Research on Signaling Pathways Reconstruction by Integrating High Content RNAi Screening and Functional Gene Network

  • Zhu-Hong You
  • Zhong Ming
  • Liping Li
  • Qiao-Ying Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7996)


The relatively new technology of RNA interference (RNAi) can be used to suppress almost any gene on a genome wide scale. Fluorescence microscopy is an ever more advancing technology that allows for the visualization of cells in multidimensional fashion. The combination of these two techniques paired with automated image analysis is emerging as a powerful tool in system biology. It can be used to study the effects of gene knockdowns on cellular phenotypes thereby lightening shadow into the understanding of complex biological processes. In this paper we propose the use of high content screen (HCS) to derive a high quality functional gene network (FGN) for Drosophila Melanogaster. This approach is based on the characteristic patterns obtained from cell images under different gene knockdown conditions. We guarantee a high coverage of the resulting network by the further integration of a large set of heterogeneous genomic data. The integration of these diverse datasets is based on a linear support vector machine. The final network is analyzed and a signal transduction pathway for the mitogen-activated protein kinase (MAPK) pathway is extracted using an extended integer linear programming algorithm. We validate our results and demonstrate that the proposed method achieves full coverage of components deposited in KEGG for the MAPK pathway. Interestingly, we retrieved a set of additional candidate genes for this pathway (e.g. including sev, tsl, hb) that we suggest for future experimental validation.


