RCM: a novel association approach to search for coronary artery disease genetic related metabolites based on SNPs and metabolic network.

Integration of genetic and metabolic network holds promise for providing insight into human disease. Coronary artery disease (CAD) is strongly heritable, but the heritability of metabolic compounds has not been evaluated in human metabolic context. Here we performed a genetic-based computational approach within eight sub-cellular networks from Edinburgh Human Metabolic Network to identify significant genetic risk compounds (SGRCs) of CAD. Our results provide the evidence that the high heritabilities of SGRCs played an important role in CAD pathogenesis. Besides, SGRCs were discovered to be strongly associated with lipid metabolism. We also established a possible disease-causing reference table to decipher genetic associations of SGRCs with CAD. Comparing with traditional method, RCM experienced better performance in CAD genetic risk compounds' identification. These findings provided novel insights into CAD pathogenesis from a genetic perspective.

Uncovering packaging features of co-regulated modules based on human protein interaction and transcriptional regulatory networks.

BACKGROUND: Network co-regulated modules are believed to have the functionality of packaging multiple biological entities, and can thus be assumed to coordinate many biological functions in their network neighbouring regions. RESULTS: Here, we weighted edges of a human protein interaction network and a transcriptional regulatory network to construct an integrated network, and introduce a probabilistic model and a bipartite graph framework to exploit human co-regulated modules and uncover their specific features in packaging different biological entities (genes, protein complexes or metabolic pathways). Finally, we identified 96 human co-regulated modules based on this method, and evaluate its effectiveness by comparing it with four other methods. CONCLUSIONS: Dysfunctions in co-regulated interactions often occur in the development of cancer. Therefore, we focussed on an example co-regulated module and found that it could integrate a number of cancer-related genes. This was extended to causal dysfunctions of some complexes maintained by several physically interacting proteins, thus coordinating several metabolic pathways that directly underlie cancer.

Predicting candidate genes based on combined network topological features: a case study in coronary artery disease.

Predicting candidate genes using gene expression profiles and unbiased protein-protein interactions (PPI) contributes a lot in deciphering the pathogenesis of complex diseases. Recent studies showed that there are significant disparities in network topological features between non-disease and disease genes in protein-protein interaction settings. Integrated methods could consider their characteristics comprehensively in a biological network. In this study, we introduce a novel computational method, based on combined network topological features, to construct a combined classifier and then use it to predict candidate genes for coronary artery diseases (CAD). As a result, 276 novel candidate genes were predicted and were found to share similar functions to known disease genes. The majority of the candidate genes were cross-validated by other three methods. Our method will be useful in the search for candidate genes of other diseases.

A novel paradigm for potential drug-targets discovery: quantifying relationships of enzymes and cascade interactions of neighboring biological processes to identify drug-targets.

Target discovery is the most crucial step in a modern drug discovery development. Our objective in this study is to propose a novel paradigm for a better discrimination of drug-targets and non-drug-targets with minimum disruptive side-effects under a biological pathway context. We introduce a novel metric, namely, "pathway closeness centrality", for each gene that jointly considers the relationships of its neighboring enzymes and cross-talks of biological processes, to evaluate its probability of being a drug-target. This metric could distinguish drug-targets with non-drug-targets. Genes with lower pathway closeness centrality values are prone to play marginal roles in biological processes and have less lethality risk, but appear to have tissue-specific expressions. Compared with traditional metrics, our method outperforms degree, betweenness and bridging centrality under the human pathway context. Analysis of the existing top 20 drugs with the most disruptive side-effects indicates that pathway closeness centrality is an appropriate index to predict the probability of the occurrence of adverse pharmacological effects. Case studies in prostate cancer and type 2 diabetes mellitus indicate that the pathway closeness centrality metric could distinguish likely drug-targets well from human pathways. Thus, our method is a promising tool to aid target identification in drug discovery.

Disease gene interaction pathways: a potential framework for how disease genes associate by disease-risk modules.

BACKGROUND: Disease genes that interact cooperatively play crucial roles in the process of complex diseases, yet how to analyze and represent their associations is still an open problem. Traditional methods have failed to represent direct biological evidences that disease genes associate with each other in the pathogenesis of complex diseases. Molecular networks, assumed as 'a form of biological systems', consist of a set of interacting biological modules (functional modules or pathways) and this notion could provide a promising insight into deciphering this topic. METHODOLOGY/PRINCIPAL FINDINGS: In this paper, we hypothesized that disease genes might associate by virtue of the associations between biological modules in molecular networks. Then we introduced a novel disease gene interaction pathway representation and analysis paradigm, and managed to identify the disease gene interaction pathway for 61 known disease genes of coronary artery disease (CAD), which contained 46 disease-risk modules and 182 interaction relationships. As demonstrated, disease genes associate through prescribed communication protocols of common biological functions and pathways. CONCLUSIONS/SIGNIFICANCE: Our analysis was proved to be coincident with our primary hypothesis that disease genes of complex diseases interact with their neighbors in a cooperative manner, associate with each other through shared biological functions and pathways of disease-risk modules, and finally cause dysfunctions of a series of biological processes in molecular networks. We hope our paradigm could be a promising method to identify disease gene interaction pathways for other types of complex diseases, affording additional clues in the pathogenesis of complex diseases.

Global risk transformative prioritization for prostate cancer candidate genes in molecular networks.

Understanding the pathogenesis of complex diseases is aided by precise identification of the genes responsible. Many computational methods have been developed to prioritize candidate disease genes, but coverage of functional annotations may be a limiting factor for most of these methods. Here, we introduce a global candidate gene prioritization approach that considers information about network properties in the human protein interaction network and risk transformative contents from known disease genes. Global risk transformative scores were then used to prioritize candidate genes. This method was introduced to prioritize candidate genes for prostate cancer. The effectiveness of our global risk transformative algorithm for prioritizing candidate genes was evaluated according to validation studies. Compared with ToppGene and random walk-based methods, our method outperformed the two other candidate gene prioritization methods. The generality of our method was assessed by testing it on prostate cancer and other types of cancer. The performance was evaluated using standard leave-one-out cross-validation.