Interplay between single resistance-associated mutations in the HIV-1 protease and viral infectivity, protease activity, and inhibitor sensitivity

Premature Activation of the HIV-1 Protease Is Influenced by Polymorphisms in the Hinge Region

HIV-1 protease inhibitors are an essential component of antiretroviral therapy. However, drug resistance is a pervasive issue motivating a persistent search for novel therapies. Recent reports found that when protease activates within the host cell's cytosol, it facilitates the pyroptotic killing of infected cells. This has led to speculation that promoting protease activation, rather than inhibiting it, could help to eradicate infected cells and potentially cure HIV-1 infection. Here, we used a nanoscale flow cytometry-based assay to characterize protease resistance mutations and polymorphisms. We quantified protease activity, viral concentration, and premature protease activation and confirmed previous findings that major resistance mutations generally destabilize the protease structure. Intriguingly, we found evidence that common polymorphisms in the hinge domain of protease can influence its susceptibility to premature activation. This suggests that viral heterogeneity could pose a considerable challenge for therapeutic strategies aimed at inducing premature protease activation in the future.

Direct-acting antiviral resistance of Hepatitis C virus is promoted by epistasis

Direct-acting antiviral agents (DAAs) provide efficacious therapeutic treatments for chronic Hepatitis C virus (HCV) infection. However, emergence of drug resistance mutations (DRMs) can greatly affect treatment outcomes and impede virological cure. While multiple DRMs have been observed for all currently used DAAs, the evolutionary determinants of such mutations are not currently well understood. Here, by considering DAAs targeting the nonstructural 3 (NS3) protein of HCV, we present results suggesting that epistasis plays an important role in the evolution of DRMs. Employing a sequence-based fitness landscape model whose predictions correlate highly with experimental data, we identify specific DRMs that are associated with strong epistatic interactions, and these are found to be enriched in multiple NS3-specific DAAs. Evolutionary modelling further supports that the identified DRMs involve compensatory mutational interactions that facilitate relatively easy escape from drug-induced selection pressures. Our results indicate that accounting for epistasis is important for designing future HCV NS3-targeting DAAs.

Definition of a New HLA B*52-Restricted Rev CTL Epitope Targeted by an HIV-1-Infected Controller

The analysis of T-cell responses in HIV-1-infected controllers may contribute to a better understanding of the protective components of the immune system. Here, we analyzed the HIV-1-specific T-cell response in a 59-year-old HIV-1-infected controller, infected for at least seven years, who presented with low viral loads ranging from <20 copies/mL to 200 copies/mL and normal CD4 counts of >800 cells/µL. In γ-IFN-ELISpot assays using freshly isolated PBMCs, he displayed a very strong polyclonal T-cell response to eight epitopes in Gag, Nef and Rev; with the dominant responses directed against the HLA-B*57-epitope AISPRTLNAW and against a so-far-unknown epitope within Rev. Further analyses using peptide-stimulated T-cell lines in γ-IFN-ELISpot assays delineated the peptide RQRQIRSI (Rev-RI8) as a newly defined HLA-B*52-restricted epitope located within a functionally important region of Rev. Peptide-stimulation assays in 15 HLA-B*52-positive HIV-1-infected subjects, including the controller, demonstrated recognition of the Rev-RI8 epitope in 6/15 subjects. CD4 counts before the start of antiviral therapy were significantly higher in subjects with recognition of the Rev-RI8 epitope. Targeting of the Rev-RI8 epitope in Rev by CTL could contribute to the positive association of HLA-B*52 with a more favorable course of HIV-1-infection.

Limits to detecting epistasis in the fitness landscape of HIV

The rapid evolution of HIV is constrained by interactions between mutations which affect viral fitness. In this work, we explore the role of epistasis in determining the mutational fitness landscape of HIV for multiple drug target proteins, including Protease, Reverse Transcriptase, and Integrase. Epistatic interactions between residues modulate the mutation patterns involved in drug resistance, with unambiguous signatures of epistasis best seen in the comparison of the Potts model predicted and experimental HIV sequence “prevalences” expressed as higher-order marginals (beyond triplets) of the sequence probability distribution. In contrast, experimental measures of fitness such as viral replicative capacities generally probe fitness effects of point mutations in a single background, providing weak evidence for epistasis in viral systems. The detectable effects of epistasis are obscured by higher evolutionary conservation at sites. While double mutant cycles in principle, provide one of the best ways to probe epistatic interactions experimentally without reference to a particular background, we show that the analysis is complicated by the small dynamic range of measurements. Overall, we show that global pairwise interaction Potts models are necessary for predicting the mutational landscape of viral proteins.

Mi3-GPU: MCMC-based Inverse Ising Inference on GPUs for protein covariation analysis

Inverse Ising inference is a method for inferring the coupling parameters of a Potts/Ising model based on observed site-covariation, which has found important applications in protein physics for detecting interactions between residues in protein families. We introduce Mi3-GPU ("mee-three", for MCMC Inverse Ising Inference) software for solving the inverse Ising problem for protein-sequence datasets with few analytic approximations, by parallel Markov-Chain Monte-Carlo sampling on GPUs. We also provide tools for analysis and preparation of protein-family Multiple Sequence Alignments (MSAs) to account for finite-sampling issues, which are a major source of error or bias in inverse Ising inference. Our method is "generative" in the sense that the inferred model can be used to generate synthetic MSAs whose mutational statistics (marginals) can be verified to match the dataset MSA statistics up to the limits imposed by the effects of finite sampling. Our GPU implementation enables the construction of models which reproduce the covariation patterns of the observed MSA with a precision that is not possible with more approximate methods. The main components of our method are a GPU-optimized algorithm to greatly accelerate MCMC sampling, combined with a multi-step Quasi-Newton parameter-update scheme using a "Zwanzig reweighting" technique. We demonstrate the ability of this software to produce generative models on typical protein family datasets for sequence lengths L ~ 300 with 21 residue types with tens of millions of inferred parameters in short running times.

Non-active site mutants of HIV-1 protease influence resistance and sensitisation towards protease inhibitors

Background

HIV-1 can develop resistance to antiretroviral drugs, mainly through mutations within the target regions of the drugs. In HIV-1 protease, a majority of resistance-associated mutations that develop in response to therapy with protease inhibitors are found in the protease’s active site that serves also as a binding pocket for the protease inhibitors, thus directly impacting the protease-inhibitor interactions. Some resistance-associated mutations, however, are found in more distant regions, and the exact mechanisms how these mutations affect protease-inhibitor interactions are unclear. Furthermore, some of these mutations, e.g. N88S and L76V, do not only induce resistance to the currently administered drugs, but contrarily induce sensitivity towards other drugs. In this study, mutations N88S and L76V, along with three other resistance-associated mutations, M46I, I50L, and I84V, are analysed by means of molecular dynamics simulations to investigate their role in complexes of the protease with different inhibitors and in different background sequence contexts.

Results

Using these simulations for alchemical calculations to estimate the effects of mutations M46I, I50L, I84V, N88S, and L76V on binding free energies shows they are in general in line with the mutations’ effect on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$IC_{50}$$\end{document}IC50 values. For the primary mutation L76V, however, the presence of a background mutation M46I in our analysis influences whether the unfavourable effect of L76V on inhibitor binding is sufficient to outweigh the accompanying reduction in catalytic activity of the protease. Finally, we show that L76V and N88S changes the hydrogen bond stability of these residues with residues D30/K45 and D30/T31/T74, respectively.

Conclusions

We demonstrate that estimating the effect of both binding pocket and distant mutations on inhibitor binding free energy using alchemical calculations can reproduce their effect on the experimentally measured \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$IC_{50}$$\end{document}IC50 values. We show that distant site mutations L76V and N88S affect the hydrogen bond network in the protease’s active site, which offers an explanation for the indirect effect of these mutations on inhibitor binding. This work thus provides valuable insights on interplay between primary and background mutations and mechanisms how they affect inhibitor binding.

GEMME: a simple and fast global epistatic model predicting mutational effects

The systematic and accurate description of protein mutational landscapes is a question of utmost importance in biology, bioengineering, and medicine. Recent progress has been achieved by leveraging on the increasing wealth of genomic data and by modeling intersite dependencies within biological sequences. However, state-of-the-art methods remain time consuming. Here, we present Global Epistatic Model for predicting Mutational Effects (GEMME) (www.lcqb.upmc.fr/GEMME), an original and fast method that predicts mutational outcomes by explicitly modeling the evolutionary history of natural sequences. This allows accounting for all positions in a sequence when estimating the effect of a given mutation. GEMME uses only a few biologically meaningful and interpretable parameters. Assessed against 50 high- and low-throughput mutational experiments, it overall performs similarly or better than existing methods. It accurately predicts the mutational landscapes of a wide range of protein families, including viral ones and, more generally, of much conserved families. Given an input alignment, it generates the full mutational landscape of a protein in a matter of minutes. It is freely available as a package and a webserver at www.lcqb.upmc.fr/GEMME/.

Intron-containing RNA from the HIV-1 provirus activates type I interferon and inflammatory cytokines

HIV-1-infected people who take drugs that suppress viremia to undetectable levels are protected from developing AIDS. Nonetheless, HIV-1 establishes proviruses in long-lived CD4⁺ memory T cells, and perhaps other cell types, that preclude elimination of the virus even after years of continuous antiviral therapy. Here we show that the HIV-1 provirus activates innate immune signaling in isolated dendritic cells, macrophages, and CD4⁺ T cells. Immune activation requires transcription from the HIV-1 provirus and expression of CRM1-dependent, Rev-dependent, RRE-containing, unspliced HIV-1 RNA. If rev is provided in trans, all HIV-1 coding sequences are dispensable for activation except those cis-acting sequences required for replication or splicing. Our results indicate that the complex, post-transcriptional regulation intrinsic to HIV-1 RNA is detected by the innate immune system as a danger signal, and that drugs which disrupt HIV-1 transcription or HIV-1 RNA metabolism would add qualitative benefit to current antiviral drug regimens.

During HIV infection, antiviral therapy can suppress viraemia to undetectable levels and hinder the progression towards AIDS; however the HIV-1 provirus can remain in long-lived CD4⁺ memory T cells. Here the authors show that intronic RNA from the HIV-1 provirus can induce type I interferon and inflammatory cytokine production.

Mechanism of Darunavir (DRV)'s High Genetic Barrier to HIV-1 Resistance: A Key V32I Substitution in Protease Rarely Occurs, but Once It Occurs, It Predisposes HIV-1 To Develop DRV Resistance

Darunavir (DRV) has bimodal activity against HIV-1 protease, enzymatic inhibition and protease dimerization inhibition, and has an extremely high genetic barrier against development of drug resistance. We previously generated a highly DRV-resistant HIV-1 variant (HIVDRVRP51). We also reported that four amino acid substitutions (V32I, L33F, I54M, and I84V) identified in the protease of HIVDRVRP51 are largely responsible for its high-level resistance to DRV. Here, we attempted to elucidate the role of each of the four amino acid substitutions in the development of DRV resistance. We found that V32I is a key substitution, which rarely occurs, but once it occurs, it predisposes HIV-1 to develop high-level DRV resistance. When two infectious recombinant HIV-1 clones carrying I54M and I84V (rHIVI54M and rHIVI84V, respectively) were selected in the presence of DRV, V32I emerged, and the virus rapidly developed high-level DRV resistance. rHIVV32I also developed high-level DRV resistance. However, wild-type HIVNL4-3 (rHIVWT) failed to acquire V32I and did not develop DRV resistance. Compared to rHIVWT, rHIVV32I was highly susceptible to DRV and had significantly reduced fitness, explaining why V32I did not emerge upon selection of rHIVWT with DRV. When the only substitution is at residue 32, structural analysis revealed much stronger van der Waals interactions between DRV and I-32 than between DRV and V-32. These results suggest that V32I is a critical amino acid substitution in multiple pathways toward HIV-1's DRV resistance development and elucidate, at least in part, a mechanism of DRV's high genetic barrier to development of drug resistance. The results also show that attention should be paid to the initiation or continuation of DRV-containing regimens in people with HIV-1 containing the V32I substitution.IMPORTANCE Darunavir (DRV) is the only protease inhibitor (PI) recommended as a first-line therapeutic and represents the most widely used PI for treating HIV-1-infected individuals. DRV possesses a high genetic barrier to development of HIV-1's drug resistance. However, the mechanism(s) of the DRV's high genetic barrier remains unclear. Here, we show that the preexistence of certain single amino acid substitutions such as V32I, I54M, A71V, and I84V in HIV-1 protease facilitates the development of high-level DRV resistance. Interestingly, all in vitro-selected highly DRV-resistant HIV-1 variants acquired V32I but never emerged in wild-type HIV (HIVWT), and V32I itself rendered HIV-1 more sensitive to DRV and reduced viral fitness compared to HIVWT, strongly suggesting that the emergence of V32I plays a critical role in the development of HIV-1's resistance to DRV. Our results would be of benefit in the treatment of HIV-1-infected patients receiving DRV-containing regimens.

Evolution of gag and gp41 in Patients Receiving Ritonavir-Boosted Protease Inhibitors

Several groups have proposed that genotypic determinants in gag and the gp41 cytoplasmic domain (gp41-CD) reduce protease inhibitor (PI) susceptibility without PI-resistance mutations in protease. However, no gag and gp41-CD mutations definitively responsible for reduced PI susceptibility have been identified in individuals with virological failure (VF) while receiving a boosted PI (PI/r)-containing regimen. To identify gag and gp41 mutations under selective PI pressure, we sequenced gag and/or gp41 in 61 individuals with VF on a PI/r (n = 40) or NNRTI (n = 20) containing regimen. We quantified nonsynonymous and synonymous changes in both genes and identified sites exhibiting signal for directional or diversifying selection. We also used published gag and gp41 polymorphism data to highlight mutations displaying a high selection index, defined as changing from a conserved to an uncommon amino acid. Many amino acid mutations developed in gag and in gp41-CD in both the PI- and NNRTI-treated groups. However, in neither gene, were there discernable differences between the two groups in overall numbers of mutations, mutations displaying evidence of diversifying or directional selection, or mutations with a high selection index. If gag and/or gp41 encode PI-resistance mutations, they may not be confined to consistent mutations at a few sites.

Decoding HIV resistance: from genotype to therapy

Genetic variation in HIV poses a major challenge for prevention and treatment of the AIDS pandemic. Resistance occurs by mutations in the target proteins that lower affinity for the drug or alter the protein dynamics, thereby enabling viral replication in the presence of the drug. Due to the prevalence of drug-resistant strains, monitoring the genotype of the infecting virus is recommended. Computational approaches for predicting resistance from genotype data and guiding therapy are discussed. Many prediction methods rely on rules derived from known resistance-associated mutations, however, statistical or machine learning can improve the classification accuracy and assess unknown mutations. Adding classifiers such as information on the atomic structure of the protein can further enhance the predictions.

HIV-1 Subtype Diversity and Prevalence of Primary Drug Resistance in a Single-Center Pediatric Cohort in Germany

OBJECTIVES

Data on drug-resistant mutations (DRMs) in HIV-1-infected therapy-naïve children are scarce. The aim of this study was to determine the HIV-1 subtype distribution and the prevalence of DRMs in therapy-naïve HIV-1-infected children who received routine care at the University Hospital Düsseldorf, Düsseldorf, Germany.

METHODS

Records of all HIV-1-infected children who received routine care between January 2005 and December 2015 were analyzed retrospectively. The collected data included demographics, clinical characteristics, CD4 cell count, viral load, HIV-1 subtype, and resistance genotype at baseline.

RESULTS

83 HIV-1-infected children received routine care during the observation period. HIV-1 subtypes were available in 61/83 patients (73.5%) and baseline HIV-1 resistance in 24 (29%). The prevalence of major DRMs was 29% (21% nucleoside reverse-transcriptase inhibitors [NRTIs], 12.5% non-NRTIs, and 4% protease inhibitors). Minor mutations in the protease gene were common (58%). Non-B subtypes were predominant (77%).

CONCLUSIONS

We report a predominance of non-subtype-B infections and a higher prevalence of DRMs compared to other pediatric cohorts from resource-rich settings. The difference in HIV-1 subtype distribution is due to the fact that a relevant proportion of pediatric patients in Germany are immigrants from high-prevalence settings in sub-Saharan Africa where non-B subtypes predominate.

Inference of Epistatic Effects Leading to Entrenchment and Drug Resistance in HIV-1 Protease

Understanding the complex mutation patterns that give rise to drug resistant viral strains provides a foundation for developing more effective treatment strategies for HIV/AIDS. Multiple sequence alignments of drug-experienced HIV-1 protease sequences contain networks of many pair correlations which can be used to build a (Potts) Hamiltonian model of these mutation patterns. Using this Hamiltonian model, we translate HIV-1 protease sequence covariation data into quantitative predictions for the probability of observing specific mutation patterns which are in agreement with the observed sequence statistics. We find that the statistical energies of the Potts model are correlated with the fitness of individual proteins containing therapy-associated mutations as estimated by in vitro measurements of protein stability and viral infectivity. We show that the penalty for acquiring primary resistance mutations depends on the epistatic interactions with the sequence background. Primary mutations which lead to drug resistance can become highly advantageous (or entrenched) by the complex mutation patterns which arise in response to drug therapy despite being destabilizing in the wildtype background. Anticipating epistatic effects is important for the design of future protease inhibitor therapies.

Incident and long-term HIV-1 infection among pregnant women in Brazil: Transmitted drug resistance and mother-to-child transmission

Primary infection, seroconversion, and transmitted drug resistance (TDR) during pregnancy may influence the risk of mother-to-child-transmission (MTCT) of HIV-1 infection. This study estimated recent seroconversion, TDR rates, HIV-1 subtypes and pregnancy outcomes among 95 recently diagnosed, antiretroviral (ARV)-naïve pregnant women recruited during antenatal care in central western Brazil. Recent seroconversion was defined by BED-capture enzyme immunoassay (<155 days) and ambiguous nucleotides base calls (<1 year) in pol sequences (protease-PR and reverse transcriptase-RT regions). TDR was evaluated by the Calibrated Population Resistance tool. HIV-1 subtypes were defined by REGA and phylogenetic analyses. The median age of participants was 25 years; the median gestational age at diagnosis was 20.5 weeks. Based on serology and sequence polymorphism, recent infection was identified in 11.6% (11/95) and, 9 of them (82%), probably seroconverted during pregnancy; one MTCT case was observed among them. Three cases of stillbirth were observed among chronic infected patients (3.6%; 3/84). Moderate rate of TDR was observed (9/90, 10%, CI95% 4.7-18.1%). Subtype B was 60% (54/90), 13.3% (12/90) was subtype C, 6.7% (6/90) was subtype F1. Recombinant B(PR) /F1(RT) and F1(PR) /B(RT) viruses comprised 15.5% (14/90); B(PR) /C(RT) mosaics represented 4.4% (4/90). Seroconversion during pregnancy, late presentation to antenatal care and moderate TDR identified in this study represent significant challenges for the MTCT elimination. J. Med. Virol. 88:1936-1943, 2016. © 2016 Wiley Periodicals, Inc.

Identifying representative drug resistant mutants of HIV

BACKGROUND

Drug resistance is one of the most important causes for failure of anti-AIDS treatment. During therapy, multiple mutations accumulate in the HIV genome, eventually rendering the drugs ineffective in blocking replication of the mutant virus. The huge number of possible mutants precludes experimental analysis to explore the molecular mechanisms of resistance and develop improved antiviral drugs.

RESULTS

In order to solve this problem, we have developed a new algorithm to reveal the most representative mutants from the whole drug resistant mutant database based on our newly proposed unified protein sequence and 3D structure encoding method. Mean shift clustering and multiple regression analysis were applied on genotype-resistance data for mutants of HIV protease and reverse transcriptase. This approach successfully chooses less than 100 mutants with the highest resistance to each drug out of about 10K in the whole database. When considering high level resistance to multiple drugs, the numbers reduce to one or two representative mutants.

CONCLUSION

This approach for predicting the most representative mutants for each drug has major importance for experimental verification since the results provide a small number of representative sequences, which will be amenable for in vitro testing and characterization of the expressed mutant proteins.

Genetic Consequences of Antiviral Therapy on HIV-1

A variety of enzyme inhibitors have been developed in combating HIV-1, however the fast evolutionary rate of this virus commonly leads to the emergence of resistance mutations that finally allows the mutant virus to survive. This review explores the main genetic consequences of HIV-1 molecular evolution during antiviral therapies, including the viral genetic diversity and molecular adaptation. The role of recombination in the generation of drug resistance is also analyzed. Besides the investigation and discussion of published works, an evolutionary analysis of protease-coding genes collected from patients before and after treatment with different protease inhibitors was included to validate previous studies. Finally, the review discusses the importance of considering genetic consequences of antiviral therapies in models of HIV-1 evolution that could improve current genotypic resistance testing and treatments design.

The triple threat of HIV-1 protease inhibitors

Newly released human immunodeficiency virus type 1 (HIV-1) particles obligatorily undergo a maturation process to become infectious. The HIV-1 protease (PR) initiates this step, catalyzing the cleavage of the Gag and Gag-Pro-Pol structural polyproteins. Proper organization of the mature virus core requires that cleavage of these polyprotein substrates proceeds in a highly regulated, specific series of events. The vital role the HIV-1 PR plays in the viral life cycle has made it an extremely attractive target for inhibition and has accordingly fostered the development of a number of highly potent substrate-analog inhibitors. Though the PR inhibitors (PIs) inhibit only the HIV-1 PR, their effects manifest at multiple different stages in the life cycle due to the critical importance of the PR in preparing the virus for these subsequent events. Effectively, PIs masquerade as entry inhibitors, reverse transcription inhibitors, and potentially even inhibitors of post-reverse transcription steps. In this chapter, we review the triple threat of PIs: the intermolecular cooperativity in the form of a cooperative dose-response for inhibition in which the apparent potency increases with increasing inhibition; the pleiotropic effects of HIV-1 PR inhibition on entry, reverse transcription, and post-reverse transcription steps; and their potency as transition state analogs that have the potential for further improvement that could lead to an inability of the virus to evolve resistance in the context of single drug therapy.

Estimating HIV-1 fitness characteristics from cross-sectional genotype data

Despite the success of highly active antiretroviral therapy (HAART) in the management of human immunodeficiency virus (HIV)-1 infection, virological failure due to drug resistance development remains a major challenge. Resistant mutants display reduced drug susceptibilities, but in the absence of drug, they generally have a lower fitness than the wild type, owing to a mutation-incurred cost. The interaction between these fitness costs and drug resistance dictates the appearance of mutants and influences viral suppression and therapeutic success. Assessing in vivo viral fitness is a challenging task and yet one that has significant clinical relevance. Here, we present a new computational modelling approach for estimating viral fitness that relies on common sparse cross-sectional clinical data by combining statistical approaches to learn drug-specific mutational pathways and resistance factors with viral dynamics models to represent the host-virus interaction and actions of drug mechanistically. We estimate in vivo fitness characteristics of mutant genotypes for two antiretroviral drugs, the reverse transcriptase inhibitor zidovudine (ZDV) and the protease inhibitor indinavir (IDV). Well-known features of HIV-1 fitness landscapes are recovered, both in the absence and presence of drugs. We quantify the complex interplay between fitness costs and resistance by computing selective advantages for different mutants. Our approach extends naturally to multiple drugs and we illustrate this by simulating a dual therapy with ZDV and IDV to assess therapy failure. The combined statistical and dynamical modelling approach may help in dissecting the effects of fitness costs and resistance with the ultimate aim of assisting the choice of salvage therapies after treatment failure.

Mutations conferring drug resistance represent major threats to the therapeutic success of highly active antiretroviral therapy (HAART) against human immunodeficiency virus (HIV)-1 infection. Viral mutants differ in their fitness and assessing viral fitness is a challenging task. In this article, we estimate drug-specific mutational pathways by learning from clinical data using statistical techniques and incorporate these into mathematical models of in vivo viral infection dynamics. This approach enables us to estimate mutant fitness characteristics. We illustrate our method by predicting fitness characteristics of mutant genotypes for two different antiretroviral therapies with the drugs zidovudine and indinavir. We recover several established features of mutant fitnesses and quantify fitness characteristics both in the absence and presence of drugs. Our model extends naturally to multiple drugs and we illustrate this by simulating a dual therapy with ZDV and IDV to assess therapy failure. Additionally, our modelling approach relies only on cross-sectional clinical data. We believe that such an approach is a highly valuable tool in assisting the choice of salvage therapies after treatment failure.

Multi-step inhibition explains HIV-1 protease inhibitor pharmacodynamics and resistance

HIV-1 protease inhibitors (PIs) are among the most effective antiretroviral drugs. They are characterized by highly cooperative dose-response curves that are not explained by current pharmacodynamic theory. An unresolved problem affecting the clinical use of PIs is that patients who fail PI-containing regimens often have virus that lacks protease mutations, in apparent violation of fundamental evolutionary theory. Here, we show that these unresolved issues can be explained through analysis of the effects of PIs on distinct steps in the viral life cycle. We found that PIs do not affect virion release from infected cells but block entry, reverse transcription, and post-reverse transcription steps. The overall dose-response curves could be reconstructed by combining the curves for each step using the Bliss independence principle, showing that independent inhibition of multiple distinct steps in the life cycle generates the highly cooperative dose-response curves that make these drugs uniquely effective. Approximately half of the inhibitory potential of PIs is manifest at the entry step, likely reflecting interactions between the uncleaved Gag and the cytoplasmic tail (CT) of the Env protein. Sequence changes in the CT alone, which are ignored in current clinical tests for PI resistance, conferred PI resistance, providing an explanation for PI failure without resistance.

Novel P2 tris-tetrahydrofuran group in antiviral compound 1 (GRL-0519) fills the S2 binding pocket of selected mutants of HIV-1 protease

GRL-0519 (1) is a potent antiviral inhibitor of HIV-1 protease (PR) possessing tris-tetrahydrofuran (tris-THF) at P2. The high resolution X-ray crystal structures of inhibitor 1 in complexes with single substitution mutants PR(R8Q), PR(D30N), PR(I50V), PR(I54M), and PR(V82A) were analyzed in relation to kinetic data. The smaller valine side chain in PR(I50V) eliminated hydrophobic interactions with inhibitor and the other subunit consistent with 60-fold worse inhibition. Asn30 in PR(D30N) showed altered interactions with neighboring residues and 18-fold worse inhibition. Mutations V82A and I54M showed compensating structural changes consistent with 6-7-fold lower inhibition. Gln8 in PR(R8Q) replaced the ionic interactions of wild type Arg8 with hydrogen bond interactions without changing the inhibition significantly. The carbonyl oxygen of Gly48 showed two alternative conformations in all structures likely due to the snug fit of the large tris-THF group in the S2 subsite in agreement with high antiviral efficacy of 1 on resistant virus.

Evolutionary analysis of human immunodeficiency virus type 1 therapies based on conditionally replicating vectors

Efforts to reduce the viral load of human immunodeficiency virus type 1 (HIV-1) during long-term treatment are challenged by the evolution of anti-viral resistance mutants. Recent studies have shown that gene therapy approaches based on conditionally replicating vectors (CRVs) could have many advantages over anti-viral drugs and other approaches to therapy, potentially including the ability to circumvent the problem of evolved resistance. However, research to date has not explored the evolutionary consequences of long-term treatment of HIV-1 infections with conditionally replicating vectors. In this study, we analyze a computational model of the within-host co-evolutionary dynamics of HIV-1 and conditionally replicating vectors, using the recently proposed ‘therapeutic interfering particle’ as an example. The model keeps track of the stochastic process of viral mutation, and the deterministic population dynamics of T cells as well as different strains of CRV and HIV-1 particles. We show that early in the co-infection, mutant HIV-1 genotypes that escape suppression by CRV therapy appear; this is similar to the dynamics observed in drug treatments and other gene therapies. In contrast to other treatments, however, the CRV population is able to evolve and catch up with the dominant HIV-1 escape mutant and persist long-term in most cases. On evolutionary grounds, gene therapies based on CRVs appear to be a promising tool for long-term treatment of HIV-1. Our model allows us to propose design principles to optimize the efficacy of this class of gene therapies. In addition, because of the analogy between CRVs and naturally-occurring defective interfering particles, our results also shed light on the co-evolutionary dynamics of wild-type viruses and their defective interfering particles during natural infections.

A long-standing challenge in efforts to control human immunodeficiency virus type 1 (HIV-1) is the rapid evolution of the virus. Any effective therapy quickly gives rise to so-called escape mutants of the virus, potentially resulting in treatment failure. A distinct class of gene therapy based on conditionally replicating vectors has been suggested to have potential to circumvent the problem of viral evolutionary escape. A conditionally replicating vector cannot replicate on its own, but when it coinfects the same cell with HIV-1, it is packaged into a virion-like particle and can be transmitted from cell to cell. Importantly, these vectors replicate using the same machinery that HIV-1 uses, and so they mutate at the same rate. This opens the possibility that conditionally replicating vectors could ‘keep up’ with HIV-1 evolution and prevent HIV-1 escape. In this study, we present mathematical analyses of the co-evolutionary dynamics of HIV-1 and conditionally replicating vectors within a patient. Our results show that with proper genetic design, conditionally replicating vectors can keep pace with HIV-1 evolution, leading to persistent reduction in HIV-1 viral loads. Therefore, this class of gene therapies shows potential for ‘evolution-proof’ control of HIV-1, and merits further investigation in laboratory trials.

The choreography of HIV-1 proteolytic processing and virion assembly

HIV-1 has been the target of intensive research at the molecular and biochemical levels for >25 years. Collectively, this work has led to a detailed understanding of viral replication and the development of 24 approved drugs that have five different targets on various viral proteins and one cellular target (CCR5). Although most drugs target viral enzymatic activities, our detailed knowledge of so much of the viral life cycle is leading us into other types of inhibitors that can block or disrupt protein-protein interactions. Viruses have compact genomes and employ a strategy of using a small number of proteins that can form repeating structures to enclose space (i.e. condensing the viral genome inside of a protein shell), thus minimizing the need for a large protein coding capacity. This creates a relatively small number of critical protein-protein interactions that are essential for viral replication. For HIV-1, the Gag protein has the role of a polyprotein precursor that contains all of the structural proteins of the virion: matrix, capsid, spacer peptide 1, nucleocapsid, spacer peptide 2, and p6 (which contains protein-binding domains that interact with host proteins during budding). Similarly, the Gag-Pro-Pol precursor encodes most of the Gag protein but now includes the viral enzymes: protease, reverse transcriptase (with its associated RNase H activity), and integrase. Gag and Gag-Pro-Pol are the substrates of the viral protease, which is responsible for cleaving these precursors into their mature and fully active forms (see Fig. 1A).

Treatment-associated polymorphisms in protease are significantly associated with higher viral load and lower CD4 count in newly diagnosed drug-naive HIV-1 infected patients

BACKGROUND

The effect of drug resistance transmission on disease progression in the newly infected patient is not well understood. Major drug resistance mutations severely impair viral fitness in a drug free environment, and therefore are expected to revert quickly. Compensatory mutations, often already polymorphic in wild-type viruses, do not tend to revert after transmission. While compensatory mutations increase fitness during treatment, their presence may also modulate viral fitness and virulence in absence of therapy and major resistance mutations. We previously designed a modeling technique that quantifies genotypic footprints of in vivo treatment selective pressure, including both drug resistance mutations and polymorphic compensatory mutations, through the quantitative description of a fitness landscape from virus genetic sequences.

RESULTS

Genotypic correlates of viral load and CD4 cell count were evaluated in subtype B sequences from recently diagnosed treatment-naive patients enrolled in the SPREAD programme. The association of surveillance drug resistance mutations, reported compensatory mutations and fitness estimated from drug selective pressure fitness landscapes with baseline viral load and CD4 cell count was evaluated using regression techniques. Protease genotypic variability estimated to increase fitness during treatment was associated with higher viral load and lower CD4 cell counts also in treatment-naive patients, which could primarily be attributed to well-known compensatory mutations at highly polymorphic positions. By contrast, treatment-related mutations in reverse transcriptase could not explain viral load or CD4 cell count variability.

CONCLUSIONS

These results suggest that polymorphic compensatory mutations in protease, reported to be selected during treatment, may improve the replicative capacity of HIV-1 even in absence of drug selective pressure or major resistance mutations. The presence of this polymorphic variation may either reflect a history of drug selective pressure, i.e. transmission from a treated patient, or merely be a result of diversity in wild-type virus. Our findings suggest that transmitted drug resistance has the potential to contribute to faster disease progression in the newly infected host and to shape the HIV-1 epidemic at a population level.

Loss of the protease dimerization inhibition activity of tipranavir (TPV) and its association with the acquisition of resistance to TPV by HIV-1

Tipranavir (TPV), a protease inhibitor (PI) inhibiting the enzymatic activity and dimerization of HIV-1 protease, exerts potent activity against multi-PI-resistant HIV-1 isolates. When a mixture of 11 multi-PI-resistant (but TPV-sensitive) clinical isolates (HIV(11MIX)), which included HIV(B) and HIV(C), was selected against TPV, HIV(11MIX) rapidly (by 10 passages [HIV(11MIX)(P10)]) acquired high-level TPV resistance and replicated at high concentrations of TPV. HIV(11MIX)(P10) contained various amino acid substitutions, including I54V and V82T. The intermolecular FRET-based HIV-1 expression assay revealed that TPV's dimerization inhibition activity against cloned HIV(B) (cHIV(B)) was substantially compromised. The introduction of I54V/V82T into wild-type cHIV(NL4-3) (cHIV(NL4-3(I54V/V82T))) did not block TPV's dimerization inhibition or confer TPV resistance. However, the introduction of I54V/V82T into cHIV(B) (cHIV(B)(I54V/V82T)) compromised TPV's dimerization inhibition and cHIV(B)(I54V/V82T) proved to be significantly TPV resistant. L24M was responsible for TPV resistance with the cHIV(C) genetic background. The introduction of L24M into cHIV(NL4-3) (cHIV(NL4-3(L24M))) interfered with TPV's dimerization inhibition, while L24M increased HIV-1's susceptibility to TPV with the HIV(NL4-3) genetic background. When selected with TPV, cHIV(NL4-3(I54V/V82T)) most readily developed TPV resistance and acquired E34D, which compromised TPV's dimerization inhibition with the HIV(NL4-3) genetic background. The present data demonstrate that certain amino acid substitutions compromise TPV's dimerization inhibition and confer TPV resistance, although the loss of TPV's dimerization inhibition is not always associated with significantly increased TPV resistance. The findings that TPV's dimerization inhibition is compromised with one or two amino acid substitutions may explain at least in part why the genetic barrier of TPV against HIV-1's development of TPV resistance is relatively low compared to that of darunavir.