Single nucleotide polymorphisms of lncRNA H19 are not associated with risk of multiple sclerosis in Iranian population

Meta Gene 21 (2019) 100592

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Single nucleotide polymorphisms of lncRNA H19 are not associated with risk of multiple sclerosis in Iranian population

T

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Mehrdokht Mazdeha, Rezvan Noroozib, Alireza Komakia, Mohammad Taheric, , ⁎ Soudeh Ghafouri-Fardb, a

Neurophysiology Research Center, Hamadan University of Medical Sciences, Hamadan, Iran Department of Medical Genetics, Shahid Beheshti University of Medical Sciences, Tehran, Iran c Urogenital Stem Cell Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran b

A R T I C LE I N FO

A B S T R A C T

Keywords: lncRNA H19 Multiple Sclerosis Single nucleotide polymorphism

Multiple Sclerosis (MS) as a chronic demyelinating disorder of the central nervous system (CNS) has been associated with dysregulation of several genetic and epigenetic factors. Long non-coding RNAs (lncRNAs) as important epigenetic regulators of gene expression might also contribute in the pathogenesis of MS. In the current study, we genotyped two single nucleotide polymorphisms (SNPs) within the lncRNA H19 (rs2839698 and rs217727) in a population of 402 MS patients and 392 healthy subjects using tetra-primer amplification-refractory mutation system-PCR technique. No significant association was found between the mentioned SNPs and MS risk in any of inheritance model. Moreover, the distributions of the four expected haplotypes were similar in MS patients and controls. Despite the previously acknowledged role of H19 in regulation of immune response, the mentioned SNPs do not contribute in the pathogenesis of MS in Iranian population. Future studies are needed to assess the associations between other SNPs within this lncRNA and risk of MS.

1. Introduction Multiple Sclerosis (MS) is a chronic demyelinating disorder of the central nervous system (CNS) described by destruction of axons (Fenoglio et al., 2018). Among dives clinical courses, relapsing-remitting (RR) course is the most frequent type (Mcfarland and Martin, 2007). Although several ethnic-based, genetic and environmental factors contribute in the pathogenesis of MS, recent researches have underscored the role of epigenetic alterations in MS (Zhang and Zhang, 2015). A number of long non-coding RNAs (lncRNAs) especially those participated in regulation of tight junctions(Dastmalchi et al., 2018), axon guidance, axon guidance receptor function, and control of endothelial cell chemotaxis have been shown to be dysregulated in MS patients (Zhang et al., 2016). The lncRNA H19 is among lncRNAs with putative roles in the regulation of immune response(Safari et al., 2019). This lncRNA is an imprinted gene that is solely expressed from the maternal allele (Ariel et al., 2000). A previous study has suggested participation of H19 in the pathogenesis of rheumatoid arthritis (RA) based on the observed overexpression of this lncRNA in RA patients and its hypersensitivity to starvation/cytokine regulation in these patients. The underlying mechanism of such overexpression might be embryonal dedifferentiation of the adult synovial tissue or response to constant ⁎

inflammatory/oxidative damages (Stuhlmuller et al., 2003). While long-term hematopoietic stem cells (LT-HSC) express high levels of H19, differentiation of these cells to short-term HSCs (ST-HSCs) and multipotent progenitors is accompanied by gradual down-regulation of this lncRNA (Venkatraman et al., 2013). H19 deletion facilitates cell cycle progression in HSCs possibly through induction of maternal insulin like growth factor 2 (Igf2), the target of H19 (Venkatraman et al., 2013). Based on the role of IGF system in the pathogenesis of MS (Chesik et al., 2007) and the importance of HSCs in the regulation of immune response (Granick et al., 2012), H19 might contribute in the pathogenesis of MS. Moreover, H19 deletion leads to induction of IGF1R signaling and subsequent loss of FOXO3 transcription factor function (Venkatraman et al., 2013). Besides, a recent study has shown over-expression of the IGF1 and IGF2 in inactive demyelinated plaques of MS patients (Zeis et al., 2018). As FOXO3 induces differentiation of pathogenic T helper-1 and alters the predisposition to experimental autoimmune encephalomyelitis (EAE), an animal model of MS (Stienne et al., 2016), H19 might also be involved in the pathology of MS. Based on these observations, we hypothesized that single nucleotide polymorphisms (SNPs) within H19 may confer susceptibility to MS. So we conducted the current study to genotype two H19 tag SNPs (rs2839698 and rs217727) in a population of Iranian patients with MS and healthy

