Genetics of Movement Disorders
In the Next-Generation Sequencing Era
Simone Olgiati
The work described in this thesis was performed at the Department of Clinical Genetics,
Erasmus MC, Rotterdam, The Netherlands.
The studies presented in this thesis were financially supported by:
Erasmus MC, The Netherlands;
Stichting ParkinsonFonds, The Netherlands;
BGI-Shenzen, China;
The Netherlands Organization for Scientific Research
(NWO, VIDI Grant, project n. 91786395).
The production costs of this thesis were entirely supported by:
ISBN: 978-94-6299-316-7
Cover: Simone Olgiati
Layout: Simone Olgiati
Printing: Ridderprint BV, Ridderkerk, The Netherlands
Copyright © S. Olgiati, 2016
All rights reserved. No part of this book may be reproduced, stored in a retrieval system or
transmitted in any form or by any means, without prior permission of the author. The copyright
of the published papers remains with the publisher
2
Genetics of Movement Disorders
in the Next-Generation Sequencing Era
Genetica van bewegingsstoornissen
in het next-generation sequencing tijdperk
Thesis
to obtain the degree of Doctor from the
Erasmus University Rotterdam
by command of the
rector magnificus
Prof.dr. H.A.P. Pols
and in accordance with the decision of the Doctorate Board.
The public defense shall be held on
Tuesday 12 April 2016 at 13:30 hrs
by
Simone Olgiati
born in Busto Arsizio, Italy
Doctoral Committee
Promotors:
Prof.dr. V. Bonifati
Prof.dr. R.M.W. Hofstra
Other members:
Prof.dr. R. Krüger
Prof.dr. C.C.W. Klaver
Prof.dr. J.C. van Swieten
4
Table of contents
List of abbreviations
6
Part I – Introduction and scope of the thesis
Chapter 1.1 Introduction11
Scope of the thesis
31
Part II – Parkinson’s disease
Chapter 2.1 DNAJC6 mutations associated with early-onset Parkinson’s disease41
Chapter 2.2 Early-onset parkinsonism caused by alpha-synuclein gene triplication:
clinical and genetic findings in a novel family
Chapter 2.3 An exome study of Parkinson’s disease in Sardinia, a Mediterranean
67
79
genetic isolate
Part III – Atypical parkinsonism
Chapter 3.1 Mutation in the SYNJ1 gene associated with autosomal recessive,
Chapter 3.2 PARK20 caused by SYNJ1 homozygous Arg258Gln mutation in
127
a new Italian family
Chapter 3.3 A new Turkish family with homozygous FBXO7 truncating mutation
105
early-onset parkinsonism
141
and juvenile atypical parkinsonism
Part IV – Other movement disorders
Chapter 4.1 Paroxysmal exercise-induced dystonia within the phenotypic
155
spectrum of ECHS1 deficiency
Chapter 4.2 A recurrent mutation in the C19orf12 gene causes MPAN in Turkey
177
Part V – General discussion
Chapter 5.1 General discussion191
Appendix
Summary, Samenvatting, Riassunto
204
Curriculum vitae
210
List of publications
211
PhD portfolio
213
Acknowledgments215
5
List of abbreviations
ACMG
American College of Medical Genetics and Genomics
AD
Autosomal dominant
AR
Autosomal recessive
BAC
Bacterial artificial chromosome
BFMRS
Burke-Fahn-Marsden dystonia rating scale
bp
Base pairs
BPAN
Beta-propeller protein-associated neurodegeneration
CCV
Clathrin-coated vesicle
ChrChromosome
CME
Clathrin-mediated endocytosis
CNV
Copy number variant
Comp Het
Compound heterozygous
CoPAN
CoA synthase protein-associated neurodegeneration
DBS
Deep brain stimulation
DNA
Deoxyribonucleic acid
dNTP
Deoxynucleotide triphosphate
ERMEzrin/radixin/moesin
EVS
NHLBI Exome Variant Server
ExAC
Exome Aggregation Consortium
FAB
Frontal assessment battery
FISH
Fluorescence in situ hybridization
FLAIR
Fluid-attenuated inversion recovery
GCGuanine-cytosine
GoNL
Genome of The Netherlands
GRCh
Genome Reference Consortium human build
GWAS
Genome-wide association studies
HetHeterozygous
HomHomozygous
ICCImmunocytochemistry
Indels
Insertions and deletions
KOKnock-out
LD
Linkage disequilibrium
LOD
Logarithm of odds
MAF
Minor allele frequency
MLPA
Multiplex ligation-dependent probe amplification
MMSE
Mini-mental state examination
MPAN
Mitochondrial-membrane protein associated neurodegeneration
MP-RAGE
Magnetization prepared rapid acquisition gradient-echo
MRI
Magnetic resonance imaging
NBIA
Neurodegeneration with brain iron accumulation
6
NGS
OR
PBS
PCR
PD
PED
PET
PKD
PxDs
qPCR
RFU
RNA
RR
SNP
SNV
SPECT
STR
UPDRS
UTR
VUS
WES
WGS
XD
XR
Next-generation sequencing
Odds ratio
Phosphate-buffered saline
Polymerase chain reaction
Parkinson’s disease
Paroxysmal exercise-induced dyskinesia
Positron emission tomography
Paroxysmal kinesigenic dyskinesia
Paroxysmal dyskinesias
Real-time quantitative PCR
Relative fluorescence unit
Ribonucleic acid
Relative risk
Single nucleotide polymorphism
Single nucleotide variant
Single photon emission computed tomography
Short tandem repeat
Unified Parkinson’s disease rating scale
Untranslated region
Variant of unknown significance
Whole-exome sequencing
Whole-genome sequencing
X-linked dominant
X-linked recessive
7
PART I
Introduction and
Scope of the Thesis
10
1.1
Introduction and Scope of the Thesis
Adapted from:
“Genetics of Movement Disorders in the Next-Generation Sequencing Era”
S. Olgiati,1 M. Quadri,1 and V. Bonifati.1,#
Published in Movement Disorders 2016, doi: 10.1002/mds.26521
# Corresponding author.
1) Department of Clinical Genetics, Erasmus MC, Rotterdam, The Netherlands.
Concerning the previously-published material:
© 2016 International Parkinson and Movement Disorder Society
Reprinted with permission of John Wiley & Sons, Inc.
11
Chapter 1.1
Movement Disorders
Movement disorders are defined as neurologic syndromes in which there is either an
excess of movements or a paucity of voluntary and automatic movements, unrelated to
weakness or spasticity [1]. This group of disorders comprises neurological conditions
that are clinically, pathologically, and genetically heterogeneous, but they all present
with defective control, planning, or execution of movements. The frequency of these
syndromes in the general population is very diverse but, overall, movement disorders are
common neurological problems, with the most common conditions being restless legs,
essential tremor, and Parkinson’s disease [2-4].
The majority of the movement disorders are associated with dysfunctions of the
basal ganglia, a group of gray matter nuclei with a primary function in the control of
movements, but having also cognitive, behavioral, and emotional functions. Exceptions
to this general concept are disorders of movement due to pathologies of the cerebellum
(ataxia, asynergia, and dysmetria). The role of basal ganglia and cerebellum is to orchestrate the adequate control of movements, based on internal and external stimuli. Traditionally, only disorders of the basal ganglia and/or cerebellum with clear consciousness
are listed as movement disorders. However, disorders of movement can also arise from
pathologies of cerebral cortex, brainstem, and spinal cord (several forms of myoclonus
and tremor) [5,6].
Movement disorders can be classified into syndromes with too little movements or
syndromes with an excess of movements. The first group, the syndromes with too little
movements, are commonly referred to as hypokinesias (decrease amplitude of movements),
but the terms bradykinesia (slowness of movements) and akinesia (loss of movements)
have also been used to describe this category. The most frequent hypokinetic syndromes
are the parkinsonisms. The second group, the syndromes with an excess of movements,
are referred to as hyperkinesias (excessive movements), but the term dyskinesia (abnormal
movements) has been used interchangeably [6]. Many different neurological conditions
are listed in this category, including tremor, dystonia, chorea, myoclonus, and tics.
Genetics of Movement Disorders
in the Next-Generation Sequencing Era
During the past few years, the advent of innovative and extremely powerful methods for
sequencing nucleic acids (DNA and RNA) has produced a major boost in our understanding of the role of genes and genetic variability in biology and pathology, and is
offering novel research and clinical opportunities for all fields of medicine, including
12
Introduction and Scope of the Thesis
the movement disorders. These new methods, collectively called Next-Generation
Sequencing (NGS) technologies, provide a way to rapidly generate an unprecedented
amount of DNA or RNA sequences at very low costs. As a consequence, new research
and diagnostic approaches have been made available for the exploration of the genetic
bases of diseases. As a result of these novel approaches, the pace of discovery of novel
disease-causing or disease-predisposing genes is accelerating (Table 1). Furthermore, the
phenotypic spectra associated with previously-known disease-genes are expanding. For
instance, ATP1A3 mutations, initially recognized as the cause of rapid-onset dystonia
parkinsonism (DYT12), have been more recently identified also as the cause of alternating hemiplegia of childhood, [7-9] and the phenotype of PRRT2 mutations, initially
considered to consist of paroxysmal kinesigenic dyskinesias, has been later expanded to
also include migraine and epileptic syndromes [10-12].
Last, the NGS technologies provide novel tools for rapid, cheap, and comprehensive
genetic testing in the clinical setting.
There are great expectations about the opportunities offered by these novel technologies.
However, their use in both research and clinical settings comes with certain challenges
and limitations that should also be taken into account. Here, we provide an update
on the current DNA sequencing technologies, focusing on their possible applications
for gene-finding in both research and diagnostic settings in the field of the movement
disorders.
The Landscape of NGS Technologies
The term NGS refers to a number of different technologies that are rapidly overcoming
the traditional way of performing DNA and RNA sequencing (the so-called Sanger
sequencing method, or first-generation sequencing) [13]. These novel technologies,
also called massively parallel sequencing or second-generation sequencing, have a broad
range of applications in disparate areas of biology and medicine. These include: I)
sequencing of individual DNA species (single fragments, one gene, gene panels, up
to entire genomes) for variants discovery by comparison to reference (normal) known
sequences; this process is also termed resequencing, and represents the main application of
NGS in human and medical genetics; II) de novo assembly of long DNA sequences (up
to entire genomes) without the comparison to other, previously determined sequences;
III) analysis of single-cell genomes, and of somatic DNA variation in normal tissues and
cancer; IV) evolutionary genetics and species classification [14]; V) investigation of the
entire set of mRNA transcripts (transcriptome) of an organism, a tissue, or a single cell
(RNA sequencing) [15]; VI) characterization of the epigenetic modifications of DNA,
13
1.1
Chapter 1.1
Table 1. Non exhaustive list of genes associated with movement disorders identified using NGS.
Independent
Gene
Disease
Inheritance
VPS35
Late onset PD (PARK17)
AD
Yes
DNAJC6
Juvenile atypical parkinsonism (PARK19)
AR
Yes
AR
Yes
SYNJ1
Juvenile atypical parkinsonism (PARK20) and
severe epilepsy with developmental delay
replication
Reference
Vilariño-Güell et al [48];
Zimprich et al [49]
Edvardson et al [42]
Quadri et al [43]; Krebs et
al [44]
DNAJC13
Late onset PD (PARK21)
AD
Pending
Vilariño-Güell et al [104]
CHCHD2
Late onset PD
AD
Pending
Funayama et al [105]
COQ2
Multiple system atrophy (MSA1)
AR
Pending
ATP6AP2
Intellectual disability, epilepsy, and parkinsonism XR
Pending
Korvatska et al [106]
RAB39B
Intellectual disability with early-onset PD
XD
Yes
Wilson et al [40]
FUS
Essential tremor (ETM4)
AD
Pending
Merner et al [107]
HTRA2
Essential tremor
AD
Pending
Unal Gulsuner et al [108]
TENM4
Essential tremor
AD
Pending
Hor et al [109]
SCN4A
Essential tremor
AD
Pending
Bergareche et al [110]
XD
Yes
Haack et al [57]
AR
Pending
Dusi et al [111]
WDR45
COASY
Neurodegeneration with brain iron
accumulation (NBIA5) - BPAN
Neurodegeneration with brain iron
accumulation (NBIA6) - CoPAN
Multiple-System Atrophy
Research Collaboration [38]
PDGFRB
Idiopathic basal ganglia calcification (IBGC4)
AD
Yes
Nicolas et al [50]
PDGFB
Idiopathic basal ganglia calcification (IBGC5)
AD
Yes
Keller et al [51]
ISG15
Idiopathic basal ganglia calcification
AR
Pending
Zhang et al [112]
XPR1
Idiopathic basal ganglia calcification (IBGC6)
AD
Pending
Legati et al [113]
PRRT2
Paroxysmal kinesigenic dyskinesia
AD
Yes
Chen et al [30]; Wang et al
[31]; Li et al [32]; Lee Li
et al [33]
HPCA
Generalized dystonia (DYT2)
AR
Pending
TUBB4A
Cranial-cervical dystonia (DYT4)
AD
Yes
CIZ1
Cranial-cervical dystonia (DYT23)
AD
Pending
Xiao et al [116]
ANO3
Cranial-cervical dystonia (DYT24)
AD
Yes
Charlesworth et al [117]
GNAL
Cranial-cervical dystonia (DYT25)
AD
Yes
Fuchs et al [118]
KCTD17
Myoclonus-dystonia (DYT26)
AD
Pending
Mencacci et al [39]
COL6A3
Cranial-cervical dystonia (DYT27)
AR
Pending
Zech et al [119]
Charlesworth et al [45]
Lohmann et al [114];
Hersheson et al [115]
PD, Parkinson’s disease; AD, autosomal dominant; AR, autosomal recessive; XD, X-linked dominant; XR,
X-linked recessive; BPAN, beta-propeller protein-associated neurodegeneration; CoPAN, CoA synthase proteinassociated neurodegeneration.
14
Introduction and Scope of the Thesis
promoter occupancy by transcription factors, and chromatin structure (methyl–seq,
DNase–seq, ChIP–seq, and others) [16,17]; and VII) analysis of microbial genomes,
for example, characterization of the entire intestinal microbiota (metagenomics) [18].
This introduction deals with the NGS applications in the area of DNA resequencing for
investigating the genetic bases of disease, with a focus on the monogenic (Mendelian)
forms of the movement disorders.
Concerning the target regions to be sequenced, one can distinguish whole-genome
sequencing (WGS), whole-exome sequencing (WES), and more focused, targeted
sequencing [19,20]. WGS is the most comprehensive approach and it is expected to
become the method of choice in the near future. Nevertheless, WGS still has limitations. Although prices are continuously dropping, the costs for WGS remain substantial
for both sequencing and data storage. Moreover, interpreting the enormous amount
of data generated by WGS poses major challenges. As a result, several protocols for
target enrichment have been developed, which allow to sequence only specific genomic
regions. WES is currently a cheaper and popular method to analyze the exome (the
coding part, equal to ~2%, or ~50 millions of bases of the human genome). Targeted
resequencing is a good strategy to screen panels of multiple candidate genes, and especially suitable for diagnostic purposes.
NGS includes different technologies and each of them is characterized by specific
chemistries, sample preparation protocols, and data analysis [21]. Readers interested
in the technological aspects are referred to dedicated reviews [21,22]. Moreover, for the
purpose of this introduction, we will focus on the more broadly-used, and commercially
available platforms, omitting the so-called third-generation sequencing (or single-molecule sequencing) technologies, that have been reviewed elsewhere [23].
Despite their differences in chemistries and other technological aspects, all NGS technologies share a basic conceptual workflow in which an initial high-molecular-weight
DNA sample (e.g., human genomic DNA) is shattered into a fragment library, and
single-strand molecules are amplified and sequenced in parallel. First, DNA fragments
(~150 to hundreds nucleotides) are generated by mechanical or enzymatic methods.
In several applications, such as exome sequencing or resequencing of customized gene
panels, an additional crucial step involves the enrichment of the target DNA fragments
(capturing). Libraries are then obtained and platform-specific adaptors are added to both
ends of each fragment. This step allows the fragments to be more easily PCR amplified
using just one pair of primers, or to be hybridized to a surface using complementary
adaptors. Then, the sequences of the fragments are read cyclically and in parallel using
different chemistries that results, in most cases, in fluorescent or electrical signals.
Last, this signal is detected by an imaging or a different sensing system coupled with a
15
1.1
Chapter 1.1
computer, which allows to simultaneously ascertain the sequences of millions of reads in
parallel. The large amount of data generated is then processed using bioinformatic tools.
For DNA resequencing, the first step of the bioinformatic analysis is the alignment of
the raw reads against the reference sequence (alignment). Second, the aligned reads are
compared against the reference in order to obtain a list of genomic variations (variant
calling). Finally, all the identified variants are compiled with various descriptive information, such as name of the gene in which the variant is located, the predicted consequence of the variant at protein level, the predicted effect on the protein function, the
frequency of the variant in population databases, and others (variant annotation). This
process allows to confidently identify and report single nucleotide variants (SNVs) and
short insertions and deletions (Indels). Currently, larger genomic rearrangements and
copy number variants (CNVs) can also be identified using NGS data by using dedicated
software [24]. However, these events are more commonly genotyped using single nucleotide polymorphisms (SNPs) array-based technologies, which are still the method of
choice, particularly in the diagnostic laboratories.
The currently available NGS platforms differ concerning throughput, read length,
read accuracy, read depth, per-base costs, and other aspects [25,26]. Their choice is
also determined on the basis of the experimental aims (e.g. de novo assembly or resequencing projects, size of the target genomes, etc.). Of note, every platform has some
biases inherent to the specific chemistry or other technical aspects, such as a reduced
sequence quality in GC-rich target regions and long stretches of the same nucleotide
(homopolymers), variable base quality along the reads, and difficulties in detecting short
or long insertions and deletions [25-27]. Such types of complex genomic regions or variations are generally difficult to capture and to sequence, often generating false positive
and false negative results.
These types of problems might have profound consequences for entire projects, such
as failures in the identification of disease-causing mutations using NGS methods. For
example, some of the PRRT2 mutations causing paroxysmal kinesigenic dyskinesia were
missed by NGS-based approaches [28,29]; however, the first identification of this gene
successfully relied on NGS [30-33]. Similarly, one of the SLC30A10 mutations causing
the manganese transport disorder with dystonia/parkinsonism was originally missed by
NGS, due to the combination of relatively low depth of coverage (the number of independent reads per DNA base), and the mutation being in a very high GC-rich region
in the first exon of the gene [34]. Because of strong publication bias, it is difficult to
appreciate the real extent of these phenomena. One way to at least reduce the amount
of false negative and false positive results is to increase the depth of coverage of the NGS
projects which of course leads to higher sequencing costs. Depths of >100-150× were
16
Introduction and Scope of the Thesis
often mentioned in recent reports of successful exome research projects.
1.1
NGS Applications in Medical Genetic Research
Genetic variants of interest for medical genetics are conveniently classified according
to their population frequency and effect-size [35]. Highly-penetrant variants that cause
disease in a Mendelian fashion are usually very rare (minor allele frequency, MAF,
far below 1%), and these can be efficiently identified by traditional linkage-based
approaches. On the other hand, risk factors of small effect-size (odds ratios <2) are
common in the population (>5%) and most of these have been identified by genomewide association studies in the past 5–10 years. However, there is a remaining category
of variants of intermediate frequency and effect-size, that remained out of reach using
traditional linkage or genome-wide association approaches. With NGS, we have the
unprecedented opportunity to investigate every type of medically-relevant variants.
Of course, the experimental design should be chosen based on the type of variants,
the frequency of the disease of interest, its mode of inheritance, penetrance, and other
features (Figure 1). In this introduction, we focus on the strategies for the identification
of highly-penetrant, disease-causing variants, that are of high relevance for the clinical
neurologists and neuro-geneticists.
Mendelian mutations have been, and can still be successfully identified using linkage
mapping and positional cloning (even, sometimes, candidate-gene) approaches [36].
Here, in principle, the power of NGS is not necessary, but it can be of great help. When
multiple affected individuals from the same family are available, the genomic region of
interest can be narrowed down dramatically by linkage mapping analysis (locus identification), and the few genes located within the locus can be quickly investigated in just one
run of a NGS instrument (Figure 1A) [37]. Several disease-causing genes have already
been identified with such a combined (linkage + NGS) approach: COQ2 in multiple
system atrophy,[38] KCTD17 in autosomal dominant myoclonus-dystonia,[39] and
RAB39B in X-linked intellectual disability with early-onset PD,[40] to name just a few.
Similarly, in the case of autosomal recessive disorders, NGS can be complemented with
homozygosity mapping, which is the traditional linkage method for the study of consanguineous pedigrees based on the analysis of homozygous regions identical-by-descent
(Figure 1B) [41]. This combination has been successful when applied to movement
disorders, allowing the identification of novel disease-causing genes for juvenile parkinsonism (DNAJC6 and SYNJ1),[42-44] dystonia (HPCA),[45] and many forms of cerebellar ataxia or hereditary spastic paraplegia [46,47]. NGS technologies can also speed
up the analysis of candidate disease-causing or disease-predisposing genes, selected on
17
Chapter 1.1
Traditional strategies
NGS not required, but convenient
B
A
C
Gene A
Gene B
Homozygosity
mapping
Gene C
Linkage mapping
Novel strategies
NGS required
Candidate gene
s
cu
Lo
cation
ntifi
ide
E
D
Identification
of novel
disease gene
Direct sequencing
of distant relatives
F
Double-hit approach
(recessive inheritance)
H
G
Cases vs controls
large NGS studies
Trio approach
(de novo mutations)
Multiple unrelated
patients or families
Figure 1. NGS strategies for gene finding. NGS-based strategies for the identification of novel disease-associated genes. The traditional mapping strategies and the novel NGS-based strategies are highlighted. Black symbols
denote affected individuals; subjects to be sequenced using NGS are highlighted in yellow frame.
the basis of their known biological or physiological roles (Figure 1C). Of note, this
approach has not been very fruitful in the past, at least if compared to the unbiased
genome-wide approaches [36].
Besides their utility in complementing the traditional linkage mapping methods, the
NGS technologies allow today novel experimental designs that were impossible during
the first-generation sequencing era. One of such approaches consists in sequencing the
exome or the genome of two distant relatives (e.g., first-cousins) affected by the same
autosomal dominant disease, looking for shared candidate variants selected according
to given filtering criteria (see below; Figure 1D and Figure 2). This approach represents
18
Introduction and Scope of the Thesis
a valid alternative to linkage mapping analysis for studying autosomal dominant
traits, with the important advantage that it can be applied also when the number of
affected relatives with DNA available is too small for a traditional linkage approach
[37]. Examples of successes using such direct-filtering strategies have been provided by
the identification of VPS35 mutations in PD [48,49] and of PDGFRB and PDGFB
mutations in primary familial brain calcifications [50,51].
Another, new experimental design made possible by NGS, is represented by the
“double-hit strategy” for studying autosomal recessive traits (Figure 1E). Here the exome
or genome of a patient with a suspected autosomal recessive disease is interrogated for all
the genes carrying two novel or otherwise likely-pathogenic mutations. This approach
is ideally conducted when the genome of both the (unaffected) parents can also be
sequenced, providing thereby the possibility to directly filter the genes carrying two
mutations that have each been transmitted by a different parent (in trans configuration
in the affected offspring), as dictated by the autosomal recessive mode of inheritance.
A third novel and important strategy is the search for mutations occurring de novo (i.e.,
a mutation present in the DNA of the patient but not detectable in the parents). This is
undertaken by WES or WGS of family trio(s): the affected offspring and both unaffected
parents (Figure 1F) [52]. De novo mutations are present by definition in patients who do
not have a positive family history of the disease (sporadic presentation), and therefore
cannot be studied by family-based linkage approaches [53]. The hypothesis of disease
caused by de novo mutations is especially valuable when studying early-onset phenotypes
that reduce the reproductive fitness of the affected individual, including for example,
several severe neuropsychiatric phenotypes, such as autism spectrum disorders, intellectual disabilities, and schizophrenia. Indeed, recent large-scale NGS studies using the
“trio” approach have been successful in the identification of de novo mutations in these
disorders [52,54]. A similar approach yielded the identification of a de novo ADCY5
mutation as the cause of a movement disorder presenting with early-onset chorea and
dystonia [55].
NGS can also be used to directly interrogate the exome or the genome of series of genealogically-unrelated patients, searching for variants occurring in the same gene (Figure
1G). This approach might be extremely powerful in finding the disease-causing gene
when the phenotype of interest is very rare, because in this situation the underlying
hypothesis is that only one (or very few) causative gene(s) are involved (limited genetic
heterogeneity). In this scenario, it has been shown that even very few patients might
be enough, because the number of genes carrying novel deleterious mutations, shared
by unrelated patients is very small [56]. This strategy was instrumental for the identification of WDR45 as the disease-causing gene in a novel X-linked dominant form of
19
1.1
Chapter 1.1
70’000–90’000
WES variants
an
ri
Va
~2’000–5’000
ts
~500–700
II) Exonic or splicing
III) Nonsynonymous
n
tio
uc
red
~300–500
I) Rare in databases
(MAF<0.5%)
~110
IV) Predicted to be pathogenic
V) Mode of inheritance
VI) Experimental strategies (see figure 1)
VII) Functional studies
Disease-causing
variant
Figure 2. Prioritization of NGS candidate variants. Variant prioritization progressively reduces the number
of candidate variants identified by NGS. Schematic representation of a general filtering workflow for NGS variants. Of note, the number of candidate variants can be further reduced by filtering for variants that are totally
absent from databases (instead of those that are present but rare, e.g. MAF <0.5%).
neurodegeneration with brain iron accumulation [57].
The genetics of complex diseases is not the focus of this introduction. However, it is
worth mentioning that the continuous drop of WES and WGS costs will make largescale NGS case-control studies a reality in the near future (Figure 1H). This will offer
the unprecedented opportunity to comprehensively explore the etiological role of every
type of genetic variants (very rare, intermediate, and common) [58,59]. Our group
recently reported the results of a first effort using this type of approach to identify novel
genetic determinants of PD in an isolated population (Sardinia) [60]. Beside the high
costs, large case-control exome or genome studies need novel statistical methods that
consider the single gene, rather than the single variant, as the target for association
with disease (“burden tests”) [61,62]. In addition to these tests, the study power can be
further increased by differentially weighting distinct classes of genetic variants or testing
larger collections of variants across functionally-related genes (pathways-based analysis)
20
Introduction and Scope of the Thesis
[62]. Similar gene- and pathway-based approaches have recently led to the identification of TUBA4A and TBK1 mutations in amyotrophic lateral sclerosis/fronto-temporal
dementia [63-65] and 18 novel candidate genes for hereditary spastic paraplegia [66].
Prioritizing Variants and Evaluating Pathogenicity
The high throughput of NGS technologies is their main advantage but, at the same time,
it creates technical challenges. The vast amount of data generated requires dedicated
hardware, software, and trained personnel. More importantly, the interpretation of the
large number of identified variants remains the biggest challenge in the NGS era.
When searching for a highly-penetrant disease-causing mutation, a typical NGS
approach usually identifies a large number of candidate variants. A substantial amount
of benign polymorphisms can be excluded from the list of disease-causing candidates
using the filters related to the experimental designs described previously (e.g., variants
shared by affected cousins). However, this is usually not enough, and dozens of candidate
variants remain, requiring additional filtering strategies (Figure 2).
The common polymorphic variants that are frequently observed in the general population are usually removed first. This fundamental step relies on large repositories of NGS
data from thousands of individuals originating from different human populations. A list
of the currently available reference databases is reported in Table 2. Moreover, data from
large cohorts of individuals sequenced in-house are often used for the same purposes.
There are some important issues to consider during this step. First, the frequency of
most genetic variants (both benign polymorphisms and disease-related variants) differs
between ethnicities and some variants are exclusive of certain populations. Of note,
individuals of different ethnic groups are included in several publicly available databases.
Therefore, it is essential that controls used to remove disease-unrelated variants are
ethnically-matched to the cases in the best way possible. Second, it is crucial to consider
that some public databases intentionally include known pathogenic variants, which are
labeled with clinical flags. More importantly, there are substantial chances that very large
databases (tens of thousands of subjects) include also pathogenic alleles present in the
general population at very low, but detectable frequencies. This fact can create problems
particularly if NGS data are filtered in the search for autosomal recessive mutations,
or for dominant disease-causing variants with reduced penetrance [67]. For all these
reasons, the database and the MAF cut-offs must be carefully chosen.
In order to facilitate the interpretation of candidate variants in known disease-causing
genes, it is possible to exploit several well-curated repositories of known disease-causing
variants and locus-specific databases (Table 2). The accuracy and completeness of these
21
1.1
Chapter 1.1
Table 2. Publicly-available population and disease-specific databases.
Population databases
dbSNP
dbVAR
1000 Genomes Project
Exome Variant Server
Exome Aggregation
Consortium (ExAC)
Database of short variants, based on multiple
submitters
Database of structural variants, based on multiple
submitters
WGS of ~2,500 individuals from ~25 different
populations
WES data from ~6,500 individuals recruited for
several genomic studies
http://www.ncbi.nlm.nih.gov/snp
http://www.ncbi.nlm.nih.gov/dbvar
http://browser.1000genomes.org
http://evs.gs.washington.edu/EVS
A collection of WES data from ~60,000
unrelated individuals recruited for several
http://exac.broadinstitute.org/
disease-specific and population genetic studies
A collection of single nucleotide variants from
KAVIAR
~70,000 individuals. It includes WGS data from
http://db.systemsbiology.net/kaviar/
about ~8,000 individuals
Disease-specific databases
OMIM
Catalog of human diseases and
disease-related genes
http://www.omim.org
ClinVar
Catalog of human pathogenic variants
Human Gene Mutation
Catalog of published human pathogenic variants
Database
(fee required for certain content)
Human Genome Variation
It includes a list of locus-specific databases for
http://www.hgvs.org/locus-specif-
Society
many genetic disorders
ic-mutation-databases
Leiden Open Variation
Database (LOVD)
PDmutDB
http://www.ncbi.nlm.nih.gov/clinvar
http://www.hgmd.org
Locus-specific database for many genetic
disorders. It also includes a useful list of
http://www.lovd.nl
third-parties locus-specific databases
Manually curated database of pathogenic and
benign variations at PD-related genes
www.molgen.vib-ua.be/PDMutDB/
Parkinson’s Disease
Manually curated database of pathogenic and
Mutation Database
benign variations at PD-related genes
PDgene
Compendium of genetic association results in PD
http://www.pdgene.org/
UMD-THAP1
THAP1 gene mutation database
http://www.umd.be/THAP1/
CACNA1A
CACNA1A gene mutation database
http://chromium.lovd.nl/LOVD2
SETX
SETX gene mutation database
http://www.LOVD.nl/SETX
SETX (UCLA)
SETX gene mutation database
http://149.142.212.78/LOVD/
ATM
ATM gene mutation database
http://www.LOVD.nl/ATM
POLG gene mutation database
http://tools.niehs.nih.gov/polg
Human DNA POLG
Mutation Database
http://grenada.lumc.nl/LOVD2/TPI/
WES, whole-exome sequencing; WGS, whole-genome sequencing; PD, Parkinson’s disease
22
Introduction and Scope of the Thesis
databases is a function of time, but it is anticipated that these tools will become more
and more important in the future. The field of movement disorders would greatly benefit
from the existence of well-curated disease-specific variant databases.
In addition, variants occurring deeply in intronic and in other non-coding genomic
regions are usually removed from the candidate list, under the assumption that highly-penetrant, disease causing-mutations should affect the coding region of the genome.
Moreover, to determine whether and how a non-coding variant is pathogenic might be
very complex and time-consuming. Nevertheless, it is good to remember that there are
some risks in removing the non-coding variants. Most of the disease-causing mutations
known so far are in coding regions [68]. However, the non-coding regions have rarely
been investigated in the era of Sanger sequencing, and this might have biased the figures
in favor of disease-causing coding alleles.
Additional strategies can be used to further prioritize the remaining candidate variants.
One robust approach is the analysis of the evolutionary conservation of the nucleotide
or amino-acid targeted by the variant (Table 3). The underlying principle is that novel
deleterious variants tend to be removed by natural selection, while neutral variants are
maintained in the genetic pool of the population during evolution. Therefore, highly
conserved nucleotides (and amino acids) are usually very relevant for the gene (or
protein) function [69].
Another common approach is based on the prediction of the functional damage caused
by the mutated amino acid using in-silico tools (Table 3). A first group of these software
relies on defined biological properties of deleterious mutations (often conservation) to
classify variants in discrete classes of pathogenicity (first-principles approaches). A second
group consists of classifiers that are trained to distinguish between a set of known deleterious mutations and a set of benign polymorphisms, using many potentially-relevant
properties (trained classifiers). Both type of approaches have pros and cons, described
elsewhere [70]. Last, the variants located in proximity to exon-intron boundaries (splice
sites) should always be assessed using in-silico splicing prediction tools (Table 3) [71].
Overall, due to the incomplete reliability of both protein prediction (accuracy 60 to
80%)[72] and splicing prediction tools (accuracy 75 to 95%),[73] the results of these
in-silico analyses should always be evaluated with caution. The results of multiple tools,
preferably based on different methods, should be taken into account.
Methods based on the functional predictions are particularly useful when looking
for disease-causing variants acting by a loss of function. This is typically the case for
recessive alleles, but also dominant alleles acting through haplo-insufficiency or
dominant-negative effects.
23
1.1
Chapter 1.1
Table 3. A list of in-silico prediction tools.
Nucleotide conservation analysis
GERP++
http://mendel.stanford.edu/sidowlab/downloads/gerp/
PhastCons
http://compgen.bscb.cornell.edu/phast/
PhyloP
http://compgen.bscb.cornell.edu/phast/
SCONE
http://genetics.bwh.harvard.edu/scone/
VISTA
http://genome.lbl.gov/vista/
Damage prediction based on evolutionary conservation
SIFT
http://sift-dna.org
FATHMM
http://fathmm.biocompute.org.uk
MutationAssessor
http://mutationassessor.org
PROVEAN
http://provean.jcvi.org/
ConSurf
http://consurftest.tau.ac.il
PANTHER
http://www.pantherdb.org/
PhD-SNP
http://snps.biofold.org/phd-snp/phd-snp.html
Damage prediction based on protein structure
SNPs&GO
http://snps-and-go.biocomp.unibo.it/snps-and-go
Damage prediction based on protein structure and evolutionary conservation
PolyPhen-2
http://genetics.bwh.harvard.edu/pph2
MutationTaster
http://www.mutationtaster.org
MutPred
http://mutpred.mutdb.org
MAPP
http://mendel.stanford.edu/SidowLab/downloads/MAPP/
Align GVGD
http://agvgd.iarc.fr/agvgd_input.php
nsSNPAnalyzer
http://snpanalyzer.uthsc.edu
PMUT
http://mmb2.pcb.ub.es/PMut/
SAPRED
http://sapred.cbi.pku.edu.cn/
SNAP
http://www.rostlab.org/services/SNAP/
SNPs3D
http://www.snps3d.org/
Damage prediction based on other/mixed methods
Condel
http://bg.upf.edu/fannsdb/
CADD
http://cadd.gs.washington.edu
Splicing-prediction tools
GeneSplicer
http://www.cbcb.umd.edu/software/GeneSplicer/gene_spl.shtml
Human Splicing Finder
http://www.umd.be/HSF/
MaxEntScan
http://genes.mit.edu/burgelab/maxent/Xmaxentscan_scoreseq.html
NetGene2
http://www.cbs.dtu.dk/services/NetGene2
NNSplice
http://www.fruitfly.org/seq_tools/splice.html
(Continues)
24
Introduction and Scope of the Thesis
(Follows)
1.1
Splicing-prediction tools
FSPLICE
http://www.softberry.com/berry.phtml?
topic=fsplice&group=programs&subgroup=gfind
GENSCAN
http://genes.mit.edu/GENSCAN.html
SpliceView
http://bioinfo4.itb.cnr.it/~webgene/wwwspliceview.html
SplicePredictor
http://bioservices.usd.edu/splicepredictor/
ASSEDA
http://splice.uwo.ca/
SplicePort
http://spliceport.cbcb.umd.edu/
CRYP-SKIP
http://cryp-skip.img.cas.cz/
SROOGLE
http://sroogle.tau.ac.il/
Spliceman
http://fairbrother.biomed.brown.edu/spliceman/
GERP, Genomic Evolutionary Rate Profiling; SCONE, Sequence CONservation Evaluation; SIFT, Sorting Intolerant from Tolerant; FATHMM, Functional Analysis through Hidden Markov Models; PROVEAN, Protein
Variation Effect Analyzer; PANTHER, Protein ANalysis THrough Evolutionary Relationships; MAPP, Multivariate Analysis of Protein Polymorphism; PMUT, Pathogenic mutation prediction; SAPRED, Single Amino
acid Polymorphisms disease-association PREdictor SNAP, screening for non-acceptable polymorphisms; Condel,
CONsensus DELeteriousness score; CADD, Combined Annotation Dependent Depletion; ASSEDA, Automated
Splice Site and Exon Definition Analyses; SROOGLE, Splicing RegulatiOn Online GraphicaL Engine.
