1. No category

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Constructing and characterizing a
bioactive small molecule and microRNA
association network for Alzheimer’s
disease
Fanlin Meng1, Enyu Dai1, Xuexin Yu1, Yan Zhang1, Xiaowen Chen1, Xinyi Liu1,
Shuyuan Wang1, Lihua Wang2 and Wei Jiang1
Research
Cite this article: Meng F, Dai E, Yu X, Zhang
Y, Chen X, Liu X, Wang S, Wang L, Jiang W.
2014 Constructing and characterizing a
bioactive small molecule and microRNA
association network for Alzheimer’s disease.
J. R. Soc. Interface 11: 20131057.
http://dx.doi.org/10.1098/rsif.2013.1057
Received: 15 November 2013
Accepted: 3 December 2013
Subject Areas:
bioinformatics, computational biology,
systems biology
Keywords:
Alzheimer’s disease, bioinformatics, drug,
microRNA, small molecule, therapeutics
Authors for correspondence:
Wei Jiang
e-mail: [email protected]
Lihua Wang
e-mail: [email protected]
Electronic supplementary material is available
at http://dx.doi.org/10.1098/rsif.2013.1057 or
via http://rsif.royalsocietypublishing.org.
1
College of Bioinformatics Science and Technology, Harbin Medical University, 194 Xuefu Road, Harbin 150081,
People’s Republic of China
2
Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, 246 Xuefu Road,
Harbin 150081, People’s Republic of China
Alzheimer’s disease (AD) is an incurable neurodegenerative disorder. Much
effort has been devoted to developing effective therapeutic agents. Recently, targeting microRNAs (miRNAs) with small molecules has become a novel therapy
for human diseases. In this study, we present a systematic computational
approach to construct a bioactive Small molecule and miRNA association
Network in AD (SmiRN-AD), which is based on the gene expression signatures
of bioactive small molecule perturbation and AD-related miRNA regulation.
We also performed topological and functional analysis of the SmiRN-AD
from multiple perspectives. At the significance level of p 0.01, 496 small
molecule–miRNA associations, including 25 AD-related miRNAs and 275
small molecules, were recognized and used to construct the SmiRN-AD. The
drugs that were connected with the same miRNA tended to share common
drug targets ( p ¼ 1.72 1024) and belong to the same therapeutic category
( p ¼ 4.22 1028). The miRNAs that were linked to the same small molecule
regulated more common miRNA targets ( p ¼ 6.07 1023). Further analysis
of the positive connections (quinostatin and miR-148b, amantadine and miR15a) and the negative connections (melatonin and miR-30e-5p) indicated
that our large-scale predictions afforded specific biological insights into AD
pathogenesis and therapy. This study proposes a holistic strategy for deciphering the associations between small molecules and miRNAs in AD, which
may be helpful for developing a novel effective miRNA-associated therapeutic
strategy for AD. A comprehensive database for the SmiRN-AD and the differential expression patterns of the miRNA targets in AD is freely available at
http://bioinfo.hrbmu.edu.cn/SmiRN-AD/.
1. Introduction
Alzheimer’s disease (AD) is an ageing-related degenerative brain disease of
unknown aetiology. This disease is prevalent in the elderly population and is
the most common form of dementia. This debilitating disorder affects more
than 24 million people worldwide today [1]. Currently, there are five prescription drugs approved by the US Food and Drug Administration to temporarily
relieve the relevant symptoms, but there are no cures for AD according to the
Alzheimer’s Association [2]. AD is characterized by the presence of amyloid plaques and neurofibrillary tangles and the loss of neurons and synapses in brain
regions, resulting in the death of neurons, memory impairment and cognitive deficits. The well-known AD-associated genes include the amyloid precursor protein
(APP) gene, the presenilin 1 (PSEN1) gene and the presenilin 2 (PSEN2) gene [3,4].
In the normal state, the APP gene is a key player in neuronal survival, synaptic
plasticity, learning and memory [5]. Mutations relevant to the APP and PSEN
genes can lead to an increase in amyloid b-peptide (Ab) deposits around neurons
& 2013 The Author(s) Published by the Royal Society. All rights reserved.
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2. Material and methods
2.1. Data source
2.1.1. Alzheimer’s disease-related microRNA and mRNA
expression profiles
We obtained four AD-related gene expression profiles from
Gene Expression Omnibus, including GSE16759, GSE12685,
GSE5281 and GSE1297. All of the four expression profiles provided
mRNA expression measurements and GSE16759 also involved
miRNA expression levels (detailed clinical information of the
samples in the electronic supplementary material, S1). AD is a
complex disease, and many factors may affect the gene expression
patterns, such as brain region, age as well as the degree of brain
destruction (Braak and Braak (B&B) stage). However, several
studies have suggested that certain important common features
could be shared among different regions in AD brain [29–31]. In
this study, to investigate the commonality among AD subjects
and to obtain stable and consistently differentially expressed
genes (CDGs), we used meta-analysis to detect differential
2
J. R. Soc. Interface 11: 20131057
Computational approaches have been more and more
widely used in drug discovery [22,23]. Bork and co-workers
[24] reviewed that computational approaches contribute to
tackle complex problems in drug discovery, including largescale prediction of drug target or drug repurposing. Many
studies have been conducted based on the hypothesis that
gene expression profiles of perturbations (gene, drugs, diseases or miRNAs) could characterize the corresponding
effects to some extent, and the perturbations could be associated based on the similarity of their induced expression
profiles. For example, Butte’s group confirmed known effective drug–disease pairs and predicted new indications for
already approved agents by comparing the expression profile
similarity [25,26]. Iorio et al. [27] constructed a drug network
based on similar expression profiles perturbed by various
compounds and identified drug communities to predict
modes of action for compounds that are still being studied
or to discover previously unreported modes of action for
well-known drugs. Our previous research investigated the
potential connections between small molecules and
miRNAs across 23 human cancers based on transcriptional
responses similarity. We constructed a complex small molecule and miRNA network among 23 cancers and explored
the molecular and functional features for small molecule
modules, as well as miRNA modules in each cancer type [28].
