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Genetic polymorphisms of IFNG and IFNGR1 in association with the risk of pulmonary tuberculosis
Gene 543 (2014) 140–144
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Genetic polymorphisms of IFNG and IFNGR1 in association with the risk of pulmonary tuberculosis Jieqiong Lü a,1, Hongqiu Pan a,b,1, Yongzhong Chen b, Shaowen Tang a, Yan Feng a, Sangsang Qiu a, Siming Zhang a, Liang Wu a, Ruobing Xu a, Xianzhen Peng a, Jianming Wang b,⁎, Cheng Lu c,⁎⁎ a b c
Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, PR China Department of Tuberculosis, Third Hospital of Zhenjiang City, Zhenjiang 212005, PR China Department of Breast, Nanjing Maternity and Child Health Hospital of Nanjing Medical University, Nanjing 210004, PR China
a r t i c l e
i n f o
Article history: Received 7 November 2013 Received in revised form 16 February 2014 Accepted 21 March 2014 Available online 25 March 2014 Keywords: Tuberculosis Infection Genetic polymorphisms Susceptibility Immunity
a b s t r a c t Objective: Genetic host factors play an important role in controlling individual's susceptibility to the pathogen. This study aims to explore the single and joint effect of genetic polymorphisms of interferon-gamma (IFNG) and its receptor (IFNGR1) in association with the pulmonary tuberculosis in a Chinese Han population. Methods: This population-based case control study consisted of 1434 pulmonary tuberculosis patients and 1412 healthy controls. Six tag SNPs in IFNG/IFNGR1 were genotyped using TaqMan allelic discrimination technology. The logistic regression model was carried out to analyze the associations between the genotypes and haplotypes and the risk of tuberculosis by calculating the odds ratio (OR) and 95% confidence interval (CI). Results: After the Bonferroni correction for multiple comparisons, three SNPs (rs2234711, rs1327475 and rs7749390) in IFNGR1 gene were observed to be significantly associated with the altered risks of tuberculosis. For the SNP rs2234711, individuals carrying C allele (vs. T) showed a decreased risk, with the adjusted OR(95% CI) of 0.82(0.76–0.91). The additive model revealed that each additional allele contributed about 14% decreased risk (OR: 0.86, 95% CI: 0.77–0.95). Moreover, we observed a strong linkage disequilibrium between rs2234711 and rs3799488. Compared with the common rs2234711C–rs3799488C haplotype, the haplotype rs2234711T– rs3799488C contributed to a significant increase in the risk of tuberculosis (adjusted OR: 1.24, 95% CI: 1.09–1.41). Conclusions: Our results suggest that genetic polymorphisms in IFNGR1 gene are involved in the risk of tuberculosis in the Chinese population. Future studies should include a comprehensive sequencing analysis to identify the specific causative sequence variants underlying the observed associations. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Tuberculosis (TB) has been declared as a global public health emergency by the World Health Organization. It was estimated that almost 8.6 million people developed active TB and 1.3 million died from it in 2012, mostly in developing countries (WHO, 2013). Although nearly one-third of the world's population has been potentially infected with the pathogen Mycobacterium tuberculosis (MTB) (Young et al., 2008), only 5–10% will develop clinical TB during their lifetimes (Frieden
Abbreviations: TB, tuberculosis; MTB, Mycobacterium tuberculosis; IFNG, interferongamma; IFNGR, interferon-gamma receptor; SNP, single nucleotide polymorphism; MAF, minor allele frequency; PCR, polymerase chain reaction; OR, odds ratio; CI, confidence interval; SD, standard deviation; LD, linkage disequilibrium; Th1, T-helper 1; UTR, untranslated region; ENCODE, Encyclopedia of DNA Elements. ⁎ Correspondence to: J. Wang, Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China. ⁎⁎ Correspondence to: C. Lu, Department of Breast, Nanjing Maternity and Child Health Hospital of Nanjing Medical University, Nanjing, China. E-mail addresses: [email protected] (J. Wang), [email protected] (C. Lu). 1 These authors contributed equally to this work.
http://dx.doi.org/10.1016/j.gene.2014.03.042 0378-1119/© 2014 Elsevier B.V. All rights reserved.
et al., 2003). The outcome of infection is influenced by many factors, such as malnutrition, co-infection with other pathogens, exposure to environmental microbes, and previous vaccination (van de Vosse et al., 2004). It is clear that genetic host factors also play an important role in controlling individual's susceptibility to the pathogen (Altare et al., 1998; Newport et al., 1996). It was estimated that the contribution of genetic factors to the phenotypic variation and immune responses in the population infected with TB ranges up to 71% (Moller and Hoal, 2010). In the past decades, there has been dramatic progress in our understanding of the innate and adaptive immunity in the human host defense to TB (Leandro et al., 2009). Current studies highlight a complex molecular network in antimycobacterial immunity, centered on interferon-gamma (IFNG) signaling pathway (Thakur et al., 2012). Individuals with inherited disorders of IFNG mediated immunity appear to be specifically vulnerable to MTB infections (Dupuis et al., 2000). The IFNG gene is located on chromosome 12q24.1. Studies have reported that IFNG can activate murine macrophages to inhibit MTB growth (Ehrt et al., 2001). It is up-regulated and secreted as a major cytokine to activate macrophages during MTB infection (Lee and Kornfeld,
J. Lü et al. / Gene 543 (2014) 140–144
2010). The human receptor of IFNG (IFNGR) is a heterodimer of IFNGR1 and IFNGR2. The IFNGR1 gene is located on chromosome 6q23.3, which encodes the ligand-binding chain (alpha) of the interferon gamma receptor. Defects in IFNGR1 have been reported as a cause of Mendelian susceptibility to mycobacterial disease, also known as familial disseminated atypical mycobacterial infection (Jouanguy et al., 1999). Previous studies mainly focused on the potentially functional polymorphisms of IFNG like +874 A/T in the intron 1 and adjacent CA repeats. He et al. analyzed seven functional SNPs in IFNG/IFNGR1 gene among a group of Chinese population, and data implied the involvement of IFNGR1 gene in susceptibility to TB (He et al., 2010). But the number of study participants of this study was relatively small (222 cases and 188 controls), resulting in lower statistical power (He et al., 2010). Considering the critical role of IFNG/IFNGR1 signaling pathway in the antimycobacterial immunity, we performed a population-based case control study with a large sample size in a Chinese Han population, with aims to systematically explore the single or joint effect of genetic polymorphisms of IFNG/IFNGR1 in the risk of pulmonary TB. 2. Methods 2.1. Study population A total of 1434 pulmonary TB patients and 1412 controls were recruited from Jiangsu province of China. All of them were geneticallyunrelated Chinese Han population. Patients were diagnosed with the following criteria: (1) sputum smear or culture positive for MTB pathogen; and/or (2) clinical–radiological and histological evidence of TB. We recruited controls from a pool of individuals who participated in the local community-based health examination program. Controls were frequency matched to the cases by sex and age. All cases and controls had no prior HIV positive history. Each subject was individually interviewed in local health facilities by using a structured questionnaire and donated a blood sample for genotyping analysis. Informed consents were obtained from all participants and the study protocol has been approved by the Institutional Review Board of Nanjing Medical University. 2.2. SNP selection and genotyping Genomic DNA was extracted from leukocytes in the peripheral blood by proteinase K digestion and phenol/chloroform extraction. We identified taq SNPs in the IFNG and IFNGR1 gene through HapMap database (http://www.hapmap.org/) based on the following criteria: (1) minor allele frequency (MAF) ≥0.05 in the Chinese Han population; and (2) P value for Hardy–Weinberg equilibrium test ≥0.05. As a result, six SNPs were selected for genotyping, including one SNP in IFNG (rs1861494) and five SNPs in IFNGR1 (rs1327475, rs2234711, rs3799488, rs7749390 and rs9376267). We genotyped these SNPs using TaqMan allelic discrimination technology on the ABI 7900 RealTime PCR System (Applied
