Abstract
While antiretroviral therapy (ART) has significantly improved disease prognosis in people with HIV (PWH), understanding the biological mechanisms underlying plasma HIV-1 RNA viral load (VL) can inform additional strategies to slow HIV/AIDS disease progression. Here, we integrated multi-omic datasets and used two machine learning network biology tools (GRIN and MENTOR) to identify biological mechanisms associated with VL across 10 cohorts from multiple omics data sets. We integrated the following gene sets: 3 genes from HIV set point VL GWAS, 258 genes whose expression was associated with set point VL in CD4+ T-cells, 143 genes based on DNA methylation associations with VL, and 8 genes previously known to affect the pharmacokinetics of ART. Using GRIN, we retained 194 VL genes based on their high network interconnectivity. We then used MENTOR to collaboratively interpret subsets of these genes and identified the following biological processes: cell cycle checkpoint pathways associated with non-AIDS defining cancers, oxidative stress, viral replication, and interferon signaling. Using these network tools for multi-omic integration, we present a conceptual model of mechanisms underlying HIV VL, and identify drug repurposing candidates to complement existing ART to enhance treatment response and reduce HIV-related comorbidities.