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Detecting MicroRNA Signatures Using Gene Expression Analysis

  • Stijn van Dongen
  • Anton J. Enright

Abstract

Small RNAs such as microRNAs (miRNAs) have been shown to play important roles in genetic regulation of plants and animals. In particular, the miRNAs of animals are capable of downregulating large numbers of genes by binding to and repressing target genes. Although large numbers of miRNAs have been cloned and sequenced, methods for analyzing their targets are far from perfect. Methods exist that can predict the likely binding sites of miRNAs in target transcripts using sequence alignment, thermodynamics or machine learning approaches. It has been widely illustrated that such de novo computational approaches suffer from high false-positive and false-negative error rates. In particular these approaches do not take into account expression information regarding the miRNA or its target transcript. In this chapter we describe the use of miRNA seed enrichment analysis approaches to this problem. In cases where gene or protein expression data are available, it is possible to detect the signature of miRNA binding events by looking for enrichment of microRNA seed binding motifs in sorted gene lists. In this chapter we introduce the concept of miRNA target analysis, the background to motif enrichment analysis, and a number of programs designed for this purpose. We focus on the Sylamer algorithm for miRNA seed enrichment analysis and its applications for miRNA target discovery with examples from real biological datasets.

Keywords

Gene Ontology Gene List miRNA Target Seed Region Hypergeometric Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Abbreviations

