Association of variation in STAT 4 gene with Rheumatoid Arthritis

by

ABSTRACT

The signal transducer and activator of transcription 4 (STAT 4) gene encodes a transcription factor, namely signal transducer and activator of transcription 4. This transcription factor is essential for mediating responses to IL12 in lymphocytes, and regulating the differentiation of T helper cells and prevents RA. Reduced production of this transcription factor due to rs7574865 single nucleotide polymorphism in STAT 4 may enhance the risk of RA. The association of rs 7574865 polymorphism with RA was investigated in the local population of Pakistan. Study also investigated the association between smoking, diabetes and hypertension with RA. Allele specific PCR based strategy was employed for the detection of different genotypes of rs7574865 polymorphism. Frequency of T allele was higher as compared to G allele in our population. Strong association between rs7574865 and RA was observed (p<0.01). TT genotype was found to enhance 7.201 times the risk of RA (OR; 7.201: 95% CI 3.830-13.540). Hypertension increased the risk of RA development by 6.036 times (OR; 6.036: 95% CI 3.236-11.260). Diabetes was found to increase the risk of RA by 4.295 times (OR; 4.295: 95% CI 2.211-8.345). Smoking increased the risk of RA development by 3.780 times (0R; 3.780: 95% CI 2.002-7.137). On the basis of present results, it can be concluded that rs 7574865 is strongly associated with RA in local population of Pakistan and T allele is higher in this polymorphism.                                                                                            

INTRODUCTION      

The human immune system is a combination of specialized cells and organs that protects the body from foreign pathogens. Its important functions are recognition, removal, regulation and memory of each infection in our body. Autoimmune disorders develop due to over activity and abnormal immune response against tissues normally present in the body. Auto antibodies are produced due to lack of regulatory T cells normal function and attack against self tissues and organs. There are more than 80 types of autoimmune diseases. Some of the more common autoimmune disorders are type-1 diabetes, rheumatoid arthritis, inflammatory bowel disease and systemic lupus erythematosus (Uraki et al., 2014).

Rheumatoid Arthritis (RA) is a systemic autoimmune disorder that causes inflammation of the joints covered by a membrane synovium and eventually lead to polyarthritis. RA is a multisystemic disorder. It damages joints and many other organs in the body. This disease is an immune mediated disease due to inflammatory cytokines and causes formation of a proliferated tissue known as rheumatoid pannus. Its main symptoms are pain, swelling, hardness and loss of function in the joints (Firestein, 2013).

The prevalence is reported to be 0.5-1% of World population and its ratio is higher in women than men (Silman & Pearson, 2002). In the urban population of southern Pakistan, Karachi, the prevalence of RA is reported to be 0.142%, while in northern Pakistan the estimated prevalence is 0.55% (Akhter et al., 2013).

RA is a multi step disorder. Classical factors such as genetic changes, environmental factors, and immune cells increase the chances of development of RA. These factors can change gene expression by changing pre and posttranslational mechanism (Ollier & Winchester, 1999).

There are multiple causes to initiate the onset of RA in susceptible organisms under pro-inflammatory conditions. Previously reported classical causes include infection, bone fracture, food sensitivities and overexposure to smoking and many other environmental toxins. Development of RA is also associated with immune cells alterations like cytokines and regulatory T cells when compared healthy controls and patients of RA (Molnar-Kimber, 2012).

Evidence for the involvement of T cells, especially CD4+ T cells, in the pathogenesis of RA is correlated between prolonged CD4+ T-cell decrease and recovery in joint disease in the absence of observable changes in the levels of rheumatoid factors in the blood and joints (Miossec, 2004).

The mechanisms by which T cells initiate joint disease in RA are not clear. Some evidence suggests that at least some T-cell cytokines (i.e., TNF alpha, IL-6) may be involved in the inflammation of synovial lining cells which cause the formation of inflammatory tissue (pannus) in the joints of patients with RA. Auto antibodies (rheumatoid factors) in the joint which are involved in the release of complement breakdown substances and secretion of cytokines like IL-1 by macrophages also participate in it. Monoclonal antibody therapy decreases the CD4+ cells which reduces joints disease but does not eliminate it. The residual joint disease activity may be affected by the continued involvement of auto antibodies there. Auto antibodies production may also be depended on decrease in the number of CD4+ cells (Faour et al., 2005).

Both genetic and environmental factors are involved in pathogenesis of RA, a common and multi step autoimmune disorder. The major susceptibility genes studies show additional evidences for association of single nucleotide polymorphism (SNP) markers in the PTPN22, STAT4, OLIG3/ TNFAIP3 and TRAF1/C5 loci with RA (Emonts et al., 2011).

Signal transducer and activator of transcription 4 (STAT4) gene is located on chromosome 2q32.2-q32.3 (Figure.1). This gene provides instructions for a protein (transcription factor) that acts as a transcription factor and has 748 amino acids polypeptide chain. This transcription factor results in attachment (binding) to specific regions of DNA. As a result it participates in controlling the activity of some genes (Orozco et al., 2008).

