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

Abstract

IntroductionPeople 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.MethodsBulk and single-cell transcriptomics, flow cytometry, proteomics, chromatin landscape analysis by ATAC-seq as well as ex vivo drug stimulation were performed in a small number of blood samples derived from PLHIV and healthy controls from the 200-HIV cohort study.ResultsSingle-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.DiscussionThese 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.

Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs

“…As a promising alternative, transcriptome-based approaches primarily rely on the identification of drug hits capable of reversing specific disease signatures ( Shukla et al, 2021 ). This is achieved by (i) the identification ( de novo or from a literature-based consensus) of a transcriptional signature characterizing the disease [i.e., usually transcripts demonstrated to be up- or downregulated in the selected disease model(s)]; (ii) the parallel acquisition of a drug-perturbed transcriptomic profile in the model(s) of interest, to be representative of the pathways and cell responses elicited in reaction to the specific agent; (iii) inspecting the expression of signature genes upon drug perturbation and the further functional assessment of drug candidates able to reverse the associated pathological signature ( Aschenbrenner et al, 2021 ; Shukla et al, 2021 ; Knoll et al, 2023 ). This principle finds its agnostic and fully-computational application in the widespread interrogation of the Connectivity Map (CMap) and the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) databases, together providing perturbed transcriptional profiles for more than 30,000 small molecule compounds screened on mostly cancer cell lines ( Lamb et al, 2006 ; Subramanian et al, 2017 ; Stathias et al, 2020 ).…”