Treatment-mediated alterations in HIV fitness preserve CD4+ T cell counts but have minimal effects on viral load

Understanding early HIV-1 rebound dynamics following antiretroviral therapy interruption: The importance of effector cell expansion

Most people living with HIV-1 experience rapid viral rebound once antiretroviral therapy is interrupted; however, a small fraction remain in viral remission for an extended duration. Understanding the factors that determine whether viral rebound is likely after treatment interruption can enable the development of optimal treatment regimens and therapeutic interventions to potentially achieve a functional cure for HIV-1. We built upon the theoretical framework proposed by Conway and Perelson to construct dynamic models of virus-immune interactions to study factors that influence viral rebound dynamics. We evaluated these models using viral load data from 24 individuals following antiretroviral therapy interruption. The best-performing model accurately captures the heterogeneity of viral dynamics and highlights the importance of the effector cell expansion rate. Our results show that post-treatment controllers and non-controllers can be distinguished based on the effector cell expansion rate in our models. Furthermore, these results demonstrate the potential of using dynamic models incorporating an effector cell response to understand early viral rebound dynamics post-antiretroviral therapy interruption.

Understanding early HIV-1 rebound dynamics following antiretroviral therapy interruption: The importance of effector cell expansion

Most people living with HIV-1 experience rapid viral rebound once antiretroviral therapy is interrupted; however, a small fraction remain in viral remission for an extended duration. Understanding the factors that determine whether viral rebound is likely after treatment interruption can enable the development of optimal treatment regimens and therapeutic interventions to potentially achieve a functional cure for HIV-1. We built upon the theoretical framework proposed by Conway and Perelson to construct dynamic models of virus-immune interactions to study factors that influence viral rebound dynamics. We evaluated these models using viral load data from 24 individuals following antiretroviral therapy interruption. The best-performing model accurately captures the heterogeneity of viral dynamics and highlights the importance of the effector cell expansion rate. Our results show that post-treatment controllers and non-controllers can be distinguished based on the effector cell expansion rate in our models. Furthermore, these results demonstrate the potential of using dynamic models incorporating an effector cell response to understand early viral rebound dynamics post-antiretroviral therapy interruption.

Modeling resistance to the broadly neutralizing antibody PGT121 in people living with HIV-1

PGT121 is a broadly neutralizing antibody in clinical development for the treatment and prevention of HIV-1 infection via passive administration. PGT121 targets the HIV-1 V3-glycan and demonstrated potent antiviral activity in a phase I clinical trial. Resistance to PGT121 monotherapy rapidly occurred in the majority of participants in this trial with the sampled rebound viruses being entirely resistant to PGT121 mediated neutralization. However, two individuals experienced long-term ART-free viral suppression following antibody infusion and retained sensitivity to PGT121 upon viral rebound. Here, we develop mathematical models of the HIV-1 dynamics during this phase I clinical trial. We utilize these models to understand the dynamics leading to PGT121 resistance and to identify the mechanisms driving the observed long-term viral control. Our modeling highlights the importance of the relative fitness difference between PGT121 sensitive and resistant subpopulations prior to treatment. Specifically, by fitting our models to data, we identify the treatment-induced competitive advantage of previously existing or newly generated resistant population as a primary driver of resistance. Finally, our modeling emphasizes the high neutralization ability of PGT121 in both participants who exhibited long-term viral control.

