• Asian Pacific Journal of Cancer Prevention
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• v.15 no.5
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• pp.2345-2351
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• 2014

Lysyl oxidase-like 2 (LOXL2), a member of the lysyl oxidase (LOX) family, is a copper-dependent enzyme that catalyzes oxidative deamination of lysine residues on protein substrates. LOXL2 was found to be overexpressed in esophageal squamous cell carcinoma (ESCC) in our previous research. We later identified a LOXL2 splicing variant LOXL2-delta72 and we overexpressed LOXL2-delta72 and its wild type counterpart in ESCC cells following microarray analyses. First, the differentially expressed genes (DEGs) of LOXL2 and LOXL2-delta72 compared to empty plasmid were applied to generate protein-protein interaction (PPI) sub-networks. Comparison of these two sub-networks showed hundreds of different proteins. To reveal the potential specific roles of LOXL2- delta72 compared to its wild type, the DEGs of LOXL2-delta72 vs LOXL2 were also applied to construct a PPI sub-network which was annotated by Gene Ontology. The functional annotation map indicated the third PPI sub-network involved hundreds of GO terms, such as "cell cycle arrest", "G1/S transition of mitotic cell cycle", "interphase", "cell-matrix adhesion" and "cell-substrate adhesion", as well as significant "immunity" related terms, such as "innate immune response", "regulation of defense response" and "Toll signaling pathway". These results provide important clues for experimental identification of the specific biological roles and molecular mechanisms of LOXL2-delta72. This study also provided a work flow to test the different roles of a splicing variant with high-throughput data.

• BMB Reports
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• v.54 no.9
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• pp.464-469
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• 2021

Cardiomyocyte differentiation occurs through complex and finely regulated processes including cardiac lineage commitment and maturation from pluripotent stem cells (PSCs). To gain some insight into the genome-wide characteristics of cardiac lineage commitment, we performed transcriptome analysis on both mouse embryonic stem cells (mESCs) and human induced PSCs (hiPSCs) at specific stages of cardiomyocyte differentiation. Specifically, the gene expression profiles and the protein-protein interaction networks of the mESC-derived platelet-derived growth factor receptor-alpha (PDGFRα)⁺ cardiac lineage-committed cells (CLCs) and hiPSC-derived kinase insert domain receptor (KDR)⁺ and PDGFRα⁺ cardiac progenitor cells (CPCs) at cardiac lineage commitment were compared with those of mesodermal cells and differentiated cardiomyocytes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that the genes significantly upregulated at cardiac lineage commitment were associated with responses to organic substances and external stimuli, extracellular and myocardial contractile components, receptor binding, gated channel activity, PI3K-AKT signaling, and cardiac hypertrophy and dilation pathways. Protein-protein interaction network analysis revealed that the expression levels of genes that regulate cardiac maturation, heart contraction, and calcium handling showed a consistent increase during cardiac differentiation; however, the expression levels of genes that regulate cell differentiation and multicellular organism development decreased at the cardiac maturation stage following lineage commitment. Additionally, we identified for the first time the protein-protein interaction network connecting cardiac development, the immune system, and metabolism during cardiac lineage commitment in both mESC-derived PDGFRα⁺ CLCs and hiPSC-derived KDR⁺PDGFRα⁺ CPCs. These findings shed light on the regulation of cardiac lineage commitment and the pathogenesis of cardiometabolic diseases.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.7
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• pp.2793-2800
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• 2015

In molecular-targeted cancer therapy, acquired resistance to gemcitabine is a major clinical problem that reduces its effectiveness, resulting in recurrence and metastasis of cancers. In spite of great efforts to reveal the overall mechanism of acquired gemcitabine resistance, no definitive genetic factors have been identified that are absolutely responsible for the resistance process. Therefore, we performed a cross-platform meta-analysis of three publically available microarray datasets for cancer cell lines with acquired gemcitabine resistance, using the R-based RankProd algorithm, and were able to identify a total of 158 differentially expressed genes (DEGs; 76 up- and 82 down-regulated) that are potentially involved in acquired resistance to gemcitabine. Indeed, the top 20 up- and down-regulated DEGs are largely associated with a common process of carcinogenesis in many cells. For the top 50 up- and down-regulated DEGs, we conducted integrated analyses of a gene regulatory network, a gene co-expression network, and a protein-protein interaction network. The identified DEGs were functionally enriched via Gene Ontology hierarchy and Kyoto Encyclopedia of Genes and Genomes pathway analyses. By systemic combinational analysis of the three molecular networks, we could condense the total number of DEGs to final seven genes. Notably, GJA1, LEF1, and CCND2 were contained within the lists of the top 20 up- or down-regulated DEGs. Our study represents a comprehensive overview of the gene expression patterns associated with acquired gemcitabine resistance and theoretical support for further clinical therapeutic studies.

• The KIPS Transactions:PartB
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• v.14B no.5
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• pp.383-390
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• 2007

Interactions between proteins and genes are often considered essential in the description of biomolecular phenomena and networks of interactions are considered as an entre for a Systems Biology approach. Recently, many works try to extract information by analyzing biomolecular text using natural language processing technology. Previous researches insist that linguistic information is useful to improve the performance in detecting gene interactions. However, previous systems do not show reasonable performance because of low recall. To improve recall without sacrificing precision, this paper proposes a new method for detection of gene interactions based on syntactic relations. Without biomolecular knowledge, our method shows reasonable performance using only small size of training data. Using the format of LLL05(ICML05 Workshop on Learning Language in Logic) data we detect the agent gene and its target gene that interact with each other. In the 1st phase, we detect encapsulation types for each agent and target candidate. In the 2nd phase, we construct verb lists that indicate the interaction information between two genes. In the last phase, to detect which of two genes is an agent or a target, we learn direction information. In the experimental results using LLL05 data, our proposed method showed F-measure of 88% for training data, and 70.4% for test data. This performance significantly outperformed previous methods. We also describe the contribution rate of each phase to the performance, and demonstrate that the first phase contributes to the improvement of recall and the second and last phases contribute to the improvement of precision.

• Journal of Communications and Networks
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• v.14 no.1
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• pp.10-14
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• 2012

When tag privacy is required in radio frequency identification (ID) system, a reader needs to identify, and optionally authenticate, a multitude of tags without revealing their IDs. One approach for identification with lightweight tags is that each tag performs pseudo-random function with his unique embedded key. In this case, a reader (or a back-end server) needs to perform a brute-force search for each tag-reader interaction, whose cost gets larger when the number of tags increases. In this paper, we suggest a simple and efficient identification technique that reduces readers computation to $O$(${\sqrt{N}}$ log$N$) without increasing communication cost. Our technique is based on the well-known "meet-in-the-middle" strategy used in the past to attack symmetric ciphers.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.787-789
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• 2003

본 논문에서는 생물체의 세포에 존재하는 방대한 단백질들 사이의 복잡한 상화작용 관계 네트워크를 개념화하기 위한 방법을 제안한다. 일반적으로 하나의 단백질은 세포의 특정한 구성요소로서 몇 개의 생물학적 작용에 참여하며 고유의 분자 기능을 수행하게 된다. 즉, 하나의 상호작용 관계 네트워크에 포함된 각각의 단백질들은 구성요소(Cellular Component), 생물학적 작용(Biological Process), 그리고 분자 기능(Molecular Function) 3가지 특징으로 개념화할 수 있다. 또한, 비슷한 특징으로 개념화되는 단백질들은 서로 클러스터링될 수 있기 때문에 단백질 상호작용 네트워크를 일반적인 의미의 개념 네트워크로 표현할 수 있다. 여기서, 단백질 특징을 개념화하기 위해 사용되는 표준개념과 이 개념들 사이의 관계를 정의하는 유전자 온톨로지(Gene Ontology)가 이용된다.

• Development and Reproduction
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• v.21 no.4
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• pp.351-359
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• 2017

Implantation is a highly organized process that involves an interaction between a receptive uterus and a competent blastocyst. In humans, natural fecundity suggests that the chance of conception per cycle is relatively low (~30%) and two-third of lost pregnancies occur because of implantation failure. Defective implantation leads to adverse pregnancy outcomes including infertility, spontaneous miscarriage, intrauterine fetal growth restriction and preeclampsia. With use of advanced scientific technologies, gene expression analysis and genetically-engineered animal models have revealed critical cellular networks and molecular pathways. But, because of ethical restrictions and the lack of a mechanistic experiment, comprehensive steps in human implantation have still not been completely understood. This review primarily focuses on the recent advances in mechanisms of implantation. Because infertility is an emerging issue these days, gaining an understanding the molecular and hormonal signaling pathway will improve the outcome of natural pregnancy and assisted reproductive technology.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.1
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• pp.138-148
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• 2018

