Abstract

   Network-based approaches are powerful and beneficial tools to study
   complex systems in their entirety, elucidating the essential factors
   that turn the multitude of individual elements into a functional
   system. In this study we used critical network topology descriptors and
   guilt-by-association rule to explore and understand the significant
   molecular players, drug targets and underlying biological mechanisms of
   Alzheimer’s disease. Analyzing two post-mortem brain gene microarrays
   ([27]GSE4757 and [28]GSE28146) with Pathway Studio software package we
   constructed and analyzed a set of protein-protein interaction, as well
   as miRNA-target networks. In a 4-step procedure the expression datasets
   were normalized using Robust Multi-array Average approach, while the
   modulation of gene expression by the disease was statistically
   evaluated by the empirical Bayes method from the limma Bioconductor
   package. Representative set of 214 seed-genes (p<0.01) common for the
   three brain sections of the two microarrays was thus created. The
   Pathway Studio analysis of the networks built identified 15 new
   potential AD-related genes and 17 novel AD-involved microRNAs. Using
   KEGG pathways relevant in Alzheimer’s disease we built an integrated
   mechanistic network from the interactions between the overlapping genes
   in these pathways. Routes of possible disease initiation process were
   thus revealed through the CD4, DCN, and IL8 extracellular ligands.
   DAVID and IPA enrichment analysis uncovered a number of deregulated
   biological processes and pathways including neuron
   projection/differentiation, aging, oxidative stress, chemokine/
   neurotrophin signaling, long-term potentiation and others. The findings
   in this study offer information of interest for subsequent experimental
   studies.

Introduction

   In 1906, Dr. Alois Alzheimer was the first one to identify and report
   the link between worsening psychological symptoms and the
   histopathological modifications in the brain [[29]1]. By late 1970s,
   Alzheimer’s disease (AD) was recognized as the most common form of
   dementia (serious progressive loss of cognitive functions), as well as
   one of the leading fatal conditions affecting elderly population.
   Initially AD patients start out with memory loss which then manifests
   into confusion and disorientation followed by slow and progressive
   decline in their standard of daily living. The disease is characterized
   by the presence of amyloid-plaques (which consist of amyloid-β
   peptides) and neurofibrillary tangles (NFTs) in the brain. In general,
   AD etiology is sporadic but a small percentage of early-onset familial
   form is also possible. Mutations in APP, APOE, PSEN1, PSEN2 and MAPT
   genes were found to cause Alzheimer’s disease pathogenesis.

   We present in [30]S1 Fig which depicts the various genes already
   implicated in Alzheimer’s disease along with different deregulated
   biological processes caused by the several abnormal protein activities.
   Many biological processes including neuroinflammation, oxidative
   stress, dysfunction of lysosomal/proteasomal degradation, mitochondrial
   dysfunction etc., have been associated with Alzheimer’s disease
   [[31]2–[32]11]. Aggregation of amyloid-plaques and tau proteins were
   suggested to be the major cause for these deregulations
   [[33]11–[34]14]. Among many dysfunctional genes the following genes
   have been found play critical role in the Alzheimer’s disease
   manifestation. APP encodes for the amyloid-β precursor protein, and the
   presenilin genes (PSEN1 and PSEN2) encode for the proteolytic enzymes
   PS1 and PS2 that cleave APP into amyloid-β and other fragments
   [[35]15]. PSEN1 mutations were attributed to more than 50% of early
   onset of familial form of AD [[36]16]. Neurofibrillary tangles consist
   of aggregations of hyperphosphorylated tau proteins (MAPT)
   [[37]13,[38]17]. Apart from these genes, APOE, CDK5, LMO4, PTEN, TGFβ 1
   etc., were found to increase the abnormal protein aggregation and other
   characteristic features of Alzheimer’s disease (Brains of individuals
   with AD manifest two characteristic lesions: extracellular amyloid
   plaques and intracellular neurofibrillary tangles of
   hyperphosphorylated tau protein. Amyloid plaques contain small, toxic
   cleavage products (denoted as amyloid β40 and amyloid β))
   [[39]18–[40]22].

   In addition, the role of miRNA regulatory mechanisms was also studied
   in Alzheimer’s disease. A recent genome-wide miRNA profiling study has
   found a substantial number of differentially expressed miRNAs in the
   cortical region of AD brains. Many of these miRNAs and their predicted
   mRNA target pairs were part of several biological processes that were
   previously reported dysfunctional in Alzheimer’s disease mechanism
   [[41]23].

   Owing to several sophisticated biomolecular research techniques many
   more deregulated genes and biological processes have been identified
   and reported in this disease paradigm. Our study is a modest effort to
   understand and explore the underlying cellular mechanisms and critical
   molecular players, as well as to identify novel drug targets using
   comprehensive network-based analysis. In this way, one could study
   these kinds of complex disease models with all its individual players
   along with the interactions among them. Network-based analysis is a
   valuable tool to understand and appreciate the underlying complexity of
   a system as a whole rather than disconnected unit. This systems biology
   approach allows searching for unifying patterns and possibly common
   mechanisms of complex diseases frequently involving distinct and
   heterogeneous datasets. The present research work on Alzheimer’s
   disease is part of a broader network-based data analysis of three
   neurodegenerative disorders (NDDs) including Parkinson’s (PD) and
   Huntington’s disease (HD) with the ultimate goal of identifying in a
   future study a unified underlying molecular mechanism of these three
   devastating NDDs. The manuscript outlining our network-based analysis
   findings of Parkinson’s disease was published earlier [[42]24]. Few
   recent research works have utilized similar network-based approach to
   identify candidate genes for Alzheimer’s disease [[43]25–[44]27].

Materials and Methods

   The network-based analysis of the three neurodegenerative diseases
   under study was performed by following the same rationale and work
   flow. For more specific details, refer the Methods and data part of our
   Parkinson’s disease network analysis paper [[45]24]. This section will
   briefly explain the different steps involved in the study data
   collection and analysis.

   The network-based analysis of Alzheimer’s disease comprises of six
   steps, beginning with the identification of microarray gene expression
   datasets. For this, we searched both National Center of Biotechnology
   Information’s (NCBI) Gene Expression Omnibus (GEO) and European
   Bioinformatics Institute’s (EBI) ArrayExpress databases for Affymetrix
   microarray gene expression datasets of Alzheimer’s disease. Two
   datasets namely [46]GSE4757 [[47]28] and [48]GSE28146 [[49]29] which
   contains post-mortem brain tissue samples from diseased (patients with
   AD) and control (normal) conditions were found and used for this
   research work. The first one is related to entorhinal cortex and the
   second one uses CA1 region in hippocampus. These two regions in the
   brain play important role in memory formation, which is essential in
   diagnostics since memory loss and disorientation are the early signs of
   AD. The [50]GSE4757 dataset had expression profile of both neurons
   containing neurofibrillary tangles and normal neurons from the
   entorhinal cortex of 10 mid-stage AD cases. The [51]GSE28146 had 30
   samples of different stages of AD cases including controls. For
   detailed sample information and other data, refer the NCBI GEO website
   and the contributor’s journal articles [[52]28–[53]31].

   Secondly, the selected microarray expression datasets were normalized
   using Robust Multi-array Average (RMA) approach [[54]32]. The
   differential gene expression changes were statistically evaluated by
   the empirical Bayes (eBayes) method [[55]33] from the limma
   Bioconductor package. Probe-sets with p-values < 0.05 were considered
   to be significantly differentially expressed genes (SDEGs). The
   statistical analysis was carried out using in-house written R software
   programming code utilizing Bioconductor package [[56]34]. The R program
   source code is available upon request by the author (SC).

   Then the two microarray datasets were subjected to the same statistical
   analysis and the significantly differentially expressed gene lists
   called “seed genes” were generated for each dataset. The statistical
   analysis of [57]GSE4757 dataset resulted in finding 725 (p-values <
   0.05) SDEGs between AD cases and control samples. The lists generated
   from the [58]GSE28146 dataset were denoted as incipient, moderate and
   severe, for the three stages of Alzheimer’s disease condition. In
   addition, differential gene expression changes found between control
   and AD cases irrespective of disease stage were denoted as “Diagnosis”.
   An overlap of 513 (p-values < 0.05) seed genes was found between the
   four sets of SDEGs as shown in [59]Fig 1. By combining the two seed
   genes lists and removing duplicates, a total of 1205 genes were found
   to be significantly differentially expressed between control and AD
   cases. In order to have a considerable number of overlapping genes as
   well as to partially compensate for not accounting for the multiple
   correlation, only those SDEGs with the p-value lower than 0.01 were
   included in network evaluation. Following this cut-off criteria, 214
   genes were treated as “seed genes” which were then subjected to
   comprehensive network analysis. (See [60]S1 Table for the list of 214
   SDEGs).

Fig 1. Four-set Venn diagram of the overlap of significantly differentially
expressed genes (SDEGs) in [61]GSE28146 gene expression datasets.

   Fig 1
   [62]Open in a new tab

   Courtesy: Oliveros, J.C. (2007–2015) Venny. An interactive tool for
   comparing lists with Venn's diagrams. Publicly available at
   [63]http://bioinfogp.cnb.csic.es/tools/venny/index.html.

   The fourth step involves the construction of various networks such as
   direct interaction, shortest-path and miRNA regulation using Pathway
   Studio 9.0 software package [[64]35],
   ([65]http://www.elsevier.com/online-tools/pathway-studio). This
   software includes a proprietary molecular interaction database namely
   ResNet 9.0 (released October 15, 2011) that has curated data of various
   molecular 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 thousands of custom built
   cell-processes, metabolic and signaling pathways. Apart from these
   networks, key 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 [[66]36,[67]37]. Based on
   these beneficial topological characteristics, along with their
   biological/ molecular functions relevant for the neurodegenerative
   process, the genes have been categorized as “already known AD-genes”
   and “genes of interest for AD”. The distinction was made by using
   sources like Online Mendelian Inheritance in Man (OMIM) database
   ([68]http://omim.org/), NCBI’s PubMed database
   ([69]http://www.ncbi.nlm.nih.gov/pubmed.com), MalaCards database
   ([70]http://malacards.org/), and Google search for the latest
   publications ([71]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 network.

   Next step was to subject the seed genes to Gene Ontology (GO)
   enrichment analysis as it provides biological functional interpretation
   of large lists of genes derived from genomic studies such as
   microarray, proteomics experiments. This analysis was accomplished
   using Database for Annotation, Visualization and Integrated Discovery
   (DAVID), a widely used web-based application focusing on GO
   classification [[72]38–[73]40]. Along with the biological functional
   interpretation, interesting information was gathered about the
   different biological pathways that could be affected in Alzheimer’s
   disease. For this, we used the core analysis in Ingenuity’s IPA
   (Ingenuity Systems, [74]http://www.ingenuity.com/) and Pathway
   Enrichment Analysis in Pathway Studio. Both the tools provided valuable
   information on several canonical pathways deregulated in Alzheimer’s
   disease.

   Finally, 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 [[75]41]. 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
   Alzheimer’s disease mechanism.

Results

   The following network-based analysis was implemented using the 214
   “seed genes” resulted from the statistical analysis of the two
   microarray gene expression datasets ([76]GSE4757 and [77]GSE28146). To
   the best of our knowledge this is the first study to carry out such an
   analysis of Alzheimer’s disease utilizing the above mentioned datasets.
   We constructed various types of network to identify critical molecular
   players and mechanisms involved in AD.

Alzheimer’s disease direct interaction network

   As the name suggests, this network describes the direct interaction
   between any two genes/proteins, if any. Out of the 214 significantly
   differentially expressed genes (SDEGs), 48 genes were directly
   connected to each other based on the different interaction types like
   regulations, promoter binding, direct regulation, protein modification
   and microRNA’s regulation. This interaction network ([78]Fig 2) has an
   average node degree of 2.20 with BCL6, DCN, JAK2, PECAM1 and SMAD3 as
   top five hub genes. Some of these hub genes (BCL6, DCN and SMAD3) along
   with C1QA and PSEN1 (a well-known gene to cause familial early-onset of
   AD) have high betweenness centrality score which position them among
   the top influential nodes in the network. 15 out of 48 SDEGs (CAMK4,
   CDK5R1, DCN, GRIN2A, HSPB2, ICAM2, JAK2, LMO4, NEFL, NEFM, PECAM1,
   PSEN1, SMAD3, SYN1 and TGFBI) are already associated in Alzheimer’s
   disease pathology. Additionally, two genes namely CAMK4, CSF1R and GAB1
   from our SDEGs set could be considered as potentially related to AD
   mechanism based on their biological function.