The STAT4 protein is turned on (activated) by presence of various cytokines like IL-12 and IL-23. When activated, the STAT4 protein increases the activity of genes that help in the maturation of T cells.  These specialized T cells are known as type 1 helper T cells. These specialized T cells are involved in the production of specific cytokines. Which play an important role in the stimulation of certain other immune cells (Kobayashi et al., 2008).

Polymorphism in STAT 4 gene may affect the production of transcription factor (protein) encoded by it. This transcription factor induces signals due several cytokines and they are considered as a genetic cause for RA, systemic lupus erythematosus (SLE), and Sjögren’s disease (SD), shows that multiple autoimmune diseases may share common biochemical pathways that lead to immune deregulation (Mori et al., 2005; Watford et al., 2004).

In a genetically homogeneous population, the association of the STAT4 rs7574865 G/T polymorphism has been shown to be associated with some of autoimmune diseases. STAT4 transmits signals induced by interleukin-12, interleukin-23 and interferon-γ, which are key cytokines and play important roles in the development of autoimmune diseases (Shen et al., 2013).

A haplotype composed of four polymorphisms tagged by the T allele of rs7574865 in the third intron of STAT4 gene was first reported to be associated with RA in the North American White population (Silman et al., 1993; Zervou et al., 2008).

  • Aims and Objectives:

The purpose of our present research was to find the allele frequency in STAT 4 gene in the local population of Pakistan. The association between SNP, rs 7574865 in STAT 4 gene and RA was also investigated.

  REVIEW OF LITERATURE

Rheumatoid arthritis (RA) is the most common systemic and chronic autoimmune disorder. RA causes severe inflammation and degeneration of the synovial joints and it is associated with disability and early death. The etiology of RA, like many other autoimmune disorders is very complicated and not clear. However, it is known that RA risk may also be resulted by an interaction between environmental and genetic susceptibilities. The worldwide epidemiology of RA and other rheumatic disorders increases their research approach because their risk factors can be easily differentiated between environmental and genetic risk factors. This ability is increased by studying the similar peoples in different geographical, climatic and social conditions (Bowes & Barton, 2008; Silman & Ollier, 1989; Turesson & Matteson, 2006).

                                   Prevalence of RA in USA (United States of America) is more than 21%. More than 46.4 million persons were considered to have self-reported and diagnosed arthritis. There are evidences that RA affects 1.3 million adults. According to National Population Health Survey and Canadian Community Health Survey (CCHS) data, the prevalence of RA in Canadian adults has increased from 13.4% to 17.6% from 1994 to 2002. Prevalence is 13.0% in the United Kingdom (Alsnih et al., 2006; Bausija et al., 2007; Gourley et al., 1997; Rasch et al., 2003).

                                   Prevalence of RA in Australia and New Zealand is 15.0% to 24.0% respectively. Prevalence of RA in developing countries has variation. Prevalence of RA in India is 0.75% which is similar in white population from Manchester (0.8%). Prevalence of RA in Pakistan varies in different populations, Northern Pakistan have high rate of RA pathogenesis as compared to Southern Pakistan (Dai et al., 2003; Farooqi & Gibson, 1998; Helmick et al., 2008; Knox et al., 2008; Mahajan et al., 2005; Malaviya et al., 1993).

                                   Hameed et al. (1995) studied prevalence of RA in affluent and poor urban areas of Pakistan. They estimated the prevalence of 0.9 and 1.98 per thousand in the poor and affluent areas of Pakistan respectively. Naqi et al. (2012) included one hundred rheumatoid patients in the study. Average age of patients was 43 years. In which 88% were females and 12% were males. In this study patients were classified into three groups of mild, moderate and severe disease activity, each group with 30, 31and 39 patients respectively. The values of laboratory parameters in mild, moderate and severe disease activity groups were studied. The parameters studied in the three disease activities estimated that entire studied laboratory markers that are RA factor, anti CCP antibodies and ESR were found to be associated with RA. Because results found that disease activity parameters increased in disease group from mild to severe.

                                   Both environmental and genetic risk factors are considered as cause of pathogenesis of RA. Most important susceptible genes associated with RA are STAT 4, HLA, PTPN22 PADI4 and TRAF1-C5 (Harney et al., 2005; Harrison et al., 2007). Many environmental factors have been associated with an increased risk of developing RA, but so far smoking is the only environmental risk factor of RA that has been mostly studied and accepted as well (Hasnis et al., 2007). Classical evidences show that tobacco smoking can enhance disease phenotype, with the increase of severe disease and more joint damage (Baka et al., 2009; Ruiz-Esquide & Sanmarti, 2012; Panoulas et al., 2007).

