Nucleic Acids Research Advance Access published online on September 28, 2009
Nucleic Acids Research, doi:10.1093/nar/gkp803
© The Author(s) 2009. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
TransmiR: a transcription factor–microRNA regulation database
Juan Wang1,2,
Ming Lu1,2,
Chengxiang Qiu1,2 and
Qinghua Cui1,2,*
1Department of Biomedical Informatics, Peking University School of Basic Medical Sciences and 2MOE Key Laboratory of Molecular Cardiology, Peking University, Beijing, 100191, China
*To whom correspondence should be addressed. Tel: +8610 82801585; Fax: +8610 82801001; Email: cuiqinghua{at}hsc.pku.edu.cn
Received June 25, 2009. Revised September 13, 2009. Accepted September 14, 2009.
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ABSTRACT
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MicroRNAs (miRNAs) regulate gene expression at the posttranscriptional
level and are therefore important cellular components. As is
true for protein-coding genes, the transcription of miRNAs is
regulated by transcription factors (TFs), an important class
of gene regulators that act at the transcriptional level. The
correct regulation of miRNAs by TFs is critical, and increasing
evidence indicates that aberrant regulation of miRNAs by TFs
can cause phenotypic variations and diseases. Therefore, a TF–miRNA
regulation database would be helpful for understanding the mechanisms
by which TFs regulate miRNAs and understanding their contribution
to diseases. In this study, we manually surveyed approximately
5000 reports in the literature and identified 243 TF–miRNA
regulatory relationships, which were supported experimentally
from 86 publications. We used these data to build a TF–miRNA
regulatory database (TransmiR,
http://cmbi.bjmu.edu.cn/transmir),
which contains 82 TFs and 100 miRNAs with 243 regulatory pairs
between TFs and miRNAs. In addition, we included references
to the published literature (PubMed ID) information about the
organism in which the relationship was found, whether the TFs
and miRNAs are involved with tumors, miRNA function annotation
and miRNA-associated disease annotation. TransmiR provides a
user-friendly interface by which interested parties can easily
retrieve TF–miRNA regulatory pairs by searching for either
a miRNA or a TF.
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INTRODUCTION
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MicroRNAs (miRNAs) are endogenous small (
22 nt) noncoding regulatory
RNAs that typically function as negative regulators of mRNA
expression at the posttranscriptional level. They act by binding
to the 3'-untranslated regions (3'-UTRs) of target mRNAs through
base pairing to complementary sequences. This binding results
in cleavage or translation inhibition of the target mRNAs (
1–3).
miRNAs play critical roles in many essential biological processes,
such as proliferation (
4,
5), metabolism (
6,
7), differentiation
(
8), development (
9,
10), apoptosis (
7,
11,
12) and cellular signaling
(
13). Because of their biological importance, the dysfunction
of specific miRNAs is associated with a variety of diseases,
such as cancer and cardiovascular diseases (
8,
14,
15).
No genes are completely independent but rather they interact with other genes. In case of miRNAs, they usually affect downstream molecules by regulating the expression of target genes. Estimates suggest that 1–4% of genes in the human genome encode miRNAs and that a single miRNA can regulate as many as 200 mRNAs (8). Furthermore, the expression of miRNAs can be activated or repressed by transcription factors (TFs), which therefore can serve as upstream regulators of miRNA. In recent years, many researchers have attempted to understand how miRNAs act to regulate target genes and what their roles are in various diseases. However, the study of miRNA regulation by TFs (TF–miRNA regulation) has been relatively limited. We reported previously that miRNAs and TFs may cooperate to tune gene expression (16). In addition, miRNAs and TFs can form feedback or feed-forward loops, which play critical roles in various biological processes. For example, Yamakuchi and Lowenstein (17) reported a feedback loop in which p53 induces expression of miR-34a, which in turn suppresses the expression of SIRT1 and thus increases p53 activity. Increasing evidence suggests that aberrant regulation of miRNAs by TFs can cause diseases (18). Therefore, TF–miRNA regulation is one of the most important aspects of the study of both miRNAs and TFs and is attracting the interest of increasing numbers of researchers. For this reason, a high-quality TF–miRNA regulation database will be of great help in the study of both the regulation of miRNAs by TFs and the roles of this regulation in diseases. However, such a database has not been available. To establish a database of TF–miRNA regulation, we manually curated the TF–miRNA regulatory relations that have been reported in literature and created a database that we named ‘TransmiR’. Finally, we gave a simple example for the application of TransmiR by analyzing the association between the conservation and degree of miRNAs. Although the ‘TransmiR’ database represents only the first step in this project, it should become a valuable ongoing resource for the study of TF–miRNA regulation.
