1. Introduction
In December 2019, the coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, broke out in China. As of 18 August 2021, the WHO reported that there had been 208,470,375 confirmed cases, including 4,377,979 deaths globally. So far, there are no specific antiviral treatments. According to Diagnosis and Treatment Protocol for COVID-19 Patients (Tentative 8th Edition) of the National Health Commission of the People’s Republic of China, confirmed cases could be classified into four clinical types: mild cases, moderate cases, severe cases and critical cases [
1]. In the early stage of the outbreak, most medical resources tended to identify and treat critical cases. With the discovery of the risk posed by asymptomatic transmission, it is of great significance to pay more attention to asymptomatic individuals to contain outbreaks.
Asymptomatic infected people refer to those whose respiratory tract specimens are positive for the nucleic acid test of SARS-CoV-2 but do not show any relevant clinical symptoms, such as fever, cough, sore throat and other self-perceived or clinically recognizable symptoms. Recently, many studies showed that there might be an increase in the asymptomatic proportion of SARS-CoV-2 globally. According to the early epidemiology report from the Chinese Center for Disease Control and Prevention (China CDC), there were 889 asymptomatic COVID-19 cases among 44,672 confirmed cases, accounting for about 2.0% of the total confirmed cases [
2]. The latest retrospective cohort study conducted by China CDC assessed the proportion of asymptomatic infection in all international entrants from 16 April to 12 October 2020. The results showed that among 3130 SARS-CoV-2 positive entrants, 1612 (51.9%) were considered to have asymptomatic infection. Furthermore, the proportion of asymptomatic infections increased significantly from 27.8% (from 16 April to 30 April) to 59.4% (from 28 September to 12 October 2020) [
3]. Additionally, Iceland reported that 43% of the participants positive for the nucleic acid test had no symptoms based on a large-scale population test, which can accurately reflect the real situation of the entire population [
4]. All this research implied a sharp increase in asymptomatic infection at a global scale. More and more evidence showed that asymptomatic individuals could greatly accelerate the spread of the virus from person to person, which caused great difficulties in the control of COVID-19 [
5,
6,
7]. Therefore, early screening, early reporting, and early separation of asymptomatic individuals may be a key breakthrough to control the COVID-19 pandemic [
8].
As we know, the clinical outcome of viral infection is closely associated with the virus itself, the amount of virus, the manner of infection, and the characteristics of the host immune response. However, our understanding of the clinical characteristics and immunophenotyping features of asymptomatic COVID-19 individuals remains somewhat mysterious. The occurrence, clinical outcome and screening of treatment targets of COVID-19 are all closely related to the host immunity. It is critically important and urgent to fully understand the immune mechanism of asymptomatic state, which will be beneficial to predict the outcome of the disease through dynamic observation of immune phenotype. Therefore, we can intervene and improve the prognosis of patients as soon as possible. Besides, the revelation of immunophenotyping characteristics will be helpful to provide a new target of immunological intervention. Overall, immunological findings of asymptomatic patients are crucial in the fight against SARS-CoV-2. This study collected 41 COVID-19 patients (including 24 asymptomatic cases and 17 symptomatic cases with moderate or severe clinical manifestation) admitted to the Fifth Hospital of Shijiazhuang from January to June 2020.
On the one hand, we carried out routine laboratory tests. On the other hand, we collected the peripheral blood before any clinical intervention and monitored the number and proportion of various immune cells and lymphocyte subsets. This study aims to explore the immunological characteristics of asymptomatic cases, understand the immune pathogenesis of COVID-19 and predict clinical outcomes more precisely.
