4.7 Article

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

期刊

ACS SYNTHETIC BIOLOGY
卷 1, 期 7, 页码 274-283

出版社

AMER CHEMICAL SOC
DOI: 10.1021/sb3000244

关键词

cross-regulation; Acinetobacter baylyi ADP1; catechol; salicylate; benzoate; LysR-type gene regulation; mathematic model; repressor

资金

  1. EPSRC [EP/H049479/1]
  2. Royal Society
  3. EU [RFCR-CT-2010-00010]
  4. Guangdong Provincial Science & Technology Project [2011B040300028]
  5. Engineering and Physical Sciences Research Council [EP/H04986X/1] Funding Source: researchfish
  6. Natural Environment Research Council [CEH010021] Funding Source: researchfish
  7. EPSRC [EP/H04986X/1] Funding Source: UKRI

向作者/读者索取更多资源

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 ADP 1, 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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