Antigenic cooperation among intrahost HCV variants organized into a complex network of cross-immunoreactivity

Fractal networks: Topology, dimension, and complexity

Over the past two decades, the study of self-similarity and fractality in discrete structures, particularly complex networks, has gained momentum. This surge of interest is fueled by the theoretical developments within the theory of complex networks and the practical demands of real-world applications. Nonetheless, translating the principles of fractal geometry from the domain of general topology, dealing with continuous or infinite objects, to finite structures in a mathematically rigorous way poses a formidable challenge. In this paper, we overview such a theory that allows to identify and analyze fractal networks through the innate methodologies of graph theory and combinatorics. It establishes the direct graph-theoretical analogs of topological (Lebesgue) and fractal (Hausdorff) dimensions in a way that naturally links them to combinatorial parameters that have been studied within the realm of graph theory for decades. This allows to demonstrate that the self-similarity in networks is defined by the patterns of intersection among densely connected network communities. Moreover, the theory bridges discrete and continuous definitions by demonstrating how the combinatorial characterization of Lebesgue dimension via graph representation by its subsets (subgraphs/communities) extends to general topological spaces. Using this framework, we rigorously define fractal networks and connect their properties with established combinatorial concepts, such as graph colorings and descriptive complexity. The theoretical framework surveyed here sets a foundation for applications to real-life networks and future studies of fractal characteristics of complex networks using combinatorial methods and algorithms.

Antigenic cooperation in viral populations: Transformation of functions of intra-host viral variants

In this paper, we study intra-host viral adaptation by antigenic cooperation - a mechanism of immune escape that serves as an alternative to the standard mechanism of escape by continuous genomic diversification and allows to explain a number of experimental observations associated with the establishment of chronic infections by highly mutable viruses. Within this mechanism, the topology of a cross-immunoreactivity network forces intra-host viral variants to specialize for complementary roles and adapt to the host's immune response as a quasi-social ecosystem. Here we study dynamical changes in immune adaptation caused by evolutionary and epidemiological events. First, we show that the emergence of a viral variant with altered antigenic features may result in a rapid re-arrangement of the viral ecosystem and a change in the roles played by existing viral variants. In particular, it may push the population under immune escape by genomic diversification towards the stable state of adaptation by antigenic cooperation. Next, we study the effect of a viral transmission between two chronically infected hosts, which results in the merging of two intra-host viral populations in the state of stable immune-adapted equilibrium. In this case, we also describe how the newly formed viral population adapts to the host's environment by changing the functions of its members. The results are obtained analytically for minimal cross-immunoreactivity networks and numerically for larger populations.

Open questions in the social lives of viruses

Social interactions among viruses occur whenever multiple viral genomes infect the same cells, hosts, or populations of hosts. Viral social interactions range from cooperation to conflict, occur throughout the viral world, and affect every stage of the viral lifecycle. The ubiquity of these social interactions means that they can determine the population dynamics, evolutionary trajectory, and clinical progression of viral infections. At the same time, social interactions in viruses raise new questions for evolutionary theory, providing opportunities to test and extend existing frameworks within social evolution. Many opportunities exist at this interface: Insights into the evolution of viral social interactions have immediate implications for our understanding of the fundamental biology and clinical manifestation of viral diseases. However, these opportunities are currently limited because evolutionary biologists only rarely study social evolution in viruses. Here, we bridge this gap by (1) summarizing the ways in which viruses can interact socially, including consequences for social evolution and evolvability; (2) outlining some open questions raised by viruses that could challenge concepts within social evolution theory; and (3) providing some illustrative examples, data sources, and conceptual questions, for studying the natural history of social viruses.

Reconstruction of Viral Variants via Monte Carlo Clustering

Identifying viral variants through clustering is essential for understanding the composition and structure of viral populations within and between hosts, which play a crucial role in disease progression and epidemic spread. This article proposes and validates novel Monte Carlo (MC) methods for clustering aligned viral sequences by minimizing either entropy or Hamming distance from consensuses. We validate these methods on four benchmarks: two SARS-CoV-2 interhost data sets and two HIV intrahost data sets. A parallelized version of our tool is scalable to very large data sets. We show that both entropy and Hamming distance-based MC clusterings discern the meaningful information from sequencing data. The proposed clustering methods consistently converge to similar clusterings across different runs. Finally, we show that MC clustering improves reconstruction of intrahost viral population from sequencing data.

