miTALOS v2: Analyzing tissue specific microRNA function

PLoS ONE

Volumn 11, Issue 3, 2016, Pages

  Preusse, Martin ^a   Theis, Fabian J ^a,b   Mueller, Nikola S ^a  

^a INSTITUTE OF EPIDEMIOLOGY   (Germany)

^b TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

MICRORNA; MICRORNA 199A 3P; MICRORNA 199B 3P; MICRORNA 200; MICRORNA 571; MITOGEN ACTIVATED PROTEIN KINASE; NOTCH RECEPTOR; UNCLASSIFIED DRUG;

ANIMAL TISSUE; ARTICLE; COMPUTER PROGRAM; CONTROLLED STUDY; DATA BASE; GENE EXPRESSION; HUMAN; HUMAN TISSUE; LIVER CELL CARCINOMA; LIVER FIBROSIS; METASTASIS; METHODOLOGY; NONHUMAN; RNA GENE; SIGNAL TRANSDUCTION; TISSUE SPECIFICITY; WEB APPLICATION; WNT SIGNALING PATHWAY; ANIMAL; ANTIBODY SPECIFICITY; COMPUTER INTERFACE; GENETICS; LIVER DISEASE; METABOLISM; MOUSE; SOFTWARE;

ANIMALS; HUMANS; LIVER DISEASES; MICE; MICRORNAS; ORGAN SPECIFICITY; SOFTWARE; USER-COMPUTER INTERFACE;

EID: 84962124791     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0151771     Document Type: Article

References (55)

