According to a large number of reported cohorts, sepsis has been observed in nearly all deceased patients with COVID-19. We and others have described sepsis, among other pathologies, to be an endotoxin tolerance (ET)–related disease. In this study, we demonstrate that the culture of human blood cells from healthy volunteers in the presence of SARS-CoV-2 proteins induced ET hallmarks, including impairment of proinflammatory cytokine production, low MHC class II (HLA-DR) expression, poor T cell proliferation, and enhancing of both phagocytosis and tissue remodeling. Moreover, we report the presence of SARS-CoV-2 blood circulating proteins in patients with COVID-19 and how these levels correlate with an ET status, the viral RNA presence of SARS-CoV-2 in plasma, as well as with an increase in the proportion of patients with secondary infections.

This article is featured in Top Reads, p.1

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a worldwide issue that has triggered significant global changes. The data indicate that 80% of coronavirus disease 2019 (COVID-19) infections are mild or asymptomatic, ∼15% are severe and require oxygen supplementation, and <10% are critical, characterized by SARS with acute respiratory distress syndrome and requiring mechanical ventilation in intensive care units (14).

According to information obtained from numerous cohorts, the principal causes of death by COVID-19 include respiratory failure and the onset of sepsis. Sepsis has been observed in nearly all deceased patients in many of the studied cohorts (58). As has been reported in sepsis, T cell exhaustion due to SARS-CoV-2 and even reduced T cell counts due to apoptosis hinder the host response to infection. In such a scenario, new infections might emerge, increasing the risk of mortality (912). In the case of sepsis, endotoxin tolerance (ET) has been defined as one of the main mechanisms involved in the appearance of secondary infections (1315). This phenomenon can cause a patent reduction in T cell proliferation, among other features, such as impairment of proinflammatory cytokine production, low Ag presentation, and high phagocytic ability, which altogether have been reported as ET hallmarks in patients with sepsis (9, 16, 17).

Structurally, SARS-CoV-2 has three main proteins, including a spike (S) glycoprotein, small envelope (E) glycoprotein, and nucleocapsid (N) protein, as well as several accessory proteins, such as papain-like protease (18, 19). These proteins are key factors in both COVID-19 physiopathology and the host response against the virus. Along this line, the balance between S- and N-specific Abs has been associated with patient survival (20). We have previously demonstrated that the sole presence of some viral proteins induced a clear human monocyte polarization toward an HLA-DRlow profile and impaired T lymphocytes’ proliferative ability (21).

In the current study, we studied PBMCs isolated from healthy volunteers (HVs) and stimulated with S and N SARS-CoV-2 proteins. We demonstrated that S and N proteins were able to bind monocytes, inducing all the reported ET hallmarks (14, 2224). These were reverted using both anti-S and anti-N mAbs and neutralizing serum. Then, we correlated S and N levels in plasma from patients with COVID-19 at different days of disease evolution with the patient’s ET status. Furthermore, we observed that patients with COVID-19 who suffered from secondary infections beyond of SARS-CoV-2 showed higher levels of plasma S and N proteins. In addition, we quantified SARS-CoV-2 RNA in plasma from patients with COVID-19 to estimate the potential correlation with circulating levels of S and N proteins. Our data shed light on the SARS-CoV-2 immune evasion mechanisms that might inform the development of targeted therapeutic interventions.

The study was conducted in accordance with the ethical guidelines of the 1975 Declaration of Helsinki and was approved by the Committee for Human Subjects of La Paz University Hospital. All the participants provided written consent for the study. Patients who fulfilled the clinical criteria for COVID-19 and were positive for a nasopharyngeal SARS-CoV-2 PCR were included in the study (7, 25). Blood samples were collected from patients who were in different days of evolution from the beginning of the infection: 0–13, 16–24, and 70–95 d after onset. The clinical data of the patients included in the study are summarized in Table I. As HVs, healthy personnel from the Emergency Department of La Paz University Hospital in Madrid, Spain, were recruited. All HVs had no history of any significant systemic diseases or malignancy, were asymptomatic for more than 14 d, and were negative for both SARS-CoV-2 acute infection (based on a routine diagnostic RT-PCR test) and COVID-19 (by anti–SARS-CoV-2 IgM/IgG Abs test).

Table I.

Demographics and baseline characteristics of HVs and patients with COVID-19 according to days after onset

Days after Onset
0–13 (n = 13)16–24 (n = 7)70–95 (n = 6)All Patients (n = 26)HV (n = 10)
Age (y) 67.69 ± 15.58 60.60 ± 12.45 63.75 ± 13.45 64.87 ± 14.12 58.28 ± 5.44 
Sex, male, n (%) 6 (46.15) 3 (42.86) 2 (33.33) 11 (42.31) 6 (60) 
Smoking history, n (%)      
 Never smoked 12 (92.31) 7 (100.0) 6 (100.0) 25 (95.45) 9 (90.0) 
 Former smoker 1 (7.69) 0 (0.0) 0 (0.0) 1 (3.85) 1 (10.0) 
 Current smoker 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 
Addicted to alcohol, n (%) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 
Heart rate (beats/min) 95.2 ± 20.5 98.6 ± 8.9 115.3 ± 7.59 101.3 ± 17.1 89.33 ± 13.4 
Respiratory rate (beats/min) 23.4 ± 6.2 28.50 ± 8.0 24.3 ± 3.1 24.9 ± 6.7 20.0 ± 5.8 
SatO2 (%) 88.5 ± 12.0 80.2 ± 16.2 92.7 ± 2.7 87.9 ± 14.1 97.8 ± 1.2 
Creatinine (mg/dl) 0.82 ± 0.41 0.50 ± 0.20 0.71 ± 0.35 0.72 ± 0.35 0.86 ± 0.18 
CRP (mg/l) 123.0 ± 92.5 175.7 ± 35.8 108.3 ± 156.6 138.0 ± 102.0 15.86 ± 11.36 
PCT (ng/ml) 2.44 ± 8.28 0.56 ± 0.27 1.20 ± 1.96 1.46 ± 6.21 0.89 ± 0.32 
Lactate (mmol/l) 1.22 ± 0.47 1.85 ± 0.61 1.19 ± 0.60 1.45 ± 0.57 0.98 ± 0.17 
Positive blood culture, n (%) 3 (23.08) 5 (71.43) 2 (33.33) 10 (38.46) — 
Positive urine culture, n (%) 5 (38.46) 4 (57.14) 3 (50.0) 12 (46.15) — 
Secondary infection, n (%) 6 (46.15) 5 (71.43) 4 (66.66) 15 (57.69) — 
Coexisting disorder, n (%)      
 Hypertension 6 (46.15) 0 (0.0) 0 (0.0) 6 (23.08) 0 (0.0) 
 Diabetes 3 (23.08) 0 (0.0) 0 (0.0) 3 (11.54) 0 (0.0) 
 Cardiovascular disease 1 (7.69) 0 (0.0) 0 (0.0) 1 (3.85) 0 (0.0) 
 Chronic renal disease 1 (7.69) 0 (0.0) 0 (0.0) 1 (3.85) 0 (0.0) 
 Obesity 5 (38.46) 1 (14.29) 1 (16.66) 7 (26.92) 1 (10.0) 
 Asthma 0 (0.0) 0 (0.0) 1 (16.66) 1 (3.85) 0 (0.0) 
 COPD 1 (7.69) 0 (0.0) 0 (0.0) 1 (3.85) 0 (0.0) 
 Oncologic disease 1 (7.69) 1 (14.29) 0 (0.0) 2 (7.69) 0 (0.0) 
 Immunodeficiency 2 (7.69) 4 (57.14) 1 (16.66) 7 (26.92) 0 (0.0) 
Days after Onset
0–13 (n = 13)16–24 (n = 7)70–95 (n = 6)All Patients (n = 26)HV (n = 10)
Age (y) 67.69 ± 15.58 60.60 ± 12.45 63.75 ± 13.45 64.87 ± 14.12 58.28 ± 5.44 
Sex, male, n (%) 6 (46.15) 3 (42.86) 2 (33.33) 11 (42.31) 6 (60) 
Smoking history, n (%)      
 Never smoked 12 (92.31) 7 (100.0) 6 (100.0) 25 (95.45) 9 (90.0) 
 Former smoker 1 (7.69) 0 (0.0) 0 (0.0) 1 (3.85) 1 (10.0) 
 Current smoker 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 
Addicted to alcohol, n (%) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 
Heart rate (beats/min) 95.2 ± 20.5 98.6 ± 8.9 115.3 ± 7.59 101.3 ± 17.1 89.33 ± 13.4 
Respiratory rate (beats/min) 23.4 ± 6.2 28.50 ± 8.0 24.3 ± 3.1 24.9 ± 6.7 20.0 ± 5.8 
SatO2 (%) 88.5 ± 12.0 80.2 ± 16.2 92.7 ± 2.7 87.9 ± 14.1 97.8 ± 1.2 
Creatinine (mg/dl) 0.82 ± 0.41 0.50 ± 0.20 0.71 ± 0.35 0.72 ± 0.35 0.86 ± 0.18 
CRP (mg/l) 123.0 ± 92.5 175.7 ± 35.8 108.3 ± 156.6 138.0 ± 102.0 15.86 ± 11.36 
PCT (ng/ml) 2.44 ± 8.28 0.56 ± 0.27 1.20 ± 1.96 1.46 ± 6.21 0.89 ± 0.32 
Lactate (mmol/l) 1.22 ± 0.47 1.85 ± 0.61 1.19 ± 0.60 1.45 ± 0.57 0.98 ± 0.17 
Positive blood culture, n (%) 3 (23.08) 5 (71.43) 2 (33.33) 10 (38.46) — 
Positive urine culture, n (%) 5 (38.46) 4 (57.14) 3 (50.0) 12 (46.15) — 
Secondary infection, n (%) 6 (46.15) 5 (71.43) 4 (66.66) 15 (57.69) — 
Coexisting disorder, n (%)      
 Hypertension 6 (46.15) 0 (0.0) 0 (0.0) 6 (23.08) 0 (0.0) 
 Diabetes 3 (23.08) 0 (0.0) 0 (0.0) 3 (11.54) 0 (0.0) 
 Cardiovascular disease 1 (7.69) 0 (0.0) 0 (0.0) 1 (3.85) 0 (0.0) 
 Chronic renal disease 1 (7.69) 0 (0.0) 0 (0.0) 1 (3.85) 0 (0.0) 
 Obesity 5 (38.46) 1 (14.29) 1 (16.66) 7 (26.92) 1 (10.0) 
 Asthma 0 (0.0) 0 (0.0) 1 (16.66) 1 (3.85) 0 (0.0) 
 COPD 1 (7.69) 0 (0.0) 0 (0.0) 1 (3.85) 0 (0.0) 
 Oncologic disease 1 (7.69) 1 (14.29) 0 (0.0) 2 (7.69) 0 (0.0) 
 Immunodeficiency 2 (7.69) 4 (57.14) 1 (16.66) 7 (26.92) 0 (0.0) 

—, empty; COPD, chronic obstructive pulmonary disease; CRP, C-reactive protein; PCT, procalcitonin; SatO2, saturation of oxygen.

The SARS-CoV-2 recombinant proteins S and N were purchased from Sino Biological. Both S and N recombinant proteins were endotoxin tested (<1.0 endotoxin unit/µg protein) by the limulus amebocyte lysate method. For heat inactivation, SARS-CoV-2 proteins were incubated at 56°C for 2 h. mAb against SARS-CoV-2 S protein was purchased from Abcam (ab273433); mAb against N protein was purchased from Sino Biological (40143-R001). Neutralizing serum (National Institute for Biological Standards and Control code 20/130) and Middle East respiratory syndrome (MERS) protein extract were kindly gifts from Dr. J. Alcamí (AIDS Immunopathology Unit, National Center for Microbiology, Madrid, Spain) and Dr. J. Manuel Honrubia Belenguer (Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas, Madrid, Spain), respectively. LPS of Escherichia coli (O111:B4) was purchased from Sigma-Aldrich. The hypoxia-inducible factor 1-α (HIF1α) inhibitor, PX-478, was purchased from Cayman Chemical. Colistimethate sodium (polymyxin E) was purchased from Genéricos Españoles and dissolved in RPMI medium.

PBMCs were isolated using Ficoll-Paque PLUS gradient (GE Healthcare Bio-Sciences) according to the manufacturer’s instructions. Plasma samples were obtained after PBMC isolation by Ficoll-Paque PLUS gradient, aliquoted, and stored at −80°C until use. The monocytes and lymphocytes were separated by adherence, comprising monocytes as adherent cells and lymphocytes as nonadherent cells (24).

PBMCs were cultured in RPMI 1640 medium, supplemented with FBS to 10 and 1% penicillin and streptomycin mix. For the in vitro model, PBMCs were treated with S and N proteins (250 ng/ml each one) for 3, 5, and 7 d. Then, cultures were washed and stimulated with LPS (10 ng/ml) for 24 h (see the scheme in (Fig. 1A). Next, cells were labeled with proper Abs for flow cytometry analysis, and supernatants were collected and stored at −80°C until cytokine measurement. In some conditions, heat-inactivated S and N proteins were used. When anti-S and anti-N mononuclear Abs were used, the recombinant proteins of SARS-CoV-2 were preincubated with them for 30 min. The treatment with nonneutralizing and neutralizing serum (National Institute for Biological Standards and Control code 20/130) was at 20% of the final volume. Gelatin from bovine skin and recombinant erythropoietin were used as negative controls.

FIGURE 1.

S and N recombinant proteins of SARS-CoV-2 induce ET in human monocytes. HVs’ PBMCs were stimulated with SARS-CoV-2 S and N proteins (250 ng/ml S + N each one) and LPS (10 ng/ml) in vitro following the scheme described in (A). TNF-α (B), IL-6 (C), and IFN-γ (D) cytokine production in supernatant of PBMCs treated with the stimuli specified in the legend for the indicated time. Monocytes from HVs were pretreated or not with a specific HIF1α inhibitor, PX-478 (15 µM), for 3 h previous to the stimulation with SARS-CoV-2 S and N proteins (250 ng/ml S + N each) or LPS (10 ng/ml). After 16 h of stimulation, monocytes were restimulated or not with LPS (10 ng/ml) for 2 h. *p < 0.05, Mann–Whitney t test versus S + N + LPS condition; n = 5. (E) HIF1α (left panel) and IRAK-M (right panel) relative expression in monocytes including all stimulation conditions. A specific HIF1α inhibitor, PX-478 (15 μM), was used in the indicated conditions. *p < 0.05, **p < 0.01, Mann–Whitney t test versus control condition (Φ) and S + N at 16 h and LPS at 2 h versus S + N at 16 h and LPS at 2 h + PX-478, and S + N at 16 h + LPS at 2 h versus S + N at 16 h + LPS at 2 h + PX-478 conditions; n = 5.

FIGURE 1.

S and N recombinant proteins of SARS-CoV-2 induce ET in human monocytes. HVs’ PBMCs were stimulated with SARS-CoV-2 S and N proteins (250 ng/ml S + N each one) and LPS (10 ng/ml) in vitro following the scheme described in (A). TNF-α (B), IL-6 (C), and IFN-γ (D) cytokine production in supernatant of PBMCs treated with the stimuli specified in the legend for the indicated time. Monocytes from HVs were pretreated or not with a specific HIF1α inhibitor, PX-478 (15 µM), for 3 h previous to the stimulation with SARS-CoV-2 S and N proteins (250 ng/ml S + N each) or LPS (10 ng/ml). After 16 h of stimulation, monocytes were restimulated or not with LPS (10 ng/ml) for 2 h. *p < 0.05, Mann–Whitney t test versus S + N + LPS condition; n = 5. (E) HIF1α (left panel) and IRAK-M (right panel) relative expression in monocytes including all stimulation conditions. A specific HIF1α inhibitor, PX-478 (15 μM), was used in the indicated conditions. *p < 0.05, **p < 0.01, Mann–Whitney t test versus control condition (Φ) and S + N at 16 h and LPS at 2 h versus S + N at 16 h and LPS at 2 h + PX-478, and S + N at 16 h + LPS at 2 h versus S + N at 16 h + LPS at 2 h + PX-478 conditions; n = 5.

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Heparinized anticoagulant venous blood from HVs and patients with COVID-19 was diluted 1:2 with RPMI 1640 medium supplemented with 1% penicillin and streptomycin and stimulated with LPS (10 ng/ml) for 3 h. Then, plasma samples were obtained by centrifugation, aliquoted, and stored at −80°C until use.

TNF-α, IL-6, and IFN-γ protein levels in the culture supernatants from reserved serum samples of patients with COVID-19 and HVs were determined using the LEGENDplex HU Essential Immune Response Panel (BioLegend), following the manufacturer’s protocol. The samples were collected by flow cytometry using a BD FACSCalibur flow cytometer (BD Biosciences). Data were analyzed using LEGENDplex (BioLegend) v.8 software.

The flow cytometry analysis was developed using the following specific Abs: CD4-PerCP and CD8–allophycocyanin (both from Immunostep) and CD14-BUV395, CD86-BUV737, PD-L1–BV421, and HLA-DR–BV711 (four from BD Biosciences). 7-Aminoactinomycin D staining was used to analyze death rate in the cultures. Cells were stained with the specific Abs in Brilliant Stain Buffer (BD Biosciences) for 30 min at 4°C in the dark and washed twice with PBS. For all assays, the labeled cells were acquired in an FACSCalibur or FACSCelesta (BD Biosciences) flow cytometer, and the data were analyzed with FlowJo (Tree Star) v. 10.6.2 software.

The monocytes were exposed to bacteria (GFP E. coli K12) for the indicated time. The cells were washed and kept in 1 mg/ml lysozyme (Thermo Fisher Scientific) for 30 min. Phagocytosis was analyzed by flow cytometry of GFP+ cells, as previously reported (24, 26).

EDTA anticoagulant venous blood samples were sent to a GlaxoSmithKline BSL3 laboratory. There, PBMCs were isolated from EDTA anticoagulant venous blood using Ficoll-Paque PLUS (GE Healthcare Bio-Sciences) gradient according to the manufacturer’s instructions. CFSE was purchased from Thermo Fisher Scientific and used following the manufacturer’s protocol to assess T cell lymphocyte proliferation. CFSE-labeled PBMCs (2 × 105 cells per well) were stimulated or not with pokeweed mitogen (2.5 μg/ml, PWD) for 5 d in RPMI 1640 medium supplemented with FBS at 10 and 1% penicillin and streptomycin mix. After incubation for 5 d, PBMCs were labeled with CD4-PerCP and CD8–allophycocyanin for 30 min and fixed with 4% paraformaldehyde for 10 min at room temperature. Fixed and labeled PBMCs were returned to our laboratory and were run in an FACSCalibur (BD Biosciences) flow cytometer.

Wound healing was performed as previously described (22, 27) with some modifications. Human adipose-derived mesenchymal stem cells (ADMSCs) obtained from lipoaspirate were cultured in DMEM media, containing 10% (v/v) FBS, 100 IU/ml penicillin, and 100 µg/ml of streptomycin in a 12-well plate. Once the maximum confluence was reached, cell monolayers were mechanically wounded with a pipette tip (200 µL; Axygen Scientific). Then, cells were washed and incubated for 16 h in presence of 25% (v/v) of supernatants from monocytes that had been treated 16 h with S + N SARS-CoV-2 proteins or S + N SARS-CoV-2 proteins with a PX-478 (15 μM) pretreatment of 3 h or vehicle. Thereafter, cells were washed, and photos were taken in bright field using Leica DMI6000B microscope at 40× magnification. The photographs were analyzed by Fiji v2.1.0 software. The percentage of healing were estimated by the following formula: (area of wound at 0 h – area of wound at 16 h)/area of wound at 0 h.

The cells were washed once with PBS, and the RNA was isolated using the High Pure RNA Isolation Kit (Roche Diagnostics) according to the manufacturer’s instructions. The cDNA was obtained by reverse transcription of 1 µg RNA using the High Capacity cDNA Reverse Transcription Kit (Applied Biosystems). The gene expression levels from cDNA were analyzed by real-time quantitative PCR using the LightCycler system (Roche Diagnostics). The real-time quantitative PCRs were performed using the QuantiMix Easy SYG kit from BioTools and specific primers. The results were normalized to the expression of β-actin, and the cDNA copy number of each gene of interest was determined using a seven-point standard curve. The products were amplified using the following specific primers: hif1a–forward (F) (5′-TTCCAGTTACGTTCCTTCGATCA-3′), hif1a–reverse (R) (5′TTTGAGGACTTGCGCTTTCA-3′), irak-3–F (5′-TTTGAATGCAGCCAGTCTGA-3′), irak-3–R (5′GCATTGCTTATGGAGCCAAT-3′), vegf-F (5′-TTCCAGTTACGTTCCTTCGATCA-3′), vegf-R (5′TTTGAGGACTTGCGCTTTCA-3′), mmp9-F (5′-TTTGAATGCAGCCAGTCTGA-3′), and mmp9-R (5′GCATTGCTTATGGAGCCAAT-3′).

Viral RNA from patients’ nasopharyngeal swabs and plasma was isolated with the NucleoSpin RNA Virus kit (740956.250; Macherey-Nagel) following the manufacturer’s instructions. SARS-CoV-2 gene E expression was assessed by quantitative RT-PCR using a qTOWER3 G real-time PCR cycler (Analytik Jena), and SuperScript III One-Step RT-PCR Platinum Taq HiFi (12574-035; Invitrogen). Primers sequences, cycling conditions, and analyses were performed as described (28).

SARS-CoV-2 S and N proteins in plasma were detected using COVID-19 S and N protein ELISA kits (both from Abcam), according to the manufacturer’s instructions.

A mouse anti-His Tag Alexa Fluor 488–conjugated mAb (αHis; R&D Systems) was purchased. S and N recombinant proteins of SARS-CoV-2 with a histidine (His) tail tag (Sino Biological) were purchased.

Binding of both recombinant proteins was performed with whole blood cells. Briefly, whole blood cells from HVs were washed twice with 10 ml of ice-cold ligand binding buffer (LBB; PBS with 1% BSA, 0.05% sodium azide, and 0.1 mM CaCl2·2H2O) and 1 × 106 cells resuspended in 1 ml LBB. Afterwards, whole blood cells were washed twice with ice-cold LBB and incubated with the S and N recombinant proteins of SARS-CoV-2 (1000 and 500 ng of S and N recombinant proteins, respectively, in 100 μl of LBB) or LBB alone for 1 h and hand-mixed each 15 min on ice. Next, both S and N recombinant protein binding was detected using αHis–Alexa Fluor 488 (2.5 in 100 μl of LBB). Possible Fc receptors were blocked using human Fc blocking (BD Biosciences) for 30 min before incubation with the recombinant proteins. Whole blood cells incubated only with αHis–Alexa Fluor 488 were used as the negative control.

The number of experiments analyzed is indicated in each figure. Data were presented as percentages, numbers, means, and SDs. We calculated the statistical significance using Student t test (Mann–Whitney or Wilcoxon if data were parametric or not) for two groups of quantitative variables or a Kruskal–Wallis test, followed by the Dunn test for multiple groups comparisons when the Kruskal–Wallis was significant. Correlations were calculated as well by Spearman correlation analysis and linear correlations using a simple linear regression analysis. We set the statistical significance at p < 0.05 and conducted the statistical analyses using Prism 8.0 software (GraphPad).

According to our previous reports, ET is characterized not only by a marked impairment in proinflammatory cytokines production after an endotoxin challenged but also by a high phagocytosis rate, poor Ag presentation, T cell proliferation reduction, and an enhanced tissue remodeling (13, 14, 2224).

Following the experimental design shown in (Fig. 1A, PBMCs from HVs were exposed to S and N recombinant proteins from SARS-CoV-2 for 3, 5, and 7 d; cells were then washed and challenged with LPS for another 24 h to verify their response against a potential bacterial infection (23, 24, 29, 30). The analysis of proinflammatory cytokine production revealed that as it happens during an ET status, TNF-α generation was impaired when cells were pre-exposed to SARS-CoV-2 proteins (Fig. 1B). Both IL-6 and IFN-γ exhibited a similar tendency to TNF-α (Fig. 1C, 1D). Note that binding of recombinant proteins from SARS-CoV-2 to blood cells were verified (Supplemental Fig. 1A, 1B), and viability of monocytes and lymphocytes was marginally affected by incubation with them (Supplemental Fig. 1C, 1D).

