Abstract

   Huntington's disease is a progressive neurodegenerative disorder
   characterized by motor disturbances, cognitive decline, and
   neuropsychiatric symptoms. In this study, we utilized network-based
   analysis in an attempt to explore and understand the underlying
   molecular mechanism and to identify critical molecular players of this
   disease condition. Using human post-mortem microarrays from three brain
   regions (cerebellum, frontal cortex and caudate nucleus) we selected in
   a four-step procedure a seed set of highly modulated genes. Several
   protein–protein interaction networks, as well as microRNA–mRNA networks
   were constructed for these gene sets with the Elsevier Pathway Studio
   software and its associated ResNet database. We applied a gene
   prioritizing procedure based on vital network topological measures,
   such as high node connectivity and centrality. Adding to these criteria
   the guilt-by-association rule and exploring their innate biomolecular
   functions, we propose 19 novel genes from the analyzed microarrays,
   from which CEBPA, CDK1, CX3CL1, EGR1, E2F1, ERBB2, LRP1, HSP90AA1 and
   ZNF148 might be of particular interest for experimental validation. A
   possibility is discussed for dual-level gene regulation by both
   transcription factors and microRNAs in Huntington's disease mechanism.
   We propose several possible scenarios for experimental studies
   initiated via the extra-cellular ligands TGFB1, FGF2 and TNF aiming at
   restoring the cellular homeostasis in Huntington's disease.

   Keywords: Huntington's disease, Computational molecular neurobiology,
   Transcriptome, Protein interaction network, miRNAs, Microarray analysis

1. Introduction

   In 1872, a young American physician named George Huntington was the
   first to recognize a specific inherited neurodegenerative disorder.
   Later it was named after him as Huntington's disease (HD) and in early
   1990s the mutant gene Huntingtin (HTT) was discovered to be the cause
   of the disease. Expansion of 36 or more CAG trinucleotide (polyQ)
   repeats in HTT gene is the hallmark characteristic of the disease.
   PolyQ expanded HTT is considered as a trigger of the neurodegeneration
   that eventually caused all the Huntington's disease symptoms. The
   disease is described by progressive motor, cognitive as well as
   emotional disturbances. The motor symptoms include chorea, dystonia,
   rigidity, postural instability, etc. Depression and personality changes
   are the major emotional disturbances part of the disorder. Like
   Alzheimer's disease (AD), short-term memory loss, confusion and
   disorientation are some of the cognitive issues found in HD patients.

   In Huntington's disease, it was suspected that the neurodegeneration is
   selective for striatal GABAergic medium-sized spiny neurons. These
   neurons project to substantia nigra and globus pallidus parts of the
   brain affecting primarily motor coordination [25][1], [26][2], [27][3].
   [28]Fig. 1 depicts HTT gene along with its interacting partners which
   trigger the striatal neuronal loss that eventually manifests all the
   Huntington's disease symptoms. Like the other neurodegenerative
   disorders Parkinson's disease and Alzheimer's disease, HD also showed
   protein misfolding, ubiquitin proteasome system deregulation, autophagy
   dysfunction, metabolic and mitochondrial dysfunction as well as
   oxidative stress, which over the years culminates into motor and
   cognitive disorders [29][4], [30][5], [31][6], [32][7]. The advances in
   understanding the molecular pathogenesis of HD, and the multiple
   research approaches undertaken since the discovery of the HD gene in
   1993, are reviewed by Zuccato et al. [33][8]. Mattson summarized the
   evidence for the role of apoptosis and related pathways of oxidative
   stress, perturbed calcium homeostasis, mitochondrial dysfunction and
   caspases activation in neurodegenerative diseases. His analysis
   involved the protecting survival signals, which suppress oxygen
   radicals and stabilize calcium homeostasis and mitochondrial function
   [34][9].

Fig. 1.

   [35]Fig. 1
   [36]Open in a new tab

   Huntington's disease pathway from KEGG database. Biological processes
   and genes implicated in the Huntington's disease. Courtesy:
   Huntington's disease pathway from KEGG database, available at
   [37]http://www.genome.jp/kegg/pathway/hsa/hsa05016.html retrieved on
   Apr. 3, 2013.

   In addition to HTT, mutations in HDL3, JPH3 and PRNP genes were also
   related to Huntington's disease pathogenesis (OMIM database, retrieved
   on Dec. 17, 2012). Other genes such as CCKBR, cytochrome c and GAPDH
   were known to contribute to the diseased state. It was reported that
   there is a possible selective loss of Cholecystokinin receptors (CCKBR)
   containing neurons in cerebral cortex of Huntington's patients
   [38][10]. Cytochrome c release from mitochondria triggers the
   downstream caspase activation leading to apoptotic neuronal death in
   many neurodegenerative diseases. This kind of neuronal death plays a
   greater role at the end stage of HD [39][11]. Due to its selective
   binding to the CAG repeats in huntingtin gene, GAPDH activity was found
   reduced in HD brains, thereby reducing the cellular energy production
   [40][12], [41][13].

   The role of FOXO gene for preserving the normal neuronal capacity, a
   role that is suppressed by the Wnt receptor Ryk in HD was studied by
   Tourette et al. [42][14]. Such models also helped to identify genes
   that regulate the dysfunction of mutant polyglutamine neurons [43][15].
   Brain-derived neurotrophic factor (BDNF) has been suggested to reduce
   amyloid-β neurotoxicity in Alzheimer's disease [44][16], [45][17],
   [46][18]. Some of the suggested beneficial pathways up-regulate the
   innate autophagy process and via increasing BDNF gene expression.
   Autophagy process protects against the toxic insults of mutant
   huntingtin proteins by enhancing its clearance from the cell. BDNF is
   necessary for the survival of striatal neurons in the brain and it
   promotes synaptic plasticity in addition to memory formation. It can
   also act as a neuromodulator affecting the pre-synaptic release of
   neurotransmitters in central nervous system. Along with BDNF, genes
   like BCL2 and G-protein coupled receptors (GPCRs) could also be part of
   therapeutic measures in Huntington's disease. Human antioxidant defense
   proteins that were strongly induced in striatum, but also detectable in
   cortex, were identified as peroxiredoxins 1, 2, and 6, as well as
   glutathione peroxidases 1 and 6 [47][19].

