Highlights
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Scientific question: This study analyzed and proposed the potential association of specific human leukocyte antigen (HLA) alleles with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.
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Evidence before this study: Several studies have investigated the association of different HLA alleles with coronavirus disease 2019 (COVID-19). However, most of these studies have shown significant differences or conflicting results regarding the clinical relevance between HLA alleles and SARS-CoV-2 infection.
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New findings: The study analyzed the correlations between specific HLA alleles and the disease severity or T cell immune memory. The results showed that the alleles HLA-B*13:02 and -B*40:01 were associated with SARS-CoV-2 infection, which may be due to their rare peptide anchors.
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Significance of the study: The findings of the study may help to identify individuals at higher risk in order to better manage and prioritize vaccination at the clinical level, and to explain the differences in epidemic trends in different regions at the epidemiological level.
Keywords: SARS-CoV-2, COVID-19, HLA, Susceptibility, Disease severity
Abstract
Understanding how human leukocyte antigen (HLA) polymorphism affects both the susceptibility and severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection will help to identify individuals at higher risk to better manage and prioritize vaccination at the clinical level and explain the differences in epidemic trends in different regions at the epidemiological level. This study compared the frequencies of HLA class I alleles (HLA-A, B) in 214 coronavirus disease 2019 (COVID-19) patients with different disease severity and 35 healthy controls and analyzed the correlations between specific HLA alleles and disease severity and T cell memory. The results showed no significant difference in HLA allele frequencies between COVID-19 patients and healthy controls (P > 0.05). The allele HLA-B*13:02 was significantly correlated with the disease severity of COVID-19 patients (P = 0.006). After adjustment for age and disease severity, the T cell responses of COVID-19 convalescents with the allele HLA-B*40:01 may be lower at six months (P = 0.044) and 12 months (P = 0.069). Moreover, these results may be due to their rare peptide anchors by analyzing the binding peptide motifs of these HLA alleles. The study may be valuable for investigating the potential association of specific HLA alleles with SARS-CoV-2 infection.
1. Introduction
The coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1] has affected more than 7 billion individuals around the world over the past three years (https://covid19.who.int/). After infection with SARS-CoV-2, the clinical manifestations vary among individuals, ranging from asymptomatic or mild respiratory infections to acute respiratory diseases or deaths [2], [3]. The severity of the disease depends not only on the viral infection but also on the host's immune response [4]. The mechanism of susceptibility to SARS-CoV-2 and the difference in disease performance is unclear [5]. Some studies have focused on the impact of host genetic variations on their susceptibility and disease severity.
Human leukocyte antigen (HLA), as the coding product of human major histocompatibility complex (MHC) genes, is a significant genetic marker for disease susceptibility [6]. The primary function of these antigens is to present antigenic peptides from pathogens to self-antigens on the cell surface to T lymphocytes, thereby triggering immune responses [7]. HLA is also a genetic system that has a clear relationship with several disorders, including diabetes [8], rheumatoid arthritis [9], tuberculosis [10], ankylosing spondylitis [11], hepatitis [12], human immunodeficiency virus (HIV) [13], etc. However, the findings for genes from MHC are often ignored or treated with caution because of their high polymorphism and extensive paralogy, which is influenced by significant differences in HLA allele frequencies worldwide and different associations for different populations with diverse ethnicities [14].
Several studies have investigated the frequency of different HLA alleles in different populations and the association of their polymorphisms with COVID-19 [14], [15], [16], [17], [18], [19]. Most of these studies evaluated the relationships between HLA alleles and susceptibility and disease severity by comparing the allele frequencies of COVID-19 patients and healthy controls. Although the studies have shown significant differences or conflicting results regarding the clinical relevance between HLA alleles and SARS-CoV-2 infection, they may help to distinguish high-risk populations to achieve the goal of clinic therapy management and vaccination prioritizing. Understanding the effects of different HLA alleles on the morbidity and mortality of the disease is also a valuable point for disease management.
