Viral assemblage composition in Yellowstone acidic hot springs assessed by network analysis

Understanding of viral assemblage structure in natural environments remains a daunting task. Total viral assemblage sequencing (for example, viral metagenomics) provides a tractable approach. However, even with the availability of next-generation sequencing technology it is usually only possible to obtain a fragmented view of viral assemblages in natural ecosystems. In this study, we applied a network-based approach in combination with viral metagenomics to investigate viral assemblage structure in the high temperature, acidic hot springs of Yellowstone National Park, USA. Our results show that this approach can identify distinct viral groups and provide insights into the viral assemblage structure. We identified 110 viral groups in the hot springs environment, with each viral group likely representing a viral family at the sub-family taxonomic level. Most of these viral groups are previously unknown DNA viruses likely infecting archaeal hosts. Overall, this study demonstrates the utility of combining viral assemblage sequencing approaches with network analysis to gain insights into viral assemblage structure in natural ecosystems.     

A viral discovery methodology for clinical biopsy samples utilising massively parallel next generation sequencing

Here we describe a virus discovery protocol for a range of different virus genera, that can be applied to biopsy-sized tissue samples. Our viral enrichment procedure, validated using canine and human liver samples, significantly improves viral read copy number and increases the length of viral contigs that can be generated by de novo assembly. This in turn enables the Illumina next generation sequencing (NGS) platform to be used as an effective tool for viral discovery from tissue samples.     

The genomic rate of molecular adaptation of the human influenza A virus

Quantifying adaptive evolution at the genomic scale is an essential yet challenging aspect of evolutionary biology. Here, we develop a method that extends and generalizes previous approaches to estimate the rate of genomic adaptation in rapidly evolving populations and apply it to a large data set of complete human influenza A virus genome sequences. In accord with previous studies, we observe particularly high rates of adaptive evolution in domain 1 of the viral hemagglutinin (HA1). However, our novel approach also reveals previously unseen adaptation in other viral genes. Notably, we find that the rate of adaptation (per codon per year) is higher in surface residues of the viral neuraminidase than in HA1, indicating strong antibody-mediated selection on the former. We also observed high rates of adaptive evolution in several nonstructural proteins, which may relate to viral evasion of T-cell and innate immune responses. Furthermore, our analysis provides strong quantitative support for the hypothesis that human H1N1 influenza experiences weaker antigenic selection than H3N2. As well as shedding new light on the dynamics and determinants of positive Darwinian selection in influenza viruses, the approach introduced here is applicable to other pathogens for which densely sampled genome sequences are available, and hence is ideally suited to the interpretation of next-generation genome sequencing data.