Giuseppe
Emeritus
[Source: Proc Natl Acad Sci USA, full text: (LINK). Abstract, edited.]
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Viral reassortment as an information exchange between viral segments
Benjamin D. Greenbaum<SUP>a</SUP>,<SUP>1</SUP>, Olive T. W. Li<SUP>b</SUP>, Leo L. M. Poon<SUP>b</SUP>, Arnold J. Levine<SUP>a</SUP>, and Raul Rabadan<SUP>c</SUP>,<SUP>d</SUP>
<SUP></SUP>
Author Affiliations: <SUP>a</SUP>The Simons Center for Systems Biology, Institute for Advanced Study, Princeton, NJ 08540; <SUP>b</SUP>State Key Laboratory of Emerging Infectious Diseases and Centre of Influenza Research, School of Public Health, University of Hong Kong, Hong Kong; and <SUP>c</SUP>Center for Computational Biology and Bioinformatics and <SUP>d</SUP>Department of Biomedical Informatics, Columbia University College of Physicians and Surgeons, New York, NY 10032
Edited by Robert A. Lamb, Northwestern University, Evanston, IL, and approved January 10, 2012 (received for review August 17, 2011)
Abstract
Viruses have an extraordinary ability to diversify and evolve. For segmented viruses, reassortment can introduce drastic genomic and phenotypic changes by allowing a direct exchange of genetic material between coinfecting strains. For instance, multiple influenza pandemics were caused by reassortments of viruses typically found in separate hosts. What is unclear, however, are the underlying mechanisms driving these events and the level of intrinsic bias in the diversity of strains that emerge from coinfection. To address this problem, previous experiments looked for correlations between segments of strains that coinfect cells in vitro. Here, we present an information theory approach as the natural mathematical framework for this question. We study, for influenza and other segmented viruses, the extent to which a virus?s segments can communicate strain information across an infection and among one another. Our approach goes beyond previous association studies and quantifies how much the diversity of emerging strains is altered by patterns in reassortment, whether biases are consistent across multiple strains and cell types, and if significant information is shared among more than two segments. We apply our approach to a new experiment that examines reassortment patterns between the 2009 H1N1 pandemic and seasonal H1N1 strains, contextualizing its segmental information sharing by comparison with previously reported strain reassortments. We find evolutionary patterns across classes of experiments and previously unobserved higher-level structures. Finally, we show how this approach can be combined with virulence potentials to assess pandemic threats.
Footnotes
Benjamin D. Greenbaum<SUP>a</SUP>,<SUP>1</SUP>, Olive T. W. Li<SUP>b</SUP>, Leo L. M. Poon<SUP>b</SUP>, Arnold J. Levine<SUP>a</SUP>, and Raul Rabadan<SUP>c</SUP>,<SUP>d</SUP>
<SUP></SUP>
Author Affiliations: <SUP>a</SUP>The Simons Center for Systems Biology, Institute for Advanced Study, Princeton, NJ 08540; <SUP>b</SUP>State Key Laboratory of Emerging Infectious Diseases and Centre of Influenza Research, School of Public Health, University of Hong Kong, Hong Kong; and <SUP>c</SUP>Center for Computational Biology and Bioinformatics and <SUP>d</SUP>Department of Biomedical Informatics, Columbia University College of Physicians and Surgeons, New York, NY 10032
Edited by Robert A. Lamb, Northwestern University, Evanston, IL, and approved January 10, 2012 (received for review August 17, 2011)
Abstract
Viruses have an extraordinary ability to diversify and evolve. For segmented viruses, reassortment can introduce drastic genomic and phenotypic changes by allowing a direct exchange of genetic material between coinfecting strains. For instance, multiple influenza pandemics were caused by reassortments of viruses typically found in separate hosts. What is unclear, however, are the underlying mechanisms driving these events and the level of intrinsic bias in the diversity of strains that emerge from coinfection. To address this problem, previous experiments looked for correlations between segments of strains that coinfect cells in vitro. Here, we present an information theory approach as the natural mathematical framework for this question. We study, for influenza and other segmented viruses, the extent to which a virus?s segments can communicate strain information across an infection and among one another. Our approach goes beyond previous association studies and quantifies how much the diversity of emerging strains is altered by patterns in reassortment, whether biases are consistent across multiple strains and cell types, and if significant information is shared among more than two segments. We apply our approach to a new experiment that examines reassortment patterns between the 2009 H1N1 pandemic and seasonal H1N1 strains, contextualizing its segmental information sharing by comparison with previously reported strain reassortments. We find evolutionary patterns across classes of experiments and previously unobserved higher-level structures. Finally, we show how this approach can be combined with virulence potentials to assess pandemic threats.
Footnotes
<SUP>1</SUP>To whom correspondence should be addressed. E-mail: beng@ias.edu.
Author contributions: B.D.G., O.T.W.L., L.L.P., A.J.L., and R.R. designed research; B.D.G. and O.T.W.L. performed research; B.D.G., O.T.W.L., L.L.P., A.J.L., and R.R. analyzed data; and B.D.G., O.T.W.L., L.L.P., A.J.L., and R.R. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1113300109/-/DCSupplemental.
Freely available online through the PNAS open access option.
-Author contributions: B.D.G., O.T.W.L., L.L.P., A.J.L., and R.R. designed research; B.D.G. and O.T.W.L. performed research; B.D.G., O.T.W.L., L.L.P., A.J.L., and R.R. analyzed data; and B.D.G., O.T.W.L., L.L.P., A.J.L., and R.R. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1113300109/-/DCSupplemental.
Freely available online through the PNAS open access option.
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