Genetic Susceptibility and Disease Activity in Ankylosing Spondylitis: The Role of GPR35 rs4676410 Polymorphism in a Turkish Population

doi:10.21203/rs.3.rs-4979847/v1

Download PDF

Research Article

Genetic Susceptibility and Disease Activity in Ankylosing Spondylitis: The Role of GPR35 rs4676410 Polymorphism in a Turkish Population

https://doi.org/10.21203/rs.3.rs-4979847/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Introduction: In East Asians, an additional genome-wide association study finally detected several new immune-related susceptibility loci for AS. There have been some investigations conducted for genetic variations, and it was found that the single nucleotide polymorphisms of GPR35 gene would play an important role in the pathophysiology of AS. The primary goal of the study was to explore the role of the rs4676410 polymorphism in the GPR35 gene in contributing to the susceptibility to Ankylosing Spondylitis (AS) and its influence on clinical and biochemical outcome parameters within the Turkish population. The study also aimed to understand how these genetic factors interact with the disease's pathogenesis and severity.

Methods:We isolated DNA and examined the rs4676410 variation in GPR35 among 200 participants (100 with AS individuals and 100 with healthy individuals) by using real time PCR. Compared the results with biochemical data and then we evaluated results using IBM SPSS 26.

Results:Our research has shown that having the AA genotype or A allele seems associated with a susceptibility or risk of developing AS in Turkish population.

Conclusion: The strong correlational basis between disease-activity scores and genetic variants supports the utility of exploring genetics, which underscores as one of main player affecting parametric severity evaluations. This is the first study conducted in Turkey.

Ankylosing Spondylitis

rs4676410

polymorphism

GPR35

disease activity

• This research is the first of its kind in Turkiye to investigate the role of the rs4676410 polymorphism in GPR35 in relation to AS. It provides novel insights into how genetic factors contribute to both the onset and severity of AS, highlighting the importance of integrating genetic testing into the diagnostic.

• The strong correlation between disease-activity scores and genetic variants highlights the importance of genetic research and underscores the role of genetic factors in parametric severity evaluations.

Ankylosing spondylitis (AS) is a chronic inflammatory disease with an unknown cause [1]. It typically begins with inflammation in the sacroiliac joints, progressing to involve the axial spine as the disease advances [1]. This inflammation results in persistent lower back pain that disrupts structure, function, and ultimately, quality of life [2]. Although AS can occur in young individuals, it most commonly affects people in their 30s, with rare cases emerging after 45 [3]. Global prevalence estimates range from 0.1–1.4% [3]. Interestingly, lower socioeconomic status seems to correlate with a higher prevalence of AS, likely due to the disease's impact on spinal function [4, 5].

While the exact cause of AS remains elusive, research suggests several contributing factors, including genetics [6], immune response [7], microbial infections [8], and hormonal imbalances [9]. Despite ongoing research, genetics are consistently highlighted as a major player [10].

Large-scale genetic studies, known as genome-wide association studies (GWAS), have identified 70 variations (SNPs) within and around the GPR35 gene [11, 12]. Notably, six of these variations are linked to an increased risk of several immune system disorders, including inflammatory bowel disease (IBD) – encompassing ulcerative colitis and Crohn's disease – and primary sclerosing cholangitis (PSC) [13, 14]. A more recent GWAS employed high-density genotyping of immune-related loci and discovered novel susceptibility loci for AS in both European and East Asian populations [15].

There is a current lack of research investigating the association between the rs4676410 variant and AS development or severity in the Turkish population. Understanding how these variant influences AS risk and severity is crucial for developing new treatment strategies. Our present study addresses this gap by examining how the rs4676410 variant in the G-protein-coupled receptor 35 gene affects AS risk and disease severity within the Turkish population. We also analyzed the correlations between disease activity scores and biochemical parameters and the association of genotype and allele frequencies with disease risk. This investigation holds particular significance as the first of its kind in Turkey.

Study Design and Patients

A total of 200 volunteers (100 healthy individuals and 100 patient individuals) participated in this study. The volunteers consisted of patients diagnosed with AS according to the Modified New York Criteria (≥ 18 years old, on follow-up treatment) and age-matched healthy individuals (≥ 18 years old, without AS or other rheumatic diseases). To isolate the effects of AS on the genetic variant, individuals with other rheumatic diseases were excluded from the patient group. Similarly, the healthy group did not include individuals with AS, other rheumatic diseases or systemic diseases such as cancer or diabetes [16, 17].

