Towards a kinetic model of the phospholipids biosynthesis of the yeast Saccharomyces cerevisiae

Lipids, as vital eukaryotic cellular components, play a major role in cell, tissue and organ physiology. Alterations of lipid enzymes and metabolic pathways are related to many human diseases, such as cancer, diabetes and neurodegenerative diseases. In this study, we focus on phospholipids, the major components of all cell membranes, which not only act as structural membrane constituents but also play a vital role in cellular energy storage and in signaling pathways. The yeast, Saccharomyces cerevisiae, has been chosen as a cell model, due to its strong homology of proteins and pathways with higher eukaryotes. The benefits of studying the glycerophospholipid metabolism of S. cerevisiae will provide critical insight into phospholipid homeostasis of this model organism in various cellular states. The reconstructed model of phospholipid metabolism consists of 118 reactions and 80 metabolites. Beyond combining knowledge of all published yeast genome-scale models, it incorporates missing reactions reported in the literature along with the current state of the art concerning the participating enzymes. Pathways regarding compounds from the glycolysis pathway and synthesis of acyl-CoAs, required for phospholipid biosynthesis, have also been included. A steady state flux analysis was performed with the growth capacity of the cell being able to vary between maintenance and a maximum experimentally observed growth rate, in aerobic carbon-limiting conditions, with mass balance constraints calculated based on experimental data reported in the most recent yeast genome scale model. A thermodynamic curation of all the reactions of the pathway followed, which constrains the allowed space of the concentration ranges of metabolites of the pathway and also provides information about the directionality of all the reactions. Finally, we applied the ORACLE methodology in order to identify enzymes that determine the lipid composition in the various steady states of the cell. Generating a comprehensive kinetic model will be a useful tool for predictive analysis, drug designing or further biomedical research.

Kinetic reconstruction and analysis of sphingolipid metabolism

Vassily Hatzimanikatis, Howard Riezman, Georgios Savoglidis

Lipids are major constituents of the cell. They are responsible for major properties of the cellular membranes: hydrophobicity, selective permeability and being the scaffold of signaling proteins. Many diseases are associated with alterations in the lipid distribution in the cell and the composition of membrane domains. Metabolic syndrome, obesity, atherosclerosis, as well as Alzheimer’s, Huntington’s diseases and cancer, have an impact in the levels of lipids, with observed alterations in their concentrations compared to the healthy state. Applying computational techniques along with systematic modeling of lipid metabolism can provide insights that can guide biomedical research and develop potential strategies for prevention and cure. In the present study we developed a comprehensive model of the sphingolipid biosynthesis in the yeast Saccharomyces cerevisiae. Sphingolipids are one of the four major lipid categories, along with (glycero)phospholipids, sterols and fatty acids synthesized in the yeast S. cerevisiae. The importance of sphingolipids present in any higher eukaryote has been demonstrated in many recent studies. For this study, S. cerevisiae has been chosen as a model organism due to its high homology of cellular processes with mammalian cells. The developed model will be an essential part towards the construction of a detailed kinetic model of the whole lipidome of the cell. We first constructed a stoichiometric model that contains all the currently known reactions for the biosynthesis of ceramides and complex sphingolipids in the yeast. Additionally, we have accounted for all five reported hydroxylation states, along with the reactions synthesizing the necessary precursor metabolites from other lipid pathways (i.e. palmitate-CoA from fatty acid synthesis and phosphatidylinositol from the phospholipids metabolism). We next developed a kinetic model and we used a large number of lipidomic measurements of wild type yeast to consistently calibrate our model. Curation of the kinetic information of the model came from the comprehensive mining of references for operation of the enzymes as well as ranges of kinetic parameters from online databases. These datasets created the pool for a sampling technique that accounts for the uncertainty in the parameters of the model. This lead to a robust dynamic model, containing mass balances for all the components of the biochemical network as well as terms that accounted for the dillution of these molecules in the cell due to growth. We performed a thorough kinetic analysis of the system by examining the impact of different assumptions in enzyme operation on the levels of ceramides and complex sphingolipids (e.g. substrate competition, (un)competitive inhibition). By applying the principles of Metabolic Control Analysis (MCA) we were able to quantify the effect of enzyme activities on the lipid profiles and we identified enzymes of the biochemical reaction network as efficient targets for metabolic engineering towards a desired state. We also applied a reverse engineering method and we were able to identify enzyme perturbations responsible for an observed altered state compared to the wild type cellular lipidomic profile. Such an approach could lead to the identification of genetic mutations by imploring the information containd within metabolomic measurements. Although we demonstrate the application of the method in models of lipid metabolism it is possible to be applied to biochemical systems of various parts of metabolism and sizes creating a modular platform for kinetic analysis of cellular operations.

