Memory cytotoxic SARS-CoV-2 spike protein-specific CD4+ T cells associate with viral control

doi:10.21203/rs.3.rs-1317569/v1

Little is known of the role of cytotoxic CD4⁺ T-cells in the control of viral replication. Here, we investigate CD4⁺ T-cell responses to three dominant SARS-CoV-2 epitopes and evaluate antiviral activity, including cytotoxicity and antiviral cytokine production. Diverse T cell receptor (TCR) usage including public TCRs were identified; surprisingly, cytotoxic CD4⁺ T-cells were found to have signalling and cytotoxic pathways distinct from classical CD8⁺ T-cells, with increased expression of chemokines and tissue homing receptors promoting migration. We show the presence of cytolytic CD4⁺ T-cells during primary infection associates with COVID-19 disease severity. Robust immune memory 6-9 months post-infection or vaccination provides CD4⁺ T-cells with potent antiviral activity. Our data support a model where CD4⁺ killer cells drive immunopathogenesis during primary infection and CD4⁺ memory responses are protective during secondary infection. Our study highlights the unique features of cytotoxic CD4⁺ T-cells that use distinct functional pathways, providing preventative and therapeutic opportunities.

Understanding the immune phenotype of SARS-CoV-2-specific T cells that are protective or pathogenic is pivotal to define therapeutic and prophylactic strategies to manage the COVID-19 pandemic¹. Early T cell responses against diverse SARS-CoV-2 proteins associate with clinical protection^{2, 3, 4, 5}. In contrast, patients with severe disease have reduced frequencies of circulating T cells and increased activation markers^{6, 7, 8, 9, 10, 11, 12}. There is an urgent need to for in-depth functional characterization of virus-specific T cells during natural infection and following vaccination.

In addition to their “helper” role, mainly through interaction with antigen presenting cells and soluble mediators, the direct effector and antiviral function of virus-specific CD4⁺ T cells have been studied in the setting of several human virus infection settings, such as influenza, cytomegalovirus (CMV) and Epstein-Barr virus (EBV)¹³. CD4⁺ cytotoxic T cell (CTL) responses were recently recognized to play an important role in the immune response to viral infections. However, most mechanistic studies investigating cytotoxic CD4⁺ T cells were conducted in murine models; therefore, it is unclear if insights gained from such studies are relevant to human CTL responses¹³. Recent SARS-CoV-2 CD4⁺ T cell studies have been focused on the regulatory and helper function of these cells, such as T follicular helper cells (Tfh) and their role in assisting antibody production^{14, 15}. Very little attention has been paid to the direct effector function and antiviral activity of SARS-CoV-2 specific CD4⁺ memory T cells, possibly due to the limited experimental tools available.

Current advances in technologies that combine population specific single cell transcriptomic profiling with TCR sequencing analysis and in vitro T cell functional assays have enabled us to study the quality of T cell responses and their ability to control virus replication⁵. Multiple immunodominant SARS-CoV-2 spike protein-specific T cell epitopes have been identified and are frequently detected in individuals who have recovered from SARS-CoV-2 infection and following vaccination^{16, 17, 18}.

In this study we focus on three most dominant CD4⁺ spike-specific epitopes identified in a cohort of individuals who had recovered from SARS-CoV-2¹⁸. We explore correlations with the quality of these spike-specific T cell responses and study their TCR repertoires, public TCR usage and their association with antiviral activity. We examine the potential mechanisms of antiviral activity in a specific cytotoxic subset of CD4⁺ T cells using single cell transcriptome analyses of expanded T cell clones bearing the same TCRs as identified in single cell analysis.

1. Identification of three immunodominant spike-specific CD4 ⁺ T cell responses with effector function in individuals recovered from COVID-19

We previously identified three dominant SARS-CoV-2 spike protein (S) CD4⁺ T cell epitopes: S_166−180 (CTFEYVSQPFLMDLE), S_751−765 (NLLLQYGSFCTQLNR) and S_866−880 (TDEMIAQYTSALLAG)¹⁸. The HLA-restriction of these epitopes was defined using interferon (IFN)-γ -ELISPOT or peptide-MHC-Class II tetramer staining (Extended Data Fig. 1). S_166−180-specific T cells are restricted by HLA-DPB1*04:01 and are also detected in vaccinated healthy donors¹⁹. The remaining two spike epitopes are presented by HLA-DRB1*15:01 as defined by peptide-MHC-Class II tetramer staining. Our cohort includes 45 individuals that had recovered from COVID-19, comprising 26 mild cases and 19 severe cases (including 6 critical cases) based on oxygen requirements during the acute illness (Supplementary Table 1 and Extended Data Fig. 2). Among them, 26 (26/41, 63.41%) were HLA-DPB1*04:01 positive (13 mild and 13 severe cases) and 17 (17/45, 37.78%) carried HLA-DRB1*15:01 (7 mild and 10 severe cases) (Fig. 1a). Our ex vivo IFN-γ ELISpot assays with convalescent samples collected 1-3 months post-infection revealed that 68% (17/25) of DPB1*04:01 individuals responded to S_166−180, while 85.71% (12/14) and 71.43% (10/14) of DRB1*15:01 positive patients showed responses to S_751−765 and S_866−880 respectively (Fig. 1b). This further confirmed the immunodominance of these three epitopes. Among S_166−180 responders, a high proportion of individuals (64.7%, 11/17) had recovered from severe disease (Fig. 1c) and) with significantly stronger responses compared to individuals who had recovered from mild symptoms (P = 0.031; Fig. 1d). Although our data does not provide statistical evidence for an association of S_751−765- and S_866−880- specific T cell responses with disease severity, we observed a higher proportion (60%, 6/10) of S_866−880 responders who had recovered from mild disease (Fig. 1c and d).

