Broad and strong memory CD4 + and CD8 + T cells induced by SARS-CoV-2 in UK convalescent COVID-19 patients

  1. Yanchun Peng
  2. Alexander J. Mentzer
  3. Guihai Liu
  4. Xuan Yao
  5. Zixi Yin
  6. Danning Dong
  7. Wanwisa Dejnirattisai
  8. Timothy Rostron
  9. Piyada Supasa
  10. Chang Liu
  11. Cesar Lopez-Camacho
  12. Jose Slon-campos
  13. Yuguang Zhao
  14. Dave Stuart
  15. Guido Paeson
  16. Jonathan Grimes
  17. Fred Antson
  18. Oliver W. Bayfield
  19. Dorothy EDP. Hawkins
  20. De-Sheng Ker
  21. Lance Turtle
  22. Krishanthi Subramaniam
  23. Paul Thomson
  24. Ping Zhang
  25. Christina Dold
  26. Jeremy Ratcliff
  27. Peter Simmonds
  28. Thushan de Silva
  29. Paul Sopp
  30. Dannielle Wellington
  31. Ushani Rajapaksa
  32. Yi-Ling Chen
  33. Mariolina Salio
  34. Giorgio Napolitani
  35. Wayne Paes
  36. Persephone Borrow
  37. Benedikt Kessler
  38. Jeremy W. Fry
  39. Nikolai F. Schwabe
  40. Malcolm G Semple
  41. Kenneth J. Baillie
  42. Shona Moore
  43. Peter JM Openshaw
  44. Azim Ansari
  45. Susanna Dunachie
  46. Ellie Barnes
  47. John Frater
  48. Georgina Kerr
  49. Philip Goulder
  50. Teresa Lockett
  51. Robert Levin
  52. Oxford Immunology Network Covid-19 Response T cell Consortium
  53. Richard J. Cornall
  54. Chris Conlon
  55. Paul Klenerman
  56. Andrew McMichael
  57. Gavin Screaton
  58. Juthathip Mongkolsapaya
  59. Julian C. Knight
  60. Graham Ogg
  61. Tao Dong

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Abstract

COVID-19 is an ongoing global crisis in which the development of effective vaccines and therapeutics will depend critically on understanding the natural immunity to the virus, including the role of SARS-CoV-2-specific T cells. We have conducted a study of 42 patients following recovery from COVID-19, including 28 mild and 14 severe cases, comparing their T cell responses to those of 16 control donors. We assessed the immune memory of T cell responses using IFNγ based assays with overlapping peptides spanning SARS-CoV-2 apart from ORF1. We found the breadth, magnitude and frequency of memory T cell responses from COVID-19 were significantly higher in severe compared to mild COVID-19 cases, and this effect was most marked in response to spike, membrane, and ORF3a proteins. Total and spike-specific T cell responses correlated with the anti-Spike, anti-Receptor Binding Domain (RBD) as well as anti-Nucleoprotein (NP) endpoint antibody titre (p<0.001, <0.001 and =0.002). We identified 39 separate peptides containing CD4 + and/or CD8 + epitopes, which strikingly included six immunodominant epitope clusters targeted by T cells in many donors, including 3 clusters in spike (recognised by 29%, 24%, 18% donors), two in the membrane protein (M, 32%, 47%) and one in the nucleoprotein (Np, 35%). CD8+ responses were further defined for their HLA restriction, including B*4001-restricted T cells showing central memory and effector memory phenotype. In mild cases, higher frequencies of multi-cytokine producing M- and NP-specific CD8 + T cells than spike-specific CD8 + T cells were observed. They furthermore showed a higher ratio of SARS-CoV-2-specific CD8 + to CD4 + T cell responses. Immunodominant epitope clusters and peptides containing T cell epitopes identified in this study will provide critical tools to study the role of virus-specific T cells in control and resolution of SARS-CoV-2 infections. The identification of T cell specificity and functionality associated with milder disease, highlights the potential importance of including non-spike proteins within future COVID-19 vaccine design.

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  1. ScreenIT

    SciScore for 10.1101/2020.06.05.134551: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Written informed consent was obtained from all patients.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    To determine EPTs to RBD and NP, immunoplates were coated with 0.125ug of Tetra-His antibody (34670; QIAGEN) followed by 2 ug/ml and 5 ug/ml of soluble RBD and NP, respectively.
    NP
    suggested: None
    On the second day, the PBMCs were stimulated with pooled or individual peptides at a final concentration of 10 μg/mL per individual peptide for 1 h in the presence of 2 μg/mL monoclonal antibodies against human CD28 (BD Pharmingen) and CD49d (BD Pharmingen) then for an additional 5h with GolgiPlug (
    human CD28
    suggested: None
    CD49d
    suggested: None
    Dead cells were first labelled for FACS analysis using LIVE/DEAD™ Fixable Aqua Dead Cell Stain Kit (Invitrogen) and with CD3-BUV395, CD8-PerCP.Cy5.5 as well as a panel of antibodies for dumping, cell activation, differentiation and inhibitory markers : CD14-BV510 (Biolegend UK), CD16-BV510 (Biolegend UK), CD19-BV510 (Biolegend UK), CD28-BV711, CD27-APC-R700, HLA-DR-BB515, CD38-BUV737, CD45RA-APC-H7, PD-1-BV650, CD57-BV785 (Biolegend, UK) and NKG2A (R&D).
    CD14-BV510
    suggested: None
    CD16-BV510
    suggested: None
    CD19-BV510
    suggested: None
    CD57-BV785
    suggested: None
    NKG2A
    suggested: None
    Software and Algorithms
    SentencesResources
    Synthetic peptides: A total of 423 15- to 18-mer peptides overlapping by 10 amino acid residues and spanning the full proteome of the SARS-CoV-2 except ORF-1 (253 spike, 29 M, 9 E, 35 ORF3a, 7ORF6, 15 ORF7a, 16 ORF8, 59 NP) were designed using the Los Alamos National Library web-based software PeptGen (http://www.hiv.lanl.gov/content/sequence/PEPTGEN/peptgen.html) and synthesized (purity >75%; Proimmune).
    PeptGen
    suggested: None
    Finally, surface markers including BUV395-anti-CD3 (BD Biosciences), BUV737-anti-CD4 (BD Biosciences), PerCP-Cy5.5-anti-CD8 (BD Biosciences), BV510-anti-CD14 (Biolegend), BV510-anti-CD16 (Biolegend) and BV510-anti-CD19 (Biolegend) were stained.
    BD Biosciences
    suggested: (BD Biosciences, RRID:SCR_013311)
    All samples were acquired on BD LSR Fortessa (BD Biosciences) flow cytometer and analyzed using FlowJo™ v.10 software for Mac (Becton, Dickinson and Company; 2019).
    FlowJo™
    suggested: (FlowJo, RRID:SCR_008520)
    Cell events were acquired on Fortessa X20 (BD Bioscience) and data files were analyzed using FlowJo software.
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    Statistical analysis: Statistical analysis was performed with IBM SPSS Statistics 25 and figures were made with GraphPad Prism 8.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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: Please consider improving the rainbow (“jet”) colormap(s) used on pages 32, 25 and 28. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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 10.1101/2020.06.05.134551 on bioRxiv