• Korean Journal of Exercise Nutrition
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• v.17 no.2
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• pp.25-34
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• 2013

This study was conducted to investigate the relationship between ACE gene polymorphism and muscle damage parameters after eccentric exercise. 80 collegiate males were instructed to take an eccentric exercise with the elbow flexor muscle through the modified preacher curl machine for 2 sets of 25 cycles (total 50 cycles). The maximal isometric strength, muscle soreness, creatine kinase (CK), and myoglobin (Mb) were measured before exercise, and 0, 24, 48, 72, and 96 hrs after exercise. The result showed that after the eccentric exercise, the maximal isometric strength significantly decreased by more than 50% (p < 0.001) and the muscle soreness, CK, and Mb significantly increased compared to those before the exercise (p < 0.001). The ACE gene polymorphism of the subjects was classified using real-time polymerase chain reaction (real-time PCR). The result showed that it consisted of 38 cases of type II (46.4%), 33 cases of type ID (43.4%), and 9 cases of type DD (10.2%). The Hardy-Weinberg equilibrium for ACE gene polymorphism was shown to have p = 0.653, which showed that each allele was evenly distributed. Although significant differences in the changes in the maximal isometric strength, muscle soreness, CK, and Mb were found according to time course (p < 0.001), no significant differences in the changes in the maximal isometric strength, muscle soreness, CK, and Mb were found according to ACE gene polymorphism. Furthermore, no significant difference in the changes in the muscle damage parameters was found according to interaction between ACE gene polymorphism and time course (p > 0.05). In conclusion, the level of the muscle damage parameters changed in the injured muscle after eccentric exercise, but these changes in the muscle damage parameters were not affected by ACE gene polymorphism. The result of this study indicates that ACE gene is not a candidate gene that explains muscle damage.

• Proceedings of the KSRS Conference
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• v.2
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• pp.623-626
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• 2006

Group of genes controls the functioning of a cell by complex interactions. These interacting gene groups are called Gene Regulatory Networks (GRNs). Two previous data mining approaches, clustering and classification have been used to analyze gene expression data. While these mining tools are useful for determining membership of genes by homology, they don't identify the regulatory relationships among genes found in the same class of molecular actions. Furthermore, we need to understand the mechanism of how genes relate and how they regulate one another. In order to detect regulatory relationships among genes from time-series Microarray data, we propose a novel approach using frequent pattern mining and chain rule. In this approach, we propose a method for transforming gene expression data to make suitable for frequent pattern mining, and detect gene expression patterns applying FP-growth algorithm. And then, we construct gene regulatory network from frequent gene patterns using chain rule. Finally, we validated our proposed method by showing that our experimental results are consistent with published results.

• Molecular & Cellular Toxicology
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• v.5 no.2
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• pp.147-152
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• 2009

This study was conducted to evaluate the inflammatory mechanisms of LPS-stimulated BV-2 microglial cells. The inflammation mechanism was evaluated in BV-2 cells with or without LPS treated using the Affymetrix microarray analysis system. The microarray analysis revealed that B cell receptor signaling pathway, cytokine-cytokine receptor interaction, Jak-STAT signaling pathway, MAPK signaling pathway, Neuro-active ligand-receptor interaction, TLR signaling path-way, and T cell receptor signaling pathway-related genes were up-regulated in LPS stimulated BV-2 cells. Selected genes were validated using real time RTPCR. These results can help an effective therapeutic approach to alleviating the progression of neuro-in-flammatory diseases.

• Journal of Microbiology and Biotechnology
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• v.18 no.4
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• pp.631-638
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• 2008

Tandem affinity purification (TAP) method combined with LC-MS/MS is the most accurate and reliable way to study the interaction of proteins or proteomics in a genome-wide scale. For the first time, we used a TAP-tag as a mutagenic tool to disrupt protein interactions at the specific site. Although lots of commonly used mutational tools exist to study functions of a gene, such as deletional mutations and site-directed mutagenesis, each method has its own demerit. To test the usefulness of a TAP-tag as a mutagenic tool, we applied a TAP-tag to RNA polymerase II, which is the key enzyme of gene expression and is controlled by hundreds of transcription factors even to transcribe a gene. Our experiment is based on the hypothesis that there will be interrupted interactions between Pol II and transcription factors owing to the TAP-tag attached at the C-terminus of each subunit of Pol II, and the abnormality caused by interrupted protein interactions can be observed by measuring a cell-cycle of each yeast strain. From ten different TAP-tagged strains, Rpb7- and Rpb12-TAP-tagged strains show severe defects in growth rate and morphology. Without a heterodimer of Rpb4/Rpb7, only the ten subunits Pol II can conduct transcription normally, and there is no previously known function of Rpb7. The observed defect of the Rpb7-TAP-tagged strain shows that Rpb7 forms a complex with other proteins or compounds and the interruption of the interaction can interfere with the normal cell cycle and morphology of the cell and nucleus. This is a novel attempt to use a TAP-tag as a proteomic tool to study protein interactions.

