Intensification of Antiretroviral Therapy with a CCR5 Antagonist in Patients with Chronic HIV-1 Infection: Effect on T Cells Latently Infected

Objective: The primary objective was to assess the effect of MVC intensification on latently infected CD4+ T cells in chronically HIV-1-infected patients receiving antiretroviral therapy. Methods: We performed an open-label pilot phase II clinical trial involving chronically HIV-1-infected patients receiving stable antiretroviral therapy whose regimen was intensified with 48 weeks of maraviroc therapy. We analyzed the latent reservoir, the residual viremia and episomal 2LTR DNA to examine the relationship between these measures and the HIV-1 latent reservoir, immune activation, lymphocyte subsets (including effector and central memory T cells), and markers associated with bacterial translocation. Results: Overall a non significant reduction in the size of the latent reservoir was found (p = 0.068). A mean reduction of 1.82 IUPM was observed in 4 patients with detectable latent reservoir at baseline after 48 weeks of intensification. No effect on plasma residual viremia was observed. Unexpectedly, all the patients had detectable 2LTR DNA circles at week 24, while none of them showed those circles at the end of the study. No changes were detected in CD4+ or CD8+ counts, although a significant decrease was found in the proportion of HLA-DR+/CD38+ CD4+ and CD8+ T-cells. LPS and sCD14 levels increased. Conclusions: Intensification with MVC was associated with a trend to a decrease in the size of the latent HIV-1 reservoir in memory T cells. No impact on residual viremia was detected. Additional studies with larger samples are needed to confirm the results

Brucella abortus Induces the Premature Death of Human Neutrophils through the Action of Its Lipopolysaccharide

Most bacterial infections induce the activation of polymorphonuclear neutrophils (PMNs), enhance their microbicidal function, and promote the survival of these leukocytes for protracted periods of time. Brucella abortus is a stealthy pathogen that evades innate immunity, barely activates PMNs, and resists the killing mechanisms of these phagocytes. Intriguing clinical signs observed during brucellosis are the low numbers of Brucella infected PMNs in the target organs and neutropenia in a proportion of the patients; features that deserve further attention. Here we demonstrate that B. abortus prematurely kills human PMNs in a dose-dependent and cell-specific manner. Death of PMNs is concomitant with the intracellular Brucella lipopolysaccharide (Br-LPS) release within vacuoles. This molecule and its lipid A reproduce the premature cell death of PMNs, a phenomenon associated to the low production of proinflammatory cytokines. Blocking of CD14 but not TLR4 prevents the Br-LPS-induced cell death. The PMNs cell death departs from necrosis, NETosis and classical apoptosis. The mechanism of PMN cell death is linked to the activation of NADPH-oxidase and a modest but steadily increase of ROS mediators. These effectors generate DNA damage, recruitments of check point kinase 1, caspases 5 and to minor extent of caspase 4, RIP1 and Ca++ release. The production of IL-1β by PMNs was barely stimulated by B. abortus infection or Br-LPS treatment. Likewise, inhibition of caspase 1 did not hamper the Br-LPS induced PMN cell death, suggesting that the inflammasome pathway was not involved. Although activation of caspases 8 and 9 was observed, they did not seem to participate in the initial triggering mechanisms, since inhibition of these caspases scarcely blocked PMN cell death. These findings suggest a mechanism for neutropenia in chronic brucellosis and reveal a novel Brucella-host cross-talk through which B. abortus is able to hinder the innate function of PMN.Fondo Especial de la Educación Superior/[0500-13]/FEES/Costa RicaFondo Especial de la Educación Superior/[0504-13]/FEES/Costa RicaFondo Especial de la Educación Superior/[0505-13]/FEES/Costa RicaFondo Especial de la Educación Superior/[0248-13]/FEES/Costa RicaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias de la Salud::Centro de Investigación en Enfermedades Tropicales (CIET)UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias de la Salud::Instituto Clodomiro Picado (ICP)UCR::Vicerrectoría de Docencia::Salud::Facultad de Microbiologí

Outcomes for efavirenz versus nevirapine-containing regimens for treatment of HIV-1 infection: a systematic review and meta-analysis.