To further characterize these three dominant T cell responses, we generated 50 S_166−180-specific T cell clones, 54 S_751−765-specific T cell clones and 49 S_866−880-specific T cell clones from convalescent samples and evaluated their functionality. T cell receptors (TCRs) of each clone were also sequenced (Supplementary Table 2). All clones expressed cytokines including TNF-α, IFN-γ and IL-2 upon antigen activation (Fig. 1e). S_866−880-specific T cell clones displayed the highest antigen-sensitivity, with the lowest EC₅₀ calculated from TNF-α, IFN-γ and IL-2 production. Interestingly, in addition to cytokine expression, we observed a substantial proportion of T cell clones (4 from S_166−180, 8 from S_751−765 and 22 from S_866−880) capable of killing target cells by more than 10%, with the highest level of killing being 60% for S_866−880 (Fig. 1f). This highlights the existence of SARS-CoV-2-specific CD4⁺ CTLs following SARS-CoV-2 infection. We defined CD4⁺ killer cells as clones with a killing capacity of > 10%. Among these spike-specific T cell clones, S_866−880-specific T cells showed the strongest cytotoxic and killing capacity (Fig. 1f, 61% P < 0.001) and proportion of killer cells (Fig. 1g, 60% P < 0.001) whereas S_166−180 T cells showed the least cytotoxic killing potential. The highest effector function and antiviral efficacy was seen for S_866−880, suggesting significant effector function of cytotoxic CD4⁺ T cell acting on virus infected cells and controlling virus replication.

2. Spike-specific CD4 ⁺ T cell antiviral activity is associated with cytotoxic activity, cytokine production and antigen load

Taking advantage of our in vitro SARS-CoV-2 virus infection system⁵, we assessed the antiviral activity of these spike-specific CD4⁺ T cells. In brief, Epstein Barr virus (EBV)-transformed B cell lines (BCLs) ectopically expressing ACE2 were infected with SARS-CoV-2 (Fig. 2a) then cocultured with spike-specific CD4⁺ T cells. T cell recognition of virus-infected cells was examined by intracellular cytokine staining (ICS), and the suppression of SARS-CoV-2 replication by T cells was assessed by quantifying the number of viral copies in the infected cells after 48hrs of co-culturing. Our data showed that CD4⁺ T cells targeting these three dominant spike epitopes can recognise virus-infected cells and produce cytokines after activation (Fig. 2b), with S_866−880−specific-T cells having the highest proportion of TNF-α (P < 0.001), IFN-γ (P < 0.001) and IL-2 (P < 0.001) producing CD4⁺ T cells after encountering virus-infected BCLs (Fig. 2c). More importantly, CD4⁺ T cell clones targeting each of the three epitopes exhibited, to varying extents, direct effector function against the virus, capable of suppressing virus replication (Fig. 2d). In particular, T cells targeting S_866−880 showed significantly better antiviral efficacy compared to T cells targeting epitope S_751−765 restricted by the same HLA-DRB1*15:01 (P = 0.002). We sought to investigate whether this strong antiviral activity was a result of high effector function or exposure to high antigen loads by examining single cell gene expression from tetramer-sorted short term cultured T cell lines. First, we compared single cell gene expression profiles of T cells targeting S_751−765 (n = 1629) and S_866−880 cells (n = 2233) (Fig. 2e). We observed significant upregulation of genes encoding effector molecules, such as cytotoxic molecules KLRK1 (P = 1.37 x 10⁻⁴⁴), GZMB (P = 6.13 x 10⁻⁴) and GZMK (P = 5.13 x 10⁻¹⁰), and cytokines CCL3 (P = 2.79 x 10⁻⁶), CCL4 (P = 4.4 x 10⁻⁸), TNF (P = 9.08 x 10⁻¹²) in S_866−800-specific T cells compared to S_751−765 -specific T cells (Fig. 2f). This further confirms the cytotoxic potential of S_866−880-specific T cells.

To estimate the antigen load of each epitope on virus-infected cells, we cultured T cells with the same number of target cells either infected with SARS-CoV-2 or loaded with variable amounts of peptide, then assessed T cell responses by ICS (Fig. 2g). The antigen-load in virus-infected cells was equivalent to the peptide concentration that elicited a similar level of response to virus-infected cells. Surprisingly, much lower concentrations (equivalent to 0.06µM) of S_866−880 peptide were presented on virus-infected cells, when compared to S_166−180 (about 0.11µM) and S_751−765 (equivalent to 2.57µM) (Fig. 2h). Our data suggests that the higher antiviral activity of S_866−880-specific T-cells is likely to result from cytotoxicity and high antigen sensitivity even when antigen load on the surface of infected cells was relatively low.

Next, we compared the cytotoxic activity of S_751−765 and S_866−880 tetramer-sorted short term cultured single cells isolated from those who had recovered from mild or severe acute COVID-19 (n = 2 mild, 2728 cells; n = 2 severe, 1823 cells; Fig. 2i). We observed that T cells isolated from patients who had recovered from severe disease were more activated and expressed higher levels of T cell effector function genes CD69 (P = 2.75 x 10⁻⁵), CCL5 (P = 1.61 x 10⁻²²), IL2RG (P = 3.27 x 10⁻³⁵), MX1 (P = 3.76 x 10⁻¹³), in particular cytotoxic molecules such as GZMB (P = 6 x 10⁻¹⁷) and GZMM (P = 3.87 x 10⁻³), compared to cells from mild cases (Fig. 2j). Collectively, these data suggest that cytotoxic CD4⁺ T cells may play a role in the immunopathogenesis of the severe disease.

3. Diverse TCR usage and public TCR clonotypes are commonly observed among immunodominant spike T cells

Single cell TCR sequences of ex vivo stimulated and cytokine sorted S_166−180 (n = 152 cells from 5 patients), tetramer-sorted S_751−765 (n = 77 cells from 4 patients) and tetramer-sorted S_866−880 (n = 100 cells from 4 patients) -specific T cells from 1-3 months convalescent patients were analysed to assess TCR diversity. Interestingly, each epitope displays a different dominant αV gene usage, with TRAV35, TRAV12-1 and TRAV26-1 being dominant for S_166−180-, S_751−765-, and S_866−880-specific T cells respectively, where each dominant αV gene pairs with multiple different βV genes (Fig. 3a). This highlighted the importance of the α chain in these spike epitope TCRs; hence we decided to focus on the αV chain when investigating TCR clonotypes further. Single cells from epitope specific T cells sampled at different timepoints (S_166−180 ex vivo acute, ex vivo 1-3 months, short term culture 9 months convalescence from total 5 patients; S_751−765 and S_866−880 ex vivo acute, ex vivo 1-3 months, short term culture 6 months and 9 months convalescence from total 6 patients) were analysed together to identify public clonotypes (CDR3 amino acid and V gene usage), which are unique clonotypes shared among more than one unrelated patient. We found that public α clonotypes (CDR3α and TRAV) were shared by many patients whereas β clonotypes (CDR3β and TRBV) were shared by a smaller number of patients. For example, the maximum number of patients with one particular α clonotype (CAGTGNNRKLIW, TRAV 25) from S_866−880-specific T cells could be 6/6 whereas the highest number of patients sharing any β clonotype from the same epitope was 3/6 (Table 1 and Supplementary Table 3). Examination of paired αβ public clonotypes revealed that the β public clonotypes were more diverse than α public clonotypes, with no clear dominant Vβ gene usage for any epitope. By focusing on α clonotypes, we reasoned that we would be better able to study the dominant α V genes for each epitope, which should also capture the diversity of β clonotypes.

