Immune cell responses are strikingly altered in patients with severe coronavirus disease 2019 (COVID-19), but the immunoregulatory process in these individuals is not fully understood. In this study, 23 patients with mild and 22 patients with severe COVID-19 and 6 asymptomatic carriers of COVID-19 were enrolled, along with 44 healthy controls (HC). Peripheral immune cells in HC and patients with COVID-19 were comprehensively profiled using mass cytometry. We found that in patients with severe COVID-19, the number of HLA-DRlow/− monocytes was significantly increased, but that of mucosal-associated invariant T (MAIT) cells was greatly reduced. MAIT cells were highly activated but functionally impaired in response to Escherichia coli and IL-12/IL-18 stimulation in patients with severe COVID-19, especially those with microbial coinfection. Single-cell transcriptome analysis revealed that IFN-stimulated genes were significantly upregulated in peripheral MAIT cells and monocytes from patients with severe COVID-19. IFN-α pretreatment suppressed MAIT cells’ response to E. coli by triggering high levels of IL-10 production by HLA-DRlow/−–suppressive monocytes. Blocking IFN-α or IL-10 receptors rescued MAIT cell function in patients with severe COVID-19. Moreover, plasma from patients with severe COVID-19 inhibited HLA-DR expression by monocytes through IL-10. These data indicate a unique pattern of immune dysregulation in severe COVID-19, which is characterized by enrichment of suppressive HLA-DRlow/− monocytes associated with functional impairment of MAIT cells through the IFN/IL-10 pathway.

The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has caused immense harm to global health and the economy. Although the majority of patients with COVID-19 have the mild and self-limited disease, ∼20% of patients with COVID-19 progress into severe illnesses, such as acute respiratory distress syndrome or septic shock (1). The mechanisms promoting severe disease are unclear and are possibly related to old age, host genetics, preexisting diseases, and dysfunctional immune responses (2). Moreover, secondary bacterial infection may amplify the inflammatory cytokine storm and likely contribute to multiple organ failure and even death in patients with severe COVID-19 (35).

Recent studies have shown that SARS-CoV-2 infection induces robust immune responses and affects the immune cell compartment (68). For example, the immature suppressive myeloid cells, hyperactivated NK cells, and innate-like T cells were increased, whereas dendritic cells and CD4+ and CD8+ T cells were significantly diminished in peripheral blood of patients with COVID-19, especially those with severe COVID-19 [(811) and I. Sánchez-Cerrillo, P. Landete, B. Aldave, S. Sanchez-Alonso, A. Sanchez-Azofra, A. Marcos-Jimenez, E. Avalos, A. Alcaraz-Serna, I. de los Santos, T. Mateu-Albero, et al., manuscript posted on medRxiv, DOI: 10.1101/2020.05.13.20100925]. Further studies revealed striking alterations in transcriptional profiles and functions in monocytes, NK cells, dendritic cells, and T cells (9, 1214) in both peripheral blood and bronchoalveolar lavage fluid from patients with COVID-19. Proinflammatory monocytes and cytotoxic CD8+ T cells were recruited to the lung during severe COVID-19, likely contributing to lung injury (15). In addition, interactions among various immune cell subsets were proposed to be involved in the immune dysregulation observed in severe COVID-19.

Mucosal-associated invariant T (MAIT) cells are innate-like T cells with a semi-invariant TCR (Va7.2-Ja12/20/33) (16, 17) restricted by the nonpolymorphic molecule MR1, which is a conserved MHC class Ib–like molecule (18, 19). MAIT cells recognize the microbial metabolite riboflavin and respond to bacterial and fungal infections (20, 21). In addition, they are stimulated by IL-12, IL-15, and IL-18 (22) during viral infection (23). Activated MAIT cells produce proinflammatory cytokines, including TNF and IFN-γ, as well as cytotoxic granzymes and perforin (24). Numerical and functional impairment of MAIT cells associated with elevated IFN-I/IL-10 axis signaling has been implicated in chronic HIV-1 infection (24) and increased susceptibility to bacterial coinfections (25). In addition, during SARS-CoV-2 infection, MAIT cells showed hyperactivation and were associated with the severity of COVID-19 (26). However, the mechanism mediating MAIT cell dysfunction, especially in patients with severe COVID-19, is still elusive.

In this study, we profiled peripheral immune cells from patients with COVID-19 by mass cytometry. We observed numerical and functional impairment of MAIT cells in patients with COVID-19, which was associated with disease severity and microbial coinfection status. By single-cell transcriptome analysis and in vitro experiments, we showed that IFN-I stimulation induced IL-10 production by HLA-DRlow/− suppressive monocytes, which subsequently dampened MAIT cell–mediated immunity in response to bacterial infection in patients with severe COVID-19. These data provide more insight into the aberrant immune function and mechanisms underlying COVID-19 immunopathogenesis, which is pivotal for the development of therapeutic strategies.

This study was conducted according to the ethical principles of the Declaration of Helsinki. Ethical approval was obtained from the Research Ethics Committee of Shenzhen Third People’s Hospital (2020-244). All participants provided written informed consent.

Forty-five patients with COVID-19 were enrolled in this study at the Shenzhen Third People’s Hospital. The procedures used for metadata and patient sample collection were similar to those previously described (15). The severity of COVID-19 was categorized as mild, moderate, severe, or critical, according to the Diagnosis and Treatment Protocol for COVID-19 (Trial Version 7) released by the National Health Commission of China. Patients with mild and moderate COVID-19 were grouped into the mild group, and those with severe and critical diseases were grouped into the severe group. Blood samples were collected from hospitalized patients with a median of 3 d (range 0–23 d) after symptom onset. Samples from convalescent patients with COVID-19 were obtained from discharged individuals, six patients with severe and eight with mild COVID-19, a median of 25 d (range 16–90 d) after symptom onset. The patient information was summarized in Supplemental Table I. Six asymptomatic carriers of COVID-19 were confirmed by fluorescence RT-PCR for SARS-CoV-2. These individuals showed no symptoms and had a median age of 33 y (range 12–51 y). Samples from a total of 44 healthy controls (HC) with a median age of 33 y (range 17–47 y) were obtained from the biological sample bank of the Shenzhen Third People’s Hospital.

PBMCs were isolated by Ficoll density gradient centrifugation. CD14+ monocytes were isolated using positive selection with CD14 MicroBeads (Miltenyi Biotec). The purity, as assessed by CD14 staining and flow cytometric analysis, was >90%.

For cell surface labeling, cells were blocked with Fc-blocking reagent (BD Biosciences). Then, the following Abs were added and incubated for 30 min: anti-CD3 (HIT3a; BioLegend), anti-CD14 (63D3; BioLegend), anti–HLA-DR (L243; BioLegend), anti-CD45 (2D1; BioLegend), anti-CD161 (HP-3G10; BioLegend), TCRVα7.2 (3C10; BioLegend), and anti-CD69 (FN50; BD Biosciences, San Jose, CA). After incubation, samples were washed and reconstituted in PBS for flow cytometric analysis in an FACSCanto II flow cytometer (BD Biosciences). For intracellular staining, cells were fixed and permeabilized using Fix/Perm Buffer (BD Biosciences). Then, the following Abs were added to the cells for intracellular labeling: anti-CD107a (LAMP-1; BioLegend), anti–IFN-γ (B27; BD Biosciences), and anti-granzyme B (GzmB) (QA16A02; BioLegend). Cells were incubated with the Abs for 60 min and were then washed and fixed with 1.6% paraformaldehyde (PFA). Data analysis was performed using FlowJo software version 10.

PBMCs were incubated with PFA-fixed Escherichia coli (multiplicity of infection [MOI] of 10) for 24 h in the presence of an anti-CD28 mAb (1.25 µg/ml) (BioLegend). For cytokine stimulation, a combination of IL-12 and IL-18 (PeproTech) was added at 100 ng/ml for 24 h. Anti-CD107a (1 µg/ml) was added at the initiation of culture for stimulation with E. coli or IL-12/IL-18. Brefeldin A (BioLegend) was added during the last 6 h of stimulation.

The Abs used in this study for mass cytometry (cytometry by time of flight [CyTOF]) are listed in Supplemental Table II. All CyTOF staining was performed at room temperature, as previously described (27). In brief, cells were incubated in 1 M cisplatin (Fluidigm) for 5 min. Fc-blocking reagent was then added to the cells for 10 min. Next, the cells were stained with Abs for 30 min for surface labeling and were then washed once in CyTOF staining buffer (Fluidigm). For intracellular labeling, cells were fixed and permeabilized using Fix/Perm Buffer (Fluidigm) before incubation with the appropriate Abs for 30 min. Then, the cells were washed twice by centrifugation with PBS, resuspended in 1.6% PFA (Fluidigm) in the presence of the iridium intercalator (125 nM; Fluidigm), and incubated overnight at 4°C. After three further washes, the cells were resuspended in PBS containing EQ calibration beads (Fluidigm) before analysis in a Helios mass cytometer. Data analysis was conducted using Cytobank (Mountain View, CA) and FlowJo (Tree Star, Ashland, OR).

A total of 97,000–360,000 cell events were collected for each sample. Files (.fcs) were uploaded into FlowJo (version V10), populations of interest were manually gated, and events of interest were exported as .fcs files. For further analysis, random sampling of 5000 cells from each FCS file was performed using the Cytofkit package (version 1.11.3) in R. Visualizations based on t-distributed stochastic neighbor embedding (t-SNE) and clustering based on the FlowSOM algorithm were then performed with these data.

PBMCs were stimulated with PFA-fixed E. coli in the presence of an anti-IFNAR2 Ab (clone MMHAR-2; PBL Assay Science) or a purified anti–IL-10R Ab (clone 3F9; BioLegend) at 10 µg/ml. Mouse IgG2a or rat IgG21 isotype control Abs were used as controls.

PBMCs or CD14+ cells were incubated alone or with plasma from the HC or patients with severe COVID-19 at different ratios or with cytokines for 24 h or 48 h. Then, cells were collected for phenotypic analysis.

IL-10 and IFN-α in plasma were examined according to the manufacturer’s instructions (Unimedica). In brief, 25 µl sonicated beads, 25 µl plasma, and 25 µl detection Abs were mixed and placed on a shaker at 500 rpm for 2 h at room temperature. Then, 25 µl streptavidin-PE was added directly to each tube. The tubes were placed on a shaker at 500 rpm for 30 min. The data were obtained by flow cytometry (Canto II; BD Biosciences) and analyzed with LEGENDplex v.8.0 (VigeneTech).

Gene expression analysis was conducted using a publicly available single-cell RNA sequencing (scRNA-seq) data set, which was from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/) or Genome Sequence Archive database (https://bigd.big.ac.cn/gsa/) (accession numbers GSE150728, GSE149689, and HRA000297) (28). MAIT cells and monocytes were defined using SLC4A10 and CD14/FEGR3A, respectively, as specific markers. Differentially expressed genes (DEGs) were analyzed using the FindMarkers function in Seurat (version 3) with the MAST algorithm. For each pairwise comparison, we ran the FindMarkers function with the parameter test.use = ‘MAST.’ The overlap of DEGs in different comparisons is shown on a Venn diagram. Genes were defined as significantly upregulated if the average natural logarithm of the fold change (logFC) was >0.25 and the adjusted p value was < 0.01. Genes with logFC < −0.25 and adjusted p < 0.01 were considered significantly downregulated. For the selected genes shown on the heat map, logFC < −0.41 or >0.41 and adjusted p < 0.01. ClusterProfiler 37 in R was used to perform gene ontology (GO) term enrichment analysis for the significantly upregulated and downregulated genes. Only the biological process GO terms are displayed.

Statistical analysis was performed using Prism software version 8.0 (GraphPad). The data are presented as the means and SEMs. The D’Agostino–Pearson omnibus or Shapiro–Wilk normality tests were used to test whether the data points were normally distributed. Paired or unpaired t tests were used to compare differences between matched or unmatched samples when the data followed a normal distribution. One-way ANOVA and the Newman-Keuls multiple-comparison tests were used to compare differences among multiple groups. An unpaired t test was used to analyze differences between the two groups. The Wilcoxon matched-pairs test was used to analyze differences between paired samples. Spearman or Pearson tests were used for correlation analyses. Differences were considered significant when p <0.05.

To obtain a global cell map of immune subsets in COVID-19, PBMCs isolated from 10 hospitalized patients (5 with moderate disease and 5 with severe disease) after admission were analyzed with a 31-color mass cytometry panel (Supplemental Table II). As reported previously, peripheral lymphocytopenia was present in patients with severe COVID-19 (Supplemental Fig. 1A, Supplemental Table I). According to the expression of the typical markers, six major immune cell types, including T cells, B cells, NK cells, monocytes, neutrophils, and non-T/B cells (Supplemental Fig. 1B, 1C), were identified. The proportions of these cells were comparable among HC subjects and patients with mild and severe COVID-19 (Supplemental Fig. 1D).

Then, we examined the T cells that play crucial roles in antipathogen immune responses. To further investigate T cell compartments, we extracted T cell clusters for in-depth analysis. Fourteen T cell subsets were identified based on 21 markers (Fig. 1A, 1B), including 5 CD4+ subsets (naive, central memory T, effector memory T [TEM], T follicular helper, and regulatory T cells), 4 CD8+ subsets (naive, TEM, TEM that re-express CD45RA, and proliferation cells), CD4/CD8 double-positive cells, γδT cells, NKT cells, CD3+CD161+ cells, and MAIT cells (Fig. 1C, 1D). Comparison of the proportions of these 14 T cell subsets in HC and patients with COVID-19 indicated that the proportion of MAIT cells was significantly decreased (Fig. 1E) and that of regulatory T cells was significantly higher in patients with COVID-19, especially those with severe disease (Supplemental Fig. 1E). By contrast, the proportions of the other CD4+ and CD8+ T cell subsets were similar between HC and patients with COVID-19 (Supplemental Fig. 1E).

FIGURE 1.

CyTOF characterizing the T cell compartments in patients with COVID-19. (A) t-SNE plot showing the 14 T cell subsets. (B) Cells are colored by the normalized expression of the indicated markers on the t-SNE plot. (C) Heat map showing the normalized expression levels of 21 T cell cluster markers among 14 PhenoGraph-defined clusters. (D) Composition of the T cell population from each analyzed subject: HC (n = 4), patients with mild COVID-19 (Mild; n = 5), and patients with severe COVID-19 (Severe; n = 5). (E) Frequencies of MAIT cells were compared among the different groups. (F) The expression levels of selected immune molecules in MAIT cells were compared among the different groups. (G) CD45RA and CD197 expression in MAIT cells was assessed (top). The frequencies of different MAIT cell populations were compared among the different groups (bottom). (H) CD69 expression levels in MAIT cells were measured in HC (n = 4), patients with mild COVID-19 (n = 5), and patients with severe COVID-19 (n = 5) by flow cytometry. The expression levels of CD69 (top) and the frequency of CD69+ cells among MAIT cells (bottom) were compared. The results are shown as the means plus SEMs. One-way ANOVA with the Newman-Keuls multiple-comparison test (E–H) was used. *p < 0.05, **p < 0.01.

FIGURE 1.

CyTOF characterizing the T cell compartments in patients with COVID-19. (A) t-SNE plot showing the 14 T cell subsets. (B) Cells are colored by the normalized expression of the indicated markers on the t-SNE plot. (C) Heat map showing the normalized expression levels of 21 T cell cluster markers among 14 PhenoGraph-defined clusters. (D) Composition of the T cell population from each analyzed subject: HC (n = 4), patients with mild COVID-19 (Mild; n = 5), and patients with severe COVID-19 (Severe; n = 5). (E) Frequencies of MAIT cells were compared among the different groups. (F) The expression levels of selected immune molecules in MAIT cells were compared among the different groups. (G) CD45RA and CD197 expression in MAIT cells was assessed (top). The frequencies of different MAIT cell populations were compared among the different groups (bottom). (H) CD69 expression levels in MAIT cells were measured in HC (n = 4), patients with mild COVID-19 (n = 5), and patients with severe COVID-19 (n = 5) by flow cytometry. The expression levels of CD69 (top) and the frequency of CD69+ cells among MAIT cells (bottom) were compared. The results are shown as the means plus SEMs. One-way ANOVA with the Newman-Keuls multiple-comparison test (E–H) was used. *p < 0.05, **p < 0.01.

