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肽组学 LC-MS/MS 数据分析策略:以尿肽组研究为例。

The Strategy for Peptidomic LC-MS/MS Data Analysis: The Case of Urinary Peptidome Study.

机构信息

Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Moscow, Russia.

Emanuel Institute for Biochemical Physics, Russian Academy of Science, Moscow, Russia.

出版信息

Methods Mol Biol. 2024;2758:389-399. doi: 10.1007/978-1-0716-3646-6_21.

DOI:10.1007/978-1-0716-3646-6_21
PMID:38549026
Abstract

The study of urinary peptidome is an important area of research, which concerns the characterization of endogenous peptides, as well as the identification of biomarkers for a wide range of socially significant diseases. First of all, this relates to renal and genitourinary pathologies and/or pathologies associated with proteinuria, such as kidney diseases, bladder, prostate and ovarian cancers, diabetic nephropathy, and pre-eclampsia. Unlike proteins, peptides do not require proteolytic hydrolysis, can be analyzed in their native form and can provide certain information about occurring (patho)physiological processes. Mass spectrometry (MS)-based approaches are the most unbiased and sensitive instruments with high multiplexing capacity and provided most of the current information about endogenous urine peptides. However, despite the large number of urine peptidomic studies, there are certain issues related to the insufficient comparability of their results due to the lack of consistent approaches to their interpretation. Also the development of a custom project-specific protein library for endogenous peptides search and identification is another important point that should be noted in the context of high-throughput peptidomic analysis. Here we propose the custom-specific urinary protein database and the grouping of endogenous urinary peptides with overlapping sequences as useful tools, which can facilitate the acquisition and analysis of LC-MS peptidomic data, as well as the comparison of results of different studies, which should facilitate their more efficient further application.

摘要

尿肽组学研究是一个重要的研究领域,涉及内源性肽的特征描述以及广泛的社会意义疾病的生物标志物的鉴定。首先,这与肾脏和泌尿生殖系统疾病以及/或与蛋白尿相关的疾病有关,如肾脏疾病、膀胱癌、前列腺癌和卵巢癌、糖尿病肾病和子痫前期。与蛋白质不同,肽不需要蛋白水解水解,可以以其天然形式进行分析,并可以提供有关发生的(病理)生理过程的某些信息。基于质谱(MS)的方法是最具客观性和敏感性的仪器,具有高多重性能力,并提供了大多数关于内源性尿肽的当前信息。然而,尽管有大量的尿肽组学研究,但由于缺乏对其解释的一致方法,存在某些与结果的可比性不足相关的问题。此外,针对内源性肽搜索和鉴定的定制项目特定蛋白质库的开发也是高通量肽组学分析背景下另一个重要的要点。在这里,我们提出了定制的尿蛋白数据库和重叠序列的内源性尿肽分组作为有用的工具,这可以促进 LC-MS 肽组学数据的获取和分析,以及不同研究结果的比较,这应该有助于它们更有效地进一步应用。

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本文引用的文献

1
Urinary Protein and Peptide Markers in Chronic Kidney Disease.慢性肾脏病的尿蛋白和肽标志物。
Int J Mol Sci. 2021 Nov 9;22(22):12123. doi: 10.3390/ijms222212123.
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Urinary peptidomic profiles to address age-related disabilities: a prospective population study.针对与年龄相关的残疾的尿肽组学特征:一项前瞻性人群研究。
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An integrated proteomic and peptidomic assessment of the normal human urinome.对正常人类尿液蛋白质组和肽组的综合评估。
Clin Chem Lab Med. 2017 Feb 1;55(2):237-247. doi: 10.1515/cclm-2016-0390.
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