Comparison between elementary flux modes analysis and 13C-metabolic fluxes measured in bacterial and plant cells

<p>Abstract</p> <p>Background</p> <p>¹³C metabolic flux analysis is one of the pertinent ways to compare two or more physiological states. From a more theoretical standpoint, the structural properties of metabolic networks can be analysed to explore feasible metabolic behaviours and to define the boundaries of steady state flux distributions. Elementary flux mode analysis is one of the most efficient methods for performing this analysis. In this context, recent approaches have tended to compare experimental flux measurements with topological network analysis.</p> <p>Results</p> <p>Metabolic networks describing the main pathways of central carbon metabolism were set up for a bacteria species (<it>Corynebacterium glutamicum</it>) and a plant species (<it>Brassica napus</it>) for which experimental flux maps were available. The structural properties of each network were then studied using the concept of elementary flux modes. To do this, coefficients of flux efficiency were calculated for each reaction within the networks by using selected sets of elementary flux modes. Then the relative differences - reflecting the change of substrate <it>i.e</it>. a sugar source for <it>C</it>. <it>glutamicum </it>and a nitrogen source for <it>B</it>. <it>napus </it>- of both flux efficiency and flux measured experimentally were compared. For both organisms, there is a clear relationship between these parameters, thus indicating that the network structure described by the elementary flux modes had captured a significant part of the metabolic activity in both biological systems. In <it>B</it>. <it>napus</it>, the extension of the elementary flux mode analysis to an enlarged metabolic network still resulted in a clear relationship between the change in the coefficients and that of the measured fluxes. Nevertheless, the limitations of the method to fit some particular fluxes are discussed.</p> <p>Conclusion</p> <p>This consistency between EFM analysis and experimental flux measurements, validated on two metabolic systems allows us to conclude that elementary flux mode analysis could be a useful tool to complement ¹³C metabolic flux analysis, by allowing the prediction of changes in internal fluxes before carbon labelling experiments.</p

Investigating Cyanobacteria Metabolism and Channeling-based Regulations via Isotopic Nonstationary Labeling and Metabolomic Analyses

Cyanobacteria have the potential to be low-cost and sustainable cell factories for bio-products; however, many challenges face cyanobacteria as biorefineries. This dissertation seeks to advance non-model photosynthetic organisms for biotechnology applications by characterizing central carbon metabolism and its regulations. Cyanobacteria phenotypes for bio-production are examined and their intracellular metabolism is quantified. Using isotopic labeling experiments, phenotypic relationships between biomass composition, central carbon fluxes, and metabolite pool sizes are investigated. Metabolic analyses of cyanobacteria led to new investigations of flux regulation mechanisms via protein spatial organizations or metabolite channeling. Metabolite channeling is further explored as a hypothesis to explain enigmatic labeling patterns and as a method to organize and regulate enzymes for robust central metabolisms. The insights reveal strategies for redirecting central metabolic fluxes for value-added chemicals as well as broad impacts for intracellular modeling approaches. First, Synechococcus UTEX 2973 was probed with isotopic nonstationary metabolic flux analysis under changing growth conditions. Despite similar genetics to Synechococcus 7942, Synechococcus UTEX 2973’s exhibits a fast growth phenotype with greater carbon fixation driven by higher energy charges, optimal ATP/NADPH ratios, low glycogen production during exponential growth, and a central metabolism that reduces CO2 loss. Unusual labeling patterns indicated metabolite channeling as a possible flux regulation mechanism. As cyanobacteria are known to have carboxysomes, a microcompartment that concentrates CO2 for RuBisCO, it was hypothesized that carboxysome mutants may reveal channeling mechanisms. Carboxysome-free mutants (high CO2 requiring phenotypes) were found to accumulate metabolites and reach higher steady state 13C enrichment, indicating more homogenous cytoplasms. Carboxysome-free mutants may provide a method for unlocking cyanobacteria flux constraints, reducing catabolic repression, and providing a way to contain genetically modified cyanobacteria. To ease the constraints of highly regulated and complex metabolic networks, platform or non-model strains can be used to provide a good starting point for small molecules of interest. To take advantage of cyanobacterial native sugar phosphate metabolisms, Synechococcus was engineered for the photoautotrophic production of a high-value polysaccharide, heparosan, which is an unsulfated polysaccharide important for cosmetic and pharmaceutical applications. Via overexpressing two key enzymes, the recombinant strain improves heparosan production by over 50 folds. Synechococcus was also found to naturally synthesize multiple glycosaminoglycans. Lastly, to further explore metabolite channeling as evidenced by isotopic labeling patterns, we developed cell-free glycolysis pathways and compared their performance with in vivo glycolysis functions in E. coli and its PTS mutants. Enzyme assays, dynamic metabolite labeling and flux analysis further confirmed the hypothesized channel of EMP enzymes where the PTS may be an anchor point to initiate enzyme assemblies. In summary, the outcomes of this thesis provide new insights into non-model phototrophic microbial chassis, reveal flux control mechanisms beyond genetic or transcriptional regulations, and offer practical guidelines for sustainable bio-production via synthetic biology approaches