DNA Isolation

For the Blood DNA Isolation, utilizing the DiaRex Blood DNA Extraction Kit (#BLD-5292-100), care was taken to ensure the blood in the tubes was mixed using a vortex and that non-coagulated blood was used. Initially, 200 µl of blood was transferred to an Eppendorf tube and 25 µl of Proteinase K was added. Subsequently, 250 µl of Lysis Buffer was added and the mixture was vortexed for 15 seconds. The sample was then incubated on a preheated heating block at 56°C for 5 minutes. After incubation, 240 µl of ethanol was added, pipetted to mix, and the mixture was transferred to columns. The sample was centrifuged at 8,000 x g for 1 minute, after which the flow-through was discarded and the washing step was initiated. For the washing step, 500 µl of Wash Buffer 1 was added and centrifuged at 8,000 x g for 1 minute. Following this, 500 µl of Wash Buffer 2 was added to the columns and centrifuged at 8,000 x g for 1 minute, with this step being repeated twice. Finally, 100 µl of Elution Buffer was added and incubated at room temperature for 2–3 minutes. The procedure was completed with a final centrifugation at 8,000 x g for 1 minute [16–18].

The nucleic acid loads (ng values) of the samples obtained from the total DNA extraction process were measured with the Colibri Microvolume Spectrometer (Titertek-Berthold, Germany) to be used in the next steps of the study.

Real-Time PCR

For the SNP with Nucleotide Database Code rs13050728, oligonucleotide designs were made, and real-time PCR analysis was performed. Primer3plus program was used in primer designs [19]. Specifically, the SNP rs4676410, which involves a G > A variant in the G protein-coupled receptor 35 gene, was analyzed.

Table 1

SNP and Primer Information for rs4676410
Oligo Name	5' Label	Sequences (5'-3')	3' Label	Tm
rs4676410_F		TCTCCAATGAACATCATGG		65 ^o C
rs4676410_R		GTTCCTGTCCCTTTCTTC
rs4676410_P1	FAM	AACCCAAGGGCGCACACTGG	BHQ1
rs4676410_P2	HEX	AACCCAAGGGCACACACTGG	BHQ1

SNP and Primer Information for rs4676410 was given in Table 1. The synthesized lyophilized primers were dissolved with nuclease free dH2O to 100 mm. SensiFAST™ Probe No-ROX Kit (Bioline, UK) was used in real time PCR reactions. 20 µl reaction mixture consisted of 2X qPCR MasterMix, 0.4 mM forward primer (10 mM) and 0.4 mM reverse primer (10 mM), 0.1 mM probe (10 mM), 5 µl DNA. The reaction was performed in Biorad CFX96 Touch real time PCR device using 8 strip tubes, first denaturation at 95 ℃ 5 min, 40 repetitions at 95 ℃ 10 s, 66 ℃ 30 s (reading). Base substitutions were shown based on allele-specific probe data obtained as a result of real time PCR [18].

Statistical analysis

Data analysis was performed using IBM SPSS (version 26) and GraphPad Prism (version 9). Descriptive statistics of the variables are given as Mean ± Standard Deviation, Median (Minimum-Maximum) values. The normality of the distribution of the variables was examined by Shapiro-Wilk test. Pearson Chi-Square test was used for nominal variables in the evaluation of intergroup relations. Mann-Whitney U test was used to compare continuous variables between two independent groups and Kruskal-Wallis H test was used to compare continuous variables between three independent groups. When significant differences were detected after the Kruskal-Wallis test, Dunn's post-hoc test was used to determine these differences. In addition, the relationships between continuous variables were evaluated by Spearman correlation analysis. Statistical significance level was accepted as p < 0.05 [19, 20].

The gender distribution between the patient and healthy groups was analyzed using the Chi-Square test. In the patient group, 74% were female and 26% were male, whereas in the healthy group, 68% were female and 32% were male. The Chi-Square analysis (χ² = 0.874, p = 0.350) revealed no statistically significant difference in gender distribution between the two groups. This indicates that gender is evenly distributed across both groups, minimizing the likelihood of gender influencing the study’s outcomes.

Table 1

Correlations Between Disease Activity Scores (BASFI, BASDAI, ASDAS-CRP) and Biochemical Parameters (Patient Group)
	BASFI	BASDAI	ASDASCRP
BASDAI	.619** (< 0.01)
ASDAS-CRP	.452** (< 0.01)	.523** (< 0.01)
Age	.227* (0.023)	0.103 (0.308)	0.124 (0.219)
CRP (mg/L)	0.023 (0.82)	-0.025 (0.804)	.450** (< 0.01)
ESR (mm/hours)	0.032 (0.754)	0.087 (0.389)	.349** (< 0.01)
WBC	-0.091 (0.366)	-0.174 (0.083)	-0.006 (0.955)
RBC	-0.072 (0.479)	− .208* (0.037)	-0.151 (0.133)
HGB	-0.036 (0.721)	-0.151 (0.134)	-0.098 (0.333)
HCT	-0.023 (0.823)	-0.132 (0.191)	-0.066 (0.517)
MCV	0.087 (0.39)	0.052 (0.606)	0.039 (0.7)
MCH	0.035 (0.733)	-0.006 (0.949)	-0.023 (0.818)
MCHC	-0.042 (0.679)	-0.092 (0.363)	-0.101 (0.318)
RDW-SW	0.013 (0.897)	-0.024 (0.811)	0.09 (0.375)
PLT	-0.153 (0.129)	-0.126 (0.212)	0.068 (0.503)
MPV	0.066 (0.511)	0.056 (0.582)	0.05 (0.619)
NEUT%	-0.125 (0.214)	-0.106 (0.294)	0.004 (0.971)
LYM %	0.067 (0.508)	0.044 (0.665)	-0.058 (0.565)
MON %	0.191 (0.057)	.264** (0.008)	0.167 (0.096)
EOS %	.206* (0.04)	0.121 (0.229)	0.193 (0.054)
BASO %	-0.084 (0.408)	-0.045 (0.659)	-0.041 (0.686)
NEUT	-0.102 (0.313)	-0.13 (0.196)	0.018 (0.861)
LYM	0.03 (0.768)	-0.069 (0.497)	-0.032 (0.75)
MON	0.102 (0.315)	0.06 (0.553)	0.099 (0.327)
EOS	.206* (0.04)	0.094 (0.353)	0.148 (0.141)
BASO	-0.087 (0.387)	-0.138 (0.17)	-0.087 (0.389)
PCT	-0.021 (0.839)	-0.014 (0.892)	0.125 (0.215)
PDW	0.075 (0.461)	0.016 (0.874)	0.093 (0.356)
ALT (U/L)	.248* (0.013)	0.104 (0.303)	0.108 (0.283)
AST (U/L)	0.113 (0.262)	0.114 (0.258)	0.025 (0.802)
ALP (U/L)	-0.094 (0.354)	-0.149 (0.139)	0.044 (0.662)
Creatinine (mg/dl)	0.072 (0.478)	-0.011 (0.913)	0.06 (0.552)
EGFR (ml/dk/1.73 m²)	-0.168 (0.095)	-0.066 (0.514)	-0.125 (0.214)
Urea (mg/dl)	0.088 (0.385)	0.111 (0.271)	.229* (0.022)
25-OH D	-0.045 (0.654)	-0.045 (0.66)	-0.012 (0.905)
Patient groups n = 100 Spearman Correlation Test Data is presented as Correlation Coefficient (p-value). Significance level was determined as p < 0.05. *Significance level was determined as p < 0.01

Table 1 explores the correlations between disease activity scores (BASFI, BASDAI, ASDAS-CRP) and various biochemical parameters. Significant positive correlations were found between BASFI, BASDAI, and ASDAS-CRP scores (p < 0.01), as well as between CRP and ESR levels and ASDAS-CRP (p < 0.01). These findings indicate that higher disease activity scores are associated with elevated inflammatory markers, suggesting that inflammation plays a key role in the progression of the disease.

