Molecular classification, prediction markers and surrogate endpoints for human type 1 diabetes (Rewers, Eisenbarth, She, Schatz, Atkinson, Pugliese and Muir):

The goals of these ongoing human studies are very similar to those described in projects 1 and 2. These studies will take advantage of two unique prospective cohorts for type 1 diabetes. At the University of Florida, the PANDA (prospective assessment in newborns for diabetes autoimmunity) program funded by NIH and JDF (PI: JX She) screen newborns for the presence of high risk diabetes genes (HLA-DR and -DQ) and monitor disease progression using immunological markers (autoantibodies) and microarray technologies. We have screened over 5,000 newborns and will continue to screen 3,000 every year. Over 200 at risk children are being monitored every 6 months starting at 6 months of age for the presence of antibodies and other markers. RNA samples from peripheral mononuclear cells (PBMCs) have also been obtained and stored at each time point. Ten children have developed autoantibodies and two have developed diabetes within the first two years of life. Expression profiling will be done on this group of children using RNA samples from multiple time points before and after the development of autoantibodies and diabetes. Microarray analysis was one of the specific aims of our funded proposal. A similar program based at Denver (PI: M. Rewers), DAISY (Diabetes autoimmunity study of the young), has screened over 20,000 newborns and identified a large number of antibody positive children that can be analyzed. Microarray analysis was not a part of the specific aims of this program. We believe that expression profiling should be done on both populations. Correlation of gene expression profiles of the samples and our comprehensive phenotyping and epidemiological data should allow us to identify markers that are associated with disease progression and molecules that contribute to the disease process. We believe that expression profiling will provide is a critical tool for disease prediction in the future. We hope to establish a diabetes prediction array that can be used for future diabetes prevention trials. In this project, we will also profile diabetic patients, normal controls, concordant and discordant twin pairs for diabetes or prediabetic markers (autoantibodies). This data set may allow us to address a number of questions. The first one is the classification of type 1 diabetes patients based on their expression profiles. It has been shown that there is extensive heterogeneity in terms of genetic susceptibility within the type 1 diabetes patient population. Genetic heterogeneity and environmental factors can result in substantially different gene expression profiles, which may determine the response to various prevention protocols that are being tested by our research groups and others. The second one is identification of genes that contribute to the disease. The comparison of patients and controls, especially the discordant twin pairs for disease or prediabetic markers may reveal important molecules involved in the disease. These studies are partly supported by existing grants. The third component of this project is to develop endpoint markers for diabetes prevention trials. To achieve these goals, we will profile prediabetic patients (autoantibody-positive) during staging and at multiple time points after therapy. We have obtained preliminary approval to study the subjects on the nation-wide Diabetes prevention Trial –1 (DPT-1). Gainesville, Denver and Miami are the major clinical centers for DPT-1 and the investigators are very interested to incorporate microarray strategies into future clinical trials. This component is a long term goal and the detailed research plan will be decided in the future. We include this component to illustrate how this technology can aid future clinical trials. We understand that these human studies may be very difficult to perform due to the heterogeneity of the PBMC population and the low frequency of disease-specific cells in the PBMCs. We intend to purify subsets of cells such as CD4+, CD8+ and B cells to increase the assay sensitivity. We may also be able to enrich diabetes-specific T cells using MHC tetramer technology.
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