MAIT cell activation and dynamics associated with COVID-19 disease severity

  1. Tiphaine Parrot
  2. Jean-Baptiste Gorin
  3. Andrea Ponzetta
  4. Kimia T. Maleki
  5. Tobias Kammann
  6. Johanna Emgård
  7. André Perez-Potti
  8. Takuya Sekine
  9. Olga Rivera-Ballesteros
  10. the Karolinska COVID-19 Study Group
  11. Sara Gredmark-Russ
  12. Olav Rooyackers
  13. Elin Folkesson
  14. Lars I. Eriksson
  15. Anna Norrby-Teglund
  16. Hans-Gustaf Ljunggren
  17. Niklas K. Björkström
  18. Soo Aleman
  19. Marcus Buggert
  20. Jonas Klingström
  21. Kristoffer Strålin
  22. Johan K. Sandberg
  23. John Tyler Sandberg
  24. Helena Bergsten
  25. Niklas K Björkström
  26. Susanna Brighenti
  27. Marcus Buggert
  28. Marta Butrym
  29. Benedict J. Chambers
  30. Puran Chen
  31. Martin Cornillet
  32. Angelica Cuapio
  33. Isabel Diaz Lozano
  34. Majda Dzidic
  35. Johanna Emgård
  36. Malin Flodström-Tullberg
  37. Jean-Baptiste Gorin
  38. Sara Gredmark-Russ
  39. Alvaro Haroun-Izquierdo
  40. Laura Hertwig
  41. Sadaf Kalsum
  42. Jonas Klingström
  43. Efthymia Kokkinou
  44. Egle Kvedaraite
  45. Hans-Gustaf Ljunggren
  46. Magdalini Lourda
  47. Kimia T Maleki
  48. Karl-Johan Malmberg
  49. Jakob Michaëlsson
  50. Jenny Mjösberg
  51. Kirsten Moll
  52. Jagadeeswara Rao Muvva
  53. Anna Norrby-Teglund
  54. Laura M. Palma Medina
  55. Tiphaine Parrot
  56. Lena Radler
  57. Emma Ringqvist
  58. Johan K. Sandberg
  59. Takuya Sekine
  60. Tea Soini
  61. Mattias Svensson
  62. Janne Tynell
  63. Andreas Von Kries
  64. David Wullimann
  65. André Perez-Potti
  66. Olga Rivera-Ballesteros
  67. Christopher Maucourant
  68. Renata Varnaite
  69. Mira Akber
  70. Lena Berglin
  71. Demi Brownlie
  72. Marco Giulio Loreti
  73. Ebba Sohlberg
  74. Tobias Kammann
  75. Elisabeth Henriksson
  76. Nicole Marquardt
  77. Kristoffer Strålin
  78. Soo Aleman
  79. Anders Sönnerborg
  80. Lena Dillner
  81. Anna Färnert
  82. Hedvig Glans
  83. Pontus Nauclér
  84. Olav Rooyackers
  85. Johan Mårtensson
  86. Lars I. Eriksson
  87. Björn P. Persson
  88. Jonathan Grip
  89. Christian Unge

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Abstract

MAIT cell activation and decline in blood are associated with COVID-19 severity, features that dynamically recover in convalescence.

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  1. Version published to 10.1126/sciimmunol.abe1670
  2. ScreenIT

    SciScore for 10.1101/2020.08.27.20182550: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: The study was approved by the Swedish Ethical Review Authority and all patients gave informed consent.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Flow cytometry and antibodies: The following antibodies were used for staining: CD69-BUV395 (clone FN50), CD38-BUV496 (clone HIT2), CD56-BUV737 (clone NCAM16.2), CD3-BUV805 (clone UCHT1), CD14-V500 (clone M5E2), CD19-V500 (clone HIB19), Vα24-BV750 (clone L243), CD161-PE-Cy5 (clone DX12), GrzB-AF700 (clone GB119) from BD Biosciences, PD1-BV421 (clone EH12.2H7), CD8-BV570 (clone RPA-T8), IL7R-BV605 (clone A019D5), CXCR3-BV650 (clone G025H7), CD4-BV711 (clone OKT4), HLA-DR-BV785 (clone L243), Ki-67-AF488, GrzA-PercCP-Cy5.5 (clone CB9), TCRγδ-PE/Dazzle564 (clone B1), Va7.2-PE-Cy7 (clone 3C10), CXCR6-AF647 (clone K041E5) from Biolegend.
    CD38-BUV496
    suggested: None
    CD56-BUV737
    suggested: None
    CD3-BUV805
    suggested: None
    CD161-PE-Cy5
    suggested: None
    PD1-BV421
    suggested: None
    CD8-BV570
    suggested: None
    CXCR3-BV650
    suggested: None
    CD4-BV711
    suggested: None
    GrzA-PercCP-Cy5.5
    suggested: None
    TCRγδ-PE/Dazzle564
    suggested: None
    Software and Algorithms
    SentencesResources
    Samples were acquired on a BD FACSymphony A5 flow cytometer (BD Biosciences) and analysed with FlowJo software version 10.6.2 (FlowJo, LLC).
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    Stainings were performed on freshly isolated (Atlas and convalescent cohorts) or cryopreserved (Biobank cohort
    Biobank
    suggested: (HIV Biobank, RRID:SCR_004691)
    UMAP and PhenoGraph analysis: FCS3.0 files exported from BD FACSDiva software were imported into FlowJo software and automated compensation matrix was generated using the acquired single-stained compensation beads.
    BD FACSDiva
    suggested: (BD FACSDiva Software, RRID:SCR_001456)
    For cluster identification, the FlowJo plugin PhenoGraph (V2.4) was run on the resulting UMAP using the default settings (nearest neighbors K=30) and including the following parameters: CD69, CD38, HLA-DR, PD1, CXCR3, CXCR6, IL7R, Ki-67, CD56, CD4, CD8, GrzB, and GrzA.
    PhenoGraph
    suggested: (Phenograph, RRID:SCR_016919)
    Principal Component Analysis: PCA were performed in Python, using scikit-learn 0.22.1.
    scikit-learn
    suggested: (scikit-learn, RRID:SCR_002577)
    Statistical analysis: Prism V7.0 (GraphPad Software) and python were used for statistical analysis.
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Correlation heat maps were generated in Python using the pingouin package v0.3.6 (https://pingouin-stats.org) for computing Spearman and rank-biserial correlations as well as the associated p-values.
    Python
    suggested: (IPython, RRID:SCR_001658)

    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:
    However, the study at the same time has several limitations. COVID-19 is heterogeneous in its presentation with a wide range of disease severity, symptoms and kinetics of disease. While the patient groups studied here were all well-defined, they still do not reflect the full complexity of the disease. Furthermore, the cross-sectional design does not capture the full disease dynamics. In particular, sampling very early following infection or symptom debut would be valuable in future studies. Nevertheless, despite these limitations the current study suggests a role for MAIT cells in COVID-19 and opens new avenues to be explored for better understanding of the immunopathogenesis of this disease.

    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.

  3. Version published to 10.1101/2020.08.27.20182550 on medRxiv