• Korean Journal of Fisheries and Aquatic Sciences
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• v.28 no.2
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• pp.209-218
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• 1995

Bacteriophage T7 gene 2.5 protein, a single-stranded DNA binding protein, has been implicated in T7 DNA replication, recombination, and repair. Purified gene 2.5 protein has been shown to interact with the phage encoded gene 5 protein (DNA polymerase) and gene 4 proteins (helicase and primase) and stimulates their activities. Genetic analysis of T7 phage defective in gene 2.5 shows that the gene 2.5 protein is essential for T7 DNA replication and growth. T7 phage that contain null mutants of gene 2.5 were constructed by homologous recombination. These mutant phage $(T7\Delta2.5)$ cannot grow in Escherichia coli. After infection of E. coli with $T7\Delta2.5$, host DNA synthesis is shut off, and $T7\Delta2.5$ DNA synthesis is reduced to less than $1\%$ of wild-type phage DNA synthesis (Kim and Richardson, 1993, Proc. Natl. Aca. Sci. USA, 90, 10173-10177). A truncated gene 2.5 protein $(GP2.5-\Delta21C)$ deleted the 21 carboxyl terminal amino acids was constructed by in vitro mutagenesis. $GP2.5-\Delta21C$ cannot substitute for wild-type gene 2.5 protein in vivo; the phage are not viable and exhibit less than $1\%$ of the DNA synthesis observed in wild-type phage-infected cells. $GP2.5-\Delta21C$ has been purified to apparent homogeneity from cells overexpressing its cloned gene. Purified $GP2.5-\Delta21C$ does not physically into「act with T1 gene 4 protein as measured by affinity chromatography and immunoblot analysis. The mutant protein cannot stimulate T7 gene 4 protein activity on RNA-primed DNA synthesis and primer synthesis. These results suggest that C-terminal domain of gene 2.5 protein is essential for protein-protein interactions.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.339-343
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• 2005

Boolean networks(BN) construction is one of the commonly used methods for building gene networks from time series microarray data. However, BN has two major drawbacks. First, it requires heavy computing times. Second, the binary transformation of the microarray data may cause a loss of information. This paper propose two methods using liner regression to construct gene regulatory networks. The first proposed method uses regression based BN variable selection method, which reduces the computing time significantly in the BN construction. The second method is the regression based network method that can flexibly incorporate the interaction of the genes using continuous gene expression data. We construct the network structure from the simulated data to compare the computing times between Boolean networks and the proposed method. The regression based network method is evaluated using a microarray data of cell cycle in Caulobacter crescentus.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.8
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• pp.4553-4557
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• 2013

Objective: The purpose of this study was to identify genes related to bladder cancer with samples from normal and disease cases by microarray chip. Methods: After downloading the gene expression profile GSE3167 from Gene Expression Omnibus database which includes 50 bladder samples, comprising 9 normal and 41 disease samples, differentially expressed genes were identified with packages in R language. The selected differentially expressed genes were further analyzed using bioinformatics methods. Firstly, molecular functions, biological processes and cell component analysis were researched by software Gestalt. Then, software String was used to search interaction relationships among differentially expressed genes, and hub genes of the network were selected. Finally, by using plugins of software Cytoscape, Mcode and Bingo, module analysis of hub-genes was performed. Results: A total of 221 genes were identified as differentially expressed by comparing normal and disease bladder samples, and a network as well as the hub gene C1QBP was obtained from the network. The C1QBP module had the closest relationship to production of molecular mediators involved in inflammatory responses. Conclusion: We obtained differentially expressed genes of bladder cancer by microarray, and both PRDX2 and YWHAZ in the module with hub gene C1QBP were most significantly related to production of molecular mediators involved in inflammatory responses. From knowledge of inflammatory responses and cancer, our results showed that, the hub gene and its module could induce inflammation in bladder cancer. These related genes are candidate bio-markers for bladder cancer diagnosis and might be helpful in designing novel therapies.

• Genomics & Informatics
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• v.9 no.3
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• pp.114-120
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• 2011

Chronic inflammation has been implicated as one of the important etiological factors in insulin resistance and type 2 diabetes mellitus (T2DM). To investigate the role of anti-inflammatory cytokines in the development of T2DM, we conducted a case-control study to assess the association between IL4/IL4R polymorphisms and disease risk. We firstly identified single nucleotide poly-morphisms (SNP) at IL4 and IL4RA loci by sequencing the loci in Korean participants. Case-control studies were conducted by genotyping the SNPs in 474 T2DM cases and 470 non-diabetic controls recruited from community-based cohorts. Replication of the associated signals was performed in 1,216 cases and 1,352 controls. We assessed effect of IL4 -IL4RA interaction on T2DM using logistic regression method. The functional relevance of the SNP associated with disease risk was determined using a reporter expression assay. We identified a strong association between the IL4 promoter variant rs2243250 and T2DM risk (OR=0.77; 95% CI, 0.67~0.88; p=$1.65{\times}10^{－4}$ in the meta-analysis). The reporter gene expression assay demonstrated that the presence of rs2243250 might affect the gene expression level with ~1.5-fold allele difference. Our findings contribute to the identification of IL4 as a T2D susceptibility locus, further supporting the role of anti-inflammatory cytokines in T2DM disease development.

