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RESEARCH Open Access
Exploring the pathogenetic association between
schizophrenia and type 2 diabetes mellitus
diseases based on pathway analysis
Yanli Liu
1
, Zezhi Li
2
, Meixia Zhang
3
, Youping Deng
4
, Zhenghui Yi
5*
, Tieliu Shi
1*
From The 2011 International Conference on Bioinformatics and Computational Biology (BIOCOMP’11)
Las Vegas, NV, USA. 18-21 July 2011
Abstract
Background: Schizophrenia (SCZ) and type 2 diabetes mellitus (T2D) are both complex diseases. Accumulated
studies indicate that schizophrenia patients are prone to present the type 2 diabetes symptoms, but the potential
mechanisms behind their association remain unknown. Here we explored the pathogenetic association between
SCZ and T2D based on pathway analysis and protein-protein interaction.
Results: With sets of prioritized susceptibility genes for SCZ and T2D, we identified significant pathways (with adjusted
p-value < 0.05) specific for SCZ or T2D and for both diseases based on pathway enrichment analysis. We also
constructed a network to explore the crosstalk among those significant pathways. Our results revealed that some
pathways are shared by both SCZ and T2D diseases through a number of susceptibility genes. With 382 unique
susceptibility proteins for SCZ and T2D, we further built a protein-protein interaction network by extracting their nearest
interacting neighbours. Among 2,104 retrieved proteins, 364 of them were found simultaneously interacted with
susceptibility proteins of both SCZ and T2D, and proposed as new candidate risk factors for both diseases. Literature
mining supported the potential association of partial new candidate proteins with both SCZ and T2D. Moreover, some
proteins were hub proteins with high connectivity and interacted with multiple proteins involved in both diseases,
implying their pleiotropic effects for the pathogenic association. Some of these hub proteins are the components of
our identified enriched pathways, including calcium signaling, g-secretase mediated ErbB4 signaling, adipocytokine
signaling, insulin signaling, AKT signaling and type II diabetes mellitus pathways. Through the integration of multiple
lines of information, we proposed that those signaling pathways, which contain susceptibility genes for both diseases,
could be the key pathways to bridge SCZ and T2D. AKT could be one of the important shared components and may
play a pivotal role to link both of the pathogenetic processes.
Conclusions: Our study is the first network and pathway-based systematic analysis for SCZ and T2D, and provides
the general pathway-based view of pathogenetic association between two diseases. Moreover, we identified a set
of candidate genes potentially contributing to the linkage between these two diseases. This research offers new
insights into the potential mechanisms underlying the co-occurrence of SCZ and T2D, and thus, could facilitate the
inference of novel hypotheses for the co-morbidity of the two diseases. Some etiological factors that exert
pleiotropic effects shared by the significant pathways of two diseases may have important implications for the
diseases and could be therapeutic intervention targets.
* Correspondence: yizhenghui1971@gmail.com; tieliushi01@gmail.com
1
Center for Bioinformatics and Computational Biology, and The Institute of
Biomedical Sciences, School of Life Sciences, East China Normal University,
500 Dongchuan Road, Shanghai 200241, China
5
Schizophrenia Program, Shanghai Mental Health Center, Shanghai Jiao Tong
University School of Medicine, 600 Wan Ping Nan Road, Shanghai 200030,
China
Full list of author information is available at the end of the article
Liu et al.BMC Medical Genomics 2013, 6(Suppl 1):S17
http://www.biomedcentral.com/1755-8794/6/S1/S17
© 2013 Liu et al.; li censee BioMed Central Ltd. This is an open access article distributed under the terms of the Crea tive Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unr estricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.
Background
Schizophrenia (SCZ) is a chronic, severe, and disabling
brain disorder that has affected people with lifelong dis-
ability. The phenotype is heterogeneous and complex,
with multiple genes and environmental exposures likely
involved. It is characterized by a breakdown of thought
processes and by poor emotional responsiveness. It most
commonly manifests itself as auditory hallucinations,
paranoid or bizarre delusions, or disorganized speech
and thinking, and it is accompanied by significant social
or occupational dysfunction. The onset of symptoms
typically occurs in young adulthood with 1% prevalence
in the general population [1]. Recently, researchers have
identified specific genes/markers and chromosomal
regions for SCZ through numerous genetic studies, such
as linkage scans and their meta-analyses, candidate gene
association analyses, gene expression and genome-wide
association studies (GWAS) [2-5].
Type 2 diabetes mellitus (T2D) is characterized by per-
sistent high blood glucose in the context of insulin resis-
tance and relative insulin deficiency, due to pancreatic
beta-cell dysfunction. Cardiovascular diseases, chronic
renal failure, retinal, and nerve damage are usual compli-
cations of this illness. Many genes and pathways have
also been implicated with the T2D, but the mechanisms
underlying the connections remain further investigation.
Recently studies indicate that the prevalence of T2D
among individuals suffering from schizophrenia or schi-
zoaffective disorders is significant higher than that of the
general population [6,7]. For instance, a recent study
reported that T2D is more common in schizophrenics
than normal controls in Canada, especially in young males
and females [8]. Another recent study also reported an ele-
vated risk of T2D in schizophrenic individuals in Taiwan
[9].
