Osteoarthritis (OA), a degenerative joint disorder characterized by articular cartilage damage and alterations to the structure of subchondral bone, is the most common joint disease worldwide1. Numerous genetic loci2 and environmental factors such as biomechanical stress3?8 have been associated with joint health and may modulate the regulation of gene expression on the road to mediate disease. However, how these factors interact to initiate OA pathogenesis is still unclear. To evaluate the effects of gene-by-environment interactions on gene regulation in the context of human OA development, the work proposed here will study inter-individual variation in gene expression responses to biomechanical stress in chondrocytes, the primary cells of cartilage. Specifically, in Aim 1, I will characterize gene expression in stressed and control chondrocytes using an iPSC-derived biomechanical strain model of OA. I have optimized a cyclic tensile strain treatment regimen model of OA9?12 for use on iPSC-derived chondrocytes13. I have further applied these methods to three individuals from a panel of 58 Yoruba (abbreviation: YRI) human iPSC lines14. Using bulk and single-cell RNA-seq data from this experiment, I have identified patterns of differential gene expression between biomechanical strain conditions. In the continuation of this work, I will differentiate all 58 YRI iPSC lines into chondrocytes and characterize bulk and single-cell transcription in strained and control chondrocytes.
In Aim 2, I will identify biomechanical strain dynamic expression quantitative trait loci (eQTLs) in differentiated chondrocytes. I have used data from a small-scale pilot study to establish the viability of this strain model of OA for mapping eQTLs. I will use RNA- seq data collected in Aim 1 to identify dynamic eQTLs that vary in effect between treatment conditions while assessing and accounting for disparities in differentiation efficiency and heterogeneity in the response to cyclic tensile strain.
In Aim 3, I will integrate mapped dynamic eQTLs with genome-wide association study (GWAS) and epigenetic data to better understand the functional consequences of variation at genetic loci associated with OA. I will test for enrichment and colocalization of my dynamic eQTLs among published significant OA GWAS loci15 to determine if OA genetic associations could influence OA risk through context- specific gene regulation. I will also evaluate the tissue-specificity of my dynamic eQTLs using data from the Genotype-Tissue Expression Project16. Finally, I will collect DNA methylation and chromatin accessibility data from my in vitro system to assess how these molecular phenotypes change in response to biomechanical stress and potentially mediate transcriptional changes. Overall, my work will elucidate the genetic basis of how biomechanical stress impacts human joint health. More broadly, my project will deepen our understanding of how genetic associations with complex diseases may be mediated through gene-by-environment interactions. At the same time, this work will provide me abundant opportunities to grow my wet lab and computational research skills while benefitting from the support of a wide group of collaborators and mentors.
Osteoarthritis (OA) is the most common joint disorder in the United States, but the mechanisms leading to OA are still unclear. To better understand the influence of genetic and environmental factors on OA pathogenesis, this research will use a large panel of differentiated human chondrocytes to identify inter-individual variation in gene expression and epigenetic responses to biomechanical strain treatments that induce OA phenotypes. This work will yield insight into the genetic basis of how biomechanical stress contributes to human OA and may inform efforts to screen for and treat this disorder.
