Toll-like receptor (TLR) signaling in macrophages is essential for generating effective innate immune responses. Quantitative differences dependent on the dose and timing of the stimulus critically affect cell function and often involve proteins that are not components of widely shared transduction pathways. Mathematical modeling is an important approach to better understand how these signaling networks function in time and space. Wehave successfully modeled the S1P signaling pathway in macrophages using selected reaction monitoring (SRM) to measure the absolute abundance of the pathway proteins. The resulting values became parameters in a computational pathway model. To model the TLR signaling networks, we developed assays for the canonical TLR signaling pathway and related proteins and phosphoproteins and used parallel reaction monitoring (PRM) with heavy-labeled internal peptide standards to quantify protein and phosphorylated protein moleculenumbers per cell in untreated and LPS-stimulated macrophages. The absolute protein abundance values were entered into a model of the TLR pathway developed using Simmune, the rule-based modeling tool with a visual interface. To reach beyond basal level quantification, the TLR signaling network model is tested further and combined with global proteomic approaches to discover biologically important new proteins, protein complexes and PTMs involved in TLR and inflammasome pathways, of which some examples will be given. The protein and PTM levels are quantified in macrophages under diverse, but well-defined conditions (different TLR ligands, whole pathogens, and cells with mutations in specific signaling molecules). These data will allow to parameterize and test the TLR network model under a variety of conditions. Together, the interconnected projects will lead to the better understanding how the immune signaling pathways are regulated and activated during an infection. This research was supported by the Intramural Research Program of NIAID, NIH.

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