Background
Elite Controllers (ECs) are individuals who suppress HIV replication without treatment. Understanding these mechanisms can provide insights for developing novel therapeutic strategies. By learning from their responses, we can identify key pathways and immune functions that could enhance viral control in the HIV-infected population. This study aims to investigate ECs using a multiomics approach to understand how they achieve viral suppression.
Methods
Samples from 13 ECs, 27 VCs, 10 Viremic Non-Controllers (VNCs), and 20 Healthy Donors (HDs) were collected. Plasma and PBMCs were analyzed for cytokine levels, plasma metabolites, and bulk RNAseq. A linear regression model was employed. Enrichment mapping identified transcriptomic modules, and K-means clustering unveiled heterogeneity among controllers. Bulk RNA modules were validated using an independent scRNAseq dataset and confirmed via in-silico deconvolution. Gradient boosting identified plasma cytokines and metabolites predictive of controller subgroups. Multiomics integration was performed using Diablo.
Results
Transcriptomic analysis revealed 2 EC clusters and 3 VC clusters. CD4 counts and viral load within cohort between clusters (esp cluster 1 vs 2) are not confounding factors. Cluster 1 included ECs and VCs whose profiles resembled HDs (Figure 1A), enriched in classical monocytes with trained immunity, inflammasome and epigenetic remodeling signatures. These individuals showed equal or higher expression of these signatures compared to HDs. Cluster 2 comprised ECs and VCs with distinct profiles, with higher proportions of effector CD8 T cells with antiviral responses (Figure 1B). Cluster 3 included VCs and VNCs with higher expression of effector CD8 and antiviral responses. IFN-γ was the best predictor of the two EC classes, being lower in ECs with trained immunity and higher in those with effector CD8 (84% accuracy). Ceramides, primary bile acids, and fatty acids were metabolite predictors, with the first two higher in ECs with effector CD8 signatures, and fatty acids higher in ECs with trained immunity signatures (100% accuracy).
Conclusions
The study reveals two distinct EC populations with differing immunological, metabolomic, and transcriptomic profiles. Recognizing this heterogeneity is crucial, as personalized therapeutic strategies could target specific pathways to enhance viral control. These findings suggest strategies to shift VCs and VNCs toward beneficial EC profiles, advancing efforts toward a functional HIV cure.