High-throughput sample generation; Liquid chromatography; Metabolomics; Saccharomyces cerevisiae; Stable isotope labelling; Untargeted high-resolution mass spectrometry; Metabolome; Chromatography, Liquid; Mass Spectrometry; Chromatography, High Pressure Liquid/methods; Saccharomyces cerevisiae/metabolism; Metabolomics/methods; Disease etiology; High resolution mass spectrometry; High-throughput; Metabolic genes; Model organisms; Sample generations; Stable-isotope labeling; Chromatography, High Pressure Liquid; Analytical Chemistry; Biochemistry
Résumé :
[en] Identifying metabolites in model organisms is critical for many areas of biology, including unravelling disease aetiology or elucidating functions of putative enzymes. Even now, hundreds of predicted metabolic genes in Saccharomyces cerevisiae remain uncharacterized, indicating that our understanding of metabolism is far from complete even in well-characterized organisms. While untargeted high-resolution mass spectrometry (HRMS) enables the detection of thousands of features per analysis, many of these have a non-biological origin. Stable isotope labelling (SIL) approaches can serve as credentialing strategies to distinguish biologically relevant features from background signals, but implementing these experiments at large scale remains challenging. Here, we developed a SIL-based approach for high-throughput untargeted metabolomics in S. cerevisiae, including deep-48 well format-based cultivation and metabolite extraction, building on the peak annotation and verification engine (PAVE) tool. Aqueous and nonpolar extracts were analysed using HILIC and RP liquid chromatography, respectively, coupled to Orbitrap Q Exactive HF mass spectrometry. Of the approximately 37,000 total detected features, only 3-7% of the features were credentialed and used for data analysis with open-source software such as MS-DIAL, MetFrag, Shinyscreen, SIRIUS CSI:FingerID, and MetaboAnalyst, leading to the successful annotation of 198 metabolites using MS2 database matching. Comparable metabolic profiles were observed for wild-type and sdh1Δ yeast strains grown in deep-48 well plates versus the classical shake flask format, including the expected increase in intracellular succinate concentration in the sdh1Δ strain. The described approach enables high-throughput yeast cultivation and credentialing-based untargeted metabolomics, providing a means to efficiently perform molecular phenotypic screens and help complete metabolic networks.
Disciplines :
Biochimie, biophysique & biologie moléculaire
Auteur, co-auteur :
FAVILLI, Lorenzo ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Environmental Cheminformatics
GRIFFITH, Corey ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Enzymology and Metabolism
H2020 - 814418 - SinFonia - Synthetic biology-guided engineering of Pseudomonas putida for biofluorination
Projet FnR :
FNR12341006 - Environmental Cheminformatics To Identify Unknown Chemicals And Their Effects, 2018 (01/10/2018-30/09/2023) - Emma Schymanski
Organisme subsidiant :
Fonds National de la Recherche Luxembourg European Commission Union Européenne
Subventionnement (détails) :
The Environmental Cheminformatics and the Enzymology and Metabolism Groups of the LCSB, University of Luxembourg, Randolph R. Singh of the Institut Français de Recherche pour l’Exploitation de la Mer, and Rainer Schuhmacher of the Universität für Bodenkultur Wien, are acknowledged, for the valuable and constructive suggestions during the planning and development of this research work, along with the metabolomics platform of the LCSB for the technical support during the LC-MS analysis.LF and ELS acknowledge funding support from the Luxembourg National Research Fund (FNR) for project A18/BM/12341006, while CLL and CMG acknowledge support from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 814418 (SinFonia).
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