Abstract
Background
Our understanding of the molecular mechanisms underlying Alzheimer’s
disease (AD) remains incomplete. Previous studies have revealed that
genetic factors provide a significant contribution to the pathogenesis
and development of AD. In the past years, numerous genes implicated in
this disease have been identified via genetic association studies on
candidate genes or at the genome-wide level. However, in many cases,
the roles of these genes and their interactions in AD are still
unclear. A comprehensive and systematic analysis focusing on the
biological function and interactions of these genes in the context of
AD will therefore provide valuable insights to understand the molecular
features of the disease.
Method
In this study, we collected genes potentially associated with AD by
screening publications on genetic association studies deposited in
PubMed. The major biological themes linked with these genes were then
revealed by function and biochemical pathway enrichment analysis, and
the relation between the pathways was explored by pathway crosstalk
analysis. Furthermore, the network features of these AD-related genes
were analyzed in the context of human interactome and an AD-specific
network was inferred using the Steiner minimal tree algorithm.
Results
We compiled 430 human genes reported to be associated with AD from 823
publications. Biological theme analysis indicated that the biological
processes and biochemical pathways related to neurodevelopment,
metabolism, cell growth and/or survival, and immunology were enriched
in these genes. Pathway crosstalk analysis then revealed that the
significantly enriched pathways could be grouped into three interlinked
modules—neuronal and metabolic module, cell growth/survival and
neuroendocrine pathway module, and immune response-related
module—indicating an AD-specific immune-endocrine-neuronal regulatory
network. Furthermore, an AD-specific protein network was inferred and
novel genes potentially associated with AD were identified.
Conclusion
By means of network and pathway-based methodology, we explored the
pathogenetic mechanism underlying AD at a systems biology level.
Results from our work could provide valuable clues for understanding
the molecular mechanism underlying AD. In addition, the framework
proposed in this study could be used to investigate the pathological
molecular network and genes relevant to other complex diseases or
phenotypes.
Electronic supplementary material
The online version of this article (doi:10.1186/s13195-017-0252-z)
contains supplementary material, which is available to authorized
users.
Keywords: Alzheimer’s disease, Functional enrichment analysis, Network
analysis, Pathway crosstalk
Background
Alzheimer’s disease (AD) is the most prevalent neurodegenerative
disorder and accounts for the majority of people diagnosed with
dementia [[33]1]. As a complex and chronic neurological disease, AD
affects about 6% of people aged 65 years and older [[34]2], and is
responsible for about 480,000 deaths per year around the world [[35]3].
In addition to its affect on the life quality of those suffering from
the disorder and their families, AD also causes a severe burden on
society. In the USA alone, the health-care costs related to AD are
about $172 billion per year [[36]4].
AD can be diagnosed by symptoms such as short-term memory loss, mood
swings, learning impairments, and disruptions in daily activities
[[37]5]. However, as an age-related and progressive disease, some
pathological features of AD (e.g., amyloid deposition, accumulation of
neurofibrillary tangles, as well as function and structure changes of
brain regions involved in memory) often appear many years prior to
clinical manifestations [[38]6, [39]7]. These pathological changes
eventually lead to the damage and death of specific neurons, resulting
in the emergence of clinical symptoms.
The cause of AD is still poorly understood although much effort has
been dedicated to exploring the pathological and molecular mechanisms
of AD via various approaches—e.g., animal models, gene expression
profiling, genome-wide association studies (GWAS), neuroimaging
techniques, or a systems biology framework [[40]2, [41]8–[42]11]. It is
agreed that AD develops as a result of the combination of multiple
factors, including genetic factors, a history of head injuries,
depression, or hypertension. Among these factors, it is estimated that
about 70% of the risk for AD is attributable to genetics [[43]1,
[44]12]. Established genetic causes of AD include the dominant
mutations of genes encoding amyloid precursor protein (APP), presenilin
1 (PSEN1), and presenilin 1 (PSEN2). However, these genes are only
responsible for the pathogenesis of AD in about 5% of patients with
clinical symptoms appearing in midlife. On the other hand, genetic
analyses have suggested that, in complex disorders like AD, individual
differences can be caused by many genes and their variants. Genes with
various biological functions may act in coordination to increase the
risk of AD, with a moderate or small effect exerted by each gene
[[45]1]. Consistent with this view, more and more genes—e.g.,
apolipoprotein E (APOE), glycogen synthase kinase 3 beta (GSK3B), dual
specificity tyrosine-phosphorylation-regulated kinase 1A (DYRK1A), and
Tau—have been found to be potentially associated with AD [[46]1,
[47]13]. For these genes, although a few plausible candidate genes have
been partially replicated, some are considered problematic. This is
especially true as high-throughput methods like GWAS are being more
widely applied to genetic studies of AD. Under such circumstances, a
comprehensive analysis of potential causal genes of AD within a pathway
and/or a network framework may not only provide us with important
insights beyond the conventional single-gene analyses, but also offer
consolidated validation for the individual candidate gene.
In the current study, we implemented a comprehensive curation of
AD-related genes from genetic association studies. We then conducted
biological enrichment analyses to detect the significant functional
themes within these genetic factors and analyzed the interactions among
the enriched biochemical pathways by pathway crosstalk analysis.
Furthermore, an AD-specific protein network was inferred and evaluated
with the human protein–protein interaction network as the background.
This study should offer valuable hints for understanding the molecular
mechanisms of AD from a perspective of systems biology.
Methods
Identification of AD-related genes
The genes genetically associated with AD were collected by retrieving
the human genetic association studies deposited in PubMed
([48]http://www.ncbi.nlm.nih.gov/pubmed/). We retrieved publications
associated with AD with the searching term ‘(Alzheimer’s Disease
[MeSH]) AND (Polymorphism [MeSH] OR Genotype [MeSH] OR Alleles [MeSH])
NOT (Neoplasms [MeSH])’. By July 7, 2015, a total of 5298 reports were
retrieved. After reviewing all abstracts of these publications, only
the genetic association studies on AD were selected. From the obtained
publication pool, we then concentrated on those studies reporting a
significant association of gene(s) with AD. In order to reduce the
number of potential false-positive genes, the studies reporting
insignificant or negative associations were excluded even though some
genes in these studies might actually be truly associated with AD. We
then reviewed the full reports of each selected publication to make
sure that the conclusion was consistent with its contents. In several
studies, some genes were found to function cooperatively to exert
significant influences on AD, with each gene having a small or mild
impact; these genes were also included in our list. In addition, the
genes from several GWAS analyses on AD, showing genetic association at
a genome-wide significance level, were also included.
