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Dynamic Metabolic Footprinting Reveals the Key Components of Metabolic Network in Yeast Saccharomyces cerevisiae.

Chumnanpuen P, Hansen MA, Smedsgaard J, Nielsen J - Int J Genomics (2014)

Bottom Line: 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.Both positive and negative electrospray ionization (ESI) modes were performed to obtain the complete information about the metabolite content.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Zoology, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

The cherry-picking process illustrated. Two upper figures show two selected metabolites during the first step, and the lower figure shows their corresponding correlation in time. Both metabolites will be picked. Slopes (yields) in the matrix that had a P value less than 0.01 were set to zero.
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fig2: The cherry-picking process illustrated. Two upper figures show two selected metabolites during the first step, and the lower figure shows their corresponding correlation in time. Both metabolites will be picked. Slopes (yields) in the matrix that had a P value less than 0.01 were set to zero.

Mentions: The first cherry-picking criteria identified a list of single metabolites that showed the proper change over time. After this step, the number of columns in the matrix was reduced to 145 metabolites. Two examples of identified metabolites can be seen in the top two figures in Figure 2.


Dynamic Metabolic Footprinting Reveals the Key Components of Metabolic Network in Yeast Saccharomyces cerevisiae.

Chumnanpuen P, Hansen MA, Smedsgaard J, Nielsen J - Int J Genomics (2014)

The cherry-picking process illustrated. Two upper figures show two selected metabolites during the first step, and the lower figure shows their corresponding correlation in time. Both metabolites will be picked. Slopes (yields) in the matrix that had a P value less than 0.01 were set to zero.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3926413&req=5

fig2: The cherry-picking process illustrated. Two upper figures show two selected metabolites during the first step, and the lower figure shows their corresponding correlation in time. Both metabolites will be picked. Slopes (yields) in the matrix that had a P value less than 0.01 were set to zero.
Mentions: The first cherry-picking criteria identified a list of single metabolites that showed the proper change over time. After this step, the number of columns in the matrix was reduced to 145 metabolites. Two examples of identified metabolites can be seen in the top two figures in Figure 2.

Bottom Line: 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.Both positive and negative electrospray ionization (ESI) modes were performed to obtain the complete information about the metabolite content.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Zoology, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus