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      Gene Expression Profiles of Beta-Cell Enriched Tissue Obtained by Laser Capture Microdissection from Subjects with Type 2 Diabetes

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Changes in gene expression in pancreatic beta-cells from type 2 diabetes (T2D) should provide insights into their abnormal insulin secretion and turnover.

          Methodology/Principal Findings

          Frozen sections were obtained from cadaver pancreases of 10 control and 10 T2D human subjects. Beta-cell enriched samples were obtained by laser capture microdissection (LCM). RNA was extracted, amplified and subjected to microarray analysis. Further analysis was performed with DNA-Chip Analyzer (dChip) and Gene Set Enrichment Analysis (GSEA) software. There were changes in expression of genes linked to glucotoxicity. Evidence of oxidative stress was provided by upregulation of several metallothionein genes. There were few changes in the major genes associated with cell cycle, apoptosis or endoplasmic reticulum stress. There was differential expression of genes associated with pancreatic regeneration, most notably upregulation of members of the regenerating islet gene (REG) family and metalloproteinase 7 (MMP7). Some of the genes found in GWAS studies to be related to T2D were also found to be differentially expressed. IGF2BP2, TSPAN8, and HNF1B (TCF2) were upregulated while JAZF1 and SLC30A8 were downregulated.

          Conclusions/Significance

          This study made possible by LCM has identified many novel changes in gene expression that enhance understanding of the pathogenesis of T2D.

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          Most cited references 77

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          Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection.

            Recent advances in cDNA and oligonucleotide DNA arrays have made it possible to measure the abundance of mRNA transcripts for many genes simultaneously. The analysis of such experiments is nontrivial because of large data size and many levels of variation introduced at different stages of the experiments. The analysis is further complicated by the large differences that may exist among different probes used to interrogate the same gene. However, an attractive feature of high-density oligonucleotide arrays such as those produced by photolithography and inkjet technology is the standardization of chip manufacturing and hybridization process. As a result, probe-specific biases, although significant, are highly reproducible and predictable, and their adverse effect can be reduced by proper modeling and analysis methods. Here, we propose a statistical model for the probe-level data, and develop model-based estimates for gene expression indexes. We also present model-based methods for identifying and handling cross-hybridizing probes and contaminating array regions. Applications of these results will be presented elsewhere.
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              Beta-cell deficit and increased beta-cell apoptosis in humans with type 2 diabetes.

              Type 2 diabetes is characterized by impaired insulin secretion. Some but not all studies suggest that a decrease in beta-cell mass contributes to this. We examined pancreatic tissue from 124 autopsies: 91 obese cases (BMI >27 kg/m(2); 41 with type 2 diabetes, 15 with impaired fasting glucose [IFG], and 35 nondiabetic subjects) and 33 lean cases (BMI <25 kg/m(2); 16 type 2 diabetic and 17 nondiabetic subjects). We measured relative beta-cell volume, frequency of beta-cell apoptosis and replication, and new islet formation from exocrine ducts (neogenesis). Relative beta-cell volume was increased in obese versus lean nondiabetic cases (P = 0.05) through the mechanism of increased neogenesis (P < 0.05). Obese humans with IFG and type 2 diabetes had a 40% (P < 0.05) and 63% (P < 0.01) deficit and lean cases of type 2 diabetes had a 41% deficit (P < 0.05) in relative beta-cell volume compared with nondiabetic obese and lean cases, respectively. The frequency of beta-cell replication was very low in all cases and no different among groups. Neogenesis, while increased with obesity, was comparable in obese type 2 diabetic, IFG, or nondiabetic subjects and in lean type 2 diabetic or nondiabetic subjects. However, the frequency of beta-cell apoptosis was increased 10-fold in lean and 3-fold in obese cases of type 2 diabetes compared with their respective nondiabetic control group (P < 0.05). We conclude that beta-cell mass is decreased in type 2 diabetes and that the mechanism underlying this is increased beta-cell apoptosis. Since the major defect leading to a decrease in beta-cell mass in type 2 diabetes is increased apoptosis, while new islet formation and beta-cell replication are normal, therapeutic approaches designed to arrest apoptosis could be a significant new development in the management of type 2 diabetes, because this approach might actually reverse the disease to a degree rather than just palliate glycemia.
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                Author and article information

                Affiliations
                [1 ]Section on Islet Transplantation and Cell Biology, Research Division, Joslin Diabetes Center and the Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
                [2 ]Molecular Pathology Unit, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
                [3 ]Section of Endocrinology and Metabolism of Organ Transplantation, Department of Endocrinology and Metabolism, University of Pisa, Pisa, Italy
                Uppsala University, Sweden
                Author notes

                Conceived and designed the experiments: LM SBW PM GCW. Performed the experiments: LM JT SD DCS. Analyzed the data: LM JT AS SBW GCW. Contributed reagents/materials/analysis tools: PM. Wrote the paper: LM GCW.

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2010
                13 July 2010
                : 5
                : 7
                2903480
                20644627
                10-PONE-RA-17289R1
                10.1371/journal.pone.0011499
                (Editor)
                Marselli 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.
                Counts
                Pages: 13
                Categories
                Research Article
                Molecular Biology
                Cell Biology/Gene Expression
                Diabetes and Endocrinology/Type 2 Diabetes

                Uncategorized

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