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Simultaneous Reconstruction of Multiple Signaling Pathways via the Prize-Collecting Steiner Forest Problem

  • Nurcan Tuncbag
  • Alfredo Braunstein
  • Andrea Pagnani
  • Shao-Shan Carol Huang
  • Jennifer Chayes
  • Christian Borgs
  • Riccardo Zecchina
  • Ernest Fraenkel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7262)

Abstract

Signaling networks are essential for cells to control processes such as growth and response to stimuli. Although many “omic” data sources are available to probe signaling pathways, these data are typically sparse and noisy. Thus, it has been difficult to use these data to discover the cause of the diseases. We overcome these problems and use “omic” data to simultaneously reconstruct multiple pathways that are altered in a particular condition by solving the prize-collecting Steiner forest problem. To evaluate this approach, we use the well-characterized yeast pheromone response. We then apply the method to human glioblastoma data, searching for a forest of trees each of which is rooted in a different cell surface receptor. This approach discovers both overlapping and independent signaling pathways that are enriched in functionally and clinically relevant proteins, which could provide the basis for new therapeutic strategies.

Keywords

Prize-collecting Steiner forest signaling pathways multiple network reconstruction 

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References

  1. 1.
    Lan, A., Smoly, I.Y., Rapaport, G., Lindquist, S., Fraenkel, E., Yeger-Lotem, E.: ResponseNet: Revealing signaling and regulatory networks linking genetic and transcriptomic screening data. Nucleic Acids Res. (2011)Google Scholar
  2. 2.
    Yeger-Lotem, E., Riva, L., Su, L.J., Gitler, A.D., Cashikar, A.G., King, O.D., Auluck, P.K., Geddie, M.L., Valastyan, J.S., Karger, D.R., et al.: Bridging high-throughput genetic and transcriptional data reveals cellular responses to alpha-synuclein toxicity. Nat. Genet. 41(3), 316–323 (2009)CrossRefGoogle Scholar
  3. 3.
    Vanunu, O., Magger, O., Ruppin, E., Shlomi, T., Sharan, R.: Associating genes and protein complexes with disease via network propagation. PLoS Comput. Biol. 6(1), e1000641 (2010)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Bailly-Bechet, M., Borgs, C., Braunstein, A., Chayes, J., Dagkessamanskaia, A., Francois, J.M., Zecchina, R.: Finding undetected protein associations in cell signaling by belief propagation. Proc. Natl. Acad. Sci. U.S.A. 108(2), 882–887 (2010)CrossRefGoogle Scholar
  5. 5.
    Dittrich, M.T., Klau, G.W., Rosenwald, A., Dandekar, T., Muller, T.: Identifying functional modules in protein-protein interaction networks: an integrated exact approach. Bioinformatics 24(13), i223–i231 (2008)CrossRefGoogle Scholar
  6. 6.
    Huang, S.S., Fraenkel, E.: Integrating proteomic, transcriptional, and interactome data reveals hidden components of signaling and regulatory networks. Sci. Signal 2(81), ra40 (2009)CrossRefGoogle Scholar
  7. 7.
    Friedman, N.: Inferring cellular networks using probabilistic graphical models. Science 303(5659), 799–805 (2004)CrossRefGoogle Scholar
  8. 8.
