Imaging Spatiotemporal Regulation of Acetyl-CoA

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PROJECT SUMMARY Cellular metabolism is highly compartmentalized, and subcellular localization of metabolites determines use. A central metabolite, acetyl-CoA, is involved in many biochemical reactions, including the production of fatty acids, steroids, ketone bodies, and acetylation reactions, dependent on cellular metabolic state and subcellular localization. Accordingly, the production and use of acetyl-CoA is tightly regulated by nutrient availability and signaling networks regulating metabolism. Therefore, acetyl-CoA represents a nexus for cellular metabolism and signal transduction. While bulk measurements have shown acetyl-CoA to be highly compartmentalized, the dy- namic regulation of acetyl-CoA across subcellular compartments at the single cell level remains elusive. An approach for overcoming these limitations and illuminating subcellular dynamics of acetyl-CoA is live cell imaging using genetically encoded fluorescent protein-based biosensors. These tools provide an opportunity to investi- gate cellular dynamics in real-time with high spatial and temporal resolution while maintaining the cellular envi- ronment. Biosensors have traditionally been used to study kinases or neurotransmitters and have only just begun to be used to study metabolism. As biosensors enable dynamic visualization of cellular processes in real-time in single cells, a biosensor for acetyl-CoA would illuminate acetyl-CoA dynamics and support an enhanced under- standing of this key metabolite. In this proposal we will develop a biosensor for acetyl-CoA using protein engi- neering and directed evolution. Then, the acetyl-CoA biosensor will be used to study compartmentalized acetyl- CoA dynamics and interrogate mechanisms for the spatiotemporal regulation of acetyl-CoA by cellular signaling networks. The success of this proposal will result in an enhanced understanding of the central metabolite acetyl- CoA and introduce a new tool into the field, further establishing a new paradigm for studying metabolism in real- time in single cells using biosensors.