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Biosystems, Foster City, CA, USA) (Teuber et al., 2009). The primer and probe sequences for each SNP were designed by Nanjing Steed BioTechnologies Co. (Table 1). 2.3. Statistical analysis Data were entered with EpiData 3.1 software (Denmark) and analyzed by using STATA 10.0 (StataCorp, College Station, TX, USA). Student t-test (for continuous variables) and χ2-test (for categorical variables) were used to analyze the differences in demographic variables and potential risk factors between cases and controls. Hardy–Weinberg equilibrium was tested using a goodness-of-fit χ2-test by comparing the observed genotype frequencies with the expected ones among the controls to make sure that alleles were independently segregated. Logistic regression model was carried out to analyze the associations between the genotypes and the risk of TB by calculating the odds ratio (OR) and 95% confidence interval (CI). To control the potential confounding factors, we adjusted the OR(95% CI) for age, sex, tobacco smoking and alcohol drinking. To analyze the effect of SNPs comprehensively, we applied three different genetic models: additive model, dominant model and recessive model (Lettre et al., 2007; Salanti et al., 2009). We further performed a haplotype analysis by constructing haplotypes using phase 2.1 software. Considering the potential false positive rate incurred by multiple comparisons of SNPs, we used the Bonferroni correction method to adjust the P value. 3. Results 3.1. General characteristics The basic characteristics of the cases and controls are shown in Table 2. This study included 1434 TB cases (73.90% males and 26.10% females) and 1412 controls (72.03% males and 27.97 females). The mean (± SD) age was 52.04(± 17.57) years for cases and 52.27 ± (17.09) years for controls, respectively. Due to the prior frequency matching, there was no significant difference in the distribution of age and sex between the two groups. The proportion of ever smokers was 53.19% among cases, which was significantly higher than that among controls (34.21%) (P b 0.001). As to the history of alcohol drinking, the proportion in cases (22.52%) was significantly lower than that in controls (26.54%) (p = 0.014). 3.2. Genotype analysis The genotype distributions of six SNPs were all in Hardy–Weinberg equilibrium in the controls (P = 0.37 for rs1861494, P = 0.60 for rs1327475, P = 0.47 for rs2234711, P = 0.09 for rs3799488, P = 0.78 for rs7749390, and P = 0.11 for rs9376267). As shown in Table 3, after the Bonferroni correction for multiple comparisons, three SNPs
Table 1 Primers and probes designed for genotyping. SNPs
Primer (5′–3′)
Probe
rs1861494 CNT rs1327475 CNT rs2234711 CNT rs3799488 CNT rs7749390 ANG rs9376267 CNT
F-AGCAAGGGACAATGAGAGAACTG R-GTAAAGACAGGTGAGTTGACAAATCC F-GTGGCCATAATATAATTGTGATAATGTAATAAA R-CTCCATATTTAAATTGGAATTGGAGAAG F-AAAGAGGAGAGCCATGCTGCTA R-CGGTGACGGAAGTGACGTAA F-TTTTGAGGGTAGGCACTTAAGCTT R-TGGCTGGTATGACGTGATGAG F-GGTGTGAGCAGGGCTGAGAT R-CTAGGGCGACCTCGGAGAA F-GATTGAACAATGGAGCCACACA R-GCAAGAAGAAATGTTGGGTATGTTT
C: FAM-TACTCCCCGCTTCT-MGB T: HEX-TACTCCCTGCTTCTT-MGB T: FAM-AAGCAGATGTTTTTGAAG-MGB C: HEX-AAGCAGATGCTTTTGAA-MGB A: FAM-AGCCCAGCACTGC-MGB G: HEX-CAGCCCAGCGCTG-MGB G: FAM-TTCTCCCCGTAGATCT-MGB A: HEX-TTCTCCCCATAGATCT-MGB C: FAM-TACCGTCGCTCGC-MGB T: HEX-TACCGTCGTTCGC-MGB C: FAM-CATCAACACTCTGCTCT-MGB T: HEX-TCATCAACATTCTGCTCT-MGB
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the dominant model showed a 20% decreased risk among individuals carrying variant genotypes (AG/AA), with the adjusted OR of 0.80(95% CI: 0.68–0.94, P = 0.006).
Table 2 Basic characteristics of the cases and controls. Variables Age (years) Mean ± SD Sex Male Female Smoking Ever Never Drink Ever Never a b
Case (n = 1434) N(%)
Control (n = 1412) N(%)
52.04 ± 17.57
52.27 ± 17.09
1059(73.90) 374(26.10)
1017(72.03) 395(27.97)
758(53.19) 667(46.81)
483(34.21) 929(65.79)
318(22.52) 1094(77.48)
367(26.54) 1016(73.46)
P
0.724a 0.260b
b0.001b
0.014b
Student t test. χ2 test.
3.3. Haplotype analysis Linkage disequilibrium (LD) analysis was carried out on the 5 SNPs of IFNGR1. We found that rs2234711 and rs3799488 were in strong LD (D′ = 0.98, r2 = 0.94); thus we performed a haplotype analysis based on these two SNPs. The haplotype frequencies were presented in Table 4. Compared with the common rs2234711C–rs3799488C haplotype, the rs2234711T–rs3799488C haplotype was associated with a significantly increased risk of TB (adjusted OR: 1.24, 95% CI: 1.09–1.41, P = 0.001). 4. Discussion
(rs2234711, rs1327475 and rs7749390) in IFNGR1 gene were significantly associated with the altered risks of TB. For the SNP rs2234711, individuals carrying C allele (vs. T) showed a decreased risk, with the adjusted OR of 0.82(95% CI: 0.76–0.91, P b 0.001). The additive model revealed that each additional allele contributed about 14% decreased risk (OR: 0.86, 95% CI: 0.77–0.95). For the SNP rs1327475, C allele was related to an increased risk as compared with T, with the adjusted OR of 1.28 (95% CI: 1.07–1.53, P = 0.007). The additive model analysis showed that each additional allele contributed about 25% increased risk (OR: 1.25, 95% CI: 1.04–1.50). For the SNP rs7749390 of IFNGR1,
The clinical outcome of TB infection varies substantially among individuals (Girardi et al., 2000). Host factors may be involved in susceptibility or resistance at various stages of infection or disease (van Helden et al., 2006). Previous studies focused on immune reactivity in those with the disease, but attention is now turning to why the majority of infected persons remain healthy. In this context, it is important to determine whether those who remain healthy have a genetically endowed high level of resistance to TB or whether resistance is affected by environmental or other exogenous factors (Davies and Grange, 2001).
Table 3 Genotype distributions of the six SNPs between cases and controls. SNP
rs1861494 C T CC CT TT rs1327475 T C TT CT CC rs2234711 T C TT CT CC rs3799488 C T CC CT TT rs7749390 G A GG AG AA rs9376267 C T CC CT TT a b c
OR(95%CI)a
Case
Control
n(%)
n(%)
1781(62.10) 1087(37.90) 552(38.49) 677(47.21) 205(14.30)
1727(61.15) 1097(38.85) 536(37.96) 655(46.39) 221(15.65)
1 0.96(0.86–1.07) 1 1.02(0.86–1.20) 0.85(0.67–10.7)
2560(89.26) 308(10.74) 1227(85.56) 200(13.95) 7(0.49)
2581(91.40) 243(8.60) 1242(87.96) 156(11.05) 14(0.99)
1 1.28(1.07–1.53) 1 1.20(0.98–1.47) 2.10(0.98–1.47)
1694(60.76) 1094(39.24) 526(36.68) 642(44.77) 266(18.55)
1586(55.81) 1256(44.19) 442(31.30) 684(48.44) 286(20.25)
1 0.82(0.76–0.91) 1 0.78(0.65–0.92) 0.76(0.61–0.94)
2123(74.02) 745(25.98) 795(55.44) 533(37.17) 106(7.39)
2075(73.48) 749(26.52) 750(53.12) 575(40.72) 87(6.16)
1 0.97(0.86–1.10) 1 0.88(0.75–1.03) 1.19(0.87–1.62)
1682(58.65) 1186(41.35) 515(35.91) 652(45.47) 267(18.62)
1581(55.98) 1243(44.02) 440(31.16) 701(49.65) 271(19.19)
1 0.90(0.81–1.00) 1 0.79(0.66–0.93) 0.82(0.66–1.02)
1550(54.04) 1518(53.75) 440(30.68) 670(46.72) 324(22.59)
1318(45.96) 1306(46.25) 393(27.83) 732(51.84) 287(20.33)
1 0.99(0.89–1.10) 1 1.85(0.71–1.02) 1.02(0.83–1.28)
OR: odds ratio; CI: confidence interval, adjusted for age, sex, smoking and drinking. Dom: dominant model; Rec: recessive model; Add: additive model. Significant after the Bonferroni correction for multiple comparisons (P b 0.008).
P
Model Modelb
OR(95%CI)a
P
Add Dom Rec
0.94(0.84–1.50) 0.97(0.83–1014) 0.84(0.68–1.04)
0.298 0.746 0.115
Add Dom Rec
1.25(1.04–1.50) 1.24(1.02–1.51) 2.03(0.90–4.57)
0.018 0.034 0.088
Add Dom Rec
0.86(0.77–0.95) 0.77(0.66–0.91) 0.87(0.72–1.06)
0.005c 0.002c 0.172
Add Dom Rec
0.98(0.87–1.11) 0.92(0.79–1.08) 1.25(0.92–1.70)
0.808 0.303 0.150
Add Dom Rec
0.89(0.80–0.99) 0.80(0.68–0.94) 0.94(0.77–1.14)
0.029 0.006c 0.536
Add Dom Rec
1.00(0.90–1.12) 0.90(0.76–1.07) 1.14(0.91–1.37)
0.988 0.239 0.183
0.460 0.834 0.172
0.007c 0.073 0.075
b0.001c 0.004c 0.012
0.640 0.128 0.280
0.423 0.006c 0.072
0.826 0.085 0.800
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Table 4 Haplotype analysis on the risk of tuberculosis. Haplotypea
Case(%)
Control(%)
OR(95%CI)
P
aOR(95%CI)b
Pb
CC TC TT CT
1168(40.73%) 955(33.30) 739(25.77) 6(0.21)
1244(44.05) 831(29.43) 737(26.10) 12(0.42)
1 1.22(1.08–1.38) 1.07(0.93–1.21) 0.53(0.20–1.42)
0.001 0.32 0.209
1 1.24(1.09–1.41) 1.09(0.95–1.24) 0.53(0.19–1.16)
0.001 0.214 0.221
a b
Haplotype: rs2234711–rs3799488. aOR: adjusted odds ratio, adjusted for age, sex, smoking and drinking.
