Quantitative differences between intra-host HCV populations from persons with recently established and persistent infections.

Detection of incident hepatitis C virus (HCV) infections is crucial for identification of outbreaks and development of public health interventions. However, there is no single diagnostic assay for distinguishing recent and persistent HCV infections. HCV exists in each infected host as a heterogeneous population of genomic variants, whose evolutionary dynamics remain incompletely understood. Genetic analysis of such viral populations can be applied to the detection of incident HCV infections and used to understand intra-host viral evolution. We studied intra-host HCV populations sampled using next-generation sequencing from 98 recently and 256 persistently infected individuals. Genetic structure of the populations was evaluated using 245,878 viral sequences from these individuals and a set of selected features measuring their diversity, topological structure, complexity, strength of selection, epistasis, evolutionary dynamics, and physico-chemical properties. Distributions of the viral population features differ significantly between recent and persistent infections. A general increase in viral genetic diversity from recent to persistent infections is frequently accompanied by decline in genomic complexity and increase in structuredness of the HCV population, likely reflecting a high level of intra-host adaptation at later stages of infection. Using these findings, we developed a machine learning classifier for the infection staging, which yielded a detection accuracy of 95.22 per cent, thus providing a higher accuracy than other genomic-based models. The detection of a strong association between several HCV genetic factors and stages of infection suggests that intra-host HCV population develops in a complex but regular and predictable manner in the course of infection. The proposed models may serve as a foundation of cyber-molecular assays for staging infection, which could potentially complement and/or substitute standard laboratory assays.

Complex genetic encoding of the hepatitis B virus on-drug persistence.

Tenofovir disoproxil fumarate (TDF) is one of the nucleotide analogs capable of inhibiting the reverse transcriptase (RT) activity of HIV and hepatitis B virus (HBV). There is no known HBV resistance to TDF. However, detectable variation in duration of HBV persistence in patients on TDF therapy suggests the existence of genetic mechanisms of on-drug persistence that reduce TDF efficacy for some HBV strains without affording actual resistance. Here, the whole genome of intra-host HBV variants (N = 1,288) was sequenced from patients with rapid (RR, N = 5) and slow response (SR, N = 5) to TDF. Association of HBV genomic and protein polymorphic sites to RR and SR was assessed using phylogenetic analysis and Bayesian network methods. We show that, in difference to resistance to nucleotide analogs, which is mainly associated with few specific mutations in RT, the HBV on-TDF persistence is defined by genetic variations across the entire HBV genome. Analysis of the inferred 3D-structures indicates no difference in affinity of TDF binding by RT encoded by intra-host HBV variants that rapidly decline or persist in presence of TDF. This finding suggests that effectiveness of TDF recognition and binding does not contribute significantly to on-drug persistence. Differences in patterns of genetic associations to TDF response between HBV genotypes B and C and lack of a single pattern of mutations among intra-host variants sensitive to TDF indicate a complex genetic encoding of the trait. We hypothesize that there are many genetic mechanisms of on-drug persistence, which are differentially available to HBV strains. These pervasive mechanisms are insufficient to prevent viral inhibition completely but may contribute significantly to robustness of actual resistance. On-drug persistence may reduce the overall effectiveness of therapy and should be considered for development of more potent drugs.

HCV adaptation to HIV coinfection.

