Dataset Information

Absolute Protein Quantitation of the Mouse Macrophage Toll-Like Receptor and Chemotaxis Pathways

ABSTRACT: The Toll-like receptor and chemotaxis pathways are key components of the innate immune system. Computational modeling and simulation at the molecular interaction level can be used to study complex biological pathways, but protein concentration values must be input as model parameters. In this investigation, targeted mass spectrometry assays were developed and used to measure the absolute abundance (copies/cell) of proteins of the mouse macrophage Toll-like receptor 4 (TLR4) and chemotaxis pathways. The data produced by this investigation can be used for pathway modeling and simulation, as well as for other systems biology research.

ORGANISM(S): Mus Musculus

SUBMITTER: Nathan Manes

PROVIDER: PXD031697 | panorama | Mon Aug 15 00:00:00 BST 2022

REPOSITORIES: PanoramaPublic

ACCESS DATA

Publications

Absolute protein quantitation of the mouse macrophage Toll-like receptor and chemotaxis pathways.

Manes Nathan P NP Calzola Jessica M JM Kaplan Pauline R PR Fraser Iain D C IDC Germain Ronald N RN Meier-Schellersheim Martin M Nita-Lazar Aleksandra A

Scientific data 20220812 1

The Toll-like receptor (TLR) and chemotaxis pathways are key components of the innate immune system. Subtle variation in the concentration, timing, and molecular structure of the ligands are known to affect downstream signaling and the resulting immune response. Computational modeling and simulation at the molecular interaction level can be used to study complex biological pathways, but such simulations require protein concentration values as model parameters. Here we report the development and ...[more]

PMID: 35961990

Similar Datasets

Project description:Successful host defense against pathogens requires innate immune recognition of the correct pathogen associated molecular patterns (PAMPs) by pathogen recognition receptors (PRRs) to trigger the appropriate gene program tailored to the pathogen. While many PRR pathways have been shown to contribute to the innate immune response to specific pathogens, the relative importance of each pathway for the complete transcriptional program elicited has not been examined in detail. Herein, we used RNA-sequencing with wildtype and mutant macrophages to delineate the innate immune pathways responsible for the early transcriptional response to Staphylococcus aureus, a ubiquitous microorganism that can activate a wide variety of PRRs. Unexpectedly, only two PRR pathways – the Toll-like receptor (TLR) and Stimulator of Interferon Gene (STING) pathways - were identified as dominant regulators of approximately 95% of the genes that were potently induced within the first four hours of macrophage infection with live S. aureus. TLR signaling predominantly activated an inflammatory program, STING signaling activated an antiviral/type I interferon response, and both pathways contributed to a program linking innate and adaptive immunity. Only a small number of genes were induced in the absence of TLR or STING signaling, and these genes possessed a strong hypoxia signature. STING pathway activation required live S. aureus and was largely dependent on the DNA sensor cyclic guanosine-adenosine synthase (cGAS) recognition of S. aureus DNA. Interestingly, using a cutaneous infection model, we found that the TLR and STING pathways played opposite roles in host defense to S. aureus, with TLR signaling being required for protective interleukin (IL)-1 and neutrophil recruitment and STING signaling having an opposite effect. These results provide novel insights into the complex interplay of innate immune signaling pathways triggered byS. aureus and uncover opposing roles of TLR and STING in cutaneous host defense to S. aureus.

Dataset Information

Absolute Protein Quantitation of the Mouse Macrophage Toll-Like Receptor and Chemotaxis Pathways

Publications

Absolute protein quantitation of the mouse macrophage Toll-like receptor and chemotaxis pathways.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets