• Proceedings of the Botanical Society of Korea Conference
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• 1987.07a
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• pp.349-356
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• 1987

Insertions of transposable elements in or near a structural gene give rise to null phenotypes, reduced levels of gene expression, or alteration on the tissue-specific pattern of gene expression. Null phenotypes often result from insertions in exons. Reduced levels of gene expression results from insertions in various regions such as promoter region, 5' non-translated region, exon and intron. The maize allele of Adh1-3F1124 is an example of alteration in the tissue-specific patetern of gene expression. Adh1-3F1124 contains a Mu element inserted 31 bp 5' to the transcriptional start site of the wild-type Adh1 activity in seeds and anaerobically-treated seedlings but normal levels in the pollen. Upon the insertion of a transposable element a certain number of host DNA sequences at the insertion site is duplcated. When transposable elements excise, all element sequences are deleted. However, the duplicated host sequences may be left intact or deleted to various extents. This results in null phenotypes, restoration of original levels of gene expression, or altered levels of gene expression. On the basis of effects of transposable-element insertions or excisions on gene expression, the usefulness of transposable ellements for studies on gene expression is discussed.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.10
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• pp.830-836
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• 2005

Application of microarray technologies which monitor simultaneously the expression pattern of thousands of individual genes in different biological systems results in a tremendous increase of the amount of available gene expression data and have provided new insights into gene expression during drug development, within disease processes, and across species. There is a great need of data mining methods allowing straightforward interpretation, visualization and analysis of the relevant information contained in gene expression profiles. Specially, classifying biological samples into known classes or phenotypes is an important practical application for microarray gene expression profiles. Gene expression profiles obtained from tissue samples of patients thus allowcancer classification. In this research, molecular classification of microarray gene expression data is applied for multi-class cancer using computational biology such gene selection, principal component analysis and fuzzy clustering. The proposed method was applied to microarray data from leukemia patients; specifically, it was used to interpret the gene expression pattern and analyze the leukemia subtype whose expression profiles correlated with four cases of acute leukemia gene expression. A basic understanding of the microarray data analysis is also introduced.

• Proceedings of the Korean Society of Plant Biotechnology Conference
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• 2005.11a
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• pp.142-142
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• 2005

• Proceedings of the Korean Society of Plant Biotechnology Conference
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• 2005.11a
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• pp.156-156
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• 2005

• Proceedings of the Korean Society of Plant Biotechnology Conference
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• 2005.11a
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• pp.154-154
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• 2005

• Molecules and Cells
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• v.42 no.8
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• pp.579-588
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• 2019

Gene set enrichment analysis (GSEA) is a popular tool to identify underlying biological processes in clinical samples using their gene expression phenotypes. GSEA measures the enrichment of annotated gene sets that represent biological processes for differentially expressed genes (DEGs) in clinical samples. GSEA may be suboptimal for functional gene sets; however, because DEGs from the expression dataset may not be functional genes per se but dysregulated genes perturbed by bona fide functional genes. To overcome this shortcoming, we developed network-based GSEA (NGSEA), which measures the enrichment score of functional gene sets using the expression difference of not only individual genes but also their neighbors in the functional network. We found that NGSEA outperformed GSEA in identifying pathway gene sets for matched gene expression phenotypes. We also observed that NGSEA substantially improved the ability to retrieve known anti-cancer drugs from patient-derived gene expression data using drug-target gene sets compared with another method, Connectivity Map. We also repurposed FDA-approved drugs using NGSEA and experimentally validated budesonide as a chemical with anti-cancer effects for colorectal cancer. We, therefore, expect that NGSEA will facilitate both pathway interpretation of gene expression phenotypes and anti-cancer drug repositioning. NGSEA is freely available at www.inetbio.org/ngsea.

• Asian-Australasian Journal of Animal Sciences
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• v.20 no.2
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• pp.153-159
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• 2007