ARE

AU-rich element

BDGP

Berkeley Drosophila genome project

BSED

binding site enrichment detection

CAGE

cap analysis gene expression

DNA

deoxyribonucleic acid

DRIM

discovery of rank-imbalanced motifs

ENU

N-ethyl-N-nitrosourea

EST

expressed sequence tag

GC

Granger causality

GFP

green fluorescent protein

GO

gene ontology

GSEA

gene set enrichment analysis

HeLa

Henrietta Lacks

HiTS-CLIP

highthroughput sequencing with crosslinking and immunoprecipitation

IUPAC

International Union of Pure and Applied Chemistry

KS

Kolmogorov–Smirnov

MEME

multiple expectation maximization for motif elicitation

PSSM

position-specific scoring matrix

PSWM

position-specific weight matrix

REDUCE

regulatory element detection using correlation with expression

RNA

ribonucleic acid

RNAi

RNA interference

RSAT

regulatory sequence analysis tools

Th1

T-helper cell

UTR

untranslated regions

cDNA

complementary DNA

dmdo

diminuendo

log

logistic regression

mRNA

messenger RNA

miRNA

microRNA

pSILAC

protein stable labeling by amino acids in cell culture

par-CLIP

photoactivatable ribunocleoside enhanced-CLIP

siRNA

small interfering RNA

References

  1. 9.1.
    A. Kozomara, S. Griffiths-Jones: miRBase: Integrating microRNA annotation and deep-sequencing data, Nucleic Acids Res. 39, D152–D157 (2011)CrossRefGoogle Scholar
  2. 9.2.
    P. Sethupathy, B. Corda, A.G. Hatzigeorgiou: TarBase: A comprehensive database of experimentally supported animal microRNA targets, RNA 12, 192–197 (2006)CrossRefGoogle Scholar
  3. 9.3.
    S.W. Chi, J.B. Zang, A. Mele, R.B. Darnell: Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps, Nature 460, 479–486 (2009)Google Scholar
  4. 9.4.
    M. Hafner, M. Landthaler, L. Burger, M. Khorshid, J. Hausser, P. Berninger, A. Rothballer, M. Ascano, A.C. Jungkamp, M. Munschauer, A. Ulrich, G.S. Wardle, S. Dewell, M. Zavolan, T. Tuschl: PAR-CliP – A method to identify transcriptome-wide the binding sites of RNA binding proteins, J. Vis Exp. 41, e2034 (2010), video articleGoogle Scholar
  5. 9.5.
    R.C. Lee, V. Ambros: An extensive class of small RNAs in Caenorhabditis elegans, Science 294, 862–864 (2001)CrossRefGoogle Scholar
  6. 9.6.
    A.J. Enright, B. John, U. Gaul, T. Tuschl, C. Sander, D.S. Marks: MicroRNA targets in Drosophila, Genome Biol. 5, R1 (2003)CrossRefGoogle Scholar
  7. 9.7.
    B.P. Lewis, I.-H. Shih, M.W. Jones-Rhoades, D.P. Bartel, C.B. Burge: Prediction of mammalian microRNA targets, Cell 115, 787–798 (2003)CrossRefGoogle Scholar
  8. 9.8.
    A. Krek, D. Grün, M.N. Poy, R. Wolf, L. Rosenberg, E.J. Epstein, P. MacMenamin, I. da Piedade, K.C. Gunsalus, M. Stoffel, N. Rajewsky: Combinatorial microRNA target predictions, Nat. Genet. 37, 495–500 (2005)CrossRefGoogle Scholar
  9. 9.9.
    J. Brennecke, D.R. Hipfner, A. Stark, R.B. Russell, S.M. Cohen: Bantam encodes a developmentally regulated microRNA that controls cell proliferation and regulates the proapoptotic gene hid in Drosophila, Cell 113, 25–36 (2003)CrossRefGoogle Scholar
  10. 9.10.
    B. John, A.J. Enright, A. Aravin, T. Tuschl, C. Sander, D.S. Marks: Human MicroRNA targets, PLoS Biol. 2, e363 (2004)CrossRefGoogle Scholar
  11. 9.11.
    P. Mazière, A.J. Enright: Prediction of microRNA targets, Drug Discov. Today 12, 452–458 (2007)CrossRefGoogle Scholar
  12. 9.12.
    B.J. Reinhart, F.J. Slack, M. Basson, A.E. Pasquinelli, J.C. Bettinger, A.E. Rougvie, H.R. Horvitz, G. Ruvkun: The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans, Nature 403, 901–906 (2000)CrossRefGoogle Scholar
  13. 9.13.
    P.H. Olsen, V. Ambros: The lin-4 regulatory RNA controls developmental timing in Caenorhabditis elegans by blocking LIN-14 protein synthesis after the initiation of translation, Dev. Biol. 216, 671–680 (1999)CrossRefGoogle Scholar
  14. 9.14.
    S. Karlin, S.F. Altschul: Methods for assessing the statistical significance of molecular sequence features by using general scoring schemes, Proc. Natl. Acad. Sci. USA 87, 2264–2268 (1990)CrossRefzbMATHGoogle Scholar
  15. 9.15.
    J. Brennecke, A. Stark, R.B. Russell, S.M. Cohen: Principles of microRNA-target recognition, PLoS Biol. 3, e85 (2005)CrossRefGoogle Scholar
  16. 9.16.
    B.P. Lewis, C.B. Burge, D.P. Bartel: Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets, Cell 120, 15–20 (2005)CrossRefGoogle Scholar
  17. 9.17.
