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New Approaches for in Silico Identification of Cytokine‐Modified β Cell Gene Networks
Author(s) -
KUTLU BURAK,
NAAMANE NAJIB,
BERTHOU LAURENCE,
EIZIRIK DECIO L.
Publication year - 2004
Publication title -
annals of the new york academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.712
H-Index - 248
eISSN - 1749-6632
pISSN - 0077-8923
DOI - 10.1196/annals.1337.007
Subject(s) - in silico , transcription factor , microarray analysis techniques , biology , dna binding site , computational biology , gene , cytokine , gene expression , gene regulatory network , effector , bioinformatics , genetics , microbiology and biotechnology , promoter
A bstract : Beta cell dysfunction and death in type 1 diabetes mellitus (T1DM) is caused by direct contact with activated macrophages and T lymphocytes and by exposure to soluble mediators secreted by these cells, such as cytokines and nitric oxide. Cytokine‐induced apoptosis depends on the expression of pro‐ and anti‐apoptotic genes that remain to be characterized. Using microarray analyses, we identified several transcription factor and “effector” gene networks regulated by interleukin‐1β and/or interferon‐γ in β cells. This suggests that β cell fate following exposure to cytokines is a complex and highly regulated process, depending on the duration and severity of perturbation of key gene networks. In order to draw correct conclusions from these massive amounts of data, we need to utilize novel bioinformatics and statistical tools. Thus, we are presently performing in silico analysis for the localization of binding sites for the transcription factor NF‐κB (previously shown to be pivotal for β cell apoptosis) in 15 temporally related gene clusters, identified by time‐course microarray analysis. In silico analysis is based on a broad range of computational techniques used to detect motifs in a DNA sequence corresponding to the binding site of a transcription factor. These computer‐based findings must be validated by use of positive and negative controls, and by “ChIP on chip” analysis. Moreover, new statistical approaches are required to decrease false positive findings. These novel approaches will constitute a “proof of principle” for the integrated use of bioinformatics and functional genomics in the characterization of relevant cytokine‐regulated β cell gene networks leading to β cell apoptosis in T1DM.