A Densely Interconnected Genome-Wide Network of MicroRNAs and Oncogenic Pathways Revealed Using Gene Expression Signatures
MicroRNAs (miRNAs) are important components of cellular signaling pathways, acting either as pathway regulators or pathway targets. Currently, only a limited number of miRNAs have been functionally linked to specific signaling pathways. Here, we explored if gene expression signatures could be used to represent miRNA activities and integrated with genomic signatures of oncogenic pathway activity to identify connections between miRNAs and oncogenic pathways on a high-throughput, genome-wide scale. Mapping >300 gene expression signatures to >700 primary tumor profiles, we constructed a genome-wide miRNA–pathway network predicting the associations of 276 human miRNAs to 26 oncogenic pathways. The miRNA–pathway network confirmed a host of previously reported miRNA/pathway associations and uncovered several novel associations that were subsequently experimentally validated. Globally, the miRNA–pathway network demonstrates a small-world, but not scale-free, organization characterized by multiple distinct, tightly knit modules each exhibiting a high density of connections. However, unlike genetic or metabolic networks typified by only a few highly connected nodes (“hubs”), most nodes in the miRNA–pathway network are highly connected. Sequence-based computational analysis confirmed that highly-interconnected miRNAs are likely to be regulated by common pathways to target similar sets of downstream genes, suggesting a pervasive and high level of functional redundancy among coexpressed miRNAs. We conclude that gene expression signatures can be used as surrogates of miRNA activity. Our strategy facilitates the task of discovering novel miRNA–pathway connections, since gene expression data for multiple normal and disease conditions are abundantly available.
Vyšlo v časopise:
A Densely Interconnected Genome-Wide Network of MicroRNAs and Oncogenic Pathways Revealed Using Gene Expression Signatures. PLoS Genet 7(12): e32767. doi:10.1371/journal.pgen.1002415
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pgen.1002415
Souhrn
MicroRNAs (miRNAs) are important components of cellular signaling pathways, acting either as pathway regulators or pathway targets. Currently, only a limited number of miRNAs have been functionally linked to specific signaling pathways. Here, we explored if gene expression signatures could be used to represent miRNA activities and integrated with genomic signatures of oncogenic pathway activity to identify connections between miRNAs and oncogenic pathways on a high-throughput, genome-wide scale. Mapping >300 gene expression signatures to >700 primary tumor profiles, we constructed a genome-wide miRNA–pathway network predicting the associations of 276 human miRNAs to 26 oncogenic pathways. The miRNA–pathway network confirmed a host of previously reported miRNA/pathway associations and uncovered several novel associations that were subsequently experimentally validated. Globally, the miRNA–pathway network demonstrates a small-world, but not scale-free, organization characterized by multiple distinct, tightly knit modules each exhibiting a high density of connections. However, unlike genetic or metabolic networks typified by only a few highly connected nodes (“hubs”), most nodes in the miRNA–pathway network are highly connected. Sequence-based computational analysis confirmed that highly-interconnected miRNAs are likely to be regulated by common pathways to target similar sets of downstream genes, suggesting a pervasive and high level of functional redundancy among coexpressed miRNAs. We conclude that gene expression signatures can be used as surrogates of miRNA activity. Our strategy facilitates the task of discovering novel miRNA–pathway connections, since gene expression data for multiple normal and disease conditions are abundantly available.
Zdroje
1. KloostermanWPPlasterkRH 2006 The diverse functions of microRNAs in animal development and disease. Developmental Cell 11 441 450
2. BartelDP 2009 MicroRNAs: target recognition and regulatory functions. Cell 136 215 233
3. PetroccaFVecchioneACroceCM 2008 Emerging Role of miR-106b-25/miR-17-92 Clusters in the Control of Transforming Growth Factor β Signaling. Cancer Research 68 8191 8191
4. ChangTCYuDLeeYSWentzelEAArkingDE 2008 Widespread microRNA repression by Myc contributes to tumorigenesis. Nat Genet 40 43 50
5. ParkS-YLeeJHHaMNamJ-WKimVN 2008 miR-29 miRNAs activate p53 by targeting p85alpha and CDC42. Nature Structural & Molecular Biology 16 23 29
6. JimaDDZhangJJacobsCRichardsKLDunphyCH 2010 Deep sequencing of the small RNA transcriptome of normal and malignant human B cells identifies hundreds of novel microRNAs. Blood 116 e118 127
7. TsangJEbertMvan OudenaardenA 2010 Genome-wide dissection of microRNA functions and cotargeting networks using gene set signatures. Mol Cell 38 140 153
8. PapadopoulosGLAlexiouPMaragkakisMReczkoMHatzigeorgiouAG 2009 DIANA-mirPath: Integrating human and mouse microRNAs in pathways. Bioinformatics 25 1991 1993
9. WangX 2008 miRDB: a microRNA target prediction and functional annotation database with a wiki interface. RNA 14 1012 1017
10. YueDLiuHHuangY 2009 Survey of Computational Algorithms for MicroRNA Target Prediction. Curr Genomics 10 478 492
