One More Piece in the VACV Ecological Puzzle: Could Peridomestic Rodents Be the Link between Wildlife and Bovine Vaccinia Outbreaks in Brazil?

BACKGROUND: Despite the fact that smallpox eradication was declared by the World Health Organization (WHO) in 1980, other poxviruses have emerged and re-emerged, with significant public health and economic impacts. Vaccinia virus (VACV), a poxvirus used during the WHO smallpox vaccination campaign, has been involved in zoonotic infections in Brazilian rural areas (Bovine Vaccinia outbreaks - BV), affecting dairy cattle and milkers. Little is known about VACV's natural hosts and its epidemiological and ecological characteristics. Although VACV was isolated and/or serologically detected in Brazilian wild animals, the link between wildlife and farms has not yet been elucidated. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we describe for the first time, to our knowledge, the isolation of a VACV (Mariana virus - MARV) from a mouse during a BV outbreak. Genetic data, in association with biological assays, showed that this isolate was the same etiological agent causing exanthematic lesions observed in the cattle and human inhabitants of a particular BV-affected area. Phylogenetic analysis grouped MARV with other VACV isolated during BV outbreaks. CONCLUSION/SIGNIFICANCE: These data provide new biological and epidemiological information on VACV and lead to an interesting question: could peridomestic rodents be the link between wildlife and BV outbreaks

The Hepatitis E Virus Polyproline Region Is Involved in Viral Adaptation

Genomes of hepatitis E virus (HEV), rubivirus and cutthroat virus (CTV) contain a region of high proline density and low amino acid (aa) complexity, named the polyproline region (PPR). In HEV genotypes 1, 3 and 4, it is the only region within the non-structural open reading frame (ORF1) with positive selection (4–10 codons with dN/dS>1). This region has the highest density of sites with homoplasy values >0.5. Genotypes 3 and 4 show ∼3-fold increase in homoplastic density (HD) in the PPR compared to any other region in ORF1, genotype 1 does not exhibit significant HD (p<0.0001). PPR sequence divergence was found to be 2-fold greater for HEV genotypes 3 and 4 than for genotype 1. The data suggest the PPR plays an important role in host-range adaptation. Although the PPR appears to be hypervariable and homoplastic, it retains as much phylogenetic signal as any other similar sized region in the ORF1, indicating that convergent evolution operates within the major HEV phylogenetic lineages. Analyses of sequence-based secondary structure and the tertiary structure identify PPR as an intrinsically disordered region (IDR), implicating its role in regulation of replication. The identified propensity for the disorder-to-order state transitions indicates the PPR is involved in protein-protein interactions. Furthermore, the PPR of all four HEV genotypes contains seven putative linear binding motifs for ligands involved in the regulation of a wide number of cellular signaling processes. Structure-based analysis of possible molecular functions of these motifs showed the PPR is prone to bind a wide variety of ligands. Collectively, these data suggest a role for the PPR in HEV adaptation. Particularly as an IDR, the PPR likely contributes to fine tuning of viral replication through protein-protein interactions and should be considered as a target for development of novel anti-viral drugs

Improved identification of metabolites in complex mixtures using HSQC NMR spectroscopy

The automated and robust identification of metabolites in a complex biological sample remains one of the greatest challenges in metabolomics. In our experiments, HSQC carbon-proton correlation NMR data with a model that takes intensity information into account improves upon the identification of metabolites that was achieved using COSY proton-proton correlation NMR data with the binary model of [1]. In addition, using intensity information results in easier-to-interpret “grey areas” for cases where it is not clear if the compound might be present. We report on highly successful experiments that identify compounds in chemically defined mixtures as well as in biological samples, and compare our 2-dimensional HSQC analyses against quantification of metabolites in the corresponding 1-dimensional proton NMR spectra. We show that our approach successfully employs a fully automated algorithm for identifying the presence or absence of pre-defined compounds (held within a library) in biological HSQC spectra, and in addition calculates upper bounds on the compound intensities