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Association of novel polymorphisms in TMEM39A gene with systemic lupus erythematosus in a Chinese Han population

BMC Medical GeneticsBMC series – open, inclusive and trusted201718:43

https://doi.org/10.1186/s12881-017-0405-8

Received: 20 July 2016

Accepted: 13 April 2017

Published: 20 April 2017

Abstract

Background

This study aimed to assess the association between 14 single nucleotide polymorphisms (SNPs) in six genes (IRF8, TMEM39A, IKZF3, ORMDL3, GSDMB, and ZPBP2) and systemic lupus erythematosus (SLE) in a Chinese Han population sample.

Methods

We carried out a case-control study of 415 patients with SLE and 470 healthy controls without autoimmune disease or cancer. DNA for genetic analysis was isolated from the blood of all subjects using standard phenol-chloroform method. TagSNPs were identified using genotype data from the panel (Han Chinese in Beijing) of the HapMap Project and were selected using the Haploview program. Genotyping assay was conducted using the Sequenom MassARRAY iPLEX Gold platform. The frequencies of the alleles and genotypes were calculated and analyzed. Association studies and haplotype analysis were also performed.

Results

The genotypic frequencies of rs12493175 and rs13062955 were significantly different between the SLE patients and the healthy controls. Compared with the common homozygous genotype, the CT and CT + TT genotypes in rs12493175 and the AC and AC + AA genotypes in rs13062955 was observed to significantly reduce the risk of SLE. The haplotype analysis of TMEM39A polymorphisms showed that the CGTA haplotype frequency was significantly low in the SLE patients.

Conclusion

Our findings identified three novel associations in SNPs located in the TMEM39A gene associated with SLE susceptibility in a Chinese Han population.

Keywords

Single nucleotide polymorphism Systemic lupus erythematosus Susceptibility

Background

Systemic lupus erythematosus (SLE) is typically characterized by the dysregulation of T cell response and B cell activation which usually causes the formation of immune complexes in multiple organs and tissues [1]. Although the pathogenesis of SLE is largely unknown to date, it most likely involves environmental and genetic factors. Several candidate-gene studies and genome-wide association (GWA) scans have successfully discovered multiple susceptibility genes that fall into key pathways implicating immune complex clearance, immune signal transduction and interferon pathways contributing to the development of SLE [2, 3]. However, much of the heritable risk needs to be identified.

Recently, multiethnic approach was utilized to find that three SLE risk loci exceeded the genome-wide significance threshold, including interferon regulatory factor 8 (IRF8), transmembrane protein 39A (TMEM39A), and 17q21 between IKAROS family of zinc finger 3 (IKZF3) and zona pellucida binding protein 2 (ZPBP2) [4]. The 17q21 region was originally associated with asthma in family linkage study [57]. More single nucleotide polymorphisms (SNPs) in the 17q21 region have been identified as being associated with the susceptibility to autoimmune diseases, including rheumatoid arthritis, ankylosing spondylitis and SLE [4, 810]. IRF8 is a family member of transcription factors that play a critical role in the regulation of cell apoptosis and immune response [11]. It is required for promoting type I interferon responses which can induce the overexpression of genes reported in SLE, and several variants within IRF8 could influence binding to the regulatory elements [4, 12]. Although very limited biological data of TMEM39A is published so far, its polymorphisms have been found to be associated with multiple sclerosis and SLE [4, 1315]. Additionally, several studies found genetic variants in orosomucoid like 3 (ORMDL3) and gasdermin B (GSDMB) were associated with the risk of autoimmune disease [16, 17]. On the basis of these studies, we hypothesized that certain novel variants in the loci described previously may contribute to the susceptibility to SLE.

In this study, we selected the six candidate genes, namely, IRF8, TMEM39A, IKZF3, ORMDL3, GSDMB, and ZPBP2, and screened for the putatively functional tagSNPs. We aimed to determine the association between the polymorphisms and susceptibility to SLE in a Chinese population.

