# Genetic Polymorphisms ofORAI1 and Chronic Kidney Disease in Taiwanese Population

Authors: Daw-Yang Hwang; Shu-Chen Chien; Yu-Wen Hsu; Chih-Chin Kao; Shih-Ying Cheng; Hui-Chen Lu; Mai-Szu Wu; Jer-Ming Chang
Journal: BioMed Research International
 (2014)

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## Abstract

Taiwan has very high incidence and prevalence of chronic kidney disease (CKD), which easily progresses to end-stage renal disease (ESRD). The association between inflammation and CKD has been explored in several studies.ORAI1 functions as a pore-forming subunit of the store-operated calcium channels which are involved in the regulation of immune system. Hence, we conducted a case-control study to determine whether the genetic polymorphisms of ORAI1 gene is a susceptibility factor to CKD and its clinical features in a Taiwanese population. Five hundred seventy-nine CKD patients from a hospital-based CKD care program were included in the study. Five tagging single nucleotide polymorphisms (tSNPs) of ORAI1 were selected from the genotyping data of the Han Chinese population from the HapMap project. Among these polymorphisms, rs12313273 was found to be significantly associated with elevated serum calcium levels, which has been linked to increased risk of death in CKD patients. To have a better management of serum calcium, we suggest that ORAI1 polymorphisms might be used as a potential biomarker for initiating non-calcium-based phosphate binder in CKD patients in the future.


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## Body

## 1. Introduction

Chronic kidney disease (CKD) is an important global public health concern because of its high incidence, prevalence, morbidity, and mortality [1]. According to the US Renal Data System (USRDS) report, Taiwan has the highest incidence and prevalence of end-stage renal disease (ESRD) [2]. The prevalence of CKD in Taiwan was 9.8–11.9% and owing to the differences in the data sources, study subjects, and definition of CKD, the reasons behind this high incidence and prevalence are multifactorial [3].CKD has been well known to be associated with low-grade inflammation, endothelial dysfunction, and platelet activation, even among those in the early stage of CKD [4]. Serum levels of the proinflammatory cytokines, such as IL-1, IL-6, CRP, and TNF-α were significantly high in CKD patients [5–8], and these inflammation markers may replace albumin, which is currently used as the predictive marker for mortality, to predict patient outcomes [9].Calcium signaling controls diverse cellular functions such as enzyme metabolism, muscle contraction, immune response, and cell cycle regulation [10, 11]. In nonexcitable cells such as T cells and B cells, immunological reactions are regulated via Ca2+ entry mainly through store-operated calcium channels [12].ORAI1 consists of four transmembrane domains and functions as a pore-forming subunit of the store-operated calcium channels [13]. Functional analysis ofORAI1- (also calledCRACM1-) deficient mice revealed dysfunction of mast cells and attenuation of cytokine (TNF-α and IL-6) release [14].Recent studies on the genetic susceptibility and the progression of CKD have yielded promising results [15–17]. The results of a genome-wide association study showed that several loci were associated with CKD and estimated glomerular filtration rate (eGFR) [16]. The evolution of ApoL1 variants as survival factors may have contributed to the high prevalence of renal disease among African Americans [17]. To the best of our knowledge, there is no previous research established regarding the association between genetic polymorphism of ORAl1 and the severity of CKD in Taiwanese population. Therefore, in this case-control study, we examined the association of theORAI1 genetic polymorphisms with CKD susceptibility, eGFR, and serum phosphorus and calcium levels.


