key: cord-0959775-y48mfuky
authors: Zhou, Yonggang; Zhang, Jinhe; Wang, Dongyao; Wang, Dong; Guan, Wuxiang; Qin, Jingkun; Xu, Xiuxiu; Fang, Jingwen; Fu, Binqing; Zheng, Xiaohu; Wang, Dongsheng; Zhao, Hong; Chen, Xianxiang; Tian, Zhigang; Xu, Xiaoling; Wang, Guiqiang; Wei, Haiming
title: Profiling of the immune repertoire in COVID-19 patients with mild, severe, convalescent, or retesting-positive status
date: 2021-01-14
journal: J Autoimmun
DOI: 10.1016/j.jaut.2021.102596
sha: b981ca2646de7ebe977c5bce322a2dee02ccb825
doc_id: 959775
cord_uid: y48mfuky

Forty-seven samples of peripheral blood mononuclear cells from four groups of coronavirus disease (COVID)-19 patients (mild, severe, convalescent, retesting-positive) and healthy controls were applied to profile the immune repertoire of COVID-19 patients in acute infection or convalescence by transcriptome sequencing and immune-receptor repertoire (IRR) sequencing. Transcriptome analyses showed that genes within principal component group 1 (PC1) were associated with infection and disease severity whereas genes within PC2 were associated with recovery from COVID-19. A “dual-injury mechanism” of COVID-19 severity was related to an increased number of proinflammatory pathways and activated hypercoagulable pathways. A machine-learning model based on the genes associated with inflammatory and hypercoagulable pathways had the potential to be employed to monitor COVID-19 severity. Signature analyses of B-cell receptors (BCRs) and T-cell receptors (TCRs) revealed the dominant selection of longer V–J pairs (e.g., IGHV3-9–IGHJ6 and IGHV3-23–IGHJ6) and continuous tyrosine motifs in BCRs and lower diversity of TCRs. These findings provide potential predictors for COVID-19 outcomes, and new potential targets for COVID-19 treatment.

Major infectious diseases pose huge threats to human health and great challenges to 89 the security of global public health, and can cause more premature deaths than any 90 other disease (1, 2) . In the last two decades, infectious diseases caused by members of 91 the coronavirus family have raged three times in the population: severe acute 92 respiratory syndrome coronavirus (SARS-CoV)(3), middle East respiratory syndrome 93 coronavirus (MERS-CoV) (4) and SARS-CoV-2 (5) .

At present, the outbreak caused by SARS-CoV-2 infection has been upgraded to a 96 global pandemic. Worldwide, the number of known cases has exceeded 83 million, 97 and led to >1.8 million deaths, according to data released by the World Health 98 Organization (WHO) on 3 January 2021. This situation has placed extreme constraints 99 on public-health resources worldwide (6) (7) (8) . The pandemic has not been brought under 100 control effectively worldwide.

The acute infectious pneumonia caused by SARS-CoV-2 was named "coronavirus 103 disease 2019" (COVID-19) by the WHO on 11 February 2020(9). The initial 104 symptoms of COVID-19 are mostly mild (fever, dry cough, fatigue) and some 105 patients develop severe symptoms (e.g., dyspnea) gradually (8) . Although most people serine protease 2 to infect the host (8, 10) . Pulmonary cells infected with SARS-CoV-2 114 release inflammatory signals that activate the body's antiviral immune response. 115 However, SARS-CoV-2 infection causes an excessive non-effective response and 116 severe inflammation in some COVID-19 patients by pathogenic T helper (Th)1 cells 117 and inflammatory cluster of differentiation (CD)14 + CD16 + monocytes (11). The level 118 of proinflammatory cytokines such as granulocyte-macrophage colony-stimulating 119 factor (GM-CSF), interleukin (IL)-1 and IL-6 released by these cells in the serum of 120 patients with severe COVID-19 is increased, which results in recruitment of more 121 activated immune cells into the lung to form "cytokine release syndrome" (CRS) (8, 122 11-14) . Monoclonal-antibody drugs that target these proinflammatory cytokines, such 123 as tocilizumab (anti-IL6 receptor) or mavrilimumab (anti-GM-CSF receptor-α) can 124 alleviate CRS and improve survival chances in patients with severe 15, 125 known as "retesting-positive" (RTP) patients. Upon re-admission to hospital, these 140 patients show no significant clinical symptoms or disease progression, with normal or 141 improved computed-tomography images and normal levels of proinflammatory 142 cytokines(18). In a clinical follow-up study from Wuhan (China), the prevalence of 143 RTP patients was ~3%, which increased the risk of . We wondered if we could identify markers that have the potential to predict 156 COVID-19 outcomes. We employed comprehensive immune analyses using Helsinki 1964 and its later amendments. 170 We profiled the transcriptome and the repertoire of immune receptors of PBMCs of 171 COVID-19 patients, and to find potential clues to analyze COVID-19 outcomes. We 

