A Proteomic Platform to identify and Validate Biomarkers in Metabolic Syndrome and Coronary Artery Disease

用于识别和验证代谢综合征和冠状动脉疾病生物标志物的蛋白质组学平台

• 批准号: 9144821
• 负责人: W. Andy Tao
• 金额: $ 30.16万
• 依托单位: PURDUE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9144821
• 关键词: Adopted; Animal Model; Atherogenic Diet; Automation; Biochemical; Biological Assay; Biological Markers; Biological Models; Chemicals; Clinical; Coronary Arteriosclerosis; Detection; Development; Diagnosis; Disease; Early Diagnosis; Employee Strikes; Family suidae; Fractionation; Genotype; Goals; Immune; Ions; Life; Link; Measurable; Measures; Metabolic syndrome; Metals; Methods; Modeling; Molecular; Obesity; Patient Monitoring; Patients; Physiological; Plasma; Plasma Proteins; Polymers; Preventive screening; Procedures; Proteins; Proteome; Proteomics; Protocols documentation; Reproducibility; Sampling; Source; Staging; Testing; Therapeutic; Time; Tissues; United States National Institutes of Health; Validation; base; biomarker discovery; candidate marker; clinical application; clinical biomarkers; cost; data acquisition; design; disease diagnosis; feeding; high risk; improved; meetings; nanopolymer; novel; outcome forecast; protein biomarkers; tool; verification and validation

Blood plasma/serum has remained a major sample source for the detection of disease biomarker candidates with its easy accessibility and measurable tissue-derived proteins that hold potential to uncover physiological and pathological changes during diseases. Proteomics-based biomarker discovery using plasma/serum, however, is severely limited by the high complexity and dynamic range of protein concentrations (10-12 orders of magnitude). Presently, a majority of plasma/serum proteomic strategies are lack of adequate reproducibility, sensitivity, and robustness for clinical tests. Solutions for these serious technological barriers to develop a reliable detection platform toward clinical implementation are elusive. The long term goal of this project is to establish a robust proteomic platform that is translational, sensitive, and reproducible for biomarker discovery, verification and validation, with an emphasis on the identification and validation of plasma biomarkers for metabolic syndrome and early detection of coronary artery disease (CAD). We propose to develop a distinctive platform based on metal ion functionalized soluble nanopolymers for efficient and selective isolation of subsets of low abundant plasma proteins. We will test this approach by focusing on Ossabaw swine as our model system and will establish protocols that will ultimately provide a powerful method for any biofluid-based biomarker discovery. We propose to profile plasma proteomes as a function of feeding time and to correlate with physiological and pathological parameters over the period in which the Ossabaw swine develop metabolic syndrome. There is an increasingly urgent and strong push of proteomic discoveries to clinical applications. This project is based on a distinctive new concept and once developed, it will dramatically simplify the procedure and enhance our ability to sensitively identify candidate markers. As a consequence, the results of this study will have an important positive impact, not only on the development of novel tools for proteomics-based biomarker discovery, but also toward improving disease therapeutics and facilitating more effective diagnosis.

血浆/血清仍然是检测疾病生物标志物的主要样品来源 候选人具有易于获得和可测量的组织衍生蛋白， 揭示疾病过程中的生理和病理变化。基于蛋白质组学的生物标志物 然而，使用血浆/血清的发现受到高复杂性和动态范围的严重限制 蛋白质浓度（10-12个数量级）。目前，大多数血浆/血清蛋白质组学 策略缺乏足够的重现性、灵敏度和临床测试的稳健性。解 这些严重的技术障碍，以发展一个可靠的检测平台，向临床 但执行却难以实现。该项目的长期目标是建立一个强大的蛋白质组学平台 其对于生物标志物发现、验证和确认是平移的、灵敏的和可再现的， 强调代谢综合征的血浆生物标志物的鉴定和验证， 冠状动脉疾病（CAD）的早期检测。我们建议开发一个基于 金属离子官能化的可溶性纳米聚合物，用于有效和选择性分离低浓度的 丰富的血浆蛋白。我们将测试这种方法的重点是奥萨堡猪作为我们的模型 系统，并将建立协议，最终将提供一个强大的方法，任何生物流体为基础的 生物标志物发现我们建议分析血浆蛋白质组作为喂养时间的函数， 与Ossabaw猪的生理和病理参数相关， 患上代谢综合征蛋白质组学的发现正受到越来越紧迫和强大的推动 临床应用。该项目是基于一个独特的新概念，一旦开发，它将 大大简化了程序，并提高了我们灵敏地识别候选标记的能力。 因此，这项研究的结果将产生重要的积极影响，不仅对 开发基于蛋白质组学的生物标志物发现的新工具，但也有助于改善 疾病治疗和促进更有效的诊断。