Nrf2 regulates activation driven-expansion of CD4+T-cells by differentially modulating glucose and glutamine metabolism [RNA-Seq]

Upon antigenic stimulation, CD4+T-cells undergo clonal expansion elevating their bioenergetic demands and utilization of nutrients like glucose and glutamine The nuclear factor erythroid 2-related factor2 (Nrf2) is a well-known regulator of oxidative stress, but its involvement in modulating the metabolism of CD4+T-cells remain unexplored. We report that Nrf2 protein levels are temporally regulated in activated CD4+T-cells, with elevated expression during early activation followed by a decline. T-cell-specific constitutive activation of Nrf2 by deletion of its regulator Keap1, enhances early activation and IL-2 secretion, upregulates TCR-signaling, and increases activation-driven expansion of CD4+T-cells. Metabolically, high Nr2 alters glucose metabolism and promotes glutamine metabolism via glutaminolysis to support increased CD4+T-cell proliferation. Mechanistically, elevated Nrf2 activity in activated CD4+T-cells leads to increased chromatin accessibility and proliferation-associated gene expression. In summary, we elucidate the role of Nrf2 beyond traditional antioxidation in modulating the activation-driven expansion of CD4+T-cells by influencing their nutrient metabolism. Overall design: Mouse naïve CD4+T-cells were activated in vitro in presence of anti-CD3 and anti-CD28 to mimic T-cell activation for 24 and 48h. After 24 and 48 hours, total RNA was extracted using RNeasy Plus Micro kit (Qiagen) and processed at the Genomics Core Facility at the University of Kansas Medical Center. 100ng of the RNA was used to perform Agilent TapeStation QC analysis to verify RNA integrity and generate RNA Integrity number (RIN). Using Nova-Seq 6000 S1 200 cycle reagent kit, 25 million pair-ended reads were recorded for construction of mRNA libraries. For bioinformatic analysis, following quality check through fastQC data were aligned to human reference genome using RSEM (RNA-Seq by Expectation Maximization) resulting in the generation of gene count matrix. Further differential expression analysis was performed between Keap1-KO (KKO) and WT CD4+T-cells for gene ontology (GO) enrichment using ClusterProfiler R-package, to identify significant biological processes, molecular pathways and metabolic pathways associated with the samples. The enrichment results were prioritized based on significance and the top GO terms were visualized using dotplots. Box-plots for genes were also generated using ggplot2 to illustrate the variability of the genes between the 2 groups with mean P-values annotated on each plot, respectively.