The South African national combination antiretroviral therapy (cART) roll-out program started in 2006, with over 4.4 million people accessing treatment since it was first introduced. HIV-1 drug resistance can hamper the success of cART. This study determined the patterns of HIV-1 drug-resistance associated mutations (RAMs) in People Living with HIV-1 (PLHIV-1). Receiving first (for children below 3 years of age) and second-line (for adults) cART regimens in South Africa. During 2017 and 2018, 110 patients plasma samples were selected, 96 samples including those of 17 children and infants were successfully analyzed. All patients were receiving a boosted protease inhibitor (bPI) as part of their cART regimen. The viral sequences were analyzed for RAMs through genotypic resistance testing. We performed genotypic resistance testing (GRT) for Protease inhibitors (PIs), Reverse transcriptase inhibitors (RTIs) and Integrase strand transfer inhibitors (InSTIs). Viral sequences were subtyped using REGAv3 and COMET. Based on the PR/RT sequences, HIV-1 subtypes were classified as 95 (99%) HIV-1 subtype C (HIV-1C) while one sample as 02_AG. Integrase sequencing was successful for 89 sequences, and all the sequences were classified as HIV-1C (99%, 88/89) except one sequence classified CRF02_AG, as observed in PR/RT. Of the 96 PR/RT sequences analyzed, M184V/I (52/96; 54%) had the most frequent RAM nucleoside reverse transcriptase inhibitor (NRTI). The most frequent non-nucleoside reverse transcriptase inhibitor (NNRTI) RAM was K103N/S (40/96, 42%). Protease inhibitor (PI) RAMs M46I and V82A were present in 12 (13%) of the sequences analyzed. Among the InSTI major RAM two (2.2%) sequences have Y143R and T97A mutations

Resistance associated mutations (RAMs) threaten the long-term success of combination antiretroviral therapy (cART) outcomes for HIV-1 treatment. HIV-1 Integrase (IN) strand transfer inhibitors (INSTIs) have proven to be a viable option for highly specific HIV-1 therapy. The INSTI, Dolutegravir is recommended by the World Health Organization for use as first-line cART. This study aims to understand how RAMs affect the stability of IN, as well as the binding of the drug Dolutegravir to the catalytic pocket of the protein. A homology model of HIV-1 subtype C IN was successfully constructed and validated. The site directed mutator webserver was used to predict destabilizing and/or stabilizing effects of known RAMs while FoldX confirmed any changes in protein energy upon introduction of mutation. Also, interaction analysis was performed between neighbouring residues. Three mutations known to be associated with Raltegravir, Elvitegravir and Dolutegravir resistance were selected; E92Q, G140S and Y143R, for molecular dynamics simulations. The structural quality assessment indicated high reliability of the HIV-1C IN tetrameric structure, with more than 90% confidence in modelled regions. Change in free energy for the three mutants indicated different effects, while simulation analysis showed G140S to have the largest affect on protein stability and flexibility. This was further supported by weaker non-bonded pairwise interaction energy and binding free energy values between the drug DTG and E92Q, Y143R and G140S mutants suggesting reduced binding affinity, as indicated by interaction analysis in comparison to the WT. Our findings suggest the G140S mutant has the strongest effect on the HIV-1C IN protein structure and Dolutegravir binding. To the best of our knowledge, this is the first study that uses the consensus wild type HIV-1C IN sequence to build an accurate 3D model to understand the effect of three known mutations on DTG drug binding in a South Africa context.

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Background More than 15 million people in sub-Saharan Africa receive ART. Treatment failure is common, but the role of HIV drug resistance in treatment failure is largely unknown because drug resistance testing is not routinely done. This study determined the prevalence and patterns of HIV drug resistance in patients with suspected virological failure. Materials and methods A single high viral load of >1000 viral RNA copies/mL of plasma at any point during ART was considered as suspected virological failure. HIV-1 RNA was extracted from plasma samples of these patients using the QIAamp Viral RNA kit. The protease and part of the RT regions of the HIV pol gene were characterized. Results Viral load was determined in 317 patients; 64 (20.2%) had suspected virological failure. We successfully genotyped 56 samples; 48 (85.7%) had at least one major resistance-associated mutation (RAM). Common mutations in RT were M184V (75%), T215Y (41.1%), K103N (39.3%), M41L (32.1%), D67DN (30.3%), G190A (28.6%) and A98G (26.8%). No RAMs were detected in ART regimens based on a ritonavir-boosted PI. Conclusions The Tanzanian national guidelines define ‘virological failure’ as two consecutive viral load measurement results, at 3 month intervals, above the WHO threshold (1000 copies/mL). Here, we show that a single viral load above the WHO threshold is associated with high rates of RAMs. This suggests that a single high viral load measurement could be used to predict virological failure and avoid delays in switching patients from first-line to higher genetic barrier second-line regimens.

AbstractResistance associated mutations (RAMs) threaten the long-term success of combination antiretroviral therapy (cART) outcomes for HIV-1 treatment. HIV-1 Integrase (IN) strand transfer inhibitors (INSTIs) have proven to be a viable option for highly specific HIV-1 therapy. The INSTI, Dolutegravir is recommended by the World Health Organization for use as first-line cART. This study aims to understand how RAMs affect the stability of IN, as well as the binding of the drug Dolutegravir to the catalytic pocket of the protein. Molecular modelling of HIV-1C IN was performed using the SWISS-MODEL webserver; with quality assessment performed using internal methods and external software tools. The site directed mutator webserver was used to predict destabilizing and/or stabilizing effects of known RAMs while FoldX confirmed any changes in protein energy upon introduction of mutation. Interaction analysis between neighbouring residues was done using PyMOL. Three randomly selected mutations were chosen for molecular dynamic simulation studies using Gromacs. Trajectory analysis included Root mean square deviation and fluctuation, Radius of gyration, Principal component analysis and Interaction analysis between Dolutegravir and protein residues. The structural quality assessment indicated high reliability of the HIV-1C IN tetrameric structure, with more than 90% confidence in modelled regions. Change in free energy for the G140S mutant indicated a stabilizing effect and simulation analysis showed it to affect structural stability and flexibility of the protein structure. This was further supported by the drug being expelled from the G140S mutant active site, as indicated by interaction analysis. Our findings suggest the G140S mutant has a strong effect on the HIV-1C IN protein structure and Dolutegravir binding and should be validated using laboratory-based experiments. This approach can be applied to determine the effect of other mutations/variants on HIV-1C integrase drug binding.