Influenza virus drug resistance: A time-sampled population genetics perspective

Foll, Matthieu; Poh, Yu-Ping; Renzette, Nicholas; Ferrer-Admetlla, Anna; Bank, Claudia; Shim, Hyunjin; Malaspinas, Anna-Sapfo; Ewing, Gregory; Liu, Ping; Wegmann, Daniel; Caffrey, Daniel R.; Zeldovich, Konstantin B.; Bolon, Daniel N.; Wang, Jennifer P.; Kowalik, Timothy F.; Schiffer, Celia A.; Finberg, Robert W.; Jensen, Jeffrey D.

doi:10.1371/journal.pgen.1004185

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Journal article

Influenza virus drug resistance: A time-sampled population genetics perspective

Foll, Matthieu School of Life Sciences, EPF), Lausanne, Switzerland - Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
Poh, Yu-Ping Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland - Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, USA
Renzette, Nicholas Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, Massachusetts, USA
Ferrer-Admetlla, Anna School of Life Sciences, EPF), Lausanne, Switzerland - Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland - Department of Biology, Biochemistry Unit, University of Fribourg, Switzerland
Bank, Claudia School of Life Sciences, EPF), Lausanne, Switzerland - Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
Shim, Hyunjin School of Life Sciences, EPF), Lausanne, Switzerland - Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
Malaspinas, Anna-Sapfo Center for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Denmark
Ewing, Gregory School of Life Sciences, EPF), Lausanne, Switzerland - Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
Liu, Ping Department of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA
Wegmann, Daniel Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland - Department of Biology, Biochemistry Unit, University of Fribourg, Switzerland
Caffrey, Daniel R. Department of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA
Zeldovich, Konstantin B. Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, USA
Bolon, Daniel N. Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts, USA
Wang, Jennifer P. Department of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA
Kowalik, Timothy F. Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, Massachusetts, USA
Schiffer, Celia A. Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts, USA
Finberg, Robert W. Department of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA
Jensen, Jeffrey D. School of Life Sciences, EPF), Lausanne, Switzerland - Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland

27.02.2014

Published in:

PLoS Genet. - 2014, vol. 10, no. 2, p. e1004185

English 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.

Faculty

Faculté des sciences et de médecine

Department

Département de Biologie

Language