Abstract
Inferring drug-disease associations is critical in unveiling disease
mechanisms, as well as discovering novel functions of available drugs,
or drug repositioning. Previous work is primarily based on
drug-gene-disease relationship, which throws away many important
information since genes execute their functions through interacting
others. To overcome this issue, we propose a novel methodology that
discover the drug-disease association based on protein complexes.
Firstly, the integrated heterogeneous network consisting of drugs,
protein complexes, and disease are constructed, where we assign weights
to the drug-disease association by using probability. Then, from the
tripartite network, we get the indirect weighted relationships between
drugs and diseases. The larger the weight, the higher the reliability
of the correlation. We apply our method to mental disorders and
hypertension, and validate the result by using comparative
toxicogenomics database. Our ranked results can be directly reinforced
by existing biomedical literature, suggesting that our proposed method
obtains higher specificity and sensitivity. The proposed method offers
new insight into drug-disease discovery. Our method is publicly
available at
[35]http://1.complexdrug.sinaapp.com/Drug_Complex_Disease/Data_Download
.html.
Keywords: Drug-disease associations, protein complexes, drug
repositioning, weighted network
Background
Diseases are often caused by congenital disorder or expression of
abnormal genes, which induces multi-factor-driven alterations and
disrupts functional modules [[36]1]. Drugs accomplish their therapeutic
effect by changing downstream processes of their targets, which contend
with the alterations of the abnormal genes. Drug development is
expensive, time consuming and has a high risk of failures. By
conservative estimates, it now takes ~15 years [[37]2] and $800 ~ $1000
million to bring a single drug to market [[38]3]. This situation
hampers the pharmaceutical industry to find innovative strategies
against currently incurable diseases. Drug repositioning (or drug
repurposing) attempts to find previously unknown targets for drugs
already established on the market or drugs currently in advanced
development stages. Several examples throughout history have shown that
such repositioning can be very successful (one example is Sildenafil,
also known as Viagra) [[39]4]. Therefore, more and more research is
focusing on inferring drug-disease associations by computational
methods.
Several network-based methods have been studied to infer the
relationships between drugs and disease (for a review, see [[40]5]).
Matteo indicated that the combination of bipartite network projections,
weighted integration of different pharmacological spaces and kernelized
score functions with random walk kernels play a key role in
significantly improving the drug ranking results with respect to
DrugBank therapeutic categories [[41]6]. Cheng [[42]7] integrated three
networks, chemical, gene and disease, to infer chemical hazard
profiles, identify exposure data gaps, and incorporate genes and
disease networks into chemical safety evaluations. Lee established a
database PharmDB, an integrated tripartite database, coupled with
Shared Neighborhood Scoring (SNS) algorithm, to find new indication of
known drugs [[43]8]. With increasing evidence in genetic and molecular
biology, we know that protein complexes and pathways are not affected
by a single gene, instead a group of interacting genes underlying
similar diseases, which point out the therapeutic importance of those
modules [[44]9]. Therefore, it is of great importance to investigate
how drugs and disease phenotypes are associated on the basis of gene
modules [[45]10]. In 2004, different tumor types were tentatively
characterized by predefined gene modules using gene expression data
[[46]11]. Wong et al. defined a module map to connect gene modules with
human cancers, which was shown to guide new disease therapies [[47]12].
PREDICT is based on the observation that similar drugs are indicated
for similar diseases, and utilizes multiple drug-drug and
disease-disease similarity measures for the prediction task [[48]13].
It allows easy integration of additional similarity measures among
diseases and drugs. In 2012, Daminelli constructed a
drug-target-disease network and extracted the bi-cliques where every
drug is linked to every target and disease [[49]14]. This method can
reposition drugs and predict a drug's off targets simultaneously. Ye
integrated known drug target information and proposed a
disease-oriented strategy for evaluating the relationships between
drugs and specific diseases based on their pathway profile [[50]15].
Zhao et al. developed a Bayesian partition method to discover
drug-gene-disease co-modules. Such a co-module approach offered a
systematic and holistic view to study drug-disease relationships and
their molecular basis [[51]16]. A huge amount of chemical, genomic and
disease phenotype data is rapidly accumulated, but the drug-diseases
associations are still not clear.
Protein complexes are key molecular entities that integrate multiple
gene products to perform cellular functions. CORUM provides a
comprehensive dataset of protein complexes for discoveries in systems
biology, analyses of protein networks and protein complex-associated
diseases [[52]17]. Therefore, based on the known complexes in CORUM
database, we design a method to infer drug-complex-disease phenotype
relationships using a network model, where protein complexes are
related to not only drugs but also to the disease phenotype.
In our study, based on a symmetrical conditional probability model, we
construct a weighted tripartite hetero-network of drugs, protein
complexes, and diseases. From this drug-complex-disease tripartite
network, we are able to obtain indirect weighted relationships between
drugs and diseases, which is a bipartite hetero-network. A drug which
has high correlation with a complex set receives a higher closeness
score with disease, which also highly related to the same complex set.
We rank the associations between drugs and diseases in descending
order, by edge weights, in drug-disease network. The larger the weight
of the association, the greater the degree of reliability, thus the
greater the possibility of relation of drug to disease. We select
mental disorders and hypertension as our test data. We use the both
curated and inferred drug-disease associations from Comparative
Toxicogenomics Database (CTD; [53]http://ctd.mdibl.org)[[54]18]as our
benchmark. Our ranked results show that our proposed method obtain
higher specificity and sensitivity. Our approach renders a promising
perspective to investigate drug-disease associations and provides
computational evidence in revealing their mechanism basis.
