Hyperuricemia (HUA) is an important risk factor for renal diseases and contributes to gout. Arhalofenate (Arha) has been proved to have uricosuric activity as an inhibitor of URAT1, organic anion transporter 4 (OAT4) and OAT10. However, the effects of Arha on HUA remain unknown. The objective of this study was to investigate whether Arha could alleviate HUA and uncovered the underlying mechanism in vitro. HK‐2 cells were exposed to uric acid (UA) to simulate HUA in vitro. Then cells were treated with Arha, caspase‐1 inhibitor Belnacasan (Beln), caspase‐11 inhibitor Wedelolactone (Wede) and PPARγ inhibitor Mifobate, respectively. The alteration of cell proliferation, inflammation, pyroptosis and expression of related proteins were detected. Results showed that UA exposure inhibited cell viability and increased IL‐1β and IL‐18 generation in a concentration dependent manner. Meanwhile, UA activated the cleavage of gasdermin D (GSDMD), enhanced the protein expression of URAT1, OAT4, TLR4, caspase‐1, and caspase‐11 and reduced PPARγ expression. While the presence of Arha or Beln enhanced cell viability and inhibited cleavage of GSDMD. Wede slightly increased cell viability but failed to prevent GSDMD cleavage. The expression of related proteins except caspase‐11was also recovered by Arha. Beln and Wede partially rescued related proteins level except PPARγ compared with model group. Besides, the co‐treatment of Mifobate blunted the effects of Arha on cell viability and expression of GSDMD, TLR4, and caspase‐1. In conclusion, Arha inhibited UA transport as well as preventing inflammation and pyroptosis via activating PPARγ thereby blocking caspase‐1 activation of HUA in vitro.
This study aims to explore the impact of interleukin (IL)-10 single nucleotide polymorphisms (SNPs) and its interaction with environment on the risk of systemic lupus erythematosus (SLE). Chi-square testing method was used to investigate whether the distributions for genotype of four SNPs were differed from Hardy-Weinberg equilibrium (HWE). Logistic regression was used to test the association between IL-10 SNPs and SLE risk. The best interaction combinations between IL-10 SNPs and environmental factors were assessed by generalized multifactor dimensionality reduction (GMDR). Both rs1800896-G and rs1800871-T alleles were associated with increased risk of SLE, the odds ratios (ORs) (95% confidence interval (CI)) for the two SNPs were 1.68 (1.25–2.09) and 1.47 (1.12–1.94), respectively. Then, we used the GMDR method to analyze the high-order interactions of four SNPs within IL-10 gene and environmental factors on SLE risk. We found a significant interaction combination (two-locus model with P = 0.001) between rs1800896 and smoking, after adjusting for gender, age, body mass index (BMI), and alcohol drinking. We also used two-variable stratified analysis by logistic regression to analyze the synergistic effect between two variables (rs1800896 and smoking), which had significant significance in GMDR model. We found that current smokers with rs1800896-AG or GG genotype have the highest SLE risk, compared with never smokers with the rs1800896-AA genotype, OR (95% CI) = 2.24 (1.52–3.58). The rs1800896-G and rs1800871-T alleles and interaction between rs1800896 and current smoking were all associated with increased risk of SLE.
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