Landscape and selection of vaccine epitopes in SARS-CoV-2

  1. Christof C. Smith
  2. Kelly S. Olsen
  3. Kaylee M. Gentry
  4. Maria Sambade
  5. Wolfgang Beck
  6. Jason Garness
  7. Sarah Entwistle
  8. Caryn Willis
  9. Steven Vensko
  10. Allison Woods
  11. Misha Fini
  12. Brandon Carpenter
  13. Eric Routh
  14. Julia Kodysh
  15. Timothy O’Donnell
  16. Carsten Haber
  17. Kirsten Heiss
  18. Volker Stadler
  19. Erik Garrison
  20. Adam M. Sandor
  21. Jenny P. Y. Ting
  22. Jared Weiss
  23. Krzysztof Krajewski
  24. Oliver C. Grant
  25. Robert J. Woods
  26. Mark Heise
  27. Benjamin G. Vincent
  28. Alex Rubinsteyn

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Background

Early in the pandemic, we designed a SARS-CoV-2 peptide vaccine containing epitope regions optimized for concurrent B cell, CD4 + T cell, and CD8 + T cell stimulation. The rationale for this design was to drive both humoral and cellular immunity with high specificity while avoiding undesired effects such as antibody-dependent enhancement (ADE).

Methods

We explored the set of computationally predicted SARS-CoV-2 HLA-I and HLA-II ligands, examining protein source, concurrent human/murine coverage, and population coverage. Beyond MHC affinity, T cell vaccine candidates were further refined by predicted immunogenicity, sequence conservation, source protein abundance, and coverage of high frequency HLA alleles. B cell epitope regions were chosen from linear epitope mapping studies of convalescent patient serum, followed by filtering for surface accessibility, sequence conservation, spatial localization near functional domains of the spike glycoprotein, and avoidance of glycosylation sites.

Results

From 58 initial candidates, three B cell epitope regions were identified. From 3730 (MHC-I) and 5045 (MHC-II) candidate ligands, 292 CD8 + and 284 CD4 + T cell epitopes were identified. By combining these B cell and T cell analyses, as well as a manufacturability heuristic, we proposed a set of 22 SARS-CoV-2 vaccine peptides for use in subsequent murine studies. We curated a dataset of ~ 1000 observed T cell epitopes from convalescent COVID-19 patients across eight studies, showing 8/15 recurrent epitope regions to overlap with at least one of our candidate peptides. Of the 22 candidate vaccine peptides, 16 (n = 10 T cell epitope optimized; n = 6 B cell epitope optimized) were manually selected to decrease their degree of sequence overlap and then synthesized. The immunogenicity of the synthesized vaccine peptides was validated using ELISpot and ELISA following murine vaccination. Strong T cell responses were observed in 7/10 T cell epitope optimized peptides following vaccination. Humoral responses were deficient, likely due to the unrestricted conformational space inhabited by linear vaccine peptides.

Conclusions

Overall, we find our selection process and vaccine formulation to be appropriate for identifying T cell epitopes and eliciting T cell responses against those epitopes. Further studies are needed to optimize prediction and induction of B cell responses, as well as study the protective capacity of predicted T and B cell epitopes.

Article activity feed

  1. Version published to 10.1186/s13073-021-00910-1
  2. ScreenIT

    SciScore for 10.1101/2020.06.04.135004: (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

    Antibodies
    SentencesResources
    Antibody epitope curation: Linear B cell epitopes on the SARS-CoV-2 surface glycoprotein were curated from four published studies56–59.
    SARS-CoV-2 surface glycoprotein
    suggested: None
    One study characterized the epitopes of monoclonal neutralizing antibodies57.
    antibodies57
    suggested: None
    Software and Algorithms
    SentencesResources
    HLA ligand prediction: The SARS-CoV-2 protein sequence FASTA was retrieved from the NCBI reference database
    NCBI
    suggested: (NCBI, RRID:SCR_006472)
    The proportion of ACE2+ cells expressing the immunomodulatory genes were plotted with the circlize package95.
    circlize
    suggested: (circlize, RRID:SCR_002141)
    Figures 4C, 4D, and 5 were generated using the following Python packages: NumPy118, pandas119, Matplotlib120, and Jupyter121.
    Python
    suggested: (IPython, RRID:SCR_001658)
    NumPy118
    suggested: None

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    One limitation of our study is that, while we use epitope mapping data with direct biological evidence for B cell epitopes in SARS-CoV-2, the T cell epitopes we report were all derived from computational prediction. In an effort to partially overcome this weakness, we applied binding affinity and immunogenicity prediction filters grounded in validated IEDB binding and tetramer studies. Reassuringly, the two extant studies examining T cell responses in COVID-19 patients have identified recurrent T cell epitopes which overlap with the vaccine peptides presented here. Le Bert et al. looked for T cell epitopes within the nucleocapsid (N), NSP-7 and NSP-13 proteins in PBMCs of recovered COVID-19 patients using an IFN-γ ELISpot assay87. They identified two recurrent epitope regions (N101-120, N321-340) which overlap with multiple 27mer vaccine peptides in this paper (Figure 5B, peptides 4-8). Shomuradova et al. also identified COVID-19 patient T cell epitopes, but using A*02:01 tetramers loaded with 13 distinct peptides from the surface glycoprotein (S)88. Two of these 13 peptides showed recurrent reactivity across 14 A*02:01 positive patients (S269-277 and S1000-1008). Both of these epitopes are also included in multiple 27mer vaccine peptides (Figure 5B, peptides 11 and 15). Another potential limitation of this study is the insensitivity of our experiments to the total potential space of SARS-CoV-2 antibody epitopes. Our B cell epitope analyses start with only 58 identified linea...

    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 10.1101/2020.06.04.135004v1 on bioRxiv