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

Virology Journal volume 12, Article number: 24 (2015) Cite this article

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.

Background

Human immunodeficiency virus (HIV), the etiological agent of acquired immunodeficiency syndrome (AIDS), was first isolated more than 30 years ago [1]. By 2013, an estimated 35 million people were living with HIV-1 globally, of which 24.7 million were living in sub Saharan Africa [2]. During this time period, the HIV-1 prevalence in South Africa was 12.2% (6.4 million people), with 469 000 new infections occurring, suggesting that the epidemic is not only generalized, but also explosive [3].

The HIV-1 epidemic in South Africa is characterized by limited subtype diversity with subtype C accounting for the majority of infections [4,5]. Other non-C subtypes, particularly subtypes B and D, have also been identified [6-8] as well as the occasional unique recombinant forms (URFs) [9-15]. Molecular epidemiological investigations in South Africa have largely focused on provinces with major metropolitan centers such as Johannesburg in Gauteng, Cape Town in the Western Cape and Durban in Kwa-Zulu Natal. No subtype information is available for the Eastern Cape, North West and Northern Cape provinces and limited information is available for the Free State, Limpopo and Mpumalanga Provinces. HIV-1 prevalence in South Africa is also characterized by extreme heterogeneity and there is considerable variation in prevalence amongst the different provinces and districts in each province [16]. The highest prevalence is in Kwa-Zulu Natal with the lowest in the Western Cape Province. South Africa not only has a generalized and explosive HIV/AIDS epidemic, its impact also varies significantly in terms of race, age, gender, and between regions of the country, with poor, young, African women in rural Kwa-Zulu Natal bearing a disproportionate burden of HIV infection [16].

The overall HIV prevalence in Mpumalanga in 2012 was 35.6% [16]. The province consists of 3 districts: Ehlanzeni, Nkangala and Gert Sibande. The Bushbuckridge Local Municipality in the Ehlanzeni District in Mpumalanga Province is a predominantly rural, impoverished area, with only 14% of the adult population employed and over 85% of households living below the house hold subsistence level. Half of males and 14% of females between the ages of 25 and 59 are long-term migrant workers and provide a source of remittances, which comprise the largest proportion of the income of the population of Bushbuckridge [17].

Molecular investigations of HIV diversity can help enhance interventions to contain and combat the HIV-1 epidemic. With this study, we investigated for the first time, HIV-1 diversity in Bushbuckridge, Mpumalanga, as well as the possible occurrence and extent of transmitted antiretroviral drug resistance mutations.

Methods

Study population and RNA extraction

In preparation for HIV prevention trials, a cohort was developed for enrollment. Ethics approval were obtained from the Human Research Ethics Committees (HRECs) from the University of the Witwatersrand (M061129) and Stellenbosch University (N11/02/054), following internationally recognized guidelines. The entry point for this cohort was via a free voluntary counseling and testing service. After HIV testing, individuals were offered the opportunity to be part of the pre-screening cohort. Both HIV negative and HIV positive individuals were allowed to join the cohort in preparation for preventative and therapeutic HIV vaccine trials. Fifty-one samples were obtained with informed consent as part of this pre-screening protocol from 43 HIV positive participants in Bushbuckridge, Mpumalanga (Figure 1). RNA was extracted from stored plasma samples using a QIAamp MinElute Virus Spin Kit in a QIAcube automated extractor (QIAGEN, Dusseldorf, Germany), according to the manufacturer’s instructions. RNA samples were stored at −70°C until used.

Figure 1
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.

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Reverse transcriptase polymerase chain reaction (RT-PCR) of HIV-1 gene fragments

Four genomic regions were targeted for amplification: the gag p24 region (HXB2 nucleotides 1248 to 1707); a part of the pol gene, that includes the Protease (PR) and a partial segment of the Reverse Transcriptase (RT) region (HXB2 nucleotides 2114 to 3335), the Integrase (IN) region (HXB2 nucleotides 4202 to 5096) and the partial env gp 41 region (HXB2 nucleotides 7877 to 8282). PCR amplification and purification was done using previously described primers and methods for the partial gag, pol integrase (IN) and env [18] genes. The partial pol PR/RT gene was also amplified using primers and a method previously described [19,20]. Briefly, cDNA synthesis and first round PCR amplification was done with the Access-RT PCR system (Promega, Wisconsin, USA), while second round nested PCR amplification was done with the GoTaq DNA polymerase system (Promega, Wisconsin, USA). The oligonucleotide primers used in the amplification of the gene fragments are listed in Table 1.