Molecular inference and GWAS studies also point out
that SCZ shares substantial polygenetic component with
T2D. Increased attention is now being given to a possible
genetic basis for co-morbidity of SCZ and T2D [10]. The
pathogenetic association between SCZ and T2D has been
recognized but the potential mechanism behind the asso-
ciation has not been fully explored [10]. Recently, more
and more researchers have paid their attentions to iden-
tify the candidate genes for human diseases, including
T2D and SCZ, mainly through genome-wide association,
transcriptomic and proteomic expression studies. These
have greatly facilitated the research of genetic basis for
pathogenetic association between SCZ and T2D. It is
well accepted that genes or proteins usually interact with
each other to form complexes or pathways within a cell,
rather than function alone to carry out biological func-
tions [11]. Considering that SCZ and T2D are both com-
plex diseases, their pathogenesis is believed coupled with
lots of factors. Lin has proposed three models for hypoth-
eses concerning the co-morbidity between SCZ and T2D
[10]. One of the models suggested that T2D and SCZ are
caused by shared etiological factors, which is consistent
with other research result that T2D and SCZ are caused
by multiple genetic variants [12]. From this perspective,
we can link these two diseases by their shared susceptibil-
ity genes. Those genes may exert pleiotropic effects; it
means they play roles in two different pathological path-
ways, one related to SCZ and the other associated with
T2D. For example, TCF7L2, one of the best confirmed
susceptibility genes for T2D, has been also inferred to
strongly relate to SCZ. On one hand, TCF7L2 acts a role
in pancreatic beta cell function; on the other hand, it is a
transcription factor involved in the Wnt/beta-catenin sig-
naling [13]. Since Wnt signaling pathway plays a role in
the development of central nervous system (CNS) [14],
and has been also associated with SCZ [15,16], TCF7L2
may contribute to the co-morbidity of SCZ and T2D
through Wnt signaling pathway [17]. In addition to
genetic factors, environmental factors may also influence
susceptibility to both SCZ and T2D, and anti-psychotic
medications can also trigger the pathogenetic association
between SCZ and T2D. Although significant attentions
have been paid to explore the association between SCZ
and T2D, not much progress has been made and the
potential mechanisms remain unclear.
It is hypothesized that many genes may contribute
major risk to SCZ through their interaction and com-
bined effects, with each gene might contribute a small or
moderate risk. Similarly, T2D has also been regarded as a
complex disease and associated with the dysfunctions of
multiple genes. Therefore, we assumed that proteins that
interact with both SCZ proteins and T2D proteins should
also be the potential ones to contribute to both diseases.
Accordingly, in this study, we used those susceptibility
genes that have been implicated for SCZ or T2D in gen-
ome wide association study (GWAS) as the basis and
retrieved their nearest interactive partners from human
protein interaction data to construct a protein-protein
interaction network. Next, we selected those novel candi-
date genes from the network that interact with both SCZ
related proteins and T2D related proteins. In this way,
we prioritized a set of new candidate genes related to
both diseases. Moreover, considering that different biolo-
gical processes for these two diseases may share the same
susceptibility genes, we conducted pathway enrichment
analysis with those susceptibility genes related to two dis-
eases, and identified the pathways common to these two
diseases and those genes participating into those path-
ways. Through the pathway analysis, we tried to link the
pathogenetic association between the two diseases at the
molecular level.
Liu et al.BMC Medical Genomics 2013, 6(Suppl 1):S17
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Materials and methods
Susceptibility gene sets of SCZ and T2D
SCZ susceptibility genes were extracted from two publicly
available databases: Genetic Association Database [18,19]
and A Catalog of Published Genome-Wide Association
Studies [20,21]. The former is an archive of human genetic
association studies of complex diseases and disorders,
which includes summary data extracted from published
papers in peer reviewed journals on candidate gene and
GWAS studies (updated to June 9, 2012); the latter is an
online catalogue of SNP-trait associations from published
genome-wide association studies for use in investigating
genomic characteristics of trait/disease-associated SNPs
(TASs) (updated by June 22, 2012). T2D susceptibility
genes were collected from three main sources: the first
was Type 2 Diabetes Genetic Association Database
[22,23], and this database provides specialized information
on the genetic risk factors involved in the development of
T2D. Among the data in this database, we only selected
genes reported in more than two independent studies.
The other two data sources were the same as SCZ genes.
The follow-up analyses are based on these two susceptibil-
ity genes sets. A detailed flow chart of my methodology is
illustrated in Figure 1.
Significant pathway enrichment analysis
To carry out the pathway enrichment analysis, we
uploaded SCZ and T2D susceptibility genes into Cytos-
cape as cluster 1 and cluster 2, respectively, and ClueGO
was used for pathway enrichment analysis for all those
genes [24]. Two pathway databases, Kyoto Encyclopedia of
Genes and Genomes (KEGG) pathway [25,26] and Bio-
Carta pathway [27,28], were selected for pathway enrich-
ment analysis. Those susceptibility genes were mapped to
their enriched pathways based on the hypergeometric test,
and p-value was corrected by Benjamini-Hochberg
method [29]. It is possible that genes from both clusters
are associated with one pathway, but in different propor-
tions. Here we defined an enriched pathway specific to
one of the clusters if over 66% genes in the pathway are
from this cluster. Pathways with adjusted p-value < 0.05
were regarded as significant enriched pathways and were
selected for further analysis.
Pathway-pathway interaction network construction
To visually represent relationships between the selected
significant pathways, a pathway-pathway interaction net-
work was created, in which the node represented the sig-
nificant pathway, the edge between the significant
pathways was defined according to kappa scores which
were calculated based on any pathway pair shared genes
in a similar way as described by DAVID software [30].
The different proportion of the genes from the analyzed
clusters was represented with a colour gradient from blue
for the first cluster genes, to red for the second cluster.
Approximately equal proportions of the two clusters
were represented in light-yellow. The genes shared by
any pathway pair and those mapped to corresponding
significant pathways were also displayed in this network
as small nodes with different colours to distinguish them
from pathway nodes. The network was automatically laid
out using the Organic layout algorithm supported by
Cytoscape.
Protein-protein interaction data
Protein-Protein interaction data was downloaded from
Human Protein Reference Database (HPRD, version: 13
April, 2010) [31,32]. After removing self interactions and
disperse nodes, we ended up with 36,727 interactions
which cover 9,205 human genes. All proteins encoded by
unique susceptibility genes of two diseases were mapped
into HPRD, and then we extracted those proteins that
directly interact with our susceptibility proteins, and con-
structed a protein-protein interaction network in which a
node is a protein and an edge represents interaction
between two proteins.
New candidate genes prediction
Among all the nearest interacting proteins, those simul-
taneously interacting with both SCZ and T2D suscept-
ibility gene products were selected, then we constructed
a sub-network with them and their interacted susceptibil-
ity proteins. Next, we performed extensively literature
mining in PubMed to determine whether the relationship
between a candidate protein and SCZ or T2D has been
supported by previous studies. Based on these two
aspects evidence we predicted those genes with pleiotro-
pic effects as the risk factors that may contribute to the
pathogenetic association between SCZ and T2D.
