Longitudinal characteristics of lymphocyte responses and cytokine profiles in the peripheral blood of SARS-CoV-2 infected patients

Jing Liu, Sumeng Li, Jia Liu, Boyun Liang, Xiaobei Wang, Hua Wang, Wei Li, Qiaoxia Tong, Jianhua Yi, Lei Zhao, Lijuan Xiong, Chunxia Guo, Jin Tian, Jinzhuo Luo, Jinghong Yao, Ran Pang, Hui Shen, Cheng Peng, Ting Liu, Qian Zhang, Jun Wu, Ling Xu, Sihong Lu, Baoju Wang, Zhihong Weng, Chunrong Han, Huabing Zhu, Ruxia Zhou, Helong Zhou, Xiliu Chen, Pian Ye, Bin Zhu, Lu Wang, Wenqing Zhou, Shengsong He, Yongwen He, Shenghua Jie, Ping Wei, Jianao Zhang, Yinping Lu, Weixian Wang, Li Zhang, Ling Li, Fengqin Zhou, Jun Wang, Ulf Dittmer, Mengji Lu, Yu Hu, Dongliang Yang, Xin Zheng, Jing Liu, Sumeng Li, Jia Liu, Boyun Liang, Xiaobei Wang, Hua Wang, Wei Li, Qiaoxia Tong, Jianhua Yi, Lei Zhao, Lijuan Xiong, Chunxia Guo, Jin Tian, Jinzhuo Luo, Jinghong Yao, Ran Pang, Hui Shen, Cheng Peng, Ting Liu, Qian Zhang, Jun Wu, Ling Xu, Sihong Lu, Baoju Wang, Zhihong Weng, Chunrong Han, Huabing Zhu, Ruxia Zhou, Helong Zhou, Xiliu Chen, Pian Ye, Bin Zhu, Lu Wang, Wenqing Zhou, Shengsong He, Yongwen He, Shenghua Jie, Ping Wei, Jianao Zhang, Yinping Lu, Weixian Wang, Li Zhang, Ling Li, Fengqin Zhou, Jun Wang, Ulf Dittmer, Mengji Lu, Yu Hu, Dongliang Yang, Xin Zheng

Abstract

Background: The dynamic changes of lymphocyte subsets and cytokines profiles of patients with novel coronavirus disease (COVID-19) and their correlation with the disease severity remain unclear.

Methods: Peripheral blood samples were longitudinally collected from 40 confirmed COVID-19 patients and examined for lymphocyte subsets by flow cytometry and cytokine profiles by specific immunoassays.

Findings: Of the 40 COVID-19 patients enrolled, 13 severe cases showed significant and sustained decreases in lymphocyte counts [0·6 (0·6-0·8)] but increases in neutrophil counts [4·7 (3·6-5·8)] than 27 mild cases [1.1 (0·8-1·4); 2·0 (1·5-2·9)]. Further analysis demonstrated significant decreases in the counts of T cells, especially CD8+ T cells, as well as increases in IL-6, IL-10, IL-2 and IFN-γ levels in the peripheral blood in the severe cases compared to those in the mild cases. T cell counts and cytokine levels in severe COVID-19 patients who survived the disease gradually recovered at later time points to levels that were comparable to those of the mild cases. Moreover, the neutrophil-to-lymphocyte ratio (NLR) (AUC=0·93) and neutrophil-to-CD8+ T cell ratio (N8R) (AUC =0·94) were identified as powerful prognostic factors affecting the prognosis for severe COVID-19.

Interpretation: The degree of lymphopenia and a proinflammatory cytokine storm is higher in severe COVID-19 patients than in mild cases, and is associated with the disease severity. N8R and NLR may serve as a useful prognostic factor for early identification of severe COVID-19 cases.

Funding: The National Natural Science Foundation of China, the National Science and Technology Major Project, the Health Commission of Hubei Province, Huazhong University of Science and Technology, and the Medical Faculty of the University of Duisburg-Essen and Stiftung Universitaetsmedizin, Hospital Essen, Germany.

Keywords: COVID-19; Coronavirus; Inflammatory cytokine; Lymphopenia; SARS-CoV-2.

Conflict of interest statement

Declaration of Competing Interest The authors disclose no conflicts of interest.

Copyright © 2020 The Author(s). Published by Elsevier B.V. All rights reserved.

