Expression of suppressor of cytokine signaling 1 (SOCS1) - eprints

Journal of the Neurological Sciences 350 (2015) 40–45

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Expression of suppressor of cytokine signaling 1 (SOCS1) gene dramatically increases in relapsing–remitting multiple sclerosis Majid Pahlevan Kakhki a,b, Nahid Rakhshi c, Masoumeh Heidary a, Mehrdad Behmanesh b, Abbas Nikravesh d,⁎ a

Department of Basic Sciences, Gonabad University of Medical Sciences, Gonabad, Islamic Republic of Iran Department of Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Islamic Republic of Iran Department of Nursing and Midwifery, Bojnourd Branch, Islamic Azad University, Bojnourd, Islamic Republic of Iran d Department of Molecular Sciences, Faculty of Medicine, North Khorasan University of Medical Sciences, Bojnurd, Islamic Republic of Iran b c

a r t i c l e

i n f o

Article history: Received 17 November 2014 Received in revised form 29 January 2015 Accepted 2 February 2015 Available online 8 February 2015 Keywords: Multiple sclerosis SOCS1 Genotyping Gene expression Cytokine JAK–STAT

a b s t r a c t Suppressor of cytokine signaling 1 (SOCS1) is a key regulator of cytokines signaling and plays the most important role in the regulation of the autoimmune responses. The absence of SOCS1 leads to aberrant thymocyte development and systemic inflammation. This study was conducted to evaluate the expression level of SOCS1 mRNA in peripheral blood mononuclear cells (PBMCs) of relapsing–remitting (RR)-multiple sclerosis (MS) patients. In addition, the association of rs243324 SNP with MS and the assessment of this SNP role on the expression level of SOCS1 were aimed to be evaluated. Our results revealed that, SOCS1 mRNA overexpressed (24.5 times) in MS patients versus healthy controls. The rs243324 SNP showed no association with MS susceptibility and this SNP was not in Hardy–Weinberg equilibrium in MS patients. Moreover, there was a significant correlation between SOCS1 expression levels with age of female control group (r = −0.43, P = 0.03). Thus, we have probably shown some new evidences for the complex role of SOCS1 gene in the pathogenesis of MS. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Multiple sclerosis (MS) is a chronic progressive inflammatory disease in the central nervous system (CNS) in which some autoimmunity responses against myelin sheaths lead to plaques. Such as many other autoimmune diseases, genetics and environmental factors have specific effects on its susceptibility [1]. Suppressor of cytokine signaling 1 (SOCS1) which is a member of intracellular cytokine-inducible proteins consisting of 8 members (CIS and SOCS1–SOCS7) plays the most important role in the regulation of cytokine signaling and the autoimmune responses [2,3]. SOCS proteins have an N-terminal domain with variable length and sequence, a central SH2 domain containing kinase inhibitory region (KIR) domain, and a conserved C-terminal SOCS box. The SOCS1 box plays a key role in the recruitment of the ubiquitin-transferase system and the degradation mediation of proteins [4]. Interestingly, SOCS1 is located on chromosome 16 and in about 70 kb distance from the previously confirmed MS risk gene (CLEC16A) [5] and is associated with T cell development, differentiation and maturation which is one of the major immune subsets involved in MS pathogenesis [6,7]. SOCS1 regulates the signaling of cytokines via JAK–STAT pathway which leads to

⁎ Corresponding author at: Department of Molecular Sciences, School of Medicine, North Khorasan University of Medical Sciences, Bojnurd, Islamic Republic of Iran. Tel./fax: + 98 5842296764. E-mail addresses: [email protected], [email protected] (A. Nikravesh).

http://dx.doi.org/10.1016/j.jns.2015.02.005 0022-510X/© 2015 Elsevier B.V. All rights reserved.

