ABSTRACTCombination anti-retroviral drug therapy (ART) potently suppresses HIV-1 replication but does not result in virus eradication or a cure. A major contributing factor is the long-term persistence of a reservoir of latently infected cells. To study this reservoir, we established a humanized mouse model of HIV-1 infection and ART suppression based on an oral ART regimen. Similar to humans, HIV-1 levels in the blood of ART-treated animals were frequently suppressed below the limits of detection. However, the limited timeframe of the mouse model and the small volume of available samples makes it a challenging model with which to achieve full viral suppression and to investigate the latent reservoir. We therefore used anex vivolatency reactivation assay that allows a semiquantitative measure of the latent reservoir that establishes in individual animals, regardless of whether they are treated with ART. Using this assay, we found that latently infected human CD4 T cells can be readily detected in mouse lymphoid tissues and that latent HIV-1 was enriched in populations expressing markers of T cell exhaustion, PD-1 and TIGIT. In addition, we were able to use theex vivolatency reactivation assay to demonstrate that HIV-specific TALENs can reduce the fraction of reactivatable virus in the latently infected cell population that establishesin vivo, supporting the use of targeted nuclease-based approaches for an HIV-1 cure.IMPORTANCEHIV-1 can establish latent infections that are not cleared by current antiretroviral drugs or the body’s immune responses and therefore represent a major barrier to curing HIV-infected individuals. However, the lack of expression of viral antigens on latently infected cells makes them difficult to identify or study. Here, we describe a humanized mouse model that can be used to detect latent but reactivatable HIV-1 in both untreated mice and those on ART and therefore provides a simple system with which to study the latent HIV-1 reservoir and the impact of interventions aimed at reducing it.
Improving the estimation of the death rate of infected cells from time course data during the acute phase of virus infections: application to acute HIV-1 infection in a humanized mouse model