Influenza Virus Drug Resistance: A Time-Sampled Population Genetics Perspective

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

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bayes Theorem
  • Drug Resistance, Viral / genetics*
  • Genetic Drift
  • Genetics, Population*
  • Humans
  • Influenza A Virus, H1N1 Subtype / drug effects
  • Influenza A Virus, H1N1 Subtype / genetics*
  • Influenza, Human / genetics
  • Influenza, Human / virology
  • Mutation
  • Oseltamivir / pharmacology
  • Selection, Genetic*

Substances

  • Oseltamivir

Grant support

The authors wish to acknowledge the support of DARPA (Prophecy Program, Defense Advanced Research Agency (http://www.darpa.mil/), Defense Sciences Office (DSO), Contract No. HR0011-11-C-0095) and the contributions of all the members of the ALiVE (Algorithms to Limit Viral Epidemics) working group. Additional funding came from grants from the Swiss National Science Foundation (http://www.snf.ch/E/Pages/default.aspx), and a European Research Council Starting Grant (ERC; http://erc.europa.eu/) to JDJ. ASM was funded by an Early Postdoc Mobility fellowship from the Swiss National Science Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.