Clifford Lane

Inflammatory and Coagulation Biomarkers and Mortality in Patients with HIV Infection

Background In the Strategies for Management of Anti-Retroviral Therapy trial, all-cause mortality was higher for participants randomized to intermittent, CD4-guided antiretroviral treatment (ART) (drug conservation [DC]) than continuous ART (viral suppression [VS]). We hypothesized that increased HIV-RNA levels following ART interruption induced activation of tissue factor pathways, thrombosis, and fibrinolysis. Methods and Findings Stored samples were used to measure six biomarkers: high sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), amyloid A, amyloid P, D-dimer, and prothrombin fragment 1+2. Two studies were conducted: (1) a nested case–control study for studying biomarker associations with mortality, and (2) a study to compare DC and VS participants for biomarker changes. For (1), markers were determined at study entry and before death (latest level) for 85 deaths and for two controls (n = 170) matched on country, age, sex, and date of randomization. Odds ratios (ORs) were estimated with logistic regression. For each biomarker, each of the three upper quartiles was compared to the lowest quartile. For (2), the biomarkers were assessed for 249 DC and 250 VS participants at study entry and 1 mo following randomization. Higher levels of hsCRP, IL-6, and D-dimer at study entry were significantly associated with an increased risk of all-cause mortality. Unadjusted ORs (highest versus lowest quartile) were 2.0 (95% confidence interval [CI], 1.0–4.1; p = 0.05), 8.3 (95% CI, 3.3–20.8; p < 0.0001), and 12.4 (95% CI, 4.2–37.0; p < 0.0001), respectively. Associations were significant after adjustment, when the DC and VS groups were analyzed separately, and when latest levels were assessed. IL-6 and D-dimer increased at 1 mo by 30% and 16% in the DC group and by 0% and 5% in the VS group (p < 0.0001 for treatment difference for both biomarkers); increases in the DC group were related to HIV-RNA levels at 1 mo (p < 0.0001). In an expanded case–control analysis (four controls per case), the OR (DC/VS) for mortality was reduced from 1.8 (95% CI, 1.1–3.1; p = 0.02) to 1.5 (95% CI, 0.8–2.8) and 1.4 (95% CI, 0.8–2.5) after adjustment for latest levels of IL-6 and D-dimer, respectively. Conclusions IL-6 and D-dimer were strongly related to all-cause mortality. Interrupting ART may further increase the risk of death by raising IL-6 and D-dimer levels. Therapies that reduce the inflammatory response to HIV and decrease IL-6 and D-dimer levels may warrant investigation. Trial Registration: ClinicalTrials.gov (NCT00027352).

Efficacy of Low-Dose Subcutaneous Interleukin-2 to Treat Advanced Human Immunodeficiency Virus Type 1 in Persons with ⩽250/μL CD4 T Cells and Undetectable Plasma Virus Load

The immunologic efficacy of low-dose recombinant interleukin-2 (rIL-2) administered subcutaneously (sc) once a day in combination with highly active antiretroviral therapy (HAART) was assessed in a pilot study in patients with advanced human immunodeficiency virus (HIV) disease. Twenty-five persons with </=250 CD4 cells/microL and plasma HIV-1 RNA levels </=500 copies/mL for >24 weeks were randomly assigned to receive sc rIL-2 (3 x 10(6) IU once a day) with their previous antiretroviral regimen (n=13) or to continue with the same treatment (n=12). The level of CD4 T cells was significantly higher in the IL-2 group at week 24 (105+/-65/microL; P<.05) but not in the control group (30+/-78/microL). Memory T cells initially contributed to the CD4 T cell increase at week 4 (P<.05). Naive T cell increases (99+/-58/microL) in the IL-2 group became statistically significant at week 24 compared with the control group (28+/-27/microL; P<.05). Subcutaneous rIL-2 once a day in combination with HAART was well tolerated and improved immunologic surface markers in patients with advanced HIV infection.

CD4 and CD8 T Cell Immune Activation during Chronic HIV Infection: Roles of Homeostasis, HIV, Type I IFN, and IL-7

Immune activation plays an important role in the pathogenesis of HIV disease. Although the causes are not fully understood, the forces that lead to immune dysfunction differ for CD4 and CD8 T cells. In this study, we report that the molecular pathways that drive immune activation during chronic HIV infection are influenced by differences in the homeostatic regulation of the CD4 and CD8 T cell pools. Proliferation of CD4 T cells is controlled more tightly by CD4 T cell numbers than is CD8 T cell proliferation. This difference reflects the importance of maintaining a polyclonal CD4 T cell pool in host surveillance. Both pools of T cells were found to be driven by viral load and its associated state of inflammation. In the setting of HIV-induced lymphopenia, naive CD4 T cells were recruited mainly into the proliferating pool in response to CD4 T cell depletion, whereas naive CD8 T cell proliferation was driven mainly by levels of HIV RNA. RNA analysis revealed increased expression of genes associated with type I IFN and common γ chain cytokine signaling in CD4 T cell subsets and only type I IFN-associated genes in CD8 T cell subsets. In vitro studies demonstrated enhanced STAT1 phosphorylation in response to IFN-α and increased expression of the IFNAR1 transcripts in naive and memory CD4 T cells compared with that observed in CD8 T cells. CD4 T cell subsets also showed enhanced STAT1 phosphorylation in response to exogenous IL-7.