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Next, we comprehensively characterized MAIT cells in patients with COVID-19. There were significantly decreased levels of CD183, CXC3R1, CD8, and CD56 expression by MAIT cells in patients with severe COVID-19, suggesting a potential functional impairment (Fig. 1F). CD45RA and CD197 (CCR7) expression levels were also decreased in severe patients. Based on their expression patterns, MAIT cells were divided into four subsets: TEM that re-express CD45RA (CD45RA+CD197)–, naive (CD45RA+CD197+)–, central memory T (CD45RACD197+)–, and TEM (CD45RA-CD197)–like cells. The proportion of TEM-like MAIT cells was significantly increased in patients with mild and severe COVID-19, and the proportion of naive-like cells was significantly decreased in patients with severe COVID-19 compared with HC (Fig. 1G). In addition, the expression of CD69 in MAIT cells was increased in patients with mild and severe COVID-19 (Fig. 1H), indicating that MAIT cells are highly activated in patients with COVID-19.

MAIT cells play key roles in the immune defense against pathogens. MAIT cells were largely depleted in the peripheral blood of patients with COVID-19, and the depletion was partially rescued in convalescent patients with COVID-19. In asymptomatic carriers with COVID-19, a trend of reduction in the MAIT cell population was found (Fig. 2A). Upon E. coli stimulation, MAIT cell from patients with severe COVID-19 produced much lower levels of IFN-γ, GzmB, and CD107a than those from HC, whereas MAIT cells from patients with mild COVID-19 displayed lower production of GzmB and CD107a than those from HC (Supplemental Fig. 1F, Fig. 2B). Similar results were found in MAIT cells from patients in response to IL-12/IL-18 stimulation (Fig. 2C). Notably, the function of MAIT cells remained largely unchanged in asymptomatic carriers with COVID-19. Importantly, IFN-γ and GzmB production by MAIT cells was fully restored in convalescent patients upon both E. coli and IL-12/IL-18 stimulation (Fig. 2B, 2C). Subsequent analysis of paired samples from patients with active disease and convalescent patients further confirmed the recovery of the MAIT cells frequency and function in patients with severe disease (Fig. 2D). Intriguingly, the frequency of MAIT cells was significantly reduced, and the levels of IFN-γ, GzmB, and CD107a in MAIT cells were lower in patients with severe COVID-19 with microbial coinfections (Fig. 2E). Consistently, we found that the incidence of coinfection increased among patients with decreased peripheral MAIT cells (Fig. 2E), supporting that MAIT cell dysregulation may be a potential contributing factor to bacterial/fungal coinfections in patients with COVID-19. Thus, MAIT cells from patients with severe COVID-19, especially those with microbial coinfections, displayed numerical and functional impairment, and recovery from the disease was accompanied by restoration of MAIT cells.

FIGURE 2.

Functional analysis of MAIT cells from patients with COVID-19. (A) The frequency of peripheral MAIT cells was determined in HC (n = 30), asymptomatic carriers of COVID-19 (AS; n = 6), patients with mild COVID-19 (Mild; n = 14), patients with severe COVID-19 (Severe; n = 17), and convalescent patients with COVID-19 (CP; n = 20) by flow cytometry. Representative FACS plots (left) and a statistical summary (right) are shown. (B and C) PBMCs isolated from HC and patients with COVID-19 were stimulated with PFA-fixed E. coli (MOI = 10; HC, n = 16; AS, n = 6; Mild, n = 16; Severe, n = 15; CP, n = 17) or IL-12/IL-18 (HC, n = 12; AS, n = 6; Mild, n = 9; Severe, n = 7; CP, n = 12) for 24 h, and IFN-γ, GzmB, and CD107a expression in MAIT cells was then assessed. (D) The frequencies of MAIT cells and IFN-γ+, GzmB+, and CD107a+ MAIT cells in patients with mild (n = 6) and severe (n = 6) COVID-19 before and after recovery. (E) The frequencies of MAIT cells (left) and IFN-γ+, GzmB+, and CD107a+ MAIT cells (right) from patients with severe COVID-19 with (n = 7) or without (n = 15) microbial coinfections. The middle panel showed the increased coinfection incidence in patients with low MAIT cell frequency (p < 0.05). According the frequency of MAIT cells, the 22 patients with severe COVID-19 were divided into lower (lower than mean value) and higher group (higher than mean value). The solid line represents the mean level of MAIT cells in patients with severe COVID-19. The results are shown as the means plus SEMs. One-way ANOVA with the Newman-Keuls multiple-comparison test (A–C), the Wilcoxon matched-pairs test (D), unpaired Student t test (E, left and right), or χ2 test (E, middle) was used. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

FIGURE 2.

