Literature DB >> 32093075

Absolute Quantification of the Central Carbon Metabolome in Eight Commonly Applied Prokaryotic and Eukaryotic Model Systems.

Lisa M Røst1, Lilja Brekke Thorfinnsdottir1, Kanhaiya Kumar1, Katsuya Fuchino1, Ida Eide Langørgen1, Zdenka Bartosova1, Kåre Andre Kristiansen1, Per Bruheim1.   

Abstract

Absolute quantification of intracellular metabolite pools is a prerequisite for modeling and in-depth biological interpretation of metabolomics data. It is the final step of an elaborate metabolomics workflow, with challenges associated with all steps-from sampling to quantifying the physicochemically diverse metabolite pool. Chromatographic separation combined with mass spectrometric (MS) detection is the superior platform for high coverage, selective, and sensitive detection of metabolites. Herein, we apply our quantitative MS-metabolomics workflow to measure and present the central carbon metabolome of a panel of commonly applied biological model systems. The workflow includes three chromatographic methods combined with isotope dilution tandem mass spectrometry to allow for absolute quantification of 68 metabolites of glycolysis, the pentose phosphate pathway, the tricarboxylic acid cycle, and the amino acid and (deoxy) nucleoside pools. The biological model systems; Bacillus subtilis, Saccharomyces cerevisiae, two microalgal species, and four human cell lines were all cultured in commonly applied culture media and sampled in exponential growth phase. Both literature and databases are scarce with comprehensive metabolite datasets, and existing entries range over several orders of magnitude. The workflow and metabolite panel presented herein can be employed to expand the list of reference metabolomes, as encouraged by the metabolomics community, in a continued effort to develop and refine high-quality quantitative metabolomics workflows.

Entities:  

Keywords:  B. subtilis; S. cerevisiae; absolute quantification; central carbon metabolism; human cell lines; intracellular metabolite pools; metabolome database; microalgae; tandem mass spectrometry; targeted metabolite profiling

Year:  2020        PMID: 32093075     DOI: 10.3390/metabo10020074

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


  15 in total

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Journal:  Nat Commun       Date:  2020-11-23       Impact factor: 14.919

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