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

Abstract

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.

Keywords: cyber-molecular assay; infection stage; machine learning; quasispecies; viral infection.

Figure 1.

(A) Examples of genetic viral networks for a persistently infected individual and (B) for a recently infected. The viral network of the recently infected host has the structural properties typical for scale-free networks.

Figure 2.

(A) Heatmap of absolute values of pairwise correlations between features. (B) 3-Dimensional projection of recently and persistently infected hosts (with highly correlated features removed).

Figure 3.

Box plots of feature distributions for persistent (left box plot on each graph) and recent (right box plot on each graph) intra-host HCV populations. The plots are in the same order as in Supplementary Table S1.

Figure 4.

ROC curves of classification models.