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Abstract
Purpose To evaluate the contributions of human leucocyte antigen (HLA) class I and II genes in the development of Graves’ ophthalmopathy (GO) in a Southern Chinese population.
Methods Eight HLA loci were genotyped and analysed in 272 unrelated patients with Graves’ disease (GD) or the proptosis and myogenic phenotypes of GO, and 411 ethnically matched control subjects.
Results The allele frequencies of HLA-DRB1*16:02 and -DQB1*05:02 in the GD, proptosis and myogenic groups, HLA-B*38:02 and -DQA1*01:02 in the myogenic group were significantly higher than those in the control group, respectively (all corrected p values <0.05, OR >2.5). The haplotype frequencies of HLA-DRB1*16:02-DQA1*01:02-DQB1*05:02 and HLA-DRB1*16:02-DQA1*01:02-DQB1*05:02-DPA1*02:02-DPB1*05:01 in the proptosis and myogenic groups, and HLA-A*02:03-B*38:02-C*07:02 and HLA-A*02:03-B*38:02-C*07:02-DRB1*16:02-DQA1*01:02-DQB1*05:02-DPA1*02:02-DPB1*05:01 in the myogenic group were significantly higher than those in the control group respectively (all corrected p values <0.05, OR >2.5). The potential epitopes (‘FLGIFNTGL’ of TSHR, ‘IRHSHALVS’, ‘ILYIRTNAS’ and ‘FVFARTMPA’ of IGF-1R) were fitted exactly in the peptide-binding groove between HLA-DRA1-DRB1*16:02 heterodimer, and the epitopes (‘ILEITDNPY’ of THSR, ‘NYALVIFEM’ and ‘NYSFYVLDN’ of IGF-1R) were also fitted exactly in the peptide-binding groove between HLA-DQA1*01:02-DQB1*05:02 heterodimer.
Conclusions The HLA-DRB1*16:02 and -DQB1*01:02 alleles might be risk factors for GD including the proptosis and myogenic phenotypes of GO. The alleles HLA-B*38:02, -DQA1*01:02, the HLA haplotypes consisting of HLA-B*38:02, -DRB1*16:02, -DQA1*01:02 and -DQB1*05:02 might be susceptibility risk factors for GO. Simultaneously, some epitopes of TSHR and IGF-1R tightly binding to groove of HLA-DRA1-DRB1*16:02 or HLA-DQA1*01:02-DQB1*05:02 heterodimers might provide some hints on presenting the pathological antigen in GO.
- Genetics
- Immunology
- Orbit
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INTRODUCTION
Graves’ ophthalmopathy (GO) is a common autoimmune inflammatory eye disease of adulthood associated with Graves’ disease (GD), whose predisposition is widely attributed to interplay of genetic and environmental factors.1 The common symptoms of GO are dry, photophobia, double vision and pressure sensation behind the eyes.1 The clinical phenotypes of GO are proptosis and restrictive extraocular myopathy.2 Over the years, genome-wide association studies have identified a growing list of credible candidate human leucocyte antigen (HLA) loci modestly associated with GD.3 4 The main function of HLA molecules is to present antigenic peptides to the immune system and thus regulate the immune response.5 The highly polymorphic HLA system is one of key immunogenetics associated with GO.6 The genetic variations of classical class I region (eg, HLA-B) and the class II region (eg, HLA-DRB1, -DQ1, and -DPB1) are risk or protective factors in some ethnic populations, resulting in differences with GO prevalence.6 In the Brazilian population, HLA-DRB1*16 was over-represented in myogenic subtype of GO patients, and HLA-DRB1*03 was over-represented in non-myogenic subtype of GO patients.7 In the Polish population, the HLA-DRB1*03 allele was found to be positively associated with GO.8 Previous studies have shown that low-resolution HLA genotypes and serological HLA antigens are associated with GO. In the Japanese population, the antigens (HLA-DR14 and -DQ1) might be the genetic markers of predisposition to GO.9 In the British population, the antigens (HLA-B8, -DQw 3.1 and -DPB 2.1/8) might confer protective effects in patients with GO.10 11 Associations of HLA-B8 and -DR3 genotypes with GO were also reported in the Hungarian population.12
