Abstract
We present a method for identifying connected gene subnetworks significantly enriched for genes that are dysregulated in specimens of a disease. These subnetworks provide a signature of the disease potentially useful for diagnosis, pinpoint possible pathways affected by the disease, and suggest targets for drug intervention. Our method uses microarray gene expression profiles derived in clinical case-control studies to identify genes significantly dysregulated in disease specimens, combined with protein interaction data to identify connected sets of genes. Our core algorithm searches for minimal connected subnetworks in which the number of dysregulated genes in each diseased sample exceeds a given threshold. We have applied the method in a study of Huntington’s disease caudate nucleus expression profiles and in a meta-analysis of breast cancer studies. In both cases the results were statistically significant and appeared to home in on compact pathways enriched with hallmarks of the diseases.
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References
Bansal, M., Belcastro, V., Ambesi-Impiombato, A., di Bernardo, D.: How to infer gene networks from expression profiles. Molecular Systems Biology 3, 78 (2007)
van’t Veer, L., Dai, H., van de Vijver, M., He, Y., Hart, A., Mao, M., Peterse, H., van der Kooy, K., Marton, M., Witteveen, A., et al.: Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536 (2002)
Segal, E., Friedman, N., Kaminski, N., Regev, A., Koller, D.: From signatures to models: understanding cancer using microarrays. Nat Genet 37(suppl.), S38–S45 (2005)
Subramanian, A., Tamayo, P., Mootha, V., Mukherjee, S., Ebert, B., Gillette, M., Paulovich, A., Pomeroy, S., Golub, T., Lander, E., Mesirov, J.: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. PNAS 102, 15545–15550 (2005)
Rapaport, F., Zinovyev, A., Dutreix, M., Barillot, E., Vert, J.: Classification of microarray data using gene networks. BMC Bioinformatics 8, 35 (2007)
Ulitsky, I., Shamir, R.: Identification of functional modules using network topology and high-throughput data. BMC Systems Biology 1 (2007)
Segal, E., Wang, H., Koller, D.: Discovering molecular pathways from protein interaction and gene expression data. Bioinformatics 19, I264–I272 (2003)
Ideker, T., Ozier, O., Schwikowski, B., Siegel, A.F.: Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 18, S233–S240 (2002)
Rajagopalan, D., Agarwal, P.: Inferring pathways from gene lists using a literature-derived network of biological relationships. Bioinformatics 21, 788–793 (2005)
Cabusora, L., Sutton, E., Fulmer, A., Forst, C.: Differential network expression during drug and stress response. Bioinformatics 21, 2898–2905 (2005)
Nacu, S., Critchley-Thorne, R., Lee, P., Holmes, S.: Gene expression network analysis and applications to immunology. Bioinformatics 23, 850 (2007)
Liu, M., Liberzon, A., Kong, S., Lai, W., Park, P., Kohane, I., Kasif, S.: Network-based analysis of affected biological processes in type 2 diabetes models. PLoS Genetics 3, e96+ (2007)
Breitling, R., Amtmann, A., Herzyk, P.: Graph-based iterative group analysis enhances microarray interpretation. BMC Bioinformatics 5, 100 (2004)
Chuang, H., Lee, E., Liu, Y., Lee, D., Ideker, T.: Network-based classification of breast cancer metastasis. Mol. Syst. Biol. 3 (2007)
Hochbaum, D.S.: Approximating covering and packing problems: set cover, vertex cover, independent set, and related problems. In: Hochbaum, D.S. (ed.) Approximation algorithms for NP-hard problems, PWS, Boston, pp. 94–143 (1997)
Dobson, G.: Worst-case analysis of greedy heuristics for integer programming with non-negative data. Mathematics of Operations Research 7, 515–531 (1982)
Shuai, T., Hu, X.: Connected set cover problem and its applications. In: Cheng, S.-W., Poon, C.K. (eds.) AAIM 2006. LNCS, vol. 4041, pp. 243–254. Springer, Heidelberg (2006)
