Julia Drylewicz

Comparison of viro-immunological marker changes between HIV-1 and HIV-2-infected patients in France.

Background:HIV-2 is known to be less pathogenic than HIV-1, although the underlying mechanisms are still debated. We compared the changes over time in viro-immunological markers in HIV-1 and HIV-2-infected patients living in France during natural history and after initiation of the first combination antiretroviral therapy (CART). Method:Patients were included in the ANRS CO3 HIV-1 cohort (N = 6707) or the ANRS CO5 HIV-2 cohort (N = 572). HIV-1-infected patients were matched to HIV-2 patients according to sex, age, HIV transmission group and period of treatment initiation. Changes in markers were estimated using linear mixed models. Results:Analyses were performed for three groups of patients: those with estimated date of contamination (98 HIV-1 and 49 HIV-2-seroincident patients); untreated seroprevalent patients (320 HIV-1 and 160 HIV-2); and those initiating a first CART (59 HIV-1 and 63 HIV-2). In group 1, CD4 T-cell counts decreased less rapidly in HIV-2 than HIV-1 patients (−9 versus −49 cells/μl per year, P < 10−4). Results were similar in group 2. Baseline CD4 cell count at CART initiation was not different according to the type of infection. During the first 2 months of treatment, the CD4 cell count increased by +59 cells/μl per month (CI 34; 84) for HIV-1 and +24 (CI 6; 42) for HIV-2. The plasma viral load drop was threefold more important in HIV-1 patients: −1.56 log10/ml per month versus −0.62 among HIV-2 patients (P < 10−4). Conclusion:Differences between the two infections during natural history are similar to those previously described in Africa. Once treatment is started, response is poorer in HIV-2 than in HIV-1 patients.

Closing the gap between T-cell life span estimates from stable isotope-labeling studies in mice and humans

Quantitative knowledge of the turnover of different leukocyte populations is a key to our understanding of immune function in health and disease. Much progress has been made thanks to the introduction of stable isotope labeling, the state-of-the-art technique for in vivo quantification of cellular life spans. Yet, even leukocyte life span estimates on the basis of stable isotope labeling can vary up to 10-fold among laboratories. We investigated whether these differences could be the result of variances in the length of the labeling period among studies. To this end, we performed deuterated water-labeling experiments in mice, in which only the length of label administration was varied. The resulting life span estimates were indeed dependent on the length of the labeling period when the data were analyzed using a commonly used single-exponential model. We show that multiexponential models provide the necessary tool to obtain life span estimates that are independent of the length of the labeling period. Use of a multiexponential model enabled us to reduce the gap between human T-cell life span estimates from 2 previously published labeling studies. This provides an important step toward unambiguous understanding of leukocyte turnover in health and disease.

Lymphocyte maintenance during healthy aging requires no substantial alterations in cellular turnover

In healthy humans, lymphocyte populations are maintained at a relatively constant size throughout life, reflecting a balance between lymphocyte production and loss. Given the profound immunological changes that occur during healthy aging, including a significant decline in T‐cell production by the thymus, lymphocyte maintenance in the elderly is generally thought to require homeostatic alterations in lymphocyte dynamics. Surprisingly, using in vivo 2H2O labeling, we find similar dynamics of most lymphocyte subsets between young adult and elderly healthy individuals. As the contribution of thymic output to T‐cell production is only minor from young adulthood onward, compensatory increases in peripheral T‐cell division rates are not required to maintain the T‐cell pool, despite a tenfold decline in thymic output. These fundamental insights will aid the interpretation of further research into aging and clinical conditions related to disturbed lymphocyte dynamics.

Moving toward endotypes in atopic dermatitis: Identification of patient clusters based on serum biomarker analysis

