2020.11.17.378992.Full.Pdf

bioRxiv preprint doi: https://doi.org/10.1101/2020.11.17.378992; this version posted November 18, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

The expression of PDCD1 and CD274 in T cells and macrophages correlated positively with COVID-19 severity

Qianqian Gao1,2#, Shang Liu1,3, Renpeng Ding1,2,3, Huanyi Chen1, Xuan Dong1,2, Jiarui Xie1, Yijian Li1, Lei Chen1,2, Huan Liu1, Feng Mu1

1BGI-Shenzhen, Shenzhen 518083, China; 2Guangdong Provincial Key Laboratory of Human Disease Genomics, Shenzhen Key Laboratory of Genomics 3BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China; 4 MGI，BGI-Shenzhen, China #Corresponding author: Qianqian Gao, [email protected]

Abstract

The immune responses underlying the infection of severe acute respiratory syndrome coronavirus

2 (SARS-CoV-2) remain unclear. To help understand the pathology of coronavirus disease 2019

(COVID-19) pandemics, public data were analyzed and the expression of PDCD1 (encoding PD-1)

and CD274 (encoding PD-L1) in T cells and macrophages were identified to correlate positively

with COVID-19 severity.

Introduction

COVID-19 has led to the global pandemic and infected millions of people worldwide [1]. It’s urgent

to better understand the pathophysiology of the infection caused by SARS-CoV-2 [2-4]. The PD-

1/PD-L1 signaling plays an essential role not only in regulating tumor immune responses but also

in balancing homeostasis and tolerance in virus infection [5], but it’s role in COVID-19 is currently

unclear. Thus, it is necessary to investigate how PD-1/PD-L1 signaling works during COVID-19

progress in order to deal with it.

Single-cell immune profiling was explored in COVID-19 patients using the public data, which is bioRxiv preprint doi: https://doi.org/10.1101/2020.11.17.378992; this version posted November 18, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

essential for understanding the potential mechanisms underlying COVID-19 pathogenesis.

Methods

Data Sets and Processing

We downloaded the gene expression matrix from GEO database (GSE145926 and GSE128033).

This dataset contained 63734 cells from four healthy people, three patients with moderate infection

by bilateral pneumonia and seven patients with severe infection. The detailed clinical information

of patients can be obtained in the reference [6]. Single cell clustering and cell annotation was

performed as previously described [6].

Statistical analysis

The Student’s t-test (t.test in R 3.5.1) was used across three experiment designs. *P<0.05,

**P<0.005, ***P＜0.001. Values are presented as mean Standard deviation (SD). Error bars

represented the SD.

Results

Single-cell analysis revealed distinct immune cell subsets

Publicly available data of bronchoalveolar cells from three moderate (M1-M3) and six severe (S1-

S6) COVID-19 patients, and four healthy controls (HC1-HC4) were collected for analysis (66630

cells, Table S1) [6]. 31 clusters were identified by classical signature genes according to the

reference (Fig. 1) [6]. CD68-Macrophage (0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 15, 19, 21, 24, 30)

dominated among these populations, followed by CD3D-T cell (4, 10, 16).

Various expression patterns of PDCD1 and CD274 in different cell subpopulations bioRxiv preprint doi: https://doi.org/10.1101/2020.11.17.378992; this version posted November 18, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Expression of PDCD1 was first analyzed at the patient group level in different cell subpopulations,

and four trends of their expression dynamics were observed (Fig. 2). PDCD1 expression was

gradually elevated in T cell, B cell, myeloid dendritic cells (mDCs), and macrophages from HC to

mild cases then to severe patients (Fig. 2A). In the 2nd trend, PDCD1 expression was specifically

increased in plasma cells and epithelial cells in severe patients but not in mild patients (Fig. 2B).

For the 3rd trend, PDCD1 expression was upregulated in mild patients but slightly reduced in severe

patients in NK and plasmacytoid dendritic cells (pDCs) (Fig. 2C). No expression of PDCD1 was

detected in mast cells and neutrophils in the 4th trend (Fig. 2D).

The CD274 expression in macrophages, mast cells, pDC, and T cells (1st trend) correlated well with

COVID-19 severity (Fig. 3A) and was specifically increased in plasma cells of severe patients (2nd

trend) (Fig. 3B). However, in neutrophil, mDC, B cells and NK cells, the expression of CD274 was

upregulated in patients with mild COVID-19, then decreased in severe patients (Fig. 3C). In contrary

to the 3rd trend, the expression of CD274 was decreased in mild patients but increased in severe

patients (Fig. 3D).

When analyzed at the individual level, expression of PDCD1 and CD274 was also elevated in mild

and severe patients (Fig. 4A, 4B). Overall, PDCD1 expression in T cells, B cells, mDCs, and

macrophages and CD274 expression in macrophages, mast cells, pDC, and T cells correlated well

with COVID-19 severity. Furthermore, PDCD1 and CD274 expression was specifically increased

in epithelial and plasma cells of severe patients.

