimal blood concentrations of the drug. Genes and the SNPs observed to have an influence on the blood concentration of certain cardiomyopathy medicines could be included in the cardiomyopathy gene panel tests in the future. This would mean that, in parallel with the molecular diagnosis of a cardiomyopathy patient, we could also obtain sequence information to help in immediately adjusting the dose of the drug, and this would facilitate complex counselling (as attempted following personal genome sequencing by Ashley et al). Yet, at this moment, alleles of known SNPs of genes associated with slower/faster drug metabolism can be much faster, cheaper and more easily identified using a genotyping array of limited size. Also, the complete lack of knowledge about the truly functional genetic variants means it is currently not worthwhile to apply sequencing for patient stratification. In the past decades, another very exciting research area of stratified medicine related to cardiomyopathies has been the struggle to find out why certain drugs used for the treatment of other diseases lead to cardiomyopathy as a result of cardiotoxic side-effects (reviewed by Ky et al). An example is the dilated cardiomyopathy frequently observed after anticancer treatment using anthracycline molecules (briefly touched upon in chapter 3.2). Even though some patients are in danger of being sensitive to the cardiotoxicity of, for example, doxorubicin, they might not have any alternative treatment option available. Different methods of drug formulation, and hopefully better preventive combinations will soon be available to alleviate the toxic sideeffects (reviewed by Octavia et al and Carvalho et al).

CONCLUSIONS Cardiomyopathy is both a clinically and genetically complex disorder. Even though currently 76 genes are known to be involved in the heritable forms of the disease, we cannot explain the familial accumulation of the phenotype

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in many cases. This thesis provides an overview of the development of molecular genetic methods implemented during recent years in the research and diagnostics of cardiomyopathies. It contributes to the field through the discovery of novel disease genes as well as through the establishment of new and highly effective methods for molecular diagnostics. In spite of the recent technological advances, the genetic cause of the disease often remains unknown in affected families, as do the complex interactions of environmental and genetic factors. Hopefully the molecular pathways underlying the disease will be extensively studied in the future, ultimately leading to novel translational solutions and practical implications for patients.

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genicity of human genetic variants. Nat Genet 2014;46:310-5 Knollmann BC: Induced pluripotent stem cell-derived cardiomyocytes – Boutique Science or valuable arrhythmia model? Circ Res 2013;112:969-976 Ky B, Vejpongsa P, Yeh ET et al. Emerging paradigms in cardiomyopathies associated with cancer therapies. Circ Res 2013;113:754-64 Li Y, Huang TT, Carlson EJ et al. Dilated cardiomyopathy and neonatal lethality in mutant mice lacking manganese superoxide dismutase. Nat Genet 1995;11:376-81 Li Mura IE, Bauce B, Nava A et al. Identification of a PKP2 gene deletion in a family with arrhythmogenic right ventricular cardiomyopathy. Eur J Hum Genet 2013;21:1226-31 Limphong P, Zhang H, Christians E et al. Modeling human protein aggregation cardiomyopathy using murine induced pluripotent stem cells. Stem Cells Transl Med 2013;2(3):161-6 Matsumoto Y, Hayashi T, Inagaki N et al. Functional analysis of titin/connectin N2-B mutations found in cardiomyopathy. J Muscle Res Cell Motil 2005;26:367-74 Menke DB: Engineering subtle targeted mutations into the mouse genome. Genesis 2013;51(9):605-18 Meyer S, van der Meer P, van Tintelen JP et al. Sex differences in cardiomyopathies. Eur J Heart Fail. 2014;16(3):238-47 Meyer T, Pankuweit S, Richter A et al. Detection of a large duplication mutation in the mysin-binding protein C3 gene in a case of hypertrophic cardiomyopathy. Gene 2013;527:416-20 Norton N, Li D, Rieder MJ et al. Genome-wide studies of copy number variation and exome sequencing identify rare variants in BAG3 as a cause of dilated cardiomyopathy. Am J Hum Genet 2011;88(3):273-82 Octavia Y, Tocchetti CG, Gabrielson KL et al. Doxorubicin-induced cardiomyopathy: from molecular mechanisms to therapeutic strategies. J Mol Cell Cardiol 2012;52:1213-25 Palojoki E, Kaartinen M, Kaaja R et al. Pregnancy and childbirth in carriers of the lamin A/Cgene mutation. Eur J Heart Fail 2010;12:630-3 Pankuweit S, Ruppert V, Jónsdóttir T et al. The HLA class II allele DQB1 0309 is associated with dilated cardiomyopathy. Gene 2013;531(2):180-3 Perrin MJ, Angaran P, Laksman Z et al. Exercise testing in asymptomatic gene carriers exposes a latent electrical substrate of arrhythmogenic right ventricular cardiomyopathy. J Am Coll Cardiol 2013;62:1772-9

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Posafalvi A, Herkert JC, Sinke RJ et al. Clinical Utility gene card for: dilated cardiomyopathy (CMD). Eur J Hum Genet 2012; doi:10.1038/ ejhg.2012.276 Ravanelli AM & Klingensmith J: The actin nucleator Cordon-bleu is required for development of motile cilia in zebrafish. Dev Biol 2011;350(1):101-11 Sanbe A, Osinska H, Saffitz JE et al. Desmin-related cardiomyopathy in transgenic mice: a cardiac amyloidosis. Proc Natl Acad Sci USA 2004;101:10132-6 Sliwa K, Blauwet L, Tibazarwa K et al. Evaluation of bromocriptine in the treatment of acute severe peripartum cardiomyopathy: a proof-of-concept pilot study. Circulation 2010;121(13):1465-73 Sinnecker D, Goedel A, Laugwitz KL et al. Induced pluripotent stem cell-derived cardiomyocytes – A versatile tool for arrhythmia research. Circ Res 2013;112:961-968 Teekakirikul P, Kelly MA, Rehm HL et al. Inherited cardiomyopathies: molecular genetics and clinical genetic testing in the postgenomic era. J Mol Diagn 2013;15(2):158-170 van der Zwaag PA, van Rijsingen IA, de Ruiter R et al. Recurrent and founder mutations in the Netherlands-Phospholamban p.Arg14del mutation causes arrhythmogenic cardiomyopathy. Neth Heart J 2013;21(6):286-93 Van Hove JL, Freehauf C, Miyamoto S et al. Infantile cardiomyopathy caused by the T14709C mutation in the mitochondrial tRNA glutamic acid gene. Eur J Pediatr 2008;167(7):771-6 Winter L & Wiche G: The many faces of plectin and plectinopathies: pathology and mechanisms. Acta Neuropathol 2013;125(1):77-93 Winter L, Staszewska I, Mihailovska E et al. Chemical chaperone ameliorates pathological protein aggregation in plectin-deficient muscle. J Clin Invest 2014;124(3):1144-57) Xu X, Meiler SE, Zhong TP et al. Cardiomyopathy in zebrafish due to mutation in an alternatively spliced exon of titin. Nat Genet 2002;30:205-9