1. Friedman RC, Farh KK-H, Burge CB, Bartel DP. Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 2009; 19: 92-105. doi: 10.1101/gr.082701.108 PMID: 18955434
2. Thomas M, Lieberman J, Lal A. Desperately seeking microRNA targets. Nat Struct Mol Biol. Nature Publishing Group; 2010; 17:1169-74. doi: 10.1038/nsmb.1921
3. Chi SW, Zang JB, Mele A, Darnell RB. Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature. Nature Publishing Group; 2009; 460: 479-86. doi: 10.1038/nature08170
4. Hafner M, Landthaler M, Burger L, Khorshid M, Hausser J, Berninger P, et al. Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell. Elsevier Ltd; 2010; 141: 129-41. doi: 10.1016/j.cell.2010.03.009
5. Li J-H, Liu S, Zhou H, Qu L-H, Yang J-H. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res. 2013; 1-6.
6. Jungkamp AC, Stoeckius M, Mecenas D, Grün D, Mastrobuoni G, Kempa S, et al. In vivo and transcrip-tome-wide identification of RNA binding protein target sites. Mol Cell. 2011; 44: 828-840. doi: 10.1016/j.molcel.2011.11.009 PMID: 22152485
7. Vidigal JA., Ventura A. The biological functions of miRNAs: lessons from in vivo studies. Trends Cell Biol. Elsevier Ltd; 2014; 25:137-147. doi: 10.1016/j.tcb.2014.11.004
8. Baek D, Villén J, Shin C, Camargo FD, Gygi SP, Bartel DP. The impact of microRNAson protein output. Nature. 2008; 455: 64-71. doi: 10.1038/nature07242 PMID: 18668037
9. Cui Q, Yu Z, Purisima EO, Wang E. Principles of microRNA regulation of a human cellular signaling network. Mol Syst Biol. 2006; 2: 46. doi: 10.1038/msb4100089 PMID: 16969338
10. Inui M, Martello G, Piccolo S. MicroRNA control of signal transduction. Nat Rev Mol Cell Biol. 2010; 11: 252-63. doi: 10.1038/nrm2868 PMID: 20216554
11. Rinck A, Preusse M, Laggerbauer B, Lickert H, Engelhardt S, Theis FJ. The human transcriptome is enriched for miRNA-binding sites located in cooperativity-permitting distance. RNA Biol. 2013; 10: 1125-35. doi: 10.4161/rna.24955 PMID: 23696004
12. Su AI, Wiltshire T, Batalov S, Lapp H, Ching Ka, Block D, et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Natl Acad Sci USA. 2004; 101: 6062-7. doi: 10.1073/pnas.0400782101 PMID: 15075390
13. Farh KK-H, Grimson A, Jan C, Lewis BP, Johnston WK, Lim LP, et al. The widespread impact of mammalian MicroRNAson mRNA repression and evolution. Science. 2005; 310:1817-21. doi: 10.1126/science.1121158 PMID: 16308420
14. Vlachos IS, Kostoulas N, Vergoulis T, Georgakilas G, Reczko M, Maragkakis M, et al. DIANA miRPath v.2.0: Investigating the combinatorial effect of microRNAs in pathways. Nucleic Acids Res. 2012; 40: 498-504. doi: 10.1093/nar/gks494
15. Petryszak R, Burdett T, Fiorelli B, Fonseca N a, Gonzalez-Porta M, Hastings E, et al. Expression Atlas update - a database of gene and transcript expression from microarray- and sequencing-based functional genomics experiments. Nucleic Acids Res. 2014; 42: D926-32. doi: 10.1093/nar/gkt1270 PMID: 24304889
16. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000; 28: 27-30. doi: 10.1093/nar/28.1.27 PMID: 10592173
17. Kelder T, Van Iersel MP, Hanspers K, Kutmon M, Conklin BR, Evelo CT, et al. WikiPathways: Building research communities on biological pathways. Nucleic Acids Res. 2012; 40:1301-1307. doi: 10.1093/nar/gkr1074
18. Croft D, O'Kelly G, Wu G, Haw R, Gillespie M, Matthews L, et al. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res. 2011; 39: D691-7. doi: 10.1093/nar/gkq1018 PMID: 21067998
19. Cho S, Jang I, Jun Y, Yoon S, Ko M, Kwon Y, et al. MiRGatorv3.0: a microRNA portal for deep sequencing, expression profiling and mRNA targeting. Nucleic Acids Res. 2013; 41: D252-7. doi: 10.1093/nar/gks1168 PMID: 23193297
20. Wu C, Bardes EE, Jegga AG, Aronow BJ. ToppMiR: ranking microRNAs and their mRNA targets based on biological functions and context. Nucleic Acids Res. 2014; 42: W107-13. doi: 10.1093/nar/gku409 PMID: 24829448