Local Immunodeficiency: Combining Cross-Immunoreactivity Networks

This article continues the analysis of the recently observed phenomenon of local immunodeficiency (LI), which arises as a result of antigenic cooperation among intrahost viruses organized into a network of cross-immunoreactivity (CR). We study here what happens as the result of combining (connecting) the simplest CR networks, which have a stable state of LI. It turned out that many possibilities occur, particularly resulting in a change of roles of some viruses in the CR network. Our results also give some indications about a boundary of the set of CR networks with stable state of LI in the entire collection of all possible CR networks. Such borderline CR networks are characterized by only a marginally stable (neutral rather than stable) state of the LI, or by the existence of such subnetworks in a CR network that evolve independently of each other (although being connected).

Two-step fitness selection for intra-host variations in SARS-CoV-2

Spontaneous mutations introduce uncertainty into coronavirus disease 2019 (COVID-19) control procedures and vaccine development. Here, we perform a spatiotemporal analysis on intra-host single-nucleotide variants (iSNVs) in 402 clinical samples from 170 affected individuals, which reveals an increase in genetic diversity over time after symptom onset in individuals. Nonsynonymous mutations are overrepresented in the pool of iSNVs but underrepresented at the single-nucleotide polymorphism (SNP) level, suggesting a two-step fitness selection process: a large number of nonsynonymous substitutions are generated in the host (positive selection), and these substitutions tend to be unfixed as SNPs in the population (negative selection). Dynamic iSNV changes in subpopulations with different gender, age, illness severity, and viral shedding time displayed a varied fitness selection process among populations. Our study highlights that iSNVs provide a mutational pool shaping the rapid global evolution of the virus.

The evolution of cheating in viruses

The success of many viruses depends upon cooperative interactions between viral genomes. However, whenever cooperation occurs, there is the potential for 'cheats' to exploit that cooperation. We suggest that: (1) the biology of viruses makes viral cooperation particularly susceptible to cheating; (2) cheats are common across a wide range of viruses, including viral entities that are already well studied, such as defective interfering genomes, and satellite viruses. Consequently, the evolutionary theory of cheating could help us understand and manipulate viral dynamics, while viruses also offer new opportunities to study the evolution of cheating.

Technology dictates algorithms: recent developments in read alignment

Aligning sequencing reads onto a reference is an essential step of the majority of genomic analysis pipelines. Computational algorithms for read alignment have evolved in accordance with technological advances, leading to today's diverse array of alignment methods. We provide a systematic survey of algorithmic foundations and methodologies across 107 alignment methods, for both short and long reads. We provide a rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read alignment. We discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology.

Accurate assembly of minority viral haplotypes from next-generation sequencing through efficient noise reduction

Rapidly evolving RNA viruses continuously produce minority haplotypes that can become dominant if they are drug-resistant or can better evade the immune system. Therefore, early detection and identification of minority viral haplotypes may help to promptly adjust the patient’s treatment plan preventing potential disease complications. Minority haplotypes can be identified using next-generation sequencing, but sequencing noise hinders accurate identification. The elimination of sequencing noise is a non-trivial task that still remains open. Here we propose CliqueSNV based on extracting pairs of statistically linked mutations from noisy reads. This effectively reduces sequencing noise and enables identifying minority haplotypes with the frequency below the sequencing error rate. We comparatively assess the performance of CliqueSNV using an in vitro mixture of nine haplotypes that were derived from the mutation profile of an existing HIV patient. We show that CliqueSNV can accurately assemble viral haplotypes with frequencies as low as 0.1% and maintains consistent performance across short and long bases sequencing platforms.

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.

Analysis of heterogeneous genomic samples using image normalization and machine learning

BACKGROUND

Analysis of heterogeneous populations such as viral quasispecies is one of the most challenging bioinformatics problems. Although machine learning models are becoming to be widely employed for analysis of sequence data from such populations, their straightforward application is impeded by multiple challenges associated with technological limitations and biases, difficulty of selection of relevant features and need to compare genomic datasets of different sizes and structures.

RESULTS

We propose a novel preprocessing approach to transform irregular genomic data into normalized image data. Such representation allows to restate the problems of classification and comparison of heterogeneous populations as image classification problems which can be solved using variety of available machine learning tools. We then apply the proposed approach to two important problems in molecular epidemiology: inference of viral infection stage and detection of viral transmission clusters using next-generation sequencing data. The infection staging method has been applied to HCV HVR1 samples collected from 108 recently and 257 chronically infected individuals. The SVM-based image classification approach achieved more than 95% accuracy for both recently and chronically HCV-infected individuals. Clustering has been performed on the data collected from 33 epidemiologically curated outbreaks, yielding more than 97% accuracy.