Instead, in case of mutations with gain-of-function (usually dominant), the altered
amino acids might introduce novel deleterious protein functions, and therefore they
might not be predicted as deleterious by these tools. Here, filtering variants based on
functional prediction is more risky. Some examples of highly-penetrant disease-causing
mutations that are predicted as benign by the majority of the in-silico tools include the
SNCA (PARK1) p.Ala53Thr, the PRKRA (DYT16) p.Pro222Leu, and the TARDBP
p.Ala382Thr.
All the previously mentioned methods reduce the number of candidate disease-causing
variants. Additional criteria are important for conclusively claim the pathogenicity of
mutations in novel genes. These include: independent genetic evidence (identification of independent patients/families with similar phenotype, carrying the same or
other mutations in the same gene); biological plausibility (e.g., gene/protein should be
expressed in the organ targeted by the disease; gene/protein might have a recognized
role in a pathway already known to be involved in the disease); and functional studies
(cell systems, model organisms). These traditional concepts remain and are even more
valuable in the NGS era.
Despite the use of all the above-mentioned approaches, some of the variants identified
by NGS are of more difficult interpretation. For these variants, also termed variants of
unknown significance (VUS), the available evidence is not conclusive to claim pathoge25
Chapter 1.1
nicity nor to exclude pathogenicity. Clear and universally valid guidelines on how to
deal with this type of variants in the clinical practice are not available yet. Functional
studies using patient-derived material, in-vitro systems, or animal models, can help in
interpreting the role of these variants. Such approaches could evaluate expression, localization, and functionality of the protein(s). Very often, however, functional assays are
not available simply because the role of the investigated protein-product is also unknown
or unclear. The neurologist should keep in mind that the results of the functional tests
might not always be conclusive.
In these scenarios, the clinicians should carefully explain to the patients that the interpretation of these findings is currently unclear or inconclusive. Moreover, and very
importantly, the patients should also be informed that the significance of the variant(s)
might change in the future, as our clinical and genetic knowledge evolves over time.
Applications of NGS in the clinical practice
NGS technologies have recently entered the genetic diagnostic field. Many laboratories
are implementing diagnostic NGS methods, or they are offering NGS solutions (such
as diagnostic gene-panels, or diagnostic WES, or even WGS). Of course, these new
NGS-based methods must adhere to the existing professional and regulatory quality
standards required for the genetic testing in diagnostic laboratories [74,75]. Furthermore, the diagnostic application of NGS technologies raises novel important issues,
among which, how to deal with incidental findings and long-term data storage for
future reanalyses.
Incidental findings are those that are unrelated to the disease that has generated the
request for diagnostic sequencing, but that are nonetheless of medical value for the
patient. This is an important and complex issue when dealing with WGS and WES. Local
or country-specific regulations might apply,[76] and guidelines have been proposed by
the relevant genetic organizations [77-79]. According to these, the clinician ordering
the analyses must be sufficiently trained and must inform the patients about the risk
of incidental findings. For example, according to the American College of Medical
Genetics and Genomics (ACMG), the diagnostic laboratory should seek and report to
the clinician certain variants in a minimum set of 56 genes associated with 24 disorders
that are considered clinically actionable, regardless of the age of the patient. Of note, no
genes related to movement disorders are currently included in this list. In addition to
the ACMG, also the European Society of Human Genetics and the Canadian College
of Medical Geneticists have released their own recommendations about management of
incidental findings [80,81].
26
Introduction and Scope of the Thesis
The storage and management of WGS and WES data for the purpose of future reanalyses (e.g., VUS, disease-associated genes that are yet-to-be discovered) is another relevant
issue. In theory, NGS data can be periodically reanalyzed to identify pathogenic variants
in novel genes, and/or re-assess the role of variants previously considered as VUS, but
no clear recommendations for this reanalyses have been established. In addition to
that, performing this work in a systematic way appears to be an untenable task for the
diagnostic laboratories. According to the ACMG, the primary care physician would be
responsible to inform the patient about the necessity to re-contact the clinical geneticist,
who should discuss with the patient the possibility of reanalysis [75,82].
Although a debate about the clinical application of WGS or WES is ongoing, much
of the current diagnostic work makes use of targeted resequencing panels [83]. These
methods avoid the problem of incidental findings and are particularly cost- and time-effective when dealing with diseases associated with a high degree of allelic and locus
heterogeneity (i.e., that might be caused by many types of mutations in different genes).
This is the case of several movement disorders, but only very few studies have focused so
far on the application of NGS technologies for diagnostic purposes.
Because of the high number of causative genes (>40), the use of resequencing panels
would be well suited for the diagnosis of cerebellar ataxias patients [84]. In 2010, a first
pilot study reported the feasibility of this approach by screening seven ataxia-causing
genes in five patients with known mutations [85]. The results showed that the identification of causative variants was possible, but the depth of coverage was crucial to
achieve enough sensitivity. Recently, another study investigated 118 genes (including
established and candidate disease-causing genes) in a cohort of 50 familial or early-onset
patients with ataxia, in whom mutations in the most common ataxia-causing genes
had been previously excluded. An overall diagnostic rate of 18% was achieved, which is
comparable to the diagnostic rate obtained by the analysis of the know ataxia-causing
genes using WES [86,87]. Similar results have been reported also for the hereditary
spastic paraplegias, and for PD [88,89].
Collectively, these studies show that targeted resequencing using panels of genes is a
good compromise between reliability and throughput [90]. Nevertheless, NGS has
limitations in detecting complex mutations, such as repeat expansions or large insertions and deletions. Therefore, appropriate techniques should be used prior NGS for
screening patients affected by such type of genetic conditions (e.g., ataxias due to DNA
repeat expansions).
For these reasons, and for the relatively-low costs, NGS-based methods represent a
valid alternative to traditional gene testing by Sanger sequencing. However, Sanger is
still considered the reference “gold standard” for the confirmation of NGS findings,
27
1.1
Chapter 1.1
because of its higher accuracy. Because NGS tests are expected to become much cheaper
than other laboratory tests (e.g., imaging), their application in the near future might
become more widespread, and the overall role of genetic testing in the clinical practice
should increase. Targeted resequencing (WES in the short future and WGS later on)
could become an important component in the differential diagnosis and personalized
medicine.
New trends in data sharing and phenotyping
The pace of discovery of novel disease-related genes has been substantially increased by
the advent of WES and WGS. This revolutionizing wave of gene discovery holds the
promise to solve the genetic basis of all human Mendelian phenotypes within a few
years (an estimated 50% of these conditions still lack a cause). However, although NGS
has increased the success rate of cloning projects, finding a novel disease-causing gene is
still a challenging endeavor (current success rates are estimated at ~25–30%) [91]. An
important and emerging issue is that the disease-genes that still remain to be discovered represent rare or extremely rare causes of the disease. Finding a second confirmatory family that carries the same or a different mutation in a given gene might be very
difficult. In this scenario, sharing of unpublished information between research groups
or organizations is becoming a key factor. To boost this process, web-based platforms
such as GeneMatcher[92] or DECIPHER[93] have been created. These types of tools
are already playing a fundamental role in the current research, but they are expected to
gain even more value in the near future. Of note, when sharing NGS individual data,
guaranteeing the anonymity and protecting the individual privacy are crucial issues that
remain to be fully addressed, and currently hampers the effective data sharing between
researchers [94,95].
The heterogeneous clinical description and the existence of overlapping phenotypes is
another critical issue that is hampering the identification of novel disease-causing genes,
particularly in the case of rare disorders. The use of univocal medical terms is crucial for
the rapid recognition, description, and ascertainment of patients worldwide. Recently,
there have been efforts to harmonize the phenotypic description of Mendelian disorders
by creating a list of standard terms to describe human phenotypes [96]. Moreover,
several software have been developed to assist in the generation of medical information
in a standardize way, as PhenoTips [97] and PhenoDB [92]. These tools should provide
assistance in clustering the patients with the same phenotypes and in understanding
the disorder pleiotropy, and they will consequently facilitate the identification of novel
disease-causing genes [98].
28
Introduction and Scope of the Thesis
Table 4. Challenges of NGS that should be considered by the movement disorders specialist.
NGS pitfall
Action/Solution
Incidental findings.
Refer to country-specific regulations. Targeted
1.1
resequencing panels might be a preferable NGS choice.
Variants of unknown significance.
Consider functional studies, if possible.
Variants interpretation might change over time.
Consider periodic reanalysis of the data.
Missing mutations due to regions with low depth of
Increase the depth of coverage, and monitor the quality
coverage.
of the coverage (particularly in the first exons of genes,
and in high guanine-cytosine-rich regions).
Some type of mutations are currently not well detected
Different laboratory techniques should be used to
or undetectable (copy number variants, long insertions/
investigate such type of mutations.
deletions, structural variants, aneuploidy).
A final word of caution
There are great expectations about the opportunities offered by the NGS technologies. However, their use in both research and clinical settings comes with certain challenges and limitations that should be taken into account (Table 4). Despite continuous improvements, the overall accuracy and reproducibility of NGS methods remain
sub-optimal, and the concordance between different platforms is still low, especially
when analyzing WES or WGS data [99]. Furthermore, the quality of the data provided
by different laboratories can be very variable. A recent study compared the performance
of 30 different NGS laboratories in the identification and reporting of disease-causing
mutations in three families with heritable genetic disorders. Despite the fact that all
the laboratories started from the same data, only two of them were able to adequately
complete the assigned task for all the families, highlighting the complexities inherent in
the current analyses and report of NGS data [100].
Because the interpretation of the data remains a complex matter, genetic findings
should be prudently reported. The analysis of WGS and WES data with the approaches
described above can help to distinguish disease-causing variants from benign genomic
variation, but the evidence is often not sufficient to conclusively declare the pathogenicity of novel variants, and several variants remains labeled as VUS. Erroneous interpretations might have serious consequences for the patient. To prevent such circumstances, the interpretation and reporting of variants in clinical settings should follow
the guidelines of relevant genetic organizations [101]. Besides the direct consequences
for a patient, erroneous assignments have a negative impact on the research field: up
29
Chapter 1.1
to 27% of the genetic variants in the scientific literature have been estimated to have
a misleading designation [102]. Claims of the identification of a novel disease-causing
gene should be considered with caution, until confirmation is provided in independent
datasets, or by independent, functional methods. This caveat is particularly important
for claims of disease-causality based on findings in single patients or single pedigrees
(examples are included in the table 1).
To avoid the proliferation of erroneously annotated variants, researchers have set up
recommendations to declare variants pathogenicity. According to these recommendations, assignments of variants pathogenicity should include the overall genetic, bioinformatic, and functional evidence, using most of the approaches already described in this
chapter [103]. The application of these rules will hopefully help to prevent erroneous
assignments in the future.
These are exciting times for those interested in the genetics of movement disorders. It
is hoped that the application of NGS technologies will result in a fuller understanding
of the genetic basis of movement disorders. This knowledge will ultimately provide the
rationale for novel clinical and therapeutic interventions.
Funding sources
This work was supported by grants from the Stichting ParkinsonFonds (The Netherlands) to V.B.
Author Contributions
1) Research project: A. Conception, B. Organization, C. Execution;
2) Statistical Analysis: A. Design, B. Execution, C. Review and Critique;
3) Manuscript Preparation: A. Writing of the first draft, B. Review and Critique.
SO: 1BC, 3A;
MQ: 1BC, 3B;
VB: 1A, 3B.
All the authors approved the final version, and took responsibility for the conduct of the work.
30
Introduction and Scope of the Thesis
Scope of the thesis
Movement disorders are a heterogeneous group of neurologic syndromes. In the past, a
large number of genetic factors have been associated with Mendelian forms of movement
disorders. Many of these genetic factors were recently identified using next-generation
sequencing (NGS) technologies (chapter 1.1).
The scope of this thesis is the study of genetic factors involved in the etiology of
movement disorders. We apply a combination of traditional positional cloning methods
and novel NGS-based approaches to identify and characterize the genetic determinants
of movement disorders.
In part II of this thesis, we investigate the genetic factors associated with Parkinson’s
disease (PD). For the first time, we identify pathogenic DNAJC6 mutations in families
with early-onset PD (chapter 2.1). Our work delineate a novel form of autosomal
recessive early-onset PD, caused by mutations in the DNAJC6 gene, and has important
implications for the diagnostic work-up of early-onset PD patients. In chapter 2.2, we
report a novel family with triplication of the SNCA locus, a very rare form of early-onset
parkinsonism. Finally, in chapter 2.3, we report a large exome sequencing study of
PD in the isolated population of Sardinia. This work identifies a list of novel candidate
genetic determinants of PD, a resource for replication in follow-up studies.
In part III, we use family-based strategies to study atypical forms of parkinsonism. First,
by studying two siblings affected by early-onset atypical parkinsonism, we identify a
novel form of parkinsonism caused by a homozygous mutation in the SYNJ1 gene
(chapter 3.1). Then, in chapter 3.2, we report an additional family (the third known so
far) with a milder clinical phenotype caused by the same SYNJ1 homozygous mutation
and we suggest that the two mutations originated from different mutational events.
Last, in chapter 3.3, we report a family with juvenile-onset atypical parkinsonism due
to a homozygous mutation in the FBXO7 gene, contributing to the delineation of the
genetic and clinical features of this rare form of parkinsonism.
In part IV of this thesis, we focus our attention on movement disorders other than
primary parkinsonisms. We identify ECHS1 compound heterozygous mutations as
the disease cause in two siblings with different dystonic disorders. This work shows
that the broad phenotype associated with ECHS1 mutations can also include isolated
paroxysmal exercise-induced dystonia (chapter 4.1). Moreover, we identify a recurrent
C19orf12 mutation in Turkish patients with neurodegeneration with brain iron accumulation, with implications for the diagnosis and genetic counseling of the patients
with Turkish ancestry (chapter 4.2).
Finally, in the last chapter (chapter 5.1), we discuss the results of this thesis in a broader
31
1.1
Chapter 1.1
context and examine how genetics studies are contributing to reshape our knowledge of
the genetic and clinical aspects of movement disorders.
References
1 Fahn S. Classification of movement disorders. Mov
Disord. 2011;26:947-57.
kinesigenic dyskinesia and infantile convulsions.
Neurology. 2012;79:777-84.
2 Rothdach AJ, Trenkwalder C, Haberstock J, Keil 12 Cloarec R, Bruneau N, Rudolf G, Massacrier A,
Salmi M, Bataillard M, et al. PRRT2 links infantile
U, Berger K. Prevalence and risk factors of RLS in
convulsions and paroxysmal dyskinesia with migraine.
an elderly population: the MEMO study. Memory
Neurology. 2012;79:2097-103.
and Morbidity in Augsburg Elderly. Neurology.
2000;54:1064-8.
13 Sanger F, Nicklen S, Coulson AR. DNA sequencing
with chain-terminating inhibitors. Proc Natl Acad Sci
3 Louis ED, Ottman R, Hauser WA. How common is
U S A. 1977;74:5463-7.
the most common adult movement disorder? estimates
of the prevalence of essential tremor throughout the 14 McCormack JE, Hird SM, Zellmer AJ, Carstens
world. Mov Disord. 1998;13:5-10.
BC, Brumfield RT. Applications of next-generation
4 de Lau LM, Breteler MM. Epidemiology of Parkinson’s disease. Lancet Neurol. 2006;5:525-35.
5
6
sequencing to phylogeography and phylogenetics.
Mol Phylogenet Evol. 2013;66:526-38.
Wolters E, Baumann C. Parkinson disease and other 15 Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet.
movement disorders: motor behavioral disorders and
2009;10:57-63.
behavioral motor disorders. Amsterdam: VU University Press; 2014.
16 Tsompana M, Buck MJ. Chromatin accessibility: a
window into the genome. Epigenetics Chromatin.
Fahn S, Jankovic J, Hallet M. Principles and Practice
2014;7:33.
of Movement Disorders. 2nd ed. Philadephia: Elsevier;
2011.
17 Park PJ. ChIP-seq: advantages and challenges of a
maturing technology. Nat Rev Genet. 2009;10:6697 de Carvalho Aguiar P, Sweadner KJ, Penniston JT,
80.
Zaremba J, Liu L, Caton M, et al. Mutations in the
Na+/K+ -ATPase alpha3 gene ATP1A3 are associated 18 Petrosino JF, Highlander S, Luna RA, Gibbs RA,
with rapid-onset dystonia parkinsonism. Neuron.
Versalovic J. Metagenomic pyrosequencing and
2004;43:169-75.
microbial identification. Clin Chem. 2009;55:85666.
8 Rosewich H, Thiele H, Ohlenbusch A, Maschke
U, Altmuller J, Frommolt P, et al. Heterozygous 19 Mamanova L, Coffey AJ, Scott CE, Kozarewa I,
de-novo mutations in ATP1A3 in patients with
Turner EH, Kumar A, et al. Target-enrichment stratalternating hemiplegia of childhood: a whole-exome
egies for next-generation sequencing. Nat Methods.
sequencing gene-identification study. Lancet Neurol.
2010;7:111-8.
2012;11:764-73.
20 Morey M, Fernandez-Marmiesse A, Castineiras D,
9 Heinzen EL, Swoboda KJ, Hitomi Y, Gurrieri F,
Fraga JM, Couce ML, Cocho JA. A glimpse into past,
Nicole S, de Vries B, et al. De novo mutations in
present, and future DNA sequencing. Mol Genet
ATP1A3 cause alternating hemiplegia of childhood.
Metab. 2013;110:3-24.
Nat Genet. 2012;44:1030-4.
21 Metzker ML. Sequencing technologies - the next
10 Marini C, Conti V, Mei D, Battaglia D, Lettori D,
generation. Nat Rev Genet. 2010;11:31-46.
Losito E, et al. PRRT2 mutations in familial infantile
22Mardis ER. Next-generation DNA sequencing
seizures, paroxysmal dyskinesia, and hemiplegic
methods. Annu Rev Genomics Hum Genet.
migraine. Neurology. 2012;79:2109-14.
2008;9:387-402.
11 van Vliet R, Breedveld G, de Rijk-van Andel J, Brilstra
23 Thompson JF, Milos PM. The properties and appliE, Verbeek N, Verschuuren-Bemelmans C, et al.
cations of single-molecule DNA sequencing. Genome
PRRT2 phenotypes and penetrance of paroxysmal
Biol. 2011;12:217.
32
Introduction and Scope of the Thesis
24 Valsesia A, Mace A, Jacquemont S, Beckmann JS, 36 Botstein DR, N. Discovering genotypes underlying
human phenotypes: past successes for mendelian
Kutalik Z. The Growing Importance of CNVs: New
disease, future approaches for complex disease. Nat
Insights for Detection and Clinical Interpretation.
Genet. 2003;33:228-37.
Front Genet. 2013;4:92.
25 Lam HY, Clark MJ, Chen R, Chen R, Natsoulis G, 37 Ott J, Wang J, Leal SM. Genetic linkage analysis in
the age of whole-genome sequencing. Nat Rev Genet.
O’Huallachain M, et al. Performance comparison of
2015;16:275-84.
whole-genome sequencing platforms. Nat Biotechnol.
2012;30:78-82.
38 Multiple-System Atrophy Research C. Mutations
in COQ2 in familial and sporadic multiple-system
26 Loman NJ, Misra RV, Dallman TJ, Constantinidou
atrophy. N Engl J Med. 2013;369:233-44.
C, Gharbia SE, Wain J, et al. Performance comparison
of benchtop high-throughput sequencing platforms. 39 Mencacci NE, Rubio-Agusti I, Zdebik A, Asmus F,
Nat Biotechnol. 2012;30:434-9.
Ludtmann MH, Ryten M, et al. A Missense Mutation
in KCTD17 Causes Autosomal Dominant Myoclo27 Ross MG, Russ C, Costello M, Hollinger A, Lennon
nus-Dystonia. Am J Hum Genet. 2015;96:938-47.
NJ, Hegarty R, et al. Characterizing and measuring
bias in sequence data. Genome Biol. 2013;14:R51.
40 Wilson GR, Sim JC, McLean C, Giannandrea M,
Galea CA, Riseley JR, et al. Mutations in RAB39B
28 Heron SE, Grinton BE, Kivity S, Afawi Z, Zuberi
cause X-linked intellectual disability and early-onset
SM, Hughes JN, et al. PRRT2 mutations cause benign
Parkinson disease with alpha-synuclein pathology. Am
familial infantile epilepsy and infantile convulsions
J Hum Genet. 2014;95:729-35.
with choreoathetosis syndrome. Am J Hum Genet.
2012;90:152-60.
41 Alkuraya FS. The application of next-generation
sequencing in the autozygosity mapping of human
29 Schubert J, Paravidino R, Becker F, Berger A, Bebek
recessive diseases. Hum Genet. 2013;132:1197-211.
N, Bianchi A, et al. PRRT2 mutations are the major
cause of benign familial infantile seizures. Hum 42 Edvardson S, Cinnamon Y, Ta-Shma A, Shaag A,
Mutat. 2012;33:1439-43.
Yim YI, Zenvirt S, et al. A deleterious mutation
30 Chen WJ, Lin Y, Xiong ZQ, Wei W, Ni W, Tan GH, et
al. Exome sequencing identifies truncating mutations
in PRRT2 that cause paroxysmal kinesigenic dyskinesia. Nat Genet. 2011;43:1252-5.
43
31 Wang JL, Cao L, Li XH, Hu ZM, Li JD, Zhang
JG, et al. Identification of PRRT2 as the causative
gene of paroxysmal kinesigenic dyskinesias. Brain.
2011;134:3493-501.
44
32 Li J, Zhu X, Wang X, Sun W, Feng B, Du T, et al.
Targeted genomic sequencing identifies PRRT2
mutations as a cause of paroxysmal kinesigenic
choreoathetosis. J Med Genet. 2012;49:76-8.
in DNAJC6 encoding the neuronal-specific clathrin-uncoating co-chaperone auxilin, is associated with
juvenile parkinsonism. PLoS One. 2012; 7:e36458.
Quadri M, Fang M, Picillo M, Olgiati S, Breedveld
GJ, Graafland J, et al. Mutation in the SYNJ1 gene
associated with autosomal recessive, early-onset
Parkinsonism. Hum Mutat. 2013;34:1208-15.
Krebs CE, Karkheiran S, Powell JC, Cao M, Makarov
V, Darvish H, et al. The Sac1 Domain of SYNJ1 Identified Mutated in a Family with Early-Onset Progressive Parkinsonism with Generalized Seizures. Hum
Mutat. 2013;34:1200-7.
33 Lee HY, Huang Y, Bruneau N, Roll P, Roberson ED, 45 Charlesworth G, Angelova PR, Bartolome-Robledo F,
Ryten M, Trabzuni D, Stamelou M, et al. Mutations
Hermann M, et al. Mutations in the gene PRRT2
in HPCA cause autosomal-recessive primary isolated
cause paroxysmal kinesigenic dyskinesia with infantile
dystonia. Am J Hum Genet. 2015;96:657-65.
convulsions. Cell Rep. 2012;1:2-12.
34 Quadri M, Federico A, Zhao T, Breedveld GJ, Battisti 46 Smeets CJ, Verbeek DS. Cerebellar ataxia and functional genomics: Identifying the routes to cereC, Delnooz C, et al. Mutations in SLC30A10 cause
bellar neurodegeneration. Biochim Biophys Acta.
parkinsonism and dystonia with hypermanganesemia,
2014;1842:2030-8.
polycythemia, and chronic liver disease. Am J Hum
Genet. 2012;90:467-77.
47 Tesson C, Koht J, Stevanin G. Delving into the
complexity of hereditary spastic paraplegias: how
35Manolio TA, Collins FS, Cox NJ, Goldstein
unexpected phenotypes and inheritance modes
DB, Hindorff LA, Hunter DJ, et al. Finding the
are revolutionizing their nosology. Hum Genet.
missing heritability of complex diseases. Nature.
2015;134:511-38.
2009;461:747-53.
33
1.1
Chapter 1.1
Neurogenetics. 2015;16:55-64.
48 Vilarino-Guell C, Wider C, Ross OA, Dachsel JC,
Kachergus JM, Lincoln SJ, et al. VPS35 mutations in 61 Bansal V, Libiger O, Torkamani A, Schork NJ. StatisParkinson disease. Am J Hum Genet. 2011;89:162-7.
tical analysis strategies for association studies involving
rare variants. Nat Rev Genet. 2010;11:773-85.
49 Zimprich A, Benet-Pages A, Struhal W, Graf E,
Eck SH, Offman MN, et al. A mutation in VPS35, 62 Sham PC, Purcell SM. Statistical power and signifiencoding a subunit of the retromer complex, causes
cance testing in large-scale genetic studies. Nat Rev
late-onset Parkinson disease. Am J Hum Genet.
Genet. 2014;15:335-46.
2011;89:168-75.
63 Smith BN, Ticozzi N, Fallini C, Gkazi AS, Topp S,
50 Nicolas G, Pottier C, Maltete D, Coutant S, RoveletKenna KP, et al. Exome-wide rare variant analysis
Lecrux A, Legallic S, et al. Mutation of the PDGFRB
identifies TUBA4A mutations associated with familial
gene as a cause of idiopathic basal ganglia calcification.
ALS. Neuron. 2014;84:324-31.
Neurology. 2013;80:181-7.
64 Cirulli ET, Lasseigne BN, Petrovski S, Sapp PC,
51 Keller A, Westenberger A, Sobrido MJ, Garcia-MuDion PA, Leblond CS, et al. Exome sequencing in
rias M, Domingo A, Sears RL, et al. Mutations in the
amyotrophic lateral sclerosis identifies risk genes and
gene encoding PDGF-B cause brain calcifications in
pathways. Science. 2015;347:1436-41.
humans and mice. Nat Genet. 2013;45:1077-82.
65 Freischmidt A, Wieland T, Richter B, Ruf W, Schaeffer
52 Veltman JA, Brunner HG. De novo mutations in
V, Muller K, et al. Haploinsufficiency of TBK1 causes
human genetic disease. Nat Rev Genet. 2012;13:565familial ALS and fronto-temporal dementia. Nat
75.
Neurosci. 2015;18:631-6.
53 Roach JC, Glusman G, Smit AF, Huff CD, Hubley R, 66 Novarino G, Fenstermaker AG, Zaki MS, Hofree M,
Shannon PT, et al. Analysis of genetic inheritance in a
Silhavy JL, Heiberg AD, et al. Exome sequencing links
family quartet by whole-genome sequencing. Science.
corticospinal motor neuron disease to common neuro2010;328:636-9.
degenerative disorders. Science. 2014;343:506-11.
54 Gratten J, Wray NR, Keller MC, Visscher PM. Large- 67 Bamshad MJ, Ng SB, Bigham AW, Tabor HK, Emond
scale genomics unveils the genetic architecture of
MJ, Nickerson DA, et al. Exome sequencing as a tool
psychiatric disorders. Nat Neurosci. 2014;17:782-90.
for Mendelian disease gene discovery. Nat Rev Genet.
2011;12:745-55.
55 Carapito R, Paul N, Untrau M, Le Gentil M, Ott L,
Alsaleh G, et al. A de novo ADCY5 mutation causes 68 Stenson PD, Mort M, Ball EV, Shaw K, Phillips A,
early-onset autosomal dominant chorea and dystonia.
Cooper DN. The Human Gene Mutation Database:
Mov Disord. 2015;30:423-7.
building a comprehensive mutation repository for
56 Gilissen C, Hoischen A, Brunner HG, Veltman
JA. Disease gene identification strategies for exome
sequencing. Eur J Hum Genet. 2012;20:490-7.
clinical and molecular genetics, diagnostic testing
and personalized genomic medicine. Hum Genet.
2014;133:1-9.
57 Haack TB, Hogarth P, Kruer MC, Gregory A, 69 Cooper GM, Brown CD. Qualifying the relationship between sequence conservation and molecular
Wieland T, Schwarzmayr T, et al. Exome sequencing
function. Genome Res. 2008;18:201-5.
reveals de novo WDR45 mutations causing a phenotypically distinct, X-linked dominant form of NBIA. 70 Cooper GM, Shendure J. Needles in stacks of needles:
Am J Hum Genet. 2012;91:1144-9.
finding disease-causal variants in a wealth of genomic
data. Nat Rev Genet. 2011;12:628-40.
58 Price AL, Kryukov GV, de Bakker PI, Purcell SM,
Staples J, Wei LJ, et al. Pooled association tests for 71 Jian X, Boerwinkle E, Liu X. In silico tools for splicing
rare variants in exon-resequencing studies. Am J Hum
defect prediction: a survey from the viewpoint of end
Genet. 2010;86:832-8.
users. Genet Med. 2014;16:497-503.
59 Cirulli ET, Goldstein DB. Uncovering the roles of rare 72 Thusberg J, Olatubosun A, Vihinen M. Performance
variants in common disease through whole-genome
of mutation pathogenicity prediction methods on
sequencing. Nat Rev Genet. 2010;11:415-25.
missense variants. Hum Mutat. 2011;32:358-68.
60 Quadri M, Yang X, Cossu G, Olgiati S, Saddi VM, 73 Houdayer C, Caux-Moncoutier V, Krieger S, Barrois
Breedveld GJ, et al. An exome study of Parkinson’s
M, Bonnet F, Bourdon V, et al. Guidelines for splicing
disease in Sardinia, a Mediterranean genetic isolate.
analysis in molecular diagnosis derived from a set of
34
Introduction and Scope of the Thesis
327 combined in silico/in vitro studies on BRCA1 and
BRCA2 variants. Hum Mutat. 2012;33:1228-38.
Schnekenberg R, Becker EB, Bera KD, et al. Next
generation sequencing for molecular diagnosis of
neurological disorders using ataxias as a model. Brain.
2013;136:3106-18.
74 Gargis AS, Kalman L, Berry MW, Bick DP, Dimmock
DP, Hambuch T, et al. Assuring the quality of
next-generation sequencing in clinical laboratory 87 Fogel BL, Lee H, Deignan JL, Strom SP, Kantarci
S, Wang X, et al. Exome sequencing in the clinical
practice. Nat Biotechnol. 2012;30:1033-6.
diagnosis of sporadic or familial cerebellar ataxia.
75 Rehm HL, Bale SJ, Bayrak-Toydemir P, Berg JS,
JAMA Neurol. 2014;71:1237-46.
Brown KK, Deignan JL, et al. ACMG clinical laboratory standards for next-generation sequencing. Genet 88 Kumar KR, Blair NF, Vandebona H, Liang C, Ng
K, Sharpe DM, et al. Targeted next generation
Med. 2013;15:733-47.
sequencing in SPAST-negative hereditary spastic para76 Knoppers BM, Zawati MH, Senecal K. Return of
plegia. J Neurol. 2013;260:2516-22.
genetic testing results in the era of whole-genome
89 Petersen MS, Guella I, Bech S, Gustavsson E,
sequencing. Nat Rev Genet. 2015;16:553-9.
Farrer MJ. Parkinson’s disease, genetic variability
77 Green RC, Berg JS, Grody WW, Kalia SS, Korf BR,
and the Faroe Islands. Parkinsonism Relat Disord.
Martin CL, et al. ACMG recommendations for
2015;21:75-8.
reporting of incidental findings in clinical exome and
90 Sikkema-Raddatz B, Johansson LF, de Boer EN,
genome sequencing. Genet Med. 2013;15:565-74.
Almomani R, Boven LG, van den Berg MP, et al.
78 Directors ABo. ACMG policy statement: updated
Targeted next-generation sequencing can replace
recommendations regarding analysis and reporting
Sanger sequencing in clinical diagnostics. Hum
of secondary findings in clinical genome-scale
Mutat. 2013;34:1035-42.
sequencing. Genet Med. 2015;17:68-9.
79 American College of Medical G, Genomics. Incidental 91 Chong JX, Buckingham KJ, Jhangiani SN, Boehm
C, Sobreira N, Smith JD, et al. The Genetic Basis of
findings in clinical genomics: a clarification. Genet
Mendelian Phenotypes: Discoveries, Challenges, and
Med. 2013;15:664-6.
Opportunities. Am J Hum Genet. 2015;97:199-215.
80 van El CG, Cornel MC, Borry P, Hastings RJ,
92
Sobreira N, Schiettecatte F, Boehm C, Valle D,
Fellmann F, Hodgson SV, et al. Whole-genome
Hamosh A. New tools for Mendelian disease gene
sequencing in health care: recommendations of the
identification: PhenoDB variant analysis module; and
European Society of Human Genetics. Eur J Hum
GeneMatcher, a web-based tool for linking investigaGenet. 2013;21:580-4.
tors with an interest in the same gene. Hum Mutat.
81 Boycott K, Hartley T, Adam S, Bernier F, Chong
2015;36:425-31.
K, Fernandez BA, et al. The clinical application of
93
Bragin E, Chatzimichali EA, Wright CF, Hurles ME,
genome-wide sequencing for monogenic diseases in
Firth HV, Bevan AP, et al. DECIPHER: database
Canada: Position Statement of the Canadian College
for the interpretation of phenotype-linked plausibly
of Medical Geneticists. J Med Genet. 2015;52:431-7.
pathogenic sequence and copy-number variation.
82 Hirschhorn K, Fleisher LD, Godmilow L, Howell
Nucleic Acids Res. 2014;42:D993-D1000.
RR, Lebel RR, McCabe ER, et al. Duty to re-contact.
94
Kaye J. The tension between data sharing and the
Genet Med. 1999;1:171-2.
protection of privacy in genomics research. Annu Rev
83 Rehm HL. Disease-targeted sequencing: a cornerstone
Genomics Hum Genet. 2012;13:415-31.
in the clinic. Nat Rev Genet. 2013;14:295-300.
95 Gymrek M, McGuire AL, Golan D, Halperin E,
84 Hersheson J, Haworth A, Houlden H. The inherited
Erlich Y. Identifying personal genomes by surname
ataxias: genetic heterogeneity, mutation databases, and
inference. Science. 2013;339:321-4.
future directions in research and clinical diagnostics.
96
Kohler S, Doelken SC, Mungall CJ, Bauer S, Firth
Hum Mutat. 2012;33:1324-32.
HV, Bailleul-Forestier I, et al. The Human Phenotype
85 Hoischen A, Gilissen C, Arts P, Wieskamp N, van der
Ontology project: linking molecular biology and
Vliet W, Vermeer S, et al. Massively parallel sequencing
disease through phenotype data. Nucleic Acids Res.
of ataxia genes after array-based enrichment. Hum
2014;42:D966-74.