    Bailly-Bechet, M., Braunstein, A., Pagnani, A., Weigt, M., Zecchina, R.: Inference of sparse combinatorial-control networks from gene-expression data: a message passing approach. BMC Bioinformatics 11, 355 (2010)CrossRefGoogle Scholar
  9. 9.
    Ourfali, O., Shlomi, T., Ideker, T., Ruppin, E., Sharan, R.: SPINE: a framework for signaling-regulatory pathway inference from cause-effect experiments. Bioinformatics 23(13), i359–i366 (2007)CrossRefGoogle Scholar
  10. 10.
    Yeang, C.H., Ideker, T., Jaakkola, T.: Physical network models. J. Comput. Biol. 11(2-3), 243–262 (2004)CrossRefGoogle Scholar
  11. 11.
    Kim, Y.A., Wuchty, S., Przytycka, T.M.: Identifying causal genes and dysregulated pathways in complex diseases. PLoS Comput. Biol. 7(3), e1001095 (2011)CrossRefGoogle Scholar
  12. 12.
    Missiuro, P.V., Liu, K., Zou, L., Ross, B.C., Zhao, G., Liu, J.S., Ge, H.: Information flow analysis of interactome networks. PLoS Comput. Biol. 5(4), e1000350 (2009)CrossRefGoogle Scholar
  13. 13.
    Suthram, S., Beyer, A., Karp, R.M., Eldar, Y., Ideker, T.: eQED: an efficient method for interpreting eQTL associations using protein networks. Mol. Syst. Biol. 4, 162 (2008)CrossRefGoogle Scholar
  14. 14.
    Sharan, R., Ideker, T.: Modeling cellular machinery through biological network comparison. Nat. Biotechnol. 24(4), 427–433 (2006)CrossRefGoogle Scholar
  15. 15.
    Akavia, U.D., Litvin, O., Kim, J., Sanchez-Garcia, F., Kotliar, D., Causton, H.C., Pochanard, P., Mozes, E., Garraway, L.A., Pe’er, D.: An integrated approach to uncover drivers of cancer. Cell 143(6), 1005–1017 (2010)CrossRefGoogle Scholar
  16. 16.
    Bayati, M., Borgs, C., Braunstein, A., Chayes, J., Ramezanpour, A., Zecchina, R.: Statistical mechanics of steiner trees. Phys. Rev. Lett. 101(3), 037208 (2008)CrossRefGoogle Scholar
  17. 17.
    Gruhler, A., Olsen, J.V., Mohammed, S., Mortensen, P., Faergeman, N.J., Mann, M., Jensen, O.N.: Quantitative phosphoproteomics applied to the yeast pheromone signaling pathway. Mol. Cell Proteomics 4(3), 310–327 (2005)CrossRefGoogle Scholar
  18. 18.
    Issel-Tarver, L., Christie, K.R., Dolinski, K., Andrada, R., Balakrishnan, R., Ball, C.A., Binkley, G., Dong, S., Dwight, S.S., Fisk, D.G., et al.: Saccharomyces Genome Database. Methods Enzymol. 350, 329–346 (2002)CrossRefGoogle Scholar
  19. 19.
    Breitkreutz, A., Choi, H., Sharom, J.R., Boucher, L., Neduva, V., Larsen, B., Lin, Z.Y., Breitkreutz, B.J., Stark, C., Liu, G., et al.: A global protein kinase and phosphatase interaction network in yeast. Science 328(5981), 1043–1046 (2010)CrossRefGoogle Scholar
  20. 20.
    Jensen, L.J., Kuhn, M., Stark, M., Chaffron, S., Creevey, C., Muller, J., Doerks, T., Julien, P., Roth, A., Simonovic, M., et al.: STRING 8–a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res. 37(database issue), 412–416 (2009)CrossRefGoogle Scholar
  21. 21.
    Ben-Shlomo, I., Yu Hsu, S., Rauch, R., Kowalski, H.W., Hsueh, A.J.: Signaling receptome: A genomic and evolutionary perspective of plasma membrane receptors involved in signal transduction. Sci. STKE 2003(187), 9 (2003)CrossRefGoogle Scholar
  22. 22.
    Huang, P.H., Mukasa, A., Bonavia, R., Flynn, R.A., Brewer, Z.E., Cavenee, W.K., Furnari, F.B., White, F.M.: Quantitative analysis of EGFRvIII cellular signaling networks reveals a combinatorial therapeutic strategy for glioblastoma. Proc Natl. Acad. Sci. U.S.A. 104(31), 12867–12872 (2007)CrossRefGoogle Scholar