CONCLUSIONS

Sequence image normalization method allows for a robust conversion of genomic data into numerical data and overcomes several issues associated with employing machine learning methods to viral populations. Image data also help in the visualization of genomic data. Experimental results demonstrate that the proposed method can be successfully applied to different problems in molecular epidemiology and surveillance of viral diseases. Simple binary classifiers and clustering techniques applied to the image data are equally or more accurate than other models.

Antigenic Variability

Protective vaccines for hypervariable pathogens are urgently needed. It has been proposed that amputating highly variable epitopes from vaccine antigens would induce the production of broadly protective antibodies targeting conserved epitopes. However, so far, these approaches have failed, partially because conserved epitopes are occluded in vivo and partially because co-localizing patterns of immunodominance and antigenic variability render variable epitopes the primary target for antibodies in natural infection. In this Perspective, to recast the challenge of vaccine development for hypervariable pathogens, I evaluate convergent mechanisms of adaptive variation, such as intrahost immune-mediated diversification, spatiotemporally defined antigenic space, and infection-enhancing cross-immunoreactivity. The requirements of broadly protective immune responses targeting variable pathogens are formulated in terms of cross-immunoreactivity, stoichiometric thresholds for neutralization, and the elicitation of antibodies targeting physicochemically conserved signatures within sequence variable domains.

Graph fractal dimension and the structure of fractal networks

Fractals are geometric objects that are self-similar at different scales and whose geometric dimensions differ from so-called fractal dimensions. Fractals describe complex continuous structures in nature. Although indications of self-similarity and fractality of complex networks has been previously observed, it is challenging to adapt the machinery from the theory of fractality of continuous objects to discrete objects such as networks. In this article, we identify and study fractal networks using the innate methods of graph theory and combinatorics. We establish analogues of topological (Lebesgue) and fractal (Hausdorff) dimensions for graphs and demonstrate that they are naturally related to known graph-theoretical characteristics: rank dimension and product dimension. Our approach reveals how self-similarity and fractality of a network are defined by a pattern of overlaps between densely connected network communities. It allows us to identify fractal graphs, explore the relations between graph fractality, graph colourings and graph descriptive complexity, and analyse the fractality of several classes of graphs and network models, as well as of a number of real-life networks. We demonstrate the application of our framework in evolutionary biology and virology by analysing networks of viral strains sampled at different stages of evolution inside their hosts. Our methodology revealed gradual self-organization of intra-host viral populations over the course of infection and their adaptation to the host environment. The obtained results lay a foundation for studying fractal properties of complex networks using combinatorial methods and algorithms.

Social evolution of innate immunity evasion in a virus

Antiviral immunity has been studied extensively from the perspective of virus-cell interactions, yet the role of virus-virus interactions remains poorly addressed. Here we demonstrate that viral escape from interferon (IFN)-based innate immunity is a social process in which IFN-stimulating viruses determine the fitness of neighbor viruses. We propose a general and simple social-evolution framework to analyze how natural selection acts on IFN shutdown, and validate it in cell cultures and mice infected with vesicular stomatitis virus (VSV). Additionally, we find that IFN shutdown is costly because it reduces short-term viral progeny production, thus fulfilling the definition of an altruistic trait. Hence, in well-mixed populations the IFN-blocking wild-type virus is susceptible to invasion by IFN-stimulating variants, and spatial structure consequently determines whether IFN shutdown can evolve. Our findings reveal that fundamental social evolution rules govern viral innate immunity evasion and virulence, and suggest possible antiviral interventions.

Local immunodeficiency: Minimal networks and stability

Some basic aspects of the recently discovered phenomenon of local immunodeficiency (Skums et al. [1]) generated by antigenic cooperation in cross-immunoreactivity (CR) networks are investigated. We prove that local immunodeficiency (LI) that is stable under perturbations already occurs in very small networks and under general conditions on their parameters. Therefore our results are applicable not only to Hepatitis C where CR networks are known to be large (Skums et al. [1]), but also to other diseases with CR. A major necessary feature of such networks is the non-homogeneity of their topology. It is also shown that one can construct larger CR networks with stable LI by using small networks with stable LI as their building blocks. Our results imply that stable LI occurs in networks with quite general topology. In particular, the scale-free property of a CR network, assumed in Skums et al. [1], is not required.

