• Title/Summary/Keyword: gene expression microarray

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Heterologous Microarray Hybridization Used for Differential Gene Expression Profiling in Benzo[a]pyrene-exposed Marine Medaka

  • Woo, Seon-Ock;Won, Hyo-Kyoung;Jeon, Hye-Young;Kim, Bo-Ra;Lee, Taek-Kyun;Park, Hong-Seog;Yum, Seung-Shic
    • Molecular & Cellular Toxicology
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    • v.5 no.4
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    • pp.283-290
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    • 2009
  • Differential gene expression profiling was performed in the hepatic tissue of marine medaka fish (Oryzias javanicus) after exposure to benzo[a]pyrene (BaP), a polycyclic aromatic hydrocarbon (PAH), by heterologous hybridization using a medaka cDNA microarray. Thirty-eight differentially expressed candidate genes, of which 23 were induced and 15 repressed (P<0.01), were identified and found to be associated with cell cycle, development, endocrine/reproduction, immune, metabolism, nucleic acid/protein binding, signal transduction, or non-categorized. The presumptive physiological changes induced by BaP exposure were identified after considering the biological function of each gene candidate. The results obtained in this study will allow future studies to assess the molecular mechanisms of BaP toxicity and the development of a systems biology approach to the stress biology of organic chemicals.

Learning Graphical Models for DNA Chip Data Mining

  • Zhang, Byoung-Tak
    • Proceedings of the Korean Society for Bioinformatics Conference
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    • 2000.11a
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    • pp.59-60
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    • 2000
  • The past few years have seen a dramatic increase in gene expression data on the basis of DNA microarrays or DNA chips. Going beyond a generic view on the genome, microarray data are able to distinguish between gene populations in different tissues of the same organism and in different states of cells belonging to the same tissue. This affords a cell-wide view of the metabolic and regulatory processes under different conditions, building an effective basis for new diagnoses and therapies of diseases. In this talk we present machine learning techniques for effective mining of DNA microarray data. A brief introduction to the research field of machine learning from the computer science and artificial intelligence point of view is followed by a review of recently-developed learning algorithms applied to the analysis of DNA chip gene expression data. Emphasis is put on graphical models, such as Bayesian networks, latent variable models, and generative topographic mapping. Finally, we report on our own results of applying these learning methods to two important problems: the identification of cell cycle-regulated genes and the discovery of cancer classes by gene expression monitoring. The data sets are provided by the competition CAMDA-2000, the Critical Assessment of Techniques for Microarray Data Mining.

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Increased Gene Expression in Cultured BEAS-2B Cells Treated with Metal Oxide Nanoparticles

  • Park, Eun-Jung;Park, Kwang-Sik
    • Toxicological Research
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    • v.25 no.4
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    • pp.195-201
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    • 2009
  • Recent publications showed that metal nanoparticles which are made from $TiO_2,\;CeO_2,\;Al_2O_3,\;CuCl_2,\;AgNO_3$ and $ZnO_2$ induced oxidative stress and pro-inflammatory effects in cultured cells and the responses seemed to be common toxic pathway of metal nanoparticles to the ultimate toxicity in animals as well as cellular level. In this study, we compared the gene expression induced by two different types of metal oxide nanoparticles, titanium dioxide nanoparticles (TNP) and cerium dioxide nanoparticles (CNP) using microarray analysis. About 50 genes including interleukin 6, interleukin 1, platelet-derived growth factor $\beta$, and leukemia inhibitory factor were induced in cultured BEAS2B cells treated with TNP 40 ppm. When we compared the induction levels of genes in TNP-treated cells to those in CNP-treated cells, the induction levels were very correlated in various gene categories (r=0.645). This may suggest a possible common toxic mechanism of metal oxide nanoparticles.

