Abstract

   Network-based systems biology tools including Pathway Studio 9.0 were
   used to identify Parkinson's disease (PD) critical molecular players,
   drug targets, and underlying biological processes. Utilizing several
   microarray gene expression datasets, biomolecular networks such as
   direct interaction, shortest path, and microRNA regulatory networks
   were constructed and analyzed for the disease conditions. Network
   topology analysis of node connectivity and centrality revealed in
   combination with the guilt-by-association rule 17 novel genes of
   PD-potential interest. Seven new microRNAs (miR-132, miR-133a1,
   miR-181-1, miR-182, miR-218-1, miR-29a, and miR-330) related to
   Parkinson's disease were identified, along with more microRNA targeted
   genes of interest like RIMS3, SEMA6D and SYNJ1. David and IPA
   enrichment analysis of KEGG and canonical pathways provided valuable
   mechanistic information emphasizing among others the role of chemokine
   signaling, adherence junction, and regulation of actin cytoskeleton
   pathways. Several routes for possible disease initiation and neuro
   protection mechanisms triggered via the extra-cellular ligands such as
   CX3CL1, SEMA6D and IL12B were thus uncovered, and a dual regulatory
   system of integrated transcription factors and microRNAs mechanisms was
   detected.

1. Introduction

   James Parkinson has been the first to observe this disease in adults in
   the year 1817. In his essay entitled “An Essay of the Shaking Palsy” he
   described this disease as initiated with slow, progressive involuntary
   tremors, followed by difficulty in walking, swallowing and speech
   [[26]1]. Apart from motor symptoms, Parkinson's disease patients
   experience significant non-motor symptoms including mood and cognition
   decline, sleep disturbances, and other autonomic dysfunctions [[27]2].
   With the help of modern-day molecular and cellular research
   advancement, progressive degeneration of the dopaminergic (DA) neurons
   of the Substantia nigra (SN) brain region were found in Parkinson's
   disease brains [[28]3], in addition to the accumulation of misfolded
   protein aggregates. Both environmental factors and genetic mutations
   were suspected to cause PD [[29]4, [30]5]. One of the distinctive
   features of Parkinson's disease is severe damage to the nigrostriatal
   dopaminergic system. Neurotoxic agents such as manganese and
   1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) were suspected for
   this type of neuronal damage. MPTP induced Parkinson's disease animal
   models were extensively used to study the neurodegeneration process as
   well as to identify potential therapeutic drug targets [[31]6]. Soluble
   fractalkine (CX3CL1, chemokine ligand 1) isoform was shown to reduce
   impairment of motor coordination, decrease dopaminergic neuron loss,
   and ameliorate microglial (macrophages of brain) activation and
   proinflammatory cytokine release resulting from MPTP exposure [[32]7].

   Long time belief was that Parkinson's disease etiology is sporadic (not
   genetically inherited) in nature. However, a small percentage of the PD
   patients were now known to inherit gene mutations. Genes including
   ATP13A2, DJ-1, GIGYF2, HTRA2, LRRK2, PARK2 (parkin), PINK1, SNCA and
   UCHL1 were associated with either autosomal dominant or recessive form
   of Parkinson's disease [[33]5]. From the listed genes SNCA (α-synuclein
   or α-syn) is critical to the pathogenesis in the early-onset of the
   rare familial form of PD. Insoluble form of α-syn fibrils were
   discovered in the protein aggregates called Lewy bodies (LBs), the
   hallmark pathological characteristics of Parkinson's disease. The
   aggregation and accumulation of abnormal α-syn in dopaminergic neurons
   have been postulated to be responsible for the neurodegeneration that
   ultimately leads to cell death [[34]8, [35]9]. Synucleins were also
   found in the amyloid-plaques in Alzheimer's disease brains. In general,
   alpha-synuclein is highly expressed in brain at presynaptic terminals,
   particularly in the neocortex, hippocampus, striatum, thalamus, and
   cerebellum components. They function as molecular chaperones and
   interact with many proteins thus modifying their cellular activity. Due
   to its versatile interacting behavior, mutant alpha-synuclein has been
   implicated in the deregulation of many biological processes including
   oxidation, neuroinflammation, mitochondrial function, ubiquitination
   etc. [[36]3, [37]10–[38]12]. [39]Figure 1 depicts the various genes
   already implicated in Parkinson's disease along with different
   deregulated biological processes caused by the several abnormal protein
   activities.

Figure 1.

   [40]Figure 1
   [41]Open in a new tab

   Biological processes and genes implicated in the Parkinson's disease.
   Courtesy: Parkinson's disease pathway from KEGG database, retrieved on
   April 3, 2013.

   To date, many genetic modifiers of PD and their role in PD pathogenesis
   have been described [[42]13–[43]17]. Some of these genes relate to
   neuronal growth and neuroprotective mechanisms in Parkinson's disease.
   FGFs (fibroblast growth factors) have potent neurotrophic properties
   for dopaminergic neurons [[44]18]. They promote DA neuron's development
   and neurite outgrowth, rescue damaged DA neurons after different toxic
   insults, and prevent apoptosis. Overexpression of L1CAM (L1 cell
   adhesion molecule) enhances the survival of imperiled endogenous
   dopaminergic neurons in the Substantia nigra [[45]19]. RAB3A (member of
   RAS oncogene family) has been shown to suppress α-syn toxicity in
   neuronal models of PD [[46]20]. Fractalkines produced by neurons
   suppress the activation of microglia and play a neuroprotective role in
   6-OHDA-induced (synthetic neurotoxic compound) dopaminergic lesions
   [[47]21]. In general, metallothioneins (cysteine-rich, heavy
   metal-binding protein molecules) have been considered ‘defensive
   proteins’ with a role in neuroprotection. Metallothioneins 1 and 2
   (MT1F, MT2A) have been shown to scavenge reactive oxygen species and
   free radicals in central nervous system [[48]22].

   Other genes have been implicated in PD pathogenesis. Neuroinflammation
   is suspected to play a major role in Parkinson's disease progression.
   MAPK signaling pathways contribute to neuroinflammatory responses and
   neuronal death triggered by synuclein-alpha aggregates or functional
   deficiencies in parkin or DJ-1 genes in the pathogenesis of PD
   [[49]23]. RNF11 (ring finger protein 11) was suggested to play major
   role in the Parkinson's disease pathology since it was found highly
   enriched in SN dopamergic neurons as well as its co-localization within
   Lewy bodies (abnormal aggregates of protein) in PD brains [[50]24].
   Earlier study by Galvin et al., (1999) had shown that β- and
   γ-synuclein are associated with hippocampal axon pathology in
   Parkinson's disease and dementia with Lewy bodies [[51]25]. Recent
   genome-wide studies have found that mutations in at least 13 PARK loci
   and related genes increase both early- and late-onset PD susceptibility
   [[52]15, [53]26, [54]27].

   Genome-wide approaches were also used to identify microRNAs-target mRNA
   interactions in PD domain. MicroRNAs (miRNAs) are a class of small RNAs
   (∼22 nucleotides) that act as post-transcriptional regulators of gene
   expression by binding to the complementary sequences in target mRNAs.
   In recent years, miRNAs have emerged as potential drug targets in a
   variety of diseases including infections, metabolism and inflammation
   etc. [[55]28]. A recent genome-wide miRNA profiling study for
   Parkinson's disease has reported several miRNAs to be differentially
   expressed in PD blood samples. The hundreds predicted genes targeted by
   these miRNAs belong to various biological pathways including synaptic
   long-term potentiation, semaphorin signaling in neurons and protein
   ubiquitination pathway, etc., many of which were previously found
   deregulated in Parkinson's disease mechanism [[56]29].

   Even though there were some new treatment options available to PD
   patients, oral administration of levodopa (precursor of dopamine) has
   been the gold standard medication for Parkinson's disease. But
   prolonged use of levodopa increases the risk of developing
   levodopa-induced dyskinesias (involuntary movement) [[57]30, [58]31].
   Recently, deep brain stimulation (DBS) has been offered as a secondary
   treatment option in Parkinson's disease where the benefits of
   medication have failed/diminished. DBS therapy has been shown to
   increase the neuron firing rate, blood flow and to promote
   neurotransmitter release as well as to stimulate neurogenesis. Although
   deep brain stimulation improves the motor symptoms of Parkinson's
   disease, it is a serious surgical intervention with major side effects
   of infection and intracranial hemorrhage including the risk of death
   [[59]32].