## 2. Materials and Methods

### 2.1. Study Subjects and Data Collection

Five hundred seventy-nine unrelated CKD patients (323 (55.8%) men; age range, 18–90 years old; mean age,61
±
14 years old) were included in the study at the time of their enrolment for the CKD Care Program at the Kaohsiung Medical University Hospital, Kaohsiung, Taiwan; written informed consent was obtained from all patients. All included patients were >18 years of age, and their detailed clinical history was recorded as part of the CKD Care Program. The study protocol conformed to the Declaration of Helsinki and was approved by the Institutional Review Board of the Kaohsiung Medical University Hospital. Serum creatinine levels were calculated using a modified kinetic Jaffe reaction. eGFR was estimated using the abbreviated equation developed in the Modification of Diet in Renal Disease Study [18], and the cases were categorized according to the staging system described in the Kidney/Dialysis Outcome Quality Initiative Clinical Practice Guidelines for CKD: Evaluation, Classification, and Stratification [19]. The patients were divided into two groups according to their eGFR: patients with eGFR above 45 mL/min/1.73 m2 were classified as having early-stage CKD [3, 20, 21], whereas those with lower eGFR were classified as having late-stage CKD. In Taiwan, the “nationwide CKD preventive project with multidisciplinary care program” implemented by Health Promotion Administration divided CKD patients into “early” and “pre-ESRD” stages, according to the eGFR ≥45 mL/min/1.73 m2 or <45 mL/min/1.73 m2 [22]. Different treatment strategy and management plans are applied in those two groups. In our study, we divided patients into two groups as above to investigate the differences of genetic polymorphism. Their clinical history and biochemical data were recorded.


### 2.2. DNA Extraction

Venous blood was collected from the patients during medical visit, stored at 4°C, and processed on the same day. The blood was centrifuged to separate serum and cells. DNA extraction from the blood cells involved an initial treatment with 0.5% SDS lysis buffer followed by treatment with protease K (1 mg/mL, for the digestion of nuclear protein) for 4 h at 60°C. Total DNA was harvested using the Gentra extraction kit and was precipitated using 70% alcohol.


### 2.3. SNP Selection

From the HapMap database (http://www.hapmap.org, HapMap Data Rel 27 PhaseII+III, Freb09, on NCBI B36 assembly, dbSNP b126), five tagging single nucleotide polymorphisms (tSNPs) ofORAI1 (rs12313273, rs6486795, rs7135617, rs12320939, and rs712853) with minor allele frequency (MAF) >10% and r
2 > 0.8 were selected from chromosomal region 120,545,838–120,561,329 of the Han Chinese population in Beijing (CHB). A graphical overview of the physical and chromosomal location of the five tSNPs is shown in Figure 1. TwoORAI1 polymorphisms (rs12313273 and rs1232093) were located in the promoter region, two (rs6486795 and rs7135617) in the intron region, and one (rs712853) in the 3′-untranslated region (UTR).Figure 1
A graphical overview of the genotyped polymorphisms identified in relation to the exon/intron structure of the humanORAI1gene.


### 2.4. Genotyping

Genotyping was performed using TaqMan PCR. In brief, TaqMan probes were first labeled with different fluorescent markers. PCR primers and TaqMan probes were designed to target the 5 tSNPs. Reactions were performed in 96-well microplates in the ABI 9700 Thermal Cycler (Applied Biosystems, Foster City, USA) and fluorescence was detected and analyzed using the System SDS software version 1.2.3.


### 2.5. Statistical Analysis

The genotype distribution of the five tSNPs was tested for Hardy-Weinberg equilibrium (HWE). The Chi-square test was used for comparing the genotype distribution or allele frequencies of the early-stage and late-stage CKD patients. One-way ANOVA was used to assess the difference in mean values of the eGFR and the serum levels of calcium and phosphate in the groups created based on genotyping results. All statistical analyses above were performed using the JMP 8.0 statistical software. Linear regression and logistic regression were used to adjust the influence of age in eGFR and CKD staging, which were performed using the SNPassoc 1.9-1 statistical software. AP value < 0.05 was considered significant.


## 2.1. Study Subjects and Data Collection

Five hundred seventy-nine unrelated CKD patients (323 (55.8%) men; age range, 18–90 years old; mean age,61
±
14 years old) were included in the study at the time of their enrolment for the CKD Care Program at the Kaohsiung Medical University Hospital, Kaohsiung, Taiwan; written informed consent was obtained from all patients. All included patients were >18 years of age, and their detailed clinical history was recorded as part of the CKD Care Program. The study protocol conformed to the Declaration of Helsinki and was approved by the Institutional Review Board of the Kaohsiung Medical University Hospital. Serum creatinine levels were calculated using a modified kinetic Jaffe reaction. eGFR was estimated using the abbreviated equation developed in the Modification of Diet in Renal Disease Study [18], and the cases were categorized according to the staging system described in the Kidney/Dialysis Outcome Quality Initiative Clinical Practice Guidelines for CKD: Evaluation, Classification, and Stratification [19]. The patients were divided into two groups according to their eGFR: patients with eGFR above 45 mL/min/1.73 m2 were classified as having early-stage CKD [3, 20, 21], whereas those with lower eGFR were classified as having late-stage CKD. In Taiwan, the “nationwide CKD preventive project with multidisciplinary care program” implemented by Health Promotion Administration divided CKD patients into “early” and “pre-ESRD” stages, according to the eGFR ≥45 mL/min/1.73 m2 or <45 mL/min/1.73 m2 [22]. Different treatment strategy and management plans are applied in those two groups. In our study, we divided patients into two groups as above to investigate the differences of genetic polymorphism. Their clinical history and biochemical data were recorded.


## 2.2. DNA Extraction

Venous blood was collected from the patients during medical visit, stored at 4°C, and processed on the same day. The blood was centrifuged to separate serum and cells. DNA extraction from the blood cells involved an initial treatment with 0.5% SDS lysis buffer followed by treatment with protease K (1 mg/mL, for the digestion of nuclear protein) for 4 h at 60°C. Total DNA was harvested using the Gentra extraction kit and was precipitated using 70% alcohol.


## 2.3. SNP Selection

From the HapMap database (http://www.hapmap.org, HapMap Data Rel 27 PhaseII+III, Freb09, on NCBI B36 assembly, dbSNP b126), five tagging single nucleotide polymorphisms (tSNPs) ofORAI1 (rs12313273, rs6486795, rs7135617, rs12320939, and rs712853) with minor allele frequency (MAF) >10% and r
2 > 0.8 were selected from chromosomal region 120,545,838–120,561,329 of the Han Chinese population in Beijing (CHB). A graphical overview of the physical and chromosomal location of the five tSNPs is shown in Figure 1. TwoORAI1 polymorphisms (rs12313273 and rs1232093) were located in the promoter region, two (rs6486795 and rs7135617) in the intron region, and one (rs712853) in the 3′-untranslated region (UTR).Figure 1
A graphical overview of the genotyped polymorphisms identified in relation to the exon/intron structure of the humanORAI1gene.


## 2.4. Genotyping

Genotyping was performed using TaqMan PCR. In brief, TaqMan probes were first labeled with different fluorescent markers. PCR primers and TaqMan probes were designed to target the 5 tSNPs. Reactions were performed in 96-well microplates in the ABI 9700 Thermal Cycler (Applied Biosystems, Foster City, USA) and fluorescence was detected and analyzed using the System SDS software version 1.2.3.


## 2.5. Statistical Analysis

The genotype distribution of the five tSNPs was tested for Hardy-Weinberg equilibrium (HWE). The Chi-square test was used for comparing the genotype distribution or allele frequencies of the early-stage and late-stage CKD patients. One-way ANOVA was used to assess the difference in mean values of the eGFR and the serum levels of calcium and phosphate in the groups created based on genotyping results. All statistical analyses above were performed using the JMP 8.0 statistical software. Linear regression and logistic regression were used to adjust the influence of age in eGFR and CKD staging, which were performed using the SNPassoc 1.9-1 statistical software. AP value < 0.05 was considered significant.


## 3. Results

### 3.1. Association betweenORAI1 tSNPs and eGFR in the CKD Patients

Patient characteristics are shown in Table1. We tested whether genetic polymorphisms inORAI1 are associated with eGFR in CKD patients. None of the tSNPs were found to be significantly associated with CKD susceptibility. We further adjusted our result by age which showed no significant associations (Table 2).Table 1
Basal characteristics of patients with chronic kidney disease.
Characteristics
Patients with CKD
Number of subjects
579
Gender: male, number (%)
323 (55.8%)
Age (years)a
61.1 ± 13.7
Range (years)
18–90
aMeans ± SD.Table 2
Difference in the value of eGFR among CKD patients stratified by differentORAI1 genotypes.
SNP
Genotype
Sample number (%)
eGFRa
rs12320939
TT
122 (21.2)
31.75 ± 24.28
TG
295 (51.2)
32.26 ± 24.04
GG
159 (27.6)
31.34 ± 24.28
P value
0.9229
AdjustedP valueb
0.8764
CC
50 (8.7)
34.64 ± 23.93
rs12313273
CT
245 (42.5)
31.08 ± 22.45
TT
281 (48.8)
31.90 ± 24.70
P value
0.6229
AdjustedP valueb
0.7209
TT
98 (17.0)
29.06 ± 20.71
rs7135617
TG
285 (49.6)
32.71 ± 24.31
GG
192 (33.4)
32.29 ± 25.44
P value
0.4138
AdjustedP valueb
0.3691
CC
71 (12.3)
33.49 ± 25.23
rs6486795
CT
271 (47.0)
31.79 ± 22.61
TT
235 (40.7)
31.93 ± 24.90
P value
0.8619
AdjustedP valueb
0.8608
CC
56 (9.7)
34.36 ± 26.12
rs712853
CT
238 (41.4)
31.95 ± 24.39
TT
281 (48.9)
31.53 ± 23.09
P value
0.7220
AdjustedP valueb
0.7038
aMeans ± SD. bAdjusted age by linear regression.


### 3.2. Association ofORAI1 tSNPs in Early- and Late-Stage CKD Patients

Next, we evaluated whether the genotype and allele frequency ofORAI1 were associated with the stage of CKD. After being adjusted by age using logistic regression, no association was observed between tSNPs and the stage of CKD (Table 3).Table 3
Genotyping and allele frequency ofORAI1 gene in chronic kidney disease patients.
Genotype
Late stage (%)(n
=
453)
Early stage (%)(n
=
126)
Allele
Late stage (%)(n
=
453)
Early stage (%)(n
=
126)
Genotype
P value
Dominant
P value
Recessive
P value
Allelic
P value
rs12320939
TT
94 (20.8)
28 (22.4)
T
424 (47.0)
115 (46.0)
0.6482
0.4482
0.7955
0.7363
TG
236 (52.3)
59 (47.2)
G
478 (53.0)
135 (54.0)
GG
121 (26.8)
38 (30.4)
rs12313273
CC
36 (8.0)
14 (11.3)
C
271 (30.0)
74 (29.8)
0.3780
0.5522
0.3148
0.9939
CT
199 (44.0)
46 (37.1)
T
633 (70.0)
174 (70.2)
TT
217 (48.0)
64 (51.6)
rs7135617
TT
80 (17.7)
18 (14.5)
T
378 (41.9)
103 (41.5)
0.4551
0.6285
0.3425
0.8567
TG
218 (48.3)
67 (54.0)
G
524 (58.1)
145 (58.5)
GG
153 (33.9)
39 (31.5)
rs6486795
CC
53 (11.8)
18 (14.2)
C
323 (35.8)
90 (35.7)
0.5856
0.6638
0.4536
0.9574
CT
217 (48.1)
54 (42.9)
T
579 (64.2)
162 (64.3)
TT
181 (40.1)
54 (42.9)
rs712853
CC
46 (10.2)
10 (8.0)
C
274 (30.4)
76 (30.4)
0.6072
0.6717
0.4667
0.9998
CT
182 (40.4)
56 (44.8)
T
626 (69.6)
174 (69.6)
TT
222 (49.3)
59 (47.2)
Late stage: eGFR <45, early stage: eGFR ≥45.
AllP values had been adjusted by age using logistic regression.