Although the initiating trigger of GO is still unclear, the excessive inflammation responses in orbital soft tissue might be the key pathogenesis.1 13 Thyroid stimulating hormone receptor (TSHR) and type 1 insulin-like growth factor receptor (IGF-1R) might be two major antigens in orbital microenvironment with GO.14 15 The extracellular domain of TSHR (TSHR-ECD) peptides epitopes bind to the groove of HLA molecules, forming HLA-THSR-complexes, which are presented to CD4+ T cells in Graves’ disease (GD).13 15–18 Krieger et al have reported that bidirectional TSHR/IGF-1R cross-talk can mediate GO pathogenesis.19 Mahdavi et al found that some peptides of IGF-1R-ECD (86 and 249 epitopes) were the best HLA-binding peptide epitopes to both HLA class I and II molecules based on molecular docking analyses.20 As a result, activated orbital fibroblasts and infiltrating lymphocytes might contribute to the pathological phenotype of GO by producing glycosaminoglycan and differentiate into myofibroblasts or adipocytes.1 13
In the present study, we comprehensively conducted high-resolution genotyping for eight HLA loci including HLA-A, -B, -C, -DRB1, -DQA1, -DQB1, -DPA1 and -DPB1 in samples collected from 272 unrelated GD patient with and without clinical GO and 411 control subjects with high resolution at both allelic and haplotypic levels using PCR-sequence-based typing (SBT). We aimed to evaluate whether specific alleles, haplotypes and the dock condition of the THSR-ECD or IGF-1R-ECD segment in eight HLA loci contribute to the development of GO in Southern Chinese.
MATERIALS AND METHODS
Participants
A total of 272 unrelated patients with GD were recruited from the patients attending the Clinic of Shenzhen Eye Hospital, China between December 2018 and December 2019. Diagnosis of GD was based on the typical clinical features of hyperthyroidism, diffuse goitre, suppressed TSHR levels, detectable THSR autoantibodies and/or increased radioiodine uptake.3 8 9 The clinical GO was defined as class 3 or higher in the American Thyroid Association mnemonic NOSPECS scheme. The GO patients divided into proptosis (the proptosis ≥18 mm or the difference between two eyes ≥2 mm) and the myogenic (strabismus caused by extraocular-muscle dysfunction without orbital fat compartment enlargement) groups according to the presence of clinically evident ophthalmopathy, as previously described.2 8 GD patients without GO were selected from those who did not have any features of ophthalmopathy (including features of class 1–2). The control group consisted of 411 unrelated healthy volunteers randomly chosen from the Shenzhen Blood Centre, China. All patients and controls were Han Chinese from Southern China. The exclusion criteria were setting as follows: the heathy volunteer without autoimmunity systemic disease history including GD or GO, and the patient who was diagnosed as GD or GO without other systemic diseases were enrolled as the study cohort. Peripheral blood samples (with 5% EDTA as anti-coagulant) were collected from each subject and stored at −20°C. The study protocol was approved by the Ethics Committee of Shenzhen Eye Hospital and was in accordance with the tenets of the Declaration of Helsinki and its later amendments or comparable ethical standards. Written informed consent was obtained from all study participants.
Subgroups of participants
On the basis of the presence of clinically evident ophthalmopathy, four subgroups were considered: Group 1: GD without clinical eye phenotype group (no changes in ophthalmologic findings had been detected according to 3 years follow-up data); Group 2: the proptosis group (the proptosis ≥18 mm or the difference between two eyes ≥2 mm); Group 3: the myogenic group (strabismus caused by extraocular-muscle dysfunction without orbital fat compartment enlargement); Group 4: the control group.
DNA extraction and sequence-based typing for HLA genes
Blood DNAs were obtained from all 683 participants. The IPD-IMGT/HLA Database (http://www.ebi.ac.uk/ipd/imgt/hla/, Release 3.40.0, 2020 April) was applied for the genotyping of each locus. Genotyping for HLA-A, -B, -C, -DRB1 and -DQB1 was performed using the AlleleSEQR HLA SBT commercial kit (Atria Genetics, San Francisco) according to the manufacturer’s instructions. Exons 2~4 for HLA-A, -B, -C and exons 2, 3 for -DRB1 and -DQB1 were sequenced. HLA-DQA1 SBT covered exons 1~4 as described by Voorter et al. 21 HLA-DPA1 and -DPB1 SBT was performed according to the protocol developed by the 13th International Histocompatibility Workshop. PCR amplification was performed using a GeneAmp PCR system 9700 (Applied Biosystems, Foster City, CA, USA). PCR products were purified using ExoSAP-IT (Atria Genetics) and sequenced with the ABI PRISM BigDye Terminator Cycle Sequencing Ready Reaction kits (Applied Biosystems, Foster City, CA, USA) on an ABI 3730XL DNA sequencer (Applied Biosystems, Foster City, CA, USA). Four-digit HLA genotypes were assigned with the help of the ASSIGN 3.5 software (Conexio Genomics, Applecross, Australia). Ambiguous allele combinations from the SBT results were further examined using the HLA PCR-SSP kit (Olerup, Stockholm, Sweden).
HLA peptide-binding prediction and in silico docking
The sequences of TSHR and IGF-1R (NP_000360.1 and NP_000866.1) were submitted to the NetMHCIIpan 3.2 server for predicting major HLA-II peptide-binding affinities.22 HLA-DRA1-DRB1*16:02 and HLA-DQA1*01:02-DQB1*05:02 heterodimers were selected for predicting epitopes within the protein. Threshold for weak and strong binding peptides was set as 1% and 10% ranks, respectively. The computing docking of the TSHR-ECD and IGF-1R-ECD segment into HLA-DRA1-DRB1*16:02 and HLA-DQA1*01:02-DQB1*05:02 heterodimers in silico was performed using the AutoDock Vina software, as described by Shu et al. 23
Statistical analysis
Statistical analysis was performed using SPSS (version 20.0, SPSS Inc., Chicago, IL). Hardy-Weinberg equilibrium (HWE) was tested using the χ² test. Haplotype frequencies were estimated from the genotyping results by the Excoffier-Laval-Balding (ELB) algorithm using the Arlequin software package version 3.5 (Laurent Excoffier, CMPG, Zoological Institute, University of Bern, Switzerland).24 The difference in allele frequency and haplotype frequency was tested using the χ² test or Fisher’s exact test. Linkage disequilibrium (LD), defined as D’ and r2, was calculated between each intra-gene haplotype block as described by Lewontin et al. 25 Multiple testings were corrected using the Bonferroni method and the corrected p value was calculated by multiplying the p value with the number of tests performed. The corrected p values (Pc) <0.05 were considered statistically significant. OR and 95% CI were calculated whenever applicable.
RESULTS
Demographic and clinical features of participants
In the GD without clinical eye phenotype group, there were 17 (23.0%) males and 57 (77.0%) females (n=74), with an average age of 38.7 (±10.4) years. In the proptosis group (n=82), there were 32 (39.0%) males and 50 (61.0%) females, with an average age of 34.9 (±12.1) years. In the myogenic group (n=116), there were 59 (51.0%) males and 57 (49.1%) females, with an average age of 44.2 (±11.4) years. In the control group (n=411), there were 411 healthy adults in Southern Chinese, with an average age of 40.6 (±12.0) years. No significant difference in age was found between patients with GD and controls (39.9±12.0 vs. 40.6±12.0 years, p value = 0.414).
