The role of DNA methylation for disease severity in patients with heterozygous mutations in the Mediterranean Fever Gene MEFV

Familial Mediterranean Fever (FMF) is an autosomal dominant disease with mutations in the MEFV gene that encodes for Pyrin, an important innate immunity regulator. However, some heterozygous individuals also show an FMF phenotype, which leads to the assumption that other modifying factors lead to a manifestation of the phenotype1. In recent years, DNA methylation has demonstrated its potential for disease diagnosis by several studies.
The main goal of this study was to evaluate DNA methylation in patients carrying heterozygous mutations in the MEFV gene but show different phenotypes. We hypothesis that alterations in DNA methylation can add important and valuable information about the disease etiopathogenesis.
The study included a total of 55 patients: 23 homozygous FMF patients, 12 heterozygous mutation carriers presenting with an FMF phenotype, 9 heterozygous without FMF phenotype and 12 healthy controls without any mutation in the MEFV gene. We performed a genome wide DNA methylation analysis using Illumina’s EPIC BeadArray
We revealed over 30000 significant cpgs (p<0.05) where between 28 and 75 CpG sites showed at least 15% differences in mean methylation between the groups. Using these features, the patient groups separated well in hierarchical clustering. More specifically, group comparisons between heterozygous disease vs heterozygous healthy revealed 71 differentially methylated sites (p<0.05, difference ≥ 15%), indicating that these two groups can be separated well using DNA methylation data. We show that the identified CpG sites can help diagnose patients and provide valuable information to the etiopathogenesis of FMF.
References:
[1] Sönmez et al.. Familial Mediterranean fever. Current perspectives. In: Journal of Inflammation Research 9; 2016. S. 13–20. DOI: 10.2147/JIR.S91352.
[2] Pidsley et al. “Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling.” Genome Biology 17 (1), S. 208; 2016. DOI: 10.1186/s13059-016-1066-1.
[3] Morris et al. “Champ: 450k chip analysis methylation pipeline.” Bioinformatics, 30(3), 428-30; 2014. DOI: 10.1093/bioinformatics/btt684.
[4] Ritchie ME et al. “limma powers differential expression analyses for RNA-sequencing and microarray studies.” Nucleic Acids Research, 43(7), e47; 2015. DOI: 10.1093/nar/gkv007