This work compared the HLA allele frequencies (HLA-A, B) in 214 COVID-19 patients with different disease severity and 35 healthy controls. We analyzed the correlations between specific HLA alleles and the disease severity or T cell memory to identify the alleles that may relate to higher disease susceptibility and T cell immune responses and to further explore the potential mechanism.
2. Materials and methods
2.1. Study participants
We recruited a total of 114 COVID-19 convalescents from Macheng, Hubei Province, in July 2020 (6 months, n = 76) and January 2021 (12 months, n = 73) and 110 COVID-19 convalescents from Shijiazhuang, Hebei Province in July 2021. Moreover, 35 healthy controls who had not been infected with SARS-CoV-2 were also recruited from Macheng. A questionnaire survey was conducted for each participant to collect the essential information (Table S1). Peripheral venous blood was collected, and peripheral blood mononuclear cells (PBMCs) were isolated. Written informed consent was obtained from all participants (or legal guardians of minors). The National Institute for Viral Disease Control and Prevention Ethics Committee of the Chinese Center for Disease Control and Prevention approved all human sample manipulations.
2.2. Clinical classification
The clinical classification standards of COVID-19 patients referred to the Diagnosis and Treatment Guideline for New Coronavirus Pneumonia (Version 6) and the Control and Prevention Guideline for New Coronavirus Pneumonia (Version 5) (https://www.nhc.gov.cn/) released by National Health Commission of the People's Republic of China. According to the clinical manifestations of COVID-19 patients in the acute stage, they were confirmed to be asymptomatic, mild, moderate, or severe.
2.3. DNA extraction and HLA typing
Genomic DNA was extracted from the peripheral blood samples of the COVID-19 convalescents with a Blood Genomic Cowin DNA extraction kit (CWBIO). HLA typing of the participants (Locus A and B) was performed using Olerup sequence-specific oligonucleotide (SSO) typing technology. All the participants' HLA-A and -B typing information is shown in Table S2.
2.4. Data on T cell responses
The data on T cell responses in the study were based on COVID-19 convalescents from Macheng at six months (6 m, n = 76) and 12 months (12 m, n = 73) after disease onset in the research previously reported in our laboratory [20]. The T cell responses of the convalescents were detected by enzyme-linked immunoblot assay (ELISpot), and peripheral blood mononuclear cells (PBMCs) were stimulated with the four pools of overlapping peptides spanning the S (including S1 and S2), M, and N proteins of SARS-CoV-2 prototype. The results were expressed as spot-forming cells (SFCs) among PBMCs (SFCs/106 PBMCs), and the T cell responses were determined by subtracting the SFCs of the negative control well from the SFCs of the detection. The total responses of COVID-19 convalescents are the sum of the responses to S1, S2, M, and N peptide pools.
2.5. Statistical analysis
Statistical analyses were performed using SPSS 24.0 and GraphPad Prism 8.0. The data were presented as median (IQR) or frequency (%). The Chi-square, Fisher’s exact, and Mann-Whitney U tests were used for inter-group comparisons. The ordered logistic regression was used to analyze the relationship between HLA alleles and disease severity in COVID-19 patients. The generalized linear model was used to evaluate the effects of HLA alleles on immune indices. The significance was set at a level of 0.05. All tests were two-tailed.
3. Results
3.1. The comparison of HLA allele frequencies
The HLA-A and -B allele typing results of 114 COVID-19 patients and 35 healthy controls from Macheng, Hubei Province, showed that the alleles with the highest frequency of HLA-A were A*11:01 and A*02:07, while the alleles with the highest frequency of HLA-B were B*46:01 and B*40:01 in both patients and healthy controls. To explore the impact of HLA alleles on individual susceptibility to the disease, we compared the frequencies of HLA alleles (N ≥ 10) between COVID-19 patients and healthy controls. However, none of these HLA allele frequencies showed significant differences (P > 0.05) (Table 1).
Table 1.