Mutat. 2010;31:494-9.
97 Girdea M, Dumitriu S, Fiume M, Bowdin S, Boycott
86 Nemeth AH, Kwasniewska AC, Lise S, Parolin
KM, Chenier S, et al. PhenoTips: patient phenotyping
35
1.1
Chapter 1.1
software for clinical and research use. Hum Mutat.
2013;34:1057-65.
tremor and Parkinson disease. Proc Natl Acad Sci U S
A. 2014;111:18285-90.
98Hennekam RC, Biesecker LG. Next-generation 109Hor H, Francescatto L, Bartesaghi L, Ortega-Cubero
S, Kousi M, Lorenzo-Betancor O, et al. Missense
sequencing demands next-generation phenotyping.
mutations in TENM4, a regulator of axon guidance
Hum Mutat. 2012;33:884-6.
and central myelination, cause essential tremor. Hum
99 O’Rawe J, Jiang T, Sun G, Wu Y, Wang W, Hu J, et al.
Mol Genet. 2015;24:5677-86.
Low concordance of multiple variant-calling pipelines:
practical implications for exome and genome 110Bergareche A, Bednarz M, Sanchez E, Krebs CE,
Ruiz-Martinez J, De La Riva P, et al. SCN4A pore
sequencing. Genome Med. 2013;5:28.
mutation pathogenetically contributes to autosomal
100Brownstein CA, Beggs AH, Homer N, Merriman B,
dominant essential tremor and may increase susceptiYu TW, Flannery KC, et al. An international effort
bility to epilepsy. Hum Mol Genet. 2015;24:7111-20.
towards developing standards for best practices in
analysis, interpretation and reporting of clinical 111Dusi S, Valletta L, Haack TB, Tsuchiya Y, Venco P,
Pasqualato S, et al. Exome sequence reveals mutations
genome sequencing results in the CLARITY
in CoA synthase as a cause of neurodegeneration
Challenge. Genome Biol. 2014;15:R53.
with brain iron accumulation. Am J Hum Genet.
101Richards S, Aziz N, Bale S, Bick D, Das S, Gasti2014;94:11-22.
er-Foster J, et al. Standards and guidelines for the
interpretation of sequence variants: a joint consensus 112Zhang X, Bogunovic D, Payelle-Brogard B, Francois-Newton V, Speer SD, Yuan C, et al. Human
recommendation of the American College of Medical
intracellular ISG15 prevents interferon-alpha/beta
Genetics and Genomics and the Association for
over-amplification and auto-inflammation. Nature.
Molecular Pathology. Genet Med. 2015;17:405-24.
2015;517:89-93.
102Bell CJ, Dinwiddie DL, Miller NA, Hateley SL,
Ganusova EE, Mudge J, et al. Carrier testing for 113Legati A, Giovannini D, Nicolas G, Lopez-Sanchez U, Quintans B, Oliveira JR, et al. Mutations
severe childhood recessive diseases by next-generation
in XPR1 cause primary familial brain calcification
sequencing. Sci Transl Med. 2011;3:65ra4.
associated with altered phosphate export. Nat Genet.
103MacArthur DG, Manolio TA, Dimmock DP, Rehm
2015;47:579-81.
HL, Shendure J, Abecasis GR, et al. Guidelines for
investigating causality of sequence variants in human 114Lohmann K, Wilcox RA, Winkler S, Ramirez A,
Rakovic A, Park JS, et al. Whispering dysphonia
disease. Nature. 2014;508:469-76.
(DYT4 dystonia) is caused by a mutation in the
104Vilarino-Guell C, Rajput A, Milnerwood AJ, Shah
TUBB4 gene. Ann Neurol. 2013;73:537-45.
B, Szu-Tu C, Trinh J, et al. DNAJC13 mutations in
Parkinson disease. Hum Mol Genet. 2014;23:1794- 115 Hersheson J, Mencacci NE, Davis M, MacDonald N,
Trabzuni D, Ryten M, et al. Mutations in the auto801.
regulatory domain of beta-tubulin 4a cause hereditary
105Funayama M, Ohe K, Amo T, Furuya N, Yamaguchi
dystonia. Ann Neurol. 2013;73:546-53.
J, Saiki S, et al. CHCHD2 mutations in autosomal
dominant late-onset Parkinson’s disease: a genome- 116Xiao J, Uitti RJ, Zhao Y, Vemula SR, Perlmutter
JS, Wszolek ZK, et al. Mutations in CIZ1 cause
wide linkage and sequencing study. Lancet Neurol.
adult onset primary cervical dystonia. Ann Neurol.
2015;14:274-82.
2012;71:458-69.
106 Korvatska O, Strand NS, Berndt JD, Strovas T, Chen
DH, Leverenz JB, et al. Altered splicing of ATP6AP2 117Charlesworth G, Plagnol V, Holmstrom KM, Bras
J, Sheerin UM, Preza E, et al. Mutations in ANO3
causes X-linked parkinsonism with spasticity (XPDS).
cause dominant craniocervical dystonia: ion channel
Hum Mol Genet. 2013;22:3259-68.
implicated in pathogenesis. Am J Hum Genet.
107Merner ND, Girard SL, Catoire H, Bourassa CV,
2012;91:1041-50.
Belzil VV, Riviere JB, et al. Exome sequencing identifies FUS mutations as a cause of essential tremor. Am 118Fuchs T, Saunders-Pullman R, Masuho I, Luciano
MS, Raymond D, Factor S, et al. Mutations in
J Hum Genet. 2012;91:313-9.
GNAL cause primary torsion dystonia. Nat Genet.
108Unal Gulsuner H, Gulsuner S, Mercan FN, Onat
2013;45:88-92.
OE, Walsh T, Shahin H, et al. Mitochondrial serine
protease HTRA2 p.G399S in a kindred with essential 119Zech M, Lam DD, Francescatto L, Schormair B,
36
Introduction and Scope of the Thesis
Salminen AV, Jochim A, et al. Recessive mutations
in the alpha3 (VI) collagen gene COL6A3 cause
early-onset isolated dystonia. Am J Hum Genet.
2015;96:883-93.
1.1
37
PART II
Parkinson’s Disease
40
2.1
DNAJC6 Mutations Associated with
Early-Onset Parkinson’s Disease
S. Olgiati,*,1 M. Quadri,*,1 M. Fang,*,2 J.P.M.A. Rood,3 J.A. Saute,4 H. Fen Chien,5
C.G. Bouwkamp,1,6 J. Graafland,1 M. Minneboo,1 G.J. Breedveld,1 J. Zhang,2
The International Parkinsonism Genetics Network,† F.W. Verheijen,1
A.J.W. Boon,3 A.J.A. Kievit,1 L.B. Jardim,4,7 W. Mandemakers,1 E.R. Barbosa,5
C.R.M. Rieder,8 K.L. Leenders,9 J. Wang,2,10,11,12,# and V. Bonifati.1,#
Published in Annals of Neurology 2016; 79(2):244-56.
* These authors contributed equally to this work, as first authors;
# These authors contributed equally to this work, as senior authors and corresponding authors;
† Members of the network are listed in the Supplementary Material.
1) Department of Clinical Genetics, Erasmus MC, Rotterdam, The Netherlands;
2) BGI-Shenzhen, Shenzhen, China;
3) Department of Neurology, Erasmus MC, Rotterdam, The Netherlands;
4) Medical Genetics Service, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil;
5) Department of Neurology, University of São Paulo, São Paulo, Brazil;
6) Department of Psychiatry, Erasmus MC, Rotterdam, The Netherlands;
7) Department of Internal Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil;
8) Neurology Service, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil;
9) Department of Neurology, University Medical Center Groningen, Groningen, The Netherlands;
10) Department of Biology, University of Copenhagen, Copenhagen, Denmark;
11) Princess Al Jawhara Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Jeddah,
Saudi Arabia;
12) Macau University of Science and Technology, Macau, China.
© 2016 American Neurological Association
Reprinted with permission of John Wiley & Sons, Inc.
41
Chapter 2.1
Abstract
Objective DNAJC6 mutations were recently described in two families with autosomal
recessive juvenile parkinsonism (onset age < 11), prominent atypical signs, poor or
absent response to levodopa, and rapid progression (wheelchair-bound within ~10
years from onset). Here, for the first time, we report DNAJC6 mutations in early-onset
Parkinson’s disease (PD).
Methods The DNAJC6 open reading frame was analyzed in 274 patients with earlyonset sporadic, or familial PD. Selected variants were followed up by cosegregation,
homozygosity mapping, linkage analysis, whole-exome sequencing, and protein studies.
Results We identified two families with different novel homozygous DNAJC6 mutations
segregating with PD. In each family, the DNAJC6 mutation was flanked by long runs
of homozygosity within highest linkage peaks. Exome sequencing did not detect additional pathogenic variants within the linkage regions. In both families, patients showed
severely decreased steady-state levels of the auxilin protein in fibroblasts. We also identified a sporadic patient carrying two rare non-coding DNAJC6 variants possibly effecting
RNA splicing. All these cases fulfilled the criteria for a clinical diagnosis of early-onset
PD, had symptoms onset in the third-to-fifth decade, and slow disease progression.
Response to dopaminergic therapies was prominent but, in some patients, limited by
psychiatric side-effects. The phenotype overlaps that of other monogenic forms of earlyonset PD.
Interpretation Our findings delineate a novel form of hereditary early-onset PD.
Screening of DNAJC6 is warranted in all patients with early-onset PD compatible with
autosomal recessive inheritance. Our data provide further evidence for the involvement
of synaptic vesicles endocytosis and trafficking in PD pathogenesis.
42
DNAJC6 Mutations Associated with Early-Onset Parkinson’s Disease
Introduction
Parkinson’s disease (PD) is a progressive, neurodegenerative disorder manifesting
with bradykinesia, resting tremor, muscular rigidity, postural instability, and a good,
prolonged response to levodopa and dopaminergic agonists. The ongoing discovery of
Mendelian forms of PD provides important clues to understand the disease pathogenesis. Mutations in Parkin, PINK1, and DJ-1 cause autosomal recessive forms of earlyonset PD (symptoms onset <age 45), which display slow disease progression, absence
of atypical signs, and good response to dopaminergic therapies [1-3]. Different types
of autosomal recessive parkinsonisms are caused by mutations in ATP13A2, PLA2G6,
FBXO7, DNAJC6, or SYNJ1 [4-11]. Unlike the previous forms, mutations in these
genes cause juvenile-onset (<age 20) parkinsonism with atypical clinical signs and poor
or absent response to levodopa.
Mutations in DNAJC6 [DnaJ (Hsp40) Homolog, Subfamily C, Member 6] were recently
described in two consanguineous families with juvenile-onset, atypical parkinsonism,
termed PARK19 (MIM 615528). The patients from a Palestinian family carried a
homozygous splice-site mutation, while those from a Turkish family had a homozygous
truncating mutation [8-9]. In both families parkinsonism manifested during childhood
(<age 11) and the disease progression was very rapid, leading to wheelchair-bound state
within ~10 years from onset. The affected family members had poor or absent response
to levodopa or additional prominent atypical signs (i.e., mental retardation, seizures,
dystonia, and pyramidal signs). Furthermore, a 7-years-old patient carrying a homozygous genomic deletion (including most of the DNAJC6 exons, but also two neighboring
genes) was reported with early-onset obesity, mental retardation, and epilepsy [12]. No
clinical signs of parkinsonism were present, but the patient was still very young and his
clinical presentation possibly incomplete.
DNAJC6 encodes the brain-specific isoform of auxilin. Auxilins have a well-established
role in the clathrin-mediated endocytosis (CME), the process responsible for the uptake
of material into the cells through clathrin-coated vesicles. In neurons, CME is an
important mechanism for the formation of new vesicles at the presynaptic terminal and
synaptic vesicles recycling [13].
Previous mutational screenings of DNAJC6 in PD patients were negative, but they
included only small numbers of patients or tested only a single variant [8,14,15]. The
role and frequency of DNAJC6 mutations in PD remained therefore unknown.
Here, by combining direct sequencing, linkage analysis, whole-exome sequencing,
and expression studies of the auxilin protein, we identify novel DNAJC6 pathogenic
mutations in patients with early-onset PD. Our data delineate a novel monogenic form
43
2.1
Chapter 2.1
of the disease and have broad implications for understanding the disease pathogenesis,
and for improving the diagnostic work-up and the genetic counselling of early-onset
PD.
Subjects and Methods
Subjects Included in the Study
We studied 274 unrelated probands with PD, including 92 cases with familial PD compatible
with autosomal recessive inheritance (mean onset age: 54.65; standard deviation [SD], 12.34;
range 19–84), and 182 patients affected by early-onset sporadic PD (mean onset age: 35.20;
SD, 7.98; range 13–50). The diagnosis of clinically definite PD was made according to the
criteria of the UK PD Society Brain Bank (with the exception that a positive family history of
PD was not considered an exclusion criterion) [16]. The patients were examined and recruited
in several movement disorder centers within the International Parkinsonism Genetics Network
(members of the network are listed in the Supplementary Material). The patients originated from
Italy (n=147), The Netherlands (n=78), Brazil (n=39), Portugal (n=7), Spain (n=2), and Turkey
(n=1). Written informed consent was obtained from all the included individuals. This study was
approved by appropriate Institutional Review Boards and complied with the legal requirements
of the different jurisdictions involved.
DNAJC6 Sequencing
Genomic DNA was isolated from peripheral blood or saliva using standard protocols. DNA
samples underwent Sanger sequencing of the entire DNAJC6 coding region and exons-intron
boundaries (MIM 608375). Exons and intron-exon boundaries of the DNAJC6 gene were
amplified using PCR (primers are reported in Suppl Table 1). Amplification reactions were
performed in a total volume of 20 µl, containing 1× FastStart Taq DNA Polymerase buffer, 200
µM of each dNTP, 0.5 µM forward primer, 0.5 µM reverse primer, 0.5 units FastStart Taq DNA
Polymerase (Roche, Basel, Switzerland) and 40 ng of genomic DNA. PCR conditions: 5 min
94°C initial denaturation followed by 30 or 28 cycles of 30 sec 94°C; 30 sec 60°C; 90 sec 72°C
with a final extension for 5 min 72°C.
PCR reactions were purified using 5 units ExoI and 1 unit Fast AP (Life Technologies, Carlsbad,
California), 45 min 37°C, 15 min 80°C. Direct sequencing was performed using Big Dye Terminator chemistry ver. 3.1 (Life Technologies) as recommended by the manufacturer. Dye terminators were removed using SephadexG50 (GE Healthcare, Little Chalfont, United Kingdom)
and loaded on an ABI 3130XL Genetic Analyzer (Life Technologies). For sequence analysis the
software packages Seqscape v2.6 (Life Technologies) and Sequencing Analysis v5.1 (Life Technologies) were used.
Variants identified were annotated according to the GenBank accession numbers
NM_001256864.1 and NP_001243793.1, corresponding to the longest protein isoform
(auxilin isoform 1).
44
DNAJC6 Mutations Associated with Early-Onset Parkinson’s Disease
Additional Genetic Studies in DNAJC6 Variant Carriers
Variants were selected for further investigations if they were I) exonic or with a possible effect
on splicing (< 100 base pairs [bp] from splice sites), II) rare in 1000 Genomes (allele frequency
< 0.005), and III) segregating with PD (if additional family members were available). Patients
carrying the selected variants were screened for mutations in genes known to cause autosomal
recessive, early-onset PD (parkin, PINK1, and DJ-1) by DNA Sanger sequencing and copy number
analysis using multiplex ligation-dependent probe amplification (MLPA) kits P051 and P052
(MRC Holland, Amsterdam, The Netherlands). Furthermore, in these patients we sequenced
DNAJC6 5’UTR (untranslated region), 3’UTR, and promoter regions (1000 bp upstream of
the translation initiation codon of NM_014787.3), and we performed DNAJC6 copy number
analysis by real-time quantitative PCR (qPCR). Five qPCR assays targeting different exons (2,
5, 10, 15, and 19) of the DNAJC6 gene were carried out. qPCR reactions contained 200nM
primer, 1× KAPA SYBR FAST qPCR Master Mix (Kapa Biosystems, Wilmington, Massachusetts), and 20 ng genomic DNA. All reactions were performed in triplicates on a Bio-Rad CFX96
Real-Time System. Data was analysed using CFX Manager Software v3.0 (Bio-Rad Laboratories,
Hercules, California). The assays SEL1L and RBM11 were used as reference targets assuming to
detect two chromosomal copies in all the tested samples. PCR conditions: 3 min 94°C initial
denaturation followed by 30 cycles of 5 sec 94°C; and 30 sec 60°C with relative fluorescence
units (RFU) data collection. qPCR primers are reported in Supplementary Table 2. All assays
were validated by testing linearity over 3 orders of magnitude and by observation of a single
peak in RFU data collected during a melting curve as a function of temperature. Intensities were
compared to at least two control DNA samples from unrelated or unaffected individuals and two
genome reference targets without copy number variations. All copy number values were defined
within the normal range for relative quantity values between 0.8 and 1.2.
Genetic Studies of Families With DNAJC6 Biallelic Mutations
We identified two familial and one sporadic PD probands carrying biallelic DNAJC6 variants.
To further support the pathogenicity of these variants, all available and consenting affected and
unaffected relatives were examined and genomic DNA samples were obtained. The two families
with biallelic DNAJC6 variants were investigated using homozygosity mapping, linkage analysis,
and whole-exome sequencing.
Homozygosity Mapping
DNA samples of the two families underwent genome-wide SNP array genotyping. For the
GPS-0313 family we used Illumina Infinium CytoSNP-850K BeadChip (Illumina, San Diego,
California; 851,274 SNPs at a median distance of 1.8 Kb); the DNA samples of the two parents
and the three siblings were included. For the PAL-50 family we employed Illumina HumanOmniExpress BeadChip (Illumina; 730,525 SNPs at a median distance of 2.1 kb); the DNA samples
of four siblings were included.
Analysis of homozygosity was performed using Nexus Copy Number, Discovery Edition, v7
(BioDiscovery, El Segundo, California). For the identification of regions of homozygosity shared
between affected siblings, the minimum length for homozygous runs was set to 1 Mb.
45
2.1
Chapter 2.1
Linkage Analysis
SNP array data were used to perform parametric multipoint linkage analysis with the software
MERLIN (v1.1.2) [17]. We assumed an autosomal recessive model, with the following settings:
markers allele frequencies set to equal; disease allele frequency set to 0.0001; and penetrances set
to 0.0001/0.0001/1.0.
Both the GPS-0313 and PAL-50 families are of white (Caucasian) ethnicity. In both families, the
parents of the patients denied consanguinity within at least two previous generations. However,
in both families the parents originate from very small villages in peripheral regions of The Netherlands and South Brazil, respectively. Their offspring show several extended (>3Mb) runs of
homozygosity, supporting parental consanguinity (Supplementary Table 3). For these reasons,
the two families were modeled as consanguineous with the two parents being third cousins. Since
the parents of the patients denied consanguinity within at least two previous generations, we
considered unlikely a closer consanguineous loop (first or second cousins), and assumed a thirdcousins marriage as the closest relatedness. Of note, more distant relatedness between the parents
would yield even higher logarithm of odds (LOD) scores at the linkage peak on chromosome 1.
Whole-Exome Sequencing
We performed whole-exome sequencing of one affected member of each family (GPS-0314
and PAL-54) at BGI Europe. Sequencing was performed using in-solution capturing (Agilent
SureSelect Human All Exon V5 50Mb, Agilent Technologies, Santa Clara, CA) and Illumina
HiSeq2000, 90 bases paired-end sequencing (Illumina). Reads were aligned to the human
reference genome hg19 (build GRCh37.p13) using Burrows-Wheeler Alignment Tool and
variants were called using Genome Analysis Toolkit (GATK) [18,19]. Variants were filtered using
Cartagenia Bench Lab NGS 4.0 (Agilent Technologies).
Genomic regions of interest were previously determined based on linkage and homozygosity
mapping. We selected all the variants that were I) within the regions of interest, II) homozygous, III) nonsynonymous exonic or close to splice sites (< 8 bases), and IV) rare in databases
(allele frequency lower than 0.01 in 1000 genomes, Exome Variant Server ESP6500SI-V2, and
dbSNP141).
Additional Linkage and Exome Data Analyses
To exclude the presence of additional novel likely-deleterious variants in the rest of the genome,
in both the GPS-0313 and PAL-50 families we also performed genome-wide linkage analysis
under autosomal recessive inheritance using the parameters set as above, but assuming no
parental relatedness.
Regions with positive values of LOD scores were then inspected using whole-exome sequencing
data (obtained in the GPS-0314 and the PAL-54 patient). We selected all the variants that were:
I) within linkage regions; II) nonsynonymous exonic or close to splice sites (< 8 bases); III) absent
from public databases (1000 genomes, Exome Variant Server ESP6500SI-V2, dbSNP141,
Exome Aggregation Consortium); and IV) homozygous or double heterozygous (heterozygous
variants located in genes with multiple heterozygous variants fulfilling criteria I, II, and III).
46
DNAJC6 Mutations Associated with Early-Onset Parkinson’s Disease
In Silico and Structural Analysis
The effect of the missense variants identified in the study was investigated using five different
protein damage prediction software: Sorting Intolerant From Tolerant (SIFT), PolyPhen-2,
likelihood ratio test (LRT), MutationTaster, and MutationAssessor [20-24]. Moreover, variants
with a possible effect on splicing were tested using five splicing prediction tools (SpliceSiteFinderlike, MaxEntScan, NNSPLICE, GeneSplicer, and Human Splicing Finder) integrated in Alamut
Visual v4.2 (Interactive Biosoftware, Rouen, France) [25-28].
The effect of the DNAJC6 missense mutation (p.Arg927Gly) was investigated using
publicly-available X-ray crystallographic structures of the bovine auxilin J domain. The sequences
of the auxilin protein homologs in Homo sapiens and Bos Taurus were aligned using the software
T-Coffee [29]. The homologs were completely identical in the domain under study (J domain).
Crystallographic structures of the auxilin J domain, and the complex between auxilin J domain
and the ATPase domain of Hsc70 (AMPPNP form) were retrieved from the Protein Data Bank
(PDB ID 1NZ6 and 2QWR) [30,31]. A modified structure with the mutant amino acid was
generated with the UCSF Chimera package (v1.10.1). Chimera is developed by the Resource
for Biocomputing, Visualization, and Informatics at the University of California, San Francisco
(supported by NIGMS P41-GM103311) [32]. Molecular graphics and analyses were performed
with the Pymol Molecular Graphics System v1.5.0.5 (Schrödinger, New York, New York).
Protein Expression Analysis
To investigate further the pathogenicity of the DNAJC6 variants identified, we analyzed the
expression of the auxilin protein in patient-derived fibroblasts. Primary fibroblasts were obtained
from skin biopsies of both the Dutch patients (GPS-0313 and GPS-0314) and one of the two
Brazilian patients with novel homozygous DNAJC6 mutations (PAL-54), one unaffected relative
of the Dutch family, who was a heterozygous mutation carrier (GPS-0315), and four unrelated
unaffected control donors. The expression of auxilin was studied in protein extracts from fibroblasts using Western blotting.
Fibroblast cell lines were expanded in growth medium (Dulbecco’s modified Eagle’s medium,
15% fetal calf serum, penicillin, streptavidin), and at 90% confluence lysed in RIPA lysis buffer
(150 mM NaCl, 1.0% IGEPAL CA-630, 0.5% sodium deoxycholate, 0.1% SDS, and 50 mM
Tris, pH 8.0.) containing protease inhibitor Complete (Roche). Lysates were cleared by spinning
10,000g for 10 min. For Western blotting, proteins were precipitated using the TCA protein
precipitation protocol, [33] separated on 4% to 15% Criterion TGX precast gels (Bio-Rad Laboratories), and transferred to nitrocellulose using the Trans-Blot® Turbo Transfer System (Bio-Rad
Laboratories). Blots were blocked using 5% Protifar (Nutricia, Amsterdam, The Netherlands) in
phosphate-buffered saline (PBS), 0.1% (v/v) TWEEN 20 (Sigma-Aldrich, St. Louis, Missouri).
Primary antibodies used were: rabbit anti-Auxilin (1:1000; a gift from Dr. Pietro De Camilli,
Yale University, New Haven, Connecticut; of note, this antibody is validated for the detection
of endogenous auxilin in Western blotting) [34]; rabbit anti-gamma-tubulin (1:2000; Sigma-Aldrich T5192); mouse anti-vinculin (Clone V284; 1:5000; Santa Cruz Biotechnology, Dallas,
Texas). After washing in PBS, 0.1% (v/v) TWEEN® 20, blots were incubated with fluorescently
conjugated goat anti-mouse (IRDye 800) and goat anti-rabbit (IRDye 680) secondary antibodies
47
2.1
Chapter 2.1
(LI-COR Biosciences, Lincoln, Nebraska). After washing in PBS, 0.1% (v/v) TWEEN 20, the
blots were imaged and analyzed using an Odyssey Imaging system (LI-COR).
Images were analyzed by removing the median background from the total intensity of each
band. Auxilin intensities were divided by intensities of the loading control vinculin and normalized using the sum of all data points in each replicate [35]. Four independent biological replicates of each individual were analyzed by signal quantification of multiple Western blots (12
technical replicates in total). The technical replicates belonging to the same biological replicate
were averaged and used for the statistical analysis. We performed one-way analysis of variance
(ANOVA) using eight groups, one for each subject (four unrelated healthy donors, GPS-0313,
GPS-0314, GPS-0315, and PAL-54). Post hoc tests between the control subjects and the
remaining individuals were conducted using pair-wise t tests and correcting for multiple comparisons (Bonferroni’s correction). Statistical analyses were conducted in R v3.2.1 (R Foundation for
Statistical Computing, Vienna, Austria).
Results
DNAJC6 Genetic Studies
The results of the DNAJC6 sequencing analysis in the 274 PD probands are displayed
in Table 1 and Figure 2C. We identified 14 samples with a total of 15 exonic or possible-splicing variants passing our filtering criteria. To exclude the possibility that any
of these variants was in linkage disequilibrium with other variants in the gene, we also
screened the entire DNAJC6 5’UTR, 3’UTR, and part of the promoter in these 14
samples, but no additional pathogenic variants were detected. Moreover, qPCR experiments could not detect any copy number variants in DNAJC6. Also, none of these 14
samples carried pathogenic variants in parkin, PINK1, and DJ-1.
Three probands carried DNAJC6 homozygous or double heterozygous variants (Fig 1).
GPS-0313 and PAL-50, two patients with familial early-onset PD, carried novel homozygous mutations in DNAJC6, while BR-2652, a patient with early-onset sporadic PD,
carried two heterozygous variants with possible effect on splicing. The clinical features
in these patients are reported in Table 2.
Family GPS-0313
A novel homozygous DNAJC6 c.2779A>G variant (predicted protein effect: p.Arg927Gly) was present in GPS-0313, a Dutch proband with early-onset, familial PD
compatible with autosomal recessive inheritance (Fig 2A). Sanger sequencing of his
affected sister (GPS-0314) revealed the same homozygous mutation, while his parents
and one healthy sibling were heterozygous mutation carriers (Fig 1A).
Genome wide-linkage analysis revealed a single, nearly-significant peak on chromosome
1 (maximum LOD: 3.07) including the DNAJC6 locus. In this region, homozygosity
48
Familial
Familial
Sporadic
Sporadic
Sporadic
Familial
Sporadic
Familial
Sporadic
Sporadic
Sporadic
Familial
Sporadic
Sporadic
GPS-0313
PAL-50
BR-2652
COMO-18
FI-03
IT53-RM590
GPS-60
IT59-RM613
GPS-107B
SAO-X35
COMO-05
PV-16
TH-20
TOR-6A
N/A
N/A
N/A
N/A
N/A
N/A
IT59-RM622
N/A
IT53-RM603
N/A
N/A
N/A
PAL-54
GPS-0314
relative ID
44
30
64
24
35
42
65/65
27
54/58
33
29
33
42/31
26
2
6
35
16
9
2/11
35
19/15
3
21
24
20/15
27/15
(y.rs) a
(y.rs)a
21/29
duration
Disease
onset
Age at
Comp Het
c.1468+83del
Het
c.2634+37A>G
c.2108-21A>G
c.995+86C>T
c.801-84C>A
c.513G>A (p.Lys171=)
c.1492T>A (p.Cys498Ser)
c.961A>G (p.Ile321Val)
c.397A>T (p.Met133Leu)
Het
Het
Het
Het
Het
Het
Het
Het
c.2517del (p.Phe839Leufs*22) Het
c.1855C>T (p.Arg619Cys)
Het
Comp Het
c.2038+3A>G
c.626T>C (p.Leu209Pro)
Hom
Hom
Zygosity
c.2223A>T (p.Thr741=)
c.2779A>G (p.Arg927Gly)
DNAJC6 variant
Biallelic mutations and mutations predicted to be deleterious are bolded.
a
For familial cases, the column reports data of proband/affected relative.
Hom = homozygous; Het = heterozygous; Comp Het = compound heterozygous; N/A = data not available.
Presentation
Proband ID
Affected
Table 1. PD patients and DNAJC6 variants reported in the study.
frequency
Allele
Absent
Absent
Absent
0.000400
0.001200
0.002803
Absent
0.000200
Absent
Absent
Absent
0.004010
Absent
Absent
Absent
Absent
0.000017
Absent
Absent
0.000305
0.001274
N/A
N/A
N/A
rs552001060
rs148204207
rs145329294
N/A
rs61757223
Absent
N/A
0.000281
N/A
Absent
N/A
0.000045
rs555883956
N/A
N/A
N/A
number
rs
Absent
Absent
Absent
Absent
Absent
1000 Genomes ExAC
frequency
Allele
DNAJC6 Mutations Associated with Early-Onset Parkinson’s Disease
2.1
49
Chapter 2.1
A
GPS-0313 Family
c.2779A>G (p.Arg927Gly)
5
GPS
0316
M/R
GPS
0313
M/M
GPS
0315
M/R
GPS
0317
M/R
GPS
0314
M/M
B
6
4
C
PAL-50 Family
c.2223A>T (p.Thr741=)
6
BR-2652 Family
c.1468+83del
c.2038+3A>G
4
c.1468+83del
c.2038+3A>G
PAL
50
M/M
PAL
55
R/R
PAL
56
M/R
PAL
54
M/M
4
c.1468+83del
c.2038+3A>G
BR
2652
R/M
M/R
BR
2652F
R/R
R/R
BR
2652A
R/M
R/R
Figure 1. Pedigrees of the PD patients with biallelic DNAJC6 mutations. The genotypes of the available
family members are reported. To protect subjects’ privacy, the gender of some individuals has been disguised. Black
symbols denote individuals affected by early-onset PD. PD = Parkinson’s disease; M = mutation; R = reference
(normal sequence).
mapping showed a 10.7-Mb run of homozygosity (from rs6660751 to rs10789400)
present in both affected siblings but absent from the unaffected family members (Fig
3A,D). Exome sequencing in one of the affected siblings, GPS-0314, achieved an
average coverage of 132×, with 91% of the target regions covered at least 10× (Supplementary Table 4). According to our filtering criteria, the p.Arg927Gly was the only
likely-pathogenic variant within the linkage region. Also, this variant was absent from
all the databases tested, including the Exome Aggregation Consortium (ExAC; 121,412
alleles), and was predicted to be pathogenic by all the prediction software (Supplementary Table 5) [36]. Notably, the variant was also absent in the Genome of The Netherlands (GoNL) database [37].
These two patients developed the first motor symptoms, bradykinesia and tremor, during
the third decade of life. In one of them, GPS-0313, parkinsonism signs developed after
six months of neuroleptic treatment, prescribed for psychotic disturbances since the age
of 21. Over the years, the motor disorder progressed slowly. Their magnetic resonance
imaging (MRI) and F-18 fluorodeoxyglucose/positron emission tomography (18F-FDG
PET) were unremarkable, whereas 18F-DOPA PET imaging revealed nigrostriatal
abnormalities compatible with PD. Supplementary Video 1and 2 display these patients
after 27 and 16 years of disease evolution. GPS-0313 had more severe parkinsonism and
was wheelchair-bound, while, remarkably, his sister had very little postural instability
and relatively preserved gait. Of note, both patients responded well to dopaminergic
therapies, but the dosages had to be limited to control psychiatric side effects.
50
DNAJC6 Mutations Associated with Early-Onset Parkinson’s Disease
Table 2. Clinical features in PD patients with biallelic DNAJC6 mutations.
Patient code
GPS-0313
GPS-0314
PAL-50
PAL-54
BR-2652
Gender
male
female
male
female
male
Age at last
48
44
62
46
57
~21
29
42
31
33
15
20
15
24
hand tremor
motor slowness
hand tremor
hand tremor
-
+
+
+
+
Hoehn-Yahr stage
5
3
3
3
4
Bradykinesia
+
+
+
+
+
Rest tremor
-
+
+
+
+
Rigidity
+
+
+
+
+
Postural instability
+
+
+
+
+
Levodopa response
+
+
+
+
+
Motor fluctuations
-
-
+
+
+
Levodopa-induced
-
-
+
+
+
+
+
+
-
-
Pyramidal signs
-
-
-
-
-
Cerebellar signs
-
-
-
-
-
Autonomic signs
-
-
-
-
-
Cognitive decline
-
-
+
-
-
Seizures
-
-
-
-
-
Brain MRI
normal
normal
normal
normal
normal
2.1
examination (y.rs)
Onset age (y.rs)
Disease duration (y.rs) ~27
Symptoms at onset
motor slowness
a
hand tremorsa
Asymmetric onset
Signs at examination
dyskinesias
Levodopa-induced
hallucinations
Brain F-Dopa PET
abnormal
abnormal
N/P
N/P
N/P
Brain FDG PET
normal
normal
N/P
N/P
N/P
Surgical therapies
none
none
none
STN-DBS
pallidotomy
18
b
b
+ present; - absent; N/P = not performed
a
parkinsonism symptoms initially emerged after treatment with neuroleptics
b
severe striatal uptake deficit, particularly at putamen level, as seen in PD
PD = Parkinson’s disease; MRI = magnetic resonance imaging; PET = positron emission tomography;
FDG = F-18 fluorodeoxyglucose; STN-DBS - treated with deep-brain stimulation of the subthalamic nuclei.
51
Chapter 2.1
A
B
c.2779A>G
p.Arg927Gly
c.2223A>T
p.Thr741=
G A AGGTGTA CGGGA AGGCTGT
A CCTTGGGACT CTAGGTACA A
C
c.397A>T
p.Met133Leu
Isoform 1
NM_001256864.1
Isoform 2
NM_014787.3
Isoform 3
NM_001256865.1
PVGMADLVTPEQVKKVYRKAVLVVHPDKATGQPYE
PVGMADLVTPEQVKKVYRKAVLVVHPDKATGQPYE
PVGMADLVTPEQVKKVYRKAVLVVHPDKATGQPYE
PVGMADLVTPEQVKKVYRRAVLVVHPDKATGQPYE
PVGMADLVTPEQVKKVYRKAVLVVHPDKATGQPYE
PVGMADLVTPEQVKKVYRRAVLVVHPDKATGQPYE
PVGMADLVTPEQVKKVYRRAVLVVHPDKATGQPYE
PVSMADLVTPEQVKKVYRRAVLVVHPDKATGQPYE
PVGMADLVTPEQVKKVYRKAVLVVHPDKATGQPYE
PVSMADLVTPEQVKKVYRKAVLVVHPDKATGQPYE
c.1855C>T
p.Arg619Cys
c.1468+83del
DNAJC6
Arg927
H.sapiens
P.troglodytes
M.mulatta
C.lupus
B.taurus
M.musculus
R.norvegicus
G.gallus
D.rerio
X.tropicalis
c.626T>C
p.Leu209Pro
c.2517del
p.Phe839Leufs*22
c.2038+3A>G
c.2223A>T
p.Thr741=
c.2779A>G
p.Arg927Gly
5'
3'
1kb
c.801-2A>G
c.2371C>T
p.Gln791*
Figure 2. DNAJC6 mutations. (A) Electropherograms of the two DNAJC6 homozygous mutations.(B)
Alignment of the auxilin protein homologs in different species (protein accession numbers are reported in
Supplementary Table 8). Protein homologs in different species were aligned using the software T-Coffee [29].