Results
SCZ and T2D susceptibility gene sets
All the susceptibility genes were selected based on the
Genome-Wide Association Studies (GWAS). For SCZ
susceptibility genes, we retrieved 169 genes from
Genetic Association Database and 57 genes from data-
base of A Catalog of Published Genome-Wide Associa-
tion Studies. For T2D related genes, we extracted 26
genes and 79 genes from each of above databases,
respectively. In addition, we collected 143 genes from
Type 2 Diabetes Genetic Association Database. After
removing redundancy, we obtained 196 susceptibility
genes for SCZ and 200 for T2D, among them, 14 genes
are in common for both diseases (Additional file 1).
Enrichment pathway analysis
To perform functional enrichment tests of the suscept-
ibility genes, we uploaded SCZ and T2D related genes,
Liu et al.BMC Medical Genomics 2013, 6(Suppl 1):S17
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named as cluster 1 and cluster 2 respectively, into
ClueGO, a Cytoscape plug-in to decipher biological net-
works, and mapped them to their enrichment pathways.
Here, considering the incomplete of each pathway anno-
tation system, we selected two main pathway databases,
KEGG and BioCarta to conduct our analysis. As a result,
we ended up with 10 significant pathways (with adjusted
p-value < 0.05) specific to SCZ, 11 significant pathways
specific to T2D, and 7 pathways for both diseases (Table
1). Here we defined an enriched pathway specific to one
of the clusters if over 66% genes (the default value in
the system) in the pathway are from this cluster. Inter-
estingly, some of the enriched pathways, even though
they were classified as one of the clusters based on the
statistics, they included genes for both SCZ and T2D,
such as Adipocytokine signaling pathway and PPAR sig-
naling pathway, both of them were clustered as T2D
pathways. In fact, for 18 susceptibility genes in the Adi-
pocytokine signaling pathway, 4 of them are related to
SCZ, while 12 of them are identified to T2D related
genes, and the rest 2 genes have been linked to both
SCZ and T2D. PPAR signaling pathway includes 13
T2D related genes and 2 SCZ related genes. Neuroactive
ligand-receptor interaction pathway and Calcium
signaling pathway [33] were enriched as SCZ pathways.
There are 35 genes in Neuroactive ligand-receptor inter-
action pathway, and 26 of them are related to SCZ,
while the rest 9 genes come from T2D gene list. Cal-
cium signaling pathway contains 18 genes implicated to
SCZ, and 5 genes linked to T2D.
Next, to explore the association and crosstalk between
those different enriched pathways, we constructed a
pathway-based network with all those 28 significant path-
ways in which a large node is a pathway and an edge
represents crosstalk between two pathways through their
shared genes (Figure 2). The genes shared by any path-
way pair and those mapped to corresponding significant
pathways were displayed in this network as small nodes
with different colours to distinguish them from pathway
nodes. From the pathway-pathway interaction network, it
can be observed that many genes are shared by multiple
pathways, such as TNF shared by over 12 different signal-
ing pathways, AKT1 participating into 4 different signal-
ling pathways (Additional file 2).
New candidate risk gene inference
To infer new genes associated with both SCZ and T2D,
we conducted network analysis based on protein-protein
Genetic Association Database ˖169
A Catalog of Published Genome-Wide Association Studies˖57
196 SCZ susceptibility genes sources
Genetic Association Database ˖26
A Catalog of Published Genome-Wide Association Studies˖79
Type 2 Diabetes Genetic Association Database:143
200 T2D susceptibility genes sources
Unique SCZ susceptibility genes ˖182
Unique T2D susceptibility genes˖186
Common susceptibility genes:14
Pathway Enrichment Analysis
Cytoscape:ClueGO
The Hypergeometric Test
Significant Pathways Specific to SCZ:10
Common Significant
Pathways:7
Significant Pathways Specific to T2D:11
P-value< 0.05
Adjusted by Benjamini-Hochberg metho
d
Pathway-Pathway Interaction Network
Protein-Protein Interaction Network
Human Protein Reference Database
SCZ and T2D Molecular Network
Systematic Literature Mining
PubMed
New Candidate Genes Prediction
PubMed
Figure 1 A detailed flow chart for the analysis process. The green, pink, blue and yellow denote gene sources, pathway enrichment analysis,
network construction and new candidate gene prediction, respectively.
Liu et al.BMC Medical Genomics 2013, 6(Suppl 1):S17
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interaction (PPI). First, we downloaded human PPI data
from HPRD. Next, we mapped all 382 unique SCZ and
T2D susceptibility gene related proteins (susceptibility
proteins) to the human PPI data, only proteins that have
their interacting partners in the HPRD data were selected
in our further analysis. Then we retrieved those suscept-
ibility proteins with their nearest interacting neighbours
from the PPI data. After removing self-interaction and
duplicates, the final network included a total of 2,104
nodes and 3,155 interactions (Additional file 3). Those
2,104 proteins included 143 SCZ susceptibility proteins,
138 T2D susceptibility proteins, 12 common susceptibil-
ity proteins and 1,811 their direct interaction partners.
Among the 1,811 protein partners, there were 1,108 pro-
teins that interact with more than one SCZ susceptibility
proteins, 1,067 proteins with more than one T2D
susceptibility proteins, and 364 proteins with both dis-
eases’susceptibility proteins. We proposed those 364
proteins as new candidate risk factors for both SCZ and
T2D according to function association (guilt by associa-
tion) rule. Function association refers to that if two pro-
teins interact with one another, they usually participate
in the same, or related, cellular functions. Based on this
assumption, new functions of proteins can be inferred
with their interaction partners.
The 364 candidate proteins and their interacted suscept-
ibility proteins may provide new relationship for elucidat-
ing the common molecular pathways that may underlie
both SCZ and T2D. So we extracted those 364 candidate
proteins and their interacted susceptibility proteins from
the entire network to construct a sub-network (Additional
file 4). In this sub-network, among all 364 candidate
Table 1 The 28 significant pathways analysed using ClueGO.