   Huntington's disease research benefited considerably from animal models
   (C. elegans and particularly mouse). These models expand the knowledge
   base of the disease, act as a valuable experimental tool for
   preclinical therapeutic trials, and provide biomarkers of disease
   progression which could be detectable in skin, muscle, blood or other
   peripherally accessible tissue [48][20], [49][21]. This resulted in few
   valuable therapeutic measures to alleviate the disease symptoms. HD
   animal model data suggest a possible interaction between genetic and
   environmental factors [50][22]. Novel transcriptional changes in
   several genes that were involved in synaptic integrity and function
   were found in HD mice [51][23]. Another HD animal model study
   demonstrated that the mutant huntingtin directly or indirectly reduces
   the expression of a distinct set of genes involved in signaling
   pathways that were known to be critical for the functioning of striatal
   neurons [52][24]. When administered systemically or delivered via
   genetically-grafted cells, BDNF has shown to prevent striatal neurons
   from cell death in HD animal models [53][25]. FGF2 (fibroblast growth
   factor 2) was shown to improve motor performance and extend the
   lifespan by 20% by reducing the accumulation of polyQ aggregates in the
   brain [54][26], [55][27]. Zhang et al. [56][28] found that mouse
   caspase activation precedes pro-apoptotic changes in Bcl-2 family
   members. Understanding the chronology of apoptotic events provides
   important information for appropriate therapeutic targeting in this
   devastating and untreatable disease. Decreasing IRS2 (insulin receptor
   substrate 2) signaling could be part of a therapeutic approach to slow
   down the progression of HD [57][29]. Thier abundant presence in central
   nervous system as well as their complex interactions with many
   downstream targets have made GPCRs potential drug targets in many
   neurological diseases including Huntington's disease [58][30]. The
   transcription factor XBP1 deficiency was found to lead in animal
   studies to augmented expression of FOXO1, a key transcription factor
   regulating autophagy in neurons [59][31].

   Recently, three papers used one of the two microarray datasets analyzed
   in our study ([60]GSE3790 with Affymetrix GeneChip Human Genome
   HG-U133A). Neueder and Bates [61][32] uncovered previously unidentified
   transcription dysregulation in the HD cerebellum, and found that genes
   implicated in mitochondrial function, glycolysis, intracellular protein
   transport, proteasome and synaptic vesicles are commonly negatively
   correlated with HD in the cerebellum, frontal and caudate networks.
   Studying molecular mechanisms of HD Kalathur et al. [62][33] found
   indications for potential relevance of the cell cycle processes, RNA
   splicing, Wnt and ErbB signaling, and proposed a candidate set of 24
   novel genetic modifiers. Alcaraz et al. [63][34] used the [64]GSE3790
   dataset in one of the case studies to prove the efficiency of their
   KeyPathwayMiner computational tool. An interesting moment in their
   analysis is that some of the proposed new HD-relevant genes (termed
   “exception” genes) are statistically insignificant. This approach
   (although not rigorously defined) parallels one part of the strategy of
   the present authors used in preceding articles [65][35], [66][36],
   [67][37] under the name “connecting” proteins (vide infra).

   Since its discovery, a multitude of genetic and biomolecular research
   studies have contributed valuable information about the Huntington's
   disease underlying mechanism and the critical genes that were
   deregulated in the process. In this research work, we have utilized
   extensive network techniques to further expand the knowledge base of
   the Huntington's disease's biological mechanisms and the vital
   molecular players. Network-based analysis is a useful tool to
   understand and appreciate the underlying complexity of a system as a
   whole rather than a disconnected unit. This research work on
   Huntington's disease is part of a comprehensive study to understand the
   underlying common molecular mechanisms and genes involved in
   neurodegenerative diseases including Parkinson's disease and
   Alzheimer's disease. Our more important network-based findings about
   the latter two diseases can be found in Refs. [68][36], [69][37].

2. Material and methods

   The study rationale and workflow are similar for all three
   neurodegenerative disorders (NDDs) (Parkinson's, Alzheimer's and
   Huntington's diseases) that we chose to study in understanding the
   underlying common biomolecular mechanisms and genes. Detailed
   information about the study methodology is presented in our Parkinson's
   disease journal article [70][36]. Below, we describe the various steps
   involved in network-based analysis of Huntington's disease.

   Microarray gene expression data is the fundamental means to carry out
   network-based analysis. Such datasets are available in major public
   data repositories like National Center of Biotechnology Information's
   (NCBI) Gene Expression Omnibus (GEO) and European Bioinformatics
   Institute's (EBI) ArrayExpress databases. For uniformity and
   cross-validation, Affymetrix microarray gene expression datasets were
   used for all three NDDs. For Huntington's disease, NCBI GEO's
   [71]GSE3790 dataset [72][38] which contained post-mortem human brain
   tissue samples from both HD patients and controls (44 and 36 samples,
   respectively) taken from three brain regions namely cerebellum, frontal
   cortex and caudate nucleus was used. For a detailed information about
   the samples, refer to Refs. [73][38], [74][39]. The microarray
   expression dataset was then normalized using the Robust Multi-array
   Average (RMA) approach [75][40]. The differential gene expression
   changes were statistically evaluated by the empirical Bayes (eBayes)
   method from the limma Bioconductor package [76][41], [77][42].
   Probe-sets with p-values of < 0.05 were considered to be significantly
   differentially expressed genes (SDEGs).

   The [78]GSE3790 microarray gene dataset had both Affymetrix GeneChip
   Human Genome HG-U133A and B. Following the above mentioned statistical
   plan, the two datasets were analyzed and the significantly
   differentially expressed genes lists called “seed genes” were
   generated. The lists generated from the [79]GSE3790 dataset were
   denoted as CE, FL and CN, for the three types of brain tissue samples:
   cerebellum, frontal cortex and caudate nucleus, respectively. In
   addition, a “diagnosis” differential genes list was generated utilizing
   all the 201 microarray samples in which there were 87 controls and 114
   HD cases. By including the diagnosis set, we were able to filter our
   SDEGs in a little more stringent and concise way.

