Wang Limin, Candia Julián, Ma Lichun, Zhao Yongmei, Imberti Luisa, Sottini Alessandra, Dobbs Kerry, Lisco Andrea, Sereti Irini, Su Helen C, Notarangelo Luigi D, Wang Xin Wei
Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland 20892.
These authors contributed equally.
medRxiv. 2020 Sep 7:2020.09.04.20187088. doi: 10.1101/2020.09.04.20187088.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the pandemic respiratory infectious disease COVID-19. However, clinical manifestations and outcomes differ significantly among COVID-19 patients, ranging from asymptomatic to extremely severe, and it remains unclear what drives these disparities. Here, we studied 159 hospitalized Italian patients with pneumonia from the NIAID-NCI COVID-19 Consortium using a phage-display method to characterize circulating antibodies binding to 93,904 viral peptides encoded by 1,276 strains of human viruses. SARS-CoV-2 infection was associated with a marked increase in individual's immune memory antibody repertoires linked to trajectories of disease severity from the longitudinal analysis also including anti-spike protein antibodies. By applying a machine-learning-based strategy, we developed a viral exposure signature predictive of COVID-19-related disease severity linked to patient survival. These results provide a basis for understanding the roles of memory B-cell repertoires in COVID-19-related symptoms as well as a predictive tool for monitoring its clinical severity.
严重急性呼吸综合征冠状病毒2(SARS-CoV-2)引发了大流行性呼吸道传染病COVID-19。然而,COVID-19患者的临床表现和预后差异显著,从无症状到极其严重不等,目前尚不清楚导致这些差异的原因。在此,我们使用噬菌体展示方法,对美国国立过敏与传染病研究所-美国国立癌症研究所COVID-19联盟中159名住院的意大利肺炎患者进行了研究,以表征与1276株人类病毒编码的93904种病毒肽结合的循环抗体。通过纵向分析,包括抗刺突蛋白抗体,SARS-CoV-2感染与个体免疫记忆抗体库的显著增加有关,这些抗体库与疾病严重程度轨迹相关。通过应用基于机器学习的策略,我们开发了一种病毒暴露特征,可预测与COVID-19相关的疾病严重程度以及患者生存情况。这些结果为理解记忆B细胞库在COVID-19相关症状中的作用提供了基础,也为监测其临床严重程度提供了一种预测工具。