Experimental validation of miRNA targets
Introduction
MicroRNAs (miRNAs) are a family of small nonprotein coding RNAs that have emerged as important regulators of gene expression [1], [2], [3]. Currently 474 human miRNAs have been characterized [4]; however, recent reports estimate that the human genome harbors ∼1000 miRNA genes [5], [6]. MiRNAs are expressed as long hairpin-forming precursor RNAs that get cleaved into partially double-stranded RNAs that are further processed into mature miRNAs (∼22 nucleotides) [reviewed in 2]. Mature miRNAs recognize their target mRNAs by base-pairing interactions between nucleotides 2–8 of the miRNA (the seed region) and complementary nucleotides in the 3′-untranslated region (3′-UTR) of mRNAs. MiRNAs inhibit gene expression by targeting mRNAs for translational repression or cleavage [7], [8], [9]. Each miRNA has hundreds of evolutionarily conserved targets and several times that number of non-conserved targets [5]. It is currently estimated that 30% of all human genes may be regulated by miRNAs [10].
Identification of miRNA target genes has been a great challenge. Computational algorithms have been the major driving force in predicting miRNA targets [11], [12], [13], [14]. These approaches are mainly focused on programming alignment to identify complementary elements in the 3′-UTR with the seed sequence of the miRNA and the phylogenetic conservation of the complementary sequences in the 3′-UTRs of orthologous genes. However, evidence suggests that perfect seed pairing may not necessarily be a reliable predictor for miRNA interactions [15], which may explain why some predicted target sites are nonfunctional. Hence, with few exceptions, most physiologic, and clinically relevant, targets for miRNAs remain to be identified or verified experimentally.
Currently there is no clear consensus as to what criteria should be followed to determine miRNA targets and to confirm their biological efficacy. Therefore, one goal of this review is to establish a set of guidelines that investigators can follow to validate that a given miRNA regulates a specific mRNA target. This review will also discuss a number of experimental procedures that can be utilized to confirm miRNA targets. Based on our previous experience with miRNAs [21], [22], we propose a working scheme (Fig. 1) to be followed in order to reduce the number of bioinformatically predicted miRNA binding sites and to expedite the biological validation of the sites of interest. We also advocate that after miRNA/mRNA interactions have been demonstrated, at least three additional criteria should be met before confirmation of a given mRNA target is complete as shown in Fig. 1. Although all of these criteria might not be met under all conditions, it is advisable that as many be achieved as possible.
Section snippets
Bioinformatic prediction of miRNA targets
To begin to investigate a predetermined gene as a target of miRNA regulation, individual gene sequences (i.e. 3′-UTR mRNA sequences) may be analyzed by various computational algorithms which utilize distinct parameters to predict the probability of a functional miRNA binding site within a given mRNA target. Due to performance values (i.e. sensitivity and specificity in target prediction) [30], we suggest that the following three bioinformatic algorithms be utilized to predict miRNA target
Concluding remarks
A growing body of evidence suggests that miRNAs are important regulators of cell growth [39], differentiation [40], and apoptosis [41]. In support of the significance of miRNAs in normal development and physiology, recent mouse miRNA “knockout” studies demonstrated that depending upon which miRNA gene was deleted, mice were left immuno-deficient [42], [43] or with heart defects [24], [44]. Implications drawn from these studies support the hypothesis that dysregulation of miRNA function may lead
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