SARS-CoV-2-reactive IFN-γ CD4 + and CD8 + T cells, clinical severity biomarkers and mortality in unvaccinated critically ill COVID-19 patients

DOI: https://doi.org/10.21203/rs.3.rs-1481837/v1

Abstract

Objectives: We examined the relationship between peripheral blood levels of SARS-CoV-2 S1/M-reactive IFN-γ-producing CD4+ and CD8+ T cells, serum levels of biomarkers of clinical severity and mortality in critically ill COVID-19 patients.  

Methods: Immune responses were monitored in 71 non-consecutive patients (49 male and 22 female; median age, 65 years) by whole-blood flow cytometry (326 specimens). SARS-CoV-2 RNA loads in paired tracheal aspirates [TA] (n=147) were available from 54 patients. Serum levels of interleukin-6, ferritin, D-Dimer, lactose dehydrogenase and C-reactive protein in paired sera were known.

Results: SARS-CoV-2 T cells (either CD4+, CD8+ or both) were detectable in 70 patients. SARS-CoV-2 IFN-γ CD4+ T-cell responses were documented more frequently than their CD8+ counterparts (62 vs. 56 patients) and were of greater magnitude overall. Detectable SARS-CoV-2 S1/M-reactive CD8+ and CD4+ T-cell responses were associated with higher SARS-CoV-2 RNA loads in TA. SARS-CoV-2 RNA load in TA decreased over time, irrespective of the dynamics of SARS-CoV-2-reactive CD8+ and CD4+ T cells.

Conclusion: Enumeration of peripheral blood levels of SARS-CoV-2-S1/M-reactive IFN-γ CD4+ and CD8+ T cells does not predict viral clearance from the lower respiratory tract or poor clinical outcomes in critically ill COVID-19 patients.

Introduction

Analogously to other respiratory viruses, SARS-CoV-2 elicits robust functional T-cell responses that seemingly play a critical role in promoting virus clearance and thus affording protection against severe disease [1–7]. Soon after natural SARS-CoV-2 infection, both SARS-CoV-2 CD8+ and CD4+ T cells expand, targeting most of the viral proteome, with those recognizing epitopes within the spike (S), membrane (M) and nucleoprotein (N) structural proteins being immunodominant in many subjects [6,8–10]. Qualitative and quantitative differences in SARS-CoV-2 T-cell responses have been reported across individuals contracting asymptomatic infection or presenting with mild or severe COVID-19; specifically, delayed appearance, weak IFN-γ/IL-2-producing, “misfiring”, dysfunctional or “exhausted” T-cell responses were seen more frequently in severe compared to mild or asymptomatic COVID-19 cases [4,5,7,8,10–14]. More recently, a strong association was found between presence of NP (nucleoprotein) 105−113-B*07:02-specific CD8+ T-cell responses and mild disease [15]. Nevertheless, there is limited and contradictory information as to the relationship between peripheral blood SARS-CoV-2 T-cell levels and clinical outcomes among critically ill patients [4,14,16–20]. In fact, while some studies reported increased antiviral T-cell responses in patients requiring ICU admission, as opposed to those presenting with severe disease without ICU admission, irrespective of patient outcome (death vs. survival) [14,16], others reported impaired T-cell responses, which nevertheless were inconsistently associated with increased mortality [4,17–20]. To gain further insight into this issue, here we examined the relationship between peripheral blood SARS-CoV-2 IFN-γ-producing CD4+ and CD8+ T-cell responses targeting the S and M proteins, plasma levels of biomarkers of clinical severity and mortality in this setting.

Patients And Methods

Patients and specimens

In this observational prospective and longitudinal study, a total of 71 non-consecutive critically ill patients [21] (49 male and 22 female; median age, 65 years; range, 21 to 80 years) with COVID-19 microbiologically documented by RT-PCR in nasopharyngeal specimens were recruited at the intensive care unit (ICU) between October 2020 and February 2021. These patients have been included in previous studies [22–24]. The only patient inclusion criterion was availability of whole blood specimens for T-cell immunity analyses, which were scheduled for once-weekly collection during ICU stay. No patient had received COVID-19 vaccination at ICU admission. Medical history and laboratory data were retrospectively reviewed. The most relevant patient characteristics are shown in Table 1. The current study was approved by the Research Ethics Committee of Hospital Clínico Universitario INCLIVA (May 2020). Informed consent was obtained from participants either on the hospital ward or at the time of ICU admission.