Functional enrichment analysis of genes related to AD
WebGestalt [[49]14] and ToppGene [[50]15] were utilized to detect the
biological themes of the AD-related genes. As a web-based
bioinformation-mining platform, WebGestalt integrates information from
multiple resources to determine the biological themes, including
identifying the overrepresented Gene Ontology (GO) terms, amid the
candidate gene listing. In this study, only the GO biological process
terms with false discovery rate (FDR) value smaller than 0.05 were kept
as the significantly enriched ones. ToppGene was used to identify and
analyze the enriched biological pathways in the input genes. Pathways
with FDR < 0.05 were considered to be significantly enriched.
Analysis of crosstalks among pathways
We further built crosstalks among pathways to investigate interlinks
and interactions of the enriched pathways. To measure the overlap
between two pathways, the overlap coefficient (OC) and the Jaccard
coefficient (JC) were calculated using the corresponding formulas:
[MATH: OC=A∩BminAB :MATH]
and
[MATH: JC=A∩BA<
/mi>∪B, :MATH]
in which A and B are the lists of genes of the two examined pathways.
Briefly, the following procedure was adopted to construct the pathway
crosstalks:
1. Only pathways with FDR < 0.05 were kept for crosstalk analysis.
Meanwhile, pathways with five or fewer candidate genes were
discarded because pathways with too few candidate genes might
present few or biased connections with other pathways.
2. Counting the common candidate genes of each pathway pair—those
pathway pairs with less than two overlapped genes were removed.
3. Measuring the overlap in every pathway pair by the corresponding JC
and OC values.
4. Constructing the pathway crosstalk with Cytoscape software
[[51]16].
Compilation of the human protein–protein interaction network
To explore the correlation and interaction among the AD-related genes,
we compiled a comprehensive protein–protein interaction (PPI) network,
based on which the protein network topological properties of the gene
set related to AD were calculated and analyzed. Briefly, the human
protein–protein interaction data were obtained from the Protein
Interaction Network Analysis (PINA) database (latest release version:
May 21, 2014) [[52]17] by pooling and curating the unique physical
interaction information from six main public protein interaction
databases: BioGRID, IntAct, DIP, MINT, MIPS/MPact, and HPRD. In the
meantime, another interactome for Homo sapiens [[53]18] that contained
141,296 edges (physical protein interactions) among 13,460 nodes
(proteins), consisting of metabolic pathway-related interactions,
regulatory and protein–protein interactions, and interaction pairs for
kinase and specific substrate, was selected as an additional source of
interactome data. After merging the two interactome data by excluding
the self-interacting and redundant pairs, the proteins in the list were
mapped onto Entrez protein-coding genes for Homo sapiens via the
Uniprot ID mapping tool ([54]http://www.uniprot.org/uploadlists).
Finally, we compiled a relatively comprehensive human physical
interactome, which comprised 16,022 genes/proteins and 228,122
interactions (see Additional file [55]1).
Construction of the AD-specific protein subnetwork
A subnetwork specific to a given disease can provide us with hints for
how the disease-related molecules interact with each other. A network
parsimony principle has been demonstrated in the context of biological
processes [[56]19]; that is, the molecular networks/pathways often
follow the shortest molecular paths between known disease-associated
components (disease-related genes or proteins in our case). The Steiner
minimal tree algorithm coincides with this biological principle, which
uses a greedy heuristic strategy to iteratively link the smaller trees
to larger ones until there is only one tree connecting all seed nodes
[[57]20]. GenRev [[58]21] was utilized to identify the pathological
subnetwork from the human interactome using the curated AD-related
genes as input. To assess the non-randomness of the constructed
network, 1000 random networks with the same number of vertices and
interactions as the AD-specific network were generated using the
Erdos-Renyi model in R igraph package [[59]22].
Results
Compilation of genes associated with AD
Genes associated with AD were compiled through searching the published
genetic association studies on AD in PubMed. Only the publications
reporting gene(s) significantly associated with the disease were
pooled, and those reporting a negative or insignificant association
were excluded. Altogether, from 823 reports, we collected 430 genes
reported to be associated with AD (Additional file [60]2: Table S1; the
gene list is referred to as Alzgset). Among them were seven
apolipoprotein genes (APOA1, APOA4, APOC1, APOC2, APOC4, APOD, and
APOE), five genes encoding subunits of nicotinic acetylcholine
receptors (CHRNA3, CHRNA4, CHRNA7, CHRNB2, and CHRFAM7A), four
adrenoceptors (ADRA2B, ADRB1, ADRB2, and ADRB3), two serotonin
receptors (HTR2A and HTR6), three dopamine degradation genes (COMT,
DBH, and MAOA), and one dopamine receptor (DRD4). A few
transport-related genes were also collected, such as ATP-binding
cassette transporters (ABCA1, ABCA2, ABCA7, ABCC2, ABCG1, and ABCG2), a
dopamine transporter (SLC6A3), a serotonin transporter (SLC6A4), two
glucose transporters (SLC2A9 and SLC2A14), a folate transporter
(SLC19A1), and ion transporters (SLC24A4). The other genes were those
involved in the biological processes related to nitric oxide synthesis
(NOS1 and NOS3), immune response (e.g., IL1A, IL6, IL10, and NLRC3), as
well as mitochondria-specific function (e.g., MT-ATP6, MT-CO1, MT-CYB,
and MTHFR). Clearly, the genes significantly associated with AD were
diverse in function, consistent with the complexity of this mental
disorder.
Biological function enrichment analysis of Alzgset
Functional enrichment analysis revealed a more detailed biological
function spectrum of these AD-related genes (see Additional file [61]2:
Table S2). Among the GO terms overrepresented in Alzgset, those related
to lipid and/or lipoprotein-related processes, drug reactions, neural
development, or synaptic transmission were included. GO terms
associated with drug reactions (e.g., response to ethanol, response to
nicotine, and response to cocaine) and metabolic processes (e.g.,
xenobiotic metabolic process) were overrepresented. These results were
in line with previous findings that complicated correlations existed
between the pathophysiological state of AD and drug abuse [[62]23,
[63]24]. Of significance, top-ranked terms included some
lipid/lipoprotein-related processes, including phospholipid efflux,
reverse cholesterol transport, cholesterol homeostasis, and lipoprotein
metabolic processes. Biological process terms related to synaptic
transmission (e.g., positive regulation of transmission of nerve
impulse; synaptic transmission, cholinergic; regulation of synaptic
transmission, dopaminergic; and regulation of neurotransmitter
secretion), dopamine metabolism (dopamine metabolic process), and other
neural functions (e.g., synaptic vesicle transport, regulation of
neuronal synaptic plasticity, neuron migration, and memory) were also
enriched. Meanwhile, GO terms related to immunological function (e.g.,
T-helper 1 type immune response, positive regulation of interleukin-6
production, and chronic inflammatory response) were overrepresented.
