综合分析ICU和非ICU患者的人冠状病毒抗体反应,发现抗SARS-CoV-2刺突蛋白IgG3是疾病严重程度的关键生物标志物。

IF 2
Fatma H Ali, Giusy Gentilcore, Hadeel T Al-Jighefee, Sara Ahmad Taleb, Ali Ait Hssain, Hamda A Qotba, Asmaa A Al Thani, Laith J Abu Raddad, Gheyath K Nasrallah, Jean-Charles Grivel, Hadi M Yassine
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引用次数: 0

摘要

介绍。对人类冠状病毒(hcov)的预先免疫可能会影响COVID-19患者的免疫反应。越来越多的证据表明,SARS-CoV-2与其他冠状病毒之间的免疫交叉反应可能决定临床预后。SARS-CoV-2疾病严重程度受预先存在的hcov免疫的影响,具有不同的抗体谱和交叉反应模式。目的:探讨重症监护室和非重症监护室SARS-CoV-2患者对不同HCoV蛋白的抗体反应,并评估既往免疫对SARS-CoV-2疾病结局的潜在影响。本研究采用综合hcov抗原头阵列检测了70例ICU和63例非ICU患者对致病性中东呼吸综合征冠状病毒(MERS-CoV)、SARS-CoV、SARS-CoV-2和4种季节性hcov的抗体反应。我们的分析显示,ICU患者的抗体反应总体上高于非ICU患者。有趣的是,ICU患者的抗s1 IgG和IgA明显高于非ICU患者。同样,针对NL63的抗s1 IgG在ICU患者中的应答较非ICU患者低。SARS-CoV-2抗体和SARS-CoV抗体之间存在明显的交叉反应性,但与MERS-CoV和季节性hcov之间没有交叉反应性。识别SARS-CoV-2的抗体亚类分析显示,ICU患者的抗s1抗体IgG1、IgG3、IgA1和IgA2明显高于非ICU患者。SARS-CoV-2患者的主要IgA亚型为IgA1。我们应用机器学习算法对血清学反应进行亚分类,以建立能够区分ICU患者和轻度COVID-19患者的分类器。在两种不同类型模型使用的90个变量中,对ICU状态影响最大的变量是抗SARS-CoV-2 S的IgG3,影响最大的8个变量包括抗S-三聚体的IgG3和抗SARS-CoV-2 S的IgA。了解各种患者体液免疫的复杂性对于早期医疗干预、疾病管理、选择性疫苗接种和被动免疫治疗至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comprehensive analysis of human coronavirus antibody responses in ICU and non-ICU COVID-19 patients reveals IgG3 against SARS-CoV-2 spike protein as a key biomarker of disease severity.

Introduction. Pre-existing immunity to human coronaviruses (HCoVs) may shape the immune response in COVID-19 patients. Increasing evidence suggests that immune cross-reactivity between SARS-CoV-2 and other coronaviruses may determine clinical prognosis.Hypothesis. SARS-CoV-2 disease severity is influenced by pre-existing immunity to HCoVs, with distinct antibody profiles and cross-reactivity patterns.Aim. To investigate the antibody response of ICU and non-ICU SARS-CoV-2 patients against different HCoV proteins and assess the potential impact of pre-existing immunity on SARS-CoV-2 disease outcomes.Methodology. This study used a comprehensive HCoVs antigen bead array to measure antibody response to pathogenic Middle East respiratory syndrome coronavirus (MERS-CoV), SARS-CoV, SARS-CoV-2 and the four seasonal HCoVs in 70 ICU and 63 non-ICU COVID-19 patients.Results. Our analysis demonstrates an overall higher antibody response in ICU than in non-ICU COVID-19 patients. Interestingly, the anti-S1 IgG and IgA were significantly higher among ICU than in non-ICU patients. Similarly, the anti-S1 IgG against NL63 showed a lower response among ICU compared to non-ICU. Cross-reactivity was evident between SARS-CoV-2 and SARS-CoV antibodies but not with MERS-CoV and seasonal HCoVs. The subclass analysis of antibodies recognizing SARS-CoV-2 revealed that anti-S1 IgG1, IgG3, IgA1 and IgA2 were significantly higher in ICU compared to non-ICU. The predominant IgA subtype among SARS-CoV-2 patients was IgA1. We applied machine learning algorithms to subclass serological responses to build classifiers that could distinguish between ICU patients and patients with milder COVID-19. Out of 90 variables used in two different types of models, the variable of highest influence in determining the ICU status was IgG3 against SARS-CoV-2 S, and the top 8 variables of influence included the presence of IgG3 against S-trimer as well as IgA against SARS-CoV-2 S.Conclusion. Understanding the complexities of humoral immunity in various patients is critical for early medical intervention, disease management, selective vaccination and passive immunotherapy.

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