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      The Human Pancreatic Islet Transcriptome: Expression of Candidate Genes for Type 1 Diabetes and the Impact of Pro-Inflammatory Cytokines

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          Abstract

          Type 1 diabetes (T1D) is an autoimmune disease in which pancreatic beta cells are killed by infiltrating immune cells and by cytokines released by these cells. Signaling events occurring in the pancreatic beta cells are decisive for their survival or death in diabetes. We have used RNA sequencing (RNA–seq) to identify transcripts, including splice variants, expressed in human islets of Langerhans under control conditions or following exposure to the pro-inflammatory cytokines interleukin-1β (IL-1β) and interferon-γ (IFN-γ). Based on this unique dataset, we examined whether putative candidate genes for T1D, previously identified by GWAS, are expressed in human islets. A total of 29,776 transcripts were identified as expressed in human islets. Expression of around 20% of these transcripts was modified by pro-inflammatory cytokines, including apoptosis- and inflammation-related genes. Chemokines were among the transcripts most modified by cytokines, a finding confirmed at the protein level by ELISA. Interestingly, 35% of the genes expressed in human islets undergo alternative splicing as annotated in RefSeq, and cytokines caused substantial changes in spliced transcripts. Nova1, previously considered a brain-specific regulator of mRNA splicing, is expressed in islets and its knockdown modified splicing. 25/41 of the candidate genes for T1D are expressed in islets, and cytokines modified expression of several of these transcripts. The present study doubles the number of known genes expressed in human islets and shows that cytokines modify alternative splicing in human islet cells. Importantly, it indicates that more than half of the known T1D candidate genes are expressed in human islets. This, and the production of a large number of chemokines and cytokines by cytokine-exposed islets, reinforces the concept of a dialog between pancreatic islets and the immune system in T1D. This dialog is modulated by candidate genes for the disease at both the immune system and beta cell level.

          Author Summary

          Pancreatic beta cells are destroyed by the immune system in type 1 diabetes mellitus, causing insulin dependence for life. Candidate genes for diabetes contribute to this process by acting both at the immune system and, as we suggest here, at the pancreatic beta cell level. We have utilized a novel technology, RNA sequencing, to define all transcripts expressed in human pancreatic islets under basal conditions and following exposure to cytokines, pro-inflammatory mediators that contribute to trigger diabetes. Our observations double the number of known genes present in human islets and indicate that >60% of the candidate genes for type 1 diabetes are expressed in beta cells. The data also show that pro-inflammatory cytokines modify alternative splicing in human islets, a process that may generate novel RNAs and proteins recognizable by the immune system. This, taken together with the findings that pancreatic beta cells themselves express and release many cytokines and chemokines (proteins that attract immune cells), indicates that early type 1 diabetes is characterized by a dialog between beta cells and the immune system. We suggest that candidate genes for diabetes function at least in part as “writers” for the beta cell words in this dialog.

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          DAVID: Database for Annotation, Visualization, and Integrated Discovery.

          Functional annotation of differentially expressed genes is a necessary and critical step in the analysis of microarray data. The distributed nature of biological knowledge frequently requires researchers to navigate through numerous web-accessible databases gathering information one gene at a time. A more judicious approach is to provide query-based access to an integrated database that disseminates biologically rich information across large datasets and displays graphic summaries of functional information. Database for Annotation, Visualization, and Integrated Discovery (DAVID; http://www.david.niaid.nih.gov) addresses this need via four web-based analysis modules: 1) Annotation Tool - rapidly appends descriptive data from several public databases to lists of genes; 2) GoCharts - assigns genes to Gene Ontology functional categories based on user selected classifications and term specificity level; 3) KeggCharts - assigns genes to KEGG metabolic processes and enables users to view genes in the context of biochemical pathway maps; and 4) DomainCharts - groups genes according to PFAM conserved protein domains. Analysis results and graphical displays remain dynamically linked to primary data and external data repositories, thereby furnishing in-depth as well as broad-based data coverage. The functionality provided by DAVID accelerates the analysis of genome-scale datasets by facilitating the transition from data collection to biological meaning.
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            The transcriptional landscape of the yeast genome defined by RNA sequencing.