Next, we sought to compare the proportion of public and private clonotypes from each epitope. S_166−180-specific T cells have higher proportions of TCRs matching public clonotypes compared to the other two epitopes (n = 21 S_166−180 public α clonotypes, n = 16 S_751−765 public, n = 19 S_866−880 public; Fig. 3b). We also investigated if there were any differences between public and private clonotype expansion for each epitope at different timepoints (acute, 1-3 months and 6-9 months convalescence) and found that T cells with public clonotypes were present at higher frequencies compared to cells with private clonotypes, with the exception of S_866−880 at 6-9 months convalescence (acute: S_166−180 P = 1.7 x 10⁻⁶, S_751−765 P = 0.04, S_866−880 P = 0.0098; 1-3 months: S_166−180 P = 1.4 x 10⁻¹⁵, S_751−765 P = 6.3 x 10⁻⁸, S_866−880 P = 3.8 x 10⁻⁶; 6-9 months: S_166−180 P = 3.1 x 10⁻⁹, S_751−765 P = 0.026, S_866−880 P = 0.15; Fig. 3c). To further investigate differences between T cells with public and those with private TCRs, we looked at single cell gene expression between these two groups. Ex vivo stimulated and cytokine sorted S_166−180-specific single cells with public TCRs (n =128 cells) had higher expression of activation markers [IFNG (P =2.92 x 10⁻⁵), FASLG (P = 9 x 10⁻⁸), CD40LG (P = 4.86 x 10⁻¹⁶), BTLA (P = 1.48 x 10⁻³), PDCD1 (P = 1.61 x 10⁻³)], Th1 activators [ID2 (P = 1.07 x 10⁻⁵)] and Th2 suppressors [NR4A2 (P = 1.94 x 10⁻¹⁴) and IRF4 (P = 1.18 x 10⁻⁶), Fig. 3d] compared with T cells with private TCRs (n = 285 cells). This may indicate that high expansion of T cells with public clonotypes might drive a Th1 response in convalescent COVID-19 patients. Conversely, tetramer-sorted S_866−880-specific T cells from short term culture with private TCRs (n = 1325 cells) display higher expression of cytotoxic genes [KLRK1 (P = 5.65 x 10⁻⁸), GZMA (P = 1.22 x 10⁻³), GZMK (P = 2.95 x 10⁻⁵), NKG7 (P = 3.92 x 10⁻⁴), CCL5 (P = 1.19 x 10⁻⁸), Fig. 3e] compared with T cells with public TCRs (n = 194 cells). Similar results were observed with S_751−765-specific T cells (Extended Data Fig. 3). In summary, these results highlight the potential different functional activities between epitope-specific T cells.

4. Cytotoxicity and function of spike-specific CD4 ⁺ T cells are regulated by more than TCR usage alone.

We next investigated whether the effector function of spike-specific CD4⁺ T cells was due to their broad TCR usage by comparing antigen sensitivity and killing capacity of T cell clones bearing the same TCR. We found that T cells with the same TCRs had very different antigen sensitivities, reflected in a wide range of EC₅₀ values for IFN-γ, TNF-α and IL-2 production (Fig. 4a). These T cell clones also produced different cytokine profiles upon antigen stimulation (Extended Data Fig. 4). Moreover, S_866−880-specific T cell clones with shared TCRs had distinct killing capacities (Fig. 4b), for example, clone 2 had 40% killing capacity while clone 3 showed minimal killing despite sharing the same TCR (TRAV12-1/TRBV5-1). This suggested that the cytolytic activity of these spike-specific CD4⁺ T cells was due to factors beyond TCR usage. This observation was further confirmed by variable expression levels of cytotoxic molecule genes, such as PRF1, GZMA and GZMB, in tetramer-sorted S_866−880 single cells (n = 399 cells) with the same TCR usage (Fig. 4c). Indeed, we noted a positive association between killing ability with degranulation activity, as measured by CD107a expression, upon antigen stimulation (R = 0.413, P = 0.001, Fig. 4d). This suggests that high expression of CD107a can act as a potential marker for CD4⁺ CTLs.

5. Antiviral activity of spike-specific CD4 ⁺ T cells strongly correlates with their killing capacity and IL-2 production

As previously highlighted, a number of spike-specific CD4⁺ T cell clones were capable of suppressing virus replication (Fig. 2d). We further examined whether this antiviral effector function was mediated via direct killing of virus-infected cells or by the expression of soluble inhibitory factors. Firstly, our data demonstrated that the antiviral activity of the CD4⁺ T cell clones strongly correlated to the proportion of cells producing IL-2 upon stimulation with virus-infected cells (R = 0.226, P = 0.030, Fig. 5a), but did not correlate with the proportion of cells producing IFN-γ or TNF-α (Extended Data Fig. 5). Secondly, we found a significant association between the killing capacity of T cells and their potential to control virus replication. Indeed, we observed that killer cells (more than 10% killing capacity) could suppress virus replication more efficiently than non-killers (less than 10% of killing) (Fig. 5b R = 0.390, P < 0.001), suggesting the importance of direct killing of virus-infected cells in viral control by CD4⁺ CTLs. Strikingly, some non-killer cells were capable of inhibiting viral replication as efficiently as killer cells, indicating the potential contribution of soluble factors to viral control (Fig. 5c). Subsequently we compared cytokine and chemokine production of these viral suppressing non-killer CD4⁺ T cells with other non-killer cells (Fig. 5d). We noticed that non-killer CD4⁺ T cells capable of suppressing virus replication (suppressor clone) produced significantly higher concentrations of IL-2 (P = 0.040) and IFN-γ (P = 0.040) than non-killer CD4⁺ T cells incapable of viral suppression (non-suppressor clone), highlighting a role for IL-2 and possibly IFN-γ in CD4⁺ T cell control of SARS-CoV-2 infection.