   A four-step strategy was applied for selection of “seed” genes in our
   analysis. It starts with the strongly modulated genes in each of the
   three types of brain tissues, then identifying the overlapping genes
   between different tissues from the same microarray, next eliminating
   the redundant genes from the overlap between the selected sets of the
   two microarrays. In [80]GSE3790 HG-U133A, an overlap of 617 seed genes
   was found between the four sets of significantly differentially
   expressed genes (SDEGs), as shown in [81]Fig. 2a. In a similar way, an
   overlap of 351 seed genes was found in the [82]GSE3790 HG-U133B
   microarray gene expression dataset ([83]Fig. 2b). Altogether, 925 seed
   genes were found after removing duplicates in [84]GSE3790 U133A and B
   datasets. In order to partially compensate for not accounting for the
   multiple correlation, and still have a considerable number of
   overlapping genes, the fourth selection step included in the network
   evaluation only those SDEGs with the p-value lower than 0.01. Following
   this new cut-off criteria, 531 genes were treated as “seed genes” which
   were then subjected to comprehensive network analysis (See
   Supplementary [85]Table S1 for the list of 531 SDEGs).

Fig. 2.

   [86]Fig. 2
   [87]Open in a new tab

   (a) and (b). Four-set Venn diagram of the overlap of significantly
   differentially expressed genes (SDEGs) in (a) [88]GSE3790 HG-U133A and
   (b) [89]GSE3790 HG-U133B gene expression datasets. Courtesy: Oliveros,
   J.C. (2007–2015) Venny. An interactive tool for comparing lists with
   Venn's diagrams.
   [90]http://bioinfogp.cnb.csic.es/tools/venny/index.html.

   Pathway Studio 9.0 software package
   ([91]http://www.elsevier.com/online-tools/pathway-studio) along with
   its proprietary molecular interaction database namely ResNet 9.0
   (released October 15, 2011) was utilized to construct various networks
   such as direct interaction, shortest-path and miRNA regulation
   [92][43]. In addition, critical network topological characteristics
   such as node degree (local connectivity), closeness centrality (network
   monitoring) and betweenness centrality (traffic-influential) scores
   were also calculated using the Pajek software package [93][44],
   [94][45]. Node degree is defined through the number of nearest
   neighbors in the network. The larger this number, the stronger the
   influence of this network node on its neighborhood in case of positive
   or negative modulation. Closeness centrality is defined as reciprocal
   of the sum of distances from a node to all other nodes in the network.
   High closeness centrality means more effective monitoring the network
   from a given node. Betweenness centrality measures another aspect of
   central location—the possibility to influence a larger portion of the
   network node–node communications. In short, these three measures of
   local, respectively global connectivity and centrality determine the
   speed with which harmful or/and beneficial signals will be transmitted
   through the entire system. Based on these prioritizing topological
   characteristics, as well as on their biological/molecular functions
   relevant for the neurodegenerative process, the seed genes were
   categorized as “already known HD-genes” and “genes of interest for HD”.
   Such categorization was possible after careful review of various
   sources like Online Mendelian Inheritance in Man (OMIM) database
   ([95]http://omim.org/), NCBI's PubMed database
   ([96]http://www.ncbi.nlm.nih.gov/pubmed.com), MalaCards database
   (http://[97]malacards.org/), and Google search for the latest
   publications ([98]http://www.google.com). Later, the 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 network.

   Further the seed genes were subjected to Gene Ontology (GO) enrichment
   analysis using Database for Annotation, Visualization and Integrated
   Discovery (DAVID), a widely used Web-based application focusing on GO
   classification [99][46], [100][47], [101][48]. GO enrichment analysis
   provides biological functional interpretation of large lists of genes
   derived from genomic studies such as microarray, proteomics
   experiments, etc. We also used the core analysis in Ingenuity's IPA
   (Ingenuity Systems, [102]http://www.ingenuity.com/) and Pathway
   Enrichment Analysis in Pathway Studio to explore the various canonical
   pathways that could be affected in Huntington's disease.

   In conclusion, an integrated mechanistic disease network was
   constructed using the genes/proteins found in common in all enriched
   Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways resulted from
   DAVID analysis [103][49]. Again, Pathway Studio software was used to
   construct the direct interaction network using the “mechanism genes”
   (genes found in common in all enriched KEGG pathways) to investigate
   the integrated Huntington's disease mechanism.

3. Results and discussion

   Using the [104]GSE3790 dataset, the original authors had performed gene
   set enrichment analysis using DAVID tools to identify biological
   processes and pathways significantly affected in HD [105][39]. One of
   the major conclusions of this study was that the differential gene
   expression in HD brains showed distinct regional pattern similar to an
   already known pattern of neuronal loss. The greatest number and
   magnitude of differentially expressed genes/proteins were detected in
   the caudate nucleus, followed by motor cortex, then in cerebellum
   tissue types.

   Our statistical analysis with renormalized data revealed a similar gene
   expression difference in these neuronal tissue samples. In this study,
   we expanded upon their research work by subjecting the differentially
   expressed genes to various network techniques to explore the underlying
   cellular mechanisms and molecular players of Huntington's disease. We
   initiated our HD network analysis using 531 “seed genes”.

3.1. Huntington's disease direct interaction network

   Constructing networks using larger gene set such as 531 “seed genes”
   are beneficial in a couple of ways. First, one could have a broader
   view of the network neighborhood of any gene of interest along with all
   its complex interactions. Next, one could also shrink the network size
   in order to have a closer look on the proximity of those nodes that are
   critical. [106]Fig. 3 shows the primary direct interaction (DI) network
   of the 531 Huntington's disease “seed genes”. In this network, 224 of
   these genes directly interact with each other and it included such
   types of interactions like regulation, physical binding, co-expression,
   promoter binding, protein modification, molecular transport and direct
   regulation.

Fig. 3.

   [107]Fig. 3
   [108]Open in a new tab

   Huntington's disease direct interaction network. The 26 genes/proteins
   implicated in HD pathology are highlighted in green and the eight
   genes/proteins of potential interest for that disease are highlighted
   in blue.

   After exhausted literature review, we found in the direct interaction
   network 26 genes that have already been associated to Huntington's
   disease (some of them are discussed in Introduction). Another eight
   genes (CNTNAP1, CX3CL1, DPYSL5, FDFT1, FGFR1, FKBP5, RCAN2 and ZNF148)
   were identified as being of potential interest in Huntington's disease
   pathogenesis due to their specific molecular functions as detailed
   below. More details on how we conducted our literature search and gene
   classification are given in methods and data section above, as well as
   in our Parkinson's paper [109][36]. We constructed a compact direct
   interaction network using these 34 genes/proteins to understand their
   inter-connectivity, and to validate the eight genes of interest by the
   guilt-by-association rule. The network shown in [110]Fig. 4 clearly
   reveals that except for FKBP5 and DPYSL5, all other genes/proteins of
   potential interest are connected to the HD-known ones.

Fig. 4.

   [111]Fig. 4
   [112]Open in a new tab

   Huntington's disease compact direct interaction network. The 26
   genes/proteins implicated in HD pathology are highlighted in green and
   the eight genes/proteins of potential interest for that disease are
   highlighted in blue.

   Their innate physiological roles along with their vital network
   attributes, increases the chance of the eight candidate genes to be
   involved in the HD pathology, elucidating the neuroprotective
   mechanisms in Huntington's disease realm. For instance, fractalkine
   (CX3CL1) is a known Parkinson's disease gene where it exhibited neuro
   protective role against microglia activation as well as reduced motor
   coordination impairment [113][50], [114][51]. Being a known
   neuroprotective agent for a similar neurodegeneration disease with
   movement disorder, it may have a potential therapeutic role in
   Huntington's disease domain too. As mentioned earlier, FGFs were
   proposed to improve the motor performance and to extend the lifespan in
   HD mouse model study. Being a receptor for fibroblast growth factors,
   FGFR1 could up-regulate FGF's beneficial activities in the cell.
   Studies have found that PPAR-γ together with PGC-1α (a transcriptional
   co-activator) is required for the regulation of mitochondrial
   biogenesis. PPAR-γ agonists are thought to be neuroprotective in
   amyotrophic lateral sclerosis (ALS) and HD [115][52]. Currently there
   is no cure available for HD patients. In a search for such a cure, zinc
   finger proteins were designed in such a way that these proteins were
   able to recognize and bind specifically with CAG repeats in mouse DNA
   [116][53]. The study reported that there was considerable reduction of
   the mutant huntingtin gene expression at both protein and mRNA levels
   (95% and 78% reduction, respectively). Many zinc finger proteins
   including ZBTB10, ZFP36L1 and ZNF148 were found significantly
   differentially expressed in the microarray dataset used in our study.
   These zinc finger proteins among which ZNF148 directly interacts with
   three known-HD genes (BCL2, CASP6 and IRS2) could emerge as a promising
   new gene therapy tool for Huntington's disease which could be extended
   and tested in human HD patients.

   A number of the candidate genes shown in [117]Fig. 4 are direct
   interacting partners of many already HD-associated genes. The CX3CL1
   interacts with five already known-HD genes namely, BCL2, CLU, FGF2,
   GAPDH and PPARA. The modulation of these previously known-HD genes have
   been shown beneficial in reducing the disease pathogenesis. This makes
   this protein a significant player in the HD-related biomolecular
   mechanisms.

3.2. Huntington's disease shortestpath network (SPNW)

   Different from the previous direct interaction network type,
   shortest-path network help us to identify indirect protein–protein
   interactions that take place through intermediary nodes in the absence
   of direct relationship. With this in mind, we built the shortest-path
   network using only those seed genes which had at least 25 neighbors in
   Pathway Studio ResNet 9.0 database (in order to have concise and yet
   meaningful network). Two hundred fifty-eight out of 531 seed genes met
   this cut-off criteria and the resulted shortest-path network included
   208 Pathway Studio software-added connecting genes. Following our
   methodology, we categorized these connecting genes into two groups. The
   genes that were already implicated in Huntington's disease belong to
   the known-HD genes group and those genes which could be of potential
   interest in HD due to their cellular functions were grouped separately.
   [118]Table 1 shows the different categories and the number of genes in
   each.

Table 1.

   Summary of genes of interest and genes already known in Huntington's
   disease.
   Different categories No. of genes Node color code in figures
   Genes of interest from SDEGs 13 blue
   Known HD genes from SDEGs 25 green
   Genes of interest in SPNW connecting nodes 23 orange
   Known HD genes in SPNW connecting nodes 24 red
   [119]Open in a new tab

   We constructed a compact shortest-path network (CSPNW), using the 85
   genes from [120]Table 1 along with few additional connecting genes that
   were needed to have a unified well-connected network. The average node
   degree of this compact shortest-path network was 7.10. In this CSPNW,
   many of the known-HD genes such as AKT1, AR, BCL2, INSR and SP1 were
   among the top 25 nodes with node degree ≥ 10, as well as the top 25
   with highest closeness (network monitors) and betweenness (traffic
   influential) centrality measures. [121]Fig. 5 illustrates the
   interactions between the known and the genes of interest in the HD
   compact SPNW.

Fig. 5.

   [122]Fig. 5
   [123]Open in a new tab

   Huntington's disease compact shortest path network. The genes/proteins
   implicated in HD pathology are highlighted in green and red. The
   genes/proteins of potential interest are highlighted in blue and orange
   (see [124]Table 1. for details). Genes/proteins causing neuronal loss
   are highlighted in purple and those that help in neuronal survival are
   in yellow.

   Interestingly, PRNP gene/protein was not statistically differentially
   expressed in our microarray dataset, but it emerged as connecting
   gene/protein in the shortest-path network. PRNP (prion protein) is a
   glycoprotein that tends to aggregate into rod-like structures causing
   neuronal cell death. Prion proteins have been associated with many
   neurodegenerative disorders including Huntington's, Creutzfeldt–Jakob
   diseases in human, and “mad cow” disease in cattle [125][54],
   [126][55], [127][56]. We found that PRNP was of importance for network
   topology as one of the top 15 nodes with highest visibility (closeness)
   and most influence (betweenness) in the compact shortest-path network.

   In the next few paragraphs, we summarize the innate molecular
   characteristics of various genes that could be of potential interest in
   HD pathogenesis, in addition to their “guilt-by-association”
   relationship to some of the already implicated HD genes. [128]Table 2
   lists our proposed Huntington's disease candidate genes along with the
   number of known-HD genes to which they directly interact with (see
   [129]Fig. 5).

