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
{"title":"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。","authors":"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","doi":"10.1099/jmm.0.002012","DOIUrl":null,"url":null,"abstract":"<p><p><b>Introduction.</b> 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.<b>Hypothesis.</b> SARS-CoV-2 disease severity is influenced by pre-existing immunity to HCoVs, with distinct antibody profiles and cross-reactivity patterns.<b>Aim.</b> 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.<b>Methodology.</b> 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.<b>Results.</b> 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.<b>Conclusion.</b> Understanding the complexities of humoral immunity in various patients is critical for early medical intervention, disease management, selective vaccination and passive immunotherapy.</p>","PeriodicalId":94093,"journal":{"name":"Journal of medical microbiology","volume":"74 5","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12075857/pdf/","citationCount":"0","resultStr":"{\"title\":\"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.\",\"authors\":\"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\",\"doi\":\"10.1099/jmm.0.002012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><b>Introduction.</b> 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.<b>Hypothesis.</b> SARS-CoV-2 disease severity is influenced by pre-existing immunity to HCoVs, with distinct antibody profiles and cross-reactivity patterns.<b>Aim.</b> 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.<b>Methodology.</b> 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.<b>Results.</b> 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.<b>Conclusion.</b> Understanding the complexities of humoral immunity in various patients is critical for early medical intervention, disease management, selective vaccination and passive immunotherapy.</p>\",\"PeriodicalId\":94093,\"journal\":{\"name\":\"Journal of medical microbiology\",\"volume\":\"74 5\",\"pages\":\"\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2025-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12075857/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of medical microbiology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1099/jmm.0.002012\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of medical microbiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1099/jmm.0.002012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.