            The identification of untranslated regions, introns, and coding regions within an organism remains challenging. We developed a quantitative sequencing-based method called RNA-Seq for mapping transcribed regions, in which complementary DNA fragments are subjected to high-throughput sequencing and mapped to the genome. We applied RNA-Seq to generate a high-resolution transcriptome map of the yeast genome and demonstrated that most (74.5%) of the nonrepetitive sequence of the yeast genome is transcribed. We confirmed many known and predicted introns and demonstrated that others are not actively used. Alternative initiation codons and upstream open reading frames also were identified for many yeast genes. We also found unexpected 3'-end heterogeneity and the presence of many overlapping genes. These results indicate that the yeast transcriptome is more complex than previously appreciated.
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              Differential targeting of prosurvival Bcl-2 proteins by their BH3-only ligands allows complementary apoptotic function.

              Apoptosis is initiated when Bcl-2 and its prosurvival relatives are engaged by proapoptotic BH3-only proteins via interaction of its BH3 domain with a groove on the Bcl-2-like proteins. These interactions have been considered promiscuous, but our analysis of the affinity of eight BH3 peptides for five Bcl-2-like proteins has revealed that the interactions vary over 10,000-fold in affinity, and accordingly, only certain protein pairs associate inside cells. Bim and Puma potently engaged all the prosurvival proteins comparably. Bad, however, bound tightly to Bcl-2, Bcl-xL, and Bcl-w but only weakly to A1 and not to Mcl-1. Strikingly, Noxa bound only Mcl-1 and A1. In accord with their complementary binding, Bad and Noxa cooperated to induce potent killing. The results suggest that apoptosis relies on selective interactions between particular subsets of these proteins and that it should be feasible to discover BH3-mimetic drugs that inactivate specific prosurvival targets.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                1553-7390
                1553-7404
                March 2012
                March 2012
                8 March 2012
                : 8
                : 3
                : e1002552
                Affiliations
                [1 ]Laboratory of Experimental Medicine, Université Libre de Bruxelles (ULB), Brussels, Belgium
                [2 ]Functional Bioinformatics (FBI), Centre Nacional d'Anàlisi Genòmica (CNAG), Barcelona, Spain
                [3 ]Cell Differentiation Unit, Diabetes Research Centre, Vrije Universiteit Brussel (VUB), Brussels, Belgium
                [4 ]Oxford Centre for Diabetes, Endocrinology, and Metabolism (OCDEM), Churchill Hospital, Oxford, United Kingdom
                [5 ]Department of Endocrinology and Metabolism, University of Pisa, Pisa, Italy
                [6 ]Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
                [7 ]Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, United Kingdom
                [8 ]Division of Endocrinology, Erasmus Hospital, Université Libre de Bruxelles (ULB), Brussels, Belgium
                Georgia Institute of Technology, United States of America
                Author notes

                Conceived and designed the experiments: DLE MIM MC. Performed the experiments: TB GS MI-E FO IS MLC JB LB LH LG GL LM PM MC. Analyzed the data: DLE MS GB MIM MC. Contributed reagents/materials/analysis tools: DLE MS LH LG GL LM PM MIM MC. Wrote the paper: DLE MS GB MC.

                Article
                PGENETICS-D-11-02435
                10.1371/journal.pgen.1002552
                3297576
                22412385
                266b8462-726d-435b-a29f-23f5061a4050
                Eizirik et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 12 November 2011
                : 10 January 2012
                Page count
                Pages: 17
                Categories
                Research Article
                Biology
                Genomics
                Genome Analysis Tools
                Medicine
                Endocrinology
                Diabetic Endocrinology

                Genetics
                Genetics

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