Corresponding authors. E-mail addresses: [email protected] (M. Taheri), [email protected] (S. Ghafouri-Fard).

https://doi.org/10.1016/j.mgene.2019.100592 Received 5 March 2019; Received in revised form 12 April 2019; Accepted 28 May 2019 Available online 30 May 2019 2214-5400/ © 2019 Elsevier B.V. All rights reserved.

Meta Gene 21 (2019) 100592

M. Mazdeh, et al.

subjects. These two SNPs have been previously shown to be associated with gastric cancer risk in Chinese population (Yang et al., 2015).

Table 2 The general data of patients and controls. Variable

MS patients

Controls

Female/Male [no. (%)]

302 (75%)/100 (25%) 34.61 ± 0.8 15–59 25.2 ± 1.9 100 (100%) 5.9 ± 5.2 4.9 ± 1.0

298 (76%)/94 (24%) 32.6 ± 1.1 18–61 – – – –

2. Materials and methods 2.1. Study participants

Age (mean ± SD, Y) Age range (Y) Age at onset (mean ± SD, Y) Relapsing-remitting course (no. %) Duration (mean ± SD, Y) Expanded Disability Status Scale score (mean ± SD)

The current study was conducted on peripheral blood samples obtained from 402 RRMS patients and 392 healthy subjects. Individuals enrolled in the control group had no personal or family history of autoimmune disorders. MS patients with other forms of disease such as primary/secondary progressive and progressive relapsing forms were excluded from the study. The study protocol was approved by the ethical committee of Hamadan University of Medical Sciences (IR.UMSHA.REC.1396.579). Written informed consent forms were obtained from all study participants. 2.2. Genotyping Genomic DNA was isolated from peripheral blood using salting out technique. The rs2839698 and rs217727 were genotyped using Taq DNA Polymerase Master Mix RED (Ampliqon, Denmark) and tetraprimer amplification-refractory mutation system (ARMS)-PCR technique. In this technique, we used four primers in a single reaction and electrophoresed PCR products on 2.5% agarose gel. Allele specific primers had mismatches in the middle of their nucleotide sequences and were designed by Primer1 software (Collins and Ke, 2012). The mismatches were deliberately incorporated to destabilize the base paring between the primers and non-targets DNA sequences and consequently enhance the specificity of the reaction. Table 1 shows the nucleotide sequences of the primers used in ARMS-PCR and the related annealing temperature. The PCR program consisted a preliminary denaturing step at 95 °C for 5 min; 35 cycles at 95 °C for 30 s, specific annealing temperature for 40 s, 72 °C for 40 s and a final extension at 72 °C for 5 min. 2.3. Statistical analyses The SNPAnalyzer 2.0 online tool was used for assessment of harmony with Hardy–Weinberg principle, haplotype estimation, linkage disequilibrium (LD) blocking. Associations were analyzed in allelic, dominant, co-dominant and recessive models. Odds ratios (OR), 95% confidence interval of OR (95% CI), P-value and Bonferroni adjusted Pvalues were measured. D' and r factors were measured for appraisal of the linkage between rs2839698 and rs217727 in the assessed population. The Bonferroni adjusted P values were calculated by multiplying the P values by the number of SNPs. P-values less than 0.05 were considered statistically significant.

Fig. 1. A. Different genotypes of rs217727 are shown in lanes A-C. Lane D is size marker; B. Different genotypes of rs2839698 are shown in lanes A-C. Lane D is size marker.

age ( ± SD) of study participants was 34.61 ( ± 0.8) and 32.6 ( ± 1.1) in cases and controls respectively. All cases had relapsing-remitting course of the disease with mean disease duration of 5.9 ( ± 5.2) and disability score of 4.9 ( ± 1.0).

