BioMed Research International

BioMed Research International / 2021 / Article

Review Article | Open Access

Volume 2021 |Article ID 6693909 | https://doi.org/10.1155/2021/6693909

Mina Mobini Kesheh, Sara Shavandi, Parastoo Hosseini, Rezvan Kakavand-Ghalehnoei, Hossein Keyvani, "Bioinformatic HLA Studies in the Context of SARS-CoV-2 Pandemic and Review on Association of HLA Alleles with Preexisting Medical Conditions", BioMed Research International, vol. 2021, Article ID 6693909, 12 pages, 2021. https://doi.org/10.1155/2021/6693909

Bioinformatic HLA Studies in the Context of SARS-CoV-2 Pandemic and Review on Association of HLA Alleles with Preexisting Medical Conditions

Academic Editor: Cassiano Felippe Gonçalves-de-Albuquerque
Received16 Nov 2020
Revised10 Feb 2021
Accepted06 May 2021
Published28 May 2021

Abstract

After the announcement of a new coronavirus in China in December 2019, which was then called SARS-CoV-2, this virus changed to a global concern and it was then declared as a pandemic by WHO. Human leukocyte antigen (HLA) alleles, which are one of the most polymorphic genes, play a pivotal role in both resistance and vulnerability of the body against viruses and other infections as well as chronic diseases. The association between HLA alleles and preexisting medical conditions such as cardiovascular diseases and diabetes mellitus is reported in various studies. In this review, we focused on the bioinformatic HLA studies to summarize the HLA alleles which responded to SARS-CoV-2 peptides and have been used to design vaccines. We also reviewed HLA alleles that are associated with comorbidities and might be related to the high mortality rate among COVID-19 patients. Since both genes and patients’ medical conditions play a key role in both severity of the disease and the mortality rate in COVID-19 patients, a better understanding of the connection between HLA alleles and SARS-CoV-2 can provide a wider perspective on the behavior of the virus. Such understanding can help scientists, especially in terms of protecting healthcare workers and designing effective vaccines.

1. Introduction

In late December 2019, China publicly revealed the occurrence of a new coronavirus, later called SARS-CoV-2. Human-to-human transmission was reported only after the zoonotic transmission of the new virus via animals in the seafood market [1]. Finally, having a higher transmission rate than SARS-CoV and MERS-CoV, and the high affinity of the viral spike protein for its receptor angiotensin-converting enzyme 2 (ACE-2), WHO announced SARS-CoV-2 as a pandemic on 12 March 2020 [2, 3]. Currently, SARS-CoV-2 has spread globally and led to all research focusing on improving methods of the diagnosis, treatment, prognosis, vaccine design, and control of COVID-19 disease.

Human leukocyte antigen (HLA) alleles are encoded by genes at region 6p21 of the human genome [4]. The nomenclature of HLA alleles is performed by WHO Nomenclature Committee for Factors of the HLA System. Each HLA allele name may contain four sets of digits separated by colons. A specific HLA locus comes after an HLA prefix, e.g., HLA-DQA1. The HLA locus is separated by a star from the first two digits (HLA-DQA1). The first two digits are assigned to the allele group (HLA-DQA101). The third and fourth digits indicate a particular HLA allele (HLA-DQA101 : 02). The second four digits describe the mutations in the allele [5]. HLA alleles are involved in presenting 8–25-mer peptide antigens, self- or pathogen-derived peptides, to T cells that elicit the immune responses [6, 7]. HLA alleles are the most highly polymorphic genes, and the heterozygote advantage of HLA genotypes affects their ability to respond to different pathogens and even control or intensify an infectious disease [8, 9]. The association of HLA alleles with some viral infections (such as HIV, HBV, H1N1, and HCV) and some chronic diseases is well established [7, 1013]. Wang et al. showed that HLA-Cw 15 : 02, DR 03 : 01 and HLA-B46 : 01, B07 : 03, DRB112 : 02 were associated with resistance and severity of SARS-CoV, respectively [14, 15].

Although most efforts to design an effective vaccine against SARS-CoV-2 are concentrating on B cell epitopes and produced antibodies, a study indicated that SARS-CoV-2 antibodies declined after 2-3 months in some recovered patients [16]. However, 40-60% of T cells in unexposed individuals reacted to the viral proteins S, M, N, and other ORFs due to the cross-immunity with other common cold coronaviruses [17]. These findings illustrate the possible role of T cells in the SARS-CoV-2 infection. Moreover, according to an analysis conducted on in-hospital deaths, the highest number of deaths was observed in COVID-19 patients who had hypertension, diabetes mellitus, chronic obstructive pulmonary disease, obesity, an underlying immunosuppressed condition, and cardiovascular diseases including coronary artery disease, cardiac arrhythmia, congestive heart failure, and cerebrovascular disease [10, 1821]. Also, patients with chronic kidney disease are more likely to be tested positive for SARS-CoV-2 [22]. Additionally, as reported by WHO, among 345 children with confirmed COVID-19 infection, 23% had an underlying condition such as chronic lung disease (asthma), cardiovascular diseases, and immunosuppressed conditions [23].

In this review, based on published information till now, we aimed to illustrate the bioinformatic HLA restriction profile that may affect the resistance or severity of COVID-19 disease and also the association of HLA alleles with preexisting medical conditions.

2. Methods

We found the literature on this review by searching the following online databases: bioRxiv, medRxiv, Google scholar, WHO, CDC, Scopus, and PubMed. We found these publications from December 2019 to May 2020. The keywords were SARS-CoV-2, COVID-19, HLA, diabetes mellitus, chronic obstructive pulmonary disease, asthma, obesity, cardiovascular disease, chronic kidney disease, and Kawasaki disease. We included all relevant literature published in English. The data were extracted and recorded in an excel spreadsheet for this review.

3. Results and Discussion

3.1. SARS-CoV-2 and Predicted HLA Alleles

We reviewed the literature that used various bioinformatics tools to predict the HLA restriction that would trigger a robust immune response to SARS-CoV-2 peptides [10, 2454]. Indeed, HLA alleles listed in Table 1 can be used for vaccine design due to their high binding affinity for antigens. Some of these studies used the SARS-CoV matched peptides to predict the SARS-CoV-2 MHC I and II molecules, and the others used SARS-CoV-2 unique peptides, albeit with some conflicts. Nguyen et al. assessed the binding of unique peptides of SARS-CoV-2 to 145 MHC I molecules. They considered the for HLA prediction, while the proper affinity threshold for some of the MHC I molecules was suggested to be higher than 500 nm [10, 55]. They reported that HLA-B46 : 01 could be associated with the severity to SARS-CoV-2 due to its lowest binding affinity for SARS-CoV-2 peptides [10], consistent with the study conducted by Lin et al. for SARS-CoV [56]. In Lin et al.’s study, among 33 probable SARS-CoV-1 patients, only six severe cases carried HLA-B46 : 01 [56], although the sample size was too small for monitoring a certain consequent. On the contrary, in Kiyotani et al.’s study that predicted T cell epitopes for SARS-CoV-2 in the Japanese population, HLA-B46 : 01 represented a strong binding affinity with 0.5%-ranked epitopes which is the top binding score in NetMHC tools which predict the binding of MHC classes I and II using artificial neural networks (ANNs) [35]. Also, in a study to design a multiepitope vaccine, Feng et al. found that epitopes in spike and envelope proteins are able to bind to HLA-B46 : 01 with a high HLA score (0.102). The authors used both NetMHCpan and an in-house prediction tool (iNeo-Pred), the latter of which was used to predict the epitopes binding to specific HLA alleles [46]. Based on bioinformatic models, HLA-A24 : 02 and DPB105 : 01 may elicit T cells’ immunity responses (Table 1), but Warren and Birol identified HLA-A24 : 02 and DPB105 : 01 as related to susceptibility to SARS-CoV-2. They predicted HLA alleles using HLAminer ( and ) on metagenomics RNA bronchoalveolar lavage (BAL) fluid samples from five COVID-19 patients [57]. The small sample size (only five patients) and the prevalence of HLA-A24 : 02 in the population of China could affect the results using their data. Thus, due to these conflicts, in vitro and in vivo experiments are needed to confirm the suggested MHC : peptide bindings, which are conducted on in silico conditions without experimental validation. Also, more genome-wide association studies (GWASs) on COVID-19 patients are required. According to a GWAS, HLA-DQA1509 was related to severe disease in COVID-19 patients from the United Kingdom [58].

