• Korean Journal of Acupuncture
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• v.23 no.3
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• pp.133-160
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• 2006

Objectives : Scolopendrae Corpus has a broad array of clinical applications in Korean medicine, including treatment of inflammatory conditions such as arthritis. To explore the global gene expression profiles in human Raw cell lines treated with Scolopendrae Corpus herbal-acupuncture solution (SCHAS), cDNA microarray analysis was performed. Methods : The Raw 264.7 cells were treated with lipopolysaccharide (LPS), SCHAS, or both. The primary data was normalized by the total spots of intensity between two groups, and then normalized by the intensity ratio of reference genes such as housekeeping genes in both groups. The expression ratio was converted to log2 ratio. Normalized spot intensities were calculated into gene expression ratios between the control and treatment groups. Greater than 2 fold changes between two groups were considered to be of significance. Results : Of the 8 K genes profiled in this study, with a cut-off level of two-fold change in the expression, 20 genes (BCL2-related protein A1, MARCKS-like 1, etc.) were upregulated and 5 genes (activated RNA polymerase II transcription cofactor 4, calcium binding atopy-related autoantigen 1, etc.) downregulated following LPS treatment. 139 genes (kell blood group precursor (McLeod phenotype), ribosomal protein S7, etc.) were upregulated and 42 genes (anterior gradient 2 homolog (xenopus laevis), phosphodiesterase 8B, etc.) were downregulated following SCHAS treatment. And 10 genes (yeast saccharomyces cerevisiae intergeneic sequence 4-1, mitogen-activated protein kinase 1, etc.) were upregulated and 8 genes (spermatid perinuclear RNA binding protein, nuclear receptor binding protein 2, etc.) were downregulated following co-stimulation of SCHAS and LPS. Discussions : It is thought that microarrays will play an ever-growing role in the advance of our understanding of the pharmacological actions of SCHAS in the treatment of arthritis. But further studies are required to concretely prove the effectiveness of SCHAS.

• Horticultural Science & Technology
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• v.34 no.1
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• pp.102-111
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• 2016

The goal of this study was to characterize the BrDSR (Drought Stress Resistance in B. rapa) gene and to identify the expression network of drought-inducible genes in Chinese cabbage under drought stress. Agrobacterium-mediated transformation was conducted using a B. rapa inbred line ('CT001') and the pSL100 vector containing the BrDSR full length CDS (438 bp open reading frame). Four transgenic plants were selected by PCR and the expression level of BrDSR was approximately 1.9-3.4-fold greater than that in the wild-type control under drought stress. Phenotypic characteristics showed that BrDSR over-expressing plants were resistant to drought stress and showed normal growth habit. To construct a co-expression network of drought-responsive genes, B. rapa 135K cDNA microarray data was analyzed to identify genes associated with BrDSR. BrDSR was directly linked to DARK INDUCIBLE 2 (DIN2, AT3G60140) and AUTOPHAGY 8H (ATG8H, AT3G06420) previously reported to be leaf senescence and autophagy-related genes in plants. Taken together, the results of this study indicated that BrDSR plays a significant role in enhancement of tolerance to drought conditions.

• Proceedings of the Ginseng society Conference
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• 2007.12a
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• pp.57-58
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• 2007

Purpose: Ginseng is a well-known medical plant used in traditional Oriental medicine. Korean red ginseng (KRG) has been known to have potent biological activities such as radical scavenging, vasodilating, anti-tumor and anti-diabetic activities. However, the mechanism of the beneficial effects of KRG on diabetes is yet to be elucidated. The present study was designed to investigate the anti-diabetic effect and mechanism of KRG extract in C57BL/KsJ db/db mice. Methods: The db/db mice were randomly divided into six groups: diabetic control group (DC), red ginseng extract low dose group (RGL, 100 mg/kg), red ginseng extract high dose group (RGH, 200 mg/kg), metformin group (MET, 300 mg/kg), glipizide group (GPZ, 15 mg/kg) and pioglitazone group (PIO, 30 mg/kg), and treated with drugs once per day for 10 weeks. During the experiment, body weight and blood glucose levels were measured once every week. At the end of treatment, we measured Hemoglobin A1c (HbA1c), blood glucose, insulin, triglyceride (TG), adiponectin, leptin, non-esterified fatty acid (NEFA). Morphological analyses of liver, pancreas and white adipose tissue were done by histological observation through hematoxylin-eosin staining. Pancreatic islet insulin and glucagon levels were detected by double-immunofluorescence staining. To elucidate an action of mechanism of KRG, DNA microarray analyses were performed, and western blot and RT-PCR were conducted for validation. Results: Compared to the DC group mice, body weight gain of PIO treated group mice showed 15.2% increase, but the other group mice did not showed significant differences. Compared to the DC group, fasting blood glucose levels were decreased by 19.8% in RGL, 18.3% in RGH, 67.7% in MET, 52.3% in GPZ, 56.9% in PIO-treated group. With decreased plasma glucose levels, the insulin resistance index of the RGL-treated group was reduced by 27.7% compared to the DC group. Insulin resistance values for positive drugs were all markedly decreased by 80.8%, 41.1% and 68.9%, compared to that of DC group. HbA1c levels in RGL, RGH, MET, GPZ and PIO-treated groups were also decreased by 11.0%, 6.4%, 18.9%, 16.1% and 27.9% compared to that of DC group, and these figure revealed a similar trend shown in plasma glucose levels. Plasma TG and NEFA levels were decreased by 18.8% and 16.8%, respectively, and plasma adiponectin and leptin levels were increased by 20.6% and 12.1%, respectively, in the RGL-treated group compared to those in DC group. Histological analysis of the liver of mice treated with KRG revealed a significantly decreased number of lipid droplets compared to the DC group. The control mice exhibited definitive loss and degeneration of islet, whereas mice treated with KRG preserved islet architecture. Compared to the DC group mice, KRG resulted in significant reduction of adipocytes. From the pancreatic islet double-immunofluorescence staining, we observed KRG has increased insulin production, but decreased glucagon production. KRG treatment resulted in stimulation of AMP-activated protein kinase (AMPK) phosphorylation in the db/db mice liver. To elucidate mechanism of action of KRG extract, microarray analysis was conducted in the liver tissue of mice treated with KRG extract, and results suggest that red ginseng affects on hepatic expression of genes responsible for glycolysis, gluconeogenesis and fatty acid oxidation. In summary, multiple administration of KRG showed the hypoglycemic activity and improved glucose tolerance. In addition, KRG increased glucose utilization and improved insulin sensitivity through inhibition of lipogenesis and activation of fatty acid $\beta$-oxidation in the liver tissue. In view of our present data, we may suggest that KRG could provide a solid basis for the development of new anti-diabetic drug.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.8
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• pp.372-377
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• 2008

Multiclass cancer classification has been actively investigated based on gene expression profiles, where it determines the type of cancer by analyzing the large amount of gene expression data collected by the DNA microarray technology. Since gene expression data include many genes not related to a target cancer, it is required to select informative genes in order to obtain highly accurate classification. Conventional rank-based gene selection methods often use ideal marker genes basically devised for binary classification, so it is difficult to directly apply them to multiclass classification. In this paper, we propose a novel method for multiclass gene selection, which does not use ideal marker genes but directly analyzes the distribution of gene expression. It measures the class-discriminability by discretizing gene expression levels into several regions and analyzing the frequency of training samples for each region, and then classifies samples by using the naive Bayes classifier. We have demonstrated the usefulness of the proposed method for various representative benchmark datasets of multiclass cancer classification.

• Journal of Microbiology and Biotechnology
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• v.24 no.8
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• pp.1143-1147
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• 2014

Human papillomavirus (HPV) infection is considered to play a critical role in the development of cervical carcinoma, which is the third most common cancer among Korean females. Here, we performed a baseline study of HPV infection and genotyping using an HPV DNA chip, which is a type of oligonucleotide microarray. A total of 6,855 cervical swab specimens from 5,494 women attending Dankook University Hospital Health Improvement Center in Cheonan, Korea between 2006 and 2012, originally collected for HPV infection screening, were genotyped for HPV. The extracted DNA from the cervical specimens was investigated by an HPV DNA chip designed to detect 41 different HPV types. HPV was identified as positive in 1,143 (16.7%) of the 6,855 samples. The most frequently detected HPV genotypes were HPV types 16, 53, 56, 58, 39, 52, 70, 84, 68, 62, 35, 54, 81, 18, and 30, in descending order of incidence. The proportions of single and multiple HPV infections in the HPV-positive specimens were 78.1% and 21.9%, respectively. The average age of HPV-positive patients was 39.9 years, with the positive rate of HPV being the highest in the 10-29 age group (20.6%). We report here on the prevalence and distribution of 41 different genotypes of HPV according to age among women in Cheonan, Korea. These data may be of use as baseline data for the assessment of public health-related issues and for the development of area-specific HPV vaccines.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.3
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• pp.1629-1633
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• 2013

Background: Gene set analysis (GSA) incorporates biological with statistical knowledge to identify gene sets which are differentially expressed that between two or more phenotypes. Materials and Methods: In this paper gene sets differentially expressed between acute lymphoblastic leukaemia (ALL) with BCR-ABL and those with no observed cytogenetic abnormalities were determined by GSA methods. The BCR-ABL is an abnormal gene found in some people with ALL. Results: The results of two GSAs showed that the Category test identified 30 gene sets differentially expressed between two phenotypes, while the Hotelling's $T^2$ could discover just 19 gene sets. On the other hand, assessment of common genes among significant gene sets showed that there were high agreement between the results of GSA and the findings of biologists. In addition, the performance of these methods was compared by simulated and ALL data. Conclusions: The results on simulated data indicated decrease in the type I error rate and increase the power in multivariate (Hotelling's $T^2$) test as increasing the correlation between gene pairs in contrast to the univariate (Category) test.

• Molecules and Cells
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• v.27 no.3
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• pp.271-277
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• 2009

Proteins interact with each other within a cell, and those interactions give rise to the biological function and dynamical behavior of cellular systems. Generally, the protein interactions are temporal, spatial, or condition dependent in a specific cell, where only a small part of interactions usually take place under certain conditions. Recently, although a large amount of protein interaction data have been collected by high-throughput technologies, the interactions are recorded or summarized under various or different conditions and therefore cannot be directly used to identify signaling pathways or active networks, which are believed to work in specific cells under specific conditions. However, protein interactions activated under specific conditions may give hints to the biological process underlying corresponding phenotypes. In particular, responsive functional modules consist of protein interactions activated under specific conditions can provide insight into the mechanism underlying biological systems, e.g. protein interaction subnetworks found for certain diseases rather than normal conditions may help to discover potential biomarkers. From computational viewpoint, identifying responsive functional modules can be formulated as an optimization problem. Therefore, efficient computational methods for extracting responsive functional modules are strongly demanded due to the NP-hard nature of such a combinatorial problem. In this review, we first report recent advances in development of computational methods for extracting responsive functional modules or active pathways from protein interaction network and microarray data. Then from computational aspect, we discuss remaining obstacles and perspectives for this attractive and challenging topic in the area of systems biology.

• Molecular & Cellular Toxicology
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• v.3 no.3
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• pp.208-213
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• 2007

With the help of a development and popularization of microarray technology that enable to us to simultaneously investigate the expression pattern of thousands of genes, the toxicogenomics experimenters can interpret the genome-scale interaction between genes exposed in toxicant or toxicant-related environment. The ultimate and primary goal of toxicogenomics identifies functional context among the group of genes that are differentially or similarly coexpressed under the specific toxic substance. On the other side, public reference databases with transcriptom, proteom, and biological pathway information are needed for the analysis of these complex omics data. However, due to the heterogeneous and independent nature of these databases, it is hard to individually analyze a large omics annotations and their pathway information. Fortunately, several web sites of the public database provide information linked to other. Nevertheless it involves not only approriate information but also unnecessary information to users. Therefore, the systematically integrated database that is suitable to a demand of experimenters is needed. For these reasons, we propose SOP (Search of Omics Pathway) database system which is constructed as the integrated biological database converting heterogeneous feature of public databases into combined feature. In addition, SOP offers user-friendly web interfaces which enable users to submit gene queries for biological interpretation of gene lists derived from omics experiments. Outputs of SOP web interface are supported as the omics annotation table and the visualized pathway maps of KEGG PATHWAY database. We believe that SOP will appear as a helpful tool to perform biological interpretation of genes or proteins traced to omics experiments, lead to new discoveries from their pathway analysis, and design new hypothesis for a next toxicogenomics experiments.

• Molecules and Cells
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• v.43 no.6
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• pp.551-571
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• 2020

Nuclear receptor-related 1 (Nurr1) protein has been identified as an obligatory transcription factor in midbrain dopaminergic neurogenesis, but the global set of human NURR1 target genes remains unexplored. Here, we identified direct gene targets of NURR1 by analyzing genome-wide differential expression of NURR1 together with NURR1 consensus sites in three human neural stem cell (hNSC) lines. Microarray data were validated by quantitative PCR in hNSCs and mouse embryonic brains and through comparison to published human data, including genome-wide association study hits and the BioGPS gene expression atlas. Our analysis identified ~40 NURR1 direct target genes, many of them involved in essential protein modules such as synapse formation, neuronal cell migration during brain development, and cell cycle progression and DNA replication. Specifically, expression of genes related to synapse formation and neuronal cell migration correlated tightly with NURR1 expression, whereas cell cycle progression correlated negatively with it, precisely recapitulating midbrain dopaminergic development. Overall, this systematic examination of NURR1-controlled regulatory networks provides important insights into this protein's biological functions in dopamine-based neurogenesis.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.802-808
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• 2009

Recently many researches have been presented to improve the clustering performance of gene expression data by incorporating Gene Ontology into the process of clustering. In particular, Kustra et al. showed higher performance improvement by exploiting Biological Process Ontology compared to the typical expression-based clustering. This paper extends the work of Kustra et al. by performing extensive experiments on the way of incorporating GO structures. To this end, we used three ontological distance measures (Lin's, Resnik's, Jiang's) and three GO structures (BP, CC, MF) for the yeast expression data. From all test cases, We found that clustering performances were remarkably improved by incorporating GO; especially, Resnik's distance measure based on Biological Process Ontology was the best.