The Role of Phylogenetics in Discerning HIV-1 Mixing among Vulnerable Populations and Geographic Regions in Sub-Saharan Africa: A Systematic Review.

George M Nduva,Jamirah Nazziwa,Eduard J Sanders,Amin S Hassan,Joakim Esbjörnsson

doi:10.3390/v13061174

George M Nduva, Jamirah Nazziwa + Show 3 more

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https://doi.org/10.3390/v13061174

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Abstract

To reduce global HIV-1 incidence, there is a need to understand and disentangle HIV-1 transmission dynamics and to determine the geographic areas and populations that act as hubs or drivers of HIV-1 spread. In Sub-Saharan Africa (sSA), the region with the highest HIV-1 burden, information about such transmission dynamics is sparse. Phylogenetic inference is a powerful method for the study of HIV-1 transmission networks and source attribution. In this review, we assessed available phylogenetic data on mixing between HIV-1 hotspots (geographic areas and populations with high HIV-1 incidence and prevalence) and areas or populations with lower HIV-1 burden in sSA. We searched PubMed and identified and reviewed 64 studies on HIV-1 transmission dynamics within and between risk groups and geographic locations in sSA (published 1995–2021). We describe HIV-1 transmission from both a geographic and a risk group perspective in sSA. Finally, we discuss the challenges facing phylogenetic inference in mixed epidemics in sSA and offer our perspectives and potential solutions to the identified challenges.

Highlights

Molecular phylogenetic approaches have evolved into powerful tools in understanding pathogens and how they cause disease in human populations [1]
Sixty-four articles were considered eligible for full-text review, including 29 articles assessing geographic dispersion (Table 1) and 35 assessing HIV-1 mixing between HIV-1 populations in Sub-Saharan Africa (sSA)
Phylogenetic methods could help in characterising such drivers but rely on the availability of large numbers of sequences obtained from wellcharacterised cohorts

Summary

Introduction

Molecular phylogenetic approaches have evolved into powerful tools in understanding pathogens and how they cause disease in human populations [1]. Based on genetic relatedness between pathogen strains, these studies have been coupled with epidemiological data to decipher transmission events in infected hosts [2]. This approach has several applications and has been used to understand the geographic distribution of a large number of pathogens The epidemic in sSA is mostly spread out among heterosexuals (HET, presumed heterosexuals) who have lower HIV-1 incidence and prevalence compared to key populations [24,25,26].

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