Association of sequence polymorphism in the mitochondrial D-loop with chronic kidney disease

Abstract

Background: The mitochondrial displacement loop (D-loop) is known to accumulate mutations and single nucleotide polymorphisms (SNPs) at a higher frequency than other regions of mitochondrial DNA (mtDNA). Methods: This is a case–control study. We sequenced SNPs in the D-loop of mtDNA and investigated their association with the risk of chronic kidney disease (CKD). Results: A total of 144 SNPs referring to the positions of the Revised Cambridge Reference Sequence (rCRS) for mitochondrial genome were identified in a case–control study. The minor alleles of nucleotides 73G, 146C, 150T, 194T, 195C and 310C were associated with an increased risk for CKD patients. Conclusion: Analysis of genetic polymorphisms in the mitochondrial D-loop can help identify the people who are at a high risk of developing chronic kidney disease. These SNPs can be considered as potential predictors for CKD.

• Chronic kidney disease
• D-loop
• mtDNA
• SNP

Introduction

Chronic kidney disease (CKD) is becoming a major public health concern. The worldwide prevalence of end stage renal disease (ESRD) is expected to continue to rise at an annual rate of 7% and exceed 2 million by 2010, which will cost ∼1.1 trillion dollars in medical expenditures.1 CKD is highly prevalent in developing countries.2,3 Recently, based on a report published on the Lancet in 2012, China had a higher incidence and prevalence rate of ESRD patients, predicting the overall prevalence of CKD to be 10.8%.4 But the mechanism involved in the developing of CKD remains unclear. CKD can result from a variety of ecologically distinct causes. Presently, diabetes and hypertension are the two leading causes of CKD, although infectious glomerulonephritis, renal vascular, genetic alterations, autoimmune disease and oxidative stress are also common causes of CKD.5 Increased oxidative stress has been observed in CKD patients before the hemodialysis.6 Oxidative stress is defined as a disturbance in cellular and molecular function caused by an imbalance between production of reactive species and the natural anti-oxidant ability of cells. Reactive oxygen species (ROS) often act to create a state of oxidative stress and contribute to DNA damage. During the pathogenesis of CKD, ROS influence downstream cell signaling pathways of nuclear factor kappa B (NF-кB) and, in the kidney, promote renal cell apoptosis and senescence, decreased regenerative ability of cells and fibrosis.7 Mitochondria are major intracellular sources of ROS and highly susceptible to oxidative damage.8,9 Hence, we aimed to investigate the possible link between CKD and mitochondrial DNA (mtDNA) mutations.

The human mitochondrial genome is 16-kb circular double-stranded DNA molecule. It contains 12 coding genes for engaged in respiration and oxidative phosphorylation, 2 rRNAs, and a set of 22 tRNAs that are essential for the protein synthesis in mitochondria.10 mtDNA is believed to be more susceptible to DNA damage and acquires mutations at a higher rate than nuclear DNA, due to high levels of ROS, a lack of protective histones and a limited capacity for DNA repair in the mitochondria.11,12 In addition, mtDNA contains a non-coding region that includes a unique displacement loop (D-loop) that controls replication and transcription of mtDNA, because it contains the initial site of heavy chain replication and the promoters for heavy and light chain transcription. The D-loop is highly polymorphic, and some polymorphisms are associated with aging,13 coronary artery disease,14 and cancers.15,16 However, D-loop polymorphisms have not been systematically characterized in CKD patients from the Chinese mainland.

The D-loop contains a length of 1122 bps (nucleotide 16,024–16,569 and 1–576) refers to mitochondria database http://www.mitomap.org. Chen et al.17 found that hemodialysis-associated single nucleotide polymorphisms (SNPs) in the D-loop locus in 16,463, 16,519,185 in Taiwanese group. In this study, we targeted the Chinese mainland population to look for CKD risk-associated D-loop SNPs.

Methods

Tissue specimens and DNA extraction

Blood samples were obtained from 119 CKD patients from the inpatient of the Fourth Hospital of Hebei Medical University between 2002 and 2008. Blood samples were also collected from 159 healthy controls. The presence of CKD was defined in accordance with the 2002 National Kidney Foundation Kidney Disease Outcomes Quality Initiative (K-DOQI) classification18 on the basis of an estimated glomerular filtration rate (eGFR) of <90 mL/min/1.73 m² for at least 90 days. Laboratories in China report eGFR using the four-variable modified diet in renal disease formula.19 The etiologies of CKD in these patients included chronic glomerulonephritis, diabetic nephropathy and hypertension nephropathy. Total DNA was extracted using a Wizard Genomic DNA extraction kit (Promega, Madison, WI) and stored at −20 °C. The study was approved by the Human Tissue Research Committee of the Fourth Hospital of Hebei Medical University. All patients provided written informed consent for the collection of samples and subsequent analysis.