The most classical evidence for a genetic role is in monozygotic twins. In case of identical twins the concordance rate is 12% to 15% which is very higher as compared to general population of 1%. While in case of fraternal twins RA is also observed high about (2% to 5%) but this is less as compared to first-degree relatives (Aho et al., 1986).

Single nucleotide polymorphisms (SNPs) in signal transducer and activator of transcription 4 (STAT4) gene have been reported to be associated with RA. Many studies confirmed the association between the polymorphism rs7574865 in STAT4 gene with RA. It was also investigated whether the associations that have been reported in these studies differ between ethnic groups or not (Choi et al., 2006; Frucht et al., 2000; Imboden, 2008; Lynn et al., 1995).

The SNP located within STAT4 gene encodes a regulatory protein necessary in the immune system. STAT4 sends signals into the blood stream by turning on other genes that control other immune responses. The same SNP has been associated with other autoimmune disorders like Systemic Lupus Erythematosus and Scleroderma (Thomson et al., 2007).

The two copies risk variants of STAT 4 gene are associated with double risk of lupus 60% of RA than those having one copy of variant. However people with a particular variant had a 35% increased risk of RA pathogenesis as compared to those without the variant (Wallin et al., 1991; Yamashit et al., 1986).

STAT proteins (transcription factors) are important cytoplasmic transcription factors and are activated by phosphorylation and subsequently dimerize and transferred to the nucleus, where they contact with DNA-binding sites. STAT-4 is essential for the signal transduction of various pro inflammatory cytokines like interleukin-12 (IL-12), IL-15, and IL-23 and therefore is essential in the production of a Th1 cell immune response (Kay & Calabrese, 2004; Mathur et al., 2007; Watford et al., 2004).

                                   Smoking and RA association is studied worldwide and smoking is considered to be a major risk factor for RA. RA patients who exhibit anti-citrullinated protein antibodies have smoking as a risk factor of 1 in 5 of RA patients. Patients with ACPA positive RA have increased risk of RA with longer and higher intensity of smoking. Smoking was also considered a risk factor at the earlier age (Abhishek et al., 2010; Soderline et al., 2012).

                                   Type 1 diabetes (T1D) is an organ-specific autoimmune disorder. It is due to selective destruction of pancreatic cells by the activity of T-cells. STAT4 gene is one of the most important gene for the pathogenesis of autoimmune diseases such as T1D and RA. In this study Han population in northeastern China the association of two SNPs in STST 4 gene in T1D patients was studied. The study includes 410 T1D patients and 407 healthy controls. STAT4 SNPs rs7574865 and rs3024866 were genotyped. It was concluded that that one of the two SNPs rs7574865 was strongly associated with T1D and RA (Bi et al., 2013; Lee et al., 2008).      

                                   Lee et al. 2010 noticed the T allele prevalence and evaluated that Europeans had the lowest (21.4%) while Asians had the highest (32.0%) prevalence of the T allele. In conclusion, this study confirmed that different ethnic groups have STAT4 rs7574865 polymorphism with RA.

                                   According to Remmers et al. 2009 a SNP haplotype in the third intron of STAT4 was associated with pathogenesis of both RA and systemic lupus erythematosus. Patients have 27% minor alleles of polymorphism while healthy controls were 22%. In Caucasians and Asians a meta-analysis showed an association between the STAT4 polymorphism rs 7575865 and RA (Kelley et al., 2011).

                                   Scott et al., 2013 studied rs 7574865 polymorphism of STAT4 association with RA in populations with different social groups. A meta-analysis was evaluated on the T allele of the STAT4 rs7574865 polymorphism in 15 studies included 16,088 RA patients and 16,509 healthy control persons. Meta-analysis confirmed an association between RA and the STAT4 rs7574865 T allele in all persons. In Europeans and Asians STAT4 rs7574865 T allele was confirmed to be significantly associated with RA.

                                   Zhao et al. (2013) studied 640 RA patients and 662 healthy controls in his genotyping. Direct sequencing was used for DNA samples for their STAT4 rs7574865 genotyping. This study evaluated the association of rs 7574865 with RA pathogenesis and the relationship between rs7574865 polymorphism and RA subgroups was calculated by clinical features. In the northern Chinese Han population a significant association of STAT4 rs7574865 polymorphism with RA pathogenesis was found. The frequency of the minor T allele in RA was significantly higher than in healthy controls.

                                   A study in Chinese included 520 RA patients and 520 controls. Polymorphism of rs 7574865 in STAT 4 gene was genotyped in patients and controls. This study confirmed the association of rs 7574865 G/T polymorphism with RA (Shen et al., 2013).

                                   Martinez et al. 2008 reported polymorphism of STAT 4 gene SNP rs 7574865 in spainish population. This study includes 575 RA patients and 723 healthy controls. This study evaluated that T allele was significantly associated with RA. Stark et al. (2009) found a significant association between rs 7574865 and RA in Slovak population. This study includes 520 controls and 520 RA patients in which 87 were male and 433 were females.