Significant Pathways Specific To
cluster
Adjust P-
Value
Total
number
Gene source
unique SCZ
gene count
unique T2D
gene count
Common
gene count
Adipocytokine signaling pathway T2D 3.91E-09 18 4 12 2
Neuroactive ligand-receptor interaction SCZ 4.01E-09 35 26 9 0
Maturity onset diabetes of the young T2D 1.94E-08 11 0 11 0
Type II diabetes mellitus T2D 1.98E-08 14 1 12 1
PPAR signaling pathway T2D 9.33E-07 15 2 13 0
Calcium signaling pathway SCZ 5.03E-06 23 18 5 0
Visceral Fat Deposits and the Metabolic Syndrome T2D 6.57E-05 5 0 4 1
Type I diabetes mellitus SCZ 7.12E-05 10 8 1 1
Corticosteroids and cardioprotection BOTH 8.96E-05 6 3 2 1
Low-density lipoprotein (LDL) pathway during
atherogenesis
T2D 3.28E-04 4 0 4 0
Insulin signaling pathway T2D 9.20E-04 16 1 15 0
Graft-versus-host disease SCZ 0.001587097 8 6 1 1
IL-10 Anti-inflammatory Signaling Pathway BOTH 0.001671861 4 1 1 2
Basic mechanism of action of PPARa, PPARb(d) and
PPARg and effects on gene expression
T2D 0.001780493 3 0 3 0
Actions of Nitric Oxide in the Heart BOTH 0.002369651 5 2 3 0
Erythropoietin mediated neuroprotection through NF-kB BOTH 0.003426765 4 2 1 1
Allograft rejection SCZ 0.003509 7 5 0 2
g-Secretase mediated ErbB4 Signaling Pathway SCZ 0.005740212 3 3 0 0
Msp/Ron Receptor Signaling Pathway BOTH 0.005740212 3 1 1 1
Autoimmune thyroid disease SCZ 0.020029548 7 6 0 1
Alzheimer’s disease BOTH 0.020096651 5 1 2 2
Free Radical Induced Apoptosis SCZ 0.020961818 3 2 0 1
Role of PPAR-gamma Coactivators in Obesity and
Thermogenesis
T2D 0.020961818 3 0 3 0
Asthma SCZ 0.023911463 5 3 0 2
AKT Signaling Pathway SCZ 0.024540904 4 3 1 0
Hematopoietic cell lineage BOTH 0.027660527 9 4 4 1
Regulation of PGC-1a T2D 0.029865431 3 1 2 0
Role of ERBB2 in Signal Transduction and Oncology T2D 0.030339933 4 1 3 0
SCZ: schizophrenia; T2D: type 2 diabetes mellitus. The p-value is adjusted by Benjamini-Hochberg method.
Liu et al.BMC Medical Genomics 2013, 6(Suppl 1):S17
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proteins, 9 proteins closely interacted with both multiple
SCZ and T2D susceptibility proteins (with both interacting
partners ≥5) and were regarded as hub proteins, these
hub proteins include SRC, PRKACA, PRKCA, GRB2,
PTPN11, SMAD3, YWHAZ, PIK3R1 and PLCG1 (Figure
3). Some of these hub proteins are the components of our
identified enriched pathways (Table 2).
To verify whether the function association approach is
reasonable to infer the function relationships of those
proteins to the two diseases, we performed systematic lit-
erature mining to survey whether those candidate genes
are reported in PubMed articles for SCZ and T2D. As a
result, we found that 59 candidate genes have been con-
nected to SCZ [34-38], 77 candidate genes have been
linked to T2D [39-43], while 25 candidate genes [44-49]
have been implicated to both SCZ and T2D with various
studies (Additional file 5). Totally, 161 candidate genes
(~45% of all candidate genes) have been related to either
SCZ or T2D or both diseases with various experimental
approaches, further proving the rationale of “function
association”in the application of disease related gene
inference. We proposed that genes encoding those 33
proteins (9 hub proteins and 25 proteins, with one com-
mon protein) could be high-priority candidate genes con-
tributing to pathogenetic association between SCZ and
T2D.
SCZ and T2D molecular network construction
Last, to explore the potential relationships of those iden-
tified genes and two diseases, based on our constructed
GAD2
GAD1
HNF4A
HNF1A
NEUROD1
HHEX
VEGFA
PDE3B
HLA-E
KEGG:05330
TNF
CTLA4
INS
KEGG:05320
HLA-DQA1
IL10RA
BioCarta:163
PRKAA2
IRS2
KEGG:04910
ANXA1
AKT1
BioCarta:41
CHRNA7
BioCarta:141
NOS3
CACNA1C
KEGG:04080
GNAS
CACNA1E
SORBS1
POMC
BioCarta:135
ADIPOR1
KEGG:04930
KEGG:04920
ADIPOR2
NPY
GCK
PDX1
SLC2A2
PRKCZ
INSR
KCNJ11
KEGG:04950
ABCC8
IAPP
NEUROG3
HNF1B
HLA-DQB1
KEGG:04940
BioCarta:213
IRS1
SREBF1
PTPN1
PPP1R3A
FOXO1
HLA-A
IL10
IL1B
HLA-DRB1
KEGG:05310
CSF2RA
KEGG:05332
KEGG:05010
IDE APOE
IL3RA
KEGG:04640
DBI
BioCarta:312
BioCarta:203
PLIN1
KEGG:03320
APOA5
RETN
APOC3
IL6
RXRB
ACSL6
PCK1
PRNP
ADIPOQ
PPARGC1A
UCP1
PPARA
FABP2
LEP
GRIN1
ADRB2
BioCarta:119
CHRM1
BioCarta:232
HIF1A
SOD2
PPP3CC
NFKB1
NOS1
YWHAH
KEGG:04020
LEPR
DRD5
GRIN2A
ADRA1A
ADRB3
HTR4
HTR2C
HTR2A
HTR5A
CCKAR
GRM8
GRIK3
GABBR1
GRIN2B
MC3R
GRM3
GABRB2
GCGR
GRIA4
BioCarta:121
DRD3
CNR1
GABRA5
NRG2
NRG3
DRD4
DRD2
BioCarta:241
ERBB4
MC4R GRM5
AGTR1
DRD1
PARD3
TAAR6
MTNR1B
LDLR
ESR1
PPARD
IL6R
PPARG
BioCarta:16
BioCarta:153
CCL2
CD14
CR2
LPL
BioCarta:243
KEGG:04920: Adipocytokine signaling pathway
