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Abstract
IntroductionIn Cameroon, a manifold diversity of HIV strains exists with CRF02_AG and unique recombinant forms (URFs) being the predominant strains. In recent years, a steady increase in URFs and clade F2 viruses has been monitored through partial genome sequencing. There is an information gap in the characterization of emerging URFs along the full genome, which is needed to address the challenges URFs pose towards diagnosis, treatment and HIV‐1 vaccine design.MethodEighteen Cameroonian URFs from samples collected between the years 2000 and 2015 were studied using a newly developed near full genome sequencing (NFGS) protocol based on variable nested RT‐PCRs with a versatile primer set. Near full genomes were characterized for recombination patterns and sequence signatures with possible impact on antiretroviral treatment or Env‐directed immune responses. Third‐generation sequencing (3GS) of near full or half genomes (HGs) gave insight into intra‐patient URF diversity.ResultsThe characterized URFs were composed of a broad variety of subtypes and recombinants including A, F, G, CRF01_AE, CRF02_AG and CRF22_01A1. Phylogenetic analysis unveiled dominant CRF02_AG and F2 recombination patterns. 3GS indicated a high intra‐patient URF diversity with up to four distinct viral sub‐populations present in plasma at the same time. URF pol genomic analysis revealed a number of accessory drug resistance mutations (DRMs) in the ART‐naïve participants. Genotypic env analysis suggests CCR5 usage in 14/18 samples and identified deviations at residues, critical for gp120/gp41 interphase and CD4 binding site broadly neutralizing antibodies in more than half of the studied URFs. V1V2 sites of immune pressure in the human RV144 vaccine study varied in more than a third of URFs.ConclusionsThis study identified novel mosaic patterns in URFs in Cameroon. In line with the regional predominance of CRF_02AG and the increased prevalence of clade F2, prominent CRF_02AG and F2 background patterns were observed underlying the URFs. In the context of the novel mosaic genomes, the impact of the identified accessory DRMs and Env epitope variations on treatment and immune control remains elusive. The evolving diversity of HIV‐1 URFs in Cameroon requires continuous monitoring to respond to the increasing challenges for diagnosis, antiretroviral treatment and prevention.
Highlights
In Cameroon, a manifold diversity of HIV strains exists with CRF02_AG and unique recombinant forms (URFs) being the predominant strains
Circulating virus lineages include virtually every known HIV-1 group M (HIV-1 M) pure subtype, many Circulating recombinant form (CRF), and a variety of URFs composed of pure, CRF and/or non-classifiable sequences making this country a unique source of rare and emerging HIV-1 M viral variants [5,9,10,11,12,13,14,15,16,17,18]
To assess whether the URFs potentially inherited impaired sensitivities to antiretroviral treatment, the entire pol regions were studied for mutations conferring resistance to protease inhibitors (PI), reverse transcriptase inhibitors (RTI), and integrase inhibitors (INSTI) (Table 2)
Summary
Based on the latest HIV molecular surveillance report, recombinant forms account for almost every fourth HIV infection globally (22.8%) [1]. Circulating virus lineages include virtually every known HIV-1 group M (HIV-1 M) pure subtype, many CRFs, and a variety of URFs composed of pure, CRF and/or non-classifiable sequences making this country a unique source of rare and emerging HIV-1 M viral variants [5,9,10,11,12,13,14,15,16,17,18]. The range of subtypes and the extent of viral diversity within a geographical region considerably impact HIV diagnosis, treatment and prevention [19] It is, crucial to monitor and genetically characterize HIV globally, at HIV diversity hot spot regions like Cameroon. Partial genome sequencing bears the risk of missing recombination sites and parental subtypes, which results in an inaccurate or obscured determination of HIV-1 M genetic diversity. Multiple reads per cluster were analysed using highlighter plots and Simplot for consistent breakpoint patterns within each cluster and differing breakpoint patterns between clusters. 3GS (sub)populations were averaged to consensus (con) sequences using Consensus Maker [34] or SeqMan Pro
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