Bottlenecks in the transmission of porcine reproductive and respiratory syndrome virus (PRRSV1) to naïve pigs and the quasi-species variation of the virus during infection in vaccinated pigs

This paper describes the results of two experiments regarding porcine reproductive and respiratory syndrome virus (PRRSV1): the first one studied the existence of bottlenecks in an experimental one-to-one model of transmission in pigs; while the second analysed the differences between viral quasi-species in vaccinated pigs that developed shorter or longer viraemias after natural challenge. Serum samples, as well as the initial inoculum, were deep-sequenced and a viral quasi-species was constructed per sample. For the first experiment, the results consistently reported a reduction in the quasi-species diversity after a transmission event, pointing to the existence of bottlenecks during PRRSV1 transmission. However, despite the identified preferred and un-preferred transmitted variants not being randomly distributed along the virus genome, it was not possible to identify any variant producing a structural change in any viral protein. In contrast, the mutations identified in GP2, nsp9 and M of the second experiment pointed to changes in the amino acid charges and the viral RNA-dependent RNA polymerase structure. The fact that the affected proteins are known targets of the immunity against PRRSV, plus the differential level of neutralizing antibodies present in pigs developing short or long viraemias, suggests that the immune response selected those changes.

Sociovirology: Conflict, Cooperation, and Communication among Viruses

Viruses are involved in various interactions both within and between infected cells. Social evolution theory offers a conceptual framework for how virus-virus interactions, ranging from conflict to cooperation, have evolved. A critical examination of these interactions could expand our understanding of viruses and be exploited for epidemiological and medical interventions.

Beneficial coinfection can promote within-host viral diversity

In many viral infections, a large number of different genetic variants can coexist within a host, leading to more virulent infections that are better able to evolve antiviral resistance and adapt to new hosts. But how is this diversity maintained? Why do faster-growing variants not outcompete slower-growing variants, and erode this diversity? One hypothesis is if there are mutually beneficial interactions between variants, with host cells infected by multiple different viral genomes producing more, or more effective, virions. We modelled this hypothesis with both mathematical models and simulations, and found that moderate levels of beneficial coinfection can maintain high levels of coexistence, even when coinfection is relatively rare, and when there are significant fitness differences between competing variants. Rare variants are more likely to be coinfecting with a different variant, and hence beneficial coinfection increases the relative fitness of rare variants through negative frequency dependence, and maintains diversity. We further find that coexisting variants sometimes reach unequal frequencies, depending on the extent to which different variants benefit from coinfection, and the ratio of variants which leads to the most productive infected cells. These factors could help drive the evolution of defective interfering particles, and help to explain why the different segments of multipartite viruses persist at different equilibrium frequencies.

Transmissibility of intra-host hepatitis C virus variants

Background

Intra-host hepatitis C virus (HCV) populations are genetically heterogeneous and organized in subpopulations. With the exception of blood transfusions, transmission of HCV occurs via a small number of genetic variants, the effect of which is frequently described as a bottleneck. Stochasticity of transmission associated with the bottleneck is usually used to explain genetic differences among HCV populations identified in the source and recipient cases, which may be further exacerbated by intra-host HCV evolution and differential biological capacity of HCV variants to successfully establish a population in a new host.

Results

Transmissibility was formulated as a property that can be measured from experimental Ultra-Deep Sequencing (UDS) data. The UDS data were obtained from one large hepatitis C outbreak involving an epidemiologically defined source and 18 recipient cases. k-Step networks of HCV variants were constructed and used to identify a potential association between transmissibility and network centrality of individual HCV variants from the source. An additional dataset obtained from nine other HCV outbreaks with known directionality of transmission was used for validation.

Transmissibility was not found to be dependent on high frequency of variants in the source, supporting the earlier observations of transmission of minority variants. Among all tested measures of centrality, the highest correlation of transmissibility was found with Hamming centrality (r = 0.720; p = 1.57 E-71). Correlation between genetic distances and differences in transmissibility among HCV variants from the source was found to be 0.3276 (Mantel Test, p = 9.99 E-5), indicating association between genetic proximity and transmissibility. A strong correlation ranging from 0.565–0.947 was observed between Hamming centrality and transmissibility in 7 of the 9 additional transmission clusters (p < 0.05).