KEGG:04080: Neuroactive ligand-receptor interaction
KEGG:04950：Maturity onset diabetes of the young
KEGG:04930: Type II diabetes mellitus
KEGG:03320: PPAR signaling pathway
KEGG:04020: Calcium signaling pathway
BioCarta:312: Visceral Fat Deposits and the Metabolic Syndrome
KEGG:04940: Type I diabetes mellitus
BioCarta:141: Corticosteroids and cardioprotection
BioCarta:16: Low-density lipoprotein (LDL) pathway during atherogenesis
KEGG:04910: Insulin signaling pathway
KEGG:05332: Graft-versus-host disease
BioCarta:163: IL-10 Anti-inflammatory Signaling Pathway
BioCarta:241: Basic mechanism of action of PPARa, PPARb(d) and PPARg and
effects on gene expression
BioCarta:213: Actions of Nitric Oxide in the Heart
BioCarta:119: Erythropoietin mediated neuroprotection through NF-kB
KEGG:05330: Allograft rejection
BioCarta:121：g-Secretase mediated ErbB4 Signaling Pathway
BioCarta:203: Msp/Ron Receptor Signaling Pathway
KEGG:05320：Autoimmune thyroid disease
KEGG:05010: Alzheimer's disease
BioCarta:135: Free Radical Induced Apoptosis
BioCarta:243: Role of PPAR-gamma Coactivators in Obesity and Thermogenesi
s
KEGG:05310: Asthma
BioCarta:41: AKT Signaling Pathway
KEGG:04640: Hematopoietic cell lineage
BioCarta:232: Regulation of PGC-1a
BioCarta:153: Role of ERBB2 in Signal Transduction and Oncology
Figure 2 Pathway-pathway interaction network. The large node represents pathway, blue for SCZ, red for T2D, light-yellow for both. The
small node represents gene mapped to corresponding pathways. Small nodes in green and purple are genes from SCZ and T2D susceptibility
gene lists, respectively. The edge represents crosstalk between any two pathways. The network is automatically laid out using the Organic layout
algorithm supported by Cytoscape.
Liu et al.BMC Medical Genomics 2013, 6(Suppl 1):S17
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pathway network, protein-protein interaction and litera-
ture survey, we developed a SCZ & T2D molecular net-
work (STMN), in which the relationships between those
susceptibility genes/proteins and the two diseases have
been inferred (Figure 4).
Discussion
As complex diseases, both SCZ and T2D have attracted
more and more attentions in the research communities
for their significant increasing prevalence during past
decades. Clinical studied have reported that the risk of
T2D is increased in schizophrenic patients and T2D is
one of the leading causes of morbidity and mortality in
individuals affected with SCZ-related disorders (i.e.,
SCZ, schizoaffective disorder, and schizophreniform dis-
order) [50,51]. There have been numerous reports of
susceptibility genes or loci to SCZ or T2D, however, few
genes have been confirmed to link to the two diseases
MPZL1
GRIN2B
CXCR4
PTPN11
SLAMF
1
LEPR
DDR1
GRIN1
CTLA4
INSR
PLCG1
PKN2
ALK
AGTR1
CSF2RA
CHRNA7
NOTCH4
HNF4A
TGM2
SPRY2
YWHAH
ADRB3
HNF1A
IL6R
PPARD
PDE3B
TH
LMNA
IRS1
IRS2
SYK
GRIN2A
TPH1 ESR1
SRC
UCP2 YWHAZ
GABBR1
SMAD3
PARD3
UCP3
FOXO1
ANXA1
WWOX
PTPN1
ADRB2
PRKCA
GRB2
EGF
ERBB4
DRD3
PRNP
GRM5
OGG1
GFPT1
NRGN
PPARA
GRIA4
PRKACA
SLC2A2
ABCA1
NFKB1
MC4R
PLIN1
SYN2
CACNA1C
NOS1 PRKCZ
HLA-A DRD4
HMGA2
AKT1
PIK3R1
ANK3
VDR
Figure 3 The sub-network of 9 hub proteins and their interacted susceptibility proteins. Nodes in blue represent SCZ susceptibility
proteins; nodes in light-green represent T2D susceptibility proteins; nodes in yellow represent SCZ and T2D common susceptibility proteins.
Large nodes in red are 9 hub proteins. The size of hub proteins reflects the degree in the network.
Table 2 Six hub proteins that involved in previous enriched pathways.
Previous enriched pathways Adjusted P-Value Hub proteins
Calcium signaling pathway 7.42E-06 PLCG1 PRKACA PRKCA
g-Secretase mediated ErbB4 Signaling Pathway 4.33E-03 PRKCA
Adipocytokine signaling pathway 6.41E-08 PTPN11
Insulin signaling pathway 5.46E-06 GRB2 PIK3R1 PRKACA
AKT Signaling Pathway 4.26E-04 PIK3R1
Type II diabetes mellitus 6.67E-03 PRKACA PIK3R1
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and the mechanisms for the association remain unclear.
The limited success in detection of genetic factors for
both diseases has indicated that the diseases are not
caused by the dysfunction of a specific molecule or
pathway, most likely both diseases are caused by the
altered function or expression of many genes, which
mayindividuallycontributetoonlyasmallrisk,but
their collective dysfunctional effects interfere with the
function of several biological pathways that eventually
produce the clinical outcome [52]. Therefore, studies
based on network and pathway interaction naturally are
the choice for both of the diseases and their association.
