Expanding the Dynamic Range of a Transcription Factor-Based Biosensor in Saccharomyces cerevisiae
Journal article, 2019

Metabolite biosensors are useful tools for high-throughput screening approaches and pathway regulation approaches. An important feature of biosensors is the dynamic range. To expand the maximum dynamic range of a transcription factor-based biosensor in Saccharomyces cerevisiae, using the fapO/FapR system from Bacillus subtilis as an example case, five native promoters, including constitutive and glucose-regulated ones, were modified. By evaluating different binding site (BS) positions in the core promoters, we identified locations that resulted in a high maximum dynamic range with low expression under repressed conditions. We further identified BS positions in the upstream element region of the TEF1 promoter that did not influence the native promoter strength but resulted in repression in the presence of a chimeric repressor consisting of FapR and the yeast repressor Mig1. These modified promoters with broad dynamic ranges will provide useful information for the engineering of future biosensors and their use in complex genetic circuits.

Saccharomyces cerevisiae

fapO/FapR

promoter engineering

biosensor

maximum dynamic range

malonyl-CoA

Author

Yasaman Dabirian

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Novo Nordisk Foundation

Xiaowei Li

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Yun Chen

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Florian David

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Jens B Nielsen

Beijing University of Chemical Technology

Technical University of Denmark (DTU)

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Verena Siewers

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

ACS Synthetic Biology

2161-5063 (eISSN)

Vol. 8 9 1968-1975

Subject Categories

Medical Genetics

Bioinformatics and Systems Biology

Genetics

DOI

10.1021/acssynbio.9b00144

PubMed

31373795

More information

Latest update

5/26/2023