The comparison of HLA allele frequencies between 114 COVID-19 patients and 35 healthy controls from Macheng, Hubei Province.
| Locus | HLA | Patients |
Healthy controls |
Pc | ||
|---|---|---|---|---|---|---|
| Na | Fb (%) | N | F (%) | |||
| Locus A | A*11:01 | 57 | 25.00 | 14 | 20.00 | 0.390 |
| A*02:07 | 40 | 17.54 | 12 | 17.14 | 0.938 | |
| A*24:02 | 31 | 13.60 | 11 | 15.71 | 0.656 | |
| A*02:01 | 17 | 7.46 | 6 | 8.57 | 0.760 | |
| A*31:01 | 15 | 6.58 | 4 | 5.71 | 1.000 | |
| A*33:03 | 14 | 6.14 | 5 | 7.14 | 0.781 | |
| Locus B | B*46:01 | 40 | 17.70 | 9 | 13.64 | 0.464 |
| B*40:01 | 35 | 15.49 | 12 | 18.18 | 0.703 | |
| B*51:01 | 14 | 6.19 | 3 | 4.55 | 0.771 | |
| B*58:01 | 14 | 6.19 | 3 | 4.55 | 0.771 | |
| B*15:01 | 13 | 5.75 | 2 | 3.03 | 0.533 | |
| B*13:02 | 11 | 4.87 | 6 | 9.09 | 0.231 | |
| B*37:01 | 10 | 4.42 | 2 | 3.03 | 1.000 | |
Abbreviations: COVID-19, coronavirus disease 2019; HLA, human leukocyte antigen.
N: number of alleles.
F: allele frequency, in percent.
The Chi-square or Fisher’s exact test was performed and the corresponding P value was listed (α = 0.05).
We then grouped COVID-19 patients according to the severity of the disease, and there were 8 asymptomatic, 58 mild, 36 moderate, and 12 severe cases among 114 COVID-19 patients. The HLA allele frequencies between COVID-19 patients with different disease severity were compared to explore the impact of HLA alleles on the disease severity. After analyzing the results, a significant difference was found for the allele HLA-B*13:02 among different groups (P = 0.035), with frequencies of 0.00%, 1.75%, 8.33%, and 12.50% in the asymptomatic, mild, moderate and severe groups, respectively (Table 2), indicating that the allele HLA-B*13:02 may be related to the increased disease severity in patients with COVID-19.
Table 2.
The comparison of HLA allele frequencies between COVID-19 patients with different disease severity from Macheng, Hubei Province.
| Locus | HLA | Asymptomatic |
Mild |
Moderate |
Severe |
Pc | ||||
|---|---|---|---|---|---|---|---|---|---|---|
| Na | Fb (%) | N | F (%) | N | F (%) | N | F (%) | |||
| Locus A | A*11:01 | 5 | 31.25 | 27 | 23.28 | 21 | 29.17 | 4 | 16.67 | 0.559 |
| A*02:07 | 3 | 18.75 | 21 | 18.10 | 13 | 18.06 | 3 | 12.50 | 0.958 | |
| A*24:02 | 3 | 18.75 | 16 | 13.79 | 9 | 12.50 | 3 | 12.50 | 0.904 | |
| A*02:01 | 0 | 0.00 | 10 | 8.62 | 6 | 8.33 | 1 | 4.17 | 0.795 | |
| A*31:01 | 3 | 18.75 | 7 | 6.03 | 4 | 5.56 | 1 | 4.17 | 0.258 | |
| A*33:03 | 1 | 6.25 | 7 | 6.03 | 4 | 5.56 | 2 | 8.33 | 0.909 | |
| Locus B | B*46:01 | 3 | 18.75 | 21 | 18.42 | 15 | 20.83 | 1 | 4.17 | 0.286 |
| B*40:01 | 4 | 25.00 | 16 | 14.04 | 9 | 12.50 | 6 | 25.00 | 0.299 | |
| B*51:01 | 0 | 0.00 | 9 | 7.89 | 4 | 5.56 | 1 | 4.17 | 0.859 | |
| B*58:01 | 1 | 6.25 | 7 | 6.14 | 2 | 2.78 | 4 | 16.67 | 0.103 | |
| B*15:01 | 1 | 6.25 | 4 | 3.51 | 6 | 8.33 | 2 | 8.33 | 0.350 | |
| B*13:02 | 0 | 0.00 | 2 | 1.75 | 6 | 8.33 | 3 | 12.50 | 0.035* | |
| B*37:01 | 1 | 6.25 | 8 | 7.02 | 0 | 0.00 | 1 | 4.17 | 0.069 | |
*Statistically significant (P < 0.05).
