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Detection of deregulated modules using deregulatory linked path.

Hu Y, Gao L, Shi K, Chiu DK - PLoS ONE (2013)

Bottom Line: To demonstrate the effectiveness of our approach, we apply the method to expression data associated with different states of Human Epidermal Growth Factor Receptor 2 (HER2).The experimental results show that the genes as well as the links in the deregulated modules are significantly enriched in multiple KEGG pathways and GO biological processes, most of which can be validated to suffer from impact of this oncogene based on previous studies.Additionally, we find the regulatory mechanism associated with the crucial gene SNAI1 significantly deregulated resulting from the activation of HER2.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, China.

ABSTRACT
The identification of deregulated modules (such as induced by oncogenes) is a crucial step for exploring the pathogenic process of complex diseases. Most of the existing methods focus on deregulation of genes rather than the links of the path among them. In this study, we emphasize on the detection of deregulated links, and develop a novel and effective regulatory path-based approach in finding deregulated modules. Observing that a regulatory pathway between two genes might involve in multiple rather than a single path, we identify condition-specific core regulatory path (CCRP) to detect the significant deregulation of regulatory links. Using time-series gene expression, we define the regulatory strength within each gene pair based on statistical dependence analysis. The CCRPs in regulatory networks can then be identified using the shortest path algorithm. Finally, we derive the deregulated modules by integrating the differential edges (as deregulated links) of the CCRPs between the case and the control group. To demonstrate the effectiveness of our approach, we apply the method to expression data associated with different states of Human Epidermal Growth Factor Receptor 2 (HER2). The experimental results show that the genes as well as the links in the deregulated modules are significantly enriched in multiple KEGG pathways and GO biological processes, most of which can be validated to suffer from impact of this oncogene based on previous studies. Additionally, we find the regulatory mechanism associated with the crucial gene SNAI1 significantly deregulated resulting from the activation of HER2. Hence, our method provides not only a strategy for detecting the deregulated links in regulatory networks, but also a way to identify concerning deregulated modules, thus contributing to the target selection of edgetic drugs.

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Related in: MedlinePlus

Estimation of transcriptional time lag.The figure shows the estimation of transcriptional time lag. In this example, with respect to the - gene pair, the time points of the initial changes in their expression of the control group are  and , respectively. Thus, the transcriptional time lag is .
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pone-0070412-g002: Estimation of transcriptional time lag.The figure shows the estimation of transcriptional time lag. In this example, with respect to the - gene pair, the time points of the initial changes in their expression of the control group are and , respectively. Thus, the transcriptional time lag is .

Mentions: The scoring method is explained as follows. Estimating the transcriptional time lag of each gene pair, we determine the time points of the initial changes in their expression of the control group. Here, the expression of the case group is not used for the estimation due to the deregulation effect induced by the oncogenes. Based on the work of Zou et al. [14], we compute the fold changes of the gene expression at each time point compared to the baseline expression. A 1.2 fold (up regulation) or 0.8 fold (down regulation) is used as a cutoff. For example, with respect to the i-th gene pair in the network DG, the time points of their initial changes and are determined according to the following equations:(1)(2)where denotes the gene expression of or at time point in the control group. The time-series includes N evenly spaced time points . Since most transcriptional regulators exhibit either an earlier or simultaneous change in the expression when compared to their targets [16], we only consider the time from to when computing the initial change time point of target . The difference, , is defined as the transcriptional time lag. As an example, we use the expression of a regulator and its target and is shown in Figure 2.


Detection of deregulated modules using deregulatory linked path.

Hu Y, Gao L, Shi K, Chiu DK - PLoS ONE (2013)

Estimation of transcriptional time lag.The figure shows the estimation of transcriptional time lag. In this example, with respect to the - gene pair, the time points of the initial changes in their expression of the control group are  and , respectively. Thus, the transcriptional time lag is .
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3722188&req=5

pone-0070412-g002: Estimation of transcriptional time lag.The figure shows the estimation of transcriptional time lag. In this example, with respect to the - gene pair, the time points of the initial changes in their expression of the control group are and , respectively. Thus, the transcriptional time lag is .
Mentions: The scoring method is explained as follows. Estimating the transcriptional time lag of each gene pair, we determine the time points of the initial changes in their expression of the control group. Here, the expression of the case group is not used for the estimation due to the deregulation effect induced by the oncogenes. Based on the work of Zou et al. [14], we compute the fold changes of the gene expression at each time point compared to the baseline expression. A 1.2 fold (up regulation) or 0.8 fold (down regulation) is used as a cutoff. For example, with respect to the i-th gene pair in the network DG, the time points of their initial changes and are determined according to the following equations:(1)(2)where denotes the gene expression of or at time point in the control group. The time-series includes N evenly spaced time points . Since most transcriptional regulators exhibit either an earlier or simultaneous change in the expression when compared to their targets [16], we only consider the time from to when computing the initial change time point of target . The difference, , is defined as the transcriptional time lag. As an example, we use the expression of a regulator and its target and is shown in Figure 2.

Bottom Line: To demonstrate the effectiveness of our approach, we apply the method to expression data associated with different states of Human Epidermal Growth Factor Receptor 2 (HER2).The experimental results show that the genes as well as the links in the deregulated modules are significantly enriched in multiple KEGG pathways and GO biological processes, most of which can be validated to suffer from impact of this oncogene based on previous studies.Additionally, we find the regulatory mechanism associated with the crucial gene SNAI1 significantly deregulated resulting from the activation of HER2.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, China.

ABSTRACT
The identification of deregulated modules (such as induced by oncogenes) is a crucial step for exploring the pathogenic process of complex diseases. Most of the existing methods focus on deregulation of genes rather than the links of the path among them. In this study, we emphasize on the detection of deregulated links, and develop a novel and effective regulatory path-based approach in finding deregulated modules. Observing that a regulatory pathway between two genes might involve in multiple rather than a single path, we identify condition-specific core regulatory path (CCRP) to detect the significant deregulation of regulatory links. Using time-series gene expression, we define the regulatory strength within each gene pair based on statistical dependence analysis. The CCRPs in regulatory networks can then be identified using the shortest path algorithm. Finally, we derive the deregulated modules by integrating the differential edges (as deregulated links) of the CCRPs between the case and the control group. To demonstrate the effectiveness of our approach, we apply the method to expression data associated with different states of Human Epidermal Growth Factor Receptor 2 (HER2). The experimental results show that the genes as well as the links in the deregulated modules are significantly enriched in multiple KEGG pathways and GO biological processes, most of which can be validated to suffer from impact of this oncogene based on previous studies. Additionally, we find the regulatory mechanism associated with the crucial gene SNAI1 significantly deregulated resulting from the activation of HER2. Hence, our method provides not only a strategy for detecting the deregulated links in regulatory networks, but also a way to identify concerning deregulated modules, thus contributing to the target selection of edgetic drugs.

Show MeSH
Related in: MedlinePlus