HCV adaptation to HIV coinfection

Functional HIV-1/HCV cross-reactive antibodies isolated from a chronically co-infected donor

Despite prolific efforts to characterize the antibody response to human immunodeficiency virus type 1 (HIV-1) and hepatitis C virus (HCV) mono-infections, the response to chronic co-infection with these two ever-evolving viruses is poorly understood. Here, we investigate the antibody repertoire of a chronically HIV-1/HCV co-infected individual using linking B cell receptor to antigen specificity through sequencing (LIBRA-seq). We identify five HIV-1/HCV cross-reactive antibodies demonstrating binding and functional cross-reactivity between HIV-1 and HCV envelope glycoproteins. All five antibodies show exceptional HCV neutralization breadth and effector functions against both HIV-1 and HCV. One antibody, mAb688, also cross-reacts with influenza and coronaviruses, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We examine the development of these antibodies using next-generation sequencing analysis and lineage tracing and find that somatic hypermutation established and enhanced this reactivity. These antibodies provide a potential future direction for therapeutic and vaccine development against current and emerging infectious diseases. More broadly, chronic co-infection represents a complex immunological challenge that can provide insights into the fundamental rules that underly antibody-antigen specificity.

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.