• Proceedings of the Korean Society of Developmental Biology Conference
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• 2003.10a
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• pp.75-75
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• 2003

Transition of the resting primordial follicle to the growing primary follicle is a critical process for female reproduction, but its mechanism is poorly understood. The present study was conducted to investigate gene expression profile at the primordial-primary follicle transition process. We isolated total RNA of female mouse ovary at day1 (contains only primordial follicles) and day5 (contains primordial and primary follicles) and synthesized cDNA using annealing control primers (ACP; Seegene, Inc., Seoul, Korea). ACP provides annealing specificity and sensitivity to the template and allows to identify only authentic differentially expressed genes (DEGs). We used total 80 ACPs for PCR, observed PCR products on 2% agarose gel, cloned 42 DEGs using TOPO TA cloning vector, sequenced, and analyzed by BLAST search. Sequences of 34 clones significantly matched database entries while 4 clones were novel and 4 clones were EST. Two of 34 genes were specifically expressed only in day 5 ovaries (Sui1-rs1, Apg3p/Aut1p-like), and rest of 32 genes were expressed in both stages but were differential in amount. Differential expression was confirmed using semiquantitative RT-PCR, and there was no false positive. Anx11 and Pepp2-pending were highly expressed genes in day1-, while BPOZ, Ches1, Kcmf1, NHE3, Nid2, Ninj1, SENP3 and Survivin were highly expressed genes in day5-ovary. List of genes would provide insight for further study of mechanism regulating primordial-primary follicle transition.

• Communications for Statistical Applications and Methods
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• v.28 no.2
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• pp.135-150
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• 2021

We propose principal differential analysis based classification methods. Computations of squared multiple correlation function (RSQ) and principal differential analysis (PDA) scores are reviewed; in addition, we combine principal differential analysis results with the logistic regression for binary classification. In the numerical study, we compare the principal differential analysis based classification methods with functional principal component analysis based classification. Various scenarios are considered in a simulation study, and principal differential analysis based classification methods classify the functional data well. Gene expression data is considered for real data analysis. We observe that the PDA score based method also performs well.

• Molecular & Cellular Toxicology
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• v.4 no.2
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• pp.132-137
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• 2008

Differential gene expression profiling was carried out in the hepatic tissue of medaka fish, Oryzias latipes, after exposure to an organophosphorus pesticide (OPP), Iprobenfos (IBP), a widely used pesticide in agri- and fish-culture, using a medaka cDNA micro array. Twenty six kinds of differentially expressed candidate genes, with 15 and 11 induced and repressed in their gene expressions, respectively, were associated with cytoskeleton (3.8%), development (7.7%), immune (7.7%), metabolism (30.8%), nucleic acid/protein binding (42.3%) and reproduction (7.7%). Of these genes, changes at the transcription level of five were re-evaluated by real-time quantitative PCR (qRT-PCR). Considering the known function of authentic genes, the effects of IBP on the biological activity and pathological aspects in medaka fish were discussed. The identified genes could be used as molecular biomarkers for biological responses to OPPs contamination in an aquatic environment.

• International Journal of Industrial Entomology and Biomaterials
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• v.17 no.2
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• pp.223-228
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• 2008

A novel beetle antimicrobial protein from stimulated Copris tripartitus and the corresponding gene were isolated in parallel through differential display-PCR and expression in Escherichia coli. To find cDNA clones responsible for bacteria resistance, the suppression subtractive hybridization and GeneFishing differentially expressed genes system were employed in the dung beetle, Copris tripartitus immunized with lipopolysaccaride. One cDNA clone from eight subtracted clones was selected through dot blot analysis and confirmed by northern blot analysis. The 516-bp, selected cDNA clone was determined by 5' and 3' rapid amplication of cDNA ends and cloned into the GST fusion expression vector pGEX-4T-1 for expression of the protein. The expressed protein was predicted 14.7 kDa and inhibited the growth of gram-negative bacteria such as Escherichia coli and Pseudomonas aeruginosa. These results implied that the expressed protein is related to immune defense mechanism against microorganism.

• Korean Journal of Plant Tissue Culture
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• v.22 no.3
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• pp.169-173
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• 1995

Border cells are differentiated cells which originate from meristematic cells in The root cap. Experimentally border cells can be released from the root cap by a physical treatment, for example dipping the root tip in the waters After 20-25 hours of release, the new border cell layer forms in the root cap. During the border cell differentiation, new gene expressions were observed in the root cap of pea which was determined by mRNA differential display These new gene expressions may be involved in the border cell differentiation Border cells had unique gene expressions which were determined by mRNA differential display, This suggests that border cells are differentiated cells which are different from the other tissues (ie., leaves, stems, roots or root caps).