PCR amplification and sequence analysis

The forward primer 5′-CCCCATGCTTACAAGCAAGT-3′ (nucleotide 16,190–16,209) and reverse primer 5′-GCTTTGAGGAGGTAAGCTAC-3′ (nucleotide 602–583) were used for amplification of a 982-bp product from the mtDNA D-loop region. PCR was performed according to the protocol of PCR Master Mix Kit (Promega) and purified prior to sequencing. The PCR condition consisted of one incubation of 2 min at 95 °C, followed by 35 cycles of a 30-s denaturation at 95 °C, a 30 s an annealing at 55 °C, and a 45-s extension at 72 °C, and a final extension at 72 °C for 5 min. Cycle sequencing was performed with the Dye Terminator Cycle Sequencing Ready Reaction Kit (Applied Biosystem, Foster City, CA) and the products were then separated on the ABIPRISM Genetic Analyzer 3100 (Applied Biosystem). Polymorphisms were confirmed by repeated analyses from both strands.

Statistical analysis

The χ² test was used to analyze dichotomous values, such as the presence or absence of an individual SNP in CKD patients and healthy controls. All statistical analyses were performed using the SPSS 17.0 software (SPSS Company, Chicago, IL). For all the statistical tests, p < 0.05 was considered statistically significant.

Results

A total of 119 CKD patients, including 104 diagnosed with chronic glomerulonephritis, 8 with diabetic nephropathy and 7 with hypertension nephropathy, and 159 healthy controls were enrolled in this study. The clinical characteristics of the CKD patients and healthy controls are listed in Table 1, which shows that there was no difference in age, sex and smoking between CKD and control subjects.

Table 1. The relationships for clinical characteristic of CKD patients and controls with disease risk.

	CKD (n = 119)	Control (n = 159)	χ^2	p Value
Age (years)
≤55	56	56	2.937	0.087
>55	63	103
Sex
Male	67	96	0.466	0.495
Female	52	63
Smoking
Yes	39	61	0.924	0.336
No	80	98

Single nucleotide polymorphisms in reference to GenBank accession AC-000021 were detected at 144 sites of the 982-bp mitochondria D-loop region from the CKD patients and the healthy controls. The 20 SNPs (73, 146, 150, 194, 195, 199, 235, 310, 16,254, 16,256, 16,260, 16,261, 16,266, 16,274, 16,290, 16,298, 16,304, 16,311, 16,319, and 16,327) with a minor allele frequency >5% in the CKD patients or controls were used for disease risk analysis by χ² test (Figure 1). The distribution of these SNPs was all followed Hardy–Weinberg Equilibrium. When individual SNPs were analyzed between CKD patients and controls, a statistically significant increase in SNP frequency for the 73G, 146C, 150T, 194T, 195C and 310C alleles were observed in CKD patients (p < 0.05), which indicated that the patients who carry these alleles were susceptible to CKD (Table 2).

Table 2. SNP sites showing the frequency difference between CKD patients and controls.

Nucleotide	CKD (n = 119)	Control (n = 159)	χ^2	p Value
73A\G	1\118 (99.1%)	159\0 (0%)	273.941	<0.001
146T\C	99\20 (16.8%)	159\0 (0%)	28.794	<0.001
150C\T	101\18 (15.1%)	159\0 (0%)	25.715	<0.001
194C\T	112\7 (5.9%)	157\2 (1.3%)	4.646	0.031
195T\C	112\7 (5.9%)	159\0 (0%)	9.595	0.002
199T\C	115\4 (3.4%)	151\8 (5.0%)	0.460	0.498
235A\G	111\8 (6.7%)	142\17 (10.7%)	1.310	0.252
310T\C	87\32 (26.9%)	159\0 (0%)	48.318	<0.001
16254A\G	116\3 (2.5%)	154\5 (3.1%)	0.095	0.758
16256C\T	115\4 (3.4%)	154\5 (3.1%)	0.010	0.920
16260C\T	116\3 (2.5%)	153\6 (3.8%)	0.341	0.559
16261C\T	111\8 (6.7%)	146\13 (8.2%)	0.206	0.650
16266C\T	116\3 (2.5%)	152\7 (4.4%)	0.695	0.405
16274G\A	116\3 (2.5%)	155\4 (2.5%)	0.000	0.998
16290C\T	112\7 (5.9%)	141\18 (9.5%)	2.460	0.117
16298T\C	112\7 (5.9%)	141\18 (9.5%)	2.460	0.117
16304T\C	109\10 (8.4%)	136\23 (14.5%)	2.391	0.122
16311T\C	112\7 (5.9%)	144\15 (9.4%)	1.178	0.278
16319G\A	108\11 (9.2%)	134\25 (15.7%)	2.535	0.111
16327C\T	110\9 (7.6%)	148\11 (6.9%)	0.042	0.837

