Elevated complement C3 and increased CD8 and type 1 helper lymphocyte T populations in patients with post-COVID-19 condition

Cytokine 169 (2023) 156295

Available online 14 July 2023

1043-4666/© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by- nc-nd/4.0/).

Elevated complement C3 and increased CD8 and type 1 helper lymphocyte T populations in patients with post-COVID-19 condition

Mercedes Garcia-Gasalla

, Maria Berman-Riu

, Adrian Rodriguez

,

Amanda Iglesias

, Pablo A. Fraile-Ribot

, Nuria Toledo-Pons

, Elisabet Pol-Pol

, Adrian Ferr ´ e-Beltr ´ an

, Francisca Artigues-Serra

, M.Luisa Martin-Pena

, Jaime Pons

, Javier Murillas

, Antonio Oliver

, Melchor Riera

, Joana M. Ferrer

aDepartment of Internal Medicine, Hospital Universitari Son Espases, Palma, Spain

bBalearic Islands Health Research Institute (IdISBa), Palma, Spain

cDepartment of Internal Medicine, Hospital Universitari Son Ll`atzer, Palma, Spain

dCentro de Investigaci´on Biomedica en Red (CIBER) de Enfermedades Respiratorias, Hospital Universitari Son Espases, Palma, Spain

eDepartment of Microbiology, Hospital Universitari Son Espases, Palma, Spain

fDepartment of Pneumology, Hospital Universitari Son Espases, Palma, Spain

gDepartment of Immunology, Hospital Universitari Son Espases, Palma, Spain

hUniversitat de les Illes Balears. Palma de Mallorca, Illes Balears, Spain

A R T I C L E I N F O Keywords:

post-COVID-19 condition Lymphocyte populations Complement factor Cytokines

A B S T R A C T

Background: Biological markers associated to post-COVID-19 condition (PCC) have not been clearly identified.

Methods: Eighty-two patients attending our post-COVID-19 outpatient clinic were recruited and classified as fully recovered (40.2%) or presenting with PCC (59.8%). Clinical and radiological data, laboratory markers, cyto- kines, and lymphocyte populations were analyzed.

Results: Median number of days after hospitalization was 78.5 [p25-p75: 60–93] days. PCC was significantly more frequent in women, in patients with a previously critical COVID-19, and in those with two or more comorbidities. No differences were found in lymphocyte counts, ferritin, C-reactive protein, D-dimer or sCD25, IL-1β, IL-1Ra, IL-6, CXCL8, IL-17A, IL-18, IL-22, IFN-γ, TNF-α, and IL-10 cytokines levels. PCC patients showed significantly higher levels of complement factor C3 than fully recovered patients: median C3 128 mg/dL [p25- p75:107–135] vs 111 mg/dL [p25-p75: 100–125] (p =.005), respectively. In the flow cytometry assessment of peripheral blood lymphocyte subpopulations, PCC patients showed significantly increased CD8 populations compared to fully recovered patients: median CD8: 529 [p25-p75: 384–683] vs 370/mm³ [p25-p75:280–523], p

=.007. When type 1, 2, 17/22, and 17.1 helper and follicular T lymphocyte subpopulations were analyzed, the frequency of Th1 was significantly higher in PCC patients compared to fully recovered patients (30% vs 38.5%, p

=.028).

Conclusion: Patients with a post-COVID-19 condition showed significantly increased immunological parameters of inflammation (complement factor C3 and CD8 and Th1 T lymphocyte populations) compared to fully recovered patients. These parameters could be used as biological markers of this condition.

1. Introduction

Persistent and disabling symptoms may occur after asymptomatic, mild, severe, or critical COVID-19. A wide range of symptoms persisting at least four weeks after being infected with SARS-CoV-2 have been

recognized, among which tiredness, respiratory and heart symptoms, or neurocognitive disorders are some of the most frequently reported manifestations [1,2]. The prevalence of these clinical features varies between studies due to the different definitions used, follow-up times evaluated, and populations surveyed. A variety of names have been

* Corresponding author at: Department of Internal Medicine, Hospital Universitari Son Espases, Carretera de Valldemossa, 79, 07210, Palma, Spain.