Table 1 Primers used in the amplification of the partial HIV-1 gene products
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Sequencing of HIV-1 gene fragments

The cycle sequencing reactions of the partial gene fragments were done with the Big Dye® Terminator v 3.1 Cycle Sequencing Kit (Applied BioSystems, Foster City, CA, USA) and run on an ABI Prism 3130xl Genetic Analyzer (Applied Biosystems, Foster City, CA, USA), according to the manufacturer’s instructions. Both strands were sequenced using overlapping primers. Sequencher v 5.1 (Gene Codes Corporation, Ann Arbor, MI, USA) was used to assemble the trace data into contiguous fragments, which were then verified, edited and saved as text files for subsequent analysis. All sequences were checked for quality assurance using the Los Alamos HIV-1 Sequence Quality Analysis tool (http://www.hiv.lanl.gov/content/sequence/QC/index.html) before further analyses and submission to GenBank.

Preliminary HIV-1 subtyping using online tools

The REGA HIV-1 subtyping Tool Version 3.0 (http://dbpartners.stanford.edu:8080/RegaSubtyping/stanford-hiv/typingtool) was used to preliminary subtype the sequences [21]. To detect recombinants, we used the jumping profile Hidden Markov Model (jpHMM-HIV) tool (http://jphmm.gobics.de) [22]. Subtype Classification Using Evolutionary Algorithms (SCUEAL) was used to test for both intra and inter subtype recombinants in the partial HIV-1 pol and IN sequences (http://www.datamonkey.org/dataupload_scueal.php ) [23].

Phylogenetic inference using Maximum likelihood (ML)

The 2010 HIV-1 Group M reference sequence dataset (n = 39), from the LANL database (http://www.hiv.lanl.gov/), was used to subtype our sequences, using phylogenetic inference. Multiple sequence alignments, comprising the partial gag, pol and env sequences, and the reference dataset, were constructed using MAFFT v7.017 [24] as implemented in Geneious version R7 created by Biomatters (http://www.geneious.com). These multiple sequence alignments were subsequently codon aligned using Codon Alignment v1.1.0 (http://www.hiv.lanl.gov/content/sequence/CodonAlign/codonalign.html) and manually checked.

The phylogenetic trees for the different HIV-1 genetic fragments were inferred using ML methods implemented in MEGA version 6 [25]. To find the most appropriate evolutionary model for phylogenetic inference, we used Model Selection (ML) as implemented in MEGA [25]. For each model, BIC scores (Bayesian Information Criterion), AICc value (Akaike Information Criterion, corrected), Maximum Likelihood value (lnL), and a number of different parameters were presented. Models with the lowest BIC scores were considered to describe the substitution pattern the best [25]. For the partial pol PR/RT region, the Integrase (IN) region and the partial env gp 41 region, the BIC, AICc and lnL scores indicated that the General Time Reversible model of evolution with Gamma distribution and invariant rate among sites (GTR + G + I), was the best model. For the gag region, the lnL method indicated the use of the GTR + G + I model and BIC and AICc indicated the use of the TN93 + G + I model. All nucleotide positions in the alignments with less than 95% site coverage were eliminated, thus fewer than 5% alignment gaps, missing data, and ambiguous bases were allowed at any position. The reliability of the inferred trees was evaluated using bootstrap resampling and branches with a bootstrap value of 70% or greater were considered reliable (n = 100) [26].

HIV-1 antiretroviral drug resistance mutations using HIVdb

HIV-1 PR and RT antiretroviral drug resistance mutations were determined using the Stanford University HIV Drug Resistance Database (HIVdb), http://www.hivdb.stanford.edu/ [27].

GenBank accession numbers

GenBank accession numbers of the gag sequences were KM218392 to KM218428; pol sequences, KM218448 to KM218460; integrase sequences, KM218429 to KM218447 and for the env sequences, KM218357 to KM218391.

Results

Demographic information

The demographic and clinical information of the cohort, together with the subtyping, are summarized in Table 2. The study involved 51 plasma samples, collected from 43 participants in Bushbuckridge, between February and July 2009. Forty samples were collected at the recruitment visit and 11 samples at visit one. Only one sample per participant was included in the study. All participants, except for 0064A and 0206A, were female and none were on HIV-1 antiretroviral treatment. The average age of the cohort was 26.7 years and ranged from 16 to 41 years. The CD4 lymphocyte count ranged from 105 to 1263 with an average of 450.

Table 2 Demographic and clinical information of the participants
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PCR amplification, sequence data and quality assurance

PCR amplification was successful for most of the samples, with 93% (n = 40) of the partial gag p24 gene, 48.8% (n = 21) of the partial IN p32 gene, and 83.7% (n = 36) of the partial env gp41 showing positive bands in an agarose gel after electrophoresis. However, the PCR amplification of the partial pol PR/RT gene was considerably less successful at 34.8% (n = 15). PCR amplification of 10 samples were positive in all 4 gene regions and only one sample, 0116A, could not be amplified in any of the primers.

Thirty-seven (86.0%) of the gag p24, 13 (30.2%) of the pol, RT/PR, 18 (41.8%) of the pol IN and 35 (81.3%) of the env gp41 amplicons were successfully sequenced. The LANL QC tool indicated no stop codons, and no hypermutation was detected in any of the sequences.

