Optimization of the production of methyl ethyl ketone in recombinant Pseudomonas putida using large-scale kinetic models

The production of second generation of biofuels by conversion of inexpensive feedstocks is a basis for future bio-sustainable economy. Among many proposed molecules, methyl ethyl ketone (MEK) emerged as one of the most prominent fuel candidates due to its high-energy density, low emissions, and good transport properties. There is no known natural producer of MEK, but there were some recent studies to express its production pathway in different organisms. In all of these attempts, reported yields were very low. Among industrial workhorses such as E. coli and S. cerevisiae, P. putida has emerged as one of the most promising biofuel production hosts due to its tolerance to high toxicity compounds. For instance, it is reported that P. putida can grow in the presence of high concentrations of butanol1. This highly adaptive bacterium has been found to survive and grow on a broad range of substrates from pure caffeine to toxic industrial waste. In this study, we used the ORACLE framework to build, for the first time, a population of large-scale kinetic models of recombinant P. putida producing MEK. ORACLE2,3 (Optimization and Risk Analysis of Complex Living Entities) is a suite of computational tools capable of identifying enzymes with the highest impact on the production of target molecules. We started by embedding the MEK production pathway in the genome-scale model iJN7464 of P. putida, and then we derived a consistently reduced stoichiometric model. The obtained core model consists of 302 reactions and 210 mass balances distributed over cytosol, periplasm and extracellular environment. We then performed a series of tests to verify the consistency of the core model with its genome-scale counterpart. We next integrated fluxomics and metabolomics data from experiments and literature and performed Thermodynamic-Based Flux Analysis (TFA) to compute the thermodynamically feasible flux profiles that are consistent with the observed data. We further integrated the available information about the kinetic properties of enzymes involved in the modeled metabolic network. For reactions with missing or incomplete information about kinetic parameters, we performed a Monte Carlo sampling to generate missing information. We then constructed a population of 250’000 models, and we used these models to simultaneously optimize specific productivity of MEK and yield of MEK from glucose as a sole carbon source while taking into consideration the metabolic parameters such as catabolic and anabolic reduction charges. This way, we were able to identify enzymes that are potential candidates for metabolic engineering strategies towards obtaining a P. putida strain with improved specific productivity of MEK and its yield from glucose. This work demonstrates the potential and usefulness of ORACLE for optimizing production of biofuels and biochemicals in metabolic engineering and synthetic biology applications. 1. Rühl, J., Schmid, A. & Blank, L. M. Selected Pseudomonas putida strains able to grow in the presence of high butanol concentrations. Appl. Environ. Microbiol. 75, 4653–6 (2009). 2. Miskovic, L. & Hatzimanikatis, V. Production of biofuels and biochemicals: in need of an ORACLE. Trends in biotechnology 28, 391–7 (2010). 3. Chakrabarti, A., Miskovic, L., Soh, K. C. & Hatzimanikatis, V. Towards kinetic modeling of genome-scale metabolic networks without sacrificing stoichiometric, thermodynamic and physiological constraints. Biotechnol. J. 8, 1043–57 (2013). 4. Nogales, J., Palsson, B. Ø. & Thiele, I. A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: iJN746 as a cell factory. BMC Syst. Biol. 2, 79 (2008).

Discovery and Evaluation of Novel Pathways for Production of the Second Generation of Biofuels

Meriç Ataman, Noushin Hadadi, Vassily Hatzimanikatis, Ljubisa Miskovic, Milenko Tokic

In an effort of overcoming the limited availability of fossil energy resources the focus of the research and development in the area of biofuels has moved towards developing the second generation of fuels that should be produced via microbial fermentation. The idea is to use as a feedstock inexpensive and abundant waste materials such as lignocellulosic biomass. The second generation biofuels should satisfy several criteria such as lower emission, higher energy density and should be less corrosive to engines. However, currently used industrial workhorses such as E. coli and S. cerevisiae do not produce many of these molecules naturally. Furthermore, for majority of these molecules there are no known biochemical pathways. The need for discovery of novel biosynthetic pathways towards desired molecules sparked the development of computational tools that are capable of reconstructing feasible reaction steps between a given set of starting compounds (precursors) and a molecule of interest (a target compound). In this study, we performed the retrobiosynthesis analysis for all the candidates applying Biochemical Network Integrated Computational Explorer (BNICE.ch) and we reconstructed the metabolic network for 69 fuel compounds. We analyzed compounds (biological or chemical) and reactions one-step away from these target molecules and the appearance of the potential substrates in their reconstructed metabolic network. Based on the results of this analysis, coupled with other criteria such as the Gibes free energy of formation, combustion potential and expert opinion, we chose the 5 highest ranked fuel candidates for further pathway reconstruction studies. For these 5 fuel candidates we have created 1.8 million de novo pathways with different number of reaction steps for further evaluation. We have then embedded the reconstructed pathways in the genome-°©‐scale models of the two chassis organisms E. coli and P. putida and we performed the pathway evaluation with respect to their maximum theoretical yield using two methods (i) Flux Balance Analysis (FBA), and (ii) Thermodynamic-based Flux Analysis (TFA). All the pathways that did not satisfy the thermodynamic constraints were rejected. Our results indicated that in the evaluation and ranking of the pathways the thermodynamics is a necessary consideration. Specifically, we showed that the majority of the pathways that were determined as feasible based on FBA, they were not feasible based on TFA. Moreover, we also showed that some pathways that were determined as feasible in both organisms based on FBA, they were not feasible in both of them based on TFA. Also, the yield of the feasible pathways can differ depending on the organism. These results demonstrate how TFA can also guide the selection of a suitable chassis organism. For all discovered reactions in the feasible pathways we have also identified known enzymes from the KEGG database with the closest reaction similarity that could be engineered for the implementation of novel reactions. This study shows the full potential of computational tools for discovery and design of novel synthetic pathways and their relevance for future developments in the area of metabolic engineering and synthetic biology.

2016

Metabolic control analysis of the central carbon pathway in optimally grown E. coli

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

The engineering of the metabolism requires the building of reliable kinetic models of the metabolic pathways through the integration of the information from heterogeneous data sources. Unfortunately, the quantitative knowledge about fluxes, mechanisms or kinetic parameters of the constituent reactions of the metabolic network is most of the times uncertain or missing, due to limitations in the measurement techniques of the relevant biological properties of the cell and variability in experimental conditions. In this contribution, we employ ORACLE (Optimization and Risk Analysis of Complex Living Entities), the computational framework for the analysis and optimization of metabolic networks that addresses these issues. ORACLE allows us to generate populations of kinetic models that account explicitly for the physico-chemical and thermodynamic features and constraints of metabolic networks. We show how ORACLE incorporates fluxomics and metabolomics data and constraints into the central carbon core model of E. coli to uncover its possible steady state behaviors, when the cells are cultivated under the physiological condition of optimal growth on different carbon sources, both anaerobically and aerobically. Alternative cases of possible intracellular flux and thermodynamic states, corresponding to the given experimental data, are identified and subsequently the kinetic analysis is performed to evaluate how the control over fluxes and concentrations is distributed over the metabolic network. We demonstrate that ORACLE methodology provides guidance for metabolic engineering in spite of uncertain/missing information in the local/global properties of the metabolic network, integrating a statistical account on the semi-quantitative available data with consistent thermodynamic information and plausible hypotheses.

2012

Discovery and evaluation of novel pathways for production of methyl ethyl ketone

Meriç Ataman, Noushin Hadadi, Vassily Hatzimanikatis, Ljubisa Miskovic, Milenko Tokic

The limited supply of fossil fuels and environmental reasons shifted research in industry and academia towards finding their replacements. An important part of these efforts is focused on a sustainable production of the 2nd generation of biofuels. Among many proposed molecules, one of the most prominent fuel candidates is Methyl Ethyl Ketone (MEK). MEK, also referred in the literature as 2-butanone, is the most important commercially produced ketone with a broad application as a solvent for paints and adhesives. MEK shows superior characteristics compared the existing fuels regarding thermo-physical and transport properties, increased combustion stability at low engine load and cold boundary conditions while overcoming their limitations on the particle emissions. There is no known native producer of MEK, and there were recent attempts to introduce biosynthetic pathways and produce this molecule in E. coli and yeast. However, none of these attempts resulted in commercially viable amounts of produced MEK. In this study, we used Biochemical Network Integrated Computational Explorer (BNICE.ch) to explore the space of potential biotransformations around MEK. Out of 1325 identified compounds one reaction step away from MEK, we chose 3-oxopentanoic acid, But-3-en-2-one, But-1-en-2-olate and Butylamine for further study as they can: (i) easily be chemically converted to MEK; (ii) be used as precursor metabolites for a range of other valuable chemicals. We have also chosen (2R)-2-Hydroxy-2-methylbutanenitrile for further analysis as it was the only compound with known biotransformation to MEK. We reconstructed 3’679’610 novel biosynthetic pathways up to a length of 4 reaction steps from 157 central carbon metabolites of E. coli toward these 5 compounds. We then embedded the reconstructed pathways in the genome-scale model of the E. coli, and we performed the pathway evaluation with respect to their maximum theoretical yield using first Flux Balance Analysis (FBA) and then Thermodynamic-based Flux Analysis (TFA). Our results showed that thermodynamics is a necessary step in pathway feasibility analysis because a majority of FBA feasible pathways were infeasible in TFA due to the thermodynamic constraints. More precisely, 13.25% (487’411) of all reconstructed pathways were FBA feasible, whereas only 0.51% (18’925) were TFA feasible. We next identified enzymes from the KEGG database with the closest reaction similarity to the novel reactions in the TFA feasible pathways as candidates for direct implementation or enzyme engineering of novel reactions. Furthermore, we classified the TFA feasible pathways depending on the reactions and precursors that were essential for production of the target molecules. Finally, all the feasible pathways were ranked based on the yield, length, number of known reaction steps and enzyme similarity score. In this way, the highest ranked pathways can be considered for experimental implementation. In this study, we illustrated the application of BNICE.ch for discovery and design of novel synthetic pathways and its potential for future developments in the area of metabolic engineering and synthetic biology.

2017

Discovery and Evaluation of Novel Pathways for Production of the Second Generation of Biofuels

Meriç Ataman, Noushin Hadadi, Vassily Hatzimanikatis, Ljubisa Miskovic, Milenko Tokic

2016

Discovery and evaluation of novel pathways for production of methyl ethyl ketone

Meriç Ataman, Noushin Hadadi, Vassily Hatzimanikatis, Ljubisa Miskovic, Milenko Tokic

2017