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 have been found to 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.
We have successfully modeled the S1P signaling pathway in macrophages using selected reaction monitoring (SRM) to measure the absolute abundance of the pathway proteins and were able to use the resulting values as parameters in a computational pathway model. RNA-seq was performed to identify expressed transcripts. Shotgun mass spectrometry was used to identify proteotypic peptides. Now, to model the TLR signaling networks SRM assays for the canonical TLR signaling pathway and related proteins and phosphoproteins have been developed. SRM with heavy-labeled internal peptide standards was used to quantify protein and phosphorylated protein molecule numbers per cell in both untreated and LPS-stimulated macrophages. These absolute protein abundance values were entered into a model of the TLR pathway that has been developed using Simmune, the rule-based modeling tool with a visual interface. To reach beyond basal level quantification to further develop and test the TLR signaling network model we use global proteomic approaches to discover biologically important proteins, protein complexes and PTMs involved in this pathway. The protein and PTM levels are quantified in macrophages under diverse, but well-defined conditions. Our data will allow us to parameterize and test the TLR network model under a variety of conditions. Tohe model will improve our understanding of the regulation of the immune signaling pathways activated during an infection, and enable immune system modulation for an appropriate immune response. This research was supported by the Intramural Research Program of NIAID, NIH.