PLoS ONE (Jan 2015)

Programmed evolution for optimization of orthogonal metabolic output in bacteria.

  • 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

DOI
https://doi.org/10.1371/journal.pone.0118322
Journal volume & issue
Vol. 10, no. 2
p. e0118322

Abstract

Read online

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.