Identification of metabolic engineering targets through analysis of optimal and sub-optimal routes

Identification of optimal genetic manipulation strategies for redirecting substrate uptake towards a desired product is a challenging task owing to the complexity of metabolic networks, esp. in terms of large number of routes leading to the desired product. Algorithms that can exploit the whole range of optimal and suboptimal routes for product formation while respecting the biological objective of the cell are therefore much needed. Towards addressing this need, we here introduce the notion of structural flux, which is derived from the enumeration of all pathways in the metabolic network in question and accounts for the contribution towards a given biological objective function. We show that the theoretically estimated structural fluxes are good predictors of experimentally measured intra-cellular fluxes in two model organisms, namely, Escherichia coli and Saccharomyces cerevisiae. For a small number of fluxes for which the predictions were poor, the corresponding enzyme-coding transcripts were also found to be distinctly regulated, showing the ability of structural fluxes in capturing the underlying regulatory principles. Exploiting the observed correspondence between in vivo fluxes and structural fluxes, we propose an in silico metabolic engineering approach, iStruF, which enables the identification of gene deletion strategies that couple the cellular biological objective with the product flux while considering optimal as well as sub-optimal routes and their efficiency.This work was supported by the Portuguese Science Foundation [grant numbers MIT-Pt/BS-BB/0082/2008, SFRH/BPD/44180/2008 to ZS] (http://www.fct.pt/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Development of 13C Fingerprint Tool and Its Application for Exploring Carbon and Energy Metabolism in Cyanobacterium Synechocystis sp. PCC 6803

Cyanobacteria are important microbial cell factories that are widely used in the biotechnology filed nowadays. They can use light as the sole energy source to fix CO2, accumulate biomass, and produce various valuable bio-products. Engineered cyanobacterial species can uptake nutrients from wastes to further reduce the cost. Recently, it is reported that cyanobacteria will provide much higher carbon yield than heterotrophs by co-utilizing organic carbons and CO2. However, the quantitative information of such `photo-fermentation\u27 process is still limited. Decoding the carbon metabolism of cyanobacteria during the photo-fermentation process can reveal the functional pathways, carbon distribution, and the energy requirement, all of which will provide guidelines for rational design of metabolic engineering strategies. The emerging of multiple omics tools, e.g. genomics, transcriptomics, proteinomics, and metabolomics analysis, allowed the comprehensive determination of microbial metabolisms. This dissertation describes the development of 13C fingerprint-based method to characterize the carbon metabolic network in cyanobacteria model species Synechocystis sp. PCC 6803 and the integration of this method with metabolic flux analysis and transcriptomics analysis to quantify the diverse carbon and energy metabolism regulation under different internal or external stimuli. The project mainly consists of four aspects: (1) developing the GC-MS based low-cost 13C fingerprint method; (2) exploring the carbon metabolic network structure and quantifying the central carbon metabolism under different environmental conditions; (3) determining the energy requirement for cell maintenance in cyanobacteria; (4) investigating the effects of light conditions on cyanobacterial carbon metabolism. These new findings not only improve our understandings of the flexible carbon metabolism employed by cyanobacteria, but also offer evolutionary insight into photosynthesis and potential applications of photo-fermentation

Revelation of Yin-Yang Balance in Microbial Cell Factories by Data Mining, Flux Modeling, and Metabolic Engineering