E-mail addresses: [email protected], [email protected] (M. Garcia-Gasalla).

1 These authors share first authorship.

Contents lists available at ScienceDirect

Cytokine

journal homepage: www.elsevier.com/locate/cytokine

https://doi.org/10.1016/j.cyto.2023.156295

Received 14 March 2023; Received in revised form 28 June 2023; Accepted 5 July 2023

proposed for this syndrome that was listed in September 2020 in the World Health Organization (WHO) International Classification of Dis- eases 10 (ICD-10) and ICD-11 as post-COVID-19 condition (PCC) [3].

PCC was defined following a Delphi consensus process as a “condition occurring in individuals with a history of probable or confirmed SARS- CoV-2 infection, usually 3 months from the onset of COVID-19, with symptoms that last for at least 2 months and cannot be explained by an alternative diagnosis. Common symptoms include, but are not limited to, fatigue, shortness of breath, and cognitive dysfunction, and generally have an impact on everyday functioning. Symptoms might be new onset after initial recovery from an acute COVID-19 episode or persist from the initial illness. Symptoms might also fluctuate or relapse over time.” [4].

The public health impact of this medical condition affecting 20–30%

of SARS-CoV-2 infected patients is high. Despite several guidelines and recommendations [5–7], PCC has become a diagnostic challenge for healthcare providers in clinical practice because of the difficulty of recognizing and differentiating it from other medical conditions and of the absence of laboratory markers supporting the diagnosis.

Different underlying biological factors, none of which are mutually exclusive, are probably associated to these heterogeneous long COVID symptoms. Virus-specific damage and a persistent immune activation with production of proinflammatory cytokines both play an important role [8–9]. A better understanding of these pathogenic effects is needed to identify laboratory markers associated to PCC and potential new therapeutic targets.

The purpose of our study was to investigate demographic, clinical, and routine laboratory features, complement factors C3 and C4, cyto- kines, and lymphocyte subpopulations in two groups of patients dis- charged after being hospitalized with COVID-19: fully recovered patients and those with PCC.

2. Methods 2.1. Patients

Patients attending the post-COVID-19 outpatient clinic 8–12 weeks after hospital discharge were recruited between May 25th and August 3rd, 2020. At the time of the study, no COVID-19 vaccination was available. Clinical and demographic data retrieved from the partici- pants’ electronic medical records included age, gender, comorbidities (hypertension, obesity, diabetes, dyslipidemia, cardiomyopathy or immunodepression), and previous COVID-19 severity during hospitali- zation, which was categorized as mild/moderate (grade 1), severe (grade 2), and critical (grade 3) [10]. Patients were classified as fully recovered (n =33) or presenting with PCC (n =49). Symptoms referred by patients with PCC were classified as respiratory symptoms (dyspnea, pleuritic chest pain, cough), general symptoms (asthenia and fatigue), and neurological symptoms (dizziness, headache, memory issues, brain fog). A blood test, chest X-ray, and pulmonary ultrasound were per- formed during this visit. In chest radiography and lung ultrasonography, abnormal findings were considered if persistent ground-glass opacities, horizontal linear opacities, and/or consolidation were found in the chest radiography or B-lines and/or pleural abnormalities were found in the ultrasonography.

2.2. Hematological and biochemical parameters

Routine blood examinations included leukocyte, neutrophil, and lymphocyte counts (cells*10^3/µL) and percentages. Serum biochemical parameters recorded were ferritin (ng/L) determined by a chem- iluminescence immunoassay, C-reactive protein (CRP) (mg/dL), D- dimer (µg/L), and complement factors C3 and C4 quantified by nephe- lometry (Table 3).

