# Coordination of cellular responses to SARS-CoV-2 during severe COVID-19 illness

Assia Samri, Raphael Lhote, Alice Rousseau, Véronique Morin, Nadine Tarantino, Stéphane Marot, Aude Jary, Delphine Sterlin, Anne-Geneviève Marcelin, Zahir Amoura, et al.

► To cite this version:

Assia Samri, Raphael Lhote, Alice Rousseau, Véronique Morin, Nadine Tarantino, et al. Coordination of cellular responses to SARS-CoV-2 during severe COVID-19 illness. Immunology Letters, 2025, 277, pp.107090. 10.1016/j.imlet.2025.107090. hal-05364526

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# Coordination of cellular responses to SARS-CoV-2 during severe COVID-19 illness

Assia Samri<sup>a,†</sup>, Raphael Lhote<sup>b,†</sup>, Alice Rousseau<sup>a</sup>, Véronique Morin<sup>a</sup>, Nadine Tarantino<sup>a</sup>, Stéphane Marot<sup>c</sup>, Aude Jary<sup>c,‡</sup>, Delphine Sterlin<sup>a,d</sup>, Anne-Geneviève Marcelin<sup>c</sup>, Zahir Amoura<sup>a,b</sup>, Guy Gorochov<sup>a,d</sup>, Vincent Vieillard<sup>a,†</sup>, Amélie Guihot<sup>a,d,*,†</sup>

## ARTICLE INFO

Keywords:

SARS-CoV-2

COVID-19

T cells

CD4⁺ T cells

NK cells

Antibody

## ABSTRACT

The early cellular and humoral immune responses to SARS-CoV-2 result in a wide range of COVID-19 disease severity. Here we conducted an observational study of these three arms of the immune responses (T, B and NK) to SARS-CoV-2 in 17 patients hospitalized for severe COVID-19 at median of 31 days after first symptoms. We found that the main T cell response was directed against two specific regions of the spike viral protein, called B and E, which are inversely correlated with the expression of the KIR2DL1 inhibitory receptor on NK cells (p = 0.03, and p = 0.0001 respectively). Furthermore, expression of the inhibitory receptor ILT2 on NK cells was only correlated with T cell responses against the specific E region (p = 0.02), suggesting that HLA-G may play a role in the extinction of the NK response to the T cell response. Moreover, the antibody response was mainly directed against the nucleocapsid, whereas the antibody neutralizing response was inversely correlated with the cellular response to spike (p < 0.003). Taken together these data suggest a strong coordination between the innate and adaptive immune responses during the acute phase of infection, which could reflect the further resolution of COVID-19 patients with severe disease.

## 1. Introduction

Since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 2019, more than 775 million confirmed cases and more than 7 million deaths caused by coronavirus disease 2019 (COVID-19) have been reported in the wild [1] (http://www.who.int/publications/m/item/covid-19-epidemiological-update-edition-169). Nowadays, COVID-19 remains a major threat, and WHO urges Member States to maintain health surveillance. The infection turned to be severe in 10% of cases, mainly elderly, obese and high blood pressure patients. Therefore, it is of high interest to decipher the immunological mechanisms underlying the severity of COVID if we are to face another wave of severe coronavirus infections. Numerous studies suggest a key role for T cell responses, particularly SARS-CoV-2-specific CD4⁺ T cell responses [2–4] in the resolution of COVID-19 disease [2,3,

5–10]. These CD4⁺ T cell responses are directed against the immunodominant proteins spike, nucleocapsid (NC) and membrane [8,11]. Moreover, in our laboratory, access to bronchoalveolar lavages enabled us to demonstrate a strong local pulmonary immune response directed against spike [12]. Concerning the antibody response, which is mainly directed against spike and NC [13], it has been shown that the more severe the COVID, the higher the antibody levels are high [14,15], and this is also true for the neutralizing response [16]. Finally, the NK response to the virus in severe cases shows a NK lymphopenia, an overexpression of activation markers in patients [17], and we showed an impaired cytotoxicity and IFNγ production by NK cells in most severe cases [18].