Objective: Fusarium mycotoxins deoxynivalenol (DON) and zearalenone (ZEN), common contaminants in the feed of farm animals, cause immune function impairment and organ inflammation. Consequently, the main objective of this study was to elucidate DON and ZEN effects on the mRNA expression of pro-inflammatory cytokines and other immune related genes in the kidneys of piglets. Methods: Fifteen 6-week-old piglets were randomly assigned to three dietary treatments for 4 weeks: control diet, and diets contaminated with either 8 mg DON/kg feed or 0.8 mg ZEN/kg feed. Kidney samples were collected after treatment, and RNA-seq was used to investigate the effects on immune-related genes and gene networks. Results: A total of 186 differentially expressed genes (DEGs) were screened (120 upregulated and 66 downregulated). Gene ontology analysis revealed that the immune response, and cellular and metabolic processes were significantly controlled by these DEGs. The inflammatory stimulation might be an effect of the following enriched Kyoto encyclopedia of genes and genomes pathway analysis found related to immune and disease responses: cytokine-cytokine receptor interaction, chemokine signaling pathway, toll-like receptor signaling pathway, systemic lupus erythematosus (SLE), tuberculosis, Epstein-Barr virus infection, and chemical carcinogenesis. The effects of DON and ZEN on genome-wide expression were assessed, and it was found that the DEGs associated with inflammatory cytokines (interleukin 10 receptor, beta, chemokine [C-X-C motif] ligand 9, CXCL10, chemokine [C-C motif] ligand 4), proliferation (insulin like growth factor binding protein 4, IgG heavy chain, receptor-type tyrosine-protein phosphatase C, cytochrome P450 1A1, ATP-binding cassette sub-family 8), and other immune response networks (lysozyme, complement component 4 binding protein alpha, oligoadenylate synthetase 2, signaling lymphocytic activation molecule-9, ${\alpha}$-aminoadipic semialdehyde dehydrogenase, Ig lambda chain c region, pyruvate dehydrogenase kinase, isozyme 4, carboxylesterase 1), were suppressed by DON and ZEN. Conclusion: In summary, our results indicate that high concentrations of DON and ZEN suppress the inflammatory response in kidneys, leading to potential effects on immune homeostasis.

• Journal of Life Science
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• v.26 no.8
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• pp.904-910
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• 2016

Complex traits are determined by the combined effects of many loci and are affected by gene networks or biological pathways. Systems biology approaches have an important role in the identification of candidate genes related to complex diseases or traits at the system level. The gene network analysis has been performed by diverse types of methods such as gene co-expression, gene regulatory relationships, protein-protein interaction (PPI) and genetic networks. Moreover, the network-based methods were described for predicting gene functions such as graph theoretic method, neighborhood counting based methods and weighted function. However, there are a limited number of researches in livestock. The present study systemically analyzed genes associated with 102 types of economic traits based on the Animal Trait Ontology (ATO) and identified their relationships based on the gene co-expression network and PPI network in cattle. Then, we constructed the two types of gene network databases and network visualization system (http://www.nabc.go.kr/cg). We used a gene co-expression network analysis from the bovine expression value of bovine genes to generate gene co-expression network. PPI network was constructed from Human protein reference database based on the orthologous relationship between human and cattle. Finally, candidate genes and their network relationships were identified in each trait. They were typologically centered with large degree and betweenness centrality (BC) value in the gene network. The ontle program was applied to generate the database and to visualize the gene network results. This information would serve as valuable resources for exploiting genomic functions that influence economically and agriculturally important traits in cattle.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.10
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• pp.1483-1490
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• 2019

Objective: To explore the molecular mechanisms of fat metabolism and deposition in pigs, an experiment was conducted to identify hepatic mRNAs and miRNAs expression and determine the potential interaction of them in two phenotypically extreme pig breeds. Methods: mRNA and miRNA profiling of liver from 70-day Jinhua (JH) and Landrace (LD) pigs were performed using RNA sequencing. Blood samples were taken to detect results of serum biochemistry. Bioinformatics analysis were applied to construct differentially expressed miRNA-mRNA network. Results: Serum total triiodothyronine and total thyroxine were significantly lower in Jinhua pigs, but the content of serum total cholesterol (TCH) and low-density lipoprotein cholesterol were strikingly higher. A total of 467 differentially expressed genes (DEGs) and 35 differentially expressed miRNAs (DE miRNAs) were identified between JH and LD groups. Gene ontology analysis suggested that DEGs were involved in oxidation-reduction, lipid biosynthetic and lipid metabolism process. Interaction network of DEGs and DE miRNAs were constructed, according to target prediction results. Conclusion: We generated transcriptome and miRNAome profiles of liver from JH and LD pig breeds which represent distinguishing phenotypes of growth and metabolism. The potential miRNA-mRNA interaction networks may provide a comprehensive understanding in the mechanism of lipid metabolism. These results serve as a basis for further investigation on biological functions of miRNAs in the porcine liver.