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Rational Diversification of a Promoter Providing Fine-Tuned Expression and Orthogonal Regulation for Synthetic Biology

Overview of attention for article published in PLOS ONE, March 2012
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Title
Rational Diversification of a Promoter Providing Fine-Tuned Expression and Orthogonal Regulation for Synthetic Biology
Published in
PLOS ONE, March 2012
DOI 10.1371/journal.pone.0033279
Pubmed ID
Authors

Benjamin A. Blount, Tim Weenink, Serge Vasylechko, Tom Ellis

Abstract

Yeast is an ideal organism for the development and application of synthetic biology, yet there remain relatively few well-characterised biological parts suitable for precise engineering of this chassis. In order to address this current need, we present here a strategy that takes a single biological part, a promoter, and re-engineers it to produce a fine-graded output range promoter library and new regulated promoters desirable for orthogonal synthetic biology applications. A highly constitutive Saccharomyces cerevisiae promoter, PFY1p, was identified by bioinformatic approaches, characterised in vivo and diversified at its core sequence to create a 36-member promoter library. TetR regulation was introduced into PFY1p to create a synthetic inducible promoter (iPFY1p) that functions in an inverter device. Orthogonal and scalable regulation of synthetic promoters was then demonstrated for the first time using customisable Transcription Activator-Like Effectors (TALEs) modified and designed to act as orthogonal repressors for specific PFY1-based promoters. The ability to diversify a promoter at its core sequences and then independently target Transcription Activator-Like Orthogonal Repressors (TALORs) to virtually any of these sequences shows great promise toward the design and construction of future synthetic gene networks that encode complex "multi-wire" logic functions.

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Geographical breakdown

Country Count As %
United States 12 4%
United Kingdom 11 3%
Belgium 4 1%
France 3 <1%
Germany 1 <1%
India 1 <1%
Canada 1 <1%
Austria 1 <1%
Slovenia 1 <1%
Other 3 <1%
Unknown 304 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 89 26%
Researcher 79 23%
Student > Master 43 13%
Student > Bachelor 30 9%
Other 14 4%
Other 57 17%
Unknown 30 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 179 52%
Biochemistry, Genetics and Molecular Biology 88 26%
Engineering 14 4%
Computer Science 6 2%
Social Sciences 4 1%
Other 14 4%
Unknown 37 11%