Target region sequencing and gene polymorphism of Chinese Uyghur individuals with autoimmune thyroid diseases AITDs

INTRODUCTION

Autoimmune thyroid diseases (AITDs) are a series of typical organ-specific autoimmune endocrine disorders, among which GD (Graves’ disease) and HT (Hashimoto’s thyroiditis) are most common [1]. They are characterized by T-cell and B-cell infiltration of the thyroid parenchyma, which lead to production of thyrotropin receptor antibodies (TRAb) in GD and thyroid peroxidase antibodies (TPOAb) in HT. Generally, the clinical manifestation of GD is hyperthyroidism and of HT is hypothyroidism or euthyroidism [2, 3].

AITDs are multifactorial diseases in which environmental exposure and genetic susceptibility interact as principal parts toward the development [2]. Previous studies have shown that environmental triggers contribute for about 20–30% and genetic factors for about 70%-80% to the occurrence of AITDs [4, 5]. Environmental agents, such as iodine, selenium, vitamin D, radiation, cigarette smoking, viral infections, chemical contaminants and intestinal dysbiosis have been reported to be closely related to the appearance of AITDs [6–8]. Over the past decades, numerous advances have been made by using various techniques (such as candidate gene studies and whole genome screenings) to identify a large number of susceptibility genes and loci that are associated with AITDs. In 2011, Oliver J. Brand et al. had reviewed and summarized the advances in genetic study of AITDs [9]. The valued genes were classified into three groups: HLA genes, non-HLA genes (e.g., CTLA-4, SCGB3A2, PTPN22 and IL2R) and thyroid-specific genes (e.g., TSHR and TG). Matthew J. Simmonds also sorted out AITDs susceptibility loci from 1970 to 2013 [10]. Previous research have identified the associations between the polymorphisms of reported genes (CTLA4, PTPN22, FCRL3, and ZFAT) with AITDs prognosis [11]. They found the polymorphisms of CD40 and FCRL3 were associated with GD and the polymorphisms of ZFAT were associated with HT, which demonstrated that multiple genes could be common to both diseases and some were unique. In the last few years, more and more AITDs susceptibility genes and loci have been discovered, such as AITDs with rs6479778 (located within the ARID5B gene); GD with rs12147587 (located within the NRXN3 gene) and rs2284720 (located within TSHR genes) [12]; HT with rs7537605 (located within the VAV3 gene) [13]; AITDs with polymorphisms of TRAF1 [14] and Interleukin-22 [15]. In short, identifying more important genes and loci of AITDs is urgently needed.

As we all know, Xinjiang Uyghur Autonomous Region has its own unique environment and Uyghur people in there possess their special diet and lifestyle. As the major ethnic group of Xinjiang (46.06%), the Uyghur share their special genetic characteristics [16]. To the best of our knowledge, no study has investigated the genes and loci of AITDs Uyghur patients. To better understand the etiology of AITDs, our aim of the present study is to identify novel genes and loci which are common or unique for GD and HT Uyghur patients.

RESULTS

Clinical characteristics of subjects

In our study, we collected 100 AITDs Uyghur patients (50 GD and 50 HT) and 50 Uyghur healthy controls from the People’s Hospital of Xinjiang Uyghur Autonomous Region (Xinjiang, China). As shown in Table 1, the average age of GD was 40 ± 12.8, HT was 42.9 ± 10.8 and controls was 39.3 ± 12.7. In the GD group, 37 (74%) patients were females and 13 (26%) patients were males. The HT group consisted of 34 (68%) female patients and 16 (32%) male patients. In the control group, 27 (54%) individuals were females and 23 (46%) individuals were males.

Genotypic and allelic frequencies of AITDs Uyghur patients versus controls

The case-control association analysis was performed by Chi-square and logistic regression tests. Among all data, seven SNPs of four genes showed significant with AITDs at P < 0.05 analyzed by Chi-square (Table 2). The major mutational allele C in rs62117031, the major mutational allele T in rs62117032, the major mutational allele A in rs4278970 and the major mutational allele A in rs4927631 of TPO gene and the major mutational allele G in rs2702998 of TG gene increased in AITDs patients compared with controls. While the major mutational allele G in rs2284735 of TSHR gene and the major mutational allele C in rs16843742 of PTPRC gene decreased in AITDs patients. All of above SNPs were confirmed to HWE in controls (P > 0.05) (Supplementary Table 1). What’s more, AITDs-controls association analysis of above SNPs were confirmed by logistic regression test (Table 3), which affirmed that the rs62117031, rs62117032, rs4278970, rs4927631, rs4278970, rs4927631, rs2702998, rs2284735 and rs16843742 were associated with AITDs patients under different inherited models.