The arginine at position 927 is also indicated. (C) Schematic representation of the DNAJC6 transcripts with the
mutations previously identified in the two PARK19 families (bottom) and the novel mutations identified here in
patients with early-onset PD (top). Mutations detected in homozygous or compound heterozygous state are bolded.
PD=Parkinson’s disease.
Family PAL-50
A different, novel DNAJC6 homozygous variant, c.2223A>T (p.Thr741=; Fig 2A),
was present in PAL-50, a Brazilian proband with early-onset, familial PD compatible
with autosomal recessive inheritance. This variant is located 5 bases before the end
of exon 15. Sanger sequencing confirmed the mutation in homozygous state in the
affected sister (PAL-54), while two unaffected siblings were heterozygous (PAL-56)
or non-carriers (PAL-55; Fig 1B). Also in this family, linkage analysis identified a
single, nearly-significant peak on chromosome 1 (LOD score 3.18), corresponding to
a 16.1-Mb homozygous run (from rs3118027 to rs6672527; Fig 3B and D). Exome
sequencing in PAL-54 achieved an average coverage of 141× with 91 % of the target
regions covered at least 10× (Supplementary Table 4). Within this linkage region, exome
sequencing revealed 2 additional homozygous variants besides the DNAJC6 c.2223A>T:
a c.589A>G (p.Arg197Gly) in L1TD1 (NM_001164835.1) with an allele frequency of
0.003 in 1000 genomes and ExAC, and a novel c.2065G>A (p.Gly689ser) in HOOK1
(NM_015888.4). Both variants were excluded from further investigations because
predicted to be benign by all the protein damage prediction tools, and because conservation analysis showed that species other than humans carry the mutated amino acids
52
DNAJC6 Mutations Associated with Early-Onset Parkinson’s Disease
A
Chromosome
3
4
5
6
7
8
9 10 11 12
13
14
15
16
17
19 21
18 20 22
2.1
LOD
1
2
3
100 cM
1
2
LOD
−1
4
0
6
C
0
LOD
1
2
3
2
B
0
chr1 60
80
100
120
−1
Genomic position [cM]
DNAJC6
D
1
GPS
0313
0
1
GPS
0314
0
1
GPS
0315
0
1
GPS
0317
0
1
PAL
50
0
1
PAL
54
B-allele frequency
0
1
GPS
0316
0
1
PAL
55
0
1
PAL
56
0
chr1 54
56
58
60
62
64
66
68
Genomic position [Mb]
70
72
74
76
Figure 3. Genome-wide linkage analysis and homozygosity mapping. Genome-wide linkage analysis
of the GPS-0313 (A) and PAL-50 (B) families. Values of LOD score (red lines) where calculated for all the
autosomes applying a grid of 1 cM. (C) Linkage analysis of the chromosome 1 locus. Red line, GPS-0313 family;
blue line, PAL-50 family; black line, cumulative LOD score. Dashed lines mark the LOD score threshold of 3.0.
(D) Homozygosity mapping of GPS-0313 and PAL-50 families showing the overlapping regions of homozygosity
on chromosome 1. For each available sample of the two families, and for each probe in the region, probe B-allele
frequency (vertical axis) is represented as a function of its genomic position (horizontal axis). Dashed line indicates
the position of the DNAJC6 locus; red probes and orange areas indicate regions of homozygosity longer that 1 Mb.
LOD = logarithm of odds.
53
Chapter 2.1
(Supplementary Table 6). On the contrary, the DNAJC6 c.2223A>T variant was absent
from all the available databases, including the ExAC, and three out of the five prediction
software suggested a splicing effect: the introduction of an aberrant splicing acceptor
site one base after the constitutive donor site, at position 2227+1 (Supplementary Table
7). These two patients developed PD symptoms at the age of 42 and 31, respectively.
Their clinical course was typical for PD, including good response to levodopa and severe
motor complications (wearing off and dyskinesia). Due to these, PAL-54 also underwent
bilateral subthalamic nucleus DBS, with marked improvement. Supplementary Video 3
and 4 display the patients at the age of 62 and 46 years old.
Proband BR-2652
Two DNAJC6 heterozygous variants, a novel c.2038+3A>G and a c.1468+83del with
allele frequency of 0.004 in 1000 genomes, were present in BR-2652, a sporadic
Brazilian patient with early-onset PD (Fig 1C). Genotyping of available relatives was
consistent with the patient being compound heterozygous. Four out of five splicing
prediction software suggested that the c.2038+3A>G variant may cause loss of the
splicing donor site at position 2038, while the same analysis for the c.1468+83del did
not suggest an effect on splicing (Supplementary Table 7). This proband developed PD
at the age of 33, with right hand tremor. The parkinsonism was responsive to levodopa,
and displayed a prolonged course. He also received left side pallidotomy in 2006 and
right side pallidotomy in 2010. Supplementary Video 5 displays the patient at the age
of 44, after 11 years of disease.
Additional Linkage and Exome Data Analyses
Our linkage analysis assuming no parental relatedness identified positive LOD scores in
33 genomic regions (total 671 Mb, maximum LOD 0.23) in the GPS-0313 family, and
32 regions (total 383 Mb, maximum LOD 0.24) in the PAL-50 family. The analysis of
the whole exome sequencing data revealed the DNAJC6 c.2779A>G (p.Arg927Gly) as
the only variant surpassing our filtering criteria in GPS-0314.
In PAL-54, this analysis revealed only two variants, which were already identified by
our homozygosity-based strategy, within the chromosome 1 linkage peak: the DNAJC6
c.2223A>T and the HOOK1 c.2065G>A (p.Gly689ser). Additional novel variants were
not detected. Further inspection of the exome data revealed an heterozygous variant
in the glucocerebrosidase gene (GBA) (NM_000157.3: c.1088T>C, p.Leu363Pro) in
PAL-54, which was present also in PAL-50. GBA variants were not found in the exome
data of GPS-0314. Heterozygous GBA variants are known to be risk factors for the
development of PD. However, the p.Leu363Pro variant has never been reported in
patients with this disease.
54
DNAJC6 Mutations Associated with Early-Onset Parkinson’s Disease
Table 3. Statistical analysis of DNAJC6 expression in subject-derived fibroblasts.
GPS-0313
GPS-0314
GPS-0315
PAL-54
Control 1
6.6×10
-05
1.3×10
0.02702
1.7×10-04
Control 2
3.0×10-04
5.8×10-04
0.40799
0.00715
Control 3
2.3×10
-05
4.5×10
0.07662
6.0×10-04
Control 4
1.3×10-06
2.5×10-06
0.00600
3.1×10-05
-06
-05
2.1
The table displays the p values for pair-wise t test comparisons between controls
and subjects with DNAJC6 mutations. p values are not corrected for multiple
testing. Values surpassing the Bonferroni-corrected threshold (p < 0.0125) are
bolded.
Expression and Structural Studies of the Auxilin Protein
The results of our protein expression studies showed that, in primary fibroblasts, the
steady-state levels of auxilin are markedly and significantly lower in patients with homozygous DNAJC6 mutations than in control subjects (ANOVA p= 1.08×10-06, Table 3,
Fig 4A,B). The unaffected heterozygous mutation carrier (GPS-0315) had auxilin levels
intermediate between those of controls and patients.
Furthermore, our in-silico structural studies showed that the Arginine at position p.927
(replaced by Glycine in the patients of the Dutch family) is located in the J domain,
which is a known crucial functional domain of auxilin, and it is facing the interacting
partner, Hsc70. The mutation has a drastic steric effect and reduces the charge potential
of a positively-charged patch at the protein surface, a region possibly involved in the
interaction with Hsc70, or with other molecular partners (Fig 4C,D).
Discussion
Our study reveals a novel role for DNAJC6 mutations in early-onset PD. Two out of the
92 probands (2.2%) with familial PD compatible with autosomal recessive inheritance
carried novel homozygous mutations.
Several lines of evidence support the contention that the two homozygous variants identified are disease-causing. First, each of these variants is located within the highest, and
nearly-significant linkage peak obtained in the respective family (Fig 3A–C), and also
supported by the results of the homozygosity mapping. Second, within these linkage
regions, exome sequencing revealed no evidence of disease-causing variants in genes
other than DNAJC6. Moreover, novel pathogenic variants were not found by exome
sequencing in any other genomic loci detected in the two families under an autosomal
recessive mode of inheritance, without assuming parental consanguinity. Third, both
variants are absent from all the available databases (more than 120,000 alleles). Fourth,
the two variants are predicted to be deleterious by our in-silico analyses. Last, the
55
Chapter 2.1
B
Mutant
3
4
-0
PS
Wild type
G
G
C
PS
-0
31
40
-0
31
PA 5
L5
C
on 4
tro
l
C
on 1
tro
l
C
on 2
tro
l
C
on 3
tro
l4
0
γ-Tubulin
31
50
50
PS
Vinculin
130
100
G
Auxilin
110
150
Auxilin levels
[% of controls average]
130
G
KDa
PS
G -03
PS 15
G -03
PS 14
C -03
on 13
t
C rol
on 1
t
C rol
on 2
t
C rol
on 3
t
PA rol
L- 4
54
A
D
Auxilin
J domain
N
Arg927
Gly927
C
Hsc70
ATPase domain
N
Arg927
C
Figure 4. Protein analyses. (A) Auxilin expression in human fibroblasts; a representative Western blot is shown;
vinculin and γ-tubulin are included as loading controls. (B) Quantitative analysis of auxilin levels in patients’
fibroblasts, expressed as percentage of the average expression in four unrelated control individuals. All data are
from four biological replicates. The plots of the patients with DNAJC6 homozygous mutations are in red; the
heterozygous carrier of the Dutch missense mutation (GPS-0315) is in gray; the control subjects are in blue.(C)
Crystal structure of the bovine auxilin J domain with visualization of the electrostatic potential surface for the
wild type (left) and mutant (right) proteins (PDB accession n. 1NZ6). The replacement of the Arginine-927 with
a Glycine modifies a large positively-charged patch on the protein surface, with drastic sterical and electrostatic
consequences. The more positively charged regions are displayed in blue, while negatively charged regions are displayed in red; the position of the residue homolog to the human Arginine-927 is indicated. (D) Crystal structure
of the bovine auxilin J domain complexed with the Hsc70 ATPase domain and ATP analog (PDB accession n.
2QWR). The residue homolog to the human Arginine-927 is located in the auxilin domain (red) that interacts
with Hsc70 (blue). The Arginine-927 points towards Hsc70, suggesting a possible involvement in the interaction
between the two proteins.
steady-state levels of auxilin are markedly and significantly decreased in fibroblasts of
patients from both families (Fig 4A,B), providing compelling experimental evidence
for the pathogenicity of the DNAJC6 homozygous mutations. Of note, these results
also delineate a loss-of-function mechanism for these mutations, in keeping with the
recessive disease inheritance in these families.
The c.2779A>G (p.Arg927Gly) mutation is classified as disease causing by all the
prediction tools, it replaces an amino acid which has been highly conserved in evolution
(Fig 2B), and is located within the J domain. Moreover, the mutation appears to cause a
substantial modification of the biochemical properties of the domain (Fig 4C,D). This
could affect the protein stability, or binding to Hsc70, or both, and would be in keeping
56
DNAJC6 Mutations Associated with Early-Onset Parkinson’s Disease
with the observed decreased levels of auxilin protein in patients’ fibroblasts. The synonymous c.2223A>T is predicted to affect splicing by the majority of the prediction software
tested. To investigate the effect of the c.2223A>T, we also undertook DNAJC6 mRNA
expression analysis in fibroblasts, but the expression levels in control subjects were unreliable (data not shown). This is in keeping with the known fact that DNAJC6 mRNA
expression is essentially brain-specific and very low in any other tissues (EST profile in
NCBI UniGene: Hs.647643) [8,38]. Furthermore, we cannot exclude the possibility
that this synonymous variant is in linkage disequilibrium with another causative variant
located in introns or other non-coding regulatory elements of DNAJC6, which were not
detectable by our screening.
We also identified a sporadic patient with early-onset PD with two rare, compound
heterozygous DNAJC6 variants. Due to its novelty and the results of the splicing prediction software, the c.2038+3A>G variant appear to be likely deleterious. Instead, the
c.1468+83del is only moderately rare in databases, and the evidence of its splicing effect
is less compelling. Despite being unable to conclusively prove their pathogenicity, the
coexistence in the same patient of these two rare variants in trans is intriguing, and
suggests a pathogenic nature. Furthermore, as said before, we cannot exclude the possibility that one or both of these variants are in linkage disequilibrium with other causative
variants located in introns or other non-coding regulatory elements, which were not
detectable by the methods used here. Unfortunately, fibroblasts from this patient were
unavailable to perform protein expression studies.
We also detected 11 patients with rare heterozygous variants in DNAJC6, but sequencing
and gene dosage assays did not reveal additional mutations in these samples. Interestingly,
at least four of the heterozygous variants identified (p.Phe839Leufs*22, p.Leu209Pro,
p.Met133Leu, and p.Arg619Cys) are either novel or extremely rare in databases, and
are predicted to have a deleterious effect for the protein function (Supplementary Table
5), which is dramatic in the case of the frameshift variant (p.Phe839Leufs*22). These
variants might be a coincidental finding. However, we cannot exclude that additional
variants in trans were missed because they are outside the sequenced regions or undetectable by our assays. Another hypothesis is that single heterozygous variants in DNAJC6
act as risk factors for PD. Similar hypotheses have been proposed for single heterozygous
variants in other genes causing autosomal recessive PD [39]. It will be interesting to test
this hypothesis for DNAJC6 in future studies.
From the clinical standpoint, our patients with homozygous DNAJC6 mutations have a
very different phenotype than the PARK19 patients reported earlier. The Palestinian and
Turkish patients developed motor symptoms during childhood (before <11 years-old)
and were all wheelchair-bound or bedridden within ~10 years from onset. Furthermore,
57
2.1
Chapter 2.1
they displayed atypical clinical signs, such as mental retardation, seizures, and dystonia,
or they had poor or absent response to levodopa [8,9]. On the contrary, the patients
identified in our study developed parkinsonism in their third to fifth decade of life,
experienced a much slower disease progression, and they all responded well to dopaminergic therapies, including DBS in one case. These clinical features are compatible with
the diagnosis of early-onset PD, and overlap the phenotype of other monogenic forms
of PD caused by parkin, PINK1, or DJ-1 mutations. It is possible that the phenotypic
variability is due to the fact that the novel mutations identified here might allow some
residual activity to the auxilin protein. In other words, our data suggest a possible genotype-phenotype correlation in patients with DNAJC6 mutations, with complete loss-offunction variants causing the juvenile, atypical form (PARK19), while milder mutations
(described in our study) cause early-onset PD. The observation of severely reduced but
detectable residual levels of auxilin protein in the fibroblasts of our patients fits very well
with this hypothesis.
The established role of auxilin in the CME is crucial for neurons, that need CME for
the formation of new vesicles at the presynaptic terminal and synaptic vesicles recycling
[13]. Together with their molecular partner, Hsc70, auxilins are essential to complete
the last step of the CME, the uncoating of the vesicles after their separation from the
plasma membrane [13,40].
Our findings add to a growing body of evidence for the involvement of synaptic vesicles
recycling in PD. First, recessive mutations in SYNJ1, encoding another close molecular
partner of auxilin, cause early-onset parkinsonism (PARK20) [10,11]. The knockout
mice models of DNAJC6 and SYNJ1 display similar severe neurological phenotypes,
with defective synaptic vesicle recycling, and clathrin-coated vesicles accumulation
[41,42]. Second, common variants in GAK (encoding auxilin-2, a different ubiquitously expressed form of auxilin) have been identified by GWAS studies as a risk factor
for late-onset idiopathic PD [43,44]. Last, the protein products of other genes causing
Mendelian PD have been linked to the recycling of synaptic vesicles. The LRRK2
and parkin protein interact with endophilinA [45,46]; the VPS35 protein is part of
the retromer complex, that recycles membrane-associated protein to the trans-Golgi
network [47]; and α-synuclein has been implicated in several ways to vesicles recycling
and release [48,49].
In conclusion, the identification of early-onset PD patients with DNAJC6 mutations
changes the current view of DNAJC6, from being the cause of a rare and atypical
juvenile parkinsonism, to be the fourth gene involved in autosomal recessive, early-onset
PD. Screening of DNAJC6 is therefore warranted in all patients with early-onset PD
compatible with autosomal recessive inheritance, with broad implications for the diag58
DNAJC6 Mutations Associated with Early-Onset Parkinson’s Disease
nostic work-up and genetic counseling of patients. Our data provide further support for
disturbed synaptic vesicle endocytosis and trafficking in the pathogenesis of PD.
Acknowledgments
This work was supported by research grants from the Stichting ParkinsonFonds (The Netherlands) to AJWB and to VB, and by
the Beijing Genomics Institute (BGI). We thank all the patients involved in the study and their families. We also would like to
thank Dr. Pietro De Camilli for kindly providing the anti-auxilin antibody.
Author Contributions
SO, MQ, MF, JG, GJB, and JZ acquired, analysed, and interpreted the genetic data. JPMAR, JAS, HFC, CGB, AJWB, AJAK,
LBJ, ERB, CRMR, KLL, and members of the International Parkinsonism Genetics Network acquired, analysed, and interpreted
the clinical data. MM, FWV, and WM acquired, analysed, and interpreted the protein expression data. JW and VB conceived
and designed the study and interpreted the clinical and laboratory data. SO, MQ, JW, and VB drafted the manuscript. All the
authors critically revised the article for important intellectual content and approved the version to be published.
Potential Conflicts of Interest
Nothing to report.
References
1 Kitada T, Asakawa S, Hattori N, Matsumine H,
Yamamura Y, Minoshima S, et al. Mutations in the
parkin gene cause autosomal recessive juvenile parkinsonism. Nature. 1998;392:605-8.
8
2 Valente EM, Abou-Sleiman PM, Caputo V, Muqit
MM, Harvey K, Gispert S, et al. Hereditary earlyonset Parkinson’s disease caused by mutations in
PINK1. Science. 2004;304:1158-60.
mutations cause autosomal recessive, early-onset
parkinsonian-pyramidal
syndrome.
Neurology.
2009;72:240-5.
3 Bonifati V, Rizzu P, van Baren MJ, Schaap O, 9
Breedveld GJ, Krieger E, et al. Mutations in the DJ-1
gene associated with autosomal recessive early-onset
parkinsonism. Science. 2003;299:256-9.
Koroglu C, Baysal L, Cetinkaya M, Karasoy H, Tolun
A. DNAJC6 is responsible for juvenile parkinsonism
with phenotypic variability. Parkinsonism Relat
Disord. 2013;19:320-4.
Edvardson S, Cinnamon Y, Ta-Shma A, Shaag A,
Yim YI, Zenvirt S, et al. A deleterious mutation
in DNAJC6 encoding the neuronal-specific clathrin-uncoating co-chaperone auxilin, is associated with
juvenile parkinsonism. PLoS One. 2012; 7:e36458.
4 Ramirez A, Heimbach A, Grundemann J, Stiller B, 10 Quadri M, Fang M, Picillo M, Olgiati S, Breedveld
GJ, Graafland J, et al. Mutation in the SYNJ1 gene
Hampshire D, Cid LP, et al. Hereditary parkinsonism
associated with autosomal recessive, early-onset
with dementia is caused by mutations in ATP13A2,
Parkinsonism. Hum Mutat. 2013;34:1208-15.
encoding a lysosomal type 5 P-type ATPase. Nat
Genet. 2006;38:1184-91.
11 Krebs CE, Karkheiran S, Powell JC, Cao M, Makarov
V, Darvish H, et al. The Sac1 Domain of SYNJ1 Iden5 Paisan-Ruiz C, Bhatia KP, Li A, Hernandez D, Davis
tified Mutated in a Family with Early-Onset ProgresM, Wood NW, et al. Characterization of PLA2G6
sive Parkinsonism with Generalized Seizures. Hum
as a locus for dystonia-parkinsonism. Ann Neurol.
Mutat. 2013;34:1200-7.
2009;65:19-23.
6 Shojaee S, Sina F, Banihosseini SS, Kazemi MH, 12 Vauthier V, Jaillard S, Journel H, Dubourg C, Jockers
R, Dam J. Homozygous deletion of an 80 kb region
Kalhor R, Shahidi GA, et al. Genome-wide linkage
comprising part of DNAJC6 and LEPR genes on
analysis of a Parkinsonian-pyramidal syndrome
chromosome 1P31.3 is associated with early onset
pedigree by 500 K SNP arrays. Am J Hum Genet.
obesity, mental retardation and epilepsy. Mol Genet
2008;82:1375-84.
Metab. 2012;106:345-50.
7 Di Fonzo A, Dekker MC, Montagna P, Baruzzi
A, Yonova EH, Correia Guedes L, et al. FBXO7 13 Kononenko NL, Haucke V. Molecular mechanisms of
59
2.1
Chapter 2.1
presynaptic membrane retrieval and synaptic vesicle
reformation. Neuron. 2015;85:484-96.
27
14 Foo JN, Liany H, Tan LC, Au WL, Prakash KM, Liu
J, et al. DNAJ mutations are rare in Chinese Parkinson’s disease patients and controls. Neurobiol Aging. 28
2014; DOI:10.1016/j.neurobiolaging.2013.09.018.
Biol. 1997;4:311-23.
Pertea M, Lin X, Salzberg SL. GeneSplicer: a new
computational method for splice site prediction.
Nucleic Acids Res. 2001;29:1185-90.
Desmet FO, Hamroun D, Lalande M, Collod-Beroud
G, Claustres M, Beroud C. Human Splicing Finder:
an online bioinformatics tool to predict splicing
signals. Nucleic Acids Res. 2009;37:e67.
15 Jesus S, Gomez-Garre P, Carrillo F, Caceres-Redondo
MT, Huertas-Fernandez I, Bernal-Bernal I, et al.
Analysis of c.801-2A>G mutation in the DNAJC6 29 Notredame C, Higgins DG, Heringa J. T-Coffee: A
gene in Parkinson’s disease in southern Spain. Parkinnovel method for fast and accurate multiple sequence
sonism Relat Disord. 2014;20:248-9.
alignment. J Mol Biol. 2000;302:205-17.
16 Hughes AJ, Daniel SE, Kilford L, Lees AJ. Accuracy 30 Jiang J, Taylor AB, Prasad K, Ishikawa-Brush Y, Hart
of clinical diagnosis of idiopathic Parkinson’s disease:
PJ, Lafer EM, et al. Structure-function analysis of the
a clinico-pathological study of 100 cases. J Neurol
auxilin J-domain reveals an extended Hsc70 interacNeurosurg Psychiatry. 1992;55:181-4.
tion interface. Biochemistry. 2003;42:5748-53.
17 Abecasis GR, Cherny SS, Cookson WO, Cardon LR. 31 Jiang J, Maes EG, Taylor AB, Wang L, Hinck AP, Lafer
Merlin--rapid analysis of dense genetic maps using
EM, et al. Structural basis of J cochaperone binding
sparse gene flow trees. Nat Genet. 2002;30:97-101.
and regulation of Hsp70. Mol Cell. 2007;28:422-33.
18 Li H, Durbin R. Fast and accurate short read 32 Pettersen EF, Goddard TD, Huang CC, Couch GS,
alignment with Burrows-Wheeler transform. BioinforGreenblatt DM, Meng EC, et al. UCSF Chimera--a
matics. 2009;25:1754-60.
visualization system for exploratory research and
analysis. J Comput Chem. 2004;25:1605-12.
19 McKenna A, Hanna M, Banks E, Sivachenko A,
Cibulskis K, Kernytsky A, et al. The Genome Analysis 33 Link AJ, LaBaer J. Trichloroacetic acid (TCA)
Toolkit: a MapReduce framework for analyzing
precipitation of proteins. Cold Spring Harb Protoc.
next-generation DNA sequencing data. Genome Res.
2011;2011:993-4.
2010;20:1297-303.
34 Milosevic I, Giovedi S, Lou X, Raimondi A, Collesi
20 Kumar P, Henikoff S, Ng PC. Predicting the effects of
C, Shen H, et al. Recruitment of endophilin to clathcoding non-synonymous variants on protein function
rin-coated pit necks is required for efficient vesicle
using the SIFT algorithm. Nat Protoc. 2009;4:1073uncoating after fission. Neuron. 2011;72:587-601.
81.
35 Degasperi A, Birtwistle MR, Volinsky N, Rauch J,
21 Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE,
Kolch W, Kholodenko BN. Evaluating strategies to
Gerasimova A, Bork P, et al. A method and server
normalise biological replicates of Western blot data.
for predicting damaging missense mutations. Nat
PLoS One. 2014; e87293.
Methods. 2010;7:248-9.
36 Exome Aggregation Consortium (ExAC), Cambridge,
22Chun S, Fay JC. Identification of deleterious
MA (URL: http://exac.broadinstitute.org) [accessed
mutations within three human genomes. Genome
August 31st, 2015].
Res. 2009;19:1553-61.
37 Genome of the Netherlands C. Whole-genome
23 Schwarz JM, Rodelsperger C, Schuelke M, Seelow D.
sequence variation, population structure and demoMutationTaster evaluates disease-causing potential of
graphic history of the Dutch population. Nat Genet.
sequence alterations. Nat Methods. 2010;7:575-6.
2014;46:818-25.
24 Reva B, Antipin Y, Sander C. Predicting the functional 38 Ishikawa K, Nagase T, Nakajima D, Seki N, Ohira M,
impact of protein mutations: application to cancer
Miyajima N, et al. Prediction of the coding sequences
genomics. Nucleic Acids Res. 2011;39:e118.
of unidentified human genes. VIII. 78 new cDNA
25 Yeo G, Burge CB. Maximum entropy modeling of
short sequence motifs with applications to RNA
splicing signals. J Comput Biol. 2004;11:377-94.
39
26 Reese MG, Eeckman FH, Kulp D, Haussler D.
Improved splice site detection in Genie. J Comput
60
clones from brain which code for large proteins in
vitro. DNA Res. 1997;4:307-13.
Klein C, Lohmann-Hedrich K, Rogaeva E, Schlossmacher MG, Lang AE. Deciphering the role of heterozygous mutations in genes associated with parkin-
DNAJC6 Mutations Associated with Early-Onset Parkinson’s Disease
sonism. Lancet Neurol. 2007;6:652-62.
45 Matta S, Van Kolen K, da Cunha R, van den Bogaart
G, Mandemakers W, Miskiewicz K, et al. LRRK2
controls an EndoA phosphorylation cycle in synaptic
endocytosis. Neuron. 2012;75:1008-21.
40Ungewickell E, Ungewickell H, Holstein SE,
Lindner R, Prasad K, Barouch W, et al. Role of
auxilin in uncoating clathrin-coated vesicles. Nature.
46
1995;378:632-5.
41 Yim YI, Sun T, Wu LG, Raimondi A, De Camilli P,
Eisenberg E, et al. Endocytosis and clathrin-uncoating
defects at synapses of auxilin knockout mice. Proc
Natl Acad Sci U S A. 2010;107:4412-7.
Trempe JF, Chen CX, Grenier K, Camacho EM,
Kozlov G, McPherson PS, et al. SH3 domains from a
subset of BAR proteins define a Ubl-binding domain
and implicate parkin in synaptic ubiquitination. Mol
Cell. 2009;36:1034-47.
42 Cremona O, Di Paolo G, Wenk MR, Luthi A, Kim 47 Bonifacino JS, Hurley JH. Retromer. Curr Opin Cell
Biol. 2008;20:427-36.
WT, Takei K, et al. Essential role of phosphoinositide metabolism in synaptic vesicle recycling. Cell. 48 Nemani VM, Lu W, Berge V, Nakamura K, Onoa B,
Lee MK, et al. Increased expression of alpha-synu1999;99:179-88.
clein reduces neurotransmitter release by inhibiting
43 Pankratz N, Wilk JB, Latourelle JC, DeStefano AL,
synaptic vesicle reclustering after endocytosis. Neuron.
Halter C, Pugh EW, et al. Genomewide association
2010;65:66-79.
study for susceptibility genes contributing to familial
Parkinson disease. Hum Genet. 2009;124:593-605.
44 Nalls MA, Pankratz N, Lill CM, Do CB, Hernandez
DG, Saad M, et al. Large-scale meta-analysis of
genome-wide association data identifies six new risk
loci for Parkinson’s disease. Nat Genet. 2014;46:98993.
49 Burre J, Sharma M, Tsetsenis T, Buchman V,
Etherton MR, Sudhof TC. Alpha-synuclein promotes
SNARE-complex assembly in vivo and in vitro.
Science. 2010;329:1663-7.
Supplementary Materials
Members of The International Parkinsonism Genetics Network
V. Bonifati, J.A. Kievit, J.P.M.A. Rood, and A.J.W. Boon, Erasmus MC, Rotterdam, The Netherlands; B. van
de Warrenburg, C. Delnooz, A. Rietveld, and B. Bloem, Radboud University Medical Centre, Nijmegen,
The Netherlands; J. Ferreira and L. Correia Guedes, Institute of Molecular Medicine, Lisbon, Portugal;
E. Tolosa, Hospital Clínic de Barcelona, University of Barcelona, IDIBAPS, Centro de Investigacíon
Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Catalonia, Spain; M.M. Kurtiz,
Hospital Ruber Internacional, Madrid, Spain; J. Obeso, University of Navarra, Pamplona, Spain, and
Centro de Investigación en Redes sobre Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain;
M. Emre, H. Hanagasi, B. Bilgic, and Z. Tufekcioglu, Istanbul Faculty of Medicine, Istanbul, Turkey; B.
Elibol, Hacettepe University, Ankara, Turkey; J.A. Saute, C. Rieder, and L. Jardim, Hospital de Clínicas de
Porto Alegre, Brazil; Hsin F. Chien and Egberto R. Barbosa, University of São Paulo, Brazil; Chin-Song Lu,
Yah-Huei Wu-Chou, and Tu-Hsueh Yeh, Chang Gung Memorial Hospital, Taipei, Taiwan; M. Atadzhanov
and P. Kelly, University of Zambia, Lusaka, Zambia. Italian members: L. Lopiano, University of Torino;
C. Tassorelli, C. Pacchetti, and G. Nappi, IRCCS “Mondino”, Pavia; G. Riboldazzi and G. Bono, Insubria
University, Varese; C. Comi, University of Eastern Piedmont “Amedeo Avogadro”, Novara; A. Padovani
and B. Borroni, University of Brescia; F. Raudino, “Valduce” Hospital, Como; E. Fincati and L. Bertolasi,
University of Verona; M. Tinazzi and A. Bonizzato, Hospital “Borgo Trento”, Verona; C. Ferracci, Hospital
of Belluno; A. Dalla Libera, “Boldrini” Hospital, Thiene; P. Marini and F. Massaro, University of Firenze; A.
Federico, I. Taglia, and C. Battisti, University of Siena; R. Marconi, “Misericordia” Hospital, Grosseto; M.
Onofrj and A. Thomas, University of Chieti-Pescara; N. Vanacore, National Institute of Health, Roma; G.
Meco, G. Fabbrini, E. Fabrizio and A. Berardelli, “Sapienza” University, Roma; F. Stocchi, L. Vacca, IRCCS
“San Raffaele Pisana”, Roma; M. Picillo, A. De Rosa, C. Criscuolo, G. De Michele, and A. Filla, Federico
II University, Naples; P. Barone, CEMAND, University of Salerno; M. De Mari, C. Dell’Aquila, Hospital
61
2.1
Chapter 2.1
of Andria; G. Iliceto and P. Lamberti, University of Bari; V. Toni and G. Trianni, Hospital of Casarano; M.
Gagliardi, G. Annesi, and A. Quattrone, National Research Council, Institute of Molecular Bioimaging and
Physiology, Germaneto, and Institute of Neurology, Department of Medical Sciences, University Magna
Graecia, Catanzaro; V. Saddi, Neurology Division, S. Francesco Hospital, Nuoro; G. Cossu, and M. Melis,
Hospital S. Michele AOB “G. Brotzu”, Cagliari, Italy.
Supplementary videos are available on the journal website.
Supplementary Table 1. DNAJC6 sequencing primers.
Primer name
Sequence
Use
PCR cycles
DNAJC6_exon1A_F
DNAJC6_exon1A_R
CGGTTCCATGCCATGTTTTG
GAAAGCTGAATGACACAATATTTGGAAAC
PCR
PCR+Sequencing
30
DNAJC6_exon1B_F
DNAJC6_exon1B_R
GTGGCCGCTCCTTCTTTTCC
CCGGCTCTCTCGCAAACC
PCR
PCR+Sequencing
30
DNAJC6_exon2_F
DNAJC6_exon2_R
GAGGGACTGACCTGGGTCTCC
TGAGCCAGACAAGCAGTCAGC
PCR+Sequencing
PCR
28
DNAJC6_exon3-4_F
DNAJC6_exon3-4_R
GGAGGGGAGTCGAAACATGC
CACGTGCTTTAAGGGTATCTCTGAGG
PCR+Sequencing
PCR
30
DNAJC6_exon5_F
DNAJC6_exon5_R
TCACTTCCAGAAGATGTTGGTTGG
CACACAAAAGTCAATGGCATTTTTAGC
PCR+Sequencing
PCR
30
DNAJC6_exon6_F
DNAJC6_exon6_R
DNAJC6_exon6_seq_R
AATGAAAAGCACCCACAGTTAAGAGG
CCAATTCAGTTTTTGCATAGTTCAGG
TTGCTCCTCCTTCCCCTTCC
PCR
PCR
PCR+Sequencing
30
DNAJC6_exon7_F
DNAJC6_exon7_R
CATTGGGGAAGTACAGCAATCTCAG
TCATTTGAAAAAGGGCCATAACATC
PCR+Sequencing
PCR
30
DNAJC6_exon8_F
DNAJC6_exon8_R
CTGGGCGAACAGTCCTCTGG
TGGGGAGAAAAGATTCAGAAATACTCC
PCR+Sequencing
PCR
30
DNAJC6_exon9_F
DNAJC6_exon9_R
GGGTTGGAACAGGGCATCC
ACAGCCTGGTGCCATTGAGC
PCR+Sequencing
PCR
28
DNAJC6_exon10_F
DNAJC6_exon10_R
TGACTTCTGTGCCAAACATCATACC
GAGGGATATCTATTGGAGCCTGTCC
PCR+Sequencing
PCR
30
DNAJC6_exon11_F
DNAJC6_exon11_R
DNAJC6_exon11_seq_R
TGGCCTTAGGAGGTATGAGTCACC
GGCCTTTTGCATCAGTCTAAGAGC
ACAGGTGGGAGTCCGTGTGC
PCR
PCR
Sequencing
30
DNAJC6_exon12A_F
DNAJC6_exon12A_R
TGTTTGCTCAGGGCTGAATCC
ACCTCCACCCCCAAACAGG
PCR+Sequencing
PCR
30
DNAJC6_exon12B_F
DNAJC6_exon12B_R
GTCAAGAAGCCCAGCAAAAAGC
AGCTCAAACGTCCACTCTTACACTACC
PCR
PCR+Sequencing
30
DNAJC6_exon13_F
DNAJC6_exon13_R
GAAATTCTTGGAGTCCACTGTTAGCC
TGTTCTTTGGAATACCAATTTTGTGG
PCR+Sequencing
PCR
28
DNAJC6_exon14_F
DNAJC6_exon14_R
TGTCAAGCCTCATGTGAACAACC
TCATCAAGTGCATTGGGAAAAGC
PCR+Sequencing
PCR
30
DNAJC6_exon15_F
DNAJC6_exon15_R
TGTTCAAGGCCACCATTCACC
TTCATGCTACACATCCAGAATTAGGC
PCR+Sequencing
PCR
30
DNAJC6_exon16_F
DNAJC6_exon16_R
TTTTGCTCTCCTTCAGTGAAATGC
TTCAGGCAGTATGACAACATAGACAGG
PCR
PCR+Sequencing
30
DNAJC6_exon17_F
DNAJC6_exon17_R
GCATGAGGGTTAAGGACTGAATGC
TGTGTGCAACCTCCTCAGAGC
PCR+Sequencing
PCR
30
DNAJC6_exon18_F
DNAJC6_exon18_R
GATTGAGGAGGAGAAGCATTCCAG
TTAAAAATAGGCCATATGAACCAGCAC
PCR+Sequencing
PCR
28
DNAJC6_exon19_F
DNAJC6_exon19_R
GAAATGGCCAAATAGAAAAACATTTCC
TGTTACTGGAGTTTTTGGTTCATCTGC
PCR
PCR+Sequencing
30
62
DNAJC6 Mutations Associated with Early-Onset Parkinson’s Disease
Supplementary Table 2. qPCR primers.
