Thanks to the advent of combination antiretroviral therapies that block viral replication to undetectable levels in the body, HIV infection is now considered a chronically manageable condition. Antiretroviral therapies however fail to completely eliminate HIV from the body, and need to be maintained indefinitely. As lifelong treatment has obvious drawbacks, an HIV cure is actively being pursued. The reason HIV is able to persist in the body is because of latent HIV reservoirs, long-lived cells that harbor an integrated - though otherwise dormant - copy of the HIV genome within them. HIV is able to persist in these cells, undetected by the host immune system, for years or even decades. But, these cells may reactivate at any time to produce infectious HIV particles, thus re-seeding infection. Establishment of latent HIV reservoirs is a continuous process that begins shortly after infection. Moreover, within a given person, the transmitted (infecting) HIV strain(s) also continually evolve during infection. Thus, by the time an individual reaches chronic infection, their latent reservoir is likely to comprise a diverse pool of HIV sequences that differ in terms of their age and genetics. Moreover, these latently-infected cells are also likely to differ in key respects including their ability to become reactivated to produce new virions and their susceptibility to immune recognition. Methods to simultaneously characterize age, genetic and functional diversity within the HIV reservoir, and to identify key characteristics that tend to associate with reservoir reactivation potential, would aid efforts towards an HIV cure. The present proposal describes a rapid, accurate and versatile framework for dating HIV reservoir sequences within a host. The framework is novel in that it is based on the calculation of a host-specific HIV evolutionary rate inferred from an individual's own data; as such, it does not require infection date to be known nor does it require sequences similar to the reservoir to have been previously sampled from the individual. Our proposal seeks to refine the framework for application to a wide range of individual scenarios (in terms of clinical, treatment and sampling histories), data types (including next-generation sequencing) and model assumptions. We further propose to apply the framework to HIV reservoir sequences isolated from individuals with long-term viral suppression on ART, alongside detailed genotypic and functional assessments of these HIV sequences. We believe our approach will provide a flexible, robust and potentially transformative addition to the growing armamentarium of tools in HIV cure research, particularly to personalized strategies aimed at tailoring immunotherapeutic approaches for HIV reservoir elimination.
The proposed studies are highly relevant to public health. Specifically, our research applies a novel evolutionary framework to infer the archiving dates of individual latent HIV reservoir sequences within an individual, combined with detailed genetic and functional assessments of these sequences. Results will enhance our understanding of latent HIV reservoirs and may contribute to the ultimate goal of curing HIV.
