Rios-Vazquez, V.; Delporte, M.; Otten, T.; Matzaraki, V.; Vos, W. A. J. W.; Blaauw, M.; van Eekeren, L. E.; Groenendijk, A. E.; Knoll, R.; Aschenbrenner, A. C.; Arts, R. J. W.; dos Santos, J. C.; Navas, A.; Gerlo, S.; van Lunzen, J.; Joosten, L. A. B.; Netea, M. G.; van der Ven, A. J. A. M.; Vandekerckhove, L.

HIV reservoirs are heterogeneous across individuals, yet host determinants of this variability remain unclear. Applying multi-omics clustering to 1,230 people with HIV, integrating omics and functional data from circulating immune cells (transcriptomics, DNA methylation, immune phenotyping, ex-vivo cytokine production capacity), plasma proteomics, and CD4+ T-cell reservoir measurements (total and intact HIV-DNA copies), revealed three immunologically distinct endotypes: All Low (low total/low intact reservoir size), All High (high total/high intact reservoir size) and Mixed (high total/low intact reservoir size). Per endotype, distinct immune landscapes were noticed in single-layer analyses as well as differences in clinical signatures. Applying non-linear machine learning across all layers, key predictors not captured by linear single-layer approaches showed IFN-{gamma} production and TCF7/AK5 expression as well as IL-1{beta}/MCP-1 production and MAN1C1/EDAR expression, linked to intact and total reservoir size, respectively. This host-virus integrative multi-omics framework provides a systems-level resource that may help to personalize reservoir-reducing intervention studies aiming for HIV cure and/or comorbidity reductions.