Preliminary subtype analysis using online tools

REGA and jpHMM online tools were used to assign subtypes to all the sequences and to detect possible recombinant forms. REGA 3.0 assigned all gag, pol PR/RT, pol IN, and env sequences to subtype C, except for env 0143A, which was assigned subtype B. Similar results were obtained with jpHMM, with the exception of the IN region of 0193A which was assigned as a CK recombinant form.

The SCUEAL subtyping of the pol PR/RT and IN gene fragments revealed that all the PR/RT and IN sequences were HIV-1 subtype C. Six of the sequences (18.75%) were intra-subtype C recombinant forms (Table 3).

Table 3 Intra-subtype C recombinants detected using SQUEAL
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ML Phylogenetic inference

Model Selection (ML) using the BIC, implemented in MEGA, indicated the use of the (GTR + G + I) model for the pol and env regions and the use of the TN93 + G + I model for the gag region (Additional files 1, 2, 3 and 4: Table S1, Table S2, Table S3 and Table S4. Maximum Likelihood fits of 24 different nucleotide substitution models for gag, pol PR/RT, pol IN and env gp41, respectively). ML phylogenetic trees were inferred from the multiple sequence alignments, and branches with a bootstrap value of 70% or greater were considered reliable. None of the sub-genomic regions supported a monophyletic South African lineage.

In the gag ML tree (Figure 2A and B) all the sequences clustered within subtype C. Except for slight differences in the bootstrap values, there were no differences in the gag tree topologies inferred with either the GTR + G + I or TN93 + G + I models. Interestingly the 2 outliers to the main subtype C cluster, 0042A and 0143A, were possible intra subtype C recombinants in the pol region. Sequence 0119A had a long branch and 3 sets of sequences, 0189A/0203A, 0064A/190A and 0085A/0101A clustered closely together. This may indicate that these samples may be a possible PCR contamination or that they are epidemiologically linked.

Figure 2
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).

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The ML phylogenetic tree for the pol PR/RT gene comprised 49 sequences and all the Mpumalanga sequences clustered with HIV-1 subtype C (Figure 3). The ML phylogenetic tree for the IN region contained 55 sequences and all Mpumalanga sequences clustered with HIV-1 subtype C reference sequences (Figure 4). Sequence 0098A clustered as an outlier to subtype C and SQUEAL indicated that the sequence is an intra-subtype C recombinant with 3 breakpoints. Sequence 0193A had a long branch and jpHMM indicated a possible CK recombinant form.

Figure 3
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.

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Figure 4
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.

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The ML phylogenetic tree for the env gp41 contained 74 sequences and all sequences, except for 0143A, clustered with HIV-1 subtype C sequences (Figure 5). Sequence 0143A clustered with subtype B in the env region and as an outlier to subtype C in the gag region. SQUEAL indicated that 0143A was an intra-subtype C recombinant in the pol region. This is the first indication of a putative unique BC recombinant sequence in Bushbuckridge, Mpumalanga.

Figure 5
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.

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HIV-1 antiretroviral drug resistance mutations

Although the participants were from an antiretroviral treatment naïve cohort, some antiretroviral drug mutations were detected (Table 4). The NNRTI mutation K103N detected on the 0143A sequence causes high-level resistance to nevirapine (NVP), and efavirenz (EFV). The NNRTI mutation, E138A, detected on the 0143A sequence is a polymorphism that may contribute to reduced etravirine (ETR) and rilpivirine (RPV) susceptibility in combination with other NNRTI-resistance mutations. The K101E mutation found on the 0189A sequence causes intermediate resistance to NVP and low-level resistance to EFV, ETR, and RPV. No major PI mutations were detected in the Bushbuckridge, Mpumalanga sequences. The T74S minor PI mutation occurs in 5% of untreated persons with subtype C viruses and is associated with reduced NFV susceptibility [28-31].

Table 4 HIV-1 drug resistance mutations detected in the Mpumalanga cohort
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E157Q is an integrase polymorphic accessory mutation that is weakly selected in patients receiving raltegravir (RAL) and causes low level resistance to RAL and elvitegravir (EVG). L74I is an accessory mutation for integrase.

Discussion

The investigation of the HIV subtype diversity of samples obtained from a cohort in Bushbuckridge, Mpumalanga revealed, first, that the HIV-1 from these samples belong almost entirely to HIV-1 subtype C with one BC recombinant; second, that the way in which the sequences derived from these samples cluster in phylogenetic trees suggests there has been multiple introductions of HIV-1 into Bushbuckridge; and third, the prevalence of antiretroviral drug resistance mutations and drug resistance-associated polymorphisms in Bushbuckridge is extremely low.

Bushbuckridge HIV epidemic is predominantly subtype C, with one BC URF

The fact that the HIV samples from Bushbuckridge, Mpumalanga, belong almost entirely to subtype C is consistent not only with the explosive HIV-1 epidemic in southern Africa, but also it’s very limited subtype diversity. HIV-1 subtype C is the most common subtype, accounting for the majority of HIV infections in southern Africa [4,5], while subtype B is responsible for infections in MSM [9,12]. One putative subtype BC unique recombinant form was detected. This indicates that BC URFs are not only found in the Western Cape Province [15], but also in Mpumalanga province.

