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Article

T-Cell Epitope Mapping of SARS-CoV-2 Reveals Coordinated IFN-γ Production and Clonal Expansion of T Cells Facilitates Recovery from COVID-19

by
Xing Fan
1,†,
Jin-Wen Song
1,2,†,
Wen-Jing Cao
1,3,
Ming-Ju Zhou
1,
Tao Yang
1,
Jing Wang
1,
Fan-Ping Meng
1,
Ming Shi
1,
Chao Zhang
1,* and
Fu-Sheng Wang
1,2,*
1
Senior Department of Infectious Diseases, The Fifth Medical Center of PLA General Hospital, National Clinical Research Center for Infectious Diseases, Beijing 100039, China
2
Medical School of Chinese PLA, Beijing 100853, China
3
The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Viruses 2024, 16(7), 1006; https://doi.org/10.3390/v16071006
Submission received: 23 April 2024 / Revised: 1 June 2024 / Accepted: 14 June 2024 / Published: 22 June 2024
(This article belongs to the Special Issue Coronaviruses Pathogenesis, Immunity, and Antivirals)
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Abstract

:
Background: T-cell responses can be protective or detrimental during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection; however, the underlying mechanism is poorly understood. Methods: In this study, we screened 144 15-mer peptides spanning the SARS-CoV-2 spike, nucleocapsid (NP), M, ORF8, ORF10, and ORF3a proteins and 39 reported SARS-CoV-1 peptides in peripheral blood mononuclear cells (PBMCs) from nine laboratory-confirmed coronavirus disease 2019 (COVID-19) patients (five moderate and four severe cases) and nine healthy donors (HDs) collected before the COVID-19 pandemic. T-cell responses were monitored by IFN-γ and IL-17A production using ELISA, and the positive samples were sequenced for the T cell receptor (TCR) β chain. The positive T-cell responses to individual SARS-CoV-2 peptides were validated by flow cytometry. Results: COVID-19 patients with moderate disease produced more IFN-γ than HDs and patients with severe disease (moderate vs. HDs, p < 0.0001; moderate vs. severe, p < 0.0001) but less IL-17A than those with severe disease (p < 0.0001). A positive correlation was observed between IFN-γ production and T-cell clonal expansion in patients with moderate COVID-19 (r = 0.3370, p = 0.0214) but not in those with severe COVID-19 (r = −0.1700, p = 0.2480). Using flow cytometry, we identified that a conserved peptide of the M protein (Peptide-120, P120) was a dominant epitope recognized by CD8+ T cells in patients with moderate disease. Conclusion: Coordinated IFN-γ production and clonal expansion of SARS-CoV-2-specific T cells are associated with disease resolution in COVID-19. Our findings contribute to a better understanding of T-cell-mediated immunity in COVID-19 and may inform future strategies for managing and preventing severe outcomes of SARS-CoV-2 infection.

1. Introduction

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is associated with significant morbidity and mortality worldwide. With the global spread of SARS-CoV-2, the emergence of variants of concern (VOCs) poses significant challenges to epidemic control and development of vaccines and therapeutics [1]. For example, mutations in the spike protein in the Omicron strain, particularly within the Receptor Binding Domain (RBD), lead to a reduction in neutralizing antibody affinity by 36–40 times compared to the wild-type strain [2,3,4,5]. Moreover, the levels of SARS-CoV-2-specific B cells and neutralizing antibodies declined significantly over time in COVID-19 convalescents and vaccine recipients [6,7,8]. Thus, neutralizing antibodies offer limited protection against VOCs.
The antigen-specific T-cell response plays a critical role in clearing SARS-CoV-2 and controlling disease progression [9,10]. Early studies have shown that robust T-cell responses are associated with milder disease courses in COVID-19. Individuals with mild or asymptomatic COVID-19 have demonstrated significant T-cell responses despite having low antibody titers [11]. Moreover, the critical role of T-cell responses in controlling the virus has been shown in B-cell-depleted COVID-19 patients and SARS-CoV-2-infected non-human primate models [12,13,14]. However, dysregulation of the T-cell response has also been observed in severe COVID-19 cases. Evidence of increased total activated CD8+ T cells has been observed in some individuals with severe COVID-19, possibly indicating bystander CD8+ T-cell activation [15]. An imbalance between circulating regulatory and cytotoxic SARS-CoV-2-specific CD4+ T cells is associated with greater COVID-19 disease [16]. Therefore, a comprehensive investigation of the T-cell immune response in individuals with natural SARS-CoV-2 infection is crucial for understanding viral control and COVID-19 disease progression.
Over several hundred T-cell epitopes of SARS-CoV-2 have been described, and SARS-CoV-2 infection induces vigorous CD4+ and CD8+ T-cell responses, which exhibit remarkable breadth in most subjects [17]. Some mutated T-cell epitopes in SARS-CoV-2 VOCs were not recognized by CD8+ T cells in infected or vaccinated individuals, and some individuals even lost the CD8+ T-cell response to the Omicron strain [18,19]. Tarke et al. showed that more than 80% of T-cell reactivity was preserved at the population level and that, in most cases, no decrease was apparent [20]. These results indicate that the remarkable breadth of human T-cell responses makes it exceedingly difficult for variants to escape T-cell recognition at the population level [21]. However, the phenotypic diversity of epitope-specific T cells and their association with disease outcomes are poorly understood.
Thus, to study the T-cell response to SARS-CoV-2 antigens in COVID-19 patients and its correlation with disease severity, we synthesized 144 SARS-CoV-2 peptides covering spike, NP, M, ORF8, and ORF10 proteins, as well as 39 reported SARS-CoV-1 peptides. Subsequently, we screened their reactivity with peripheral blood mononuclear cells (PBMCs) of COVID-19 patients with moderate or severe disease, as well as healthy donors (HDs), by measuring IFN-γ and IL-17A production. We also analyzed T-cell expansion through T-cell receptor (TCR) sequencing and flow cytometry. Our results will improve our understanding of T-cell responses to SARS-CoV-2 infection.

2. Methods

2.1. Study Subjects

Thirteen laboratory-confirmed COVID-19 patients were hospitalized at the Fifth Medical Center of the Chinese PLA General Hospital in Beijing between 23 January and 26 May 2020, and nine healthy donors (HDs) were enrolled before the COVID-19 pandemic. All the subjects had no vaccination and no previous infection history. These patients were classified into two clinical groups, namely moderate and severe groups, according to the World Health Organization guidelines. Patients in the moderate disease group exhibited obvious clinical symptoms and pneumonia and were admitted to general wards but did not require intensive care. Patients in the severe disease group required critical care and met at least one of these criteria: dyspnea and respiratory rate ≥ 30/min; blood oxygen saturation ≤ 93%; PaO2/FiO2 ratio < 300 mmHg; lung infiltrates on CT scan >50% within 24–48 h; or who exhibited respiratory failure, septic shock, and/or multiple organ dysfunction/failure. Discharge criteria were consistent with the guideline, including the resolution of respiratory symptoms, substantial improvement in chest CT images, afebrile for more than 3 days, and two consecutive negative RT-PCR results from respiratory tract swab samples collected at least 24 h apart.

2.2. Sample Collection

Blood samples were collected from COVID-19 patients and HDs and centrifuged at 400× g for 5 min at room temperature. Plasma samples were stored at −80 °C until use. After plasma collection, PBMCs were isolated via Ficoll density gradient centrifugation and stored at −80 °C and in liquid nitrogen subsequently.

