Longitudinal analyses reveal age-specific immune correlates of COVID-19 severity

  1. Sloan A. Lewis
  2. Suhas Sureshchandra
  3. Michael Z. Zulu
  4. Brianna Doratt
  5. Amanda Pinski
  6. Micaila Curtis
  7. Allen Jankeel
  8. Izabela Ibraim
  9. Nicholas Rhoades
  10. Xiwen Jiang
  11. Delia Tifrea
  12. Frank Zaldivar
  13. Weining Shen
  14. Robert Edwards
  15. Daniel Chow
  16. Dan Cooper
  17. Alpesh Amin
  18. Ilhem Messaoudi

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Abstract

Severe COVID-19 disproportionately impacts older individuals and those with comorbidities. It is estimated that approximately 80% of COVID-19 deaths are observed among individuals >65 years of age. However, the immunological underpinnings of severe COVID-19 in the aged have yet to be defined. This study captures the longitudinal immune response to SARS-CoV-2 infection in a cohort of young and aged patients with varying disease severity. Phenotypic transcriptional and functional examination of the peripheral mononuclear cells revealed age-, time, and disease severity-specific adaptations. Gene expression signatures within memory B cells suggest qualitative differences in the antibody responses in aged patients with severe disease. Examination of T cells showed profound lymphopenia, that worsened over time and correlated with lower levels of plasma cytokines important for T cell survival in aged patients with severe disease. Single cell RNA sequencing revealed augmented signatures of activation, exhaustion, cytotoxicity, and type-I interferon signaling in memory T cells and NK cells. Although hallmarks of a cytokine storm were evident in both groups, older individuals exhibited elevated levels of chemokines that mobilize inflammatory myeloid cells, notably in those who succumbed to disease. Correspondingly, we observed a re-distribution of DC and monocytes with severe disease that was accompanied by a rewiring towards a more regulatory phenotype. Several of these critical changes, such as the reduction of surface HLA-DR on myeloid cells, were reversed in young but not aged patients over time. In summary, the data presented here provide novel insights into the impact of aging on the host response to SARS-CoV2 infection.

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  1. ScreenIT

    SciScore for 10.1101/2021.01.25.21250189: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Ethics Statement: This study was approved by University of California Irvine Institutional Review Boards.
    Consent: Informed consent was obtained from all enrolled subjects.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Antibody ELISA: Clear 96 well, high-binding polystyrene ELISA plates were coated with 100 uL/well of 500 ng/mL SARS-CoV-2 Spike-protein Receptor-Binding Domain (RBD) or 1 ug/mL SARS-CoV-2 Nucleocapsid Protein (NP) (GenScrip) in PBS overnight at 4C.
    1 ug/mL SARS-CoV-2 Nucleocapsid Protein ( NP ) ( GenScrip )
    suggested: None
    For the innate panel, the following antibodies were used: CD3 (SP34, BD Pharmingen) and CD20 (2H7, BioLegend) for the exclusion of T & B lymphocytes, respectively.
    CD3
    suggested: (BD Biosciences Cat# 556610, RRID:AB_396483)
    CD20
    suggested: None
    For the adaptive panel, the following antibodies were used: CD4 (OKT4, BioLegend), CD8b (2ST8.5H7, Beckman Coulter), CD45RA (HI100, TONBO Biosciences), CCR7 (G043H7, BD Biosciences), CD19 (HIB19, BioLegend), IgD (IA6-2, BioLegend), CD27 (M-T271, BioLegend), KLRG1 (SA231A2, BioLegend) and PD-1 (Eh12.2h7, BioLegend).
    CD4
    suggested: (BD Biosciences Cat# 557939, RRID:AB_2802162)
    CD8b
    suggested: (Abcam Cat# ab34397, RRID:AB_2291359)
    CD45RA
    suggested: None
    CCR7
    suggested: None
    CD19
    suggested: None
    HIB19
    suggested: None
    IA6-2
    suggested: None
    CD27
    suggested: None
    M-T271
    suggested: None
    KLRG1
    suggested: None
    SA231A2
    suggested: None
    PD-1
    suggested: None
    Cells were then washed twice in FACS buffer and surface stained using the following antibody cocktail - CD14 (M5E2, BioLegend), HLA-DR (L243, BioLegend), CD11b (ICRF44, BioLegend) for 30 minutes at 4C.
    CD14
    suggested: (BD Biosciences Cat# 340827, RRID:AB_400137)
    HLA-DR
    suggested: None
    L243
    suggested: None
    CD11b
    suggested: None
    ICRF44
    suggested: None
    Stained cells were then fixed and permeabilized using Fixation buffer (BioLegend) and incubated overnight with a cocktail of intracellular antibodies - IL-6 (MQ2-6A3, BioLegend)
    IL-6
    suggested: None
    Single cell RNA library preparation: Cryopreserved PBMC from each patient (n=4/group for HD and Mild; n=4/time point for severe) were thawed, washed, and stained with 1 ug/test cell-hashing antibody (TotalSeq B0251,B0254, B0256, B0260, clones LNH-95, 2M2, BioLegend) for 30 minutes at 4C.
    B0254
    suggested: None
    B0256
    suggested: None
    2M2
    suggested: None
    Software and Algorithms
    SentencesResources
    Data were analyzed using FlowJo v10 (TreeStar
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    Single cell RNA-Seq data analysis: Raw reads were aligned and quantified using the Cell Ranger Single-Cell Software Suite with Feature Barcode addition (version 4.0, 10X Genomics) against the GRCh38 human reference genome using the STAR aligner.
    STAR
    suggested: (STAR, RRID:SCR_015899)
    Data objects from all groups were integrated using Seurat
    Seurat
    suggested: (SEURAT, RRID:SCR_007322)
    Differential expression analyses: Differential expression analysis was performed using MAST using default settings in Seurat.
    MAST
    suggested: (MAST, RRID:SCR_016340)
    Module Scoring and functional enrichment: For gene scoring analysis, we compared gene signatures and pathways from KEGG (https://www.genome.jp/kegg/pathway.html)
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    Over representative gene ontologies were identified using 1-way, 2-way, 4-way and 8-way enrichment of differential signatures using Metascape (42)
    Metascape
    suggested: (Metascape, RRID:SCR_016620)
    Functional enrichment networks were edited and annotated using Cytoscape (version 3.6.1).
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)
    All plots were generated using ggplot2 and Seurat.
    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: We detected the following sentences addressing limitations in the study:
    We acknowledge several limitations in our study design and implementation. Firstly, we analyzed immune parameters by days post symptom onset, which being self-reported can be rather inaccurate and arbitrary. Secondly, we broadly defined patients with mild disease as ones with a positive PCR test with either no symptoms associated with COVID-19 or a mild disease not requiring extensive (>3 days) hospital stay. Lack of longitudinal samples from patients from this category prevented us from modeling disease dynamics with varied severity. Thirdly, given the nature of this pandemic, there are some biases in patient and healthy donor cohorts. For example, healthy donor subjects were predominantly female (68% in young; 58% in aged) whereas a significant number of patients with severe disease were Hispanic (69% in young; 75% in aged). Additionally, patients in severe categories presented with a wide array of underlying conditions that might have played a role in disease severity/outcome. A significant number of patients were treated with remdesivir, however, there is limited evidence suggesting its role in either immune activation or suppression in blood. Due to limited statistical power, we pooled patients with severe disease at any DPS timepoint into one category for initial analysis before regressing the data with time. Future studies will stratify young and aged patients by clinical scores to identify innate immune correlates of disease severity and identify determinants of disea...

    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.

  2. Version published to 10.1101/2021.01.25.21250189v1 on medRxiv