Absolute Quantification of Molecular Representation and Interaction

National Institute Of Allergy And Infectious Diseases

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Abstract

Explanation We have adapted the methods for absolute quantification based on the targeted proteomics combined with the use of peptide standards and used the data for robust predictive modeling of the signaling pathways in the immune system. We used sphingosine-1-phosphate-mediated macrophage chemotaxis as an initial model system. S1P regulates the chemoattraction and chemorepulsion of osteoclast precursors to and from bones. The murine macrophage RAW 264.7 cells, used here as a model, express two receptors for S1P: S1PR1 and S1PR2. These receptors have markedly different affinity to S1P and cause the opposite effects upon exposure to low/high concentrations of S1P. To develop a deeper understanding of mammalian cell chemotaxis, we used transcriptomics, shotgun proteomics, targeted proteomics, and pathway simulation to investigate S1P-mediated chemotaxis of osteoclast precursors. Transcriptomics using RNA-seq enabled the identification and quantitation of RNA transcripts and shotgun proteomics enabled the identification of proteotypic peptides selected based on peptide proteotypic qualities, sequence uniqueness, and vulnerability to modification (e.g., oxidation and deamidation), eliminating many theoretically possible peptides, which could be non-compatible with mass spectrometric analysis. We used the quantitative data obtained from osteoclast precursors by shotgun proteomics to find the peptides amenable to analysis in our mass spectrometers. SPOT synthesis was used to prepare a set of 409 standard, synthetic peptides, which we used to assess the protein expressions in macrophages. Single Reaction Monitoring (SRM) of RAW264.7 cell lysates spiked with the standard peptides resulted in the confident identification and semi-quantitation of 208 of the 409 peptide targets from proteins in the chemokine signaling network. The SRM analysis of a smaller set of 65 heavy-labeled, quantitated internal peptide standards from proteins differentially expressed under different experimental conditions provided absolute numbers of molecules. These data were then used to design targeted proteomics assays of the proteins of the mouse chemotaxis pathway. Targeted proteomics assays using nano-flow liquid chromatography coupled to selected reaction monitoring mass spectrometry (LC-SRM) were performed to produce absolute abundance values (in units of copies/cell) for each of the target proteins within RAW 264.7 cells. RAW cells were again used as model osteoclast precursors because they have very similar S1P-directed chemotaxis behavior. Rules-based pathway modeling enabled the simulation of the mouse chemotaxis pathway based on bi-molecular interactions within the geometry of a three-dimensional in silico RAW cell. 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 towards stimuli that were not used for parameterization were consistent with experimental findings. These findings demonstrated the feasibility and value of combining targeted mass spectrometry with pathway modeling for advancing biological insight and defined our experimental approach to modeling other immune system signaling pathways (1, 2, 3). In the TLR signaling network modeling study, we utilize targeted proteomics with transcriptomics to aid in constructing a computational model of the LPS-TLR4 signaling pathway in a mouse monocyte-macrophage cell line RAW264.7. A set of protein targets was identified from a review of current literature and KEGG pathways describing LPS-TLR4 signaling. Corresponding peptides were selected after scoring based on several criteria including length, shotgun proteomics identification, and potential PTM sites as determined by literature mining by motif prediction (Pubmed). Peptides were analyzed in both shotgun-mode and SRM-mode to determine the potential for success in biological samples. RAW cell samples stimulated with LPS for different times were analyzed for the selected peptides. We performed semi-quantitative analysis with the external peptide standards and obtained proteotypic peptides for most of the proteins in the canonical TLR signaling network. Based on these results, we have obtained and heavy-labeled internal peptide standards against corresponding protein targets for absolute quantitation measurements. Additionally, we designed, obtained and tested a set of peptides phosphorylated at the crucial regulatory residues of the proteins in the TLR signaling network. We have performed robust quantitative measurements with the heavy-labeled peptide standards spiked into the cell lysates. Using SRM and PRM (Parallel Reaction Monitoring) we examined unstimulated controls and cells stimulated with LPS for 30 minutes. We obtained absolute protein measurements and phosphosite occupancy measurements for both and made the comprehensive datasets publicly available (4). We have created the network of essential proteins and their interactions in the innate immune signaling (5) for the Simmune-based model and began modeling the network changes following TLR stimulation with LPS. The model we are building in collaboration with Dr. Meier-Schellersheim and the Computational Biology Section incorporates also the measurements of PTM changes obtained from project AI001084-11 and the binding constants we are getting using the modeling with protein structure data. The current efforts are focused on determining the association constants for the complexes in the TLR pathway in collaboration with Dr. Sergio Hassan, using Alpha Fold. We have also begun to look at proteome changes at the single cell level using the combination of a single-cell proteomics methodology recently developed by Slavov and Budnik (SCoPE-MS). In this project, we can reach beyond basal level quantification to further develop and test the TLR signaling network model under a variety of biologically relevant perturbations (different and modified TLR ligands, whole pathogens, and cells with mutations in specific signaling molecules). 1. Manes NP, et al. (2015) Mol Cell Proteomics. 2015 Oct;14(10):2661-81. doi: 10.1074/mcp.M115.048918. 2. Manes NP, Mann JM, and Nita-Lazar A. (2015) J Vis Exp 102, doi: 10.3791/529 3. Manes NP, Nita-Lazar A (2018) J Proteomics. 2018 Oct 30;189:75-90. doi: 10.1016/j.jprot.2018.02. 4. Manes NP et al. (2022) Sci Data. 2022 Aug 12;9(1):491. 5. Manes NP, Nita-Lazar A (2021) mSystems, Jun 29;6(3):e0033621.