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Article

Association of KIR Genes with Middle East Respiratory Syndrome Coronavirus Infection in South Koreans

by
In-Cheol Baek
1,
Eun-Jeong Choi
1,
Hyoung-Jae Kim
1,
Haeyoun Choi
1,2,
Hyoung-Shik Shin
3,
Dong-Gyun Lim
4,* and
Tai-Gyu Kim
1,2,*
1
Catholic Hematopoietic Stem Cell Bank, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
2
Department of Microbiology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
3
Department of Infectious Diseases, College of Medicine, Eulji University, Daejeon 34824, Republic of Korea
4
Translational Research Center, Research Institute of Public Health, National Medical Center, Seoul 04564, Republic of Korea
*
Authors to whom correspondence should be addressed.
J. Clin. Med. 2024, 13(1), 258; https://doi.org/10.3390/jcm13010258
Submission received: 26 October 2023 / Revised: 8 December 2023 / Accepted: 27 December 2023 / Published: 2 January 2024
(This article belongs to the Section Infectious Diseases)
Browse Figures
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Abstract

:
Background: Middle East respiratory syndrome (MERS) is a lower respiratory tract disease caused by a beta coronavirus (CoV) called MERS-CoV, characterized by a high mortality rate. We aimed to evaluate the association between genetic variation in killer cell immunoglobulin-like receptors (KIRs) and the risk of MERS in South Koreans. Methods: KIR genes were genotyped by multiplex polymerase chain reaction with sequence-specific primers (PCR-SSP). A case-control study was performed to identify the odds ratios (OR) of KIR genes for MERS and the association of KIR genes and their ligands, human leukocyte antigens (HLA) genes. Results: KIR2DS4D and KIR3DP1F showed higher frequencies in the group of all patients infected with MERS-CoV than in the control group (p = 0.023, OR = 2.4; p = 0.039, OR = 2.7). KIR2DL1, KIR2DP1, and KIR3DP1D were significantly associated with moderate/mild (Mo/Mi) cases. KIR2DL2, KIR2DS1, and KIR3DP1F were affected in severe cases. When we investigated the association between KIR genes and their ligands in MERS patient and control groups, KIR3DL1+/Bw4(80I)+, KIR3DL1+/Bw6+, KIR3DL1+/Bw6−, KIR2DS1+/C2+, and KIR3DS+/Bw4(80I)+ were associated with MERS. KIR3DL1+/Bw6− was found in Mo/Mi cases. KIR2DS1+/C2+ and KIR2DS2+/C1+ were found in severe cases. Conclusion: Further investigations are needed to prove the various immune responses of MERS-CoV-infected cells according to variations in the KIR gene and ligand gene. A treatment strategy based on current research on the KIR gene and MERS-CoV will suggest potential treatment targets.

1. Introduction

Middle East Respiratory Syndrome (MERS) is a new lower respiratory tract disease with a high mortality rate [1]. It is caused by the high-resolution structures of the trimeric beta coronavirus (CoV) MERS-CoV [2]. The first case of MERS-CoV was reported in Saudi Arabia [3] and has since spread to other countries. From 2013 to April 2018, MERS-CoV infection was confirmed in dromedary camels (Camelus dromedarius) in the field. Serological evidence of viral circulation was found in 20 countries and molecular evidence in 13 countries [4].
As of 19 June 2015, South Korea had the highest MERS-CoV infection rate outside of the Arabian Peninsula, with 166 confirmed cases (including 24 deaths). The mean incubation period was 6.7 days and the mean illness duration was 12.6 days. Infection does not appear before symptoms begin. There is an approximately 21% risk of death in all cases of MERS (95% confidence interval: 14–31%) [5]. Hospital-to-hospital transmission started at one hospital in Korea and expanded to 17 hospitals [6]. The outbreak occurred entirely in hospitals and was largely due to failures in infection control and policy. The average age of MERS patients in Korea was 55 years old (the world average is 50 years old). In most countries, including Korea, men were more affected than women. The death rate in Korea was 19.9%, lower than that of global citizens (38.7%) and Saudi Arabians (36.5%). Most Korean patients were infected in hospitals and there was no community spread. Respiratory disease, cancer, and hypertension were the major underlying diseases [7].
Natural killer (NK) cells, large granular cytotoxic lymphocytes that account for 2–18% of human peripheral lymphocytes, play an important role in innate immunity against bacteria, parasites, viruses, and malignant cells [8]. Effector functions of NK cells are regulated by a delicate balance between signals transmitted from activating and inhibitory cell surface receptors [8]. NK cells play an essential role in preventing the formation and progression of leukemia cells [9,10,11].
On the surface of NK cells are different receptors that transmit inhibitory or activating signals [12]. The receptor family is the highly polygenic and polymorphic killer cell immunoglobulin-like receptors (KIRs) with different ligand specificities for human leukocyte antigen class I (HLA-I) molecules [13]. KIRs play a dominant role in the development and functioning of NK cells [14,15]. These receptors are divided into two groups according to the intracellular length of the KIR tail: inhibitory (i) KIRs and activating (a) KIRs. Long-tailed receptors induce inhibition signals via immunoreceptor tyrosine-based inhibitory motifs (ITIMs), while short-tailed receptors transmit activation signals using DAP12 adapter proteins via immunoreceptor tyrosine-based activation motifs (ITAMs) [15,16].
The KIR gene cluster is located on chromosome 19 (19q13.4) and encodes 16 different molecules containing two pseudogenes: KIR2DL1, KIR2DL2/L3, KIR2DL4, KIR2DL5A, KIR2DL5B, KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, KIR2DS5, KIR3DL1/S1, KIR3DL2, KIR3DL3, KIR2DP1, and KIR3DP1 [16,17,18,19]. The KIR gene contains two or three extracellular domains (2D or 3D), membrane permeation regions, and long (with inhibitor motifs), or short (with docking sites for molecular activation) tails [17]. Depending on the KIR gene content, there are two KIR haplotypes. “A” haplotypes contain five iKIRs (2DL3, 2DL1, 2DL4, 3DL1, and 3DL2) and one aKIR (2DS4) [20]. In addition to the full-length form (fl), KIR2DS4 has a truncated variant with a 22-bp deletion at exon five (del), which produces a secretory molecule with no known function [21,22]. However, “B” haplotypes mainly possess aKIRs (2DL2, 2DL5, 2DS1, 2DS2, 2DS3, 2DS5, and 3DS1). Both haplotypes have four framework genes (3DL3, 3DP1, 2DL4, and 3DL2) [20]. Individuals can be the AA or Bx (AB or BB) genotype according to their inherited haplotypes. KIR haplotypes are divided into centromeric and telomeric regions via a rearrangement site [17]. Two pseudogenes (KIR2DP1 and KIR3DP1) are contained [23].
HLA-I molecules are the most important ligands for the KIRs. NK cell development and response strongly depend on KIR/HLA-I interaction [24]. HLA-C alleles encode two forms of ligands based on amino acid residues at positions 77 and 80 in their α1 domains. HLA-C1 has serine and aspartic acid, but HLA-C2 has aspartic acid and lysine at these positions, respectively. Therefore, they can interact with different receptors via these residues. HLA-C1 interacts with KIR2DL2/3, and HLA-C2 ligates with KIR2DL1 [15,25]. HLA-B alleles are also classified into Bw4 and Bw6 motifs, and just the former group can bind to KIR3DL1. The affinity of Bw4-bearing allotypes to KIR3DL1 is determined by a dimorphic site at amino acid position 80 (isoleucine or threonine), as HLA-Bw4Ile80 binds to the receptor with greater affinity [26,27,28]. The Bw4 motif is also found in HLA-A*23/24/32 alleles, called HLA-A Bw4 [29,30]. Furthermore, HLA-A*03/11 allotypes can interact with KIR3DL2 [31]. Although the extracellular domains of some aKIRs have high sequence homology with some iKIRs (2DS1-2DL1, 2DS2-2DL2, and 3DS1-3DL1 pairs), little information is available about the ligands of aKIRs, and their ligands are mostly unknown except for KIR2DS1 which binds to HLA-C2 [32,33]. Recently, it has been found that some HLA-I ligands can also bind to aKIRs but with lower affinity than the corresponding iKIRs [15].
Downregulation of HLA class I expression due to viral infection mitigates the inhibitory effect on NK cells, allowing NK cells to lyse target cells [34]. Usually, Middle East respiratory syndrome coronavirus (MERS-CoV) and severe acute respiratory syndrome are commonly reported in comparison to COVID-19 (SARS-CoV-2). This was a multi-epitope vaccine against MERS-CoV in silico prediction, bioinformatics analysis, and drug research [35,36,37,38,39,40,41,42]. Cytokine response profiles were analyzed in plasma samples from confirmed MERS-CoV patients [43]. Studies of KIR have been investigated with a focus on SARS-CoV-2 [44,45].
In this study, because no specific KIR association has yet been reported against MERS, we evaluated the association between genetic variation in KIR using polymerase chain reaction with sequence-specific primers (PCR-SSP) and the risk of MERS in South Koreans.