The diversity in the function of AD-related genes demonstrated the
complexity of the disease.
Biochemical pathway enriched in Alzgset
Detecting the biological pathways overrepresented among Alzgset may
provide useful information about the pathogenic molecular mechanism
underlying AD. For Alzgset, 68 enriched pathways were identified
(Table [64]1). Among them, several pathways related to immune processes
were included (e.g., cytokines and inflammatory response, cytokine
network, dendritic cells in regulating TH1 and TH2 development, and
IL-5 signaling), consistent with previous studies [[65]25, [66]26].
Also, neurotransmitter signaling-related pathways were identified, such
as cholinergic synapse, dopaminergic synapse, serotonergic synapse, and
so forth. Additionally, in the Alzgset enriched pathway list, there
were some pathways related to cell growth and/or survival, including
neurotrophin signaling, PI3K-Akt signaling, mTOR signaling, Notch
signaling, and so forth, which are vital for cell growth/survival state
of neurons in the process of AD [[67]27, [68]28]. Moreover,
metabolism-related pathways, consisting of drug metabolism (cytochrome
P450), glutathione metabolism, and metabolism of xenobiotics by
cytochrome P450, were also significantly enriched, indicating that
related metabolism processes were involved in the etiology and
development processes of AD. What is more, the pathway of the
intestinal immune network for IgA production was enriched, which might
suggest a connection between AD and the intestinal microbiota [[69]29,
[70]30]. Furthermore, pathways involved in osteoclast differentiation
and adipocytokine signaling were also detected, complying with prior
studies [[71]31–[72]33].
Table 1.
Pathways enriched in Alzgset^a
Pathway p value^b p [BH] value^c Genes included in the pathway^d
Cytokines and inflammatory response 1.03 × 10^–9 8.79 × 10^–8 CXCL8,
HLA-DRA, HLA-DRB1, IL10, IL12A, IL12B, IL1A, IL4, IL6, TGFB1, TNF
cytokine network 9.89 × 10^–9 3.84 × 10^–7 CXCL8, IL10, IL12A, IL12B,
IL18, IL1A, IL4, IL6, TNF
Hematopoietic cell lineage 1.92 × 10^–7 5.46 × 10^–6 CD14, CD33, CD36,
CD44, CR1, HLA-DRA, HLA-DRB1, HLA-DRB5, IL1A, IL1B, IL4, IL6, IL6R,
MME, TNF
Dendritic cells in regulating TH1 and TH2 Development 3.11 × 10^–7
8.29 × 10^–6 CD33, IL10, IL12A, IL12B, IL4, TLR2, TLR4, TLR9
Ovarian steroidogenesis 5.88 × 10^–6 1.09 × 10^–4 ALOX5, CYP19A1, FSHR,
IGF1, INS, LDLR, LHCGR, PLA2G4A, PTGS2, STAR
IL-5 signaling pathway 9.00 × 10^–6 1.60 × 10^–4 HLA-DRA, HLA-DRB1,
IL1B, IL4, IL6
Neurotrophin signaling pathway 1.08 × 10^–5 1.77 × 10^–4 BDNF, CAMK2D,
GSK3B, IRS1, NGF, NGFR, NTF3, NTRK1, NTRK2, PIK3R1, PSEN1, PSEN2, SOS2,
TP53, TP73
HIF-1 signaling pathway 1.12 × 10^–5 1.77 × 10^–4 CAMK2D, EIF4EBP1,
GAPDH, HMOX1, IGF1, IL6, IL6R, INS, NOS3, PIK3R1, RPS6KB2, TF, TLR4,
VEGFA
NOD-like receptor signaling pathway 1.66 × 10^–5 2.37 × 10^–4 CARD8,
CCL2, CXCL8, IL18, IL1B, IL6, MEFV, NLRP1, NLRP3, TNF
Mechanism of gene regulation by peroxisome proliferators via PPARα
1.95 × 10^–5 2.69 × 10^–4 APOA1, CD36, INS, LPL, PIK3R1, PPARA, PTGS2,
RXRA, SP1, TNF
Th1/Th2 differentiation 2.54 × 10^–5 3.19 × 10^–4 HLA-DRA, HLA-DRB1,
IL12A, IL12B, IL18, IL4
Antigen-dependent B-cell activation 2.68 × 10^–5 3.26 × 10^–4 FAS,
HLA-DRA, HLA-DRB1, IL10, IL4
Oxidative phosphorylation 3.74 × 10^–5 4.39 × 10^–4 COX10, COX15,
MT-ATP6, MT-ATP8, MT-CO1, MT-CO2, MT-CO3, MT-CYB, MT-ND1, MT-ND2,
MT-ND3, MT-ND4, MT-ND4L, MT-ND5, MT-ND6
PI3K-Akt signaling pathway 3.80 × 10^–5 4.39 × 10^–4 COL11A1, EFNA5,
EIF4EBP1, FGF1, GNB3, GSK3B, IGF1, IL4, IL6, IL6R, INS, IRS1, NGF,
NGFR, NOS3, PCK1, PIK3R1, PPP2R2B, RELN, RPS6KB2, RXRA, SOS2, TLR2,
TLR4, TP53, VEGFA, YWHAQ
NF-κB signaling pathway 4.83 × 10^–5 5.42 × 10^–4 CD14, CXCL8, ICAM1,
IL1B, LCK, PARP1, PLAU, PTGS2, TLR4, TNF, TRAF2, UBE2I
Phagosome 7.77 × 10^–5 8.29 × 10^–4 CD14, CD36, CTSS, HLA-A, HLA-DQB1,
HLA-DRA, HLA-DRB1, HLA-DRB5, MBL2, MPO, NOS1, OLR1, RAB7A, TAP2, TLR2,
TLR4
Erythrocyte differentiation pathway 9.33 × 10^–5 9.49 × 10^–4 CCL3,
IGF1, IL1A, IL6, TGFB1
IL-10 anti-inflammatory signaling pathway 1.82 × 10^–4 1.69 × 10^–3
HMOX1, IL10, IL1A, IL6, TNF
Cells and molecules involved in local acute inflammatory response
1.82 × 10^–4 1.69 × 10^–3 CXCL8, ICAM1, IL1A, IL6, TNF
Toll-like receptor signaling pathway 2.15 × 10^–4 1.95 × 10^–3 CCL3,
CD14, CXCL8, IL12A, IL12B, IL1B, IL6, PIK3R1, TLR2, TLR4, TLR9, TNF
Free radical induced apoptosis 2.22 × 10^–4 1.97 × 10^–3 CXCL8, GPX1,
SOD1, TNF