SUMMARY SAMENVATTING MAGYAR NYELVŰ ÖSSZEFOGLALÓ

SUMMARY

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In my doctoral thesis several aspects of the genetic background of cardiomyopathy, an insidious, complex group of hereditary heart diseases, were examined. This disorder usually develops in adulthood and manifests with diverse symptoms. While some patients have shortness of breath, chest pain or oedema, others may suffer from arrhythmia, embolism, and other severe symptoms. A relatively rare but extreme sign of the disease is sudden cardiac death, which most often occurs in athletes and football players. Although there are many environmental factors and/or other diseases (including muscular abnormalities, hormonal changes, some types of chemotherapeutic drugs, pregnancy-related cardiovascular challenges, alcoholism and drug abuse) known to cause or trigger cardiomyopathy, certain genetic factors also increase susceptibility to the disorder. Presently, we know of about 75 genes which, when mutated, play a role in the molecular pathomechanism and onset of cardiomyopathy. However, a significant subset of these genes have only been studied in limited numbers of patients and mostly in a research setting. The pathogenicity of the respective mutations is often based on in silico predictions but not yet supported by functional proof. Despite the known heterogeneity of disease, some genes were only studied within the context of specific cardiomyopathy subtypes, and there are still a considerable number of patients or families whose phenotypes cannot be explained by having mutations in these genes. Therefore, the studies in this thesis aimed to (1) provide a better understanding of the genetic background and the molecular pathomechanism of familial cardiomyopathies, (2) identify novel disease genes in unsolved families, and (3) improve existing methods of molecular diagnostic testing. The preface provides an easy-to-understand general introduction to cardiomyopathies and the challenges of the related genetic research. Chapter 1 provides a more detailed, scientific introduction to the field of cardiogenetics. It reviews congenital and late-onset inherited heart diseases, categorizes the genes involved in different types of heritable heart diseases, and thoroughly describes those research methods with a great potential for future diagnostic application in cardiovascular diseases. In the studies reported in chapter 2, the classical candidate gene screening approach via the traditional method of Sanger sequencing was applied to study the involvement of candidate genes in disease development of two cardiomyopathy subtypes: dilated cardiomyopathy (DCM) and arrhythmogenic right ventricular cardiomyopathy (ARVC).

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In chapter 2.1, we report on our studies of the role of the DCM-related gene RNA-binding motif protein 20 (RBM20) in Dutch patients. We identified five known mutations of the arginine-serine(RS)-rich domain, and 18 novel missense variants. In total, 10 variants were classified as likely pathogenic or pathogenic. We then performed a functional follow-up of ten ‘interesting’ variants by using an in-house-developed splicing assay to study the transcripts produced by one of the recently identified heart-specific RBM20 targets, LDB3. Unfortunately, our results could not confirm differential splicing of LDB3 in HEK293 cells transfected with either wild type or mutant RBM20 encoding plasmids, neither could these studies support evaluation of the potential pathogenicity of variants identified outside of the RS-rich domain. Interestingly, two of our RBM20 mutation-carrying families manifest DCM in combination with peripartum cardiomyopathy (PPCM). This novel observation is not completely unexpected, since RBM20 is known to control splicing of the titin gene (TTN), and, in another study reported in this thesis, we have shown that mutations in this gene are the frequent underlying cause of familial PPCM/DCM (see details in chapter 4.2). Our findings suggests that abnormal titin isoform composition could be an essential mutual pathway leading to PPCM in both TTN and RBM20 mutation carriers. In chapter 2.2, the screening of the plectin gene (PLEC) as a novel desmosome-related candidate gene for ARVC is reported. Though plectin was formerly shown to be an essential component of the desmosomes and hemidesmosomes in skin, muscle and heart, and was known for a decade to carry homozygous truncating/frameshift mutations in blistering skin diseases with muscular involvement, we were the first to investigate its potential disease-causing role in cardiomyopathy. We identified numerous missense variants in patients, and compared the patient-related variation with the general variation in PLEC in the Genome of the Netherlands control cohort. We identified one region of PLEC rich in mostly novel variants with high predicted pathogenicity level in ARVC patients in both the Dutch and British patient cohort that shows a “variant desert” in controls. This region is localized in the homo-dimerizing ROD domain, underscoring the particular importance of the mechanical resistance of this cytolinker protein and suggesting a role for mutations in this domain in disease progression. In conclusion, missense variants (in particular the ones located in this region) might play a risk-factor-role in the oligogenic background of the disease, contributing to the various genetic and non-genetic factors that can then exceed the threshold needed for the manifestation of ARVC.

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In chapter 3, we describe our studies utilizing the novel exome sequencing (ES) technique in order to identify novel cardiomyopathy genes (mostly private mutations of unsolved families, who were formerly screened for known disease genes). We performed ES in 12 families suffering from autosomal dominant cardiomyopathy and report on the results in chapter 3.1. We identified the potentially causal genetic variations in 6/12 families in the TTN (in two families), FHL2, FLNC, COBL, and STARD13 genes. Importantly, earlier functional studies of the encoded proteins support their putative involvement in heart disease (though to different extents). Additionally, by evaluating the potential coexpression of these genes using a large expression array database, we found that all these genes are interconnected by a complex network of co-expressed genes. This network of 166 proteins contains 28 genes that are already known to be associated with cardiomyopathy. Furthermore, 100 of them are listed in the Cardiovascular Gene Ontology Annotation database, suggesting a potential cardiac function and providing an excellent basis for genetic and functional follow-up studies. It is interesting to note that many of these proteins are involved in the sarcomeric pathway that is known to be the most prominent one involved in the molecular pathomechanism of several subtypes of cardiomyopathy. We have investigated a consanguineous family with a child who passed away due to severe DCM a few days after birth and show the results in chapter 3.2. The nature and severity of the symptoms suggested a mitochondrial disease. Applying ES in combination with homozygosity mapping, we identified a homozygous missense mutation affecting the Mn-binding pocket of a mitochondrial protein encoded by the autosomal superoxide-dismutase gene SOD2, which is located in the longest autosomal homozygous region on chromosome 6. The absence of the gene was previously shown to lead to DCM in knock-out mice, but had not yet been found to underlie the disease in humans. Here, we confirmed the accumulation of oxygen radical substrates via functional experiments performed on fibroblast samples of the deceased patient, while excluding dysfunction of the mitochondrial respiratory chain complexes. Excitingly, the same pathway of SOD2-dependent accumulation of oxygen radicals has been known for about 20 years to be involved in the pathomechanism of cardiomyopathy that arises as a complication of anthracycline chemotherapy in cancer patients. We present a short report on the unusual case of a distantly consanguineous family with several patients affected by two distinct cardiomyopathy

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subtypes (late-onset DCM and neonatal DCM) and having three different genes (MYL2, SOD2, and JUP) underlying their diseases in chapter 3.3. The case presented is an excellent example of how essential genealogical linking and pedigree construction are for the proper interpretation of genetic findings, subsequent counselling and individual genetic follow-up studies in families affected by different forms of cardiomyopathies. In chapter 4, we demonstrate the application of next generation sequencing in routine diagnostic screenings. In order to do so, we applied a different technical approach when compared to that described in chapter 3. Instead of capturing and enriching for the whole exome, we performed targeted enrichment for a set of already known, previously published cardiomyopathy disease genes during the sample preparation. This minimized both the costs of the NGS experiments and the time needed for the interpretation of the results, while the coverage is highly optimized for standardized screening. An additional advantage of this targeted enrichment method is that the longest gene of the entire human genome, TTN, which has been known for a decade or two to be involved in DCM but which was, in the past, too large to be routinely screened for using Sanger sequencing, could now also be included in the enrichment kit. In chapter 4.1, we report on the quantitative advantages of this method complemented with a rigorous variant classification system over the ‘oldfashioned’ Sanger-sequencing in diagnostics: we have managed to solve 107/206 (52%) index cases of different types of cardiomyopathies with the help of this ‘cardiomyopathy panel’-based approach, and the yield was especially high for patients with DCM and DCM-like phenotypes. In our sample 30/206 (15%) patients had multiple mutations detected, pinpointing the importance of broadening the spectrum of inherited cardiomyopathies from a classical Mendelian inherited disease towards a more oligogenic disorder. Finally, in at least half of the cases the mutations were identified in genes that would not have been selected for candidate screening in the earlier era when such decisions were based on the phenotype of the patient and the low or unknown frequencies of mutations in those genes. In chapter 4.2, we report on solving the genetic cause of 10/18 PPCM families via identifying (likely) pathogenic variants in one or more of the 48 sequenced known (mostly dilated) cardiomyopathy genes. Our results support the former, phenotype-based hypothesis that PPCM is not an independent subtype of cardiomyopathy, but rather a pregnancy-related

