The role of regulatory T cells in the pathogenesis of acute kidney injury

Abstract

The incidence of acute kidney injury (AKI) is on the rise and is associated with high mortality; however, there are currently few effective treatments. Moreover, the relationship between Tregs and other components of the immune microenvironment (IME) in the pathogenesis of AKI remains unclear. We downloaded four publicly accessible AKI datasets, GSE61739, GSE67401, GSE19130, GSE81741, GSE19288 and GSE106993 from the gene expression omnibus (GEO) database. Additionally, we gathered two kidney single-cell sequencing (scRNA-seq) samples from the Department of Organ Transplantation at Zhujiang Hospital of Southern Medical University to investigate chronic kidney transplant rejection (CKTR). Moreover, we also collected three samples of normal kidney tissue from GSE131685. By analysing the differences in immune cells between the AKI and Non-AKI groups, we discovered that the Non-AKI group contained a significantly greater number of Tregs than the AKI group. Additionally, the activation of signalling pathways, such as inflammatory molecules secretion, immune response, glycolytic metabolism, NOTCH, FGF, NF-κB and TLR4, was significantly greater in the AKI group than in the Non-AKI group. Additionally, analysis of single-cell sequencing data revealed that Tregs in patients with chronic kidney rejection and in normal kidney tissue have distinct biology, including immune activation, cytokine production, and activation fractions of signalling pathways such as NOTCH and TLR4. In this study, we found significant differences in the IME between AKI and Non-AKI, including differences in Tregs cells and activation levels of biologically significant signalling pathways. Tregs were associated with lower activity of signalling pathways such as inflammatory response, inflammatory molecule secretion, immune activation, glycolysis.

Keywords: Tregs; acute kidney injury; immune microenvironment; inflammatory.

FIGURE 1

The overall design of the study.

FIGURE 2

The differences between the AKI and non‐AKI groups with regard to Tregs. Heatmap (A) and boxplot (B) illustrated the differences in Tregs between the AKI and Non‐AKI group in GSE61739. Heatmap (C) and boxplot (D) illustrated the differences in Tregs between the AKI and Non‐AKI group in GSE67401. (E) The boxplot illustrated the differences in Tregs between the AKI and Non‐AKI groups in GSE192883.

FIGURE 3

The gene set enrichment analysis (GSEA) of the AKI group compared with the Non‐AKI group.

FIGURE 4

The activity of signalling pathways in AKI and the correlation between Tregs and signalling pathway scores. (A) Correlation between the signalling pathway scores estimated by ssGSEA and the Tregs in GSE67401. (B) The differences in signalling pathway scores between the AKI and Non‐AKI groups in the GSE67401, as estimated by ssGSEA. (C) The correlation between the signalling pathway scores determined by ssGSEA and the Tregs in the GSE19130. The differences in signalling pathway scores were estimated by ssGSEA between the AKI and Non‐AKI groups in the GSE19130 (D), GSE81741 (E) and GSE106993 (F).

FIGURE 5

The biological function of Tregs as determined by single‐cell sequencing. (A) The UMAP plot displayed T cell clusters. (B) UMAP plot colour‐coded for the expression of the marker genes for CD4+ T cells (A), CD8+ T cells (B), gamma delta T cells (C), memory T cells (D), and Tregs (E, F). (G) Tregs' sample origin is colour‐coded on their UMAP plot. (H) The UMAP plot illustrated the Tregs clusters. (I) Heatmap of the ssGSEA score for three Tregs clusters, as estimated using gene sets from MsiDB. (J) The proportion of the cell types of each sample. (K) The differences in the Tregs subtypes between the normal and CKTR group.