In-Silico Algorithms for the Screening of Possible microRNA Binding Sites and Their Interactions

Scan for Motifs: a webserver for the analysis of post-transcriptional regulatory elements in the 3' untranslated regions (3' UTRs) of mRNAs

Background

Gene expression in vertebrate cells may be controlled post-transcriptionally through regulatory elements in mRNAs. These are usually located in the untranslated regions (UTRs) of mRNA sequences, particularly the 3′UTRs.

Results

Scan for Motifs (SFM) simplifies the process of identifying a wide range of regulatory elements on alignments of vertebrate 3′UTRs. SFM includes identification of both RNA Binding Protein (RBP) sites and targets of miRNAs. In addition to searching pre-computed alignments, the tool provides users the flexibility to search their own sequences or alignments. The regulatory elements may be filtered by expected value cutoffs and are cross-referenced back to their respective sources and literature. The output is an interactive graphical representation, highlighting potential regulatory elements and overlaps between them. The output also provides simple statistics and links to related resources for complementary analyses. The overall process is intuitive and fast. As SFM is a free web-application, the user does not need to install any software or databases.

Conclusions

Visualisation of the binding sites of different classes of effectors that bind to 3′UTRs will facilitate the study of regulatory elements in 3′ UTRs.

Variations in mismatch repair genes and colorectal cancer risk and clinical outcome

DNA mismatch repair (MMR) deficiency is one of the best understood forms of genetic instability in colorectal cancer (CRC). CRC is routinely cured by 5-fluorouracil (5-FU)-based chemotherapy, with a prognostic effect and resistance to such therapy conferred by MMR status. In this study, we aimed to analyse the effect of genetic variants in classical coding regions or in less-explored predicted microRNA (miRNA)-binding sites in the 3' untranslated region (3'UTR) of MMR genes on the risk of CRC, prognosis and the efficacy of 5-FU therapy. Four single nucleotide polymorphisms (SNPs) in MMR genes were initially tested for susceptibility to CRC in a case-control study (1095 cases and 1469 healthy controls). Subsequently, the same SNPs were analysed for their role in survival on a subset of patients with complete follow-up. Two SNPs in MLH3 and MSH6 were associated with clinical outcome. Among cases with colon and sigmoideum cancer, carriers of the CC genotype of rs108621 in the 3'UTR of MLH3 showed a significantly increased survival compared to those with the CT + TT genotype (log-rank test, P = 0.05). Moreover, this polymorphism was also associated with an increased risk of relapse or metastasis in patients with heterozygous genotype (log-rank test, P = 0.03). Patients carrying the CC genotype for MSH6 rs1800935 (D180D) and not undergoing 5-FU-based chemotherapy showed a decreased number of recurrences (log-rank test, P = 0.03). No association with CRC risk was observed. We provide the first evidence that variations in potential miRNA target-binding sites in the 3'UTR of MMR genes may contribute to modulate CRC prognosis and predictivity of therapy.

Common features of microRNA target prediction tools

The human genome encodes for over 1800 microRNAs (miRNAs), which are short non-coding RNA molecules that function to regulate gene expression post-transcriptionally. Due to the potential for one miRNA to target multiple gene transcripts, miRNAs are recognized as a major mechanism to regulate gene expression and mRNA translation. Computational prediction of miRNA targets is a critical initial step in identifying miRNA:mRNA target interactions for experimental validation. The available tools for miRNA target prediction encompass a range of different computational approaches, from the modeling of physical interactions to the incorporation of machine learning. This review provides an overview of the major computational approaches to miRNA target prediction. Our discussion highlights three tools for their ease of use, reliance on relatively updated versions of miRBase, and range of capabilities, and these are DIANA-microT-CDS, miRanda-mirSVR, and TargetScan. In comparison across all miRNA target prediction tools, four main aspects of the miRNA:mRNA target interaction emerge as common features on which most target prediction is based: seed match, conservation, free energy, and site accessibility. This review explains these features and identifies how they are incorporated into currently available target prediction tools. MiRNA target prediction is a dynamic field with increasing attention on development of new analysis tools. This review attempts to provide a comprehensive assessment of these tools in a manner that is accessible across disciplines. Understanding the basis of these prediction methodologies will aid in user selection of the appropriate tools and interpretation of the tool output.

miRWalk database for miRNA-target interactions

miRWalk (http://mirwalk.uni-hd.de/) is a publicly available comprehensive resource, hosting the predicted as well as the experimentally validated microRNA (miRNA)-target interaction pairs. This database allows obtaining the possible miRNA-binding site predictions within the complete sequence of all known genes of three genomes (human, mouse, and rat). Moreover, it also integrates many novel features such as a comparative platform of miRNA-binding sites resulting from ten different prediction datasets, a holistic view of genetic networks of miRNA-gene pathway, and miRNA-gene-Online Mendelian Inheritance in Man disorder interactions, and unique experimentally validated information (e.g., cell lines, diseases, miRNA processing proteins). In this chapter, we describe a schematic workflow on how one can access the stored information from miRWalk and subsequently summarize its applications.

A schematic workflow for collecting information about the interaction between copy number variants and microRNAs using existing resources

MicroRNAs (miRNAs) and copy number variations (CNVs) are two extensively studied genomic components in the field of modern biology-as they have been found to be associated with many disorders such as cancer, Alzheimer, pancreatitis, HIV susceptibility, beta-thalassemia, and glomerulonephritis. Several studies suggested that an alteration in CNV-miRNA interaction could result in some human diseases such as cancer. Therefore, the possible miRNA-binding site information within the CNV genes opens new avenues in understanding such disorders. In this chapter, we present a schematic approach for collecting the information on CNV-miRNA interactions using miRWalk and TargetScan databases.