Fig 2. Alzheimer’s disease direct interaction network.

   [79]Fig 2
   [80]Open in a new tab

   The 15 genes/proteins implicated in AD pathology are highlighted in
   green and the two genes/proteins of potential interest for that disease
   are highlighted in blue.

Molecular functions of some well-known and candidate genes

   The molecular functions of some genes that were known to contribute the
   neurodegeneration process in Alzheimer’s disease were briefly explained
   below. CDK5R1 is an activator of CDK5, a known gene that leads to the
   accumulation of aberrantly phosphorylated protein tau, a classic
   feature of AD pathology. From the direct interaction network (see
   [81]Fig 2), GRIN2A, NEFM, NEFL and SYN1 genes were first level
   interacting partners of CDK5R1. Their regional alteration and reduced
   expression were thought to influence the severity of Alzheimer’s
   disease [[82]42–[83]44]. The network also revealed the direct
   interaction of CDK5R1 with PSEN1 (well-known as important AD
   regulator), as provided by the Pathway Studio ResNet database:
   Cyclin-dependent kinase-5/p35 phosphorylates Presenilin 1 to regulate
   carboxy-terminal fragment stability. CAMK4 is regulated by the Wnt
   signaling pathway and its expression could play a role in the
   neuroprotective function of the Wnt signaling against the Alzheimer's
   amyloid peptide. This role was confirmed in mouse AD model studies
   [[84]45,[85]46].

   With their innate physiological roles as well as being direct
   interacting partners (guilt-by-association) to already known AD genes
   (JAK2 and PECAM1) increases the chances of the two genes (CSF1R and
   GAB1) as novel candidate genes for Alzheimer’s disease. Many suggest
   that oxidative stress is involved in the pathogenesis of most of the
   neurodegenerative disorders, including AD [[86]12,[87]48–[88]52]. GAB1
   has been identified as an important component in oxidative stress
   signaling and influencing the cell survival [[89]47]. Even though CSFIR
   (colony stimulating factor 1 receptor) is a second-level interacting
   partner to AD known gene, based on its intrinsic neuroprotective
   function, it could also be a potential gene of interest in Alzheimer’s
   disease condition. Recent study show that CSF1R depleted mice exhibit
   increased neuronal loss after injury and vice versa. Its ligands CSF1
   and IL34 exhibit neuroprotection against excitotoxicity induced by
   neurodegeneration [[90]53]. Thus suggesting, up-regulation of CSF1R
   could play potential role in neuronal survival after injury and
   degeneration.

Alzheimer’s disease shortest-path network (SPNW)

   Unlike direct interaction network, shortest-path network (SPNW) enables
   us to find indirect relationship between two or more entities through
   intermediary nodes in the absence of direct relationship. One could
   identify genes/proteins that might contribute to the neurodegenerative
   process based on the inference (guilt-by-association) that when
   genes/proteins with well-defined biological functions (e.g., oxidative
   stress) interact with other genes/proteins, the latter have a higher
   probability to share that function, as compared to those selected at
   random [[91]54,[92]55].

   One limitation of the shortest-path network is that sometimes it could
   bring in more intermediary nodes in order to have a unified network.
   Therefore to have a concise and yet meaningful network, we selected
   only those SDEGs with node degree ≥ 25 as given in the ResNet database
   of the Pathway Studio software. This cut-off criterion was satisfied by
   96 out of 214 SDEGs and the resulted shortest-path network included an
   additional of 131 software-added from the ResNet database connecting
   genes/proteins. Following our categorization technique (See [93]Methods
   and Data section), the 96 SDEGs along with the 131 software-added
   connecting genes were classified into the genes that were already
   related to AD and the genes that could be of potential interest in AD
   diagnosis and medical treatment. [94]Table 1 summarizes the different
   categories and the corresponding numbers of genes.

Table 1. Summary of genes of interest and genes already known in Alzheimer's
disease.

   Different categories No. of genes Node color code in figures
   Genes of interest from SDEGs 4 Blue
   Known AD genes from SDEGs 18 Green
   Genes of interest in SPNW connecting nodes 23 Orange
   Known AD genes in SPNW connecting nodes 27 Red
   [95]Open in a new tab

   Compared to direct interaction network, shortest-path network offered a
   better means to evaluate the complexity and the interconnectedness of
   the significantly differentially expressed genes in the Alzheimer’s
   disease. However, the SPNW was still complex to derive any valuable
   information regarding critical players and molecular mechanisms of AD.
   We decided to further reduce the network size and construct a compact
   version of the SPNW network that would reveal the key contributors of
   Alzheimer’s disease. Such a compact SPNW was built using the genes
   summarized in [96]Table 1 along with few additional connecting genes
   without which some of the genes of interest would remain unconnected.
   The resulted compact SPNW had an average node degree of 8.81 and genes
   like CREB1, MAPK1, JUN, SP1 and TP53 were highly connected nodes
   (hubs). In biomolecular networks, hub genes tend to be part of critical
   functions or pathways (for example CREB1, MAPK1 and TP53). In this
   network, we found 24 such genes with node degree > 10. Many of these
   hub genes were also the nodes with high closeness (better
   accessibility) and betweenness (traffic-influential) centrality scores
   in this compact SPNW. [97]Fig 3 illustrates the interactions between
   the known and the genes of interest in the AD compact SPNW.

Fig 3. Alzheimer’s disease compact shortest path network.

   [98]Fig 3
   [99]Open in a new tab

   The genes/proteins implicated in AD pathology are highlighted in green
   and red. The genes/proteins of potential interest are highlighted in
   blue and orange. (see [100]Table 1 for gene highlighting details).

Guilt-by-association analysis

   The guilt-by-association analysis, based on the immediate proximity to
   some of the already known AD genes revealed some genes (listed in
   [101]Table 2) that could be of interest in Alzheimer’s disease
   paradigm. Further, we explored earlier bimolecular research works on
   these genes to understand their innate molecular functions and
   mechanisms, if any.

Table 2. Genes of interest for Alzheimer’s disease identified by
“guilt-by-association” with the known AD-related genes.