Table 1

Baseline clinical characteristics of the study population at Intensive Care Unit admission

Variable

no. (%)

Sex

Male

49 (69.0)

Female

22 (31.0)

Acute physiology and chronic health evaluation (APACHE) II score

< 10

14 (19.7)

10–14

27 (38.0)

15–29

30 (42.3)

Comorbidities

  

Diabetes mellitus

17 (23.9)

Asthma/ Chronic lung disease

10 (14.1)

Hypertension

32 (45.1)

Obesity

37 (52.1)

Chronic heart disease

8 (11.3)

Vascular disease

7 (9.8)

Cancer

3 (4.2)

Hematologic disease

3 (4.2)

Number of comorbidity conditions

One

21 (29.6)

Two or more

32 (45.1)

None

18 (25.3)

Oxygenation and ventilator support

Invasive mechanical ventilation

63 (88.7)

PiO2/FiO2 < 150 mmHg

56 (78.9)

Acute kidney disfunction

17 (23.9)

Antiviral or anti-inflammatory treatment

  

Remdesivir

16 (22.5)

Corticosteroids

69 (97.2)

Tocilizumab

27 (38.0)

SARS-CoV-2 RNA load in tracheal aspirates

SARS-CoV-2 RNA quantitation in TA was carried out by the Abbott RealTime SARS-CoV-2 assay Abbott Molecular (Des Plaines, IL, USA) [23,24]. SARS-CoV-2 viral loads (in copies/ml) were estimated using the AMPLIRUN® TOTAL SARS-CoV-2 RNA Control (Vircell SA, Granada, Spain).

SARS-CoV-2-reactive IFN-γ CD4+ and CD8+ T cells

SARS-CoV-2-reactive IFNγ-producing-CD4+ and CD8+ T cells stimulated with two sets of 15-mer overlapping peptides (11 mer overlap) encompassing the SARS-CoV-2 Spike glycoprotein N-terminal 1-643 amino acid sequence (158 peptides) and the entire sequence of SARS-CoV-2 M protein (53 peptides) were enumerated in whole blood by flow cytometry for intracellular cytokine staining (ICS) (BD Fastimmune, BD Biosciences, San Jose, CA, USA) as previously described [25,26]. T-cell counts relative to total CD8+ and CD4+ T cell numbers are reported throughout the study.

Laboratory measurements

Clinical laboratory investigation included serum levels of interleukin-6 (IL-6), ferritin, D-Dimer, lactose dehydrogenase (LDH), C-reactive protein (CRP).

Statistical methods

Frequency comparisons for categorical variables were carried out using the Fisher exact test. Differences between medians were compared using the Mann–Whitney U-test. Spearman’s rank test was used for analysis of correlation between continuous variables. Two-sided exact P-values were reported. A P-value < 0.05 was considered statistically significant. The analyses were performed using SPSS version 20.0 (SPSS, Chicago, IL, USA).

Results

Patient clinical features

Patients were recruited at a median of 3 days (range, 0–27 days) after ICU admission, corresponding to a median of 12 days (range, 3–38 days) after onset of COVID-19 symptoms. Sixty-three patients (88.7%) underwent mechanical ventilation. Median time of ICU stay was 19 days (range, 1–67). All patients were treated at some point with anti-inflammatory drugs (Table 1). Remdesivir was administered to 16 patients.

