Global Absence and Targeting of Protective Immune States in Severe COVID-19

  1. Alexis J. Combes
  2. Tristan Courau
  3. Nicholas F. Kuhn
  4. Kenneth H. Hu
  5. Arja Ray
  6. William S. Chen
  7. Simon J. Cleary
  8. Nayvin W. Chew
  9. Divyashree Kushnoor
  10. Gabriella C. Reeder
  11. Alan Shen
  12. Jessica Tsui
  13. Kamir J. Hiam-Galvez
  14. Priscila Muñoz-Sandoval
  15. Wandi S Zhu
  16. David S. Lee
  17. Yang Sun
  18. Ran You
  19. Mélia Magnen
  20. Lauren Rodriguez
  21. Aleksandra Leligdowicz
  22. Colin R. Zamecnik
  23. Rita P. Loudermilk
  24. Michael R. Wilson
  25. Chun J. Ye
  26. Gabriela K. Fragiadakis
  27. Mark R. Looney
  28. Vincent Chan
  29. Alyssa Ward
  30. Sidney Carrillo
  31. The UCSF COMET Consortium
  32. Michael Matthay
  33. David J. Erle
  34. Prescott G. Woodruff
  35. Charles Langelier
  36. Kirsten Kangelaris
  37. Carolyn M. Hendrickson
  38. Carolyn Calfee
  39. Arjun Arkal Rao
  40. Matthew F. Krummel

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Abstract

While SARS-CoV-2 infection has pleiotropic and systemic effects in some patients, many others experience milder symptoms. We sought a holistic understanding of the severe/mild distinction in COVID-19 pathology, and its origins. We performed a whole-blood preserving single-cell analysis protocol to integrate contributions from all major cell types including neutrophils, monocytes, platelets, lymphocytes and the contents of serum. Patients with mild COVID-19 disease display a coordinated pattern of interferon-stimulated gene (ISG) expression across every cell population and these cells are systemically absent in patients with severe disease. Severe COVID-19 patients also paradoxically produce very high anti-SARS-CoV-2 antibody titers and have lower viral load as compared to mild disease. Examination of the serum from severe patients demonstrates that they uniquely produce antibodies with multiple patterns of specificity against interferon-stimulated cells and that those antibodies functionally block the production of the mild disease-associated ISG-expressing cells. Overzealous and auto-directed antibody responses pit the immune system against itself in many COVID-19 patients and this defines targets for immunotherapies to allow immune systems to provide viral defense.

One Sentence Summary

In severe COVID-19 patients, the immune system fails to generate cells that define mild disease; antibodies in their serum actively prevents the successful production of those cells.

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  1. ScreenIT

    SciScore for 10.1101/2020.10.28.359935: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Patients, or a designated surrogate, provided informed consent to participate in the study.
    IRB: The study is approved by the Institutional Review board: IRB#
    RandomizationThe average expression of the genes in each signature are compared to a background list of randomly selected from the bins and used to generate a score per cell for each signature.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Samples were harvested and unconjugated AffiniPure Fab Fragment Goat anti-human IgG (H+L) (Jackson Immunoresearch; #109-007-003) and Human TruStain FcX block (BioLegend; #422302) were used to block pre-bound antibodies and Fc receptors.
    anti-human IgG
    suggested: (Jackson ImmunoResearch Labs Cat# 109-007-003, RRID:AB_2337555)
    After washing the cells with FACS buffer, cell-bound antibodies were detected using an AffiniPure Donkey anti-human IgG-Alexa Fluor 647 antibody (Jackson Immunoresearch; #709-605-149), which was incubated with the cells for 30 min on ice.
    anti-human IgG-Alexa Fluor 647
    suggested: None
    The following antibodies were used for flow cytometric analysis: anti-human CD3-BB700 (clone SK7; BD Biosciences;
    anti-human CD3-BB700
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    The supernatant was collected and viral titer was quantified using a plaque assay in Vero E6 cells.
    Vero E6
    suggested: RRID:CVCL_XD71)
    Software and Algorithms
    SentencesResources
    Bulk RNASeq library preparation for Genotyping: RNA was extracted from aliquots of 250K Peripheral Blood Mononuclear Cells (PBMCs) utilizing the ZYMO Research Quick RNA MagBead kit (R2133) on a Thermofisher KingFisher Flex system following manufacturer’s procedures.
    Thermofisher KingFisher
    suggested: None
    Computational Processing for Genotyping: Sequencing reads were aligned to the human reference genome and Ensembl annotation (GRCh38 genome build, Ensembl annotation version 95) using STAR v2.7.5c (PMID: 23104886) with the following parameters: --outFilterType BySJout --outFilterMismatchNoverLmax 0.04 --outFilterMismatchNmax 999 --alignSJDBoverhangMin 1 --outFilterMultimapNmax 1 --alignIntronMin 20 --alignIntronMax 1000000 --alignMatesGapMax 1000000.
    Ensembl
    suggested: (Ensembl, RRID:SCR_002344)
    STAR
    suggested: (STAR, RRID:SCR_015899)
    Duplicate reads were removed and read groups assigned by individual for variant calling using Picard Tools v2.23.3 (
    Picard
    suggested: (Picard, RRID:SCR_006525)
    Nucleotide variants were identified from the resulting bam files using the Genome Analysis Tool Kit (GATK, v4.0.11.0) following the best practices for RNA-seq variant calling (PMID: 25431634; PMID: 21478889).
    GATK
    suggested: (GATK, RRID:SCR_001876)
    Processed, annotated Seurat objects were processed using the DoubletFinder package (8).
    DoubletFinder
    suggested: (DoubletFinder, RRID:SCR_018771)
    The R package EnrichR were used to generate volcano plot from differential gene expression using FindMarkers function in Seurat.
    EnrichR
    suggested: (Enrichr, RRID:SCR_001575)
    20 μL each of plasma collected from the COMET patient cohort (21 COVID-19 positive, 13 COVID-19 negative, and 14 healthy individuals) were analyzed using the Olink® Target 96 Inflammation panel, which is a set of 92 inflammation-related protein biomarkers.
    COMET
    suggested: (CoMet, RRID:SCR_011925)
    After surface staining and addition of fixable live/dead violet dye (ThermoFisher; #L34955), intracellular detection of IFITM3 was done using the eBioscience Foxp3 /
    ThermoFisher
    suggested: (ThermoFisher; SL 8; Centrifuge, RRID:SCR_020809)
    Samples were harvested and unconjugated AffiniPure Fab Fragment Goat anti-human IgG (H+L) (Jackson Immunoresearch; #109-007-003) and Human TruStain FcX block (BioLegend; #422302) were used to block pre-bound antibodies and Fc receptors.
    BioLegend
    suggested: (BioLegend, RRID:SCR_001134)
    Statistical Analysis and Data visualization: Statistical analyses were performed using GraphPad prism or the R software package.
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)
    The R packages Seurat, ggplot2
    Seurat
    suggested: (SEURAT, RRID:SCR_007322)
    2 (version 3.1.0) (Wickham, 2016) GraphPad Prism and Adobe Illustrator were used to generate figures.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Adobe Illustrator
    suggested: (Adobe Illustrator, RRID:SCR_010279)

    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. Version published to 10.1101/2020.10.28.359935 on bioRxiv