Materials and methods
The integrated network, including three heterogeneous data of drug,
disease, protein complex are illustrated in Figure [55]1.
Figure 1.
Figure 1
[56]Open in a new tab
The overview of our proposed method. Firstly, we construct a
drug-complex network. If the target set of a drug has at least one
common protein with a complex, there will be an edge between the drug
and the complex. Then, we construct a complex-disease network. If there
is an edge between a complex and a disease, at least one protein of the
complex is also a protein related to the disease. In this way, we get a
drug-complex-disease tripartite network. Based on the tripartite, we
can extract the associations between drugs and diseases. If a drug and
a disease have at least one common protein complex neighbor, there will
be a connection between them.
Materials
Data sources
Drug data
The DrugBank database combines detailed drug (i.e. chemical,
pharmacological and pharmaceutical) data with comprehensive drug target
(i.e. sequence, structure, and pathway) information [[57]19,[58]20]. We
collect FDA-approved drugs in the latest release of DrugBank database
(version 4.0) [[59]21].
Protein complexes data
The CORUM database is a comprehensive resource of manually annotated
protein complexes from mammalian organisms. All the information is
obtained from individual experiments published in scientific articles,
and data from high-throughput experiments are excluded. We download the
all Complexes from CORUM [[60]17](the release February 2012).
Disease data
The disease data is downloaded from FunDO
([61]http://django.nubic.northwestern.edu/fundo/) [[62]22]. FunDO takes
a list of genes and finds relevant diseases based on statistical
analysis of the Disease Ontology annotation database [[63]23].
Protein-protein interaction network
We obtain relationships between genes (or equivalently, proteins) as
demonstrated by Liu et al. [[64]24]. The final binary protein-protein
interaction network contains 7,533 nodes and 22, 345 edges. Genes are
identified by their NCBI gene IDs. We use the PPI network to filter the
predicted drug-disease associations. If a drug and a disease are
associated with two different genes in a same complex, and there is a
direct connection between the two genes in the PPI network, we will
track the association, or else we discard it.
Benchmark of drug-disease associations
We extract all the known associations between chemicals (or
equivalently, drugs) and disorders or its descendants from Comparative
Toxicogenomics Database (CTD) in May 2014 as our benchmark [[65]25].
CTD contains curated and inferred chemical-disease associations.
Curated chemical-disease associations are extracted from the published
literature by CTD biocurators. Inferred associations are established
via CTD-curated chemical-gene interactions. In our research the curated
and inferred associations have been identified, and they can help
researchers develop hypotheses about environmental diseases and their
underlying mechanisms.
Functional enrichment analysis
In order to evaluate our method further, we perform functional
enrichment analysis using DAVID [[66]26,[67]27] on the target sets of
predicted drugs. With the target genes as inputs, we observe
gene-disease associations and the enriched KEGG pathways on the related
biological process. With Benjamin multiple testing correction method
[[68]28], the enrichment p-value was corrected to control family-wide
false discovery rate under certain rate (e.g. ≥ 0.05).
Methods
Weighted network construction
To construct a weighted tripartite network of drugs, protein complexes,
and diseases, we map the UniProt ID of each drug target to the Entrez
gene ID. We obtain a list of gene targets for each drug. There are
6,039 relations between 1,481 drugs and 1,583 targets (additional file
[69]1). We collect the list of protein subunits for each complex in the
all Complexes set, which are referenced by their Entrez IDs (additional
file [70]2). The same operation is conducted for all genes related to
diseases, resulting in a list of Entrez gene identifiers for each
disease (additional file [71]3). The relations between drugs, protein
complexes, and diseases can be represented as a tripartite network,
which can be expressed as:
[MATH: GTPD=(T,P,D,ET,ED) :MATH]
(1)
T, P, and D are finite sets of drug, protein complex, and disease; E[T
]and E[D ]denote the two types of undirected links in the network:
drug-complex and complex-disease. The relevance between drug t[i ](t[i
]∈ T, i = 1,...,|T|) and complex p[j ](p[j ]∈ P, j = 1,...,|P| ),
w[T](t[i], p[j]) is calculated by symmetrical conditional probability,
as in equation (2).
[MATH: wT(ti,pj)=pro(ti|pj )⋅pro(pj |ti) :MATH]
(2)
Equation (2) indicates that the relevance between t[i ]and p[j ]is
determined jointly by their conditional probabilities on each other.
Suppose that g(t[i], p[j]) denotes the number of elements shared by the
target set of the drug t[i ]and the complex set p[j], g(t[i]) and
g(p[j]) stand for the number of targets of the drug t[i ]and the number
of proteins in complex p[j ]respectively. Accordingly, equation (2) can
be expressed as:
[MATH: wT(ti,pj)=g(ti,pj )g(ti)⋅g(ti,pj)g(pj) :MATH]
(3)
Similarly, we can obtain the weight w[D](p[i], d[j]) (p[i ]∈ P, d[j ]∈
D, i = 1,...,|P|, j = 1,...,|D|) to the links between complexes and
diseases. (p[i], d[j]) ∈ E[D ]if at least one protein of the complex
p[i ]is also a protein related to the disease d[j], where p[i ]∈ P, d[j
]∈ D, i = 1,...,|P|, j = 1,...,|D|.
