title: In silico identification of NF-kappaB-regulated genes in pancreatic beta-cells.
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)-1beta and interferon (IFN)-gamma. 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-1beta-activated transcription factor (TF) nuclear factor (NF)-kappaB, and previous studies by our group have shown that cytokine-induced NF-kappaB activation is pro-apoptotic in beta-cells. To identify NF-kappaB-regulated gene networks in beta-cells we presently used a discriminant analysis-based approach to predict NF-kappaB responding genes on the basis of putative regulatory elements.
  
  
  RESULTS
  The performance of linear and quadratic discriminant analysis (LDA, QDA) in identifying NF-kappaB-responding genes was examined on a dataset of 240 positive and negative examples of NF-kappaB 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-kappaB putative target genes in insulin-producing INS-1E cells, primary rat beta-cells and human pancreatic islets, respectively. Predicted NF-kappaB targets had a significant enrichment in genes regulated by cytokines (IL-1beta or IL-1beta + IFN-gamma) and double stranded RNA (dsRNA), as compared to genes not regulated by these NF-kappaB-dependent stimuli. We increased the confidence of the predictions by selecting only evolutionary stable genes, i.e. genes with homologs predicted as NF-kappaB targets in rat, mouse, human and chimpanzee.
  
  
  CONCLUSIONS
  The present in silico analysis allowed us to identify novel regulatory targets of NF-kappaB 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.
authors.affiliation: Laboratory of Experimental Medicine, Université Libre de Bruxelles, Route de Lennik, 808, CP 618, B-1070 Brussels, Belgium. nnaamane@ulb.ac.be <nnaamane@ulb.ac.be>
authors.forename: Najib
authors.initials: N
authors.lastname: Naamane
authors.forename: Jacques
authors.initials: J
authors.lastname: van Helden
authors.forename: Decio L
authors.initials: DL
authors.lastname: Eizirik
mesh_terms: D000465:Algorithms; D000818:Animals; D016207:Cytokines / Q000378:metabolism*; D016002:Discriminant Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D006801:Humans; D050417:Insulin-Secreting Cells / Q000378:metabolism*; D051379:Mice; D008954:Models, Biological; D016328:NF-kappa B / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D002679:Pan troglodytes; D051381:Rats; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D016207:Cytokines; D016328:NF-kappa B