Jewett, Michael Christopher1; Workman, Christopher4; Nookaew, Intawat5; Pizarro, Francisco A5; Agosin, Eduardo1; Hellgren, Lars6; Nielsen, Jens7
1 Department of Systems Biology, Technical University of Denmark2 Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark3 Department of Biotechnology, Technical University of Denmark4 Regulatory Genomics, Department of Biotechnology and Biomedicine, Technical University of Denmark5 Technical University of Denmark6 Systems Metabolic Lipidology, Department of Biotechnology and Biomedicine, Technical University of Denmark7 Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark
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 13C-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.