Table 2.

   Genes of interest for Huntington's disease identified by
   “guilt-by-association” with the known HD-related genes.
   Genes of interest Interacts with no. of known HD genes
   EGR1 13
   CEBPA 9
   CDK1 8
   HSP90AA1 7
   PRKCZ 6
   E2F1, STAT5A 5
   SRF 4
   ERBB2, FGFR1, IL2, INSR, LRP1, PRKCB, TNF, ZNF148 3
   CX3CL1 2
   CNTNAP1, RCAN2 1
   [130]Open in a new tab

   From the compact shortest-path network, we found that EGR1 (early
   growth response 1) was the nearest interacting partner to an unusually
   high number (13) of previously known HD genes (AKT1, AR, BCL2, CLU,
   CTNNB1, CYCS, FGF2, MAOB, MMP9, SOD1, SP1, TGFB1 and TP53), which
   according to the “guilt-of-association” rule makes it gene of
   considerable interest for the HD-disease mechanisms. BIM (BCL2-like 11
   apoptosis facilitator) plays an important role in neuronal apoptosis, a
   hallmark feature of many neurological diseases including Alzheimer's
   and Parkinson's diseases. Previous research study had demonstrated that
   EGR1 directly transactivate BIM gene expression to promote neuronal
   apoptosis. EGR1/BIM pathway has been suggested as a pro-apoptotic
   mechanism in neurological diseases. Mithramycin A, a U.S. Food and Drug
   Administration clinically approved drug has been studied to improve
   motor symptoms and extend life span in a mouse model of Huntington's
   disease. This drug was suspected to exploit the EGR1/BIM pathway to
   promote neuroprotective mechanism in HD models and thus could be a
   promising drug for the treatment for the same [131][57], [132][58].

   Our next HD candidate gene is CEBPA (CCAAT/enhancer binding protein,
   alpha). It has been shown to bind to the promoter and modulate the
   expression of the gene encoding for leptin, a protein that plays an
   important role in body weight homeostasis. Leptin receptors are found
   in various brain regions such as the hippocampus and cerebral cortex,
   and have known roles in neural development and neuroendocrine
   functions. Studies have indicated that leptin could be neuroprotective
   and thus enhance neuronal survival [133][59]. CEBPA, the promoter of
   leptin gene could also play a critical role in this neuroprotective
   mechanism. In the compact shortest-path network, CEBPA was the
   first-level interacting partners with nine known-HD genes.

   Another recommendation for HD candidate gene is CDK1 (cyclin-dependent
   kinase 1).The abnormal activation of CDK1 is likely to be involved in
   the neuronal cell loss in neurodegenerative diseases including
   Alzheimer's disease and HIV [134][60]. Earlier, CDK5 was suspected to
   contribute to the deleterious protein accumulation in Alzheimer's
   disease [135][61], [136][62], [137][63]. In the compact SP network,
   CDK1 directly interacts with eight known-HD genes. CDK1 is a part of
   the kinase family that is actively contributing to the
   neurodegeneration process in similar disease conditions; it could have
   potential role in HD neurodegeneration mechanism too.

   Thus, undeniably, these candidate genes should be further investigated
   for their molecular role in HD. However, we expect many of these novel
   genes to surpass the experimental verification due to their
   “guilt-by-association” with previously established Huntington's disease
   genes.

   Apart from novel connecting genes, it is valuable to note that genes
   like EGFR, ESR1, HSBP1 and MAPT were also included in the compact SP
   network as connecting genes. They are previously known contributors as
   well as therapeutic agents in neurodegenerative disorders.
   Hyperphosphorylated tau (MAPT) is the major component of the
   neurofibrillary tangles, one of the hallmarks of neurodegenerative
   diseases [138][62], [139][64], [140][65], [141][66]. Similarly,
   huntingtin gene, which mutates strongly in HD was suggested to be
   indirectly associated with EGFR, thus deregulating the downstream
   actions of EGFR leading to cell death [142][67]. Considering the
   treatment measures, HSBP1 and ESR1 are suggested to offer such
   mechanisms. In general, heat shock proteins (HSBP1) are evaluated as
   therapeutic targets in mitigating or preventing protein aggregate
   formations [143][68]. One of the major conclusions of an animal HD
   model study was that the female sex hormone, estrogen (ESR1) could be a
   target for neuroprotective therapy aiming at postponing the onset and
   reducing the severity of HD. A similar pattern of late onset was also
   shown in a human HD study [144][69], [145][70]. Moreover, these four
   connecting genes were among the top 25 nodes with highest connectivity
   (degree > 10) as well as one of the top 25 nodes with highest
   visibility as measured by the closeness centrality scores. Except
   HSBP1, the other three genes were also among the top 25 nodes with
   highest accessibility to other nodes in the network as determined by
   the betweenness centrality in the compact SP network.

   Neueder and Bates [146][32] employed weighted correlation network
   analysis to a number of post-mortem brain tissues in human, as well as
   in mouse samples. They identified extensive transcriptional
   dysregulation in the cerebellum of HD patients, similar to that
   observed in the frontal cortex and caudate nucleus. A common signature
   of gene expression changes in all three brain tissues networks of HD
   patients has been proposed. Our study generally confirms the disruption
   of a number of biological processes and first of all that of
   mitochondrial functions. However, our unweighted correlation network
   analysis did not show HD importance of some of the genes proposed in
   their work, e.g., E2F, NRF1, SF1, E4F1, and ELK1 were excluded from our
   “seed genes” list, due to insufficient statistical significance (p >
   0.05).

   Kalathur et al. [147][33] used the same [148]GSE3970 database, along
   with a couple of other HD mouse model datasets. There is a considerable
   overlap of the enriched KEGG pathways and GO categories with our study.
   20 out of 26 Kalahari's selected genes were significantly expressed in
   our analysis (p < 0.05), all of them expressed in caudate nucleus
   region of the brain. However, due to the more stringent p-value cut off
   criterion in our work (p < 0.01), only one gene (CDK5R2) out of these
   20 was included in our network analysis. We added also MAP3K5 (p <
   0.05), as the only gene with more than 25 neighbors.