Intestinal immune network for IgA production 2.65 × 10^–4 2.26 × 10^–3
HLA-DQB1, HLA-DRA, HLA-DRB1, HLA-DRB5, IL10, IL4, IL6, TGFB1
Selective expression of chemokine receptors during T-cell polarization
3.35 × 10^–4 2.68 × 10^–3 CCL3, CCR2, IL12A, IL12B, IL4, TGFB1
B lymphocyte cell surface molecules 3.39 × 10^–4 2.68 × 10^–3 CR1,
HLA-DRA, HLA-DRB1, ICAM1
Phosphorylation of MEK1 by cdk5/p35 downregulates the MAP kinase
pathway 3.39 × 10^–4 2.68 × 10^–3 CDK5, CDK5R1, NGF, NGFR
Complement and coagulation cascades 4.61 × 10^–4 3.58 × 10^–3 A2M, C4A,
C4B, CFH, CR1, F13A1, MBL2, PLAU, SERPINA1
ABC transporters 5.87 × 10^–4 4.32 × 10^–3 ABCA1, ABCA2, ABCA7, ABCC2,
ABCG1, ABCG2, TAP2
Signal transduction through IL-1R 6.97 × 10^–4 5.05 × 10^–3 IL1A, IL1B,
IL1RN, IL6, TGFB1, TNF
mTOR signaling pathway 8.19 × 10^–4 5.83 × 10^–3 EIF4EBP1, IGF1, INS,
IRS1, PIK3R1, RPS6KB2, TNF, VEGFA
Adhesion and diapedesis of granulocytes 9.49 × 10^–4 6.65 × 10^–3
CXCL8, ICAM1, IL1A, TNF
TNF signaling pathway 1.12 × 10^–3 7.69 × 10^–3 CCL2, FAS, ICAM1, IL1B,
IL6, MAGI2, MMP3, PIK3R1, PTGS2, TNF, TRAF2
MAPK signaling pathway 1.13 × 10^–3 7.69 × 10^–3 BDNF, CD14, FAS, FGF1,
IL1A, IL1B, MAPK8IP1, MAPT, MEF2C, NGF, NTF3, NTRK1, NTRK2, PLA2G4A,
SOS2, TGFB1, TNF, TP53, TRAF2
The IGF-1 receptor and longevity 1.26 × 10^–3 8.28 × 10^–3 IGF1,
PIK3R1, SOD1, SOD2
Glutathione metabolism 1.45 × 10^–3 8.95 × 10^–3 GPX1, GSTM1, GSTM3,
GSTO1, GSTO2, GSTP1, GSTT1
Cytokine–cytokine receptor interaction 1.48 × 10^–3 8.95 × 10^–3 CCL2,
CCL3, CCR2, CXCL8, FAS, IL10, IL12A, IL12B, IL18, IL1A, IL1B, IL23R,
IL4, IL6, IL6R, NGFR, TGFB1, TNF, VEGFA
Serotonergic synapse 1.50 × 10^–3 8.95 × 10^–3 ALOX5, APP, CYP2D6,
GNB3, HTR2A, HTR6, KCNJ6, MAOA, PLA2G4A, PTGS2, SLC6A4
Antigen processing and presentation 1.63 × 10^–3 9.53 × 10^–3 CTSS,
HLA-A, HLA-DQB1, HLA-DRA, HLA-DRB1, HLA-DRB5, HSPA5, TAP2, TNF
Drug metabolism—cytochrome P450 1.88 × 10^–3 1.05 × 10^–2 CYP2D6,
GSTM1, GSTM3, GSTO1, GSTO2, GSTP1, GSTT1, MAOA
Cell cycle: G1/S check point 2.13 × 10^–3 1.18 × 10^–2 CDK1, CDKN2A,
GSK3B, TGFB1, TP53
Fcε RI signaling pathway 2.26 × 10^–3 1.23 × 10^–2 FCER1G, GAB2, IL4,
INPP5D, PIK3R1, PLA2G4A, SOS2, TNF
Apoptosis 2.28 × 10^–3 1.23 × 10^–2 FAS, IL1A, IL1B, NGF, NTRK1,
PIK3R1, TNF, TP53, TRAF2
Role of Erk5 in neuronal survival 2.61 × 10^–3 1.39 × 10^–2 MEF2A,
MEF2C, NTRK1, PIK3R1
Bioactive peptide-induced signaling pathway 2.90 × 10^–3 1.52 × 10^–2
CAMK2D, CDK5, GNA11, MAPT, MYLK, PTK2B
Control of skeletal myogenesis by HDAC and calcium/calmodulin-dependent
kinase (CaMK) 2.93 × 10^–3 1.52 × 10^–2 IGF1, INS, MEF2A, MEF2C, PIK3R1
Metabolism of xenobiotics by cytochrome P450 3.22 × 10^–3 1.62 × 10^–2
CYP2D6, GSTM1, GSTM3, GSTO1, GSTO2, GSTP1, GSTT1, HSD11B1
Ras-independent pathway in NK cell-mediated cytotoxicity 3.92 × 10^–3
1.88 × 10^–2 HLA-A, IL18, PIK3R1, PTK2B
Dopaminergic synapse 4.48 × 10^–3 2.11 × 10^–2 CAMK2D, CLOCK, COMT,
DRD4, GNB3, GRIN2B, GSK3B, KCNJ6, MAOA, PPP2R2B, SLC6A3
Cholinergic synapse 4.57 × 10^–3 2.12 × 10^–2 CAMK2D, CHAT, CHRNA3,
CHRNA4, CHRNA7, CHRNB2, GNA11, GNB3, KCNJ6, PIK3R1
The co-stimulatory signal during T-cell activation 4.72 × 10^–3
2.17 × 10^–2 HLA-DRA, HLA-DRB1, LCK, PIK3R1
Adhesion and diapedesis of lymphocytes 5.03 × 10^–3 2.28 × 10^–2 CXCL8,
ICAM1, IL1A
Notch signaling pathway 5.07 × 10^–3 2.28 × 10^–2 APH1A, APH1B, NCSTN,
PSEN1, PSEN2, PSENEN
Role of ERBB2 in signal transduction and oncology 5.61 × 10^–3
2.50 × 10^–2 ESR1, IL6, IL6R, PIK3R1
Aminoacyl-tRNA biosynthesis 6.37 × 10^–3 2.80 × 10^–2 MT-TG, MT-TH,
MT-TL2, MT-TQ, MT-TR, MT-TS2, MT-TT
Trka receptor signaling pathway 6.55 × 10^–3 2.80 × 10^–2 NGF, NTRK1,
PIK3R1
Rac 1 cell motility signaling pathway 6.62 × 10^–3 2.80 × 10^–2 CDK5,
CDK5R1, MYLK, PIK3R1
CTCF: first multivalent nuclear factor 6.62 × 10^–3 2.80 × 10^–2
CDKN2A, PIK3R1, TGFB1, TP53
Regulation of PGC-1a 7.74 × 10^–3 3.21 × 10^–2 CAMK2D, MEF2A, MEF2C,
PPARA
Calcium signaling pathway 7.85 × 10^–3 3.22 × 10^–2 ADRB1, ADRB2,
ADRB3, CAMK2D, CHRNA7, GNA11, HTR2A, HTR6, LHCGR, MYLK, NOS1, NOS3,
PTK2B
Lck and Fyn tyrosine kinases in initiation of TCR activation
8.30 × 10^–3 3.38 × 10^–2 HLA-DRA, HLA-DRB1, LCK
Adipocytokine signaling pathway 8.75 × 10^–3 3.52 × 10^–2 CD36, IRS1,
PCK1, PPARA, RXRA, TNF, TRAF2
Ras signaling pathway 9.43 × 10^–3 3.76 × 10^–2 EFNA5, EXOC2, FGF1,
GAB2, GNB3, GRIN2B, IGF1, INS, NGF, NGFR, PIK3R1, PLA2G3, PLA2G4A,
SOS2, VEGFA
Prolactin signaling pathway 1.02 × 10^–2 3.96 × 10^–2 ESR1, ESR2,
GSK3B, INS, LHCGR, PIK3R1, SOS2
Catecholamine biosynthesis,
tyrosine → dopamine → noradrenaline → adrenaline 1.05 × 10^–2
3.99 × 10^–2 DBH, PNMT
Fat digestion and absorption 1.14 × 10^–2 4.32 × 10^–2 ABCA1, APOA1,
APOA4, CD36, PLA2G3
Stress induction of HSP regulation 1.26 × 10^–2 4.63 × 10^–2 FAS, IL1A,
TNF
Regulation of hematopoiesis by cytokines 1.26 × 10^–2 4.63 × 10^–2
CXCL8, IL4, IL6
CTL-mediated immune response against target cells 1.26 × 10^–2
4.63 × 10^–2 FAS, HLA-A, ICAM1
Osteoclast differentiation 1.32 × 10^–2 4.81 × 10^–2 GAB2, IL1A, IL1B,
LCK, PIK3R1, PPARG, TGFB1, TNF, TRAF2, TREM2
[73]Open in a new tab
^aAlzheimer’s disease-related genes gene set
^bCalculated by Fisher’s exact test
^cAdjusted by the Benjamini and Hochberg (BH) method
^dGenes among Alzgset included in the specific pathway
Crosstalks among significantly enriched pathways
To explore the correlations between the pathways, we implemented a
pathway crosstalk analysis for the 68 enriched pathways. Here we
assumed that crosstalk existed in a pathway pair if they had a
proportion of common genes in Alzgset [[74]34]. There were 41 pathways
including six or more members in Alzgset, of which 37 pathways met the
criterion for crosstalk analysis; that is, each pathway shared at least
two genes with one or more other pathways. All of the pathway pairs
(207 crosstalks among 37 pathways) were used for constructing the
pathway crosstalk network and the overlap significance of each pathway
pair was evaluated based on the average of JC and OC.
Based on their crosstalks, these pathways could be roughly divided into
three major modules, with pathways in each group having more crosstalks
with each other than with those outside of this module and more likely
being related to the same or similar biological process (Fig. [75]1).
The first module primarily included neuronal-related and xenobiotic or
drug metabolism-related pathways (e.g., calcium signaling, dopaminergic
synapse, cholinergic synapse, serotonergic synapse and neurotrophin
signaling, metabolism of xenobiotics by cytochrome P450, and drug
metabolism—cytochrome P450). The major theme of the second module was
cell growth/survival and neuroendocrine-related pathways (e.g.,
PI3K-Akt signaling, mTOR signaling, notch signaling, prolactin
signaling, etc.). The third module included immune response-related
pathways (e.g., toll-like receptor signaling, Fc epsilon RI signaling
pathway). At the same time, the three modules were interlinked with
each other, indicating the existence of an AD-specific
immune-endocrine-neuronal regulatory network.
Fig. 1.
Fig. 1
[76]Open in a new tab
Crosstalk network amid Alzgset-overrepresented pathways. Vertices,
biological pathways; lines, crosstalks among pathways. Width of one
line (edge) shows direct proportion with the crosstalk level of a given
pathway pair. Nodes tagged with numbers represent the following
corresponding pathways: 1, intestinal immune network for IgA
production; 2, toll-like receptor signaling pathway; 3,
cytokine–cytokine receptor interaction; 4, hematopoietic cell lineage;
5, TNF signaling pathway; 6, apoptosis; 7, Fcε RI signaling pathway
AD-specific protein network
To further examine the potential pathological protein network of
Alzgset, we constructed a subnetwork for AD from the human
protein–protein interaction network via the Steiner minimal tree
algorithm. This method tries to connect the largest number of input
nodes (genes included in Alzgset in our case) via the least number of
interlinking nodes. As shown in Fig. [77]2, the protein network of AD
comprised 496 nodes and 1521 edges (interactions).
Fig. 2.
Fig. 2
[78]Open in a new tab
AD-specific protein network built by means of the Steiner minimal tree
algorithm, including 496 vertices and 1521 lines. Circular vertices,
genes of Alzgset; triangular vertices, expanding genes. Color of a
typical vertex designates its corresponding degree under the background
of the human protein interactome. Darkness of color for a vertex is
directly proportional to the corresponding degree value (Color figure
online)
As shown, 393 out of 430 Alzgset genes were included in the AD-specific
network, which accounted for 79.2% of 496 genes in the network and
91.4% of Alzgset, demonstrating a high coverage of Alzgset in the
subnetwork. There were 103 genes in the AD-specific molecular network
outside of Alzgset (Table [79]2). Given that these intermediate genes
interacted closely with those known to be related to AD, they might
also be involved in the pathological process of the disease phenotype.
