Skip to main content
Log in

Principles of miRNA–mRNA interactions: beyond sequence complementarity

  • Review
  • Published:
Cellular and Molecular Life Sciences Aims and scope Submit manuscript

Abstract

MicroRNAs (miRNAs) are small non-coding RNAs that post-transcriptionally regulate gene expression by altering the translation efficiency and/or stability of targeted mRNAs. In vertebrates, more than 50 % of all protein-coding RNAs are assumed to be subject to miRNA-mediated control, but current high-throughput methods that reliably measure miRNA–mRNA interactions either require prior knowledge of target mRNAs or elaborate preparation procedures. Consequently, experimentally validated interactions are relatively rare. Furthermore, in silico prediction based on sequence complementarity of miRNAs and their corresponding target sites suffers from extremely high false positive rates. Apparently, sequence complementarity alone is often insufficient to reflect the complex post-transcriptional regulation of mRNAs by miRNAs, which is especially true for animals. Therefore, combined analysis of small non-coding and protein-coding RNAs is indispensable to better understand and predict the complex dynamics of miRNA-regulated gene expression. Single-nucleotide polymorphisms (SNPs) and alternative polyadenylation (APA) can affect miRNA binding of a given transcript from different individuals and tissues, and especially APA is currently emerging as a major factor that contributes to variations in miRNA–mRNA interplay in animals. In this review, we focus on the influence of APA and SNPs on miRNA-mediated gene regulation and discuss the computational approaches that take these mechanisms into account.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Carthew RW, Sontheimer EJ (2009) Origins and mechanisms of miRNAs and siRNAs. Cell 136(4):642–655

    CAS  PubMed Central  PubMed  Google Scholar 

  2. Cullen BR (2006) Viruses and microRNAs. Nature Genet 38:S25–S30

    CAS  PubMed  Google Scholar 

  3. Huang PJ, Lin WC, Chen SC, Lin YH, Sun CH, Lyu PC, Tang P (2012) Identification of putative miRNAs from the deep-branching unicellular flagellates. Genomics 99(2):101–107

    CAS  PubMed  Google Scholar 

  4. Lee HC, Li L, Gu W, Xue Z, Crosthwaite SK, Pertsemlidis A, Lewis ZA, Freitag M, Selker EU, Mello CC, Liu Y (2010) Diverse pathways generate microRNA-like RNAs and Dicer-independent small interfering RNAs in fungi. Mol Cell 38(6):803–814

    CAS  PubMed Central  PubMed  Google Scholar 

  5. Molnar A, Schwach F, Studholme DJ, Thuenemann EC, Baulcombe DC (2007) miRNAs control gene expression in the single-cell alga Chlamydomonas reinhardtii. Nature 447(7148):1126–1129

    CAS  PubMed  Google Scholar 

  6. Yao Y, Nair V (2014) Role of virus-encoded microRNAs in Avian viral diseases. Viruses 6(3):1379–1394

    CAS  PubMed Central  PubMed  Google Scholar 

  7. Salmena L, Poliseno L, Tay Y, Kats L, Pandolfi PP (2011) A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language? Cell 146(3):353–358

    CAS  PubMed Central  PubMed  Google Scholar 

  8. Kozomara A, Griffiths-Jones S (2013) miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. doi:10.1093/nar/gkt1181

    PubMed Central  PubMed  Google Scholar 

  9. Kozomara A, Griffiths-Jones S (2010) miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. doi:10.1093/nar/gkq1027

    PubMed Central  PubMed  Google Scholar 

  10. Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ (2006) miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res 34(Database issue):D140–D144

  11. Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ (2008) miRBase: tools for microRNA genomics. Nucleic Acids Res 36(Database issue):D154–D158

  12. Bazzini AA, Lee MT, Giraldez AJ (2012) Ribosome profiling shows that miR-430 reduces translation before causing mRNA decay in zebrafish. Science 336(6078):233–237

    CAS  PubMed Central  PubMed  Google Scholar 

  13. Bethune J, Artus-Revel CG, Filipowicz W (2012) Kinetic analysis reveals successive steps leading to miRNA-mediated silencing in mammalian cells. EMBO Rep 13(8):716–723

    CAS  PubMed Central  PubMed  Google Scholar 

  14. Djuranovic S, Nahvi A, Green R (2012) miRNA-mediated gene silencing by translational repression followed by mRNA deadenylation and decay. Science 336(6078):237–240