Human immunodeficiency virus (HIV) infection is rising as a leading cause of morbidity and mortality among hepatitis C virus (HCV)-infected patients. Both viruses interact in co-infected hosts, which may affect their intra-host evolution, potentially leading to differing genetic composition of viral populations in co-infected (CIP) and mono-infected (MIP) patients. Here, we investigate genetic differences between intra-host variants of the HCV hypervariable region 1 (HVR1) sampled from CIP and MIP. Nucleotide (nt) sequences of intra-host HCV HVR1 variants (N = 28,622) obtained from CIP (N = 112) and MIP (n = 176) were represented using 148 physical-chemical (PhyChem) indexes of DNA nt dimers. Significant (p < .0001) differences in the means and frequency distributions of 7 PhyChem properties were found between HVR1 variants from both groups. Linear projection analysis of 29 PhyChem features extracted from such PhyChem properties showed that the CIP and MIP HVR1 variants have a distinct distribution in the modeled 2D-space, with only ~1.3% of PhyChem profiles (N = 6782), shared by all HVR1 variants, being found in both groups. Probabilistic neural network (PNN) and naïve Bayesian (NB) classifiers trained on the PhyChem features accurately classified HVR1 variants by the group in cross-validation experiments (AUROC ≥ 0.96). Similarly, both models showed a high accuracy (AUROC ≥ 0.95) when evaluated on a test dataset of HVR1 sequences obtained from 10 patients, data from whom were not used for model building. Both models performed at the expected lower accuracy on randomly labeled datasets in cross-validation experiments (AUROC = 0.50). The random-label trained PNN showed a similar drop in accuracy on the test dataset (AUROC = 0.48), indicating that the detected associations were unlikely due to random correlations. Marked differences in genetic composition of HCV HVR1 variants sampled from CIP and MIP suggest differing intra-host HCV evolution in the presence of HIV infection. PhyChem features identified here may be used for detection of HIV infection from intra-host HCV variants alone in co-infected patients, thus facilitating monitoring for HIV introduction to high-risk populations with high HCV prevalence.

Identification of recent cases of hepatitis C virus infection using physical-chemical properties of hypervariable region 1 and a radial basis function neural network classifier.

BACKGROUND: Identification of acute or recent hepatitis C virus (HCV) infections is important for detecting outbreaks and devising timely public health interventions for interruption of transmission. Epidemiological investigations and chemistry-based laboratory tests are 2 main approaches that are available for identification of acute HCV infection. However, owing to complexity, both approaches are not efficient. Here, we describe a new sequence alignment-free method to discriminate between recent (R) and chronic (C) HCV infection using next-generation sequencing (NGS) data derived from the HCV hypervariable region 1 (HVR1). RESULTS: Using dinucleotide auto correlation (DAC), we identified physical-chemical (PhyChem) features of HVR1 variants. Significant (p < 9.58 × 10-4) differences in the means and frequency distributions of PhyChem features were found between HVR1 variants sampled from patients with recent vs chronic (R/C) infection. Moreover, the R-associated variants were found to occupy distinct and discrete PhyChem spaces. A radial basis function neural network classifier trained on the PhyChem features of intra-host HVR1 variants accurately classified R/C-HVR1 variants (classification accuracy (CA) = 94.85%; area under the ROC curve, AUROC = 0.979), in 10-fold cross-validation). The classifier was accurate in assigning individual HVR1 variants to R/C-classes in the testing set (CA = 84.15%; AUROC = 0.912) and in detection of infection duration (R/C-class) in patients (CA = 88.45%). Statistical tests and evaluation of the classifier on randomly-labeled datasets indicate that classifiers' CA is robust (p < 0.001) and unlikely due to random correlations (CA = 59.04% and AUROC = 0.50). CONCLUSIONS: The PhyChem features of intra-host HVR1 variants are strongly associated with the duration of HCV infection. Application of the PhyChem biomarkers to models for detection of the R/C-state of HCV infection in patients offers a new opportunity for detection of outbreaks and for molecular surveillance. The method will be available at https://webappx.cdc.gov/GHOST/ to the authenticated users of Global Hepatitis Outbreak and Surveillance Technology (GHOST) for further testing and validation.

Modeling the functional state of the reverse transcriptase of hepatitis B virus and its application to probing drug-protein interaction.

