Literature DB >> 15791937

Applying metabolic profiling techniques for stimulus-response experiments: chances and pitfalls.

M Oldiges1, Ralf Takors.   

Abstract

So far it is mainly transcriptome and proteome analysis that has been applied to elucidate the correlation between genotype and phenotype although thorough metabolome studies can provide substantial information on the control of the metabolism at the biochemical level. Stimulus-response experiments, i.e. the investigation of metabolism dynamics after a glucose pulse (pulse experiment), can be used to study the in vivo enzyme kinetics offering insight into underlying reaction mechanisms. Usually, this requires rapid cell quenching combined with cell inactivation to'freeze' the microbial metabolism response at a definite time-lag after pulse stimulation. To access the 'frozen' metabolic reply, adequate analytical methods are needed to measure intracellular metabolite concentrations in the cell extract. As shown in the introductory review part, stimulus-response experiments were usually applied to study central metabolism dynamics in wildtype strains. Our own results, presented in the second part of the contribution, indicate that stimulus-response experiments should also be applied to analyse pathway dynamics in anabolic routes. Using the example of the aromatic amino acid pathway, an LC-MS/MS technique is presented that allows the quantification of intracellular pools of central metabolism as well as of the aromatic amino acid pathway. Based on the analytical approach metabolic profiling is performed to monitor the metabolism dynamics after a glucose pulse experiment allowing the conclusion that pulse stimulation is transmitted to the anabolic pathway of interest.

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Year:  2005        PMID: 15791937     DOI: 10.1007/b98913

Source DB:  PubMed          Journal:  Adv Biochem Eng Biotechnol        ISSN: 0724-6145            Impact factor:   2.635


  6 in total

1.  Conservation of the metabolomic response to starvation across two divergent microbes.

Authors:  Matthew J Brauer; Jie Yuan; Bryson D Bennett; Wenyun Lu; Elizabeth Kimball; David Botstein; Joshua D Rabinowitz
Journal:  Proc Natl Acad Sci U S A       Date:  2006-12-11       Impact factor: 11.205

2.  Repetitive Short-Term Stimuli Imposed in Poor Mixing Zones Induce Long-Term Adaptation of E. coli Cultures in Large-Scale Bioreactors: Experimental Evidence and Mathematical Model.

Authors:  Alexander Nieß; Michael Löffler; Joana D Simen; Ralf Takors
Journal:  Front Microbiol       Date:  2017-06-28       Impact factor: 5.640

3.  Metabolic control analysis of L-tryptophan producing Escherichia coli applying targeted perturbation with shikimate.

Authors:  Kristin Schoppel; Natalia Trachtmann; Fabian Mittermeier; Georg A Sprenger; Dirk Weuster-Botz
Journal:  Bioprocess Biosyst Eng       Date:  2021-09-14       Impact factor: 3.210

4.  Metabolic control analysis enables rational improvement of E. coli L-tryptophan producers but methylglyoxal formation limits glycerol-based production.

Authors:  Kristin Schoppel; Natalia Trachtmann; Emil J Korzin; Angelina Tzanavari; Georg A Sprenger; Dirk Weuster-Botz
Journal:  Microb Cell Fact       Date:  2022-10-04       Impact factor: 6.352

5.  13C labeling experiments at metabolic nonstationary conditions: an exploratory study.

Authors:  Sebastian Aljoscha Wahl; Katharina Nöh; Wolfgang Wiechert
Journal:  BMC Bioinformatics       Date:  2008-03-18       Impact factor: 3.169

6.  Evaluation of sample extracting methods of FCSM by Lactobacillus acidophilus based on a UPLC-Q-TOF-MS global metabolomics analysis.

Authors:  Yongqiang Wang; Wenju Zhang; Cunxi Nie; Cheng Chen; Xiaoyang Zhang; Jianhe Hu
Journal:  Braz J Microbiol       Date:  2017-10-31       Impact factor: 2.476

  6 in total

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