Summary

By leveraging access to a large, existing sample of fresh eye tissues, we examine genes expressed, their sequences, and the metabolites that are present in individuals with and without naturally-occurring glaucoma in a closely related animal model. These data are essentially impossible to obtain for humans and provide a direct way to probe the biological functions that are impacted by glaucoma, along with genetic risk factors. These data have large promise to guide genetic screening panels used in diagnosis and prognosis of glaucoma in humans, and to identify molecules in our blood that can be used for early detection and treatment.

Project Details

The goal of our project is to understand genetic and metabolic differences in ocular tissues of individuals with and without naturally occurring open-angle glaucoma (OAG) and ocular hypertension (OHT) to improve our understanding of the causes and consequences of these diseases. Our first specific aim is diagnose OAG and OHT in an animal model. We are using a comprehensive, multiplatform approach that combines the latest imaging technologies with traditional ocular exams. Our second aim is to identify gene expression and metabolite profiles associated with OAG and OHT. We are conducting a transcriptomic (RNA-sequencing) and metabolomic study of glaucoma-relevant ocular tissues from a subset of individuals with OAG/OHT and from closely related individuals with health visual systems. For genes and metabolites associated with disease, we will examine the linked biological pathways relevant for cell death and inflammation; pathways implicated in human glaucoma, a leading cause of blindness globally. To our knowledge, we are the first to unite OAG status with the transcriptomics and metabolomics of affected ocular tissues originating from the same samples. Furthermore, we accomplish this in an exceptional animal model with naturally occurring disease. Our research will provide new foundational knowledge concerning the biology and pathophysiology of glaucoma, essential data for understanding this diverse and multifaceted disease. With this information, we hope to improve early diagnostic tools, and identify druggable targets for disease treatments.