Limits...
Mapping condition-dependent regulation of lipid metabolism in Saccharomyces cerevisiae.

Jewett MC, Workman CT, Nookaew I, Pizarro FA, Agosin E, Hellgren LI, Nielsen J - G3 (Bethesda) (2013)

Bottom Line: Here, we map condition-dependent regulation controlling lipid metabolism in Saccharomyces cerevisiae by measuring 5636 mRNAs, 50 metabolites, 97 lipids, and 57 (13)C-reaction fluxes in yeast using a three-factor full-factorial design.To query this network, we developed integrative methods for correlation of multi-omics datasets that elucidate global regulatory signatures.Our data highlight many characterized regulators of lipid metabolism and reveal that sterols are regulated more at the transcriptional level than are amino acids.

View Article: PubMed Central - PubMed

Affiliation: Center for Microbial Biotechnology, DTU Systems Biology, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark.

ABSTRACT
Lipids play a central role in cellular function as constituents of membranes, as signaling molecules, and as storage materials. Although much is known about the role of lipids in regulating specific steps of metabolism, comprehensive studies integrating genome-wide expression data, metabolite levels, and lipid levels are currently lacking. Here, we map condition-dependent regulation controlling lipid metabolism in Saccharomyces cerevisiae by measuring 5636 mRNAs, 50 metabolites, 97 lipids, and 57 (13)C-reaction fluxes in yeast using a three-factor full-factorial design. Correlation analysis across eight environmental conditions revealed 2279 gene expression level-metabolite/lipid relationships that characterize the extent of transcriptional regulation in lipid metabolism relative to major metabolic hubs within the cell. To query this network, we developed integrative methods for correlation of multi-omics datasets that elucidate global regulatory signatures. Our data highlight many characterized regulators of lipid metabolism and reveal that sterols are regulated more at the transcriptional level than are amino acids. Beyond providing insights into the systems-level organization of lipid metabolism, we anticipate that our dataset and approach can join an emerging number of studies to be widely used for interrogating cellular systems through the combination of mathematical modeling and experimental biology.

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A systems approach to mapping condition-dependent lipid metabolism in the yeast Saccharomyces cerevisiae. (A) Cartoon representation of the 2 × 3 factorial design showing each experimental condition as a point on a cube (C-limited, C; N-limited, N; aerobic, O; anaerobic, A; 30°, T; and 15°, t). (B) For each condition (e.g., COT), mRNAs, metabolites, lipids, and reaction fluxes were measured and mapped onto a metabolic model visualized in Cytoscape. (C–F) Integrative analyses used to query the measurement data. These included the identification of correlation networks (C), co-regulated gene neighborhoods (D), co-regulated pathway neighborhoods (E), and transcription factor (TF) correlated modules (F). (D and E) M1 represents a target metabolite in the reaction network. Node colors are meant to indicate a log2 color bar for measurement ratios, such as aerobic vs. anaerobic conditions.
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fig1: A systems approach to mapping condition-dependent lipid metabolism in the yeast Saccharomyces cerevisiae. (A) Cartoon representation of the 2 × 3 factorial design showing each experimental condition as a point on a cube (C-limited, C; N-limited, N; aerobic, O; anaerobic, A; 30°, T; and 15°, t). (B) For each condition (e.g., COT), mRNAs, metabolites, lipids, and reaction fluxes were measured and mapped onto a metabolic model visualized in Cytoscape. (C–F) Integrative analyses used to query the measurement data. These included the identification of correlation networks (C), co-regulated gene neighborhoods (D), co-regulated pathway neighborhoods (E), and transcription factor (TF) correlated modules (F). (D and E) M1 represents a target metabolite in the reaction network. Node colors are meant to indicate a log2 color bar for measurement ratios, such as aerobic vs. anaerobic conditions.

Mentions: In this study, we set out to elucidate global regulatory structure controlling lipid metabolism under different environmental conditions (Figure 1). Toward this goal, we created a model of lipid metabolism that allows for the integration of mRNA, metabolite, lipid, and flux data with known networks of protein-DNA interactions and metabolic reaction stoichiometry. We present the results of our measurements together with integrative methods for analyzing high-throughput experimental datasets. The condition specificity of the measured data provides the possibility to infer some insights into the global regulatory architecture of lipid metabolism (i.e., regulation structure that is general and can be applied over different environmental perturbations) and into how fluxes toward different lipid species are controlled.


