人类自身抗原的分子组成规律及转化应用研究

自身抗体对于维持机体平衡和清除异常增殖细胞扮演非常重要的角色，与自身免疫性疾病和肿瘤发生发展密切相关。自1945年以来有>12万篇文献开展了自身抗体研究，如何系统研究这些文献中发现的自身抗原并揭示他们的分子机制和规律以及在人类疾病中的作用将为推动生命组学和精准医学研究具有重要的意义！本课题组将在前期人类自身抗原组数据库AAgAtlas1.0的基础上，1)采用知识挖掘技术系统收集、整理、归纳和构建国际上第一个全面的人类自身抗原组数据库；2）系统分析自身抗原组的遗传、生化细胞、基因蛋白表达和系统进化特性，探索这些自身抗原的产生、分子组成和与人类疾病的相关规律；3）建立疾病自身抗体标志物分子预测和验证体系，显著降低高通量自身抗原组学研究的技术门槛和成本, 为生物标志物研究提供一条新的途径。本项目的实施将为理解人类自身抗原的分子机制、组成规律和发现可用于重大疾病精准诊治的标志物分子具有重要的价值。

Autoantibodies play very important roles in maintaining body balance and eliminating abnormally proliferating cells, which are closely related to the development of autoimmune diseases and tumors. More than 120,000 scientific papers studying autoantibodies or autoantigens had been published since 1945. How to systematically study the autoantigens found in these documents and reveal their molecular mechanisms and their roles in human diseases will be of great significance in promoting life science and precision medicine! Based on our previous work on the AAgAtlas1.0 database, 1) we will continue to collect, annonate, summarize and construct the first comprehensive database of human autoantigens in the world using the knowledge mining technology; 2) we will explore the production of these autoantigens, molecular composition and association with human diseases according to the characteristics of genetics, biochemistry, cell biology，gene and protein expression as well as systematic evolution; 3) based on those investigations, we will develop a new method that enable the prediction of autoantibody biomarkers for different human diseases. The successful execution of this project will allow us to systematically understand the molecular mechanism of human autoantigens (AAgeome), significantly reduce effort for the High-Throughput autoantibody biomarker study, and to discover the potential autoantibody biomarkers for the diagnosis and treatment of human cancer and autoimmune diseases.