Literature DB >> 25304508

Use of adaptive laboratory evolution to discover key mutations enabling rapid growth of Escherichia coli K-12 MG1655 on glucose minimal medium.

Ryan A LaCroix1, Troy E Sandberg1, Edward J O'Brien1, Jose Utrilla1, Ali Ebrahim1, Gabriela I Guzman1, Richard Szubin1, Bernhard O Palsson2, Adam M Feist3.   

Abstract

Adaptive laboratory evolution (ALE) has emerged as an effective tool for scientific discovery and addressing biotechnological needs. Much of ALE's utility is derived from reproducibly obtained fitness increases. Identifying causal genetic changes and their combinatorial effects is challenging and time-consuming. Understanding how these genetic changes enable increased fitness can be difficult. A series of approaches that address these challenges was developed and demonstrated using Escherichia coli K-12 MG1655 on glucose minimal media at 37°C. By keeping E. coli in constant substrate excess and exponential growth, fitness increases up to 1.6-fold were obtained compared to the wild type. These increases are comparable to previously reported maximum growth rates in similar conditions but were obtained over a shorter time frame. Across the eight replicate ALE experiments performed, causal mutations were identified using three approaches: identifying mutations in the same gene/region across replicate experiments, sequencing strains before and after computationally determined fitness jumps, and allelic replacement coupled with targeted ALE of reconstructed strains. Three genetic regions were most often mutated: the global transcription gene rpoB, an 82-bp deletion between the metabolic pyrE gene and rph, and an IS element between the DNA structural gene hns and tdk. Model-derived classification of gene expression revealed a number of processes important for increased growth that were missed using a gene classification system alone. The methods described here represent a powerful combination of technologies to increase the speed and efficiency of ALE studies. The identified mutations can be examined as genetic parts for increasing growth rate in a desired strain and for understanding rapid growth phenotypes.
Copyright © 2015, American Society for Microbiology. All Rights Reserved.

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Year:  2014        PMID: 25304508      PMCID: PMC4272732          DOI: 10.1128/AEM.02246-14

Source DB:  PubMed          Journal:  Appl Environ Microbiol        ISSN: 0099-2240            Impact factor:   4.792


  56 in total

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Journal:  Genetica       Date:  1999       Impact factor: 1.082

2.  Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth.

Authors:  Rafael U Ibarra; Jeremy S Edwards; Bernhard O Palsson
Journal:  Nature       Date:  2002-11-14       Impact factor: 49.962

3.  Gene replacement without selection: regulated suppression of amber mutations in Escherichia coli.

Authors:  Christopher D Herring; Jeremy D Glasner; Frederick R Blattner
Journal:  Gene       Date:  2003-06-05       Impact factor: 3.688

4.  Metabolic gene-deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes.

Authors:  Stephen S Fong; Bernhard Ø Palsson
Journal:  Nat Genet       Date:  2004-09-26       Impact factor: 38.330

5.  Allele replacement in Escherichia coli by use of a selectable marker for resistance to spectinomycin: replacement of the lexA gene.

Authors:  S A Hill; J W Little
Journal:  J Bacteriol       Date:  1988-12       Impact factor: 3.490

Review 6.  Bacterial mutator genes and the control of spontaneous mutation.

Authors:  E C Cox
Journal:  Annu Rev Genet       Date:  1976       Impact factor: 16.830

7.  Aerobic fermentation of D-glucose by an evolved cytochrome oxidase-deficient Escherichia coli strain.

Authors:  Vasiliy A Portnoy; Markus J Herrgård; Bernhard Ø Palsson
Journal:  Appl Environ Microbiol       Date:  2008-10-24       Impact factor: 4.792

8.  Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity.

Authors:  Q K Beg; A Vazquez; J Ernst; M A de Menezes; Z Bar-Joseph; A-L Barabási; Z N Oltvai
Journal:  Proc Natl Acad Sci U S A       Date:  2007-07-24       Impact factor: 11.205

9.  The COG database: an updated version includes eukaryotes.

Authors:  Roman L Tatusov; Natalie D Fedorova; John D Jackson; Aviva R Jacobs; Boris Kiryutin; Eugene V Koonin; Dmitri M Krylov; Raja Mazumder; Sergei L Mekhedov; Anastasia N Nikolskaya; B Sridhar Rao; Sergei Smirnov; Alexander V Sverdlov; Sona Vasudevan; Yuri I Wolf; Jodie J Yin; Darren A Natale
Journal:  BMC Bioinformatics       Date:  2003-09-11       Impact factor: 3.169

10.  Evolution of Escherichia coli to 42 °C and subsequent genetic engineering reveals adaptive mechanisms and novel mutations.

