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通过生物信息学分析和分子实验鉴定与验证脓毒症患者中与坏死性凋亡相关的基因

Identification and Verification of Necroptosis-Related Genes in Patients With Sepsis by Bioinformatic Analysis and Molecular Experiments.

作者信息

Choi Hayoung, Lee Jin Young, Yoo Hongseok, Jeon Kyeongman

机构信息

Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Internal Medicine, Hallym University Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea.

Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.

出版信息

J Cell Mol Med. 2025 May;29(9):e70582. doi: 10.1111/jcmm.70582.

DOI:10.1111/jcmm.70582
PMID:40318009
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12049152/
Abstract

Although necroptosis is an emerging mechanism of multiple organ dysfunction in sepsis, data on the mechanistic link between necroptosis and sepsis are scarce. Bioinformatic analysis was performed to compare the gene profiles between the sepsis (n = 133) and healthy control (n = 12) groups and identify necroptosis-related differentially expressed genes (DEGs). The identified necroptosis-related DEGs were verified by three-step molecular experiments: (1) quantitative real-time PCR and enzyme-linked immunosorbent assay; (2) cell culture, transfection and Western blotting; and (3) cytokine array with apoptosis inhibition. Additionally, receiver-operating characteristic curve analyses were performed to evaluate the performance of the corresponding proteins to the necroptosis-related DEGs in diagnosing sepsis and in predicting in-hospital mortality of patients with sepsis. Eight necroptosis-related DEGs, including five upregulated (PYGL, TNF, CYLD, FADD and TLR3) and three downregulated (TP53, FASLG and NLRP6) DEGs, were identified. Moreover, the levels of the corresponding proteins to necroptosis-related DEGs showed excellent or considerable accuracy in diagnosing sepsis and in predicting the mortality of sepsis patients. In cell culture media transfected with plasma from the sepsis and control groups, Western blotting revealed that the levels of the corresponding proteins were increased in the upregulated DEGs and decreased in the downregulated DEGs. The cytokine array revealed cytokines in cell culture media transfected with plasma from patients with sepsis while preventing apoptosis by inhibiting the caspase-8 activity, wherein the transfected cells potentially underwent necroptosis. Eight necroptosis-related DEGs were identified in patients with sepsis by bioinformatic analysis and verified by molecular experiments, implying that necroptosis may be a key mechanism of sepsis.

摘要

尽管坏死性凋亡是脓毒症中多器官功能障碍的一种新机制,但关于坏死性凋亡与脓毒症之间机制联系的数据却很匮乏。进行了生物信息学分析,以比较脓毒症组(n = 133)和健康对照组(n = 12)之间的基因谱,并鉴定与坏死性凋亡相关的差异表达基因(DEG)。通过三步分子实验验证了鉴定出的与坏死性凋亡相关的DEG:(1)定量实时PCR和酶联免疫吸附测定;(2)细胞培养、转染和蛋白质印迹法;(3)抑制凋亡的细胞因子阵列。此外,还进行了受试者工作特征曲线分析,以评估相应蛋白质对与坏死性凋亡相关的DEG在诊断脓毒症和预测脓毒症患者院内死亡率方面的表现。鉴定出8个与坏死性凋亡相关的DEG,包括5个上调的(PYGL、TNF、CYLD、FADD和TLR3)和3个下调的(TP53、FASLG和NLRP6)DEG。此外,与坏死性凋亡相关的DEG的相应蛋白质水平在诊断脓毒症和预测脓毒症患者死亡率方面显示出极佳或相当高的准确性。在转染了脓毒症组和对照组血浆的细胞培养基中,蛋白质印迹法显示上调的DEG中相应蛋白质水平升高,下调的DEG中相应蛋白质水平降低。细胞因子阵列显示,在用脓毒症患者血浆转染的细胞培养基中,通过抑制半胱天冬酶-8活性来防止细胞凋亡,其中转染的细胞可能发生了坏死性凋亡。通过生物信息学分析在脓毒症患者中鉴定出8个与坏死性凋亡相关的DEG,并通过分子实验进行了验证,这意味着坏死性凋亡可能是脓毒症的关键机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0549/12049152/f63533cd26a2/JCMM-29-e70582-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0549/12049152/4861d568702f/JCMM-29-e70582-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0549/12049152/ee569a7661b6/JCMM-29-e70582-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0549/12049152/3d61ba8a564f/JCMM-29-e70582-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0549/12049152/0cebc085fcf2/JCMM-29-e70582-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0549/12049152/f63533cd26a2/JCMM-29-e70582-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0549/12049152/4861d568702f/JCMM-29-e70582-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0549/12049152/ee569a7661b6/JCMM-29-e70582-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0549/12049152/3d61ba8a564f/JCMM-29-e70582-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0549/12049152/0cebc085fcf2/JCMM-29-e70582-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0549/12049152/f63533cd26a2/JCMM-29-e70582-g005.jpg

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本文引用的文献

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Receiver operating characteristic curve analysis in diagnostic accuracy studies: A guide to interpreting the area under the curve value.诊断准确性研究中的受试者工作特征曲线分析:曲线下面积值解读指南。
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Bioinformatics Analysis of Gene Expression Profiles for Diagnosing Sepsis and Risk Prediction in Patients with Sepsis.基于基因表达谱的生物信息学分析用于诊断脓毒症和预测脓毒症患者的风险。
Int J Mol Sci. 2023 May 27;24(11):9362. doi: 10.3390/ijms24119362.
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Identification of featured necroptosis-related genes and imbalanced immune infiltration in sepsis machine learning.
脓毒症机器学习中特征性坏死性凋亡相关基因的鉴定及免疫浸润失衡
Front Genet. 2023 Apr 6;14:1158029. doi: 10.3389/fgene.2023.1158029. eCollection 2023.
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