Multiple introductions of HIV-1 into Bushbuckridge

The fact that HIV-1 subtype C sequences from South Africa tend to intermingle with HIV-1 subtype C sequences from Botswana, Malawi and Zambia suggests they may have a common evolutionary origin [32,33]. The possibility of an underlying common evolutionary origin of isolates in southern Africa is consistent with the history of the population dynamics of the southern African region. While the HIV-1 subtype C isolates from Brazil and Ethiopia tend to cluster separately, the fact that the subtype C isolate from India tends to cluster with the subtype C isolates from southern Africa [34,35] can be explained by the historical connections between the Indian subcontinent and southern Africa, which arises from the roles of both regions as former British colonial territories.

Countries in southern Africa in which adult national HIV prevalence rates exceeded 15% in 2007 were all linked by the migrant labor system. This system, which under pinned the population dynamics of both South Africa and the broader southern African region, was critical in shaping the patterns of population mobility and integration that characterizing the entire region. The migrant labor system was integral to the development and structure of the South African economy and apartheid. Botswana, Lesotho, Namibia, South Africa, Swaziland, Zambia, and Zimbabwe, were all historically linked through the migrant labor system that brought men from as far as Zambia and Malawi to the mines initially on the Reef and subsequently elsewhere in the country [36-39]. Migrants are more vulnerable to HIV infection than people who hardly move, both in southern Africa as in other African countries [40-42]. A 1985 survey of workers in the gold mines originating from the entire southern African region found HIV prevalence to be very low among South African miners but among Malawian miners prevalence was already at 3% [36]. High infection levels are being found in Gaza province in Mozambique, where large numbers of migrants working in South Africa originate [43]. Before and after independence foreign migrant workers also crossed borders to work in mines in Namibia, Botswana, Zambia, and Zimbabwe [44-46].

Many of the countries in southern African with explosive HIV/AIDS epidemic are also landlocked, which entails that the region’s road transport networks does not only link these landlocked countries to the ports in Durban, Richards Bay and Maputo, but also facilitate the rapid spread of HIV in the region by ensuring the sexual networks that drive the epidemic transcend national boundaries. The Ehlanzeni District in Mpumalanga Province straddles the Maputo Corridor, a major trade route which connects the Gauteng, Limpopo, and Mpumalanga provinces of South Africa with Maputo, the capital of Mozambique that also has a major port. In Mozambique, HIV is spreading more rapidly in provinces linked by major transport routes to Malawi, South Africa and Zimbabwe. High infection rates have been found in Sofia province, which is traversed by Zimbabwe’s main export route [43].

The peculiarly explosive HIV-1 epidemic in southern Africa could also stem from the unique biological properties of subtype C. HIV-1 subtype C has an additional NF-binding site in the long terminal repeat (LTR), a prematurely truncated Rev protein, a 5′-amino-acid insertion in Vpu, and a more active, catalytically efficient protease, which may influence viral gene expression and alter the transmissibility and pathogenesis of subtype C isolates [31,47-52]. These unique biological properties, including those related to viral entry and pathogenesis such as the CCR5 and non-syncytium-inducing phenotype, may account for the explosive epidemic of HIV-1 subtype C in southern Africa [53-55]. However, the additional NF-κB site in HIV-1 subtype C may be biologically inactive, and enhanced activity of these individual functions may still not be sufficient to overcome the decreased replicative capacity of the CCR5-tropic non-syncytium-inducing phenotype [53].

Drug resistance mutations and polymorphisms

Combination antiretroviral therapy can suppress HIV-1 replication to undetectable levels with concomitant significant clinical outcomes. However, suboptimal suppression HIV-1 replication can result in the emergence of drug resistant virus strains. HIV-1 isolates that have acquired mutations conferring reduced susceptibility to antiretroviral drugs can be can be transmitted, potentially limiting options for first line therapy in untreated individuals [56]. The proportion of patients without prior antiretroviral therapy and who are infected with a virus resistant to at least one antiretroviral drug in Australia, Europe, Japan and the United States of America is 10% to 17%, while data between 2006 and 2010 suggests that transmitted antiretroviral drug resistance among those starting antiretroviral treatment in low- and middle-income countries increasing [2].

South Africa has the largest antiretroviral treatment program in the world. Besides its unprecedented scale, the antiretroviral treatment programme in South Africa is also being rolled out rapidly, such that while only 833653 adults and 86270 children were on antiretroviral treatment through the public sector in South Africa by the end of 2009, the number of those on treatment by 2012 had increased to 2010340 adults and 140541 children [2,3,16].