DNAJC6 assays
Primer name
Sequence
DNAJC6_exon2_F
ACCAGGTACGCACATTCTTCC
DNAJC6_exon2_R
GAGCCAGACAAGCAGTCAGC
DNAJC6_exon5_F
CAACCTTTTTGCTGTGTGTCG
DNAJC6_exon5_R
TGACCAAACCAAACCATCACC
DNAJC6_exon10-11_F
GGTTTTGTTTTTCAGGATGAAGC
DNAJC6_exon10-11_R
ATGCCTGGGGTTGTCTGG
DNAJC6_exon15_F
TCCTTTTCAGGAGGCTTTGG
DNAJC6_exon15_R
GATGGGTGGGAGTACCATGC
DNAJC6_exon19_F
TCTACAGGCTACTGGGCAACC
DNAJC6_exon19_R
ATAAGGGCTTTTGGCCTTGG
Reference target assays
Primer name
Sequence
SEL1L_F
TTAAGGCCTTTTTCTAGCCATCC
SEL1L_R
ACCAAGTTTCCCACTCTTAAGTCC
RBM11_F
TCTTCAAAGTGTTTTGTGGTTGG
RBM11_R
TGAGAAAATTTAACAATGACCAGAAGG
2.1
Supplementary Table 3. Homozygous runs in GPS-0313 and PAL-50 families.
Sample ID
Homozygous runs
Amount
Total length (Mb)
GPS-0313
5
46.46
GPS-0314
5
24.39
GPS-0317
5
26.51
PAL-50
4
32.77
PAL-54
7
51.73
PAL-55
3
13.64
PAL-56
1
3.78
Only regions of homozygosity longer than 3 Mb are included.
Sex chromosomes are excluded.
63
Chapter 2.1
Supplementary Table 4. Whole-exome sequencing statistics.
Patient ID
GPS-0314
PAL-54
Bases in target regions
51,543,125
51,543,125
Reads mapped to genome
103,600,897
109,622,834
Reads uniquely mapped to genome
101,594,992
107,435,979
Reads uniquely mapped to target regions
78,991,916
84,134,111
Bases uniquely mapped to target regions(Mb)
6,679.86
7,138.77
Mean depth of target regions
131.79
140.92
Coverage of target regions (%)
96.81
96.67
Fraction of target regions covered >=4X (%)
94.27
93.96
Fraction of target regions covered >=10X (%)
91
90.54
Fraction of target regions covered >=20X (%)
86.02
85.58
Fraction of flanking regions covered >=4X (%)
50.45
48.15
Fraction of flanking regions covered >=10X (%)
35.91
34.32
Fraction of flanking regions covered >=20X (%)
26.08
25.2
Duplication rate (%)
12.21
12.76
Statistics regarding to depth and coverage in this table are calculated from de-duplicated data. Target region refers to the
regions that are covered by the designed probes. Flanking region refers to a 200bp window on both sides of each target region.
Duplication rate refers to the fraction of reads that are PCR duplicates, meaning all reads that have the same start and end for
both mates.
Supplementary Table 5. Prediction of the protein effects caused by the DNAJC6 missense variants.
Polyphen2
HDIV
Polyphen2
HVAR
LRT
MutationTaster
Mutation
Assessor
Sample ID
Variant
SIFT
GPS-0313
c.2779A>G
p.Arg927Gly
Deleterious Probably Damaging Probably Damaging Deleterious Disease Causing High
0
1.0
1.0
5.16×10-9
1.000
4.17
GPS-60
Deleterious Probably Damaging Probably Damaging Deleterious Disease Causing Medium
c.397A>T
0.975
0.979
1.42×10-7
1.000
2.595
p.Met133Leu 0.05
COMO-18
c.626T>C
p.Leu209Pro
Deleterious Probably Damaging Probably Damaging Deleterious Disease Causing High
1.000
3.665
0
1.0
0.999
4.62×10-7
IT59-RM613
c.961A>G
p.Ile321Val
Tolerated
1
Benign
0.025
Benign
0.067
Deleterious Disease Causing Neutral
2.26×10-9
1.000
0.025
GPS-107B
c.1492T>A
p.Cys498Ser
Tolerated
0.69
Benign
0.001
Benign
0.0
Neutral
5.61×10-2
FI-03
c.1855C>T
p.Arg619Cys
Tolerated
0.06
Probably Damaging Possibly Damaging
0.994
0.784
Polymorphism
1.000
Neutral
-0.895
Deleterious Disease Causing Low
1.77×10-4
1.000
1.39
For each variant, the results of the prediction software are reported, with predicted effect and score. Red, deleterious effects; green,
benign effects.
64
DNAJC6 Mutations Associated with Early-Onset Parkinson’s Disease
Supplementary Table 6. Prediction of the protein effect for the missense variants identified in PAL-54 by
whole-exome sequencing.
Gene
Variant
SIFT
Polyphen2
HDIV
Polyphen2
HVAR
LRT
MutationTaster
Mutation
Assessor
HOOK1
c.2065G>A
p.Gly689Ser
Tolerated
0.32
Benign
0.0
Benign
0.001
Neutral
3.77×10-1
Polymorphism Low
1.000
1.21
L1TD1
c.589A>G
p.Arg197Gly
Tolerated
0.51
Benign
0.0
Benign
0.0
N/A
Polymorphism Neutral
1.000
0
For each variant, the results of the prediction software are reported, with predicted effect and score. Red, deleterious effects; green,
benign effects.
Supplementary Table 7. Results of the splicing prediction analysis.
Variant
(Patient ID)
c.2223A>T
(PAL-50)
c.2038+3A>G
(BR-2652)
c.1468+83del
(BR-2652)
c.397A>T
(GPS-60)
c.513G>A
(SAO-X35)
c.801-84C>A
(COMO-05)
c.995+86C>T
(PV-16)
Splicing prediction method
[Range], Threshold
Effect
position
Type
c.2164
Donor
c.2227 ‡
Donor
c.2227+1
Acceptor
— ► 74.45
c.2038 ‡
Donor
c.2038+6
SSF
[0-100], ≥ 70
MaxEnt
[0-12], ≥ 0
NNSPLICE
[0-1], ≥ 0.4
GeneSplicer
[0-15], ≥ 0
HSF
[0-100], ≥ 65
3.28
=
1.00 ► 0.96
(-4.2%)
71.00
=
3.23
=
0.87 ► 0.88
(+1.6%)
2.51 ► 5.10
(+103.1%)
77.47 ► 81.03
(+4.6%)
86.16 ► 81.80 9.33 ► 7.61
(-5.1%)
(-18.4%)
0.99 ► 0.96
(-3.1%)
87.99 ► 87.33
(-0.7%)
Acceptor
71.50 ► 70.60
(-1.3%)
c.1468+61
Acceptor
82.32
=
4.78
=
0.68
=
4.19 ► 4.33
(+3.2%)
81.18
=
c.1468+92
Acceptor
c.1468+95
Acceptor
c.395-2
Donor
69.26 ► —
c.395 ‡
Acceptor
74.72
=
8.87 ► 9.42
(+6.2%)
0.82 ► 0.80
(-3.2%)
7.10 ► 7.63
(+7.4%)
82.23
=
c.543 ‡
Donor
86.02
=
10.17
=
1.00
=
2.01 ► 1.62
(-19.6%)
93.21
=
c.514
Acceptor
c.801-80
Acceptor
71.83 ► 71.27
(-0.8%)
c.801-77
Acceptor
73.22 ► 70.65
(-3.5%)
c.995+84
Donor
— ► 73.14
— ► 4.07
— ► 0.79
— ► 82.05
c.995+90
Acceptor
76.99
=
1.63 ► 1.75
(+7.3%)
80.51 ► 79.85
(-0.8%)
74.71
=
73.48 ► 74.11
(+0.9%)
1.07 ► —
75.89 ► 79.24
(+4.4%)
77.01 ► —
(Continues)
65
2.1
Chapter 2.1
(Follows)
Variant
(Patient ID)
c.2108-21A>G
(TH-20) c.2634+37A>G
(TOR-6A)
Effect
position
Type
c.2108-22
Donor
c.2164
Donor
c.2108-91
Acceptor
c.2108-20
Acceptor
c.2108 ‡
Splicing prediction method
[Range], Threshold
SSF
[0-100], ≥ 70
MaxEnt
[0-12], ≥ 0
NNSPLICE
[0-1], ≥ 0.4
GeneSplicer
[0-15], ≥ 0
HSF
[0-100], ≥ 65
— ► 67.34
3.28
=
1.00 ► 1.07
(+7.6%)
71.00
=
76.09
=
6.23
=
2.71 ► 2.49
(-8.1%)
79.03
=
— ► 80.23
— ► 5.33
Acceptor
97.07
=
11.45
=
0.98 ► 0.98
(-0.0%)
11.38 ► 10.20 96.31
(-10.4%)
=
c.2634 ‡
Donor
95.33
=
10.47
=
1.00
=
14.38 ► 14.35 97.89
(-0.2%)
=
c.2634+36
Donor
c.2634+46
Acceptor
— ► 84.38
— ► 71.64
70.24 ► 70.36
(+0.2%)
For each variant, the list of genomic sites with altered scores in a ±100bp interval is displayed. For each prediction tool, the score
in the reference and mutated sequences, as well as the percentage difference are reported. The complete range of possible values
for each method is reported; only values passing the method-specific threshold are reported.
‡, Natural splice site; SSF, SpliceSiteFinder-like; MaxEnt, MaxEntScan; HSF, Human Splicing Finder. The interval between
squared brackets indicates the range of values for each method; the cut-off value after the comma is the threshold applied for
filtering the results.
Supplementary Table 8. Accession numbers used for the auxilin protein alignment.
Species
DNAJC6 homolog accession number
H. sapiens
NP_001243793.1
P. troglodytes
XP_001161657.1
M. mulatta
XP_001090057.1
C. lupus
XP_546673.2
B. taurus
NP_777261.1
M. musculus
NP_001158055.1
R. norvegicus
NP_001101419.1
G. gallus
NP_001170886.1
D. rerio
XP_005165940.1
X. tropicalis
NP_001120147.1
66
2.2
Early-Onset Parkinsonism Caused by Alpha-Synuclein Gene
Triplication: Clinical and Genetic Findings in a Novel Family
S. Olgiati,*,1 A. Thomas,*,2 M. Quadri,1 G.J. Breedveld,1 J. Graafland,1
H. Eussen,1 H. Douben,1 A. de Klein,1 M. Onofrj,2 and V. Bonifati.1,#
Published in Parkinsonism & Related Disorders 2015, 21(8):981-6.
* These authors contributed equally to this work, as first authors;
# Corresponding author.
1) Department of Clinical Genetics, Erasmus MC, Rotterdam, The Netherlands;
2) Department of Neuroscience, Imaging, and Medical Sciences, G. d’Annunzio University, Chieti/Pescara, Italy; Aging
Research Centre, Ce.S.I., G. d’Annunzio University Foundation, Chieti, Italy.
© 2015 Elsevier Ltd. All rights reserved.
Reprinted with permission from Elsevier
67
78
2.3
An Exome Study of Parkinson’s Disease in Sardinia,
a Mediterranean Genetic Isolate
M. Quadri,*,1 X. Yang,*,2 G. Cossu,*,3 S. Olgiati,*,1 V.M. Saddi,4 G.J. Breedveld,1
L. Ouyang,2 J. Hu,2 N. Xu,2 J. Graafland,1 V. Ricchi,3 D. Murgia,3 L.C. Guedes,5
C. Mariani,6 M.J. Marti,7 P. Tarantino,8 R. Asselta,9 F. Valldeoriola,7 M. Gagliardi,8,10
G. Pezzoli,6 M. Ezquerra,11 A. Quattrone,8,10 J. Ferreira,5 G. Annesi,8 S. Goldwurm,6
E. Tolosa,7 B.A. Oostra,1 M. Melis,3 J. Wang,2,12,13,14,# V. Bonifati1,#
Published in Neurogenetics 2015, 16(1):55-64.
* These authors contributed equally to this work, as first authors;
# These authors contributed equally to this work, as senior authors and corresponding authors.
1) Department of Clinical Genetics, Erasmus MC, Rotterdam, The Netherlands;
2) BGI-Shenzhen, Shenzhen, China;
3) Neurology Service and Stroke Unit, General Hospital S. Michele AOB “G. Brotzu”, Cagliari, Italy;
4) Department of Neurology and Stroke Unit, San Francesco Hospital , ASL No.3 Nuoro, Italy;
5) Clinical Pharmacology Unit, Instituto de Medicina Molecular, University of Lisbon, Lisbon, Portugal;
6) Parkinson Institute, Istituti Clinici di Perfezionamento, Milan, Italy;
7) Neurology Service, Hospital Clínic de Barcelona, Universitat de Barcelona, IDIBAPS, Centro de Investigación Biomédica
en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Barcelona, Catalonia, Spain;
8) Institute of Molecular Bioimaging and Physiology, National Research Council, Section of Germaneto, Catanzaro, Italy;
9) Dipartimento di Biotecnologie Mediche e Medicina Traslazionale (BIOMETRA), Università degli Studi di Milano, Milan,
Italy;
10) Institute of Neurology, Department of Medical Sciences, University Magna Graecia, Catanzaro, Italy;
11) Laboratory of Neurodegenerative Disorders, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)-Fundació
Clinic, Barcelona, Spain;
12) Department of Biology, University of Copenhagen, Copenhagen, Denmark;
13) King Abdulaziz University, Jeddah, Saudi Arabia;
14) The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark.
© Springer-Verlag Berlin Heidelberg 2014
Reprinted with permission of Springer
79
PART III
Atypical Parkinsonism
104
3.1
Mutation in the SYNJ1 Gene Associated With
Autosomal Recessive, Early-Onset Parkinsonism
M. Quadri,*,1 M. Fang,*,2 M. Picillo,*,3 S. Olgiati,*,1 G.J. Breedveld,1
J. Graafland,1 B. Wu,2 F. Xu,2 R. Erro,3 M. Amboni,4,5 S. Pappatà,6
M. Quarantelli,6 G. Annesi,7 A. Quattrone,8 H.F. Chien,9
E.R. Barbosa,9 The International Parkinsonism Genetics Network,‡
B.A. Oostra,1 P. Barone,5,# J. Wang,2,10,11,12,# and V. Bonifati.1,#
Published in Human Mutation 2013, 34(9):1208-15.
* These authors contributed equally to this work, as first authors;
# These authors contributed equally to this work, as senior authors and corresponding authors;
‡ Members of the network are listed in the Appendix of this chapter.
1) Department of Clinical Genetics, Erasmus MC, Rotterdam, The Netherlands;
2) BGI-Shenzhen, Shenzhen, China;
3) Department of Neurological Sciences, University of Naples “Federico II”, Naples, Italy;
4) IDC Hermitage-Capodimonte Institute, Naples, Italy;
5) Department of Medicine and Surgery, CEMAND, University of Salerno, Italy;
6) Biostructure and Bioimaging Institute, National Research Council, Naples, Italy;
7) Institute of Neurological Science, National Research Council, Cosenza, Italy;
8) Institute of Neurology, University Magna Græcia, Catanzaro, Italy;
9) Department of Neurology, University of São Paulo, Brazil;
10) Department of Biology, University of Copenhagen, Copenhagen, Denmark;
11) King Abdulaziz University, Jeddah, Saudi Arabia;
12) The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark.
© 2013 Wiley Periodicals, Inc.
Reprinted with permission of John Wiley & Sons, Inc.
105
Chapter 3.1
Abstract
Autosomal recessive, early-onset parkinsonism is clinically and genetically heterogeneous.
Here, we report the identification, by homozygosity mapping and exome sequencing,
of a SYNJ1 homozygous mutation (p.Arg258Gln) segregating with disease in an Italian
consanguineous family with parkinsonism, dystonia, and cognitive deterioration.
Response to levodopa was poor, and limited by side effects. Neuroimaging revealed
brain atrophy, nigrostriatal dopaminergic defects, and cerebral hypometabolism. SYNJ1
encodes synaptojanin 1, a phosphoinositide phosphatase protein with essential roles in
the post-endocytic recycling of synaptic vesicles. The mutation is absent in variation
databases and in ethnically-matched controls, is damaging according to all prediction
programs, and replaces an amino acid that is extremely conserved in the synaptojanin 1
homologues and in SAC1-like domains of other proteins. Sequencing the SYNJ1 ORF
in unrelated patients revealed another heterozygous mutation (p.Ser1422Arg), predicted
as damaging, in a patient who also carries a heterozygous PINK1 truncating mutation.
The SYNJ1 gene is a compelling candidate for parkinsonism; mutations in the functionally linked protein auxilin cause a similar early-onset phenotype, and other findings
implicate endosomal dysfunctions in the pathogenesis. Our data delineate a novel form
of human Mendelian parkinsonism, and provide further evidence for abnormal synaptic
vesicle recycling as a central theme in the pathogenesis.
106
Mutation in the SYNJ1 Gene Associated With Autosomal Recessive, Early-Onset Parkinsonism
Introduction
Parkinson’s disease (PD), one of the most common neurodegenerative disorders, is
clinically characterized by the combination of slowness of movement (bradykinesia),
muscular rigidity, resting tremor, and postural instability, and a beneficial response to
levodopa or other dopaminergic therapies [1]. Brain pathology shows neuronal loss and
gliosis in the substantia nigra and other areas, with pathological protein aggregates (Lewy
bodies) in the surviving neurons. During the past few years, several Mendelian forms of
human parkinsonism have been identified [2]. Mutations in each of the following three
genes: parkin (PRKN, PARK2) [3], PTEN induced putative kinase 1 (PINK1, PARK6)
[4], and Parkinson protein 7 (DJ-1, PARK7) [5] can cause autosomal recessive forms
of early-onset parkinsonism, with a good and prolonged response to dopaminergic
therapy without additional, atypical clinical signs, and clinically diagnosed as earlyonset PD. Mutations in other genes, including ATPase type 13A2 (ATP13A2, PARK9)
[6], phospholipase A2, group VI (PLA2G6, PARK14) [7], F-box only protein 7 (FBXO7,
PARK15) [8,9], and DNAJC6 [10,11] cause early onset atypical parkinsonism, with
poor or absent response to levodopa and variable combinations of additional clinical
signs (such as dystonias, oculomotor disturbances, pyramidal signs, dementia). Here,
we report the identification, by homozygosity mapping and exome sequencing, of a
homozygous mutation in the SYNJ1 gene in an Italian consanguineous family with
early-onset atypical parkinsonism, delineating a novel disease form.
Subjects and Methods
This study was approved by the appropriate institutional review boards, and written informed
consent was obtained from all subjects. We studied one white consanguineous family originating
from Sicily (Italy) with two siblings affected by early-onset atypical parkinsonism, and no history
of neurological diseases in the previous generations; a pattern compatible with autosomal-recessive
inheritance (Fig. 1A). Additional DNA samples from 118 unrelated patients with early-onset
parkinsonism compatible with autosomal recessive inheritance, collected at several movement
disorders centers contributing to the International Parkinsonism Genetics Network, were also
analyzed. These patients originate from Italy (n=77), Brazil (n=31), The Netherlands (n=6),
Portugal (n=2), Spain (n=1) and Belgium (n=1). According to the UK Brain Bank diagnostic
criteria [12], most of these patients (n=92) had early-onset, typical PD; their average onset age
was 30 ± 5.3 years (range 13–36). The remaining 26 patients had early-onset parkinsonism in
combination with some atypical features (poor levodopa response, oculomotor disturbances,
pyramidal signs, or dementia); their average onset age was 34.1 ± 16 years (range 6–49). Last,
DNA samples from 180 unrelated control subjects from Southern Italy were analyzed.
107
3.1
Chapter 3.1
Figure 1. The Italian pedigree with SYNJ1 mutation and neuroimaging findings. Panel (A) shows the
Italian pedigree with the SYNJ1 mutation. The black symbols denote individuals affected by early-onset, atypical
parkinsonism. Imaging of the brain dopamine transporter density values (V˝3) using single photon emission
computed tomography (DaTSCAN-SPECT) shows severe presynaptic nigrostriatal dopaminergic deficit in the
NAPO-16 patient (B). A control subject (C) is also shown. Representative brain magnetic resonance images
(MRI, 3 Tesla) are shown for the NAPO-16 (D,H,L) and NAPO-17 (E,I,M) patient. Axial images at the
level of the basal ganglia acquired using a Fluid Attenuated Inversion Recovery (FLAIR) sequence (D,E) show
cortical atrophy, more severe in NAPO-16. Mid-sagittal images from Magnetization Prepared Rapid Acquisition
Gradient-Echo (MPRAGE) T1-weighted volumes (H,I) show thinning of the quadrigeminal plate in both patients
(more prominent in NAPO-16) (white arrows). Coronal FLAIR images (L,M) show diffuse hyperintensity of the
hyppocampi (white arrows) and enlarged lateral ventricles and sulci of the convexity, consistent with diffuse
atrophy, more severe in NAPO-16. Imaging of the brain metabolic activity using [18F]fluorodeoxyglucose and
positron emission tomography (PET) at the level of the basal ganglia (F,J,N) and of the centrum semiovale
(G,K,O); representative axial images of the NAPO-16 patient (F,G), the NAPO-17 patient (J,K), and a control
subject (N,O) are shown. Metabolic deficit are present in both patients, much more severe and widespread in
NAPO-16, in several cortical areas and the caudate nucleus, with relative preservation of the putamen.
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Mutation in the SYNJ1 Gene Associated With Autosomal Recessive, Early-Onset Parkinsonism
Neuroimaging Studies
In the patients of the Italian family, brain MRI studies were performed at 3 Tesla (TRIO,
Siemens Medical Solutions, Erlangen, Germany). Imaging sequences included a 3D T1-weighted
Gradient-echo volume with isotropic voxels, axial diffusion-weighted images, and axial and
coronal T2-weighted turbo spin-echo and fluid-attenuated inversion recovery (FLAIR) images.
Brain single photon emission computed tomography (SPECT) studies were performed 4 h
after intravenous administration of 185 MBq of [123I]FP-CIT (DaTSCAN; GE-Healthcare,
Wauwatosa, WI) using a dual-headed camera (E.CAM; Siemens Medical Systems, Hoffman
Estates, IL) equipped with low-energy high-resolution collimators. Outcome measures were the
specific-to-nondisplaceable binding ratio, V˝3, for the putamen and caudate. [18F] FDG-PET
brain images were acquired for 15 minutes in 3D mode between 45 and 60 minutes after intravenous administration of 213–250 MBq of [18F]fluorodeoxyglucose in resting state and eyes closed
condition using a whole body PET/CT scanner (Discovery LS; GE Medical System, Milwaukee,
WI). Images were reconstructed with iterative reconstruction (FORE-Iterative) and corrected for
attenuation using the CT scan. Outcome measure was relative glucose metabolism. Both SPECT
and PET images were spatially normalized in the Montreal Neurological Institute (MNI) space
using SPM2 (Wellcome Department of Imaging Neuroscience, London, UK).
Genetic Studies
Genomic DNA was isolated from peripheral blood with standard protocols. In the Italian family,
our screening of known genes causing early-onset parkinsonism (Sanger sequencing of all exons
and exon-intron boundaries of parkin, PINK1, DJ-1, ATP13A2, PLA2G6, FBXO7, DNAJC6,
and exon dosage of SNCA, parkin, PINK1, DJ-1, and ATP13A2) revealed no mutations. A
Filipin test for Niemann-Pick disease, type C, was also negative. We then performed genomewide homozygosity mapping using Illumina (San Diego, CA) HumanOmniExpress BeadChip
array data (730,525 SNPs at a median distance of 2.1 kb). Samples from the two affected siblings
(NAPO-16 and NAPO-17), two unaffected siblings (NAPO-24 and NAPO-25), and the unaffected mother (NAPO-18) were included. Data were analysed using ALLEGRO (version 1.2c)
through the graphical user interface EasyLinkage Plus v.5.08 [13]. SNPs with homozygous and
identical genotypes in all tested individuals were removed. The marker spacing was set at 0.3 cM
in a computation set of 100 markers. We specified an autosomal recessive-homozygous model
(HOMOZ scoring function), 0.0001 disease allele frequency, and complete penetrance. Homozygosity within the linkage peaks was further analyzed with Nexus Copy Number, Discovery
Edition, ver. 7 (BioDiscovery, El Segundo, CA).
Exome sequencing was performed in both affected individuals (NAPO-16 and NAPO-17) at
BGI-Shenzhen, using in-solution capturing (Agilent SureSelect Human All Exon 50Mb Kit;
Agilent Technologies, Santa Clara, CA) and Illumina HiSeq2000, 90 paired-end sequencing.
Reads were aligned to the human reference genome hg18 (build 36.3). SOAPaligner/SOAP2
aligner and SOAPsnp were used for SNP identification, while Burrows-Wheeler-Aligner and
GATK (Genome-Analysis-Tool-Kit) were used for indels detection. For the selection of candidate
variants, we first identified chromosomal regions of interest by homozygosity mapping. Within
these regions, the variants detected by the exome sequencing, that fulfilled all the following
109
3.1
Chapter 3.1
criteria were selected: homozygous, coding non-synonymous or affecting splice-sites, absent in
dbSNP132 and in 1000Genomes, present in both the affected individuals of the family. Last, we
performed Sanger sequencing of the positional candidate gene SYNJ1 (MIM #604297) in order
to confirm a mutation identified in the exome sequencing, and to screen additional patients.
Primers and PCR protocols are reported in the Supp. Table S1. The SYNJ1 variants are annotated
according to the GenBank accession numbers NM_003895.3 and NP_003886.3, corresponding
to the longest isoform (synaptojanin-1, isoform a), and numbered at nucleotide level from the
‘‘A’’ of the ATG-translation initiation codon. The variants identified have also been submitted to
a locus-specific database (http://www.lovd.nl/SYNJ1), and to the NCBI SNP database.
Six different web-tools were used in order to predict the functional effects of novel missense
variants: PolyPhen-2 (http://genetics.bwh.harvard.edu/pph2/), SIFT (http://sift.jcvi.org/www/
SIFT_enst_submit.html), PROVEAN (http://provean.jcvi.org/seq_submit.php), SNPs&GO
(http://snps-and-go.biocomp.unibo.it/snps-and-go/index.html), SNAP (http://rostlab.org/
services/snap/submit), and Mutation Taster (http://www.mutationtaster.org/).
Furthermore, for the analysis of the amino acid conservation, the closest evolutionary homologues of the human synaptojanin 1 protein, as well as additional human proteins containing
a suppressor-of actin1 (SAC1)-like phosphatase domain, were identified by blasting the human
sequence using the BLASTp program. Retrieved sequences were saved in FASTA format and
aligned using the program T-Coffee. GenBank accession numbers are reported in the Supp.
Table S2.
Results
Clinical Studies
Two healthy first cousins from a small Sicilian village had four offspring, two of whom
developed a progressive neurological disease. The first patient (NAPO-16) is a 47-yearold man with an uneventful birth and normal development until adulthood. At 22-years
old he developed rapidly progressive slowness of movements, fatigue, leg stiffness, gait
impairment, and involuntary movements in the upper arm. Within a few months he
was unable to work, and within one year he needed assistance in his daily living activities. Cognitive decline and severe dysarthria also developed, progressing to an anarthric
state in about 3 years. Levodopa therapy, initiated within 2 years since the onset of
symptoms, yielded unclear benefit on his motor disturbances, but had to be stopped due
to the emergence of disabling side-effects, such as oro-mandibular and feet dystonias,
that were further interfering with feeding and gait.
On our first examination, twenty-five years after the onset of the disease, he presented
with a stooped posture, and a severely disturbed, short-stepped, shuffling gait (Supp.
Videos 1-2). There was severe axial and four-limb rigidity, more marked on the left side.
The postural reflexes were also impaired. Dystonic postures were present in both hands
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Mutation in the SYNJ1 Gene Associated With Autosomal Recessive, Early-Onset Parkinsonism
(left>right). Irregular, wide-amplitude tremor was present at rest and during action in
the upper limbs; wide-amplitude jerky involuntary movements were also observed in
the facial muscles. His gaze was markedly staring. There was eyelid apraxia, and a clear
supranuclear vertical gaze limitation (downward more than upward). There was also
mild dysphagia, allowing feeding with semiliquid food. The deep tendon reflexes were
normal, and the Babinski sign was absent. There were no signs of cerebellar dysfunction. The score of the Unified Parkinson’s disease rating scale (UPDRS-III, motor scale)
was 78 (max score is 108, with higher scores indicating more severe parkinsonism). An
acute levodopa challenge (200 mg Melevodopa) yielded no relevant benefit, but induced
marked oro-mandibular and limbs dystonias, and hypotension (blood pressure dropped
from 110/80 to 80/60 mmHg). The Mini-Mental State Examination (MMSE) scale
could not be administered, due to the severe motor and cognitive disability. Three years
later, he remained untreated and his clinical picture was substantially unchanged.
The second patient (NAPO-17) is a 31-year-old woman with normal birth and developmental milestones. At 28 years-old she developed slowness of movements, speech and gait
difficulties, and involuntary movements in the right arm. Like in her brother, levodopa
therapy had to be stopped because of marked side-effects, such as oro-mandibular and
feet dystonias, which were seriously interfering with her feeding and gait.
On our first examination, three years after the onset of symptoms, she presented with
a stooped posture, and a short-stepped, shuffling gait. Postural reflexes were markedly
impaired. Her gaze was markedly staring and there was a moderate supranuclear vertical
gaze limitation (Supp. Videos 3-4). There was no eyelid apraxia. Almost continuous,
irregular, small-amplitude jerky involuntary movements were present in the lips and
tongue. Intermittent, wide-amplitude tremor appeared in the right arm at rest and
during action. There was moderate axial and four-limb rigidity (more marked on the
left side), and dystonic postures were present in the hands and feet (again more severe on
the left side). She also presented marked dysarthria, hypophonia, and mild dysphagia.
She was fed with a semiliquid diet. The deep tendon reflexes were brisk, but the Babinski
sign was absent. There were no signs of cerebellar dysfunction. The UPDRS-III score was
57. As seen in her brother, an acute levodopa challenge (200 mg Melevodopa) induced
severe dystonias in both the oro-mandibular district and in the feet, and a drop of her
blood pressure from 110/70 to 70/50 mmHg. The MMSE score was 26 (max 30, lower
scores indicating lower cognitive performance). Three years later, she was still untreated
by antiparkinsonian drugs; we noticed a worsening of the parkinsonism, of the trunk
dystonia, and of the supranuclear gaze palsy. Her UPDRS-III score had increased to 68,
while her MMSE score declined to 24/30.
In both patients, blood chemistry was normal. Moreover, in both siblings, needle elec111
3.1
Chapter 3.1
tromyography, sensory and motor nerve conduction studies, somatosensory evoked
potentials with median and tibial nerve stimulation were all unremarkable. Similarly,
in both sibs, the motor evoked potentials measured in both the upper and lower limbs
yielded normal amplitude and conduction time values.
Brain MRI showed diffuse cerebral cortex atrophy, which was more severe in the brother
(Fig. 1D,E). Diffuse hyperintensity of the hyppocampi and thinning of the midbrain
quadrigeminal plate was appreciated in both patients (more prominent in NAPO-16),
with no significant atrophy of the midbrain tegmentum (Fig. 1H,I,L,M). SPECT
imaging was available for the NAPO-16 patient, and it showed a marked decrease of
dopamine transporter density in the striatum (V˝3 reduced by 87% in the putamen
and by 77% in the caudate nucleus), compared to control values (Fig. 1B). Moreover,
FDG-PET imaging showed in both patients a relative cerebral cortex hypometabolism,
more evident in the NAPO-16 patient, and in the frontal and parieto-occipital regions,
and the caudate nuclei (Fig. 1F,G,J,K).
Genetic Studies
Genome-wide homozygosity mapping revealed five interesting loci, with LOD>2, on
chromosome 4, 7, 16, 19, and 21, that contained extended homozygous regions in the
two patients and not in the unaffected relatives (Fig. 2A,B). The exome sequencing
achieved an average coverage of 50× and 55× in the NAPO-16 and NAPO-17 patient,
respectively, with 71% and 73% of the exome target region covered at least 20×.
Within the five regions of extended homozygosity, the exome sequencing detected
two homozygous exonic or splice site variants, shared by the two patients,
which were not annotated in dbSNP132 or in the 1000 Genomes project.
The first variant is a c.931C>T substitution in ZNF439, leading to p.Arg311Cys. This
was confirmed by Sanger sequencing, and is not present in public databases. However,
it is consistently considered as benign by the prediction programs (with the exception of
SIFT, which yields a non-conclusive score) (Supp. Table S3). Although nothing is known
about the function of the protein encoded by this gene, inspection of the ZNF439
variation in the NHLBI Exome Variant Server (EVS) (http://evs.gs.washington.edu/
Figure 2. Homozygosity mapping, SYNJ1 mutation, and in-silico protein analyses. (A) Genome-wide
LOD score plot show five suggestive regions on chromosome 4, 7, 16, 19, and 21. (B) Homozygosity view of the
chromosome 21 region. A large homozygous area is shared by the NAPO16 and NAPO-17 patients. The same
region is heterozygous in the unaffected mother NAPO-18, and unaffected siblings (not shown). The position of
the SYNJ1 gene is indicated. (C) Electropherograms showing the SYNJ1 c.773G>A (p.Arg258Gln) mutation in
homozygous and heterozygous state, and a wild type, control sequence. (D) Schematic representation of the Synj1
protein domains (SAC – suppressor-of actin1-like phosphatase domain; INPP5c – inositol-5-phosphatase domain). (E) Aligmnent of the Synj1 protein homologs and of other human SAC1-like domain containing proteins.
112
Mutation in the SYNJ1 Gene Associated With Autosomal Recessive, Early-Onset Parkinsonism
3.1
113
Chapter 3.1
EVS/) reveals many frequent and rare deleterious variants (including truncating, splicesite, and frameshift mutations). Due to these evidences, we consider that ZNF439 is not
a candidate gene for our phenotype of interest.
