title: Non-random reassortment in human influenza A viruses.
abstract: BACKGROUND
  The influenza A virus has two basic modes of evolution. Because of a high error rate in the process of replication by RNA polymerase, the viral genome drifts via accumulated mutations. The second mode of evolution is termed a shift, which results from the reassortment of the eight segments of this virus. When two different influenza viruses co-infect the same host cell, new virions can be released that contain segments from both parental strains. This type of shift has been the source of at least two of the influenza pandemics in the 20th century (H2N2 in 1957 and H3N2 in 1968).
  
  
  OBJECTIVE
  The methods to measure these genetic shifts have not yet provided a quantitative answer to questions such as: what is the rate of genetic reassortment during a local epidemic? Are all possible reassortments equally likely or are there preferred patterns?
  
  
  METHODS
  To answer these questions and provide a quantitative way to measure genetic shifts, a new method for detecting reassortments from nucleotide sequence data was created that does not rely upon phylogenetic analysis. Two different sequence databases were used: human H3N2 viruses isolated in New York State between 1995 and 2006, and human H3N2 viruses isolated in New Zealand between 2000 and 2005.
  
  
  RESULTS
  Using this new method, we were able to reproduce all the reassortments found in earlier works, as well as detect, with very high confidence, many reassortments that were not detected by previous authors. We obtain a lower bound on the reassortment rate of 2-3 events per year, and find a clear preference for reassortments involving only one segment, most often hemagglutinin or neuraminidase. At a lower frequency several segments appear to reassort in vivo in defined groups as has been suggested previously in vitro.
  
  
  CONCLUSIONS
  Our results strongly suggest that the patterns of reassortment in the viral population are not random. Deciphering these patterns can be a useful tool in attempting to understand and predict possible influenza pandemics.
authors.affiliation: Institute for Advanced Study, Einstein Dr, Princeton, NJ 08540, USA. rabadan@ias.edu
authors.forename: Raul
authors.initials: R
authors.lastname: Rabadan
authors.forename: Arnold J
authors.initials: AJ
authors.lastname: Levine
authors.forename: Michael
authors.initials: M
authors.lastname: Krasnitz
mesh_terms: D019295:Computational Biology; D030561:Databases, Nucleic Acid; D006801:Humans; D053122:Influenza A Virus, H3N2 Subtype / Q000235:genetics* / Q000302:isolation & purification; D007251:Influenza, Human / Q000821:virology*; D009518:New York; D009520:New Zealand; D016865:Reassortant Viruses / Q000235:genetics* / Q000302:isolation & purification