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A systems approach to infectious disease

Abstract

Ongoing social, political and ecological changes in the 21st century have placed more people at risk of life-threatening acute and chronic infections than ever before. The development of new diagnostic, prophylactic, therapeutic and curative strategies is critical to address this burden but is predicated on a detailed understanding of the immensely complex relationship between pathogens and their hosts. Traditional, reductionist approaches to investigate this dynamic often lack the scale and/or scope to faithfully model the dual and co-dependent nature of this relationship, limiting the success of translational efforts. With recent advances in large-scale, quantitative omics methods as well as in integrative analytical strategies, systems biology approaches for the study of infectious disease are quickly forming a new paradigm for how we understand and model host–pathogen relationships for translational applications. Here, we delineate a framework for a systems biology approach to infectious disease in three parts: discovery — the design, collection and analysis of omics data; representation — the iterative modelling, integration and visualization of complex data sets; and application — the interpretation and hypothesis-based inquiry towards translational outcomes.

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Fig. 1: Interdependence of host and pathogen.
Fig. 2: A systems biology framework.
Fig. 3: Systems biology technologies for infectious disease research.
Fig. 4: Assembly and representation of a network model.

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Acknowledgements

J.F.H. is supported by amfAR grant 109504-61-RKRL with funds raised by generationCURE, the Gilead Sciences Research Scholars Program in HIV, US National Institutes of Health (NIH) grant K22 AI136691, a supplement from the NIH-supported Third Coast Center for AIDS Research (P30 AI117943) and a supplement from the NIH-sponsored HARC Center (P50 AI150476). R.M.K. is supported by the NIH-sponsored HARC Center (P50 AI150476) and the NIH-sponsored Host-Pathogen Mapping Initiative (U19 AI135990). R.H. is supported by the US Department of Defense Advanced Research Projects Agency (HR0011-19-2-0020). N.J.K. is supported by the NIH-sponsored HARC Center (P50 AI150476), the NIH-sponsored Host-Pathogen Mapping Initiative (U19 AI135990), the NIH-sponsored FluOMICs consortium (U19 AI135972) and NIH grant P01 AI063302.

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M.E., J.F.H, R.M.K. and R.H. researched the literature. M.E., J.F.H, R.M.K., R.H. and N.J.K. wrote the article, provided substantial contributions to discussions of the content and reviewed and/or edited the manuscript before submission.

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Correspondence to Judd F. Hultquist or Nevan J. Krogan.

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Nature Reviews Genetics thanks T. Baumert and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Glossary

Primary model systems

Types of host models that rely on cells taken directly from living tissue (such as from biopsy material or blood) for growth and maintenance ex vivo.

Laboratory-adapted strain

A genetically distinct strain of a pathogen that has been selected for enhanced fitness ex vivo and for use in laboratory experiments even though it is not found as a major strain in the natural world.

Clinical isolates

Genetic strains of pathogens isolated directly from patients or clinical samples.

Technical replicates

Repeated experiments analysing the same sample with the same instrumentation to measure the variability inherent in the testing protocol.

Biological replicates

Repeated experiments analysing different samples that represent the same thing (such as samples collected from different patients with the same disease outcome) to determine the variability in the sample pools.

Confounding effects

The influence of one or more unmonitored variables on a system’s components or the relationships between those components that can alter experimental interpretation.

Saturating mutagenesis

A genetic screening technique wherein a codon or set of codons is randomized to produce all possible amino acids at a position or positions.

Host–pathogen co-evolution

Iterative rounds of adaptation and counter-adaptation between a pathogen and its host over evolutionary history as a result of the ability of pathogens to elicit selective pressure on their host populations and vice versa.

Transposon mutagenesis

A method for the random disruption of gene function by the untargeted insertion of transposable retroelements into a genome.

Metadata

Information that describes a set of data.

Multiplicity of infection

The ratio of infectious agents (such as virions or bacteria) to infection targets (such as cells).

Nodes

A connection point in a network representing a component of the system.

Edges

A connection between nodes in a network representing a relationship between two components.

Enrichment analysis

An approach for identifying over-represented classifications of components by comparing the frequency of a given annotation in a data set with a predefined reference list.

k-means clustering

A method of data clustering that aims to partition a set of components into a total of k clusters, wherein each component belongs to the cluster with the nearest mean value.

Principal component analysis

A statistical procedure often used in the development of predictive models, which describes a data set as a series of uncorrelated variables called ‘principal components’ that account for sources of variability.

Support vector machines

A machine learning method related to regression analysis that seeks to identify the separation boundary between clusters of data given predefined clusters in a prelabelled set of input data.

Neural networks

A machine learning method that seeks to cluster and classify data on the basis of similarities and differences extracted from a prelabelled set of input data.

Random forests

A machine learning algorithm that seeks to cluster and classify data on the basis of the ensemble output of a series of decision trees formulated from a prelabelled set of input data.

Mutual information

A measurement of dependency between two variables that is used in machine learning to determine how much can be assumed about one component on the basis of the observed behaviour of another.

Phenotypic selection

Isolation of a given cell population based on an observed trait or characteristic (such as fluorescence or resistance to a toxic compound).

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Eckhardt, M., Hultquist, J.F., Kaake, R.M. et al. A systems approach to infectious disease. Nat Rev Genet 21, 339–354 (2020). https://doi.org/10.1038/s41576-020-0212-5

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