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manifestation of DCM, since these two ‘types’ of the disease also exhibit considerable overlap in their genetic background. Interestingly, seven of the ten families in which a (likely) pathogenic variant was identified carry the mutation in the TTN gene. Drastically decreased passive force development in single cardiomyocytes, as well as the switch in titin isoform composition measured in explanted heart tissue of one of these patients, support upgrading of the classification of (at least) the p.K15664Vfs*13 truncating variant to the pathogenic mutation category. Taken together, our results suggest a prominent role for TTN mutations in the development of PPCM. In chapter 5 the tremendous technical advances influencing cardiogenetics during the past few years are discussed, the current workflow of routine diagnostics and research of cardiomyopathies in our department is illustrated, and many unsolved questions and potential future directions in this field of research are addressed.

LAY SUMMARY

LAY SUMMARY

LAY SUMMARY

Cardiomyopathy is an insidious disease of the heart that can cause mild symptoms like dizziness and chest pain, but might result in irregular heart rhythm, and sometimes even heart failure or sudden cardiac death without any previous warning sign. This disease is largely influenced by heritable factors, and our aim was to gain more insight into the genetic causes of the disease. We applied various DNA sequencing methods (determining the genetic code of certain genes or regions) to identify novel mutations in known genes as well as novel disease genes in currently unexplained families with a repeated history of the disease. We have shown that the production of incorrect forms of an important building block of the heart muscle machinery leads to pregnancy-related cardiomyopathy, while variants in the rod domain of a novel gene may increase the fragility of the protein complex that “glues” together neighbouring cardiac muscle cells, leading to arrhythmogenic cardiomyopathy. Moreover, in a newborn patient we identified increased oxygen radical levels due to a mutation to be the likely cause of the disease. From a diagnostic point of view, our panel of 55 known disease genes can be easily and reliably sequenced using our targeted sequencing method, which results in much improved diagnostic yield, facilitating the screening of healthy looking family members of patients to identify those who have a risk to develop the disease too.

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NEDERLANDSE SAMENVATTING In dit proefschrift heb ik verschillende aspecten van de genetische achtergrond van cardiomyopathie onderzocht, een groep van erfelijke hartafwijkingen. Cardiomyopathie ontwikkelt zich meestal op volwassen leeftijd, en kan zich uiten met verschillende symptomen. Sommige patiënten hebben kortademigheid, pijn op de borst of oedeem, anderen hebben last van hartritmestoornissen, embolieën, of andere ernstige symptomen. Een zeldzame maar extreme uiting is plotse hartdood, wat meestal voorkomt bij atleten of voetballers. Verschillende factoren kunnen cardiomyopathie veroorzaken. Naast omgevingsfactoren en andere ziektes (spierafwijkingen, hormonale veranderingen, chemotherapie, zwangerschapsgerelateerde hart- en vaatproblemen, alcoholisme en drugsgebruik) die de ziekte kunnen veroorzaken, spelen genetische (erfelijke) factoren hierbij ook een belangrijke rol. Op dit moment zijn er ’ongeveer 75 genen bekend waarvan duidelijk is dat mutaties (foutjes) in deze genen een rol kunnen spelen bij het ontstaan en ziektemechanisme van cardiomyopathie. Het effect van veel van deze genen is vaak enkel bestudeerd bij een kleine groep patiënten, en meestal alleen binnen wetenschappelijke onderzoeksprojecten. Hoe schadelijk de mutaties zijn voor de functie van de genen is vaak slechts gebaseerd op voorspellingen met de computer, maar nog niet ondersteund door bewijs uit functionele studies. Ondanks dat bekend is dat de ziekte genetisch sterk heterogeen is (een groot aantal genen kunnen potentieel bij de ziekte betrokken zijn), is de rol van sommige genen alleen nog maar bestudeerd in de context van één type cardiomyopathie. Bovendien, zijn er nog veel patiënten en families bij wie de ziekte niet verklaard wordt door mutaties in de nu bekende cardiomyopathie genen. Het onderzoek in dit proefschrift had daarom tot doel om: (1) ons inzicht te vergoten in de genetische achtergrond en de oorzaken van erfelijke cardiomyopathieën, (2) nieuwe genen te ontdekken in families waarvan nog niet bekend was welke mutaties hun ziekte veroorzaken en (3) de bestaande methodes van moleculaire diagnostiek (het stellen/bevestigen van de juiste diagnose op basis van DNA-onderzoek) te verbeteren. De “preface” is een algemene introductie over cardiomyopathieën en de uitdagingen van het genetische onderzoek naar deze ziektes. Hoofdstuk 1 is een meer gedetailleerde, wetenschappelijke introductie van het veld van de cardiogenetica. Hierin wordt een overzicht gegeven van aangeboren en verworven erfelijke hartafwijkingen en worden de genen die betrokken zijn bij verschillende soorten erfelijke hartziektes gegroepeerd. Ook wordt

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een uitgebreide beschrijving gegeven van recent beschikbaar gekomen en veelbelovende onderzoeksmethoden die gebruikt kunnen worden voor (toekomstige) diagnostiek bij erfelijke hartaandoeningen. In hoofdstuk 2 zijn studies beschreven waarbij kandidaatgenen (genen waarvan vermoed wordt dat zij een rol spelen bij cardiomyopathieën) op mogelijke mutaties onderzocht zijn op de “klassieke” manier: via een techniek die Sanger Sequencing heet. Hierbij werd de rol van deze kandidaatgenen bij twee types cardiomyopathie onderzocht: Dilaterende Cardiomyopathie (DCM) en Aritmogene Rechter Ventrikel Cardiomyopathie (ARVC). In hoofdstuk 2.1 is de rol van het RNA-binding motif protein 20 (RBM20) gen, waarvan betrokkenheid bij DCM eerder beschreven was , bij Nederlandse DCM patiënten bestudeerd. Hierbij hebben we vijf al eerder beschreven mutaties in het Arginine-Serine (RS)-rijke domein gevonden en 18 nog niet eerder gerapporteerde missense mutaties, zowel in als buiten dit RS-rijke domein. Van deze 23 mutaties, werden in totaal 10 als pathogeen of waarschijnlijk pathogeen (schadelijk) beoordeeld. Vervolgens hebben we 10 variaties/mutaties verder bestudeerd met behulp van een zelf ontwikkelde methode (een splicing assay genoemd). Hierbij hebben we gekeken of deze variaties/mutaties invloed hebben op de samenstelling van transcripten afkomstig van het LDB3 gen, dat één van de genen is waarvan de variërende RNA splicing door RBM20 gereguleerd wordt. Helaas waren we niet in staat om middels deze methode verschillen in aanwezigheid van wild type of gemuteerd RBM20 aan te tonen. Hierdoor was het ook niet mogelijk om bewijs te vinden voor een mogelijke rol van nieuwe, buiten het RS-rijke domein gevonden variaties. In twee families met mutaties in het RMB20 gen werd DCM in combinatie met peripartum cardiomyopathie (PPCM) aangetoond. Omdat bekend is dat RMB20 ook bij de variërende splicing van het gen titine (TTN) betrokken is, en we weten dat mutaties in TTN vaak de onderliggende oorzaak zijn van familiaire PPCM/DCM (zie hoofdstuk 4.2) is dit niet onverwachts. Onze bevindingen suggereren dat een afwijkende samenstelling van titine isovormen de onderliggende oorzaak is van de ontwikkeling van PPCM bij dragers van mutaties in zowel TTN als RBM20. In hoofdstuk 2.2 beschrijven we ons onderzoek naar het plectine gen (PLEC), als mogelijk nieuw kandidaatgen voor ARVC. Hoewel eerder aangetoond is dat plectine een essentiële rol speelt in desmosomen en hemidesmosomen in de huid, spier en het hart en het al langere tijd bekend is dat homozygote en “compound” heterozygote, truncerende mutaties in dit gen blaarziektes met spierproblemen veroorzaakten, waren wij de eersten die een mogelijke