  23. 23.
    Chekuri, C., Ene, A., Korula, N.: Prize-Collecting Steiner Tree and Forest in Planar Graphs. Data Structures and Algorithms (2010)Google Scholar
  24. 24.
    Gupta, A., Konemann, J., Leonardi, S., Ravi, R., Schaefer, G.: An efficient cost-sharing mechanism for the prize-collecting Steiner forest problem. In: SODA 2007 Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms (2007)Google Scholar
  25. 25.
    Bailly-Bechet, M., Bradde, S., Braunstein, A., Flaxman, A., Foini, F., Zecchina, R.: Clustering with shallow trees. J. Stat. Mech., 12010 (2009)Google Scholar
  26. 26.
    Maere, S., Heymans, K., Kuiper, M.: BiNGO: A Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 21(16), 3448–3449 (2005)CrossRefGoogle Scholar
  27. 27.
    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 Res. 13(11), 2498–2504 (2003)CrossRefGoogle Scholar
  28. 28.
    Buehrer, B.M., Errede, B.: Coordination of the mating and cell integrity mitogen-activated protein kinase pathways in Saccharomyces cerevisiae. Mol. Cell Biol. 17(11), 6517–6525 (1997)Google Scholar
  29. 29.
    Zarzov, P., Mazzoni, C., Mann, C.: The SLT2(MPK1) MAP kinase is activated during periods of polarized cell growth in yeast. EMBO. J. 15(1), 83–91 (1996)Google Scholar
  30. 30.
    Garcia, R., Bermejo, C., Grau, C., Perez, R., Rodriguez-Pena, J.M., Francois, J., Nombela, C., Arroyo, J.: The global transcriptional response to transient cell wall damage in Saccharomyces cerevisiae and its regulation by the cell integrity signaling pathway. J. Biol. Chem. 279(15), 15183–15195 (2004)CrossRefGoogle Scholar
  31. 31.
    Baetz, K., Moffat, J., Haynes, J., Chang, M., Andrews, B.: Transcriptional coregulation by the cell integrity mitogen-activated protein kinase Slt2 and the cell cycle regulator Swi4. Mol. Cell Biol. 21(19), 6515–6528 (2001)CrossRefGoogle Scholar
  32. 32.
    Kaffman, A., Rank, N.M., O’Shea, E.K.: Phosphorylation regulates association of the transcription factor Pho4 with its import receptor Pse1/Kap121. Genes Dev. 12(17), 2673–2683 (1998)CrossRefGoogle Scholar
  33. 33.
    Bhoite, L.T., Allen, J.M., Garcia, E., Thomas, L.R., Gregory, I.D., Voth, W.P., Whelihan, K., Rolfes, R.J., Stillman, D.J.: Mutations in the pho2 (bas2) transcription factor that differentially affect activation with its partner proteins Bas1, Pho4, and Swi5. J. Biol. Chem. 277(40), 37612–37618 (2002)CrossRefGoogle Scholar
  34. 34.
    Cancer Genome Atlas Research Network: Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455(7216), 1061–1068 (2008)Google Scholar
  35. 35.
    Macaulay, V.M.: The IGF receptor as anticancer treatment target. In: Novartis Found Symp., 262:235-243; discussion 243-236, 265-238 (2004)Google Scholar
  36. 36.
    Kiaris, H., Schally, A.V., Varga, J.L.: Antagonists of growth hormone-releasing hormone inhibit the growth of U-87MG human glioblastoma in nude mice. Neoplasia 2(3), 242–250 (2000)CrossRefGoogle Scholar
  37. 37.
    Adams, T.E., McKern, N.M., Ward, C.W.: Signalling by the type 1 insulin-like growth factor receptor: interplay with the epidermal growth factor receptor. Growth Factors 22(2), 89–95 (2004)CrossRefGoogle Scholar
  38. 38.