Derivative Network
To identify the drug-disease association, a derived drug-disease
network can be extract with an immediate purpose to facilitate the
association identification. A bipartite network G[TD ]= (T, D, E[TD])
is used to illustrate their associations, where T, D are finite sets of
drug and disease respectively. E[TD ]denotes the undirected links
between drugs and diseases. The drug-disease interaction exists if and
only if the following two constraints are met simultaneously: i) the
drug and the disease have at least one common protein complex neighbor
in G[TPD ]network; ii) at least one protein target of the drug was also
a subunit of the protein complex. Specifically, it is defined as
[MATH: ETD={(t,d)|(∃p∈P)((t,p)∈ET∧(p,d)∈ED)∧t∈T∧d∈D} :MATH]
(4)
where P is the set of protein complexes. For each edge (t, d) ∈ E[TD],
its weight w[TD](t, d) can be calculated by equation (5):
[MATH: wTD(t,d)=gT(t,C)g(t)⋅gD(d,C)g(d) :MATH]
(5)
Suppose C represents the set of protein complexes that both drug t and
disease d connect to in G[TPD ]network, then:
[MATH: C={p|p∈P∧(t,p)∈ET∧(p,d)∈ED∧t∈T∧d∈D} :MATH]
(6)
g[T](t, C) represents the sum of edge weights between drug t and
protein complexes in set C. The formulas of g[T](t, C) and g[D](d, C)
are given as follows:
[MATH: gT(t,C)=
∑p′∈CwT(t,p′)
:MATH]
(7)
[MATH: gD(d,C)=
∑p′∈CwD(p′,d) :MATH]
(8)
g(t) and g(d) in equation (5) respectively indicate the sum of edge
weights between drug t, disease d and protein complexes in set P.
Therefore:
[MATH: g(t)=
∑p′∈PwT(t,p′)
:MATH]
(9)
[MATH: g(d)=
∑p′∈PwD(p′,d) :MATH]
(10)
If drug t^' and disease d^' cannot be connected by common complex
neighbors, but at least one protein target of drug t^' is also a
protein related to disease d^', a connection will be created between
t^' and d^'. Similarly, the weight of edge (t^', d^') can be calculated
by equation (3).
Network conversions
In order to verify the predicted drug-disease correlations by
modularity, we first need to convert G[TD ]into two networks. Each
converted network is composed of a single type of node. The bipartite
network for drugs and diseases G[TD ]is converted into two independent
networks, which are denoted by G[1 ]= (V[1],E[1]) and G[2 ]=
(V[2],E[2]). G[1 ]and G[2 ]are the drugs and the diseases networks
respectively. In G[1], nodes of V[1 ]are connected together if they
have at least one common neighbor (D) in G[TD]. The set of edges E[1
]can be defined as:
[MATH: E1={(t,t′)|(∃d∈D)((t,d)∈ETD∧(t′,d)∈ETD∧t≠t′)} :MATH]
(11)
The set of edges E[2 ]is defined similarly. The weight of edge (t, t^')
∈ E[1], w(t, t^') is defined as:
[MATH: w(t,t′)=
∑d∈Dmin<
/mtext>(wTD(t,d),wTD(t′,d)) :MATH]
(12)
Edge weights in G2 have a similar definition. Therefore, we get two
weighted networks: a drug-drug network and a disease-disease network.
Module structure in converted network
We use ClusterONE (Clustering with Overlapping Neighborhood Expansion)
[[72]29] to obtain modules in converted networks. ClusterONE is a graph
clustering algorithm that is able to handle weighted graphs. Owing to
these properties, ClusterONE is especially useful for detecting modules
in networks with associated confidence values.
Results
Bipartite network of drugs and diseases
The weighted tripartite network of drug-complex-disease consists of two
bipartite networks: drug-complex and complex-disease. The drug-complex
network contains 1,229 nodes (628 drugs and 601 complexes) and 3,405
weighted edges (additional file [73]4). The complex-disease network
contains 1932 nodes (1,472 complexes and 460 diseases) and 14,848
weighted edges (additional file [74]5). The bipartite network of
drug-disease obtained from the tripartite network includes 1,634 nodes
(1,127 drugs and 507 diseases) and 30,722 weighted edges (additional
file [75]6). In order to improve the reliability of the predicted
correlations between drugs and diseases, we first use PPI network to
filter the results, then we discard the edges whose weights are lower
than 0.50. The final network consists of 353 nodes (231 drugs and 122
diseases) and 594 weighted edges (weight ≥ 0.50) (additional file
[76]7). This is a scale-free network, with a small number of nodes
connected to many edges and the majority of nodes connected to few
edges (Figure [77]2).
Figure 2.
Figure 2
[78]Open in a new tab
Bipartite network of drugs and diseases. A drug is connected to a
disease if they share at least one complex and the value of
relationship is not lower than 0.5. Drugs are represented by triangles
and diseases by squares. Different types of nodes also distinguish from
each other by color. Every connected subgraph is a module. Drugs and
diseases are labeled by their DrugBank identifier and name in FunDO,
respectively.
All network visualizations were produced using the Cytoscape software
[[79]30]. Every connected subgraph represents a module, resulting in 29
modules with bipartite structure as shown in Figure [80]2. Nodes with a
large degree can be seen among both drugs and diseases (See Table
[81]1).
Table 1.
