During alcoholic fermentation Saccharomyces cerevisiae metabolizes L-arginine to ornithine and urea. S. cerevisiae can metabolize urea through the action of urea amidolyase, encoded by the DUR1,2 gene; however, DUR1,2 is subject to nitrogen catabolite repression (NCR) in the presence of high quality nitrogen sources during fermentation. Being cytotoxic at high concentrations, urea is exported into wine where it spontaneously reacts with ethanol, and forms the carcinogen ethyl carbamate (EC). Urea degrading yeast strains were created by integrating a linear cassette containing the DUR1,2 gene under the control of the S. cerevisiae PGK1 promoter and terminator signals into the URA3 locus of the Sake yeast strains K7 and K9. The ‘self-cloned’ strains K7EC- and K9EC- produced Sake wine with 68% less EC. The Sake strains K7EC- and K9EC- did not efficiently reduce EC in Chardonnay wine due to the evolutionary adaptation of said strains to the unique nutrients of rice mash; therefore, the functionality of engineered yeasts must be tested in their niche environments as to correctly characterize new strains. S. cerevisiae possesses an NCR controlled high affinity urea permease (DUR3). Urea importing yeast strains were created by integrating a linear cassette containing the DUR3 gene under the control of the PGK1 promoter and terminator signals into the TRP1 locus of the yeast strains K7 (Sake) and 522 (wine). In Chardonnay wine, the urea importing strains K7D3 and 522D3 reduced EC by 7% and 81%, respectively; reduction by these strains was equal to reduction by the urea degrading strains K7EC- and 522EC-. In Sake wine, the urea degrading strains K7EC- and 522EC- reduced EC by 87% and 84% respectively, while the urea importing strains K7D3 and 522D3 were significantly less capable of reducing EC (15% and 12% respectively). In Chardonnay and Sake wine, engineered strains that constitutively co-expressed DUR1,2 and DUR3 did not reduce EC more effectively than strains in which either gene was expressed solely. Uptake of 14C-urea under non-inducing conditions was enhanced in urea importing strains; parental strains failed to incorporate any 14C-urea thus confirming the functionality of the urea permease derived from the integrated DUR3 cassette. / Medicine, Faculty of / Medical Genetics, Department of / Graduate

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Plant natural products include a diverse array of compounds that play important roles in plant metabolism and physiology. After elucidation of biosynthetic pathways and regulatory factors, it has become possible to metabolically engineer new capabilities in planta as well as successfully engineer whole pathways into microbial systems. Microbial expression systems for producing valuable plant compounds have evolved to incorporate polyculture and co-culture consortiums for carrying out robust biosynthesis strategies. This review focuses on four classes of plant secondary metabolites and the recent advances in generating useful compounds in microbial expression platforms and in plant metabolic engineering. They are the flavonoids, alkaloids, betalains, and glucosinolates.

abstract: Aromatic compounds have traditionally been generated via petroleum feedstocks and have wide ranging applications in a variety of fields such as cosmetics, food, plastics, and pharmaceuticals. Substantial improvements have been made to sustainably produce many aromatic chemicals from renewable sources utilizing microbes as bio-factories. By assembling and optimizing native and non-native pathways to produce natural and non-natural bioproducts, the diversity of biochemical aromatics which can be produced is constantly being improved upon. One such compound, 2-Phenylethanol (2PE), is a key molecule used in the fragrance and food industries, as well as a potential biofuel. Here, a novel, non-natural pathway was engineered in Escherichia coli and subsequently evaluated. Following strain and bioprocess optimization, accumulation of inhibitory acetate byproduct was reduced and 2PE titers approached 2 g/L – a ~2-fold increase over previously implemented pathways in E. coli. Furthermore, a recently developed mechanism to allow E. coli to consume xylose and glucose, two ubiquitous and industrially relevant microbial feedstocks, simultaneously was implemented and systematically evaluated for its effects on L-phenylalanine (Phe; a precursor to many microbially-derived aromatics such as 2PE) production. Ultimately, by incorporating this mutation into a Phe overproducing strain of E. coli, improvements in overall Phe titers, yields and sugar consumption in glucose-xylose mixed feeds could be obtained. While upstream efforts to improve precursor availability are necessary to ultimately reach economically-viable production, the effect of end-product toxicity on production metrics for many aromatics is severe. By utilizing a transcriptional profiling technique (i.e., RNA sequencing), key insights into the mechanisms behind styrene-induced toxicity in E. coli and the cellular response systems that are activated to maintain cell viability were obtained. By investigating variances in the transcriptional response between styrene-producing cells and cells where styrene was added exogenously, better understanding on how mechanisms such as the phage shock, heat-shock and membrane-altering responses react in different scenarios. Ultimately, these efforts to diversify the collection of microbially-produced aromatics, improve intracellular precursor pools and further the understanding of cellular response to toxic aromatic compounds, give insight into methods for improved future metabolic engineering endeavors. / Dissertation/Thesis / Doctoral Dissertation Chemical Engineering 2019

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学位プログラム名: 京都大学大学院思修館 / 京都大学 / 0048 / 新制・課程博士 / 博士(総合学術) / 甲第22613号 / 総総博第13号 / 新制||総総||2(附属図書館) / 京都大学大学院総合生存学館総合生存学専攻 / (主査)教授 山口 栄一, 教授 山敷 庸亮, 教授 植田 充美, 大嶌 幸一郎 / 学位規則第4条第1項該当 / Doctor of Philosophy / Kyoto University / DFAM

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Systems metabolic engineering has enabled systematic studying of microbes for modifying their genetic contents, analyzing their metabolism, and designing new capabilities. One of the most commonly used approaches in systems metabolic engineering involves genome-scale metabolic flux modeling. These models allow generation of predictions of the global metabolic flux distribution in the metabolic network of organisms, in silico. With the current advances in genome sequencing technologies and the global demand for bio-based commodity chemicals and fuels, genome-scale models can help metabolic engineers propose design strategies while considering holistic behavior of the organism. In this research, novel tools and methodologies were developed to improve the future prospective of systems metabolic engineering with genome-scale modeling. To do this, an online web application (Synthetic Metabolic Pathway Builder and Genome-Scale Model Database, SyM- GEM) was first developed enabling the construction of synthetic metabolic pathway(s) and addition of those to synchronized genome-scale models. This addresses the need for an easy and universal way of creating models of engineered microbes with improved properties without the time-consuming inconvenience of synchronizing different formats and representations of genome- scale models prepared by different laboratories. The web application is freely available at http:www.mesb.bse.vt.edu/SyM-GEM. Then, a computational framework (Total Membrane Influx-Flux Balance Analysis, ToMI-FBA) was developed to allow for evaluating synthetic pathway use by different models. This enabled, for the first time, a computational guide for optimal host selection (for a specific metabolic engineering problem) and culture media formulation design to achieve the solution. Results showed that (i) L-valine improves isobutanol production by Bacillus subtilis, (ii) cellobiose increases ethanol selectivity by Clostridium acetobutylicum ATCC 824, and (iii) B. subtilis is an optimal host for artimisinate production. To further expand the capability of genome-scale models, an algorithm was developed (Genetic Algorithm-Flux Balance Analysis minimizing Total Unconstrained eXchange Flux, GA-FBA minimizing TUX) to help improve the fitness between metabolic fluxes predicted by genome-scale modeling and those obtained by 13C-tracing methods. Application of this method to the cyanobacterium Synechocystis PCC 6803 improved model accuracy by more than 50% for both heterotrophic and autotrophic growth. To generate even more realistic predictions of metabolic flux from genome-scale modeling, Raman spectroscopy was employed to help design biomass equations of microbial cells in different environmental conditions. To do this, the cellulose- consuming anaerobe Clostridium cellulolyticum ATCC 35319 was grown on cellobiose, and samples were obtained at different points of differentiation due to sporulation. Biomass composition was determined through Raman spectroscopy and traditional chemical analyses. A new genome-scale model of this organism (iCCE557) served as the basis for genome-scale model calculations. Model fitness improved upto 95% with these methods. Finally, to implement metabolic engineering strategies, regulatory RNA molecules (antisense RNAs) were designed to help target desired mRNA molecules in the metabolic network. Thermodynamic binding calculations were found to correlate with the efficiency of asRNA-mRNA binding and inhibition of mRNA translation. / Ph. D.

In metabolic engineering of prokaryotes, combinatorial approaches have developed recently that induce random genetic perturbations to achieve a desired cell phenotype. A screening strategy follows the randomized genetic manipulations to select strain(s) with the more optimal phenotype of interest. This screening strategy is often divided into two categories: (i) a growth competition assay and (ii) selection by high-throughput screening. The growth competition assay involves culturing strains together. The strain with the highest growth rate will ultimately dominate the culture. This strategy is ideal for selecting strain with cellular fitness (e.g., solvent tolerance), but it does not work for selecting a strain that can over-produce a product (e.g., an amino acid). For the case of selecting highly productive phenotypes, high-throughput screening is used. This method analyzes strains individually and is costly and time-consuming. In this research, a synthetic genetic circuit was developed to select highly productive phenotypes using a growth competition assay rather than high-throughput screening. This novel system is called Feed-back Inhibition of Transcription for Growth Selection (FITSelect), and it uses a natural feedback inhibition mechanism in the L-arginine production pathway to select strains (transformed with a random genomic library) that can over-produce L-arginine in E. coli DH10B. With FITSelect, the cell can thrive in the growth competition assay when L-arginine is over-produced (i.e., growth is tied to L-arginine production). Cell death or reduced growth results if L-arginine is not over-produced by the cell. This system was created by including an L-arginine concentration responsive argF promoter to control a ccdB cell death gene in the FITSelect system. The effects of ccdB were modulated by the antidote ccdA gene under control of an L-tryptophan responsive trp promoter. Several insights and construction strategies were required to build a system that ties the growth rate of the cell to L-arginine concentrations. / Master of Science