3. Discussion
A vast proportion of asymptomatic COVID-19 individuals may cause an exponential increase in the number of COVID-19 cases globally. As we know, individual immune responses potentially orchestrate the pathology and play important roles in determining different clinical courses of SARS-CoV-2 infection. A recent study showed an extensive and durable remodeling of immune cells during and after SARS-CoV-2 infection [
11]. However, the immune mechanisms that are associated with asymptomatic infection are still largely elusive. To reveal possible mechanisms by which a large fraction of COVID-19 patients remain asymptomatic, we aimed to investigate whether the clinical characteristics and immunophenotyping of innate and adaptive immune cells in asymptomatic and symptomatic COVID-19 patients can help us identify pathological or protective immune responses during COVID-19 infection.
Firstly, we tried to find the demographic characteristics and laboratory indicators of asymptomatic individuals based on comprehensive clinical data. Asymptomatic individuals seemed to be younger than symptomatic ones (27.4 ± 9.6 versus 44.9 ± 18.4 years). We found a higher rate of asymptomatic infection in the younger group through age-stratified analysis and a higher rate of symptomatic infection in the older group, which is consistent with the results of Davies et al. [
12]. Additionally, the group with chronic diseases had a higher rate of symptomatic infection but a lower rate of asymptomatic infection. This result implied that chronic diseases, such as hypertension and cardiovascular disease, may promote the risk of COVID-19. Many other studies also reported this phenomenon [
13,
14], which may explain to some extent why those asymptomatic individuals have a shorter hospital stay (16.8 ± 5.2 versus 21.9 ± 8.4 days) and better prognosis. However, there was no significant difference in the re-positive rate between the two groups (20.8% versus 23.5% in asymptomatic and symptomatic individuals, respectively).
In addition, we focused on some parameters reflecting inflammation. The mean value of CRP far exceeded the normal upper limit in symptomatic cases, while it was in the normal range in asymptomatic individuals. Besides, although the value of D-dimer and fibrinogen were in the normal range in the two groups, they were higher in the symptomatic group. Our results implied that the systemic inflammatory response was relatively strong in symptomatic individuals. Consistently, Zhao and colleagues reported that CRP, IL6, LDH and ferritin increased dramatically in COVID-19 patients, leading to disease progression [
15].
SARS-CoV-2-infected epithelial cells could recruit and activate various innate immune cells, including macrophages, NK, neutrophils, and others, and adaptive immune cells to orchestrate antiviral immunity and immunopathological effect closely associated with disease severity and progression [
16,
17]. NK cells play an important role in early immune defense against viral infection, which can lyse virus-infected cells at an early stage of infection and prevent viral dissemination [
18]. The decline in the number and function of NK cells likely allows viruses to proliferate intracellularly to trigger NLRP3 activation and aggravate inflammation. Among several pathogenesis mechanisms of COVID-19, hypercytokinemia has taken center stage [
19,
20]. Recruited monocytes could cause an “atypical” cytokine storm [
21], which is characterized by reduced type II interferon signaling, and finally cause local or systemic damage [
22]. The balance between monocytes and NK cells may be closely related to the clinical outcome of COVID-19. The results of flow cytometry showed that NK percentage was beyond the normal range and the monocytes percentage was lower than that of healthy control in asymptomatic cases, which could lead to the decrease in the monocytes to NK cells ratio (MNKR) in asymptomatic cases. Similar to our results, Rita et al. also demonstrated the loss of NK cells and the expansion of monocytes in mild or severe COVID-19 [
23]. However, Diao et al. and Zheng et al. observed NK cell cytopenia in severe COVID-19 patients [
24,
25], which was contrary to our results. These results suggest that the change pattern of NK and monocytes may be one of the sensitive and important factors affecting the prognosis of the disease, which still needs to be further clarified.
Additionally, the progression of the disease is not only related to the frequency of NK and monocytes, but also to their phenotypes. A single cell sequencing study identified 7 monocyte clusters and 13 major NK cell clusters in PBMC from COVID-19 patients, respectively. Different clusters had different transcriptional characteristics and functional phenotypes. The proportion of different subpopulations was closely related with severity of COVID-19 [
11]. In the future, to accurately reflect the characteristics of immune phenotype of asymptomatic and symptomatic cases, it is necessary to analyze NK and monocyte clusters.