Authors:  Troy E Sandberg; Margit Pedersen; Ryan A LaCroix; Ali Ebrahim; Mads Bonde; Markus J Herrgard; Bernhard O Palsson; Morten Sommer; Adam M Feist
Journal:  Mol Biol Evol       Date:  2014-07-10       Impact factor: 16.240

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  82 in total

1.  Adaptive Evolution of Thermotoga maritima Reveals Plasticity of the ABC Transporter Network.

Authors:  Haythem Latif; Merve Sahin; Janna Tarasova; Yekaterina Tarasova; Vasiliy A Portnoy; Juan Nogales; Karsten Zengler
Journal:  Appl Environ Microbiol       Date:  2015-06-05       Impact factor: 4.792

2.  Fast growth phenotype of E. coli K-12 from adaptive laboratory evolution does not require intracellular flux rewiring.

Authors:  Christopher P Long; Jacqueline E Gonzalez; Adam M Feist; Bernhard O Palsson; Maciek R Antoniewicz
Journal:  Metab Eng       Date:  2017-09-23       Impact factor: 9.783

3.  Tools and systems for evolutionary engineering of biomolecules and microorganisms.

Authors:  Sungho Jang; Minsun Kim; Jaeseong Hwang; Gyoo Yeol Jung
Journal:  J Ind Microbiol Biotechnol       Date:  2019-05-27       Impact factor: 3.346

4.  Laboratory Evolution to Alternating Substrate Environments Yields Distinct Phenotypic and Genetic Adaptive Strategies.

Authors:  Troy E Sandberg; Colton J Lloyd; Bernhard O Palsson; Adam M Feist
Journal:  Appl Environ Microbiol       Date:  2017-06-16       Impact factor: 4.792

5.  A Model for Designing Adaptive Laboratory Evolution Experiments.

Authors:  Ryan A LaCroix; Bernhard O Palsson; Adam M Feist
Journal:  Appl Environ Microbiol       Date:  2017-03-31       Impact factor: 4.792

6.  Multiple Optimal Phenotypes Overcome Redox and Glycolytic Intermediate Metabolite Imbalances in Escherichia coli pgi Knockout Evolutions.

Authors:  Douglas McCloskey; Sibei Xu; Troy E Sandberg; Elizabeth Brunk; Ying Hefner; Richard Szubin; Adam M Feist; Bernhard O Palsson
Journal:  Appl Environ Microbiol       Date:  2018-09-17       Impact factor: 4.792

Review 7.  Experimental Design, Population Dynamics, and Diversity in Microbial Experimental Evolution.

Authors:  Bram Van den Bergh; Toon Swings; Maarten Fauvart; Jan Michiels
Journal:  Microbiol Mol Biol Rev       Date:  2018-07-25       Impact factor: 11.056

8.  Deciphering the essentiality and function of the anti-σM factors in Bacillus subtilis.

Authors:  Heng Zhao; Daniel M Roistacher; John D Helmann
Journal:  Mol Microbiol       Date:  2019-03-13       Impact factor: 3.501

9.  Strain-Specific Metabolic Requirements Revealed by a Defined Minimal Medium for Systems Analyses of Staphylococcus aureus.

Authors:  Henrique Machado; Liam L Weng; Nicholas Dillon; Yara Seif; Michelle Holland; Jonathan E Pekar; Jonathan M Monk; Victor Nizet; Bernhard O Palsson; Adam M Feist
Journal:  Appl Environ Microbiol       Date:  2019-10-16       Impact factor: 4.792

10.  Global Rebalancing of Cellular Resources by Pleiotropic Point Mutations Illustrates a Multi-scale Mechanism of Adaptive Evolution.

Authors:  Jose Utrilla; Edward J O'Brien; Ke Chen; Douglas McCloskey; Jacky Cheung; Harris Wang; Dagoberto Armenta-Medina; Adam M Feist; Bernhard O Palsson
Journal:  Cell Syst       Date:  2016-04-27       Impact factor: 10.304

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