. A grand challenge of the 21st century is to gain a complete understanding of the brain's most fundamental characteristics, which include its potential for plasticity and its ability to learn from experience. Activity- dependent gene expression is central to neural plasticity, learning, and memory; however, efforts to elucidate the underlying mechanisms in the brain have remained elusive due to conceptual limitations in appreciating how our genome rapidly adapts to environmental changes and technical limitations with regard to molecular tools to interrogate this process. For example, the transcriptional programs of individual neurons are highly specific, and this is obscured by the massive diversity of cell-types in the adult brain that are also organized in a region specific manner. Therefore, a major hurdle has been the lack of tools to study dynamic gene expression programs in the brain, especially in real-time. The primary goal of this R21 proposal is to develop a novel experimental program to track nascent transcription in two cell types (neurons and astrocytes), simultaneously within a living brain. Our strong preliminary data demonstrates that we have identified orthogonal neucleoside/nucleobase-enzyme pairs that can be exploited for nascent metabolic labeling of RNA.
Our specific aims are to optimize this system within cultured neuronal cells in vitro, and to transfer our optimized protocols into a living mouse brain. Successful completion of our aims is sure to empower us with tools and preliminary data to tackle RNA transcription in a living brain during learning and memory formation.
. Our goal within this proposal is to develop novel tools to understand gene expression programs, in real time, within a living mammalian brain. Our approach is not limited by cell type; it can be applied to many different cell types inside the brain, and through the use of our general chemical and genomic strategies, can be applied to many different diseases. These goals are in line with a variety of NIH programs, including EUREKA, CEBRA and the BRAIN Initiatives, and we anticipate that many labs will be able to use these tools and analysis with the goal of understanding neuronal cell organization, its impact on neurobiology, and in designing RNA-based therapies to correct neurological diseases. Such a progressive approach will have a tremendous impact on the neuroscience community.
| Nguyen, Kim; Aggarwal, Mahima B; Feng, Chao et al. (2018) Spatially Restricting Bioorthogonal Nucleoside Biosynthesis Enables Selective Metabolic Labeling of the Mitochondrial Transcriptome. ACS Chem Biol 13:1474-1479 |
| Zajaczkowski, Esmi L; Zhao, Qiong-Yi; Zhang, Zong Hong et al. (2018) Bioorthogonal Metabolic Labeling of Nascent RNA in Neurons Improves the Sensitivity of Transcriptome-Wide Profiling. ACS Chem Neurosci 9:1858-1865 |
