Understanding the role of memory re-activation and cross-reactivity in the defense against SARS-CoV-2

  1. Viola Denninger
  2. Catherine K. Xu
  3. Georg Meisl
  4. Alexey S. Morgunov
  5. Sebastian Fiedler
  6. Alison Ilsley
  7. Marc Emmenegger
  8. Anisa Y. Malik
  9. Monika A. Piziorska
  10. Matthias M. Schneider
  11. Sean R. A. Devenish
  12. Vasilis Kosmoliaptsis
  13. Adriano Aguzzi
  14. Heike Fiegler
  15. Tuomas P. J. Knowles

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Abstract

Recent efforts in understanding the course and severity of SARS-CoV-2 infections have highlighted both potential beneficial as well as detrimental effects of cross-reactive antibodies derived from memory immunity. Specifically, due to a significant degree of sequence similarity between SARS-CoV-2 and other members of the coronavirus family, memory B-cells that emerged from previous infections with endemic human coronaviruses (HCoVs) could be re-activated upon encountering the newly emerged SARS-CoV-2, thus prompting the production of cross-reactive antibodies. Understanding the affinity and concentration of these potentially cross-reactive antibodies to the new SARS-CoV-2 antigens is therefore particularly important when assessing both existing immunity against common HCoVs and adverse effects like antibody-dependent enhancement (ADE) in COVID-19. However, these two fundamental parameters cannot easily be deconvoluted by surface-based assays like enzyme-linked immunosorbent assays (ELISAs) which are routinely used to assess cross-reactivity.

Here, we have used microfluidic antibody-affinity profiling (MAAP) to quantitatively evaluate the humoral immune response in COVID-19 convalescent patients by determining both antibody affinity and concentration against spike antigens of SARS-CoV-2 directly in nine convalescent COVID-19 patient and three pre-pandemic sera that were seropositive for common HCoVs. All 12 sera contained low concentrations of high affinity antibodies against spike antigens of HCoV-NL63 and HCoV-HKU1, indicative of past exposure to these pathogens, while the affinity against the SARS-CoV-2 spike protein was lower. These results suggest that cross-reactivity as a consequence of memory re-activation upon an acute SARS-CoV-2 infection may not be a significant factor in generating immunity against SARS-CoV-2.

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  1. ScreenIT

    SciScore for 10.1101/2021.07.23.453352: (What is this?)

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

    Table 1: Rigor

    EthicsConsent: BioIVT sought informed consent from each subject, or the subjects legally authorized representative and appropriately documented this in writing.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    After sample incubation for 2 h at RT, the wells were washed five times with wash buffer and the presence of IgGs directed against above-defined SARS-CoV-2 antigens was detected using an HRP-linked anti-human IgG antibody (Peroxidase AffiniPure Goat Anti-Human IgG, Fcγ Fragment Specific, Jackson, 109-035-098, at 1:4000 dilution in sample buffer) at 20 μL/well.
    Anti-Human IgG
    suggested: (Jackson ImmunoResearch Labs Cat# 109-035-098, RRID:AB_2337586)
    Equilibrium affinity binding curves of recombinant antibodies against S1 proteins of HCoV-HKU1 and HCoV-NL63: For affinity measurements of anti-human coronavirus spike glycoprotein HKU1 (40021-MM07-100, Sino Biological) to HCoV-HKU1 S1 protein, the antibody was reconstituted according to the manufacturer’s instructions and diluted into PBS, containing 0.05% Tween 20 and 5% human serum albumin (HSA), to achieve a two-fold concentration series ranging from 60 pM to 1 μM.
    anti-human coronavirus spike glycoprotein HKU1
    suggested: None
    For cross-reactivity measurements, anti-SARS-CoV-2 neutralizing antibody (SAD-S35, Acro Biosystems) was reconstituted according to the manufacturer’s instructions.
    anti-SARS-CoV-2
    suggested: None
    The binding affinities and concentrations of antigen-specific antibodies in serum samples were determined by monitoring the fraction of labeled antigen that diffused into the distal chamber of the microfluidic device.
    antigen-specific
    suggested: None
    As serum samples were derived from convalescent patients, potentially containing different classes of antibodies with variable numbers of binding sites (IgG = 2, IgM = 10), antibody concentration is expressed in terms of binding sites, rather than molecules.
    IgG = 2 , IgM = 10) ,
    suggested: None
    SARS-CoV-2 RBD and HCoV-NL63-RBD competition assay: 10 nM Alexa Fluor 647 labeled HCoV-NL63 RBD in PBS containing 0.05% Tween 20 was combined with serum sample corresponding to a final anti-NL63 RBD antibody concentration of 20 nM (based on previous MAAP of each serum sample).
    anti-NL63 RBD
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    Pre-COVID sera were seropositive for the following coronaviruses: serum 1 (HCoV-229E, HCoV-NL63), serum 2 (HCoV-229E, HCoV-NL63, HCoV-OC43, HCoV-HKU1) and serum 3 (HCoV-229E).
    HCoV-NL63
    suggested: RRID:CVCL_RW88)
    For profiling of serum against SARS-CoV-2 S1, SARS-CoV-2 S2, HCoV-NL63 S1 and HCoV-HKU1 S1, the buffer additionally contained 5% human serum albumin (HSA).
    HCoV-NL63 S1
    suggested: None
    Software and Algorithms
    SentencesResources
    alignment: Sequence alignment of coronavirus full length spikes was performed using the Clustal Omega Sequence Alignment Tool.
    Clustal Omega
    suggested: (Clustal Omega, RRID:SCR_001591)
    The p(EC50) values for all samples and antigens were visualized using the ggplot2 package in R.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    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.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2021.07.23.453352v1 on bioRxiv