2.3. Epitopes Screening

One hundred and forty-four peptides spanning the spike (n = 83) and NP protein (n = 28) without overlap and the M (n = 7), ORF8 (n = 19), and ORF10 (n = 5) proteins with an 8-amino acid overlap were synthesized. Two ORF3a peptides were also synthesized. A total of 39 reported SARS-CoV-1 peptides were synthesized, including 17 spike protein peptides, 14 NP protein peptides, 7 M protein peptides, and 1 ORF3a protein peptide. The peptide sequence information is available in Table A1. All peptides were resuspended in DMSO and stored at −80 °C.
PBMCs were thawed and rested overnight in AIM medium (Gibco, New York, NY, USA) supplemented with 10% heat-inactivated human AB serum (Thermo Fisher Scientific, Waltham, MA, USA). PBMCs (1.5 × 105) were seeded in a 96-well U bottom plate in AIM medium supplemented with 100 U/mL penicillin (Gibco), 0.1 mg/mL streptomycin (Gibco), 1000 IU/mL IL-2 (PeproTech, Rocky Hill, NJ, USA), 5 ng/mL IL-15 (PeproTech), 0.5 ng/mL IL-21 (PeproTech), and 1 µg/mL of one peptide (Genescript, Nanjing, China) and cultured at 37 °C in 5% CO2. An equimolar amount of DMSO was used to stimulate the cells as a negative control, and 1 mg/mL phytohemagglutinin (PHA, Roche, Basel, Switzerland) was used as the positive control. Heat-inactivated human AB serum was added the following morning to a final concentration of 5%. Seven days after incubation, the supernatant was collected and stored at −80 °C for enzyme-linked immunosorbent assay (ELISA). IFN-γ and IL-17A in the supernatant were assessed via ELISA (DAKEWE, Shenzhen, China) following the manufacturer’s instructions. The remaining cells were lysed using TRIzol reagent and stored at −80 °C for TCR sequencing and human leukocyte antigen (HLA) genotyping.

2.4. SARS-CoV-2 Specific Short-Term Cell Line Expansion and IFN-γ Expression Assessment via Flow Cytometry

PBMCs (1 × 105) were stimulated with individual SARS-CoV-2 peptide in AIM medium with 5% AB serum and incubated at 37 °C in 5% CO2. IL-2 (10 U/mL; PeproTech) was added 4, 7, and 11 d after initial antigenic stimulation (1 μg/mL). On day 13, the culture medium was replaced with AIM containing 5% AB serum without any cytokines. On day 14, the short-term cell line was restimulated using SARS-CoV-2 peptides (10 μg/mL) for 6 h and GolgiPlug was added 5 h before harvest. For surface marker staining, T cells were stained with CD3-BV510, CD4-PerCP, and CD8-FITC antibodies (Biolegend, San Diego, CA, USA) for 30 min at 4 °C. For intracellular marker staining, cells were permeabilized using a Cytofix/Cytoperm Kit (BD Bioscience, Franklin Lakes, NJ, USA) and then stained with IFN-γ-PE-Cy7 antibody (Biolegend). Cells were fixed in 0.5% formaldehyde and analyzed by flow cytometry using a BD Canto II instrument and the data were analyzed using FlowJo software V10 (Tree Star Inc., Ashland, OR, USA).

2.5. Next-Generation Sequencing (NGS) of the TCR β Gene

Total RNA was extracted from each sample and subjected to RT-PCR amplification of the TCRβ gene using a QIAGEN one-step RT-PCR kit (QIAGEN, Hilden, Germany), with Cβ anti-sense primers and 36 Vβ mix primers used for two rounds of PCR. The resulting size-corrected PCR products were purified using magnetic beads (BioMagBeads, Wuxi, China). A sequencing library was prepared and purified using a Thermo Fisher Ion Plus Fragment Library Kit (Thermo Fisher Scientific), Ion Xpress Barcode Adaptors 1-16 Kit (Thermo Fisher Scientific), and Agencourt AMPure XP (Beckman Coulter, Brea, CA, USA). NGS was performed on an Ion S5 system using an Ion 530 Chip kit (Thermo Fisher Scientific).

2.6. TCR β Gene Sequence Analyses

The quality of the sequences obtained was monitored by cross-analyzing for potential contamination. Sequences shorter than 150 bp were excluded from analysis. TCR Vβ, Dβ, and Jβ germline gene assignment was conducted using a locally operating IgBlast program at Chengdu ExAb Biotechnology, Ltd. Non-functional TCR β VDJ sequences were removed, and 30,000 randomly selected TCR genes were analyzed.

2.7. HLA Genotyping

Genomic DNA (200 ng) from each sample was sheared using a Biorupter (Diagenode, Liège, Belgium) to acquire 150~200 bp fragments. The ends of the DNA fragments were repaired, and an Illumina Adaptor was added (Fast Library Prep Kit; iGeneTech, Beijing, China). After constructing the sequencing library, the target region was captured using an AI-HLA-Cap Enrichment Kit (iGeneTech) and sequenced on an Illumina platform (Illumina, San Diego, CA, USA) with 150 base paired-end reads. The raw reads were filtered using FastQC to remove low-quality reads. Clean reads were then mapped to the reference sequences in the HLA dictionary and typed to generate HLA types for HLA-A, -B, -C, -DPB1, -DQB1, and -DRB1 using HLA-HD Software (Version 1.5.0, Tokyo, Japan).

2.8. Peptide Homology Analysis

The P120 peptide sequences of different coronaviruses were obtained from the NCBI or Uniprot database, as follows: SARS-CoV-2 original strain M protein (P0DTC5), SARS-CoV-2 betta strain M protein (B.1.351, YP_009724393.1), SARS-CoV-2 delta strain M protein (B.1.617.2, QUX81285.1), SARS-CoV-2 Omicron Strain (B.1.1.529, UFO69282), SARS-CoV-1 M protein (P59596), 229E M protein(P15422), HKU1 M protein (Q5MQC7), NL63 M protein (K4P0W4), and OC43 M protein (YP_009555244.1). The CLUSTALW online tool was used to analyze the homology of P120 peptide among different coronaviruses (https://www.genome.jp/tools-bin/clustalw, accessed on 15 March 2024).

2.9. Ethical Approval

This study was approved by the Ethics Committee of the Fifth Medical Center of the Chinese PLA General Hospital (S2020-044-02). Informed consent was obtained from all enrolled participants or their legal guardians.

2.10. Statistical Analysis

GraphPad Prism statistical software (version 8.0; GraphPad Software, USA) and SPSS version 25 (SPSS Inc., USA) were used for statistical analysis. Spearman’s rank correlation was performed using the ‘ggcor’ package of the R software. Categorical variables were compared using the χ2 test or Fisher’s exact test and are reported as counts and percentages. Continuous variables were compared using the Mann–Whitney U test and Kruskal–Wallis H test and are presented as the median and interquartile range (IQR). Statistical significance was defined as a two-tailed p-value < 0.05.

3. Results

3.1. T Cells of Patients with Moderate Disease Had a Robust Response towards SARS-CoV-2 Antigens

To reveal the spectrum of T-cell responses to SARS-CoV-2 in naturally infected patients, we synthesized 144 peptides spanning the spike, NP, M, ORF8, ORF10, and ORF3a proteins. As the SARS-CoV-2 genome is highly homogenous with that of the SARS-CoV-1 virus, 39 reported SARS-CoV-1 peptides were synthesized (Table A1). Nine laboratory-confirmed and hospitalized COVID-19 patients (four severe and five moderate cases) were enrolled for epitope screening. Blood samples from nine HDs collected before the SARS-CoV-2 pandemic were used as controls. Demographic information and HLA alleles are listed in Table 1 and Table A2, and the timeline of the course of the disease is shown in Figure A1. PBMCs from HDs and COVID-19 patients were stimulated with each peptide and cultured for 7 days. IFN-γ in the supernatant was assessed using ELISA, and the experimental procedure is shown in Figure 1A. A positive response was defined as an IFN-γ concentration two-fold higher than that in the unstimulated sample. All samples that had a positive response were sequenced for the TCR β chain V-D-J gene usage and HLA allele. The IL-17A concentration in these positive samples was also analyzed using ELISA. The corresponding peptide responses were validated in short-term cells via flow cytometry.
The IFN-γ secretion of PBMCs in patients with moderate COVID-19 was higher than that in patients with severe COVID-19 and HDs (moderate vs. HD, p < 0.0001; moderate vs. severe, p < 0.0001) (Figure 1B,C). Venn diagrams showed the number of responsive peptides detected in healthy donors (HDs), COVID-19 patients, as well as COVID-19 patients with moderate or severe disease. Of the 183 peptides, 92 exhibited reactivity in both HDs and COVID-19 patients, while 34 were recognized by HD and 23 were recognized by COVID-19 patients (Figure A2A). Notably, PBMCs from patients with moderate disease recognized more peptides than those from patients with severe disease (84 vs. 63) (Figure A2C). However, no obvious pattern of antigen specificity was observed between HDs and COVID-19 patients or between patients with moderate and severe disease (Figure A2B,D).

