Association of Genetic Variants of RANK, RANKL, and OPG with Ankylosing Spondylitis Clinical Features in Taiwanese.
Chin-Man WangShu-Chun TsaiJing-Chi LinYeong-Jian Jan WuJianming WuJi-Yih ChenPublished in: Mediators of inflammation (2019)
Ankylosing spondylitis (AS) is a chronic inflammatory disease that leads to spinal ankylosis. The receptor activator of the nuclear factor-kappa (RANK), RANK ligand, and osteoprotegerin (OPG) (RANK/RANKL/OPG) pathway plays critical roles in bone metabolism and the immune system. The current study was aimed at investigating whether six single-nucleotide polymorphisms (SNPs) within the RANK, RANKL, and OPG genes essential for bone homeostasis are associated with AS. Genotype distributions, allele and haplotype frequencies, were compared between 1120 AS patients and 1435 healthy controls and among AS patients with stratification by syndesmophyte formation, onset age, and HLA-B27 positivity. We found that RANKL SNPs were associated with AS syndesmophyte formation. Notably, the RANKL SNP haplotype rs7984870C/rs9533155G/rs9525641C was negatively associated with AS susceptibility and appeared to protect against syndesmophyte formation in AS. Functionally, RANKL promoter SNPs (rs9525641 C/T and rs9533155 G/C) affected DNA-protein complex formation and promoter activity in promoter reporter analyses. The OPG SNP haplotype rs2073618G/rs3102735T was significantly associated with HLA-B27 negativity in AS patients. Furthermore, AS patients with syndesmophyte formation had significantly lower levels of soluble RANKL levels than those without syndesmophyte formation. Our data suggested a role for RANKL in AS susceptibility and severity.
Keyphrases
- nuclear factor
- toll like receptor
- ankylosing spondylitis
- genome wide
- bone loss
- end stage renal disease
- dna methylation
- ejection fraction
- newly diagnosed
- gene expression
- chronic kidney disease
- rheumatoid arthritis
- transcription factor
- oxidative stress
- prognostic factors
- immune response
- machine learning
- inflammatory response
- disease activity
- small molecule
- spinal cord
- artificial intelligence
- systemic lupus erythematosus
- spinal cord injury
- binding protein
- big data
- deep learning
- soft tissue
- nucleic acid