Human immunodeficiency virus type I (HIV-1) has the ability to enter a long-lived dormant state, termed proviral latency. Proviral latency is the primay barrier to eradication of HIV-1 from infected individuals, owing to viral reactivation from reservoirs of latently infected cells upon interruption of antiretroviral treatment. It is known tht HIV-1 transcription significantly influences the regulation of HIV-1 latency. The proposed research will utilize high-throughput single-cell measurements to quantify modulated HIV-1 gene expression. A combination of drug perturbations and mutations of cis regulatory elements within the promoter will be used to modulate gene expression and stability of the latent state. Modulations using both approaches will be characterized by high-throughput flow cytometry to measure HIV-1 gene-expression from an array of HIV-1 constructs expressing GFP. Diverse modulations of gene expression will be tested for their ability to reactivate latent HIV as a function of their transcriptional modulation, with the aim of using the data to develop a detailed mechanistic model of transcriptionally driven latency and reactivation. With a deeper mechanistic understanding of latency and the potential discovery of novel reactivating compounds, this research has applications to the fields of HIV, systems biology, and pharmaceutical sciences.
The discovery of a cure for HIV is currently out of reach, hindered by the virus's ability to lay silent in a dormant state within the DNA of a human cell. This inactive or latent state reactivates at a later time, causing elevated levels of HIV, AIDS, an ultimately the death of an infected individual. By building on evidence that latency is heavily influenced by HIV-1 gene expression, this project aims to mechanistically understand how altering viral gene expression affects HIV latent stability using diverse biochemical approaches that modulate its expression and latent reactivation resulting in research of interests to the HIV and systems biology communities.
|Dar, Roy D; Hosmane, Nina N; Arkin, Michelle R et al. (2014) Screening for noise in gene expression identifies drug synergies. Science 344:1392-6|