   There is no overlap of genes or biological pathways reported in the
   study of Alcaraz et al. [149][34] with those identified in our work.
   Out of the 11 new genes proposed by these authors seven genes (CTNNB1,
   GNAQ, GRB2, OPTN, TP53, UBE2K and YWHAB) have p-value of < 0.01 but
   were not significantly expressed in all tissue types so they were not
   included into our 531 SDEGs.

   Continuing with our network analysis, we subjected the genes in the
   compact shortest-path network to DAVID analysis to identify various
   enriched biological processes and pathways in Huntington's disease.
   [150]Table 3 lists some of the Gene Ontology categories/subcategories
   related to nervous system and functions that were statistically
   significantly enriched in HD (with Benjamini-Hochberg multiple
   correction). DAVID analysis uncovered biological processes involving in
   oxidative stress, reactive oxygen species, deregulation in inflammatory
   response, steroid hormone receptor signaling, lipid binding and insulin
   receptor signaling pathways to be significantly affected in
   Huntington's disease, some of which were mentioned above. Other
   neurodegenerative signaling pathway including Alzheimer's and ALS were
   also considerably affected in Huntington's disease which reinforce our
   view for similar underlying molecular pattern in all these diseases. In
   the next section, we will provide detailed information about our
   proposed model for Huntington's disease mechanism based on the various
   biological pathways that were affected in this disease.

Table 3.

   Gene set DAVID enrichment analysis of Huntington's disease compact
   shortest-path network.
   Term Gene count Fold enrichment Benjamini
   GO:0048666 ~ neuron development 14 6.50 5.99E − 06
   GO:0030182 ~ neuron differentiation 15 5.39 1.44E − 05
   GO:0046324 ~ regulation of glucose import 6 28.60 4.33E − 05
   GO:0043005 ~ neuron projection 13 6.07 1.37E − 04
   GO:0050994 ~ regulation of lipid catabolic process 5 30.25 3.09E − 04
   GO:0010001 ~ glial cell differentiation 6 17.81 3.09E − 04
   GO:0043523 ~ regulation of neuron apoptosis 7 12.23 3.22E − 04
   GO:0031175 ~ neuron projection development 10 6.14 4.40E − 04
   GO:0043627 ~ response to estrogen stimulus 7 10.49 6.42E − 04
   GO:0042063 ~ gliogenesis 6 14.52 6.76E − 04
   GO:0048812 ~ neuron projection morphogenesis 9 6.65 6.99E − 04
   GO:0006979 ~ response to oxidative stress 8 7.67 8.94E − 04
   GO:0001836 ~ release of cytochrome c from mitochondria 4 29.96 0.003
   GO:0048667 ~ cell morphogenesis involved in neuron differentiation 8
   6.02 0.003
   GO:0030425 ~ dendrite 8 7.84 0.004
   GO:0030424 ~ axon 8 8.04 0.004
   GO:0007568 ~ aging 6 8.58 0.006
   GO:0008289 ~ lipid binding 11 3.82 0.009
   GO:0000302 ~ response to reactive oxygen species 5 10.49 0.010
   GO:0050727 ~ regulation of inflammatory response 5 10.35 0.011
   GO:0007409 ~ axonogenesis 7 5.71 0.011
   GO:0008286 ~ insulin receptor signaling pathway 4 17.01 0.013
   GO:0050804 ~ regulation of synaptic transmission 6 6.94 0.013
   GO:0031644 ~ regulation of neurological system process 6 6.17 0.020
   GO:0016192 ~ vesicle-mediated transport 11 3.00 0.023
   GO:0050767 ~ regulation of neurogenesis 6 5.69 0.027
   GO:0030518 ~ steroid hormone receptor signaling pathway 4 10.85 0.037
   GO:0006874 ~ cellular calcium ion homeostasis 6 5.16 0.038
   GO:0030136 ~ clathrin-coated vesicle 6 7.26 0.039
   GO:0055114 ~ oxidation reduction 11 2.71 0.042
   GO:0045121 ~ membrane raft 6 6.70 0.045
   [151]Open in a new tab

3.3. Integrated huntington's disease mechanism

   In addition to various biological processes, DAVID analysis also
   recommended several KEGG pathways to be significantly (p-values of
   < 0.05 with Benjamini-Hochberg multiple correction) affected in
   Huntington's disease of which we selected 10 pathways for further
   evaluation. These pathways were selected (listed in [152]Table 4) on
   the basis of previous implications in Huntington's disease research
   work. Either the entire pathway or many important players of the
   pathways were found deregulated in HD pathogenesis [153][71],
   [154][72], [155][73], [156][74], [157][75], [158][76], [159][77].

Table 4.

   Enriched KEGG pathways in Huntington's disease resulted from DAVID
   analysis.
   Term Gene count Fold enrichment Benjamini Genes
   hsa05016:Huntington's disease 12 5.14 4.40E − 04 CASP3, GNAQ, SP1,
   CYCS, PPARG, CYC1, TP53, COX4I1, REST, CLTC, SOD1, PPARGC1A
   hsa04010:MAPK signaling pathway 14 4.04 5.86E − 04 EGFR, FGFR1, TNF,
   TP53, SRF, TGFB1, PRKCB, AKT1, CASP3, PAK2, MAPT, HSPB1, TRAF6, FGF2
   hsa04012:ErbB signaling pathway 8 7.08 0.002 EGFR, AKT1, PAK2, ERBB2,
   STAT5A, MTOR, SRC, PRKCB
   hsa05010:Alzheimer's disease 10 4.73 0.003 CASP3, TNF, LRP1, GNAQ,
   MAPT, CYCS, CYC1, COX4I1, GAPDH, CDK5
   hsa05014:Amyotrophic lateral sclerosis (ALS) 6 8.72 0.006 CASP3, TNF,
   BCL2, CYCS, TP53, SOD1
   hsa04910:Insulin signaling pathway 8 4.57 0.011 AKT1, PRKCZ, IRS2,
   PRKCI, FOXO1, MTOR, INSR, PPARGC1A
   hsa04920:Adipocytokine signaling pathway 6 6.90 0.012 AKT1, PPARA,
   IRS2, TNF, MTOR, PPARGC1A
   hsa04930:Type II diabetes mellitus 5 8.20 0.015 PRKCZ, IRS2, TNF, MTOR,
   INSR
   hsa04520:Adherens junction 6 6.00 0.016 EGFR, FGFR1, ERBB2, INSR, SRC,
   CTNNB1
   hsa04115:p53 signaling pathway 5 5.67 0.049 CDK1, CASP3, CYCS, TP53,
   SIAH1
   [160]Open in a new tab

   Enriched KEGG pathways belong to endocrine system, cell communication,
   cell growth and death, signal transduction, neurodegenerative diseases,
   and endocrine and metabolic diseases classification. We used the 10
   pathways (see [161]Table 4) to search for any underlying molecular
   mechanism that could either cause or mitigate Huntington's disease
   pathology. To accomplish this task, we constructed an integrated HD
   mechanism network using the 41 genes found in common in all the 10
   enriched KEGG pathways (see [162]Fig. 6).