Genome Wide Expression Profile of Asiasarum sieboldi in LPS-stimulated BV-2 Microglial Cells

  • Sohn, Sung-Hwa;Ko, Eun-Jung;Kim, Yang-Seok;Shin, Min-Kyu;Hong, Moo-Chang;Bae, Hyun-Su
    • Molecular & Cellular Toxicology
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    • v.4 no.3
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    • pp.205-210
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    • 2008
  • Recent studies suggest that activated microglial cells play an essential role in the inflammatory responses and neurodegenerative disorders such as Alzheimer’s and Parkinson’s disease. This study was conducted to evaluate the protective mechanisms of Asiasarum sieboldi (AS) on LPS-induced activation of BV-2 microglial cells. The effects of AS on gene expression profiles in activated BV-2 microglial cells were evaluated using microarray analysis. BV-2 microglial cells were cultured in a 100 mm dish ($1{\times}10^7$/mL) for 24 h and then pretreated with 1 ${\mu}g$/mL AS or left untreated for 30 min. Next, 1 ${\mu}g$/mL LPS was added to the samples and the cells were reincubated at $37^{\circ}C$ for 30 min and 1 hr. The gene expression profiles of the BV-2 microglial cells varied depending on the AS. The microarray analysis revealed that MAPK signaling pathway-related genes were downregulated in AS-treated BV-2 microglial cells. AS can affect the neuroinflammatory-related pathway such as MAPK signaling pathway in activated BV-2 microglial cells.

A Genome-wide Approach for Functional Analysis Using Rice Mutant

  • Yim, Won-Cheol;Kim, Dong-Sub;Moon, Jun-Cheol;Jang, Cheol-Seong;Lee, Byung-Moo
    • KOREAN JOURNAL OF CROP SCIENCE
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    • v.54 no.3
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    • pp.332-338
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    • 2009
  • Rapid extension of genomic database leads to the remarkable advance of functional genomics. This study proposes a novel methodology of functional analysis using 5-methyltrytophan (5 MT) mutant together with their 2-DE analysis and public microarray database. A total of 24 proteins was changed in 5 MT mutant and four remarkably different expressed proteins were identified. Among them, three spots were converted to Affymetrix probe. A total of 155 microarray samples from Gene Expression Omnibus (GEO) in NCBI was retrieved and followed by constructing gene co-expression networks over a broad range of biological issues through Self-Organising Tree Algorithm. Three co-expressing gene clusters were retrieved and each functional categorization with differential expression pattern was exhibited from 5 MT resistance mutant rice. It was indicated new co-expression networks in the mutant. This study suggests that on investigating possibility which correspond 2-DE to microarray database with their full potential.

The Gene Expression Profile of Cyst Epithelial Cells in Autosomal Dominant Polycystic Kidney Disease Patients

  • Lee, Jae-Eun;Park, Min-Ha;Park, Jong-Hoon
    • BMB Reports
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    • v.37 no.5
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    • pp.612-617
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    • 2004
  • Autosomal dominant polycystic kidney disease (ADPKD) is a common genetic disorder characterized by the formation of fluid-filled cysts in the kidney and progressive renal failure. Other manifestations of ADPKD include the formation of cysts in other organs (liver, pancreas, and spleen), hypertension, cardiac defects, and cerebral aneurysms. The loss of function of the polycystin -1 and -2 results in the formation of epithelium-lined cysts, a process that depends on initial epithelial proliferation. cDNA microarrays powerfully monitor gene expression and have led to the discoveries of pathways regulating complex biological processes. We undertook to profile the gene expression patterns of epithelial cells derived from the cysts of ADPKD patients using the cDNA microarray technique. Candidate genes that were differently expressed in cyst tissues were identified. 19 genes were up-regulated, and 6 down-regulated. Semi-quantitative RT-PCR results were consistent with the microarray findings. To distinguish between normal and epithelial cells, we used the hierarchical method. The results obtained may provide a molecular basis for understanding the biological meaning of cytogenesis.

Transcriptional profiles of Rhizobium vitis-inoculated and salicylic acid-treated 'Tamnara' grapevines based on microarray analysis

