Hepatitis C is a pandemic human RNA virus, which commonly causes chronic infection and liver disease. The characterization of viral populations that successfully initiate infection, and also those that drive progression to chronicity is instrumental for understanding pathogenesis and vaccine design. A comprehensive and longitudinal analysis of the viral population was conducted in four subjects followed from very early acute infection to resolution of disease outcome. By means of next generation sequencing (NGS) and standard cloning/Sanger sequencing, genetic diversity and viral variants were quantified over the course of the infection at frequencies as low as 0.1%. Phylogenetic analysis of reassembled viral variants revealed acute infection was dominated by two sequential bottleneck events, irrespective of subsequent chronicity or clearance. The first bottleneck was associated with transmission, with one to two viral variants successfully establishing infection. The second occurred approximately 100 days post-infection, and was characterized by a decline in viral diversity. In the two subjects who developed chronic infection, this second bottleneck was followed by the emergence of a new viral population, which evolved from the founder variants via a selective sweep with fixation in a small number of mutated sites. The diversity at sites with non-synonymous mutation was higher in predicted cytotoxic T cell epitopes, suggesting immune-driven evolution. These results provide the first detailed analysis of early within-host evolution of HCV, indicating strong selective forces limit viral evolution in the acute phase of infection.

BackgroundGemSIM, or General Error-Model based SIMulator, is a next-generation sequencing simulator capable of generating single or paired-end reads for any sequencing technology compatible with the generic formats SAM and FASTQ (including Illumina and Roche/454). GemSIM creates and uses empirically derived, sequence-context based error models to realistically emulate individual sequencing runs and/or technologies. Empirical fragment length and quality score distributions are also used. Reads may be drawn from one or more genomes or haplotype sets, facilitating simulation of deep sequencing, metagenomic, and resequencing projects.ResultsWe demonstrate GemSIM's value by deriving error models from two different Illumina sequencing runs and one Roche/454 run, and comparing and contrasting the resulting error profiles of each run. Overall error rates varied dramatically, both between individual Illumina runs, between the first and second reads in each pair, and between datasets from Illumina and Roche/454 technologies. Indels were markedly more frequent in Roche/454 than Illumina and both technologies suffered from an increase in error rates near the end of each read.The effects of these different profiles on low-frequency SNP-calling accuracy were investigated by analysing simulated sequencing data for a mixture of bacterial haplotypes. In general, SNP-calling using VarScan was only accurate for SNPs with frequency > 3%, independent of which error model was used to simulate the data. Variation between error profiles interacted strongly with VarScan's 'minumum average quality' parameter, resulting in different optimal settings for different sequencing runs.ConclusionsNext-generation sequencing has unprecedented potential for assessing genetic diversity, however analysis is complicated as error profiles can vary noticeably even between different runs of the same technology. Simulation with GemSIM can help overcome this problem, by providing insights into the error profiles of individual sequencing runs and allowing researchers to assess the effects of these errors on downstream data analysis.

c Norovirus (NoV) is an emerging RNA virus that has been associated with global epidemics of gastroenteritis. Each global epidemic arises with the emergence of novel antigenic variants. While the majority of NoV infections are mild and self-limiting, in the young, elderly, and immunocompromised, severe and prolonged illness can result. As yet, there is no vaccine or therapeutic treatment to prevent or control infection. In order to design effective control strategies, it is important to understand the mechanisms and source of the new antigenic variants. In this study, we used next-generation sequencing (NGS) technology to investigate genetic diversification in three contexts: the impact of a NoV transmission event on viral diversity and the contribution to diversity of intrahost evolution over both a short period of time (10 days), in accordance with a typical acute NoV infection, and a prolonged period of time (288 days), as observed for NoV chronic infections of immunocompromised individuals. Investigations of the transmission event revealed that minor variants at frequencies as low as 0.01% were successfully transmitted, indicating that transmission is an important source of diversity at the interhost level of NoV evolution. Our results also suggest that chronically infected immunocompromised subjects represent a potential reservoir for the emergence of new viral variants. In contrast, in a typical acute NoV infection, the viral population was highly homogenous and relatively stable. These results indicate that the evolution of NoV occurs through multiple mechanisms.

Generation of genetic diversity is a prerequisite for bacterial evolution and adaptation. Short-term diversification and selection within populations is, however, largely uncharacterised, as existing studies typically focus on fixed substitutions. Here, we use whole-genome deep-sequencing to capture the spectrum of mutations arising during biofilm development for two Pseudomonas aeruginosa strains. This approach identified single nucleotide variants with frequencies from 0.5% to 98.0% and showed that the clinical strain 18A exhibits greater genetic diversification than the type strain PA01, despite its lower per base mutation rate. Mutations were found to be strain specific: the mucoid strain 18A experienced mutations in alginate production genes and a c-di-GMP regulator gene; while PA01 acquired mutations in PilT and PilY1, possibly in response to a rapid expansion of a lytic Pf4 bacteriophage, which may use type IV pili for infection. The Pf4 population diversified with an evolutionary rate of 2.43 × 10 −3 substitutions per site per day, which is comparable to single-stranded RNA viruses. Extensive within-strain parallel evolution, often involving identical nucleotides, was also observed indicating that mutation supply is not limiting, which was contrasted by an almost complete lack of noncoding and synonymous mutations. Taken together, these results suggest that the majority of the P. aeruginosa genome is constrained by negative selection, with strong positive selection acting on an accessory subset of genes that facilitate adaptation to the biofilm lifecycle. Long-term bacterial evolution is known to proceed via few, nonsynonymous, positively selected mutations, and here we show that similar dynamics govern shortterm, within-population bacterial diversification.dispersal | prophage | haplotypes D iversifying selection within bacterial populations underpins a range of ecological and clinical phenomena, for example niche adaptation (1-3) and antibiotic resistance (4). During diversifying selection, a single population explores multiple fitness peaks, resulting in subpopulations with different adaptive mutations. This kind of within-population genetic diversity can provide the raw material on which long-term evolution acts. However, diversifying selection of bacterial populations is still poorly understood, because previous experimental evolution and epidemiological studies typically have focused on fixed substitutions at the end point of evolution (5), ignoring short-term or withinpopulation effects.Laboratory-grown Pseudomonas aeruginosa biofilms provide an ideal model for investigating within-population diversification. The bacterium P. aeruginosa is a widespread, Gram-negative generalist and can have either a planktonic, motile lifestyle or exist as a biofilm (i.e., a surface-attached cells embedded within an extracellular polymeric matrix). P. aeruginosa has been the focus of extensive research, because of both its status as a model organism and its ability to form opportunistic, chronic, often lethal biof...

Deep sequencing harnesses the high throughput nature of next generation sequencing technologies to generate population samples, treating information contained in individual reads as meaningful. Here, we review applications of deep sequencing to pathogen evolution. Pioneering deep sequencing studies from the virology literature are discussed, such as whole genome Roche-454 sequencing analyses of the dynamics of the rapidly mutating pathogens hepatitis C virus and HIV. Extension of the deep sequencing approach to bacterial populations is then discussed, including the impacts of emerging sequencing technologies. While it is clear that deep sequencing has unprecedented potential for assessing the genetic structure and evolutionary history of pathogen populations, bioinformatic challenges remain. We summarise current approaches to overcoming these challenges, in particular methods for detecting low frequency variants in the context of sequencing error and reconstructing individual haplotypes from short reads.