Fig. 6.

   [163]Fig. 6
   [164]Open in a new tab

   Integrated Huntington's disease mechanism. The 41 genes/proteins are
   found in common in all 10 enriched KEGG pathways. Genes/proteins
   implicated in HD pathology are highlighted in green/red and the
   genes/proteins of potential interest are highlighted in blue/orange.
   Genes/proteins causing neuronal loss are highlighted in purple and
   those that help in neuronal survival are in yellow.

   We then performed a Huntington's disease literature search to classify
   these 41 genes into two groups, namely, genes that aid in the neuronal
   survival or cause loss. Twenty-three out of 41 were implicated in
   neuronal loss and the remaining 18 genes were related to neuronal
   survival. This classification is depicted in [165]Fig. 6 where genes
   are highlighted in purple and yellow, respectively. Similar to our
   integrated neurodegenerative disease mechanism networks of Parkinson's
   and Alzheimer's disease [166][36], [167][37], we found a pattern of
   three extra-cellular ligands (FGF2, TNF, and TGFB1) initiating various
   downstream signaling cascades in the integrated Huntington's disease
   mechanism network as well.

   As explained earlier, FGF2s are pursued as promising drug targets for
   its neuroprotective and neuroproliferative roles. TNF (tumor necrosis
   factor) and TGFB1 (transforming growth factor, beta 1) belong to
   inflammatory cytokine family which is involved in the regulation of a
   wide variety of biological processes including cell proliferation,
   differentiation, adhesion, apoptosis, lipid metabolism, and
   coagulation. Neuroinflammation has been implicated in many neurological
   disorders including Huntington's disease. In general, cytokines are
   required for normal functioning of cells. However, the formation of
   protein aggregates inside the cell triggers inflammatory mechanism
   which leads to increased cytokine activities thereby causing chronic
   cell stress [168][78], [169][79]. A delicate balance has to be
   maintained in order to sustain homeostasis within cell. On examining
   the integrated network, we propose that the hemostasis in Huntington's
   disease environment could be restored by regulating the three
   extra-cellular ligands FGF2, TGFB1 and TNF thereby controlling their
   downstream signaling cascades of various target genes expression. On
   the other hand, even though the integrated HD mechanism network size
   was relatively small, due to the high interconnectedness of all the
   nodes it was difficult to suggest specific intracellular pathway(s) to
   implement in detail the proposed strategy for neuronal restoration in
   Huntington's disease via the three ligands.

   Once again we went back to HD research literature looking for some
   molecular mechanisms and/or therapeutic pathways that are currently
   being utilized in this field. A recent review article by Zuccato et al.
   in 2010 [170][8] described the past achievements, the current status
   along with suspected disease mechanisms and therapeutic measures
   available in Huntington's disease realm. Several research works suggest
   few important players in HD whose regulation could promote
   neurogenesis. Under normal physiological conditions, HTT interacts with
   many genes/proteins including BDNF, MTOR and REST to promote the
   survival of striatal neurons, the ones that are subjected to cell death
   in Huntington's disease. Interaction between BDNF (brain-derived
   neurotrophic factor) and HTT is important for the survival of striatal
   neurons as well as promoting synapse formation. In addition, HTT binds
   and sequesters mechanistic target of rapamycin (MTOR) inside the
   cytoplasm inhibiting MTOR's downstream regulation. In general, MTORs
   are negative regulators of autophagy. Autophagy is an essential,
   homeostatic process by which cells break down their own components.
   They are the debris clearance machineries in the cell that is required
   to protect against infections, autoimmune and inflammatory diseases
   [171][80]. Likewise, REST (RE1-silencing transcription factor) and HTT
   interaction is also important in HD pathogenesis. HTT binds with REST
   to maintain low levels of REST gene expression inside the cytoplasm
   thereby not affecting the transcription of BDNF gene.

   In Huntington's disease, mutation in HTT causes protein aggregation
   formations which were not properly cleared from the cell thus
   disrupting the normal functioning of the stratial neurons. Due to
   transcription suppression by REST, the BDNF level was found reduced in
   neurodegenerative diseases including Alzheimer's, Parkinson's and
   Huntington's diseases [172][81], [173][82]. Mutant HTTs were found
   inducing neuronal death via distinct but complementary pathways
   including deregulation of apoptosis and/or autophagy, altered
   transcription, metabolism and cellular stress responses. Currently one
   of the therapeutic measures suggested in Huntington's disease domain is
   clearing the HTT protein aggregates from the cell through the induction
   of autophagy by the MTOR inhibitor rapamycin. Another indicated
   treatment is through increasing the beneficial BDNF gene expression
   [8]. Animal HD model studies have shown that the use of rapamycin (MTOR
   inhibitor) improved striatal neuron survival and motor performance.
   However, due to deleterious side effects of rapamycin, it was not
   recommended for use as an exclusive drug in HD treatment. A
   combinatorial strategy with rapamycin or other drugs promoting
   autophagy has been suggested as relevant treatment for HD and other
   related diseases [174][83].

   Following these HD literature suggested treatment ideas, we modified
   our integrated Huntington's disease mechanism network to include only
   those nodes (13 genes: AKT1, BCL2, GAPDH, EGFR, FGF2, FGFR1, INSR,
   MTOR, PPARGC1A, REST, SP1, TGFB1 and TNF) that might play a critical
   role in both inhibiting MTOR and improving BDNF gene expression. BDNF
   was added to the reduced network, as was done with HTT. The resulted
   enriched integrated HD mechanism network is shown in [175]Fig. 7.