3.2. T-Cell Response of Patients with Severe Disease Produced Higher Levels of IL-17A

In the response-positive samples, the IFN-γ secreted by PBMCs of patients with moderate disease was higher compared to that of patients with severe disease (203.49 pg/mL [102.45, 286.08] vs. 50.22 pg/mL [33.25, 47.38]; p < 0.0001) (Figure 2A). As IL-17A is reported to be involved in COVID-19 disease progression [22], we analyzed the levels of IL-17A in the supernatant of positive samples. Interestingly, PBMCs from patients with severe disease expressed higher levels of IL-17A than those with moderate disease (50.03 pg/mL [22.91, 79.75] vs. 21.00 pg/mL [13.89, 29.93]; p = 0.0002) (Figure 2B). Then, we analyzed the bias of epitopes to induce IFN-γ or IL-17A production. The data in Figure 2C reveal a complex tendency in the secretion of IFN-γ or IL-17A induced by individual peptides, which is dependent on disease severity. IL-17A was more pronounced in patients with severe disease than in those with moderate disease.
To assess the antigen-specific T-cell expansion after antigen peptide stimulation, we sequenced the TCR β chain of the responsive sample and found that the proportion of the top immunodominant TCR β V-J gene pair (top TCR clone) was comparable between patients with moderate and severe disease (8.10% [4.97, 10.62] vs. 7.21% [5.62, 13.38], p = 0.47) (Figure 2D). The correlation between the proportion of top TCR clones and cytokine production was analyzed. The data in Figure 2E show a positive correlation between TCR expansion and IFN-γ production in patients with moderate disease (r = 0.38, p = 0.021) but not in those with severe disease (r = −0.17, p = 0.25) (Figure 2E, Left). No correlation between TCR expansion and IL-17A expression was observed in patients with moderate or severe disease (Figure 2E, Right). These data suggest that concerted IFN-γ production and TCR expansion in patients with moderate disease may favor disease resolution.

3.3. COVID-19 Patients Broadly Recognized Conserved P120 Peptide

We then validated the T-cell response towards SARS-CoV-2 peptides in short-term T cells of COVID-19 patients using flow cytometry. Twenty broadly recognized peptides identified (Table A4) in Figure 1D were validated in short-term T-cell lines from six COVID-19 patients, including four with moderate and two with severe disease. The demographic information and HLA alleles are listed in Table A2 and Table A3, and the timeline of the course of the disease is also shown in Figure A1. Compared to other peptides, the P120-peptide (P120) from the M protein (M148–162: HLRIAGHHLGRCDIK) elicited a broad and robust IFN-γ expression in CD8+ T cells in COVID-19 (Figure 3A). The P120 peptide was primarily recognized by CD8+ T cells of patients with moderate COVID-19 compared to those with severe COVID-19 (Figure 3B,C). It also induced a slight CD4+ T-cell response in patients with moderate COVID-19 (Figure 3D). Furthermore, the data in Figure 1B show that P120 induced higher IFN-γ secretion in the supernatant of PBMCs from patients with moderate disease. The median concentration of IFN-γ was 17.28 pg/mL in HDs, 64.78 pg/mL in patients with moderate disease, and 28.91 pg/mL in patients with severe disease. We used the Immune Epitope Database & Analysis Resource (IEDB) to predict potential epitopes within P120. Table A5 showed that P120 contains four epitopes (HLRIAGHHL, HLRIAGHHLGR, LRIAGHHLGR, and RIAGHHLGR) that could be presented by 15 HLA alleles. Combined analysis of IFN-γ production induced by P120 (assessed by ELISA or flow cytometry) and HLA alleles of P120-responsive patients revealed that HLA-A*30:01, HLA-B*13:02, HLA-B*46:01, HLA-B*08:01, HLA-C*01:02, HLA-C*03:03, and HLA-C*03:04 may present the P120 peptide. The epitope presented by these seven HLA alleles in P120 was the HLRIAGHHL peptide, which may contribute the CD8+ T-cell activation by P120 (Table A6).

3.4. P120 Peptide Was Conserved among SARS-CoV-2 Variants and Induced Strong TCR Expansion

To assess the VDJ gene usage of P120-responsive T cells, the TCR β chain of positive samples was sequenced. The result showed that in the P120-responsive samples of Pt02, the dominant D-J gene pair comprised TRBJ2-5 and TRBV18, constituting 25.99% of all D-J gene pairs (Figure 4A,B). We analyzed the level of the TRBV18-TRBJ2-5 TCR and seven other dominant TCRs in all the positive samples of Pt02, and the results showed that the levels of TRBV18-TRBJ2-5 TCR, TRBV18-TRBJ2-1 TCR, and TRBV12-3-TRBJ2-1 TCR were exclusively high in their corresponding samples than the other five dominant TCRs (Figure A3). This result indicated that TRBV18-TRBJ2-5 TCR, TRBV18-TRBJ2-1 TCR, and TRBV12-3-TRBJ2-1 TCR may be P120-, P143-, or P150-specific TCRs, respectively. Furthermore, the P120 peptide was not mutated among the reported SARS-CoV-2 variants (Figure 4C), rendering it a promising candidate for T-cell activation to confer protection against prevalent SARS-CoV-2 variants.