Methods

Sample description

A total of 415 patients with SLE diagnosed according to the criteria of the 1982 American College of Rheumatology were enrolled [18]. Additionally, 470 healthy controls without autoimmune disease or cancer were recruited, who were sex- and age-matched with the patients. All the study participants were from the Chinese Han population, with the age ranging from 16 to 65 years. Demographic and clinical characteristics of SLE patients and controls are shown in Table 1. This project was approved by the Human Ethics Review Committee of China Medical University. Written informed consent was obtained from all the participants, including the guardians on behalf of the children enrolled in the study.
Table 1

General characteristics of the study population

 

SLE patients (n = 415)

Healthy controls (n = 470)

P value

Female/male

312 (103)

362 (108)

P > 0.05

Age (mean ± SD), (years)

38.2 ± 11.4

35.8 ± 13.6

P > 0.05

Fever, n (%)

57 (13.7)

-

 

Baldness, n (%)

148 (35.7)

-

 

Light sensitivity, n (%)

74 (17.8)

-

 

Facial erythema, n (%)

161 (38.8)

-

 

Oral ulcer, n (%)

67 (16.1)

-

 

Arthritis, n (%)

62 (14.9)

-

 

Lupus nephritis, n (%)

246 (59.3)

-

 

SNP selection

TagSNPs are representative SNPs which can capture most of the genetic variation in a region of the genome on the basis that they are in high linkage disequilibrium (LD) with other SNPs [19]. TagSNPs genotyped in this study were selected by analyzing the genotype data of Chinese Han population from HapMap dbSNP (http://www.hapmap.ncbi.nlm.nih.gov) using LD-based tagSNP selection with a pairwise algorithm LDSelect, available in the Tagger function implemented in Haploview version 4.2 (http://www.broadinstitute.org/mpg/haploview) [20, 21].

First, genotype data of HapMap Chinese Han Beijing population (Release 27, Phase I + II + III) were extracted and the chromosomal regions including the six candidate genes within the extended gene regions encompassing 3000 bp upstream and 1500 bp downstream flanking sequence (to capture the 5’ and 3’ UTR) were searched. TagSNPs were chosen based on a minor allele frequency (MAF) of at least 5% and a pairwise LD threshold of r 2 > 0.8 using Haploview 4.2. Second, the F-SNP program (http://compbio.cs.queensu.ca/F-SNP) and SNP Function Prediction (FuncPred) software (http://snpinfo.niehs.nih.gov/snpinfo/snpfunc.htm) were applied to prioritize the tagSNPs for genotyping based on their putative functions. Accordingly, 14 tagSNPs with predicted functional effects were selected for genotyping. The common SNPs captured using the selected tagSNPs in the six candidate genes are presented in Table 2.
Table 2

Common SNPs captured using the selected 14 tagSNPs in the six candidate genes based on the HapMap population data for Chinese in Beijing (Release 27)

Gene

tagSNP_ID

SNP captured

Position (hg19)

IKZF3

rs3816470

rs9635726, rs3816470, rs9303277, rs10445308, rs9909593

chr17:37985801

rs907092

rs907092

chr17:37922259

GSDMB

rs9303281

rs11078927, rs1008723, rs4795400, rs9303281, rs2305480, rs7219923, rs869402, rs2305479, rs7224129, rs11078926, rs2290400, rs7216389

chr17:38074046

ORMDL3

rs4795402

rs4795403, rs4795402, rs3744246, rs4795404

chr17:38085385

rs8076131

rs4378650, rs8076131, rs12603332

chr17:38080912

ZPBP2

rs11557466

rs11557467, rs12936231, rs11078925, rs1054609, rs11557466, rs11870965, rs10852936, rs9907088

