Survey of network-based approaches to research of cardiovascular diseases

Drug Repurposing Using Biological Networks

Drug repositioning is a strategy to identify new uses for existing, approved, or research drugs that are outside the scope of its original medical indication. Drug repurposing is based on the fact that one drug can act on multiple targets or that two diseases can have molecular similarities, among others. Currently, thanks to the rapid advancement of high-performance technologies, a massive amount of biological and biomedical data is being generated. This allows the use of computational methods and models based on biological networks to develop new possibilities for drug repurposing. Therefore, here, we provide an in-depth review of the main applications of drug repositioning that have been carried out using biological network models. The goal of this review is to show the usefulness of these computational methods to predict associations and to find candidate drugs for repositioning in new indications of certain diseases.

Disease network delineates the disease progression profile of cardiovascular diseases

OBJECTIVE

As Electronic Health Records (EHR) data accumulated explosively in recent years, the tremendous amount of patient clinical data provided opportunities to discover real world evidence. In this study, a graphical disease network, named progressive cardiovascular disease network (progCDN), was built to delineate the progression profiles of cardiovascular diseases (CVD).

MATERIALS AND METHODS

The EHR data of 14.3 million patients with CVD diagnoses were collected for building disease network and further analysis. We applied a new designed method, progression rates (PR), to calculate the progression relationship among different diagnoses. Based on the disease network outcome, 23 disease progression pair were selected to screen for salient features.

RESULTS

The network depicted the dominant diseases in CVD development, such as the heart failure and coronary arteriosclerosis. Novel progression relationships were also discovered, such as the progression path from long QT syndrome to major depression. In addition, three age-group progCDNs identified a series of age-associated disease progression paths and important successor diseases with age bias. Furthermore, a list of important features with sufficient abundance and high correlation was extracted for building disease risk models.

DISCUSSION

The PR method designed for identifying the progression relationship could be widely applied in any EHR database due to its flexibility and robust functionality. Meanwhile, researchers could use the progCDN network to validate or explore novel disease relationships in real world data.

CONCLUSION

The first-time interrogation of such a huge CVD patients cohort enabled us to explore the general and age-specific disease progression patterns in CVD development.

Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network

A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network (interactome) indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy (IDCM) data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field.

Network wiring of pleiotropic kinases yields insight into protective role of diabetes on aneurysm

Recent studies suggest a protective role of diabetes in the development of aneurysm, but the biological mechanisms behind this are still unknown. This type of association is not present in the case of diabetes and atherosclerosis despite similar risk factors for aneurysm and atherosclerosis. We postulate the existence of genes that disrupt the pathways needed for the onset of aneurysm in the presence of diabetes. Motivated by the significance of genetic interactions in understanding disease-disease associations, we tackle this problem by integrating protein-protein interaction and genetic interaction data, i.e., we examine the biological pathways related to the three diseases that contain genes involved in the following genetic interactions: one gene in a genetic interaction is part of a diabetes pathway, the other gene is part of an aneurysm, or an atherosclerosis pathway. We create a protein-protein interaction sub-network that contains disease pathways described above. We then use a "brokerage" measure - a topological measure that identifies proteins in this sub-network whose removal severely affects the interconnectedness of their neighbourhood, enabling such proteins to disrupt the pathway they are in. We identify a set of proteins with high brokerage values and find this set to be enriched in biological functions, including cell-matrix adhesion, which facilitates mechanisms that have already been suggested as possible causes of diabetes-aneurysm association. We further narrow down our set to 16 proteins that are involved in an aneurysm or an atherosclerosis pathway and are encoded by genes participating in genetic interactions with a gene in a diabetes pathway. This set is enriched in kinases and phosphorylation processes, with two pleiotropic kinases that are involved in both aneurysm and atherosclerosis pathways. Kinases can turn on or off proteins, explaining how functional changes of such proteins could result in the disruption of pathways. So if in an aneurysm-related pathway a gene is turned off, the onset of the disease could be prevented. However, mutations of pleiotropic genes could have effects only on one of the traits, which explains why pleiotropic kinases that are involved in both aneurysm and atherosclerosis pathways could disrupt aneurysm pathways explaining the reduced risk of aneurysm in diabetes patients, but not affect the atherosclerosis pathways.