Abbreviations: COVID-19, coronavirus disease 2019; HLA, human leukocyte antigen.
N: number of alleles.
F: allele frequency, in percent.
Chi-square or Fisher’s exact test was performed and the corresponding P value was listed (α = 0.05).
3.2. The relationship between allele HLA-B*13:02 and the disease severity
We analyzed the relationship between the allele HLA-B*13:02 and disease severity with an ordered logistic regression. The dependent variable was the disease severity of COVID-19 patients in the acute phase, which was converted to a rank variable, and the independent variables were age and allele HLA-B*13:02. Although both had statistical significance, the unadjusted model for age (-2 Log Likelihood = 18.420, Table 3) fit better than the adjusted model (−2 Log Likelihood = 183.477, Table S3). The results showed that the risk of increasing disease severity by at least one level in patients with the allele HLA-B*13:02 compared to those without HLA-B*13:02 was 5.323 times higher (P = 0.006). It is worth noting that this result did not change after adding 110 COVID-19 patients in Shijiazhuang (P = 0.023) (Table 3).
Table 3.
The ordered logistic regression models of alleles HLA-B*13:02, -B*40:01 and the disease severity.
| Modela | Variable | β | S.E | Wald | P | ORd | 95% CIe |
|
|---|---|---|---|---|---|---|---|---|
| Lower | Upper | |||||||
| Model 1 | Allele B*13:02b | |||||||
| Yes | 1.672 | 0.612 | 7.458 | 0.006* | 5.323 | 1.603 | 17.655 | |
| Noc | – | – | – | – | 1.000 | – | – | |
| Model 2 | Allele B*40:01b | |||||||
| Yes | −0.175 | 0.394 | 0.197 | 0.657 | 0.839 | 0.388 | 1.817 | |
| Noc | – | – | – | – | 1.000 | – | – | |
| Model 3 | Allele B*13:02b | |||||||
| Yes | 1.094 | 0.482 | 5.155 | 0.023* | 2.986 | 1.162 | 7.675 | |
| Noc | – | – | – | – | 1.000 | – | – | |
| Model 4 | Allele B*40:01b | |||||||
| Yes | −0.100 | 0.309 | 0.106 | 0.745 | 0.905 | 0.494 | 1.655 | |
| Noc | – | – | – | – | 1.000 | – | – | |
*Statistically significant (P < 0.05). Abbreviation: HLA, human leukocyte antigen.
Model 1 and 2 were conducted on 114 COVID-19 patients from Macheng. Model 3 and 4 were conducted on 224 COVID-19 patients from Macheng and Shijiazhuang. Model 1: -2 Log Likelihood = 18.420; Model 3: −2 Log Likelihood = 22.622.
Yes: individuals with specific allele; No: individuals without specific allele.
Represent reference group.
OR: odds ratio.
CI: 95% confidence interval.
3.3. The relationship between the allele HLA-B*13:02 and T cell memory
In addition, we also used the generalized linear model to analyze the relationship between the allele HLA-B*13:02 and the T cell memory of COVID-19 convalescents. The dependent variable was the total T cell responses (the sum of the responses to S1, S2, M, and N peptide pools) of COVID-19 convalescents at 6 m (n = 76) and 12 m (n = 73) after disease onset and the independent variables were age, disease severity and allele HLA-B*13:02. There were only 7 COVID-19 convalescents with the allele HLA-B*13:02 at 6 m and 6 at 12 m. After adjusting the age and disease severity, the allele HLA-B*13:02 did not affect T cell responses at 6 m or 12 m (P > 0.05) (Table 4). Moreover, there were also no significant differences in the T cell responses to S1, S2, M, and N peptide pools and the total responses of COVID-19 convalescents grouped by HLA-B*13:02 at 6 m or 12 m according to the Mann-Whitney U test (Fig. 1A, B and E).
Table 4.
The generalized linear models of alleles HLA-B*13:02, -B*40:01 and T cell memory in 114 COVID-19 convalescents from Macheng.
| Model | Variable | 6 m (n = 76) |
12 m (n = 73) |
||||
|---|---|---|---|---|---|---|---|
| β | Wald | P | β | Wald | P | ||
| Model 1 | Intercept | 2,362.218 | 0.715 | 0.398 | 3,704.663 | 1.510 | 0.219 |
| Allele B*13:02a | |||||||
| Yes | −66.625 | 0.002 | 0.969 | −652.063 | 0.148 | 0.700 | |
| Nob | – | – | – | – | – | – | |
| Age | 36.792 | 1.141 | 0.285 | 36.089 | 0.968 | 0.325 | |
| Disease severity | 1,764.743 | 8.719 | 0.003* | 673.220 | 1.202 | 0.273 | |
| Model 2 | Intercept | 728.147 | 0.109 | 0.742 | 3,186.655 | 1.764 | 0.184 |
| Allele B*40:01a | |||||||
| Yes | −2,118.465 | 4.050 | 0.044* | −1,725.357 | 3.313 | 0.069 | |
| Nob | – | – | – | – | – | – | |
| Age | 40.775 | 1.471 | 0.225 | 38.903 | 1.174 | 0.279 | |
| Disease severity | 1,750.910 | 9.404 | 0.002* | 609.385 | 1.055 | 0.304 | |
*Statistically significant (P < 0.05). Abbreviations: COVID-19, coronavirus disease 2019; HLA, human leukocyte antigen.
Yes: individuals with specific allele; No: individuals without specific allele.
Represent reference group.
Fig. 1.
The T cell responses of COVID-19 convalescents from Macheng grouped by HLA alleles. A-B) The T cell responses to 4 different peptide pools (S1, S2, M and N) of SARS-CoV-2 prototype in COVID-19 convalescents grouped by allele HLA-B*13:02 at 6 m (A) and 12 m (B). C-D) The T cell responses to 4 different peptide pools (S1, S2, M and N) of COVID-19 convalescents grouped by allele HLA-B*40:01 at 6 m (C) and 12 m (D). E-F) The total responses (sum of the responses to S1, S2, M and N) of COVID-19 convalescents grouped by alleles HLA-B*13:02 (E) and -B*40:01 (F) at 6 m and 12 m. A Mann-Whitney U test was used in (A-F). The significance was set at a level of 0.05. All tests were two-tailed. Abbreviations: COVID-19, coronavirus disease 2019; HLA, human leukocyte antigen; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; PBMCs, peripheral blood mononuclear cells; SFCs, spot-forming cells.
3.4. The HLA-B*40:01 relationship to T cell memory and the disease severity
When investigating the relationship between the HLA alleles and the disease severity and T cell memory of convalescents with COVID-19, we found that after adjusting the age and disease severity, the allele HLA-B*40:01 may be related to the T cell responses of COVID-19 convalescents at 6 m (P = 0.044) and 12 m (P = 0.069) (Table 4), within the generalized linear model. Consequently, we also grouped the COVID-19 convalescents according to the presence of the allele HLA-B*40:01 and compared their T cell responses by the Mann-Whitney U test. For the T cell responses to S1, S2, M, and N peptide pools and the total responses, although there were no significant differences between the two groups, the T cell responses of the convalescents without allele HLA-B*40:01 were higher than those with HLA-B*40:01 (Fig. 1C, D and F).
