HIV-1 phenotypic reverse transcriptase inhibitor drug resistance test interpretation is not dependent on the subtype of the virus backbone

Evolution Driven By A Varying Host Environment Selects For Distinct HIV-1 Entry Phenotypes and Other Informative Variants

HIV-1 generates remarkable intra- and inter-host viral diversity during infection. In response to dynamic selective pressures of the host environment, HIV-1 will evolve distinct phenotypes - biological features that provide fitness advantages. The transmitted form of HIV-1 has been shown to require a high density of CD4 on the target cell surface (as found on CD4+ T cells) and typically uses CCR5 as a co-receptor during entry. This phenotype is referred to as R5 T cell-tropic (or R5 T-tropic); however, HIV-1 can switch to a secondary co-receptor, CXCR4, resulting in a X4 T cell-tropic phenotype. Macrophage-tropic (or M-tropic) HIV-1 can evolve to efficiently enter cells expressing low densities of CD4 on their surface (such as macrophages/microglia). So far only CCR5-using M-tropic viruses have been found. M-tropic HIV-1 is most frequently found within the central nervous system, and infection of the CNS has been associated with neurological impairment. It has been shown that interferon resistance phenotypes have a selective advantage during transmission, but the underlying mechanism of this is still unclear. During untreated infection, HIV-1 evolves under selective pressure from both the humoral/antibody response and CD8+ T cell killing. Sufficiently potent antiviral therapy will suppress viral replication, but if the antiviral drugs are not sufficiently potent to stop replication then the replicating virus will evolve drug resistance. HIV-1 phenotypes are highly relevant to treatment efforts, clinical outcomes, vaccine studies, and cure strategies. Therefore, it is critical to understand the dynamics of the host environment that drive these phenotypes and how they affect HIV-1 pathogenesis. This review will provide a comprehensive discussion of HIV-1 entry, transmission, and drug resistance phenotypes. Finally, we will assess the methods used in previous and current research to characterize these phenotypes.

The Effect of Treatment-Associated Mutations on HIV Replication and Transmission Cycles

HIV/AIDS mortality has been decreasing over the last decade. While promising, this decrease correlated directly with increased use of antiretroviral drugs. As a natural consequence of its high mutation rate, treatments provide selection pressure that promotes the natural selection of escape mutants. Individuals may acquire drug-naive strains, or those that have already mutated due to treatment. Even within a host, mutation affects HIV tropism, where initial infection begins with R5-tropic virus, but the clinical transition to AIDS correlates with mutations that lead to an X4-tropic switch. Furthermore, the high mutation rate of HIV has spelled failure for all attempts at an effective vaccine. Pre-exposure drugs are currently the most effective drug-based preventatives, but their effectiveness is also threatened by viral mutation. From attachment and entry to assembly and release, the steps in the replication cycle are also discussed to describe the drug mechanisms and mutations that arise due to those drugs. Revealing the patterns of HIV-1 mutations, their effects, and the coordinated attempt to understand and control them will lead to effective use of current preventative measures and treatment options, as well as the development of new ones.

Drug Resistance Mutations Against Protease, Reverse Transcriptase and Integrase Inhibitors in People Living With HIV-1 Receiving Boosted Protease Inhibitors in South Africa

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 while one sample had T66I. The accessory RAM E157Q was identified in two (2.2%). The data indicates that the majority of the patients failed on bPIs didn't have any mutation; therefore adherence could be major issue in these groups of individuals. We propose continued viral load monitoring for better management of infected PLHIV.

Ensemble Classifiers for Predicting HIV-1 Resistance from Three Rule-Based Genotypic Resistance Interpretation Systems

Resistance to antiretrovirals (ARVs) is a major problem faced by HIV-infected individuals. Different rule-based algorithms were developed to infer HIV-1 susceptibility to antiretrovirals from genotypic data. However, there is discordance between them, resulting in difficulties for clinical decisions about which treatment to use. Here, we developed ensemble classifiers integrating three interpretation algorithms: Agence Nationale de Recherche sur le SIDA (ANRS), Rega, and the genotypic resistance interpretation system from Stanford HIV Drug Resistance Database (HIVdb). Three approaches were applied to develop a classifier with a single resistance profile: stacked generalization, a simple plurality vote scheme and the selection of the interpretation system with the best performance. The strategies were compared with the Friedman's test and the performance of the classifiers was evaluated using the F-measure, sensitivity and specificity values. We found that the three strategies had similar performances for the selected antiretrovirals. For some cases, the stacking technique with naïve Bayes as the learning algorithm showed a statistically superior F-measure. This study demonstrates that ensemble classifiers can be an alternative tool for clinical decision-making since they provide a single resistance profile from the most commonly used resistance interpretation systems.