Conclusions

Transmission is not an exclusively stochastic process. Transmissibility, as formally measured in this study, is associated with certain biological properties that also define location of variants in the genetic space occupied by the HCV strain from the source. The measure may also be applicable to other highly heterogeneous viruses. Besides improving accuracy of outbreak investigations, this finding helps with the understanding of molecular mechanisms contributing to establishment of chronic HCV infection.

Transmission Bottleneck Size Estimation from Pathogen Deep-Sequencing Data, with an Application to Human Influenza A Virus

The bottleneck governing infectious disease transmission describes the size of the pathogen population transferred from the donor to the recipient host. Accurate quantification of the bottleneck size is particularly important for rapidly evolving pathogens such as influenza virus, as narrow bottlenecks reduce the amount of transferred viral genetic diversity and, thus, may decrease the rate of viral adaptation. Previous studies have estimated bottleneck sizes governing viral transmission by using statistical analyses of variants identified in pathogen sequencing data. These analyses, however, did not account for variant calling thresholds and stochastic viral replication dynamics within recipient hosts. Because these factors can skew bottleneck size estimates, we introduce a new method for inferring bottleneck sizes that accounts for these factors. Through the use of a simulated data set, we first show that our method, based on beta-binomial sampling, accurately recovers transmission bottleneck sizes, whereas other methods fail to do so. We then apply our method to a data set of influenza A virus (IAV) infections for which viral deep-sequencing data from transmission pairs are available. We find that the IAV transmission bottleneck size estimates in this study are highly variable across transmission pairs, while the mean bottleneck size of 196 virions is consistent with a previous estimate for this data set. Furthermore, regression analysis shows a positive association between estimated bottleneck size and donor infection severity, as measured by temperature. These results support findings from experimental transmission studies showing that bottleneck sizes across transmission events can be variable and influenced in part by epidemiological factors.IMPORTANCE The transmission bottleneck size describes the size of the pathogen population transferred from the donor to the recipient host and may affect the rate of pathogen adaptation within host populations. Recent advances in sequencing technology have enabled bottleneck size estimation from pathogen genetic data, although there is not yet a consistency in the statistical methods used. Here, we introduce a new approach to infer the bottleneck size that accounts for variant identification protocols and noise during pathogen replication. We show that failing to account for these factors leads to an underestimation of bottleneck sizes. We apply this method to an existing data set of human influenza virus infections, showing that transmission is governed by a loose, but highly variable, transmission bottleneck whose size is positively associated with the severity of infection of the donor. Beyond advancing our understanding of influenza virus transmission, we hope that this work will provide a standardized statistical approach for bottleneck size estimation for viral pathogens.

Toward a microbial Neolithic revolution in buildings

The Neolithic revolution--the transition of our species from hunter and gatherer to cultivator--began approximately 14,000 years ago and is essentially complete for macroscopic food. Humans remain largely pre-Neolithic in our relationship with microbes but starting with the gut we continue our hundred-year project of approaching the ability to assess and cultivate benign microbiomes in our bodies. Buildings are analogous to the body and it is time to ask what it means to cultivate benign microbiomes in our built environment. A critical distinction is that we have not found, or invented, niches in buildings where healthful microbial metabolism occurs and/or could be cultivated. Key events affecting the health and healthfulness of buildings such as a hurricane leading to a flood or a burst pipe occur only rarely and unpredictably. The cause may be transient but the effects can be long lasting and, e.g., for moisture damage, cumulative. Non-invasive "building tomography" could find moisture and "sentinel microbes" could record the integral of transient growth. "Seed" microbes are metabolically inert cells able to grow when conditions allow. All microbes and their residue present actinic molecules including immunological epitopes (molecular shapes). The fascinating hygiene and microbial biodiversity hypotheses propose that a healthy immune system requires exposure to a set of microbial epitopes that is rich in diversity. A particular conjecture is that measures of the richness of diversity derived from microbiome next-generation sequencing (NGS) can be mechanistically coupled to--rather than merely correlated with some measures of--human health. These hypotheses and conjectures inspire workers and funders but an alternative is also consequent to the first Neolithic revolution: That the genetic uniformity of contemporary foods may also decrease human exposure to molecular biodiversity in a heath-relevant manner. Understanding the consequences--including the unintended consequences of the first Neolithic revolution--will inform and help us benignly implement the second--the microbial--Neolithic revolution.