### 3.3. Association between theORAI1 Polymorphisms and Serum Calcium Levels in CKD Patients

Abnormalities in the levels of calcium, phosphorus, and intact parathyroid hormone (PTH) are evident early in CKD patients who are not on dialysis [19]. Since abnormalities in calcium and phosphate levels are associated with increased mortality and CKD progression in non-dialysis-dependent CKD patients [23, 24], we also investigated the associations betweenORAI1 genetic polymorphisms and serum calcium concentration. We found that rs12313273 was significantly associated with serum calcium levels in CKD patients (Table 4). We also observed that patients with the CC genotype of rs12313273 showed significantly higher calcium levels than those with other genotypes did. However, we found no correlation between the genetic polymorphisms and the serum phosphorus levels.Table 4
Difference in the value of Ca2+ and phosphorous among CKD patients stratified by different ORAI1genotype.
SNP
Genotype
Sample number (%)
Calcium (mg/dL)a
P value
Phosphorous (mg/dL)a
P value
rs12320939
TT
122 (21.2)
9.32 ± 0.53
0.0528
4.26 ± 1.02
0.5243
TG
295 (51.2)
9.10 ± 0.94
4.25 ± 1.02
GG
159 (27.6)
9.17 ± 0.80
4.37 ± 1.02
rs12313273
CC
50 (8.7)
9.32 ± 0.61
0.0389
*
4.33 ± 0.89
0.0831
CT
245 (42.5)
9.23 ± 0.57
4.18 ± 1.01
TT
281 (48.8)
9.08 ± 1.03
4.38 ± 1.03
rs7135617
TT
98 (17.0)
9.21 ± 0.87
0.1017
4.42 ± 1.05
0.3290
TG
285 (49.6)
9.09 ± 0.81
4.24 ± 0.97
GG
192 (33.4)
9.25 ± 0.85
4.29 ± 1.07
rs6486795
CC
71 (12.3)
9.33 ± 0.60
0.1586
4.36 ± 1.08
0.2622
CT
271 (47.0)
9.17 ± 0.77
4.21 ± 1.03
TT
235 (40.7)
9.12 ± 0.96
4.35 ± 0.98
rs712853
CC
56 (9.7)
8.99 ± 1.27
0.2356
4.47 ± 1.06
0.3133
CT
238 (41.4)
9.16 ± 0.87
4.30 ± 1.12
TT
281 (48.9)
9.20 ± 0.68
4.24 ± 0.92
*Significant (P
<
0.05) values are in bold.aMeans ± SD.


## 3.1. Association betweenORAI1 tSNPs and eGFR in the CKD Patients

Patient characteristics are shown in Table1. We tested whether genetic polymorphisms inORAI1 are associated with eGFR in CKD patients. None of the tSNPs were found to be significantly associated with CKD susceptibility. We further adjusted our result by age which showed no significant associations (Table 2).Table 1
Basal characteristics of patients with chronic kidney disease.
Characteristics
Patients with CKD
Number of subjects
579
Gender: male, number (%)
323 (55.8%)
Age (years)a
61.1 ± 13.7
Range (years)
18–90
aMeans ± SD.Table 2
Difference in the value of eGFR among CKD patients stratified by differentORAI1 genotypes.
SNP
Genotype
Sample number (%)
eGFRa
rs12320939
TT
122 (21.2)
31.75 ± 24.28
TG
295 (51.2)
32.26 ± 24.04
GG
159 (27.6)
31.34 ± 24.28
P value
0.9229
AdjustedP valueb
0.8764
CC
50 (8.7)
34.64 ± 23.93
rs12313273
CT
245 (42.5)
31.08 ± 22.45
TT
281 (48.8)
31.90 ± 24.70
P value
0.6229
AdjustedP valueb
0.7209
TT
98 (17.0)
29.06 ± 20.71
rs7135617
TG
285 (49.6)
32.71 ± 24.31
GG
192 (33.4)
32.29 ± 25.44
P value
0.4138
AdjustedP valueb
0.3691
CC
71 (12.3)
33.49 ± 25.23
rs6486795
CT
271 (47.0)
31.79 ± 22.61
TT
235 (40.7)
31.93 ± 24.90
P value
0.8619
AdjustedP valueb
0.8608
CC
56 (9.7)
34.36 ± 26.12
rs712853
CT
238 (41.4)
31.95 ± 24.39
TT
281 (48.9)
31.53 ± 23.09
P value
0.7220
AdjustedP valueb
0.7038
aMeans ± SD. bAdjusted age by linear regression.


