A Novel Dual Florescent HIV-1 to Study Latency
Award amount: 40,000.00
Emily Battivelli, PhD, Recipient
HIV-1 latency is a state of reversible, non-productive infection that occurs primarily in long-lived memory CD4+ T cells. Latency allows infected cells to evade both the host immune response and antiretroviral drugs, thereby making it one of the most significant barriers to viral eradication. HIV-1 latency is a product of proviral transcriptional silencing and can occur both directly upon infection (primary latency), as well as by progressive silencing of productive infections (secondary latency). Numerous mechanisms for secondary latency have been independently identified; however, there is minimal overlap in the mechanisms identified in different model systems. Furthermore, the design of these model systems precludes studying primary latency. Consequently, it is unclear if and how various mechanisms interact, which are dominant, and which are responsible for primary latency establishment in newly infected cells. As a result, we hypothesize that only a systems-level approach will reveal the cumulative action and interplay of factors involved in latency establishment. Accordingly, we propose to use our dual-labeled latency model, which is capable of detecting latently infected cells in their native state, in conjunction with mass cytometry (CyTOF) to obtain a comprehensive dataset of cellular parameters relevant to HIV-1 transcription. We will use two panels of CyTOF validated antibodies to evaluate cellular phenotype, function, activation, and signaling pathways in both productively and latently infected cells. We will then use this information to perform cluster and correlation analysis in order to identify biomarkers correlated with the HIV-1 latency switch. Together, the goals outlined in this proposal will provide a first-of-its-kind systems-level, comprehensive view of HIV-1 latency. Indeed, determining molecular signatures and biomarkers for latency establishment is an important step in designing relevant model systems to identify therapeutics targeting HIV-1 latency.