Primary resistance to integrase strand-transfer inhibitors in Europe.

OBJECTIVES: The objective of this study was to define the natural genotypic variation of the HIV-1 integrase gene across Europe for epidemiological surveillance of integrase strand-transfer inhibitor (InSTI) resistance. METHODS: This was a multicentre, cross-sectional study within the European SPREAD HIV resistance surveillance programme. A representative set of 300 samples was selected from 1950 naive HIV-positive subjects newly diagnosed in 2006-07. The prevalence of InSTI resistance was evaluated using quality-controlled baseline population sequencing of integrase. Signature raltegravir, elvitegravir and dolutegravir resistance mutations were defined according to the IAS-USA 2014 list. In addition, all integrase substitutions relative to HXB2 were identified, including those with a Stanford HIVdb score ≥ 10 to at least one InSTI. To rule out circulation of minority InSTI-resistant HIV, 65 samples were selected for 454 integrase sequencing. RESULTS: For the population sequencing analysis, 278 samples were retrieved and successfully analysed. No signature resistance mutations to any of the InSTIs were detected. Eleven (4%) subjects had mutations at resistance-associated positions with an HIVdb score ≥ 10. Of the 56 samples successfully analysed with 454 sequencing, no InSTI signature mutations were detected, whereas integrase substitutions with an HIVdb score ≥ 10 were found in 8 (14.3%) individuals. CONCLUSIONS: No signature InSTI-resistant variants were circulating in Europe before the introduction of InSTIs. However, polymorphisms contributing to InSTI resistance were not rare. As InSTI use becomes more widespread, continuous surveillance of primary InSTI resistance is warranted. These data will be key to modelling the kinetics of InSTI resistance transmission in Europe in the coming years.

Characterization of amino acids Arg, Ser and Thr at position 70 within HIV-1 reverse transcriptase.

OBJECTIVES: The amino acid position 70 in HIV-1 reverse transcriptase (RT) plays an important role in nucleoside RT inhibitor (NRTI) resistance. K70R is part of the thymidine analog mutations, but also other amino acid changes have been associated with NRTI resistance, such as K70E and K70G. In this study, we investigated the in vivo selection of the HIV-1 RT mutations K70S and K70T and their in vitro effect on drug resistance and replication capacity. METHODS: Recombinant viruses with RT mutations were generated to measure the in vitro drug susceptibility and replication capacity. Bayesian network analysis and three-dimensional modeling were performed to understand the selection and impact of the RT70 mutations. RESULTS: K70S and K70T were found at a low frequency in RTI-experienced HIV-1 patients (0.10% and 0·20%). Baeyesian network learning identified no direct association with the in vivo exposure to any specific RTI. However, direct associations of K70S with mutations within the Q151M-complex and of K70T with K65R were observed. In vitro phenotypic testing revealed only minor effects of K70R/S/T as single mutations, associated with Q151M and within the context of the Q151M-complex. DISCUSSION: These results suggest that the selection of K70S/T and their phenotypic impact are influenced by the presence of other mutations in RT. However, the low impact on in vitro phenotype here observed, alongside with the low in vivo prevalence, the exclusive direct association with known major RTI mutations and the unknown correlation with in vivo response, do not yet necessitate the inclusion of K70S/T in drug resistance interpretation systems.

Prediction of response to antiretroviral therapy by human experts and by the EuResist data-driven expert system (the EVE study).

OBJECTIVES: The EuResist expert system is a novel data-driven online system for computing the probability of 8-week success for any given pair of HIV-1 genotype and combination antiretroviral therapy regimen plus optional patient information. The objective of this study was to compare the EuResist system vs. human experts (EVE) for the ability to predict response to treatment. METHODS: The EuResist system was compared with 10 HIV-1 drug resistance experts for the ability to predict 8-week response to 25 treatment cases derived from the EuResist database validation data set. All current and past patient data were made available to simulate clinical practice. The experts were asked to provide a qualitative and quantitative estimate of the probability of treatment success. RESULTS: There were 15 treatment successes and 10 treatment failures. In the classification task, the number of mislabelled cases was six for EuResist and 6-13 for the human experts [mean±standard deviation (SD) 9.1±1.9]. The accuracy of EuResist was higher than the average for the experts (0.76 vs. 0.64, respectively). The quantitative estimates computed by EuResist were significantly correlated (Pearson r=0.695, P<0.0001) with the mean quantitative estimates provided by the experts. However, the agreement among experts was only moderate (for the classification task, inter-rater κ=0.355; for the quantitative estimation, mean±SD coefficient of variation=55.9±22.4%). CONCLUSIONS: With this limited data set, the EuResist engine performed comparably to or better than human experts. The system warrants further investigation as a treatment-decision support tool in clinical practice.

Resistance pathways of human immunodeficiency virus type 1 against the combination of zidovudine and lamivudine.