Notably, a number of the genes—e.g., epidermal growth factor receptor
(EGFR), nuclear respiratory factor 1 (NRF1), somatostatin receptor 2
(SSTR2), and sortilin 1 (SORT1)—had been shown related to AD in several
previous studies [[80]35–[81]38]. Some of these genes have not been
reported to be directly involved in the pathophysiological condition of
AD, but genes linking to them or other members of the same protein
family may have been found to play a role in such processes. For
instance, ATP binding cassette subfamily G member 5 (ABCG5), a member
of a transport system superfamily, involved in ATP binding and
transporting of substrates across cytomembranes, was a node in the
AD-specific network but was out of Alzgset. However, six members from
the same family were included in Alzgset (ABCA1, ABCA2, ABCA7, ABCC2,
ABCG1, and ABCG2), and there was experimental evidence for their
involvement in AD; for example, the expression reduction or loss of
function of ABCA7 could alter Alzheimer amyloid processing [[82]39].
Solute carrier family 40 member 1 (SLC40A1), encoding a cytomembrane
protein that may be linked to iron export from duodenal epithelial
cells, was also included in the AD-specific network. SLC40A1can
interact with Golgi membrane protein 1 (GOLM1) and hepcidin
antimicrobial peptide (HAMP). The former was a gene in Alzgset and its
mutation may be related to reduced regional gray matter volume in AD
patients [[83]40], and the expression of HAMP was significantly reduced
in hippocampal lysates from AD brains [[84]41]. Thus, it is likely that
some of the 103 genes in the AD-specific network may play roles in AD
susceptibility and can be novel targets for further exploration.
Table 2.
Genes included in the AD-specific network but not in Alzgset^a
Gene symbol Gene name
ABCG5 ATP binding cassette subfamily G member 5
ACHE Acetylcholinesterase (Yt blood group)
ADAMTSL4 ADAMTS-like 4
ADRA1D Adrenoceptor alpha 1D
ALB Albumin
ARFGAP3 ADP-ribosylation factor GTPase activating protein 3
ARG1 Arginase 1
ATP1B2 ATPase, Na^+/K^+ transporting, beta 2 polypeptide
BEND7 BEN domain containing 7
BMP2 Bone morphogenetic protein 2
BRI3BP BRI3 binding protein
CA8 Carbonic anhydrase VIII
CARD16 Caspase recruitment domain family, member 16
CDH2 Cadherin 2, type 1, N-cadherin (neuronal)
CGB Chorionic gonadotropin, beta polypeptide
CHGB Chromogranin B
CLEC7A C-type lectin domain family 7, member A
COLQ Collagen-like tail subunit (single strand of homotrimer) of
asymmetric acetylcholinesterase
COPS5 COP9 signalosome subunit 5
COX6B2 Cytochrome c oxidase subunit VIb polypeptide 2 (testis)
CRK V-crk avian sarcoma virus CT10 oncogene homolog
CTAG1B Cancer/testis antigen 1B
CTNNA1 Catenin (cadherin-associated protein), alpha 1, 102 kDa
CTSA Cathepsin A
DAO d-amino-acid oxidase
DDR1 Discoidin domain receptor tyrosine kinase 1
DPYSL5 Dihydropyrimidinase-like 5
DYNC1LI2 Dynein, cytoplasmic 1, light intermediate chain 2
EDN1 Endothelin 1
EFNA1 Ephrin-A1
EGFR Epidermal growth factor receptor
ELF3 E74-like factor 3 (ets domain transcription factor,
epithelial-specific)
ERAP1 Endoplasmic reticulum aminopeptidase 1
ERP44 Endoplasmic reticulum protein 44
ETNPPL Ethanolamine-phosphate phospho-lyase
FBXO2 F-box protein 2
FCGR2B Fc fragment of IgG, low affinity IIb, receptor (CD32)
FGFBP1 Fibroblast growth factor binding protein 1
FGG Fibrinogen gamma chain
FOXRED2 FAD-dependent oxidoreductase domain containing 2
GNAS GNAS complex locus
GPLD1 Glycosylphosphatidylinositol specific phospholipase D1
GSTM2 Glutathione S-transferase mu 2 (muscle)
HCRT Hypocretin (orexin) neuropeptide precursor
HIST1H2AG Histone cluster 1, H2ag
HIST1H2AM Histone cluster 1, H2am
HLA-DQA1 Major histocompatibility complex, class II, DQ alpha 1
HSD17B14 Hydroxysteroid (17-beta) dehydrogenase 14
HSPA1L Heat shock 70 kDa protein 1-like
IFNA5 Interferon, alpha 5
IFNAR2 Interferon (alpha, beta and omega) receptor 2
IL18RAP Interleukin-18 receptor accessory protein
IL1R2 Interleukin-1 receptor, type II
IL23A Interleukin-23, alpha subunit p19
KCNJ9 Potassium channel, inwardly rectifying subfamily J, member 9
KIAA0513 KIAA0513
L3MBTL3 L(3)mbt-like 3 (Drosophila)
MAGEA11 Melanoma antigen family A11
MICAL2 Microtubule associated monooxygenase, calponin and LIM domain
containing 2
MLLT4 Myeloid/lymphoid or mixed-lineage leukemia; translocated to, 4
MUM1 Melanoma associated antigen (mutated) 1
MYC V-myc avian myelocytomatosis viral oncogene homolog
NRF1 Nuclear respiratory factor 1
NRXN1 Neurexin 1
OCIAD1 OCIA domain containing 1
PIM1 Pim-1 proto-oncogene, serine/threonine kinase
PKNOX2 PBX/knotted 1 homeobox 2
PLCZ1 Phospholipase C, zeta 1
PLD1 Phospholipase D1, phosphatidylcholine-specific
POR P450 (cytochrome) oxidoreductase
PPP1CA Protein phosphatase 1, catalytic subunit, alpha isozyme
PVR Poliovirus receptor
RAB26 RAB26, member RAS oncogene family
REST RE1-silencing transcription factor
RNF19A Ring finger protein 19A, RBR E3 ubiquitin protein ligase
RNF2 Ring finger protein 2
RPSA Ribosomal protein SA
SCNN1A Sodium channel, non voltage gated 1 alpha subunit
SDHA Succinate dehydrogenase complex, subunit A, flavoprotein (Fp)
SEPT12 Septin 12
SEPT6 Septin 6
SFN Stratifin
SIRPB1 Signal-regulatory protein beta 1
SLC40A1 Solute carrier family 40 member 1
SORT1 Sortilin 1
SSTR2 Somatostatin receptor 2
STK11IP Serine/threonine kinase 11 interacting protein
TMEM173 Transmembrane protein 173
TNFRSF11A Tumor necrosis factor receptor superfamily, member 11a, NFKB
activator
TNFRSF12A Tumor necrosis factor receptor superfamily, member 12A
TOMM7 Translocase of outer mitochondrial membrane 7 homolog (yeast)
TRMT6 tRNA methyltransferase 6
TSPY2 Testis specific protein, Y-linked 2
TYROBP TYRO protein tyrosine kinase binding protein
UBC Ubiquitin C
UBE3A Ubiquitin protein ligase E3A
UBIAD1 UbiA prenyltransferase domain containing 1
VKORC1 Vitamin K epoxide reductase complex, subunit 1
VSTM2L V-set and transmembrane domain containing 2 like
WIPF3 WAS/WASL interacting protein family, member 3
YEATS4 YEATS domain containing 4
ZNF423 Zinc finger protein 423
ZNHIT1 Zinc finger, HIT-type containing 1
[85]Open in a new tab
AD Alzheimer’s disease
^aAlzheimer’s disease-related genes gene set
Discussion
We have made great progress in exploring the molecular mechanisms of
Alzheimer’s disease in recent years. With the advancement and maturity
of high-throughput technology, we are able to identify the elements
related to this disease on much larger scales. Although more and more
genes/proteins potentially involved in the disease have been reported,
a thorough analysis of the biochemical processes associated with the
pathogenesis of AD from the molecular aspect is still missing. In such
cases, a systematic analysis of AD-related genes via a pathway-based
and network-based analytical framework will provide us with insight
into the disease beyond the single candidate gene-based analyses
[[86]42–[87]44]. In this study, by pooling and curating human genes
related to AD from genetic studies, and systematically delineating the
interconnection of these genes by means of pathway-based and
network-based analyses, we analyzed AD-related biochemical processes
and their interactions.