The development of an expert system to predict virological response to HIV therapy as part of an online treatment support tool.

Objective:The optimum selection and sequencing of combination antiretroviral therapy to maintain viral suppression can be challenging. The HIV Resistance Response Database Initiative has pioneered the development of computational models that predict the virological response to drug combinations. Here we describe the development and testing of random forest models to power an online treatment selection tool. Methods:Five thousand, seven hundred and fifty-two treatment change episodes were selected to train a committee of 10 models to predict the probability of virological response to a new regimen. The input variables were antiretroviral treatment history, baseline CD4 cell count, viral load and genotype, drugs in the new regimen, time from treatment change to follow-up and follow-up viral load values. The models were assessed during cross-validation and with an independent set of 50 treatment change episodes by plotting receiver–operator characteristic curves and their performance compared with genotypic sensitivity scores from rules-based genotype interpretation systems. Results:The models achieved an area under the curve during cross-validation of 0.77–0.87 (mean = 0.82), accuracy of 72–81% (mean = 77%), sensitivity of 62–80% (mean = 67%) and specificity of 75–89% (mean = 81%). When tested with the 50 test cases, the area under the curve was 0.70–0.88, accuracy 64–82%, sensitivity 62–80% and specificity 68–95%. The genotypic sensitivity scores achieved an area under the curve of 0.51–0.52, overall accuracy of 54–56%, sensitivity of 43–64% and specificity of 41–73%. Conclusion:The models achieved a consistent, high level of accuracy in predicting treatment responses, which was markedly superior to that of genotypic sensitivity scores. The models are being used to power an experimental system now available via the Internet.

Prevention of Ebola virus disease through vaccination: where we are in 2018

Yves Lévy, Clifford Lane, Peter Piot, Abdul Habib Beavogui, Mark Kieh, Bailah Leigh, Seydou Doumbia, Eric D’Ortenzio, Claire Lévy-Marchal, Jerome Pierson, Deborah WatsonJones, Vinh-Kim Nguyen, Heidi Larson, Julia Lysander, Christine Lacabaratz, Rodolphe Thiebaut, Augustin Augier, David Ishola, Stephen Kennedy, Geneviève Chêne, Brian Greenwood, James Neaton, and Yazdan Yazdanpanah French Institute for Health and Medical Research (Inserm), Paris, France (Prof Y Lévy MD, E D’Ortenzio MD, C Lévy-Marchal MD, C Lacabaratz PhD, R Thiebaut PhD, G Chêne MD, Y Yazdanpanah MD);National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA (C Lane MD, J Pierson PhD); London School of Hygiene & Tropical Medicine, London, UK (P Piot PhD, D Watson-Jones PhD, H Larson PhD, D Ishola MD, B Greenwood PhD); Centre National de Formation et de Recherche en Santé Rurale de Mafèrinyah, Mafèrinyah, Guinea (A H Beavogui MD); Partnership for Research on Ebola Virus in Liberia (PREVAIL), Monrovia, Liberia (M Kieh MD, J Lysander MSc, S Kennedy MD); College of Medicine and Allied Health Sciences (COMAHS), University of Sierra Leone, Freetown, Sierra Leone (B Leigh MD); University of Sciences, Technique and Technology of Bamako, Bamako, Mali (S Doumbia MD); Graduate Institute of International and Development Studies, Geneva, Switzerland (V-K Nguyen MD); The Alliance for International Medical Action, Dakar, Senegal (A Augier MSc); School of Public Health, University of Minnesota, Minneapolis, MN, USA (J Neaton PhD)

INA-RESPOND: a multi-centre clinical research network in Indonesia

Nationally representative observational and translational research is needed to address the public health challenges in Indonesia due to the geographic disparity, recently decentralized health system, and diverse infectious disease priorities. To accomplish this, the Indonesian Ministry of Health in collaboration with the US National Institute of Health has established INA-RESPOND (Indonesia Research Partnership on Infectious Disease) – a clinical research network comprising 9 referral hospitals, 7 medical faculties, and 2 research centres across Indonesia. The network provides a forum to conduct research at a national scale and to address scientific questions that would be difficult to address in smaller research settings. Further, it is currently conducting multi-centre research on the etiologies of fever, sepsis, and tuberculosis. There are opportunities to leverage existing network resources for other public health research needs. INA-RESPOND is an Indonesian-led network in a country with diverse population groups and public health needs which is poised to collaborate with researchers, universities, donors, and industry worldwide. This paper describes the network and its goals and values, as well as the management structure, process for collaboration, and future vision.