Abstract
We propose an integrative network-based meta-analysis strategy to enable the selection of potential gene markers for one of the most prevalent diseases worldwide, Type 2 diabetes (T2D), formally known as the non-insulin dependent diabetes mellitus. Comprehensive elucidation of the genes regulated through this disorder and their wiring will provide a more complete understanding of the overall gene network topology and their role in disease progression and treatment. The proposed strategy was able to find conservative gene modules which play interesting role in T2D, pointing to gene markers such as NR3C1, ADIPOR1 and CDC123. Network-based meta-analysis by enumerating conserved gene modules pave a practical approach to the identification of candidate gene markers across several related transcriptomic studies. The NEMESIS R pipeline for network-based meta-analysis is also provided.
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References
Liu, M., Liberzon, A., Kong, S.W., Lai, W.R., Park, P.J., Kohane, I.S., Kasif, S.: Network-based analysis of affected biological processes in type 2 diabetes models. PLoS Genet. 3(6), e96 (2007)
Stumvoll, M., Goldstein, B.J., van Haeften, T.W.: Type 2 diabetes: principles of pathogenesis and therapy. Lancet 365(9467), 1333–1346 (2005)
Kaur, P., Reis, M.D., Couchman, G.R., Forjuoh, S.N., Greene, J.F., Asea, A.: Serpine 1 links obesity and diabetes: A pilot study. J. Proteomics Bioinform. 3(6), 191–199 (2010)
Goh, K.I., Cusick, M.E., Valle, D., Childs, B., Vidal, M., Barabási, A.L.: The human disease network. Proc. Natl. Acad. Sci. U. S. A. 104(21), 8685–8690 (2007)
Keller, M.P., Choi, Y., Wang, P., Davis, D.B., Rabaglia, M.E., Oler, A.T., Stapleton, D.S., Argmann, C., Schueler, K.L., Edwards, S., Steinberg, H.A., Chaibub Neto, E., Kleinhanz, R., Turner, S., Hellerstein, M.K., Schadt, E.E., Yandell, B.S., Kendziorski, C., Attie, A.D.: A gene expression network model of type 2 diabetes links cell cycle regulation in islets with diabetes susceptibility. Genome Res. 18(5), 706–716 (2008)
Park, K.S.: Prevention of type 2 diabetes mellitus from the viewpoint of genetics. Diabetes Res. Clin. Pract. 66(suppl. 1), S33–S35 (2004)
Jain, P., Vig, S., Datta, M., Jindel, D., Mathur, A.K., Mathur, S.K., Sharma, A.: Systems biology approach reveals genome to phenome correlation in type 2 diabetes. PLoS One 8(1), e53522 (2013)
Calderon, B., Suri, A., Pan, X.O., Mills, J.C., Unanue, E.R.: Ifn-gamma-dependent regulatory circuits in immune inflammation highlighted in diabetes. J. Immunol. 181(10), 6964–6974 (2008)
Casas, S., Gomis, R., Gribble, F.M., Altirriba, J., Knuutila, S., Novials, A.: Impairment of the ubiquitin-proteasome pathway is a downstream endoplasmic reticulum stress response induced by extracellular human islet amyloid polypeptide and contributes to pancreatic beta-cell apoptosis. Diabetes 56(9), 2284–2294 (2007)
Song, G.Y., Wu, Y.J., Yang, Y.J., Li, J.J., Zhang, H.L., Pei, H.J., Zhao, Z.Y., Zeng, Z.H., Hui, R.T.: The accelerated post-infarction progression of cardiac remodelling is associated with genetic changes in an untreated streptozotocin-induced diabetic rat model. Eur. J. Heart Fail 11(10), 911–921 (2009)
Almon, R.R., DuBois, D.C., Lai, W., Xue, B., Nie, J., Jusko, W.J.: Gene expression analysis of hepatic roles in cause and development of diabetes in goto-kakizaki rats. J. Endocrinol. 200(3), 331–346 (2009)
Langfelder, P., Horvath, S.: Wgcna: an r package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008)
Zhang, B., Horvath, S.: A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, Article17 (2005)
Alves, R., Rodriguez-Baena, D.S., Aguilar-Ruiz, J.S.: Gene association analysis: a survey of frequent pattern mining from gene expression data. Brief. Bioinform. 11(2), 210–224 (2010)
Yu, W., Clyne, M., Khoury, M.J., Gwinn, M.: Phenopedia and genopedia: disease-centered and gene-centered views of the evolving knowledge of human genetic associations. Bioinformatics 26(1), 145–146 (2010)
Tomas, E., Tsao, T.S., Saha, A.K., Murrey, H.E., Zhang, C.C., Itani, S.I., Lodish, H.F., Ruderman, N.B.: Enhanced muscle fat oxidation and glucose transport by acrp30 globular domain: acetyl-coa carboxylase inhibition and amp-activated protein kinase activation. Proc. Natl. Acad. Sci. U. S. A. 99(25), 16309–16313 (2002)
Grarup, N., Andersen, G., Krarup, N.T., Albrechtsen, A., Schmitz, O., Jørgensen, T., Borch-Johnsen, K., Hansen, T., Pedersen, O.: Association testing of novel type 2 diabetes risk alleles in the jazf1, cdc123/camk1d, tspan8, thada, adamts9, and notch2 loci with insulin release, insulin sensitivity, and obesity in a population-based sample of 4,516 glucose-tolerant middle-aged danes. Diabetes 57(9), 2534–2540 (2008)
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Alves, R., Mendes, M., Bonnato, D. (2013). A Network-Based Meta-analysis Strategy for the Selection of Potential Gene Modules in Type 2 Diabetes. In: Setubal, J.C., Almeida, N.F. (eds) Advances in Bioinformatics and Computational Biology. BSB 2013. Lecture Notes in Computer Science(), vol 8213. Springer, Cham. https://doi.org/10.1007/978-3-319-02624-4_15
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DOI: https://doi.org/10.1007/978-3-319-02624-4_15
Publisher Name: Springer, Cham
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