Australian National Phenome Centre, Computational and Systems Medicine, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, Western Australia 6150, Australia.
Perron Institute for Neurological and Translational Science, Nedlands, Western Australia 6009, Australia.
J Proteome Res. 2020 Nov 6;19(11):4442-4454. doi: 10.1021/acs.jproteome.0c00519. Epub 2020 Sep 14.
The metabolic effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on human blood plasma were characterized using multiplatform metabolic phenotyping with nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS). Quantitative measurements of lipoprotein subfractions, α-1-acid glycoprotein, glucose, and biogenic amines were made on samples from symptomatic coronavirus disease 19 (COVID-19) patients who had tested positive for the SARS-CoV-2 virus ( = 17) and from age- and gender-matched controls ( = 25). Data were analyzed using an orthogonal-projections to latent structures (OPLS) method and used to construct an exceptionally strong (AUROC = 1) hybrid NMR-MS model that enabled detailed metabolic discrimination between the groups and their biochemical relationships. Key discriminant metabolites included markers of inflammation including elevated α-1-acid glycoprotein and an increased kynurenine/tryptophan ratio. There was also an abnormal lipoprotein, glucose, and amino acid signature consistent with diabetes and coronary artery disease (low total and HDL Apolipoprotein A1, low HDL triglycerides, high LDL and VLDL triglycerides), plus multiple highly significant amino acid markers of liver dysfunction (including the elevated glutamine/glutamate and Fischer's ratios) that present themselves as part of a distinct SARS-CoV-2 infection pattern. A multivariate training-test set model was validated using independent samples from additional SARS-CoV-2 positive patients and controls. The predictive model showed a sensitivity of 100% for SARS-CoV-2 positivity. The breadth of the disturbed pathways indicates a systemic signature of SARS-CoV-2 positivity that includes elements of liver dysfunction, dyslipidemia, diabetes, and coronary heart disease risk that are consistent with recent reports that COVID-19 is a systemic disease affecting multiple organs and systems. Metabolights study reference: MTBLS2014.
采用基于核磁共振（NMR）光谱和液相色谱-质谱（LC-MS）的多平台代谢表型分析方法，研究了严重急性呼吸综合征冠状病毒 2（SARS-CoV-2）感染对人血浆的代谢影响。对经 SARS-CoV-2 病毒检测呈阳性的有症状的 2019 冠状病毒病（COVID-19）患者（n=17）和年龄、性别匹配的对照者（n=25）的样本进行了脂蛋白亚组分、α-1-酸性糖蛋白、葡萄糖和生物胺的定量测量。采用正交投影到潜在结构（OPLS）方法分析数据，并构建了一个异常强大（AUROC=1）的混合 NMR-MS 模型，能够对两组样本进行详细的代谢区分及其生化关系分析。关键判别代谢物包括炎症标志物，如升高的α-1-酸性糖蛋白和增加的犬尿氨酸/色氨酸比值。此外，还存在异常的脂蛋白、葡萄糖和氨基酸特征，与糖尿病和冠状动脉疾病一致（总胆固醇和高密度脂蛋白载脂蛋白 A1 降低，高密度脂蛋白甘油三酯升高，低密度脂蛋白和极低密度脂蛋白甘油三酯升高），以及多个肝功能失调的高度显著氨基酸标志物（包括升高的谷氨酰胺/谷氨酸和 Fischer 比值），这些标志物是 SARS-CoV-2 感染模式的一部分。使用来自其他 SARS-CoV-2 阳性患者和对照者的独立样本对多元训练-测试集模型进行了验证。预测模型对 SARS-CoV-2 阳性的灵敏度为 100%。受干扰途径的多样性表明 SARS-CoV-2 阳性存在系统性特征，包括肝功能失调、血脂异常、糖尿病和冠心病风险等元素，这与 COVID-19 是一种影响多个器官和系统的全身性疾病的最近报道一致。代谢组学研究参考资料：MTBLS2014。