Top diseases and drugs with a large degree in the bipartite
drug-disease network
Disease Name Number of direct neighbors Sum of weight on edges Drug ID
Drug name Number of directed edges Sum of weight on edges
Mental disorders 51 39.58 DB00098 Antithymocyte globulin 27 19.90
__________________________________________________________________
Cystic fibrosis 33 18.56 DB01259 Lapatinib 18 15.07
__________________________________________________________________
Primary biliary cirrhosis 30 20.28 DB08916 Afatinib 17 13.79
__________________________________________________________________
Attention deficit hyperactivity disorder 26 16.40 DB00054 Abciximab 16
13.49
__________________________________________________________________
Anorexia nervosa 25 17.14 DB00775 Tirofiban 15 15.31
__________________________________________________________________
Panic disorder 25 16.77 DB00072 Trastuzumab 12 9.13
__________________________________________________________________
Sudden infant death syndrome 25 18.27
__________________________________________________________________
Epilepsy 24 13.37
__________________________________________________________________
Hypertension 24 13.26
__________________________________________________________________
Migraine 21 11.10
__________________________________________________________________
Supranuclear palsy, progressive 21 11.51
__________________________________________________________________
Mucocutaneous lymph node syndrome 11 8.45
__________________________________________________________________
Subacute sclerosing panencephalitis 10 5
[82]Open in a new tab
Table [83]1 shows the number of edges directly related to the hubs
(column: Number of directed edges) and the sum of weight on these edges
(column: Sum of weight on edges). We find that the sum of weights of
edges may more accurately reflect the role of nodes in the network. For
example, cystic fibrosis has more direct neighbors than primary biliary
cirrhosis in bipartite network. But, the correlation between the drugs
and primary biliary cirrhosis is greater than that between the drugs
and cystic fibrosis. In Table [84]1 the most connected disease is
mental disorders (Synonym: behavior disease), which is a mental or
behavioral pattern, or an anomaly that causes either suffering or an
impaired ability to function in ordinary life (disability). The most
connected drug is anti-thymocyte globulin (ATG). It is an infusion of
horse or rabbit-derived antibodies against human T cells, which is used
in the prevention and treatment of acute rejection in organ
transplantation and therapy of aplastic anemia.
Case study: Mental Disorders
Potential drugs and Mental Disorders relations
Mental disorders are one aspect of mental health [[85]31], which are
generally defined by a combination of how a person feels, acts, thinks
and perceives. This may be associated with particular regions or
functions of the brain, or any part of the nervous system, often in a
social context. 226 drug-mental disorders relations are found in our
candidate sets (additional file [86]8). In order to improve the
accuracy of the prediction, an association will not be considered if
its weight is below 0.5. The reason is that based on the experiments,
0.5 as threshold can conserve more real correlations, as well as avoid
including too many false-positive ones. Finally, 51 drug-mental
disorders correlations are obtained (see Table [87]2).
Table 2.
Drug-mental disorders associations (weight ≥ 0.5)
ID Drug ID Drug Name Weight ID Drug ID Drug Name Weight
1 DB00904 Ondansetron 0.89 27 DB00334 Olanzapine 0.82
__________________________________________________________________
2 DB00669 Sumatriptan 0.85 28 DB01186 Pergolide 0.82
__________________________________________________________________
3 DB00734 Risperidone 0.73 29 DB01618 Molindone 0.82
__________________________________________________________________
4 DB00490 Buspirone 0.77 30 DB06684 Vilazodone 0.82
__________________________________________________________________
5 DB01149 Nefazodone 0.93 31 DB01621 Pipotiazine 0.82
__________________________________________________________________
6 DB01142 Doxepin 0.83 32 DB01616 Alverine 0.82
__________________________________________________________________
7 DB01392 Yohimbine 0.87 33 DB01200 Bromocriptine 0.82
__________________________________________________________________
8 DB00540 Nortriptyline 0.89 34 DB00216 Eletriptan 0.81
__________________________________________________________________
9 DB01224 Quetiapine 0.82 35 DB01622 Thioproperazine 0.79
__________________________________________________________________
10 DB00363 Clozapine 0.82 36 DB01614 Acepromazine 0.79
__________________________________________________________________
11 DB00477 Chlorpromazine 0.78 37 DB00960 Pindolol 0.77
__________________________________________________________________
12 DB00571 Propranolol 0.75 38 DB04946 Iloperidone 0.76
__________________________________________________________________
13 DB00321 Amitriptyline 0.69 39 DB08807 Bopindolol 0.75
__________________________________________________________________
14 DB00726 Trimipramine 0.90 40 DB08815 Lurasidone 0.75
__________________________________________________________________
15 DB00247 Methysergide 0.87 41 DB06216 Asenapine 0.74
__________________________________________________________________
16 DB00656 Trazodone 0.86 42 DB01049 Ergoloid mesylate 0.74
__________________________________________________________________
17 DB00315 Zolmitriptan 0.85 43 DB05271 Rotigotine 0.72
__________________________________________________________________
18 DB00952 Naratriptan 0.85 44 DB01267 Paliperidone 0.72
__________________________________________________________________
19 DB08810 Cinitapride 0.84 45 DB01359 Penbutolol 0.60
__________________________________________________________________
20 DB00589 Lisuride 0.84 46 DB00866 Alprenolol 0.60
__________________________________________________________________
21 DB00268 Ropinirole 0.84 47 DB00696 Ergotamine 0.56
__________________________________________________________________
22 DB00413 Pramipexole 0.84 48 DB00998 Frovatriptan 0.55
__________________________________________________________________
23 DB00714 Apomorphine 0.83 49 DB00918 Almotriptan 0.55
__________________________________________________________________
24 DB00248 Cabergoline 0.83 50 DB00953 Rizatriptan 0.53
__________________________________________________________________
25 DB01238 aripiprazole 0.82 51 DB00320 Dihydroergotamine 0.50
__________________________________________________________________
26 DB00246 ziprasidone 0.82
[88]Open in a new tab
Drug ID represents the unique DrugBank accession number of a drug. Drug
Name represents the corresponding name of a Drug ID. Weight represents
the correlation between a drug and the mental disorder. 40 drugs are
approved by our benchmark, 9 predicted drugs are supported by
literature (in bold italic), and 2 are not directly supported by the
literatures (in underlined bold).
