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. Primary hepatitis C (HCV) infection is typically asymptomatic and commonly results in persistent infection. The characteristics of early infection remain undefined. Four subjects were studied longitudinally from within a few weeks of transmission until resolution of outcome, via a full genome analysis of viral evolution. In the acute phase (<100 days post-infection) there were two periods with a major reduction in genetic diversity (i.e. a bottleneck) irrespective of subsequent clearance (n = 2) or chronic infection (n = 2). The first bottleneck was associated with transmission, with generally only one ‘founder’ virus successfully establishing infection. The second occurred following the primary peak in viraemia, concomitant with seroconversion, approximately 100 days post-infection. In the subjects who became chronically infected, the second bottleneck was followed by emergence of a new cluster of variants, which evolved from the founder(s), and carried only a small number of mutated residues that reached fixation. Some fixations occurred in known targets of CD8 cytotoxic T cell and neutralizing antibody responses. These results indicate a common evolutionary pattern, independent of disease outcome in the acute phase of HCV infection, with strong signatures of selective pressures driving the transition into chronic infection. These novel data will inform preventative vaccine strategies.

GemSIM, 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.

With the goal of assessing the environmental risk of pharmaceuticals, we have previously observed that a mixture of 13 different drugs at environmentally relevant concentrations had adverse consequences on human and zebra fish cells in vitro. Here we aimed to identify both main and interaction effects within the same environmentally relevant mixture of pharmaceuticals. We studied in vitro cytotoxicity in Escherichia coli, human embryonic HEK293, and estrogen-responsive OVCAR3 tumor cells using fractional-factorial experimental design. Our approach identified a subset of compounds of primary environmental concern, namely atenolol, bezafibrate, ciprofloxacin, and lincomycin, that had statistically significant effects on prokaryotic and eukaryotic cells at environmentally relevant exposure levels (ng/l). Drugs could interact and behave as chemosensitizers, with joint effects representing a statistically significant element of mixture toxicity. Effects and interactions were concentration dependent, confirming the difficulty of dose extrapolation in mixture toxicity data. This study suggests that a thorough investigation of mixture effects can direct environmental concerns toward a handful of pharmaceuticals, which may represent an actual risk at environmental concentrations. We indicate that risk identification may strongly depend on the use of environmentally relevant exposure scenarios. Antagonistic-synergistic interactions and dose dependency of effects may hamper the modeling and prediction of mixture toxicity with pharmaceuticals. Hazard identification for micropollutants depends heavily on appropriate study designs, and we indicate the use of in vitro cytotoxicity threshold and statistical design of experiments (DOEs) as a valid approach.

Tuberculosis can be studied at the population level by genotyping strains of Mycobacterium tuberculosis isolated from patients. We use an approximate Bayesian computational method in combination with a stochastic model of tuberculosis transmission and mutation of a molecular marker to estimate the net transmission rate, the doubling time, and the reproductive value of the pathogen. This method is applied to a published data set from San Francisco of tuberculosis genotypes based on the marker IS6110. The mutation rate of this marker has previously been studied, and we use those estimates to form a prior distribution of mutation rates in the inference procedure. The posterior point estimates of the key parameters of interest for these data are as follows: net transmission rate, 0.69/year [95% credibility interval (C.I.) 0.38, 1.08]; doubling time, 1.08 years (95% C.I. 0.64, 1.82); and reproductive value 3.4 (95% C.I. 1.4, 79.7). These figures suggest a rapidly spreading epidemic, consistent with observations of the resurgence of tuberculosis in the United States in the 1980s and 1990s.

The emergence of antibiotic resistance in Mycobacterium tuberculosis has raised the concern that pathogen strains that are virtually untreatable may become widespread. The acquisition of resistance to antibiotics results in a longer duration of infection in a host, but this resistance may come at a cost through a decreased transmission rate. This raises the question of whether the overall fitness of drug-resistant strains is higher than that of sensitive strains—essential information for predicting the spread of the disease. Here, we directly estimate the transmission cost of drug resistance, the rate at which resistance evolves, and the relative fitness of resistant strains. These estimates are made by using explicit models of the transmission and evolution of sensitive and resistant strains of M. tuberculosis , using approximate Bayesian computation, and molecular epidemiology data from Cuba, Estonia, and Venezuela. We find that the transmission cost of drug resistance relative to sensitivity can be as low as 10%, that resistance evolves at rates of ≈0.0025–0.02 per case per year, and that the overall fitness of resistant strains is comparable with that of sensitive strains. Furthermore, the contribution of transmission to the spread of drug resistance is very high compared with acquired resistance due to treatment failure (up to 99%). Estimating such parameters directly from in vivo data will be critical to understanding and responding to antibiotic resistance. For instance, projections using our estimates suggest that the prevalence of tuberculosis may decline with successful treatment, but the proportion of cases associated with resistance is likely to increase.

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.