MATERIALS AND METHODS

Population studied:

All procedures were in agreement with the declaration of Helsinki. The Advanced Research and Study Board, University of Sargodha approved the protocol of the present study. Permission from Ethical Committee, University of Sargodha was also obtained before the start of research work. The subjects were divided into two groups. One group consisted of patients of RA and other comprised of age matched controls. For each group, data for, gender, age, smoking habit and diabetes was collected. Study consisted of 150-200 subjects out of which 100 samples were RA patients and 100 were healthy persons. Group of RA patients was named as RA group while group of healthy individuals was called as control group.

            3.2: Sample collection

          Samples were collected from October 2013 to February 2014. After taking proper consent and completion of ethical criteria, RA patients and control (healthy individuals) were selected for the present study.

3.3: Venipuncture

            All blood samples were taken between 8-11 am. To draw blood samples cubital vein was punctured by using sterilized syringe (BD, USA). The collected blood samples were stored in EDTA coated vials (BD, USA) as whole blood for genetic analysis. For long term storage, the samples were kept stored at -20 ºC until further analysis.

3.4: Genetic analysis

          Genetic study was based on the SNP (rs7574865) detection in STAT 4 gene. Major techniques used in the genetic analysis included genomic DNA islolation, agarose gel electrophoresis and allele specific PCR.

3.5: Other relevant information

          Information about age, gender, diabetes, smoking, and hypertension of each sample was recorded. Performa for data collection was prepared as given in Table 3.1. Information of diabetes and smoking was obtained from hospitals from where samples were collected.

Table 3.1: PERFORMA FOR DATA COLLECTION

                   DATA COLLECTION PERFORMA

S.NO                   NAME:                 AGE:                GENDER:

GROUP:
 Status Yes No
Smoker    
Diabetes    
Hypertension    

 

The subjects were considered as smoker if they were smoking at the time of sample collection with smoking history of 5 year. Hypertensive individuals were defined on the basis of systolic and diastolic blood pressure. Hypertensive individuals were having systolic blood pressure values more than 140 mmHg and their diastolic blood pressure values were more than 90 mmHg. Persons were considered as diabetic on the basis of their blood sugar levels. If blood sugar level was higher than 120 mg/ dl (fasting), the individual were considered as diabetic.

3.6: Genomic DNA isolation

          Genomic DNA was isolated by using kit method (vivantis Cat. No. GF-BD-100, USA). Protocol was as follow:

3.6.1: Preparation of Buffers:

          For the preparation of wash buffer 1, 30ml absolute ethanol was added in the bottle marked as Wash Buffer 1. For preparation of wash buffer 2, 80ml of absolute ethanol was added in the bottle marked as Wash Buffer 2.

3.6.2: Blood Lysis

            200µl of blood sample was mixed thoroughly with 200µl of buffer BB in an eppendrof by pulsed vortexing. Then 20µl of proteinase K was added and mixed without delay. Then it was incubated for 10 minutes at 65 ºC.

3.6.3: Ethanol Addition

          After incubation, 200µl of absolute ethanol was added and mixed immediately to get a homogenous mixture. Immediate mixing was done to prevent unnecessary precipitations of nucleic acids.

3.6.4: Column Loading

          The samples were loaded into a column assembled in a clean collection tube. After loading, column was centrifuged (Mikro 120-hittich, Germany) at 5,000 rpm for 1 min. Outflow was discarded.

3.6.5: Column washing with buffer 1

          The columns were then washed by adding 500µl of wash buffer 1 through centrifugation at 5,000 rpm for 1 min. The outflow was discarded.

3.6.6: Column washing with buffer 2

          The columns were washed by adding 500µl of wash buffer 2 through centrifugation at 5,000 rpm for 1 min. The outflow was discarded. The columns were again washed using 500µl of wash buffer 2 and centrifuged for 3 minutes at maximum speed.

3.6.7: DNA elution

          The columns were positioned into clean eppendrof. Elution buffer (preheated at 65 ºC) was directly added into the membrane of column and waited for 2 minutes. After that, it was centrifuged at 5,000 rpm for 1 minute to elute DNA. The DNA was stored at 4ºC or -20ºC.

3.7: Agarose gel electrophoresis

          Genomic DNA and PCR products were detected by using agarose gel electrophoresis. Agarose gel was prepared in 0.5X TBE (Table 3.2).