To establish a model for evaluating COVID-19 severity based on the dual-injury 234 mechanism, we created the learning set based on the 632 inflammation-and vascular 235 injury mechanism-related genes from non-ICU and ICU groups using the 236 leave-one-out method. With glyceraldehyde 3-phosphate dehydrogenase (GAPDH) as 237 an internal reference, the relative gene expression was calculated. Using the P value 238 as the screening condition, a logistic regression algorithm was used to model, and the 239 final 16 featured genes and data models were determined through 100 permutations.

The transcriptome data of 10 COVID-19 patients who were RTP and the dynamic data the PC2 area (Fig. 1D) . Furthermore, the PC score was used to assess the correlation 311 between the patients in the five groups and PC1 or PC2. The PC1 score increased in 312 HCs, non-ICU patients and ICU patients, and rose to a peak in ICU patients, whereas 313 CPs and RTP patients showed a low correlation with PC1 (Fig. 1E ). These data 314 showed that PC1 was a gene set related to SARS-CoV-2 infection and COVID-19 315 severity. The PC2 score was higher in CPs and RTP patients than that in the other 316 three groups, and highest in CPs. These observations showed that PC2 was a gene set 317 related to convalescence from COVID-19. 

336 pathways is present in severe COVID-19 337 A decrease in the number of T cells (especially CD4 + T cells) is an obvious feature of 338 COVID-19 (8, 11, 22) . Based on the xCell algorithm, contrary to the decrease in the factors, we found that from patients with mild disease to severe disease, the immune 376 imbalance developed to CRS, but the immune balance was restored in the 377 convalescent period (Fig. 2D, right) .

In addition to inflammation, decreased SpO 2 due to dyspnea is a typical feature of 379 severe COVID-19 (8) . Differential analyses of the top-100 genes in PC1 showed 380 increased expression of the hemoglobin-1 gene in the ICU group ( Supplementary Fig.   381 4A, B). Upon functional-enrichment analyses of these genes using the Gene Ontology 382 database, we found that these genes were not only involved in the immune response 383 but also related to the hypoxic response of erythrocytes ( Supplementary Fig. 4C ).

GSVA of these genes showed that expression of blood oxygen-related pathways (e.g., Finally, a feature set containing 16 genes was obtained (Fig. 4A, B) . Among them, 427 TGFB1, TGFBR1, TGFBR2 and CD74 (as immune-negative signals) and CD44, IRF8 428 and CD244 (as antiviral-activity signals) could inhibit COVID-19 severity, whereas 429 IL-1, TNF, CCL3 and CCL4 (as proinflammatory cytokines) promoted COVID-19 430 severity (Fig. 4B ). These genes also had an important role in defining the dual-injury 431 mechanism based on inflammatory and hypercoagulable pathways in patients with 432 severe COVID-19 (Fig. 2E, 3C ).

To test the accuracy of this MLM, we first used all 10 RTP samples that were mild or 434 asymptomatic as the test set. After MLM operation, the score of COVID-19 severity 435 in all 10 RTP samples was <0.5, and they were considered to be patients with "mild" Fig. 6A, B) . These genes were involved mainly in the regulation of immune balance 466 and repair of various tissue injuries ( Supplementary Fig. 6C ). Furthermore, we used 467 GSVA to assess differences in enrichment of multiple pathways related to immune 468 recognition, immune activation, immune regulation, and vascular repair in PC2 469 ( Supplementary Fig. 7) . Compared with other groups, the signaling pathways of TCRs also decreased significantly in non-ICU and ICU groups, and recovered in 536 convalescence ( Supplementary Fig. 9A , B, C). GSVA of transcriptome data suggested 537 that T cells had more obvious activation of the antiviral response in convalescence 538 than that in infection (Supplementary Fig. 3 and Supplementary Fig. 7 ). Focusing on 539 convalescent patients, we found that the TCR diversity of the RTP group was greater 540 than that of the CP group ( Supplementary Fig. 9A , B, C). Next, to further understand 541 the rearrangement of TCRs, we separately analyzed the dominant selection of V and J 542 regions. In the V region, TRBV28 was selected more often in the CP group compared 543 with that in the RTP group ( Supplementary Fig. 9D ). In the J region, TRBJ2-7 was 544 selected more often in the CP group, whereas TRBJ1-5 and TRBJ1-4 were selected 545 more often in the RTP group ( Supplementary Fig. 9E ). Further evaluation of the 546 diversity of the pairs of the dominant selected V and J regions revealed that, compared 547 with that in HCs, V-J combinations were more concentrated and less diverse in the 548 convalescent period (Fig. 6A) . These observations further suggested that T-cell clones four groups of patients may have been related to SARS-CoV-2 infection (Fig. 6C) .