To our knowledge, our study is the first network and
pathway-based systematic analyses for the pathogenetic
association between SCZ and T2D by using susceptibil-
ity genes generated from various researches. For many
Figure 4 The SCZ & T2D molecular network. The potential underlying common molecular mechanism between SCZ and T2D. Nodes in blue,
pink and green background are related to SCZ, T2D and common progression, respectively. Solid and dashed lines represent direct and indirect
regulation; lines with arrow and spot represent activation and inhibition, respectively.
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complex diseases, including SCZ and T2D, there are no
applicable gene signatures in clinical to detect them in
early stages. The new discovered common susceptible
genes related to the pathogenetic association between
SCZ and T2D could be potentially used as candidates to
signify the co-occurrence of SCZ with T2D.
From our enrichment pathway analysis results and the
pathway-pathway interaction network, we observed that
many genes are shared by several pathways, such as
TNF shared by 12 enriched pathways and AKT1 shared
by 4 enriched pathways. Those genes that participate in
several pathways could be the key components for the
pathway crosstalks and the potential risk factors for the
SCZ and T2D association.
As a serine/threonine kinase, AKT is a key regulator of
many signal transduction processes mediated by protein
phosphorylation and a central molecule in regulating mul-
tiple cellular processes such as glucose metabolism, tran-
scription, apoptosis, cell proliferation, angiogenesis, and
cell motility [53]. AKT is activated by phosphoinositide
3-kinase (PI3K), which itself is activated by several
upstream signaling pathways, Neuroactive ligand-receptor
interaction pathway is the major one for the activation of
PI3K. Through PI3K, AKT is regulated by many proteins,
such as insulin receptors, receptor tyrosine kinases, G pro-
tein coupled receptors, cytokine receptors, etc., then con-
trols diverse biological responses such as programmed cell
death, cell proliferation, migration, and metabolic pro-
cesses. Recently, accumulating evidences suggest that
impaired AKT signaling plays a role in the pathogenesis of
SCZ [54]. The potential molecular mechanisms underlying
the role of AKT signaling in SCZ has contributed to the
AKT dysfunction. Activated AKT can phosphorylate a
number of other molecules, one of them is the strong
clinically relevant target, glycogen synthase kinase-3
(GSK3) [53]. GSK3 has been confirmed to play several
roles in glucose metabolism, differentiation and develop-
ment, intracellular trafficking, apoptosis, and regulation of
gene transcription [55]. In the brain, both GSK3 and AKT
have been proposed to modulate synaptic plasticity [56].
AKT1 activation has been reported to be reduced in the
hippocampus and frontal cortex of SCZ patients compared
with healthy controls [54]. Other studies have further pro-
vided the evidence of a reduction of AKT1 mRNA and
protein levels in peripheral blood, prefrontal cortex, and
hippocampus in SCZ patients [57,58]. Moreover, the single
SNP that is associated with reduced expression of AKT1
in peripheral lymphocytes is associated with brain volume
reductions in caudate and right prefrontal cortex [59].
The AKT signaling pathway also plays a pivotal role in
the metabolic functions of insulin in the liver. AKT regu-
lates glycogenesis through the phosphorylation of GSK3,
GSK3 phosphorylates glycogen synthase and converts it
to the less-active (glucose-6-phosphate dependent) form,
thus inhibits glycogen synthesis. In contrast to the phos-
phorylation of AKT for its activation [53], constitutively
activated GSK3 in resting cells requires phosphorylation
by kinases such as AKT to inactivate it [60]. Interestingly,
68% less expression of AKT1 has been detected in the
lymphocytes of SCZ patients compared with healthy con-
trols [54]. Significant reduction of AKT1 expression and
deregulation of AKT1-associated pathways have recently
also been reported in peripheral blood cells of schizo-
phrenia patients [61]. The impaired activation of AKT in
SCZ patients could result in the higher activity of GSK3
in blood, which eventually causes the reduction of glyco-
gen and inhibition of glucose with the increase of blood
glucose levels. In addition, AKT1 has also been associated
with other signaling pathways, such Dopamine pathways,
Wnt signalling pathway and Adipocytokine signaling
pathway. The dysfunction of these signaling pathways
with impaired AKT1 all has significant impact on the
SCZ or T2D, which is consistent with our analysis result.
Taken together, AKT signaling pathway could be one of
the pivotal pathways to bridge the association between
SCZ and T2D, AKT1 gene, together with GSK3 gene in
this pathway, may be responsible for the co-occurrence
of SCZ and T2D.
Leptin (LEP) gene (Figure 2) is involved in the pathways
of Neuroactive ligand-receptor interaction and Adipocyto-
kine signaling in our pathway-pathway interaction net-
work. Leptin is secreted by adipose tissue and signifies the
endocrine function of adipose tissue. An increase in leptin
signals can affect the neuronal targets in the hypothala-
mus. Leptin activates Janus-activating kinase2 (Jak2) and
STAT3, leading to activate alpha-MSH and CART in
POMC/CART neuron, and inhibit NPY and AGRP in
NPY/AGRP neuron. The Neuroactive ligand-receptor
interaction pathway contains G protein-coupled receptors
(GPCRs) of dopamine and serotonin which have been pro-
posed to play an important role in the pathophysiology of
SCZ. Previous studies have suggested that LEP may associ-
ate with SCZ [62,63]. Adipocytokine signaling pathway has
been specifically linked to T2D. As a component for Adi-
pocytokine signalling pathway, LEP is considered to be an
important regulator in the pathophysiology of T2D dis-
eases. In our constructed STMN, we also observed a
crosstalk between leptin and insulin in the hypothalamus.
In addition, leptin can activate AKT1 through the activa-
tion of PI3K, and possibly through JAK2, thus providing a
mechanism for regulation of target genes, the same as in
Insulin signaling pathway. Therefore, the crosstalk
between above two pathways also implies the underlying
pathogenetic association between SCZ and T2D.