In our previous study, overexpression of extracellular proteinase inhibitor (Expi) gene accelerated apoptosis of mammary epithelial cells, and induced expression of B cell activating factor (BAFF) gene. In this study, we found induction of BAFF-receptor (BAFF-R) gene expression in the Expi-transfected cells. A proliferation-inducing ligand (APRIL) gene is another TNF family member and the closest known relative of BAFF. We found induction of APRIL gene expression in the Expi-overexpressed apoptotic cells. NF-${\kappa}$B gene was also induced in the Expi-overexpressed cells. Expression patterns of BAFF and APRIL pathway-related genes were examined in in vivo mouse mammary gland at various reproductive stages. Expression levels of BAFF gene were very low at early pregnancy, increased from mid-pregnancy, and peaked at lactation, and thereafter decreased at involution stages of mammary gland. Expression of BAFF-R gene was highly induced in involution stages compared to lactation stages. Thus, expression patterns of BAFF-R gene were correlated to apoptotic status of mammary gland: active apoptosis of mammary epithelial cells occurs at involution stage of mammary gland. Expression levels of NF-${\kappa}$B gene were higher in involution stages compared to lactation stages. We analyzed mRNA levels of bcl-2 family genes from different stages of mammary development. Bcl-2 gene expression was relatively constant during lactation and involution stages. There was a slight increase in bcl-xL gene expression in involution stages compared to lactation state. Bax gene expression was highly induced in involution stage. Our results suggest that signaling pathways activated by both BAFF and ARRIL in mammary gland point towards NF-${\kappa}$B activation which causes upregulation of bax.

• KOGO NEWS
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• v.6 no.1
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• pp.29-33
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• 2006

Gene set analysis is a new concept and method. to analyze and interpret microarray gene expression data and tries to extract biological meaning from gene expression data at gene set level rather than at gene level. Compared with methods which select a few tens or hundreds of genes before gene ontology and pathway analysis, gene set analysis identifies important gene ontology terms and pathways more consistently and performs well even in gene expression data sets with minimal or moderate gene expression changes. Moreover, gene set analysis is useful for comparing multiple gene expression data sets dealing with similar biological questions. This review briefly summarizes the rationale behind the gene set analysis and introduces several algorithms and tools now available for gene set analysis.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.138-149
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• 2004

In this study, we analyzed the gene expression data of Saccharomyces cerevisiae obtained from Holstege et al. 1998 to understand the relationship between expression level and nucleotide sequence of a gene. First, the correlation between gene expression and percent composition of each type of nucleotide was computed. It was observed that nucleotide 'G' and 'C' show positive correlation (r ${\geq}$ 0.15), 'A' shows negative correlation (r ${\approx}$ -0.21) and 'T' shows no correlation (r ${\approx}$ 0.00) with gene expression. It was also found that 'G+C' rich genes express more in comparison to 'A+T' rich genes. We observed the inverse correlation between composition of a nucleotide at genome level and level of gene expression. Then we computed the correlation between dinucleotides (e.g. AA, AT, GC) composition and gene expression and observed a wide variation in correlation (from r = -0.45 for AT to r = 0.35 for GT). The dinucleotides which contain 'T' have wide range of correlation with gene expression. For example, GT and CT have high positive correlation and AT have high negative correlation. We also computed the correlation between trinucleotides (or codon) composition and gene expression and again observed wide range of correlation (from r = -0.45 for ATA r = 0.45 for GGT). However, the major codons of a large number of amino acids show positive correlation with expression level, but there are a few amino acids whose major codons show negative correlation with expression level. These observations clearly indic ate the relationship between nucleotides composition and expression level. We also demonstrate that codon composition can be used to predict the expression of gene in a given condition. Software has been developed for calculating correlation between expression of gene and codon usage.

• Molecular & Cellular Toxicology
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• v.1 no.4
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• pp.248-255
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• 2005

To understand the response of cancer cells to anticancer drugs at the gene expression level, we examined the gene expression changes in response to five anticancer drugs, 5-fluorouracil, cytarabine, cisplatin, paclitaxel, and cytochalasin D in NCI-H460 human lung cancer cells. Of the five drugs, 5-fluorouracil had the most distinctive gene expression signature. By clustering genes whose expression changed significantly, we identified three clusters with unique gene expression patterns. The first cluster reflected the up-regulation of gene expression by cisplatin, and included genes involved in cell death and DNA repair. The second cluster pointed to a general reduction of gene expression by most of the anticancer drugs tested. A number of genes in this cluster are involved in signal transduction that is important for communication between cells and reception of extracellular signals. The last cluster represented reduced gene expression in response to 5-fluorouracil, the genes involved being implicated in DNA metabolism, the cell cycle, and RNA processing. Since the gene expression signature of 5-fluorouracil was unique, we investigated it in more detail. Significance analysis of microarray data (SAM) identified 808 genes whose expression was significantly altered by 5-fluorouracil. Among the up-regulated genes, those affecting apoptosis were the most noteworthy. The down-regulated genes were mainly associated with transcription-and translation-related processes which are known targets of 5-fluorouracil. These results suggest that the gene expression signature of an anticancer drug is closely related to its physiological action and the response of caner cells.