In my first article on this topic we delved into what miRNAs are, how they are generated, and their function. Now, we are going to discuss how to identify miRNAs and their targets.
Why do you want to look at something so small anyhow?
miRNAs play a crucial role in most physiological processes. It’s not surprising that defective regulation of miRNAs has been linked to many diseases such as cancers, metabolic syndromes and neurodegenerative disorders.
Bioinformatic analyses suggest that a single miRNA can actually bind up to 200 diverse gene targets. miRNAs combined could potentially regulate expression of one third of all human genes (1). Analysis of miRNA expression in specific cell types could hold huge diagnostic value (see our recent webinar on isolation of circulating biomarkers) and correction of altered miRNA levels in disease states presents promising therapeutic prospects (2).
How can we find new miRNAs and their targets?
A number of strategies have emerged allowing you to identify new animal miRNAs and their targets. These range from small-scale to large-scale genetic, biochemical, and bioinformatics approaches.
You usually carry out a phenotypic suppression screen to look for candidate genes that are able to rescue a miRNA loss-of-function phenotype. This method was used to identify miRNA let-7’s role in negatively regulating lin-41 (3,4).
The basics of suppression screens
You can generate a mutant strain that lacks your miRNA of interest through traditional mutagenesis, or gene deletion, or RNAi. Then, you look for genes that are upregulated (usually your target genes). If you are very lucky, loss of the miRNA will result in measurable phenotypes. To see if your candidate target gene might be the cause, you can knockdown the ‘target upregulated’ gene and see if the phenotype is partially or completely rescued.
These approaches lead to identification of target genes and can reveal physiologically relevant phenotypes. However, it’s difficult to discriminate between direct and indirect targets of miRNAs. miRNA target gene products often work as part of complex biochemical cascades. Because of this, genetic approaches are being replaced by more modern computational and biochemical methods for finding new miRNAs and their targets.
2) Computational methods
Computational (or bioinformatic) methods use complex algorithms that employ a diverse set of criteria for the identification of candidate miRNAs and their targets.
miRNAs are identified from sequence data uploaded or entered by the user. Sequencing data could be derived from a variety of sequencing projects. For instance, in a comparative sequencing project, miRNA expression in tissue samples could be examined with and without a transcription factor silenced by siRNA. Using comparative set-ups, you can get a lot of hints about miRNA pathways and functions.
While bioinformatic approaches can be extremely powerful, they are predictory and usually not based on experimental evidence. Therefore, they carry the risk of false negatives and positives. Most of these algorithms attempt to overcome this by using a set of common experimentally derived conclusions. If you would like to read more about the development of computational methods for miRNA identification, Min and Yoon (2010) provide an excellent review (6). Other reviews on the current status of bioinformatics methods can also be found (7,8).
Let’s look at a two of the publicly available and widely used miRNA databases:
miRBase is managed by the University of Manchester, UK, and is the main online database for miRNA sequences and annotation. Predicted miRNAs are added to the database only if they fulfill certain criteria. At a minimum, all entries contain a predicted hairpin portion of a miRNA transcript. The database provides information on the genomic location and sequence of the mature miRNA. Entries can be searched according to name, keyword, references and annotation, and all retrieved data can be downloaded by the user (5,9).
miRDB is an online database for miRNA target prediction and functional annotation. The miRNA targets have all been predicted using MirTarget, which was developed following the analysis of thousands of miRNA-target interactions derived from high-throughput sequencing experiments. miRDB contains predicted miRNA targets covering five animal species: human, mouse, rat, dog and chicken (10).
3) Biochemical methods
Although biochemical methods to identify miRNAs and their targets can be time-consuming and expensive, they offer certain advantages. These approaches tend to be more sensitive and potentially identify miRNAs and targets that might not be detected by computational methods. Furthermore, biochemical methods are less likely to reveal false positives/negatives. When biochemical approaches are combined with bioinformatic analyses, the outcome can be very powerful!
Nowadays, biochemical approaches to identify miRNAs and their targets involves a combination of 1) immunopurification of RISC complexes and subsequent isolation of the associated mRNAs, and 2) identification of target mRNAs via microarray analysis. The most modern strategies include UV cross-linking and immunopurification coupled to deep sequencing (e.g. CLIP-seq) or high-throughput sequencing, to isolate intact miRNA target sequences within endogenous RNAs. These methods can provide nucleotide-level resolution of the targeted sequences ((11) and references within).
Quantitative PCR (qPCR) and Western blotting.
qPCR and western blotting may be useful in visualising the downstream effects of differential miRNA expression on target transcript and protein levels, respectively. While these approaches don’t discriminate between direct and secondary miRNA targets, they can be useful in indicating miRNA regulation of mRNAs and, thereby, proteins.
Luciferase assays may demonstrate a link between a miRNA and suspected target sequence. The expression of a reporter construct is monitored while altering levels of the miRNA of interest. You can further test for direct miRNA effects by mutating miRNA target sites within the construct and monitoring the luminescence from the reporter gene. However, reporter assays can be time-consuming and are sensitive to subtle alterations in protocol. Small changes should be interpreted with caution.
You should now have an idea of the great potential that lies within understanding miRNAs and their targets! Research in this area has exploded within the last 2 decades. It’s likely that more exciting roles for miRNAs will emerge in the future. New approaches to study miRNAs are developing and evolving all the time and I predict that great advances are on the way in the field of miRNA biology!
So far, it hasn’t been possible to apply the information gleaned from animal miRNA studies to plant miRNA biology. Although not discussed so far in this series, plant miRNAs are extremely important in regulating many processes such as plant development, signal transduction pathways, and protein degradation. Stay tuned for the last, but not least, article in this miniseries to find out more about miRNAs in plants!
Additional useful miRNA resources:
StarBase and Cupid – Special focus on cancerous tissue and associated miRNAs.
miRGen 3.0 – miRNA genomic information and regulation.
Esquela-Kerscher, A, Slack, F.J. (2006). Oncomirs – microRNAs with a role in cancer. Nat Rev Cancer 6:259-
Witkos T.M.,Koscianska E, Krzyzosiak W.J. (2011). Aspects of microRNA Target Prediction. Curr Mol Med. 11(2):93-109.
Garzon R,Calin G.A., Croce C.M. (2009). MicroRNAs in Cancer. Annu Rev Med. 60:167-79.
Slack F.J., Basson M., Liu Z., Ambros V., Horvitz H.R., Ruvkun G. (2000) The lin-41RBCC gene acts in the elegans heterochronic pathway between the let-7regulatory RNA and the LIN-29 transcription factor. Mol. Cell 5:659–669.
Griffiths-Jones S, Saini H.K., van Dongen S, Enright A.J. (2008). miRBase: tools for microRNA genomics.Nucleic Acids Res. 36:D154-D158.
Min Hand Yoon (2010). Got target?: computational methods for microRNA target prediction and their extension. Experimental & Molecular Medicine 42, 233-244.
Hamzeiy H, Allmer J, Yousef M. (2014). Computational methods for microRNA target prediction. Methods Mol. Biol. 1107:201-221.
Hertel J, Langenberger D, Stadler P.F. (2014). Computational prediction of microRNA genes. Methods Mol Biol. 1097:437-56.
Kozomara A, Griffiths-Jones S. 2014. miRBASE: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 42:D68-D73.
Wong N, Wang X. (2015). miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic Acids Res. 43(D1):D146-152.
Clark P.M., Loher P, Quann K, Brody J, Londin E.R, Rigoutsos I. (2014). Argonaute CLIP-Seq reveals miRNA targetome diversity across tissue type. Scientific Reports 4, Article number: 5947.
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