The other homozygous variant was a c.773G>A substitution in the SYNJ1 gene (#MIM
604297), leading to the p.Arg258Gln missense change. The SYNJ1 gene encodes synaptojanin 1 (Synj1), a phosphoinositide phosphatase protein with a major, established
role in the clathrin-mediated endocytosis in the adult brain [14]. Two main isoforms
(synaptojanin 1, isoform a and b) of 170 and 145 kDa, containing 1612 and 1350 amino
acids, are encoded by SYNJ1 as a result of alternative splicing. Both are ubiquitously
expressed but the 145 kDa isoform (NM_203446.2, NP_982271.2) is the predominant isoform expressed in the presynaptic terminals in the brain [15,16]; it possesses
a distinct and shorter C-terminus compared to the 170 kDa isoform (NM_003895.3,
NP_003886.3). Two additional, shorter isoforms (synaptojanin 1, isoform c and
d) of unknown functional relevance, (NM_001160302.1, NP_001153774.1;
NM_001160306.1, NP_001153778.1) are also annotated in Genbank.
SYNJ1 represents therefore an excellent candidate for the phenotype of interest in our
study. Sanger sequencing confirmed the presence of the mutation in homozygous state
in both patients and in heterozygous state in the unaffected siblings and the mother (Fig.
2C). Furthermore, sequencing of all SYNJ1 exons and exon-intron boundaries revealed
no additional novel variants in these two patients. The p.Arg258Gln mutation is not
present in dbSNP132, the 1000 Genomes, nor in the EVS database (>13,000 chromosomes). We did not detect any carrier in control chromosomes from Calabria (Southern
Italy) (n=360). Furthermore, this mutation is damaging according to all prediction
programs (Supp. Table S3). The affected amino acid is located in the SAC1-like phosphatase domain of Synj1 (Fig. 2D), and is extremely conserved in the evolution of the
Synj1 homologues, and in the SAC1-like domain of heterologous proteins (Fig. 2E),
suggesting a very important functional role for this phosphatase domain.
In order to identify mutations in additional patients, we performed Sanger sequencing of
the entire SYNJ1 coding region (32 exons) and exon-intron boundaries in 118 unrelated
patients with early-onset parkinsonism compatible with autosomal recessive inheritance
(26 of them with atypical clinical features, and 92 with early-onset typical PD). Novel
variants, or known rare variants (MAF<1%), in homozygous or compound heterozygous state, were not detected in any of these patients. The complete list of the variants
detected, and their allelic frequencies are reported in the Supp. Table S4.
Only one novel heterozygous, non-synonymous variant was found, in one Brazilian
patient (SAO-X20), a c.4264A>C substitution in the last 3’exon, leading to a missense
change (p.Ser1422Arg), affecting only one of the two predominant Synj1 isoforms (170
114
Mutation in the SYNJ1 Gene Associated With Autosomal Recessive, Early-Onset Parkinsonism
kDa isoform a). This mutation was not present in dbSNP132, the 1000 Genomes, nor
in the EVS database, and is considered as damaging by several (though not all) prediction programs (Supp. Table S3). The amino acid replaced is incompletely conserved
in the evolution (Supp. Figure S1). Of note, our previous screening (including Sanger
sequencing and exon dosage) revealed that this patient carries also a heterozygous
truncating PINK1 mutation (p.Trp437Stop). Clinical data concerning this patient
are limited, and DNA samples from additional family members are not available. He
was diagnosed with sporadic early-onset PD (onset age 35 years) and was treated with
levodopa with satisfactory response. Atypical features are not reported.
Another Brazilian patient (SAO-X41) with early-onset typical PD (onset age 35) carried
a heterozygous c.3755G>A substitution, leading to a missense change (p.Arg1252Gln).
This variant is rare but present in dbSNP (Supp. Table S4), and considered as damaging
by some prediction programs (Supp. Table S3). In this patient, our previous screening
of the other genes causing early-onset PD revealed no mutations. All the remaining
non-synonymous variants detected in these 118 patients were either very frequent
polymorphisms present in dbSNP, or rare known variants considered to be neutral by
the prediction programs (Supp. Table S3 and Supp. Table S4).
Discussion
Endocytosis is essential for the neurons in order to recycle membranes of synaptic
vesicles. The phosphoinositides, especially the phosphatidylinositol-4,5-bisphosphate
[PI(4,5)P2] play important roles in the membrane traffic processes, such as endocytosis
and exocytosis, among other cellular functions [17]. PI(4,5)P2 is concentrated in discrete
regions on the cytosolic leaflet of the presynaptic membranes, where it functions in
the early steps of the clathrin-mediated endocytosis by recruiting the clathrin adaptor
proteins, which in turn, recruit clathrin, leading to the formation of the clathrin-coated
pits (CCPs). The maturation of the CCPs and their fission from the plasma membrane
is coordinated by the endophilins and the dynamin proteins. This chain of events
generates the clathrin-coated vesicles (CCVs). On their transit to target sites the CCVs
are uncoated by means of the coordinated action of Synj1, the chaperone Hsc70 and its
co-chaperone auxilin. Hsc70 and auxilin mediate the clathrin disassembly [18], while
Synj1 dephosphorylates PI(4,5)P2 , thereby shedding the adaptor proteins [19,20]. The
complete inactivation of the Synj1 gene in mice is associated with increased PI(4,5)
P2 levels, defective uncoating of clathrin, accumulation of CCVs, endocytic delays,
and severe neurological phenotypes (weakness, ataxia), leading to perinatal lethality
[19]. Strikingly similar phenotypes are seen in the auxilin and endophilin KO mice,
confirming a close functional interaction between these proteins [21,22].
115
3.1
Chapter 3.1
Several arguments support the contention that the SYNJ1 mutation is the cause of the
disease in the Italian family. First, the identification was based on an unbiased strategy
of genome-wide homozygosity mapping followed by exome sequencing. This is the
only homozygous, damaging mutation that survived our strict filtering steps. Second,
the mutation is absent from all variant databases and from our population-matched
controls. Third, it replaces an extremely conserved amino acid, not only in the evolution
of the synaptojanin 1, but also in other SAC1-like domains of different proteins, in
keeping with a very important role for the phosphatase activity of this domain (Fig. 2E).
Fourth, recessive mutations in the DNAJC6, encoding auxilin, the direct functional
partner of Synj1 in the uncoating of CCVs, cause a very similar human phenotype of
early-onset, atypical parkinsonism [10,11]. Fifth, the mice KO models of these two
genes (DNAJC6 and SYNJ1) show nearly identical phenotypes of CCVs accumulation, defective synaptic vesicle recycling, and severe neurological phenotypes [19,22].
Sixth, common variants in the GAK gene, encoding auxilin 2 (another co-chaperone for
Hsc70) were identified in GWAS studies and replicated with genome-wide significance
as risk factor for late-onset PD [23,24].
Additional evidences point to defective recycling of synaptic vesicles in the pathogenesis
of the more common forms of PD. The LRRK2 kinase protein, which is mutated in the
most common, known form of autosomal dominant PD, was recently identified as a
regulator of endophilinA functions in the synaptic vesicle endocytosis [25].
Moreover, parkin, an ubiquitin ligase mutated in the most common form of autosomal-recessive, early-onset PD, was also reported to interact with endophilinA [26]. The
biology of alpha-synuclein, a pre-synaptic protein mutated in rare autosomal dominant
forms of PD, and the major component of the aggregates found in all the patients
with typical late-onset PD, has several ties with the traffic and recycling of the synaptic
vesicles [27,28]. The VPS35 protein, mutated in a rare form of autosomal dominant
parkinsonism [29,30], is part of the retromer complex involved in retrograde sorting
steps on early endosomes [31,32].
The perinatal lethality in mice with complete ablation of Synj1, or of the directly interacting proteins auxilin or endophilins, indicates that these proteins are essential for
postnatal life. Interestingly, however, the partial loss of endophilin causes severe neurological defects, including epilepsy and neurodegeneration [21]. In keeping with this
evidence, the mutation identified in our Italian family is predicted to affect only the
SAC1-like phosphatase domain, leaving the other 5’-phosphatase domain intact. It will
be interesting to study the effects of this partial loss-of-function in model organisms.
Our sequencing of the entire SYNJ1 ORF in 118 patients with early-onset parkinsonism has not identified additional homozygous or compound heterozygous mutations,
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Mutation in the SYNJ1 Gene Associated With Autosomal Recessive, Early-Onset Parkinsonism
suggesting that SYNJ1 mutations might be an uncommon cause of disease. However,
only 26 of the 118 patients had atypical parkinsonism. Screening of additional patients
with similar phenotypes is therefore warranted, in order to identify the same or different
mutations and support our contention that SYNJ1 mutation is the cause of this novel
form of parkinsonism.
The interpretation of the heterozygous SYNJ1 mutation detected in the Brazilian patient
(SAO-X20) is difficult. We did not identify a compound-heterozygous mutation, but
did not screen for small heterozygous duplications or deletions. A compound-heterozygous mutation could also be present in the promoter or intronic regulatory elements.
Although the mutation replaces a conserved residue and is predicted to be damaging
in silico, it is located in the variable C-terminus region present in the 170 kDa, but
not in the 145 kDa isoform, this last being the major synaptic Synj1 isoform. This
mutation, could represent a coincidental finding, unrelated to the disease. However, its
presence together with a single heterozygous mutation in another gene causing earlyonset parkinsonism (PINK1) might suggest a possible additive or interactive effect in
the pathogenesis of the disease in this case. The other, rare SYNJ1 heterozygous variant
detected in the SAO-X41 patient might also be a coincidental finding.
The clinical phenotype in the Italian family is characterized by prominent, early-onset
parkinsonism, dystonias, oculomotor disturbances and cognitive decline. Pyramidal or
cerebellar signs were not present. The response of the motor symptoms to levodopa
was poor and limited by the appearance of disabling side effects, such as involuntary movements (dystonias) and severe hypotension. The results of the neuroimaging
investigations disclosed cerebral atrophy and hypometabolism, and severe nigrostriatal
dopaminergic defects. The atrophy of the quadrigeminal plate of the midbrain might
underlie the oculomotor disturbances, as this area is important for the control of eye
movements. The widespread cerebral cortical atrophy, the hippocampal sclerosis, and
the cortical and caudate nucleus hypometabolism are all in keeping with the cognitive
decline present in these patients. Because of these clinical and neuroimaging features,
this phenotype is clearly different from typical early-onset PD, and close to the atypical
early-onset forms, caused by mutations in the PARK9, PARK14, and PARK15 [33]. The
absence of pyramidal disturbances in the Italian family is of interest, as these are often
present in the other above-mentioned forms. Of note, a very similar phenotype of earlyonset parkinsonism and cognitive deterioration is associated with mutations in auxilin
(encoded by DNAJC6), a direct functional interactor of Synj1 [10,11]. Response to
levodopa and pyramidal disturbances are variable in the patients with auxilin mutations,
and seizures are also described in some. Seizures have not been observed in our patients
with Synj1 mutation. However, wide clinical variability is observed across all these forms
of atypical parkinsonism [8,33,34], and the identification of additional families with
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3.1
Chapter 3.1
SYNJ1 mutations is warranted to better define the phenotypic spectrum of this novel
form.
Conclusions
This work delineates a novel genetic form of early-onset parkinsonism associated with
Synj1 mutation, pointing to defective post-endocytic uncoating and recycling of the
synaptic vesicles in the pathogenesis. These findings might also have implications
for understanding the pathogenesis of more common forms of neurodegenerative
parkinsonisms.
Acknowledgments
The Authors thank all the patients and relatives for their contributions.
Grant sponsors
This study was supported by BGI-Shenzhen, China, and by research grants from the “Internationaal Parkinson Fonds” – Netherlands, and the Netherlands Organization for Scientific Research (NWO, VIDI grant, project n. 91786395) to V.B.
Disclosure of conflicts of interest
The authors declare that they have no conflicts of interest.
Appendix
Members of the International Parkinsonism Genetics Network
V. Bonifati, A. Maat-Kievit, J. Rood, and A. Boon, Erasmus MC, Rotterdam, The Netherlands; B. van
de Warrenburg, C. Delnooz, A. Rietveld, and B. Bloem, Radboud University Medical Centre, Nijmegen,
The Netherlands; J. Ferreira and L. Correia Guedes, Institute of Molecular Medicine, Lisbon, Portugal;
E. Tolosa, Hospital Clínic de Barcelona, Universitat de Barcelona, IDIBAPS, Centro de Investigacíon
Biomédicaen Red sobre Enfermedades Neurodegenerativas (CIBERNED), Catalonia, Spain; S. Janssens,
Ghent University Hospital, Ghent, Belgium; M. Emre, H. Hanagasi, and B. Bilgic, Istanbul Faculty of
Medicine, Istanbul, Turkey; B. Elibol, Hacettepe University, Ankara, Turkey; M. Socal and L. Jardim,
Hospital de Clínicas de Porto Alegre, Brazil; Hsin F. Chien and Egberto R. Barbosa, University of São Paulo,
Brazil; Chin-Song Lu, Yah-Huei Wu-Chou, and Tu-Hsueh Yeh, Chang Gung Memorial Hospital, Taipei,
Taiwan; Masharip Atadzhanov and Paul Kelly, University of Zambia, Lusaka, Zambia. Italianmembers:
L. Lopiano,University of Torino;C. Tassorelli, C. Pacchetti, andG.Nappi,IRCCS “Mondino”, Pavia; G.
Riboldazzi and G. Bono, Insubria University, Varese; A.Padovani and B. Borroni, University of Brescia; F.
Raudino, Hospital “Valduce”, Como; A. Di Fonzo, University of Milan, Italy; A. Volonte’, San Raffaele
Institute, Milan; E. Fincati and L. Bertolasi, University of Verona; M. Tinazzi and A. Bonizzato, Hospital
“Borgo Trento”, Verona; C. Ferracci, Hospital of Belluno; A. Dalla Libera, “Boldrini” Hospital, Thiene;
P. Cortelli and S. Capellari, University of Bologna; P. Marini and F. Massaro, University of Firenze; A.
Federico, I. Taglia, and C. Battisti, University of Siena; R. Marconi, “Misericordia” Hospital, Grosseto; M.
Onofrj and A. Thomas, University of Chieti-Pescara; N. Vanacore, National Institute of Health, Roma;
G.Meco, G. Fabbrini, E. Fabrizio, M. Manfredi, and A. Berardelli, “Sapienza” University, Roma; F. Stocchi,
L. Vacca, IRCCS “San Raffaele Pisana”, Roma; G. De Michele, C. Criscuolo, L. Santoro, A. Filla, Federico
II University, Naples; M. DeMari, C. Dell’Aquila,Hospital of Andria; G. Iliceto and P. Lamberti, University
of Bari; V. Toni and G. Trianni, Hospital of Casarano; M. Gagliardi and G. Annesi, Institute of Neurological
118
Mutation in the SYNJ1 Gene Associated With Autosomal Recessive, Early-Onset Parkinsonism
Science, National Research Council, Cosenza; A. Quattrone, Institute of Neurology, University Magna
Græcia, Catanzaro; V. Saddi, Neurology Division, S. Francesco Hospital, Nuoro; G. Cossu, and M. Melis,
Hospital S. Michele AOB “G. Brotzu”, Cagliari, Italy.
References
1
2
Fahn S. Description of Parkinson’s disease as a clinical
syndrome. Ann N Y Acad Sci. 2003;991:1-14.
A. DNAJC6 is responsible for juvenile parkinsonism
with phenotypic variability. Parkinsonism Relat
Disord. 2013;19:320-4.
Singleton AB, Farrer MJ, Bonifati V. The genetics of
Parkinson’s disease: progress and therapeutic implica- 12 Hughes AJ, Daniel SE, Kilford L, Lees AJ. Accuracy
of clinical diagnosis of idiopathic Parkinson’s disease:
tions. Mov Disord. 2013;28:14-23.
a clinico-pathological study of 100 cases. J Neurol
3 Kitada T, Asakawa S, Hattori N, Matsumine H,
Neurosurg Psychiatry. 1992;55:181-4.
Yamamura Y, Minoshima S, et al. Mutations in the
parkin gene cause autosomal recessive juvenile parkin- 13 Hoffmann K, Lindner TH. easyLINKAGE-Plus--automated linkage analyses using large-scale SNP data.
sonism. Nature. 1998;392:605-8.
Bioinformatics. 2005;21:3565-7.
4 Valente EM, Abou-Sleiman PM, Caputo V, Muqit
MM, Harvey K, Gispert S, et al. Hereditary early- 14McPherson PS, Garcia EP, Slepnev VI, David
C, Zhang X, Grabs D, et al. A presynaptic inosionset Parkinson’s disease caused by mutations in
tol-5-phosphatase. Nature. 1996;379:353-7.
PINK1. Science. 2004;304:1158-60.
5 Bonifati V, Rizzu P, van Baren MJ, Schaap O, 15 Perera RM, Zoncu R, Lucast L, De Camilli P, Toomre
D. Two synaptojanin 1 isoforms are recruited to clathBreedveld GJ, Krieger E, et al. Mutations in the DJ-1
rin-coated pits at different stages. Proc Natl Acad Sci
gene associated with autosomal recessive early-onset
U S A. 2006;103:19332-7.
parkinsonism. Science. 2003;299:256-9.
6 Ramirez A, Heimbach A, Grundemann J, Stiller B, 16 Ramjaun AR, McPherson PS. Tissue-specific alternative splicing generates two synaptojanin isoforms
Hampshire D, Cid LP, et al. Hereditary parkinsonism
with differential membrane binding properties. J Biol
with dementia is caused by mutations in ATP13A2,
Chem. 1996;271:24856-61.
encoding a lysosomal type 5 P-type ATPase. Nat
Genet. 2006;38:1184-91.
17 Di Paolo G, De Camilli P. Phosphoinositides in
cell regulation and membrane dynamics. Nature.
7 Paisan-Ruiz C, Bhatia KP, Li A, Hernandez D, Davis
2006;443:651-7.
M, Wood NW, et al. Characterization of PLA2G6
as a locus for dystonia-parkinsonism. Ann Neurol. 18Ungewickell E, Ungewickell H, Holstein SE,
2009;65:19-23.
Lindner R, Prasad K, Barouch W, et al. Role of
auxilin in uncoating clathrin-coated vesicles. Nature.
8 Di Fonzo A, Dekker MC, Montagna P, Baruzzi
1995;378:632-5.
A, Yonova EH, Correia Guedes L, et al. FBXO7
mutations cause autosomal recessive, early-onset 19 Cremona O, Di Paolo G, Wenk MR, Luthi A, Kim
parkinsonian-pyramidal
syndrome.
Neurology.
WT, Takei K, et al. Essential role of phosphoinos2009;72:240-5.
itide metabolism in synaptic vesicle recycling. Cell.
1999;99:179-88.
9 Shojaee S, Sina F, Banihosseini SS, Kazemi MH,
Kalhor R, Shahidi GA, et al. Genome-wide linkage 20 Mani M, Lee SY, Lucast L, Cremona O, Di Paolo G,
analysis of a Parkinsonian-pyramidal syndrome
De Camilli P, et al. The dual phosphatase activity of
pedigree by 500 K SNP arrays. Am J Hum Genet.
synaptojanin1 is required for both efficient synaptic
2008;82:1375-84.
vesicle endocytosis and reavailability at nerve
terminals. Neuron. 2007;56:1004-18.
10 Edvardson S, Cinnamon Y, Ta-Shma A, Shaag A,
Yim YI, Zenvirt S, et al. A deleterious mutation 21 Milosevic I, Giovedi S, Lou X, Raimondi A, Collesi
in DNAJC6 encoding the neuronal-specific clathC, Shen H, et al. Recruitment of endophilin to clathrin-uncoating co-chaperone auxilin, is associated with
rin-coated pit necks is required for efficient vesicle
juvenile parkinsonism. PLoS One. 2012;7:e36458.
uncoating after fission. Neuron. 2011;72:587-601.
11 Koroglu C, Baysal L, Cetinkaya M, Karasoy H, Tolun 22 Yim YI, Sun T, Wu LG, Raimondi A, De Camilli P,
119
3.1
Chapter 3.1
Eisenberg E, et al. Endocytosis and clathrin-uncoating
defects at synapses of auxilin knockout mice. Proc
Natl Acad Sci U S A. 2010;107:4412-7.
clein reduces neurotransmitter release by inhibiting
synaptic vesicle reclustering after endocytosis. Neuron.
2010;65:66-79.
23 Nalls MA. Imputation of sequence variants for iden- 29 Vilarino-Guell C, Wider C, Ross OA, Dachsel JC,
Kachergus JM, Lincoln SJ, et al. VPS35 mutations in
tification of genetic risks for Parkinson’s disease: a
Parkinson disease. Am J Hum Genet. 2011;89:162-7.
meta-analysis of genome-wide association studies.
International Parkinson Disease Genomics Consor- 30 Zimprich A, Benet-Pages A, Struhal W, Graf E,
tium. Lancet. 2011;377:641-9.
Eck SH, Offman MN, et al. A mutation in VPS35,
encoding a subunit of the retromer complex, causes
24 Pankratz N, Wilk JB, Latourelle JC, DeStefano AL,
late-onset Parkinson disease. Am J Hum Genet.
Halter C, Pugh EW, et al. Genomewide association
2011;89:168-75.
study for susceptibility genes contributing to familial
Parkinson disease. Hum Genet. 2009;124:593-605.
31 Bonifacino JS, Hurley JH. Retromer. Curr Opin Cell
Biol. 2008;20:427-36.
25 Matta S, Van Kolen K, da Cunha R, van den Bogaart
G, Mandemakers W, Miskiewicz K, et al. LRRK2 32 Popoff V, Mardones GA, Tenza D, Rojas R, Lamaze
controls an EndoA phosphorylation cycle in synaptic
C, Bonifacino JS, et al. The retromer complex and
endocytosis. Neuron. 2012;75:1008-21.
clathrin define an early endosomal retrograde exit site.
J Cell Sci. 2007;120:2022-31.
26 Trempe JF, Chen CX, Grenier K, Camacho EM,
Kozlov G, McPherson PS, et al. SH3 domains from a 33 Bonifati V. Autosomal recessive parkinsonism. Parkinsubset of BAR proteins define a Ubl-binding domain
sonism Relat Disord. 2012;18 Suppl 1:S4-6.
and implicate parkin in synaptic ubiquitination. Mol
34 Santoro L, Breedveld GJ, Manganelli F, Iodice R,
Cell. 2009;36:1034-47.
Pisciotta C, Nolano M, et al. Novel ATP13A2
27 Ben Gedalya T, Loeb V, Israeli E, Altschuler Y, Selkoe
(PARK9) homozygous mutation in a family with
DJ, Sharon R. Alpha-synuclein and polyunsaturated
marked phenotype variability. Neurogenetics.
fatty acids promote clathrin-mediated endocytosis and
2011;12:33-9.
synaptic vesicle recycling. Traffic. 2009;10:218-34.
28 Nemani VM, Lu W, Berge V, Nakamura K, Onoa B,
Lee MK, et al. Increased expression of alpha-synu-
120
Mutation in the SYNJ1 Gene Associated With Autosomal Recessive, Early-Onset Parkinsonism
Supporting online material
PCR Protocol
The amplification reactions were performed in a total volume of 20 µl, containing 1× FastStart Taq DNA
Polymerase buffer, 200 µM of each dNTP, 0.5 µM forward primer, 0.5 µM reverse primer, 0.5 unit FastStart
Taq DNA Polymerase (Roche Diagnostics) and 20 ng genomic DNA. SYNJ1_1F/R was amplified with
addition of 1XGC-melt (Roche Diagnostics).
The PCR conditions were as follows: initial denaturation, 7 min 30 sec at 96ºC, followed by 9 cycles of: 30
sec denaturation at 96ºC; 30 sec annealing (1st cycle at 70ºC, with 1ºC/cycle decrease); 1 min extension at
72ºC. Then, 25 cycles of: 30 sec denaturation at 96ºC; 30 sec annealing at 60ºC; 1 min extension at 72ºC.
Final extension: 5 min at 72ºC. The PCR reactions were purified using 5 units ExoI (Fermentas) and 1 unit
Fast AP (Fermentas), 45’ 37oC, 15’ 80oC.
Direct sequencing was performed using Big Dye Terminator chemistry ver. 3.1 (Applied Biosystems) as
recommended by the manufacturer. Dye terminators were removed using SephadexG50 (GE Healthcare)
and loaded on an ABI 3130XL Genetic Analyzer (Applied Biosystems). For sequence analysis the software
packages Sequence Analysis version 5.3 (Applid Biosystems) and Seqscape version 2.6 (Applied Biosystems)
were used.
H.
P.
P.
C.
O.
M.
G.
X.
sapiens
troglodytes
abelii
lupus
cuniculus
musculus
gallus
tropicalis
3.1
SSVSCMPTMPPIPARSQSQENMRSSPNPFIT
SSVSCMPTMPPIPARSQSQENMRSSPNPFIT
XVCKCMPTMPPIQLES-SQEXYAKFSNLFIT
SSVSC---MPPIPARSKSQENMRCSPNPFIT
GSVSCMPTMPPIPARSKSQENMRSSPNPFIT
SSVSCMPTRPPGPEESKSQESMGSSANPFPNTVSCMPAMPPAPTLNISQEHTRSSPNPFVT
SGITCMATLPPVPARTGFQDSTRISPNPFVN
Supp. Figure S1. Analysis of the conservation of Ser1422 in the Synj1 protein homologs.
Note. In other species, such as R. norvegicus, D. rerio, D. melanogaster, B. taurus, and C. elegans, the Ser1422 residue and the
flanking region are not present. The protein sequences from these species are therefore not shown.
Supplementary videos are available on the journal website.
121
Chapter 3.1
Supp. Table S1. PCR primers and protocol for the amplification and sequencing of SYNJ1 fragments.
Amplification primersa
Primer name
Primer sequence
Amplicon size
SYNJ1_1F
SYNJ1_1R
TGAAGAGCCCACGCAAAAGG
CTGCGGAGGCAGAGACTGG
430 bp
SYNJ1_2F
SYNJ1_2R
ATTAAACGGGAAACTCAACCAGACC
GAGGAAATACAGTGAAAGGGTTGTCG
535 bp
SYNJ1_3F
SYNJ1_3R
GAGTTCAGATCAGTGGACAGGATGG
GCATGTAGCACGCCTTTTTGG
502 bp
SYNJ1_4F
SYNJ1_4R
AGAGGTGCAAGAAGAAATGGAATAGC
GGCATTCTTTCCAACACGTATACCAC
615 bp
SYNJ1_5F
SYNJ1_5R
TCATAGATCACATGTTGAAGGATCTCG
AAGGCCCATAAGTAACCAAGAACAATC
567 bp
SYNJ1_6F
SYNJ1_6R
CAAACCTGTATAGTGGCCTGTTTTGG
AACTCTGTACTCGTCTAGACCCTTATTTGC
397 bp
SYNJ1_7F
SYNJ1_7R
GTTATTTGATTAACGACTGTGTGTTCAGC
AAGACCCACACTTCCAGCACTACC
367 bp
SYNJ1_8F
SYNJ1_8R
GCCTCCACAGCTCCAACAGG
GAAAGCTTTTTAGAATCCCAATTTCAGC
541 bp
SYNJ1_9F
SYNJ1_9R
CAGGGATAGTTTTTGAGGCTTTAGGG
CACTGCCTGCTCCATAAAATGC
843 bp
SYNJ1_10F
SYNJ1_10R
GGAGAGGGAAGGTGGATGCTACA
TTTCCTCCAAAAGACAGGGAAAAA
483 bp
SYNJ1_11F
SYNJ1_11R
GTTTTCAGAAGCTAGTCCCAAATAGTGC
CCACTGTGAAAGGAAGTATCAAAGAAGG
448 bp
SYNJ1_12F
SYNJ1_12R
GCTTTTTCATTTCTTTGTGCAGAGC
AACAGACAAACTTCCCTTGGAGAATG
546 bp
SYNJ1_13F
SYNJ1_13R
ACTGTAGGATTCTTCTTAGGTGCAGAAGG
GCAATTGCTACAGGAAGACAAAGAGC
470 bp
SYNJ1_14F
SYNJ1_14R
TTGCCTCATACCAGGCTTTGC
TCATCAATTAATGCATCCTCTGAAGTG
475 bp
SYNJ1_15F
SYNJ1_15R
AATTTCATTCCACTCAAGAGTGTTTGC
TTTTACAGTTTTACGAGCACCCTTCC
374 bp
SYNJ1_16F
SYNJ1_16R
TTAGATTTTGAAATGTGGTTTTCTTTTGG
TGGTATGGTACTTTGAGGGCAAGC
534 bp
SYNJ1_17F
SYNJ1_17R
ATGGGGTTTTGGGGGTTTTG
TTCTAGGAACTATCTTAAATTTCCAAAAACATTG
475 bp
SYNJ1_18F
SYNJ1_18R
CAGGACATGGGAGGAGACAAGC
TGCATATTTAAGTACAAAAAGACCCCAAG
376 bp
SYNJ1_19F
SYNJ1_19R
GGGACCCACCCACTTCTGC
TTTGATATTATGTCATTCCCTGCTTCC
472 bp
SYNJ1_20F
SYNJ1_20R
CCAGCCTCAAACCACTGAAGC
AACTTCACCAAAACTTGCAAACTGC
533 bp
SYNJ1_21F
SYNJ1_21R
CTCCAGGCACTTTGCTGCAC
AATTCTCTCCTTCCCCAAATAAGGTG
459 bp
SYNJ1_22F
SYNJ1_22R
TTTTTACTTCCTGGCCTCTTGTGG
TCCCCTCCCATTTAGATAATCTTGC
296 bp
SYNJ1_23F
SYNJ1_23R
AAACTTGCATCTCTGGGCATCC
CCAAGAAGAAGAAGAAAGATGAACTTGG
515 bp
SYNJ1_24F
SYNJ1_24R
TCATGCTTAATCCATGTGTGTTTGC
GAGAAAACAGGGTGCACTTTATCACC
463 bp
(Continues)
122
Mutation in the SYNJ1 Gene Associated With Autosomal Recessive, Early-Onset Parkinsonism
(Follows)
Amplification primersa
a
b
Primer name
Primer sequence
Amplicon size
SYNJ1_25F
SYNJ1_25R
TGAAAATACATGCCCTTTCTTTACACC
TGAAAACACCTTAGAGGAATCTACTCTGC
481 bp
SYNJ1_26F
SYNJ1_26R
CAAAATCCTCATTTCTGGATGATTTATAAGC
CAGCCTACCTGGGAAATGAAAGC
520 bp
SYNJ1_27F
SYNJ1_27R
GGATTCAGATTTGGGCCAAGC
AAGGAGGGGAAAGAGAAGTTTACAGC
374 bp
SYNJ1_28F
SYNJ1_28R
TCTCATGCCCCCATTTCTGC
GCTTGAGGTGAAGGCTGACTGC
303 bp
SYNJ1_29F
SYNJ1_29R
TGAATGTAGCTATTCACCCTTAAAACTGG
AAATGATAAAAAGCTGCCCAAATCC
492 bp
SYNJ1_30F
SYNJ1_30R
CAAAAGCAAATGTTTTCACAGAATTTTACC
TGTAGAAGGTGGGAGATGACAGAAGG
566 bp
SYNJ1_31F
SYNJ1_31R
TCTCTTTTTCCTTCTGTCATCTCC
ACTTCTTATAACAGGCAATAATTAAAATAGG
511 bp
SYNJ1_32A_F
SYNJ1_32A_R
AATTTACATGCTTGCCCCTAAAAGG
TTGCCTCTGATTCTTCAGACTTGG
470 bp
SYNJ1_32B_F
SYNJ1_32B_R
ACCTGTTCCCACCCCAGACC
GCTGATTACCCAGTAAGTCTGAACAAGC
488 bp
SYNJ1_32C_F
SYNJ1_32C_R
TCTACATTAAAAATTAGCAACCCGAAAGG
CAATGAGAAGGGTTTTTCCTTATTTCC
500 bp
Sanger sequencing primersb
Primer name
Primer sequence
SYNJ1_S4R
CTGCTGAAAAGGGGCATCG
SYNJ1_S9R
GCCTGGGCAACAAAGTGAGG
3.1
primers used for PCR amplification and Sanger sequencing reactions.
primers used only for Sanger sequencing.
Supp. Table S2. Protein accession numbers
Accession numbers for the Synj1 homologs
|NP_003886.3|
|XP_531429.2| |XP_002830685.2|
|NP_776893.2|
|XP_535580.2|
|XP_416706.3|
|NP_001164888.1|
|NP_445928.2|
|NP_001157955.1|
|XP_002938495.1|
|NP_001007031.1|
|NP_726155.1|
|NP_001023266.1|
synaptojanin-1 isoform a [Homo sapiens]
PREDICTED: synaptojanin-1 isoform 4 [Pan troglodytes]
PREDICTED: LOW QUALITY PROTEIN: synaptojanin-1 [Pongo abelii]
synaptojanin-1 [Bos taurus]
PREDICTED: synaptojanin-1 isoform 1 [Canis lupus familiaris]
PREDICTED: synaptojanin-1 [Gallus gallus]
synaptojanin-1 [Oryctolagus cuniculus]
synaptojanin-1 [Rattus norvegicus]
synaptojanin-1 isoform a [Mus musculus]
PREDICTED: synaptojanin-1-like [Xenopus (Silurana) tropicalis]
synaptojanin-1 [Danio rerio]
synaptojanin, isoform A [Drosophila melanogaster]
Protein UNC-26, isoform b [Caenorhabditis elegans]
Accession numbers for different human proteins containing the SAC1-like phosphatase domain
|NP_003889.1|
|NP_054735.3|
|NP_055660.1|
|NP_055752.1|
|NP_054735.3|
synaptojanin-2 isoform 1 [Homo sapiens]
phosphatidylinositide phosphatase SAC1 [Homo sapiens]
polyphosphoinositide phosphatase [Homo sapiens]
phosphatidylinositide phosphatase SAC2 isoform 1 [Homo sapiens]
phosphatidylinositide phosphatase SAC1 [Homo sapiens]
123
Chapter 3.1
Supp. Table S3. In-silico prediction of functional effects of variants.
Synj1 p.Arg258Gln
Synj1 p.Ser1422Arg
ZNF439 p.Arg311Cys
PolyPhen-2
HumDiva
PROBABLY DAMAGING
PROBABLY DAMAGING
BENIGN
HumVarb
POSSIBLY DAMAGING
POSSIBLY DAMAGING
BENIGN
SIFT
DAMAGING (score=0)
DAMAGING (score=0,01)
DAMAGING (score=0,05)c
PROVEAN
DELETERIOUS
NEUTRAL
NEUTRAL
SNP-and-GO
DISEASE
NA
NEUTRAL
SNAP
NON-NEUTRAL
NON-NEUTRAL
NEUTRAL
MUTATION TASTING
DISEASE-CAUSING
POLYMORPHISM
POLYMORPHISM
Synj1 p.Arg1252Gln
Synj1 p.Arg1429Gln
Synj1 p.Gly1453Glu
PROBABLY DAMAGING
BENIGN
PROBABLY DAMAGING
PolyPhen-2
HumDiva
HumVar
SIFT
b
PROBABLY DAMAGING
BENIGN
PROBABLY DAMAGING
TOLERATED (score=0,07)
TOLERATED (score=0,22)
DAMAGING (score=0,01)
PROVEAN
NEUTRAL
NEUTRAL
NEUTRAL
SNP-and-GO
NA
NA
NA
SNAP
NON-NEUTRAL
NON-NEUTRAL
NON-NEUTRAL
MUTATION TASTING
DISEASE-CAUSING
POLYMORPHISM
DISEASE-CAUSING
preferred model for evaluating rare alleles, dense mapping of regions identified by GWAS and analysis of natural selection.
preferred model for the evaluation of Mendelian disease-causing variants, which requires distinguishing mutations with drastic
effects from the remaining human variation, including abundant mildly deleterious alleles.
c
the substitution is predicted to be damaging if the score is ≤ 0.05, and tolerated if the score is > 0.05.