chr17:38024626

IRF8

rs188602

rs188602, rs170033, rs2270502, rs381139

chr16:85932351

rs4843860

rs4843860, rs12926854, rs4843861

chr16:85950921

rs2270501

rs2270501, rs12924316

chr16:85932988

rs191022

rs191022

chr16:85932132

TMEM39A

rs13062955, rs12493175

rs12492859, rs13094625, rs13081197, rs13078312, rs12493326, rs16829853, rs13081067, rs2282170, rs13062955, rs12492315, rs12493175, rs13096213, rs12496277, rs12492609

chr3:119159658,chr3:119160413

rs4687859

rs7629750, rs2282171, rs3772136, rs9846088, rs4687859, rs9872589, rs3195852

chr3:119170371

rs2282175

rs17281647, rs2282175, rs1132202

chr3:119182259

Genotyping assay

Genomic DNA was isolated from peripheral blood leukocytes using the standard phenol-chloroform method. Each DNA sample was diluted to working concentration of 50 ng/μl for genotyping. The selected tagSNP genotyping was performed by BGI (Shenzhen, China) using the Sequenom MassARRAY iPLEX Gold platform (Sequenom, San Diego, California) according to the manufacturer’s instructions [22]. The primers for polymorphism genotyping were designed using MassARRAY Assay Design 3.1 software and are shown in Table 3. All samples were randomized on 384-well plates and blinded for case or control status. A random selection of samples was repeatedly genotyped using direct sequencing validate the accuracy of the SNP genotyping assays and the results were 100% concordant.
Table 3

Details of the primers used in the polymorphism genotyping by MassArray

tagSNP_ID

Alleles

Forward and reverse primer

Extension primer

rs2282175

C/T

ACGTTGGATGGAAAGCGGCGACAACTTTAC

CGCTGGGAGGGAGTTC

ACGTTGGATGCTGGTTTGCAGCGTTCCAAC

rs4687859

A/G

ACGTTGGATGCATGCCTGGCCTCATTTTTC

TTTTCCCTGCCTCATTG

ACGTTGGATGAGAAAGCACATTTCCCTGCC

rs12493175

C/T

ACGTTGGATGGTTATGGGACAGCTTCTTTC

CCCAAACGTATGAAGGTTAACAG

ACGTTGGATGGAGAGGTGAGAAAGCTACAG

rs13062955

A/C

ACGTTGGATGGGCAAATACAGGCATACCTC

GGACTACAGTATCTGGGAAGCACAAT

ACGTTGGATGGGGTTGCCACAAACCTTCAG

rs9303281

A/G

ACGTTGGATGACCCCTTTTTTGGACTCAGC

CTCTTCCATGTGAAGAGAGTCCA

ACGTTGGATGACGTGCGTCCATGTGAAGAG

Reverse transcriptase–PCR of candidate gene mRNA levels

To examine the relation between the associated polymorphisms and the gene mRNA levels, forty patients stratified by polymorphic genotypes were randomly selected. The relative expression levels of twenty patients with common homogenous genotype carriers were set to a unity, and the relative expression levels of twenty patients with heterogeneous and rare homogenous genotypes were expressed relative to those of the common homogenous genotype carriers. Total RNA prepared from peripheral blood mononuclear cells were reversely transcribed using the TaqMan reverse transcription reagents (Applied Biosystems, Weiterstadt, Germany). RT-PCR was carried out using the ABI Universal Master Mix on an ABI PRISM 7000 Sequence Detection System.

Statistical analysis

Data were managed and stored using the SPSS software 16.0. Allele and genotype frequencies were compared between patient and control groups by the chi-square (χ 2) test. The quality of the genotype data was evaluated by Hardy-Weinberg equilibrium (HWE) in the case and control subjects using Fisher’s exact test (P > 0.05). The association between each polymorphism and risk of SLE was estimated by logistic regression and was expressed as odds ratio (OR) with 95% confidence intervals (95% CI). The haplotypes were assigned using the online software platform SHEsis (http://www.analysis.bio-x.cn). The haplotype construction element is based on the standard Full-Precise-Iteration (FPI) algorithm [23]. All tests were two-tailed, and P values < 0.05 were considered as statistically significant.

