Introduction
Parkinson’s disease (PD) is a progressive debilitating movement disorder that affects approximately 1 % of the population older than 65 years of age worldwide [
1]. Clinically, most patients present resting tremor, bradykinesia, stiffness of movement and postural instability. These major symptoms derive from the profound and selective loss of dopaminergic neurons in the substantia nigra pars compacta (SNc), coupled with the accumulation of eosinophilic intracytoplasmic aggregates termed Lewy bodies (LBs) [
1]. Like other complex diseases, PD is believed to be a multifactorial syndrome, resulting from an elaborate interplay of numerous elements (genes, susceptibility alleles, environmental exposures and gene-environment interactions), and its molecular aetiology remains incompletely understood [
2].
In recent years, the intensive efforts of the scientific community and the significant and rapid advancement of biotechnologies have fuelled several steps towards the elucidation of the genetic components of PD. Genome-wide linkage scans and exome sequencing of well-characterized PD families have been successful in discovering disease-causing mutations in dominant (
SNCA,
LRRK2,
VPS35 and the recent
TMEM230), recessive (
PARK2,
PINK1,
DJ1,
DNAJC6) [
2‐
4] and X-linked (
RAB39B) PD genes [
5,
6]. Other genes, such as
CHCHD2 and
EIF4G1, are associated with familial PD inheritance but still require independent confirmations [
7,
8]. Moreover, a set of genes related to atypical parkinsonian forms is known and includes
ATP13A2, whose mutations cause the Kufor-Rakeb syndrome (PARK9) [
9]. Despite the existence of these rare Mendelian monogenic forms, it is now clear that PD is a genetically heterogeneous and most likely complex disorder. This complexity is underlined by the notion that we are currently aware of dozens of loci, genes and risk factors that seem to contribute to PD [
2,
10]. These genes are involved in numerous cellular pathways, such as the ubiquitin-proteasome system, synaptic transmission, autophagy, lysosomal autophagy, endosomal trafficking, mitochondrial metabolism, apoptosis and inflammatory mechanisms, all of which are generally implicated in neuronal cell death [
11].
While the major pathogenic mutations are single nucleotide polymorphisms (SNPs) in the coding regions of PD-linked genes, the contribution of other types of DNA molecular defects (e.g. structural chromosome abnormalities such as CNVs) to the genomic architecture is less emphasized but equally significant [
12,
13]. CNVs are unbalanced rearrangements larger than 50 bp and arise from genomic instability [
12]. They are recognized as critical elements for the development and maintenance of the nervous system and appear to contribute to hereditable or sporadic neurological diseases, including neuropathies, epilepsy, autistic syndromes, psychiatric illnesses and neurodegenerative diseases, such as PD [
14‐
16]. In this regard, several CNVs have been reported in PD patients, including specific pathogenic anomalies mapped in PD loci or involving candidate PD-related genes [
17]. To mention the most recurrent,
SNCA copy-number gains have been proven to play a major role in the disease severity of PARK1, while
PARK2 homozygous or compound heterozygous exon copy number changes are very common among the early-onset cases, rendering the gene-dosage assay essential in mutational screening.
Currently, the detection of CNVs and gene dosage imbalances mainly relies on traditional methodological approaches (karyotyping and PCR-based approaches such as quantitative PCR and multiple ligation probe analysis). However, these methodologies bear objective limits: they are time-consuming and labour-intensive, require multiple phase steps and severe equipment costs and, above all, do not provide a complete genomic overview of structural imbalances at sufficiently high resolution. The development of the array-based comparative genomic hybridization (aCGH) technology has dramatically improved and catalysed the detection and characterization of multiple CNVs, offering high reproducibility, high resolution and scalability for complete genome-wide mapping of imbalances [
18]. The aCGH technique has been refined to the most advanced aCGH plus SNP edition, a widely used array able to simultaneously perform SNP genotyping and CNV detection. This methodology shows higher sensitivity for the detection of low-level mosaic aneuploidies and chimerism and offers the ability to detect loss of heterozygosity, but it has a limited ability to detect single-exon CNVs due to the distribution of SNPs across the genome. For this reason, several customized aCGHs suitably designed to focus on specific clinically relevant chromosomal locations have been developed and are already applied to different human diseases, including neuromuscular diseases, cancer, autism, epilepsy, multiple sclerosis, mitochondrial and metabolic disorders [
19‐
24].
