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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

Key metabolite network on PC1; there are 32 key metabolites linking to each other with 4 hubs, that is, glucose, cyclic AMP, cyclic dAMP, and NAMN in PC1 network. These key metabolites can be used as PC1 to separate the sample taken from glucose culture from ethanol and glycerol cultures. Since there are 3 different patterns of changing in metabolite profile, these nodes are shown in 3 different colors; red (increasing), (decreasing), and blue (nonmonotonous or constant).
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fig6: Key metabolite network on PC1; there are 32 key metabolites linking to each other with 4 hubs, that is, glucose, cyclic AMP, cyclic dAMP, and NAMN in PC1 network. These key metabolites can be used as PC1 to separate the sample taken from glucose culture from ethanol and glycerol cultures. Since there are 3 different patterns of changing in metabolite profile, these nodes are shown in 3 different colors; red (increasing), (decreasing), and blue (nonmonotonous or constant).

Mentions: PC1 ions primarily separate the glucose samples from the ethanol and glycerol samples and PC2 mainly separates the ethanol samples from the glycerol samples. The metabolite pairs from PC1 represent a network as illustrated in Figure 6, and the metabolite pairs from PC2 represent two networks as illustrated in Figure 7. In these networks the edges represent correlations between the corresponding key metabolites. It is observed that most of the key metabolites are amino acids that are linked to cyclic AMP or cyclic dAMP, but glucose is also clearly correlated to several metabolites. The color code indicates the slope with green indicating decreasing concentration with time and red increasing concentration with time. A few metabolites do not change much in concentration profile or do not have a monotonous concentration profile during the whole fermentation, for example, their concentration are increasing in one part of the fermentation and decreasing in another part of the fermentation, and these are marked blue in the networks. It is interesting to note that there is a positive correlation between cAMP and most amino acids, whereas the glucose concentration is negatively correlated with the concentration of several metabolites, for example, glycerol.


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)

Key metabolite network on PC1; there are 32 key metabolites linking to each other with 4 hubs, that is, glucose, cyclic AMP, cyclic dAMP, and NAMN in PC1 network. These key metabolites can be used as PC1 to separate the sample taken from glucose culture from ethanol and glycerol cultures. Since there are 3 different patterns of changing in metabolite profile, these nodes are shown in 3 different colors; red (increasing), (decreasing), and blue (nonmonotonous or constant).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig6: Key metabolite network on PC1; there are 32 key metabolites linking to each other with 4 hubs, that is, glucose, cyclic AMP, cyclic dAMP, and NAMN in PC1 network. These key metabolites can be used as PC1 to separate the sample taken from glucose culture from ethanol and glycerol cultures. Since there are 3 different patterns of changing in metabolite profile, these nodes are shown in 3 different colors; red (increasing), (decreasing), and blue (nonmonotonous or constant).
Mentions: PC1 ions primarily separate the glucose samples from the ethanol and glycerol samples and PC2 mainly separates the ethanol samples from the glycerol samples. The metabolite pairs from PC1 represent a network as illustrated in Figure 6, and the metabolite pairs from PC2 represent two networks as illustrated in Figure 7. In these networks the edges represent correlations between the corresponding key metabolites. It is observed that most of the key metabolites are amino acids that are linked to cyclic AMP or cyclic dAMP, but glucose is also clearly correlated to several metabolites. The color code indicates the slope with green indicating decreasing concentration with time and red increasing concentration with time. A few metabolites do not change much in concentration profile or do not have a monotonous concentration profile during the whole fermentation, for example, their concentration are increasing in one part of the fermentation and decreasing in another part of the fermentation, and these are marked blue in the networks. It is interesting to note that there is a positive correlation between cAMP and most amino acids, whereas the glucose concentration is negatively correlated with the concentration of several metabolites, for example, glycerol.

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