Structurally and functionally distinct early antibody responses predict COVID-19 disease trajectory and mRNA vaccine response

  1. Saborni Chakraborty
  2. Joseph C. Gonzalez
  3. Benjamin L. Sievers
  4. Vamsee Mallajosyula
  5. Srijoni Chakraborty
  6. Megha Dubey
  7. Usama Ashraf
  8. Bowie Yik-Ling Cheng
  9. Nimish Kathale
  10. Kim Quyen Thi Tran
  11. Courtney Scallan
  12. Aanika Sinnott
  13. Arianna Cassidy
  14. Steven T. Chen
  15. Terri Gelbart
  16. Fei Gao
  17. Yarden Golan
  18. Xuhuai Ji
  19. Seunghee Kim-Schulze
  20. Mary Prahl
  21. Stephanie L. Gaw
  22. Sacha Gnjatic
  23. Thomas U. Marron
  24. Miriam Merad
  25. Prabhu S. Arunachalam
  26. Scott D. Boyd
  27. Mark M. Davis
  28. Marisa Holubar
  29. Chaitan Khosla
  30. Holden T. Maecker
  31. Yvonne Maldonado
  32. Elizabeth D. Mellins
  33. Kari C. Nadeau
  34. Bali Pulendran
  35. Upinder Singh
  36. Aruna Subramanian
  37. Paul J. Utz
  38. Robert Sherwood
  39. Sheng Zhang
  40. Prasanna Jagannathan
  41. Gene S. Tan
  42. Taia T. Wang

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Abstract

A damaging inflammatory response is strongly implicated in the pathogenesis of severe COVID-19 but mechanisms contributing to this response are unclear. In two prospective cohorts, early non-neutralizing, afucosylated, anti-SARS-CoV-2 IgG predicted progression from mild, to more severe COVID-19. In contrast to the antibody structures that predicted disease progression, antibodies that were elicited by mRNA SARS-CoV-2 vaccines were low in Fc afucosylation and enriched in sialylation, both modifications that reduce the inflammatory potential of IgG. To study the biology afucosylated IgG immune complexes, we developed an in vivo model which revealed that human IgG-FcγR interactions can regulate inflammation in the lung. Afucosylated IgG immune complexes induced inflammatory cytokine production and robust infiltration of the lung by immune cells. By contrast, vaccine elicited IgG did not promote an inflammatory lung response. Here, we show that IgG-FcγR interactions can regulate inflammation in the lung and define distinct lung activities associated with the IgG that predict severe COVID-19 and protection against SARS-CoV-2.

One Sentence Summary

Divergent early antibody responses predict COVID-19 disease trajectory and mRNA vaccine response and are functionally distinct in vivo .

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  1. Version published to 10.1101/2021.05.25.445649v4 on bioRxiv
  2. Version published to 10.1101/2021.05.25.445649v3 on bioRxiv
  3. Version published to 10.1101/2021.05.25.445649v2 on bioRxiv
  4. ScreenIT

    SciScore for 10.1101/2021.05.25.445649: (What is this?)

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

    Table 1: Rigor

    EthicsIACUC: In vivo lung inflammation: All in vivo experiments were performed in compliance with federal laws and institutional guidelines and have been approved by the Stanford University Institutional Animal Care and Use Committee.
    IRB: Clinical cohorts and samples: Characterization of these samples was performed under a protocol approved by the Institutional Review Board of Stanford University (protocol #55718)
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Following primary incubation with the sera, 25 μl of 1:5000 diluted horse radish peroxidase (HRP) conjugated anti-Human IgG and IgA secondary antibodies (Southern Biotech) were added and incubated for 1h at RT.
    anti-Human IgG
    suggested: None
    IgA
    suggested: None
    Cells were then stained for viability with Live/Dead Fixable Staining Kit (ThermoFisher) as well as the following antibody cocktail: anti-CD3 (BioLegend; clone OKT3), anti-CD11c (BioLegend; clone S-HCL-3), anti-CD14 (BioLegend; clone M5E2), anti-CD16 (BioLegend; clone 3G8), anti-CD19 (BioLegend; clone SJ25C1), anti-CD32 (STEMCELL; clone IV.3), anti-CD32B/C (BioLegend; clone S18005H), anti-CD56 (BioLegend; 5.1H11), and anti-HLA-DR (BioLegend; clone L243).
    anti-CD3
    suggested: (BioLegend Cat# 348805, RRID:AB_2889063)
    anti-CD11c
    suggested: None
    anti-CD14
    suggested: None
    anti-CD16
    suggested: (BioLegend Cat# 158402, RRID:AB_2876547)
    anti-CD19
    suggested: None
    anti-CD32
    suggested: None
    STEMCELL
    suggested: (STEMCELL Technologies Cat# 60021, RRID:AB_2891082)
    anti-CD32B/C
    suggested: None
    anti-CD56
    suggested: None
    anti-HLA-DR
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    Cell lines: Human embryonic kidney (HEK) 293T (American Type Culture Collection, ATCC; CRL-3216) and Vero (ATCC; CCL-81) cells were grown and maintained in 1X Dulbecco’s Modified Eagle Medium (DMEM; ThermoFisher Scientific) supplemented with 10% fetal bovine serum (FBS).
    HEK
    suggested: None
    Vero
    suggested: None
    After poly-D-lysine treatment, plates were washed three times with sterile water and then seeded with 1.5e6 cells of HEK 293T per well.
    HEK 293T
    suggested: CCLV Cat# CCLV-RIE 1018, RRID:CVCL_0063)
    Recombinant DNA
    SentencesResources
    We did this by modifying a pCAGGS mammalian expression vector encoding the full-length Wuhan S and deleting its last 18 amino acids of the cytoplasmic domain, which we call pCAGGS-SΔ18.
    pCAGGS
    suggested: RRID:Addgene_18926)
    pCAGGS-SΔ18
    suggested: None
    Data and Statistical analysis: The log10 transformed half-maximal serum neutralization titers (pNT50) were used to generate the heatmap to show the evolution of antibody responses in longitudinal samples collected at different time-points after COVID-19 disease symptom onset.
    pNT50
    suggested: None
    Software and Algorithms
    SentencesResources
    A non-linear curve and the half-maximal neutralization titer (NT50) were generated using GraphPad Prism.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    anti-human CD64 (BioLegend; clone 10.1), anti-B220 (BioLegend), anti-anti-Ly6G (BioLegend; clone 1A8), anti-MERTK (BioLegend; clone 2B10C42)
    BioLegend
    suggested: (BioLegend, RRID:SCR_001134)
    Python version 3.8.5 was used for machine learning.
    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.

    Results from scite Reference Check: We found no unreliable references.

    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.

  5. Version published to 10.1101/2021.05.25.445649v1 on bioRxiv