Dynamics of Neutralizing Antibody Titers in the Months After Severe Acute Respiratory Syndrome Coronavirus 2 Infection

  1. Katharine H D Crawford
  2. Adam S Dingens
  3. Rachel Eguia
  4. Caitlin R Wolf
  5. Naomi Wilcox
  6. Jennifer K Logue
  7. Kiel Shuey
  8. Amanda M Casto
  9. Brooke Fiala
  10. Samuel Wrenn
  11. Deleah Pettie
  12. Neil P King
  13. Alexander L Greninger
  14. Helen Y Chu
  15. Jesse D Bloom

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Most individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) develop neutralizing antibodies that target the viral spike protein. In this study, we quantified how levels of these antibodies change in the months after SARS-CoV-2 infection by examining longitudinal samples collected approximately 30–152 days after symptom onset from a prospective cohort of 32 recovered individuals with asymptomatic, mild, or moderate-severe disease. Neutralizing antibody titers declined an average of about 4-fold from 1 to 4 months after symptom onset. This decline in neutralizing antibody titers was accompanied by a decline in total antibodies capable of binding the viral spike protein or its receptor-binding domain. Importantly, our data are consistent with the expected early immune response to viral infection, where an initial peak in antibody levels is followed by a decline to a lower plateau. Additional studies of long-lived B cells and antibody titers over longer time frames are necessary to determine the durability of immunity to SARS-CoV-2.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.08.06.20169367: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Participants or their legally authorized representatives completed electronic informed consent.
    IRB: This study was approved by the University of Washington Human Subjects Institutional Review Board.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Gemini Biosciences, 100-110, lot H86W03J, pooled from 75 donors), and a CR3022 monoclonal antibody positive control dilution series starting at 1 ug/mL.
    CR3022
    suggested: None
    Plates were then washed three times, and 50 μL of goat anti-human IgG-Fc horseradish peroxidase (HRP)-conjugated antibody (Bethyl Labs, A80-104P) diluted 1:3,000 in PBS-T containing 1% milk was added to each well and incubated for 1 hour at room temperature.
    anti-human IgG-Fc
    suggested: (Bethyl Cat# A80-104P, RRID:AB_67064)
    The IgA secondary antibody was Peroxidase AffiniPure Goat Anti-Human Serum IgA, α chain specific (Jackson Labs, 109-035-011), and the IgM secondary antibody was goat Anti-Human IgM (μ-chain specific)–Peroxidase antibody (Sigma Aldrich, A6907); both were diluted 1:3000 in PBS-T containing 1 % milk.
    IgA
    suggested: (Jackson ImmunoResearch Labs Cat# 109-035-011, RRID:AB_2337580)
    Anti-Human Serum IgA
    suggested: (Jackson ImmunoResearch Labs Cat# 109-035-011, RRID:AB_2337580)
    IgM
    suggested: (Thermo Fisher Scientific Cat# MA1-4803, RRID:AB_612248)
    Anti-Human IgM
    suggested: (Sigma-Aldrich Cat# A6907, RRID:AB_258318)
    μ-chain specific)–Peroxidase
    suggested: None
    Software and Algorithms
    SentencesResources
    Sociodemographic and clinical data were collected from electronic chart review and from participants via a data collection questionnaire (Project REDCap [24]) at the time of enrollment.
    REDCap
    suggested: (REDCap, RRID:SCR_003445)
    After calculating fraction infectivities, we used the neutcurve Python package (https://jbloomlab.github.io/neutcurve/) to calculate the plasma dilution that inhibited infection by 50% (IC50) by fitting a Hill curve with the bottom fixed at 0 and the top fixed at 1.
    Python
    suggested: (IPython, RRID:SCR_001658)

    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: We detected the following sentences addressing limitations in the study:
    The limitations of this study include the small number of samples, particularly in the asymptomatic and symptomatic hospitalized groups, and recruitment of participants from a single study site, which potentially limits the generalizability of these results. Furthermore, since symptom-onset date relies on individual recollections, it is difficult to precisely match the timing of blood draws across all participants. Additionally, we only had follow-up to about four months post-symptom onset and only measured plasma antibody responses. Further studies over longer time frames and with direct interrogation of plasma and memory B cells will be necessary to determine longer term durability of immunity to SARS-CoV-2, as well as its relationship to protection against re-infection. Despite these limitations, our study shows that titers of neutralizing and binding antibodies targeting SARS-CoV-2 spike remain detectable in most individuals out to >90 days post-symptom onset. While titers decline modestly from ~30 to >90 days post-symptom onset, we found that the dynamics of the antibody response to SARS-CoV-2 in the first several months following infection are consistent with what would be expected from knowledge of other acute viral infections [13-18].

    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

    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 10.1093/infdis/jiaa618
  3. Version published to 10.1101/2020.08.06.20169367v1 on medRxiv