the phosphorylation of the STAT transcription factors [8] and acts as a regulator of cytokine-mediated homeostasis, including innate and adaptive immunity by mediating negative-feedback inhibition of cytokine signaling in complex ways [8,9]. Some previous studies found the aberrant expression of SOCS1 gene in various inflammatory and autoimmune disease such as systemic lupus erythematosus (SLE) [4], rheumatoid arthritis (RA) [9] and Parkinson [10]. Association between MS and SOCS1 gene has been indicated [11] and confirmed for rs243324 SNP in some population [5]. Collectively, SOCS1 influences the signaling of many cytokines related to inflammatory diseases. The negative regulation of cytokine signaling may be impaired if the protein expression level of SOCS1 changed which may be associated with the development of inflammatory diseases such as MS. Three independent previous studies have shown the increased expression of SOCS1 mRNA in the EAE mouse model of MS [12–14]. Therefore, we hypothesized that SOCS1 expression – as the major regulator of JAK–STAT signaling pathway – is altered in PBMCs of MS patients in order to regulate uncontrolled expression of cytokines and associated with MS susceptibility in Sistan and Baluchistan province, where the number of MS cases has significantly increased since 1999 to 2009 like many other regions [15]. To further explore the association between SOCS1 and MS, in the present study, we investigated the mRNA expression level of SOCS1 in PBMCs and genotyped the rs243324 SNP in MS patients and healthy controls; but, whereas the potential effect of non-coding SNPs on the expression level of genes leading to disease development was confirmed [16,17] we investigated the effect of rs243324 SNP on the

M. Pahlevan Kakhki et al. / Journal of the Neurological Sciences 350 (2015) 40–45

expression level of SOCS1. Moreover, we analyzed the correlation between normalized expression of SOCS1 with age of participants, age of onset, and disease duration. 2. Materials and methods 2.1. Patients and controls To evaluate the expression level of SOCS1, RNA samples obtained from 30 MS patients and 30 age and gender matched healthy controls. All patients were taking Cinnovex which is an interferon-beta based drug, as a general treatment strategy in a weekly period. To optimize the role of this treatment on the expression of SOCS1, we designed a new strategy and collected the blood samples after a week of injection and just before a new injection. To genotype the rs243324 SNP, DNA samples were isolated from whole blood of 112 MS patients and 125 age and gender matched controls, containing those participated in expression analysis. According to the McDonald criteria, all subtypes of MS patients were recruited in this study [18]. This group has at least one episode of MS such as optic neuritis, sensory sings and weakness. The investigation was performed in keeping with the Helsinki declaration on research with human participant. Also, this study was approved by the Ethics committee of the University of Zabol, and the informed consent was obtained from all of the participants.

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2.4. Statistical analysis The expression level of SOCS1 mRNA was analyzed using ΔΔCT method [20] and compared between patients and controls via independent t-test in the statistical package for the social sciences version 20 (SPSS, Version 20; SPSS Inc., Chicago, IL). Hardy–Weinberg equilibrium (HWE) was evaluated using Chi-Square Test. Moreover, allelic and genotypic frequencies were compared via Chi-Square test for cases and controls with a Bonferroni-corrected statistical significance level to analyze the association of rs243324 SNP with MS. Three groups of genotypes, including: major allele homozygous, heterozygous and minor allele homozygous were generated and for each genotype, OR (Odds Ratio) and 95% CI (Confidence Interval) were calculated. The correlation between SOCS1 expression levels and genotypes was assessed by twosided Mann–Whitney U-test using GraphPad Prism 6.01 (GraphPad Software, Inc., San Diego, CA, USA). Also, the correlation between normalized expression of SOCS1 with age of participants, age of onset and disease duration was calculated by Pearson's correlation coefficient. Finally, the Receiver Operating Characteristic (ROC) curve was performed by GraphPad Prism 6.01 to analyze the specificity and sensitivity of the expression level of SOCS1 as a potential biomarker which the area under the ROC curve (AUC) was used to represent an overall summary of its diagnostic accuracy. A P-value ≤ 0.05 was considered significant. 3. Results

