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本文引用的文献

1
Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification.用于绝对蛋白质定量的选择反应监测质谱分析
J Vis Exp. 2015 Aug 17(102):e52959. doi: 10.3791/52959.
2
Targeted Proteomics-Driven Computational Modeling of Macrophage S1P Chemosensing.基于靶向蛋白质组学的巨噬细胞S1P化学传感计算模型
Mol Cell Proteomics. 2015 Oct;14(10):2661-81. doi: 10.1074/mcp.M115.048918. Epub 2015 Jul 21.
3
Phosphoproteome profiling of the macrophage response to different toll-like receptor ligands identifies differences in global phosphorylation dynamics.巨噬细胞对不同Toll样受体配体反应的磷酸化蛋白质组分析揭示了整体磷酸化动力学的差异。
J Proteome Res. 2014 Nov 7;13(11):5185-97. doi: 10.1021/pr5002466. Epub 2014 Jun 18.
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Status of large-scale analysis of post-translational modifications by mass spectrometry.基于质谱的大规模翻译后修饰分析的研究现状。
Mol Cell Proteomics. 2013 Dec;12(12):3444-52. doi: 10.1074/mcp.O113.034181. Epub 2013 Nov 1.
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Septic shock: desperately seeking treatment.脓毒性休克:急需治疗。
Clin Sci (Lond). 2014 Jan 1;126(1):31-9. doi: 10.1042/CS20120668.
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The history of Toll-like receptors - redefining innate immunity.Toll 样受体的历史——重新定义固有免疫。
Nat Rev Immunol. 2013 Jun;13(6):453-60. doi: 10.1038/nri3446. Epub 2013 May 17.
7
Signaling organelles of the innate immune system.先天免疫系统的信号细胞器。
Cell. 2012 Dec 7;151(6):1168-78. doi: 10.1016/j.cell.2012.11.011.
8
Structural biology of the Toll-like receptor family. Toll 样受体家族的结构生物学。
Annu Rev Biochem. 2011;80:917-41. doi: 10.1146/annurev-biochem-052909-141507.
9
Secretome proteomics for discovery of cancer biomarkers.外泌体蛋白质组学在癌症生物标志物发现中的应用。
J Proteomics. 2010 Nov 10;73(12):2291-305. doi: 10.1016/j.jprot.2010.07.001. Epub 2010 Jul 15.
10
Variation matters: TLR structure and species-specific pathogen recognition.变异至关重要:Toll样受体结构与物种特异性病原体识别
Trends Immunol. 2009 Mar;30(3):124-30. doi: 10.1016/j.it.2008.12.001. Epub 2009 Feb 9.

巨噬细胞中Toll样受体信号通路的综合分析方法

A Methodology for Comprehensive Analysis of Toll-Like Receptor Signaling in Macrophages.

作者信息

Koppenol-Raab Marijke, Nita-Lazar Aleksandra

机构信息

Cellular Networks Proteomics Unit, Laboratory of Systems Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA.

出版信息

Methods Mol Biol. 2017;1636:301-312. doi: 10.1007/978-1-4939-7154-1_19.

DOI:10.1007/978-1-4939-7154-1_19
PMID:28730487
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6371964/
Abstract

A combination of high-throughput, multiplexed, quantitative methods with computational modeling and statistical approaches is required to obtain system-level understanding of biological function. Mass spectrometry (MS)-based proteomics has emerged as a preferred tool for the analysis of changes in protein abundance and their post-translational modification (PTM) levels at a global scale, comparable with genomic experiments and generating data suitable for use in mathematical modeling of signaling pathways. Here we describe a set of parallel bottom-up proteomic approaches to detect and quantify the global protein changes in total intracellular proteins, their phosphorylation, and the proteins released by active and passive secretion or shedding mechanisms (referred to as the secretome as reviewed in Makridakis and Vlahou, J Proteome 73:2291-2305, 2010) in response to the stimulation of Toll-like receptors (TLRs) with specific ligands in cultured macrophages. The method includes protocols for metabolic labeling of cells (SILAC: stable isotope labeling by amino acids in cell culture; Ong et al., Mol Cell Proteomics 1:376-386, 2002), ligand stimulation, cell lysis and media collection, in-gel and in-solution modification and digestion of proteins, phosphopeptide enrichment for phosphoproteomics, and LC-MS/MS analysis. With these methods, we can not only reliably quantify the relative changes in the TLR signaling components (Sjoelund et al., J Proteome Res 13:5185-5197, 2014) but also use the data as constraints for mathematical modeling.

摘要

需要将高通量、多重、定量方法与计算建模和统计方法相结合,以获得对生物功能的系统层面理解。基于质谱(MS)的蛋白质组学已成为一种首选工具,可在全球范围内分析蛋白质丰度变化及其翻译后修饰(PTM)水平,与基因组实验相当,并生成适用于信号通路数学建模的数据。在这里,我们描述了一套平行的自下而上蛋白质组学方法,用于检测和量化培养的巨噬细胞中总细胞内蛋白质、其磷酸化以及通过主动和被动分泌或脱落机制释放的蛋白质(如Makridakis和Vlahou在《蛋白质组学杂志》73:2291 - 2305, 2010中综述的那样,称为分泌组)在受到特定配体刺激Toll样受体(TLR)时的全局蛋白质变化。该方法包括细胞代谢标记方案(SILAC:细胞培养中氨基酸稳定同位素标记;Ong等人,《分子细胞蛋白质组学》1:376 - 386, 2002)、配体刺激、细胞裂解和培养基收集、蛋白质的凝胶内和溶液内修饰与消化、磷酸蛋白质组学的磷酸肽富集以及LC - MS/MS分析。通过这些方法,我们不仅可以可靠地量化TLR信号成分的相对变化(Sjoelund等人,《蛋白质组研究杂志》13:5185 - 5197, 2014),还可以将数据用作数学建模的约束条件。