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PLoS Genet. 2014 Feb 27;10(2):e1004185. doi: 10.1371/journal.pgen.1004185. eCollection 2014 Feb.

Influenza virus drug resistance: a time-sampled population genetics perspective.

Author information

1
School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland ; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
2
Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland ; Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America.
3
Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America.
4
School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland ; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland ; Department of Biology and Biochemistry, University of Fribourg, Fribourg, Switzerland.
5
Center for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Denmark.
6
Department of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America.
7
Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland ; Department of Biology and Biochemistry, University of Fribourg, Fribourg, Switzerland.
8
Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America.
9
Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America.

Abstract

The challenge of distinguishing genetic drift from selection remains a central focus of population genetics. Time-sampled data may provide a powerful tool for distinguishing these processes, and we here propose approximate Bayesian, maximum likelihood, and analytical methods for the inference of demography and selection from time course data. Utilizing these novel statistical and computational tools, we evaluate whole-genome datasets of an influenza A H1N1 strain in the presence and absence of oseltamivir (an inhibitor of neuraminidase) collected at thirteen time points. Results reveal a striking consistency amongst the three estimation procedures developed, showing strongly increased selection pressure in the presence of drug treatment. Importantly, these approaches re-identify the known oseltamivir resistance site, successfully validating the approaches used. Enticingly, a number of previously unknown variants have also been identified as being positively selected. Results are interpreted in the light of Fisher's Geometric Model, allowing for a quantification of the increased distance to optimum exerted by the presence of drug, and theoretical predictions regarding the distribution of beneficial fitness effects of contending mutations are empirically tested. Further, given the fit to expectations of the Geometric Model, results suggest the ability to predict certain aspects of viral evolution in response to changing host environments and novel selective pressures.

PMID:
24586206
PMCID:
PMC3937227
DOI:
10.1371/journal.pgen.1004185
[Indexed for MEDLINE]
Free PMC Article

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