4. Discussion

In this study, we evaluated the reactivity of 183 SARS-CoV-2 and SARS-CoV-1 derived peptides towards PBMCs from nine COVID-19 patients and nine HDs. We found that T cells in patients with moderate disease had strong IFN-γ production with SARS-CoV-2 peptides, while patients with severe disease had higher IL-17A production. Although TCR amplification following antigen restimulation in vitro did not differ by COVID-19 disease severity, a positive correlation between TCR expansion and IFN-γ secretion was observed in patients with moderate but not severe disease. We identified one peptide (P120, M148–162) from the M protein broadly recognized by COVID-19 patients, especially those with moderate disease. The P120 peptide is conserved among SARS-CoV-2 variants, such as Delta and Omicron strains, and is presented by diverse HLA alleles. These results indicate that it is a promising target for promoting T-cell response and COVID-19 disease control.
Antigen-specific T cells are critical in the fight against viral infections. Associations between early T-cell responses and less severe COVID-19 outcomes have been observed [23,24]. We found that PBMCs in patients with severe disease produced less IFN-γ but higher levels of IL-17A compared to those of patients with moderate disease, despite similar levels of T-cell clone expansion between these two groups. Furthermore, a positive correlation was observed between the abundance of the top TCR β chain and IFN-γ production in patients with moderate disease, suggesting that coordinated IFN-γ production and clonal expansion of SARS-CoV-2-specific T cells are associated with disease control in COVID-19 patients. Conversely, such correlation was absent in severe cases, indicating that Th17 may be involved in the expanded T cells’ population, which may contribute to disease progression. COVID-19 patients with severe disease are characterized by inflammation and tissue damage in the respiratory tract, with monocyte and lymphocyte infiltration observed in the lungs [25,26,27,28]. Th17 cells characterized by high IL-17A and GM-CSF expression are enriched and clonally expanded in the lungs of COVID-19 patients with severe disease, potentially contributing to lung damage through interacting with CD8+ T cells and macrophages [22]. Additionally, IL-17A and Th17 cells are involved in the pathogenesis of MERS-CoV, SARS-CoV-1, and H1N1 infection [29,30,31].
Spike, NP, M, and NSP3 proteins are the major T-cell antigens of the SARS-CoV-2 virus, and differences exist in the phenotype and functional characteristics of T cells specific to different antigens [32]. Spike-specific T cells tend to differentiate into T follicular helper (TFH) cells, suggesting a crucial role in generating potent antibody responses, while M protein-specific and NP-specific CD4+ T cells are skewed toward a Th1 or Th1/Th17 profile [11]. Most studies have not distinguished the differences in T-cell responses induced by different SARS-CoV-2 antigens. We found that the IFN-γ and IL-17A production induced by the SARS-CoV-2 peptide was severity-dependent but not epitope-dependent. The bias of IL-17A production in patients with severe disease persisted even after several days of in vitro culture, and this phenomenon indicated that a stable program toward IL-17A production was established in patients with severe disease. This program may contribute to the elevated IL-17A in critical patients and be associated with fatal outcomes [22,33]. Furthermore, IL-17A has been implicated in promoting alveolar epithelial cell apoptosis and exacerbating the progression of pulmonary fibrosis, disrupting normal alveolar architecture and impairing alveolar–capillary gas exchange. Consequently, these processes adversely affect the normal oxygenation process, contributing to the respiratory symptoms [34].
After careful screening and validation, we identified a peptide (P120, M148–162: HLRIAGHHLGRCDIK) from the M protein, which was broadly recognized by COVID-19 patients with moderate disease and may contribute to disease control in SARS-CoV-2 infection. Similar to a prior study, a prevalent HLA-A*24:02-restricted epitope (M198–206) from the M protein was associated with convalescence in COVID-19 patients admitted to intensive care. While the reactivity of M198–206 was observed in all COVID-19 patients, CD8+ T cells specific to the M198–206 epitope exhibited an exhausted phenotype in severe cases compared to those in the moderate group [35]. The dysfunction of T cells in SARS-CoV-2 patients with severe disease has also been reported in the HLA-B*07:02-restricted NP105–113 epitope-responsive CD8+ T cells, which had a weaker response in patients who recovered from severe disease compared to those with mild disease [36]. The mechanism underlying the diminished responses of antigen-specific T cells in COVID-19 patients with severe disease remains unclear. It is hypothesized that the dysregulation of Tumor Necrosis Factor-α/Tumor Necrosis Factor Receptor 1 signaling, upregulation of inhibitory receptors, and prolonged antigen exposure may contribute to the dysfunction observed in antigen-specific T-cell responses in COVID-19 patients with severe disease [35,37,38].
Previous studies found that the P120 peptide could be presented by HLA-DRB1*04:04, HLA-DRB1*07:01, HLA-DRB1*11:01, or HLA-DRB1*15:01 alleles and promote CD4+ T-cell response [39,40]. However, in our study, four patients in the validation cohort with HLA-DRB1*15:01 or HLA-DRB1*11:01 alleles exhibited a very weak CD4+ T-cell expansion after being cultured with P120. Four epitopes within the P120 peptide (HLRIAGHHLGR, RIAGHHLGR, HLRIAGHHL, and LRIAGHHLGR) were predicted by the HLA binding algorithm and reported by other studies [41,42,43,44,45,46]. We found that the HLRIAGHHL peptide may be represented by HLA-A*30:01 and drive TRBV18-TRBJ2-5 TCR expansion in Pt02, consistent with another study [47].
The TCR β chain of a P120-responsive sample was sequenced, revealing that the predominant TCR V-J gene pair was TRBV18-TRBJ2-5, constituting 26% of the TCR β chains. TRBV18-TRBJ2-5 TCR was exclusively and prominently expanded in the P120-responsive sample compared with other positive samples in Pt02. We speculated that the HLRIAGHHL epitope in P120 might be presented by HLA-A*30:01 and drive the TRBV18-TRBJ2-5 TCR expansion in the in vitro culture assay. In a study on human immunodeficiency virus, researchers noted a significant TCR bias toward the HLA-B*0702-FPQGEAREL epitope, with TRBV18 and TRBJ2-5 being preferentially utilized. The TRBV18/CASSPRGREETQY/TRBJ2-5 clonotype retains functionality and persistence, even when antigen levels decay dramatically after antiretroviral therapy [48]. Therefore, the T cell of TRBV18-TRBJ2-5 expanded by the P120 peptide may preferentially persist under conditions of limited antigenic stimulation and provide long-term protection.
Our study had several limitations. While our peptide synthesis strategy avoided the bioinformatics bias of the epitope prediction algorithm, we only screened peptides derived from SARS-CoV-2 spike, NP, M, ORF8, and ORF10 proteins, with no overlap in spike and NP protein peptides. In addition, the epitopes of the ORF1a and ORF1b antigens were not screened. We only analyzed the correlation between TCR expansion and cytokine production. A comprehensive and detailed analysis of the relationship among HLA alleles, individual peptide responses, and TCR β chain sequences is needed to elucidate the comprehensive immune response of SARS-CoV-2. Moreover, our epitope screening included only nine COVID-19 patients and nine healthy donors (HDs), with significant variation in sampling time among COVID-19 patients. Additionally, glucocorticoid usage in COVID-19 patients with severe disease may weaken our conclusions. Our findings need to be validated in a larger cohort of COVID-19 patients.

Author Contributions

Conceptualization, X.F., C.Z. and F.-S.W.; methodology, X.F. and J.-W.S.; validation, W.-J.C., M.-J.Z. and J.W.; formal analysis, T.Y. and C.Z.; investigation, X.F. and J.-W.S., data curation, X.F.; writing—original draft preparation, X.F.; writing—review and editing, X.F., J.-W.S., C.Z., F.-P.M., M.S. and F.-S.W.; visualization, X.F.; supervision, C.Z. and F.-S.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by PLA General Hospital (22QNCZ011).

Institutional Review Board Statement

The study was conducted in accordance with the Decla-ration of Helsinki and approved by the Ethics Committee of the Fifth Medical Center, Chinese PLA General Hospital (S2020-044-02).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author/s.

