A trans-acting locus regulates an anti-viral expression network and type 1 diabetes risk

Combined analyses of gene networks and DNA sequence variation can provide new insights into the aetiology of common diseases that may not be apparent from genome-wide association studies alone. Recent advances in rat genomics are facilitating systems-genetics approaches. Here we report the use of integrated genome-wide approaches across seven rat tissues to identify gene networks and the loci underlying their regulation. We defined an interferon regulatory factor 7 (IRF7)-driven inflammatory network (IDIN) enriched for viral response genes, which represents a molecular biomarker for macrophages and which was regulated in multiple tissues by a locus on rat chromosome 15q25. We show that Epstein-Barr virus induced gene 2 (Ebi2, also known as Gpr183), which lies at this locus and controls B lymphocyte migration, is expressed in macrophages and regulates the IDIN. The human orthologous locus on chromosome 13q32 controlled the human equivalent of the IDIN, which was conserved in monocytes. IDIN genes were more likely to associate with susceptibility to type 1 diabetes (T1D)-a macrophage-associated autoimmune disease-than randomly selected immune response genes (P = 8.85 x 10(-6)). The human locus controlling the IDIN was associated with the risk of T1D at single nucleotide polymorphism rs9585056 (P = 7.0 x 10(-10); odds ratio, 1.15), which was one of five single nucleotide polymorphisms in this region associated with EBI2 (GPR183) expression. These data implicate IRF7 network genes and their regulatory locus in the pathogenesis of T1D

Fruit Distribution in the Canadian West

Transcription factors, by binding to particular DNA sequences termed transcription factor-binding sites, play an important role in regulating gene expression in both prokaryotic and eukaryotic organisms. These binding sites lie within promoters (which are located just upstream of a gene and promote transcription of that gene) and enhancers (short DNA elements enhancing transcription levels of genes in a gene cluster, and which need not be particularly close to the genes they act on, or even located on the same chromosome). Binding of transcription factors in these genomic regulatory regions can influence gene transcription rates either positively or negatively. The binding may also be dependant on the interaction with co-activators and co-repressors, in addition to context (e.g. particular histone modifications in the vicinity of the regulatory element). Identifying all transcription factors and their respective binding sites would be an important step towards a more thorough understanding of gene regulation. Regular expression type patterns, as well as nucleotide distribution matrices, have both been used for describing transcription factor-binding sites, e.g. (Bucher 1990; Ghosh 1990; Chen et al. 1995; Wingender et al. 1996). Here we will discuss some of the computational approaches that are used in binding site identification.SCOPUS: ch.binfo:eu-repo/semantics/publishe