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鉴定脓毒症中涉及的 TF-miRNA-mRNA 共调控网络。

Identification of the TF-miRNA-mRNA co-regulatory networks involved in sepsis.

机构信息

The Department of SICU, The Second Affiliated Hospital of Zhejiang University School of Medicine, Jiefang street 88th, Hangzhou, 310009, Zhejiang Province, China.

The Department of SICU, The First Affiliated Hospital, Zhejiang University School of Medicine, Qingchun street 79th, Hangzhou, 310003, Zhejiang Province, China.

出版信息

Funct Integr Genomics. 2022 Aug;22(4):481-489. doi: 10.1007/s10142-022-00843-x. Epub 2022 Mar 24.

Abstract

Sepsis is a life-threatening medical condition caused by a dysregulated host response to infection. Recent studies have found that the expression of miRNAs is associated with the pathogenesis of sepsis and septic shock. Our study aimed to reveal which miRNAs may be involved in the dysregulated immune response in sepsis and how these miRNAs interact with transcription factors (TFs) using a computational approach with in vitro validation studies. To determine the network of TFs, miRNAs, and target genes involved in sepsis, GEO datasets GSE94717 and GSE131761 were used to identify differentially expressed miRNAs and DEGs. TargetScan and miRWalk databases were used to predict biological targets that overlap with the identified DEGs of differentially expressed miRNAs. The TransmiR database was used to predict the differential miRNA TFs that overlap with the identified DEGs. The TF-miRNA-mRNA network was constructed and visualized. Finally, qRT-PCR was used to verify the expression of TFs and miRNA in HUVECs. Between the healthy and sepsis groups, there were 146 upregulated and 98 downregulated DEGs in the GSE131761 dataset, and there were 1 upregulated and 183 downregulated DEMs in the GSE94717 dataset. A regulatory network of the TF-miRna target genes was established. According to the experimental results, RUNX3 was found to be downregulated while MAPK14 was upregulated, which corroborates the result of the computational expression analysis. In a HUVECs model, miR-19b-1-5p and miR-5009-5p were found to be significantly downregulated. Other TFs and miRNAs did not correlate with our bioinformatics expression analysis. We constructed a TF-miRNA-target gene regulatory network and identified potential treatment targets RUNX3, MAPK14, miR-19b-1-5p, and miR-5009-5p. This information provides an initial basis for understanding the complex sepsis regulatory mechanisms.

摘要

脓毒症是一种危及生命的医学病症,由宿主对感染的失调反应引起。最近的研究发现,miRNAs 的表达与脓毒症和感染性休克的发病机制有关。我们的研究旨在使用计算方法并结合体外验证研究,揭示哪些 miRNAs 可能参与脓毒症中失调的免疫反应,以及这些 miRNAs 如何与转录因子 (TF) 相互作用。为了确定与脓毒症相关的 TF、miRNAs 和靶基因的网络,使用 GEO 数据集 GSE94717 和 GSE131761 来识别差异表达的 miRNAs 和 DEGs。TargetScan 和 miRWalk 数据库用于预测与鉴定的差异表达 miRNA 的 DEGs 重叠的生物学靶标。TransmiR 数据库用于预测与鉴定的 DEGs 重叠的差异 miRNA TF。构建并可视化了 TF-miRNA-mRNA 网络。最后,使用 qRT-PCR 验证了 HUVECs 中 TF 和 miRNA 的表达。在 GSE131761 数据集的健康组和脓毒症组之间,有 146 个上调和 98 个下调的 DEG,在 GSE94717 数据集有 1 个上调和 183 个下调的 DEM。建立了 TF-miRna 靶基因的调控网络。根据实验结果,发现 RUNX3 下调而 MAPK14 上调,这与计算表达分析的结果一致。在 HUVECs 模型中,发现 miR-19b-1-5p 和 miR-5009-5p 显著下调。其他 TF 和 miRNAs 与我们的生物信息学表达分析没有相关性。我们构建了 TF-miRNA-靶基因调控网络,并鉴定了潜在的治疗靶点 RUNX3、MAPK14、miR-19b-1-5p 和 miR-5009-5p。这些信息为理解复杂的脓毒症调控机制提供了初步基础。

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