Hyung Seok Choi

In silico identification of gene amplification targets for improvement of lycopene production.

ABSTRACT The identification of genes to be deleted or amplified is an essential step in metabolic engineering for strain improvement toward the enhanced production of desired bioproducts. In the past, several methods based on flux analysis of genome-scale metabolic models have been developed for identifying gene targets for deletion. Genome-wide identification of gene targets for amplification, on the other hand, has been rather difficult. Here, we report a strategy called flux scanning based on enforced objective flux (FSEOF) to identify gene amplification targets. FSEOF scans all the metabolic fluxes in the metabolic model and selects fluxes that increase when the flux toward product formation is enforced as an additional constraint during flux analysis. This strategy was successfully employed for the identification of gene amplification targets for the enhanced production of the red-colored antioxidant lycopene. Additional metabolic engineering based on gene knockout simulation resulted in further synergistic enhancement of lycopene production. Thus, FSEOF can be used as a general strategy for selecting genome-wide gene amplification targets in silico.

Systems-level analysis of genome-scalein silico metabolic models using MetaFluxNet

The systems-level analysis of microbes with myriad of heterologous data generated by omics technologies has been applied to improve our understanding of cellular function and physiology and consequently to enhance production of various bioproducts. At the heart of this revolution residesin silico genome-scale metabolic model. In order to fully exploit the power of genome-scale model, a systematic approach employing user-friendly software is required. Metabolic flux analysis of genome-scale metabolic network is becoming widely employed to quantify the flux distribution and validate model-driven hypotheses. Here we describe the development of an upgraded MetaFluxNet which allows (1) construction of metabolic models connected to metabolic databases, (2) calculation of fluxes by metabolic flux analysis, (3) comparative flux analysis with flux-profile visualization, (4) the use of metabolic flux analysis markup language to enable models to be exchanged efficiently, and (5) the exporting of data from constraints-based flux analysis into various formats. MetaFluxNet also allows cellular physiology to be predicted and strategies for strain improvement to be developed from genome-based information on flux distributions. This integrated software environment promises to enhance our understanding on metabolic network at a whole organism level and to establish novel strategies for improving the properties of organisms for various biotechnological applications.

Serum-stable quantum dot--protein hybrid nanocapsules for optical bio-imaging

We introduce shell cross-linked protein/quantum dot (QD) hybrid nanocapsules as a serum-stable systemic delivery nanocarrier for tumor-targeted in vivo bio-imaging applications. Highly luminescent, heavy-metal-free Cu0.3InS2/ZnS (CIS/ZnS) core-shell QDs are synthesized and mixed with amine-reactive six-armed poly(ethylene glycol) (PEG) in dichloromethane. Emulsification in an aqueous solution containing human serum albumin (HSA) results in shell cross-linked nanocapsules incorporating CIS/ZnS QDs, exhibiting high luminescence and excellent dispersion stability in a serum-containing medium. Folic acid is introduced as a tumor-targeting ligand. The feasibility of tumor-targeted in vivo bio-imaging is demonstrated by measuring the fluorescence intensity of several major organs and tumor tissue after an intravenous tail vein injection of the nanocapsules into nude mice. The cytotoxicity of the QD-loaded HSA-PEG nanocapsules is also examined in several types of cells. Our results show that the cellular uptake of the QDs is critical for cytotoxicity. Moreover, a significantly lower level of cell death is observed in the CIS/ZnS QDs compared to nanocapsules loaded with cadmium-based QDs. This study suggests that the systemic tumor targeting of heavy-metal-free QDs using shell cross-linked HSA-PEG hybrid nanocapsules is a promising route for in vivo tumor diagnosis with reduced non-specific toxicity.

Highly luminescent, off-stoichiometric CuxInyS2/ZnS quantum dots for near-infrared fluorescence bio-imaging

Quantum dots (QDs) are very attractive for in vivo bio-imaging and therapeutic applications due to their relatively large absorption coefficient, high quantum yield, low level of photo bleaching, and large Stokes shift. However, two technical issues need to be resolved before they can be practically applied to in vivo bio-imaging applications: ensuring both reduced toxicity and efficient emission in the near-infrared (NIR) frequency range. Here we report a simple and reliable method to synthesize highly luminescent, NIR-emitting CuxInyS2/ZnS (CIS/ZnS) core–shell QDs for deep-tissue bio imaging applications. Off-stoichiometric effects are utilized with 1-dodecanethiol as a reaction medium for thermolytic synthesis. The most important finding in our work is that at a high Cu/In ratio, the emission spectrum of CIS/ZnS QDs can be tuned to NIR frequencies with a high quantum yield up to approximately 65%. The maximum emission wavelengths of the synthesized QDs are 589 nm (QD589) and 726 nm (QD726) at a Cu/In ratio of 0.25 and of 1.8, respectively. Their feasibility for optical bio-imaging in a deep-tissue condition is investigated by the intramuscular injection of QD-loaded polymer microspheres in a mouse model. Our results show that more than 30% of the original emission of the QD726 can be detected through biological tissue of 0.9 cm, whereas emission from the QD589 is not detectable. Our investigation on the off-stoichiometric effects of CIS QDs will contribute to the development of highly luminescent, NIR-emitting, cadmium-free QDs in the areas of tissue-level imaging, sensing, and therapeutics.

Image Cytometric Analysis of Algal Spores for Evaluation of Antifouling Activities of Biocidal Agents

Chemical biocides have been widely used as marine antifouling agents, but their environmental toxicity impose regulatory restriction on their use. Although various surrogate antifouling biocides have been introduced, their comparative effectiveness has not been well investigated partly due to the difficulty of quantitative evaluation of their antifouling activity. Here we report an image cytometric method to quantitatively analyze the antifouling activities of seven commercial biocides using Ulva prolifera as a target organism, which is known to be a dominant marine species causing soft fouling. The number of spores settled on a substrate is determined through image analysis using the intrinsic fluorescence of chlorophylls in the spores. Pre-determined sets of size and shape of spores allow for the precise determination of the number of settled spores. The effects of biocide concentration and combination of different biocides on the spore settlement are examined. No significant morphological changes of Ulva spores are observed, but the amount of adhesive pad materials is appreciably decreased in the presence of biocides. It is revealed that the growth rate of Ulva is not directly correlated with the antifouling activities against the settlement of Ulva spores. This work suggests that image cytometric analysis is a very convenient, fast-processable method to directly analyze the antifouling effects of biocides and coating materials.

Determination of the Metabolic Networks Fluxes Using Carbon Isotopomer Labeling and Metabolic Flux Analysis

To determine intracellular fluxes using carbon labeled experimental data, adequate techniques are needed. Metabolic flux analysis (MFA) is a useful method to simulate metabolic networks. Because the measurable extracellular flux data is always insufficient, there are more unknowns than equations. Further information or constraints are required to make fully determined system of which the degrees of freedom (DOF) is zero. It is possible to obtain mass distribution data using the carbon isotope labeling experiment for fermentation experiments. Carbon isotope labeled data can be obtained from C isotope tracer technique and GC-MS (Gas Chromatography-Mass Spectrometry) measurements. In this work, we have developed a metabolic networks simulation tool which can exactly determine intracellular fluxes using carbon isotopomer labeling data. The result can provide strict insight into complex metabolic networks