3. Results

3.2. Genotyping

3.1. General information of study participants

Fig. 1 shows the results of tetra primer-ARMS PCR for genotyping

Table 2 shows the general data of patients and controls. The mean Table 1 The nucleotide sequences of primers used for SNP genotyping. SNP

Primer sequence

Tm

Annealing temperature

PCR product size (bp)

rs2839698

Forward inner primer (C allele): CTGATGTCAGTGAGGAGTGTGGAGTATGC Reverse inner primer (T allele): GCCCTGTCTACACGATGCCTGGACA Forward outer primer: GAAAAAGACCTGGCTAGGACCGAGGAG Reverse outer primer: ATCAAACCCTGCCCACCAGCTCCCCTC Forward inner primer (T allele): ACATCTTCATCGCCACCCCCTGCTGT Reverse inner primer (C allele): TGTGATGGCTGGTGGTCAACCGTTCG Forward outer primer: GACTAAGGAATCGGCTCTGGAAGGTGAG Reverse outer primer: GATGGAGGAAACAGAGTCGTGGAGGCTT

75 °C 75 °C 75 °C 77 °C 76 °C 76 °C 76 °C 76 °C

62 °C

187 bp (C allele) 273 bp (T allele) 404 bp (two outer primers)

69 °C

205 bp (T allele) 248 bp (C allele) 399 bp (two outer primers)

rs217727

2

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M. Mazdeh, et al.

the role of HSCs in the treatment of MS (Kuan et al., 2017), H19 is a putative target in this regard. Further evidences for contribution of H19 in the pathophysiology of MS have emerged from the observed role of this lncRNA in the regulation of expression or cellular localization of IGFs and FOXO3 (Venkatraman et al., 2013). FOXO3 induces expression of FOXP3 which has crucial role in differentiation of regulatory T cells and their immunosuppressive roles (Becher et al., 2018). Based on the above-mentioned studies, we hypothesized that H19 SNPs might alter susceptibility to MS. Consequently, we genotyped two H19 SNPs in Iranian MS patients and healthy subjects. However, we did not detect any significant difference in genotypes, alleles and haplotypes frequencies between MS patients and healthy subjects. Although we detected a significant difference between one SNP and MS in male subjects, based on the low number of individuals in this subgroup, such data should be interpreted with caution. These two SNPs have been previously associated with gastric cancer risk (Yang et al., 2015). Moreover, individuals having the CT and TT genotypes of the rs2839698 had higher serum H19 transcript levels compared with those having the CC genotype (Yang et al., 2015). Two other H19 SNPs (rs3741216, rs3741219) have also been genotyped in cancer patients (Yang et al., 2015). However, based on the lack of associations between these two SNPs and cancer risk and lower probability of functionality of these SNPs (Yang et al., 2015), we did not genotype them. Consequently, we suggest assessment of genotypes of other SNPs within this gene (for instance: rs3741216, rs3741219 and rs2107425) in MS patients and healthy subjects to explore probable associations between these variants/haplotypes and MS risk. The latter SNP has been associated with cancer risk as revealed by a meta-analysis (Chu et al., 2016). Taken together, although we did not detect any association between two H19 SNPs and MS risk in Iranian population, these results do not necessarily exclude participation of H19 in the pathogenesis of MS. Other SNPs within this gene might also affect expression of function of H19. Alternatively, as H19 is regarded as a microRNA (miRNA) precursor (Cai and Cullen, 2007), SNPs within this gene might affect the expression of resulting miRNAs. Our study has some limitations. First, we only genotyped two tag SNPs within this gene. Second, as MS patients were under treatment with dissimilar disease modifying treatments, we could not assess H19 serum levels to find associations between genotypes/haplotypes and H19 levels. Assessment of H19 levels during different phases of MS course (relapse and remission) as well in drug-naïve MS patients might explore the role of this lncRNA in the pathogenesis of MS. Finally, since the H19 gene is among imprinted genes being only expressed from maternal chromosome, the chromosome source of the alleles is important. With the used method in the current study, it was not possible to define the allelic source (paternal or maternal). However, when we

