HIV-1 diversity in an antiretroviral treatment naïve cohort from Bushbuckridge, Mpumalanga Province, South Africa

Abstract

Background: South Africa has a generalized and explosive HIV/AIDS epidemic with the largest number of people infected with HIV-1 in the world. Molecular investigations of HIV-1 diversity can help enhance interventions to contain and combat the HIV/AIDS epidemic. However, many studies of HIV-1 diversity in South Africa tend to be limited to the major metropolitan centers and their surrounding provinces. Hardly any studies of HIV diversity have been undertaken in Mpumalanga Province, and this study sought to investigate the HIV-1 diversity in this province, as well as establish the occurrence and extent of transmitted antiretroviral drug resistance mutations.

Methods: HIV-1 gag p24, pol p10 and p66/p51, pol p31 and env gp41 gene fragments from 43 participants were amplified and sequenced. Quality control on the sequences was carried out using the LANL QC online tool. HIV-1 subtype was preliminary assigned using the REGA 3.0 and jpHMM online tools. Subtype for the pol gene fragment was further designated using the SCUEAL online tool. Phylogenetic analysis was inferred using the Maximum Likelihood methods in MEGA version 6. HIV-1 antiretroviral drug resistance mutations were determined using the Stanford database.

Results: Phylogenetic analysis using Maximum Likelihood methods indicated that all sequences in the study clustered with HIV-1 subtype C. The exception was one putative subtype BC unique recombinant form. Antiretroviral drug resistance mutations K103N and E138A were also detected, indicating possible transmission of anti-retroviral drug resistance mutations.

Conclusions: The phylogenetic analysis of the HIV sequences revealed that, by 2009, patients in the Bushbuckridge, Mpumalanga were predominantly infected with HIV-1 subtype C. However, the generalized, explosive nature of the HIV/AIDS epidemic in South Africa, in the context of extensive mobility by South Africans who inhabit rural areas, renders the continued molecular monitoring and surveillance of the epidemic imperative.

Figure 1

Geographical location of samples collected in this study. The South African map with 9 provinces is indicated and the Bushbuckridge local municipality in the Ehlanzeni district of Mupumalanga is enlarged. The “Maputo corridor” or N4 trunk roadway is highlighted in blue.

Figure 2

Phylogenetic analysis of the partial gag gene, using MEGA 6. A. The evolutionary history was inferred by using the ML method based on the GTR model. The tree with the highest log likelihood (−5337.9653) is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. A discrete Gamma distribution was used to model evolutionary rate differences among sites (5 categories (+G, parameter = 1.2415)). The rate variation model allowed for some sites to be evolutionarily invariable ([+I], 52.1407% sites). The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 76 nucleotide sequences and included all codon positions. There were a total of 451 positions in the final dataset. B. The evolutionary history was inferred by using the ML method based on the Tamura-Nei model (TN93 + G + I). The tree with the highest log likelihood (−5340.4505) is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. A discrete Gamma distribution was used to model evolutionary rate differences among sites (5 categories (+G, parameter = 1.1914)). The rate variation model allowed for some sites to be evolutionarily invariable ([+I], 51.4597% sites).

Figure 3

Phylogenetic analysis of the partial pol gene, using MEGA 6. The evolutionary history was inferred by using the ML method based on the GTR model. The tree with the highest log likelihood (−9574.7386) is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. A discrete Gamma distribution was used to model evolutionary rate differences among sites (5 categories (+G, parameter = 1.1121)). The rate variation model allowed for some sites to be evolutionarily invariable ([+I], 45.2893% sites). The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 49 nucleotide sequences and included all codon positions. There were a total of 1062 positions in the final dataset.

Figure 4

Phylogenetic analysis of the integrase gene, using MEGA 6. The evolutionary history was inferred by using the ML method based on the GTR model. The tree with the highest log likelihood (−7480.4899) is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. A discrete Gamma distribution was used to model evolutionary rate differences among sites (5 categories (+G, parameter = 0.3186)). The rate variation model allowed for some sites to be evolutionarily invariable ([+I], 42.6830% sites). The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 55 nucleotide sequences and there were a total of 849 positions in the final dataset.

Figure 5

Phylogenetic analysis of the partial env gene, using MEGA 6. The evolutionary history was inferred by using the ML method based on the GTR model. The tree with the highest log likelihood (−7290.5638) is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. A discrete Gamma distribution was used to model evolutionary rate differences among sites (5 categories (+G, parameter = 0.6134)). The rate variation model allowed for some sites to be evolutionarily invariable ([+I], 32.1349% sites). The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 74 nucleotide sequences and there were a total of 402 positions in the final dataset.