Integrated Bioinformatics and Clinical Correlation Analysis of Key Genes, Pathways, and Potential Therapeutic Agents Related to Diabetic Nephropathy

Chen, Shengnan; Chen, Lei; Jiang, Hongli

doi:10.1155/2022/9204201

Cited by 6 publications

(6 citation statements)

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“…In addition, NPHS1 is an essential slit diaphragm protein and plays and important role in the development and function of glomerular filtration barrier. Jiang et al reported that the abundance of NPHS1 was downregulated in DN and significantly positively correlated with eGFR. , In addition, the integrated analysis of mRNA expression profiles and clinical characteristics have identified many hub genes underlying DN, including the ECM components of COL1A1, FN1, COL6A3, COL1A2, THBS2, CD44, TNC, LUM, and POSTN, and the complement-related genes of C1q, C3, C7, CLU, and CFH, − and these hub genes have also been proved to play crucial functions in the pathological process of DN at protein levels. − …”

Section: Discussionmentioning

confidence: 99%

“…Jiang et al reported that the abundance of NPHS1 was downregulated in DN and significantly positively correlated with eGFR. 25,26 In addition, the integrated analysis of mRNA expression profiles and clinical characteristics have identified many hub genes underlying DN, including the ECM components of COL1A1, FN1, COL6A3, COL1A2, THBS2, CD44, TNC, LUM, and POSTN, and the complement-related genes of C1q, C3, C7, CLU, and CFH, 27−29 and these hub genes have also been proved to play crucial functions in the pathological process of DN at protein levels. 30−33 The other 788 differentially expressed mRNA-protein pairs (70%, 788/1152) showed poor or even negative correlation, which indicated that there were complex regulatory mechanisms in DN to controlling the abundance of mRNA and protein, including mRNA stability and decay, translation efficiency, and protein degradation, particularly post-transcriptional regulation.…”

Section: ■ Discussionmentioning

confidence: 99%

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The Glomerulus Multiomics Analysis Provides Deeper Insights into Diabetic Nephropathy

Zhao,

Cheng,

Zhan

et al. 2023

J. Proteome Res.

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Although diabetic nephropathy (DN) is the leading cause of the end-stage renal disease, the exact regulation mechanisms remain unknown. In this study, we integrated the transcriptomics and proteomics profiles of glomeruli isolated from 50 biopsy-proven DN patients and 25 controls to investigate the latest findings about DN pathogenesis. First, 1152 genes exhibited differential expression at the mRNA or protein level, and 364 showed significant association. These strong correlated genes were divided into four different functional modules. Moreover, a regulatory network of the transcription factors (TFs)−target genes (TGs) was constructed, with 30 TFs upregulated at the protein levels and 265 downstream TGs differentially expressed at the mRNA levels. These TFs are the integration centers of several signal transduction pathways and have tremendous therapeutic potential for regulating the aberrant production of TGs and the pathological process of DN. Furthermore, 29 new DNspecific splice-junction peptides were discovered with high confidence; these peptides may play novel functions in the pathological course of DN. So, our in-depth integrative transcriptomicsproteomics analysis provided deeper insights into the pathogenesis of DN and opened the potential avenue for finding new therapeutic interventions. MS raw files were deposited into the proteomeXchange with the dataset identifier PXD040617.

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Section: Discussionmentioning

confidence: 99%

Section: ■ Discussionmentioning

confidence: 99%

The Glomerulus Multiomics Analysis Provides Deeper Insights into Diabetic Nephropathy

Zhao,

Cheng,

Zhan

et al. 2023

J. Proteome Res.

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“…In our model, FF age-independently elevated the renal levels of VEGFA protein, despite decreased Vegfa mRNA after low-dose FF, suggestive of post-transcriptional mechanisms of regulation. Using bioinformatics tools, FF has been previously predicted to increase VEGFA expression in diabetic nephropathy [ 39 ], while the drug has been shown to increase VEGFA levels in the visceral adipose tissue of FF-treated normolipemic rabbits [ 40 ]. Moreover, in both young and old rats in our model, low-dose FF inhibited the expression of Serpine1 , activated by HIF-1 under hypoxic conditions [ 15 ].…”

Section: Discussionmentioning

confidence: 99%

High-Dose Fenofibrate Stimulates Multiple Cellular Stress Pathways in the Kidney of Old Rats

Wrońska,

Kieżun,

Kmieć

2024

IJMS

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We investigated the age-related effects of the lipid-lowering drug fenofibrate on renal stress-associated effectors. Young and old rats were fed standard chow with 0.1% or 0.5% fenofibrate. The kidney cortex tissue structure showed typical aging-related changes. In old rats, 0.1% fenofibrate reduced the thickening of basement membranes, but 0.5% fenofibrate exacerbated interstitial fibrosis. The PCR array for stress and toxicity-related targets showed that 0.1% fenofibrate mildly downregulated, whereas 0.5% upregulated multiple genes. In young rats, 0.1% fenofibrate increased some antioxidant genes’ expression and decreased the immunoreactivity of oxidative stress marker 4-HNE. However, the activation of cellular antioxidant defenses was impaired in old rats. Fenofibrate modulated the expression of factors involved in hypoxia and osmotic stress signaling similarly in both age groups. Inflammatory response genes were variably modulated in the young rats, whereas old animals presented elevated expression of proinflammatory genes and TNFα immunoreactivity after 0.5% fenofibrate. In old rats, 0.1% fenofibrate more prominently than in young animals induced phospho-AMPK and PGC1α levels, and upregulated fatty acid oxidation genes. Our results show divergent effects of fenofibrate in young and old rat kidneys. The activation of multiple stress-associated effectors by high-dose fenofibrate in the aged kidney warrants caution when applying fenofibrate therapy to the elderly.

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“…In addition, the database used in this work is commonly utilized in other bioinformatics studies. Some scholars have identified VEGFA, NPHs1, WT1, CTGF, SYNPO and POD XL as promising biomarkers to diagnose DKD using GSE30528 and GSE1009 databases ( 44 ). Others used GSE30528 database and WGCNA method to identify six key genes related to DKD ( 22 ).…”

Section: Discussionmentioning

confidence: 99%

“…There has been evidence that changes in the HNF1B gene contribute to a slight predisposition to type 2 diabetes, Possibly caused by mutations in the HNFB1 gene that create a protein that is incapable of binding DNA or fails to transactivate DNA following binding ( 71 ). It is clear from these evidences that key genes are important in the pathogenesis of DKD, as well as potential diagnostic and therapeutic targets ( 44 , 72 , 73 ).…”

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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Integrated Bioinformatics and Clinical Correlation Analysis of Key Genes, Pathways, and Potential Therapeutic Agents Related to Diabetic Nephropathy

Cited by 6 publications

References 50 publications

The Glomerulus Multiomics Analysis Provides Deeper Insights into Diabetic Nephropathy

The Glomerulus Multiomics Analysis Provides Deeper Insights into Diabetic Nephropathy

High-Dose Fenofibrate Stimulates Multiple Cellular Stress Pathways in the Kidney of Old Rats

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

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