Niu Jingjing, Qin Bingyu, Wang Cunzhen, Chen Chao, Yang Jianxu, Shao Huanzhang
Department of Critical Care Medicine, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Zhengzhou, China.
Front Genet. 2021 Nov 3;12:668527. doi: 10.3389/fgene.2021.668527. eCollection 2021.
Septic shock is the severe complication of sepsis, with a high mortality. The inflammatory response regulates the immune status and mediates the progression of septic shock. In this study, we aim to identify the key immune-related genes (IRGs) of septic shock and explore their potential mechanism. Gene expression profiles of septic shock blood samples and normal whole blood samples were retrieved from the Gene Expression Omnibus (GEO) and Genotype-Tissue Expression Portal (GTEx). The differential expression genes (DEGs) and septic shock-specific immune-related genes (SSSIRGs) were evaluated and identified, along with the immune components by "cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT, version x)" algorithm. Additionally, in order to explore the key regulatory network, the relationship among SSSIRGs, upstream transcription factors (TFs), and downstream signaling pathways were also identified by Gene Set Variation Analysis (GSVA) and co-expression analysis. Moreover, the Connectivity Map (CMap) analysis was applied to find bioactive small molecules against the members of regulation network while Chromatin Immunoprecipitation sequencing (ChIP-seq) and Assay for Targeting Accessible-Chromatin with high-throughput sequencing (ATAC-seq) data were used to validate the regulation mechanism of the network. A total of 14,843 DEGs were found between 63 septic shock blood samples and 337 normal whole blood samples. Then, we identified septic shock-specific 839 IRGs as the intersection of DEGs and IRGs. Moreover, we uncovered the regulatory networks based on co-expression analysis and found 28 co-expression interaction pairs. In the regulation network, protein phosphatase 3, catalytic subunit, alpha isozyme (PPP3CA) may regulate late estrogen response, glycolysis and TNFα signaling NFκB and HLA; Kirsten rat sarcoma viral oncogene homolog (KRAS) may be related to late estrogen response and HLA; and Toll-like receptor 8 (TLR8) may be associated with TNFα signaling NFκB. And the regulation mechanisms between TFs and IRGs (TLR8, PPP3CA, and KRAS) were validated by ChIP-seq and ATAC-seq. Our data identify three SSSIRGs (TLR8, PPP3CA, and KRAS) as candidate therapeutic targets for septic shock and provide constructed regulatory networks in septic shock to explore its potential mechanism.
脓毒性休克是脓毒症的严重并发症,死亡率很高。炎症反应调节免疫状态并介导脓毒性休克的进展。在本研究中,我们旨在鉴定脓毒性休克的关键免疫相关基因(IRGs)并探索其潜在机制。从基因表达综合数据库(GEO)和基因型-组织表达数据库(GTEx)中检索脓毒性休克血样和正常全血样本的基因表达谱。通过“通过估计RNA转录本的相对亚群进行细胞类型鉴定(CIBERSORT,x版本)”算法评估和鉴定差异表达基因(DEGs)和脓毒性休克特异性免疫相关基因(SSSIRGs)以及免疫成分。此外,为了探索关键调控网络,还通过基因集变异分析(GSVA)和共表达分析确定了SSSIRGs、上游转录因子(TFs)和下游信号通路之间的关系。此外,应用连通性图谱(CMap)分析来寻找针对调控网络成员的生物活性小分子,同时利用染色质免疫沉淀测序(ChIP-seq)和高通量测序靶向可及染色质分析(ATAC-seq)数据来验证网络的调控机制。在63份脓毒性休克血样和337份正常全血样本之间共发现14,843个DEGs。然后,我们将839个脓毒性休克特异性IRGs鉴定为DEGs和IRGs的交集。此外,我们基于共表达分析揭示了调控网络并发现了28个共表达相互作用对。在调控网络中,蛋白磷酸酶3催化亚基α同工酶(PPP3CA)可能调节晚期雌激素反应、糖酵解和TNFα信号通路、NFκB和HLA; Kirsten大鼠肉瘤病毒癌基因同源物(KRAS)可能与晚期雌激素反应和HLA有关;Toll样受体8(TLR8)可能与TNFα信号通路、NFκB有关。并且通过ChIP-seq和ATAC-seq验证了TFs与IRGs(TLR8、PPP3CA和KRAS)之间的调控机制。我们的数据确定了三个SSSIRGs(TLR8、PPP3CA和KRAS)作为脓毒性休克的候选治疗靶点,并提供了脓毒性休克中构建的调控网络以探索其潜在机制。