In the vast majority of individuals, innate immune pathways and T-helper 1 (Th1) cell mediated immunity are activated resulting in the lysis of the bacterium. Certain cytokines, including IFNG, mainly produced by Th1 cells, plays an important part in the cell mediated immune response to intracellular pathogens and induced organ-specific autoimmunity (Abbas et al., 1996). Experimental studies on knockout mice as well as clinical observation on severe mycobacterial infections in patients with defects of IFNG signaling pathway have proved its essential role in controlling MTB infections (Cooper et al., 1993; Filipe-Santos et al., 2006; van de Vosse et al., 2004). In this case control study, we genotyped six taq SNPs in the IFNG/ IFNGR1 gene among a group of Chinese Han population. Findings indicated that genetic polymorphisms of rs2234711, rs1327475 and rs7749390 were associated with an altered risk of TB. For the SNP rs2234711, individuals carrying C allele (vs. T) showed a decreased risk. For the SNP rs1327475, C allele was related to an increased risk. For the SNP rs7749390, the dominant model showed a 20% decreased risk among individuals carrying variant genotypes (AG/AA). Moreover, we observed that the haplotype rs2234711T–rs3799488C also contributed a significantly increased risk of the disease. IFNG is highly conserved and few SNPs are found in the intragenic region (Pacheco et al., 2008). The most studied one is IFNG +874T/A, which is located in the first intron and adjacent to a CA repeat region (Pravica et al., 2000). Findings from previous studies have suggested the association between IFNG + 874A allele and the susceptibility to TB, but with inconsistent results (Pacheco et al., 2008; Wang et al., 2010). The taq SNP rs1861494 is located in the intron region of IFNG gene. Abhimanyu et al. observed a significant risk of this locus in susceptibility to TB in Indian populations (Abhimanyu et al., 2012). However, our study does not replicate this finding in the Chinese population. It might be attributed to the variations in allelic frequencies and therefore it is not surprising that genetic polymorphisms associated with TB have yielded conflicting results in different ethnic groups (Ansari et al., 2009). IFN-gamma signaling is mediated through the ligand binding to IFNGR1. Control of IFNGR1 expression level is one of the mechanisms by which cells modulate the potency of IFN-gamma signaling (Zhou et al., 2009). It is conceivable that natural selection might favor different levels of IFNGR1 expression, depending on the type of infectious pathogens to which a population is exposed (He et al., 2010). The SNP rs2234711 (C/T) is located in the 5′-UTR region of the IFNGR1 gene, which encodes the human interferon-gamma receptor ligand-binding chain I. It is also the binding site of transcription factors, including HNF4A, HMGN3, HEY1, and EGR1, etc. (Table 5). The minor allele frequency of the SNP s2234711 varies greatly in different areas, ranging from 0% in European populations to 60% in African–American populations. In Mandinka, the major Gambian ethnic group, heterozygotes for the IFNGR1 rs2234711 polymorphism were protected against cerebral malaria (OR: 0.54) and against death resulting from cerebral malaria (OR: 0.22) (Koch et al., 2002). However, such a tendency or heterozygote advantage was not observed in another study of the Thai malaria patients (Naka et al., 2009). Our data showed that C allele is more frequent in the control group and related to a decreased risk of TB. Moreover, we observed a combined effect of the SNP rs2234711 together
with another SNP rs3799488, where the haplotype rs2234711T– rs3799488C showed a significantly increased risk of TB. The SNP rs1327475 is located in the intron region of IFNGR1 gene. It has been revealed to be associated with SLE in a Norwegian cohort (Nordang et al., 2012) and ADEH + (atopic dermatitis patients suffer from disseminated viral infections, i.e. eczema herpeticum) phenotype (Leung et al., 2011). Blanton et al. analyzed 40 Egyptian pedigrees in which more than 1 sib had hepatic fibrosis due to S. mansoni infection and found significant linkage with a polymorphism rs1327475 in IFNGR1 gene (Blanton et al., 2005). Our study was the first one to explore the polymorphism at this locus on the susceptibility to TB. We found that the C allele was related to a 28% increased risk as compared with T allele. As this SNP locates in the intron region of the gene IFNGR1, the real function of this SNP or whether it is related to other functional loci is unclear. Consistent with our findings, He et al. also observed a significant association between rs7749390 and the susceptibility to TB (He et al., 2010). This SNP is located on the exon/intron splice site and seems to have an influence on the intron–exon splicing process (He et al., 2010). This SNP has also been explored in post kala-azar dermal leishmaniasis in Sudan, but without significant findings (Salih et al., 2007). There are several limitations in this study. First, we only selected taq SNPs in the IFNG and IFNGR1 gene. Some of these SNPs are located in the intron region and their real functions are unclear. Analysis of the Encyclopedia of DNA Elements (ENCODE) as implemented in Regulome DB indicated that some SNPs might influence the binding of specified transcription factors (Table 5). Further work with both knockout and overexpression models of IFNG/IFNGR1 and the neighboring genes is likely to provide the most fruitful approach to understand the mechanisms and pathways whereby these variants influence the risk of TB. Secondly, due to the weak effect of a single genetic polymorphism, other genes in the immunity pathway, together with environmental factors are all interesting. Replications in larger populations are needed to conclusively confirm or reject our findings. Thirdly, due to the technical issues, not all TB centers in China especially in rural areas, could
Table 5 Functional annotation for the six SNPs based on Regulome DB. SNP
Regulome DB Scorea
Hits
Binding protein
rs1861494 rs1327475 rs3799488 rs7749390
4 7 5 4
Chromatin_Structure NA Motifs Chromatin_Structure
rs9376267 rs2234711
7 4
NA Chromatin_Structure
CEBPB NA RFX3 HNF4A, HEY1, POU2F2, ETS1, TBP, SIN3A, GABPA, CHD2, POLR2A, CDX2 NA HNF4A, HMGN3, HEY1, EGR1, MYC, POU2F2, TRIM28, ELF1, E2F6, ETS1, TBP, TAF1, SIN3A, GABPA, MAX, IRF1, E2F4, CCNT2, SP1, JUND, YY1, STAT3, POLR2A, CDX2, NR2C2
a Description of Regulome DB Score: 4, TF binding + DNase peak; 5, TF binding or DNase peak; 7, No data supporting (NA).
