JoVE Journal > Biochemistry
Summary
Abstract
Introduction
Protocol
Results
Discussion
Disclosures
Acknowledgements
Materials
References
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생화학

JUMPn：蛋白质共表达聚类和网络分析在蛋白质组学中的简化应用

Published: October 19, 2021
doi:
10.3791/62796
, , , , , , , ,
1Departments of Structural Biology and Developmental Neurobiology,St. Jude Children’s Research Hospital, 2Center for Proteomics and Metabolomics,St. Jude Children’s Research Hospital, 3Department of Computational Biology,St. Jude Children’s Research Hospital

Summary

我们提出了一个系统生物学工具JUMPn，用于执行和可视化定量蛋白质组学数据的网络分析，其详细的方案包括数据预处理，共表达聚类，途径富集和蛋白质 – 蛋白质相互作用网络分析。

Abstract

随着基于质谱的蛋白质组学技术的最新进展，对数百个蛋白质组进行深度分析变得越来越可行。然而，从这些有价值的数据集中获取生物学见解是具有挑战性的。在这里，我们介绍了一个基于生物学的系统软件JUMPn及其相关协议，以将蛋白质组组织成跨样品的蛋白质共表达簇和由模块连接的蛋白质 – 蛋白质相互作用（PPI）网络（例如，蛋白质复合物）。使用R/Shiny平台，JUMPn软件通过集成的数据可视化和用户友好的界面，简化了共表达聚类、通路富集和PPI模块检测的分析。该协议的主要步骤包括安装JUMPn软件，定义差异表达的蛋白质或（dys）调节的蛋白质组，确定有意义的共表达簇和PPI模块，以及结果可视化。虽然该方案使用基于等压标记的蛋白质组谱进行演示，但JUMPn通常适用于广泛的定量数据集（例如，无标记蛋白质组学）。因此，JUMPn软件和协议为定量蛋白质组学中的生物学解释提供了强大的工具。

Introduction

基于质谱的鸟枪鱼蛋白质组学已成为分析复杂样品蛋白质组多样性的关键方法1。随着质谱仪器2，3，色谱4，5，离子淌度检测6，采集方法（与数据无关的7和数据依赖的采集8），定量方法（多重等压肽标记方法，例如TMT9，10和无标记定量11，12）和数据分析策略的最新进展/软件开发13，14，15，16，17，18，整个蛋白质组（例如，超过10，000个蛋白质）的定量现在是常规的19，20，21。然而，如何从如此深入的定量数据集中获得机械洞察力仍然是一个挑战22。研究这些数据集的最初尝试主要依赖于对数据中各个元素的注释，独立处理每个组分（蛋白质）。然而，生物系统及其行为不能仅仅通过检查单个组分23来解释。因此，将量化的生物分子置于相互作用网络背景下的系统方法对于理解复杂系统和相关过程（例如胚胎发生，免疫反应和人类疾病的发病机制）至关重要24。

基于网络的系统生物学已成为分析大规模定量蛋白质组学数据25，26，27，28，29，30，31，32，33的强大范式。从概念上讲，诸如哺乳动物细胞之类的复杂系统可以建模为分层网络34，35，其中整个系统以层表示：首先由许多大型组件表示，然后由较小的子系统迭代建模。从技术上讲，蛋白质组动力学的结构可以通过共表达蛋白质簇的相互连接的网络（因为共表达的基因/蛋白质通常具有相似的生物学功能或调节机制36）和物理相互作用的PPI模块37来呈现。作为最近的示例25，我们在T细胞活化过程中生成了整个蛋白质组和磷酸蛋白质组的时间谱，并使用具有PPI的整合共表达网络来鉴定介导T细胞静止退出的功能模块。突出了多个生物能量相关模块并进行了实验验证（例如，线粒体和复合IV模块25，以及单碳模块38）。在另一个示例26中，我们进一步扩展了我们的方法来研究阿尔茨海默病的发病机制，并成功地优先考虑与疾病进展相关的蛋白质模块和分子。重要的是，我们的许多无偏倚发现都得到了独立患者队列26，29和/或疾病小鼠模型26的验证。这些例子说明了系统生物学方法在通过定量蛋白质组学和其他组学整合来解剖分子机制方面的力量。