2.2. SOCS1 expression analysis Peripheral blood mononuclear cells (PBMCs) were obtained from fresh blood with a gradient of Ficoll-Paque solution (Sigma Chemical Co, St Louis, MO, USA). Total RNA extracted from 5–10 × 107 cells via TRIZOL reagent (IsoGene Lab, Moscow, Russia) according to the Chomczynski protocol [19]. RNA concentration, quality and integrity were verified by spectrophotometry (Eppendorf BioPhotometer plus, Eppendorf, Germany) and 1% agarose gel electrophoresis. Reverse transcription of RNA was performed using the manufacturer's instructions of 2-Steps RT-PCR kit (Vivantis Technologies, Selangor, Malaysia) with OligodT and Random Hexamer primers, generating cDNA for subsequent gene expression analysis. Real time PCR performed using realtime PCR standard curve method and Applied Biosystems 7500 Real Time PCR System (Applied Biosystem/MDS SCIEX, Foster City, CA, USA). The relative expression of SOCS1 gene (Forward: CTCCTTCCCCTT CCAGATTTGACC Reverse: TCGCCCCTACACCCATCCGCTCC) was normalized to Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) (Forward: CCATGAGAAGTATGACAAC Reverse: GAGTC-CTTCCACGATACC) which was used as the endogenous control. All of the samples were tested in duplicate and the specificity of qPCR reaction was verified by a single band after polyacrylamide gel electrophoresis. 2.3. Genotyping of SOCS1 Total genomic DNA extracted from whole blood using the boiling method and stored in − 20 °C for future analysis. The sequence of rs243324 SNP was obtained from the SNP database (available at http://www.ncbi.nlm.nih.gov/snp). Quality and quantity control of extracted DNA was measured by visualization on 1% agarose gel and spectrophotometry (Eppendorf BioPhotometer plus, Eppendorf, Germany). Genotyping was performed via polymerase chain reaction (PCR) with subsequently restriction fragment length polymorphism (RFLP) analysis. We designed a mismatch PCR-RFLP strategy for genotyping of rs243324 SNP (Forward: 5-ACATGAGGAATCTTCCG(T)GC-3 and Reverse: 5-GCACAGTATTCCAACAAGCC-3). After the amplification of approximately 100 ng of genomic DNA, PCR products were digested using BsnI restriction enzyme (Vivantis Technologies, Selangor, Malaysia) according to the manufacturer's instructions. Digested samples run on 12% polyacrylamide gel and stained with ethidium bromide.

3.1. Patients and controls The mean age of disease onset was 29.5 (ranging from 17 to 46) years, and the mean duration of disease was 5.46 (ranging from 1 to 31) years. Some clinical features of patients and controls are presented in Table 1. The patients who resided in Sistan and Baluchistan province for generations, aged between 19 and 55 years and only treated with Cinnovex were included, while the patients treated with other drugs, migrated from other provinces, hospitalized at the time of study, pregnant females and any patient and control who had any inflammation situation were excluded. Patients and controls were matched by age, gender and time of blood sampling. 3.2. Gene expression Statistical analysis of Ct (Cycle threshold) and ΔCt values revealed a significant difference between the expression of SOCS1 gene in MS patients versus healthy controls (P = 0.0001). Using 2−ΔΔCt method, our results revealed that the expression of SOCS1 gene in PBMCs of RR-MS patients was 24.5 times higher than the control group. Moreover, in order to evaluate the ability of SOCS1 mRNA expression to discriminate the MS patients from healthy controls, we performed a ROC curve analysis. The AUC was 0.913 ± 0.03 (CI: 0.846–0.98) (Fig. 1), with a P value of b 0.0001. The optimal cut-off value of SOCS1 expression to discriminate between healthy controls and MS patients was 1.348, with a sensitivity of 83.33% and specificity of 86.67%. 3.3. Genotyping After the Bonferroni's correction, the significance level was P ≤ 0.05. All genotype frequencies were in HWE in the control group (P = 0.944), but the genotype distribution of rs243324 SNP deviated from HWE in the MS patients due to an excess of heterozygotes (P b 0.05) which precluded further analysis in stratification for gender and subtypes. Association analysis of rs243324 SNP with MS susceptibility showed no significant association in MS patients versus healthy controls (P = 0.348, OR: 0.995, 95% CI = 0.526–1.734). Moreover, there were no significant differences between allelic frequencies in MS patients and controls (P = 0.825, OR: 0.95, 95% CI = 0.65–1.41) (Table 2).

