Abstract
MicroRNAs are 18–22 bp long non-coding sequences and play a critical role in diverse biological processes, through modulation of gene expression at the post-transcriptional level by binding at the 3′-untranslated region of target mRNA. Consequent upon the discovery of structural and functional features of miRNA targeting, several molecular methods have been developed to identify miRNA targets. However, these methods suffer several drawbacks, including technical challenges, requirement of high cell volumes, inability to differentiate between direct and indirect targets, cell/tissue as well as experimental-specificity and imprecise binding site information. Alternatively in silico approach enables the exploration of the potential miRNA-mRNA pairs to investigate signature miRNA and proteins involved in the signaling of various diseases. Here, we describe micronome-based standard method for identification of miRNA-mRNA pairs as well as validation of key regulator miRNA.
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References
Farazi TA, Hoell JI, Morozov P, Tuschl T (2013) MicroRNAs in human cancer. In: MicroRNA cancer regulation. Springer, Dordrecht, p 1–20
Liu Z, Sall A, Yang D (2008) MicroRNA: an emerging therapeutic target and intervention tool. Int J Mol Sci 9(6):978–999
Drakaki A, Iliopoulos D (2009) MicroRNA gene networks in oncogenesis. Curr Genomics 10(1):35–41
Chi SW, Zang JB, Mele A, Darnell RB (2009) Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature 460:479–486
Murakami Y, Tanahashi T, Okada R, Toyoda H, Kumada T, Enomoto M et al (2014) Comparison of hepatocellular carcinoma miRNA expression profiling as evaluated by next generation sequencing and microarray. PLoS One 9:e106314 1-9
Kelly AD, Hill KE, Correll M, Hu L, Wang YE, Rubio R et al (2013) Next-generation sequencing and microarray-based interrogation of microRNAs from formalin-fixed, paraffin-embedded tissue: preliminary assessment of cross-platform concordance. Genomics 1:8–14
Yoon S, De Micheli G (2006) Computational identification of microRNAs and their targets. Birth Defects Res C Embryo Today 78:118–128
Bar M, Wyman SK, Fritz BR, Qi J, Garg KS, Parkin RK et al (2008) MicroRNA discovery and profiling in human embryonic stem cells by deep sequencing of small RNA libraries. Stem Cells 26:2496–2505
Li L, Xu J, Yang D, Tan X, Wang H (2010) Computational approaches for microRNA studies: a review. Mamm Genome 21:1–12
Akhtar MM, Micolucci L, Islam MS, Olivieri F, Procopio AD (2016) Bioinformatic tools for microRNA dissection. Nucleic Acids Res 44:24–44
Christopher AF, Kaur RP, Kaur G, Kaur A, Gupta V, Bansal P (2016) MicroRNA therapeutics: discovering novel targets and developing specific therapy. Perspect Clin Res 7(2):68–74
Christopher AF, Gupta M, Bansal P (2016) Micronome revealed miR-19a/b as key regulator of SOCS3 during cancer related inflammation of oral squamous cell carcinoma. Gene 594(1):30–40
Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Cepas JH et al (2015) STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43:D447–D452
Hsu SD, Lin FM, Wu WC, Liang C, Huang WC, Chan WL et al (2011) miRTarBase: a database curates experimentally validated microRNA–target interactions. Nucleic Acids Res 39:D163–D169
Vergoulis T, Vlachos IS, George PA, Maragkakis GM, Reczko M, Gerangelos S et al (2012) TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support. Nucleic Acids Res 40:D222–D229
Xiao F, Zuo Z, Cai G, Kang S, Gao X, Li T et al (2009) miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res 37:D105–D110
Jiang Q, Wang Y, Hao Y, Juan L, Teng M, Zhang X et al (2009) miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res 37:D98–D104
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ranage D et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504
Agarwal V, Bell GW, Nam J, Bartel DP (2015) Predicting effective microRNA target sites in mammalian mRNAs. E Life 4:e05005 1-38
Shirdel EA, Xie W, Mak TW, Jurisica I (2011) Navigating the micronome–using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs. PLoS One 6:e17429 1-17
Assenov Y, Ramírez F, Schelhorn SE, Lengauer T, Albrecht M (2008) Computing topological parameters of biological networks. Bioinformatics 24:282–284
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Bansal, P., Kumar, A., Chandna, S., Arora, M., Bansal, R. (2018). Targeting miRNA for Therapeutics Using a Micronome Based Method for Identification of miRNA-mRNA Pairs and Validation of Key Regulator miRNA. In: Ørom, U. (eds) miRNA Biogenesis. Methods in Molecular Biology, vol 1823. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-8624-8_14
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DOI: https://doi.org/10.1007/978-1-4939-8624-8_14
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