Acknowledgments

We thank participating subjects for supporting the study and their valuable input. We thank Jin-Hong Yuan and Chun-Bao Zhou for flow cytometry analysis. We thank Markus Maeurer for his assistance in designing the SARS-CoV-2 peptide libraries. We also thank the team of the Department of Infectious Diseases at Fifth Medical Center of Chinese PLA General Hospital for their support.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. SARS-CoV-2 and SARS-CoV-1 peptides list.
Table A1. SARS-CoV-2 and SARS-CoV-1 peptides list.
Peptide IDNameSpeciesAntigenSequenceLengthReference
P1spike-1 SARS-CoV-2SpikeMFVFLVLLPLVSSQC15
P2spike-2SARS-CoV-2SpikeVNLTTRTQLPPAYTN15
P3spike-3SARS-CoV-2SpikeSFTRGVYYPDKVFRS15
P4spike-4SARS-CoV-2SpikeSVLHSTQDLFLPFFS15
P5spike-5SARS-CoV-2SpikeNVTWFHAIHVSGTNG15
P6spike-6SARS-CoV-2SpikeTKRFDNPVLPFNDGV15
P7spike-7SARS-CoV-2SpikeYFASTEKSNIIRGWI15
P8spike-8SARS-CoV-2SpikeFGTTLDSKTQSLLIV15
P9spike-9SARS-CoV-2SpikeNNATNVVIKVCEFQF15
P10spike-10SARS-CoV-2SpikeCNDPFLGVYYHKNNK15
P11spike-11SARS-CoV-2SpikeSWMESEFRVYSSANN15
P12spike-12SARS-CoV-2SpikeCTFEYVSQPFLMDLE15
P13spike-13SARS-CoV-2SpikeGKQGNFKNLREFVFK15
P14spike-14SARS-CoV-2SpikeNIDGYFKIYSKHTPI15
P15spike-15SARS-CoV-2SpikeNLVRDLPQGFSALEP15
P16spike-16SARS-CoV-2SpikeLVDLPIGINITRFQT15
P17spike-17SARS-CoV-2SpikeLLALHRSYLTPGDSS15
P18spike-18SARS-CoV-2SpikeSGWTAGAAAYYVGYL15
P19spike-19SARS-CoV-2SpikeQPRTFLLKYNENGTI15
P20spike-20SARS-CoV-2SpikeTDAVDCALDPLSETK15
P21spike-21SARS-CoV-2SpikeCTLKSFTVEKGIYQT15
P22spike-22SARS-CoV-2SpikeSNFRVQPTESIVRFP15
P23spike-23SARS-CoV-2SpikeNITNLCPFGEVFNAT15
P24spike-24SARS-CoV-2SpikeRFASVYAWNRKRISN15
P25spike-25SARS-CoV-2SpikeCVADYSVLYNSASFS15
P26spike-26SARS-CoV-2SpikeTFKCYGVSPTKLNDL15
P27spike-27SARS-CoV-2SpikeCFTNVYADSFVIRGD15
P28spike-28SARS-CoV-2SpikeEVRQIAPGQTGKIAD15
P29spike-29SARS-CoV-2SpikeYNYKLPDDFTGCVIA15
P30spike-30SARS-CoV-2SpikeWNSNNLDSKVGGNYN15
P31spike-31SARS-CoV-2SpikeYLYRLFRKSNLKPFE15
P32spike-32SARS-CoV-2SpikeRDISTEIYQAGSTPC15
P33spike-33SARS-CoV-2SpikeNGVEGFNCYFPLQSY15
P34spike-34SARS-CoV-2SpikeGFQPTNGVGYQPYRV15
P35spike-35SARS-CoV-2SpikeVVLSFELLHAPATVC15
P36spike-36SARS-CoV-2SpikeGPKKSTNLVKNKCVN15
P37spike-37SARS-CoV-2SpikeFNFNGLTGTGVLTES15
P38spike-38SARS-CoV-2SpikeNKKFLPFQQFGRDIA15
P39spike-39SARS-CoV-2SpikeDTTDAVRDPQTLEIL15
P40spike-40SARS-CoV-2SpikeDITPCSFGGVSVITP15
P41spike-41SARS-CoV-2SpikeGTNTSNQVAVLYQDV15
P42spike-42SARS-CoV-2SpikeNCTEVPVAIHADQLT15
P43spike-43SARS-CoV-2SpikePTWRVYSTGSNVFQT15
P44spike-44SARS-CoV-2SpikeRAGCLIGAEHVNNSY15
P45spike-45SARS-CoV-2SpikeECDIPIGAGICASYQ15
P46spike-46SARS-CoV-2SpikeTQTNSPRRARSVASQ15
P47spike-47SARS-CoV-2SpikeSIIAYTMSLGAENSV15
P48spike-48SARS-CoV-2SpikeAYSNNSIAIPTNFTI15
P49spike-49SARS-CoV-2SpikeSVTTEILPVSMTKTS15
P50spike-50SARS-CoV-2SpikeVDCTMYICGDSTECS15
P51spike-51SARS-CoV-2SpikeNLLLQYGSFCTQLNR15
P52spike-52SARS-CoV-2SpikeALTGIAVEQDKNTQE15
P53spike-53SARS-CoV-2SpikeVFAQVKQIYKTPPIK15
P54spike-54SARS-CoV-2SpikeDFGGFNFSQILPDPS15
P55spike-55SARS-CoV-2SpikeKPSKRSFIEDLLFNK15
P56spike-56SARS-CoV-2SpikeVTLADAGFIKQYGDC15
P57spike-57SARS-CoV-2SpikeLGDIAARDLICAQKF15
P58spike-58SARS-CoV-2SpikeNGLTVLPPLLTDEMI15
P59spike-59SARS-CoV-2SpikeAQYTSALLAGTITSG15
P60spike-60SARS-CoV-2SpikeWTFGAGAALQIPFAM15
P61spike-61SARS-CoV-2SpikeQMAYRFNGIGVTQNV15
P62spike-62SARS-CoV-2SpikeLYENQKLIANQFNSA15
P63spike-63SARS-CoV-2SpikeIGKIQDSLSSTASAL15
P64spike-64SARS-CoV-2SpikeGKLQDVVNQNAQALN15
P65spike-65SARS-CoV-2SpikeTLVKQLSSNFGAISS15
P66spike-66SARS-CoV-2SpikeVLNDILSRLDKVEAE15
P67spike-67SARS-CoV-2SpikeVQIDRLITGRLQSLQ15
P68spike-68SARS-CoV-2SpikeTYVTQQLIRAAEIRA15
P69spike-69SARS-CoV-2SpikeSANLAATKMSECVLG15
P70spike-70SARS-CoV-2SpikeQSKRVDFCGKGYHLM15
P71spike-71SARS-CoV-2SpikeSFPQSAPHGVVFLHV15
P72spike-72SARS-CoV-2SpikeTYVPAQEKNFTTAPA15
P73spike-73SARS-CoV-2SpikeICHDGKAHFPREGVF15
P74spike-74SARS-CoV-2SpikeVSNGTHWFVTQRNFY15
P75spike-75SARS-CoV-2SpikeEPQIITTDNTFVSGN15
P77spike-77SARS-CoV-2SpikeLQPELDSFKEELDKY15
P78spike-78SARS-CoV-2SpikeFKNHTSPDVDLGDIS15
P79spike-79SARS-CoV-2SpikeGINASVVNIQKEIDR15
P80spike-80SARS-CoV-2SpikeLNEVAKNLNESLIDL15
P81spike-81SARS-CoV-2SpikeQELGKYEQYIKWPWY15
P83spike-83SARS-CoV-2SpikeTIMLCCMTSCCSCLK15
P84spike-84SARS-CoV-2SpikeGCCSCGSCCKFDEDD15
P85spike-85SARS-CoV-2SpikeSEPVLKGVKLHYT13
P86nucleocapcid-1SARS-CoV-2NPMSDNGPQNQRNAPRI15
P87nucleocapcid-2SARS-CoV-2NPTFGGPSDSTGSNQNG15
P88nucleocapcid-3SARS-CoV-2NPERSGARSKQRRPQGL15
P89nucleocapcid-4SARS-CoV-2NPPNNTASWFTALTQHG15
P90nucleocapcid-5SARS-CoV-2NPKEDLKFPRGQGVPIN15
P91nucleocapcid-6SARS-CoV-2NPTNSSPDDQIGYYRRA15
P92nucleocapcid-7SARS-CoV-2NPTRRIRGGDGKMKDLS15
P93nucleocapcid-8SARS-CoV-2NPPRWYFYYLGTGPEAG15
P94nucleocapcid-9SARS-CoV-2NPLPYGANKDGIIWVAT15
P95nucleocapcid-10SARS-CoV-2NPEGALNTPKDHIGTRN15
P96nucleocapcid-11SARS-CoV-2NPPANNAAIVLQLPQGT15
P97nucleocapcid-12SARS-CoV-2NPTLPKGFYAEGSRGGS15
P98nucleocapcid-13SARS-CoV-2NPQASSRSSSRSRNSSR15
P99nucleocapcid-14SARS-CoV-2NPNSTPGSSRGTSPARM15
P100nucleocapcid-15SARS-CoV-2NPAGNGGDAALALLLLD15
P101nucleocapcid-16SARS-CoV-2NPRLNQLESKMSGKGQQ15
P102nucleocapcid-17SARS-CoV-2NPQQGQTVTKKSAAEAS15
P103nucleocapcid-18SARS-CoV-2NPKKPRQKRTATKAYNV15
P104nucleocapcid-19SARS-CoV-2NPTQAFGRRGPEQTQGN15
P105nucleocapcid-20SARS-CoV-2NPFGDQELIRQGTDYKH15
P106nucleocapcid-21SARS-CoV-2NPWPQIAQFAPSASAFF15
P107nucleocapcid-22SARS-CoV-2NPGMSRIGMEVTPSGTW15
P108nucleocapcid-23SARS-CoV-2NPLTYTGAIKLDDKDPN15
P109nucleocapcid-24SARS-CoV-2NPFKDQVILLNKHIDAY15
P110nucleocapcid-25SARS-CoV-2NPKTFPPTEPKKDKKKK15
P111nucleocapcid-26SARS-CoV-2NPADETQALPQRQKKQQ15
P112nucleocapcid-27SARS-CoV-2NPTVTLLPAADLDDFSK15
P113nucleocapcid-28SARS-CoV-2NPQLQQSMSSADSTQA14
P114membrane-1SARS-CoV-2MMADSNGTITVEELKK15
P115membrane-2SARS-CoV-2MITVEELKKLLEQWNL15
P116membrane-3SARS-CoV-2MKKLLEQWNLVIGFLF15
P117membrane-4SARS-CoV-2MEQWNLVIGFLFLTWI15
P118membrane-5SARS-CoV-2MLLESELVIGAVILRG15
P119membrane-6SARS-CoV-2MIGAVILRGHLRIAGH15
P120membrane-7SARS-CoV-2MHLRIAGHHLGRCDIK15
P121SARS_2016.05.006SARS-CoV-1NPLLNKHIDAYKTFP13PMID:27287409
P123SARS_10.1128_2SARS-CoV-1SpikeNYNYKYRYLRGKLRPF16PMID:18832706
P124SARS_10.1128_3SARS-CoV-1SpikeAGCLIGAEHVDTSYECDI18PMID:18832706
P125SARS_10.1128_4SARS-CoV-1NPGETALALLLLDRLNQ15PMID:18832706
P126SARS_10.1128_5SARS-CoV-1MGHLRMAGHSLGRCDI15PMID:18832706
P127SARS_10.1128_6SARS-CoV-1SpikeNFNGLTGTGVLTPSSKRF18PMID:18832706
P128SARS_10.1128_7SARS-CoV-1SpikeDIPIGAGICASYHTVSLL18PMID:18832706
P129SARS_10.1128_8SARS-CoV-1SpikeSWFITQRNFFSPQII15PMID:18832706
P130SARS_1SARS-CoV-1SpikeFIAGLIAIV9PMID: 15972696
P131SARS_2SARS-CoV-1SpikeLITGRLQSL9PMID: 15972696
P132SARS_3SARS-CoV-1SpikeRLNEVAKNL 11PMID: 15972696
P133SARS_4SARS-CoV-1SpikeILPDPLKPT9PMID: 15016646
P134SARS_5SARS-CoV-1SpikeVVFLHVTYV9PMID: 15016646
P135SARS_6SARS-CoV-1SpikeKLPDDFMGCV10PMID: 16887973
P136SARS_7SARS-CoV-1SpikeVLNDILSRL9PMID: 15016646
P137SARS_8SARS-CoV-1NPLLLDRLNQL9PMID: 16887973
P138SARS_9SARS-CoV-1NPRLNQLESKV9PMID: 16887973
P139SARS_10SARS-CoV-1NPGMSRIGMEV9PMID: 16887973
P140SARS_11SARS-CoV-1NPWLTYHGAIKLDDKDPQF17PMID: 15528730
P141SARS_12SARS-CoV-1NPQFKDNVILLNKHIDAYK17PMID: 15528730
P142SARS_13SARS-CoV-1NPMASGGGETALALLLLDRL
NQLESKV		25PMID: 17183651
P143SARS_14SARS-CoV-1NPTWLTYHGAIKLDDKDPQF
KDNVILL		25PMID: 17183651
P144SARS_15SARS-CoV-1NPGETALALLLL10PMID: 21813600
P145SARS_16SARS-CoV-1MLVIGFLFLAWIMLLQFAYS
NRNRF		24PMID: 17183651
P147SARS_18SARS-CoV-1MILLNVPLRGTIVTRPLME
SELVIG		24PMID: 17183651
P148SARS_19SARS-CoV-1MIGNYKLNTDHAGSNDN
IALLV		21PMID: 17183651
P149SARS_20SARS-CoV-1MGHLRMAGHPLGRCDI15PMID: 17183651
P150SARS-21SARS-CoV-1ORF3aPLQASLPFGWLVIGV15PMID:18832706
P151SARS_01026-XSARS-CoV-1SpikeDVNCTDVSTAIHADQLTP
AWR		21 PMID: 15629033
P152SARS_01025-8_1SARS-CoV-1NPKDKKKKTDEAQPLPQRQ
KKQ		20PMID: 15629033
P153SARS_01025-8_2SARS-CoV-1NPQRQKKQPTVTLLPAAD
MDDFSRQ		23PMID: 15629033
P154SARS_HLA-DR0401_1SARS-CoV-1SpikeNAFNCTFEYISDAFSLDV18PMID: 19050106
P155SARS_HLA-DR0401_2SARS-CoV-1SpikeYISDAFSLDVSEKSGNFK18PMID: 19050106
P156SARS_HLA-DR0401_3SARS-CoV-1SpikeYLRHGKLRPFERDISNVP18PMID: 19050106
P157SARS_HLA-DR0401_4SARS-CoV-1SpikeRPFERDISNVPFSPDGK17PMID: 19050106
P158SARS_3726.2005_1SARS-CoV-1MMADNGTITVEELKQLLE
QWNLVIGFLFLAWI		31PMID: 16081901
P159SARS_3726.2005_2SARS-CoV-1MLMESELVIGAVIIRGHLR
MAGHPLGRCDIK		30PMID: 16081901
P160SARS_5314.2004_1SARS-CoV-1NPNNNAATVLQLPQGTTLP
KGFYAEGSR		26 PMID: 15528730
P161SARS_5314.2004_2SARS-CoV-1NPKTFPPTEPK9 PMID: 15528730
P162ORF8_1SARS-CoV-2ORF8MKFLVFLGIITTVAA15
P163ORF8_2SARS-CoV-2ORF8GIITTVAAFHQECSL15
P164ORF8_3SARS-CoV-2ORF8AFHQECSLQSCTQHQ15
P165ORF8_4SARS-CoV-2ORF8LQSCTQHQPYVVDDP15
P166ORF8_5SARS-CoV-2ORF8QPYVVDDPCPIHFYS15
P167ORF8_6SARS-CoV-2ORF8PCPIHFYSKWYIRVG15
P168ORF8_7SARS-CoV-2ORF8SKWYIRVGARKSAPL15
P169ORF8_8SARS-CoV-2ORF8GARKSAPLIELCVDE15
P170ORF8_9SARS-CoV-2ORF8LIELCVDEAGSKSPI15
P171ORF8_10SARS-CoV-2ORF8EAGSKSPIQYIDIGN15
P172ORF8_11SARS-CoV-2ORF8IQYIDIGNYTVSCLP15
P173ORF8_12SARS-CoV-2ORF8NYTVSCLPFTINCQE15
P174ORF8_13SARS-CoV-2ORF8PFTINCQEPKLGSLV15
P175ORF8_14SARS-CoV-2ORF8EPKLGSLVVRCSFYE15
P176ORF8_15SARS-CoV-2ORF8VVRCSFYEDFLEYHD15
P177ORF8_16SARS-CoV-2ORF8EDFLEYHDVRVVLDF15
P178ORF8_17SARS-CoV-2ORF8DVRVVLDFI9
P179ORF8_ASARS-CoV-2ORF8GNYTVSCSPFTINCQ15
P180ORF8_BCSARS-CoV-2ORF8GNYTVSCLPFTINCQ15
P181ORF10_1SARS-CoV-2ORF10MGYINVFAFPFTIYS15
P182ORF10_2SARS-CoV-2ORF10AFPFTIYSLLLCRMN15
P183ORF10_3SARS-CoV-2ORF10SLLLCRMNSRNYIAQ15
P184ORF10_4SARS-CoV-2ORF10NSRNYIAQVDVVNFN15
P185ORF10_5SARS-CoV-2ORF10QVDVVNFNLT10
P186ORF3a_ABSARS-CoV-2ORF3aVQIHTIDGSSGVVNP15
P187ORF3a_CSARS-CoV-2ORF3aVQIHTIDVSSGVVNP15

