Single-cell RNA and transcriptome sequencing profiles identify immune-associated key genes in the development of diabetic kidney disease

Abstract

BackgroundThere is a growing public concern about diabetic kidney disease (DKD), which poses a severe threat to human health and life. It is important to discover noninvasive and sensitive immune-associated biomarkers that can be used to predict DKD development. ScRNA-seq and transcriptome sequencing were performed here to identify cell types and key genes associated with DKD.MethodsHere, this study conducted the analysis through five microarray datasets of DKD (GSE131882, GSE1009, GSE30528, GSE96804, and GSE104948) from gene expression omnibus (GEO). We performed single-cell RNA sequencing analysis (GSE131882) by using CellMarker and CellPhoneDB on public datasets to identify the specific cell types and cell-cell interaction networks related to DKD. DEGs were identified from four datasets (GSE1009, GSE30528, GSE96804, and GSE104948). The regulatory relationship between DKD-related characters and genes was evaluated by using WGCNA analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) datasets were applied to define the enrichment of each term. Subsequently, immune cell infiltration between DKD and the control group was identified by using the “pheatmap” package, and the connection Matrix between the core genes and immune cell or function was illuminated through the “corrplot” package. Furthermore, RcisTarget and GSEA were conducted on public datasets for the analysis of the regulation relationship of key genes and it revealed the correlation between 3 key genes and top the 20 genetic factors involved in DKD. Finally, the expression of key genes between patients with 35 DKD and 35 healthy controls were examined by ELISA, and the relationship between the development of DKD rate and hub gene plasma levels was assessed in a cohort of 35 DKD patients. In addition, we carried out immunohistochemistry and western blot to verify the expression of three key genes in the kidney tissue samples we obtained.ResultsThere were 8 cell types between DKD and the control group, and the number of connections between macrophages and other cells was higher than that of the other seven cell groups. We identified 356 different expression genes (DEGs) from the RNA-seq, which are enriched in urogenital system development, kidney development, platelet alpha granule, and glycosaminoglycan binding pathways. And WGCNA was conducted to construct 13 gene modules. The highest correlations module is related to the regulation of cell adhesion, positive regulation of locomotion, PI3K-Akt, gamma response, epithelial-mesenchymal transition, and E2F target signaling pathway. Then we overlapped the DEGs, WGCNA, and scRNA-seq, SLIT3, PDE1A and CFH were screened as the closely related genes to DKD. In addition, the findings of immunological infiltration revealed a remarkable positive link between T cells gamma delta, Macrophages M2, resting mast cells, and the three critical genes SLIT3, PDE1A, and CFH. Neutrophils were considerably negatively connected with the three key genes. Comparatively to healthy controls, DKD patients showed high levels of SLIT3, PDE1A, and CFH. Despite this, higher SLIT3, PDE1A, and CFH were associated with an end point rate based on a median follow-up of 2.6 years. And with the gradual deterioration of DKD, the expression of SLIT3, PDE1A, and CFH gradually increased.ConclusionsThe 3 immune-associated genes could be used as diagnostic markers and therapeutic targets of DKD. Additionally, we found new pathogenic mechanisms associated with immune cells in DKD, which might lead to therapeutic targets against these cells.

Identification of necroptosis-related diagnostic biomarkers in coronary heart disease

“…Based on the defined significance threshold: |log 2 fold change (FC)| > 0 and P < 0.05, CHD-related DEGs were confirmed by R package limma (version 3.50.3) [ 18 ] in the CHD group (n = 99) vs. healthy control group (n = 99). The volcano map was drawn with the ggplot2 package (version 3.3.6) [ 19 ], and employed the pheatmap package (version 1.0.12) [ 20 ] to generate the heatmap.…”

Crosstalk among podocytes, glomerular endothelial cells and mesangial cells in diabetic kidney disease: an updated review

“…5) Relying on the affymetrix microarray dataset to infer the pathogenesis of diabetes nephropathy, and the human transcriptome data is limited. [ 204 , 205 ] CellChat: a tool that can quantitatively infer and analyze cellular interaction networks from scRNA-seq data and performs well in predicting stronger interactions, which helps to narrow the range of interactions for further experimental validation. It has been applied to discover the dysfunctional signaling and metabolic pathways in the thin endometrium, which provides insights into the mechanisms and treatment strategies of atrophic endometrium; [ 206 , 207 ] ICELLNET: a global, versatile, biologically validated, and easy-to-use framework to analyze cell crosstalk from individual or multiple cell-based transcriptomic profiles.…”

“…CellPhoneDB, a novel repository of ligands, receptors, and their interactions, offers a more accurate representation of heterogeneous complexes compared to other repositories. It has been employed to analyze scRNA-seq data from a public dataset to discern the cell-cell crosstalk networks in DKD [ 204 ]. Researchers have provided a step-by-step guide for implementing the CellPhoneDB protocol, which allows for the inference of cellular crosstalk networks from scRNA-seq data [ 205 ].…”

The Role of Immune Cells in DKD: Mechanisms and Targeted Therapies

“…Correlation analysis revealed that both were associated with active DC, CD8 + T cells, CD4 + T effector memory, CD8 + T effector memory, and mast cells. 135 Xueqin Zhang et al 136 identified SLIT3, PDE1A , and CFH as hub genes closely associated with DKD, which are significantly positively correlated with γδ T cell, M2 macrophage, and resting mast cell levels. Mingming Xu et al 137 identified three immunological and oxidative stress-related hub genes ( CD36, SLC1A3 , and ITGB2 ) by merging weighted gene co-expression network analysis (WGCNA), protein–protein interaction (PPI) networks, and machine learning data; however, because their results were based solely on bioinformatics analysis, with no basic experiments to validate them, it is unclear whether the conclusions are truly valid.…”