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Infectious bursal disease in Nigeria: continuous circulation of reassortant viruses.

O A ArowoloUwem Etop GeorgePam Dachung LukaN A MauriceY J AtumanJ J ShallmizhiliOlufemi D OlaleyeDaniel Oladimeji Oluwayelu
Published in: Tropical animal health and production (2021)
Outbreaks of infectious bursal disease (IBD), a highly contagious immunosuppressive disease of young chickens, are still reported globally despite vaccination efforts. This study investigated the genetic characteristics of infectious bursal disease virus (IBDV) from 26 reported outbreaks in 2019 in Nigeria. Nucleotide sequences of VP2 hypervariable (hvVP2) region (n=26) and VP1 (n=23) of Nigerian IBDVs were determined. Our results revealed the detection of reassortant strains with segment A related to very virulent IBDV (vvIBDV) having virulence marker (222A, 242I, 256I, 294I and 299S), whereas their segment B were closely related to previously detected IBDV strains having QEG substitution at positions 145-147. Phylogenetic analysis of the hvVP2 region revealed that all the Nigerian IBDV clustered with vvIBDV (genogroup 3) and were independent of the Asian/European lineage. Interestingly, in the hvVP2, all the viruses had a G-S substitution at residue 254. Additionally, one isolate had an A321T substitution at the PHI loop, which has been suggested to play a key role in antigenicity. Four of the viruses (Bauchi=3 and Plateau=1) had a unique A-T substitution at residue 144 on the VP1 region. We also observed a T174S substitution in nine of the Nigerian viruses from Bauchi and Plateau state that were not found in any outbreak viruses from Oyo and Akwa Ibom. This report demonstrates the circulation of reassortant strains in commercial and backyard poultry farms in Nigeria despite sustained vaccination efforts. Our data suggest that the Nigerian outbreak viruses have mutations that may affect antigenicity and contribute to antigenic drift.
Keyphrases
  • disease virus
  • escherichia coli
  • genetic diversity
  • single cell
  • staphylococcus aureus
  • pseudomonas aeruginosa
  • antimicrobial resistance
  • dna methylation
  • machine learning