A Graph Community Approach for Constructing microRNA Networks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9196)

Abstract

Network integration methods are critical in understanding the underlying mechanisms of genetic perturbations and susceptibility to disease. Often, expression quantitative trait loci (eQTL) mapping is used to integrate two layers of genomic data. However, eQTL associations only represent the direct associations among eQTLs and affected genes. To understand the downstream effects of eQTLs on gene expression, we propose a network community approach to construct eQTL networks that integrates multiple data sources. By using this approach, we can view the genetic networks consisting of genes affected directly or indirectly by genetic variants. To extend the eQTL network, we use a protein-protein interaction network as a base network and a spin glass community detection algorithm to find hubs of genes that are indirectly affected by eQTLs. This method contributes a novel approach to identifying indirect targets that may be affected by variant perturbations. To demonstrate its application, we apply this approach to study how microRNAs affect the expression of target genes and their indirect downstream targets in ovarian cancer.

Keywords

Network integration Graph community detection Spin glass microRNA networks 

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References

  1. 1.
    Gamazon, E.R., et al.: Genetic architecture of microRNA expression: implications for the transcriptome and complex traits. Am J. Hum Genet 90(6), 1046–1063 (2012)CrossRefGoogle Scholar
  2. 2.
    Lappalainen, T., et al.: Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501(7468), 506–511 (2013)CrossRefGoogle Scholar
  3. 3.
    Huan, T., et al.: Genome-wide identification of microRNA expression quantitative trait loci. Nat Commun. 6, 6601 (2015)CrossRefGoogle Scholar
  4. 4.
    Tian, L., Quitadamo, A., Lin, F., Shi, X.: Methods for Population Based eQTL Analysis in Human Genetics. Tsinghua Science and Technology 19(6), 624–634 (2014)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Chen, X., Shi, X., Xu, X., Wang, Z., Mills, R.E., Lee, C., Xu, J.: A two-graph guided multi-task lasso approach for eQTL mapping. Proceedings of the 15th International Conference of Artificial Intelligence and Statistics (AISTATS), Journal of Machine Learning Research (JMLR) W&CP 22, 208–217 (2012)Google Scholar
  6. 6.
    Online Mendelian Inheritance in Man (OMIM). URL: http://omim.org/
  7. 7.
    Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D., Amin, N., Schwikowski, B., Ideker, T.: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Research 13(11), 2498–2504 (2003)CrossRefGoogle Scholar
  8. 8.
    Cancer Genome Atlas Research Network: Integrated genomic analyses of ovarian carcinoma. Nature 474, 609–615 (2011)Google Scholar
  9. 9.
    Ryan, B.M., Robles, A.I., Harris, C.C.: Genetic variation in microRNA networks: the implications for cancer research. Nat. Rev. Cancer 10(6), 389–402 (2010)CrossRefGoogle Scholar
  10. 10.
    Csardi, G., Nepusz, T.: The igraph software package for complex network research. InterJournal, Complex Systems 1695 (2006)Google Scholar
  11. 11.
    Shabalin, A.A.: Matrix eqtl: Ultra fast eqtl analysis via large matrix operations. Bioinformatics 28(10), 1353–1358 (2012)CrossRefGoogle Scholar
  12. 12.
    Xie, B., et al.: miRCancer: a microRNA cancer association database constructed by text mining on literature. Bioinformatics, btt014 (2013)Google Scholar
  13. 13.
    Ho, Y.-Y., Cope, L.M., Parmigiani, G.: Modular network construction using eqtl data: an analysis of computational costs and benefits. Frontiers in genetics 5, 40–40 (2014)CrossRefGoogle Scholar
  14. 14.
    Huang, Y., Wuchty, S., Przytycka, T.M.: Eqtl epistasis - challenges and computational approaches. Frontiers in Genetics 4, 51–51 (2013)Google Scholar
  15. 15.
    Liu, C., Guo, J., Dung-Chul, K., Wang, J.: Inference of snp-gene regulatory networks by integrating gene expressions and genetic perturbations. BioMedical Research InternationalGoogle Scholar
  16. 16.
    Lage, K., Karlberg, E.O., Størling, Z.M., Olason, P.I., Pedersen, A.G., Rigina, O., Hinsby, A.M., Tümer, Z.: A human phenome-interactome network of protein complexes implicated in genetic disorders. Nature biotechnology 25(3), 309–316 (2007)CrossRefGoogle Scholar
  17. 17.
    Li, Y., Sheu, C.-C., Ye, Y., de Andrade, M., Wang, L., Chang, S.-C., Aubry, M.C., Aakre, J.A., Allen, M.S., Chen, F., et al.: Genetic variants and risk of lung cancer in never smokers: a genome-wide association study. The lancet oncology 11(4), 321–330 (2010)CrossRefGoogle Scholar
  18. 18.
