Division of Nephrology, San Francisco Veterans Affairs Medical Center, University of California, San Francisco, California; and.
Nephrology Division and Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts.
Clin J Am Soc Nephrol. 2020 Mar 6;15(3):404-411. doi: 10.2215/CJN.07420619. Epub 2019 Oct 21.
In this review of the application of proteomics and metabolomics to kidney disease research, we review key concepts, highlight illustrative examples, and outline future directions. The proteome and metabolome reflect the influence of environmental exposures in addition to genetic coding. Circulating levels of proteins and metabolites are dynamic and modifiable, and thus amenable to therapeutic targeting. Design and analytic considerations in proteomics and metabolomics studies should be tailored to the investigator's goals. For the identification of clinical biomarkers, adjustment for all potential confounding variables, particularly GFR, and strict significance thresholds are warranted. However, this approach has the potential to obscure biologic signals and can be overly conservative given the high degree of intercorrelation within the proteome and metabolome. Mass spectrometry, often coupled to up-front chromatographic separation techniques, is a major workhorse in both proteomics and metabolomics. High-throughput antibody- and aptamer-based proteomic platforms have emerged as additional, powerful approaches to assay the proteome. As the breadth of coverage for these methodologies continues to expand, machine learning tools and pathway analyses can help select the molecules of greatest interest and categorize them in distinct biologic themes. Studies to date have already made a substantial effect, for example elucidating target antigens in membranous nephropathy, identifying a signature of urinary peptides that adds prognostic information to urinary albumin in CKD, implicating circulating inflammatory proteins as potential mediators of diabetic nephropathy, demonstrating the key role of the microbiome in the uremic milieu, and highlighting kidney bioenergetics as a modifiable factor in AKI. Additional studies are required to replicate and expand on these findings in independent cohorts. Further, more work is needed to understand the longitudinal trajectory of select protein and metabolite markers, perform transomics analyses within merged datasets, and incorporate more kidney tissue-based investigation.
在这篇关于蛋白质组学和代谢组学在肾脏疾病研究中的应用的综述中，我们回顾了关键概念，突出了说明性的例子，并概述了未来的方向。蛋白质组和代谢组反映了环境暴露对遗传编码的影响。蛋白质和代谢物的循环水平是动态的和可调节的，因此可以进行治疗靶向。蛋白质组学和代谢组学研究中的设计和分析考虑因素应根据研究者的目标进行调整。对于临床生物标志物的鉴定，需要对所有潜在的混杂变量进行调整，特别是肾小球滤过率（GFR），并严格遵循显著性阈值。然而，这种方法有可能掩盖生物学信号，并且由于蛋白质组和代谢组内高度的相关性，可能过于保守。质谱分析，通常与前置色谱分离技术相结合，是蛋白质组学和代谢组学的主要工具。高通量抗体和适体为基础的蛋白质组学平台已经成为检测蛋白质组的另一种强大方法。随着这些方法的广度不断扩大，机器学习工具和途径分析可以帮助选择最感兴趣的分子，并将它们分类为不同的生物学主题。迄今为止的研究已经产生了重大影响，例如阐明膜性肾病的靶抗原，确定尿肽谱，为 CKD 中的尿白蛋白增加预后信息，提示循环炎症蛋白可能是糖尿病肾病的潜在介质，证明了微生物组在尿毒症环境中的关键作用，并强调了肾脏生物能学作为 AKI 的可调节因素。需要进一步的研究来在独立队列中复制和扩展这些发现。此外，还需要更多的工作来了解选定的蛋白质和代谢物标记物的纵向轨迹，在合并数据集内进行跨组学分析，并纳入更多基于肾脏组织的研究。