   In our study we construct a variety of biomolecular networks proceeding
   from several gene expression datasets covering different areas of brain
   affected by Parkinson's disease. Two such sets were reported by Moran
   et al. in 2006, who provided a whole genome analysis of the Substantia
   nigra (SN), found considerable difference in the gene expressions
   compared to control, reported several new genes that map to PARK loci,
   and identified 570 “priority genes” after using the Benjamini-Hochberg
   FDR correction [[60]33]. Two years later, the same group published a
   network-based analysis based on Pathway Studio's ResNet database
   version 5.0. Several direct interaction networks have been constructed
   for the interactions between priority and known-PD genes. Cancer,
   diabetes and inflammation disease conditions have been associated with
   the top up-regulated priority genes. Another set was published by
   Zhang, et al. in 2005 [[61]34], highlighting some of the deregulated
   genes responsible for either disease aggravation (MKNK2) or
   neuroprotection (HSBP1, SMA5, and FGF13). Deregulation was noticed in
   various genes belonging to metallothionein group and the heat shock
   protein group. These patterns of multiple molecular process
   deregulations have been found across different brain regions studied.
   Another expression pattern discovered supports the hypothesis for
   ubiquitin/proteasome system (UPS) dysfunction in Parkinson's disease. A
   decrease in Complex I activity has also being found to reinforce the
   suspected mitochondrial deregulation in PD.

   With current advancement of different “omics” technologies along with
   effective in-silico testing options, finding successful molecular
   therapeutic targets for Parkinson's disease seems much closer than
   before. Along this avenue, the current paper presents a comprehensive
   network-based analysis of Parkinson's disease (PD) related microarray
   datasets. Helped by the latest accumulated knowledge of gene/protein
   interactions and sophisticated software for network analysis we were
   able to expand upon the previous analyses of this disease paradigm,
   underlying cellular mechanisms and critical molecular players, as well
   as to identify novel drug targets. This research work on Parkinson's
   disease is part of a broader network-based data analysis of three
   neurodegenerative disorders (NDDs) including Alzheimer's (AD) and
   Huntington's disease (HD) with the final goal the identification of
   unified underlying molecular mechanisms of these three devastating
   NDDs. Manuscripts outlining our research findings of AD and HD,
   including the unified molecular mechanisms of NDDs, are in preparation
   and will be submitted for publication subsequently.

2. Methods and Data

   The work flow followed in this study is illustrated in [62]Figure 2.

Figure 2.

   [63]Figure 2
   [64]Open in a new tab

   The study workflow.

Step 1: Microarray gene expression data

   DNA microarray is a powerful technology that provides a high throughput
   and detailed view of the entire genome and transcriptome of an organism
   by measuring the relative mRNA abundance intensity. Due to their ready
   availability, high volume capacity and parallel testing, microarrays
   have dramatically accelerated many types of molecular biology
   investigation. The known limitation of using microarrays is that mRNA
   level does not necessarily correlates with its functional protein level
   in the cell. Also, post-translational modifications essential for
   determining protein function are not present on DNA microarray.
   However, these limitations could be partially overcome by careful
   handling of arrays, probe selections and repeat experiments. Moreover,
   microarray assays are inexpensive and less-time consuming when compared
   with proteomics experiments. Better results in understanding the
   underlying biological mechanisms are yielded by integrating gene
   expression along with proteomics data. Such high quality proteomic data
   are expected to be provided by the Human Brain Proteome Atlas, a
   project launched by 10th HUPO World Congress in 2011 [[65]35].

   Microarray gene expression of post-mortem brain tissue samples from
   diseased and control conditions were used. The three Affymetrix
   GeneChips sets used were [66]GSE8397 U133A and U133B
   ([67]http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE8397) and
   [68]GSE20295 U133A
   ([69]http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20295)
   arrays. This specific selection was influenced by our extended research
   plan to search for the unified underlined mechanism of
   neurodegenerative diseases. Only Affymetrix post-mortem datasets were
   found to cover the three most characteristic neurodegenerative diseases
   - Parkinson's, Alzheimer's and Huntington ones. The samples were
   initially selected after careful review of the cases neuropsychological
   and/or neuropathological data, and matched by age and sex. The control
   subjects were with no known neurodegenerative disease history. The
   [70]GSE8397 arrays included 15 cases and 8 controls each with male to
   female ratios 9:6 and 6:2, respectively. The mean age of cases reported
   was 80±5.7 whereas that of controls 70.6±12.5. The brain
   tissues/regions involved were Superior Frontal Gyrus (SFG), Medial
   Substantia Nigra (MSN) and Lateral Substantia Nigra (LSN). The
   [71]GSE20295 array has equal number of 15 cases and controls. The male
   to female ratios for the two groups were 9:6 and 10:5, while the mean
   age was 76.7±6.2 and 71.2±11.1, respectively. Broadman Area 9 (BA9),
   Putamen (PT) and Substantia Nigra (SN) were the brain tissue/regions
   involved.

Step 2: Detection of significantly differentially expressed genes

   The microarray data analysis was focused on genes differentially
   expressed across different tissues. For consistency between the
   selected datasets, the latter were subjected to the same techniques for
   pre-processing, normalizing and post-normalizing. Bioconductor software
   for analysis and comprehension of genomic data based on R programming
   language [[72]36] ([73]http://www.bioconductor.org/) was implemented in
   written in-house R. The raw microarray CEL files were downloaded from
   the GEO/ArrayExpress databases, and the microarray chip quality was
   assessed using arrayQualityMetrics [[74]37]. More specifically,
   GeneChip reproducibility was assessed, signal-to-noise ratio was
   determined and no extreme outliers were detected. Relevant quality
   assessment figures/plots were obtained

   All microarray expression datasets were normalized to correct for
   systematic differences due to sample preparation, batch processing,
   etc. between genes or arrays. A multi-array average (RMA) expression
   measure was used [[75]38], which consists of three steps: background
   correction, quantile normalization (each performed at the individual
   probe level), and robust linear model fit using median polish
   (log-transformed intensities at the probesets level). The standard RMA
   approach was utilized to enable more direct comparisons with other
   similar research results.

   The differential gene expression changes were statistically evaluated
   by the empirical Bayes (eBayes) method [[76]39] from the limma
   Bioconductor package. Probe-sets with p-values < 0.05 were considered
   to be significantly differentially expressed genes (SDEGs). R source
   code for statistical analysis of such microarray gene expression
   dataset, including graphical output of the differentially expressed
   gene, can be obtained from the authors by request.

Step 3: Generation of “Seed genes” Set

   The microarray datasets were subjected to the same statistical
   procedures given above and significantly differentially expressed genes
   lists called “seed genes” were generated for each dataset. The lists
   generated from the [77]GSE8397 dataset were denoted as SFG, MSN and
   LSN, for the three types of brain tissue samples: superior frontal
   gyrus, medial and lateral Substantia nigra, respectively. In addition,
   differential gene expression changes found between control and PD cases
   irrespective of tissue types were denoted as “Diagnosis”. An overlap of
   414 seed genes was found between the four sets of significantly
   differentially expressed genes (SDEGs), as shown in [78]Figure 3a. In a
   similar way, an overlap of 225 seed genes was found in the [79]GSE8397
   HG-U133B microarray gene expression dataset ([80]Figure 3b).
   Altogether, 631 seed genes were found after removing duplicates in
   [81]GSE8397 U133A and U133B datasets.

Figure 3.

   [82]Figure 3
   [83]Open in a new tab

   Four-set Venn diagram of the overlap of significantly differentially
   expressed genes (SDEGs) in (a) [84]GSE8397 HG-U133A (b) [85]GSE8397
   HG-U133B and (c) [86]GSE20295 HG-133A gene expression datasets.