While the HIV-1 sequences used in this study are derived from treatment-naïve participants from Bushbuckridge, Mpumalanga, the K103N antiretroviral drug resistance mutation was detected. This suggests the participants from Bushbuckridge, Mpumalanga may either have undergone antiretroviral treatment or that they were infected with antiretroviral drug resistant strains [28-31]. The E138A mutation selected for by riplivirine/etravirine, must also be a transmitted mutation. Both riplivirine/etravirine are not part of the first and second line ART regimens in South Africa, while etravirine is part of the third line regimen. Patients in Mpumalanga only started receiving third line ART 2013.

Limitations of this study

The limitations of the study include a relatively small sample size; DNA amplification was not successful for up to 71% of the samples of the partial pol PR/RT sub-genomic region; use of partial gene regions to assign viral subtypes, potentially allowing recombinant viruses to be missed, the use of direct; population sequencing may result in the lack of detection of minority-population viruses; which can lead to an underestimation of viral diversity and drug resistance mutations.

Conclusions

HIV diversity may have implications for diagnosis, pathogenesis, transmission, clinical management and vaccine development. Phylogenetic analysis of HIV sequence diversity has allowed vital insights into the origin, evolution and spread of HIV, which suggests it is imperative to maintain HIV-1 molecular epidemiology surveillance. The extensive population mobility arising from the historical and structural migrant labor system characterizing South Africa, and the concomitant overlapping of sexual networks, seems to have precluded the possibility of distinct geographical lineages developing. However, the demise of apartheid, in particular the end of influx control measures, may have a significant impact on patterns of population mobility and settlement in South Africa, which in turn may affect the patterns of transmission of HIV and ultimately it’s evolution. The possible emergence of various HIV-1 recombinants could suggest that the migration of people into South Africa from Central, West and eastern Africa could also impact on the character and dynamics of the HIV/AIDS epidemic in South Africa.

References

  1. Barré-Sinoussi F, Chermann JC, Rey F, Nugeyre MT, Chamaret S, Gruest J, et al. Isolation of a T-lymphotropic retrovirus from a patient at risk for acquired immune deficiency syndrome (AIDS). Science. 1983;220:868–71.

    Article  PubMed  Google Scholar 

  2. UNAIDS: The gap report. Geneva 2014. http://www.unaids.org/en/resources/documents/2014/name,97466,en.asp

  3. Shisana O, Rehle T, Simbayi LC, Zuma K, Jooste S, Zungu N, et al. South African National HIV prevalence, incidence and behaviour survey, 2012. Cape Town: HSRC Press; 2014.

    Google Scholar 

  4. Van Harmelen JH, Van der Ryst E, Loubser AS, York D, Madurai S. A predominantly HIV-1 subtype C-restricted epidemic in South African urban populations. AIDS Res Hum Retroviruses. 1999;15:395–8.

    Article  PubMed  Google Scholar 

  5. Williamson C, Engelbrecht S, Lambrick M, Van Rensburg EJ, Wood R, Bredell W, et al. HIV-1 subtypes in different risk groups in South Africa. Lancet. 1995;346:782.

    Article  CAS  PubMed  Google Scholar 

  6. Engelbrecht S, Laten JD, Smith TL, van Rensburg EJ. Identification of env subtypes in fourteen HIV type 1 isolates from South Africa. AIDS Res Hum Retroviruses. 1995;11(10):1269–71.

    Article  CAS  PubMed  Google Scholar 

  7. Loxton AG, Treurnicht F, Laten A, Van Rensburg EJ, Engelbrecht S. Sequence analysis of near full-length HIV type 1 subtype D primary strains isolated in Cape Town, South Africa, from 1984 to 1986. AIDS Res Hum Retroviruses. 2005;21:410–3.

    Article  CAS  PubMed  Google Scholar 

  8. Jacobs GB, De Beer C, Fincham JE, Adams V, Dhansay MA, van Rensburg EJ, et al. Serotyping and genotyping of HIV-1 infection in residents of Khayelitsha, Cape Town, South Africa. J Med Virol. 2006;78(12):1529–36.

    Article  CAS  PubMed  Google Scholar 

  9. Jacobs GB, Loxton AG, Laten A, Robson B, van Rensburg EJ, Engelbrecht S. Emergence and diversity of different HIV-1 subtypes in South Africa, 2000–2001. J Med Virol. 2009;81(11):1852–9.

    Article  CAS  PubMed  Google Scholar 

  10. Bredell H, Hunt G, Casteling A, Cilliers T, Rademeyer C, Coetzer M, et al. HIV-1 subtype A, D, G, AG and unclassified sequences identified in South Africa. AIDS Res Hum Retroviruses. 2002;18(9):681–3.

    Article  CAS  PubMed  Google Scholar 

  11. Iweriebor BC, Bessong PO, Mavhandu LG, Masebe TM, Nwobegahay J, Moyo SR, et al. Genetic analysis of the near full-length genome of an HIV type 1 A1/C unique recombinant form from northern South Africa. AIDS Res Hum Retroviruses. 2011;27(8):911–5.