Table 2

Distribution of Genetic Variants in Patient and Healthy Groups
	Groups		χ2	p
	Patient Individuals (n = 100)	Healthy Indiviuals (n = 100)	χ2	p
AA	19 (19%)	6 (6%)	7.726	0.021
GA	25 (25%)	29 (29%)
GG	56 (56%)	65 (65%)
The data are given as n (%) (total number of genotypes (percentage of genotype)). p: Chi-Square test. Significance is set at p < 0.05

Genetic analysis revealed a significant association between the rs4676410 polymorphism and the disease (p = 0.021). The AA genotype was more prevalent in the patient group (19%) compared to the healthy group (6%), while the GG and GA genotypes were less common in patients. This suggests that the AA genotype might be associated with a higher risk of developing the disease, whereas the GG and GA genotypes could be associated with a lower risk (Table 2 and Fig. 1).

Table 3

Genotype and Allele Frequencies in Patient and Healthy Groups
		Patient Individuals	Healthy Individuals	χ²	p	OR	95% CI
Genotypes	GG	56 (69.1%)	65 (69.1%)	0.000^a	0.999	0.999	0.525–1.902
	GA	25 (30.9%)	29 (30.9%)	0.000^a	0.999	0.999	0.525–1.902
	GG	56 (74.7%)	65 (91.5%)	6.184^b	0.013	0.272	0.102–0.729
	AA	19 (25.3%)	6 (8.5%)	6.184^b	0.013	0.272	0.102–0.729
	GG	56 (56%)	65 (65%)	1.695^a	0.193	0.685	0.388–1.212
	GA + AA	44 (44%)	35 (35%)	1.695^a	0.193	0.685	0.388–1.212
Alleles	G	137 (68.5%)	159 (79.5%)	6.289^a	0.012	0.561	0.356–0.884
Alleles	A	63 (31.5%)	41 (20.5%)	6.289^a	0.012	0.561	0.356–0.884
CI: Confidence Interval ve OR: Odds Ratio ^a: Pearson Chi-Square Value ve ^b: Continuity Correction Value p: Chi-Square test. Significance is set at p < 0.05 The data are given as n (%) (total number of genotypes/alleles (percentage of genotypes/alleles)

Table 5

Distribution of Genetic Variants in Patient and Healthy Groups
	Groups		χ2	p
	Patient Individuals (n = 100)	Healthy Indiviuals (n = 100)	χ2	p
AA	19 (19%)	6 (6%)	7.726	0.021
GA	25 (25%)	29 (29%)
GG	56 (56%)	65 (65%)
The data are given as n (%) (total number of genotypes (percentage of genotype)). p: Chi-Square test. Significance is set at p < 0.05
Genetic analysis revealed a significant association between the rs4676410 polymorphism and the disease (p = 0.021). The AA genotype was more prevalent in the patient group (19%) compared to the healthy group (6%), while the GG and GA genotypes were less common in patients. This suggests that the AA genotype might be associated with a higher risk of developing the disease, whereas the GG and GA genotypes could be associated with a lower risk (Table 5 and Fig. 1).

The analysis of genotype and allele frequencies showed that the GG genotype was equally common in both groups. However, the GA + AA genotype combination was less frequent in the healthy group. Regarding allele frequencies, the G allele was more prevalent in the healthy group, while the A allele was more common in the patient group (p = 0.012). These findings imply that the A allele may be associated with an increased risk of the disease, whereas the G allele could have a protective effect (Table 3).

Figure 2 shows the distribution of BASFI, BASDAI, and ASDAS-CRP scores among patients with different genotypes (AA, GA, GG), which we evaluated for statistical significance using the Kruskal-Wallis test. While no statistically significant differences were observed between the genotype groups regarding BASFI and BASDAI scores (p > 0.05), a significant difference was found for ASDAS-CRP scores among the genotypes (p = 0.043).

Ankylosing spondylitis is a chronic inflammatory disease characterized by a range of symptoms, including persistent back pain and stiffness, which can significantly impair mobility and overall quality of life. The disease primarily affects the axial skeleton but can also involve peripheral joints and other organs. Ankylosing spondylitis is a disease with various symptoms and requires multidisciplinary treatment coordinated by a specialized physician [21]. The main goal of treatment is to control symptoms and inflammation, prevent progressive structural damage and maximize health-related quality of life in the long term [22].

