This is a preprint. Preprints are preliminary reports that have not undergone peer review. They should not be considered conclusive, used to inform clinical practice, or referenced by the media as validated information.
Research Article
SARS-CoV-2 T-cell epitopes define heterologous and COVID-19-induced T-cell recognition
Juliane S. Walz
Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany
Corresponding Author
ORCiD: https://orcid.org/0000-0001-6404-7391
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This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
Lay Summary
Lay Summary
created by Research Square
SARS-CoV-2, the virus causing COVID-19, has dramatically altered life around the globe. For antiviral therapeutics like vaccines to work, scientists need to know which areas of the virus - or epitopes - can activate immune cells called T cells. The authors of this study used computer algorithms to predict which epitopes of SARS-CoV-2 could activate T cells. To confirm those predictions, they measured how T cells responded using blood samples from two groups of people: 180 individuals previously infected with SARS-CoV-2 and 185 individuals not exposed to the virus. The team discovered that some epitopes were specific to SARS-CoV-2, while others were cross-reactive—they were similar enough to epitopes on common-cold-causing viruses to prompt a T-cell response. In fact, 81% of unexposed individuals had some T-cell response to these epitopes. While this suggests at least some built-in immune protection from SARS-CoV-2, T-cell responses in previously infected people were much more robust than those in unexposed individuals. Individuals with a broader T-cell response to viral epitopes reported less-severe COVID-19 symptoms, suggesting that people with some T-cell recognition of SARS-CoV-2 prior to infection may exhibit less severe illness. Additional studies comparing the same subjects before and after infection are needed to understand how T-cell cross-reactivity relates to immunity and symptom severity. Nevertheless, this study suggests that promoting T-cell responses to SARS-CoV-2 may be important for designing effective therapeutic and preventive measures.
Lay Summary
The SARS-CoV-2 pandemic calls for the rapid development of diagnostic, preventive, and therapeutic approaches. CD4+ and CD8+ T cell-mediated immunity is central for control of and protection from viral infections[1-3]. A prerequisite to characterize T-cell immunity, but also for the development of vaccines and immunotherapies, is the identification of the exact viral T-cell epitopes presented on human leukocyte antigens (HLA)[2-8]. This is the first work identifying and characterizing SARS-CoV-2-specific and cross-reactive HLA class I and HLA-DR T-cell epitopes in SARS-CoV-2 convalescents (n = 180) as well as unexposed individuals (n = 185) and confirming their relevance for immunity and COVID-19 disease course. SARS-CoV-2-specific T-cell epitopes enabled detection of post-infectious T-cell immunity, even in seronegative convalescents. Cross-reactive SARS-CoV-2 T-cell epitopes revealed preexisting T-cell responses in 81% of unexposed individuals, and validation of similarity to common cold human coronaviruses provided a functional basis for postulated heterologous immunity[9] in SARS-CoV-2 infection[10,11]. Intensity of T-cell responses and recognition rate of T-cell epitopes was significantly higher in the convalescent donors compared to unexposed individuals, suggesting that not only expansion, but also diversity spread of SARS-CoV-2 T-cell responses occur upon active infection. Whereas anti-SARS-CoV-2 antibody levels were associated with severity of symptoms in our SARS-CoV-2 donors, intensity of T-cell responses did not negatively affect COVID-19 severity. Rather, diversity of SARS-CoV-2 T-cell responses was increased in case of mild symptoms of COVID-19, providing evidence that development of immunity requires recognition of multiple SARS-CoV-2 epitopes. Together, the specific and cross-reactive SARS-CoV-2 T-cell epitopes identified in this work enable the identification of heterologous and post-infectious T-cell immunity and facilitate the development of diagnostic, preventive, and therapeutic measures for COVID-19.
Keywords
SARS-CoV-2, COVID-19, T-cell epitopes, HLA peptides, vaccine design, T-cells, immunity
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T cells control viral infections and provide immunological memory that enables long-lasting protection1-3. Whereas CD4+ T helper cells orchestrate the immune response and enable B cells to produce antibodies, CD8+ cytotoxic T cells eliminate virus-infected cells. For both, recognition of viral antigens in the form of short peptides presented on human leukocyte antigens (HLA) is fundamental. In consequence, characterization of such viral T-cell epitopes4,5,8 is crucial for the understanding of immune defense mechanisms, but also a prerequisite for the development of vaccines and immunotherapies2,6,7,12.
The SARS-CoV-2 coronavirus causes COVID-19, which has become a worldwide pandemic with dramatic socioeconomic consequences13,14. Available treatment options are limited, and despite intensive efforts a vaccine is so far not available. Knowledge obtained from the two other zoonotic coronaviruses SARS-CoV-1 and MERS-CoV indicates that coronavirus (CoV)-specific T-cell immunity is an important determinant for recovery and long-term protection15-18. This is even more important since studies on humoral immunity to SARS-CoV-1 provided evidence that antibody responses are short-lived and can even cause or aggravate virus-associated lung pathology19,20. With regard to SARS-CoV-2, two very recent studies10,11 described CD4+ and CD8+ T-cell responses to viral peptide megapools in donors that had recovered from COVID-19 and individuals not exposed to SARS-CoV-2, the latter being indicative of potential T-cell cross-reactivity9,21. The exact viral epitopes that mediate these T-cell responses against SARS-CoV-2, however, were not identified in these studies, but are prerequisite (i) to delineate the role of post-infectious and heterologous T- cell immunity in COVID-19, (ii) for establishing diagnostic tools to identify SARS-CoV-2 immunity, and, most importantly, (iii) to define target structures for the development of SARS-CoV-2-specific vaccines and immunotherapies. In this study, we define SARS-CoV-2-specific and cross-reactive CD4+ and CD8+ T-cell epitopes in a large collection of SARS-CoV-2 convalescents as well as non-exposed individuals and their relevance for immunity and the course of COVID-19 disease.
Identification of SARS-CoV-2-derived HLA class I- and HLA-DR-binding peptides
A novel prediction and selection workflow, based on the integration of the algorithms SYFPEITHI and NetMHCpan, identified 1,739 and 1,591 auspicious SARS-CoV-2-derived HLA class I- and HLA-DR-binding peptides across all 10 viral open-reading frames (ORFs, Fig. 1a, Extended Data Fig. 1a, b). Predictions were performed for the 10 and 6 most common HLA class I (HLA-A*01:01, -A*02:01, -A*03:01, -A*11:01, -A*24:02, -B*07:02, -B*08:01, -B*15:01,-B*40:01, and -C*07:02) and HLA-DR (HLA-DRB1*01:01, -DRB1*03:01, -DRB1*04:01,-DRB1*07:01, -DRB1*11:01, and -DRB1*15:01) allotypes covering 91.7% and 70.6% of the world population with at least one allotype, respectively22,23 (Extended Data Fig. 1c and 2a). To identify broadly applicable SARS-CoV-2-derived T-cell epitopes, we selected 100 SARS-CoV-2-derived HLA class I-binding peptides comprising 10 peptides per HLA class I allotype across all 10 viral ORFs for immunogenicity screening (range 3 - 20 peptides per ORF, mean 10, Fig. 1b, c, Extended Data Fig. 1d-m, Supplementary Table 1). In addition, 20 SARS-CoV-2-derived promiscuous HLA-DR-binding peptides across all ORFs from peptide clusters of various HLA-DR allotype restrictions representing 99 different peptide-allotype combinations were included (Fig. 1d, e, Extended Data Fig. 2b-k, Supplementary Tables 2 and 3). Of these HLA-DR-binding peptides, 14/20 (70%) contained embedded SARS-CoV-2- derived HLA class I-binding peptides for 7/10 HLA class I allotypes. The complete panel of 120 SARS-CoV-2-derived peptides comprised 10% of the total SARS-CoV-2 proteome (57%and 12% of nucleocapsid and spike protein, respectively; Extended Data Fig. 2l) and showed an equally distributed origin of structural ORF proteins (61/120 (51%)) encompassing spike, envelope, membrane and nucleocapsid proteins as well as non-structural or accessory ORFs (59/120 (49%)). The broad HLA class I and HLA-DR allotype-restriction of the selected SARS-CoV-2-derived peptides allowed for a total coverage of at least one HLA allotype in 97.6% of the world population (Fig. 1f). Recurrent mutations of SARS-CoV-224,25 affected only a minority of selected SARS-CoV-2-derived peptides with 14/120 (12%) sequences (1.7% at anchor position) including reported mutation sites (Fig. 1g, Supplementary Tables 4 and 5).
Validation and characterization of SARS-CoV-2-derived CD8+ and CD4+ T-cell epitopes
IFNγ ELISPOT screening of SARS-CoV-2 convalescents (SARS, group 1, n = 116, Extended Data Table 1, Supplementary Table 6) and donors never exposed to SARS-CoV-2 (PRE, group A, n = 104, samples collected prior to SARS-CoV-2 pandemic, Extended Data Table 1, Supplementary Table 7) validated 29/100 (29%) SARS-CoV-2-derived HLA class I- (3/10 HLA- A*01, 2/10 HLA-A*02, 3/10 HLA-A*03, 2/10 HLA-A*11, 5/10 HLA-A*24, 2/10 HLA-B*07, 4/10 HLA-B*08, 0/10 HLA-B*15, 5/10 HLA-B*40, 3/10 HLA-C*07) and 20/20 (100%) HLA-DR-binding peptides as naturally occurring T-cell epitopes (Fig. 2a-f, Extended Data Tables 2 and 3, Supplementary Fig. 1 and 2, Supplementary Table 8). Flow cytometry revealed that T-cell responses directed against HLA class I-binding peptides were mainly driven by IFNγ+CD8+ T cells, whereas HLA-DR-binding peptides were recognized by multifunctional (IFNγ+TNF+CD107a+) CD4+ T cells and in single donors additionally by CD8+ T cells (Fig. 2b, d). 12/29 (41%) and 11/20 (55%) SARS-CoV-2-derived CD8+ and CD4+ T-cell epitopes were dominant epitopes (recognized by ≥ 50% of SARS donors) with recognition frequencies up to 83% (A01_P01) and 95% (DR_P16), respectively (Fig. 2e, f, Extended Data Tables 2 and 3).
