<it>In silico </it>identification of NF-kappaB-regulated genes in pancreatic beta-cells

Abstract

Abstract Background Pancreatic beta-cells are the target of an autoimmune attack in type 1 diabetes mellitus (T1DM). This is mediated in part by cytokines, such as interleukin (IL)-1β and interferon (IFN)-γ. These cytokines modify the expression of hundreds of genes, leading to beta-cell dysfunction and death by apoptosis. Several of these cytokine-induced genes are potentially regulated by the IL-1β-activated transcription factor (TF) nuclear factor (NF)-κB, and previous studies by our group have shown that cytokine-induced NF-κB activation is pro-apoptotic in beta-cells. To identify NF-κB-regulated gene networks in beta-cells we presently used a discriminant analysis-based approach to predict NF-κB responding genes on the basis of putative regulatory elements. Results The performance of linear and quadratic discriminant analysis (LDA, QDA) in identifying NF-κB-responding genes was examined on a dataset of 240 positive and negative examples of NF-κB regulation, using stratified cross-validation with an internal leave-one-out cross-validation (LOOCV) loop for automated feature selection and noise reduction. LDA performed slightly better than QDA, achieving 61% sensitivity, 91% specificity and 87% positive predictive value, and allowing the identification of 231, 251 and 580 NF-κB putative target genes in insulin-producing INS-1E cells, primary rat beta-cells and human pancreatic islets, respectively. Predicted NF-κB targets had a significant enrichment in genes regulated by cytokines (IL-1β or IL-1β + IFN-γ) and double stranded RNA (dsRNA), as compared to genes not regulated by these NF-κB-dependent stimuli. We increased the confidence of the predictions by selecting only evolutionary stable genes, i.e. genes with homologs predicted as NF-κB targets in rat, mouse, human and chimpanzee. Conclusion The present in silico analysis allowed us to identify novel regulatory targets of NF-κB using a supervised classification method based on putative binding motifs. This provides new insights into the gene networks regulating cytokine-induced beta-cell dysfunction and death.