Results

In the screening stage, a few of tagSNPs for the six candidate genes were excluded from further analysis because they were found to have no polymorphic sites or to exhibited MAFs < 0.05 in Chinese Han Beijing population. Finally, 14 tagSNPs with predicted functional effects were selected for genotyping in a total of 885 subjects (Table 2). The details of the five identified genetic single-nucleotide variants in two genes, namely, TMEM39A rs2282175, rs4687859, rs12493175, rs13062955, and GSDMB rs9303281 were presented (Table 4). Additionally, there was no significant difference concerning the call rates between the SLE group and the control (p > 0.05). However, as shown in Table 5, the allelic distributions of rs4687859 and rs9303281 showed significant departure from the Hardy-Weinberg law for the controls. The allelic distributions of the three selected tag-SNPs, rs2282175, rs12493175, and rs13062955, of the TMEM39A gene met the Hardy-Weinberg principle (Table 5). Thus, we focused on the three selected tag-SNPs, rs2282175, rs12493175, and rs13062955, of the TMEM39A gene in the following analysis (Table 6).
Table 4

The details of the identified genetic single-nucleotide variants

SNP

Chr

Position

Func.refGene

CADD.Score

rs13062955

chr3

119159658

intronic

CADD = 3.564

rs12493175

chr3

119160413

intronic

CADD = 2.429

rs4687859

chr3

119170371

intronic

CADD = 8.630

rs2282175

chr3

119182259

UTR5

CADD = 4.988

rs9303281

chr17

38074046

intronic

CADD = 0.720

Func.refGene functional gene element, CADD Combined Annotation Dependent Depletion

Table 5

The five SNPs call rates in patients and control individuals and HWE p-values

SNP_ID

Call rate (%)

HWE p-value

SLE

Control

SLE

Control

rs2282175

98

99

0.539349

0.056740

rs4687859

98

95

0.792124

0.000719

rs12493175

99

99

0.004372

0.295414

rs13062955

97

92

0.002761

0.107666

rs9303281

93

99

7.77E-16

2.66E-15

Table 6

Genotype and allele association analysis of three tagSNPs

tagSNP_ID

Genotype/Allele

SLE, n(%)

CON, n(%)

χ 2

P value

OR (95% CI)

P value

P.adjust

rs2282175

CC

339 (83.7)

415 (88.9)

5.1

0.08

1

  

CT

62 (15.3)

48 (10.3)

  

1.63 (1.06–2.38)

0.026

0.054

TT

4 (1.0)

4 (0.9)

  

1.21 (0.30–5.00)

0.776

0.817

CT/TT

    

1.56 (1.05–2.27)

0.027

0.054

C

740 (91.4)

878 (94.0)

4.5

0.033

1

  

T

70 (8.6)

56 (6.0)

  

1.49 (1.03–2.12)

0.033

0.06

rs12493175

CC

311 (75.7)

308 (66.2)

12.7

0.002

1

  

CT

85 (20.7)

145 (31.2)

  

0.58 (0.42–0.79)

0.001

0.005

TT

15 (3.6)

12 (2.6)

  

1.23 (0.57–2.70)

0.589

0.736

CT/TT

    

0.62 (0.46–0.84)

0.002

0.007

C

707 (86.0)

761 (81.8)

5.6

0.017

1

  

T

115 (14.0)

169 (18.2)

  

0.73 (0.56–0.95)

0.017

0.0486

rs13062955

CC

305 (75.9)

281 (66.0)

15.1

0.001

1

  

AC

82 (20.4)

136 (31.9)

  

0.55 (0.40–0.76)

2.95 × 10−4

0.002

AA

15 (3.7)

9 (2.1)

  

1.53 (0.66–3.57)

0.318

0.424

AC/AA

    

0.61 (0.45–0.84)

0.002

0.007

C

692 (86.1)

698 (81.9)

5.2

0.021

1

  

A

112 (13.9)

154 (18.1)

  

0.73 (0.56–0.96)

0.021

0.053

P.adjust: the Bonferroni corrected P value

The genotypic frequencies of rs12493175 and rs13062955 located in TMEM39A gene were significantly different between the SLE patients and the healthy controls. Compared with the common homozygous genotype, the CT and CT + TT genotypes in rs12493175 (p.adjust = 0.005, odds ratio (OR) 0.58, 95% CI 0.42 to 0.79; p.adjust = 0.007, OR 0.62, 95% CI 0.46 to 0.84, respectively) and the AC and AC + AA genotypes in rs13062955 (p.adjust = 0.002, OR 0.55, 95% CI 0.40 to 0.76; p.adjust = 0.007, OR 0.61, 95% CI 0.45 to 0.84, respectively) was observed to significantly reduce the risk of SLE. On the other hand, the difference in the frequency of rs2282175 was only marginal. The CT and CT + TT genotypes in rs2282175 was observed to modestly increase the risk of SLE (p.adjust = 0.054, OR 1.63, 95% CI 1.06 to 2.38; p.adjust = 0.054, OR 1.56, 95% CI 1.05 to 2.27, respectively). However, we did not find any other tagSNP associated with SLE risk in the genes of IRF8, IKZF3, ORMDL3, GSDMB and ZPBP2. We also evaluated the relation between the associated polymorphisms and the gene mRNA levels in peripheral blood mononuclear cells from 40 patients. Nevertheless, we failed to find any correlation between them (data not shown).

Haplotypes were constructed in both SLE and healthy controls and the haplotypes with frequency of > 3% were built from TMEM39A rs2282175, rs12493175 and rs13062955 (Table 7). The results show that the CGTA haplotype frequency was significantly low in the SLE patients (p = 0.019, OR 0.72, 95% CI 0.55 to 0.95). No difference was detected in the other haplotypes.
Table 7

Frequencies of the haplotypes formed by TMEM39A rs2282175, rs12493175 and rs13062955 SNPs

Haplotype

SLE, n(%)

CON, n(%)

P value

OR (95% CI)

CACC

492.8 (62.4)

527.7 (63.0)

0.813

0.97 (0.79–1.19)

CGCC

119.4 (15.1)

103.3 (12.3)

0.101

1.26 (0.95–1.68)

CGTA

108.7 (13.8)

151.0 (18.0)

0.019

0.72 (0.55–0.95)

TGCC

65.5 (8.3)

52.7 (6.3)

0.11

1.34 (0.92–1.96)

Discussion

IRF8, TMEM39A and IKZF3-ZPBP2 were previously identified as susceptibility loci for SLE in the multiracial replication study [4], Besides, ORMDL3 and GSDMB were found to have susceptibility loci for autoimmune diseases [16, 17]. Thus we hypothesized certain novel associations in SNPs located in these genes could be identified in Chinese populations. To test this hypothesis, we selected 14 tagSNPs in these candidate genes to determine the association between the polymorphisms and SLE susceptibility in a Chinese Han population. Our findings showed that TMEM39A rs2282175, rs12493175, and rs13062955 were associated with SLE risk.