    CAS  PubMed Central  PubMed  Google Scholar 

  15. Vasudevan S, Tong Y, Steitz JA (2007) Switching from repression to activation: microRNAs can up-regulate translation. Science 318(5858):1931–1934

    CAS  PubMed  Google Scholar 

  16. Morlando M, Ballarino M, Gromak N, Pagano F, Bozzoni I, Proudfoot NJ (2008) Primary microRNA transcripts are processed co-transcriptionally. Nat Struct Mol Biol 15(9):902–909

    CAS  PubMed  Google Scholar 

  17. Fahlgren N, Howell MD, Kasschau KD, Chapman EJ, Sullivan CM, Cumbie JS, Givan SA, Law TF, Grant SR, Dangl JL, Carrington JC (2007) High-throughput sequencing of Arabidopsis microRNAs: evidence for frequent birth and death of MIRNA genes. PLoS One 2(2):e219

    PubMed Central  PubMed  Google Scholar 

  18. Denli AM, Tops BB, Plasterk RH, Ketting RF, Hannon GJ (2004) Processing of primary microRNAs by the Microprocessor complex. Nature 432(7014):231–235

    CAS  PubMed  Google Scholar 

  19. Kim VN (2005) MicroRNA biogenesis: coordinated cropping and dicing. Nat Rev Mol Cell Biol 6(5):376–385

    CAS  PubMed  Google Scholar 

  20. Yi R, Qin Y, Macara IG, Cullen BR (2003) Exportin-5 mediates the nuclear export of pre-microRNAs and short hairpin RNAs. Genes Dev 17(24):3011–3016

    CAS  PubMed Central  PubMed  Google Scholar 

  21. Ketting RF, Fischer SE, Bernstein E, Sijen T, Hannon GJ, Plasterk RH (2001) Dicer functions in RNA interference and in synthesis of small RNA involved in developmental timing in C. elegans. Genes Dev 15(20):2654–2659

    CAS  PubMed Central  PubMed  Google Scholar 

  22. Lee YS, Nakahara K, Pham JW, Kim K, He Z, Sontheimer EJ, Carthew RW (2004) Distinct roles for Drosophila Dicer-1 and Dicer-2 in the siRNA/miRNA silencing pathways. Cell 117(1):69–81

    CAS  PubMed  Google Scholar 

  23. Czech B, Hannon GJ (2011) Small RNA sorting: matchmaking for Argonautes. Nat Rev Genet 12(1):19–31

    CAS  PubMed Central  PubMed  Google Scholar 

  24. Voinnet O (2009) Origin, biogenesis, and activity of plant microRNAs. Cell 136(4):669–687

    CAS  PubMed  Google Scholar 

  25. Ladewig E, Okamura K, Flynt AS, Westholm JO, Lai EC (2012) Discovery of hundreds of mirtrons in mouse and human small RNA data. Genome Res 22(9):1634–1645

    CAS  PubMed Central  PubMed  Google Scholar 

  26. Ruby JG, Jan CH, Bartel DP (2007) Intronic microRNA precursors that bypass Drosha processing. Nature 448(7149):83–86

    CAS  PubMed Central  PubMed  Google Scholar 

  27. Meng Y, Shao C (2012) Large-scale identification of mirtrons in Arabidopsis and rice. PLoS One 7(2):e31163

    CAS  PubMed Central  PubMed  Google Scholar 

  28. Zhu QH, Spriggs A, Matthew L, Fan L, Kennedy G, Gubler F, Helliwell C (2008) A diverse set of microRNAs and microRNA-like small RNAs in developing rice grains. Genome Res 18(9):1456–1465

    CAS  PubMed Central  PubMed  Google Scholar 

  29. Hertel J, Langenberger D, Stadler PF (2014) Computational prediction of microRNA genes. Methods Mol Biol 1097:437–456

    CAS  PubMed  Google Scholar 

  30. Liu J, Carmell MA, Rivas FV, Marsden CG, Thomson JM, Song JJ, Hammond SM, Joshua-Tor L, Hannon GJ (2004) Argonaute2 is the catalytic engine of mammalian RNAi. Science 305(5689):1437–1441