A better understanding of human immunodeficiency virus type 1 drug-resistance evolution under the selective pressure of combination treatment is important for the design of long-term effective treatment strategies. We applied Bayesian network learning to sequences from patients treated with the reverse transcriptase inhibitor combination of zidovudine (AZT) and lamivudine (3TC) to identify the role of many treatment-selected mutations in the development of resistance. Based on the Bayesian network structure, an in vivo fitness landscape was built, reflecting the necessary selective pressure under treatment, to evolve naive sequences to sequences obtained from patients treated with the combination. This landscape, combined with an evolutionary model, was used to predict resistance evolution in longitudinal sequence pairs. In our analysis, mutations 41L, 70R, 184V and 215F/Y were identified as major resistance mutations to the combination of AZT and 3TC, as they were associated directly with treatment experience. The network also suggested a possible role in resistance development for a number of novel mutations. Estimated fitness, using the landscape, correlated significantly with in vitro resistance phenotype in genotype-phenotype pairs (R(2)=0.70). Variation in predicted evolution under selective pressure correlated significantly with observed in vivo evolution during AZT plus 3CT treatment. In conclusion, we confirmed current knowledge on resistance development to the combination of AZT and 3CT, but additional novel mutations were identified. Moreover, a model to predict resistance evolution during AZT and 3CT treatment has been built and validated.

Estimation of an in vivo fitness landscape experienced by HIV-1 under drug selective pressure useful for prediction of drug resistance evolution during treatment.

MOTIVATION: HIV-1 antiviral resistance is a major cause of antiviral treatment failure. The in vivo fitness landscape experienced by the virus in presence of treatment could in principle be used to determine both the susceptibility of the virus to the treatment and the genetic barrier to resistance. We propose a method to estimate this fitness landscape from cross-sectional clinical genetic sequence data of different subtypes, by reverse engineering the required selective pressure for HIV-1 sequences obtained from treatment naive patients, to evolve towards sequences obtained from treated patients. The method was evaluated for recovering 10 random fictive selective pressures in simulation experiments, and for modeling the selective pressure under treatment with the protease inhibitor nelfinavir. RESULTS: The estimated fitness function under nelfinavir treatment considered fitness contributions of 114 mutations at 48 sites. Estimated fitness correlated significantly with the in vitro resistance phenotype in 519 matched genotype-phenotype pairs (R(2) = 0.47 (0.41 - 0.54)) and variation in predicted evolution under nelfinavir selective pressure correlated significantly with observed in vivo evolution during nelfinavir treatment for 39 mutations (with FDR = 0.05). AVAILABILITY: The software is available on request from the authors, and data sets are available from http://jose.med.kuleuven.be/~kdforc0/nfv-fitness-data/.

Bayesian network analysis of resistance pathways against HIV-1 protease inhibitors.

Interpretation of Human Immunodeficiency Virus 1 (HIV-1) genotypic drug resistance is still a major challenge in the follow-up of antiviral therapy in infected patients. Because of the high degree of HIV-1 natural variation, complex interactions and stochastic behaviour of evolution, the role of resistance mutations is in many cases not well understood. Using Bayesian network learning of HIV-1 sequence data from diverse subtypes (A, B, C, F and G), we could determine the specific role of many resistance mutations against the protease inhibitors (PIs) nelfinavir (NFV), indinavir (IDV), and saquinavir (SQV). Such networks visualize relationships between treatment, selection of resistance mutations and presence of polymorphisms in a graphical way. The analysis identified 30N, 88S, and 90M for nelfinavir, 90M for saquinavir, and 82A/T and 46I/L for indinavir as most probable major resistance mutations. Moreover we found striking similarities for the role of many mutations against all of these drugs. For example, for all three inhibitors, we found that the novel mutation 89I was minor and associated with mutations at positions 90 and 71. Bayesian network learning provides an autonomous method to gain insight in the role of resistance mutations and the influence of HIV-1 natural variation. We successfully applied the method to three protease inhibitors. The analysis shows differences with current knowledge especially concerning resistance development in several non-B subtypes.

Analysis of HIV-1 pol sequences using Bayesian Networks: implications for drug resistance.

Human Immunodeficiency Virus-1 (HIV-1) antiviral resistance is a major cause of antiviral therapy failure and compromises future treatment options. As a consequence, resistance testing is the standard of care. Because of the high degree of HIV-1 natural variation and complex interactions, the role of resistance mutations is in many cases insufficiently understood. We applied a probabilistic model, Bayesian networks, to analyze direct influences between protein residues and exposure to treatment in clinical HIV-1 protease sequences from diverse subtypes. We can determine the specific role of many resistance mutations against the protease inhibitor nelfinavir, and determine relationships between resistance mutations and polymorphisms. We can show for example that in addition to the well-known major mutations 90M and 30N for nelfinavir resistance, 88S should not be treated as 88D but instead considered as a major mutation and explain the subtype-dependent prevalence of the 30N resistance pathway.

Optimization of a genotypic assay applicable to all human immunodeficiency virus type 1 protease and reverse transcriptase subtypes.

Genotypic assays are used often to guide clinicians in decisions concerning the treatment of patients. An optimized sequence-based genotypic assay was used to determine the whole protease and reverse transcriptase (RT) gene, including the gag cleavage site region and RNase H region. Since non-B subtypes are increasing in countries where subtype B was the most prevalent subtype, and treatment becomes more available in developing countries where the epidemic is characterized by a high prevalence of non-B subtypes, it was important that the genotypic test was evaluated using a panel of different subtypes. Amplification was successful for different subtypes: A, B, C, D, F, G, H, J, CRF01_AE, CRF02_AG, CRF11_cpx, CRF13_cpx and an uncharacterized recombinant sample. The detection limit of the PCR was 1000 copies/ml, except for 1 subtype C sample (PL3) and 1 CRF02_AG sample (PL8). The detection limit for these samples was 5000 copies/ml. A sequence could be obtained in both directions for most of the samples.