Figures

Fig. 1
Fig. 1
Kinetic analysis of cell counts of different populations of WBCs in COVID-19 patients. The absolute numbers of total WBCs (a), neutrophils (b), lymphocytes (c) and monocytes (d) in the peripheral blood of mild (blue line) and severe (red line) COVID-19 patients were analyzed at different time points after hospital admission. Error bars, mean ± SD.; Statistics, repeated measures (mixed model) ANOVA. *p<0·05. The upper dotted lines show the upper normal limit of each parameter, and the lower dotted lines show the lower normal limit of each parameter. Number of cases for each time point, M: Mild patients, S: Severe patients. Time≤3d 27(M),13(S); 4-6d 10(M), 1(S); 7-9d 9 (M), 4 (S); 10-12d 6 (M), 3(S); 13-15d 4 (M), 6 (S); ≥16d 2 (M), 7 (S).
Fig. 2
Fig. 2
Kinetic analysis of cell counts of different lymphocyte subsets in COVID-19 patients. The absolute numbers of CD3+ T cells (a), CD8+ T cells (b), CD4+ T cells (c), B cells (d) and NK cells (e) in the peripheral blood of mild (blue line) and severe (red line) COVID-19 patients were analyzed at different time points after hospital admission. Error bars, mean ± SD.; Statistics, repeated measures (mixed model) ANOVA. *p<0·05. Number of cases for each time point, M: Mild patients, S: Severe patients. Time≤3d 27(M),13(S); 4-6d 10(M), 1(S); 7-9d 9 (M), 4 (S); 10-12d 6 (M), 3(S); 13-15d 4 (M), 6 (S); ≥16d 2 (M), 7 (S).
Fig. 3
Fig. 3
Kinetic analysis of the serum levels of inflammatory cytokines in COVID-19 patients. The concentrations of IL-6 (a), IL-10 (b), IL-2 (c), IL-4 (d), TNF-α (e) and IFN-γ (f) in the serum of mild (blue line) and severe (red line) COVID-19 patients were analyzed at different time points after hospital admission. Error bars, mean ± SD.; Statistics, repeated measures (mixed model) ANOVA. *p<0·05. The upper dotted lines show the upper normal limit of each parameter, and the lower dotted lines show the lower normal limit of each parameter. Number of cases for each time point, M: Mild patients, S: Severe patients. Time≤3d 27(M),13(S); 4-6d 10(M), 1(S); 7-9d 9 (M), 4 (S); 10-12d 6 (M), 3(S); 13-15d 4 (M), 6 (S); ≥16d 2 (M), 7 (S).
Fig. 4
Fig. 4
Prognostic factors of severe COVID-19. (a) Principal component analysis was performed by R package “factoextra” to identify correlated variables for distinguishing severe patients from mild COVID-19 patients. Four mostly contributing variables, neutrophil-to-CD8+ T cell ratio (N8R), neutrophil-to-lymphocyte ratio (NLR), neutrophil counts (NE) and White Blood Cells counts (WBC) were identified. (b) ROC curve and AUC were calculated for these 4 selected parameters by using R package “pROC”. The results of this analysis identified N8R with a higher AUC (0·94) than NLR (0·93), NE (0·91) and WBC (0·85).

References

    1. World Health Organization. Coronavirus disease (COVID-19) outbreak situation. 2020. Available at:. Accessed March 26, 2020.
    1. National Health Commission of the People's Republic Of China. Update on the epidemic situation of novel coronavirus pneumonia as of 24:00 on March 25. Available at: . Accessed March 26, 2020.
    1. Huang C., Wang Y., Li X. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):514–523.
    1. Chan J.F., Yuan S., Kok K.H. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet. 2020;395(10223):514–523.
    1. Wang D., Hu B., Hu C. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA. 2020;395(10223):514–523.
    1. de Wit E., van Doremalen N., Falzarano D., Munster V.J. SARS and MERS: recent insights into emerging coronaviruses. Nat Rev Microbiol. 2016;14(8):523–534.
    1. Chien J.Y., Hsueh P.R., Cheng W.C., Yu C.J., Yang P.C. Temporal changes in cytokine/chemokine profiles and pulmonary involvement in severe acute respiratory syndrome. Respirology. 2006;11(6):715–722.
    1. Chen N., Zhou M., Dong X. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395(10223):507–513.
    1. Qin C., Zhou L., Hu Z. Dysregulation of immune response in patients with COVID-19 in Wuhan, China. Clin Infect Dis. 2020
    1. Chen G., Wu D., Guo W. Clinical and immunologic features in severe and moderate coronavirus disease 2019. J Clin Invest. 2020
    1. Chang D., Lin M., Wei L. Epidemiologic and clinical characteristics of novel coronavirus infections involving 13 patients outside Wuhan, China. JAMA. 2020;323(11):1092.
    1. Chu H., Zhou J., Wong B.H. Productive replication of middle east respiratory syndrome coronavirus in monocyte-derived dendritic cells modulates innate immune response. Virology. 2014;454:197–205.
    1. Zhou J., Chu H., Li C. Active replication of middle east respiratory syndrome coronavirus and aberrant induction of inflammatory cytokines and chemokines in human macrophages: implications for pathogenesis. J Infect Dis. 2014;209(9):1331–1342.
    1. Kong S.L., Chui P., Lim B., Salto-Tellez M. Elucidating the molecular physiopathology of acute respiratory distress syndrome in severe acute respiratory syndrome patients. Virus Res. 2009;145(2):260–269.
    1. Kim K.D., Zhao J., Auh S. Adaptive immune cells temper initial innate responses. Nat Med. 2007;13(10):1248–1252.
    1. Palm N.W., Medzhitov R. Not so fast: adaptive suppression of innate immunity. Nat Med. 2007;13(10):1142–1144.
    1. Liu J., Liu Y., Xiang P. Neutrophil-to-lymphocyte ratio predicts severe illness patients with 2019 novel coronavirus in the early stage. medRxiv. 2020 doi: 10.1101/2020.02.10.20021584. [Preprint]. February 12, 2020 [cited 2020 Feb 16]. Available from:
    1. Wang F., Nie J., Wang H. Characteristics of peripheral lymphocyte subset alteration in COVID-19 pneumonia. J Infect Dis. 2020
    1. Channappanavar R., Perlman S. Pathogenic human coronavirus infections: causes and consequences of cytokine storm and immunopathology. Semin Immunopathol. 2017;39(5):529–539.

Source: PubMed

3
Subskrybuj