Table 3 Exact test for assessment of compliance of genotypes and alleles frequencies with Hardy-Weinberg principle. Study groups

MS patients Healthy controls

rs2839698

P-value

CC

CT

TT

202 203

174 153

26 36

0.15 0.36

rs217727

P-value

CC

CT

TT

216 181

151 176

35 35

0.32 0.40

rs217727 (A) and rs2839698 (B). Both SNPs were in Hardy-Weinberg equilibrium in both study groups (P values of 0.15 and 0.36 for rs2839698 distribution in cases and controls; P values of 0.32 and 0.40 for rs217727 distribution in cases and controls respectively) (Table 3). No significant association was found between SNPs and MS risk in any of inheritance models after correction for multiple comparisons (Table 4). However, there was a trend towards association between the rs217727 and MS risk in dominant model (OR (95% CI) = 0.74 (0.56–0.98), adjusted P value = .07). Afterwards, we performed sex-based analysis. Such analyses revealed significant association between the rs2839698 and disease in allelic, co-dominant and dominant models in male subjects (Table 5). However, this association was not detected between female subjects (Table 6). The mentioned SNPs were not in strong linkage disequilibrium in the assessed population (r = 0.003, D' = 0.13). Assessment of association between expected haplotypes and risk of MS revealed no significant difference between any haplotype and susceptibility to MS after correction for multiple comparisons which means that CC, CT, TC and TT haplotypes are similarly distributed in cases and controls (Table 7). 4. Discussion Previous studies have linked several SNPs with risk of MS. For instance, De Jager et al. have introduced an algorithm conjoining 16 SNPs that “modestly predict” susceptibility to this disorder. Among these SNPs, the rs3135388 in HLA DRB1 gene and the rs2300747 in CD58 gene had the highest total weight (De Jager et al., 2009). Several lines of evidence suggest participation of HSCs in the regulation of immune response. These cells might function as sentries of infection, providing easily accessible precursors of mature blood leukocytes, or directly contributing as immune effectors. Notably, these cells react to and produce chemokines, cytokines and growth factors (Granick et al., 2012). The lncRNA H19 has a prominent role in the regulation of HSCs as its silencing in HSCs has resulted in increased cell proliferation and reduced self-renewal capacity (Venkatraman et al., 2013). Considering

Table 4 Assessment of associations between H19 SNPs and risk of MS in four inheritance models in total subjects. SNP

Model

rs2839698

Allelic

T vs. C

Co-dominant Dominant

TT vs. CC CT vs. CC CT + TT vs. CC

Recessive

TT vs. CT + CC

Allelic

T vs. C

Co-dominant Dominant

TT vs. CC CT vs. CC CT + TT vs. CC

Recessive

TT vs. CT + CC

rs217727

MS (%)

Control (%)

OR (95% CI)

P-value

Adjusted P-value

226 (28) 578 (72) 26 (6.5) 174 (43.3) 200 (49.8) 202 (50.2) 26 (6.5) 376 (93.5) 221 (27) 583 (73) 35 (8.7) 151 (37.6) 186 (46.3) 216 (53.7) 35 (8.7) 367 (91.3)

225 (29) 559 (71) 36 (9.2) 153 (39) 189 (48.2) 203 (51.8) 36 (9.2) 356 (90.8) 246 (31) 538 (69) 35 (8.9) 176 (44.9) 211 (53.8) 181 (46.2) 35 (8.9) 357 (91.1)

0.97 (0.78–1.21)

0.79

1.00

0.72 (0.42–1.25) 1.15 (0.85–1.54) 1.06 (0.81–1.40)

0.24

0.48

0.66

1.00

0.97 (0.59–1.6)

0.91

1.00

0.83 (0.67–1.03)

0.09

0.18

0.84 (0.51–1.39) 0.72 (0.53–0.96) 0.74 (0.56–0.98)

0.09

0.17

0.03

0.07

0.68 (0.4–1.16)

