Network medicine in Cardiovascular Research

Abstract

The ability to generate multi-omics data coupled with deeply characterizing the clinical phenotype of individual patients promises to improve understanding of complex cardiovascular pathobiology. There remains an important disconnection between the magnitude and granularity of these data and our ability to improve phenotype-genotype correlations for complex cardiovascular diseases. This shortcoming may be due to limitations associated with traditional reductionist analytical methods, which tend to emphasize a single molecular event in the pathogenesis of diseases more aptly characterized by crosstalk between overlapping molecular pathways. Network medicine is a rapidly growing discipline that considers diseases as the consequences of perturbed interactions between multiple interconnected biological components. This powerful integrative approach has enabled a number of important discoveries in complex disease mechanisms. In this review, we introduce the basic concepts of network medicine and highlight specific examples by which this approach has accelerated cardiovascular research. We also review how network medicine is well-positioned to promote rational drug design for patients with cardiovascular diseases, with particular emphasis on advancing precision medicine.

Keywords: Cardiovascular disease; Network medicine; Omics; Pathobiology; Precision medicine.

Figure 1

Biological networks and basic network components. (A) Biological networks can represent a wide spectrum of biological dimensions. (B) In biological networks, distinct biological components, such as genes or metabolites, are represented as individual nodes. Edges connect two nodes and represent the interaction between them. Hubs are defined as nodes that are highly connected to other nodes. When a node and its neighbours are highly connected to one another, the resulting network neighbourhood is defined as a cluster or network submodule. Adapted from refs with permission.

Figure 2

High-throughput methods for determining protein-protein interactions. (A) In the yeast two-hybrid system, a plasmid is created expressing a ‘bait’ protein linked to the DNA binding domain (BD) of a transcription factor (TF) required for expression of a reporter gene. Various ‘prey’ proteins are then linked to the activation domain (AD) of this TF in separate plasmids. Plasmids are co-transfected in pairs into yeast cells. If the ‘bait’ and ‘prey’ proteins interact, the BD and AD domains of the TF will be in close enough proximity to translocate to the nucleus and initiate reporter gene expression. If the ‘bait’ and ‘prey’ do not interact, the reporter gene will not be expressed. (B) In co-complex discovery, a ‘bait’ protein is linked to a tag and incubated with target cell or tissue lysates to allow ‘prey’ proteins to associate. The ‘bait’-‘prey’ complexes are isolated through affinity purification utilizing the linked tag. The ‘prey’ proteins can then be identified using mass spectrometry.

Figure 3

Network-based methods to predict disease genes. (A) Linked genetic elements, for example through shared expression quantitative trait loci (eQTLs) containing a known disease gene, have increased probability that other genes within the linkage interval contribute to the disease process. Linkage methods can be utilized to uncover these candidate disease genes and proteins. (B) Disease modules identify network regions that contribute to a disease. Proteins contained within these network elements can be investigated as candidate disease proteins through methods such as the seed connector algorithm. (C) Diffusion propagation methods start with known disease proteins and then assign probabilities to proteins in the interactome based on the likelihood they are associated with the disease. This analysis is based on frequency of candidate proteins’ interactions with and network distance from the known disease proteins. Adapted from ref. with permission.

Figure 4

Network analysis to identify novel disease mediators. A network approach has enabled a number of novel disease mechanism discoveries. Notable examples include the vitamin D receptor (VitDR) and interleukin (IL)-10 pathways in pre-eclampsia (upper left), NEDD9 in PAH (upper right), 26S proteasome non-ATPase regulatory subunit 3 (PSMD3) in valvular calcification (lower left), and nuclear factor of activated T cells 4 (NFATC4) in type 2 diabetes (DM2) (lower right). (upper left) Network analysis of 348 differentially expressed vitamin D-associated genes in peripheral blood of pre-eclampsia patients identified the network modules specific to the changes in maternal immune responses. Of these, the IL-10 signalling pathway was noted to be closely interacting with the vitamin D signalling pathway with notable down-regulation of IL-10 signalling in pre-eclampsia patients. (upper right) Betweenness centrality analysis of a novel network consisting of ALDO-regulated genes associated with vascular fibrosis identified NEDD9 as a novel mediator of PAH pathogenesis. (lower left) Construction of the proteomics subnetworks involving the proteins overrepresented in the calcific stages of human calcific aortic valve disease in the fibrosa layers and betweenness centrality analysis identified fibronectin-1, PSMD3, and PSMA1 as the potential key disease mediators involved in valvular calcification. (lower right) Control centrality analysis of the human pancreatic islet tissue-specific gene regulatory network developed from diabetic and non-diabetic donors identified multiple biological pathways driving the type 2 diabetes disease network with NFATC4 as a key controller of these pathways. Adapted from refs^,,, with permission.

Figure 5

Exercise correlation network from invasive cardiopulmonary exercise testing. Patients undergoing invasive cardiopulmonary exercise testing for unexplained exertional intolerance were used to construct an exercise network. Clinical measurements collected at the time of the study were used to construct this network with 39 nodes and 98 edges, grouped into 7 broader physiological domains. A subnetwork of 10 variables was used to identify 4 distinct patient clusters that differed in exercise profiles and clinical outcomes. Adapted from ref. with permission.

Figure 6

Reticulotype analysis and precision medicine for cardiovascular patients. Complex sets of patient-specific molecular and clinical data can be integrated into a set of biological networks unique to each individual (reticulotype). The study of such network behaviour and the sequelae of its perturbation may facilitate genotype–phenotype correlation and predict treatment response in a patient-specific manner. Adapted from ref. with permission.