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provide sputum culture services. Thus we didn't perform a subgroup analysis by considering the proven and probable cases. 5. Conclusions Taken together, our results suggest that genetic polymorphism in IFNGR1 gene is involved in the risk of TB in the Chinese population. Future studies should include a comprehensive sequencing analysis to identify the specific causative sequence variants underlying the observed associations. Conflict of interest The authors declare that they have no conflict of interest. Acknowledgments This study was partly supported by the National Natural Science Foundation of China (81072351), Jiangsu Science Supported Planning/ Social Development Foundation (BE2011841), Jiangsu Natural Science Foundation (BE2012694), and Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). References Abbas, A.K., Murphy, K.M., Sher, A., 1996. Functional diversity of helper T lymphocytes. Nature 383, 787–793. Abhimanyu, Bose, M., Jha, P., Indian Genome Variation Consortium, 2012. Footprints of genetic susceptibility to pulmonary tuberculosis: cytokine gene variants in north Indians. Indian Journal of Medical Research 135, 763–770. Altare, F., Durandy, A., Lammas, D., Emile, J.F., Lamhamedi, S., Le Deist, F., Drysdale, P., Jouanguy, E., Doffinger, R., Bernaudin, F., Jeppsson, O., Gollob, J.A., Meinl, E., Segal, A. W., Fischer, A., Kumararatne, D., Casanova, J.L., 1998. Impairment of mycobacterial immunity in human interleukin-12 receptor deficiency. Science 280, 1432–1435. Ansari, A., Talat, N., Jamil, B., Hasan, Z., Razzaki, T., Dawood, G., Hussain, R., 2009. Cytokine gene polymorphisms across tuberculosis clinical spectrum in Pakistani patients. PLoS One 4, e4778. Blanton, R.E., Salam, E.A., Ehsan, A., King, C.H., Goddard, K.A., 2005. Schistosomal hepatic fibrosis and the interferon gamma receptor: a linkage analysis using singlenucleotide polymorphic markers. European Journal of Human Genetics 13, 660–668. Cooper, A.M., Dalton, D.K., Stewart, T.A., Griffin, J.P., Russell, D.G., Orme, I.M., 1993. Disseminated tuberculosis in interferon gamma gene-disrupted mice. Journal of Experimental Medicine 178, 2243–2247. Davies, P.D., Grange, J.M., 2001. Factors affecting susceptibility and resistance to tuberculosis. Thorax 56 (Suppl. 2), ii23–ii29. Dupuis, S., Doffinger, R., Picard, C., Fieschi, C., Altare, F., Jouanguy, E., Abel, L., Casanova, J.L., 2000. Human interferon-gamma-mediated immunity is a genetically controlled continuous trait that determines the outcome of mycobacterial invasion. Immunological Reviews 178, 129–137. Ehrt, S., Schnappinger, D., Bekiranov, S., Drenkow, J., Shi, S., Gingeras, T.R., Gaasterland, T., Schoolnik, G., Nathan, C., 2001. Reprogramming of the macrophage transcriptome in response to interferon-gamma and Mycobacterium tuberculosis: signaling roles of nitric oxide synthase-2 and phagocyte oxidase. Journal of Experimental Medicine 194, 1123–1140. Filipe-Santos, O., Bustamante, J., Chapgier, A., Vogt, G., de Beaucoudrey, L., Feinberg, J., Jouanguy, E., Boisson-Dupuis, S., Fieschi, C., Picard, C., Casanova, J.L., 2006. Inborn errors of IL-12/23- and IFN-gamma-mediated immunity: molecular, cellular, and clinical features. Seminars in Immunology 18, 347–361. Frieden, T.R., Sterling, T.R., Munsiff, S.S., Watt, C.J., Dye, C., 2003. Tuberculosis. Lancet 362, 887–899. Girardi, E., Raviglione, M.C., Antonucci, G., Godfrey-Faussett, P., Ippolito, G., 2000. Impact of the HIV epidemic on the spread of other diseases: the case of tuberculosis. AIDS 14 (Suppl. 3), S47–S56. He, J., Wang, J., Lei, D., Ding, S., 2010. Analysis of functional SNP in ifng/ifngr1 in Chinese Han population with tuberculosis. Scandinavian Journal of Immunology 71, 452–458. Jouanguy, E., Lamhamedi-Cherradi, S., Lammas, D., Dorman, S.E., Fondaneche, M.C., Dupuis, S., Doffinger, R., Altare, F., Girdlestone, J., Emile, J.F., Ducoulombier, H.,
Edgar, D., Clarke, J., Oxelius, V.A., Brai, M., Novelli, V., Heyne, K., Fischer, A., Holland, S.M., Kumararatne, D.S., Schreiber, R.D., Casanova, J.L., 1999. A human IFNGR1 small deletion hotspot associated with dominant susceptibility to mycobacterial infection. Nature Genetics 21, 370–378. Koch, O., Awomoyi, A., Usen, S., Jallow, M., Richardson, A., Hull, J., Pinder, M., Newport, M., Kwiatkowski, D., 2002. IFNGR1 gene promoter polymorphisms and susceptibility to cerebral malaria. Journal of Infectious Diseases 185, 1684–1687. Leandro, A.C., Rocha, M.A., Cardoso, C.S., Bonecini-Almeida, M.G., 2009. Genetic polymorphisms in vitamin D receptor, vitamin D-binding protein, Toll-like receptor 2, nitric oxide synthase 2, and interferon-gamma genes and its association with susceptibility to tuberculosis. Brazilian Journal of Medical and Biological Research 42, 312–322. Lee, J., Kornfeld, H., 2010. Interferon-gamma regulates the death of M. tuberculosisinfected macrophages. Journal of Cell Death 3, 1–11. Lettre, G., Lange, C., Hirschhorn, J.N., 2007. Genetic model testing and statistical power in population-based association studies of quantitative traits. Genetic Epidemiology 31, 358–362. Leung, D.Y., Gao, P.S., Grigoryev, D.N., Rafaels, N.M., Streib, J.E., Howell, M.D., Taylor, P.A., Boguniewicz, M., Canniff, J., Armstrong, B., Zaccaro, D.J., Schneider, L.C., Hata, T.R., Hanifin, J.M., Beck, L.A., Weinberg, A., Barnes, K.C., 2011. Human atopic dermatitis complicated by eczema herpeticum is associated with abnormalities in IFN-gamma response. Journal of Allergy and Clinical Immunology 127 (965–73), e1–e5. Moller, M., Hoal, E.G., 2010. Current findings, challenges and novel approaches in human genetic susceptibility to tuberculosis. Tuberculosis (Edinburgh, Scotland) 90, 71–83. Naka, I., Patarapotikul, J., Hananantachai, H., Tokunaga, K., Tsuchiya, N., Ohashi, J., 2009. IFNGR1 polymorphisms in Thai malaria patients. Infection, Genetics and Evolution 9, 1406–1409. Newport, M.J., Huxley, C.M., Huston, S., Hawrylowicz, C.M., Oostra, B.A., Williamson, R., Levin, M., 1996. A mutation in the interferon-gamma-receptor gene and susceptibility to mycobacterial infection. New England Journal of Medicine 335, 1941–1949. Nordang, G.B., Viken, M.K., Amundsen, S.S., Sanchez, E.S., Flato, B., Forre, O.T., Martin, J., Kvien, T.K., Lie, B.A., 2012. Interferon regulatory factor 5 gene polymorphism confers risk to several rheumatic diseases and correlates with expression of alternative thymic transcripts. Rheumatology (Oxford) 51, 619–626. Pacheco, A.G., Cardoso, C.C., Moraes, M.O., 2008. IFNG + 874T/A, IL10 − 1082G/A and TNF − 308G/A polymorphisms in association with tuberculosis susceptibility: a meta-analysis study. Human Genetics 123, 477–484. Pravica, V., Perrey, C., Stevens, A., Lee, J.H., Hutchinson, I.V., 2000. A single nucleotide polymorphism in the first intron of the human IFN-gamma gene: absolute correlation with a polymorphic CA microsatellite marker of high IFN-gamma production. Human Immunology 61, 863–866. Salanti, G., Southam, L., Altshuler, D., Ardlie, K., Barroso, I., Boehnke, M., Cornelis, M.C., Frayling, T.M., Grallert, H., Grarup, N., Groop, L., Hansen, T., Hattersley, A.T., Hu, F.B., Hveem, K., Illig, T., Kuusisto, J., Laakso, M., Langenberg, C., Lyssenko, V., McCarthy, M.I., Morris, A., Morris, A.D., Palmer, C.N., Payne, F., Platou, C.G., Scott, L.J., Voight, B. F., Wareham, N.J., Zeggini, E., Ioannidis, J.P., 2009. Underlying genetic models of inheritance in established type 2 diabetes associations. American Journal of Epidemiology 170, 537–545. Salih, M.A., Ibrahim, M.E., Blackwell, J.M., Miller, E.N., Khalil, E.A., ElHassan, A.M., Musa, A.M., Mohamed, H.S., 2007. IFNG and IFNGR1 gene polymorphisms and susceptibility to post-kala-azar dermal leishmaniasis in Sudan. Genes and Immunity 8, 75–78. Teuber, M., Wenz, M.H., Schreiber, S., Franke, A., 2009. GMFilter and SXTestPlate: software tools for improving the SNPlex genotyping system. BMC Bioinformatics 10, 81. Thakur, A., Pedersen, L.E., Jungersen, G., 2012. Immune markers and correlates of protection for vaccine induced immune responses. Vaccine 30, 4907–4920. van de Vosse, E., Hoeve, M.A., Ottenhoff, T.H., 2004. Human genetics of intracellular infectious diseases: molecular and cellular immunity against mycobacteria and salmonellae. Lancet Infectious Diseases 4, 739–749. van Helden, P.D., Moller, M., Babb, C., Warren, R., Walzl, G., Uys, P., Hoal, E., 2006. TB epidemiology and human genetics. Novartis Foundation Symposium 279, 17–31 (discussion 31–41, 216–9). Wang, J., Tang, S., Shen, H., 2010. Association of genetic polymorphisms in the IL12-IFNG pathway with susceptibility to and prognosis of pulmonary tuberculosis in a Chinese population. European Journal of Clinical Microbiology & Infectious Diseases 29, 1291–1295. WHO, 2013. Global Tuberculosis Report 2013. WHO. Young, D.B., Perkins, M.D., Duncan, K., Barry III, C.E., 2008. Confronting the scientific obstacles to global control of tuberculosis. Journal of Clinical Investigation 118, 1255–1265. Zhou, J., Chen, D.Q., Poon, V.K., Zeng, Y., Ng, F., Lu, L., Huang, J.D., Yuen, K.Y., Zheng, B.J., 2009. A regulatory polymorphism in interferon-gamma receptor 1 promoter is associated with the susceptibility to chronic hepatitis B virus infection. Immunogenetics 61, 423–430.
