Bio-succinic acid production: Escherichia coli strains design from genome-scale perspectives

Escherichia coli (E. coli) has been established to be a native producer of succinic acid (a platform chemical with different applications) via mixed acid fermentation reactions. Genome-scale metabolic models (GEMs) of E. coli have been published with capabilities of predicting strain design strategies for the production of bio-based succinic acid. Proof-of-principle strains are fundamentally constructed as a starting point for systems strategies for industrial strains development. Here, we review for the first time, the use of E. coli GEMs for construction of proof-of-principles strains for increasing succinic acid production. Specific case studies, where E. coli proof-of-principle strains were constructed for increasing bio-based succinic acid production from glucose and glycerol carbon sources have been highlighted. In addition, a propose systems strategies for industrial strain development that could be applicable for future microbial succinic acid production guided by GEMs have been presented

METABOLIC ENGINEERING OF THE DEOXYXYLULOSE PHOSPHATE PATHWAY FOR ISOPRENOID PRODUCTION IN E.COLI

Ph.DDOCTOR OF PHILOSOPH

Mapping interactions between metabolites and transcriptional regulators at a genome-scale

The control and regulation of cellular metabolism is required to maintain the biosynthesis of building blocks and energy, but also to prevent the loss of energy and to be able to quickly adjust to changing conditions. Hence, the metabolic network and the flow of genetic information has multiple layers of regulation and information is transmitted between gene expression and metabolism. For this purpose, metabolites serve as key signals of the regulatory network to balance metabolism via the adjustment of protein levels and the activity of enzymes. Understanding these regulations and interplays of bacterial metabolism will enable us to improve the modelling and engineering of metabolic networks and ultimately to develop new antibiotics and production strains. The aim of this thesis is to investigate which regulatory mechanisms are used by the cell to respond to genetic perturbations. Moreover, we develop new methods to map protein-metabolite interactions and to prove their functionality in the cell. After introducing the fundamentals of metabolic network regulation, we investigate in chapter 1 how Escherichia coli (E. coli) reacts to genetic perturbations. We use a library of 7177 CRISPRi strains to perform a pooled fitness growth assay, demonstrating the buffering effects of metabolism. Additionally, measuring the metabolome and proteome of 30 arrayed CRISPRi strains enables us to elucidate three gene-specific buffering mechanisms. In chapter 2, we use our new insights about genetic perturbations of chapter 1 to develop a method for systematically mapping interactions between metabolites and transcriptional regulators. CRISPRi leads to a knockdown of a gene and therefore induces specific changes in the metabolome and proteome of the cell. We therefore combine the pooled CRISPRi library with a fluorescent reporter for transcription factor activity and extract cells, which show a response of the reporter to the changing conditions, via FACS from the pooled library. By analyzing proteome and metabolome data, we confirm previously reported and discover new interactions. With chapter 3, we provide a detailed protocol of how to work with CRISPRi libraries. We explain the design and construction of sgRNAs of arrayed as well as pooled CRISPRi strains and how to perform growth assays. Furthermore, we explain the execution and analysis of Illumina Next-generation sequencing of pooled libraries. We also explain the sorting of cells from pooled libraries via FACS. In chapter 4, we show how to find new interactions between metabolites and transcription factors by external perturbations. By switching a growing E. coli culture between growth and glucose limitation, we provoke strong changes of metabolite levels and transcript levels. Calculating the transcription factor activity from gene expression levels and correlating them with metabolite levels, enables us to recover known interactions but also to discover new interactions, of which we prove five in in vitro binding assays. In chapter 5, we investigate the function of allosteric regulation of metabolic enzymes in amino acid pathways of E. coli. We constructed 7 mutants of allosteric enzymes to remove the allosteric feedback regulation. By metabolomics, proteomics and flux profiling analysis we show how allostery helps to adjust enzyme levels of the cell. Furthermore, using a metabolic model and the application of CRISPRi we show how well-adjusted enzyme levels make the cell more stable towards genetic perturbations

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

Understanding and engineering cofactor metabolism: a quest for improved biochemical production