0.15

0.31

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Table 5 Assessment of associations between H19 SNPs and risk of MS in four inheritance models in male subjects. SNP

Model

rs2839698

Allele

T vs. C

Co-dominant Dominant

TT vs. CC CT vs. CC CT + TT vs. CC

Recessive

TT vs. CT + CC

Allele

T vs. C

Co-dominant Dominant

TT vs. CC CT vs. CC CT + TT vs. CC

Recessive

TT vs. CT + CC

rs217727

Case (%)

Control (%)

OR (95% CI)

P-value

Adjusted P-value

69 (34) 131 (66) 13 (13) 43 (43) 56 (56) 44 (44) 13 (13) 87 (87) 56 (28) 144 (72) 5 (5) 46 (46) 51 (51) 49 (49) 5 (5) 95 (95)

43 (23) 145 (77) 14 (14.9) 15 (16) 29 (30.9) 65 (691) 14 (14.9) 80 (85.1) 58 (31) 130 (69) 5 (5.3) 48 (51.1) 53 (56.4) 41 (43.6) 5 (5.3) 89 (94.7)

1.78 (1.13–2.78)

0.01

0.02

0.73 (0.31–1.69) 4.23 (2.10–8.54) 2.85 (1.58–5.14)

1.6E-4

3.3E-4

4.2E-4

8.4E-4

0.85 (0.38–1.93)

0.70

1.00

0.87 (0.56–1.35)

0.54

1.00

0.84 (0.23–3.09) 0.8 (0.45–1.43) 0.81 (0.46–1.42)

0.81

1.00

0.45

0.90

0.94 (0.26–3.35)

0.82

1.00

Table 6 Assessment of associations between H19 SNPs and risk of MS in four inheritance models in female subjects. SNP

Model

rs2839698

Allele

T vs. C

Co-dominant Dominant

TT vs CC CT vs CC CT + TT vs CC

Recessive

TT vs CT + CC

Allele

T vs C

Co-dominant Dominant

TT vs CC CT vs CC CT + TT vs CC

Recessive

TT vs CT + CC

rs217727

Case (%)

Control (%)

OR (95% CI)

P-value

Adjusted P-value

157 (26) 447 (74) 13 (4.3) 131 (43.4) 144 (47.7) 158 (52.3) 13 (4.3) 289 (95.7) 165 (27) 439 (73) 30 (9.9) 105 (34.8) 135 (44.7) 167 (55.3) 30 (9.9) 272 (90.1)

182 (31) 414 (69) 22 (7.4) 138 (46.3) 160 (53.7) 138 (46.3) 22 (7.4) 276 (92.6) 188 (32) 408 (68) 30 (10.1) 128 (43) 158 (53) 140 (47) 30 (10.1) 268 (89.9)

0.8 (0.62–1.03)

0.08

0.16

0.52 (0.25–1.06) 0.83 (0.6–1.15) 0.79 (0.57–1.08)

0.15

0.3

0.14

0.28

0.56 (0.28–1.14)

0.11

0.22

0.82 (0.64–1.05)

0.12

0.22

0.84 (0.48–1.46) 0.69 (0.49–0.97) 0.72 (0.52–0.99)

0.1

0.2

0.04

0.08

0.98 (0.58–1.68)

0.96

1.00

Table 7 Frequencies of expected H19 haplotypes in study groups. rs2839698

rs217727

Frequency in MS patients

Frequency in controls

Total frequency

OR (95% CI)

P-value

Adjusted P-value

C C T T

C T C T

0.52 0.20 0.20 0.08

0.46 0.25 0.22 0.07

0.49 0.22 0.21 0.08

1.24 0.78 0.92 1.32

0.03 0.03 0.49 0.31

0.12 0.11 1.00 1.00

analyzed the association in the co-dominant model, we considered homozygotes for each genotype and found no significant difference between study groups which might solve the problem of not defining the allelic source at least to some extent. Future studies with assessment of the allelic source would reach to more precise results.

(1.02–1.51) (0.62–0.97) (0.73–1.16) (0.77–2.29)

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