Deep-sequence phylogenetics to quantify patterns of HIV transmission in the context of a universal testing and treatment trial - BCPP/ Ya Tsie trial

  1. Lerato E Magosi  Is a corresponding author
  2. Yinfeng Zhang
  3. Tanya Golubchik
  4. Victor DeGruttola
  5. Eric Tchetgen Tchetgen
  6. Vladimir Novitsky
  7. Janet Moore
  8. Pam Bachanas
  9. Tebogo Segolodi
  10. Refeletswe Lebelonyane
  11. Molly Pretorius Holme
  12. Sikhulile Moyo
  13. Joseph Makhema
  14. Shahin Lockman
  15. Christophe Fraser
  16. Myron (Max) Essex
  17. Marc Lipsitch  Is a corresponding author
  18. on behalf of the Botswana Combination Prevention Project and the PANGEA consortium
  1. Harvard University, United States
  2. University of Pittsburgh Medical Center, United States
  3. University of Oxford, United Kingdom
  4. Harvard T H Chan School of Public Health, United States
  5. University of Pennsylvania, United States
  6. Centers for Disease Control and Prevention, United States
  7. Centers for Disease Control and Prevention, Botswana
  8. Ministry of Health, Botswana
  9. Botswana Harvard AIDS Institute Partnership, Botswana
  10. Brigham and Women's Hospital, United States
  11. Harvard TH Chan School of Public Health, United States

Abstract

Background: Mathematical models predict that community-wide access to HIV testing-and-treatment can rapidly and substantially reduce new HIV infections. Yet several large universal test-and-treat HIV prevention trials in high-prevalence epidemics demonstrated variable reduction in population-level incidence.

Methods: To elucidate patterns of HIV spread in universal test-and-treat trials we quantified the contribution of geographic-location, gender, age and randomized-HIV-intervention to HIV transmissions in the 30-community Ya Tsie trial in Botswana. We sequenced HIV viral whole genomes from 5,114 trial participants among the 30 trial communities.

Results: Deep-sequence phylogenetic analysis revealed that most inferred HIV transmissions within the trial occurred within the same or between neighboring communities, and between similarly-aged partners. Transmissions into intervention communities from control communities were more common than the reverse post-baseline (30% [12.2 - 56.7] versus 3% [0.1 - 27.3]) than at baseline (7% [1.5 - 25.3] versus 5% [0.9 - 22.9]) compatible with a benefit from treatment-as-prevention.

Conclusion: Our findings suggest that population mobility patterns are fundamental to HIV transmission dynamics and to the impact of HIV control strategies.

Funding: This study was supported by the National Institute of General Medical Sciences (U54GM088558); the Fogarty International Center (FIC) of the U.S. National Institutes of Health (D43 TW009610); and the President’s Emergency Plan for AIDS Relief through the Centers for Disease Control and Prevention (CDC) (Cooperative agreements U01 GH000447 and U2G GH001911).

Data availability

All relevant data are within the paper, figures and tables. HIV-1 viral whole genome consensus sequences are provided as a Dryad dataset (https://doi.org/10.5061/dryad.0zpc86706). HIV-1 reads are available on reasonable request through a concept sheet proposal to the PANGEA consortium. Contact details are provided on the consortium website (www.pangea-hiv.org).Code availability: Algorithms to estimate HIV transmission flows within and between population groups accounting for sampling variability and corresponding confidence intervals have been implemented as an R package, bumblebee that will be made available at the following URL: https://magosil86.github.io/bumblebee . A step-by-step tutorial on how to estimate HIV transmission flows with bumblebee and accompanying example datasets can be accessed at: https://github.com/magosil86/bumblebee/blob/master/vignettes/bumblebee-estimate-transmission-flows-and-ci-tutotial.md .

The following data sets were generated

Article and author information

Author details

  1. Lerato E Magosi

    Department of Epidemiology, Harvard University, Boston, United States
    For correspondence
    lmagosi@hsph.harvard.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3388-9892
  2. Yinfeng Zhang

    Division of Molecular and Genomic Pathology, University of Pittsburgh Medical Center, Pittsburgh, United States
    Competing interests
    No competing interests declared.
  3. Tanya Golubchik

    Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2765-9828
  4. Victor DeGruttola

    Department of Biostatistics, Harvard T H Chan School of Public Health, Boston, United States
    Competing interests
    No competing interests declared.
  5. Eric Tchetgen Tchetgen

    Department of Statistics, University of Pennsylvania, Philadelphia, United States
    Competing interests
    No competing interests declared.
  6. Vladimir Novitsky