The expression of STAT1 was increased in patients with mild and severe COVID-19

Inflammatory signaling participates in modulating PD-L1 expression, particularly, STAT1, which

can be activated by IFNγ or interleukin 6 (IL-6), is a crucial regulator for PD-L1 expression [7, 8]. bioRxiv preprint doi: https://doi.org/10.1101/2020.11.17.378992; this version posted November 18, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Furthermore, plasma IFNγ level [3] and the IL-6 level in bronchoalveolar lavage fluid (BALF) [6]

were reported to be increased in COVID-19 patients. Consistently, STAT1 was found upregulated in

both mild and severe patients (Fig. 5), suggesting increased CD274 expression might at least partly

resulting from increased STAT1 level in COVID-19 patients.

The landscape of immune checkpoint molecules in COVID-19 patients

To further elucidate the immune checkpoint landscape of COVID-19 patients, expression of

classical inhibitory and stimulatory checkpoint molecules was assessed. For inhibitory checkpoint

molecules (ICMs), expression of CD160, CD244, PDCD1, BTLA, TIGIT, LAG3, KLRG1, and

ADORA2A were increased in mild patients compared to HC and severe patients while expression of

CTLA4, HAVCR2, IDO1, and CD276 were highest in severe patients (Fig. 6A). Regarding

stimulatory checkpoint molecules (SCMs), expression of TNFRSF9, CD28, ICOS, and CD27 was

elevated in mild patients in comparison to HC and severe patients while expression of TNFRSF18

and CD40 were highest in severe patients (Fig. 6B).

When analyzed at the cell subpopulation level, unique expression patterns of ICMs and SCMs were

demonstrated in each cell subpopulation (Fig. 7, Fig. 8). Expression patterns of ICMs were different

between mild and severe patients (Fig. 7). Most categories and the highest intensity of ICMs were

enriched in severe patients, except in T cells and NK cells. The results indicated that the immune

cells might be more exhausted in severe patients with COVID-19 compared to healthy people and

even mild patients. In terms of SCMs, macrophages, mast cells, and pDC of severe patients got

more enriched than that of mild patients and healthy donors (Fig. 8). In T cell population, more

ICMs and SCMs were enriched in patients with mild and severe COVID-19 than that of healthy

donors, but there was no dramatic difference between mild and severe patients themselves (Fig. 7, bioRxiv preprint doi: https://doi.org/10.1101/2020.11.17.378992; this version posted November 18, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Fig. 8).

Discussion

Since COVID-19 is pandemic and threatening thousands of people’s life, it is urgent and essential

to investigate the molecular mechanism of the immune pathogenesis of the disease. Compared to

healthy controls, PDCD1 expression in T cells, B cells, mDCs, and macrophages (Fig. 2) and CD274

expression in macrophages, mast cells, pDCs, and T cells (Fig. 3) were upregulated in COVID-19

patients, and correlated well with COVID-19 severity. Moreover, expression of PDCD1 and CD274

was specifically increased in plasma cells of severe patients (Fig. 2, 3), which could serve as a

biomarker for prognosing the severity of COVID-19. Many clinical trials for treating COVID-19

are ongoing. Among them, one clinical trial uses PD-1 monoclonal antibody to block PD-1 in

COVID-19 patients (NCT04268537). Based on our results, PDCD1 expression was dramatically

upregulated in T cells and macrophages especially in severe patients (Fig. 2) and its blockade would

further increase the secretion of multiple pro-inflammatory cytokines, which will enhance the

cytokine release syndrome reported in COVID-19 patients and possibly associated with disease

severity [2, 3], leading to further tissue damage or even more death especially in severe COVID-19

patients [9, 10]. A current study supports that checkpoint inhibitor immunotherapy is risky for severe

outcomes in SARS-CoV-2-infected cancer patients, though these patients were treated with immune

checkpoint inhibitors (ICI) before SARS-CoV-2 infection [11].

Conclusion

Our research provides valuable information about COVID-19 and its severity correlated well with

the expression of PDCD1 and CD274.

Competing interests

The authors declare that they have no competing interests.

Author contributions

Gao Q. supervised the project, designed the bioinformatic analysis, wrote and revised the

manuscript. Liu S. performed the bioinformatic analysis. Ding R. dealt with the data. Chen H., Dong

X., Xie J., Li Y., Chen L., and Liu H. helped with the data management.

Acknowledgments

We sincerely thank the support provided by China National GeneBank and this research was

supported by the Guangdong Enterprise Key Laboratory of Human Disease Genomics

(2020B1212070028), and Science, Technology and Innovation Commission of Shenzhen

Municipality (JSGG20180508152912700).

