Innate lymphoid cells and COVID-19 severity in SARS-CoV-2 infection

  1. Noah J Silverstein  Is a corresponding author
  2. Yetao Wang  Is a corresponding author
  3. Zachary Manickas-Hill
  4. Claudia Carbone
  5. Ann Dauphin
  6. Brittany P Boribong
  7. Maggie Loiselle
  8. Jameson Davis
  9. Maureen M Leonard
  10. Leticia Kuri-Cervantes
  11. MGH COVID-19 Collection & Processing Team
  12. Nuala J Meyer
  13. Michael R Betts
  14. Jonathan Z Li
  15. Bruce D Walker
  16. Xu G Yu
  17. Lael M Yonker
  18. Jeremy Luban  Is a corresponding author
  1. Program in Molecular Medicine, University of Massachusetts Medical School, United States
  2. Medical Scientist Training Program, University of Massachusetts Medical School, United States
  3. Massachusetts Consortium on Pathogen Readiness, United States
  4. Ragon Institute of MGH, MIT and Harvard, United States
  5. Massachusetts General Hospital, Mucosal Immunology and Biology Research Center, United States
  6. Massachusetts General Hospital, Department of Pediatrics, United States
  7. Harvard Medical School, United States
  8. Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, United States
  9. Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, United States
  10. Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, United States
  11. Department of Medicine, Brigham and Women’s Hospital, United States
  12. Howard Hughes Medical Institute, United States
  13. Department of Biology and Institute of Medical Engineering and Science, Massachusetts Institute of Technology, United States
  14. Department of Biochemistry and Molecular Biotechnology, University of Massachusetts Medical School, United States
  15. Broad Institute of Harvard and MIT, United States

Abstract

Background:

Risk of severe COVID-19 increases with age, is greater in males, and is associated with lymphopenia, but not with higher burden of SARS-CoV-2. It is unknown whether effects of age and sex on abundance of specific lymphoid subsets explain these correlations.

Methods:

Multiple regression was used to determine the relationship between abundance of specific blood lymphoid cell types, age, sex, requirement for hospitalization, duration of hospitalization, and elevation of blood markers of systemic inflammation, in adults hospitalized for severe COVID-19 (n = 40), treated for COVID-19 as outpatients (n = 51), and in uninfected controls (n = 86), as well as in children with COVID-19 (n = 19), recovering from COVID-19 (n = 14), MIS-C (n = 11), recovering from MIS-C (n = 7), and pediatric controls (n = 17).

Results:

This observational study found that the abundance of innate lymphoid cells (ILCs) decreases more than 7-fold over the human lifespan – T cell subsets decrease less than 2-fold – and is lower in males than in females. After accounting for effects of age and sex, ILCs, but not T cells, were lower in adults hospitalized with COVID-19, independent of lymphopenia. Among SARS-CoV-2-infected adults, the abundance of ILCs, but not of T cells, correlated inversely with odds and duration of hospitalization, and with severity of inflammation. ILCs were also uniquely decreased in pediatric COVID-19 and the numbers of these cells did not recover during follow-up. In contrast, children with MIS-C had depletion of both ILCs and T cells, and both cell types increased during follow-up. In both pediatric COVID-19 and MIS-C, ILC abundance correlated inversely with inflammation. Blood ILC mRNA and phenotype tracked closely with ILCs from lung. Importantly, blood ILCs produced amphiregulin, a protein implicated in disease tolerance and tissue homeostasis. Among controls, the percentage of ILCs that produced amphiregulin was higher in females than in males, and people hospitalized with COVID-19 had a lower percentage of ILCs that produced amphiregulin than did controls.

Conclusions:

These results suggest that, by promoting disease tolerance, homeostatic ILCs decrease morbidity and mortality associated with SARS-CoV-2 infection, and that lower ILC abundance contributes to increased COVID-19 severity with age and in males.

Funding:

This work was supported in part by the Massachusetts Consortium for Pathogen Readiness and NIH grants R37AI147868, R01AI148784, F30HD100110, 5K08HL143183.

Editor's evaluation

The manuscript shows striking data that circulating innate lymphoid cells (ILCs) decline over human age, more in males than in females, and that a further decline in their prominence is associated with severe COVID-19 in both elderly and paediatric situations. While the findings remain correlative as yet, they shed further light on the complex immune dysregulation of COVID-19, and underline the potential importance of innate lymphoid cells.

https://doi.org/10.7554/eLife.74681.sa0

Introduction

The outcome of SARS-CoV-2 infection is highly variable with only a minority progressing to severe COVID-19, characterized by acute respiratory distress syndrome, multi-organ dysfunction, elevated inflammatory cytokines, lymphopenia, and other abnormalities of the immune system (Bonnet et al., 2021; Giamarellos-Bourboulis et al., 2020; Huang and Pranata, 2020; Kaneko et al., 2020; Kuri-Cervantes et al., 2020; Lucas et al., 2020; Mathew et al., 2020; Mudd et al., 2020; Zhou et al., 2020). The risk of severe COVID-19 and death in people infected with SARS-CoV-2 increases with age and is greater in men than in women (Alkhouli et al., 2020; Bunders and Altfeld, 2020; Gupta et al., 2021; Laxminarayan et al., 2020; Mauvais-Jarvis, 2020; O’Driscoll et al., 2021; Peckham et al., 2020; Richardson et al., 2020; Scully et al., 2020). These trends have been observed in people infected with SARS-CoV (Chen and Subbarao, 2007; Donnelly et al., 2003; Karlberg et al., 2004), or with MERS-CoV (Alghamdi et al., 2014), and in laboratory animals challenged with SARS-CoV or SARS-CoV-2 (Channappanavar et al., 2017; Leist et al., 2020). The mechanisms underlying these effects of age and sex on COVID-19 morbidity and mortality remain poorly understood.

The composition and function of the human immune system changes with age and exhibits sexual dimorphism (Darboe et al., 2020; Klein and Flanagan, 2016; Márquez et al., 2020; Patin et al., 2018; Solana et al., 2012), with consequences for survival of infection, response to vaccination, and susceptibility to autoimmune disease (Flanagan et al., 2017; Giefing-Kröll et al., 2015; Márquez et al., 2020; Mauvais-Jarvis, 2020; Patin et al., 2018; Piasecka et al., 2018). Better understanding of these effects might provide clues as to why the clinical outcome of SARS-CoV-2 infection is so variable, ranging from asymptomatic to lethal (Cevik et al., 2021; He et al., 2020; Jones et al., 2021; Lee et al., 2020; Lennon et al., 2020; Ra et al., 2021; Richardson et al., 2020; Yang et al., 2021).

Survival after infection with a pathogenic virus such as SARS-CoV-2 requires not only that the immune system control and eliminate the pathogen, but that disease tolerance mechanisms limit tissue damage caused by the pathogen or by host inflammatory responses (Ayres, 2020a; McCarville and Ayres, 2018; Medzhitov et al., 2012; Schneider and Ayres, 2008). Research with animal models has demonstrated that genetic and environmental factors can promote host fitness without directly inhibiting pathogen replication (Ayres, 2020a; Cumnock et al., 2018; Jhaveri et al., 2007; McCarville and Ayres, 2018; Medzhitov et al., 2012; Råberg et al., 2007; Sanchez et al., 2018; Schneider and Ayres, 2008; Wang et al., 2016). Although in most cases the underlying mechanism is unknown, some of these models suggest that subsets of innate lymphoid cells (ILCs) contribute to disease tolerance (Artis and Spits, 2015; Branzk et al., 2018; Califano et al., 2018; Diefenbach et al., 2020; McCarville and Ayres, 2018; Monticelli et al., 2015; Monticelli et al., 2011). Some ILC subsets produce the epidermal growth factor family member amphiregulin (AREG) that maintains the integrity of epithelial barriers in the lung and intestine (Branzk et al., 2018; Jamieson et al., 2013; Monticelli et al., 2015; Monticelli et al., 2011), and promotes tissue repair (Artis and Spits, 2015; Cherrier et al., 2018; Klose and Artis, 2016; Rak et al., 2016). In models of influenza infection in mice, homeostatic ILCs and exogenous AREG promote lung epithelial integrity, decrease disease severity, and increase survival, without decreasing pathogen burden (Califano et al., 2018; Jamieson et al., 2013; Monticelli et al., 2011).

Little is known about disease tolerance in the context of human infectious diseases. Interestingly, SARS-CoV-2 viral load does not reliably discriminate symptomatic from asymptomatic infection (Cevik et al., 2021; Jones et al., 2021; Lee et al., 2020; Lennon et al., 2021; Ra et al., 2021; Yang et al., 2021). This discrepancy between SARS-CoV-2 viral load and the severity of COVID-19 is especially pronounced in children, who rarely have severe COVID-19 (Bailey et al., 2021; Li et al., 2020; Lu et al., 2020; Poline et al., 2020), although viral load may be comparable to that in adults with severe COVID-19 (Heald-Sargent et al., 2020; LoTempio et al., 2021; Yonker et al., 2020). These observations suggest that age-dependent, disease tolerance mechanisms influence the severity of COVID-19. In mice, homeostatic ILCs decrease in abundance in the lung with increasing age, and lose their ability to maintain disease tolerance during influenza infection (D’Souza et al., 2019). Although the distribution of ILCs within human tissues differs from mice and is heterogeneous among individuals (Yudanin et al., 2019), human ILCs share many features with those in mice (Vivier et al., 2018) and therefore may perform similar roles in maintaining tissue homeostasis and disease tolerance.

ILCs in peripheral blood have been reported to be depleted in individuals with severe COVID-19 (García et al., 2020; Kuri-Cervantes et al., 2020), but it is difficult to determine the extent to which ILCs are decreased independently from the overall lymphopenia associated with COVID-19 (Chen et al., 2020; Huang et al., 2020; Huang and Pranata, 2020; Zhang et al., 2020; Zhao et al., 2020), or from changes in other blood cell lineages (Giamarellos-Bourboulis et al., 2020; Huang et al., 2020; Kuri-Cervantes et al., 2020; Lucas et al., 2020; Mathew et al., 2020; Mudd et al., 2020; Zheng et al., 2020). In addition, assessment of lymphoid cell abundance, in the context of a disease for which age and sex are risk factors for severity, is confounded by programmed differences in lymphocyte abundance with age and sex (Márquez et al., 2020; Patin et al., 2018). The goal of this study was to determine whether the abundance of any blood lymphoid cell population was altered in COVID-19, independent of age, sex, and global lymphopenia, and whether abundance of any lymphoid cell population correlated with clinical outcome in SARS-CoV-2 infection.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
AntibodyAnti-Human BDCA1 (mouse monoclonal)BiolegendCat# 354,208Clone: 201 A (FITC) (1:200 dilution)
AntibodyAnti-Human CD117 (mouse monoclonal)BiolegendCat# 313,206Clone: 104D2 (APC) (1:200 dilution)
AntibodyAnti-Human CD11c (mouse monoclonal)BiolegendCat# 301,604Clone: 3.9 (FITC) (1:200 dilution)
AntibodyAnti-Human CD123 (mouse monoclonal)BiolegendCat# 306,014Clone: 6H6 (FITC) (1:200 dilution)
AntibodyAnti-Human CD127 (mouse monoclonal)BiolegendCat# 351,320Clone: A019D5 (PE/Cyanine7) (1:200 dilution)
AntibodyAnti-Human CD14 (mouse monoclonal)BiolegendCat# 325,604Clone: HCD14 (FITC) (1:200 dilution)
AntibodyAnti-Human CD16 (mouse monoclonal)BiolegendCat# 980,104Clone: 3G8 (APC) (1:400 dilution)
AntibodyAnti-Human CD19 (mouse monoclonal)BiolegendCat# 302,206Clone: HIB19 (FITC) (1:200 dilution)
AntibodyAnti-Human CD1a (mouse monoclonal)BiolegendCat# 300,104Clone: HI149 (FITC) (1:200 dilution)
AntibodyAnti-Human CD20 (mouse monoclonal)BiolegendCat# 302,304Clone: 2H7a (FITC) (1:200 dilution)
AntibodyAnti-Human CD22 (mouse monoclonal)BiolegendCat# 363,508Clone: S-HCL-1 (FITC) (1:200 dilution)
AntibodyAnti-Human CD3 (mouse monoclonal)BiolegendCat# 317,306Clone: OKT3 (FITC) (1:200 dilution)
AntibodyAnti-Human CD34 (mouse monoclonal)BiolegendCat# 343,504Clone: 581 (FITC) (1:200 dilution)
AntibodyAnti-Human CD4 (mouse monoclonal)BiolegendCat# 317,428Clone: OKT4 (PerCP/Cyanine5.5) (1:200 dilution)
AntibodyAnti-Human CD4 (mouse monoclonal)BiolegendCat# 317,408Clone: OKT4 (FITC) (1:200 dilution)
AntibodyAnti-Human CD45 (mouse monoclonal)BDCat# 560,178Clone: 2D1 (APC/H7) (1:200 dilution)
AntibodyAnti-Human CD56 (mouse monoclonal)BiolegendCat# 318,306Clone: HCD56 (PE) (1:200 dilution)
AntibodyAnti-Human CD8 (mouse monoclonal)BiolegendCat# 300,924Clone: HIT8a (PerCP/Cyanine 5.5) (1:200 dilution)
AntibodyAnti-Human CRTH2 (rat monoclonal)BiolegendCat# 350,116Clone: BM16 (PerCP/Cyanine5.5) (1:200 dilution)
AntibodyAnti-Human FcεR1α (mouse monoclonal)BiolegendCat# 334,608Clone: AER-37 (FITC) (1:200 dilution)
AntibodyAnti-Human TBX21 (mouse monoclonal)ebioscienceCat# 25-5825-82Clone: ebio4B10 (PE/Cyanine7) (1:200 dilution)
AntibodyAnti-Human TCRα/β (mouse monoclonal)BiolegendCat# 306,706Clone: IP26 (FITC) (1:200 dilution)
AntibodyAnti-Human TCRγ/δ (mouse monoclonal)BiolegendCat# 331,208Clone: B1 (FITC) (1:200 dilution)
AntibodyAnti-Human TCF7 (rabbit monoclonal)Cell SignalingCat# 37,636 sClone: C63D9 (APC) (1:200 dilution)
AntibodyAnti-Human IL-13 (rat monoclonal)BiolegendCat# 501,908Clone: JES10-5A2 (APC) (1:200 dilution)
AntibodyAnti-Human AREG (mouse monoclonal)ebioscienceCat# 17-5370-42Clone: AREG559
Antibodymouse IgG1, k isotype control (mouse monoclonal)BiolegendCat# 400,112Clone: MOPC-21 (PE) (1:200 dilution)
Antibodymouse IgG1, k isotype control (mouse monoclonal)BiolegendCat# 400,120Clone: MOPC-21 (APC) (1:200 dilution)
AntibodyRabbit IgG, isotype control (rabbit monoclonal)Cell SignalingCat# 3,452 S(Alexa Fluor 647) (1:200 dilution)
AntibodyRat IgG1, k isotype control (rat monoclonal)BiolegendCat# 400,412Clone: RTK2071 (APC) (1:200 dilution)
Biological Samples (Homo sapiens)PBMCsNew York Biologicshttps://www.newyorkbiologics.com/
Biological Samples (Homo sapiens)PBMCsMassCPRhttps://masscpr.hms.harvard.edu/
Biological Samples (Homo sapiens)PBMCsMGH Pediatric COVID-19 Biorepository
Commercial assay or kitCell stimulation cocktaileBioscienceCat# 00-4970-03
Chemical compound, drugProtein transport inhibitoreBioscienceCat# 00-4980-03
Chemical compound, drugTRIzol reagentInvitrogenCat# 15596018
Commercial assay or kitAMPure XP beadsBeckman CulterCat# A63880
Commercial assay or kitExoSAP-ITAffymetrixCat# 78,200
Commercial assay or kitLive and Dead violet viability kitInvitrogenCat# L-34963
Commercial assay or kitFoxp3 /Transcription Factor Staining Buffer SeteBioscienceCat# 00-5523-00
Commercial assay or kitHiScribe T7 High Yield RNA Synthesis KitNEBCat# E2040S
Commercial assay or kitNEBNext Ultra II Non directional Second Strand Synthesis ModuleNEBCat# E6111L
Software, algorithmR computer software environment (version 4.0.2)The R Foundation R Development Core Team, 2020https://www.r-project.org/
Software, algorithmFlowJoFlowJo, LLChttps://www.flowjo.com/
Software, algorithmtidyverse v1.3.1Wickham et al., 2019https://www.tidyverse.org
Software, algorithmggplot2 v3.3.3Wickham, 2016https://ggplot2.tidyverse.org
Software, algorithmggpubr v0.4.0Kassambara, 2020https://rpkgs.datanovia.com/ggpubr/
Software, algorithmComplexHeatmap v2.4.3Gu et al., 2016https://github.com/jokergoo/ComplexHeatmap
Software, algorithmemmeans v1.6.0Wieduwilt et al., 2020https://CRAN.R-project.org/package=emmeans
Software, algorithmlme4 v1.1–27Bates et al., 2015https://cran.r-project.org/web/packages/lme4/index.html
Software, algorithmlmerTest v3.1–3Kuznetsova et al., 2017https://cran.r-project.org/web/packages/lmerTest/index.html
Software, algorithmDolphinNext RNA-Seq pipeline (Revision 4)Yukselen et al., 2020https://github.com/UMMS-Biocore/dolphinnext
Software, algorithmSTAR v2.1.6Dobin et al., 2013https://github.com/alexdobin/STAR
Software, algorithmRSEM v1.3.1Li and Dewey, 2011http://deweylab.github.io/RSEM/
Software, algorithmDESeq2 v1.28.1Love et al., 2014https://bioconductor.org/packages/release/bioc/html/DESeq2.html
Software, algorithmclusterProfiler v3.16.1Yu et al., 2012https://guangchuangyu.github.io/software/clusterProfiler/

Human blood collection

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As part of a COVID-19 observational study, peripheral blood samples were collected between March 31st and June 3rd of 2020 from 91 adults with SARS-CoV-2 infection, either after admission to Massachusetts General Hospital for the hospitalized cohort, or while at affiliated outpatient clinics for the outpatient cohort. Request for access to coded patient samples was reviewed by the Massachusetts Consortium for Pathogen Readiness (https://masscpr.hms.harvard.edu/) and approved by the University of Massachusetts Medical School IRB (protocol #H00020836). Pediatric participants with COVID-19 or MIS-C were enrolled in the Massachusetts General Hospital Pediatric COVID-19 Biorepository (MGB IRB # 2020P000955). Healthy pediatric controls were enrolled in the Pediatric Biorepository (MGB IRB # 2016P000949). Samples were collected after obtaining consent from the patient if 18 years or older, or from the parent/guardian, plus assent when appropriate. Demographic, laboratory, and clinical outcome data were included with the coded samples. Samples from 86 adult blood donors and 17 pediatric blood donors were included as controls; these were either collected prior to the SARS-CoV-2 outbreak or from healthy individuals screened at a blood bank.

Isolation of human peripheral blood mononuclear cells (PMBCs)

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Human peripheral blood was diluted in an equal volume of RPMI-1640 (Gibco), overlaid on Lymphoprep (STEMCELL Technologies, #07851), and centrifuged at 500 x g at room temperature for 30 min. Mononuclear cells were washed three times with MACS buffer (0.5% BSA and 2 mM EDTA in PBS) and frozen in FBS containing 10% DMSO.

Flow cytometry

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PBMCs were first stained with Live and Dead violet viability kit (Invitrogen, L-34963). To detect surface molecules, cells were stained in MACS buffer with antibodies (Supplementary file 1a) for 30 min at 4°C in the dark. To detect IL-13 or AREG, cells were stimulated with PMA and ionomycin (eBioscience, 00-4970-03) for 3 hr with Brefeldin A and Monensin (eBioscience, 00-4980-03) present during the stimulation. To detect transcription factors or cytokines, cells were fixed and permeabilized using Foxp3 staining buffer kit (eBioscience, 00-5523-00), then intracellular molecules were stained in permeabilization buffer with antibodies. Cells were detected on a BD Celesta flow cytometer using previously established gating strategies (Wang et al., 2020). Cell subsets were identified using FlowJo software (Becton, Dickson and Company). Representative gating strategies are shown in Figure 2—figure supplement 1.

Bulk RNA-Seq library preparation

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The sequencing libraries were prepared using CEL-Seq2 (Hashimshony et al., 2016). RNA from sorted cells was extracted using TRIzol reagent (ThermoFisher, 15596018). Ten ng RNA was used for first strand cDNA synthesis using barcoded primers (the specific primers for each sample were listed in Supplementary file 1b). The second strand was synthesized by NEBNext Second Strand Synthesis Module (NEB, E6111L). The pooled dsDNA was purified with AMPure XP beads (Beckman Coulter, A63880), and subjected to in vitro transcription (IVT) using HiScribe T7 High Yield RNA Synthesis Kit (NEB, E2040S), then treated with ExoSAP-IT (Affymetrix, 78200). IVT RNA was fragmented using RNA fragmentation reagents (Ambion) and underwent another reverse transcription step using random hexamer RT primer-5’-GCC TTG GCA CCC GAG AAT TCC ANN NNN N-3’ to incorporate the second adapter. The final library was amplified with indexed primers: RP1 and RPI1 (Supplementary file 1b), and the bead purified library was quantified with 4,200 TapeStation (Agilent Technologies) and paired end sequenced on Nextseq 500 V2 (Illumina), Read 1: 15 cycles; index 1: 6 cycles; Read 2: 60 cycles.

Analysis of RNA-Seq data

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Pooled reads from PBMC-derived ILCs were separated by CEL-Seq2 barcodes, and demultiplexed reads from RNA-Seq of ILCs from lung (Ardain et al., 2019), spleen, and intestine (Yudanin et al., 2019), were downloaded from GSE131031 and GSE126107. Within the DolphinNext RNA-Seq pipeline (Revision 4) (Yukselen et al., 2020), reads were aligned to the hg19 genome using STAR (version 2.1.6) (Dobin et al., 2013) and counts of reads aligned to RefSeq genes were quantified using RSEM (version 1.3.1) (Li and Dewey, 2011). Normalized transcript abundance in the form of TPMs were used to filter out low abundance transcripts with an average of <3 TPMs across libraries. RSEM-generated expected counts were normalized and differential analysis was performed using DESeq2 (Love et al., 2014) in R, with significant genes defined as a greater than 1.5-fold difference and an adjusted p-value < 0.01. GO Enrichment Analysis was performed in R using the enrichGO function in the clusterProfiler R package (Yu et al., 2012). Data were transformed using vsd within DESeq2 both for the heatmap visualization with ComplexHeatmap (Gu et al., 2016) and for principal component analysis (PCA) with prcomp on the top 250 most variable genes. Normalized counts were generated for plotting using the counts command in DESeq2.

Statistical analysis and data visualization

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Data were prepared for analysis with tidyverse packages (Wickham et al., 2019) and visualized using the ggplot2 (Wickham, 2016), ggpubr (Kassambara, 2020), and ComplexHeatmap (Gu et al., 2016) packages, within the R computer software environment (version 4.0.2) (R Development Core Team, 2020). Group differences were determined with pairwise, two-sided, Wilcoxon rank-sum tests, or Fisher’s exact test, as indicated, with Bonferroni correction for multiple comparisons. Multiple linear regression analyses were performed with dependent and independent variables as indicated in the text, using the lm function in R. Pairwise group comparisons on estimated marginal means generated from multiple linear regression were performed using the emmeans package (Wieduwilt et al., 2020) in R, with multiple comparison correction using the Tukey adjustment. Multiple logistic regressions were performed using the glm function in R. Longitudinal follow-up analyses on pediatric COVID-19 and MIS-C was performed with linear mixed-effect models using lme4 (Bates et al., 2015) in R with the equation: log2 (lymphoid cell abundance) ~ Age + Sex + Group + Group:Follow_up + (1|Patient_ID). This model tested the effect of followup on ILC abundance in the pediatric COVID-19 and MIS-C groups while accounting for age, sex, and group. Statistical significance was determined with lmerTest (Kuznetsova et al., 2017) in R, using the Satterthwarte’s degrees of freedom method. p < 0.05 was considered significant. United States SARS-CoV-2 infection and mortality data were downloaded from (CDC Case Surveillance Task Force, 2020) and cases with age group and outcome available were plotted by age group as indicated. Mortality rate was calculated by dividing the number of fatal cases by the total number of cases with known outcome in each age group as indicated.

