James Lara

A Novel Hepacivirus with an Unusually Long and Intrinsically Disordered NS5A Protein in a Wild Old World Primate

ABSTRACT 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.

Spatial Configuration of Hepatitis E Virus Antigenic Domain

ABSTRACT 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.

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.

Coevolution of the Hepatitis C Virus Polyprotein Sites in Patients on Combined Pegylated Interferon and Ribavirin Therapy

ABSTRACT 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.

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.

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

Genetic host specificity of hepatitis E virus.

Abstract 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.

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

BackgroundChronic 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).MethodsA 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.ResultsBoth 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.ConclusionsThe 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.

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.