Six immunoassays for detecting immunoglobulin M antibodies to hepatitis E virus were evaluated. Serum samples representing acute infection by each of the 4 viral genotypes as well as nonacute hepatitis E virus infection constituted the test panels. Diagnostic sensitivities and specificities as well as interassay agreement varied widely. Analytical sensitivity limits also were determined and were found to be particularly disparate.

The intrahost evolution of hepatitis C virus (HCV) holds keys to understanding mechanisms responsible for the establishment of chronic infections and to development of a vaccine and therapeutics. In this study, intrahost variants of two variable HCV genomic regions, HVR1 and NS5A, were sequenced from four treatment-naïve chronically infected patients who were followed up from the acute stage of infection for 9 to 18 years. Median-joining network analysis indicated that the majority of the HCV intrahost variants were observed only at certain time points, but some variants were detectable at more than one time point. In all patients, these variants were found organized into communities or subpopulations. We hypothesize that HCV intrahost evolution is defined by two processes: incremental changes within communities through random mutation and alternations between coexisting communities. The HCV population was observed to incrementally evolve within a single community during approximately the first 3 years of infection, followed by dispersion into several subpopulations. Two patients demonstrated this pattern of dispersion for the rest of the observation period, while HCV variants in the other two patients converged into another single subpopulation after ϳ9 to 12 years of dispersion. The final subpopulation in these two patients was under purifying selection. Intrahost HCV evolution in all four patients was characterized by a consistent increase in negative selection over time, suggesting the increasing HCV adaptation to the host late in infection. The data suggest specific staging of HCV intrahost evolution.Hepatitis C virus (HCV) infection is a major cause of liver disease in the world. It is estimated that ϳ130 million people are infected with HCV globally (2). HCV is a heterogeneous single-stranded (plus-strand) RNA virus belonging to the Flaviviridae. The HCV genome contains one large open reading frame that encodes a polyprotein which can be processed into 10 mature proteins (34). HCV causes chronic infection in 70 to 85% of infected adults. There is no vaccine against HCV, and current antiviral therapy is effective in only 50 to 70% of chronically infected patients (18).HCV intrahost evolution is frequently compared to an "arms race," implying that the HCV genome constantly changes in order to escape from neutralizing adaptive immunoresponses (53). This concept of constant change is seemingly different from the HIV model, according to which intrahost evolution slows down with CD4 ϩ T-cell depletion and quasispecies diversity decreases with the development of AIDS (48). Our limited understanding of the dynamics of HCV intrahost evolution impedes the development of efficient therapeutic and prophylactic interventions.Analysis of HCV longitudinal evolution is significantly hindered by difficulties with identifying and with long-term follow-up of patients, starting from the acquisition of infection. In the present study, we explored HCV evolutionary processes during long-term chronic infection in four treatment-...

Hepatitis C is a major public health problem in the United States and worldwide. Outbreaks of hepatitis C virus (HCV) infections are associated with unsafe injection practices, drug diversion, and other exposures to blood, being difficult to detect and investigate. Here, we developed and validated a simple approach for molecular detection of HCV transmissions in outbreak settings. We obtained sequences from the HCV hypervariable region 1 (HVR1) using End-Point Limiting-Dilution (EPLD) from 127 cases involved in 32 epidemiologically defined HCV outbreaks and 193 individuals with unrelated HCV strains. We compared several types of genetic distances and calculated a threshold using minimal Hamming distances that identifies transmission clusters in all tested outbreaks with 100% accuracy. The approach was also validated on sequences from 239 individuals obtained using next-generation sequencing, showing the same accuracy as EPLD. In average, nucleotide diversity of the intra-host population was 6.2-times greater in the source than in any incident case, allowing the correct detection of transmission direction in 8 outbreaks for which source cases were known. A simple and accurate distance-based approach for detecting HCV transmissions developed here streamlines molecular investigation of outbreaks, thus improving the public health capacity for rapid and effective control of hepatitis C.

No abstract

Supplementary data are available at Bioinformatics online.