Allele frequencies of HLA-A, -B, -C, -DRB1, -DQA1, -DQB1 and -DPB1
The genotype distributions of HLA-A, -B, -C, -DRB1, -DQA1, -DQB1 and -DPB1 in the control group was in accordance with HWE (all p values >0.05) (data not shown). A total of 31 HLA-A alleles, 63 HLA-B alleles, 29 HLA-C alleles, 40 HLA-DRB1 alleles, 18 HLA-DQA1 alleles, 17 HLA-DQB1 alleles, 7 HLA-DPA1 alleles, and 24 HLA-DPB1 alleles were identified in all participants including patients and controls (data not shown). The allele frequencies of HLA-DRB1*16:02 and -DQB1*05:02 in GD patients without clinical eye phenotype, the proptosis and myogenic groups were significantly higher than those in the control group respectively, which survived the Bonferroni correction (all Pc <0.05, OR >2.5). The allele frequencies of HLA-B*38:02 and -DQA1*01:02 in the myogenic group were significantly higher than those in the control group, respectively, which survived the Bonferroni correction (8.6% vs 3.0%, Pc=0.01, OR=3.0; 32.8% vs 19.8%, Pc =5.9×10−4, OR=2.0) (table 1).
Allele and haplotype frequencies of HLA in cases and controls
Haplotype frequencies
We identified 404 HLA class I haplotypes (A-C-B), 93 HLA-DRB1-DQA1-DQB1 haplotypes, 44 HLA-DPA1-DPB1 haplotypes, 338 HLA class II haplotypes (HLA-DRB1-DQA1-DQB1-DPA1-DPB1) and 974 HLA eight loci haplotypes (HLA A−C-B-DRB1-DQA1-DQB1-DPA1-DPB1) in all participants including patients and controls (data not shown). The haplotype frequencies of HLA-DRB1*16:02-DQA1*01:02-DQB1*05:02 and HLA-DRB1*16:02-DQA1*01:02-DQB1*05:02-DPA1*02:02-DPB1*05:01 in the proptosis and myogenic groups were significantly higher than those in the control group, respectively, which survived the Bonferroni correction (all Pc <0.05, OR >4.0). The haplotype frequencies of HLA-A*02:03-B*38:02-C*07:02 and HLA-A*02:03-B*38:02-C*07:02-DRB1*16:02-DQA1*01:02-DQB1*05:02-DPA1*02:02-DPB1*05:01 in the myogenic group were significantly higher frequencies than those in the control group, respectively, which survived the Bonferroni correction (6.5 vs 2.7%, Pc=0.02, OR=4.3; 4.7 vs 0.4%, Pc=1.7×10−3, OR=13.6) (table 1).
Since the three disease-associated HLA class II alleles HLA-DRB1*16:02, HLA-DQA1*01:02 and HLA-DQB1*05:02 formed a haplotype, we further investigated the associations between eight combinations of HLA-DRB1*16:02, HLA-DQA1*01:02 and HLA-DQB1*05:02 with disease groups. As shown in table 2, HLA-DRB1*16:02+-DQA1*01:02+-DQB1*05:02+ was associated with the proptosis and myogenic groups, respectively, which survived the Bonferroni correction (all Pc <0.05, OR =3.96 and 4.83 respectively). HLA-DRB1*16:02–DQA1*01:02–DQB1*05:02- was a protective factor for the myogenic group, which survived the Bonferroni correction (Pc <0.05, OR =0.53). Our results suggested that HLA-DRB1*16:02, HLA-DQA1*01:02 and HLA-DQB1*05:02 were associated with the development of clinical GO.
Association of the DRB1*16:02-DQA1*01:02-DQB1*05:02 haplotype with GD with or without clinical eye disease
Comparation of OR and linkage disequilibrium
The OR of significant HLA allele and haplotype between the disease groups and control groups are shown in figure 1, respectively. The ORs of HLA-DRB1*16:02 and HLA-DQB1*05:02 alleles of ‘the myogenic group vs control group’ showed the top odd (OR: 4.8 and 14.5), followed by ‘the proptosis group vs control group’ (OR: 4.0 and 10.7) and ‘GD without clinical eye phenotype group vs control group’ (OR: 2.6 and 9.3), respectively. The ORs of HLA-DRB1*16:02-DQA1*01:02-DQB1*05:02 and HLA-DRB1*16:02-DQA1*01:02-DQB1*05:02-DPA1*02:02-DPB1*05:01 haplotypes of ‘the myogenic group vs control group’ (OR: 4.8 and 6.6) were higher than that in ‘the proptosis group vs control group’ (OR: 4.0 and 4.0).
Association analysis OR for significant HLA alleles and haplotypes. The magnitude of OR is indicated by height of column. The OR of ‘GD without clinical eye phenotype group vs Controls’, ‘Proptosis group vs Controls’, and ‘Myogenic group vs Controls’ are shown in pink, blue and green columns, respectively. The grey column represents no significance between two groups.
Thirty-five alleles had strong LD (r2 > 0.33, 95% CI 0.70 to 0.98) (data not shown). The allele HLA-DRB1*16:02 was linked to HLA-DQA1*01:02 and HLA-DQB1*05:02 tightly (D’=1, 95% CI 0.95 to 1.00, r2=0.62; D’=0.99, 95% CI 0.96 to 1.00, r2=0.34).
Computational epitope prediction and binding of TSHR-ECD and IGF-1R-ECD to HLA
It has been reported that in GO, the pathogenic auto-antigenic peptide epitope of TSHR and IGF-1R could be matched to the antigen-binding pocket of HLA molecule and be recognised by T cell receptors.13 We predicted the potential epitopes of THSR-ECD (amino acids 1–418, AA 1–418) and IGF-1R-ECD (AA 1–902), which could be recognised by the HLA-DRA1-DRB1*16:02 and HLA-DQA1*01:02-DQB1*05:02 heterodimers.
We identified 1 epitope of THSR-ECD and 3 epitopes of IGF-1R-ECD showing strong binding to HLA-DRA1-DRB1*16:02 heterodimer respectively, including ‘FLGIFNTGL’, ‘IRHSHALVS’, “ILYIRTNAS’ and ‘FVFARTMPA’. Similarly, 1 epitope of THSR-ECD and 2 epitopes of IGF-1R-ECD also showing strong binding to HLA-DQA1*01:02-DQB1*05:02 heterodimer respectively, including ‘ILEITDNPY’, ‘NYALVIFEM’ and “NYSFYVLDN’ (figure 2). The position of the epitopes with the target sequences ‘FLGIFNTGL’ and ‘ILEITDNPY’ were located between AA130–138 and AA155–163 of TSHR-ECD, and ‘IRHSHALVS’, ‘ILYIRTNAS’, ‘FVFARTMPA’, ‘NYALVIFEM’ and ‘NYSFYVLDN’ were located between AA390–398, AA601–609, AA820–828, AA114–122, and AA417–425 of IGF-1R-ECD. Furthermore, we predicted the binding affinity between the HLA pocket and epitopes of ‘FLGIFNTGL’, ‘IRHSHALVS’, ‘ILYIRTNAS’, ‘FVFARTMPA’, ‘ILEITDNPY’, ‘NYALVIFEM’ and ‘NYSFYVLDN’. The potential sequences ‘FLGIFNTGL’, ‘IRHSHALVS’, ‘ILYIRTNAS’ and ‘FVFARTMPA’ fitted exactly in the peptide-binding groove between HLA-DRA1-DRB1*16:02 heterodimer, with a docking score (ΔG) of −9.1, −9.6, −5.1 and −5.9 kcal/mol, respectively (figure 3). In addition, the target sequences ‘ILEITDNPY’, ‘NYALVIFEM’ and ‘NYSFYVLDN’ also fitted exactly in the peptide-binding groove between HLA-DQA1*01:02-DQB1*05:02 heterodimer. The ΔG scores between HLA-DQB1*05:02 and ‘ILEITDNPY’, ‘NYALVIFEM’ and ‘NYSFYVLDN’ were −6.1, −5.7 and −5.9 kcal/mol, respectively (figure 4).