GCRs play a significant role in pathophysiology of many diseases such as cardiovascular diseases [23], autoimmune diseases [24, 25], cancer [26] and neurodegenerative and cerebrovascular diseases [27]. These receptors found the largest family of membrane receptors constitute on the cell surface and are encoded by approximately one-thousand genes [28–30]. GPCRs share a conserved structure characterized by seven transmembrane (7TM) helices, which are connected by three intracellular and three extracellular loops. This structural configuration allows GPCRs to transduce extracellular signals into intracellular responses, influencing various physiological processes, including inflammation and immune response, which are central to the pathogenesis of ankylosing spondylitis [31]. Some studies showed that rs4676410 polymorphism in GPR35 is susceptibility with inflammatory bowel diseases [32, 33]. Another study has shown that rs4676410 and rs2531875 have been identified as genetic markers associated with the risk of AS in the Han Chinese population [17]. In this study, we first analyzed the association between the rs4676410 polymorphism in the GPR35 gene and the risk of AS in a Turkish population, and the second evaluated and compared the correlations between disease activity scores and biochemical parameters and the association of genotype and allele frequencies with disease risk.

Our results showed a significant association between the rs4676410 polymorphism and AS, particularly highlighting the increased prevalence of the AA genotype in the patient group compared to the healthy group. This suggests that individuals carrying the AA genotype may have a higher susceptibility to AS, whereas those with the GG and GA genotypes may have a lower risk. These results are consistent with previous studies that have identified certain SNPs within or near the GPR35 gene as being associated with various inflammatory conditions, such as inflammatory bowel disease and primary sclerosing cholangitis [17, 32–34]. When we analyzed the genotype and allele frequencies, we found that the A allele was more common in the patient group, while the G allele was more common in the healthy group. This finding is also correlated with the literature [17]. The role of the GPR35 gene in modulating immune responses may support its involvement in AS pathogenesis. Moreover, our study found important links between disease activity scores (BASFI, BASDAI, ASDAS-CRP) and higher levels of inflammatory markers (CRP and ESR), underscoring the pivotal role of inflammation in AS progression. The significant difference in ASDAS-CRP scores among different genotypes further supports the hypothesis that genetic factors can not only affect the risk of developing AS but also affect the severity of the disease [35]. The biochemical analyses showed that patients with AS had much greater levels of CRP, ESR, WBC, PLT, NEUT%, and other inflammatory markers compared to the healthy group, alongside lower levels of RBC, HGB, and HCT. These alterations in hematological parameters suggest an enhanced inflammatory response in AS patients, which is a direct consequence of the disease's chronic inflammatory nature.

In conclusion, our study provides the first evidence of an association between the rs4676410 polymorphism in the GPR35 gene and AS risk in a Turkish population. The presence of the AA genotype and A allele appears to be linked with an increased or susceptibility risk of AS. Furthermore, the correlation between genetic variants and disease activity scores highlights the potential of using genetic markers to predict disease severity and alter treatment strategies. Further research with larger sample sizes and diverse populations is necessary to validate these findings and to explore the underlying mechanisms by which the GPR35 gene influences AS pathogenesis.

In conclusion, our study was the first report to describe an association between rs4676410 polymorphism in GPR35 gene and may be a genetic susceptibility for AS risk among Turkish population. The strong correlational basis between disease-activity scores and genetic variants vindicates the usefulness of investigating genetics, which underscores as one of major player influencing parametric severity assessments. More investigation that includes larger and varied populations is required to validate these links as well as elucidate the ways in which GPR35 influences AS.

Author Contribution

D.K. designed the study, supervised the experiments, collected the data, wrote the initial draft, revised the paper, F.H. and B.G contributed in performing the concept and design of the study and revised the paper B.D. and S.K.T made contributions in performing the concept and design of the study and revising the paper. All these authors have substantial contributions to the final manuscript and approved this submission.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Funding

This study was carried out by the authors' own means and was not supported by any external funding.

Data Availability

No datasets were generated or analysed during the current study

Competing interests The authors declare no conflict of interest.

