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Knowledge-based analysis of functional impacts of mutations in microRNA seed regions

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Abstract

MicroRNAs are a class of important post-transcriptional regulators. Genetic and somatic mutations in miRNAs, especially those in the seed regions, have profound and broad impacts on gene expression and physiological and pathological processes. Over 500 SNPs were mapped to the miRNA seeds, which are located at position 2–8 of the mature miRNA sequences. We found that the central positions of the miRNA seeds contain fewer genetic variants and therefore are more evolutionary conserved than the peripheral positions in the seeds. We developed a knowledge-based method to analyse the functional impacts of mutations in miRNA seed regions. We computed the gene ontology-based similarity score GOSS and the GOSS percentile score for all 517 SNPs in miRNA seeds. In addition to the annotation of SNPs for their functional effects, in the present article we also present a detailed analysis pipeline for finding the key functional changes for seed SNPs. We performed a detailed gene ontology graph-based analysis of enriched functional categories for miRNA target gene sets. In the analysis of a SNP in the seed region of hsa-miR-96 we found that two key biological processes for progressive hearing loss ‘Neurotrophin TRK receptor signaling pathway’ and ‘Epidermal growth factor receptor signaling pathway’ were significantly and differentially enriched by the two sets of allele-specific target genes of miRNA hsa-miR-96.

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References

  • Bao L et al. 2007 PolymiRTS database: linking polymorphisms in microRNA target sites with complex traits. Nucleic Acids Res. 35 D51–D54

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Bartel DP 2004 MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116 281–297

    Article  CAS  PubMed  Google Scholar 

  • Bhattacharya A, Ziebarth JD and Cui Y 2013 SomamiR: a database for somatic mutations impacting microRNA function in cancer. Nucleic Acids Res. 41 D977–D982

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Bhattacharya A, Ziebarth JD and Cui Y 2014 PolymiRTS Database 3.0: linking polymorphisms in microRNAs and their target sites with human diseases and biological pathways. Nucleic Acids Res. 42 D86–D91

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Enright AJ et al. 2003 MicroRNA targets in drosophila. Genome Biol. 5 R1

    Article  PubMed Central  PubMed  Google Scholar 

  • Furness DN et al. 2013 Progressive hearing loss and gradual deterioration of sensory hair bundles in the ears of mice lacking the actin-binding protein Eps8L2. Proc. Natl. Acad. Sci. USA 110 13898–13903

  • Hill CG et al. 2014 Functional and evolutionary significance of human MicroRNA seed region mutations. PLoS One 9 e115241

    Article  PubMed Central  PubMed  Google Scholar 

  • Iliff BW, Riazuddin SA and Gottsch JD 2012 A single-base substitution in the seed region of miR-184 causes EDICT syndrome. Invest. Ophthalmol. Vis. Sci. 53 348–353

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Jevsinek Skok D et al. 2013 Genome-wide in silico screening for microRNA genetic variability in livestock species. Anim. Genet. 44 669–677

    Article  CAS  PubMed  Google Scholar 

  • Karolchik D et al. 2004 The UCSC Table Browser data retrieval tool. Nucleic Acids Res. 32 D493–D496

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Kozomara A and Griffiths-Jones S 2014 miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 42 D68–D73

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Lewis BP, Burge CB and Bartel DP 2005 Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are MicroRNA targets. Cell 120 15–20

    Article  CAS  PubMed  Google Scholar 

  • Mencia A et al. 2009 Mutations in the seed region of human miR-96 are responsible for nonsyndromic progressive hearing loss. Nat. Genet. 41 609–613

    Article  CAS  PubMed  Google Scholar 

  • Peterson SM et al. 2014 Common features of microRNA target prediction tools. Front. Genet. 5 23

    Article  PubMed Central  PubMed  Google Scholar 

  • Reimand J, Arak T and Vilo J 2011 g:Profiler--a web server for functional interpretation of gene lists (2011 update). Nucleic Acids Res. 39 W307–W315

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Reimand J et al. 2007 g:Profiler--a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res. 35 W193–W200

    Article  PubMed Central  PubMed  Google Scholar 

  • Sato T et al. 2006 Progressive hearing loss in mice carrying a mutation in the p75 gene. Brain Res. 1091 224–234

    Article  CAS  PubMed  Google Scholar 

  • Schroder MS et al. 2013 RamiGO: an R/Bioconductor package providing an AmiGO visualize interface. Bioinformatics 29 666–668

    Article  PubMed Central  PubMed  Google Scholar 

  • Sherry ST et al. 2001 dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29 308–311

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Siomi H and Siomi MC 2010 Posttranscriptional regulation of microRNA biogenesis in animals. Mol. Cell. 38 323–332

    Article  CAS  PubMed  Google Scholar 

  • Wang JZ et al. 2007 A new method to measure the semantic similarity of GO terms. Bioinformatics 23 1274–1281

    Article  CAS  PubMed  Google Scholar 

  • Wheeler BM et al. 2009 The deep evolution of metazoan microRNAs. Evol. Dev. 11 50–68

    Article  CAS  PubMed  Google Scholar 

  • Yu G et al. 2010 GOSemSim: an R package for measuring semantic similarity among GO terms and gene products. Bioinformatics 26 976–978

    Article  CAS  PubMed  Google Scholar 

  • Ziebarth JD et al 2012 PolymiRTS Database 2.0: linking polymorphisms in microRNA target sites with human diseases and complex traits. Nucleic Acids Res. 40 D216–D221

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Acknowledgements

The authors gratefully acknowledge the editorial assistance of Dr Martha M. Howe provided through the Writing Assistance Program of the College of Graduate Health Sciences. This work was partly supported by The University of Tennessee Center for Integrative and Translational Genomics.

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Correspondence to Anindya Bhattacharya or Yan Cui.

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[Bhattacharya A and Cui Y 2015 Knowledge-based analysis of functional impacts of mutations in microRNA seed regions. J. Biosci.] DOI 10.1007/s12038-015-9560-2

Supplementary materials pertaining to this article are available on the Journal of Biosciences Website at http://www.ias.ac.in/jbiosci/oct2015/supp/Bhattacharya.pdf

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Bhattacharya, A., Cui, Y. Knowledge-based analysis of functional impacts of mutations in microRNA seed regions. J Biosci 40, 791–798 (2015). https://doi.org/10.1007/s12038-015-9560-2

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