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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

  • 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*
  • *Corresponding author for this work
  • Qatar University
  • Sidra Medicine
  • Hamad Medical Corporation
  • Primary Health Care Centers
  • Weill Cornell Medicine-Qatar

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number002012
Number of pages17
JournalJournal of Medical Microbiology
Volume74
Issue number5
DOIs
Publication statusPublished - May 2025

Keywords

  • Antibody responses
  • Antigen bead array
  • Biomarker
  • Coronaviruses
  • Covid-19
  • Cross-reactivity

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