The long-held assumption of never-ending rapid growth in biotechnology and especially in synthetic biology has been recently questioned, due to lack of substantial return of investment. One of the main reasons for failures in synthetic biology and metabolic engineering is the metabolic burdens that result in resource losses. Metabolic burden is defined as the portion of a host cells resources either energy molecules (e.g., NADH, NADPH and ATP) or carbon building blocks (e.g., amino acids) that is used to maintain the engineered components (e.g., pathways). As a result, the effectiveness of synthetic biology tools heavily dependents on cell capability to carry on the metabolic burden. Although genetic modifications can effectively engineer cells and redirect carbon fluxes toward diverse products, insufficient cell ATP powerhouse is limited to support diverse microbial activities including product synthesis. Here, I employ an ancient Chinese philosophy (Yin-Yang) to describe two contrary forces that are interconnected and interdependent, where Yin represents energy metabolism in the form of ATP, and Yang represents carbon metabolism. To decipher Yin-Yang balance and its implication to microbial cell factories, this dissertation applied metabolic engineering, flux analysis, data mining tools to reveal cell physiological responses under different genetic and environmental conditions. Firstly, a combined approach of FBA and 13C-MFA was employed to investigate several engineered isobutanol-producing strains and examine their carbon and energy metabolism. The result indicated isobutanol overproduction strongly competed for biomass building blocks and thus the addition of nutrients (yeast extract) to support cell growth is essential for high yield of isobutanol. Based on the analysis of isobutanol production, \u27Yin-Yang\u27 theory has been proposed to illustrate the importance of carbon and energy balance in engineered strains. The effects of metabolic burden and respiration efficiency (P/O ratio) on biofuel product were determined by FBA simulation. The discovery of energy cliff explained failures in bioprocess scale-ups. The simulation also predicted that fatty acid production is more sensitive to P/O ratio change than alcohol production. Based on that prediction, fatty acid producing strains have been engineered with the insertion of Vitreoscilla hemoglobin (VHb), to overcome the intracellular energy limitation by improving its oxygen uptake and respiration efficiency. The result confirmed our hypothesis and different level of trade-off between the burden and the benefit from various introduced genetic components. On the other side, a series of computational tools have been developed to accelerate the application of fluxomics research. Microbesflux has been rebuilt, upgraded, and moved to a commercial server. A platform for fluxomics study as well as an open source 13C-MFA tool (WUFlux) has been developed. Further, a computational platform that integrates machine learning, logic programming, and constrained programming together has been developed. This platform gives fast predictions of microbial central metabolism with decent accuracy. Lastly, a framework has been built to integrate Big Data technology and text mining to interpret concepts and technology trends based on the literature survey. Case studies have been performed, and informative results have been obtained through this Big Data framework within five minutes. In summary, 13C-MFA and flux balance analysis are only tools to quantify cell energy and carbon metabolism (i.e., Yin-Yang Balance), leading to the rational design of robust high-producing microbial cell factories. Developing advanced computational tools will facilitate the application of fluxomics research and literature analysis

\u3ci\u3eIn silico\u3c/i\u3e Driven Metabolic Engineering Towards Enhancing Biofuel and Biochemical Production

The development of a secure and sustainable energy economy is likely to require the production of fuels and commodity chemicals in a renewable manner. There has been renewed interest in biological commodity chemical production recently, in particular focusing on non-edible feedstocks. The fields of metabolic engineering and synthetic biology have arisen in the past 20 years to address the challenge of chemical production from biological feedstocks. Metabolic modeling is a powerful tool for studying the metabolism of an organism and predicting the effects of metabolic engineering strategies. Various techniques have been developed for modeling cellular metabolism, with the underlying principle of mass balance driving the analysis. In this dissertation, two industrially relevant organisms were examined for their potential to produce biofuels. First, Saccharomyces cerevisiae was used to create biodiesel in the form of fatty acid ethyl esters (FAEEs) through expression of a heterologous acyl-transferase enzyme. Several genetic manipulations of lipid metabolic and / or degradation pathways were rationally chosen to enhance FAEE production, and then culture conditions were modified to enhance FAEE production further. The results were used to identify the rate-limiting step in FAEE production, and provide insight to further optimization of FAEE production. Next, Clostridium thermocellum, a cellulolytic thermophile with great potential for consolidated bioprocessing but a weakly understood metabolism, was investigated for enhanced ethanol production. To accomplish the analysis, two models were created for C. thermocellum metabolism. The core metabolic model was used with extensive fermentation data to elucidate kinetic bottlenecks hindering ethanol production. The genome scale metabolic model was constructed and tuned using extensive fermentation data as well, and the refined model was used to investigate complex cellular phenotypes with Flux Balance Analysis. The work presented within provide a platform for continued study of S. cerevisiae and C. thermocellum for the production of valuable biofuels and biochemicals

Understanding the Adaptive Growth Strategy of Lactobacillus plantarum by In Silico Optimisation

