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Network biology concepts in complex disease comorbidities

Key Points

  • Disease progression patterns of patients with more than one disease have recently received increasing attention, as disease co-occurrences can help to elucidate the interaction between the molecular level and external exposures such as diet, lifestyle and patient care. Additionally, they can provide information about the underlying network biology of shared and multifunctional genes and pathways.

  • The concepts of pleiotropy, robustness and rewiring are central to the investigation of comorbidity and network dynamics and should be viewed together, as they all relate to the disease trajectory of an individual.

  • The temporal disease progression of the non-idealized patient can be described in terms of trajectories in a multimorbidity space, in which each dimension corresponds to a quantitative phenotype.

  • Dynamic network models can be constructed to study complex disease progression and are increasingly becoming feasible with the advances in high-throughput omics, single-cell technologies and sophisticated analysis tools.

  • The utility of network concepts has been hampered by confusion and inconsistent terminology. This can be mediated by the clear delineation of the concepts, especially in regards to context, including the clear specification of timeframe, phenotype and organizational level.

  • The increased collection of health transaction data combined with advances in omics technologies require a further concerted view on how robustness, rewiring and pleiotropy come together in frameworks that can rationalize comorbidities and their relationships at the molecular level, knowledge that can also facilitate drug repositioning and the development of targeted therapeutic strategies.

Abstract

The co-occurrence of diseases can inform the underlying network biology of shared and multifunctional genes and pathways. In addition, comorbidities help to elucidate the effects of external exposures, such as diet, lifestyle and patient care. With worldwide health transaction data now often being collected electronically, disease co-occurrences are starting to be quantitatively characterized. Linking network dynamics to the real-life, non-ideal patient in whom diseases co-occur and interact provides a valuable basis for generating hypotheses on molecular disease mechanisms, and provides knowledge that can facilitate drug repurposing and the development of targeted therapeutic strategies.

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Figure 1: The pervasiveness of dynamic concepts.
Figure 2: Multimorbidity space and dynamic disease progression.
Figure 3: Five models of multifunctionality and pleiotropy.
Figure 4: Rewiring in differential versus dynamic networks.

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Acknowledgements

The authors thank L. J. Jensen, C. Workman and H. V. Cook for comments on the manuscript, and D. Westergaard, J. M. Gonzalez-Izarzugaza and K. Banasik for useful discussions and suggestions. The work was supported by the Novo Nordisk Foundation (grant agreement NNF14CC0001), as well as the Innovation Fund Denmark.

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FURTHER INFORMATION

BUHMBOX

Comorbidity-View

GIANT

PowerPoint slides

Glossary

Pleiotropy

The property of a genetic locus that affects more than one trait.

Robustness

The property that allows a system to maintain its functions against internal and external perturbations.

Rewiring

Restructuring of interactions between biological components due to conditional changes.

Complex disease

A disease that is a result of complex interactions between genetics and environment that is hard to explain by a few factors.

Multifunctionalities

Properties of a biological component that have multiple distinct roles.

Comorbidities

Diseases that co-occur on top of a primary disease of interest in an individual.

Genetic interaction networks

Networks in which nodes are genes and edges are their epistatic interactions.

Physical interaction networks

Networks in which nodes physically interact. In biology interactions may be between and among, for example, proteins, DNA and RNA.

Differential networks

Analytical approaches to identify edge changes between two static network states.

Hub

A hub node in a network has a high degree of edges, meaning that it interacts with many other nodes in the network.

Organizational levels

Levels in the hierarchy of biological structures and systems such as protein, cell, tissue, organ or organism.

Dynamic network

A network that continuously changes topology over time.

Multimorbidity

The coexistence of two or more diseases in the same individual without disease prioritization.

Health transaction data

Data describing patients' contacts with the health care system. Data accumulates in electronic patient records and registries.

Inversely comorbid

Diseases that co-occur less often in an individual than expected given their individual frequencies in the population.

Drug repurposing

The application of a known drug to new indications. Synonymous with the term drug repositioning.

Scale-free

A network structure that has a degree distribution following a power law.

Bow tie

A multi-layered network structure where intermediate layers have far fewer components than input and output layers.

Modularity

A network structure with dense connections between clusters of nodes and sparse connections between nodes in different clusters.

Homeostasis

The ability to sustain various physiological parameters in a steady state.

Plasticity

Variation of a phenotype as a response to a given environmental exposure.

Epistasis

A phenomenon in which the function of one gene affects the function of another gene in a non-additive manner.

Penetrance

From a genome-wide association study perspective, penetrance describes the proportion of individuals for which a genetic variant results in a changed phenotype.

Network topology

The layout of nodes and edges in a network.

Microsatellites

Polymorphic DNA loci containing repeated nucleotide sequences of typically 2–7 nucleotides per unit.

Cryptic variation

Genetic variation that has little or no effect on phenotypic variation under normal conditions, but can generate heritable phenotypic variation when circumstances change.

Edge

An edge represents the interaction between nodes in a network. In biological systems an edge can represent a physical interaction between two proteins or the co-occurrence of two diseases.

Wearables

Personal portable devices that monitor the state of an individual.

Nodes

In biological networks nodes are connection points, for example, of proteins, genes or diseases. They may or may not directly interact.

Bottleneck

A bottleneck node in a network has a high degree of intersections (high betweenness), meaning that it will often be a linker between different subnetworks.

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Hu, J., Thomas, C. & Brunak, S. Network biology concepts in complex disease comorbidities. Nat Rev Genet 17, 615–629 (2016). https://doi.org/10.1038/nrg.2016.87

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