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Title: Phylogenetic patterns recover known HIV epidemiological relationships and reveal common transmission of multiple variants

Abstract

The growth of human immunodeficiency virus (HIV) sequence databases resulting from drug resistance testing has motivated efforts using phylogenetic methods to assess how HIV spreads. Such inference is potentially both powerful and useful for tracking the epidemiology of HIV and the allocation of resources to prevention campaigns. We recently used simulation and a small number of illustrative cases to show that certain phylogenetic patterns are associated with different types of epidemiological linkage. Our original approach was later generalized for large next-generation sequencing datasets and implemented as a free computational pipeline. Previous work has claimed that direction and directness of transmission could not be established from phylogeny because one could not be sure that there were no intervening or missing links involved. We address this issue by investigating phylogenetic patterns from 272 previously identified HIV transmission chains with 955 transmission pairs representing diverse geography, risk groups, subtypes, and genomic regions. These HIV transmissions had known linkage based on epidemiological information such as partner studies, mother-to-child transmission, pairs identified by contact tracing, and criminal cases. Here, we show that the resulting phylogeny inferred from real HIV genetic sequences indeed reveals distinct patterns associated with direct transmission contra transmissions from a common source.more » Thus, our results establish how to interpret phylogenetic trees based on HIV sequences when tracking who-infected-whom, when and how genetic information can be used for improved tracking of HIV spread. We also investigate limitations that stem from limited sampling and genetic time-trends in the donor and recipient HIV populations.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1479964
Report Number(s):
LA-UR-18-28043
Journal ID: ISSN 2058-5276
Grant/Contract Number:  
AC52-06NA25396
Resource Type:
Accepted Manuscript
Journal Name:
Nature Microbiology
Additional Journal Information:
Journal Volume: 3; Journal Issue: 9; Journal ID: ISSN 2058-5276
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Biological Science

Citation Formats

Leitner, Thomas Kenneth, and Romero-Severson, Ethan. Phylogenetic patterns recover known HIV epidemiological relationships and reveal common transmission of multiple variants. United States: N. p., 2018. Web. doi:10.1038/s41564-018-0204-9.
Leitner, Thomas Kenneth, & Romero-Severson, Ethan. Phylogenetic patterns recover known HIV epidemiological relationships and reveal common transmission of multiple variants. United States. doi:10.1038/s41564-018-0204-9.
Leitner, Thomas Kenneth, and Romero-Severson, Ethan. Mon . "Phylogenetic patterns recover known HIV epidemiological relationships and reveal common transmission of multiple variants". United States. doi:10.1038/s41564-018-0204-9. https://www.osti.gov/servlets/purl/1479964.
@article{osti_1479964,
title = {Phylogenetic patterns recover known HIV epidemiological relationships and reveal common transmission of multiple variants},
author = {Leitner, Thomas Kenneth and Romero-Severson, Ethan},
abstractNote = {The growth of human immunodeficiency virus (HIV) sequence databases resulting from drug resistance testing has motivated efforts using phylogenetic methods to assess how HIV spreads. Such inference is potentially both powerful and useful for tracking the epidemiology of HIV and the allocation of resources to prevention campaigns. We recently used simulation and a small number of illustrative cases to show that certain phylogenetic patterns are associated with different types of epidemiological linkage. Our original approach was later generalized for large next-generation sequencing datasets and implemented as a free computational pipeline. Previous work has claimed that direction and directness of transmission could not be established from phylogeny because one could not be sure that there were no intervening or missing links involved. We address this issue by investigating phylogenetic patterns from 272 previously identified HIV transmission chains with 955 transmission pairs representing diverse geography, risk groups, subtypes, and genomic regions. These HIV transmissions had known linkage based on epidemiological information such as partner studies, mother-to-child transmission, pairs identified by contact tracing, and criminal cases. Here, we show that the resulting phylogeny inferred from real HIV genetic sequences indeed reveals distinct patterns associated with direct transmission contra transmissions from a common source. Thus, our results establish how to interpret phylogenetic trees based on HIV sequences when tracking who-infected-whom, when and how genetic information can be used for improved tracking of HIV spread. We also investigate limitations that stem from limited sampling and genetic time-trends in the donor and recipient HIV populations.},
doi = {10.1038/s41564-018-0204-9},
journal = {Nature Microbiology},
number = 9,
volume = 3,
place = {United States},
year = {2018},
month = {7}
}

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Cited by: 7 works
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Works referenced in this record:

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    Works referencing / citing this record:

    Inferring HIV-1 transmission networks and sources of epidemic spread in Africa with deep-sequence phylogenetic analysis
    journal, March 2019


    Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases
    journal, May 2019

    • Alamil, M.; Hughes, J.; Berthier, K.
    • Philosophical Transactions of the Royal Society B: Biological Sciences, Vol. 374, Issue 1775
    • DOI: 10.1098/rstb.2018.0258

    Inferring HIV-1 transmission networks and sources of epidemic spread in Africa with deep-sequence phylogenetic analysis
    journal, March 2019


    Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases
    journal, May 2019

    • Alamil, M.; Hughes, J.; Berthier, K.
    • Philosophical Transactions of the Royal Society B: Biological Sciences, Vol. 374, Issue 1775
    • DOI: 10.1098/rstb.2018.0258