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Table 1 Clinical and demographic features of controls and patients. rs243324 SNP genotyping

SOCS1 expression a b c

Controls

Cases Male/female (%)

RRa/SPb/PPc (%)

112

33/67

63.4/24.1/12.5

29.5 ± 6.45

30

26.7/73.3

100/0.0/0.0

27.26 ± 7.14

Number

Male/female (%)

Number

125

40/60

30

20/80

Age of onset (Average ± sd)

Relapsing remitting. Secondary progressive. Primary progressive.

3.4. The effect of rs243324 SNP on SOCS1 expression We compared the individuals being homozygous for the protective C allele (CC) with consistently grouped individuals homozygous for the SOCS1 T risk allele (TT) together with those that were heterozygous for this allele (CT) in total MS and control groups and in female groups. Finally, our results revealed that the presence of rs243324 T allele in SOCS1 gene did not affect the expression level of SOCS1 mRNA in PBMCs of RR-MS patients (P = 0.18), controls (P = 0.9) and female groups (Pcontrols = 0.61 and Ppatients = 0.15) (Fig. 2). Unfortunately, we could not perform this analysis in the male group due to the low number of cases in MS and control groups of expression study. 3.5. Correlation between normalized expression of SOCS1 with age of participants, age of onset and disease duration A Pearson correlation was performed to find a possible relation between the expression of SOCS1 gene with age of participants, age of onset and disease duration (Table 3). Our results revealed a significant correlation between normalized expression of SOCS1 and age in female control group (r = −0.43, P = 0.03). Also, we found a tendency to correlation in total MS patients and disease duration (r = −0.34, P = 0.06) (Fig. 3). But, we did not find any significant correlation between MS patients and controls with age of onset. 4. Discussion Inflammation is one of the major aspects of MS which many therapies are trying to decrease the level of the pro-inflammatory cytokines that up-regulated in MS patients. Although the cause of this inflammation is remained to be cleared, many studies revealed some molecular aspects of its controlling. SOCS1 that is the major controller of cytokine signaling in the JAK–STAT pathway is an interesting candidate in this way which we aimed to determine its relative expression in the 100

Sensitivity %

80 60 40

Table 2 The genotypes and allele distribution of SOCS1 rs243324 C N T polymorphism in MS cases and controls. Genotypic and allelic frequencies do not shown any significant differences between cases and controls.

A U C = 0 .9 1 3 3 9 5 % C I= 0 .8 4 6 to 0 .9 8

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PBMCs of the MS patients and healthy controls. After the statistical analysis, in agreement with other reports on EAE model [12–14], our results revealed that SOCS1 expression level dramatically increased (24.5 times) in patients versus controls (P = 0.0001). Hormones, colony stimulatory factors, growth factors and many cytokines, including IL-2, IL-4, IL-6, IL-9, IL-13, IFNα, IFNβ, IFNγ and TNFα, may induce SOCS1 expression [21]. SOCS1 is involved in innate immunity and plays a significant role in the pathogenesis of inflammatory diseases [22]. The absence of SOCS1 gene in a mouse model, leads to aberrant thymocyte development and inflammation [23]. Additionally, Foxp3+ CD4+ T-regulatory cell differentiation in the thymus is influenced by SOCS1 [24]. Interestingly, loss of SOCS1 resulted in increased differentiation of Th17 cells, which is a pro-inflammatory CD4+ T-cell subset [25]. Moreover, deficiencies in SOCS1 resulted in lymphopenia, T-cells activation, liver necrosis, and multi-organ failure associated with severe inflammation [26,27]. SOCS1-restored mutant mice have insufficient expression of SOCS1 which induces the dysfunction of CD25+ CD4+ regulatory T cells and aberrant activation of CD4+ T cells [28]. All these evidences showed that the proper expression of SOCS1 is essential for the immune homeostasis and the prevention of autoimmunity. Therefore, dysfunction of SOCS1 may be a pathologic factor in MS. Such as many other previous studies, our results potentiate this hypothesis that the cells which reside in whole blood are triggers for inflammatory responses in the brain plaques, as the expression of SOCS1 increases in the blood cells possibly due to increased expression of inflammatory cytokines that destroy the blood–brain barrier and finally, may cause plaques. The significant increase in the expression of SOCS1 suggests that it can serve as one of the best targets to control cytokines, which are the frontiers of pathogenesis in MS. Also, this result indicated that the gene expression level was characteristic for individuals and may be useful for personalizing therapy. Moreover, the area under the ROC curve was N 0.9 and therefore implies that SOCS1 expression level in the PBMCs may have a good predictive power to diagnose MS patients from normal individuals. However, there were no previous studies either on EAE model or MS patients that evaluate the ROC curve analysis of SOCS1 expression. Now, further prospective studies are necessary to verify and test the usefulness of this cutoff value to distinguish MS patients and healthy controls. In the other aspect, we performed our genotyping analysis in a specific racial group which has a high rate of consanguineous marriage

P v a lu e < 0 .0 0 0 1

rs243324

0 0

20

40

60

80

100

100% - Specificity% Fig. 1. ROC curve analysis of SOCS1 gene expression for discriminating MS patients and healthy controls. ROC curve analysis performed by plotting sensitivity versus (1-specificity) for each threshold value. The area under the curve shows good accuracy (N0.9) (P-value b 0.0001).

Genotype CC CT TT Allele C T

Cases (%)

47 (41) 58 (51) 7 (6) 152 (67.86) 72 (32.14)

Controls (%)

59 (47) 54 (43) 12 (9) 172 (68.8) 78 (31.2)

OR (95% CI)

P-value

0.995 (0.526–1.734)

0.348

0.95 (0.65–1.41)

0.825


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Fig. 2. The effect of rs243324 SNP on SOCS1 mRNA expression. These scatter graphs show relative expression of SOCS1 normalized to GAPDH in PBMCs of RR-MS patients and healthy controls (A, B) and also in stratification for female group (C, D), versus genotype of rs243324 T risk SNP. Individuals carrying the risk allele were compared with individuals homozygous for the protective allele. No significant association was found for correlation between gene expression levels and genotypes.

(85%), resulted in a specific genetically background [29,30]. Repetition of previously reported genetic associations in other populations is required to determine the associations of the genetic risk in each population. When we evaluated the association of rs243324 SNP, we were not able to replicate the recently reported association of this SNP with MS in total [5]. Also, we didn't find any significant differences between the allelic distribution in MS patients and controls. These differences may be due to differences in the population or sample size. Interestingly, we found that the case group in our study was not in Hardy–Weinberg equilibrium due to an excess of heterozygotes. This can arise because of a strong association between an allele and disease state, undetected population stratification, genetic mistyping or inadequate sample size. The potential for population stratification cannot be excluded completely. But the possibility of a false positive association is reduced by randomly genotyping of some samples with designing a new mismatch PCR-RFLP using fast digest Hinf I restriction enzyme (Fermentas), Table 3 Correlation analysis between the expression levels of SOCS1 gene with age of participants, age of onset, and disease duration in patients and controls. We found a significant correlation between SOCS1 expression levels and age of female control group. Correlation

r

P-value

MS — Age MS — Onset MS — duration Control-Age Female MS — age Female MS — onset Female MS — duration Female control — age Male MS — age Male MS — Onset Male MS — duration Male Control — age

−0.25 −0.01 −0.3 −0.29 −0.25 −0.13 −0.2 −0.43 −0.14 0.36 −0.45 0.27

0.17 0.94 0.06 0.11 0.24 0.54 0.2 0.03 0.72 0.37 0.25 0.59

which confirms our previous results (data not shown) and usually a sample size in excess of 30 individuals is adequate to obtain HWE [31]. Savage et al. concluded that immunologic SNPs are often under a degree of selective pressure based on the population studied [32], which is probably a contributing factor to the results seen in this study. Non-coding SNPs may increase the susceptibility of disease development by affecting the expression of nearby genes. For example, some SNPs in the CLEC16A gene affect expression of SOCS1 in thymic samples of MS patients [33]. Our analysis failed to show any significant correlation between SOCS1-dependent genotypes and its expression, in total and female group of patients. In line with Leikfoss et al., we think that the small number of samples and the heterogeneous combination of cells in whole blood may overshadow the variation of SOCS1 expression. Furthermore, a significant correlation between the expression levels of SOCS1 with age of female participants in the control group were observed, suggesting a new complexity level in the role of SOCS1 gene in the pathogenesis of female MS patients. Gender specific prevalence of MS might be associated with differences in the sexual hormones between males and females. Although, we do not perform the measurement of sexual hormones in our samples, previous studies showed that these hormones such as estrogen, prolactin and progesterone play an important role in MS pathogenesis [34,35] and can affect the expression of SOCS1. For example, prolactin induces expression of SOCS1 [36] and in turn, SOCS1 suppresses prolactin-induced STAT5dependent gene transcription and act as a negative regulator of prolactin signaling [37]. Our results in combination with other mentioned evidences may shed further light into the combinational role of SOCS1 and sexual hormones in the pathogenesis of multiple sclerosis. Moreover, the correlation of SOCS1 expression and disease duration tends to be significant in total MS patients which must to be further studied. It is worth mentioning to acknowledge the fact that this study performed in a closed racial population. Consequently, low sample size is one of the main limitations in this study. More precisely, there were

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Fig. 3. Correlation analysis between some clinical features and SOCS1 expression. Significant correlation between the SOCS1 mRNA expression with age of participants in female control group (A). A tendency to correlation in total MS patients and disease duration (B).

low number of MS patients who registered at the MS society and medical network in this province. Furthermore, we were not able to have access to the EDSS scores of all patients and therefore, we failed to perform the correlation of SOCS1 expression with EDSS. In conclusion, we found that SOCS1 gene overexpressed in RR-MS patients; but, our findings in such a small cohort of MS patients may shed further light into pathogenesis of MS and once our results must be validated by other scientists in larger cohorts of patients. There were no significant differences in the genotype frequencies of SOCS1 rs243324 SNP between MS patients and the healthy controls. Moreover, the rs243324 SNP did not affect the expression level of SOCS1 mRNA in PBMCs of RR-MS patients. Our data is still preliminary due to the study sample size and additional studies in other populations could help define the true role of SOCS1 expression and association with MS. Conflicts of interest The authors declare that they have no conflict of interest. Acknowledgments The authors gratefully acknowledge Zeinab Shirvani Farsani and Zohre Ghabimi for their valuable laboratory supports as well as the contribution of the patients and healthy controls for their blood donations and also the MS Society of Sistan & Baluchistan province and University of Zabol for their assistance.

[11]

[12]

[13]

[14]

[15] [16]

[17] [18]

[19]

[20] [21] [22]

[23]