We used a chemiluminescence assay (IMMULITE, Siemens, Ger- many) to determine serum soluble IL-2 receptor alpha (sIL-2rα or sCD25) and a human cytokine magnetic bead panel (Merck Millipore,

Billerica, MA, USA) to measure levels of other cytokines associated with

“cytokine storm”: IL-1β, IL-1 receptor antagonist (IL-1Ra), IL-6, CXCL8, IL-17A, IL-18, IL-22, interferon gamma (IFN-γ), tumor necrosis factor alpha (TNF-α), and IL-10.

Testing of IgG concentration against the receptor binding domain of SARS-CoV-2 spike (S) protein was performed using either serum or plasma samples. Samples were run on the Alinity i System using the SARS-CoV-2 IgG II Quant®assay, a chemiluminescent microparticle immunoassay (CMIA) used for the qualitative and quantitative deter- mination of these antibodies, according to manufacturer’s recommen- dations. A concentration greater than or equal to 60 arbitrary units (AU)/mL or 8.5 binding antibody units (BAU)/mL was defined as posi- tive (supplier’s threshold).

2.3. Flow cytometry

Cell surface marker expression was analyzed by flow cytometry using a FACS-Lyric cytometer, and data analysis was performed with the FlowJo software, both from Becton-Dickinson. A surface staining pro- tocol was applied to study the distribution of different cell sub- populations in peripheral blood. Briefly, 50 □L of peripheral whole blood was incubated with different combinations of fluorochrome con- jugated monoclonal antibodies for 20 min at room temperature (25 ^◦C).

Red blood cells were lysed for 10 min with 2 mL of FACS Lysing solution (Becton Dickinson) and washed with phosphate-buffered saline (PBS) before flow cytometry analysis. Combinations of the following mono- clonal antibodies were used: anti-CD3-PerCPCy5.5, anti-CD3-APC, anti- CD4-V500, anti-CD4-V450, anti-CD8-APCR700, anti-CD8-FITC, anti- CD19-PECy7, anti-CD56-PE, anti-CD45-APCH7, anti-CD45-V500, anti- HLA-DR-V450, anti-CD25-PECy7, anti-CD127-APC, anti-CD45RA- BV605, anti-CXCR5-BB515, anti-CXCR3-APC, anti-CCR6-PE, anti-CCR7- APCR700, anti-CD27-APC, anti-IgD-V450, anti-CD38-PE, anti-CD38- PerCPCy5.5, and anti-CD24-PE, all from Becton-Dickinson. Gating strategy of the analyzed subpopulations is depicted in supplementary figures S1 and S2.

2.4. Statistical analysis

Categorical variables were expressed as numbers and percentages, and continuous variables as median and percentiles p25 and p75 since a non-normal distribution was found for most variables. Proportions for categorical variables were compared using the χ2 test. The independent group Mann-Whitney U test was used for the comparison of continuous variables. All statistical analyses were performed using SPSS (Statistical Package for the Social Sciences) version 22.0 software (SPSS Inc.). Two- sided p-values of<0.05 were considered statistically significant.

3. Results

3.1. Study participants

Eighty-two patients accepted to participate in the study, signed an informed consent, and were recruited in the post-COVID-outpatient clinic at Hospital Universitari Son Espases (Mallorca, Spain) to take part in the study. Median age was 56.5 years (p25-p75: 48–67), and 42 (51.22%) were men. Median number of days after hospital discharge was 78.5 (p25-p75: 60–93). Thirty-three (40.2%) participants were classified as fully recovered and 49 (59.8%) had symptoms of PCC.

COVID-19 had been mild/moderate in 36 (43.9%), severe in 25 (30.5%), and critical in 21 (25.6 %). Clinical, epidemiological, and radiological data of fully recovered and PCC patients are presented in Table 1. PCC was found to be significantly more frequent in women (29 [59.2%]

women vs 20 [40.8%] men) even though critical COVID-19 had been slightly more frequent in men: 14 (33.3%) men had had a critical COVID-19 vs 7 (17.5%) women. PCC was more common in patients previously hospitalized with a critical COVID-19 and patients with two

or more comorbidities. PCC and respiratory symptoms were not found to be related to abnormal chest X-ray or lung ultrasonography, or to pos- itive or negative serology (Table 1).

PCC patients reported respiratory symptoms in 29 cases (58%), mainly dyspnea and pleuritic chest pain, fatigue in 21 (42%), and neurological symptoms consisting of memory issues and brain fog in 9 (18%). A patient who was pregnant developed hyperthyroidism. No statistically significant relationship was found between these symptoms and sex, previous COVID-19 severity, or any comorbidity (Table 2).

3.2. Laboratory biomarkers

We did not find any significant differences in lymphocyte counts or neutrophil/lymphocyte ratios between fully recovered and PCC pa- tients, and no differences in the serum biochemical parameters ferritin CRP or D-dimer were found either (Table 3). Patients with PCC showed significantly higher levels of complement factor C3 than asymptomatic patients: median C3 in PCC patients was 128 mg/dL [p25-p75:107–135]

vs 111 mg/dL [p25-p75: 100–125] in fully recovered patients (p =.005).

No significant differences were observed in complement factor C4.

3.3. Cytokines

Levels of serum soluble IL-2 receptor alpha (sIL-2rα or sCD25), IL-1β, IL-1 receptor antagonist (IL-1Ra), IL-6, CXCL8, IL-17A, IL-18, IL-22, IFN- γ, TNF-α, and IL-10 were compared between fully recovered and PCC patients. We did not find significant differences in any of these cytokines (Table 4).

3.4. Lymphocyte subsets

The flow cytometric analysis of peripheral blood did not reveal dif- ferences in the total number or percentage of T, B, and natural killer (NK) cells from fully recovered compared with PCC patients. However, in the T lymphocytes subpopulation study (Fig. 1), symptomatic patients showed significantly increased CD8 populations with a lower CD4/CD8 ratio compared to fully recovered patients (median CD8: 529 [p25-p75:

384–683] vs 370/mm³ [p25-p75:280–523] p =.027,and CD4/CD8: 1.9 [p25-p75: 1.3–2.9 ] vs 2.5 [p25-p75: 1–9-3.7] p =ns in symptomatic and fully recovered patients respectively). Moreover, if PCC patients with respiratory symptoms were compared with fully recovered patients, the difference reached statistical significance as the CD4/CD8 median ratio Table 1

Clinical, epidemiological, and radiological data of fully recovered and post-COVID-19 condition patients.

Fully recovered n ¼33 (%) PCC n ¼49 (%)) p

COVID-19 severity:

Mild/mod þsevere 17 (52) +12 (36) 19 (39) +13 (26) 0.037

Critical 4 (12) 17 (35)

Days after discharge 73.8 (DS: 24.3) 76.9 (DS: 21,3) 0.54

Comorbidities (number)

<2 =23 (70) <2 =28 (58) 0.036

≥2 =10 (30) ≥2 =21 (32)

Age 60.0 (SD: 15.5) 56.6 (SD: 12.5) 0.27

Sex Male: 22 (67) Male: 20 (41) 0.026

Female: 11 (33) Female: 29 (59)

SARS-CoV-2 anti-S IgG (n ¼80) Negative: 8 (25) Negative: 9 (19) 0.58

Positive: 24 (75) Positive:39 (81)

Chest X-ray

(n ¼71) Normal: 16 (55)

Abnormal: 13 (45) NP: 4 (12)

Normal: 15 (36) Abnormal: 27 (64) NP: 7 (14)

Patients with respiratory symptoms (n =19) Normal: 5 (22)

Abnormal: 10 (52) NP: 4 (21)

0.25

0.16

Lung ultrasonography Normal: 10 (30)

Abnormal: 20 (61) NP: 3 (9)

Normal: 10 (20) Abnormal: 37 (76) NP: 2 (4)

Patients with respiratory symptoms (n =19) Normal: 3 (16)

Abnormal: 16 (84)

0.32

0.17 Abbreviations: mod: moderate; NP, not performed; PCC, post-COVID-19 condition; SD, standard deviation.

Table 2

Symptoms reported in 49 post-COVID-19 condition patients according to sex, COVID-19 severity, and number of comorbidities.

