key: cord-0946804-mtjvd2tt
authors: Zenarruzabeitia, Olatz; Astarloa-Pando, Gabirel; Terrén, Iñigo; Orrantia, Ane; Pérez-Garay, Raquel; Seijas-Betolaza, Iratxe; Nieto-Arana, Javier; Imaz-Ayo, Natale; Pérez-Fernández, Silvia; Arana-Arri, Eunate; Borrego, Francisco
title: T cell activation, highly armed cytotoxic cells and a sharp shift in monocytes CD300 receptors expression is characteristic of patients with severe COVID-19
date: 2020-12-22
journal: bioRxiv
DOI: 10.1101/2020.12.22.423917
sha: 80d029b75ef64d814106149ecd7854b3f36971a6
doc_id: 946804
cord_uid: mtjvd2tt

COVID-19 manifests with a wide diversity of clinical phenotypes characterized by dysfunctional and exaggerated host immune responses. Many results have been described on the status of the immune system of patients infected with SARS-CoV-2, but there are still aspects that have not been fully characterized. In this study, we have analyzed a cohort of patients with mild, moderate and severe disease. We performed flow cytometric studies and correlated the data with the clinical features and clinical laboratory values of patients. Both conventional and unsupervised data analyses concluded that patients with severe disease are characterized, among others, by a higher state of activation in all T cell subsets, higher expression of perforin and granzyme B in cytotoxic cells, expansion of adaptive NK cells and the accumulation of activated and immature dysfunctional monocytes which are identified by a low expression of HLA-DR and an intriguing abrupt change in the expression pattern of CD300 receptors. More importantly, correlation analysis showed a strong association between the alterations in the immune cells and the clinical signs of severity. These results indicate that patients with severe COVID-19 have a broad perturbation of their immune system, and they will help to understand the immunopathogenesis of severe COVID-19 as well as could be of special value for physicians to decide which specific therapeutic options are most effective for their patients.

for its therapeutic implications, but also to better understand the immunopathology of 150 the disease. Therefore, it is essential to entirely define the immune response 151 characteristics related to disease features and determine at which stage of the disease 152 specific therapeutic options may be most effective. 153 We have characterized lymphocytes (T, B and NK cells) and monocytes of patients with 169 Our aim was to evaluate the impact of acute SARS-CoV-2 infection in circulating 170 leukocytes. To this end, we performed a cross-sectional study. Forty four patients with 171 COVID-19 disease were recruited for the study. To correlate laboratory findings, 172 including frequencies and phenotype of circulating leukocytes and the severity of the Table S1 and Table S2 . Inclusion and exclusion criteria were followed to guarantee the 177 homogeneity of the cohort, including age, gender, severity of the disease and time from 178 the onset of symptoms to sample collection. In addition, twelve healthy controls (HC) 179 were included in the study.

No significant differences were found between COVID-19 patients and HC in relation 181 to age (median ages of 64 and 59.5, respectively). There were also no significant 182 differences between the three groups of patients (severe, moderate and mild) in relation 183 to the number of days from the appearance of symptoms and the sample collection: (Fig. 1B) . Specifically, 26% of patients exhibited IL-6 192 levels above the normal range (>40 pg/mL). Interestingly, all HC had IL-6 levels below 193 the limit of detection (<3 pg/mL), while 69% of patients had >3 pg/mL of IL-6. On the 194 other hand, 66% of patients exhibited CRP levels above the normal range (>11 mg/L) 195 and 57% of patients exhibited ferritin levels above the normal range (>300 ng/mL) (Fig. 196 1B). Furthermore, although white blood cell (WBC) counts were mostly normal in mild 197 and moderate COVID-19 patients, some moderate and severe patients exhibited high 198 WBC counts (Fig. 1C) . Also, and in accordance with the literature (Hadjadj et al., 2020; 199 Huang et al., 2020), we observed frequencies and absolute numbers of lymphocytes 200 8 below the normal values, and frequencies and absolute number of neutrophils above the 201 normal values associated with the severity of the disease (Fig. 1C) . Finally, increased 202 levels of IL-6 (>40 pg/mL), CRP (>11 mg/L), ferritin (>300 ng/mL), fibrinogen (>400 203 mg/dL) and D-dimer (>500 ng/mL) and lower levels of hemoglobin (<13 g/dL) were 204 observed mostly in moderate and severe patients (Fig. 1D ).

