Genome-Wide Discovery of Modulators of Transcriptional Interactions in Human B Lymphocytes

  • Kai Wang
  • Ilya Nemenman
  • Nilanjana Banerjee
  • Adam A. Margolin
  • Andrea Califano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3909)


Transcriptional interactions in a cell are modulated by a variety of mechanisms that prevent their representation as pure pairwise interactions between a transcription factor and its target(s). These include, among others, transcription factor activation by phosphorylation and acetylation, formation of active complexes with one or more co-factors, and mRNA/protein degradation and stabilization processes.

This paper presents a first step towards the systematic, genome-wide computational inference of genes that modulate the interactions of specific transcription factors at the post-transcriptional level. The method uses a statistical test based on changes in the mutual information between a transcription factor and each of its candidate targets, conditional on the expression of a third gene. The approach was first validated on a synthetic network model, and then tested in the context of a mammalian cellular system. By analyzing 254 microarray expression profiles of normal and tumor related human B lymphocytes, we investigated the post transcriptional modulators of the MYC proto-oncogene, an important transcription factor involved in tumorigenesis. Our method discovered a set of 100 putative modulator genes, responsible for modulating 205 regulatory relationships between MYC and its targets. The set is significantly enriched in molecules with function consistent with their activities as modulators of cellular interactions, recapitulates established MYC regulation pathways, and provides a notable repertoire of novel regulators of MYC function. The approach has broad applicability and can be used to discover modulators of any other transcription factor, provided that adequate expression profile data are available.


Mutual Information Candidate Modulator Synthetic Network Putative Modulator Expression Range 


