Dysregulated adaptive immune response contributes to severe COVID-19

Dear Editor, The outbreak of the new coronavirus SARS-CoV-2 has resulted in a global pandemic. Due to the lack of a speci ﬁ c drug against this virus, the current clinical management of this disease mainly depends on supportive care to reduce in ﬂ ammatory responses and to keep the lung functioning. 1 Understanding the underlying immunopathology of coronavirus disease 2019 (COVID-19) is therefore of paramount importance for improving the current treatment. In this study, we found a distinct feature of adaptive immunity in severely affected patients, the coincidence of impaired cellular and enhanced humoral immune responses, suggesting that dysregulated adaptive immune responses advanced severe COVID-19. Interestingly, expression of Prothymosin alpha (PTMA), the proprotein of Thymosin alpha-1 (T α 1), was increased in a group of CD8 T memory stem cells accumulated during severe disease. We further showed that T α 1 slightly reduced T cell activation in vitro and promoted proliferation of effector T cells. Moreover, T α 1 treatment relieved the lymphopenia in COVID-19 patients. Our data suggest that early intervention of adaptive immune response might be critical for the prevention of severe COVID-19. A high rate of severe COVID-19 was reported in immunocom- promised patients, 2 suggesting that an insuf ﬁ cient rather than an overactive antiviral immunity caused this disease. Meanwhile, lymphopenia, a reduction in the number of lymphocytes in the blood, was associated with the severity of COVID-19. 3 We analyzed the incidence of lymphopenia in 284 patients infected with SARS- CoV-2 (Supplementary information, Table S1), and found that a reduction of lymphocytes was more frequently observed in aged patients except for the group between 0 – 9 years old who may have an immature immune system (Fig. 1a). These ﬁ ndings denote the pivotal role of the adaptive immunity in the viral clearance and disease control.


Antibodies and reagents
The antibodies for flow cytometry were purchased from Biolegend or BD. Tα1 (ZADAXIN) was purchased from SciClone Pharmaceuticals. Chromium TM Single cell 5' Library & Gel Bead Kit (1000006), Chromium TM Single cell 3'/5' Library Construction Kit (1000020) was purchased from 10x Genomics. Cytokines were purchased from Peprotech. All cell culture reagents were purchased from Gibco unless otherwise indicated.

Patients and study approval
This study enrolled a total of 10 COVID-19 patients. The 4 patients with severe disease were reported in our previous study 1 . The 6 patients with mild disease (3 at post mild disease stage and 3 at convalescence stage) were recruited from the Fifth Affiliated Hospital (Zhuhai, China) and the Third Affiliated Hospital (Guangzhou, China) of Sun Yat-sen University.
Lymphocyte count data from 25 SARS-CoV-2 patients were collected in Huoshenshan hospital (Wuhan, China) by managing doctors. Lymphocyte count data from 256 SARS-CoV-2 patients were collected from designated hospitals in Fujian province during January 3 2020 to march 13 by managing doctors. Informed consent has been obtained from all participated patients. This study received approval from the Research Ethics Committee of the Sun Yat-sen University Cancer Center, the Fifth Affiliated Hospital, the Third Affiliated Hospital of Sun Yat-sen University, the Huoshenshan hospital and the Fujian Provincial Hospital, China (K176-1, HSSLL030, K2020-03-01).

PBMCs Isolation
All procedures were carried out within P2+ laboratory certified for studies of infectious materials. PBMCs were isolated from peripheral venous whole blood samples density gradient cell separation (Ficoll, TBD Science, China), and were preceded for Single cell transcriptome sequencing immediately.

Cell culture
Fresh PBMCs from healthy donors were cultured in IL-2 (200U/ml) containing media in 5 96-well u-bottomed plates coated with anti-CD3/CD28 (5ug/ml respectively, Peprotech). To test the effect on T cell activation, Tα1 (200ng/ml) was added into T cells culture media while seeding. Cell numbers were recorded at day 3, 6 and 9. Cells harvested after 3 days activation were analyzed by flow cytometry.

