Targeted Proteomics-Driven Computational Modeling of Macrophage S1P Chemosensing

Abstract

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. Molecular & Cellular Proteomics 14: 10.1074/mcp.M115.048918, 2661-2681, 2015.Chemotaxis is defined as directed movement of a cell (or of an organism) resulting from stimulation by a chemokine or other chemotactic chemical. Eukaryotic cells employ intricate intracellular pathways to sense concentration differences of chemoattractants at their surface and move along such gradients using multiple synchronized cellular processes, including cell protrusion and adhesion at the leading end, de-adhesion at the trailing end, and mechanical force generation at both the leading and trailing ends (1-4).Activation of chemotactic receptors through chemoattractant concentration gradients results in nonuniform intracellular signaling responses that depend on numerous feedback mechanisms (5-7). Downstream, F-actin synthesis at the leading end causes the formation of cell protrusions (for example, filopodia, lamellipodia, and lamellae) that, in concert with actomyosin contraction, generates force at both the leading and trailing ends (8 -10). Chemotactic traction is produced by cell protrusions interacting with a confined environment and/or by adhesions that bind to the extracellular matrix and/or to cell adhesion molecules (11).Chemotaxis plays a major role in a wide range of physiological and pathophysiological processes. Sphingosine-1-phosphate (S1P), a phosphosphingolipid, mediates chemotaxis of many circulating cell types, including osteoclast precursors (OPs) (12)(13)(14). Osteoclasts are monocyte-derived multinuclear cells that directly attach to the bone matrix and resorb bone. They are solely responsible for bone resorption, and their misregulated activity has been imp...

Systems biology markup language (SBML) level 3 package: multistate, multicomponent and multicompartment species, version 1, release 2

“…Rule-based, domain-detailed modeling has been extremely valuable in systems biology related studies [Manes et al (2015) and Miskov-Zivanov1 et al (2013)]. Rule-based, domain-detailed modeling approaches (BioNetGen [Faeder et al (2009)], Kappa [Danos and Laneve (2004)], and Simmune ; Meier-Schellersheim et al (2006)]) define rules for transformations of molecular domains and interactions between pairs of molecule domains, specifying how the transformations/interactions depend on particular states of the molecules (pattern) and their locations in specific compartments.…”

Mechanistic Models of Cellular Signaling, Cytokine Crosstalk, and Cell-Cell Communication in Immunology

“…Among them, “rule-based” approaches permit specifying details such as the binding sites that mediate the molecular interactions (20–22), thereby incorporating aspects that can, for instance, help identify molecular binding motives as potential targets for pharmacological modulation through small molecule inhibitors. Finally, constructing models step-by-step by specifying the interactions among its components and then letting algorithms assemble the computational representations of the resulting networks will allow us to consider models that would be too complex for manual construction because of the number of components or because they span several scales (23) or utilize as experimental input very large data sets, for instance based on proteomic studies (24).…”

Advances in targeted proteomics and applications to biomedical research

“…Besides application in biomarker studies, SRM has also been used for accurate quantification of the dynamics of signaling pathways or networks in response to a given perturbation to improve our understanding of molecular mechanisms of signal transduction [7,[119][120][121][122][123][124][125][126][127][128][129]. Using the combination of affinity purification (AP) and quantitative SRM temporal regulation of EGF signaling networks by the scaffold protein Shc1 was investigated in detail, resulting in a significant finding that the Shc1 directs the temporal flow of signaling information after EGF stimulation [7].…”

“…The nearly complete quantitative pathway data revealed strain‐specific changes in the mouse insulin and central metabolic pathways after a sustained high‐fat diet. Very recently, SRM was combined with RNA‐seq and rule‐based pathway modeling to quantitatively explore the chemotaxis signaling pathway mediated by sphingosine‐1‐phosphate . Using absolute protein concentrations measured by SRM as input parameters of a mathematic model, the resulting in silico pathway behavior matched experimental measurements.…”