Arthritis represents a family of complex joint pathologies responsible for the majority of musculoskeletal conditions. Nearly all diseases within this family, including osteoarthritis, rheumatoid arthritis, and juvenile idiopathic arthritis, are chronic conditions with few or no disease-modifying therapeutics available. Advances in genome engineering technology, most recently with CRISPR-Cas9, have revolutionized our ability to interrogate and validate genetic and epigenetic elements associated with chronic diseases such as arthritis. These technologies, together with cell reprogramming methods, including the use of induced pluripotent stem cells, provide a platform for human disease modeling. We summarize new evidence from genome-wide association studies and genomics that substantiates a genetic basis for arthritis pathogenesis. We also review the potential contributions of genome engineering in the development of new arthritis therapeutics. Arthritis represents the most prevalent cause of disability in the USA, but the genetic basis of disease etiology remains poorly understood. Recent GWAS and candidate association studies have identified several loci associated with arthritis. These studies suggest that unique loci may play distinct roles in the development of various types of arthritis. Advances in genome editing technologies enable the precise modification of candidate causal loci and functional validation in disease pathogenesis. Recently, epigenome editing has been used to uncover the function of regulatory elements near disease susceptibility loci. Concurrent advances in tissue engineering from pluripotent stem cells have facilitated arthritis disease modeling. Gene-editing tools have been used in other fields for both regenerative medicine and disease modeling. Given the wealth of data ascribing genetic variation to arthritis, at this time, there is significant potential for using gene editing in conjunction with tissue engineering, to discover mechanisms underlying genetic drivers of arthritis.
- drug screening
- genome engineering