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The relationship between ASIC3 gene polymorphism and fibromyalgia syndrome

  • Cemile Zontul ORCID logo , Ayca Tas ORCID logo , Emrullah Hayta ORCID logo and Yavuz Silig ORCID logo EMAIL logo
Published/Copyright: October 19, 2023
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Turkish Journal of Biochemistry
From the journal Turkish Journal of Biochemistry Volume 49 Issue 1

Abstract

Objectives

Fibromyalgia syndrome (FMS) is a chronic pain syndrome characterized by widespread body pain over a long period, the cause of which is not yet clearly known. FMS patients usually have high pain sensitivity. We aimed to investigate whether rs4148855 and rs2288646 polymorphisms of acid-sensing ion channel 3 (ASIC3), one of the factors contributing to pain, cause a predisposition to FMS in the Turkish population.

Methods

ASIC3 gene rs4148855 and rs2288646 polymorphisms in DNA samples obtained from blood samples of 175 patients with FMS and 176 healthy individuals were analyzed by real-time polymerase chain reaction (RT-PCR) using a hydrolysis probe. Statistical data were obtained by chi-square (χ2) test and logistic regression analysis.

Results

No significant association was found between ASIC3 gene rs4148855 and rs2288646 polymorphisms and the Turkish population’s FMS group and control group (p>0.05).

Conclusions

As a result, no significant association was found between the genotype and allele distributions of ASIC3 polymorphism (rs4148855 and rs2288646) in patients with FMS compared to controls in the Turkish population. Further studies are needed to elucidate the relationship between ion channels and FMS to elucidate the mechanisms of FMS.

Keywords: fibromyalgia syndrome; single nucleotide polymorphism; acid-sensing ion channel 3; Turkish population

Introduction

Fibromyalgia syndrome (FMS) is a chronic pain syndrome characterized by long-lasting widespread body pain and tenderness in at least 11 of 18 specific areas identified. FMS is associated with fatigue, sleep disturbance, somatic syndromes, psychological disorders, disability and impaired quality of life [1]. The prevalence of FMS in the general population is 2.9–4.7 %. Although FMS can be seen in all ages and genders, it usually affects women aged 40–60 [2, 3]. Gender and advanced age are risk factors for FMS [4]. Physical and mechanical traumas and stress factors in the social environment are influential in the emergence of FMS [5]. Although the etiology of FMS has been attributed to infection, genetic and autoimmune factors, the mechanism is not fully known. Micro-muscle trauma, immunologic, psychological, neuro-hormonal, central nervous system, and environmental factors have been suggested to play a role in the development of the disease [6, 7]. It has been suggested that widespread chronic pain in FMS patients is caused by genetic factors affecting peripheral and central pain mechanisms [8]. The classification of FMS as neuropathic pain and its connection with pathophysiology is becoming increasingly important [9]. Despite many studies on FMS, its pathophysiology still needs to be clarified. In addition, according to genetic studies, there is a predisposition to FMS. Several pain-related genes have been identified, and there is evidence that many polymorphisms are associated with FMS [10].

ASIC3 are sodium channels activated by protons from outside the cell and are a member of acid-sensitive channel proteins structurally related to epithelial sodium channel proteins [11]. ASIC3 is predominantly expressed in both the central and peripheral nervous systems and is activated by cellular proteins [12], [13], [14]. They are the most sensitive acid sensors with high expression in peripheral nerves [15, 16]. In studies on rats, peripheral ASIC3 channels are the primary sensors of acidic pain in inflammatory conditions [17]. ASIC3 regulates myocardial ischemic pain, muscle pressure reflex, and possibly unstable autonomic regulation. Acidosis in muscle tissue is implicated in chronic muscle pain [12, 18]. ASIC3 ion channels activate muscle pain receptors and cause chronic muscle pain [19, 20].

Therefore, in our study, we examined the relationship between ASIC3 gene polymorphisms (rs4148855 and rs2288646), which is one of the subunits of ASICs, which is important in pain perception, and there is no study in this area in our country, and FMS. This gene is located in the 7q35-36.1 region of the chromosome and is also localized in the intron region of ASIC3 in the first SNP rs4148855 we investigated. Nucleotide polymorphism occurs with the conversion of – in this 110 bp (base pair) long region to GTC. The other one, the SNP rs2288646 region, is also located in the exon region of ASIC3, and a single nucleotide polymorphism occurs when the adenine (A) base in this 106bp-long region converts to the guanine (G) base. As a result of this polymorphism, the presence of the G allele in the exon region resulted in a splicing-reducing effect, and the presence of the A allele resulted in a splicing-enhancing impact [21].

In addition, genetic variants of ASIC3, rs4148855 and rs2288646, were found to be associated with diseases such as pain, hypertension, anxiety and insulin resistance.

Materials and methods

Study population

This study consisted of 175 patients with FMS and 176 healthy controls admitted to Sivas Cumhuriyet University Faculty of Medicine, Department of Physical Medicine and Rehabilitation, and Sivas Numune Hospital, Physical Therapy Clinic. Patients who had no previous history of FMS and were diagnosed with FMS for the first time were included in the study, and the patients were from the same ethnic group and geographical region. The control group consisted of individuals admitted to the same hospitals, were similar to the selected patient group in terms of age and gender, were not diagnosed with FMS, and did not have any other disease. The diagnosis of FMS was made by a physician according to the American College of Rheumatology classification criteria [22]. Various questions such as name, surname, age, gender, height, weight, occupation, sleep disturbance, fatigue, headache, morning fatigue, dry mouth, leg numbness, dry eyes, difficulty concentrating, feeling of swelling in soft tissues, family history of FMS were filled in the questionnaire in question-answer form. Our study was approved by Sivas Cumhuriyet University Clinical Research Ethics Committee (Date: 12.05.2015, Decision no: 2015-05/01) and was conducted by the Declaration of Helsinki.

