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Controlling the response: predictive modeling of a highly central, pathogen-targeted core response module in macrophage activation.

McDermott JE, Archuleta M, Thrall BD, Adkins JN, Waters KM - PLoS ONE (2011)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, Washington, United States of America. Jason.McDermott@pnl.gov

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.

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Response set analysis in macrophages.A. Genes (rows) with shared differential expression in response to multiple stimuli (columns) are shown with black boxes indicating differential expression. The plot is ordered from genes differentially regulated in all conditions examined (9*, the core response module), to those differentially regulated in three conditions (bottom). A dendrogram showing the similarity between stimuli is shown at top; N10, 10 nm nanoparticle; N300, 300 nm nanoparticle; STM, Salmonella infection. B. The percentage of pathogen targets (bars) in each group of genes (blue bars) or in background (not in the group; purple bars) is shown for each group of genes regulated by N or more stimuli (X axis). The corresponding analysis is shown for Human homologs (lines) for the group (red line) or background (green line) in each group. Asterisks by each group on the X axis indicates that these groups are statistically enriched in both homologs and pathogen targets, other values were statistically significant after multiple hypothesis correction. These results show that groups of genes that are differentially regulated in response to a broad range of stimuli are more likely to be targets of pathogens and are more conserved than other genes.
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pone-0014673-g003: Response set analysis in macrophages.A. Genes (rows) with shared differential expression in response to multiple stimuli (columns) are shown with black boxes indicating differential expression. The plot is ordered from genes differentially regulated in all conditions examined (9*, the core response module), to those differentially regulated in three conditions (bottom). A dendrogram showing the similarity between stimuli is shown at top; N10, 10 nm nanoparticle; N300, 300 nm nanoparticle; STM, Salmonella infection. B. The percentage of pathogen targets (bars) in each group of genes (blue bars) or in background (not in the group; purple bars) is shown for each group of genes regulated by N or more stimuli (X axis). The corresponding analysis is shown for Human homologs (lines) for the group (red line) or background (green line) in each group. Asterisks by each group on the X axis indicates that these groups are statistically enriched in both homologs and pathogen targets, other values were statistically significant after multiple hypothesis correction. These results show that groups of genes that are differentially regulated in response to a broad range of stimuli are more likely to be targets of pathogens and are more conserved than other genes.

Mentions: The broad spectrum of stimuli in our data set gave us the opportunity to identify the essential conserved components of macrophage activation. Differentially regulated genes were identified using a 1.5 fold expression change threshold for probes that passed a significance test up to 360 minutes post-treatment. We observed that the responses of macrophages to nanoparticles were delayed relative to the other stimuli, and that very few differentially expressed genes overlapped with the compendium; therefore, we considered the entire time course, up to 24 hours post-treatment. To elucidate the components of macrophage activation, we identified groups of genes that are regulated by different numbers of conditions (). The results of this analysis are shown in Figure 3A, where black indicates that genes (rows) are differentially regulated in a given response (columns). Table S1 provides the complete list of genes, the conditions under which we found differential regulation, the network properties of the gene, and its status as a pathogen target.


Controlling the response: predictive modeling of a highly central, pathogen-targeted core response module in macrophage activation.

McDermott JE, Archuleta M, Thrall BD, Adkins JN, Waters KM - PLoS ONE (2011)

Response set analysis in macrophages.A. Genes (rows) with shared differential expression in response to multiple stimuli (columns) are shown with black boxes indicating differential expression. The plot is ordered from genes differentially regulated in all conditions examined (9*, the core response module), to those differentially regulated in three conditions (bottom). A dendrogram showing the similarity between stimuli is shown at top; N10, 10 nm nanoparticle; N300, 300 nm nanoparticle; STM, Salmonella infection. B. The percentage of pathogen targets (bars) in each group of genes (blue bars) or in background (not in the group; purple bars) is shown for each group of genes regulated by N or more stimuli (X axis). The corresponding analysis is shown for Human homologs (lines) for the group (red line) or background (green line) in each group. Asterisks by each group on the X axis indicates that these groups are statistically enriched in both homologs and pathogen targets, other values were statistically significant after multiple hypothesis correction. These results show that groups of genes that are differentially regulated in response to a broad range of stimuli are more likely to be targets of pathogens and are more conserved than other genes.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3038849&req=5

pone-0014673-g003: Response set analysis in macrophages.A. Genes (rows) with shared differential expression in response to multiple stimuli (columns) are shown with black boxes indicating differential expression. The plot is ordered from genes differentially regulated in all conditions examined (9*, the core response module), to those differentially regulated in three conditions (bottom). A dendrogram showing the similarity between stimuli is shown at top; N10, 10 nm nanoparticle; N300, 300 nm nanoparticle; STM, Salmonella infection. B. The percentage of pathogen targets (bars) in each group of genes (blue bars) or in background (not in the group; purple bars) is shown for each group of genes regulated by N or more stimuli (X axis). The corresponding analysis is shown for Human homologs (lines) for the group (red line) or background (green line) in each group. Asterisks by each group on the X axis indicates that these groups are statistically enriched in both homologs and pathogen targets, other values were statistically significant after multiple hypothesis correction. These results show that groups of genes that are differentially regulated in response to a broad range of stimuli are more likely to be targets of pathogens and are more conserved than other genes.
Mentions: The broad spectrum of stimuli in our data set gave us the opportunity to identify the essential conserved components of macrophage activation. Differentially regulated genes were identified using a 1.5 fold expression change threshold for probes that passed a significance test up to 360 minutes post-treatment. We observed that the responses of macrophages to nanoparticles were delayed relative to the other stimuli, and that very few differentially expressed genes overlapped with the compendium; therefore, we considered the entire time course, up to 24 hours post-treatment. To elucidate the components of macrophage activation, we identified groups of genes that are regulated by different numbers of conditions (). The results of this analysis are shown in Figure 3A, where black indicates that genes (rows) are differentially regulated in a given response (columns). Table S1 provides the complete list of genes, the conditions under which we found differential regulation, the network properties of the gene, and its status as a pathogen target.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, Washington, United States of America. Jason.McDermott@pnl.gov

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.

Show MeSH
Related in: MedlinePlus