Quantifying prevalence and risk factors of HIV multiple infection in Uganda from population-based deep-sequence data

People living with HIV can acquire secondary infections through a process called superinfection, giving rise to simultaneous infection with genetically distinct variants (multiple infection). Multiple infection provides the necessary conditions for the generation of novel recombinant forms of HIV and may worsen clinical outcomes and increase the rate of transmission to HIV seronegative sexual partners. To date, studies of HIV multiple infection have relied on insensitive bulk-sequencing, labor intensive single genome amplification protocols, or deep-sequencing of short genome regions. Here, we identified multiple infections in whole-genome or near whole-genome HIV RNA deep-sequence data generated from plasma samples of 2,029 people living with viremic HIV who participated in the population-based Rakai Community Cohort Study (RCCS). We estimated individual- and population-level probabilities of being multiply infected and assessed epidemiological risk factors using the novel Bayesian deep-phylogenetic multiple infection model (deep - phyloMI) which accounts for bias due to partial sequencing success and false-negative and false-positive detection rates. We estimated that between 2010 and 2020, 4.09% (95% highest posterior density interval (HPD) 2.95%-5.45%) of RCCS participants with viremic HIV multiple infection at time of sampling. Participants living in high-HIV prevalence communities along Lake Victoria were 2.33-fold (95% HPD 1.3-3.7) more likely to harbor a multiple infection compared to individuals in lower prevalence neighboring communities. This work introduces a high-throughput surveillance framework for identifying people with multiple HIV infections and quantifying population-level prevalence and risk factors of multiple infection for clinical and epidemiological investigations.

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Publication Details

Journal
PLoS Pathogens
Published
2025-04-22
DOI
https://doi.org/10.1371/journal.ppat.1013065
Primary Topic
HIV Research and Treatment
Type
article
Field-Weighted Citation Impact
0.00

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article

Quantifying prevalence and risk factors of HIV multiple infection in Uganda from population-based deep-sequence data

David Serwadda, Thomas C. Quinn, Ronald Moses Galiwango, Xiaoyue Xi et al.
PLoS Pathogens
HIV Research and Treatment
article

Quantifying prevalence and risk factors of HIV multiple infection in Uganda from population-based deep-sequence data

David Serwadda, Thomas C. Quinn, Ronald Moses Galiwango, Xiaoyue Xi, Alexandra Blenkinsop, Andrea Brizzi, Lucie Abeler‐Dörner, Deogratius Ssemwanga, Michael A. Martin, M. Kate Grabowski, Oliver Ratmann, Joseph Kagaayi, Gertrude Nakigozi, Andrew D. Redd, Sikhulile M. Moyo, Steven J. Reynolds, David Bonsall, Godfrey Kigozi
article en

Abstract

People living with HIV can acquire secondary infections through a process called superinfection, giving rise to simultaneous infection with genetically distinct variants (multiple infection). Multiple infection provides the necessary conditions for the generation of novel recombinant forms of HIV and may worsen clinical outcomes and increase the rate of transmission to HIV seronegative sexual partners. To date, studies of HIV multiple infection have relied on insensitive bulk-sequencing, labor intensive single genome amplification protocols, or deep-sequencing of short genome regions. Here, we identified multiple infections in whole-genome or near whole-genome HIV RNA deep-sequence data generated from plasma samples of 2,029 people living with viremic HIV who participated in the population-based Rakai Community Cohort Study (RCCS). We estimated individual- and population-level probabilities of being multiply infected and assessed epidemiological risk factors using the novel Bayesian deep-phylogenetic multiple infection model (deep - phyloMI) which accounts for bias due to partial sequencing success and false-negative and false-positive detection rates. We estimated that between 2010 and 2020, 4.09% (95% highest posterior density interval (HPD) 2.95%-5.45%) of RCCS participants with viremic HIV multiple infection at time of sampling. Participants living in high-HIV prevalence communities along Lake Victoria were 2.33-fold (95% HPD 1.3-3.7) more likely to harbor a multiple infection compared to individuals in lower prevalence neighboring communities. This work introduces a high-throughput surveillance framework for identifying people with multiple HIV infections and quantifying population-level prevalence and risk factors of multiple infection for clinical and epidemiological investigations.

PLoS PathogensVol. 21(4)
National Institutes of Health (US), Centre for Human Genetics (GB), Harvard University (US), Johns Hopkins University (US), University of Cape Town (ZA), University of Cambridge (GB), Johns Hopkins Medicine (US), University of Oxford (GB), Rakai Health Sciences Program (UG), National Institute of Allergy and Infectious Diseases (US), MRC Biostatistics Unit (GB), Botswana Harvard AIDS Institute Partnership (BW), Imperial College London (GB), Makerere University (UG), Medical Research Council (GB), Uganda Virus Research Institute (UG)
Bill and Melinda Gates Foundation, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, U.S. President’s Emergency Plan for AIDS Relief, Center for AIDS Research, Johns Hopkins University, National Heart, Lung, and Blood Institute, National Institute of Allergy and Infectious Diseases, Fogarty International Center, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Division of Loan Repayment
Good health and well-being
Openalex Percentile: Top 91%
HIV Research and Treatment
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