pathway reconstruction high content screen functional gene networks 


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  1. 1.
    Baudot, J.B., Angelelli, G.A., et al.: Defining a Modular Signalling network from the fly interactome. BMC Syst. Biol. 2(45) (2008)Google Scholar
  2. 2.
    You, Z.H., Yin, Z., Han, K., Huang, D.S., Zhou, X.B.: A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network. BMC Bioinformatics 11(343) (2010)Google Scholar
  3. 3.
    Zhao, X.M., Wang, R.S., Chen, L.: Uncovering signal transduction networks from high-throughput data by integer linear programming. Nucleic Acids Res. 36(9) (2008)Google Scholar
  4. 4.
    Scott, J., Ideker, T., Karp, R.M.: Efficient algorithms for detecting signaling pathways in protein interaction networks. J. Comput. Biol. 13(2), 133–144 (2006)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Aoki, K.F., Kanehisa, M.: Using the KEGG database resource. Curr Protoc Bioinformatics, ch.1, pp. 1– 12 (2005) Google Scholar
  6. 6.
    Li, F., Zhou, X.B., Ma, J.: An automated feedback system with the hybrid model of scoring and classification for solving over-segmentation problems in RNAi high content screening. Journal of Microscopy-Oxford 226(2), 121–132 (2007)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Bakal, C., Aach, J., Church, G.: Quantitative morphological signatures define local signaling networks regulating cell morphology. Science 316(5832), 1753–1756 (2007)CrossRefGoogle Scholar
  8. 8.
    Yin, Z., Zhou, X.B., Bakal, C., et al.: Using iterative cluster merging with improved gap statistics to perform online phenotype discovery in the context of high-throughput RNAi screens. BMC Bioinformatics 9(264) (2008)Google Scholar
  9. 9.
    Perrimon, N., Mathey-Prevot, B.: Applications of high-throughput RNA interference screens to problems in cell and developmental biology. Genetics 175(1), 7–16 (2007)CrossRefGoogle Scholar
  10. 10.
    Lee, I., Lehner, B., Crombie, C.: A single gene network accurately predicts phenotypic effects of gene perturbation in Caenorhabditis elegans. Nature Genetics 40(2), 181–188 (2008)CrossRefGoogle Scholar
  11. 11.
    You, Z.H., Lei, Y.K., Huang, D.S., Zhou, X.B.: Using manifold embedding for assessing and predicting protein interactions from high-throughput experimental data. Bioinformatics 26(21), 2744–2751 (2010)CrossRefGoogle Scholar
  12. 12.
    Edgar, R., Domrachev, M., Lash, A.E.: Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30(1), 207–210 (2002)CrossRefGoogle Scholar
  13. 13.
    Maraziotis, I.A., Dimitrakopoulou, K., Bezerianos, A.: Growing functional modules from a seed protein via integration of protein interaction and gene expression data. BMC Bioinformatics 8, 408 (2007)CrossRefGoogle Scholar
  14. 14.
    Mering, C., Jensen, L.J., Kuhn, M.: STRING 7 - recent developments in the integration and prediction of protein interactions. Nucleic Acids Research 35, D358–D362 (2007)CrossRefGoogle Scholar
  15. 15.
    You, Z.H., Lei, Y.K., Zhu, L., Xia, J.F., Wang, B.: Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis. BMC Bioinformatics 14(S10) (2013)Google Scholar
  16. 16.
    Lei, Y.K., You, Z.H., Ji, Z., Zhu, L., Huang, D.S.: Assessing and predicting protein interactions by combining manifold embedding with multiple information integration. BMC Bioinformatics 13(S3) (2012)Google Scholar
  17. 17.
    Zheng, C.H., Huang, D.S., Zhang, L., Kong, X.Z.: Tumor clustering using non-negative matrix factorization with gene selection. IEEE Transactions on Information Technology in Biomedicine 13(4), 599–607 (2009)CrossRefGoogle Scholar
  18. 18.
    Bader, G.D., Betel, D., Hogue, C.W.: BIND: the Biomolecular Interaction Network Database. Nucleic Acids Res. 31(1), 248–250 (2003)CrossRefGoogle Scholar
  19. 19.
    Igaki, T., Kanda, H., Yamamoto-Goto, Y.: Eiger, a TNF superfamily ligand that triggers the Drosophila JNK pathway. EMBO J. 21(12), 3009–3018 (2002)CrossRefGoogle Scholar
  20. 20.
    Lim, Y.M., Nishizawa, K., Nishi, Y., et al.: Genetic analysis of rolled, which encodes a Drosophila mitogen-activated protein kinase. Genetics 153(2), 763–771 (1999)Google Scholar
  21. 21.
    Maus, M., Medgyesi, D., Kovesdi, D.: Grb2 associated binder 2 couples B-cell receptor to cell survival. Cell Signal 21(2), 220–227 (2009)CrossRefGoogle Scholar
  22. 22.
    Sawamoto, K., Okabe, M., Tanimura, T.: The Drosophila secreted protein Argos regulates signal transduction in the Ras/MAPK pathway. Dev. Biol. 178(1), 13–22 (1996)CrossRefGoogle Scholar
  23. 23.
    Janody, F., Sturny, R., Catala, F.: Phosphorylation of bicoid on MAP-kinase sites: contribution to its interaction with the torso pathway. Development 127(2), 279–289 (2000)Google Scholar
  24. 24.
    Spirov, A.V., Holloway, D.M.: Making the body plan: precision in the genetic hierarchy of Drosophila embryo segmentation. Silico Biol. 3(1-2), 89–100 (2003)Google Scholar
  25. 25.
    Davies, S.A., Stewart, E.J., Huesmann, G.R.: Neuropeptide stimulation of the nitric oxide signaling pathway in Drosophila melanogaster Malpighian tubules. Am J. Physiol. 273(2 Pt 2), R823–R827 (1997)Google Scholar
  26. 26.
    Linghu, B., Snitkin, E.S., Holloway, D.T.: High-precision high-coverage functional inference from integrated data sources. BMC Bioinformatics 9(119) (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Zhu-Hong You
    • 1
  • Zhong Ming
    • 1
  • Liping Li
    • 2
  • Qiao-Ying Huang
    • 1
  1. 1.College of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina
  2. 2.The Institute of Soil and Water Conservation of GansuLanzhouChina

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