The major obstacle to an 'HIV cure' is the persistence of viral reservoirs (VR) harboring replication competent viral genomes that have the capacity to produce infectious virus. These VR persist for long periods of time, and even after years of suppressive ART, the systemic spread of virus resumes within a few weeks upon cessation of ART in all but exceptional cases. Effective cure strategies will need to dramatically reduce or eliminate VR through safe and scalable approaches. It is currently thought that the major VR are long-lived latently infected resting memory CD4+ T cells, which remain quiescent until they are stimulated by external cues to produce virus. In addition to the truly latent VR, emerging data shows that in individuals on suppressive ART a subset of VR transcribe viral RNA (vRNA+) at variable levels (termed ?active VR?). In some individuals, this might lead to residual levels of HIV replication, particularly in tissue microenvironments where drug concentrations are suboptimal. Even without full viral replication, this residual expression of virus may have adverse consequences and contribute to chronic immune activation/inflammation and non-AIDS defining clinical events. Eradicating HIV will require targeting both the ?latent? and ?active? VR, however, our current understanding of HIV reservoirs comes mostly from studies performed in peripheral blood, but the blood contains only a small fraction of VR during ART. We reason that to maximize efficacy of ?HIV cure? strategies, we need to first better characterize both the tissue compartments and the cellular subsets from which infection might rebound in HIV-infected individuals after ART is interrupted. Thus, the overarching goals of this research proposal in response to FOA PA-17-305 Imaging the Persistent HIV Reservoir are to validate and apply novel microscopic and flow cytometric RNA and DNA in situ hybridization (ISH) platforms that allow multiparametric single-cell characterization of VR with various levels of residual transcription and/or translation. We will use these approaches in HIV-infected samples and a NHP model of SIV infection, which allows detailed studies of diverse tissues.
In Aim 1, we will optimize our unique ISH platforms to identify and characterize ?latent? and ?active? VR with putatively intact viral genomes and distinguish VR with different levels of transcriptional and translational activities. We will define relationships between classical VR (e.g., PCR) and ISH-based measurements and determine key differences between VR measures in the blood compared to different tissue environments, as well as a comprehensive analysis of the ?immune neighborhoods? and ?inflammatory landscapes in which VR reside.
In Aim 2, we will perform an interventional study using infection with SIVmac239M barcoded virus and determine the relationship between novel ISH measures of VR in blood and tissues with time to viral rebound in SIV-infected RMs after ART cessation. We hypothesize that these ISH measures are more likely to predict time to viral rebound and the number of rebounding viruses upon ART interruption than conventional assays and believe that these powerful new approaches will be important in monitoring VR in future clinical trials.
Although lifelong suppression of HIV replication with antiretroviral therapy (ART) seems possible, side effects, need for strict adherence, resistance, stigma and cost all contribute to the necessity of finding an ?HIV cure?. As a model for HIV infection, and to better understand viral reservoirs in different organs and cell types, we propose to apply novel imaging techniques called microscopic and flow cytometric RNA and DNA in situ hybridization (ISH), to monkeys infected with the simian immunodeficiency virus (SIV), allowing an in-depth single-cell characterization of viral reservoirs with various levels of residual activity in terms of the expression of viral products. We will conduct these studies: i) to better understand the relationships between measures of viral persistence in blood compared to tissue compartments and ii) to better predict time to viral rebound upon ART cessation.