To examine potential associations between these general laboratory values and other 206 clinical features, we performed correlation analysis (Fig. 1E ). The analysis revealed 

216 higher levels of PD-1 and perforin 217 We next performed a detailed multiparametric flow cytometry analysis to further 218 investigate circulating leukocytes status in COVID-19 patients (see gating strategy for 219 each cell population in Fig. S1 ). Given the important role of T cells in the defense 220 against viral infections and in the establishment of an immunological memory, as well 221 as in the immunopathology and damage that may occur, we studied T cell 222 subpopulations. We did not observe significant differences in the frequency of the major 223 T cells subsets, i.e. CD4, CD8 and double negative (DN) neither in the CD4/CD8 ratio 224 between the patients and compared with the HC (Fig. S2 ). Four major CD4 T cell memory. But also, highly differentiated CD8 T cells have been suggested to induce 301 damage in SARS-CoV-2 infected lungs in an antigen-independent manner (AN and 302 DW, 2020). Therefore, we next examined the four major subpopulations (naïve, 303 memory, effector-memory and TEMRA). We observed no significant differences in the 304 frequencies of naïve, memory and TEMRA subsets between HC and COVID-19 305 patients. Nevertheless, the frequency of CD8 effector-memory cells was significantly 306 higher in patients with severe disease (Fig. 3A) . 307 Then, we determined the activation status of CD8 T cells. We observed that COVID-19 308 patients exhibited a significant expansion of activated (CD38+HLA-DR+) cells, In contrast to the CD4 T cells, we did not observe a correlation between CD38+HLA-321 DR+ cells and PD-1+ cells in COVID-19 patients (Fig. S5C ), probably suggesting that 322 PD-1 expression on CD8 T cells is more a marker of exhaustion than of activation.

Nevertheless, more studies are required to confirm this statement. When we looked at the total CD8 T cell population we observed a significant increase 328 in the frequency of cells containing perforin in patients with severe disease (Fig. S5D) . CD300c and CD300e on monocytes from patients with COVID-19 (Fig. 4D) . Results 385 showed a differential expression of these receptors between HC and patients. Very with COVID-19 patients (Fig. 6A) . 445 Next, we analyzed the expression of perforin and granzyme B in NK cells (Fig. 6B) .

Results showed that both CD56 bright and CD56 dim NK cells from COVID-19 patients (Fig. 6C) . Nevertheless, the increased expression of perforin and granzyme B that 458 associated with the severity of the disease was also evident in each of the four subsets 459 (Fig. S9A ). 460 We also performed high-dimensional mapping of the eight parameter flow cytometry 461 data using tSNE representation and it was evident that some regions were preferentially 462 found in COVID-19 patients when compared with HC (Fig. 6D ). To gain more insight 463 into the NK cell alterations observed in COVID-19, we used the FlowSOM clustering 464 tool and compared the expression of the eight markers to define 16 populations (Fig. 465 6E). Using this approach, we were able to identify some populations that were 466 differentially expressed between COVID-19 patients and HC (Fig. 6F and S9B) . Pop6, are more than 5% of circulating CD56 dim cells. In addition, we have also determined 504 that there is an expansion of adaptive FcRγ-NK cells when they represent more than 505 7% of the CD56 dim cells. Results in Fig. 7C shows that CMV-seronegative individuals 506 do not have expansions of adaptive NK cells, except for one patient with moderate 507 disease, in which the NKG2C+CD57+ cells represented more than 5%, and another 508 patient with severe disease, in which the FcRγ-cells were more than 7%. When only the 509 CMV-seropositive individuals were taken into account, we observed a significant 510 expansion of adaptive NK cells in patients with moderate and severe COVID-19, which 511 was more pronounced when NKG2C+CD57+ cells were taken into account instead of 512 FcRγ-cells (Fig. 7C) . 513 Finally, we performed high-dimensional mapping using tSNE representation and we 514 observed that some regions were preferentially found in CMV-seropositive individuals 515 compared with CMV-seronegative donors (Fig. 7D) . Then, to better understand the 516 differences in NK cell subsets between CMV-seropositive and CMV-seronegative 517 individuals we used the FlowSOM clustering tool and compared the expression of seven 518 markers to define 8 populations (Fig. 7E ). Using this approach, we were able to identify 519 some populations that were differentially expressed between the two groups of donors 520 ( Fig. 7F and S10C) . Specifically, the adaptive NK cells Pop6 and Pop7 were 521 characterized by the phenotype NKG2C+FcRγ-, and while Pop6 was CD57+, Pop7 was 