   Correspondingly, using [87]GSE20295 HG-U133A ([88]Figure 3c) microarray
   dataset another four sets of seed genes, namely diagnosis, BA9, PT and
   SN (for tissue samples used), were generated and an overlap of 110
   genes were considered SDEGs (p-values < 0.05). Finally, combining the
   three Parkinson's microarray datasets ([89]GSE8397 (HG-U133A and B) and
   20295) we found a total of 719 (p-values < 0.05) genes to be
   significantly differentially expressed.

   The p-values shown above were obtained with paired p-test without
   correction for multiple correlations. Due to the specificity of the
   post-mortem expression datasets no statistically significant expressed
   genes were found after Bonferoni correction, while with the less
   stringent Benjamini-Hochberg correction the number of SDEGs was not
   large enough to allow for meaningful analysis. However, to compensate
   partially for not taking into account the probes correlation, we
   selected to consider a more stringent p-value cut-off of 0.01 for the
   paired p-test value. With the new cut-off the total number of “seed
   genes” for Parkinson's disease was reduced from 719 to 267 (see
   Supplementary Table S1 for details).

Step 4: Construction of various types of neurodegenerative disorder networks.
Identification of novel PD-genes and drug targets

   For constructing and analyzing networks relevant to neurodegenerative
   diseases we selected Pathway Studio 9.0 software package [[90]40],
   ([91]http://www.elsevier.com/online-tools/pathway-studio). It offers
   options to construct various kinds of networks such as direct
   interaction, shortest-path, common targets and regulators of pairs or
   multiple genes, and others. The molecular interaction data used in the
   study were supplied by the ResNet 9.0 database (released October 15,
   2011), provided jointly with the software. It covers human, mouse and
   rat proteins. The database is compiled by using MedScan technology from
   over 20 million NCBI's PubMed abstracts and over 880,000 full-text
   articles as of May 27, 2011. Currently the database covers 125342
   entities, such as cell process, complex, disease, functional class,
   treatment and small molecules including over 110000 genes/proteins. It
   offers over a million interactions like binding, chemical reaction,
   direct regulation, expression, miRNA regulation, molecular synthesis,
   molecular transport, promoter binding, protein modification and
   regulations, as well as information about almost 5600 custom built
   cell-processes, metabolic and signaling pathways.

   In this study, we constructed direct interaction (DI) and shortest-path
   (SP) networks to analyze interactions between the SDEGs and with
   connecting genes/proteins that could be of interest in
   neurodegeneration process. Direct regulatory interactions of five
   different types were used only, including among others promoter
   binding, protein modification and miRNA regulation

   By applying the shortest-path (SP) network strategy with the list of
   SDEGs, we were able to identify connecting genes/proteins that might
   contribute to the neurodegenerative process but have not been related
   so far to it. This approach is based on the inference that
   genes/proteins with well-defined biological functions when interacting
   with other genes/proteins known of importance for given disease like
   Parkinson's have a higher probability to share that function, as
   compared to those selected at random (guilt-by-association). One
   limitation of the shortest-path network approach is that sometimes it
   could bring in a large amount of intermediary nodes in order to have a
   unified network. Such a huge network is not only impractical for
   further analysis, but it also diminishes the importance of the seed
   genes in the selected scenario. Thus, care was taken to reduce the
   number of connecting nodes in the shortest-path network producing
   compact shortest-path networks. The last task was accomplished by
   setting up a cut-off rule, to include only seed genes with a large
   number (≥ 25) of neighbors in the Pathway Studio ResNet 9.0 database,
   thus focusing on genes having a better chance to be connected to known
   Parkinson's disease genes. The 267 seed genes were thus reduced to 105
   genes and the ratio of connecting to seed genes/proteins ranged from
   1.5:1 to 2:1 for all the datasets. The construction of the compacted SP
   network was finalized by adding few generic genes without which some of
   the genes of interest would still remain unconnected.

   Special attention in the network analysis was paid to identifying the
   key players - nodes with high network topology scores of node degree
   (local connectivity), closeness centrality (network monitoring) and
   betweeness centrality (traffic-influential) scores [[92]41]. The
   calculation of these topological descriptors was executed with the
   Pajek software package [[93]42]. Nodes with such favorable topological
   characteristics, along with biological/ molecular functions relevant
   for the neurodegenerative process, have been considered in two
   categories “already known PD-genes” and “genes of interest for PD”. The
   distinction was made by using sources like Online Mendelian Inheritance
   in Man (OMIM) database ([94]http://omim.org/), NCBI's PubMed database
   ([95]http://www.ncbi.nlm.nih.gov/pubmed.com), MalaCards database
   ([96]http://malacards.org/), and Google search for the latest
   publications ([97]http://www.google.com). Each of these two categories
   were further divided in two subcategories, those found among the
   significantly differentially expression genes (SDEGs) and such emerging
   from the connecting proteins in shortest-path and common regulator
   networks.

   Differential gene expression was analyzed through complex regulatory
   networks that are controlled by two types of regulators: transcription
   factors (TFs) and microRNAs (miRNAs). In order to identify the
   microRNAs that target our seed genes we constructed shortest-path
   network with only miRNA regulation type of interactions using Pathway
   Studio's ResNet 9.0 database.Then, in order to construct a miRNA
   regulatory network we used the direct interaction network option in
   Pathway Studio utilizing the seed genes and the corresponding miRNAs
   identified in the earlier step. We identified many microRNA regulations
   of our seed genes which will be discussed in detail in the following
   sections. The microRNA regulatory network also revealed an integrated
   regulation in neurodegeneration process by both transcription factors
   and microRNAs. However, the miRNA regulatory analysis should be offered
   with some caution, because currently a high percentage of miRNA-mRNA
   interactions in Pathway Studio ResNet 9.0 database are based on
   predictions but not on experimental validation.

Step 5: Gene Ontology (GO) analysis. Identification of enriched pathways

   Gene ontology (GO), an expert-curated database, assigns a list of genes
   into various biologically meaningful categories such as biological
   process, molecular function, and cellular component. p-values are used
   to rank the significantly modulated genes into GO categories. We used
   the Database for Annotation, Visualization and Integrated Discovery
   (DAVID) [[98]43–[99]45], which provides biological functional
   interpretation of large lists of genes derived from genomic studies
   such as microarray, proteomics experiments, etc. Core analysis in
   Ingenuity's IPA (Ingenuity Systems, [100]www.ingenuity.com) and Pathway
   Enrichment Analysis in Pathway Studio were then applied to identify
   enriched canonical pathways in Parkinson's disease, and the genes from
   the lists of SDEGs and network generated lists that take part in the
   enriched pathways.

Step 6: Mechanistic Analysis

   The results from DAVID analysis were examined in an attempt to
   characterize the integrated molecular mechanisms involved in
   neurodegeneration process. The output includes those GO categories and
   KEGG pathways that are enriched in a given list of genes. Kyoto
   Encyclopedia of Genes and Genomes (KEGG) is a basic database resource
   for understanding high-level functions of biological systems from
   molecular-level information, especially large-scale molecular datasets
   generated by genome sequencing and other high-throughput experimental
   technologies ([101]http://www.genome.jp/kegg/) [[102]46]. The KEGG
   pathways that were significantly enriched (p-value ≤ 0.05 after
   Benjamini-Hochberg FDR adjustments) and previously known in
   neurodegenerative disorders under study were identified and further
   investigated. Google and NCBI's PubMed databases were used to search
   for such previously known biological pathways in neurodegenerative
   disorders. After that, all the genes from the enriched KEGG pathways
   were combined into a list of “mechanism genes”. Based on their
   molecular functions we further classified these “mechanism genes” as
   either disease causing (leading to neuronal loss/death) or disease
   alleviating (helps in neuronal survival) agents. Once again, Google and
   NCBI's PubMed databases were used to identify such previous
   implications. For easy understanding, the loss versus survival
   classification is represented in the figures of next sections by
   highlighting the “mechanism genes” in purple or yellow, respectively.
   Using the “mechanism genes” direct interaction network was constructed
   as well as investigated for integrated disease mechanism. As will be
   shown in Section 3 we outlined three possible mechanisms for initiating
   the Parkinson's disease from extracellular signaling.

