Genome-wide DNA methylation analysis of articular chondrocytes reveals a cluster of osteoarthritic patients
- Juan Fernández-Tajes1,
- Angel Soto-Hermida1,
- Maria E Vázquez-Mosquera1,
- Estefania Cortés-Pereira1,
- Alejandro Mosquera2,
- Mercedes Fernández-Moreno1,
- Natividad Oreiro1,
- Carlos Fernández-López1,
- Jose Luis Fernández2,
- Ignacio Rego-Pérez1,
- Francisco J Blanco1,3,4
- 1Rheumatology Division, INIBIC-Complejo Hospitalario Universitario A Coruña (CHUAC), Coruña, Spain
- 2Genetics Unit, INIBIC-Complejo Hospitalario Universitario A Coruña (CHUAC), Coruña, Spain
- 3Proteo-Red/ISCIII, Madrid, Spain
- 4CIBER-BBN-ISCIII, Madrid, Spain
- Correspondence to Dr Francisco J Blanco and Dr Ignacio Rego-Pérez Rheumatology Division, Hospital Universitario A Coruña, Coruña 15006-A, Spain;
- Accepted 16 February 2013
- Published Online First 16 March 2013
Objective Alterations in DNA methylation patterns have been found to correlate with several diseases including osteoarthritis (OA). The aim of this study was to identify, for the first time, the genome-wide DNA methylation profiles of human articular chondrocytes from OA cartilage and healthy control cartilage samples.
Methods DNA methylation profiling was performed using Illumina Infinium HumanMethylation27 in 25 patients with OA and 20 healthy controls. Subsequent validation was performed by genome-wide expression analysis using the Affymetrix Human Gene 1.1 ST array in an independent cohort of 24 patients with OA. Finally, the most consistent genes in both assays were amplified by quantitative reverse transcriptase PCR in a validation cohort of 48 patients using microfluidic real-time quantitative PCR. Appropriate bioinformatics analyses were carried out using R bioconductor software packages and qBase plus software from Biogazelle.
Results We found 91 differentially methylated (DM) probes, which permitted us to separate patients with OA from healthy controls. Among the patients with OA, we detected 1357 DM probes that identified a tight cluster of seven patients who were different from the rest. This cluster was also identified by genome-wide expression in which 450 genes were differentially expressed. Further validation of the most consistent genes in an independent cohort of patients with OA permitted us to identify this cluster, which was characterised by increased inflammatory processes.
Conclusions We were able to identify a tight subgroup of patients with OA, characterised by an increased inflammatory response that could be regulated by epigenetics. The identification and isolation of this subgroup may be critical for the development of effective treatment and disease prevention.