Flow cytometry
The antibodies used for cell surface labeling were BUV737 anti-human CD4 (564305, BD),

Single cell transcriptome sequencing and data preprocessing
The single cell RNA libraries were prepared using the Chromium TM Single cell 3' Reagent Kit of Chromium platform (10x Genomics, USA) following the manufacturer's instruction.
Generated single cell RNA libraries were sequenced on the Illumina HiSeq X Ten platform.
The CellRanger software (version 3.1.0) was used for preprocessing of the PE150 Illumina sequencing reads. Briefly, raw reads in bcl format were converted to FASTQ format using "cellranger mkfastq", and then the reads in FASTQ format were aligned to human genome reference (hg38, GRCh38) using STAR, and then "cellranger count" was used to derive gene expression matrix for each sample.

Determination of cell types from single cell transcriptome sequencing data
6 Seurat (v3.1.3) R toolkit was used to analyze the single cell transcriptome sequencing data.
Firstly, cells with low quality were filtered out. Briefly, the dead or dying cells with more than 20% mitochondrial RNA content were removed, and the cells with too low number (less than 200) were also removed. Cell doublets were predicted using DoubletFinder. For each patient, a 4% (true) doublet rate was assumed, 5 principal components were used, and the default value of 20% was used for pN (the number of generated artificial doublets expressed as a proportion of the merged real-artificial data). For each library, the PC neighborhood size pK was estimated using as the maxima of the distribution of mean-variance normalized bimodality coefficient scores. Cells expressed more than one marker among the three markers (CD2, CD79A, CD68) were also defined as doublets and removed. Then, the filtered gene expression matrix for each sample was normalized using "NormalizeData" function in Seurat, and only highly variable genes were remained using "FindVariableFeatures" function in Seurat. Next, "FindIntegrationAnchors" and "Integratedata" functions in Seurat were used to integrate the gene expression matrices of all samples, where batch effects between different samples have been adjusted. Next, "RunPCA" function was used to perform the principal component analysis (PCA) and "FindNeighbors" function was used to construct a K-nearest-neighbor graph. Next, the most representative principle components (PCs) selected based on PCA were used for clustering analysis with "FindCluster" function to determine different cell types. Lastly, tSNE was used to visualize the different cell types.
We annotated the cell types using the following rules: Based on the most 10 differentially expressed genes that were derived using "FindAllMarkers" function in Seurat, genes such as CD2, CD3D, CD3E, CD3G and CD247 were used as T cell markers, and genes such as CD19, CD79A, CD79B, BLNK, FCRL5, MS4A1 were used as B cell markers, and genes such as CD14, CD163, CD68, CSF1R, FCGR2A, and CD33 were used as myeloid cell markers. The percentage of CD4 gene expression and CD8A was counted to define CD4+ T cells or CD8+ T cells.

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The CD19+, CD79A+, CD79B+, CD3D+, CD3E+, and CD3G+ lymphocytes were further clustered using the single cell analysis pipeline as described above. To get higher resolution clusters, the "resolution" parameter used in FindCluster was set from 0.3 to 0.5.

Differential gene expression analysis between different cell types
Seurat v3 was used to perform differential gene expression analysis between different cell types. For each cell type, DEGs were obtained relative to all of the other cell types using "FindCluster" function in Seurat. DEGs between Tm-2 and Te were obtained using R package edgeR with log2 Fold change > 0.58 and P value < 0.05.

Statistical analysis
All sample sizes were large enough to ensure proper statistical analysis. Statistical analyses were performed using GraphPad Prism (GraphPad Software, Inc.). P values < 0.05 were considered as statistically significant. All t-test analyses were one-tailed t-tests (paired or unpaired depending on the experiments). The number of replicates (n), number of independent experiments performed, and p values for each experiment are reported in the corresponding figure legends.

Data Accession
The raw data files were deposited in the Genome Sequencing Archive of the National Genomics Data Center with the accession number of CRA002572.