In this study, we effectively extended and supplemented
our previous approach to identify the positive and negative associations between small molecules and AD-related
miRNAs (ADMs) by incorporating miRNA expression profiles.
The differentially expressed target genes, which were regulated
by the aberrantly expressed miRNAs, were defined as the gene
expression signatures of ADM regulation. By comparing these
signatures with transcriptional responses to drug treatment,
we constructed the small molecule and miRNA association network in AD (SmiRN-AD), which was further analysed with
respect to its topological characteristics and functional properties. Lastly, a web server was built for querying and visualizing
the predicted associations between small molecules and ADMs
and the differentially expressed targets of ADMs. Thus, our
method and its application provided a novel viewpoint with
respect to the treatment of AD.
rsif.royalsocietypublishing.org
[6]. The intracellular neurofibrillary tangles result from the
accumulations of the microtubule-associated protein Tau and
lead to apoptosis of the neuron.
miRNAs are non-coding short RNA molecules that are
involved in post-transcriptional gene regulation. Generally,
these RNA molecules operate as guides for RISC to cleave
a target mRNA or to block the target mRNA translation [7].
Certain analyses have shown the spatial or temporal distribution of miRNAs in the nervous system and have indicated
that miRNAs may be involved in the fine-tuning of neuronal gene expression. MiRNA-mediated post-transcriptional
regulations in cellular pathways and functions have effects
on dendrite spine development, neuronal differentiation,
memory and synaptic plasticity [8]. Moreover, accumulating
evidence suggests that the alterations in miRNAs expression
have potential contributions to AD pathophysiology [9].
Hebert et al. investigated alterations in miRNA expression
profiles of patients with AD and recognized the miR-29a/
29b-1 cluster as a potential suppressor of BACE1, based on
theoretical predictions and in vitro validation in cells. They
proposed that the loss of specific miRNAs could be responsible for the elevations of BACE1 and Ab levels in patients
with AD [10]. Wang et al. [11] examined miRNA expression
levels of the brain tissue of patients with AD and detected
approximately 70 differentially expressed miRNAs. They
suggested that miR-107 might contribute to AD progression
by the regulation of BACE1, which was validated using
Northern blot and target prediction algorithm.
In addition, miRNAs have recently elicited a growing
interest as new potential therapeutic targets and anti-cancer
agents [12]. Reconstitution of underexpressed miRNAs
or restriction of overexpressed miRNAs could become
treatments for human diseases [13]. On the one hand,
miRNAs have the potential to shift the philosophy of drug
design from ‘one drug, one target’ to ‘one drug, multiple
targets’ owing to their ability to target multiple genes and
regulate a variety of cellular functions [14]. The approaches to inhibit the function of oncogenic miRNAs include
the use of anti-miRNA oligonucleotides, miRNA sponges,
miRNA masking and small molecule inhibitors [15], in
which small molecules are inclined to possess ideal drug
properties, including good solubility, bioavailability and
metabolism [16]. Gumireddy et al. [17] identified diazobenzene and its derivatives as inhibitors of miR-21 after
screening approximately 1000 compounds for increases in
the luminescent signalling of luciferase expression and after
performing RT-PCR studies. Similar virtual screening and
RT-PCR experiments were conducted against liver-specific
miR-122. Young et al. [18] reported compounds NSC158959
and NSC5476 to be miR-122 inhibitors and identified
compound NSC308847 to be an activator of this miRNA.
On the other hand, as many miRNAs are implicated in
tumour suppressive effects, restoring their expression
(endogenously or exogenously) may yield therapeutic effects
[15,19]. Wiggins et al. [20] demonstrated that synthetic miR34a (a tumour-suppressor miRNA) is effective against
tumours by restoring normal levels of miR-34a. The formulated synthetic miR-34a can be delivered in tumour
xenografts, and also does not produce toxicity in mice. Reid
et al. [21] verified that miR-16 mimics could be effectively
delivered using EGFR-targeted minicells resulting in
tumour regression in vivo, which represented a novel
therapeutic approach for malignant pleural mesothelioma.
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Table 1. Information on AD-related gene expression profiles
the no.
normal
samples
the no.
