Virologica Sinica

, Volume 32, Issue 4, pp 342–345 | Cite as

Reconciling individual and population levels of porcine reproductive and respiratory syndrome virus evolution

  • Giovanni FranzoEmail author
  • Claudia Maria Tucciarone
  • Mattia Cecchinato
  • Michele Drigo

Dear Editor,

Porcine reproductive and respiratory syndrome virus (PRRSV), a member of the family Arteriviridae, represents one of the most challenging pathogens in the swine industry, with serious economic impact. Unfortunately, despite widespread application, vaccination has not been successful in effectively controlling the virus, mainly because of the limited cross-protection among different strains (Kimman et al., 2009).

A longitudinal study revealed a gradual rise in genetic divergence among field isolates of PRRSV type 2 (Brar et al., 2015). Similar to other RNA viruses, PRRSV displays high mutation and recombination rates, which give rise to a plethora of new variants able to rapidly explore the fitness landscape. The substantial viral population size at the host and the population level should create optimal conditions for natural selection to act. Accordingly, different studies have pointed out selective pressures, mainly related to host immunity, which affect PRRSV evolution...

Supplementary material

12250_2017_3981_MOESM1_ESM.pdf (1.6 mb)
Reconciling individual and population levels of porcine reproductive and respiratory syndrome virus evolution


  1. Abascal F, Zardoya R, Telford MJ. 2010. Nucleic Acids Res, 38: W7–W13.CrossRefGoogle Scholar
  2. Brar MS, Shi M, Murtaugh MP, et al. 2015. J Gen Virol, 96: 1570–1580.CrossRefGoogle Scholar
  3. Chen N, Trible B., Kerrigan MA, et al 2016. Infect Genet Evol, 40: 167–175.CrossRefGoogle Scholar
  4. Costers S, Vanhee M, Van Breedam W, et al. 2010. Virus Res, 154: 104–113.CrossRefGoogle Scholar
  5. Darwich L, Díaz I, Mateu E. 2010. Virus Res, 154: 123–132.CrossRefGoogle Scholar
  6. Franzo G, Cortey M, Segales J, et al. 2016. Mol Phylogenet Evol, 100: 269–280.CrossRefGoogle Scholar
  7. Franzo G, Dotto G, Cecchinato M, et al. 2015. Infect Genet Evol, 31: 149–157.CrossRefGoogle Scholar
  8. Goldberg TL, Lowe JF, Milburn SM, et al. 2003. Virology, 317: 197–207.CrossRefGoogle Scholar
  9. Guindon S, Dufayard JFF, Lefort V, et al. 2010. Syst Biol, 59: 307–321.CrossRefGoogle Scholar
  10. Katoh K, Standley DM. 2013. Mol Biol Evol, 30: 772–780.CrossRefGoogle Scholar
  11. Kimman TG, Cornelissen LA, Moormann R, et al. 2009. Vaccine, 27: 3704–3718.CrossRefGoogle Scholar
  12. Kosakovsky Pond SL, Posada D, Gravenor MB, et al. 2006. Bioinformatics, 22: 3096–3098.CrossRefGoogle Scholar
  13. Kosakovsky Pond SL, Frost S, Muse SV. 2005. Bioinformatics, 21: 676–679.CrossRefGoogle Scholar
  14. Paradis E, Claude J, Strimmer K. 2004. Bioinformatics, 20: 289–290.CrossRefGoogle Scholar
  15. Pybus OG, Rambaut A. 2009. Nat Rev Genet, 10: 540–550.CrossRefGoogle Scholar

Copyright information

© Wuhan Institute of Virology, CAS and Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Department of Animal Medicine, Production and Health (MAPS)University of PaduaLegnaroItaly

Personalised recommendations