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ziekteveroorzakende rol van dit gen bij cardiomyopathie onderzochten. We hebben in Nederlandse en Britse patiënten een groot aantal missense variaties in dit gen gevonden, en hebben de variatie in patiënten vergeleken met de variatie die voorkomt bij gezonde mensen uit het “Genoom van Nederland” cohort. Hierbij bleek er sprake te zijn van de verrijking van variaties, die als waarschijnlijk pathogeen geclassificeerd worden op basis van “in silico” voorspellingen, in één gebied van het PLEC gen, in vergelijking tot de variaties in dit gebied bij de gezonde controles. Bij de laatste is er in dit gebied vrijwel geen variatie aanwezig. Dit gebied bevindt zich in het zogenaamde ROD domein dat belangrijk is voor homodimerisatie van het PLEC eiwit. We denken dat mutaties in dit domein een rol kunnen spelen in de ziekte ARVC en de mechanische resistentie van het eiwit aantast. Samenvattend, suggereren onze resultaten dat mutaties in PLEC een risicofactor kunnen zijn die een rol spelen bij de oligogene basis van ARVC, en kunnen bijdragen aan de verschillende genetische en niet-genetische factoren die ervoor zorgen dat iemand de ziekte ontwikkelt. In hoofdstuk 3 beschrijven we het gebruik van de nieuwe techniek exome sequencing (ES) om nieuwe genen, betrokken bij cardiomyopathie, te identificeren – waarbij voornamelijk mutaties gevonden werden die uniek zijn voor de onderzochte families, die eerder zonder resultaat gescreend waren voor bekende ziektegenen. We hebben deze techniek toegepast bij 12 families waarbij een erfelijke vorm van cardiomyopathie voorkwam en de resultaten hiervan staan beschreven in hoofdstuk 3.1. In 6 van deze 12 families hebben we genetische variaties gevonden die waarschijnlijk de ziekte veroorzaken, te weten in de genen TTN (in 2 families), FHL2, FLNC, COBL en STARD13). Eerder functioneel onderzoek maakte al duidelijk dat deze genen betrokken zouden kunnen zijn bij erfelijke hartziektes. Daarnaast hebben we de gegevens van een grote gen expressie database gebruikt om aan te tonen dat deze genen onderdeel zijn van een complex netwerk van genen die gezamenlijk tot expressie komen. Dit suggereert dat ze een gezamenlijke functie kunnen hebben. Het netwerk, bestaande uit 166 genen, bevat 28 genen waarvan al bekend is dat ze betrokken zijn bij cardiomyopathieën. Ook zijn 100 van deze genen beschreven in de Cardiovascular Gene Ontology Annotation database. Dit wijst er dus op dat de genen in dit netwerk een potentiële rol spelen bij een normale hartfunctie. Dit netwerk van genen vormt dan ook een mooie basis voor verdere genetische en functionele vervolg studies. Bovendien, viel het ons op dat een aanzienlijk deel van deze genen betrokken zijn bij het functioneren van het sarcomeer,

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de structuur waarin mutaties een prominente rol spelen bij het moleculaire ziektemechanisme bij verschillende cardiomyopathie subtypes. Ook werd een familie bestudeerd waarin een kind met consanguine ouders een paar dagen na de geboorte overleed aan een ernstige vorm van DCM. De resultaten van deze studie staan beschreven in hoofdstuk 3.2. De ernst van en het type symptomen suggereerden dat het om een mitochondriële ziekte zou gaan. Door exome sequencing in combinatie met “homozygosity mapping” te gebruiken, hebben we een homozygote missense mutatie geïdentificeerd, gelegen in het langste, autosomale homozygote gebied in het DNA van de patiënt en gelocaliseerd op chromosoom 6. Deze mutatie beïnvloedt het Mnbindende domein van het mitochondriële eiwit gecodeerd door het superoxide dismutase gen SOD2. Het was al bekend dat het ontbreken van dit gen in knockout muizen leidt tot DCM; dat dit gen ook de humane ziekte kon veroorzaken was nog niet bekend. Wij hebben, door cellen van de overleden patiënt te bestuderen op accumulatie van zuurstof radicalen en het correct functioneren van de mitochondriële ademhalingsketen, bevestigd dat de functie van het gen inderdaad verstoord was bij deze patiënt. Dit is ook een interessante waarneming met het oog op de rol van SOD2-afhankelijke accumulatie van zuurstofradicalen die al jaren geleden beschreven is bij het ontstaan van cardiomyopathie als bijwerking van anthracycline-gebaseerde chemotherapie bij kanker patiënten. In hoofdstuk 3.3 beschrijven we het ongebruikelijke geval van een consanguine familie met meerdere patiënten met twee verschillende vormen van cardiomyopathie (volwassen DCM en neonatale DCM). We laten zien dat er drie verschillende genen bij hun ziekte betrokken zijn (MYL2, SOD2 en JUP). Deze casus is een goed voorbeeld van hoe genealogische koppeling en stamboom reconstructie gebruikt kunnen worden voor de juiste interpretatie van genetisch resultaten, en hoe deze kennis kan helpen bij goede voorlichting van en genetische vervolgstudies bij families waarin verschillende vormen van cardiomyopathie voorkomen. In hoofdstuk 4 demonstreren we hoe de next generation sequencing (NGS) techniek gebruikt kan worden in de routine diagnostiek. Hiervoor hebben we een andere aanpak gebruikt dan beschreven in hoofdstuk 3. In plaats van het verrijken en bestuderen van álle genen hebben we de gerichte verrijking gebruikt van een set genen, waarvan bekend was dat ze betrokken zijn bij cardiomyopathie. Hierdoor werden de kosten behoorlijk lager, en konden de resultaten sneller geïnterpreteerd worden; terwijl dankzij de hoge horizontale en goede verticale dekking resultaten van hoge kwaliteit verkregen

