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Predicting Novel Features of Toll-Like Receptor 3 Signaling in Macrophages

Overview of attention for article published in PLOS ONE, March 2009
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Title
Predicting Novel Features of Toll-Like Receptor 3 Signaling in Macrophages
Published in
PLOS ONE, March 2009
DOI 10.1371/journal.pone.0004661
Pubmed ID
Authors

Mohamed Helmy, Jin Gohda, Jun-ichiro Inoue, Masaru Tomita, Masa Tsuchiya, Kumar Selvarajoo

Abstract

The Toll-like receptor (TLR) 3 plays a critical role in mammalian innate immune response against viral attacks by recognizing double-stranded RNA (dsRNA) or its synthetic analog polyinosinic-polycytidylic acid (poly (IratioC)). This leads to the activation of MAP kinases and NF-kappaB which results in the induction of type I interferons and proinflammatory cytokines to combat the viral infection. To understand the complex interplay of the various intracellular signaling molecules in the regulation of NF-kappaB and MAP kinases, we developed a computational TLR3 model based upon perturbation-response approach. We curated literature and databases to determine the TLR3 signaling topology specifically for murine macrophages. For initial model creation, we used wildtype temporal activation profiles of MAP kinases and NF-kappaB and, for model testing, used TRAF6 KO and TRADD KO data. From dynamic simulations we predict i) the existence of missing intermediary steps between extracellular poly (IratioC) stimulation and intracellular TLR3 binding, and ii) the presence of a novel pathway which is essential for JNK and p38, but not NF-kappaB, activation. Our work shows activation dynamics of signaling molecules can be used in conjunction with perturbation-response models to decipher novel signaling features of complicated immune pathways.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 8%
United States 1 3%
Turkey 1 3%
Unknown 33 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 29%
Researcher 10 26%
Student > Master 6 16%
Professor > Associate Professor 4 11%
Other 2 5%
Other 3 8%
Unknown 2 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 22 58%
Medicine and Dentistry 4 11%
Biochemistry, Genetics and Molecular Biology 3 8%
Computer Science 2 5%
Business, Management and Accounting 1 3%
Other 4 11%
Unknown 2 5%