Ethical approval Hamidiye Scientific Research Ethic Committee 2022/28

Informed consent Hamidiye Scientific Research Ethic Committee 2022/28

Acknowledgement

Wenker KJ, Quint JM. Ankylosing Spondylitis.(2023). In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan–. PMID: 29261996.
Ozgül, A., Peker, F., Taskaynatan, M. A., Tan, A. K., Dinçer, K., & Kalyon, T. A. (2006). Effect of ankylosing spondylitis on health-related quality of life and different aspects of social life in young patients. Clinical rheumatology, 25(2), 168–174. https://doi.org/10.1007/s10067-005-1150-5
Akkoc N. (2008). Are spondyloarthropathies as common as rheumatoid arthritis worldwide? A review. Current rheumatology reports, 10(5), 371–378. https://doi.org/10.1007/s11926-008-0060-3
Braun, J., & Sieper, J. (2007). Ankylosing spondylitis. Lancet (London, England), 369(9570), 1379–1390. https://doi.org/10.1016/S0140-6736(07)60635-7
Montacer Kchir, M., Mehdi Ghannouchi, M., Hamdi, W., Azzouz, D., Kochbati, S., Saadellaoui, K., Daoud, L., Hamida, A. B., & Zouari, M. B. (2009). Impact of the ankylosing spondylitis on the professional activity. Joint bone spine, 76(4), 378–382. https://doi.org/10.1016/j.jbspin.2008.08.010
Hanson, A., & Brown, M. A. (2017). Genetics and the Causes of Ankylosing Spondylitis. Rheumatic diseases clinics of North America, 43(3), 401–414. https://doi.org/10.1016/j.rdc.2017.04.006
Duan, Z., Gui, Y., Li, C., Lin, J., Gober, H. J., Qin, J., Li, D., & Wang, L. (2017). The immune dysfunction in ankylosing spondylitis patients. Bioscience trends, 11(1), 69–76. https://doi.org/10.5582/bst.2016.01171
Zhang, X., Sun, Z., Zhou, A., Tao, L., Chen, Y., Shi, X., Yin, J., Sun, Z., & Ding, G. (2021). Association Between Infections and Risk of Ankylosing Spondylitis: A Systematic Review and Meta-Analysis. Frontiers in immunology, 12, 768741. https://doi.org/10.3389/fimmu.2021.768741
Bertoldo, E., Adami, G., Rossini, M., Giollo, A., Orsolini, G., Viapiana, O., Gatti, D., & Fassio, A. (2021). The Emerging Roles of Endocrine Hormones in Different Arthritic Disorders. Frontiers in endocrinology, 12, 620920. https://doi.org/10.3389/fendo.2021.620920
Reveille J. D. (2006). The genetic basis of ankylosing spondylitis. Current opinion in rheumatology, 18(4), 332–341. https://doi.org/10.1097/01.bor.0000231899.81677.04
Mackenzie, A. E., Lappin, J. E., Taylor, D. L., Nicklin, S. A., & Milligan, G. (2011). GPR35 as a Novel Therapeutic Target. Frontiers in endocrinology, 2, 68. https://doi.org/10.3389/fendo.2011.00068
Horikawa, Y., Oda, N., Cox, N. J., Li, X., Orho-Melander, M., Hara, M., Hinokio, Y., Lindner, T. H., Mashima, H., Schwarz, P. E., del Bosque-Plata, L., Horikawa, Y., Oda, Y., Yoshiuchi, I., Colilla, S., Polonsky, K. S., Wei, S., Concannon, P., Iwasaki, N., Schulze, J., … Bell, G. I. (2000). Genetic variation in the gene encoding calpain-10 is associated with type 2 diabetes mellitus. Nature genetics, 26(2), 163–175. https://doi.org/10.1038/79876
Imielinski, M., Baldassano, R. N., Griffiths, A., Russell, R. K., Annese, V., Dubinsky, M., Kugathasan, S., Bradfield, J. P., Walters, T. D., Sleiman, P., Kim, C. E., Muise, A., Wang, K., Glessner, J. T., Saeed, S., Zhang, H., Frackelton, E. C., Hou, C., Flory, J. H., Otieno, G., … Hakonarson, H. (2009). Common variants at five new loci associated with early-onset inflammatory bowel disease. Nature genetics, 41(12), 1335–1340. https://doi.org/10.1038/ng.489