Since the predictions are merely assumptions, we need to further
examine these predictions using external literature support: 40 known
associations agree with the benchmark (CTD), 9 predicted associations
are supported by the literature (in bold italic). We find the 9
predicted drugs for the treatment of mental disorders may have a good
effect. For example, vilazodone [[89]32] (ID = 30) is approved for
treatment of acute episodes of major depression. Major depressive
disorder (MDD) is a mental disorders characterized by a pervasive and
persistent low mood that is accompanied by low self-esteem and by a
loss of interest or pleasure in normally enjoyable activities.
Pipotiazine (ID = 31) is a typical antipsychotic of the phenothiazine
class [[90]33] used in the United Kingdom and other countries for the
treatment of schizophrenia. Thioproperazine (ID = 35) is an
antipsychotic. Antipsychotics [[91]34] are a class of psychiatric
medication primarily used to manage psychosis, in and concentration
[[92]35,[93]36]. Certain mental health problems, such as depression and
disturbances, including hallucinations, delusions and paranoia, are
possible complications of Parkinson's disease and/or its treatment.
Rotigotine (ID = 43) is for treatment in neurologic disorders and
Parkinson's disease, as well as moderate-to-severe primary Restless
Legs Syndrome [[94]37]. Paliperidone (ID = 44) is the major active
metabolite of risperidone. It is used for schizophrenia and
schizoaffective cinitapride (ID = 19) and penbutolol (ID = 45), there
is no direct support in literature. However, we are confident that they
maybe effective in the treatment of mental disorders. Cinitapride is a
substituted benzamide with 5-HT receptor antagonist and agonist
activity [[95]38]. The 5-HT receptors are the target of a variety of
pharmaceutical drugs, including many antidepressants, antipsychotics,
etc [[96]39], so cinitapride may be effective in the treatment of
mental disorders. Similarly, penbutolol is able to bind both β-1
adrenergic receptors (ARs) and β-2 adrenergic receptors [[97]40], and
the interaction between β-1 ARs and testosterone has been shown in
anxiolytic behaviors in the basolateral amygdale [[98]41]. β-2 receptor
is also involved in brain-immune-communication [[99]42]. Therefore, we
can conclude that penbutolol has a high correlation with mental
disorders.
The significant modules related to mental disorders in drug-drug network
Modular structure is one of the emerging properties of complex
networks. A module is associated to sets of nodes with specific
function. In order to further validate the effectiveness of our
algorithm, we run ClusterONE with parameter Minimum density set to 0.35
and other parameters using default values in drug-drug network. We get
23 clusters from drug-drug network (additional file [100]9); nodes
representing drugs. All drugs associated with mental disorders are
scattered into two overlapping modules (cluster 1 and cluster 3, i.e.
Cluster Label = 1 and Cluster Label = 3 in additional file [101]9). To
analyze drugs associated with mental disorders, we merge these two
modules (shown in Figure [102]3). Diamonds represent overlapping drugs
of cluster 1 and cluster 3. In Figure [103]3, drugs colored pink have
been shown to be associated with mental disorders by the benchmark
(CTD). Purple nodes are drugs predicted by our method. They are listed
in Table [104]2, and their correlations with mental disorders are not
lower than 0.5 in drug-disease network. They are closely linked with
known drugs (pink nodes), which further confirms that they have a high
functional similarity with known drugs. That is, the 11 predicted drugs
also have a strong association with mental disorders. The 3 green nodes
are new predicted drugs by clustering the drug-drug network. They are
also closely connected with known drugs, and are supported by
literature. For example, dexmethylphenidate (DB06701) is used as a
treatment for Attention Deficit Hyperactivity Disorder (ADHD), ideally
in conjunction with psychological, educational, behavioral or other
forms of treatment [[105]43] Levomilnacipran (DB08918) is an
antidepressant developed by Forest Laboratories and Pierre Fabre Group
for the treatment of depression [[106]44-[107]46]. For ephedra
(DB01363), studies have shown that it may cause serious mental illness
[[108]47]. Maglione et al. reviewed all 1,820 adverse event reports
related to dietary supplements containing herbal ephedra from FDA
MedWatch files as of Sept. 30, 2001. Fifty-seven serious psychiatric
events were reported. Therefore, clinicians should be aware that
serious psychiatric symptoms could be associated with ephedra use.
Figure 3.
Figure 3
[109]Open in a new tab
Drugs associated with mental disorder within the module after merging
cluster 1 and cluster 3. Nodes represent drugs. Diamond nodes represent
the overlap of cluster 1 and cluster 3. Nodes colored pink represent
drugs that have been shown to be associated with mental disorder by the
benchmark (CTD). Purple nodes represent drugs predicted by our method.