Results

Characteristics of adult blood donors, either hospitalized for COVID-19, treated for COVID-19 as outpatients, or SARS-CoV-2-uninfected controls

The first group of blood donors in this study included SARS-CoV-2-infected adults hospitalized for severe COVID-19 (N = 40), among whom 33 (82.5%) were admitted to the ICU, 32 (80%) required intubation with mechanical ventilation, and 7 (17.5%) died (Table 1). Aside from intubation, information regarding treatment during hospitalization was not available. This group had a mean age of 57.6 (range 24–83) and 60% were males. The second group consisted of adults infected with SARS-CoV-2 who were treated for COVID-19 as outpatients (N = 51). This group had a mean age of 36.8 years (range 23–77) and was 25.5% male (Table 1). Differences between these two SARS-CoV-2-infected groups, in terms of median age (P = 5.2 x 10–8), sex ratio (P = 3.7 x 10–3), and diagnosis of diabetes mellitus (p = 1.05 x 10–3), were consistent with established risk factors for severe COVID-19 (Figure 1 and Table 1; Alkhouli et al., 2020; Bunders and Altfeld, 2020; Gupta et al., 2021; Laxminarayan et al., 2020; Mauvais-Jarvis, 2020; O’Driscoll et al., 2021; Peckham et al., 2020; Petrilli et al., 2020; Richardson et al., 2020; Scully et al., 2020). Available information concerning ethnicity and race of the blood donors was insufficient for statistical comparisons among the groups (Supplementary file 2a). Finally, 86 adults who donated blood prior to the SARS-CoV-2 outbreak, or who were screened at a blood donation center, were included as controls for SARS-CoV-2 infection. The age of this group spanned the range of the two groups of SARS-CoV-2-infected people (mean age 50.9; range 23–79), and the percentage of males (55.8%) was similar to that of the group hospitalized for COVID-19 (Table 1 and Figure 1). Complete information regarding ethnicity, race, and comorbidity was not available for control blood donors.

Table 1
Demographic and clinical characteristics of adult blood donor groups.
CharacteristicControlN = 86HospitalizedN = 40OutpatientN = 51
Mean age (range) - years50.9 (23–79)57.6 (24–83)36.8 (23–77)
Sex – number (%)
Male48 (55.8)24 (60)13 (25.5)
Female38 (44.2)16 (40)38 (74.5)
Mean symptom duration at sample collection (range) – days21.8 (5–66)26.9 (1–61)
Diabetes mellitus diagnosis – number (%)11 (27.5)1 (2)
ICU admission – number (%)33 (82.5)
Intubation with mechanical ventilation – number (%)32 (80)
Deaths – number (%)7 (17.5)
Mean time hospitalized (range) – days34.2 (4–87)
Max lab value – mean (range)
CRP – mg/L228.6 (6.5–539.5)
ESR – mm/h89.0 (15–146)
D-dimer – (ng/mL)5700 (351–11923)
Age and sex of control and SARS-CoV-2-infected blood donors.

Age of the subjects is shown, along with the number of subjects and fraction male in each group, for adult (left) and pediatric (right) cohorts, as indicated. The p-values are from pairwise, two-sided, Wilcoxon rank-sum test for ages and Fisher’s exact test for fraction male, with Bonferroni correction for multiple comparisons. Adjusted p-values < 0.05 are shown.

Characteristics of pediatric blood donors with COVID-19, MIS-C, or SARS-CoV-2-uninfected controls

Children are less likely than adults to have severe disease when infected with SARS-CoV-2 despite having viral loads as high as adults (Bailey et al., 2021; Heald-Sargent et al., 2020; Li et al., 2020; LoTempio et al., 2021; Lu et al., 2020; Poline et al., 2020; Yonker et al., 2020). Rarely, after SARS-CoV-2 clearance from the upper airways, children can develop severe Multisystem Inflammatory Syndrome in Children (MIS-C), a life-threatening condition distinct from COVID-19 that presents with high fevers and multiorgan injury, often including coronary aneurysms, ventricular failure, or myocarditis (Cheung et al., 2020; Feldstein et al., 2021; Feldstein et al., 2020; Licciardi et al., 2020; Riphagen et al., 2020; Verdoni et al., 2020; Whittaker et al., 2020).

The first cohort of pediatric blood donors in this study consisted of patients with COVID-19 who were treated in hospital (N = 11) or as outpatients (N = 8). The second cohort of pediatric blood donors was patients hospitalized for MIS-C (N = 11). Seventeen SARS-CoV-2-uninfected pediatric blood donors constituted a control group. No significant differences in age or percentage of males were detected among the pediatric COVID-19, MIS-C, or pediatric control groups (Supplementary file 2b and Figure 1).

Blood ILC abundance decreases exponentially across the lifespan and is sexually dimorphic

Lymphoid cell abundance in peripheral blood changes with age and is sexually dimorphic (Márquez et al., 2020; Patin et al., 2018). Previous studies reporting the effect of COVID-19 on the abundance of blood lymphoid cell subsets have not fully accounted for the association of age and sex with COVID-19 severity. To isolate the effect of COVID-19 on cell abundance from effects of age and sex, PBMCs were collected from 103 SARS-CoV-2-negative blood donors distributed from 2 to 79 years of age, with a nearly equal ratio of males to females (Supplementary file 2c). Abundance of lymphoid cell types was plotted by 20-year age groups (Figure 2A), as well as by sex (Figure 2B). Lymphoid cell types assessed here included CD4+ T cells, CD8+ T cells, ILCs, and FcγRIII (CD16)-positive NK cells. Like CD8+ T cells, NK cells kill virus-infected cells using perforin and granzyme (Artis and Spits, 2015; Cherrier et al., 2018). Additionally, by binding virus-specific immunoglobulins that target virus-infected cells for antibody-dependent cellular cytotoxicity, CD16+ NK cells link innate and acquired immunity (Anegón et al., 1988).

Figure 2 with 1 supplement see all
Blood ILC abundance decreases exponentially across the lifespan mirroring the mortality rate from SARS-CoV-2 infection.

(A–B) Log2 abundance per million lymphocytes of the indicated lymphoid cell populations in combined pediatric and adult control data plotted by 20 year bin or by sex, as indicated. Each dot represents an individual blood donor. Boxplots represent the distribution of the data with the center line drawn through the median with the upper and lower bounds of the box at the 75th and 25th percentiles respectively. The upper and lower whiskers extend to the largest or smallest values within 1.5 x the interquartile range (IQR). The p-values are from two-sided, Wilcoxon rank-sum tests with Bonferroni correction for multiple comparisons. Adjusted p-values < 0.05 are shown. (C) Case numbers and mortality rate within the indicated age ranges for cases reported in the United States between Jan 1, 2020, and June 6, 2021.

Figure 2—source data 1

Combined demographic, clinical, and flow cytometry data for adult COVID-19 and control cohorts.

Data included in this file are associated with Figures 13; Figure 3—figure supplement 1; Figure 6; Tables 1–4; and Supplementary file 2d,e . File name: Adult_COVIDandControl_data.xlsx.

https://cdn.elifesciences.org/articles/74681/elife-74681-fig2-data1-v2.xlsx

All cell types examined here were affected by age, but ILCs were the only subset with significant differences among all age groups, falling approximately twofold in median abundance every 20 years, with a greater than sevenfold decrease from the youngest to oldest age groups (p = 1.64 x 10–11) (Figure 2A). This magnitude decrease was unique to ILCs and corresponded inversely with the exponential increase in COVID-19 mortality with age (O’Driscoll et al., 2021; Figure 2C). In addition, both ILCs and CD4+ T cells were less abundant in males (Figure 2B). These findings highlight the importance of accounting for effects of age and sex when assessing group differences in lymphoid cell abundance, particularly in the context of a disease such as COVID-19 that disproportionately affects older males (O’Driscoll et al., 2021).

Adults hospitalized with COVID-19 have fewer total lymphocytes even after accounting for effects of age and sex

Severe COVID-19 is associated with lymphopenia (Chen et al., 2020; Huang et al., 2020; Huang and Pranata, 2020; Zhang et al., 2020; Zhao et al., 2020) but it remains unclear if this effect is due to reduction in particular lymphoid cell subpopulations, or whether this effect is explained by the more advanced age and higher proportion of males among people with severe COVID-19. As a first step to assess the specificity of lymphocyte depletion, the effect of COVID-19 on total lymphocyte abundance was addressed with multiple linear regression. After accounting for effects of age and sex, individuals hospitalized with severe COVID-19 had 1.33-fold (95% CI: 1.49–1.19; p = 1.22 x 10–6) fewer total lymphocytes among PBMCs than did controls (Supplementary file 2d). Lymphocyte abundance in people infected with SARS-CoV-2 who were treated as outpatients was not different from controls (Supplementary file 2d). In addition, total lymphocytes decreased with age and were less abundant in males (Supplementary file 2d). Subsequent analyses of lymphoid cell subsets took into account the depletion in total lymphocytes associated with COVID-19 by assessing lymphoid subsets as a fraction of total lymphocytes.

After accounting for age and sex, only innate lymphoid cells are depleted in severe COVID-19

To determine whether there were independent associations between lymphoid cell subsets and COVID-19, multiple linear regression was performed on the abundance of lymphoid cell subsets, with age, sex, and group (control, hospitalized, and outpatient) as independent variables. Across all three groups of adult blood donors, CD4+ T cells, CD8+ T cells, and ILCs decreased with age, while CD16+ NK cells increased with age, and both CD4+ T cells and ILCs were less abundant in males (Table 2 and Figure 3A).

Table 2
Change in cell abundance due to age, sex, and COVID-19 severity fold difference (log2) [± 95% CI].
CD4+ T*ILC*CD8+ T*CD16+ NK*
Age–0.012***–0.043***–0.009*0.021***
[–0.018,–0.005][–0.053,–0.033][–0.016,–0.002][0.010, 0.032]
Male–0.409***–0.334*–0.1770.184
[–0.618,–0.201][–0.659,–0.010][–0.406, 0.051][–0.169, 0.538]
Hospitalized0.168–0.835***0.227–1.205***
[–0.084, 0.421][–1.228,–0.441][–0.050, 0.503][–1.633,–0.778]
Outpatient0.332*–0.088–0.023–0.522*
[0.082, 0.581][–0.478, 0.302][–0.298, 0.253][–0.948,–0.095]
R20.2750.4780.0700.232
  1. *p< 0.05, **p < 0.01, ***p < 0.001.

  2. *

    per 106 lymphocytes.

Figure 3 with 2 supplements see all
Innate lymphoid cells are depleted in adults hospitalized with COVID-19 and ILC abundance correlates inversely with disease severity.

(A) Effect of age (X-axis) on log2 abundance per million total lymphocytes of the indicated lymphoid cell populations (Y-axis), as determined by the regression analysis in Table 2. Each dot represents an individual blood donor, with yellow for female and blue for male. Shading represents the 95% CI. The p-values are from the regression analysis for comparisons to the control group. (B) Log2 abundance per million lymphocytes of the indicated lymphoid cell populations, shown as estimated marginal means with 95% CI, generated from the multiple linear regressions in Table 2, and averaged across age and sex. The p-values represent pairwise comparisons on the estimated marginal means, adjusted for multiple comparisons with the Tukey method. Adjusted p-values < 0.05 are shown. (C) Association of the indicated clinical parameters with log2 abundance of ILCs per million lymphoid cells. Regression lines are from simplified multiple regression models to permit visualization on a two-dimensional plane. Shading represents the 95% CI. Results of the full models accounting for effects of both age and sex, are reported in Table 4 and the text.

When effects of age and sex were held constant, adults hospitalized with COVID-19 had 1.78-fold fewer ILCs (95% CI: 2.34–1.36; p = 4.55 x 10–5) and 2.31-fold fewer CD16+ natural killer (NK) cells (95% CI: 3.1–1.71; p = 1.04 x 10–7), as compared to controls (Table 2 and Figure 3B). Similar effects were also seen with ILC precursors (ILCP) (Figure 3—figure supplement 1). Neither CD4+ T cells nor CD8+ T cells were depleted further than expected for age and sex (Table 2 and Figure 3B). As compared with controls, SARS-CoV-2-infected adults with less severe COVID-19 who were treated as outpatients had no reduction in ILCs, but 1.44-fold fewer CD16+ NK cells (95% CI: 1.93–1.07; p = 0.018), and 1.26-fold higher CD4+ T cells (95% CI: 1.06–1.5; p = 9.59 x 10–3) (Table 2 and Figure 3B). As these analyses were performed on lymphoid cell abundance normalized to total lymphocyte number, it is possible that T cells were not lower in patients hospitalized with COVID-19 because the amount of depletion was not in excess of the change in total lymphocytes. However, the cell-type-specific results remained unchanged even when the analyses were repeated using the less stringent threshold of normalizing to total PBMC number (Supplementary file 2e).

When data from an independent, previously published cohort (Kuri-Cervantes et al., 2020) were analyzed to account for total lymphocyte abundance, age, and sex, people hospitalized with acute respiratory distress syndrome due to COVID-19, had 1.7-fold fewer ILCs (95% CI: 2.38–1.22; p = 0.002) than controls (Figure 3—figure supplement 2). Also consistent with the main adult cohort studied here, ILC abundance was not significantly reduced in the group of patients with less severe disease (Figure 3—figure supplement 2).

Odds of hospitalization in adults infected with SARS-CoV-2 increases with decreasing number of ILCs

Multiple logistic regression was used next to determine whether differences in abundance of any lymphoid cell subset was associated with odds of hospitalization in people infected with SARS-CoV-2. The adjusted odds ratio was calculated using lymphoid cell subset abundance, age, sex, diagnosis of diabetes mellitus, and duration of symptoms at the time of blood draw, each as independent variables. Abundance of ILCs, but not of CD16+ NK cells, CD4+ T cells, or CD8+ T cells was associated with odds of hospitalization: the odds ratio for hospitalization, adjusted for age, sex, diagnosis of diabetes mellitus, and symptom duration, was 0.454 (95%CI: 0.213–0.808; p = 0.018), an increase of 54.6% for each twofold decrease in ILC abundance (Table 3).

Table 3
Odds of hospitalization*.
Cell countOdds ratio95% Confidence intervalp-value
CD4+ T0.5760.211–1.280.198
ILC0.4540.213–0.8080.018
CD8+ T1.20.584–3.060.652
CD16+ NK0.8410.538–1.270.412
  1. *

    Adjusted for age, sex, diagnosis of diabetes mellitus, and symptom duration at time of sample collection.

  2. per 106 lymphocytes.

  3. per twofold increase in cell population abundance.

Duration of hospital stay in adults with COVID-19 increases with decreasing ILC abundance

The relationship between lymphoid cell abundance and duration of hospitalization was assessed to determine whether the association between ILC abundance and COVID-19 severity extended to clinical outcomes within the hospitalized adults. This relationship was assessed with multiple linear regression, including age, sex, and cell abundance as independent variables. Holding age and sex constant, abundance of ILCs, but not of CD16+ NK cells, CD4+ T cells, or CD8+ T cells, was associated with length of time in the hospital: each twofold decrease in ILC abundance was associated with a 9.38-day increase in duration of hospital stay (95% CI: 15.76–3.01; p = 0.0054) (Figure 3C and Table 4).

Table 4
Association of cell type abundance with time in hospital and laboratory values*.
Cell countDays hospitalizedCRP (mg/L)ESR (mm/h)D-dimer (ng/mL)
CD4+ T–10.843–3.335–2.674–1868.847*
[–22.511, 0.825][–56.162, 49.492][–23.840, 18.492][–3375.630, –362.063]
ILC–9.381**–46.288***–11.035*–1098.515*
[–15.755,–3.008][–71.337, –21.238][-21.936,–0.134][–1932.842, –264.188]
CD8+ T3.36632.24715.317486.192
[–8.992, 15.724][–16.509, 81.003][–4.127, 34.761][–1049.836, 2022.221]
CD16+ NK–4.775–14.619–5.159–404.873
[–11.251, 1.701][–44.011, 14.774][–16.809, 6.491][–1316.261, 506.516]
  1. * p< 0.05, ** p < 0.01, *** p < 0.001.

  2. *

    coefficients are for each two-fold increase in cell population abundance, adjusted for age and sex [ ± 95% CI].

  3. per 106 lymphoid cells.

  4. Maximum lab value recorded during course of hospitalization.

ILC abundance correlates inversely with markers of inflammation in adults hospitalized with COVID-19

To further characterize the extent to which lymphoid cell abundance predicted COVID-19 severity, multiple regression with age, sex, and cell abundance, as independent variables, was performed on peak blood levels of inflammation markers indicative of COVID-19 severity: C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR), and the fibrin degradation product D-dimer (Gallo Marin et al., 2021; Gupta et al., 2021; Luo et al., 2020; Zhang et al., 2020; Zhou et al., 2020). Holding age and sex constant, each two-fold decrease in ILC, but not in CD16+ NK cell, CD4+ T cell, or CD8+ T cell abundance, was associated with a 46.29 mg/L increase in blood CRP (95% CI: 71.34–21.24; p = 6.25 x 10–4) and 11.04 mm/hr increase in ESR (95% CI: 21.94–0.13; p = 0.047) (Figure 3C and Table 4). Abundance of both ILCs and CD4+ T cells was associated with blood levels of D-dimer, with each two-fold decrease in cell abundance associated with an increase in D-dimer by 1098.52 ng/mL (95% CI: 1932.84–264.19; p = 0.011) and 1868.85 ng/mL (95% CI: 3375.63–362.06; p = 0.016), respectively (Table 4).

ILCs are depleted in children and young adults with COVID-19 or MIS-C

Given the decline in ILC abundance with age (Figures 2A and 3A, and Table 2), and the inverse relationship between ILC abundance and disease severity in adults (Figure 3C, and Tables 3 and 4), it was hypothesized that children as a group have less severe COVID-19 because ILC abundance is higher at younger ages, and that pediatric cases with symptomatic SARS-CoV-2 infection, or with MIS-C, are accompanied by significantly lower numbers of ILCs. To test these hypotheses, the abundance of lymphoid cell subsets in pediatric COVID-19 or MIS-C was compared with that from pediatric controls, using multiple linear regression with age, sex, and group as independent variables. Consistent with the findings in adults, blood ILCs in the pediatric cohort decreased with age (Table 5 and Figure 4A), demonstrating that the decrease in ILC abundance across the lifespan is already evident within the first two decades of life. In contrast, significant change over this age range was not detected in the abundance of CD4+ T cells, CD8+ T cells, or CD16+ NK cells (Table 5 and Figure 4A).

Figure 4 with 2 supplements see all
ILCs are depleted in children with COVID-19 or MIS-C.

(A) Effect of age (X-axis) on log2 abundance per million lymphocytes of the indicated lymphoid cell populations (Y-axis), as determined by the regression analysis in Table 5. Each dot represents an individual blood donor, with yellow for female and blue for male. Shading represents the 95% CI. The p-values are from the regression analysis for comparisons to the control group. (B) Log2 abundance per million lymphocytes of the indicated lymphoid cell populations, shown as estimated marginal means with 95% CI, generated from the multiple linear regressions in Supplementary file 2g that included the combined pediatric and adult control data, and averaged across age and sex. The p-values represent pairwise comparisons on the estimated marginal means, adjusted for multiple comparisons with the Tukey method. Adjusted p-values < 0.05 are shown. (C) Association of CRP with log2 abundance of ILCs per million lymphocytes. Shading represents the 95% CI. Each dot represents a single blood donor, orange for COVID-19, magenta for MIS-C. The p-value is for the effect of ILC abundance on CRP as determined by linear regression. (D) Log2 ILC abundance per million lymphocytes in longitudinal pairs of samples collected during acute presentation and during follow-up, from individual children with COVID-19 or MIS-C. Each pair of dots connected by a line represents an individual blood donor. The p-values are for change in ILC abundance at follow-up, as determined with a linear mixed model, adjusting for age, sex, and group, and with patient as a random effect. (E) Effect of time to follow-up (X-axis) on log2 abundance per million lymphocytes of the indicated lymphoid cell populations (Y-axis). The p-values are for the difference between the COVID-19 and MIS-C follow-up groups, independent of time to follow-up as determined by multiple linear regression. Shading represents the 95% CI.

Figure 4—source data 1

Combined demographic, clinical, and flow cytometry data for pediatric COVID-19, MIS-C, and control cohorts.

Data included in this file are associated with Figures 1 and 4; Figure 4—figure supplement 1; Figure 4—figure supplement 2; Table 5, and Supplementary file 2f,g. File name: Pediatric_ COVID_MISC_andControl_data.xlsx.

https://cdn.elifesciences.org/articles/74681/elife-74681-fig4-data1-v2.xlsx
Table 5
Change in pediatric cohort cell abundance due to age, sex, and group.

Fold difference (log2) [± 95% CI].

CD4+T*ILC*CD8+T*CD16+NK*
Age–0.004–0.083**0.0120.004
[–0.027, 0.019][–0.135,–0.032][–0.014, 0.039][–0.060, 0.068]
Male–0.219–0.0270.060–0.343
[–0.492, 0.054][–0.640, 0.586][–0.249, 0.370][–1.098, 0.413]
COVID0.018–0.754*–0.0880.416
[–0.290, 0.327][–1.447,–0.061][–0.432, 0.257][–0.424, 1.257]
MIS-C–0.678***–1.098**–0.503*–0.498
[–1.028,–0.328][–1.884,–0.313][–0.904,–0.101][–1.479, 0.483]
R20.3590.3420.1690.106
  1. * p < 0.05, ** p < 0.01, *** p < 0.001

  2. *

    aper 106 lymphocytes

Among pediatric patients with COVID-19, no difference in abundance of the lymphoid cell subsets was associated with hospitalization (Supplementary file 2f), so all pediatric patients treated for COVID-19 were analyzed as a single group. After accounting for effects of age and sex, pediatric patients with COVID-19 had 1.69-fold fewer ILCs (95% CI: 2.73–1.04; p = 0.034) than controls (Figure 4A and Table 5). Neither CD4+ T cells, CD8+ T cells, nor CD16+ NK cells were depleted in pediatric COVID-19 patients (Figure 4A and Table 5).

As with pediatric COVID-19, ILCs were also lower in MIS-C, with 2.14-fold fewer ILCs (95% CI: 3.69–1.24; p = 0.007) than controls (Figure 4A and Table 5). However, unlike pediatric COVID-19, individuals with MIS-C had reduced numbers of T cells as compared with pediatric controls, with 1.6-fold fewer CD4+ T cells (95% CI: 2.04–1.26; p = 3.28 × 10–4) and 1.42-fold fewer CD8+ T cells (95% CI: 1.87–1.07; p = 0.016) (Figure 4A and Table 5). Depletion of T cells, then, distinguished MIS-C from both pediatric and adult COVID-19. Additionally, consistent with the finding in adults hospitalized with COVID-19 (Figure 3C and Table 4), after accounting for effect of group, each twofold decrease in ILC abundance in pediatric patients hospitalized with COVID-19 or MIS-C was associated with a 40.5 mg/L increase in blood CRP (95% CI: 77.87–3.13; p = 0.035) (Figure 4C), and no such association was detected with CD4+ T cells, CD8+ T cells, or CD16+ NK cells.

The above analysis of lymphoid cell subsets in pediatric COVID-19 and MIS-C was performed in comparison to pediatric controls alone. Results were essentially unchanged when multiple linear regression was repeated with combined pediatric and adult control groups (Figure 4B, Figure 4—figure supplement 1, and Supplementary file 2g).

Pediatric MIS-C is distinguished from COVID-19 by recovery of ILCs during follow-up

The availability of follow-up samples in this pediatric cohort provided the opportunity to assess the abundance of lymphoid subsets after recovery from illness. To this end, a linear mixed model was fit to determine the change in ILC abundance from acute illness to follow-up in 10 individuals (5 COVID-19 and 5 MIS-C) for whom both acute and follow-up samples were available. After accounting for effects of age, sex, and group, individuals recovering from MIS-C had a 2.39-fold increase in ILC abundance (95% CI: 1.49–3.81; p = 6.6 × 10–3) but there was no significant change in ILC abundance for individuals recovering from COVID-19 (Figure 4D). Both CD4+ and CD8+ T cells, which were depleted in MIS-C but not in COVID-19, also increased during recovery from MIS-C and remained unchanged during recovery from COVID-19 (Figure 4—figure supplement 2).

