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Top1. Introduction
The expression of protein-coding genes is regulated via a variety of mechanisms. While regulation of gene transcription is central to the cellular metabolism, post-transcriptional control of gene expression has also gained significant attention in the recent years. Post-transcriptional processes like translational regulation and mRNA turnover are fairly complex mechanisms and are being recognized as equally if not more sophisticated than the transcriptional regulation of gene expression.
Several cis-acting signals are known to regulate mRNA stability. In the 3’ untranslated regions, AU-rich elements (AREs) play dominant roles in the regulated decay of short-lived human mRNAs via specific interactions with proteins. While some of these proteins promote ARE-mediated mRNA degradation (AMD) (Stoecklin et al., 2002), others delay it (Peng, Chen, Xu, & Shyu, 1998). The mRNAs containing AREs generally code for proteins that are involved in transient processes such as cellular proliferation, stress response or development, which require delicate but strict regulation. The expression of these genes requires stringent and prompt controls which can be best achieved at the post-transcriptional level (Barreau, Paillard, & Osborne, 2005).
The post-transcriptional expression of a large number of metazoan genes is also influenced by microRNAs (miRNAs) which have received significant attention recently as important regulators of translation and destabilization of mRNAs. For reviews, see Bushati and Cohen (2007), Filipowicz, Bhattacharyya, and Sonenberg (2008), Bartel (2009), and Zhang and Su (2009). The impact on gene expression is mediated by annealing of the miRNAs to complementary segments of the targeted mRNAs, especially in the 3’ untranslated regions (UTRs). Approximately 30-50% of the human protein-coding genes appear to be regulated by miRNAs (John et al., 2004; John, Sander, & Marks, 2006; Filipowicz, Bhattacharyya, & Sonenberg, 2008; Friedman, Farh, Burge, & Bartel, 2009). Since miRNAs are considered to be involved in almost all types of cellular processes, an alteration in their own expression could lead to human diseases (Esquela-Kerscher & Slack, 2006; Kloosterman & Plasterk, 2006; Chang & Mendell, 2007; Rodriguez et al., 2007). Therefore, investigating the mechanism of miRNA-mediated regulation of gene expression is critical to the understanding of cellular function and the diseases associated with altered miRNA function.
Several studies in the recent years have suggested that miRNAs can regulate post-transcriptional gene expression by targeting ARE-mediated mRNA turnover (reviewed in von Roretz & Gallouzi, 2008; Asirvatham, Magner, & Tomasi, 2009). For example, expression of cytokine genes could be regulated via miRNA targeting of proteins involved in AMD (Asirvatham, Gregorie, Hu, Magner, & Tomasi, 2008). Although more than 3,000 human genes are predicted to contain AREs (Bakheet, Williams, & Khabar, 2006), what proportion of these cis-acting elements require interactions with miRNAs or miRNPs for mRNA destabilization is not clear.
Owing to a lack of full length complementarity between the ~22 nucleotide miRNAs and their corresponding target sites in the animal mRNAs, accurate prediction of miRNA binding sites has been challenging, especially when attempted at a genomic scale. Prediction of miRNA targeted AREs in the 3’UTR is even trickier because the ARE-complementary region may not be in the 5’-region of the miRNA. It has been widely accepted that successful target recognition in animal mRNAs is dependent on pairing with bases in the 5’-region of the miRNAs (Bartel, 2004; Brennecke, Stark, Russell, & Cohen, 2005; Mendes, Freitas, & Sagot, 2009). This principle has been adopted by several miRNA target prediction tools.