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Controlling the Response: Predictive Modeling of a Highly Central, Pathogen-Targeted Core Response Module in Macrophage Activation

Overview of attention for article published in PLOS ONE, February 2011
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Title
Controlling the Response: Predictive Modeling of a Highly Central, Pathogen-Targeted Core Response Module in Macrophage Activation
Published in
PLOS ONE, February 2011
DOI 10.1371/journal.pone.0014673
Pubmed ID
Authors

Jason E. McDermott, Michelle Archuleta, Brian D. Thrall, Joshua N. Adkins, Katrina M. Waters

Abstract

We have investigated macrophage activation using computational analyses of a compendium of transcriptomic data covering responses to agonists of the TLR pathway, Salmonella infection, and manufactured amorphous silica nanoparticle exposure. We inferred regulatory relationship networks using this compendium and discovered that genes with high betweenness centrality, so-called bottlenecks, code for proteins targeted by pathogens. Furthermore, combining a novel set of bioinformatics tools, topological analysis with analysis of differentially expressed genes under the different stimuli, we identified a conserved core response module that is differentially expressed in response to all studied conditions. This module occupies a highly central position in the inferred network and is also enriched in genes preferentially targeted by pathogens. The module includes cytokines, interferon induced genes such as Ifit1 and 2, effectors of inflammation, Cox1 and Oas1 and Oasl2, and transcription factors including AP1, Egr1 and 2 and Mafb. Predictive modeling using a reverse-engineering approach reveals dynamic differences between the responses to each stimulus and predicts the regulatory influences directing this module. We speculate that this module may be an early checkpoint for progression to apoptosis and/or inflammation during macrophage activation.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 4%
United States 2 4%
Japan 1 2%
Netherlands 1 2%
Unknown 45 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 25%
Student > Ph. D. Student 11 22%
Other 5 10%
Professor 4 8%
Student > Bachelor 4 8%
Other 10 20%
Unknown 4 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 37%
Biochemistry, Genetics and Molecular Biology 8 16%
Medicine and Dentistry 6 12%
Computer Science 3 6%
Engineering 3 6%
Other 6 12%
Unknown 6 12%