Longitudinal proteomic profiling of dialysis patients with COVID-19 reveals markers of severity and predictors of death
Longitudinal proteomic profiling of dialysis patients with COVID-19 reveals markers of severity and predictors of death
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COVID-19 透析患者的纵向蛋白质组学分析揭示了严重程度标志物和死亡预测因素
DOI：
10.1101/2020.11.05.20223289
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Gisby J
中科院分区：
文献类型：
--
作者：
Gisby J
终末期肾病（ESKD）患者是严重COVID-19的高风险人群。我们测量了来自住院和未住院的ESKD COVID-19患者的系列血液样本中的436种循环蛋白（n = 55例患者的256份样本）。与51名未感染患者的比较显示了221种差异表达的蛋白质，在46名COVID-19患者的单独子队列中结果一致。203种蛋白质与临床严重程度相关，包括IL 6、单核细胞募集标志物（例如CCL 2、CCL 7）、中性粒细胞活化（例如蛋白酶-3）和上皮损伤（例如KRT 19）。机器学习确定了严重程度的预测因子，包括IL 18 BP、CTSD、GDF 15和KRT 19。联合模型的生存分析揭示了69个死亡预测因子。线性混合模型的纵向建模揭示了32种蛋白质在严重与非严重疾病中表现出不同的时间分布，包括整合素和粘附分子。这些数据暗示了严重COVID-19病理学中的上皮损伤、先天免疫激活和白细胞-内皮相互作用，并为确定药物靶点提供了资源。
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.
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影响因子：
19.6
作者：
Ng, Jia H.;Hirsch, Jamie S.;Fishbane, Steven
通讯作者：
Fishbane, Steven
影响因子：
3.7
作者：
Gandolfo LC;Speed TP
通讯作者：
Speed TP
影响因子：
39
作者：
Wu, Chaomin;Chen, Xiaoyan;Song, Yuanlin
通讯作者：
Song, Yuanlin
DOI：
10.2215/cjn.03880411
发表时间：
2012-01-01
影响因子：
9.8
作者：
Usvyat, Len A.;Carter, Mary;Kotanko, Peter
通讯作者：
Kotanko, Peter
影响因子：
19.6
作者：
Massry, SG;Smogorzewski, M
通讯作者：
Smogorzewski, M