Functional analysis of MAIT cells from patients with COVID-19. (A) The frequency of peripheral MAIT cells was determined in HC (n = 30), asymptomatic carriers of COVID-19 (AS; n = 6), patients with mild COVID-19 (Mild; n = 14), patients with severe COVID-19 (Severe; n = 17), and convalescent patients with COVID-19 (CP; n = 20) by flow cytometry. Representative FACS plots (left) and a statistical summary (right) are shown. (B and C) PBMCs isolated from HC and patients with COVID-19 were stimulated with PFA-fixed E. coli (MOI = 10; HC, n = 16; AS, n = 6; Mild, n = 16; Severe, n = 15; CP, n = 17) or IL-12/IL-18 (HC, n = 12; AS, n = 6; Mild, n = 9; Severe, n = 7; CP, n = 12) for 24 h, and IFN-γ, GzmB, and CD107a expression in MAIT cells was then assessed. (D) The frequencies of MAIT cells and IFN-γ+, GzmB+, and CD107a+ MAIT cells in patients with mild (n = 6) and severe (n = 6) COVID-19 before and after recovery. (E) The frequencies of MAIT cells (left) and IFN-γ+, GzmB+, and CD107a+ MAIT cells (right) from patients with severe COVID-19 with (n = 7) or without (n = 15) microbial coinfections. The middle panel showed the increased coinfection incidence in patients with low MAIT cell frequency (p < 0.05). According the frequency of MAIT cells, the 22 patients with severe COVID-19 were divided into lower (lower than mean value) and higher group (higher than mean value). The solid line represents the mean level of MAIT cells in patients with severe COVID-19. The results are shown as the means plus SEMs. One-way ANOVA with the Newman-Keuls multiple-comparison test (A–C), the Wilcoxon matched-pairs test (D), unpaired Student t test (E, left and right), or χ2 test (E, middle) was used. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

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Next, we further explore the mechanisms underlying the functional impairment of MAIT cells in COVID-19. Depletion of monocytes from the PBMCs population significantly decreased the ability of MAIT cells to produce IFN-γ, GzmB, and CD107a in response to E. coli infection, indicating that CD14+ monocytes are required for MAIT cells function in response to bacterial infection (Fig. 3A). We and others recently reported that monocytes exhibited a myeloid-derived suppressor cell (MDSC)–like phenotype in patients with severe COVID-19 (13, 14, 29), indicated by the decreased levels of HLA-DR expression (Supplemental Fig. 2A, 2B). The decreased HLA-DR expression was observed in both classical and intermediate monocytes in patients with severe COVID-19 (Supplemental Fig. 2C, 2D). In an independent cohort examined by conventional flow cytometry, the diminished HLA-DR expression and increased frequency of HLA-DRlow/−CD14+ cells in patients with severe COVID-19 were further validated. A slight decrease in HLA-DR expression was also found in asymptomatic carriers and patients with mild COVID-19. Effective treatment of patients with COVID-19 failed to fully restore the expression of HLA-DR (Fig. 3B, 3C).

FIGURE 3.

Monocytes from patients with severe COVID-19 inhibited MAIT cell function. (A) MAIT cell responses to PFA-fixed E. coli (MOI = 10) stimulation for 24 h in the absence (Mo) or presence (Mo+) of monocytes (CD14+ cells) isolated from PBMCs of HC (n = 6). The expression of HLA-DR on CD14+ cells (B) and the frequency of HLA-DRlow/−CD14+ cells (C) were determined by flow cytometry in PBMCs from HC (n = 15), asymptomatic carriers (AS; n = 5), mild (n = 8), severe (n = 8), and convalescent patients with COVID-19 (CP; n = 10). (D) PBMCs from HC (n = 6) and patients with severe COVID-19 (n = 6) were divided into CD14+ and CD14 populations. The two separate populations were then cocultured (ratio of CD14+ to CD14 = 1:2) as indicated and stimulated with PFA-fixed E. coli for 24 h. The production of IFN-γ and GzmB by MAIT cells was examined. Representative FACS plots (left) and statistical analyses (right) are shown. The results are shown as the means plus SEM. The Wilcoxon matched-pairs test (A) and one-way ANOVA with the Newman-Keuls multiple-comparison test (B–D) were used. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

FIGURE 3.

Monocytes from patients with severe COVID-19 inhibited MAIT cell function. (A) MAIT cell responses to PFA-fixed E. coli (MOI = 10) stimulation for 24 h in the absence (Mo) or presence (Mo+) of monocytes (CD14+ cells) isolated from PBMCs of HC (n = 6). The expression of HLA-DR on CD14+ cells (B) and the frequency of HLA-DRlow/−CD14+ cells (C) were determined by flow cytometry in PBMCs from HC (n = 15), asymptomatic carriers (AS; n = 5), mild (n = 8), severe (n = 8), and convalescent patients with COVID-19 (CP; n = 10). (D) PBMCs from HC (n = 6) and patients with severe COVID-19 (n = 6) were divided into CD14+ and CD14 populations. The two separate populations were then cocultured (ratio of CD14+ to CD14 = 1:2) as indicated and stimulated with PFA-fixed E. coli for 24 h. The production of IFN-γ and GzmB by MAIT cells was examined. Representative FACS plots (left) and statistical analyses (right) are shown. The results are shown as the means plus SEM. The Wilcoxon matched-pairs test (A) and one-way ANOVA with the Newman-Keuls multiple-comparison test (B–D) were used. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

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Then, we sought to determine whether the dysfunction of MAIT cells is related to the MDSC-like monocytes in COVID-19. We separated CD14+ and CD14 cell populations from PBMCs of HC and patients with severe COVID-19, respectively. Then, the separated cell populations were cocultured and stimulated with E. coli to test the effect of monocytes on MAIT cell function (Fig. 3D). We found that IFN-γ and GzmB production by MAIT cells from HC subjects was significantly impaired when they were cocultured with CD14+ cells from patients with severe COVID-19 (Fig. 3D). These results indicate that monocytes from patients with severe COVID-19 are suppressive and possibly contribute to the functional impairment of MAIT cells.