Stochastic investigation of HIV infection and the emergence of drug resistance

Drug-resistant HIV-1 has caused a growing concern in clinic and public health. Although combination antiretroviral therapy can contribute massively to the suppression of viral loads in patients with HIV-1, it cannot lead to viral eradication. Continuing viral replication during sub-optimal therapy (due to poor adherence or other reasons) may lead to the accumulation of drug resistance mutations, resulting in an increased risk of disease progression. Many studies also suggest that events occurring during the early stage of HIV-1 infection (i.e., the first few hours to days following HIV exposure) may determine whether the infection can be successfully established. However, the numbers of infected cells and viruses during the early stage are extremely low and stochasticity may play a critical role in dictating the fate of infection. In this paper, we use stochastic models to investigate viral infection and the emergence of drug resistance of HIV-1. The stochastic model is formulated by a continuous-time Markov chain (CTMC), which is derived based on an ordinary differential equation model proposed by Kitayimbwa et al. that includes both forward and backward mutations. An analytic estimate of the probability of the clearance of HIV infection of the CTMC model near the infection-free equilibrium is obtained by a multitype branching process approximation. The analytical predictions are validated by numerical simulations. Unlike the deterministic dynamics where the basic reproduction number R₀ serves as a sharp threshold parameter (i.e., the disease dies out if R₀<1 and persists if R₀>1), the stochastic models indicate that there is always a positive probability for HIV infection to be eradicated in patients. In the presence of antiretroviral therapy, our results show that the chance of clearance of the infection tends to increase although drug resistance is likely to emerge.

Mechanistic basis of post-treatment control of SIV after anti-α4β7 antibody therapy

Treating macaques with an anti-α4β7 antibody under the umbrella of combination antiretroviral therapy (cART) during early SIV infection can lead to viral remission, with viral loads maintained at < 50 SIV RNA copies/ml after removal of all treatment in a subset of animals. Depletion of CD8⁺ lymphocytes in controllers resulted in transient recrudescence of viremia, suggesting that the combination of cART and anti-α4β7 antibody treatment led to a state where ongoing immune responses kept the virus undetectable in the absence of treatment. A previous mathematical model of HIV infection and cART incorporates immune effector cell responses and exhibits the property of two different viral load set-points. While the lower set-point could correspond to the attainment of long-term viral remission, attaining the higher set-point may be the result of viral rebound. Here we expand that model to include possible mechanisms of action of an anti-α4β7 antibody operating in these treated animals. We show that the model can fit the longitudinal viral load data from both IgG control and anti-α4β7 antibody treated macaques, suggesting explanations for the viral control associated with cART and an anti-α4β7 antibody treatment. This effective perturbation to the virus-host interaction can also explain observations in other nonhuman primate experiments in which cART and immunotherapy have led to post-treatment control or resetting of the viral load set-point. Interestingly, because the viral kinetics in the various treated animals differed—some animals exhibited large fluctuations in viral load after cART cessation—the model suggests that anti-α4β7 treatment could act by different primary mechanisms in different animals and still lead to post-treatment viral control. This outcome is nonetheless in accordance with a model with two stable viral load set-points, in which therapy can perturb the system from one set-point to a lower one through different biological mechanisms.

Some macaques treated with an anti-α4β7 monoclonal antibody along with antiretroviral therapy during the early stages of simian immunodeficiency virus infection had their viral load become undetectable (below 50 SIV RNA copies/ml) after all treatment was stopped, whereas animals not given the antibody all had their viral loads rebound to high levels. Using a mathematical model, we examined four potential ways in which the antibody could have altered the balance between viral growth and immune control to maintain an undetectable viral load. We show that a shift to controlled infection can occur through multiple biologically reasonable mechanisms of action of the anti-α4β7 antibody.

The association between detected drug resistance mutations and CD4+ T-cell decline in HIV-positive individuals maintained on a failing treatment regimen

BACKGROUND

To analyse the effect of drug resistance mutations (DRM) on CD4⁺ T-cell (CD4) trends in HIV-positive people maintained on virologically failing antiretroviral therapy (ART).

METHODS

Individuals from two large cohorts experiencing virological failure (VF) while maintained on ART with ≥1 CD4 count and ≥1 resistance test were included. CD4 slopes were estimated using linear mixed models. Principal component analysis (PCA) was used to assess the effect of clusters of mutations, defined using extracted component based scores from the PCA, on CD4 decline.