The relationship between time to follow-up and lymphoid cell abundance was then examined for all available follow-up samples whether or not a paired sample from the acute illness was available (COVID-19, N = 14; MIS-C, N = 7). This analysis found no relationship between time to follow-up and abundance of any lymphoid subset, and that individuals recovering from MIS-C had 2.28-fold more ILCs (95% CI: 1.11–4.69; p = 0.0265) than individuals recovering from COVID-19 (Figure 4E). Of note, ILC abundance rebounded by 2 months of follow-up for the MIS-C patients, whereas ILC abundance still had not rebounded after 9 months of follow-up for the COVID-19 patients. There was no difference between the follow-up groups in CD4+ T cells, CD8+ T cells, or CD16+ NK cells. Interestingly, prior to being hospitalized with MIS-C, only one of these patients had COVID-19 symptoms and, despite low ILC abundance in the COVID-19 follow-up cohort, only 28.6% of this group had been ill enough to require hospitalization (Supplementary file 2b).

Differences between COVID-19 and MIS-C in regard to T cell depletion and ILC recovery during follow-up indicate that the underlying processes causing lower ILC abundance in these two SARS-CoV-2-associated diseases are different.

Blood ILCs resemble homeostatic ILCs isolated from lung

In response to the above results with blood ILCs, attempts were made to profile ILCs from the lungs of people with fatal COVID-19, but isolation of ILCs from available samples was unsuccessful. Given that ILCs circulate from tissues to the bloodstream via the thoracic duct (Buggert et al., 2020), blood ILC levels might reflect tissue-resident cells and serve as a surrogate for lung ILCs. While ILCs might be decreased in the blood as a result of sequestration within the COVID-19-damaged lung (Ardain et al., 2019), reduced numbers of ILCs within the intestinal lamina propria of people living with HIV-1 is paralleled by reduction in blood ILCs (Wang et al., 2020).

Given the inability to assess lung samples from people with COVID-19, RNA sequencing (RNA-Seq) was performed on blood ILCs from nine healthy controls and these data were compared to previously published RNA-Seq profiles of ILCs sorted from lung, spleen, and intestine (Ardain et al., 2019; Yudanin et al., 2019). Unbiased principal component analysis demonstrated overlap of blood ILCs with ILCs from the lung, with clear separation from ILCs of jejunum or spleen origin (Figure 5A).

Blood ILCs are transcriptionally similar to lung ILCs.RNA-Seq of ILCs sorted from blood of 9 SARS-CoV-2-uninfected controls in comparison to RNA-Seq data of ILCs sorted from jejunum, lung, and spleen.

(A) PCA plot of first two principal components calculated from the top 250 most variable genes across all samples. Each dot represents an individual sample with blue for ILCs sorted from blood, green for lung, yellow for jejunum, and grey for spleen. (B) Heatmap of 355 genes differentially expressed (fold-change >1.5, padj <0.01 as determined with DESeq2) between either blood or lung ILCs and ILCs from the other tissues. (C) Select genes from (B) plotted as DESeq2 normalized counts. Each dot represents an individual sample with blue for ILCs sorted from blood, green for lung, yellow for jejunum, and grey for spleen. Boxplots represent the distribution of the data with the center line drawn through the median with the upper and lower bounds of the box at the 75th and 25th percentiles, respectively. The upper and lower whiskers extend to the largest or smallest values within 1.5 x the interquartile range (IQR).

Based on expression of characteristic transcription factors and specific inducible cytokines, ILCs are classified into ILC1, ILC2, and ILC3 subsets that are analogous to TH1, TH2, and TH17 cells, respectively (Artis and Spits, 2015; Cherrier et al., 2018; Vivier et al., 2018; Yudanin et al., 2019). A total of 355 genes were consistently differentially expressed (fold-change >1.5, padj <0.01) when either blood or lung ILCs were compared to ILCs from the other tissues (Figure 5B and C). Gene ontology analysis demonstrated enrichment for terms associated with type two immunity (Supplementary file 2h). Genes significantly higher in both blood and lung ILCs included the ILC2-defining genes GATA3 and PTGDR2 (CRTH2), as well as other genes important for ILC development such as TCF7 (Yang et al., 2013; Figure 5B and C).

TCF7- and CRTH2-encoded proteins were detected in blood ILCs by flow cytometry, confirming the RNA signature of ILC2s (Figure 6A). To assess the function of blood ILCs, PBMCs were stimulated with PMA and ionomycin, and assayed by flow cytometry for production of IL-13 after intracellular cytokine staining and gating on ILCs. IL-13 was detected in the stimulated ILC population (Figure 6B), demonstrating that the majority of blood ILCs function as ILC2s. Additionally, the blood ILCs produced amphiregulin (Figure 6B), a protein implicated in the promotion of disease tolerance by ILCs in animal models (Branzk et al., 2018; Diefenbach et al., 2020; Jamieson et al., 2013; McCarville and Ayres, 2018; Monticelli et al., 2015; Monticelli et al., 2011).

Figure 6 with 1 supplement see all
Peripheral blood ILCs exhibit homeostatic ILC2 functions.

(A–B) Flow cytometry for the indicated proteins. Cells in (A) were assayed at steady-state and cells in (B) were assayed either at steady-state or after stimulation with PMA and ionomycin, as indicated. Detection of surface proteins was performed on ILCs gated as Lin-CD56-CD127+ and detection of intracellular proteins was performed on ILCs gated as Lin-TBX21-CD127+. (C) Percent of AREG+ ILCs in blood of control blood donors after stimulation with PMA and ionomycin and gated as Lin-TBX21-CD127+. Each dot represents an individual blood donor (N Female = 13, N Male = 25). Boxplots represent the distribution of the data with the center line drawn through the median with the upper and lower bounds of the box at the 75th and 25th percentiles respectively. The upper and lower whiskers extend to the largest or smallest values within 1.5 x the interquartile range (IQR). The p-value is from a two-sided, Wilcoxon rank-sum test. (D) Log2 percent AREG+ ILCs in blood of controls or people hospitalized with COVID-19 after stimulation with PMA and ionomycin and gated as Lin-TBX21-. Data shown as estimated marginal means with 95% CI, generated from the multiple linear regression reported in the text and averaged across age and sex. The p-value is from the regression analysis.

Figure 6—source data 1

Amphiregulin (AREG) flow cytometry data presented in Figure 6 and Figure 6—figure supplement 1.

File name: AREG_in_ILCs.xlsx.

https://cdn.elifesciences.org/articles/74681/elife-74681-fig6-data1-v2.xlsx

Effect of sex and COVID-19 on fraction of blood ILCs that produce amphiregulin

Given the role that AREG-producing ILCs play in maintaining disease tolerance in animal models (Branzk et al., 2018; Diefenbach et al., 2020; McCarville and Ayres, 2018; Monticelli et al., 2015; Monticelli et al., 2011), sex differences in the functional capability of these ILCs could contribute to the greater risk for severe COVID-19 in males (O’Driscoll et al., 2021). To address this hypothesis, ILCs isolated from the peripheral blood of controls were stimulated with PMA and ionomycin, and assayed by flow cytometry for AREG production. Consistent with the apparently lower disease tolerance in males, males had a lower median fraction of AREG+ ILCs than did females (p = 0.018) (Figure 6C). This difference was also reflected in a significantly lower AREG Mean Fluorescent Intensity (MFI) in males, and neither fraction of AREG+ ILCs nor AREG MFI was affected by age (Figure 6—figure supplement 1). Multiple linear regression was performed, with age, sex, and group as independent variables, to determine whether hospitalization with COVID-19 was associated with differences in the percentage of AREG+ ILCs. This analysis showed that, after accounting for effects of age and sex, patients hospitalized with COVID-19 had a 1.91-fold lower percentage of AREG+ ILCs (95% CI: 1.19–3.06; p = 8.06 x 10–3) than controls (Figure 6D).

Discussion

The outcome of SARS-CoV-2 infection ranges from entirely asymptomatic to lethal COVID-19 (Cevik et al., 2021; He et al., 2020; Jones et al., 2021; Lee et al., 2020; Lennon et al., 2020; Ra et al., 2021; Richardson et al., 2020; Yang et al., 2021). Yet, viral load does not reliably discriminate asymptomatic from symptomatic or hospitalized populations (Cevik et al., 2021; Jones et al., 2021; Lee et al., 2020; Lennon et al., 2020; Ra et al., 2021; Yang et al., 2021). In contrast, demographic factors, including increasing age and male sex, predict worse outcome of SARS-CoV-2 infection (Alkhouli et al., 2020; Bunders and Altfeld, 2020; Gupta et al., 2021; Laxminarayan et al., 2020; Mauvais-Jarvis, 2020; O’Driscoll et al., 2021; Peckham et al., 2020; Richardson et al., 2020; Scully et al., 2020). These demographic risk factors could be due to sexual dimorphism and changes with aging in composition and function of the human immune system (Darboe et al., 2020; Klein and Flanagan, 2016; Márquez et al., 2020; Patin et al., 2018; Solana et al., 2012). Therefore it is necessary to account for effects of age and sex to determine if there are additional, independent, effects of SARS-CoV-2-associated disease.

This study collected and analyzed 245 blood samples from 177 adult and 58 pediatric patients and controls, spanning the ages of 0.7–83 years, with approximately equal numbers of males and females. It was therefore possible to characterize the independent effects of age, sex, COVID-19, and MIS-C on blood lymphoid cell populations. After accounting for effects of age and sex, ILCs, but not CD4+ or CD8+ T cells, were lower in individuals hospitalized with COVID-19 when compared with controls (Table 2 and Figure 3A, B). Lower numbers of ILCs were also observed in children with COVID-19 (Table 5 and Figure 4A, B), as well as in an independent cohort of adult patients (Figure 3—figure supplement 2). Among adults infected with SARS-CoV-2, lower abundance of ILCs, but not of the other lymphoid cell subsets, was associated with increased odds of hospitalization, longer duration of hospitalization, and higher blood level of factors associated with systemic inflammation, including CRP (Tables 3 and 4, and Figure 3C). This inverse relationship between ILC abundance and CRP was also evident in children with COVID-19 or MIS-C (Figure 4C).

The identification of reduced ILC numbers as uniquely related to COVID-19 severity is important as these cells mediate disease tolerance in animal models (Artis and Spits, 2015; Branzk et al., 2018; Califano et al., 2018; Diefenbach et al., 2020; McCarville and Ayres, 2018; Monticelli et al., 2015; Monticelli et al., 2011). The results here therefore indicate that loss of ILCs from blood correlates with loss of ILC-associated homeostatic functions, thereby allowing more severe COVID-19. Although this study examined circulating blood lymphoid cells, and does not provide direct information about processes occurring within tissues, transcriptional and functional characterization of blood ILCs demonstrated that these cells are similar to ILCs isolated from lung tissue (Figure 5). Human ILCs circulate in lymphatic fluid draining from the tissues to the blood via the thoracic duct (Buggert et al., 2020), raising the possibility that some ILCs in the blood originate from, or traffic to, lung tissue. Further characterization of these blood ILCs showed that they are functional ILC2s capable of producing the protein AREG (Figure 6A and B). Given the tissue homeostatic role AREG plays in animal models of disease tolerance (Branzk et al., 2018; Diefenbach et al., 2020; Jamieson et al., 2013; McCarville and Ayres, 2018; Monticelli et al., 2015; Monticelli et al., 2011), the discovery here that males have a smaller fraction than females of blood ILCs capable of producing AREG (Figure 6C) could explain why males are at greater risk of death from SARS-CoV-2 infection (O’Driscoll et al., 2021). This sexual dimorphism in ILC function would be amplified further by the lower overall abundance of ILCs in males (Figure 2B and Table 2).

Although the inverse relationship between the number of blood ILCs and severity of COVID-19 suggests that loss of ILC homeostatic function results in breakdown of disease tolerance (Arpaia et al., 2015; Artis and Spits, 2015; Branzk et al., 2018; Diefenbach et al., 2020; McCarville and Ayres, 2018; Monticelli et al., 2015; Monticelli et al., 2011), this observational study cannot determine whether ILC depletion preceded SARS-CoV-2 infection or whether ILC numbers are depleted as a consequence of SARS-CoV-2 infection. However, several observations support the hypothesis that individuals with lower ILC numbers at the time of SARS-CoV-2 infection are at greater risk of developing severe disease. ILC numbers in uninfected controls decrease exponentially with age; this decrease is much larger than that seen with other lymphoid cell types (Figure 2A), and much more closely mirrors the exponential increase in COVID-19 mortality with age (O’Driscoll et al., 2021; Figure 2C). In addition, the greater risk of COVID-19 mortality in males (O’Driscoll et al., 2021) correlates with lower abundance of blood ILCs (Figure 2B and Table 2) and smaller fraction of ILCs capable of producing AREG (Figure 6C). Finally, patients hospitalized with COVID-19 have a smaller fraction of AREG+ ILCs than controls (Figure 6D) and conditions independently associated with lower ILC abundance, such as HIV-1 infection (Kløverpris et al., 2016; Wang et al., 2020) and obesity (Brestoff et al., 2015; Yudanin et al., 2019), increase the risk for worse outcomes from SARS-CoV-2 infection (Biccard et al., 2021; Kompaniyets et al., 2021; Tesoriero et al., 2021).

