Literature DB >> 29702276

RapidRIP quantifies the intracellular metabolome of 7 industrial strains of E. coli.

Douglas McCloskey1, Julia Xu2, Lars Schrübbers1, Hanne B Christensen1, Markus J Herrgård3.   

Abstract

Fast metabolite quantification methods are required for high throughput screening of microbial strains obtained by combinatorial or evolutionary engineering approaches. In this study, a rapid RIP-LC-MS/MS (RapidRIP) method for high-throughput quantitative metabolomics was developed and validated that was capable of quantifying 102 metabolites from central, amino acid, energy, nucleotide, and cofactor metabolism in less than 5 minutes. The method was shown to have comparable sensitivity and resolving capability as compared to a full length RIP-LC-MS/MS method (FullRIP). The RapidRIP method was used to quantify the metabolome of seven industrial strains of E. coli revealing significant differences in glycolytic, pentose phosphate, TCA cycle, amino acid, and energy and cofactor metabolites were found. These differences translated to statistically and biologically significant differences in thermodynamics of biochemical reactions between strains that could have implications when choosing a host for bioprocessing.
Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

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Year:  2018        PMID: 29702276     DOI: 10.1016/j.ymben.2018.04.009

Source DB:  PubMed          Journal:  Metab Eng        ISSN: 1096-7176            Impact factor:   9.783


  7 in total

1.  A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action.

Authors:  Jason H Yang; Sarah N Wright; Meagan Hamblin; Douglas McCloskey; Miguel A Alcantar; Lars Schrübbers; Allison J Lopatkin; Sangeeta Satish; Amir Nili; Bernhard O Palsson; Graham C Walker; James J Collins
Journal:  Cell       Date:  2019-05-09       Impact factor: 41.582

2.  Engineering Escherichia coli for a high yield of 1,3-propanediol near the theoretical maximum through chromosomal integration and gene deletion.

Authors:  Nonthaporn Wong; Kaemwich Jantama
Journal:  Appl Microbiol Biotechnol       Date:  2022-04-13       Impact factor: 4.813

Review 3.  Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis.

Authors:  Svetlana Volkova; Marta R A Matos; Matthias Mattanovich; Igor Marín de Mas
Journal:  Metabolites       Date:  2020-07-24

4.  Mapping enzyme catalysis with metabolic biosensing.

Authors:  Linfeng Xu; Kai-Chun Chang; Emory M Payne; Cyrus Modavi; Leqian Liu; Claire M Palmer; Nannan Tao; Hal S Alper; Robert T Kennedy; Dale S Cornett; Adam R Abate
Journal:  Nat Commun       Date:  2021-11-23       Impact factor: 14.919

5.  Analysis of Growth Phases of Enterotoxigenic Escherichia coli Reveals a Distinct Transition Phase before Entry into Early Stationary Phase with Shifts in Tryptophan, Fucose, and Putrescine Metabolism and Degradation of Neurotransmitter Precursors.

Authors:  Enrique Joffré; Xue Xiao; Mário S P Correia; Intawat Nookaew; Samantha Sasse; Daniel Globisch; Baoli Zhu; Åsa Sjöling
Journal:  Microbiol Spectr       Date:  2022-07-25

6.  Metabolic Footprinting of Microbial Systems Based on Comprehensive In Silico Predictions of MS/MS Relevant Data.

Authors:  Alexander Reiter; Jian Asgari; Wolfgang Wiechert; Marco Oldiges
Journal:  Metabolites       Date:  2022-03-17

7.  A Comprehensive Evaluation of Metabolomics Data Preprocessing Methods for Deep Learning.

Authors:  Krzysztof Jan Abram; Douglas McCloskey
Journal:  Metabolites       Date:  2022-02-24
  7 in total

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