2. Materials and Methods

2.1. Subjects

2.1.1. MERS Patients

This study comprised 32 patients with MERS admitted to or followed up after recovery at the National Medical Center (NMC) in Seoul, Republic of Korea using the previous study [46]. All patients were confirmed to have MERS-CoV infection by real-time reverse transcription-polymerase chain reaction (PCR) of nasopharyngeal swabs or tracheal aspirates. The patients were classified into two groups depending on the clinical severity during their acute stage of infection. Severe disease (n = 16) included fatalities and patients who required mechanical ventilation to relieve respiratory failure. Moderate and mild disease (Mo/Mi) encompassed patients who reported symptoms such as fever, headache, cough, and malaise with or without pulmonary lesions in the absence of pulmonary failure. Demographic characteristics of the patients are shown in Table 1. Peripheral blood was collected from patients at the acute/convalescent phases of infection or after recovery. Peripheral blood mononuclear cells (PBMCs) were isolated from heparinized whole blood by density gradient centrifugation using Ficoll–Paque solution (GE Healthcare, Uppsala, Sweden) and stored in liquid nitrogen until use. The study was approved by the NMC Ethical Committee and written informed consent was obtained from the patients (Institutional Review Board [IRB]: H-1801-086-002, H-1712-085-005).

2.1.2. Control Group

The control group included 200 genetically unrelated healthy Korean adults with no history of MERS and little or no gender distribution (age: 38 ± 8 years; 84 females and 116 males). The control subjects provided written informed consent to participate and mainly comprised students and staff from the Medical College of the Catholic University of Korea and Hematopoietic Stem Cell Transplantation Center. The use of the material was reviewed and approved by the IRB of The Catholic University of Korea with written informed consent obtained for all samples collected (IRB: MC13SISI0126).

2.2. Genomic DNA Preparation

We performed this process as previously described [46]. Genomic DNA was isolated from PBMCs using the QIAamp DNA Blood Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. DNA yields and purity were determined by measuring the absorbance at 260 and 280 nm with a NanoDrop 2000 spectrophotometer (Thermo Scientific, Waltham, MA, USA).

2.3. Multiplex PCR-SSP for KIR Genotyping and Gel Analysis of PCR Products

A multiplex PCR-SSP method for KIR genotyping has been developed with recently introduced novel primers [47]. Briefly, PCR products were generated by mixing primers for two to four different KIR genes and separating them using an agarose gel. Finally, sixteen KIR genes and the major subtypes of three KIR genes were detected in a set of eight PCR reactions, including the genes encoding human growth hormone (HGH) and adenomatous polyposis (APC) used as an internal amplification control (IC). Amplified PCR products were separated for KIR genotyping using electrophoresis on 3% agarose gel (Bio-rad, Hercules, CA, USA) gel according to a previous study [47].

2.4. Statistical Analysis

The observed carrier frequency (OF) of the KIR gene was determined as the number of positive typing responses divided by the total number of individuals typed in the previous study [47]. Estimated gene frequencies (GF) based on the assumption of Hardy–Weinberg equilibrium were calculated using the formula GF = 1 − ( 1 − O F ) , where OF is the carrier frequency of the KIR gene observed in an individual [47].
For analysis of the case-control study, the previously described analysis methods were used, such as a chi-square test (χ2), Fisher’s exact test, corrected p-values of Bonferroni’s method, 95% confidence intervals (Cis) and odds ratios (ORs) of Haldane’s modification of Woolf’s method [46,48,49,50,51,52,53,54,55,56,57,58]. We defined significant data as p < 0.05 and 95% CI ≠ 1 (not containing 1; e.g., 95% CI of a risk effect is 1.01–6.1 or 95% CI of a protective effect is 0.01–0.99).

3. Results

3.1. Frequencies of KIR Gene Clusters in Patients with MERS-CoV Infection and Controls

The frequency of KIR2DS4D (HLA-C2 group) was higher in the group of all patients with MERS-CoV infection (MERS total) compared with the controls (88 of 200 [44.0%] versus 21 of 32 [65.6%]; p = 0.023; OR = 2.4; 95% CI 1.1–5.3). KIR3DP1F frequencies were higher in the group of all patients with MERS-CoV infection (MERS total) compared with the controls (19 of 200 [9.5%] versus 7 of 32 [21.9%]; p = 0.039; OR = 2.7; 95% CI 1.02–7.0). KIR2DL1 (HLA-C2 group), KIR2DP1, and KIR3DP1D were lower in Mo/Mi cases (each 200 of 200 [100%] versus 15 of 16 [93.8%]; p = 0.018; OR = 0.04; 95% CI 0.003–0.5). KIR2DL2, HLA-C1 group, was higher in severe cases of MERS-CoV infection compared with the control group (25 of 200 [12.5%] versus 5 of 16 [31.3%]; p = 0.040; OR = 3.2; 95% CI 1.02–9.9). KIR2DS2 and KIR3DP1F were higher (31 of 200 [15.5%] versus 6 of 16 [37.5%]; p = 0.025; OR = 3.3; 95% CI 1.1–9.7, 19 of 200 [9.5%] versus 5 of 16 [31.3%]; p = 0.017; OR = 4.3; 95% CI 1.4–13.8) (Table 2).

3.2. The Association between KIR Genes and Their Ligands in MERS Patients versus Control Groups

When we investigated the association between KIR genes and their ligands in MERS patients versus control groups, we found the following results: KIR3DL1+/Bw4(80I)+ (68 of 200 [34.0%] versus 17 of 32 [53.1%]; p = 0.037; OR = 2.2; 95% CI 1.04–4.7), KIR3DL1+/Bw6+ (143 of 200 [71.5%] versus 17 of 32 [53.1%]; p = 0.037; OR = 0.5; 95% CI 0.2–0.97), KIR3DL1+/Bw6− (40 of 200 [20.0%] versus 13 of 32 [40.6%]; p = 0.010; OR = 2.7; 95% CI 1.2–6.0), KIR2DS1+/C2+ (23 of 200 [11.5%] versus 8 of 32 [25.0%]; p = 0.037; OR = 2.6; 95% CI 1.03–6.4), and KIR3DS+/Bw4(80I)+ (29 of 200 [14.5%] versus 10 of 32 [31.3%]; p = 0.019; OR = 2.7; 95% CI 1.2–6.2) were associated with the group of all patients with MERS-CoV infection (MERS total). KIR3DL1+/Bw6− was also found more often in Mo/Mi cases (40 of 200 [20.0%] versus 7 of 32 [43.8%]; p = 0.019; OR = 2.7; 95% CI 1.2–6.2). KIR2DS1+/C2+ (23 of 200 [11.5%] versus 5 of 32 [31.3%]; p = 0.040; OR = 3.5; 95% CI 1.1–11.0) and KIR2DS2+/C1+ (31 of 200 [15.5%] versus 6 of 32 [37.5%]; p = 0.025; OR = 3.3; 95% CI 1.1–8.9) were both found more often in severe cases (Table 3).
We updated the previous study [46] as the association analysis of the HLA-A, -B, and -C genes between the added control group (n = 200) and the group of patients with MERS (n = 32) (Tables S1–S5). At the allele-level, the HLA-C*01:02 allele frequency was lower in the Mo/Mi cases of MERS-CoV infection compared with the controls but did not show statistical significance by Bonferroni’s correction (72 of 200 [36.0%] versus 1 of 16 [6.3%]; p = 0.009; OR = 0.1; 95% CI 0.015–0.92; pc = NS). Genotypes C1C1, C1C2, and C2C2 showed no significant association with MERS-CoV infection (Table S5).

