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Targeted Proteomics‐Driven Computational Modeling of Macrophage Microbial Sensing Pathways
Author(s) -
NitaLazar Aleksandra,
Manes Nathan P.,
Mann Jessica M.,
Kaplan Pauline,
MeierSchellersheim Martin,
Fraser Iain D. C.,
Germain Ronald N.
Publication year - 2018
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.2018.32.1_supplement.261.1
Subject(s) - signal transduction , computational biology , quantitative proteomics , proteomics , biology , receptor , function (biology) , innate immune system , immune system , microbiology and biotechnology , biological pathway , cell signaling , phosphorylation , chemistry , gene , immunology , biochemistry , gene expression
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 involve proteins that are not components of widely shared transduction pathways. These features make mathematical modeling an important approach to a better understanding of how these signaling networks function in time and space. To model the TLR signaling networks, we are using selected reaction monitoring (SRM) to measure the absolute abundance of TLR pathway proteins, with the resulting values used as pathway model parameters. RNA‐seq was performed to identify expressed transcripts, and shotgun mass spectrometry was used to identify proteotypic peptides. SRM assays for the canonical TLR signaling pathway and related proteins and phosphoproteins have been successfully 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. A preliminary model of the TLR pathway has been developed using Simmune and the resulting estimated protein abundance values. Support or Funding Information This research was supported by the Intramural Research Program of the National Institute of Allergy and Infectious Diseases, NIH. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .