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Cardiomyocytes pp 27–49Cite as

Deep Sequencing of Cardiac MicroRNA-mRNA Interactomes in Clinical and Experimental Cardiomyopathy

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1299))

Abstract

MicroRNAs are a family of short (~21 nucleotide) noncoding RNAs that serve key roles in cellular growth and differentiation and the response of the heart to stress stimuli. As the sequence-specific recognition element of RNA-induced silencing complexes (RISCs), microRNAs bind mRNAs and prevent their translation via mechanisms that may include transcript degradation and/or prevention of ribosome binding. Short microRNA sequences and the ability of microRNAs to bind to mRNA sites having only partial/imperfect sequence complementarity complicate purely computational analyses of microRNA-mRNA interactomes. Furthermore, computational microRNA target prediction programs typically ignore biological context, and therefore the principal determinants of microRNA-mRNA binding: the presence and quantity of each. To address these deficiencies we describe an empirical method, developed via studies of stressed and failing hearts, to determine disease-induced changes in microRNAs, mRNAs, and the mRNAs targeted to the RISC, without cross-linking mRNAs to RISC proteins. Deep sequencing methods are used to determine RNA abundances, delivering unbiased, quantitative RNA data limited only by their annotation in the genome of interest. We describe the laboratory bench steps required to perform these experiments, experimental design strategies to achieve an appropriate number of sequencing reads per biological replicate, and computer-based processing tools and procedures to convert large raw sequencing data files into gene expression measures useful for differential expression analyses.

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References

  1. Lynn FC, Skewes-Cox P, Kosaka Y et al (2007) MicroRNA expression is required for pancreatic islet cell genesis in the mouse. Diabetes 56:2938–2945

    Article  CAS  PubMed  Google Scholar 

  2. Kuehbacher A, Urbich C, Zeiher AM et al (2007) Role of Dicer and Drosha for endothelial microRNA expression and angiogenesis. Circ Res 101:59–68

    Article  CAS  PubMed  Google Scholar 

  3. Chen JF, Murchison EP, Tang R et al (2008) Targeted deletion of Dicer in the heart leads to dilated cardiomyopathy and heart failure. Proc Natl Acad Sci U S A 105:2111–2116

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  4. Melkman-Zehavi T, Oren R, Kredo-Russo S et al (2011) miRNAs control insulin content in pancreatic β-cells via downregulation of transcriptional repressors. EMBO J 30:835–845

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  5. van Rooij E, Olson EN (2007) MicroRNAs: powerful new regulators of heart disease and provocative therapeutic targets. J Clin Invest 117:2369–2376

    Article  PubMed Central  PubMed  Google Scholar 

  6. Liu N, Olson EN (2010) MicroRNA regulatory networks in cardiovascular development. Dev Cell 18:510–525

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  7. Chen CY, Zheng D, Xia Z et al (2009) Ago-TNRC6 triggers microRNA-mediated decay by promoting two deadenylation steps. Nat Struct Mol Biol 16:1160–1166

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  8. Kozomara A, Griffiths-Jones S (2011) miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res 39:D152–D157

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  9. Matkovich SJ, Hu Y, Dorn GW II (2013) Regulation of cardiac microRNAs by cardiac microRNAs. Circ Res 113:62–71

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  10. Matkovich SJ, Zhang Y, Van Booven D et al (2010) Deep mRNA sequencing for in vivo functional analysis of cardiac transcriptional regulators. Application to Gαq. Circ Res 106:1459–1467

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  11. Hu Y, Matkovich SJ, Hecker PA et al (2012) Epitranscriptional orchestration of genetic reprogramming is an emergent property of stress-regulated cardiac microRNAs. Proc Natl Acad Sci U S A 109:19864–19869

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  12. Matkovich SJ, Van Booven DJ, Youker KA et al (2009) Reciprocal regulation of myocardial microRNAs and messenger RNA in human cardiomyopathy and reversal of the microRNA signature by biomechanical support. Circulation 119:1263–1271

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  13. Ikeda S, Kong SW, Lu J et al (2007) Altered microRNA expression in human heart disease. Physiol Genomics 31:367–373

    Article  CAS  PubMed  Google Scholar 

  14. van Rooij E, Sutherland LB, Liu N et al (2006) A signature pattern of stress-responsive microRNAs that can evoke cardiac hypertrophy and heart failure. Proc Natl Acad Sci U S A 103:18255–18260

    Article  PubMed Central  PubMed  Google Scholar 

  15. Putt ME, Hannenhalli S, Lu Y et al (2009) Evidence for coregulation of myocardial gene expression by MEF2 and NFAT in human heart failure. Circ Cardiovasc Genet 2:212–219

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  16. Margulies KB, Matiwala S, Cornejo C et al (2005) Mixed messages: transcription patterns in failing and recovering human myocardium. Circ Res 96:592–599

    Article  CAS  PubMed  Google Scholar 

  17. van Rooij E, Sutherland LB, Thatcher JE et al (2008) Dysregulation of microRNAs after myocardial infarction reveals a role of miR-29 in cardiac fibrosis. Proc Natl Acad Sci U S A 105:13027–13032

