Detection of deregulated modules using deregulatory linked path

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.

Figure 1. Workflow of our approach.

The figure shows the workflow of our approach. Given the time-series gene expression datasets of the case and the control group, 15 deregulated genes are selected to map to the nodes of KEGG human regulatory network G. Allowing for the integrity of links among genes, we construct the differentially regulated network DG consisting of 17 nodes and 25 directed edges. For each regulator-target gene pair, we compute the regulatory strength of the control (colored by blue) and the case (colored by red) and the two weighted differentially regulated network and are constructed. Centered on the seed gene ‘g10’, which is colored by yellow, the two regulatory subsystems and are extracted. The deregulated module is derived by integrating the differential edges of the CCRPs. Here, the CCRP from ‘g4’ to ‘g11’ is switched. In the control group, the CCRP is ‘g4→g7→g10→g11’. While in the case group, it is ‘g4→g10→g16→g11’.

Figure 2. 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 .

Figure 3. Construction of expression level matrix.

As is shown in Figure 2, the transcriptional time lag between and is 3 time unit. After discretizing the expression of the gene pair, we organize and into the expression level matrix, where the expression level of at time point is aligned with the expression level of at time point .

Figure 4. Statistical distribution of differentially expressed scores of all the genes in the microarray.

SAM is used to compute the differentially expressed score of each gene. And the score of 3.6 (P<0.05) is considered as a cutoff to select deregulated genes.

Figure 5. Integrated deregulated module.

The figure shows the integrated deregulated module consisting of 49 gene nodes and 86 directed edges, which include both 43 edges belonging respectively to the control (colored by blue) and the case group (colored by red). The seed genes are colored by yellow. Note that not all the 20 seed genes appear in the deregulated module.

Figure 6. SNAI1 associated deregulated module.

The figure shows the SNAI1 associated deregulated module consisting of 9 gene nodes and 13 directed edges. This includes 7 and 6 edges belonging respectively to the control (colored by blue) and the case group (colored by red). The seed gene SNAI1 is colored by yellow.