BackgroundNext-generation sequencing allows the analysis of an unprecedented number of viral sequence variants from infected patients, presenting a novel opportunity for understanding virus evolution, drug resistance and immune escape. However, sequencing in bulk is error prone. Thus, the generated data require error identification and correction. Most error-correction methods to date are not optimized for amplicon analysis and assume that the error rate is randomly distributed. Recent quality assessment of amplicon sequences obtained using 454-sequencing showed that the error rate is strongly linked to the presence and size of homopolymers, position in the sequence and length of the amplicon. All these parameters are strongly sequence specific and should be incorporated into the calibration of error-correction algorithms designed for amplicon sequencing.ResultsIn this paper, we present two new efficient error correction algorithms optimized for viral amplicons: (i) k-mer-based error correction (KEC) and (ii) empirical frequency threshold (ET). Both were compared to a previously published clustering algorithm (SHORAH), in order to evaluate their relative performance on 24 experimental datasets obtained by 454-sequencing of amplicons with known sequences. All three algorithms show similar accuracy in finding true haplotypes. However, KEC and ET were significantly more efficient than SHORAH in removing false haplotypes and estimating the frequency of true ones.ConclusionsBoth algorithms, KEC and ET, are highly suitable for rapid recovery of error-free haplotypes obtained by 454-sequencing of amplicons from heterogeneous viruses.The implementations of the algorithms and data sets used for their testing are available at: http://alan.cs.gsu.edu/NGS/?q=content/pyrosequencing-error-correction-algorithm

Hepatitis C virus is a genetically heterogeneous RNA virus that is a major cause of liver disease worldwide. Here, we show that, despite its extensive heterogeneity, the evolution of hepatitis C virus is primarily shaped by negative selection and that numerous coordinated substitutions in the polyprotein can be organized into a scale-free network whose degree of connections between sites follows a power-law distribution. This network shares all major properties with many complex biological and technological networks. The topological structure and hierarchical organization of this network suggest that a small number of amino acid sites exert extensive impact on hepatitis C virus evolution. Nonstructural proteins are enriched for negatively selected sites of high centrality, whereas structural proteins are enriched for positively selected sites located in the periphery of the network. The complex network of coordinated substitutions is an emergent property of genetic systems with implications for evolution, vaccine research, and drug development. In addition to such properties as polymorphism or strength of selection, the epistatic connectivity mapped in the network is important for typing individual sites, proteins, or entire genetic systems. The network topology may help devise molecular intervention strategies for disrupting viral functions or impeding compensatory changes for vaccine escape or drug resistance mutations. Also, it may be used to find new therapeutic targets, as suggested in this study for the NS4A protein, which plays an important role in the network.complex systems ͉ scale-free network ͉ covariation ͉ natural selection ͉ epistasis H epatitis C virus (HCV) is a major cause of liver disease worldwide. The global prevalence of HCV infection is estimated to be 2.2%, representing 130 million people (1). HCV causes chronic infection in 70-85% of infected adults (2). There is no vaccine against HCV and current antiviral therapy is relatively toxic, being effective in 50-60% of patients treated (3). HCV is a single-stranded RNA virus of Ϸ9.4 kb belonging to the Flaviviridae family (4). The positive-sense genome of HCV contains one large ORF that encodes a polyprotein that can undergo proteolytic cleavage into 10 mature proteins (C-E1-E2-P7-NS2-NS3-NS4A-NS4B-NS5A-NS5B). The structural proteins, the core (C) and envelope glycoproteins E1 and E2, are present in the N-terminal part of the polyprotein and presumably self-assemble to form the virion. The nonstructural (NS) proteins have various functions and form the replication complex (5).The HCV genome continually mutates during virus replication. Although a high rate of mutation significantly contributes to the enormous adaptability of RNA viruses, it also limits the size of viral genomes by causing error catastrophe (6). The small size of viral genomes imposes strong evolutionary constraints on their organization, as a result of which each genomic region may encode multiple and often conflicting functions. Such genomic organization requires a tight coor...

Treatment with lamivudine of patients infected with hepatitis B virus (HBV) results in a high rate of drug resistance, which is primarily associated with the rtM204I/V substitution in the HBV reverse transcriptase domain. Here we show that the rtM204I/V substitution, although essential, is insufficient for establishing resistance against lamivudine. The analysis of 639 HBV whole-genome sequences obtained from 11 patients shows that rtM204I/V is independently acquired by more than one intra-host HBV variant, indicating the convergent nature of lamivudine resistance. The differential capacity of HBV variants to develop drug resistance suggests that fitness effects of drug-resistance mutations depend on the genetic structure of the HBV genome. An analysis of Bayesian networks that connect rtM204I/V to many sites of HBV proteins confirms that lamivudine resistance is a complex trait encoded by the entire HBV genome rather than by a single mutation. These findings have implications for public health and offer a more general framework for understanding drug resistance.