Proteomic changes/progression of human type 1 diabetes

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(T1D)是一个关键但知之甚少的过程，导致一系列分子和细胞变化。这些变化的识别无疑将为疾病预测和疾病机制的阐明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下来，因为它们可能发生在不同的时间和不同的组织或细胞。传统的一次分析单个基因/蛋白质的方法在揭示与自身免疫级联反应有关的复杂分子途径方面取得的成功有限。因此，我们建议使用高通量蛋白质组学技术系统地鉴定血清和外周血单核细胞中与T1D进展相关的蛋白质组变化。R21应用程序旨在使用两种免费的蛋白质组技术：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)，筛选和验证存在于人类血清样本和选定的PBMC亚群中的假定生物标记物。筛查将使用糖尿病、糖尿病前期和对照受试者的大量横断面队列进行，而假定的生物标志物的验证将使用大型独立横断面数据集完成。这些研究有望发现一些可能与T1D的发病机制有关和/或对风险评估有用的蛋白质。在R33阶段，我们将使用前瞻性队列进一步验证在R21阶段发现的生物标记物，这对于预测标记物的开发至关重要。前瞻性数据集应该允许我们直接估计T1D进展阶段之间转换的机会，并将其他风险因素，如人类白细胞抗原和胰岛自身抗体数据纳入基于蛋白质组的风险评估模型。我们还将为感兴趣的新蛋白质开发和验证高度重复性和经济的分析方法。新的分析方法将作为对发现工具确定的蛋白质组变化的独立确认，更重要的是，可以用作适当的临床测试。

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.