We comprehensively analyzed the number and percentage of various peripheral blood leukocytes (including lymphocytes, granulocytes and monocytes) of asymptomatic and symptomatic SARS-CoV-2-infected people. Previous studies have shown that most COVID-19 patients have lymphopenia, and the degree of lymphocyte reduction is closely related to the severity of COVID-19 [
26,
27]. It has been shown that the number of neutrophils and monocytes increased abnormally with the decrease in the number of lymphocytes in patients with COVID-19 [
28,
29], which can lead to the increase in the neutrophil to lymphocyte ratio (NLR) and monocyte to lymphocyte ratio (MLR), especially in patients with severe COVID-19. Our results showed the differences of changes in lymphocytes and monocytes between symptomatic and asymptomatic cases. Symptomatic cases had an abnormally low lymphocyte count and percentage, and a normal monocyte percentage. In contrast, asymptomatic cases had a normal level of lymphocyte and a low percentage of monocyte, which was near the normal lower limit. This immune phenotype of asymptomatic cases cannot only help patients maintain a protective immune response, but also avoid inflammatory injury mediated by monocytes, which may be one of the reasons for their asymptomatic state.
As an intracellular parasitic microorganism, cellular immunity plays an important role in antiviral immunity. Besides, cellular immunity can regulate innate immune and humoral immune response [
30]. Therefore, it is important to investigate cellular immune function to understand the immune mechanisms of asymptomatic infection and disease progression. This study also focused on the changes in CD4
+ T cell subsets, including Th1, Th2, Th17, and Treg, which play different roles in the antiviral immunity. Th1 subsets play an important role in activating macrophage-mediated inflammation and cell-mediated immunity [
31]. In addition to mediating humoral immune response, the Th2 population can inhibit inflammation, which Th1 and Th17 trigger by regulating IFN-mediated response [
32]. Th17 mediates antiviral inflammation by secreting IL17. Some studies have shown that Th17 and IL17 increased significantly in patients with COVID-19, which participate in cytokine storm and disease progression. Th17/IL17 axis may be used as a clinical therapeutic target for COVID-19 [
33]. Treg could suppress the immune response and maintain immune balance by secreting anti-inflammatory cytokines such as TGFβ, IL10 and IL35. Th1/Th2 and Th17/Treg balance play crucial roles in various diseases, such as autoimmune diseases and viral infections. The imbalance of the immune system can increase various inflammatory cytokines such as MIP-1, GM-CSF, and IL1β, leading to respiratory injury and accelerating COVID-19 [
34]. Do asymptomatic individuals establish their asymptomatic carrier state by inducing T cell response towards an anti-inflammatory phenotype and maintaining immune balance?
We investigated the changes in Th1, Th2, Th17, Treg, Th1/Th2 ratio, Th17/Treg ratio in asymptomatic and symptomatic patients with COVID-19. Our results showed that symptomatic and asymptomatic individuals both had an abnormally high percentage of Th2 and Th17, which can lead to the abnormally low ratio of Th1 to Th2 and high ratio of Th17 to Treg, suggesting that virus infection can lead to immune imbalance regardless of clinical symptoms. Compared to the symptomatic cases, asymptomatic individuals had a relatively lower Th17 percentage and a higher Treg percentage, which suggested that asymptomatic cases tend to establish a relatively balanced immune phenotype by decreasing Th17/Treg ratio. A study of RNA sequencing recently explored differentially expressed genes of symptomatic and asymptomatic patients with COVID-19. The results showed a higher expression of the genes involved in the Th2 pathway, together with the down-regulation of the genes in Th1 and Th17 pathways in asymptomatic individuals [
35]. Our results are consistent with theirs. Both high-throughput data and our results suggest that immune homeostasis plays a crucial role in COVID-19 progression. Asymptomatic individuals may be prone to establishing a relatively balanced immune response by decreasing Th17 populations and increasing Treg populations, which can regulate the excessive increase in inflammatory responses of COVID-19 patients.
