Proteomic changes/progression of human type 1 diabetes

人类 1 型糖尿病的蛋白质组变化/进展

• 批准号: 7491195
• 负责人: JIN-XIONG SHE
• 金额: $ 49.4万
• 依托单位: AUGUSTA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-25 至 2010-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7491195
• 关键词: Antigens; Appearance; Autoantibodies; Autoimmune Diseases; Autoimmune Process; Autoimmunity; Beta Cell; Biological Assay; Biological Markers; Cells; Clinical; Complex; Data; Data Set; Dendritic Cells; Development; Disease; Disease Progression; Economics; Environment; Gene Proteins; Genes; Genetic Predisposition to Disease; Human; Immune system; Individual; Infection; Insulin-Dependent Diabetes Mellitus; Islet Cell; Islets of Langerhans; Modeling; Molecular; Mononuclear; Numbers; Pathogenesis; Pathway interactions; Patients; Peripheral Blood Mononuclear Cell; Phase; Polyacrylamide Gel Electrophoresis; Process; Proteins; Proteomics; Range; Relative (related person); Research Design; Risk; Risk Assessment; Risk Factors; Sampling; Screening procedure; Serum; Staging; Surface; Susceptibility Gene; T-Lymphocyte; Technology; Testing; Time; Tissues; Two-Dimensional Polyacrylamide Gel Electrophoresis; Validation; base; cohort; design; diabetic; high throughput technology; interest; ionization; islet; monocyte; novel; numb protein; peripheral blood; prospective; success; surface enhanced laser desorption ionization; tool; two-dimensional

DESCRIPTION (provided by applicant): 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.

描述（由申请人提供）： 从遗传易感性到β细胞自身免疫，然后是1型糖尿病（T1 D）的进展是一个关键但知之甚少的过程，导致分子和细胞变化的级联反应。这些变化的鉴定无疑将为疾病预测和阐明疾病机制提供有用的生物标志物。不幸的是，与疾病进展相关的变化很难记录，因为它们可能发生在不同的时间和不同的组织或细胞。一次分析单个基因/蛋白质的常规方法在揭示自身免疫级联中涉及的复杂分子途径方面仅取得有限的成功。因此，我们建议使用高通量蛋白质组学技术来系统地鉴定血清和外周血单核细胞中与T1 D进展相关的蛋白质组学变化。R21应用程序旨在使用两种互补的蛋白质组学技术筛选和验证人血清样本和选定PBMC亚群中存在的推定生物标志物：表面增强激光解吸/电离（SELDI）和二维聚丙烯酰胺凝胶电泳（2D-PAGE）。筛选将使用糖尿病、前驱糖尿病和对照受试者的大型横断面队列进行，而推定生物标志物的验证将使用大型独立横断面数据集完成。这些研究有望发现许多可能与T1 D发病机制有关和/或可用于风险评估的蛋白质。在R33阶段，我们将使用前瞻性队列进一步验证在R21阶段发现的生物标志物，这对于预测标志物的开发至关重要。前瞻性数据集应允许我们直接估计T1 D进展阶段之间的转换机会，并将其他风险因素（如HLA和胰岛自身抗体数据）纳入基于蛋白质组学的风险评估模型。我们还将开发和验证用于感兴趣的新型蛋白质的高度可重复和经济的检测方法。新的检测方法将作为发现工具所识别的蛋白质组学变化的独立确认，更重要的是可以用作合适的临床测试。