    Department of Immunology and Infectious Disease, Harvard T H Chan School of Public Health, Boston, United States
    Competing interests
    No competing interests declared.
  7. Janet Moore

    Division of Global HIV/AIDS and TB, Centers for Disease Control and Prevention, Atlanta, United States
    Competing interests
    No competing interests declared.
  8. Pam Bachanas

    Division of Global HIV/AIDS and TB, Centers for Disease Control and Prevention, Atlanta, United States
    Competing interests
    No competing interests declared.
  9. Tebogo Segolodi

    HIV Prevention Research Unit, Centers for Disease Control and Prevention, Gaborone, Botswana
    Competing interests
    No competing interests declared.
  10. Refeletswe Lebelonyane

    Ministry of Health, Gaborone, Botswana
    Competing interests
    No competing interests declared.
  11. Molly Pretorius Holme

    epartment of Immunology and Infectious Disease, Harvard T H Chan School of Public Health, Boston, United States
    Competing interests
    No competing interests declared.
  12. Sikhulile Moyo

    Botswana Harvard AIDS Institute Partnership, Gaborone, Botswana
    Competing interests
    No competing interests declared.
  13. Joseph Makhema

    Botswana Harvard AIDS Institute Partnership, Gaborone, Botswana
    Competing interests
    No competing interests declared.
  14. Shahin Lockman

    Division of Infectious Diseases, Brigham and Women's Hospital, Boston, United States
    Competing interests
    Shahin Lockman, participates in a data safety monitoring board for NIH-funded study of PK of TB drugs and antiretrovirals in children and on a scientific advisory board for observational study of DTG programmatic rollout in Botswana. Is also a member of the Finance Board and a member of the Board of Directors for the Botswana Harvard AIDS Institute Partnership. Receives no financial compensation for these roles, and has no other competing interests to declare..
  15. Christophe Fraser

    Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
  16. Myron (Max) Essex

    Department of Immunology and Infectious Disease, Harvard T H Chan School of Public Health, Boston, United States
    Competing interests
    No competing interests declared.
  17. Marc Lipsitch

    Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Boston, United States
    For correspondence
    mlipsitc@hsph.harvard.edu
    Competing interests
    Marc Lipsitch, is a Reviewing Editor for eLife. Has received consultancy fees from Merck, University of Virginia Miller Center and Janssen, and has performed unpaid consultancy work for Janssen, Pfizer and Astra Zeneca. Has also received payments or honoraria from Sanofi Pasteur and Bristol Myers Squibb. ML has no other competing interests to declare..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1504-9213

Funding

Fogarty International Center (D43 TW009610)

  • Lerato E Magosi

Centers for Disease Control and Prevention (U01 GH000447 and U2G GH001911)

  • Lerato E Magosi
  • Janet Moore
  • Pam Bachanas
  • Refeletswe Lebelonyane
  • Molly Pretorius Holme
  • Shahin Lockman
  • Myron (Max) Essex

National Institutes of Health

  • Christophe Fraser
  • Marc Lipsitch

Bill and Melinda Gates Foundation

  • Christophe Fraser

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: The BCPP study was approved by the Botswana Health Research and Development Committee and the institutional review board of the Centers for Disease Control and Prevention; and was monitored by a data and safety monitoring board and Westat. Written informed consent for enrollment in the study and viral HIV genotyping was obtained from all participants.

Reviewing Editor

  1. Francesca Di Giallonardo

Publication history

  1. Preprint posted: June 23, 2021 (view preprint)
  2. Received: July 30, 2021
  3. Accepted: February 8, 2022
  4. Accepted Manuscript published: March 1, 2022 (version 1)
  5. Version of Record published: March 10, 2022 (version 2)

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

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  1. Lerato E Magosi
  2. Yinfeng Zhang
  3. Tanya Golubchik
  4. Victor DeGruttola
  5. Eric Tchetgen Tchetgen
  6. Vladimir Novitsky
  7. Janet Moore
  8. Pam Bachanas
  9. Tebogo Segolodi
  10. Refeletswe Lebelonyane
  11. Molly Pretorius Holme
  12. Sikhulile Moyo
  13. Joseph Makhema
  14. Shahin Lockman
  15. Christophe Fraser
  16. Myron (Max) Essex
  17. Marc Lipsitch
  18. on behalf of the Botswana Combination Prevention Project and the PANGEA consortium
(2022)
Deep-sequence phylogenetics to quantify patterns of HIV transmission in the context of a universal testing and treatment trial - BCPP/ Ya Tsie trial
eLife 11:e72657.
https://doi.org/10.7554/eLife.72657
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