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Reverse engineering of regulatory networks in human B cells

Nature Genetics volume 37, pages 382–390 (2005)Cite this article

Abstract

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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Figure 1: Comparison of the performance of Bayesian networks and ARACNe algorithms in the analysis of a synthetic genetic network.
Figure 2: Schematic representation of the germinal center reaction and related normal and malignant B cell phenotypes.
Figure 3: Identification of key regulatory hubs in the human B cell network.
Figure 4: The MYC subnetwork.
Figure 5: Distribution of known MYC target genes among first and second neighbors in the MYC subnetwork.
Figure 6: Identification of new direct MYC targets by ChIP analysis.

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Acknowledgements

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.

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Authors and Affiliations

  1. Institute for Cancer Genetics, 1300 St. Nicholas Avenue, Room 912, New York, 10032, New York, USA

    Katia Basso, Ulf Klein & Riccardo Dalla-Favera

  2. Joint Centers for Systems Biology, 1300 St. Nicholas Avenue, Room 912, New York, 10032, New York, USA

    Adam A Margolin & Andrea Califano

  3. IBM T.J. Watson Research Center, Yorktown Heights, New York, 10598, New York, USA

    Gustavo Stolovitzky

  4. Departments of Pathology and Genetics & Development, Columbia University, New York, 10032, New York, USA

    Riccardo Dalla-Favera

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  1. Katia Basso
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Correspondence to Andrea Califano.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Table 1

List of samples included in the gene expression profiles database. (PDF 14 kb)

Supplementary Table 2

Adjacency matrix for the B cell network inferred by ARACNe. (PDF 1318 kb)

Supplementary Table 3

List of 5% largest hubs in the B cell network and their distribution in the Gene Ontology categories. (PDF 44 kb)

Supplementary Table 4

c-MYC network genes identified by ARACNe. (PDF 115 kb)

Supplementary Table 5

Identification of E-boxes and ChIP validation in 34 c-MYC first neighbors. (PDF 10 kb)

Supplementary Table 6

Analysis of the Gene Ontology terms enrichment in the 10 largest c-MYC sub-hubs. (PDF 380 kb)

Supplementary Table 7

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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