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Comparative Study
. 2010 Jan 26;107 Suppl 1(Suppl 1):1742-6.
doi: 10.1073/pnas.0906193106. Epub 2009 Oct 26.

Evolution in health and medicine Sackler colloquium: The comparative genomics of viral emergence

Edward C Holmes  1
Affiliations
Comparative Study

Evolution in health and medicine Sackler colloquium: The comparative genomics of viral emergence

Edward C Holmes. Proc Natl Acad Sci U S A. .

Abstract

RNA viruses are the main agents of emerging and re-emerging diseases. It is therefore important to reveal the evolutionary processes that underpin their ability to jump species boundaries and establish themselves in new hosts. Here, I discuss how comparative genomics can contribute to this endeavor. Arguably the most important evolutionary process in RNA virus evolution, abundant mutation, may even open up avenues for their control through "lethal mutagenesis." Despite this remarkable mutational power, adaptation to diverse host species remains a major adaptive challenge, such that the most common outcome of host jumps are short-term "spillover" infections. A powerful case study of the utility of genomic approaches to studies of viral evolution and emergence is provided by influenza virus and brought into sharp focus by the ongoing epidemic of swine-origin H1N1 influenza A virus (A/H1N1pdm). Research here reveals a marked lack of surveillance of influenza viruses in pigs, coupled with the possibility of cryptic transmission before the first reported human cases, such that the exact genesis of A/H1N1pdm (where, when, how) is uncertain.

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Conflict of interest statement

The author declares no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Relationship between mutation rate per nucleotide site and genome size for different genomic systems including viruses. [Reproduced with permission from ref. (Copyright 2009, AAAS).]
Fig. 2.
Fig. 2.
Maximum clade credibility tree of a sample of 147 complete genomes (13,130 nt) of A/H1N1pdm showing the spatial diffusion of this virus. The tree was estimated by using the Bayesian Markov Chain Monte Carlo method available in the BEAST package (67). The data were analyzed by assuming a relaxed (uncorrelated lognormal) molecular clock under the HKY85 model of nucleotide substitution with a different substitution rate for each codon position and a Bayesian skyline coalescent prior. The chain was run for 200 million generations (with a 10% burn-in). Branches are color-coded by place of origin. The bar at the root node represents the 95% highest probability density for the age of that node (x axis). In all cases tip times reflect the time of sampling. Although there is a heavy sampling bias toward American strains, there have clearly been multiple exportation events to localities such as Asia and Europe. Data kindly provided by Andrew Rambaut (University of Edinburgh, Edinburgh, Scotland, UK).
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