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Genetic polymorphisms of IFNG and IFNGR1 in association with the risk of pulmonary tuberculosis Jieqiong Lü a,1, Hongqiu Pan a,b,1, Yongzhong Chen b, Shaowen Tang a, Yan Feng a, Sangsang Qiu a, Siming Zhang a, Liang Wu a, Ruobing Xu a, Xianzhen Peng a, Jianming Wang b,⁎, Cheng Lu c,⁎⁎ a b c
Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, PR China Department of Tuberculosis, Third Hospital of Zhenjiang City, Zhenjiang 212005, PR China Department of Breast, Nanjing Maternity and Child Health Hospital of Nanjing Medical University, Nanjing 210004, PR China
a r t i c l e
i n f o
Article history: Received 7 November 2013 Received in revised form 16 February 2014 Accepted 21 March 2014 Available online 25 March 2014 Keywords: Tuberculosis Infection Genetic polymorphisms Susceptibility Immunity
a b s t r a c t Objective: Genetic host factors play an important role in controlling individual's susceptibility to the pathogen. This study aims to explore the single and joint effect of genetic polymorphisms of interferon-gamma (IFNG) and its receptor (IFNGR1) in association with the pulmonary tuberculosis in a Chinese Han population. Methods: This population-based case control study consisted of 1434 pulmonary tuberculosis patients and 1412 healthy controls. Six tag SNPs in IFNG/IFNGR1 were genotyped using TaqMan allelic discrimination technology. The logistic regression model was carried out to analyze the associations between the genotypes and haplotypes and the risk of tuberculosis by calculating the odds ratio (OR) and 95% confidence interval (CI). Results: After the Bonferroni correction for multiple comparisons, three SNPs (rs2234711, rs1327475 and rs7749390) in IFNGR1 gene were observed to be significantly associated with the altered risks of tuberculosis. For the SNP rs2234711, individuals carrying C allele (vs. T) showed a decreased risk, with the adjusted OR(95% CI) of 0.82(0.76–0.91). The additive model revealed that each additional allele contributed about 14% decreased risk (OR: 0.86, 95% CI: 0.77–0.95). Moreover, we observed a strong linkage disequilibrium between rs2234711 and rs3799488. Compared with the common rs2234711C–rs3799488C haplotype, the haplotype rs2234711T– rs3799488C contributed to a significant increase in the risk of tuberculosis (adjusted OR: 1.24, 95% CI: 1.09–1.41). Conclusions: Our results suggest that genetic polymorphisms in IFNGR1 gene are involved in the risk of tuberculosis in the Chinese population. Future studies should include a comprehensive sequencing analysis to identify the specific causative sequence variants underlying the observed associations. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Tuberculosis (TB) has been declared as a global public health emergency by the World Health Organization. It was estimated that almost 8.6 million people developed active TB and 1.3 million died from it in 2012, mostly in developing countries (WHO, 2013). Although nearly one-third of the world's population has been potentially infected with the pathogen Mycobacterium tuberculosis (MTB) (Young et al., 2008), only 5–10% will develop clinical TB during their lifetimes (Frieden
Abbreviations: TB, tuberculosis; MTB, Mycobacterium tuberculosis; IFNG, interferongamma; IFNGR, interferon-gamma receptor; SNP, single nucleotide polymorphism; MAF, minor allele frequency; PCR, polymerase chain reaction; OR, odds ratio; CI, confidence interval; SD, standard deviation; LD, linkage disequilibrium; Th1, T-helper 1; UTR, untranslated region; ENCODE, Encyclopedia of DNA Elements. ⁎ Correspondence to: J. Wang, Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China. ⁎⁎ Correspondence to: C. Lu, Department of Breast, Nanjing Maternity and Child Health Hospital of Nanjing Medical University, Nanjing, China. E-mail addresses: [email protected] (J. Wang), [email protected] (C. Lu). 1 These authors contributed equally to this work.
http://dx.doi.org/10.1016/j.gene.2014.03.042 0378-1119/© 2014 Elsevier B.V. All rights reserved.
et al., 2003). The outcome of infection is influenced by many factors, such as malnutrition, co-infection with other pathogens, exposure to environmental microbes, and previous vaccination (van de Vosse et al., 2004). It is clear that genetic host factors also play an important role in controlling individual's susceptibility to the pathogen (Altare et al., 1998; Newport et al., 1996). It was estimated that the contribution of genetic factors to the phenotypic variation and immune responses in the population infected with TB ranges up to 71% (Moller and Hoal, 2010). In the past decades, there has been dramatic progress in our understanding of the innate and adaptive immunity in the human host defense to TB (Leandro et al., 2009). Current studies highlight a complex molecular network in antimycobacterial immunity, centered on interferon-gamma (IFNG) signaling pathway (Thakur et al., 2012). Individuals with inherited disorders of IFNG mediated immunity appear to be specifically vulnerable to MTB infections (Dupuis et al., 2000). The IFNG gene is located on chromosome 12q24.1. Studies have reported that IFNG can activate murine macrophages to inhibit MTB growth (Ehrt et al., 2001). It is up-regulated and secreted as a major cytokine to activate macrophages during MTB infection (Lee and Kornfeld,
J. Lü et al. / Gene 543 (2014) 140–144
2010). The human receptor of IFNG (IFNGR) is a heterodimer of IFNGR1 and IFNGR2. The IFNGR1 gene is located on chromosome 6q23.3, which encodes the ligand-binding chain (alpha) of the interferon gamma receptor. Defects in IFNGR1 have been reported as a cause of Mendelian susceptibility to mycobacterial disease, also known as familial disseminated atypical mycobacterial infection (Jouanguy et al., 1999). Previous studies mainly focused on the potentially functional polymorphisms of IFNG like +874 A/T in the intron 1 and adjacent CA repeats. He et al. analyzed seven functional SNPs in IFNG/IFNGR1 gene among a group of Chinese population, and data implied the involvement of IFNGR1 gene in susceptibility to TB (He et al., 2010). But the number of study participants of this study was relatively small (222 cases and 188 controls), resulting in lower statistical power (He et al., 2010). Considering the critical role of IFNG/IFNGR1 signaling pathway in the antimycobacterial immunity, we performed a population-based case control study with a large sample size in a Chinese Han population, with aims to systematically explore the single or joint effect of genetic polymorphisms of IFNG/IFNGR1 in the risk of pulmonary TB. 2. Methods 2.1. Study population A total of 1434 pulmonary TB patients and 1412 controls were recruited from Jiangsu province of China. All of them were geneticallyunrelated Chinese Han population. Patients were diagnosed with the following criteria: (1) sputum smear or culture positive for MTB pathogen; and/or (2) clinical–radiological and histological evidence of TB. We recruited controls from a pool of individuals who participated in the local community-based health examination program. Controls were frequency matched to the cases by sex and age. All cases and controls had no prior HIV positive history. Each subject was individually interviewed in local health facilities by using a structured questionnaire and donated a blood sample for genotyping analysis. Informed consents were obtained from all participants and the study protocol has been approved by the Institutional Review Board of Nanjing Medical University. 2.2. SNP selection and genotyping Genomic DNA was extracted from leukocytes in the peripheral blood by proteinase K digestion and phenol/chloroform extraction. We identified taq SNPs in the IFNG and IFNGR1 gene through HapMap database (http://www.hapmap.org/) based on the following criteria: (1) minor allele frequency (MAF) ≥0.05 in the Chinese Han population; and (2) P value for Hardy–Weinberg equilibrium test ≥0.05. As a result, six SNPs were selected for genotyping, including one SNP in IFNG (rs1861494) and five SNPs in IFNGR1 (rs1327475, rs2234711, rs3799488, rs7749390 and rs9376267). We genotyped these SNPs using TaqMan allelic discrimination technology on the ABI 7900 RealTime PCR System (Applied
