13C-assisted metabolic flux analysis to investigate heterotrophic and mixotrophic metabolism in Cupriavidus necator H16

Introduction. Cupriavidus necator H16 is a gram-negative bacterium, capable of lithoautotrophic growth by utilizing hydrogen as an energy source and fixing carbon dioxide (CO2) through Calvin-Benson-Bassham (CBB) cycle. The potential to utilize synthesis gas (Syngas) and the prospects of rerouting carbon from polyhydroxybutyrate synthesis to value-added compounds makes C. necator an excellent chassis for industrial application. Objectives. In the context of lack of sufficient quantitative information of the metabolic pathways and to advance in rational metabolic engineering for optimized product synthesis in C. necator H16, we carried out a metabolic flux analysis based on steady-state 13C-labelling. Methods. In this study, steady-state carbon labelling experiments, using either D-[1-13C]fructose or [1,2-13C]glycerol, were undertaken to investigate the carbon flux through the central carbon metabolism in C. necator H16 under heterotrophic and mixotrophic growth conditions, respectively. Results. We found that the CBB cycle is active even under heterotrophic condition, and growth is indeed mixotrophic. While Entner-Doudoroff (ED) pathway is shown to be the major route for sugar degradation, tricarboxylic acid (TCA) cycle is highly active in mixotrophic condition. Enhanced flux is observed in reductive pentose phosphate pathway (redPPP) under the mixotrophic condition to supplement the precursor requirement for CBB cycle. The flux distribution was compared to the mRNA abundance of genes encoding enzymes involved in key enzymatic reactions of the central carbon metabolism. Conclusion. This study leads the way to establishing 13C-based quantitative fluxomics for rational pathway engineering in C. necator H16

The Design of FluxML: A Universal Modeling Language for 13C Metabolic Flux Analysis

13C metabolic flux analysis (MFA) is the method of choice when a detailed inference of intracellular metabolic fluxes in living organisms under metabolic quasi-steady state conditions is desired. Being continuously developed since two decades, the technology made major contributions to the quantitative characterization of organisms in all fields of biotechnology and health-related research. 13C MFA, however, stands out from other “-omics sciences,” in that it requires not only experimental-analytical data, but also mathematical models and a computational toolset to infer the quantities of interest, i.e., the metabolic fluxes. At present, these models cannot be conveniently exchanged between different labs. Here, we present the implementation-independent model description language FluxML for specifying 13C MFA models. The core of FluxML captures the metabolic reaction network together with atom mappings, constraints on the model parameters, and the wealth of data configurations. In particular, we describe the governing design processes that shaped the FluxML language. We demonstrate the utility of FluxML to represent many contemporary experimental-analytical requirements in the field of 13C MFA. The major aim of FluxML is to offer a sound, open, and future-proof language to unambiguously express and conserve all the necessary information for model re-use, exchange, and comparison. Along with FluxML, several powerful computational tools are supplied for easy handling, but also to maintain a maximum of flexibility. Altogether, the FluxML collection is an “all-around carefree package” for 13C MFA modelers. We believe that FluxML improves scientific productivity as well as transparency and therewith contributes to the efficiency and reproducibility of computational modeling efforts in the field of 13C MFA

Characterizing nonmodel microorganisms and reconstructing catabolic pathways for bio-productions

In the 1970s, spikes in oil prices sparked early interests into first-generation biofuels (i.e., the production of ethanol from corn). Four decades later, biofuel manufacturing has under-performed as developed industrial strains suffer from poor performance and high costs of feedstocks. While the advancement in molecular biology has greatly benefited pathway engineering, synthetic biology hosts still cannot overcome the obstacles for profitable microbial bio-production. One approach may be to study non-model organisms and employ their metabolic features towards an improved bio-production pipeline. Here, this thesis aims to develop 13C-metabolism analysis tools to characterize novel organisms and re-program cell metabolism using novel pathways for better biosynthesis. First, the thesis introduces fast 13C-metabolism analysis via parallel 13C-fingerprinting for pathway delineation as well as quantitative 13C-MFA using WUflux or other published software. Second, genome-to-phenome mapping was performed for an oil-rich and inhibitor-tolerant bacterium, Rhodococcus opacus PD630, to improve conversion of lignocellulose to biodiesel. One major discovery was the simultaneous use of the Entner-Doudoroff pathway (EDP) and gluconeogenesis for co-utilizing of phenolic compounds and sugar without catabolic repression. Third, we analyzed the fastest-growing cyanobacterial strain isolated, Synechococcus elongatus UTEX 2973, through isotopically non-stationary 13C-Metabolic Flux Analysis. The flux results indicate that Synechococcus 2973 has efficient catabolic pathways with minimal carbon loss after CO2 fixation comparing to the model cyanobacterial chassis Synechocystis 6803. This inspired the engineering of Synechocystis 6803 for improved carbon fixation efficiency by overexpression of EDP and knockout of CO2 re-generating pathways. Fourth, the thermodynamically favorable EDP pathway was also engineered in E. coli for improving carbon utilization and biomass growth. However, the EDP engineering only re-directed a small portion of its flux, but through a downregulation of Embden-Meyerhof-Parnas Pathway via pfkA knockout the flux was significantly elevated. Also, the _pfkA mutants showed co-utilization of acetate and xylose without glucose catabolic repression. Furthermore, 13C-pulse experiments suggested a possible presence of glycolysisosome, in which sugar phosphate metabolites can be passed between enzymes without mixing with the bulk phase

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

<it>iMS2Flux</it> – a high–throughput processing tool for stable isotope labeled mass spectrometric data used for metabolic flux analysis

<p>Abstract</p> <p>Background</p> <p>Metabolic flux analysis has become an established method in systems biology and functional genomics. The most common approach for determining intracellular metabolic fluxes is to utilize mass spectrometry in combination with stable isotope labeling experiments. However, before the mass spectrometric data can be used it has to be corrected for biases caused by naturally occurring stable isotopes, by the analytical technique(s) employed, or by the biological sample itself. Finally the MS data and the labeling information it contains have to be assembled into a data format usable by flux analysis software (of which several dedicated packages exist). Currently the processing of mass spectrometric data is time-consuming and error-prone requiring peak by peak cut-and-paste analysis and manual curation. In order to facilitate high-throughput metabolic flux analysis, the automation of multiple steps in the analytical workflow is necessary.</p> <p>Results</p> <p>Here we describe <it>iMS2Flux,</it> software developed to automate, standardize and connect the data flow between mass spectrometric measurements and flux analysis programs. This tool streamlines the transfer of data from extraction via correction tools to ¹³C-Flux software by processing MS data from stable isotope labeling experiments. It allows the correction of large and heterogeneous MS datasets for the presence of naturally occurring stable isotopes, initial biomass and several mass spectrometry effects. Before and after data correction, several checks can be performed to ensure accurate data. The corrected data may be returned in a variety of formats including those used by metabolic flux analysis software such as <it>13CFLUX</it>, <it>OpenFLUX</it> and <it>13CFLUX2</it>.</p> <p>Conclusion</p> <p><it>iMS2Flux</it> is a versatile, easy to use tool for the automated processing of mass spectrometric data containing isotope labeling information. It represents the core framework for a standardized workflow and data processing. Due to its flexibility it facilitates the inclusion of different experimental datasets and thus can contribute to the expansion of flux analysis applications.</p