Mapping condition-dependent regulation of lipid metabolism in Saccharomyces cerevisiae

• Published in: G3 : genes - genomes - genetics Vol. 3; no. 11; pp. 1979 - 1995
• Main Authors: Jewett, Michael C, Workman, Christopher T, Nookaew, Intawat, Pizarro, Francisco A, Agosin, Eduardo, Hellgren, Lars I, Nielsen, Jens
• Format: Journal Article
• Language: English
• Published: United States Genetics Society of America 01.11.2013
• Subjects: Amino Acids - chemistry; Amino Acids - metabolism; Carbon Isotopes - chemistry; Carbon Isotopes - metabolism; chemostat; cultures; DNA - metabolism; escherichia-coli; fatty-acid synthesis; Gene Expression Profiling; Genome, Fungal; genome-reduced bacterium; growth temperature; integrated systems biology; Investigations; lipid metabolism; Lipid Metabolism - genetics; Metabolic Networks and Pathways - genetics; metabolome; Metabolome - genetics; Models, Theoretical; omics; phospholipid-synthesis; Principal Component Analysis; regulation; RNA, Messenger - metabolism; Saccharomyces cerevisiae - genetics; Saccharomyces cerevisiae - metabolism; Saccharomyces cerevisiae Proteins - genetics; Saccharomyces cerevisiae Proteins - metabolism; sterol synthesis; transcriptional; yeast; omics; regulation; integrated systems biology; metabolome; lipid metabolism
• ISSN: 2160-1836; 2160-1836
• DOI: 10.1534/g3.113.006601

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.