新冠肺炎代谢紊乱的系统发现和途径分析

Infectious diseases & immunity Pub Date : 2021-06-09 eCollection Date: 2021-07-01 DOI:10.1097/ID9.0000000000000010
Bo-Wen Li, Xing Fan, Wen-Jing Cao, He Tian, Si-Yu Wang, Ji-Yuan Zhang, Sin Man Lam, Jin-Wen Song, Chao Zhang, Shao-Hua Zhang, Zhe Xu, Ruo-Nan Xu, Jun-Liang Fu, Lei Huang, Tian-Jun Jiang, Ming Shi, Fu-Sheng Wang, Guang-Hou Shui
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引用次数: 0

摘要

背景:2019冠状病毒病(COVID-19)全球大流行正在对世界各国构成严重的公共卫生威胁。了解疾病的发病机制和宿主免疫反应将有助于发现治疗靶点和更好地管理感染患者。代谢组学技术可以提供一种无偏倚的工具来探索代谢扰动。方法:选取2020年1月22日至2月16日在解放军总医院第五医学中心就诊的26名健康对照者和50名轻、中、重度症状的新冠肺炎患者为研究对象。采集空腹血样,采用液相色谱-质谱联用法进行代谢组学分析。代谢物丰度以峰面积测定,并在统计分析前进行对数变换。采用R软件包进行主成分分析、差异表达分析和代谢途径分析。通过加权相关网络分析和Spearman相关系数确定共调节代谢物及其与临床指标的关系。使用随机森林模型分析潜在代谢物生物标志物。结果:我们发现了100多种与COVID-19疾病相关的代谢物,其中许多与疾病严重程度相关。鉴定出了一系列高度相关的代谢物,并给出了它们与临床指标的相关性。进一步的分析将差异代谢物与生化反应、代谢途径和生物医学MeSH术语联系起来,为疾病发病机制和宿主反应提供了背景见解。最后,一组代谢物被发现能够区分COVID-19患者和健康对照者,以及另一组轻度和更严重病例的清单。我们的研究结果显示,新冠肺炎患者的柠檬酸循环、鞘脂代谢中的鞘氨醇1-磷酸和类固醇激素生物合成下调,嘌呤代谢和色氨酸代谢受到干扰。结论:本研究发现了与COVID-19发病机制和宿主免疫反应相关的关键代谢物及其相关的生物学和医学概念,有助于选择潜在的预后生物标志物和发现治疗靶点。
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Systematic Discovery and Pathway Analyses of Metabolic Disturbance in COVID-19.

Background: The ongoing global coronavirus disease 2019 (COVID-19) pandemic is posing a serious public health threat to nations worldwide. Understanding the pathogenesis of the disease and host immune responses will facilitate the discovery of therapeutic targets and better management of infected patients. Metabolomics technology can provide an unbiased tool to explore metabolic perturbation.

Methods: Twenty-six healthy controls and 50 COVID-19 patients with mild, moderate, and severe symptoms in the Fifth Medical Center of PLA General Hospital from January 22 to February 16, 2020 were recruited into the study. Fasting blood samples were collected and subject to metabolomics analysis by liquid chromatography-mass spectrometry. Metabolite abundance was measured by peak area and was log-transformed before statistical analysis. The principal component analysis, different expression analysis, and metabolic pathway analysis were performed using R package. Co-regulated metabolites and their associations with clinical indices were identified by the weighted correlation network analysis and Spearman correlation coefficients. The potential metabolite biomarkers were analyzed using a random forest model.

Results: We uncovered over 100 metabolites that were associated with COVID-19 disease and many of them correlated with disease severity. Sets of highly correlated metabolites were identified and their correlations with clinical indices were presented. Further analyses linked the differential metabolites with biochemical reactions, metabolic pathways, and biomedical MeSH terms, offering contextual insights into disease pathogenesis and host responses. Finally, a panel of metabolites was discovered to be able to discriminate COVID-19 patients from healthy controls, and also another list for mild against more severe cases. Our findings showed that in COVID-19 patients, citrate cycle, sphingosine 1-phosphate in sphingolipid metabolism, and steroid hormone biosynthesis were downregulated, while purine metabolism and tryptophan metabolism were disturbed.

Conclusion: This study discovered key metabolites as well as their related biological and medical concepts pertaining to COVID-19 pathogenesis and host immune response, which will facilitate the selection of potential biomarkers for prognosis and discovery of therapeutic targets.

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