Compared with the candidate gene(s)-based approach, a comprehensive
analysis on AD-related genes conducted in this study has its own
advantages. By implementing an extensive compilation and curation of
human genes from genetic association studies on AD, we could obtain
valuable gene source data for further analysis. Especially, because the
risk of AD susceptibility can be attributed to many genes, with
multiple genes functioning in a concerted manner and each gene exerting
a small effect [[88]45], we took this into consideration by also
retrieving genes jointly showing significant genetic association with
AD. At the same time, by focusing on the biological correlation of
genes, pathway and network analysis can not only give us a more
comprehensive view for the pathological mechanisms of AD, but are also
more robust to the influence of false-positive genes.
As revealed by function enrichment analysis, genes in Alzgset may play
important roles in lipid/lipoprotein-related procedures, the immune
system, the metabolic process, drug response processes, and
neurodevelopment. For example, terms such as reverse cholesterol
transport, positive regulation of interleukin-6 production, response to
ethanol, lipoprotein metabolic process, diol metabolic process,
xenobiotic metabolic process, and regulation of neuronal synaptic
plasticity were overrepresented among Alzgset genes, implying the
important roles of these processes in the pathological processes of AD.
Furthermore, we noticed several terms of memory, visual learning,
social behavior, sleep, axon regeneration, and axon guidance also
emerged in the enriched list, concurrent with a-priori biological
findings for AD [[89]46–[90]50].
Our biochemical pathway analysis showed that immune-related pathways
were enriched among Alzgset, which further highlighted the connections
between AD and immune-related biological activities. Previous studies
have shown the involvement of neuroinflammation in AD pathology, with
inflammatory cytokines exerting central efforts [[91]51, [92]52].
Simultaneously, four pathways associated with neurotransmitters were
found to be overrepresented in Alzgset, coinciding with their essential
roles in the etiology and progression of AD. Acetylcholine, dopamine,
and serotonin are major neurotransmitters, involved in advanced
neuronal functions (e.g., learning, memory, language, etc.), exerting
key effects in the pathologic processes of AD. These neurotransmitters
could be involved in the damaging procedure of synaptic plasticity like
long-term potentiation and long-term depression in AD subjects or
animal models, which in turn may impair some synapse-based higher brain
functions such as memory and cognition [[93]53–[94]55]. Moreover, our
results detected several pathways pertaining to neuroendocrine
activities (i.e., ovarian steroidogenesis and prolactin signaling),
cuing endocrine processes for the pathogenesis of AD [[95]56, [96]57].
In addition, the adipocytokine signaling pathway was enriched in
Alzgset. Adipocytokines, including leptin, adiponectin, NAMPT, RBP-4,
and other proinflammatory cytokines, have attracted much attention due
to their close connection with AD [[97]32, [98]57, [99]58]. Detection
of the adipocytokine signaling pathway in this study provides further
evidence for the relationship between adipocytokine and the development
and progression of AD, and may also support the idea that AD could be a
metabolic disease [[100]59–[101]61]. As suggested by the results shown,
the molecular mechanisms underlying AD are pretty complicated, calling
for further thorough studies to decode the underlying pathologic
mechanisms.
Of significance, we detected three major pathway groups through pathway
crosstalk analysis. One group basically involved the pathways related
to the nervous system and metabolism-related activities. Amid these
pathways, cholinergic synapse, the calcium signaling pathway,
dopaminergic synapse, serotonergic synapse, and neurotrophin signaling
have been well dissected to function in the progress of AD
[[102]62–[103]65]. In the second module, pathways were largely
dominated by immune response or related functions, and by cell
growth/survival and neuroendocrine pathways for the third group.
Furthermore, we could notice that these three pathway modules were
interconnected and acted as an immune-endocrine-neuronal regulatory
network for the AD-related pathological conditions. Of note, one
pathway (i.e., intestinal immune network for IgA production) was found
to be a component part of the immune module. These results might
suggest that the gut–brain axis, made up of immune, neuroendocrine, and
neuronal components, was involved in the pathogenesis of AD
[[104]66–[105]68], in line with results from pathway crosstalk analysis
(i.e., there being three similar modules containing Alzgset-enriched
pathways). Subsequently, via in-depth examination, we observed that the
immune module has plenty of pathway crosstalks and plenty of crosstalk
strength. In turn, the cell growth/survival and neuroendocrine module
has lower number and less strength, compared with the immune module;
however, in terms of the neural module, the number and strength of
crosstalks are greater and larger. In spite of the limited number of
crosstalks, there exist paramount crosstalk levels among metabolic
pathways. These observed results might provide causal and regulatory
hints for AD. Integrating results from biochemical pathway and pathway
crosstalk analyses and the a-priori biological knowledge base, the
major pathways related to AD could be summarized in a diagram
(Fig. [106]3).
Fig. 3.
Fig. 3
[107]Open in a new tab
Main biochemical pathways related to AD. Numbers of genetics-based
studies have revealed the fact that AD is actually a complex disorder.
These major biochemical pathways involved in AD were connected based on
their biological relations
Further, we extracted an AD-specific protein network on the basis of
the human protein–protein interaction network. It is worth noting that
some linking genes outside Alzgset but included in the human
protein–protein interaction network may be potentially related to AD.
