Byoungjin Kim

Bio-based production of C2-C6 platform chemicals.

Platform chemicals composed of 2–6 carbons derived from fossil resources are used as important precursors for making a variety of chemicals and materials, including solvents, fuels, polymers, pharmaceuticals, perfumes, and foods. Due to concerns regarding our environment and the limited nature of fossil resources, however, increasing interest has focused on the development of sustainable technologies for producing these platform chemicals from renewable resources. The techniques and strategies for developing microbial strains for chemicals production have advanced rapidly, and it is becoming feasible to develop microbes for producing additional types of chemicals, including non‐natural molecules. In this study, we review the current status of the bio‐based production of major C2–C6 platform chemicals, focusing on the microbial production of platform chemicals that have been used for the production of chemical intermediates, building block compounds, and polymers. Biotechnol. Bioeng. 2012; 109: 2437–2459.

Metabolic engineering of Escherichia coli for the production of phenol from glucose

Phenol is an industrially versatile commodity chemical and is currently produced from fossil resources. Phenols biological production from renewable resources has been limited due to its toxicity to microorganisms. Here, we simultaneously engineered 18 Escherichia coli strains for the production of phenol using synthetic regulatory small RNA (sRNA) technology. sRNA-based knock-down of the two regulators and overexpression of the genes involved in the tyrosine biosynthetic pathway together with tyrosine phenol-lyase (TPL) in E. coli strains resulted in the production of phenol from glucose. The 18 engineered E. coli strains showed significant differences in the production of tyrosine (i.e. the immediate precursor for phenol), TPL activity, and tolerance to phenol. Among the engineered E. coli strains, the BL21 strain produced phenol most efficiently: 419 mg/L by flask culture and 1.69 g/L by fed-batch culture. The final titer and productivity were further improved through biphasic fed-batch fermentation using glycerol tributyrate as an extractant of phenol. The concentration of phenol in the glycerol tributyrate phase and fermentation broth reached 9.84 and 0.3 g/L, respectively, in 21 hours, which translates into the final phenol titer and productivity of 3.79 g/L and 0.18 g/L/h, respectively. This is the highest titer achieved by microbial fermentation. Although further engineering is required to be competitive with the current petro-based process, the strategies used for the development of the engineered strain and fermentation process will provide a valuable framework for the microbial production of toxic chemicals.

Applications of genome-scale metabolic network model in metabolic engineering

Genome-scale metabolic network model (GEM) is a fundamental framework in systems metabolic engineering. GEM is built upon extensive experimental data and literature information on gene annotation and function, metabolites and enzymes so that it contains all known metabolic reactions within an organism. Constraint-based analysis of GEM enables the identification of phenotypic properties of an organism and hypothesis-driven engineering of cellular functions to achieve objectives. Along with the advances in omics, high-throughput technology and computational algorithms, the scope and applications of GEM have substantially expanded. In particular, various computational algorithms have been developed to predict beneficial gene deletion and amplification targets and used to guide the strain development process for the efficient production of industrially important chemicals. Furthermore, an Escherichia coli GEM was integrated with a pathway prediction algorithm and used to evaluate all possible routes for the production of a list of commodity chemicals in E. coli. Combined with the wealth of experimental data produced by high-throughput techniques, much effort has been exerted to add more biological contexts into GEM through the integration of omics data and regulatory network information for the mechanistic understanding and improved prediction capabilities. In this paper, we review the recent developments and applications of GEM focusing on the GEM-based computational algorithms available for microbial metabolic engineering.

Effects of introducing heterologous pathways on microbial metabolism with respect to metabolic optimality

Although optimality of microbial metabolism under genetic and environmental perturbations is well studied, the effects of introducing heterologous reactions on the overall metabolism are not well understood. This point is important in the field of metabolic engineering because heterologous reactions are more frequently introduced into various microbial hosts. The genome-scale metabolic simulations of Escherichia coli strains engineered to produce 1,4-butanediol, 1,3-propanediol, and amorphadiene suggest that microbial metabolism shows much different responses to the introduced heterologous reactions in a strain-specific manner than typical gene knockouts in terms of the energetic status (e.g., ATP and biomass generation) and chemical production capacity. The 1,4-butanediol and 1,3-propanediol producers showed greater metabolic optimality than the wild-type strains and gene knockout mutants for the energetic status, while the amorphadiene producer was metabolically less optimal. For the optimal chemical production capacity, additional gene knockouts were most effective for the strain producing 1,3-propanediol, but not for the one producing 1,4-butanediol. These observations suggest that strains having heterologous metabolic reactions have metabolic characteristics significantly different from those of the wild-type strain and single gene knockout mutants. Finally, comparison of the theoretically predicted and 13C-based flux values pinpoints pathways with non-optimal flux values, which can be considered as engineering targets in systems metabolic engineering strategies. To our knowledge, this study is the first attempt to quantitatively characterize microbial metabolisms with different heterologous reactions. The suggested potential reasons behind each strain’s different metabolic responses to the introduced heterologous reactions should be carefully considered in strain designs.

Metabolic engineering of Escherichia coli for the enhanced production of l-tyrosine: KIM et al.

Despite wide applications of l‐tyrosine in the market, microbial overproduction of l‐tyrosine has been a great challenge due to the complex gene regulations involved in its biosynthetic pathway. To this end, effects of knocking out tyrR on the l‐tyrosine production were further explored during the strain development. Also, blocking cellular uptake of l‐tyrosine by knocking out tyrosine transporters was examined with respect to l‐tyrosine production. Using feedback‐resistant aroG and tyrA genes (aroGfbr and tyrAfbr hereafter) as initial overexpression targets, which encode 3‐deoxy‐7‐phosphoheptulonate synthase and chorismate mutase or prephenate dehydrogenase, respectively, various combinations of genes were subsequently overexpressed in the Escherichia coli wild‐type and tyrR knockout strain, and their effects on the l‐tyrosine production were examined. Co‐overexpression of aroGfbr, aroL and tyrC, a gene from Zymomonas mobilis functionally similar to tyrAfbr, but insensitive to l‐tyrosine, led to the greatest l‐tyrosine production regardless of the strains and plasmid constructs examined in this study. The strain BTY2.13 overexpressing the abovementioned three genes together with the removal of the l‐tyrosine‐specific transporter (tyrP) produced 43.14 g/L of l‐tyrosine by fed‐batch fermentation using the exponential feeding followed by DO‐stat feeding method. This outcome suggested that the tyrR gene knockout was not mandatory for the l‐tyrosine overproduction, but the production performance of strains having tyrR appeared to be highly affected by vector systems and feeding methods. With an optimal vector system and a feeding method, tyrP knockout appeared to be more effective in enhancing the l‐tyrosine than tyrR knockout.