We still conducted an ordered logistic regression to investigate the COVID-19 susceptibility of HLA-B*40:01. The dependent variable was the disease severity of COVID-19 patients in the acute phase, and the independent variable was the allele HLA-B*40:01. The results showed that there was no significant difference in the allele HLA-B*40:01 (P > 0.05). It did not change after adding 110 COVID-19 patients in Shijiazhuang (P > 0.05) (Table 3).
3.5. The rare anchor residues of the epitope peptides presented by the HLA class I allele may contribute to the correlation with disease severity and T cell memory
We explored whether the relationship between the alleles and the disease severity and T cell memory of convalescents with COVID-19 is related to the preference of their anchor residues. We predicted all possible overlapping 9-mer peptides that can bind to alleles HLA-B*13:02 and -B*40:01 (%Rank ≤ 2) derived from different viruses using the NetMHCpan-4.1, including 7 human coronaviruses and 3 influenza viruses, whose length refers to the total length of amino acids of proteomes. The sequence logos of the two alleles based on these peptides were generated to intuitively clarify the peptide binding properties.
We found that Gln (Q) and Glu (E) appeared in the anchor residues in the second position of both alleles HLA-B*13:02 and -B*40:01 (Fig. 2), both of which are relatively rare anchor residues due to the characteristics of amino acids. In addition, the anchor residue in the second position of allele HLA-B*40:01 is more conserved, mainly Glu (E) (Fig. 2B). We further analyzed the proportions of Gln (Q) and Glu (E) in these viruses. The results showed that the proportions of Gln (Q) and Glu (E) in SARS-CoV-2 were higher than those of other human coronaviruses but much lower than those of influenza viruses (Table 5).
Fig. 2.
The characteristics of HLA alleles-binding peptides. A-B) The sequence logo diagrams of alleles HLA-B*13:02 (A) and -B*40:01 (B)-binding peptide motif. Abbreviations: HLA, human leukocyte antigen; HCoV, human coronavirus; SARS-CoV, severe acute respiratory syndrome coronavirus; MERS-CoV, Middle East respiratory syndrome coronavirus.
Table 5.
The proportions of Gln (Q) and Glu (E) in different viruses.
| Virusesa | Lengthb | Q (%) | E (%) | Q + E (%)c | Pd |
|---|---|---|---|---|---|
| SARS-CoV-2 | 9,085 | 340 (3.7) | 409 (4.5) | 8.2 | / |
| SARS-CoV | 9,047 | 328 (3.6) | 413 (4.6) | 8.2 | 0.895 |
| MERS-CoV | 9,145 | 332 (3.6) | 353 (3.9) | 7.5 | 0.059 |
| HCoV-OC43 | 9,214 | 326 (3.5) | 325 (3.5) | 7.1 | 0.003* |
| HCoV-HKU1 | 9,273 | 280 (3.0) | 311 (3.4) | 6.4 | <0.001* |
| HCoV-229E | 8,622 | 260 (3.0) | 352 (4.1) | 7.1 | 0.004* |
| HCoV-NL63 | 8,765 | 226 (2.6) | 291 (3.3) | 5.9 | <0.001* |
| H1N1 | 4,454 | 168 (3.8) | 324 (7.3) | 11.0 | <0.001* |
| H3N2 | 4,465 | 178 (4.0) | 321 (7.2) | 11.2 | <0.001* |
| H7N9 | 4,442 | 179 (4.0) | 328 (7.4) | 11.4 | <0.001* |
NCBI reference sequence: SARS-CoV-2(MN908947.3); SARS-CoV(AY274119.3); MERS-CoV(NC_019843.3); OC43(JN129834.1); HKU1(KT779555.1); 229E(NC_002645.1); NL63(NC_005831.2); H1N1(GCF_001343775.1); H3N2(GCF_000865075.1); H7N9(GCF_000928545.1).
*Statistically significant (P < 0.05). Abbreviations: SARS-CoV, severe acute respiratory syndrome coronavirus; HCov, human coronavirus; MERS-CoV, Middle East respiratory syndrome coronavirus.