3. Results

3.1 Parkinson's disease direct interaction network

   We initiated our Parkinson's disease network analysis using the 267
   “seed genes”, selected as explained in Methods and Data. Out of the 267
   significantly differentially expressed genes (SDEGs) 67 genes were
   directly connected to each other by interactions such as regulations,
   promoter binding, direct regulation, protein modification and miRNA
   regulation. This interaction network ([103]Figure 4) has a relatively
   low average node degree of 2.84. Genes like MAPK8, RAB3A, STXBP1, SYN1
   and VAMP2 are the top five most highly connected nodes with node degree
   ≥ 7. One of the well-known Parkinson's gene SNCA (α-synuclein) was
   among the top five most influential (betweeness centrality) and highest
   accessible (closeness centrality) nodes in the network. 15 of the 67
   genes/proteins (ACHE, ATR, CX3CL1, FGFR1, GRIA1, L1CAM, MAPK8, MT1F,
   MT2A, PRDX2, RAB3A, RNF11, SNCA, SNCG and SPTAN1), have already been
   implicated in Parkinson's disease paradigm either as neuroprotective
   and therapeutic agents or as disease aggravating ones. In [104]Figure
   4, these previously PD-known genes are highlighted in green.

Figure 4.

   [105]Figure 4
   [106]Open in a new tab

   Parkinson's disease direct interaction network. The 15 genes/proteins
   implicated previously in PD pathology are highlighted in green and the
   12 genes/proteins of potential interest for that disease are
   highlighted in blue. Different interactions are represented as follows:
   regulation – dashed grey, molecular transport – dotted red,
   co-expression – solid blue, protein modification – solid green, and
   protein-protein binding – solid purple.

   Based on their characteristic physiological roles 12 genes (BSN, DCLK1,
   KCNQ2, NCAM1, NEDD4L, PAK1, PCDH8, STXBP1, SYN1, UBE2N, UNC13A and
   VAMP2) colored in blue in [107]Figure 4 were classified as potentially
   involved in Parkinson's disease. The molecular functions of some of
   these candidate genes are summarized here. NCAM1 (neural cell adhesion
   molecule 1) is important in cognitive processes such as learning and
   memory. It plays a major role in brain immune surveillance system
   [[108]47]. NCAM1 also facilitates the release, repositioning, and/or
   expansion of the synaptic complex. BSN (bassoon presynaptic cytomatrix
   protein), is a scaffolding protein involved in organizing the
   presynaptic cytoskeleton, the specialized sites where neurotransmitters
   are released from the synaptic vesicles. (Retrieved on 25-Feb-2013 from
   [109]http://www.ncbi.nlm.nih.gov/gene/8927). Campbell et al., (2012)
   [[110]48] have shown that STXBP1 (syntaxin binding protein 1) is a
   vital part of the process of calcium ion–dependent exocytosis in
   neurons, as well as in neuroendocrine cells. It facilitates membrane
   fusion and neurotransmitter release. SYN1 (synapsin I) is known to be a
   key player in synapse formation and plasticity [[111]49]. During an
   action potential (an important part of the neuron firing process),
   synapsins are phosphorylated by PKA (cAMP dependent protein kinase),
   releasing the synaptic vesicles and allowing them to move to the
   membrane and release their neurotransmitter. VAMP2 (vesicle-associated
   membrane protein 2), gene is thought to participate in neurotransmitter
   release at a step between docking and fusion. A recent study has shown
   that single nucleotide polymorphisms in UNC13A (unc-13 homolog A) gene
   may be associated with sporadic amyotrophic lateral sclerosis (ALS)
   [[112]50]. It regulates neurotransmitter release at synapses, including
   at neuromuscular junctions. α-synuclein was shown to promote disruption
   of ubiquitin proteasome system [[113]51]. UBE2N (ubiquitin-conjugating
   enzyme E2N) targets proteins for degradation via the proteasome. In
   recent years, synaptic vesicle trafficking defects have been
   increasingly implicated as an important factor in many PD models,
   either via direct interactions with the synaptic vesicle (SV) cycling
   machinery or via indirect effects caused by mitochondrial dysfunction
   [[114]52]. Even though genes BSN, NCAM1, STXBP1, SYN1, VAMP2 and UNC13A
   are not shown to be directly related to PD, they all seems to play an
   important role in the regulation as well as the release of
   neurotransmitters and synaptic vesicles during the SV cycle process.

   Additional arguments for considering the above mentioned genes as
   associated with Parkinson's disease are provided from network
   perspective. [115]Figure 4 reveals that BSN, STXBP1, SYN1, VAMP2, and
   UNC13A directly interact with RAB3A, a gene well-known in PD, where
   RAB3A is able to provide substantial rescue against α-syn-induced
   degeneration of dopaminergic neurons. Besides with RAB3A, SYN1 is also
   directly connected to GRIA1 and SNCA, two known PD genes. Studies have
   suggested glutamate receptor (GRIA1) antagonists as potential treatment
   agent for Parkinson's disease [[116]53].

   In the direct interaction network, potential candidate genes like PAK1
   and UBE2N are among the top five nodes with high closeness (visibility)
   centrality score. Another of the proposed candidate genes SYN1, was
   among the top five hub nodes as well as among the top five nodes with
   highest betweenness (traffic-influential) centrality score. Being a
   first-level direct interacting neighbors of a known gene
   (guilt-by-association), makes also BSN, NCAM1, PAK1, PCDH8, STXBP1,
   SYN1, UBE2N, UNC13A or VAMP2 genes of potential interest in Parkinson's
   disease. The physiological role these genes play in synaptic vesicle
   trafficking, neurotransmitter release, and ubiquitination, as well as
   their other network attributes like being hubs, network
   traffic-influential and/or monitoring nodes, increases the chance of
   these genes to be involved in the PD pathology, which reinforces the
   arguments in favor of their experimental validation.

3.2 Parkinson's disease shortest path network (SPNW)

   A shortest path network (SP) was built by selecting 105 out of the 267
   significant differentially expressed genes (SDEGs), which have a higher
   chance to be connected to some of the known PD genes (See Methods).
   Interaction types included promoter binding, protein modification and
   direct regulation. 193 genes were added by the Pathway Studio 9.0
   software to connect the 105 seed genes along the shortest paths between
   any pair of genes. The connecting genes were examined in sources like
   OMIM and PubMed, along with Google search to verify whether they have
   already been implicated or not in PD. In the second case, whether they
   could be of potential interest in PD diagnosis was decided based on the
   gene's physiology/molecular characteristics and network location
   (guilt-by-association).

   A more compact version of this 298 genes SP type network was
   constructed using only the genes from the four categories of [117]Table
   1, and adding few generic genes without which some of the genes of
   interest would remain unconnected. The compact SP network (see
   [118]Figure 5) is considerably better connected (average node degree
   6.79) than the one based on direct interactions. Many of the known PD
   genes, such as AKT1, CASP3, CDK5, MAPK1, MAPT and SNCA are highly
   connected in this network. From those, CDK5 and MAPK1 are among the 10
   hub genes (AKT1, CASP3, CDK5, CREB1, CTNNB1, EGFR, MAPK1, SP1, SRC and
   TP53) with node degree > 15. In biomolecular networks highly connected
   nodes tend to be part of critical functions or pathways, some of the
   found hubs like TP53, MAPK1, AKT1 and CASP3 being a typical example.

Table 1.

   Summary of genes of interest and genes already known in Parkinson's
   disease.
   Different categories No. of genes Node color code in figures
   Genes of interest from SDEGs 16 blue
   Known PD genes from SDEGs 15 green
   Genes of interest in SPNW connecting nodes 36 orange
   Known PD genes in SPNW connecting nodes 20 red
   [119]Open in a new tab

Figure 5.