  • Choi, Youn Jung;Yun, Hae Keun
    • Journal of Plant Biotechnology
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    • v.43 no.1
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    • pp.37-48
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    • 2016
  • The transcriptional profiles of 'Tamnara' grapevine (Vitis labruscana L.) to Rhizobium vitis were determined using 12,000 gene oligonucleotide microarray chips constructed with 6,776 unigenes based on the EST sequencing. Among them, 95 clones were up-regulated more than three times and 90 were down-regulated more than 5-times in the R. vitis-inoculated grapevines relative to the control vines. Treatment of salicylic acid showed that 337 clones were upregulated and 52 clones were down regulated in grapevines. Microarray analysis, reverse transcription-polymer chain reaction, and slot blot hybridization analysis revealed that 5, 14, and 64 clones were up-regulated and 10, 12, and 61 clones were down-regulated in wounded, salicylic acid-treated, and R. vitis-inoculated 'Tamnara' grapevine leaves, respectively. The expression patterns of ${\beta}$-1,3-glucanase, proline-rich protein, and lipoxygenase genes of 'Tamnara' moderately resistant to R. vitis were similar to those of resistant 'Concord' and 'Delaware' grapevines. However, chalcone synthase genes in 'Tamnara' grapevines showed similar expression patterns to susceptible grapevines 'Neomuscat' and 'Rizamat'. Further expression studies with various clones for each gene should be conducted to elucidate their roles in resistant responses against pathogens or other stimuli in grapevines. These results could provide better resources for understanding the mechanism of defense responses against crown gall disease and clues for identifying new genes that may play a role in defense against R. vitis in grapevines.

Detection of Differentially Expressed Genes by Clustering Genes Using Class-Wise Averaged Data in Microarray Data

  • Kim, Seung-Gu
    • Communications for Statistical Applications and Methods
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    • v.14 no.3
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    • pp.687-698
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    • 2007
  • A normal mixture model with which dependence between classes is incorporated is proposed in order to detect differentially expressed genes. Gene clustering approaches suffer from the high dimensional column of microarray expression data matrix which leads to the over-fit problem. Various methods are proposed to solve the problem. In this paper, use of simple averaging data within each class is proposed to overcome the various problems due to high dimensionality when the normal mixture model is fitted. Some experiments through simulated data set and real data set show its availability in actuality.

Identifying statistically significant gene sets based on differential expression and differential coexpression (특이발현과 특이공발현을 고려한 유의한 유전자 집단 탐색)

  • Lee, Sunho
    • The Korean Journal of Applied Statistics
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    • v.29 no.3
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    • pp.437-448
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    • 2016
  • Gene set analysis utilizing biologic information is expected to produce more interpretable results because the occurrence of tumors (or diseases) is believed to be associated with the regulation of related genes. Many methods have been developed to identify statistically significant gene sets across different phenotypes; however, most focus exclusively on either the differential gene expression or the differential correlation structure in the gene set. This research provides a new method that simultaneously considers the differential expression of genes and differential coexpression with multiple genes in the gene set. Application of this NEW method is illustrated with real microarray data example, p53; subsequently, a simulation study compares its type I error rate and power with GSEA, SAMGS, GSCA and GSNCA.

Differential Gene Expression after Adenovirus-Mediated p16 Gene Transfer in Human Non-Small Cell Lung Cancer Cells (폐암세포주에서 아데노바이러스 매개 p16 유전자 전달로 인한 유전자 발현의 변화)

  • 박미선;김옥희;박현신;지승완;엄미옥;염태경;강호일
    • Toxicological Research
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    • v.20 no.2
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    • pp.109-116
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    • 2004
  • For the safety evaluation of adenovirus-mediated gene transfer, we investigated differential gene expressions after transfecting adenoviral vector containing p16 tumor suppressor gene (Ad5CMV-p16) into human non-small cell lung cancer cells. In the previous study, we showed adenovirus-mediated $p16^{INK4a}$ gene transfer resulted in significant inhibition of cancer cell growth. We investigated gene expression changes after transfecting Ad5CMV-p16, Ad5CMV (null type, a mock vector) into A549 cells by using cDNA chip and oligonucleotide microarray chip (1200 genes) which carries genes related with signal transduction pathways, cell cycle regulations, oncogenes and tumor suppressor genes. We found that $p16^{INK4a}$ gene transfer down regulated 5 genes (cdc2, cyclin D3, cyclin B, cyclin E, cdk2) among 26 genes involved in cell cycle regulations. Compared with serum-free medium treated cells, Ad5CMV-p16 changed 27 gene expressions, two fold or more on oligonucleotide chip. In addition, Ad5CMV-p16 did not seem to increase the tumorigenicity-related gene expression in A549 cells. Further studies will be needed to investigate the effect of Ad5CMV-p16 on normal human cells and tissues for safety evaluation.