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Genetically encoded sensors for metabolites
Author(s) -
Deuschle Karen,
Fehr Marcus,
Hilpert Melanie,
Lager Ida,
Lalonde Sylvie,
Looger Loren L.,
Okumoto Sakiko,
Persson Jörgen,
Schmidt Anja,
Frommer Wolf B.
Publication year - 2005
Publication title -
cytometry part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.316
H-Index - 90
eISSN - 1552-4930
pISSN - 1552-4922
DOI - 10.1002/cyto.a.20119
Subject(s) - computational biology , metabolomics , biology , functional genomics , in silico , multicellular organism , mcherry , model organism , function (biology) , mutagenesis , metabolite , genomics , biochemistry , genome , microbiology and biotechnology , gene , mutant , bioinformatics , green fluorescent protein
Background 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. Methods 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. Results Prototypes of these sensors were successfully used in vitro and also in vivo, i.e., to measure sugar levels in fungal and animal cells. Conclusions 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.