   [120]Figure 5
   [121]Open in a new tab

   Parkinson's disease compact shortest path network. The genes/proteins
   implicated in PD pathology are highlighted in green and red. The
   genes/proteins of potential interest are highlighted in blue and orange
   (green and blue refer to SDEGs, while red and orange to SP network
   connecting genes, respectively). Different interactions are represented
   as follows: protein modification – solid green, promoter binding –
   dotted green and direct regulation – solid grey.

   The nodes included in the network were then subjected to enrichment
   analysis using DAVID software tool which systematically maps the given
   gene list to the associated biological annotation terms. The
   statistically significant enriched Gene Ontology categories and
   pathways related to brain and nervous system, assessed with the
   Benjamini-Hochberg multiple correction, are presented in [122]Table 2.
   Several clusters of genes were thus identified to be involved in neuron
   development, differentiation, projection and apoptosis, synaptic
   transmission, vesicle transport and regulation as biological processes
   affected by Parkinson's disease. Indeed, many of the enriched genes
   like CDK5, FGFR1, L1CAM, NR4A2, PRKCA, RAB3A, RAC1 and SNCA have
   already been studied as mediators, suppressors or regulators of
   neurodegeneration. Pathways such as ErbB signaling and Neurotrophin
   signaling are enriched in this PD related gene list. Both these
   pathways were considered as major avenues to promote survival of
   dopaminergic neurons [[123]54]. Synaptosomes, axons, and
   membrane-bounded vesicles are some of the cellular components that are
   found affected by PD.

Table 2.

   Gene set enrichment analysis of Parkinson's disease compact shortest
   path network.
   Term Gene Count Fold Enrichment Benjamini
   GO:0031175∼neuron projection development 16 9.61 1.37E-08
   GO:0030182∼neuron differentiation 19 6.67 2.88E-08
   GO:0048666∼neuron development 17 7.71 4.11E-08
   GO:0048489∼synaptic vesicle transport 8 37.27 1.11E-07
   GO:0043005∼neuron projection 16 7.38 2.10E-07
   GO:0007268∼synaptic transmission 15 7.74 3.66E-07
   GO:0060627∼regulation of vesicle-mediated transport 10 16.01 5.66E-07
   GO:0048812∼neuron projection morphogenesis 13 9.38 5.81E-07
   GO:0048488∼synaptic vesicle endocytosis 6 65.88 9.59E-07
   GO:0007409∼axonogenesis 12 9.56 1.88E-06
   GO:0019717∼synaptosome 9 16.71 2.17E-06
   GO:0030424∼axon 11 10.92 2.52E-06
   GO:0045202∼synapse 14 6.22 7.42E-06
   GO:0050804∼regulation of synaptic transmission 10 11.3 7.83E-06
   GO:0007611∼learning or memory 9 12.46 1.62E-05
   GO:0051588∼regulation of neurotransmitter transport 6 35.48 1.64E-05
   GO:0016192∼vesicle-mediated transport 17 4.54 1.83E-05
   GO:0046928∼regulation of neurotransmitter secretion 5 36.6 1.80E-04
   GO:0001764∼neuron migration 6 14.41 9.20E-04
   GO:0021955∼central nervous system neuron axonogenesis 4 40.99 0.002
   GO:0031982∼vesicle 14 3.3 0.004
   GO:0030665∼clathrin coated vesicle membrane 5 14.89 0.004
   GO:0007411∼axon guidance 6 8.62 0.007
   GO:0030136∼clathrin-coated vesicle 6 7.17 0.014
   GO:0001963∼synaptic transmission, dopaminergic 3 51.24 0.014
   GO:0016358∼dendrite development 4 17.57 0.014
   GO:0021952∼central nervous system projection neuron axonogenesis 3
   38.43 0.023
   GO:0012506∼vesicle membrane 6 6.27 0.023
   GO:0030425∼dendrite 6 5.81 0.028
   GO:0030426∼growth cone 4 11.69 0.035
   [124]Open in a new tab

   [125]Table 3 lists the genes identified in our study as possibly
   related to Parkinson's disease, based on their moderate-to-considerably
   high connectivity to known PD genes. CTNNB1 (catenin, beta 1) has the
   record environment of ten (!) nearest neighbors in the compacted
   shortest path network (CSPNW, [126]Figure 5) all of which known to be
   involved in Parkinson's disease (AKT1, CASP3, CASP6, CDK5, CREB1,
   MAPK8, NR4A2, PTEN, RAC1 and SMAD3). This makes CTNNB1 number one
   candidate gene of interest. This gene, along with Wnt1 and Fzd-1
   critically contributes to the survival and protection of adult midbrain
   DA neurons [[127]55]. In addition, it has a high betweeness centrality
   which increases its global influence in the network.

Table 3.

   Genes of interest for Parkinson's disease identified by
   “guilt-by-association” with the known PD-related genes.
   Genes of Interest Interacts with no. of known PD genes
   CTNNB1 10
   EGFR 6
   PAK1 5
   CEBPA, CTNND1, ADAM17 4
   CDKN1B, KLF1, ROCK1, SYN1 3
   AKT2, BAK1, DNM1, DYRK1A, NF2, TUBB3 2
   EIF4E, ITSN1, MECP2, NCAM1, NEDD4L, NOS1AP, RASGRF1, RHOA, STX1A,
   STXBP1, SYNJ1, UBE2N 1
   [128]Open in a new tab

   The next strongest candidate for implication with Parkinson's disease
   is EGFR (epidermal growth factor receptor) gene having six PD-related
   neighbors (CASP3, CDK5, PRKCA, RNF11 and TP53). It is one of the top
   ten nodes with highest node degree, closeness as well as betweeness
   centrality scores. This greatly contributes EGFR to be one of the
   critical positions in the compact shortest path network with greater
   visibility and traffic-control. Many studies have shown that EFGR
   signaling play a major role in neurogenesis, neuron survival and
   maintenance [[129]56–[130]59]. In a recent study, EGFR has been
   suggested as a preferred target for treating amyloid-beta induced
   memory loss in Alzheimer's disease [[131]60].

   Third interesting PD candidate is PAK1 (p21 protein
   (Cdc42/Rac)-activated kinase 1) gene having five PD-related neighbors
   (AKT1, CASP3, CDK5, RAC1, and TP53). PAK1 regulates neuronal polarity,
   morphology, migration and synaptic function [[132]61]. The gallery of
   Parkinson's disease potentially related genes from [133]Table 3
   includes also CEBPA (CCAAT/enhancer binding protein (C/EBP), alpha),
   which interacts with four known PD genes (GATA2, IL12B, MT2A, and
   TP53). CEBPA has been shown to bind to the promoter and modulate the
   expression of leptin, a hormone having easy accessibility to the brain.
   It is important to note that leptin receptors are expressed in neurons
   and other brain regions and are known to regulate neural development.
   Thus, leptin could be a potential drug candidate for neurodegeneration
   [[134]62].

   The compact shortest path network included many noteworthy connecting
   proteins like APP, CREB1, HSP90AA1, MAPT and PTEN which were previously
   implicated to play critical roles in many neurodegeneration disease
   pathogenesis and couple of them were indicated to have neuroprotective
   mechanism. APP (amyloid beta (A4) precursor protein), is the major
   component of the filamentous inclusions found in the Lewy bodies and
   Lewy neuritis, the characteristic hallmark features of many
   neurodegenerative diseases including Parkinson's, Alzheimer's, dementia
   with Lewy bodies and multiple system atrophy (MSA). Neurodegenerative
   diseases caused by abnormal aggregations of alpha-synuclein proteins
   are specially classified as alpha-synucleinopathies [[135]63–[136]66].
   Similarly, tauopathies are a class of neurodegenerative diseases that
   are associated with the aberrant accumulations of tau proteins (MAPT)
   in the brain. Hyper phosphorylated tau proteins are the main component
   of neurofibrillary tangles (NFTs), another typical pathological feature
   of neurodegeneration. Tau proteins deformation are found in both
   genetic and sporadic forms of Parkinson's and Alzheimer's diseases in
   addition to other neurodegenerative diseases such as progressive
   supranuclear palsy (PSP), Down's syndrome, Pick's disease
   [[137]67–[138]70]. Many molecular evidence suggests potential
   interaction between alpha-syncluein and tau proteins [[139]71]. PTEN
   gene mutations also contribute to the NFT formations and the
   deregulation of tau phosphorylation [[140]72–[141]74]. Detailed
   biochemical and genetic studies about APP, MAPT and PTEN molecular
   processing will be crucial to the development of therapeutic targets to
   treat many neurodegenerative diseases.

