Literature DB >> 30054360

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

Douglas McCloskey1,2, Sibei Xu1, Troy E Sandberg1, Elizabeth Brunk1, Ying Hefner1, Richard Szubin1, Adam M Feist1,2, Bernhard O Palsson3,2.   

Abstract

A mechanistic understanding of how new phenotypes develop to overcome the loss of a gene product provides valuable insight on both the metabolic and regulatory functions of the lost gene. The pgi gene, whose product catalyzes the second step in glycolysis, was deleted in a growth-optimized Escherichia coli K-12 MG1655 strain. The initial knockout (KO) strain exhibited an 80% drop in growth rate that was largely recovered in eight replicate, but phenotypically distinct, cultures after undergoing adaptive laboratory evolution (ALE). Multi-omic data sets showed that the loss of pgi substantially shifted pathway usage, leading to a redox and sugar phosphate stress response. These stress responses were overcome by unique combinations of innovative mutations selected for by ALE. Thus, the coordinated mechanisms from genome to metabolome that lead to multiple optimal phenotypes after the loss of a major gene product were revealed.IMPORTANCE A mechanistic understanding of how microbes are able to overcome the loss of a gene through regulatory and metabolic changes is not well understood. Eight independent adaptive laboratory evolution (ALE) experiments with pgi knockout strains resulted in eight phenotypically distinct endpoints that were able to overcome the gene loss. Utilizing multi-omics analysis, the coordinated mechanisms from genome to metabolome that lead to multiple optimal phenotypes after the loss of a major gene product were revealed.
Copyright © 2018 American Society for Microbiology.

Entities:  

Keywords:  Escherichia coli; adaptive laboratory evolution; multi-omics analysis; mutation analysis; pgi gene knockout; systems biology

Mesh:

Substances:

Year:  2018        PMID: 30054360      PMCID: PMC6146989          DOI: 10.1128/AEM.00823-18

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


  80 in total

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Journal:  Bioinformatics       Date:  2014-01-11       Impact factor: 6.937

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Review 5.  Physiological consequences of small RNA-mediated regulation of glucose-phosphate stress.

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Journal:  Curr Opin Microbiol       Date:  2007-03-23       Impact factor: 7.934

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7.  A comprehensive genome-scale reconstruction of Escherichia coli metabolism--2011.

Authors:  Jeffrey D Orth; Tom M Conrad; Jessica Na; Joshua A Lerman; Hojung Nam; Adam M Feist; Bernhard Ø Palsson
Journal:  Mol Syst Biol       Date:  2011-10-11       Impact factor: 11.429

8.  Systematic phenome analysis of Escherichia coli multiple-knockout mutants reveals hidden reactions in central carbon metabolism.

Authors:  Kenji Nakahigashi; Yoshihiro Toya; Nobuyoshi Ishii; Tomoyoshi Soga; Miki Hasegawa; Hisami Watanabe; Yuki Takai; Masayuki Honma; Hirotada Mori; Masaru Tomita
Journal:  Mol Syst Biol       Date:  2009-09-15       Impact factor: 11.429

9.  optGpSampler: an improved tool for uniformly sampling the solution-space of genome-scale metabolic networks.

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10.  RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond.

Authors:  Socorro Gama-Castro; Heladia Salgado; Alberto Santos-Zavaleta; Daniela Ledezma-Tejeida; Luis Muñiz-Rascado; Jair Santiago García-Sotelo; Kevin Alquicira-Hernández; Irma Martínez-Flores; Lucia Pannier; Jaime Abraham Castro-Mondragón; Alejandra Medina-Rivera; Hilda Solano-Lira; César Bonavides-Martínez; Ernesto Pérez-Rueda; Shirley Alquicira-Hernández; Liliana Porrón-Sotelo; Alejandra López-Fuentes; Anastasia Hernández-Koutoucheva; Víctor Del Moral-Chávez; Fabio Rinaldi; Julio Collado-Vides
Journal:  Nucleic Acids Res       Date:  2015-11-02       Impact factor: 16.971

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  8 in total

Review 1.  Rapid Growth and Metabolism of Uropathogenic Escherichia coli in Relation to Urine Composition.

Authors:  Larry Reitzer; Philippe Zimmern
Journal:  Clin Microbiol Rev       Date:  2019-10-16       Impact factor: 26.132

2.  Causal mutations from adaptive laboratory evolution are outlined by multiple scales of genome annotations and condition-specificity.

Authors:  Patrick V Phaneuf; James T Yurkovich; David Heckmann; Muyao Wu; Troy E Sandberg; Zachary A King; Justin Tan; Bernhard O Palsson; Adam M Feist
Journal:  BMC Genomics       Date:  2020-07-25       Impact factor: 3.969

3.  NetFlow: A tool for isolating carbon flows in genome-scale metabolic networks.

Authors:  Sean G Mack; Ganesh Sriram
Journal:  Metab Eng Commun       Date:  2020-12-02

4.  Evolutionary dynamics and structural consequences of de novo beneficial mutations and mutant lineages arising in a constant environment.

Authors:  Margie Kinnersley; Katja Schwartz; Dong-Dong Yang; Gavin Sherlock; Frank Rosenzweig
Journal:  BMC Biol       Date:  2021-02-04       Impact factor: 7.431

5.  Phosphoglucose Isomerase Plays a Key Role in Sugar Homeostasis, Stress Response, and Pathogenicity in Aspergillus flavus.

Authors:  Yao Zhou; Chao Du; Arome Solomon Odiba; Rui He; Chukwuemeka Samson Ahamefule; Bin Wang; Cheng Jin; Wenxia Fang
Journal:  Front Cell Infect Microbiol       Date:  2021-12-15       Impact factor: 5.293

6.  Predictive evolution of metabolic phenotypes using model-designed environments.

Authors:  Paula Jouhten; Dimitrios Konstantinidis; Filipa Pereira; Sergej Andrejev; Kristina Grkovska; Sandra Castillo; Payam Ghiachi; Gemma Beltran; Eivind Almaas; Albert Mas; Jonas Warringer; Ramon Gonzalez; Pilar Morales; Kiran R Patil
Journal:  Mol Syst Biol       Date:  2022-10       Impact factor: 13.068

7.  Evolution of gene knockout strains of E. coli reveal regulatory architectures governed by metabolism.

Authors:  Douglas McCloskey; Sibei Xu; Troy E Sandberg; Elizabeth Brunk; Ying Hefner; Richard Szubin; Adam M Feist; Bernhard O Palsson
Journal:  Nat Commun       Date:  2018-09-18       Impact factor: 14.919

8.  Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers.

Authors:  David Heckmann; Anaamika Campeau; Colton J Lloyd; Patrick V Phaneuf; Ying Hefner; Marvic Carrillo-Terrazas; Adam M Feist; David J Gonzalez; Bernhard O Palsson
Journal:  Proc Natl Acad Sci U S A       Date:  2020-09-01       Impact factor: 11.205

  8 in total

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