Ellinghaus, D., Folseraas, T., Holm, K., Ellinghaus, E., Melum, E., Balschun, T., Laerdahl, J. K., Shiryaev, A., Gotthardt, D. N., Weismüller, T. J., Schramm, C., Wittig, M., Bergquist, A., Björnsson, E., Marschall, H. U., Vatn, M., Teufel, A., Rust, C., Gieger, C., Wichmann, H. E., … Karlsen, T. H. (2013). Genome-wide association analysis in primary sclerosing cholangitis and ulcerative colitis identifies risk loci at GPR35 and TCF4. Hepatology (Baltimore, Md.), 58(3), 1074–1083. https://doi.org/10.1002/hep.25977
International Genetics of Ankylosing Spondylitis Consortium (IGAS), Cortes, A., Hadler, J., Pointon, J. P., Robinson, P. C., Karaderi, T., Leo, P., Cremin, K., Pryce, K., Harris, J., Lee, S., Joo, K. B., Shim, S. C., Weisman, M., Ward, M., Zhou, X., Garchon, H. J., Chiocchia, G., Nossent, J., Lie, B. A., … Brown, M. A. (2013). Identification of multiple risk variants for ankylosing spondylitis through high-density genotyping of immune-related loci. Nature genetics, 45(7), 730–738. https://doi.org/10.1038/ng.2667
Mesahel, R. M. I., Fotoh, D. S., Hadhoud, M. M., & Ali Assar, M. F. (2024). A study of the association between single nucleotide polymorphisms of the endoplasmic reticulum aminopeptidase 2 (ERAP2) gene and the risk of ankylosing spondylitis in Egyptians. Molecular biology reports, 51(1), 614. https://doi.org/10.1007/s11033-024-09404-w
Wang Q, Li P, Wang J, Lin Q, Wang L, et al. (2014) Rs4676410 and Rs2531875 are Associated with the Risk of Ankylosing Spondylitis in the Han Chinese Population. Rheumatology (Sunnyvale) S5: 007. doi:10.4172/2161-1149.S5-007
Mellati, A., Soltani, S., Kazemi, T., Ahmadzadeh, N., Akhtari, M., Madreseh, E., Jamshidi, A., Farhadi, E., & Mahmoudi, M. (2024). Determination of IL-23 receptor expression and gene polymorphism (rs1884444) in Iranian patients with ankylosing spondylitis. BMC rheumatology, 8(1), 14. https://doi.org/10.1186/s41927-024-00383-w
Kalkanli Tas, S., Kirkik, D., Tanoglu, A., Kahraman, R., Ozturk, K., Esen, M. F., Coskunpinar, M. E., & Cagiltay, E. (2020). Polymorphisms in Toll-like receptors 1, 2, 5, and 10 are associated with predisposition to Helicobacter pylori infection. European journal of gastroenterology & hepatology, 32(9), 1141–1146. https://doi.org/10.1097/MEG.0000000000001797
Bostanci, E., Kirkik, D., Kalkanli Tas, S., & Uyeturk, U. (2024). Genetic insights into bladder cancer: the impact of SIRT1 gene polymorphism. Nucleosides, nucleotides & nucleic acids, 1–12. Advance online publication. https://doi.org/10.1080/15257770.2024.2310710
Wenker, K. J., & Quint, J. M. (2023). Ankylosing Spondylitis. In StatPearls. StatPearls Publishing.
Lavie, F., Pavy, S., Dernis, E., Goupille, P., Cantagrel, A., Tebib, J., Claudepierre, P., Flipo, R. M., Le Loët, X., Maillefert, J. F., Mariette, X., Saraux, A., Schaeverbeke, T., Wendling, D., & Combe, B. (2007). Pharmacotherapy (excluding biotherapies) for ankylosing spondylitis: development of recommendations for clinical practice based on published evidence and expert opinion. Joint bone spine, 74(4), 346–352. https://doi.org/10.1016/j.jbspin.2007.04.003
Kaur, G., Verma, S. K., Singh, D., & Singh, N. K. (2023). Role of G-Proteins and GPCRs in Cardiovascular Pathologies. Bioengineering (Basel, Switzerland), 10(1), 76. https://doi.org/10.3390/bioengineering10010076