ET induction by S and N proteins was abolished when the recombinant proteins were preincubated with mAbs after 10 freeze–thaw cycles and after heat inactivation, but not in presence of the LPS scavenger polymyxin E, discarding the presence of LPS traces (Fig. 2A–C). In line, an SARS-CoV-2 neutralizing serum significantly reduced the observed effect on TNF-α production after LPS challenge in the presence of S and N recombinant proteins (Fig. 2D). In addition, as we have reported during sepsis (13, 14, 22, 23, 29, 31) expressions of HLA-DR, CD86, but not PD-L1, were downregulated by recombinant proteins from SARS-CoV-2 (Supplemental Fig. 2A). However, those effected were not observed in presence of nonrelated proteins, such as gelatin from bovine skin and recombinant erythropoietin (Supplemental Fig. 2B). Noteworthy, a protein extract of MERS also induced an ET status (Supplemental Fig. 3).

FIGURE 2.

Effect of different pretreatments on S and N recombinant proteins of SARS-CoV-2 in the ET induction. HVs’ PBMCs were stimulated with SARS-CoV-2 S and N proteins (250 ng/ml S + N each one) for 5 d after been pretreated with different conditions in vitro following the scheme described in (A). Then, cells were stimulated or not with LPS (10 ng/ml) for 24 h. TNF-α (B) and IL-6 (C) cytokine production in supernatant of PBMCs. *p < 0.05, Mann–Whitney t test LPS versus S + N + LPS condition; n = 5. (D) TNF-α (left panel) and IL-6 (right panel) cytokine production in supernatant of PBMCs treated or not with S and N (250 ng/ml S + N each) proteins of SARS-CoV-2 in presence or not of a neutralizing serum for 5 d and then stimulated with LPS (10 ng/ml) for 24 h. *p < 0.05, Mann–Whitney t test control condition (Φ) versus S + N + LPS with nonneutralizing and neutralizing serum conditions; n = 5.

FIGURE 2.

Effect of different pretreatments on S and N recombinant proteins of SARS-CoV-2 in the ET induction. HVs’ PBMCs were stimulated with SARS-CoV-2 S and N proteins (250 ng/ml S + N each one) for 5 d after been pretreated with different conditions in vitro following the scheme described in (A). Then, cells were stimulated or not with LPS (10 ng/ml) for 24 h. TNF-α (B) and IL-6 (C) cytokine production in supernatant of PBMCs. *p < 0.05, Mann–Whitney t test LPS versus S + N + LPS condition; n = 5. (D) TNF-α (left panel) and IL-6 (right panel) cytokine production in supernatant of PBMCs treated or not with S and N (250 ng/ml S + N each) proteins of SARS-CoV-2 in presence or not of a neutralizing serum for 5 d and then stimulated with LPS (10 ng/ml) for 24 h. *p < 0.05, Mann–Whitney t test control condition (Φ) versus S + N + LPS with nonneutralizing and neutralizing serum conditions; n = 5.

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FIGURE 3.

S and N recombinant proteins of SARS-CoV-2 enhance phagocytosis ability of human monocytes. HVs’ monocytes were prestimulated with SARS-CoV-2 S and N proteins (250 ng/ml S + N each one) in vitro for 16 h and 5 d, then washed and cultured with GFP-labeled E. coli for 0.5 and 2 h. (A) Schematic procedure of the in vitro model. MFI of GFP on gated CD14+ cells from 16 h (B) or 5 d (C) S + N prestimulated monocytes (left panels). Representative histograms are shown in right panels. (D) TNF-α (left panel), IL-6 (central panel), and IFN-γ (right panel) production of monocytes prestimulated with SARS-CoV-2 S and N proteins (250 ng/ml S + N each one) in vitro for 16 h and 5 d. *p < 0.05, **p < 0.01, ***p < 0.001, Mann–Whitney t test versus control; n = 3.

FIGURE 3.

S and N recombinant proteins of SARS-CoV-2 enhance phagocytosis ability of human monocytes. HVs’ monocytes were prestimulated with SARS-CoV-2 S and N proteins (250 ng/ml S + N each one) in vitro for 16 h and 5 d, then washed and cultured with GFP-labeled E. coli for 0.5 and 2 h. (A) Schematic procedure of the in vitro model. MFI of GFP on gated CD14+ cells from 16 h (B) or 5 d (C) S + N prestimulated monocytes (left panels). Representative histograms are shown in right panels. (D) TNF-α (left panel), IL-6 (central panel), and IFN-γ (right panel) production of monocytes prestimulated with SARS-CoV-2 S and N proteins (250 ng/ml S + N each one) in vitro for 16 h and 5 d. *p < 0.05, **p < 0.01, ***p < 0.001, Mann–Whitney t test versus control; n = 3.

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Our previous data have showed that HIF1α governs most of the monocyte reprogramming that takes place during an ET state, including the downregulation of inflammation, a considerable increase in phagocytosis, antimicrobial activity, remodeling, tissue repair, IL-1R–associated kinase (IRAK)–M and PD-L1 expression, and T cell proliferation impairment (13, 14, 2224). Along these lines, we analyzed the expression of HIF1α and IRAK-M after 16 h of exposition to S and N proteins from SARS-CoV-2 (Fig. 1E). These two factors were upregulated after exposure to SARS-CoV-2 proteins, with the highest increase shown after a short second stimulus with LPS. Note that the upregulation of them was impaired with the specific HIF1α inhibitor PX-478.

Previous reports indicated that phagocytosis is increased during ET. In this study, we analyzed the monocytes’ phagocytic ability when these cells were exposed to S and N recombinant proteins for 16 h and 5 d (Fig. 3A). (Fig. 3B and (3C shows that the presence of SARS-CoV-2 proteins induced a significant increase of CD14+GFP+ mean fluorescence intensity (MFI), indicating a higher rate of phagocytosis of GFP+ bacteria by monocytes. Moreover, the S and N recombinant proteins induced proinflammatory cytokine production (Fig. 3D). Proliferation was also tested in this in vitro model using the classical mitogen lectin PWD as inducer. Both CD4+ and CD8+ lymphocytes showed a reduced proliferation capability in the presence of SARS-CoV-2 proteins (Fig. 4). Eventually, we tested the ability for tissue remodeling by the action of release matrix metalloproteases (MMPs) and angiogenic factors such as vascular endothelial growth factor (VEGF) (22). We have found that the presence of recombinant SARS-CoV-2 proteins increased both VEGF and MMP-9 expression in human monocytes from HV (Fig. 5A). Note that this effect was inhibited again by a pretreatment of a specific HIF1α inhibitor, PX-478. Along these lines, supernatants from S + N–stimulated monocytes increased healing of wounded human ADMSCs as compared with vehicle-treated monocytes supernatants (Fig. 5B, 5C). A patent inhibition of this effect was observed when monocytes were pretreated with PX-478.

