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.
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Acknowledgements
The authors would like to thank Dr. Nathan Manes for critical reading of the manuscript and helpful comments and Dr. Virginie Sjoelund for the optimization of the phosphoproteome and proteome analyses. The original research was supported by the Intramural Research Program of the NIH, National Institute of Allergy and Infectious Diseases.
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Koppenol-Raab, M., Nita-Lazar, A. (2017). A Methodology for Comprehensive Analysis of Toll-Like Receptor Signaling in Macrophages. In: Tan, AC., Huang, P. (eds) Kinase Signaling Networks. Methods in Molecular Biology, vol 1636. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7154-1_19
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DOI: https://doi.org/10.1007/978-1-4939-7154-1_19
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