DNA isolation

A 4 mL blood sample was collected from healthy control subjects and FMS patients in sterile citrated blood tubes. Genomic DNA isolation was performed manually using the precipitation method at high salt concentrations when the blood samples reached the laboratory [23].

ASIC3 genotyping

ASIC3 polymorphisms (rs4148855 and rs2288646) were determined by real-time polymerase chain reaction (RT-PCR) using hydrolysis probes (GT0196, Qiagen-Rs 2288646; GT0195, Qiagen-Rs 4148855).

Statistical analysis

The relationship between fibromyalgia and ASIC3 gene polymorphism, age, gender, height, weight, occupation, sleep disturbance, fatigue, headache, morning fatigue, dry mouth, leg numbness, dry eyes, difficulty concentrating, soft tissue swelling, and family history of fibromyalgia was determined using the Chi-square (χ2) test (bias value α=0.05). Risk estimates for fibromyalgia occurrence were determined by applying a logistic regression test. The degree of association was described as 95 % CI (confidence interval) OR (Odds Ratio). Statistical data analysis was performed using the Statistical Package for the Social Sciences (SPSS) program (Version 22).

Results

Demographic data and statistical analysis of the people included in the study

Demographic information and statistical findings of patients diagnosed with FMS and healthy individuals are given in Table 1. According to the data, 10 (5.7 %) of the patients were male, and 165 (94.3 %) were female. Among the controls, 10 (5.1 %) were male, and 166 (94.9 %) were female. The mean ages of patients and controls were 40.33 ± 16.30 and 39.69 ± 17.27 years for males and 43.89 ± 9.76 and 44.05 ± 8.87 years for females, respectively.

Table 1:

Demographic information of patients with FMS and healthy group.

Control group, n (%) FMS group, n (%) p-Valuea χ2 OR (95 % CI) Adjusted OR (95 % CI)b
Number of individuals 176 175 – – – –
Gender – – – –
 Male 10 (5.1) 10 (5.7)
 Female 166 (94.9) 165 (94.3)
Age – – – –
 Range 22–61 20–62
Average age
 Male 39.69 ± 17.27 40.33 ± 16.30
 Female 44.05 ± 8.87 43.89 ± 9.76
Family history of fibromyalgia – – – –
 Present 0 (0.0) 78 (44.5)
 Male 0 (0.0) 3 (3.90)
 Woman 0 (0.0) 75 (96.1)
Fatigue 0.001 21.94 6.77 (2.75–16.50) 6.86 (2.79–16.85)
 Present 142 (81.1) 169 (96.5)
 Male 6 (4.20) 10 (5.90)
 Female 136 (95.8) 159 (94.1)
Headache 0.001 29.63 3.83 (2.33–6.29) 3.87 (2.35–6.43)
 Present 100 (56.8) 146 (83.4)
 Male 2 (2.00) 8 (5.50)
 Female 98 (98.0) 138 (94.5)
Morning fatigue 0.001 68.09 12.83 (6.34–25.96) 13.23 (6.50–26.92)
 Present 99 (56.5) 165 (94.2)
 Male 4 (4.10) 10 (6.10)
 Female 95 (95.9) 155 (93.9)
Dry mouth 0.001 21.31 2.86 (1.82–4.51) 2.20 (1.84–4.64)
 Present 91 (52.0) 132 (75.4)
 Male 1 (1.10) 7 (5.30)
 Female 90 (98.9) 125 (94.7)
Leg numbness 0.001 31.63 3.57 (2.27–5.62) 3.8 (2.37–6.10)
 Present 79 (44.8) 131 (74.8)
 Male 1 (1.30) 4 (3.10)
 Female 78 (98.7) 127 (96.9)
Dry eye 0.001 13.73 2.31 (1.48–3.61) 2.28 (1.45–3.58)
 Present 47 (26.8) 80 (45.7)
 Male 1 (2.10) 4 (5.00)
 Female 46 (97.9) 76 (95.0)
Difficulty concentrating 0.001 37.98 4.3 (2.69–7.09) 4.6 (2.80–7.67)
 Present 89 (50.6) 143 (81.7)
 Male 3 (3.40) 3 (2.10)
 Female 86 (96.6) 140 (97.9)
Swelling in soft tissues 0.001 20.76 2.71 (1.76–4.18) 3.02 (1.90–4.76)
 Present 74 (42.2) 116 (66.2)
 Male 2 (2.70) 3 (2.60)
 Female 72 (97.3) 113 (97.4)

  1. ap-Values were calculated using the χ2 test. bAdjusted for age, job, gendering status.

Analysis of patients and controls for ASIC3 polymorphisms (rs4148855, rs2288646)

Statistical analysis of patients and controls in terms of ASIC3 (rs2288646, rs4148855) polymorphisms is given in Table 2. Accordingly, 169 (96.6 %) patients had AA, 6 (3.4 %) AG, and 0 (0 %) GG genotypes in terms of rs2288646. Among controls, 170 (96.6 %) had AA, 4 (2.3 %) AG, and 2 (1.1 %) GG genotypes. When the patients and controls were evaluated by the χ2 method in terms of rs4148855, no statistically significant difference was observed (χ2: 2.40, p: 0.310). In terms of rs4148855, 75 (40.9 %) patients had −/GTC, 88 (50.3 %) −/−, and 12 (6.9 %) patients had GTC/GTC genotypes. Regarding rs4148855, 89 (50.6 %) of the controls had −/GTC, 72 (40.9 %) had −/−, and 15 (8.5 %) had GTC/GTC genotype. No statistically significant difference was observed when the patients and controls were evaluated by the χ2 method regarding rs2288646 (χ2: 3.12, p: 0.210).