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  1. 1.
    Friedman, N.: Inferring cellular networks using probabilistic graphical models. Science 303, 799–805 (2004)CrossRefGoogle Scholar
  2. 2.
    Gardner, T.S., di Bernardo, D., Lorenz, D., Collins, J.J.: Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301, 102–105 (2003)CrossRefGoogle Scholar
  3. 3.
    Elkon, R., Linhart, C., Sharan, R., Shamir, R., Shiloh, Y.: Genome-Wide In Silico Identification of Transcriptional Regulators Controlling the Cell Cycle in Human Cells. Genome Res. 13, 773–780 (2003)CrossRefGoogle Scholar
  4. 4.
    Stuart, J.M., Segal, E., Koller, D., Kim, S.K.: A gene-coexpression network for global discovery of conserved genetic modules. Science 302, 249–255 (2003)CrossRefGoogle Scholar
  5. 5.
    Basso, K., Margolin, A.A., Stolovitzky, G., Klein, U., Dalla-Favera, R., Califano, A.: Reverse engineering of regulatory networks in human B cells. Nature Genetics 37, 382–390 (2005)CrossRefGoogle Scholar
  6. 6.
    Zeitlinger, J., Simon, I., Harbison, C.T., Hannett, N.M., Volkert, T.L., Fink, G.R., Young, R.A.: Program-Specific Distribution of a Transcription Factor Dependent on Partner Transcription Factor and MAPK Signaling. Cell 113, 395–404 (2003)CrossRefGoogle Scholar
  7. 7.
    Luscombe, N.M., Babu, M.M., Yu, H., Snyder, M., Teichmann, S.A., Gerstein, M.: Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 431, 308–312 (2004)CrossRefGoogle Scholar
  8. 8.
    Segal, E., Shapira, M., Regev, A., Pe’er, D., Botstein, D., Koller, D., Friedman, N.: Module networks: identifying regulatory modules and their condition-specific regulators from expression data. Nature Genetics 34, 166–176 (2003)CrossRefGoogle Scholar
  9. 9.
    de Lichtenberg, U., Jensen, L.J., Brunak, S., Bork, P.: Dynamic Complex Formation During the Yeast Cell Cycle. Science 307, 724–727 (2005)CrossRefGoogle Scholar
  10. 10.
    Pe’er, D., Regev, A., Tanay, A.: Minreg: Inferring an active regulator set. Bioinformatics 18, S258–S267 (2002)Google Scholar
  11. 11.
    Margolin, A., Nemenman, I., Basso, K., Klein, U., Wiggins, C., Stolovitzky, G., Dalla-Favera, R., Califano, A.: ARACNE: An algorithm for reconstruction of genetic networks in a mammalian cellular context. BMC Bioinformatics (in press, 2005), manuscript available online at:
  12. 12.
    Nemenman, I.: Information theory, multivariate dependence, and genetic network inference KITP, UCSB, NSF-KITP-04-54, Santa Barbara, CA (2004), manuscript available online at:
  13. 13.
    Butte, A.J., Kohane, I.S.: Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac. Symp. Biocomput., 418–429 (2000)Google Scholar
  14. 14.
    Friedman, N., Linial, M., Nachman, I., Pe’er, D.: Using Bayesian networks to analyze expression data. Journal of Computational Biology 7, 601–620 (2000)CrossRefGoogle Scholar
  15. 15.
    Mendes, P.: Biochemistry by numbers: simulation of biochemical pathways with Gepasi 3. Trends Biochem Sci. 22, 361–363 (1997)CrossRefGoogle Scholar
  16. 16.
    Ashburner, M., et al.: Gene Ontology: tool for the unification of biology. Nature Genetics 25, 1061–4036 (2000)Google Scholar
  17. 17.
    Sears, R., Nuckolls, F., Haura, E., Taya, Y., Tamai, K., Nevins, J.R.: Multiple Ras-dependent phosphorylation pathways regulate Myc protein stability. Genes Dev. 14, 2501–2514 (2000)CrossRefGoogle Scholar
  18. 18.
    Patel, J.H., et al.: The c-MYC Oncoprotein Is a Substrate of the Acetyltransferases hGCN5/PCAF and TIP60. Mol. Cell. Biol. 24, 10826–10834 (2004)CrossRefGoogle Scholar
  19. 19.
    Levens, D.L.: Reconstructing MYC. Genes Dev. 17, 1071–1077 (2003)CrossRefGoogle Scholar
  20. 20.
    Amati, B., Brooks, M.W., Levy, N., Littlewood, T.D., Evan, G.I., Land, H.: Oncogenic activity of the c-Myc protein requires dimerization with Max. Cell 72, 233–245 (1993)CrossRefGoogle Scholar
  21. 21.
    Peukert, K., et al.: An alternative pathway for gene regulation by Myc. EMBO J. 16, 5672–5686 (1977)CrossRefGoogle Scholar
  22. 22.
    Luscher, B., Kuenzel, E.A., Krebs, E.G., Eisenman, R.N.: Myc oncoproteins are phosphorylated by casein kinase II. EMBO J. 8, 1111–1119 (1989)Google Scholar
  23. 23.
    Bousset, K., Henriksson, M., Luscher-Firzlaff, J.M., Litchfield, D.W., Luscher, B.: Identification of casein kinase II phosphorylation sites in Max: effects on DNA-binding kinetics of Max homo- and Myc/Max heterodimers. Oncogene 8, 3211–3220 (1993)Google Scholar
  24. 24.
    Noguchi, K., et al.: Regulation of c-Myc through Phosphorylation at Ser-62 and Ser-71 by c-Jun N-Terminal Kinase. J. Biol. Chem. 274, 32580–32587 (1999)CrossRefGoogle Scholar
  25. 25.
    Gregory, M.A., Qi, Y., Hann, S.R.: Phosphorylation by glycogen synthase kinase-3 controls c-myc proteolysis and subnuclear localization. J. Biol. Chem. 278, 51606–51612 (2003)CrossRefGoogle Scholar
  26. 26.
    Niiro, H., Clark, E.A.: Regulation of B-cell fate by antigen-receptor signals. Nature Reviews Immunology 2, 945–956 (2002)CrossRefGoogle Scholar
  27. 27.
    Machida, N., et al.: Mitogen-activated Protein Kinase Kinase Kinase Kinase 4 as a Putative Effector of Rap2 to Activate the c-Jun N-terminal Kinase. J. Biol. Chem. 279, 15711–15714 (2004)CrossRefGoogle Scholar
  28. 28.
    Salghetti, S.E., Kim, S.Y., Tansey, W.P.: Destruction of Myc by ubiquitin-mediated proteolysis: cancer-associated and transforming mutations stabilize Myc. EMBO J. 18, 717–726 (1999)CrossRefGoogle Scholar
  29. 29.
    Anant, S., Davidson, N.O.: An AU-Rich Sequence Element (UUUN[A/U]U) Downstream of the Edited C in Apolipoprotein B mRNA Is a High-Affinity Binding Site for Apobec-1: Binding of Apobec-1 to This Motif in the 3’ Untranslated Region of c-myc Increases mRNA Stability. Mol. Cell. Biol. 20, 1982–1992 (2000)CrossRefGoogle Scholar
  30. 30.
    Brenner, C., et al.: Myc represses transcription through recruitment of DNA methyltransferase corepressor. EMBO J. 24, 336–346 (2005)CrossRefMathSciNetGoogle Scholar
  31. 31.
    Robertson, K.D., et al.: DNMT1 forms a complex with Rb, E2F1 and HDAC1 and represses transcription from E2F-responsive promoters. Nature Genetics 25, 338–342 (2000)CrossRefGoogle Scholar
  32. 32.
    Wingender, E., et al.: The TRANSFAC system on gene expression regulation Nucl. Acids Res. 29, 281–283 (2001)CrossRefGoogle Scholar
  33. 33.
    Karolchik, D., et al.: The UCSC Genome Browser Database. Nucl. Acids Res. 31, 51–54 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kai Wang
    • 1
    • 2
  • Ilya Nemenman
    • 2
  • Nilanjana Banerjee
    • 2
  • Adam A. Margolin
    • 1
    • 2
  • Andrea Califano
    • 1
    • 2
    • 3
  1. 1.Department of Biomedical InformaticsColumbia UniversityNew York
  2. 2.Joint Centers for Systems BiologyColumbia UniversityNew York
  3. 3.Institute of Cancer GeneticsColumbia University, Russ Berrie PavilionNew York

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