Genotypic and allelic frequencies of GD patients versus controls

As for GD patients, nine SNPs of four genes showed significant (P < 0.05) (Table 4). The deletion of allele A in rs3835894, the major mutational allele A in rs1181388 and the major mutational allele T in rs3181113 of CD28 gene; the major mutational allele C in rs62117031 of TPO gene and the major mutational allele T in rs2403883 of TG gene increased in GD patients compared with controls. While the major mutational allele T in rs2013278 of CD28 gene, the major mutational allele A in rs12144388 as well as the major mutational allele A in rs1326279 of PTPRC gene and the major mutational allele T in rs4236899 of TG gene decreased in GD patients. All of above SNPs were confirmed to HWE in controls (P > 0.05) (Supplementary Table 2). What’s more, logistic regression test showed that the rs3835894, rs1181388, rs2013278, rs3181113, rs62117031, rs12144388, rs1326279, rs4236899 and rs2403883 were related to GD patients under different inherited models (Table 5).

Genotypic and allelic frequencies of patients with HT versus controls

As for HT patients, seven SNPs of three genes showed significant (P < 0.05) (Table 6). The major mutational allele T in rs4736434 of TG gene; the major mutational allele T in rs10905668, the major mutational allele T in rs1107345 and the major mutational allele T in rs10905669 of IL2RA gene elevated in HT patients. While the major mutational allele C in rs957971 of STAT3 gene, the major mutational allele A in rs791587 and the major mutational allele G in rs791588 of IL2RA gene declined in HT patients. All SNPs of controls were in HWE (P > 0.05) (Supplementary Table 3). Logistic regression test also confirmed that the rs4736434, rs957971, rs10905668, rs791587, rs791588, rs1107345 and rs10905669 were correlated with HT patients under different inherited models (Table 7).

Genotypic and allelic frequencies of patients with HT versus GD

Additionally, we were also interested in exploring the different allelic frequencies between HT and GD. The result of Chi-square test was shown in Table 8. Five SNPs of four genes showed significant (P < 0.05). The major mutational allele A in rs12888772 of TSHR gene; the major mutational allele T in rs1295686 and the major mutational allele T in rs847 of IL13 gene; the major mutational allele T in rs2013278 of CD28 gene increased in HT patients. While the major mutational allele C in rs6602368 of IL2RA gene decreased in HTs. All of above SNPs were confirmed to HWE both in HT and GD (P > 0.05) (Supplementary Table 4). Finally, HTs-GDs association analysis of above SNPs were confirmed by logistic regression test (Table 9). It verified that the rs12888772, rs1295686, rs847, rs2013278 and rs6602368 were associated with HT patients compared with GD patients under different inherited models.

DISCUSSION

Autoimmune thyroid diseases (AITDs) are archetypal organ-specific disorders, such as Graves’ disease (GD) and Hashimoto’s thyroiditis (HT), which mainly caused by genetic and environmental factors [1, 6, 17]. Although the phenotypes of GD and HT are contrasting, their pathogenesis shared immune-genetic mechanisms [18]. Indeed, the genetic function between GD and HT has not been fully investigated till now. In our current study with aim to explore special genes and loci that contribution to AITDs Uyghur individuals of Xinjiang China, target region sequencing was performed to discriminate expression profiles of particular genes and loci in comparing 100 AITDs Uyghur patients (50 GD and 50HT) with 50 healthy Uyghur individuals. Following Hardy–Weinberg, Chi-square and logistic regression tests, the data of case-control association analysis were obtained. It should be noted that Hardy–Weinberg test in this study was used for quality control. Generally speaking, the frequency of each inherited allele and genotype are stable, that is, in Hardy–Weinberg equilibrium (HWE) (P value >0.05). In our research, all SNPs of controls were confirmed to HWE (P > 0.05), but not all SNPs of cases were (Supplementary Tables 1 to 4), which could be precisely important to disease’s causation [19]. From our data we can see that there really exists several common as well as unique arresting genes and loci as potential genetic regions for GD and HT Uyghur patients.

