A method for analysis and design of metabolism using metabolomics data and kinetic models: Application on lipidomics using a novel kinetic model of sphingolipid metabolism.
We present a model-based method, designated Inverse Metabolic Control Analysis (IMCA), which can be used in conjunction with classical Metabolic Control Analysis for the analysis and design of cellular metabolism. We demonstrate the capabilities of the method by first developing a comprehensively curated kinetic model of sphingolipid biosynthesis in the yeast Saccharomyces cerevisiae. Next we apply IMCA using the model and integrating lipidomics data. The combinatorial complexity of the synthesis of sphingolipid molecules, along with the operational complexity of the participating enzymes of the pathway, presents an excellent case study for testing the capabilities of the IMCA. The exceptional agreement of the predictions of the method with genome-wide data highlights the importance and value of a comprehensive and consistent engineering approach for the development of such methods and models. Based on the analysis, we identified the class of enzymes regulating the distribution of sphingolipids among species and hydroxylation states, with the D-phospholipase SPO14 being one of the most prominent. The method and the applications presented here can be used for a broader, model-based inverse metabolic engineering approach.
Laboratory of Computational Systems Biology, Department of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland.
Georgios
G
Savoglidis
Department of Biochemistry, University of Geneva, CH-1211 Geneva, Switzerland; National Center of Competence in Research "Chemical Biology", CH-1211 Geneva, Switzerland.
Aline Xavier
AX
da Silveira Dos Santos
Department of Biochemistry, University of Geneva, CH-1211 Geneva, Switzerland.
Isabelle
I
Riezman
Laboratory of Computational Systems Biology, Department of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland.
Paolo
P
Angelino
Department of Biochemistry, University of Geneva, CH-1211 Geneva, Switzerland; National Center of Competence in Research "Chemical Biology", CH-1211 Geneva, Switzerland.
Howard
H
Riezman
Laboratory of Computational Systems Biology, Department of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland. Electronic address: vassily.hatzimanikatis@epfl.ch.
Vassily
V
Hatzimanikatis
D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods; D007700:Kinetics; D060847:Metabolic Engineering / Q000379:methods; D064688:Metabolic Flux Analysis / Q000379:methods*; D053858:Metabolic Networks and Pathways / Q000502:physiology; D055442:Metabolome / Q000502:physiology*; D008954:Models, Biological; D010739:Phospholipase D / Q000235:genetics / Q000378:metabolism*; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism*; D013107:Sphingolipids / Q000235:genetics / Q000378:metabolism*
D029701:Saccharomyces cerevisiae Proteins; D013107:Sphingolipids; D010739:Phospholipase D; C471177:SPO14 protein, S cerevisiae
Inverse metabolic engineering; Lipid metabolism; Lipidomics; Nonlinear kinetic models; Sphingolipid regulation; Strain design