We performed RNA-seq and high-resolution mass spectrometry on 128 blood samples from COVID-19-positive and COVID-19-negative patients with diverse disease severities and outcomes. Quantified transcripts, proteins, metabolites, and lipids were associated with clinical outcomes in a curated relational database, uniquely enabling systems analysis and cross-ome correlations to molecules and patient prognoses. We mapped 219 molecular features with high significance to COVID-19 status and severity, many of which were involved in complement activation, dysregulated lipid transport, and neutrophil activation. We identified sets of covarying molecules, e.g., protein gelsolin and metabolite citrate or plasmalogens and apolipoproteins, offering pathophysiological insights and therapeutic suggestions. The observed dysregulation of platelet function, blood coagulation, acute phase response, and endotheliopathy further illuminated the unique COVID-19 phenotype. We present a web-based tool (covid-omics.app) enabling interactive exploration of our compendium and illustrate its utility through a machine learning approach for prediction of COVID-19 severity.

我们对来自不同疾病严重程度和结果的 COVID-19 阳性和 COVID-19 阴性患者的 128 份血液样本进行了 RNA 测序和高分辨率质谱分析。量化的转录本、蛋白质、代谢物和脂质与精心策划的关系数据库中的临床结果相关，独特地实现了系统分析以及分子和患者预后的跨组相关性。我们绘制了 219 个对 COVID-19 状态和严重程度具有重要意义的分子特征，其中许多与补体激活、脂质转运失调和中性粒细胞激活有关。我们鉴定了一组共变分子，例如蛋白质凝溶胶蛋白和代谢物柠檬酸盐或缩醛磷脂和载脂蛋白，提供病理生理学见解和治疗建议。观察到的血小板功能、凝血、急性期反应和内皮病的失调进一步阐明了独特的 COVID-19 表型。我们提出了一个基于网络的工具 (covid-omics.app)，可以对我们的纲要进行交互式探索，并通过机器学习方法来说明其在预测 COVID-19 严重程度方面的实用性。