Transcriptional impact of iron in ILC3 [RNA-seq]

Iron metabolism is pivotal for cell fitness in the mammalian host. However, its role in group 3 innate lymphoid cells (ILC3s) is unknown. Here we show that transferrin receptor CD71-mediated iron metabolism cell-intrinsically controls ILC3 maintenance, cytokine production and host protection against Citrobacter rodentium infection, and metabolically affects mitochondrial respiration by switching of oxidative phosphorylation toward glycolysis. Iron deprivation or Tfrc deficiency reduces the expression and/or activity of the aryl hydrocarbon receptor (Ahr), a ligand-dependent transcription factor and a key ILC3 regulator. Furthermore, consistent with its role in generation of Ahr ligand, microbiome exerts a negative impact on CD71 expression in an Ahr-dependent manner. Genetic ablation or activation of Ahr in ILC3s leads to CD71 upregulation or downregulation, respectively, suggesting an active suppression of CD71 by Ahr. Iron overload partially restores defective ILC3 compartment in the small intestine of Ahr-deficient mice, representing compensatory action of CD71 upregulation during Ahr deficiency. Mechanistically, Ahr directly binds to the promoter region of the Tfrc locus to inhibit Tfrc transcription. These data collectively demonstrate an under-appreciated role of the Ahr-CD71-iron axis in regulation of ILC3 maintenance and function. Overall design: ILC3 cells (Lin–CD90hiCD45lo) were FACS-sorted from the gut of indicated mice, and MNK-3 cells were collected after in vitro treatment. RNA was extracted using the RNAeasy Micro Kit (TaKaRa), followed by complementary DNA (cDNA) generation with the SMART-Seq® HT Kit (TaKaRa). Sequencing libraries were generated with the Nextera® XT DNA Library Preparation Kit (Illumina), and sequenced on the Illumina HiSeq 2500 instrument. FastQC (v0.11.5) was used to ensure high per-base sequence quality of reads. Sequenced reads were mapped and raw count values quantified with STAR78 to the Mus musculus genome (GRCm38/mm10 assembly) and filtered for uniquely mapped reads. Quantitated mRNA expression levels, FPKM aligned reads, were calculated based on exon regions using RSEM (v1.2.25). Differentially expressed genes (max FPKM = 1, fold change = 1.5, q-value = 0.05) were identified by DESeq2 (v1.26.0) analysis. Gene Set Enrichment Analysis (GSEA) was performed for Gene Ontology (GO) analysis. Log2 transformed FPKM values were used for principal component analysis in R (v3.6.2) with the prcomp function and then visualized using the factoextra package (v1.0.7). Heatmaps were created using the R package pheatmap (v1.0.12).