(a)

MHC I

01 : 01
02 : 01
02 : 02
02 : 03
02 : 04
02 : 05
02 : 06
02 : 07
02 : 17
03 : 01
11 : 01
23 : 01
24 : 02
24 : 07
24 : 10
24 : 23
25 : 01
26 : 01
29 : 01
29 : 02
30 : 01
30 : 02
31 : 01
32 : 01
33 : 01
33 : 02
33 : 03
34 : 01
66 : 01
68 : 01
68 : 02

07 : 02
07 : 05
08 : 01
14 : 02
15 : 01
15 : 02
15 : 03
15 : 21
18 : 01
27 : 02
27 : 05
35 : 01
35 : 02
35 : 03
35 : 05
35 : 30
37 : 01
38 : 01
39 : 01
39 : 02
40 : 01
44 : 02
44 : 03
46 : 01
51 : 01
51 : 02
51 : 03
52 : 01
53 : 01
54 : 01
56 : 01
56 : 07
57 : 01
57 : 03
58 : 01

01 : 02
02 : 01
03 : 01
03 : 03
03 : 04
04 : 01
05 : 01
06 : 02
07 : 01
07 : 02
08 : 01
12 : 02
12 : 03
14 : 02
14 : 03
15 : 02

(b)

MHC II

01 : 01
01 : 02
01 : 13
03 : 01
03 : 05
03 : 06
03 : 07
03 : 08
03 : 09
03 : 11
04 : 01
04 : 02
04 : 04
04 : 05
04 : 08
04 : 10
04 : 21
04 : 23
04 : 26
07 : 01
07 : 03
08 : 01
08 : 02
08 : 04
08 : 06
08 : 13
08 : 17
09 : 01
10 : 01
11 : 01
11 : 02
11 : 04
11 : 06
11 : 07
11 : 14
11 : 20
11 : 21
11 : 28
12 : 01
13 : 01
13 : 02
13 : 04
13 : 05
13 : 07
13 : 11
13 : 21
13 : 22
13 : 23
13 : 27
13 : 28
13 : 41
14 : 31
15 : 01
15 : 02
15 : 03
15 : 06
16 : 01
16 : 02

01 : 01
02 : 02
03 : 01

01 : 01
01 : 03

01 : 01
01 : 05

01 : 01

01 : 03
02 : 01
03 : 01

01 : 01
02 : 01
03 : 01
04 : 01
04 : 02
05 : 01
06 : 01
14 : 01

01 : 01
01 : 02
01 : 03
01 : 04
02 : 01
03 : 01
04 : 01
04 : 02
05 : 01
06 : 01

02 : 01
02 : 02
03 : 01
03 : 02
03 : 03
04 : 01
04 : 02
05 : 01
05 : 03
06 : 02
06 : 03

Laboratory HLA typing methods on blood samples from patients of different ethnic backgrounds who are admitted to the intensive care unit (ICU) are suggested; also, asymptomatic or recovered patients can be used as control groups. These methods, provided in large sample sizes, are useful for estimating either the HLA frequencies or resistance/severity of COVID-19 disease.

3.2. Strength and Limitation of Bioinformatics Tools

Bioinformatic analytic tools were created to assist the scientists in different biomedical issues, especially in the characterization of immune epitopes, MHC I/II allele prediction, and therapeutics and vaccine development. Many of these tools are freely available and contain an extensive collection of epitopes for infectious agents, cancers, autoimmune disorders, and also HLA alleles in understudied animals and humans with distinct ethnicity. Hence, epitope discovery is a significant part of HLA allele binding predictions. The most frequently used tool for the prediction of HLA alleles in conducted studies related to the design of SARS-CoV-2 vaccine was the Immune Epitope Database and Analysis Resource (IEDB) server, especially the NetMHC/NetMHCpan tools [59] (Supplementary Table 1). IEDB is one of the top datasets with a huge size of training data which contribute in measuring MHC binding affinities. In the past years, new or updated tools were added to this server. New tools for T cell epitopes and MHC binding prediction include MHC-NP, MHCII-NP, and CD4EpiScore, while updated versions of NetMHCpan, NetMHC, PickPocket, SMM, and NetMHCIIpan are available [60]. Each of these tools uses different algorithms to predict the binding of peptides to MHC molecules class I and II. The NetMHC tool that adopts an allele-specific approach can be used to make acceptable predictions of the affinity of peptides 8-mer to 11-mer long, for which there is no sufficient data on the affinity. Being based on ANNs, the NetMHC server predicts peptides’ binding to a large number of HLA alleles [61].

NetMHCpan generates quantitative predictions of the interactions between peptides and MHC class I, which covers human HLA-A, B, and nonhuman MHC alleles in a wide range of animals such as cow, mouse, and chimpanzee [62]. The tool applies both epitope sequences and MHC binding groove to train ANNs for the prediction of MHC molecules which have not been previously identified. This tool, as well as NetMHCIIpan, uses a pan-specific method to measure MHC molecules depending on training data which have close similarities to their neighbors that can lead to a biased measurement [63].

Compared with MHC I molecules, MHC II groove can bind to peptides with different lengths; hence, the prediction of MHC II epitopes is complicated due to the fact that it needs a highly specific match. Among MHC II epitope prediction datasets, such as SMM-align, ProPred, NetMHCIIpan, and RANKPEP, ProPred predicts MHC II epitopes based on a quantitative matrix and TEPITOPE methods with high accuracy of its position-specific scoring matrix (PSSM), which is trained by the data from empirical experiments [64]. The prediction of only HLA-DR epitopes is the major drawback of this database [65]. Another top tool is SMM-align that utilizes the IEBD server for MHC II binding predictions based on quantitative matrices [64]. SMM-align performs the prediction of peptide : MHC binding affinities based on pan-specific receptors even with very restricted binding data; also, this method contains the data related to the peptide flanking residues (PFRs) on either side of the binding core sequences, improving the stability of the binding as well as its prediction [66]. The RANKPEP tool, like NetMHCIIpan, can predict MHC II binding affinities of HLA-DR, DQ, and DP through the PSSM method based on peptide sequence alignments, but its sensitivity is lower than the two former databases [62, 65, 67].

Another tool applied in the SARS-CoV-2 bioinformatic studies was PickPocket that uses PSSM and pan-specific methods for binding prediction. PickPocket outperforms other tools in predicting the ligands binding distant alleles to MHC molecules like nonhuman alleles [63]; therefore, the tool can be used for more investigation in terms of either T cell responses or vaccine design in animals vulnerable to SARS-CoV-2.

Despite the advantage of these tools, they still have high numbers of false positive predictions. Also, some peptides which have high immunogenicity may not obtain a high score in analysis with the bioinformatics tools. Indeed, although all these bioinformatics tools facilitate epitope discovery, false negative and false positive predictions can also occur depending on the trained algorithms, specific alleles, and the affinity threshold used for peptide selection [68]. Moreover, these predicting tools differ in performance, so a combination of different tools or algorithms, such as CONSENSUS, can provide significant advances in peptide : MHC binding prediction.

Furthermore, the bioinformatic studies related to SARS-CoV-2 had the following limitations: (i)The affinity threshold is dependent on the HLA alleles; therefore, the selected score for binding affinity (IC50) cutoff may lead to an over-/underestimation of the number of HLA alleles. For example, the binding affinity threshold that should be used for HLA-A02 : 06 is 60 nm vs. 944 nm for HLA-B38 : 01 [55](ii)SARS-CoV-2 shares 76% of its amino acids with SARS-CoV [69]. In some of these studies, completely matched SARS-CoV peptides were often applied to predict the HLA restrictions of SARS-CoV-2 that leads to a biased selection of the peptides to be assayed(iii)Computing a peptide made up of a specific number of amino acids leads to other mer-peptides being missed(iv)There is a lack of tools for predicting the HLA alleles not belonging to any superfamily

Some ways exist to improve the tools’ potency for MHC molecule epitope predictions, like combining diverse algorithms and consensus approaches. Also, using docking tools for MHC : epitope bindings can improve the predictions. On the other hand, these tools not only contain a massive collection of epitopes but also contain MHC molecules which predominantly cover a high percentage of the total global human population; therefore, they can be used as a starting point for the development of universal vaccines against emerging pathogens like SARS-CoV-2. Finding proper peptides that can bind to MHC molecules and, consequently, stimulate T cells plays a part in the development of appropriate vaccines. Therefore, using computational studies in addition to experimental methods may assist epitope discovery.