BACKGROUND: Herein, the predicted atomic structures of five representative sequence variants of the reverse transcriptase protein (RT) of hepatitis B virus (HBV), sampled from patients with rapid or slow response to tenofovir disoproxil fumarate (TDF) treatment, have been examined to identify structural variations between them in order to assess structural and functional properties of HBV-RT variants associated with the differential responses to TDF treatment. RESULTS: We utilized a hybrid computational approach to model the atomistic structures of HBV-RT/DNA-RNA/dATP and HBV-RT/DNA-RNA/TFV-DP (tenofovir diphosphate) complexes with the native hybrid DNA-RNA substrate in place. Multi-nanosecond molecular dynamics (MD) simulations of HBV-RT/DNA-RNA/dATP complexes revealed strong coupling of the natural nucleotide substrate, dATP, to the active site of the RT, and the differential involvement of the two putative magnesium cations (Mg(2+)) at the active site, whereby one Mg(2+) directly bridges the interaction between dATP and HBV-RT and the other serves as a coordinator to maintain an optimal configuration of the active site. Solvated interaction energy (SIE) calculated in MD simulations of HBV-RT/DNA-RNA/TFV-DP complexes indicate no differential binding affinity between TFV-DP and HBV-RT variants identified in patients with slow or rapid response to TDF treatment. CONCLUSION: The predicted atomic structures accurately represent functional states of HBV-RT. The equivalent interaction between TFV-DP and each examined HBV-RT variants suggests that binding affinity of TFV-DP to HBV-RT is not associated with delayed viral clearance.

Computational models of liver fibrosis progression for hepatitis C virus chronic infection.

BACKGROUND: Chronic infection with hepatitis C virus (HCV) is a risk factor for liver diseases such as fibrosis, cirrhosis and hepatocellular carcinoma. HCV genetic heterogeneity was hypothesized to be associated with severity of liver disease. However, no reliable viral markers predicting disease severity have been identified. Here, we report the utility of sequences from 3 HCV 1b genomic regions, Core, NS3 and NS5b, to identify viral genetic markers associated with fast and slow rate of fibrosis progression (RFP) among patients with and without liver transplantation (n = 42). METHODS: A correlation-based feature selection (CFS) method was used to detect and identify RFP-relevant viral markers. Machine-learning techniques, linear projection (LP) and Bayesian Networks (BN), were used to assess and identify associations between the HCV sequences and RFP. RESULTS: Both clustering of HCV sequences in LP graphs using physicochemical properties of nucleotides and BN analysis using polymorphic sites showed similarities among HCV variants sampled from patients with a similar RFP, while distinct HCV genetic properties were found associated with fast or slow RFP. Several RFP-relevant HCV sites were identified. Computational models parameterized using the identified sites accurately associated HCV strains with RFP in 70/30 split cross-validation (90-95% accuracy) and in validation tests (85-90% accuracy). Validation tests of the models constructed for patients with or without liver transplantation suggest that the RFP-relevant genetic markers identified in the HCV Core, NS3 and NS5b genomic regions may be useful for the prediction of RFP regardless of transplant status of patients. CONCLUSIONS: The apparent strong genetic association to RFP suggests that HCV genetic heterogeneity has a quantifiable effect on severity of liver disease, thus presenting opportunity for developing genetic assays for measuring virulence of HCV strains in clinical and public health settings.

Genetic host specificity of hepatitis E virus.

Hepatitis E virus (HEV) causes epidemic and sporadic cases of hepatitis worldwide. HEV genotypes 3 (HEV3) and 4 (HEV4) infect humans and animals, with swine being the primary reservoir. The relevance of HEV genetic diversity to host adaptation is poorly understood. We employed a Bayesian network (BN) analysis of HEV3 and HEV4 to detect epistatic connectivity among protein sites and its association with the host specificity in each genotype. The data imply coevolution among ∼70% of polymorphic sites from all HEV proteins and association of numerous coevolving sites with adaptation to swine or humans. BN models for individual proteins and domains of the nonstructural polyprotein detected the host origin of HEV strains with accuracy of 74-93% and 63-87%, respectively. These findings, taken together with lack of phylogenetic association to host, suggest that the HEV host specificity is a heritable and convergent phenotypic trait achievable through variety of genetic pathways (abundance), and explain a broad host range for HEV3 and HEV4.

A novel hepacivirus with an unusually long and intrinsically disordered NS5A protein in a wild Old World primate.