In the past year, multiple studies were conducted to evaluate the role of CD4 T cell subsets in the pathogenesis of COVID-19. However, these studies report conflicting results. Some studies showed that Th1 subsets and their cytokines increased significantly in patients with COVID-19 [
36]. In contrast, some studies have yielded opposite results [
37,
38]. However, in our investigation, the percentage of Th1 in asymptomatic individuals is equal to that in symptomatic patients. Several studies showed that the levels of Th2 subsets and their cytokines in COVID-19 patients were significantly increased, especially in severe COVID-19 patients [
39]. In contrast, another study found that theTh2 population and their cytokines were decreased in hospitalized patients compared to asymptomatic individuals [
28], which is consistent with our results. Most studies reported that Th17 subsets and their cytokines were increased, while the proportion of Treg was decreased in patients with COVID-19 [
40,
41,
42,
43], which may lead to cytokine storm and promote disease progression. However, another piece of research observed a significant increase in the number of Treg cells in patients with COVID-19. As discussed above, various studies have conflicting results. The possible reasons are as follows: firstly, the subjects included in different studies have a diverse demographic and genetic background and disease severity, but they are not divided into different groups according to the severity of the disease. Secondly, the type of detection techniques used in different studies may affect the results. Thirdly, the peripheral blood sample may be collected before or after clinical treatment, influencing the results.
Antiviral drugs used in this study, such as lopinavir or interferon, have immunomodulatory effects. Lopinavir can inhibit the production of TNFα and other pro-inflammatory cytokines in human endothelial cells, inhibit T lymphocytes proliferation and reactivity, and exhibit an inhibitory effect on proteasome activities to reduce transcription of genes encoding pro-inflammatory cytokines [
44,
45,
46]. Surely it can improve inflammatory status in COVID-19 patients and be a confounder for our results. Type Ⅰ interferon is acknowledged for its antiviral roles. Additionally, interferon may confer immunopathological effects by inducing excessive inflammation in viral infection. IFN can trigger the expression of a diverse suite of IFN-stimulated genes (ISGs), including a large number of proinflammatory cytokines and chemokines, such as CXCL10, MCP1, MIP1β, and IL12. These chemokines can recruit monocytes and CXCR3 expressing Th1 cells producing many proinflammatory cytokines and chemokines, which finally skewed the host immune response [
47]. Therefore, lopinavir or interferon may differentially regulate inflammatory mediators of COVID-19. To avoid the impact of medical treatment in our results, we collect blood samples before any clinical intervention. In addition, small sample size may increase the bias. In our study, some indicators, such as the NK percentage, were higher in asymptomatic cases but without statistical significance. The possible reason is that the number of samples is too small to result in negative results, which is certainly a limitation of our study. Therefore, more studies need to be conducted in the future.
Overall, symptomatic individuals were prone to establishing a non-protective immune phenotype by abnormally decreasing the lymphocyte count and percentage, abnormally increasing the Th17 percentage and decreasing Treg percentage. In contrast, asymptomatic individuals tended to establish a more effective and protective immune phenotype by maintaining a normal level of lymphocyte and a high level of NK cells. At the same time, asymptomatic individuals can establish a relatively balanced immune response through maintaining a low level of monocytes, a relatively low level of Th17 and high level of Treg, which therefore lead to the decrease in MNKR and Th17/Treg ratio and finally avoidance of excessive inflammatory responses. This may be one of the reasons for their asymptomatic states. This study is helpful to reveal the immunological characteristics of asymptomatic individuals, understand immune pathogenesis of COVID-19 and predict clinical outcomes more precisely. However, owing to small sample sizes, a future study with larger sample size is still warranted.