AD samples
array
type
4
4
mRNA
GSE1297
9
22
miRNA
mRNA
GSE12685
8
6
mRNA
GSE5281
13
13
10
10
mRNA
12
13
16
9
11
23
12
95
19
119
Total
expression in nine case–control studies that correspond to
different microarray studies (table 1).
2.1.2. microRNA target genes
We integrated the miRNA target gene dataset from seven prediction algorithms [32], which involved DIANA-microT, miRanda,
RNA22, RNAhybrid, TargetScan, miRBase Targets and Pictar. In
total, 289 469 miRNA–target mRNA pairs including 776 miRNAs
and 15 185 miRNA target genes were predicted by at least two of
the seven algorithms.
2.1.3. mRNA expression profiles perturbed with small molecules
The connectivity map (cMap, build 02) provides mRNA expression profiles of four cell lines that were treated with 1309
bioactive small molecules (potential drugs or compounds) [33].
For each small molecule, we could obtain the differential gene
expression levels based on the instances of the small molecule.
Each instance comprises a pair of treatment and vehicle expression
profiles for one small molecule.
2.1.4. Chemical structures of small molecules
PubChem is an accessible repository for biological information of
small molecules [34]. We searched chemical structures of small molecules and downloaded two-dimensional structure information in
the form of ‘sdf’ files from PubChem Compound.
2.2. Data analysis
We presented a computational pipeline to investigate the connections between small molecules and miRNAs in AD through
comparative analysis of transcriptional responses of small molecule
perturbation and miRNA regulation in AD. Compared with our
previous study [28], we here introduced miRNA expression profiles
to gain insights into the biological effects of small molecules and
miRNAs more deeply in this study. The workflow of our approach
is shown in the electronic supplementary material, figure S1. (See
details in the electronic supplementary material, S2.)
2.2.1. Differential expression analysis
To obtain the CDGs, we conducted meta-analysis to merge the
nine case–control microarray studies related to AD. We first separately normalized the nine sets of mRNA expression data using
2.2.2. Alzheimer’s disease-related microRNA signatures and small
molecule signatures
In order to identify the transcriptional responses of miRNA
regulations in AD, the gene expression profiles of miRNA perturbation should be used. However, there were no sufficient
microarray data of miRNA perturbation. Here, we simulated
the presence and absence of miRNA in miRNA perturbation as
significantly upregulated and downregulated miRNAs in AD.
Thus, for each ADM, we produced miRNA signatures through
intersecting the CDGs and corresponding target gene sets. One
ADM signature represented the genome-wide expression pattern
regulated by the miRNA.
For each small molecule, the corresponding signatures comprised all of the genes that were ranked in descending order
according to their differential expression levels in expression profiles after perturbation by the small molecule. We obtained the
drug signatures based on the connectivity map (cMap, http://
www.broadinstitute.org/cmap/) which contains drug perturbed
and vehicle-treated (as control) gene expression profiles. Therefore, the small molecule signatures denoted the genome-wide
expression alterations after small molecule perturbation. The
set of small molecule signatures (as a reference set) collectively
characterize the transcriptional responses of corresponding
small molecules, which were applied to compare with the
miRNA signatures through statistics test.
2.2.3. Associations between small molecules and microRNAs in
Alzheimer’s disease
We performed a rank-based, non-parametric test to associate small
molecules with ADMs based on their gene expression signatures.
For each ADM, if the upregulated target genes of the ADM
tended to appear at the bottom of the ranked small molecule signatures, while the downregulated target genes tended to appear at
the top of the ranked signatures, we inferred that the ADMmediated expression alterations were opposite to that of the
small molecule perturbation. In this situation, a negative similarity
score was calculated to measure the connection between the small
molecule and the ADM (termed as ‘negative connection’), which
might be helpful in the development anti-miRNA drugs because
the gene expression pattern of corresponding ADM was opposite
to that of small molecule as above. On the contrary, if the upregulated target genes fell near the top of the ranked small molecule
signature, and the downregulated appeared near the bottom of
the ranked small molecule signature, the small molecule and
ADM pair was assigned with a positive similarity score representing similar transcriptional responses from the small molecule
treatment and the miRNA perturbation. The small molecule and
ADM connection that acquired a positive similarity score is
called a ‘positive connection’, which may be helpful in developing
miRNA mimic drugs.
The similarity (S) score of one small molecule and ADM pair
was calculated as follows. First, we calculated the Kolmogorov–
Smirnov (KS) values for the upregulated (KSup) and downregulated
J. R. Soc. Interface 11: 20131057
GSE16759
3
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GSE
accession
number
median normalization via ArrayTools [35]. We next used the R
package of ‘metaMA’ to implement meta-analysis based on the
theory of effect size combination, in which the ‘SMVar’ method
was applied to calculate the gene differential expression levels
for each case–control study (details in the electronic supplementary material, S3).
For miRNAs, the differentially expressed miRNAs were selected
based on the empirical Bayes procedure, which was performed in
Nunez-Iglesias et al. [36]. We then reserved the miRNAs, which
were licensed by miRBase (v. 9.1) and provided targets in our integrated miRNAs target genes dataset. The differentially expressed
miRNAs were considered to be ADMs.