Corticosteroids and cardioprotection pathway, a path-
way both for SCZ and T2D, was reported to be asso-
ciated with SCZ [64,65] and T2D [66]. It interlinks to
Calcium signaling pathway and Insulin signaling
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pathway. Interestingly, the crosstalk between Corticos-
teroids and cardioprotection pathway and Insulin signal-
ing pathway is mediated by AKT according to our
pathway-based network. Previous study also has shown
that Calcium signaling pathway is associated with dopa-
mine-induced cortical neuron apoptosis which is consid-
ered as an important mechanism in SCZ pathogenesis
[67]. Meanwhile, Actions of Nitric Oxide in the Heart,
another pathway for both SCZ and T2D, is a crosstalk
between Calcium signaling pathway and Insulin signal-
ing pathway either. Previous study indicated that Nitric
oxide was involved in pathophysiology of SCZ [68].
IL-10 Anti-inflammatory signaling pathway is an
immune-related pathway. Accumulated evidence from
epidemiological, clinical and animal studies suggests that
immune-related pathway may play a key role in the
development of mental diseases including SCZ and
mood disorders [69,70]. IL-10 Anti-inflammatory Signal-
ing Pathway has been reported previously to be involved
in pathophysiology of SCZ [71] and T2D [72], respec-
tively. Therefore, the above evidence suggests that IL-10
Anti-inflammatory signaling pathway may be involved in
the pathogenetic association between SCZ and T2D. In
another perspective, due to inflammation contributes to
injury or enhances CNS vulnerability, and acute inflam-
mation can also be shifted to a chronic inflammatory
state and adversely affect brain development, therefore,
through efficient anti-inflammatory and reparative pro-
cesses, inflammation may resolve without any harmful
effects on the brain. Alternatively, intervention of TNF-
a, before the progressive loss of beta cell function, may
yield promising results in the treatment of T2D. Since
IL-10 is a cytokine with potent anti-inflammatory prop-
erties, it represses the expression of inflammatory cyto-
kines such as TNF-a, IL-6 and IL-1 by activated
macrophages. The anti-inflammatory actions of IL-10
may be therapeutically useful by intervention of TNF-a,
IL-1 or IL-6 to avoid inflammatory response, then to
decrease the CNS vulnerability, further to reduce the
chance to trigger T2D.
In our inferred new candidate risk factors, 9 proteins
interact with multiple proteins involved in both diseases
with high connectivity, 6 of them are found to be the
components of our enriched pathways (Table 2). Among
them, PRKACA is shared by Type II diabetes mellitus,
Insulin signaling pathway and Calcium signaling path-
ways; PIK3R1 is a common molecule of AKT signaling,
Insulin signaling and Type II diabetes mellitus pathways;
PRKCA is a component for both of Calcium signaling
and g-Secretase mediated ErbB4 signaling pathways
while PLCG1 for Calcium signaling pathway, PTPN11 for
Adipocytokine signaling pathway and GRB2 for Insulin
signaling pathway. All of those proteins may be asso-
ciated with both SCZ and T2D through participating into
related signaling pathways and interacting with other dis-
ease related susceptibility genes, then further enhancing
the linkage between SCZ and T2D.
For the rest of three hub proteins, SRC, SMAD3 and
YWHAZ, they may also play some role in contributing to
pathogenic association between SCZ and T2D. Src is a
tyrosine kinase. In the sub-network, it interacts with 7
and 13 SCZ and T2D related proteins, respectively. Src
has been associated with SCZ, the potential molecular
mechanism is that the NRG1-ErbB4 pathway, which is a
candidate pathway participated in cognitive dysfunction
in SCZ, affects NMDAR hypofunction through modula-
tion of Src activity. In mouse model, NRG1-ErbB4 signal-
ing blocks Src enhancement of NMDAR-mediated
synaptic currents [73]. Although there has no report
about Src implicated with T2D, from the sub-network,
we observed that Src links to multiple T2D related pro-
teins, such as INSR, an insulin receptor, and AKT1.
Given that the Src protein is a tyrosine kinase, which
plays critical roles in the activiation of multiple signaling
pathways [74], we speculate that SRC is a potential candi-
date gene with pleiotropic effects that affects both SCZ
and T2D.
SMAD3 is a member of SMAD protein family that are
signal transducers and transcriptional modulators that
mediate multiple signaling pathways. One of those sig-
naling pathway is the transforming growth factor beta
(TGF-b) pathway [75], TGF-bplays an important role in
regulation of insulin gene transcription and b-cell func-
tion [39], it is also a key mediator in the development of
diabetic complications. TGF-bexerts its biological
effects by activating downstream mediators, called
Smad2 and Smad3. Recent studies have demonstrated
that under disease conditions Smad3 act as signal inte-
grators and interact with other signaling pathways, such
as the MAPK and NF-B pathways [76]. In adult Smad3
null mice, TGF-bsignaling through Smad3 is needed to
maintain the rate of cell division of neuronal precursors
in the adult brain and hence the amount of neurogen-
esis [77]. Another Smad family member - Smad4 has
been proven to be related to SCZ, since forebrain-speci-
fic Smad4 knock-out mice shows typical endophenotype
of schizophrenia [78]. Taken together, these data add
new evidence to support our hypothesis that the Smad3
may link to both SCZ and T2D by interacting with mul-
tiple signaling pathways as a signal integrator.
YWHAZ gene product belongs to the 14-3-3 family of
proteins which mediate signal transduction by binding to
phosphoserine-containing proteins. The encoded protein
interacts with IRS1 protein, and is a negative regulator
for insulin signal transduction, suggesting its role in regu-
lating insulin sensitivity [79]. Previous study has also
indicated that the YWHAZ gene is a potential risk factor
for paranoid SCZ, although the potential mechanism of
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how this gene affected biological functions in the brain is
unknown [80]. Therefore, our hypothesis tentatively
assumes that the YWHAZ may also be a pleiotropic
gene, which participates in the pathogenetic linkage
between SCZ and T2D diseases.
For the rest of new candidate genes, although the
number of interaction partners for them is various and
less than those hub proteins in the PPI network, 25 of
them, including well known genes, TP53, GSK3 and
RXRA, are still supportedly associated with SCZ and
T2D by text mining. Various data have indicated that
they all have been implicated in both of the diseases
(Additional file 5). For those genes without literature
support, they may also be involved in differential but
intertwined SCZ and T2D pathogenetic processes.
Further experiments need to carry out to verify those
associations.