Table 2:

Analysis of ASIC3 (rs2288646 and rs4148855) genotypes of the study groups.

Rs2288646 GG AG AA
Patient 0 (0.00) 6 (3.4) 169 (96.6)
Control 2 (1.1) 4 (2.3) 170 (96.6)
 χ2 2.40
 p 0.210
Rs4148855 GTC/GTC −/GTC −/−
Patient 12 (6.9) 75 (42.9) 88 (50.93)
Control 15 (8.5) 89 (50.6) 72 (40.09)
 χ2 3.12
 p 0.210

  1. χ2, chi-square test. p<0.05 determined as statistically significant

Comparison of ASIC3 polymorphism (rs4148855 and rs2288646) genotypes and allele frequencies in study groups

The statistical analysis of the association of rs4148855 polymorphism of the ASIC3 gene with genotype and allele distributions in FMS patients and healthy individuals in the control group is given in Table 3. According to these data, the genotypes of 176 individuals in the control group in terms of rs4148855 were found to be 15 GTC/GTC, 89 −/GTC, 72 −/−. Genotypes of 175 individuals in the FMS patient group were determined as 12 GTC/GTC, 75 −/GTC, 88 −/−. In the analysis of ASIC3 (rs4148855) polymorphism, it was not found statistically significant when FMS patients and controls were evaluated in terms of GTC and – allele (χ2: 2.50 p: 0.114, Raw OR: 1.29 95 % CI: 0.93–1.81). In terms of FMS patients and controls ASIC3 (rs4148855), no statistically significant correlation was found in the analysis performed with the χ2 method according to GTC/GTC and −/GTC status (χ2: 0.01 p: 0.901, Crude OR: 1.05 % 95 % CI: 0.46–2.49). No statistically significant correlation was found in the analysis performed with the χ2 method according to GTC/GTC and −/− status (χ2: 1.03 p: 0.309, Raw OR: 1.15 % 95 % CI: 0.67–3.47). No statistically significant correlation was found in the analysis performed with the χ2 method according to GTC/GTC and −/GTC+ −/− status (χ2: 0.34 p: 0.055, Raw OR: 1.27 % 95 % CI: 0.58–2.79).

Table 3:

Genotype and allele frequency distribution of rs4148855 polymorphism of ASIC3 gene in the study groups.

Rs4148855 (GTC/GTC/−/−) Control group (n=176) (%) Patient group (n=175) (%) χ2 p-Value OR (95 % CI) Adjusted OR (95 % CI)
 GTC 119 (33.8) 99 (28.2) Reference
  − 233 (66.2) 251 (71.8) 2.50 0.114 1.29 (0.93–1.81) –
Codominant
 GTC/GTC 15 (8.52) 12 (6.85) Reference
 −/GTC 89 (50.57) 75 (42.85) 0.01 0.901 1.05 (0.46–2.39) 1.02 (0.45–2.34)
 −/− 72 (40.90) 88 (50.29) 1.03 0.309 1.15 (0.67–3.47) 1.18 (0.78–1.80)
Dominant
 GTC/GTC 15 (8.52) 12 (6.86) Reference
 GTC/− + −/− 161 (91.48) 163 (93.14) 0.34 0.055 1.27 (0.58–2.79) 1.10 (0.74–1.64)
Recessive
 GTC/GTC + GC/- 104 (59.09) 87 (49.71) Reference
 −/− 72 (40.91) 88 (50.29) 3.11 0.078 1.46 (0.96–2.23) 1.20 (0.97–1.48)
Overdominant
 GTC/GTC + −/− 87 (49.43) 100 (57.14) Reference
 −/GTC 89 (50.57) 75 (42.86) 2.09 0.148 0.73 (0.48–1.12) 0.74 (0.48–1.13)

  1. n, number of individuals. p<0.05 determined as statistically significant. χ2, chi-square test; OR, odds ratio; CI, confidence interval.

The statistical analysis of the relationship between rs2288646 polymorphism of the ASIC3 gene and genotype and allele distributions of FMS patients and healthy individuals in the control group is given in Table 4. In the statistics of ASIC3 (rs2288646), the genotypes of 176 individuals in the control group were determined as 2 GG, 4 as AG, and 170 as AA. Genotypes of 175 individuals in the FMS patient group were found to be 0 as GG, 6 as AG, and 170 as AA. In the analysis of ASIC3 polymorphism with the χ2 method, no statistically significant correlation was found when FMS patients and controls were evaluated in terms of G and A alleles (χ2: 0.28 p: 0.597, Raw OR: 1.33 % 95 % CI: 0.42–4.37). When patients and controls were evaluated in terms of ASIC3 (rs2288646), the analysis of GG and AG using the χ2 method was not significant (χ2: 2.40 p: 0.121 Raw OR: 0.67 % 95 % CI: 0.38–1.17). The analysis of GG and AG with the χ2 method was not found significant (χ2: 1.98 p: 0.160 Raw OR: 0.99 % 95 % CI: 0.97–1.00). The analysis of GG and AG + AA using the χ2 method was insignificant (χ2: 2.00 p: 0.157 Raw OR: 0.99 % 95 % CI: 0.97–1.00).

