Network Medicine in Pathobiology

Abstract

The past decade has witnessed exponential growth in the generation of high-throughput human data across almost all known dimensions of biological systems. The discipline of network medicine has rapidly evolved in parallel, providing an unbiased, comprehensive biological framework through which to interrogate and integrate systematically these large-scale, multi-omic data to enhance our understanding of disease mechanisms and to design drugs that reflect a deep knowledge of molecular pathobiology. In this review, we discuss the key principles of network medicine and the human disease network and explore the latest applications of network medicine in this multi-omic era. We also highlight the current conceptual and technological challenges, which serve as exciting opportunities by which to improve and expand the network-based applications beyond the artificial boundaries of the current state of human pathobiology.

Figure 1

Basic network properties and network types. Nodes represent distinct biological entities and are connected by edges. Hubs are highly connected nodes and often represent essential biological elements. Shortest path lengths represent the minimal number of edges connecting two nodes. A and B: Edges can be unweighted or weighted (with varying thicknesses) to signify the different strengths of given biological interactions. C: Edges can also be used to depict the directionality of chosen molecular interactions. D: The relationships between more than one type of node (depicted as circles and squares) can be represented in a bipartite network. E and F: Scale-free properties of biological networks. In random networks, the degree distribution [P(k)] follows a binomial distribution. Most biological networks are scale-free networks with their P(k) following a power law distribution. Only a small number of nodes (hubs) are highly connected. Adapted from Loscalzo et al with permission. Network Medicine: Complex Systems in Human Disease and Therapeutics, edited by Joseph Loscalzo, Albert-László Barabási, and Edwin K. Silverman, Cambridge, Mass.: Harvard University Press, Copyright © 2017 by the President and Fellows of Harvard College. k, degree of each node; P(k), distribution of the degrees involving all nodes.

Figure 2

The human interactome and disease network modules. A: The human interactome represents unbiased mapping of all known biological interactions. The disease network modules can be constructed by placing a set of genes or gene products that are differentially expressed in specific disease states in the context of the interactome. B: This subnetwork of the human interactome contains the disease modules specific to multiple sclerosis (MS), peroxisomal disorders (PD), and rheumatoid arthritis (RA). The molecular relationships among different disease entities can be examined in this context. C: The degree of topological overlap between the MS and RA disease modules is represented by a Venn diagram. The network-based separation of a disease pair, A and B, is defined as S_AB = <d_AB> − (<d_AA>+ <d_BB>)/2. This compares the mean shortest distances of the protein pairings between the diseases A and B (d_AB) and the shortest distances of the protein pairings within the disease A (d_AA) and B (d_BB). Negative S_AB indicates overlap between the diseases A and B; S_AB for MS and RA is −0.2. The graph below depicts the probability distribution [P(d)] as a function of the shortest distances. D: The MS and PD disease modules show no topologic overlap with positive network-based separation value (S_AB = 1.3). From Menche J, Sharma A, Kitsak M, Ghiassian SD, Vidal M, Loscalzo J, Barabasi AL: Disease networks: uncovering disease-disease relationships through the incomplete interactome. Science 2015, 347:1257601. Adapted with permission from AAAS. From Leopold JA, Loscalzo J: Emerging role of precision medicine in cardiovascular disease. Circ Res 2018, 122:1302–1315. Adapted with permission from Wolters Kluwer Health Inc.

Figure 3

Integrating multidimensional biological networks. Studying the cross-talk among networks representing different biological domains and their integration (networks of networks) enables us to approach complex disease mechanisms and therapeutic decisions with greater precision. This integration should also involve network interactions with long-term and short-term environmental exposures, as well as microbiome interactions.

Figure 4

Network approach to precision phenotype and rational polypharmacy. A: The conventional reductionistic therapy decision involves identifying patients with a common endophenotype (eg, obesity) who may otherwise have distinctive underlying biological phenotypes (depicted in red, blue, or green). Some patients may undergo limited genotyping. Drug therapies are initiated in this heterogeneous patient group based on population-based clinical trial results with limited consideration for the individual's underlying biology. B: The precision medicine approach involves deep phenotyping of each individual with a common endophenotype using multi-omic platforms and clinical assessments. These data are integrated using network analysis to determine more precise phenotypes with their key molecular components (gray nodes). On the basis of such analysis in the context of the interactome, a drug target(s) (red, blue, or green nodes) can be determined for each phenotype. From Leopold JA, Loscalzo J: Emerging role of precision medicine in cardiovascular disease. Circ Res 2018, 122:1302–1315. Adapted with permission from Wolters Kluwer Health Inc.

Figure 5

Network approach to molecular- and phenotype-based disease classification. The integrated disease network is constructed from the multiple disease networks built based on database-curated disease-disease molecular or phenotypic associations. This integrated network consisted of 1857 nodes depicting distinct disease entities and 35,114 links depicting molecular or phenotypic associations among the disease pairs. A total of 233 overlapping communities were identified from this network. They represent novel disease subcategories that are distinct from the International Classification of Diseases, Ninth Revision (https://www.cdc.gov/nchs/icd/icd9cm.htm) chapters or their subcategories. These disease subcategories subsequently are placed in a network based on the shared disease entities (depicted by weighted links). Nonoverlapping community detection analysis leads to the identification of 17 distinct clusters of disease subcategories that represent the new chapter-level disease categories. These chapters contain varying numbers of disease entities and subcategories.

Figure 6

Reticulotype analysis and individualized medicine. Patient-specific genotype-phenotype relationships can be assessed with greater precision by network-based reticulotype analysis. Each individual's unique molecular perturbation findings are examined within the context of his or her unique integrative biological network (reticulome) derived from multi-omic studies. From Leopold JA, Loscalzo J: Emerging role of precision medicine in cardiovascular disease. Circ Res 2018, 122:1302–1315. Adapted with permission from Wolters Kluwer Health Inc.