Single-cell RNA and transcriptome sequencing profiles identify immune-associated key genes in the development of diabetic kidney disease
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 sequencin...
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1030198/full |
| _version_ | 1797857573835112448 |
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| author | Xueqin Zhang Peng Chao Lei Zhang Lin Xu Xinyue Cui Shanshan Wang Miiriban Wusiman Hong Jiang Hong Jiang Chen Lu Chen Lu |
| author_facet | Xueqin Zhang Peng Chao Lei Zhang Lin Xu Xinyue Cui Shanshan Wang Miiriban Wusiman Hong Jiang Hong Jiang Chen Lu Chen Lu |
| author_sort | Xueqin Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T20:58:58Z |
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| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-04-09T20:58:58Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Immunology |
| spelling | doaj.art-b741809c41444e8d90da8581e65da6a62023-03-29T12:53:37ZengFrontiers Media S.A.Frontiers in Immunology1664-32242023-03-011410.3389/fimmu.2023.10301981030198Single-cell RNA and transcriptome sequencing profiles identify immune-associated key genes in the development of diabetic kidney diseaseXueqin Zhang0Peng Chao1Lei Zhang2Lin Xu3Xinyue Cui4Shanshan Wang5Miiriban Wusiman6Hong Jiang7Hong Jiang8Chen Lu9Chen Lu10Department of Nephropathy, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumuqi, Xinjiang Uygur Autonomous Region, ChinaDepartment of Cardiology, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumuqi, Xinjiang Uygur Autonomous Region, ChinaDepartment of Endocrine, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumuqi, Xinjiang Uygur Autonomous Region, ChinaDepartment of Rheumatology Immunology, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumuqi, Xinjiang Uygur Autonomous Region, ChinaDepartment of Nephropathy, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumuqi, Xinjiang Uygur Autonomous Region, ChinaDepartment of Nephropathy, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumuqi, Xinjiang Uygur Autonomous Region, ChinaDepartment of Nephropathy, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumuqi, Xinjiang Uygur Autonomous Region, ChinaDepartment of Nephropathy, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumuqi, Xinjiang Uygur Autonomous Region, ChinaNephrology Clinical Research Center, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumuqi, Xinjiang Uygur Autonomous Region, ChinaNephrology Clinical Research Center, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumuqi, Xinjiang Uygur Autonomous Region, ChinaDepartment of Nephropathy, The First Affiliated Hospital of Xinjiang Medical University, Urumuqi, Xinjiang Uygur Autonomous Region, ChinaBackgroundThere 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.https://www.frontiersin.org/articles/10.3389/fimmu.2023.1030198/fulldiabetic kidney disease (DKD)single-cell RNA and transcriptome sequencingimmune cellsdiagnostic markersWGCNA (weighted gene co- expression network analyses) |
| spellingShingle | Xueqin Zhang Peng Chao Lei Zhang Lin Xu Xinyue Cui Shanshan Wang Miiriban Wusiman Hong Jiang Hong Jiang Chen Lu Chen Lu Single-cell RNA and transcriptome sequencing profiles identify immune-associated key genes in the development of diabetic kidney disease Frontiers in Immunology diabetic kidney disease (DKD) single-cell RNA and transcriptome sequencing immune cells diagnostic markers WGCNA (weighted gene co- expression network analyses) |
| title | Single-cell RNA and transcriptome sequencing profiles identify immune-associated key genes in the development of diabetic kidney disease |
| title_full | Single-cell RNA and transcriptome sequencing profiles identify immune-associated key genes in the development of diabetic kidney disease |
| title_fullStr | Single-cell RNA and transcriptome sequencing profiles identify immune-associated key genes in the development of diabetic kidney disease |
| title_full_unstemmed | Single-cell RNA and transcriptome sequencing profiles identify immune-associated key genes in the development of diabetic kidney disease |
| title_short | Single-cell RNA and transcriptome sequencing profiles identify immune-associated key genes in the development of diabetic kidney disease |
| title_sort | single cell rna and transcriptome sequencing profiles identify immune associated key genes in the development of diabetic kidney disease |
| topic | diabetic kidney disease (DKD) single-cell RNA and transcriptome sequencing immune cells diagnostic markers WGCNA (weighted gene co- expression network analyses) |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1030198/full |
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