Identification of drug candidates targeting monocyte reprogramming in people living with HIV

Immunology Immunology
Infectious Disease Infectious Disease
Alex K. Shalek Alex K. Shalek

Knoll et al.▾ Knoll, A., Bonaguro, L. Santos, J. C. D., Warnat-Herresthal, S., Jacobs-Cleophas, M. C. P., Blumen, E., Reusch, N., Horne, A., Herbert, M., Nuesch-Germano, M., Otten, T., Heiden, W. A. V. D., Wijer, L. V. D., Shalek, A.K., Handler, K., Becker, M., Beyer, M. D., Netter, M. G., Joosten, L. A. B., Ven, A. J. A. M. V. D., Schultze, J. L., Aschenbrenner, A. C.,

Frontiers in Immunology , Volume 14

November, 2023


Introduction: People living with HIV (PLHIV) are characterized by functional reprogramming of innate immune cells even after long-term antiretroviral therapy (ART). In order to assess technical feasibility of omics technologies for application to larger cohorts, we compared multiple omics data layers. Methods: Bulk and single-cell transcriptomics, flow cytometry, proteomics, chromatin landscape analysis by ATAC-seq as well as ex vivodrug stimulation were performed in a small number of blood samples derived from PLHIV and healthy controls from the 200-HIV cohort study. Results: Single-cell RNA-seq analysis revealed that most immune cells in peripheral blood of PLHIV are altered in their transcriptomes and that a specific functional monocyte state previously described in acute HIV infection is still existing in PLHIV while other monocyte cell states are only occurring acute infection. Further, a reverse transcriptome approach on a rather small number of PLHIV was sufficient to identify drug candidates for reversing the transcriptional phenotype of monocytes in PLHIV. Discussion: These scientific findings and technological advancements for clinical application of single-cell transcriptomics form the basis for the larger 2000-HIV multicenter cohort study on PLHIV, for which a combination of bulk and single-cell transcriptomics will be included as the leading technology to determine disease endotypes in PLHIV and to predict disease trajectories and outcomes.