In our first genome screen, performed with 100 AITDs Uyghur patients (50 GD and 50 HT) and 50 Uyghur controls, seven SNPs of four genes showed evidence for linkage with AITDs Uyghur patients (Tables 2 and 3): four SNPs (rs62117031, rs62117032, rs4278970 and rs4927631) of TPO gene and one SNP (rs2702998) of TG gene elevated in AITDs patients; otherwise rs2284735 of TSHR gene and rs16843742 of PTPRC gene received an opposite consequence. These results seemed to reflect that certain loci of above genes could be associated with AITDs Uyghur patients in Xinjiang China. Accumulated evidences have indicated that the polymorphisms of TPO, TG and TSHR genes could be associated with AITDs. The TPO gene is nearly 150 kb in length and regional localization to human chromosome 2p25, whose mutations have been found in patients with hypothyroidism and could be related to thyroid destruction [20, 21]. In recent decades, a number of SNPs in the TPO gene have been genotyped and different loci showed different effect on ATIDs (both GD and HT, or respectively), which further clarified the important role of TPO played in the development of AITDs [21–24]. TG is a 660-kDa glycoprotein, which accounts for about 75%-80% of the total thyroid protein [25, 26] and deserves to be a major AITDs susceptibility gene. Earlier researches have recognized that TG SNPs were related to the development of AITDs [27–31]. In 2011, Mihaela Stefan et al. revealed a new mechanism of interaction between the TG promoter SNP variant with viral infection to AITDs, which laid the foundation for further exploration [32]. However, an explicit link between TG SNPs with the complex etiology of AITDs was not yet established. So more researches are needed. Moreover, TSHR, another obvious candidate gene for AITDs, whose polymorphisms have been reported to affect the etiology of AITDs, especially of GD [33–37]. But which SNPs of TSHR can confer risk for AITDs remains an intractable problem. PTPRC (CD45) is a protein coding gene, which can encode a member of the protein tyrosine phosphatase (PTP) family. So far, even though the role of PTPRC gene plays in the AITDs is not very clear, but the genetic variations of PTPRC have been reported as risk factors for another autoimmune disease, Juvenile idiopathic arthritis (JIA) [38]. Above mentioned SNPs harboring AITDs susceptibility genes we have identified have not been reported in other researches before, suggesting AITDs Uyghur patients could be more likely to owe their special genetic characteristics.

As we mentioned earlier, GD and HT may be influenced by distinct genetic susceptibility. So in the second stage we analyzed the genotypic and allelic frequencies of Uyghur patients with GD or HT versus controls, respectively. Our data demonstrated that there actually may present distinct genes and loci compared GD Uyghur patients with HTs (Tables 4 to 7). In GD patients, nine SNPs of four genes (CD28, TPO, TG and PTPRC) showed significant and in HT patients, there were seven SNPs of three genes (TG, IL2RA and STAT3). Similarly, SNPs harboring above genes we identified have not been reported, either (Tables 4 to 7). The important claim here is that the major mutational allele C in rs62117031 of TPO gene, which was up-regulated in GD, was consistent in AITDs Uyghur patients (Tables 2 and 4). The validation of this locus will be implemented in our further study. Nevertheless, no uniform locus showed evidence for association with both GD and HT Uyghur patients, this may be due to genetic heterogeneity [39]. Consistent with previous studies, the polymorphisms of CD28 gene were considered as good candidates for GD patients [40], and the polymorphisms of STAT3 gene were good for HT [41]. Previously, the polymorphisms of IL2RA (CD25) gene was investigated in GD [42, 43] and whether it is related to the aetiological variant of HT need more identification.

Subsequently, we performed a direct comparison of the two diseases during discovery stage to select candidate genes and loci that would distinguish between HT and GD. Our analysis suggested that the expression of TSHR SNPs rs12888772, IL13 SNPs rs1295686, IL13 SNPs rs847 and CD28 SNPs rs2013278 were significantly higher in Uyghur patients with HT than in those with GD. While IL2RA SNPs rs6602368 was lower in HT patients than GDs (Tables 8 and 9). In addition those genes noted above, previous study has also demonstrated the key role of the IL13 SNPs played in the etiology of AITDs [44]. More importantly, we identified a major mutational allele T in rs2013278 of CD28, which elevated in HT Uyghur patients, happened to decline in GDs (Tables 4 and 8). The consistency of the experimental results not only proved the accuracy of the current genetic test but pointed out the direction for our further research.

In summary, this is the first target region sequencing and genetic association analysis performed in AITDs Uyghur patients. Here we report a series of genes and loci which are shared or special between GD and HT, may provide novel insight into understanding the etiology and pathogenesis of Uyghur patients. Besides, replicated, epidemiological and functional studies are needed to validate our findings. In our further research, we are willing to collect samples as large as possible to better illustrate our findings.

Clinical participants and methods

Subjects

The case-control participants included 100 AITDs Uyghur patients (50 GD and 50 HT) and 50 Uyghur healthy controls. All of those participants were collected from the People’s Hospital of Xinjiang Uyghur Autonomous Region (Xinjiang, China). All patients with GD were diagnosed by presence of clinical history of thyrotoxicosis and positive expression of thyrotropin receptor antibodies (TRAb). All patients with HT were positive for thyroid peroxidase antibodies (TPOAb) and/or antibodies against thyroglobulin (TGAb) and showed hypothyroidism or euthyroidism with palpable diffuse goiters. All healthy controls were euthyroid and had no history or any family history of thyroid disease and were negative for all kinds of thyroid autoantibodies. Written informed consent had been obtained from all participants and the clinical characteristics of AITDs patients and controls were shown in Table1.

Genomic DNA (GDNA) preparation and target region sequencing

GDNA was extracted from 2ml peripheral venous blood in an EDTA tube of each participant. The purity (optical density (OD) 260/280 was 1.8–2.0), concentration (≥50 ng/ul) and total content (>2ug) of each GDNA reached the requirements of sequencing. GDNA of each sample was checked and sheared, followed by ending repair, adenylating 3’ ends and adaptor ligation according to the manufacturer’s protocols. The targeted size (300–400bp) of adaptor-ligated DNA fragments were recovered using TIANGEN Gel Extraction kit (TIANGEN, Beijing, China). The PCR amplification experiment was performed to amplify the extracted DNA in 10 PCR cycles (10cycles of: 10 seconds at 98°C, 30 seconds at 60°C, 30 seconds at 72°C, 5 minutes at 72°C, Hold at 4°C ). Then the GDNA library was normalized, pooled and hybridized in accordance with the manufacturer’s instructions. After being purified by Agencourt AMPure XP (Beckman Coulter), enriched elution was amplified using PCR. The amplicons were checked and quantitated, and then sequenced by Illumina Hiseq2500 (Illumina, San Diego, CA, USA).

Raw data analysis

Raw data were collected and analyzed through bioinformatical resources. FastQC was performed to quality control and detect sequencing quantity. BWA (http://bio-bwa.sourceforge.net/) was used for comparing the reference genome and GATK (https://software.broadinstitute.org/gatk/best-practices/) was applied to correct the initial comparison results obtained by the BWA software. In the end, combining the phenotypic and mutated information of the samples, the genetic analysis based on family patterns or disease patterns was used for finding genes and loci associated with AITDs.

Statistical analysis

Statistical analysis was executed with SPSS version 18.0 software (SPSS, Inc., Chicago, IL, USA). For each SNP, following the assessment of Hardy–Weinberg equilibrium (HWE), Chi-square and logistic regression tests were used for detecting the different frequencies of genotypes and alleles between cases and controls. Chi-square analysis contains four hypothetical genetic models, including Codominant, Dominant, Recessive and Allele. Whereas, Logistic regression analysis contains five: Dominant, Recessive, Log-additive and HOM / HET. The association between SNPs and AITDs was firstly evaluated, and then stratified analysis were performed based on the types of AITDs. P < 0.05 was considered statistical criteria.

Author contributions

Xin-Ling Wang and Yan-Ying Guo conceived and designed the experiments; Xin-Ling Wang, Meng-He Wang and Yan-Ying Guo wrote the article. Su-Li Li and Jing Zhang collected and prepared the samples. Meng-He Wang, Yuan Chen and Jamilam Mamtiming performed the experiments. Xin-Ling Wang, Yan-Ying Guo, Meng-He Wang, Su-Li Li, Jing Zhang and Yun-Zhi Luo analyzed the data.

ACKNOWLEDGMENTS

We thank all clinical participants and their family members. Besides, we are particularly grateful to our students for their timely help.

CONFLICTS OF INTEREST

The authors declare no conflicts of interest.

FUNDING

This study was supported by grants from the Natural Science Foundation of Xinjiang Uyghur Autonomous Region (2017D01C105).

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