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konden worden. Een additioneel voordeel van de inzet van NGS was dat de inclusie van het TTN gen, het langste gen in het hele humane genoom en te groot om routine-matig te screenen met Sanger sequencing, mogelijk werd. Hierdoor kan ook dit gen, waarvan bekend is dat het betrokken is bij DCM, meegenomen worden bij de routine diagnostiek. De voordelen van deze methode ten opzichte van de ‘ouderwetse’ techniek (Sanger sequencing) worden beschreven in hoofdstuk 4.1. Met deze “gen panel”gebaseerde aanpak is het ons gelukt om 107/206 (52%) index patiënten met cardiomyopathie op te lossen (d.w.z.: het bijbehorende gen te vinden). De diagnostische opbrengst was voornamelijk hoog bij patiënten met klinisch bewezen DCM of patiënten die verdacht zijn voor DCM. Bovendien werd bij 30/206 (15%) patiënten meer dan één (waarschijnlijk) ziekteverwekkende mutatie gedetecteerd. Deze observatie ondersteunt de eerdere suggesties van het bestaan van een spectrum van klassiek Mendeliaanse tot oligogeen overervende cardiomyopathieën. In ten minste de helft van de patiënten werden mutaties gevonden in genen die in het verleden niet onderzocht zouden zijn met Sanger sequencing, toen beslissingen voor genetische screening gebaseerd werden op de symptomen van de patiënt en de gerapporteerde frequentie van mutaties in genen. In hoofdstuk 4.2 beschrijven we hoe we de genetische oorzaak van de ziekte bij 10/18 PPCM-families vinden door het zoeken naar schadelijke varianten in één of meer van de 48 bekende genen die betrokken zijn bij (met name dilaterende) cardiomyopatie. Onze resultaten bevestigen het idee dat PPCM geen subtype van cardiomyopathie is, maar een zwangerschap-gerelateerde vorm van DCM, aangezien deze twee ‘types’ van de ziekte ook een significante overlap vertonen in hun genetische achtergrond. Daarnaast werden in zeven van de tien families waarin een schadelijke variant geïdentificeerd is, een mutatie in het TTN gen gevonden. In hartweefsel van één van deze patiënten werd inderdaad een andere samenstelling van de verschillende isovormen van titine gevonden en bleek het functioneren van dit eiwit verstoord. Onze resultaten suggereren dat mutaties in TTN een belangrijke rol spelen bij de ontwikkeling van PPCM. In hoofdstuk 5 wordt een overzicht van de enorme technische vooruitgang van de aflopen jaren gegeven, en de invloed daarvan op de cardiogenetica beschreven. Ook illustreren we het gebruik van NGS methodes binnen onze afdeling in de routine-diagnostiek en bij het wetenschappelijk onderzoek naar de genetische achtergrond van cardiomyopathieën, en worden vele onbeantwoorde vragen en mogelijke toekomstige onderzoekslijnen besproken. (Translated by Dr Eva Teuling)

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MAGYAR NYELVŰ ÖSSZEFOGLALÓ

MAGYAR NYELVŰ ÖSSZEFOGLALÓ

MAGYAR NYELVŰ ÖSSZEFOGLALÓ

Doktori disszertációmban az örökletes cardiomyopathiák genetikai hátterét vizsgáltam. Ez a betegség (ami szó szerint átfogóan csupán annyit jelent: szívizomrendellenesség) leggyakrabban felnőtt korban jelentkezik változatos és eltérő súlyosságú tünetekkel - egyeseknél szédülés, nehézlégzés, ödéma és mellkasi fájdalom jelzi, míg mások súlyos szívritmuszavaroktól és thromboembolizációtól is szenvedhetnek. A betegség ritka és szélsőséges megnyilvánulási formája a futball- és jégkorongjátékosok réme, a hirtelen szívhalál. Ugyan számos környezeti tényező és egyéb betegség (mint például izombetegségek és hormonális változások, egyfajta kemoterápiás kezelés, terhesség során felmerülő keringési nehézség, alkoholizmus és egyes drogok használata) is kiválthatja vagy gyorsíthatja a betegség progresszióját, genetikai tényezők is hajlamosíthatnak cardiomyopathiára. Pillanatnyilag mintegy 75 olyan gént ismerünk, amelynek bizonyos módosulatai szerepet játszanak a betegség molekuláris mechanizmusában és kialakulásában. Sajnos ezen gének jelentős hányadát eddig kis esetszámban tanulmányozták a kutatók. A felfedezett mutációk valódi betegségkiváltó hatását gyakran nem erősítik meg funkcionális kísérletek, azt pusztán számítógépes predikciók alapján feltételezhetjük. A betegség rendkívüli összetettsége és sokszínűsége ellenére egyes gének szerepét eddig csak bizonyos cardiomyopathia típusok eseteiben vizsgálták, továbbá a betegek és családok jelentős hányadában a fenotípus kialakulását nem magyarázza az eddig ismert gének egyikének potenciális eltérése sem. Éppen ezért kutatásom célja volt (1) alaposabban feltárni a betegség örökletes formájának genetikai és molekuláris hátterét, (2) a megoldatlan, családi halmozódást mutató esetekben eddig nem ismert gének mutációinak azonosítása, illetve (3) a jelenleg használatos molekuláris genetikai, diagnosztikai módszerek továbbfejlesztése, hatásfokának javítása. A betegséget nagyvonalakban bemutató előszót követően az első fejezetben részletes irodalmi áttekintést adunk a kardiogenetikáról, csoportosítjuk a géneket aszerint, hogy a betegség mely típusában ismert mutációjuk, és részletesen leírjuk azokat a kutatási módszereket, amelyeket a szív- és érrendszeri betegségek diagnosztikájában akár a közeljövőben is lehetne sikeresen használni. A második fejezetben a hagyományos Sanger szekvenálási módszerrel vizsgáltuk nagy betegcsoportokban, hogy egyes – korábbi ismereteink alapján potenciálisan érdekesnek tűnő – gének szerepet játszhatnak-e a cardiomyopathia kialakulásában.

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A 2.1 cikkben egy néhány éve felfedezett dilatatív cardiomyopathia (DCM) gént, az RBM20-at (egy RNS-kötő fehérjét kódoló gént) szekvenáltuk holland betegekben, és számos olyan génváltozatot találtunk, amely egészséges kontrollokban nem megfigyelhető: 5 ismert mutációt az arginin-szerin (RS) gazdag doménben, valamint további 18 korábban ismeretlen missense (egy darab aminosavat másik aminosavra cserélő) variánst. Összesen 10 variánst találtunk pathogénnek (betegséget kiváltónak) vagy lehetségesen pathogénnek. Ezt követően nyomon követtük 10 „ígéretes” variáns hatását egy saját tervezésű splicing assay segítségével, mely módszer az egyik nemrég azonosított szívspecifikus RBM20-célpont, az LDB3 különböző hosszúságú transzkriptjeinek jelenlétén és arányán alapul transzfektált HEK293 sejtekben. Sajnos nem találtunk egyértelmű különbséget a vad típusú és mutáns RBM20plazmidokat hordozó sejtek között, és nem sikerült igazolnunk az RS-gazdag doménen kívül elhelyezkedő variánsok esetleges pathogén mivoltát sem. Érdekes viszont, hogy az RBM20 mutációt hordozó családok közül kettőben is a DCM terhességi cardiomyopathiával (TCM) kombinálva jelenik meg. Ez a megfigyelés nem volt teljesen váratlan, hiszen az RBM20 által kódolt fehérje az RNS-molekulák megkötésében és átszabásában (splicing) játszik szerepet, és fő molekuláris célpontja a titin (TTN), amelynek mutációit a 4.2 fejezetben gyakran kulcsfontosságúnak találtuk örökletes TCM/DCM kialakulásában. Így valószínű, hogy a TTN izoformák arányának megbomlása a közös molekuláris út, amely TCM-hez vezethet mind TTN, mind RBM20 mutációt hordozó betegekben. A 2.2 fejezetben a plektin (PLEC) gént szekvenáltuk meg arrythmogén cardiomyopathiátiól (ACM) szenvedő betegek vérből kivont DNS-mintájában. Habár a plektin bőrben, izmokban és szívben játszott, szomszédos sejteket egymással összekapcsoló funkciója évtizedek óta ismert, a bőr felhólyagosodásával és a vázizmok elsorvadásával járó súlyos betegségben – az epidermolysis bullosában – ismertek frameshift (“kereteltolásos”) és nonsense (“csonkoló”) mutációi, mi voltunk az elsők, akik megkísérelték szívbetegséghez is kötni a PLEC genetikai változatait. Rengeteg missense típusú variánst találtunk a génben ACM-es betegekben, majd összevetettük ezen variánsok elhelyezkedését a gén általános variánsaival, amelyeket egészséges emberekben azonosítottak be a Genome of the Netherlands kohortban. Találtunk egy olyan PLEC szakaszt, amelyben több, korábban ismeretlen, vélhetően pathogén variáns halmozódott fel mind a brit, mind a holland beteg kohortban, ám „variáns sivatagnak” bizonyult kontrollokban.

