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Construction of the influenza A virus infection-induced cell-specific inflammatory regulatory network based on mutual information and optimization.

Jin S, Zou X - BMC Syst Biol (2013)

Bottom Line: Moreover, we used the average relative error and sensitivity analysis to evaluate the effectiveness of our proposed approach.Most of these regulatory interactions are statistically significant by Z-statistic.The pathway analysis results from the Kyoto Encyclopaedia of Genes and Genomes (KEGG) showed that 8 pathways are enriched significantly.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China. xfzou@whu.edu.cn.

ABSTRACT

Background: Influenza A virus (IAV) infection-induced inflammatory regulatory networks (IRNs) are extremely complex and dynamic. Specific biological experiments for investigating the interactions between individual inflammatory factors cannot provide a detailed and insightful multidimensional view of IRNs. Recently, data from high-throughput technologies have permitted system-level analyses. The construction of large and cell-specific IRNs from high-throughput data is essential to understanding the pathogenesis of IAV infection.

Results: In this study, we proposed a computational method, which combines nonlinear ordinary differential equation (ODE)-based optimization with mutual information, to construct a cell-specific optimized IRN during IAV infection by integrating gene expression data with a prior knowledge of network topology. Moreover, we used the average relative error and sensitivity analysis to evaluate the effectiveness of our proposed approach. Furthermore, from the optimized IRN, we confirmed 45 interactions between proteins in biological experiments and identified 37 new regulatory interactions and 8 false positive interactions, including the following interactions: IL1β regulates TLR3, TLR3 regulates IFN-β and TNF regulates IL6. Most of these regulatory interactions are statistically significant by Z-statistic. The functional annotations of the optimized IRN demonstrated clearly that the defense response, immune response, response to wounding and regulation of cytokine production are the pivotal processes of IAV-induced inflammatory response. The pathway analysis results from the Kyoto Encyclopaedia of Genes and Genomes (KEGG) showed that 8 pathways are enriched significantly. The 5 pathways were validated by experiments, and 3 other pathways, including the intestinal immune network for IgA production, the cytosolic DNA-sensing pathway and the allograft rejection pathway, are the predicted novel pathways involved in the inflammatory response.

Conclusions: Integration of knowledge-driven and data-driven methods allows us to construct an effective IRN during IAV infection. Based on the constructed network, we have identified new interactions among inflammatory factors and biological pathways. These findings provide new insight into our understanding of the molecular mechanisms in the inflammatory network in response to IAV infection. Further characterization and experimental validation of the interaction mechanisms identified from this study may lead to a novel therapeutic strategy for the control of infections and inflammatory responses.

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The significance test for the selected threshold value of the MI and CMI. The x-axis is the Z-statistic values of the deleted edges when the threshold value of MI and CMI is set to be 0.1. The y-axis represents the number of edges whose Z-statistic fall into the corresponding bins. The blue dashed line is the inverse cumulative distribution function of N(0,1) when using significant level α=0.1.
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Figure 2: The significance test for the selected threshold value of the MI and CMI. The x-axis is the Z-statistic values of the deleted edges when the threshold value of MI and CMI is set to be 0.1. The y-axis represents the number of edges whose Z-statistic fall into the corresponding bins. The blue dashed line is the inverse cumulative distribution function of N(0,1) when using significant level α=0.1.

Mentions: We simplified the initial IRN based on PCA-CMI. We deleted the edges of the initial IRN with independent correlations recursively (i.e., from low to high order of independent correlation until no edge can be deleted). The threshold value θ of MI and CMI was set to 0.1, which was statistically tested by Z-statistic [15,20,38] (Figure 2). The simplified network, which contains 50 nodes and 142 directed edges, is depicted in Figure 3.


Construction of the influenza A virus infection-induced cell-specific inflammatory regulatory network based on mutual information and optimization.

Jin S, Zou X - BMC Syst Biol (2013)

The significance test for the selected threshold value of the MI and CMI. The x-axis is the Z-statistic values of the deleted edges when the threshold value of MI and CMI is set to be 0.1. The y-axis represents the number of edges whose Z-statistic fall into the corresponding bins. The blue dashed line is the inverse cumulative distribution function of N(0,1) when using significant level α=0.1.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4016583&req=5

Figure 2: The significance test for the selected threshold value of the MI and CMI. The x-axis is the Z-statistic values of the deleted edges when the threshold value of MI and CMI is set to be 0.1. The y-axis represents the number of edges whose Z-statistic fall into the corresponding bins. The blue dashed line is the inverse cumulative distribution function of N(0,1) when using significant level α=0.1.
Mentions: We simplified the initial IRN based on PCA-CMI. We deleted the edges of the initial IRN with independent correlations recursively (i.e., from low to high order of independent correlation until no edge can be deleted). The threshold value θ of MI and CMI was set to 0.1, which was statistically tested by Z-statistic [15,20,38] (Figure 2). The simplified network, which contains 50 nodes and 142 directed edges, is depicted in Figure 3.

Bottom Line: Moreover, we used the average relative error and sensitivity analysis to evaluate the effectiveness of our proposed approach.Most of these regulatory interactions are statistically significant by Z-statistic.The pathway analysis results from the Kyoto Encyclopaedia of Genes and Genomes (KEGG) showed that 8 pathways are enriched significantly.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China. xfzou@whu.edu.cn.

ABSTRACT

Background: Influenza A virus (IAV) infection-induced inflammatory regulatory networks (IRNs) are extremely complex and dynamic. Specific biological experiments for investigating the interactions between individual inflammatory factors cannot provide a detailed and insightful multidimensional view of IRNs. Recently, data from high-throughput technologies have permitted system-level analyses. The construction of large and cell-specific IRNs from high-throughput data is essential to understanding the pathogenesis of IAV infection.

Results: In this study, we proposed a computational method, which combines nonlinear ordinary differential equation (ODE)-based optimization with mutual information, to construct a cell-specific optimized IRN during IAV infection by integrating gene expression data with a prior knowledge of network topology. Moreover, we used the average relative error and sensitivity analysis to evaluate the effectiveness of our proposed approach. Furthermore, from the optimized IRN, we confirmed 45 interactions between proteins in biological experiments and identified 37 new regulatory interactions and 8 false positive interactions, including the following interactions: IL1β regulates TLR3, TLR3 regulates IFN-β and TNF regulates IL6. Most of these regulatory interactions are statistically significant by Z-statistic. The functional annotations of the optimized IRN demonstrated clearly that the defense response, immune response, response to wounding and regulation of cytokine production are the pivotal processes of IAV-induced inflammatory response. The pathway analysis results from the Kyoto Encyclopaedia of Genes and Genomes (KEGG) showed that 8 pathways are enriched significantly. The 5 pathways were validated by experiments, and 3 other pathways, including the intestinal immune network for IgA production, the cytosolic DNA-sensing pathway and the allograft rejection pathway, are the predicted novel pathways involved in the inflammatory response.

Conclusions: Integration of knowledge-driven and data-driven methods allows us to construct an effective IRN during IAV infection. Based on the constructed network, we have identified new interactions among inflammatory factors and biological pathways. These findings provide new insight into our understanding of the molecular mechanisms in the inflammatory network in response to IAV infection. Further characterization and experimental validation of the interaction mechanisms identified from this study may lead to a novel therapeutic strategy for the control of infections and inflammatory responses.

Show MeSH
Related in: MedlinePlus