Background Atopic dermatitis (AD) is a complex, chronic, inflammatory skin disease with a diverse clinical presentation. However, it is unclear whether this diversity exists at a biological level. Objective We sought to test the hypothesis that AD is heterogeneous at the biological level of individual inflammatory mediators. Methods Sera from 193 adult patients with moderate‐to‐severe AD (six area, six sign atopic dermatitis [SASSAD] score: geometric mean, 22.3 [95% CI, 21.3‐23.3] and 39.1 [95% CI, 37.5‐40.9], respectively) and 30 healthy control subjects without AD were analyzed for 147 serum mediators, total IgE levels, and 130 allergen‐specific IgE levels. Population heterogeneity was assessed by using principal component analysis, followed by unsupervised k‐means cluster analysis of the principal components. Results Patients with AD showed pronounced evidence of inflammation compared with healthy control subjects. Principal component analysis of data on sera from patients with AD revealed the presence of 4 potential clusters. Fifty‐seven principal components described approximately 90% of the variance. Unsupervised k‐means cluster analysis of the 57 largest principal components delivered 4 distinct clusters of patients with AD. Cluster 1 had high SASSAD scores and body surface areas with the highest levels of pulmonary and activation‐regulated chemokine, tissue inhibitor of metalloproteinases 1, and soluble CD14. Cluster 2 had low SASSAD scores with the lowest levels of IFN‐&agr;, tissue inhibitor of metalloproteinases 1, and vascular endothelial growth factor. Cluster 3 had high SASSAD scores with the lowest levels of IFN‐&bgr;, IL‐1, and epithelial cytokines. Cluster 4 had low SASSAD scores but the highest levels of the inflammatory markers IL‐1, IL‐4, IL‐13, and thymic stromal lymphopoietin. Conclusion AD is a heterogeneous disease both clinically and biologically. Four distinct clusters of patients with AD have been identified that could represent endotypes with unique biological mechanisms. Elucidation of these endotypes warrants further investigation and will require future intervention trials with specific agents, such as biologics.

Maraviroc Intensification of cART in Patients with Suboptimal Immunological Recovery: A 48-Week, Placebo-Controlled Randomized Trial

Objective The immunomodulatory effects of the CCR5-antagonist maraviroc might be beneficial in patients with a suboptimal immunological response, but results of different cART (combination antiretroviral therapy) intensification studies are conflicting. Therefore, we performed a 48-week placebo-controlled trial to determine the effect of maraviroc intensification on CD4+ T-cell counts and immune activation in these patients. Design Double-blind, placebo-controlled, randomized trial. Methods Major inclusion criteria were 1. CD4+ T-cell count <350 cells/μL while at least two years on cART or CD4+ T-cell count <200 cells/μL while at least one year on cART, and 2. viral suppression for at least the previous 6 months. HIV-infected patients were randomized to add maraviroc (41 patients) or placebo (44 patients) to their cART regimen for 48 weeks. Changes in CD4+ T-cell counts (primary endpoint) and other immunological parameters were modeled using linear mixed effects models. Results No significant differences for the modelled increase in CD4+ T-cell count (placebo 15.3 CD4+ T cells/μL (95% confidence interval (CI) [1.0, 29.5] versus maraviroc arm 22.9 CD4+ T cells/μL (95% CI [7.4, 38.5] p = 0.51) or alterations in the expression of markers for T-cell activation, proliferation and microbial translocation were found between the arms. However, maraviroc intensification did increase the percentage of CCR5 expressing CD4+ and CD8+ T-cells, and the plasma levels of the CCR5 ligand MIP-1β. In contrast, the percentage of ex-vivo apoptotic CD8+ and CD4+ T-cells decreased in the maraviroc arm. Conclusions Maraviroc intensification of cART did not increase CD4+ T-cell restoration or decrease immune activation as compared to placebo. However, ex-vivo T-cell apoptosis was decreased in the maraviroc arm. Trial Registration ClinicalTrials.gov NCT00875368

Quantification of naive and memory T-cell turnover during HIV-1 infection

Background:In HIV infection, the homeostasis of CD4+ and CD8+ T cells is dramatically disturbed, and several studies have pointed out that T-cell turnover rates are increased. To understand how the CD4+ and CD8+ T-cell pools are affected, it is important to have quantitative insights into the lifespans of the cells constituting the different T-lymphocyte populations. Methods:We used long-term in-vivo 2H2O labeling and mathematical modeling to estimate the average lifespans of naive and memory CD4+ and CD8+ T cells in untreated (n = 4) and combination antiretroviral therapy-treated (n = 3) HIV-1-infected individuals. Results:During untreated chronic HIV-1 infection, naive CD4+ and CD8+ T cells lived on average 618 and 271 days, whereas memory CD4+ and CD8+ T cells had average lifespans of 53 and 43 days, respectively. These lifespans were at least three-fold shorter than those in healthy controls (n = 5). In patients on effective combination antiretroviral therapy with total CD4+ T-cell counts in the normal range, we found that naive CD4+ and CD8+ T-cell lifespans had not completely normalized and were still two-fold shortened. Conclusion:The average lifespan of both naive and memory CD4+ and CD8+ T cells decreased during untreated chronic HIV-1 infection. Although the turnover of the memory T-cell populations nearly normalized during effective treatment, the turnover of naive CD4+ and CD8+ T cells did not seem to normalize completely.