11. OoiCHIvanovaTWuJLeeMTanIB 2009 Oncogenic pathway combinations predict clinical prognosis in gastric cancer. PLoS Genet 5 e1000676 doi:10.1371/journal.pgen.1000676
12. MillerLDSmedsJGeorgeJVegaVBVergaraL 2005 An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. Proc Natl Acad Sci U S A 102 13550 13555
13. LobodaANebozhynMKlinghofferRFrazierJChastainM 2010 A gene expression signature of RAS pathway dependence predicts response to PI3K and RAS pathway inhibitors and expands the population of RAS pathway activated tumors. BMC Med Genomics 3 26
14. BildAHYaoGChangJTWangQPottiA 2006 Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 439 353 357
15. SmythGK 2005 Limma: linear models for microarray data. R. GentlemanVCDudoitSIrizarryRHuberW Bioinformatics and Computational Biology Solutions using R and Bioconductor New York Springer 397 420
16. O'DonnellKAWentzelEAZellerKIDangCVMendellJT 2005 c-Myc-regulated microRNAs modulate E2F1 expression. Nature 435 839 843
17. GuoMMaoXJiQLangMLiS 2010 miR-146a in PBMCs modulates Th1 function in patients with acute coronary syndrome. Immunology and Cell Biology doi:10.1038/icb.2010.16
18. KuhnASchlauchKLaoRHalaykoAGerthofferWT 2010 MicroRNA expression in human airway smooth muscle cells: role of miR-25 in regulation of airway smooth muscle phenotype. Am J Respir Cell Mol Biol 42 506 513
19. LiuXNelsonAWangXKanajiNKimM 2009 MicroRNA-146a modulates human bronchial epithelial cell survival in response to the cytokine-induced apoptosis. Biochem Biophys Res Commun 380 177 182
20. TaganovKDBoldinMPChangKJBaltimoreD 2006 NF-kappaB-dependent induction of microRNA miR-146, an inhibitor targeted to signaling proteins of innate immune responses. Proc Natl Acad Sci U S A 103 12481 12486
21. LoAKLoKWTsaoSWWongHLHuiJW 2006 Epstein-Barr virus infection alters cellular signal cascades in human nasopharyngeal epithelial cells. Neoplasia 8 173 180
22. TarasovVJungPVerdoodtBLodyginDEpanchintsevA 2007 Differential regulation of microRNAs by p53 revealed by massively parallel sequencing: miR-34a is a p53 target that induces apoptosis and G1-arrest. Cell Cycle 6 1586 1593
23. YanHLXueGMeiQWangYZDingFX 2009 Repression of the miR-17-92 cluster by p53 has an important function in hypoxia-induced apoptosis. EMBO J 28 2719 2732
24. CoombsGSYuJCanningCAVeltriCACoveyTM 2010 WLS-dependent secretion of WNT3A requires Ser209 acylation and vacuolar acidification. J Cell Sci 123 3357 3367
25. KorinekVBarkerNMorinPJvan WichenDde WegerR 1997 Constitutive transcriptional activation by a β-catenin -Tcf complex in APC-/- colon carcinoma. Science 275 1784 1787
26. SubramanianATamayoPMoothaVKMukherjeeSEbertBL 2005 Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 102 43 15545 15550
27. KennyPAEnverTAshworthA 2005 Receptor and secreted targets of Wnt-1/β-catenin signalling in mouse mammary epithelial cells. BMC Cancer 5 3
28. LinYMOnoKSatohSIshiguroHFujitaM 2001 Identification of AF17 as a downstream gene of the β-catenin T-cell factor pathway and its involvement in colorectal carcinogenesis. Cancer Research 61 6345 6349
29. LeeHHsuASajdakJQinJPavlidisP 2004 Coexpression analysis of human genes across many microarray data sets. Genome Res 14 1085 1094
30. BrenneckeJStarkARussellRBCohenSM 2005 Principles of MicroRNA–Target Recognition. PLoS Biol 3 e85 doi:10.1371/journal.pbio.0030085
31. YuJWangFYangG-HWangF-LMaY-N 2006 Human microRNA clusters: Genomic organization and expression profile in leukemia cell lines. Biochemical and Biophysical Research Communications 349 59 68
32. HumphriesMDGurneyKPrescottTJ 2006 The brainstem reticular formation is a small-world, not scale-free, network. Proceedings of the Royal Society B: Biological Sciences 273 503 511
33. WattsDJStrogatzSH 1998 Collective dynamics of ‘small-world’ networks. Nature 393 440 442
34. MontoyaJMSolRV 2002 Small world patterns in food webs. J Theor Biol 214 405 412
35. BarabasiALOltvaiZN 2004 Network biology: understanding the cell's functional organization. Nature Review Genetics 5 101 113
36. van NoortVSnelBHuynenMA 2004 The yeast coexpression network has a small-world, scale-free architecture and can be explained by a simple model. EMBO Reports 5 280 284
37. LambJCrawfordEDPeckDModellJWBlatIC 2006 The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313 5795 1929 1935
38. AlbertRBarabasiA-L 2002 Statistical mechanics of complex networks. REVIEWS OF MODERN PHYSICS 74 47 97
39. SkalskyRLSamolsMAPlaisanceKBBossIWRivaA 2007 Kaposi's sarcoma-associated herpesvirus encodes an ortholog of miR-155. J Virol 81 12836 12845
40. BenjaminiYHochbergY 1995 Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B (Methodological) 57 289 300
41. StoreyJDTibshiraniR 2003 Statistical significance for genome-wide experiments. Proc Natl Acad Sci U S A 100 9440 9445
42. GanesanKIvanovaTWuYRajasegaranVWuJ 2008 Inhibition of Gastric Cancer Invasion and Metastasis by PLA2G2A, a Novel β-catenin/TCF Target Gene. Cancer Res 68 4277 4286
43. JohnsonWELiCRabinovicA 2007 Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8 118 127
44. WestDB 2001 Introduction to Graph Theory: Prentice Hall
45. RavaszEBarabásiAL 2003 Hierarchical organization in complex networks. Phys Rev E Stat Nonlin Soft Matter Phys 67 026112
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Genetika Reprodukčná medicínaČlánok vyšiel v časopise
PLOS Genetics
2011 Číslo 12
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