在这里，我们介绍 JUMPn，这是一款简化的软件，它使用基于网络的系统生物学方法探索定量蛋白质组学数据。JUMPn作为已建立的JUMP蛋白质组学软件套件13，14，39的下游组件，旨在使用系统生物学方法填补从单个蛋白质定量到生物学上有意义的途径和蛋白质模块的空白。通过以差异表达（或最可变）蛋白质的定量基质作为输入，JUMPn旨在将蛋白质组组织成跨样品和密集连接的PPI模块（例如，蛋白质复合物）共表达的蛋白质簇的分层层次结构，这些模块通过过度表示（或富集）分析进一步注释公共途径数据库（图1）。JUMPn与R/Shiny平台40 一起开发，具有用户友好的界面，并集成了三个主要功能模块：共表达聚类分析，途径富集分析和PPI网络分析（图1）。每次分析后，结果都会自动可视化，并可通过R / shiny小部件功能进行调整，并可轻松下载为Microsoft Excel格式的发布表。在以下实验方案中，我们使用定量全蛋白质组数据作为示例，并描述使用JUMPn的主要步骤，包括安装JUMPn软件，定义差异表达的蛋白质或（dys）调节的蛋白质组，共表达网络分析和PPI模块分析，结果可视化和解释以及故障排除。JUMPn 软件在 GitHub41 上免费提供。

Protocol

注意：在该协议中，JUMPn的使用通过利用由TMT等压标记试剂27定量的B细胞分化期间全蛋白质组分析的已发表数据集来说明。 1. JUMPn 软件的设置 注：为设置 JUMPn 软件提供了两个选项：（i） 在本地计算机上安装以供个人使用;（ii） 在本地计算机上安装以供个人使用;（iii） 在本地计算机上安装以供个人使用;（ii）在远程闪亮服务器上为多个…

Representative Results

我们使用已发布的深度蛋白质组学数据集25、26、27、30 （图 5 和 图 6）以及数据模拟57 （表 1）来优化和评估 JUMPn 性能。对于通过WGCNA进行的共表达蛋白聚类分析，我们建议使用样品之间显着变化的蛋白质作为输入（例如，通…

Discussion

在这里，我们介绍了我们的JUMPn软件及其协议，它们已应用于多个项目中，使用深度定量蛋白质组学数据25，26，27，30，64解剖分子机制。JUMPn软件和实验方案已经过全面优化，包括考虑用于共表达网络分析的DE蛋白，综合和高质量PPI网络的汇编，严格的统计分析（例如，通过?…

Disclosures

The authors have nothing to disclose.

Acknowledgements

美国国立卫生研究院（NIH）（R01AG047928，R01AG053987，RF1AG064909，RF1AG068581和U54NS110435）和ALSAC（美国黎巴嫩叙利亚联合慈善机构）提供了资金支持。MS分析在圣裘德儿童研究医院的蛋白质组学和代谢组学中心进行，该中心由NIH癌症中心支持补助金（P30CA021765）部分支持。内容完全由作者负责，并不一定代表美国国立卫生研究院的官方观点。

Materials

MacBook Pro with a 2.3 GHz Quad-Core Processor running OS 10.15.7. Apple Inc. MacBook Pro 13'' Hardware used for software development and testing
Anoconda Anaconda, Inc. version 4.9.2 https://docs.anaconda.com/anaconda/install/
miniconda Anaconda, Inc. version 4.9.2 https://docs.conda.io/en/latest/miniconda.html
RStudio RStudio Public-benefit corporation version 4.0.3 https://www.rstudio.com/products/rstudio/download/
Shiny Server RStudio Public-benefit corporation https://shiny.rstudio.com/articles/shinyapps.html
DOWNLOAD MATERIALS LIST

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Tags

Keywords: Protein Co-expressionClusteringNetwork AnalysisProteomicsJUMPn SoftwareCo-expression ClusteringPathway EnrichmentPPI Module DetectionUser-friendly InterfaceInteractive VisualizationPhosphoproteomicsInteractome DataWGCNA AlgorithmCo-expression PatternsTrends PlotPathway Ontology Enrichment Heatmap
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Vanderwall, D., Suresh, P., Fu, Y., Cho, J., Shaw, T. I., Mishra, A., High, A. A., Peng, J., Li, Y. JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics. J. Vis. Exp. (176), e62796, doi:10.3791/62796 (2021).
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