Literature DB >> 27667363

Multi-omics Quantification of Species Variation of Escherichia coli Links Molecular Features with Strain Phenotypes.

Jonathan M Monk1, Anna Koza2, Miguel A Campodonico3, Daniel Machado2, Jose Miguel Seoane2, Bernhard O Palsson4, Markus J Herrgård2, Adam M Feist5.   

Abstract

Escherichia coli strains are widely used in academic research and biotechnology. New technologies for quantifying strain-specific differences and their underlying contributing factors promise greater understanding of how these differences significantly impact physiology, synthetic biology, metabolic engineering, and process design. Here, we quantified strain-specific differences in seven widely used strains of E. coli (BL21, C, Crooks, DH5a, K-12 MG1655, K-12 W3110, and W) using genomics, phenomics, transcriptomics, and genome-scale modeling. Metabolic physiology and gene expression varied widely with downstream implications for productivity, product yield, and titer. These differences could be linked to differential regulatory structure. Analyzing high-flux reactions and expression of encoding genes resulted in a correlated and quantitative link between these sets, with strain-specific caveats. Integrated modeling revealed that certain strains are better suited to produce given compounds or express desired constructs considering native expression states of pathways that enable high-production phenotypes. This study yields a framework for quantitatively comparing strains in a species with implications for strain selection.
Copyright © 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Escherichia coli; genome-scale modeling; metabolic engineering; systems biology

Mesh:

Substances:

Year:  2016        PMID: 27667363      PMCID: PMC5058344          DOI: 10.1016/j.cels.2016.08.013

Source DB:  PubMed          Journal:  Cell Syst        ISSN: 2405-4712            Impact factor:   10.304


  115 in total

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3.  Glucose metabolism at high density growth of E. coli B and E. coli K: differences in metabolic pathways are responsible for efficient glucose utilization in E. coli B as determined by microarrays and Northern blot analyses.

Authors:  Je-Nie Phue; Santosh B Noronha; Ritabrata Hattacharyya; Alan J Wolfe; Joseph Shiloach
Journal:  Biotechnol Bioeng       Date:  2005-06-30       Impact factor: 4.530

4.  Biases in Illumina transcriptome sequencing caused by random hexamer priming.

Authors:  Kasper D Hansen; Steven E Brenner; Sandrine Dudoit
Journal:  Nucleic Acids Res       Date:  2010-04-14       Impact factor: 16.971

5.  Understanding the differences between genome sequences of Escherichia coli B strains REL606 and BL21(DE3) and comparison of the E. coli B and K-12 genomes.

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Journal:  J Mol Biol       Date:  2009-09-15       Impact factor: 5.469

6.  Generation of an atlas for commodity chemical production in Escherichia coli and a novel pathway prediction algorithm, GEM-Path.

Authors:  Miguel A Campodonico; Barbara A Andrews; Juan A Asenjo; Bernhard O Palsson; Adam M Feist
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7.  Metabolic deficiences revealed in the biotechnologically important model bacterium Escherichia coli BL21(DE3).

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9.  Phylogenetic and genomic diversity of human bacteremic Escherichia coli strains.

Authors:  Françoise Jaureguy; Luce Landraud; Virginie Passet; Laure Diancourt; Eric Frapy; Ghislaine Guigon; Etienne Carbonnelle; Olivier Lortholary; Olivier Clermont; Erick Denamur; Bertrand Picard; Xavier Nassif; Sylvain Brisse
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10.  optGpSampler: an improved tool for uniformly sampling the solution-space of genome-scale metabolic networks.

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  27 in total

1.  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

2.  Automatic construction of metabolic models with enzyme constraints.

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Journal:  BMC Bioinformatics       Date:  2020-01-14       Impact factor: 3.169

3.  Application of the Metabolic Modeling Pipeline in KBase to Categorize Reactions, Predict Essential Genes, and Predict Pathways in an Isolate Genome.

Authors:  Benjamin H Allen; Nidhi Gupta; Janaka N Edirisinghe; José P Faria; Christopher S Henry
Journal:  Methods Mol Biol       Date:  2022

4.  A kinetic framework for modeling oleochemical biosynthesis in Escherichia coli.

Authors:  Jackson Peoples; Sophia Ruppe; Kathryn Mains; Elia C Cipriano; Jerome M Fox
Journal:  Biotechnol Bioeng       Date:  2022-08-24       Impact factor: 4.395

5.  The impact of technical failures on recombinant production of soluble proteins in Escherichia coli: a case study on process and protein robustness.

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6.  Assessing glycolytic flux alterations resulting from genetic perturbations in E. coli using a biosensor.

Authors:  Christina E Lehning; Solvej Siedler; Mostafa M H Ellabaan; Morten O A Sommer
Journal:  Metab Eng       Date:  2017-07-12       Impact factor: 9.783

7.  Parametric studies of metabolic cooperativity in Escherichia coli colonies: Strain and geometric confinement effects.

Authors:  Joseph R Peterson; John A Cole; Zaida Luthey-Schulten
Journal:  PLoS One       Date:  2017-08-18       Impact factor: 3.240

8.  Broad-Host-Range Expression Reveals Native and Host Regulatory Elements That Influence Heterologous Antibiotic Production in Gram-Negative Bacteria.

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9.  Gene expression models based on a reference laboratory strain are poor predictors of Mycobacterium tuberculosis complex transcriptional diversity.

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Review 10.  Genome-scale modeling of yeast: chronology, applications and critical perspectives.

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Journal:  FEMS Yeast Res       Date:  2017-08-01       Impact factor: 2.796

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