Minyoung Kim

Characterization of a gene encoding cellulase from uncultured soil bacteria.

To detect cellulases encoded by uncultured microorganisms, we constructed metagenomic libraries from Korean soil DNAs. Screenings of the libraries revealed a clone pCM2 that uses carboxymethyl cellulose (CMC) as a sole carbon source. Further analysis of the insert showed two consecutive ORFs (celM2 and xynM2) encoding proteins of 226 and 662 amino acids, respectively. A multiple sequence analysis with the deduced amino acid sequences of celM2 showed 36% sequence identity with cellulase from the Synechococcus sp., while xynM2 had 59% identity to endo-1,4-beta-xylanase A from Cellulomonas pachnodae. The highest enzymatic CMC hydrolysis was observable at pH 4.0 and 45 degrees C with recombinant CelM2 protein. Although the enzyme CelM2 additionally hydrolyzed avicel and xylan, no substrate hydrolysis was observed on oligosaccharides such as cellobiose, pNP-beta-cellobioside, pNP-beta-glucoside, and pNP-beta-xyloside. These results showed that CelM2 is a novel endo-type cellulase.

Structural and functional analysis of a novel hormone-sensitive lipase from a metagenome library

Structural and functional analysis of a novel hormone-sensitive lipase from a metagenome library Ki Hyun Nam,1y Min-Young Kim,2y Soo-Jin Kim, Amit Priyadarshi, Suk-Tae Kwon, Bon-Sung Koo, Sang-Hong Yoon, and Kwang Yeon Hwang* 1Division of Biotechnology, College of Life Sciences and Biotechnology, Korea University, Seoul 136-701, Korea 2National Agrobiodiversity Center, National Academy of Agricultural Science, Rural Development Administration, Suwon 441-707, Korea 3 Biomedical Research Center, Korea Institute of Science and Technology, Seoul 136-791, Korea 4Department of Genetic Engineering, Sungkyunkwan University, Jangan-gu, Suwon 440-746, Korea 5 Fermentation and Food Processing Division, Department of Korean Food Research for Globalization, National Academy of Agricultural Science,

Crystal structure of engineered β‐glucosidase from a soil metagenome

Crystal structure of engineered b-glucosidase from a soil metagenome Ki Hyun Nam,1y Soo-Jin Kim,2y Min-Young Kim, Jae Hee Kim, Yun-Soo Yeo, Chang-Muk Lee, Hong-Ki Jun, and Kwang Yeon Hwang* 1Division of Biotechnology, College of Life Sciences and Biotechnology, Korea University, Seoul, Korea 2Microbial Genetics Division, National Institute of Agricultural Biotechnology, Rural Development Administration, Suwon, Korea 3Department of Microbiology, Pusan National University, Pusan, Korea

Assessment of physically-based and data-driven models to predict microbial water quality in open channels.

In the present study, a physically-based hydraulic modeling tool and a data-driven approach using artificial neural networks (ANNs) were evaluated for their ability to simulate the fate and transport of microorganisms in a water system. To produce reliable data, a pipe network was constructed and a series of experiments using a fecal coliform indicator (Escherichia coli 15597) was conducted. For the physically-based model, morphological (pipe size, link length, slope, etc.) and hydraulic (flow rate) conditions were used as input variables, and for ANNs, water quality parameters (conductivity, pH, and turbidity) were used. Both approaches accurately described the fate and transport of microorganisms (physically-based model: correlation coefficient (R) in the range of 0.914-0.977 and ANNs: R in the range of 0.949 - 0.980), with the exception of one case at a low flow rate (q = 31.56 cm3/sec). This study also indicated that these approaches could be complementarily utilized to assess the vulnerability of water facilities and to establish emergency plans based on hypothetical scenarios.

Wastewater retreatment and reuse system for agricultural irrigation in rural villages.

Climate changes and continuous population growth increase water demands that will not be met by traditional water resources, like surface and ground water. To handle increased water demand, treated municipal wastewater is offered to farmers for agricultural irrigation. This study aimed to enhance the effluent quality from worn-out sewage treatment facilities in rural villages, retreat effluent to meet water quality criteria for irrigation, and assess any health-related and environmental impacts from using retreated wastewater irrigation on crops and in soil. We developed the compact wastewater retreatment and reuse system (WRRS), equipped with filters, ultraviolet light, and bubble elements. A pilot greenhouse experiment was conducted to evaluate lettuce growth patterns and quantify the heavy metal concentration and pathogenic microorganisms on lettuce and in soil after irrigating with tap water, treated wastewater, and WRRS retreated wastewater. The purification performance of each WRRS component was also assessed. The study findings revealed that existing worn-out sewage treatment facilities in rural villages could meet the water quality criteria for treated effluent and also reuse retreated wastewater for crop growth and other miscellaneous agricultural purposes.

