Changes in DNA methylation accompany changes in gene expression during chondrocyte hypertrophic differentiation in vitro.

BACKGROUND: During osteoarthritis (OA), articular chondrocytes undergo phenotypic changes that resemble developmental patterns characteristic of growth plate chondrocytes. These phenotypic alterations lead to a hypertrophy-like phenotype characterized by altered production of extracellular matrix constituents and increased collagenase activity, which in turn results in cartilage destruction in OA disease. PURPOSE: To identify transcriptomic and epigenomic changes in murine primary articular chondrocytes undergoing hypertrophy-like differentiation in vitro. METHODS: We paired an in vitro 3D/pellet culture system that mimics chondrocyte hypertrophy with RNA sequencing (RNA-Seq) and enhanced reduced representation of bisulfite sequencing (ERRBS). RESULTS: We identified hypertrophy-associated changes in DNA methylation patterns in vitro. Integration of RNA-Seq and ERRBS datasets identified associations between changes in methylation and gene expression. CONCLUSION: Our integrative analyses showed that hypertrophic differentiation of articular chondrocytes is accompanied by transcriptomic and epigenomic changes in vitro. Chondrocytes were isolated from articular cartilage obtained from 5-to-6 days old C57BL/6J mice. 3D/pellet culture systems were used to drive chondrocyte hypertrophy in vitro. Pellets were harvested at 7 and 21 days for RNA (n=3 per time-point) or DNA isolation (n=3 per time-point). Total RNA was used for RNAseq, and DNA was used for Enhanced Reduced Representation Bisulfite Sequencing (ERRBS) at the Epigenomics Core at Weill Cornell Medical College. CpG sites in the resulting ERRBS data were interrogated for methylation patterns and differential methylation using methylKit. The differential methylation data was queried for differentially methylated regions using eDMR. RNA-Seq reads passing Illumina’s purity filter were adapter trimmed using FLEXBAR barcode and adapter processing tool. The trimmed reads were aligned to the Gencode mm10/ GRC38.p3 build of the mouse genome using STAR aligner with default parameters. Read counts for RefSeq (NCBI) transcripts were then quantified from the alignments using the feature Counts software package. Significant differential expression (adjusted p-value < 0.05) was assessed between the read counts for the control and DMM sample groups using the DESeq2 R Bioconductor package. Gene Ontology (GO) enrichment analysis for Biological Processes and Molecular Functions was performed for differentially expressed genes and genes associated with differentially methylated regions using the clusterProfiler Biocondutor R package.