3.3. Association of KIR Haplotype and Genotype with MERS Risk

When we classified genomic data into the AA and Bx haplotypes, AA genotype ID one haplotype was lower in the group of all patients with MERS-CoV infection (MERS total) compared with the controls (111 of 200 [55.5%] versus 11 of 32 [34.4%]; p = 0.026; OR = 0.4; 95% CI 0.19–0.92). However, Bx genotype ID 4 haplotype was higher in severe cases of MERS-CoV infection compared with the controls (9 of 200 [4.5%] versus 3 of 16 [18.8%]; p = 0.042; OR = 4.9; 95% CI 1.2–20.3). Bx genotype ID 7 and 36 showed risk effects in severe cases and Bx genotype ID 72 showed risk effects in Mo/Mi cases of MERS-CoV infection compared with the controls (each 0 of 200 [0.0%] versus 1 of 16 [6.3%]; p = 0.018; OR = 25.1; 95% CI 2.2–292.3) (Table S6).

4. Discussion

4.1. KIR Genes

We investigated the association of KIR gene clusters with MERS-CoV in 32 patients with MERS. We summarized the risk effects of MERS stages against KIR clusters and NK-target cell interactions (Figure 1 and Figure 2) based on Table 1 and Table 2. To date, no research has been conducted on the association of KIR genes with MERS-CoV infection. We were able to discuss the current controversial COVID-19 situation indirectly. Moreover, similar to SARS and MERS [59], this extrapolation can be made because three human beta-coronaviruses share common features: (i) similar genomes (80% homogeneity between SARS-CoV-2 and SARS-CoV; and 50% with MERS-CoV); (ii) high transmission rates and similar transmission modes; and (iii) severe clinical infection spectrum with development of lung damage and cytokine storms [60,61,62]. Reduced lymphocytes, particularly CD4+CD8+ T lymphocytes, have been found in patients with early-stage COVID-19. Decreased lymphocytes are also important predictors of disease severity [63,64]. In the recent SARS-CoV-2 (COVID-19) association study, it has been reported that an individualized constellation of killer cell immunoglobulin-like receptors and their cognate HLA class I ligands, which control natural killer cell antiviral immunity, causes COVID-19 [65].
Frequencies of inhibitor KIR2DL1 (C2) are lower in Mo/Mi cases of MERS-CoV infection compared to the control group (p = 0.018; OR = 0.04) (Table 2). In the COVID-19 study, the authors characterized the expansion of the NK subset with low cell degradation activity in relation to the immunological scenario of severe COVID-19 infection, enhanced expression of 2DL1 inhibitory receptors, and C2 ligand (KIR2DL1-C2) recovered from mild/moderate/severe SARS-CoV-2 infection in 30 patients suffering from neurological conditions. The control group had only 10 cases of SARS-CoV-2 infections [66].
A recent COVID-19 study suggested that KIR2DL2/HLA-C1C1 gene pairs could be a risk factor for SARS-CoV-2 infection [67]. When comparing 158 mild COVID-19 patients, 99 severely ill patients, and 98 healthy controls, KIR2DL2, KIR2DS3, HLA-C1C1, and KIR2DL2/HLA-C1 pairing frequencies were significantly higher in the COVID-19 patients than in healthy controls. The frequency of KIR2DL3+KIR2DL2-/HLA-C1+ other+ was lower in COVID-19 patients than in healthy individuals. These results suggested that the protective effect of KIR2DL3 on SARS-CoV-2 infection is associated with the absence of the KIR2DL2 gene. No correlation was found between the frequency of these genes and the outbreak of COVID-19 [67]. In this study, clusters of KIR2DL1/2/3 and 2DS1/2 genes also showed an independent association with HLA-C alleles (Table S5).

4.2. HLA Class I Ligands

Inhibitor KIR3DL1+/HLA-Bw4(80I)+ and KIR3DL1+/HLA-Bw4(80I)- showed conflicting results among patients with MERS-CoV infection (p = 0.037; OR = 2.2and p = 0.037; OR = 0.5, respectively). A recent study investigated the effect of HLA and KIR genotypes and HLA-KIR combinations on COVID-19 outcomes and found that the peptide affinity of HLA alleles was not correlated with COVID-19 severity [68]. Predicted bad binders for SARS-CoV-2 peptides encode KIR ligands containing Bw4 and C1 (introduced by B*46:01) which have small F pockets and cannot accommodate SARS-CoV-2. It belongs to the HLA-B subtype cytotoxic T lymphocytes (CTL) epitope. However, HLA-Bw4 weak binders were beneficial for COVID-19 results, and individuals without HLA-Bw4 motifs had a higher risk of developing serious diseases due to COVID-19. The presence of a combination of HLA-Bw4 and KIR3DL1 lowered the risk of developing severe COVID-19 by 58.8% (OR = 0.412, 95% CI = 0.165–0.904, p = 0.02). This suggests that HLA-Bw4 alleles that impair the ability to load SARS-CoV-2 peptides will be targeted for NK-mediated destruction [68]. Ninety-six Sicilian patients were tested for KIR and HLA ligands, including 56 chronic hepatitis B (CHB) patients who were human immunodeficiency virus-1 (HIV) positive and 92 SARS-CoV-2 positive Sicilian patients [69]. Inhibitive KIR3DL1 gene and KIR3DL1/HLA-B Bw4 pairs were more common in individual CHB patients. The frequency of HLA-C2 was higher in CHB compared to other groups. The HLA-C1 ligand did not appear to contribute to CHB progression but had a significantly higher frequency in COVID-19 and HIV-positive than in healthy controls [69]. Additionally, we investigated the influence on specific locations, KIR3DL1+/HLA-Bw4(80I)+, and in all patients with MERS-CoV infection, so we suggest that synergistic reactions of CTLs and NK cells can efficiently control MERS-CoV infection and replication, and that NK cell-mediated anti-MERS-CoV immune responses are primarily involved in severe infections according to the study of SARS-CoV-2 (Table 3).

4.3. MICA Molecules

As an important ligand of human NKG2D, as well as another activating cell surface receptor expressed on NK cells and some T-cell subsets, the MHC class I chain-related gene A (MICA) molecule is expressed on several tumors, especially on epithelium-derived cancer cells [70]. Ligand binding of MICA to the NKG2D receptor mediates innate immune response by stimulating NK cells and γδT cells. Tumor cells expressing high levels of membrane MICA molecule and other NKG2D ligands are rejected by NK cells and CD8aβT cells and stimulate antitumor activity, while reduced expression of membrane MICA molecule, as well as increased soluble MICA molecules in the serum, leads to inactivation of NKG2D-mediated antitumor response [71,72,73,74,75]. It is assumed that the reduction in membrane MICA molecules containing MICA*002:01 and A9 alleles by release or shedding from the cell surface would potentially reduce the immunogenetic signals of tumor cells, and then the tumor cells would become less detectable by NK and T cells. Finding that strong linkage disequilibrium (LD) between HLA-B58 and MICA-STR A9 existed, the haplotype HLA-B58-MICA-STR A9 did not impose a higher risk for leukemia [76]. Similar results have been reported in many autoimmune diseases [77,78], including the previous study on the association between MICA/B and leukemia [51], and the reason why the MICA gene seemed to be associated with diseases independent of the HLA gene remains unknown. Further work needs to be completed to functionally resolve the possible association between other HLA-MICA haplotypes and MERS-CoV infection.