Next, we sought to determine how monocytes suppress the function of MAIT cells in patients with severe COVID-19. Analysis of publicly available scRNA-seq data set from PBMCs allowed identification of MAIT cells and monocytes using SLC4A10 and CD14/FEGR3A (28). GO enrichment analysis showed that the upregulated genes in both MAIT cells and monocytes from patients with severe COVID-19 were enriched in energy metabolism (e.g., oxidative phosphorylation and ATP metabolic process), cellular activation, and type I IFN pathways (Fig. 4A, 4B). IFN-stimulating genes, such as IFITM1, IFITM2, or IFITM3, were expressed at higher levels in both MAIT cells and monocytes from patients with COVID-19 than in those from HC (Fig. 4C, 4D). In contrast, Ag processing and presentation pathways, such as those involving the MHC II molecules HLA-DPA1, HLA-DRA, and CD74, were significantly downregulated in monocytes from patients with severe COVID-19 (Fig. 4D).

FIGURE 4.

IFN signaling was enriched in MAIT cells and monocytes from patients with severe COVID-19. DEGs between MAIT cells (A) and monocytes (B) from HC (n = 11) and patients with severe COVID-19 (n = 11). GO analysis of upregulated and downregulated genes. Expression levels of selected genes in MAIT cells (C) and monocytes (D) are presented. The results are shown as the means plus SEMs. One-way ANOVA with the Newman-Keuls multiple comparison test (C and D) was used. *p < 0.05, **p < 0.01, ****p < 0.0001.

FIGURE 4.

IFN signaling was enriched in MAIT cells and monocytes from patients with severe COVID-19. DEGs between MAIT cells (A) and monocytes (B) from HC (n = 11) and patients with severe COVID-19 (n = 11). GO analysis of upregulated and downregulated genes. Expression levels of selected genes in MAIT cells (C) and monocytes (D) are presented. The results are shown as the means plus SEMs. One-way ANOVA with the Newman-Keuls multiple comparison test (C and D) was used. *p < 0.05, **p < 0.01, ****p < 0.0001.

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The upregulation of IFN-stimulating genes in MAIT cells and monocytes from patients with severe COVID-19 was reminiscent of our recent findings in HIV-infected patients (25) and raised the possibility that IFN-I–activated monocytes might suppress the function of MAIT cells in patients with COVID-19. Indeed, we observed functional suppression of MAIT cells in response to E. coli infection after sustained IFN-α pretreatment (Fig. 5A). In contrast, blocking IFNAR2 restored the responsiveness of MAIT cells from patients with severe COVID-19 to E. coli infection (Fig. 5B). The level of IFN-α in the plasma from patients with COVID-19, especially patients with severe disease, was higher than that in HC. Similarly, the level of IL-10, which can be induced by IFN-I, was higher in patients with severe COVID-19 than in the HC (Fig. 5C) and was positively correlated with the level of IFN-α (Fig. 5D). Moreover, increasing levels of IL-10 were detected in the supernatant of PBMCs treated with IFN-α (Fig. 5E). As reported, monocytes are the major cellular source of IL-10 (30), suggesting the involvement of monocyte-derived IL-10 in suppressing MAIT cell function during severe COVID-19. We confirmed that the function of MAIT cells in response to E. coli infection could be suppressed by IL-10 pretreatment (Fig. 5F). Moreover, blocking IL-10R significantly elevated IFN-γ and GzmB production by MAIT cells from patients with severe COVID-19 (Fig. 5G). In our previous study, we found that IFN-α–induced MAIT cell dysfunction could be corrected by the addition of an IL-10R Ab (25). Collectively, these results indicate that HLA-DRlow/− monocytes may mediate functional impairment of MAIT cells through IFN-I–induced IL-10 signaling in severe COVID-19.

FIGURE 5.

IFN-α–induced IL-10 suppressed MAIT cell function and induced HLA-DRlow/−CD14+ cell expansion. (A) PBMCs from HC (n = 5) were treated with IFN-α (100 ng/ml) or untreated for 72 h. Then, the cells were stimulated with PFA-fixed E. coli for 24 h, and the production of IFN-γ and GzmB by MAIT cells was assessed. (B) Responses of MAIT cells among PBMCs from patients with severe COVID-19 (n = 5) were treated with the anti-IFNAR2 (5 µg/ml) Ab or isotype control (Iso) for 3 d before stimulation with PFA-fixed E. coli for 24 h and assessment of the production of IFN-γ and GzmB by MAIT cells. (C) The levels of IFN-α (left) and IL-10 (right) in plasma of HC (n = 11), mild patients (n = 13), and severe patients (n = 17) were detected. (D) Spearman correlations between the levels of IFN-α and IL-10 in plasma (n = 41). The Spearman correlation coefficients (R) and the associated calculated p values are indicated. (E) The level of IL-10 in the supernatant of PBMCs (n = 6) after IFN-α treatment (100 ng/ml) for 48 h was detected. (F) PBMCs from HC (n = 6) were pretreated with IL-10 (100 ng/ml) or untreated for 72 h. Then, the cells were stimulated with E. coli for 24 h, and IFN-γ and GzmB production by MAIT cells was assessed. (G) PBMCs from patients with severe COVID-19 (n = 6) were treated with the anti–IL-10 receptor mAb (10 µg/ml) or isotype control (Iso), followed by E. coli stimulation to assess MAIT cell function. (H) CD14+ cells from HC (n = 9) were cultured in medium with or without plasma (ratio of medium to plasma = 3:1) from patients with severe COVID-19 for 24 h. HLA-DR expression on CD14+ monocytes (left) and the frequency of HLA-DRlow/−CD14+ cells (right) were determined. (I) CD14+ cells (n = 5) from HC were cultured in medium with or without IL-10 (100 ng/ml) for 48 h. HLA-DR expression on CD14+ monocytes (left) and the frequency of HLA-DRlow/−CD14+ cells (right) were determined. The results are shown as the means plus SEMs. Wilcoxon matched-pairs test (A, B, and E–I) or one-way ANOVA with the Newman-Keuls multiple-comparison test (C) were used. *p < 0.05, **p < 0.01, ****p < 0.0001.