Binding affinity prediction of the TSHR-ECD and IGF-1R-ECD to the HLA- DRB1*16:02 and HLA-DQA1*01:02-DQB1*05:02 heterodimers.
The NetMHCIIpan 3.2 Server was used to predict epitopes within TSHR and IGF-1R protein (TSHR-NP_000360.1 and IGF-1R-NP_000866.1). The y-axis presents the binding affinity represented as 1 divided by %Rank of predicted affinity compared with random natural peptides, while the x-axis represents the position of amino acids.
Computational docking of TSHR-ECD and IGF-1R-ECD segments to the HLA-DRA1-DRB1*16:02 heterodimer.
(A) ‘FLGIFNTGL’ of TSHR-ECD binding to the HLA-DRA1-DRB1*16:02 heterodimer; (B) ‘IRHSHALVS’ of IGF-1R-ECD binding to the HLA-DRA1-DRB1*16:02 heterodimer; (C) ‘ILYIRTNAS’ of IGF-1R-ECD binding to the HLA-DRA1-DRB1*16:02 heterodimer; (D) ‘FVFARTMPA’ of IGF-1R-ECD binding to the HLA-DRA1-DRB1*16:02 heterodimer. HLA-DRA1, HLA-DRB1, TSHR-ECD and IGF-1R-ECD segments are shown in blue, green and purple, respectively.
Computational docking of TSHR-ECD and IGF-1R-ECD segments to the HLA-DQA1*01:02-DQB1*05:02 heterodimer.
(A) ‘ILEITDNPY’ of TSHR-ECD binding to the HLA-DQA1*01:02-DQB1*05:02 heterodimer; (B) ‘NYALVIFEM’ of IGF-1R-ECD binding to the HLA-DQA1*01:02-DQB1*05:02 heterodimer; (C) ‘NYSFYVLDN’ of IGF-1R-ECD binding to the HLA-DQA1*01:02-DQB1*05:02 heterodimer. HLA-DQA1, HLA-DQB1, TSHR-ECD and IGF-1R-ECD segments are shown in blue, green and purple, respectively.
DISCUSSION
In this study, we investigated the association of eight HLA loci with GD and GO in the Southern Chinese population. Our results demonstrated that the alleles HLA-B*38:02, -DRB1*16:02, -DQA1*01:02 and -DQB1*05:02 and the special haplotypes formed by these HLA alleles might contribute to the development of different types of ocular phenotypes in GD patients.
The allele frequencies of HLA-DRB1*16:02 and -DQB1*05:02 in the GD patient in the Southern Chinese population were significantly higher than those in the control group (table 1). Similar findings had been reported in GD patient in Thai, Korea and Han populations in Taiwan.26–28 We hypothesised that the alleles HLA-DRB1*16:02 and -DQB1*05:02 might be risk factors in common for GD in the Southern Chinese population or the other Asian population. However, previous associations did not consider different ocular phenotypes in GD. Chen et al revealed that HLA-DRB1*16:02 carried a unique motif of amino acid residues at position 67–74, which might increase the ability to present immunogenic autoantigens.29 Alleles HLA-DRB1*16:02 and -DQB1*05:02 were tightly linked in our study due to linkage disequilibrium, which has also been reported in the Asian population.30 The combinations of alleles HLA-DRB1*16:02 and -DQB1*05:02 might enhance the susceptibility to some diseases.23 30 31 HLA-DRB1*16 has been associated with the pathogenesis of the myopathic process of GO in Brazilian patients.7 Beside allele HLA-DRB1*16:02, the allele frequencies of HLA-B*38:02 and -DQA1*01:02 only in the myogenic group in Southern Chinese population were significantly higher than those in the control group (table 1). We hypothesised that alleles HLA-B*38:02 and -DQA1*01:02 might be key risk factors in the development of extraocular-muscle dysfunction in Southern Chinese population with GO.
