bioRxivEvolutionary Biology
High resolution evolutionary analysis of within-host hepatitis C virus infection
J. Raghwani, C.-H. Wu, C. K. Ho, M. de Jong, R. Molenkamp, J. Schinkel, O. G. Pybus, K. A. Lythgoe
Abstract
Despite the breakthroughs in the treatment of HCV infection in recent years, we have a limited understanding of how virus diversity generated within individuals impacts the evolution and spread of HCV variants at the population scale. Addressing this gap will be important for building models for molecular epidemiology, which can identify main sources of disease transmission and evaluate the risks of drug-resistance mutations emerging and disseminating in a population. Here, we have undertaken a high-resolution analysis of HCV within-host evolution from four individuals co-infected with HIV. Specifically, we used long-read, deep-sequenced data of the full-length HCV envelope glycoprotein, longitudinally sampled from acute to chronic HCV infection to investigate the underlying viral evolutionary dynamics. In three individuals we found strong statistical support for population structure maintaining within-host HCV genetic diversity. Furthermore, we found significant variation in rates of molecular evolution among different regions of the HCV envelope region, both within and between individuals. Lastly, we report the first estimate of the within-host population genetic rate of recombination for HCV (0.28 x 10-7 recombinations per site per day; interquartile range: 0.13-1.05 x 10-7), which is two orders of magnitude lower than that estimated for HIV-1, and four orders of magnitude lower than the nucleotide substitution rate of the HCV envelope gene. Together, these observations indicate that population structure and strong genetic linkage shapes within-host HCV evolutionary dynamics. These results will guide the future investigation of potential HCV drug resistance adaptation during infection, and at the population scale.
- organism: human
- assay: long-read sequencing
- research focus: chromatin & epigenetic regulation
- approach: statistical model
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