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Analysis of MicroRNA and Transcription Factor Regulation

  • Wei-Li Guo
  • Kyungsook Han
  • De-Shuang HuangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9771)

Abstract

Gene regulatory networks in different tissues offer insight into the mechanism of tissue identity and function. Here we construct regulatory networks in 10 human tissues including regulations among miRNAs, transcription factors and genes. The results reveal that TS miRNAs are regulated largely by non-tissue specific TFs. TS miRNAs connect with more TFs compared with trivial miRNAs, inferring tight co-regulation of gene expression for TS miRNAs and TFs. Both TS miRNAs and TSTFs tend to regulate broad sets of genes involved in tissue specific functions. In particular, we identified tissue specific regulations instrumental to defining tissue specific functions, and some pathways important to tissue identity or disease, which cannot be explained by only tissue specific genes, can be captured in our tissue specific regulations.

Keywords

Gene regulatory network miRNA  Transcription factor Tissue specific regulation Tissue specificity 

Notes

Acknowledgements

This work was supported by the grants of the National Science Foundation of China, Nos. 61133010, 61520106006, 31571364, 61532008, 61572364, 61373105, 61303111, 61411140249, 61402334, 61472282, 61472280, 61472173, 61572447, and 61373098, China Postdoctoral Science Foundation Grant, Nos. 2014M561513 and 2015M580352.

