Abstract：
Acute kidney injury (AKI) accounts for up to 29% of severe COVID-19 cases and increases mortality among these patients. Viral infections participate in the pathogenesis of diseases by changing the expression profile of normal transcriptome. This study attempts to identify LncRNA-miRNA-gene and TF-gene networks as gene expression regulating networks in the kidney tissues of COVID-19 patients.
In this analysis, four kidney libraries from the GEO repository were considered. To conduct the preprocessing, Deseq2 software in R was used for the purpose of data normalization and log2 transformation. In addition, pre- and post-normalization, PCA and box plots were developed using ggplot2 software in R for quality control. The expression profiles of the kidney samples of COVID-19 patients and control individuals were compared using DEseq2 software in R. The considered significance thresholds for DEGs were Adj P value < 0.05 and |logFC| >2. Then, to predict molecular interactions in lncRNA-miRNA-gene networks, different databases, including DeepBase v3.0, miRNATissueAtlas2, DIANA-LncBase v3, and miRWalk, were used. Furthermore, by employing ChEA databases, interactions at the TF-Gene level were obtained. Finally, the obtained networks were plotted using Stringdb and Cytoscape v8.
Results obtained from the comparison of the post-mortem kidney tissue samples of the COVID-19 patients with the healthy kidney tissue samples showed significant changes in the expression of more than 2000 genes. In addition, predictions regarding the miRNA-gene interaction network based on DEGs obtained from this meta-analysis showed that 11 miRNAs targeted the obtained DEGs. Interestingly, in the kidney tissue, these 11 miRNAs interacted with LINC01874, LINC01788, and LINC01320, which have high specificity for this tissue. Moreover, four transcription factors of EGR1, SMAD4, STAT3, and CHD1 were identified as key transcription factors regulating DEGs. Taken together, the current study showed several dysregulated genes in the kidney of patients affected with COVID-19.
This study suggests lncRNA-miRNA-gene networks and key TFs as new diagnostic and therapeutic targets for experimental and preclinical studies.
In this analysis, four kidney libraries from the GEO repository were considered. To conduct the preprocessing, Deseq2 software in R was used for the purpose of data normalization and log2 transformation. In addition, pre- and post-normalization, PCA and box plots were developed using ggplot2 software in R for quality control. The expression profiles of the kidney samples of COVID-19 patients and control individuals were compared using DEseq2 software in R. The considered significance thresholds for DEGs were Adj P value < 0.05 and |logFC| >2. Then, to predict molecular interactions in lncRNA-miRNA-gene networks, different databases, including DeepBase v3.0, miRNATissueAtlas2, DIANA-LncBase v3, and miRWalk, were used. Furthermore, by employing ChEA databases, interactions at the TF-Gene level were obtained. Finally, the obtained networks were plotted using Stringdb and Cytoscape v8.
Results obtained from the comparison of the post-mortem kidney tissue samples of the COVID-19 patients with the healthy kidney tissue samples showed significant changes in the expression of more than 2000 genes. In addition, predictions regarding the miRNA-gene interaction network based on DEGs obtained from this meta-analysis showed that 11 miRNAs targeted the obtained DEGs. Interestingly, in the kidney tissue, these 11 miRNAs interacted with LINC01874, LINC01788, and LINC01320, which have high specificity for this tissue. Moreover, four transcription factors of EGR1, SMAD4, STAT3, and CHD1 were identified as key transcription factors regulating DEGs. Taken together, the current study showed several dysregulated genes in the kidney of patients affected with COVID-19.
This study suggests lncRNA-miRNA-gene networks and key TFs as new diagnostic and therapeutic targets for experimental and preclinical studies.
摘要：
目的：急性肾损伤（AKI）占严重COVID-19病例的29%，并增加这些患者的死亡率。病毒感染通过改变正常转录组的表达谱参与疾病的发病机制。本研究试图确定LncRNA-miRNA-基因和TF-基因网络是所有COVID-19患者肾组织中的基因表达调控网络。
方法：在此分析中，考虑了来自GEO存储库的四个肾脏库。进行预处理，将R中的Deseq2软件用于数据归一化和log2转换的目的。此外,标准化前和标准化后，使用R中的ggplot2软件开发PCA和箱线图用于质量控制。使用DEseq2软件在R中比较了COVID-19患者和对照组肾脏样本的表达谱。DEGs的考虑显著性阈值为AdjP值<0.05和|logFC|>2。然后，预测lncRNA-miRNA-基因网络中的分子相互作用，不同的数据库，包括DeepBasev3.0、miRNATissueAtlas2、DIANA-LncBasev3和miRWalk，被使用。此外，通过使用ChEA数据库，获得TF-基因水平的相互作用。最后,使用Stringdb和Cytoscapev8绘制获得的网络。
结果：COVID-19患者验尸后肾组织样本与健康肾组织样本的比较结果显示，超过2000个基因的表达发生了显着变化。此外,基于从该荟萃分析获得的DEGs对miRNA-基因相互作用网络的预测显示,11种miRNA靶向获得的DEGs.有趣的是,在肾脏组织中，这11种miRNA与LINC01874、LINC01788和LINC01320相互作用,对该组织具有高度特异性。此外，EGR1、SMAD4、STAT3和CHD14个转录因子被确定为调节DEGs的关键转录因子。一起来看,目前的研究表明，COVID-19患者的肾脏中有几个基因失调。
结论：这项研究表明lncRNA-miRNA-基因网络和关键TFs是实验和临床前研究的新诊断和治疗靶标。
方法：在此分析中，考虑了来自GEO存储库的四个肾脏库。进行预处理，将R中的Deseq2软件用于数据归一化和log2转换的目的。此外,标准化前和标准化后，使用R中的ggplot2软件开发PCA和箱线图用于质量控制。使用DEseq2软件在R中比较了COVID-19患者和对照组肾脏样本的表达谱。DEGs的考虑显著性阈值为AdjP值<0.05和|logFC|>2。然后，预测lncRNA-miRNA-基因网络中的分子相互作用，不同的数据库，包括DeepBasev3.0、miRNATissueAtlas2、DIANA-LncBasev3和miRWalk，被使用。此外，通过使用ChEA数据库，获得TF-基因水平的相互作用。最后,使用Stringdb和Cytoscapev8绘制获得的网络。
结果：COVID-19患者验尸后肾组织样本与健康肾组织样本的比较结果显示，超过2000个基因的表达发生了显着变化。此外,基于从该荟萃分析获得的DEGs对miRNA-基因相互作用网络的预测显示,11种miRNA靶向获得的DEGs.有趣的是,在肾脏组织中，这11种miRNA与LINC01874、LINC01788和LINC01320相互作用,对该组织具有高度特异性。此外，EGR1、SMAD4、STAT3和CHD14个转录因子被确定为调节DEGs的关键转录因子。一起来看,目前的研究表明，COVID-19患者的肾脏中有几个基因失调。
结论：这项研究表明lncRNA-miRNA-基因网络和关键TFs是实验和临床前研究的新诊断和治疗靶标。