A simple and cost-saving phenotypic drug susceptibility testing of HIV-1

It is essential to monitor the occurrence of drug-resistant strains and to provide guidance for clinically adapted antiviral treatment of HIV/AIDS. In this study, an individual patient's HIV-1 pol gene encoding the full length of protease and part of the reverse transcriptase was packaged into a modified lentivirus carrying dual-reporters ZsGreen and luciferase. The optimal coefficient of correlation between drug concentration and luciferase activity was optimized. A clear-cut dose-dependent relationship between lentivirus production and luciferase activity was found in the phenotypic testing system. Fold changes (FC) to a wild-type control HIV-1 strain ratios were determined reflecting the phenotypic susceptibility of treatment-exposed patient's HIV-1 strains to 12 HIV-1 inhibitors including 6 nucleoside reverse-transcriptase inhibitors (NRTIs), 4 non-nucleoside reverse transcriptase inhibitors (NNRTIs) and 2 protease inhibitors (PIs). Phenotypic susceptibility calls from 8 HIV-1 infected patients were consistent with 80-90% genotypic evaluations, while phenotypic assessments rectified 10-20% genotypic resistance calls. By a half of replacement with ZsGreen reporter, the consumption of high cost Bright-Glo Luciferase Assay is reduced, making this assay cheaper when a large number of HIV-1 infected individuals are tested. The study provides a useful tool for interpreting meaningful genotypic mutations and guiding tailored antiviral treatment of HIV/AIDS in clinical practice.

HIV-1 genetic variation and drug resistance development

Up until 10 years ago, basic and clinical HIV-1 research was mainly performed on HIV-1 subtype B that predominated in resource-rich settings. Over the past decade, HIV-1 care and therapy has been scaled up substantially in Latin America, Africa and Asia. These regions are largely dominated by non-B subtype infections, and especially the African continent is affected by the HIV pandemic. Insight on the potency of antiviral drugs and regimens as well as on the emergence of drug resistance in non-B subtypes was lacking triggering research in this field, also partly driven by the introduction and spreading of HIV-1 non-B subtypes in Europe. The scope of this article was to review and discuss the state-of-the-art on the impact of HIV-1 genetic variation on the in vitro activity of antiviral drugs and in vivo response to antiviral therapy; as well as on the in vitro and in vivo emergence of drug resistance.

Impact of Y181C and/or H221Y mutation patterns of HIV-1 reverse transcriptase on phenotypic resistance to available non-nucleoside and nucleoside inhibitors in China

BACKGROUND

The aim of this study was to investigate the role of K101Q, Y181C and H221Y emerging in HIV-1 reverse transcriptase with different mutations patterns in phenotypic susceptibility to currently available NNRTIs (nevirapine NVP, efavirenz EFV) and NRTIs (zidovudine AZT, lamivudine 3TC, stavudine d4T) in China.

METHODS

Phenotype testing of currently available NNRTIs (NVP, EFV) and NRTIs (AZT, 3TC, d4T) was performed on TZM-b1 cells using recombined virus strains. P ≤ 0.05 was defined significant considering the change of 50% inhibitory drug concentration (IC50) compared with the reference, while P ≤ 0.01 was considered to be statistically significant considering multiple comparisons.

RESULTS

Triple-mutation K101Q/Y181C/H221Y and double-mutation K101Q/Y181C resulted in significant increase in NVP resistance (1253.9-fold and 986.4-fold), while only K101Q/Y181C/H221Y brought a 5.00-fold significant increase in EFV resistance. Remarkably, K101Q/H221Y was hypersusceptible to EFV (FC = 0.04), but was significantly resistant to the three NRTIs. Then, the interaction analysis suggested the interaction was not significant to NVP (F = 0.77, P = 0.4061) but significant to EFV and other three NRTIs.

CONCLUSION

Copresence of mutations reported to be associated with NNRTIs confers significant increase to NVP resistance. Interestingly, some may increase the susceptibility to EFV. Certainly, the double mutation (K101Q/H221Y) also changes the susceptibility of viruses to NRTIs. Interaction between two different sites makes resistance more complex.