In the study of metabolic networks, optimization techniques are often used to predict flux distributions, and hence, metabolic phenotype. Flux balance analysis in particular has been successful in predicting metabolic phenotypes. However, an inherent limitation of a stoichiometric approach such as flux balance analysis is that it can predict only flux distributions that result in maximal yields. Hence, previous attempts to use FBA to predict metabolic fluxes in Lactobacillus plantarum failed, as this lactic acid bacterium produces lactate, even under glucose-limited chemostat conditions, where FBA predicted mixed acid fermentation as an alternative pathway leading to a higher yield. In this study we tested, however, whether long-term adaptation on an unusual and poor carbon source (for this bacterium) would select for mutants with optimal biomass yields. We have therefore adapted Lactobacillus plantarum to grow well on glycerol as its main growth substrate. After prolonged serial dilutions, the growth yield and corresponding fluxes were compared to in silico predictions. Surprisingly, the organism still produced mainly lactate, which was corroborated by FBA to indeed be optimal. To understand these results, constraint-based elementary flux mode analysis was developed that predicted 3 out of 2669 possible flux modes to be optimal under the experimental conditions. These optimal pathways corresponded very closely to the experimentally observed fluxes and explained lactate formation as the result of competition for oxygen by the other flux modes. Hence, these results provide thorough understanding of adaptive evolution, allowing in silico predictions of the resulting flux states, provided that the selective growth conditions favor yield optimization as the winning strategy

Machine and deep learning meet genome-scale metabolic modeling

Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine learning. In parallel, constraint-based metabolic modeling has established itself as the main tool to investigate large-scale relationships between genotype, phenotype, and environment. The development and application of these methodological frameworks have occurred independently for the most part, whereas the potential of their integration for biological, biomedical, and biotechnological research is less known. Here, we describe how machine learning and constraint-based modeling can be combined, reviewing recent works at the intersection of both domains and discussing the mathematical and practical aspects involved. We overlap systematic classifications from both frameworks, making them accessible to nonexperts. Finally, we delineate potential future scenarios, propose new joint theoretical frameworks, and suggest concrete points of investigation for this joint subfield. A multiview approach merging experimental and knowledge-driven omic data through machine learning methods can incorporate key mechanistic information in an otherwise biologically-agnostic learning process

Genome-scale reconstruction and system level investigation of the metabolic network of Methylobacterium extorquens AM1

Background: Methylotrophic microorganisms are playing a key role in biogeochemical processes - especially the global carbon cycle - and have gained interest for biotechnological purposes. Significant progress was made in the recent years in the biochemistry, genetics, genomics, and physiology of methylotrophic bacteria, showing that methylotrophy is much more widespread and versatile than initially assumed. Despite such progress, system-level description of the methylotrophic metabolism is currently lacking, and much remains to understand regarding the network-scale organization and properties of methylotrophy, and how the methylotrophic capacity emerges from this organization, especially in facultative organisms. Results: In this work, we report on the integrated, system-level investigation of the metabolic network of the facultative methylotroph Methylobacterium extorquens AM1, a valuable model of methylotrophic bacteria. The genome-scale metabolic network of the bacterium was reconstructed and contains 1139 reactions and 977 metabolites. The sub-network operating upon methylotrophic growth was identified from both in silico and experimental investigations, and 13C-fluxomics was applied to measure the distribution of metabolic fluxes under such conditions. The core metabolism has a highly unusual topology, in which the unique enzymes that catalyse the key steps of C1 assimilation are tightly connected by several, large metabolic cycles (serine cycle, ethylmalonyl- CoA pathway, TCA cycle, anaplerotic processes). The entire set of reactions must operate as a unique process to achieve C1 assimilation, but was shown to be structurally fragile based on network analysis. This observation suggests that in nature a strong pressure of selection must exist to maintain the methylotrophic capability. Nevertheless, substantial substrate cycling could be measured within C2/C3/C4 inter-conversions, indicating that the metabolic network is highly versatile around a flexible backbone of central reactions that allows rapid switching to multi-carbon sources. Conclusions: This work emphasizes that the metabolism of M. extorquens AM1 is adapted to its lifestyle not only in terms of enzymatic equipment, but also in terms of network-level structure and regulation. It suggests that the metabolism of the bacterium has evolved both structurally and functionally to an efficient but transitory utilization of methanol. Besides, this work provides a basis for metabolic engineering to convert methanol into value-added products