In contrast to individuals with COVID-19, children with MIS-C had lower numbers of T cells as well as ILCs (Table 5 and Figure 4A and B), and longitudinal follow-up samples for pediatric COVID-19 and MIS-C patients showed persistence of low ILC numbers after COVID-19, but normalization of all depleted cell types after recovery from MIS-C (Figure 4D and E and Figure 4—figure supplement 2). These differences imply that the reversible lymphopenia in MIS-C is due to different underlying processes than the more specific and persistent lower ILC abundance seen in individuals with COVID-19. This difference is made more interesting by the fact that none of the children with MIS-C had required hospitalization for COVID-19 and only one experienced any COVID-19 symptoms. The other children with MIS-C were therefore unaware that they had been infected. It is possible that children with pre-existing lower ILC numbers are at risk of developing COVID-19 if infected with SARS-CoV-2, while other factors such as prolonged exposure to SARS-CoV-2 antigens in the gastrointestinal tract (Yonker et al., 2021), or rare inborn errors of immunity (Sancho-Shimizu et al., 2021), promote inflammatory processes in MIS-C that drive nonspecific lymphoid cell depletion, which ultimately normalizes after recovery.

Although ILC depletion and recovery has been reported in rheumatoid arthritis (Rauber et al., 2017), inflammation-driven ILC-depletion is not necessarily reversible, as ILCs appear permanently depleted after HIV-1 infection, possibly by the high levels of common γ-chain cytokines that are present during acute infection (Wang et al., 2020). Better understanding of the processes that drive down ILC abundance in populations susceptible to COVID-19 could potentially allow for development of interventions that increase ILC abundance and restore homeostatic disease tolerance mechanisms.

In conclusion, considering the established functions of ILCs (Artis and Spits, 2015; Branzk et al., 2018; Klose and Artis, 2016; Monticelli et al., 2015; Monticelli et al., 2011), and the host homeostatic responses necessary to survive pathogenic infection (López-Otín and Kroemer, 2021; McCarville and Ayres, 2018; Medzhitov et al., 2012; Schneider and Ayres, 2008), the findings reported here support the hypothesis that loss of disease tolerance mechanisms attributable to ILCs increase the risk of morbidity and mortality with SARS-CoV-2 infection. The findings of this observational study warrant establishment of prospective cohorts to determine whether abundance of ILCs or of other lymphoid cell subsets associated with disease tolerance (Arpaia et al., 2015; Artis and Spits, 2015; Branzk et al., 2018; Diefenbach et al., 2020; McCarville and Ayres, 2018; Monticelli et al., 2015; Monticelli et al., 2011), predict clinical outcome for infection with SARS-CoV-2 or other lethal pathogens. Understanding the mechanisms that allow an individual to tolerate high-level viral replication without experiencing symptoms, and how these mechanisms can fail and thereby allow for progression to severe disease, will provide the foundation for development of therapeutic interventions that maintain health and improve survival of pathogenic viral infection (Ayres, 2020b).

Data availability

The data that support the findings of this study are available within the manuscript and in its supplementary information data files. Bulk RNA-Seq datasets generated here can be found at: NCBI Gene Expression Omnibus (GEO): GSE168212. Bulk RNA-Seq data generated by previously published studies are available from NCBI GEO: GSE131031 and GSE126107. This study did not generate unique code.

The following data sets were generated
    1. Wang Y
    2. Lifshitz LM
    3. Luban J
    (2021) NCBI Gene Expression Omnibus
    ID GSE168212. Systematic analysis of innate lymphoid cells and natural killer cells in context of HIV-1 infection.
The following previously published data sets were used
    1. Leslie A
    2. Khader S
    (2019) NCBI Gene Expression Omnibus
    ID GSE131031. Group 3 innate lymphoid cells mediate early protective immunity against Mycobacterium tuberculosis.
    1. Yudanin N
    2. Latorre I
    3. Covington C
    (2019) NCBI Gene Expression Omnibus
    ID GSE126107. Spatial and Temporal Mapping of Human Innate Lymphoid Cells Reveals Elements of Tissue Specificity.

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Decision letter

  1. Evangelos J Giamarellos-Bourboulis
    Reviewing Editor; National and Kapodistrian University of Athens, Medical School, Greece
  2. Satyajit Rath
    Senior Editor; Indian Institute of Science Education and Research (IISER), India
  3. Evangelos J Giamarellos-Bourboulis
    Reviewer; National and Kapodistrian University of Athens, Medical School, Greece
  4. Evdoxia Kyriazopoulou
    Reviewer; National and Kapodistrian University of Athens, Medical School, Greece

Our editorial process produces two outputs: i) public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Innate lymphoid cells and disease tolerance in SARS-CoV-2 infection" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including Evangelos J Giamarellos-Bourboulis as Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Satyajit Rath as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Evdoxia Kyriazopoulou (Reviewer #2).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

– The Introduction is too long and it is based on references of the past before the arrival of COVID-19.

– One main concern is that the authors suggest that depletion of innate lymphoid cells (ILC) is the main driver of the lymphopenia of severe COVID-19. To prove this the authors need to provide the absolute counts of all subpopulations and to provide serial follow-up data. They also need to explain how hypoglobulinemia of patients is explained if depletion involves only ILC. There are at least two publications (PMID 32320677 and 34678611) suggesting defect in antigen presentation and they are not even referenced in this submission.

– The authors need to provide data on the cytokine production capacity of ILC.

– The authors should clarify when exactly blood sampling took place (at admission? during hospitalization? Before any treatment start?).

– According to Table 1 median duration of symptoms is about 20 days which is quiet a long period; normally patients are admitted after the first week. If sampling is not done at admission, administered therapy especially corticosteroids may influence flow cytometry results. If therapy was administered this has to be part of the regression results in Table 3.

– Another concern is the absence in presentation of comorbidities as they can perhaps also influence results. If these data are unavailable, this is for sure a limitation and should be discussed.

– In SARS-CoV2 infected adults, as reported widely, infections were highest in young adults, and mortality was highest in the elderly and increased approximately 1 log for each 20 year age group. Linear regression revealed that age, sex, and ILC and NK cell frequency were inversely correlated with hospitalization, and the association between ILC and NK frequency was retained when age and sex were adjusted for. Subsequent multiple logistic regression analysis showed that adjusting for age, sex, and symptom duration, only ILC frequency was significantly associated with an increased risk of hospitalization, the duration of hospital stay, and an increase in CRP. The same association between ILC frequency, but not other lymphocytes, and COVID was observed in an additional paediatric cohort. However, in rare Paediatric cases who developed MIS-C, both ILCs and T cells were significantly reduced. To link blood ILCs to what is happening on the lung, bioinformatic analysis of sequence data generated from sorted blood ILCS in compared to that of published gut and lung ILC datasets. Although, the authors argue this shows blood ILCs are transcriptionally more closely aligned to lung ILCs, the validity of this analysis is hard to judge without more detail and, as the data does not come from COVID subjects, its value in supporting the manuscript is unclear. Finally, ILCs from uninfected males produced less amphiregulin, important in lung homeostasis and repair.

– The loss of ILCs from circulation has been shown in several diseases, including HIV, TB, and COVID, as has the association between circulating ILCs and age. The strength of this manuscript is in using multiple regression to show that the association between ILC loss and disease severity is retained when age is controlled for, as age is such an important factor in COVID. However, the overall conclusion that elevated circulating ILCs support disease tolerance, while interesting, is not directly supported by any data. For example, a reduction in blood ILCs may indicate the recruitment of these cells to the lung, as has been shown for TB (Ardain et al., Nature 2019). In which case, an increased frequency of blood ILCs may not be protective per se, but just reflective of less lung involvement. Therefore, I suggest the title and abstract be altered to reflect the data presented more directly.

– The study consistently refers to cell abundance but is actually reporting the frequency of cells. It is an important distinction in this context, as the total lymphocyte count declines with age. Therefore, if there are differences in the rates of decline, it can create the impression of an increase in specific subsets which are actually declining but just at a slower than the median (as may be happening with NK cells for example). To avoid any potential confusion, I suggest the authors present and discuss the data as frequencies of total lymphocytes (or PBMC as appropriate).

– For the lymphocyte specific analysis, what is lacking is reporting of the other lymphocyte subsets, such as B-cells. It may not be possible to represent these as individual populations, depending on the markers used, but they could be presented as the remainder of the whole. In this way, the reader can get a sense of what is happening in the lymphocyte compartment as a whole. This is important given that absolute values are not available.

-I am a bit concerned that the synchronization of the paeds sample is too big an issue in this small sample size to give much confidence in the effects observed – the MISC subjects are measured at about 2months and 6-7 months, and the COVID at 6-8 months and one at 11 months. Given this, how much weight do the authors think this analysis carries?

– The authors compare the RNA read-out of the peripheral ILCs and they use previous arrays from lung ILCs to suggest that they are coming from the lung. This is far too arbitrary since this is a different cohort. Ardain et al., sorted ILC2s and ILC3s from the lung whilst Yudanin sorted ILC1, 2 and 3 from the gut – and I think only sequenced 3 and 1. ILC2 are the dominant population in blood. Is it possible that the greater apparent overlap between the blood and lung relates to the type of ILCs sequenced rather than the biological overlap between these compartments?. More generally, does this analysis add useful information to the current study? Analysis of the transcriptional activity of ILCs during COVID may have provided evidence of their role in disease tolerance, as hypothesised by the authors. However, as the PBMC sequenced were uninfected healthy controls, this is impossible.

– Figure 2.B – it might be informative to plot males and females separately in each age bin. We know there is an equal ratio of males and females but we don’t know if this is equally distributed by age. From Figure 3 it looks like there may be a bias toward younger female and older male controls? Presenting it this way will make it easy for the reader to assess.

https://doi.org/10.7554/eLife.74681.sa1

Author response

Essential Revisions (for the authors):

– The Introduction is too long and it is based on references of the past before the arrival of COVID-19.

To address these points, the Introduction was edited to start with a brief but more general statement about the features of severe COVID-19 (page 3, lines 89-92) and to include the two references suggested in comment #2 (PMID 32320677 and 34678611). Several sentences on innate lymphoid cells were deleted to focus the Introduction on COVID-19.

– One main concern is that the authors suggest that depletion of innate lymphoid cells (ILC) is the main driver of the lymphopenia of severe COVID-19. To prove this the authors need to provide the absolute counts of all subpopulations and to provide serial follow-up data. They also need to explain how hypoglobulinemia of patients is explained if depletion involves only ILC. There are at least two publications (PMID 32320677 and 34678611) suggesting defect in antigen presentation and they are not even referenced in this submission.

We thank the reviewers for the chance to clarify these points.

– It was not our intention to suggest that ILC defects are the primary cause of immune abnormalities such as lymphopenia or the defects in antigen presentation that the reviewer mentioned. As we wrote (page 6, lines 157-161), “The goal of this study was to determine whether the abundance of any blood lymphoid cell population was altered in COVID-19, independent of age, sex, and global lymphopenia, and whether abundance of any lymphoid cell population correlated with clinical outcome in SARS-CoV-2 infection.” After taking into account the effects of age and sex, our main finding was that reduction in ILC number was significantly associated with COVID-19 severity, risk and duration of hospitalization, and with increase in blood inflammatory markers such as CRP.

– To clarify this point we modified our abstract to say that our study “suggests” that lower ILC abundance, “contributes” to increased COVID-19 severity with age and in males (page 2, line 76).

– As stated above in response to point #1, we have expanded our introduction about the abnormalities associated with severe COVID-19 and have incorporated the two suggested references (page 3, lines 89-92).

– The authors need to provide data on the cytokine production capacity of ILC.

In response to this comment we have added a new panel showing that, in addition to being decreased in abundance, blood ILCs in people hospitalized for COVID-19 have significantly decreased capacity to produce amphiregulin (Figure 6D). The text of the abstract (page 2, line 69-71), results (page 44, lines 676-681), discussion (page 49, lines 786-788), and figure legends (page 46, line 703-706), have been modified accordingly.

