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Low escape-rate genome safeguards with minimal molecular perturbation of Saccharomyces cerevisiae

Agmon, N and Tang, Z and Yang, K and Sutter, B and Ikushima, S and Cai, Y and Caravelli, K and Martin, JA and Sun, X and Choi, WJ and Zhang, A and Stracquadanio, G and Hao, H and Tu, BP and Fenyo, D and Bader, JS and Boeke, JD (2017) 'Low escape-rate genome safeguards with minimal molecular perturbation of Saccharomyces cerevisiae.' Proceedings of the National Academy of Sciences of the United States of America, 114 (8). E1470 - E1479. ISSN 0027-8424

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Abstract

© 2017, National Academy of Sciences. All rights reserved. As the use of synthetic biology both in industry and in academia grows, there is an increasing need to ensure b iocontainment. There is growing interest in engineering bacterial- and yeast-based safeguard (SG) strains. First-generation SGs were based on metabolic auxotrophy; however, the risk of cross-feeding and the cost of growth-controlling nutrients led researchers to look for other avenues. Recent strategies include bacteria engineered to be dependent on nonnatural amino acids and yeast SG strains that have both transcriptional- and recombinational-based biocontainment. We describe improving yeast Saccharomyces cerevisiae-based transcriptional SG strains, which have near-WT fitness, the lowest possible escape rate, and nanomolar ligands controlling growth. We screened a library of essential genes, as well as the best-performing promoter and terminators, yielding the best SG strains in yeast. The best constructs were fine-tuned, resulting in two tightly controlled inducible systems. In addition, for potential use in the prevention of industrial espionage, we screened an array of possible "decoy molecules" that can be used to mask any proprietary supplement to the SG strain, with minimal effect on strain fitness.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QD Chemistry
Q Science > QR Microbiology
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Giovanni Stracquadanio
Date Deposited: 23 May 2017 09:29
Last Modified: 28 Nov 2017 18:15
URI: http://repository.essex.ac.uk/id/eprint/19041

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