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Synthetic Morphology Using Alternative Inputs

Overview of attention for article published in PLOS ONE, September 2009
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Title
Synthetic Morphology Using Alternative Inputs
Published in
PLOS ONE, September 2009
DOI 10.1371/journal.pone.0006946
Pubmed ID
Authors

Hiromasa Tanaka, Tau-Mu Yi

Abstract

Designing the shape and size of a cell is an interesting challenge for synthetic biology. Prolonged exposure to the mating pheromone alpha-factor induces an unusual morphology in yeast cells: multiple mating projections. The goal of this work was to reproduce the multiple projections phenotype in the absence of alpha-factor using a gain-of-function approach termed "Alternative Inputs (AIs)". An alternative input is defined as any genetic manipulation that can activate the signaling pathway instead of the natural input. Interestingly, none of the alternative inputs were sufficient to produce multiple projections although some produced a single projection. Then, we extended our search by creating all combinations of alternative inputs and deletions that were summarized in an AIs-Deletions matrix. We found a genetic manipulation (AI-Ste5p ste2Delta) that enhanced the formation of multiple projections. Following up this lead, we demonstrated that AI-Ste4p and AI-Ste5p were sufficient to produce multiple projections when combined. Further, we showed that overexpression of a membrane-targeted form of Ste5p alone could also induce multiple projections. Thus, we successfully re-engineered the multiple projections mating morphology using alternative inputs without alpha-factor.

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Geographical breakdown

Country Count As %
Germany 2 7%
Portugal 1 4%
Italy 1 4%
Brazil 1 4%
United Kingdom 1 4%
Mexico 1 4%
United States 1 4%
Unknown 19 70%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 33%
Student > Ph. D. Student 5 19%
Student > Bachelor 4 15%
Student > Master 3 11%
Unspecified 1 4%
Other 3 11%
Unknown 2 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 52%
Computer Science 4 15%
Social Sciences 2 7%
Biochemistry, Genetics and Molecular Biology 2 7%
Unspecified 1 4%
Other 2 7%
Unknown 2 7%