• Genomics & Informatics
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• v.6 no.3
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• pp.153-156
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• 2008

Complex diseases such as stroke and cancer have two or more genetic loci and are affected by environmental factors that contribute to the diseases. Due to the complex characteristics of these diseases, identifying candidate genes requires a system-level analysis of the following: gene ontology, pathway, and interactions. A database and user interface, termed StrokeBase, was developed; StrokeBase provides queries that search for pathways, candidate genes, candidate SNPs, and gene networks. The database was developed by using in silico data mining of HGNC, ENSEMBL, STRING, RefSeq, UCSC, GO, HPRD, KEGG, GAD, and OMIM. Forty candidate genes that are associated with cerebrovascular disease were selected by human experts and public databases. The networked cerebrovascular disease gene maps also were developed; these maps describe genegene interactions and biological pathways. We identified 1127 genes, related indirectly to cerebrovascular disease but directly to the etiology of cerebrovascular disease. We found that a protein-protein interaction (PPI) network that was associated with cerebrovascular disease follows the power-law degree distribution that is evident in other biological networks. Not only was in silico data mining utilized, but also 250K Affymetrix SNP chips were utilized in the 320 control/disease association study to generate associated markers that were pertinent to the cerebrovascular disease as a genome-wide search. The associated genes and the genes that were retrieved from the in silico data mining system were compared and analyzed. We developed a well-curated cerebrovascular disease-associated gene network and provided bioinformatic resources to cerebrovascular disease researchers. This cerebrovascular disease network can be used as a frame of systematic genomic research, applicable to other complex diseases. Therefore, the ongoing database efficiently supports medical and genetic research in order to overcome cerebrovascular disease.

• Proceedings of the Zoological Society Korea Conference
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• 1995.10b
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• pp.82-82
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• 1995

Regulation of enzyme synthesis by transcriptional and translational control systems provides rather stable adaptation to change of amino acid level in the growth medium, while manipulation of enzyme activity through endproduct feedback inhibition represents rather short-term and reversible ways of adjusting metabolic fluctuation of amino acid level. Various control mechanisms interplay to regulate genes encoding enzymes for amino acid biosynthesis in the yeast, Sacchromyces cerevisiae. When amino acids are in short supply, genes under a cross-pathway regulatory mechanism Or general amino acid control (general control) increase their action, in which Gcn4p is the major positive regulator of gene expression. When cells are cultured in minimal medium, basal level expression is also regulated by supplementary control elements, where inorganic phosphate level is additionally involved. Most of amino acid biosynthetic genes are also regulated by the level of endproduct of the pathway. This pathway-specific regulatory mechanism is called specific amino acid control (specific controD, under which gene expression is reduced when endproduct is present in the medium. Derepression of a gene through general control can be usually overridden by repression through specific control, where the endproduct level of that particular pathway is high and not limiting. In this presentation, regulatory factors for basal level expression and general control of yeast amino acid biosynthesis will be discussed, m addition to pathway-specific repression patterns and interaction between CrOSS- and specific-control mechanisms. Preliminary results are also presented from the investigation of the cloned genes in the threonine biosynthetic pathway of the yeast. yeast.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.1
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• pp.457-461
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• 2013

Prostate cancer is a leading cause of death in male populations across the globe. With the advent of gene expression arrays, many microarray studies have been conducted in prostate cancer, but the results have varied across different studies. To better understand the genetic and biologic mechanisms of prostate cancer, we conducted a meta-analysis of two studies on prostate cancer. Eight key genes were identified to be differentially expressed with progression. After gene co-expression analysis based on data from the GEO database, we obtained a co-expressed gene list which included 725 genes. Gene Ontology analysis revealed that these genes are involved in actin filament-based processes, locomotion and cell morphogenesis. Further analysis of the gene list should provide important clues for developing new prognostic markers and therapeutic targets.

