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

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.