Green nodes are newly predicted drugs related to mental disorder. Drugs
are labeled by their DrugBank identifiers.
Functional enrichment analysis on target genes of potential drugs of mental
disorder
Functional analysis are performed on the target sets of eleven drugs,
which are not approved by CTD (see Table [110]2, drugs in bold italic
and underlined bold). Gene-disease associations and KEGG pathway
enrichment analysis are made on them with the functional annotation
tool of DAVID. We find ten target sets of them are directly associated
with mental disorder or the same type of diseases, such as depressive
disorder, and personality disorders. In addition, the same ten target
sets of drugs are significantly enriched in the mental disorder related
pathways: neuroactive ligand-receptor interaction. Adkins et al.
systematically screened associations between 58 neuroactive
ligand-receptor interaction pathways and antipsychotic treatment
efficacy by bioinformatics tools [[111]48]. The target set of
vilazodone (Drug ID=DB06684) is not obtained annotations from DAVID. We
infer the reason is that the set only includes one gene (HTR1A). In
fact, vilazodone is already approved for treatment of acute episodes of
major depression [[112]32].
Case study: Hypertension
Potential drugs and Hypertension relations
Hypertension, also referred to as high blood pressure, is a condition
in which the arteries have persistently elevated blood pressure. A
blood pressure of 140/90 or above is considered hypertension.
Hypertension can lead to damaged organs, as well as several illnesses,
such as renal failure (kidney failure), aneurysm, heart failure,
stroke, or heart attack [[113]49].
We find 339 drug-hypertension relations in our candidate sets in all
(additional file [114]10). 69.3% of the weight is less than 0.1, and
there are 31 associations with high confidence (weight ≥ 0.5, see Table
[115]3). Among them, 26 known associations agree with the benchmark
(CTD). Through in-depth analysis of the other 5 associations (in bold
italic), there are two types of correlation between diseases and drugs:
positive and negative correlations. Positive correlations refer to the
positive effect of drugs on diseases. For example, drugs can treat
diseases. Negative correlations, for example, are that drugs can cause
diseases, namely, side effects of drugs, or drugs that worsen diseases,
etc. Both are very important in discovering the causes of a disease or
in using drugs safely, so that we can treat diseases more effectively.
Using SIDER (Side Effect Resource, [116]http://sideeffects.embl.de)
[[117]50], we find asenapine (ID = 1) has the side effect of
hypertension [[118]50]. For trimipramine (ID = 29) and paliperidone (ID
= 31), although there is no clear evidence showing they have side
effect of hypertension, there have been some indications that they are
likely to lead to high blood pressure [[119]50,[120]51]. Mehtysergide
(ID = 20) is metabolised into methylergometrine in humans [[121]52].
Adverse effects of methylergometrine include cholinergic effects,
pulmonary hypertension, and severe systemic hypertension, etc
[[122]53]. The last drug, iloperidone (ID = 30), plays an active role
in the treatment of hypertension. Considering the alpha1 antagonism
characteristics of iloperidone, the effect of anti-hypertensive agents
would be potentiated when administered concomitantly [[123]54]. This
shows that iloperidone has certain effects on lower blood pressure.
Table 3.
Drug-hypertension associations (weight ≥ 0.5)
ID Drug ID Drug Name Weight ID Drug ID Drug Name Weight
1 DB06216 Asenapine 0.71 17 DB00413 Pramipexole 0.52
__________________________________________________________________
2 DB00571 Propranolol 0.66 18 DB00589 Lisuride 0.52
__________________________________________________________________
3 DB08807 Bopindolol 0.66 19 DB01149 Nefazodone 0.52
__________________________________________________________________
4 DB00960 Pindolol 0.64 20 DB00247 Methysergide 0.52
__________________________________________________________________
5 DB00866 Alprenolol 0.59 21 DB01049 Ergoloid mesylate 0.52
__________________________________________________________________
6 DB01359 Penbutolol 0.59 22 DB00714 Apomorphine 0.52
__________________________________________________________________
7 DB01200 Bromocriptine 0.54 23 DB00656 Trazodone 0.51
__________________________________________________________________
8 DB00248 Cabergoline 0.54 24 DB01142 Doxepin 0.50
__________________________________________________________________
9 DB00246 Ziprasidone 0.53 25 DB00904 Ondansetron 0.50
__________________________________________________________________
10 DB00334 Olanzapine 0.53 26 DB08815 Lurasidone 0.50
__________________________________________________________________
11 DB01238 Aripiprazole 0.53 27 DB00216 Eletriptan 0.50
__________________________________________________________________
12 DB00363 Clozapine 0.53 28 DB00734 Risperidone 0.50
__________________________________________________________________
13 DB01224 Quetiapine 0.53 29 DB00726 Trimipramine 0.50
__________________________________________________________________
14 DB01186 Pergolide 0.53 30 DB04946 Iloperidone 0.50
__________________________________________________________________
15 DB01392 Yohimbine 0.52 31 DB01267 Paliperidone 0.50
__________________________________________________________________
16 DB00268 Ropinirole 0.52
[124]Open in a new tab
Drug ID represents the unique DrugBank accession number of a drug. Drug
Name represents the corresponding name of a Drug ID. Weight represents
the value of correlation between a drug and the mental disorders. 26
drugs are approved by our benchmark. Among the remaining 5 drugs, 4 (in
bold italic) have negative relationships with the mental disorders and
1 (in underlined bold) has a positive relationship with the mental
disorders.
