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Cited by 31

The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.

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

McDermott, Jason E., Archuleta, Michelle, Thrall, Brian D., Adkins, Joshua N., Waters, Katrina M.·2011·PLoS ONE
DOI 10.1371/journal.pone.0014673PMC PMC3038849
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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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Crossref · 10.1371/journal.pone.0014673

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Figures

Figure 1 Overview of computational approaches.
Figure 1 Overview of computational approaches.CC BY · PubMed Central
Figure 2 Topological bottlenecks in inferred networks are enriched in pathogen targets. A. Hubs and bottlenecks (top 20% of degree and betweenness values, respectively) were analyzed for their enrichment in known targets of pathogens (blue bars). Bottlenecks are significantly enriched (p-value 0.004) in pathogen targets, but not human homologs, and hubs were enriched in neither. Additionally, the mean enrichment of bottlenecks from 100 randomized networks is shown, with error bars representing +/− one standard deviation. B. Bottlenecks were identified using between 1 and 100% of the top ranked betweenness values in the network (x axis) and the enrichment in pathogen targets versus non-bottlenecks is shown (black line). The betweenness values are shown as a blue line. Significant fold change values (p-value<0.05) are indicated by asterisks at the top of the figure. The dotted line indicates the location of the peak of greatest enrichment. These results indicate that bottlenecks from inferred networks are more important to the functioning of the system than other genes.
Figure 2 Topological bottlenecks in inferred networks are enriched in pathogen targets. A. Hubs and bottlenecks (top 20% of degree and betweenness values, respectively) were analyzed for their enrichment in known targets of pathogens (blue bars). Bottlenecks are significantly enriched (p-value 0.004) in pathogen targets, but not human homologs, and hubs were enriched in neither. Additionally, the mean enrichment of bottlenecks from 100 randomized networks is shown, with error bars representing +/− one standard deviation. B. Bottlenecks were identified using between 1 and 100% of the top ranked betweenness values in the network (x axis) and the enrichment in pathogen targets versus non-bottlenecks is shown (black line). The betweenness values are shown as a blue line. Significant fold change values (p-value<0.05) are indicated by asterisks at the top of the figure. The dotted line indicates the location of the peak of greatest enrichment. These results indicate that bottlenecks from inferred networks are more important to the functioning of the system than other genes.CC BY · PubMed Central
Figure 3 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.
Figure 3 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.CC BY · PubMed Central
Figure 4 Dynamics of core response module. A) Temporal regulation of gene expression levels for a cluster of upregulated genes in the core response module. The three conditions LPS, Np (nanoparticle), and Salmonella (STM) are labeled with blue, purple, and green lines. Error bars signify 95% confidence and the average is over all gene expression profiles within a cluster. B) Individual gene expression levels of Ifit1 (dashed line) and Fos (solid line) under each condition LPS (blue), Np (purple), and STM (green).
Figure 4 Dynamics of core response module. A) Temporal regulation of gene expression levels for a cluster of upregulated genes in the core response module. The three conditions LPS, Np (nanoparticle), and Salmonella (STM) are labeled with blue, purple, and green lines. Error bars signify 95% confidence and the average is over all gene expression profiles within a cluster. B) Individual gene expression levels of Ifit1 (dashed line) and Fos (solid line) under each condition LPS (blue), Np (purple), and STM (green).CC BY · PubMed Central
Figure 5 Modeling the dynamics of the core response module. A. Heatmap representation of the expression of the core response module. Each row represents a gene and each column represents a time series. The values in the heatmap are the maximum absolute value of differential expression from all time points. Shown at right is a dendrogram indicating the relationships between the genes and the color bars indicate sub-clusters that were used for further modeling. B. Predictive dynamics of regulatory cluster. The observed (red lines) versus predicted (dashed black lines) expression for cluster 1 (the regulatory cluster) is shown over a 24 hour time period after exposure to LPS or 300 nm nanoparticles, or infection with Salmonella. Given the sparse Salmonella infection data in mouse macrophages we use the expression of genes (grey lines) from the cluster in a study of infection of human macrophages to illustrate cluster dynamics. The mean expression of the genes is shown as a blue line. C. Inferred regulatory influences for core response sub-clusters. The correlation of predicted to observed expression is listed for each sub-cluster. Predicted regulators for the cluster are listed; black indicates a positive influence, blue indicates a negative influence, and pairs of regulators separated by a slash denote inferred combinatorial influences.
Figure 5 Modeling the dynamics of the core response module. A. Heatmap representation of the expression of the core response module. Each row represents a gene and each column represents a time series. The values in the heatmap are the maximum absolute value of differential expression from all time points. Shown at right is a dendrogram indicating the relationships between the genes and the color bars indicate sub-clusters that were used for further modeling. B. Predictive dynamics of regulatory cluster. The observed (red lines) versus predicted (dashed black lines) expression for cluster 1 (the regulatory cluster) is shown over a 24 hour time period after exposure to LPS or 300 nm nanoparticles, or infection with Salmonella. Given the sparse Salmonella infection data in mouse macrophages we use the expression of genes (grey lines) from the cluster in a study of infection of human macrophages to illustrate cluster dynamics. The mean expression of the genes is shown as a blue line. C. Inferred regulatory influences for core response sub-clusters. The correlation of predicted to observed expression is listed for each sub-cluster. Predicted regulators for the cluster are listed; black indicates a positive influence, blue indicates a negative influence, and pairs of regulators separated by a slash denote inferred combinatorial influences.CC BY · PubMed Central

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Research works printed but not linked: 1

  1. JE McDermott, Separating the drivers from the driven: Integrative network and pathway approaches aid identification of disease biomarkers from high-throughput data., Dis Markers, 28, 253, 10.1155/2010/708932 · DOI

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Metadata

Authors
McDermott, J. E., Archuleta, M., Thrall, B. D., Adkins, J. N., & Waters, K. M.
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2011-02-14
Volume
6
Issue
2
Pages
e14673
ISSN
1932-6203
DOI
10.1371/journal.pone.0014673
PMC
PMC3038849
Cited by
31
References
45
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (unspecified)

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