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.

Show MeSH

Related in: MedlinePlus

Metabolic map for interrogating the connectivity of mRNAs, metabolites, lipids, and reaction fluxes based on the genome-scale model iIN800. This network contains gene (enzyme)-reaction, metabolite/lipid-reaction, and lipid distribution edges (see node type and edge key). Measurement ratios can be visualized with log2-fold changes (see node color key; here, shown for aerobic vs. anaerobic conditions) and gray indicates the lack of a measurement for that node. Individual pathway networks (e.g., phospholipid biosynthesis, sterol biosynthesis, and amino acid biosynthesis) are highlighted. Findings for individual pathways are discussed in the text.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3815060&req=5

fig3: Metabolic map for interrogating the connectivity of mRNAs, metabolites, lipids, and reaction fluxes based on the genome-scale model iIN800. This network contains gene (enzyme)-reaction, metabolite/lipid-reaction, and lipid distribution edges (see node type and edge key). Measurement ratios can be visualized with log2-fold changes (see node color key; here, shown for aerobic vs. anaerobic conditions) and gray indicates the lack of a measurement for that node. Individual pathway networks (e.g., phospholipid biosynthesis, sterol biosynthesis, and amino acid biosynthesis) are highlighted. Findings for individual pathways are discussed in the text.

Mentions: To determine the conditional dependence of mRNA, metabolite, and lipid data, we first constructed a visual map based on the genome-scale metabolic model, iIN800 (Nookaew et al. 2008). Because we lacked measurements for many of the metabolites in iIN800, we centered our biosynthetic network on central metabolism, the tricarboxylic acid (TCA) cycle, amino acid biosynthesis, sterol biosynthesis, steryl ester biosynthesis, triacylglyceride (TAG), biosynthesis, FFA biosynthesis, phospholipid biosynthesis, and sphingolipid biosynthesis. In total, our visual network map consisted of 368 gene, 174 metabolite/lipid, and 131 reaction nodes connected by 849 gene (enzyme)-reaction and metabolite/lipid-reaction edges (Table S6). Nine distribution nodes were also included for mapping information on lipid acyl-chain composition. A distribution node is connected to all of the acyl-chain species involving that particular lipid or fatty acid type. For the reaction nodes, we performed flux balance analysis to estimate values of reaction fluxes in silico under the constraints of maximized biomass production, a steady-state metabolic network, and fixed protein composition (Table S7) (Nookaew et al. 2008). Our network model linking all measurement types was visualized in Cytoscape (Cline et al. 2007) as shown in Figure 3 [with color mapping for measurement log-fold-changes, such as aerobic (O) vs. anaerobic (A) conditions]. Although the interplay between different metabolic pathways could be missed in our visualization approach, we have previously shown that such a model provides a useful framework for exploring the connectivity of mRNAs, metabolites, lipids, and fluxes (Moxley et al. 2009). Additionally, subsequent integrative analyses we performed for this work were not solely based on this visualization and contain the entire metabolic network (see Data integration through hypothesis testing across multiple cellular levels section).


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)

Metabolic map for interrogating the connectivity of mRNAs, metabolites, lipids, and reaction fluxes based on the genome-scale model iIN800. This network contains gene (enzyme)-reaction, metabolite/lipid-reaction, and lipid distribution edges (see node type and edge key). Measurement ratios can be visualized with log2-fold changes (see node color key; here, shown for aerobic vs. anaerobic conditions) and gray indicates the lack of a measurement for that node. Individual pathway networks (e.g., phospholipid biosynthesis, sterol biosynthesis, and amino acid biosynthesis) are highlighted. Findings for individual pathways are discussed in the text.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: Metabolic map for interrogating the connectivity of mRNAs, metabolites, lipids, and reaction fluxes based on the genome-scale model iIN800. This network contains gene (enzyme)-reaction, metabolite/lipid-reaction, and lipid distribution edges (see node type and edge key). Measurement ratios can be visualized with log2-fold changes (see node color key; here, shown for aerobic vs. anaerobic conditions) and gray indicates the lack of a measurement for that node. Individual pathway networks (e.g., phospholipid biosynthesis, sterol biosynthesis, and amino acid biosynthesis) are highlighted. Findings for individual pathways are discussed in the text.
Mentions: To determine the conditional dependence of mRNA, metabolite, and lipid data, we first constructed a visual map based on the genome-scale metabolic model, iIN800 (Nookaew et al. 2008). Because we lacked measurements for many of the metabolites in iIN800, we centered our biosynthetic network on central metabolism, the tricarboxylic acid (TCA) cycle, amino acid biosynthesis, sterol biosynthesis, steryl ester biosynthesis, triacylglyceride (TAG), biosynthesis, FFA biosynthesis, phospholipid biosynthesis, and sphingolipid biosynthesis. In total, our visual network map consisted of 368 gene, 174 metabolite/lipid, and 131 reaction nodes connected by 849 gene (enzyme)-reaction and metabolite/lipid-reaction edges (Table S6). Nine distribution nodes were also included for mapping information on lipid acyl-chain composition. A distribution node is connected to all of the acyl-chain species involving that particular lipid or fatty acid type. For the reaction nodes, we performed flux balance analysis to estimate values of reaction fluxes in silico under the constraints of maximized biomass production, a steady-state metabolic network, and fixed protein composition (Table S7) (Nookaew et al. 2008). Our network model linking all measurement types was visualized in Cytoscape (Cline et al. 2007) as shown in Figure 3 [with color mapping for measurement log-fold-changes, such as aerobic (O) vs. anaerobic (A) conditions]. Although the interplay between different metabolic pathways could be missed in our visualization approach, we have previously shown that such a model provides a useful framework for exploring the connectivity of mRNAs, metabolites, lipids, and fluxes (Moxley et al. 2009). Additionally, subsequent integrative analyses we performed for this work were not solely based on this visualization and contain the entire metabolic network (see Data integration through hypothesis testing across multiple cellular levels section).

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