The significant modules related to hypertension in drug-drug network
Of the 23 drug modules, 11 are found to be related to hypertension.
Five predicted drugs (purple rectangle nodes: DB06216, DB00247,
DB00726, DB04946, DB01267) are in the same cluster (Figure [125]4).
They are listed in Table [126]2 and their associations with
hypertension is not lower than 0.5. The pink circular nodes have been
confirmed to be associated with hypertension by CTD. It can be seen
that the interactions between the five predicted drugs and the known
drugs are very frequent. These results further indicate that they are
highly correlated with hypertension. In addition, twenty-six nodes in
Figure [127]4 are shown in Table [128]4. They includes two types of
drugs: (1) predicted by our method, but their association with
hypertension is lower than 0.5; (2) new drugs predicted by clustering
drug-drug network. The first sixteen drugs (ID = 1 to ID = 16) were
predicted by our method previously. The remaining ten drugs (ID = 17 to
ID = 26) are newly predicted by clustering drug-drug network. They have
high accuracy: nine of them are approved by CTD database
(Correlation=CTD, see Table [129]4); one is supported by literature
[[130]55] (ephedra (ID = 17)). Ephedra containing products (ECPs),
which are most often found in sources of caffeine alkaloids, may be an
under-recognized cause of hypertension. For the previously predicted
drugs with lower weights (ID = 1 to ID = 16), seven of them may cause
high blood pressure, and are negatively correlated with hypertension
(Correlation = N, see Table [131]4). Milnacipran (ID = 3) for example,
researchers presented the case of a patient with major depressive
disorder (MDD) who developed hypertension during treatment with regular
therapeutic doses of milnacipran [[132]56]. Desvenlafaxine (ID = 6) is
similar to venlafaxine, its use may worsen preexisting hypertension
[[133]57]. For the remaining eight drugs, there are no evidence
suggesting drug-hypertension relations. From the results, we derived
two indications: 1) as a metric, our definition of weight is reasonable
in assessing the credibility of drug-disease correlation - the greater
the degree of reliability, the larger the weight, while the smaller the
weight, the lower the reliability; 2) combined with modularity in
projected network, our method is very effective in predicting
drug-disease associations.
Figure 4.
Figure 4
[134]Open in a new tab
Drugs associated with hypertension. Nodes represent drugs. Circular
nodes represent drugs that have been shown to be associated with
hypertension by the benchmark (CTD). Rectangle and diamond nodes
respectively represent our predicted drugs whose relationship with
hypertension are higher than 0.5 and lower than 0.5. Drugs are labeled
by their DrugBank identifiers.
Table 4.
Correlations of twenty-six Drugs with hypertension
ID Drug ID Drug Name Correlation (CTD, N, or Unknown) ID Drug ID Drug
Name Correlation (CTD, N, or Unknown)
1 DB00315 Zolmitriptan[[135]60] N 14 DB01622 Thioproperazine Unknown
__________________________________________________________________
2 DB00476 Duloxetine[[136]61] N 15 DB06701 Dexmethylphenidate Unknown
__________________________________________________________________
3 DB04896 Milnacipran[[137]56] N 16 DB08810 Cinitapride Unknown
__________________________________________________________________
4 DB06204 Tapentadol[[138]62] N 17 DB01363 Ephedra [[139]55] N
__________________________________________________________________
5 DB06684 Vilazodone[[140]63] N 18 DB00472 Fluoxetine CTD
__________________________________________________________________
6 DB06700 Desvenlafaxine [[141]57] N 19 DB00176 Fluvoxamine CTD
__________________________________________________________________
7 DB08918 Levomilnacipran [[142]64] N 20 DB00543 Amoxapine CTD
__________________________________________________________________
8 DB00805 Minaprine Unknown 21 DB01104 Sertraline CTD
__________________________________________________________________
9 DB00952 Naratriptan Unknown 22 DB06148 Mianserin CTD
__________________________________________________________________
10 DB00998 Frovatriptan Unknown 23 DB01577 Methamphetamine CTD
__________________________________________________________________
11 DB01614 Acepromazine Unknown 24 DB01151 Desipramine CTD
__________________________________________________________________
12 DB01616 Alverine Unknown 25 DB00852 Pseudoephedrine CTD
__________________________________________________________________
13 DB01621 Pipotiazine Unknown 26 DB00514 Dextromethorphan CTD
[143]Open in a new tab
Drug ID represents the unique DrugBank accession number of a drug. Drug
Name represents the corresponding name of a Drug ID. There are three
types of correlation: (a) CTD represents the drug-hypertension
correlation that can be found in the CTD database; (b) N represents the
drug-hypertension correlation that may be negative, supported by
literature; (c) Unknown means that there is no evidence suggesting the
drug-hypertension relation as yet.
Functional enrichment analysis on target genes of potential drugs of
hypertension
There are five drugs predicted by our method, but not approved by the
benchmark (see Table [144]3, drugs in bold italic and underlined bold).
We perform the gene-disease associations and KEGG pathway enrichment
analysis on their target sets with DAVID. The enrichment result thus
obtained show that three target sets of them are directly associated
with hypertension. But all of them are significantly enriched in the
hypertension related pathway, such as gap junction. It is instructive
to note that the gap junction has been proved to be relevant to
hypertension [[145]58].
Comparison with other method
To evaluate the performance of our method, we compare it with a popular
web tool, PROMISCUOUS [[146]59]. PROMISCUOUS contains three different
types of entities: drugs, proteins and side-effects as well as
relations between them. It is kind of knowledge-based drug
repositioning method, which offers exploits known interactions between
a drug and a target and combine this information with new knowledge
about the target's role in a new indication.