Table 4:

Genotype and allele frequency distribution of rs2288646 polymorphism of ASIC3 gene in the study groups.

Rs 2288646, GG/AA Control group (n=176) (%) Patient group (n=175) (%) χ2 p-Value OR (95 % CI) Adjusted OR (95 % CI)
 G 8 (2.27) 6 (1.71) Referans
 A 344 (97.73) 344 (98.29) 0.28 0.597 1.33 (0.42–4.37) –
Codominant
 GG 2 (1.14) 0 (0) Referans
 AG 4 (2.27) 6 (3.43) 2.40 0.121 0.67 (0.38–1.17) –
 AA 170 (96.59) 169 (96.57) 1.98 0.160 0.99 (0.97–1.00) –
Dominant
 GG 2 (1.13) 0 (0) Referans
 AG + AA 174 (98.87) 175 (100) 2.00 0.157 0.99 (0.97–1.00) –
Recessive
 GG + AG 6 (3.40) 6 (3.43) Referans
 AA 170 (96.60) 169 (96.57) 0.00 0.992 0.99 (0.31–3.14) 0.99 (0.55–1.77)
Overdominant
 GG + AA 172 (97.73) 169 (96.57) Referans
 AG 4 (2.27) 6 (3.43) 0.42 0.515 1.53 (0.42–5.51) 1.46 (0.40–5.30)

  1. n, number of individuals. p<0.05 determined as statistically significant. χ2, chi-square test; OR, odds ratio; CI, confidence interval.

Gene variants between ASIC3 polymorphism (rs4148855) and fibromyalgia syndromes

In patients with Fibromyalgia given in Table 5, ASIC3 polymorphism (rs4148855) with sleep disturbance, fatigue, headache, dry mouth, numbness in legs, dry eyes, difficulty concentrating, swelling in soft tissues and familial history of fibromyalgia were statistically evaluated by χ2 method. No significant difference was found between them. However, when morning fatigue was analyzed by the χ2 method, a meaningful relationship was obtained between gene variants. (χ2: 7.74, p: 0.02).

Table 5:

ASIC3 polymorphism (rs4148855) genotype distributions in terms of fibromyalgia syndromes.

FMS syndromes ASIC3 polymorphism (rs4148855) n (%) χ2 p-Value
GTC/GTC −/GTC −/−
Sleeping disorder Yes 17 (63.0) 88 (53.7) 96 (60.0) 1.71 0.42
No 10 (37.0) 76 (46.3) 64 (40.0)
Fatigue Yes 27 (100.0) 143 (87.2) 141 (88.1) 3.83 0.14
No 0 (00.0) 21 (12.8) 19 (11.9)
Headache Yes 19 (70.4) 109 (66.5) 118 (73.8) 2.05 0.35
No 8 (29.6) 55 (33.5) 42 (26.3)
Morning fatigue Yes 17 (63.0) 116 (70.7) 131 (81.9) 7.74 0.02
No 10 (37.0) 48 (29.3) 29 (18.1)
Dry mouth Yes 18 (66.7) 103 (62.8) 102 (63.8) 0.15 0.92
No 9 (33.3) 61 (37.2) 58 (36.3)
Leg numbness Yes 14 (51.9) 97 (59.1) 100 (62.5) 1.21 0.54
No 13 (48.1) 67 (40.9) 60 (37.5)
Dry eye Yes 10 (37.5) 59 (36.0) 58 (36.3) 0.01 0.99
No 17 (63.0) 105 (64.0) 102 (63.8)
Difficulty concentrating Yes 18 (66.7) 105 (64.0) 109 (68.1) 0.61 0.73
No 9 (33.3) 59 (36.0) 51 (31.9)
Swelling in soft tissues Yes 14 (51.9) 87 (53.0) 89 (55.6) 0.27 0.87
No 13 (48.1) 77 (47.0) 71 (44.4)
Family history of fibromyalgia Yes 7 (25.9) 38 (23.2) 33 (20.6) 0.53 0.76
No 20 (74.1) 126 (76.8) 127 (79.4)

  1. n, number of individuals. p<0.05 determined as statistically significant. χ2, chi-square test

Gene variants between ASIC3 polymorphism (rs2288646) and fibromyalgia syndromes

In patients with Fibromyalgia given in Table 6, no significant difference was found between gene variants when the ASIC3 polymorphism (rs2288646) and clinical parameters were evaluated statistically by the χ2 method.

Table 6:

ASIC3 polymorphism (rs288646) genotype distributions in terms of fibromyalgia syndromes.