Virus reference sequence.
The length of the coronavirus is the total length of ORF1ab, S, M, N, and E proteins. The length of influenza virus is the total length of PB1, PB2, PA, HA, NA, NP, M1, M2, NS1, NEP proteins.
The proportion of amino acids.
The proportions of Gln (Q) and Glu (E) in other viruses were compared with that in SARS-CoV-2 with the Chi-square test.
4. Discussion
The infection of SARS-CoV-2 can cause extensive activation of humoral and cellular immunity, with neutralizing antibodies and T lymphocytes serving as the primary protective factors [21], [22]. By recognizing the virus-derived peptides presented by MHC molecules [23], these T lymphocytes can influence viral infection and provide immune memory, allowing the body to develop long-term protection [24], [25]. Herein, we analyzed the relationship between the HLA alleles of COVID-19 patients and their disease severity or T cell memory and further explored possible mechanisms which may provide beneficial information for vaccination and clinical treatment strategies.
An increasing surge of studies on SARS-CoV-2 and HLA alleles have been reported since the pandemic outbreak, demonstrating the presence of positive associations of specific HLA alleles with susceptibility to the disease. One study showed that alleles HLA-A*02:02, -B*15:03, and -C*12:03 had the most significant predicted capacities to present SARS-CoV-2 epitopes with an in silico analysis, whereas alleles HLA-A*25:01, -B*46:01, and -C*01:02 had the lowest, which may be susceptible alleles [19] which was similar to the predictive study by Rodrigo et al., in which they predicted the affinities of 438 HLA molecules with different viral peptides using a bioinformatics approach [26]. Yusuke et al. using in silico analysis, have also reported a possible association between the allele HLA-A*02:01 and an increased risk for COVID-19 [18]. However, the bioinformatic predictions on HLA molecules with viral peptides alone are of limited functional significance.
Population studies on associations between specific HLA alleles and SARS-CoV-2 infection are also available. A significant association between the allele HLA-B*38 and susceptibility to the disease was found in the studies by Shekarkar et al. [27] and Farahani et al. [15]. The allele HLA-A*11 was has been associated with disease severity [17], [28], [29], and its association with increased mortality in ICU COVID-19 patients has been reported previously [30]. A study of HLA frequency distribution in 82 Chinese patients showed a significant increase in HLA-B*15:27 and -C*07:29 alleles in COVID-19 patients [31]. Novelli et al. reported a significant association of disease severity with the allele HLA-B*27:07 in a cohort of 99 Italian patients with severe or highly severe cases [16]. Except for HLA class I alleles, Novelli et al. found a significant association between HLA-DRB1*15:01 and -DQB1*06:02 [16]. Moreover, the allele DQB1*06 was also reported to be related to the disease severity by analyzing data from 80 hospitalized COVID-19 patients of mixed ethnicity from the UK [32]. Furthermore, alleles HLA-DRB1*01:01, -DRB1*12:01, and -DRB1*14:04 have also been associated with COVID-19 severity in the Chinese population [29]. In one study, the allele HLA-DPA1*02:02 was observed at a higher frequency in the COVID-19-positive cohort when compared to the COVID-negative control group [17]. Nevertheless, a genome-wide association study (GWAS) conducted on 1,610 COVID-19 patients with 2,305 controls showed no significant associations in HLA alleles with susceptibility to COVID-19 [33].
In addition, the influence of HLA alleles on severity or immune response may be due to a combination of them. In addition, some studies have also analyzed the synergistic effect of HLA alleles on SARS-CoV-2 infection through HLA haplotypes, which include HLA class I and II alleles [34], [35]. Moreover, we did not observe both alleles HLA-B*13:02 and -B*40:01 in the same COVID convalescent individual, so that the synergistic effect may be limited. Further studies are needed to explore the potential mechanisms. A recent study identified a strong association between HLA-B*15:01 and asymptomatic infection, possibly due to pre-existing T-cell immunity. They found that T cells from pre-pandemic samples of individuals carrying HLA-B*15:01 were reactive to the immunodominant peptide NQKLIANQF derived from SARS-CoV-2 and a peptide NQKLIANAF from seasonal coronaviruses [36].