   Genes of Interest       Interacts with no. of known AD genes
   SRC                     13
   MDM2                    11
   NRF1                    5
   ESRRA, PTK2B, SRF       4
   CR2, NOX4               2
   CHRM3, CSF1R, HEY2, IL3 1
   [102]Open in a new tab

   SRC (v-src sarcoma (Schmidt-Ruppin A-2) viral oncogene homolog (avian))
   a tyrosine kinase is a very generic gene/protein, which controls a wide
   variety of processes, pathways, and actions, and is responsible for key
   events in the body, we found it to be the first-level neighbor to 13
   already known Alzheimer’s disease genes (ADRBK1, AR, CASP3, CDH1, JAK2,
   JUN, NFE2L2, NOS1, PECAM1, SP1, STAT1, STAT3 and VAV1). This
   association with many known AD genes makes SRC a strong candidate for a
   role in Alzheimer’s pathology. Moreover, it has been demonstrated that
   accumulation of amyloid-β stimulates microglial (macrophages of brain)
   activation via SRC tyrosine kinase pathway. Murine AD model study has
   shown that a cancer drug named Dasatinib could inhibit SRC tyrosine
   kinase activity of microgliosis in the AD diseased brains. Decreased
   microglial activity was correlated with improved cognitive performance
   in mouse [[103]56]. Cell culture study also suggests that SRC tyrosine
   kinase inhibition shows promising route in mitigating the effects of
   amyloid-β accumulation in brains which could be tested in human
   Alzheimer’s disease brains [[104]57].

   Based on its high proximity to AD related genes, another important
   player in AD is MDM2 which is directly connected to eleven already
   known Alzheimer’s disease genes. MDM2 (p53 E3 ubiquitin protein ligase
   homolog (mouse)) was shown in 2010 in possible relation to AD in the
   pathway map of Proctor and Gray [[105]58]. It has E3 ubiquitin ligase
   activity, which targets tumor suppressor protein p53 for proteasomal
   degradation. It was shown that amyloid-β signaling pathway disrupts
   biochemical pathways involving lipid metabolism, ultimately affecting
   tau phosphorylation which contributes to AD [[106]59]. Perturbation of
   lipid metabolism, especially of cholesterol homeostasis, has been
   illustrated in an another severe progressive neurodegeneration called
   Niemann-Pick Type C (NPC) disease [[107]60]. In a mouse NPC model, it
   has been demonstrated that cholesterol perturbation-induced axonal
   growth cone collapse and decrease in phosphorylated p53 were reduced by
   inhibition of MAPK and MDM2 E3 ligase.

   Another proposed candidate gene is NRF1which neighbors with five
   (CREB1, ESR1, NFE2L2, NOS1 and TP53) genes involved in Alzheimer’s
   disease mechanism. One of the distinctive characteristic features of
   several neurodegenerative diseases is the aberrant accumulation of
   protein (like α-synuclein, amyloid-β) aggregations. This protein
   buildup leads to mitochondrial dysfunction which eventually increases
   the ROS production in the brain [[108]49,[109]61]. Both NRF1 and NRF2
   are transcription factors that play a pivotal role in the protection
   against the toxic effects of reactive oxygen species (ROS). Studies
   have suggested that NRF1 and NRF2 promote mitochondrial biogenesis as
   well as play crucial role in neuronal survival after acute brain injury
   [[110]62,[111]63]. Our finding for NRF1 as a potential important factor
   in AD, reported in May 2013 during the defense of the PhD Thesis of Dr.
   Chandrasekaran, was confirmed five months later in the independent
   study of Satoh et al. [[112]64] based on analysis of ChIP-Seq set of
   genes. Summarizing the analysis done in the foregoing it is fair to
   conclude that, due to their neighborhood connectivity, as well as by
   their known physiological roles, the genes listed in [113]Table 2
   appear as credible genes of potential importance in Alzheimer’s
   disease, needing experimental validation.

Molecular functions and network attributes of some of the critical connecting
genes

   Other than the potential candidate genes, the compact SPNW also
   included many significant connecting proteins like APP, BACE1, CCNA2,
   CREB1, E2F1, EGR1 and MDM2 which were previously implicated to play
   critical roles in many neurodegeneration disease pathogenesis including
   Alzheimer’s. Abnormal proliferation of APP is the major contributor of
   the neurodegeneration process in AD. Pathogenic alterations of BACE1,
   CCNA2, CREB1, E2F1 and EGR1 are evident in the disease progression.
   BACE1 initiates the amyloid-β formation [[114]65]. In general, cyclins
   (CCNA2) were shown to increase the severity of Alzheimer-related
   pathology and other types of dementia [[115]66]. In addition, cyclins
   are regulators of CDK kinases. It has been indicated that APP and tau,
   the major two proteins implicated in AD, are the physiological
   substrates for cyclin-dependent kinase especially by a neuron-specific
   cyclin kinase called CDK5 [[116]67]. Similarly, E2F1 were found
   co-localized with amyloid-β plaques. It may ultimately contribute to
   neuronal cell death in AD [[117]68]. CREB1 along with CREM were
   suggested to play major therapeutic role in neurodegeneration
   [[118]69].

   Apart from having pivotal role in neurodegeneration mechanism, the
   above mentioned connecting genes interact with many previously known-AD
   genes. Except BACE1, all other genes are among the top 30 nodes with
   highest connectivity (degree > 8) in the compact shortest-path network.
   CREB1 turns out to be one of the critical nodes in the network. It has
   many advantages being one of the top five highly connected nodes,
   having better visibility as measured the closeness centrality as well
   as by its capacity to control the flow of traffic within the network as
   assessed by the betweenness centrality.

DAVID enrichment analysis

   Additionally, we also carried out gene set enrichment analysis using
   DAVID software tool to identify different biological processes and/or
   pathways affected in Alzheimer’s disease. [119]Table 3 lists some of
   the Gene Ontology categories/subcategories related to nervous system
   and functions that were statistically significantly enriched in AD
   (with Benjamini-Hochberg multiple correction).

Table 3. Gene set DAVID enrichment analysis of Alzheimer’s disease compact
shortest-path network.