    CAS  PubMed  Google Scholar 

  31. Song JJ, Smith SK, Hannon GJ, Joshua-Tor L (2004) Crystal structure of Argonaute and its implications for RISC slicer activity. Science 305(5689):1434–1437

    CAS  PubMed  Google Scholar 

  32. Brodersen P, Sakvarelidze-Achard L, Bruun-Rasmussen M, Dunoyer P, Yamamoto YY, Sieburth L, Voinnet O (2008) Widespread translational inhibition by plant miRNAs and siRNAs. Science 320(5880):1185–1190

    CAS  PubMed  Google Scholar 

  33. Bartel DP (2009) MicroRNAs: target recognition and regulatory functions. Cell 136(2):215–233

    CAS  PubMed Central  PubMed  Google Scholar 

  34. Axtell MJ, Westholm JO, Lai EC (2011) Vive la difference: biogenesis and evolution of microRNAs in plants and animals. Genome Biol 12(4):221

    CAS  PubMed Central  PubMed  Google Scholar 

  35. Rhoades MW, Reinhart BJ, Lim LP, Burge CB, Bartel B, Bartel DP (2002) Prediction of plant microRNA targets. Cell 110(4):513–520

    CAS  PubMed  Google Scholar 

  36. Grimson A, Farh KK-H, Johnston WK, Garrett-Engele P, Lim LP, Bartel DP (2007) MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol Cell 27(1):91–105

    CAS  PubMed Central  PubMed  Google Scholar 

  37. Stroynowska-Czerwinska A, Fiszer A, Krzyzosiak WJ (2014) The panorama of miRNA-mediated mechanisms in mammalian cells. Cell Mol Life Sci 71(12):2253–2270

    CAS  PubMed Central  PubMed  Google Scholar 

  38. Nicoloso MS, Sun H, Spizzo R, Kim H, Wickramasinghe P, Shimizu M, Wojcik SE, Ferdin J, Kunej T, Xiao L et al (2010) Single-nucleotide polymorphisms inside microRNA target sites influence tumor susceptibility. Cancer Res 70(7):2789–2798

    CAS  PubMed Central  PubMed  Google Scholar 

  39. Chen K, Song F, Calin GA, Wei Q, Hao X, Zhang W (2008) Polymorphisms in microRNA targets: a gold mine for molecular epidemiology. Carcinogenesis 29(7):1306–1311

    CAS  PubMed  Google Scholar 

  40. Elkon R, Ugalde AP, Agami R (2013) Alternative cleavage and polyadenylation: extent, regulation and function. Nat Rev Genet 14(7):496–506

    CAS  PubMed  Google Scholar 

  41. Tian B, Manley JL (2013) Alternative cleavage and polyadenylation: the long and short of it. Trends Biochem Sci 38(6):312–320

    CAS  PubMed Central  PubMed  Google Scholar 

  42. Sandberg R, Neilson JR, Sarma A, Sharp PA, Burge CB (2008) Proliferating cells express mRNAs with shortened 3’ untranslated regions and fewer microRNA target sites. Science 320(5883):1643–1647

    CAS  PubMed Central  PubMed  Google Scholar 

  43. Millevoi S, Vagner S (2010) Molecular mechanisms of eukaryotic pre-mRNA 3′ end processing regulation. Nucleic Acids Res 38(9):2757–2774

    CAS  PubMed Central  PubMed  Google Scholar 

  44. Proudfoot NJ (2011) Ending the message: poly(A) signals then and now. Genes Dev 25(17):1770–1782

    CAS  PubMed Central  PubMed  Google Scholar 

  45. Hu J, Lutz CS, Wilusz J, Tian B (2005) Bioinformatic identification of candidate cis-regulatory elements involved in human mRNA polyadenylation. RNA 11(10):1485–1493

    CAS  PubMed Central  PubMed  Google Scholar 

  46. Scorilas A (2002) Polyadenylate polymerase (PAP) and 3′ end pre-mRNA processing: function, assays, and association with disease. Crit Rev Clin Lab Sci 39(3):193–224

    CAS  PubMed  Google Scholar 

  47. Di Giammartino DC, Manley JL (2014) New links between mRNA polyadenylation and diverse nuclear pathways. Mol Cells 37(9):644–649