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2.2.4. The evaluation of common characteristics of pairwise
small molecules
We attempted to detect common characteristics with respect
to chemical structures, targets and therapeutic categories
between all of the possible small molecule pairs. We first evaluated the structural similarity between any two small molecules in
the SmiRN-AD. The Tanimoto coefficient was used to detect the
structural similarity for all possible small molecule pairs, which
was implemented using small molecule subgraph detector
(SMSD) software [37]. We then applied the unpaired t-test to
evaluate the differences in the Tanimoto scores between small
molecule pairs that were connected with the same ADM and
small molecule pairs that were linked with different ADMs in
the SmiRN-AD.
Second, we detected the common characteristics of drug targets between all possible small molecule pairs. Information
regarding the small molecules’ drug targets and therapeutic categories was obtained from DrugBank [38]. On the one hand, all
of the possible small molecule pairs were divided into pairs that
shared and did not share common targets. On the other hand,
all of the possible small molecule pairs were divided into pairs
that were connected and not connected to the same ADM. We
hypothesized that the proportion of small molecule pairs that
shared common drug targets was higher in pairwise small
molecules that were connected with the same ADM. We
attempted to test whether any difference in the proportions was
significant by Fisher’s exact test. Similarly, we also performed
the same statistical analysis of the pairwise small molecules on
the therapeutic categories.
2.2.5. The evaluation of the common target similarities of
pairwise Alzheimer’s disease-related microRNAs
We also evaluated the proportions of common target genes
between two miRNAs in the SmiRN-AD. For miRNA i and
miRNA j, their differentially expressed target gene sets were
denoted as target(i) and target( j), respectively. The ‘Meet/Min’
score was calculated as follows:
jtargetsðiÞ > targetsð jÞj
:
minðjtargetsðiÞj; jtargetsð jÞjÞ
The unpaired t-test was used to evaluate the difference in the
‘Meet/Min’ scores between ADM pairs that were connected to
the same small molecule and ADMs pairs that were linked to
different small molecules.
4
3.1. Differentially expressed mRNAs and microRNAs
We collected AD-related gene expression profiles that
involved GSE16759, GSE1297, GSE12685 and GSE5281,
from the Gene Expression Omnibus. Using the R package
of ‘metaMA’, we performed meta-analysis and identified
3506 CDGs that corresponded to 4503 differentially expressed
probes (FDR 0.05). The details of CDGs are presented in
the electronic supplementary material, S4. The CDGs represented the common characteristics that were potentially
shared by different AD patients.
Moreover, we determined 26 differentially expressed
miRNAs (hsa-miR-15a, -20b, -29b, -29c, -30e-5p, -95 –101,
-130a, -134, -148b, -181c, -185, -188, -320, -376a, -368, -374,
-382, -432, -494, -572, -575, -598, -601, -617, -765) among AD
and normal subjects (FDR 0.05) based on the miRNA
expression profiles from GSE16759 (details in the electronic
supplementary material, S5).
3.2. Bioactive small molecule and microRNA association
network in Alzheimer’s disease
To identify the relationships between small molecules and
miRNAs in AD, we conducted a KS statistics test based on
the signatures of 1309 bioactive small molecules (also
known as potential drugs or compounds) and ADMs. As the
implementation of cMap requires both up- and downregulated
genes for each ADM, we excluded one ADM (hsa-miR-374)
from the list of 26 ADMs, which has no upregulated genes.
Thus, the final 25 ADMs were applied to identify the associations with small molecules. For each ADM, we calculated
the similarity scores for all of the possible small molecule–
miRNA connections and reserved only the significant associations at p 0.01. As a result, 496 small molecule–miRNA
connections including 25 ADMs and 275 small molecules
were recognized and used to construct the SmiRN-AD
(figure 1), in which the ADMs and small molecules were represented as triangles and circles, respectively. Grey and red
lines in the online version indicate positive and negative
similarity scores, respectively.
We next investigated the topological properties of the
SmiRN-AD. The degree distribution of the small molecules is
displayed in figure 2a, which shows that the majority of the
small molecules (263/275, 95.6%) were connected with only a
small number of ADMs (less than or equal to 5), which was
consistent with the observations in a previous study of
23 types of cancers [28]. Here, 189 out of 275 small molecules
were linked with only one ADM alone. That is, most of the
small molecules were associated with the ADMs in a specificmiRNA manner. However, the degree distribution of miRNA
was different. The degrees of miRNAs ranged from 12 to 28
(figure 2b). There were no miRNAs that associated with only
one or even a small number of the small molecules, suggesting
that miRNA may participate in multiple drug-related pathways. In addition, according to the integrated miRNAs’
target predictions, 12 out of the 25 ADMs regulated the wellknown AD genes (APP and PSEN2) and beta-site APP cleaving
enzyme 1 (BACE1), in which BACE1 is involved in two sequential cleavages of the APP gene and play key roles in forming
of the Ab deposits [39]. Highly connected nodes are listed
in table 2.