Mapping condition-dependent regulation of lipid metabolism in Saccharomyces cerevisiae.

Jewett MC, Workman CT, Nookaew I, Pizarro FA, Agosin E, Hellgren LI, Nielsen J - G3 (Bethesda) (2013)

A systems approach to mapping condition-dependent lipid metabolism in the yeast Saccharomyces cerevisiae. (A) Cartoon representation of the 2 × 3 factorial design showing each experimental condition as a point on a cube (C-limited, C; N-limited, N; aerobic, O; anaerobic, A; 30°, T; and 15°, t). (B) For each condition (e.g., COT), mRNAs, metabolites, lipids, and reaction fluxes were measured and mapped onto a metabolic model visualized in Cytoscape. (C–F) Integrative analyses used to query the measurement data. These included the identification of correlation networks (C), co-regulated gene neighborhoods (D), co-regulated pathway neighborhoods (E), and transcription factor (TF) correlated modules (F). (D and E) M1 represents a target metabolite in the reaction network. Node colors are meant to indicate a log2 color bar for measurement ratios, such as aerobic vs. anaerobic conditions.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3815060&req=5

fig1: A systems approach to mapping condition-dependent lipid metabolism in the yeast Saccharomyces cerevisiae. (A) Cartoon representation of the 2 × 3 factorial design showing each experimental condition as a point on a cube (C-limited, C; N-limited, N; aerobic, O; anaerobic, A; 30°, T; and 15°, t). (B) For each condition (e.g., COT), mRNAs, metabolites, lipids, and reaction fluxes were measured and mapped onto a metabolic model visualized in Cytoscape. (C–F) Integrative analyses used to query the measurement data. These included the identification of correlation networks (C), co-regulated gene neighborhoods (D), co-regulated pathway neighborhoods (E), and transcription factor (TF) correlated modules (F). (D and E) M1 represents a target metabolite in the reaction network. Node colors are meant to indicate a log2 color bar for measurement ratios, such as aerobic vs. anaerobic conditions.
Mentions: In this study, we set out to elucidate global regulatory structure controlling lipid metabolism under different environmental conditions (Figure 1). Toward this goal, we created a model of lipid metabolism that allows for the integration of mRNA, metabolite, lipid, and flux data with known networks of protein-DNA interactions and metabolic reaction stoichiometry. We present the results of our measurements together with integrative methods for analyzing high-throughput experimental datasets. The condition specificity of the measured data provides the possibility to infer some insights into the global regulatory architecture of lipid metabolism (i.e., regulation structure that is general and can be applied over different environmental perturbations) and into how fluxes toward different lipid species are controlled.

Bottom Line: Here, we map condition-dependent regulation controlling lipid metabolism in Saccharomyces cerevisiae by measuring 5636 mRNAs, 50 metabolites, 97 lipids, and 57 (13)C-reaction fluxes in yeast using a three-factor full-factorial design.To query this network, we developed integrative methods for correlation of multi-omics datasets that elucidate global regulatory signatures.Our data highlight many characterized regulators of lipid metabolism and reveal that sterols are regulated more at the transcriptional level than are amino acids.

View Article: PubMed Central - PubMed

Affiliation: Center for Microbial Biotechnology, DTU Systems Biology, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark.

ABSTRACT
Lipids play a central role in cellular function as constituents of membranes, as signaling molecules, and as storage materials. Although much is known about the role of lipids in regulating specific steps of metabolism, comprehensive studies integrating genome-wide expression data, metabolite levels, and lipid levels are currently lacking. Here, we map condition-dependent regulation controlling lipid metabolism in Saccharomyces cerevisiae by measuring 5636 mRNAs, 50 metabolites, 97 lipids, and 57 (13)C-reaction fluxes in yeast using a three-factor full-factorial design. Correlation analysis across eight environmental conditions revealed 2279 gene expression level-metabolite/lipid relationships that characterize the extent of transcriptional regulation in lipid metabolism relative to major metabolic hubs within the cell. To query this network, we developed integrative methods for correlation of multi-omics datasets that elucidate global regulatory signatures. Our data highlight many characterized regulators of lipid metabolism and reveal that sterols are regulated more at the transcriptional level than are amino acids. Beyond providing insights into the systems-level organization of lipid metabolism, we anticipate that our dataset and approach can join an emerging number of studies to be widely used for interrogating cellular systems through the combination of mathematical modeling and experimental biology.

Show MeSH
Related in: MedlinePlus