Adaptive immune responses to SARS-CoV-2 infection in severe versus mild individuals

Signal Transduction and Targeted Therapy - Tập 5 Số 1
Fan Zhang1, Rui Gan1, Ziqi Zhen1, Xiaoli Hu2, Xiang Li1, Fengxia Zhou1, Ying Liu3, Chuangeng Chen1, Shuangyu Xie1, Bailing Zhang1, WU Xiao-ke4, Zhiwei Huang1
1HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China
2Department of Infectious Diseases, Heilongjiang Provincial Hospital, Harbin Institute of Technology, Harbin, 150030, China
3Harbin Blood Center, Harbin, 150056, China
4Centre for Reproductive Medicine, Heilongjiang Provincial Hospital, Harbin Institute of Technology, Harbin, 150030, China

Tóm tắt

AbstractThe global Coronavirus disease 2019 (COVID-19) pandemic caused by SARS-CoV-2 has affected more than eight million people. There is an urgent need to investigate how the adaptive immunity is established in COVID-19 patients. In this study, we profiled adaptive immune cells of PBMCs from recovered COVID-19 patients with varying disease severity using single-cell RNA and TCR/BCR V(D)J sequencing. The sequencing data revealed SARS-CoV-2-specific shuffling of adaptive immune repertories and COVID-19-induced remodeling of peripheral lymphocytes. Characterization of variations in the peripheral T and B cells from the COVID-19 patients revealed a positive correlation of humoral immune response and T-cell immune memory with disease severity. Sequencing and functional data revealed SARS-CoV-2-specific T-cell immune memory in the convalescent COVID-19 patients. Furthermore, we also identified novel antigens that are responsive in the convalescent patients. Altogether, our study reveals adaptive immune repertories underlying pathogenesis and recovery in severe versus mild COVID-19 patients, providing valuable information for potential vaccine and therapeutic development against SARS-CoV-2 infection.

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Tài liệu tham khảo

Long, Q. X. et al. Antibody responses to SARS-CoV-2 in patients with COVID-19. Nat. Med. https://doi.org/10.1038/s41591-020-0897-1 (2020).

Yu, F., Du, L., Ojcius, D. M., Pan, C. & Jiang, S. Measures for diagnosing and treating infections by a novel coronavirus responsible for a pneumonia outbreak originating in Wuhan, China. Microbes Infect. 22, 74–79 (2020).

Liao, M. et al. Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19. Nat. Med. https://doi.org/10.1038/s41591-020-0901-9 (2020).

Wilk, A. J. et al. A single-cell atlas of the peripheral immune response to severe COVID-19. https://doi.org/10.1101/2020.04.17.20069930 (2020).

Li, T. et al. Significant changes of peripheral T lymphocyte subsets in patients with severe acute respiratory syndrome. J. Infect. Dis. 189, 648–651 (2004).

Ko, J. H. et al. Predictive factors for pneumonia development and progression to respiratory failure in MERS-CoV infected patients. J. Infect. 73, 468–475 (2016).

Mohn, K. G. et al. Immune responses in acute and convalescent patients with mild, moderate and severe disease during the 2009 influenza pandemic in Norway. PLoS ONE 10, e0143281 (2015).

Wolfel, R. et al. Virological assessment of hospitalized patients with COVID-2019. Nature https://doi.org/10.1038/s41586-020-2196-x (2020).

Ni, L. et al. Detection of SARS-CoV-2-specific humoral and cellular immunity in COVID-19 convalescent individuals. Immunity. https://doi.org/10.1016/j.immuni.2020.04.023 (2020).

Wen, W. et al. Immune cell profiling of COVID-19 patients in the recovery stage by single-cell sequencing. Cell Discov. 6, 31 (2020).

Li, T. et al. Long-term persistence of robust antibody and cytotoxic T cell responses in recovered patients infected with SARS coronavirus. PLoS ONE 1, e24 (2006).

Cao, Y. et al. Potent neutralizing antibodies against SARS-CoV-2 identified by high-throughput single-cell sequencing of convalescent patients’ B cells. Cell 182, 73–84. e16 (2020).

Chao, A., Hsieh, T. C., Chazdon, R. L., Colwell, R. K. & Gotelli, N. J. Unveiling the species-rank abundance distribution by generalizing the good-turing sample coverage theory. Ecology 96, 1189–1201 (2015).

Hill, M. O. Diversity and evenness: a unifying notation and its consequences. Ecology 54, 2 (1973).

Yaari, G., Uduman, M. & Kleinstein, S. H. Quantifying selection in high-throughput Immunoglobulin sequencing data sets. Nucleic Acids Res. 40, e134 (2012).

Hershberg, U., Uduman, M., Shlomchik, M. J. & Kleinstein, S. H. Improved methods for detecting selection by mutation analysis of Ig V region sequences. Int. Immunol. 20, 683–694 (2008).