NA = not available
a
b
124
Mutation in the SYNJ1 Gene Associated With Autosomal Recessive, Early-Onset Parkinsonism
Supp. Table S4. Variants detected in the SYNJ1 gene.
SYNJ1 positiona
Ref SNPb
Nucleotide change
Protein change
Frequencyc
exon1
exon2
intron2
intron2
intron2
intron3
intron4
intron7
ss778182305#
rs61750221
ss778182306#
rs74625247
ss778182298#
rs184110321
rs76502784
rs73194279
c.54C>T
c.123G>A
c.241+47T>C
c.242-148A>C
c.242-99A>G
c.328+134A>G
c.596+9A>G
c.968+43A>G
p.18Gly=
p.41Ala=
exon8
rs2254562
c.1001A>G
p.Lys334Arg
intron8
intron8
intron10
intron10
intron10
intron10
intron11
intron12
intron13
intron15
intron15
intron18
intron18
intron21
exon25
intron25
intron25
intron25
rs844975
rs143229035
rs75924460
rs111884129
rs73194278
rs844988
rs80299581
rs116389986
ss778182299#
rs114386610
rs844998
ss778182300#
rs2833936
ss778182301#
ss778182302#
rs2406
rs189532601
rs34001349
c.1065+81G>A
c.1066-38A>C
c.1317+55T>C
c.1317+69T>C
c.1317+273A>G
c.1318-3A>T
c.1470+27C>T
c.1628-59T>A
c.1651+61G>A
c.1928+51A>G
c.1929-100T>C
c.2421+55C>T
c.2422-110T>A
c.2913-23G>T
c.3486A>G
c.3508+99A>T
c.3509-129G>T
c.3509-96T>A
c.3509-202_3509199delGTTT
c.3596-29T>G
c.3635-7G>T
c.3705+14C>T
c.3705+128G>A
0.004
0.008
0.004
0.059
0.004
0.004
0.017
0.025
0.326
MAF, dbSNP137: 0.293
MAF, EVS: 0.297
0.525
0.008
0.038
0.034
0.004
0.076
0.004
0.004
0.004
0.004
0.072
0.004
0.038
0.004
0.004
0.591d
0.013
0.038
intron25
rs142688189
intron27
intron28
intron29
intron29
rs146753923
rs2833929
rs11702774
rs56238975
p.1162 Pro=
3.1
0.008
exon30
rs144048853*
c.3755G>A
intron30
exon31
rs147026277
rs2230766
exon32
rs71640263
exon32
ss778182303#
c.3815-251G>C
c.3838C>T
c.4214_4215insAATACT
c.4264 A>C
p.Arg1252Gln
p.Ser1422Arg
exon32
rs112469776*
c.4286G>A
p.Arg1429Gln
exon32
rs61750217*
c.4358G>A
p.Gly1453Glu
exon32
rs146425050
c.4581G>A
p.Glu1527=
p.Leu1280=
p.Val1405delinsValAsnThr
0.008
0.305
0.229
0.025
0.004
MAF, EVS: 0.00007
0.004
0.085
0.555
MAF, dbSNP137: 0.511
0.004
0.004
MAF, dbSNP137: 1 ID het
(Bushman); no additional
IDs available
0.004
MAF, dbSNP137: 0.008
MAF, EVS: 0.014
0.004
References for annotation of variants: GenBank n. NM_003895.3 and NP_003886.3
SNP reference number in dbSNP137
c
Minor allele frequency in 92 early-onset Parkinson’s disease and 26 atypical parkinsonism patients
d
Minor allele frequency in 29 early-onset Parkinson’s disease and 26 atypical parkinsonism patients
#
Submitter SNP (ss) numbers provided by dbSNP Group NCBI/NLM/NIH (http://www.ncbi.nlm.nih.gov/projects/SNP/)
* Web tools were used to predict the functional effects of these rare variants (See Supp. Table S3)
a
b
125
126
3.2
PARK20 Caused by SYNJ1 Homozygous Arg258Gln
Mutation in a New Italian Family
S. Olgiati,1,* A. De Rosa,2,* M. Quadri,1 C. Criscuolo,2
G.J. Breedveld,1 M. Picillo,2 S. Pappatà,3 M. Quarantelli,3
P. Barone,4 G. De Michele,2,# and V. Bonifati.1,#
Published in Neurogenetics 2014,15(3):183-8.
* These authors contributed equally to this work, as first authors;
# These authors contributed equally to this work, as senior authors and corresponding authors.
1) Department of Clinical Genetics, Erasmus MC, Rotterdam, The Netherlands;
2) Department of Neurosciences and Reproductive and Odontostomatological Sciences, “Federico II” University, Naples, Italy;
3) Biostructure and Bioimaging Institute, National Research Council, Naples, Italy;
4) Department of Medicine and Surgery, CEMAND, University of Salerno, Salerno, Italy.
© Springer-Verlag Berlin Heidelberg 2014
Reprinted with permission of Springer
127
140
3.3
A New Turkish Family With Homozygous FBXO7
Truncating Mutation and Juvenile Atypical Parkinsonism
G. Yalcin-Cakmakli,1 S. Olgiati,2 M. Quadri,2 G.J. Breedveld,2
P. Cortelli,3 V. Bonifati,2,# and B. Elibol.1,#
Published in Parkinsonism & Related Disorders 2014, 20(11):1248-52.
# Corresponding authors.
1) Department of Neurology, Hacettepe University, Ankara, Turkey;
2) Department of Clinical Genetics, Erasmus MC, Rotterdam, The Netherlands;
3) Department of Biomedical and NeuroMotor Sciences (DIBINEM), Alma Mater Studiorum - Università di Bologna, and
IRCCS Istituto delle Scienze Neurologiche di Bologna, Italy.
© 2014 Elsevier Ltd. All rights reserved.
Reprinted with permission from Elsevier
141
PART IV
Other Movement Disorders
154
4.1
Paroxysmal Exercise-Induced Dystonia Within the
Phenotypic Spectrum of ECHS1 Deficiency
S. Olgiati,*,1 M. Skorvanek,*,2,3 M. Quadri,1 M. Minneboo,1 J. Graafland,1
G.J. Breedveld,1 R. Bonte,1 Z. Ozgur,4 M.C.G.N. van den Hout,4
K. Schoonderwoerd,1 F.W. Verheijen,1 W.F.J. van IJcken,4 H. Fen Chien,5
E.R. Barbosa,5 H.C. Chang,6 S.C. Lai,6 T.H. Yeh,6 C.S. Lu,6 Y.H. Wu-Chou,7
A.J.A. Kievit,1 V. Han,2,3 Z. Gdovinova,2,3 R. Jech,8 R.M.W. Hofstra,1
G.J.G. Ruijter,1 W. Mandemakers,1 and V. Bonifati.1,#
Manuscript in press, Movement Disorders 2016
* These authors contributed equally to this work, as first authors;
# Corresponding author.
1) Department of Clinical Genetics, Erasmus MC, Rotterdam, The Netherlands;
2) Department of Neurology, Safarik University, Kosice, Slovakia;
3) Department of Neurology, University Hospital L. Pasteur, Kosice, Slovakia;
4) Center for Biomics, Erasmus MC, Rotterdam, The Netherlands;
5) Department of Neurology, University of São Paulo, São Paulo, Brazil;
6) Neuroscience Research Center, Division of Movement Disorders, Department of Neurology, Chang Gung Memorial Hospital
and Chang Gung University, Taoyuan, Taiwan;
7) Human Molecular Genetics Laboratory, Department of Medical Research, Chang Gung Memorial Hospital and Chang
Gung University, Taoyuan, Taiwan;
8) Department of Neurology, Charles University in Prague, First Faculty of Medicine, Prague, Czech Republic.
155
176
4.2
A Recurrent Mutation in the C19orf12 Gene
Causes MPAN in Turkey
S. Olgiati,1 O. Doğu,2 H.A. Hanagasi,3 Y. Diler,4 M. Gultekin,5
D. Kuipers, M. Quadri,1 G.J. Breedveld,1 J. Graafland,1 R. Sürmeli,4 G. Sünter,4
A.D. Yalçin,4 Z. Tufekcioglu,3 B. Bilgiç,3 B. Elibol,6 M. Emre,3 and V. Bonifati.1,#
1
Manuscript in preparation
# Corresponding author.
1) Department of Clinical Genetics, Erasmus MC, Rotterdam, The Netherlands;
2) Department of Neurology, Mersin University, Mersin, Turkey;
3) Department of Neurology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey;
4) Department of Neurology, Istanbul Education and Research Hospital, Istanbul, Turkey;
5) Department of Neurology, School of Medicine, Erciyes university, Kayseri, Turkey;
6) Department of Neurology, Hacettepe University, Ankara, Turkey.
177
PART V
General Discussion
190
5.1
General Discussion
191
Chapter 5.1
During the last three decades, linkage analysis and positional cloning have been largely
applied to human diseases, including movement disorders, allowing the identification
of many highly penetrant genetic factors. For many disorders, these findings have laid
the foundation for understanding the pathogenetic mechanisms of the disease. More
recently, genome-wide association studies (GWAS) have been very successful in identifying risk factors for complex diseases. However, for most disorders, GWAS have
not explained large proportions of the disease burden so far. Now, the field of human
genetics is undergoing a third technological revolution. Next-generation sequencing
technologies (NGS) are fueling a new wave of gene discovery, holding great promises
for the movement disorders field. The applications of these tools to movement disorders
are discussed in chapter 1.1 of this thesis.
The aim of this thesis was to investigate the genetic factors involved in the etiology
of movement disorders. To achieve this goal, we applied different genetic approaches,
including NGS-based strategies, but also traditional linkage and positional-cloning
methods, and candidate gene analyses. This work allowed us to expand our current
knowledge about movement disorders by I) identifying novel disease-causing genes,
II) expanding the phenotype associated with known disease genes, III) proposing
novel candidate risk genes, and IV) contributing to the clinical characterization of
previously-described forms. These findings provide insights into the mechanisms of
these disorders by supporting novel or previously-postulated hypotheses. Moreover, our
results have direct clinical relevance, and will be important for the diagnostic work-up
and the genetic counseling of the patients and their relatives.
The Genetic Architecture of Movement Disorders
During the last twenty years, a large number of genetics factors have been implicated in
the etiology of movement disorders, including rare Mendelian factors, moderately-rare
factors with moderate effect size, and common variants with low effect size. The identification of such genetic factors has been very important, and it has allowed the scientific
community to formulate novel hypotheses about the disease mechanisms. However, with
few notable exceptions, these findings have not been translated into novel therapies for
these disorders so far. Moreover, despite the successful identification of genetic factors,
the genetic architecture of many movement disorders is still incompletely understood.
In movement disorders with a clear monogenic etiology (especially rare, autosomal
recessive disorders), many of the disease genes are now known. Moreover, it is anticipated
that the application of NGS technologies and DNA arrays methods to family-based
studies will resolve most of the remaining conditions in the near future. A good example
192
General Discussion
of this type of disorders is neurodegeneration with brain iron accumulation (NBIA).
This group of genetically heterogeneous disorders is now associated with mutations in
ten genes, accounting for the majority of the cases (~70%) [1-3]. The identification and
characterization of these different NBIA forms have been instrumental for the ongoing
development of specific diagnostic algorithms that take clinical signs and brain imaging
features into account. Our work described in chapter 4.2 contributes to the characterization of one of these forms, caused by mutations in the C19orf12 gene.
For movement disorders with a more complex genetic architecture, our genetic
knowledge is still quite limited. Parkinson’s disease (PD) is a perfect example. The
disorder is characterized by a strong familial aggregation (the relative risk of PD for
having a first-degree relative with PD versus having no first-degree relative with PD
is 2.9), suggesting a genetic component in PD [4]. Consistently, GWAS studies have
identified common variants associated with the disease in 24 loci [5]. However, these
variants have only small effect size (0.7>OR<2.3) and they explain only a small proportion of the heritable component of PD [6]. In addition to variants with a small effect
size, moderate risk factors for PD have also been identified. Mutations in GBA and one
mutation in LRRK2 (p.Gly2019Ser) are moderate-to-strong risk factors for PD (OR
~5–10), and their prevalence is especially high in some populations [7,8]. Moreover,
family-based studies have been successful in the identification of Mendelian factors for
PD. Mutations in seven genes have been confirmed to cause clinically typical late-onset
or early-onset PD: SNCA [9], LRRK2 [10,11], VPS35 [12,13], parkin [14], PINK1
[15], DJ-1 [16], and DNAJC6 [17]. Mutations in these genes are Mendelian causes for
PD, but they are extremely rare in the general population and they explain only a small
proportion of the patients. Of note, mutations in one of these genes, DNAJC6, were
first identified in association with early-onset PD by the work described in chapter 2.1.
In addition, mutations in multiple genes (ATP13A2 [18], PLA2G6 [19], FBXO7
[20,21], DNAJC6 [22,23], SYNJ1 [24,25], and RAB39B [26]) have been associated to
juvenile forms of parkinsonism with additional, atypical clinical signs. Despite some
clinical similarities with the typical PD, the presentation of these forms usually includes
other clinical features that are not observed in typical PD, and they are likely representing different disease entities. One of these forms, caused by a mutation in SYNJ1,
was identified for the first time and characterized by the work reported in chapter 3.1
and 3.2.
Despite the identification of several disease-causing genes for PD and juvenile atypical
parkinsonism, mutations in these genes are not found in most of the PD cases, including
some large families with multiple affected individuals. This suggests that additional
genes are likely to be discovered in the future.
193
5.1
Chapter 5.1
Parkinson’s Disease: One or Many?
The heritable component of complex movement disorders, like PD, seems to be the
result of a multitude of different genetic factors, covering the entire spectrum of risk,
from low to high. Small effect-size factors are very common, but have limited clinical
relevance. On the other side of the spectrum, high risk alleles are often good diagnostic
and prognostic markers, but they explain only small minorities of patients. Moreover,
the fundamental question arises of whether these genetic forms and the so called
“idiopathic” patients share the same disease mechanisms, and ultimately, whether they
are the same disease. To answer this question, we should first consider how movement
disorders are currently defined.
For most movement disorders, including PD, the disease is usually diagnosed by a combination of many criteria, including clinical presentation, response to therapies, pathological signs, and brain imaging patterns. The problem with this definition is that most of
these features are not specific to one disease. Instead, they are usually observed also in
other neurological disorders or even in healthy individuals. Taking PD as example, the
disease definition is based on the clinical presentation (presence of parkinsonism), the
response to dopaminergic therapies, and the neuronal loss in the substantia nigra pars
compacta and in other brain areas, with the presence of Lewy bodies and Lewy neurites
in the remaining neurons [27,28].
However, parkinsonism can be observed also in other lesions or dysfunctions of the
basal ganglia; response to dopaminergic therapies is also a feature of other disorders (e.g.,
dopa-responsive dystonia); and Lewy bodies are present also in other disorders, including
Alzheimer’s disease, dementia with Lewy bodies, and some forms of NBIA, but also in
elderly individuals without neurological disorders (i.e., incidental Lewy bodies) [29,30].
Based on these observations, it is reasonable to say that the genetic heterogeneity of PD
might reflect an intrinsic heterogeneity of the disease. In other words, using etiology
to define the disorder, one could say that PD is likely made of different disease entities
having different etiologies, but similar clinical and pathological outcomes.
At the same time, some of the different disease entities that are diagnosed as PD are clinically indistinguishable. For example, some of the patients with Mendelian forms, like
LRRK2 mutation carriers, are clinically indistinguishable from the rest of the patients
(with no mutations in known genes). Therefore, one could argue that LRRK2 mutation
carriers do not have a different disease entity, and instead, they also have PD. In this
view, the disorder can be seen as the result of many different factors, or a combination of
them, all acting through one, or more likely few, molecular pathways. These factors can
be genetic (e.g., LRRK2 mutations), but also aging, and most likely environmental and
stochastic factors. Similarly to PD, other movement disorders can be described using
194
General Discussion
these two opposite approaches: etiologic or pathogenetic.
In my view, these two interpretations of the complexity of PD must coexist. For
unknown reasons, multiple etiologic factors produce similar, clinical outcomes. Both
the etiologic and the pathogenetic approach can be used to describe this complex disease
entity. The etiologic approach helps us to better understand the complexity of this
disorder by allowing the study of the disease mechanisms. At the same time, because
the different etiologic factors involved in PD might alter only a limited number of
molecular pathways, a pathogenetic approach might be more successful in the development of novel therapeutic strategies broadly applicable to the patients.
The Contribution of This Thesis
In the complex scenario of the genetics of movement disorders, the identification and
characterization of the genetic factors associated with these diseases is paving the way to
a better understanding of these conditions. In this thesis we provide an original contribution to the current knowledge of the Mendelian factors for Parkinson’s disease (Part
II), atypical parkinsonisms (Part III), paroxysmal exercise-induced dystonia, and NBIA
(Part IV). Our findings provide novel insights into the pathological mechanisms of these
disorders.
The identification of DNAJC6 mutations in early-onset Parkinson’s disease (chapter
2.1) provide strong evidence for the involvement of synaptic vesicles recycling in PD.
Previously, mutations in this gene were known to cause an atypical form of parkinsonism (PARK19) with juvenile onset (<11 years) [22,23]. We reported, for the first
time, DNAJC6 mutations in patients with onset in the third-to-fifth decade, slow disease
progression, absence of atypical clinical signs, and prominent response to dopaminergic therapies. This phenotype is similar to other monogenic forms of early-onset PD
caused by mutations in parkin, PINK1, and DJ-1. Interestingly, the DNAJC6 mutations
reported by us might allow some residual protein activity, while the mutations causing
juvenile, atypical parkinsonism are predicted to cause a complete loss-of-function. This
allowed us to propose a genotype-phenotype correlation in patients with DNAJC6
mutations, with complete loss-of-function variants causing the juvenile, atypical form
(PARK19), whereas milder mutations (described in our study) cause early-onset PD.
Our hypothesis has been partially confirmed by another group that reported a new
patient with atypical juvenile parkinsonism caused by a novel homozygous truncating
mutation in DNAJC6 [31,32].
DNAJC6 encodes for the brain-specific isoform of auxilin, a protein involved in the
clathrin-mediated endocytosis (CME). In neurons, this process is an important
195
5.1
Chapter 5.1
mechanism for the formation of clathrin-coated vesicles, and for the recycling of synaptic
vesicles at the presynaptic terminal [33].
Consistently, another finding reported in this thesis points to the involvement of the
CME pathway in the pathogenesis of parkinsonisms. In chapter 3.1 and 3.2, we identified and characterized a novel form of early-onset, atypical parkinsonism caused by
a mutation in SYNJ1. The protein encoded by this gene, synaptojanin 1, is a close
molecular partner of auxilin. Both proteins act in the last step of the CME, the vesicles
uncoating. The uncoating process requires the coordinated actions of three proteins:
the chaperone Hsc70, its co-chaperone auxilin, and the phosphatase Synj1. Hsc70
and auxilin operate in the clathrin disassembly [34], while Synj1 dephosphorylates
PI(4,5)P2, a molecule acting in the early-stages of CME to recruit the clathrin adaptor
proteins [35,36].
The identification of mutations in DNAJC6 and SYNJ1 in patients with parkinsonism
strongly supports the involvement of the synaptic vesicles recycling in the pathogenesis of the disorder. These findings add to a growing body of evidence supporting the
involvement of this pathway. Common variants in GAK (encoding a ubiquitously
expressed form of auxilin, called auxilin-2) were identified by GWAS as low-risk factors
for PD [5,37]. The Lrrk2 kinase was recently described as a modulator of the activity of
endophilin-A [38], a protein involved in vesicle formation and uncoating [39-42]. In
addition, endophilin-A was also reported to interact with parkin [43]. Moreover, Vps35
is part of the retromer, a protein complex that mediates the transport of transmembrane
protein from endosomes to the trans-Golgi network [44]. Finally, α-synuclein has been
involved in the several step of the CME process [45-47]. In light of the findings reported
in this thesis, and the previously existing evidence, the dynamic of vesicle endocytosis
and trafficking at the synaptic terminal appears to have a central role in the pathogenesis
of parkinsonism.
In order to identify novel risk factors for PD, we also performed a large exome sequencing
study in the genetically isolated population of Sardinia (chapter 2.3). Taking advantage
of the unique genetic architecture of the isolated populations, we aimed at the identification of moderately rare variants with strong effect size for PD. To our knowledge, this
was the first exome-wide attempt to identify moderately rare variants of moderate effect
size for PD. Although no variants achieved genome-wide significance after Bonferroni correction, we reported a catalog of variants exclusively present in PD patients or
enriched among them. These variants might point to genetic determinants of PD with
moderate-to-strong effect size. However, these findings are not conclusive, and they
should be further validated by independent studies. Of note, similar inconclusive results
were achieved by another, recently published, large exome sequencing project in PD
196
General Discussion
[48]. Regarding the genomic regions investigated, the results of our study support the
high degree of genetic heterogeneity of PD, even in the isolated population of Sardinia.
This suggests that the identification of moderately-rare risk factors for PD using large
exome sequencing projects requires much larger cohorts of patients and controls, or the
application of gene-based or pathway-based analyses.
To conclude the discussion of our work on parkinsonisms, part of this thesis was also
devoted to the clinical and genetic characterization of the known forms of parkinsonism
associated to SNCA triplications (chapter 2.2) and FBXO7 mutations (chapter 3.3).
Both SNCA triplications and FBXO7 mutations are very rare causes of parkinsonism,
with only few families described so far. Our identification and description of these
families contributes to the ongoing characterization of these rare Mendelian disorders.
In addition to that, our study on SNCA triplication emphasizes the possible role of copy
number variants in the pathogenesis of movement disorders and shows the importance
of gene dosage assays, also in the NGS era.
In part IV of this thesis, we focused on to the study of movement disorders other
than primary parkinsonisms. In chapter 4.1, we suggested ECHS1 mutations as one
of the causes of paroxysmal exercise-induced dystonia (PED). PED are rare forms
of paroxysmal dyskinesias in which attacks of abnormal involuntary movements are
triggered by prolonged or sustained exercise. Mutations in ECHS1 were recently
reported as a novel cause of Leigh syndrome (subacute, necrotizing encephalopathy),
or of atypical forms with later onset (Leigh-like syndromes), with a broad range of
clinical presentations [49,50]. In our study, we showed that the phenotype associated
with ECHS1 mutations might be milder than reported earlier, and includes isolated
paroxysmal exercise-induced dystonia. Therefore, the screening of this gene should be
considered in the patients with otherwise unexplained PED. Most importantly, our
findings have potential therapeutic implications. Indeed, diet regimens and detoxifying
agents represent potential therapeutic strategies for ECHS1 deficiency, but first, their
efficacy and safety should be investigated.
Finally, in chapter 4.2 we studied the role of C19orf12 mutations in Turkish patients
with NBIA. Mutations in C19orf12 have been recently associated with an autosomal
recessive form of NBIA called mitochondrial-membrane protein associated neurodegeneration (MPAN) [51]. Our work identified a single C19orf12 mutation in eight
unrelated Turkish patients with NBIA (40% of our cohort). MPAN is a rare disease,
and complete neurological evaluations have been reported only in a limited number of
patients. Therefore, our work represents a novel contribution to the clinical characterization of this form of NBIA. Moreover, we identified a recurrent mutation in C19orf12
in Turkish MPAN patients. This finding has important consequences for the genetic
197
5.1
Chapter 5.1
counseling of NBIA patients with Turkish origins.
Genetics of Movement Disorders: the Road Ahead
The genetic findings of the past years helped us to better understand the pathogenetic
mechanisms of movement disorders but, in order to more fully understand the genetic
architecture of these conditions, further work remains ahead. In the coming years, novel
genetic factors will likely be identified and the previously-identified factors will be better
characterized.
For the most common movement disorders, like PD, GWAS have identified several loci
associated with the disease. However, GWAS results usually nominate large genomic
regions associated with the disease, not single genes. Moreover, the disease-associated
variants are often not located in the coding regions. As a result, for most GWAS loci, it
is difficult to pinpoint the gene responsible for the association. In the future, the GWAS
loci identified should be characterized in order to understand the biological bases for the
association. This process poses major challenges and will likely require the integration of
different genomics and transcriptomics technologies.
Despite the successes of GWAS, these studies are limited by the possibility to investigate
only common variants (minor allele frequency, MAF > ~1% in the general population).
The application of NGS technologies to large association studies will solve this major
limitation, allowing the study of variants that are too rare to be investigated by GWAS
[52]. In chapter 3.2, we reported the first example of this type of studies in PD. As
discussed above, our results have been so far inconclusive, suggesting that PD has a
high degree of genetic heterogeneity. For this reason, future studies should rely on larger
cohorts of patients and controls and should apply gene-based or pathway-based analyses
in order to increase the statistical power. Several methods for conducting such types of
analyses have been already proposed [53]. In addition, the power of the study could be
increased by evaluating rare variants pathogenicity, using the same tools described in the
introduction of this thesis [54]. Last, another way to possibly increase the power of large
NGS studies in PD would be the inclusion of patients with more extreme phenotypes,
like cases with earlier onset or more aggressive disease progression.
In disorders with a high level of genetic heterogeneity, the study of families with
multiple affected individuals can represent a more effective experimental design. For
this reason, the identification of novel genetic factors in movement disorders might
also rely on family based studies. In chapters 3.1 and 4.1, we have applied a combination of classical positional cloning methods and NGS technologies in two families with
movement disorders. In both studies, this approach successfully identified the disease198
General Discussion
causing mutations. Moreover, these findings have direct clinical relevance. In the future,
both population studies and family-based studies might still be applied to movement
disorders, and should serve as complementary strategies to explore the whole spectrum
of genetic factors associated with these conditions.
Finally, it is important to remember that the identification of disease-causing genes
should also rely on the important biological information that can be gathered by
studying the effect of candidate mutations at the RNA and protein levels, and by using
in vitro and in vivo models. Most importantly, these studies are fundamental to understand the pathogenetic mechanism of the disease and to translate the genetic findings
into novel therapeutic strategies.
Conclusions
Despite the great progresses of the last 20 years, much is still unknown about the etiology
of movement disorders. Genetic studies are leading the way towards the identification
of the molecular mechanisms of these disorders, but most likely, many genetic factors
are still to be found. A more complete knowledge about the genetic architecture of these
disorders could be the cornerstone for mechanistic studies of the involved proteins and
molecular pathways. A clear understanding of the molecular mechanisms should provide
hints for the development of novel and personalized therapies for these disorders.
References
1 Rouault TA. Iron metabolism in the CNS: impli- 6 Keller MF, Saad M, Bras J, Bettella F, Nicolaou N,
Simon-Sanchez J, et al. Using genome-wide complex
cations for neurodegenerative diseases. Nat Rev
trait analysis to quantify ‘missing heritability’ in
Neurosci. 2013;14:551-64.
Parkinson’s disease. Hum Mol Genet. 2012;21:49962 Dusi S, Valletta L, Haack TB, Tsuchiya Y, Venco P,
5009.
Pasqualato S, et al. Exome sequence reveals mutations
3
in CoA synthase as a cause of neurodegeneration 7 Healy DG, Falchi M, O’Sullivan SS, Bonifati V,
Durr A, Bressman S, et al. Phenotype, genotype,
with brain iron accumulation. Am J Hum Genet.
and worldwide genetic penetrance of LRRK2-associ2014;94:11-22.
ated Parkinson’s disease: a case-control study. Lancet
Gregory A, Hayflick S. Neurodegeneration with Brain
Neurol. 2008;7:583-90.
Iron Accumulation Disorders Overview. GeneReviews®. 1993-2015.
4 Thacker EL, Ascherio A. Familial aggregation of
Parkinson’s disease: a meta-analysis. Mov Disord.
2008;23:1174-83.
5
8 Sidransky E, Nalls MA, Aasly JO, Aharon-Peretz J,
Annesi G, Barbosa ER, et al. Multicenter analysis of
glucocerebrosidase mutations in Parkinson’s disease. N
Engl J Med. 2009;361:1651-61.
Nalls MA, Pankratz N, Lill CM, Do CB, Hernandez 9 Polymeropoulos MH, Lavedan C, Leroy E, Ide SE,
Dehejia A, Dutra A, et al. Mutation in the alpha-syDG, Saad M, et al. Large-scale meta-analysis of
nuclein gene identified in families with Parkinson’s
genome-wide association data identifies six new risk
disease. Science. 1997;276:2045-7.
loci for Parkinson’s disease. Nat Genet. 2014;46:98910 Zimprich A, Biskup S, Leitner P, Lichtner P, Farrer M,
93.
199
5.1
Chapter 5.1
2009;72:240-5.
Lincoln S, et al. Mutations in LRRK2 Cause Autosomal-Dominant Parkinsonism with Pleomorphic 22 Edvardson S, Cinnamon Y, Ta-Shma A, Shaag A,
Pathology. Neuron. 2004;44:601-7.
Yim YI, Zenvirt S, et al. A deleterious mutation
in DNAJC6 encoding the neuronal-specific clath11 Paisan-Ruiz C, Jain S, Evans EW, Gilks WP, Simon J,
rin-uncoating co-chaperone auxilin, is associated with
van der Brug M, et al. Cloning of the Gene Containing
juvenile parkinsonism. PLoS One. 2012; 7:e36458.
Mutations that Cause PARK8-Linked Parkinson’s
Disease. Neuron. 2004;44:595-600.
23 Koroglu C, Baysal L, Cetinkaya M, Karasoy H, Tolun
12 Vilarino-Guell C, Wider C, Ross OA, Dachsel JC,
Kachergus JM, Lincoln SJ, et al. VPS35 mutations in
Parkinson disease. Am J Hum Genet. 2011;89:162-7.
A. DNAJC6 is responsible for juvenile parkinsonism
with phenotypic variability. Parkinsonism Relat
Disord. 2013;19:320-4.
13 Zimprich A, Benet-Pages A, Struhal W, Graf E, 24 Quadri M, Fang M, Picillo M, Olgiati S, Breedveld
GJ, Graafland J, et al. Mutation in the SYNJ1 gene
Eck SH, Offman MN, et al. A mutation in VPS35,
associated with autosomal recessive, early-onset
encoding a subunit of the retromer complex, causes
Parkinsonism. Hum Mutat. 2013;34:1208-15.
late-onset Parkinson disease. Am J Hum Genet.
2011;89:168-75.
25 Krebs CE, Karkheiran S, Powell JC, Cao M, Makarov
V, Darvish H, et al. The Sac1 Domain of SYNJ1 Iden14 Kitada T, Asakawa S, Hattori N, Matsumine H,
tified Mutated in a Family with Early-Onset ProgresYamamura Y, Minoshima S, et al. Mutations in the
sive Parkinsonism with Generalized Seizures. Hum
parkin gene cause autosomal recessive juvenile parkinMutat. 2013;34:1200-7.
sonism. Nature. 1998;392:605-8.
15 Valente EM, Abou-Sleiman PM, Caputo V, Muqit 26 Wilson GR, Sim JC, McLean C, Giannandrea M,
Galea CA, Riseley JR, et al. Mutations in RAB39B
MM, Harvey K, Gispert S, et al. Hereditary earlycause X-linked intellectual disability and early-onset
onset Parkinson’s disease caused by mutations in
Parkinson disease with alpha-synuclein pathology. Am
PINK1. Science. 2004;304:1158-60.
J Hum Genet. 2014;95:729-35.
16 Bonifati V, Rizzu P, van Baren MJ, Schaap O,
Breedveld GJ, Krieger E, et al. Mutations in the DJ-1 27 Tolosa E, Wenning G, Poewe W. The diagnosis of
Parkinson’s disease. Lancet Neurol. 2006;5:75-86.
gene associated with autosomal recessive early-onset
parkinsonism. Science. 2003;299:256-9.
28 Hughes AJ, Daniel SE, Kilford L, Lees AJ. Accuracy
of clinical diagnosis of idiopathic Parkinson’s disease:
17 Olgiati S, Quadri M, Fang M, Rood JP, Saute JA,
a clinico-pathological study of 100 cases. J Neurol
Fen Chien H, et al. DNAJC6 mutations associated
Neurosurg Psychiatry. 1992;55:181-4.
with early-onset Parkinson’s disease. Ann Neurol.
2016;79:244-56.
29 Gibb WR, Lees AJ. The relevance of the Lewy body
to the pathogenesis of idiopathic Parkinson’s disease. J
18 Ramirez A, Heimbach A, Grundemann J, Stiller B,
Neurol Neurosurg Psychiatry. 1988;51:745-52.
Hampshire D, Cid LP, et al. Hereditary parkinsonism
with dementia is caused by mutations in ATP13A2, 30 Wakabayashi K, Tanji K, Odagiri S, Miki Y, Mori
encoding a lysosomal type 5 P-type ATPase. Nat
F, Takahashi H. The Lewy body in Parkinson’s
Genet. 2006;38:1184-91.
disease and related neurodegenerative disorders. Mol
Neurobiol. 2013;47:495-508.
19 Paisan-Ruiz C, Bhatia KP, Li A, Hernandez D, Davis
M, Wood NW, et al. Characterization of PLA2G6 31 Elsayed LE, Drouet V, Usenko T, Mohammed IN,
as a locus for dystonia-parkinsonism. Ann Neurol.
Hamed AA, Elseed MA, et al. A novel nonsense
2009;65:19-23.
mutation in DNAJC6 expands the phenotype of
autosomal recessive juvenile-onset Parkinson disease.
20 Shojaee S, Sina F, Banihosseini SS, Kazemi MH,
Ann Neurol. 2016;79(2):335-7.
Kalhor R, Shahidi GA, et al. Genome-wide linkage
analysis of a Parkinsonian-pyramidal syndrome 32 Olgiati S, Quadri M, Mandemakers W, Bonifati V.
pedigree by 500 K SNP arrays. Am J Hum Genet.
Reply to Letter by Dr. Elsayed and colleagues. Ann
2008;82:1375-84.
Neurol. 2016;79:337-8.
21 Di Fonzo A, Dekker MC, Montagna P, Baruzzi 33 Kononenko NL, Haucke V. Molecular mechanisms of
A, Yonova EH, Correia Guedes L, et al. FBXO7
presynaptic membrane retrieval and synaptic vesicle
mutations cause autosomal recessive, early-onset
reformation. Neuron. 2015;85:484-96.
parkinsonian-pyramidal
syndrome.
Neurology.
200
General Discussion
34Ungewickell E, Ungewickell H, Holstein SE, 44 Bonifacino JS, Hurley JH. Retromer. Curr Opin Cell
Biol. 2008;20:427-36.
Lindner R, Prasad K, Barouch W, et al. Role of
auxilin in uncoating clathrin-coated vesicles. Nature. 45 Nemani VM, Lu W, Berge V, Nakamura K, Onoa B,
1995;378:632-5.
Lee MK, et al. Increased expression of alpha-synuclein reduces neurotransmitter release by inhibiting
35 Cremona O, Di Paolo G, Wenk MR, Luthi A, Kim
synaptic vesicle reclustering after endocytosis. Neuron.
WT, Takei K, et al. Essential role of phosphoinos2010;65:66-79.
itide metabolism in synaptic vesicle recycling. Cell.
1999;99:179-88.
46 Burre J, Sharma M, Tsetsenis T, Buchman V,
Etherton MR, Sudhof TC. Alpha-synuclein promotes
36 Mani M, Lee SY, Lucast L, Cremona O, Di Paolo G,
SNARE-complex assembly in vivo and in vitro.
De Camilli P, et al. The dual phosphatase activity of
Science. 2010;329:1663-7.
synaptojanin1 is required for both efficient synaptic
vesicle endocytosis and reavailability at nerve 47 Ben Gedalya T, Loeb V, Israeli E, Altschuler Y, Selkoe
terminals. Neuron. 2007;56:1004-18.