The eight TCR sequences was consistent with the activation time of T cells, which 559 increased in infection and decreased in convalescence, and was higher in the CP 560 group than that in the RTP group (Fig. 6D) . These findings suggested that a reduction 561 in the number of these TCRs may be related to the weak clearance of virus by T cells 562 in the RTP group, which must be confirmed in future studies. Inflammation is a major cause of COVID-19 severity (8, 11) . Transcriptome data 573 showed that the increased inflammation shown in patients after SARS-CoV-2 574 infection was related to immune imbalance. In ICU patients, the imbalance was more Fig. 2F ). However, this seems to be only part of 586 the cause of hypercoagulation. On the basis of inflammation, we showed that the 587 increased peroxide accumulation and hypercoagulable pathways associated with 588 hypoxia and inflammation caused further dysfunction of vascular endothelial cells 589 (Fig. 3) . Clinically, endothelial dysfunction is associated with hypercoagulation (35).

Endothelial dysfunction was closely related to an increase in vascular permeability in 591 patients with severe COVID-19 (Fig. 3) . This increase in vascular permeability would 592 allow more inflammatory factors and inflammatory cells to enter tissues, thereby 593 aggravating tissue injury and even causing multi-organ injury. Our results suggest that 594 loss of the don't-eat-me signal may also be an important mechanism of endothelial 595 J o u r n a l P r e -p r o o f dysfunction in COVID-19 patients (Fig. 3) . The classic don't-eat-me signaling 596 molecule (36), CD24Fc, has been used to test alleviation of the pathological injury 597 caused by . We propose a dual-injury mechanism based on 598 inflammatory and hypercoagulable pathways in the mechanism of COVID-19 599 severity.

Gradually, COVID-19 is being recognized as a systemic disease (8, 38) . In addition to 601 an excessive inflammatory response and increased vascular-injury signals, 602 transcriptome analyses suggested that the sense of smell and taste may also be 603 disturbed in patients after SARS-CoV-2 infection (Supplementary Fig. 5) . Also, 604 anosmia and dysgeusia are being considered as new typical symptoms of COVID-19 605 (39). We also found that the bone-mineralization pathway was downregulated and a 606 cartilage-development pathway was upregulated in convalescent patients, suggesting 607 that osteoporosis is a potential sequela of COVID-19 ( Supplementary Fig. 7) . that CPs had a longer V region and J region (e.g., V H 3-9 and J H 6) to achieve longer 635 V-J pairing (Fig. 5B ). SARS-CoV-2 is a highly glycosylated spherical particle whose 636 degree of glycosylation is much higher than that of influenza viruses and the HIV, so 637 many neutralizing antibodies cannot block SARS-CoV-2 from invading cells (42, 43) . NAT "+" NAT "+" NAT "-" NAT "+" PCs (1−10) Explained variances (%) IL6  IL1A  IL1B  IFIT1B  IFI27  TNFSF10   MXI1  ISG15  TRIM58  TRIM10  CCL3  CCL4  CCL3L1  CXCL8  BCL2L1  KLF1  GATA1  IL10  IL4  TGFBR1  TGFBR2  TGFBR3  TGFB3  TGFA  LILRB1  LILRB3  SOCS1  SOCS3  SOCS4  FOXO1  FOXO3B  FOXP4  FPR2  MT1XP1  ADRB1 

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The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine

The CAS Key Laboratory of Innate Immunity and Chronic Disease, Division of Life Sciences and Medicine

Xiaoshan Innovation Polis

These authors contributed equally. * Correspondence: john131212@sina