Appendix B

Table A2. COVID-19 patients’ HLA alleles.
Table A2. COVID-19 patients’ HLA alleles.
PatientsHLA-AHLA-BHLA-CHLA-DPB1HLA-DRB1HLA-DQB1
Pt01A*11:01:01;
A*01:01:01		B*40:01:02;
B*37:01:01		C*07:02:01;
C*06:02:01		DPB1*13:01:01;
DPB1*04:02:01		DRB1*01:01:01G;
DRB1*12:02:01		DQB1*03:01:01;
DQB1*05:01:01
Pt02A*01:01:01;
A*30:01:01		B*15:17:01;
B*13:02:01		C*07:01:02;
C*06:02:01		DPB1*05:01:01G;
DPB1*14:01:01		DRB1*13:02:01;
DRB1*09:01:02		DQB1*03:03:02;
DQB1*06:04:01G
Pt03A*24:02:01;
A*29:01:01		B*07:02:01;
B*35:03:01		C*07:02:01;
C*04:01:01		DPB1*10:01:01;
DPB1*02:01:02		DRB1*15:01:01;
DRB1*11:04:01G		DQB1*06:02:01;
DQB1*03:01:01
Pt04A*11:01:01;
A*30:01:01		B*13:02:01;
B*46:01:01G		C*06:02:01;
C*01:02:01		DPB1*05:01:01;
DPB1*17:01:01		DRB1*07:01:01;
DRB1*09:01:02		DQB1*03:03:02;
DQB1*02:02:01
Pt05A*11:01:01;
A*11:01:01		B*38:02:01G;
B*15:01:01		C*07:02:01;
C*04:01:01		DPB1*05:01:01;
DPB1*02:01:02		DRB1*15:01:01;
DRB1*12:02:01		DQB1*06:02:01;
DQB1*03:01:01
Pt06A*24:02:01;
A*33:03:01		B*40:01:02;
B*58:01:01		C*03:02:02;
C*03:04:01		DPB1*04:01:01;
DPB1*04:02:01		DRB1*15:01:01;
DRB1*03:01:01		DQB1*05:02:01;
DQB1*02:01:01G
Pt07A*02:06:01;
A*02:01:01		B*13:02:01;
B*15:11:01G		C*03:03:01;
C*03:04:01		DPB1*02:01:02;
DPB1*17:01:01		DRB1*07:01:01;
DRB1*09:01:02		DQB1*03:03:02;
DQB1*02:02:01
Pt08A*24:02:01;
A*26:01:01		B*08:01:01;
B*46:01:01G		C*07:02:01;
C*01:02:01		DPB1*02:01:02;
DPB1*02:01:02		DRB1*15:02:01;
DRB1*03:01:01		DQB1*05:02:01;
DQB1*02:01:01G
Pt09A*02:07:01G;
A*02:06:01		B*15:01:01;
B*46:01:01G		C*01:02:01;
C*08:01:01		DPB1*05:01:01;
DPB1*04:02:01		DRB1*09:01:02;
DRB1*09:01:02		DQB1*03:03:02;
DQB1*03:03:02
Pt10NANANANANANA
Pt11A*01:01:01;
A*26:01:01		B*40:01:02;
B*40:01:02		C*07:02:01;
C*03:04:01		DPB1*05:01:01;
DPB1*05:01:01G		DRB1*11:01:01;
DRB1*08:03:02		DQB1*06:01:01;
DQB1*03:01:01
Pt12A*02:06:01;
A*24:02:01		B*40:02:01;
B*51:01:01		C*14:02:01;
C*03:03:01		DPB1*05:01:01;
DPB1*02:01:02		DRB1*16:02:01;
DRB1*11:01:01		DQB1*05:02:01;
DQB1*03:01:01
Pt13A*02:01:01;
A*01:01:01		B*37:01:01;
B*46:01:01G		C*01:03:01G;
C*06:02:01		DPB1*05:01:01;
DPB1*04:01:01		DRB1*09:01:02;
DRB1*10:01:01		DQB1*03:03:02;
DQB1*05:01:01
Pt: patient.

