Chromatin accessibility landscape of articular knee cartilage reveals aberrant enhancer regulation in osteoarthritis View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-10-19

AUTHORS

Ye Liu, Jen-Chien Chang, Chung-Chau Hon, Naoshi Fukui, Nobuho Tanaka, Zhenya Zhang, Ming Ta Michael Lee, Aki Minoda

ABSTRACT

Osteoarthritis (OA) is a common joint disorder with increasing impact in an aging society. While genetic and transcriptomic analyses have revealed some genes and non-coding loci associated to OA, the pathogenesis remains incompletely understood. Chromatin profiling, which provides insight into gene regulation, has not been reported in OA mainly due to technical difficulties. Here, we employed Assay for Transposase-Accessible Chromatin with high throughput sequencing (ATAC-seq) to map the accessible chromatin landscape in articular knee cartilage of OA patients. We identified 109,215 accessible chromatin regions for cartilages, of which 71% were annotated as enhancers. By overlaying them with genetic and DNA methylation data, we have determined potential OA-relevant enhancers and their putative target genes. Furthermore, through integration with RNA-seq data, we characterized genes that are altered both at epigenomic and transcriptomic levels in OA. These genes are enriched in pathways regulating ossification and mesenchymal stem cell (MSC) differentiation. Consistently, the differentially accessible regions in OA are enriched for MSC-specific enhancers and motifs of transcription factor families involved in osteoblast differentiation. In conclusion, we demonstrate how direct chromatin profiling of clinical tissues can provide comprehensive epigenetic information for a disease and suggest candidate genes and enhancers of translational potential. More... »

PAGES

15499

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41598-018-33779-z

    DOI

    http://dx.doi.org/10.1038/s41598-018-33779-z

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1107650258

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30341348


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