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呼吸机诱导肺损伤小鼠模型的转录组全基因表达。

Transcriptome-Wide Gene Expression in a Murine Model of Ventilator-Induced Lung Injury.

机构信息

Department of Anesthesiology, East Hospital, Tongji University School of Medicine, Shanghai 200120, China.

Department of Anesthesiology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362000, China.

出版信息

Dis Markers. 2021 Apr 7;2021:5535890. doi: 10.1155/2021/5535890. eCollection 2021.

Abstract

BACKGROUND

Mechanical ventilation could lead to ventilator-induced lung injury (VILI), but its underlying pathogenesis remains largely unknown. In this study, we aimed to determine the genes which were highly correlated with VILI as well as their expressions and interactions by analyzing the differentially expressed genes (DEGs) between the VILI samples and controls.

METHODS

GSE11434 was downloaded from the gene expression omnibus (GEO) database, and DEGs were identified with GEO2R. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using DAVID. Next, we used the STRING tool to construct protein-protein interaction (PPI) network of the DEGs. Then, the hub genes and related modules were identified with the Cytoscape plugins: cytoHubba and MCODE. qRT-PCR was further used to validate the results in the GSE11434 dataset. We also applied gene set enrichment analysis (GSEA) to discern the gene sets that had a significant difference between the VILI group and the control. Hub genes were also subjected to analyses by CyTargetLinker and NetworkAnalyst to predict associated miRNAs and transcription factors (TFs). Besides, we used CIBERSORT to detect the contributions of different types of immune cells in lung tissues of mice in the VILI group. By using DrugBank, small molecular compounds that could potentially interact with hub genes were identified.

RESULTS

A total of 141 DEGs between the VILI group and the control were identified in GSE11434. Then, seven hub genes were identified and were validated by using qRT-PCR. Those seven hub genes were largely enriched in TLR and JAK-STAT signaling pathways. GSEA showed that VILI-associated genes were also enriched in NOD, antigen presentation, and chemokine pathways. We predicted the miRNAs and TFs associated with hub genes and constructed miRNA-TF-gene regulatory network. An analysis with CIBERSORT showed that the proportion of M0 macrophages and activated mast cells was higher in the VILI group than in the control. Small molecules, like nadroparin and siltuximab, could act as potential drugs for VILI.

CONCLUSION

In sum, a number of hub genes associated with VILI were identified and could provide novel insights into the pathogenesis of VILI and potential targets for its treatment.

摘要

背景

机械通气可导致呼吸机相关性肺损伤(VILI),但其潜在发病机制仍知之甚少。在这项研究中,我们旨在通过分析 VILI 样本与对照之间的差异表达基因(DEGs),确定与 VILI 高度相关的基因及其表达和相互作用。

方法

从基因表达综合数据库(GEO)下载 GSE11434,使用 GEO2R 识别 DEGs。使用 DAVID 进行基因本体论(GO)和京都基因与基因组百科全书(KEGG)通路富集分析。接下来,我们使用 STRING 工具构建 DEGs 的蛋白质-蛋白质相互作用(PPI)网络。然后,使用 Cytoscape 插件 cytoHubba 和 MCODE 识别枢纽基因和相关模块。进一步使用 qRT-PCR 验证 GSE11434 数据集的结果。我们还应用基因集富集分析(GSEA)来区分 VILI 组和对照组之间有显著差异的基因集。枢纽基因也通过 CyTargetLinker 和 NetworkAnalyst 进行分析,以预测相关的 miRNA 和转录因子(TF)。此外,我们使用 CIBERSORT 检测 VILI 组小鼠肺组织中不同类型免疫细胞的贡献。通过使用 DrugBank,确定了可能与枢纽基因相互作用的小分子化合物。

结果

在 GSE11434 中,我们鉴定了 VILI 组和对照组之间的 141 个 DEGs。然后,通过 qRT-PCR 验证了 7 个枢纽基因。这 7 个枢纽基因主要富集在 TLR 和 JAK-STAT 信号通路中。GSEA 表明,VILI 相关基因也富集在 NOD、抗原呈递和趋化因子途径中。我们预测了与枢纽基因相关的 miRNA 和 TF,并构建了 miRNA-TF-基因调控网络。CIBERSORT 分析显示,VILI 组中 M0 巨噬细胞和活化肥大细胞的比例高于对照组。纳屈肝素和西妥昔单抗等小分子可能成为 VILI 的潜在药物。

结论

总之,确定了一些与 VILI 相关的枢纽基因,为 VILI 的发病机制提供了新的见解,并为其治疗提供了潜在的靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2adf/8049808/771696302121/DM2021-5535890.001.jpg

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