Dynamics of SARS-CoV-2-reactive IFN-γ CD4+ and CD8+ T cells in intensive care COVID-19 patients

A total of 326 whole blood specimens were available for assessment of SARS-CoV-2 S1/M-reactive IFN-γ T-cell responses (a median of 4 specimens/patient; range 1–16; detailed in Supplementary Table 1), of which 211 from 70 patients had detectable SARS-CoV-2 T cells (either CD4+, CD8+ or both). The time to the first whole blood specimen displaying measurable SARS-CoV-2-reactive T cells since symptoms onset and ICU admission was 13 days (range, 3–42) and 3 days (range, 0–27), respectively. SARS-CoV-2 IFN-γ T- CD4+ T cells responses were documented more frequently (169 specimens from 62 patients, 87.3%) than their CD8+ counterparts (140 specimens from 56 patients, 78.9%) over different arbitrarily defined time frames since symptoms onset (Fig. 1A). Overall, CD4+ T-cell responses were of greater magnitude than CD8+ T-cell responses within all time windows explored (Fig. 1B and Supplementary Table 2). SARS-CoV-2 IFN-γ T- CD4+ T-cell levels appeared to fluctuate over the first five weeks, and increased at later times; in contrast, SARS-CoV-2 IFN-γ T- CD8+ T cells waned over time. Importantly, neither the use of remdesivir nor that of tocilizumab had an impact on median levels of SARS-CoV-2 CD4+ and CD8+ T cells (not shown). Next, we examined the kinetics of SARS-CoV-2 T-cell responses at the individual level in 47 patients with ≥ 3 available specimens (Supplementary Table 3). Qualitatively, many patients exhibited fluctuating CD8+ and CD4+ T-cell responses (n = 25 for both T-cell subsets), while fewer tested positive (4 for CD8+ and 8 for CD4+) or negative (10 for CD8+ and 3 for CD4+) systematically over time.

SARS-CoV-2-reactive IFN-γ CD4  +  and CD8+ T cells and SARS-CoV-2 RNA load in the lower respiratory tract

We next investigated the potential impact of SARS-CoV-2 RNA load in tracheal aspirates on the detection rate and magnitude of SARS-CoV-2 S1/M-reactive IFN-γ T-cell responses. A total of 147 paired TA and whole blood specimens from 54 patients were available for analyses; these specimens were collected at a median of 21 days (range, 2–71 days) since symptoms onset. As shown in Fig. 2, higher SARS-CoV-2 RNA loads in TA were associated with measurable SARS-CoV-2 S1/M-reactive CD8+ (Fig. 2A) and CD4+ (Fig. 2B) T-cell responses (P = 0.01 and P = 0.06, respectively) in paired whole-blood specimens. Moreover, a trend towards higher SARS-CoV-2 S1/M-reactive IFN-γ CD8+ and CD4+ T-cell counts (P = 0.13 and P = 0.15) was documented when SARS-COV-2 RNA (at any level) could be detected in paired TA specimens (Fig. 2, panels C and D, respectively). However, SARS-CoV-2 CD8+ and CD4 + T cells correlated either poorly (Rho = 0.20) or not at all (Rho = 0.09) with SARS-CoV-2 RNA loads (Supplementary Fig. 1).

We next examined the dynamics of SARS-CoV-2 S1/M-reactive IFN-γ T-cell responses in peripheral blood relative to that of SARS-COV-2 RNA load in the lower respiratory tract. This analysis involved 14 patients (n = 53 specimens) with ≥ 3 whole blood and TA paired specimens. As shown in Table 2, SARS-CoV-2 RNA load decreased over time, with no obvious relationship to the dynamics of peripheral levels of SARS-CoV-2-reactive CD8+ and CD4+ T cells.

Table 2

Kinetics of SARS-CoV-2 RNA load in tracheal aspirates and SARS-CoV-2 S1/M-reactive IFN- CD8+ and CD4 + T cells in paired whole blood specimens from critically ill patients

Days after symptoms onset (no. of specimens available)

SARS-CoV-2 RNA load in tracheal aspirates. Median copies/ml (range)

SARS-CoV-2 S1/M-reactive IFN-γ CD8+ T cell counts. Median cells/µl (range)

SARS-CoV-2 S1/M-reactive IFN-γ CD4+ T-cell counts. Median cells/µl (range)

1–10 (5)

9.4 (7.8–10.1)

0.02 (0–0.3)

0.33 (0–10.2)

11–20 (12)

7.4 (3–10.6)

0.12 (0–10.47)

0.31 (0–5.2)

21–30 (14)

5.1 (0–8.1)

0.05 (0–11.65)

0 (0–7.8)

31–40 (9)

0 (0–6.5)

0 (0–1.14)