Discussion

Polymorphisms accumulate in the mitochondrial D-loop region when cells suffer from increased oxidative stress.20 SNPs in the D-loop become popular candidates for the cause of several diseases, including non-Hodgkin lymphoma,21 renal cell carcinoma22 and CKD.14,17,23 In the past few years, selected SNPs in the D-loop region have been examined for the ability to predict cancer risk in a variety of cancers, including non-Hodgkin lymphoma,21 hepatocellular carcinoma,24 and renal cell carcinoma.22 Our previous study suggested that SNPs in the mtDNA D-loop region may be a predictor for renal cell carcinoma risk25 and outcome.26 In the present study, six SNP sites including 73, 146, 150, 194, 195 and 310 were identified for their association with CKD risk. These SNPs may be of great potential use for future studies of their biological functions.

Figure 1. Distribution of D-loop SNPs at 20 sites (X axis) and their relative frequencies in percentage within each group (Y axis).

In the previous study, Chen et al.17 found that three SNPs (185, 16,463 and 16,519) in the mtDNA D-loop were associated with the CKD risk for Taiwanese. The difference of CKD-associated D-loop SNPs between Chinese mainland and Taiwan are as might be due to the different mitochondrial genetic background between northern and southern areas in China. It has been reported that the SNPs located in 73, 146 sites were associated with the age-related macular degeneration,27 and 150, 194, 195 related with cancers,28,29 which indicated that SNPs in the D-loop appear to exhibit different effects in different diseases.

A previous study pointed out that the mutations of mtDNA correlated positively with p53 mutations.30 Other studies also found that the mutations of the D-loop were significantly associated with betel quid chewing.31 The D-loop is the most variable region in mtDNA. There are two hypervariable (HV) regions (HV-I: 16,024–16,365, HV-II: 73–340) in the D-loop region.32 In our study, all of the CKD risk associated SNPs are located in the HV-II region of the D-loop. The functional significance of HV segments, mutational hotspots at which germline and mtDNA mutations preferentially occur,33 is not known, but our data suggest that these SNPs are potential genetic markers for this disease.

The D-loop region of mtDNA is important for the regulation of mitochondrial genome replication and expression. SNPs in the D-loop region might affect mtDNA replication and lead to electron transport chain alteration, which is responsible for the release of highly ROS and could contribute to the nuclear genome damage.34,35 The extensive oxidative damage to the pol cytidine sequences may cause slipping and/or disincorporation during replication or repair of mtDNA by mitochondrial DNA polymerase. The ROS may also promote kidney disease by activating the signal pathways of NF-кB.7 NF-кB is activated by ROS and upregulates of ROS-induced granulocyte macrophage-colony-stimulating factor (GM-CSF), which can enhance glomerular inflammation and induce mesangial proliferation.7

Single nucleotide polymorphisms in the mtDNA D-loop were found to be risk markers for CKD. The utility of mtDNA SNPs for prediction of CKD risks is a promising area for future CKD prevention. Our results link the SNPs of mtDNA D-loop and the risk of CKD. The future experiments with a large sample size are needed to explore the usage of those minor alleles and to validate the predictive values of SNPs identified in this pilot study.

Conclusion

In conclusion, analysis of genetic polymorphisms in the D-loop may help to identify people at a higher risk for developing CKD, thereby helping to refine therapeutic decisions for these people.

Declaration of interest

All of the authors disclose any financial, consulting and personal relationships with other people or organizations that could influence the author’s work. This work was supported by the project of the Hebei Natural Science Fund (H2012206157).