and monocytes with disease severity in COVID-19 patients. 528 We first performed a bivariate analysis of 203 clinical laboratory and flow cytometry 529 variables (Table S3) . We selected the statistically significant variables for a multivariate 530 analysis. Then, to reduce the number of variables to include in the multivariate analysis 531 we performed a principal component analysis (PCA) (Fig. 8A) . Components 1 to 4 532 explained around 73.7% of the variance, and components 1 and 2 explained around 533 60.8% of the variance (Fig. S11A ). In Figure S11B different between patients with mild and moderate disease (Fig. 8B, upper panel) . On 545 the other hand, when we compared patients with moderate disease with those with a 546 mild and severe disease, we could see that component 1 was significantly different 547 between patients with a moderate and severe disease and, as expected, component 2 was 548 different between patients with moderate and mild disease (Fig. 8B, lower panel) . 549 Next, we performed correlation analysis. A different correlogram pattern was observed 550 between HC and patients groups when we looked at the correlation between the 551 significant flow cytometry variables (Fig. S12) . Then, the analysis was performed to Table S1 and S2). All donors Table S4 ). test.

Bivariate analyses were performed (Table S3) . First, using the Shapiro-Wilks normality 809 test we determined if variables followed a normal distribution. If they did, the average Whitney U test otherwise. To take into account multiple comparisons, we also presented 814 the adjusted p-values using the Benjamini & Hochberg test (Table S3) 

The CD300a (IRp60) 823 inhibitory receptor is rapidly up-regulated on human neutrophils in response to 824 inflammatory stimuli and modulates CD32a (FcγRIIa) mediated signaling

Aging immunity may exacerbate COVID-19

Imbalanced Host Response 830 to SARS-CoV-2 Drives Development of COVID-19

The CD300 molecules: an emerging family of regulators of the 832 immune system

Cytotoxic CD4+CD28− T Cells Drive Excess Cardiovascular Mortality in Rheumatoid 835

Arthritis and Other Chronic Inflammatory Conditions and Are Triggered by CMV 836

Expression of NKp30, NKp46 and DNAM-1 activating receptors on 839 resting and IL-2 activated NK cells from healthy donors according to CMV-serostatus 840 and age

Whole blood 843 immunophenotyping uncovers immature neutrophil-to-VD2 T-cell ratio as an early 844 marker for severe COVID-19

Natural killer cell memory in infection, 846 inflammation and cancer

T Follicular Helper Cell Biology: A Decade of Discovery and 848 Diseases

Identification of druggable inhibitory immune 851 checkpoints on Natural Killer cells in COVID-19

Complex Immune Dysregulation in COVID-19 Patients with Severe

Impaired type I 858 interferon activity and inflammatory responses in severe COVID-19 patients

Mechanisms and Dynamics of T Cell-861

Mediated Cytotoxicity In Vivo

Elevated levels of IL-6 and CRP 864 predict the need for mechanical ventilation in COVID-19

Clinical features of patients infected with

Sphingomyelin and ceramide are 871 physiological ligands for human LMIR3/CD300f, inhibiting FcεRI-mediated mast cell 872 activation

COVID-19 pneumonia: CD8+ T and NK cells are decreased in 875 number but compensatory increased in cytotoxic potential

Incidence of thrombotic complications in critically ill ICU patients 879 with COVID-19

Comprehensive mapping of immune perturbations associated with severe COVID-19

A 886 dynamic COVID-19 immune signature includes associations with poor prognosis

NK cells in host responses to viral infections

Natural Killer Cells Are Involved 891 in Acute Lung Immune Injury Caused by Respiratory Syncytial Virus Infection

Elevated Exhaustion Levels of NK and CD8+ T Cells as 895 Indicators for Progression and Prognosis of COVID-19 Disease

Gene Editing Reprograms Conventional NK Cells to Display Key Features of Adaptive 898

Venous and arterial 901 thromboembolic complications in COVID-19 patients admitted to an academic hospital 902 in

Dysregulated myelopoiesis and 904 hematopoietic function following acute physiologic insult

Longitudinal immune 908 profiling reveals key myeloid signatures associated with COVID-19

Deep 912 immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic 913 implications

Natural killer cell 916 immunotypes related to COVID-19 disease severity

Impaired immune cell cytotoxicity 919 in severe COVID-19 is IL-6 dependent

Cytokine release syndrome in severe COVID-19

Impaired 924 natural killer cell counts and cytolytic activity in patients with severe COVID-19

Megakaryocytes and 928 platelet-fibrin thrombi characterize multi-organ thrombosis at autopsy in COVID-19: A 929 case series

Immune Adaptation to Environmental Influence: The 931 Case of NK Cells and HCMV

COVID-19 severity associates with pulmonary redistribution of CD1c+

DCs and inflammatory transitional and nonclassical monocytes

Cytotoxic CD4+ T-cells during HIV infection: Targets or 939 weapons?

The PD-1/PD-L1 Axis and Virus Infections: A 941 Delicate Balance

Robust T 947

Abnormal Myeloid Cell Subsets Discriminate Severe from Mild COVID-19

Human CD300a binds to phosphatidylethanolamine and phosphatidylserine, and 955 modulates the phagocytosis of dead cells

CD300c is an Activating Receptor Expressed on Human Monocytes

Myeloid Cells during Viral 961 Infections and Inflammation

Multi-Omics Resolves a Sharp Disease-State 964 Shift between Mild and Moderate COVID-19

Abnormal coagulation parameters are 966 associated with poor prognosis in patients with novel coronavirus pneumonia

Differential Expression of CD163 on 970

Monocyte Subsets in Healthy and HIV-1 Infected Individuals

Immunology of COVID-19: Current 973 State of the Science

An 976 inflammatory cytokine signature predicts COVID-19 severity and survival

Myeloid cells in sepsis-979 acquired immunodeficiency

CD300 981 receptor family in viral infections

On the Way to Become a Natural 983 Killer

A Granulocytic 986

Signature Identifies COVID-19 and Its Severity

Natural killer cells 988 act as rheostats modulating antiviral T cells

A single-cell atlas of severe COVID-19

Plasma IP-10 and MCP-3 levels are highly associated with disease 995 severity and predict the progression of COVID-19

The Biology and Disease Relevance of CD300a, an Inhibitory Receptor for 999

The expression and function of 1002 human CD300 receptors on blood circulating mononuclear cells are distinct in neonates 1003 and adults

Functional exhaustion of antiviral lymphocytes in COVID-19 patients

Clinical course and risk factors for mortality of adult inpatients with 1009

COVID-19 in Wuhan, China: a retrospective cohort study

Acute SARS-CoV-2 Infection Impairs Dendritic Cell 1012 and T Cell Responses

Acknowledgements: We thank all patients and healthy controls who participated in this 1017 study and the staff from the Basque Biobank for Research. This work was supported by 1018 a grant from the

OZ is recipient of a postdoctoral contract funded by 1020 "Instituto de Salud Carlos III-Contratos Sara Borrell

The ESF invests in your future. GA-P is recipient of a 1022 fellowship from the BBK Fundazioa (1543/2006_0001) and from the Jesús de Gangoiti 1023

IT is recipient of a predoctoral contract funded by 1024 the Department of Education, Basque Government (PRE_2019_2_0109)

Basque Foundation for Science. 1027 1028 Author contribution: FB conceived the project; OZ and FB designed experiments

obtained the clinical samples and clinical data from COVID-1030 19 patients; OZ and FB obtained samples from healthy controls

CMV serology from patients and healthy controls; OZ stained and acquired flow 1032 cytometry samples; GA-P, and FB performed flow cytometry analysis; GA-P, and SP-F 1033 performed computational and statistical analysis

Clinical features of patients, quantification of leukocyte subsets and 1044 inflammation markers. (A) Left: age and gender distribution of patient cohorts in this 1045 study, including healthy controls (HC) and patients with mild (green), moderate (blue) 1046 and severe (red) COVID-19

