redLips: a comprehensive mechanistic model of the lipid metabolic network of yeast

Over the last decades, yeast has become a key model organism for the study of lipid biochemistry. Because the regulation of lipids has been closely linked to various physiopathologies, the study of these biomolecules could lead to new diagnostics and treatments. Before the field can reach this point, however, sufficient tools for integrating and analyzing the ever-growing availability of lipidomics data will need to be developed. To this end, genome-scale models (GEMs) of metabolic networks are useful tools, though their large size and complexity introduces too much uncertainty in the accuracy of predicted outcomes. Ideally, therefore, a model for studying lipids would contain only the pathways required for the proper analysis of these biomolecules, but would not be an ad hoc reduction. We hereby present a metabolic model that focuses on lipid metabolism constructed through the integration of detailed lipid pathways into an already existing GEM of Saccharomyces cerevisiae. Our model was then systematically reduced around the subsystems defined by these pathways to provide a more manageable model size for complex studies. We show that this model is as consistent and inclusive as other yeast GEMs regarding the focus and detail on the lipid metabolism, and can be used as a scaffold for integrating lipidomics data to improve predictions in studies of lipid-related biological functions.

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

Mapping and Analysis of the Complexity of Lipid Metabolism for Applications in Health and Sustainability

Vassily Hatzimanikatis, Sofia Tsouka

Metabolism is a network of biotransformations that sustain life in cells. Over the past decades, metabolism has been studied in multiple contexts, ranging from medicine to industrial manufacturing. In particular, lipid metabolism has been linked to various physiopathologies, and its study could lead to new diagnostics and treatments. Metabolic networks are extremely complex and a systems approach is essential in deciphering the different levels of cellular organization and regulation that an organism possesses. Genome-scale metabolic models (GEMs) encompass all the available information for an organism, though their large size and complexity introduce a lot of uncertainty in the accuracy of predictions. GEM reductions are usually done in an ad hoc manner to produce context-specific models, and cannot serve multiple studies. The study of regulatory mechanisms requires the development of kinetic models that can accurately capture dynamic responses to perturbations. To this end, constraint-based models can be enhanced through the integration of kinetic information. Constructing consistent large-scale kinetic models is a challenging endeavor, since detailed knowledge about regulatory mechanisms and kinetic parameter values is scarce.Examining the mechanisms of enzymatic and metabolic restructuring in states of mutation could increase our fundamental understanding of how diseases evolve, and facilitate phenotype mapping. Metabolic Control Analysis (MCA) has been a well-established tool for the prediction and evaluation of genetic modification strategies. However, it fails to account for the physiological limitations of an organism and can often lead to unrealistic predictions.In this thesis, we developed computational models, tools, and methodologies to facilitate the study of lipids and their regulatory mechanisms. Firstly, we constructed and curated a metabolic model that focuses on lipid metabolism, through the integration of detailed lipid pathways into a GEM of S. cerevisiae. This model was then systematically reduced around these pathways to provide a more manageable model size for complex studies. We show that this model is as consistent and inclusive as other yeast GEMs, and can be used as a scaffold for integrating lipidomics data to improve predictions in studies of lipid-related biological functions. Secondly, we used this model as a basis to build a large-scale kinetic model. Enzymes in the lipid metabolism are typically promiscuous and multifunctional, giving rise to enzymatic coupling. To accurately capture these properties, we assigned suitable kinetic rate expressions to the reactions in the network. We generated populations of kinetic parameter sets through a sampling-based workflow and we demonstrate how the consideration of enzymatic coupling is essential for factual predictions. Thirdly, we developed a constraint-based formulation that utilizes MCA-based control coefficients for the consistent derivation of metabolic engineering strategies. We show how the parametrization and introduction of biological constraints enhances this formulation in comparison to the classical MCA approach, and we highlight its ability to generate alternative optimal strategies. Fourthly, we defined and introduced additional mathematical objectives and constraints to this formulation to enable the mapping of enzymes that are responsible for different phenotypes. Concluding, we discuss the contribution and potential applications of this thesis.

EPFL2020

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