   CREB1 and HSPs were suggested for such therapeutic measures in
   neurodegenerative disorders. In a mice model study [[142]75], it is
   indicated that postnatal disruption of CREB1 along with CREM showed
   progressive neurodegeneration in the hippocampus and in the
   dorsolateral striatum. This evidences that both CREB1 and CREM can
   promote nerve cell survival globally in developing brain while more
   selectively in adult brain. Earlier studies have demonstrated that
   increase in the expression of HSPs, and especially HSP70, by gene
   transfer or HSPs inducers can reduce the aberrant protein misfolding
   and inhibit the pro-apoptotic pathway to attenuate dopaminergic neuron
   degeneration [[143]76].

   Besides being major contributors of neurodegeneration process, APP,
   CREB1, HSP90AA1, MAPT and PTEN have varying degree of interactions with
   many known Parkinson's disease genes (see CSPNW, [144]Figure 5). Among
   these five genes, CREB1 appears to have major network advantage as
   being one of the top ten nodes with highest local connectivity,
   visibility and traffic-influential node in the compact shortest-path
   network. In addition, genes like APP, MAPT and HSP90AA1 are among the
   top 25 nodes with highest connectivity and higher accessibility to all
   other nodes as measured from their node degree and closeness centrality
   score. Other genes from [145]Table 3 might also be investigated for
   possible relations to Parkinson's disease, including the generic genes
   MAPK1 and EGFR, which are also interacting with many known PD genes.

3.4 Integrated Parkinson's disease mechanism

   The genes used to construct the compact shortest-path network were
   subjected to Ingenuity's IPA and DAVID pathway enrichment analysis, the
   latter software utilizing KEGG pathway classifications (Kyoto
   Encyclopedia of Genes and Genomes, [146]http://www.genome.jp/kegg/)
   [[147]46]. IPA produced 25 enriched pathways vs. 34 for DAVID, and
   after elimination of the cancer- and infection disease related
   pathways, the ratio reduced to 18:21. After reviewing Parkinson's
   disease literature we selected sixteen of the David enriched pathways
   ([148]Table 4) belonging to categories of signal transduction, cell
   motility, cell communication, immune system, nervous system and
   neurodegenerative diseases.

Table 4.

   Enriched KEGG pathways in Parkinson's disease resulted from DAVID
   analysis.
   Term Gene Count Fold Enrichment Benjamini Genes
   hsa04510:Focal adhesion 17 6.62 1.49E-07 PRKCA, EGFR, ROCK1, XIAP,
   PTEN, SRC, CTNNB1, AKT1, MAPK1, FYN, RASGRF1, RAC1, RHOA, RAP1A, MAPK8,
   PAK1, AKT2
   hsa04520:Adherens junction 10 10.16 6.08E-06 EGFR, FGFR1, MAPK1, FYN,
   RAC1, RHOA, SMAD3, CTNND1, SRC, CTNNB1
   hsa04010:MAPK signaling pathway 15 4.39 4.57E-05 PRKCA, EGFR, FGFR1,
   TP53, AKT1, MAPK1, CASP3, RASGRF1, MAPT, RAC1, CACNA1G, RAP1A, MAPK8,
   PAK1, AKT2
   hsa04360:Axon guidance 11 6.67 5.10E-05 DCC, MAPK1, ROCK1, PLXNA1,
   SEMA6D, FYN, RAC1, RHOA, L1CAM, PAK1, CDK5
   hsa04012:ErbB signaling pathway 9 8.09 1.12E-04 PRKCA, EGFR, AKT1,
   MAPK1, CDKN1B, MAPK8, PAK1, SRC, AKT2
   hsa04062:Chemokine signaling pathway 11 4.6 6.61E-04 AKT1, MAPK1,
   ROCK1, TIAM1, RAC1, RHOA, RAP1A, CX3CL1, GRK5, PAK1, AKT2
   hsa04310:Wnt signaling pathway 9 4.66 0.002 PRKCA, ROCK1, RAC1, RHOA,
   TP53, SMAD3, SIAH1, MAPK8, CTNNB1
   hsa04722:Neurotrophin signaling pathway 8 5.05 0.004 AKT1, MAPK1, RAC1,
   RHOA, TP53, RAP1A, MAPK8, AKT2
   hsa05010:Alzheimer's disease 9 4.32 0.004 MAPK1, APP, CASP3, NOS1,
   MAPT, SNCA, ADAM17, CDK5, CAPN1
   hsa04530:Tight junction 8 4.67 0.005 PRKCA, AKT1, RHOA, PTEN, SRC,
   SPTAN1, CTNNB1, AKT2
   hsa04115:p53 signaling pathway 6 6.9 0.005 CASP3, TP53, SIAH1, ATR,
   PTEN, ATM
   hsa04370:VEGF signaling pathway 6 6.26 0.008 PRKCA, AKT1, MAPK1, RAC1,
   SRC, AKT2
   hsa05014:Amyotrophic lateral sclerosis (ALS) 5 7.38 0.012 CASP3, NOS1,
   GRIA1, RAC1, TP53
   hsa04540:Gap junction 6 5.27 0.014 PRKCA, EGFR, MAPK1, DRD1, SRC, TUBB3
   hsa04620:Toll-like receptor signaling pathway 6 4.65 0.022 AKT1, MAPK1,
   RAC1, MAPK8, IL12B, AKT2
   hsa04810:Regulation of actin cytoskeleton 8 2.91 0.044 EGFR, FGFR1,
   MAPK1, ROCK1, TIAM1, RAC1, RHOA, PAK1
   [149]Open in a new tab

   Directly shared between IPA and DAVID were the pathways for p53, axonal
   guidance, gap junction and adherence junction signaling. Many signaling
   pathways (see [150]Figure 6) including 14-3-3 mediated, neuregulin,
   semaphorin, ephrin, gap-junction, axonal guidance, as well as different
   growth factor signaling like EGF/EGFR, FGF, and NGF, were found
   enriched in Parkinson's disease pathology. This finding extends over
   the recent report [[151]77]. Neuregulins along with epidermal growth
   factors play a diverse role in neuronal development and
   differentiation. Systemic administration of neuregulin-1β1 protects
   dopaminergic neurons in a mouse model of Parkinson's disease [[152]78].
   Semaphorins and ephrins are prominent families of axon guidance cues
   during normal nerve growth and also after injury. Binding interactions
   were reported between 14-3-3 proteins Synuclein-alpha and LRRK2
   (leucine-rich repeat protein kinase 2), genes linked to sporadic and
   familial form of PD [[153]79]. It was symptomatic to find out major
   neurodegenerative conditions like Alzheimer's disease, Amyotrophic
   lateral sclerosis (ALS) and Huntington's disease signaling, to be
   enriched in Parkinson's disease conditions as well. Discovering these
   overlapping pathways will help to better understand the complex
   neurodegenerative diseases mechanism and to search for therapeutic
   agents common for the entire family of these diseases.

Figure 6.

   [154]Figure 6
   [155]Open in a new tab

   Parkinson's disease enriched canonical pathways as produced by IPA
   analysis.

   The analysis of genes involved in the selected DAVID/IPA pathways
   revealed more genes related to Parkinson's disease manifestation, such
   as FYN (protein-tyrosine kinase oncogene belonging to focal adhesion
   pathway) and VEGF (from VEGF signaling pathway). FYN-mediated signaling
   [[156]80], activates phosphorylation of alpha-synuclein, and the
   accumulation of this phosphorylated protein in the brainstems of
   patients with Parkinson's disease is a signature mark of this disease.
   VEGF (vascular endothelial growth factor) is known to promote
   microglial proliferation, neurogenesis and angiogenesis providing thus
   neuroprotective effects via both direct and indirect mechanisms with
   other players of VEGF signaling pathway [[157]81]. This was one more
   argument to use the 46 genes/proteins found in common in all the 16
   KEGG pathways from [158]Table 4 as an essential part of the integrated
   Parkinson's disease mechanism. The network built on this basis is shown
   in [159]Figure 7.

