Untargeted saliva metabolomics by liquid chromatography-Mass spectrometry reveals markers of COVID-19 severity.
Untargeted saliva metabolomics by liquid chromatography-Mass spectrometry reveals markers of COVID-19 severity.
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DOI：
10.1371/journal.pone.0274967
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发表时间：
2022
期刊：
影响因子：
3.7
通讯作者：
Bailey, Melanie J.
中科院分区：
文献类型：
--
作者：
Frampas, Cecile F.;Longman, Katie;Spick, Matt;Lewis, Holly-May;Costa, Catia D. S.;Stewart, Alex;Dunn-Walters, Deborah;Greener, Danni;Evetts, George;Skene, Debra J.;Trivedi, Drupad;Pitt, Andy;Hollywood, Katherine;Barran, Perdita;Bailey, Melanie J.
鉴于新变种的可能性、疫苗逃逸以及消除该疾病所有宿主的可能性较低，COVID-19大流行可能是一个持续存在的全球卫生问题。虽然诊断测试进展迅速，但结果的代谢驱动因素-以及是否可以在不同的生物液体中找到标记物-尚不清楚。最近的研究表明，血清代谢组学对疾病进展的预后有潜在的影响。在医院环境中，唾液样本的收集对工作人员和患者都更方便，因此提供了血清的另一种采样基质。收集了临床怀疑为COVID-19的住院患者的唾液样本以及临床元数据。采用RT-PCR检测确诊COVID-19，并采用临床描述符（呼吸频率、外周氧饱和度评分和c反应蛋白水平）对COVID-19的严重程度进行分类。使用高分辨率液相色谱-质谱法提取和分析代谢物，并使用多变量技术分析所得峰面积矩阵。采用6个特征（其中5个是氨基酸，1个只能通过公式识别）的偏最小二乘-判别分析代谢组学模型与COVID-19严重程度的临床诊断之间实现了1.00的正一致性。与临床严重程度诊断的负百分比也为1.00，导致确定的特征面板的受试者操作特征曲线下的面积为1.00。在这项探索性工作中，我们发现唾液代谢组学，特别是氨基酸可以区分高严重程度的COVID-19患者和低严重程度的COVID-19患者。这扩大了COVID-19代谢失调图谱，并可能在未来为快速和非侵入性取样患者提供基础，旨在补充现有的临床试验，目的是为可能预后不良的患者提供及时治疗。
The COVID-19 pandemic is likely to represent an ongoing global health issue given the potential for new variants, vaccine escape and the low likelihood of eliminating all reservoirs of the disease. Whilst diagnostic testing has progressed at a fast pace, the metabolic drivers of outcomes–and whether markers can be found in different biofluids–are not well understood. Recent research has shown that serum metabolomics has potential for prognosis of disease progression. In a hospital setting, collection of saliva samples is more convenient for both staff and patients, and therefore offers an alternative sampling matrix to serum. Saliva samples were collected from hospitalised patients with clinical suspicion of COVID-19, alongside clinical metadata. COVID-19 diagnosis was confirmed using RT-PCR testing, and COVID-19 severity was classified using clinical descriptors (respiratory rate, peripheral oxygen saturation score and C-reactive protein levels). Metabolites were extracted and analysed using high resolution liquid chromatography-mass spectrometry, and the resulting peak area matrix was analysed using multivariate techniques. Positive percent agreement of 1.00 between a partial least squares–discriminant analysis metabolomics model employing a panel of 6 features (5 of which were amino acids, one that could be identified by formula only) and the clinical diagnosis of COVID-19 severity was achieved. The negative percent agreement with the clinical severity diagnosis was also 1.00, leading to an area under receiver operating characteristics curve of 1.00 for the panel of features identified. In this exploratory work, we found that saliva metabolomics and in particular amino acids can be capable of separating high severity COVID-19 patients from low severity COVID-19 patients. This expands the atlas of COVID-19 metabolic dysregulation and could in future offer the basis of a quick and non-invasive means of sampling patients, intended to supplement existing clinical tests, with the goal of offering timely treatment to patients with potentially poor outcomes.
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10.1016/j.metabol.2021.154922
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