    PubMed Central  PubMed  Google Scholar 

  48. Shi Y, Di Giammartino DC, Taylor D, Sarkeshik A, Rice WJ, Yates JR 3rd, Frank J, Manley JL (2009) Molecular architecture of the human pre-mRNA 3’ processing complex. Mol Cell 33(3):365–376

    CAS  PubMed Central  PubMed  Google Scholar 

  49. Elkon R, Drost J, van Haaften G, Jenal M, Schrier M, Oude Vrielink JA, Agami R (2012) E2F mediates enhanced alternative polyadenylation in proliferation. Genome Biol 13(7):R59

    CAS  PubMed Central  PubMed  Google Scholar 

  50. Han T, Kim JK (2014) Driving glioblastoma growth by alternative polyadenylation. Cell Res 24(9):1023–1024

    CAS  PubMed  Google Scholar 

  51. Miura P, Shenker S, Andreu-Agullo C, Westholm JO, Lai EC (2013) Widespread and extensive lengthening of 3′ UTRs in the mammalian brain. Genome Res 23(5):812–825

    CAS  PubMed Central  PubMed  Google Scholar 

  52. Di Giammartino DC, Nishida K, Manley JL (2011) Mechanisms and consequences of alternative polyadenylation. Mol Cell 43(6):853–866

    PubMed Central  PubMed  Google Scholar 

  53. Kaida D, Berg MG, Younis I, Kasim M, Singh LN, Wan L, Dreyfuss G (2010) U1 snRNP protects pre-mRNAs from premature cleavage and polyadenylation. Nature 468(7324):664–668

    CAS  PubMed Central  PubMed  Google Scholar 

  54. Berg MG, Singh LN, Younis I, Liu Q, Pinto AM, Kaida D, Zhang Z, Cho S, Sherrill-Mix S, Wan L et al (2012) U1 snRNP determines mRNA length and regulates isoform expression. Cell 150(1):53–64

    CAS  PubMed Central  PubMed  Google Scholar 

  55. Huang H, Chen J, Liu H, Sun X (2013) The nucleosome regulates the usage of polyadenylation sites in the human genome. BMC genomics 14912

  56. Shepard PJ, Choi EA, Lu J, Flanagan LA, Hertel KJ, Shi Y (2011) Complex and dynamic landscape of RNA polyadenylation revealed by PAS-Seq. RNA 17(4):761–772

    CAS  PubMed Central  PubMed  Google Scholar 

  57. Derti A, Garrett-Engele P, Macisaac KD, Stevens RC, Sriram S, Chen R, Rohl CA, Johnson JM, Babak T (2012) A quantitative atlas of polyadenylation in five mammals. Genome Res 22(6):1173–1183

    CAS  PubMed Central  PubMed  Google Scholar 

  58. Jan CH, Friedman RC, Ruby JG, Bartel DP (2011) Formation, regulation and evolution of Caenorhabditis elegans 3 [prime] UTRs. Nature 469(7328):97–101

    CAS  PubMed Central  PubMed  Google Scholar 

  59. Zawada AM, Rogacev KS, Muller S, Rotter B, Winter P, Fliser D, Heine GH (2014) Massive analysis of cDNA Ends (MACE) and miRNA expression profiling identifies proatherogenic pathways in chronic kidney disease. Epigenetics 9(1):161–172

    CAS  PubMed Central  PubMed  Google Scholar 

  60. Hoque M, Ji Z, Zheng D, Luo W, Li W, You B, Park JY, Yehia G, Tian B (2013) Analysis of alternative cleavage and polyadenylation by 3′ region extraction and deep sequencing. Nat Methods 10(2):133–139

    CAS  PubMed Central  PubMed  Google Scholar 

  61. Müller S, Rycak L, Afonso-Grunz F, Winter P, Zawada AM, Damrath E, Scheider J, Schmäh J, Koch I, Kahl G, others (2014) APADB: a database for alternative polyadenylation and microRNA regulation events. Database. doi:10.1093/database/bau076

  62. Leslie C (2014) Context-specific 3′UTR isoform expression and miRNA regulation. In Intergrative RNA Biology Special Interest Group Meeting, p 16

  63. Ulitsky I, Shkumatava A, Jan CH, Subtelny AO, Koppstein D, Bell GW, Sive H, Bartel DP (2012) Extensive alternative polyadenylation during zebrafish development. Genome Res 22(10):2054–2066