6. Cytotoxic spike-specific CD4 ⁺ CTLs utilise distinct cytolytic pathways with increased migration potential

Activated CD8⁺ CTLs carry out their killing function primarily by releasing cytotoxic granules such as perforin and granzymes, which subsequently induce apoptosis of target cells. To determine whether the killing of target cells by spike-specific CD4⁺ CTLs was also mediated through the perforin-dependent pathway, S_866−880−specific CD4⁺ T cell clones were treated with concanamycin A (CMA), an inhibitor of perforin, prior to adding to target cells loaded with peptide. The cytolytic activity mediated by CD4⁺ T cell clones was completely blocked by CMA, resulting in decreased or ablated killing capacity (Fig. 6a), and reduced or no viral suppression (Fig. 6b). To identify other factors involved with this perforin-mediated effective viral control, we grouped tetramer-sorted S_866−880−specific single cells from short term cultured lines into perforin-high (n = 693 cells) and perforin-low (n = 724 cells) subsets according to their perforin module scores (Supplementary Table 4) and compared their gene expression profiles (Fig. 6c). Perforin-high T cells not only upregulate cytotoxic associated genes such as GZMA (P = 4.43 x 10⁻¹⁴⁴), NKG7 (P = 1.03 x 10⁻¹³⁹), GZMK (P = 4.34 x 10⁻³⁹), KLRD1 (P = 2.01 x 10⁻²) and CTSW (P = 6.54 x 10⁻³⁵) but also genes associated with migration for example: chemokines such as CCL3 (P = 8.48 x 10⁻¹⁶), CCL4 (P = 2.26 x 10⁻⁵⁹), CCL5 (P = 9.29 x 10⁻⁸²), chemokine receptors CCR3 (P = 4.6 x 10⁻³) and IL2RG (P = 3.17 x 10⁻²⁴), tissue homing receptors ITGB1 (P = 1.19 x 10⁻⁸), ITGA4 (P = 1.26 x 10⁻³), ITGAL (P = 3.78 x 10⁻⁴) and inhibitory receptors such as TIGIT (P = 2.82 x 10⁻⁴) and KLRG1 (P = 1.94 x 10⁻²) (Fig. 6d). This suggests there is increased migration potential of activated cytotoxic CD4⁺ T cells to infected tissue.

To understand whether these spike-specific CD4⁺ CTLs utilise similar cytolytic pathways as CD8⁺ CTLs, we compared single cell gene expression, in particular the expression of effector molecules, between perforin-high CD4⁺ cytotoxic T cells (tetramer-sorted S_866−880 specific, n = 693) and CD8⁺ cytotoxic T cells (Pentamer-sorted HLA-B*0702/NP_105−113-specific, n = 1041) from short term cultured lines⁵ (Fig. 6e). We found that CD8⁺ CTLs expressed significantly higher levels of granzyme B and H (GZMB, average log₂ fold change = -2.68, P = 2.24 x 10⁻²⁵; GZMH, average log₂ fold change = -0.57, P = 2.09 x 10⁻⁵), classic cytolytic molecules secreted by CD8⁺ T cells and induce apoptosis in target cells (Fig. 6f). Compared to CD8⁺ CTLs, CD4⁺ CTLs displayed upregulated expression of other cytolytic molecules such as GZMM (P = 1.91 x 10⁻¹⁰), GZMK (P = 1.78 x 10⁻¹⁰), NKG7 (P = 9.51 x 10⁻¹²), KLRK1 (P = 8.3 x10⁻⁶) and CTSW (P = 2.86 x 10⁻¹¹), suggesting the involvement of different cytolytic machineries in CD4⁺ CTL killing.

7. Dominant spike-specific CD4 ⁺ T cells are maintained nine months after infection with diverse TCR repertoire and preserved antiviral activity.

To examine whether memory T cells established following natural infection could provide sufficient protection against secondary viral infection, we collected peripheral blood mononuclear cells (PBMCs) from the same patients 6-9 months after infection. Although T cell responses to these three spike epitopes (S_166−180, S_751−765 and S_866−880) significantly declined six months after infection (Fig. 7a), we were able to sort, sequence, and expand these spike-specific CD4⁺ T cells after in vitro antigen stimulation. We discovered that the diversity of the TCR repertoire of these CD4⁺ T cells were maintained 6-9 months after infection (Fig. 7b). We then assessed the antiviral efficacy of these bulk spike-specific T cell lines using our in vitro SARS-CoV-2 infection assays. The bulk lines targeting all three epitopes elicited strong responses against BCLs infected with SARS-CoV-2 (Victoria strain) and variants of concerns (VOCs; Delta and Omicron). The CD4⁺ T cell lines produced profoundly elevated level of IFN-γ, TNF-α and IL-2 (Fig. 7c and Extended Fig. 6) and showed significant cytotoxic potential with upregulated CD107a expression (Fig. 7d). In addition, we found that these antigen-specific CD4⁺ bulk cell lines are capable of suppressing SARS-CoV-2 replication and showed strong inhibition against VOCs (Fig. 7e). Our data highlight the protective role of these dominant spike-specific CD4⁺ T cells in secondary infection against different SARS-CoV-2 variants.

In this study, we focus on the three most dominant spike-specific CD4⁺ T cell responses identified in our cohort restricted by common HLA Class II alleles (S_166−180-DPB1*04:01, S_751−765-DRB1*15:01 and S_866−880-DRB1*15:01). Significant differences are observed between these responses in functional avidity/antigen sensitivity, cytotoxic capacity, cytokine profile and their effector function in response to virus infected cells and suppression of virus replication. S_866−880 specific T cells show the strongest responses to virus infected cells and direct control of virus replication, which was associated with potent cytotoxic capacity and high antigen sensitivity to cytokine production, such as IL-2. S_166−180 responses feature dominant public TCR usage, in particular TRAV35, that is biased to TH1 cell subset²⁰ (i.e. high ID2 expression) and suppressed TH2 transcriptomic profiles²¹ (i.e. high TH2 suppressor IRF4 and NR4A2 expression). TCR repertoire analysis surprisingly reveals that over 30% of TCRs are public in all three epitope specific responses at all time points studied, including public clonotypes with shared αV and βV sequences. There are also different dominant TRAV usages across these three epitope specific T cells, with S_166−180 dominated by TRAV35, S_751−765 by TRAV12-1 and S_866−880 by TRAV25, TRAV26-1 and TRAV12-1 clonotypes.

