Proteomic approaches to central nervous system disorders

Abstract The discovery, design and evaluation of new medicines is critically dependent on the elucidation of protein mechanisms involved in human diseases. Since the proteome of a cell or tissue is not a simple reflection of its transcriptome, direct protein-based analysis is needed. Advances in proteomic technologies are improving the analysis of membrane proteins and signaling complexes with increased speed and molecular detail. Changes in protein isoforms due to post-translational modifications, such as phosphorylation induced by cell signaling events and alternative splice forms of receptors, may be mapped to an altered protein expression pattern in clinically relevant cell populations with a causative or diagnostic disease link. A CNS proteome database derived from primary human tissues may avoid ambiguities of experimental models. It will also accelerate the development of more specific diagnostic and prognostic disease markers as well as new selective therapeutics. Proteomics is also being applied to resolve in silico gene prediction uncertainties by direct open reading frame verification. These advances hold great promise for improvements in the understanding, diagnosis and therapy of central nervous system disorders.摘要 新药的发现、设计与评价，关键依赖于对人类疾病相关蛋白质致病机制的阐明。由于细胞或组织的蛋白质组（proteome）并非其转录组（transcriptome）的简单复刻，因此亟需开展直接基于蛋白质的分析。蛋白质组学技术的进步正以更快的分析速度与更精细的分子分辨率，优化膜蛋白与信号复合物的分析流程。由翻译后修饰（post-translational modifications）——如细胞信号事件诱导的磷酸化修饰——以及受体可变剪接体所引发的蛋白质同工型变化，可与临床相关细胞群中与疾病存在致病或诊断关联的蛋白质表达谱改变建立精准对应。基于原代人类组织构建的中枢神经系统（central nervous system, CNS）蛋白质组数据库，可规避实验模型固有的歧义性缺陷；同时还将加速特异性更强的疾病诊断、预后标志物，以及新型选择性治疗药物的开发进程。此外，蛋白质组学还可通过直接验证开放阅读框（open reading frame），解决计算机模拟（in silico）基因预测中的不确定性问题。上述技术进展有望极大推动中枢神经系统疾病的认知、诊断与治疗水平的提升。