COMPARATIVE STUDY
JOURNAL ARTICLE

Molecular characterization of enterovirus 71 and coxsackievirus A16 using the 5' untranslated region and VP1 region

Fei Zhou, Fanrong Kong, Bin Wang, Kenneth McPhie, Gwendolyn L Gilbert, Dominic E Dwyer
Journal of Medical Microbiology 2011, 60: 349-58
21148280
Enterovirus 71 (EV71) and coxsackievirus A16 (CVA16) are members of the species Human enterovirus A, and are both major and independent aetiological agents of hand-foot-and-mouth disease. The human enterovirus (HEV) 5' untranslated region (UTR) is fundamentally important for efficient virus replication and for virulence, whilst the VP1 region correlates well with antigenic typing by neutralization, and can be used for virus identification and evolutionary studies. A comparison was undertaken of the 5'UTR and VP1 nucleotide sequences of five EV71 clinical isolates and 10 CVA16 clinical isolates from one laboratory with the 5'UTR and VP1 sequences of 104 EV71 strains and 45 CVA16 strains available in GenBank. The genetic relationships were analysed using standard phylogenetic methods. The EV71 phylogenetic analysis showed that the VP1 sequences were clustered into three genogroups, A, B and C, with genogroups B and C further divided into five subgenogroups, B1-B5 and C1-C5, respectively. All EV71 strains were clustered similarly in the 5'UTR and VP1 trees, except for one Taiwanese strain, which demonstrated different clustering in the two trees, suggesting a recombination event in the phylogeny. The CVA16 phylogenetic analysis showed that the VP1 sequences were clustered into two genogroups, A and B, with genogroup B further divided into B1 (B1a and B1b), B2 and a possible B3; and that a similar pattern and grouping of all strains were displayed in the 5'UTR tree. This study demonstrated that comparing the two regions provides evidence of epidemiological linkage of HEV-A strains, and that mutation in the two regions plays a vital role in the evolution of these viruses. The combination of molecular typing and phylogenetic sequence analysis will be beneficial in both individual patient diagnosis and public health measures.

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