FIGURE 4.

S and N recombinant proteins of SARS-CoV-2 reduce mitogenic T cell responses. CFSE-labeled PBMCs from HVs were stimulated or not with the mitogen PWD (2.5 µg/ml) in the presence or absence of SARS-CoV-2 S and N proteins (250 ng/ml S + N each) for 5 d. Percentages of proliferative population (defined as CFSE dim/low) on CD4+ (A) and CD8+ (B) cells measured by FACS (n = 4). Representative flow cytometry histogram plots of CFSE fluorescence in proliferative CD4+ (A) and CD8+ (B) cells are shown in right panels. *p < 0.05, Mann–Whitney t test versus PWD.

FIGURE 4.

S and N recombinant proteins of SARS-CoV-2 reduce mitogenic T cell responses. CFSE-labeled PBMCs from HVs were stimulated or not with the mitogen PWD (2.5 µg/ml) in the presence or absence of SARS-CoV-2 S and N proteins (250 ng/ml S + N each) for 5 d. Percentages of proliferative population (defined as CFSE dim/low) on CD4+ (A) and CD8+ (B) cells measured by FACS (n = 4). Representative flow cytometry histogram plots of CFSE fluorescence in proliferative CD4+ (A) and CD8+ (B) cells are shown in right panels. *p < 0.05, Mann–Whitney t test versus PWD.

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FIGURE 5.

Supernatants from monocytes treated with SARS-CoV-2 S and N recombinant proteins enhance wound healing. Monocytes from HVs (n = 5) were pretreated or not with a specific HIF1α inhibitor, PX-478 (15 µM), for 3 h previous to the stimulation with SARS-CoV-2 S and N proteins (250 ng/ml S + N proteins [S + N] each one) or LPS (10 ng/ml). After 16 h of stimulation, monocytes were restimulated or not with LPS (10 ng/ml) for 2 h. (A) Bar graph of VEGF (left panel) and MMP-9 (right panel) mRNA relative expression including all stimulation conditions are shown. *p < 0.05, **p < 0.01 in Mann–Whitney t test, n = 5. (B) ADMSCs were wounded and cultured with 25% (v/v) of supernatant from monocytes stimulated for 16 h with vehicle (control), S + N, and S + N with PX-478 pretreatment (SN + PX-478). Percentage of healing are shown (n = 3) with three replicates in each condition. ns, not significant, **p < 0.01, ***p < 0.001, Kruskal–Wallis test, followed by the Dunn multiple comparison test comparing control versus S + N conditions and S + N versus SN 1 PX-478 conditions, n = 3. (C) Representative images at 40× magnification of wound healing of ADMSCs cultured with 25% (v/v) of supernatant from monocytes stimulated for 16 h with vehicle (control), S + N, and SN + PX-478 are shown.

FIGURE 5.

Supernatants from monocytes treated with SARS-CoV-2 S and N recombinant proteins enhance wound healing. Monocytes from HVs (n = 5) were pretreated or not with a specific HIF1α inhibitor, PX-478 (15 µM), for 3 h previous to the stimulation with SARS-CoV-2 S and N proteins (250 ng/ml S + N proteins [S + N] each one) or LPS (10 ng/ml). After 16 h of stimulation, monocytes were restimulated or not with LPS (10 ng/ml) for 2 h. (A) Bar graph of VEGF (left panel) and MMP-9 (right panel) mRNA relative expression including all stimulation conditions are shown. *p < 0.05, **p < 0.01 in Mann–Whitney t test, n = 5. (B) ADMSCs were wounded and cultured with 25% (v/v) of supernatant from monocytes stimulated for 16 h with vehicle (control), S + N, and S + N with PX-478 pretreatment (SN + PX-478). Percentage of healing are shown (n = 3) with three replicates in each condition. ns, not significant, **p < 0.01, ***p < 0.001, Kruskal–Wallis test, followed by the Dunn multiple comparison test comparing control versus S + N conditions and S + N versus SN 1 PX-478 conditions, n = 3. (C) Representative images at 40× magnification of wound healing of ADMSCs cultured with 25% (v/v) of supernatant from monocytes stimulated for 16 h with vehicle (control), S + N, and SN + PX-478 are shown.

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To confirm our in vitro data, blood samples from HVs and patients with COVID-19, recruited from admission at the Emergency Department of the La Paz University Hospital, were stimulated ex vivo with LPS for 3 h. Next, TNF-α, IL-6, and IFN-γ levels were analyzed. Although the immune response in circulating cells from the HVs was as expected, TNF-α production was considerably impaired after ex vivo LPS challenge in samples from patients with COVID-19 (Fig. 6A). In contrast to findings in the in vitro model, this effect was not observed with either IL-6 or IFN-γ (Fig. 6B, 6C). In line with the TNF-α regulation after ex vivo LPS challenge, the analysis of HLA-DR expression on CD14+ cells also revealed a critical downregulation, and CD14+ monocytes from patients with COVID-19 exhibited an HLA-DRlow profile (Fig. 6D). Note that a tendency toward a recovered response, both in cytokine production and HLA-DR expression on CD14+ cells, was observed in those samples from long-term patients, 70–95 d after onset. Furthermore, T cell proliferation was also impaired in samples from patients with COVID-19, both in CD4+ and CD8+ T cells (Fig. 7). All these features suggest a potential ET status in patients with COVID-19.

FIGURE 6.

