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Evolutionary histories of coxsackievirus B5 and swine vesicular disease virus reconstructed by phylodynamic and sequence variation analyses.

ABSTRACT: Coxsackievirus (CV)-B5 is a common human enterovirus reported worldwide; swine vesicular disease virus (SVDV) is a porcine variant of CV-B5. To clarify the transmission dynamics and molecular basis of host switching between CV-B5 and SVDV, we analysed and compared the VP1 and partial 3D^pol gene regions of these two viruses. Spatiotemporal dynamics of viral transmission were estimated using a Bayesian statistical inference framework. The detected selection events were used to analyse the key molecules associated with host switching. Analyses of VP1 sequences revealed six CV-B5 genotypes (A1-A4 and B1-B2) and three SVDV genotypes (I-III). Analyses of partial 3D^pol revealed five clusters (A-E). The genotypes evolved sequentially over different periods, albeit with some overlap. The major hub of CV-B5 transmission was in China whereas the major hubs of SVDV transmission were in Italy. Network analysis based on deduced amino acid sequences showed a diverse extension of the VP1 structural protein, whereas most sequences were clustered into two haplotypes in the partial 3D^pol region. Residue 178 of VP1 showed four epistatic interactions with residues known to play essential roles in viral host tropism, cell entry, and viral decoating.

SUBMITTER: Huang HW

PROVIDER: S-EPMC5995886 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Admixture is a fundamental evolutionary process that has influenced genetic patterns in numerous species. Maximum-likelihood approaches based on allele frequencies and linkage-disequilibrium have been extensively used to infer admixture processes from genome-wide data sets, mostly in human populations. Nevertheless, complex admixture histories, beyond one or two pulses of admixture, remain methodologically challenging to reconstruct. We developed an Approximate Bayesian Computation (ABC) framework to reconstruct highly complex admixture histories from independent genetic markers. We built the software package MetHis to simulate independent SNPs or microsatellites in a two-way admixed population for scenarios with multiple admixture pulses, monotonically decreasing or increasing recurring admixture, or combinations of these scenarios. MetHis allows users to draw model-parameter values from prior distributions set by the user, and, for each simulation, MetHis can calculate numerous summary statistics describing genetic diversity patterns and moments of the distribution of individual admixture fractions. We coupled MetHis with existing machine-learning ABC algorithms and investigated the admixture history of admixed populations. Results showed that random forest ABC scenario-choice could accurately distinguish among most complex admixture scenarios, and errors were mainly found in regions of the parameter space where scenarios were highly nested, and, thus, biologically similar. We focused on African American and Barbadian populations as two study-cases. We found that neural network ABC posterior parameter estimation was accurate and reasonably conservative under complex admixture scenarios. For both admixed populations, we found that monotonically decreasing contributions over time, from Europe and Africa, explained the observed data more accurately than multiple admixture pulses. This approach will allow for reconstructing detailed admixture histories when maximum-likelihood methods are intractable.

Dataset Information

Evolutionary histories of coxsackievirus B5 and swine vesicular disease virus reconstructed by phylodynamic and sequence variation analyses.

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