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Mutants of Influenza Virus

  • B. W. J. Mahy

Abstract

Influenza virus was the first animal virus with which genetic recombination was demonstrated, when Burnet and Lind (1949), taking advantage of naturally occurring variation, found recombination in mouse brain between the neurotropic strain NWS and the non-neurotropic strain WSM. Further quantitative studies by Burnet and co-workers in Australia and by Hirst and Gotlieb in the U.S.A. established that recombination between any two strains of influenza virus occurred at a high frequency, around 35–40% (Burnet and Lind, 1952; Hirst and Gotlieb, 1955). The reason for this high frequency recombination (gene reassortment) was not suggested until 1961, when Barry (1961) demonstrated the phenomenon of multiplicity reactivation of UV-inactivated influenza virus and proposed that the virus genome might exist as “six independent radiation sensitive units”. Hirst (1962) reached a similar conclusion on the basis of experiments in which the plaque-forming capacity of UV-inactivated WSN strain influenza virus was rescued by a variety of non-plaque-forming strains (Simpson and Hirst, 1961). Chemical inactivation studies of the influenza virus genome also supported the notion that there were several independent genetic units (Scholtissek and Rott, 1964). Physical analyses of the influenza virus genome carried out at this time were consistent with the idea that it was fragmented, since RNA species larger than about 18s-20s could not be obtained from virions (Davies and Barry, 1966; Duesberg and Robinson, 1967; Pons, 1970), but ten years were to elapse before the true structure of the influenza virus genome, consisting of eight single stranded RNA pieces, was established (reviewed by Palese, 1977; Barry and Mahy, 1979; Chapter 2, this volume).

Keywords

Influenza Virus Restrictive Temperature Permissive Temperature Influenza Virus Strain Host Range Mutant 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer-Verlag/Wien 1983

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  • B. W. J. Mahy

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