Table 3.2: 10X TBE electrophoresis buffer               

  Items concentration (1X) Amount per liter (10X)  Final
            Tris base               108 gram         90 mM
           Boric acid               55 gram         90 mM
           0.5 M EDTA              40 mL         2 mM
           H2O                 To 1 liter  

 

Gel concentration was made according to the size of the sample. For genomic DNA detection, 0.8 % agarose (Bio BASIC INC, D0012) gel was prepared. For PCR product, 2 % gel was used for analysis. 1µl of ethidium bromide (Invitrogen, Cat.No. 15585-011, USA) (10 ng/ml) was added in the gel and mixed. Running buffers were of similar composition and concentration. The gel was poured in the tray with closed clips and comb was inserted to create wells. Gel was allowed to polymerize for 30 minutes and then comb was removed after placing gel in the gel tank (Thermo SCIENTIFIC, EC300XL2, China) until it submerged in running buffer. The samples were mixed with loading buffer (Table 3.3) and then loaded in the wells.

Table 3.3: Recipe of loading buffer

Ingredients Concentration
Cyan orange 33µl
Nuclease free H2O 100µl

 

In first well, low range DNA ladder was added for the analysis (Gene on GmbH Germany, Cat. No, 300009, USA). Gel tank was closed and gel was run with 5V/cm. To visualize the DNA fragments, gel was removed from the gel tank and visualized under UV Transilluminator (BIOTOP Transilluminator, TU1002, China). Digital camera (SONY, 7.2 Mega Pixel, Japan) was used for saving pictures.

3.8: Allele specific PCR:

          PCR tubes of 200 µl were used for the required amplification of genomic sequence. 50µl reaction mixture was prepared. PCR Master Mix (Invitrogen, Cat. No. 12341-012, USA) was used for amplification. The composition of the master mix is given in the table 3.4.

  Table 3.4: Composition of 2X PCR Master Mix

Ingredients Concentrations                       
Taq DNA polymerase                          0.5u/µl
Mg Cl2                          3.0Mm
dNTPs (dATP, dCTP, dGTP, dTTP)                         0.4Mm each
2X ViBuffer A*  

   *contain 100mM KCl, 20mM Tris-HCl (Ph 9.1 at 20ºC) and (0.02% TritonTM     X-100)

           

            Primers used in the study were synthesized by Invitrogen, USA through local representative. These were prepared for PCR using the nuclease free water. Table 3.5 shows the list of primers used. The storage temperature for primers and PCR mixture was -20ºC and before use they were thawed on ice. Recipe of reaction mixture is shown in table 3.6.

             PCR reactions were carried out in XP thermal cycler (BIOER TECHNOLOGY CO., LTD., TC-XP-G, China). The PCR program installed in the thermal cycler for amplification is shown in the table 3.7. To visualize the fragments of PCR products UV Transilluminator was used. Before visualization it was run on 2.0% gel (agarose) and stained with ethidium bromide. The data was saved by using digital camera. The size of the product was compared with the ladder.

        Table 3.5: Specifications of PCR for rs7574865 genetic study

Polymorphism Primers Primer sequence Annealing temperature Product size(base pairs)
 

               

 

rs 7574865

 Forward1

(F1)

 5`AAAGTTGGTGACCAAAATGTG3`  

 

 

 

 

   59.2ºC

  

 

 

 

 

   311bp
Forward2 (F2) 5`AAAGTTGGTGACCAAAATGTT3`
Reverse

(R)

 5`TTGCTTCGTAAATGTCAGCA3`

 

        Table 3.6: Reaction mixture for allele specific PCR (50µl)

Components Quantity
Primer F1/F2 3 µl
Reverse Primer R 3 µl
Master Mix 40 µl
Genomic DNA 4 µl

 

        Table 3.7: Program for amplification reactions in thermal cycler

STEP    TEMPERATURE TIME LID

    TEMPERATURE
Step 1 94 ºC 2 minutes Lid temperature 105 ºC
 

Step 2-5 (32 cycles)

 94 ºC 30 seconds Lid temperature 105 ºC
59.2 ºC 1 minute Lid temperature 105 ºC
68 ºC 1 minute Lid temperature 105 ºC
Step 6 68 ºC 12 minute Lid temperature 105 ºC
Step 7 4 ºC Storage Lid temperature off

 

  3.9: rs 7574865 polymorphism:

          To resolve PCR products, UV Transilluminator was used. In the presence of GG homozygotes bands (311bp) appeared with F1 primer, in TT homozygotes bands (311bp) appeared with F2 primer and in case of GT heterozygotes bands (311bp) appeared with both F1 and F2 primers.

         

           3.10: Statistical analysis

          The sample size was calculated by using online calculator provided by Creative Research Systems (http://www.surveysystem.com/sscalc.htm). For the analysis of Hardy Weinberg equilibrium chi square test was used. Genetic frequencies, allelic frequencies and difference in genotypic and allelic frequencies among different groups were also examined by chi-square analysis. Chi-square test and other non parametric tests were applied by SPSS® Software version 13 for window (SPSS Inc., Chicago Illinois, USA 1989-2003). Odd ratios were calculated using an online calculator (Bland and Altman, 2000).