• BMB Reports
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• v.36 no.1
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• pp.28-34
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• 2003

Breast cancer is the most prevalent cancer among women in Western countries, and its prevalence is also increasing in Asia. The major risk factor for breast cancer can be traced to reproductive events that influence the lifetime levels of hormones. However, a large percentage of breast cancer cases cannot, be explained by these risk factors. The identification of susceptibility factors that predispose individuals to breast cancer (for instance, if they are exposed to particular environmental agents) could possibly give further insight into the etiology of this malignancy and provide targets for the future development of therapeutics. The most interesting candidate genes include those that mediate a range of functions. These include carcinogen metabolism, DNA repair, steroid hormone metabolism, signal transduction, and cell cycle control. We conducted a hospital-based case-control study in South Korea to evaluate the potential modifying role of the genetic polymorphisms of selected low penetrance genes that are involved in carcinogen metabolisms (i.e., CYP1A1, CYP2E1, GSTM1/T1/P1, NAT1/2, etc.), estrogen synthesis and metabolism (i.e., CYP19, CYP17, CYP1B1, COMT, ER-$\alpha$, etc.), DNA repair (i.e., XRCC1/3, ERCC2/4, ATM, AGT, etc.), and signal transduction as well as others (i.e., TGF-$\beta$, IGF-1, TNF-$\beta$, IL-1B, IL-1RN, etc.). We also took into account the potential interaction between these and the known risk factors of breast cancer. The results of selected genes will be presented in this mini-review.

• Journal of Microbiology and Biotechnology
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• v.14 no.4
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• pp.836-843
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• 2004

Nontypeable H. influenzae (NTHi), a Gram-negative obligate human pathogen, causes pneumonia, chronic bronchitis, and otitis media, and the respiratory epithelium is the first line of defense that copes with the pathogen. In an effort to identify transcriptional responses of human respiratory epithelial cells to infection with NTHi, we examined its differential gene expression using high density cDNA microarrays. BEAS-2B human bronchial epithelial cells were exposed to NTHi for 3 hand 24 h, and the alteration of mRNA expression was analyzed using microarrays consisting of 8,170 human cDNA clones. The results indicated that approximately 2.6% of the genes present on the microarrays increased in expression over 2-fold and 3.8% of the genes decreased during the 24-h infection period. Upregulated genes included cytokines (granulocyte-macrophage colony stimulating factor 2, granulocyte chemotactic protein 2, IL-6, IL-10, IL-8), transcription factors (Kruppel-like factor 7, CCAAT/enhancer binding protein $\beta$, E2F-1, NF-$\kappa$B, cell surface molecules (CD74, ICAM-1, ICAM-2, HLA class I), as well as those involved in signal transduction and cellular transport. Selected genes were further confirmed by reverse-transcription-PCR. These data expand our knowledge of host cellular responses during NTHi infection and should provide a molecular basis for the study of host-NTHi interaction.

• Korean Journal of Clinical Laboratory Science
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• v.53 no.4
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• pp.353-362
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• 2021

Cancer is a global health problem. There are diverse types of cancers, but there are several common pathways which lead to the development of cancer. Changes in gene expression might be the most common similarity found in almost all cancers. An understanding of the underlying changes in gene expression during cancer progression could lay a valuable foundation for the development of cancer therapeutics and even cancer vaccines. In this study, a well-known carcinogen, N-methyl-N'-nitro-N-nitrosoguanidine (MNNG), was employed to induce changes in gene expression in normal stomach cells. MNNG is known to cause cancer by inducing damage to DNA in MNNG-treated mammalian cells and animals fed with this carcinogen. An analysis was performed by comparing the differentially expressed genes (DEGs) caused by MNNG treatment with DEGs in stomach cancer cell lines. To this end, methods of analysis for functional categorization and protein-protein interaction networks, such as gene ontology (GO), the database for annotation, visualization, and integrated discovery (DAVID), Kyoto encyclopedia of genes and genomics (KEGG) and search tool for the retrieval of interacting genes/proteins (STRING), were used. As a result of these analyses, MNNG-regulated specific genes and interaction networks of their protein products that contributed to stomach cancer were identified.

