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Investigating miRNA–lncRNA Interactions: Computational Tools and Resources

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MicroRNA Target Identification

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1970))

Abstract

In the last two decades noncoding RNAs have been the recipients of increasing scientific interest. In particular, miRNAs, short (~22 nts) noncoding transcripts, have been thoroughly investigated since their essential role in posttranscriptional gene expression regulation had been established in the early 2000s. With the advent and the advancements of high-throughput sequencing technologies in recent years, long noncoding RNAs have also started to emerge as important actors in cellular functions and processes. Such transcripts, on average longer than 200 nt, whose functions have yet to be fully characterized, have recently been identified as regulatory elements of the RNAi pathway, harboring several miRNA response elements, uncovering the phenomena of competing endogenous RNAs (ceRNAs), or “sponge RNAs.” The present chapter aims to provide a brief update on the actual biomedical relevance of ceRNAs, together with a summary of resources, tools, and practical examples of their application to aid researchers in the discovery and further elucidation of lncRNA–miRNA interactions.

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References

  1. Kang W, Friedländer MR (2015) Computational prediction of miRNA genes from small RNA sequencing data. Front Bioeng Biotechnol 3:7

    Article  PubMed  PubMed Central  Google Scholar 

  2. Veneziano D et al (2016) Noncoding RNA: current deep sequencing data analysis approaches and challenges. Hum Mutat 37(12):1283–1298

    Article  CAS  PubMed  Google Scholar 

  3. Veneziano D, Nigita G, Ferro A (2015) Computational approaches for the analysis of ncRNA through deep sequencing techniques. Front Bioeng Biotechnol 3:77

    Article  PubMed  PubMed Central  Google Scholar 

  4. Kozomara, A. And S. Griffiths-Jones miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res, 2014. 42(database issue): p. D68-D73

    Article  CAS  PubMed  Google Scholar 

  5. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Massone S et al (2012) NDM29, a RNA polymerase III-dependent non coding RNA, promotes amyloidogenic processing of app and amyloid beta secretion. Biochim Biophys Acta 1823(7):1170–1177

    Article  CAS  PubMed  Google Scholar 

  7. Ruiz-orera J et al (2018) Translation of neutrally evolving peptides provides a basis for de novo gene evolution. Nat Ecol Evol 2(5):890–896

    Article  PubMed  Google Scholar 

  8. Housman, G. And I. Ulitsky, Methods for distinguishing between protein-coding and long noncoding RNAs and the elusive biological purpose of translation of long noncoding RNAs. Biochim Biophys Acta, 2016. 1859(1): p. 31-40

    Article  CAS  PubMed  Google Scholar 

  9. Nelson BR et al (2016) A peptide encoded by a transcript annotated as long noncoding RNA enhances SERCA activity in muscle. Science 351(6270):271–275

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Anderson DM et al (2015) A micropeptide encoded by a putative long noncoding RNA regulates muscle performance. Cell 160(4):595–606

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Harrow J et al (2012) Gencode: the reference human genome annotation for the encode project. Genome Res 22(9):1760–1774

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Quek XC et al (2015) lncRNAdb v2.0: expanding the reference database for functional long noncoding RNAs. Nucleic Acids Res 43(database issue):D168–D173

    Article  CAS  PubMed  Google Scholar 

  13. Jarroux J, Morillon A, Pinskaya M (2017) History, discovery, and classification of lncRNAs. Adv Exp Med Biol 1008:1–46

    Article  CAS  PubMed  Google Scholar 

  14. St Laurent G, Wahlestedt C, Kapranov P (2015) The landscape of long noncoding RNA classification. Trends Genet 31(5):239–251

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Dey BK, Pfeifer K, Dutta A (2014) The h19 long noncoding RNA gives rise to microRNAs mir-675-3p and mir-675-5p to promote skeletal muscle differentiation and regeneration. Genes Dev 28(5):491–501

    Article  PubMed  PubMed Central  Google Scholar 

  16. Ounzain S et al (2015) Genome-wide profiling of the cardiac transcriptome after myocardial infarction identifies novel heart-specific long non-coding RNAs. Eur Heart J 36(6):353–68a

