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Aims/hypothesis: It is thought that enterovirus infections initiate or facilitate the pathogenetic processes leading to type 1 diabetes. Exposure of cultured human islets to cytolytic enterovirus strains kills beta cells after a protracted period, suggesting a role for secondary virusinduced factors such as cytokines. Methods: To clarify the molecular mechanisms involved in virus-induced beta cell destruction, we analysed the global pattern of gene expression in human islets. After 48 h, RNA was extracted from three independent human islet preparations infected with coxsackievirus B5 or exposed to interleukin 1β (50 U/ml) plus interferon γ (1,000 U/ml), and gene expression profiles were analysed using Affymetrix HG-U133A gene chips, which enable simultaneous analysis of 22,000 probe sets. Results: As many as 13,077 genes were detected in control human islets, and 945 and 1293 single genes were found to be modified by exposure to viral infection and the indicated cytokines, respectively. Four hundred and eighty-four genes were similarly modified by the cytokines and viral infection. Conclusions/ interpretation: The large number of modified genes observed emphasises the complex responses of human islet cells to agents potentially involved in insulitis. Notably, both cytokines and viral infection significantly (p<0.02) increased the expression of several chemokines, the cytokine IL-15 and the intercellular adhesion molecule ICAM-1, which might contribute to the homing and activation of mononuclear cells in the islets during infection and/or an early autoimmune response. The present results provide novel insights into the molecular mechanisms involved in viral-and cytokine-induced human beta cell dysfunction and death.

Background: Gene expression patterns provide a detailed view of cellular functions. Comparison of profiles in disease vs normal conditions provides insights into the processes underlying disease progression. However, availability and integration of public gene expression datasets remains a major challenge. The aim of the present study was to explore the transcriptome of pancreatic islets and, based on this information, to prepare a comprehensive and open access inventory of insulinproducing beta cell gene expression, the Beta Cell Gene Atlas (BCGA).

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Aims/hypothesis. Viral infections and local production of IFN-γ might contribute to beta-cell dysfunction/ death in Type 1 Diabetes. Double stranded RNA (dsRNA) accumulates in the cytosol of viral-infected cells, and exposure of purified rat beta cells to dsRNA (tested in the form of polyinosinic-polycytidylic acid, PIC) in combination with IFN-γ results in beta-cell dysfunction and apoptosis. To elucidate the molecular mechanisms involved in PIC + IFN-γ-effects, we determined the global profile of genes modified by these agents in primary rat beta cells. Methods. FACS-purified rat beta cells were cultured for 6 or 24 h in control condition or with IFN-γ, PIC or a combination of both agents. The gene expression profile was analysed in duplicate by high-density oligonucleotide arrays representing 5000 full-length genes and 3000 EST's. Changes of greater than or equal to 2.5-fold were considered as relevant.Results. Following a 6-or 24-h treatment with IFN-γ, PIC or IFN-γ and PIC, we observed changes in the expression of 51 to 189 genes. IFN-γ modified the expression of MHC-related genes, and also of genes involved in beta-cell metabolism, protein processing, cytokines and signal transduction. PIC affected preferentially the expression of genes related to cell adhesion, cytokines and dsRNA signal transduction, transcription factors and MHC. PIC and/or IFN-γ up-regulated the expression of several chemokines and cytokines that could contribute to mononuclear cell homing and activation during viral infection, while IFN-γ induced a positive feedback on its own signal transduction. PIC + IFN-γ inhibited insulin and GLUT-2 expression without modifying pdx-1 mRNA expression. Conclusion/interpretation. This study provides the first comprehensive characterization of the molecular responses of primary beta cells to dsRNA + IFN-γ, two agents that are probably present in the beta cell milieu during the course of virally-induced insulitis and Type 1 Diabetes. Based on these findings, we propose an integrated model for the molecular mechanisms involved in dsRNA + IFN-γ induced beta-cell dysfunction and death. [Diabetologia (2003[Diabetologia ( ) 46:1641[Diabetologia ( -1657

IBD and experimental murine colitis have a high degree of similarity in the colonic transcriptional profile, probably secondary to non-specific inflammatory processes. However, differences do exist between models, emphasising the need for careful selection and interpretation of qualified animal models in preclinical research.

BackgroundBiomolecular pathways and networks are dynamic and complex, and the perturbations to them which cause disease are often multiple, heterogeneous and contingent. Pathway and network visualizations, rendered on a computer or published on paper, however, tend to be static, lacking in detail, and ill-equipped to explore the variety and quantities of data available today, and the complex causes we seek to understand.ResultsRCytoscape integrates R (an open-ended programming environment rich in statistical power and data-handling facilities) and Cytoscape (powerful network visualization and analysis software). RCytoscape extends Cytoscape's functionality beyond what is possible with the Cytoscape graphical user interface. To illustrate the power of RCytoscape, a portion of the Glioblastoma multiforme (GBM) data set from the Cancer Genome Atlas (TCGA) is examined. Network visualization reveals previously unreported patterns in the data suggesting heterogeneous signaling mechanisms active in GBM Proneural tumors, with possible clinical relevance.ConclusionsProgress in bioinformatics and computational biology depends upon exploratory and confirmatory data analysis, upon inference, and upon modeling. These activities will eventually permit the prediction and control of complex biological systems. Network visualizations -- molecular maps -- created from an open-ended programming environment rich in statistical power and data-handling facilities, such as RCytoscape, will play an essential role in this progression.