3.3. Medical Preexisting Conditions and HLA Alleles

Regardless of bioinformatic HLA alleles against SARS-CoV-2-derived immunogenic peptides, we also reviewed the association of some underlying medical conditions and HLA alleles which results in a higher risk to get infected with COVID-19 or higher death rate of this disease.

3.3.1. Cardiovascular Disease (CVD)

CVD represents different subphenotypes from hypertension, coronary syndromes, congestive heart failure, cerebrovascular disease, peripheral arterial disease, thrombosis, and ischemic heart disease [4]. Some studies investigated the connection between HLA alleles and the severity of these chronic diseases. Zhu et al. reported the role of HLA-DRB104 in immunogenic mechanisms involved in essential hypertension [70]. Moreover, some HLA alleles have a predisposition toward coronary artery disease (Table 2), and other genotypes of HLA alleles such as HLA-DRB101 have a protective role against atherosclerosis [71, 72]. The fatality rate of COVID-19 in patients with CVD and hypertension was reported to be significantly high, at 10.5% and 6%, respectively [73]. Genetic markers (such as blood group, polymorphisms in the ACE2 gene, and probably HLA alleles), ethnicity, and comorbidities play key roles in the vulnerability to COVID-19 disease [21, 58, 7476]. ACE2, as the significant receptor of SARS-CoV-2, is highly expressed in the heart and renal endothelium surfaces and is mainly involved in the regulation of heart function [77]. The association of ACE2 single-nucleotide polymorphisms (SNPs) with hypertension vulnerability in different ethnicities is mentioned by Luo et al. [78]. Compared to Whites, Blacks (African Americans) and Asians are considered to be at higher risk for COVID-19 disease [21, 58], as it was observed that hypertension is more prevalent in Africans and African Americans (AAs) than other white European and US descents [79, 80]. Also, SNPs in loci near HLA-B and some other genes may contribute to the blood pressure among AAs [79] (Table 2).


Disease/disorderRef

Cardiovascular disease
(i) Congestive heart failure[12]
(ii) Cardiomyopathy[126]
(iii) Ischemic HF[13, 72, 127, 128]
(iv) Cardiovascular disease
(v) Coronary artery disease, , , ,
(vi) Hypertension[70, 129131]
Obese, , [117, 118]
Kawasaki disease (KD), [132134]
Diabetes mellitus
(i) Type 1, , , , [86, 135137]
(ii) Type 2, , , , , ,
Respiratory disease
(i) COPD[95, 96]
(ii) Asthma[102, 103]
Kidney disease
(i) Chronic kidney disease[138, 139]
(ii) End stage renal disease

indicates that only alleles which are likely markers of susceptibility for these chronic diseases are mentioned.
3.3.2. Diabetes Mellitus (DM)

Diabetes is a metabolic disorder often characterized by hyperglycemia [81]. DM is classified into two main groups: Type 1—a disorder with the autoimmune destruction of insulin-producing cells in the pancreas and Type 2—a complex metabolic disorder that accounts for a very high percentage of the population compared to Type 1 [82]. Patients with diabetes may be prone to many infections during their lifetime. It seems that factors such as genetic characteristics, weak innate immune systems, and changes in metabolism are attributed to the development of diabetes [83]. Interestingly, some races like Native Americans and AAs are more likely to develop diabetes [84]. According to GWASs, HLA-B loci are related to Type 2 diabetes (T2D) in AAs [85]. In addition to HLA-II, HLA-I affects susceptibility for diabetes independently [86] (Table 2). DM patients have accounted for a high fatality rate in COVID-19 infection [20]. The expression of ACE2 is upregulated in the early stages of diabetes while it decreases in later stages. Further, most people with diabetes have high blood pressure, and these comorbidities provide proper conditions for progressive and severe COVID-19 disease [87]. Also, SARS-CoV-2 infection causes changes in blood glucose amount that contributes to developing hypoglycemia and hyperglycemia in diabetic patients [88]. An increase in ALT, which happens in DM patients, may be used as a marker to diagnose the severity of the COVID-19 disease. Therefore, there is a possible link between ALT, DM, and SARS-CoV-2 infection [89, 90].

3.3.3. Chronic Obstructive Pulmonary Disease (COPD)

COPD is one of the leading causes of death in the world [91], with limited airflow and systemic inflammation [92]. The disease includes two categories, namely, chronic bronchitis and emphysema [93], and the symptoms are shortness of breath, sputum, and cough [94]. The connection between HLA and COPD is not yet clear, and few studies mentioned this issue (Table 2) [9597]. The exact mechanism through which COPD makes people more vulnerable to getting COVID-19 or developing a more severe disease was not completely understood [98]; nonetheless, ACE2 expression was enhanced in the lower respiratory tract of the COPD patients and smokers [99]. Also, the impaired renin-angiotensin-aldosterone system caused acute pulmonary hypertension and edema [98, 100]. Thus, these possible reasons make patients with COPD vulnerable to SARS-CoV-2 infection.

3.3.4. Asthma

Asthma is a complex bronchial disorder with three distinct features, including respiratory hypersensitivity, airway obstruction, and airway inflammation [101]. Unlike COPD, lung function is not lost in asthma, and its airflow obstruction is usually reversible [102]. Although the etiologies of asthma are various, the relations of HLA genes, especially MHC class II, are introduced as important candidates (reviewed in detail in [102] (Table 2). In companion with genetic variation, viral or bacterial infections, and environmental influences, ethnical diversity is implicated as having a crucial impact on asthma susceptibility [103, 104]. For example, SNPs within HLA-DQA1/HLA-DQB1 regions are associated with asthma susceptibility in non-Hispanic whites [103]. A delay or a deficiency of innate antiviral responses, like interferons, which is reported in individuals with uncontrolled asthma, has been noted to be a risk factor for a more severe course of COVID-19 disease [105, 106]. TMPRSS2, a transmembrane protease serine 2, is essential for SARS-CoV-2 cell entry through viral spike protein cleavage. Increased expression of TMPRSS2 in asthmatic patients may predispose them to COVID-19 [106, 107]. Further, in patients with asthma and who were confirmed to be positive after a COVID-19 test, being male and ethnic African American were affective factors for higher expression of ACE2 and TMPRSS2 [106].

3.3.5. Chronic Kidney Disease (CKD)

In chronic kidney disease, one of the most common diseases globally, the function of the kidneys becomes abnormal [108]. Preexisting medical conditions like hypertension, heart diseases, and especially diabetes are involved in expanding CKD [109]. Robson et al. reviewed the link between HLA and different types of kidney diseases in detail, either the autoimmune disorders or diseases of native kidneys [110] (Table 2). Based on a meta-analysis study, CKD increases the mortality risk for COVID-19 disease [111]. Although a cytokine storm is rare in patients with end-stage kidney disease, they have less ability to fight with SARS-CoV-2 infection and show high fatality due to impaired immune responses. Further, they are more prone to respiratory infections [112, 113]. The viral injury of kidneys is possible through a high level of ACE2 expression in renal tubular cells [114]. On the other hand, it has been reported that the elevation of serum creatine kinase in COVID-19 patients with kidney involvement [115] resulted in high levels of creatine kinase that led to acute renal failure [116].

3.3.6. Obesity

Obesity (high ) is a heritable risk factor for several chronic diseases like cardiovascular disease, hypertension, and diabetes mellitus [117]. There is a correlation between obesity and HLA genotypes in relation to the risk of multiple sclerosis, latent autoimmune diabetes in adults, and T2D [118, 119] (Table 2). Obesity may influence the immune responses to infections by impairing the balance between metabolic and immunity systems [120, 121]. Like influenza virus, SARS-CoV-2 can develop into a severe illness in obese patients [122]. In obese patients, not only the overexpression of ACE2 in adipose tissue but also the overexpression of CD147 (another proposed SARS-CoV-2 receptor that may be involved in systemic spread of the virus) in whole blood were implicated [107, 122]. Also, the low blood levels of 25-hydroxyvitamin D cause vitamin D insufficiency in individuals with high BMI [123]. According to these reasons, there is a predictable high mortality rate among SARS-CoV-2-infected obese patients.