Fatigue n (%)

Dyspnea n (%)

Pleuritic chest pain

n (%) Neurological symptoms

n (%)

Sex

Male (n ¼20) 9 (45) 13 (65) 2 (10) 4 (20)

Female (n ¼29) 12 (40) 15 (53) 2 (7) 5 (17)

COVID-19 severity. Grade

1 (n ¼19) 7 (37) 9 (47) 4 (21) 2 (11)

2 (n ¼13) 3 (23) 8 (62) 0 3 (23)

3 (n ¼17) 6 (35) 11 (65) 0 4 (24)

Number of comorbidities

≤1 (n ¼29) 13 (45) 16 (55) 4 (14) 6 (21)

≥2 (n ¼20) 8 (40) 13 (65) 0 3 (15)

Total n ¼49 21 (42) 29 (58) 4 (8) 9 (18)

was 1.60 [p25-p75: 0.99–2.46] in this subgroup (p =.039). We did not find differences in the proportion of activated (CD38 +HLA-DR + ) memory T cell, effector memory CD45RA+(EMRA phenotype) CD4 and T regulatory cell frequencies (data not shown). Finally, type 1, 2, 17/22,

and 17.1 helper and follicular T lymphocyte subpopulations were analyzed (Fig. 2). We found a statistically significant higher median percentage of type 1 helper (Th1) lymphocytes among the T helper lymphocytes in symptomatic compared to fully recovered patients Table 3

Laboratory biomarkers in fully recovered patients and in patients with a post-COVID-19 condition -Median and percentiles [p25-p75].

Fully recovered

(n ¼33) PCC (n ¼49) p

Lymphocyte count (mm³) 1960.0 [1652.5–2642.5] 2200 [ 1720.0–2890.0] 0.257

NLR 1.9 [1.4–2.6] 1.5 [1.2–2.0] 0.150

Ferritin (ng/mL) 49.0 [32.7–170.2] 57.0 [21.0–105.0] 0.938

D-dimer (ng/mL) 68 [ 53.7–169.0] 91 [63.0–139.0] 0.807

CRP (mg/dL) 0.1 [0.1–0.2] 0.2 [0.1–0.6] 0.048

Complement C3 (mg/dL) 111.0 [99.0–125.0] 128.5 [107.2–135.0] 0.005

Complement C4 (mg/dL) 24.5 [20.7–27.7] 25.5 [21.0–33.7] 0.212

Abbreviations: CRP, C-reactive protein; NLR; neutrophil to lymphocyte ratio; PCC, post-COVID-19 condition.

Normal values: Lymphocyte count 1600–4500 cells/ mm³; Ferritin: 20–274 ng/ml; D-dimer: 0–230 ng/ml; CRP: 0,0–0,5 mg/dl; complement factor C3:80–120 mg/dl;

complement factor C4: 20–40 mg/dl.

Table 4

Cytokine levels -median and percentiles [p25-p75]- in fully recovered patients and patients with a post-COVID-19 condition.

Cytokine levels (pg/mL) Median [p25-p75] Fully recovered (n ¼33) PCC (n ¼48) p

IL-6 0.25 [0.0–9.5] 1.9 [0.0–5.1] 0.518

IL-10 0.0 [0.0–0.0] 0.0 [0.0–0.0] 0.553

sCD25 300.0 [256.0–415.0] 368.5 [288.5–523.7] 0.209

IFN-γ 1.6 [0.9–63.0] 2.6 [0.2–13.4] 0.860

IL-1β 0.6 [0.0–30.4] 1.1 [0.0–7.6] 0.676

IL-1Ra 24.9 [13.0–45.6] 32.0 [15.6–73.8] 0.204

CXCL8 7.9 [5.9–10.8] 10.4 [4.5–15.8 ] 0.531

IL-17A 0.0 [0.0–3.8] 0.0 [0.0–2.3] 0.639

IL-18 37.8 [21.2–93.8] 41.8 [87.0] 0.559

IL-22 0.0 [0.0–14.8] 0.0 [0.0–0.0] 0.535

TNF-α 12.7 [9.6–74.6] 14.5 [2.1–31.6] 0.136

Abbreviations: PCC, post-COVID-19 condition.

