Transforming growth factor-β1 gene polymorphism rs1800471 and end-stage renal disease.

Abstract

The mortality rate in the ultimate form of chronic kidney disease (CKD), that is, end-stage renal disease (ESRD), is high, the main cause being cardiovascular disease (CVD). While typical risk factors such as hypertension, diabetes mellitus, dyslipidemia, age, and smoking are prevalent in ESRD patients on dialysis, the high prevalence of CVD in these patients can only be partly explained. In the development of CKD and its complications, as in other multifactorial disorders, genetic factors interact with environmental factors [1]. Several studies have linked chronic inflammation and morbidity and mortality in maintenance-haemodialysis patients with ESRD. Proinflammatory cytokines play an important role, providing a link between accelerated atherogenesis, and excessive morbidity and mortality in ESRD [2]. Specific single-nucleotide polymorphisms (SNPs) in genes may directly or indirectly lead to variations in their activity and have significant impacts in different diseases [3–8]. There are several SNPs in the transforming growth factor-β1 (TGF-β1) gene located at 19q13, some of which affect TGF-β1 protein levels. At position 74 of the TGF-β1 signal chain, the G/C transition causes the amino acid sequence to shift in codon 25 from arginine (CCG) to proline (CCG). TGF-β1 overexpression reduces the accumulation of macrophages and T cells and decreases the release of inflammatory mediators in renal disease. A typical pathological phenomenon characteristic of ESRD is progressive fibrosis of kidney tissue and subsequent sclerosis. TGF-β1 exerts its profibrotic activity by inducing fibroblast proliferation, extracellular matrix synthesis and epithelial-tomesenchymal transformation. Although studies have investigated the effects of the TGF-β1 SNPs in the pathogenesis of different diseases few have investigated the influence of TGF-β1 SNPs on ESRD, and the results are contradictory. This study tested the hypothesis of a link between TGF-β1 and its product with SNPs in rs1800471 with ESRD. The hypothesis was tested in 150 patients with ESRD (>1 year dialysis) and 150 healthy controls free of any renal disease. Common co-morbidities in the patients were high blood pressure, loss of appetite, and fatigue. Written informed consent was obtained from all participants. The study was approved by the high graduate committees of Hawler Medical University, Erbil, Kurdistan Region-Iraq. Seven millilitres of blood samples were taken from all participants and put in two different tubes, some to obtain serum for the determination of serum TGF-β1 by ELISA (R&D Systems, Minneapolis, USA) and for routine renal indices and some (into EDTA) for DNA analysis by amplification refractory mutation system PCR. The genomic DNA was isolated and extracted from the venous blood of the studied samples according to standard salting out procedures. Primer sequences were a TGF-β1 generic primer, 5 ́-GG CGAGCCGCAGCTTGGACA-3 ́, TGF-β1 (G) allele primer 5 ́-TGGTGCTGACGCCTGGCCG-3 ́ and TGF-β1 (C) allele primer 5 ́-TGGTGCTGACGCCTGGCCC-3 ́. The PCR reaction was carried out in the thermal cycler (PX2) with the following program. The samples were placed in a 20 μL reaction volume containing 40 ng genomic DNA, 1.5 mM dNTPs, 25 mM MgCl2, 1 μL of 10 pmol of each primer and 0.4 units of Taq polymerase (Fermentas, Maryland, USA) in 1X reaction buffer. Cycling conditions were a primary 4-min denaturation at 95 °C, followed by 35 30-s cycles at 95°C, 60°C and 74°C. The final extension step was at 74°C for 6 min. The amplified products resulted 125 bp were analysed on a 2% agarose gel. To evaluate the biological function of rs1800471 polymorphism, in silico analysis was also conducted. Different bioinformatics tools were used to assess the effects of this polymorphism on serum TGF-β1. All statistical analyses were done using both SNPAlyze software (ver.8.1, Dynacom, Japan) and SPSS (ver.22). Allele and genotype frequencies among control and case groups were compared and checked using Pearson χ statistic. Analyses were also performed assuming recessive, codominant and dominant models of inheritance and crude odds ratio (OR)