Exploring Biosynthetic Control in Lipid Metabolism
Michael C. Jewett1, Intawat Nookaew2, Francisco A. Pizarro3, Lars I. Hellgren4, Eduardo Agosin3, and Jens Nielsen5. (1) Center for Microbial Biotechnology, Technical University of Denmark, BioCentrum-DTU, Kgs. Lyngby, 2800, Denmark, (2) Department of Chemical Engineering, King Mongkut’s University of Technology Thonburi, Bangkok, 10140, Thailand, (3) Department of Chemical and Bioprocess Engineering, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, Santiago, Chile, (4) BioCentrum-DTU, Biochemistry and Nutrition Group, Technical University of Denmark, Building 224, Kgs. Lyngby, 2800, Denmark, (5) Chemical and Biological Engineering, Chalmers University of Technology, Kemivägen 10, Gothenburg, 412 96, Sweden
Lipid metabolism, organization, and transport have been implicated in numerous human diseases ranging from atherosclerosis to cancer to Alzheimer's. Although much is known about the role of lipids and sterols in regulating the biophysical properties of membranes, integrating multiple state measurements which link the genetic architecture to physiology promises to aid in the development of models describing biosynthetic control. Here, we coupled gene expression profiling with metabolomic, lipidomic, and fluxomic techniques to generate a large-scale experimental data set in the yeast Saccharomyces cerevisiae, a model organism for studying higher eukaryotic cells. Our investigation explores the systems-level response of S. cerevisiae grown in chemostat cultures at eight different growth conditions where three factors were varied: 1) high temperature (30°C) versus low temperature (15°C), 2) carbon versus nitrogen limitation, and 3) aerobic versus anaerobic growth. While transcriptional response alone was insufficient to predict metabolic phenotype, characterizing the molecular inventory and operation of the cell in the framework of an expanded genome-scale metabolic model of lipid metabolism clarified existing, and revealed new, regulatory motifs underlying lipid biosynthesis. These results underscore the importance of interrogating genomic, metabolomic, and network information in large-scale models and promises to impact strategies for treatment of obesity related diseases and engineering yeast strains with improved physiological performance at low temperatures.