MicroRNA-Related Polymorphism and Their Association with Fibromyalgia.
Fabian BergDirk A MoserVerena HagenaFabian StreitBenjamin MoschRobert KumstaStephan HerpertzMartin DiersPublished in: Genes (2023)
MicroRNAs are tissue-specific expressed short RNAs that serve post-transcriptional gene regulation. A specific microRNA can bind to mRNAs of different genes and thereby suppress their protein production. In the context of the complex phenotype of fibromyalgia, we used the Axiom miRNA Target Site Genotyping Array to search genome-wide for DNA variations in microRNA genes, their regulatory regions, and in the 3'UTR of protein-coding genes. To identify disease-relevant DNA polymorphisms, a cohort of 176 female fibromyalgia patients was studied in comparison to a cohort of 162 healthy women. The association between 48,329 markers and fibromyalgia was investigated using logistic regression adjusted for population stratification. Results show that 29 markers had p -values < 1 × 10 -3 , and the strongest association was observed for rs758459 ( p -value of 0.0001), located in the Neurogenin 1 gene which is targeted by hsa-miR-130a-3p. Furthermore, variant rs2295963 is predicted to affect binding of hsa-miR-1-3p. Both microRNAs were previously reported to be differentially expressed in fibromyalgia patients. Despite its limited statistical power, this study reports two microRNA-related polymorphisms which may play a functional role in the pathogenesis of fibromyalgia. For a better understanding of the disease pattern, further functional analyses on the biological significance of microRNAs and microRNA-related polymorphisms are required.
Keyphrases
- genome wide
- end stage renal disease
- ejection fraction
- dna methylation
- chronic kidney disease
- newly diagnosed
- transcription factor
- prognostic factors
- gene expression
- high throughput
- genome wide identification
- peritoneal dialysis
- adipose tissue
- single molecule
- circulating tumor
- patient reported outcomes
- metabolic syndrome
- binding protein
- mass spectrometry
- single cell
- bioinformatics analysis
- atomic force microscopy
- high density