High-Resolution Evolutionary Analysis of Within-Host Hepatitis C Virus Infection.


Journal

The Journal of infectious diseases
ISSN: 1537-6613
Titre abrégé: J Infect Dis
Pays: United States
ID NLM: 0413675

Informations de publication

Date de publication:
05 05 2019
Historique:
received: 18 09 2018
accepted: 28 12 2018
pubmed: 3 1 2019
medline: 17 3 2020
entrez: 3 1 2019
Statut: ppublish

Résumé

Despite recent breakthroughs in treatment of hepatitis C virus (HCV) infection, we have limited understanding of how virus diversity generated within individuals impacts the evolution and spread of HCV variants at the population scale. Addressing this gap is important for identifying the main sources of disease transmission and evaluating the risk of drug-resistance mutations emerging and disseminating in a population. We have undertaken a high-resolution analysis of HCV within-host evolution from 4 individuals coinfected with human immunodeficiency virus 1 (HIV-1). We used long-read, deep-sequenced data of full-length HCV envelope glycoprotein, longitudinally sampled from acute to chronic HCV infection to investigate the underlying viral population and evolutionary dynamics. We found statistical support for population structure maintaining the within-host HCV genetic diversity in 3 out of 4 individuals. We also report the first population genetic estimate of the within-host recombination rate for HCV (0.28 × 10-7 recombination/site/year), which is considerably lower than that estimated for HIV-1 and the overall nucleotide substitution rate estimated during HCV infection. Our findings 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.

Sections du résumé

BACKGROUND
Despite recent breakthroughs in treatment of hepatitis C virus (HCV) infection, we have limited understanding of how virus diversity generated within individuals impacts the evolution and spread of HCV variants at the population scale. Addressing this gap is important for identifying the main sources of disease transmission and evaluating the risk of drug-resistance mutations emerging and disseminating in a population.
METHODS
We have undertaken a high-resolution analysis of HCV within-host evolution from 4 individuals coinfected with human immunodeficiency virus 1 (HIV-1). We used long-read, deep-sequenced data of full-length HCV envelope glycoprotein, longitudinally sampled from acute to chronic HCV infection to investigate the underlying viral population and evolutionary dynamics.
RESULTS
We found statistical support for population structure maintaining the within-host HCV genetic diversity in 3 out of 4 individuals. We also report the first population genetic estimate of the within-host recombination rate for HCV (0.28 × 10-7 recombination/site/year), which is considerably lower than that estimated for HIV-1 and the overall nucleotide substitution rate estimated during HCV infection.
CONCLUSIONS
Our findings 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.

Identifiants

pubmed: 30602023
pii: 5269822
doi: 10.1093/infdis/jiy747
pmc: PMC6500553
mid: EMS81302
doi:

Substances chimiques

Antiviral Agents 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1722-1729

Subventions

Organisme : Wellcome Trust
ID : 107652
Pays : United Kingdom
Organisme : European Research Council
ID : 614725
Pays : International

Informations de copyright

© The Author(s) 2019. Published by Oxford University Press for the Infectious Diseases Society of America.

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Auteurs

Jayna Raghwani (J)

Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, United Kingdom.

Chieh-Hsi Wu (CH)

Department of Statistics, University of Oxford, United Kingdom.

Cynthia K Y Ho (CKY)

Department of Medical Microbiology, Amsterdam University Medical Center, the Netherlands.

Menno De Jong (M)

Department of Medical Microbiology, Amsterdam University Medical Center, the Netherlands.

Richard Molenkamp (R)

Department of Medical Microbiology, Amsterdam University Medical Center, the Netherlands.

Janke Schinkel (J)

Department of Medical Microbiology, Amsterdam University Medical Center, the Netherlands.

Oliver G Pybus (OG)

Department of Zoology, University of Oxford, United Kingdom.

Katrina A Lythgoe (KA)

Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, United Kingdom.

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Classifications MeSH