RESULTS

5,357 individuals contributing 7,661 VF episodes were included: any DRM were detected in 88.8% of episodes. After adjustment, CD4 counts declined less steeply during episodes where DRM were detected compared to episodes with no DRM (difference =28 cells/mm³/year, 95% CI =18, 39; P<0.001). Among individuals with at least one DRM, we found evidence that any nucleoside/nucleotide reverse transcriptase inhibitor (NRTI) resistance, the reverse transcriptase (RT) mutations M184V, D67N and T215Y as well as the protease mutations V82A and I54V were associated with reduced CD4 declines. The detection of any non-nucleoside reverse transcriptase inhibitor resistance, the RT mutations V179D and L74V were associated with steeper CD4 declines. The presence of some mutation patterns similar to the clusters identified by the PCA also affected the CD4 decline.

CONCLUSIONS

Detection of resistance and of certain DRM during VF of ART has a small but significant favourable effect on CD4 decline.

Estimation of the HIV-1 backward mutation rate from transmitted drug-resistant strains

One of the serious threats facing the administration of antiretroviral therapy to human immunodeficiency virus (HIV-1) infected patients is the reported increasing prevalence of transmitted drug resistance. However, given that HIV-1 drug-resistant strains are often less fit than the wild-type strains, it is expected that drug-resistant strains that are present during the primary phase of the HIV-1 infection are replaced by the fitter wild-type strains. This replacement of HIV-1 resistant mutations involves the emergence of wild-type strains by a process of backward mutation. How quickly the replacement happens is dependent on the class of HIV-1 mutation group. We estimate the backward mutation rates and relative fitness of various mutational groups known to confer HIV-1 drug resistance. We do this by fitting a stochastic model to data for individuals who were originally infected by an HIV-1 strain carrying any one of the known drug resistance-conferring mutations and observed over a period of time to see whether the resistant strain is replaced. To do this, we seek a distribution, generated from simulations of the stochastic model, that best describes the observed (clinical data) replacement times of a given mutation. We found that Lamivudine/Emtricitabine-associated mutations have a distinctly higher, backward mutation rate and low relative fitness compared to the other classes (as has been reported before) while protease inhibitors-associated mutations have a slower backward mutation rate and high relative fitness. For the other mutation classes, we found more uncertainty in their estimates.

Impact of early treatment programs on HIV epidemics: An immunity-based mathematical model

While studies on pre-exposure prophylaxis (PrEP) and post-exposure prophylaxis (PEP) have demonstrated substantial advantages in controlling HIV transmission, the overall benefits of the programs with early initiation of antiretroviral therapy (ART) have not been fully understood and are still on debate. Here, we develop an immunity-based (CD4+ T cell count based) mathematical model to study the impacts of early treatment programs on HIV epidemics and the overall community-level immunity. The model is parametrized using the HIV prevalence data from South Africa and fully analyzed for stability of equilibria and infection persistence criteria. Using our model, we evaluate the effects of early treatment on the new infection transmission, disease death, basic reproduction number, HIV prevalence, and the community-level immunity. Our model predicts that the programs with early treatments significantly reduce the new infection transmission and increase the community-level immunity, but the treatments alone may not be enough to eliminate HIV epidemics. These findings, including the community-level immunity, might provide helpful information for proper implementation of HIV treatment programs.

Dynamics of an HIV Model with Multiple Infection Stages and Treatment with Different Drug Classes