## 3.2. Association ofORAI1 tSNPs in Early- and Late-Stage CKD Patients

Next, we evaluated whether the genotype and allele frequency ofORAI1 were associated with the stage of CKD. After being adjusted by age using logistic regression, no association was observed between tSNPs and the stage of CKD (Table 3).Table 3
Genotyping and allele frequency ofORAI1 gene in chronic kidney disease patients.
Genotype
Late stage (%)(n
=
453)
Early stage (%)(n
=
126)
Allele
Late stage (%)(n
=
453)
Early stage (%)(n
=
126)
Genotype
P value
Dominant
P value
Recessive
P value
Allelic
P value
rs12320939
TT
94 (20.8)
28 (22.4)
T
424 (47.0)
115 (46.0)
0.6482
0.4482
0.7955
0.7363
TG
236 (52.3)
59 (47.2)
G
478 (53.0)
135 (54.0)
GG
121 (26.8)
38 (30.4)
rs12313273
CC
36 (8.0)
14 (11.3)
C
271 (30.0)
74 (29.8)
0.3780
0.5522
0.3148
0.9939
CT
199 (44.0)
46 (37.1)
T
633 (70.0)
174 (70.2)
TT
217 (48.0)
64 (51.6)
rs7135617
TT
80 (17.7)
18 (14.5)
T
378 (41.9)
103 (41.5)
0.4551
0.6285
0.3425
0.8567
TG
218 (48.3)
67 (54.0)
G
524 (58.1)
145 (58.5)
GG
153 (33.9)
39 (31.5)
rs6486795
CC
53 (11.8)
18 (14.2)
C
323 (35.8)
90 (35.7)
0.5856
0.6638
0.4536
0.9574
CT
217 (48.1)
54 (42.9)
T
579 (64.2)
162 (64.3)
TT
181 (40.1)
54 (42.9)
rs712853
CC
46 (10.2)
10 (8.0)
C
274 (30.4)
76 (30.4)
0.6072
0.6717
0.4667
0.9998
CT
182 (40.4)
56 (44.8)
T
626 (69.6)
174 (69.6)
TT
222 (49.3)
59 (47.2)
Late stage: eGFR <45, early stage: eGFR ≥45.
AllP values had been adjusted by age using logistic regression.


## 3.3. Association between theORAI1 Polymorphisms and Serum Calcium Levels in CKD Patients

Abnormalities in the levels of calcium, phosphorus, and intact parathyroid hormone (PTH) are evident early in CKD patients who are not on dialysis [19]. Since abnormalities in calcium and phosphate levels are associated with increased mortality and CKD progression in non-dialysis-dependent CKD patients [23, 24], we also investigated the associations betweenORAI1 genetic polymorphisms and serum calcium concentration. We found that rs12313273 was significantly associated with serum calcium levels in CKD patients (Table 4). We also observed that patients with the CC genotype of rs12313273 showed significantly higher calcium levels than those with other genotypes did. However, we found no correlation between the genetic polymorphisms and the serum phosphorus levels.Table 4
Difference in the value of Ca2+ and phosphorous among CKD patients stratified by different ORAI1genotype.
SNP
Genotype
Sample number (%)
Calcium (mg/dL)a
P value
Phosphorous (mg/dL)a
P value
rs12320939
TT
122 (21.2)
9.32 ± 0.53
0.0528
4.26 ± 1.02
0.5243
TG
295 (51.2)
9.10 ± 0.94
4.25 ± 1.02
GG
159 (27.6)
9.17 ± 0.80
4.37 ± 1.02
rs12313273
CC
50 (8.7)
9.32 ± 0.61
0.0389
*
4.33 ± 0.89
0.0831
CT
245 (42.5)
9.23 ± 0.57
4.18 ± 1.01
TT
281 (48.8)
9.08 ± 1.03
4.38 ± 1.03
rs7135617
TT
98 (17.0)
9.21 ± 0.87
0.1017
4.42 ± 1.05
0.3290
TG
285 (49.6)
9.09 ± 0.81
4.24 ± 0.97
GG
192 (33.4)
9.25 ± 0.85
4.29 ± 1.07
rs6486795
CC
71 (12.3)
9.33 ± 0.60
0.1586
4.36 ± 1.08
0.2622
CT
271 (47.0)
9.17 ± 0.77
4.21 ± 1.03
TT
235 (40.7)
9.12 ± 0.96
4.35 ± 0.98
rs712853
CC
56 (9.7)
8.99 ± 1.27
0.2356
4.47 ± 1.06
0.3133
CT
238 (41.4)
9.16 ± 0.87
4.30 ± 1.12
TT
281 (48.9)
9.20 ± 0.68
4.24 ± 0.92
*Significant (P
<
0.05) values are in bold.aMeans ± SD.