The HLA haplotype combination of alleles HLA-B*38:02, -DRB1*16:02, -DQA1*01:02 and -DQB1*05:02 might contribute to the development of ocular phenotype in GD patients. Firstly, the haplotypes HLA-DRB1*16:02-DQA1*01:02-DQB1*05:02 and HLA-DRB1*16:02-DQA1*01:02-DQB1*05:02-DPA1*02:02-DPB1*05:01 might be common risk factors for the proptosis and myogenic phenotypes of GO in Southern Chinese population (table 1). Secondly, the frequencies of class I haplotype HLA-A*02:03-B*38:02-C*07:02 and eight HLA loci haplotype HLA-A*02:03-B*38:02-C*07:02-DRB1*16:02-DQA1*01:02-DQB1*05:02-DPA1*02:02-DPB1*05:01 in the myogenic group were significantly higher than those in the control group (table 1). Interestingly, the frequency of the haplotype lacking of alleles HLA-DRB1*16:02, -DQA1*01:02 and -DQB1*05:02 in the myogenic group was significantly lower than that in the control group (table 2). Lastly, the ORs of the significant HLA alleles and haplotypes in myogenic group were higher than those in the proptosis and GD without clinical eye phenotype group respectively (table 1, figure 1). Therefore, the combination of alleles HLA-B*38:02, -DRB1*16:02, -DQA1*01:02 and -DQB1*05:02 might contribute to different phenotypes of GO with different degrees.
Notably, some disease-causing autoantigenic peptides were only fitted into the peptide- binding pockets of specific kind of HLA-DRA1-DRB1 and HLA-DQA1-DQB1 heterodimers, and recognised by the CD4+ T-cell receptor.13 15–18 23 32 Some previous studies showed that the alleles HLA-DRB1*16:02 and/or HLA-DQB1*05:02 might be mediated by autoantibodies in GD, anti-NMDAR encephalitis and anti-IFN-γ disseminated nontuberculous mycobacterial infections myasthenia gravis, and juvenile ocular myasthenia gravis.23 29 30 Shu et al found that the epitope ‘FRAITSTLA’ of autoantibody to the NR1 subunit of NMDAR binds to HLA-DRA1-DRB1*16:02 heterodimer on the surface of APCs, which might provoke the pathogenesis of anti-NMDAR encephalitis.23 Our study also discovered the association between heterodimers (HLA-DRA1-DRB1*16:02 and HLA-DQA*01:02-DQB1*05:02) and the antigens (TSHR and IGF-1R). Interestingly, bioinformatic analyses suggested that some epitopes (‘FLGIFNTGL’ of TSHR-ECD, ‘IRHSHALVS’, ‘ILYIRTNAS’ and ‘FVFARTMPA’ of IGF-1R-ECD) strongly bind to the HLA-DRα-DRβ1 pocket groove, encoded by the HLA-DRA1-DRB1*16:02 heterodimer. Simultaneously, the HLA-DQA*01:02-DQB1*05:02 heterodimer encoded the pocket groove of HLA-DQα1-DQβ1 peptide-binding groove combined with the potential epitopes (‘ILEITDNPY’ of THSR-ECD, ‘NYALVIFEM’ and ‘NYSFYVLDN’ of IGF-1R-ECD), strongly. These peptides were fitted tightly in the peptide-binding groove of HLA-DRα-DRβ1 or HLA-DQα1-DQβ1 with relatively lower docking scores (especial peptides, ‘FLGIFNTGL’ of TSHR-ECD and ‘IRHSHALVS’ of IGF-1R-ECD). Previous studies have found that the epitopes (‘FLGIFNTGL’ and ‘ILEITDNPY’) of THSR-ECD might be the immunodominant T-cell epitope that binds to the HLA-DR3 molecule in silico and in vitro.16–18 33 34 These epitopes of TSHR-ECD or IGF-1R-ECD might favour binding to the pocket groove of special heterodimers (HLA-DRα-DRβ1 or HLA-DQα-DQβ1), increasing the capacity in APCs, to present the disease-causing autoantigenic peptides to the T-cell receptors, which might predispose to GD with and without clinical GO in immunogenicity.