Plasma levels of IL-6, C reactive protein (CRP) and ferritin in HC

The ranges of normal clinical laboratory values are represented in light green. 1049 (C) White blood cells (WBC) counts, leukocyte subsets frequencies and counts in 1050 patients with mild, moderate and severe COVID-19. The light green region represents 1051 the normal range for healthy people in the clinical laboratory

Spearman correlation of the indicated clinical features for COVID-19 patients

1C and 1D are represented as boxplot graphs with the median and 25 th to 75 th 1056 percentiles, and the whiskers denote lowest and highest values

CD4 T cell subsets, activation status and perforin expression in COVID-19 1062 patients. (A) Left: pseudocolor plots of concatenated peripheral CD4 T cells from 1063 healthy controls (HC) and patients with mild, moderate and severe disease

Pseudocolor plots of 1068 concatenated peripheral CD4 T cells from HC and COVID-19 patients and boxplot 1069 graphs of the frequencies of activated naïve, memory, effector-memory and TEMRA 1070 cells. Numbers in the quadrants are the average of each subset. Activated T cells are 1071 identified by the coexpression of CD38 and HLA-DR. (C) Pseudocolor plots of 1072 concatenated peripheral CD4 T cells and boxplot graphs showing the frequencies of PD-1073 1+ naïve, memory, effector-memory and TEMRA cells. Numbers in the gates are the 1074 average of PD-1+ cells in each subset. (D) Spearman correlation of activated 1075 (CD38+HLA-DR+) with PD-1+ CD4 T cells from patients with mild, moderate and 1076 severe COVID-19. (E) Pseudocolor plots of concatenated peripheral CD4 T cells and 1077 boxplot graphs of the frequencies of perforin positive naïve, memory, effector-memory 1078 and TEMRA cells

CD4 T cells for all HC and COVID-19 patients. (G) tSNE projection of non-naïve CD4

T cell populations (Pop) identified by FlowSOM clustering tool. (H) Fluorescence 1082 intensity of each Pop as indicated in the column-scaled z-score and boxplot graphs 1083 showing the frequencies of Pop0, Pop2, Pop6 and Pop10 in HC and COVID-19

Boxplots show the median and 25 th to 75 th percentiles, and the whiskers denote 1085 lowest and highest values

2B, 2C, 2E and 2H was determined by the Kruskal-Wallis test followed by Dunn´s 1087 multiple comparison test. *p <0.05, **p <0.01, and ***p <0

CD8 T cell subsets, perforin expression and activated cells in COVID-19 1091 patients. (A) Left: pseudocolor plots of concatenated peripheral CD8 T cells from 1092 healthy controls (HC) and patients with mild, moderate and severe COVID-19

effector-memory (CD27−CD45RA−), and terminal differentiated effector-memory 1095 (TEMRA) (CD27−CD45RA+). Numbers in the gates are the average of each subset

Pseudocolor plots of concatenated 1097 peripheral CD8 T cells from HC and COVID-19 patients and boxplot graphs of the 1098 frequencies of activated naïve, memory, effector-memory and TEMRA cells. Numbers 1099 in the quadrants are the average of each subset. Activated T cells are identified by the 1100 coexpression of CD38 and HLA-DR

1102 effector-memory and TEMRA cells. Numbers in the gates are the average of PD-1+ 1103 cells in each subset. (D) Pseudocolor plots of concatenated peripheral CD8 T cells and 1104 boxplot graphs of the frequencies of perforin positive naïve, memory, effector-memory 1105 and TEMRA cells. Numbers in the gates are the average of perforin positive cells in 1106 each subset. (E) tSNE projection of the indicated markers and density plots in non

CD8 T cells for all HC and COVID-19 patients. (F) tSNE projection of non-naïve CD8

T cell populations (Pop) identified by FlowSOM clustering tool. (G) Fluorescence 1109 intensity of each Pop as indicated in the column-scaled z-score and boxplot graphs 1110 showing the frequencies of Pop7, Pop8, Pop9, Pop10 and Pop11 in HC and COVID-19

Boxplots show the median and 25 th to 75 th percentiles, and the whiskers denote 1112 lowest and highest values

3G was determined by the Kruskal-Wallis test followed by Dunn´s 1114 multiple comparison test