• Genomics & Informatics
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• v.12 no.4
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• pp.216-224
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• 2014

Gastritis is a common but a serious disease with a potential risk of developing carcinoma. Helicobacter pylori infection is reported as the most common cause of gastritis, but other genetic and genomic factors exist, especially single-nucleotide polymorphisms (SNPs). Association studies between SNPs and gastritis disease are important, but results on epistatic interactions from multiple SNPs are rarely found in previous genome-wide association (GWA) studies. In this study, we performed computational GWA case-control studies for gastritis in Korea Associated Resource (KARE) data. By transforming the resulting SNP epistasis network into a gene-gene epistasis network, we also identified potential gene-gene interaction factors that affect the susceptibility to gastritis.

• Proceedings of the Korean Society of Plant Pathology Conference
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• 2003.10a
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• pp.29-29
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• 2003

Incompatible plant-pathogen interactions result in the rapid cell death response known as hypersensitive response (HR) and activation of host defense-related genes. To understand the molecular and cellular mechanism controlling defense response better, several approaches including isolation and characterization of novel genes, promoter analysis of those genes, protein-protein interaction analysis and reverse genetic approach etc. By using the yeast two-hybrid system a clone named Tsipl, Tsil -interacting protein 1, was isolated whose translation product apparently interacted with Tsil, an EREBP/AP2 type DNA binding protein. RNA gel blot analysis showed that the expression of Tsipl was increased by treatment with NaCl, ethylene, salicylic acid, or gibberellic acid. Transient expression analysis using a Tsipl::smGFP fusion gene in Arabidopsis protoplasts indicated that the Tsipl protein was targeted to the outer surface of chloroplasts. The targeted Tsipl::smGFP proteins were diffused to the cytoplasm of protoplasts in the presence of salicylic acid (SA) The PEG-mediated co-transfection analysis showed that Tsipl could interact with Tsil in the nucleus. These results suggest that Tsipl-Tsil interaction might serve to regulate defense-related gene expression. Basically the useful promoters are valuable tools for effective control of gene expression related to various developmental and environmental condition.(중략)

• ALGAE
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• v.30 no.4
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• pp.303-312
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• 2015

The intimate physical interaction between food algae and sacoglossan sea slug is a pertinent system to test the theory that “you are what you eat.” Some sacoglossan mollusks ingest and maintain chloroplasts that they acquire from the algae for photosynthesis. The basis of photosynthesis maintenance in these sea slugs was often explained by extensive horizontal gene transfer (HGT) from the food algae to the animal nucleus. Two large-scale expressed sequence tags databases of the green alga Bryopsis plumosa and sea slug Placida dendritica were established using 454 pyrosequencing. Comparison of the transcriptomes showed no possible case of putative HGT, except an actin gene from P. dendritica, designated as PdActin04, which showed 98.9% identity in DNA sequence with the complementary gene from B. plumosa, BpActin03. Highly conserved homologues of this actin gene were found from related green algae, but not in other photosynthetic sea slugs. Phylogenetic analysis showed incongruence between the gene and known organismal phylogenies of the two species. Our data suggest that HGT is not the primary reason underlying the maintenance of short-term kleptoplastidy in Placida dendritica.

• Genes and Genomics
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• v.40 no.12
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• pp.1363-1371
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• 2018

One of the main concerns in biology is extracting sophisticated features from DNA sequence for gene interaction determination, receiving a great deal of researchers' attention. The epigenetic modifications along with their patterns have been intensely recognized as dominant features affecting on gene expression. However, studying sequenced-based features highly correlated to this key element has remained limited. The main objective in this research was to propose a new feature highly correlated to epigenetic modifications capable of classification of genes. In this paper, classification of 34 genes in PPAR signaling pathway associated with muscle fat tissue in human was performed. Using different statistical outlier detection methods, we proposed that 5-mers highly correlated to epigenetic modifications can correctly categorize the genes involved in the same biological pathway or process. Thirty-four genes in PPAR signaling pathway were classified via applying a proposed feature, 5-mers strongly associated to 17 different epigenetic modifications. For this, diverse statistical outlier detection methods were applied to specify the group of thoroughly correlated genes. The results indicated that these 5-mers can appropriately identify correlated genes. In addition, our results corresponded to GeneMania interaction information, leading to support the suggested method. The appealing findings imply that not only epigenetic modifications but also their highly correlated 5-mers can be applied for reconstructing gene regulatory networks as supplementary data as well as other applications like physical interaction, genes prioritization, indicating some sort of data fusion in this analysis.

• Genomics & Informatics
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• v.11 no.4
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• pp.200-210
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• 2013

Studying biological networks, such as protein-protein interactions, is key to understanding complex biological activities. Various types of large-scale biological datasets have been collected and analyzed with high-throughput technologies, including DNA microarray, next-generation sequencing, and the two-hybrid screening system, for this purpose. In this review, we focus on network-based approaches that help in understanding biological systems and identifying biological functions. Accordingly, this paper covers two major topics in network biology: reconstruction of gene regulatory networks and network-based applications, including protein function prediction, disease gene prioritization, and network-based genome-wide association study.

• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.137-147
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• 2015

This study proposes an algorithm for predicting breast cancer prognosis based on genetic network. We identify prognosis-specific network using gene expression data and PPI(protein-protein interaction) data. To acquire the network, we calculate Pearson's correlation coefficient(PCC) between genes in all PPI pairs using gene expression data. We develop a prediction model for breast cancer patients with estrogen-receptor-negative using the network as a classifier. We compare classification performance of our algorithm with existing algorithms on independent data and shows our algorithm is improved. In addition, we make an functionality analysis on the genes in the prognosis-specific network using GO(Gene Ontology) enrichment validation.