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MAGYAR NYELVŰ ÖSSZEFOGLALÓ

MAGYAR NYELVŰ ÖSSZEFOGLALÓ

Valószínűsíthető tehát, hogy a szakasz genetikai módosulatai a betegségben szerepet játszhatnak. Ez a szakasz a homodimerizációban szerepet játszó rod doménben helyezkedik el, és e kapocsként működő protein mechanikai ellenállásban betöltött különleges funkcióját sugallja. Összegezve, a PLEC gén missense variánsai (főként az említett régióban találhatók) hajlamosíthatnak az ACM-re, hozzájárulva a számos örökletes és környezeti tényezőhöz, amelyek összeadódva és egymást felerősítve a betegség oligogénes kialakulásához vezethetnek. A harmadik fejezetben exom szekvenálás modern technikáját alkalmaztuk. Exomnak nevezzük az összes gén exonjainak összességét, vagyis a (jelen esetben emberi) genom szétszórtan elhelyezkedő, fehérjekódoló DNS-darabkáinak összességét. A rendkívül ígéretes új módszer használatával célunk volt új cardiomyopathia gének felfedezése, elsősorban olyan családok szekvenálása révén, amelyeket korábban az már ismert génekre szűrve nem sikerült mutációt azonosítani. Először is (3.1) autoszomális domináns cardiomyopathiás családokban kutattunk a betegségben eddig nem ismert gének mutációi után. A valószínűleg pathogén genetikai módosulatot a vizsgált 12-ből 6 családban megtaláltuk a TTN (2x), FHL2, FLNC, COBL és STARD13 génekben. Ezekről korábbi tanulmányok alapján eltérő mennyiségű információ állt rendelkezésünkre, amely potenciálisan magyarázhatja az adott gének által kódolt fehérjék normál szívfunkcióban játszott szerepét (és mutációiknak betegségkiváltó hatását). Ezt követően koexpressziós hálózatot építettünk egy microarray adatbázis segítségével, vagyis hozzákapcsoltuk az 5 fent nevezett génhez azokat, amelyekkel egyidőben szokták egyes sejtekben, szövetekben fehérje termékeiket kifejezni. A hálózatban található 166 gén közül már 28 korábban is ismert volt cardiomyopathiákban, míg összesen 100 megtalálható egy génontológiai adatbázisban mint potenciális kardiovaszkuláris betegségért felelős gén. Ez a nagy arányú szívspecifitás, és a citoszkeletális molekuláris útvonal erős reprezentáltsága alátámasztja az 5 gén variánsainak cardiomyopathiával való vélhető összefüggését. Egy feltehetően 8 generációra visszamenőleg vérrokon házaspár születés után pár nappal, nagyon súlyos DCM-ben elhunyt gyermekének betegségét vizsgáltuk a 3.2 fejezetben. A tünetek típusa és súlyossága alapján elképzelhető volt, hogy a gyermek mitokondriális betegségben szenvedett. Exom szekvenálással találtunk egy homozigóta missense mutációt a SOD2 génben, amely egy mitokondriális funkciójú, oxigén szabadgyököket semlegesítő

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enzimet kódol. A gén a családban talált leghosszabb autoszomális homozigóta régióban, a 6. kromoszómán van kódolva, és a mutáció a génről átírt enzim fontos szerepű “mangán-kötő zsebének” szerkezetét torzíthatja el. A SOD2 gént már korábban egy egér modellen azonosították a DCM kiváltójaként (a SOD2 gén “kiütésével” létrehozott állatok cardiomyopathia tüneteit mutatták), de korábban még nem találták jelentősnek humán betegségekben. A betegtől vett, petri csészében kitenyésztett fibroblaszt sejtekben az oxigén szabadgyökök felhalmozódásával és a SOD2 enzimaktivitásának közvetett mérésével igazolódott, hogy az enzim nem funkcionál normálisan a mintában, míg egyéb mitokondriális enzimkomplexek funkcióképtelenségét kizártuk. Érdekes módon, a SOD2 hibájával összefüggő reaktív oxigéngyök felhalmozódás egy évtizedek óta ismert molekuláris pathológiai útvonal az antraciklin kemoterápiával kezelt betegeknél hosszútávú szövődményként jelentkező cardiomyopathiában. Egy rövid esetismertetés következik a 3.3 fejezetben: egy családot mutatunk be, amelyben a cardiomyopathia két elkülöníthető formája (a későn kezdődő és az újszülöttkori DCM) is jelen van, és három gént (MYL2, SOD2 és RYR2) találtunk, amelyek szerepet játszanak a betegség kialakulásában. Ez az eset annak jó példája, hogyan segítheti a genetikai információk helyes értelmezését és a személyre szabott tanácsadást a családfakészítés cardiomyopathiában szenvedő családok esetében. A negyedik fejezetben bemutatjuk az újgenerációs szekvenálás lehetséges rutindiagnosztikai használatát. Ehhez egy technikai módosítás szükséges: a teljes genom vagy teljes exom DNS-darabkáinak feldúsítása és szekvenálása helyett célzottan egy (ismert, szívbetegségekben szerepet játszó génekből álló) génkészletre koncentrálunk a mintaelőkészítés során. Ilyen módon a szekvenálás költségei éppúgy, mint a bonyolult elemzéshez szükséges idő is nagymértékben csökkenthető, miközben a szekvenált területek lefedettsége bőségesen megfelel a standardizált mérésekhez. Egy további előnye a célzott szekvenálási módszernek, hogy a humán genom leghosszabb génje, a titin (TTN), mely már évtizedek óta ismert a cardiomyopathia különböző típusaiban, de hossza miatt nagyon nehéz volt a korábbi módszerekkel megszekvenálni, szintén bekerülhetett a kiválasztott gének körébe. A 4.1 fejezetben ezen módszer, és a hozzá kapcsolódó szigorú variáns osztályozó rendszer előnyeit tárgyaljuk és hasonlítjuk a hagyományos Sanger szekvenáláshoz: 206 beteg genetikai szűrése során 107 esetben (52%) találtuk meg a cardiomyopathia genetikai okát a vizsgált 55 gén valamelyikében, és ez