DJ, Sharon R. Alpha-synuclein and polyunsaturated
fatty acids promote clathrin-mediated endocytosis and
37 Pankratz N, Wilk JB, Latourelle JC, DeStefano AL,
synaptic vesicle recycling. Traffic. 2009;10:218-34.
Halter C, Pugh EW, et al. Genomewide association
study for susceptibility genes contributing to familial 48 Farlow JL, Robak LA, Hetrick K, Bowling K, BoerParkinson disease. Hum Genet. 2009;124:593-605.
winkle E, Coban-Akdemir ZH, et al. Whole-Exome
Sequencing in Familial Parkinson Disease. JAMA
38 Matta S, Van Kolen K, da Cunha R, van den Bogaart
Neurol. 2016;73:68-75.
G, Mandemakers W, Miskiewicz K, et al. LRRK2
controls an EndoA phosphorylation cycle in synaptic 49 Peters H, Buck N, Wanders R, Ruiter J, Waterham
endocytosis. Neuron. 2012;75:1008-21.
H, Koster J, et al. ECHS1 mutations in Leigh disease:
a new inborn error of metabolism affecting valine
39 Gallop JL, Jao CC, Kent HM, Butler PJ, Evans PR,
metabolism. Brain. 2014;137:2903-8.
Langen R, et al. Mechanism of endophilin N-BAR
domain-mediated membrane curvature. EMBO J. 50 Haack TB, Jackson CB, Murayama K, Kremer LS,
2006;25:2898-910.
Schaller A, Kotzaeridou U, et al. Deficiency of ECHS1
40 Masuda M, Takeda S, Sone M, Ohki T, Mori H,
Kamioka Y, et al. Endophilin BAR domain drives
membrane curvature by two newly identified structure-based mechanisms. EMBO J. 2006;25:2889-97. 51
41 Milosevic I, Giovedi S, Lou X, Raimondi A, Collesi
C, Shen H, et al. Recruitment of endophilin to clathrin-coated pit necks is required for efficient vesicle
uncoating after fission. Neuron. 2011;72:587-601.
causes mitochondrial encephalopathy with cardiac
involvement. Ann Clin Transl Neurol. 2015;2:492509.
Hartig MB, Iuso A, Haack T, Kmiec T, Jurkiewicz E,
Heim K, et al. Absence of an orphan mitochondrial
protein, c19orf12, causes a distinct clinical subtype of
neurodegeneration with brain iron accumulation. Am
J Hum Genet. 2011;89:543-50.
42 Verstreken P, Kjaerulff O, Lloyd TE, Atkinson 52 Cirulli ET, Goldstein DB. Uncovering the roles of rare
variants in common disease through whole-genome
R, Zhou Y, Meinertzhagen IA, et al. Endophilin
sequencing. Nat Rev Genet. 2010;11:415-25.
mutations block clathrin-mediated endocytosis but
not neurotransmitter release. Cell. 2002;109:101-12. 53 Lee S, Abecasis GR, Boehnke M, Lin X. Rare-variant
association analysis: study designs and statistical tests.
43 Trempe JF, Chen CX, Grenier K, Camacho EM,
Am J Hum Genet. 2014;95:5-23.
Kozlov G, McPherson PS, et al. SH3 domains from a
subset of BAR proteins define a Ubl-binding domain 54 Sham PC, Purcell SM. Statistical power and signifiand implicate parkin in synaptic ubiquitination. Mol
cance testing in large-scale genetic studies. Nat Rev
Cell. 2009;36:1034-47.
Genet. 2014;15:335-46.
201
5.1
Appendix
Summary
Movement disorders are neurologic syndromes in which there is either an excess of
movements or a paucity of voluntary and automatic movements, unrelated to weakness
or spasticity. These disorders are associated with dysfunctions of the basal ganglia and
cerebellum, brain structures that together orchestrate the control of movements. During
the last two decades, genetic studies of movement disorders identified many genetics
factors associated with these diseases, including rare Mendelian factors, moderately-rare
factors with moderate effect size, and common variants with low effect size. In the
coming years, the application of novel DNA sequencing technologies to the study of
movement disorders promises to unveil even more genetic factors, possibly leading to a
full understanding of the genetic architecture of these disorders.
The introduction (Part I) of this thesis reviews the modern strategies for the identification of genetic factors associated with Mendelian forms of movement disorders. After a
brief definition of these disorders, we focus on the use of the next-generation sequencing
technologies to identify Mendelian mutations in movement disorders (chapter 1.1).
Our experimental work is presented in the next three parts of this thesis. Part II is focused
on the genetic studies of Parkinson’s disease (PD). In chapter 2.1, for the first time, we
report the identification of DNAJC6 mutations in families with early-onset PD. Our
work delineate a novel form of autosomal recessive early-onset PD, caused by mutations
in the DNAJC6 gene, and has important implications for the diagnostic work-up and
genetic counseling of early-onset PD patients. In chapter 2.2, we report genetic and
clinical findings in a novel family with early-onset parkinsonism associated with triplication of the SNCA locus. This study contributes to the genetic and clinical characterization of this very rare form. In chapter 2.3, we perform a large exome sequencing
project in the isolated population of Sardinia to identify novel risk factors for PD with
intermediate or strong effect size. In this study we identify a list of candidate variants
highly enriched or exclusively present in PD, a resource of candidates for future studies.
Notably, this was the first study analyzing a large cohort of PD patients at exome-wide
level.
Part III of this thesis focuses on the study of atypical forms of parkinsonisms. In chapter
3.1, we report the identification of a novel form of early-onset atypical parkinsonism
caused by a homozygous mutation in SYNJ1. This study was the first to report the
association between a SYNJ1 mutation and parkinsonism. In chapter 3.2, we identify a
family with a milder phenotype caused by the same homozygous mutation in a different
haplotype, suggesting that the two SYNJ1 mutations originated from different mutational events. Interestingly, the proteins encoded by SYNJ1 and DNAJC6 both act in the
molecular pathway of the clathrin-mediated endocytosis, suggesting an involvement of
204
Appendix
this pathway in the pathogenesis of early-onset parkinsonisms. Chapter 3.3 is focused
on another form of parkinsonism, caused by mutations in the FBXO7 gene. In this
study, we describe a novel family with juvenile atypical parkinsonism due to a homozygous mutation in the FBXO7 gene, contributing to the clinical and genetic characterization of this rare form.
Part IV of the thesis contains our work in movement disorders other than parkinsonian syndromes. In chapter 4.1, we present clinical, genetics, functional, and metabolic
findings in two siblings with dystonic disorders caused by compound heterozygous
ECHS1 mutations. This work expands the phenotype associated with ECHS1 mutations,
showing that mutations in this gene can also cause primary paroxysmal exercised-induced
dystonia. In chapter 4.2, we report a recurrent C19orf12 mutation in Turkish patients
with neurodegeneration with brain iron accumulation. Our work contributes to the
phenotypic characterization of this recently identified form of neurodegeneration, and
has important implications for the genetic testing of the patients with Turkish origins.
Appendix
Finally, in the last part of the thesis (chapter 5.1), we discuss the genetic architecture of
movement disorders and the contribution of this thesis in the characterization of these
disorders and in understanding their pathogenetic mechanisms.
205
Samenvatting
Bewegingsstoornissen zijn neurologische syndromen waarbij er een overschot dan wel
een tekort is aan vrijwillige en automatische bewegingen die niet gerelateerd zijn
aan spierzwakte of spasticiteit. Deze bewegingsstoornissen zijn geassocieerd met een
gestoorde functie van de basale ganglia en het cerebellum. Dit zijn de hersenstructuren
die samen de controle over bewegingen orkestreren. In de afgelopen twee decennia
hebben genetische studies naar bewegingsstoornissen onderliggende erfelijke factoren
geïdentificeerd, variërend van zeldzame Mendeliaanse factoren met een groot effect tot
veelvoorkomende varianten met een kleine effectgrootte. Met het toepassen van nieuwe
DNA sequencing technieken zullen in de komende jaren nog veel meer genetische factoren
geïdentificeerd worden en dit zal mogelijk leiden tot het volledig in kaart brengen van de
genetische architectuur van deze stoornissen.
In de introductie van dit proefschrift (deel I) worden de moderne strategieën voor de
identificatie van genetische factoren besproken die zijn geassocieerd met Mendeliaanse
vormen van bewegingsstoornissen. Na een korte beschrijving van deze stoornissen
focussen we op het gebruik van next-generation sequencing technologieën voor de
identificatie van Mendeliaanse mutaties in bewegingsstoornissen (hoofdstuk 1.1).
De volgende drie delen van dit proefschrift beschrijven ons experimentele werk. In
Deel II focussen we op het genetische onderzoek naar de ziekte van Parkinson (PD). In
hoofdstuk 2.1 beschrijven wij de identificatie van DNAJC6 mutaties in families met
vroeg opgetreden PD. Ons werk beschrijft een nieuwe vorm van autosomaal recessieve
vroeg opgetreden PD veroorzaakt door mutaties in DNAJC6 en dat deze mutaties
belangrijke consequenties hebben voor de genetische diagnostiek en counseling van
patiënten met deze vorm van PD. In hoofdstuk 2.2 beschrijven we de genetische en
klinische bevindingen in een nieuwe familie met vroeg opgetreden parkinsonisme welke
geassocieerd is met een triplicatie van de SNCA locus. Deze studie draagt bij aan de
genetische en klinische karakterisatie van deze zeldzame vorm van parkinsonisme. In
hoofdstuk 2.3 beschrijven we de resultaten van een groot exome sequencing project
in de geïsoleerde populatie van het eiland Sardinië met het doel nieuwe risicofactoren
te identificeren met gematigde tot grote effectgrootte. In deze studie hebben we
kandidaat-varianten geïdentificeerd die verrijkt dan wel exclusief aanwezig zijn in
patiënten met PD. Deze varianten zijn een belangrijke bron voor toekomstig onderzoek.
Opmerkelijk is dat dit de eerste studie is waarin een groot cohort patiënten met PD op
exome-breed niveau wordt geanalyseerd.
Deel III van dit proefschrift is gefocust op de studie van atypische vormen van
parkinsonismen. In hoofdstuk 3.1 rapporteren we identificatie van een nieuwe vroeg
opgetreden vorm van atypisch parkinsonisme veroorzaakt door een homozygote
206
Appendix
mutatie in het gen SYNJ1. Dit was de eerste studie waarin de associatie tussen een
SYNJ1 mutatie en parkinsonisme werd gerapporteerd. In hoofdstuk 3.2 beschrijven we
een familie met een milder fenotype veroorzaakt door dezelfde homozygote mutatie in
een ander haplotype. Dit suggereert dat de twee SYNJ1 mutaties voortkomen uit twee
verschillende mutationele gebeurtenissen. Interessant is dat het eiwitten die worden
gecodeerd door SYNJ1 en DNAJC6 beiden een rol spelen in clathrin-gemedieerde
endocytose. Dit suggereert dat deze cascade een rol speelt in de pathogenese van vroeg
opgetreden parkinsonismen.
In hoofdstuk 3.3 focussen we op een andere vorm van parkinsonisme, veroorzaakt door
mutaties in het gen FBXO7. In deze studie beschrijven we een nieuwe familie met een
juveniele atypische vorm van parkinsonisme veroorzaakt door een homozygote mutatie
in FBXO7. Deze studie draagt bij aan de genetische en klinische karakterisatie van deze
zeldzame vorm van parkinsonisme.
Deel IV van dit proefschrift bevat ons werk in het veld van bewegingsstoornissen,
anders dan parkinsonistische syndromen. In hoofdstuk 4.1 presenteren we klinische,
genetische, functionele en metabole bevindingen van de studie van twee broers met
dystone stoornissen veroorzaakt door een compound-heterozygote mutatie in ECHS1.
Dit werk breidt het fenotype uit dat geassocieerd kan worden met mutaties in ECHS1
en laat zien dat mutaties in dit gen ook paroxysmale inspannings-geïnduceerde dystonie
kan veroorzaken. In hoofdstuk 4.2 beschrijven we een vaak voorkomende C19orf12
mutatie in Turkse patiënten met neurodegeneratie met ijzerstapeling in de hersenen. Ons
werk draagt bij aan de fenotypische karakterisatie van deze recent geïdentificeerde vorm
van neurodegeneratie en heeft belangrijke implicaties voor de genetische diagnostiek
van patiënten met een Turkse achtergrond.
Appendix
Ten slotte, in het laatste deel van dit proefschrift (hoofdstuk 5.1) bespreken we de
genetische architectuur van bewegingsstoornissen en de bijdrage van dit proefschrift aan
de karakterisatie en het begrijpen van de onderliggende pathogenetische mechanismen
van deze stoornissen.
207
Riassunto
I disordini del movimento sono sindromi neurologiche che presentano un eccesso di
movimenti o una scarsezza di movimenti sia volontari che automatici, in assenza di
debolezza o spasticità. Queste malattie sono associate a disfunzioni dei gangli della base
e del cervelletto, le strutture cerebrali che si occupano di orchestrare il controllo dei
movimenti. Vent’anni di studi hanno portato all’identificazione di molti fattori genetici
associati ai disturbi del movimento. Questi includono sia fattori Mendeliani, che
fattori comuni nella popolazione che modificano di poco la probabilità di sviluppare la
malattia, ma anche fattori con frequenza intermedia ed effetto intermedio. Nei prossimi
anni, grazie all’applicazione delle nuove tecnologie di sequenziamento del DNA, un
numero crescente di fattori verrà probabilmente identificato nel campo dei disturbi del
movimento. Questo ci permetterà di comprendere appieno l’architettura genetica di
queste malattie.
L’introduzione (Parte I) di questa tesi tratta le strategie moderne per l’identificazione
di fattori genetici associati a forme Mendeliane di disordini del movimento. Dopo una
breve definizione di queste malattie, approfondiamo l’uso delle cosiddette tecnologie di
sequenziamento di prossima generazione (next-generation sequencing) per l’identificazione
di mutazioni Mendeliane nei disordini del movimento (capitolo 1.1).
Le successive tre parti di questa tesi illustrano il lavoro sperimentale svolto. La seconda
parte è dedicata agli studi genetici sulla malattia di Parkinson (MP). Nel capitolo
2.1 riportiamo, per la prima volta, l’identificazione di mutazioni nel gene DNAJC6
in famiglie con MP. Il nostro lavoro delinea una nuova forma autosomica recessiva di
MP ad esordio precoce causata da mutazioni in DNAJC6. Questa scoperta ha delle
importanti implicazioni per la diagnosi e la consulenza genetica di pazienti con MP
ad esordio precoce. Nel capitolo 2.2 riportiamo studi genetici e clinici in una nuova
famiglia affetta da parkinsonismo ad esordio precoce associata ad una triplicazione del
locus del gene alpha sinucleina (SNCA). Questo studio contribuisce alla caratterizzazione
genetica e clinica di questa forma rara di parkinsonismo. Nel capitolo 2.3, al fine
d’identificare nuovi fattori di rischio per la MP con effetto intermedio o pronunciato,
applichiamo su larga scala la tecnica di exome sequencing alla popolazione sarda, un
isolato genetico. Il nostro lavoro ha portato all’identificazione di una serie di varianti
che, in questo studio, sono più frequenti in individui con la MP. Questi dati forniscono
una risorsa di candidati genetici che potranno essere validati in studi futuri. Questo è
stato il primo studio di popolazione a riportare l’analisi dell’intero esoma in una larga
coorte di pazienti con la MP.
La terza parte di questa tesi è focalizzata sullo studio di diverse forme di parkinsonismi
atipici. Nel capitolo 3.1 riportiamo l’identificazione di una nuova forma genetica di
208
Appendix
parkinsonismo atipico ad esordio precoce causata da una mutazione omozigote del gene
sinaptogianina (SYNJ1). Questo studio è stato il primo ad associare una mutazione in
SYNJ1 a questo tipo di disordine. Nel capitolo 3.2 presentiamo una famiglia con un
quadro clinico meno severo, causato però dalla stessa mutazione in omozigosi. Dopo
aver analizzato la serie di alleli presenti in questa regione genomica nei pazienti delle
due famiglie (geneticamente definito come “aplotipo”), suggeriamo che la mutazione
sia probabilmente originata da due distinti eventi mutazionali. È interessante notare
che sinaptogianina e auxilina, ovvero le proteine codificate dai geni SYNJ1 e DNAJC6,
hanno entrambe un ruolo essenziale nel processo cellulare di endocitosi mediato da
clatrina. Da qui l’importante scoperta che questo processo sia direttamente coinvolto
nella patogenesi dei parkinsonismi ad esordio precoce. Il capitolo 3.3 è focalizzato
su un’altra forma di parkinsonismo, quella causata da mutazioni nel gene FBXO7. In
questo studio contribuiamo alla caratterizzazione clinica e genetica di questa forma rara
descrivendo una nuova famiglia con parkinsonismo giovanile causato da una mutazione
omozigote nel gene FBXO7.
La quarta parte della tesi contiene il nostro lavoro sui disturbi del movimento diversi
dalle sindromi parkinsoniane. Nel capitolo 4.1 descriviamo due fratelli con disturbi
distonici differenti causati da mutazioni eterozigote composte del gene ECHS1 e
supportiamo questa scoperta con dati clinici, genetici, funzionali e metabolici. Questo
lavoro espande il fenotipo associato a mutazioni in ECHS1 e mostra come mutazioni
in questo gene possano anche causare distonia parossistica primaria indotta da esercizio
fisico (primary paroxysmal exercised-induced dystonia). Nel capitolo 4.2 riportiamo una
mutazione ricorrente nel gene C19orf12 in pazienti turchi affetti da neurodegenerazione
con accumulo cerebrale di ferro (neurodegeneration with brain iron accumulation).
Il nostro lavoro contribuisce alla caratterizzazione fenotipica di questa forma di
neurodegenerazione recentemente identificata e ha importanti implicazioni per le analisi
genetiche di pazienti con origini turche.
Appendix
Per finire, nell’ultima parte (capitolo 5.1), discutiamo l’architettura genetica dei
disordini del movimento ed il contributo di questa tesi nella caratterizzazione di queste
malattie e nella comprensione dei loro meccanismi patogenetici.
209
Appendix
List of Publications
1) Olgiati S,* Skorvanek M,* Quadri M, Minneboo M, Graafland J, Breedveld GJ, Bonte R,
Ozgur Z, van den Hout MCGN, Schoonderwoerd K, Verheijen FW, van Ijcken WFJ, Fen
Chien H, Barbosa ER, Chang HC, Lai SC, Yeh TH, Lu CS, Wu-Chou YH, Kievit AJA,
Han V, Gdovinova Z, Jech R, Hofstra RMW, Ruijter GJG, Mandemakers W, Bonifati V.
Paroxysmal exercise-induced dystonia within the phenotypic spectrum of ECHS1 deficiency.
Mov Disord. 2016. In press
2) Quadri M, Olgiati S, Sensi M, Gualandi F, Groppo E, Rispoli V, Graafland J, BSc,1 Breedveld
GJ, Fabbrini G, Berardelli A, Bonifati V. PRKRA founder mutation causing childhood-onset
generalized dystonia-parkinsonism (DYT16) in an Italian family. Mov Disord. 2016. In
press
3) Olgiati S, Quadri M, Bonifati V. Genetics of movement disorders in the next-generation
sequencing era. Mov Disord. 2016; doi: 10.1002/mds.26521. Epub ahead of print
4) Olgiati S, Quadri M, Mandemakers W, Bonifati V. Reply to Letter by Dr. Elsayed and
colleagues. Ann Neurol. 2016 Feb;79(2):337-8.
5) Olgiati S,* Quadri M,* Fang M,* Rood JP, Saute JA, Fen Chien H, Bouwkamp CG,
Graafland J, Minneboo M, Breedveld GJ, Zhang J; International Parkinsonism Genetics
Network, Verheijen FW, Boon AJ, Kievit AJ, Jardim LB, Mandemakers W, Barbosa ER,
Rieder CR, Leenders KL, Wang J,# Bonifati V.# DNAJC6 mutations associated with
early-onset Parkinson’s disease. Ann Neurol. 2016 Feb;79(2):244-56.
6) Olgiati S,* Thomas A,* Quadri M, Breedveld GJ, Graafland J, Eussen H, Douben H, de
Klein A, Onofrj M, Bonifati V. Early-onset parkinsonism caused by alpha-synuclein gene
triplication: Clinical and genetic findings in a novel family. Parkinsonism Relat Disord.
2015 Aug;21(8):981-6.
7) Quadri M,* Kamate M,* Sharma S,* Olgiati S, Graafland J, Breedveld GJ, Kori I, Hattiholi
V, Jain P, Aneja S, Kumar A, Gulati P, Goel M, Talukdar B, Bonifati V. Manganese
transport disorder: novel SLC30A10 mutations and early phenotypes. Mov Disord. 2015
Jun;30(7):996-1001.
9) Quadri M,* Yang X,* Cossu G,* Olgiati S,* Saddi VM, Breedveld GJ, Ouyang L, Hu J, Xu
N, Graafland J, Ricchi V, Murgia D, Guedes LC, Mariani C, Marti MJ, Tarantino P, Asselta
R, Valldeoriola F, Gagliardi M, Pezzoli G, Ezquerra M, Quattrone A, Ferreira J, Annesi
G, Goldwurm S, Tolosa E, Oostra BA, Melis M, Wang J, # Bonifati V.# An exome study
of Parkinson’s disease in Sardinia, a Mediterranean genetic isolate. Neurogenetics. 2015
Jan;16(1):55-64.
211
Appendix
8) Lewthwaite AJ,* Lambert TD,* Rolfe EB, Olgiati S, Quadri M, Simons EJ, Morrison KE,
Bonifati V, Nicholl DJ. Novel GCH1 variant in Dopa-responsive dystonia and Parkinson’s
disease. Parkinsonism Relat Disord. 2015 Apr;21(4):394-7.
10)Taglia I,* Mignarri A,* Olgiati S, Menci E, Petrocelli PL, Breedveld GJ, Scaglione C,
Martinelli P, Federico A, Bonifati V, Dotti MT. Primary familial brain calcification: Genetic
analysis and clinical spectrum. Mov Disord. 2014 Nov;29(13):1691-5.
11)Yalcin-Cakmakli G, Olgiati S, Quadri M, Breedveld GJ, Cortelli P, Bonifati V, Elibol B.
A new Turkish family with homozygous FBXO7 truncating mutation and juvenile atypical
parkinsonism. Parkinsonism Relat Disord. 2014 Nov;20(11):1248-52.
12) Olgiati S,* De Rosa A,* Quadri M, Criscuolo C, Breedveld GJ, Picillo M, Pappatà S,
Quarantelli M, Barone P, De Michele G, # Bonifati V.# PARK20 caused by SYNJ1 homozygous
Arg258Gln mutation in a new Italian family. Neurogenetics. 2014 Aug;15(3):183-8.
13)Verhoeven WM, Egger JI, Koolen DA, Yntema H, Olgiati S, Breedveld GJ, Bonifati V,
van de Warrenburg BP. Beta-propeller protein-associated neurodegeneration (BPAN), a rare
form of NBIA: novel mutations and neuropsychiatric phenotype in three adult patients.
Parkinsonism Relat Disord. 2014 Mar;20(3):332-6.
14) Quadri M,* Fang M,* Picillo M,* Olgiati S,* Breedveld GJ, Graafland J, Wu B, Xu F, Erro
R, Amboni M, Pappatà S, Quarantelli M, Annesi G, Quattrone A, Chien HF, Barbosa ER;
International Parkinsonism Genetics Network, Oostra BA, Barone P, Wang J,# Bonifati V. #
Mutation in the SYNJ1 gene associated with autosomal recessive, early-onset Parkinsonism.
Hum Mutat. 2013 Sep;34(9):1208-15.
15) Alberio T, Pippione AC, Zibetti M, Olgiati S, Cecconi D, Comi C, Lopiano L, Fasano M.
Discovery and verification of panels of T-lymphocyte proteins as biomarkers of Parkinson’s
disease. Sci Rep. 2012;2:953.
16)Alberio T, Pippione AC, Comi C, Olgiati S, Cecconi D, Zibetti M, Lopiano L, Fasano
M. Dopaminergic therapies modulate the T-CELL proteome of patients with Parkinson’s
disease. IUBMB Life. 2012 Oct;64(10):846-52.
* These authors contributed equally, as first authors
#
These authors contributed equally, as senior authors
212
Appendix
PhD Portfolio
Summary of PhD training and teaching
Name: Simone Olgiati
Department: Clinical Genetics, Erasmus MC
Research school: MGC, Medisch-Genetisch Centrum zuid-west Nederland
PhD period: February 2012 – March 2016
Promotors: Prof.dr. V. Bonifati, and Prof.dr. R.M.W. Hofstra
1) Training
Year
Workload
(ECTS/duration)
Courses
PhD Teaching Program of Biomedical Science:
Genetics
Biochemistry and Biophysics
Cell and Developmental Biology
Literature
2012
2012
2013
2013
3
3
3
2
Safely Working in the Lab
Next-Generation Sequencing Data Analysis
Statistics
Biomedical English Writing and Communication
Browsing Genes and Genomes with UCSC – advanced
2012
2012
2013
2014
2015
0.25
1.4
2
3
0.2
Seminars and workshops
Population Genomics and Complex Traits and Disease – Rotterdam, The Netherlands
Sophia Research Days – Rotterdam, The Netherlands
Masterclass prof. dr. Eric Lander – Amsterdam, The Netherlands
2013
2014
2015
0.9
0.4
0.25
2013
2014
2015
2015
2012-2015
1
1
1
1
4
2012
2012
2013
2013
2013
2014
2014
2015
2015
1
1
1
1
1
1
1
1
1
2) Congresses & presentations
Oral Presentations
World Congress of Parkinsonism and Related Disorders – Geneve, Switzerland
MGC symposium – Rotterdam, The Netherlands
American Society of Human Genetics Annual Meeting – Baltimore, USA
World Congress of Parkinsonism and Related Disorders – Milan, Italy
Dept. Clinical Genetics Research Meeting
International and national conferences
World Congress of Parkinson’s Disease and Movement Disorders – Dublin, Ireland
MGC PhD Workshop – Düsseldorf, Germany
American Society of Human Genetics Annual Meeting – Boston, USA
NVHG Autumn symposium – Arnhem, The Netherlands
World Congress of Parkinsonism and Related Disorders – Geneve, Switzerland
World Congress of Parkinson’s Disease and Movement Disorders – Stockholm, Sweden
MGC symposium – Rotterdam, The Netherlands
American Society of Human Genetics Annual Meeting – Baltimore, USA
World Congress of Parkinsonism and Related Disorders – Milan, Italy
Appendix
(Continues)
213
(Follows)
Year
Workload
(ECTS/duration)
Lecturing
Module MNEU-2.10, Genetics and Neurological Diseases, MSc Program Neuroscience
2013-2016
0.5/year
Supervising practicals
Overerving in de praktijk, Thema Ba.3.A
2014-2016
0.25/year
2013
2014
9 months
6 months
3) Teaching
Supervising students
Ilaria Taglia’s internship (visiting PhD student, University of Siena, Italy)
Demy Kuipers’s internship (student, Hogeschool Rotterdam, The Netherlands)
214
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Acknowledgments
Many colleagues and friends contributed to this thesis with their work, their inspiring
passion, and their support. I wish to thank them all! Some people were particularly
important and I would like to mention them.
Dear Prof. Bonifati, dear Vincenzo, thank you for your essential contribution to this
thesis and for everything you have done for me. During these years, your contagious
passion for science and your rigorous experimental approach inspired me to become
a better scientist. Most importantly, you believed in my potential since the beginning,
and you gave me a great opportunity that changed my life for good. I will never forget
that, grazie!
Dear Prof. Hofstra, dear Robert, thank you for the nice scientific discussions and for
your comments on this thesis. I really appreciated the time you invested in being my
promotor. Your suggestions have improved this book.
I also would like to thank Prof. Reiko Krüger, Prof. Caroline Klaver, Prof. John van
Swieten, Prof. Berry Kremer, and Dr. Anneke Kievit for their comments on this thesis
and for taking part in my dissertation committee. It is an honor to have you all in my
PhD committee.
A great thank goes to all the patients and family members who were involved in our
projects. Moreover, I thank the Stichting ParkinsonFonds and its president, Mr. Rene
Kruijff, for their generous financial support to our research over the years.
I wish to thank the many researchers and clinicians who collaborated with us during these
years. A special thank goes to the colleagues directly involved in the projects presented
here, and especially those involved in multiple studies of this thesis. Among these, I wish
to thank Dr. Anneke Kievit, Dr. Frans Verheijen, and Prof. Ben Oostra (Department
of Clinical Genetics, Erasmus MC); Prof. Egberto Reis Barbosa and Dr. Hsin Fen
215
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I would like to thank my paranymphs for their fundamental help in arranging everything for the defence, but also for their great support during these years. Dear Dr.
Quadri, dear Marialuisa, your contribution to this thesis goes much beyond your role
as paranymph. I cannot thank you enough. You started helping me even before I arrived
in the Netherlands and you initiated me to the lab (and to the Dutch habits!). During
the last four years you have been both a great colleague, and one of my best friends.
Thank you for being such a nice person! Dear Christian Bouwkamp, you really are a
terrific colleague and friend, thanks for everything you have done for me! During our
discussions about science and life, you always showed me novel and interesting perspectives. I will miss your brightness, your revealing opinions, and your scientific jokes.
Chien (University of São Paulo, Brazil); Prof. Bülent Elibol and co-workers (Hacettepe
University, Ankara, Turkey); Prof. Aldo Quattrone, Dr. Grazia Annesi, and co-workers
(University Magna Græcia, Catanzaro, Italy). Moreover, for the fruitful collaborations
with the BGI-Shenzen (China), I wish to thank Prof. Jun Wang, Mingyan Fang, Xu
Yang, and associates.
For our study on DNAJC6 mutations in Parkinson’s disease, I wish to thank Dr. Agnita
Boon and Dr. Janneke Rood (Department of Neurology, Erasmus MC); Prof. Klaus
Leenders (University Medical Center Groningen, The Netherlands); Prof. Laura
Bannach Jardim, Prof. Carlos Rieder, and Dr. Jonas Saute (Hospital de Clinícas
de Porto Alegre, Brazil). For our study on a family with alpha-synuclein triplication, I
wish to thank Prof. Marco Onofrj and Prof. Astrid Thomas (G. d’Annunzio University, Chieti/Pescara, Italy); Dr. Annelies de Klein, Hannie Douben, and Bert Eussen
(Department of Clinical Genetics, Erasmus MC). For the large exome sequencing
project in Sardinia, I wish to thank Dr. Giovanni Cossu, Dr. Maurizio Melis, and
co-workers (General Hospital S. Michele, AOB “G. Brotzu”, Cagliari, Italy); Dr. Valeria
Saddi (San Francesco Hospital, Nuoro, Italy); Dr. Stefano Goldwurm, Prof. Gianni
Pezzoli, and their associates (Istituti Clinici di Perfezionamento, Milan, Italy); Dr.
Mario Ezquerra, Prof. Eduardo Tolosa, and co-workers (Hospital Clínic de Barcelona,
Spain); Prof. Joaquim Ferreira and Dr. Leonor Correia Guedes (University of Lisbon,
Portugal). For their contribution to the SYNJ1 studies, I would like to thank Prof. Paolo
Barone, Dr. Marina Picillo, and co-workers (University of Salerno, Italy); Prof. Mario
Quarantelli and co-workers (National Research Council, Naples, Italy); Prof. Giuseppe
De Michele, Dr. Anna De Rosa, and co-workers (University of Naples “Federico II”,
Italy). For their contribution to the identification of ECHS1 mutations in patients with
dystonia, I would like to thank Dr. Matej Skorvanek, Prof. Zuzana Gdovinova, and
co-workers (Safarik University and University Hospital L. Pasteur, Kosice, Slovakia);
Prof. Robert Jech (Charles University in Prague, Czech Republic); Dr. George Rujiter
and colleagues (Department of Clinical Genetics, Erasmus MC); Dr. Wilfred van
IJcken and associates (Center for Biomics, Erasmus MC); Prof. Chin-Song Lu, Prof.
Yah-Huei Wu-Chou, and co-workers (Chang Gung Memorial Hospital and Chang
Gung University, Taoyuan, Taiwan). For their work on Turkish patients with neurodegeneration with brain iron accumulation, I wish to thank Prof. Murat Emre, Prof.
Hasmet A. Hanagasi, and colleagues (Istanbul University, Turkey); Prof. Okan Doğu
(Mersin University, Turkey); Dr. Reyhan Sürmeli and colleagues (Istanbul Education
and Research Hospital, Turkey); and Prof. Murat Gultekin (Erciyes university, Kayseri,
Turkey). Finally, for his contribution in the FBXO7 study, I would like to thank Prof.
Pietro Cortelli (University of Bologna, Italy).
In addition, I would like to thank Prof. Steven Kushner (Department of Psychiatry,
216
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Erasmus MC) for the projects on the psychiatric families. Moreover, I would like to
thank Prof. Mauro Fasano (University of Insubria, Varese, Italy), not only for his crucial
role during my undergraduate education, but especially for his great support in finding a
position as PhD student and for suggesting Vincenzo’s lab to me. Grazie Mauro!
Scientific research is a team effort and I was lucky to work in a group of nice and talented
people. Therefore, I wish to thank all the members of the PD group: Guido, Josja, Isa,
Christian, Wim, Michelle, Roy, Tianna, Demy, Victor, Stephanie, Martyna, Ilaria,
and Monica. Your work was essential for this thesis and I cannot thank you enough!
I learned a lot from you all, both scientifically and personally and your dedication to
science inspires me every day. Thank you for being always so nice and helpful with me!
I also would like to thank the roommate of the office Ee-914: Agnese, Friedemann,
Renske, Ruben, Cristina, Joachim, Gert, and Gert-Jan. Thank you for being always
very kind with me, and for listening to my (often silly) questions.
I also would like to thank all the members of the Department of Clinical Genetics
(especially the Ee-930 and Ee-922 labs) for creating a nice working environment. In
particular, I would like to thank the people who helped me with scientific comments
and key questions on my work, especially Prof. Rob Willemsen, Dr. Alan Burns, Dr.
Pim Pijnappel, Dr. Grazia Mancini, Dr. Tjakko van Ham, and Dr. Renate Hukema.
A special thank goes to Bep Hoogstraten, Jeannette Lokker, and Cindy Jongen
for their secretarial support, to Sjozef van Baal, Mario Redeker, Ton Verkerk, and
Nils Wijgers for the IT support, and to Ruud Koppenol and Tom de Vries Lentsch
for the photographic support. Moreover, I would like to thank the members of the
Department of Developmental Biology for being such friendly office neighbors.
Finishing a PhD is much easier if you have a good balance between work and social life.
Therefore, I am grateful to all my friends, especially the members of the international
community in Rotterdam. Thank you guys for all the dinners and parties, especially the
great evenings at Franco’s.
L’ultimo ringraziamento va alla mia compagna. Cara Flavia, grazie per esserti lanciata
senza dubbi in questa avventura olandese con me. Insieme abbiamo condiviso gioie e dolori
di questa scelta complicata. Senza le tue attenzioni e il tuo supporto quotidiano non ce l’avrei
mai fatta. Questo libro é per entrambi.
217
Appendix
Un ringraziamento speciale va infine a tutta la mia famiglia (includendo nonni, zii, cugini
e famiglia Niglio!) ed in particolare ai miei genitori. La vostra spinta é stata fondamentale
per convincermi ad iniziare un dottorato in Olanda (grazie mamma!). Il vostro entusiasmo
e il vostro amore incondizionato mi hanno permesso di proseguire questo percorso fino alla
fine. Grazie per tutto quello che avete fatto per me.

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