FMS syndromes ASIC3 polymorphism (rs2288646) n (%) χ2 p-Value
AA AG GG
Sleeping disorder Yes 1 (50.0) 6 (60.0) 194 (57.2) 0.07 0.96
No 1 (50.0) 4 (40.0) 145 (42.8)
Fatigue Yes 2 (100.0) 9 (90.0) 300 (88.5) 0.28 0.86
No 0 (00.0) 1 (10.0) 39 (11.5)
Headache Yes 0 (00.0) 8 (80.0) 238 (70.0) 5.15 0.07
No 2 (100.0) 2 (20.0) 101 (29.8)
Morning fatigue Yes 2 (100.0) 7 (70.0) 255 (75.2) 0.80 0.66
No 0 (00.0) 3 (30.0) 84 (24.8)
Dry mouth Yes 2 (100.0) 9 (90.0) 212 (62.5) 4.31 0.11
No 0 (00.0) 1 (10.0) 127 (37.5)
Leg numbness Yes 2 (100.0) 7 (70.0) 202 (59.6) 1.77 0.41
No 0 (00.0) 3 (30.0) 137 (40.4)
Dry eye Yes 0 (00.0) 4 (40.0) 123 (36.3) 1.19 0.54
No 2 (100.0) 6 (60.0) 216 (63.7)
Difficulty concentrating Yes 1 (50.0) 7 (70.0) 224 (66.1) 0.29 0.86
No 1 (50.0) 3 (30.0) 115 (33.9)
Swelling in soft tissues Yes 0 (00.0) 5 (50.0) 185 (54.6) 2.45 0.29
No 2 (100.0) 5 (50.0) 154 (45.4)
Family history of fibromyalgia Yes 0 (00.0) 4 (40.0) 74 (21.8) 2.43 0.29
No 2 (100.0) 6 (60.0) 265 (78.2)

  1. n, number of individuals. p<0.05 determined as statistically significant. χ2, chi-square test

Discussion

FMS is known to cause widespread skeletal-muscular pain, fatigue, sleep disturbance, depression, headache, morning fatigue, and bowel dysfunction [2, 24]. In Turkey, the prevalence of FMS among 20–64 year-olds was 3.6 % [25]. In this study, the age range of patients diagnosed with fibromyalgia (n=175) was 20–62 years. The age ranges of other studies conducted in Turkey, and our current study is similar. FMS symptoms are usually observed in women and are found in the age range of 30–50 years [26]. In our study, 165 of 175 FMS patients were female. There are biomarker studies for FMS, but no specific biomarker has been identified yet [10]. According to a study, high serum prolidase activities were observed in FMS [27]. In research by Taş et al., the relationship between FMS and potassium ion channels was investigated, and determined that the level of potassium voltage-gated channel subfamily H member 2 (KCNH2) was low in plasma [28]. In a study by Koçak et al., serum cathepsin S (CatS) and cystatin C (CysC) levels were evaluated in FMS, and both were found to be elevated [29]. In addition, a significant correlation was observed for calcitonin gene-related peptide (CGRP), calcitonin receptor-like receptor (CLR), and receptor component protein (RCP) levels in FMS [30]. In a study conducted by Zontul et al. within the scope of artificial intelligence with FMS, classification was performed with support vector machines and achieved high success [31].

Today, it is suggested that the susceptibility to chronic pain and the response to pain sensitivity are related to the individual’s genetic structure. In recent genetic studies, the fact that pain varies from person to person is explained by genetic polymorphism [7]. ASIC3 is expressed in peripheral sensory neurons and is essential in pain perception and acid-induced hyperalgesia [32]. Our study examined the relationship between the rs4148855 and rs2288646 polymorphisms of the ASIC3 gene, one of the subunits of ASICs, which is essential in pain perception and which has not been studied in the Turkish population, and FMS. Yu-Linko et al. investigated the relationship between ASIC3 gene polymorphisms (rs2288646 and rs4148855) and blood pressure in a Taiwanese population. They found that individuals carrying the rs2288646-A allele (AA + AG genotype) were associated with high blood pressure [33]. In another study, the relationship between the rs1042717 polymorphism of the beta-2 adrenergic receptor gene between patients with FMS and healthy individuals was examined, and no significant association was found [34]. In a study by Kaydok et al., the relationship between estrogen receptor 1 (ESR1) gene polymorphisms (rs2228480 and rs2295190) and FMS was investigated. As a result of the evaluation of the relationship between rs2295190 polymorphism and FMS, it was found that patients with CG and GG genotypes had a lower risk of FMS than CC genotypes. No significant difference was observed between rs2228480 polymorphism and FMS [35]. It has been reported that polymorphism of transient receptor potential vanilloid 2 (TRPV2), an ion channel in FMS, makes FMS more susceptible. TRPV3 has been suggested to increase the occurrence of FMS symptoms [36]. In a study examining polymorphisms of the sodium voltage-gated channel alpha subunit 9 (SCN9A) gene, which encodes voltage-gated sodium channel, in FMS, rs6754031 polymorphism was found to be significantly significant [37]. In a study by Yu et al., an FMS mouse model was created, electroacupuncture was applied, and the ASIC3 receptor was examined. It was found that mechanical hyperalgesia decreased in the mechanism involving ASIC3. It was also found that hyperalgesia and nociceptive signaling decreased in mice in which the ASIC3 gene was silenced [38]. Hung et al. suggested that ASIC3 is activated by lysophosphatidylcholine (16:0) in chronic hyperalgesia caused by repeated and intermittent sound stress [39]. In a mouse model of FMS, therapeutic ultrasound treatment activated ASIC3 in muscle afferents and produced an analgesic effect [40]. ASIC3 expression levels have been reported to be increased in patients with chronic fatigue syndrome [41].

This study determined the genotype distribution of ASIC3 gene (rs4148855 and rs2288646) polymorphisms in patients with FMS and healthy control subjects by RT-PCR. In terms of rs2288646, 169 (96.6 %) FMS patients were found to have GG, 6 (3.4 %) AG, and 0 (0 %) AA genotypes. Among the controls, 170 (96.6 %) had GG, 4 (2.3 %) AG, and 2 (1.1 %) AA genotypes. Our study observed no statistically significant difference between ASIC3 gene (rs4148855 and rs2288646) polymorphisms in patients with FMS and control subjects.