    Article  CAS  PubMed  Google Scholar 

  17. Zhu M et al (2017) Lnc-mg is a long non-coding RNA that promotes myogenesis. Nat Commun 8:14718

    Article  PubMed  PubMed Central  Google Scholar 

  18. Zhang G et al (2018) Characterization of dysregulated lncRNA-mRNA network based on ceRNA hypothesis to reveal the occurrence and recurrence of myocardial infarction. Cell Death discov 4:35

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Zhou X et al (2017) lncRNA MIAT functions as a competing endogenous RNA to upregulate DAPK2 by sponging mir-22-3p in diabetic cardiomyopathy. Cell Death Dis 8(7):E2929

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Fan M et al (2013) A long non-coding RNA, ptcsc3, as a tumor suppressor and a target of miRNAs in thyroid cancer cells. Exp Ther Med 5(4):1143–1146

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Yu G et al (2014) Pseudogene PTENP1 functions as a competing endogenous RNA to suppress clear-cell renal cell carcinoma progression. Mol Cancer Ther 13(12):3086–3097

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Li JH et al (2014) StarBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale clip-seq data. Nucleic Acids Res 42(database issue):D92–D97

    Article  CAS  PubMed  Google Scholar 

  23. Roberts JT, Borchert GM (2017) Computational prediction of microRNA target genes, target prediction databases, and web resources. Methods Mol Biol 1617:109–122

    Article  CAS  PubMed  Google Scholar 

  24. Zarringhalam K et al (2017) Identification of competing endogenous RNAs of the tumor suppressor gene PTEN: a probabilistic approach. Sci Rep 7(1):7755

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Niland CN, Merry CR, Khalil AM (2012) Emerging roles for long non-coding RNAs in cancer and neurological disorders. Front Genet 3:25

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Straniero L et al (2017) The GBAP1 pseudogene acts as a ceRNA for the glucocerebrosidase gene GBA by sponging mir-22-3p. Sci Rep 7(1):12702

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Cheng Y et al (2009) MicroRNA-21 protects against the H(2)O(2)-induced injury on cardiac myocytes via its target gene PDCD4. J Mol Cell Cardiol 47(1):5–14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Xiao J et al (2016) Cardiac progenitor cell-derived exosomes prevent cardiomyocytes apoptosis through exosomal miR-21 by targeting PDCD4. Cell Death Dis 7(6):E2277

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Yan H et al (2017) Long non-coding RNA MEG3 functions as a competing endogenous RNA to regulate ischemic neuronal death by targeting miR-21/PDCD4 signaling pathway. Cell Death Dis 8(12):3211

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Zhang Y et al (2018) Critical effects of long non-coding RNA on fibrosis diseases. Exp Mol Med 50(1):E428

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Wang J et al (2012) MicroRNA-24 regulates cardiac fibrosis after myocardial infarction. J Cell Mol Med 16(9):2150–2160

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Stawowy p et al (2004) Regulation of matrix metalloproteinase MT1-MMP/MMP-2 in cardiac fibroblasts by TGF-beta1 involves furin-convertase. Cardiovasc Res 63(1):87–97

    Article  CAS  PubMed  Google Scholar 

  33. Qu X et al (2017) MIAT is a pro-fibrotic long non-coding RNA governing cardiac fibrosis in post-infarct myocardium. Sci Rep 7:42657

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Engelman JA et al (2008) Effective use of PI3K and MEK inhibitors to treat mutant Kras G12D and PIK3CA H1047R murine lung cancers. Nat Med 14(12):1351–1356

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Pandolfi PP (2004) Breast cancer—loss of PTEN predicts resistance to treatment. N Engl J Med 351(22):2337–2338

    Article  CAS  PubMed  Google Scholar 

  36. Li RK et al (2017) PTENP1 acts as a ceRNA to regulate PTEN by sponging miR-19b and explores the biological role of PTENP1 in breast cancer. Cancer Gene Ther 24(7):309–315

    Article  CAS  PubMed  Google Scholar 

  37. Lal-Nag M, Morin PJ (2009) The claudins. Genome Biol 10(8):235

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Jung H et al (2011) The expression of claudin-1, claudin-2, claudin-3, and claudin-4 in gastric cancer tissue. J Surg Res 167(2):E185–E191

    Article  CAS  PubMed  Google Scholar 

  39. Lin X et al (2013) Regulation of the epithelial-mesenchymal transition by claudin-3 and claudin-4. PLoS One 8(6):E67496

