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A Mathematical Model of CR3/TLR2 Crosstalk in the Context of Francisella tularensis Infection

Overview of attention for article published in PLoS Computational Biology, November 2012
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Title
A Mathematical Model of CR3/TLR2 Crosstalk in the Context of Francisella tularensis Infection
Published in
PLoS Computational Biology, November 2012
DOI 10.1371/journal.pcbi.1002757
Pubmed ID
Authors

Rachel Leander, Shipan Dai, Larry S. Schlesinger, Avner Friedman

Abstract

Complement Receptor 3 (CR3) and Toll-like Receptor 2 (TLR2) are pattern recognition receptors expressed on the surface of human macrophages. Although these receptors are essential components for recognition by the innate immune system, pathogen coordinated crosstalk between them can suppress the production of protective cytokines and promote infection. Recognition of the virulent Schu S4 strain of the intracellular pathogen Francisella tularensis by host macrophages involves CR3/TLR2 crosstalk. Although experimental data provide evidence that Lyn kinase and PI3K are essential components of the CR3 pathway that influences TLR2 activity, additional responsible upstream signaling components remain unknown. In this paper we construct a mathematical model of CR3 and TLR2 signaling in response to F. tularensis. After demonstrating that the model is consistent with experimental results we perform numerical simulations to evaluate the contributions that Akt and Ras-GAP make to ERK inhibition. The model confirms that phagocytosis-associated changes in the composition of the cell membrane can inhibit ERK activity and predicts that Akt and Ras-GAP synergize to inhibit ERK.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Hungary 1 3%
Brazil 1 3%
Unknown 30 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 28%
Researcher 8 25%
Student > Postgraduate 5 16%
Student > Doctoral Student 2 6%
Student > Bachelor 1 3%
Other 3 9%
Unknown 4 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 31%
Mathematics 6 19%
Immunology and Microbiology 6 19%
Biochemistry, Genetics and Molecular Biology 2 6%
Business, Management and Accounting 1 3%
Other 2 6%
Unknown 5 16%