Interestingly, the estimated antigen load from antigen sensitivity assays and virus suppression assays reveals significant differences among these three epitopes. Despite over 10 times less estimated S_166−180 epitope peptide loaded on the MHCII of infected cells than S_751−765, S_166−180 and S_751−765 have similar antiviral activity for both cytokine production and direct viral control. The fact that S_166−180 responses display much higher overall antigen sensitivity than S_751−765 may compensate the lower antigen load on the cell surface. S_866−880 displays the strongest antiviral responses and antigen sensitivity among all three epitope responses, despite having the lowest antigen load. These results suggest that both antigen sensitivity and viral load on infected cells or antigen presenting cells are likely to play critical roles in viral control by these spike-specific CD4⁺ T cells, and need to be considered carefully when peptide-based approaches are only used to measure T cell responses.

CD4⁺ cytotoxic T cell responses observed in virus infection and cancer may contributed to disease pathogenesis^{22, 23} or protection^{24, 25, 26}. We observe potent cytotoxic capacity and antiviral efficacy of S_866−880 CD4+ T cells. Looking further into this response, we first find that cytotoxicity is likely primarily mediated by perforin, which is in agreement with previous reports^{27, 28, 29}. When we compare cells at a single cell level, we find a significant association of CD4⁺ cytotoxic T cells in individuals recovered from severe in comparison with mild disease; however, the number of patients studied are small, therefore further large-scale studies are needed. Using single cells with high and low perforin scores as a marker for CD4⁺ cytotoxic T cells, we find that perforin-high cells have the potential to migrate to affected tissue efficiently by expressing high levels of chemokines (CCL3, CCL4 and CCL5) and tissue homing receptors (ITGB1, ITGA4 and ITGAL), in line with the observation that CD4⁺ CTLs are expanded in the lung of patients with severe COVID-19³⁰. Our data highlights the potential pathogenic role of these potent cytotoxic T cells during primary virus infection. These cells may accumulate in the infected organ/tissue, causing excessive inflammation and bystander killing of cells with elevated MHC Class II expression, including professional antigen presenting cells. This hypothesis merits further investigation.

We also find that cytotoxic CD4⁺ T cells may use different signalling and cytotoxic molecules compared to classical CD8⁺ T cells; CD4⁺ CTLs express GZMK and GZMM significantly higher whereas CD8⁺ CTLs express much higher GZMB and GZMH. In addition, NK activation receptor NKG2D (KLRK1) and NKG7, known to be important for cytotoxic degranulation³¹, are highly expressed on CD4⁺ CTLs but not classical CD8⁺ CTLs.. Importantly, it is known that NKG2D ligands are expressed in the lung affected by COVID-19, therefore the high NKG2D expression on cytotoxic CD4⁺ T cells could potentially contribute to potent cytotoxicity in infected lungs and provide additional protection in mild COVID-19 cases or excessive inflammation in severe cases.

Surprisingly, we find that the functionality of these CD4⁺ T cells, in particular cytotoxicity, appears to involve mechanisms beyond TCR usage alone. S_866−880 –specific T cell clones sharing the same TCR exhibit a broad range of killing capacity, which correlate with their ability to control virus replication in infected cells. However, T cells with no killing activity can still suppress virus replication and express high levels of IL-2 and IFN-γ. In addition, we observe an overall significant association between IL-2 and IFN-γ and the antiviral efficacy of all three epitope responses, suggesting direct antiviral effector function of CD4⁺ spike-specific T cells independent of cytotoxicity.

Finally, the immune memory responses from 6 - 9 months post-infection demonstrate potent antiviral efficacy to the original SARS-CoV-2, Delta and Omicron Variants, suggesting memory responses to spike protein, induced by vaccine or natural infection, may contribute to protection against secondary infections to all VOCs, including Omicron, by direct killing of virus infected cells and/or antiviral cytokine and chemokine production.

In summary, our study provides evidence of cytotoxic CD4⁺ T cells in SARS-CoV-2 virus infection and new insights on the potential mechanisms related to this important group of CD4⁺ cells at a single cell level. Induction of potent CD4⁺ killer cells by vaccination could be an attractive approach for novel vaccine designs to support early viral control.

Acknowledgments:

We are grateful to all the participants for donating their samples and data for these analyses, and the research teams involved in the consenting, recruitment, and sampling of these participants. The manuscript was edited at Life Science Editors.

This work is supported by Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (CIFMS), China (grant number: 2018-I2M-2-002) (T.D, Y.P, S.L.F, X.Y, G.L, D.D, J.C.K, G.O, G.R.S); UK Medical Research Council (T.D, G.O, Y.P); National Institutes of Health, National Key R&D Program of China (2020YFE0202400) (T.D); China Scholarship Council (Z.Y, G.L, C.L). The study is also funded by the NIHR Oxford Biomedical Research Centre (G.O, J.C.K), Wellcome Trust Investigator Award (204969/Z/16/Z) (J.C.K), Wellcome Trust Grants (090532/Z/09/Z and 203141/Z/16/Z) to core facilities Wellcome Centre for Human Genetics, Senior Investigator Award (G.O) and Clinical Research Network (G.O); Schmidt Futures (G.R.S), The McKeating laboratory is funded by a Wellcome Investigator Award (IA) 200838/Z/16/Z, UK Medical Research Council (MRC) project grant MR/R022011/1.

This work uses data provided by patients and collected by the NHS as part of their care and support #DataSavesLives.

Author contributions:

T.D conceptualized the project; T.D and Y.P designed and supervised T cell experiments; J.C.K supervised bioinformatic analysis, G.L,Y.P, X.Y, Z.Y, D.D and W.W performed all T cell experiments and data analysis; S.L.F performed single cell data analysis; P.A.C.W and J.A.M assisted with virus infection; T.R performed HLA typing and next generation sequencing; P.H and R.B made the ACE2 constructs and lentivirus; J.W.F provided MHC Class II Tetramers; J.C.K, A.J.M, A.F established clinical cohorts and collected clinical samples and data; C.W, S-A.C, K.C, P.S, W.D, P.S, C.L, J.M, G.R.S provided technical assistance and critical reagents; T.D, J.C.K and Y.P supervised data analysis, T.D , Y.P, G.L, S.L.F, wrote the original draft. J.C.K, G.O and S.L.F reviewed and edited the manuscript and figures.

