Targets of T Cell Responses to SARS-CoV-2 Coronavirus in Humans with COVID-19 Disease and Unexposed Individuals

SARS-CoV-2 Peptides and Predicted Class I and Class II Epitopes

We recently predicted SARS-CoV-2 T cell epitopes utilizing the Immune Epitope Database and Analysis Resource (IEDB) (Dhanda et al., 2019, Vita et al., 2019). Utilizing bioinformatic approaches, we identified specific peptides in SARS-CoV-2 with increased probability of being T cell targets (Grifoni et al., 2020). We previously developed the megapool (MP) approach to allow simultaneous testing of large numbers of epitopes. By this technique, numerous epitopes are solubilized, pooled, and re-lyophilized to avoid cell toxicity problems (Carrasco Pro et al., 2015). These MPs have been used in human T cell studies of a number of indications, including allergies (Hinz et al., 2016), tuberculosis (Lindestam Arlehamn et al., 2016), tetanus, pertussis (Bancroft et al., 2016, da Silva Antunes et al., 2017), and dengue virus, for both CD4⁺ and CD8⁺ T cell epitopes (Grifoni et al., 2017, Weiskopf et al., 2015). Here, we generated MPs based on predicted SARS-CoV-2 epitopes. Specifically, one MP corresponds to 221 predicted HLA class II CD4⁺ T cell epitopes (Grifoni et al., 2020) covering all proteins in the viral genome, apart from the spike (S) antigen (CD4_R MP). The prediction strategy utilized is geared to capture ∼50% of the total response (Dhanda et al., 2018, Paul et al., 2015) and was designed and validated to predict dominant epitopes independently of ethnicity and HLA polymorphism. This approach takes advantage of the extensive cross-reactivity and repertoire overlap between different HLA class II loci and allelic variants to predict promiscuous epitopes, capable of binding many of the most common HLA class II prototypic specificities (Greenbaum et al., 2011, O’Sullivan et al., 1991, Sidney et al., 2010a, Sidney et al., 2010b, Southwood et al., 1998).

For the spike protein, to ensure that all T cell reactivity against this important antigen can be detected, we generated a separate MP covering the entire antigen with 253 15-mer peptides overlapping by 10-residues (MP_S, Table S1). As stated above, the MP used to probe the non-spike regions is expected to capture ∼50% of the total response. The use of overlapping peptides spanning entire open reading frames (ORFs) instead allows for a more complete characterization but also requires more cells. This factor should be kept in mind in terms of comparison of the magnitude of the CD4⁺ T cell responses to those pools.

In the case of CD8 epitopes, since the overlap between different HLA class I allelic variants and loci is more limited to specific groups of alleles, or supertypes (Sidney et al., 2008), we targeted a set of the 12 most prominent HLA class I A and B alleles, which together allow broad coverage (>85%) of the general population. Two class I MPs were synthesized based on epitope predictions for those 12 most common HLA A and B alleles (Grifoni et al., 2020), which collectively encompass 628 predicted HLA class I CD8⁺ T cell epitopes from the entire SARS-CoV-2 proteome (CD8 MP-A and MP-B).

Immunological Phenotypes of Recovered COVID-19 Patients

To test for the generation of SARS-CoV-2 CD4⁺ and CD8⁺ T cell responses following infection, we initially recruited 20 adult patients who had recovered from COVID-19 disease (Table 1 ). We also utilized peripheral blood mononuclear cell (PBMC) and plasma samples from local healthy control donors collected in 2015–2018 (see STAR Methods). Blood samples were collected at 20–35 days post-symptoms onset from non-hospitalized COVID-19 patients who were no longer symptomatic. SARS-CoV-2 infection was determined by swab test viral PCR during the acute phase of the infection. Verification of SARS-CoV-2 exposure was attempted both by lateral flow serology and SARS-CoV-2 spike protein receptor binding domain (RBD) ELISA (Stadlbauer et al., 2020), using plasma from the convalescence stage blood draw. Most patients were confirmed positive by lateral flow immunoglobulin (Ig) tests (Table 1). All patients were confirmed COVID-19 cases by SARS-CoV-2 RBD ELISA (Figures 1 and S1 ). All cases were IgG positive; anti-RBD IgM and IgA was also detected in the large majority of cases (Figures 1 and S1).

We defined a 21-color flow cytometry panel of mononuclear leukocyte lineage and phenotypic markers (Table S2) to broadly assess the immunological cellular profile of recovered COVID-19 patients (Figures 1 and S2 ). The frequency of CD3⁺ cells was slightly increased in recovered COVID-19 patients relative to non-exposed controls, while no significant differences overall were observed in the frequencies of CD4⁺ or CD8⁺ T cells between the two groups. Frequencies of CD19⁺ cells were somewhat decreased, while no differences were observed in the frequencies of CD3^–CD19^– cells or CD14⁺CD16^– monocytes (Figures 1 and S2). No evidence of general lymphopenia was observed in the convalescing patients, consistent with the literature. Next, we utilized the SARS-CoV-2 MPs to probe CD4⁺ and CD8⁺ T cell responses.

Identification and Quantitation of SARS-CoV-2-Specific CD4⁺ T Cell Responses

We utilized T cell receptor (TCR) dependent activation induced marker (AIM) assays to identify and quantify SARS-CoV-2-specific CD4⁺ T cells in recovered COVID-19 patients. Initial definition and assessment of human antigen-specific SARS-CoV-2 T cell responses are best made with direct ex vivo T cell assays using broad-based epitope pools, such as MPs, and assays capable of detecting T cells of unknown cytokine polarization and functional attributes. AIM assays are cytokine-independent assays to identify antigen-specific CD4⁺ T cells (Havenar-Daughton et al., 2016, Reiss et al., 2017). AIM assays have been successfully used to identify virus-specific, vaccine-specific, or tuberculosis-specific CD4⁺ T cells in a range of studies (Dan et al., 2016, Dan et al., 2019, Herati et al., 2017, Morou et al., 2019).

We stimulated PBMCs from 10 COVID-19 cases and 11 healthy controls (SARS-CoV-2 unexposed, collected in 2015–2018) with a spike MP (MP_S) and the class II MP covering the remainder of the SARS-CoV-2 orfeome (“non-spike,” MP CD4_R). A CMV MP was used as a positive control, while DMSO was used as the negative control (Figures 2 and S3 ). SARS-CoV-2 spike-specific CD4⁺ T cell responses (OX40⁺CD137⁺) were detected in 100% of COVID-19 cases (p < 0.0001 versus unexposed donors spike MP, Figures 2A and 2B. p = 0.002 versus DMSO control, Figure 2C). CD4⁺ T cell responses to the remainder of the SARS-CoV-2 orfeome were also detected in 100% of COVID-19 cases (p < 0.0079 versus unexposed donors non-spike MP, Figures 2A and 2B. p = 0.002, non-spike versus DMSO control, Figure 2C). The magnitude of the SARS-CoV-2-specific CD4⁺ T cell responses measured was similar to that of the CMV MP (Figure S3C). The concordance between SARS-CoV-2-specific CD4⁺ T cell measurements in independent experiments was high (p < 0.0002, Figure S3D). To assess functionality and polarization of the SARS-CoV-2-specific CD4⁺ T cell response, we measured cytokines secreted in response to MP stimulation. The SARS-CoV-2-specific CD4⁺ T cells were functional, as the cells produced IL-2 in response to non-spike and spike MPs (Figure 2D). Polarization of the cells appeared to be a classic T_H1 type, as substantial interferon (IFN)-γ was produced (Figure 2E), while little to no IL-4, IL-5, IL-13, or IL-17α was expressed (Figures S3G–S3J).

Thus, recovered COVID-19 patients consistently generated a substantial CD4⁺ T cell response against SARS-CoV-2. Similar conclusions were reached using stimulation index as the metric (Figures S3E and S3F). In terms of total CD4⁺ T cell response per donor (Figure 2A), on average ∼50% of the detected response was directed against the spike protein, and ∼50% was directed against the MP representing the remainder of the SARS-CoV-2 orfeome (Figure 2A). This is of significance, since the SARS-CoV-2 spike protein is a key component of the vast majority of candidate COVID-19 vaccines under development. Of note, given the nature of the MP_R peptide predictions, the actual CD4⁺ T cell response to be ascribed to non-spike ORFs was likely to be higher, addressed in further experiments below.

Identification and Quantitation of SARS-CoV-2-Specific CD8⁺ T Cell Responses

To measure SARS-CoV-2-specific CD8⁺ T cells in the recovered COVID-19 patients, we utilized two complementary methodologies, AIM assays and intracellular cytokine staining (ICS). The two SARS-CoV-2 class I MPs were used, CD8-A and CD8-B, with CMV MP and DMSO serving as positive and negative controls, respectively (Figures 3 and S4 ). CD8⁺ T cell responses were detected by AIM (CD69⁺CD137⁺) in 70% of COVID-19 cases (p < 0.0011 versus unexposed donors “CD8 total,” Figures 3A and 3B; p = 0.002, CD8-A or CD8-B versus DMSO control, Figure S4B). MP CD8-A contains spike epitopes, among epitopes to other proteins. The magnitude of the SARS-CoV-2 reactive CD8⁺ T cell responses measured by AIM was somewhat lower than the CMV MP (Figure S4C). Similar conclusions were reached using stimulation index (Figures S3D and S3E).

Independently, ICS assays detected IFN-γ⁺ SARS-CoV-2-specific CD8⁺ T cells in the majority of COVID-19 cases (Figures 3C and 3D). The majority of IFN-γ⁺ cells co-expressed granzyme B (Figures 3D and 3E). A substantial fraction of the IFN-γ⁺ cells expressed tumor necrosis factor (TNF) but not IL-10 (Figure 3D). Thus, the majority of recovered COVID-19 patients generated a CD8⁺ T cell response against SARS-CoV-2.

Relationship between SARS-CoV-2-Specific CD4⁺ T Cell Responses and IgG and IgA Titers

Most protective antibody responses are dependent on CD4⁺ T cell help. Therefore, we assessed whether stronger SARS-CoV-2-specific CD4⁺ T cell responses were associated with higher antibody titers in COVID-19 cases. Given that spike is the primary target of SARS neutralizing antibodies, we examined spike-specific CD4⁺ T cells. Spike-specific CD4⁺ T cell responses correlated well with the magnitude of the anti-spike RBD IgG titers (R = 0.81; p < 0.0001; Figure 4 A). Similar results were obtained using stimulation index (Figure S5 A). The non-spike SARS-CoV-2-specific CD4⁺ T cell response did not correlate as well with anti-spike RBD IgG titers (Figures 4B and S5B), consistent with a common requirement for intramolecular CD4⁺ T cell help (Sette et al., 2008). Anti-spike IgA titers also correlated with spike-specific CD4⁺ T cells (p < 0.0002, Figure S5). Thus, COVID-19 patients make anti-spike RBD antibody responses commensurate with the magnitude of their spike-specific CD4⁺ T cell response. We then assessed the relationship between the CD4⁺ and CD8⁺ T cell responses to SARS-CoV-2. SARS-CoV-2-specific CD4⁺ and CD8⁺ T cell responses were well correlated (R = 0.62. p = 0.0025, Figures 4C and S5). Thus, antibody, CD4⁺, and CD8⁺ T cell responses to SARS-CoV-2 were generally well correlated.

Pre-existing Cross-Reactive Coronavirus-Specific T Cells

While spike- and non-spike-specific CD4⁺ T cell responses were detectable in all COVID-19 cases, cells were also detected in unexposed individuals (Figures 3A and 3B). These responses were statistically significant for non-spike-specific CD4⁺ T cell reactivity (non-spike, p = 0.039; spike, p = 0.067; Figures 5A and 5B). Non-spike-specific CD4⁺ T cell responses were above the limit of detection in 50% of donors based on stimulation index (SI) (Figure S3E). All of the donors were recruited between 2015 and 2018, excluding any possibility of exposure to SARS-CoV-2. Four human coronaviruses are known causes of seasonal “common cold” upper-respiratory tract infections: HCoV-OC43, HCoV-HKU1, HCoV-NL63, and HCoV-229E. We tested the SARS-CoV-2 unexposed donors for seroreactivity to HCoV-OC43 and HCoV-NL63 as a representative betacoronavirus and alphacoronavirus, respectively. All donors were IgG seropositive to HCoV-OC43 and HCoV-NL63 RBD, to varying degrees (Figure 5C), consistent with the endemic nature of these viruses (Gorse et al., 2010, Huang et al., 2020, Severance et al., 2008). We therefore examined whether these represented true pan-coronavirus T cells capable of recognizing SARS-CoV-2 epitopes.

SARS-CoV-2 ORF Targets of CD4⁺ and CD8⁺ T Cells

A most pressing, yet unresolved, set of issues in understanding SARS-CoV-2 immune responses is what antigens are targeted by CD4⁺ and CD8⁺ T cells, whether the corresponding antigens are the same or different, and how do they reflect the antigens currently considered for COVID-19 vaccine development. We synthesized sets of overlapping peptides spanning the entire sequence of SARS-CoV-2 and pooled them separately so that each pool would represent one antigen (with the exception of nsp3, for which two pools were made; Table S1).

In the case of CD4⁺ T cell responses, no obvious pattern of antigen specificity was observed based on SARS-CoV-2 genome organization; however, coronaviruses increase protein synthesis of certain ORFs in infected cells via subgenomic RNAs. Accounting for the relative abundance of subgenomic RNAs (Figure 6 A) (Irigoyen et al., 2016, Snijder et al., 2003, Xie et al., 2020), the ORFs were re-ordered based on predicted protein abundance (Figure 6B). A clear hierarchy of SARS-CoV-2-specific CD4⁺ T cell targets was then apparent, with the majority of the CD4⁺ T cell response in COVID-19 cases directed against highly expressed SARS-CoV-2 ORFs spike, M, and N. On average, these antigens accounted for 27%, 21%, and 11% of the total CD4⁺ T cell response, respectively. Most COVID-19 cases also had CD4⁺ T cells specific for SARS-CoV-2 nsp3, nsp4, and ORF8 (Figure 6B), on average each accounting for ∼5% of the total CD4⁺ T cell response (Figure 6C). E, ORF6, hypothetical ORF10, and nsp1 are all small antigens (or potentially not expressed, in the case of ORF10) and were most likely predominantly unrecognized as a result. These results are somewhat unexpected, because data for other coronaviruses, from 27 different studies curated in the IEDB, reported that spike accounted for nearly two-thirds of reported CD4⁺ T cell reactivity (Table S3). N accounted for most of the remaining epitopes in the published literature, although human N-specific CD4⁺ T cell responses were not observed in one of the most comprehensive studies of human SARS-CoV-1 T cell responses (Li et al., 2008). Coronavirus M has not previously been described as a prominent target of CD4⁺ T cell responses (Table S3). In sum, these results, fully scanning the SARS2 orfeome, demonstrate a pattern of robust and diverse SARS-CoV-2-specific CD4⁺ T cell reactivity in convalescing COVID-19 cases that correlated largely with predicted viral protein abundance in infected cells.

When examining the non-exposed donors, the pattern of CD4⁺ T cell targets changed. While S was still a relatively prominent target (23% of total, on average), there was no, or marginal, reactivity against SARS-CoV-2 N and M. Among donors with detectable CD4⁺ T cells, a shift in reactivity was observed toward SARS-CoV-2 nsp14 (25%), nsp4 (15%) and nsp6 (14%) (Figures 6B and 6C). SARS-CoV-2-reactive CD4⁺ T cells were detected in at least six different unexposed donors, demonstrating that the cross-reactivity is relatively widely distributed (Figure S6 A).

Having scanned the full SARS-CoV-2 orfeome for CD4⁺ T cell reactivity in multiple donors, it was possible to assess whether the epitope prediction MP approach successfully enriched for SARS-CoV-2 epitopes targeted by human CD4⁺ T cells. When the total reactivity observed with the CD4_R MP was plotted versus the sum total of all antigen pools (excluding spike, given that spike predictions were not included in the CD4_R MP), a significant correlation was observed (p < 0.0002, Figure S6C). The single MP-R captured ∼50% (44% +/− range 28%–80%) of the non-spike response per COVID-19 donor, demonstrating the success of the prediction approach, which, as mentioned above, was devised to attempt to capture approximately 50% of the total response (Dhanda et al., 2018, Paul et al., 2015).

In the case of CD8⁺ T cell responses, the data in the literature from other coronaviruses (57 different studies curated in the IEDB; Table S3) reported spike accounting for 50% and N accounting for 36% of the defined epitopes. In a large study of human SARS-CoV-1 responses, spike was reported as essentially the only target of CD8⁺ T cell responses (Li et al., 2008), while in a study of MERS CD8⁺ T cells, responses were noted for spike, N and a pool of M/E peptides (Zhao et al., 2017). Few epitopes have been reported from other coronavirus antigens (Table S3). Here, we scanned the full SARS-CoV-2 orfeome for CD8⁺ T cell recognition. Our data indicate a somewhat different pattern of immunodominance for SARS-CoV-2 CD8⁺ T cell reactivity (Figures 6D and 6E), with spike protein accounting for ∼26% of the reactivity, and N accounting for ∼12%. Significant reactivity in COVID-19 recovered subjects was derived from other antigens, such as M (22%), nsp6 (15%), ORF8 (10%), and ORF3a (7%) (Figures 6D and 6E). In unexposed donors, SARS-CoV-2-reactive CD8⁺ T cells were detected in at least four different donors (Figure S7 ), with less clear targeting of specific SARS-CoV-2 proteins than was observed for CD4⁺ T cells, suggesting that coronavirus CD8⁺ T cell cross-reactivity exists but is less widespread than CD4⁺ T cell cross-reactivity.

Limitations and Future Directions

Caveats of this study include the sample size and the focus on non-hospitalized COVID-19 cases. Sample size was limited by expediency. The focus on non-hospitalized cases of COVID-19 is a strength, in that these donors had uncomplicated disease of moderate duration, and thus it was encouraging that substantial CD4⁺ T cell and antibody responses were detected in all cases, and CD8⁺ T cell responses in the majority of cases. Complementing these data with MP T cell data from acute patients and patients with complicated disease course will also be of clear value, as will studies on the longevity of SARS-CoV-2 immunological memory. Additionally, lack of detailed information on common cold history or matched blood samples pre-exposure to SARS-CoV-2 prevents conclusions regarding the abundance of cross-reactive coronavirus T cells before exposure to SARS-CoV-2 and any potential protective efficacy of such cells. Finally, full epitope mapping in the future will add important detailed resolution of the human coronavirus-specific T cell responses.

In sum, we measured SARS-CoV-2-specific CD4⁺ and CD8⁺ T cells responses in COVID-19 cases. Using multiple experimental approaches, SARS-CoV-2-specific CD4⁺ T cell and antibody responses were observed in all COVID-19 cases, and CD8⁺ T cell responses were observed in most. Importantly, pre-existing SARS-CoV-2-cross-reactive T cell responses were observed in healthy donors, indicating some potential for pre-existing immunity in the human population. ORF mapping of T cell specificities revealed valuable targets for incorporation in candidate vaccine development and revealed distinct specificity patterns between COVID-19 cases and unexposed healthy controls.

Key Resources Table

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Experimental Model and Subject Details

Method Details

Quantification and Statistical Analysis

Data and statistical analyses were done in FlowJo 10 and GraphPad Prism 8.4, unless otherwise stated. The statistical details of the experiments are provided in the respective figure legends. Data plotted in linear scale were expressed as Mean + Standard Deviation (SD). Data plotted in logarithmic scales were expressed as Geometric Mean + Geometric Standard Deviation (SD). Correlation analyses were performed using Spearman, while Mann-Whitney or Wilcoxon tests were applied for unpaired or paired comparisons, respectively. Details pertaining to significance are also noted in the respective legends. T cell data have been calculated as background subtracted data or stimulation index. Background subtracted data were derived by subtracting the percentage of AIM⁺ cells after SARS-CoV-2 stimulation from the DMSO stimulation. Stimulation Index was calculated instead by dividing the percentage of AIM+ cells after SARS-CoV-2 stimulation with the percentage of AIM⁺ cells derived from DMSO stimulation. If the AIM⁺ cells percentage after DMSO stimulation was equal to 0, the minimum value across each cohort was used. When two stimuli were combined together, the percentage of AIM⁺ cells after SARS-CoV-2 stimulation was combined and either subtracted twice or divided by twice the value of the percentage of AIM+ cells derived from DMSO stimulation. Additional data analysis techniques are described in the STAR Methods sections above.