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  • Review Article
  • Published:

Identifying genetic markers of adaptation for surveillance of viral host jumps

Nature Reviews Microbiology volume 8, pages 802–813 (2010)Cite this article

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This article has been updated

Key Points

  • Viral host jumps can lead to major public health threats. The most recent pandemics were caused by viruses that were transmitted from animal reservoirs to humans, such as influenza A viruses and severe acute respiratory syndrome-coronavirus. Adaptation of the virus to the new host is often cited as the cause of such emergence.

  • Distinguishing the genetic changes that are due to adaptation from those that are due to random events is hard in any biological context; virus host jumps are no exception. We present four different mechanisms by which viruses may emerge in a new host. Although all four mechanisms could produce the same genetic pattern in new hosts, only two are due to adaptation. We illustrate which data need to be collected to distinguish between the four mechanisms.

  • Future risk of viral host jumps to humans could be assessed by genetic surveillance of viruses in reservoir hosts, but only when genetic adaptation is required for a host jump and when precursors of this adaptation can be detected.

  • Bioinformatic analyses of surveillance data are key stepping stones for identifying putative genetic markers of viral adaptation from enormous pools of genetic data. Confirmation of which of these putative markers are due to adaptation requires experimental validation by using reverse genetics and host models from reservoir and new host species, and corroborating results with epidemiological and ecological data.

  • Our review of the current literature on four well-studied viral host jumps shows that research on host-jump processes unfolds in four broad stages: virus sample collection and genetic analysis; experiments in vitro or in cell culture; in vivo experiments in model hosts; and in vivo experiments in natural hosts. We evaluate the issues in using these types of data for validating adaptive hypotheses, and identify opportunities to collect further data that would enable better discrimination among emergence mechanisms.

  • A detailed understanding of viral host jumps and the assessment of future risk requires multidisciplinary research efforts with input from field ecologists, microbiologists, immunologists, epidemiologists, bioinformaticians and evolutionary biologists, and the use of use of diverse approaches (field sampling, laboratory experiments, data analysis and mathematical modelling).

Abstract

Adaptation is often thought to affect the likelihood that a virus will be able to successfully emerge in a new host species. If so, surveillance for genetic markers of adaptation could help to predict the risk of disease emergence. However, adaptation is difficult to distinguish conclusively from the other processes that generate genetic change. In this Review we survey the research on the host jumps of influenza A, severe acute respiratory syndrome-coronavirus, canine parvovirus and Venezuelan equine encephalitis virus to illustrate the insights that can arise from combining genetic surveillance with microbiological experimentation in the context of epidemiological data. We argue that using a multidisciplinary approach for surveillance will provide a better understanding of when adaptations are required for host jumps and thus when predictive genetic markers may be present.

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Figure 1: Mechanisms of viral emergence in new hosts.
Figure 2: Origin of strains studied in the surveyed literature.
Figure 3: Types of relevant data collected by surveyed articles.

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Change history

  • 12 October 2010

    In the sentence "For example, H5N influenza transmission from avian species to humans has caused 498 cases (294 deaths) in 15 countries worldwide since 2003, but no sustained chains of human–human transmission have been observed66" H5N was corrected to H5N1.

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Acknowledgements

This work was supported by the Research and Policy for Infectious Diseases Dynamics (RAPIDD) programme of the Science and Technology Directorate, the Department of Homeland Security and Fogarty International Center and the National Institutes of Health. K.M.P. was also supported by National Science Foundation grant 0742373; S.L. was supported by National Science Foundation grant DEB-0520468 to P. Hudson; J.L.-S. was supported by National Science Foundation grant EF-0928690 and the De Logi Chair in Biological Sciences.

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Author notes

  1. Sandra Lass and Juliet R. C. Pulliam: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Physics, Pennsylvania State University, University Park, 16802, Pennsylvania, USA

    Kim M. Pepin

  2. Kirchenhölzle 18, 79104, Freiburg, Germany

    Sandra Lass

  3. Fogarty International Center, National Institutes of Health, Bethesda, 20892, Maryland, USA

    Juliet R. C. Pulliam, Andrew F. Read & James O. Lloyd-Smith

  4. Department of Ecology and Evolutionary Biology, University of California Los Angeles, 90095, California, USA

    Juliet R. C. Pulliam & James O. Lloyd-Smith

  5. Departments of Biology and Entomology, Centre for Infectious Disease Dynamics, Pennsylvania State University, University Park, 16827, Pennsylvania, USA

    Andrew F. Read

Authors
  1. Kim M. Pepin
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  2. Sandra Lass
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  3. Juliet R. C. Pulliam
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Correspondence to Kim M. Pepin.

