Assessing in vivo mutation frequencies and creating a high-resolution genome-wide map of fitness costs of Hepatitis C virus.

Autor: Tisthammer KH; Department of Biology, San Francisco State University, San Francisco, California, United States of America., Solis C; Department of Biology, San Francisco State University, San Francisco, California, United States of America., Orcales F; Department of Biology, San Francisco State University, San Francisco, California, United States of America., Nzerem M; Department of Biology, San Francisco State University, San Francisco, California, United States of America., Winstead R; Department of Biology, San Francisco State University, San Francisco, California, United States of America., Dong W; BC Centre for Excellence in HIV/AIDS, Vancouver, British Colombia, Canada., Joy JB; BC Centre for Excellence in HIV/AIDS, Vancouver, British Colombia, Canada.; Division of Infectious Diseases, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.; Bioinformatics Programme, University of British Columbia, Vancouver, British Colombia, Canada., Pennings PS; Department of Biology, San Francisco State University, San Francisco, California, United States of America.
Jazyk: angličtina
Zdroj: PLoS genetics [PLoS Genet] 2022 May 02; Vol. 18 (5), pp. e1010179. Date of Electronic Publication: 2022 May 02 (Print Publication: 2022).
DOI: 10.1371/journal.pgen.1010179
Abstrakt: Like many viruses, Hepatitis C Virus (HCV) has a high mutation rate, which helps the virus adapt quickly, but mutations come with fitness costs. Fitness costs can be studied by different approaches, such as experimental or frequency-based approaches. The frequency-based approach is particularly useful to estimate in vivo fitness costs, but this approach works best with deep sequencing data from many hosts are. In this study, we applied the frequency-based approach to a large dataset of 195 patients and estimated the fitness costs of mutations at 7957 sites along the HCV genome. We used beta regression and random forest models to better understand how different factors influenced fitness costs. Our results revealed that costs of nonsynonymous mutations were three times higher than those of synonymous mutations, and mutations at nucleotides A or T had higher costs than those at C or G. Genome location had a modest effect, with lower costs for mutations in HVR1 and higher costs for mutations in Core and NS5B. Resistance mutations were, on average, costlier than other mutations. Our results show that in vivo fitness costs of mutations can be site and virus specific, reinforcing the utility of constructing in vivo fitness cost maps of viral genomes.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
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