Appendix C

Table A3. Demographic characteristics of COVID-19 patients for validation assay.
Table A3. Demographic characteristics of COVID-19 patients for validation assay.
PatientsGenderAge SeveritySampling (Day)Sampling Status Glucocorticoid before Sampling
Pt05Female85Severe16dischargeNo
Pt06Female60Severe38dischargeNo
Pt09Male37Moderate27dischargeNo
Pt11Male58Moderate14inpatientNo
Pt12Male42Moderate50inpatientNo
Pt13Female53Moderate14inpatientYes
Pt: patient.

Appendix D

Table A4. List of twenty broadly responsive peptides.
Table A4. List of twenty broadly responsive peptides.
Peptide IDNameSpeciesAntigenSequenceLength
P13spike-13SARS-CoV-2SpikeGKQGNFKNLREFVFK15
P31spike-31SARS-CoV-2SpikeYLYRLFRKSNLKPFE15
P32spike-32SARS-CoV-2SpikeRDISTEIYQAGSTPC15
P36spike-36SARS-CoV-2SpikeGPKKSTNLVKNKCVN15
P51spike-51SARS-CoV-2SpikeNLLLQYGSFCTQLNR15
P52spike-52SARS-CoV-2SpikeALTGIAVEQDKNTQE15
P59spike-59SARS-CoV-2SpikeAQYTSALLAGTITSG15
P60spike-60SARS-CoV-2SpikeWTFGAGAALQIPFAM15
P62spike-62SARS-CoV-2SpikeLYENQKLIANQFNSA15
P70spike-70SARS-CoV-2SpikeQSKRVDFCGKGYHLM15
P71spike-71SARS-CoV-2SpikeSFPQSAPHGVVFLHV15
P77spike-77SARS-CoV-2SpikeLQPELDSFKEELDKY15
P83spike-83SARS-CoV-2SpikeTIMLCCMTSCCSCLK15
P99nucleocapcid-14SARS-CoV-2NPNSTPGSSRGTSPARM15
P120membrane-7SARS-CoV-2MHLRIAGHHLGRCDIK15
P129SARS_10.1128_8SARS-CoV-1SpikeSWFITQRNFFSPQII15
P143SARS_14SARS-CoV-1NPTWLTYHGAIKLDDKD
PQFKDNVILL		25
P150SARS-21SARS-CoV-1ORF3aPLQASLPFGWLVIGV15
P152SARS_01025-8_1SARS-CoV-1NPKDKKKKTDEAQPLPQRQKKQ20
P158SARS_3726.2005_1SARS-CoV-1MMADNGTITVEELKQ
LLEQWNLVIGFLFLAWI		31

Appendix E

Table A5. Predicted epitopes within P120 peptides.
Table A5. Predicted epitopes within P120 peptides.
CatAlleleLengthPeptideScoreRank
1HLA-A*11:019RIAGHHLGR0.4795260.33
2HLA-A*30:019RIAGHHLGR0.2768170.49
3HLA-A*30:019HLRIAGHHL0.1875640.84
4HLA-A*33:039RIAGHHLGR0.4624950.36
5HLA-A*33:0311HLRIAGHHLGR0.3278840.62
6HLA-A*33:0310LRIAGHHLGR0.1698751.4
7HLA-B*07:029HLRIAGHHL0.3694520.37
8HLA-B*08:019HLRIAGHHL0.5082220.15
9HLA-B*13:029HLRIAGHHL0.1677931.1
10HLA-B*15:019HLRIAGHHL0.2795090.68
11HLA-B*46:019HLRIAGHHL0.1232460.92
12HLA-C*01:029HLRIAGHHL0.0675420.92
13HLA-C*03:029HLRIAGHHL0.0585911.9
14HLA-C*03:039HLRIAGHHL0.0294971.4
15HLA-C*03:049HLRIAGHHL0.0294971.4
16HLA-C*07:019RIAGHHLGR0.0070381.8
17HLA-C*07:029RIAGHHLGR0.0114051.9
18HLA-C*14:029HLRIAGHHL0.0527571.3

Appendix F

Table A6. HLA genotyping of P120-responsive patients.
Table A6. HLA genotyping of P120-responsive patients.
PatientsHLA-AHLA-BHLA-CDPB1DRB1DQB1IFN-γ Level Detection Assay
Pt11A*26:01;
A*01:01		B*40:01;
B*40:01		C*03:04;
C*07:02		DPB1*05:01;
DPB1*05:01		DRB1*08:03;
DRB1*11:01		DQB1*03:01;
DQB1*06:01		6.37%Flow cytometry
Pt12A*24:02;
A*02:06		B*51:01;
B*40:02		C*03:03;
C*14:02		DPB1*02:01;
DPB1*05:01		DRB1*11:01;
DRB1*16:02		DQB1*03:01;
DQB1*05:02		8.82%Flow cytometry
Pt02A*01:01;
A*30:01		B*15:17;
B*13:02		C*07:01;
C*06:02		DPB1*05:01;
DPB1*14:01		DRB1*13:02;
DRB1*09:01		DQB1*03:03;
DQB1*06:04		32.13 pg/mLELISA
Pt08A*26:01;
A*24:02		B*46:01;
B*08:01		C*01:02;
C*07:02		DPB1*02:01;
DPB1*02:01		DRB1*03:01;
DRB1*15:02		DQB1*02:01;
DQB1*05:02		64.78 pg/mLELISA
HLA alleles in bold and grey shadow represent predicted HLA alleles with P120 peptide binding activity.

