Applying RNA Logic in Space and Time to Neurologic Disease - 2024 R35 Project Summary This proposal presents a series of interrelated new techniques and concepts aimed at discovering otherwise hidden therapeutic targets for neurologic diseases. This treatment-oriented approach combines the urgency of a practicing neurologist with the knowledge and technology that modern molecular biology brings to neuroscience. From the basic science perspective, understanding the fundamental root mechanisms of disease is an uncompromising goal. From the neurologist’s perspective, the perfect should not be the enemy of the good. This leads to several fundamental points: • Genetic (DNA) etiologies of brain disease set the stage for our focus—the downstream manifestations of such defects. • Different cell types contribute to different brain disorders, but the difference between individual cells is unknown. These differences are manifest at the level of RNA, mediated by the stoichiometry, variation and localization of cell-specific regulatory systems, and their consequent effects on proteins within affected cells. • Neurons are spatially complex and temporally dynamic, requiring commensurate focus and tool development that address these aspects of RNA regulation. • Developing and validating new model systems will lead to unexpected discoveries. • Understanding human neurologic disease is complicated; thus, the best model system for understanding neurologic disorders is the human. Therefore model systems must be complimented and validated by studying human neurons. • Building the bridge between model systems and human neurobiology alongside computational biologic analysis of large datasets is necessary to develop new insights and predictions fully. • Providing new strategies for fundamental discovery and focusing on actionable subsets for neurologic disease is our primary strategy. A significant innovation our lab has developed supplements the standard analysis of RNA quantity (RNAseq) to understand the regulation of RNA quality through the development of CLIP, a rapidly expanding technology. Recent work has established the potential of this technology, and we propose to further map the ability to study RNA regulation in single neuronal cell types in the brain, subcellular domains of neurons, and during the rapid changes that accompany neuronal depolarization. Importantly, we propose to integrate data from model systems with a novel concept to study molecular variation in neurons obtained from human autopsy. We will analyze complex molecular datasets with computational biology. Success in the treatment of brain diseases to date emphasizes the importance of focusing on accessible molecules which will guide our approach to big data analysis.