J. R. Soc. Interface 11: 20131057
Here, both KSup and KSdown are in the range [21,1]. The variable
t represents the number of genes in the ADM signature, j represents the jth gene based on the rank of the differential
expression in the ADM signature, N denotes the magnitude of
the ranked small molecule signature and the element V( j ) of a
vector V is the position of the jth target gene in the ordered
small molecule signature. Second, the corresponding KSup and
KSdown generated were integrated into the S score, as follows:
S is equal to 0 when KSup and KSdown have the same sign and
is equal to KSup 2 KSdown otherwise.
In this study, we used cMap to implement the procedure.
Based on the identified associations between small molecules
and miRNAs, we constructed the SmiRN-AD.
3. Results
rsif.royalsocietypublishing.org
(KSdown) target genes of ADM, respectively.
t
j Vð jÞ
;
a ¼ Max j¼1 t
N
t
VðjÞ ðj 1Þ
b ¼ Max
j¼1
N
t
a; ða . bÞ
and
KSup=down ¼
b; ðb . aÞ:
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nordihydroguaiaretic
sulfacetamide
thiostrepton
acid
clonidine
nifenazone
glimepiride
solanine
tacrolimus
meptazinol
tolmetin
benzonatate
clopamide
levodopa
emetine
vincamine
griseofulvin
tribenoside
clorsulon
tolnaftate
sulfaquinoxaline
Prestwick-642
nomegestrol iocetamic acid
betazole
MG-262
mometasone
josamycin
practolol
anisomycin astemizole
Prestwick-984
rotenone
mifepristone
terfenadine
dilazep
proadifen
gelsemine
ivermectin mir-188
cefalexin
CP-944629
clemizole
cefuroxime
clotrimazole
proxyphylline
myosmine
nisoxetine
mir-598 prenylamine
cyclizine
mir-130a
triamterene trifluridine
thioperamide
bumetanide
benzamil
dobutamine
lomustine
mir-134
levonorgestrel
0316684-0000
acetylsalicylsalicylic
niclosamide oxolamine
zimeldine
tanespimycin
propranolol
alsterpaullone
metitepine
atractyloside
acid
mir-765
semustine
camptothecin
alfaxalone
methylbenzethonium
terguride
pramocaine
wortmannin
3-acetamidocoumarin
chloride terazosin perhexiline
Prestwick-1082
mebeverine
CP-690334-01
diperodon
quinisocaine
quinostatin
ergocalciferol
bezafibrate
mir-494
apigenin
protoveratrine A
ampicillin
trimethobenzamide
cephaeline
NS-398
niflumic acid
flucytosine
ceforanide
propantheline bromide mir-181c
luteolin
harmol
meclofenamic acid
zidovudine
SR-95639A
Gly-His-Lys
mir-368
chenodeoxycholic
mir-101
0198306-0000
bepridil
probenecid
vorinostat
acid
thioridazine
molindone ketotifen
tetrandrine
pseudopelletierine
3-hydroxy-DL-kynurenine
vigabatrin
mir-382
thioproperazine
levothyroxine sodium
novobiocin
0225151-0000
LY-294002
PNU-0230031
metacycline nicotinic acid
nitrofural
buspirone
15-delta
ouabain
triflupromazine
desipramine
metrizamide
disopyramide
resveratrol
prostaglandin J2
ifenprodil
nocodazole
acenocoumarol
estropipate
etidronic acid moroxydine
amoxapine
MS-275
remoxipride
spaglumic acid
colchicine
Prestwick-857
amikacin
vinburnine
mir-601
indapamide
clobetasol
mir-20b
tonzonium bromide
thapsigargin
guanadrel
ketorolac
mafenide
trichostatin A
mexiletine
mir-95
penbutolol
pivmecillinam
antazoline
mir-432
aceclofenac
terbutaline
mebendazole
lanatoside C
mir-185
isoxicam
tolfenamic acid
meglumine
prochlorperazine
mevalolactone
harmine
PHA-00851261E
fusaric acid
etacrynic
acid
mir-575
flecainide
carbimazole
5279552
mebhydrolin
paromomycin
amantadine
melatonin
ciclosporin sanguinarine
captopril
dinoprost
digoxin
beta-escin sirolimus
rottlerin
kaempferol
5230742
mir-376a
puromycin
alvespimycin
lovastatin
ethosuximide
pimozide
iproniazid
5707885
fenofibrate
SC-58125
tetroquinone
cefepime
pyrazinamide
STOCK1N-28457
gemfibrozil
harmalol
suloctidil syrosingopine fluphenazine
mir-29b
articaine
Prestwick-675
hexylcaine
hydrastine
mir-30e-5p
pilocarpine Y-27632
clemastine
trifluoperazine
atropine
pentoxyverine
pyrvinium
tropine
hydrochloride
citalopram
15(S)-15-methylprostaglandin
Prestwick-967
hydrocortisone
diphemanil
geldanamycin
6-bromoindirubin-3'-oxime
0317956-0000 acacetin mir-617
E2
hycanthone
Prestwick-860
mir-572
metilsulfate
promazine
helveticoside
H-7
16,16-dimethylprostaglandin
amiodarone
mir-320
digoxigenin
quipazine
trimethoprim
idoxuridine
E2
lasalocid
Prestwick-685
loxapine
colforsin
SR-95531
mycophenolic acid
mir-29c
mir-15a
IC-86621
mesalazine
TTNPB
proscillaridin
sulfamethizole
ursodeoxycholic acid
estrone
chlorzoxazone
Prestwick-1080
rilmenidine
phenindione
latamoxef
monensin
1,4-chrysenequinone
nifuroxazide
mir-148b
tridihexethyl
dorzolamide
quercetin cobalt chloride
sulindac
methoxamine
staurosporine
loperamide
cefotetan
5182598
dosulepin
deptropine digitoxigenin 4-hydroxyphenazone
amprolium
oxymetazoline
fenbendazole
cefmetazole
5
hecogenin
(b) 3
180
140
no. miRNAs
no. small molecules
160
120
100
80
60
2
1
40
20
0
1 2 3 4 5 6 7 8 9 1213
small molecule degree
21
0
10 12 14 16 18 20 22 24 26 28 30
miRNA degree
Figure 2. Degree distribution of small molecule and miRNA nodes. (a) Degree distribution of the small molecule nodes. (b) Degree distribution of miRNA nodes.