Appendix G

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Figure A1. Timeline of the disease course for the enrolled 13 COVID-19 patients in our study. RT–qPCR indicates PCR test for SARS-CoV-2 nucleic acids. RT–qPCR positive indicates nasopharyngeal or sputum samples that were positive for SARS-CoV-2 nucleic acids.
Figure A1. Timeline of the disease course for the enrolled 13 COVID-19 patients in our study. RT–qPCR indicates PCR test for SARS-CoV-2 nucleic acids. RT–qPCR positive indicates nasopharyngeal or sputum samples that were positive for SARS-CoV-2 nucleic acids.
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Appendix H

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Figure A2. (A,C) Venn diagram illustrating the numbers of peptides recognized by COVID-19 patient and healthy donor cohorts (A), or by moderate and severe patients (C). (B,D) Response rate of SARS-CoV-2 peptides classified according to antigens in HDs and COVID-19 patients (B), or moderate and severe patients (D). SARS: SARS-CoV-1. Data are expressed as mean ± SD.
Figure A2. (A,C) Venn diagram illustrating the numbers of peptides recognized by COVID-19 patient and healthy donor cohorts (A), or by moderate and severe patients (C). (B,D) Response rate of SARS-CoV-2 peptides classified according to antigens in HDs and COVID-19 patients (B), or moderate and severe patients (D). SARS: SARS-CoV-1. Data are expressed as mean ± SD.
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Appendix I

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Figure A3. Eight dominant TCRs among all positive samples of Pt02 are presented. Different colors of the dot represent different TCRs as indicated on the right side of the panel.
Figure A3. Eight dominant TCRs among all positive samples of Pt02 are presented. Different colors of the dot represent different TCRs as indicated on the right side of the panel.
Viruses 16 01006 g0a3

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Figure 1. T cells of patients with moderate disease had a robust response towards SARS-CoV-2 antigens. (A) A schematic showing the overall study design. (B) IFN-γ in supernatant of cultured PBMCs from HD and COVID-19 patients with moderate or severe disease were assessed by ELISA, and antigen information was labeled along the X axis. (C) The mean value of IFN-γ concentration of cultured PBMCs from HD and COVID-19 patients with moderate or severe disease induced by each peptide assessed by ELISA. (D) Violin plot showing the response rate of each peptide in HD and COVID-19 patients with moderate or severe disease, two times higher IFN-γ concentration than the unstimulated sample was recognized as a positive response. S1: spike S1 domain; S2: spike S2 domain. Data are expressed as mean ± SD. and **** p < 0.0001 by two-tailed Mann–Whitney U-test. ns, non-significant.
Figure 1. T cells of patients with moderate disease had a robust response towards SARS-CoV-2 antigens. (A) A schematic showing the overall study design. (B) IFN-γ in supernatant of cultured PBMCs from HD and COVID-19 patients with moderate or severe disease were assessed by ELISA, and antigen information was labeled along the X axis. (C) The mean value of IFN-γ concentration of cultured PBMCs from HD and COVID-19 patients with moderate or severe disease induced by each peptide assessed by ELISA. (D) Violin plot showing the response rate of each peptide in HD and COVID-19 patients with moderate or severe disease, two times higher IFN-γ concentration than the unstimulated sample was recognized as a positive response. S1: spike S1 domain; S2: spike S2 domain. Data are expressed as mean ± SD. and **** p < 0.0001 by two-tailed Mann–Whitney U-test. ns, non-significant.
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Viruses 16 01006 g002
Figure 2. T-cell response of patients with severe disease produced higher levels of IL-17A. (A,B) IFN-γ (A) and IL-17A (B) of the positive-response sample was assessed by ELISA assay. (C) Spearman’s rank correlation between IFN-γ and IL-17A concentration in the supernatant of 6 representative samples was shown in dot plot. The dot shapes indicated representative peptides, the red indicated patients with severe disease, and the blue indicated patients with moderate disease. (D) The top TCR clone (immunodominant TCR β chain V-J gene pair) was analyzed between patients with moderate and severe disease. (E) Spearman’s rank correlation of the top TCR clone and IFN-γ or IL-17A was analyzed between patients with moderate and severe disease. Data are expressed as mean ± SD. **** p < 0.0001 by two-tailed Mann–Whitney U-test.
Figure 2. T-cell response of patients with severe disease produced higher levels of IL-17A. (A,B) IFN-γ (A) and IL-17A (B) of the positive-response sample was assessed by ELISA assay. (C) Spearman’s rank correlation between IFN-γ and IL-17A concentration in the supernatant of 6 representative samples was shown in dot plot. The dot shapes indicated representative peptides, the red indicated patients with severe disease, and the blue indicated patients with moderate disease. (D) The top TCR clone (immunodominant TCR β chain V-J gene pair) was analyzed between patients with moderate and severe disease. (E) Spearman’s rank correlation of the top TCR clone and IFN-γ or IL-17A was analyzed between patients with moderate and severe disease. Data are expressed as mean ± SD. **** p < 0.0001 by two-tailed Mann–Whitney U-test.
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Figure 3. COVID-19 patients broadly recognized the conserved P120 peptide. (A) The short-term cell line was incubated with 20 peptides (10 ug/mL) for 5 h, and IFN-γ expression was measured by flow cytometry. (B) Representative flow cytometry results of IFN-γ expression in CD4+ or CD8+ T cells induced by P120 peptide in patients with moderate and severe disease. (C,D) The P120-specific CD4+ or CD8+ T-cell level was analyzed by flow cytometry. The value of antigen-specific T cells was calculated by subtracting the negative control. Data are expressed as mean ± SD.
Figure 3. COVID-19 patients broadly recognized the conserved P120 peptide. (A) The short-term cell line was incubated with 20 peptides (10 ug/mL) for 5 h, and IFN-γ expression was measured by flow cytometry. (B) Representative flow cytometry results of IFN-γ expression in CD4+ or CD8+ T cells induced by P120 peptide in patients with moderate and severe disease. (C,D) The P120-specific CD4+ or CD8+ T-cell level was analyzed by flow cytometry. The value of antigen-specific T cells was calculated by subtracting the negative control. Data are expressed as mean ± SD.
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Figure 4. P120 peptide was conserved among SARS-CoV-2 variants and induces strong TCR expansion. (A) Circos plot depicting each β VJ gene usage of P120 responsive sample. (B) The proportion of the top 4 CDR3 sequences of the short-term cell of the P120 peptide. (C) Alignment of the amino acid sequences of original SARS-CoV-2 P120 peptides with Delta, Omicron variant, SARS-CoV-1, and endemic human coronaviruses.
Figure 4. P120 peptide was conserved among SARS-CoV-2 variants and induces strong TCR expansion. (A) Circos plot depicting each β VJ gene usage of P120 responsive sample. (B) The proportion of the top 4 CDR3 sequences of the short-term cell of the P120 peptide. (C) Alignment of the amino acid sequences of original SARS-CoV-2 P120 peptides with Delta, Omicron variant, SARS-CoV-1, and endemic human coronaviruses.
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Table 1. Demographic characteristics of COVID-19 patients and HDs.
Table 1. Demographic characteristics of COVID-19 patients and HDs.
PatientsGenderAge SeveritySampling (Day)Sampling Status Glucocorticoid before Sampling
Pt01male46Moderate17dischargeNo
Pt02male39Severe33inpatientYes
Pt03male48Severe30dischargeYes
Pt04male48Moderate30dischargeNo
Pt05female85Severe16dischargeNo
Pt06female60Severe38dischargeNo
Pt07male37Moderate24dischargeYes
Pt08female44Moderate23dischargeNo
Pt10female48Moderate16inpatientNo
HD-1Male29NANAHDNA
HD-2Male30NANAHDNA
HD-3Female27NANAHDNA
HD-4Female27NANAHDNA
HD-5Female28NANAHDNA
HD-6Male28NANAHDNA
HD-7Male26NANAHDNA
HD-8Female26NANAHDNA
HD-9Female25NANAHDNA
Pt: patient.
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Fan, X.; Song, J.-W.; Cao, W.-J.; Zhou, M.-J.; Yang, T.; Wang, J.; Meng, F.-P.; Shi, M.; Zhang, C.; Wang, F.-S. T-Cell Epitope Mapping of SARS-CoV-2 Reveals Coordinated IFN-γ Production and Clonal Expansion of T Cells Facilitates Recovery from COVID-19. Viruses 2024, 16, 1006. https://doi.org/10.3390/v16071006

AMA Style

Fan X, Song J-W, Cao W-J, Zhou M-J, Yang T, Wang J, Meng F-P, Shi M, Zhang C, Wang F-S. T-Cell Epitope Mapping of SARS-CoV-2 Reveals Coordinated IFN-γ Production and Clonal Expansion of T Cells Facilitates Recovery from COVID-19. Viruses. 2024; 16(7):1006. https://doi.org/10.3390/v16071006

Chicago/Turabian Style

Fan, Xing, Jin-Wen Song, Wen-Jing Cao, Ming-Ju Zhou, Tao Yang, Jing Wang, Fan-Ping Meng, Ming Shi, Chao Zhang, and Fu-Sheng Wang. 2024. "T-Cell Epitope Mapping of SARS-CoV-2 Reveals Coordinated IFN-γ Production and Clonal Expansion of T Cells Facilitates Recovery from COVID-19" Viruses 16, no. 7: 1006. https://doi.org/10.3390/v16071006

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