0.6 (0–4.8)

≥ 41 (13)

0 (0–4.9)

0 (0.0–4.1)

0.1 (0–5.3)

SARS-CoV-2-reactive IFN-γ CD4+ and CD8+ T cells and serum levels of clinical severity biomarkers

Since development of adaptive immunity responses may be modulated in magnitude and breadth by the net state of inflammation [27], we next investigated whether serum levels of IL-6, ferritin D-Dimer, LDH and CRP correlated with SARS-CoV-2-reactive IFN-γ CD4+ and CD8+ T-cell levels in paired whole blood specimens. Median levels of all these biomarkers were comparable across patients either with or without detectable T-cell responses at the corresponding sample time (Table 3). Furthermore, no correlation was found between whole blood T-cell counts and biomarker serum levels (Table 4).


Table 3

SARS-CoV-2 S1/M-reactive IFN- CD8+ and CD4+ T-cell counts in whole blood and serum levels of clinical severity biomarkers in critically ill patients

Biomarker of clinical severity

SARS-CoV-2 S1/M-reactive IFN-γ CD8+ T-cell responses

P-value

SARS-CoV-2 S1/M-reactive IFN-γ CD4+ T-cell responses

P-value

Qualitative result

No. of specimens

Median cell counts in cells/µl (range)

Qualitative result

No. of specimens

Median cell counts in cells/µl (range)

IL-6 (pg/mL)

Detectable

27

36 (0–3,548)

0.55

Detectable

30

28 (0–3,548)

0.30

Undetectable

37

22 (0–3,437)

Undetectable

34

37 (0–3,303)

Ferritin (ng/ml)

Detectable

106

604 (0.0–6,440)

0.45

Detectable

125

607 (0–6,440)

0.45

Undetectable

139

602 (0–3,616)

Undetectable

120

561 (0–3,616)

D-dimer (ng/ml)

Detectable

129

1,640 (0–51,919)

0.32

Detectable

156

1865,00 (0.0–51919,00)

0.19

Undetectable

167

1,740 (270–29,940)

Undetectable

140

1,635 (270–21,420)

LDH (IU/L)

Detectable

133

653 (93–1,720)

0.74

Detectable

162

611 (0.0–2,132)

0.06

Undetectable

171

637 (0–2,132)

Undetectable

142

670 (101–1,685)

PCR (mg/L)

Detectable

138

33 (0–746)

0.74

Detectable

168

35 (0–746)

0.734

Undetectable

184

31 (0–606)

Undetectable

154

28 (0–606)
Table 4

Correlation between SARS-CoV-2 S1/M-reactive IFN- CD8+ T-cell counts in whole blood and serum levels of biomarkers of clinical severity in critically ill patients

Parameter

Biomarker (no. of specimens)

  

Interleukin-6 (64)

Ferritin (245)

D-Dimer (296)

Lactose dehydrogenase (304)

C-reactive protein (322)

SARS-CoV-2 S1/M-reactive IFN-γ CD8+ T cells

Rho valuea

0.09

0.06

-0-08

0.002

-0.05

P value

0.47

0.34

0.15

0.97

0.29

SARS-CoV-2 S1/M-reactive IFN-γ CD4+ T cells

Rho valuea

0.16

0.03

0.06

-0.12

-0.01

P value

0.19

0.55

0.24

0.37

0.85

aSpearman Rank test

SARS-CoV-2-reactive IFN-γ CD4+ and CD8+ T cells and mortality

Out of 71 patients, 28 died (at a median of 32 days; range, 12–91 days since symptoms onset). A comparable number of whole blood specimens (n = 115 from 28 deceased patients and n = 211 from 43 surviving patients) were examined for presence of SARS-CoV-2 T cells. Time to symptoms onset from collection of first specimen was also similar (P = 0.66). No difference was found in the rate of detectable (at least in one specimen) SARS-CoV-2-reactive IFN-γ CD8+ (22/28 vs. 34/43, respectively; P = 0.96) and CD4+ (23/28 vs. 39/43, respectively; P = 0.29) T cells between patients in the two comparison groups. Moreover, when considering all analyzed samples, median levels of both functional T-cell subsets were comparable across groups; in detail, for SARS-CoV-2-reactive IFN-γ CD8+ T cells the figures were: median 0 cell/µl (95% CI, 0-4.9) in deceased patients and 0.08 cell/µl (95% CI, 0–5.0) in survivors (P = 0.22), and for SARS-CoV-2-reactive IFN-γ CD4+ T cells they were: median 0.22 cell/µl (95% CI, 0-10.8) and median 0.29 cell/µl (95% CI, 0-10.1), respectively (P = 0.45). We next compared SARS-CoV-2 T-cell responses in deceased and surviving individuals over different time intervals since symptoms onset, finding no consistent association between the dynamics of virus-reactive CD8+ and CD4+ T-cell counts over time and mortality (Fig. 3).