GB virus B (GBV-B; family Flaviviridae, genus Hepacivirus) has been studied in New World primates as a model for human hepatitis C virus infection, but the distribution of GBV-B and its relatives in nature has remained obscure. Here, we report the discovery of a novel and highly divergent GBV-B-like virus in an Old World monkey, the black-and-white colobus (Colobus guereza), in Uganda. The new virus, guereza hepacivirus (GHV), clusters phylogenetically with GBV-B and recently described hepaciviruses infecting African bats and North American rodents, and it shows evidence of ancient recombination with these other hepaciviruses. Direct sequencing of reverse-transcribed RNA from blood plasma from three of nine colobus monkeys yielded near-complete GHV genomes, comprising two distinct viral variants. The viruses contain an exceptionally long nonstructural 5A (NS5A) gene, approximately half of which codes for a protein with no discernible homology to known proteins. Computational structure-based analyses indicate that the amino terminus of the GHV NS5A protein may serve a zinc-binding function, similar to the NS5A of other viruses within the family Flaviviridae. However, the 521-amino-acid carboxy terminus is intrinsically disordered, reflecting an unusual degree of structural plasticity and polyfunctionality. These findings shed new light on the natural history and evolution of the hepaciviruses and on the extent of structural variation within the Flaviviridae.

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.

Convergence and coevolution of hepatitis B virus drug resistance.

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.

Epistatic connectivity among HCV genomic sites as a genetic marker of interferon resistance.

Until recently, the standard-of-care therapy of patients with HCV infection involves treatment with interferon (IFN) and ribavirin (RBV). Host demographic and genetic factors as well as HCV genetic heterogeneity have been shown to be associated with outcomes of therapy. Although resistance to IFN/RBV remains an important clinical and public health problem, there are no reliable genetic markers for the prediction of the therapy outcomes. Recently, it was shown that adaptation to IFN, a major constituent of the host innate immunity, is reflected in the HCV genetic composition and epistatic connectivity among polymorphic genomic sites, thus providing novel genetic markers of IFN resistance. Consideration of coordinated evolution among HCV genomic sites allows for identification of these genetic markers from short regions of the HCV genome and for accurate prediction of therapeutic outcomes. HCV genomic co-evolution offers a general framework for the detection of predisposition to IFN resistance, and possibly to resistance to direct-acting antivirals.

Coevolution of the hepatitis C virus polyprotein sites in patients on combined pegylated interferon and ribavirin therapy.

Genotype-specific sensitivity of the hepatitis C virus (HCV) to interferon-ribavirin (IFN-RBV) combination therapy and reduced HCV response to IFN-RBV as infection progresses from acute to chronic infection suggest that HCV genetic factors and intrahost HCV evolution play important roles in therapy outcomes. HCV polyprotein sequences (n = 40) from 10 patients with unsustainable response (UR) (breakthrough and relapse) and 10 patients with no response (NR) following therapy were identified through the Virahep-C study. Bayesian networks (BNs) were constructed to relate interrelationships among HCV polymorphic sites to UR/NR outcomes. All models showed an extensive interdependence of HCV sites and strong connections (P ≤ 0.003) to therapy response. Although all HCV proteins contributed to the networks, the topological properties of sites differed among proteins. E2 and NS5A together contributed ∼40% of all sites and ∼62% of all links to the polyprotein BN. The NS5A BN and E2 BN predicted UR/NR outcomes with 85% and 97.5% accuracy, respectively, in 10-fold cross-validation experiments. The NS5A model constructed using physicochemical properties of only five sites was shown to predict the UR/NR outcomes with 83.3% accuracy for 6 UR and 12 NR cases of the HALT-C study. Thus, HCV adaptation to IFN-RBV is a complex trait encoded in the interrelationships among many sites along the entire HCV polyprotein. E2 and NS5A generate broad epistatic connectivity across the HCV polyprotein and essentially shape intrahost HCV evolution toward the IFN-RBV resistance. Both proteins can be used to accurately predict the outcomes of IFN-RBV therapy.

Association of antigenic properties to structure of the hepatitis C virus NS3 protein.

