Different pattern of pre-existing SARS-COV-2 specific T cell immunity in SARS-recovered and uninfected individuals

  1. Nina Le Bert
  2. Anthony T Tan
  3. Kamini Kunasegaran
  4. Christine Y L Tham
  5. Morteza Hafezi
  6. Adeline Chia
  7. Melissa Chng
  8. Meiyin Lin
  9. Nicole Tan
  10. Martin Linster
  11. Wan Ni Chia
  12. Mark I-Cheng Chen
  13. Lin-Fa Wang
  14. Eng Eong Ooi
  15. Shirin Kalimuddin
  16. Paul Anantharajal Tambyah
  17. Jenny Guek-Hong Low
  18. Yee-Joo Tan
  19. Antonio Bertoletti

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Abstract

Memory T cells induced by previous infections can influence the course of new viral infections. Little is known about the pattern of SARS-CoV-2 specific pre-existing memory T cells in human. Here, we first studied T cell responses to structural (nucleocapsid protein, NP) and non-structural (NSP-7 and NSP13 of ORF1) regions of SARS-CoV-2 in convalescent from COVID-19 (n=24). In all of them we demonstrated the presence of CD4 and CD8 T cells recognizing multiple regions of the NP protein. We then show that SARS-recovered patients (n=23), 17 years after the 2003 outbreak, still possess long-lasting memory T cells reactive to SARS-NP, which displayed robust cross-reactivity to SARS-CoV-2 NP. Surprisingly, we observed a differential pattern of SARS-CoV-2 specific T cell immunodominance in individuals with no history of SARS, COVID-19 or contact with SARS/COVID-19 patients (n=18). Half of them (9/18) possess T cells targeting the ORF-1 coded proteins NSP7 and 13, which were rarely detected in COVID-19- and SARS-recovered patients. Epitope characterization of NSP7-specific T cells showed recognition of protein fragments with low homology to “common cold” human coronaviruses but conserved among animal betacoranaviruses.

Thus, infection with betacoronaviruses induces strong and long-lasting T cell immunity to the structural protein NP. Understanding how pre-existing ORF-1-specific T cells present in the general population impact susceptibility and pathogenesis of SARS-CoV-2 infection is of paramount importance for the management of the current COVID-19 pandemic.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.26.115832: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Ethics statement: All donors provided written consent.
    IRB: The study was conducted in accordance with the Declaration of Helsinki and approved by the NUS institutional review board (H-20-006) and the SingHealth Centralised Institutional Review Board (reference CIRB/F/2018/2387).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    , anti-CD4 (clone SK3), and anti-CD8 (clone SK1) antibodies.
    anti-CD4
    suggested: None
    anti-CD8
    suggested: None
    Cells were subsequently fixed and permeabilized using the Cytofix/Cytoperm kit (BD Biosciences-Pharmingen) and stained with anti-IFN-γ (clone 25723, R&D Systems) and anti-TNF-α (clone MAb11) antibodies and analyzed on a BD-LSR 11 FACS Scan.
    anti-IFN-γ
    suggested: None
    anti-TNF-α
    suggested: None
    Software and Algorithms
    SentencesResources
    Data were analyzed by FlowJo (Tree Star Inc.)
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    Sequences were aligned using the MUSCLE algorithm with default parameters and percentage identity was calculated in Geneious Prime 2020.1.2 (
    MUSCLE
    suggested: (MUSCLE, RRID:SCR_011812)
    (https://www.geneious.com).
    https://www.geneious.com
    suggested: (Geneious, RRID:SCR_010519)
    Alignment figures were made in Snapgene 5.1 (GSL Biotech).
    Snapgene
    suggested: (SnapGene, RRID:SCR_015052)

    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.

    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. ScreenIT

    SciScore for 10.1101/2020.05.26.115832: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.Randomizationnot detected.Blindingnot detected.Power Analysisnot detected.Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    The blood samples were collected either before July 2019 or were serologically negative for both SARS-CoV-2 neutralizing antibodies and SARS-CoV-2 NP antibodies19 .
    SARS-CoV-2 NP
    suggested: (Sino Biological Cat# 40143-R019, AB_2827973)

    Results from OddPub: We did not find a statement about open data. We also did not find a statement about open code. Researchers are encouraged to share open data when possible (see Nature blog).

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.05.26.115832 on bioRxiv