Cormen, T.H., Leiserson, C.E., Rivest, R.L.: Introduction to Algorithms. MIT Press, Cambridge (1990)
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: simple building blocks of complex networks. Science 298, 824–827 (2002)
Hodges, A., Strand, A., Aragaki, A., Kuhn, A., Sengstag, T., Hughes, G., Elliston, L., Hartog, C., Goldstein, D., Thu, D., et al.: Regional and cellular gene expression changes in human Huntington’s disease brain. Human Molecular Genetics 15, 965 (2006)
Kaltenbach, L., Romero, E., et al.: Huntingtin interacting proteins are genetic modifiers of neurodegeneration. PLoS Genet 3, e82 (2007)
Rockabrand, E., Slepko, N., Pantalone, A., Nukala, V., Kazantsev, A., Marsh, J., Sullivan, P., Steffan, J., Sensi, S., Thompson, L.: The first 17 amino acids of Huntingtin modulate its sub-cellular localization, aggregation and effects on calcium homeostasis. Human Molecular Genetics 16, 61 (2007)
Borrell-Pagès, M., Zala, D., Humbert, S., Saudou, F.: Huntington’s disease: from huntingtin function and dysfunction to therapeutic strategies. Cellular and Molecular Life Sciences (CMLS) 63, 2642–2660 (2006)
Giuliano, P., De Cristofaro, T., et al.: DNA damage induced by polyglutamine-expanded proteins. Human Molecular Genetics 12, 2301–2309 (2003)
Hoshino, M., Tagawa, K., et al.: Histone deacetylase activity is retained in primary neurons expressing mutant huntingtin protein. J. Neurochem. 87, 257–267 (2003)
Butler, R., Bates, G.: Histone deacetylase inhibitors as therapeutics for polyglutamine disorders. Nat. Rev. Neurosci. 7, 784–796 (2006)
Ferrante, R., Kubilus, J., Lee, J., Ryu, H., Beesen, A., Zucker, B., Smith, K., Kowall, N., Ratan, R., Luthi-Carter, R., et al.: Histone deacetylase inhibition by sodium butyrate chemotherapy ameliorates the neurodegenerative phenotype in Huntington’s disease mice. Journal of Neuroscience 23, 9418–9427 (2003)
Borovecki, F., Lovrecic, L., Zhou, J., Jeong, H., Then, F., Rosas, H.D., Hersch, S.M., Hogarth, P., Bouzou, B., Jensen, R.V., Krainc, D.: Genome-wide expression profiling of human blood reveals biomarkers for Huntington’s disease. Proc. Natl. Acad. Sci. USA 102, 11023–11028 (2005)
Sotiriou, C., Wirapati, P., Loi, S., Harris, A., Fox, S., Smeds, J., Nordgren, H., Farmer, P., Praz, V., Haibe-Kains, B., et al.: Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J. Natl. Cancer Inst. 98, 262–272 (2006)
Affar, E., Gay, F., Shi, Y., Liu, H., Huarte, M., Wu, S., Collins, T., Li, E., Shi, Y.: Essential Dosage-Dependent Functions of the Transcription Factor Yin Yang 1 in Late Embryonic Development and Cell Cycle Progression. Molecular and Cellular Biology 26, 3565–3581 (2006)
Begon, D., Delacroix, L., Vernimmen, D., Jackers, P., Winkler, R.: Yin Yang 1 Cooperates with Activator Protein 2 to Stimulate ERBB2 Gene Expression in Mammary Cancer Cells. Journal of Biological Chemistry 280, 24428–24434 (2005)
Li, L., Shaw, P.: Autocrine-mediated activation of STAT3 correlates with cell proliferation in breast carcinoma lines. Journal of Biological Chemistry 277, 17397–17405 (2002)
Futreal, P., Coin, L., Marshall, M., Down, T., Hubbard, T., Wooster, R., Rahman, N., Stratton, M.: A census of human cancer genes. Nature Reviews Cancer 4, 177–183 (2004)
Efron, B., Tibshirani, R.: An introduction to the bootstrap. Chapman & Hall, New York (1993)
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Ulitsky, I., Karp, R.M., Shamir, R. (2008). Detecting Disease-Specific Dysregulated Pathways Via Analysis of Clinical Expression Profiles. In: Vingron, M., Wong, L. (eds) Research in Computational Molecular Biology. RECOMB 2008. Lecture Notes in Computer Science(), vol 4955. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78839-3_30
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DOI: https://doi.org/10.1007/978-3-540-78839-3_30
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