Highly active antiretroviral therapy can effectively control HIV replication in infected individuals. Some clinical and modeling studies suggested that viral decay dynamics may depend on the inhibited stages of the viral replication cycle. In this paper, we develop a general mathematical model incorporating multiple infection stages and various drug classes that can interfere with specific stages of the viral life cycle. We derive the basic reproductive number and obtain the global stability results of steady states. Using several simple cases of the general model, we study the effect of various drug classes on the dynamics of HIV decay. When drugs are assumed to be 100% effective, drugs acting later in the viral life cycle lead to a faster or more rapid decay in viremia. This is consistent with some patient and experimental data, and also agrees with previous modeling results. When drugs are not 100% effective, the viral decay dynamics are more complicated. Without a second population of long-lived infected cells, the viral load decline can have two phases if drugs act at an intermediate stage of the viral replication cycle. The slopes of viral load decline depend on the drug effectiveness, the death rate of infected cells at different stages, and the transition rate of infected cells from one to the next stage. With a second population of long-lived infected cells, the viral load decline can have three distinct phases, consistent with the observation in patients receiving antiretroviral therapy containing the integrase inhibitor raltegravir. We also fit modeling prediction to patient data under efavirenz (a nonnucleoside reverse-transcriptase inhibitor) and raltegravir treatment. The first-phase viral load decline under raltegravir therapy is longer than that under efavirenz, resulting in a lower viral load at initiation of the second-phase decline in patients taking raltegravir. This explains why patients taking a raltegravir-based therapy were faster to achieve viral suppression than those taking an efavirenz-based therapy. Taken together, this work provides a quantitative and systematic comparison of the effect of different drug classes on HIV decay dynamics and can explain the viral load decline in HIV patients treated with raltegravir-containing regimens.

Residual Viremia in Treated HIV+ Individuals

Antiretroviral therapy (ART) effectively controls HIV infection, suppressing HIV viral loads. However, some residual virus remains, below the level of detection, in HIV-infected patients on ART. The source of this viremia is an area of debate: does it derive primarily from activation of infected cells in the latent reservoir, or from ongoing viral replication? Observations seem to be contradictory: there is evidence of short term evolution, implying that there must be ongoing viral replication, and viral strains should thus evolve. However, phylogenetic analyses, and rare emergent drug resistance, suggest no long-term viral evolution, implying that virus derived from activated latent cells must dominate. We use simple deterministic and stochastic models to gain insight into residual viremia dynamics in HIV-infected patients. Our modeling relies on two underlying assumptions for patients on suppressive ART: that latent cell activation drives viral dynamics and that the reproductive ratio of treated infection is less than 1. Nonetheless, the contribution of viral replication to residual viremia in patients on ART may be non-negligible. However, even if the portion of viremia attributable to viral replication is significant, our model predicts (1) that latent reservoir re-seeding remains negligible, and (2) some short-term viral evolution is permitted, but long-term evolution can still be limited: stochastic analysis of our model shows that de novo emergence of drug resistance is rare. Thus, our simple models reconcile the seemingly contradictory observations on residual viremia and, with relatively few parameters, recapitulates HIV viral dynamics observed in patients on suppressive therapy.

In HIV⁺ individuals, antiretroviral therapy (ART) effectively controls HIV viral loads to below levels detectable by routine tests. However, more sensitive tests can detect some residual viremia. The source of this virus is a matter of debate: does it derive from ongoing viral replication, or from viral production following activation of latently infected cells? Experimental observations support both sides of the argument: in patients on therapy, HIV shows no long-term evolution, and emergence of drug-resistant mutants is rare, implying no ongoing viral replication, but there remains short-term evolution, implying the opposite. To reconcile these observations, we propose a mathematical model of latently and productively infected cells and virus. Using our models we predict that, in patients on suppressive ART, the contribution of viral replication to residual virus, while small, yields short term-evolution. But even if the contribution is large, for example if adherence to therapy is poor, long-term evolution can still be limited, and de novo emergence of drug resistance is rare. Thus, our simple models reconcile the seemingly contradictory observations on residual viremia.