    P. Flicek, M.R. Amode, D. Barrell, K. Beal, S. Brent, Y. Chen, P. Clapham, G. Coates, S. Fairley, S. Fitzgerald, L. Gordon, M. Hendrix, T. Hourlier, N. Johnson, A. Kähäri, D. Keefe, S. Keenan, R. Kinsella, F. Kokocinski, E. Kulesha, P. Larsson, I. Longden, W. McLaren, B. Overduin, B. Pritchard, H.S. Riat, D. Rios, G.R. Ritchie, M. Ruffier, M. Schuster, D. Sobral, G. Spudich, Y.A. Tang, S. Trevanion, J. Vandrovcova, A.J. Vilella, S. White, S.P. Wilder, A. Zadissa, J. Zamora, B.L. Aken, E. Birney, F. Cunningham, I. Dunham, R. Durbin, X.M. Fernández-Suarez, J. Herrero, T.J. Hubbard, A. Parker, G. Proctor, J. Vogel, S.M. Searle: Ensembl 2011, Nucleic Acids Res. 39, D800–D806 (2011)CrossRefGoogle Scholar
  18. 9.18.
    L. David, W. Huber, M. Granovskaia, J. Toedling, C.J. Palm, L. Bofkin, T. Jones, R.W. Davis, L.M. Steinmetz: A high-resolution map of transcription in the yeast genome, Proc. Natl. Acad. Sci. USA 103, 5320–5325 (2006)CrossRefGoogle Scholar
  19. 9.19.
    A.J. Giraldez, Y. Mishima, J. Rihel, R.J. Grocock, S. Van Dongen, K. Inoue, A.J. Enright, A.F. Schier: Zebrafish MiR-430 promotes deadenylation and clearance of maternal mRNAs, Science 312, 75–79 (2006)CrossRefGoogle Scholar
  20. 9.20.
    S. Griffiths-Jones, H.K. Saini, S. van Dongen, A.J. Enright: miRBase: Tools for microRNA genomics, Nucleic Acids Res. 36, D154–D158 (2008)CrossRefGoogle Scholar
  21. 9.21.
    K.K.-H. Farh, A. Grimson, C. Jan, B.P. Lewis, W.K. Johnston, L.P. Lim, C.B. Burge, D.P. Bartel: The widespread impact of mammalian MicroRNAs on mRNA repression and evolution, Science 310, 1817–1821 (2005)CrossRefGoogle Scholar
  22. 9.22.
    L.P. Lim, N.C. Lau, P. Garrett-Engele, A. Grimson, J.M. Schelter, J. Castle, D.P. Bartel, P.S. Linsley, J.M. Johnson: Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs, Nature 433, 769–773 (2005)CrossRefGoogle Scholar
  23. 9.23.
    A.J. Giraldez, R.M. Cinalli, M.E. Glasner, A.J. Enright, J.M. Thomson, S. Baskerville, S.M. Hammond, D.P. Bartel, A.F. Schier: MicroRNAs regulate brain morphogenesis in zebrafish, Science 308, 833–838 (2005)CrossRefGoogle Scholar
  24. 9.24.
    M. Selbach, B. Schwanhäusser, N. Thierfelder, Z. Fang, R. Khanin, N. Rajewsky: Widespread changes in protein synthesis induced by microRNAs, Nature 455, 58–63 (2008)CrossRefGoogle Scholar
  25. 9.25.
    A. Subramanian, P. Tamayo, V.K. Mootha, S. Mukherjee, B.L. Ebert, M.A. Gillette, A. Paulovich, S.L. Pomeroy, T.R. Golub, E.S. Lander, J.P. Mesirov: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles, Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005)CrossRefGoogle Scholar
  26. 9.26.
    M. Ashburner, S. Lewis: On ontologies for biologists: The Gene Ontology – Untangling the web, Novartis Found. Symp. 247, 66–80 (2002), discussion 80–3, 84–90, 244–52CrossRefGoogle Scholar
  27. 9.27.
    E. Eden, D. Lipson, S. Yogev, Z. Yakhini: Discovering motifs in ranked lists of DNA sequences, PLoS Comput. Biol. 3, e39 (2007)MathSciNetCrossRefGoogle Scholar
  28. 9.28.
    J. van Helden: Regulatory sequence analysis tools, Nucleic Acids Res. 31, 3593–3596 (2003)CrossRefGoogle Scholar
  29. 9.29.
    M. Defrance, R. Janky, O. Sand, J. van Helden: Using RSAT oligo-analysis and dyad-analysis tools to discover regulatory signals in nucleic sequences, Nat. Protoc. 3, 1589–1603 (2008)CrossRefGoogle Scholar
  30. 9.30.
    M. Thomas-Chollier, M. Defrance, A. Medina-Rivera, O. Sand, C. Herrmann, D. Thieffry, J. van Helden: RSAT 2011: Regulatory sequence analysis tools, Nucleic Acids Res. 39, W86–91 (2011)CrossRefGoogle Scholar
  31. 9.31.
    S. van Dongen, C. Abreu-Goodger, A.J. Enright: Detecting microRNA binding and siRNA off-target effects from expression data, Nat. Methods 5, 1023–1025 (2008)CrossRefGoogle Scholar
  32. 9.32.
    G.E. Crooks, G. Hon, J.-M. Chandonia, S.E. Brenner: WebLogo: A sequence logo generator, Genome Res. 14, 1188–1190 (2004)CrossRefGoogle Scholar
  33. 9.33.
    T.L. Bailey, M. Boden, F.A. Buske, M. Frith, C.E. Grant, L. Clementi, J. Ren, W.W. Li, W.S. Noble: MEME SUITE: Tools for motif discovery and searching, Nucleic Acids Res. 37, W202–W208 (2009)CrossRefGoogle Scholar
  34. 9.34.
    X.S. Liu, D.L. Brutlag, J.S. Liu: An algorithm for finding protein-DNA binding sites with applications to chromatin-immunoprecipitation microarray experiments, Nat. Biotechnol. 20, 835–839 (2002)CrossRefGoogle Scholar
  35. 9.35.
