Antibody Responses to SARS-CoV-2 in Patients With Novel Coronavirus Disease 2019

  1. Juanjuan Zhao
  2. Quan Yuan
  3. Haiyan Wang
  4. Wei Liu
  5. Xuejiao Liao
  6. Yingying Su
  7. Xin Wang
  8. Jing Yuan
  9. Tingdong Li
  10. Jinxiu Li
  11. Shen Qian
  12. Congming Hong
  13. Fuxiang Wang
  14. Yingxia Liu
  15. Zhaoqin Wang
  16. Qing He
  17. Zhiyong Li
  18. Bin He
  19. Tianying Zhang
  20. Yang Fu
  21. Shengxiang Ge
  22. Lei Liu
  23. Jun Zhang
  24. Ningshao Xia
  25. Zheng Zhang

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

Background

The novel coronavirus SARS-CoV-2 is a newly emerging virus. The antibody response in infected patients remains largely unknown, and the clinical value of antibody testing has not been fully demonstrated.

Methods

173 patients with SARS-CoV-2 infection were enrolled. Their serial plasma samples (n = 535) collected during hospitalization were tested for total antibodies (Ab), IgM, and IgG against SARS-CoV-2. The dynamics of antibodies with disease progress were analyzed.

Results

Among 173 patients, the seroconversion rates for Ab, IgM, and IgG were 93.1%, 82.7%, and 64.7%, respectively. The reason for the negative antibody findings in 12 patients might be due to the lack of blood samples at the later stage of illness. The median seroconversion times for Ab, IgM, and then IgG were days 11, 12, and 4, respectively. The presence of antibodies was <40% among patients within 1 week of onset, and rapidly increased to 100.0% (Ab), 94.3% (IgM), and 79.8% (IgG) by day 15 after onset. In contrast, RNA detectability decreased from 66.7% (58/87) in samples collected before day 7 to 45.5% (25/55) during days 15–39. Combining RNA and antibody detection significantly improved the sensitivity of pathogenic diagnosis for COVID-19 (P < .001), even in the early phase of 1 week from onset (P = .007). Moreover, a higher titer of Ab was independently associated with a worse clinical classification (P = .006).

Conclusions

Antibody detection offers vital clinical information during the course of SARS-CoV-2 infection. The findings provide strong empirical support for the routine application of serological testing in the diagnosis and management of COVID-19 patients.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.03.02.20030189: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study was reviewed and approved by the Medical Ethical Committee of Shenzhen Third People’s Hospital (approval number 2020-0018).
    Consent: Written informed consent was obtained from each enrolled patient.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Antibody measurement: The total antibody (Ab), IgM antibody and IgG antibody against SARS-CoV-2 in plasma samples were tested using enzyme linked immunosorbent assay (ELISA) kits supplied by Beijing Wantai Biological Pharmacy Enterprise Co., Ltd., China, according to the manufacturer’s instructions.
    IgM
    suggested: None
    Briefly, the ELISA for total antibodies detection was developed based on double-antigens sandwich immunoassay (Ab-ELISA), using mammalian cell expressed recombinant antigens contained the receptor binding domain (RBD) of the spike protein of SARS-CoV-2 as the immobilized and HRP-conjugated antigen.
    HRP-conjugated antigen.
    suggested: None
    The IgG antibodies were tested using indirect ELISA kit (IgG-ELISA) based on a recombinant nucleoprotein antigen.
    IgG
    suggested: None
    IgG-ELISA) based on a recombinant nucleoprotein antigen.
    suggested: None
    Software and Algorithms
    SentencesResources
    All statistical analysis was conducted by SAS 9.4 (SAS Institute, Cary, NC, USA).
    SAS Institute
    suggested: (Statistical Analysis System, RRID:SCR_008567)

    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:
    It should be noted that there were some limitations of this study. First, for most of RNA tests of the patients were based on upper respiratory tract specimens, the positive rate may be higher in detection using lower respiratory tract specimens, such as bronchoalveolar lavage fluid, deep tracheal aspirates, and induced sputum may yield higher sensitivity for RNA tests. Second, we cannot evaluate the persistence of antibodies because samples were collected during the acute illness course of patients. Third, although it had shown good specificity in healthy people, the cross-reactivity among the different coronaviruses cannot be accurately assessed because we cannot obtain blood samples from SARS-CoV-1 and other coronaviruses infection patients. Future studies are needed to a better understanding of the antibody response profile of SARS-CoV-2 infection. In conclusion, the findings demonstrate that antibody tests have important diagnosis value in addition to RNA tests. These findings provide strong evidence for the routine application of serological antibody assays in the diagnosis and clinical management of COVID-19 patients.

    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/cid/ciaa344
  3. Version published to 10.1101/2020.03.02.20030189 on medRxiv