Dissection of human MiRNA regulatory influence to subpathway
Xia Li
Wei Jiang
Wei Li
Baofeng Lian
Shuyuan Wang
Mingzhi Liao
Xiaowen Chen
Yanqiu Wang
Yingli Lv
Shiyuan Wang
Lei Yang
The global insight into the relationships between miRNAs and their regulatory influences remains poorly understood. And most of complex diseases may be attributed to certain local areas of pathway (subpathway) instead of the entire pathway. Here, we reviewed the studies on miRNA regulations to pathways and constructed a bipartite miRNAs and subpathways network for systematic analyzing the miRNA regulatory influences to subpathways. We found that a small fraction of miRNAs were global regulators, environmental information processing pathways were preferentially regulated by miRNAs, and miRNAs had synergistic effect on regulating group of subpathways with similar function. Integrating the disease states of miRNAs, we also found that disease miRNAs regulated more subpathways than nondisease miRNAs, and for all miRNAs, the number of regulated subpathways was not in proportion to the number of the related diseases. Therefore, the study not only provided a global view on the relationships among disease, miRNA and subpathway, but also uncovered the function aspects of miRNA regulations and potential pathogenesis of complex diseases. A web server to query, visualize and download for all the data can be freely accessed at http://bioinfo.hrbmu.edu.cn/miR2Subpath.
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INTRODUCTION
The mechanism of post-transcriptional regulation of
coding genes is rising as one of the new challenges
in systems biology. miRNAs are single-stranded
RNAs, which regulate gene expression by translation
inhibition or degradation of mRNAs in
posttranscriptional level [1]. Some investigations have
been reported that miRNAs take part in a lot of
important biological functions and a broad spectrum
of human diseases, such as cell proliferation,
differentiation, apoptosis [24], immune response [5],
tumor development [6], cardiac diseases [7] and
so on.
More and more studies have demonstrated that
one miRNA can regulate several hundred genes on
average [8]. Currently, the functional interpretation
of miRNAs mainly relies on the functions of their
target genes. Several studies predicted the functions
of miRNAs based on the enrichment analysis of their
target genes from a number of functional categories.
For example, miRGator [9] and DIANA-mirPath
[10] provided the statistically enriched Gene
Ontology functions, KEGG/GenMAPP/BioCarta
pathways, or diseases from Ingenuity Pathway
Analysis [11]. Another comprehensive analysis
established a dictionary on miRNAs and their putative
target pathways and found that differentially
expressed genes in cancer were statistically significant
enriched with targets of certain miRNAs [12]. They
are effective tools for study on miRNAs and
pathways, but they do not pay more attention to local
areas of pathway and the properties of miRNA
regulations to pathways, especially in human diseases.
Recently, we have demonstrated that subpathway
(local area of the entire biological pathway)-based
analysis may give us much more detailed
explanations of type-specific functions for pathology of
complex diseases, because the focused genes may
not be significantly enriched in the entire pathway
but the subpathways [13].
In this study, we constructed the bipartite graph of
miRNAsubpathway interactions to explore the
rules of miRNA regulatory influence on
subpathway. The results indicated that miRNAs have
synergistic effect to regulate a group of subpathways
with similar function. Through integrating the
disease information of miRNAs, the characteristics of
disease miRNAs regulation were also uncovered. All
the findings can help us to understand the detail
mechanisms of miRNAs regulations and identify
novel disordered miRNAs or subpathways in
human diseases.
MATERIALS AND METHODS
Data Source
MiRNA target genes
We acquired human miRNA target genes from
seven miRNA target predicting tools, which were
PicTar [14], RNAhybrid [15], DIANA-microT [16],
RNA22 [17], miRBase Targets [18], miRanda [19],
TargetScan [20]. In order to improve the reliability
of the predicted miRNA regulations, we only
extracted the regulations that were predicted by at least
two tools. In the final, we obtained 776 miRNAs,
15 185 miRNA target genes and 289 469 miRNA
regulations.
Disease information of miRNAs
We downloaded the miR2Disease database (August
2009) [21], which contained the disease-miRNA
relationships extracted from literatures. Disease
miRNAs are defined by miRNAs themselves
deregulation in various human diseases. In total,
there are 123 diseases, 414 miRNAs and 2047
miRNA-disease pairs in the whole data file. We
classified diseases according to the rules in the online
book of Genes and Disease (http://www.ncbi.nlm
.nih.gov/books/NBK22183/). In the final, the 123
diseases were grouped into 15 disease categories. The
concrete classes were Hematological (Hem), Cancer,
Chromosomal (Chr), Ophthamological (Oph),
Immunological (Imm), (...truncated)