bioRxivImmunologyPreprint
Metabolic reprogramming of immune cells in HIV infection and treatment
Abstract
BACKGROUNDImmune cells undergo metabolic reprogramming, altering their energy production in response to HIV infection. During acute HIV infection, CD4+ T cells increase glycolysis to fuel viral replication and defense, but prolonged activation leads to immune exhaustion, with reduced T cell function and impaired pathogen elimination, contributing to AIDS progression. However, the details Metabolic dynamics of Immune Cells during HIV Infection (UT) and after receiving antiretroviral therapy (ART) remains unknown. Communication between Immune cells will be modified to response HIV infection, which can be mediated by metabolites. In this study, we deciphered Metabolic pathway dynamics of all Immune Cells by comparing UT and ART with seronegative controls (SN). Additionally, we investigate alteration of metabolite-mediated cell-cell communications in HIV Infection. METHODSSingle cell RNA sequencing (scRNA-seq) data of PBMC from SN, UT and ART were analyzed with KEGG Metabolic pathways by irGSEA and algorithm MEBOCOST to study cell-cell communications mediated by metabolites from sender cells and sensor proteins on receiver cells. RESULTSlots of differentially expressed Metabolic Pathways were identified for immune cells, including Energy Production and Utilization, Lipid Metabolism, Amino Acid Metabolism, Glycosylation and Glycan Biosynthesis and Oxidative Stress and Antioxidant Defense. CONCLUSIONSThe analysis highlights the intricate metabolic interplay between monocytes and other immune cells, reflecting their central role in orchestrating immune responses through metabolite exchange. These insights may open avenues for therapeutic interventions, targeting metabolic pathways to enhance immune function or inhibit pathogen survival.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: cell-cell communication
- approach: deep learning
- approach: statistical model
- Citations
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