Literature DB >> 24482123

Mass spectrometry-based workflow for accurate quantification of Escherichia coli enzymes: how proteomics can play a key role in metabolic engineering.

Mathieu Trauchessec1, Michel Jaquinod, Aline Bonvalot, Virginie Brun, Christophe Bruley, Delphine Ropers, Hidde de Jong, Jérôme Garin, Gwenaëlle Bestel-Corre, Myriam Ferro.   

Abstract

Metabolic engineering aims to design high performance microbial strains producing compounds of interest. This requires systems-level understanding; genome-scale models have therefore been developed to predict metabolic fluxes. However, multi-omics data including genomics, transcriptomics, fluxomics, and proteomics may be required to model the metabolism of potential cell factories. Recent technological advances to quantitative proteomics have made mass spectrometry-based quantitative assays an interesting alternative to more traditional immuno-affinity based approaches. This has improved specificity and multiplexing capabilities. In this study, we developed a quantification workflow to analyze enzymes involved in central metabolism in Escherichia coli (E. coli). This workflow combined full-length isotopically labeled standards with selected reaction monitoring analysis. First, full-length (15)N labeled standards were produced and calibrated to ensure accurate measurements. Liquid chromatography conditions were then optimized for reproducibility and multiplexing capabilities over a single 30-min liquid chromatography-MS analysis. This workflow was used to accurately quantify 22 enzymes involved in E. coli central metabolism in a wild-type reference strain and two derived strains, optimized for higher NADPH production. In combination with measurements of metabolic fluxes, proteomics data can be used to assess different levels of regulation, in particular enzyme abundance and catalytic rate. This provides information that can be used to design specific strains used in biotechnology. In addition, accurate measurement of absolute enzyme concentrations is key to the development of predictive kinetic models in the context of metabolic engineering.

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Year:  2014        PMID: 24482123      PMCID: PMC3977194          DOI: 10.1074/mcp.M113.032672

Source DB:  PubMed          Journal:  Mol Cell Proteomics        ISSN: 1535-9476            Impact factor:   5.911


  52 in total

1.  The interface between biomarker discovery and clinical validation: The tar pit of the protein biomarker pipeline.

Authors:  Amanda G Paulovich; Jeffrey R Whiteaker; Andrew N Hoofnagle; Pei Wang
Journal:  Proteomics Clin Appl       Date:  2008-10-01       Impact factor: 3.494

2.  Multiple reaction monitoring-based, multiplexed, absolute quantitation of 45 proteins in human plasma.

Authors:  Michael A Kuzyk; Derek Smith; Juncong Yang; Tyra J Cross; Angela M Jackson; Darryl B Hardie; N Leigh Anderson; Christoph H Borchers
Journal:  Mol Cell Proteomics       Date:  2009-05-01       Impact factor: 5.911

3.  Synergies between synthetic biology and metabolic engineering.

Authors:  Jens Nielsen; Jay D Keasling
Journal:  Nat Biotechnol       Date:  2011-08-05       Impact factor: 54.908

4.  Identification and quantification of DNA repair proteins by liquid chromatography/isotope-dilution tandem mass spectrometry using their fully 15N-labeled analogues as internal standards.

Authors:  Miral Dizdaroglu; Prasad T Reddy; Pawel Jaruga
Journal:  J Proteome Res       Date:  2011-06-20       Impact factor: 4.466

5.  Accurate quantification of cardiovascular biomarkers in serum using Protein Standard Absolute Quantification (PSAQ™) and selected reaction monitoring.

Authors:  Céline Huillet; Annie Adrait; Dorothée Lebert; Guillaume Picard; Mathieu Trauchessec; Mathilde Louwagie; Alain Dupuis; Luc Hittinger; Bijan Ghaleh; Philippe Le Corvoisier; Michel Jaquinod; Jérôme Garin; Christophe Bruley; Virginie Brun
Journal:  Mol Cell Proteomics       Date:  2011-11-11       Impact factor: 5.911

6.  High sensitivity detection of plasma proteins by multiple reaction monitoring of N-glycosites.

Authors:  Jianru Stahl-Zeng; Vinzenz Lange; Reto Ossola; Katrin Eckhardt; Wilhelm Krek; Ruedi Aebersold; Bruno Domon
Journal:  Mol Cell Proteomics       Date:  2007-07-20       Impact factor: 5.911

7.  Responses of the central metabolism in Escherichia coli to phosphoglucose isomerase and glucose-6-phosphate dehydrogenase knockouts.

Authors:  Qiang Hua; Chen Yang; Tomoya Baba; Hirotada Mori; Kazuyuki Shimizu
Journal:  J Bacteriol       Date:  2003-12       Impact factor: 3.490

8.  Systems metabolic engineering, industrial biotechnology and microbial cell factories.

Authors:  Sang Yup Lee; Diethard Mattanovich; Antonio Villaverde
Journal:  Microb Cell Fact       Date:  2012-12-11       Impact factor: 5.328

9.  EcoCyc: a comprehensive database resource for Escherichia coli.

Authors:  Ingrid M Keseler; Julio Collado-Vides; Socorro Gama-Castro; John Ingraham; Suzanne Paley; Ian T Paulsen; Martín Peralta-Gil; Peter D Karp
Journal:  Nucleic Acids Res       Date:  2005-01-01       Impact factor: 16.971

10.  Protein abundance profiling of the Escherichia coli cytosol.

Authors:  Yasushi Ishihama; Thorsten Schmidt; Juri Rappsilber; Matthias Mann; F Ulrich Hartl; Michael J Kerner; Dmitrij Frishman
Journal:  BMC Genomics       Date:  2008-02-27       Impact factor: 3.969

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  4 in total

Review 1.  Application of targeted mass spectrometry in bottom-up proteomics for systems biology research.

Authors:  Nathan P Manes; Aleksandra Nita-Lazar
Journal:  J Proteomics       Date:  2018-02-13       Impact factor: 4.044

2.  Inference of quantitative models of bacterial promoters from time-series reporter gene data.

Authors:  Diana Stefan; Corinne Pinel; Stéphane Pinhal; Eugenio Cinquemani; Johannes Geiselmann; Hidde de Jong
Journal:  PLoS Comput Biol       Date:  2015-01-15       Impact factor: 4.475

3.  Enzyme I facilitates reverse flux from pyruvate to phosphoenolpyruvate in Escherichia coli.

Authors:  Christopher P Long; Jennifer Au; Nicholas R Sandoval; Nikodimos A Gebreselassie; Maciek R Antoniewicz
Journal:  Nat Commun       Date:  2017-01-27       Impact factor: 14.919

4.  WellInverter: a web application for the analysis of fluorescent reporter gene data.

Authors:  Yannick Martin; Michel Page; Christophe Blanchet; Hidde de Jong
Journal:  BMC Bioinformatics       Date:  2019-06-11       Impact factor: 3.169

  4 in total

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