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Song YX et al (2017) Non-coding RNAs participate in the regulatory network of CLDN4 via ceRNA mediated miRNA evasion. Nat Commun 8(1):289

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Cai H et al (2017) Long non-coding RNA taurine upregulated 1 enhances tumor-induced angiogenesis through inhibiting microRNA-299 in human glioblastoma. Oncogene 36(3):318–331

    Article  CAS  PubMed  Google Scholar 

  42. Yang JH et al (2011) starBase: a database for exploring microRNA-mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data. Nucleic Acids Res 39.(database issue:D202–D209

    Article  CAS  PubMed  Google Scholar 

  43. Jeggari A, Marks DS, Larsson E (2012) miRcode: a map of putative microRNA target sites in the long non-coding transcriptome. Bioinformatics 28(15):2062–2063

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Paraskevopoulou MD et al (2013) DIANA-lncBase: experimentally verified and computationally predicted microRNA targets on long non-coding RNAs. Nucleic Acids Res 41(database issue):D239–D245

    Article  CAS  PubMed  Google Scholar 

  45. Paraskevopoulou MD et al (2013) DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows. Nucleic Acids Res 41.(web server issue:W169–W173

    Article  PubMed  PubMed Central  Google Scholar 

  46. Griffiths-Jones S et al (2006) miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res 34(database issue):D140–D144

    Article  CAS  PubMed  Google Scholar 

  47. Paraskevopoulou MD et al (2016) DIANA-LncBase v2: indexing microRNA targets on non-coding transcripts. Nucleic Acids Res 44(D1):D231–D238

    Article  CAS  PubMed  Google Scholar 

  48. Vlachos IS et al (2012) DIANA mirpath v.2.0: investigating the combinatorial effect of microRNAs in pathways. Nucleic Acids Res 40.(web server issue:W498–W504

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Dweep H, Gretz N (2015) miRWalk2.0: a comprehensive atlas of microRNA-target interactions. Nat Methods 12(8):697

    Article  CAS  PubMed  Google Scholar 

  50. Dweep H et al (2011) miRWalk—database: prediction of possible miRNA binding sites by “walking” the genes of three genomes. J Biomed Inform 44(5):839–847

    Article  CAS  PubMed  Google Scholar 

  51. John B et al (2004) Human microRNA targets. PLoS Biol 2(11):E363

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Kertesz M et al (2007) The role of site accessibility in microRNA target recognition. Nat Genet 39(10):1278–1284

    Article  CAS  PubMed  Google Scholar 

  53. Heyne S et al (2012) GraphClust: alignment-free structural clustering of local RNA secondary structures. Bioinformatics 28(12):I224–I232

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. 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

    Article  CAS  PubMed  Google Scholar 

  55. Chou CH et al (2018) miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res 46(d1):D296–d302

    Article  CAS  PubMed  Google Scholar 

  56. Li Y et al (2014) HMDD v2.0: a database for experimentally supported human microRNA and disease associations. Nucleic Acids Res 42(database issue):D1070–D1074

    Article  CAS  PubMed  Google Scholar 

  57. O’leary NA et al (2016) Reference sequence (refseq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res 44(D1):D733–D745

    Article  PubMed  CAS  Google Scholar 

  58. Maglott D et al (2005) Entrez gene: gene-centered information at NCBI. Nucleic Acids Res 33(database issue):D54–D58

    Article  CAS  PubMed  Google Scholar 

  59. Tomczak K, Czerwinska P, Wiznerowicz M (2015) The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Pozn) 19(1A):A68–A77

    Google Scholar 

  60. Barrett T et al (2013) NCBI geo: archive for functional genomics data sets—update. Nucleic Acids Res 41(database issue):D991–D995

    CAS  PubMed  Google Scholar 

  61. Agarwal V et al (2015) Predicting effective microRNA target sites in mammalian mRNAs. eLife 4

    Google Scholar 

  62. Betel D et al (2010) Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biol 11(8):R90

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Huang da W, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4(1):44–57

    Article  PubMed  CAS  Google Scholar 

  64. Kanehisa M et al (2016) kegg as a reference resource for gene and protein annotation. Nucleic Acids Res 44(D1):D457–D462