Competing interests: The authors declare no competing interests.

Data and materials availability: All data are available in the main text or the supplementary materials.

Study participants and clinical definitions. Patients were recruited from the John Radcliffe Hospital in Oxford, UK, between March 2020 and September 2021 by identification of patients hospitalized during the SARS-CoV-2 pandemic and recruited into the Sepsis Immunomics study. Patients were sampled at least 28 days after symptom onset. Written informed consent was obtained from all patients. Ethical approval was given by the South Central-Oxford C Research Ethics Committee in England (ref. 19/SC/0296). Clinical definitions were defined as previously described¹.

Generation of ACE2-transduced EBV-transformed BCLs. EBV-transformed BCLs³² and ACE2-transduced BCLs⁵ were established as described previously. In brief, the cDNA for the human ACE2 gene (ENSG00000130234) was cloned into a lentiviral vector backbone (Addgene plasmid ID 17488), then co-transfected with packaging plasmids pMD2.G and psPAX2 into HEK293-TLA using PEIpro (Polyplus) to produce lentivirus. EBV-transformed BCLs were infected with ACE2-coding lentivirus followed by cell sorting via flow cytometry to enrich ACE2-expressing B cells. B cells with stable expression of ACE2 were maintained with 0.5 μg ml⁻¹ of puromycin (Thermo Fisher Scientific). Mycoplasma testing was carried out every 4 weeks with all cell lines using MycoAlert detection kit (Lonza).

Generation of T cell lines and clones. Short-term SARS-CoV-2-specific T cell lines were generated as described previously³³. Briefly, 2 x 10⁶ PBMCs were pulsed with 10 μM peptides at 37°C for 1hr and cultured in R10 (RPMI 1640 medium with 10% human serum, 2 mM glutamine and 100 mg ml⁻¹ of penicillin–streptomycin) at 2 x 10⁶ cells per well in a 24-well plate (Costar). IL-2 was added to a final concentration of 100 IU ml^-1 on day3. S_751-765- and S_866-880-specific T cell clones were established by sorting tetramer⁺ CD4⁺ T cells from thawed PBMCs or short-term T cell lines on day 10-14. S_166-180-specific T cell clones were generated by cell sorting with TNF-α, IFN-γ, and IL-2 secretion assay (Miltenyi). T cell clones were then expanded with irradiated allogeneic PBMCs every 2-3 weeks as described previously³⁴.

IFNγ ELISpot assay. Ex vivo assays were carried out using either freshly isolated or cryopreserved PBMCs as described previously¹⁸. Peptides were added to 2 x 10⁵ PBMCs at a final concentration of 2 μM for 16-18hrs. For in vitro ELISpot assays, autologous and allogeneic EBV-transformed BCLs were loaded with peptides, and subsequently cocultured with T cell clones or bulks at an effector:target (E:T) ratio of 1:50 for at least 6hrs. To quantify antigen-specific responses, mean spots of the control wells were subtracted from the sample wells, and the results expressed as spot forming units (SFU) per 10⁶ PBMCs. Responses were considered positive if results were at least 3x the mean of the quadruplicate negative control wells and >25 SFU/10⁶ PBMCs. If negative control wells had >30 SFU/10⁶ PBMCs or positive control wells (PHA stimulation) were negative, the results were excluded from further analysis.

Cell sorting for single cell RNA sequencing (scRNA-seq). S_166-180-specific CD4⁺ T cells were sorted using cytokine secretion assay following the manufacturer’s instructions (Miltenyi Biotec). Briefly, 1-3 x 10⁶ cells were stimulated with S_166-180 peptide at a final concentration of 10 μM for 5hrs. Subsequently, cells were washed and incubated with TNF-α, IFN-γ, and IL-2 catching antibody for 45 mins, followed by staining with CD3-BV785 (BioLegend), CD4-APC, CD14-PE-CF594, CD19-PE-CF594 and CD16-PE-CF594 (BD Biosciences). Before sorting, cells were stained with Propidium Iodide (PI) (eBioscience) to exclude nonviable cells. S_751-765- and S_866-880- specific CD4⁺ T cells were sorted with peptide-Class II tetramers. In brief, 1-3 x 10⁶ cells were stained with APC-conjugated HLA-DRB1*15:01 S_751-765 and S_866-880 tetramers (ProImmune) respectively. Live/dead fixable Aqua dye (Invitrogen) was used to exclude nonviable cells from the analysis. Cells were washed and stained with the following surface antibodies: CD4-PE (BD Biosciences), CD3-BV785, CD14-BV510, CD19-BV510 and CD16-BV510 (BioLegend). After exclusion of nonviable/CD14⁺/CD19⁺/CD16⁺cells, CD3⁺CD4⁺TNF-α⁺/IFN-γ⁺/IL-2⁺ cells or CD3⁺CD4⁺Tetramer⁺ were sorted for scRNA-seq using a BD FACSAria Fusion sorter or BD FACS Aria III (BD Biosciences). Single cells were directly sorted into 96-well PCR plates (Thermo Fisher Scientific) and stored at −80°C for further SmartSeq2 analysis as described previously⁵; bulk cells were sorted into 1.5ml Eppendorf tubes (Eppendorf) for subsequent Chromium Single Cell Immune Profiling analysis following manufacturer’s instruction (10x Genomics).

SmartSeq2 scRNA-seq data processing. BCL files were converted to FASTQ format using bcl2fastq v2.20.0.422 (Illumina). FASTQ files were aligned to human genome hg19 using STAR v2.6.1d. Reads were counted using featureCounts (subread v2.0.0). The resulting counts matrix was analyzed in R v4.0.1 using Seurat v4.0.1.

10x scRNA-seq data processing. BCL files were converted to FASTQ files using cellranger mkfastq (Cellranger v.5.0.0). Counts matrices and sample demultiplexing was carried out using cellranger count (CellRanger) using with Feature Barcode options. For additional donor deconvolution, cellSNP v.0.3.2 was used to generate a list of SNPs from Cellranger output (BAM files), followed by Vireo v.0.5.6 to demultiplex sequencing data into individual patients from pooled sequenced libraries. The resulting counts matrix was analyzed in R using Seurat.

Single cell RNA sequencing analysis. Cells were filtered using the following criteria: minimum number of cells expressing specific gene = 3, minimum number of genes expressed by cell = 200 and maximum number of genes expressed by cell = 4000. Cells were excluded if they expressed more than 5 – 10% mitochondrial genes. Patient-specific cells were integrated using Harmony v.1.0 to remove batch effects. The AddModuleScore function (Seurat) was used to look at the expression of specific gene sets (Supplementary Table 4). The average expression of a gene set was calculated, and the average expression levels of control gene sets were subtracted to generate a score for each cell relating to that particular gene set. Higher scores indicate that that specific signature is more highly expressed in a particular cell compared with the rest of the population. Cells with a module score ≥ 1 were defined as perforin-high; cells with a score ≤ 0.25 were defined as perforin-low. The FindMarkers function (Seurat) was used to evaluate differentially expressed genes (DEGs) between two conditions using MAST (Model-based Analysis of Single Cell Transcriptomics) statistical test, with different sequencing batches as latent variables. DEGs were visualized on volcano plots using EnhancedVolcano v1.6.0 and VlnPlot (Seurat).

SmartSeq2 and 10x TCR processing. TCR sequences were reconstructed from SmartSeq2 scRNA-seq FASTQ files using MiXCR v.3.0.13 to produce separate TRA and TRB output files for analysis. The output files were parsed into R using tcR v.2.3.2. TCR sequences were extracted from 10x VDJ sequencing using cellranger vdj (Cellranger). The resulting filtered_contig_annotations.csv file was analysed in R.

Single cell TCR repertoire analysis. TCRs were filtered to retain 1/2α or 1β; paired αβ cells consist of 1α1β or 2α1β. Clonotypes were defined as α (CDR3α amino acid + TRAV), β (CDRβ amino acid +TRBV) or paired αβ (CDR3α amino acid + TRAV + CDRβ amino acid +TRBV). Public clonotypes were defined as shared clonotypes between 2 or more patients. Circo plots were plotted using circlize v.0.4.12 showing paired TRAV-TRBV. Pie charts showing α clonotypes were plotted using ggplot2 v.3.3.2. Box plots comparing public/private clonotypes were plotted using ggplot2 and ggpubr v.0.4.0. Longitudinal TCRs were represented graphically using ggalluvial v.0.12.2.

Deep sequencing of TCR repertoire of T cell clones. TCR usage of T cell clones was sequenced as described⁵. Total RNA was extracted from 5 x 10⁵ cells of each clone using RNeasy Micro Kit (QIAGEN), and 100-300ng of the RNA from each clone was used for the generation of full-length TCR repertoire libraries using SMARTer Human TCR a/b Profiling Kit/v2 (TAKARA) following the supplier’s instruction. After purification, libraries of all clones were pooled and sequenced using MiSeq reagent Kit v.3 (600 cycles) on MiSeq (Illumina) with MiSeq Control Software v.2.6.2.1. Sequencing BCL files were converted to FASTQ files using bcl2fastq. TCRs were extracted using MiXCR and parsed into R as described earlier. TCRs were filtered to retain 1α1β or 2α1β for each clone.

Intracellular cytokine staining. Intracellular cytokine staining (ICS) was performed as described previously¹⁸. T cells were cocultured with peptide-pulsed or virus-infected BCLs at 37°C for 6hrs with GolgiPlug and GolgiStop then stained with CD107a-PE (BD Bioscience). Cells were stained with Live/Dead Fixable Aqua dye (Invitrogen) followed by surface staining with CD4-PE-Cy7 (BD, Biosciences). After subsequent permeabilisation with Fixation/Permeabilisation solution (BD, Biosciences), cells were stained with IFN-γ-AF488 (BD Biosciences), TNF-α-APC (eBioscence) and IL-2-BV421 (BioLegend). Negative controls without peptide or without virus infection were set up for each sample. Samples were run on Attune NxT Flow Cytometer (software v.3.2.1) and analysed using FlowJo v.10 software (FlowJo LLC).

CFSE-based cytotoxic T lymphocyte killing assay. EBV-transformed BCLs were labelled with 0.5 μM carboxyfluoroscein succinimidyl ester (CFSE, ThermoFisher) then pulsed with 10 μM of peptide at 37°C for 1hr. Subsequently, cells were washed, counted and cocultured with T cells at an E:T ratio of 4:1 at 37°C for 6hrs. Samples were then stained with 7-AAD (eBioscience) and CD19-BV421 (BioLegend). Cell death was assessed based on the presence of CFSE⁺CD19⁺7-AAD- (live) cells. Negative controls containing BCLs without peptide pulse and T cells were included for each sample. Samples were run on Attune NxT Flow Cytometer (software v.3.2.1) and analysed using FlowJo v.10 software (FlowJo LLC).

T cell responses to live SARS-CoV-2 infection. SARS-CoV-2 was propagated and titrated as previously described⁵. The Omicron variant was provided by G. Screaton³⁵. AEC2-transduced BCLs were infected with SARS-CoV-2 viruses at MOI 0.1 for 2hrs at 37°C, after which cells were washed and incubated at 37°C for 24hrs. Subsequently, cells were washed and cocultured with T cells at an E:T ratio of 1:1. CD107a expression and cytokine production of T cells were evaluated by ICS as described above.

Live virus suppression assay. As described previously⁵, BCLs expressing ACE2 were infected with SARS-CoV-2 viruses at MOI 0.1 for 2hrs at 37°C. Cells were then washed and cocultured with T cells at an E:T ratio of 4:1 at 37°C. Control wells containing only virus-infected targets were included. After 48hrs incubation, cells were washed with PBS and lysed with RLT buffer (QIAGEN), followed by RNA extraction using RNeasy 96 Kit (QIAGEN). Virus copies were quantified using real-time qPCR, and the virus suppression rate was calculated by the reduction of viral copies when antigen-specific T cells present.

