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基于 UPLC-MS/MS 的广泛靶向代谢组学揭示 COVID-19 的代谢特征。

Metabolite profile of COVID-19 revealed by UPLC-MS/MS-based widely targeted metabolomics.

机构信息

Medical Research Center, Yue Bei People's Hospital, Shantou University Medical College, Shaoguan, China.

The Central Laboratory, Yangjiang People's Hospital, Yangjiang, China.

出版信息

Front Immunol. 2022 Jul 18;13:894170. doi: 10.3389/fimmu.2022.894170. eCollection 2022.

DOI:10.3389/fimmu.2022.894170
PMID:35924246
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9339702/
Abstract

The metabolic characteristics of COVID-19 disease are still largely unknown. Here, 44 patients with COVID-19 (31 mild COVID-19 patients and 13 severe COVID-19 patients), 42 healthy controls (HC), and 42 patients with community-acquired pneumonia (CAP), were involved in the study to assess their serum metabolomic profiles. We used widely targeted metabolomics based on an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The differentially expressed metabolites in the plasma of mild and severe COVID-19 patients, CAP patients, and HC subjects were screened, and the main metabolic pathways involved were analyzed. Multiple mature machine learning algorithms confirmed that the metabolites performed excellently in discriminating COVID-19 groups from CAP and HC subjects, with an area under the curve (AUC) of 1. The specific dysregulation of AMP, dGMP, -glycero-3-phosphocholine, and carnitine was observed in the severe COVID-19 group. Moreover, random forest analysis suggested that these metabolites could discriminate between severe COVID-19 patients and mild COVID-19 patients, with an AUC of 0.921. This study may broaden our understanding of pathophysiological mechanisms of COVID-19 and may offer an experimental basis for developing novel treatment strategies against it.

摘要

新型冠状病毒肺炎（COVID-19）的代谢特征仍知之甚少。本研究纳入 44 例 COVID-19 患者（31 例轻症 COVID-19 患者和 13 例重症 COVID-19 患者）、42 例健康对照者（HC）和 42 例社区获得性肺炎（CAP）患者，评估其血清代谢组学特征。我们采用基于超高效液相色谱-串联质谱（UPLC-MS/MS）的广泛靶向代谢组学方法。筛选轻症和重症 COVID-19 患者、CAP 患者和 HC 受试者血浆中差异表达的代谢物，并分析其涉及的主要代谢途径。多种成熟的机器学习算法证实，这些代谢物在区分 COVID-19 组与 CAP 和 HC 组方面表现出色，曲线下面积（AUC）为 1。在重症 COVID-19 组中观察到 AMP、dGMP、-甘油磷酸胆碱和肉碱的特异性失调。此外，随机森林分析表明，这些代谢物可区分重症 COVID-19 患者和轻症 COVID-19 患者，AUC 为 0.921。本研究可能拓宽我们对 COVID-19 病理生理机制的理解，并为开发针对 COVID-19 的新型治疗策略提供实验基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc6f/9339702/d269727d459e/fimmu-13-894170-g008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc6f/9339702/d269727d459e/fimmu-13-894170-g008.jpg
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