4.4. Limitation

The number of samples is very limited at 32. However, considering the scarcity of samples, it can be assumed that collecting samples would not have been easy, so it is reasonable to place meaning on the samples themselves rather than the number of samples. Further work needs to be completed to functionally resolve the possible association between KIR gene clusters and MERS-CoV infection.

5. Conclusions

In conclusion, it remains unclear why some patients suffer from MERS-CoV. Genetic variations of the KIR genes and their ligand genes may affect MERS-CoV infection in Koreans. Further investigations are needed to demonstrate the different immune responses of NK cells against MERS-CoV infection according to genetic variation in KIR genes and their ligand genes. This evidence will help inform NK cell-based immunotherapy for patients with MERS-CoV infection. Current research in KIR gene clusters and treatment strategies based on their association with MERS-CoV can provide potential treatment targets.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jcm13010258/s1, Table S1: Genetic influence of HLA-A in MERS patients. Table S2: Genetic influence of HLA-B in MERS patients. Table S3: Genetic influence of HLA-Bw4 in MERS patients; Table S4: Genetic influence of HLA-Bw6 in MERS patients. Table S5: Genetic influence of HLA-C in MERS patients. Table S6. Genetic influence of KIR haplotypes in MERS patients.

Author Contributions

I.-C.B., D.-G.L. and T.-G.K. contributed to the conception of the study. I.-C.B. was involved in data acquisition and formal analysis. I.-C.B., D.-G.L. and T.-G.K. were involved in funding acquisition. I.-C.B., E.-J.C. and H.C. performed the investigation and methodology. D.-G.L. and T.-G.K. were involved in project administration. H.-S.S. and D.-G.L. acquired resources. T.-G.K. provided supervision. I.-C.B., H.-J.K. and H.C. were involved in validation. I.-C.B. performed statistical analysis. I.-C.B. wrote the original draft, as well as reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by a grant from the National Medical Center of the Republic of Korea (grant number NMC2018-MS-01), the Korean Health Technology R&D Project of the Ministry for Health & Welfare of the Republic of Korea (grant number HI14C3417), and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (grant number 2019R1I1A1A01064049). The funders had no role in this study.