We firstly conducted high-resolution genotyping for eight HLA loci in 272 unrelated GD patient with and without clinical GO in Southern Chinese at both allelic and haplotypic levels. These results showed that the specific HLA alleles and haplotypes of GO were prevalent in patients with GD or GO in Southern Chinese, which is a different region and ethnic group from previous studies. In addition, we found that the HLA alleles and haplotypes contributed to different progression of GO. Furthermore, we performed an in-silico analysis for the binding affinities between HLA class II and peptides from the two most extensively studied autoantigens primarily. Our results indicated that different HLA alleles and haplotypes could be used as biomarkers and predict the progression of different phenotypes of GO in Southern Chinese. However, there were some limitations in this study. The number of patients with GD and GO was relatively small, and the participation seems to be population-specific, which might have limited our results generalising to the general population. We found the female frequency of the patients was significantly higher than those in the control group. However, the sex mismatch might not impact the HLA contribution to GD since the inheritance of HLA loci is autosomal dominant. We should have proven that the binding condition might provoke the progression to GO between the potential peptides of THSR or IGF-1R and HLA-DRA1-DRB1*16:02 or HLA-DQA*01:02-DQB1*05:02 heterodimers in vitro and in vivo.
CONCLUSIONS
Taken together, the current study suggested that alleles HLA-B*38:02, -DRB1*16:02, -DQA1*01:02 and -DQB1*05:02 and corresponding haplotypes might contribute to the development of GD, proptosis and myogenic phenotypes of GO in the Southern Chinese population. In addition, we predicted that some potential critical epitopes of TSHR-ECD or IGF-1R-ECD favoured binding to HLA-DRA1-DRB1*16:02 and HLA-DQA*01:02-DQB1*05:02 heterodimers tightly, by bioinformatic analysis. These findings indicated that the associations of alleles HLA-B*38:02, -DRB1*16:02, -DQA1*01:02 and -DQB1*05:02 and corresponding haplotypes with the development of GD and clinical GO might be due to affecting the affinity between HLA peptide-binding groove and antigenic peptides of TSHR-ECD and IGF-1R-ECD.
Acknowledgments
We are indebted to the participants for their excellent co-operation and support. We would like to thank the volunteer blood donors for generously providing blood samples for this study.
REFERENCES
Footnotes
XH and GL contributed equally
ZD and JZ contributed equally
Contributors XH and GL contributed equally to this work. ZD and JZ contributed equally to this work. XH, JZ and ZD contributed to study design, data analysis, result interpretation and manuscript drafting. GL contributed to provide the information and blood samples of participates. XH, SM, JC, JR, TZ, WC, SP, YW and YY contributed to data acquisition and management. XH, MT, TZ and JZ contributed to data analysis. All authors contributed to manuscript drafting and its critical revision for final content.
Funding This study was supported by Science, Technology and Innovation Commission of Shenzhen Municipality under Grant (number GJHZ20180420180937076, number JCYJ20180228164400218 and number JCYJ20190806152001762), Sanming Project of Medicine in Shenzhen Grant (number SZSM201812090), and Shenzhen Key Medical Discipline Construction Fund (number SZXK070).
Competing interests None declared.
Patient consent for publication Not require.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availabilty statement Data are available upon reasonable request.
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