Recombination Enhances HIV-1 Envelope Diversity by Facilitating the Survival of Latent Genomic Fragments in the Plasma Virus Population

HIV-1 is subject to immune pressure exerted by the host, giving variants that escape the immune response an advantage. Virus released from activated latent cells competes against variants that have continually evolved and adapted to host immune pressure. Nevertheless, there is increasing evidence that virus displaying a signal of latency survives in patient plasma despite having reduced fitness due to long-term immune memory. We investigated the survival of virus with latent envelope genomic fragments by simulating within-host HIV-1 sequence evolution and the cycling of viral lineages in and out of the latent reservoir. Our model incorporates a detailed mutation process including nucleotide substitution, recombination, latent reservoir dynamics, diversifying selection pressure driven by the immune response, and purifying selection pressure asserted by deleterious mutations. We evaluated the ability of our model to capture sequence evolution in vivo by comparing our simulated sequences to HIV-1 envelope sequence data from 16 HIV-infected untreated patients. Empirical sequence divergence and diversity measures were qualitatively and quantitatively similar to those of our simulated HIV-1 populations, suggesting that our model invokes realistic trends of HIV-1 genetic evolution. Moreover, reconstructed phylogenies of simulated and patient HIV-1 populations showed similar topological structures. Our simulation results suggest that recombination is a key mechanism facilitating the persistence of virus with latent envelope genomic fragments in the productively infected cell population. Recombination increased the survival probability of latent virus forms approximately 13-fold. Prevalence of virus with latent fragments in productively infected cells was observed in only 2% of simulations when we ignored recombination, while the proportion increased to 27% of simulations when we allowed recombination. We also found that the selection pressures exerted by different fitness landscapes influenced the shape of phylogenies, diversity trends, and survival of virus with latent genomic fragments. Our model predicts that the persistence of latent genomic fragments from multiple different ancestral origins increases sequence diversity in plasma for reasonable fitness landscapes.

Increasing evidence suggests that HIV-1 released from activated latent cells survives in productively infected cells in patient plasma despite competition against better adapted virus variants that have evolved in response to the host immune pressure. Long-term survival requires that latent virus forms adapt to the host immune response so that they are not outcompeted. We simulated the dynamics of HIV-1 envelope sequence evolution in response to host immune pressure to investigate how virus from activated latent cells can survive despite having reduced fitness compared to the more evolved virus variants in patient plasma. The evolutionary trends of our simulated virus populations followed closely those observed in HIV-1 sequence data from 16 patients. Our simulation results suggest that recombination facilitates the survival of genomic fragments originating from virus activated from latent cells. Our model further predicts that sequence diversity increases with the number of latent genomic fragments from different origins that persist in plasma.

Post-treatment control of HIV infection

Antiretroviral therapy (ART) for HIV is not a cure. However, recent studies suggest that ART, initiated early during primary infection, may induce post-treatment control (PTC) of HIV infection with HIV RNA maintained at <50 copies per mL. We investigate the hypothesis that ART initiated early during primary infection permits PTC by limiting the size of the latent reservoir, which, if small enough at treatment termination, may allow the adaptive immune response to prevent viral rebound (VR) and control infection. We use a mathematical model of within host HIV dynamics to capture interactions among target cells, productively infected cells, latently infected cells, virus, and cytotoxic T lymphocytes (CTLs). Analysis of our model reveals a range in CTL response strengths where a patient may show either VR or PTC, depending on the size of the latent reservoir at treatment termination. Below this range, patients will always rebound, whereas above this range, patients are predicted to behave like elite controllers. Using data on latent reservoir sizes in patients treated during primary infection, we also predict population-level VR times for noncontrollers consistent with observations.

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.

The majority of CD4+ T-cell depletion during acute simian-human immunodeficiency virus SHIV89.6P infection occurs in uninfected cells

Untreated human immunodeficiency virus (HIV) infection is characterized by depletion of CD4(+) T cells, ultimately leading to the impairment of host immune defenses and death. HIV-infected CD4(+) T cells die from direct virus-induced apoptosis and CD8 T-cell-mediated elimination, but a broader and more profound depletion occurs in uninfected CD4(+) T cells via multiple indirect effects of infection. We fit mathematical models to data from experiments that tested an HIV eradication strategy in which five macaques with a proportion of CD4(+) T cells resistant to simian-human immunodeficiency virus (SHIV) entry were challenged with SHIV89.6P, a highly pathogenic dual-tropic chimeric SIV-HIV viral strain that results in rapid loss of both SHIV-susceptible and SHIV-resistant CD4(+) T cells. Our results suggest that uninfected (bystander) cell death accounts for the majority of CD4(+) T-lymphocyte loss, with at least 60% and 99% of CD4(+) T cell death occurring in uninfected cells during acute and established infection, respectively. Mechanisms to limit the profound indirect killing effects associated with HIV infection may be associated with immune preservation and improved long-term survival.