Quantifying and Predicting the Effect of Exogenous Interleukin-7 on CD4+T Cells in HIV-1 Infection

Exogenous Interleukin-7 (IL-7), in supplement to antiretroviral therapy, leads to a substantial increase of all CD4+ T cell subsets in HIV-1 infected patients. However, the quantitative contribution of the several potential mechanisms of action of IL-7 is unknown. We have performed a mathematical analysis of repeated measurements of total and naive CD4+ T cells and their Ki67 expression from HIV-1 infected patients involved in three phase I/II studies (N = 53 patients). We show that, besides a transient increase of peripheral proliferation, IL-7 exerts additional effects that play a significant role in CD4+ T cell dynamics up to 52 weeks. A decrease of the loss rate of the total CD4+ T cell is the most probable explanation. If this effect could be maintained during repeated administration of IL-7, our simulation study shows that such a strategy may allow maintaining CD4+ T cell counts above 500 cells/µL with 4 cycles or fewer over a period of two years. This in-depth analysis of clinical data revealed the potential for IL-7 to achieve sustained CD4+ T cell restoration with limited IL-7 exposure in HIV-1 infected patients with immune failure despite antiretroviral therapy.

Inference in HIV dynamics models via hierarchical likelihood

HIV dynamical models are often based on non-linear systems of ordinary differential equations (ODE), which do not have an analytical solution. Introducing random effects in such models leads to very challenging non-linear mixed-effects models. To avoid the numerical computation of multiple integrals involved in the likelihood, a hierarchical likelihood (h-likelihood) approach, treated in the spirit of a penalized likelihood is proposed. The asymptotic distribution of the maximum h-likelihood estimators (MHLE) for fixed effects is given. The MHLE are slightly biased but the bias can be made negligible by using a parametric bootstrap procedure. An efficient algorithm for maximizing the h-likelihood is proposed. A simulation study, based on a classical HIV dynamical model, confirms the good properties of the MHLE. The method is applied to the analysis of a clinical trial.

Score Tests for Exploring Complex Models: Application to HIV Dynamics Models

In biostatistics, more and more complex models are being developed. This is particularly the case in system biology. Fitting complex models can be very time-consuming, since many models often have to be explored. Among the possibilities are the introduction of explanatory variables and the determination of random effects. The particularity of this use of the score test is that the null hypothesis is not itself very simple; typically, some random effects may be present under the null hypothesis. Moreover, the information matrix cannot be computed, but only an approximation based on the score. This article examines this situation with the specific example of HIV dynamics models. We examine the score test statistics for testing the effect of explanatory variables and the variance of random effect in this complex situation. We study type I errors and the statistical powers of this score test statistics and we apply the score test approach to a real data set of HIV-infected patients.

NIMROD: A program for inference via a normal approximation of the posterior in models with random effects based on ordinary differential equations

Models based on ordinary differential equations (ODE) are widespread tools for describing dynamical systems. In biomedical sciences, data from each subject can be sparse making difficult to precisely estimate individual parameters by standard non-linear regression but information can often be gained from between-subjects variability. This makes natural the use of mixed-effects models to estimate population parameters. Although the maximum likelihood approach is a valuable option, identifiability issues favour Bayesian approaches which can incorporate prior knowledge in a flexible way. However, the combination of difficulties coming from the ODE system and from the presence of random effects raises a major numerical challenge. Computations can be simplified by making a normal approximation of the posterior to find the maximum of the posterior distribution (MAP). Here we present the NIMROD program (normal approximation inference in models with random effects based on ordinary differential equations) devoted to the MAP estimation in ODE models. We describe the specific implemented features such as convergence criteria and an approximation of the leave-one-out cross-validation to assess the model quality of fit. In pharmacokinetics models, first, we evaluate the properties of this algorithm and compare it with FOCE and MCMC algorithms in simulations. Then, we illustrate NIMROD use on Amprenavir pharmacokinetics data from the PUZZLE clinical trial in HIV infected patients.