T cell perturbations persist for at least 6 months following hospitalization for COVID-19

  1. Melissa Govender
  2. Francis R. Hopkins
  3. Robin Göransson
  4. Cecilia Svanberg
  5. Esaki M. Shankar
  6. Maria Hjorth
  7. Åsa Nilsdotter-Augustinsson
  8. Johanna Sjöwall
  9. Sofia Nyström
  10. Marie Larsson

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Abstract

COVID-19 is being extensively studied, and much remains unknown regarding the long-term consequences of the disease on immune cells. The different arms of the immune system are interlinked, with humoral responses and the production of high-affinity antibodies being largely dependent on T cell immunity. Here, we longitudinally explored the effect COVID-19 has on T cell populations and the virus-specific T cells, as well as neutralizing antibody responses, for 6-7 months following hospitalization. The CD8 + TEMRA and exhausted CD57 + CD8 + T cells were markedly affected with elevated levels that lasted long into convalescence. Further, markers associated with T cell activation were upregulated at inclusion, and in the case of CD69 + CD4 + T cells this lasted all through the study duration. The levels of T cells expressing negative immune checkpoint molecules were increased in COVID-19 patients and sustained for a prolonged duration following recovery. Within 2-3 weeks after symptom onset, all COVID-19 patients developed anti-nucleocapsid IgG and spike-neutralizing IgG as well as SARS-CoV-2-specific T cell responses. In addition, we found alterations in follicular T helper (TFH) cell populations, such as enhanced TFH-TH2 following recovery from COVID-19. Our study revealed significant and long-term alterations in T cell populations and key events associated with COVID-19 pathogenesis.

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  1. Version published to 10.3389/fimmu.2022.931039
  2. ScreenIT

    SciScore for 10.1101/2022.03.17.484640: (What is this?)

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

    Table 1: Rigor

    EthicsConsent: The participants were ≥18-years-old male and female subjects who provided written informed consent before enrollment.
    Sex as a biological variableThe participants were ≥18-years-old male and female subjects who provided written informed consent before enrollment.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Flow Cytometry of Whole Blood: The levels of CD3+ CD4+ T cells and CD3+ CD8+ T cells/µl were measured using BD Trucount™ Tubes (BD Multitest™ 340491, BD Biosciences) with FITC-conjugated anti-CD3, PE-conjugated anti-CD8, PerCP-conjugated anti-CD45 and APC-conjugated anti-CD4 antibodies (BD Multitest™, 342417, BD Biosciences).
    anti-CD3
    suggested: None
    anti-CD8,
    suggested: None
    PerCP-conjugated
    suggested: None
    anti-CD45
    suggested: (BD Biosciences Cat# 342417, RRID:AB_2868785)
    anti-CD4
    suggested: None
    IFNγ-ELISPOT Assay: 96-well plates (Millipore Multiscreen Filtration, Merck Millipore, Sweden) were pre-treated with coating buffer (0.1M Na-Carbonate–Bicarbonate buffer pH 9.5), after which, wells were coated with human anti-IFN-γ monoclonal antibodies (1-D1K, Mabtech Sweden) at a concentration of 5μg/ml and diluted with the same coating buffer.
    anti-IFN-γ
    suggested: None
    After 48 hours of incubation, the plates were washed four times with 0.05% Tween 20 in PBS, followed by 2 hours of incubation at 37°C with biotinylated anti-human IFN-γ monoclonal antibodies (clone 7-B6-1, Mabtech) diluted at 1μg/ml in PBS.
    anti-human IFN-γ
    suggested: None
    Anti-SARS-CoV-2 Spike and Nucleocapsid IgG: Qualitative measurement of anti-SARS-CoV-2 spike IgG, including anti-spike neutralizing IgG antibodies directed against the receptor binding domain (RBD) of S1 subunit was performed using a commercial chemiluminescent microparticle-based immune assay (Abbott SARS-CoV-2 IgG II Quant/6S60 ARCHITECT SARS-CoV-2 IgG kit) and anti-SARS-CoV-2 nucleocapsid IgG (6R86 ARCHITECT SARS-CoV-2 IgG) using a commercial ARCHITECT Abbott (Abbott Laboratories Diagnostics Division, Abbott Scandinavia AB) kit.
    Nucleocapsid IgG: Qualitative measurement of anti-SARS-CoV-2 spike IgG, including anti-spike neutralizing IgG antibodies
    suggested: None
    anti-SARS-CoV-2 spike IgG
    suggested: None
    anti-spike neutralizing IgG
    suggested: (Sino Biological Cat# 40592-R001, RRID:AB_2857936)
    anti-SARS-CoV-2 nucleocapsid IgG
    suggested: None
    The anti-SARS-CoV-2 spike IgG are expressed as standardized binding antibody units (BAU)/mL, calibrated to the WHO International Standards for anti-SARS-CoV-2 immunoglobulin (human) (NIBSC Code 20-136) [43] with a positivity cut-off of 7.1 BAU/mL.
    anti-SARS-CoV-2 immunoglobulin
    suggested: None
    Software and Algorithms
    SentencesResources
    The PBMCs were washed again and resuspended in 200μl FACS buffer and run/acquired on a Cytek® Aurora (Cytek Biosciences, France) flow cytometry system.
    Cytek Biosciences
    suggested: None
    The spectral flow data was analyzed/processed using OMIQ (OMIQ, Inc, Santa Clara, CA, USA) and FlowJo™ v10.8 Software (BD Life Sciences).
    FlowJo™
    suggested: (FlowJo, RRID:SCR_008520)
    After overnight incubation at 4°C, the wells were washed 4 times with PBS (Cytiva HyClone™ Dulbecco’s PBS, Fisher Scientific), and quenched in 5% PHS in RPMI supplemented with gentamicin (20 µg/ml) and HEPES (10 mM), before seeding the wells with PBMCs at up to 300,000 per well.
    Fisher Scientific
    suggested: (Thermo Fisher Scientific, RRID:SCR_008452)
    Anti-SARS-CoV-2 Spike and Nucleocapsid IgG: Qualitative measurement of anti-SARS-CoV-2 spike IgG, including anti-spike neutralizing IgG antibodies directed against the receptor binding domain (RBD) of S1 subunit was performed using a commercial chemiluminescent microparticle-based immune assay (Abbott SARS-CoV-2 IgG II Quant/6S60 ARCHITECT SARS-CoV-2 IgG kit) and anti-SARS-CoV-2 nucleocapsid IgG (6R86 ARCHITECT SARS-CoV-2 IgG) using a commercial ARCHITECT Abbott (Abbott Laboratories Diagnostics Division, Abbott Scandinavia AB) kit.
    Abbott Laboratories
    suggested: None
    Statistical Analysis: Data and statistical analyses were carried out using GraphPad PRISM v9.0 (GraphPad Software, CA, USA).
    GraphPad PRISM
    suggested: (GraphPad Prism, RRID:SCR_002798)
    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: We detected the following sentences addressing limitations in the study:
    It is important to note that the current investigation has limitations. A critical factor to consider is the pre-selection of patients for this cohort, with most of the patients included already having moderate to severe COVID-19. It is also noteworthy to consider that health systems vary across geographical locations so patients in other counties might be more diverse due to different thresholds of illness required for hospital admission. Additionally, due to severe disease presentations, there were challenges with obtaining clinical samples from some patients and, in some cases, the cell numbers were low and/or of poor quality. During the study some COVID-19 patients were vaccinated against SARS-CoV-2 and therefore were excluded from our data set as this interfered with some results. Despite the immensely challenging conditions during the pandemic, we do believe that the cohort presented in this investigation is well characterized and of high quality and value. Altogether, this study highlights the alterations in the immune response incurred during hospital treated SARS-CoV-2 infection and convalescence. These novel longitudinal data illustrate the substantial changes to the T cell landscape, with a persistent increase in markers of activation, exhaustion and senescence lasting for more than 6 months. We further showed an accompanying decay in SARS-CoV-2-specific antibody responses at the same time. Our findings, in combination with others, are valuable in providing insight...

    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.

  3. Version published to 10.1101/2022.03.17.484640v1 on bioRxiv