21. Chen R, Alveroa B, Silasi Da, Kelly MG, Fest S, Visintin I, et al. Regulation of IKKbetaby miR-199a affects NF-kappaB activity in ovarian cancer cells. Oncogene. 2008; 27: 4712-23. doi: 10.1038/onc.2008.112 PMID: 18408758
22. Murakami Y, Yasuda T, Saigo K, Urashima T, Toyoda H, Okanoue T, et al. Comprehensive analysis of microRNA expression patterns in hepatocellular carcinoma and non-tumorous tissues. Oncogene. 2006; 25: 2537-45. doi: 10.1038/sj.onc.1209283 PMID: 16331254
23. Hou J, Lin L, Zhou W, Wang Z, Ding G, Dong Q, et al. Identification of miRNomes in human liver and hepatocellular carcinoma reveals miR-199a/b-3p as therapeutic target for hepatocellular carcinoma. Cancer Cell. Elsevier Inc.; 2011; 19: 232-43. doi: 10.1016/j.ccr.2011.01.001
24. Callegari E, Elamin BK, D'Abundo L, Falzoni S, Donvito G, Moshiri F, et al. Anti-tumor activity of amiR-199-dependent oncolytic adenovirus. PLoS One. 2013; 8: e73964. doi: 10.1371/journal.pone.0073964 PMID: 24069256
25. Duan Z, Choy E, Harmon D, Liu X, Susa M, Mankin H, et al. MicroRNA-199a-3p Is Downregulated in Human Osteosarcoma and Regulates Cell Proliferation and Migration. Mol Cancer Ther. 2011; 10: 1337-1345. doi: 10.1158/1535-7163.MCT-11-0096 PMID: 21666078
26. Bonet F, Dueñas Á, López-Sánchez C, García-Martínez V, Aránega AE, Franco D. MiR-23b and miR-199a impair epithelial-to-mesenchymal transition during atrioventricular endocardial cushion formation. Dev Dyn. 2015; 244:1259-1275. doi: 10.1002/dvdy.24309 PMID: 26198058
27. Song J, Gao L, Yang G, Tang S, Xie H, Wang Y, et al. MiR-199a regulates cell proliferation and survival by targeting FZD7. PLoS One. 2014; 9: e110074. doi: 10.1371/journal.pone.0110074 PMID: 25313882
28. Gregory Pa, Bert AG, Paterson EL, Barry SC, Tsykin A, Farshid G, et al. The miR-200 family and miR-205 regulate epithelial to mesenchymal transition by targeting ZEB1 and SIP1. Nat Cell Biol. 2008; 10: 593-601. doi: 10.1038/ncb1722 PMID: 18376396
29. Humphries B, Yang C. The microRNA-200 family: small molecules with novel roles in cancer development, progression and therapy. Oncotarget. 2015; 6: 6472-98. PMID: 25762624
30. Wu Z-Q, Li X-Y, Hu CY, Ford M, Kleer CG, Weiss SJ. Canonical Wnt signaling regulates Slug activity and links epithelial-mesenchymal transition with epigenetic Breast Cancer 1, Early Onset (BRCA1) repression. Proc Natl Acad Sci USA. 2012; 109:16654-9. doi: 10.1073/pnas.1205822109 PMID: 23011797
31. Chen H-C, Zhu Y-T, Chen S-Y, Tseng SCG. Wnt signaling induces epithelial-mesenchymal transition with proliferation in ARPE-19cells upon loss of contact inhibition. Lab Invest. Nature Publishing Group; 2012; 92: 676-87. doi: 10.1038/labinvest.2011.201
32. Jiang Y-G, Luo Y, He D, Li X, Zhang L, Peng T, et al. Role of Wnt/beta-catenin signaling pathway in epithelial-mesenchymal transition of human prostate cancer induced by hypoxia-inducible factor-1alpha. Int J Urol. 2007; 14: 1034-9. doi: 10.1111/j.1442-2042.2007.01866.x PMID: 17956532
33. Schmidt CM, McKillop IH, Cahill Pa, Sitzmann J V. Increased MAPK expression and activity in primary human hepatocellular carcinoma. Biochem Biophys Res Commun. 1997; 236: 54-8. doi: 10.1006/bbrc.1997.6840 PMID: 9223425
34. Huynh H, Nguyen TTT, Chow K-HP, Tan PH, Soo KC, Tran E. Over-expression of the mitogen-activated protein kinase (MAPK) kinase (MEK)-MAPK in hepatocellular carcinoma: its role in tumor progression and apoptosis. BMC Gastroenterol. 2003; 3:19. doi: 10.1186/1471-230X-3-19 PMID: 12906713
35. Liu L, Cao Y, Chen C, Zhang X, McNabola A, Wilkie D, et al. Sorafenib blocks the RAF/MEK/ERK pathway, inhibits tumor angiogenesis and induces tumor cell apoptosis in hepatocellular carcinoma model PLC/PRF/5. Cancer Res. 2006; 66:11851-11858. doi: 10.1158/0008-5472.CAN-06-1377 PMID: 17178882
36. Noetel A, Kwiecinski M, Elfimova N, Huang J, Odenthal M. microRNA are Central Players in Anti- and Profibrotic Gene Regulation during Liver Fibrosis. Front Physiol. 2012; 3: 49. doi: 10.3389/fphys.2012.00049 PMID: 22457651