TCR gene-modified T cells can efficiently treat established hepatitis C-associated hepatocellular carcinoma tumors

The success in recent clinical trials using T cell receptor (TCR)-genetically engineered T cells to treat melanoma has encouraged the use of this approach toward other malignancies and viral infections. Although hepatitis C virus (HCV) infection is being treated with a new set of successful direct anti-viral agents, potential for virologic breakthrough or relapse by immune escape variants remains. Additionally, many HCV+ patients have HCV-associated disease, including hepatocellular carcinoma (HCC), which does not respond to these novel drugs. Further exploration of other approaches to address HCV infection and its associated disease are highly warranted. Here, we demonstrate the therapeutic potential of PBL-derived T cells genetically engineered with a high-affinity, HLA-A2-restricted, HCV NS3:1406-1415-reactive TCR. HCV1406 TCR-transduced T cells can recognize naturally processed antigen and elicit CD8-independent recognition of both peptide-loaded targets and HCV+ human HCC cell lines. Furthermore, these cells can mediate regression of established HCV+ HCC in vivo. Our results suggest that HCV TCR-engineered antigen-reactive T cells may be a plausible immunotherapy option to treat HCV-associated malignancies, such as HCC.

Network Analysis of the Chronic Hepatitis C Virome Defines Hypervariable Region 1 Evolutionary Phenotypes in the Context of Humoral Immune Responses

Hypervariable region 1 (HVR1) of hepatitis C virus (HCV) comprises the first 27 N-terminal amino acid residues of E2. It is classically seen as the most heterogeneous region of the HCV genome. In this study, we assessed HVR1 evolution by using ultradeep pyrosequencing for a cohort of treatment-naive, chronically infected patients over a short, 16-week period. Organization of the sequence set into connected components that represented single nucleotide substitution events revealed a network dominated by highly connected, centrally positioned master sequences. HVR1 phenotypes were observed to be under strong purifying (stationary) and strong positive (antigenic drift) selection pressures, which were coincident with advancing patient age and cirrhosis of the liver. It followed that stationary viromes were dominated by a single HVR1 variant surrounded by minor variants comprised from conservative single amino acid substitution events. We present evidence to suggest that neutralization antibody efficacy was diminished for stationary-virome HVR1 variants. Our results identify the HVR1 network structure during chronic infection as the preferential dominance of a single variant within a narrow sequence space.

IMPORTANCE

HCV infection is often asymptomatic, and chronic infection is generally well established in advance of initial diagnosis and subsequent treatment. HVR1 can undergo rapid sequence evolution during acute infection, and the variant pool is typically seen to diverge away from ancestral sequences as infection progresses from the acute to the chronic phase. In this report, we describe HVR1 viromes in chronically infected patients that are defined by a dominant epitope located centrally within a narrow variant pool. Our findings suggest that weakened humoral immune activity, as a consequence of persistent chronic infection, allows for the acquisition and maintenance of host-specific adaptive mutations at HVR1 that reflect virus fitness.

Comparative Immunogenicity in Rabbits of the Polypeptides Encoded by the 5' Terminus of Hepatitis C Virus RNA

Recent studies on the primate protection from HCV infection stressed the importance of immune response against structural viral proteins. Strong immune response against nucleocapsid (core) protein was difficult to achieve, requesting further experimentation in large animals. Here, we analyzed the immunogenicity of core aa 1–173, 1–152, and 147–191 and of its main alternative reading frame product F-protein in rabbits. Core aa 147–191 was synthesized; other polypeptides were obtained by expression in E. coli. Rabbits were immunized by polypeptide primes followed by multiple boosts and screened for specific anti-protein and anti-peptide antibodies. Antibody titers to core aa 147–191 reached 10⁵; core aa 1–152, 5 × 10⁵; core aa 1–173 and F-protein, 10⁶. Strong immunogenicity of the last two proteins indicated that they may compete for the induction of immune response. The C-terminally truncated core was also weakly immunogenic on the T-cell level. To enhance core-specific cellular response, we immunized rabbits with the core aa 1–152 gene forbidding F-protein formation. Repeated DNA immunization induced a weak antibody and sustained proliferative response of broad specificity confirming a gain of cellular immunogenicity. Epitopes recognized in rabbits overlapped those in HCV infection. Our data promotes the use of rabbits for the immunogenicity tests of prototype HCV vaccines.