Institutional Review Board Statement

[MERS] The study was approved by the NMC Ethical Committee and written informed consent was obtained from the patients (institutional review board [IRB]: H-1801-086-002, H-1712-085-005). [Controls] The control subjects provided written informed consent to participate and were mainly of comprised students and staff from the Medical College of the Catholic University of Korea and Hematopoietic Stem Cell Transplantation Center. The use of the material was reviewed and approved by the local IRB of The Catholic University of Korea with written informed consent obtained for all samples collected (IRB: MC13SISI0126).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are available on request due to privacy/ethical restrictions.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Lu, G.; Hu, Y.; Wang, Q.; Qi, J.; Gao, F.; Li, Y.; Zhang, Y.; Zhang, W.; Yuan, Y.; Bao, J.; et al. Molecular basis of binding between novel human coronavirus MERS-CoV and its receptor CD26. Nature 2013, 500, 227–231. [Google Scholar] [CrossRef]
  2. Pallesen, J.; Wang, N.; Corbett, K.S.; Wrapp, D.; Kirchdoerfer, R.N.; Turner, H.L.; Cottrell, C.A.; Becker, M.M.; Wang, L.; Shi, W.; et al. Immunogenicity and structures of a rationally designed prefusion MERS-CoV spike antigen. Proc. Natl. Acad. Sci. USA 2017, 114, E7348–E7357. [Google Scholar] [CrossRef] [PubMed]
  3. Zaki, A.M.; van Boheemen, S.; Bestebroer, T.M.; Osterhaus, A.D.; Fouchier, R.A. Isolation of a novel coronavirus from a man with pneumonia in Saudi Arabia. N. Engl. J. Med. 2012, 367, 1814–1820. [Google Scholar] [CrossRef] [PubMed]
  4. Sikkema, R.S.; Farag, E.; Islam, M.; Atta, M.; Reusken, C.; Al-Hajri, M.M.; Koopmans, M.P.G. Global status of Middle East respiratory syndrome coronavirus in dromedary camels: A systematic review. Epidemiol. Infect. 2019, 147, e84. [Google Scholar] [CrossRef] [PubMed]
  5. Cowling, B.J.; Park, M.; Fang, V.J.; Wu, P.; Leung, G.M.; Wu, J.T. Preliminary epidemiological assessment of MERS-CoV outbreak in South Korea, May to June 2015. Euro Surveill. Eur. Commun. Dis. Bull. 2015, 20, 7–13. [Google Scholar] [CrossRef]
  6. Ki, M. 2015 MERS outbreak in Korea: Hospital-to-hospital transmission. Epidemiol. Health 2015, 37, e2015033. [Google Scholar] [CrossRef]
  7. Kim, K.H.; Tandi, T.E.; Choi, J.W.; Moon, J.M.; Kim, M.S. Middle East respiratory syndrome coronavirus (MERS-CoV) outbreak in South Korea, 2015: Epidemiology, characteristics and public health implications. J. Hosp. Infect. 2017, 95, 207–213. [Google Scholar] [CrossRef]
  8. Robertson, M.J.; Ritz, J. Biology and clinical relevance of human natural killer cells. Blood 1990, 76, 2421–2438. [Google Scholar] [CrossRef]
  9. Hanna, N. The role of natural killer cells in the control of tumor growth and metastasis. Biochim. Biophys. Acta 1985, 780, 213–226. [Google Scholar] [CrossRef]
  10. Hu, W.; Wang, G.; Huang, D.; Sui, M.; Xu, Y. Cancer Immunotherapy Based on Natural Killer Cells: Current Progress and New Opportunities. Front. Immunol. 2019, 10, 1205. [Google Scholar] [CrossRef]
  11. Allison, M.; Mathews, J.; Gilliland, T.; Mathew, S.O. Natural Killer Cell-Mediated Immunotherapy for Leukemia. Cancers 2022, 14, 843. [Google Scholar] [CrossRef] [PubMed]
  12. Lanier, L.L. NK cell recognition. Annu. Rev. Immunol. 2005, 23, 225–274. [Google Scholar] [CrossRef] [PubMed]
  13. Parham, P.; Guethlein, L.A. Genetics of Natural Killer Cells in Human Health, Disease, and Survival. Annu. Rev. Immunol. 2018, 36, 519–548. [Google Scholar] [CrossRef] [PubMed]
  14. Rajalingam, R. Human diversity of killer cell immunoglobulin-like receptors and disease. Korean J. Hematol. 2011, 46, 216–228. [Google Scholar] [CrossRef] [PubMed]
  15. Pende, D.; Falco, M.; Vitale, M.; Cantoni, C.; Vitale, C.; Munari, E.; Bertaina, A.; Moretta, F.; Del Zotto, G.; Pietra, G.; et al. Killer Ig-Like Receptors (KIRs): Their Role in NK Cell Modulation and Developments Leading to Their Clinical Exploitation. Front. Immunol. 2019, 10, 1179. [Google Scholar] [CrossRef]
  16. Trowsdale, J.; Barten, R.; Haude, A.; Stewart, C.A.; Beck, S.; Wilson, M.J. The genomic context of natural killer receptor extended gene families. Immunol. Rev. 2001, 181, 20–38. [Google Scholar] [CrossRef] [PubMed]
  17. Cooley, S.; Weisdorf, D.J.; Guethlein, L.A.; Klein, J.P.; Wang, T.; Le, C.T.; Marsh, S.G.; Geraghty, D.; Spellman, S.; Haagenson, M.D.; et al. Donor selection for natural killer cell receptor genes leads to superior survival after unrelated transplantation for acute myelogenous leukemia. Blood 2010, 116, 2411–2419. [Google Scholar] [CrossRef] [PubMed]
  18. Wilson, M.J.; Torkar, M.; Haude, A.; Milne, S.; Jones, T.; Sheer, D.; Beck, S.; Trowsdale, J. Plasticity in the organization and sequences of human KIR/ILT gene families. Proc. Natl. Acad. Sci. USA 2000, 97, 4778–4783. [Google Scholar] [CrossRef]
  19. Trowsdale, J. Genetic and functional relationships between MHC and NK receptor genes. Immunity 2001, 15, 363–374. [Google Scholar] [CrossRef]
  20. Dębska-Zielkowska, J.; Moszkowska, G.; Zieliński, M.; Zielińska, H.; Dukat-Mazurek, A.; Trzonkowski, P.; Stefańska, K. KIR Receptors as Key Regulators of NK Cells Activity in Health and Disease. Cells 2021, 10, 1777. [Google Scholar] [CrossRef]
  21. Maxwell, L.D.; Wallace, A.; Middleton, D.; Curran, M.D. A common KIR2DS4 deletion variant in the human that predicts a soluble KIR molecule analogous to the KIR1D molecule observed in the rhesus monkey. Tissue Antigens 2002, 60, 254–258. [Google Scholar] [CrossRef] [PubMed]
  22. Middleton, D.; Williams, F.; Halfpenny, I.A. KIR genes. Transpl. Immunol. 2005, 14, 135–142. [Google Scholar] [CrossRef] [PubMed]
  23. Middleton, D.; Gonzelez, F. The extensive polymorphism of KIR genes. Immunology 2010, 129, 8–19. [Google Scholar] [CrossRef] [PubMed]
  24. Parham, P. MHC class I molecules and KIRs in human history, health and survival. Nat. Rev. Immunol. 2005, 5, 201–214. [Google Scholar] [CrossRef]
  25. Varbanova, V.; Naumova, E.; Mihaylova, A. Killer-cell immunoglobulin-like receptor genes and ligands and their role in hematologic malignancies. Cancer Immunol. Immunother. 2016, 65, 427–440. [Google Scholar] [CrossRef]
  26. D’Andrea, A.; Chang, C.; Franz-Bacon, K.; McClanahan, T.; Phillips, J.H.; Lanier, L.L. Molecular cloning of NKB1. A natural killer cell receptor for HLA-B allotypes. J. Immunol. 1995, 155, 2306–2310. [Google Scholar] [CrossRef]