37. Roderburg C, Mollnow T, Bongaerts B, Elfimova N, Vargas Cardenas D, Berger K, et al. Micro-RNA profiling in human serum reveals compartment-specific roles of miR-571 andmiR-652in liver cirrhosis. Lafrenie R, editor. PLoS One. 2012; 7: e32999. doi: 10.1371/journal.pone.0032999 PMID: 22412969
38. Sweetwyne MT, Tao J, Susztak K. Kick it up a notch: Notch signaling and kidney fibrosis. Kidney Int Suppl. 2014; 4: 91-96. doi: 10.1038/kisup.2014.17
39. Morell CM, Strazzabosco M. Notch signaling and new therapeutic options in liver disease. J Hepatol. 2014; 60: 885-90. doi: 10.1016/j.jhep.2013.11.028 PMID: 24308992
40. Garcia DM, Baek D, Shin C, Bell GW, Grimson A, Bartel DP. Weak seed-pairing stability and high target-site abundance decrease the proficiency of lsy-6 and other microRNAs. Nat Struct Mol Biol. Nature Publishing Group; 2011; 18:1139-1146. doi: 10.1038/nsmb.2115
41. Betel D, Koppal A, Agius P, Sander C, Leslie C. Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biol. 2010; 11: R90. doi: 10.1186/gb-2010-11-8-r90 PMID: 20799968
42. de Brevern AG, Meyniel J-P, Fairhead C, Neuvéglise C, Malpertuy A. Trends in IT Innovation to Build a Next Generation Bioinformatics Solution to Manage and Analyse Biological Big Data Produced by NGS Technologies. Biomed Res Int. 2015; 2015: 904541. doi: 10.1155/2015/904541 PMID: 26125026
43. Vicknair C, Macias M, Zhao Z, Nan X, Chen Y, Wilkins D. A comparison of a graph database and a relational database. Proceedings of the 48th Annual Southeast Regional Conference on - ACM SE '10. New York, New York, USA: ACM Press; 2010. p. 1.
44. Jouili S, Vansteenberghe V. An Empirical Comparison of Graph Databases. 2013 International Conference on Social Computing. IEEE; 2013. pp. 708-715.
45. Vlachos IS, Zagganas K, Paraskevopoulou MD, Georgakilas G, Karagkouni D, Vergoulis T, et al. DIANA-miRPath v3.0: deciphering microRNA function with experimental support. Nucleic Acids Res. 2015; 43: W460-6. doi: 10.1093/nar/gkv403 PMID: 25977294
46. Hsu S-D, Lin F-M, Wu W-Y, Liang C, Huang W-C, Chan W-L, et al. miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res. 2011; 39: D163-9. doi: 10.1093/nar/gkq1107 PMID: 21071411
47. Xiao F, Zuo Z, Cai G, Kang S, Gao X, Li T. miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res. 2009; 37: D105-10. doi: 10.1093/nar/gkn851 PMID: 18996891
48. Sethupathy P, Megraw M, Hatzigeorgiou AG. A guide through present computational approaches for the identification of mammalian microRNA targets. Nat Methods. 2006; 3: 881-6. doi: 10.1038/nmeth954 PMID: 17060911
49. Alexiou P, Maragkakis M, Papadopoulos GL, Reczko M, Hatzigeorgiou AG. Lost in translation: an assessment and perspective for computational microRNA target identification. Bioinformatics. 2009; 25: 3049-55. doi: 10.1093/bioinformatics/btp565 PMID: 19789267
50. Selbach M, Schwanhäusser B, Thierfelder N, Fang Z, Khanin R, Rajewsky N. Widespread changes in protein synthesis induced by microRNAs. Nature. 2008; 455: 58-63. doi: 10.1038/nature07228 PMID: 18668040
51. Fan X, Kurgan L. Comprehensive overview and assessment of computational prediction of microRNA targets in animals. Brief Bioinform. Oxford University Press; 2015; 16: 780-94. doi: 10.1093/bib/bbu044
52. Tay Y, Rinn J, Pandolfi PP. The multilayered complexity of ceRNA crosstalk and competition. Nature. 2014; 505: 344-52. doi: 10.1038/nature12986 PMID: 24429633
53. Yang J, Li T, Gao C, Lv X, Liu K, Song H, et al. FOXO1 3'UTR functions as a ceRNA in repressing the metastases of breast cancer cells via regulating miRNA activity. FEBS Lett. Federation of European Biochemical Societies; 2014; 588: 3218-24. doi: 10.1016/j.febslet.2014.07.003
54. Yuan Y, Liu B, Xie P, Zhang MQ, Li Y, Xie Z, et al. Model-guided quantitative analysis of microRNA-mediated regulation on competing endogenous RNAs using a synthetic gene circuit. Proc Natl Acad Sci USA. 2015; 112: 3158-63. doi: 10.1073/pnas.1413896112 PMID: 25713348
55. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995; 57: 289-300.