Discussion

While impaired SARS-CoV-2-reactive functional T-cell responses have been linked to progression from mild to severe forms of COVID-19 [4,5,7,8,10–15], it remains to be elucidated whether these are associated with clinical outcomes among critically ill patients. Here, we prospectively monitored SARS-CoV-2 S1/M-reactive IFN-γ T-cell responses using an in-house-developed flow cytometry assay in a cohort of 71 patients admitted to ICU, of whom most were mechanically ventilated (88%) and 28 died. Our antigen choice was based upon previously published data showing that a wide array of highly immunogenic T-cell epitopes map within S1 and M proteins that elicit immunodominant responses [2, 4–8]. In addition to further characterizing the dynamics of these T-cell subsets in this population group, which currently remains poorly defined, we aimed to establish whether SARS-CoV-2 S1/M-reactive IFN-γ T-cell counts were related to serum levels of biomarkers predicting poor outcomes and mortality across critically ill patients, and could thus be used as a surrogate prognostic marker. Our data allowed us to draw the following conclusions. First, virtually all patients developed SARS-CoV-2 S1/M-reactive IFN-γ T-cell responses (either CD8+, CD4+ or both) during ICU stay, yet CD4+ T-cell responses were detected more frequently and at higher levels than their CD8+ counterparts. Furthermore, overall, CD4+ T-cell responses appeared to fluctuate over time, while those involving CD8+ T cells tended to wane. Despite this  general landscape, we noted wide variations at the individual level. In fact, fluctuating responses were observed more frequently than consistent (either detectable or undetectable) ones over time. Second, data obtained in the rhesus macaque experimental model clearly underscore the crucial role of SARS-CoV-2-reactive T-cell responses in contributing to virus clearance from the lower respiratory tract [27,28]. To determine whether this could be the case in our patient population, we compared the dynamics of SARS-CoV-2 load in TA to that of SARS-CoV-2 S1/M-reactive IFN-γ T cells in paired whole-blood specimens. Our data indicated that although the rate of detection and magnitude of SARS-CoV-2 T-cell responses appeared directly related to the level of virus replication in the lower respiratory tract, as inferred by viral RNA load in TA, the dynamics of virus clearance from this compartment was not consistently associated with that of peripheral blood SARS-CoV-2 S1/M-reactive IFN-γ T cells. This suggested that enumeration of these T-cell subset specificities in whole blood provides no reliable information on the course of virus infection in the lungs. Naturally, our findings do not detract from the role of T cells in affording protection against severe forms of COVID-19, but rather suggest that examination of SARS-CoV-2-driven immune responses at the lower respiratory tract could offer a better perspective of the interplay between virus replication and host immune responses during severe COVID-19. Indeed, different cellular immune profiles in the airways and blood have been documented in critically ill COVID-19 patients [30,31]. Fourth, sustained high serum levels of several biomarkers of inflammation (IL-6, ferritin, CRP), coagulation and fibrinolysis (D-dimer) and tissue damage (LDH) are associated with poor COVID-19 prognosis across critically ill patients [32,33]. Moreover, hyperinflammatory states may down-regulate ongoing T-cell responses [27]. In this context, we investigated whether (qualitative and quantitative) SARS-CoV-2 S1/M-reactive IFN-γ T-cell responses in our patients were somehow related to levels of the aforementioned biomarkers. This was found not to be the case, as serum levels of all biomarkers were similar regardless of detected or absent SARS-CoV-2 CD8+ or CD4+ T-cell responses; moreover, no correlation was found between SARS-CoV-2 T-cell counts and biomarker levels in paired specimens. Fifth, in our cohort, mortality was not consistently associated with either detection rate or the magnitude of SARS-CoV-2 S1/M-reactive IFN-γ T-cell responses. This is in line with data reported by Thieme and colleagues [16], who found that development of robust T-cell responses toward spike, membrane, and nucleocapsid SARS-CoV-2 proteins was not associated with survival in a small cohort of critical COVID-19 patients.  In a more comprehensive study, Saris et al. [30] found high levels of TNF-α-producing S-reactive CD8+ T cells to be associated with increased mortality, while mono-functional CD4+ T- cell subsets could not be related to survival; nevertheless, survivors appeared to display broader and stronger virus-reactive poly-functional CD4+ T-cell responses than those who died; yet, as stated by the authors, no obvious combination of effector functions of CD4+ T cells could be linked to prognosis. The key finding of the study was that mucosal-associated invariant T (MAIT) cell activation is an independent and significant predictor of mortality. Likewise, in a very small study critically ill patients with hypertension who died exhibited prolonged low peripheral blood counts of SARS-CoV-2-S-reactive CD8+ and CD4+ T cells [20].