Whole blood cells of patients with COVID-19 exhibit an ET status after LPS challenge. Quantification of plasma TNF-α (A), IL-6 (B), and IFN-γ (C) levels in whole blood of HVs (n = 10) and patients with COVID-19 (n = 22) stimulated ex vivo with LPS (10 ng/ml) for 3 h. (D) MFI of HLA-DR on CD14+ cells from patients with COVID-19 (n = 22) and HVs (n = 10) analyzed by FACS. (A and D) *p < 0.05, **p < 0.01, ***p < 0.001, Kruskal–Wallis test, followed by the Dunn multiple comparison test comparing HVs + LPS versus patients with COVID-19 + LPS (A) and HVs versus patients with COVID-19 (B).

FIGURE 6.

Whole blood cells of patients with COVID-19 exhibit an ET status after LPS challenge. Quantification of plasma TNF-α (A), IL-6 (B), and IFN-γ (C) levels in whole blood of HVs (n = 10) and patients with COVID-19 (n = 22) stimulated ex vivo with LPS (10 ng/ml) for 3 h. (D) MFI of HLA-DR on CD14+ cells from patients with COVID-19 (n = 22) and HVs (n = 10) analyzed by FACS. (A and D) *p < 0.05, **p < 0.01, ***p < 0.001, Kruskal–Wallis test, followed by the Dunn multiple comparison test comparing HVs + LPS versus patients with COVID-19 + LPS (A) and HVs versus patients with COVID-19 (B).

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FIGURE 7.

T cells from patients with COVID-19 show reduced proliferative responses. Proliferation levels (defined as CFSEdim/low) of PWD-stimulated CD4+ (A) and CD8+ (B) of patients with COVID-19 for 5 d were measured by FACS (n = 22). *p < 0.05, one-way ANOVA and Tukey post hoc test comparing HVs versus patients with COVID-19.

FIGURE 7.

T cells from patients with COVID-19 show reduced proliferative responses. Proliferation levels (defined as CFSEdim/low) of PWD-stimulated CD4+ (A) and CD8+ (B) of patients with COVID-19 for 5 d were measured by FACS (n = 22). *p < 0.05, one-way ANOVA and Tukey post hoc test comparing HVs versus patients with COVID-19.

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To verify our data, the presence of S and N proteins from SARS-CoV-2 was evaluated in blood samples from HVs and patients. High levels of both proteins were detected in plasma from early-onset patients, but not from HVs (Fig. 8A, 8B). Moreover, a negative correlation between levels of TNF-α after ex vivo LPS stimulation and plasma concentration of proteins N and S was observed (Fig. 8C, 8D). Similar results were obtained when we analyzed the correlations between the presence of proteins from SARS-CoV-2 in plasma from patients with COVID-19 and the ability for ex vivo T cell proliferation (Fig. 9). Furthermore, a positive correlation between viral SARS-CoV-2 mRNA and levels of S and N proteins of SARS-CoV-2 on plasma was also observed (Supplemental Fig. 4A, 4B). In contrast, we did not find any correlation between levels of S and N proteins on plasma and the viral load in nasopharyngeal specimens (Supplemental Fig. 4C, 4D).

FIGURE 8.

SARS-CoV-2 S and N proteins are detected in plasma during early phase of COVID-19 and inversely correlate with TNF-α production after LPS challenge. Plasma levels of SARS-CoV-2 S (A) or N (B) protein in HV (n = 10) and patients with COVID-19 (n = 22) according to the days after symptom onset. (C) Correlation between plasmatic SARS-CoV-2 S (C) or N (D) protein levels and TNF-α production after 3 h of LPS challenge. *p < 0.05, Spearman correlation test. R, Spearman rank correlation coefficient.

FIGURE 8.

SARS-CoV-2 S and N proteins are detected in plasma during early phase of COVID-19 and inversely correlate with TNF-α production after LPS challenge. Plasma levels of SARS-CoV-2 S (A) or N (B) protein in HV (n = 10) and patients with COVID-19 (n = 22) according to the days after symptom onset. (C) Correlation between plasmatic SARS-CoV-2 S (C) or N (D) protein levels and TNF-α production after 3 h of LPS challenge. *p < 0.05, Spearman correlation test. R, Spearman rank correlation coefficient.

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FIGURE 9.

Plasma SARS-CoV-2 S and N protein levels are inversely correlated with ex vivo T cell response. Correlations between plasma levels of SARS-CoV-2 S protein and PWD-stimulated CD4+ (A) or CD8+ (B) T cells for 5 d. Correlations between plasma levels of SARS-CoV-2 N protein and PWD-stimulated CD4+ T (C) or CD8+ (D) cells for 5 d. *p < 0.05, **p < 0.01, Spearman correlation test. R, Spearman rank correlation coefficient.

FIGURE 9.

Plasma SARS-CoV-2 S and N protein levels are inversely correlated with ex vivo T cell response. Correlations between plasma levels of SARS-CoV-2 S protein and PWD-stimulated CD4+ (A) or CD8+ (B) T cells for 5 d. Correlations between plasma levels of SARS-CoV-2 N protein and PWD-stimulated CD4+ T (C) or CD8+ (D) cells for 5 d. *p < 0.05, **p < 0.01, Spearman correlation test. R, Spearman rank correlation coefficient.

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Eventually, given that ET is strongly associated with the occurrence of secondary infections, we classified patients according to whether they developed overinfections during their hospital stay. We found that plasma levels of SARS-CoV-2 S and N proteins from patients with secondary infections were higher than those detected in non-overinfected patients’ group (Fig. 10A, 10B). Moreover, those patients showing detectable plasma levels of both S and N proteins suffered secondary infections more frequently (Fig. 10C, 10D).

FIGURE 10.

Frequency of detectable SARS-CoV-2 S and N proteins were higher in patients with COVID-19 that suffered secondary infections. Plasma levels of S (A) or N (B) SARS-CoV-2 proteins in patients with COVID-19 who suffered a secondary infection or not. Frequency of detectable SARS-CoV-2 S and N proteins in patients with COVID-19 with nonsecondary infections (C) (n = 11) or with secondary infections (D) (n = 15). *p < 0.05, **p < 0.01, Mann–Whitney t test versus patients without secondary infections. nd, not detected.

FIGURE 10.

Frequency of detectable SARS-CoV-2 S and N proteins were higher in patients with COVID-19 that suffered secondary infections. Plasma levels of S (A) or N (B) SARS-CoV-2 proteins in patients with COVID-19 who suffered a secondary infection or not. Frequency of detectable SARS-CoV-2 S and N proteins in patients with COVID-19 with nonsecondary infections (C) (n = 11) or with secondary infections (D) (n = 15). *p < 0.05, **p < 0.01, Mann–Whitney t test versus patients without secondary infections. nd, not detected.

