Machine learning approaches identify immunologic signatures of total and intact HIV DNA during long-term antiretroviral therapy.

ABSTRACT: Understanding the interplay between the HIV reservoir and the host immune system may yield insights into HIV persistence during antiretroviral therapy (ART) and inform strategies for a cure. Here, we applied machine learning (ML) approaches to cross-sectional high-parameter HIV reservoir and immunology data in order to characterize host-reservoir associations and generate new hypotheses about HIV reservoir biology. High-dimensional immunophenotyping, quantification of HIV-specific T cell responses, and measurement of genetically intact and total HIV proviral DNA frequencies were performed on peripheral blood samples from 115 people with HIV (PWH) on long-term ART. Analysis demonstrated that both intact and total proviral DNA frequencies were positively correlated with T cell activation and

SUBMITTER: Semenova L 

PROVIDER: S-EPMC11383529 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature