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Detection of molecular paths associated with insulitis and type 1 diabetes in non-obese diabetic mouse.

ABSTRACT: Recent clinical evidence suggests important role of lipid and amino acid metabolism in early pre-autoimmune stages of type 1 diabetes pathogenesis. We study the molecular paths associated with the incidence of insulitis and type 1 diabetes in the Non-Obese Diabetic (NOD) mouse model using available gene expression data from the pancreatic tissue from young pre-diabetic mice. We apply a graph-theoretic approach by using a modified color coding algorithm to detect optimal molecular paths associated with specific phenotypes in an integrated biological network encompassing heterogeneous interaction data types. In agreement with our recent clinical findings, we identified a path downregulated in early insulitis involving dihydroxyacetone phosphate acyltransferase (DHAPAT), a key regulator of ether phospholipid synthesis. The pathway involving serine/threonine-protein phosphatase (PP2A), an upstream regulator of lipid metabolism and insulin secretion, was found upregulated in early insulitis. Our findings provide further evidence for an important role of lipid metabolism in early stages of type 1 diabetes pathogenesis, as well as suggest that such dysregulation of lipids and related increased oxidative stress can be tracked to beta cells.

SUBMITTER: Lindfors E

PROVIDER: S-EPMC2749452 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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Detection of molecular paths associated with insulitis and type 1 diabetes in non-obese diabetic mouse.

Lindfors Erno E Gopalacharyulu Peddinti V PV Halperin Eran E Oresic Matej M

PloS one 20091002 10

Recent clinical evidence suggests important role of lipid and amino acid metabolism in early pre-autoimmune stages of type 1 diabetes pathogenesis. We study the molecular paths associated with the incidence of insulitis and type 1 diabetes in the Non-Obese Diabetic (NOD) mouse model using available gene expression data from the pancreatic tissue from young pre-diabetic mice. We apply a graph-theoretic approach by using a modified color coding algorithm to detect optimal molecular paths associate ...[more]

PMID: 19798418

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Project description:Despite the 2019 Executive Order on Advancing American Kidney Health Initiative, kidney disease has moved up in rank from the 9th to the 8th leading cause of death in the United States. A recent push in the field of nephrology has been to identify molecular markers and/or molecular profiles involved in kidney disease process or injury that can help identify the cause of injury and predict patient outcomes. While these studies have had moderate success, they have not yet considered that many of the health conditions that cause kidney disease (diabetes, hypertension, etc.) can also be caused by environmental factors (such as viruses), which in and of themselves can cause kidney disease. Thus, the goal of this study was to identify molecular and phenotypic profiles that can differentiate kidney injury caused by diabetes (a health condition resulting in kidney disease) and coxsackievirus B4 (CVB4) exposure (which can cause diabetes and/or kidney disease), both alone and together. Non-obese diabetic (NOD) mice were used for this study due to their susceptibility to both type 1 diabetes (T1D)- and CVB4-mediated kidney injury, in order to glean a better understanding of how hyperglycemia and viral exposure, when occurring on their own and in combination, may alter the kidneys' molecular and phenotypic profiles. While no changes in kidney function were observed, molecular biomarkers of kidney injury were significantly up- and downregulated based on T1D and CVB4 exposure, both alone and together, but not in a predictable pattern. By combining individual biomarkers with function and phenotypic measurements (i.e., urinary albumin creatinine ratio, serum creatinine, kidney weight, and body weight), we were able to perform an unbiased separation of injury group based on the type of injury. This study provides evidence that unique kidney injury profiles within a kidney disease health condition are identifiable, and will help us to identify the causes of kidney injury in the future.

Project description:Model organisms are becoming increasingly important for the study of complex diseases such as type 1 diabetes (T1D). The non-obese diabetic (NOD) mouse is an experimental model for T1D having been bred to develop the disease spontaneously in a process that is similar to humans. Genetic analysis of the NOD mouse has identified around 50 disease loci, which have the nomenclature Idd for insulin-dependent diabetes, distributed across at least 11 different chromosomes. In total, 21 Idd regions across 6 chromosomes, that are major contributors to T1D susceptibility or resistance, were selected for finished sequencing and annotation at the Wellcome Trust Sanger Institute. Here we describe the generation of 40.4 mega base-pairs of finished sequence from 289 bacterial artificial chromosomes for the NOD mouse. Manual annotation has identified 738 genes in the diabetes sensitive NOD mouse and 765 genes in homologous regions of the diabetes resistant C57BL/6J reference mouse across 19 candidate Idd regions. This has allowed us to call variation consequences between homologous exonic sequences for all annotated regions in the two mouse strains. We demonstrate the importance of this resource further by illustrating the technical difficulties that regions of inter-strain structural variation between the NOD mouse and the C57BL/6J reference mouse can cause for current next generation sequencing and assembly techniques. Furthermore, we have established that the variation rate in the Idd regions is 2.3 times higher than the mean found for the whole genome assembly for the NOD/ShiLtJ genome, which we suggest reflects the fact that positive selection for functional variation in immune genes is beneficial in regard to host defence. In summary, we provide an important resource, which aids the analysis of potential causative genes involved in T1D susceptibility. Database URLs: http://www.sanger.ac.uk/resources/mouse/nod/; http://vega-previous.sanger.ac.uk/info/data/mouse_regions.html#Idd

Dataset Information

Detection of molecular paths associated with insulitis and type 1 diabetes in non-obese diabetic mouse.

Publications

Detection of molecular paths associated with insulitis and type 1 diabetes in non-obese diabetic mouse.

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