    Article  PubMed Central  PubMed  Google Scholar 

  18. Ozsolak F, Milos PM (2011) RNA sequencing: advances, challenges and opportunities. Nat Rev Genet 12:87–98

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  19. Jensen KB, Darnell RB (2008) CLIP: crosslinking and immunoprecipitation of in vivo RNA targets of RNA-binding proteins. Methods Mol Biol 488:85–98

    Article  CAS  PubMed  Google Scholar 

  20. Hafner M, Landthaler M, Burger L et al (2010) PAR-CliP—a method to identify transcriptome-wide the binding sites of RNA binding proteins. J Vis Exp 41:e2034

    Google Scholar 

  21. Hafner M, Landthaler M, Burger L et al (2010) Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell 141:129–141

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  22. Licatalosi DD, Mele A, Fak JJ et al (2008) HITS-CLIP yields genome-wide insights into brain alternative RNA processing. Nature 456:464–469

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  23. Chi SW, Zang JB, Mele A et al (2009) Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature 460:479–486

    PubMed Central  CAS  PubMed  Google Scholar 

  24. Karginov FV, Conaco C, Xuan Z et al (2007) A biochemical approach to identifying microRNA targets. Proc Natl Acad Sci U S A 104:19291–19296

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  25. Matkovich SJ, Hu Y, Eschenbacher WH et al (2012) Direct and indirect involvement of microRNA-499 in clinical and experimental cardiomyopathy. Circ Res 111:521–531

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  26. Dorn GW II, Matkovich SJ, Eschenbacher WH et al (2012) A human 3′ miR-499 mutation alters cardiac mRNA targeting and function. Circ Res 110:958–967

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  27. Buermans HP, Ariyurek Y, van Ommen G et al (2010) New methods for next generation sequencing based microRNA expression profiling. BMC Genomics 11:716

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  28. An J, Lai J, Lehman ML et al (2013) miRDeep*: an integrated application tool for miRNA identification from RNA sequencing data. Nucleic Acids Res 41:727–737

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  29. Langmead B, Trapnell C, Pop M et al (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10:R25

    Article  PubMed Central  PubMed  Google Scholar 

  30. Trapnell C, Pachter L, Salzberg SL (2009) TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25:1105–1111

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  31. Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11:R106

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  32. Matkovich SJ, Van Booven DJ, Eschenbacher WH et al (2011) RISC RNA sequencing for context-specific identification of in vivo microRNA targets. Circ Res 108:18–26

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  33. Mortazavi A, Williams BA, McCue K et al (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5:621–628

    Article  CAS  PubMed  Google Scholar 

  34. Toung JM, Morley M, Li M et al (2011) RNA-sequence analysis of human B-cells. Genome Res 21:991–998

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  35. Labaj PP, Leparc GG, Linggi BE et al (2011) Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling. Bioinformatics 27:i383–i391

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  36. Trapnell C, Roberts A, Goff L et al (2012) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 7:562–578

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  37. Bullard JH, Purdom E, Hansen KD et al (2010) Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics 11:94

    Article  PubMed Central  PubMed  Google Scholar 

  38. Oshlack A, Wakefield MJ (2009) Transcript length bias in RNA-seq data confounds systems biology. Biol Direct 4:14

    Article  PubMed Central  PubMed  Google Scholar 

  39. Trapnell C, Williams BA, Pertea G et al (2010) Transcript assembly and abundance estimation from RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 28:511–515

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  40. Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139–140

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  41. Anders S, Reyes A, Huber W (2012) Detecting differential usage of exons from RNA-seq data. Genome Res 22:2008–2017

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  42. Dillies MA, Rau A, Aubert J et al (2013) A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform 14:671–683

    Article  CAS  PubMed  Google Scholar 

  43. Kim YK, Yeo J, Kim B et al (2012) Short structured RNAs with low GC content are selectively lost during extraction from a small number of cells. Mol Cell 46:893–895

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

Related work in the authors’ laboratories is supported by the NIH-sponsored Diabetes Research Center at Washington University, grant 5 P30 DK020579 (to S.J.M.) and NIH grant R01 HL108943 (to G.W.D.).

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Authors and Affiliations

  1. Center for Pharmacogenomics, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA

    Scot J. Matkovich Ph.D. & Gerald W. Dorn II

Authors
  1. Scot J. Matkovich Ph.D.
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  2. Gerald W. Dorn II
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Corresponding author

Correspondence to Scot J. Matkovich Ph.D. .

Editor information

Editors and Affiliations

  1. Rochester Institute of Technology, Rochester, New York, USA

    Gary R. Skuse

  2. Rochester Institute of Technology, Rochester, New York, USA

    Maureen C. Ferran

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Matkovich, S.J., Dorn, G.W. (2015). Deep Sequencing of Cardiac MicroRNA-mRNA Interactomes in Clinical and Experimental Cardiomyopathy. In: Skuse, G., Ferran, M. (eds) Cardiomyocytes. Methods in Molecular Biology, vol 1299. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2572-8_3

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  • DOI: https://doi.org/10.1007/978-1-4939-2572-8_3

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-2571-1

  • Online ISBN: 978-1-4939-2572-8

  • eBook Packages: Springer Protocols

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