title: Controlling the response: predictive modeling of a highly central, pathogen-targeted core response module in macrophage activation.
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.
authors.affiliation: Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, Washington, United States of America. Jason.McDermott@pnl.gov
authors.forename: Jason E
authors.initials: JE
authors.lastname: McDermott
authors.forename: Michelle
authors.initials: M
authors.lastname: Archuleta
authors.forename: Brian D
authors.initials: BD
authors.lastname: Thrall
authors.forename: Joshua N
authors.initials: JN
authors.lastname: Adkins
authors.forename: Katrina M
authors.initials: KM
authors.lastname: Waters
mesh_terms: D056704:Adaptive Immunity / Q000235:genetics* / Q000502:physiology; D000818:Animals; D002478:Cells, Cultured; D016000:Cluster Analysis; D005544:Forecasting / Q000379:methods; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D005786:Gene Expression Regulation / Q000502:physiology; D053263:Gene Regulatory Networks / Q000276:immunology / Q000502:physiology; D054884:Host-Pathogen Interactions / Q000235:genetics* / Q000276:immunology*; D006801:Humans; D007249:Inflammation / Q000235:genetics; D008262:Macrophage Activation / Q000235:genetics* / Q000276:immunology* / Q000502:physiology; D051379:Mice; D046228:Microarray Analysis / Q000706:statistics & numerical data; D008954:Models, Biological; D015233:Models, Statistical; D053758:Nanoparticles / Q000633:toxicity; D012480:Salmonella Infections / Q000235:genetics / Q000276:immunology; D015398:Signal Transduction / Q000235:genetics / Q000276:immunology / Q000502:physiology; D012822:Silicon Dioxide / Q000276:immunology / Q000633:toxicity
chemical_list: D012822:Silicon Dioxide