IMPORTANCE

HIV infection induces a massive depletion of CD4(+) T cells, leading to profound immunodeficiency, opportunistic infections, and eventually death. While HIV induces apoptosis (programmed cell death) by directly entering and replicating in CD4(+) T cells, uninfected CD4(+) T cells also undergo apoptosis due to ongoing toxic inflammation in the region of infection. In this paper, we use mathematical models in conjunction with data from simian-human immunodeficiency virus SHIV89.6P infection in macaques (a model of HIV infection in humans) to estimate the percentage of cell death that occurs in uninfected cells during the initial period of infection. We reveal that the vast majority of cell death occurs in these cells, which are not infected. The "bystander effects" that lead to enormous reductions in the number of uninfected CD4(+) T cells may be a target for future interventions that aim to limit the extent of damage caused by HIV.

The role of backward mutations on the within-host dynamics of HIV-1

The quality of life for patients infected with human immunodeficiency virus (HIV-1) has been positively impacted by the use of antiretroviral therapy (ART). However, the benefits of ART are usually halted by the emergence of drug resistance. Drug-resistant strains arise from virus mutations, as HIV-1 reverse transcription is prone to errors, with mutations normally carrying fitness costs to the virus. When ART is interrupted, the wild-type drug-sensitive strain rapidly out-competes the resistant strain, as the former strain is fitter than the latter in the absence of ART. One mechanism for sustaining the sensitive strain during ART is given by the virus mutating from resistant to sensitive strains, which is referred to as backward mutation. This is important during periods of treatment interruptions as prior existence of the sensitive strain would lead to replacement of the resistant strain. In order to assess the role of backward mutations in the dynamics of HIV-1 within an infected host, we analyze a mathematical model of two interacting virus strains in either absence or presence of ART. We study the effect of backward mutations on the definition of the basic reproductive number, and the value and stability of equilibrium points. The analysis of the model shows that, thanks to both forward and backward mutations, sensitive and resistant strains co-exist. In addition, conditions for the dominance of a viral strain with or without ART are provided. For this model, backward mutations are shown to be necessary for the persistence of the sensitive strain during ART.

Modeling the within-host dynamics of HIV infection

The new field of viral dynamics, based on within-host modeling of viral infections, began with models of human immunodeficiency virus (HIV), but now includes many viral infections. Here we review developments in HIV modeling, emphasizing quantitative findings about HIV biology uncovered by studying acute infection, the response to drug therapy and the rate of generation of HIV variants that escape immune responses. We show how modeling has revealed many dynamical features of HIV infection and how it may provide insight into the ultimate cure for this infection.

A new system to measure and compare hepatitis C virus replication capacity using full-length, replication competent viruses

Measuring the in vitro replication capacity of viruses is an important tool for assessing the effects of selective pressure of immune responses and drug therapy. Measuring hepatitis C virus (HCV) replication capacity utilizing primarily sub-genomic reporter constructs is limited. To overcome some of these limitations a quantitative reverse transcriptase PCR (RT-qPCR) was designed to measure simultaneously the growth rate of 2 whole genome HCV variants under identical culture conditions. The assay demonstrates 100% specificity of detection of each variant and a linear detection range from 200 to 2×10(8) copies. The system was validated using a panel of HCV mutants, including the NS3 protease inhibitor drug resistance mutants R155K and T54A. The creation of a unique sequence tag results in highly sensitive and specific discrimination of parental JFH-FNX and modified clones using distinct probes in a RT-qPCR allowing for comparison of the effect of drug resistance or immune escape mutations on HCV replication. This system has advantages over existing methods both by permitting direct comparison of the replication capacity of fully replication-competent HCV mutants under identical culture conditions and by measuring effects on replication capacity due to mutations affecting all stages of the viral life cycle including entry and egress.