4. Materials and Methods
4.1. Study Design and Participants
The study group enrolled patients with COVID-19 (aged 17–67 years) hospitalized in the Fifth Hospital of Shijiazhuang from January to June 2020. A total of 41 COVID-19-infected individuals were included for flow cytometry detection in this study (including 24 asymptomatic individuals and 17 symptomatic cases). For immunological indexes with normal reference values, we did not set a healthy control, and compared the results with the normal reference values. For immunological indicator without normal reference values, such as the percentage of monocyte in karyocyte, we detected 20 healthy individuals who participated in physical examination in the Fifth Hospital of the Shijia Zhuang at the same time to be as the baseline. For the blood routine examination, to better observe the changing trend, we collected the blood routine results of 50 age-matched healthy controls in the same period to be the baseline.
The symptomatic COVID-19 infections were defined as follows: positive test results for SARS-CoV-2 by qPCR in oro/nasopharyngeal swab samples plus being symptomatic. The asymptomatic infection was defined as follows: positive test for SARS-CoV-2 by qPCR but no symptoms throughout the 14-day quarantine according to the standard definition in China, which is different from that of the World Health Organization. According to the management standards for an asymptomatic infected person launched by The Joint Prevention and Control Mechanism of the State Council of China, there are two types of asymptomatic carriers: those who are medically quarantined for 14 days without any self-perceiving or clinically identifiable symptoms, and those who are in the incubation period of symptomatic infection. The second type of asymptomatic carrier may become a symptomatic patient during quarantine. In our study, only the first type of asymptomatic case was included.
Asymptomatic individuals are at risk of spreading the virus. In order to better control the epidemic of COVID-19, the Chinese government has adopted very strict centralized isolation of fixed medical organization for suspected cases, confirmed cases and asymptomatic cases. According to Prevention and Control of COVID-19 (Tentative 4th Edition) of the National Health Commission of the People’s Republic of China, 14 days of centralized isolation and medical observation shall be carried out for asymptomatic cases identified by medical care and health institutions at various levels and of different types. Additionally, to determine different clinical types of confirmed COVID-19 and predict the progression of asymptomatic cases precisely, CT chest images are carried out routinely for all cases. These strict isolation measures for asymptomatic individuals conducted in China may be different from other countries.
All patients received comprehensive clinical examination. The following variables were collected for each patient: age, sex, complication, epidemiological data, past or present medical history, clinical manifestations, severity assessment on admission, laboratory findings, the results of chest computed tomography (CT) scan, treatment, hospitalization days and clinical outcome, etc. Additionally, peripheral blood was collected to detect the number and percentage of various immune cells and their subsets by flow cytometry.
4.2. Flow Cytometry
Venous blood samples were collected from the patients on hospital admission and transferred to the laboratory within 1 h. Immunophenotyping of immune cells and lymphocyte subsets was performed using a 3-laser, 10-color FACSCanto II flow cytometer (Beckman-Coulter, Brea, CA, USA). All ethylenediaminetetraacetic acid (EDTA)-anticoagulated blood samples were analyzed immediately as soon as the blood was collected. Sample preparation and analysis were performed according to the manufacturer’s instructions. About 100 µL aliquots of blood were incubated with 2 mL of BD FACSTM Lysing Solution (Cat No. 349202, BD Biosciences, Franklin Lakes, NJ, USA) in the dark for 10 min to lyse the red blood cells. After washing and re-suspending the cells in 100 µL PBS, the cells were vortexed and incubated in the dark with combinations of the following fluorochrome-conjugated antibodies for 30 min at 4 °C. CD16/32 monoclonal antibody was pre-incubated with the cells at room temperature for 10 min to block FcR of B cells and monocytes. Finally, stained samples were analyzed with FACSCanto II flow cytometer immediately after completion of sample staining. Data analyses were performed with the BD FACS Diva software. The antibodies used in this study are as follows: CD56-BB700 (566573, BD), CD45-APC (555485, BD), CD8-PE-Cy7 (557746, BD), CD4-PE-Cy7 (557852, BD), CD16-APC-H7 (560195, BD), CD45-Percp-Cy5.5 (564105, BD), CD196-APC (560619, BD), CD4-PE-Cy7 (560909, BD), CD4-PE (555347, BD), CD19-APC (555415, BD), CD25-PE (555415, BD), CD86-PE (555658, BD), CD3-FITC (555916, BD), CD183-PE (557185, BD), CD127-Alexa Fluor® 647 (558598, BD), CD14-PE-Cy7 (557742, BD), CD16/CD32 antibody (553141, BD), BD FACS lysing solution (349202, BD).