Bryson, S. et al. Structures of preferred human IgV genes-based protective antibodies identify how conserved residues contact diverse antigens and assign source of specificity to CDR3 loop variation. J. Immunol. 196, 4723–4730 (2016).

Fu, Y. et al. A broadly neutralizing anti-influenza antibody reveals ongoing capacity of haemagglutinin-specific memory B cells to evolve. Nat. Commun. 7, 12780 (2016).

Ehrhardt, S. A. et al. Polyclonal and convergent antibody response to Ebola virus vaccine rVSV-ZEBOV. Nat. Med. 25, 1589–1600 (2019).

Tsay, G. J. & Zouali, M. The interplay between innate-like B cells and other cell types in autoimmunity. Front. Immunol. 9, 1064 (2018).

Pizzolla, A. et al. Influenza-specific lung-resident memory T cells are proliferative and polyfunctional and maintain diverse TCR profiles. J. Clin. Investig. 128, 721–733 (2018).

Attaf, M. et al. Major TCR repertoire perturbation by immunodominant HLA-B (*) 44:03-restricted CMV-specific T cells. Front. Immunol. 9, 2593 (2018).

Price, D. A. et al. T cell receptor recognition motifs govern immune escape patterns in acute SIV infection. Immunity 21, 793–803 (2004).

Gupta, N. T. et al. Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. Bioinformatics 31, 3356–3358 (2015).

Galson, J. D. et al. In-depth assessment of within-individual and inter-individual variation in the B cell receptor repertoire. Front. Immunol. 6, 531 (2015).

Robbiani, D. F. et al. Convergent antibody responses to SARS-CoV-2 infection in convalescent individuals. https://doi.org/10.1101/2020.05.13.092619 (2020).

Glanville, J. et al. Identifying specificity groups in the T cell receptor repertoire. Nature 547, 94–98. https://doi.org/10.1038/nature22976 (2017).

Waltman, L. & van Eck, N. J. A smart local moving algorithm for large-scale modularity-based community detection. Eur. Phys. J. B 86, 471 (2013).

Li, C. et al. SciBet as a portable and fast single cell type identifier. Nat. Commun. 11, 1818 (2020).

Aran, D. et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol. 20, 163–172 (2019).

Ussher, J. E., Willberg, C. B. & Klenerman, P. MAIT cells and viruses. Immunol. Cell Biol. 96, 630–641 (2018).

Zhao, J. et al. Antibody responses to SARS-CoV-2 in patients of novel coronavirus disease 2019. Clin. Infect. Dis. https://doi.org/10.1093/cid/ciaa344 (2020).

Gralinski, L. E. et al. Complement activation contributes to severe acute respiratory syndrome coronavirus pathogenesis. mBio 9. https://doi.org/10.1128/mBio.01753-18 (2018).

Chen, G. et al. Clinical and immunological features of severe and moderate coronavirus disease 2019. J. Clin. Investig. 130, 2620–2629 (2020).

Huang, C. et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395, 497–506 (2020).

Huang, L. et al. Blood single cell immune profiling reveals the interferon-MAPK pathway mediated adaptive immune response for COVID-19.https://doi.org/10.1101/2020.03.15.20033472 (2020).

Prachar, M. et al. COVID-19 vaccine candidates: prediction and validation of 174 SARS-CoV-2 epitopes. https://doi.org/10.1101/2020.03.20.000794 (2020).

Campbell, K. M., Steiner, G., Wells, D. K., Ribas, A. & Kalbasi, A. Prediction of SARS-CoV-2 epitopes across 9360 HLA class I alleles. https://doi.org/10.1101/2020.03.30.016931 (2020).

Fast, E. & Chen, B. Potential T-cell and B-cell epitopes of 2019-nCoV. https://doi.org/10.1101/2020.02.19.955484 (2020).

Grifoni, A. et al. Targets of T cell responses to SARS-CoV-2 coronavirus in humans with COVID-19 disease and unexposed individuals. Cell. https://doi.org/10.1016/j.cell.2020.05.015 (2020).

Hadjadj, J. et al. Impaired type I interferon activity and exacerbated inflammatory responses in severe Covid-19 patients. https://doi.org/10.1101/2020.04.19.20068015 (2020).

Channappanavar, R. et al. Dysregulated type I interferon and inflammatory monocyte-macrophage responses cause lethal pneumonia in SARS-CoV-infected mice. Cell Host Microbe 19, 181–193 (2016).

Zhang, J. et al. Association of hypertension with the severity and fatality of SARS-CoV-2 infection: a meta-analysis. Epidemiol. Infect. 148, e106 (2020).

Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

Stern, J. N. et al. B cells populating the multiple sclerosis brain mature in the draining cervical lymph nodes. Sci. Transl. Med. 6, 248ra107 (2014).

Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

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Signal Transduction and Targeted Therapy

Tập 5 Số 1

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