The drivers of critical coronavirus disease 2019 (COVID-19) remain unknown. Given major confounding factors such as age and comorbidities, true mediators of this condition have remained elusive. We used a multi-omics analysis combined with artificial intelligence in a young patient cohort where major comorbidities were excluded at the onset. The cohort included 47 “critical” (in the intensive care unit under mechanical ventilation) and 25 “non-critical” (in a non-critical care ward) patients with COVID-19 and 22 healthy individuals. The analyses included whole-genome sequencing, whole-blood RNA sequencing, plasma and blood mononuclear cell proteomics, cytokine profiling, and high-throughput immunophenotyping. An ensemble of machine learning, deep learning, quantum annealing, and structural causal modeling were used. Patients with critical COVID-19 were characterized by exacerbated inflammation, perturbed lymphoid and myeloid compartments, increased coagulation, and viral cell biology. Among differentially expressed genes, we observed up-regulation of the metalloprotease ADAM9. This gene signature was validated in a second independent cohort of 81 critical and 73 recovered patients with COVID-19 and was further confirmed at the transcriptional and protein level and by proteolytic activity. Ex vivo ADAM9 inhibition decreased severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) uptake and replication in human lung epithelial cells. In conclusion, within a young, otherwise healthy, cohort of individuals with COVID-19, we provide the landscape of biological perturbations in vivo where a unique gene signature differentiated critical from non-critical patients. We further identified ADAM9 as a driver of disease severity and a candidate therapeutic target.

中文翻译：

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在具有深度表型的年轻患者队列中鉴定关键形式 COVID-19 的驱动基因

2019 年严重冠状病毒病 (COVID-19) 的驱动因素仍然未知。鉴于年龄和合并症等主要混杂因素，这种情况的真正介质仍然难以捉摸。我们在一个年轻患者队列中使用了多组学分析和人工智能相结合，其中主要合并症在开始时被排除在外。该队列包括 47 名“重症”（在机械通气下的重症监护病房）和 25 名“非重症”（在非重症监护病房）的 COVID-19 患者和 22 名健康个体。分析包括全基因组测序、全血 RNA 测序、血浆和血液单核细胞蛋白质组学、细胞因子分析和高通量免疫表型分析。使用了机器学习、深度学习、量子退火和结构因果建模的集合。重症 COVID-19 患者的特征是炎症加剧、淋巴和骨髓隔室紊乱、凝血增加和病毒细胞生物学。在差异表达的基因中，我们观察到金属蛋白酶的上调亚当9。该基因特征在第二个独立队列中得到验证，该队列由 81 名危重患者和 73 名康复的 COVID-19 患者组成，并在转录和蛋白质水平以及蛋白水解活性得到进一步证实。体外 ADAM9 抑制降低了人类肺上皮细胞中严重急性呼吸综合征冠状病毒 2 (SARS-CoV-2) 的摄取和复制。总之，在一个年轻、健康的 COVID-19 患者群体中，我们提供了体内生物扰动的情况，其中独特的基因特征区分了危重患者和非危重患者。我们进一步确定ADAM9是疾病严重程度的驱动因素和候选治疗靶点。