In this study, the number of male patients with FMS was not the same as the number of female patients. When ASIC3 gene polymorphisms are evaluated, the effect of these gene polymorphisms on protein level cannot be investigated due to the need for a project budget. These are the limitations of our study.

Conclusions

In the Turkish population, no significant association was found between the genotype and allele distributions of ASIC3 polymorphism (rs4148855 and rs2288646) in patients with FMS compared to controls. Further studies are needed to elucidate the relationship between ion channels and FMS to elucidate the mechanisms of FMS.

Corresponding author: Yavuz Silig, Department of Biochemistry, Faculty of Medicine, Sivas Cumhuriyet University, Sivas, Türkiye, E-mail: .

Acknowledgments

The authors thank to Dr Ziynet Cinar for her assistance with the statistical analysis.

  1. Research ethics: The study was approved by Sivas Cumhuriyet University Clinical Research Ethics Committee (Date: 12.05.2015, Decision no: 2015-05/01) and was conducted by the Declaration of Helsinki.

  2. Informed consent: Informed consent was obtained from all individuals included in this study.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Competing interests: Authors state no conflict of interest.

  5. Research funding: This work is supported by the Scientific Research Project Fund of Sivas Cumhuriyet University (CUBAP) under Project number T-656.

References

1. Pagliai, G, Giangrandi, I, Dinu, M, Sofi, F, Colombini, B. Nutritional interventions in managing fibromyalgia syndrome. Nutrients 2020;12:2525. https://doi.org/10.3390/nu12092525.Search in Google Scholar PubMed PubMed Central

2. Yılmaz, H, Uğurlu, H, Sallı, A. Fibromiyalji Sendromlu Hastalarda Kas Performansı. Romatizma/Rheumatism 2007;22:43–7.Search in Google Scholar

3. Marcus, DA, Dawn, A. Analgesics and opioids. Chronic pain: a primary care guide to practical management. Totowa, NJ: Humana Press; 2009:349–65 pp.10.1007/978-1-60327-465-4_19Search in Google Scholar

4. McBeth, J, Jones, K. Epidemiology of chronic musculoskeletal pain. Best Pract Res Clin Rheumatol 2007;21:403–25. https://doi.org/10.1016/j.berh.2007.03.003.Search in Google Scholar PubMed

5. Gormsen, L, Rosenberg, R, Bach, FW, Jensen, TS. Depression, anxiety, health-related quality of life, and pain in chronic fibromyalgia and neuropathic pain patients. Eur J Pain 2010;14:127-e1.10.1016/j.ejpain.2009.03.010Search in Google Scholar PubMed

6. Yunus, MB. Towards a model of pathophysiology of fibromyalgia: aberrant central pain mechanisms with peripheral modulation. J Rheumatol 1992;19:846–50.Search in Google Scholar

7. Reisine, S, Fifield, J, Walsh, SJ, Feinn, R. Do employment and family work affect the health status of women with fibromyalgia? J Rheumatol 2003;30:2045–53.Search in Google Scholar

8. Barcak, O, Karkucak, M, Capkin, E, Karagüzel, G, Dilber, B, Dedeoglu, S. Prevalence of generalized joint hypermobility and fibromyalgia syndrome in the children population of Trabzon: a Turkish study. Turk Fiz Tip ve Rehabil Derg-Turk J Phys Med Rehabil 2015;61:6–11. https://doi.org/10.5152/tftrd.2015.65002.Search in Google Scholar

9. Kato, K, Sullivan, PF, Evengård, B, Pedersen, NL. Importance of genetic influences on chronic widespread pain. Arthritis Rheum 2006;54:1682–6. https://doi.org/10.1002/art.21798.Search in Google Scholar PubMed

10. D’Agnelli, S, Arendt-Nielsen, L, Gerra, MC, Zatorri, K, Boggiani, L, Baciarello, M, et al.. Fibromyalgia: genetics and epigenetics insights may provide the basis for the development of diagnostic biomarkers. Mol Pain 2019;15:1–12. https://doi.org/10.1177/1744806918819944.Search in Google Scholar PubMed PubMed Central

11. Babinski, K, Lê, KT, Séguéla, P. Molecular cloning and regional distribution of a human proton receptor subunit with biphasic functional properties. J Neurochem 1999;72:51–7. https://doi.org/10.1046/j.1471-4159.1999.0720051.x.Search in Google Scholar PubMed

12. Yagi, J, Wenk, HN, Naves, LA, McCleskey, EW. Sustained currents through ASIC3 ion channels at the modest pH changes that occur during myocardial ischemia. Circ Res 2006;99:501–9. https://doi.org/10.1161/01.res.0000238388.79295.4c.Search in Google Scholar PubMed

13. Cheng, CF, Yang, KC, Kuo, TB, Chen, CC. S29-3 loss of Asic3 results in abnormal cardiac autonomic regulation and increased cardiac ischemia susceptibility in mice. Int J Cardiol 2007;122:47. https://doi.org/10.1016/s0167-5273(08)70475-3.Search in Google Scholar

14. Tan, ZY, Lu, Y, Whiteis, CA, Benson, CJ, Chapleau, MW, Abboud, FM. Acid-sensing ion channels contribute to the transduction of extracellular acidosis in rat carotid body glomus cells. Circ Res 2007;101:1009–19. https://doi.org/10.1161/circresaha.107.154377.Search in Google Scholar

15. Law, LAF, Sluka, KA, McMullen, T, Lee, J, Arendt-Nielsen, L, Graven-Nielsen, T. Acidic buffer-induced muscle pain evokes referred pain and mechanical hyperalgesia in humans. Pain 2008;140:254–64. https://doi.org/10.1016/j.pain.2008.08.014.Search in Google Scholar PubMed PubMed Central

