Genetically encoded sensors for metabolites
Article first published online: 1 FEB 2005
Copyright © 2005 Wiley-Liss, Inc.
Cytometry Part A
Volume 64A, Issue 1, pages 3–9, March 2005
How to Cite
Deuschle, K., Fehr, M., Hilpert, M., Lager, I., Lalonde, S., Looger, L. L., Okumoto, S., Persson, J., Schmidt, A. and Frommer, W. B. (2005), Genetically encoded sensors for metabolites. Cytometry, 64A: 3–9. doi: 10.1002/cyto.a.20119
- Issue published online: 17 FEB 2005
- Article first published online: 1 FEB 2005
- Manuscript Accepted: 6 DEC 2004
- Manuscript Revised: 23 NOV 2004
- Manuscript Received: 21 MAY 2004
- Carnegie Institution of Washington
- National Institutes of Health (NIH)
- Körber Foundation, Hamburg
- fluorescence resonance energy transfer;
- periplasmic binding protein
Metabolomics, i.e., the multiparallel analysis of metabolite changes occurring in a cell or an organism, has become feasible with the development of highly efficient mass spectroscopic technologies. Functional genomics as a standard tool helped to identify the function of many of the genes that encode important transporters and metabolic enzymes over the past few years. Advanced expression systems and analysis technologies made it possible to study the biochemical properties of the corresponding proteins in great detail. We begin to understand the biological functions of the gene products by systematic analysis of mutants using systematic PTGS/RNAi, knockout and TILLING approaches. However, one crucial set of data especially relevant in the case of multicellular organisms is lacking: the knowledge of the spatial and temporal profiles of metabolite levels at cellular and subcellular levels.
We therefore developed genetically encoded nanosensors for several metabolites to provide a basic set of tools for the determination of cytosolic and subcellular metabolite levels in real time by using fluorescence microscopy.
Prototypes of these sensors were successfully used in vitro and also in vivo, i.e., to measure sugar levels in fungal and animal cells.
One of the future goals will be to expand the set of sensors to a wider spectrum of substrates by using the natural spectrum of periplasmic binding proteins from bacteria and by computational design of proteins with altered binding pockets in conjunction with mutagenesis. This toolbox can then be applied for four-dimensional imaging of cells and tissues to elucidate the spatial and temporal distribution of metabolites as a discovery tool in functional genomics, as a tool for high-throughput, high-content screening for drugs, to test metabolic models, and to analyze the interplay of cells in a tissue or organ. © 2005 Wiley-Liss, Inc.