3.3. Functional characteristics of small molecule
and microRNA association network in
Alzheimer’s disease
To investigate the characteristics of small molecules (or miRNAs)
that were associated with the same miRNAs (or small molecules)
in the SmiRN-AD, we performed comparative analysis for small
molecule (or miRNA) pairs.
For ADM pairs, we introduced the ‘Meet/Min’ score to
evaluate the ratio of the ADM pairs’ common target genes.
We obtained a significant difference ( p ¼ 6.07 1023) between
ADM pairs that were linked to the same small molecule and
that were connected with different small molecules. For small
molecule pairs, we observed that small molecule pairs that connected with the same miRNA tended to share common drug
targets ( p ¼ 1.72 1024) and tended to belong to the same
therapeutic category ( p ¼ 4.22 1028). In addition, we calculated the similarity of two-dimensional chemical structures
for each small molecule pair, which was measured via the
Tanimoto coefficient provided by the SMSD software. The
results indicated that small molecule pairs that were associated
with the same miRNA in SmiRN-AD had moderate structure
similarity ( p-value ¼ 0.27).
The distribution of the small molecules in our predicted
results across different classes according to the anatomical,
therapeutic and chemical (ATC) classification system is
shown in figure 3. Notably, we predicted significantly more
small molecules that belong to the ATC classes of the cardiovascular system (C) and the nervous system (N) compared
with the other classes. We also found evidence to support
an insidious and likely causal association between cardiovascular disease (CVD) and AD [40]. According to autopsy
reports on AD brains, approximately 60 –90% of the cases
exhibited variable cerebrovascular pathology that was analogous to CVD [41]. Furthermore, a series of studies revealed
that the very same risk factors for heart disease also put
J. R. Soc. Interface 11: 20131057
(a) 200
rsif.royalsocietypublishing.org
Figure 1. Small molecules – miRNAs network in AD. Network organization of miRNA and small molecule associations. Triangles indicate miRNAs and circles signify
small molecules. (Online version in colour.)
Downloaded from http://rsif.royalsocietypublishing.org/ on July 7, 2015
V
2%
R
7%
S
9%
A
9%
C
17%
N
16%
D
9%
J
12%
H
2%
L
4%
G
4%
Figure 3. Distribution of ATC top-level classes among small molecules. Alimentary tract and metabolism (A), blood and blood-forming organs (B), cardiovascular
system (C), dermatologicals (D), genito-urinary system and sex hormones (G), systemic hormonal preparations, excluding sex hormones and insulins (H), antiinfectives for systemic use (J), antineoplastic and immunomodulating agents (L), musculo-skeletal system (M), nervous system (N), antiparasitic products, insecticides
and repellents (P), respiratory system (R), sensory organs (S) and various (V). (Online version in colour.)
Table 2. Highly connected small molecules and miRNAs in the SmiRN-AD
small molecule
nodes
degree
miRNA
nodes
degree
thioridazine
quinostatin
21
13
hsa-miR-148b
hsa-miR-617
28
26
perhexiline
metrizamide
12
9
hsa-miR-368
hsa-miR-181c
25
24
MS-275
9
hsa-miR-382
24
digoxin
lanatoside C
8
8
hsa-miR-188
hsa-miR-376a
23
23
melatonin
ouabain
8
8
hsa-miR-765
hsa-miR-95
23
22
beta-escin
prochlorperazin
7
6
hsa-miR-432
hsa-miR-601
21
21
vorinostat
6
hsa-miR-320
20
individuals at greater risk of developing AD. For example, de
Toledo Ferraz Alves et al. [42] reported that cardiovascular
risk factors (such as hypertension, heart failure and hypercholesterolemia) could trigger AD pathology. All of the above
evidence generally supports our predictions. In total, the
majority of the small molecules in the SmiRN-AD belonged
to ATC classes of the nervous system (N) and cardiovascular
system (C), suggesting that these small molecules should be
further examined for extended indications, especially for AD.