    Article  CAS  PubMed  Google Scholar 

  65. Furio-tari P et al (2016) SpongeScan: a web for detecting microRNA binding elements in lncRNA sequences. Nucleic Acids Res 44(w1):W176–W180

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Cunningham F et al (2015) ENSEMBL. Nucleic Acids Res 43(database issue):D662–D669

    Article  CAS  PubMed  Google Scholar 

  67. Li R et al (2018) GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, miRNA, and mRNA data in GDC. Bioinformatics 34(14):2515–2517

    Article  PubMed  Google Scholar 

  68. Team RC (2013) R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. 2014(18)

    Google Scholar 

  69. Gentleman RC et al (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5(10):R80

    Article  PubMed  PubMed Central  Google Scholar 

  70. Grossman RL et al (2016) Toward a shared vision for cancer genomic data. N Engl J Med 375(12):1109–1112

    Article  PubMed  PubMed Central  Google Scholar 

  71. Sean M.M.A.D. (2017) Genomic data commons: NIH/NCI genomic data commons access.

    Google Scholar 

  72. Robinson MD, mccarthy DJ, Smyth GK (2010) edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1):139–140

    Article  CAS  PubMed  Google Scholar 

  73. Mccarthy DJ, Chen Y, Smyth GK (2012) Differential expression analysis of multifactor RNA-seq experiments with respect to biological variation. Nucleic Acids Res 40(10):4288–4297

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Ritchie ME et al (2015) Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43(7):E47

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  75. Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with deseq2. Genome Biol 15(12):550

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  76. Paci P, Colombo T, Farina L (2014) Computational analysis identifies a sponge interaction network between long non-coding RNAs and messenger RNAs in human breast cancer. BMC Syst Biol 8:83

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  77. Yu G et al (2012) Clusterprofiler: an R package for comparing biological themes among gene clusters. OMICS 16(5):284–287

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Yu G et al (2015) Dose: an R/bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics 31(4):608–609

    Article  CAS  PubMed  Google Scholar 

  79. Nigita G, Veneziano D, Ferro A (2015) A-to-I RNA editing: current knowledge sources and computational approaches with special emphasis on non-coding RNA molecules. Front Bioeng Biotechnol 3:37

    PubMed  PubMed Central  Google Scholar 

  80. Nishikura K (2016) A-to-I editing of coding and non-coding RNAs by adars. Nat Rev Mol Cell Biol 17(2):83–96

    Article  CAS  PubMed  Google Scholar 

  81. Wahlstedt H, Ohman M (2011) Site-selective versus promiscuous A-to-I editing. Wiley Interdiscip Rev RNA 2(6):761–771

    Article  CAS  PubMed  Google Scholar 

  82. Picardi E et al (2014) Uncovering RNA editing sites in long non-coding RNAs. Front Bioeng Biotechnol 2:64

    Article  PubMed  PubMed Central  Google Scholar 

  83. Picardi E et al (2015) Profiling RNA editing in human tissues: towards the inosinome atlas. Sci Rep 5:14941

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Gong J et al (2017) LNCediting: a database for functional effects of RNA editing in lncRNAs. Nucleic Acids Res 45(d1):D79–d84

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Cancer Biology and Genetics, Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA

    Dario Veneziano, Gioacchino P. Marceca, Giovanni Nigita, Rosario Distefano & Carlo M. Croce

  2. Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy

    Gioacchino P. Marceca

  3. NMS Oncology, Nerviano Medical Sciences Srl, Nerviano, Italy

    Sebastiano Di Bella

Authors
  1. Dario Veneziano
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  2. Gioacchino P. Marceca
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  3. Sebastiano Di Bella
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  4. Giovanni Nigita
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  5. Rosario Distefano
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  6. Carlo M. Croce
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Correspondence to Dario Veneziano .

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Editors and Affiliations

  1. Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY, USA

    Alessandro Laganà

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Veneziano, D., Marceca, G.P., Di Bella, S., Nigita, G., Distefano, R., Croce, C.M. (2019). Investigating miRNA–lncRNA Interactions: Computational Tools and Resources. In: Laganà, A. (eds) MicroRNA Target Identification. Methods in Molecular Biology, vol 1970. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9207-2_14

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  • DOI: https://doi.org/10.1007/978-1-4939-9207-2_14

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