key: cord-1054983-6tcytxdu authors: Wei, Lin‐lin; Wang, Wen‐jing; Chen, De‐xi; Xu, Bin title: Dysregulation of the immune response affects the outcome of critical COVID‐19 patients date: 2020-06-16 journal: J Med Virol DOI: 10.1002/jmv.26181 sha: 93029948341507916e5ca60e5b0e21858b3578bc doc_id: 1054983 cord_uid: 6tcytxdu OBJECTIVES: Critical cases of coronavirus disease 2019 (COVID‐19) are associated with a high risk of mortality. It remains unclear why patients with the same critical condition have different outcomes. We aimed to explore relevant factors that may affect the prognosis of critical COVID‐19 patients. METHODS: Six critical COVID‐19 inpatients were included in our study. The 6 patients were divided into two groups based on whether they had a good or poor prognosis. We collected peripheral blood samples at admission and the time point of exacerbation to compare differences in the phenotypes and functions of major populations of immune cells between the groups. RESULTS: On admission, compared to patients with poor prognoses, those with good prognoses had significantly higher counts of monocytes (p < 0.05), macrophages (p < 0.05), higher frequency of CD3(+)CD4(+)CD45RO(+)CXCR3(+) subsets (p < 0.05), higher frequency of CD14(+)CD11C(+)HLA‐DR(+) subset of dendritic cells (DCs) (p < 0.05), and a lower count of neutrophils (p < 0.05). At the time point of exacerbation, the proportions of naïve CD4(+) T cells (p < 0.05), Tregs, and Th2 cells in the poor prognosis group were relatively higher than those in the good prognosis group, and CD4(+) memory T cells were relatively lower (p < 0.05). CONCLUSION: According to our results, the poor prognosis group showed a worse immune response than the good prognosis group at the time of admission and at exacerbation. Dysregulation of the immune response affects the outcome of critical COVID‐19 patients. This article is protected by copyright. All rights reserved. immune response, including innate and adaptive immunity against SARS-CoV-2, seems crucial to controlling and resolving viral infection [5] . There is evidence that COVID-19 is more likely to occur in older men with comorbidities who have weaker immune function [6] [7] [8] . However, little is known about the lymphocyte subsets and immune response in patients with COVID-19 [9] . In this study, we aimed to investigate the phenotypes and functions of major populations of immune cells of critical COVID-19 patients to explore possible factors that may affect the prognosis of the disease. In this retrospective cohort study, we included 6 adult inpatients from Beijing YouAn Hospital, Capital Medical University, from January 23 to February 8, 2020. Outcomes were followed up until March 8, 2020. All of the patients were clinically diagnosed with critical infections. The 6 patients were divided into two groups based on whether they had a good prognosis (3 patients) or a poor prognosis (3 patients). The criterion for poor prognosis is a hospital stay for over 30 days or death during hospitalization. We collected peripheral blood samples on admission and at the time point of exacerbation to compare differences in lymphocyte function between the two groups. Next, we analyzed clinical characteristics, expression of infection-related This article is protected by copyright. All rights reserved. biomarkers, and lymphocyte subsets between the groups. Epidemiological, demographic, clinical, laboratory test, treatment, and efficacy data were obtained from electronic medical records. Two physicians (LLW and BX) checked all the data carefully. This study was approved by the ethics committee of Beijing YouAn Hospital, Capital Medical University. All patients signed informed consent forms. On admission, critical illness was defined according to the "Guidelines of the Diagnosis and Treatment of New Coronavirus Pneumonia" (Revision 7) by the National Health Commission of China, as follows [6] : (1) a breathing rate ≥30 times/min; (2) a pulse oximeter oxygen saturation (SpO 2 ) ≤93% at rest; and (3) a ratio of the partial pressure of arterial oxygen (PaO 2 ) to the fraction of inspired oxygen (FiO 2 ) ≤300 mmHg. The criterion for disease exacerbation refers to cases with rapid deterioration with severe hypoxemic respiratory failure: (1) pulse oximeter oxygen saturation cannot reach 93% on 15 L/min flow of oxygen via a face mask; (2) chest computed tomography (CT) scan revealed diffuse ground-glass opacities and consolidation in the dependent segments of both lungs consistent with acute respiratory distress syndrome (ARDS); and (3) patients need to be initiated on invasive mechanical ventilation if no signs of improvement were observed under the standard treatments. Blood cells were collected from the 6 critical COVID-19 patients. Peripheral blood mononuclear cells (PBMCs) from healthy donors were used as a control This article is protected by copyright. All rights reserved. group. All blood cells were cultured with 2 μM cisplatin (195-Pt, Fluidigm) for 2 min before quenching with CSB (Fluidigm) to identify viability during the mass cytometry analysis and fixed using a fix I (Fluidigm) buffer according to a previous publication[10]. A metal-conjugated antibody cocktail was used to stain the cells. The cells were counted and diluted to 1× 10 6 cells/mL in PBS, permeabilized before being washed three times in CSB, cultured with an antibody cocktail in a total of 50 μL of CSB for 30 min at RT, washed three times in CSB and incubated with a 0.125-μm intercalator in a fixation and permeabilization buffer (Fluidigm) at 4 ℃ overnight. The cells were then washed three times with ice-cold PBS and three times with deionized water. Prior to data acquisition, the samples were resuspended in deionized water containing 10% EQ 4 Element Beads (Fluidigm), and the concentration of the cells was adjusted to 1× 10 6 cells/mL. Data acquisition was performed using a Helios mass cytometer (Fluidigm). All fcs files were uploaded to Cytobank, data cleaning was performed according to a previous paper [11] , and the populations of single living cells were exported as fcs files for further analysis. Arcsinh transform was performed to determine the signal intensities of all channels. A viSNE analysis method was performed as previously described. PhenoGraph analysis was carried out as described [12] . Continuous variables are described as means and standard deviations (SDs) or medians and interquartile