Zhao, M., Wang, Z., Yang, M., Ding, Y., Zhao, M., Wu, H., Zhang, Y., & Lu, Q. (2021). The Roles of Orphan G Protein-Coupled Receptors in Autoimmune Diseases. Clinical reviews in allergy & immunology, 60(2), 220–243. https://doi.org/10.1007/s12016-020-08829-y
Yuan, Y., Ma, Y., Zhang, X., Han, R., Hu, X., Yang, J., Wang, M., Guan, S. Y., Pan, G., Xu, S. Q., Jiang, S., & Pan, F. (2019). Genetic polymorphisms of G protein-coupled receptor 65 gene are associated with ankylosing spondylitis in a Chinese Han population: A case-control study. Human immunology, 80(2), 146–150. https://doi.org/10.1016/j.humimm.2018.12.001
Dorsam, R. T., & Gutkind, J. S. (2007). G-protein-coupled receptors and cancer. Nature reviews. Cancer, 7(2), 79–94. https://doi.org/10.1038/nrc2069
Leiser, S. C., Li, Y., Pehrson, A. L., Dale, E., Smagin, G., & Sanchez, C. (2015). Serotonergic Regulation of Prefrontal Cortical Circuitries Involved in Cognitive Processing: A Review of Individual 5-HT Receptor Mechanisms and Concerted Effects of 5-HT Receptors Exemplified by the Multimodal Antidepressant Vortioxetine. ACS chemical neuroscience, 6(7), 970–986. https://doi.org/10.1021/cn500340j
Eichel, K., & von Zastrow, M. (2018). Subcellular Organization of GPCR Signaling. Trends in pharmacological sciences, 39(2), 200–208. https://doi.org/10.1016/j.tips.2017.11.009
Rosenbaum, D. M., Rasmussen, S. G., & Kobilka, B. K. (2009). The structure and function of G-protein-coupled receptors. Nature, 459(7245), 356–363. https://doi.org/10.1038/nature08144
Katritch, V., Cherezov, V., & Stevens, R. C. (2012). Diversity and modularity of G protein-coupled receptor structures. Trends in pharmacological sciences, 33(1), 17–27. https://doi.org/10.1016/j.tips.2011.09.003
Tsui, F. W., Tsui, H. W., Akram, A., Haroon, N., & Inman, R. D. (2014). The genetic basis of ankylosing spondylitis: new insights into disease pathogenesis. The application of clinical genetics, 7, 105–115. https://doi.org/10.2147/TACG.S37325
Yansen, Z., Lingang, Z., Dali, L., & Mingyao, L. (2021). Inflammatory bowel disease susceptible gene GPR35 promotes bowel inflammation in mice. Yi chuan = Hereditas, 43(2), 169–181. https://doi.org/10.16288/j.yczz.20-392
Lee, J. J., Essers, J. B., Kugathasan, S., Escher, J. C., Lettre, G., Butler, J. L., Stephens, M. C., Ramoni, M. F., Grand, R. J., & Hirschhorn, J. (2010). Association of linear growth impairment in pediatric Crohn's disease and a known height locus: a pilot study. Annals of human genetics, 74(6), 489–497. https://doi.org/10.1111/j.1469-1809.2010.00606.x
Manganis, C. D., Chapman, R. W., & Culver, E. L. (2020). Review of primary sclerosing cholangitis with increased IgG4 levels. World journal of gastroenterology, 26(23), 3126–3144. https://doi.org/10.3748/wjg.v26.i23.3126
Kasapoğlu Aksoy, M., Altan, L., Görükmez, O., Güner, A., & Ayar, K. (2020). The relationship between CRP gene polymorphism (rs2794521, rs3091244), ASDAS-CRP and ASDAS-ESR in ankylosing spondylitis. Modern rheumatology, 30(4), 715–720. https://doi.org/10.1080/14397595.2019.1639916

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Genetic Susceptibility and Disease Activity in Ankylosing Spondylitis: The Role of GPR35 rs4676410 Polymorphism in a Turkish Population

Status:

Version 1

Abstract

Figures

Key Points

INTRODUCTION

MATERIAL AND METHODS

Study Design and Patients

DNA Isolation

Real-Time PCR

Statistical analysis

RESULTS

DISCUSSION

CONCLUSION

Declarations

Acknowledgement

References

Additional Declarations

Status:

Version 1