The question of the coordination between the different arms of the immune response (T, B, NK) is crucial to explain the immunopathology of severe COVID; notably, severe cases correlate with a lack of

<footnote><sup>a</sup> Sorbonne Université, Inserm, U1135, CNRS ERL8255, Centre d'Immunologie et des Maladies Infectieuses (CIMI-Paris), Paris, France</footnote>

<footnote><sup>b</sup> Service de Médecine Interne 2, Institut E3M, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France</footnote>

<footnote><sup>c</sup> Sorbonne Université, Inserm, U1136, Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), AP-HP, Hôpital Pitié-Salpêtrière, Laboratoire de Virologie, Paris, France</footnote>

<footnote><sup>d</sup> Assistance Publique-Hôpitaux de Paris (AP-HP), Hôpital Pitié-Salpêtrière, Département d'Immunologie, Paris, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: amelie.guihot@aphp.fr (A. Guihot).</footnote>

<footnote>† These authors contributed equally.</footnote>
coordination of cellular and antibody responses and delayed kinetics of adaptive responses [7,10]. However, it remains unexplored whether the T and the NK cell responses are coordinated, complementary or independent explaining the recovery from COVID infection.

We therefore set up a study with the aim to study and correlate NK, serology and CD4⁺ and CD8⁺ T cell responses in the same cohort of 42 unvaccinated hospitalized patients infected by SARS-CoV-2.

## 2. Results and discussion

### 2.1. Anti-SARS-CoV-2 antibodies and NK cell phenotype in severe COVID-19 disease

We enrolled 42 unvaccinated hospitalized patients infected by SARS-CoV-2 between May and July 2021 during the alpha variant wave in France. All patients recovered from SARS-CoV-2 infection except one patient who died. The patient's characteristics are shown in Table 1. We first evaluated the humoral IgG response to spike and NC in the entire cohort of patients at inclusion (T1) and at discharge (T2). Among them 70 % (29 out of 41) had anti-NC and anti-spike positive serology at T1 which, increased significantly at T2, being detectable in 100 % (20 out of 20) and 95 % of patients (19 out of 20) (p = 0.0004 and p = 0.0014), for anti-NC and anti-spike, respectively (Fig. 1A and B). We then performed a neutralization assay against the spike RBD domain and found that 68 % (28 out of 41) were positive at T1, which, increased significantly to be detectable in all 20 patients at T2 (p = 0.0004, Fig. 1C). Thus, we show here that the antibody response is mainly directed against the NC and that a neutralizing response is observed in early severe forms [14,15].

Concomitantly, we found that NK cells were not different from healthy donors (HD) and COVID patients at T1 and T2, in terms of ILT-2 and KIR inhibitory receptor expression (KIR2DL1, KIR2DL2DL3 and KIR3DL1), except for KIR2DL1 at T1 (p = 0.0005) (Fig. 1D, E, F and G), suggesting a hypofunctional profile of NK with an upregulation of KIR2DL1, as previously described [18,19].

### 2.2. T-cell responses to SARS-CoV-2 in severe COVID-19 disease

We then monitored the T cell responses to ancestral SARS-CoV-2 spike and NC using an IFNγ-ELISpot assay on available PMBCs collected from a blood sample at T2 obtained 30.5 days [range, 21.8-41] after symptom onset, this for 17 of 20 patients. Responses to spike were measured against 5 pools of peptides (A through E) spanning the entire spike sequence (1273 amino acids) and against one pool of the nucleocapsid (NC) (419 amino acid). Fig. 1H shows the sum of all responses against the spike pools. All patients recognized at least one of the spike pools. Spike pool B (aa 309-665), containing the RBD region (319-541), was recognized with the higher magnitude of response ([median: 290 (range, 168–750) SFC/10⁶ PBMC]) (Fig. 1H). No response to spike pools was observed in 4 blood donors enrolled between 2008 and 2009 prior to the COVID-19 pandemic and used as controls (data not shown). The ELISpot response to the NC was detected in 16 out of 17 patients and was significantly lower than the spike's [median sum of responses 317 vs 1190 SFC/10⁶ PBMC] (p = 0.041) (data not shown).

In parallel to the IFNγ-ELISpot assay, a multiparametric flow-cytometry analysis of SARS-CoV-2-specific T cells with intracellular cytokine assays was performed in five patients (3 infected with alpha variant and 2 with E484Q) using a pool of all spike peptides (sum of spike) and the pool of NC peptides. The CD4⁺ T cell polyfunctionality was defined as co-production of IFNγ, and/or IL-2 and/or TNFα. Note-worthy, in the 5 patients tested, classified as COVID-19 disease (WHO = 4; n = 4 and WHO = 3; n = 1), the TNFα⁺-specific CD4⁺ T cell response to spike was the predominant response, particularly when compared to the IFNγ⁺ CD4⁺ T cell response (p = 0.016, Fig. 1I). In patients with COVID-19, TNFα was associated with disease severity and mortality [12, 20–22]. The CD4⁺ T cells specific for spike or NC produce one to three