    Article  CAS  PubMed  Google Scholar 

  12. Middelkoop K, Rademeyer C, Brown BB, Cashmore TJ, Marais JC, Scheibe AP, et al. Epidemiology of HIV-1 subtypes among men who have sex with men in Cape Town, South Africa. J Acquir Immune Defic Syndr. 2014;65(4):473–80.

    Article  PubMed  Google Scholar 

  13. Papathanasopoulos MA, Cilliers T, Morris L, Mokili JL, Dowling W, Birx DL, et al. Full-length genome analysis of HIV-1 subtype C utilizing CXCR4 and intersubtype recombinants isolated in South Africa. AIDS Res Hum Retroviruses. 2002;18(12):879–86.

    Article  CAS  PubMed  Google Scholar 

  14. Wilkinson E, Engelbrecht S. Molecular characterization of non-subtype C and recombinant HIV-1 viruses from Cape Town, South Africa. Infect Genet Evol. 2009;9:840–6.

    Article  CAS  PubMed  Google Scholar 

  15. Jacobs GB, Wilkinson E, Isaacs S, Spies G, de Oliveira T, Seedat S, et al. HIV-1 subtypes B and C unique recombinant forms (URFs) and transmitted drug resistance identified in the Western Cape Province, South Africa. PLoS One. 2014;9(6):e90845.

    Article  PubMed Central  PubMed  Google Scholar 

  16. South African Government. The 2012 National Antenatal Sentinel HIV and Herpes Simplex Type-2 Prevalence Survey in South Africa. Pretoria, South Africa: Department of Health; 2013.

    Google Scholar 

  17. South African Government. Bushbuckridge Nodal Economic Development Profile Mpumalanga South Africa. Pretoria: South Africa: Department of Cooperative Governance and Traditional Affairs (Formerly Department of Provincial and Local Government); 2005.

    Google Scholar 

  18. Swanson P, Devare SG, Hackett Jr J. Molecular characterization of 39 HIV-1 isolates representing group M (subtypes A-G) and group O: Sequence analysis of gag p24, pol integrase, and env gp41. AIDS Res Hum Retroviruses. 2003;19(7):625–9.

    Article  CAS  PubMed  Google Scholar 

  19. Jacobs GB, Laten A, van Rensburg EJ, Bodem J, Weissbrich B, Rethwilm A, et al. Phylogenetic diversity and low level antiretroviral resistance mutations in HIV type 1 treatment-naive patients from Cape Town, South Africa. AIDS Res Hum Retroviruses. 2008;24(7):1009–12.

    Article  CAS  PubMed  Google Scholar 

  20. Plantier JC, Dachraoui R, Lemee V, Gueudin M, Borsa-Lebas F, Caron F, et al. HIV-1 resistance genotyping on dried serum spots. AIDS. 2005;19(4):391–7.

    Article  CAS  PubMed  Google Scholar 

  21. Pineda-Peña AC, Faria NR, Imbrechts S, Libin P, Abecasis AB, Deforche K, et al. Automated subtyping of HIV-1 genetic sequences for clinical and surveillance purposes: performance evaluation of the new REGA version 3 and seven other tools. Infect Genet Evol. 2013;19:337–48.

    Article  PubMed  Google Scholar 

  22. Schultz AK, Zhang M, Bulla I, Leitner T, Korber B, Morgenstern B, et al. jpHMM: Improving the reliability of recombination prediction in HIV-1. Nucleic Acids Res. 2009;37(Web Server Issue):W647–51. Erratum in: Nucleic Acids Res 2010, 38(3):1059.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  23. Kosakovsky Pond SL, Posada D, Stawiski E, Chappey C, Poon AF, Hughes G, et al. An evolutionary model-based algorithm for accurate phylogenetic breakpoint mapping and subtype prediction in HIV-1. PLoS Comput Biol. 2009;5(11):e1000581.

    Article  PubMed Central  PubMed  Google Scholar 

  24. Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30(4):772–80.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  25. Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: Molecular Evolutionary Genetics Analysis version 6.0. Mol Biol Evol. 2013;30(12):2725–9.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  26. Felsenstein J. Confidence limits on phylogenies: An approach using the bootstrap. Evolution. 1985;39:738–91.

    Article  Google Scholar 

  27. Rhee SY, Gonzales MJ, Kantor R, Betts BJ, Ravela J, Shafer RW. Human immunodeficiency virus reverse transcriptase and protease sequence database. Nucleic Acids Res. 2003;31(1):298–303.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  28. Barth RE, Wensing AM, Tempelman HA, Moraba RR, Schuurman R. Rapid accumulation of non-nucleoside reverse transcriptase inhibitor-associated resistance: evidence of transmitted resistance in rural South Africa. AIDS. 2008;22(16):2210–2.