16. Birdsong, WT, Fierro, L, Williams, FG, Spelta, V, Naves, LA, Knowles, M, et al.. Sensing muscle ischemia: coincident detection of acid and ATP via interplay of two ion channels. Neuron 2010;68:739–49. https://doi.org/10.1016/j.neuron.2010.09.029.Search in Google Scholar PubMed PubMed Central

17. Lingueglia, E. Acid-sensing ion channels (ASICs) in pain. Biol Aujourd’hui 2014;208:13–20. https://doi.org/10.1051/jbio/2014001.Search in Google Scholar PubMed

18. Waldmann, R, Champigny, G, Bassilana, F, Voilley, N, Lazdunski, M. Molecular cloning and functional expression of a novel amiloride-sensitive Na+ channel. J Biol Chem 1995;270:27411–14. https://doi.org/10.1074/jbc.270.46.27411.Search in Google Scholar PubMed

19. Kellenberger, S, Schild, L. International Union of Basic and Clinical Pharmacology. XCI. structure, function, and pharmacology of acid-sensing ion channels and the epithelial Na+ channel. Pharmacol Rev 2015;67:1–35. https://doi.org/10.1124/pr.114.009225.Search in Google Scholar PubMed

20. Jensen, JE, Cristofori-Armstrong, B, Anangi, R, Rosengren, KJ, Lau, CHY, Mobli, M, et al.. Understanding the molecular basis of toxin promiscuity: the analgesic sea anemone peptide APETx2 interacts with acid-sensing ion channel 3 and hERG channels via overlapping pharmacophores. J Med Chem 2014;57:9195–203. https://doi.org/10.1021/jm501400p.Search in Google Scholar PubMed

21. Fairbrother, WG, Yeh, RF, Sharp, PA, Burge, CB. Predictive identification of exonic splicing enhancers in human genes. Science 2002;297:1007–13. https://doi.org/10.1126/science.1073774.Search in Google Scholar PubMed

22. Wolfe, F, Clauw, DJ, Fitzcharles, MA, Goldenberg, DL, Häuser, W, Katz, RL, et al.. Revisions to the 2010/2011 fibromyalgia diagnostic criteria. Semin Arthritis Rheum 2016;46:319–29. https://doi.org/10.1016/j.semarthrit.2016.08.012.Search in Google Scholar PubMed

23. MWer, S, Dykes, D, Polesky, H. A simple salting-out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res 1988;16:1215. https://doi.org/10.1093/nar/16.3.1215.Search in Google Scholar PubMed PubMed Central

24. Smith, HS, Barkin, RL. Fibromyalgia syndrome: a discussion of the syndrome and pharmacotherapy. Am J Therapeut 2010;17:418–39. https://doi.org/10.1097/mjt.0b013e3181df8e1b.Search in Google Scholar

25. Topbas, M, Cakirbay, H, Gulec, H, Akgol, E, Ak, I, Can, G. The prevalence of fibromyalgia in women aged 20–64 in Turkey. Scand J Rheumatol 2005;34:140–4.Search in Google Scholar

26. Busch, AJ, Overend, TJ, Schachter, CL. Fibromyalgia treatment: the role of exercise and physical activity. Int J Clin Rheumtol 2009;4:343–80. https://doi.org/10.2217/ijr.09.23.Search in Google Scholar

27. Ozturk, A, Agbektas, T, Tas, A, Gul, MA, Karadag, A, Silig, Y. The relationship between serum prolidase activity and histone H3 protein levels and fibromyalgia. Eur Rev Med Pharmacol Sci 2022;26:9098–106. https://doi.org/10.26355/eurrev_202212_30659.Search in Google Scholar PubMed

28. Tas, A, Hayta, E, Karadag, A, Zontul, C, Ozmen, E, Aydin, S, et al.. Potassium ion channel protein (KCNH) levels in patients with Fibromyalgia Syndrome. Cell Mol Biol 2021;67:451–7. https://doi.org/10.14715/cmb/2021.67.5.57.Search in Google Scholar PubMed

29. Kocak, I, Hizmetli, S, Tas, A, Karadag, A, Zontul, C, Silig, Y. High levels of cathepsin S and cystatin C in patients with fibromyalgia syndrome. Int J Rheum Dis 2020;23:966–9. https://doi.org/10.1111/1756-185x.13840.Search in Google Scholar

30. Korucu, RU, Karadağ, A, Taş, A, Özmen, E, Hayta, E, Siliğ, Y. Serum calcitonin gene-related peptide and receptor protein levels in patients with fibromyalgia syndrome: a cross-Sectional study. Arch Rheumatol 2020;35:463–7. https://doi.org/10.46497/archrheumatol.2020.7783.Search in Google Scholar PubMed PubMed Central

31. Zontul, C, Hayta, E, Zontul, M, Taş, A, Silig, Y. Destek Vektör Makineleri ile Fibromiyalji Sendromu Sınıflaması. Acta Infologica 2017;1:2602–3563.Search in Google Scholar

32. Giesecke, T, Gracely, RH, Williams, DA, Geisser, ME, Petzke, FW, Clauw, DJ. The relationship between depression, clinical pain, and experimental pain in a chronic pain cohort. Arthritis Rheum 2005;52:1577–84. https://doi.org/10.1002/art.21008.Search in Google Scholar PubMed