  27. Saunders, P.M.; Pymm, P.; Pietra, G.; Hughes, V.A.; Hitchen, C.; O’Connor, G.M.; Loiacono, F.; Widjaja, J.; Price, D.A.; Falco, M.; et al. Killer cell immunoglobulin-like receptor 3DL1 polymorphism defines distinct hierarchies of HLA class I recognition. J. Exp. Med. 2016, 213, 791–807. [Google Scholar] [CrossRef]
  28. Boudreau, J.E.; Mulrooney, T.J.; Le Luduec, J.B.; Barker, E.; Hsu, K.C. KIR3DL1 and HLA-B Density and Binding Calibrate NK Education and Response to HIV. J. Immunol. 2016, 196, 3398–3410. [Google Scholar] [CrossRef]
  29. Stern, M.; Ruggeri, L.; Capanni, M.; Mancusi, A.; Velardi, A. Human leukocyte antigens A23, A24, and A32 but not A25 are ligands for KIR3DL1. Blood 2008, 112, 708–710. [Google Scholar] [CrossRef]
  30. Saunders, P.M.; MacLachlan, B.J.; Widjaja, J.; Wong, S.C.; Oates, C.V.L.; Rossjohn, J.; Vivian, J.P.; Brooks, A.G. The Role of the HLA Class I α2 Helix in Determining Ligand Hierarchy for the Killer Cell Ig-like Receptor 3DL1. J. Immunol. 2021, 206, 849–860. [Google Scholar] [CrossRef]
  31. Hansasuta, P.; Dong, T.; Thananchai, H.; Weekes, M.; Willberg, C.; Aldemir, H.; Rowland-Jones, S.; Braud, V.M. Recognition of HLA-A3 and HLA-A11 by KIR3DL2 is peptide-specific. Eur. J. Immunol. 2004, 34, 1673–1679. [Google Scholar] [CrossRef] [PubMed]
  32. Stewart, C.A.; Laugier-Anfossi, F.; Vély, F.; Saulquin, X.; Riedmuller, J.; Tisserant, A.; Gauthier, L.; Romagné, F.; Ferracci, G.; Arosa, F.A.; et al. Recognition of peptide-MHC class I complexes by activating killer immunoglobulin-like receptors. Proc. Natl. Acad. Sci. USA 2005, 102, 13224–13229. [Google Scholar] [CrossRef]
  33. Chewning, J.H.; Gudme, C.N.; Hsu, K.C.; Selvakumar, A.; Dupont, B. KIR2DS1-positive NK cells mediate alloresponse against the C2 HLA-KIR ligand group in vitro. J. Immunol. 2007, 179, 854–868. [Google Scholar] [CrossRef] [PubMed]
  34. Ljunggren, H.G.; Kärre, K. In search of the ‘missing self’: MHC molecules and NK cell recognition. Immunol. Today 1990, 11, 237–244. [Google Scholar] [CrossRef] [PubMed]
  35. Shi, J.; Zhang, J.; Li, S.; Sun, J.; Teng, Y.; Wu, M.; Li, J.; Li, Y.; Hu, N.; Wang, H.; et al. Epitope-Based Vaccine Target Screening against Highly Pathogenic MERS-CoV: An In Silico Approach Applied to Emerging Infectious Diseases. PLoS ONE 2015, 10, e0144475. [Google Scholar] [CrossRef] [PubMed]
  36. Badawi, M.M.; SalahEldin, M.A.; Suliman, M.M.; AbduRahim, S.A.; Mohammed, A.A.e.; SidAhmed, A.S.A.; Osman, M.M.; Salih, M.A. In Silico Prediction of a Novel Universal Multi-epitope Peptide Vaccine in the Whole Spike Glycoprotein of MERS CoV. Am. J. Microbiol. Res. 2016, 4, 101–121. [Google Scholar]
  37. Liu, W.J.; Zhao, M.; Liu, K.; Xu, K.; Wong, G.; Tan, W.; Gao, G.F. T-cell immunity of SARS-CoV: Implications for vaccine development against MERS-CoV. Antivir. Res. 2017, 137, 82–92. [Google Scholar] [CrossRef]
  38. Cong, Y.; Hart, B.J.; Gross, R.; Zhou, H.; Frieman, M.; Bollinger, L.; Wada, J.; Hensley, L.E.; Jahrling, P.B.; Dyall, J.; et al. MERS-CoV pathogenesis and antiviral efficacy of licensed drugs in human monocyte-derived antigen-presenting cells. PLoS ONE 2018, 13, e0194868. [Google Scholar] [CrossRef]
  39. Srivastava, S.; Kamthania, M.; Singh, S.; Saxena, A.K.; Sharma, N. Structural basis of development of multi-epitope vaccine against Middle East respiratory syndrome using in silico approach. Infect. Drug Resist. 2018, 11, 2377–2391. [Google Scholar] [CrossRef]
  40. Shokri, S.; Mahmoudvand, S.; Taherkhani, R.; Farshadpour, F. Modulation of the immune response by Middle East respiratory syndrome coronavirus. J. Cell. Physiol. 2019, 234, 2143–2151. [Google Scholar] [CrossRef]
  41. Tahir Ul Qamar, M.; Saleem, S.; Ashfaq, U.A.; Bari, A.; Anwar, F.; Alqahtani, S. Epitope-based peptide vaccine design and target site depiction against Middle East Respiratory Syndrome Coronavirus: An immune-informatics study. J. Transl. Med. 2019, 17, 362. [Google Scholar] [CrossRef] [PubMed]
  42. Nallusamy, S.; Mannu, J.; Ravikumar, C.; Angamuthu, K.; Nathan, B.; Nachimuthu, K.; Ramasamy, G.; Muthurajan, R.; Subbarayalu, M.; Neelakandan, K. Exploring Phytochemicals of Traditional Medicinal Plants Exhibiting Inhibitory Activity Against Main Protease, Spike Glycoprotein, RNA-dependent RNA Polymerase and Non-Structural Proteins of SARS-CoV-2 Through Virtual Screening. Front. Pharmacol. 2021, 12, 667704. [Google Scholar] [CrossRef] [PubMed]
  43. Mahallawi, W.H.; Khabour, O.F.; Zhang, Q.; Makhdoum, H.M.; Suliman, B.A. MERS-CoV infection in humans is associated with a pro-inflammatory Th1 and Th17 cytokine profile. Cytokine 2018, 104, 8–13. [Google Scholar] [CrossRef] [PubMed]
  44. Market, M.; Angka, L.; Martel, A.B.; Bastin, D.; Olanubi, O.; Tennakoon, G.; Boucher, D.M.; Ng, J.; Ardolino, M.; Auer, R.C. Flattening the COVID-19 Curve With Natural Killer Cell Based Immunotherapies. Front. Immunol. 2020, 11, 1512. [Google Scholar] [CrossRef] [PubMed]
  45. Soleimanian, S.; Yaghobi, R. Harnessing Memory NK Cell to Protect Against COVID-19. Front. Pharmacol. 2020, 11, 1309. [Google Scholar] [CrossRef] [PubMed]
  46. Baek, I.C.; Choi, E.J.; Shin, D.H.; Kim, H.J.; Choi, H.; Shin, H.S.; Lim, D.G.; Kim, T.G. Association of HLA class I and II genes with Middle East respiratory syndrome coronavirus infection in Koreans. Immun. Inflamm. Dis. 2022, 10, 111–116. [Google Scholar] [CrossRef] [PubMed]
  47. Choi, E.J.; Baek, I.C.; Park, S.; Kim, H.J.; Kim, T.G. Development of cost-effective and fast KIR genotyping by multiplex PCR-SSP. Hla 2023, 103, e15191. [Google Scholar] [CrossRef]
  48. Kim, H.J.; Choi, H.B.; Jang, J.P.; Baek, I.C.; Choi, E.J.; Park, M.; Kim, T.G.; Oh, S.T. HLA-Cw polypmorphism and killer cell immunoglobulin-like receptor (KIR) gene analysis in Korean colorectal cancer patients. Int. J. Surg. 2014, 12, 815–820. [Google Scholar] [CrossRef]
  49. Jang, J.P.; Cho, W.K.; Baek, I.C.; Choi, E.J.; Shin, D.H.; Suh, B.K.; Kim, T.G. Comprehensive analysis of cytokine gene polymorphisms defines the association of IL-12 gene with ophthalmopthy in Korean children with autoimmune thyroid disease. Mol. Cell. Endocrinol. 2016, 426, 43–49. [Google Scholar] [CrossRef]
  50. Cho, W.K.; Jang, J.P.; Choi, E.J.; Ahn, M.; Kim, S.H.; Cho, K.S.; Park, S.H.; Baek, I.C.; Jung, M.H.; Kim, T.G.; et al. Association of Polymorphisms in Toll-Like Receptors 4 and 9 with Autoimmune Thyroid Disease in Korean Pediatric Patients. Int. J. Endocrinol. 2017, 2017, 2304218. [Google Scholar] [CrossRef]
  51. Baek, I.C.; Shin, D.H.; Choi, E.J.; Kim, H.J.; Yoon, J.H.; Cho, B.S.; Kim, Y.J.; Lee, S.; Min, W.S.; Kim, H.J.; et al. Association of MICA and MICB polymorphisms with the susceptibility of leukemia in Korean patients. Blood Cancer J. 2018, 8, 58. [Google Scholar] [CrossRef] [PubMed]
