Viral Diversity Based on Next-Generation Sequencing of HIV-1 Provides Precise Estimates of Infection Recency and Time Since Infection.

HIV-1 diversity infection recency next-generation sequencing time since 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:
19 06 2019
Historique:
received: 06 12 2018
accepted: 01 03 2019
pubmed: 6 3 2019
medline: 6 3 2019
entrez: 6 3 2019
Statut: ppublish

Résumé

Human immunodeficiency virus type 1 (HIV-1) genetic diversity increases over the course of infection and can be used to infer the time since infection and, consequently, infection recency, which are crucial for HIV-1 surveillance and the understanding of viral pathogenesis. We considered 313 HIV-infected individuals for whom reliable estimates of infection dates and next-generation sequencing (NGS)-derived nucleotide frequency data were available. Fractions of ambiguous nucleotides, obtained by population sequencing, were available for 207 samples. We assessed whether the average pairwise diversity calculated using NGS sequences provided a more exact prediction of the time since infection and classification of infection recency (<1 year after infection), compared with the fraction of ambiguous nucleotides. NGS-derived average pairwise diversity classified an infection as recent with a sensitivity of 88% and a specificity of 85%. When considering only the 207 samples for which fractions of ambiguous nucleotides were available, the NGS-derived average pairwise diversity exhibited a higher sensitivity (90% vs 78%) and specificity (95% vs 67%) than the fraction of ambiguous nucleotides. Additionally, the average pairwise diversity could be used to estimate the time since infection with a mean absolute error of 0.84 years, compared with 1.03 years for the fraction of ambiguous nucleotides. Viral diversity based on NGS data is more precise than that based on population sequencing in its ability to predict infection recency and provides an estimated time since infection that has a mean absolute error of <1 year.

Sections du résumé

BACKGROUND
Human immunodeficiency virus type 1 (HIV-1) genetic diversity increases over the course of infection and can be used to infer the time since infection and, consequently, infection recency, which are crucial for HIV-1 surveillance and the understanding of viral pathogenesis.
METHODS
We considered 313 HIV-infected individuals for whom reliable estimates of infection dates and next-generation sequencing (NGS)-derived nucleotide frequency data were available. Fractions of ambiguous nucleotides, obtained by population sequencing, were available for 207 samples. We assessed whether the average pairwise diversity calculated using NGS sequences provided a more exact prediction of the time since infection and classification of infection recency (<1 year after infection), compared with the fraction of ambiguous nucleotides.
RESULTS
NGS-derived average pairwise diversity classified an infection as recent with a sensitivity of 88% and a specificity of 85%. When considering only the 207 samples for which fractions of ambiguous nucleotides were available, the NGS-derived average pairwise diversity exhibited a higher sensitivity (90% vs 78%) and specificity (95% vs 67%) than the fraction of ambiguous nucleotides. Additionally, the average pairwise diversity could be used to estimate the time since infection with a mean absolute error of 0.84 years, compared with 1.03 years for the fraction of ambiguous nucleotides.
CONCLUSIONS
Viral diversity based on NGS data is more precise than that based on population sequencing in its ability to predict infection recency and provides an estimated time since infection that has a mean absolute error of <1 year.

Identifiants

pubmed: 30835266
pii: 5369731
doi: 10.1093/infdis/jiz094
doi:

Types de publication

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

Langues

eng

Sous-ensembles de citation

IM

Pagination

254-265

Informations de copyright

© The Author(s) 2019. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.

Auteurs

Louisa A Carlisle (LA)

Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich.
Institute of Medical Virology, University of Zurich, Zurich.

Teja Turk (T)

Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich.
Institute of Medical Virology, University of Zurich, Zurich.

Katharina Kusejko (K)

Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich.
Institute of Medical Virology, University of Zurich, Zurich.

Karin J Metzner (KJ)

Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich.
Institute of Medical Virology, University of Zurich, Zurich.

Christine Leemann (C)

Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich.
Institute of Medical Virology, University of Zurich, Zurich.

Corinne D Schenkel (CD)

Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich.
Institute of Medical Virology, University of Zurich, Zurich.

Nadine Bachmann (N)

Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich.
Institute of Medical Virology, University of Zurich, Zurich.

Susana Posada (S)

Department of Biosystems Science and Engineering, ETH Zurich.
SIB Swiss Institute of Bioinformatics, University of Basel, Basel.

Niko Beerenwinkel (N)

Department of Biosystems Science and Engineering, ETH Zurich.
SIB Swiss Institute of Bioinformatics, University of Basel, Basel.

Jürg Böni (J)

Institute of Medical Virology, University of Zurich, Zurich.
Swiss National Center for Retroviruses, University of Zurich, Zurich.

Sabine Yerly (S)

Laboratory of Virology and Division of Infectious Diseases, Geneva University Hospital, Geneva.

Thomas Klimkait (T)

Molecular Virology, Department of Biomedicine-Petersplatz, University of Basel, Basel.

Matthieu Perreau (M)

Division of Immunology and Allergy, Lausanne University Hospital, Lausanne.

Dominique L Braun (DL)

Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich.
Institute of Medical Virology, University of Zurich, Zurich.

Andri Rauch (A)

Department of Infectious Diseases, Bern University Hospital, University of Bern, Bern.

Alexandra Calmy (A)

Laboratory of Virology and Division of Infectious Diseases, Geneva University Hospital, Geneva.

Matthias Cavassini (M)

Division of Infectious Diseases, Lausanne University Hospital, Lausanne.

Manuel Battegay (M)

Division of Infectious Diseases and Hospital Epidemiology, University Hospital Basel, Basel.

Pietro Vernazza (P)

Division of Infectious Diseases, Cantonal Hospital St. Gallen, St. Gallen.

Enos Bernasconi (E)

Division of Infectious Diseases, Regional Hospital Lugano, Lugano, Switzerland.

Huldrych F Günthard (HF)

Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich.
Institute of Medical Virology, University of Zurich, Zurich.

Roger D Kouyos (RD)

Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich.
Institute of Medical Virology, University of Zurich, Zurich.

Classifications MeSH