T-cell responses showed high inter-individual as well as inter-peptide intensity variation(Supplementary Fig. 3). Overall, the intensity of HLA-DR-specific T-cell responses in the SARS group was significantly more pronounced compared to those directed against HLA class I T-cell epitopes (median 414 versus 56 calculated spot counts, Fig. 2g). All SARS- CoV-2-derived HLA-DR-binding peptides were found to be immunogenic, independently of the source ORF. SARS-CoV-2-derived HLA class I T-cell epitopes showed an equally distributed origin from structural (13/29 (45%)) and non-structural or accessory (16/29 (55%)) ORFs (Extended Data Table 2). However, ORF-specific differences regarding the proportion of validated HLA class I T-cell epitopes were observed, revealing the highest frequencies for ORF9 (50%, nucleocapsid protein), ORF1 (45%), and ORF3 (38%, Fig. 2h). The highest recognition rate in SARS donors was observed for HLA class I T-cell epitopes derived from ORF2 (55%, spike protein), ORF5 (52%, membrane protein), and ORF3 (45%), as well as for HLA-DR T-cell epitopes derived from ORF5 (95%, membrane protein), ORF8 (68%), and ORF4 (55%, envelope protein, Fig. 2i).
Cross-reactive T-cell responses to SARS-CoV-2-derived HLA class I and HLA-DR T-cell epitopes in unexposed individuals
Upon screening PRE group A, cross-reactive T-cell responses to 9/29 (31%) of the validated HLA class I and to 14/20 (70%) HLA-DR T-cell epitopes were detected. Recognition frequencies of single SARS-CoV-2 HLA class I and HLA-DR T-cell epitopes in PRE donors were lower compared to that of SARS group 1 (up to 27% for B08_P05 and 44% for DR_P01, Fig. 2e, f, Extended Data Tables 2 and 3). Recognition frequencies of HLA class I and HLA-DR T-cell epitopes in individual donors differed profoundly between the PRE and the SARS group within the different ORFs. ORF1-derived HLA class I (9%) and ORF8-derived HLA-DR (25%) T-cell epitopes showed the highest recognition frequencies in the PRE group, whereas noneof the T-cell epitopes from ORF5 (membrane protein) and ORF10 that were frequentlyrecognized in SARS donors were detected by T cells in PRE donors (Fig. 2i). In line with the lower recognition frequencies of single SARS-CoV-2 T-cell epitopes (Fig. 2e, f), donor-specific recognition rates of HLA class I and HLA-DR SARS-CoV-2 T-cell epitopes were significantly lower in the PRE group (HLA class I, mean 26 ± 9; HLA-DR, mean 10 ± 5) than in the SARS group (HLA class I, mean 52 ± 23; HLA-DR, mean 52 ± 23, Fig. 3a). Alignments of the SARS- CoV-2 T-cell epitopes recognized by unexposed individuals revealed similarities to the four seasonal common cold human coronaviruses (HCoV-OC43, HCoV-229E, HCoV-NL63, HCoV- HKU1) with regard to amino acid sequences, physiochemical and/or HLA-binding properties for 14/20 (70%) of the epitopes, thereby providing clear evidence for SARS-CoV-2 T-cell cross-reactivity (Fig. 3b, Supplementary Tables 9 and 10, Supplementary Data 1).
Frequency of SARS-CoV-2 T-cell responses in COVID-19 convalescents and unexposed individuals
Epitope screening in SARS and PRE donors enabled the identification of SARS-CoV-2-specific T-cell epitopes recognized exclusively in convalescents after SARS-CoV-2 infection and of cross-reactive T-cell epitopes recognized by both, convalescents and SARS-CoV-2 unexposed individuals (Fig. 2e, f). To allow for standardized evaluation and determination of T-cell response frequencies to SARS-CoV-2, we designed broadly applicable HLA class I and HLA-DR SARS-CoV-2-specific and cross-reactive T-cell epitope compositions (EC, Fig. 3c, Extended Data Table 4). These EC were utilized for IFNγ ELISPOT assays in groups of convalescents (SARS group 2, n = 86, Extended Data Table 1, Supplementary Table 6) and unexposed donors (PRE group B, n = 94, Extended Data Table 1, Supplementary Table 7). Of the SARS donors, 100% showed T-cell responses to cross-reactive and/or specific EC (Fig. 3d, e), whereas 81% of PRE donors showed HLA class I (16%) and/or HLA-DR (77%) T-cell responsesto cross-reactive EC (Fig. 3d). In line with the findings obtained with the screening group(SARS group 1), the intensity of HLA class I T-cell responses was significantly lower compared to HLA-DR T-cell responses, both for specific (median calculated spot count HLA class I 379, HLA-DR 760) and cross-reactive EC (median calculated spot count HLA class I 86, HLA-DR 846, Fig. 3f, g). In line with the differences in recognition rates observed between SARS group 1 and PRE group A, the intensity of T-cell responses to cross-reactive EC was significantly lower in the PRE group (median calculated spot count HLA class I 14, HLA-DR346) compared to the SARS group (Fig. 3g).
Relationship of SARS-CoV-2 T-cell and antibody responses
Anti-SARS-CoV-2 IgG antibody responses in SARS donors were analyzed in two independent assays. The S1 IgG ELISA assay revealed 149/178 (84%), 7/178 (4%), and 22/178 (12%) donors with positive, borderline, and no anti S1 antibody response, respectively (Fig. 4a). Of the borderline/none responders, 18/29 (62%) were also negative in a second, independent anti-nucleocapsid immunoassay (Fig. 4b). However, SARS-CoV-2-specific CD8+ and/or CD4+ T- cell responses were detected in 10/18 (56%) of these “antibody double-negative” donors (Fig. 4c). The intensity of SARS-CoV-2-specific and cross-reactive HLA-DR T-cell responses correlated with antibody levels (Fig. 4d, e), whereas no correlation was observed with HLA class I T-cell responses (Extended Data Fig. 3a, b). No correlation between antibody titers directed against the nucleocapsid of human common cold coronaviruses (HCoV-229E, HCoV- NL63, HCoV-OC43), as determined by bead-based serological multiplex assays and the intensity of cross-reactive CD4+ and CD8+ T-cell responses in the SARS group, was detected (Extended Data Fig. 3c-h).
Association of SARS-CoV-2-directed antibody and T-cell responses with clinical characteristics in COVID-19
Finally, the association of anti-SARS-CoV-2 antibody and T-cell responses with disease severity as assessed by a combinatorial symptom score (SC) of objective (fever ≥ 38.0°C) and patient-subjective disease symptoms was determined (Extended Data Table 1). Alike in critically ill patients26, independently of age high antibody ratios significantly associated with disease severity in our collection of convalescent SARS donors (n = 180), which in general were in good health condition and had not been hospitalized (Fig. 4f, Extended Data Fig. 4a). Neither the intensity of SARS-CoV-2-specific nor of cross-reactive T-cell responses to HLA class I or HLA-DR EC correlated with disease severity (Fig. 4g). Rather, diversity of T-cell responses in terms of recognition rate of SARS-CoV-2 T-cell epitopes was decreased in patients with more severe COVID-19 symptoms (Fig. 4h, Extended Data Fig. 4b), providing evidence that development of protective immunity requires recognition of multiple SARS- CoV-2 epitopes.
This study reports the first characterization of broadly applicable SARS-CoV-2-specific and cross-reactive T-cell epitopes of various HLA allotype restrictions across all viral ORFs identified in two large collections of donors recovered from SARS-CoV-2 infection as well as unexposed individuals. Our findings aid SARS-CoV-2 research with regard to the understanding of SARS-CoV-2 post-infectious and heterologous T-cell responses, but also regarding the development of prophylactic and therapeutic measures.
Cross-reactivity of T cells for different virus species or even amongst different pathogens is a well-known phenomenon27,28 postulated to enable heterologous immunity to a pathogenafter exposure to a non-identical pathogen9,21,29. Using predicted or randomSARS-CoV-2--derived peptide pools, two very recent studies reported preexisting SARS-CoV-2-directed T-cell responses in small groups of unexposed as well as SARS-CoV-2 seronegative individuals, thereby suggesting cross-reactivity between human common cold coronaviruses and SARS-CoV-210,11. In our study we identified and characterized the exact T-cell epitopes that govern SARS-CoV-2 cross-reactivity and proved similarity to human common cold coronaviruses regarding individual peptide sequences, physiochemical and HLA-binding properties30,31. Notably, we detected SARS-CoV-2 cross-reactive T cells in 81% of unexposed individuals. To determine if these T-cells indeed mediate heterologous immunity and whether this explains the relatively small proportion of severely ill or, even in general, infected patients during this pandemic32,33, a dedicated study using e.g. a matched case control, or retrospective cohort design applying our cross-reactive SARS-CoV-2 T-cell epitopes would be required. Our observation that intensity of T-cell responses and recognition rate of T-cell epitopes was significantly higher in convalescents compared to unexposed individuals suggests that not only expansion, but also a spread of SARS-CoV-2 T-cell response diversity occurs upon active infection.
At present, determination of immunity to SARS-CoV-2 relies on the detection of SARS-CoV-2 antibody responses. However, despite the high sensitivity reported for several assays there is still a substantial percentage of patients with negative or borderline antibody responses and thus unclear immunity status after SARS-CoV-2 infection34. Our SARS-CoV-2-specific T- cell epitopes, which are not recognized by T cells of unexposed donors, allowed for detection of specific T-cell responses even in donors without antibody responses, thereby providing evidence for T-cell immunity upon infection.
In line with previous data on acute and chronic viral infection35,36, our data indicate an important role of SARS-CoV-2 CD4+ T-cell responses in the natural course of infection, with the identification of multiple dominant HLA class-DR T-cell epitopes that elicit more frequent and intense immune response in SARS donors compared to the HLA class I T-cell epitopes. This guides selection of T-cell epitopes for vaccine design, also in light of the dependency of protective antibody responses on CD4+ T cell help.
The pathophysiological involvement of the immune response in the course of COVID-19 is a matter of intense debate. Our finding that also in mainly non-hospitalized patients with a mild disease course high level antibody responses are associated with more severe symptoms of COVID-19 is in line with recent data on the correlation of antibody titers with disease severity in hospitalized patients26. Our data provide the first evidence that, on the contrary, the intensity of T-cell responses does not correlate with disease severity. This is of high relevance for the design of vaccines, as it provides evidence that disease aggravating effects might not hamper the development of prophylactic and therapeutic vaccination approaches aiming to induce SARS-CoV-2-specific T-cell responses. In contrast to the intensity of the T-cell response, recognition rates of SARS-CoV-2 T-cell epitopes by individual donors were lower in individuals with more severe COVID-19 symptoms. This observation, together with our data on increased T-cell epitope recognition rates after SARS-CoV-2 infection compared to preexisting T-cell responses in unexposed individuals and reports from other active or chronic viral infections associating diversity of T-cell response with anti- viral defense37-39, provide evidence that natural development and vaccine-based induction of immunity to SARS-CoV-2 requires recognition of multiple SARS-CoV-2 epitopes. Confirmation of this observation in a larger SARS cohort including hospitalized patients is warranted andrequires single epitope-based methods to determine T-cell epitope recognition rates asenabled by our SARS-CoV-2 T-cell epitopes. Moreover, our data underline the high importance of the identified T-cell epitopes for further studies of SARS-CoV-2 immunity, but also for the development of preventive and therapeutic COVID-19 measures. Using the SARS-CoV-2 T-cell epitopes we are currently preparing two clinical first-in-man studies (EudraCT 2020-002502-75; EudraCT 2020-002519-23) to evaluate a multi-peptide vaccine for induction of broad T-cell immunity to SARS-CoV-2 to combat COVID-19.