For example, nuclear respiratory factor-1 (NRF1) could be affected by
early changes in genes participating in the insulin and energy
metabolism pathways in an APP/PS1 transgenic mouse model of AD
[[108]69]. TYROBP, a transmembrane signaling protein, appeared in our
AD-specific subnetwork. By constructing gene regulatory networks in
1647 postmortem brain tissues from late-onset Alzheimer’s disease
(LOAD) patients and normal subjects, an immune and microglia-related
module dominated by genes participating in pathogen phagocytosis was
identified, with TYROBP as a key causal regulator upregulated in LOAD
[[109]70]. CDH2, a classical cadherin playing roles in the development
of the nervous system, was found with the pathogenic copy number
variations from 261 early-onset familial Alzheimer’s disease and
early/mixed-onset pedigree individuals using high-density DNA
microarrays [[110]71]. By applying cell-based studies and FBXO2
knockout mice, it was found that FBXO2 could regulate amyloid precursor
protein-related activities in the brain and might modulate AD
pathogenesis, coupling with our result to consolidate its involvement
in AD [[111]72]. Although no evidence indicated that VSTM2L, one of the
intermediate genes, was directly related to AD, it interacted with
ataxin 1 (ATXN1) of Alzgset [[112]73], whose biological function is
presently unknown, and also might be a secreted antagonist of Humanin
(HN) [[113]74] which mediated attenuation of AD-related memory
impairment and Aβ-induced AD-like pathological changes [[114]75,
[115]76]. As specified by the results detailed, this protein subnetwork
predicting approach could not only engender a significant predicted
subnetwork of Alzgset for AD, but could also possess the potentiality
to detect promising relevant genes.
There have been several available datasets or projects focused on the
curation of AD-related genes, including AlzGene [[116]77], Alzheimer’s
Disease Neuroimaging Initiative (ADNI) [[117]78], the Alzheimer Disease
& Frontotemporal Dementia Mutation Database (AD&FTDMDB) [[118]79], and
AlzBase [[119]80]. While AlzGene maintains a comprehensive catalog of
genetic association studies on AD and also includes results from
meta-analysis of polymorphisms with genotype data available in several
GWAS projects on AD, AD&FTDMDB is dedicated to the known mutations of
genes associated with AD and frontotemporal dementias from the
published reports or presentations at scientific meetings. The ADNI
project aims at facilitating the investigation of genetic influences on
AD onset and progression reflected in imaging changes, fluid
biomarkers, and cognitive status. It has reported several neuroimaging
GWAS with imaging quotas as quantitative phenotypes, such as
hippocampal volume and hippocampal gray matter density. On the other
hand, AlzBase is an integrative database for genes dysregulated in AD
and related diseases, and comprises annotations and expression
information on more than 7800 differentially expressed genes collected
from multiple microarray datasets. These datasets with different
features provide valuable information on genes and/or phenotypes for
exploring and understanding AD and its mechanisms.
Similar to AlzGene, Alzgset is also a compilation of AD-related genes
identified in genetic association studies. While AlzGene includes both
genes showing positive and negative association with AD, Alzgset
focuses only on the genes reported to be positively associated with AD
by the original authors. Because AlzGene has not been updated since
April 2011, results from many recent genetic association studies may
not be included. In association with studies on candidate genes, some
genes may each possess a mild to moderate p value, but two or more
genes could collectively show a more significant association with AD
due to the fact they probably act in a concerted manner. In such cases,
all of these candidates were included in Alzgset as long as the
original authors could provide sufficient evidence. On the other hand,
the genes in AlzGene were selected from meta-analyses for each
polymorphism and a relative uniform criterion was adopted, so the genes
mentioned may be neglected. Thus, Alzgset should offer an informative
supplement for AlzGene and serve as a useful dataset for AD
investigation.
However, there were several limitations in this study. First, our
pathway-based and network-based analyses results relied on genes in the
publications reported to be associated with AD. In view of the fact
that identification of risk genes for AD is still an ongoing task, the
GO biological process terms, biochemical pathways, and results derived
from network analysis should also be treated in the similar manner.
Second, we adopted the results and conclusions offered by the original
authors of each selected report when collecting the genes, which
inevitably impacts our results due to possible bias and insufficiency
in the available reports. Then, in order to decrease the false-positive
rate of AD-associated genes, we eliminated reports with insignificant
or negative results. Nevertheless, we cannot avoid the fact that some
genes in those studies might be actually associated with the disease
phenotype. Additionally, although the GO terms enriched in Alzgset
could provide valuable hints and might serve as an important resource
for understanding the molecular mechanisms of AD, it should be noted
that GO is biased towards fields like cancer biology and the concepts
related to neurology are underrepresented [[120]81]. Thus, some
important neurological processes related to AD may be missed in our
analysis. At the same time, despite overall levels of protein–protein
interaction databases having been greatly improved, the present human
interactome is still incomplete and some false-positive data may also
be included [[121]82]. Thus, the present research status of the human
interactome may also influence our results. It can be expected that, as
the protein–protein interaction data become more comprehensive and
accurate, the inferred AD-specific subnetwork can become more reliable
and valuable.
Conclusions
In summary, via a systems biology approach, we investigated the
pathways and molecular networks related to AD based on the genes
associated with the disease. Integrating biological function,
biochemical pathway, and pathway crosstalk analyses, we identified that
biochemical processes and pathways linked with lipid and/or
lipoprotein-related processes, metabolism, the immune system, and
neural development were overrepresented among Alzgset and there existed
three inter-connected pathway modules: neuronal and metabolic module,
cell growth/survival and neuroendocrine clique, and immunological
cluster. What is more, an AD-specific protein network was built via the
Steiner minimal tree algorithm and some novel genes latently associated
with AD were predicted. Such analysis of genes involved in AD will not
only improve our understanding of the contribution of genetic factors
and their interaction with environmental factors to the pathogenesis of
this disease, but will also help us to identify potential biomarkers
for further exploration. It could be anticipated that as more genetic
factors related to AD are identified, a systematic and comprehensive
analysis such as the one adopted in this study will be more useful to
explore the molecular mechanisms underlying AD.
Additional files
[122]Additional file 1:^ (5.2MB, txt)
Is a list of the human interactome utilized in this study. The human
protein interaction network contains 16,022 genes/proteins and 228,122
interactions. (TXT 5293 kb)
[123]Additional file 2: Table S1.^ (990.5KB, doc)
Is presenting a list of genes associated with Alzheimer’s disease and
Table S2 presenting the GO biological process terms enriched in
Alzgset. (DOC 990 kb)
Acknowledgements