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Programmed Evolution for Optimization of Orthogonal Metabolic Output in Bacteria
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 bacter...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4340930/ https://www.ncbi.nlm.nih.gov/pubmed/25714374 http://dx.doi.org/10.1371/journal.pone.0118322 |
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author | Eckdahl, Todd T. Campbell, A. Malcolm Heyer, Laurie J. Poet, Jeffrey L. Blauch, David N. Snyder, Nicole L. Atchley, Dustin T. Baker, Erich J. Brown, Micah Brunner, Elizabeth C. Callen, Sean A. Campbell, Jesse S. Carr, Caleb J. Carr, David R. Chadinha, Spencer A. Chester, Grace I. Chester, Josh Clarkson, Ben R. Cochran, Kelly E. Doherty, Shannon E. Doyle, Catherine Dwyer, Sarah Edlin, Linnea M. Evans, Rebecca A. Fluharty, Taylor Frederick, Janna Galeota-Sprung, Jonah Gammon, Betsy L. Grieshaber, Brandon Gronniger, Jessica Gutteridge, Katelyn Henningsen, Joel Isom, Bradley Itell, Hannah L. Keffeler, Erica C. Lantz, Andrew J. Lim, Jonathan N. McGuire, Erin P. Moore, Alexander K. Morton, Jerrad Nakano, Meredith Pearson, Sara A. Perkins, Virginia Parrish, Phoebe Pierson, Claire E. Polpityaarachchige, Sachith Quaney, Michael J. Slattery, Abagael Smith, Kathryn E. Spell, Jackson Spencer, Morgan Taye, Telavive Trueblood, Kamay Vrana, Caroline J. Whitesides, E. Tucker |
author_facet | Eckdahl, Todd T. Campbell, A. Malcolm Heyer, Laurie J. Poet, Jeffrey L. Blauch, David N. Snyder, Nicole L. Atchley, Dustin T. Baker, Erich J. Brown, Micah Brunner, Elizabeth C. Callen, Sean A. Campbell, Jesse S. Carr, Caleb J. Carr, David R. Chadinha, Spencer A. Chester, Grace I. Chester, Josh Clarkson, Ben R. Cochran, Kelly E. Doherty, Shannon E. Doyle, Catherine Dwyer, Sarah Edlin, Linnea M. Evans, Rebecca A. Fluharty, Taylor Frederick, Janna Galeota-Sprung, Jonah Gammon, Betsy L. Grieshaber, Brandon Gronniger, Jessica Gutteridge, Katelyn Henningsen, Joel Isom, Bradley Itell, Hannah L. Keffeler, Erica C. Lantz, Andrew J. Lim, Jonathan N. McGuire, Erin P. Moore, Alexander K. Morton, Jerrad Nakano, Meredith Pearson, Sara A. Perkins, Virginia Parrish, Phoebe Pierson, Claire E. Polpityaarachchige, Sachith Quaney, Michael J. Slattery, Abagael Smith, Kathryn E. Spell, Jackson Spencer, Morgan Taye, Telavive Trueblood, Kamay Vrana, Caroline J. Whitesides, E. Tucker |
author_sort | Eckdahl, Todd T. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4340930 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-43409302015-03-04 Programmed Evolution for Optimization of Orthogonal Metabolic Output in Bacteria Eckdahl, Todd T. Campbell, A. Malcolm Heyer, Laurie J. Poet, Jeffrey L. Blauch, David N. Snyder, Nicole L. Atchley, Dustin T. Baker, Erich J. Brown, Micah Brunner, Elizabeth C. Callen, Sean A. Campbell, Jesse S. Carr, Caleb J. Carr, David R. Chadinha, Spencer A. Chester, Grace I. Chester, Josh Clarkson, Ben R. Cochran, Kelly E. Doherty, Shannon E. Doyle, Catherine Dwyer, Sarah Edlin, Linnea M. Evans, Rebecca A. Fluharty, Taylor Frederick, Janna Galeota-Sprung, Jonah Gammon, Betsy L. Grieshaber, Brandon Gronniger, Jessica Gutteridge, Katelyn Henningsen, Joel Isom, Bradley Itell, Hannah L. Keffeler, Erica C. Lantz, Andrew J. Lim, Jonathan N. McGuire, Erin P. Moore, Alexander K. Morton, Jerrad Nakano, Meredith Pearson, Sara A. Perkins, Virginia Parrish, Phoebe Pierson, Claire E. Polpityaarachchige, Sachith Quaney, Michael J. Slattery, Abagael Smith, Kathryn E. Spell, Jackson Spencer, Morgan Taye, Telavive Trueblood, Kamay Vrana, Caroline J. Whitesides, E. Tucker PLoS One Research Article 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. Public Library of Science 2015-02-25 /pmc/articles/PMC4340930/ /pubmed/25714374 http://dx.doi.org/10.1371/journal.pone.0118322 Text en © 2015 Eckdahl et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Eckdahl, Todd T. Campbell, A. Malcolm Heyer, Laurie J. Poet, Jeffrey L. Blauch, David N. Snyder, Nicole L. Atchley, Dustin T. Baker, Erich J. Brown, Micah Brunner, Elizabeth C. Callen, Sean A. Campbell, Jesse S. Carr, Caleb J. Carr, David R. Chadinha, Spencer A. Chester, Grace I. Chester, Josh Clarkson, Ben R. Cochran, Kelly E. Doherty, Shannon E. Doyle, Catherine Dwyer, Sarah Edlin, Linnea M. Evans, Rebecca A. Fluharty, Taylor Frederick, Janna Galeota-Sprung, Jonah Gammon, Betsy L. Grieshaber, Brandon Gronniger, Jessica Gutteridge, Katelyn Henningsen, Joel Isom, Bradley Itell, Hannah L. Keffeler, Erica C. Lantz, Andrew J. Lim, Jonathan N. McGuire, Erin P. Moore, Alexander K. Morton, Jerrad Nakano, Meredith Pearson, Sara A. Perkins, Virginia Parrish, Phoebe Pierson, Claire E. Polpityaarachchige, Sachith Quaney, Michael J. Slattery, Abagael Smith, Kathryn E. Spell, Jackson Spencer, Morgan Taye, Telavive Trueblood, Kamay Vrana, Caroline J. Whitesides, E. Tucker Programmed Evolution for Optimization of Orthogonal Metabolic Output in Bacteria |
title | Programmed Evolution for Optimization of Orthogonal Metabolic Output in Bacteria |
title_full | Programmed Evolution for Optimization of Orthogonal Metabolic Output in Bacteria |
title_fullStr | Programmed Evolution for Optimization of Orthogonal Metabolic Output in Bacteria |
title_full_unstemmed | Programmed Evolution for Optimization of Orthogonal Metabolic Output in Bacteria |
title_short | Programmed Evolution for Optimization of Orthogonal Metabolic Output in Bacteria |
title_sort | programmed evolution for optimization of orthogonal metabolic output in bacteria |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4340930/ https://www.ncbi.nlm.nih.gov/pubmed/25714374 http://dx.doi.org/10.1371/journal.pone.0118322 |
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