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Archives of Virology

, Volume 159, Issue 12, pp 3185–3196 | Cite as

Analysis of the ORF2 of human astroviruses reveals lineage diversification, recombination and rearrangement and provides the basis for a novel sub-classification system

  • Vito MartellaEmail author
  • Pierfrancesco Pinto
  • Fabio Tummolo
  • Simona De Grazia
  • Giovanni M. Giammanco
  • Maria C. Medici
  • Balasubramanian Ganesh
  • Yvan L’Homme
  • Tibor Farkas
  • Ferenc Jakab
  • Krisztián Bányai
Original Article

Abstract

Canonical human astroviruses (HAstVs) are important enteric pathogens that can be classified genetically and antigenically into eight types. Sequence analysis of small diagnostic regions at either the 5′ or 3′ end of ORF2 (capsid precursor) is a good proxy for prediction of HAstV types and for distinction of intratypic genetic lineages (subtypes), although lineage diversification/classification has not been investigated systematically. Upon sequence and phylogenetic analysis of the full-length ORF2 of 86 HAstV strains selected from the databases, a detailed classification of HAstVs into lineages was established. Three main lineages could be defined in HAstV-1, four in HAstV-2, two in HAstV-3, three in HAstV-4, three in HAstV-5 and two in HAstV-6. Intratypic (inter-lineages) ORF2 recombinant strains were identified in type 1 (1b/1d) and type 2 (2c/2b) with distinct crossover points. Other potential intratypic recombinant strains were identified in type 3, type 5 and type 6. In addition, a type-1b strain with a large insertion (~600 bp) of heterologous RNA in the N-terminal region and a type-6 strain with a large RNA rearrangement in the hypervariable region were identified. The classification scheme was integrated in a novel nomenclature system suitable for designation of HAstV strains.

Keywords

ORF2 Sequence Crossover Site Capsid Precursor Human Astroviruses High Bayesian Posterior Probability 
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.

Notes

Acknowledgements

This investigation was supported by the Grant “MicroMap (PON01_02589)”. KB was supported by the Momentum program awarded by the Hungarian Academy of Sciences.

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Copyright information

© Springer-Verlag Wien 2014

Authors and Affiliations

  • Vito Martella
    • 1
    Email author
  • Pierfrancesco Pinto
    • 1
  • Fabio Tummolo
    • 2
  • Simona De Grazia
    • 3
  • Giovanni M. Giammanco
    • 3
  • Maria C. Medici
    • 2
  • Balasubramanian Ganesh
    • 4
  • Yvan L’Homme
    • 5
  • Tibor Farkas
    • 6
  • Ferenc Jakab
    • 7
  • Krisztián Bányai
    • 8
  1. 1.Dipartimento di Medicina VeterinariaUniversità Aldo Moro di BariValenzanoItaly
  2. 2.Unità di Microbiologia e Virologia, Dipartimento di Medicina Clinica e SperimentaleUniversità degli Studi di ParmaParmaItaly
  3. 3.Dipartimento di Scienze per la Promozione della Salute e Materno Infantile “G. D’Alessandro”Università di PalermoPalermoItaly
  4. 4.Division of VirologyNational Institute of Cholera and Enteric Diseases (NICED)KolkataIndia
  5. 5.Canadian Food Inspection Agency, St-Hyacinthe LaboratorySt-HyacintheCanada
  6. 6.Cincinnati Children’s Hospital Medical CenterCincinnatiUSA
  7. 7.Virological Research Group, Faculty of Sciences, Institute of BiologyUniversity of PécsPecsHungary
  8. 8.Centre for Agricultural Research, Veterinary Medical Research InstituteHungarian Academy of SciencesBudapestHungary

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