Kinetics of the Severe Acute Respiratory Syndrome Coronavirus 2 Antibody Response and Serological Estimation of Time Since Infection

  1. Stéphane Pelleau
  2. Tom Woudenberg
  3. Jason Rosado
  4. Françoise Donnadieu
  5. Laura Garcia
  6. Thomas Obadia
  7. Soazic Gardais
  8. Yasmine Elgharbawy
  9. Aurelie Velay
  10. Maria Gonzalez
  11. Jacques Yves Nizou
  12. Nizar Khelil
  13. Konstantinos Zannis
  14. Charlotte Cockram
  15. Sarah Hélène Merkling
  16. Annalisa Meola
  17. Solen Kerneis
  18. Benjamin Terrier
  19. Jerome de Seze
  20. Delphine Planas
  21. Olivier Schwartz
  22. François Dejardin
  23. Stéphane Petres
  24. Cassandre von Platen
  25. Sandrine Fernandes Pellerin
  26. Laurence Arowas
  27. Louise Perrin de Facci
  28. Darragh Duffy
  29. Clíona Ní Cheallaigh
  30. Jean Dunne
  31. Niall Conlon
  32. Liam Townsend
  33. Veasna Duong
  34. Heidi Auerswald
  35. Laurie Pinaud
  36. Laura Tondeur
  37. Marija Backovic
  38. Bruno Hoen
  39. Arnaud Fontanet
  40. Ivo Mueller
  41. Samira Fafi-Kremer
  42. Timothée Bruel
  43. Michael White

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Abstract

Background

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induces a complex antibody response that varies by orders of magnitude between individuals and over time.

Methods

We developed a multiplex serological test for measuring antibodies to 5 SARS-CoV-2 antigens and the spike proteins of seasonal coronaviruses. We measured antibody responses in cohorts of hospitalized patients and healthcare workers followed for up to 11 months after symptoms. A mathematical model of antibody kinetics was used to quantify the duration of antibody responses. Antibody response data were used to train algorithms for estimating time since infection.

Results

One year after symptoms, we estimate that 36% (95% range, 11%–94%) of anti-Spike immunoglobulin G (IgG) remains, 31% (95% range, 9%–89%) anti-RBD IgG remains, and 7% (1%–31%) of anti-nucleocapsid IgG remains. The multiplex assay classified previous infections into time intervals of 0–3 months, 3–6 months, and 6–12 months. This method was validated using data from a seroprevalence survey in France, demonstrating that historical SARS-CoV-2 transmission can be reconstructed using samples from a single survey.

Conclusions

In addition to diagnosing previous SARS-CoV-2 infection, multiplex serological assays can estimate the time since infection, which can be used to reconstruct past epidemics.

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  1. Version published to 10.1093/infdis/jiab375
  2. ScreenIT

    SciScore for 10.1101/2021.03.04.21252532: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Sample collection in Hôpital Cochin was approved by the Research Ethics Commission of Necker-Cochin Hospital.
    IACUC: Use of the Peruvian negative controls was approved by the Institutional Ethics Committee from the Universidad Peruana Cayetano Heredia (SIDISI 100873).
    Consent: Informed written consent was obtained from all participants or their next of kin in accordance with the Declaration of Helsinki.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Serological assays: We set up a 9-plex bead-based assay allowing simultaneous detection of antibody responses to five SARS-CoV-2 antigens and four seasonal coronaviruses (Spike proteins of NL63, 229E, HKU1, OC43) in 1µL serum or plasma samples that were heat-inactivated at 56°C for 30 minutes.
    HKU1
    suggested: None
    Secondary antibodies conjugated to R-phycoerythrin (Jackson Immunoresearch) were used at 1/120, 1/200 or 1/400 for detection of specific IgG, IgA and IgM respectively.
    R-phycoerythrin
    suggested: (Cell Sciences Cat# CMC400PE, RRID:AB_418620)
    Secondly, we implemented a foci reduction neutralization test (FRNT) based on the detection of neutralizing antibodies directed against SARS-CoV-2.
    SARS-CoV-2
    suggested: None
    Classification algorithms: Measurements of antibodies of three isotypes (IgG, IgM, IgA) to multiple SARS-CoV-2 antigens were used to create a training dataset.
    IgM, IgA
    suggested: None

    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:
    There are several limitations to this study. Estimates of the duration of antibody responses are based on data from multiple studies, each using a unique immunoassay6,7,8,9,10,11,12. Every immunoassay may differ in terms of background reactivity, cross-reactivity with other pathogens, protein formulation, dynamic range and reproducibility. We believe that the benefit of drawing on multiple data sources outweighs the benefit of having a smaller more homogeneous database, especially since the mathematical model of antibody kinetics is sufficiently flexible to incorporate data from multiple assays. Our selection of a mechanistic mathematical model of antibody kinetics is a potential limitation. The model is based on a mechanistic understanding of the immunological processes underlying the generation and persistence of antibodies, and imposes a flexible functional form on how antibody levels change over time. Although this approach has been validated in a range of applications16,17,18,19, there will be instances where the model fails to capture the true pattern of antibody kinetics, for example in immune-deficient individuals. An advantage of a mechanistic model versus a non-parametric statistical model is the ability to make projections forward in time. We have provided predictions up to two years following infection, for example by estimating that 16% (5%, 48%) of anti-RBD IgG antibodies remain after two years. There is a risk to providing predictions beyond the timescale of th...

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04325646RecruitingSero-epidemiological Study of the SARS-CoV-2 Virus Responsib…
    NCT04262921RecruitingFrench COVID Cohort
    NCT04441684RecruitingSeroprevalence of SARS-CoV-2 in Strasbourg University Hospit…

    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 10.1101/2021.03.04.21252532 on medRxiv