Antibody response patterns in COVID‐19 patients with different levels of disease severity in Japan

  1. Kazuo Imai
  2. Yutaro Kitagawa
  3. Sakiko Tabata
  4. Katsumi Kubota
  5. Mayu Nagura‐Ikeda
  6. Masaru Matsuoka
  7. Kazuyasu Miyoshi
  8. Jun Sakai
  9. Noriomi Ishibashi
  10. Norihito Tarumoto
  11. Shinichi Takeuchi
  12. Toshimitsu Ito
  13. Shigefumi Maesaki
  14. Kaku Tamura
  15. Takuya Maeda

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Abstract

We analyzed antibody response patterns according to the level of disease severity in patients with novel coronavirus disease 2019 (COVID‐19) in Japan. We analyzed 611 serum specimens from 231 patients with COVID‐19 (mild, 170; severe, 31; critical, 30). Immunoglobulin M (IgM) and IgG antibodies against nucleocapsid protein (N) and spike 1 protein (S1) were detected by enzyme‐linked immunosorbent assays. The peaks of fitting curves for the optical density (OD) values of IgM and IgG antibodies against N appeared simultaneously, while those against S1 were delayed compared with N. The OD values of IgM against N and IgG against both N and S1 were significantly higher in the severe and critical cases than in the mild cases at 11 days after symptom onset. The seroconversion rates of IgG were higher than those of IgM against both N and S1 during the clinical course based on the optimal cut‐off values defined in this study. The seroconversion rates of IgG and IgM against N and S1 were higher in the severe and critical cases than in the mild cases. Our findings show that a stronger antibody response occurred in COVID‐19 patients with greater disease severity and there were low seroconversion rates of antibodies against N and S1 in the mild cases.

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  1. Version published to 10.1002/jmv.26899
  2. ScreenIT

    SciScore for 10.1101/2020.11.20.20231696: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Ethics statement: This study was reviewed and approved by the Institutional Review Board of Saitama Medical University (approval number 1917), Institutional Review Board of Saitama Medical University Hospital (approval numbers 20064.01 and 20001), and Institutional Review Board of the Self-Defense Forces Central Hospital (approval number 01-011).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableBriefly, the patients’ age ranged from 18 to 93 years (median, 49 years; interquartile range [IQR], 38–66 years), and 138 patients (59.7%) were male and 93 (40.3%) were female.

    Table 2: Resources

    Antibodies
    SentencesResources
    Detection of antibodies against SARS-CoV-2 by ELISAs: To measure antibody titers against N and S1, a QuaResearch COVID-19 Human IgM IgG ELISA Kit (Nucleocapsid Protein) (RCOEL961N; Cellspect Co., Ltd., Iwate, Japan) and QuaResearch COVID-19
    S1
    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:
    This study has some limitations. First, we used only commercial ELISAs to detect the antibodies; thus, it is possible that the antibody response patterns are assay-specific. There is a possibility that the absence of seroconversion in several patients was caused by the test conditions and antigens used in the ELISAs utilized in our study. Second, cross-reactivity between SARS-CoV-2 and seasonal HCoVs in COVID-19 was suspected, but it was not examined in this study. Further investigations are required to verify the antibody response patterns by using several different serological methods and to examine cross-reactivity between SARS-CoV-2 and seasonal HCoVs.

    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.

  3. Version published to 10.1101/2020.11.20.20231696v1 on medRxiv