In this study, we developed a customized exon-centric aCGH (hereafter called NeuroArray), tailored to detect single/multi-exon deletions and duplications in a large panel of PD-related genes. We will first report the design strategy and the applied analysis methods. Then, we will show two representative PD cases tested on NeuroArray. Our findings show the advantages of the NeuroArray platform in terms of results, time and costs, as well as for the discovery of new potential genetic biomarkers underlying the pathogenic mechanisms of PD and commonly shared genetic signatures with other neurological diseases.
Discussion
In recent years, several studies have highlighted the key role of CNVs in the development of hereditable or sporadic neurological diseases, including PD [
14‐
16]. Many gene-dosage anomalies have been previously mapped in PD patients, including familiar genes (
SNCA,
PARK2,
PINK1,
PARK7,
ATP13A2) [
48,
49], as well as several rare CNVs in candidate regions [
45]. The aCGH biotechnology currently represents a useful tool for the detection of unbalanced chromosomal changes across the human genome, and its applications to screen common benign and rare pathogenetic CNVs are extensively growing [
19‐
23]. The classical methodologic approaches are a gold-standard test when applied to monogenic disorders, but when applied to multigenic complex pathologies (such as PD), they require higher equipment costs, time, steps and personnel [
50]. Conversely, targeted aCGH is rapid, relatively inexpensive, highly sensitive and an accurate method to simultaneously detect single- and multi-exon CNVs in numerous genes on a unique common platform. For this reason, several whole-genome and exon-targeted aCGH platforms have already been implemented in human diseases [
19‐
24], and their utility has been demonstrated in patients with various clinical complex phenotypes [
51‐
53].
In this study, we have designed and validated a targeted exon-centric aCGH platform (NeuroArray) as a molecular testing tool to simultaneously screen CNV imbalances in a large set of clinically relevant genes for PD and other complex neurological diseases. This customized design offers some considerable advantages: it allows an exon-focused evaluation of structural imbalances in clinically relevant regions at a higher resolution than whole-genome commercially available platforms and lowers the costs of an “exon by exon” analysis through PCR-based approaches, simultaneously providing an extensive window of further potentially involved genetic alterations.
In addition to the customized design, we also applied several approaches for data analysis. The first interesting result was the need to integrate data from both the ADM-1 and ADM-2 algorithms for CNV calling aberrations in order to reduce the number of false positives and to bring out relevant CNVs that otherwise would have been lost. We have also employed a one-probe analysis to reveal small imbalances at the single-exon level. Although this approach has the potential to detect crucial genetic variations ignored by multi-probe analysis, it largely increases the quantity of false-positive probe signals. Therefore, the single-probe analysis would be a useful validation strategy for NGS experiments or to investigate exon copy number changes in a smaller set of causative genes (as we performed with the script in the R-platform).
The use of dedicated high-throughput genotyping platforms like our
NeuroArray could offer new opportunities for the PD genomic research field, mainly for familiar PD cases with an incomplete molecular diagnosis or sporadic cases without any detected genetic anomalies. The large-scale screening of genes that are involved in nervous system dysfunctions could allow for differential diagnosis with other common neurological disorders, refine the genotype-phenotype correlations and explore the potential genetic overlapping signatures among different neurological conditions [
54]. Specifically, the PD panel shares a good number of genes with other neurological diseases (Fig.
1). Given the existence of PD patients with combined clinical and pathological features [
55‐
57], this strategy could be useful to investigate common genetic anomalies underlying very complex phenotypes.
Similarly to other aCGH-based technology,
NeuroArray has some limitations, such as the inability to detect mosaicism poorly represented, balanced structural chromosomal abnormalities, nucleotide repeat expansions (e.g. in
C9orf72 or
ATXN2 genes) and mutations included in regions not covered by probes. To overcome some of these limits and reduce the number of false-positive signals, we are developing a second version of the
NeuroArray design with the aim of improving probe coverage in non-targeted genomic regions, including (where necessary) the intronic flanking regions and the alternatively spliced cassette exons of relevant PD genes [
58‐
60].
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