Differential patterns of cross-reactive antibody response against SARS-CoV-2 spike protein detected for chronically ill and healthy COVID-19 naïve individuals

  1. Mariliis Jaago
  2. Annika Rähni
  3. Nadežda Pupina
  4. Arno Pihlak
  5. Helle Sadam
  6. Jürgen Tuvikene
  7. Annela Avarlaid
  8. Anu Planken
  9. Margus Planken
  10. Liina Haring
  11. Eero Vasar
  12. Miljana Baćević
  13. France Lambert
  14. Eija Kalso
  15. Pirkko Pussinen
  16. Pentti J. Tienari
  17. Antti Vaheri
  18. Dan Lindholm
  19. Tõnis Timmusk
  20. Amir M. Ghaemmaghami
  21. Kaia Palm

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Abstract

Immunity to previously encountered viruses can alter response to unrelated pathogens. We reasoned that similar mechanism may also involve SARS-CoV-2 and thereby affect the specificity and the quality of the immune response against the virus. Here, we employed high-throughput next generation phage display method to explore the link between antibody immune response to previously encountered antigens and spike (S) glycoprotein. By profiling the antibody response in COVID-19 naïve individuals with a diverse clinical history (including cardiovascular, neurological, or oncological diseases), we identified 15 highly antigenic epitopes on spike protein that showed cross-reactivity with antigens of seasonal, persistent, latent or chronic infections from common human viruses. We observed varying degrees of cross-reactivity of different viral antigens with S in an epitope-specific manner. The data show that pre-existing SARS-CoV-2 S1 and S2 cross-reactive serum antibody is readily detectable in pre-pandemic cohort. In the severe COVID-19 cases, we found differential antibody response to the 15 defined antigenic and cross-reactive epitopes on spike. We also noted that despite the high mutation rates of Omicron (B.1.1.529) variants of SARS-CoV-2, some of the epitopes overlapped with the described mutations. Finally, we propose that the resolved epitopes on spike if targeted by re-called antibody response from SARS-CoV-2 infections or vaccinations can function in chronically ill COVID-19 naïve/unvaccinated individuals as immunogenic targets to boost antibodies augmenting the chronic conditions. Understanding the relationships between prior antigen exposure at the antibody epitope level and the immune response to subsequent infections with viruses from a different strain is paramount to guiding strategies to exit the COVID-19 pandemic.

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  1. Version published to 10.1038/s41598-022-20849-6
  2. ScreenIT

    SciScore for 10.1101/2020.05.18.20105189: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Antibodies
    SentencesResources
    Amino acids on Spike glycoprotein (S) of SARS-CoV-2 important for binding to human ACE2 were recently reported by different groups and anti-Spike RBD neutralizing antibody CR3022 discontinuous epitope were reported by Yuan and colleagues18.
    anti-Spike RBD
    suggested: None
    Software and Algorithms
    SentencesResources
    The 22,949 motif sequences were aligned to primary sequence of SARS-CoV-2 Spike glycoprotein (S) (UniProtKB accession P0DTC2).
    UniProtKB
    suggested: (UniProtKB, RRID:SCR_004426)
    To assess conservation of S from SARS-CoV-2, SARS-CoV, OC43 and HKU1 sequence alignment was performed with Clustal Omega tool (version 1.2.4) with default settings (https://www.ebi.ac.uk/Tools/msa/clustalo/).
    Clustal Omega tool
    suggested: None
    https://www.ebi.ac.uk/Tools/msa/clustalo/
    suggested: (Clustal Omega, RRID:SCR_001591)
    Statistical analysis: All statistical analyses (ANOVA, t-Test, correlation analyses, ROC analysis) were done using R statistical programming language and RStudio environment (RStudio Team, 2015;
    RStudio
    suggested: (RStudio, RRID:SCR_000432)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  3. Version published to 10.1101/2020.05.18.20105189v1 on medRxiv