<table><caption>**Table 1**
Demographic and clinical characteristics of patients.</caption><thead><tr><th></th><th>Patients at inclusion n = 42</th><th>Patient with last sample n = 20</th></tr></thead><tbody><tr><td>Age (yr), (median, [IQR])</td><td>56.5 [48.3-69.8]</td><td>60.5 [52-70.3]</td></tr><tr><td>Gender (Female) (n, %)</td><td>13 (30.95)</td><td>5 (25)</td></tr><tr><td>Lymphocyte count /mL (median (IQR) at inclusion</td><td>1060 [760-1667]</td><td>970 [668-1591]</td></tr><tr><td>CD4⁺ Lymphocyte count /mL (median (IQR) at inclusion</td><td>463 [293-760]</td><td>456 [283-717]</td></tr><tr><td>Time from onset of symptoms to inclusion (days) (median, [IQR])</td><td>9 [6-11]</td><td>7.5 [4-10.3]</td></tr><tr><td>Time from onset of symptoms and positive RT-PCR test (days) (median, [IQR])</td><td>4 [1-9]</td><td>3.5 [0-6.3]</td></tr><tr><td>Time from first symptoms to first sample (days) (median [IQR])</td><td>11 [6.5-15]</td><td>10 [4.5-15]</td></tr><tr><td>Duration (days) of hospitalization (median [IQR])</td><td>6 [4-9]</td><td>6 [3-9]</td></tr><tr><td>Time between first symptoms and end of hospitalization (days) (median [IQR])</td><td>18 [12.3-21.8]</td><td>17.5 [11-22]</td></tr><tr><td>Time between first blood sample and last blood sample (days) (median [IQR])</td><td>22 [10.5-26.3]</td><td>22 [10.5-26.3]</td></tr><tr><td>Time from first symptoms to last sample (days) (median [IQR]) *</td><td>30.5 [21.8-41]</td><td>30.5 [21.8-41]</td></tr><tr><td><strong>Variants (n, %)</strong></td><td></td><td></td></tr><tr><td>Alpha variant</td><td>24 (57)</td><td>14(70)</td></tr><tr><td>484+</td><td>4 (10)</td><td>2 (10)</td></tr><tr><td>20D/C.36.3+</td><td>1 (2)</td><td>0</td></tr><tr><td>PCR + Variant undetermined (n, %)</td><td>13 (31)</td><td>4 (20)</td></tr><tr><td>PCR ND, positive serology (n, %)</td><td>10 (77)</td><td>4 (100)</td></tr><tr><td>PCR ND, negative serology (n, %) **</td><td>3 (23)</td><td></td></tr><tr><td><strong>Co-morbidities, (n, %)</strong></td><td></td><td></td></tr><tr><td>Cardiovascular disease</td><td>11 (26)</td><td>6 (30)</td></tr><tr><td>High blood pressure (HBP)</td><td>18 (43)</td><td>8 (40)</td></tr><tr><td>Diabetes</td><td>12 (29)</td><td>7 (35)</td></tr><tr><td>Obesity</td><td>12 (29)</td><td>8 (40)</td></tr><tr><td>Immunosuppressive treatment</td><td>6 (14)</td><td>2 (10)</td></tr><tr><td>Chronic kidney injury</td><td>2 (5)</td><td>1 (5)</td></tr><tr><td><strong>Symptoms (n, %)</strong></td><td></td><td></td></tr><tr><td>Dyspnea</td><td>32 (76)</td><td>15 (75)</td></tr><tr><td>Fever</td><td>29 (69)</td><td>11 (55)</td></tr><tr><td>Cough</td><td>25 (59.5)</td><td>14 (70)</td></tr><tr><td>Myalgia</td><td>15 (36)</td><td>7 (35)</td></tr><tr><td>Dysguesia</td><td>9 (21)</td><td>4 (20)</td></tr><tr><td>Anosmia</td><td>8 (19)</td><td>3 (15)</td></tr><tr><td><strong>Severity (n, %)</strong></td><td></td><td></td></tr><tr><td>Acute respiratory distress syndrome (ARDS), n (%)</td><td>3 (7)</td><td>1 (5)</td></tr><tr><td>Optiflow®</td><td>6 (14)</td><td>1 (5)</td></tr><tr><td>Oxygen therapy</td><td>34 (81)</td><td>16 (80)</td></tr><tr><td>WHO classification</td><td></td><td></td></tr><tr><td>WHO = 3 without oxygen therapy</td><td>8 (19)</td><td>4 (20)</td></tr><tr><td>WHO = 4 only oxygen therapy</td><td>28 (67)</td><td>15 (75)</td></tr><tr><td>WHO ≥5***</td><td>6 (14)</td><td>1 (5)</td></tr><tr><td>Mortality at hospital</td><td>1 (2.4)****</td><td>0 (0)</td></tr></tbody></table>

* Some patients returned for sampling after discharge from hospital.

