Modeling dynamic CD8+ T cell-virus interactions in post-treatment control of HIV

About This Grant

SUMMARY Nearly 40 million people globally and 1.2 million people in the United States are living with HIV. Although antiretroviral therapy (ART) has transformed HIV from a fatal disease to a manageable chronic condition, lifelong treatment poses substantial challenges, and ART does not cure the infection. Based on data in humans and non- human primate models linking high quality CD8+ T cell responses with low viral loads, there is strong rationale for targeting HIV-specific CD8+ T cells to promote control of HIV. However, due in part to limitations of existing CD8+ T cell assays, the field currently lacks a mechanistic understanding of which features of the CD8+ T cell response might drive effective control of HIV rebound. The overarching goal of this project is to comprehensively define the mechanistic features of CD8+ T cell responses that most strongly relate to control of HIV after stopping ART. To do this, we will study peripheral blood samples collected before analytic treatment interruption (ATI) and longitudinally after rebound in participants from ATI sub-studies within the San Franscisco SCOPE cohort, including several who maintained low viral loads (<2,000 copies/mL) for several months after stopping ART. We recently developed a novel nanoparticle class I peptide:Human Leukocyte Antigen (pHLA) pool assay that enables simultaneous measurement of pHLA specificity, T cell receptor (TCR) avidity and breadth of peptide recognition, and transcriptional signature at a clonotype level from up to 1000 HIV-specific CD8+ T cell responses per sample. In Aim 1, we will use this tool to identify features of HIV-specific CD8+ T cell clonotypes that respond as HIV reactivates during viral rebound. In Aim 2, we will apply newly-developed single-copy sequencing methods to plasma HIV sequences in order to characterize the development of HIV escape to autologous CD8+ T cell responses during and after rebound. Finally, to connect these distinct but inter-related data types, in Aim 3, we will utilize mathematical models that describe viral dynamics and evolution simultaneously to model dynamic CD8+ T cell-virus interactions that promote control of HIV after ART is stopped. This highly collaborative project with clinical, immunology, virology, and mathematical investigators will identify the mechanistic properties of CD8+ T cell responses required for successful control rebound HIV across a large group of post-treatment controllers. Our work will provide a target for the next generation of immunotherapies for HIV cure and inform T cell-based therapeutics for other chronic infections and cancers.