3.3.7. Kawasaki Disease

Kawasaki disease (KD) or Kawasaki syndrome is a systemic vasculitis and serious complexity with an unknown cause that occurs mainly in boys and children under 5 years. On 14 May 2020, CDC released an advisory and warned healthcare providers about multisystem inflammatory syndrome (MIS-C) associated with COVID-19 [124]. MIS-C presents Kawasaki disease-like features. The HLA region, HLA-B, and HLA-C variants were one of the genes that were considered as KD susceptibility genes [125] (Table 2).

4. Conclusion

Cross-reactivity was observed between some SARS-CoV-2 antigens such as E protein and other common cold coronaviruses [28]. T cell epitope-based peptide vaccines even based on other coronavirus antigens may be candidates that can elicit the common HLA restriction in the worldwide population. The elderly or having preexisting medical conditions were identified as risk factors for COVID-19 disease; however, death in some youths and children without an underlying condition is currently an obscure question that is probably tied to immunity and genetic markers.

In addition, compared to Whites, Blacks (African Americans) and Asians were at higher risk for COVID-19 disease [21, 58]. In contrast, the mortality and infectious cases related to COVID-19 disease in Japan were reported to be lower than other countries in this pandemic due to either their culture or genetic markers [140]. Since ethnicity may be considered as a factor in COVID-19 disease, more investigation on HLA alleles, polymorphisms in the ACE2 gene, comparisons of ACE2 expression in the upper respiratory tract and other tissues which are attacked by the virus, and other genetic markers as GWASs were suggested.

In summary, access to the HLA-I and HLA-II alleles of CD8+ and CD4+ T cell responses to SARS-CoV-2 antigens, especially in certain clinical samples, is the main gap which may be used as a biomarker for prognosis of COVID-19 disease. It may also be used to help or protect the healthcare workers and to set up even better plans for vaccine development. To gain insight into the HLA alleles in COVID-19 patients, further experimental investigation is required.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Supplementary Materials

Supplementary Table 1. List of SARS-CoV-2 proteins binding to HLA restrictions, predicted T cell HLA alleles, and bioinformatics tools used in the reviewed studies. (Supplementary Materials)