** Patients with symptoms.

*** WHO ≥5: Optiflow® ± ARDS.

**** death caused by pneumonia.

cytokines (Fig. 1J). The only patient classified as COVID-19 WHO≥ 5, out of 17 tested in ELISpot, showed spike and NC responses in the median responses detected. Unfortunately, this patient did not have enough cells to test multiparametric flow-cytometry analysis of SARS-CoV-2-specific T cells with intracellular cytokine assays (IFNγ, IL-2 or TNFα).

Subsequently, T cell responses against VOC spike variants were also studied and compared with wild-type (WT) spike sequences. First, the T-cell responses observed against the spike region specifically impacted by deletions or mutations that occurred in the VOC are very weak suggesting low immunogenicity of spike VOC mutations. Second, equivalent responses are observed between WT and mutant sequences in terms of cytokine production (IFNγ, IL2 and/or TNFα) in both assays (Fig. S1A
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threshold

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Number of functions

(caption on next page)
**Fig. 1.** B and T cell responses to SARS-CoV-2 and NK cells phenotype during COVID-19 disease.
Production of anti-N (A) anti-S (B) SARS-CoV-2 IgG antibodies. Neutralization against RBD (C). The dotted line represents the cutoff at 30 %, according to the manufacturer. Data are shown at inclusion (T1) (n = 41) and at the discharge (T2) (n = 20).
(D) (E) (F) (G) Percentage of cells expressing cell-surface markers on peripheral CD3$^+$CD56$^+$ NK cells. Data are shown for healthy donors (HD), and COVID-19 patients at inclusion (T1) and at the discharge (T2). Black lines represent the median values.
(H) T cell responses were evaluated in an IFN$\gamma$-ELISpot assay against 5 pools of peptides (Spike A to E) spanning the entire spike sequence. Data are shown at T2 for 17 patients. Results are expressed as spot-forming cells per million peripheral blood mononuclear cells (SFC/10$^6$ PBMC). Data are plotted after background subtraction. The threshold of positivity of 50 SFC/10$^6$ PBMC is shown by a dotted line.
(I) Polyfunctional profiles of CD4$^+$ T cells were evaluated by a multiparametric flow-cytometry analyzing the co-production of IFN$\gamma$, and/or IL-2 and/or TNF$\alpha$ against spike (pools A+B+C+D+E (Sum of Spike)) and the nucleocapsid (NC) in 5 patients. Detailed results for each cytokine production; IFN$\gamma$, IL2 and TNF$\alpha$ by specific CD4$^+$ T cells to sum of spike and NC are shown. Each symbol represents a subject and the medians are shown. Comparisons were done by Wilcoxon signed rank test.
(J) The pie charts represent the percentage of Sum of Spike and NC-specific CD4$^+$ T cells producing one to three cytokines (1–3 functions).

and B). Taken together these data suggest cross-reactivity between the
ancestral SARS-CoV-2 strain and SARS-CoV-2 variants, and are in
accordance with previous results showing T-cell cross-reactivity be-
tween the original Wuhan strain of SARS-CoV-2 and later variants
[23–25].

## 2.3. Coordination of the T, B, NK responses during severe COVID-19 disease

We next explored the correlation between T cell responses to spike and NC and the humoral IgG response to spike and NC and neutralization at T1 against the spike RBD domain. We found a negative correlation between the neutralization assay results and the effector T cell response to the Spike A pool of peptides (N-terminal portion of the protein) (p = 0.0023 and rho = -0.6863, Fig. 2A) and the Spike C peptide pool (p = 0.0038 and rho = -0.6622, Fig. 2B). We show for the first time that a low neutralizing response is associated with a high T cell response against spike (pool A and pool C). This could suggest that during severe COVID, the adaptive immune system is either committed to a neutralizing antibody response, or committed to an effector T cell response.

Regarding the comparisons with NK markers, we found a very sig-
nificant negative correlation between the expression of the inhibitory
receptor KIR2DL1 on NK cells and T cell responses to Spike B or Spike E
(C-terminal portion of the protein) (p = 0.0347, rho = -0.68 and, p <
0.0001, rho = -0.96, respectively) (Fig. 2C and D). Notably, we observed
a lack of correlation between KIR2DL1 expression on CD3⁺ T cells and T
cell responses (data not shown), suggesting a specificity of the response
mediated by NK cells.

Conversely, we found a positive correlation between the expression of ILT-2 another inhibitory receptor on NK cells and the effector T cell response to the Spike E pool peptides (p = 0.0234, rho = 0.71, Fig. 2E). ILT-2 recognizes all MHC class 1 molecules, except HLA-C, which is recognized by KIR2DL1, whereas it specifically binds HLA-G with high affinity [26]. Interestingly, high levels of soluble HLA-G were measured during SARS-CoV-2 infection [27], which could block the inhibitory effect of ILT-2 and thus represent a viral escape mechanism. We have therefore measured soluble HLA-G (sHLA-G) levels at T1 (n = 37) and T2 (n = 19) and as expected high levels of sHLA-G were detected 1042 U/mL at T1 and significantly decrease 685 U/mL at T2 (p = 0.0006) (Fig. 2F) and the difference remains significant (p = 0.004) in the 19 subjects with 2 follow-up points (T1 and T2) (Fig. 2G). Thus, a decrease in sHLA-G levels appears to be associated with SARS-CoV-2 infection resolution. Furthermore, no significant association between immunological cellular and clinical/demographic parameters with sHLA-G levels could be demonstrated. The initially high levels of plasma HLA-G suggest that when the levels fall subsequently, the ILT-2 inhibitory signals on NK cells are triggered in the same patients that have high T cells responses (Fig. 2E). This suggests a putative transition from NK to T cell responses during severe COVID-19.

Taken together, these data may explain the antagonistic effect of
both inhibitory KIR2DL1 and ILT2 receptors on the specific T cell
response to Spike E.

Finally, we sought to determine whether the negative correlation

between KIR2DL1 expression on NK cells and Spike-specific T cell re-
sponses was associated with competition for HLA-C binding.

In silico, 543 epitopes were predicted for Spike that could potentially represent strong interactions with HLA-C1 and HLA-C2 class I molecules, including 157, 124, 69, 106, and 87 located in pools A, B, C, D, and E, respectively (data not shown). Compiled analysis of these different sequences using WebLogo software (weblogo.berkeley.edu) allowed visualization of the variation between sequences of the C1 and C2 HLA-C allotypes for the different Spike peptide pools. For pools B and E, we observed that the peptide anchoring profiles (positions 2 and 9) were different and more pronounced for the HLA-C\*06:02, compared to the Spike peptide pools A, C and D (Fig. 2H and I). In contrast, no difference was observed for the other C2 genotypes: HLA-C\*02:02, HLA-C-04:01, HLA-C\*05:01, C\*15:02 and HLA-C\*17:01 (Fig. S2A to J). These data may suggest that infected cells presenting spike peptides on HLA-C\*06:02 molecules may be involved in the inhibition of the KIR2DL1-mediated NK response and the specific T responses.

Taken together, these data show that the NK and T cell responses to
SARS-CoV-2 are coordinated during COVID-19 disease and may
contribute to resolution of the infection.

3. Conclusion

This study which analyzed the coordination between immune re-
sponses to SARS-CoV-2 during severe COVID-19 disease seems to show
that the coordination of NK and T cell responses contributes to resolu-
tion of the infection.

3.1. *Data limitations and perspectives*

Despite this study was limited by sample size, our data improve our
understanding of the T cell response to SARS-CoV-2, showing that there
was a strong T response to SARS-CoV-2 mediated by IFNγ-producing
cells, including CD4+ and CD8+ T cells. In this study, we also showed
that the CD4+TNFα+ response dominated the T cell response landscape.
We also show here that there was almost perfect T cell cross-reactivity
against the ancestral Wuhan strain and SARS-CoV-2 variant genomes.
Here, the coordination between T, B and NK immune responses to SARS-
CoV-2 was shown during COVID-19 disease. This coordination needs to
be further explored in other infectious diseases.