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Supplementary information

Supplementary information S1 (table)

Articles used in analysis (XLS 112 kb)

Supplementary information S2 (box)

Methods used to analyse articles (PDF 394 kb)

Supplementary information S3 (figure)

Research effort on viral host jumps. (PDF 279 kb)

Supplementary information S4 (figure)

Inclusion of experiment in studies with relevant data. (PDF 279 kb)

Supplementary information S5 (figure)

Types of data used for analyses of evolutionary processes. (PDF 325 kb)

Supplementary information S6 (figure)

Replication and experimental evolution in experimental studies. (PDF 288 kb)

Supplementary information S7 (figure)

Model systems in experimental studies. (PDF 334 kb)

Glossary

Reservoir

Host population in which a virus is maintained long term.

Host jump

The complete process of a virus transmitting between a reservoir host and a new host followed by sustained transmission among individuals of the new host population.

Emergence

The appearance of new viruses in a population, particularly the appearance and sustained transmission of viruses in a new host species (as opposed to new strains in host populations in which the virus is endemic).

Ecological change

A shift in frequency, nature or outcome of host species contact caused by factors such as demography, migration, invasion or environmental change.

Adaptation

Genetic change driven by natural selection.

Genetic markers of adaptation

Changes in the viral genome that are indicative of adaptation to a new host species. These may include point mutation, insertion, deletion, recombination, reassortment or any combination of these.

Viral fitness

Genetic contribution to future generations of the entire virus population circulating in an entire population of hosts.

Cross-species transmission

Transmission of infection between different host species. This does not include subsequent transmission among hosts in the new host population. Cross-species transmission is a necessary precondition for a host jump but is not sufficient to be called a host jump.

Convergent evolution

Independent occurrence of the same trait in multiple lineages (that is, same genetic change evolves as a result of a similar selective pressure).

Adaptive fine-tuning

Fixation of mutations that increase fitness in situations in which fitness is already high enough for sustained transmission (that is, a change from adapted to more adapted).

Contact-tracing data

Determination of the occurrence and nature of contacts between individual hosts, often used in an attempt to reconstruct the host–host transmission chain for a set of infections.

Founder effects

A shift in genetic composition owing to sampling effects.

Neutral evolution

Fixation of mutations that do not affect fitness.

Positive selection

The force that causes an increase in the frequency of fitness-enhancing mutations.

Selective pressures

Environmental conditions, either biotic or abiotic, that decrease genetic variation by excluding deleterious mutations or increasing the frequency of beneficial mutations. In viruses, these pressures operate at two main scales: within hosts (through cell receptor structure and host immune response) and between hosts (through host contact rates and population heterogeneity in immunity).

Adaptive constraints

Forces that restrict upward movement between genetic coordinates on the fitness landscape.

Trait performance

Functionality of individual virus life-history traits such as receptor binding, replication rate and virion packaging rate.

Within-host fitness

Genetic contribution to future generations of the virus population within a host.

Basic reproductive number

The average number of secondary infections caused by an infected individual in a population of completely susceptible hosts.

Stuttering transmission

A short-lived chain of transmission that can arise when R0 < 1 but not ≈ 0 (that is, for R0 > 0.5 it is likely that at least one transmission event will occur). The total number of cases is determined by chance, and extinction of the outbreak is certain if the virus does not adapt, but the additional exposure to new hosts can facilitate adaptive emergence if the appropriate mutations arise.

Longitudinal sampling

Sampling over time to obtain a time series of viral strains.

Mutation–selection balance

Steady-state frequency of deleterious genotypes determined by the balance between their continual creation by mutation and their exclusion by selection.

Experimental evolution

Measuring evolutionary change in real-time by applying evolutionary forces experimentally and observing the outcome.

Evolutionary process

A factor that drives genetic change, including genetic drift, mutation, gene flow and natural selection.

Fitness landscape

The relationship of genotype and reproductive success. Often depicted in three dimensional space, in which the X and Y axes are the coordinates that describe all possible genetic combinations in the genome and the height of the Z axis gives the reproductive success for a given set of genetic coordinates.

Experimental transmission

Transmission of a pathogen by placing infected and uninfected animals in close proximity; this is quasi-natural transmission as it excludes the role of contact probability in transmission.

Parallel evolution

Independent occurrence of the same trait in lineages that arose from a common ancestor (that is, same genetic change evolves independently from same genetic starting point as a result of a similar selective pressure). This is a special case of convergent evolution.

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Pepin, K., Lass, S., Pulliam, J. et al. Identifying genetic markers of adaptation for surveillance of viral host jumps. Nat Rev Microbiol 8, 802–813 (2010). https://doi.org/10.1038/nrmicro2440

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