3.4. Biological insights into small molecule
and microRNA association network in
Alzheimer’s disease
In the SmiRN-AD, we observed several ‘AD or neuron
system-specific’ miRNAs through a literature review, such
as miR-134, miR-101 and miR-15a. By both northern blotting
and an RNase protection assay, Schratt et al. revealed that
miR-134 was specially expressed in the brain and had a
specific expression pattern in the hippocampus. MiR-134
levels gradually increased with development, peaking when
synaptic maturation occurs [43]. Long and Lahiri applied
qPCR to assay miR-101 levels, finding that miR-101 appears
to be preferentially expressed in model CNS neurons [44].
At the same time, in additional research on rat hippocampal
neurons that were cultured in vitro, Vilardo et al. [45] demonstrated that miR-101 negatively tuned the APP gene in
hippocampal neurons in a luciferase assay. Thus, overexpressed
miR-101 could reduce the APP gene levels and decrease
the abundance on Ab deposits. Additionally, miR-15a is a
Parkinson-related miRNAs according to Kim et al. study [46].
Moreover, miR-15a often results in a severe dementia that is
similar to AD, when Parkinson’s disease progresses [2].
Furthermore, we concentrated on several positive and
negative small molecule–miRNA connections in the SmiRNAD. Quinostatin is known to have an antiproliferative effect
and is named after its quinoline core structure. Yang et al.
screened a list of small molecule modulators of the mammalian
target of rapamycin (mTOR) signalling network from a compound library of approximately 20 000 molecules. These
authors identified quinostatin as a novel mTOR inhibitor
through a high-throughput and cell-based assay [47]. The
mTOR signalling pathway is relevant to various human diseases, including AD [48]. In vitro studies showed that Ab was
an activator of the PI3K/AKT pathway, which in turn activated
mTOR [49]. Caccamo et al. proposed that the interplays
between mTOR signalling and Ab impinged on cognitive disturbances. A reduction in the mTOR signalling caused positive
regulation of autophagy, which decreased the accumulation of
Ab [50]. Thus, as an inhibitor of mTOR, quinostatin has a
potential effect on AD via the regulation of the mTOR pathway.
In our SmiRN-AD, miR-148b has a positive connection with quinostatin. MiR-148b is located at chromosome
12q13. Recent studies have found that it is downregulated
in multiple human diseases using microarray analysis
[51,52]. Using TaqMan low density miRNA arrays, Schonrock
et al. observed that miR-148b was downregulated in subjects
with AD, as well as another 19 downregulated miRNAs.
The authors also verified the downregulation of miR-148b
with in vivo analysis of APP23 hippocampus [53]. Based on
enrichment analysis, we also found that the differential
target mRNAs of miR-148b were overrepresented in the
mTOR signalling pathway (hsa04150, figure 4) in the KEGG
database ( p ¼ 0.042). Pink genes in the online version of the
figure denote the differential targets of miR-148b that are
overrepresented in the mTOR signalling pathway.
6
J. R. Soc. Interface 11: 20131057
M
6%
alimentary tract and metabolism
blood and blood-forming organs
cardiovascular system
dermatologicals
genito-urinary system and sex hormones
systemic hormonal preparations, excluding sex hormones and insulins
anti-infectives for systemic use
antineoplastic and immunomodulating agents
musculo-skeletal system
nervous system
antiparasitic products, insecticides and repellents
respiratory system
sensory organs
various
rsif.royalsocietypublishing.org
P
1%
B
2%
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7
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J. R. Soc. Interface 11: 20131057
Figure 4. MiR-148b in the mTOR signalling pathway. The mTOR signalling pathway from KEGG. (Online version in colour.)
Given that our strategy reflected biological consequences
by comparing the gene expression patterns of miRNA regulation and the transcriptional responses to small molecules,
the positive connection indicated that miR-148b regulation
and quinostatin treatment could lead to similar biological
changes. That is, miR-148b potentially has degradative effects
on Ab that are similar to quinostatin. Both miR-148b and quinostatin may become therapy for AD or other nervous system
disorders based on the underlying molecular machinery.
Especially, administration of synthetic miR-148b mimics
might become a therapeutic treatment through ameliorating
influence induced by AD-specific miR-148b downregulation.
Another positive linkage is the amantadine and miR-15a
connection. Generally, amantadine acts as an N-methyl-D-aspartate receptor antagonist and a dopamine agonist. Many
researchers have observed that amantadine is associated
with the treatment of behavioural disturbances, synaptic and
cholinergic receptors, neurotransmitter deficits and improving neurorecovery [54,55]. MiR-15a was identified as an
SA-miRNA (senescence-associated miRNA) and was downregulated in AD patients’ brains [56]. This miRNA is a member
of the miR15/107 group, which has been implicated in neuronal
degenerative diseases, including AD [57]. Moreover, the differentially expressed target genes of miR-15a are involved in AD
pathway (hsa05010, figure 5) in the KEGG database. Five pink
(online version) genes are miR-15a-targeted differentially
expressed genes which are annotated in the hsa05010 pathway.