RESULTS

Present study includes 200 samples. These samples gave results of PCR amplification for genotype identification. Table 4.1 shows the baseline characters of studied groups. It provides the information of parameters like age, gender, smoking habit, presence of disease like hypertension and diabetes. It can be learnt that groups were almost similar in terms of age and gender (p>0.05). It was observed that groups were significantly different on the basis of hypertension (p<0.01) and diabetes (p<0.01). Smoking was also significantly different in these groups (p<0.01).

TABLE 4.1: BASELINE CHARACTERISTICS

Characteristics RA patients

(N = 100)

 Normal

(N = 100)

 Total

(N = 200)

 p value
Age (Years) a 46.31±13.392 41.80±13.770 44.05±13.735 0.890
Gender Femaleb          61        50           111 0.55
Smokers  Yesb          47        19            66 0.000*
Diabetic Yesb          45        16            61 0.000*
Hypertensive Yesb          63        22            85 0.005*

aData are shown as mean ± standard deviation. Students T test was used for comparison of groups of RA and Normal.

b Chi square test of the difference between the two groups (RA and Normal) defined in terms of disease presence.

 *p <0.01, ψ p < 0.05

Figure 4.1 shows the results of genomic DNA detection. Sample in which DNA was detected were used for PCR amplification. Band was not detected in well No 1 so this sample was not use for PCR amplification.

 

    1                 2                    3                      4                  5                    6                       7      

FIGURE 4.1: RESULTS OF GENOMIC DNA DETECTION

Figure 4.2 shows the results of PCR amplification DNA ladder (L) was loaded in the well NO 6. Bands appeared in the well no 7, 8, 9, 10 and 11 with F2 primer amplification. Bands did not appear in well No 1, 2, 3, 4 and with F1 primer. These results confirm the TT homozygous condition due to absence of bands with F1 primer.

Table 4.2 shows the frequencies of G and T allele in RA and normal group and results of Hardy Weinberg Equilibrium (HWE). The results indicate that G allele frequency was higher in case of normal individuals and T allele was comparatively smaller. In case of RA patients results were different. T allele was higher as compared to G allele. When data was analyzed collectively, T allele was found to be higher as compared to G allele.

Results for HWE estimation show that the allele frequencies in normal individuals were deviant from HWE. Similar results were noticed in RA patients. Results remained unchanged when the stratification impact of disease presence was removed for the analysis.                    

4.2: GENOTYPE AND ALLELE FREQUENCIES

 

Allele Normal (N=100) RA (N=100) Total (N=200)
GG             62             24              86
TT             21             67              88
GT             12               9              26
G             0.71             0.28              0.5
T             0.3             0.72              0.51
HWE (p)     34.96 (0.000)       60.71 (0.000)       109.5 (0.000)

 

Table 4.3 shows the results of association between the polymorphism and RA. Association is estimated in terms of Chi-square test and odd ratio with 95% confidence interval (95% CI). The analysis indicate that a strong association between the rs7574865 polymorphism and RA (p<0.01). GG genotype was noticed to have protective effects against the disease development. It lowers the risk of RA 0.188 times (OR: 0.188; 95% CI 0.102-0.347). TT genotype shows 7.201 times the increased risk of RA development (OR: 7.201; 95% CI 3.830-13.540). GT also has association with the RA but at lower level (OR: 0.482; 95% CI 0.204-1.142).

TABLE 4.3: ASSOCIATION OF GENETIC POLYMORPHISM AND RA

WITHOUT ADJUSTMENT OF DATA FOR AGE AND GENDER

 

GENOTYPE    RA NORMAL ODDS RATIO 95%CI Chi square (p- value)
GG      24 62 0.1886 0.1025-0.3472 43.298 (0.000)
TT      67 21 7.2014 3.8301-13.540
GT       9 17 0.4829 0.2041-1.1423

 

Table 4.4 presents the results of association between polymorphism and RA but after adjusting the same data for age and gender. It also indicates the strong association between the rs7574865 polymorphism and RA (p<0.01). Results for the odd ratio estimation remained same for GG and TT genotypes. GG genotype was found to have protective effects against the development of RA. It decreases the risk of RA for 0.1569 times (OR; 0.1569: 95% CI 0.071-0.346). TT genotype was suspected to have 3.469 times increased risk of RA development (OR; 3.469: 95% CI 1.673-7.190). Results for odd ratio of GT were changed after adjustment of data. GT was non-significantly associated with the RA (OR; 0.4006: 95% CI 0.1306-1.229).

 

TABLE 4.4: ASSOCIATION OF GENETIC POLYMORPHISM AND CAD

WITH ADJUSTMENT OF DATA FOR AGE AND GENDER

GENOTYPE RA NORMAL ODDS RATIO 95%CI Chi square

 (p-value)
GG 13        39 0.1569 0.071-0.3469  33.70            (0.000)
TT 46        13 3.469 1.673-7.190
GT 5        11 0.4006 0.1306-1.229

 

Table 4.5 shows the association of smoking habit with RA without adjustment of data for age and gender. The results suggested the strong association between smoking and RA (p<0.01). Analysis with odd ratio revealed that smoking habit increased 3.780 times the risk of RA development (OR; 3.780: 95% CI 2.002-7.137).