The new candidate genes are inferred from the PPI,
however, it is worth pointing out that the PPI we used
in the study represents a static relationship between
each protein pair. In real biological processes, such as
pathogenetic conditions or different development stages,
gene expression has spatiotemporal pattern, the same as
protein-protein interaction. Therefore, different impli-
cated genes may participate into SCZ and T2D diseases
in different stages and play different roles in the associa-
tion with the SCZ and T2D. By integrating multiple
dimensional data, it can be expected that network-based
approach, combined with other multiple resources, will
provide great help to decipher the coordination and
functional roles of those implicated genes in complex
diseases. Furthermore, it is well known that many pro-
teins in signaling pathways are drug targets. Our path-
way-based network has revealed that many susceptible
genes linking SCZ and T2D participate into different
signaling pathways and have pleiotropic effects, their
encoded proteins could be good candidates as drug tar-
gets to treat this complex disease, and selectively target-
ing those dysfunctional proteins in different signaling
pathways with synergetic effect could potentially have
better treatment outcome.
There are certain limitations in our study. First, those
prioritized SCZ genes and T2D related genes we used
are all from GWAS. Considering the inherent drawbacks
of GWAS approach with its noise and high false positive
rate, some of the genes may not be truly associated with
both of the diseases, which will certainly affect the path-
way enrichment analysis result and our inference of new
candidate risk genes for the association of SCZ and
T2D. Second, the incomplete pathway annotation sys-
tems for each pathway database could also negatively
contribute to the pathway network construction and the
pathway crosstalk interpretation. Nevertheless, our
results still present novel and promising explanation for
the association between SCZ and T2D, these novel rela-
tionships could offer new insights into these two dis-
eases’etiology.
Conclusions
We have successfully built the pathogenetic association
between SCZ and T2D based on their enriched pathway
crosstalk. Through the integration of multiple level ana-
lysis results, including pathway crosstalk, PPI and litera-
ture survey, we revealed some potential molecular
mechanisms and multiple susceptibility genes that could
exert pleiotropic effects shared by two diseases. Totally
364 candidate proteins that directly interacted with both
our SCZ and T2D susceptibility proteins have been
identified, 33 of them have been prioritized as high sig-
nificant genes linking to both of SCZ and T2D.
Although there are certain limitations for our analysis
processes, our preliminary findings can provide an alter-
native direction for other researchers to explore the
linkage between these two diseases.
Currently, many chromosomal intergenic regions and
SNPs on human genome have been associated with dis-
eases. However, no gene has been identified in those
regions or to host those SNPs. It can be anticipated that
with the emergence and significant progress of new
technologies, such as next generation sequence technol-
ogy [81,82], more and more genes and transcribed
regions will be discovered in human genome [83,84] and
those unrealized expression genes in the current inter-
genic regions will be indentified and linked to the dis-
eases. Those will definitely facilitate the investigation of
those complex diseases, and help us to reshape the
potential underlying genetic mechanisms for those com-
plex diseases.
Additional material
Additional file 1: 196 SCZ and 200 T2D susceptibility gene sets.
Additional file 2: Pathway shared genes and their involved
pathways.
Additional file 3: Protein-Protein Interaction Network. This network
consists of 2,104 nodes and 3,155 edges, nodes represent proteins, node
size stands for its degree, edges represent interaction between two
proteins. Nodes in blue are 143 SCZ susceptibility proteins; nodes in
green are 138 T2D susceptibility proteins; nodes in yellow are common
susceptibility proteins; remainder nodes in purple are 1,811 candidate
proteins.
Additional file 4: Sub-network extracted from Additional file 3. This
network consists of 580 nodes and 1,266 edges, node attributes refer to
Additional file 3.
Additional file 5: Literature mining results for the 364 new
candidate genes. 364 candidate genes are in the first column; the
second column hosts the number of interactions from SCZ susceptibility
proteins; the third column lists the PubMed ID for the reported protein
associated with SCZ; the fourth column is the interactions of SCZ. The
corresponding results of T2D are displayed in the following columns.
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Genes in pink background are 25 candidate genes which have been
implicated in both SCZ and T2D with various studies.
Authors’contributions
TS and ZY conceived and designed the study. YL, ZL, MZ performed
analyses. TS, ZY, YL, ZL and YD wrote the manuscript, TS finalized the
manuscript.
Competing interests
The authors declare that they have no competing interests.
Acknowledgements
This work was supported by the National 973 Key Basic Research Program
(Grant Nos. 2010CB945401 and 2012CB910400), the National Natural Science
Foundation of China (Grant No. 31171264, 31071162, 31000590 and
81171272) and the Science and Technology Commission of Shanghai
Municipality (11DZ2260300).
This article has been published as part of BMC Medical Genomics Volume 6
Supplement 1, 2013: Proceedings of the 2011 International Conference on
Bioinformatics and Computational Biology (BIOCOMP’11). The full contents
of the supplement are available online at http://www.biomedcentral.com/
bmcmedgenomics/supplements/6/S1. Publication of this supplement has
been supported by the International Society of Intelligent Biological
Medicine.
Author details
1
Center for Bioinformatics and Computational Biology, and The Institute of
Biomedical Sciences, School of Life Sciences, East China Normal University,
500 Dongchuan Road, Shanghai 200241, China.
2
Department of Neurology,
Shanghai Changhai Hospital, Secondary Military Medical University, 168
Changhai Road, Shanghai, China.
3
Department of Ophthalmology, West
China Hospital, Sichuan University 37 Guoxuexiang, Chengdu, Sichuan,
610041, China.
4
Rush University Cancer Center, Department of Internal
Medicine, Rush University Medical Center, Chicago, IL 60612, USA.
5
Schizophrenia Program, Shanghai Mental Health Center, Shanghai Jiao Tong
University School of Medicine, 600 Wan Ping Nan Road, Shanghai 200030,
China.
Published: 23 January 2013
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doi:10.1186/1755-8794-6-S1-S17
Cite this article as: Liu et al.: Exploring the pathogenetic association
between schizophrenia and type 2 diabetes mellitus diseases based on
pathway analysis. BMC Medical Genomics 2013 6(Suppl 1):S17.
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