• Journal of Plant Biotechnology
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• v.32 no.2
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• pp.73-83
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• 2005

Disease resistance in plants is often controlled by gene-for-gene mechanism in which avirulence (avr) gene products encoding by pathogens are specifically recognized, either directly or indirectly by plant disease resistance (R) gene products and sequential signal transduction pathways activating defense responses are rapidly triggered. As a results, not only exhibit a resistance against invading pathogens but also plants maintain the systemic acquired resistance (SAR) to various other pathogens. This molecular interaction between pathogen and plant is commonly compared to innate immune system of animal. Recent studies arising from molecular characterization of a number of R genes from various plant species that confer resistance to different pathogens and corresponding avr genes from various pathogens resulted in the accumulation of a wealth of knowledge on molecular mechanism of gene-for-gene interaction. Furthermore, new technologies of genomics and proteomics make it possible to monitor the genome-wide gene regulation and protein modification during activation of disease resistance, expanding our ability to understand the plant immune response and develop new crops resistant to biotic stress.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.12
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• pp.1684-1688
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• 2017

Objective: From a review of published information on genetic association studies, a meta-analysis was conducted to determine the influence of the genes ${\kappa}-casein$ (CSN3) and ${\beta}-lactoglobulin$ (LGB) on milk yield traits in Holstein, Jersey, Brown Swiss, and Fleckvieh. Methods: The GLIMMIX procedure was used to analyze milk production and percentage of protein and fat in milk. Models included the main effects and all their possible two-way interactions; not estimable effects and non-significant (p>0.05) two-way interactions were dropped from the models. The three traits analyzed used Poisson distribution and a log link function and were determined with the Interactive Data Analysis of SAS software. Least square means and multiple mean comparisons were obtained and performed for significant main effects and their interactions (p<0.0255). Results: Interaction of breed by gene showed that Holstein and Fleckvieh were the breeds on which CSN3 ($6.01%{\pm}0.19%$ and $5.98%{\pm}0.22%$), and LGB ($6.02%{\pm}0.19%$ and $5.70%{\pm}0.22%$) have the greatest influence. Interaction of breed by genotype nested in the analyzed gene indicated that Holstein and Jersey showed greater influence of the CSN3 AA genotype, $6.04%{\pm}0.22%$ and $5.59%{\pm}0.31%$ than the other genotypes, while LGB AA genotype had the largest influence on the traits analyzed, $6.05%{\pm}0.20%$ and $5.60%{\pm}0.19%$, respectively. Furthermore, interaction of type of statistical model by genotype nested in the analyzed gene indicated that CSN3 and LGB genes had similar behavior, maintaining a difference of more than 7% across analyzed genotypes. These results could indicate that both Holstein and Jersey have had lower substitution allele effect in selection programs that include CSN3 and LGB genes than Brown Swiss and Fleckvieh. Conclusion: Breed determined which genotypes had the greatest association with analyzed traits. The mixed model based in Bayesian or Ridge Regression was the best alternative to analyze CSN3 and LGB gene effects on milk yield and protein and fat percentages.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.12
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• pp.5005-5006
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• 2015

Gastric cancer as one of the most common cancers worldwide has various genetic and environmental risk factors including Helicobacter pylori (H.pylori) infection. Recently, loss of a tumor suppressor gene named promyelocytic leukemia (PML) has been identified in gastric cancer. However, no mutation has been found in this gene in gastric cancer samples. Cag A H.pylori protein has been shown to exert post transcriptional regulation of some tumor suppressor genes. In order to assess such a mechanism for PML degradation, we performed in silico analyses to establish any interaction between PML and Cag A proteins. In silico interaction and docking studies showed that these two proteins may have stable interactions. In addition, we showed that imatinib kinase inhibitor can restore PML function by inhibition of casein kinase 2.

• Genomics & Informatics
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• v.16 no.4
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• pp.38.1-38.3
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• 2018

Gene-gene interaction (GGI) analysis is known to play an important role in explaining missing heritability. Many previous studies have already proposed software to analyze GGI, but most methods focus on a binary phenotype in a case-control design. In this study, we developed "Hierarchical structural CoMponent analysis of Gene-Gene Interactions" (HisCoM-GGI) software for GGI analysis with a continuous phenotype. The HisCoM-GGI method considers hierarchical structural relationships between genes and single nucleotide polymorphisms (SNPs), enabling both gene-level and SNP-level interaction analysis in a single model. Furthermore, this software accepts various types of genomic data and supports data management and multithreading to improve the efficiency of genome-wide association study data analysis. We expect that HisCoM-GGI software will provide advanced accessibility to researchers in genetic interaction studies and a more effective way to understand biological mechanisms of complex diseases.