Cellular phenotypes are determined by the differential activity of networks linking coregulated genes. Available methods for the reverse engineering of such networks from genome-wide expression profiles have been successful only in the analysis of lower eukaryotes with simple genomes. Using a new method called ARACNe (algorithm for the reconstruction of accurate cellular networks), we report the reconstruction of regulatory networks from expression profiles of human B cells. The results are suggestive a hierarchical, scale-free network, where a few highly interconnected genes (hubs) account for most of the interactions. Validation of the network against available data led to the identification of MYC as a major hub, which controls a network comprising known target genes as well as new ones, which were biochemically validated. The newly identified MYC targets include some major hubs. This approach can be generally useful for the analysis of normal and pathologic networks in mammalian cells.
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Gene Expression Omnibus
Han, J.D. et al. Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature 430, 88–93 (2004).
Jeong, H., Tombor, B., Albert, R., Oltvai, Z.N. & Barabasi, A.L. The large-scale organization of metabolic networks. Nature 407, 651–654 (2000).
Jordan, I.K., Marino-Ramirez, L., Wolf, Y.I. & Koonin, E.V. Conservation and coevolution in the scale-free human gene coexpression network. Mol. Biol. Evol. 21, 2058–2070 (2004).
Lukashin, A.V., Lukashev, M.E. & Fuchs, R. Topology of gene expression networks as revealed by data mining and modeling. Bioinformatics 19, 1909–1916 (2003).
Friedman, N., Linial, M., Nachman, I. & Pe'er, D. Using Bayesian networks to analyze expression data. J. Comput. Biol. 7, 601–620 (2000).
Hartemink, A.J., Gifford, D.K., Jaakkola, T.S. & Young, R.A. Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks. Pac. Symp. Biocomput. 2001, 422–433 (2001).
Gat-Viks, I. & Shamir, R. Chain functions and scoring functions in genetic networks. Bioinformatics 19 Suppl 1, i108–i117 (2003).
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).
Yeung, M.K., Tegner, J. & Collins, J.J. Reverse engineering gene networks using singular value decomposition and robust regression. Proc. Natl. Acad. Sci. USA 99, 6163–6168 (2002).
Ideker, T. et al. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 292, 929–934 (2001).
Butte, A.J. & Kohane, I.S. Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac. Symp. Biocomput. 2000, 418–429 (2000).
Butte, A.J., Tamayo, P., Slonim, D., Golub, T.R. & Kohane, I.S. Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proc. Natl. Acad. Sci. USA 97, 12182–12186 (2000).
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).
Hughes, T.R. et al. Functional discovery via a compendium of expression profiles. Cell 102, 109–126 (2000).
MacLennan, I.C. Germinal centers. Annu. Rev. Immunol. 12, 117–139 (1994).
Barabasi, A.L. & Bonabeau, E. Scale-free networks. Sci. Am. 288, 60–69 (2003).
Friedman, N. Inferring cellular networks using probabilistic graphical models. Science 303, 799–805 (2004).
Smith, V.A., Jarvis, E.D. & Hartemink, A.J. Influence of network topology and data collection on network inference. Pac. Symp. Biocomput. 2003, 164–175 (2003).
Yu, J., Smith, A.V., Wang, P.P., Hartemink, A.J. & Jarvis, E.D. Using Bayesian network inference algorithms to recover molecular genetic regulatory networks. in 3rd International Conference on Systems Biology (Karolinska Institute, Stockholm, Sweden, 2002).
Smith, V.A., Jarvis, E.D. & Hartemink, A.J. Evaluating functional network inference using simulations of complex biological systems. Bioinformatics 18, S216–S224 (2002).
Jarvis, E.D. et al. A framework for integrating the songbird brain. J. Comp. Physiol. A. Neuroethol. Sens. Neural. Behav. Physiol. 188, 961–980 (2002).
Zeeberg, B.R. et al. GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome Biol. 4, R28 (2003).
Zeller, K.I., Jegga, A.G., Aronow, B.J., O'Donnell, K.A. & Dang, C.V. An integrated database of genes responsive to the Myc oncogenic transcription factor: identification of direct genomic targets. Genome Biol. 4, R69 (2003).
Fernandez, P.C. et al. Genomic targets of the human c-Myc protein. Genes Dev. 17, 1115–1129 (2003).
Dang, C.V. c-Myc target genes involved in cell growth, apoptosis, and metabolism. Mol. Cell. Biol. 19, 1–11 (1999).
O'Connell, B.C. et al. A large scale genetic analysis of c-Myc-regulated gene expression patterns. J. Biol. Chem. 278, 12563–12573 (2003).
D'Haeseleer, P., Wen, X., Fuhrman, S. & Somogyi, R. Linear modeling of mRNA expression levels during CNS development and injury. Pac. Symp. Biocomput. 1999, 41–52 (1999).
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).