– The authors should clarify when exactly blood sampling took place (at admission? During hospitalization? Before any treatment start?).

We now mention that samples from hospitalized patients were collected during admission (page 12, lines 165-168). In addition, we now mention that the COVID-19 blood samples were collected during the first wave of COVID-19, between March 31 and June 3rd, 2020 (page 12, lines 165-166); this time was before corticosteroids were demonstrated to be efficacious for severe COVID-19. Records of treatment interventions other than intubation were not available to us for these patients and this point has been added to the text (page 17, lines 271-272).

– According to Table 1 median duration of symptoms is about 20 days which is quiet a long period; normally patients are admitted after the first week. If sampling is not done at admission, administered therapy especially corticosteroids may influence flow cytometry results. If therapy was administered this has to be part of the regression results in Table 3.

We agree that timing of corticosteroids would be an important variable to include in Table 3. As discussed in response to point #4, this information was not available, and could not be included in the analysis. That being said, as indicated in the text (page 30, lines 457-463) and in the footnote to Table 3 (page 30), we included symptom duration at the time of sample collection as a covariate in the regression analysis presented in Table 3.

– Another concern is the absence in presentation of comorbidities as they can perhaps also influence results. If these data are unavailable, this is for sure a limitation and should be discussed.

In response to this suggestion we obtained information about diabetes mellitus. These data are now listed in Table 1 (page 18), mentioned in the text (page 17, lines 276-278), and included in the calculations for the odds of hospitalization presented Table 3 (page 30) and discussed on page 30 (lines 457-463). Accounting for diabetes mellitus diagnosis had minimal effect on the statistical results.

– In SARS-CoV2 infected adults, as reported widely, infections were highest in young adults, and mortality was highest in the elderly and increased approximately 1 log for each 20 year age group. Linear regression revealed that age, sex, and ILC and NK cell frequency were inversely correlated with hospitalization, and the association between ILC and NK frequency was retained when age and sex were adjusted for. Subsequent multiple logistic regression analysis showed that adjusting for age, sex, and symptom duration, only ILC frequency was significantly associated with an increased risk of hospitalization, the duration of hospital stay, and an increase in CRP. The same association between ILC frequency, but not other lymphocytes, and COVID was observed in an additional paediatric cohort. However, in rare Paediatric cases who developed MIS-C, both ILCs and T cells were significantly reduced. To link blood ILCs to what is happening on the lung, bioinformatic analysis of sequence data generated from sorted blood ILCS in compared to that of published gut and lung ILC datasets. Although, the authors argue this shows blood ILCs are transcriptionally more closely aligned to lung ILCs, the validity of this analysis is hard to judge without more detail and, as the data does not come from COVID subjects, its value in supporting the manuscript is unclear. Finally, ILCs from uninfected males produced less amphiregulin, important in lung homeostasis and repair.

We thank the reviewer for the extensive summary of our findings. The comment they make here is addressed in point #12 below.

– The loss of ILCs from circulation has been shown in several diseases, including HIV, TB, and COVID, as has the association between circulating ILCs and age. The strength of this manuscript is in using multiple regression to show that the association between ILC loss and disease severity is retained when age is controlled for, as age is such an important factor in COVID. However, the overall conclusion that elevated circulating ILCs support disease tolerance, while interesting, is not directly supported by any data. For example, a reduction in blood ILCs may indicate the recruitment of these cells to the lung, as has been shown for TB (Ardain et al., Nature 2019). In which case, an increased frequency of blood ILCs may not be protective per se, but just reflective of less lung involvement. Therefore, I suggest the title and abstract be altered to reflect the data presented more directly.

In response to this comment we have added new data and changed the text as recommended:

– The term “disease tolerance” has been removed from the title (page 1, line 3).

– The abstract now mentions that blood ILCs from people hospitalized with COVID-19 have significantly decreased capacity to produce amphiregulin (page 2, lines 69-71). See point number 3 above regarding new Figure 6D.

– The possibility that ILCs are sequestered in the lung is now mentioned in the text (page 40, lines 614-615).

– The study consistently refers to cell abundance but is actually reporting the frequency of cells. It is an important distinction in this context, as the total lymphocyte count declines with age. Therefore, if there are differences in the rates of decline, it can create the impression of an increase in specific subsets which are actually declining but just at a slower than the median (as may be happening with NK cells for example). To avoid any potential confusion, I suggest the authors present and discuss the data as frequencies of total lymphocytes (or PBMC as appropriate).

As suggested by the reviewer, the majority of the data are reported as ILCs per million lymphocytes. This is stated in the text and figure legends, and indicated in the Y axis labels on the figures. As the reviewer also points out, certain analyses were per million PBMCs; critical findings such as the effect of COVID-19 on ILCs were essentially the same per million lymphocytes or PBMCs (page 27, lines 418-420; Supplementary file 2e). The same was true for the other lymphoid cell subsets, including the significant increase in NK cells with age that was referred to by the reviewer.

– For the lymphocyte specific analysis, what is lacking is reporting of the other lymphocyte subsets, such as B-cells. It may not be possible to represent these as individual populations, depending on the markers used, but they could be presented as the remainder of the whole. In this way, the reader can get a sense of what is happening in the lymphocyte compartment as a whole. This is important given that absolute values are not available.

The reviewer makes an important point. To provide a sense of the lymphocyte compartment as a whole, we had analyzed lymphocyte abundance as a fraction of total PBMCs. This was described in the text on page 25, line 379-384,

“the effect of COVID-19 on total lymphocyte abundance was addressed with multiple linear regression. After accounting for effects of age and sex, individuals hospitalized with severe COVID-19 had 1.33-fold (95%CI: 1.49–1.19; p = 1.22 x 10-6) fewer total lymphocytes among PBMCs than did controls (Supplementary file 2d)”.

-I am a bit concerned that the synchronization of the paeds sample is too big an issue in this small sample size to give much confidence in the effects observed – the MISC subjects are measured at about 2months and 6-7 months, and the COVID at 6-8 months and one at 11 months. Given this, how much weight do the authors think this analysis carries?

We thank the reviewer for raising this question regarding the difference in timing of follow-up blood collections for the MISC-C subjects vs the COVID-19 subjects in Figure 4E (page 38). If the results had been different, the difference in timing would have been a problem. But, given the directionality of the effect we observed, this difference in timing highlights the divergence in rate of recovery of ILC numbers between these two groups: ILC abundance rebounded by 2 months of follow-up for the MIS-C patients, whereas ILC abundance had still not rebounded after 9 months of follow-up for the COVID-19 patients. We now emphasize this point in the text (page 36, lines 570-572).

– The authors compare the RNA read-out of the peripheral ILCs and they use previous arrays from lung ILCs to suggest that they are coming from the lung. This is far too arbitrary since this is a different cohort. Ardain et al., sorted ILC2s and ILC3s from the lung whilst Yudanin sorted ILC1, 2 and 3 from the gut – and I think only sequenced 3 and 1. ILC2 are the dominant population in blood. Is it possible that the greater apparent overlap between the blood and lung relates to the type of ILCs sequenced rather than the biological overlap between these compartments?. More generally, does this analysis add useful information to the current study? Analysis of the transcriptional activity of ILCs during COVID may have provided evidence of their role in disease tolerance, as hypothesised by the authors. However, as the PBMC sequenced were uninfected healthy controls, this is impossible.

Several changes were made to the manuscript in response to these comments:

– We agree that it would have been better if we had presented RNA-Seq data on lung ILCs from people hospitalized with COVID-19. The text now states that our attempts to profile ILCs from COVID-19 lung samples were unsuccessful (page 40, lines 610-612). In our previous work concerning intestinal ILCs from people living with HIV-1, biopsies had to be processed fresh within the hour (Nat Immunol 21:274); we were unable to profile intestinal ILCs from frozen or fixed tissues and suspect a similar issue holds for the viability of lung ILCs in COVID-19.

– We also agree that it would have been better if we had RNA-Seq data for ILCs isolated from the blood of people hospitalized with COVID-19. This was not possible because the limited sample available was prioritized for the FACS experiments. These samples had low numbers of ILCs which increased the difficulty of sorting viable cells from this population that is already rare in the blood of uninfected people.

– Given the significant reductions in blood ILCs in our study, and our inability to profile these rare cells from people with severe COVID, we felt it was important to provide some additional characterization of blood ILCs, even if only from uninfected individuals. Our analysis of global gene expression in blood ILCs from 9 donors shows close similarity to the expression profile of ILCs from the lung. While the reviewer is correct that this is driven in part by the ILC2 character of these cells, many of the 355 genes that distinguish blood and lung ILCs from ILCs of other tissues are not obvious ILC2 genes and could indicate other biologically relevant features in common between blood and lung ILCs (Figure 5B and C, page 42).

– The transcriptional analysis guided our phenotypic analysis of the blood ILCs (Figure 6 page 45), and, in the revised manuscript, we now show that blood ILCs in people hospitalized for COVID-19 have significantly decreased capacity to produce amphiregulin (Figure 6D). The text of the abstract (page 2, line 69-71), results (page 44, lines 676-681), discussion (page 49, lines 786-788), and figure legends (page 46, line 703-706), have been modified accordingly.

– Figure 2.B – it might be informative to plot males and females separately in each age bin. We know there is an equal ratio of males and females but we don't know if this is equally distributed by age. From Figure 3 it looks like there may be a bias toward younger female and older male controls? Presenting it this way will make it easy for the reader to assess.

To address the reviewer’s question about our control group we compared the male/female ratio for each age group against each of the other age groups in Figure 2. There were no significant differences in this ratio among any of the groups. A table showing this analysis has been added to the supplement (Supplementary file 2c).

https://doi.org/10.7554/eLife.74681.sa2

Article and author information

Author details

  1. Noah J Silverstein

    1. Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, United States
    2. Medical Scientist Training Program, University of Massachusetts Medical School, Worcester, United States
    3. Massachusetts Consortium on Pathogen Readiness, Boston, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Software, Visualization, Writing – original draft, Writing – review and editing
    Contributed equally with
    Yetao Wang
    For correspondence
    noah.silverstein@umassmed.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5688-9978
  2. Yetao Wang

    1. Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, United States
    2. Massachusetts Consortium on Pathogen Readiness, Boston, United States
    Contribution
    Conceptualization, Formal analysis, Investigation, Methodology, Writing – review and editing
    Contributed equally with
    Noah J Silverstein
    For correspondence
    yetao.wang@umassmed.edu
    Competing interests
    No competing interests declared
  3. Zachary Manickas-Hill

    1. Massachusetts Consortium on Pathogen Readiness, Boston, United States
    2. Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    Contribution
    Data curation, Investigation
    Competing interests
    No competing interests declared
  4. Claudia Carbone

    Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  5. Ann Dauphin

    Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  6. Brittany P Boribong

    1. Massachusetts General Hospital, Mucosal Immunology and Biology Research Center, Boston, United States
    2. Massachusetts General Hospital, Department of Pediatrics, Boston, United States
    3. Harvard Medical School, Boston, United States
    Contribution
    Data curation, Investigation
    Competing interests
    No competing interests declared
  7. Maggie Loiselle

    Massachusetts General Hospital, Mucosal Immunology and Biology Research Center, Boston, United States
    Contribution
    Data curation, Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1051-2072
  8. Jameson Davis

    Massachusetts General Hospital, Mucosal Immunology and Biology Research Center, Boston, United States
    Contribution
    Data curation, Investigation
    Competing interests
    No competing interests declared
  9. Maureen M Leonard

    1. Massachusetts General Hospital, Mucosal Immunology and Biology Research Center, Boston, United States
    2. Massachusetts General Hospital, Department of Pediatrics, Boston, United States
    3. Harvard Medical School, Boston, United States
    Contribution
    Data curation, Investigation
    Competing interests
    No competing interests declared
  10. Leticia Kuri-Cervantes

    1. Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    2. Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    Contribution
    Data curation, Investigation
    Competing interests
    No competing interests declared
  11. MGH COVID-19 Collection & Processing Team