    Article  CAS  PubMed  Google Scholar 

  29. Cane PA, De Ruiter A, Rice P, Wiselka M, Fox R, Pillay D. Resistance associated mutations in the human immunodeficiency virus type 1 subtype C protease gene from treated and untreated patients in the United Kingdom. J Clin Microbiol. 2001;39(7):2652–4.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  30. Soares EA, Santos AF, Gonzalez LM, Lalonde MS, Denis M, Tebit DM, et al. Mutation T74S in HIV-1 subtype B and C proteases resensitizes them to ritonavir and indinavir and confers fitness advantage. J Antimicrob Chemother. 2009;64(5):938–44.

    Article  CAS  PubMed  Google Scholar 

  31. Velazquez-Campoy A, Todd MJ, Vega S, Freire E. Catalytic efficiency and vitality of HIV-1 proteases from African viral subtypes. Proc Natl Acad Sci U S A. 2001;98(11):6062–7.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  32. Novitsky VA, Montano MA, McLane MF, Renjifo B, Vannberg F, Foley BT, et al. Molecular cloning and phylogenetic analysis of human immunodeficiency virus type 1 subtype C: a set of 23 full-length clones from Botswana. J Virol. 1999;73(5):4427–32.

    PubMed Central  CAS  PubMed  Google Scholar 

  33. Rousseau CM, Birditt BA, McKay AR, Stoddard JN, Lee TC, McLaughlin S, et al. Large-scale amplification, cloning and sequencing of near full-length HIV-1 subtype C genomes. J Virol Methods. 2006;136(1–2):118–25.

    Article  CAS  PubMed  Google Scholar 

  34. Gaschen B, Taylor J, Yusim K, Foley B, Gao F, Lang D, et al. Diversity considerations in HIV-1 vaccine selection. Science. 2002;296(5577):2354–60.

    Article  CAS  PubMed  Google Scholar 

  35. Shankarappa R, Chatterjee R, Learn GH, Neogi D, Ding M, Roy P, et al. Human immunodeficiency virus type 1 env sequences from Calcutta in eastern India: identification of features that distinguish subtype C sequences in India from other subtype C sequences. J Virol. 2001;75(21):10479–87.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  36. Abdool Karim Q, Abdool Karim SS. The evolving HIV epidemic in South Africa. Int J Epidemiol. 2002;31:37–40.

    Article  PubMed  Google Scholar 

  37. Bauer G, Taylor SD. Politics in Southern Africa: state and society in transition. Boulder: Lynne Rienner Publishers; 2005.

    Google Scholar 

  38. Dusheiko GM, Brink BA, Conradie JD, Marimuthu T, Sher R. Regional prevalence of hepatitis B, delta, and human immunodeficiency virus infection in southern Africa: a large population survey. Am J Epidemiol. 1989;129(1):138–45.

    CAS  PubMed  Google Scholar 

  39. Huang KH, Goedhals D, Fryer H, van Vuuren C, Katzourakis A, De Oliveira T, et al. Prevalence of HIV type-1 drug-associated mutations in pre-therapy patients in the Free State South Africa. Antivir Ther. 2009;14(7):975–84.

    Article  CAS  PubMed  Google Scholar 

  40. Abdool Karim SS, Abdool Karim Q. Changes in HIV seroprevalence in a rural black community in KwaZulu Natal. S Afr Med J. 1992;82(6):484.

    CAS  PubMed  Google Scholar 

  41. Decosas J, Kane F, Anarfi JK, Sodji KD, Wagner HU. Migration and AIDS. Lancet. 1995;346(8978):826–8.

    Article  CAS  PubMed  Google Scholar 

  42. Lurie MN, Williams BG, Zuma K, Mkaya-Mwamburi D, Garnett G, Sturm AW, et al. The impact of migration on HIV-1 transmission in South Africa: a study of migrant and nonmigrant men and their partners. Sex Transm Dis. 2003;30(2):149–56.

    Article  PubMed  Google Scholar 

  43. Ministry of Health, Mozambique: Ministry of Health National Control Program ITS/HIV-SIDA. Relatório sobre a Revisão dos Dadosde Vigilância Epidemiológica do HIV-Ronda 2004 [Report regarding the revision of the epidemiological surveillance data on HIV - Round 2004]. Maputo, Mozambique: Ministério da Saúde; 2005.

    Google Scholar 

  44. Lurie M. Migration and AIDS in southern Africa: a review. S Afr J Sci. 2000;96:343–7.

    Google Scholar 

  45. Lurie MN, Williams BG, Zuma K, Mkaya-Mwamburi D, Garnett GP, Sweat MD, et al. Who infects whom? HIV-1 concordance and discordance among migrant and non-migrant couples in South Africa. AIDS. 2003;17(15):2245–52.

    Article  PubMed  Google Scholar 

  46. Ramjee G, Gouws E. Prevalence of HIV among truck drivers visiting sex workers in KwaZulu-Natal, South Africa. Sex Transm Dis. 2002;29(1):44–9.

    Article  PubMed  Google Scholar 

  47. De Oliveira T, Engelbrecht S, Van Rensburg E, Gordon M, Bishop K, Zur Megede J, et al. Variability at HIV- 1 subtype C protease cleavage sites and indication of viral fitness? J Virol. 2003;77(17):9422–30.

