To explore the effect of gene polymorphisms of propofol GABA A receptor and metabolic enzyme on drug susceptibility during the induction period of general anesthesia. Patients and Methods: A total of 294 female patients aged 18-55 years, ASA I-II, who underwent hysteroscopy with intravenous general anesthesia, were included in the study. Anesthesia was induced by continuous intravenous infusion of propofol at 40 mg•kg −1 •h −1 . Infusion of propofol was ended when both the Modified Observer's Assessment of Awareness/Sedation scale (MOAA/S scale) decreased to 1 and the BIS index decreased to 60. The time when the MOAA/S scale decreased to 1 and the time when BIS index decreased to 60 was recorded to assess the susceptibility to the sedation effect. The maximum decreased percentage in mean arterial pressure (MAP) within 5 minutes was recorded to assess the susceptibility of cardiovascular response. Venous blood of each patient was collected to identify the presence of genetic variants in the GABRA1, GABRA2, GABRB2, GABRB3, GABRG2, CYP2B6, and UGT1A9 genes using the Sequenom MassARRAY ® platform. Results: After receiving propofol infusion, carriers of polymorphic GABRA1 rs4263535 G allele required significantly less time for BIS decreased to 60, while carriers of polymorphic GABRB2 rs3816596 T allele required significantly more time for BIS decreased to 60, carriers of polymorphic GABRA1 rs1157122 C allele and carriers of polymorphic GABRB2 rs76774144 T allele had a significantly less change in MAP. Conclusion: GABRB2 rs3816596 and GABRA1 rs4263535 polymorphisms are associated with susceptibility to the sedation effect of propofol. GABRA1 rs1157122 and GABRB2 rs76774144 polymorphisms are associated with the degree of drop in blood pressure after propofol infusion.
Background Increasing evidence has suggested an association between carotid atherosclerosis (CAS) and periodontitis (PD); however, the mechanisms have not been fully understood. This study aims to investigate the shared genes and molecular mechanisms underlying the co-pathogenesis of CAS and PD. Methods Gene Expression Omnibus (GEO) datasets GSE100927 and GSE10334 were downloaded, and differentially expressed genes (DEGs) shared by both datasets were identified. The functional enrichment analysis of these overlapping DEGs was then conducted. A protein-protein interaction (PPI) network was created using the STRING database and Cytoscape software, and PPI key genes were identified using the cytoHubba plugin. Then, weighted gene co-expression network analysis (WGCNA) was performed on GSE100927 and GSE10334, and the gene modules most correlated with CAS and PD were identified as key modules. The genes in key modules overlapping with PPI key genes were determined to be the key crosstalk genes. Subsequently, the key crosstalk genes were validated in three independent external datasets (GSE43292 [CAS microarray dataset], GSE16134 [PD microarray dataset], and GSE28829 [CAS microarray dataset]). In addition, the immune cell patterns of PD and CAS were evaluated by single-sample gene set enrichment analysis (ssGSEA), and the correlation of key crosstalk genes with each immune cell was calculated. Finally, we investigated the transcription factors (TFs) that regulate key crosstalk genes using NetworkAnalyst 3.0 platform. Results 355 overlapping DEGs of CAS and PD were identified. Functional enrichment analysis highlighted the vital role of immune and inflammatory pathways in CAS and PD. The PPI network was constructed, and eight PPI key genes were identified by cytoHubba, including CD4, FCGR2A, IL1B, ITGAM, ITGAX, LCK, PTPRC, and TNF. By WGCNA, the turquoise module was identified as the most correlated module with CAS, and the blue module was identified as the most correlated module with PD. Ultimately, ITGAM and LCK were identified as key crosstalk genes as they appeared both in key modules and PPI key genes. Expression levels of ITGAM and LCK were significantly elevated in the case groups of the test datasets (GSE100927 and GSE10334) and validation datasets (GSE43292, GSE16134, and GSE28829). In addition, the expression of multiple immune cells was significantly elevated in PD and CAS compared to controls, and the two key crosstalk genes were both significantly associated with CD4 T cells. Finally, SPI1 was identified as a potential key TF, which regulates the two key crosstalk genes. Conclusion This study identified the key crosstalk genes and TF in PD and CAS, which provides new insights for further studies on the co-morbidity mechanisms of CAS and PD from an immune and inflammatory perspective.
BackgroundStroke is the leading cause of death and disability worldwide, with ischemic stroke (IS) being the most prevalent type. Circular RNAs (circRNAs) are involved in the pathological process of IS and are promising biomarkers for the diagnosis of IS. However, studies focusing on circRNAs acting as microRNAs (miRNAs) sponges in regulating mRNA expression are currently scarce.MethodsIn this study, expression profiles of circRNAs (GSE195442), miRNAs (GSE117064), and mRNAs (GSE58294) from the Gene Expression Omnibus (GEO) database were analyzed. Differentially expressed circRNAs (DEcircRNAs), differentially expressed miRNAs (DEmiRNAs), and differentially expressed mRNAs (DEmRNAs) were identified by R software. The target miRNAs and target genes were predicted by several bioinformatics methods. Then, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the DEmRNAs. Subsequently, the protein-protein interaction (PPI) network and the competing endogenous RNA (ceRNA) regulatory network were visualized by Cytoscape software. Finally, we further constructed an immune-related circRNA-miRNA-mRNA regulatory sub-network in IS.ResultsA total of 35 DEcircRNAs, 141 DEmiRNAs, and 356 DEmRNAs were identified. By comprehensive analysis of bioinformatics methods, we constructed a circRNA-miRNA-mRNA regulatory network, including 15 DEcircRNAs, eight DEmiRNAs, and 39 DEmRNAs. FGF9 was identified as an immune-related hub gene. Immune cell analysis indicated a significantly higher level of neutrophils in IS, and the expression of FGF9 was significantly negatively correlated with the level of neutrophils. Eventually, miR-767-5p was predicted as the upstream molecules of FGF9, and circ_0127785 and circ_0075008 were predicted as the upstream circRNAs of miR-767-5p.ConclusionOur study provides novel insights into the molecular mechanisms governing the progression of IS from the perspective of immune-related ceRNA networks.
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