  52. Shin, D.H.; Baek, I.C.; Kim, H.J.; Choi, E.J.; Ahn, M.; Jung, M.H.; Suh, B.K.; Cho, W.K.; Kim, T.G. HLA alleles, especially amino-acid signatures of HLA-DPB1, might contribute to the molecular pathogenesis of early-onset autoimmune thyroid disease. PLoS ONE 2019, 14, e0216941. [Google Scholar] [CrossRef] [PubMed]
  53. Cho, W.K.; Shin, H.R.; Lee, N.Y.; Kim, S.K.; Ahn, M.B.; Baek, I.C.; Kim, T.G.; Suh, B.K. GPR174 and ITM2A Gene Polymorphisms rs3827440 and rs5912838 on the X chromosome in Korean Children with Autoimmune Thyroid Disease. Genes 2020, 11, 858. [Google Scholar] [CrossRef] [PubMed]
  54. Min, G.J.; Kim, H.J.; Kim, T.G.; Hyun, Y.S.; Hyun, S.J.; Baek, I.C.; Yoon, S.Y.; Park, S.S.; Park, S.; Yoon, J.H.; et al. Specific donor HLA allotypes as predictors of cytomegalovirus disease risk in acute myeloid leukemia. Hla 2020, 96, 445–455. [Google Scholar] [CrossRef] [PubMed]
  55. Shin, H.R.; Cho, W.K.; Baek, I.C.; Lee, N.Y.; Lee, Y.J.; Kim, S.K.; Ahn, M.B.; Suh, B.K.; Kim, T.G. Polymorphisms of IRAK1 Gene on X Chromosome Is Associated with Hashimoto Thyroiditis in Korean Children. Endocrinology 2020, 161, bqaa088. [Google Scholar] [CrossRef] [PubMed]
  56. Cho, W.K.; Baek, I.C.; Kim, S.E.; Kim, M.; Kim, T.G.; Suh, B.K. Association of ITM2A rs1751094 polymorphism on X chromosome in Korean pediatric patients with autoimmune thyroid disease. Immun. Inflamm. Dis. 2023, 11, e800. [Google Scholar] [CrossRef]
  57. Choi, S.S.; Choi, H.; Baek, I.C.; Park, S.A.; Park, J.S.; Kim, T.G.; Jeun, S.S.; Ahn, S. HLA polymorphisms and risk of glioblastoma in Koreans. PLoS ONE 2021, 16, e0260618. [Google Scholar] [CrossRef]
  58. Choi, H.; Baek, I.C.; Park, S.A.; Park, J.S.; Jeun, S.S.; Kim, T.G.; Ahn, S. Polymorphisms of Killer Ig-like Receptors and the Risk of Glioblastoma. J. Clin. Med. 2023, 12, 4780. [Google Scholar] [CrossRef]
  59. Cui, W.; Fan, Y.; Wu, W.; Zhang, F.; Wang, J.Y.; Ni, A.P. Expression of lymphocytes and lymphocyte subsets in patients with severe acute respiratory syndrome. Clin. Infect. Dis. 2003, 37, 857–859. [Google Scholar] [CrossRef]
  60. Zawawi, A.; Naser, A.Y.; Alwafi, H.; Minshawi, F. Profile of Circulatory Cytokines and Chemokines in Human Coronaviruses: A Systematic Review and Meta-Analysis. Front. Immunol. 2021, 12, 666223. [Google Scholar] [CrossRef]
  61. Petrosillo, N.; Viceconte, G.; Ergonul, O.; Ippolito, G.; Petersen, E. COVID-19, SARS and MERS: Are they closely related? Clin. Microbiol. Infect. 2020, 26, 729–734. [Google Scholar] [CrossRef] [PubMed]
  62. Liu, J.; Zheng, X.; Tong, Q.; Li, W.; Wang, B.; Sutter, K.; Trilling, M.; Lu, M.; Dittmer, U.; Yang, D. Overlapping and discrete aspects of the pathology and pathogenesis of the emerging human pathogenic coronaviruses SARS-CoV, MERS-CoV, and 2019-nCoV. J. Med. Virol. 2020, 92, 491–494. [Google Scholar] [CrossRef] [PubMed]
  63. Tan, L.; Wang, Q.; Zhang, D.; Ding, J.; Huang, Q.; Tang, Y.Q.; Wang, Q.; Miao, H. Lymphopenia predicts disease severity of COVID-19: A descriptive and predictive study. Signal Transduct. Target. Ther. 2020, 5, 33. [Google Scholar] [CrossRef]
  64. He, R.; Lu, Z.; Zhang, L.; Fan, T.; Xiong, R.; Shen, X.; Feng, H.; Meng, H.; Lin, W.; Jiang, W.; et al. The clinical course and its correlated immune status in COVID-19 pneumonia. J. Clin. Virol. 2020, 127, 104361. [Google Scholar] [CrossRef] [PubMed]
  65. Maruthamuthu, S.; Rajalingam, K.; Kaur, N.; Morvan, M.G.; Soto, J.; Lee, N.; Kong, D.; Hu, Z.; Reyes, K.; Ng, D.; et al. Individualized Constellation of Killer Cell Immunoglobulin-Like Receptors and Cognate HLA Class I Ligands that Controls Natural Killer Cell Antiviral Immunity Predisposes COVID-19. Front. Genet. 2022, 13, 845474. [Google Scholar] [CrossRef] [PubMed]
  66. Saresella, M.; Trabattoni, D.; Marventano, I.; Piancone, F.; La Rosa, F.; Caronni, A.; Lax, A.; Bianchi, L.; Banfi, P.; Navarro, J.; et al. NK Cell Subpopulations and Receptor Expression in Recovering SARS-CoV-2 Infection. Mol. Neurobiol. 2021, 58, 6111–6120. [Google Scholar] [CrossRef]
  67. Hu, S.; Shao, Z.; Ni, W.; Sun, P.; Qiao, J.; Wan, H.; Huang, Y.; Liu, X.; Zhai, H.; Xiao, M.; et al. The KIR2DL2/HLA-C1C1 Gene Pairing Is Associated With an Increased Risk of SARS-CoV-2 Infection. Front. Immunol. 2022, 13, 919110. [Google Scholar] [CrossRef]
  68. Wang, R.; Sun, Y.; Kuang, B.H.; Yan, X.; Lei, J.; Lin, Y.X.; Tian, J.; Li, Y.; Xie, X.; Chen, T.; et al. HLA-Bw4 in association with KIR3DL1 favors natural killer cell-mediated protection against severe COVID-19. Emerg. Microbes Infect. 2023, 12, 2185467. [Google Scholar] [CrossRef]
  69. Ligotti, M.E.; Aiello, A.; Accardi, G.; Calabrò, A.; Ciaccio, M.; Colomba, C.; Di Bona, D.; Lo Sasso, B.; Pojero, F.; Tuttolomondo, A.; et al. Distribution of KIR Genes and Their HLA Ligands in Different Viral Infectious Diseases: Frequency Study in Sicilian Population. Int. J. Mol. Sci. 2022, 23, 15466. [Google Scholar] [CrossRef]
  70. Raulet, D.H. Roles of the NKG2D immunoreceptor and its ligands. Nat. Rev. Immunol. 2003, 3, 781–790. [Google Scholar] [CrossRef]
  71. Beetz, S.; Marischen, L.; Kabelitz, D.; Wesch, D. Human gamma delta T cells: Candidates for the development of immunotherapeutic strategies. Immunol. Res. 2007, 37, 97–111. [Google Scholar] [CrossRef] [PubMed]
  72. Busche, A.; Goldmann, T.; Naumann, U.; Steinle, A.; Brandau, S. Natural killer cell-mediated rejection of experimental human lung cancer by genetic overexpression of major histocompatibility complex class I chain-related gene A. Hum. Gene Ther. 2006, 17, 135–146. [Google Scholar] [CrossRef] [PubMed]
  73. Germain, C.; Larbouret, C.; Cesson, V.; Donda, A.; Held, W.; Mach, J.P.; Pelegrin, A.; Robert, B. MHC class I-related chain A conjugated to antitumor antibodies can sensitize tumor cells to specific lysis by natural killer cells. Clin. Cancer Res. 2005, 11, 7516–7522. [Google Scholar] [CrossRef] [PubMed]
  74. Lo, S.S.; Lee, Y.J.; Wu, C.W.; Liu, C.J.; Huang, J.W.; Lui, W.Y. The increase of MICA gene A9 allele associated with gastric cancer and less schirrous change. Br. J. Cancer 2004, 90, 1809–1813. [Google Scholar] [CrossRef]
  75. Salih, H.R.; Rammensee, H.G.; Steinle, A. Cutting edge: Down-regulation of MICA on human tumors by proteolytic shedding. J. Immunol. 2002, 169, 4098–4102. [Google Scholar] [CrossRef]
  76. Luo, Q.Z.; Lin, L.; Gong, Z.; Mei, B.; Xu, Y.J.; Huo, Z.; Yu, P. Positive association of major histocompatibility complex class I chain-related gene A polymorphism with leukemia susceptibility in the people of Han nationality of Southern China. Tissue Antigens 2011, 78, 178–184. [Google Scholar] [CrossRef]