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Biosystems, Foster City, CA, USA) (Teuber et al., 2009). The primer and probe sequences for each SNP were designed by Nanjing Steed BioTechnologies Co. (Table 1). 2.3. Statistical analysis Data were entered with EpiData 3.1 software (Denmark) and analyzed by using STATA 10.0 (StataCorp, College Station, TX, USA). Student t-test (for continuous variables) and χ2-test (for categorical variables) were used to analyze the differences in demographic variables and potential risk factors between cases and controls. Hardy–Weinberg equilibrium was tested using a goodness-of-fit χ2-test by comparing the observed genotype frequencies with the expected ones among the controls to make sure that alleles were independently segregated. Logistic regression model was carried out to analyze the associations between the genotypes and the risk of TB by calculating the odds ratio (OR) and 95% confidence interval (CI). To control the potential confounding factors, we adjusted the OR(95% CI) for age, sex, tobacco smoking and alcohol drinking. To analyze the effect of SNPs comprehensively, we applied three different genetic models: additive model, dominant model and recessive model (Lettre et al., 2007; Salanti et al., 2009). We further performed a haplotype analysis by constructing haplotypes using phase 2.1 software. Considering the potential false positive rate incurred by multiple comparisons of SNPs, we used the Bonferroni correction method to adjust the P value. 3. Results 3.1. General characteristics The basic characteristics of the cases and controls are shown in Table 2. This study included 1434 TB cases (73.90% males and 26.10% females) and 1412 controls (72.03% males and 27.97 females). The mean (± SD) age was 52.04(± 17.57) years for cases and 52.27 ± (17.09) years for controls, respectively. Due to the prior frequency matching, there was no significant difference in the distribution of age and sex between the two groups. The proportion of ever smokers was 53.19% among cases, which was significantly higher than that among controls (34.21%) (P b 0.001). As to the history of alcohol drinking, the proportion in cases (22.52%) was significantly lower than that in controls (26.54%) (p = 0.014). 3.2. Genotype analysis The genotype distributions of six SNPs were all in Hardy–Weinberg equilibrium in the controls (P = 0.37 for rs1861494, P = 0.60 for rs1327475, P = 0.47 for rs2234711, P = 0.09 for rs3799488, P = 0.78 for rs7749390, and P = 0.11 for rs9376267). As shown in Table 3, after the Bonferroni correction for multiple comparisons, three SNPs
Table 1 Primers and probes designed for genotyping. SNPs
Primer (5′–3′)
Probe
rs1861494 CNT rs1327475 CNT rs2234711 CNT rs3799488 CNT rs7749390 ANG rs9376267 CNT
F-AGCAAGGGACAATGAGAGAACTG R-GTAAAGACAGGTGAGTTGACAAATCC F-GTGGCCATAATATAATTGTGATAATGTAATAAA R-CTCCATATTTAAATTGGAATTGGAGAAG F-AAAGAGGAGAGCCATGCTGCTA R-CGGTGACGGAAGTGACGTAA F-TTTTGAGGGTAGGCACTTAAGCTT R-TGGCTGGTATGACGTGATGAG F-GGTGTGAGCAGGGCTGAGAT R-CTAGGGCGACCTCGGAGAA F-GATTGAACAATGGAGCCACACA R-GCAAGAAGAAATGTTGGGTATGTTT
C: FAM-TACTCCCCGCTTCT-MGB T: HEX-TACTCCCTGCTTCTT-MGB T: FAM-AAGCAGATGTTTTTGAAG-MGB C: HEX-AAGCAGATGCTTTTGAA-MGB A: FAM-AGCCCAGCACTGC-MGB G: HEX-CAGCCCAGCGCTG-MGB G: FAM-TTCTCCCCGTAGATCT-MGB A: HEX-TTCTCCCCATAGATCT-MGB C: FAM-TACCGTCGCTCGC-MGB T: HEX-TACCGTCGTTCGC-MGB C: FAM-CATCAACACTCTGCTCT-MGB T: HEX-TCATCAACATTCTGCTCT-MGB
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the dominant model showed a 20% decreased risk among individuals carrying variant genotypes (AG/AA), with the adjusted OR of 0.80(95% CI: 0.68–0.94, P = 0.006).
Table 2 Basic characteristics of the cases and controls. Variables Age (years) Mean ± SD Sex Male Female Smoking Ever Never Drink Ever Never a b
Case (n = 1434) N(%)
Control (n = 1412) N(%)
52.04 ± 17.57
52.27 ± 17.09
1059(73.90) 374(26.10)
1017(72.03) 395(27.97)
758(53.19) 667(46.81)
483(34.21) 929(65.79)
318(22.52) 1094(77.48)
367(26.54) 1016(73.46)
P
0.724a 0.260b
b0.001b
0.014b
Student t test. χ2 test.
3.3. Haplotype analysis Linkage disequilibrium (LD) analysis was carried out on the 5 SNPs of IFNGR1. We found that rs2234711 and rs3799488 were in strong LD (D′ = 0.98, r2 = 0.94); thus we performed a haplotype analysis based on these two SNPs. The haplotype frequencies were presented in Table 4. Compared with the common rs2234711C–rs3799488C haplotype, the rs2234711T–rs3799488C haplotype was associated with a significantly increased risk of TB (adjusted OR: 1.24, 95% CI: 1.09–1.41, P = 0.001). 4. Discussion
(rs2234711, rs1327475 and rs7749390) in IFNGR1 gene were significantly associated with the altered risks of TB. For the SNP rs2234711, individuals carrying C allele (vs. T) showed a decreased risk, with the adjusted OR of 0.82(95% CI: 0.76–0.91, P b 0.001). The additive model revealed that each additional allele contributed about 14% decreased risk (OR: 0.86, 95% CI: 0.77–0.95). For the SNP rs1327475, C allele was related to an increased risk as compared with T, with the adjusted OR of 1.28 (95% CI: 1.07–1.53, P = 0.007). The additive model analysis showed that each additional allele contributed about 25% increased risk (OR: 1.25, 95% CI: 1.04–1.50). For the SNP rs7749390 of IFNGR1,
The clinical outcome of TB infection varies substantially among individuals (Girardi et al., 2000). Host factors may be involved in susceptibility or resistance at various stages of infection or disease (van Helden et al., 2006). Previous studies focused on immune reactivity in those with the disease, but attention is now turning to why the majority of infected persons remain healthy. In this context, it is important to determine whether those who remain healthy have a genetically endowed high level of resistance to TB or whether resistance is affected by environmental or other exogenous factors (Davies and Grange, 2001).
Table 3 Genotype distributions of the six SNPs between cases and controls. SNP
rs1861494 C T CC CT TT rs1327475 T C TT CT CC rs2234711 T C TT CT CC rs3799488 C T CC CT TT rs7749390 G A GG AG AA rs9376267 C T CC CT TT a b c
OR(95%CI)a
Case
Control
n(%)
n(%)
1781(62.10) 1087(37.90) 552(38.49) 677(47.21) 205(14.30)
1727(61.15) 1097(38.85) 536(37.96) 655(46.39) 221(15.65)
1 0.96(0.86–1.07) 1 1.02(0.86–1.20) 0.85(0.67–10.7)
2560(89.26) 308(10.74) 1227(85.56) 200(13.95) 7(0.49)
2581(91.40) 243(8.60) 1242(87.96) 156(11.05) 14(0.99)
1 1.28(1.07–1.53) 1 1.20(0.98–1.47) 2.10(0.98–1.47)
1694(60.76) 1094(39.24) 526(36.68) 642(44.77) 266(18.55)
1586(55.81) 1256(44.19) 442(31.30) 684(48.44) 286(20.25)
1 0.82(0.76–0.91) 1 0.78(0.65–0.92) 0.76(0.61–0.94)
2123(74.02) 745(25.98) 795(55.44) 533(37.17) 106(7.39)
2075(73.48) 749(26.52) 750(53.12) 575(40.72) 87(6.16)
1 0.97(0.86–1.10) 1 0.88(0.75–1.03) 1.19(0.87–1.62)
1682(58.65) 1186(41.35) 515(35.91) 652(45.47) 267(18.62)
1581(55.98) 1243(44.02) 440(31.16) 701(49.65) 271(19.19)
1 0.90(0.81–1.00) 1 0.79(0.66–0.93) 0.82(0.66–1.02)
1550(54.04) 1518(53.75) 440(30.68) 670(46.72) 324(22.59)
1318(45.96) 1306(46.25) 393(27.83) 732(51.84) 287(20.33)
1 0.99(0.89–1.10) 1 1.85(0.71–1.02) 1.02(0.83–1.28)
OR: odds ratio; CI: confidence interval, adjusted for age, sex, smoking and drinking. Dom: dominant model; Rec: recessive model; Add: additive model. Significant after the Bonferroni correction for multiple comparisons (P b 0.008).
P
Model Modelb
OR(95%CI)a
P
Add Dom Rec
0.94(0.84–1.50) 0.97(0.83–1014) 0.84(0.68–1.04)
0.298 0.746 0.115
Add Dom Rec
1.25(1.04–1.50) 1.24(1.02–1.51) 2.03(0.90–4.57)
0.018 0.034 0.088
Add Dom Rec
0.86(0.77–0.95) 0.77(0.66–0.91) 0.87(0.72–1.06)
0.005c 0.002c 0.172
Add Dom Rec
0.98(0.87–1.11) 0.92(0.79–1.08) 1.25(0.92–1.70)
0.808 0.303 0.150
Add Dom Rec
0.89(0.80–0.99) 0.80(0.68–0.94) 0.94(0.77–1.14)
0.029 0.006c 0.536
Add Dom Rec
1.00(0.90–1.12) 0.90(0.76–1.07) 1.14(0.91–1.37)
0.988 0.239 0.183
0.460 0.834 0.172
0.007c 0.073 0.075
b0.001c 0.004c 0.012
0.640 0.128 0.280
0.423 0.006c 0.072
0.826 0.085 0.800
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Table 4 Haplotype analysis on the risk of tuberculosis. Haplotypea
Case(%)
Control(%)
OR(95%CI)
P
aOR(95%CI)b
Pb
CC TC TT CT
1168(40.73%) 955(33.30) 739(25.77) 6(0.21)
1244(44.05) 831(29.43) 737(26.10) 12(0.42)
1 1.22(1.08–1.38) 1.07(0.93–1.21) 0.53(0.20–1.42)
0.001 0.32 0.209
1 1.24(1.09–1.41) 1.09(0.95–1.24) 0.53(0.19–1.16)
0.001 0.214 0.221
a b
Haplotype: rs2234711–rs3799488. aOR: adjusted odds ratio, adjusted for age, sex, smoking and drinking.