   Term Gene Count Fold Enrichment Benjamini
   GO:0030424~axon 13 15.60 4.84E-09
   GO:0043005~neuron projection 15 8.37 1.58E-07
   GO:0007568~aging 8 13.48 8.02E-05
   GO:0043523~regulation of neuron apoptosis 7 14.41 2.76E-04
   GO:0030425~dendrite 8 9.36 3.87E-04
   GO:0030182~neuron differentiation 12 5.08 5.43E-04
   GO:0007169~transmembrane receptor protein tyrosine kinase signaling
   pathway 9 7.45 6.43E-04
   GO:0006979~response to oxidative stress 8 9.04 6.66E-04
   GO:0042325~regulation of phosphorylation 12 4.77 8.15E-04
   GO:0001944~vasculature development 9 6.64 0.001
   GO:0045202~synapse 10 5.37 0.001
   GO:0048666~neuron development 10 5.47 0.002
   GO:0031594~neuromuscular junction 4 34.69 0.002
   GO:0007611~learning or memory 6 10.02 0.005
   GO:0031175~neuron projection development 8 5.79 0.006
   GO:0008637~apoptotic mitochondrial changes 4 23.91 0.008
   GO:0000302~response to reactive oxygen species 5 12.35 0.009
   GO:0007005~mitochondrion organization 6 8.06 0.010
   GO:0035235~ionotropic glutamate receptor signaling pathway 3 61.77
   0.011
   GO:0007268~synaptic transmission 8 4.97 0.011
   GO:0007259~JAK-STAT cascade 4 19.01 0.013
   GO:0019226~transmission of nerve impulse 8 4.24 0.024
   GO:0007409~axonogenesis 6 5.76 0.032
   GO:0008088~axon cargo transport 3 30.89 0.035
   GO:0050877~neurological system process 15 2.30 0.036
   GO:0051402~neuron apoptosis 3 29.26 0.038
   GO:0048667~cell morphogenesis involved in neuron differentiation 6 5.32
   0.042
   GO:0048812~neuron projection morphogenesis 6 5.22 0.045
   [120]Open in a new tab

   DAVID analysis revealed some of the biological processes like oxidative
   stress, neuron apoptosis, tyrosine kinase signaling pathway, regulation
   of phosphorylation and aging affected in Alzheimer’s disease, which
   were also discussed above as part of AD mechanisms. It is valuable to
   note that the ALS pathway, characterizing another debilitating
   neurodegenerative condition called Amyotrophic lateral sclerosis, was
   also enriched among Alzheimer’s disease genes. Enrichment of ALS
   disease pathway along with Huntington’s disease pathway was reported by
   Ingenuity’s IPA analysis. One more promising step towards our study
   goal of identifying and reporting common genes and pathways in major
   neurodegenerative disorders was thus achieved.

Integrated Alzheimer’s disease mechanism

   The results of DAVID analysis were further evaluated in an attempt to
   elucidate details of the underlying molecular mechanism of Alzheimer’s
   disease. DAVID analysis revealed 22 KEGG pathways that were
   significantly (p-value <0.05 with Benjamini-Hochberg multiple
   correction) affected in AD. These pathways were found to take part in
   signal transduction, immune system, cell communications, nervous system
   and in general neurodegenerative diseases categories. Twelve out of 22
   enriched KEGG pathways (listed in [121]Table 4) were selected and the
   genes from these pathways were then consolidated into a list of 37
   common genes. The pathways selection was helped by previous Alzheimer’s
   disease research literature reviews [[122]70–[123]81].

Table 4. Enriched KEGG pathways in Alzheimer’s disease resulted from DAVID
analysis.

   Term Gene Count Fold Enrichment Benjamini Genes
   hsa05014:Amyotrophic lateral sclerosis (ALS) 7 12.21 6.67E-04 CASP3,
   NOS1, GRIA2, GRIN2A, TP53, NEFL, NEFM
   hsa04062:Chemokine signaling pathway 10 4.94 0.002 MAPK1, IL8, GRB2,
   ARRB1, PTK2B, JAK2, ADRBK1, STAT1, VAV1, STAT3
   hsa04722:Neurotrophin signaling pathway 7 5.22 0.014 MAPK1, PSEN1,
   CAMK4, GRB2, JUN, GAB1, TP53
   hsa05010:Alzheimer's disease 8 4.54 0.015 MAPK1, APP, CDK5R1, CASP3,
   NOS1, PSEN1, BACE1, GRIN2A
   hsa04650:Natural killer cell mediated cytotoxicity 7 4.87 0.017 MAPK1,
   CASP3, GRB2, PTK2B, ICAM2, LCK, VAV1
   hsa04720:Long-term potentiation 5 6.80 0.029 MAPK1, EP300, CAMK4,
   GRIA2, GRIN2A
   hsa04660:T cell receptor signaling pathway 6 5.14 0.030 MAPK1, GRB2,
   JUN, LCK, CD4, VAV1
   hsa04662:B cell receptor signaling pathway 5 6.16 0.034 MAPK1, CR2,
   GRB2, JUN, VAV1
   hsa04520:Adherens junction 5 6.00 0.036 MAPK1, EP300, SMAD3, CDH1, SRC
   hsa04020:Calcium signaling pathway 7 3.68 0.040 NOS1, CAMK4, SPHK2,
   CHRM3, PTK2B, GRIN2A, NOS3
   hsa04012:ErbB signaling pathway 5 5.31 0.049 MAPK1, GRB2, JUN, GAB1,
   SRC
   hsa04350:TGF-beta signaling pathway 5 5.31 0.049 MAPK1, EP300, SP1,
   SMAD3, DCN
   [124]Open in a new tab

KEGG analysis

   An integrated Alzheimer’s disease mechanism network was constructed
   (see [125]Fig 4) to analyze the interactions among these 37 common
   genes. We found a number of routes of disease initiation via three
   extra-cellular proteins namely, CD4, DCN and IL8. Here, we summarize
   some of the biological functions of the three proteins and their role
   in Alzheimer’s disease pathogenesis. Increased accumulation of
   amyloid-β plaques in AD brain has been shown to attract a surge of
   inflammatory mediators including chemokines, cytokines and interleukins
   leading to chronic inflammation that triggers neuronal death [[126]82].
   IL8 (interleukin 8) is a member of the CXC chemokine family and one of
   the major mediators of the inflammatory response. Studies have shown
   overproduction of IL8 along with other interleukins found in specific
   regions affected in Alzheimer’s disease. Under normal conditions, IL8
   promotes cell-cell communications in central nervous system, but
   increased amyloid-β production leads to chronic stimulation of IL8
   response which is suspected as cause for abnormal physiology in AD
   brains [[127]83]. Similarly, overproduction of CD4 has been associated
   with indirect neuronal damage in infectious and immune- mediated
   diseases of the central nervous system. Persistent amyloid-β stimulus
   in AD brains has been implicated in chronic CD4 activation which
   results in premature immunosenescence (gradual deterioration of the
   immune system brought on by natural age advancement) manifestation
   [[128]84]. DCN (decorin) is a small cellular or pericellular matrix
   proteoglycan. Earlier immunohistological study of AD brain tissues has
   found that decorin was primarily localized to the periphery of the
   amyloid plaque and to the edges of amyloid fibril bundles within the
   plaque periphery. It has been suggested that decorin molecules may
   contribute to regulating the size of amyloid accumulation and/or in
   maintaining their spherical shape [[129]85]. In order to achieve
   continued homeostasis, a delicate balance has to be maintained between
   APP gene expression and various inflammatory mediators.