In our study, we found that the distribution of HLA alleles in COVID-19 patients conformed to the distribution characteristics of the Chinese population [37], and there was no significant difference compared with healthy controls. The frequency of allele HLA-B*13:02 varied in patients with different disease severity, and patients with B*13:02 may have more severe clinical manifestations in the acute phase. However, the allele HLA-B*13:02 may not be related to the T cell memory of COVID-19 convalescents. In addition, the allele HLA-B*40:01, although not associated with the disease severity, may affect the T cell memory of COVID-19 convalescents. Concordant to the study by Rodrigo et al. [26], alleles HLA-B*13:02 and -B*40:01 belong to the HLA weakest binders of SARS-CoV-2 peptides, which means they do not or weakly bind >99% of the peptides on SARS-CoV-2. These results may be due to their amino acid preference by analyzing the binding peptide motifs of the two alleles. The anchoring residues in the second position of both alleles HLA-B*13:02 and -B*40:01 were Gln (Q) and Glu (E). No other HLA alleles with a similar peptide-binding motif were found among the study's 13 HLA alleles in the study (N ≥ 10). Furthermore, as in the investigations in other studies, the alleles using residues Gln (Q) and Glu (E) as P2 anchors are rare [38].
Our research also has some limitations. Firstly, the sample size of the study is small, and there might represent a risk of a false positive or negative result. The HLA-B*13:02 allele has nothing to do with the T cell memory of COVID-19 convalescents, which may be because too few convalescents carry HLA-B*13:02 at 6 months or 12 months. Secondly, the sequencing of genomic DNA covers fewer loci, and HLA class II alleles and haplotypes are still needed in the future. In addition, further in vitro and in vivo studies are also needed to explore and identify the mechanism of how these HLA alleles affect the susceptibility and disease severity of COVID-19.
Similar studies have been conducted on associations between HLA alleles and SARS-CoV-2 infection, with a more significant sample size and diverse findings and conclusions [15], [16], [17], [27], [28], [29], [30], [31], [32], [33]. Based on these, our study further evaluated the relationships between HLA alleles and T cell memory using the data on T cell responses of COVID convalescents and explored the potential mechanisms by analyzing the binding motif of HLA alleles. Our observations may contribute to verifying the potential relevance of specific HLA alleles interacting with SARS-CoV-2 and identify high-risk populations to manage and prioritize individuals for vaccination and clinical treatment.
Ethics statement
The National Institute for Viral Disease Control and Prevention Ethics Committee, China CDC, approved the study (NO: IVDC2021-007). Written informed consent was obtained from all participants (or legal guardians of minors).
Acknowledgements
The National Key Research and Development Program of China (2022YFC2604100) and the National Natural Science Foundation of China (92269203) supported the study.
Conflict of interest statement
The authors declare that there are no conflicts of interest.
Author contributions
Xin Wang: Methodology, Data curation, Formal analysis, Visualization, Writing – original draft. Jie Zhang: Investigation, Data curation, Formal analysis. Peipei Guo: Visualization, Data curation. Yuanyuan Guo: Data curation. Xiaonan Yang: Data curation. Maoshun Liu: Data curation. Danni Zhang: Data curation. Yaxin Guo: Data curation. Jianbo Zhan: Investigation, Data curation. Kun Cai: Investigation, Data curation. Jikun Zhou: Investigation, Data curation. Shaobo Dong: Investigation, Data curation. Jun Liu: Conceptualization, Writing – review & editing, Funding acquisition, Supervision.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.bsheal.2023.09.002.
Contributor Information
Jikun Zhou, Email: 13933880581@163.com.
Shaobo Dong, Email: 464881568@qq.com.
Jun Liu, Email: liujun@ivdc.chinacdc.cn.
Supplementary data
The following are the Supplementary data to this article:
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