The proposed research, based on our prior 8-year achievements, will investigate the role of new immunologic biomarkers in the largest population-based cohort of first degree relatives of T1DM patients with and without T1DM from the same geographical area. Growing number of pilot trials for T1DM are being conducted by international collaborative clinical research networks, such as TrialNet (://www.diabetestrialnet.org) and the Immune Tolerance Network (://www.immunetolerance.org) in an effort to find the cure for T1DM. There are many prevention trials in the pipeline that cannot be performed because the currently available biomarkers cannot identify a sufficient number of individuals to be enrolled in these trials. Endless discussions have taken place on how to develop new strategies to enhance sensitivity of multiple markers and in turn effectively enroll first-degree relatives in T1DM prevention trials. Based on our most recent preliminary data, a major hypothesis of the present application is that a combination of novel biomarkers detecting antibodies directed to IA-2, GAD65 specific epitopes and the newly discovered antigen ZnT8 will further enhance sensitivity and the predictive value of T1DM progression as compared to conventional islet autoantibody markers. The proposed research will be performed at the University of Michigan using the Children's Hospital of Pittsburgh cohort, which currently has 161 first degree relatives, who converted to insulin-requiring diabetes during follow-up (converters) from a pool of over 10,000 relatives of T1DM probands. This unique serum sample archive is ideal to test our immunologic hypotheses. This represents the largest number of converters of any center and as such, more than 200 converters should be available by the end of the next grant period. Because it is impossible to have access to pancreatic tissue and pancreatic lymph nodes from subjects at risk of developing T1DM, we feel that it is entirely appropriate to develop predictive models of pancreatic 2 cell destruction to understand the role of pathogenic T cell responses during the natural history of human disease. We have constructed mathematical models taking into consideration new and conventional islet autoantibody biomarkers (Specific Aim I) and high avidity T cells which leads to 2 cell destruction during T1DM progression (Specific Aim II). We have coupled laboratory-based methodology with mathematical modeling of T1DM progression and have assembled an unprecedented team of leading experts in immunology research and mathematical predictive models of 2 cell destruction. Modeling key elements of T1DM progression (islet autoantibodies, T cell avidity, ER stress and UPR interactions) fits well with the experimental design and may ultimately validate a subset of T cells and/or the UPR pathway as drug targets and prove useful in guiding drug discovery to treat T1DM. The outcome of the proposed investigation should allow for the development of new biomarkers, modeling of key elements associated with T1DM progression and facilitate major clinical trials aimed at evaluating new approaches for understanding, preventing and treating Type 1 diabetes.
The use of immuno-epidemiology studies can be applied to reliably identify the earliest signs of islet autoantibodies which indicate that the autoimmune process leading to pancreatic 2 cell injury has already initiated. It is likely that the appearance of these white cells contribute to the destructive process of pancreatic 2 cells, a prelude of clinically overt Type 1 diabetes mellitus. Our research has major public health implications and together with modeling of key elements of Type 1 diabetes progression these studies will also provide a conceptual framework for designing new algorithms used for enrolling new subjects at risk in major clinical trials aimed at preventing Type 1 diabetes.
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|Jaberi-Douraki, Majid; Pietropaolo, Massimo; Khadra, Anmar (2014) Predictive models of type 1 diabetes progression: understanding T-cell cycles and their implications on autoantibody release. PLoS One 9:e93326|
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|Schnell, Santiago; Pietropaolo, Massimo (2013) Autoimmunity treatment using pMHC-NP-based therapy: designing nanoparticle treatment of autoimmunity with quantitative biology. Immunol Cell Biol 91:333-4|
|Sugarman, Jordan; Tsai, Sue; Santamaria, Pere et al. (2013) Quantifying the importance of pMHC valency, total pMHC dose and frequency on nanoparticle therapeutic efficacy. Immunol Cell Biol 91:350-9|
|Bonner, Samantha M; Pietropaolo, Susan L; Fan, Yong et al. (2012) Sequence variation in promoter of Ica1 gene, which encodes protein implicated in type 1 diabetes, causes transcription factor autoimmune regulator (AIRE) to increase its binding and down-regulate expression. J Biol Chem 287:17882-93|
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