FIGURE 5.

IFN-α–induced IL-10 suppressed MAIT cell function and induced HLA-DRlow/−CD14+ cell expansion. (A) PBMCs from HC (n = 5) were treated with IFN-α (100 ng/ml) or untreated for 72 h. Then, the cells were stimulated with PFA-fixed E. coli for 24 h, and the production of IFN-γ and GzmB by MAIT cells was assessed. (B) Responses of MAIT cells among PBMCs from patients with severe COVID-19 (n = 5) were treated with the anti-IFNAR2 (5 µg/ml) Ab or isotype control (Iso) for 3 d before stimulation with PFA-fixed E. coli for 24 h and assessment of the production of IFN-γ and GzmB by MAIT cells. (C) The levels of IFN-α (left) and IL-10 (right) in plasma of HC (n = 11), mild patients (n = 13), and severe patients (n = 17) were detected. (D) Spearman correlations between the levels of IFN-α and IL-10 in plasma (n = 41). The Spearman correlation coefficients (R) and the associated calculated p values are indicated. (E) The level of IL-10 in the supernatant of PBMCs (n = 6) after IFN-α treatment (100 ng/ml) for 48 h was detected. (F) PBMCs from HC (n = 6) were pretreated with IL-10 (100 ng/ml) or untreated for 72 h. Then, the cells were stimulated with E. coli for 24 h, and IFN-γ and GzmB production by MAIT cells was assessed. (G) PBMCs from patients with severe COVID-19 (n = 6) were treated with the anti–IL-10 receptor mAb (10 µg/ml) or isotype control (Iso), followed by E. coli stimulation to assess MAIT cell function. (H) CD14+ cells from HC (n = 9) were cultured in medium with or without plasma (ratio of medium to plasma = 3:1) from patients with severe COVID-19 for 24 h. HLA-DR expression on CD14+ monocytes (left) and the frequency of HLA-DRlow/−CD14+ cells (right) were determined. (I) CD14+ cells (n = 5) from HC were cultured in medium with or without IL-10 (100 ng/ml) for 48 h. HLA-DR expression on CD14+ monocytes (left) and the frequency of HLA-DRlow/−CD14+ cells (right) were determined. The results are shown as the means plus SEMs. Wilcoxon matched-pairs test (A, B, and E–I) or one-way ANOVA with the Newman-Keuls multiple-comparison test (C) were used. *p < 0.05, **p < 0.01, ****p < 0.0001.

Close modal

We and others recently reported that monocytes exhibited an MDSC-like phenotype in patients with severe COVID-19 (14). We further sought to identify the factors leading to the reduction in HLA-DR expression by monocytes in patients with COVID-19. We found that incubation with plasma from patients with severe COVID-19 decreased HLA-DR expression by peripheral monocytes (Supplemental Fig. 2E, 2F). In addition, purified CD14+ cells incubated with plasma from patients with severe COVID-19 exhibited a significant decrease in HLA-DR expression (Fig. 5H). In contrast, the expression of HLA-DR on monocytes did not change when PBMCs were incubated with plasma from HC subjects (Supplemental Fig. 2G). SARS-CoV-2 infection can induce high levels of cytokine production. We sought to determine whether some cytokines inhibited the expression of HLA-DR on monocytes and, if so, which cytokines were responsible. We tested a panel of cytokines (IL-10, IFN-α, IL-6, IL-7, TNF-α, CCL2, and CCL4) reported to be highly expressed in patients with COVID-19 (3, 31, 32) and assessed their effects on HLA-DR expression (Supplemental Fig. 2H). We found that only incubation of PBMCs or CD14+ monocytes with IL-10 decreased HLA-DR expression and thus increased the proportion of CD14+HLA-DRlow/− cells (Supplemental Fig. 2I, Fig. 5I). These data indicated that IL-10 potentially downregulates HLA-DR expression by monocytes in patients with COVID-19.

Immune differences between mild and severe COVID-19 have been reported extensively (16, 33) and impairment of peripheral MAIT cells and other unconventional T cell populations was a notable feature of severe COVID-19 (26) (34, 35). In addition, one study showed that MAIT cells were enriched in the airways of patients with COVID-19, where they were hyperactivated and correlated with clinical outcomes, indicating their possible involvement in COVID-19 immunopathogenesis (26, 35). Consistent with these earlier reports, we identified numerical reduction and phenotypic activation of MAIT cells in patients with COVID-19 through the profiling of immune cells using mass cytometry and noticed that MAIT cell dysfunction could indicate COVID-19 severity. Furthermore, we discovered for the first time, to our knowledge, that the profound dysfunction of MAIT cells was linked to IFN-I–induced IL-10 production by MDSC-like cells in severe COVID-19 and reported more profound impairment of MAIT cells in patients with COVID-19 with microbial coinfections, strongly implying a crucial role of MAIT cells in preventing severe COVID-19 complications. Notably, we found a recovery of frequency and function of MAIT cells by assessing asymptomatic carriers of COVID-19 and convalescent patients, in agreement with their protective roles in preserving antimicrobial activities.