TABLE 4.5: ASSOCIATION OF SMOKING WITH CAD WITHOUT

ADJUSTMENT OF DATA FOR AGE AND GENDER

 

SMOKING STATUS RA NORMAL ODDS RATIO 95%CI Chi square (p value)
Smokers 47 19 3.7805 2.0025-7.1373 17.73 (0.000)
Non Smokers 53 81

 

Table 4.6 presents the results of association between smoking habit and RA after adjustment of data for age and gender. The results remained unchanged. A strong association was observed between smoking and RA (p<0.05). Results of odd ratio indicate 2.4 times increase in the risk of RA with smoking habit (OR; 2.4: 95% CI 1.1042-5.216).

TABLE 4.6: ASSOCIATION OF SMOKING WITH CAD WITH ADJUSTMENT OF DATA FOR AGE AND GENDER

SMOKING STATUS RA NORMAL ODDS RATIO 95%CI Chi square (p value)
Smokers 24 20      2.4 1.1042-5.216 4.976 (0.02)
Non Smokers 23 46

 

Table 4.7 shows the significant association of diabetes with RA (p<0.05) without adjustment of data for age and gender. Results suggested 4.295 times increase in risk of RA with diabetes (OR; 4.295: 95% CI 2.211-8.345).

TABLE 4.7: ASSOCIATION OF DIABETES WITH CAD WITH OUT

ADJUSTMENT OF DATA FOR AGE AND GENDER

CATEGORY RA NORMAL ODDS RATIO 95% CI Chi square  

 (p value)
Diabetic 45 16 4.2955                           2.211-8.3452

 

 19.837 (0.000)
Non Diabetic 55 84

 

Table 4.8 presents the results of association of diabetes with RA development with adjustment of data for age and gender. Results with adjusted data remained unchanged. Strong association observed with diabetes and RA (p<0.05). Odd ratio estimation presents an increase of 4.22 times in the risk of RA with diabetes (OR; 4.22: 95% CI 1.991-8.953).

 

 

 

 

 

TABLE 4.8: ASSOCIATION OF DIABETES WITH CAD WITH ADJUSTMENT OF DATA FOR AGE AND GENDER ADJUSTMENT

CATEGORY RA NORMAL ODDS RATIO 95% CI Chi square    (p value)
Diabetic 38 18 4.22 1.991-8.953 14.746 (0.00)
Non Diabetic 23 46

 

Table 4.9 shows the strong association of RA with hypertension (p<0.01) without adjustment of data for age and gender. Hypertension 6.036 times increases the risk of RA development (OR; 6.036: 95% CI 2.21-8.345).

TABLE 4.9: ASSOCIATION OF HYPERTENSION WITH CAD WITHOUT

ADJUSTMENT OF DATA FOR AGE AND GENDER

CATEGORY RA NORMAL ODDS RATIO 95%CI Chi square

(p value)
Hypertensive 63 22 6.0369 3.2364-11.260 34.394 (0.000)
Normal 37 78

 

Table 4.10 presents the results for the association of hypertension with RA with adjustment of data for age and gender. These results unchanged for the adjusted data. Strong association was observed between hypertension and RA (p<0.01). Hypertension was observed to have an increase risk of 2.740 times for RA development (OR; 2.740: 95% CI 2.211-8.345).

 

 

 

 

 

TABLE 4.10: ASSOCIATION OF HYPERTENSION WITH CAD WITH

ADJUSTMENT OF DATA FOR AGE AND GENDER ADJUSTMENT

CATEGORY RA NORMAL ODDS RATIO 95%CI Chi square

(p value)
Hypertensive 37 21 2.7407 1.3321-5.6388 7.667 (0.005)
Normal 27 42

 

                                           DISCUSSION

RA is a systemic auto-immune disorder. It causes polyarthritis and the primary clinical manifestation. RA initiates in the small joints of the hands and the feet and then spread to larger joints containing synovial membrane. Due to inflammation in synovial membrane it extends and degeneration of the cartilage and bone occurs. It causes joint deformity and leads to physical disability. Some pathogenic features of RA include synovial pannus, pulmonary fibrosis, peripheral neuropathy and amyloidosis (Gravellese, 2002; Hulkower et al., 1992; Mor et al., 2005; Zhemakova et al., 2009).

Many epidemiological studies of RA found that among adult white populations of Europe and America, the incidence of RA by the 1958-1987 is approximately 1%. The concordance incidence of RA among white populations is about 0.03% per annum. The incidence of RA is higher in women than men (Feldman et al., 1996; Smolen & Steiner, 2003).

