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Programmed evolution for optimization of orthogonal metabolic output in bacteria.

Authors :
Todd T Eckdahl
A Malcolm Campbell
Laurie J Heyer
Jeffrey L Poet
David N Blauch
Nicole L Snyder
Dustin T Atchley
Erich J Baker
Micah Brown
Elizabeth C Brunner
Sean A Callen
Jesse S Campbell
Caleb J Carr
David R Carr
Spencer A Chadinha
Grace I Chester
Josh Chester
Ben R Clarkson
Kelly E Cochran
Shannon E Doherty
Catherine Doyle
Sarah Dwyer
Linnea M Edlin
Rebecca A Evans
Taylor Fluharty
Janna Frederick
Jonah Galeota-Sprung
Betsy L Gammon
Brandon Grieshaber
Jessica Gronniger
Katelyn Gutteridge
Joel Henningsen
Bradley Isom
Hannah L Itell
Erica C Keffeler
Andrew J Lantz
Jonathan N Lim
Erin P McGuire
Alexander K Moore
Jerrad Morton
Meredith Nakano
Sara A Pearson
Virginia Perkins
Phoebe Parrish
Claire E Pierson
Sachith Polpityaarachchige
Michael J Quaney
Abagael Slattery
Kathryn E Smith
Jackson Spell
Morgan Spencer
Telavive Taye
Kamay Trueblood
Caroline J Vrana
E Tucker Whitesides
Source :
PLoS ONE, Vol 10, Iss 2, p e0118322 (2015)
Publication Year :
2015
Publisher :
Public Library of Science (PLoS), 2015.

Abstract

Current use of microbes for metabolic engineering suffers from loss of metabolic output due to natural selection. Rather than combat the evolution of bacterial populations, we chose to embrace what makes biological engineering unique among engineering fields - evolving materials. We harnessed bacteria to compute solutions to the biological problem of metabolic pathway optimization. Our approach is called Programmed Evolution to capture two concepts. First, a population of cells is programmed with DNA code to enable it to compute solutions to a chosen optimization problem. As analog computers, bacteria process known and unknown inputs and direct the output of their biochemical hardware. Second, the system employs the evolution of bacteria toward an optimal metabolic solution by imposing fitness defined by metabolic output. The current study is a proof-of-concept for Programmed Evolution applied to the optimization of a metabolic pathway for the conversion of caffeine to theophylline in E. coli. Introduced genotype variations included strength of the promoter and ribosome binding site, plasmid copy number, and chaperone proteins. We constructed 24 strains using all combinations of the genetic variables. We used a theophylline riboswitch and a tetracycline resistance gene to link theophylline production to fitness. After subjecting the mixed population to selection, we measured a change in the distribution of genotypes in the population and an increased conversion of caffeine to theophylline among the most fit strains, demonstrating Programmed Evolution. Programmed Evolution inverts the standard paradigm in metabolic engineering by harnessing evolution instead of fighting it. Our modular system enables researchers to program bacteria and use evolution to determine the combination of genetic control elements that optimizes catabolic or anabolic output and to maintain it in a population of cells. Programmed Evolution could be used for applications in energy, pharmaceuticals, chemical commodities, biomining, and bioremediation.

Subjects

Subjects :
Medicine
Science

Details

Language :
English
ISSN :
19326203
Volume :
10
Issue :
2
Database :
Directory of Open Access Journals
Journal :
PLoS ONE
Publication Type :
Academic Journal
Accession number :
edsdoj.0ac0400eb84b1c9b9669fc8650bd6c
Document Type :
article
Full Text :
https://doi.org/10.1371/journal.pone.0118322