Metabolic dysregulation induces impaired lymphocyte memory formation during severe SARS-CoV-2 infection

  1. Sanjeev Gurshaney
  2. Anamaria Morales Alvarez
  3. Kevin Ezhakunnel
  4. Andrew Manalo
  5. Thien-Huong Huynh
  6. Nhat-Tu Le
  7. Daniel S. Lupu
  8. Stephen J. Gardell
  9. Hung Nguyen

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Abstract

Cellular metabolic dysregulation is a consequence of COVID-19 infection that is a key determinant of disease severity. To understand the mechanisms underlying these cellular changes, we performed high-dimensional immune cell profiling of PBMCs from COVID-19-infected patients, in combination with single cell transcriptomic analysis of COVID-19 BALFs. Hypoxia, a hallmark of COVID-19 ARDS, was found to elicit a global metabolic reprogramming in effector lymphocytes. In response to oxygen and nutrient-deprived microenvironments, these cells shift from aerobic respiration to increase their dependence on anaerobic processes including glycolysis, mitophagy, and glutaminolysis to fulfill their bioenergetic demands. We also demonstrate metabolic dysregulation of ciliated lung epithelial cells is linked to significant increase of proinflammatory cytokine secretion and upregulation of HLA class 1 machinery. Augmented HLA class-1 antigen stimulation by epithelial cells leads to cellular exhaustion of metabolically dysregulated CD8 and NK cells, impairing their memory cell differentiation. Unsupervised clustering techniques revealed multiple distinct, differentially abundant CD8 and NK memory cell states that are marked by high glycolytic flux, mitochondrial dysfunction, and cellular exhaustion, further highlighting the connection between disrupted metabolism and impaired memory cell function in COVID-19. Our findings provide novel insight on how SARS-CoV-2 infection affects host immunometabolism and anti-viral response during COVID-19.

Graphical Abstract

Highlights

  • Hypoxia and anaerobic glycolysis drive CD8, NK, NKT dysfunction

  • Hypoxia and anaerobic glycolysis impair memory differentiation in CD8 and NK cells

  • Hypoxia and anaerobic glycolysis cause mitochondrial dysfunction in CD8, NK, NKT cells

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  1. ScreenIT

    SciScore for 10.1101/2021.12.06.471421: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Blood samples from hospitalized COVID-19 patients were collected from the AdventHealth hospital under protocols IRB# 1668907 and #1590483 approved by AdventHealth IRB committee.
    Field Sample Permit: Briefly, blood specimens were centrifuged at 700G for 7 min at RT for serum collection.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    High-dimensional Flow Cytometry Analysis: First, the flowCore package in R was used to read in compensated FCS files into the R environment61.
    flowCore
    suggested: (flowCore, RRID:SCR_002205)
    For each identified population of interest, FlowSOM was applied again on only the functional state markers with the number of expected populations set at 10.
    FlowSOM
    suggested: (FlowSOM, RRID:SCR_016899)
    Briefly, after quality control and compensation, batch effect was corrected in makers with bimodally distributed expression by the CytoNorm package in R64. UMAP and FlowSOM65 were used to identify unsupervised clusters (Fig. 1H and Fig.
    CytoNorm
    suggested: None
    Downstream Analysis: For heatmap visualizations, scaled SCTransformed values were used and the Complexheatmap package was used to generate visualization 71.
    Complexheatmap
    suggested: (ComplexHeatmap, RRID:SCR_017270)
    enrichR was used to determine over and under expressed pathways from differential expression analysis (Kuleshov) 74.
    enrichR
    suggested: (Enrichr, RRID:SCR_001575)
    Other graphical visualizations were created using ggplot2, ggpubr or plotly.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    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.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  2. Version published to 10.1101/2021.12.06.471421 on bioRxiv