Dynamic Metabolic Footprinting Reveals the Key Components of Metabolic Network in Yeast Saccharomyces cerevisiae
Metabolic footprinting offers a relatively easy approach to exploit the potentials of metabolomics for phenotypic characterization of microbial cells. To capture the highly dynamic nature of metabolites, we propose the use of dynamic metabolic footprinting instead of the traditional method which rel...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | International Journal of Genomics |
| Online Access: | http://dx.doi.org/10.1155/2014/894296 |
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| author | Pramote Chumnanpuen Michael Adsetts Edberg Hansen Jørn Smedsgaard Jens Nielsen |
| author_facet | Pramote Chumnanpuen Michael Adsetts Edberg Hansen Jørn Smedsgaard Jens Nielsen |
| author_sort | Pramote Chumnanpuen |
| collection | DOAJ |
| description | Metabolic footprinting offers a relatively easy approach to exploit the potentials of metabolomics for phenotypic characterization of microbial cells. To capture the highly dynamic nature of metabolites, we propose the use of dynamic metabolic footprinting instead of the traditional method which relies on analysis at a single time point. Using direct infusion-mass spectrometry (DI-MS), we could observe the dynamic metabolic footprinting in yeast S. cerevisiae BY4709 (wild type) cultured on 3 different C-sources (glucose, glycerol, and ethanol) and sampled along 10 time points with 5 biological replicates. In order to analyze the dynamic mass spectrometry data, we developed the novel analysis methods that allow us to perform correlation analysis to identify metabolites that significantly correlate over time during growth on the different carbon sources. Both positive and negative electrospray ionization (ESI) modes were performed to obtain the complete information about the metabolite content. Using sparse principal component analysis (Sparse PCA), we further identified those pairs of metabolites that significantly contribute to the separation. From the list of significant metabolite pairs, we reconstructed an interaction map that provides information of how different metabolic pathways have correlated patterns during growth on the different carbon sources. |
| format | Article |
| id | doaj-art-8254a0e46c294fd09dc40d201398d4d0 |
| institution | Kabale University |
| issn | 2314-436X 2314-4378 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Genomics |
| spelling | doaj-art-8254a0e46c294fd09dc40d201398d4d02025-02-03T06:01:40ZengWileyInternational Journal of Genomics2314-436X2314-43782014-01-01201410.1155/2014/894296894296Dynamic Metabolic Footprinting Reveals the Key Components of Metabolic Network in Yeast Saccharomyces cerevisiaePramote Chumnanpuen0Michael Adsetts Edberg Hansen1Jørn Smedsgaard2Jens Nielsen3Department of Zoology, Faculty of Science, Kasetsart University, Bangkok 10900, ThailandInstitut Pasteur Korea, Sampyeong-dong 696, Bundang-gu, Seongnam-si, 463-400 Gyeonggi-do, Republic of KoreaNational Food Institute, Technical University of Denmark, Mørkhøj Bygade 19, 2860 Søborg, DenmarkDepartment of Chemical and Biological Engineering, Chalmers University of Technology, Kemivägen 10, 412 96 Gothenburg, SwedenMetabolic footprinting offers a relatively easy approach to exploit the potentials of metabolomics for phenotypic characterization of microbial cells. To capture the highly dynamic nature of metabolites, we propose the use of dynamic metabolic footprinting instead of the traditional method which relies on analysis at a single time point. Using direct infusion-mass spectrometry (DI-MS), we could observe the dynamic metabolic footprinting in yeast S. cerevisiae BY4709 (wild type) cultured on 3 different C-sources (glucose, glycerol, and ethanol) and sampled along 10 time points with 5 biological replicates. In order to analyze the dynamic mass spectrometry data, we developed the novel analysis methods that allow us to perform correlation analysis to identify metabolites that significantly correlate over time during growth on the different carbon sources. Both positive and negative electrospray ionization (ESI) modes were performed to obtain the complete information about the metabolite content. Using sparse principal component analysis (Sparse PCA), we further identified those pairs of metabolites that significantly contribute to the separation. From the list of significant metabolite pairs, we reconstructed an interaction map that provides information of how different metabolic pathways have correlated patterns during growth on the different carbon sources.http://dx.doi.org/10.1155/2014/894296 |
| spellingShingle | Pramote Chumnanpuen Michael Adsetts Edberg Hansen Jørn Smedsgaard Jens Nielsen Dynamic Metabolic Footprinting Reveals the Key Components of Metabolic Network in Yeast Saccharomyces cerevisiae International Journal of Genomics |
| title | Dynamic Metabolic Footprinting Reveals the Key Components of Metabolic Network in Yeast Saccharomyces cerevisiae |
| title_full | Dynamic Metabolic Footprinting Reveals the Key Components of Metabolic Network in Yeast Saccharomyces cerevisiae |
| title_fullStr | Dynamic Metabolic Footprinting Reveals the Key Components of Metabolic Network in Yeast Saccharomyces cerevisiae |
| title_full_unstemmed | Dynamic Metabolic Footprinting Reveals the Key Components of Metabolic Network in Yeast Saccharomyces cerevisiae |
| title_short | Dynamic Metabolic Footprinting Reveals the Key Components of Metabolic Network in Yeast Saccharomyces cerevisiae |
| title_sort | dynamic metabolic footprinting reveals the key components of metabolic network in yeast saccharomyces cerevisiae |
| url | http://dx.doi.org/10.1155/2014/894296 |
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