Production of a thermostable fumarase in Saccharomyces cerevisiae for the bioconversion of fumarate to L-malate

The gene encoding fumarase (fum) from Thermus thermophilus was expressed in yeast Saccharomyces cerevisiae. The recombinant cells were heated at 70°C to inactivate indigenous enzymes and used for the bioconversion of fumaric acid to L-malic acid. By heating the host cells at 70°C, substrate is able to go across the heat-damaged membrane of the microorganism and a desired product can be formed. This new concept, called Synthetic Metabolic Engineering (SME), has already been applied successfully in Escherichia coli. Unfortunately, E. coli membrane is weakened too much during the heat treatment and enzyme leakage appears. The surface structure of yeast is more rigid than that of E. coli and this might be taken as an advantage for application of SME. When continuous or repeated-batch reaction is carried out, enzyme leakage becomes a major drawback. It is assumed that yeast cells could overcome this problem by retaining more enzymes in the cell during and after the heat treatment. In order to prove this hypothesis, a thermophilic fumarase (FUM) was over-expressed in two hosts, S. cerevisiae as well as E. coli. fum was first modified to be over-expressed in yeast cells and FUM was successfully produced in yeasts. Optimization of SME techniques was carried out for yeast cells. Then, enzyme activity and enzyme leakage was investigated for both strains. E. coli showed high level of FUM expression, though considerable amount of enzyme leaked to supernatant. On the other hand, even though the level of FUM expression in S. cerevisiae was low, yeast cells overcome leakage problem and are re-usable. This study showed the first trial of SME in yeast cells and possibility of utilization of yeast as a host strain for SME.

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

Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models

Stefano Andreozzi, Vassily Hatzimanikatis, Ljubisa Miskovic, Keng Cher Soh

Rational metabolic engineering methods are increasingly employed in designing the commercially viable processes for the production of chemicals relevant to pharmaceutical, biotechnology, and food and beverage industries. With the growing availability of omics data and of methodologies capable to integrate the available data into models, mathematical modeling and computational analysis are becoming important in designing recombinant cellular organisms and optimizing cell performance with respect to desired criteria. In this contribution, we used the computational framework ORACLE (Optimization and Risk Analysis of Complex Living Entities) to analyze the physiology of recombinant E. coli producing 1,4-butanediol (BDO) and to identify potential strategies for improved production of BDO. The framework allowed us to integrate data across multiple levels and to construct a population of large-scale kinetic models despite the lack of available information about kinetic properties of every enzyme in the metabolic pathways. We analyzed these models and we found that the enzymes that primarily control the fluxes leading to BDO production are part of central glycolysis, the lower branch of tricarboxylic acid (TCA) cycle and the novel BDO production route. Interestingly, among the enzymes between the glucose uptake and the BDO pathway, the enzymes belonging to the lower branch of TCA cycle have been identified as the most important for improving BDO production and yield. We also quantified the effects of changes of the target enzymes on other intracellular states like energy charge, cofactor levels, redox state, cellular growth, and byproduct formation. Independent earlier experiments on this strain confirmed that the computationally obtained conclusions are consistent with the experimentally tested designs, and the findings of the present studies can provide guidance for future work on strain improvement. Overall, these studies demonstrate the potential and effectiveness of ORACLE for the accelerated design of microbial cell factories.

Elsevier2016

Exploring enhancement of 1,4-butanediol production in recombinant E. coli using Large-scale kinetic models

Stefano Andreozzi, Anirikh Chakrabarti, Vassily Hatzimanikatis, Ljubisa Miskovic

Rational metabolic engineering methods are increasingly employed in designing the commercially viable processes for the production of chemicals relevant to pharmaceutical, biotechnology, and food and beverage industries. With the growing availability of omics data and of methodologies capable to integrate the available data into models, mathematical modeling and computational analysis are becoming important in designing recombinant cellular organisms and optimizing cell performance with respect to desired criteria. In this contribution, we used the computational framework ORACLE[1] (Optimization and Risk Analysis of Complex Living Entities) to analyze the physiology of recombinant E. coli producing 1,4-butanediol (BDO) and to identify potential strategies for improved production of BDO. The framework allowed us to integrate data across multiple levels and to construct a population of large-scale kinetic models despite the lack of available information about kinetic properties of every enzyme in the metabolic pathways. We analyzed these models and we found that the enzymes that primarily control the fluxes leading to BDO production are part of central glycolysis, the lower branch of tricarboxylic acid cycle and the novel BDO production route. We further analyzed these models to predict the effects of changes of the target enzymes on other intracellular states like energy charge, cofactor levels, redox state, cellular growth and byproduct formation. We also identified targets for metabolic engineering for improved BDO production and yield, while taking into consideration other intracellular states. The conclusions of the performed analysis are consistent with the experimentally tested designs, and these results demonstrate the potential and effectiveness of ORACLE for the design of microbial cell factories.

2016

Kinetic reconstruction and analysis of sphingolipid metabolism

Vassily Hatzimanikatis, Howard Riezman, Georgios Savoglidis

2014

Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models

Stefano Andreozzi, Vassily Hatzimanikatis, Ljubisa Miskovic, Keng Cher Soh

Elsevier2016

Exploring enhancement of 1,4-butanediol production in recombinant E. coli using Large-scale kinetic models

Stefano Andreozzi, Anirikh Chakrabarti, Vassily Hatzimanikatis, Ljubisa Miskovic

Rational metabolic engineering methods are increasingly employed in designing the commercially viable processes for the production of chemicals relevant to pharmaceutical, biotechnology, and food and beverage industries. With the growing availability of omics data and of methodologies capable to integrate the available data into models, mathematical modeling and computational analysis are becoming important in designing recombinant cellular organisms and optimizing cell performance with respect to desired criteria. In this contribution, we used the computational framework ORACLE[1] (Optimization and Risk Analysis of Complex Living Entities) to analyze the physiology of recombinant E. coli producing 1,4-butanediol (BDO) and to identify potential strategies for improved production of BDO. The framework allowed us to integrate data across multiple levels and to construct a population of large-scale kinetic models despite the lack of available information about kinetic properties of every enzyme in the metabolic pathways. We analyzed these models and we found that the enzymes that primarily control the fluxes leading to BDO production are part of central glycolysis, the lower branch of tricarboxylic acid cycle and the novel BDO production route. We further analyzed these models to predict the effects of changes of the target enzymes on other intracellular states like energy charge, cofactor levels, redox state, cellular growth and byproduct formation. We also identified targets for metabolic engineering for improved BDO production and yield, while taking into consideration other intracellular states. The conclusions of the performed analysis are consistent with the experimentally tested designs, and these results demonstrate the potential and effectiveness of ORACLE for the design of microbial cell factories.

2016