Statistical analysis. EC₅₀ calculations were performed with GraphPad Prism 9, all other statistics were analyzed with IBM SPSS Statistics 27. Figures were made with GraphPad Prism 9. Chi-square was used to compare ratio difference between two groups. Data distribution normality was examined with Kolmogorov-Smirnov test. Mann-Whitney U test was employed to compare two groups, and Kruskal-Wallis one-way ANOVA was used to compare three groups. Correlation analysis was performed using Spearman’s rank correlation coefficient. EC₅₀ of T cell clones was calculated by using nonlinear regression with variable slope (four parameters) in a dose–response–stimulation model with GraphPad Prism. Statistical significance was set at P<0.05 and all tests were 2-tailed. For statistical analyses conducted using R, MAST test was used to find DEGs between two conditions, taking into account variation in batches and represented by volcano plots and violin plots (adjusted P value shown). A Mann-Whitney nonparametric U-test was used for comparison between groups of public/private clonotypes (boxplots), the adjusted P value is shown (Benjamini-Hochberg). ns, not significant; * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001.

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Table 1 | Public TCR clonotypes for each spike epitope.

Epitope	CDR3α	TRAV	No. of patients
S_166-180	CAALNYGGSQGNLIF	TRAV35	5/5
	CAGLLFKAAGNKLTF	TRAV35	5/5
	CAGLNSGGSNYKLTF	TRAV35	3/5
	CAASGARGAQKLVF	TRAV29/DV5	2/5
S_751-765	CAASIAGSARQLTF	TRAV13-1	3/6
	CVVNARSSNTGKLIF	TRAV12-1	5/6
	CVVNIGSSASKIIF	TRAV12-1	5/6
	CVVNKRSSSASKIIF	TRAV12-1	3/6
	CVVNRGGYQKVTF	TRAV12-1	3/6
	CAASISGGYNKLIF	TRAV13-1	2/6
	CATGGSNYKLTF	TRAV17	3/6
	CAVGRYGGSQGNLIF	TRAV2	3/6
	CVVNKYSSNTGKLIF	TRAV12-1	2/6
	CVVNPKTSYDKVIF	TRAV12-1	2/6
	CVVNRGYSTLTF	TRAV12-1	2/6
	CVVPTGGGNKLTF	TRAV12-1	2/6
S_866-880	CAGTGNNRKLIW	TRAV25	6/6
	CAVAGNNRKLIW	TRAV25	3/6
	CIVRAANQAGTALIF	TRAV26-1	3/6
	CIVRVEYNFNKFYF	TRAV26-1	4/6
	CVASGGSNYKLTF	TRAV12-1	3/6
	CAFMKPVTQGAQKLVF	TRAV38-1	2/6
	CILRAPLSFGNEKLTF	TRAV26-2	2/6
	CIVRGLNAGNMLTF	TRAV26-1	2/6
	CIVRVGGSWGKLQF	TRAV26-1	2/6
	CIVRVLFGNEKLTF	TRAV26-1	2/6
	CIVRVLGGGYNKLIF	TRAV26-1	2/6
	CVVNPNARLMF	TRAV12-1	4/6
	CVVNRGSNYQLIW	TRAV12-1	2/6
	CVVNRGSSYKLIF	TRAV12-1	3/6
	CVVSGGSNYKLTF	TRAV12-1	3/6

Epitope	CDR3β	TRBV	No. of patients
S_166-180	CASSFRGDGYTF	TRBV5-1	2/5
	CASSPRDRVNTGELFF	TRBV9	2/5
	CASSQGVGYTF	TRBV4-1	2/5
	CASSYRGAYGYTF	TRBV6-2	2/5
	CASSYSGGQPQHF	TRBV6-5	2/5
	CAWRGGIGYTF	TRBV30	2/5
S_751-765	CASSEGAGSQPQHF	TRBV6-1	4/5
	CASSSSGGGSYEQYF	TRBV6-5	2/5
	CASSTPNRGNNQPQHF	TRBV19	2/5
	CASTEGASNQPQHF	TRBV6-1	2/5
S_866-880	CASSPLANEQFF	TRBV11-2	3/6
	CASSPTYEQYF	TRBV11-2	3/6
	CASSFGGSYGYTF	TRBV12-3	3/6
	CASSFGGNYGYTF	TRBV12-4	2/6
	CASSQVAAEQFF	TRBV3-1	3/6
	CASSLGPNSGNTIYF	TRBV5-1	2/6
	CASSLLTGSTDTQYF	TRBV5-1	2/6
	CASSLSGLDGYTF	TRBV5-1	2/6
	CASSLVSEKLFF	TRBV11-2	2/6
	CASSQVGAGTDTQYF	TRBV4-3	2/6

Epitope	CDR3_alpha	TRAV	CDR3_beta	TRBV	No of patients
S_166-180	CAGMNYGGSQGNLIF	TRAV35	CASSQGVGYTF	TRBV4-1	2/5
	CAGMNYGGSQGNLIF	TRAV35	CASSYRGAYGYTF	TRBV6-2	2/5
	CAGMNYGGSQGNLIF	TRAV35	CAWRGGIGYTF	TRBV30	2/5
	CAGQLYGGSQGNLIF	TRAV35	CASSFRGDGYTF	TRBV5-1	2/5
	CAGQLYGGSQGNLIF	TRAV35	CASSYSGGQPQHF	TRBV6-5	2/5
S_751-765	CVVNKGSSASKIIF	TRAV12-1	CASSEGASNQPQHF	TRBV6-1	4/6
S_866-880	CAGTGNNRKLIW	TRAV25	CASSFGAYGYTF	TRBV12-4	2/6
	CAGTGNNRKLIW	TRAV25	CASSFGGNYGYTF	TRBV12-3	2/6
	CAMNGGANSKLTF	TRAV12-3	CASSLGPNSGNTIYF	TRBV5-1	2/6
	CAVAGNNRKLIW	TRAV25	CASSQVGAGTDTQYF	TRBV4-3	2/6
	CIVRAANQAGTALIF	TRAV26-1	CASSQVAGEQYF	TRBV3-1	3/6
	CIVRVEYNFNKFYF	TRAV26-1	CASSPLANEQFF	TRBV11-2	3/6
	CIVRVSYNFNKFYF	TRAV26-1	CASSLVSEKLFF	TRBV11-2	2/6
	CVVNRGSSYKLIF	TRAV12-1	CASSQTYEQYF	TRBV11-2	3/6

There is NO Competing Interest.

SupplementaryInformation.docx
SupplementaryTable1.xlsx
Supplementary Table 1
SupplementaryTable2.xlsx
Supplementary Table 2

Memory cytotoxic SARS-CoV-2 spike protein-specific CD4+ T cells associate with viral control

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Abstract

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