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az arány kiemelkedően magas volt a DCM és DCM-szerű fenotípusok esetében. 30/206 páciens (15%) esetében két vagy több mutációt is azonosítottunk, mely alátámasztja, hogy az örökletes cardiomyopathiák spektrumát a klasszikus mendeli helyett egyre inkább az oligogénes betegségek irányába lehet kitolni. Végezetül, a betegek legalább felében a mutációt olyan génben találtuk meg, amely a korábbi módszerek használatának korszakában „nem jött volna számításba”, amikor az ilyen jellegű döntéseket pusztán a beteg tüneteire és az adott gén alacsony vagy ismeretlen mutáció frekvenciájára alapozták. Ezzel szemben a 4.2-ben 10/18 terhességi cardiomyopathiában (TCM) szenvedő beteg esetét sikerült megoldanunk 48, főként DCM-ben ismert gén vizsgálatával. Ezen eredményeink megerősítik azt a korábbi hipotézist, mely szerint a rendkívül ritka TCM nem egy önálló altípusa a betegségnek, hanem a terhesség során fellépő hemodinamikai (keringési) változások mint előnytelen környezeti tényezők hatására örökletesen hajlamos egyéneknél már a szokásosnál korábbi életkorban megmutatkozó DCM-ről van szó. A szívbetegség ezen két formája, a TCM és a DCM tüneteiben is nagy hasonlóságot mutat, nemcsak átfedő genetikai hátterében. Érdekesség, hogy a 10-ből 7 sikeresen elemzett családban a TTN gén mutációja felelős a betegség kialakulásáért. Egy szívátültetésen átesett beteg szövetmintáján végzett TTN izoforma arányt és ezzel összefüggésben a szívizomsejt passzív erőkifejtését vizsgáló funkcionális kísérlet alátámasztja a kereteltolásos p.K15664Vfs*13 variáns pathogén osztályzását. Az ötödik, egyben utolsó fejezetben összegezve áttekintem e könyv tartalmát, egy folyamatábrán keresztül bemutatom a jelenleg tanszékünkön használt kardiogenetikai diagnosztika és kutatás lépéseit, az elmúlt néhány év technikai fejlődésének, ezen belül is főként az újgenerációs DNS szekvenálás hatását a terület fejlődésére, és említést teszek különböző további lehetséges kutatási irányvonalakról. (Proof-read by Edit Posta and Dr Péter Mészáros)

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APPENDIX 2 List of authors and affiliations: UNIVERSITY OF GRONINGEN, UNIVERSITY MEDICAL CENTER GRONINGEN, GRONINGEN, THE NETHERLANDS Department of Genetics Ludolf G Boven Nicole Corsten-Janssen Jos Dijkhuis Johanna C Herkert Yvonne Hoedemaekers Robert MW Hofstra Jan DH Jongbloed Wilhelmina S Kerstjens-Frederikse Irene M van Langen Gerard J te Meerman Rowida Al Momani Renee C Niessen Anna Posafalvi Birgit Sikkema-Raddatz Richard J Sinke Karin Y van Spaendonck-Zwarts J Peter van Tintelen Cindy Weidijk Paul A van der Zwaag Department of Cardiology Rudolf A de Boer Peter van der Meer Maarten P van den Berg Dirk J van Veldhuisen Department of Dermatology Marieke C Bolling Marcel F Jonkman UNIVERSITY MEDICAL CENTER UTRECHT, UTRECHT, THE NETHERLANDS Department of Medical Genetics Jan G Post Jasper J van der Smagt Department of Pathology Peter GJ Nikkels Division of Heart and Lungs, Department of Cardiology Folkert W Asselbergs Judith A Groeneweg Richard NW Hauer DURRER CENTER FOR CARDIOGENETIC RESEARCH, UTRECHT, THE NETHERLANDS J Peter van Tintelen RADBOUD UNIVERSITY MEDICAL CENTRE, NIJMEGEN, THE NETHERLANDS Department of Cardiology Bert Baars

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Department of Human Genetics Carlo L Marcelis NIJMEGEN CENTRE FOR MITOCHONDRIAL DISORDERS Department of Biochemistry Peter Willems Department of Paediatrics Richard J Rodenburg LEIDEN UNIVERSITY MEDICAL CENTER, LEIDEN, THE NETHERLANDS Department of Cardiology Sebastiaan RD Piers Katja Zeppenfeld Department of Clinical Genetics Daniela QCM Barge-Schaapveld ANTONIUS HOSPITAL, SNEEK, THE NETHERLANDS Department of Cardiology Paul L van Haelst ACADEMIC MEDICAL CENTER, UNIVERSITY OF AMSTERDAM, AMSTERDAM, THE NETHERLANDS Heart Center, Department of Cardiology Arthur AM Wilde Department of Genetics Mariëlle Alders Imke Christiaans Karin Y van Spaendonck-Zwarts VU UNIVERSITY MEDICAL CENTER, AMSTERDAM, THE NETHERLANDS Department of Physiology Ilse AE Bollen Jolanda van der Velden UNIVERSITY COLLEGE LONDON, LONDON, UNITED KINGDOM Institute of Cardiovascular Science William McKenna Petros Syrris Department of Genetics Vincent Plagnol MEDICAL SCHOOL HANNOVER, HANNOVER, GERMANY Department of Cardiology and Angiology Denise Hilfiker-Kleiner UNIVERSITY OF SOUTHERN DENMARK, ODENSE, DENMARK Department of Cardiology Jens Mogensen UNIVERSITY OF CAPE TOWN, SOUTH AFRICA Hatter Institute for Cardiovascular Research in Africa, Department of Medicine & IIDMM Karen Sliwa

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ABOUT THE AUTHOR Anna Pósafalvi graduated as a Doctor of Pharmacy (PharmD) from the University of Debrecen Medical and Health Science Centre, Hungary, in 2009 with praise. Subsequently, she started her PhD in cardiogenetics at the Department of Genetics, University Medical Centre Groningen, the Netherlands, where she has learned the ins & outs of Sanger, exome, and genepanel-based next generation sequencing. She applied these methods in the research and diagnostics of the complex disease group of cardiomyopathies, and performed functional follow-up experiments on some of the genetic findings. She is currently working in the group of Professor David Kelsell at the Blizard Institute, Queen Mary University of London, where she is investigating desmosomal biology in the context of skin and heart diseases. Besides her immediate field, she also has a broad scientific interest in personalised/stratified medicine and pharmacogenetics, clinical pharmacy, herbal medicine, and the history of pharmacy. In her undergraduate years, Anna was active in organizing various student activities and events (e.g. The World Diabetes and AIDS Days), while during her PhD period, she has also given trainings and workshops to pharmacy students on topics such as creativity, emotional intelligence, cultural awareness, or academic life. In her free time, Anna plays the piano, and likes reading, dancing and hiking. She is also a passionate photographer.