References [24] [1] Milo R, Kahana E. Multiple sclerosis: geoepidemiology, genetics and the environment. Autoimmun Rev 2010;9:A387–94. [2] Kubo M, Hanada T, Yoshimura A. Suppressors of cytokine signaling and immunity. Nat Immunol 2003;4:1169–76. [3] Bullen DVR, Baldwin TM, Curtis JM, Alexander WS, Handman E. Persistence of lesions in suppressor of cytokine signaling-1-deficient mice infected with Leishmania major. J Immunol 2003;170:4267–72. [4] Qiu L-J, Xu K, Liang Y, Cen H, Zhang M, Wen P-F, et al. Decreased SOCS1 mRNA expression levels in peripheral blood mononuclear cells from patients with systemic lupus erythematosus in a Chinese population. Clin Exp Med 2014;1–7. [5] Vandenbroeck K, Alvarez J, Swaminathan B, Alloza I, Matesanz F, Urcelay E, et al. A cytokine gene screen uncovers SOCS1 as genetic risk factor for multiple sclerosis. Genes Immun 2011;13:21–8. [6] Ramanathan S, Gagnon J, Leblanc C, Rottapel R, Ilangumaran S. Suppressor of cytokine signaling 1 stringently regulates distinct functions of IL-7 and IL-15 in vivo during T lymphocyte development and homeostasis. J Immunol 2006;176:4029–41. [7] Palmer DC, Restifo NP. Suppressors of cytokine signaling (SOCS) in T cell differentiation, maturation, and function. Trends Immunol 2009;30:592–602. [8] Yoshimura A, Naka T, Kubo M. SOCS proteins, cytokine signalling and immune regulation. Nat Rev Immunol 2007;7:454–65. [9] Chan HC, K L, Liu CC, Chang LL, Tsai WC, Liu HW, et al. Increased expression of suppressor of cytokine signaling 1 mRNA in patients with rheumatoid arthritis. Kaohsiung J Med Sci 2010;26:290–8. [10] Lofrumento DD, Nicolardi G, Cianciulli A, De Nuccio F, La Pesa V, Carofiglio V, et al. Neuroprotective effects of resveratrol in an MPTP mouse model of Parkinson's-like

[25]

[26] [27]

[28]

[29]

[30]

[31]

[32]