Replication-independent expression of anti-apoptosis marker genes in human peripheral blood mononuclear cells infected with the wild-type HIV-1 and reverse transcriptase variants

Clinical trials with highly-active antiretroviral therapy (HAART) have shown that a substantial number of patients continue to show a decrease in viral load and/or increase or stable CD4(+) T-cell numbers even in the presence of multidrug resistant (MDR) viruses. We compared replication capacity (RC) and expression of anti-apoptosis marker genes (AAMGs) in human peripheral blood mononuclear (PBM) cells infected with NL4-3 (wild-type; WT) and mutant viruses. Replication kinetics assays showed a significant decrease in RC of all mutant viruses in comparison to the WT virus. The viruses containing patient-derived MDR RT without the K65R mutation (PSD5.2) replicated efficiently in comparison to the viruses with MDR RT containing the K65R mutation (PSD5.1), or the single mutations K65R and M184V. Compared with WT, a significant decrease in RCs of viruses: K65R (RC=0.39±0.02; p≤0.0001), M184V (RC=0.72±0.04; p≤0.0001), PSD5.1 (RC=0.32±0.04; p≤0.0001), and PSD5.2 (RC=0.90±0.04; p=0.002) was observed on day 10. RT-PCR-based apoptosis array was performed on total cellular RNA. Recombinant virus PSD5.2 showed a 1.5- to 6-fold upregulation in 8 AAMGs (AKT1, BAG3, BCL2A1, BFAR, BIRC2, BNIP1, BNIP3, and CFLAR) on day 1 and day 7 post-infection with respect to WT virus. PSD5.1 showed upregulation of only one gene (BAG1) on day 1 (1.75-fold) and day 7 (1.97-fold). Point mutant K65R showed a 1.5- to 4-fold upregulation of six AAMGs on day 7. Viruses with the M184V mutation showed upregulation of only one gene (BAG1). These observations indicate that the upregulation of specific AAMGs may not be dependent on the RCs of HIV-I variants, and that the possible interaction among mutated RT residues and viral and/or host proteins may induce CD4(+) T-cell-protective anti-apoptosis proteins.

Immune activation, CD4+ T cell counts, and viremia exhibit oscillatory patterns over time in patients with highly resistant HIV infection

The rates of immunologic and clinical progression are lower in patients with drug-resistant HIV compared to wild-type HIV. This difference is not fully explained by viral load. It has been argued that reductions in T cell activation and/or viral fitness might result in preserved target cells and an altered relationship between the level of viremia and the rate of CD4+ T cell loss. We tested this hypothesis over time in a cohort of patients with highly resistant HIV. Fifty-four antiretroviral-treated patients with multi-drug resistant HIV and detectable plasma HIV RNA were followed longitudinally. CD4+ T cell counts and HIV RNA levels were measured every 4 weeks and T cell activation (CD38/HLA-DR) was measured every 16 weeks. We found that the levels of CD4+ T cell activation over time were a strong independent predictor of CD4+ T cell counts while CD8+ T cell activation was more strongly associated with viremia. Using spectral analysis, we found strong evidence for oscillatory (or cyclic) behavior in CD4+ T cell counts, HIV RNA levels, and T cell activation. Each of the cell populations exhibited an oscillatory behavior with similar frequencies. Collectively, these data suggest that there may be a mechanistic link between T cell activation, CD4+ T cell counts, and viremia and lends support for the hypothesis of altered predator-prey dynamics as a possible explanation of the stability of CD4+ T cell counts in the presence of sustained multi-drug resistant viremia.