The immune vulnerability landscape of the 2019 Novel Coronavirus, SARS-CoV-2

  1. James Zhu
  2. Jiwoong Kim
  3. Xue Xiao
  4. Yunguan Wang
  5. Danni Luo
  6. Shuang Jiang
  7. Ran Chen
  8. Lin Xu
  9. He Zhang
  10. Lenny Moise
  11. Andres H. Gutierrez
  12. Anne S. De Groot
  13. Guanghua Xiao
  14. John W. Schoggins
  15. Xiaowei Zhan
  16. Tao Wang
  17. Yang Xie

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Abstract

The outbreak of the 2019 Novel Coronavirus (SARS-CoV-2) rapidly spread from Wuhan, China to more than 150 countries, areas, or territories, causing staggering numbers of infections and deaths. In this study, bioinformatics analyses were performed on 5,568 complete genomes of SARS-CoV-2 virus to predict the T cell and B cell immunogenic epitopes of all viral proteins, which formed a systematic immune vulnerability landscape of SARS-CoV-2. The immune vulnerability and genetic variation profiles of SARS-CoV were compared with those of SARS-CoV and MERS-CoV. In addition, a web portal was developed to broadly share the data and results as a resource for the research community. Using this resource, we showed that genetic variations in SARS-CoV-2 are associated with loss of B cell immunogenicity, an increase in CD4 + T cell immunogenicity, and a minimum loss in CD8 + T cell immunogenicity, indicating the existence of a curious correlation between SARS-CoV-2 genetic evolutions and the immunity pressure from the host. Overall, we present an immunological resource for SARS-CoV-2 that could promote both therapeutic/vaccine development and mechanistic research.

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  1. Version published to 10.1101/2020.02.08.939553v3 on bioRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.02.08.939553: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    DNA and protein sequence alignment: The command-line version of MUSCLE (v3.8.31
    MUSCLE
    suggested: (MUSCLE, RRID:SCR_011812)
    The protein sequence alignment between the S proteins, YP_009724390.1 (SARS-CoV-2) and NP_828851.1 (SARS-CoV), was performed using EMBOSS needle(Rice et al., 2000) with the BLOSUM62 scoring matrix.
    EMBOSS
    suggested: (EMBOSS, RRID:SCR_008493)
    The B cell epitope predictions were made by the BepiPred 2.0 software (Jespersen et al., 2017), with default parameters.
    BepiPred
    suggested: (BepiPred-2.0, RRID:SCR_018499)
    Statistical analyses: All computations and statistical analyses were carried out in the R and Python computing environment.
    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: 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

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  3. Version published to 10.1101/2020.02.08.939553v2 on bioRxiv
  4. Version published to 10.1101/2020.02.08.939553v1 on bioRxiv