Literature DB >> 33926117

Exploring the Glucose Fluxotype of the E. coli y-ome Using High-Resolution Fluxomics.

Cécilia Bergès1,2, Edern Cahoreau1,2, Pierre Millard1, Brice Enjalbert1, Mickael Dinclaux1, Maud Heuillet1,2, Hanna Kulyk1,2, Lara Gales1,2, Noémie Butin1,2,3, Maxime Chazalviel4, Tony Palama1,2, Matthieu Guionnet1,2, Sergueï Sokol1, Lindsay Peyriga1,2, Floriant Bellvert1,2, Stéphanie Heux1, Jean-Charles Portais1,2,3.   

Abstract

We have developed a robust workflow to measure high-resolution fluxotypes (metabolic flux phenotypes) for large strain libraries under fully controlled growth conditions. This was achieved by optimizing and automating the whole high-throughput fluxomics process and integrating all relevant software tools. This workflow allowed us to obtain highly detailed maps of carbon fluxes in the central carbon metabolism in a fully automated manner. It was applied to investigate the glucose fluxotypes of 180 Escherichia coli strains deleted for y-genes. Since the products of these y-genes potentially play a role in a variety of metabolic processes, the experiments were designed to be agnostic as to their potential metabolic impact. The obtained data highlight the robustness of E. coli's central metabolism to y-gene deletion. For two y-genes, deletion resulted in significant changes in carbon and energy fluxes, demonstrating the involvement of the corresponding y-gene products in metabolic function or regulation. This work also introduces novel metrics to measure the actual scope and quality of high-throughput fluxomics investigations.

Entities:  

Keywords:  E. coli; fluxomics; high throughput; high-resolution fluxotyping; y-ome phenotyping

Year:  2021        PMID: 33926117     DOI: 10.3390/metabo11050271

Source DB:  PubMed          Journal:  Metabolites        ISSN: 2218-1989


  44 in total

Review 1.  High-throughput phenomics: experimental methods for mapping fluxomes.

Authors:  Uwe Sauer
Journal:  Curr Opin Biotechnol       Date:  2004-02       Impact factor: 9.740

2.  COMPLETE-MFA: complementary parallel labeling experiments technique for metabolic flux analysis.

Authors:  Robert W Leighty; Maciek R Antoniewicz
Journal:  Metab Eng       Date:  2013-09-08       Impact factor: 9.783

Review 3.  Recent advances in high-throughput 13C-fluxomics.

Authors:  Stéphanie Heux; Cécilia Bergès; Pierre Millard; Jean-Charles Portais; Fabien Létisse
Journal:  Curr Opin Biotechnol       Date:  2016-11-10       Impact factor: 9.740

4.  Mutant phenotypes for thousands of bacterial genes of unknown function.

Authors:  Morgan N Price; Kelly M Wetmore; R Jordan Waters; Mark Callaghan; Jayashree Ray; Hualan Liu; Jennifer V Kuehl; Ryan A Melnyk; Jacob S Lamson; Yumi Suh; Hans K Carlson; Zuelma Esquivel; Harini Sadeeshkumar; Romy Chakraborty; Grant M Zane; Benjamin E Rubin; Judy D Wall; Axel Visel; James Bristow; Matthew J Blow; Adam P Arkin; Adam M Deutschbauer
Journal:  Nature       Date:  2018-05-16       Impact factor: 49.962

5.  A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations.

Authors:  L M Raamsdonk; B Teusink; D Broadhurst; N Zhang; A Hayes; M C Walsh; J A Berden; K M Brindle; D B Kell; J J Rowland; H V Westerhoff; K van Dam; S G Oliver
Journal:  Nat Biotechnol       Date:  2001-01       Impact factor: 54.908

6.  Integrated 13C-metabolic flux analysis of 14 parallel labeling experiments in Escherichia coli.

Authors:  Scott B Crown; Christopher P Long; Maciek R Antoniewicz
Journal:  Metab Eng       Date:  2015-01-14       Impact factor: 9.783

7.  High-resolution 13C metabolic flux analysis.

Authors:  Christopher P Long; Maciek R Antoniewicz
Journal:  Nat Protoc       Date:  2019-08-30       Impact factor: 13.491

Review 8.  Metabolic networks in motion: 13C-based flux analysis.

Authors:  Uwe Sauer
Journal:  Mol Syst Biol       Date:  2006-11-14       Impact factor: 11.429

9.  Genomewide landscape of gene-metabolome associations in Escherichia coli.

Authors:  Tobias Fuhrer; Mattia Zampieri; Daniel C Sévin; Uwe Sauer; Nicola Zamboni
Journal:  Mol Syst Biol       Date:  2017-01-16       Impact factor: 11.429

10.  Reframing gene essentiality in terms of adaptive flexibility.

Authors:  Gabriela I Guzmán; Connor A Olson; Ying Hefner; Patrick V Phaneuf; Edward Catoiu; Lais B Crepaldi; Lucas Goldschmidt Micas; Bernhard O Palsson; Adam M Feist
Journal:  BMC Syst Biol       Date:  2018-12-17
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  3 in total

1.  An optimization method for untargeted MS-based isotopic tracing investigations of metabolism.

Authors:  Noémie Butin; Cécilia Bergès; Jean-Charles Portais; Floriant Bellvert
Journal:  Metabolomics       Date:  2022-06-16       Impact factor: 4.747

Review 2.  Integrative metabolic flux analysis reveals an indispensable dimension of phenotypes.

Authors:  Richard C Law; Aliya Lakhani; Samantha O'Keeffe; Sevcan Erşan; Junyoung O Park
Journal:  Curr Opin Biotechnol       Date:  2022-03-09       Impact factor: 10.279

Review 3.  Fluxomics - New Metabolomics Approaches to Monitor Metabolic Pathways.

Authors:  Abdul-Hamid Emwas; Kacper Szczepski; Inas Al-Younis; Joanna Izabela Lachowicz; Mariusz Jaremko
Journal:  Front Pharmacol       Date:  2022-03-21       Impact factor: 5.810

  3 in total

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