Osteoarthritis (OA) is a joint disease characterized by the degradation of cartilage and underlying bone, and it is a major source of disability1 and financial burden2,3 worldwide. While progress has been made towards recognizing OA as a complex disorder4, the mechanisms that initiate and mediate the onset of OA pathogenesis are still unclear. Genetic markers5?19 and environmental influences such as biomechanical stress20?24 have been associated with joint health, and alterations in gene expression regulation25?36 may connect and reinforce these factors? involvement in disease progression. To better characterize the degree to which gene expression patterns in skeletal cells relate to underlying genotypes and are altered in response to biomechanical forces, we will use a large panel of differentiated human chondrocytes and osteoblasts to identify inter-individual variation in gene expression responses to mechanical stress treatments. Specifically, in Aim 1, I will differentiate chondrocytes and osteoblasts from 70 human induced pluripotent stem cells (iPSCs) and characterize gene expression in these cell types using bulk and single-cell data. The proposed study will include a previously characterized panel of iPSCs from 70 fully sequenced Hutterite individuals37,38 and will use established chondrocyte and osteoblast differentiation protocols39. Drop-seq single-cell RNA-seq data will be collected in addition to bulk RNA-seq data so that I can characterize inter-individual variability in gene expression and account for variation in cell composition across samples.
In Aim 2, I will treat differentiated chondrocytes and osteoblasts with biomechanical stress to identify response expression quantitative trait loci (eQTLs). To do this, I will subject terminally differentiated chondrocytes and osteoblasts to established cyclic tensile strain treatments that serve as an in vitro model of OA40?43. Response eQTLs will be determined using bulk RNA-seq data, and the degree to which biomechanical stress response is robust will be estimated using single-cell data. Finally, in Aim 3, I will integrate biomechanical stress response eQTLs with genome-wide association study (GWAS) data to identify variants associated with OA risk and the underlying molecular mechanisms. Several genetic associations with OA have been identified in individuals of primarily European decent6,8?19. Since the panel of cells in the proposed study consists of a homogeneous population that represents much of European genetic diversity44, it is an ideal comparative sample set. I will determine the disease- relevance of our response eQTLs by testing for enrichment of OA GWAS hits and evaluating colocalization. Overall, this research will identify and characterize inter-individual gene expression responses in a population- scale cell culture model of OA. Further, this work will produce the largest panel of human iPSC-derived chondrocytes and osteoblasts and is expected to yield substantial insight into the gene-by-environment interactions that contribute to disease phenotypes in the skeletal system.
The degenerative joint disease of osteoarthritis (OA) is a major source of disability worldwide, but the mechanisms that initiate and mediate OA pathogenesis are still unclear. To better characterize the degree to which gene expression patterns in skeletal cells relate to underlying genotypes and are altered in response to biomechanical forces, this project will use a large panel of differentiated human chondrocytes and osteoblasts to identify inter-individual variation in gene expression responses to mechanical stress treatments that induce OA phenotypes. This research will yield insight into the gene-by-environment interactions that contribute to disease phenotypes in the skeletal system.