Therefore, the amantadine-miR-15a positive connection we predicted also gives biological insights into AD therapeutics. When
formulated miR-15a is administrated, the symptoms of AD subjects might be relieved, which achieve similar affects to
amantadine. In previous investigations, researchers have proposed analogous therapeutic function in cancers. For example,
Nora et al. showed that miR-15a was frequently deleted or
downregulated in squamous cell carcinomas. They indicated
that overexpression of miR-15a could induce cell cycle arrest in
G1 –G0 [58]. Another study suggested that administrating
a combination of miR-15a and miR-34a may also potentiate
their therapeutic effects based on the demonstration that
formulated miR-34a blocked tumour growth in vivo [59].
Additionally, melatonin has a negative connection with
miR-30e-5p. Melatonin defects have been reported to occur in
neurodegenerative disorders [60]. Moreover, as a known CNS
depressant, melatonin is also implicated in the regulation of cognitive (e.g. learning and memory) impairment [61] and is
associated with Ab [62]. Rosales-Corral et al. [63] suggested
that melatonin could present multiple potential functions that
correspond to several possible pathological mechanisms including the cholinergic hypothesis. Moreover, ‘melatoninergic’ or
melatonin-type drugs may relieve dementia-related cognitive
deficits and other syndromes [64,65]. Although melatonin has
not been applied in treating AD in the clinical setting, we propose melatonin as a repositioning candidate to maximize its
potential. Furthermore, Xu et al. demonstrated that the
expression level of miR-30e in peripheral leucocytes was significantly higher comparing schizophrenia patients with the control
group, and a genetic polymorphism (ss178077483) in the genes
encoding miR-30e increased vulnerability to depression [66].
Cogswell et al. [67] concentrated on the miRNA changes in
AD brains and found the overexpression of miR-30e-5p in the
hippocampus by qRT-PCR. Melatonin has the potential to
treat AD phenotype by reverting the gene expression pattern
of miR-30e-5p ectopic regulation in AD patients.
3.5. Online database
We constructed an online database to query and visualize
the identified small molecule and miRNA associations. The
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8
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J. R. Soc. Interface 11: 20131057
Figure 5. MiR-15a in the AD pathway. The AD pathway from KEGG. (Online version in colour.)
database has a user-friendly interface to retrieve information
according to the miRNA or the small molecule. Through
the miRNA query, users can obtain the stem– loop structure
of the primary miRNAs, the consistently differentially
expressed target genes, the associated small molecules and
the corresponding p-values. In addition, for each ADM, we
illustrated the expression profiles of their consistently differentially expressed target genes using a heat map and the
expression levels of each consistently differentially expressed
target gene using a bar chart. This was done separately for
nine case –control studies. Through small molecule queries,
we provided the two-dimensional chemical structure, the
associated miRNAs and the corresponding p-values. Most
importantly, either query can return an illustration of the
small molecule and miRNA associations for the user submitted small molecule or miRNA. The database is freely
available at http://bioinfo.hrbmu.edu.cn/SmiRN-AD/. We
anticipate that users can gain insights into screening drug
candidates, drug repurposing and experimental designs
when using our online database.
4. Discussion
In this study, we effectively extended our previous study to
identify the associations between small molecules and
ADMs through comparative analysis of the transcriptional
responses (or gene expression signatures) of small molecules
perturbation and miRNA regulation in AD. We first conducted a meta-analysis to identify the CDGs by combining nine
case–control AD-related microarray studies. The ADMs were
selected from the Nunez-Iglesias’s microarray study. Second,
we created the gene expression signatures for each ADM,
which were attained by intersecting the ADM target gene set
with CDGs. Lastly, through the cMap web server, we executed the query with each of the ADM signatures against the
transcriptional responses of each small molecule, which represented the differentially expressed levels of the genes,
comparing mRNA expression profiles in treatment versus
non-treatment with small molecules. The aim was to return a
similarity score of the small molecule–miRNA connections.
According to the similarity scores, we evaluated the significance of the association. As a result, we constructed the
SmiRN-AD including 25 ADMs and 275 small molecules at a
significance level of 0.01. Through topological and functional
analysis of the SmiRN-AD, we found that the small molecules
(or miRNAs) that were connected with the same miRNA (or
small molecule) shared common targets or therapeutic
categories. Furthermore, we also investigated the biological
insights afforded by the SmiRN-AD. We found that amantadine and quinostatin had positive connections with miR-15a
and miR-148b in the SmiRN-AD, respectively. Moreover, melatonin had a negative connection with miR-30e-5p. The results
might be facilitated to make references to molecular therapy for
Downloaded from http://rsif.royalsocietypublishing.org/ on July 7, 2015
extracted from the miRNA affected expression profile, the
applicability of our method will broaden. In total, we propose
a scheme for molecular signature-based inference of small
molecule and miRNA associations in AD that is capable of
guiding both drug repositioning and drug candidate screening
from thousands of compounds. We also suggest that the analysis strategy that we used is not only appropriate for AD, but
also appropriate for other complex diseases.
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Funding statement. This work was supported by the National Natural
Science Foundation of China (grant no. 30900837), the Heilongjiang
Postdoctoral Funds for Scientific Research Initiation (grant no. LBHQ11042), the Graduate Innovation Fund of Heilongjiang Province
(grant no. YJSCX2012-249HLJ) and the Program for Young Talents of
Science and Technology in Harbin (grant no. 2013RFQXJ057).
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