Dysregulated immune responses in COVID-19 patients correlating with disease severity and invasive oxygen requirements

  1. Paulina García-González
  2. Fabián Tempio
  3. Camila Fuentes
  4. Consuelo Merino
  5. Leonardo Vargas
  6. Valeska Simon
  7. Mirliana Ramirez-Pereira
  8. Verónica Rojas
  9. Eduardo Tobar
  10. Glauben Landskron
  11. Juan Pablo Araya
  12. Mariela Navarrete
  13. Carla Bastias
  14. Rocío Tordecilla
  15. Macarena A. Varas
  16. Pablo Maturana
  17. Andrés E. Marcoleta
  18. Miguel L. Allende
  19. Rodrigo Naves
  20. Marcela A. Hermoso
  21. Flavio Salazar-Onfray
  22. Mercedes Lopez
  23. María Rosa Bono
  24. Fabiola Osorio

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Abstract

The prognosis of severe COVID-19 patients has motivated research communities to uncover mechanisms of SARS-CoV-2 pathogenesis also on a regional level. In this work, we aimed to understand the immunological dynamics of severe COVID-19 patients with different degrees of illness, and upon long-term recovery.

We analyzed immune cellular subsets and SARS-CoV-2-specific antibody isotypes of 66 COVID-19 patients admitted to the Hospital Clínico Universidad de Chile, which were categorized according to the WHO ten-point clinical progression score. These included 29 moderate patients (score 4-5) and 37 severe patients under either high flow oxygen nasal cannula (18 patients, score 6), or invasive mechanical ventilation (19 patients, score 7-9), plus 28 convalescent patients and 28 healthy controls. Furthermore, six severe patients that recovered from the disease were longitudinally followed over 300 days.

Our data indicate that severe COVID-19 patients display increased frequencies of plasmablasts, activated T cells and SARS-CoV-2-specific antibodies compared to moderate and convalescent patients. Remarkably, within the severe COVID-19 group, patients rapidly progressing into invasive mechanical ventilation show higher frequencies of plasmablasts, monocytes, eosinophils, Th1 cells and SARS-CoV-2-specific IgG than patients under high flow oxygen nasal cannula. These findings demonstrate that severe COVID-19 patients progressing into invasive mechanical ventilation show a distinctive type of immunity. In addition, patients that recover from severe COVID-19 begin to regain normal proportions of immune cells 100 days after hospital discharge and maintain high levels of SARS-CoV-2-specific IgG throughout the study, which is an indicative sign of immunological memory. Thus, this work can provide a useful benchmark for improvement of disease outcomes.

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  1. ScreenIT

    SciScore for 10.1101/2021.09.14.21263541: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Ethics statement: This study was approved by the Institutional Review Boards at Hospital Clínico Universidad de Chile and at the Faculty of Medicine, Universidad de Chile (Protocol ID.
    Consent: All patients and healthy controls were required to understand the study and sign an informed consent.
    Field Sample Permit: Sample processing and cell isolation: Whole blood for flow cytometry analysis was collected in EDTA-coated vacutainers, and all blood samples were processed the same day as collection.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Next, 100 µl per well of HRP-conjugated anti-human IgG (HRP Donkey anti-human IgG Clone: Poly24109) or anti-human IgA (Goat Anti-Human IgA alpha chain (HRP) ABCAM ab97215) secondary antibody diluted 1:10,000 in 0.1% TPB was added and incubated for 1 hour at room temperature.
    anti-human IgG
    suggested: (BioLegend Cat# 410902, RRID:AB_2686937)
    anti-human IgA
    suggested: None
    Anti-Human IgA alpha chain ( HRP
    suggested: None
    The following antibodies were used on a dilution 1/100: CD4 FITC (OKT4); CD138 PE (DL-101); CD56 PerCP/Cy5.5 (HCD56);
    CD56
    suggested: None
    Additional antibodies used on a 1/200 dilution were: CD3 PE/Dazzle 594 (UCHT1); CD45 PE/Cy7 (HI30); HLA-DR BV785 (L243); CD45RA BV650 (HI100); IgD FITC (IA6-2).
    BV785
    suggested: (BioLegend Cat# 307642, RRID:AB_2563461)
    Experimental Models: Cell Lines
    SentencesResources
    EH12.2H7); CD11b FITC (ICRF44); PD-L1 PE (29E.2A3); CD19 PE/Dazzle 594 (HIB19); CD86 BV650 (IT2.2); CD64 BV711 (10.1);
    PD-L1
    suggested: None
    Software and Algorithms
    SentencesResources
    The clinical data was stored using REDCap (Research Electronic Data Capture) software(29).
    REDCap
    suggested: (REDCap, RRID:SCR_003445)
    Unsupervised flow cytometry analysis of CD45+ cells in PBMC samples was done using Uniform Manifold Approximation Projection (UMAP) along with the FlowSOM automated clustering tool of FlowJo Software.
    FlowSOM
    suggested: (FlowSOM, RRID:SCR_016899)
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    Statistical analysis: Statistical analyses were performed using Prism 8 software (GraphPad).
    Prism
    suggested: (PRISM, RRID:SCR_005375)
    GraphPad
    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: 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.
    • Thank you for including a protocol registration statement.

    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.

  2. Version published to 10.1101/2021.09.14.21263541v1 on medRxiv