  77. Gonzalez-Gay, M.A.; Rueda, B.; Vilchez, J.R.; Lopez-Nevot, M.A.; Robledo, G.; Ruiz, M.P.; Fernandez, O.; Garcia-Porrua, C.; Gonzalez-Escribano, M.F.; Martin, J. Contribution of MHC class I region to genetic susceptibility for giant cell arteritis. Rheumatology 2007, 46, 431–434. [Google Scholar] [CrossRef]
  78. Park, Y.; Lee, H.; Sanjeevi, C.B.; Eisenbarth, G.S. MICA polymorphism is associated with type 1 diabetes in the Korean population. Diabetes Care 2001, 24, 33–38. [Google Scholar] [CrossRef]
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Figure 1. Risk effects of MERS stages against KIR clusters. OR, odd ratio; OR > 1.0, risk effect; OR < 1.0, protective effect.
Figure 1. Risk effects of MERS stages against KIR clusters. OR, odd ratio; OR > 1.0, risk effect; OR < 1.0, protective effect.
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Figure 2. Risk effects of MERS stages against NK-target cell interactions. OR, odd ratio; OR > 1.0, risk effect; OR < 1.0, protective effect; Mo/Mi, moderate and mild; −, inhibitory KIRs; +, activating KIRs.
Figure 2. Risk effects of MERS stages against NK-target cell interactions. OR, odd ratio; OR > 1.0, risk effect; OR < 1.0, protective effect; Mo/Mi, moderate and mild; −, inhibitory KIRs; +, activating KIRs.
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Table 1. Characteristics of patients with MERS [46].
Table 1. Characteristics of patients with MERS [46].
Group (Clinical Severity)Total
Mo/MiSevere
No. of patients (%)16 (50)16 (50)32
Male/female (n)8/813/321/11
Age (year), mean ± SD50 ± 2158 ± 1554 ± 19
Underlying disease, n (%)
          Diabetes1 (6.3)3 (18.8)4 (12.5)
          Chronic renal disease1 (6.3)2 (12.5)3 (9.4)
          Chronic cardiac disease1 (6.3)4 (25.0)5 (15.6)
          Chronic pulmonary disease0 (0)1 (6.3)1 (3.1)
          Hypertension2 (12.5)7 (43.8)9 (28.1)
          Chronic liver disease1 (6.3)0 (0)1 (3.1)
          Solid tumor2 (12.5)2 (12.5)4 (12.5)
          Obesity0 (0)1 (6.3)1 (3.1)
MERS, middle east respiratory syndrome; Mo/Mi, Moderate and Mild.
Table 2. Genetic influence of KIR genes in MERS patients.
Table 2. Genetic influence of KIR genes in MERS patients.
FunctionGene ClustersControlsMERS TotalMo/Mi CasesSevere Cases
n = 200 (%)n = 32 (%)n = 16 (%)n = 16 (%)
InhibitoryKIR2DL1200 (100.0)31 (96.9)15 (93.8) c16 (100.0)
KIR2DL225 (12.5)8 (25.0)3 (18.8)5 (31.3) f
KIR2DL3199 (99.5)31 (96.9)15 (93.8)16 (100.0)
KIR2DL4200 (100.0)32 (100.0)16 (100.0)16 (100.0)
KIR2DL574 (37.0)17 (53.1)9 (56.3)8 (50.0)
KIR2DL5A69 (34.5)16 (50.0)9 (56.3)7 (43.8)
KIR2DL5B14 (7.0)2 (6.3)1 (6.3)1 (6.3)
KIR3DL1183 (91.5)30 (93.8)15 (93.8)15 (93.8)
KIR3DL2200 (100.0)32 (100.0)16 (100.0)16 (100.0)
KIR3DL3200 (100.0)32 (100.0)16 (100.0)16 (100.0)
ActivatingKIR2DS172 (36.0)16 (50.0)9 (56.3)7 (43.8)
KIR2DS231 (15.5)9 (28.1)3 (18.8)6 (37.5) g
KIR2DS337 (18.5)9 (28.1)4 (25.0)5 (31.3)
KIR2DS4183 (91.5)31 (96.9)15 (93.8)16 (100.0)
KIR2DS4D88 (44.0)21 (65.6) a11 (68.8)10 (62.5)
KIR2DS4F146 (73.0)23 (71.9)10 (62.5)13 (81.3)
KIR2DS548 (24.0)9 (28.1)6 (37.5)3 (18.8)
KIR3DS173 (36.5)16 (50.0)9 (56.3)7 (43.8)
PseudogeneKIR2DP1200 (100.0)31 (96.9)15 (93.8) d16 (100.0)
KIR3DP1200 (100.0)32 (100.0)16 (100.0)16 (100.0)
KIR3DP1D200 (100.0)31 (96.9)15 (93.8) e16 (100.0)
KIR3DP1F19 (9.5)7 (21.9) b2 (12.5)5 (31.3) h
Controls versus MERS total: a OR = 2.4 (1.1–5.3), p = 0.023; b OR = 2.7 (1.02–7.0), p = 0.039. Controls versus Mo/Mi cases: c OR = 0.04 (0.003–0.5), p = 0.018; d OR = 0.04 (0.003–0.5), p = 0.018; e OR = 0.04 (0.003–0.5), p = 0.018. Controls versus Severe cases: f OR = 3.2 (1.02–9.9), p = 0.037; g OR = 3.3 (1.1–9.7), p = 0.025; h OR = 4.3 (1.4–13.8), p = 0.017.
Table 3. The association of KIR genes and their ligands between MERS patients and control groups.
Table 3. The association of KIR genes and their ligands between MERS patients and control groups.
KIR GenesLigand Genes (HLA)ControlsMERS TotalMo/Mi CasesSevere Cases
n = 200 (%)n = 32 (%)n = 16 (%)n = 16 (%)
KIR2DL1+C2+61(30.5)12(37.5)6(37.5)6(37.5)
C2−139(69.5)19(59.4)9(56.3)10(62.5)
KIR2DL2+C1+25(12.5)8(25.0)3(18.8)5(31.3)
C1−0(0.0)0(0.0)0(0.0)0(0.0)
KIR2DL3+C1+192(96.0)31(96.9)15(93.8)16(100.0)
C1−7(3.5)0(0.0)0(0.0)0(0.0)
KIR3DL1+Bw4+133(66.5)25(78.1)11(68.8)14(87.5)
BW4−50(25.0)5(15.6)4(25.0)1(6.3)
Bw4 (80I)+68(34.0)17(53.1) a8(50.0)9(56.3)
Bw4 (80I)−115(57.5)13(40.6)7(43.8)6(37.5)
Bw4 (80T)+33(16.5)5(15.6)1(6.3)4(25.0)
Bw4 (80T)−150(75.0)25(78.1)14(87.5)11(68.8)
Bw6+143(71.5)17(53.1) b8(50.0)9(56.3)
Bw6−40(20.0)13(40.6) c7(43.8) f6(37.5)
KIR3DL2+A3+ or A11+49(24.5)6(18.8)3(18.8)3(18.8)
A3− and A11−151(75.5)26(81.3)13(81.3)13(81.3)
KIR2DS1+C2+23(11.5)8(25.0) d3(18.8)5(31.3) g
C2−49(24.5)8(25.0)6(37.5)2(12.5)
KIR2DS2+C1+31(15.5)9(28.1)3(18.8)6(37.5) h
C1−0(0.0)0(0.0)0(0.0)0(0.0)
KIR3DS1+Bw4+56(28.0)13(40.6)7(43.8)6(37.5)
Bw4−17(8.5)3(9.4)2(12.5)1(6.3)
Bw4 (80I)+29(14.5)10(31.3) e5(31.3)5(31.3)
Bw4 (80I)−44(22.0)6(18.8)4(25.0)2(12.5)
Bw4 (80T)+13(6.5)4(12.5)1(6.3)3(18.8)
Bw4 (80T)−60(30.0)12(37.5)8(50.0)4(25.0)
Controls versus MERS total: a OR = 2.2 (1.04–4.7), p = 0.037; b OR = 0.5 (0.2–0.97), p = 0.037; c OR = 2.7 (1.2–6.0), p = 0.010; d OR = 2.6 (1.03–6.4), p = 0.037; e OR = 2.7 (1.2–6.2), p = 0.019. Controls versus Mo/Mi cases: f OR = 3.1 (1.1–8.9), p = 0.027. Controls versus Severe cases: g OR = 3.5 (1.1–11.0), p = 0.040; h OR = 3.3 (1.1–9.7), p = 0.025. C1 group: Ser77Asn80 allele among HLA-C alleles. C2 group: Asn77Lys80 allele among HLA-C alleles.
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MDPI and ACS Style

Baek, I.-C.; Choi, E.-J.; Kim, H.-J.; Choi, H.; Shin, H.-S.; Lim, D.-G.; Kim, T.-G. Association of KIR Genes with Middle East Respiratory Syndrome Coronavirus Infection in South Koreans. J. Clin. Med. 2024, 13, 258. https://doi.org/10.3390/jcm13010258

AMA Style

Baek I-C, Choi E-J, Kim H-J, Choi H, Shin H-S, Lim D-G, Kim T-G. Association of KIR Genes with Middle East Respiratory Syndrome Coronavirus Infection in South Koreans. Journal of Clinical Medicine. 2024; 13(1):258. https://doi.org/10.3390/jcm13010258

Chicago/Turabian Style

Baek, In-Cheol, Eun-Jeong Choi, Hyoung-Jae Kim, Haeyoun Choi, Hyoung-Shik Shin, Dong-Gyun Lim, and Tai-Gyu Kim. 2024. "Association of KIR Genes with Middle East Respiratory Syndrome Coronavirus Infection in South Koreans" Journal of Clinical Medicine 13, no. 1: 258. https://doi.org/10.3390/jcm13010258

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