Analysis of functional and pathway association of differential co-expressed genes: A
case study in drug addiction
Zi-hui Li1, Yu-feng Liu1, Ke-ning Li, Hui-zi DuanMu, Zhi-qiang Chang, Zhen-qi Li, Shan-zhen Zhang,
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
a r t i c l e i n f o
Received 4 January 2011
Accepted 24 August 2011
Available online 28 August 2011
Gene co-expression meta-analysis
a b s t r a c t
Drug addiction has been considered as a kind of chronic relapsing brain disease influenced by both
genetic and environmental factors. At present, many causative genes and pathways related to diverse
kinds of drug addiction have been discovered, while less attention has been paid to common mechanisms
shared by different drugs underlying addiction. By applying a co-expression meta-analysis method to
mRNA expression profiles of alcohol, cocaine, heroin addicted and normal samples, we identified signif-
icant gene co-expression pairs. As co-expression networks of drug group and control group constructed,
associated function term pairs and pathway pairs reflected by co-expression pattern changes were dis-
covered by integrating functional and pathway information respectively. The results indicated that respi-
ratory electron transport chain, synaptic transmission, mitochondrial electron transport, signal
transduction, locomotory behavior, response to amphetamine, negative regulation of cell migration, glu-
cose regulation of insulin secretion, signaling by NGF, diabetes pathways, integration of energy metabo-
lism, dopamine receptors may play an important role in drug addiction. In addition, the results can
provide theory support for studies of addiction mechanisms.
? 2011 Elsevier Inc. All rights reserved.
Drug addiction has been considered as a kind of chronic relaps-
ing brain disease characterized by drug craving, drug abuse and
abstinence syndrome, which has been a worldwide issue . It is
influenced by both genetic and environmental factors, in which ge-
netic factor occupies 40–60% . Prepublished work always fo-
cused on single-type drug addiction such as morphine or
heroine; however the common mechanisms shared by diverse
drugs underlying addiction have been paid little attention to. For
each kind of addictive drug which has its distinctive pharmacody-
namics effect could cause some similar abstinence syndromes,
such as negative emotion, extension of sensitization, and associa-
tive learning process induced by drug condition, studies of com-
mon mechanisms of drug addiction will contribute to the control
and cure of addictive diseases. In 2005, Nestler elaborated the com-
mon mechanisms of four drugs, namely nicotine, alcohol, opium
and hemp combining with existing studies . In 2008, Li et al.
identified 396 genes related to at least two kinds of drug addictions
as well as five signaling pathways involved in the common mech-
anisms of four drug addictions . The high throughput gene
microarray technology is an efficient method used to analyze gene
expression level under different conditions. In previous work, the
analysis of gene expression profiles had been widely used to study
cancer and other complex diseases, and their approaches mainly
focused on the identification of differential expression genes. How-
ever, differential expression analysis which could only distinguish
single gene related to diseases can hardly reflect cooperative rela-
tions among genes. In order to settle this problem, constructing
gene co-expression network based on co-expression analysis could
identify functionally related genes, i.e. they may functioning to-
gether, and take part in the processes of disease occurrence and
Since genes involved in addiction mechanism are in extensive
wide range, single data and research method may result in a bias.
In order to overcome the limits of single data, it is better to com-
bine several independent datasets for analyzing common mecha-
nisms of drug addiction. Meta-analysis is the method that
analyzes and summarizes multiple collected data by statistical
methods aiming to provide quantified averaging effects to a ques-
tion; whose advantage is that strengthen the credibility of conclu-
sions by comprising more samples and prevent the inconsistency
of results. It takes results from several independent studies on
same project and uses appropriate statistical methods for system-
atical, objective and quantitative analysis on them. Since there is a
large range of genes involved in addiction mechanisms, single
1532-0464/$ - see front matter ? 2011 Elsevier Inc. All rights reserved.
E-mail address: email@example.com (Y. Xu).
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cannot provide precise information to experimental scientists. By
contrast, in pathway association network, the number of edges
was close to our meta-analysis. Some functional terms and
pathways were missed by MRS but were found in our study which
were already verified to be related with drug addiction, such as
respiratory electron transport chain (GO:0022904), mitochondrial
electron transport, NADH to ubiquinone(GO:0006120),central ner-
(GO:0001975), negative regulation of cell migration (GO:0030336),
signaling by NGF and integration of energy metabolism. Conse-
quently, it suggests that the meta-analysis is more suitable and
reliable in identifying differential co-expressing gene pairs from
cross-platform microarray data.
In conclusion, our method can effectively identify drug addic-
tion related biological functions and pathways, which can provide
theoretical support for future studies of drug addictive mechanism.
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