    Contribution
    Resources
    Competing interests
    No competing interests declared
    1. Kendall Lavin-Parsons, Department of Emergency Medicine, Massachusetts General Hospital, Boston, United States
    2. Blair Parry, Department of Emergency Medicine, Massachusetts General Hospital, Boston, United States
    3. Brendan Lilley, Department of Emergency Medicine, Massachusetts General Hospital, Boston, United States
    4. Carl Lodenstein, Department of Emergency Medicine, Massachusetts General Hospital, Boston, United States
    5. Brenna McKaig, Department of Emergency Medicine, Massachusetts General Hospital, Boston, United States
    6. Nicole Charland, Department of Emergency Medicine, Massachusetts General Hospital, Boston, United States
    7. Hargun Khanna, Department of Emergency Medicine, Massachusetts General Hospital, Boston, United States
    8. Justin Margolin, Department of Emergency Medicine, Massachusetts General Hospital, Boston, United States
    9. Anna Gonye, Massachusetts General Hospital Cancer Center, Boston, United States
    10. Irena Gushterova, Massachusetts General Hospital Cancer Center, Boston, United States
    11. Tom Lasalle, Massachusetts General Hospital Cancer Center, Boston, United States
    12. Nihaarika Sharma, Massachusetts General Hospital Cancer Center, Boston, United States
    13. Brian C Russo, Division of Infectious Diseases, Department of Medicine, Massachusetts General Hospital, Boston, United States
    14. Maricarmen Rojas-Lopez, Division of Infectious Diseases, Department of Medicine, Massachusetts General Hospital, Boston, United States
    15. Moshe Sade-Feldman, Massachusetts General Hospital Center for Immunology and Inflammatory Diseases, Boston, United States
    16. Kasidet Manakongtreecheep, Massachusetts General Hospital Center for Immunology and Inflammatory Diseases, Boston, United States
    17. Jessica Tantivit, Massachusetts General Hospital Center for Immunology and Inflammatory Diseases, Boston, United States
    18. Molly Fisher Thomas, Massachusetts General Hospital Center for Immunology and Inflammatory Diseases, Boston, United States
    19. Betelihem A Abayneh, Massachusetts General Hospital, Boston, United States
    20. Patrick Allen, Massachusetts General Hospital, Boston, United States
    21. Diane Antille, Massachusetts General Hospital, Boston, United States
    22. Katrina Armstrong, Massachusetts General Hospital, Boston, United States
    23. Siobhan Boyce, Massachusetts General Hospital, Boston, United States
    24. Joan Braley, Massachusetts General Hospital, Boston, United States
    25. Karen Branch, Massachusetts General Hospital, Boston, United States
    26. Katherine Broderick, Massachusetts General Hospital, Boston, United States
    27. Julia Carney, Massachusetts General Hospital, Boston, United States
    28. Andrew Chan, Massachusetts General Hospital, Boston, United States
    29. Susan Davidson, Massachusetts General Hospital, Boston, United States
    30. Michael Dougan, Massachusetts General Hospital, Boston, United States
    31. David Drew, Massachusetts General Hospital, Boston, United States
    32. Ashley Elliman, Massachusetts General Hospital, Boston, United States
    33. Keith Flaherty, Massachusetts General Hospital, Boston, United States
    34. Jeanne Flannery, Massachusetts General Hospital, Boston, United States
    35. Pamela Forde, Massachusetts General Hospital, Boston, United States
    36. Elise Gettings, Massachusetts General Hospital, Boston, United States
    37. Amanda Griffin, Massachusetts General Hospital, Boston, United States
    38. Sheila Grimmel, Massachusetts General Hospital, Boston, United States
    39. Kathleen Grinke, Massachusetts General Hospital, Boston, United States
    40. Kathryn Hall, Massachusetts General Hospital, Boston, United States
    41. Meg Healy, Massachusetts General Hospital, Boston, United States
    42. Deborah Henault, Massachusetts General Hospital, Boston, United States
    43. Grace Holland, Massachusetts General Hospital, Boston, United States
    44. Chantal Kayitesi, Massachusetts General Hospital, Boston, United States
    45. Vlasta LaValle, Massachusetts General Hospital, Boston, United States
    46. Yuting Lu, Massachusetts General Hospital, Boston, United States
    47. Sarah Luthern, Massachusetts General Hospital, Boston, United States
    48. Jordan Marchewka, Massachusetts General Hospital, Boston, United States
    49. Brittani Martino, Massachusetts General Hospital, Boston, United States
    50. Roseann McNamara, Massachusetts General Hospital, Boston, United States
    51. Christian Nambu, Massachusetts General Hospital, Boston, United States
    52. Susan Nelson, Massachusetts General Hospital, Boston, United States
    53. Marjorie Noone, Massachusetts General Hospital, Boston, United States
    54. Christine Ommerborn, Massachusetts General Hospital, Boston, United States
    55. Lois Chris Pacheco, Massachusetts General Hospital, Boston, United States
    56. Nicole Phan, Massachusetts General Hospital, Boston, United States
    57. Falisha A Porto, Massachusetts General Hospital, Boston, United States
    58. Edward Ryan, Massachusetts General Hospital, Boston, United States
    59. Kathleen Selleck, Massachusetts General Hospital, Boston, United States
    60. Sue Slaughenhaupt, Massachusetts General Hospital, Boston, United States
    61. Kimberly Smith Sheppard, Massachusetts General Hospital, Boston, United States
    62. Elizabeth Suschana, Massachusetts General Hospital, Boston, United States
    63. Vivine Wilson, Massachusetts General Hospital, Boston, United States
    64. Galit Alter, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    65. Alejandro Balazs, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    66. Julia Bals, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    67. Max Barbash, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    68. Yannic Bartsch, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    69. Julie Boucau, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    70. Josh Chevalier, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    71. Fatema Chowdhury, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    72. Kevin Einkauf, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    73. Jon Fallon, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    74. Liz Fedirko, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    75. Kelsey Finn, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    76. Pilar Garcia-Broncano, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    77. Ciputra Hartana, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    78. Chenyang Jiang, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    79. Paulina Kaplonek, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    80. Marshall Karpell, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    81. Evan C Lam, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    82. Kristina Lefteri, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    83. Xiaodong Lian, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    84. Mathias Lichterfeld, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    85. Daniel Lingwood, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    86. Hang Liu, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    87. Jinqing Liu, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    88. Natasha Ly, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    89. Ashlin Michell, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    90. Ilan Millstrom, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    91. Noah Miranda, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    92. Claire O’Callaghan, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    93. Matthew Osborn, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    94. Shiv Pillai, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    95. Yelizaveta Rassadkina, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    96. Alexandra Reissis, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    97. Francis Ruzicka, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    98. Kyra Seiger, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    99. Libera Sessa, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    100. Christianne Sharr, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    101. Sally Shin, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    102. Nishant Singh, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    103. Weiwei Sun, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    104. Xiaoming Sun, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    105. Hannah Ticheli, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    106. Alicja Trocha-Piechocka, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    107. Daniel Worrall, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    108. Alex Zhu, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    109. George Daley, Harvard Medical School, Boston, United States
    110. David Golan, Harvard Medical School, Boston, United States
    111. Howard Heller, Harvard Medical School, Boston, United States
    112. Arlene Sharpe, Harvard Medical School, Boston, United States
    113. Nikolaus Jilg, Brigham and Women’s Hospital, Boston, United States
    114. Alex Rosenthal, Brigham and Women’s Hospital, Boston, United States
    115. Colline Wong, Brigham and Women’s Hospital, Boston, United States
  12. Nuala J Meyer

    Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, United States
    Contribution
    Data curation, Resources
    Competing interests
    No competing interests declared
  13. Michael R Betts

    1. Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    2. Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    Contribution
    Data curation, Resources
    Competing interests
    No competing interests declared
  14. Jonathan Z Li

    1. Massachusetts Consortium on Pathogen Readiness, Boston, United States
    2. Department of Medicine, Brigham and Women’s Hospital, Boston, United States
    Contribution
    Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  15. Bruce D Walker

    1. Massachusetts Consortium on Pathogen Readiness, Boston, United States
    2. Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    3. Howard Hughes Medical Institute, Chevy Chase, United States
    4. Department of Biology and Institute of Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, United States
    Contribution
    Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  16. Xu G Yu

    1. Massachusetts Consortium on Pathogen Readiness, Boston, United States
    2. Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    3. Department of Medicine, Brigham and Women’s Hospital, Boston, United States
    Contribution
    Data curation, Investigation, Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  17. Lael M Yonker

    1. Massachusetts General Hospital, Mucosal Immunology and Biology Research Center, Boston, United States
    2. Massachusetts General Hospital, Department of Pediatrics, Boston, United States
    3. Harvard Medical School, Boston, United States
    Contribution
    Data curation, Funding acquisition, Investigation, Resources, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  18. Jeremy Luban

    1. Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, United States
    2. Massachusetts Consortium on Pathogen Readiness, Boston, United States
    3. Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    4. Department of Biochemistry and Molecular Biotechnology, University of Massachusetts Medical School, Worcester, United States
    5. Broad Institute of Harvard and MIT, Cambridge, United States
    Contribution
    Conceptualization, Formal analysis, Funding acquisition, Project administration, Resources, Supervision, Writing – original draft, Writing – review and editing
    For correspondence
    jeremy.luban@umassmed.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5650-4054

Funding

National Institutes of Health (R37AI147868)

  • Jeremy Luban

National Institutes of Health (R01AI148784)

  • Jeremy Luban

National Institutes of Health (F30HD100110)

  • Noah J Silverstein

National Institutes of Health (5K08HL143183)

  • Lael M Yonker

Massachusetts Consortium for Pathogen Readiness

  • Jeremy Luban

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank the Massachusetts Consortium for Pathogen Readiness Specimen Collection and Processing Team listed below and members of the Yu and Luban Labs. This work was supported in part by the Massachusetts Consortium for Pathogen Readiness and NIH grants R37AI147868 and R01AI148784 to JL and Ruth L Kirschstein NRSA Fellowship F30HD100110 to NJS. The MGH/MassCPR COVID biorepository was supported by a gift from Ms. Enid Schwartz, by the Mark and Lisa Schwartz Foundation, the Massachusetts Consortium for Pathogen Readiness, and the Ragon Institute of MGH, MIT and Harvard. The Pediatric COVID-19 Biorepository was supported by the National Heart, Lung, and Blood Institute (5K08HL143183 to LMY), and the Department of Pediatrics at Massachusetts General Hospital for Children (to LMY).

Ethics

Human subjects: As part of a COVID-19 observational study, peripheral blood samples were collected between March 31st and June 3rd of 2020 from 91 adults with SARS-CoV-2 infection, either after admission to Massachusetts General Hospital for the hospitalized cohort, or while at affiliated outpatient clinics for the outpatient cohort. Request for access to coded patient samples was reviewed by the Massachusetts Consortium for Pathogen Readiness (https://masscpr.hms.harvard.edu/) and approved by the University of Massachusetts Medical School IRB (protocol #H00020836). Pediatric participants with COVID-19 or MIS-C were enrolled in the Massachusetts General Hospital Pediatric COVID-19 Biorepository (MGB IRB # 2020P000955). Healthy pediatric controls were enrolled in the Pediatric Biorepository (MGB IRB # 2016P000949). Samples were collected after obtaining consent from the patient if 18 years or older, or from the parent/guardian, plus assent when appropriate.

Senior Editor

  1. Satyajit Rath, Indian Institute of Science Education and Research (IISER), India

Reviewing Editor

  1. Evangelos J Giamarellos-Bourboulis, National and Kapodistrian University of Athens, Medical School, Greece

Reviewers

  1. Evangelos J Giamarellos-Bourboulis, National and Kapodistrian University of Athens, Medical School, Greece
  2. Evdoxia Kyriazopoulou, National and Kapodistrian University of Athens, Medical School, Greece

Publication history

  1. Preprint posted: January 15, 2021 (view preprint)
  2. Received: October 14, 2021
  3. Accepted: March 11, 2022
  4. Accepted Manuscript published: March 11, 2022 (version 1)
  5. Version of Record published: April 25, 2022 (version 2)

Copyright

© 2022, Silverstein et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Noah J Silverstein
  2. Yetao Wang
  3. Zachary Manickas-Hill
  4. Claudia Carbone
  5. Ann Dauphin
  6. Brittany P Boribong
  7. Maggie Loiselle
  8. Jameson Davis
  9. Maureen M Leonard
  10. Leticia Kuri-Cervantes
  11. MGH COVID-19 Collection & Processing Team
  12. Nuala J Meyer
  13. Michael R Betts
  14. Jonathan Z Li
  15. Bruce D Walker
  16. Xu G Yu
  17. Lael M Yonker
  18. Jeremy Luban
(2022)
Innate lymphoid cells and COVID-19 severity in SARS-CoV-2 infection
eLife 11:e74681.
https://doi.org/10.7554/eLife.74681
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