Background
Multi-omics data integration offers potential to uncover hidden patterns among people living with HIV (PLWH). This study aimed to identify clusters of individuals with distinct viral reservoir characteristics and multi-omics profiles in the 2000HIV cohort.
Methods
Multi-omics data from 1,236 PLWH in the 2000HIV cohort (NCT03994835) were analyzed, including gene expression (58,347 genes, bulk RNA-seq from PBMCs), DNA methylation (793,775 CpG sites, PBMCs), plasma proteins (2,367, Olink Explore), immune cell markers (355, flow cytometry from whole blood), and cytokine production capacity (90 markers, ex vivo stimulation assays from PBMCs). Total viral reservoir (DNA copies/million CD4 T cells) and intactness (intact reservoir DNA copies/million CD4 T cells) were assessed. Data preprocessing included normalization, filtering, and selection of top variable features. Multi-omics clustering was performed using moCluster.
Results
Three distinct clusters were identified: High Total Low Intact (HTLI, n=348), Low Total Low Intact (LTLI, n=421), and High Total High Intact (HTHI, n=467), each with distinct viral reservoir profiles and clinical characteristics. LTLI exhibited the lowest total reservoir DNA and intactness. HTHI revealed high total reservoir DNA and highest intactness, while HTLI showed high total reservoir DNA but low intactness. These differences were statistically significant (P<0.05). Top 10 driver markers were identified in each omics layer, with bulk transcriptomics showing significant contributions to intercluster differences. Key driver genes included ACVR1C, AK5, LRP6, MMP28, SLC22A17, SLC16A10, OBSCN, MAN1C1, EDAR, and LEF1, which were highly expressed (P<0.05) in LTLI compared to HTLI and HTHI. These genes are involved in processes relevant to control HIV infection, including cell signaling, T cell development, immune response and apoptosis regulation, macrophage function, iron homeostasis, mitochondrial biogenesis, and HIV-1 transcriptional regulation.
Conclusions
Multi-omics clustering revealed distinct molecular signatures associated with viral reservoir size and intactness in PLWH, identifying three clusters with different characteristics, particularly distinct in the LTLI group. These findings suggest potential for personalized therapeutic strategies informed by biomarkers from distinct multi-omics profiles. Further investigation of these and other omics markers could provide insights into HIV reservoir dynamics and tailored interventions for HIV cure research.