Fig 4. Integrated Alzheimer’s disease mechanism network.

   [130]Fig 4
   [131]Open in a new tab

   The 37 genes/proteins are found in common in all 12 enriched KEGG
   pathways. Genes/proteins implicated in AD pathology are highlighted in
   green/red and the genes/proteins of potential interest are highlighted
   in blue/orange (See [132]Table 1. for details). Genes/proteins causing
   neuronal loss are highlighted in purple and those that help in neuronal
   survival are in yellow.

   Thus, the three extra-cellular molecules of the integrated AD mechanism
   network have already been implicated in Alzheimer’s disease
   pathogenesis in one way or another. The integrated network uncovered
   direct interactions between these three extra-cellular molecules and
   amyloid- β (A4) precursor protein, APP inside the nucleus. In addition,
   the network revealed interdependent interactions between APP, BACE1 and
   PSEN1 genes/proteins. Deregulation of APP, BACE1 (beta-site
   APP-cleaving enzyme 1) and PSEN1 are well-known causes of Alzheimer’s
   disease where APP and PSEN1 mutations are associated with familial form
   of the disease and BACE1 gene codes for one of the proteases that
   cleaves APP whose accumulation is the hallmark of Alzheimer’s disease.
   Apart from direct interactions with APP, BACE1 and PSEN1, the three
   extra-cellular molecules also trigger the downstream targets of MAP
   kinases, and caspases directing cell death.

Classification of disease causing and therapeutic mediators

   Next, each of the 37 common genes of the enriched KEGG pathways was
   subjected to Google and NCBI’s PubMed search for their
   biological/molecular functions, as well as for their potential role in
   AD pathogenesis. Based on this search, we categorized these 37 genes as
   either disease causing (leading to neuronal death) or possible
   therapeutics (helping with reducing the harmful effects and/or
   preventing neuronal loss) for Alzheimer’s disease. This categorization
   is depicted in [133]Fig 5 where the genes are highlighted either in
   purple or yellow, respectively.

Fig 5. MicroRNA incorporated integrated Alzheimer’s disease mechanism.

   [134]Fig 5
   [135]Open in a new tab

   The 37 genes/proteins found in common in all 12 enriched KEGG pathways
   are regulated by 22 microRNAs. Genes/proteins implicated in AD
   pathology are highlighted in green/red and the genes/proteins of
   potential interest are highlighted in blue/orange (See [136]Table 1)
   for details. Genes/proteins causing neuronal loss are highlighted in
   purple and those that help in neuronal survival are in yellow.

   As mentioned earlier, by modulating SRC tyrosine kinase gene expression
   we could employ some inhibition of amyloid-β accumulation in AD brains.
   But in general, the integrated disease mechanism network resulted into
   a well-connected destructive pathway with almost 80% of the 37 common
   genes found to play harmful roles in AD pathogenesis. We could try
   exerting gene/protein expression regulation through some of the
   transcription factor in the integrated network. However, the
   transcription factors found in this network were very generic and they
   are required for normal functioning of the cell. Thus, it was difficult
   to find/propose a node in the integrated Alzheimer’s disease mechanism
   network through which we could modulate the expression of disease
   causing/aggravating genes. It was mentioned before that the amyloid-β
   production has to be delicately balanced in order to maintain
   homeostasis within the cell. The integrated network revealed none to
   very little room to modulate APP gene/protein expression.

Proposed treatment options

   We then sought after expanding the underlying AD mechanisms by
   superimposing the level of fine gene expression regulation by
   microRNAs, the short non-coding RNAs that regulate gene expression
   post-transcriptionally. Dysregulation of miR-125b, miR-9, miR-210 and
   others have been consistently reported in several miRNA expression
   profiling of AD-affected brain studies [[137]86–[138]88]. Even though
   many of microRNA’s biological functions are still unclear, they offer
   many attractive features from a drug development standpoint such as
   small size, specific regulation, conserved nucleotide sequence etc.,
   which makes them potential therapeutic entities. Currently, microRNAs
   as medical treatment means in various disease conditions are pursued
   either through the use of synthetic active microRNAs called mimics or
   through the use of anti-microRNAs. For example, miR-208 is used in
   cardiac diseases, miR-155 in chronic inflammatory diseases and miR-122
   in Hepatitis C Virus [[139]89]. In AD, overexpression of miR-195 can
   potentially be used as a therapeutic strategy [[140]90]. A recent AD
   mouse model study has shown that noninvasive intranasal delivery of
   AM206, a neutralizing inhibitor of miR-206 has increased brain-derived
   neurotrophic factor (BDNF) level in brain [[141]91]. In general, BDNF
   gene regulates synaptic plasticity and memory. Many microRNA clinical
   trials are also ongoing.

   Pathway Studio’s ResNet 9.0 database was used to identify the microRNAs
   that target APP, BACE1, PSEN1, as well as the three extra-cellular
   molecules CD4, DCN and IL8. Twenty two such microRNAs were found
   including five of the already AD-known microRNAs namely, miR-153,
   miR-17, miR-20a, miR-29b-1 and miR-9. For example, BACE1 (beta-site
   APP-cleaving enzyme 1) is the target gene for miR-29a/b-1. BACE1 enzyme
   is essential for the generation of amyloid-β, the hallmark
   neuropathological lesions in Alzheimer’s disease brain. Earlier study
   shows that loss of miR-103, miR-29a/b-1 and miR-9 can contribute to
   increased BACE1 and Aβ levels in sporadic AD [[142]92]. In addition to
   APP and BACE1 gene expression regulation, miR-9 was suspected to
   regulate PSEN1, a familial early-onset AD gene. Loss of PSEN1 impacts
   memory, synaptic plasticity and induces neurodegenerative changes (see
   above for more details). In a PSEN1 null mice model study, miR-9
   down-regulation was correlated to severe brain defects [[143]93]. Using
   transgenic mouse model of AD, it was found that miR-153 downregulated
   the expression of APP and APLP2 (a homologue of APP) proteins
   [[144]94]. Another study strongly suggests that miR-17-5p along with
   miR-20a and miR-106b may contribute, at least in part, to the
   developmental regulation of APP expression in brain and in
   differentiating neurons [[145]95].

   With possible therapeutic role of microRNAs in mind, we incorporated
   the 22 microRNAs into our integrated Alzheimer’s disease mechanism
   network (see [146]Fig 5). Following this we examined the microRNAs and
   their targets. BACE1 had the highest microRNA regulation potential
   being targeted by nine miRNAs, next were APP and CD4, each of which was
   targeted by five microRNAs, then IL8 with three microRNA regulations,
   and one each for PSEN1 and CD4. In addition, miR-320a was found to
   target both APP and IL8, similarly miR-181a1 targeted both CD4 and DCN.
   In conclusion, by incorporating microRNAs into the integrated network,
   we were able to suggest some critical nodes to regulate Alzheimer’s
   disease causing and/or aggravating genes. Using network techniques we
   could offer both narrow and specific range of entities through which
   the network could be modulated. Thus, we propose 17 microRNAs
   (miR-101-1, miR-107, miR-124-1, miR-135a1, miR-142, miR-146a, miR-155,
   miR-15a, miR-181a1, miR-184, miR-19a, miR-221, miR-298, miR-302a,
   miR-328, miR-520B and miR-7-1) in addition to the known AD-related five
   as potential therapeutic targets in Alzheimer’s disease. Most of these
   novel microRNAs are not yet experimentally verified but one may expect
   that after such verification a sizable part of them will be found of
   therapeutic interest.

Alzheimer’s disease microRNA regulatory network

   Besides finding the microRNAs that could regulate the critical nodes
   such as APP, BACE1, CD4, DCN, IL8 and PSEN1, we searched to uncover
   additional regulatory mechanisms of Alzheimer’s disease genes. For this
   we used all the 214 significantly differentially expressed AD genes and
   constructed a shortest-path network with only microRNA interactions
   found in Pathway Studio’s ResNet 9.0 database. This SPNW revealed that
   67 miRNAs regulate many of the 214 AD SDEGs. Utilizing these 67 miRNAs
   and 214 SDEGs, we then constructed a direct miRNA-mRNA targets
   interaction network shown in [147]Fig 6.

Fig 6. Alzheimer’s disease miRNA regulatory network.

   [148]Fig 6
   [149]Open in a new tab

   The genes/proteins and miRNAs implicated in AD pathology are
   highlighted in green and the genes/proteins of potential interest are
   highlighted in blue. Genes/proteins that code for transcription factors
   (TFs) are highlighted in yellow.

   In addition to miRNAs regulation, the network also incorporated
   regulation by ten transcription factors like BCL6, LMO4, HEY2 and SMAD3
   which were not among the very generic transcription factors (TFs)
   reported earlier. Thus, like in other complex diseases, cooperative
   two-level gene expression regulatory mechanisms occur in Alzheimer’s
   disease pathogenesis via miRNAs and transcriptional factors. On further
   examination of the microRNA regulatory network ([150]Fig 6), miR-148a
   was the node with the highest degree (hub), closeness and betweenness
   centrality scores. It regulates gene expression of ten genes including
   CDK5R1 whose expression is needed for proper CDK5 activity, as a part
   of the AD molecular mechanism discussed above. MicroRNA quantitative
   biochemical study has reported altered expression of miR-148a along
   with other microRNAs in hippocampus and medial frontal gyrus regions of
   human AD brain samples [[151]96]. In addition, our network analysis
   revealed that expression of CDK5R1 gene could also be regulated by five
   microRNAs (miR-103, miR-148a, miR-15a, miR-183, and miR-27b), of which
   regulation via miR-103 along with miR-107 has already been
   experimentally established [[152]97].

   Additional microRNA regulation of some of the genes of interest in
   Alzheimer’s disease was uncovered in the microRNA regulatory network.
   It includes modulation of CAB39 gene expression by six microRNAs
   (miR-103-1, miR-155, miR-16-1, miR-19b1, miR-203 and miR-23b), PCDH8 by
   four microRNAs (miR-200a/b, miR-218-1 and miR-96), CSF1R by three
   microRNAs (miR-155, miR-22 and miR-34a), GAB1 by three microRNAs
   (miR-17, and miR-29b1/c), CAMK4 by miR-507 and HEY2 by miR-153-1, which
   would require experimental validation. Finally, we found that miR-148a,
   miR-155 and miR-153 seem to target many of the critical genes, as well
   as genes of interest, in Alzheimer’s disease.

Discussion

   Online Mendelian Inheritance in Man (OMIM) database search for
   Alzheimer’s disease revealed many genes such as A2M, ACE, APOE, APP,
   NOS3, PSEN1, PSEN2 and SORL1 implicated in the disease mechanism.
   However, none of these genes except PSEN1 were included in our “seed
   genes” since these genes were not strongly differentially expressed,
   and also their log-fold change was minimal (ranged from -0.3 to +0.1).
   We consider the lowered gene expression intensity in the post-mortem
   brain samples compared to a functioning brain as attributing factor to
   this gene set low variability. But it is noteworthy to note that APP
   and NOS3 along with other well-known AD genes like AR, BACE1, STAT1
   etc., although not significantly changed were later included as
   connecting genes/proteins in our shortest-path network. This turned
   those genes into critical nodes in the closest network environment of
   the known significantly changed Alzheimer’s genes. These findings
   reflect upon our ideas that it is beneficial to utilize network
   techniques to study complex systems like neurodegenerative diseases
   where even in the absence of an appropriate study medium (a fully
   functional brain) we could recreate some of missing links/nodes that
   are valuable to understand the underlying system mechanism.

