Urinary Polypeptide Biomarkers of IgA Nephropathy

描述(由申请人提供)：我们的最终目标是开发一种诊断IgA肾病(IgAN)的非侵入性程序。本申请中提出的研究的具体目标是鉴定可作为免疫球蛋白肾病生物标志物的尿多肽。在初步研究中，我们用免疫复合体特异性的酶联免疫吸附试验证实了IgA和Ig G免疫球蛋白以及Ig A-Ig G复合体在Ig AN患者的尿液中水平升高。此外，我们还用毛细管电泳质谱(CE-MS)鉴定了几个潜在的IgAN尿多肽生物标志物，并用串联质谱仪对其中一些多肽进行了测序。基于这些数据，我们假设IgAN患者的尿蛋白组含有疾病特异性生物标记物。我们建议首先通过分析从特征良好的患者和对照中收集的IgAN研究期间收集的档案尿样来确认我们的初步数据，以检验这一假设。我们将使用经典的和免疫复合物特异性的ELISA法测定免疫球蛋白和免疫复合体的水平，并通过蛋白质印迹分析进行质量保证和验证。然后，这些数据将通过使用CE-MS进行分析来扩展，以确定疾病的阳性和阴性生物标记物，其中软件工具MosaCluster将与支持向量机一起用于样本分类。这种方法将定义成员，具有正值(疾病相关标记)和负值(正常标记)。将ELISA法和CE-MS法检测结果与临床结果进行对比分析。CE-MS确定的候选生物标志物将通过傅立叶变换-离子回旋共振MS进一步表征，以确定氨基酸序列和翻译后修饰。最后，候选生物标记物将使用独立的患者和对照队列进行验证。这项研究旨在确定可用于区分IgAN与其他肾脏疾病的标志物，以及疾病进展和严重程度的生物标志物。相关性：IGAN是全世界最常见的原发性肾小球肾炎。目前，诊断需要侵入性肾活检，而IgAN通常在很晚的阶段才被诊断出来。识别可用作非侵入性诊断测试的基础和/或用于监测疾病进程的生物标记物将是非常有益的。

DESCRIPTION (provided by applicant): Our ultimate goal is to develop a non-invasive procedure for the diagnosis of IgA nephropathy (IgAN). The specific goal of the studies proposed in this application is to identify urinary polypeptides that can be used as biomarkers of IgAN. In preliminary studies, we have demonstrated elevated levels of IgA and IgG immunoglobulins and IgA-IgG complexes in the urine of IgAN patients using immune complex-specific ELISA. Furthermore, we have identified several potential urinary polypeptide biomarkers for IgAN by capillary electrophoresis-mass spectrometry (CE-MS) and sequenced some of these polypeptides using tandem mass spectrometry. Based on these data, we hypothesize that the urine proteome of IgAN patients contains disease-specific biomarkers. We propose to test this hypothesis by first confirming our preliminary data through analysis of archival urine samples collected during studies of IgAN from well-characterized patients and controls. We will determine the levels of immunoglobulins and immune complexes using classical and immune complex-specific ELISA protocols with quality assurance and verification by western blot analysis. These data will then be extended by analysis using CE-MS to identify positive and negative biomarkers of disease in which the software tool, MosaCluster, with support vector machines, will be used for classification of samples. This approach will define a membership, with positive values (disease-associated marker) and negative values (marker for normal). The results of ELISA and CE-MS will be compared and correlated with the clinical findings. Candidate biomarkers identified by CE-MS will be further characterized by Fourier transform-ion cyclotron resonance MS to define the amino acid sequence and post- translational modifications. Finally, candidate biomarkers will be verified using an independent cohort of patients and controls. This study is designed to identify markers that can be used to distinguish IgAN from other renal diseases as well as biomarkers of disease progression and severity. Relevance: IgAN is the most common primary glomerulonephritis worldwide. Currently, diagnosis requires an invasive renal biopsy and IgAN is frequently diagnosed at a very late stage. The identification of biomarkers that can be used as the basis of a noninvasive diagnostic test and/or used to monitor the course of the disease would be extremely beneficial.