背景:马兜铃酸肾病(AAN)是由马兜铃酸(AA)引起的快速进行性间质性肾病。AAN与肾病和尿路上皮癌的发展有关。据估计,全球有超过1亿人面临发展AAN的风险。然而,驱动AAN肾脏恶化的潜在机制仍然知之甚少,治疗选择有限。
方法:我们从与AAN相关的基因表达综合(GEO)获得了GSE27168和GSE136276系列矩阵数据。使用RStudio环境,我们应用limma包和WGCNA包来鉴定共差异表达的基因(co-DEGs)。通过GO/KEGG/GSVA分析,我们揭示了共同的生物学途径。随后,将co-DEG置于String数据库中以构建蛋白质-蛋白质相互作用(PPI)网络。在Cytohubba插件中实现的MCC算法用于识别hub基因。将hub基因与转录因子(TF)数据库交叉引用以鉴定hubTF。利用CIBERSORT进行免疫浸润分析以鉴定关键免疫细胞群。在体内和体外验证了AAN相关hubTF的表达。最后,对两种TFs进行siRNA干预以验证其在AAN中的调节作用。
结果:我们的分析通过\"limma\"和\"WGCNA\"R包确定了88个共同DEG。构建了一个包含53个节点和34条边的PPI网络,其置信水平>0.4。ATF3和c-JUN被确定为可能与AAN相关的枢纽TFs。此外,ATF3和c-JUN的表达与单核细胞呈正相关,嗜碱性粒细胞,和船只,与嗜酸性粒细胞和内皮细胞呈负相关。我们观察到这两种hubTF的蛋白质和mRNA水平显着增加。此外,发现靶向ATF3而非c-JUN的siRNA干预,减轻AA诱导的细胞损伤。在AAN细胞模型中,ATF3的敲低保护免受氧化应激和炎症。
结论:这项研究为ATF3在AAN中的作用提供了新的见解。综合分析揭示了分子机制,并确定了AAN治疗的潜在生物标志物和药物靶标。
BACKGROUND: Aristolochic acid nephropathy (AAN) is a rapidly progressive interstitial nephropathy caused by Aristolochic acid (AA). AAN is associated with the development of nephropathy and urothelial carcinoma. It is estimated that more than 100 million people worldwide are at risk of developing AAN. However, the underlying mechanisms driving renal deterioration in AAN remain poorly understood, and the treatment options are limited.
METHODS: We obtained GSE27168 and GSE136276 series matrix data from the Gene Expression Omnibus (GEO) related to AAN. Using the R Studio environment, we applied the limma package and WGCNA package to identify co-differently expressed genes (co-DEGs). By GO/KEGG/GSVA analysis, we revealed common biological pathways. Subsequently, co-DEGs were subjected to the String database to construct a protein-protein interaction (PPI) network. The MCC algorithms implemented in the Cytohubba plugin were employed to identify hub genes. The hub genes were cross-referenced with the transcription factor (TF) database to identify hub TFs. Immune infiltration analysis was performed to identify key immune cell groups by utilizing CIBERSORT. The expressions of AAN-associated hub TFs were verified in vivo and in vitro. Finally, siRNA intervention was performed on the two TFs to verify their regulatory effect in AAN.
RESULTS: Our analysis identified 88 co-DEGs through the \"limma\" and \"WGCNA\" R packages. A PPI network comprising 53 nodes and 34 edges was constructed with a confidence level >0.4. ATF3 and c-JUN were identified as hub TFs potentially linked to AAN. Additionally, expressions of ATF3 and c-JUN positively correlated with monocytes, basophils, and vessels, and negatively correlated with eosinophils and endothelial cells. We observed a significant increase in protein and mRNA levels of these two hub TFs. Furthermore, it was found that siRNA intervention targeting ATF3, but not c-JUN, alleviated cell damage induced by AA. The knockdown of ATF3 protects against oxidative stress and inflammation in the AAN cell model.
CONCLUSIONS: This study provides novel insights into the role of ATF3 in AAN. The comprehensive analysis sheds light on the molecular mechanisms and identifies potential biomarkers and drug targets for AAN treatment.