• The KIPS Transactions:PartD
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• v.14D no.1 s.111
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• pp.9-20
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• 2007

Groups of genes control the functioning of a cell by complex interactions. Such interactions of gene groups are tailed Gene Regulatory Networks(GRNs). Two previous data mining approaches, clustering and classification, have been used to analyze gene expression data. Though these mining tools are useful for determining membership of genes by homology, they don't identify the regulatory relationships among genes found in the same class of molecular actions. Furthermore, we need to understand the mechanism of how genes relate and how they regulate one another. In order to detect regulatory relationships among genes from time-series Microarray data, we propose a novel approach using frequent pattern mining and chain rules. In this approach, we propose a method for transforming gene expression data to make suitable for frequent pattern mining, and gene expression patterns we detected by applying the FP-growth algorithm. Next, we construct a gene regulatory network from frequent gene patterns using chain rules. Finally, we validate our proposed method through our experimental results, which are consistent with published results.

• International Journal of Industrial Entomology and Biomaterials
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• v.46 no.1
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• pp.16-23
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• 2023

The silkworm's dormancy and embryonic development are accomplished through the interaction of various genes. Analysis of the expression of several interacting genes can predict the embryonic stage of silkworms. In this study, we analyzed the changes in the expression level of genes at each stage during the embryonic development of dormant silkworm eggs and selected genes that can predict the hatching time. Jam123 and Jam124 silkworms were collected after egg laying, and the silkworm eggs were preserved using a double refrigeration method and expression analysis was performed for 23 genes during embryogenesis. There were 5 genes showing significant changes during embryogenesis: UDP-glucuronosyltransferases (BmUGTs), heat shock protein hsp20.8 (BmHsp20.8), Cytochromes b5-like proteins (BmCytb5), Krüppel homolog 1 (BmKr-h1), and cuticular protein RR-1 motif 41 (BmCpr41). As a result of quantitative comparison of the expression levels of these 5 genes through real-time PCR, the BmUGTs gene showed a difference between Jam123 and Jam124, making it difficult to see it as an indicator for predicting hatching time. However, the BmHsp20.8 gene had a common expression decreased at the imminent hatching stage. In addition, it was confirmed that the expression level of the BmCytb5 gene decreased to the lowest level at the time of imminent hatching, and the expression of the BmKr-h gene was made only at the time of imminent hatching. The expression of the last BmCpr41 gene can be confirmed only at the time of imminent hatching, and it was confirmed that it shows a rapid increase right before hatching. Taken together, these results suggest that expression analysis of BmHsp20.8, BmCytb5, BmKr-h1, and BmCpr41 genes can determine the stage of embryogenesis, predict hatching time, which facilitate better management of silkworm eggs.

• The Plant Pathology Journal
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• v.36 no.6
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• pp.618-627
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• 2020

Although limited progress have been made about pathogen system of Hibiscus rosa-sinensis and Hibiscus latent Fort Pierce virus (HLFPV), interaction between plant host and pathogen remain largely unknown, which led to deficiency of effective measures to control disease of hibiscus plants caused by HLFPV. In this study, infection of HLFPV in Hibiscus rosa-sinensis was firstly confirmed for the first time by traditional electron microscopy, modern reverse transcription polymerase chain reaction and RNA-seq methods in China (HLFPV-Ch). Sequence properties analyzing suggested that the full-length sequences (6,465 nt) of HLFPV-Ch had a high sequence identity and a similar genomic structure with other tobamoviruses. It includes a 5'-terminal untranslated region (UTR), followed by four open reading frames encoding for a 128.5-kDa replicase, a 186.5-kDa polymerase, a 31-kDa movement protein, 17.6-kDa coat protein, and the last a 3'-terminal UTR. Furthermore, HLFPV-Ch-derived virus-derived siRNAs (vsiRNAs) ant its putative target genes, reported also for the first time, were identified and characterized from disease Hibiscus rosa-sinensis through sRNA-seq and Patmatch server to investigate the interaction in this pathogen systems. HLFPV-Ch-derived vsiRNAs demonstrated several general and specific characteristics. Gene Ontology classification revealed predicted target genes by vsiRNAs are involved in abroad range of cellular component, molecular function and biological processes. Taken together, for first time, our results certified the HLFPV infection in China and provide an insight into interaction between HLFPV and Hibiscus rosa-sinensis.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.51 no.7
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• pp.328-334
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• 2002

The DNA chips are devices associating the specific recognition properties of two DNA single strands through hybridization process with the performances of the microtechnology. In the literature, the "Gene chip" or "DNA chip" terminology is employed in a wide way and includes macroarrays and microarrays. Standard definitions are not yet clearly exposed. Generally, the difference between macro and microarray concerns the number of active areas and their size, Macroarrays correspond to devices containing some tens spots of 500$\mu$m or larger in diameter. microarrays concern devices containing thousnads spots of size less than 500$\mu$m. The key technical parameters for evaluating microarray-manufacturing technologies include microarray density and design, biochemical composition and versatility, repreducibility, throughput, quality, cost and ease of prototyping. Here we report, a new method in which minute particles are arranged in a random fashion on a chip pattern using random fluidic self-assembly (RFSA) method by hydrophobic interaction. We intend to improve the stability of the particles at the time of arrangement by establishing a wall on the chip pattern, besides distinction of an individual particle is enabled by giving a tag structure. This study demonstrates the fabrication of a chip pattern, immobilization of DNA to the particles and arrangement of the minute particle groups on the chip pattern by hydrophobic interaction.ophobic interaction.

• Asian-Australasian Journal of Animal Sciences
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• v.25 no.10
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• pp.1481-1492
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• 2012

The interaction of the genes involved in intestinal development is the molecular basis of the regulatory mechanisms of intestinal development. The objective of this study was to identify the significant pathways and key genes that regulate intestinal development in Landrace piglets, and elucidate their rules of operation. The differential expression of genes related to intestinal development during suckling time was investigated using a porcine genome array. Time sequence profiles were analyzed for the differentially expressed genes to obtain significant expression profiles. Subsequently, the most significant profiles were assayed using Gene Ontology categories, pathway analysis, network analysis, and analysis of gene co-expression to unveil the main biological processes, the significant pathways, and the effective genes, respectively. In addition, quantitative real-time PCR was carried out to verify the reliability of the results of the analysis of the array. The results showed that more than 8000 differential expression transcripts were identified using microarray technology. Among the 30 significant obtained model profiles, profiles 66 and 13 were the most significant. Analysis of profiles 66 and 13 indicated that they were mainly involved in immunity, metabolism, and cell division or proliferation. Among the most effective genes in these two profiles, CN161469, which is similar to methylcrotonoyl-Coenzyme A carboxylase 2 (beta), and U89949.1, which encodes a folate binding protein, had a crucial influence on the co-expression network.

• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.1057-1067
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• 2014

Many genetic variants have been identified to be associated with complex diseases such as hypertension, diabetes and cancers throughout genome-wide association studies (GWAS). However, there still exist a serious missing heritability problem since the proportion explained by genetic variants from GWAS is very weak less than 10~15%. Gene-gene interaction study may be helpful to explain the missing heritability because most of complex disease mechanisms are involved with more than one single SNP, which include multiple SNPs or gene-gene interactions. This paper focuses on gene-gene interactions with the survival phenotype by extending the multifactor dimensionality reduction (MDR) method to the accelerated failure time (AFT) model. The standardized residual from AFT model is used as a residual score for classifying multiple geno-types into high and low risk groups and algorithm of MDR is implemented. We call this method AFT-MDR and compares the power of AFT-MDR with those of Surv-MDR and Cox-MDR in simulation studies. Also a real data for leukemia Korean patients is analyzed. It was found that the power of AFT-MDR is greater than that of Surv-MDR and is comparable with that of Cox-MDR, but is very sensitive to the censoring fraction.