4. Materials and methods

4.1. Study design

We performed a single-center study carried out at Assistance Pub-
lique-Hôpitaux de Paris, Sorbonne Université. The target population for
the study was composed of unvaccinated adult and pre-Omicron SARS-
CoV-2-infected patients. Blood samples were collected longitudinally
from COVID-19 patients admitted for severe SARS-CoV-2 infection to
the hôpital Pitié-Salpetrière in the Department of Internal Medicine of
the E3M Institute over a period of approximately one month. Immuno-
logical and virological studies were performed at the same hospital, in
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(caption on next page)
**Fig. 2.** Spearman correlation test between percentage of serum SARS-CoV-2 neutralization at inclusion (T1) and T-cell responses against (A) Spike A and (B) Spike C peptide pool measured at T2. Correlation between percentage of KIR2DL1⁺ NK cells and T cell responses against (C) Spike B and (D) Spike E peptide pool. Correlation between percentage of ILT-2⁺ NK and T cell responses against Spike E, measured as described in the method section (E). Levels of sHLA-G at inclusion (T1) and at the discharge (T2) in 37 and 19 patients respectively (F) and in the 19 patients with the two time-points T1 and T2 (G). Representation of peptide anchoring profiles (positions 2 and 9) of epitopes predicted for peptide pools from spike A, C and D (H) and spike B and E with strong binding to HLA-C*06:02 (I). A significant correlation between expression of the inhibitory receptor KIR2DL1 on NK cells and T cell responses to Spike pools B and E (Positive Pools) but not for Spike pools A, C and D (Negative Pools) is observed. This analysis is based on sequence comparison using weblogo software (http://weblogo.berkeley.edu/). Polar amino acids are shown in red, basic in blue, acidic in green and hydrophobic in black.

the Departments of Immunology and Virology, respectively.

## 4.2. Patient recruitment and sample preparation

Forty-two consecutive adult patients with COVID-19 referred to Institut E3M, Hôpital Pitié-Salpêtrière, Paris were included in the study between May and July 2021. All patients presented with clinical symptoms. The diagnosis of COVID-19 was confirmed for all patients by SARS-CoV-2 carriage in the nasopharyngeal swab, as measured by real-time reverse transcription PCR (RT-PCR) analysis. The variant could not be documented in 13. Of these patients, 10 were tested positive, and 3 were negative for the presence of serum anti-SARS-CoV-2 antibodies. Demographic, clinical and virological characteristics of patients are summarized in Table 1. A first fresh blood sample was collected for all patients (n = 42) at admission (T1) in median 11 [range 6.5-15] days after symptom onset and a second sample (T2) was collected for 20 patients in median of 30.5 days [range, 21.8-41] after symptom onset. For all patients, plasma was stored at -80°C immediately. Peripheral blood mononuclear cells (PBMCs) were isolated from the blood samples after Ficoll-Hypaque density gradient centrifugation (Eurobio, Courta-boeuf, France) and analyzed immediately or cryopreserved in liquid nitrogen. In addition, 4 blood donors (D) enrolled between 2008 and 2009 were used as controls.

## 4.3. SARS-CoV-2 IgG immunoassays

Semi-quantification of anti-nucleoprotein (anti-N) IgG antibodies (SARS-CoV-2 IgG, Abbott, USA) and the quantification of anti-Spike receptor binding domain (RBD) IgG antibodies (anti-S) (SARS-CoV-2 IgG II Quant, Abbott, USA) were performed using an automated chemiluminescence assay on the Abbott Alinity i platform, according to the manufacturer's instructions. The cutoffs for positivity for anti-N and anti-RBD antibodies were 0.8 (S/Co) and 7.1 binding antibody units per milliliter (BAU/mL), respectively, as recommended by the manufacturer.

## 4.4. Neutralizing antibodies

The anti-RBD neutralizing activity of sera was measured by a semi-quantitative ELISA assay (SARS-CoV-2 Surrogate Virus Neutralization Test, GenScript, Piscataway, NJ, USA) based on the binding inhibition of labeled RBD to angiotensin-converting enzyme 2 (ACE2) by the serum anti-RBD neutralizing antibodies (Nabs). Briefly, sera are pre-incubated with horseradish peroxidase (HRP)-labeled SARS-CoV-2 RBD, and then added to ACE-2 coated ELISA plates. RBD-ACE2 interaction is quantified biochemically by adding a substrate TMB solution. The more Nabs are present, the more they inhibit the binding of labeled RBD to ACE-2, resulting in a lower signal. Inhibition greater than 30 % is considered positive by the fabricant.