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DATA SOURCES AND IMPLEMENTATION
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We first searched PubMed by the keywords ‘microRNA’
or ‘miRNA’ and then downloaded the search results
that had been recorded before April 2009 from the National Center
for Biotechnology Information (NCBI). From this search, we obtained
5000 papers that contained the words ‘microRNA’
or ‘miRNA’. Next, we curated the data manually and
retrieved TF–miRNA regulatory pairs that related to the
regulation of miRNAs by TFs. Different researchers double-checked
all TF–miRNA pairs. We also noted whether a particular
TF activated or repressed the expression of its miRNA partner.
The database includes PubMed IDs and hyperlinks to the original
PubMed articles as well as a hyperlink to NCBI (
http://www.ncbi.nlm.nih.gov/)
for each TF. This will enable researchers to easily access annotations
such as functions, cellular components and biological processes
in which the TF is involved, as well as their related disease.
We annotated each miRNA according to whether it is associated
with tumor and other diseases using the human microRNA disease
database (HMDD,
http://cmbi.bjmu.edu.cn/hmdd) (
15). We also
annotated the functions of each miRNA using the miRNA function
database UCbase (
19).
All data were organized in the ‘TransmiR’ database using SQLite, a lightweight database management system. The website is presented using Django, a Python web framework and is available at http://cmbi.bjmu.edu.cn/transmir. TransmiR provides several search options, name of the TF, miRNA ID or both.
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A CASE USING TF-miRNA DATA
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TransmiR represents a valuable resource for the study of TF–miRNA
regulation and can be used to analyze various processes, such
as the evolution of the interactions, expression patterns and
associated diseases of miRNAs. As an example, in this study
we assembled a human TF–miRNA regulatory network (
Figure 1)
and performed a preliminary analysis of it. As shown in
Figure 1,
the largest component of the network represented 67.8% of the
original network nodes, which suggests that many TFs/miRNAs
interact with other miRNAs/TFs. A single miRNA could be regulated
by different TFs and one TF could regulate multiple miRNAs.
These findings indicate that the regulatory relationships between
TFs and miRNAs are complex. Both the TF and the miRNA nodes
had skewed degree (the number of connections to a node) distribution.
These skewed distributions suggest that most TFs regulate just
a few miRNAs and, in addition, that most miRNAs are regulated
by a small number of TFs. However, it also means that some hub
TFs and miRNAs showed a very high number of connections, which
suggests that they may play essential roles in TF–miRNA
regulation. For example,
MYC regulated 26 miRNAs, and miR-20a
was regulated by seven TFs. Degree is a measure of node centrality
in a network. Those nodes that interact with a greater number
of nodes than others are normally more important in cellular
functioning and could represent factors that would be more highly
conserved in evolution (
20). Previously, we revealed a correlation
between conservation and the degree of a protein (
21). However,
no previous research has shown whether this pattern exists in
TF–miRNA network. In order to address this issue, we investigated
the correlation between conservation and degree for the miRNAs.
We evaluated miRNA conservation using data on miRNA families
and the method presented by Zhang
et al. (
22). Human miRNAs
were classified into five groups according to their level of
conservation: miRNAs that were present only in humans (G5),
conserved in primates (G4), conserved in mammals (G3), conserved
in vertebrates (G2) and those that were conserved in other more
distant species (G1, the most conserved group). We classified
the miRNAs in the network into two groups according to their
degree: the high-degree group (degree
3) and the low-degree
group (degree <3). We evaluated the level of conservation
for the miRNAs in these two groups. As expected, we found that
the miRNAs in the high-degree groups were more conserved (i.e.
a greater number were in G1,
P = 0.02, Fisher’s exact
test) than those in the low-degree group (
Table 1). This suggests
that miRNAs that are regulated by a large number of TFs tend
to be highly conserved during evolution.
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FUTURE EXTENSIONS
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The TransmiR database represents the first step in this project
and further extensions should be developed. As we described
above, feedback/feed-forward loops represent two critical local
interactions between TFs and miRNAs. Therefore, we plan to curate
feedback/feed-forward loops between TFs and miRNAs and integrate
them into TransmiR. Furthermore, we will also incorporate miRNA
target data that is supported experimentally. In addition, we
will classify both TFs and miRNAs into more detailed clusters
according to their associations with various diseases, such
as cancer or cardiovascular diseases. Finally, we will include
additional annotations, such as expression patterns (
23), and
conservation during evolution will be included in future updates.
We plan to continuously update TransmiR.
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FUNDING
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State Basic Research Development Program of China (No. 2007CB512100
partially). Funding for open access charge: The State Basic
Research Development Program of China (No. 2007CB512100).
Conflict of interest statement. None declared.
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ACKNOWLEDGEMENTS
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We thank Drs Edmund F. and Rhoda E. Perozzi for English editing
assistance.
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Footnotes
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The authors wish it to be known that, in their opinion, the
first three authors should be regarded as joint First Authors.
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ABSTRACT
INTRODUCTION