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A Biosensor Strategy for E. coli Based on Ligand-Dependent Stabilization

Overview of attention for article published in ACS Synthetic Biology, July 2018
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  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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2 X users
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1 patent

Readers on

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65 Mendeley
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Article details
Title
A Biosensor Strategy for E. coli Based on Ligand-Dependent Stabilization
Published in
ACS Synthetic Biology, July 2018
DOI 10.1021/acssynbio.8b00052
Pubmed ID
Authors
Abstract

The engineering of microorganisms to monitor environmental chemicals or to produce desirable bioproducts is often reliant on the availability of a suitable biosensor. However, the conversion of a ligand-binding protein into a biosensor has been difficult. Here, we report a general strategy for generating biosensors in Escherichia coli that act by ligand-dependent stabilization of a transcriptional activator and mediate ligand concentration-dependent expression of a reporter gene. We constructed such a biosensor by using the lac repressor, LacI, as the ligand-binding domain and fusing it to the Zif268 DNA-binding domain and RNA polymerase omega subunit transcription-activating domain. Using error-prone PCR mutagenesis of lacI and selection, we identified a biosensor with multiple mutations, only one of which was essential for biosensor behavior. By tuning parameters of the assay, we obtained a response dependent on the ligand isopropyl β-D-1-thiogalactopyranoside (IPTG) of up to a seven-fold increase in the growth rate of E. coli. The single destabilizing mutation combined with a lacI mutation that expands ligand specificity to D-fucose generated a biosensor with improved response both to D-fucose and to IPTG. However, a mutation equivalent to the one that destabilized LacI in either of two structurally similar periplasmic binding proteins did not confer ligand-dependent stabilization. Finally, we demonstrated the generality of this method by using mutagenesis and selection to engineer another ligand-binding domain, MphR, to function as a biosensor. This strategy may allow many natural proteins that recognize and bind to ligands to be converted into biosensors.

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The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 65 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 19 29%
Researcher 15 23%
Student > Bachelor 5 8%
Student > Master 5 8%
Other 2 3%
Other 3 5%
Unknown 16 25%
Readers by discipline
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 24 37%
Agricultural and Biological Sciences 9 14%
Chemistry 7 11%
Engineering 3 5%
Chemical Engineering 2 3%
Other 3 5%
Unknown 17 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 September 2022.
All research outputs
#10,157,651
of 31,393,817 outputs
Outputs from ACS Synthetic Biology
#1,972
of 3,308 outputs
Outputs of similar age
#132,490
of 354,854 outputs
Outputs of similar age from ACS Synthetic Biology
#47
of 77 outputs
Altmetric has tracked 31,393,817 research outputs across all sources so far. This one has received more attention than most of these and is in the 66th percentile.
So far Altmetric has tracked 3,308 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.9. This one is in the 39th percentile – i.e., 39% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 354,854 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.
We're also able to compare this research output to 77 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.