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Network medicine: a network-based approach to human disease

Key Points

  • A disease phenotype is rarely a consequence of an abnormality in a single effector gene product, but reflects various pathobiological processes that interact in a complex network.

  • Here we present an overview of the organizing principles that govern cellular networks and the implications of these principles for understanding disease. Network-based approaches have potential biological and clinical applications, from the identification of disease genes to better drug targets.

  • Whereas essential genes tend to be associated with hubs, or highly connected proteins, disease genes tend to segregate at the network's functional periphery, avoiding hubs.

  • Disease genes have a high propensity to interact with each other, forming disease modules. The identification of these disease modules can help us to identify disease pathways and predict other disease genes.

  • The highly interconnected nature of the interactome means that, at the molecular level, it is difficult to consider diseases as being independent of one another. The mapping of network-based dependencies between pathophenotypes has culminated in the concept of the diseasome, which represents disease maps whose nodes are diseases and whose links represent various molecular relationships between the disease-associated cellular components.

  • Diseases linked at the molecular level tend to show detectable comorbidity.

  • Network medicine has important applications to drug design, leading to the emergence of network pharmacology, and also in disease classification.

Abstract

Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.

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Figure 1: Disease and essential genes in the interactome.
Figure 2: Disease modules.
Figure 3: Identifying and validating disease modules.
Figure 4: Identifying disease gene candidates.
Figure 5: Disease networks.

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Acknowledgements

We thank Z. Oltvai, A. Sharma, D.-S. Lee and J. Park for useful discussions and suggestions. A.L.B. and N.G. were supported by the US National Institutes of Health (NIH) through the Center of Excellence in Genomic Sciences (CEGS), and J.L. was supported by NIH grants HL061795 (Merit Award), HL81587, HL70819 and HL48743.

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Correspondence to Albert-László Barabási.

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B-cell interactome (BCI)

Biochemical Genetic and Genomics knowledgebase (BIGG)

Biological General Repository for Interaction Datasets (BioGRID)

Biomolecular Interaction Network Database (BIND)

CBS prediction server

Database of Interacting Proteins (DIP)

Human Protein Reference Database (HPRD)

JASPAR

Kyoto Encyclopedia of Genes and Genomes (KEGG)

microRNA

miRBase

miRDB

miRecords

Molecular Interaction database (MINT)

Munich Information Center for Protein Sequence (MIPS) Mammalian Protein–Protein Interaction Database

NetPhorest

Phospho.ELM

Phosphorylation site database (PHOSIDA)

PhosphoSite

PicTar

Protein Interaction database (IntAct)

STRING

TarBase

TargetScan

TRANSFAC

Universal Protein Binding Microarray Resource for Oligonucleotide Binding Evaluation (UniPROBE)

Glossary

Node (or vertex)

A system component that, by interacting with other components, forms a network. In biological networks, nodes can denote proteins, genes, metabolites, RNA molecules or even diseases and phenotypes.

Link (or edge)

A link represents the interactions between the nodes of a network. In biological systems, interactions can correspond to protein–protein binding interactions or metabolic coupling, or they may represent connections between diseases based on a common genetic origin or shared phenotypic characteristics.

Degree

The degree of a node is the number of links that connect to it. The degree of a protein could represent the number of proteins with which it interacts with, whereas the degree of a disease may represent the number of other diseases that are associated with the same gene or that have a common phenotype.

Module (or community)

A dense subgraph on the network that often represents a set of nodes that have a joint role. In biology, a module could correspond to a group of molecules that interact with each other to achieve some common function.

Comorbidity

Comorbidity implies the presence of one or more disorders (or diseases) in addition to a primary disease or disorder that the patient has. Comorbidity may hide causal effects, when one disease enhances the emergence of some other disease, such as the much-studied comorbidity between diabetes and obesity.

Edgetic

Edgetic perturbations denote mutations that do not result in the complete loss of a gene product, but affect one or several interactions (and thus functions) of a protein. From a network perspective, an edgetic perturbation removes one or several links, but leaves the other links and the node unaffected.

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Barabási, AL., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat Rev Genet 12, 56–68 (2011). https://doi.org/10.1038/nrg2918

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