References

  1. 1.
    Greene, C.S., Krishnan, A., Wong, A.K., Ricciotti, E., Zelaya, R.A., Himmelstein, D.S., Zhang, R., Hartmann, B.M., Zaslavsky, E., Sealfon, S.C.: Understanding multicellular function and disease with human tissue-specific networks. Nat. Genet. 47, 569–576 (2015)CrossRefGoogle Scholar
  2. 2.
    Pierson, E., Koller, D., Battle, A., Mostafavi, S., Consortium, G.: Sharing and specificity of co-expression networks across 35 human tissues (2015)Google Scholar
  3. 3.
    Zhu, L., Guo, W.-L., Deng, S.-P., Huang, D.-S.: ChIP-PIT: enhancing the analysis of ChIP-seq data using convex-relaxed pair-wise tensor decompositionGoogle Scholar
  4. 4.
    Shalgi, R., Lieber, D., Oren, M., Pilpel, Y.: Global and local architecture of the mammalian microRNA–transcription factor regulatory network. PLoS Comput. Biol. 3(7), e131 (2007)CrossRefGoogle Scholar
  5. 5.
    Gerstein, M.B., Kundaje, A., Hariharan, M., Landt, S.G., Yan, K.-K., Cheng, C., Mu, X.J., Khurana, E., Rozowsky, J., Alexander, R.: Architecture of the human regulatory network derived from ENCODE data. Nature 489(7414), 91–100 (2012)CrossRefGoogle Scholar
  6. 6.
    Jiang, C., Xuan, Z., Zhao, F., Zhang, M.Q.: TRED: a transcriptional regulatory element database, new entries and other development. Nucleic Acids Res. 35(suppl 1), D137–D140 (2007)CrossRefGoogle Scholar
  7. 7.
    Portales-Casamar, E., Arenillas, D., Lim, J., Swanson, M.I., Jiang, S., McCallum, A., Kirov, S., Wasserman, W.W.: The PAZAR database of gene regulatory information coupled to the ORCA toolkit for the study of regulatory sequences. Nucleic Acids Res. 37(suppl 1), D54–D60 (2009)CrossRefGoogle Scholar
  8. 8.
    Han, H., Shim, H., Shin, D., Shim, J.E., Ko, Y., Shin, J., Kim, H., Cho, A., Kim, E., Lee, T.: TRRUST: a reference database of human transcriptional regulatory interactions. Sci. Rep. 5 (2015)Google Scholar
  9. 9.
    Consortium, E.P.: An integrated encyclopedia of DNA elements in the human genome. Nature 489(7414), 57–74 (2012)CrossRefGoogle Scholar
  10. 10.
    Mathelier, A., Zhao, X., Zhang, A.W., Parcy, F., Worsley-Hunt, R., Arenillas, D.J., Buchman, S., Chen, C.-Y., Chou, A., Ienasescu, H.: JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles. Nucleic Acids Res. gkt997 (2013)Google Scholar
  11. 11.
    Griffiths-Jones, S., Grocock, R.J., Van Dongen, S., Bateman, A., Enright, A.J.: miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res. 34(suppl 1), D140–D144 (2006)CrossRefGoogle Scholar
  12. 12.
    Wang, J., Lu, M., Qiu, C., Cui, Q.: TransmiR: a transcription factor–microRNA regulation database. Nucleic Acids Res. 38(suppl 1), D119–D122 (2010)CrossRefGoogle Scholar
  13. 13.
    Hsu, S.-D., Lin, F.-M., Wu, W.-Y., Liang, C., Huang, W.-C., Chan, W.-L., Tsai, W.-T., Chen, G.-Z., Lee, C.-J., Chiu, C.-M.: miRTarBase: a database curates experimentally validated microRNA–target interactions. Nucleic Acids Res. gkq1107 (2010)Google Scholar
  14. 14.
    Krek, A., Grün, D., Poy, M.N., Wolf, R., Rosenberg, L., Epstein, E.J., MacMenamin, P., da Piedade, I., Gunsalus, K.C., Stoffel, M.: Combinatorial microRNA target predictions. Nat. Genet. 37(5), 495–500 (2005)CrossRefGoogle Scholar
  15. 15.
    Chang, C.-W., Cheng, W.-C., Chen, C.-R., Shu, W.-Y., Tsai, M.-L., Huang, C.-L., Hsu, I.C.: Identification of human housekeeping genes and tissue-selective genes by microarray meta-analysis. PLoS ONE 6(7), e22859 (2011)CrossRefGoogle Scholar
  16. 16.
    Kim, M.-S., Pinto, S.M., Getnet, D., Nirujogi, R.S., Manda, S.S., Chaerkady, R., Madugundu, A.K., Kelkar, D.S., Isserlin, R., Jain, S.: A draft map of the human proteome. Nature 509(7502), 575–581 (2014)CrossRefGoogle Scholar
  17. 17.
    Landgraf, P., Rusu, M., Sheridan, R., Sewer, A., Iovino, N., Aravin, A., Pfeffer, S., Rice, A., Kamphorst, A.O., Landthaler, M.: A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 129(7), 1401–1414 (2007)CrossRefGoogle Scholar
  18. 18.
    Ravasi, T., Suzuki, H., Cannistraci, C.V., Katayama, S., Bajic, V.B., Tan, K., Akalin, A., Schmeier, S., Kanamori-Katayama, M., Bertin, N.: An atlas of combinatorial transcriptional regulation in mouse and man. Cell 140(5), 744–752 (2010)CrossRefGoogle Scholar
  19. 19.
    Esquela-Kerscher, A., Slack, F.J.: Oncomirs—microRNAs with a role in cancer. Nat. Rev. Cancer 6(4), 259–269 (2006)CrossRefGoogle Scholar
  20. 20.
    Huang, D.-S.: Systematic Theory of Neural Networks for Pattern Recognition, vol. 28, pp. 323–332. Publishing House of Electronic Industry of China, Beijing (1996)Google Scholar
  21. 21.
    Yu, X., Lin, J., Zack, D.J., Qian, J.: Computational analysis of tissue-specific combinatorial gene regulation: predicting interaction between transcription factors in human tissues. Nucleic Acids Res. 34(17), 4925–4936 (2006)CrossRefGoogle Scholar
  22. 22.
    Dennis Jr., G., Sherman, B.T., Hosack, D.A., Yang, J., Gao, W., Lane, H.C., Lempicki, R.A.: DAVID: database for annotation, visualization, and integrated discovery. Genome Biol. 4(5), P3 (2003)CrossRefGoogle Scholar
  23. 23.
    Li, J., Kozono, D., Nitta, M., Sampetrean, O., Gonda, D., Kushwaha, D.S., Merzon, D., Ramakrishnan, V., Zhu, S., Zhu, K.: Dynamic epigenetic regulation of glioblastoma tumorigenicity through LSD1 modulation of MYC expression. Cancer Res. 75(15 Supplement), 979 (2015)CrossRefGoogle Scholar
  24. 24.
    Kaur, B., Khwaja, F.W., Severson, E.A., Matheny, S.L., Brat, D.J., Van Meir, E.G.: Hypoxia and the hypoxia-inducible-factor pathway in glioma growth and angiogenesis. Neuro-oncology 7(2), 134–153 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of Machine Learning and Systems Biology, College of Electronics and Information Engineering, Tongji UniversityShanghaiChina
  2. 2.Department of Computer Science and EngineeringInha UniversityIncheonSouth Korea

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