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Identification of the TF-miRNA-mRNA co-regulatory networks involved in sepsis.
Luo, Xiaoqian; Lu, Weina; Zhao, Jianfeng; Hu, Jun; Chen, Enjiang; Fu, Shi; Fu, Qinghui.
Afiliação
  • Luo X; The Department of SICU, The Second Affiliated Hospital of Zhejiang University School of Medicine, Jiefang street 88th, Hangzhou, 310009, Zhejiang Province, China.
  • Lu W; The Department of SICU, The Second Affiliated Hospital of Zhejiang University School of Medicine, Jiefang street 88th, Hangzhou, 310009, Zhejiang Province, China.
  • Zhao J; The Department of SICU, The First Affiliated Hospital, Zhejiang University School of Medicine, Qingchun street 79th, Hangzhou, 310003, Zhejiang Province, China.
  • Hu J; The Department of SICU, The Second Affiliated Hospital of Zhejiang University School of Medicine, Jiefang street 88th, Hangzhou, 310009, Zhejiang Province, China.
  • Chen E; The Department of SICU, The Second Affiliated Hospital of Zhejiang University School of Medicine, Jiefang street 88th, Hangzhou, 310009, Zhejiang Province, China.
  • Fu S; The Department of SICU, The First Affiliated Hospital, Zhejiang University School of Medicine, Qingchun street 79th, Hangzhou, 310003, Zhejiang Province, China.
  • Fu Q; The Department of SICU, The First Affiliated Hospital, Zhejiang University School of Medicine, Qingchun street 79th, Hangzhou, 310003, Zhejiang Province, China. 2513107@zju.edu.cn.
Funct Integr Genomics ; 22(4): 481-489, 2022 Aug.
Article em En | MEDLINE | ID: mdl-35322335
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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Fatores de Transcrição / Sepse / MicroRNAs / Proteína Quinase 14 Ativada por Mitógeno Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Fatores de Transcrição / Sepse / MicroRNAs / Proteína Quinase 14 Ativada por Mitógeno Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article