The incidence and pathogenesis of RA increase with age. Accounting for 0.7% of total Years lived with Disability (YLD), RA was considered to be 40th leading cause of non-fatal burden in the World in 1990. According to Global Burden of Disease 2000 study, published in the World Health Report, RA was estimated to be 31st leading cause of YLDs at global level and its was included 0.8% of total global YLDs (Symmons et al., 1996).

50% of risk of developing RA is considered to be associated with genetic factors. Research approach has been made in identification of genetic regions that are structural variation (single nucleotide polymorphisms). It has been studied that more than 30 genetic regions are associated with pathogenesis of RA (Klareskog et al., 2006; Croon et al., 2004).

Different risk factors are involved in the development of RA. Among these most important risk factors are genetic polymorphism, age and gender, smoking, diabetes and hypertension. All of these contribute in pathogenesis of RA. Transcription factor or production of auto antibodies is considered as important factor of RA development. STAT 4 transcription factors play important role in joint inflammation in RA (Lewis-Faning, 1950).

The STAT4 protein is turned on (activated) by presence of various cytokines like IL-12 and IL-23. T cells maturation needs activation of STAT 4 protein encoding genes. These specialized T cells are known as type 1 helper T cells. These specialized T cells are important in the production of specific cytokines. They play an important role in the activation of certain other immune cells. Polymorphism in STAT 4 gene play important role in RA pathogenesis. rs7574865 polymorphism in STAT 4 gene has extensively been studied as a risk factor of RA. In the presence of T allele dysfunction in Cytokines signaling pathway and auto antibodies production enhances the risk of development (Coenon et al., 2009).

Several studies have focused on this SNP for its impact on etiology of RA in different populations. Present study has focused on this SNP for analysis of its association with RA in local population of Pakistan. These results for allele frequency suggest that T allele frequency was higher in population under observation. A strong association was observed between RA and rs7574865. These findings are in agreement with Lau et al. (1993), who found the association of T allele in Chinese of Hong Kong. The present findings also correspond with the results of Mirkazemi et al. (2013) who found a association between RA and Iranian population. Findings of Daha et al. (2009) also show correspondence with present study. Tong et al. (2010) also found significant association between RA and T allele frequency in Japanese population. Observations reported by Zervou et al. (2011) also in agreement with findings of present study. Zervou et al. (2011) found that TT genotype is associated with RA. Association of RA and rs7574865 was observed in Northern Chinese Han population (Liang et al., 2011: Su et al., 2010). Lee et al. (2007) found association of rs7574865 with RA in Korean population. Similar findings were also observed in European population (Orcozo et al., 2005). Amos et al. (2006) also observed strong association between rs7574865 and RA in Caucasian families.

Present study depicts association of TT genotype with RA and these results are in agreement with several populations. Contradictory results were not obtained. It was shown that association exists between all studied populations. Not a single population reported without this association.

Increasing age is considered as important risk factor of RA. It is more common in women than men almost 60% of the people with RA are women (Mckenna et al., 1991). Age and gender shows association with RA due to disturbance in cytokines signaling and other physiological factors associated with age and gender (Anderson, 1996; Weyand et al., 1998). Present study also shown old age females have more risk of development of RA. 

Smoking also considered as an important risk factor for development of RA because it can enhance immune dysfunction (Costenbader et al., 2006; Karlson et al., 1999; Stolt et al., 2003). Formica et al., (2003) finds significant association between smoking and risk of RA development in black women. Krishnan et al. (2003) reported that smoking as a risk factor of RA in Finland population. Kiyahara et al. (2009) also find significant association in smoking and RA. Result of present study depicts strong association between smoking and RA development in population of Pakistan.

Among traditional factors for RA diabetes shows association with RA pathogenesis. Liao wt al. (2010) observed significant association between diabetes and RA. Zervou et al. (2008) observed association of diabetes with RA in Japanese population. Munakat et al. (2005) also considered diabetes as a risk factor of RA. Present study depicts the strong association between diabetes and RA in local population of Pakistan.

Hypertension has been reported to have association with RA. It is considered to have some physiological effects on immune cells (Gasparyan et al., 2010; Young et al., 1989). Present study noticed strong association between hypertension and RA. Lopez-Majias et al. (2013) also observed strong association between hypertension and RA in Caucasian individuals. RA considered as multifactorial disorder because it is affected by various risk factors including traditional and genetic factors. Individual exposed to traditional risk factors always do not develop RA. Genetics may be there a potential factor for the development of RA.

 

5.1: Conclusion

Frequencies of GG, TT and GT genotypes were different in RA patients and normal individuals. It can be concluded on the basis of present finding that rs7574865 polymorphism play an important role in modulating the chances of RA. GG genotype shows its protective effects against RA, while TT genotypes enhance the chances of RA. Other factors like smoking habit, diabetes and hypertension were also found to increase the chances of RA. Generally stating, the smoking habit, diabetes, hypertension and genotype of individual decide the onset of RA in local population of Pakistan.

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