Development and evaluation of a decision-supporting model for identifying the source location of microbial intrusions in real gravity sewer systems

Assuming a scenario of a hypothetical pathogenic outbreak, we aimed this study at developing a decision-support model for identifying the location of the pathogenic intrusion as a means of facilitating rapid detection and efficient containment. The developed model was applied to a real sewer system (the Campbell wash basin in Tucson, AZ) in order to validate its feasibility. The basin under investigation was divided into 14 sub-basins. The geometric information associated with the sewer network was digitized using GIS (Geological Information System) and imported into an urban sewer network simulation model to generate microbial breakthrough curves at the outlet. A pre-defined amount of Escherichia coli (E. coli), which is an indicator of fecal coliform bacteria, was hypothetically introduced into 56 manholes (four in each sub-basin, chosen at random), and a total of 56 breakthrough curves of E. coli were generated using the simulation model at the outlet. Transport patterns were classified depending upon the location of the injection site (manhole), various known characteristics (peak concentration and time, pipe length, travel time, etc.) extracted from each E. coli breakthrough curve and the layout of sewer network. Using this information, we back-predicted the injection location once an E. coli intrusion was detected at a monitoring site using Artificial Neural Networks (ANNs). The results showed that ANNs identified the location of the injection sites with 57% accuracy; ANNs correctly recognized eight out of fourteen expressions with relying on data from a single detection sensor. Increasing the available sensors within the basin significantly improved the accuracy of the simulation results (from 57% to 100%).

Measurement of Aerodynamic Properties of Screens for Windbreak Fence using the Apparatus for Testing Screens

Recently, damage occurrence by wind erosion has been increasing in society. In times past, such problems only took place in desert area ; however, in recent years, the wind erosion problem is spreading out to agricultural land. Wind erosion in agricultural land can cause loss of loam soils, the disturbance of the photosynthesis of the crop fields and serious economic losses. To overcome the mentioned problems, installation of windbreak fence can be recommended which function as disturbing strong wind and wind erosion. However, there is still no proper guideline to install the windbreak fence and the installation used to rely on the intuition of the workers due to the lack of related studies. Therefore, this study measured the aerodynamic resistance of screens of the windbreak fence using the apparatus for testing screens. The apparatus for testing screens was designed to measure pressure loss around the screen. Measured pressure loss by wall friction compensated for pressure loss to calculate the aerodynamic resistance of screens. The result of pressure loss by regression analysis derived the aerodynamic coefficient of Darcy-Forchheimer equation and power law equation. The aerodynamic resistance was constant regardless of the overlapped shape when the screen was overlapped into several layers. Increasing the number of layers of the screen, internal resistance increased significantly more, and pressure loss caused by the screen also increased linearly when the wind speed was certain conditions, but permeability had no tendency. In the future, the results of this study will be applied to the computational fluid dynamics simulation. The simulation models will be also validated in advance by wind tunnel experiments. It will provide standard of a design for constructing windbreak fence.

Comparative prediction schemes using conventional and advanced statistical analysis to predict microbial water quality in runoff from manured fields.

Accurate estimations of indicator microorganisms’ concentrations are necessary to properly monitor water quality and manage contamination from agricultural land runoffs. In this study, Artificial Neural Networks (ANNs) and Multiple Regression Analysis (MRA) statistical methods were compared for accuracy in the prediction of manure-borne microorganisms’ concentrations in runoffs from agricultural plots (0.75 m × 2 m) treated with cattle or swine manure. Field rainfall simulation tests were initiated on days 4, 32, 62, 123, and 354 between June 2002 and May 2003. Each rainfall event produced 35 mm rainfall for 30 min at the intensity of 70 mm hr−1 at 24-intervals. Concentrations of microbial indicators were correlated with hydrological and environmental water quality parameters including water runoff, erosion, air temperature, relative humidity, solar radiation, pH, electric conductivity (EC) and turbidity to determine their impacts on microbial fate and transport. ANNs demonstrated a better ability to model the nonlinearity of land application of manure to ensure the safety of agricultural water environments.