HLA-DR and CD300 receptors expression in monocytes from 1117

A) Pseudocolor plots of concatenated monocytes cells from 1118 healthy controls (HC) and patients and boxplot graphs of the frequencies of classical 1119 (CD14++CD16-), transitional (CD14++CD16+) and non-classical

Pseudocolor 1121 plots of all concatenated monocytes and subsets and boxplot graphs showing the 1122 frequencies of CD163+ cells. Numbers in the gates are the average of CD163+ cells in 1123 each subset. (C) Histograms of concatenated monocytes and boxplot graphs showing 1124 the median fluorescence intensity (MFI) of HLA-DR in all and each monocyte subset. 1125 (D) Histograms of concatenated monocytes and boxplot graphs of the MFI of

Each dot 1128 represents a donor. Significance of data was determined by the Kruskal-Wallis test 1129 followed by Dunn´s multiple comparison test

Unsupervised analysis of monocytes in COVID-19 patients. (A) tSNE 1156 projection of the indicated markers and density plots in monocytes for all HC

COVID-19 patients. (B) tSNE projection of monocyte populations (Pop) identified by 1158

Fluorescence intensity of each Pop as indicated in the 1159 column-scaled z-score and boxplot graphs showing the frequencies of Pop0, Pop1, Pop4 1160 and Pop5 in HC and COVID-19 patients. Boxplots show the median and 25th to 75th 1161 percentiles, and the whiskers denote lowest and highest values. Each dot represents a 1162 donor

Dunn´s multiple comparison test. *p <0.05, **p <0.01, ***p <0.001, and ****p

Perforin and granzyme B expression in NK cell subsets from COVID-19 1167 patients. (A) Pseudocolor plots of concatenated NK cells from healthy controls (HC) 1168 and patients and boxplot graphs of the frequencies of CD56 bright (CD56++NKp80+), 1169 CD56 dim (CD56+NKp80+) and CD56 neg (CD56-NKp80+) NK cell subsets

MFI) of perforin (upper) and granzyme B 1172 (lower). (C) Pseudocolor plots of concatenated CD56 dim NK cells from HC and patients 1173 and boxplot graphs of the frequencies of the four subsets based in the expression of the 1174 CD57 and NKG2A differentiation markers. Numbers in the gates are the average of 1175 each subset. (D) tSNE projection of the indicated markers and density plots in NK cells 1176 for all HC and COVID-19 patients. (E) tSNE projection of NK cells populations (Pop) 1177 identified by FlowSOM clustering. (F) Fluorescence intensity of each Pop as

Pop14 and Pop15 in HC and COVID-19 patients. Boxplots show the 1180 median and 25 th to 75 th percentiles, and the whiskers denote lowest and highest values

Significance of data in Fig. 6A, 6B and 6C was determined 1182 by the Kruskal-Wallis test followed by Dunn´s multiple comparison test

Significance of data was determined by the Kruskal-Wallis test

Pseudocolor plots of concatenated 1193 CD56 dim NK cells from healthy controls (HC) and COVID-19 patients showing the 1194 expression of NKG2C and FcRγ. Numbers in the quadrants are the average of each 1195 subset. (C) Percentage of individuals from the indicated groups having or not having 1196

Significance of data was determined by chi-squared test

(D) tSNE projection of the 1198 indicated markers and density plots in CD56 dim NK cells from CMV-seropositive 1199 (CMV+) and CMV-seronegative (CMV-) individuals. (E) tSNE projection of CD56 dim 1200 NK cells populations (Pop) identified by FlowSOM clustering tool from CMV+

CMV-donors. (F) Fluorescence intensity of each Pop as indicated in the column-scaled 1202

z-score and boxplot graphs showing the frequencies of Pop6 and Pop7 in CMV+ and 1203 CMV-HC and COVID-19 patients

Multivariate analysis and correlation studies of immune cell phenotypes and 1207 clinical parameters. (A) Representation of the principal component analysis (PCA) 1208 results obtained with the most discriminant markers between patients groups

Lower: Patients with 1211 moderate disease versus patients with mild and severe disease. Odd ratio (OR), 95% 1212 confidence interval (CI) and p-values are indicated

Spearman correlation of the indicated flow cytometry data and clinical features for 1214

Only flow cytometry data that were statistically significant from 1215 the bivariate analysis (Table S3) were considered for the analysis