References

  1. D. Wu, T. Wu, Q. Liu, and Z. Yang, “The SARS-CoV-2 outbreak: what we know,” International Journal of Infectious Diseases, vol. 94, pp. 44–48, 2020. View at: Publisher Site | Google Scholar
  2. K. G. Andersen, A. Rambaut, W. I. Lipkin, E. C. Holmes, and R. F. Garry, “The proximal origin of SARS-CoV-2,” Nature Medicine, vol. 26, no. 4, pp. 450–452, 2020. View at: Publisher Site | Google Scholar
  3. WHO, Director-General’s Opening Remarks at the Mission Briefing on COVID-19—12 March 2020, World Health Organization: World Health Organization, 2020.
  4. J. M. Anaya, R. Cervera, Y. Shoenfeld, R. A. Levy, and A. Rojas-Villarraga, Universidad Colegio Mayor de Nuestra Señora del Rosario. Escuela de Medicina y Ciencias de la Salud. Centro de Estudio de Enfermedades Autoinmunes C. Autoimmunity: From Bench to Bedside, El Rosario University Press, 2013.
  5. WHO, System NCfFotH. Nomenclature for Factors of the HLA System, 2019, http://hla.alleles.org/nomenclature/naming.html.
  6. D. Gfeller, P. Guillaume, J. Michaux et al., “The length distribution and multiple specificity of naturally presented HLA-I ligands,” The Journal of Immunology, vol. 201, no. 12, pp. 3705–3716, 2018. View at: Google Scholar
  7. A. Sanchez-Mazas, “A review of HLA allele and SNP associations with highly prevalent infectious diseases in human populations,” Swiss Medical Weekly, vol. 150, 2020. View at: Google Scholar
  8. C. A. Dendrou, J. Petersen, J. Rossjohn, and L. Fugger, “HLA variation and disease,” Nature Reviews Immunology, vol. 18, no. 5, pp. 325–339, 2018. View at: Publisher Site | Google Scholar
  9. D. Chowell, C. Krishna, F. Pierini et al., “Evolutionary divergence of HLA class I genotype impacts efficacy of cancer immunotherapy,” Nature Medicine, vol. 25, no. 11, pp. 1715–1720, 2019. View at: Publisher Site | Google Scholar
  10. A. Nguyen, J. K. David, S. K. Maden et al., Human leukocyte antigen susceptibility map for SARS-CoV-2, medRxiv, 2020.
  11. Y. Shi, Y. Wang, C. Shao et al., “COVID-19 infection: the perspectives on immune responses,” Cell Death & Differentiation, vol. 27, no. 5, pp. 1451–1454, 2020. View at: Publisher Site | Google Scholar
  12. S. Roura, F. Rudilla, P. Gastelurrutia et al., “Determination of HLA-A, -B, -C, -DRB1 and -DQB1 allele and haplotype frequencies in heart failure patients,” ESC Heart Failure, vol. 6, no. 2, pp. 388–395, 2019. View at: Publisher Site | Google Scholar
  13. on behalf of the FinnDiane Study Group, J. Söderlund, C. Forsblom et al., “HLA class II is a factor in cardiovascular morbidity and mortality rates in patients with type 1 diabetes,” Diabetologia, vol. 55, no. 11, pp. 2963–2969, 2012. View at: Publisher Site | Google Scholar
  14. S. F. Wang, K. H. Chen, M. Chen et al., “Human-leukocyte antigen class I Cw 1502 and class II DR 0301 genotypes are associated with resistance to severe acute respiratory syndrome (SARS) infection,” Viral Immunology, vol. 24, no. 5, pp. 421–426, 2011. View at: Publisher Site | Google Scholar
  15. N. Keicho, S. Itoyama, K. Kashiwase et al., “Association of human leukocyte antigen class II alleles with severe acute respiratory syndrome in the Vietnamese population,” Human Immunology, vol. 70, no. 7, pp. 527–531, 2009. View at: Publisher Site | Google Scholar
  16. J. Seow, C. Graham, B. Merrick et al., Longitudinal evaluation and decline of antibody responses in SARS-CoV-2 infection, medRxiv, 2020.
  17. A. Grifoni, D. Weiskopf, S. I. Ramirez et al., “Targets of T cell responses to SARS-CoV-2 coronavirus in humans with COVID-19 disease and unexposed individuals,” Cell, vol. 181, no. 7, pp. 1489–1501.e15, 2020. View at: Publisher Site | Google Scholar
  18. M. R. Mehra, S. S. Desai, S. Kuy, T. D. Henry, and A. N. Patel, “Retraction: Cardiovascular disease, drug therapy, and mortality in Covid-19. N Engl J Med. DOI: 10.1056/NEJMoa2007621,” New England Journal of Medicine, vol. 382, no. 26, p. 2582, 2020, Retraction in: N Engl J Med. 2020 Jun 18;382(25):e102. View at: Publisher Site | Google Scholar
  19. W. Tan and J. Aboulhosn, “The cardiovascular burden of coronavirus disease 2019 (COVID-19) with a focus on congenital heart disease,” International Journal of Cardiology, vol. 309, pp. 70–77, 2020. View at: Publisher Site | Google Scholar
  20. C. Cristelo, C. Azevedo, J. M. Marques, R. Nunes, and B. Sarmento, “SARS-CoV-2 and diabetes: new challenges for the disease,” Diabetes Research and Clinical Practice, vol. 164, p. 108228, 2020. View at: Publisher Site | Google Scholar
  21. C. W. Yancy, “COVID-19 and African Americans,” Journal of the American Medical Association, vol. 323, no. 19, pp. 1891-1892, 2020. View at: Publisher Site | Google Scholar
  22. S. de Lusignan, J. Dorward, A. Correa et al., “Risk factors for SARS-CoV-2 among patients in the Oxford Royal College of General Practitioners Research and Surveillance Centre primary care network: a cross-sectional study,” The Lancet Infectious Diseases, vol. 20, no. 9, pp. 1034–1042, 2020. View at: Publisher Site | Google Scholar
  23. World Health Organization, Multisystem Inflammatory Syndrome in Children and Adolescents Temporally Related to COVID-19, World Health Organization, 2020.
  24. M. Bhattacharya, A. R. Sharma, P. Patra et al., “Development of epitope-based peptide vaccine against novel coronavirus 2019 (SARS-COV-2): immunoinformatics approach,” Journal of Medical Virology, vol. 92, no. 6, pp. 618–631, 2020. View at: Publisher Site | Google Scholar
  25. M. Prachar, S. Justesen, D. B. Steen-Jensen et al., COVID-19 vaccine candidates: prediction and validation of 174 SARS-CoV-2 epitopes, bioRxiv, 2020.
  26. S. Nerli and N. G. Sgourakis, Structure-based modeling of SARS-CoV-2 peptide/HLA-A02 antigens, bioRxiv, 2020.
  27. C. Hyun-Jung Lee and H. Koohy, “In silico identification of vaccine targets for 2019-nCoV,” F1000Research, vol. 9, 2020. View at: Publisher Site | Google Scholar
  28. M. I. Abdelmageed, A. H. Abdelmoneim, M. I. Mustafa et al., “Design of a multiepitope-based peptide vaccine against the E protein of human COVID-19: an immunoinformatics approach,” BioMed Research International, vol. 2020, Article ID 2683286, 12 pages, 2020. View at: Publisher Site | Google Scholar
  29. B. Sarkar, M. A. Ullah, F. T. Johora, M. A. Taniya, and Y. Araf, The essential facts of Wuhan novel coronavirus outbreak in China and epitope-based vaccine designing against 2019-nCoV, bioRxiv, 2020.
  30. A. Grifoni, J. Sidney, Y. Zhang, R. H. Scheuermann, B. Peters, and A. Sette, “A sequence homology and bioinformatic approach can predict candidate targets for immune responses to SARS-CoV-2,” Cell Host & Microbe, vol. 27, no. 4, pp. 671–680.e2, 2020. View at: Publisher Site | Google Scholar
  31. M. Enayatkhani, M. Hasaniazad, S. Faezi et al., “Reverse vaccinology approach to design a novel multi-epitope vaccine candidate against COVID-19: an in silico study,” Journal of Biomolecular Structure and Dynamics, vol. 39, no. 8, pp. 2857–2872, 2020. View at: Google Scholar
  32. S. F. Ahmed, A. A. Quadeer, and M. R. McKay, “Preliminary identification of potential vaccine targets for the COVID-19 coronavirus (SARS-CoV-2) based on SARS-CoV immunological studies,” Viruses, vol. 12, no. 3, p. 254, 2020. View at: Publisher Site | Google Scholar
  33. V. Baruah and S. Bose, “Immunoinformatics-aided identification of T cell and B cell epitopes in the surface glycoprotein of 2019-nCoV,” Journal of Medical Virology, vol. 92, no. 5, pp. 495–500, 2020. View at: Publisher Site | Google Scholar
  34. P. Yadav, V. Potdar, M. Choudhary et al., “Full-genome sequences of the first two SARS-CoV-2 viruses from India,” Indian Journal of Medical Research, vol. 151, 2020. View at: Google Scholar
  35. K. Kiyotani, Y. Toyoshima, K. Nemoto, and Y. Nakamura, “Bioinformatic prediction of potential T cell epitopes for SARS-Cov-2,” Journal of Human Genetics, vol. 65, no. 7, pp. 569–575, 2020. View at: Publisher Site | Google Scholar
  36. W. Chour, A. M. Xu, A. H. C. Ng et al., Shared antigen-specific CD8+ T cell responses against the SARS-COV-2 spike protein in HLA A02:01 COVID-19 participants, medRxiv, 2020.
  37. P. Kalita, A. K. Padhi, K. Y. J. Zhang, and T. Tripathi, “Design of a peptide-based subunit vaccine against novel coronavirus SARS-CoV-2,” Microbial Pathogenesis, vol. 145, p. 104236, 2020. View at: Publisher Site | Google Scholar
  38. H. Chen, L. Tang, X. Yu, J. Zhou, Y. Chang, and X. Wu, “Bioinformatics analysis of epitope-based vaccine design against the novel SARS-CoV-2,” Infectious Diseases of Poverty, vol. 9, no. 1, p. 88, 2020. View at: Publisher Site | Google Scholar
  39. M. T. Qamar, F. Shahid, U. A. Ashfaq et al., Structural modeling and conserved epitopes prediction against SARS-COV-2 structural proteins for vaccine development, Research Square, 2020.
  40. H. K. Manikyam and S. K. Joshi, Computational methods to develop potential neutralizing antibody Fab region against SARS-CoV-2 as therapeutic and diagnostic tool, bioRxiv, 2020.
  41. J. P. Romero-López, M. Carnalla-Cortés, D. L. Pacheco-Olvera et al., Prediction of SARS-CoeV2 spike protein epitopes reveals HLA-associated susceptibility, Research Square, 2020.
  42. B. Bravi, J. Tubiana, S. Cocco, R. Monasson, T. Mora, and A. M. Walczak, Flexible machine learning prediction of antigen presentation for rare and common HLA-I alleles, bioRxiv, 2020.
  43. M. Seema, Designing of cytotoxic and helper T cell epitope map provides insights into the highly contagious nature of the pandemic novel coronavirus SARS-CoV2, ChemRxiv, 2020. View at: Publisher Site
  44. K. Rawal, B. Abbasi, T. Sharma, R. Sinha, and S. Singh, Identification of vaccine targets & design of vaccine against SARS-CoV-2 coronavirus using computational and deep learning based approaches, OSF Preprints, 2020.
  45. R. Jakhar, S. Kaushik, and S. K. Gakhar, “3CL hydrolase-based multiepitope peptide vaccine against SARS-CoV-2 using immunoinformatics,” Journal of Medical Virology, vol. 92, no. 10, pp. 2114–2123, 2020. View at: Publisher Site | Google Scholar
  46. Y. Feng, M. Qiu, S. Zou et al., Multi-epitope vaccine design using an immunoinformatics approach for 2019 novel coronavirus in China (SARS-CoV-2), bioRxiv, 2020.
  47. B. Malone, B. Simovski, C. Moliné et al., Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2: toward universal blueprints for vaccine designs, bioRxiv, 2020.
  48. Y. Vashi, V. Jagrit, and S. Kumar, Understanding the B and T cells epitopes of spike protein of severe respiratory syndrome coronavirus-2: a computational way to predict the immunogens, bioRxiv, 2020.
  49. N. Jain, U. Shankar, P. Majee, and A. Kumar, Scrutinizing the SARS-CoV-2 protein information for designing an effective vaccine encompassing both the T-cell and B-cell epitopes, bioRxiv, 2020.
  50. M. Gustiananda, What do T cells see in SARS-CoV2? Immunoinformatics analysis to identify T cell epitopes from SARS-CoV2 ORF1ab polyprotein, 2020.
  51. A. Banerjee, D. Santra, and S. Maiti, Energetics based epitope screening in SARS CoV-2 (COVID 19) spike glycoprotein by immuno-informatic analysis aiming to a suitable vaccine development, bioRxiv, 2020.
  52. L. Li, T. Sun, Y. He, W. Li, Y. Fan, and J. Zhang, Epitope-based peptide vaccine design and target site characterization against novel coronavirus disease caused by SARS-CoV-2, bioRxiv, 2020.
  53. A. Khan, A. Alam, N. Imam, M. F. Siddiqui, and R. Ishrat, Design of an epitope-based peptide vaccine against the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2): a vaccine informatics approach, bioRxiv, 2020.
  54. A. Joshi, B. C. Joshi, M. A.-U. Mannan, and V. Kaushik, “Epitope based vaccine prediction for SARS-COV-2 by deploying immuno- informatics approach,” Informatics in Medicine Unlocked, vol. 19, p. 100338, 2020. View at: Publisher Site | Google Scholar
  55. W. Fleri, “Selecting thresholds (cut-offs) for MHC class I and II binding predictions,” https://help.iedb.org/hc/en-us/articles/114094151811-Selecting-thresholds-cut-offs-for-MHC-class-I-and-II-binding-predictions. View at: Google Scholar
  56. M. Lin, H.-K. Tseng, J. A. Trejaut et al., “Association of HLA class I with severe acute respiratory syndrome coronavirus infection,” BMC Medical Genetics, vol. 4, no. 1, p. 9, 2003. View at: Publisher Site | Google Scholar
  57. R. L. Warren and I. Birol, HLA predictions from the bronchoalveolar lavage fluid samples of five patients at the early stage of the Wuhan seafood market COVID-19 outbreak, arxiv e-prints, 2020.
  58. D. A. Kolin, S. Kulm, and O. Elemento, Clinical and genetic characteristics of Covid-19 patients from UK Biobank, medrxiv, 2020.
  59. R. Vita, S. Mahajan, J. A. Overton et al., “The immune epitope database (IEDB): 2018 update,” Nucleic Acids Research, vol. 47, no. D1, pp. D339–D343, 2019. View at: Publisher Site | Google Scholar
  60. S. K. Dhanda, S. Mahajan, S. Paul et al., “IEDB-AR: immune epitope database—analysis resource in 2019,” Nucleic Acids Research, vol. 47, no. W1, pp. W502–W506, 2019. View at: Publisher Site | Google Scholar
  61. C. Lundegaard, K. Lamberth, M. Harndahl, S. Buus, O. Lund, and M. Nielsen, “NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11,” Nucleic acids research, vol. 36, suppl_2, pp. W509–W512, 2008. View at: Publisher Site | Google Scholar
  62. R. E. Soria-Guerra, R. Nieto-Gomez, D. O. Govea-Alonso, and S. Rosales-Mendoza, “An overview of bioinformatics tools for epitope prediction: implications on vaccine development,” Journal of Biomedical Informatics, vol. 53, pp. 405–414, 2015. View at: Publisher Site | Google Scholar
  63. H. Zhang, O. Lund, and M. Nielsen, “The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding,” Bioinformatics, vol. 25, no. 10, pp. 1293–1299, 2009. View at: Publisher Site | Google Scholar
  64. P. Wang, J. Sidney, C. Dow, B. Mothé, A. Sette, and B. Peters, “A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach,” PLoS Computational Biology, vol. 4, no. 4, article e1000048, p. e1000048, 2008. View at: Publisher Site | Google Scholar
  65. A. Zawawi, R. Forman, H. Smith et al., “In silico design of a T-cell epitope vaccine candidate for parasitic helminth infection,” PLoS Pathogens, vol. 16, no. 3, article e1008243, 2020. View at: Publisher Site | Google Scholar
  66. M. Nielsen, C. Lundegaard, and O. Lund, “Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method,” BMC bioinformatics, vol. 8, no. 1, p. 238, 2007. View at: Publisher Site | Google Scholar
  67. L. Backert and O. Kohlbacher, “Immunoinformatics and epitope prediction in the age of genomic medicine,” Genome Medicine, vol. 7, no. 1, p. 119, 2015. View at: Publisher Site | Google Scholar
  68. F. A. Chaves, A. H. Lee, J. L. Nayak, K. A. Richards, and A. J. Sant, “The utility and limitations of current web-available algorithms to predict peptides recognized by CD4 T cells in response to pathogen infection,” The Journal of Immunology, vol. 188, no. 9, pp. 4235–4248, 2012. View at: Publisher Site | Google Scholar
  69. A. Poran, D. Harjanto, M. Malloy, M. S. Rooney, L. Srinivasan, and R. B. Gaynor, Sequence-based prediction of vaccine targets for inducing T cell responses to SARS-CoV-2 utilizing the bioinformatics predictor RECON, bioRxiv, 2020.
  70. F. Zhu, Y. Sun, M. Wang et al., “Correlation between HLA-DRB1, HLA-DQB1 polymorphism and autoantibodies against angiotensin AT(1) receptors in Chinese patients with essential hypertension,” Clinical Cardiology, vol. 34, no. 5, pp. 302–308, 2011. View at: Publisher Site | Google Scholar
  71. H. Golmoghaddam, S. Farjadian, S. Khosropanah, P. Dehghani, and M. Doroudchi, “Lower frequency of HLA-DRB101 in southwestern Iranian patients with atherosclerosis,” Iranian Journal of Immunology, vol. 15, no. 3, pp. 197–206, 2018. View at: Publisher Site | Google Scholar
  72. R. W. Davies, G. A. Wells, A. F. Stewart et al., “A genome-wide association study for coronary artery disease identifies a novel susceptibility locus in the major histocompatibility complex,” Circulation Cardiovascular Genetics, vol. 5, no. 2, pp. 217–225, 2012. View at: Publisher Site | Google Scholar
  73. Z. Wu and J. M. McGoogan, “Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China,” Journal of the American Medical Association, vol. 323, no. 13, pp. 1239–1242, 2020. View at: Publisher Site | Google Scholar
  74. D. Ellinghaus, F. Degenhardt, L. Bujanda, M. Buti, A. Albillos, and P. Invernizzi, “Genomewide association study of severe Covid-19 with respiratory failure,” New England Journal of Medicine, vol. 383, no. 16, 2020. View at: Google Scholar
  75. M. Bansal, “Cardiovascular disease and COVID-19,” Diabetes & metabolic syndrome, vol. 14, no. 3, pp. 247–250, 2020. View at: Publisher Site | Google Scholar
