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Characterization and Modeling of Transcriptional Cross-Regulation in Acinetobacter baylyi ADP1

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Kroto Research Institute, University of Sheffield, Broad Lane, Sheffield S3 7HQ, U.K.
BGI-Shenzhen, Shenzhen 518083, P.R. China
§ Shenzhen Key Laboratory of Environmental Microbial Genomics and Application, Shenzhen 518083, P.R. China
Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100029, China
Centre for Ecology and Hydrology, Wallingford, Banson Road, Wallingford OX10 8BB, U.K.
Cite this: ACS Synth. Biol. 2012, 1, 7, 274–283
Publication Date (Web):June 11, 2012
https://doi.org/10.1021/sb3000244
Copyright © 2012 American Chemical Society

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    Abstract

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    Synthetic biology involves reprogramming and engineering of regulatory genes in innovative ways for the implementation of novel tasks. Transcriptional gene regulation systems induced by small molecules in prokaryotes provide a rich source for logic gates. Cross-regulation, whereby a promoter is activated by different molecules or different promoters are activated by one molecule, can be used to design an OR-gate and achieve cross-talk between gene networks in cells. Acinetobacter baylyi ADP1 is naturally transformable, readily editing its chromosomal DNA, which makes it a convenient chassis for synthetic biology. The catabolic genes for salicylate, benzoate, and catechol metabolism are located within a supraoperonic cluster (-sal-are-ben-cat-) in the chromosome of A. baylyi ADP1, which are separately regulated by LysR-type transcriptional regulators (LTTRs). ADP1-based biosensors were constructed in which salA, benA, and catB were fused with a reporter gene cassette luxCDABE under the separate control of SalR, BenM, and CatM regulators. Salicylate, benzoate, catechol, and associated metabolites were found to mediate cross-regulation among sal, ben, and cat operons. A new mathematical model was developed by considering regulator-inducer binding and promoter activation as two separate steps. This model fits the experimental data well and is shown to predict cross-regulation performance.

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