Barabasi, A.L. & Albert, R. Emergence of scaling in random networks. Science 286, 509–512 (1999).
Roos, J., Luz, J.M., Centoducati, S., Sternglanz, R. & Lennarz, W.J. ENP1, an essential gene encoding a nuclear protein that is highly conserved from yeast to humans. Gene 185, 137–146 (1997).
Suzuki, N. et al. A cytoplasmic protein, bystin, interacts with trophinin, tastin, and cytokeratin and may be involved in trophinin-mediated cell adhesion between trophoblast and endometrial epithelial cells. Proc. Natl. Acad. Sci. USA 95, 5027–5032 (1998).
Chen, W., Bucaria, J., Band, D.A., Sutton, A. & Sternglanz, R. Enp1, a yeast protein associated with U3 and U14 snoRNAs, is required for pre-rRNA processing and 40S subunit synthesis. Nucleic Acids Res. 31, 690–699 (2003).
Stewart, M.J. & Nordquist, E.K. Drosophila Bys is nuclear and shows dynamic tissue-specific expression during development. Dev. Genes Evol. 215, 97–102 (2005).
Joe, H. Multivariate Models and Dependence Concepts (Chapman & Hall, Boca Raton, Florida, 1997).
Steuer, R., Kurths, J., Daub, C.O., Weise, J. & Selbig, J. The mutual information: Detecting and evaluating dependencies between variables. Bioinformatics 18 Suppl 2: S231–S240 (2002).
Cooper, G.F. & Herskovits, E. A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9, 309–347 (1992).
Klein, U. et al. Gene expression profiling of B cell chronic lymphocytic leukemia reveals a homogeneous phenotype related to memory B cells. J. Exp. Med. 194, 1625–1638 (2001).
Klein, U. et al. Transcriptional analysis of the B cell germinal center reaction. Proc. Natl. Acad. Sci. USA 100, 2639–2644 (2003).
Klein, U. et al. Gene expression profile analysis of AIDS-related primary effusion lymphoma (PEL) suggests a plasmablastic derivation and identifies PEL-specific transcripts. Blood 101, 4115–4121 (2003).
Basso, K. et al. Gene expression profiling of hairy cell leukemia reveals a phenotype related to memory B cells with altered expression of chemokine and adhesion receptors. J. Exp. Med. 199, 59–68 (2004).
Kuppers, R. et al. Identification of Hodgkin and Reed-Sternberg cell-specific genes by gene expression profiling. J. Clin. Invest. 111, 529–537 (2003).
Basso, K. et al. Tracking CD40 signaling during germinal center development. Blood 104, 4088–4096 (2004).
Niu, H., Cattoretti, G. & Dalla-Favera, R. BCL6 controls the expression of the B7-1/CD80 costimulatory receptor in germinal center B cells. J. Exp. Med. 198, 211–221 (2003).
Wu, K.J., Polack, A. & Dalla-Favera, R. Coordinated regulation of iron-controlling genes, H-ferritin and IRP2, by c-MYC. Science 283, 676–679 (1999).
Kempkes, B. et al. B-cell proliferation and induction of early G1-regulating proteins by Epstein-Barr virus mutants conditional for EBNA2. EMBO J. 14, 88–96 (1995).
Frank, S.R., Schroeder, M., Fernandez, P., Taubert, S. & Amati, B. Binding of c-Myc to chromatin mediates mitogen-induced acetylation of histone H4 and gene activation. Genes Dev. 15, 2069–2082 (2001).
We thank I. Nemenman for his expertise in Information Theory, M. Mattioli for contributing to the generation of the B-cell gene expression database and V. Miljkovic for help with the microarray hybridizations. K.B. is supported by a fellowship from the American-Italian Cancer Foundation, A.M. by the National Library of Medicine Medical Informatics Research Training Program at Columbia and U.K. by a fellowship from the Human Frontiers Science Program. This study was supported by a US National Institutes of Health grant to R.D.-F. and by the computational resources of the AMDeC Bioinformatics Core at Columbia University.
The authors declare no competing financial interests.
List of samples included in the gene expression profiles database. (PDF 14 kb)
Adjacency matrix for the B cell network inferred by ARACNe. (PDF 1318 kb)
List of 5% largest hubs in the B cell network and their distribution in the Gene Ontology categories. (PDF 44 kb)
c-MYC network genes identified by ARACNe. (PDF 115 kb)
Identification of E-boxes and ChIP validation in 34 c-MYC first neighbors. (PDF 10 kb)
Analysis of the Gene Ontology terms enrichment in the 10 largest c-MYC sub-hubs. (PDF 380 kb)
Oligonucleotide primer sequences and PCR conditions used for the ChIP assay. (PDF 12 kb)
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Basso, K., Margolin, A., Stolovitzky, G. et al. Reverse engineering of regulatory networks in human B cells. Nat Genet 37, 382–390 (2005). https://doi.org/10.1038/ng1532
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