2014

Integrating omics data into metabolic models of lipids: a platform for the identification of gene mutations from lipidomics data

Paolo Angelino, Vassily Hatzimanikatis, Howard Riezman, Georgios Savoglidis

Kinetic models of metabolic networks are mapping the biochemical reactions taking part in a cell to a mathematical representation. They are the blueprints of the biochemical network, connecting enzymatic activities and metabolic compounds. Kinetic models have been used in systems biology to produce estimates of response of an organism to stress and to reveal potentially efficient targets for cellular engineering. Lipids are major constituents of the cell membrane. They are responsible for major properties of the cellular membranes: hydrophobicity, selective permeability and being the scaffold of signaling proteins. Many diseases are associated with alterations in the lipid distribution in the cell and the composition of membrane domains. Metabolic syndrome, obesity, atherosclerosis, as well as Alzheimer’s, Huntington’s diseases and cancer, all originate from alterations in some stage of lipid biosynthesis. Therefore, advancement of knowledge in the field of lipid metabolism will provide novel insights for further biomedical research and potential strategies for drug development. In the current study we combine lipidomics data and metabolic models of lipid metabolism in yeast Saccharomyces cerevisiae. We use the models as platform for the application of sensitivity analysis and metabolic control analysis, which involves direct and inverse sensitivity analysis. In direct sensitivity analysis we quantify how the changes in enzymatic activities, either by gene deletion or RNAi, are mapped through the metabolic pathway model into variations in lipid profiles. This analysis allows us to identify the effects of the perturbations in enzyme activities (inhibition or overexpression) on the distribution of the lipids synthesized as the end products of the network. We further extend the analysis by casting the inverse problem. With the inverse sensitivity analysis framework, we consider as input an altered lipidomic profile and we are able to identify, the responsible perturbations in enzyme activities that account for such a change. The results of the analysis have been confirmed based on lipidomics analysis of mutant yeast cells. Although we demonstrate the application of the method in models of lipid metabolism it is possible to be applied to biochemical systems of various parts of metabolism and sizes creating a modular platform for identification of mutations.

2013

A comprehensive reconstruction of the fatty acid biosynthesis in Saccharomyces cerevisiae

Vassily Hatzimanikatis, Iro Oikonomidi, Georgios Savoglidis

The role of lipids in eukaryotic cells is of high importance. They are one of the main components of cell membranes, act as storage of high potential energy as well as being a scaffold of signaling proteins or participate in signaling themselves. Many diseases are associated with alterations in the lipid distribution in the cell and the composition of membrane domains. Metabolic syndrome, obesity, atherosclerosis, as well as Alzheimer’s, Huttington diseases and cancer, all originate from alterations in some stage of lipid biosynthesis. Therefore, advancement of knowledge in the field of lipid metabolism will provide novel insights for further biomedical research and potential strategies for drug development. Fatty acids are the backbone of the lipid metabolism. Being lipids themselves they also participate in the biosynthesis of the majority of the lipid classes. Thus, the study of their synthesis and biotransformation in a cell constitutes the basic step towards a systematic investigation of the phenomena attributed to lipids. In the current study we reconstructed a comprehensive representation of the biosynthesis of fatty acids in a model eukaryote such as Saccharomyces cerevisiae. The model accounts for approximately 300 reactions and 230 metabolites, taking part in 3 compartments of the cell, describing the steps of synthesis, elongation and β-oxidation of fatty acids. We make use of other systems approaches in order to identify the input fluxes to the model and we explore the diverse capabilities of the network with respect to flux profiles and distribution of fatty acids.

2013

Kinetic reconstruction and analysis of sphingolipid metabolism

Vassily Hatzimanikatis, Howard Riezman, Georgios Savoglidis

2014

Integrating omics data into metabolic models of lipids: a platform for the identification of gene mutations from lipidomics data

Paolo Angelino, Vassily Hatzimanikatis, Howard Riezman, Georgios Savoglidis

2013