Urinary proteome profiling for stratifying patients with familial Parkinson's disease.
Urinary proteome profiling for stratifying patients with familial Parkinson's disease.
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DOI：
10.15252/emmm.202013257
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发表时间：
2021-03-05
影响因子：
11.1
通讯作者：
Mann M
中科院分区：
文献类型：
--
作者：
Virreira Winter S;Karayel O;Strauss MT;Padmanabhan S;Surface M;Merchant K;Alcalay RN;Mann M
帕金森病(PD)的发病率正在增加，但早期发现PD的特异、敏感和非侵入性生物标志物将有助于开发新的治疗策略和治疗方法来改变疾病的进程。在这里，我们描述了一种可扩展和灵敏的基于质谱学(MS)的尿蛋白组谱工作流程。我们的工作流程能够使用来自两个独立患者队列的最小体积，对200多个尿样中的2000多个蛋白质进行可重复性的定量。PD患者与健康对照组、LRRK2 G2019S携带者与非携带者之间的尿蛋白质组差异均有统计学意义。有趣的是，我们的数据显示，携带LRRK2 G2019S突变的个体存在溶酶体失调。当结合机器学习时，仅尿蛋白质组数据就足以很好地对突变携带者的突变状态和疾病表现进行分类，将VGF、ENPEP和其他PD相关蛋白确定为最具区分性的特征。综上所述，我们的结果验证了尿蛋白组学是发现帕金森病生物标记物和患者分层的有价值的策略。本研究提出了一种可扩展、灵敏和可重复性的基于质谱学的尿蛋白组学工作流程，并证明它是帕金森病(PD)尿液生物标记物发现的一种很有前途的策略。
The prevalence of Parkinson's disease (PD) is increasing but the development of novel treatment strategies and therapeutics altering the course of the disease would benefit from specific, sensitive, and non‐invasive biomarkers to detect PD early. Here, we describe a scalable and sensitive mass spectrometry (MS)‐based proteomic workflow for urinary proteome profiling. Our workflow enabled the reproducible quantification of more than 2,000 proteins in more than 200 urine samples using minimal volumes from two independent patient cohorts. The urinary proteome was significantly different between PD patients and healthy controls, as well as between LRRK2 G2019S carriers and non‐carriers in both cohorts. Interestingly, our data revealed lysosomal dysregulation in individuals with the LRRK2 G2019S mutation. When combined with machine learning, the urinary proteome data alone were sufficient to classify mutation status and disease manifestation in mutation carriers remarkably well, identifying VGF, ENPEP, and other PD‐associated proteins as the most discriminating features. Taken together, our results validate urinary proteomics as a valuable strategy for biomarker discovery and patient stratification in PD. This study presents a scalable, sensitive and reproducible mass spectrometry‐based proteomics workflow for urinary proteome profiling, and demonstrates it as a promising strategy for urine biomarker discovery for Parkinson’s disease (PD).