range (IQR) values. We used unpaired t tests to compare Accepted Article differences between the good prognosis group and the poor prognosis group, as appropriate. In total, the median age of the 6 patients was 74. (Table 1) . Table 2 presents the laboratory findings for the 6 patients with COVID-19. S) of the poor prognosis group was longer than that of the good prognosis group. All the above data were not statistically significant. Compared with the good prognosis group, the poor prognosis group had worse liver injury, myocardial injury, respiratory impairment, and coagulation impairment. Renal function on admission was similar between the 2 groups. (Table 3) . Mass cytometry analyses including a 33 CyTOF marker panel were performed (Table 4 ). In brief, PBMCs from the 6 COVID-19 patients were collected at the time of admission and of exacerbation. Samples from 7 healthy donors were collected as controls. We first analyzed the frequencies of the major immune components of PBMCs, and viSNE plots were employed to visualize high-dimensional CyTOF data and the distribution of the markers used to identify subsets in two dimensions ( Figure 1A ). The mean value of each population and comparisons among groups represented as minus log10 p values are shown in the heatmap in Figure 1B patients with a good or poor prognosis, we found that the good prognosis group had significantly higher counts of monocytes and macrophages (p < 0.05, p < 0.05) and a lower count of neutrophils (p < 0.05). At time point exacerbation, the proportions of naïve CD4 + T cells (p < 0.05), Tregs, and Th 2 cells were relatively higher in the poor prognosis group than in the good prognosis group, whereas CD4 + memory T cells were relatively lower (p < 0.05). (Figure 1B ). This article is protected by copyright. All rights reserved. We then used PhenoGraph to analyze 19 subjects (7 healthy donors and the 6 patients with critical COVID-19 at two time points) with 33 markers (Figure 2A) . A total of 19,0000 cells were analyzed, and 32 clusters were identified according to marker expression ( Figure 2B ). On admission, the patients with a good prognosis had higher frequencies of CD3 + CD4 + CD45RO + CXCR3 + subsets (cluster 10, p < 0.05) and the CD14 + CD11C + HLA-DR + subset of dendritic cells (DCs) (cluster 25, p < 0.05) than the poor prognosis patients. To further explore functional differences in immune subpopulations, we also analyzed marker expression patterns in all 32 PhenoGraph metaclusters among all five groups; cluster 2 (CD14 -CD11c + HLA-DR + ) and cluster 25 (CD14 + CD11b + CD11c + HLA-DR + ) are displayed as examples in Figure 2D . We observed that the good prognosis group patients displayed relatively higher levels of markers such as CD38, CXCR3, CCR5, HLA-DR, and CD49d. Critical cases of COVID-19 are associated with a high risk of mortality. According to reports, the mortality rates of ICU patients in Jin Yin-Tan Hospital were between 38-62%, and more than 10% required ECMO [13] [14] [15] . Thus, clarifying the pathogenesis of critical COVID-19 to facilitate improvement in treatment is important [16, 17] . Several studies have described the immune response of COVID-19 patients. SARS-CoV-2 may break down antiviral immunity at an early stage. Elevated T cell exhaustion levels and reduced functional diversity of these cells in the peripheral blood may predict severe progression in COVID-19 patients [18, 19] . Our research focuses on the immune This article is protected by copyright. All rights reserved. response in critical COVID-19 patients with different outcomes, which is quite different from previous studies. In our study, the median durations from illness onset to exacerbation were similar in the 2 groups. The median number of days with critical illness (28 days vs 16 days) in the poor prognosis group was longer than that in the good prognosis group. Furthermore, laboratory investigations on admission revealed more prominent laboratory abnormalities in the poor prognosis group than in the good prognosis group, such as leukocyte counts, neutrophil counts, platelet counts, and procalcitonin levels. It seems that when patients were hospitalized, those with a poor prognosis had already exhibited a more pronounced inflammatory response and more serious multiple organ functional impairment than those with a good prognosis at the early stage of disease. To map the peripheral lymphocyte signature of COVID-19 patients diagnosed with critical disease and further identify the immune subpopulations that might be associated with different outcomes, the major immune cell components in COVID-19 patients were analyzed. At the time of admission, the good prognosis patients had higher frequencies of CD3 + CD4 + CD45RO + CXCR3 + subsets compared to the poor prognosis patients. This subset can be recognized as memory helper T-cell type 1 (Th1). Evidence has indicated that the Th1-type response plays a key role in the successful control of SARS-CoV, MERS-CoV, and SARS-CoV-2 [18] . Moreover, expansion of antigen-specific CD8 + T cells depending on Th1 cells has a significant effect on virus clearance [19] . It has been reported that the total number of natural killer (NK) cells and CD8 + T cells is markedly decreased in patients with SARS-CoV-2 infection [20, 21] . Our results indicate that memory Th1 cells may play an important protective role in defense This article is protected by copyright. All rights reserved. against fatal cases of coronavirus infection, which is consistent with previous research results. DCs are the most powerful full-time antigen presenting cells (APCs), and they can efficiently ingest, process and present antigens [22] . According to our results, good prognosis patients had higher frequencies of the CD14 + CD11C + HLA-DR + subset of DCs than did poor prognosis patients on admission. Previous studies have shown that CD11C + DCs coexpressing the monocyte marker CD14 may be directly involved in the immunopathology of some diseases. These cells can exhibit an efficient antigen-presentation capacity and constitutive secretion of TNFα, which suggests an active immune response [23, 24] . However, some viruses can hinder the body's antiviral response, resulting in more serious infection. Our study has some limitations. It is necessary to mention that our findings were based on a limited number of cases and CyTOF antibodies. 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