Host-pathogen dynamics through targeted secretome analysis of stimulated macrophages.

The pattern recognition receptors (PRRs) facilitate an organism's first line of defense against interlopers and shape the overall innate immune response through sensing and sampling pathogen-associated molecular patterns (PAMPs). The Toll-like receptor (TLR) family is the prototypic PRR family. Upon recognition of PAMPs, TLRs promote MyD88 dependent and independent responses. Understanding how different PAMPs are recognized by their specific TLRs and how pathogen recognition initiates immune activation is an intense area of research. Previously, we have reported the discovery of the temporal changes in signaling cascades of macrophage proteome and secretome post-stimulation with three different PAMPs. To extend our global proteomics approach to targeted protein abundance quantification, we describe the macrophage secretome targeted proteomics assay. We chose three different pathogens that specifically stimulate diverse TLRs (TLR2, TLR4, and TLR7). Using a simple targeted proteomics approach, combining data-dependent acquisition with an inclusion list, an array of cytokines, chemokines, and transcription factors can be profiled for their secretome abundance. This strategy facilitates the profiling and validation of pathogen-specific temporal changes in the macrophage secretome.

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

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.

Proteome and Secretome Analysis Reveals Differential Post-transcriptional Regulation of Toll-like Receptor Responses.

The innate immune system is the organism's first line of defense against pathogens. Pattern recognition receptors (PRRs) are responsible for sensing the presence of pathogen-associated molecules. The prototypic PRRs, the membrane-bound receptors of the Toll-like receptor (TLR) family, recognize pathogen-associated molecular patterns (PAMPs) and initiate an innate immune response through signaling pathways that depend on the adaptor molecules MyD88 and TRIF. Deciphering the differences in the complex signaling events that lead to pathogen recognition and initiation of the correct response remains challenging. Here we report the discovery of temporal changes in the protein signaling components involved in innate immunity. Using an integrated strategy combining unbiased proteomics, transcriptomics and macrophage stimulations with three different PAMPs, we identified differences in signaling between individual TLRs and revealed specifics of pathway regulation at the protein level.

A Targeted Mutation Identified through pKa Measurements Indicates a Postrecruitment Role for Fis1 in Yeast Mitochondrial Fission.

The tail-anchored protein Fis1 is implicated as a passive tether in yeast mitochondrial fission. We probed the functional role of Fis1 Glu-78, whose elevated side chain pKa suggests participation in protein interactions. Fis1 binds partners Mdv1 or Dnm1 tightly, but mutation E78A weakens Fis1 interaction with Mdv1, alters mitochondrial morphology, and abolishes fission in a growth assay. In fis1Δ rescue experiments, Fis1-E78A causes a novel localization pattern in which Dnm1 uniformly coats the mitochondria. By contrast, Fis1-E78A at lower expression levels recruits Dnm1 into mitochondrial punctate structures but fails to support normal fission. Thus, Fis1 makes multiple interactions that support Dnm1 puncta formation and may be essential after this step, supporting a revised model for assembly of the mitochondrial fission machinery. The insights gained by mutating a residue with a perturbed pKa suggest that side chain pKa values inferred from routine NMR sample pH optimization could provide useful leads for functional investigations.

Targeted Proteomics-Driven Computational Modeling of Macrophage S1P Chemosensing.

Osteoclasts are monocyte-derived multinuclear cells that directly attach to and resorb bone. Sphingosine-1-phosphate (S1P)(1) regulates bone resorption by functioning as both a chemoattractant and chemorepellent of osteoclast precursors through two G-protein coupled receptors that antagonize each other in an S1P-concentration-dependent manner. To quantitatively explore the behavior of this chemosensing pathway, we applied targeted proteomics, transcriptomics, and rule-based pathway modeling using the Simmune toolset. RAW264.7 cells (a mouse monocyte/macrophage cell line) were used as model osteoclast precursors, RNA-seq was used to identify expressed target proteins, and selected reaction monitoring (SRM) mass spectrometry using internal peptide standards was used to perform absolute abundance measurements of pathway proteins. The resulting transcript and protein abundance values were strongly correlated. Measured protein abundance values, used as simulation input parameters, led to in silico pathway behavior matching in vitro measurements. Moreover, once model parameters were established, even simulated responses toward stimuli that were not used for parameterization were consistent with experimental findings. These findings demonstrate the feasibility and value of combining targeted mass spectrometry with pathway modeling for advancing biological insight.