Revealing the mechanism underlying MAIT cell dysfunction is pivotal for the development of immunotherapeutic strategies in patients with severe COVID-19. Through analyzing the scRNA-seq data set, the IFN-I signaling pathway was found to be significantly upregulated in MAIT cells from patients with COVID-19. IFN-I can be persistently produced in patients with infectious diseases, leading to chronic activation of multiple types of immune cells (36, 37). This chronic activation can either promote or inhibit T cell activation, proliferation, differentiation, and function (38). We recently reported that persistent pretreatment with IFN-α substantially inhibited the functional activity of MAIT cells through a monocyte-mediated pathway (25). In this study, we demonstrated that this negative effect of IFN-α on MAIT cells was dependent on the induction of IL-10 by monocytes, which is known to inhibit T cell function during chronic viral infections (30). IL-10 has been reported to be a predictor for rapid diagnosis of patients with COVID-19 with a higher risk of disease deterioration (39). We proposed that IFN-α–induced IL-10 inhibited the function of MAIT cells, and the blockade of IL-10 may restore MAIT cell function in severe COVID-19.

We further revealed that monocytes from patients with severe COVID-19 suppressed MAIT cell function. These suppressive monocytes showed an HLA-DRlow/− MDSC-like phenotype and were significantly expanded in patients with severe COVID-19 (40). Several studies have reported that MHC class II molecules are downregulated in peripheral monocytes, which possibly suppressed T cell function in patients with severe COVID-19 (13, 40, 41). However, to date, little is known about the mechanisms underlying CD14+HLA-DRlow/− cell expansion in patients with COVID-19. Early reports suggested that these MDSC-like cells were heterogeneous populations of immature myeloid cells originating from hematopoietic stem cells in the bone marrow (42, 43). Accumulating evidence showed that inflammatory factors may induce the tissue recruitment, expansion, and activation of MDSC-like cells (44, 45). Our data showed that plasma from severe patients with COVID-19 induced the expansion of CD14+HLA-DRlow/− monocytes in vitro dependent on IL-10. Indeed, IL-10 has been shown to induce MDSC expansion in sepsis (46), tumors (47), and asthma (48). Future studies should address other inflammatory factors contributing to the development of suppressive monocytes.

Thus, IFN-I induces IL-10 production in patients with severe COVID-19, which could be the driver for both MAIT cell dysfunction and MDSC-like monocyte expansion. Collectively, considering these findings with our previous findings that IFN-I can induce monocytes to produce high levels of IL-10 (25), we further proposed that IFN-I pathway activation during SARS-CoV-2 infection induces high levels of IL-10 production by MDSC-like cells, which, on the one hand, downregulates HLA-DR expression by monocytes and generates more MDSC-like cells, and, on the other hand, inhibits the antibacterial functions of MAIT cells in patients with severe COVID-19. In addition, other mechanisms might also contribute to MAIT dysfunction by MDSC-like monocytes. For example, a recent report showed that mononuclear MDSCs isolated from patients with COVID-19 suppressed T cell proliferation and IFN-γ production partly via an arginase-1–dependent mechanism (49).

The factor leading to the numeral reduction of peripheral MAIT cells remained unknown. Several earlier studies suggested that peripheral MAIT cells could migrate to inflammatory tissues, where they were activated during COVID-19 (15, 27, 36). Thus, the pulmonary recruitment and activation of MAIT cells may contribute to MAIT cell reduction and COVID-19 immunopathogenesis. Further studies determining the number, phenotype, and functionality of pulmonary and extrapulmonary tissue-resident MAIT cells of patients with COVID-19 will reveal the fates and roles of these cells in SARS-CoV-2 infection. In addition, studies in SARS-CoV-2–infected animal models could be performed to examine the roles of MAIT cells and monocytes in vivo.

In summary, our findings showed for the first time, to our knowledge, that the functional impairment of MAIT cells in patients with COVID-19 was correlated with the expansion of MDSC-like cells, which produced high levels of IL-10 through IFN-I activation. This study has important implications for the development of anti-inflammatory therapies and control of microbial coinfections in patients with COVID-19.

We thank the biological sample bank of the Shenzhen Third People’s Hospital for the biosamples and services provided. We also thank the clinical staff at Shenzhen Third People’s Hospital and all study participants.

This work was supported by the National Science Fund for Distinguished Young Scholars (82025022), the Science and Technology Innovation Committee of Shenzhen Municipality (2020A1111350032), the Central Charity Fund of Chinese Academy of Medical Science (2020-PT310-009), the Shenzhen Bay Project (2020B1111340074 and 2020B1111340075), the Shenzhen Science and Technology Innovation Commission (JCYJ20190809170011461), the China Postdoctoral Science Foundation (2020T130122ZX and 2021M693357), and the Guangdong Province Prevention and Control of SARS-COV-2 Infection Science and Technology Emergency Special Fourth-Round Project (2020B1111340028, 2020B1111340032, and 2020B1111340040).

The online version of this article contains supplemental material.

Abbreviations used in this article

COVID-19

coronavirus disease 2019

CyTOF

cytometry by time of flight

DEG

differentially expressed gene

GO

gene ontology

GzmB

granzyme B

HC

healthy control

logFC

logarithm of the fold change

MAIT

mucosal-associated invariant T

MDSC

myeloid-derived suppressor cell

MOI

multiplicity of infection

PFA

paraformaldehyde

SARS-CoV-2

severe acute respiratory syndrome coronavirus 2

scRNA-seq

single-cell RNA sequencing

TEM

effector memory T

t-SNE

t-distributed stochastic neighbor embedding

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The authors have no financial conflicts of interest.

Supplementary data