Fig. 1. Higher CD8 þT cell absolute and frequency values in patients with post-COVID-19 condition (PCC). Absolute values (panel A) and frequency (panel B) of peripheral blood cell populations (CD3, CD4, CD8, CD19 and NK) in recovered (white circles) and post-COVID-19 condition (dark circles) groups of patients. Each dot represents an individual patient. Black horizontal lines illustrate the median of the group. Mann-Whitney test P values: *P <0.05; **P <0.01.

(32.3% vs 38.8%, p =.028).

4. Discussion

In our study, we analyzed clinical, epidemiological, radiological, laboratory, and immunological characteristics of patients with PCC compared to fully recovered patients. Female sex, previous comorbid- ities, and a critical COVID-19 were more frequent in patients with PCC.

In the laboratory parameters, we did not find differences in leukocyte counts, neutrophil to lymphocyte ratio (NLR), or levels of ferritin, CRP, or D-dimer, but higher levels of complement factor C3 were observed in symptomatic patients, suggesting a hyperinflammatory state. In the cytokine study, no statistically significant differences were found. A decreased CD4/CD8 ratio due to an increased CD8 T cell subpopulation was found in the flow cytometric assessment of peripheral blood lymphocyte subpopulations, and a higher proportion of the Th1 lymphocyte subpopulation was found in symptomatic patients.

Our clinical and epidemiological data are in agreement with other studies that have found that female sex, previous comorbidities, and critical COVID-19 are risk factors for developing a PCC: critical factors identified have been female sex and hypertension in a large series in Moscow and in another one in Saudi Arabia [11–12]; female sex and critical disease in a Spanish study [13] and in a meta-analysis [14]; and female sex, increasing age and body mass index in a study conducted in the United Kingdom [15]. In our study, no differences related to age between both groups were observed. Moreover, 9/16 (56.3%) patients over 70 years were fully recovered compared to 26/67 (38.8%) patients under 70 years.

Lung ultrasonography or chest X-ray were found to be abnormal in a high proportion of patients, but no relationship with persistent respi- ratory symptoms was found. In agreement with us, a study concluded that chest X-ray was a poor marker of recovery [16]. As for lung ultra- sonography, studies are scarce and inconclusive: an observational single-center study in London found that a lung ultrasonography score

≥3 indicated an excellent ability to discriminate patients with relevant

interstitial lung sequelae [17], and another study in Brazil showed an altered lung ultrasonography in 36 (61%) patients with persistent res- piratory symptoms two months after the diagnosis of acute COVID-19 [18].

We did not find routine laboratory inflammatory markers related to PCC. Studies reporting pro-inflammatory laboratory markers in patients with a PCC are scarce and results are conflicting. Elevated CRP and lactate dehydrogenase (LDH) in patients with post-COVID-19 pulmo- nary fibrotic changes have been described in one study, [19] and low grade inflammation with mild, but statistically significant, CRP eleva- tion in post-COVID-19 symptomatic patients was found in another publication performed in a single primary care center in Spain [20]. In contrast, no association with pro-inflammatory markers was found in persistent fatigue following SARS-CoV-2 infection in a post-COVID-19 clinic in a hospital in Dublin [21]. Accordingly, post-COVID-19 man- agement guidelines do not recommend routine determination of any inflammatory biomarkers [5,7].