Figure 7.

   [160]Figure 7
   [161]Open in a new tab

   Integrated Parkinson's disease mechanism. The 46 genes/proteins found
   in common in all 16 enriched KEGG pathways. Genes/proteins implicated
   in PD pathology are highlighted in green/red and those of potential
   interest are highlighted in blue/orange, where blue and green colored
   genes belong to the set of significantly modulated genes, while those
   colored in red and orange are from the set of connecting proteins in
   shortest path network. Different interactions: regulation – dashed
   grey, molecular transport – dotted red, co-expression – solid blue,
   protein modification – solid green, protein-protein binding – solid
   purple, promoter binding – dotted green and direct regulation – solid
   grey.

   The genes in [162]Figure 7 are classified into four categories as being
   already implicated in Parkinson's disease, such of potential interest
   to PD, as well as being disease causing (leading to neuronal
   loss/death) or disease alleviating (helping in neuronal survival. Due
   to the high network interconnectedness no separation between the loss
   and survival genes could be detected; the genes appear as part of a
   single integrated system. Visual inspection of the pathways in KEGG
   database also revealed that there is no definite compartmentalization
   of processes within a biological cell. One process/pathway feed into
   another or multiple pathways, e.g., WnT signaling pathway includes
   players from MAPK, focal adhesion, adherens junction, and Alzheimer's
   disease pathways.

   In examining the integrated mechanism network three routes emerged for
   triggering the Parkinson's disease mechanism via one of the
   extra-cellular ligands CX3CL1, IL12B and SEMA6D. In the first route,
   CX3CL1 (fractalkine) together with DRD1 (dopamine receptor D1)
   suppresses the expression of ionotropic glutamate receptor GRIA1. There
   is also interaction between CX3CL1, ADAM17 (metallopeptidase domain
   17), and LCAM1 which then follows a downstream path into cytoplasm and
   to the nucleus for subsequent regulation of gene expression. ADAM17 and
   TP53 activate the expression of the upstream positioned CX3CL1. The
   suppression of microglial activation by fractalkine contributes to
   neuronal survival. ADAM17 mediated fractalkine cleavage would
   ultimately limit activation of microglia and support neuronal survival
   [[163]82]. There is a two-ways gene expression modulation between
   CX3CL1 and SRC. Inside the cytoplasm AKT1, CASP3, MAPK1, MAPK8
   genes/proteins are direct downstream targets of CX3CL1. Except GRIA1
   and CASP3, all other downstream target genes of CX3CL1 are positively
   activated by it. Some of the players in the outlined route like ADAM17,
   CX3CL1, DRD1, GRIA1, and LCAM1 have been claimed in animal model
   studies as therapeutic targets for Parkinson's disease [[164]19,
   [165]21, [166]83, [167]84].

   Second route is initiated via SEMA6D and its receptor PLXNA1 (plexin
   A1) which in turn regulates RHOA and AKT1 gene expression inside the
   cytoplasm. The downstream activity of MAPK1 in the cytoplasm is also
   negatively modulated by SEMA6D. SEMA6D, on its turn can be negatively
   modulated as upstream target of PLXNA1. Apart from SEMA6D, CAPN1
   (calpain 1, (mu/I) large subunit) negatively regulates the expression
   of both PLXNA1 and SNCA and can thus modulate their downstream actions
   inside the cytoplasm. Semaphorins, secreted proteins involved in the
   guidance of neuronal and nonneuronal cells, interact with receptor
   complexes formed by plexins and neuropilins. There is a literature
   evidence for semaphorins and their receptors to promote or guide
   neuronal axon projection as therapeutic approaches for treatment of
   Parkinson's disease [[168]85, [169]86]. Studies in rodent and cell
   culture models of PD suggest that treatment with calpain inhibitors can
   prevent neuronal death and restore functions thus suggesting that
   calpain inhibition could be a therapeutic strategy in PD [[170]87].

   Third route of the proposed integrated Parkisnon's disease mechanism
   takes place via another extra-cellular ligand IL12B (interleukin 12B)
   which lies upstream to MAP kinases, RAC1 and AKT1, and all these genes
   negatively regulate the gene expression of IL12B. Many studies have
   suggested that neuroinflammation and activated microglia contribute to
   neurodegenerative processes. Interleukins alleviate these harmful
   effects and help in differentiation and survival of neuronal cells that
   were stressed out by activated microglial actions [[171]88, [172]89].

   Thus, from the integrated disease mechanism network we present a
   preliminary outline of three possible routes to enhance the survival of
   the dopaminergic neurons, which could be a source for potential
   therapeutic targets in Parkinson's disease. A more detailed study will
   be needed to elucidate this very complex overall mechanism.

3.5 Parkinson's disease microRNA regulatory network

   A shortest path network (SPNW) was constructed using all the 267 seed
   genes and accounting only for their direct microRNA-mRNA target
   interactions as given in the ResNet 9.0 database of Pathway Studio
   software. 71 regulatory miRNAs were thus identified ([173]Figure 8).

Figure 8.

   [174]Figure 8
   [175]Open in a new tab

   Parkinson's disease regulatory network. The genes and miRNAs implicated
   in PD pathology are highlighted in green and the genes of potential
   interest are highlighted in blue. Genes that code for transcription
   factors (TFs) are highlighted in yellow. MicroRNA-mRNA target
   interactions are represented using solid orange lines.

   [176]Table 5 shows the genes of interest in the MicroRNA Regulatory
   Network (MRN) and how many miRNAs are targeting each gene's mRNA.
   miR-218-1 was found to be the top player regulating the expression of
   16 genes of which three (PCDH8, RIMS3 and STXBP1) are of potential
   interest to Parkinson's disease. In animal model study, it was shown
   that miR-218-1 is expressed in hippocampus [[177]90], where volumetric
   MRI imaging study have found a progressive volume loss in PD human
   subjects [[178]91]. Other microRNAs like miR-29a, miR-132, miR-133a1,
   miR-182, and miR-330 were found to regulate the expression of the known
   Parkinson's related genes ACHE, CX3CL1, FGFR1, L1CAM, and SPTAN1. Being
   direct interacting partners with known PD-related genes some of these
   miRNAs could be considered as potential regulatory targets in
   Parkinson's disease mechanism.

Table 5.

   Genes of interest determined from Parkinson's disease microRNA
   regulatory network.
   Genes of Interest          Targeted by No. of miRNAs
   RIMS3, SEMA6D, SYNJ1       7
   PCDH8                      6
   AQP11, VAMP2               5
   DCLK1, PAK1                4
   BSN, NCAMP1, STXBP1, UBE2N 3
   CACNA1G, GLS, NEDD4L       2
   ACACB, KCNQ2               1
   [179]Open in a new tab

   The microRNA regulatory network also incorporates some of the already
   implicated miRNA's (miR-133b, miR-153, miR-34c, miR-7 and miR-let-7)
   mediated translation regulations of DJ-1, PARKIN, PITX3 and SNCA. These
   genes are relevant to PD pathophysiology being shown to regulate
   dopaminergic neuron differentiation and activity, oxidative stress
   mediated cell death, and mitochondrial energy production dynamics
   [[180]77–[181]79].

   On further examination, microRNA regulatory network revealed that the
   expression of candidate genes like RIMS3, SEMA6D and SYNJ1 was tightly
   regulated by multiple miRNAs. RIMS3 (regulating synaptic membrane
   exocytosis 3) and other RIM family members are generally believed to be
   RAB3 isoforms (RAB3A/B/C/D)-specific effectors that regulate synaptic
   vesicle exocytosis in neurons and in some endocrine cells [[182]95].
   Release and re-uptake of neurotransmitters in the synaptic junction is
   a highly coordinated process and RIMS3, and RAB3A along with other
   proteins play an important role during neurotransmitter release.

