Acute kidney injury (AKI) is a serious and frequently observed disease associated with high morbidity and mortality. Weighted gene co-expression network analysis (WGCNA) is a research method that converts the relationship between tens of thousands of genes and phenotypes into the association between several gene sets and phenotypes. We screened potential target genes related to AKI through WGCNA to provide a reference for the diagnosis and treatment of AKI. Key biomolecules of AKI were investigated based on transcriptome analysis. RNA sequencing data from 39 kidney biopsy specimens of AKI patients and 9 normal subjects were downloaded from the GEO database. By WGCNA, the top 20% of mRNAs with the largest variance in the data matrix were used to construct a gene co-expression network with a p-value < 0.01 as a screening condition, showing that the blue module was most closely associated with AKI. Thirty-two candidate biomarker genes were screened according to the threshold values of |MM|≥0.86 and |GS|≥0.4, and PPI and enrichment analyses were performed. The top three genes with the most connected nodes, alanine—glyoxylate aminotransferase 2(AGXT2), serine hydroxymethyltransferase 1(SHMT1) and aconitase 2(ACO2), were selected as the central genes based on the PPI network. A rat AKI model was constructed, and the mRNA and protein expression levels of the central genes in the model and control groups were verified by PCR and immunohistochemistry experiments. The results showed that the relative mRNA expression and protein levels of AGXT2, SHMT1 and ACO2 showed a decrease in the model group. In conclusion, we inferred that there is a close association between AGXT2, SHMT1 and ACO2 genes and the development of AKI, and the down-regulation of their expression levels may induce AKI.
Background In recent years, peroxisome proliferator-activated receptor γ (PPARγ) has been found to be closely associated with hypoxia renal disease. The aim of this study was to investigate the relationship between rosiglitazone and mitochondrial apoptosis in renal tissue and its associated mechanisms. Methods Twenty-four male Sprague-Dawley rats were randomly divided into three groups ( n = 8 in each): normal control group, hypoxia injury group (equal volume of 0.9% saline), and PPARγ agonist group (Rosiglitazone, 10 mg/kg · d, intraperitoneally). The hypoxia injury group and PPARγ agonist group were placed in a hypoxia chamber and the simulated altitude was set at 7,000 m for 7 days. Blood and kidney samples were collected after 7 days. The quantitative real-time polymerase chain reaction and Western blot methods were used to determine the expression of PPARγ, nuclear factor kappa-B (NF-κB), B-cell lymphoma-2 (Bcl-2), and Bax. Results The results showed that compared with the normal control group, the renal tissue of rats after hypoxia was severely damaged, as shown by massive renal tubular epithelial cell degeneration and detachment, and renal tubular dilation. The NF-κB protein expression significantly increased, the Bcl-2 protein and mRNA expression significantly decreased, and Bax protein and mRNA expression significantly increased ( p < .05 for all). Renal injury was much less severe in the PPARγ agonist group compared to the hypoxia injury group. Conclusions Rosiglitazone can alleviate hypoxia renal injury, with the possible mechanism involving attenuation of apoptosis by inhibiting the activation of the NF-κB signaling pathway in a PPARγ-dependent manner and increasing Bcl-2 and decreasing Bax expression.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.