    CAS  PubMed Central  PubMed  Google Scholar 

  64. Ji Z, Lee JY, Pan Z, Jiang B, Tian B (2009) Progressive lengthening of 3′ untranslated regions of mRNAs by alternative polyadenylation during mouse embryonic development. Proc Natl Acad Sci USA 106(17):7028–7033

    CAS  PubMed Central  PubMed  Google Scholar 

  65. Ji Z, Tian B (2009) Reprogramming of 3′ untranslated regions of mRNAs by alternative polyadenylation in generation of pluripotent stem cells from different cell types. PLoS One 4(12):e8419

    PubMed Central  PubMed  Google Scholar 

  66. Müller S (2014) In silico analysis of regulatory networks underlines the role of miR-10b-5p and its target BDNF in huntington’s disease. Transl Neurodegr 3(1):17

    Google Scholar 

  67. Zuccato C, Cattaneo E (2007) Role of brain-derived neurotrophic factor in Huntington’s disease. Prog Neurobiol 81(5–6):294–330

    CAS  PubMed  Google Scholar 

  68. Varendi K, Kumar A, Härma M-A, Andressoo JO (2014) miR-1, miR-10b, miR-155, and miR-191 are novel regulators of BDNF. Cell Mol Life Sci 71(22):4443–4456. doi:10.1007/s00018-014-1628-x

    CAS  PubMed Central  PubMed  Google Scholar 

  69. Mayr C, Bartel DP (2009) Widespread shortening of 3′ UTRs by alternative cleavage and polyadenylation activates oncogenes in cancer cells. Cell 138(4):673–684

    CAS  PubMed Central  PubMed  Google Scholar 

  70. Lee RC, Feinbaum RL, Ambros V (1993) The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 75(5):843–854

    CAS  PubMed  Google Scholar 

  71. Reinhart BJ, Slack FJ, Basson M, Pasquinelli AE, Bettinger JC, Rougvie AE, Horvitz HR, Ruvkun G (2000) The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature 403(6772):901–906

    CAS  PubMed  Google Scholar 

  72. Wightman B, Ha I, Ruvkun G (1993) Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans. Cell 75(5):855–862

    CAS  PubMed  Google Scholar 

  73. Kuhn DE, Martin MM, Feldman DS, Terry AV Jr, Nuovo GJ, Elton TS (2008) Experimental validation of miRNA targets. Methods 44(1):47–54

    CAS  PubMed Central  PubMed  Google Scholar 

  74. German MA, Pillay M, Jeong DH, Hetawal A, Luo S, Janardhanan P, Kannan V, Rymarquis LA, Nobuta K, German R, De Paoli E, Lu C, Schroth G, Meyers BC, Green PJ (2008) Global identification of microRNA-target RNA pairs by parallel analysis of RNA ends. Nat Biotechnol 26(8):941–946

    CAS  PubMed  Google Scholar 

  75. Gregory BD, O’Malley RC, Lister R, Urich MA, Tonti-Filippini J, Chen H, Millar AH, Ecker JR (2008) A link between RNA metabolism and silencing affecting Arabidopsis development. Dev Cell 14(6):854–866

    CAS  PubMed  Google Scholar 

  76. Addo-Quaye C, Eshoo TW, Bartel DP, Axtell MJ (2008) Endogenous siRNA and miRNA targets identified by sequencing of the Arabidopsis degradome. Curr Biol 18(10):758–762

    CAS  PubMed Central  PubMed  Google Scholar 

  77. Karlova R, van Haarst JC, Maliepaard C, van de Geest H, Bovy AG, Lammers M, Angenent GC, de Maagd RA (2013) Identification of microRNA targets in tomato fruit development using high-throughput sequencing and degradome analysis. J Exp Bot 64(7):1863–1878

    CAS  PubMed Central  PubMed  Google Scholar 

  78. Shamimuzzaman M, Vodkin L (2012) Identification of soybean seed developmental stage-specific and tissue-specific miRNA targets by degradome sequencing. BMC Genom 13:310

    CAS  Google Scholar 

  79. Addo-Quaye C, Miller W, Axtell MJ (2009) CleaveLand: a pipeline for using degradome data to find cleaved small RNA targets. Bioinformatics 25(1):130–131

