Australian National Phenome Centre, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, Western Australia 6150, Australia.
Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Murdoch, Western Australia 6150, Australia.
J Proteome Res. 2021 Feb 5;20(2):1382-1396. doi: 10.1021/acs.jproteome.0c00876. Epub 2021 Jan 11.
To investigate the systemic metabolic effects of SARS-CoV-2 infection, we analyzed H NMR spectroscopic data on human blood plasma and co-modeled with multiple plasma cytokines and chemokines (measured in parallel). Thus, 600 MHz H solvent-suppressed single-pulse, spin-echo, and 2D J-resolved spectra were collected on plasma recorded from SARS-CoV-2 rRT-PCR-positive patients ( = 15, with multiple sampling timepoints) and age-matched healthy controls ( = 34, confirmed rRT-PCR negative), together with patients with COVID-19/influenza-like clinical symptoms who tested SARS-CoV-2 negative ( = 35). We compared the single-pulse NMR spectral data with diagnostic research (IVDr) information on quantitative lipoprotein profiles (112 parameters) extracted from the raw 1D NMR data. All NMR methods gave highly significant discrimination of SARS-CoV-2 positive patients from controls and SARS-CoV-2 negative patients with individual NMR methods, giving different diagnostic information windows on disease-induced phenoconversion. Longitudinal trajectory analysis in selected patients indicated that metabolic recovery was incomplete in individuals without detectable virus in the recovery phase. We observed four plasma cytokine clusters that expressed complex differential statistical relationships with multiple lipoproteins and metabolites. These included the following: cluster 1, comprising MIP-1β, SDF-1α, IL-22, and IL-1α, which correlated with multiple increased LDL and VLDL subfractions; cluster 2, including IL-10 and IL-17A, which was only weakly linked to the lipoprotein profile; cluster 3, which included IL-8 and MCP-1 and were inversely correlated with multiple lipoproteins. IL-18, IL-6, and IFN-γ together with IP-10 and RANTES exhibited strong positive correlations with LDL1-4 subfractions and negative correlations with multiple HDL subfractions. Collectively, these data show a distinct pattern indicative of a multilevel cellular immune response to SARS CoV-2 infection interacting with the plasma lipoproteome giving a strong and characteristic immunometabolic phenotype of the disease. We observed that some patients in the respiratory recovery phase and testing virus-free were still metabolically highly abnormal, which indicates a new role for these technologies in assessing full systemic recovery.
为了研究 SARS-CoV-2 感染的系统性代谢效应,我们对人血浆的 1H NMR 波谱数据进行了分析,并与多种血浆细胞因子和趋化因子进行了共建模(平行测量)。因此,我们对 SARS-CoV-2 rRT-PCR 阳性患者(n=15,具有多个采样时间点)和年龄匹配的健康对照者(n=34,经 rRT-PCR 证实为阴性)的血浆记录了 600 MHz 1H 溶剂抑制单脉冲、自旋回波和 2D J 分辨谱,并与 COVID-19/流感样临床症状但 SARS-CoV-2 检测为阴性的患者(n=35)进行了比较。我们将单脉冲 NMR 光谱数据与从原始 1D NMR 数据中提取的定量脂蛋白谱(112 个参数)的诊断研究(IVDr)信息进行了比较。所有 NMR 方法都能高度显著地区分 SARS-CoV-2 阳性患者与对照者和 SARS-CoV-2 阴性患者,并且个体 NMR 方法提供了疾病诱导表型转化的不同诊断信息窗口。对选定患者的纵向轨迹分析表明,在恢复期无病毒检测的个体中,代谢恢复不完全。我们观察到四个与多种脂蛋白和代谢物表达复杂的差异统计学关系的血浆细胞因子簇。这些包括:簇 1,由 MIP-1β、SDF-1α、IL-22 和 IL-1α 组成,与多种 LDL 和 VLDL 亚组分增加相关;簇 2,包括 IL-10 和 IL-17A,与脂蛋白谱仅有微弱的关联;簇 3,包括 IL-8 和 MCP-1,与多种脂蛋白呈负相关。IL-18、IL-6 和 IFN-γ与 IP-10 和 RANTES 一起与 LDL1-4 亚组分呈强正相关,与多种 HDL 亚组分呈负相关。总的来说,这些数据显示了一种独特的模式,表明对 SARS CoV-2 感染的多层次细胞免疫反应与血浆脂蛋白组相互作用,形成了该疾病强烈而特征性的免疫代谢表型。我们观察到一些处于呼吸恢复期且无病毒检测的患者仍然存在代谢异常,这表明这些技术在评估全身系统恢复方面具有新的作用。