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

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

Xueqin Zhang et al. Front Immunol. 2023.

Abstract

Background: There 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.

Methods: Here, 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.

Results: There 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.

Conclusions: The 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.

Keywords: WGCNA (weighted gene co- expression network analyses); diabetic kidney disease (DKD); diagnostic markers; immune cells; single-cell RNA and transcriptome sequencing.

Copyright © 2023 Zhang, Chao, Zhang, Xu, Cui, Wang, Wusiman, Jiang and Lu.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1

Figure 1

Workflow of this study.

Figure 2

Figure 2

A coexpression module has been constructed by analyzing WGCNA and Metascape functional enrichment scores for MEblue genes. (A) Clustering of module hub genes in a hierarchical manner that summarizes the modules that were identified in the clustering analysis. (B) The scale independence plot, mean connectivity plot, and scale-free topology plots, 4 was an appropriate soft-power. (C) The cluster dendrogram shows the modules that make up the co-expression network. (D) Analysis of connection of the modules with immune scores. (E) GO and KEGG analysis of the model genes. (F) Differential pathway enrichment between DKD and control.

Figure 3

Figure 3

Cluster analysis of single cell sample subtypes. (A) Reduced maintenance number of main genes of PC1, PC2, PC3 and PC4. (B) The scores of cell genes on PC1, PC2, PC3 and PC4. (C) Elbowplot for identifying the optimal PCs. (D) TSNE dimensionality reduction of 20 genes in the samples. (E) Heat map of gene expression.

Figure 4

Figure 4

Annotation of cluster subtypes (A) 29 clusters were annotated to 8 cell categories: Stromal cells, Endothelial cells, NKT, B cells, Epithelial cells, Neutrophils, DC and Macrophages middle. (B) Samples in each cell subtype.

Figure 5

Figure 5

Analysis of receptor-ligand relationship pairs. (A) Receptor-ligand trafficking and intracellular signaling. (B) Interaction numbers between cell groups. (C) The number of ligand-receptor gene pairs corresponding to each cell group.

Figure 6

Figure 6

Functional analysis of different expression genes in RNA-sequencing. (A) Five GEO data sets were combined into expression profiles of 122 samples by ComBat. (B) Combat PCA for combined expression profile. (C) Volcano plot displaying differential expressed genes (DEGs) between DKD patients and healthy control for combined expression profile. (D) Gene Ontology plots of over-expressed and under-expressed terms. (E) Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis.

Figure 7

Figure 7

Screening and verification of key genes. (A) The Venn diagram displays the overlap genes obtained by three methods. (B) Box plots of the level of SLIT3, PDE1A and CFH in DKD and control samples. (C) The expression distribution of marker genes. (D) Violin plots showing the level of SLIT3, PDE1A and CFH in different identity. (E) Dot plot displaying the normalized mean level of markers. **P<0.01; ****P<0.0001.

Figure 8

Figure 8

Distribution and visualization of immune cell infiltration. (A) The relative percentage of 22 types of immune cells. (B) Box plots demonstrating 22 immune cell subtypes between DKD and healthy controls. Blue represents normal and yellow represents DKD samples. (C) The heat map demonstrated the interaction of 21 kinds of immune cells. Red showed the positive relation and blue displayed the negative relation, The correlation parameter was shown with the number. (D) GO analysis of three genes. *P<0.05; **P<0.01; ***P<0.0001.

Figure 9

Figure 9

Correlation analysis of key genes and immune infiltration.(A) Interaction between core genes and immune cells. (B) Interaction between the level of core genes and immune cells abundance. (C) Connection between core genes and immunomodulators, chemokines and cell receptors. *P<0.05; **P<0.01; ***P<0.0001.

Figure 10

Figure 10

Discussion on the specific signaling mechanism of key genes. (A) GO and KEGG analysis of CFH gene using GSEA method. (B) GO and KEGG analysis of PDE1A gene using GSEA method. (C) GO and KEGG analysis of SLIT3 gene using GSEA method.

Figure 11

Figure 11

Analysis of regulatory network of key genes. (A) Motif-TF annotation based on normalized enrichment score. (B) Optimal gene based on motif enrichment. (C) Motif enrichment and its annotation information.

Figure 12

Figure 12

Connection between the level of key genes and several DKD-related genes. (A) Box plots displaying the expression of the expression of the top 20 genes related to DKD. (B) Interaction between the level of key genes and the expression of several DKD-related genes. *P<0.05; ***P<0.0001; ****P<0.0001.

Figure 13

Figure 13

Verification of key genes in clinical population. (A) Expression levels of key genes in plasma samples of DKD patients and healthy control. (B) Kaplan-Meier chart to display the association between the key genes and DKD development rate of different expression level of the 3 genes.

Figure 14

Figure 14

Verification of key genes in clinical participants. (A) Immunohistochemistry for control and different stages of DKD participants. (B) Quantitative results of immunohistochemistry. (C) Western blotting for control and DKD participants.

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