    CAS  PubMed Central  PubMed  Google Scholar 

  80. Folkes L, Moxon S, Woolfenden HC, Stocks MB, Szittya G, Dalmay T, Moulton V (2012) PAREsnip: a tool for rapid genome-wide discovery of small RNA/target interactions evidenced through degradome sequencing. Nucleic Acids Res 40(13):e103

    CAS  PubMed Central  PubMed  Google Scholar 

  81. Willmann MR, Berkowitz ND, Gregory BD (2014) Improved genome-wide mapping of uncapped and cleaved transcripts in eukaryotes–GMUCT 2.0. Methods 67(1):64–73

    CAS  PubMed  Google Scholar 

  82. Zhai J, Arikit S, Simon SA, Kingham BF, Meyers BC (2014) Rapid construction of parallel analysis of RNA end (PARE) libraries for Illumina sequencing. Methods 67(1):84–90

    CAS  PubMed  Google Scholar 

  83. Bader AG, Brown D, Winkler M (2010) The promise of microRNA replacement therapy. Cancer Res 70(18):7027–7030

    CAS  PubMed Central  PubMed  Google Scholar 

  84. Krützfeldt J, Rajewsky N, Braich R, Rajeev KG, Tuschl T, Manoharan M, Stoffel M (2005) Silencing of microRNAs in vivo with ‘antagomirs’. Nature 438(7068):685–689

    PubMed  Google Scholar 

  85. Thomas M, Lieberman J, Lal A (2010) Desperately seeking microRNA targets. Nat Struct Mol Biol 17(10):1169–1174

    CAS  PubMed  Google Scholar 

  86. Thomson DW, Bracken CP, Goodall GJ (2011) Experimental strategies for microRNA target identification. Nucleic Acids Res 39(16):6845–6853

    CAS  PubMed Central  PubMed  Google Scholar 

  87. Chi SW, Zang JB, Mele A, Darnell RB (2009) Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature 460(7254):479–486

    CAS  PubMed Central  PubMed  Google Scholar 

  88. Hafner M, Landthaler M, Burger L, Khorshid M, Hausser J, Berninger P, Rothballer A, Ascano M Jr, Jungkamp A-C, Munschauer M et al (2010) Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell 141(1):129–141

    CAS  PubMed Central  PubMed  Google Scholar 

  89. Helwak A, Kudla G, Dudnakova T, Tollervey D (2013) Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell 153(3):654–665

    CAS  PubMed Central  PubMed  Google Scholar 

  90. Zhang C, Darnell RB (2011) Mapping in vivo protein-RNA interactions at single-nucleotide resolution from HITS-CLIP data. Nat Biotechnol 29(7):607–614

    CAS  PubMed Central  PubMed  Google Scholar 

  91. Farazi TA, Hoeve J, Brown M, Mihailovic A, Horlings HM, Vijver MVD, Tuschl T, Wessels L (2014) Identification of distinct miRNA target regulation between breast cancer molecular subtypes using AGO2-PAR-CLIP and patient datasets. Genome Biol 15:R9

    PubMed Central  PubMed  Google Scholar 

  92. Venkataraman S, Birks DK, Balakrishnan I, Alimova I, Harris PS, Patel PR, Handler MH, Dubuc A, Taylor MD, Foreman NK et al (2013) MicroRNA 218 acts as a tumor suppressor by targeting multiple cancer phenotype-associated genes in medulloblastoma. J Biol Chem 288(3):1918–1928

    CAS  PubMed Central  PubMed  Google Scholar 

  93. Lewis BP, Burge CB, Bartel DP (2005) Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120(1):15–20

    CAS  PubMed  Google Scholar 

  94. Enright AJ, John B, Gaul U, Tuschl T, Sander C, Marks DS et al (2004) MicroRNA targets in Drosophila. Genome Biol 5(1):R1

    PubMed Central  Google Scholar 

  95. John B, Enright AJ, Aravin A, Tuschl T, Sander C, Marks DS (2004) Human microRNA targets. PLoS Biol 2(11):e363

    PubMed Central  PubMed  Google Scholar 

  96. Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E (2007) The role of site accessibility in microRNA target recognition. Nat Genet 39(10):1278–1284

    CAS  PubMed  Google Scholar 

  97. Krek A, Grün D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, da Piedade I, Gunsalus KC, Stoffel M et al (2005) Combinatorial microRNA target predictions. Nat Genet 37(5):495–500