INTRODUCTION: There is conflicting evidence and practice regarding the use of the non-nucleoside reverse transcriptase inhibitors (NNRTI) efavirenz (EFV) and nevirapine (NVP) in first-line antiretroviral therapy (ART). METHODS: We systematically reviewed virological outcomes in HIV-1 infected, treatment-naive patients on regimens containing EFV versus NVP from randomised trials and observational cohort studies. Data sources include PubMed, Embase, the Cochrane Central Register of Controlled Trials and conference proceedings of the International AIDS Society, Conference on Retroviruses and Opportunistic Infections, between 1996 to May 2013. Relative risks (RR) and 95% confidence intervals were synthesized using random-effects meta-analysis. Heterogeneity was assessed using the I(2) statistic, and subgroup analyses performed to assess the potential influence of study design, duration of follow up, location, and tuberculosis treatment. Sensitivity analyses explored the potential influence of different dosages of NVP and different viral load thresholds. RESULTS: Of 5011 citations retrieved, 38 reports of studies comprising 114 391 patients were included for review. EFV was significantly less likely than NVP to lead to virologic failure in both trials (RR 0.85 [0.73-0.99] I(2) = 0%) and observational studies (RR 0.65 [0.59-0.71] I(2) = 54%). EFV was more likely to achieve virologic success than NVP, though marginally significant, in both randomised controlled trials (RR 1.04 [1.00-1.08] I(2) = 0%) and observational studies (RR 1.06 [1.00-1.12] I(2) = 68%). CONCLUSION: EFV-based first line ART is significantly less likely to lead to virologic failure compared to NVP-based ART. This finding supports the use of EFV as the preferred NNRTI in first-line treatment regimen for HIV treatment, particularly in resource limited settings

The development of artificial neural networks to predict virological response to combination HIV therapy.

Introduction: When used in combination, antiretroviral drugs are highly effective for suppressing HIV replication. Nevertheless, treatment failure commonly occurs and is generally associated with viral drug resistance. The choice of an alternative regimen may be guided by a drug-resistance test. However, interpretation of resistance from genotypic data poses a major challenge. Methods: As an alternative to current interpretation systems, we have developed artificial neural network (ANN) models to predict virological response to combination therapy from HIV genotype and other clinical information. Results: ANN models trained with genotype, baseline viral load and time to follow-up viral load (1,154 treatment change episodes from multiple clinics), produced predictions of virological response that were highly significantly correlated with actual responses (r2=0.53; P<0.00001) using independent test data from clinics that contributed training data. Augmented models, trained with the additional variables of baseline CD4+ T-cell count and four treatment history variables, were more accurate, explaining 69% of the variance in virological response. Models trained with the full input dataset, but only those data involving highly active antiretroviral therapy (three or more full-dose antiretroviral drugs in combination), performed at an intermediate level, explaining 61% of the variance. The augmented models performed less well when tested with data from unfamiliar clinics that had not contributed data to the training dataset, explaining 46% of the variance in response. Conclusion: These data indicate that ANN models can be quite accurate predictors of virological response to HIV therapy even for patients from unfamiliar clinics. ANN models therefore warrant further development as a potential tool to aid treatment selection

A comparison of three computational modelling methods for the prediction of virological response to combination HIV therapy

OBJECTIVE: HIV treatment failure is commonly associated with drug resistance and the selection of a new regimen is often guided by genotypic resistance testing. The interpretation of complex genotypic data poses a major challenge. We have developed artificial neural network (ANN) models that predict virological response to therapy from HIV genotype and other clinical information. Here we compare the accuracy of ANN with alternative modelling methodologies, random forests (RF) and support vector machines (SVM). METHODS: Data from 1204 treatment change episodes (TCEs) were identified from the HIV Resistance Response Database Initiative (RDI) database and partitioned at random into a training set of 1154 and a test set of 50. The training set was then partitioned using an L-cross (L=10 in this study) validation scheme for training individual computational models. Seventy six input variables were used for training the models: 55 baseline genotype mutations; the 14 potential drugs in the new treatment regimen; four treatment history variables; baseline viral load; CD4 count and time to follow-up viral load. The output variable was follow-up viral load. Performance was evaluated in terms of the correlations and absolute differences between the individual models' predictions and the actual DeltaVL values. RESULTS: The correlations (r(2)) between predicted and actual DeltaVL varied from 0.318 to 0.546 for ANN, 0.590 to 0.751 for RF and 0.300 to 0.720 for SVM. The mean absolute differences varied from 0.677 to 0.903 for ANN, 0.494 to 0.644 for RF and 0.500 to 0.790 for SVM. ANN models were significantly inferior to RF and SVM models. The predictions of the ANN, RF and SVM committees all correlated highly significantly with the actual DeltaVL of the independent test TCEs, producing r(2) values of 0.689, 0.707 and 0.620, respectively. The mean absolute differences were 0.543, 0.600 and 0.607log(10)copies/ml for ANN, RF and SVM, respectively. There were no statistically significant differences between the three committees. Combining the committees' outputs improved correlations between predicted and actual virological responses. The combination of all three committees gave a correlation of r(2)=0.728. The mean absolute differences followed a similar pattern. CONCLUSIONS: RF and SVM models can produce predictions of virological response to HIV treatment that are comparable in accuracy to a committee of ANN models. Combining the predictions of different models improves their accuracy somewhat. This approach has potential as a future clinical tool and a combination of ANN and RF models is being taken forward for clinical evaluation