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Systems virology: host-directed approaches to viral pathogenesis and drug targeting

Nature Reviews Microbiology volume 11, pages 455–466 (2013)Cite this article

Subjects

Key Points

  • Systems biology approaches are required to advance our understanding of virus–host interactions, how these interactions cause disease and, ultimately, how to improve diagnostics, therapeutics and vaccines.

  • Over the past decade, the field of systems virology has evolved from using first-generation microarrays to the integration of multidimensional data sets. This has resulted in significant findings, including the identification of gene expression signatures that are predictive of viral pathogenesis and vaccine efficacy, insights into how viruses disrupt cellular metabolism, and the mapping of virus–host interactomes.

  • To fulfil its initial promise of revolutionizing our understanding of virus–host interactions, the field of systems virology must move beyond just the listing of molecules that are differentially expressed following viral infection; it must now look to define the relationships between key host molecules and their interactions with viral components.

  • Several key computational challenges must be addressed in order to move into this new phase of systems virology, including consideration of nonlinear relationships such as the dynamics of the system, the integration of multidimensional data sets and the identification of causal relationships.

  • Virologists, computer scientists and mathematicians must combine their skills and expertise in applying systems approaches to untangle the complex question of how viruses kill.

Abstract

High-throughput molecular profiling and computational biology are changing the face of virology, providing a new appreciation of the importance of the host in viral pathogenesis and offering unprecedented opportunities for better diagnostics, therapeutics and vaccines. Here, we provide a snapshot of the evolution of systems virology, from global gene expression profiling and signatures of disease outcome, to geometry-based computational methods that promise to yield novel therapeutic targets, personalized medicine and a deeper understanding of how viruses cause disease. To realize these goals, pipettes and Petri dishes need to join forces with the powers of mathematics and computational biology.

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Figure 1: The systems virology paradigm.
Figure 2: A 57-gene analogue signature predicts respiratory virus pathogenicity.
Figure 3: Co-regulation networks.

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Acknowledgements

The authors thank L. Josset for generating the networks in figure 3, and M. Heise and M. Ferris for providing the data used in box 3. Research in the author's laboratory is supported by Public Health Service grants R2400011172, R2400011157, P30DA015625, P51RR00166 and U54AI081680, and by federal funds from the US National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under contract HHSN272200800060C.

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Authors and Affiliations

  1. Department of Microbiology and Washington National Primate Research Center, University of Washington, Box 358070, Seattle, 98195, Washington, USA

    G. Lynn Law, Marcus J. Korth, Arndt G. Benecke & Michael G. Katze

  2. Université Pierre et Marie Curie, Centre National de la Recherche Scientifique UMR7224, 7–9 Quai Saint Bernard, Bat. A, B 75005, Paris, France

    Arndt G. Benecke

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  1. G. Lynn Law
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Glossary

Complement system

Blood proteins that react with one another in a cascade to aid the ability of phagocytic cells to eliminate microorganisms. Complement proteins also have a role in the development of inflammation.

Small nucleolar RNAs

(snoRNAs). RNAs that guide the modification (for example, methylation or pseudouridylation) of other RNAs, particularly ribosomal RNAs.

PIWI-interacting RNAs

(piRNAs). Small RNAs that are thought to be involved in gene silencing through the formation of ribonucleoprotein complexes with PIWI proteins.

RIP–seq

Immunoprecipitation of RNA-binding proteins followed by high-throughput sequencing of the bound RNA.

CLIP–seq

(Crosslinking immunoprecipitation followed by high-throughput sequencing). A screening method used to identify RNA sequences that interact with either RNA-binding proteins or other RNAs.

Metabolic flux profiling

A measurement approach that uses liquid chromatography–tandem mass spectrometry to quantify the rate of conversion of biochemical molecules in a metabolic network after perturbing the system. Systems-level metabolic flux profiling is a high-throughput approach to quantifying changes in metabolic activity.

Short hairpin RNA

(shRNA). A type of RNA that forms a tight hairpin which has the ability to silence gene expression through RNAi.

Unfolded-protein response

A cellular stress response to the accumulation of unfolded proteins in the ER. The response is characterized by a signal transduction pathway that aims to restore homeostasis by limiting protein biosynthesis and increasing the abundance of molecular chaperones involved in protein folding.

Expression quantitative trait loci

(eQTLs). Genomic loci, as identified by gene expression profiling, that regulate mRNA expression. eQTLs are mapped by computationally connecting DNA sequence variation with variation in gene expression, providing information on how host genetics affects the function of molecular networks.

Structural equation modelling

A multivariate analysis technique for testing and estimating causal relationships among variables.

Betweenness centrality

A measure of the location of a gene in a network. Genes with high betweenness centrality, referred to as bottleneck genes, are located between and therefore connect different portions of the network (that is, different subnetworks).

Epistasis

The phenomenon in which the effects of one gene are modified by one or more other genes.

Network topology

The arrangement and connections of the various components of a network.

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Law, G., Korth, M., Benecke, A. et al. Systems virology: host-directed approaches to viral pathogenesis and drug targeting. Nat Rev Microbiol 11, 455–466 (2013). https://doi.org/10.1038/nrmicro3036

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