The complement system plays an important role in the innate im- mune response to pathogens, but also instructs the adaptive immune response [22]. The activation of the complement system has been described to have a prominent role in both COVID-19 pathogenesis and disease severity, as described by Lim in an extensive review [23]. Pro- teomic studies have confirmed the up-regulation of the three comple- ment pathways and their association with severity and mortality. In our study, complement factor C3 was up-regulated and associated with Intensive Care Unit (ICU) hospitalization and severity. Observational studies have shown complement activation in COVID-19, although levels of complement factors C3 and C4 were either decreased or not different in severe or non-survivor patients compared to non-severe or survivors. Anaphylatoxins C3a and C5a were elevated and correlated with severity and mortality.

However, studies analyzing complement factor C3 in post-COVID-19 patients are scarce. A previous publication found high levels of the complement complex C5b-9 in post-COVID-19 patients, but when pa- tients with post-COVID-19 lung fibrotic changes were specifically tested, Fig. 2. Higher Th1 frequency in patients with post-COVID-19 condition (PCC). Frequency of T helper cells (panel A) and T follicular helper cells (panel B) in recovered (white circles) and post-COVID-19 condition (dark circles) groups of patients. Each dot represents an individual patient. Black horizontal lines illustrate the median of the group. Mann-Whitney test P values: *P <0.05. Th: T helper; Tfh: T follicular helper.

no differences were found compared to healthy controls [19]. In our study, we have found a significant serum complement factor C3 eleva- tion in patients with long COVID, with no elevations in other inflam- matory markers compared to recovered subjects. In this setting, complement factor C3 elevation may reflect a low grade persistent in- flammatory state and could be a source of C3a in these patients, although this aspect should be tested in a future study. Complement factor C4 levels were not diminished, which argues against activation of the classical and lectin pathways of the complement system.

Our analysis showed no significant elevated cytokines in patients with PCC compared with asymptomatic ones. Despite a study published in the early stages of the COVID-19 pandemic describing different pat- terns of elevated pro-inflammatory cytokines in convalescent COVID-19 patients’ plasma [24], few studies have analyzed these biological markers in post-COVID-19 symptomatic patients, and results have been controversial. An important study in Germany performed in a first cohort and validated in a second cohort of more than 300 patients each found a triad of cytokines (IL-1β, IL-6, and TNFα) related to post-acute COVID-19 sequelae [25]. Another study found elevated TNF-α and IFN-γ–induced protein-10 in patients in the early phase of PCC (<90 days) and elevated IL-6 in the late recovery phase [8]. Another article described elevated IL-1α and TGF-β in patients with pulmonary fibrosis [19], while others have suggested that IL-6 may be a mediator of long- term neuropsychiatric symptoms, but results remain inconclusive [26].

Persistent inflammation and immune activation have been related to long COVID in different studies [27–28]. Flow cytometry analysis of peripheral blood lymphocyte subpopulations in acute COVID-19 pa- tients has been widely investigated [29–32], but there is a limited number of studies in the post COVID-19 state. Adaptive immunity dis- turbances such as a decrease in CD4 effector memory cells [33] or a lack of naive T and B cells attributed to bystander activation of naive subsets [27] have been found in patients with long COVID. We found decreased CD4 and increased CD8 subpopulations resulting in a lower CD4/CD8 ratio in post-COVID-19 patients compared to fully recovered patients.

No differences in the proportion of B lymphocytes, NK cells, or activated, memory or regulatory T cells were observed. When subtypes of type 1, 2, 17/22, and 17.1 in helper (Th) or follicular T lymphocyte sub- populations were analyzed, a higher proportion of Th1 cells was found in long COVID.

Georg et al [34] have demonstrated that an increased production of C3a in severe COVID-19 patients induced activated CD16 +cytotoxic T cells. Their proportion and the plasma level of complement proteins upstream of C3a have been associated to a fatal outcome. In a previous study, we found that acute severe/critical patients -the group that most contributes to the development of a PCC- showed an increase in acti- vated (HLA-DR +) and EMRA (CD45RA +CCR7-) cytotoxic CD8 +cells [32]. We speculate that C3a generated in our patients could be related to an increase in cytotoxic CD8 T cells, although CD16 +expression was not evaluated in our cohort. A low CD4/CD8 ratio has been recognized as a marker of T lymphocytes activation and senescence in people living with HIV on antiretroviral therapy and virologically suppressed, and has been related to a higher risk of non-AIDS defining events [35].