The gating strategy used in the study for immune cells identification is shown in
Figure S1. Firstly, after the standard rejection of debris, residual erythrocytes and doublets, physical gating was performed based on CD45 and side scatter (SSC) to distinguish the lymphocytes group, monocytes, and granulocytes. We gated lymphocytes as CD45
bright/SSC
low cells, monocytes as CD45
bright/SSC
mid cells, and granulocytes as CD45
+/SSC
high cells. In the second step, the following subpopulations of monocytes and lymphocytes were defined and gated as follows: B cells (CD3
−CD19
+); CD4
+T cells (CD3
+ CD4
+); CD8
+T cells (CD3
+CD8
+); Th1 lymphocyte (CD3
+CD4
+CD183
+CD196
−); Th2 lymphocyte (CD3
+ CD4
+ CD183
−CD196
−); Th17 lymphocyte (CD3
+CD4
+CD183
− CD196
+); Treg (CD3
+CD4
+CD25
+CD127
low); NK cell (CD3
−CD56
+); monocyte cells (CD3
−CD14
+).
4.3. RNA Extraction and Quantitative Real-Time PCR (qPCR) to Detect SARS-CoV-2 RNA
Clinical samples (nasopharyngeal swabs/pharyngeal swabs/anal swab) were collected according to the Technical Guidelines for COVID-19 Laboratory Testing formulated by the National Health Commission of China [
48]. Viral RNA extraction and nucleic acid detection were performed within 24 h of collecting the samples. DNA and RNA extraction kits (Zybio, Chongqing, China) were used to extract SARS-CoV-2 RNA according to the manufacturer’s instructions. RNA was extracted from 200 µL of samples, before being eluted in 50 µL of elution buffer and used as the template for qPCR. SARS-CoV-2 RNA was detected using novel coronavirus nucleic acid detection kit (DA AN Gene, Guangzhou, China) according to the manufacturer’s protocol. Briefly, 5 µL RNA samples were added into 17 µL PCR Reaction Mix A and 3 µL PCR Reaction Mix B and analyzed using ABI prism 7500 Sequence Detection System (Applied Biosystems, Waltham, MA, USA). Amplification was performed as follows: 50 °C for 2 min, 95 °C for 2 min, followed by 42 cycles consisting of 95 °C for 5 s, 60 °C for 35 s and a default melting curve step in an ABI 7500 machine. At the same time each PCR run included positive and negative control tubes.
4.4. Statistical Analysis
All data were statistically analyzed by SPSS20.0 software (SPSS lnc., IBM Corporation, Chicago, IL, USA). The measurement data were expressed by mean ± standard deviation (s.d.). Data were checked for normality and homogeneity of variances. For normally distributed data, an unpaired Student’s t-test was applied for statistical analysis between the two groups, and one-way analysis of variance (ANOVA) was used for multiple groups. Non-parametric tests were used when data were not normally distributed. Mann–Whitney U test and Kruskal–Wallis H test were applied to compare the differences between the two groups and among multiple groups, respectively. The count data were expressed in the form of a percentage, and the Pearson chi-square test or Fisher’s precision probability test was used for comparing the groups. Graph Prism software was utilized to plot the graphs. A value of p ≤ 0.05 was considered to be significant statistically.