A study of miRNAs targets prediction and experimental validation
Protein Cell
A study of miRNAs targets prediction and experimental validation
Yong Huang 0 2
Quan Zou 1
Haitai Song 0 2
Fei Song 0 2
Ligang Wang 0 2
Guozheng Zhang 0 2
Xingjia Shen 0 2
0 The Key Laboratory of Silkworm and Mulberry genetic Improvement, Ministry of Agriculture, Sericultural Research Institute, Chinese Academy of Agricultural Sciences , Zhejiang 212018 , China
1 School of information Science and Technology of Xiamen University , Xiamen 361005 , China
2 Jiang Su University of Science and Technology , Zhenjiang 212018 , China
microRNAs (miRNAs) are 20-24 nucleotide (nt) RNAs that regulate eukaryotic gene expression post-transcriptionally by the degradation or translational inhibition of their target messenger RNAs (mRNAs). To identify miRNA target genes will help a lot by understanding their biological functions. Sophisticated computational approaches for miRNA target prediction, and effective biological techniques for validating these targets now play a central role in elucidating their functions. Owing to the imperfect complementarity of animal miRNAs with their targets, it is difficult to judge the accuracy of the prediction. Complexity of regulation by miRNA-mediated targets at protein and mRNAs levels has made it more challenging to identify the targets. To date, only a few miRNAs targets are confirmed. In this article, we review the methods of miRNA target prediction and the experimental validation for their corresponding mRNA targets in animals.
microRNA; computational prediction; target; experimental validation
INTRODUCTION
The miRNAs are a widely distributed class of small
noncoding RNAs that play an integral role in gene regulation
(Elbashir et al., 2001; Hutvágner and Zamore, 2002)
. miRNA
biogenesis pathway in animals can be divided into two steps
(Fig.1). Initially, miRNAs are transcribed by RNA polymerase
II as primary miRNAs (pri-miRNAs) with hundreds to
thousands of nucleotides in length
(Cai et al., 2004; Lee
et al., 2004; Trujillo et al., 2010)
. Ribonuclease III (RNase III)
enzyme Drosha cleaves the flanks of pri-miRNAs to liberate
~70 nucleotide stem-loop structures, called precursor miRNAs
(pre-miRNAs). Pre-miRNA hairpins are exported from the
nucleus by Exportin-5
(Lee et al., 2003; Engels and
Hutvagner, 2006; Flynt and Lai, 2008)
. In the cytoplasm, the
pre-miRNAs are processed into ~22 nucleotide duplex
miRNAs (miR/miR*) by the RNase III enzyme Dicer
(Kim,
2004; Lund et al., 2004; Engels and Hutvagner, 2006)
. Next,
one strand of the miRNA duplex is loaded into the RISC
(RNA-induced silencing complex) to bind the mRNA target. If
the complementarities between the 3′-UTR mRNA and the
miRNA are extensive, the target mRNA is degraded;
whereas, if the complementarities are partial, the translation
of the target mRNA is repressed
(Brennecke et al., 2005)
.
Recent studies have shown that many miRNAs are
involved in a variety of biological processes, such as
transcriptional gene regulatory network, developmental
timing, neuronal synapses formation, cell proliferation, cell
death, viral infection, differentiation and tumor metastasis
(Sarnow et al., 2006; Hwang and Mendell, 2007; Ma et al.,
2007; Bartel, 2009; Nachmani et al., 2009; Xiao and
Rajewsky, 2009)
. Currently, more than 10,000 miRNAs have
been identified in the miRBase database (http://www.
mirbase.org/). Thus, the development of precise and fast
assays for miRNA target identification and verification will
play a significant role in the study of miRNA functions and the
biological processes in which they are involved. Several
effective algorithms have been developed for the prediction of
miRNAs targets in animals. In this review, we summarize the
prediction methods of miRNAs targets and the experimental
approaches that have been described for identification of their
targets.
For miRNAs in animals, the target prediction is more complex
because few miRNAs are perfectly complementary to their
targets. In the following only animal miRNAs are considered.
Principles of miRNA target recognition
The function of a miRNA is ultimately defined by its targets
and the effects it has on their expression. Although the
detailed target recognition mechanism is still elusive, the
consensus suggests that the base pairing of miRNA with its
target mRNA is the key. Differences in target
complementarities and target location within the mRNA could be related to
the silencing mechanism used. The prediction criteria include
the following:
1) The miRNA sequence is complementary to the 3′-UTR
sequence of potential target mRNAs. Especially, the strong
binding of the 5′ end (the first eight base pairs) of the mature
miRNA to the 3′-UTR sequence is very important for targeting,
whereas the G:U wobble pairing reduces the silencing
efficiency
(Brodersen and Voinnet, 2009)
. For example,
there are three types of target sites: 5′-dominant canonical,
5′-dominant seed only and 3′-compensatory (Fig. 2). They
differ in the level of comp (...truncated)