Regarding B lymphocytes, a significant negative correlation between the duration of COVID-19 symptoms and the frequency of memory B cells has been found [36]. Furthermore, SARS-CoV-2 vaccination improves the sequelae of the post-COVID-19 condition [37–38] by inducing a robust memory B cell response [39]. When we analyzed type 1, 2, 17/22, and 17.1 subpopulations in helper or follicular T lymphocytes, a higher proportion of Th1 cells was found in PCC patients. Different T sub- populations have different B cell helper abilities. In contrast to other subpopulations that help B cells to differentiate to antibody secreting and memory B cells, Th1 cells from both CXCR5 +and CXCR5 − com- partments lacked the capacity to help B cells [40]. Although we did not find differences in B cell subpopulations between our two patient groups, the Th1 increase in PCC patients could secondarily impair functional B cell differentiation. Further studies are needed to clarify

this finding.

Our study has some important limitations: the number of patients is small and post-COVID-19 symptoms are heterogeneous. Moreover, the study was performed during the first pandemic wave, before vaccines were available; no patients with new SARS-CoV-2 variants were included and samples for C3a determination were not available. Another limitation concerns Th2 and Tfh2 identification (Figure S2). No positive identification with anti-CCR4, in accordance with published guidelines [41], was applied to the CXCR3-CCR6-CXCR5-/+CD45RA-CD4 +CD3 +Th2 and Tfh2 cells, respectively.

In conclusion, our study suggests that increased serum complement factor C3 and increased peripheral blood CD8 and Th1 populations are potential biological markers for PCC, although studies with larger pa- tient cohorts are needed to validate these observations. A better knowledge of the risk factors, mediators, and laboratory markers involved in the development of PCC is necessary to improve diagnosis and therapeutic interventions and to mitigate this heterogeneous condition.

Declarations.

The study was approved by the local Ethics Committee (Comit´e ´Etico de Investigaci´on Clínica Illes Balears n^◦IB 4169/20 PI) and was per- formed in compliance with the Declaration of Helsinki. Informed con- sent forms were obtained from all participants.

The authors have no conflicts of interest to declare.

CRediT authorship contribution statement

Mercedes Garcia-Gasalla: Funding acquisition, Project adminis- tration, Supervision, Visualization, Writing – review & editing, Writing – original draft, Conceptualization, Methodology, Formal analysis, Re- sources. Maria Berman-Riu: Visualization, Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Conceptualiza- tion. Adrian Rodriguez: Writing – review & editing, Conceptualization, Methodology, Formal analysis. Amanda Iglesias: Writing – review &

editing. Pablo A. Fraile-Ribot: Writing – review & editing. Nuria Toledo-Pons: Resources, Writing – review & editing. Elisabet Pol-Pol:

Writing – review & editing. Adrian Ferr´e-Beltr´an: Writing – review &

editing, Conceptualization, Resources. Francisca Artigues-Serra:

Writing – review & editing, Writing – original draft, Resources. M.Luisa Martin-Pena: Writing – review & editing, Conceptualization, Re- sources, Writing – original draft. Jaime Pons: Writing – review &

editing, Conceptualization. Javier Murillas: Writing – review & editing, Conceptualization, Resources, Writing – original draft. Antonio Oliver:

Writing – review & editing. Melchor Riera: Writing – review & editing, Conceptualization, Resources, Writing – original draft. Joana M. Fer- rer: Project administration, Supervision, Writing – review & editing, Conceptualization, Methodology, Formal analysis.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

Data will be made available on request.

Acknowledgments

Writing assistance provided by Graham & Chevarria S.L. (https://

www.graham-chevarria.com)

This work was supported by a grant from Instituto Salud Carlos III [grant number COV20/00943].

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.

org/10.1016/j.cyto.2023.156295.

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