The goal of this project is develop a system for real-time in vivo spatiotemporal quantification of metabolic fluctuations at the cellular level. To achieve this, we combines four key technologies: i) genetically encoded fluorescent nanosensor proteins (GENAs) that report on specific metabolites; ii) protein engineering techniques that permit the systematic construction of nanosensors for a large number of metabolites of interest; iii) quantitative real-time fluorescent microscopy visualization techniques that permit these nanosensors to be used in living cells; iv) genetically engineered cell lines that perturb signal transduction pathways by gain-of-function (protein over-expression) and loss-of-function (deletion libraries, RNAi). Our approach is generalizable, scalable, sensitive and dynamic enabling time-resolved measurements in the millisecond range. Using these reagents, it is anticipated that over 10,000 signaling state perturbations will be analyzed for metabolic changes. It is anticipated that many of the signal pathway perturbations and alterations in metabolic profiles will provide a large database of correlative changes, recapitulation and diagnostic potential of disease states and novel biological discovery of signaling networks. We have assembled a research team consisting of four groups that each bring part of the required skills to the project. The Meyer group (Stanford University) has a long-standing involvement in the development of GENAs, and has also been heavily involved in the development of the fluorescence microscopy instrumentation for metabolic visualization. Furthermore, the Meyer group is also involved in development of reagent sets for metabolic perturbations. The Frommer group (Carnegie Institute of Washington) has developed GENAs for sensing of sugars, and has demonstrated that these can be used for in vivo metabolic imaging. The York group (Duke University) has been deeply involved in the genetic manipulation, metabolism and discovery of intracellular signaling pathways. The Hellinga group (Duke University) has been instrumental in the development and experimental validation of computational protein design tools for the radical manipulation of ligand-binding specificities. At the end of the project period, we aim to deliver i) nanosensor toolkits; ii) engineered cell line toolkits; iii) spatiotemporal imaging datasets of metabolic fluctuations as a function of genetic perturbations and exogenous changes in cellular environment.
| Otto, James C; York, John D (2010) Molecular manipulation and analysis of inositol phosphate and pyrophosphate levels in Mammalian cells. Methods Mol Biol 645:47-60 |
| Dulla, Chris; Tani, Hiroaki; Okumoto, Sakiko et al. (2008) Imaging of glutamate in brain slices using FRET sensors. J Neurosci Methods 168:306-19 |
| Lee, Young-Sam; Mulugu, Sashidhar; York, John D et al. (2007) Regulation of a cyclin-CDK-CDK inhibitor complex by inositol pyrophosphates. Science 316:109-12 |
| Mulugu, Sashidhar; Bai, Wenli; Fridy, Peter C et al. (2007) A conserved family of enzymes that phosphorylate inositol hexakisphosphate. Science 316:106-9 |
| Fridy, Peter C; Otto, James C; Dollins, D Eric et al. (2007) Cloning and characterization of two human VIP1-like inositol hexakisphosphate and diphosphoinositol pentakisphosphate kinases. J Biol Chem 282:30754-62 |
| Otto, James C; Mulugu, Sashidhar; Fridy, Peter C et al. (2007) Biochemical analysis of inositol phosphate kinases. Methods Enzymol 434:171-85 |
| Seeds, Andrew M; Frederick, Joshua P; Tsui, Marco M K et al. (2007) Roles for inositol polyphosphate kinases in the regulation of nuclear processes and developmental biology. Adv Enzyme Regul 47:10-25 |
| Kaper, Thijs; Looger, Loren L; Takanaga, Hitomi et al. (2007) Nanosensor detection of an immunoregulatory tryptophan influx/kynurenine efflux cycle. PLoS Biol 5:e257 |
| Seeds, Andrew M; York, John D (2007) Inositol polyphosphate kinases: regulators of nuclear function. Biochem Soc Symp :183-97 |
| Wiechert, Wolfgang; Schweissgut, Oliver; Takanaga, Hitomi et al. (2007) Fluxomics: mass spectrometry versus quantitative imaging. Curr Opin Plant Biol 10:323-30 |
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