Comprehensive characterization of DNA methylation changes in Fuchs endothelial corneal dystrophy.
Emily KhucRussell BainerMarie WolfSelene M ClayDaniel J WeisenbergerJacquelyn D KemmerValerie M WeaverDavid G HwangMatilda F ChanPublished in: PloS one (2017)
Transparency of the human cornea is necessary for vision. Fuchs Endothelial Corneal Dystrophy (FECD) is a bilateral, heritable degeneration of the corneal endothelium, and a leading indication for corneal transplantation in developed countries. While the early onset, and rarer, form of FECD has been linked to COL8A2 mutations, the more common, late onset form of FECD has genetic mutations linked to only a minority of cases. Epigenetic modifications that occur in FECD are unknown. Here, we report on and compare the DNA methylation landscape of normal human corneal endothelial (CE) tissue and CE from FECD patients using the Illumina Infinium HumanMethylation450 (HM450) DNA methylation array. We show that DNA methylation profiles are distinct between control and FECD samples. Differentially methylated probes (10,961) were identified in the FECD samples compared with the control samples, with the majority of probes being hypermethylated in the FECD samples. Genes containing differentially methylated sites were disproportionately annotated to ontological categories involving cytoskeletal organization, ion transport, hematopoetic cell differentiation, and cellular metabolism. Our results suggest that altered DNA methylation patterns may contribute to loss of corneal transparency in FECD through a global accumulation of sporadic DNA methylation changes in genes critical to basic CE biological processes.
Keyphrases
- dna methylation
- early onset
- genome wide
- late onset
- endothelial cells
- gene expression
- optical coherence tomography
- wound healing
- cataract surgery
- copy number
- small molecule
- newly diagnosed
- fluorescence imaging
- nitric oxide
- single molecule
- pluripotent stem cells
- ejection fraction
- transcription factor
- photodynamic therapy
- stem cells
- bioinformatics analysis
- living cells
- high resolution