  76. C. A. Devaux, J.-M. Rolain, and D. Raoult, “ACE2 receptor polymorphism: susceptibility to SARS-CoV-2, hypertension, multi-organ failure, and COVID-19 disease outcome,” Journal of Microbiology, Immunology and Infection, vol. 53, no. 3, pp. 425–435, 2020. View at: Publisher Site | Google Scholar
  77. G. Y. Oudit, M. A. Crackower, P. H. Backx, and J. M. Penninger, “The role of ACE2 in cardiovascular physiology,” Trends in Cardiovascular Medicine, vol. 13, no. 3, pp. 93–101, 2003. View at: Publisher Site | Google Scholar
  78. Y. Luo, C. Liu, T. Guan et al., “Association of ACE2 genetic polymorphisms with hypertension-related target organ damages in south Xinjiang,” Hypertension Research, vol. 42, no. 5, pp. 681–689, 2019. View at: Publisher Site | Google Scholar
  79. E. R. Fox, J. H. Young, Y. Li et al. et al., “Association of genetic variation with systolic and diastolic blood pressure among African Americans: the Candidate Gene Association Resource study,” Human Molecular Genetics, vol. 20, no. 11, pp. 2273–2284, 2011. View at: Publisher Site | Google Scholar
  80. D. Cohall, N. Ojeh, C. M. Ferrario, O. P. Adams, and M. Nunez-Smith, “Is hypertension in African-descent populations contributed to by an imbalance in the activities of the ACE2/Ang-(1-7)/Mas and the ACE/Ang II/AT1axes?” Journal of the Renin-Angiotensin-Aldosterone System : JRAAS, vol. 21, no. 1, article 147032032090818, 2020. View at: Publisher Site | Google Scholar
  81. American Diabetes Association, “Diagnosis and classification of diabetes mellitus,” Diabetes Care, vol. 37, Supplement_1, pp. S81–S90, 2014. View at: Publisher Site | Google Scholar
  82. P. Zimmet, K. Alberti, and J. Shaw, “Global and societal implications of the diabetes epidemic,” Nature, vol. 414, no. 6865, pp. 782–787, 2001. View at: Publisher Site | Google Scholar
  83. A. Lecube, G. Pachón, J. Petriz, C. Hernández, and R. Simó, “Phagocytic activity is impaired in type 2 diabetes mellitus and increases after metabolic improvement,” PLoS One, vol. 6, no. 8, article e23366, 2011. View at: Publisher Site | Google Scholar
  84. A. G. Obukhov, B. R. Stevens, R. Prasad et al., “SARS-CoV-2 infections and ACE2: clinical outcomes linked with increased morbidity and mortality in individuals with diabetes,” Diabetes, vol. 69, no. 9, pp. 1875–1886, 2020. View at: Publisher Site | Google Scholar
  85. M. C. Ng, D. Shriner, B. H. Chen et al., “Meta-analysis of genome-wide association studies in African Americans provides insights into the genetic architecture of type 2 diabetes,” PLoS Genetics, vol. 10, no. 8, article e1004517, 2014. View at: Publisher Site | Google Scholar
  86. C. L. Roark, K. M. Anderson, L. J. Simon, R. P. Schuyler, M. T. Aubrey, and B. M. Freed, “Multiple HLA epitopes contribute to type 1 diabetes susceptibility,” Diabetes, vol. 63, no. 1, pp. 323–331, 2014. View at: Publisher Site | Google Scholar
  87. M. Marhl, V. Grubelnik, M. Magdič, and R. Markovič, “Diabetes and metabolic syndrome as risk factors for COVID-19,” Diabetes & Metabolic Syndrome: Clinical Research & Reviews, vol. 14, no. 4, pp. 671–677, 2020. View at: Publisher Site | Google Scholar
  88. A. Hussain, B. Bhowmik, and N. C. do Vale Moreira, “COVID-19 and diabetes: knowledge in progress,” Diabetes Research and Clinical Practice, vol. 162, no. article 108142, 2020. View at: Google Scholar
  89. X. Jiang, M. Coffee, A. Bari et al., “Towards an artificial intelligence framework for data-driven prediction of coronavirus clinical severity,” CMC: Computers, Materials & Continua, vol. 63, pp. 537–551, 2020. View at: Google Scholar
  90. N. Sattar, O. Scherbakova, I. Ford et al., “Elevated alanine aminotransferase predicts new-onset type 2 diabetes independently of classical risk factors, metabolic syndrome, and C-reactive protein in the west of Scotland coronary prevention study,” Diabetes, vol. 53, no. 11, pp. 2855–2860, 2004. View at: Publisher Site | Google Scholar
  91. A. Lopez, K. Shibuya, C. Rao, C. Mathers, A. Hansell, and L. Held, “Chronic obstructive pulmonary disease: current burden and future projections,” European Respiratory Journal, vol. 27, no. 2, pp. 397–412, 2006. View at: Publisher Site | Google Scholar
  92. R. A. Pauwels and K. F. Rabe, “Burden and clinical features of chronic obstructive pulmonary disease (COPD),” The Lancet, vol. 364, no. 9434, pp. 613–620, 2004. View at: Publisher Site | Google Scholar
  93. N. Siafakas, P. Vermeire, N. B. Pride et al., “Optimal assessment and management of chronic obstructive pulmonary disease (COPD),” European Respiratory Journal, vol. 8, no. 8, pp. 1398–1420, 1995. View at: Publisher Site | Google Scholar
  94. E. Braunwald, A. S. Fauci, D. L. Kasper, S. L. Hauser, D. L. Longo, and J. L. Jameson, Harrison’s Principles of Internal Medicine, McGraw Hill, 2001.
  95. N. Maranetra, D. Chandanayingyong, and S. Bovornkitti, “HLA antigen and ventilatory drive in Thais with chronic obstructive pulmonary disease,” Asian Pacific Journal of Allergy and Immunology, vol. 8, no. 2, pp. 137–140, 1990. View at: Google Scholar
  96. R. Faner, B. Nuñez, J. Sauleda et al., “HLA distribution in COPD patients,” COPD: Journal of Chronic Obstructive Pulmonary Disease, vol. 10, no. 2, pp. 138–146, 2013. View at: Publisher Site | Google Scholar
  97. S. G. Pillai, D. Ge, G. Zhu et al., “A genome-wide association study in chronic obstructive pulmonary disease (COPD): identification of two major susceptibility loci,” PLoS Genetics, vol. 5, no. 3, article e1000421, 2009. View at: Publisher Site | Google Scholar
  98. D. D. Sin, “COVID-19 in COPD: a growing concern,” EClinicalMedicine, vol. 26, p. 100546, 2020. View at: Publisher Site | Google Scholar
  99. J. M. Leung, C. X. Yang, A. Tam et al., “ACE-2 expression in the small airway epithelia of smokers and COPD patients: implications for COVID-19,” European Respiratory Journal, vol. 55, no. 5, p. 2000688, 2020. View at: Publisher Site | Google Scholar
  100. Ü. Toru, C. Ayada, O. Genç, S. Sahin, Ö. Arik, and I. Bulut, Serum levels of RAAS components in COPD, European Respiratory Society, 2015.
  101. Bethesda (MD): National Heart L, and Blood Institute (US), “National Asthma Education and Prevention Program, Third Expert Panel on the Diagnosis and Management of Asthma, Expert Panel Report 3: Guidelines for the Diagnosis and Management of Asthma,” Section 2, Definition, Pathophysiology and Pathogenesis of Asthma, and Natural History of Asthma, 2007, https://www.ncbi.nlm.nih.gov/books/NBK7223/2007. View at: Google Scholar
  102. E. Kontakioti, K. Domvri, D. Papakosta, and M. Daniilidis, “HLA and asthma phenotypes/endotypes: a review,” Human Immunology, vol. 75, no. 8, pp. 930–939, 2014. View at: Publisher Site | Google Scholar
  103. A. Dahlin, J. E. Sordillo, J. Ziniti et al., “Large-scale, multiethnic genome-wide association study identifies novel loci contributing to asthma susceptibility in adults,” Journal of Allergy and Clinical Immunology, vol. 143, no. 4, pp. 1633–1635, 2019. View at: Publisher Site | Google Scholar
  104. S. Y. Yeh and R. Schwartzstein, “Asthma: pathophysiology and diagnosis,” in Asthma, Health and Society: A Public Health Perspective, A. Harver and H. Kotses, Eds., pp. 19–42, Springer US, Boston, MA, 2010. View at: Publisher Site | Google Scholar
  105. S. L. Johnston, “Asthma and COVID-19: is asthma a risk factor for severe outcomes?” Allergy, vol. 75, no. 7, pp. 1543–1545, 2020. View at: Publisher Site | Google Scholar
  106. M. C. Peters, S. Sajuthi, P. Deford et al., “COVID-19-related genes in sputum cells in asthma. Relationship to demographic features and corticosteroids,” American Journal of Respiratory and Critical Care Medicine, vol. 202, no. 1, pp. 83–90, 2020. View at: Publisher Site | Google Scholar
  107. U. Radzikowska, M. Ding, G. Tan et al., “Distribution of ACE2, CD147, CD26, and other SARS-CoV-2 associated molecules in tissues and immune cells in health and in asthma, COPD, obesity, hypertension, and COVID-19 risk factors,” Allergy, vol. 75, no. 11, pp. 2829–2845, 2020. View at: Publisher Site | Google Scholar
  108. J. Coresh, E. Selvin, L. A. Stevens et al., “Prevalence of chronic kidney disease in the United States,” Journal of the American Medical Association, vol. 298, no. 17, pp. 2038–2047, 2007. View at: Publisher Site | Google Scholar
