Identification of potential therapeutic target genes in mouse mesangial cells associated with diabetic nephropathy using bioinformatics analysis

Mou, Xin; Zhou, Di; Liu, Ying Hui; Liu, Kaiyuan; Zhou, Danyang

doi:10.3892/etm.2019.7524

Cited by 4 publications

(4 citation statements)

References 54 publications

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“…( 42 ) indicated that CFH-associated types of mesangial proliferative glomerulonephritis were seen in end-stage kidney disease caused by hypertension or glomerulosclerosis, and fosinopril with CFH demonstrated preferred binding activity. Thus, CFH might be a target for the treatment of mesangial cells associated with DKD ( 43 ). Further results of GO and KEGG indicated that the gene is enriched in peroxisome, focal adhesion and lysine degradation pathways.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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“…Among these core genes, some have been shown to play an important role in the pathogenesis of DN, C3 ( 52 , 53 ), ALB ( 54 – 56 ), EGF ( 57 ), EGR1 ( 58 – 61 ), COL1A2 ( 62 ), FN1 ( 63 , 64 ), CD44 ( 65 , 66 ), FOS ( 67 ), PLG ( 68 , 69 ), and DUSP1 ( 70 ). It is well-known that inflammation and fibrosis play an important role in the pathogenesis of the DN glomerulus ( 71 ).…”

Section: Discussionmentioning

confidence: 99%

“…As described before, some of those factors can recruit and active immune cells ( 124 ), and those active immune cells will damage glomerular cells. Some of these factors even can damage glomerular cells directly, such as TGF-β ( 125 ), TNF-α ( 126 ), ILs ( 127 ), and FN1 ( 63 ).…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics Analysis Reveals Crosstalk Among Platelets, Immune Cells, and the Glomerulus That May Play an Important Role in the Development of Diabetic Nephropathy

et al. 2021

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Diabetic nephropathy (DN) is the main cause of end stage renal disease (ESRD). Glomerulus damage is one of the primary pathological changes in DN. To reveal the gene expression alteration in the glomerulus involved in DN development, we screened the Gene Expression Omnibus (GEO) database up to December 2020. Eleven gene expression datasets about gene expression of the human DN glomerulus and its control were downloaded for further bioinformatics analysis. By using R language, all expression data were extracted and were further cross-platform normalized by Shambhala. Differentially expressed genes (DEGs) were identified by Student's t-test coupled with false discovery rate (FDR) (P < 0.05) and fold change (FC) ≥1.5. DEGs were further analyzed by the Database for Annotation, Visualization, and Integrated Discovery (DAVID) to enrich the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. We further constructed a protein-protein interaction (PPI) network of DEGs to identify the core genes. We used digital cytometry software CIBERSORTx to analyze the infiltration of immune cells in DN. A total of 578 genes were identified as DEGs in this study. Thirteen were identified as core genes, in which LYZ, LUM, and THBS2 were seldom linked with DN. Based on the result of GO, KEGG enrichment, and CIBERSORTx immune cells infiltration analysis, we hypothesize that positive feedback may form among the glomerulus, platelets, and immune cells. This vicious cycle may damage the glomerulus persistently even after the initial high glucose damage was removed. Studying the genes and pathway reported in this study may shed light on new knowledge of DN pathogenesis.

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“…Reanalyzing available public data can provide valuable clues for new research. To date, many studies have screened numerous Differentially Expressed Genes (DEGs) involved in DN [26][27][28][29][30][31]. However, the heterogeneity of experimental samples in independent studies and the application of different detection platforms and different processing methods can lead to inconsistent results.The Robust Rank Aggreg (RRA) method is suitable for comparing several sequenced gene lists based on the assumption that each gene in each dataset is randomly arranged [32].…”

Section: Introductionmentioning

confidence: 99%

Bioinformatics Facilitates the use of Microarrays to Identify Potential Markers in Diabetic Nephropathy

2022

J Urol Ren Dis

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Background: Diabetic Nephropathy (DN) is the leading cause of end-stage renal disease worldwide. Extensive studies have been performed to elucidate the underlying mechanisms of DN, which still need to be clarified. The identification of key biomarkers using integrated bioinformatics could provide a certain theoretical foundation for future research and provide experimental direction for subsequent experimental verification.Methods: GSE1009, GSE30528, and GSE96804 were downloaded from the Gene Expression Omnibus (GEO) database to screen Differentially Expressed Genes (DEGs) between normal renal tissue and DN renal tissue by using the limma package. Then, the Robust Rank Aggreg (RRA) method was used to integrate and analyze the three datasets to obtain integrated DEGs. Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to determine the molecular mechanisms of integrated DEGs involved in the progression of DN. A Protein-Protein Interaction (PPI) network of integrated DEGs was constructed via the STRING database, PPI network visualization and module analyses were performed by using Cytoscape software, and the hub genes in the PPI network were selected by topological analysis. Finally, the Nephroseq v5 online platform was utilized to explore the correlation between hub genes and clinical features of DN Results: In total, 249 integrated DEGs, including 191 upregulated genes and 58 downregulated genes, were identified and enriched in pathways involved in several functions and expression pathways, such as extracellular matrix,complement and coagulation cascades, focal adhesion, ECM-receptor interactions, cytokine-cytokine receptor interaction, the renin-angiotensin system, and chemokine signaling pathways. The top 10 hub genes identified from the PPI network were ALB, FN1, VEGFA, IGF1, JUN, FOS, CTGF, C3, COL1A2, and CLU. In addition, a KEGG pathway analysis of the top 2 modules identified from the PPI network revealed that Module 1 was mainly involved in focal adhesion and ECM-receptor interactions, while Module 2 was mainly involved in cytokine-cytokine receptor interactions, the chemokine signaling pathway, the Toll-like receptor signaling pathway, and the TGF-beta signaling pathway.Correlation and subgroup analyses of 10 hub genes and the clinical characteristics of DN indicated that ALB, FN1, VEGFA, IGF1, JUN, FOS, CTGF, C3, COL1A2, and CLU may participate in the development of DN. Conclusions:The identification of hub genes may be a key biomarker for early DN diagnosis and targeted treatment.

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Identification of potential therapeutic target genes in mouse mesangial cells associated with diabetic nephropathy using bioinformatics analysis

Cited by 4 publications

References 54 publications

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

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

Bioinformatics Analysis Reveals Crosstalk Among Platelets, Immune Cells, and the Glomerulus That May Play an Important Role in the Development of Diabetic Nephropathy

Bioinformatics Facilitates the use of Microarrays to Identify Potential Markers in Diabetic Nephropathy

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