The current study has several limitations deserving comment. First, the limited sample size, particularly regarding the number of deceased patients, clearly undermine the robustness of the analyses. Sufficiently powered studies are needed to elucidate whether monitoring SARS-CoV-2 T-cell responses in peripheral blood may have prognostic value in critically ill COVID-19 patients. Second, although blood specimens were scheduled to be collected weekly, this was unfortunately not achieved in a number of patients. Third, like other flow cytometry-based immunoassays used for measuring SARS-CoV-2 T-cell responses, ours lacks appropriate standardization. Fourth, SARS-CoV-2-reactive T cells were examined only for IFN-γ production, thus we cannot rule out the possibility that other functional T-cell specificities are associated with survival. Also, no data on the state of differentiation of reactive T cells are provided. Fifth, only SARS-CoV-2 S1 and M-reactive T cells were measured; whether enumeration of SARS-CoV-2 T cells targeting other viral proteins may help to individualize mortality risk in critical COVID-19 patients remain to be defined. Sixth, SARS-CoV-2 T-cell responses in the lower respiratory compartment were not assessed. Seventh, most patients were under corticosteroid treatment within sampling times. Eight, the impact of tocilizumab use on serum levels of inflammatory biomarkers was not apparent in our series (not shown), although it cannot be completely dismissed.

In summary, we found no association between peripheral blood levels of SARS-CoV-2-S1/M-reactive IFN-γ CD4+ and CD8+ T cells and viral clearance from the lower respiratory tract, serum levels of biomarkers of poor prognosis and mortality. Further, larger studies centered on resolving these issues are warranted. 

Declarations

ACKNOWLEDGMENTS

We are grateful to all personnel working in the Clínico-Malvarrosa Health Department and at Clinic University Hospital, in particular those at the Microbiology laboratory, for their commitment in the fight against COVID-19. Eliseo Albert and Estela Giménez hold a Juan Rodés Contract (JR20/00011 and JR18/00053, respectively) from the Carlos III Health Institute. Ignacio Torres holds a Río Hortega Contract (CM20/00090) from the Carlos III Health Institute.

AUTHOR CONTRIBUTIONS

BO, EA, IT, PA, MJR, JA, RG-R: Methodology and data validation. BO, EA, IT: Formal analysis. NC, JF, and MLB: Clinical Staff in charge of patients. DN: Conceptualization, supervision and writing the original draft. All authors reviewed and approved the original draft.  

ETHICAL STATEMENT

The current study was approved by the Ethics Committee of Hospital Clínico Universitario INCLIVA (May, 2020). All experiments were performed in accordance with relevant local guidelines and regulations. Informed consent was obtained from all participants, either on the hospital ward or at the time of ICU admission.

COMPETING INTERESTS

The authors declare no competing interests. 

FUNDING 

This work received no public or private funds.

DATA AVAILABILITY STATEMENT

The data presented in the manuscript have not been made available, but can be shared upon request.

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