In the vast majority of individuals, innate immune pathways and T-helper 1 (Th1) cell mediated immunity are activated resulting in the lysis of the bacterium. Certain cytokines, including IFNG, mainly produced by Th1 cells, plays an important part in the cell mediated immune response to intracellular pathogens and induced organ-specific autoimmunity (Abbas et al., 1996). Experimental studies on knockout mice as well as clinical observation on severe mycobacterial infections in patients with defects of IFNG signaling pathway have proved its essential role in controlling MTB infections (Cooper et al., 1993; Filipe-Santos et al., 2006; van de Vosse et al., 2004). In this case control study, we genotyped six taq SNPs in the IFNG/ IFNGR1 gene among a group of Chinese Han population. Findings indicated that genetic polymorphisms of rs2234711, rs1327475 and rs7749390 were associated with an altered risk of TB. For the SNP rs2234711, individuals carrying C allele (vs. T) showed a decreased risk. For the SNP rs1327475, C allele was related to an increased risk. For the SNP rs7749390, the dominant model showed a 20% decreased risk among individuals carrying variant genotypes (AG/AA). Moreover, we observed that the haplotype rs2234711T–rs3799488C also contributed a significantly increased risk of the disease. IFNG is highly conserved and few SNPs are found in the intragenic region (Pacheco et al., 2008). The most studied one is IFNG +874T/A, which is located in the first intron and adjacent to a CA repeat region (Pravica et al., 2000). Findings from previous studies have suggested the association between IFNG + 874A allele and the susceptibility to TB, but with inconsistent results (Pacheco et al., 2008; Wang et al., 2010). The taq SNP rs1861494 is located in the intron region of IFNG gene. Abhimanyu et al. observed a significant risk of this locus in susceptibility to TB in Indian populations (Abhimanyu et al., 2012). However, our study does not replicate this finding in the Chinese population. It might be attributed to the variations in allelic frequencies and therefore it is not surprising that genetic polymorphisms associated with TB have yielded conflicting results in different ethnic groups (Ansari et al., 2009). IFN-gamma signaling is mediated through the ligand binding to IFNGR1. Control of IFNGR1 expression level is one of the mechanisms by which cells modulate the potency of IFN-gamma signaling (Zhou et al., 2009). It is conceivable that natural selection might favor different levels of IFNGR1 expression, depending on the type of infectious pathogens to which a population is exposed (He et al., 2010). The SNP rs2234711 (C/T) is located in the 5′-UTR region of the IFNGR1 gene, which encodes the human interferon-gamma receptor ligand-binding chain I. It is also the binding site of transcription factors, including HNF4A, HMGN3, HEY1, and EGR1, etc. (Table 5). The minor allele frequency of the SNP s2234711 varies greatly in different areas, ranging from 0% in European populations to 60% in African–American populations. In Mandinka, the major Gambian ethnic group, heterozygotes for the IFNGR1 rs2234711 polymorphism were protected against cerebral malaria (OR: 0.54) and against death resulting from cerebral malaria (OR: 0.22) (Koch et al., 2002). However, such a tendency or heterozygote advantage was not observed in another study of the Thai malaria patients (Naka et al., 2009). Our data showed that C allele is more frequent in the control group and related to a decreased risk of TB. Moreover, we observed a combined effect of the SNP rs2234711 together
with another SNP rs3799488, where the haplotype rs2234711T– rs3799488C showed a significantly increased risk of TB. The SNP rs1327475 is located in the intron region of IFNGR1 gene. It has been revealed to be associated with SLE in a Norwegian cohort (Nordang et al., 2012) and ADEH + (atopic dermatitis patients suffer from disseminated viral infections, i.e. eczema herpeticum) phenotype (Leung et al., 2011). Blanton et al. analyzed 40 Egyptian pedigrees in which more than 1 sib had hepatic fibrosis due to S. mansoni infection and found significant linkage with a polymorphism rs1327475 in IFNGR1 gene (Blanton et al., 2005). Our study was the first one to explore the polymorphism at this locus on the susceptibility to TB. We found that the C allele was related to a 28% increased risk as compared with T allele. As this SNP locates in the intron region of the gene IFNGR1, the real function of this SNP or whether it is related to other functional loci is unclear. Consistent with our findings, He et al. also observed a significant association between rs7749390 and the susceptibility to TB (He et al., 2010). This SNP is located on the exon/intron splice site and seems to have an influence on the intron–exon splicing process (He et al., 2010). This SNP has also been explored in post kala-azar dermal leishmaniasis in Sudan, but without significant findings (Salih et al., 2007). There are several limitations in this study. First, we only selected taq SNPs in the IFNG and IFNGR1 gene. Some of these SNPs are located in the intron region and their real functions are unclear. Analysis of the Encyclopedia of DNA Elements (ENCODE) as implemented in Regulome DB indicated that some SNPs might influence the binding of specified transcription factors (Table 5). Further work with both knockout and overexpression models of IFNG/IFNGR1 and the neighboring genes is likely to provide the most fruitful approach to understand the mechanisms and pathways whereby these variants influence the risk of TB. Secondly, due to the weak effect of a single genetic polymorphism, other genes in the immunity pathway, together with environmental factors are all interesting. Replications in larger populations are needed to conclusively confirm or reject our findings. Thirdly, due to the technical issues, not all TB centers in China especially in rural areas, could
Table 5 Functional annotation for the six SNPs based on Regulome DB. SNP
Regulome DB Scorea
Hits
Binding protein
rs1861494 rs1327475 rs3799488 rs7749390
4 7 5 4
Chromatin_Structure NA Motifs Chromatin_Structure
rs9376267 rs2234711
7 4
NA Chromatin_Structure
CEBPB NA RFX3 HNF4A, HEY1, POU2F2, ETS1, TBP, SIN3A, GABPA, CHD2, POLR2A, CDX2 NA HNF4A, HMGN3, HEY1, EGR1, MYC, POU2F2, TRIM28, ELF1, E2F6, ETS1, TBP, TAF1, SIN3A, GABPA, MAX, IRF1, E2F4, CCNT2, SP1, JUND, YY1, STAT3, POLR2A, CDX2, NR2C2
a Description of Regulome DB Score: 4, TF binding + DNase peak; 5, TF binding or DNase peak; 7, No data supporting (NA).