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PUBLICATIONS Jongbloed JDH, Pósafalvi A, Kerstjens-Frederikse WS, Sinke RJ, van Tintelen JP: New clinical molecular diagnostic methods for congenital and inherited heart disease. Expert Opin Med Diagn. (2011) 5(1):9-24 review Posafalvi A*, Herkert JC*, Sinke RJ, van den Berg MP, Mogensen J, Jongbloed JDH, van Tintelen JP: Clinical utility gene card for: dilated cardiomyopathy (CMD) Eur J Hum Genet. (2012 Dec 19.) doi: 10.1038/ejhg.2012.276. # van Spaendonck-Zwarts KY, Posafalvi A, van den Berg MP, Hilfiker-Kleiner D, Sliwa K, Alders M, Almomani R, van Langen IM, van der Meer P, Sinke RJ, van der Velden J, van Veldhuisen DJ, van Tintelen JP§, Jongbloed JDH§: Titin gene mutations are common in families with both peripartum cardiomyopathy and dilated cardiomyopathy Eur Heart J. (2014) 35:2165-73 Posafalvi A*, Jongbloed JDH*, Niessen RC, van der Zwaag PA, Hoedemaekers Y, Sikkema-Raddatz B, Dijkhuis J, Piers SRD, Zeppenfeld K, de Boer RA, van Haelst PL, Barge-Schaapveld DQCM, Asselbergs FW, van der Smagt JJ, van den Berg MP, van Tintelen JP§, Sinke RJ§: Gene-panel based Next Generation Sequencing (NGS) substantially improves clinical genetic diagnostics in inherited cardiomyopathies. Manuscript submitted * the first two authors contributed equally, §

the last two authors contributed equally,

#

not included in this thesis

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ACKNOWLEDGEMENTS “...ekkor a megtapasztalások hihetetlen lavinája temette maga alá” (... and then she was buried under the incredible avalanche of experiences) The science of genetics has completed a long journey from crossing garden pea plants to sequencing personal genomes… and yet, the most demanding part, the proper and ethical interpretation of genomic information, is still ahead of us. Even though my contributions are tiny and insignificant, it feels like I have been on a long and crazy adventure, and I would like to thank all the people who guided me on the way. First and most, Jan and Richard, thank you for providing me with the great environment and opportunities to achieve all the things which I can present today in this book. Jan, I can only imagine what a tough job it must have been to supervise me, not something people would like to pick up as a new hobby... Thanks for all your efforts keeping me on track and not letting my brain fly around too much. And looking back after so many years, perhaps the results of our work together look just great like this, even if we had our different ways of doing things sometimes. Richard, thank you for the great brainstorming sessions, and for cheering me up with a new “dog-sitter” story or picture whenever I looked like I really needed one (actually, I might have looked like that every day?). Robert, during the first half of this 4-year marathon, you were, with the greatest respect, my “professor next door”. It was wonderful to meet such an inspiring person. Funnily enough, even though you left a few years ago, there is always a place called the “Oude Kamer van Robert” somewhere in the department. Ludolf, thank you for all your experimental help, the support in my fight against the evil labgoblin®. And of course, you also got some help from the students, especially Bastiaan and Elisabetta. Thank you guys for a great job! Cindy, you were the only student I supervised during my PhD, but you would anyway be my absolute favourite and dearest… it was amazing to work with you. :) Thank you for all your valuable contributions! Working in biological sciences always means teamwork, and this is certainly true if one is working with Next Generation Sequencing. Members of the

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ACKNOWLEDGEMENTS

Genome Analysis Facility who performed the exome sequencing; the genome diagnostic teams responsible for the targeted sequencing (Renee, Birgit, Eddy, Lennart, Krista and others); and colleagues of the Genomics Coordination Centre involved in my data analysis: thank you all for your wonderful work in setting up and running the method! Rowida, my “partner in crime”, eating carrots during every coffee break of the American Heart Scientific Sessions in LA, thank you for the collaboration on the exome sequenced families. Kristin and Pieter Neerincx, thanks for the pleasant and helpful conversations. I would like to acknowledge all the medical doctors, cardiologists and clinical geneticists in Groningen who were sedulously collecting the blood samples and medical information of cardiomyopathy patients for the cardio group research projects: Maarten, Peter, Paul, Yvonne, Anne, Nicole, Ellen, and everyone else responsible for counselling these families; as well as the doctors from Utrecht, Leiden, Nijmegen, Amsterdam, Sneek, and further afield, from Hannover and Cape Town. Peter, thank you for your feedback on the contents of my thesis. I would also like to express my gratitude to all our external collaborators: William McKenna and Petros Syrris (the Heart Hospital and University College London, UK) for collaborating on ARVC; the Nijmegen group for the mitochondrial measurements; and Marieke Bolling and Marcel Jonkman (Department of Dermatology, UMCG) for their valuable insights for the plectin manuscript. I need to mention many more colleagues who were not working with me directly, but who have all been head over heels (yes, this must be some kind of love) involved in what it took to create the Department of Genetics: Cisca, Gerard, Lude, Sebo, Klaas, Ellen, Irene, Connie, Rolf, Morris, Dineke, Sasha, Cleo, and Jingyuan, for influencing my view on scientific questions and professional matters; Jackie and Kate for correcting my sometimes rather crooked English; Bote, Mentje, Hayo, Marina, Héléne, and Joke for all their support. I enjoyed nice chats, coffee breaks, crazy and cosy moments around the department with so many: Mats the Chess master, Supertrynka, Agata, Jihane, Yunia (I miss you singing and humming around the lab and in the corridors), Bahram, Mahdi, Maria, Kaushal, Asia (the godmother of Glamorous Thursdays, my occasional Túró Rudi provider, happy family of the skateboarding lovebirds), Rodrigo, Javier, Juha (I so admire your special talent of ordering a glass of wine in fluent Hungarian, let alone your gene network magic), Isis, Harm-Jan, the always very kind and smiling Omid, Rajendra (thanks for the amazing curry! I made frozen aliquots of it and used them for cooking :) ),

ACKNOWLEDGEMENTS

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Marijke, Suzanne, Ania, Vinod; Michiel and Rutger who ensured the neverboring atmosphere in the lab, Jan, Mathieu, Astrid, and everyone else. My dear roomies, many of you were my Dutch teachers, and I remember our few fun pub quiz tours: Helga, Peter, Annemieke, Karin; Gerben and Anna, proud residents of the M&M’s Addicts’ Desk; Ettje and her amazing cakes; Jun, the expert on linguistics, Monica (lunch?), Itty and Gineke. Thanks to my mentor for her advice, to the Graduate School of Medical Sciences office for the range of courses and their general helpfulness, and to my Dutch teachers, Jenny and Joke. Becoming a pharmacist is one of the best things that has happened in my life, and no matter how I look at it, I still carry with me some of those strange personality traits and the way of thinking... A warm thanks to all those people who have shaped me into a concerned health care professional from the University of Debrecen. One of the most memorable events from my undergrad studies is my first visit to a congress of the European Pharmaceutical Students’ Association, where I got completely infected by what the insiders simply call the EPSA Spirit. I met countless amazing people (Louise, João, Uros, Nikos, Dave and Giulia, to name just a few) and got my annual dose of inspiration from those sleepless conferences and alumni weekends between 2007 and 2014. I am particularly glad for the opportunity of growing from my experiences as a member of the Trainer Team, as well as for witnessing the Science Day and Project unfold throughout the years – so, all in all, for having seeing all an EPSA dinosaur could hope to have seen. But life is luckily not all about science and studying, and I have met many wonderful people outside work, such as the organic chemistry-related, culturally interested, temporary inhabitants of Groningen: Miri my dear, no reindeers can hide from you... and you hosted the best VAl NTiNe’S party ever :), Julia and Felix, Mathieu, Céline, Nop and Tizi; people from “the Cell Biology table” of the restaurant; Indian people at and not at GISA events: Harsh, Shiva, Ashoka, Bhushan and Prachi, Milind (we had great conversations at the final countdown), Harshad, Lalitha (who draped me in a Beautifully Blue Real Sari

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