## 4.5. Peptides design

To evaluate T cell responses against SARS-CoV-2, we used overlapping peptides generated using the PeptGen Peptide Generator tool (https://www.hiv.lanl.gov/content/sequence/PEPTGEN/peptgen.html) by submitting the ancestral Wuhan-Hu-1 SARS-CoV-2 sequence.

Fifty-nine 18-mer peptides overlapping by 11 amino acids spanning

the ancestral Wuhan-Hu-1 SARS-CoV-2 Nucleocapsid (419 amino acids) (GenBank MN908947.3) were tested in a pool (Table S1). Three hundred fifteen 18-mer peptides overlapping by 14 amino acids spanning the ancestral Wuhan-Hu-1 SARS-CoV-2 Spike (1273 amino acids) (GenBank MN908947.3) were tested in 5 pools (pool A: aa 1-322; pool B: aa 309-665; pool C: aa 653-826; pool D: aa 813-1050 and pool E: aa 1037-1273) (Table S2). Pool B contains the RBD region (aa 319 to 541). In addition, 11 pools (2 to 5 peptides per pool) (named wild-type (WT) (WT-1 to WT-11)) (Table S3) were tested that were impacted by mutations/deletions occurring in the variants (VOC) (at the time of the study). By analogy, we tested the spike homologous regions including the mutations/deletions occurring in the variants (VOC) (at the time of our study) covered by the 11 WT pools. We mapped the specific mutations and deletions of the VOCs of Alpha (Alpha (B.1.1.7 GISAID:EPI_ISL_601443) (mutation N501Y, deletions 69 and 70 (Δ69-70 HV)), of Beta ((B.1.351; GISAID: EPI_ISL_736932), (mutations L18F, D80A, D215G, R246I, K417N, E484K, N501Y, deletions 242,243 and 244, Δ242-244 (LAL)), of Gamma (B.1.1.28) (mutations K417T, E484K and N501Y) and of Delta (B.1.617.2 GISAID: EPI_ISL_2020950) (mutations L452R, T478K, E484Q and P681R) as compared to the ancestral Wuhan-Hu-1 SARS-CoV-2 spike sequence. Fifty-seven 18-mer peptides overlapping by 14 amino-acids including mutated or deleted amino-acids in the spike protein were defined and tested in 11 different pools (M-1 to M-11) (Table S3). Sequences of SARS-CoV-2 variants are available on the global initiative on sharing all influenza data (GISAID) (https://outbreak.info/compare-lineages) [28].

As a control, we used a pool of forty-two optimal 9-mer peptides corresponding to the most common HLA-restricted immunodominant epitopes of Epstein-Barr Virus (EBV) (Table S4) [29]. All peptides were synthesized (Genecust, Boynes, France) and reconstituted in 10 % DMSO.

## 4.6. Interferon-gamma ELISpot assays

Cryopreserved PBMCs with a median viability > 60 % after thawing were incubated as described [12] in triplicate in 96-well flat-bottomed plates (MSIPN4550, Millipore, St Quentin en Yvelines France), coated with anti-IFNγ ☐ antibody (Diaclone, Besancon, France), with peptides at a final concentration of 2**µg/ml** of each peptide in medium with 10 % fetal calf serum for 40 h. Phytohemagglutinin (1/10 000) (Sigma-Aldrich, France) and medium alone were used as positive and negative controls, respectively. After incubation, the cells were removed and biotinylated anti-IFNγ mAb (Diaclone, Besancon, France) was added followed by streptavidin-alkaline phosphatase conjugate (Sigma-Aldrich, France) and chromogenic substrate (NBT/BCIP) (Sigma-Aldrich, France) before the plates were washed with water and the number of spots was counted using an AID-ELISpot reader (Autoimmun Diagnostika GmbH, Germany). Antigen-specific responses were considered positive when Spot Forming Cells (SFC)/10⁶ PBMC were greater than 50 after background subtraction.