To date, almost no biological data of TMEM39A have been reported and only two SNPs in TMEM39A were identified as being associated with the susceptibility of autoimmune diseases. TMEM39A rs1132200 have been found to be associated with susceptibilities to multiple sclerosis and SLE in multiracial replication study [4, 13, 14]. but the recent studies showed that TMEM39A rs12494314, instead of rs1132200, was associated with SLE susceptibility in the Chinese population [15, 24]. In our current study, we identified three novel associations in SNPs located in TMEM39A as being associated with SLE susceptibility. The genotypic frequencies of rs12493175 and rs13062955 were significantly different between the SLE patients and the healthy controls, while the difference in the frequency of rs2282175 was only marginal. Among these polymorphisms, rs12493175 T-allele and rs13062955 A-allele were found to be associated with a reduced SLE risk, suggesting a protective factor to SLE. In contrast, rs2282175 T-allele was found to be associated with an increased SLE risk, suggesting a susceptibility factor to SLE. Haplotype analysis for TMEM39A SNPs revealed that the haplotype CGTA conferred a reduced risk of SLE. It is possible that the haplotype CGTA provides protection to SLE, resulting from the rs12493175 T and rs13062955 A alleles. It is worth noting that rs2282175 is located in the region of 5' upstream in TMEM39A and predicted to be a binding site of certain transcription factor. It is speculated that the C → T allele change at the rs2282175 site may influence the DNA binding ability of transcription factor c-Rel, which was predicted according to the different variants using the search tool of Alibaba 2.1 (http://www.gene-regulation.com/pub/programs/alibaba2). Although we did not find any relation between mRNA expression and the polymorphisms, it may be required to explore the possible biological significance of the SNPs in different cell subsets.

Several limitations in the current study should also be noted. First, due to the restricted number of study subjects and limited analysis capacity, we did not analyze the SNPs with rare MAF in the Chinese Han population, including those reported as risk loci for SLE in other ancestries, and mainly focused on the SNPs with predicted functional effect. Further research on the role of the rare variants should be carried out in a larger number of samples. As different populations have different genetic backgrounds, it is still necessary to perform the genetic analysis of multiracial study. Second, we still could not determine the causality of SLE-associated SNPs. For those variants in the large strong LD region, such as chromosome 17q21, it is difficult to determine which SNP is the true functional locus that contributes to SLE susceptibility independently. Better understanding whether the SNP is functionally relevant will require mechanistic and fine-mapping experiments. Third, our study did not assess the SNP-SNP interaction (epistasis). For the genetically complex disease, multiple interacting loci could contribute to SLE susceptibility. Additionally, as a heterogenetic disease, the contribution of genetic and environmental factors is very important for the disease [25]. Therefore, interaction between genetic and environmental factors is required to further clarify the pathogenesis of SLE.

Conclusion

This study identified three novel associations in SNPs located in the TMEM39A gene associated with SLE susceptibility in a Chinese Han population. Functional study and further independent large-scale study in other racial populations are still needed to confirm our results.

Abbreviations

CI: 

Confidence interval

GSDMB: 

Gasdermin B

GWA: 

Genome-wide association

IKZF3: 

IKAROS family of zinc finger 3

IRF8: 

Interferon regulatory factor 8

MAF: 

Minor allele frequency

OR: 

Odds ratio

ORMDL3: 

Orosomucoid like 3

SLE: 

Systemic lupus erythematosus

SNP: 

Single nucleotide polymorphism

TMEM39A: 

Transmembrane protein 39A

ZPBP2: 

Zona pellucida binding protein 2

Declarations

Acknowledgment

We especially thank all SLE patients who participated for making this study possible.

Funding

The work was supported by grants from the National Natural Science Foundation of China (81401330) and the National Science and Technology Pillar Program (2011BAI10B04).

Availability of data and material

All data generated or analysed during this study are included in this published article.

Authors’ contributions

XC contributed to the collection, analysis of the clinical data, and manuscript preparation. WH and XL contributed to the analysis of the SNP results. LW and YJ coordinated the study and helped draft the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

The study was approved by the Human Ethics Committee of China Medical University and the informed consents were obtained from all donors.

Publisher’s Note

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Authors’ Affiliations

(1)
Central Laboratory, First Affiliated Hospital of China Medical University
(2)
Department of Nephrology, First Affiliated Hospital of China Medical University
(3)
Central Laboratory; Department of Dermatology, First Affiliated Hospital of China Medical University

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© The Author(s). 2017