   The gene expression of the extra-cellular ligand SEMA6D, proposed as
   one of three initiators of the integrated Parkinson's disease mechanism
   ([183]Figure 7), was found in our miRNA regulatory network to be
   regulated by seven miRNAs (miR-124-1, miR-128-1, miR-16-1, miR-19a,
   miR-23b, miR-30a and miR-9). Some of those like miR-124-1, miR-128-1
   and miR-9 have been previously shown of importance for Alzheimer's
   disease neuropathology being abundantly expressed in Alzheimer
   hippocampus [[184]96]. This may be considered as one more sign for the
   possible existence of common regulatory mechanisms in neurodegenerative
   diseases.

   Another highly microRNA-regulated gene is SYNJ1 (synaptojanin 1) (see
   [185]Table 5), a polyphosphoinositide phosphatase found enriched in the
   brain and located at nerve terminals, as well as associated with
   synaptic vesicles and coated endocytic intermediates. Synaptojanins
   were suggested to accelerate the synaptic vesicle recovery/trafficking
   process at the synapse [[186]97]. Dysfunction of synaptic transmission
   and membrane trafficking are implicated in PD. Based on its molecular
   function, SYNJ1 could play a role in Parkinson's disease molecular
   mechanism.

   Finally, in addition to miRNA mediated regulation, the network also
   included four genes (AFF1, ATF7IP, ATOH8 and TBC1D2B) that encode for
   transcription factors (TFs). These significantly differentially
   expressed TFs indicate a possible integrated TF/miRNA regulation of the
   transcription of Parkinson's related genes.

4. Summary

   The microarray expression data used in our study were a combination of
   data produced and interpreted by different authors [[187]33, [188]34]
   and referring to different regions of brain. With a long-term aim to
   search for a common molecular mechanism for neurodegenerative diseases,
   we renormalized the data for a better comparability. Then, a number of
   specific biomolecular networks were built and analyzed in a variety of
   ways. As a result, while confirming some of the previous finding,
   including part of the novel predicted Parkinson's genes, more such
   PD-related genes were proposed in this work based on
   guilt-by-association analysis and accounting for the importance of
   certain nodes in network topology.

   As well known, the guilt-by-association approach is based on analysis
   of the nearest network neighborhood of genes with proved function in
   the search of interest. Many Parkinson's disease genes were listed in
   the OMIM database. However, our list of SDEGs in all three Parkinson's
   disease datasets used in this study did not include all of the OMIM
   PD-related genes, missing such genes like LRRK2, PARK2, PARK7, PLA2G6,
   PINK1 and UCHL1, while PINK1 and UCHL1 were still both significantly
   expressed in medial Substantia nigra, and UCHL1 also in lateral
   Substantia nigra, but not in all three brain tissue types. We found
   that the log fold-change of PARK2, PARK7 and PLA2G6 was only around
   0.03, which was not significant enough to detect changes in gene
   expression. Affymetrix HG-U133A GeneChip did not contain probe for
   LRRK2 gene but instead included LRRK1 gene probe. Again, LRRK1 did not
   meet the criteria for “seed genes” list since it did not show strong
   differential gene expression and its log fold-change was also only
   around 0.03. While the lack of statistically significant presence of
   the above mentioned PD-related genes could possibly be attributed to
   the loss of expression intensity in the post-mortem brain samples
   compared to a functioning brain, in this study we focused our attention
   mainly to the genes showing considerable change in all three selected
   Parkinson's disease brain tissue samples.

   Despite the reduced base of 15 known PD-genes needed for the
   guilt-by-association predictions we were able to identify from our
   direct interaction network SYN1 neighboring three known PD genes,
   followed by UBE2N and NCAM1 with two and BSN, PAK1, PCHD8, STXBP1,
   UNC13A and VAMP2 with one such neighbor as novel Parkinson's disease
   candidate genes. Second-level interacting partners generally have much
   lesser chance to be included in the list of candidate genes. However,
   this chance may increase for some genes known to show certain functions
   that may be related to the disease of interest. Such is the case with
   DCLK1 gene via its role in synaptic plasticity and neurodevelopment and
   as being first neighbor of SYN1. Another group of novel PD gene
   candidates was found from similar analysis of the shortest path
   network. Such is the case with NEDD4L, SYNJ1, TUBB3 as direct partners,
   and ACACB, CACNA1G, KCNQ2, and SEMA6D as second-level partners to
   already known PD genes. All 17 genes listed here are significantly
   differentially expressed in PD.

   Our network analysis indicated that apart from the strongly
   differentially expressed genes some connecting genes/proteins from the
   shortest path networks could be of similar importance in the
   deregulation of the disease mechanisms. Considering such connecting
   genes/proteins via their guilt by associations to already known PD
   genes we concluded that CTNNB1, EGFR, ADAM17, CEBPA, CTNND1, CDKN1B,
   KLF1, ROCK1 and TIAM1 could also be genes of potential interest in
   Parkinson's disease realm. Some of the genes of this list were found to
   play an important role in network topology. Thus, CTNNB1 and EGFR are
   among the top ten highly connected nodes (with degree > 15), among the
   top ten nodes with higher accessibility to all other nodes as assessed
   by the closeness centrality, and among the top ten traffic influential
   nodes in the network as judged by their betweenness centrality. Genes
   like ADAM17, CEBPA and CTNND1 are among the top 25 high connectivity
   nodes (with degree ≥ 8) and also among the top 25 traffic-influential
   nodes in the network. Besides helping in identifying novel PD-related
   genes, the same line of network analysis has shown that APP, MAPT and
   PTEN, well-known contributors of many other neurodegenerative diseases
   including Alzheimer's, MSA, Pick's, PSPs etc., are important connecting
   genes/proteins in the Parkinson's shortest-path network. Finding such
   genes with common role in neurodegeneration process reinforces our
   study goal.

   We have also added another seven to the numerous miRNAs already known
   to affect the expression of PD-relevant genes [[189]92–[190]94]. With
   caution, because some of their regulatory interactions are not yet
   validated, we predict that miR-132, miR-133a1, miR-181-1, miR-182,
   miR-218-1, miR-29a, and miR-330 could be of interest as potential
   regulators in Parkinson's disease mechanisms, due to their direct
   interaction with known PD related genes. Further investigation of the
   above mentioned miRNA-related regulatory interactions of candidate and
   known PD-genes would deepen our understanding of the molecular
   mechanisms of complex diseases like Parkinson's. Examining the microRNA
   regulatory network, one may conclude that disease pathogenesis is
   complex enough and requires regulatory mechanisms mediated via both
   protein-coding genes and the small noncoding microRNAs.

   All genes listed in this summary were shown through gene set enrichment
   analysis to be key players in various cellular pathways and mechanisms
   like neuron development and differentiation, synaptic transmission,
   vesicle transport and endocytosis, apoptosis, and memory/learning,
   which are altered in the underlying Parkinson's pathophysiology and the
   potential compensatory responses. Moreover, enrichment of Alzheimer's,
   ALS and Huntington's disease signaling pathway was found to take place
   in PD brains as well. This supports the views for the presence of an
   underlying common mechanism for all neurodegenerative diseases.

   In the final stage of our systems biology approach to Parkinson's
   disease we used the KEGG pathways found enriched by DAVID analysis
   along with the enriched canonical pathways from IPA analysis to build
   an integrated mechanistic Parkinson's disease network containing 46
   genes. Three routes of triggering PD molecular mechanisms were
   identified on this basis proceeding from signaling initiated via the
   extra-cellular ligands CX3CL1, SEMA6D and IL12B. Further analysis of
   these routes could reveal novel therapeutic targets for Parkinson's
   disease. Yet, the above findings could be considered only as the tip of
   the iceberg in understanding the intertwined nature of the highly
   complex neurodegenerative diseases.

Supplementary Material

   A Network View on Parkinson's Disease
   [191]Click here for additional data file.^ (25KB, xlsx)

Competing Interests

   The authors have declared that no competing interests exist.

References