## 4.7. Intracellular cytokine staining assays

SARS-CoV-2-specific T cells were analyzed using an ICS assay [29]. Briefly 1 × 10⁶ thawed PBMC were stimulated overnight with each peptide pool (1 pool of NC, 1 pool of ancestral Wuhan-Hu-1 SARS-CoV-2 spike (pool A to E), 1 pool of ancestral Wuhan-Hu-1 SARS-CoV-2 spike
(WT-1 to WT11) and 1 pool of all spike mutated peptides homologous to WT-1 to WT11 named M-1 to M-11) (Tables S1, S2 and S3) at a final concentration of 2 µg/ml of each peptide. Negative and positive controls were medium alone and staphylococcal-enterotoxin-B (Servibio, Les Ulis Courtaboeuf, France) respectively. Staining was performed with Live/- Dead Fixable Aqua Dead Cell (Thermofisher, L34957), CD4-APC (RPA-T4) and CD8-PerCP-Cy5.5 (SK1); followed by intracellular staining was performed for interleukin-2 (IL2)-PE (5344.111), interferon-γ (IFNγ)-FITC (25723.11), tumor necrosis factor-α (TNFα)-PE-Cy7 (Mab11), and CD3-APC-H7 (SK7), all antibodies from BD Biosciences.

T cell functionality was evaluated only on cytokine-producing cells with more than 50 events. Cells were acquired on a DxFlex flow cytometer and analyzed using Kaluza 1.2 software® (Beckmann-Coulter®). Percentages of CD8⁺ or CD4⁺ T cells producing cytokines were determined after background subtraction.

## 4.8. Plasma soluble HLA-G ELISA

Plasma sHLA-G levels were determined from the patients' plasma by commercially available sHLA-G (BioVendor R&D) ELISA kit, according to the manufacturer's instructions.

## 4.9. Prediction of HLA-C-restricted SARS-CoV-2 spike T-cell epitopes

We predicted the promiscuous SARS-CoV-2 spike T-cell epitopes that are restricted to the C1 and C2 allotypes of HLA-C, using the NetCTLpan-4.1 server (https://services.healthtech.dtu.dk/services/NetMHCpan-4.1/), with a threshold for strongly binding peptides set at 0.5. Prediction was performed for nine-amino acid peptides based on the sequence of the Wuhan-Hu-1 SARS-CoV-2 Spike strain (GenBank MN908947.3) binding to 21 C1 (01:02, C*03:02, C*03:03, C*03:04, C*07:01, C*07:02, C*07:04, C*08:01, C*08:02, C*12:02, C*12:03, C*14:02, C*14:03, and C*16:01) and C2 (02:02, C*03:02, C*04:01, C*04:03, C*05:01, C*06:02, C*07:04, C*08:01, C*12:02, C*14:03C*15:02, and C*17:01) HLA-C class I molecules. Variations between the sequences of the A, B, C, D and E pools of peptides generated from spike for HLA-C1 and HLA-C2 were analyzed using the Weblogo software (http://weblogo.berkeley.edu/).

## 4.10. Statistical analysis

Data are presented as median and interquartile range. Mann-Whitney U and Kruskal-Wallis nonparametric tests were used, as appropriate, to compare continuous variables between independent groups. The Spearman correlation test was used to measure the correlation between two variables. Wilcoxon Signed Rank test was used to compare sHLA-G levels between T1 and T2 in the same patients.

Statistical analysis was performed using GraphPad software Prism 9 (GraphPad, San Diego). Differences were considered significant when $p < 0.05$.

## Ethics approval and consent to participate

The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Sorbonne Université under number CER2021-044. Informed consent was obtained from all subjects involved in the study.

## Funding

This research received no external funding.

## CRediT authorship contribution statement

**Assia Samri:** Conceptualization, Data curation, Writing – original draft. **Raphael Lhote:** Conceptualization, Investigation, Project

**administration.** **Alice Rousseau:** Data curation, Formal analysis, Investigation. **Véronique Morin:** Data curation, Investigation. **Nadine Tarantino:** Data curation, Investigation, Software. **Stéphane Marot:** Data curation, Investigation. **Aude Jary:** Conceptualization, Data curation, Investigation. **Delphine Sterlin:** Data curation, Formal analysis, Investigation, Methodology. **Anne-Genevieve Marcelin:** Conceptualization, Funding acquisition. **Zahir Amoura:** Conceptualization, Funding acquisition, Investigation. **Guy Gorochov:** Conceptualization, Funding acquisition, Investigation. **Vincent Vieillard:** Conceptualization, Data curation, Investigation, Writing – review & editing. **Amélie Guihot:** Investigation, Supervision, Validation, Writing – review & editing.

## Declaration of competing interest

The authors declare no conflicts of interest.

## Acknowledgements

We thank T4-T8 laboratory. We also thank the Dormeur Foundation, Vaduz, for providing AID ELISPOT Reader.

## Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.imlet.2025.107090.

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