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Engineering Genetically Encoded Nanosensors for Real-Time In Vivo Measurements of Citrate Concentrations

Overview of attention for article published in PLOS ONE, December 2011
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Title
Engineering Genetically Encoded Nanosensors for Real-Time In Vivo Measurements of Citrate Concentrations
Published in
PLOS ONE, December 2011
DOI 10.1371/journal.pone.0028245
Pubmed ID
Authors

Jennifer C. Ewald, Sabrina Reich, Stephan Baumann, Wolf B. Frommer, Nicola Zamboni

Abstract

Citrate is an intermediate in catabolic as well as biosynthetic pathways and is an important regulatory molecule in the control of glycolysis and lipid metabolism. Mass spectrometric and NMR based metabolomics allow measuring citrate concentrations, but only with limited spatial and temporal resolution. Methods are so far lacking to monitor citrate levels in real-time in-vivo. Here, we present a series of genetically encoded citrate sensors based on Förster resonance energy transfer (FRET). We screened databases for citrate-binding proteins and tested three candidates in vitro. The citrate binding domain of the Klebsiella pneumoniae histidine sensor kinase CitA, inserted between the FRET pair Venus/CFP, yielded a sensor highly specific for citrate. We optimized the peptide linkers to achieve maximal FRET change upon citrate binding. By modifying residues in the citrate binding pocket, we were able to construct seven sensors with different affinities spanning a concentration range of three orders of magnitude without losing specificity. In a first in vivo application we show that E. coli maintains the capacity to take up glucose or acetate within seconds even after long-term starvation.

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Mendeley readers

The data shown below were compiled from readership statistics for 126 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 2%
Switzerland 1 <1%
France 1 <1%
Brazil 1 <1%
Sweden 1 <1%
India 1 <1%
United Kingdom 1 <1%
Belgium 1 <1%
Denmark 1 <1%
Other 1 <1%
Unknown 115 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 31 25%
Researcher 26 21%
Student > Master 9 7%
Student > Doctoral Student 9 7%
Student > Bachelor 9 7%
Other 18 14%
Unknown 24 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 48 38%
Biochemistry, Genetics and Molecular Biology 25 20%
Chemistry 8 6%
Engineering 4 3%
Business, Management and Accounting 2 2%
Other 8 6%
Unknown 31 25%