  109. C. A. Johnson, A. S. Levey, J. Coresh, A. Levin, J. Lau, and G. Eknoyan, “Clinical practice guidelines for chronic kidney disease in adults: part I. definition, disease stages, evaluation, treatment, and risk factors,” American Family Physician, vol. 70, no. 5, pp. 869–876, 2004. View at: Google Scholar
  110. K. J. Robson, J. D. Ooi, S. R. Holdsworth, J. Rossjohn, and A. R. Kitching, “HLA and kidney disease: from associations to mechanisms,” Nature Reviews Nephrology, vol. 14, no. 10, pp. 636–655, 2018. View at: Publisher Site | Google Scholar
  111. B. M. Henry and G. Lippi, “Chronic kidney disease is associated with severe coronavirus disease 2019 (COVID-19) infection,” International Urology and Nephrology, vol. 52, no. 6, pp. 1193-1194, 2020. View at: Publisher Site | Google Scholar
  112. J. H. Ng, J. S. Hirsch, R. Wanchoo et al., “Outcomes of patients with end-stage kidney disease hospitalized with COVID-19,” Kidney International, vol. 98, no. 6, pp. 1530–1539, 2020. View at: Publisher Site | Google Scholar
  113. S. Carriazo, M. Kanbay, and A. Ortiz, Kidney Disease and Electrolytes in COVID-19: More than Meets the Eye, Oxford University Press, 2020.
  114. C. Fan, K. Li, Y. Ding, W. L. Lu, and J. Wang, “ACE2 expression in kidney and testis may cause kidney and testis damage after 2019-nCoV infection,” Tech. Rep., medRxiv, 2020. View at: Publisher Site | Google Scholar
  115. Y. Cheng, R. Luo, K. Wang et al., “Kidney disease is associated with in-hospital death of patients with COVID-19,” Kidney International, vol. 97, no. 5, pp. 829–838, 2020. View at: Publisher Site | Google Scholar
  116. P. Luckoor, M. Salehi, and A. Kunadu, “Exceptionally high creatine kinase (CK) levels in multicausal and complicated rhabdomyolysis: a case report,” The American Journal of Case Reports, vol. 18, pp. 746–749, 2017. View at: Publisher Site | Google Scholar
  117. J. Shen, T. Guo, T. Wang et al., “HLA-B07, HLA-DRB107, HLA-DRB112, and HLA-C03:02 strongly associate with BMI: data from 1.3 million healthy Chinese adults,” Diabetes, vol. 67, no. 5, pp. 861–871, 2018. View at: Publisher Site | Google Scholar
  118. A. K. Hedstrom, I. Lima Bomfim, L. Barcellos et al., “Interaction between adolescent obesity and HLA risk genes in the etiology of multiple sclerosis,” Neurology, vol. 82, no. 10, pp. 865–872, 2014. View at: Publisher Site | Google Scholar
  119. R. Hjort, J. E. Löfvenborg, E. Ahlqvist et al., “Interaction between overweight and genotypes of HLA, TCF7L2, and FTO in relation to the risk of latent autoimmune diabetes in adults and type 2 diabetes,” The Journal of Clinical Endocrinology & Metabolism, vol. 104, no. 10, pp. 4815–4826, 2019. View at: Publisher Site | Google Scholar
  120. S. A. Rojas-Osornio, T. R. Cruz-Hernández, M. E. Drago-Serrano, and R. Campos-Rodríguez, “Immunity to influenza: impact of obesity,” Obesity Research & Clinical Practice, vol. 13, no. 5, pp. 419–429, 2019. View at: Publisher Site | Google Scholar
  121. J. J. Milner and M. A. Beck, “The impact of obesity on the immune response to infection,” Proceedings of the Nutrition Society, vol. 71, no. 2, pp. 298–306, 2012. View at: Publisher Site | Google Scholar
  122. A. Hussain, K. Mahawar, Z. Xia, W. Yang, and S. El-Hasani, “Retracted: Obesity and mortality of COVID-19. Meta-analysis,” Obesity Research and Clinical Practice, vol. 14, no. 4, pp. 295–300, 2020. View at: Publisher Site | Google Scholar
  123. R. A. Marrie and C. A. Beck, “Obesity and HLA in multiple sclerosis: Weighty matters,” Neurology, vol. 82, no. 10, pp. 826-827, 2014. View at: Publisher Site | Google Scholar
  124. What is CDC is doing about multisystem inflammatory syndrome in children (MIS-C). CDC, May 2020, https://www.cdc.gov/mis-c/cdc-response/index.html.
  125. The Korean Kawasaki Disease Genetics Consortium, J. J. Kim, S. W. Yun et al., “A genome-wide association analysis identifies NMNAT2 and HCP5 as susceptibility loci for Kawasaki disease,” Journal of Human Genetics, vol. 62, no. 12, pp. 1023–1029, 2017. View at: Publisher Site | Google Scholar
  126. R. C. Williams, R. L. Hanson, D. J. Pettitt, M. L. Sievers, R. G. Nelson, and W. C. Knowler, “HLAA2 confers mortality risk for cardiovascular disease in Pimans,” Tissue Antigens, vol. 47, no. 3, pp. 188–193, 1996. View at: Publisher Site | Google Scholar
  127. A. Palikhe, J. Sinisalo, M. Seppänen, V. Valtonen, M. S. Nieminen, and M. L. Lokki, “Human MHC region harbors both susceptibility and protective haplotypes for coronary artery disease,” Tissue Antigens, vol. 69, no. 1, pp. 47–55, 2007. View at: Publisher Site | Google Scholar
  128. F. Takeuchi, M. Yokota, K. Yamamoto et al., “Genome-wide association study of coronary artery disease in the Japanese,” European Journal of Human Genetics : EJHG, vol. 20, no. 3, pp. 333–340, 2012. View at: Publisher Site | Google Scholar
  129. M. Luque Otero et al., “Severe hypertension in the Spanish population. Association with specific HLA antigens,” vol. 5, no. 6_part_3, p. V149, 1983. View at: Google Scholar
  130. I. K. Shkhvatsabaia, V. I. Rudnev, S. IuI, D. GIu, and S. G. Osipov, “Various immunological aspects of essential and symptomatic hypertension,” Biulleten'Vsesoiuznogo kardiologicheskogo nauchnogo tsentra AMN SSSR, vol. 11, no. 1, pp. 7–12, 1998. View at: Google Scholar
  131. Y. S. Titkov, R. A. Ziskina, A. A. Temirov, D. V. Gybladze, and L. N. Bubnova, “HLA antigens in borderline and essential hypertension,” American Journal of Hypertension, vol. 6, no. 10, pp. 885–887, 1993. View at: Publisher Site | Google Scholar
  132. J. H. Oh, J. W. Han, S. J. Lee et al., “Polymorphisms of human leukocyte antigen genes in Korean children with Kawasaki disease,” Pediatric Cardiology, vol. 29, no. 2, pp. 402–408, 2008. View at: Publisher Site | Google Scholar
  133. S. Shrestha, H. W. Wiener, B. Aissani, A. Shendre, J. Tang, and M. A. Portman, “Imputation of class I and II HLA loci using high-density SNPs from ImmunoChip and their associations with Kawasaki disease in family-based study,” International Journal of Immunogenetics, vol. 42, no. 3, pp. 140–146, 2015. View at: Publisher Site | Google Scholar
  134. C. Shimizu, J. Kim, H. Eleftherohorinou et al., “HLA-C variants associated with amino acid substitutions in the peptide binding groove influence susceptibility to Kawasaki disease,” Human Immunology, vol. 80, no. 9, pp. 731–738, 2019. View at: Publisher Site | Google Scholar
  135. Z.-J. Ma, P. Sun, G. Guo, R. Zhang, and L.-M. Chen, “Association of the HLA-DQA1 and HLA-DQB1 alleles in type 2 diabetes mellitus and diabetic nephropathy in the Han ethnicity of China,” Journal of Diabetes Research, vol. 2013, Article ID 452537, 5 pages, 2013. View at: Publisher Site | Google Scholar
  136. R. A. Scott, L. J. Scott, R. Mägi et al., “An expanded genome-wide association study of type 2 diabetes in Europeans,” Diabetes, vol. 66, no. 11, pp. 2888–2902, 2017. View at: Publisher Site | Google Scholar
  137. A. Sayeh, C. B. Cheikh, A. Mardessi et al., “HLA DRB1 03 as a possible common etiology of schizophrenia, Graves’ disease, and type 2 diabetes,” Annals of General Psychiatry, vol. 16, no. 1, pp. 1–4, 2017. View at: Google Scholar
  138. Y. M. Mosaad, M. Mansour, I. al-Muzairai et al., “Association between human leukocyte antigens (HLA-A, -B, and -DR) and end-stage renal disease in Kuwaiti patients awaiting transplantation,” Renal Failure, vol. 36, no. 8, pp. 1317–1321, 2014. View at: Publisher Site | Google Scholar
  139. B. He, G. Musk, Z. Q. Ng et al., “Outcomes of kidney transplantation by laparoscopic surgery versus open surgery,” Transplantation, vol. 102, Supplement 7, p. S528, 2018. View at: Publisher Site | Google Scholar
  140. A. Iwasaki and N. D. Grubaugh, “Why does Japan have so few cases of COVID-19?” EMBO Molecular Medicine, vol. 12, no. 5, p. e12481-e, 2020. View at: Publisher Site | Google Scholar

Copyright © 2021 Mina Mobini Kesheh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Related articles

No related content is available yet for this article.
 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder
Views685
Downloads755
Citations

Related articles

No related content is available yet for this article.

Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.