Targeted Proteomics-Driven Computational Modeling of Macrophage S1P Chemosensing

被引:15
作者
Manes, Nathan P. [1 ]
Angermann, Bastian R. [1 ]
Koppenol-Raab, Marijke [1 ]
An, Eunkyung [1 ]
Sjoelund, Virginie H. [1 ]
Sun, Jing [1 ]
Ishii, Masaru [2 ]
Germain, Ronald N. [1 ]
Meier-Schellersheim, Martin [1 ]
Nita-Lazar, Aleksandra [1 ]
机构
[1] NIAID, Lab Syst Biol, NIH, Bethesda, MD 20892 USA
[2] Osaka Univ, Immunol Frontier Res Ctr, Suita, Osaka 5650871, Japan
基金
美国国家卫生研究院;
关键词
MASS-SPECTROMETRY; CELL-MIGRATION; QUANTITATIVE PROTEOMICS; PROTEIN IDENTIFICATION; DYNAMICS; TRANSCRIPTOME; ACTIVATION; GENERATION; EXPRESSION; SPHINGOSINE-1-PHOSPHATE;
D O I
10.1074/mcp.M115.048918
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页码:2661 / 2681
页数:21
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