Proteomics Approaches to Interstitial Cystitis.

描述(申请人提供)：本申请是对申请(RFA)的回应：DK-03-010，间质性膀胱炎基础研究。间质性膀胱炎(IC)的诊断主要基于症状，因为由于缺乏已证实的生物标志物，目前还没有可用的血液或尿液测试。此外，目前还没有持续有效的治疗IC的方法，确切的病因还没有得到证实。因此，一个迫切需要的领域是确定IC的疾病标志物。可用于IC的敏感、特异检测/筛查的标志物在疾病的准确诊断以及阐明可能转化为治疗IC的作用模式的潜在病理生物学途径方面可能具有巨大的价值。疾病特征的识别是特别感兴趣的研究领域之一，也是本RFA的预期目标。因此，为了实现这一目标，我们将：1)使用表面增强激光解吸电离飞行时间(SELDI-TOF)质谱仪生成临床注释的间质性膀胱炎标本的疾病相关尿蛋白图谱；2)基于SELDI-TOF获得的结果，通过2-0差分凝胶电泳(2-D QIGE)和串联质谱仪(MSIMS)序列识别来表征潜在的疾病相关蛋白质；3)将低丰度尿蛋白在临床注释的IC标本中的相对表达水平与同位素编码的亲和标签(ICAT)和质谱学进行比较；4)挖掘蛋白质组学数据，并创建可根据临床信息和/或结果对样本进行分层的预测性生物信息学模型(即，等级聚类分析和K-均值方法、典型相关分析、判别分析、贝叶斯统计、自组织映射和神经网络)；5)开发由尿液、血清和血浆样本组成的生物库，作为未来分析的资源，包括创建以冷冻尿液、血清和血浆蛋白阵列的形式的资源，以及全球代谢组学研究的资源。通过这些具体目标，我们的目标是满足为IC开发可靠的预测和诊断工具的需求，这被NIDDK膀胱研究进展小组视为高度优先事项。

DESCRIPTION (provided by applicant): This application is in response to a Request for Applications (RFA): DK-03-010, Basic Research in Interstitial Cystitis. Diagnosis of interstitial cystitis (IC) is primarily based on symptoms, as there are no currently available blood or urine tests due to the lack of demonstrated biological markers. In addition, there are currently no consistently effective treatments for IC, and a precise etiology has not been demonstrated. Thus, one area of critical need is to identify disease markers for IC. Markers that can be used in sensitive, specific tests/screens for IC may have immense value in the accurate diagnosis of disease, as well as elucidating potential pathobiological pathways that may translate into a mode of action for the treatment of IC. The identification of disease signatures is one of the research areas of special interest, and anticipated goals of this RFA. Therefore, to fulfill this goal, we will: 1) generate disease-associated urinary protein profiles of clinically annotated interstitial cystitis specimens with surface enhanced laser desorption ionization time-of-flight (SELDI-TOF) mass spectrometry; 2) characterize potential disease-associated proteins, based on results obtained from SELDI-TOF, by 2-0 difference gel electrophoresis (2-D QIGE) and tandem mass spectrometry (msims)-based sequence identification; 3) compare relative expression levels of low- abundance urinary proteins in clinically annotated IC specimens with isotope-coded affinity tag (ICAT) and mass spectrometry; 4) mine proteomics data and to create predictive bioinformatics models (i.e., hierarchical cluster analysis and K-means methods, canonical correlations, discriminant analysis, Bayesian statistics, self-organizing maps and neural networks) that can stratify samples according to clinical information and/or outcome; 5) develop a biorepository consisting of urine, serum, and plasma specimens as a resource for future assays, including the creation of resources in the form of frozen urine, serum, and plasma protein arrays, as well as resources for global metabolomics studies. Through these specific aims, our goal is to fulfill the need to develop reliable predictive and diagnostic tools for IC, which is deemed to be a high-priority by the NIDDK Bladder Research Progress Group.