## 4. Discussion

We systematically investigated fiveORAI1 tSNPs (rs12313273, rs6486795, rs7135617, rs12320939, and rs712853) in CKD patients. None of the tSNPs ofORAI1 were associated with the risk of CKD. However, rs12313273 was found to be significantly associated with increased serum calcium levels. Patients with CC genotype showed higher serum calcium levels than those with other genotypes. Impaired calcium and phosphate homeostasis have been reported in the early stages of CKD. We frequently used calcium-based or non-calcium-based phosphate binder to manage hyperphosphatemia, yet calcium-based binders often result in hypercalcemia [25]. Recent studies showed that CKD patients with high serum calcium levels (>2.75 mmol/L) have a higher risk of death than patients with low serum calcium levels do [26, 27]. Moreover, high calcium-phosphate product is associated with increased risk of vascular calcification and cardiovascular mortality [28, 29]. Our findings showed that patients with CC genotype of rs12313273 were associated with higher calcium levels. Therefore, we may takeORAI1 polymorphism into account when prescribing calcium or non-calcium-based phosphate binder to CKD patients with hyperphosphatemia.ORAI1-mediated calcium signaling plays critical roles in inflammatory diseases. Chang et al. identified several polymorphisms inORAI1 from Taiwanese and Japanese atopic dermatitis patients [30]. In addition, the CC genotype of rs12313273 inORAI1 was strongly associated with the risk and recurrence of calcium nephrolithiasis [31]. Furthermore, theORAI1 haplotypes (rs12313273 and rs7135617) are associated with the risk of HLA-B27-positive ankylosing spondylitis [32]. Consistent with the findings of previous studies, our results confirm the functional role ofORAI1 polymorphism rs12313273 in modulating the serum calcium concentration.The calcium-dependent pathway is involved in multiple physiological and cellular functions such as modulation of immune responses, activation of inflammation, and enzyme metabolism [33, 34]. Inflammation is an important mediator of CKD progression and is a contributing factor in malnutrition and increased risk of cardiovascular morbidity [35]. A vast body of evidence supports the important role of calcium in kidney disease. Mutations in transient receptor potential canonical 6 (TRPC6) channels and polycystin-2, a prototypical member of a subfamily of the TRPC channel superfamily, have been reported to cause familial focal segmental glomerulosclerosis and autosomal dominant polycystic kidney disease, respectively [36–41].Recently, Lu et al. demonstrated a significant correlation betweenTPRC1,ORAI1,STIM1, and parathyroid cells [42]. PTH plays a key role in serum calcium regulation. PTH itself is also regulated by extracellular calcium through stimulating the calcium-sensing receptor (CaSR) expressed on the surface of parathyroid cells [43]. CaSR, a G-protein PLC-linked receptor, has been shown to be involved in the TRPC1-mediated transient calcium oscillation in human embryonic kidney cells [44]. Our results suggest that the genetic polymorphisms ofORAI1 may alterORAI1 gene expression in store-operated calcium channels, which in turn may affect PTH secretion and thereby serum calcium levels.This study has several limitations. First, we did not consider several factors that are known to influence calcium levels, such as concomitant drug usage and underlying disease. Second, the underlying comorbidities were not identified in this study, and a possible relationship between the different comorbidities and the tSNPs ofORAI1 cannot be ruled out. Our results showed that the genotype ofORAI1 was not associated with CKD susceptibility. However, owing to the moderate size of our cohort, our analyses may not have sufficient power for detecting minor genetic effects. Therefore, we cannot exclude rare causal genetic polymorphisms inORAI1. DirectORAI1 sequencing using larger samples may be useful for identifying new SNPs in theORAI1 gene and for clarifying the association ofORAI1 polymorphisms with CKD susceptibility. Further investigation on other variants of the genes of the SOC pathway and of the genes involved in calcium homeostasis are needed to fully understand CKD susceptibility and progression.In conclusion, our results showed that theORAI1 polymorphism rs12313273 is associated with higher serum calcium levels in Taiwanese CKD patients. To have a better management of serum calcium,ORAI1 polymorphism might be used as a potential biomarker for initiating non-calcium-based phosphate binder in CKD patients in the future.



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*Source: 290863-2014-03-17.xml*