SARS-CoV-2 Infection Drives a Glycan Switch of Peripheral T Cells at Diagnosis

  1. Inês Alves
  2. Manuel Machado Vicente
  3. Joana Gaifem
  4. Ângela Fernandes
  5. Ana Mendes Dias
  6. Cláudia Sousa Rodrigues
  7. José Carlos Oliveira
  8. Nair Seixas
  9. Luis Malheiro
  10. Miguel Araújo Abreu
  11. Rui Sarmento e Castro
  12. Salomé Soares Pinho

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Abstract

COVID-19 is a highly selective disease in which SARS-CoV-2 infection can result in different clinical manifestations ranging from asymptomatic/mild to severe disease that requires hospitalization. In this study, we demonstrated that SARS-CoV-2 infection results in a glycosylation reprogramming of circulating lymphocytes at diagnosis. We identified a specific glycosignature of T cells, defined upon SARS-CoV-2 infection and apparently triggered by a serological factor. This specific glycan switch of T cells is detected at diagnosis being more pronounced in asymptomatic patients. We further demonstrated that asymptomatic patients display an increased expression of a viral-sensing receptor through the upregulation of DC-SIGN in monocytes. We showed that higher levels of DC-SIGN in monocytes at diagnosis correlates with better COVID-19 prognosis. This new evidence pave the way to the identification of a novel glycan-based response in T cells that may confer protection against SARS-CoV-2 infection in asymptomatic patients, highlighting a novel prognostic biomarker and potential therapeutic target.

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  1. Version published to 10.4049/jimmunol.2100131
  2. ScreenIT

    SciScore for 10.1101/2021.02.17.21251918: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: All participants gave informed consent about all clinical procedures and research protocols were approved by the ethics committee of CHUP and CHVNG
    IRB: All participants gave informed consent about all clinical procedures and research protocols were approved by the ethics committee of CHUP and CHVNG
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    For surface marker staining, cells were stained for 30 minutes on ice while protected from light with the following antibodies: APC anti-human TCRγδ (clone B1),
    anti-human TCRγδ
    suggested: None
    Software and Algorithms
    SentencesResources
    PE-Cy7 anti-human CD206 (clone 15-2) from Biolegend; eFluor™ 450 anti-human CD4 (clone RPA-T4), PerCP-eFluor™ 710 anti-human PD-1 (clone J105)
    Biolegend
    suggested: (BioLegend, RRID:SCR_001134)
    PE-Cy5 anti-human CD86 (clone IT2.2) from eBioscience; PE anti-human TCRα/β (clone BW242/412) from Miltenyi;
    Miltenyi
    suggested: (Miltenyi Biotec, RRID:SCR_008984)
    anti-human CD3 (clone OKT3) from BD Biosciences; for DC-SIGN staining, cells were incubated with DC-SIGN rabbit IgG (Biorad) followed by incubation with polyclonal swine anti-rabbit IgG (FITC; Dako) for 30 minutes on ice.
    BD Biosciences
    suggested: (BD Biosciences, RRID:SCR_013311)
    Data were obtained on a BD FACS Canto II instrument (Becton Dickinson) and analyzed using FlowJo v10.0
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    Data visualization and statistical analysis: Data visualization and statistical analyses (non-parametric Mann-Whitney t-test) were done using GraphPad Prism 9 software.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    , IBM SPSS Statistics for Windows, Version 25.0, Armonk, NY; released 2017) The threshold used for statistical significance was p-value< 0.05.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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:
    Our study was conducted during the first wave of COVID-19 (March-July 2020) having a limitation in terms of sample size, indicating the need to validate these promising observations in larger and well-characterized multicentric cohorts as well as analysing the impact of other SARS-CoV-2 variants in T cells glycan switch. These new evidences in COVID-19 pave the way to the identification of a specific blood glycosignature able to stratify patients at diagnosis according with their risk to evolve to worsen disease. This will certainly contribute to improve vaccination strategy and patients risk stratification, optimizing an effective allocation and management of health care resources such as ventilators and intensive care facilities.

    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

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  3. Version published to 10.1101/2021.02.17.21251918v1 on medRxiv