Long-term perturbation of the peripheral immune system months after SARS-CoV-2 infection

  1. Feargal J. Ryan
  2. Christopher M. Hope
  3. Makutiro G. Masavuli
  4. Miriam A. Lynn
  5. Zelalem A. Mekonnen
  6. Arthur Eng Lip Yeow
  7. Pablo Garcia-Valtanen
  8. Zahraa Al-Delfi
  9. Jason Gummow
  10. Catherine Ferguson
  11. Stephanie O’Connor
  12. Benjamin A. J. Reddi
  13. Pravin Hissaria
  14. David Shaw
  15. Chuan Kok-Lim
  16. Jonathan M. Gleadle
  17. Michael R. Beard
  18. Simon C. Barry
  19. Branka Grubor-Bauk
  20. David J. Lynn

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Abstract

Background

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly infectious respiratory virus which is responsible for the coronavirus disease 2019 (COVID-19) pandemic. It is increasingly clear that recovered individuals, even those who had mild COVID-19, can suffer from persistent symptoms for many months after infection, a condition referred to as “long COVID”, post-acute sequelae of COVID-19 (PASC), post-acute COVID-19 syndrome, or post COVID-19 condition. However, despite the plethora of research on COVID-19, relatively little is known about the molecular underpinnings of these long-term effects.

Methods

We have undertaken an integrated analysis of immune responses in blood at a transcriptional, cellular, and serological level at 12, 16, and 24 weeks post-infection (wpi) in 69 patients recovering from mild, moderate, severe, or critical COVID-19 in comparison to healthy uninfected controls. Twenty-one of these patients were referred to a long COVID clinic and > 50% reported ongoing symptoms more than 6 months post-infection.

Results

Anti-Spike and anti-RBD IgG responses were largely stable up to 24 wpi and correlated with disease severity. Deep immunophenotyping revealed significant differences in multiple innate (NK cells, LD neutrophils, CXCR3+ monocytes) and adaptive immune populations (T helper, T follicular helper, and regulatory T cells) in convalescent individuals compared to healthy controls, which were most strongly evident at 12 and 16 wpi. RNA sequencing revealed significant perturbations to gene expression in COVID-19 convalescents until at least 6 months post-infection. We also uncovered significant differences in the transcriptome at 24 wpi of convalescents who were referred to a long COVID clinic compared to those who were not.

Conclusions

Variation in the rate of recovery from infection at a cellular and transcriptional level may explain the persistence of symptoms associated with long COVID in some individuals.

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  1. Version published to 10.1186/s12916-021-02228-6
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    SciScore for 10.1101/2021.07.30.21261234: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The protocol was approved CALHN Human Research Ethics Committee, Adelaide,
    Consent: Inclusion criteria were PCR-confirmed SARS-CoV-2 infection from nasopharyngeal swabs, the ability to attend study follow up visits, and voluntary informed consent.
    Sex as a biological variableA total of 69 COVID-19 convalescent individuals (35 male, 36 female) representing a range of prior mild, moderate, severe, critical COVID-19 cases were recruited (Table S1).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Secondary antibodies were diluted in 5% skim milk in PBST as follows: Goat anti-Human IgG (H+L) Secondary Antibody, HRP (1:30,000;
    anti-Human IgG
    suggested: None
    Sigma): anti-human IgA HRP antibody (1:5,000; Sigma) and incubated for 1 hour at room temperature.
    anti-human IgA
    suggested: None
    Pearson correlation analysis was performed using the Hmisc v4.4-2 package in R to determine correlations between anti-Spike and anti-RBD antibody titres, flow cytometry data and BTM activity scores.
    anti-Spike
    suggested: None
    anti-RBD
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    SARS-CoV-2 protein purification and ELISA: Prefusion SARS-CoV-2 ectodomain (isolate WHU1, residues1-1208) with HexaPro mutations (76) (kindly provided by Adam Wheatley) and SARS-Cov-2 receptor-binding domain (RBD) with C-terminal His-tag (77) (residues 319-541; kindly provided by Florian Krammer) were overexpressed in Expi293 cells and purified by Ni-NTA affinity and size- exclusion chromatography.
    Expi293
    suggested: RRID:CVCL_D615)
    Software and Algorithms
    SentencesResources
    AUC calculation was performed using Prism GraphPad, where the X-axis is half log10 of sera dilution against OD450 on Y-axis.
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Compensation was set with beads matched to each panel antibody combination using spectral compensation using FlowJo Software V10.
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    RNA-Seq analysis: Sequence read quality was assessed using FastQC version 0.11.4 (78) and summarised with MultiQC version 1.8 (79) prior to quality control with Trimmomatic version 0.38 (80) with a window size of 4 nucleotides and an average quality score of 25.
    FastQC
    suggested: (FastQC, RRID:SCR_014583)
    MultiQC
    suggested: (MultiQC, RRID:SCR_014982)
    Trimmomatic
    suggested: (Trimmomatic, RRID:SCR_011848)
    Reads that passed all quality control steps were then aligned to the mouse genome (GRCh38 assembly) using HISAT2 version 2.1.0 (81).
    HISAT2
    suggested: (HISAT2, RRID:SCR_015530)
    The gene count matrix was generated with FeatureCounts version 1.5.0-p2 (82) using the union model with Ensembl version 101 annotation.
    FeatureCounts
    suggested: (featureCounts, RRID:SCR_012919)
    Ensembl
    suggested: (Ensembl, RRID:SCR_002344)
    The count matrix was then imported into R version 4.0.3 for further analysis and visualisation in ggplot2 v2.3.3.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    To assess if differential gene expression was primarily driven by differences in the proportion of any major immune cell population (i.e. LD granulocytes, LD neutrophils, CXCR3+ neutrophils, monocytes, lymphocytes, CD56++ NK cells, CD19+ B cells, CD3+ T cells, NKT cells, CD4+ T cells or CD8+ T cells), we additionally fit the frequency of each population in each individual into the EdgeR model and reperformed the differential gene expression and pathway overrepresentation analysis.
    EdgeR
    suggested: (edgeR, RRID:SCR_012802)
    Gene Set Variation Analysis (GSVA) (44) was used to calculate a per sample activity score for each of the modules (excluding unannotated modules labelled as ‘TBA’). limma v3.46.0 was used to identify modules that were differentially active in at least one timepoint.
    limma
    suggested: (LIMMA, RRID:SCR_010943)
    Correlation networks were exported to Cytoscape v3.8.1 for visualisation.
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)

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

    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.

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  3. Version published to 10.1101/2021.07.30.21261234v1 on medRxiv