    Kirkpatrick, S.: Optimization by simulated annealing: Quantitative studies. Journal of statistical physics 34(5–6), 975–986 (1984)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Liu, Y., Maxwell, S., Feng, T., Zhu, X., Elston, R.C., Koyutürk, M., Chance, M.R.: Gene, pathway and network frameworks to identify epistatic interactions of single nucleotide polymorphisms derived from gwas data. BMC systems biology 6(Suppl 3), S15 (2012)CrossRefGoogle Scholar
  20. 20.
    Eaton, E., Mansbach, R.: A Spin-Glass Model for Semi-Supervised Community Detection. In: AAAI (2012)Google Scholar
  21. 21.
    Quitadamo, A., Tian, L., Hall, B., Shi, X.: An Integrated Network of microRNA and Gene Expression in Ovarian Cancer. BMC Bioinformatics 16(Suppl 5), S5 (2015)CrossRefGoogle Scholar
  22. 22.
    Rachel Wang, Y.X., Huang, H.: Review on statistical methods for gene network reconstruction using expression data. Journal of theoretical biology 04, 1–9 (2014)CrossRefGoogle Scholar
  23. 23.
    Pan, L., Wang, C., Xie, J.: A spin-glass model based local community detection method in social networks. In: 2013 IEEE 25th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE (2013)Google Scholar
  24. 24.
    Corney, D.C., Hwang, C.-I., Matoso, A., Vogt, M., Flesken-Nikitin, A., Godwin, A.K., Kamat, A.A., Sood, A.K., Ellenson, L.H., Hermeking, H., et al.: Frequent downregulation of mir-34 family in human ovarian cancers. Clinical Cancer Research 16(4), 1119–1128 (2010)CrossRefGoogle Scholar
  25. 25.
    Brüning-Richardson, A., Bond, J., Alsiary, R., Richardson, J., Cairns, D.A., McCormac, L., Hutson, R., Burns, P.A., Wilkinson, N., Hall, G.D., et al.: Numa overexpression in epithelial ovarian cancer. PloS one 7(6), e38945 (2012)CrossRefGoogle Scholar
  26. 26.
    Flutre, T., Wen, X., Pritchard, J., Stephens, M.: A Statistical Framework for Joint eQTL Analysis in Multiple Tissues. PLoS Genet 9(5), e1003486 (2013)CrossRefGoogle Scholar
  27. 27.
    He, J., Jing, Y., Wei Li, X., Qian, Q.X., Li, F.-S., Liu, L.-Z., Jiang, B.-H., Jiang, Y.: Roles and mechanism of mir-199a and mir-125b in tumor angiogenesis. PLoS One 8(2), e56647 (2013)CrossRefGoogle Scholar
  28. 28.
    Liu, T., Hou, L., Huang, Y.: Ezh2-specific microrna-98 inhibits human ovarian cancer stem cell proliferation via regulating the prb-e2f pathway. Tumor Biology 35(7), 7239–7247 (2014)CrossRefGoogle Scholar
  29. 29.
    Prokopi, M., Kousparou, C.A., Epenetos, A.A.: The Secret Role of microRNAs in Cancer Stem Cell Development and Potential Therapy: A Notch-Pathway Approach. Frontiers in Oncology 4, 389 (2014)MATHGoogle Scholar
  30. 30.
    Yan-ming, L., Shang, C., Yang-ling, O., Yin, D., Li, Y.-N., Li, X., Wang, N., Zhang, S.: mir-200c modulates ovarian cancer cell metastasis potential by targeting zinc finger e-box-binding homeobox 2 (zeb2) expression. Medical Oncology 31(8), 1–11 (2014)Google Scholar
  31. 31.
    Park, Y.T., Jeong, J.Y., Lee, M.J., Kim, K.I., Kim, T.-H., Kwon, Y.D., Lee, C., Kim, O.J., An, H.-J.: Micrornas overexpressed in ovarian aldh1-positive cells are associated with chemoresistance. J. Ovarian. Res. 6(1), 18 (2013)CrossRefGoogle Scholar
  32. 32.
    Reichardt, J., Bornholdt, S.: Statistical mechanics of community detection. Physical Review E 74(1), 016110 (2006)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Shen, W., Song, M., Liu, J., Qiu, G., Li, T., Yanjie, H., Liu, H.: Mir-26a promotes ovarian cancer proliferation and tumorigenesis. PloS one 9(1), e86871 (2014)CrossRefGoogle Scholar
  34. 34.
    Dernyi, I., Palla, G., Vicsek, T.: Clique percolation in random networks. Physical review letters 94(16), 160202 (2005)CrossRefGoogle Scholar
  35. 35.
    Prislei, S., Martinelli, E., Mariani, M., Raspaglio, G., Sieber, S., Ferrandina, G., Shahabi, S., Scambia, G., Ferlini, C.: MiR-200c and HuR in ovarian cancer. BMC Cancer 13, 72 (2013)CrossRefGoogle Scholar
  36. 36.
    Marchini, S., Cavalieri, D., Fruscio, R., Calura, E., Garavaglia, D., Nerini, I.F., Mangioni, C., Cattoretti, G., livio, L., Beltrame, L., Katsaros, D., Scarampi, L., Menato, G., Perego, P., Chiorino, G., Buda, A., Romualdi, C., D’Incalci, M.: Association between miR-200c and the survival of patients with stage I epithelial ovarian cancer: a retrospective study of two independent tumour tissue collections. The Lancet Oncology 12(3), 273–285 (2011)CrossRefGoogle Scholar
  37. 37.
    Lu, L.J., Xia, Y., Paccanaro, A., Yu, H., Gerstein, M.: Assessing the limits of genomic data integration for predicting protein networks. Genome Research 15(7), 945953 (2005)CrossRefGoogle Scholar
  38. 38.
    Nitzan, M., Steiman-Shimony, A., Altuvia, Y., Biham, O., Margalit, H.: Interactions between Distant ceRNAs in Regulatory Networks. Biophysical Journal 106(10), 2254–2266Google Scholar
  39. 39.
    Huang, D., Zhou, X., Lyon, C.J., Hsueh, W.A., Wong, S.T.C.: MicroRNA-Integrated and Network-Embedded Gene Selection with Diffusion Distance. PLoS ONE 5(10), e13748 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Bioinformatics and GenomicsUniversity of North Carolina at CharlotteCharlotteUSA

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