Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom.
Renal and Transplant Centre, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom.
Elife. 2021 Mar 11;10:e64827. doi: 10.7554/eLife.64827.
End-stage kidney disease (ESKD) patients are at high risk of severe COVID-19. We measured 436 circulating proteins in serial blood samples from hospitalised and non-hospitalised ESKD patients with COVID-19 (n = 256 samples from 55 patients). Comparison to 51 non-infected patients revealed 221 differentially expressed proteins, with consistent results in a separate subcohort of 46 COVID-19 patients. Two hundred and three proteins were associated with clinical severity, including IL6, markers of monocyte recruitment (e.g. CCL2, CCL7), neutrophil activation (e.g. proteinase-3), and epithelial injury (e.g. KRT19). Machine-learning identified predictors of severity including IL18BP, CTSD, GDF15, and KRT19. Survival analysis with joint models revealed 69 predictors of death. Longitudinal modelling with linear mixed models uncovered 32 proteins displaying different temporal profiles in severe versus non-severe disease, including integrins and adhesion molecules. These data implicate epithelial damage, innate immune activation, and leucocyte-endothelial interactions in the pathology of severe COVID-19 and provide a resource for identifying drug targets.
终末期肾病(ESKD)患者患严重 COVID-19 的风险很高。我们在住院和非住院 COVID-19 患者(55 名患者的 256 个样本)的连续血液样本中测量了 436 种循环蛋白。与 51 名未感染患者相比,发现了 221 种差异表达的蛋白质,在另外 46 名 COVID-19 患者的亚组中得到了一致的结果。203 种蛋白质与临床严重程度相关,包括 IL6、单核细胞募集标志物(如 CCL2、CCL7)、中性粒细胞活化(如蛋白酶-3)和上皮损伤(如 KRT19)。机器学习确定了包括 IL18BP、CTSD、GDF15 和 KRT19 在内的严重程度预测因子。联合模型的生存分析显示,69 个死亡预测因子。线性混合模型的纵向模型揭示了 32 种在严重疾病与非严重疾病中呈现不同时间特征的蛋白质,包括整合素和粘附分子。这些数据表明上皮损伤、先天免疫激活和白细胞-内皮相互作用与严重 COVID-19 的发病机制有关,并为识别药物靶点提供了资源。