References

1. Wilk A.J., Rustagi A., Zhao N.Q., Roque J., Martínez-Colón G.J., McKechnie J.L., et al., A single-cell atlas of the peripheral immune response in patients with severe COVID-19. Nat Med, 2020. 26: p. 1070-76. bioRxiv preprint doi: https://doi.org/10.1101/2020.11.17.378992; this version posted November 18, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

2. Chen Nanshan, Zhou Min, Dong Xuan, Qu Jieming, Gong Fengyun, Han Yang, et al., Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. The Lancet, 2020. 395(10223): p. 507- 513. 3. Huang Chaolin, Wang Yeming, Li Xingwang, Ren Lili, Zhao Jianping, Hu Yi, et al., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet, 2020. 395(10223): p. 497-506. 4. Liu Y., Zhang C., Huang F., Yang Y., Wang F., Yuan J., et al., 2019-novel coronavirus (2019- nCoV) infections trigger an exaggerated cytokine response aggravating lung injury. ChinaXiv:202002.00018, 2020. 5. Qin Weiting, Hu Lipeng, Zhang Xueli, Jiang Shuheng, Li Jun, Zhang Zhigang, et al., The Diverse Function of PD-1/PD-L Pathway Beyond Cancer. Frontiers in Immunology, 2019. 10. 6. Liao M., Liu Y., Yuan J., Wen Y., Xu G., Zhao J., et al., Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19. Nat Med, 2020. 7. Garcia-Diaz A., Shin D. S., Moreno B. H., Saco J., Escuin-Ordinas H., Rodriguez G. A., et al., Interferon Receptor Signaling Pathways Regulating PD-L1 and PD-L2 Expression. Cell Rep, 2017. 19(6): p. 1189-1201. 8. Moon J. W., Kong S. K., Kim B. S., Kim H. J., Lim H., Noh K., et al., IFNgamma induces PD- L1 overexpression by JAK2/STAT1/IRF-1 signaling in EBV-positive gastric carcinoma. Sci Rep, 2017. 7(1): p. 17810. 9. Zhang C., Wu Z., Li J. W., Zhao H.,Wang G. Q., Cytokine release syndrome in severe COVID-19: interleukin-6 receptor antagonist tocilizumab may be the key to reduce mortality. Int J Antimicrob Agents, 2020. 55(5): p. 105954. 10. Chua Robert Lorenz, Lukassen Soeren, Trump Saskia, Hennig Bianca P., Wendisch Daniel, Pott Fabian, et al., COVID-19 severity correlates with airway epithelium–immune cell interactions identified by single-cell analysis. Nat Biotechnol, 2020. 11. Robilotti E. V., Babady N. E., Mead P. A., Rolling T., Perez-Johnston R., Bernardes M., et al., Determinants of COVID-19 disease severity in patients with cancer. Nat Med, 2020.

Fig. 1 IGHG4- Plasma cell (23,25) 23 0 16 LILRA4-pDC 10 25 1 17 (28) 28 2 18 TPPP3-Ciliated 3 CD1C-mDC (17, 27) 19 CD68-Macrophage (22) 17 4 20 (0,1,2,3,5,6,7,8,9,11, 22 27 5 21 5 12,15,19,21,24,30) 9 6 22 24 14 3 19 26 KRT18-Secretory 7 23 6 (14) 8 24

UMAP-2 12 30 TPSB2-Mast cell 9 25 1 21 0 29(29) 10 26 158 11 27 11 MS4A1-B cell 4 16 2 7 (26) 13 12 28 CD3D-T cell 13 29 5 Doublet 0 (13) 10 (4,10,16) 14 30 −5 18 20 15 FCGB3B-Neutrophil KLRD1-NK cell (18) (20) −5 0 5 10 UMAP-1 Fig. 2 PDCD1 B A *** * 0.15 0.3 *** T_cell Plasma * B_cell Epithelial 0.2 mDC 0.10 1st trend Macrophage 2nd trend 0.1 0.05 p = 0.103 *** p =0.172 ** *** 0.0 *** 0.00 HC Mild Severe HC Mild Severe C D 0.050 0.20 p = 0.763 0.025 0.15 NK_cell Mast p = 0.105 0.000 3rd trend 0.10 pDC 4th trend Neutrophil

0.05 p = 0.084 −0.025 p = 0.321 0.00 −0.050 HC Mild Severe HC Mild Severe Fig. 3 CD274 B A *** 0.100 *** 0.4 *** * 0.075 0.3 Macrophage Mast 0.050 Plasma 1st trend 0.2 p = 0.493 ** pDC 2nd trend T_cell 0.1 *** 0.025 *** 0.0 p = 0.061 0.000 HC Mild Severe HC Mild Severe C D 2.0 * *** 0.15 1.5 Neutrophil mDC B_cell 0.10 1.0 NK_cell Epithelial 3rd trend *** 4th trend 0.5 p = 0.066 *** 0.0 *** 0.05 * * p = 0.113 p = 0.097 HC Mild Severe HC Mild Severe Fig.4