Fig. 7.

   [176]Fig. 7
   [177]Open in a new tab

   Enriched integrated Huntington's disease mechanism. The 15
   genes/proteins that were suggested to play a major role in HD
   treatment. Genes/proteins implicated in HD pathology are highlighted in
   green/red and the genes/proteins of potential interest are highlighted
   in blue/orange. Genes/proteins causing neuronal loss are highlighted in
   purple and those that help in neuronal survival are in yellow.

   From this enriched network we propose two pathways through which
   homeostasis in HD could be restored by initiating the downstream
   signaling cascade of various target genes expression via primarily
   through TGFB1, one of the three extra-cellular ligands. Our first
   proposal includes a two-step process of MTOR inhibition. Step 1: TGFB1
   activates PPARGC1A gene expression in the nucleus, which in turn
   increases GAPDH gene/protein activity. Step 2: Up-regulation of GAPDH
   inhibits MTOR gene expression activity. Once MTOR is inhibited,
   autophagy mechanism could be boosted up in the cell. As soon as
   autophagy process is reestablished, HTT protein aggregates will be
   effectively cleared from the cell thus leading to neuronal survival.

   Our second restoration pathway recommendation is via both FGF2 and
   TGFB1 ligand activation of EGFR receptors thereby initiating several
   downstream target genes expression. Among those upregulated genes, AKT1
   (v-akt murine thymoma viral oncogene homolog 1) is a vital downstream
   target for EGFR and has been shown to be a critical mediator of
   neuronal survival. AKT1 has been suggested to be a promising
   therapeutic target to promote cell survival [178][84]. Apart from AKT1,
   EGFR interacts with SP1 (Sp1 transcription factor) which is involved in
   many cellular processes, including cell differentiation, cell growth,
   apoptosis, immune responses, response to DNA damage, and chromatin
   remodeling. SP1 fine-tunes the transcription of many genes including
   BCL2 and REST. BDNF transcription could be increased via maintaining
   tight regulation between REST and SP1. In addition, SP1 could also
   up-regulate BCL2 gene expression, promoting anti-apoptosis. Thus,
   eventually striatal neurons could be protected by promoting BDNF
   activity, as well as by reducing the apoptotic process in the cell.
   Additionally, EGFR also promotes GAPDH gene expression eventually
   aiding in neuronal survival as detailed in our earlier pathway
   proposal.

   From network analysis stand-point, the two proposed homeostasis
   restoration pathways show promising measures towards treatment plans in
   Huntington's disease. As a first step towards translating our proposed
   therapeutic networks into real world applications, such complex
   multi-player interconnected pathways could be evaluated using advanced
   dynamic modeling tools such as cellular automata [179][85].

3.4. Huntington's disease microRNA regulatory network

   MicroRNAs perform important role in delivering post-transcriptional
   regulation of gene expression. Previous studies have found such
   microRNAs in this disease paradigm [180][86], [181][87], [182][88]. In
   order to identify the microRNAs and their potential targets in
   Huntington's disease domain, we constructed a microRNA regulatory
   network (MRN) using the 514 “seed genes”. Before proceeding with the
   MRN construction, we first identified the microRNAs that could target
   our seed genes. This was accomplished using the shortest-path network
   option in Pathway Studio software where we subjected all the 514 HD
   “seed genes” to only microRNA interactions type. We found 132 microRNAs
   to target our HD genes. In order to obtain the microRNA–target gene
   interactions, we constructed a direct interaction network using the 132
   microRNAs and the 514 “seed genes”. [Note: The figure with the microRNA
   regulatory network containing over 1000 nodes is not shown, due to its
   extreme complexity].

   The average node degree of the microRNA regulatory network was 4.3.
   Being the node with highest degree in the network, miR-9 was observed
   to target 35 genes. In addition, miR-9 was the node with highest
   closeness and betweenness centrality scores. Finding it in our microRNA
   regulatory network was exciting for a couple of reasons. First, miR-9
   was previously known in Huntington's disease mechanism, as well as
   found to target REST (RE1-silencing transcription factor), one of the
   important players of HD pathogenesis. Secondly, miR-9 regulation has
   already been identified and found reduced in both Alzheimer's and
   Huntington's disease brains [183][81], [184][82], [185][89], [186][90],
   providing thus another evidence for the conjectured unified underlined
   mechanism of the neurodegenerative diseases. Apart from miR-9
   regulation, the network included miR-132 and miR-29a/b1 miRNAs, both
   already associated with HD pathogenesis [187][91], [188][92].

   The next top five microRNAs found in the regulatory network were
   miR-124, miR-135a, miR-141, miR-182 and miR-19a. All these microRNAs
   were among the top 25 nodes with highest degree (node degree ≥ 12), and
   the top 25 nodes with highest closeness and betweenness centrality
   scores. miR-124 is one of the most abundantly expressed miRNAs in the
   nervous system, being widely expressed in neurons in the brain, retina,
   and spinal cord. It has been implicated in the modulation of neurite
   outgrowth, as well as cytoskeleton formation [189][93]. There have been
   no indications so far for involvement in neurodegenerative processes of
   miR-135a, known to target genes involved in blood pressure regulation
   [190][94]. Similarly, miR-141 and miR-182 were known to be involved
   only in DNA methylation and cancer metastasis, respectively [191][95],
   [192][96]. miR-19 and miR-21 have been found to target PTEN, a
   gene/protein found localized in the neurofibrillary tangles (NFTs) and
   senile plaques in Alzheimer's disease brains [193][97], [194][98].
   MicroRNA regulatory network also uncovered that many of these top
   regulating microRNAs modulate several known-HD genes such as CNR1,
   FOXP1, GAPDH, and IRS2. We suggest that the following nine microRNAs
   namely, miR-135 A1, miR-141, miR-153-1, miR-15 A, miR-16-1, miR-182,
   miR-19 A, miR-27 A and miR-96 could be of potential interest in HD.
   However, our microRNA regulatory analysis should be offered with some
   caution, because miRNAs that are enriched in the CNS are more likely to
   regulate targets enriched in the CNS. In addition, currently a high
   percentage of miRNA–target interactions in Pathway Studio ResNet 9.0
   database are based on predictions, as verified from the references