Diabetes, Type I

http://www.physorg.com/news198243171.html

Toward defining the autoimmune microbiome for type 1 diabetes. by Giongo A, Gano KA, Crabb DB, Mukherjee N, Novelo LL, Casella G, Drew JC, Ilonen J, Knip M, Hyöty H, Veijola R, Simell T, Simell O, Neu J, Wasserfall CH, Schatz D, Atkinson MA, Triplett EW Related Articles Toward defining the autoimmune microbiome for type 1 diabetes.

ISME J. 2010 Jul 8;

Authors: Giongo A, Gano KA, Crabb DB, Mukherjee N, Novelo LL, Casella G, Drew JC, Ilonen J, Knip M, Hyöty H, Veijola R, Simell T, Simell O, Neu J, Wasserfall CH, Schatz D, Atkinson MA, Triplett EW

Several studies have shown that gut bacteria have a role in diabetes in murine models. Specific bacteria have been correlated with the onset of diabetes in a rat model. However, it is unknown whether human intestinal microbes have a role in the development of autoimmunity that often leads to type 1 diabetes (T1D), an autoimmune disorder in which insulin-secreting pancreatic islet cells are destroyed. High-throughput, culture-independent approaches identified bacteria that correlate with the development of T1D-associated autoimmunity in young children who are at high genetic risk for this disorder. The level of bacterial diversity diminishes overtime in these autoimmune subjects relative to that of age-matched, genotype-matched, nonautoimmune individuals. A single species, Bacteroides ovatus, comprised nearly 24% of the total increase in the phylum Bacteroidetes in cases compared with controls. Conversely, another species in controls, represented by the human firmicute strain CO19, represented nearly 20% of the increase in Firmicutes compared with cases overtime. Three lines of evidence are presented that support the notion that, as healthy infants approach the toddler stage, their microbiomes become healthier and more stable, whereas, children who are destined for autoimmunity develop a microbiome that is less diverse and stable. Hence, the autoimmune microbiome for T1D may be distinctly different from that found in healthy children. These data also suggest bacterial markers for the early diagnosis of T1D. In addition, bacteria that negatively correlated with the autoimmune state may prove to be useful in the prevention of autoimmunity development in high-risk children.The ISME Journal advance online publication, 8 July 2010; doi:10.1038/ismej.2010.92.

PMID: 20613793 [PubMed - as supplied by publisher]

Chronic inflammatory diseases of the intestine, such as inflammatory bowel disease (3) and celiac disease (4), are characterized by a leaky intestinal barrier. In type I diabetes, an autoimmune disease, patients have increased small intestinal permeability (5). The incidence of diabetes can be reduced in diabetes-prone rats by preventing an increase in epithelial permeability (6). .

3. Suenaert P, Bulteel V, Lemmens L, Noman M, Geypens B, Van Assche G, Geboes K, Ceuppens JL, Rutgeerts P. Anti-tumor necrosis factor treatment restores the gut barrier in Crohn’s disease. Am J Gastroenterol. 2002;97:2000–4. 4. Vogelsang H, Schwarzenhofer M, Oberhuber G. Changes in gastrointestinal permeability in celiac disease. Dig Dis. 1998;16:333–6. 5. Damci T, Nuhoglu I, Devranoglu G, Osar Z, Demir M, Ilkova H. Increased intestinal permeability as a cause of fluctuating postprandial blood glucose levels in Type 1 diabetic patients. Eur J Clin Invest. 2003;33:397–401. 6. Watts T, Berti I, Sapone A, Gerarduzzi T, Not T, Zielke R, Fasano A. Role of the intestinal tight junction modulator zonulin in the pathogenesis of type I diabetes in BB diabetic-prone rats. Proc Natl Acad Sci USA. 2005;102:2916–21

PANGEA: pipeline for analysis of next generation amplicons

Adriana Giongo1, David B Crabb1, Austin G Davis-Richardson1, Diane Chauliac1, Jennifer M Mobberley1, Kelsey A Gano1, Nabanita Mukherjee2, George Casella2,3, Luiz FW Roesch4, Brandon Walts3,5, Alberto Riva3,5, Gary King6 and Eric W Triplett1,3

1Department of Microbiology and Cell Science, University of Florida, Gainesville, FL, USA 2Department of Statistics, University of Florida, Gainesville, FL, USA 3Genetics Institute, University of Florida, Gainesville, FL, USA 4Centro de Ciencias Agricolas, Universidade Federal do Pampa, São Gabriel, Rio Grande do Sul, Brazil 5Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL, USA 6Department of Biology, Louisiana State University, Baton Rouge, LA, USA Correspondence: EW Triplett, Department of Microbiology and Cell Science, University of Florida, 1052 Museum Road, Gainesville, FL 32611-0700, USA. E-mail: ewt@ufl.edu

Received 26 November 2009; Revised 22 January 2010; Accepted 25 January 2010; Published online 25 February 2010.

Top of page Abstract High-throughput DNA sequencing can identify organisms and describe population structures in many environmental and clinical samples. Current technologies generate millions of reads in a single run, requiring extensive computational strategies to organize, analyze and interpret those sequences. A series of bioinformatics tools for high-throughput sequencing analysis, including pre-processing, clustering, database matching and classification, have been compiled into a pipeline called PANGEA. The PANGEA pipeline was written in Perl and can be run on Mac OSX, Windows or Linux. With PANGEA, sequences obtained directly from the sequencer can be processed quickly to provide the files needed for sequence identification by BLAST and for comparison of microbial communities. Two different sets of bacterial 16S rRNA sequences were used to show the efficiency of this workflow. The first set of 16S rRNA sequences is derived from various soils from Hawaii Volcanoes National Park. The second set is derived from stool samples collected from diabetes-resistant and diabetes-prone rats. The workflow described here allows the investigator to quickly assess libraries of sequences on personal computers with customized databases. PANGEA is provided for users as individual scripts for each step in the process or as a single script where all processes, except the χ2 step, are joined into one program called the ‘backbone’.

http://www.physorg.com/news198243171.html

http://www.drugs.com/news/immune-genes-show-links-type-1-diabetes-26531.html

Nature. 2010 Sep 23;467(7314):460-4. Epub 2010 Sep 8. A trans-acting locus regulates an anti-viral expression network and type 1 diabetes risk. Heinig M, Petretto E, Wallace C, Bottolo L, Rotival M, Lu H, Li Y, Sarwar R, Langley SR, Bauerfeind A, Hummel O, Lee YA, Paskas S, Rintisch C, Saar K, Cooper J, Buchan R, Gray EE, Cyster JG; Cardiogenics Consortium, Erdmann J, Hengstenberg C, Maouche S, Ouwehand WH, Rice CM, Samani NJ, Schunkert H, Goodall AH, Schulz H, Roider HG, Vingron M, Blankenberg S, Münzel T, Zeller T, Szymczak S, Ziegler A, Tiret L, Smyth DJ, Pravenec M, Aitman TJ, Cambien F, Clayton D, Todd JA, Hubner N, Cook SA. Collaborators (35) Max-Delbrück-Center for Molecular Medicine (MDC), Berlin, Germany. Abstract 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 × 10(-6)). The human locus controlling the IDIN was associated with the risk of T1D at single nucleotide polymorphism rs9585056 (P = 7.0 × 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. PMID: 20827270