disease: possible role of SOCS-1 in reducing pro-inflammatory responses. Innate Immun 2014;20:249–60. Zuvich RL, McCauley JL, Oksenberg JR, Sawcer SJ, De Jager PL, Aubin C, et al. Genetic variation in the IL7RA/IL7 pathway increases multiple sclerosis susceptibility. Hum Genet 2010;127:525–35. Maier J, Kincaid C, Pagenstecher A, Campbell IL. Regulation of signal transducer and activator of transcription and suppressor of cytokine-signaling gene expression in the brain of mice with astrocyte-targeted production of interleukin-12 or experimental autoimmune encephalomyelitis. Am J Pathol 2002;160:271–88. Stark JL, Cross AH. Differential expression of suppressors of cytokine signaling-1 and -3 and related cytokines in central nervous system during remitting versus nonremitting forms of experimental autoimmune encephalomyelitis. Int Immunol 2006;18:347–53. Berard JL, Kerr BJ, Johnson HM, David S. Differential expression of SOCS1 in macrophages in relapsing–remitting and chronic EAE and its role in disease severity. Glia 2010;58:1816–26. Moghtaderi A, Rakhshanizadeh F, Shahraki-Ibrahimi S. Incidence and prevalence of multiple sclerosis in southeastern Iran. Clin Neurol Neurosurg 2013;115:304–8. Nicolae DL, Gamazon E, Zhang W, Duan S, Dolan ME, Cox NJ. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet 2010;6:e1000888. Cookson W, Liang L, Abecasis G, Moffatt M, Lathrop M. Mapping complex disease traits with global gene expression. Nat Rev Genet 2009;10:184–94. McDonald WI, Compston A, Edan G, Goodkin D, Hartung HP, Lublin FD, et al. Recommended diagnostic criteria for multiple sclerosis: guidelines from the International Panel on the diagnosis of multiple sclerosis. Ann Neurol 2001;50:121–7. Chomczynski P, Sacchi N. The single-step method of RNA isolation by acid guanidinium thiocyanate–phenol–chloroform extraction: twenty-something years on. Nat Protoc 2006;1:581–5. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 2001;25:402–8. Fujimoto M, Naka T. Regulation of cytokine signaling by SOCS family molecules. Trends Immunol 2003;24:659–66. Chong MM, Metcalf D, Jamieson E, Alexander WS, Kay TW. Suppressor of cytokine signaling-1 in T cells and macrophages is critical for preventing lethal inflammation. Blood 2005;106:1668–75. Catlett IM, Hedrick SM. Suppressor of cytokine signaling 1 is required for the differentiation of CD4+ T cells. Nat Immunol 2005;6:715–21. Zhan Y, Davey GM, Graham KL, Kiu H, Dudek NL, Kay TW, et al. SOCS1 negatively regulates the production of Foxp3+ CD4+ T cells in the thymus. Immunol Cell Biol 2009;87:473–80. Takahashi R, Nishimoto S, Muto G, Sekiya T, Tamiya T, Kimura A, et al. SOCS1 is essential for regulatory T cell functions by preventing loss of Foxp3 expression as well as IFN-{gamma} and IL-17A production. J Exp Med 2011;208:2055–67. Naka T, Narazaki M, Hirata M, Matsumoto T, Minamoto S, Aono A, et al. Structure and function of a new STAT-induced STAT inhibitor. Nature 1997;387:924. Starr R, Metcalf D, Elefanty AG, Brysha M, Willson TA, Nicola NA, et al. Liver degeneration and lymphoid deficiencies in mice lacking suppressor of cytokine signaling1. Proc Natl Acad Sci 1998;95:14395–9. Fujimoto M, Tsutsui H, Xinshou O, Tokumoto M, Watanabe D, Shima Y, et al. Inadequate induction of suppressor of cytokine signaling‐1 causes systemic autoimmune diseases. Int Immunol 2004;16:303–14. Heidary M, Rakhshi N, Pahlevan Kakhki M, Behmanesh M, Sanati MH, Sanadgol N, et al. The analysis of correlation between IL-1B gene expression and genotyping in multiple sclerosis patients. J Neurol Sci 2014;343:41–5. Miri-Moghaddam E, Zadeh-Vakili A, Nikravesh A, Sanei Sistani S, Naroie-Nejad M. Sistani population: a different spectrum of β-thalassemia mutations from other ethnic groups of Iran. Hemoglobin 2012;1–10. Eng J, Prichard R. A comparison of genetic polymorphism in populations of Onchocerca volvulus from untreated-and ivermectin-treated patients. Mol Biochem Parasitol 2005;142:193–202. Savage SA, Abnet CC, Haque K, Mark SD, Qiao Y-L, Dong Z-W, et al. Polymorphisms in interleukin-2,-6, and-10 are not associated with gastric cardia or esophageal cancer in a high-risk Chinese population. Cancer Epidemiol Biomarkers Prev 2004;13:1547–9.

M. Pahlevan Kakhki et al. / Journal of the Neurological Sciences 350 (2015) 40–45 [33] Leikfoss IS, Mero I-L, Dahle MK, Lie BA, Harbo HF, Spurkland A, et al. Multiple sclerosis-associated single-nucleotide polymorphisms in CLEC16A correlate with reduced SOCS1 and DEXI expression in the thymus. Genes Immun 2013;62–66. [34] Gold SM, Voskuhl RR. Estrogen and testosterone therapies in multiple sclerosis. Prog Brain Res 2009;175:239. [35] El-Etr M, Vukusic S, Gignoux L, Durand-Dubief F, Achiti I, Baulieu EE, et al. Steroid hormones in multiple sclerosis. J Neurol Sci 2005;233:49–54.

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[36] Hooghe R, Dogusan Z, Martens N, Velkeniers B, Hooghe-Peters E. Effects of prolactin on signal transduction and gene expression: possible relevance for systemic lupus erythematosus. Lupus 2001;10:719–27. [37] Lindeman GJ, Wittlin S, Lada H, Naylor MJ, Santamaria M, Zhang J-G, et al. SOCS1 deficiency results in accelerated mammary gland development and rescues lactation in prolactin receptor-deficient mice. Genes Dev 2001;15:1631–6.