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provide sputum culture services. Thus we didn't perform a subgroup analysis by considering the proven and probable cases. 5. Conclusions Taken together, our results suggest that genetic polymorphism in IFNGR1 gene is involved in the risk of TB in the Chinese population. Future studies should include a comprehensive sequencing analysis to identify the specific causative sequence variants underlying the observed associations. Conflict of interest The authors declare that they have no conflict of interest. Acknowledgments This study was partly supported by the National Natural Science Foundation of China (81072351), Jiangsu Science Supported Planning/ Social Development Foundation (BE2011841), Jiangsu Natural Science Foundation (BE2012694), and Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). References Abbas, A.K., Murphy, K.M., Sher, A., 1996. Functional diversity of helper T lymphocytes. Nature 383, 787–793. Abhimanyu, Bose, M., Jha, P., Indian Genome Variation Consortium, 2012. Footprints of genetic susceptibility to pulmonary tuberculosis: cytokine gene variants in north Indians. Indian Journal of Medical Research 135, 763–770. Altare, F., Durandy, A., Lammas, D., Emile, J.F., Lamhamedi, S., Le Deist, F., Drysdale, P., Jouanguy, E., Doffinger, R., Bernaudin, F., Jeppsson, O., Gollob, J.A., Meinl, E., Segal, A. W., Fischer, A., Kumararatne, D., Casanova, J.L., 1998. Impairment of mycobacterial immunity in human interleukin-12 receptor deficiency. Science 280, 1432–1435. Ansari, A., Talat, N., Jamil, B., Hasan, Z., Razzaki, T., Dawood, G., Hussain, R., 2009. Cytokine gene polymorphisms across tuberculosis clinical spectrum in Pakistani patients. PLoS One 4, e4778. Blanton, R.E., Salam, E.A., Ehsan, A., King, C.H., Goddard, K.A., 2005. Schistosomal hepatic fibrosis and the interferon gamma receptor: a linkage analysis using singlenucleotide polymorphic markers. European Journal of Human Genetics 13, 660–668. Cooper, A.M., Dalton, D.K., Stewart, T.A., Griffin, J.P., Russell, D.G., Orme, I.M., 1993. Disseminated tuberculosis in interferon gamma gene-disrupted mice. Journal of Experimental Medicine 178, 2243–2247. Davies, P.D., Grange, J.M., 2001. Factors affecting susceptibility and resistance to tuberculosis. Thorax 56 (Suppl. 2), ii23–ii29. Dupuis, S., Doffinger, R., Picard, C., Fieschi, C., Altare, F., Jouanguy, E., Abel, L., Casanova, J.L., 2000. Human interferon-gamma-mediated immunity is a genetically controlled continuous trait that determines the outcome of mycobacterial invasion. Immunological Reviews 178, 129–137. Ehrt, S., Schnappinger, D., Bekiranov, S., Drenkow, J., Shi, S., Gingeras, T.R., Gaasterland, T., Schoolnik, G., Nathan, C., 2001. Reprogramming of the macrophage transcriptome in response to interferon-gamma and Mycobacterium tuberculosis: signaling roles of nitric oxide synthase-2 and phagocyte oxidase. Journal of Experimental Medicine 194, 1123–1140. Filipe-Santos, O., Bustamante, J., Chapgier, A., Vogt, G., de Beaucoudrey, L., Feinberg, J., Jouanguy, E., Boisson-Dupuis, S., Fieschi, C., Picard, C., Casanova, J.L., 2006. Inborn errors of IL-12/23- and IFN-gamma-mediated immunity: molecular, cellular, and clinical features. Seminars in Immunology 18, 347–361. Frieden, T.R., Sterling, T.R., Munsiff, S.S., Watt, C.J., Dye, C., 2003. Tuberculosis. Lancet 362, 887–899. Girardi, E., Raviglione, M.C., Antonucci, G., Godfrey-Faussett, P., Ippolito, G., 2000. Impact of the HIV epidemic on the spread of other diseases: the case of tuberculosis. AIDS 14 (Suppl. 3), S47–S56. He, J., Wang, J., Lei, D., Ding, S., 2010. Analysis of functional SNP in ifng/ifngr1 in Chinese Han population with tuberculosis. Scandinavian Journal of Immunology 71, 452–458. Jouanguy, E., Lamhamedi-Cherradi, S., Lammas, D., Dorman, S.E., Fondaneche, M.C., Dupuis, S., Doffinger, R., Altare, F., Girdlestone, J., Emile, J.F., Ducoulombier, H.,
Edgar, D., Clarke, J., Oxelius, V.A., Brai, M., Novelli, V., Heyne, K., Fischer, A., Holland, S.M., Kumararatne, D.S., Schreiber, R.D., Casanova, J.L., 1999. A human IFNGR1 small deletion hotspot associated with dominant susceptibility to mycobacterial infection. Nature Genetics 21, 370–378. Koch, O., Awomoyi, A., Usen, S., Jallow, M., Richardson, A., Hull, J., Pinder, M., Newport, M., Kwiatkowski, D., 2002. IFNGR1 gene promoter polymorphisms and susceptibility to cerebral malaria. Journal of Infectious Diseases 185, 1684–1687. Leandro, A.C., Rocha, M.A., Cardoso, C.S., Bonecini-Almeida, M.G., 2009. Genetic polymorphisms in vitamin D receptor, vitamin D-binding protein, Toll-like receptor 2, nitric oxide synthase 2, and interferon-gamma genes and its association with susceptibility to tuberculosis. Brazilian Journal of Medical and Biological Research 42, 312–322. Lee, J., Kornfeld, H., 2010. Interferon-gamma regulates the death of M. tuberculosisinfected macrophages. Journal of Cell Death 3, 1–11. Lettre, G., Lange, C., Hirschhorn, J.N., 2007. Genetic model testing and statistical power in population-based association studies of quantitative traits. Genetic Epidemiology 31, 358–362. Leung, D.Y., Gao, P.S., Grigoryev, D.N., Rafaels, N.M., Streib, J.E., Howell, M.D., Taylor, P.A., Boguniewicz, M., Canniff, J., Armstrong, B., Zaccaro, D.J., Schneider, L.C., Hata, T.R., Hanifin, J.M., Beck, L.A., Weinberg, A., Barnes, K.C., 2011. Human atopic dermatitis complicated by eczema herpeticum is associated with abnormalities in IFN-gamma response. Journal of Allergy and Clinical Immunology 127 (965–73), e1–e5. Moller, M., Hoal, E.G., 2010. Current findings, challenges and novel approaches in human genetic susceptibility to tuberculosis. Tuberculosis (Edinburgh, Scotland) 90, 71–83. Naka, I., Patarapotikul, J., Hananantachai, H., Tokunaga, K., Tsuchiya, N., Ohashi, J., 2009. IFNGR1 polymorphisms in Thai malaria patients. Infection, Genetics and Evolution 9, 1406–1409. Newport, M.J., Huxley, C.M., Huston, S., Hawrylowicz, C.M., Oostra, B.A., Williamson, R., Levin, M., 1996. A mutation in the interferon-gamma-receptor gene and susceptibility to mycobacterial infection. New England Journal of Medicine 335, 1941–1949. Nordang, G.B., Viken, M.K., Amundsen, S.S., Sanchez, E.S., Flato, B., Forre, O.T., Martin, J., Kvien, T.K., Lie, B.A., 2012. Interferon regulatory factor 5 gene polymorphism confers risk to several rheumatic diseases and correlates with expression of alternative thymic transcripts. Rheumatology (Oxford) 51, 619–626. Pacheco, A.G., Cardoso, C.C., Moraes, M.O., 2008. IFNG + 874T/A, IL10 − 1082G/A and TNF − 308G/A polymorphisms in association with tuberculosis susceptibility: a meta-analysis study. Human Genetics 123, 477–484. Pravica, V., Perrey, C., Stevens, A., Lee, J.H., Hutchinson, I.V., 2000. A single nucleotide polymorphism in the first intron of the human IFN-gamma gene: absolute correlation with a polymorphic CA microsatellite marker of high IFN-gamma production. Human Immunology 61, 863–866. Salanti, G., Southam, L., Altshuler, D., Ardlie, K., Barroso, I., Boehnke, M., Cornelis, M.C., Frayling, T.M., Grallert, H., Grarup, N., Groop, L., Hansen, T., Hattersley, A.T., Hu, F.B., Hveem, K., Illig, T., Kuusisto, J., Laakso, M., Langenberg, C., Lyssenko, V., McCarthy, M.I., Morris, A., Morris, A.D., Palmer, C.N., Payne, F., Platou, C.G., Scott, L.J., Voight, B. F., Wareham, N.J., Zeggini, E., Ioannidis, J.P., 2009. Underlying genetic models of inheritance in established type 2 diabetes associations. American Journal of Epidemiology 170, 537–545. Salih, M.A., Ibrahim, M.E., Blackwell, J.M., Miller, E.N., Khalil, E.A., ElHassan, A.M., Musa, A.M., Mohamed, H.S., 2007. IFNG and IFNGR1 gene polymorphisms and susceptibility to post-kala-azar dermal leishmaniasis in Sudan. Genes and Immunity 8, 75–78. Teuber, M., Wenz, M.H., Schreiber, S., Franke, A., 2009. GMFilter and SXTestPlate: software tools for improving the SNPlex genotyping system. BMC Bioinformatics 10, 81. Thakur, A., Pedersen, L.E., Jungersen, G., 2012. Immune markers and correlates of protection for vaccine induced immune responses. Vaccine 30, 4907–4920. van de Vosse, E., Hoeve, M.A., Ottenhoff, T.H., 2004. Human genetics of intracellular infectious diseases: molecular and cellular immunity against mycobacteria and salmonellae. Lancet Infectious Diseases 4, 739–749. van Helden, P.D., Moller, M., Babb, C., Warren, R., Walzl, G., Uys, P., Hoal, E., 2006. TB epidemiology and human genetics. Novartis Foundation Symposium 279, 17–31 (discussion 31–41, 216–9). Wang, J., Tang, S., Shen, H., 2010. Association of genetic polymorphisms in the IL12-IFNG pathway with susceptibility to and prognosis of pulmonary tuberculosis in a Chinese population. European Journal of Clinical Microbiology & Infectious Diseases 29, 1291–1295. WHO, 2013. Global Tuberculosis Report 2013. WHO. Young, D.B., Perkins, M.D., Duncan, K., Barry III, C.E., 2008. Confronting the scientific obstacles to global control of tuberculosis. Journal of Clinical Investigation 118, 1255–1265. Zhou, J., Chen, D.Q., Poon, V.K., Zeng, Y., Ng, F., Lu, L., Huang, J.D., Yuen, K.Y., Zheng, B.J., 2009. A regulatory polymorphism in interferon-gamma receptor 1 promoter is associated with the susceptibility to chronic hepatitis B virus infection. Immunogenetics 61, 423–430.