Ali Fatma H, Gentilcore Giusy, Al-Jighefee Hadeel T, Taleb Sara Ahmad, Hssain Ali Ait, Qotba Hamda A, Al Thani Asmaa A, Abu Raddad Laith J, Nasrallah Gheyath K, Grivel Jean-Charles, Yassine Hadi M
Biomedical Research Center, QH Health, Qatar University, Doha, Qatar.
Department of Biomedical Sciences, College of Health Sciences, QU Health, Qatar University, Doha, Qatar.
J Med Microbiol. 2025 May;74(5). doi: 10.1099/jmm.0.002012.
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. SARS-CoV-2 disease severity is influenced by pre-existing immunity to HCoVs, with distinct antibody profiles and cross-reactivity patterns. 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. 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. 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. Understanding the complexities of humoral immunity in various patients is critical for early medical intervention, disease management, selective vaccination and passive immunotherapy.
对人类冠状病毒(HCoVs)的既往免疫力可能会影响COVID-19患者的免疫反应。越来越多的证据表明,严重急性呼吸综合征冠状病毒2(SARS-CoV-2)与其他冠状病毒之间的免疫交叉反应性可能决定临床预后。SARS-CoV-2疾病的严重程度受对HCoVs的既往免疫力影响,具有不同的抗体谱和交叉反应模式。为了研究重症监护病房(ICU)和非ICU的SARS-CoV-2患者针对不同HCoV蛋白的抗体反应,并评估既往免疫力对SARS-CoV-2疾病结局的潜在影响。本研究使用了一种全面的HCoVs抗原珠阵列,来测量70例ICU和63例非ICU COVID-19患者对致病性中东呼吸综合征冠状病毒(MERS-CoV)、严重急性呼吸综合征冠状病毒(SARS-CoV)、SARS-CoV-2以及四种季节性HCoVs的抗体反应。我们的分析表明,ICU的COVID-19患者总体抗体反应高于非ICU患者。有趣的是,ICU患者的抗S1 IgG和IgA显著高于非ICU患者。同样,与非ICU患者相比,ICU患者中针对NL63的抗S1 IgG反应较低。SARS-CoV-2与SARS-CoV抗体之间存在明显的交叉反应,但与MERS-CoV和季节性HCoVs不存在交叉反应。对识别SARS-CoV-2抗体的亚类分析显示,与非ICU患者相比,ICU患者中的抗S1 IgG1、IgG3、IgA1和IgA2显著更高。SARS-CoV-2患者中主要的IgA亚型是IgA1。我们应用机器学习算法对亚类血清学反应进行分析,以构建能够区分ICU患者和症状较轻的COVID-19患者的分类器。在两种不同类型模型中使用的90个变量中,决定ICU状态影响最大的变量是针对SARS-CoV-2 S的IgG3,影响最大的前8个变量包括针对S三聚体的IgG3以及针对SARS-CoV-2 S的IgA。了解不同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。