Fig 1.
Map of Australia and Papua New Guinea showing the geographic origins of the MVEV strains used in this study.
Australian states and territories are indicated, as well as selected regions or provinces.
Fig 2.
ML phylogenetic tree estimated using MVEV prM-Env gene sequences.
Genotypes and sub-lineages are shown and geographic origin of strains is indicated by font colour: New South Wales, blue; Queensland, red; Victoria, purple; Western Australia, green; Papua New Guinea, orange. The tree was estimated using a general time-reversible model of nucleotide substitution with a gamma distribution and invariant sites. Numbers at the nodes represent bootstrap support as a percentage of 1000 replicates; only values ≥50% are shown. The scale bar indicates 2 nucleotide substitutions per site. The tree was rooted with the prM-Env sequence of JEV, however this has been removed to improve visual resolution of the tree.
Fig 3.
Location of genotype-defining amino acid residues and motifs in the ectodomain of the MVEV Env protein, based on the crystal structure of JEV [42].
The three domains of the Env protein are shown in red (DI), yellow (DII) and blue (DIII). The fusion loop is coloured green, and the N-linked glycosylation site at position 154, the hypervariable domain in DII and the hinge connecting the αB helix of DII and the Io β-strand of DI are indicated.
Fig 4.
Bayesian MCC phylogenetic tree for MVEV prM-Env sequences.
Divergence times and branch lengths are drawn according to the time scale bar (in years), such that the tips of the tree correspond to sampling date of each virus strain. Branches and ancestral lineage of each genotype and sub-lineage are coloured blue (G1A), red (G1B), grey (remaining G1 strains), light blue (G2), green (G3) and dark red (G4). Posterior probability values are shown at the nodes. Letters at the upper left of each node correspond to divergence times and substitution rates shown in Table 1.
Table 1.
Divergence times and evolutionary rates for genotypes and sub-lineages of MVEV estimated by Bayesian MCMC analysis.