In the field of metabolic engineering, where cells are treated as “factories” that synthesise industrial compounds, it is essential for cell metabolism to accommodate the energy and re- dox cofactor demands of synthetic pathways. A balanced supply and consumption of ATP and NAD(P)H directly influences biotechnological performance. This study develops computational and experimental frameworks to explore how ATP and NAD(P)H limit the yield of synthetic pathways during bioproduction. Constraint-based modelling was used to develop a novel computational protocol, CBA (Co- factor Balance Assessment), which tracks how ATP and NAD(P)H contribute to cell target production, as opposed to cell maintenance, biomass and waste release, in the presence of a synthetic pathway. Using butanol pathways (a non-native product in E.coli) with varying cofactor demands, CBA discerned the network-wide effects of cofactor variations on butanol yield. Results indicate that yields could be boosted by up to 13% if the introduced pathway is balanced both in terms of energy and redox. CBA simplified cofactor balance assessments and provided insights into how to improve the efficiency of recombinant strains. Physiological and metabolic responses to cofactor perturbations were also experimentally assessed. The predominant phenotypes of strains harbouring the ATP synthase and PCK knockouts included high glycolytic flux, lower biomass and ATP. These strains were used to improve ethanol production, resulting in yields 10% and 29% higher than the WT overproducing ethanol and reaching over 70% of the theoretical maximum. The low but positive ATP yields boosted ethanol production and minimised unrestricted growth. This research posits that early-stage in silico cofactor usage profiling serves as an instrument to select better performing pathways. Significant yield improvements can be achieved experimentally with a small number of cofactor-driven modifications that reduce the waste of cofactors, illustrating the potential of these strains as platforms to improve bioproduction of cofactor-neutral or cofactor-surplus synthetic pathways.Open Acces

Metabolic engineering of Clostridium saccharoperbutylacetonicum for improved solvent production

In order to avert irreversible damage to the global climate, the global community has committed to reaching net zero carbon emission in the coming years. To meet this ambitious target, substantial changes will be needed. To minimise the disruption to people’s lives, there is a need for renewable technologies which are compatible with existing infrastructure, such as biofuels and drop in chemical compounds. A compound which can fulfil both roles is n-butanol. Clostridium are natural butanol producers but have fallen out of use as they have been unable to compete with fossil fuel-based production methods. The aim of this thesis was to improve the production of butanol in an asporogenic strain of Clostridium saccharoperbutylacetonicum N1-4. A systems scale approach was taken to improve butanol production. Combined analysis of Clostridium metabolism using flux balance analysis and 13C-metabolic flux analysis was used to guide metabolic engineering strategies, with the aim of increasing butanol production in asporogenic C. saccharoperbutylacetonicum N1-4 spo0AI261T. Flux balance analysis was used to gain an understanding of Clostridium metabolism and to explore manipulations that could lead to increased butanol production in wild type C. saccharoperbutylacetonicum N1-4. Key features of in silico engineered strains were compared to experimental data and identified an increase in NADH generation and key butanol synthesising genes as targets for increasing butanol production. A second round of flux balance analysis identified further manipulations relevant to C. saccharoperbutylacetonicum N1-4 spo0AI261T, mainly that the rate of glucose uptake appeared to be limiting butanol production. Simulations of strains with increased glucose uptake and butanol production suggested that ATP consuming enzymes would have to be engineered into the asporogenic strain to balance ATP. Additional investigation was performed using 13C-metabolic flux analysis, which was able to resolve intracellular fluxes of asporogenic C. saccharoperbutylacetonicum N1-4. It also identified a feature of C. saccharoperbutylacetonicum N1-4 spo0AI261T metabolism that was unexpected, that ATP was in excess to biomass synthesis requirements, resulting in a futile cycle. The results from this flux analysis confirmed the rationale of the flux balance analysis guided strategies. In the final chapter, the developed strategies were incorporated into the asporogenic strain of C. saccharoperbutylacetonicum N1-4. These mutant strains were analysed in fermentations. While none of the strains produced more butanol than the parent strain, this work incorporated several novel approaches to increase butanol production in Clostridium and will serve as a starting point for future metabolic engineering work

Understanding metabolic robustness of Escherichia coli using genetic and environmental perturbations