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ET is a phenomenon that occurs after an inflammatory stress to limit overinflammation. However, this protective mechanism can have detrimental effects, such as the generation of a refractory state in the immune system, leading to secondary infections (3133). In this study, we have described how patients suffering from COVID-19 showed an ET phenotype, characterized by low TNF-α production after LPS stimulation, low HLA-DR expression, and reduced T cell proliferation.

Although the initial hypothesis proposed an immune overreaction in COVID-19, current studies have demonstrated that patients with COVID-19 exhibit an exhausted and “collapsed” immune system characterized by immunosuppression (12, 3436). Despite this paradigm change, very little is known about the mechanisms by which SARS-CoV-2 causes these effects in the host’s immune system. One explanation would be the direct action of viral proteins, similar to that observed in other coronaviruses (3739). In this line, a study indicates that viral RNA load in plasma is correlated with key signatures of dysregulated host responses to such higher levels of PD-L1, IL-10, and lymphopenia (40). Previously, we had reported that a viral protein mixture could cause some alterations to the immune system (21). Among the most relevant structural proteins for the immune response against SARS-CoV-2 are the S and N proteins (18, 20). In this study, we found that these proteins induced an ET status impairing the TNF-α and IL-6 production of PBMCs, increasing PD-L1 expression and reducing both CD86 and HLA-DR expression on monocytes. Although such an effect is also produced by S and N proteins separately, the synergism results in a more defined phenotype.

The specificity of the observed effect was demonstrated when both blocking mAbs against S and N proteins and a neutralizing serum significantly reduced the ET status induced after incubation with the recombinant proteins from SARS-CoV-2. Other controls, such as the presence of the LPS scavenger (polymyxin E), and assays using nonrelated proteins confirmed the robustness of our findings. The fact that an extract of MERS also induces ET is a minor datum of our study that will deserve future studies.

We have also showed that recombinant proteins from SARS-CoV-2 induced both HIF1α and IRAK-M overexpression on human monocytes. Note that these molecules are responsible for the ET phenotype (13, 22, 24, 41, 42). This fact suggests that HIF1α pathway and its associated IRAK-M overexpression could be the main actors in the tolerant immune signs observed in COVID-19 monocytes.

Beyond the low cytokine production against novel inflammatory challenges, ET is also characterized by other immune function features, such as enhanced phagocytosis and tissue remodeling, and reduced T cell proliferation (2224, 30). Regarding phagocytosis, COVID-19 results are still controversial. Although some authors have found FcγR-mediated phagocytic pathways are enriched in patients with severe COVID-19 (43), others suggest that low phagocytosis due to senescence could negatively impact the pathology (44). We have found that both S and N proteins from SARS-CoV-2 enhanced bacterial phagocytosis. This phenomenon is similar to what occurs in other ET-related diseases, such as leukemia and cystic fibrosis (24, 26). Also, we observed a patent tissue remodeling of wounded human ADMSCs cultured in the presence of supernatants from SARS-CoV-2 protein–stimulated human monocytes. As we have previously described, this effect was shown to be controlled by HIF1α (22).

In contrast to phagocytosis and tissue remodeling, T cell exhaustion has been well described in patients with COVID-19 (12, 36, 4547). This phenomenon could be explained by both inhibitory immune checkpoint receptor overexpression (12, 45) and reduced Ag presentation by APCs. It is worth noting that the T cell priming ability of monocyte-derived dendritic cells is significantly suppressed when they are cultured with SARS-CoV-2–infected cells (48, 49). In full agreement with previous reports, we found that the expression of the MHC class II molecule HLA-DR was downregulated in patients with COVID-19 (16, 50, 51). We also found S and N proteins could reduce T cell responses in vitro, suggesting that viral proteins could be affecting the cellular immune response. These data were reinforced by the observed inverse correlation we found between plasma SARS-CoV-2 proteins and T cell proliferation.

The observed correlation between plasma SARS-CoV-2 proteins and the viral RNA load on the same specimens suggested that not only proteins but also SARS-CoV-2 RNA can translocate to blood (5254). Moreover, our data indicate that both S and N proteins are increased in patients with COVID-19 who subsequently undergo a secondary infection. Along these lines, other authors have described SARS-CoV-2 RNA in plasma from patients with COVID-19 and its association with both severity (40, 55) and immune markers alterations (40). However, the underlying causes of its effects on peripheral blood remain unknown. In this regard, our data shed light on how SARS-CoV-2 could affect the immune response through two of its structural proteins, generating a patent ET status. Nevertheless, our results must be validated in a larger cohort of patients because of the limited size of patients collected, as well as a number of open questions remain unanswered. Furthermore, a large longitudinal study evaluating the levels of SARS-CoV-2 proteins and viral RNA load on both nasopharyngeal and plasma samples, from the beginning of the infection, must be performed to identify the dynamics in these two types of specimens.

In summary, our data provide evidence indicating that SARS-CoV-2 infection is associated with hallmarks of ET. In addition, SARS-CoV-2 Ag levels, in early infection, could provide information about which patients are endowed with immune tolerance associated with the potential development of secondary infections.

This work was supported by Fundación Familia Alonso, Santander Bank, Reale Seguros, Fundación Mutua Madrileña, Fundación Uria, Fundación Caixa, and Ayuntamiento de Madrid (COVID-19).

The online version of this article contains supplemental material.

Abbreviations used in this article

ADMSC

adipose-derived mesenchymal stem cell

COVID-19

coronavirus disease 2019

ET

endotoxin tolerance

HIF1α

hypoxia-inducible factor 1-α

αHis

mouse anti-His Tag Alexa Fluor 488–conjugated mAb

HV

healthy volunteer

IRAK

IL-1R–associated kinase

LBB

ligand binding buffer

MERS

Middle East respiratory syndrome

MFI

mean fluorescence intensity

MMP

matrix metalloprotease

N

nucleocapsid

PWD

pokeweed mitogen

S

spike

SARS-CoV-2

severe acute respiratory syndrome coronavirus 2

VEGF

vascular endothelial growth factor

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The authors have no financial conflicts of interest.

Supplementary data