We compare our method and PROMISCUOUS on eleven potential drugs of
mental disorder one by one. They are shown in Table [147]2 (drugs in
bold italic and underlined bold). By experimentation, five of them,
pipotiazine, thioproperazine, acepromazine, ergoloid mesylate and
paliperidone, are found to be antipsychotic medications by PROMISCUOUS,
which are consistent with our prediction. Penbutolol (ID = 45) and
bopindolol (ID = 39) are not shown associated with the treatments of
mental disorders directly by PROMISCUOUS. However, for penbutolol,
based on the fact that similar drugs often act on the same targets,
PROMISCUOUS finds eight drugs similar to it. One of them is pemoline,
which is a kind of antipsychotic drugs. Moreover, PROMISCUOUS also find
penbutolol and bopindolol are related to KEGG pathways: neuroactive
ligand-receptor interaction, which is proved associated with
antipsychotic treatment [[148]48]. Therefore, one can assume that
penbutolol and bopindolol may also be effective for treatment of mental
disorders. Because PROMISCUOUS integrated multiple public database,
such as Drugbank, Protein Data Bank, KEGG, UniProt, SIDER, etc., the
comparative results show the validity of our algorithm from another
side. The last four drugs, cinitapride, vilazodone, iloperidone and
rotigotine, are not found closely related to mental disorders by
PROMISCUOUS. But with the exception of cinitapride (ID = 19), the other
three drugs are all directly supported by the literatures.
A comparison also is made between PROMISCUOUS and our method on five
potential drugs of hypertension (see Table [149]3, drugs in bold italic
and underlined bold). Among the five drugs, PROMISCUOUS finds
methysergide and paliperidone related to gap junction pathway, which is
supported to be associated with hypertension [[150]58]. The other three
drugs, asenapine, trimipramine and iloperidone, are not found by
PROMISCUOUS. More likely the reason is that they may have the side
effect of hypertension. This is also consistent with our inference.
Conclusions
We integrate the information of drugs, protein complexes and diseases
from available experimental data and knowledge as weighted
drug-complex-disease tripartite networks and obtain a derived connected
relationships network, i.e. drug-disease bipartite network. One of the
advantages of our model is its relative simplicity. It is not like
other existing algorithms that first need to construct drug and disease
similarity networks. With protein complexes as the bridge, we apply
drug-complex-disease approach for inferring and evaluating the
likelihood of the probability between drugs and diseases. In our
simulation experiment, we take mental disorders and hypertension as our
case study. The results of the experiment are encouraging. Both the
positive and negative associations can be predicted and are found to be
reinforced by existing biomedical literature. The success of our
methods can be attributed to the following factors: first, we integrate
heterogeneous data and knowledge about drugs, protein complexes, and
diseases into our model; next, we use symmetric probability modelling
dependencies between drugs, protein complexes, and diseases; last, our
method combines the information derived from other connected
hetero-networks to infer the drug-disease associations. We believe that
the integration of networks and heterogeneous data sources will help us
bring about new hypotheses to infer the drug-disease associations and
even speed up drug development processes. Our study provides
opportunities for future toxicogenomics and drug discovery
applications. However, we find that it is difficult to automatically
distinguish the positive and negative associations between drug and
disease. For the next step, we suggest: 1) for commonly used data, such
as drugs, targets, protein complexes, and diseases, we need to
integrate data sources with higher confidence to improve the accuracy
of the prediction; 2) in order to predict the positive and negative
associations automatically as much as possible, we need to integrate
data sources that can offer information about the side effects of
drugs, such as drug side effect resources, response profiles,
pharmacological data and therapeutic/toxicological expression profiles.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
LY carried out the study. LY, JZ, and YZ designed the study. LY wrote
the first draft of the manuscript. JH, ZM, and LG revised the
manuscript. All the authors read and approved the final manuscript.
Supplementary Material
Additional file 1
Table illustrating the relations between drugs and targets.
[151]Click here for file^ (88KB, pdf)
Additional file 2
Table illustrating the list of protein complexes.
[152]Click here for file^ (166.5KB, pdf)
Additional file 3
Table illustrating disease-gene dataset.
[153]Click here for file^ (158.4KB, pdf)
Additional file 4
Table illustrating the information of drug-complex network.
[154]Click here for file^ (115.7KB, PDF)
Additional file 5
Table illustrating the information of complex-disease network.
[155]Click here for file^ (632.9KB, PDF)
Additional file 6
Table illustrating the information of drug-disease network before being
filtered by PPI network and weight.
[156]Click here for file^ (1MB, PDF)
Additional file 7
Table illustrating the information of drug-disease network after being
filtered by PPI network and weight.
[157]Click here for file^ (23.3KB, PDF)
Additional file 8
Table illustrating the drug-mental disorders relations predicted by our
method.
[158]Click here for file^ (9KB, PDF)
Additional file 9
Table illustrating 23 clusters got from drug-drug network.
[159]Click here for file^ (9.2KB, PDF)
Additional file 10
Table illustrating the drug-hypertension relations predicted by our
method.
[160]Click here for file^ (10.8KB, PDF)
Contributor Information
Liang Yu, Email: lyu@xidian.edu.cn.
Lin Gao, Email: lgao@mail.xidian.edu.cn.
Acknowledgements