Sequence heterogeneity substantially affects antigenic properties of the major epitope in the hepatitis C virus (HCV) NS3 protein. To facilitate protein engineering of NS3 antigens immunologically reactive with antibody against the broad diversity of HCV variants we constructed a set of Bayesian Networks (BN) for predicting antigenicity based on structural parameters. Using homology modeling, tertiary (3D) structures of NS3 variants with known antigenic properties were predicted. Energy force field estimated using the 3D-models was found to be most strongly associated with the antigenic properties. The best BN-models showed 100% accuracy of prediction of immunological reactivity with tested serum specimens in 10-fold cross validation. Bootstrap analyses of BN's constructed using selected features showed that secondary structure and electrostatic potential assessed from 3D-models are the most robust attributes associated with immunological reactivity of NS3 antigens. The data suggest that the BN models may guide the development of NS3 antigens with improved diagnostically relevant properties.

Coordinated evolution among hepatitis C virus genomic sites is coupled to host factors and resistance to interferon.

Machine-learning methods in the form of Bayesian networks (BN), linear projection (LP) and self-organizing tree (SOT) models were used to explore association among polymorphic sites within the HVR1 and NS5a regions of the HCV genome, host demographic factors (ethnicity, gender and age) and response to the combined interferon (IFN) and ribavirin (RBV) therapy. The BN models predicted therapy outcomes, gender and ethnicity with accuracy of 90%, 90% and 88.9%, respectively. The LP and SOT models strongly confirmed associations of the HVR1 and NS5A structures with response to therapy and demographic host factors identified by BN. The data indicate host specificity of HCV evolution and suggest the application of these models to predict outcomes of IFN/RBV therapy.

Spatial configuration of hepatitis E virus antigenic domain.

Hepatitis E virus (HEV) is a human pathogen that causes acute hepatitis. When an HEV capsid protein containing a 52-amino-acid deletion at the C terminus and a 111-amino-acid deletion at the N terminus is expressed in insect cells, the recombinant HEV capsid protein can self-assemble into a T=1 virus-like particle (VLP) that retains the antigenicity of the native HEV virion. In this study, we used cryoelectron microscopy and image reconstruction to show that anti-HEV monoclonal antibodies bind to the protruding domain of the capsid protein at the lateral side of the spikes. Molecular docking of the HEV VLP crystal structure revealed that Fab224 covered three surface loops of the recombinant truncated second open reading frame (ORF2) protein (PORF2) at the top part of the spike. We also determined the structure of a chimeric HEV VLP and located the inserted B-cell tag, an epitope of 11 amino acids coupled to the C-terminal end of the recombinant ORF2 protein. The binding site of Fab224 appeared to be distinct from the location of the inserted B-cell tag, suggesting that the chimeric VLP could elicit immunity against both HEV and an inserted foreign epitope. Therefore, the T=1 HEV VLP is a novel delivery system for displaying foreign epitopes at the VLP surface in order to induce antibodies against both HEV and the inserted epitope.

Artificial neural network for prediction of antigenic activity for a major conformational epitope in the hepatitis C virus NS3 protein.

MOTIVATION: Insufficient knowledge of general principles for accurate quantitative inference of biological properties from sequences is a major obstacle in the rationale design of proteins with predetermined activities. Due to this deficiency, protein engineering frequently relies on the use of computational approaches focused on the identification of quantitative structure-activity relationship (SAR) for each specific task. In the current article, a computational model was developed to define SAR for a major conformational antigenic epitope of the hepatitis C virus (HCV) non-structural protein 3 (NS3) in order to facilitate a rationale design of HCV antigens with improved diagnostically relevant properties. RESULTS: We present an artificial neural network (ANN) model that connects changes in the antigenic properties and structure of HCV NS3 recombinant proteins representing all 6 HCV genotypes. The ANN performed quantitative predictions of the enzyme immunoassay (EIA) Signal/Cutoff (S/Co) profiles from sequence information alone with 89.8% accuracy. Amino acid positions and physicochemical factors strongly associated with the HCV NS3 antigenic properties were identified. The positions most significantly contributing to the model were mapped on the NS3 3D structure. The location of these positions validates the major associations found by the ANN model between antigenicity and structure of the HCV NS3 proteins. AVAILABILITY: Matlab code is available at the following URL address: http://bio-ai.myeweb.net/box_widget.html