    Chakravarti, A., Loeffler, J.S., Dyson, N.J.: Insulin-like growth factor receptor I mediates resistance to anti-epidermal growth factor receptor therapy in primary human glioblastoma cells through continued activation of phosphoinositide 3-kinase signaling. Cancer Res. 62(1), 200–207 (2002)Google Scholar
  39. 39.
    Kaur, B., Cork, S.M., Sandberg, E.M., Devi, N.S., Zhang, Z., Klenotic, P.A., Febbraio, M., Shim, H., Mao, H., Tucker-Burden, C., et al.: Vasculostatin inhibits intracranial glioma growth and negatively regulates in vivo angiogenesis through a CD36-dependent mechanism. Cancer Res. 69(3), 1212–1220 (2009)CrossRefGoogle Scholar
  40. 40.
    Silverstein, R.L., Febbraio, M.: CD36, a scavenger receptor involved in immunity, metabolism, angiogenesis, and behavior. Sci. Signal 2(72), re3 (2009)CrossRefGoogle Scholar
  41. 41.
    Dai, C., Celestino, J.C., Okada, Y., Louis, D.N., Fuller, G.N., Holland, E.C.: PDGF autocrine stimulation dedifferentiates cultured astrocytes and induces oligodendrogliomas and oligoastrocytomas from neural progenitors and astrocytes in vivo. Genes Dev. 15(15), 1913–1925 (2001)CrossRefGoogle Scholar
  42. 42.
    Uhrbom, L., Hesselager, G., Nister, M., Westermark, B.: Induction of brain tumors in mice using a recombinant platelet-derived growth factor B-chain retrovirus. Cancer Res. 58(23), 5275–5279 (1998)Google Scholar
  43. 43.
    Clarke, I.D., Dirks, P.B.: A human brain tumor-derived PDGFR-alpha deletion mutant is transforming. Oncogene 22(5), 722–733 (2003)CrossRefGoogle Scholar
  44. 44.
    Ziegler, D.S., Wright, R.D., Kesari, S., Lemieux, M.E., Tran, M.A., Jain, M., Zawel, L., Kung, A.L.: Resistance of human glioblastoma multiforme cells to growth factor inhibitors is overcome by blockade of inhibitor of apoptosis proteins. J. Clin. Invest. 118(9), 3109–3122 (2008)CrossRefGoogle Scholar
  45. 45.
    Li, Y., Li, A., Glas, M., Lal, B., Ying, M., Sang, Y., Xia, S., Trageser, D., Guerrero-Cazares, H., Eberhart, C.G., et al.: c-Met signaling induces a reprogramming network and supports the glioblastoma stem-like phenotype. Proc. Natl. Acad. Sci. U.S.A. 108(24), 9951–9956 (2011)CrossRefGoogle Scholar
  46. 46.
    Alam, N., Goel, H.L., Zarif, M.J., Butterfield, J.E., Perkins, H.M., Sansoucy, B.G., Sawyer, T.K., Languino, L.R.: The integrin-growth factor receptor duet. J. Cell Physiol. 213(3), 649–653 (2007)CrossRefGoogle Scholar
  47. 47.
    Cardona-Gomez, G.P., Mendez, P., DonCarlos, L.L., Azcoitia, I., Garcia-Segura, L.M.: Interactions of estrogen and insulin-like growth factor-I in the brain: molecular mechanisms and functional implications. J. Steroid Biochem. Mol. Biol. 83(1-5), 211–217 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Nurcan Tuncbag
    • 1
  • Alfredo Braunstein
    • 2
    • 3
  • Andrea Pagnani
    • 3
  • Shao-Shan Carol Huang
    • 1
  • Jennifer Chayes
    • 4
  • Christian Borgs
    • 4
  • Riccardo Zecchina
    • 2
    • 3
  • Ernest Fraenkel
    • 1
  1. 1.Department of Biological EngineeringMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.Department of Applied SciencePolitecnico di TorinoTorinoItaly
  3. 3.Human Genetics FoundationTorinoItaly
  4. 4.Microsoft Research New EnglandCambridgeUSA

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