    H.J. Bussemaker, H. Li, E.D. Siggia: Regulatory element detection using correlation with expression, Nat. Genet. 27, 167–171 (2001)CrossRefGoogle Scholar
  36. 9.36.
    B.C. Foat, A.V. Morozov, H.J. Bussemaker: Statistical mechanical modeling of genome-wide transcription factor occupancy data by MatrixREDUCE, Bioinformatics 22, e141–e149 (2006)CrossRefGoogle Scholar
  37. 9.37.
    P. Sood, A. Krek, M. Zavolan, G. Macino, N. Rajewsky: Cell-type-specific signatures of microRNAs on target mRNA expression, Proc. Natl. Acad. Sci. USA 103, 2746–2751 (2006)CrossRefGoogle Scholar
  38. 9.38.
    L.J. Jensen, S. Knudsen: Automatic discovery of regulatory patterns in promoter regions based on whole cell expression data and functional annotation, Bioinformatics 16, 326–333 (2000)CrossRefGoogle Scholar
  39. 9.39.
    Tatusov, Lipman: unpublishedGoogle Scholar
  40. 9.40.
    R.D. Palmer, M.J. Murray, H.K. Saini, S. van Dongen, C. Abreu-Goodger, B. Muralidhar, M.R. Pett, C.M. Thornton, J.C. Nicholson, A.J. Enright, N. Coleman: Childrenʼs Cancer and Leukaemia Group: Malignant germ cell tumors display common microRNA profiles resulting in global changes in expression of messenger RNA targets, Cancer Res. 70, 2911–2923 (2010)CrossRefGoogle Scholar
  41. 9.41.
    N. Bartonicek, A.J. Enright: SylArray: A web server for automated detection of miRNA effects from expression data, Bioinformatics 26, 2900–2901 (2010)CrossRefGoogle Scholar
  42. 9.42.
    A. Rodriguez, E. Vigorito, S. Clare, M.V. Warren, P. Couttet, D.R. Soond, S. van Dongen, R.J. Grocock, P.P. Das, E.A. Miska, D. Vetrie, K. Okkenhaug, A.J. Enright, G. Dougan, M. Turner, A. Bradley: Requirement of bic/microRNA-155 for normal immune function, Science 316, 608–611 (2007)CrossRefGoogle Scholar
  43. 9.43.
    E. Vigorito, K.L. Perks, C. Abreu-Goodger, S. Bunting, Z. Xiang, S. Kohlhaas, P.P. Das, E.A. Miska, A. Rodriguez, A. Bradley, K.G. Smith, C. Rada, A.J. Enright, K.M. Toellner, I.C. Maclennan, M. Turner: microRNA-155 regulates the generation of immunoglobulin class-switched plasma cells, Immunity 27, 847–859 (2007)CrossRefGoogle Scholar
  44. 9.44.
    M.A. Lewis, E. Quint, A.M. Glazier, H. Fuchs, M.H. De Angelis, C. Langford, S. van Dongen, C. Abreu-Goodger, M. Piipari, N. Redshaw, T. Dalmay, M.A. Moreno-Pelayo, A.J. Enright, K.P. Steel: An ENU-induced mutation of miR-96 associated with progressive hearing loss in mice, Nat. Genet. 41, 614–618 (2009)CrossRefGoogle Scholar
  45. 9.45.
    A. Mencía, S. Modamio-Høybjør, N. Redshaw, M. Morín, F. Mayo-Merino, L. Olavarrieta, L.A. Aguirre, I. del Castillo, K.P. Steel, T. Dalmay, F. Moreno, M.A. Moreno-Pelayo: Mutations in the seed region of human miR-96 are responsible for nonsyndromic progressive hearing loss, Nat. Genet. 41, 609–613 (2009)CrossRefGoogle Scholar
  46. 9.46.
    K.D. Rasmussen, S. Simmini, C. Abreu-Goodger, N. Bartonicek, M. Di Giacomo, D. Bilbao-Cortes, R. Horos, M. Von Lindern, A.J. Enright, D. OʼCarroll: The miR-144/451 locus is required for erythroid homeostasis, J. Exp. Med. 207, 1351–1358 (2010)CrossRefGoogle Scholar
  47. 9.47.
    A.L. Jackson, S.R. Bartz, J. Schelter, S.V. Kobayashi, J. Burchard, M. Mao, B. Li, G. Cavet, P.S. Linsley: Expression profiling reveals off-target gene regulation by RNAi, Nat. Biotechnol. 21, 635–637 (2003)CrossRefGoogle Scholar
  48. 9.48.
    I. Sudbery, A.J. Enright, A.G. Fraser, I. Dunham: Systematic analysis of off-target effects in an RNAi screen reveals microRNAs affecting sensitivity to TRAIL-induced apoptosis, BMC Genomics 11, 175 (2010)CrossRefGoogle Scholar
  49. 9.49.
    A. Birmingham, E.M. Anderson, A. Reynolds, D. Ilsley-Tyree, D. Leake, Y. Fedorov, S. Baskerville, E. Maksimova, K. Robinson, J. Karpilow, W.S. Marshall, A. Khvorova: 3ʼ UTR seed matches, but not overall identity, are associated with RNAi off-targets, Nat. Methods 3, 199–204 (2006)CrossRefGoogle Scholar
  50. 9.50.
    E.M. Anderson, A. Birmingham, S. Baskerville, A. Reynolds, E. Maksimova, D. Leake, Y. Fedorov, J. Karpilow, A. Khvorova: Experimental validation of the importance of seed complement frequency to siRNA specificity, RNA 14, 853–861 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2014

Authors and Affiliations

  1. 1.Wellcome Trust Genome CampusEMBL – European Bioinformatics InstituteHinxtonUK
  2. 2.Wellcome Trust Genome CampusEMBL – European Bioinformatics InstituteHinxtonUK

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