    CAS  PubMed  Google Scholar 

  98. Friedman RC, Farh KK-H, Burge CB, Bartel DP (2009) Most mammalian mRNAs are conserved targets of microRNAs. Genome Res 19(1):92–105

    CAS  PubMed Central  PubMed  Google Scholar 

  99. Lewis BP, Shih IH, Jones-Rhoades MW, Bartel DP, Burge CB et al (2003) Prediction of mammalian microRNA targets. Cell 115(7):787–798

    CAS  PubMed  Google Scholar 

  100. Lee D, Shin C (2012) MicroRNA-target interactions: new insights from genome-wide approaches. Ann N Y Acad Sci 1271(1):118–128

    CAS  PubMed Central  PubMed  Google Scholar 

  101. Muniategui A, Pey J, Planes FJ, Rubio A (2013) Joint analysis of miRNA and mRNA expression data. Brief Bioinform 14(3):263–278

    CAS  PubMed  Google Scholar 

  102. Dweep H, Sticht C, Pandey P, Gretz N (2011) miRWalk-database: prediction of possible miRNA binding sites by “walking” the genes of three genomes. J Biomed Inform 44(5):839–847

    CAS  PubMed  Google Scholar 

  103. Laganà A, Forte S, Giudice A, Arena M, Puglisi P, Giugno R, Pulvirenti A, Shasha D, Ferro A (2009) miRò: a miRNA knowledge base. Database 2009:bap008

  104. Giles CB, Girija-Devi R, Dozmorov MG, Wren JD (2013) mirCoX: a database of miRNA-mRNA expression correlations derived from RNA-seq meta-analysis. BMC Bioinform 14(Suppl 14):S17

    Google Scholar 

  105. Hua Y, Duan S, Murmann AE, Larsen N, Kjems J, Lund AH, Peter ME (2011) miRConnect: identifying effector genes of miRNAs and miRNA families in cancer cells. PLoS One 6(10):e26521

    CAS  PubMed Central  PubMed  Google Scholar 

  106. Xiao F, Zuo Z, Cai G, Kang S, Gao X, Li T (2009) miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res 37(suppl 1):D105–D110

    CAS  PubMed Central  PubMed  Google Scholar 

  107. Vergoulis T, Vlachos IS, Alexiou P, Georgakilas G, Maragkakis M, Reczko M, Gerangelos S, Koziris N, Dalamagas T, Hatzigeorgiou AG (2012) TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support. Nucleic Acids Res 40(D1):D222–D229

    CAS  PubMed Central  PubMed  Google Scholar 

  108. Brennecke J, Stark A, Russell RB, Cohen SM (2005) Principles of microRNA-target recognition. PLoS Biol 3(3):e85

    PubMed Central  PubMed  Google Scholar 

  109. Carmona-Saez P, Chagoyen M, Tirado F, Carazo JM, Pascual-Montano A (2007) GENECODIS: a web-based tool for finding significant concurrent annotations in gene lists. Genome Biol 8(1):R3

    PubMed Central  PubMed  Google Scholar 

  110. Li J, Min R, Bonner A, Zhang Z (2009) A probabilistic framework to improve microrna target prediction by incorporating proteomics data. J Bioinform Comput Biol 7(06):955–972

    CAS  PubMed  Google Scholar 

  111. Nam S, Li M, Choi K, Balch C, Kim S, Nephew KP (2009) MicroRNA and mRNA integrated analysis (MMIA): a web tool for examining biological functions of microRNA expression. Nucleic Acids Res 37(suppl 2):W356–W362

    CAS  PubMed Central  PubMed  Google Scholar 

  112. Ritchie W, Rajasekhar M, Flamant S, Rasko JE (2009) Conserved expression patterns predict microRNA targets. PLoS Comput Biol 5(9):e1000513

    PubMed Central  PubMed  Google Scholar 

  113. Muller S, Rycak L, Winter P, Kahl G, Koch I, Rotter B (2013) omiRas: a Web server for differential expression analysis of miRNAs derived from small RNA-Seq data. Bioinformatics 29(20):2651–2652

    PubMed  Google Scholar 

  114. Zhang Y, Xu B, Yang Y, Ban R, Zhang H, Jiang X, Cooke HJ, Xue Y, Shi Q (2012) CPSS: a computational platform for the analysis of small RNA deep sequencing data. Bioinformatics 28(14):1925–1927