    Article  PubMed Central  PubMed  Google Scholar 

  48. Gao F, Robertson DL, Carruthers CD, Morrison SG, Jian B, Chen Y, et al. A comprehensive panel of near-full-length clones and reference sequences for non-subtype B isolates of human immunodeficiency virus type1. J Virol. 1998;72(7):5680–98.

    PubMed Central  CAS  PubMed  Google Scholar 

  49. Hunt G, Tiemessen CT. Occurrence of additional NF-κB-binding motifs in the long terminal repeat region of South African HIV type 1 subtype C Isolates. AIDS Res Human Retroviruses. 2000;16(3):305–6.

    Article  CAS  Google Scholar 

  50. McCormick-Davis C, Dalton SB, Singh DK, Stephens EB. Comparisons of Vpu sequences from diverse geographical isolates of HIV type 1 identifies the presence of highly variable domains additional invariant amino acids and a signature sequence motif common to subtype C isolates. AIDS Res Hum Retroviruses. 2000;16(11):1089–95.

    Article  CAS  PubMed  Google Scholar 

  51. Rodenburg CM, Li Y, Trask SA. Near full-length clones and reference sequences for subtype C isolates of HIV type 1 from three different continents. AIDS Res Hum Retroviruses. 2001;17(2):161–8.

    Article  CAS  PubMed  Google Scholar 

  52. Ball SC, Abraha A, Collins KR, Marozsan AJ, Baird H, Quiñones-Mateu ME, et al. Comparing the ex vivo fitness of CCR5- tropic human immunodeficiency virus type 1 isolates of subtypes B and C. J Virol. 2003;77(2):1021–38.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  53. Peeters M, Vincent R, Perret JL, Lasky M, Patrel D, Liegeois F, et al. Evidence for differences in MT2 cell tropism according to genetic subtypes of HIV-1: syncytium-inducing variants seem rare among subtype C HIV-1 viruses. J Acquir Immune Defic Syndr Hum Retrovirol. 1999;20(2):115–21.

    Article  CAS  PubMed  Google Scholar 

  54. Ping LH, Nelson JA, Hoffman IF, Schock J, Lamers SL, Goodman M, et al. Characterization of V3 sequence heterogeneity in subtype C human immunodeficiency virus type 1 isolates from Malawi: underrepresentation of X4 variants. J Virol. 1999;73(8):6271–81.

    PubMed Central  CAS  PubMed  Google Scholar 

  55. Gifford RJ, Liu TF, Rhee SY, Kiuchi M, Hue S, Pillay D, et al. The calibrated population resistance tool: standardized genotypic estimation of transmitted HIV-1 drug resistance. Bioinformatics. 2009;25(9):1197–8.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  56. Shafer RW, Rhee SY, Bennett DE. Consensus drug resistance mutations for epidemiological surveillance: basic principles and potential controversies. Antivir Ther. 2008;13 Suppl 2:59–68.

    PubMed  Google Scholar 

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Acknowledgements

This research was supported by grants from the Medical Research Council (MRC), the Poliomyelitis Research foundation (PRF) and the NHLS Research Trust.

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Authors and Affiliations

  1. Division of Medical Virology, Department of Pathology, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg Hospital, P.O. Box 241, Cape Town, 8000, South Africa

    Patrick Wela Msimanga, Efthyia Vardas & Susan Engelbrecht

  2. Lancet Laboratories, P.O. Box 8475, Johannesburg, 2000, South Africa

    Efthyia Vardas

  3. National Health Laboratory Services (NHLS), Western Cape Region, Tygerberg Hospital (Coastal), Tygerberg, Cape Town, South Africa

    Susan Engelbrecht

  4. Current Address: National Department of Health, Civitas Building, Corner Thabo Sehume and Struben Streets, P.O. Box X828, Pretoria, 0001, South Africa

    Patrick Wela Msimanga

Authors
  1. Patrick Wela Msimanga
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Correspondence to Susan Engelbrecht.

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Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

PWM performed the experiments, participated in the analysis and wrote the first draft of the manuscript. EV contributed samples and clinical information. SE conceived and designed the study and analyzed the data. All authors read and approved the final manuscript.

Additional files

Additional file 1: Table S1.

ML fit of 24 different nucleotide substitution models for HIV-1 gag dataset.

Additional file 2: Table S2.

ML fit of 24 different substitution models for HIV-1 pol PR/RT dataset.

Additional file 3: Table S3.

ML fit of 24 different substitution models for HIV-1 pol IN dataset.

Additional file 4: Table S4.

ML fit of 24 different substitution models for HIV-1 env gp41 dataset.

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Msimanga, P.W., Vardas, E. & Engelbrecht, S. HIV-1 diversity in an antiretroviral treatment naïve cohort from Bushbuckridge, Mpumalanga Province, South Africa. Virol J 12, 24 (2015). https://doi.org/10.1186/s12985-015-0244-1

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ISSN: 1743-422X