Le système du complément

Daugan, Marie V.; Noé, Rémi; Fridman, Wolf Herman; Sautès-Fridman, Catheriné; Roumenina, Lubka T.

doi:10.1051/medsci/20173310019

Cited by 6 publications

(2 citation statements)

References 41 publications

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“…C1q associates with the proenzymes C1r and C1s to yield C1, the first component of the serum complement system ( Reid, 2018 ). In the blood plasma, the complement system is a proteolytic cascade that mediates innate immunity, a non-specific defense mechanism to pathogens ( Daugan et al, 2017 ). The main consequences of complement activation are the opsonization of pathogens, recruitment of inflammatory and immunocompetent cells, and direct killing of pathogens.…”

Section: Discussionmentioning

confidence: 99%

Identifying C1QB, ITGAM, and ITGB2 as potential diagnostic candidate genes for diabetic nephropathy using bioinformatics analysis

Yang

Dong

et al. 2023

PeerJ

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Background Diabetic nephropathy (DN), the most intractable complication in diabetes patients, can lead to proteinuria and progressive reduction of glomerular filtration rate (GFR), which seriously affects the quality of life of patients and is associated with high mortality. However, the lack of accurate key candidate genes makes diagnosis of DN very difficult. This study aimed to identify new potential candidate genes for DN using bioinformatics, and elucidated the mechanism of DN at the cellular transcriptional level. Methods The microarray dataset GSE30529 was downloaded from the Gene Expression Omnibus Database (GEO), and the differentially expressed genes (DEGs) were screened by R software. We used Gene Ontology (GO), gene set enrichment analysis (GSEA), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to identify the signal pathways and genes. Protein-protein interaction (PPI) networks were constructed using the STRING database. The GSE30122 dataset was selected as the validation set. Receiver operating characteristic (ROC) curves were applied to evaluate the predictive value of genes. An area under curve (AUC) greater than 0.85 was considered to be of high diagnostic value. Several online databases were used to predict miRNAs and transcription factors (TFs) capable of binding hub genes. Cytoscape was used for constructing a miRNA-mRNA-TF network. The online database ‘nephroseq’ predicted the correlation between genes and kidney function. The serum level of creatinine, BUN, and albumin, and the urinary protein/creatinine ratio of the DN rat model were detected. The expression of hub genes was further verified through qPCR. Data were analyzed statistically using Student’s t-test by the ‘ggpubr’ package. Results A total of 463 DEGs were identified from GSE30529. According to enrichment analysis, DEGs were mainly enriched in the immune response, coagulation cascades, and cytokine signaling pathways. Twenty hub genes with the highest connectivity and several gene cluster modules were ensured using Cytoscape. Five high diagnostic hub genes were selected and verified by GSE30122. The MiRNA-mRNA-TF network suggested a potential RNA regulatory relationship. Hub gene expression was positively correlated with kidney injury. The level of serum creatinine and BUN in the DN group was higher than in the control group (unpaired t test, t = 3.391, df = 4, p = 0.0275, r = 0.861). Meanwhile, the DN group had a higher urinary protein/creatinine ratio (unpaired t test, t = 17.23, df = 16, p < 0.001, r = 0.974). QPCR results showed that the potential candidate genes for DN diagnosis included C1QB, ITGAM, and ITGB2. Conclusions We identified C1QB, ITGAM and ITGB2 as potential candidate genes for DN diagnosis and therapy and provided insight into the mechanisms of DN development at transcriptome level. We further completed the construction of miRNA-mRNA-TF network to propose potential RNA regulatory pathways adjusting disease progression in DN.

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Section: Discussionmentioning

confidence: 99%

Identifying C1QB, ITGAM, and ITGB2 as potential diagnostic candidate genes for diabetic nephropathy using bioinformatics analysis

Yang

Dong

et al. 2023

PeerJ

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“…The complement system is a proteolytic cascade in the blood plasma and a mediator of innate immunity, a nonspecific defense mechanism against pathogens. 28 There are three pathways of complement activation: the classical pathway, the lectin pathway, and the alternative pathway. 29 All these pathways generate crucial enzymatic activity that, in turn, drives the activation of effector complement molecules.…”

Section: Discussionmentioning

confidence: 99%

Complement C7 is Specifically Expressed in Mesangial Cells and is a Potential Diagnostic Biomarker for Diabetic Nephropathy and is Regulated by miR-494-3p and miR-574-5p

Guo¹,

Yan²,

Hu³

et al. 2021

DMSO

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Background: Diabetic nephropathy (DN) is the leading cause of end-stage renal disease, but it remains relatively underdiagnosed. Objective: In this study, we aimed to explore the key regulatory pathways and potential biomarkers related to DN using integrated bioinformatics analysis and validation. Methods: First, the microarray data of the GSE30528 and GSE96804 datasets were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were screened. Then, weighted gene coexpression network analysis (WGCNA), gene ontology (GO) annotation, gene set enrichment analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to identify key pathways and genes. qRT-PCR and receiver operating characteristic (ROC) curves were used to validate our results. Furthermore, single-cell RNA sequencing (scRNA-seq) data were reanalyzed to investigate the expression specificity of C7 in DN cells. An online database search and luciferase reporter assay identified the target relationship between miRNAs and C7. Results: The "complement and coagulation cascades" were significantly enriched, and complement C3 and C7 were candidate markers. The receiver operating characteristic (ROC) curve revealed that C7 had significant diagnostic value (AUC=0.865) in DN. Through scRNA-seq reanalysis, we found that C7 was specifically elevated in mesangial (MES) cells of DN. Moreover, we found that the expression of C7 was regulated by miR-494-3p and miR-574-5p. Conclusion: This is the first study to reveal that C7 is specifically expressed in mesangial cells, is a potential diagnostic biomarker for diabetic nephropathy, and is regulated by miR-494-3p and miR-574-5p.

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Targeted Human Enzymes

2024

Enzymes and Drugs

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Le système du complément

Cited by 6 publications

References 41 publications

Identifying C1QB, ITGAM, and ITGB2 as potential diagnostic candidate genes for diabetic nephropathy using bioinformatics analysis

Identifying C1QB, ITGAM, and ITGB2 as potential diagnostic candidate genes for diabetic nephropathy using bioinformatics analysis

Complement C7 is Specifically Expressed in Mesangial Cells and is a Potential Diagnostic Biomarker for Diabetic Nephropathy and is Regulated by miR-494-3p and miR-574-5p

Targeted Human Enzymes

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