A B 0.15 0.6

0.10 0.4

0.05 0.2 Normalized expression Normalized expression

0.00 0.0 S1 S2 S3 S4 S5 S6 S1 S2 S3 S4 S5 S6 M1 M2 M3 M1 M2 M3 HC1 HC2 HC3 HC4 HC4 HC1 HC2 HC3

Fig. 5 Healthy Mild Severe 3 4

3 3 2

2 2

1 1 1

0 0 0 pDC Mast mDC pDC pDC Mast Mast T_cell mDC B_cell mDC T_cell B_cell T_cell B_cell Plasma NK_cell Plasma Plasma NK_cell NK_cell Epithelial Epithelial Epithelial Neutrophil Neutrophil Neutrophil Macrophage Macrophage Macrophage

Fig. 6 A B CD160 1 1 0.5 TNFRSF9 0.5 CD244 0 0 PDCD1 −0.5 CD28 −0.5 BTLA −1 −1 TIGIT ICOS LAG3 KLRG1 CD27 ADORA2A CTLA4 TNFRSF18 HAVCR2 IDO1 CD40 CD276

Healthy Mild Severe Healthy Mild Severe

Fig. 7

B cell T cell Macrophage Mast 1 1 1 1 KLRG1 CD276 CD160 CD160 0.5 0.5 0.5 0.5 CD160 CD244 CD276 0 0 0 0 CD244 LAG3 −0.5 −0.5 KLRG1 CTLA4 −0.5 −0.5 BTLA KLRG1 −1 −1 −1 −1 BTLA LAG3 CD276 HAVCR2

TIGIT KLRG1 CTLA4 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.17.378992; this version posted November 18, 2020. The copyright holder for this CTLA4 preprint (which was notPDCD1 certified by peer review) is the author/funder.ADORA2A All rights reserved. No reuse PDCD1allowed without permission. CTLA4 BTLA HAVCR2 TIGIT PDCD1 TIGIT IDO1 TIGIT HAVCR2 IDO1 LAG3 HAVCR2 IDO1 CD276 ADORA2A IDO1 ADORA2A

Healthy Mild Severe Healthy Mild Severe Healthy Mild Severe Healthy Mild Severe mDC Neutrophil NK pDC 1 1 1 TIGIT CD160 1 CD276 CD160 0.5 0.5 0.5 0.5 BTLA PDCD1 0 HAVCR2 0 0 0 CD244 PDCD1 IDO1 −0.5 −0.5 −0.5 −0.5 CD244 BTLA KLRG1 −1 −1 −1 BTLA −1 TIGIT ADORA2A CTLA4 KLRG1 HAVCR2 CD244 TIGIT ADORA2A IDO1 CTLA4 IDO1 CTLA4 PDCD1 HAVCR2 LAG3 LAG3 HAVCR2 IDO1 CD276 KLRG1 LAG3 LAG3

CD276 ADORA2A CD276 ADORA2A

Healthy Mild Severe Healthy Mild Severe Healthy Mild Severe Healthy Mild Severe

Plasma 1 HAVCR2 0.5 IDO1 0

LAG3 −0.5

ADORA2A −1

CD160

CTLA4

PDCD1

BTLA

TIGIT

KLRG1

CD276

Healthy Mild Severe Fig. 8 B cell T cell Macrophage Mast 1 1 1 1 TNFRSF9 ICOS ICOS 0.5 0.5 0.5 0.5 0 0 0 0

−0.5 ICOS −0.5 TNFRSF18 −0.5 TNFRSF18 −0.5 TNFRSF18 −1 −1 −1 −1

CD27 TNFRSF9

TNFRSF9

TNFRSF18 CD28

CD27 CD28 CD27 TNFRSF9

CD40 CD40 CD40

Healthy Mild Severe Healthy Mild Severe Healthy Mild Severe Healthy Mild Severe mDC Neutrophil NK pDC 1 1 1 1

TNFRSF9 0.5 TNFRSF18 0.5 CD28 0.5 0.5

0 0 0 0 −0.5 −0.5 CD28 −0.5 ICOS −0.5 ICOS TNFRSF18 −1 −1 −1 −1

TNFRSF18 CD40 CD27

ICOS TNFRSF9 TNFRSF18

CD27 CD28 TNFRSF9 CD40

CD40 CD27 CD40

Healthy Mild Severe Healthy Mild Severe Healthy Mild Severe Healthy Mild Severe Plasma 1

TNFRSF18 0.5 0

−0.5

CD40 −1

CD28

ICOS

CD27

Healthy Mild Severe