33. Friese, MA, Craner, MJ, Etzensperger, R, Vergo, S, Wemmie, JA, Welsh, MJ, et al.. Acid-sensing ion channel-1 contributes to axonal degeneration in autoimmune inflammation of the central nervous system. Nat Med 2007;13:1483–9. https://doi.org/10.1038/nm1668.Search in Google Scholar PubMed

34. Çakiroğlu, GŞ, Hizmetli, S, Siliğ, Y, KaradaĞ, A, Hayta, E, Özaltin, B, et al.. Comparison of beta-2 adrenergic receptor gene polymorphisms between patients with fibromyalgia syndrome and healthy controls. Arch Rheumatol 2020;35:328. https://doi.org/10.46497/archrheumatol.2020.7602.Search in Google Scholar PubMed PubMed Central

35. Kaydok, E, Ozmen, E, Sari, I, Cakıroglu, GS, Tas, A, Silig, Y, et al.. A significant association between rs2295190 polymorphism of the ESR1 gene and fibromyalgia syndrome. Cell Mol Biol 2022;68:182–7. https://doi.org/10.14715/cmb/2022.68.7.30.Search in Google Scholar PubMed

36. Park, DJ, Kim, SH, Nah, SS, Lee, JH, Kim, SK, Lee, YA, et al.. Polymorphisms of the TRPV2 and TRPV3 genes associated with fibromyalgia in a Korean population. Rheumatology 2016;55:1518–27. https://doi.org/10.1093/rheumatology/kew180.Search in Google Scholar PubMed

37. Vargas-Alarcon, G, Alvarez-Leon, E, Fragoso, JM, Vargas, Martinez, A, Vallejo, M, et al.. A SCN9A gene-encoded dorsal root ganglia sodium channel polymorphism associated with severe fibromyalgia. BMC Muscoskel Disord 2012;13:1–5. https://doi.org/10.1186/1471-2474-13-23.Search in Google Scholar PubMed PubMed Central

38. Yen, LT, Hsieh, CL, Hsu, HC, Lin, YW. Targeting ASIC3 for relieving mice fibromyalgia pain: roles of electroacupuncture, opioid, and adenosine. Sci Rep 2017;7:46663. https://doi.org/10.1038/srep46663.Search in Google Scholar PubMed PubMed Central

39. Hung, CH, Lee, CH, Tsai, MH, Chen, CH, Lin, HF, Hsu, CY, et al.. Activation of acid-sensing ion channel 3 by lysophosphatidylcholine 16: 0 mediates psychological stress-induced fibromyalgia-like pain. Ann Rheum Dis 2020;79:1644–56. https://doi.org/10.1136/annrheumdis-2020-218329.Search in Google Scholar PubMed PubMed Central

40. Han, DS, Lee, CH, Shieh, YD, Chang, KV, Lin, SH, Chu, YC, et al.. Involvement of ASIC3 and substance P in therapeutic ultrasound-mediated analgesia in mouse models of fibromyalgia. J Pain 2023;24:1493–505. https://doi.org/10.1016/j.jpain.2023.04.003.Search in Google Scholar PubMed

41. Light, AR, White, AT, Hughen, RW, Light, KC. Moderate exercise increases expression for sensory, adrenergic, and Immune genes in chronic fatigue syndrome patients but not in normal subjects. J Pain 2009;10:1099–112. https://doi.org/10.1016/j.jpain.2009.06.003.Search in Google Scholar PubMed PubMed Central

Received: 2023-05-04
Accepted: 2023-08-24
Published Online: 2023-10-19

© 2023 the author(s), published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/tjb-2023-0101
Keywords for this article
fibromyalgia syndrome; single nucleotide polymorphism; acid-sensing ion channel 3; Turkish population
Creative Commons
BY 4.0

Articles in the same Issue

  1. Frontmatter
  2. Review
  3. Metabolomics: a review of liquid chromatography mass spectrometry-based methods and clinical applications
  4. Opinion Papers
  5. Green transformation in the health sector and medical laboratories, adaptation to climate change in Türkiye
  6. Forward steps in green medical laboratory practices for a sustainable future
  7. Research Articles
  8. Comparison of the immunoassay method with the commercial and in-house LC-MS/MS methods for substance abuse in urine
  9. Peroxisome proliferator-activated receptor gamma and osteoprotegerin levels as an indicator and diagnostic predictor of endothelial dysfunction
  10. Gingival status and prophylactic oral hygiene measures modulate salivary amino acids’ profile in children with plaque-induced gingivitis
  11. FIB4 score is increased in severe preeclampsia
  12. The effects of a single dialysis session on serum hepcidin levels
  13. Gestational diabetes mellitus is associated with a low serum level of mitochondrial-derived peptide-MOTS-C
  14. Ideal timing of labor in terms of oxidative stress – which term period is best?
  15. Synergistic role of thymoquinone and 5-fluorouracil in U-251MG glioblastoma cell line
  16. Effect of oligosaccharides and aerobic training on hyperglycemia, growth and intestinal microbial diversity of diabetic rats
  17. Association of AdipoQ (G>T) gene polymorphism with obesity and hypertension in North Indian postmenopausal women of Punjab
  18. The attraction of paraoxonase-1 associated with the MAPK pathway on colon carcinoma cells
  19. Resveratrol modulates miRNA machinery proteins in different types of colon cancer cells
  20. The relationship between ASIC3 gene polymorphism and fibromyalgia syndrome
  21. Expression levels of genes involved in lipogenesis and cholesterol synthesis in adenomyosis
  22. Frequency of thrombophilia-associated mutations and polymorphisms in pregnant women with a history of thrombosis or pregnancy complications
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