   It should be mentioned that our detailed network analysis was unable to
   confirm the AD role of APOJ, TIMP3 and IRAK1 genes reported by Dunckley
   et al. 2006 [[153]31]. These genes were not statistically significant
   after the normalization we performed on their dataset, and they did not
   emerge in any way in the networks we constructed on the basis of
   well-established AD genes. We may also note that two genes having a
   significant role in oxidative stress could not be included in our
   network analysis. These are DRP1, which is not part of the microarray
   probeset, and VDAC1 which is neither significantly modulated in AD, nor
   could it satisfy the other criteria used for selection of the seed
   genes in our analysis. In contrast, the identification of PSEN1, a
   well-known familial Alzheimer’s disease gene as an important node in
   both direct interaction and shortest-path network is critical in number
   of ways. First, PSEN1 was found to be significantly differentially
   expressed gene (p-value < 0.01) in the microarray datasets. Secondly,
   it directly interacts with many already known AD genes such as APP,
   CDH1, CDK5R1, CREB1, JUN, SP1 and STAT3. It is also among the
   prioritized top 33 nodes with degree ≥ 8, and top 26 nodes with high
   closeness centrality index (high accessibility to all other nodes) in
   the compact shortest-path network, emerging thus with a major role in
   the molecular mechanism of Alzheimer disease.

   Our network analysis provided sufficient arguments in favor of GAB1 as
   novel candidate genes for Alzheimer’s disease based on its direct
   network interactions with already known AD genes. With somewhat less
   confidence we propose also CSF1R gene, which despite being a
   second-level neighbor to AD related genes, has demonstrated important
   role in neuronal survival after injury and degeneration. Both genes are
   significantly differentially expressed in the post-mortem AD samples we
   used and they could have critical role as neuroprotective agents. From
   shortest-path network assessment, being first-level interacting
   partners with three or more already known AD-related genes we conclude
   that connecting genes/proteins added by the Pathway Studio software
   such as ESRRA, NRF1, PTK2B, SRC and SRF could also be of potential
   interest in Alzheimer’s disease domain. Among the above listed genes,
   SRC was directly connected with at least ten or more previously known
   AD genes. Apart from directly interacting with a large number of known
   AD-related genes/proteins as shown in the guilt-by-association list
   above, SRC is among the top 25 hub nodes (with degree > 10), among the
   top 25 nodes with higher accessibility to all other nodes (as measured
   by closeness centrality network descriptor) and among the top 25
   traffic influential nodes in the network (as measured by the
   betweenness centrality index). All this may be considered as evidence
   for a potential important systems biology role of SRC gene in the
   integral mechanism of Alzheimer’s disease. Additionally, one may
   consider including to the list of Alzheimer’s genes five more from the
   connecting genes in the shortest-path network (CHRM3, CR2, HEY2, IL3
   and NOX4) via their guilt-by-associations to one or two already known
   AD genes. The ARRB1 and SPHK2 connecting genes, which are second-level
   interacting partners with previously implicated AD genes, may also be
   viewed of interest via their role as modulators in amyloid-β production
   [[154]98,[155]99].

   In addition to identifying novel individual genes, proceeding from our
   integrated Alzheimer’s disease mechanism network, we propose three
   disease initiating routes via extra-cellular ligands like CD4, DCN and
   IL8. These genes directly interact with many known AD genes including
   APP, BACE1 and PSEN1 and influence AD pathogenesis mechanism. Since
   this network was well-connected with almost 80% of genes promoting
   neurodegeneration process, we were unable to propose any therapeutic
   targets or mechanisms using this network. However, by incorporating
   microRNA regulations to this network, we identified 17 microRNAs
   (miR-101-1, miR-107, miR-124-1, miR-135a1, miR-142, miR-146a, miR-155,
   miR-15a, miR-181a1, miR-184, miR-19a, miR-221, miR-298, miR-302a,
   miR-328, miR-520B and miR-7-1) in addition to the known five AD-related
   miRNAs as potential therapeutic targets in Alzheimer’s disease. More
   generally, we propose a scheme of complex multi-level regulations
   taking place between the critical players of AD, such as APP, BACE1,
   and PSEN1, and other disease causing/alleviating entities. In order to
   maintain continued homeostasis, a delicate balance should be sustained
   between the genes, and with its surgically accurate mode of action the
   microRNA regulation could play an important role in this scenario.
   Perhaps, a well-designed cellular automata simulation would help to
   shed some light on this delicate but life-determining balance. The
   genes discussed in the summary were also shown by gene ontology
   analysis to be key players in numerous specific biological mechanisms
   like neuroprotection against amyloid-β accumulation, formation of
   neurofibrillary tangles, synapses, cognitive deficits, mental
   retardation, memory loss and attenuating APP accumulation, many of
   which are known to be modulated in the underlying Alzheimer’s
   pathophysiology and potential compensatory responses.

   Summarizing our study it is noteworthy mentioning that the successful
   identification of novel important disease-related genes requires a
   combination of gene expression modulation data with the knowledge
   accumulated in databases for biomolecular interactions. This systems
   biology approach makes possible the construction of diverse types of
   informative networks whose topological analysis provides reliable basis
   for prioritizing of network nodes based on their connectivity and
   centrality. In parallel, accounting for the neighborhood relations to
   already experimentally confirmed disease-related genes increases the
   chance for successful predictions, based on the guilt-by-association
   rule. In the search for molecular mechanisms of Alzheimer’s disease a
   different route seems to be fruitful. It proceeds with building an
   integrated mechanistic network by overlapping the known disease-related
   KEGG molecular pathways, adding more interactions between the network
   nodes, again provided by databases of such interactions. Identifying
   novel AD-related microRNAs adds more options in the search for
   beneficial regulatory molecular mechanisms. We intend applying such
   strategy in a future publication devoted to the search of common
   unifying mechanisms of neurodegenerative diseases.

Supporting Information

   S1 Fig. Biological processes and genes implicated in the Alzheimer’s
   disease.

   Courtesy: Alzheimer’s disease pathway from KEGG database, retrieved on
   Apr 3, 2013. Publicly available at
   [156]http://www.genome.jp/kegg-bin/show_pathway?hsa05010.

   (TIF)
   [157]Click here for additional data file.^ (86.9KB, tif)
   S1 Table. List of 214 significantly differentially expressed genes
   (SDEGs).

   (XLSX)
   [158]Click here for additional data file.^ (23.3KB, xlsx)

Data Availability

   All micro array gene expression data sets of Alzheimer’s disease used
   in this work are available from the NCBI Gene Expression Omnibus (GEO)
   database (accession numbers GSE4757, GSE28146).

Funding Statement

   The authors received no specific funding for this work.

References