Patients and blood samples
Blood and serum samples as well as questionnaire-based assessment of donor characteristics and disease symptoms from convalescent volunteers after SARS-CoV-2 infection were collected at the University Hospital Tübingen, Germany from 4/2020 - 5/2020 (SARS collection n = 180). SARS-CoV-2 infection was confirmed by PCR test after nasopharyngeal swab. SARS donor recruitment was performed by online and paper-based calls. Sample collection for each SARS donor was performed approximately three to eight weeks after the end of symptoms and/or negative virus smear. Peripheral blood mononuclear cells (PBMCs) asserted from blood donations of healthy individuals prior to the SARS-CoV-2 pandemic (06/2007 - 11/2019) at the Department of Immunology, University of Tübingen were used to assess preexisting SARS-CoV-2 T-cell responses (PRE collection, n = 185). Informed consent was obtained in accordance with the Declaration of Helsinki protocol. The study was approved by and performed according to the guidelines of the local ethics committees (179/2020/BO2). PBMCs were isolated by density gradient centrifugation and stored at -80°C until further use. Serum was separated by centrifugation for 10 min and the supernatant was stored at -80°C. HLA typing was carried out by Immatics Biotechnology GmbH and the Department of Hematology and Oncology at the University Hospital Tübingen. Symptom score (SC) was determined by combining objective (fever ≥ 38.0°C) and subjective disease symptoms of individual donors. SARS and PRE collections were split into two groups for T-cell epitope screening and standardized immunity evaluation. Detailed SARS and PRE donor characteristics as well as information on allocation of the donors to the experimental groups are provided in Extended Data Table 1 and Supplementary Tables 6and 7.
Data retrieval
The complete highly conserved and representative annotated proteome sequence of SARS-CoV-2 isolate Wuhan-Hu-1 containing ten different open-reading frames (ORFs) was retrieved from the NCBI database with the accession number MN90894740. The amino acid sequence is identical to the reference sequence (EPI_ISL_412026) defined by Wang et al. conducting multiple sequence alignments and phylogenetic analyses of 95 full-length genomic sequences24.
Prediction of SARS-CoV-2-derived HLA class I-binding peptides
The protein sequences of all ten ORFs were split into 9 - 12 amino acid long peptides covering the complete proteome of the virus. The prediction algorithms NetMHCpan 4.041-43 and SYFPEITHI 1.044 were used to predict the binding of peptides to HLA-A*01:01, -A*02:01,-A*03:01, -A*11:01, -A*24:02, -B*07:02, -B*08:01, -B*15:01, -B*40:01, and -C*07:02. Onlypeptides predicted as HLA-binding peptides by both algorithms (SYFPEITHI score ≥ 60%, NetMHCpan rank ≤ 2) for the respective allotype were further examined. Peptides containing cysteines were excluded to avoid dimerization in a potential subsequent vaccine production process. Peptides derived from the ORF1 polyprotein spanning the cleavage sites of the comprised different protein chains were excluded. An averaged rank combining NetMHCpan- and SYFPEITHI-derived prediction scores was calculated and peptides were ranked for each allotype and ORF separately. Through rank-based selection one peptide for each ORF and each allotype, respectively was selected. For peptides with equal averaged ranks, peptides with higher SYFPEITHI scores were nominated. For some HLA allotypes not every ORF gave rise to an appropriate HLA-binding peptide. To receive 10 peptides per HLA allotype and ORF, remaining slots were filled with additional peptides from the ORF9nucleocapsid protein, the ORF2 spike protein, and ORF1.
Prediction of SARS-CoV-2-derived HLA-DR-binding peptides
For HLA-DR predictions all ten ORFs were split into peptides of 15 amino acids, resulting in a total of 9,561 peptides. The prediction algorithm SYFPEITHI 1.0 was used to predict the binding to HLA-DRB1*01:01, -DRB1*03:01, -DRB1*04:01, -DRB1*07:01, -DRB1*11:01, and
-DRB1*15:01. The 5% (2% for ORF1) top-scoring peptides of each ORF (based on the total length of each ORF) and each HLA-DR allotype were selected. Position-based sorting of peptides within each ORF revealed peptide clusters of promiscuous peptides binding to several HLA-DR allotypes. Through cluster-based selection, peptide clusters of promiscuous peptides with a common core sequence of 9 amino acids were selected. Thereby, 10 and 2 clusters were selected for the ORF9 nucleocapsid and the ORF2 spike protein as well as one cluster for each of the remaining ORFs. Of each selected cluster one representative peptide was selected for immunogenicity analysis excluding cysteine containing peptides.
Sequence and physiochemical property alignments to common cold human coronaviruses
\Sequence and physiochemical property alignments of the SARS-CoV-2-derived peptide sequences with the four seasonal common cold human coronaviruses (HCoV-OC43, HCoV-229E, HCoV-NL63, HCoV-HKU1) were performed by NCBI BLAST45,46 and PepCalc (https://pepcalc.com/).
IFNγ ELISPOT assay following 12-day in vitro stimulation
Synthetic peptides were provided by EMC Microcollections GmbH and INTAVIS Bioanalytical Instruments AG. PBMCs were pulsed with HLA class I or HLA-DR peptide pools (1 µg/mL per peptide for class I or 5 µg/mL for HLA-DR) and cultured for 12 days adding 20 U/mL IL-2 (Novartis) on days 3, 5, and 7. Peptide-stimulated PBMCs were analyzed by enzyme-linked immunospot (ELISPOT) assay in duplicates (if not mentioned otherwise). 200.000 - 800.000cells per well were incubated with 1 µg/mL (class I) or 2.5 µg/mL (HLA-DR) of single peptides in 96-well plates coated with anti-IFNγ antibody (clone 1-D1K, 2 µg/mL, MabTech). PHA (Sigma-Aldrich) served as positive control. After 22 - 24 h incubation, spots were revealed with anti-IFNγ biotinylated detection antibody (clone 7-B6-1, 0.3 µg/mL, MabTech), Extraavidin−Alkaline Phosphatase (1:1,000 dilution, Sigma-Aldrich) and BCIP/NBT (5-bromo-4-chloro-3-indolyl-phosphate/nitro-blue tetrazolium chloride, Sigma-Aldrich). Spots were counted using an ImmunoSpot S5 analyzer (CTL) and T-cell responses were considered positive when mean spot count was at least three-fold higher than the mean spot count of the negative control. Calculated spot counts indicate the mean spot count of duplicates normalized to 5 x 105 cells minus the normalized mean spot count of the respective negative control. For negative control peptides see Supplementary Table 11. For HLA-C*07-restricted peptides, screening in PRE donors was performed using samples of HLA-B*07+ samples due to unavailable HLA-C typing and the known linkage disequilibrium of HLA-B*07 and -C*0747,48.
Intracellular cytokine and cell surface marker staining
Peptide-specific T cells were further characterized by intracellular cytokine and cell surface marker staining. PBMCs were incubated with 10 µg/mL of peptide, 10µg/mL Brefeldin A (Sigma-Aldrich), and a 1:500 dilution of GolgiStop (BD) for 12 - 16 h. Staining was performed using Cytofix/Cytoperm solution (BD), APC/Cy7 anti-human CD4 (BD), PE/Cy7 anti-human CD8 (Beckman Coulter), Pacific Blue anti-human TNF, FITC anti-human CD107a, and PE anti-human IFNγ monoclonal antibodies (BioLegend). PMA (5 µg/ml) and ionomycin (1 µM, Sigma-Aldrich) served as positive control. Viable cells were determined using Aqua live/dead (Invitrogen). All samples were analyzed on a FACS Canto II cytometer (BD) and evaluatedusing FlowJo software version 10.0.7 (BD).
SARS-CoV-2 IgG ELISA (EUROIMMUN)
The 96-well SARS-CoV-2 IgG ELISA assay (EUROIMMUN) was performed on an automated BEP 2000 Advance® system (Siemens Healthcare Diagnostics GmbH) according to the manufacturer’s instructions. The ELISA assay detects anti-SARS-CoV-2 IgG directed against the S1 domain of the viral spike protein and relies on an assay-specific calibrator to report a ratio of specimen absorbance to calibrator absorbance. The final interpretation of positivity is determined by ratio above a threshold value given by the manufacturer: positive (ratio≥ 1.1), borderline (ratio 0.8 - 1.0) or negative (ratio < 0.8). Quality control was performed following the manufacturer’s instructions on each day of testing.
Elecsys® anti-SARS-CoV-2 immunoassay (Roche Diagnostics GmbH)
The Elecsys® anti-SARS-CoV-2 assay is an ECLIA (electrogenerated chemiluminescence immunoassay) assay designed by Roche Diagnostics GmbH and was used according to manufacturer’s instructions. It is intended for the detection of high affinity antibodies (including IgG) directed against the nucleocapsid protein of SARS-CoV-2 in human serum. Readout was performed on the Cobas ae411 analyzer. Negative results were defined by a cut-off index (COI) of < 1.0. Quality control was performed following the manufacturer’s instructions on each day of testing.
Generation of expression constructs for the production of viral antigens
The cDNAs encoding the nucleocapsid proteins of HCoV-OC43, HCoV-NL63, and HCoV-229E (gene bank accession numbers YP_009555245.1; YP_003771.1; NP_073556.1) were produced with a N-terminal hexahistidine (His6)-tag by gene synthesis (ThermoFisher Scientific) and cloned using standard techniques into NdeI/HindIII sites of the bacterial expression vector pRSET2b (ThermoFisher Scientific).
Protein expression and purification
To express the viral nucleocapsid proteins the respective expression constructs were transformed in E.coli BL21(DE3) cells. Protein expression was induced in 1 L TB medium at an optical density (OD600 of 2.5 - 3) by addition of 0.2 mM isopropyl-β-D-thiogalactopyranoside (IPTG) for 16 h at 20°C. Cells were harvested by centrifugation (10 min, 6000 x g) and the pellets were suspended in binding buffer (1x PBS, 0.5 M NaCl, 50 mM imidazole, 2 mM PMSF, 2 mM MgCl2, 150 µg/mL lysozyme (Merck) and 625 µg/mL DNAse I (Applichem)). The cell suspensions were sonified for 15 min (Bandelin Sonopuls HD70 - power MS72/D, cycle 50%) on ice, incubated for 1 h at 4°C in a rotary shaker and sonified again. After centrifugation (30 min at 20,000 x g) urea was added to a final concentration of 6 M to the soluble protein extract. The extract was filtered through a 0.45 µm filter and loaded on a pre-equilibrated 1-ml HisTrapFF column (GE Healthcare). The bound His-tagged nucleocapsid proteins were eluted by a linear gradient (30 mL) ranging from 50 to 500 mM imidazole in elution buffer (1x PBS, pH 7.4, 0.5 M NaCl, 6 M Urea). Elution fractions (0.5 mL) containing the His-tagged nucleocapsid proteins were pooled and dialyzed (D-Tube Dialyzer Mega, Novagen) into PBS. All purified proteins were analyzed via standard SDS-PAGE followed by staining with InstantBlue (Expedeon) and immunoblotting using an anti-His antibody (Penta-His Antibody, #34660, Qiagen) in combination with a donkey-anti-mouse antibody labeled with AlexaFluor647 (Invitrogen) on a Typhoon Trio (GE Healthcare, excitation 633 nM, emission filter settings 670 nM BP 30) to confirm protein integrity.
Preparation of beads for the serological multiplex assay
Antigens were covalently immobilized on spectrally distinct populations of carboxylated paramagnetic beads (MagPlex Microspheres, Luminex Corporation, Austin, TX) using 1-Ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC) / sulfo-N-hydroxysuccinimide (sNHS)chemistry. For immobilization, a magnetic particle processor (KingFisher 96, ThermoFischer Scientific) was used. Bead stocks were vortexed thoroughly and sonificated for 15 seconds. A 96-deep-well plate and tip comb was blocked with 1.1 mL 0.5% (v/v) Triton X-100 for 10 minutes. Afterwards, 83 µL of 0.065% (v/v) Triton X-100 and 1 mL bead stock were added to each well. Finally, each well contained 0.005% (v/v) Triton X-100 and 12.5 x 107 beads of one single bead population. The beads were washed twice with 500 µL activation buffer (100 mM Na2HPO4, pH 6.2, 0.005% (v/v) Triton X-100) and beads were activated for 20 min in 300 µL activation mix containing 5 mg/mL EDC and 5 mg/mL sNHS in activation buffer. Following activation, the beads were washed twice with 500 µL coupling buffer (500 mM MES, pH 5.0 + 0.005% (v/v) Triton X-100). Antigens were diluted to 39 µg/mL in coupling buffer and incubated with the activated beads for 2 h at 21°C to immobilize the antigens on the surface. Antigen-coupled beads were washed twice with 800 µL wash buffer (1x PBS + 0.005% (v/v) Triton X-100) and were finally resuspended in 1 mL storage buffer (1x PBS + 1% (w/v) BSA + 0.05% (v/v) ProClin). The beads were stored at 4°C until further use.
Bead-based serological multiplex assay
To detect human IgG directed against nucleocapsid proteins from three different coronavirus species (HCoV-229E, HCoV-NL63, HCoV-OC43), a bead-based multiplex assay was performed. All antigens were immobilized on different bead populations as described above. The individual bead populations were combined to a bead mix. 25 µL of diluted serum sample were added to 25 µL of the bead mix resulting in a final sample dilution of 1:400 and incubated for 2 h at 21°C. Unbound antibodies were removed by washing the beads three times with 100 µL wash buffer (1x PBS + 0.05% (v/v) Tween20) per well using a microplate washer (Biotek 405TS, Biotek Instruments GmbH). Bound antibodies weredetected by incubating the beads with PE-labeled goat-anti-human IgG detection antibodiesfor 45 min at 21°C. Measurements were performed using a Luminex FlexMap 3D instrument using Luminex xPONENT Software (sample size: 80 µL, 100 events; gate: 7,500 - 15,000; reporter gain: Standard PMT). Data analysis was performed on Mean Fluorescence Intensity (MFI).
Software and statistical analysis
The population coverage of HLA allotypes was calculated by the IEDB population coverage tool (www.iedb.org). Flow cytometric data was analyzed using FlowJo 10.0.8 (Treestar). Data are displayed as mean with standard deviation, box plot as median with 25% or 75% quantiles and min/max whiskers. Continuous data were tested for distribution and individual groups were tested by use of unpaired students-t test, Mann-Whitney-U test or Kruskal- Wallis-test and corrected for multiple comparison as indicated. Spearman rho (r) was calculated for correlation between continuous data. A logistic regression model was used to calculate odds ratios and 95% confidence interval (CI). Factors before the outcome and measured continuous variables were included in the model. Missing data were included in tables and in descriptive analysis. Graphs were plotted using GraphPad Prism 8.4.0. Statistical analyses were conducted using GraphPad Prism 8.4.0 and JMP® Pro (SAS Institute Inc., version 14.2) software. P values of < 0.05 were considered statistically significant.
Acknowledgements
We thank all SARS and PRE donors for their support of our research. We thank Ulrike Schmidt, Christine Bauer, Antje Petz, Martina Storz, Isolde Riedlinger, Sabrina Sauter, Sabrina Augstein, Celine Reiß, Valentina Agrusa, Santhana Dethling, Michael Beller and Claudia Falkenburger for technical support and project coordination. This work was supported by the Bundesministerium für Bildung und Forschung (BMBF, FKZ:01KI20130), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, Grant WA 4608/1-2), the Deutsche Forschungsgemeinschaft under Germany’s Excellence Strategy (Grant EXC2180-390900677), the German Cancer Consortium (DKTK), the Wilhelm Sander Stiftung (Grant 2016.177.2), the José Carreras Leukämie-Stiftung (Grant DJCLS 05 R/2017), and the Fortüne Program of the University of Tübingen (Fortüne number 2451-0-0 and 2581-0-0). Multiplex antibody detection against common cold coronaviruses is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 101003480 - CORESMA.
Authorship Contributions
A.N., H.-G.R., S.S., C.G., J.S.W. designed the study; A.N., S.S., and J.S.W. performed in silicoprediction and selection of candidate peptides; T.Bi., Y.M., M.L, J.B., J.R., M.W., M.F., I.H.,M.M. conducted in vitro T-cell experiments; B.P., R.K., D.J.K. and V.S-Z. conducted HLA allotype analysis; B.T., P.D.K., and U.R. generated expression plasmids, purified proteins for multiplex serological Luminex assay which was developed and conducted by M.B., D.J., G.K., M.S., N.S-M., M.F.T., T.O.J; SARS-CoV-2 IgG were detected by S.H., A.P.; J.S.H., M.R., T.Ba., L.- C.G., D.R., H.R.S., J.S.W. conducted patient data and sample collection as well as medical evaluation and analysis; A.N., T.Bi., J.S.H., M.G., O.K., J.S.W. analyzed data and performedstatistical analyses; A.N., T.Bi., J.S.H., H.R.S., J.S.W. drafted the manuscript; H.-G.R., S.S.,J.S.W. supervised the study.
Data availability statement
All data that support the findings of this study are provided with the manuscript. Further source data are available from the corresponding author upon request.
Disclosure of Conflicts of Interest
Daniel Kowalewski and Vlatka Stos-Zweifel are employees of the Immatics Biotechnologies GmbH. Hans-Georg Rammensee is shareholder of Immatics Biotechnologies GmbH and Curevac AG. The other authors declare no competing financial interests.
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Juan Arkani
ORCiDcommented on 29 June, 2020
The study found by measuring recognition of the epitopes of SARS-Cov-2 that diversification had been lower in patients with more severe symptoms ”Rather, diversity of T-cell responses in terms of recognition rate of SARS-CoV-2 T-cell epitopes was decreased in patients with more severe COVID-19 symptoms ”. However, these patients had higher levels of antibodies! The question here is whether various previous epitopes "similarities to the four seasonal common cold human coronaviruses (HCoV-OC43, HCoV-229E, HCoV-NL63, HCoV-HKU1)" are responsible for a mechanism that stimulates (specifically?) and induces how T-cells react. In other words whether the presence of previous cross-reactive epitopes more or less (specifically?) dictates in which direction the cellular immune response goes and also affects the humoral branch, depending on which cell line dominates, namely the CD8 cell line or the CD4 cell line, and if the CD4 cell line goes in the direction of Th1 or Th2. In this latter case dominance of the Th2 direction with antibody production may explain the observed positive correlation between the severity of symptoms and the development of antibodies as mentioned in the study and with reference also to another study with hospitalized patients. Moreover there may be individuals who are at higher risk of severe course of COVID-19 not only on the basis of other previously identified risk factors such as age and comorbidities, but also an "immunologically high risk group" that is independent of other risk factors but is nevertheless affected by them.
Paul Taylor
commented on 01 July, 2020
As a synthetic organic chemist reading your note I was struck by the last paragraph of your discussion. It may be that I am unfamiliar with the way immunologists deliver information but are you saying that the stage after identifying the two epitopes is to develop a vaccine that instead of prompting the body to produce antibodies to attack the virus you intend to use T cells to do the attacking. I ask because this T cell treatment seems to be using just that approach. https://uk.reuters.com/article/uk-health-coronavirus-britain-tcell/scottish-firm-to-trial-t-cells-as-possible-covid-19-treatment-idUKKBN24135C If so presumably these T cells might provide ongoing protection against future coronaviruses. I that your proposed approach?
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Research Article
SARS-CoV-2 T-cell epitopes define heterologous and COVID-19-induced T-cell recognition
Juliane S. Walz
Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany
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ORCiD: https://orcid.org/0000-0001-6404-7391
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Lay Summary
Lay Summary
created by Research Square
SARS-CoV-2, the virus causing COVID-19, has dramatically altered life around the globe. For antiviral therapeutics like vaccines to work, scientists need to know which areas of the virus - or epitopes - can activate immune cells called T cells. The authors of this study used computer algorithms to predict which epitopes of SARS-CoV-2 could activate T cells. To confirm those predictions, they measured how T cells responded using blood samples from two groups of people: 180 individuals previously infected with SARS-CoV-2 and 185 individuals not exposed to the virus. The team discovered that some epitopes were specific to SARS-CoV-2, while others were cross-reactive—they were similar enough to epitopes on common-cold-causing viruses to prompt a T-cell response. In fact, 81% of unexposed individuals had some T-cell response to these epitopes. While this suggests at least some built-in immune protection from SARS-CoV-2, T-cell responses in previously infected people were much more robust than those in unexposed individuals. Individuals with a broader T-cell response to viral epitopes reported less-severe COVID-19 symptoms, suggesting that people with some T-cell recognition of SARS-CoV-2 prior to infection may exhibit less severe illness. Additional studies comparing the same subjects before and after infection are needed to understand how T-cell cross-reactivity relates to immunity and symptom severity. Nevertheless, this study suggests that promoting T-cell responses to SARS-CoV-2 may be important for designing effective therapeutic and preventive measures.
Lay Summary
The SARS-CoV-2 pandemic calls for the rapid development of diagnostic, preventive, and therapeutic approaches. CD4+ and CD8+ T cell-mediated immunity is central for control of and protection from viral infections[1-3]. A prerequisite to characterize T-cell immunity, but also for the development of vaccines and immunotherapies, is the identification of the exact viral T-cell epitopes presented on human leukocyte antigens (HLA)[2-8]. This is the first work identifying and characterizing SARS-CoV-2-specific and cross-reactive HLA class I and HLA-DR T-cell epitopes in SARS-CoV-2 convalescents (n = 180) as well as unexposed individuals (n = 185) and confirming their relevance for immunity and COVID-19 disease course. SARS-CoV-2-specific T-cell epitopes enabled detection of post-infectious T-cell immunity, even in seronegative convalescents. Cross-reactive SARS-CoV-2 T-cell epitopes revealed preexisting T-cell responses in 81% of unexposed individuals, and validation of similarity to common cold human coronaviruses provided a functional basis for postulated heterologous immunity[9] in SARS-CoV-2 infection[10,11]. Intensity of T-cell responses and recognition rate of T-cell epitopes was significantly higher in the convalescent donors compared to unexposed individuals, suggesting that not only expansion, but also diversity spread of SARS-CoV-2 T-cell responses occur upon active infection. Whereas anti-SARS-CoV-2 antibody levels were associated with severity of symptoms in our SARS-CoV-2 donors, intensity of T-cell responses did not negatively affect COVID-19 severity. Rather, diversity of SARS-CoV-2 T-cell responses was increased in case of mild symptoms of COVID-19, providing evidence that development of immunity requires recognition of multiple SARS-CoV-2 epitopes. Together, the specific and cross-reactive SARS-CoV-2 T-cell epitopes identified in this work enable the identification of heterologous and post-infectious T-cell immunity and facilitate the development of diagnostic, preventive, and therapeutic measures for COVID-19.
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T cells control viral infections and provide immunological memory that enables long-lasting protection1-3. Whereas CD4+ T helper cells orchestrate the immune response and enable B cells to produce antibodies, CD8+ cytotoxic T cells eliminate virus-infected cells. For both, recognition of viral antigens in the form of short peptides presented on human leukocyte antigens (HLA) is fundamental. In consequence, characterization of such viral T-cell epitopes4,5,8 is crucial for the understanding of immune defense mechanisms, but also a prerequisite for the development of vaccines and immunotherapies2,6,7,12.
The SARS-CoV-2 coronavirus causes COVID-19, which has become a worldwide pandemic with dramatic socioeconomic consequences13,14. Available treatment options are limited, and despite intensive efforts a vaccine is so far not available. Knowledge obtained from the two other zoonotic coronaviruses SARS-CoV-1 and MERS-CoV indicates that coronavirus (CoV)-specific T-cell immunity is an important determinant for recovery and long-term protection15-18. This is even more important since studies on humoral immunity to SARS-CoV-1 provided evidence that antibody responses are short-lived and can even cause or aggravate virus-associated lung pathology19,20. With regard to SARS-CoV-2, two very recent studies10,11 described CD4+ and CD8+ T-cell responses to viral peptide megapools in donors that had recovered from COVID-19 and individuals not exposed to SARS-CoV-2, the latter being indicative of potential T-cell cross-reactivity9,21. The exact viral epitopes that mediate these T-cell responses against SARS-CoV-2, however, were not identified in these studies, but are prerequisite (i) to delineate the role of post-infectious and heterologous T- cell immunity in COVID-19, (ii) for establishing diagnostic tools to identify SARS-CoV-2 immunity, and, most importantly, (iii) to define target structures for the development of SARS-CoV-2-specific vaccines and immunotherapies. In this study, we define SARS-CoV-2-specific and cross-reactive CD4+ and CD8+ T-cell epitopes in a large collection of SARS-CoV-2 convalescents as well as non-exposed individuals and their relevance for immunity and the course of COVID-19 disease.
Identification of SARS-CoV-2-derived HLA class I- and HLA-DR-binding peptides
A novel prediction and selection workflow, based on the integration of the algorithms SYFPEITHI and NetMHCpan, identified 1,739 and 1,591 auspicious SARS-CoV-2-derived HLA class I- and HLA-DR-binding peptides across all 10 viral open-reading frames (ORFs, Fig. 1a, Extended Data Fig. 1a, b). Predictions were performed for the 10 and 6 most common HLA class I (HLA-A*01:01, -A*02:01, -A*03:01, -A*11:01, -A*24:02, -B*07:02, -B*08:01, -B*15:01,-B*40:01, and -C*07:02) and HLA-DR (HLA-DRB1*01:01, -DRB1*03:01, -DRB1*04:01,-DRB1*07:01, -DRB1*11:01, and -DRB1*15:01) allotypes covering 91.7% and 70.6% of the world population with at least one allotype, respectively22,23 (Extended Data Fig. 1c and 2a). To identify broadly applicable SARS-CoV-2-derived T-cell epitopes, we selected 100 SARS-CoV-2-derived HLA class I-binding peptides comprising 10 peptides per HLA class I allotype across all 10 viral ORFs for immunogenicity screening (range 3 - 20 peptides per ORF, mean 10, Fig. 1b, c, Extended Data Fig. 1d-m, Supplementary Table 1). In addition, 20 SARS-CoV-2-derived promiscuous HLA-DR-binding peptides across all ORFs from peptide clusters of various HLA-DR allotype restrictions representing 99 different peptide-allotype combinations were included (Fig. 1d, e, Extended Data Fig. 2b-k, Supplementary Tables 2 and 3). Of these HLA-DR-binding peptides, 14/20 (70%) contained embedded SARS-CoV-2- derived HLA class I-binding peptides for 7/10 HLA class I allotypes. The complete panel of 120 SARS-CoV-2-derived peptides comprised 10% of the total SARS-CoV-2 proteome (57%and 12% of nucleocapsid and spike protein, respectively; Extended Data Fig. 2l) and showed an equally distributed origin of structural ORF proteins (61/120 (51%)) encompassing spike, envelope, membrane and nucleocapsid proteins as well as non-structural or accessory ORFs (59/120 (49%)). The broad HLA class I and HLA-DR allotype-restriction of the selected SARS-CoV-2-derived peptides allowed for a total coverage of at least one HLA allotype in 97.6% of the world population (Fig. 1f). Recurrent mutations of SARS-CoV-224,25 affected only a minority of selected SARS-CoV-2-derived peptides with 14/120 (12%) sequences (1.7% at anchor position) including reported mutation sites (Fig. 1g, Supplementary Tables 4 and 5).
Validation and characterization of SARS-CoV-2-derived CD8+ and CD4+ T-cell epitopes
IFNγ ELISPOT screening of SARS-CoV-2 convalescents (SARS, group 1, n = 116, Extended Data Table 1, Supplementary Table 6) and donors never exposed to SARS-CoV-2 (PRE, group A, n = 104, samples collected prior to SARS-CoV-2 pandemic, Extended Data Table 1, Supplementary Table 7) validated 29/100 (29%) SARS-CoV-2-derived HLA class I- (3/10 HLA- A*01, 2/10 HLA-A*02, 3/10 HLA-A*03, 2/10 HLA-A*11, 5/10 HLA-A*24, 2/10 HLA-B*07, 4/10 HLA-B*08, 0/10 HLA-B*15, 5/10 HLA-B*40, 3/10 HLA-C*07) and 20/20 (100%) HLA-DR-binding peptides as naturally occurring T-cell epitopes (Fig. 2a-f, Extended Data Tables 2 and 3, Supplementary Fig. 1 and 2, Supplementary Table 8). Flow cytometry revealed that T-cell responses directed against HLA class I-binding peptides were mainly driven by IFNγ+CD8+ T cells, whereas HLA-DR-binding peptides were recognized by multifunctional (IFNγ+TNF+CD107a+) CD4+ T cells and in single donors additionally by CD8+ T cells (Fig. 2b, d). 12/29 (41%) and 11/20 (55%) SARS-CoV-2-derived CD8+ and CD4+ T-cell epitopes were dominant epitopes (recognized by ≥ 50% of SARS donors) with recognition frequencies up to 83% (A01_P01) and 95% (DR_P16), respectively (Fig. 2e, f, Extended Data Tables 2 and 3).
T-cell responses showed high inter-individual as well as inter-peptide intensity variation(Supplementary Fig. 3). Overall, the intensity of HLA-DR-specific T-cell responses in the SARS group was significantly more pronounced compared to those directed against HLA class I T-cell epitopes (median 414 versus 56 calculated spot counts, Fig. 2g). All SARS- CoV-2-derived HLA-DR-binding peptides were found to be immunogenic, independently of the source ORF. SARS-CoV-2-derived HLA class I T-cell epitopes showed an equally distributed origin from structural (13/29 (45%)) and non-structural or accessory (16/29 (55%)) ORFs (Extended Data Table 2). However, ORF-specific differences regarding the proportion of validated HLA class I T-cell epitopes were observed, revealing the highest frequencies for ORF9 (50%, nucleocapsid protein), ORF1 (45%), and ORF3 (38%, Fig. 2h). The highest recognition rate in SARS donors was observed for HLA class I T-cell epitopes derived from ORF2 (55%, spike protein), ORF5 (52%, membrane protein), and ORF3 (45%), as well as for HLA-DR T-cell epitopes derived from ORF5 (95%, membrane protein), ORF8 (68%), and ORF4 (55%, envelope protein, Fig. 2i).
Cross-reactive T-cell responses to SARS-CoV-2-derived HLA class I and HLA-DR T-cell epitopes in unexposed individuals
Upon screening PRE group A, cross-reactive T-cell responses to 9/29 (31%) of the validated HLA class I and to 14/20 (70%) HLA-DR T-cell epitopes were detected. Recognition frequencies of single SARS-CoV-2 HLA class I and HLA-DR T-cell epitopes in PRE donors were lower compared to that of SARS group 1 (up to 27% for B08_P05 and 44% for DR_P01, Fig. 2e, f, Extended Data Tables 2 and 3). Recognition frequencies of HLA class I and HLA-DR T-cell epitopes in individual donors differed profoundly between the PRE and the SARS group within the different ORFs. ORF1-derived HLA class I (9%) and ORF8-derived HLA-DR (25%) T-cell epitopes showed the highest recognition frequencies in the PRE group, whereas noneof the T-cell epitopes from ORF5 (membrane protein) and ORF10 that were frequentlyrecognized in SARS donors were detected by T cells in PRE donors (Fig. 2i). In line with the lower recognition frequencies of single SARS-CoV-2 T-cell epitopes (Fig. 2e, f), donor-specific recognition rates of HLA class I and HLA-DR SARS-CoV-2 T-cell epitopes were significantly lower in the PRE group (HLA class I, mean 26 ± 9; HLA-DR, mean 10 ± 5) than in the SARS group (HLA class I, mean 52 ± 23; HLA-DR, mean 52 ± 23, Fig. 3a). Alignments of the SARS- CoV-2 T-cell epitopes recognized by unexposed individuals revealed similarities to the four seasonal common cold human coronaviruses (HCoV-OC43, HCoV-229E, HCoV-NL63, HCoV- HKU1) with regard to amino acid sequences, physiochemical and/or HLA-binding properties for 14/20 (70%) of the epitopes, thereby providing clear evidence for SARS-CoV-2 T-cell cross-reactivity (Fig. 3b, Supplementary Tables 9 and 10, Supplementary Data 1).
Frequency of SARS-CoV-2 T-cell responses in COVID-19 convalescents and unexposed individuals
Epitope screening in SARS and PRE donors enabled the identification of SARS-CoV-2-specific T-cell epitopes recognized exclusively in convalescents after SARS-CoV-2 infection and of cross-reactive T-cell epitopes recognized by both, convalescents and SARS-CoV-2 unexposed individuals (Fig. 2e, f). To allow for standardized evaluation and determination of T-cell response frequencies to SARS-CoV-2, we designed broadly applicable HLA class I and HLA-DR SARS-CoV-2-specific and cross-reactive T-cell epitope compositions (EC, Fig. 3c, Extended Data Table 4). These EC were utilized for IFNγ ELISPOT assays in groups of convalescents (SARS group 2, n = 86, Extended Data Table 1, Supplementary Table 6) and unexposed donors (PRE group B, n = 94, Extended Data Table 1, Supplementary Table 7). Of the SARS donors, 100% showed T-cell responses to cross-reactive and/or specific EC (Fig. 3d, e), whereas 81% of PRE donors showed HLA class I (16%) and/or HLA-DR (77%) T-cell responsesto cross-reactive EC (Fig. 3d). In line with the findings obtained with the screening group(SARS group 1), the intensity of HLA class I T-cell responses was significantly lower compared to HLA-DR T-cell responses, both for specific (median calculated spot count HLA class I 379, HLA-DR 760) and cross-reactive EC (median calculated spot count HLA class I 86, HLA-DR 846, Fig. 3f, g). In line with the differences in recognition rates observed between SARS group 1 and PRE group A, the intensity of T-cell responses to cross-reactive EC was significantly lower in the PRE group (median calculated spot count HLA class I 14, HLA-DR346) compared to the SARS group (Fig. 3g).
Relationship of SARS-CoV-2 T-cell and antibody responses
Anti-SARS-CoV-2 IgG antibody responses in SARS donors were analyzed in two independent assays. The S1 IgG ELISA assay revealed 149/178 (84%), 7/178 (4%), and 22/178 (12%) donors with positive, borderline, and no anti S1 antibody response, respectively (Fig. 4a). Of the borderline/none responders, 18/29 (62%) were also negative in a second, independent anti-nucleocapsid immunoassay (Fig. 4b). However, SARS-CoV-2-specific CD8+ and/or CD4+ T- cell responses were detected in 10/18 (56%) of these “antibody double-negative” donors (Fig. 4c). The intensity of SARS-CoV-2-specific and cross-reactive HLA-DR T-cell responses correlated with antibody levels (Fig. 4d, e), whereas no correlation was observed with HLA class I T-cell responses (Extended Data Fig. 3a, b). No correlation between antibody titers directed against the nucleocapsid of human common cold coronaviruses (HCoV-229E, HCoV- NL63, HCoV-OC43), as determined by bead-based serological multiplex assays and the intensity of cross-reactive CD4+ and CD8+ T-cell responses in the SARS group, was detected (Extended Data Fig. 3c-h).
Association of SARS-CoV-2-directed antibody and T-cell responses with clinical characteristics in COVID-19
Finally, the association of anti-SARS-CoV-2 antibody and T-cell responses with disease severity as assessed by a combinatorial symptom score (SC) of objective (fever ≥ 38.0°C) and patient-subjective disease symptoms was determined (Extended Data Table 1). Alike in critically ill patients26, independently of age high antibody ratios significantly associated with disease severity in our collection of convalescent SARS donors (n = 180), which in general were in good health condition and had not been hospitalized (Fig. 4f, Extended Data Fig. 4a). Neither the intensity of SARS-CoV-2-specific nor of cross-reactive T-cell responses to HLA class I or HLA-DR EC correlated with disease severity (Fig. 4g). Rather, diversity of T-cell responses in terms of recognition rate of SARS-CoV-2 T-cell epitopes was decreased in patients with more severe COVID-19 symptoms (Fig. 4h, Extended Data Fig. 4b), providing evidence that development of protective immunity requires recognition of multiple SARS- CoV-2 epitopes.
This study reports the first characterization of broadly applicable SARS-CoV-2-specific and cross-reactive T-cell epitopes of various HLA allotype restrictions across all viral ORFs identified in two large collections of donors recovered from SARS-CoV-2 infection as well as unexposed individuals. Our findings aid SARS-CoV-2 research with regard to the understanding of SARS-CoV-2 post-infectious and heterologous T-cell responses, but also regarding the development of prophylactic and therapeutic measures.
Cross-reactivity of T cells for different virus species or even amongst different pathogens is a well-known phenomenon27,28 postulated to enable heterologous immunity to a pathogenafter exposure to a non-identical pathogen9,21,29. Using predicted or randomSARS-CoV-2--derived peptide pools, two very recent studies reported preexisting SARS-CoV-2-directed T-cell responses in small groups of unexposed as well as SARS-CoV-2 seronegative individuals, thereby suggesting cross-reactivity between human common cold coronaviruses and SARS-CoV-210,11. In our study we identified and characterized the exact T-cell epitopes that govern SARS-CoV-2 cross-reactivity and proved similarity to human common cold coronaviruses regarding individual peptide sequences, physiochemical and HLA-binding properties30,31. Notably, we detected SARS-CoV-2 cross-reactive T cells in 81% of unexposed individuals. To determine if these T-cells indeed mediate heterologous immunity and whether this explains the relatively small proportion of severely ill or, even in general, infected patients during this pandemic32,33, a dedicated study using e.g. a matched case control, or retrospective cohort design applying our cross-reactive SARS-CoV-2 T-cell epitopes would be required. Our observation that intensity of T-cell responses and recognition rate of T-cell epitopes was significantly higher in convalescents compared to unexposed individuals suggests that not only expansion, but also a spread of SARS-CoV-2 T-cell response diversity occurs upon active infection.
At present, determination of immunity to SARS-CoV-2 relies on the detection of SARS-CoV-2 antibody responses. However, despite the high sensitivity reported for several assays there is still a substantial percentage of patients with negative or borderline antibody responses and thus unclear immunity status after SARS-CoV-2 infection34. Our SARS-CoV-2-specific T- cell epitopes, which are not recognized by T cells of unexposed donors, allowed for detection of specific T-cell responses even in donors without antibody responses, thereby providing evidence for T-cell immunity upon infection.
In line with previous data on acute and chronic viral infection35,36, our data indicate an important role of SARS-CoV-2 CD4+ T-cell responses in the natural course of infection, with the identification of multiple dominant HLA class-DR T-cell epitopes that elicit more frequent and intense immune response in SARS donors compared to the HLA class I T-cell epitopes. This guides selection of T-cell epitopes for vaccine design, also in light of the dependency of protective antibody responses on CD4+ T cell help.
The pathophysiological involvement of the immune response in the course of COVID-19 is a matter of intense debate. Our finding that also in mainly non-hospitalized patients with a mild disease course high level antibody responses are associated with more severe symptoms of COVID-19 is in line with recent data on the correlation of antibody titers with disease severity in hospitalized patients26. Our data provide the first evidence that, on the contrary, the intensity of T-cell responses does not correlate with disease severity. This is of high relevance for the design of vaccines, as it provides evidence that disease aggravating effects might not hamper the development of prophylactic and therapeutic vaccination approaches aiming to induce SARS-CoV-2-specific T-cell responses. In contrast to the intensity of the T-cell response, recognition rates of SARS-CoV-2 T-cell epitopes by individual donors were lower in individuals with more severe COVID-19 symptoms. This observation, together with our data on increased T-cell epitope recognition rates after SARS-CoV-2 infection compared to preexisting T-cell responses in unexposed individuals and reports from other active or chronic viral infections associating diversity of T-cell response with anti- viral defense37-39, provide evidence that natural development and vaccine-based induction of immunity to SARS-CoV-2 requires recognition of multiple SARS-CoV-2 epitopes. Confirmation of this observation in a larger SARS cohort including hospitalized patients is warranted andrequires single epitope-based methods to determine T-cell epitope recognition rates asenabled by our SARS-CoV-2 T-cell epitopes. Moreover, our data underline the high importance of the identified T-cell epitopes for further studies of SARS-CoV-2 immunity, but also for the development of preventive and therapeutic COVID-19 measures. Using the SARS-CoV-2 T-cell epitopes we are currently preparing two clinical first-in-man studies (EudraCT 2020-002502-75; EudraCT 2020-002519-23) to evaluate a multi-peptide vaccine for induction of broad T-cell immunity to SARS-CoV-2 to combat COVID-19.
Patients and blood samples
Blood and serum samples as well as questionnaire-based assessment of donor characteristics and disease symptoms from convalescent volunteers after SARS-CoV-2 infection were collected at the University Hospital Tübingen, Germany from 4/2020 - 5/2020 (SARS collection n = 180). SARS-CoV-2 infection was confirmed by PCR test after nasopharyngeal swab. SARS donor recruitment was performed by online and paper-based calls. Sample collection for each SARS donor was performed approximately three to eight weeks after the end of symptoms and/or negative virus smear. Peripheral blood mononuclear cells (PBMCs) asserted from blood donations of healthy individuals prior to the SARS-CoV-2 pandemic (06/2007 - 11/2019) at the Department of Immunology, University of Tübingen were used to assess preexisting SARS-CoV-2 T-cell responses (PRE collection, n = 185). Informed consent was obtained in accordance with the Declaration of Helsinki protocol. The study was approved by and performed according to the guidelines of the local ethics committees (179/2020/BO2). PBMCs were isolated by density gradient centrifugation and stored at -80°C until further use. Serum was separated by centrifugation for 10 min and the supernatant was stored at -80°C. HLA typing was carried out by Immatics Biotechnology GmbH and the Department of Hematology and Oncology at the University Hospital Tübingen. Symptom score (SC) was determined by combining objective (fever ≥ 38.0°C) and subjective disease symptoms of individual donors. SARS and PRE collections were split into two groups for T-cell epitope screening and standardized immunity evaluation. Detailed SARS and PRE donor characteristics as well as information on allocation of the donors to the experimental groups are provided in Extended Data Table 1 and Supplementary Tables 6and 7.
Data retrieval
The complete highly conserved and representative annotated proteome sequence of SARS-CoV-2 isolate Wuhan-Hu-1 containing ten different open-reading frames (ORFs) was retrieved from the NCBI database with the accession number MN90894740. The amino acid sequence is identical to the reference sequence (EPI_ISL_412026) defined by Wang et al. conducting multiple sequence alignments and phylogenetic analyses of 95 full-length genomic sequences24.
Prediction of SARS-CoV-2-derived HLA class I-binding peptides
The protein sequences of all ten ORFs were split into 9 - 12 amino acid long peptides covering the complete proteome of the virus. The prediction algorithms NetMHCpan 4.041-43 and SYFPEITHI 1.044 were used to predict the binding of peptides to HLA-A*01:01, -A*02:01,-A*03:01, -A*11:01, -A*24:02, -B*07:02, -B*08:01, -B*15:01, -B*40:01, and -C*07:02. Onlypeptides predicted as HLA-binding peptides by both algorithms (SYFPEITHI score ≥ 60%, NetMHCpan rank ≤ 2) for the respective allotype were further examined. Peptides containing cysteines were excluded to avoid dimerization in a potential subsequent vaccine production process. Peptides derived from the ORF1 polyprotein spanning the cleavage sites of the comprised different protein chains were excluded. An averaged rank combining NetMHCpan- and SYFPEITHI-derived prediction scores was calculated and peptides were ranked for each allotype and ORF separately. Through rank-based selection one peptide for each ORF and each allotype, respectively was selected. For peptides with equal averaged ranks, peptides with higher SYFPEITHI scores were nominated. For some HLA allotypes not every ORF gave rise to an appropriate HLA-binding peptide. To receive 10 peptides per HLA allotype and ORF, remaining slots were filled with additional peptides from the ORF9nucleocapsid protein, the ORF2 spike protein, and ORF1.
Prediction of SARS-CoV-2-derived HLA-DR-binding peptides
For HLA-DR predictions all ten ORFs were split into peptides of 15 amino acids, resulting in a total of 9,561 peptides. The prediction algorithm SYFPEITHI 1.0 was used to predict the binding to HLA-DRB1*01:01, -DRB1*03:01, -DRB1*04:01, -DRB1*07:01, -DRB1*11:01, and
-DRB1*15:01. The 5% (2% for ORF1) top-scoring peptides of each ORF (based on the total length of each ORF) and each HLA-DR allotype were selected. Position-based sorting of peptides within each ORF revealed peptide clusters of promiscuous peptides binding to several HLA-DR allotypes. Through cluster-based selection, peptide clusters of promiscuous peptides with a common core sequence of 9 amino acids were selected. Thereby, 10 and 2 clusters were selected for the ORF9 nucleocapsid and the ORF2 spike protein as well as one cluster for each of the remaining ORFs. Of each selected cluster one representative peptide was selected for immunogenicity analysis excluding cysteine containing peptides.
Sequence and physiochemical property alignments to common cold human coronaviruses
\Sequence and physiochemical property alignments of the SARS-CoV-2-derived peptide sequences with the four seasonal common cold human coronaviruses (HCoV-OC43, HCoV-229E, HCoV-NL63, HCoV-HKU1) were performed by NCBI BLAST45,46 and PepCalc (https://pepcalc.com/).
IFNγ ELISPOT assay following 12-day in vitro stimulation
Synthetic peptides were provided by EMC Microcollections GmbH and INTAVIS Bioanalytical Instruments AG. PBMCs were pulsed with HLA class I or HLA-DR peptide pools (1 µg/mL per peptide for class I or 5 µg/mL for HLA-DR) and cultured for 12 days adding 20 U/mL IL-2 (Novartis) on days 3, 5, and 7. Peptide-stimulated PBMCs were analyzed by enzyme-linked immunospot (ELISPOT) assay in duplicates (if not mentioned otherwise). 200.000 - 800.000cells per well were incubated with 1 µg/mL (class I) or 2.5 µg/mL (HLA-DR) of single peptides in 96-well plates coated with anti-IFNγ antibody (clone 1-D1K, 2 µg/mL, MabTech). PHA (Sigma-Aldrich) served as positive control. After 22 - 24 h incubation, spots were revealed with anti-IFNγ biotinylated detection antibody (clone 7-B6-1, 0.3 µg/mL, MabTech), Extraavidin−Alkaline Phosphatase (1:1,000 dilution, Sigma-Aldrich) and BCIP/NBT (5-bromo-4-chloro-3-indolyl-phosphate/nitro-blue tetrazolium chloride, Sigma-Aldrich). Spots were counted using an ImmunoSpot S5 analyzer (CTL) and T-cell responses were considered positive when mean spot count was at least three-fold higher than the mean spot count of the negative control. Calculated spot counts indicate the mean spot count of duplicates normalized to 5 x 105 cells minus the normalized mean spot count of the respective negative control. For negative control peptides see Supplementary Table 11. For HLA-C*07-restricted peptides, screening in PRE donors was performed using samples of HLA-B*07+ samples due to unavailable HLA-C typing and the known linkage disequilibrium of HLA-B*07 and -C*0747,48.
Intracellular cytokine and cell surface marker staining
Peptide-specific T cells were further characterized by intracellular cytokine and cell surface marker staining. PBMCs were incubated with 10 µg/mL of peptide, 10µg/mL Brefeldin A (Sigma-Aldrich), and a 1:500 dilution of GolgiStop (BD) for 12 - 16 h. Staining was performed using Cytofix/Cytoperm solution (BD), APC/Cy7 anti-human CD4 (BD), PE/Cy7 anti-human CD8 (Beckman Coulter), Pacific Blue anti-human TNF, FITC anti-human CD107a, and PE anti-human IFNγ monoclonal antibodies (BioLegend). PMA (5 µg/ml) and ionomycin (1 µM, Sigma-Aldrich) served as positive control. Viable cells were determined using Aqua live/dead (Invitrogen). All samples were analyzed on a FACS Canto II cytometer (BD) and evaluatedusing FlowJo software version 10.0.7 (BD).
SARS-CoV-2 IgG ELISA (EUROIMMUN)
The 96-well SARS-CoV-2 IgG ELISA assay (EUROIMMUN) was performed on an automated BEP 2000 Advance® system (Siemens Healthcare Diagnostics GmbH) according to the manufacturer’s instructions. The ELISA assay detects anti-SARS-CoV-2 IgG directed against the S1 domain of the viral spike protein and relies on an assay-specific calibrator to report a ratio of specimen absorbance to calibrator absorbance. The final interpretation of positivity is determined by ratio above a threshold value given by the manufacturer: positive (ratio≥ 1.1), borderline (ratio 0.8 - 1.0) or negative (ratio < 0.8). Quality control was performed following the manufacturer’s instructions on each day of testing.
Elecsys® anti-SARS-CoV-2 immunoassay (Roche Diagnostics GmbH)
The Elecsys® anti-SARS-CoV-2 assay is an ECLIA (electrogenerated chemiluminescence immunoassay) assay designed by Roche Diagnostics GmbH and was used according to manufacturer’s instructions. It is intended for the detection of high affinity antibodies (including IgG) directed against the nucleocapsid protein of SARS-CoV-2 in human serum. Readout was performed on the Cobas ae411 analyzer. Negative results were defined by a cut-off index (COI) of < 1.0. Quality control was performed following the manufacturer’s instructions on each day of testing.
Generation of expression constructs for the production of viral antigens
The cDNAs encoding the nucleocapsid proteins of HCoV-OC43, HCoV-NL63, and HCoV-229E (gene bank accession numbers YP_009555245.1; YP_003771.1; NP_073556.1) were produced with a N-terminal hexahistidine (His6)-tag by gene synthesis (ThermoFisher Scientific) and cloned using standard techniques into NdeI/HindIII sites of the bacterial expression vector pRSET2b (ThermoFisher Scientific).
Protein expression and purification
To express the viral nucleocapsid proteins the respective expression constructs were transformed in E.coli BL21(DE3) cells. Protein expression was induced in 1 L TB medium at an optical density (OD600 of 2.5 - 3) by addition of 0.2 mM isopropyl-β-D-thiogalactopyranoside (IPTG) for 16 h at 20°C. Cells were harvested by centrifugation (10 min, 6000 x g) and the pellets were suspended in binding buffer (1x PBS, 0.5 M NaCl, 50 mM imidazole, 2 mM PMSF, 2 mM MgCl2, 150 µg/mL lysozyme (Merck) and 625 µg/mL DNAse I (Applichem)). The cell suspensions were sonified for 15 min (Bandelin Sonopuls HD70 - power MS72/D, cycle 50%) on ice, incubated for 1 h at 4°C in a rotary shaker and sonified again. After centrifugation (30 min at 20,000 x g) urea was added to a final concentration of 6 M to the soluble protein extract. The extract was filtered through a 0.45 µm filter and loaded on a pre-equilibrated 1-ml HisTrapFF column (GE Healthcare). The bound His-tagged nucleocapsid proteins were eluted by a linear gradient (30 mL) ranging from 50 to 500 mM imidazole in elution buffer (1x PBS, pH 7.4, 0.5 M NaCl, 6 M Urea). Elution fractions (0.5 mL) containing the His-tagged nucleocapsid proteins were pooled and dialyzed (D-Tube Dialyzer Mega, Novagen) into PBS. All purified proteins were analyzed via standard SDS-PAGE followed by staining with InstantBlue (Expedeon) and immunoblotting using an anti-His antibody (Penta-His Antibody, #34660, Qiagen) in combination with a donkey-anti-mouse antibody labeled with AlexaFluor647 (Invitrogen) on a Typhoon Trio (GE Healthcare, excitation 633 nM, emission filter settings 670 nM BP 30) to confirm protein integrity.
Preparation of beads for the serological multiplex assay
Antigens were covalently immobilized on spectrally distinct populations of carboxylated paramagnetic beads (MagPlex Microspheres, Luminex Corporation, Austin, TX) using 1-Ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC) / sulfo-N-hydroxysuccinimide (sNHS)chemistry. For immobilization, a magnetic particle processor (KingFisher 96, ThermoFischer Scientific) was used. Bead stocks were vortexed thoroughly and sonificated for 15 seconds. A 96-deep-well plate and tip comb was blocked with 1.1 mL 0.5% (v/v) Triton X-100 for 10 minutes. Afterwards, 83 µL of 0.065% (v/v) Triton X-100 and 1 mL bead stock were added to each well. Finally, each well contained 0.005% (v/v) Triton X-100 and 12.5 x 107 beads of one single bead population. The beads were washed twice with 500 µL activation buffer (100 mM Na2HPO4, pH 6.2, 0.005% (v/v) Triton X-100) and beads were activated for 20 min in 300 µL activation mix containing 5 mg/mL EDC and 5 mg/mL sNHS in activation buffer. Following activation, the beads were washed twice with 500 µL coupling buffer (500 mM MES, pH 5.0 + 0.005% (v/v) Triton X-100). Antigens were diluted to 39 µg/mL in coupling buffer and incubated with the activated beads for 2 h at 21°C to immobilize the antigens on the surface. Antigen-coupled beads were washed twice with 800 µL wash buffer (1x PBS + 0.005% (v/v) Triton X-100) and were finally resuspended in 1 mL storage buffer (1x PBS + 1% (w/v) BSA + 0.05% (v/v) ProClin). The beads were stored at 4°C until further use.
Bead-based serological multiplex assay
To detect human IgG directed against nucleocapsid proteins from three different coronavirus species (HCoV-229E, HCoV-NL63, HCoV-OC43), a bead-based multiplex assay was performed. All antigens were immobilized on different bead populations as described above. The individual bead populations were combined to a bead mix. 25 µL of diluted serum sample were added to 25 µL of the bead mix resulting in a final sample dilution of 1:400 and incubated for 2 h at 21°C. Unbound antibodies were removed by washing the beads three times with 100 µL wash buffer (1x PBS + 0.05% (v/v) Tween20) per well using a microplate washer (Biotek 405TS, Biotek Instruments GmbH). Bound antibodies weredetected by incubating the beads with PE-labeled goat-anti-human IgG detection antibodiesfor 45 min at 21°C. Measurements were performed using a Luminex FlexMap 3D instrument using Luminex xPONENT Software (sample size: 80 µL, 100 events; gate: 7,500 - 15,000; reporter gain: Standard PMT). Data analysis was performed on Mean Fluorescence Intensity (MFI).
Software and statistical analysis
The population coverage of HLA allotypes was calculated by the IEDB population coverage tool (www.iedb.org). Flow cytometric data was analyzed using FlowJo 10.0.8 (Treestar). Data are displayed as mean with standard deviation, box plot as median with 25% or 75% quantiles and min/max whiskers. Continuous data were tested for distribution and individual groups were tested by use of unpaired students-t test, Mann-Whitney-U test or Kruskal- Wallis-test and corrected for multiple comparison as indicated. Spearman rho (r) was calculated for correlation between continuous data. A logistic regression model was used to calculate odds ratios and 95% confidence interval (CI). Factors before the outcome and measured continuous variables were included in the model. Missing data were included in tables and in descriptive analysis. Graphs were plotted using GraphPad Prism 8.4.0. Statistical analyses were conducted using GraphPad Prism 8.4.0 and JMP® Pro (SAS Institute Inc., version 14.2) software. P values of < 0.05 were considered statistically significant.
Acknowledgements
We thank all SARS and PRE donors for their support of our research. We thank Ulrike Schmidt, Christine Bauer, Antje Petz, Martina Storz, Isolde Riedlinger, Sabrina Sauter, Sabrina Augstein, Celine Reiß, Valentina Agrusa, Santhana Dethling, Michael Beller and Claudia Falkenburger for technical support and project coordination. This work was supported by the Bundesministerium für Bildung und Forschung (BMBF, FKZ:01KI20130), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, Grant WA 4608/1-2), the Deutsche Forschungsgemeinschaft under Germany’s Excellence Strategy (Grant EXC2180-390900677), the German Cancer Consortium (DKTK), the Wilhelm Sander Stiftung (Grant 2016.177.2), the José Carreras Leukämie-Stiftung (Grant DJCLS 05 R/2017), and the Fortüne Program of the University of Tübingen (Fortüne number 2451-0-0 and 2581-0-0). Multiplex antibody detection against common cold coronaviruses is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 101003480 - CORESMA.
Authorship Contributions
A.N., H.-G.R., S.S., C.G., J.S.W. designed the study; A.N., S.S., and J.S.W. performed in silicoprediction and selection of candidate peptides; T.Bi., Y.M., M.L, J.B., J.R., M.W., M.F., I.H.,M.M. conducted in vitro T-cell experiments; B.P., R.K., D.J.K. and V.S-Z. conducted HLA allotype analysis; B.T., P.D.K., and U.R. generated expression plasmids, purified proteins for multiplex serological Luminex assay which was developed and conducted by M.B., D.J., G.K., M.S., N.S-M., M.F.T., T.O.J; SARS-CoV-2 IgG were detected by S.H., A.P.; J.S.H., M.R., T.Ba., L.- C.G., D.R., H.R.S., J.S.W. conducted patient data and sample collection as well as medical evaluation and analysis; A.N., T.Bi., J.S.H., M.G., O.K., J.S.W. analyzed data and performedstatistical analyses; A.N., T.Bi., J.S.H., H.R.S., J.S.W. drafted the manuscript; H.-G.R., S.S.,J.S.W. supervised the study.
Data availability statement
All data that support the findings of this study are provided with the manuscript. Further source data are available from the corresponding author upon request.
Disclosure of Conflicts of Interest
Daniel Kowalewski and Vlatka Stos-Zweifel are employees of the Immatics Biotechnologies GmbH. Hans-Georg Rammensee is shareholder of Immatics Biotechnologies GmbH and Curevac AG. The other authors declare no competing financial interests.
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Juan Arkani
ORCiDcommented on 29 June, 2020
The study found by measuring recognition of the epitopes of SARS-Cov-2 that diversification had been lower in patients with more severe symptoms ”Rather, diversity of T-cell responses in terms of recognition rate of SARS-CoV-2 T-cell epitopes was decreased in patients with more severe COVID-19 symptoms ”. However, these patients had higher levels of antibodies! The question here is whether various previous epitopes "similarities to the four seasonal common cold human coronaviruses (HCoV-OC43, HCoV-229E, HCoV-NL63, HCoV-HKU1)" are responsible for a mechanism that stimulates (specifically?) and induces how T-cells react. In other words whether the presence of previous cross-reactive epitopes more or less (specifically?) dictates in which direction the cellular immune response goes and also affects the humoral branch, depending on which cell line dominates, namely the CD8 cell line or the CD4 cell line, and if the CD4 cell line goes in the direction of Th1 or Th2. In this latter case dominance of the Th2 direction with antibody production may explain the observed positive correlation between the severity of symptoms and the development of antibodies as mentioned in the study and with reference also to another study with hospitalized patients. Moreover there may be individuals who are at higher risk of severe course of COVID-19 not only on the basis of other previously identified risk factors such as age and comorbidities, but also an "immunologically high risk group" that is independent of other risk factors but is nevertheless affected by them.
Paul Taylor
commented on 01 July, 2020
As a synthetic organic chemist reading your note I was struck by the last paragraph of your discussion. It may be that I am unfamiliar with the way immunologists deliver information but are you saying that the stage after identifying the two epitopes is to develop a vaccine that instead of prompting the body to produce antibodies to attack the virus you intend to use T cells to do the attacking. I ask because this T cell treatment seems to be using just that approach. https://uk.reuters.com/article/uk-health-coronavirus-britain-tcell/scottish-firm-to-trial-t-cells-as-possible-covid-19-treatment-idUKKBN24135C If so presumably these T cells might provide ongoing protection against future coronaviruses. I that your proposed approach?
View 1 reply
Juliane Walz
replied on 03 July, 2020
Thank you very much for your interest in our study. Indeed we are currently planning a peptide vaccination study based on our preclinical work. In contrast to the T-cell transfer pursued by the scottish firm you mentioned our approach aims to induce SARS-CoV-2 T-cell responses in vivo applying the identified T-cell epitopes as a peptide vaccine.
View 1 reply
Paul Taylor
replied on 03 July, 2020
Thanks for the reply, I find this approach an truly exciting one and hope it is successful, If there were a choice of vaccines in the battle against Covid-19 it would be my vaccine of choice. The potential of this approach is of course that the T cells generated to fight the current pandemic might well offer a cross ‘protection’ against future coronaviral epidemics. This will give any vaccine developed following this approach a unique advantage over any other vaccine simply inducing antibodies. I have a website that reviews coronavirus developments, https:coronaviruswales.uk I’ll post an article there to help advance your profile and look forward to the full paper in due course. Great research thank you.
Juliane Walz
replied on 03 July, 2020
Thank you very much for your interest in our study. Indeed we are currently planning a peptide vaccination study based on our preclinical work. In contrast to the T-cell transfer pursued by the scottish firm you mentioned our approach aims to induce SARS-CoV-2 T-cell responses in vivo applying the identified T-cell epitopes as a peptide vaccine.
View 1 reply
Paul Taylor
replied on 03 July, 2020
Thanks for the reply, I find this approach an truly exciting one and hope it is successful, If there were a choice of vaccines in the battle against Covid-19 it would be my vaccine of choice. The potential of this approach is of course that the T cells generated to fight the current pandemic might well offer a cross ‘protection’ against future coronaviral epidemics. This will give any vaccine developed following this approach a unique advantage over any other vaccine simply inducing antibodies. I have a website that reviews coronavirus developments, https:coronaviruswales.uk I’ll post an article there to help advance your profile and look forward to the full paper in due course. Great research thank you.