• Genomics & Informatics
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• v.15 no.4
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• pp.156-161
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• 2017

Transcriptome analysis has been widely used to make biomarker panels to diagnose cancers. In breast cancer, the age of the patient has been known to be associated with clinical features. As clinical transcriptome data have accumulated significantly, we classified all human genes based on age-specific differential expression between normal and breast cancer cells using public data. We retrieved the values for gene expression levels in breast cancer and matched normal cells from The Cancer Genome Atlas. We divided genes into two classes by paired t test without considering age in the first classification. We carried out a secondary classification of genes for each class into eight groups, based on the patterns of the p-values, which were calculated for each of the three age groups we defined. Through this two-step classification, gene expression was eventually grouped into 16 classes. We showed that this classification method could be applied to establish a more accurate prediction model to diagnose breast cancer by comparing the performance of prediction models with different combinations of genes. We expect that our scheme of classification could be used for other types of cancer data.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.5
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• pp.2519-2525
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• 2016

There are several existing reports of microarray chip use for assessment of altered gene expression in different diseases. In fact, there have been over 1.5 million assays of this kind performed over the last twenty years, which have influenced clinical and translational research studies. The most commonly used DNA microarray platforms are Affymetrix GeneChip and Quality Control Software along with their GeneChip Probe Arrays. These chips are created using several quality controls to confirm the success of each assay, but their actual impact on gene expression profiles had not been previously analyzed until the appearance of several bioinformatics tools for this purpose. We here performed a data mining analysis, in this case specifically focused on ovarian cancer, as well as healthy ovarian tissue and ovarian cell lines, in order to confirm quality control results and associated variation in gene expression profiles. The microarray data used in our research were downloaded from ArrayExpress and Gene Expression Omnibus (GEO) and analyzed with Expression Console Software using RMA, MAS5 and Plier algorithms. The gene expression profiles were obtained using Partek Genomics Suite v6.6 and data were visualized using principal component analysis, heat map, and Venn diagrams. Microarray quality control analysis showed that roughly 40% of the microarray files were false negative, demonstrating over- and under-estimation of expressed genes. Additionally, we confirmed the results performing second analysis using independent samples. About 70% of the significant expressed genes were correlated in both analyses. These results demonstrate the importance of appropriate microarray processing to obtain a reliable gene expression profile.

• Genes and Genomics
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• v.40 no.11
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• pp.1127-1136
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• 2018

The Greater Sudbury Region has been known as one of the most ecologically disturbed areas in Canada for the past century. Plant adaptation to environmental stressors often results in modifications in gene expression at the transcriptional level. The main objective of the present study was to compare the expression of genes associated with nickel resistance in Acer rubrum and Populus tremuloides growing in areas contaminated and uncontaminated with metals. Primers targeting Nramps4, Nas 3, At2G, MRP4 and alpha-tubulin genes were used to amplify cDNA of both species. The expression of the At2G gene, was $2{\times}$ and $9{\times}$ higher in P. tremuloides than in A. rubrum for St. Charles (uncontaminated site) and Kelly Lake (metal contaminated site), respectively. There was a much smaller difference between the two species for the Nramps 4 gene as its expression was $2.5{\times}$ and $3{\times}$ higher in P. tremuloides compared to A. rubrum from St. Charles and Kelly Lake, respectively. The same trend was observed for the MRP4 gene whose expression was $2{\times}$ and $14{\times}$ higher in P. tremuloides than in A. rubrum from St. Charles and Kelly Lake, respectively. For the Nas 3 gene, the expression was similar in both sites. This gene was upregulated $11{\times}$ and $10{\times}$ in P. tremuloides compared to A. rubrum in samples from St. Charles and Kelly Lake, respectively. In general, no significant difference was observed between the metal contaminated and uncontaminated sites for gene expression. In depth analysis revealed that AT2G and MRP4 genes were significantly down regulated in A. rubrum from the metal contaminated sites compared to those from uncontaminated areas, but environmental factors driving this differential gene expression couldn't be established.

• Journal of Microbiology and Biotechnology
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• v.20 no.4
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• pp.789-797
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• 2010

The limited information on differential gene expression in the different serotypes of Actinobacillus pleuropneumoniae has significantly hampered the research on the pathogenic mechanisms of this organism and the development of multivalent vaccines against A. pleuropneumoniae infection. To compare the gene expressions in the A. pleuropneumoniae strains CVCC259 (serotype 1) and CVCC261 (serotype 3), we screened the differentially expressed genes in the two strains by performing representational difference analysis (RDA). Northern blot analyses were used to confirm the results of RDA. We identified 22 differentially expressed genes in the CVCC259 strain and 20 differentially expressed genes in the CVCC261 strain, and these genes were classified into 11 groups: (1) genes encoding APX toxins; (2) genes encoding transferrin-binding protein; (3) genes involved in lipopolysaccharide (LPS) biosynthesis; (4) genes encoding autotransporter adhesin; (5) genes involved in metabolism; (6) genes involved in the ATP-binding cassette (ABC) transporter system; (7) genes encoding molecular chaperones; (8) genes involved in bacterial transcription and nucleic acid metabolism; (9) a gene encoding protease; (10) genes encoding lipoprotein/membrane protein; and (11) genes encoding various hypothetical proteins. This is the first report on the systematic application of RDA for the analysis of differential gene expression in A. pleuropneumoniae serotypes 1 and 3. The determination of these differentially expressed genes will serve as an indicator for future research on the pathogenic mechanisms of A. pleuropneumoniae and the development of a multivalent vaccine against A. pleuropneumoniae infection.

• Proceedings of the Korean Society of Potoscience Conference
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• spring
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• pp.18-18
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• 1996