    CAS  PubMed  Google Scholar 

  115. Chen CJ, Servant N, Toedling J, Sarazin A, Marchais A, Duvernois-Berthet E, Cognat V, Colot V, Voinnet O, Heard E, Ciaudo C, Barillot E (2012) ncPRO-seq: a tool for annotation and profiling of ncRNAs in sRNA-seq data. Bioinformatics 28(23):3147–3149

    CAS  PubMed  Google Scholar 

  116. Doerr A (2013) Mass spectrometry-based targeted proteomics. Nat Methods 10(1):23

    PubMed  Google Scholar 

  117. Nam J-W, Rissland OS, Koppstein D, Abreu-Goodger C, Jan CH, Agarwal V, Yildirim MA, Rodriguez A, Bartel DP (2014) Global analyses of the effect of different cellular contexts on MicroRNA targeting. Mol Cell 53(6):1031–1043

    CAS  PubMed Central  PubMed  Google Scholar 

  118. Thomas LF, S\aetrom Pa (2012) Single nucleotide polymorphisms can create alternative polyadenylation signals and affect gene expression through loss of microRNA-regulation. PLoS Comput Biol 8(8):e1002621

    CAS  PubMed Central  PubMed  Google Scholar 

  119. Prasad MK, Bhalla K, Pan ZH, O’Connell JR, Weder AB, Chakravarti A, Tian B, Chang YP (2013) A polymorphic 3′UTR element in ATP1B1 regulates alternative polyadenylation and is associated with blood pressure. PLoS One 8(10):e76290

    CAS  PubMed Central  PubMed  Google Scholar 

  120. Barenboim M, Zoltick BJ, Guo Y, Weinberger DR (2010) MicroSNiPer: a web tool for prediction of SNP effects on putative microRNA targets. Hum Mutat 31(11):1223–1232

    CAS  PubMed Central  PubMed  Google Scholar 

  121. Bruno AE, Li L, Kalabus JL, Pan Y, Yu A, Hu Z (2012) miRdSNP: a database of disease-associated SNPs and microRNA target sites on 3′UTRs of human genes. BMC Genom 13(1):44

    CAS  Google Scholar 

  122. Hiard S, Charlier C, Coppieters W, Georges M, Baurain D (2010) Patrocles: a database of polymorphic miRNA-mediated gene regulation in vertebrates. Nucleic Acids Res 38(suppl 1):D640–D651

    CAS  PubMed Central  PubMed  Google Scholar 

  123. Ziebarth JD, Bhattacharya A, Chen A, Cui Y (2011) PolymiRTS Database 2.0: linking polymorphisms in microRNA target sites with human diseases and complex traits. Nucleic Acids Res. doi:10.1093/nar/gkr1026

    PubMed Central  PubMed  Google Scholar 

  124. Liu C, Zhang F, Li T, Lu M, Wang L, Yue W, Zhang D (2012) MirSNP, a database of polymorphisms altering miRNA target sites, identifies miRNA-related SNPs in GWAS SNPs and eQTLs. BMC Genom 13(1):661

    CAS  Google Scholar 

  125. Sherry ST, Ward M, Sirotkin K (1999) dbSNP-database for single nucleotide polymorphisms and other classes of minor genetic variation. Genome Res 9(8):677–679

    CAS  PubMed  Google Scholar 

  126. Deveci M, Catalyürek ÜV, Toland AE (2014) mrSNP: software to detect SNP effects on microRNA binding. BMC Bioinform 15(1):73

    Google Scholar 

  127. Rands CM, Meader S, Ponting CP, Lunter G (2014) 8.2% of the Human genome is constrained: variation in rates of turnover across functional element classes in the human lineage. PLoS Genet 10(7):e1004525

    PubMed Central  PubMed  Google Scholar 

Download references

Conflict of interest

The authors declare no competing financial interests.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fabian Afonso-Grunz.

Additional information

F. Afonso-Grunz and S. Müller contributed equally to the publication.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Afonso-Grunz, F., Müller, S. Principles of miRNA–mRNA interactions: beyond sequence complementarity. Cell. Mol. Life Sci. 72, 3127–3141 (2015). https://doi.org/10.1007/s00018-015-1922-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00018-015-1922-2

Keywords

Navigation