Metabolism provides the essential biochemical intermediates and energy that enable life and its growth. In this thesis we studied robustness of Escherichia coli metabolism, by perturbing it with different methods and measuring the response at a molecular level. In Chapter 1, we introduce the latest insight into metabolic regulation and optimality in microbial model organisms. Overall, we identified and described two major gaps in knowledge: the limited amount of known metabolite-protein interactions and the unknown objectives towards which cells optimize their enzyme levels. Moreover, we provide a short introduction to the relevant methods utilized in this thesis. In Chapter 2, we describe a series of experiments which confirmed that CRISPRi is a reliable tool to specifically perturb metabolism in E. coli. We showcase the advantage of using a CRISPRi system integrated in the genome, which is suitable to apply inducible knockdowns of essential genes. We demonstrate this by characterizing growth for a library of over 100 strains and verifying inducibility and specificity with proteomics data. In Chapter 3 we applied the validated CRISPRi setup to perturb and study metabolism systematically. First, we used a pooled CRISPRi library to knock down all metabolic genes in E. coli. By following the appearance of growth defects with next generation sequencing, we show that metabolic enzymes are expressed at higher levels than strictly necessary. We then focused on a panel of 30 CRISPRi strains and characterize their response to lower enzyme levels with metabolomics and proteomics. We show that the metabolome can buffer perturbations of enzyme levels in two different stages: first, metabolites increase enzyme activity to maintain optimal growth and only later they activate gene regulatory feedbacks to specifically upregulate perturbed pathways. In Chapter 4 we employed a different approach to perturb bacterial metabolism, by growing E. coli in different environmental conditions and measuring the response at the metabolome level. We could show that in exponentially growing cells key biosynthetic products as amino acids and nucleotides are kept at relatively stable levels across different environments. We compared our dataset to a matching published proteomics dataset, showing that unlike the proteome, metabolite levels are independent from growth effects

Metabolic engineering of microorganisms for the overproduction of fatty acids

Fatty acids naturally synthesized in many organisms are promising starting points for the catalytic production of industrial chemicals and diesel-like biofuels. However, bio-production of fatty acids in microbial hosts relies heavily on manipulating tightly regulated fatty acid biosynthetic pathways, thus complicating the engineering for higher yields. With the advent of systems metabolic engineering, we demonstrated an iterative metabolic engineering effort that integrates computationally driven predictions and metabolic flux analysis (MFA) was demonstrated to meet this challenge. With wild type E. coli fluxomic data, the OptForce procedure was employed to suggest genetic manipulations for fatty acid overproduction. In accordance with the OptForce prioritization of interventions, fabZ and acyl-ACP thioesterase were upregulated and fadD was deleted to arrive at a strain that produces 1.70 g/L and 0.14 g fatty acid/g glucose of C14-16 fatty acid in minimal medium. However, OptForce does not infer gene regulation, enzyme inhibition and metabolic toxicity. Along with transcriptomics and metabolomics analysis, we re-deployed OptForce simulation using the redefined flux distribution as constraints to generate predictions for the second generation fatty acid-overproducing strain. MFA identified the up-regulation of the TCA cycle and down-regulation of pentose phosphate pathway under fatty acid overproduction to replenish the need of energy and reducing molecules. The elevation of intracellular metabolite levels in the TCA cycle complemented the flux findings. With re-defined flux boundary of the first generation strain, OptForce suggested the interruption of TCA cycle such as removal of succinate dehydrogenase as the most prioritized genetic intervention to further improve fatty acid production. Meanwhilem, the whole genome transcriptional analysis revealed acid stress response, membrane disruption, colanic acid and biofilm formation during fatty acid production, thus pinpointing the targets for future metabolic engineering effort. These results highlight the benefit of using computational strain design and system metabolic engineering tools in systematically guiding the strain design to produce free fatty acids. Nonetheless, Saccharomyces cerevisiae is another attractive host organism for the production of biochemicals and biofuels. However, S. cerevisiae is very susceptible to octanoic acid toxicity. Transcriptomics analysis revealed membrane stress and intracellular acidification during octanoic acid stress. MFA illustrated the increase of flux in the TCA cycle possibly to facilitate the ATP-binding-cassette transporter activities. Further efforts can focus on improving membrane integrity or explore oleaginious yeasts to enhance the tolerance against fatty acids