The progression from genetic predisposition to beta-cell autoimmunity and then Type 1 diabetes (T1D) is a critical but poorly understood process, resulting in a cascade of molecular and cellular changes. Identification of these changes will undoubtedly provide useful biomarkers for disease prediction and elucidation of disease mechanisms. Unfortunately, the changes associated with disease progression are difficult to document as they can occur at different times and different tissues or cells. The conventional approaches of analyzing a single gene/protein a time have had only limited success in uncovering the complex molecular pathways implicated in the autoimmune cascade. Therefore, we propose to use high throughput proteomic technologies to systematically identify proteomic changes associated with T1D progression in the serum and peripheral blood mononuclear ceils. The R21 application is designed to screen and validate putative biomarkers present in human serum samples and selected PBMC subsets using two complimentary proteomic technologies: surface-enhanced laser desorption/ionization (SELDI) and 2-dimensional polyacrylamide gel electrophoresis (2D-PAGE). The screening will be done using a large cross-sectional cohort of diabetic, pre-diabetic and control subjects, while and the validation of putative biomarkers will be accomplished using a large independent cross-sectional data set. These studies are expected to discover a number of proteins that are likely implicated in the pathogenesis of T1D and/or useful for risk assessment. In the R33 phase, we will further validate the biomarkers discovered in the R21 phase using a prospective cohort, essential for the development of predictive markers. The prospective data set should allow us to directly estimate the chance of transitions between T1D progression stages and to incorporate other risk factors such as HLA and islet autoantibody data into proteomic-based risk assessment models. We will also develop and validate highly reproducible and economic assays for the novel proteins of interest. The new assays will serve as independent confirmation of the proteomic changes identified by the discovery tools and more importantly can be used as suitable clinical tests.

Agency
National Institute of Health (NIH)
Institute
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21HD050196-02
Application #
6954093
Study Section
Special Emphasis Panel (ZDK1-GRB-9 (O1))
Program Officer
Grave, Gilman D
Project Start
2004-09-28
Project End
2006-08-31
Budget Start
2005-09-01
Budget End
2006-08-31
Support Year
2
Fiscal Year
2005
Total Cost
$357,501
Indirect Cost
Name
Georgia Health Sciences University
Department
Pathology
Type
Schools of Medicine
DUNS #
966668691
City
Augusta
State
GA
Country
United States
Zip Code
30912
Purohit, Sharad; Sharma, Ashok; Zhi, Wenbo et al. (2018) Proteins of TNF-? and IL6 Pathways Are Elevated in Serum of Type-1 Diabetes Patients with Microalbuminuria. Front Immunol 9:154
Törn, Carina; Hadley, David; Lee, Hye-Seung et al. (2015) Role of Type 1 Diabetes-Associated SNPs on Risk of Autoantibody Positivity in the TEDDY Study. Diabetes 64:1818-29
Jin, Yulan; Sharma, Ashok; Carey, Colleen et al. (2013) The expression of inflammatory genes is upregulated in peripheral blood of patients with type 1 diabetes. Diabetes Care 36:2794-802
Lu, Shangsu; Purohit, Sharad; Sharma, Ashok et al. (2012) Serum insulin-like growth factor binding protein 6 (IGFBP6) is increased in patients with type 1 diabetes and its complications. Int J Clin Exp Med 5:229-37
Jin, Yulan; Purohit, Sharad; Chen, Xueqin et al. (2012) Over-expression of Stat5b confers protection against diabetes in the non-obese diabetic (NOD) mice via up-regulation of CD4(+)CD25(+) regulatory T cells. Biochem Biophys Res Commun 424:669-74
Jin, Yulan; She, Jin-Xiong (2012) Novel biomarkers in type 1 diabetes. Rev Diabet Stud 9:224-35
Guan, Ruili; Purohit, Sharad; Wang, Hongjie et al. (2011) Chemokine (C-C motif) ligand 2 (CCL2) in sera of patients with type 1 diabetes and diabetic complications. PLoS One 6:e17822
Carey, Colleen; Purohit, Sharad; She, Jin-Xiong (2010) Advances and challenges in biomarker development for type 1 diabetes prediction and prevention using omic technologies. Expert Opin Med Diagn 4:397-410
Lai, Yinglei; Eckenrode, Sarah E; She, Jin-Xiong (2009) A statistical framework for integrating two microarray data sets in differential expression analysis. BMC Bioinformatics 10 Suppl 1:S23
Jin, Yulan; Chen, Xueqin; Podolsky, Robert et al. (2009) APC dysfunction is correlated with defective suppression of T cell proliferation in human type 1 diabetes. Clin Immunol 130:272-9

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