• Asian-Australasian Journal of Animal Sciences
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• v.22 no.6
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• pp.871-884
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• 2009

Gene expression profiling is a useful tool for identifying critical genes and pathways in metabolism. The objective of this study was to determine the major differences in the expression of genes associated with metabolism and metabolic regulation in liver and mammary tissues of lactating cows. We used the Michigan State University bovine metabolism (BMET) microarray; previously, we have designed a bovine metabolism-focused microarray containing known genes of metabolic interest using publicly available genomic internet database resources. This is a high-density array of 70mer oligonucleotides representing 2,349 bovine genes. The expression of 922 genes was different at p<0.05, and 398 genes (17%) were differentially expressed by two-fold or more with 222 higher in liver and 176 higher in mammary tissue. Gene ontology categories with a high percentage of genes more highly expressed in liver than mammary tissues included carbohydrate metabolism (glycolysis, glucoenogenesis, propanoate metabolism, butanoate metabolism, electron carrier and donor activity), lipid metabolism (fatty acid oxidation, chylomicron/lipid transport, bile acid metabolism, cholesterol metabolism, steroid metabolism, ketone body formation), and amino acid/nitrogen metabolism (amino acid biosynthetic process, amino acid catabolic process, urea cycle, and glutathione metabolic process). Categories with more genes highly expressed in mammary than liver tissue included amino acid and sugar transporters and MAPK, Wnt, and JAK-STAT signaling pathways. Real-time PCR analysis showed consistent results with those of microarray analysis for all 12 genes tested. In conclusion, microarray analyses clearly identified differential gene expression profiles between hepatic and mammary tissues that are consistent with the differences in metabolism of these two tissues. This study enables understanding of the molecular basis of metabolic adaptation of the liver and mammary gland during lactation in bovine species.

• Genomics & Informatics
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• v.15 no.1
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• pp.28-37
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• 2017

Obesity is a highly prevalent, chronic disorder that has been increasing in incidence in young patients. Both epigenetic and genetic aberrations may play a role in the pathogenesis of obesity. Therefore, in-depth epigenomic and genomic analyses will advance our understanding of the detailed molecular mechanisms underlying obesity and aid in the selection of potential biomarkers for obesity in youth. Here, we performed microarray-based DNA methylation and gene expression profiling of peripheral white blood cells obtained from six young, obese individuals and six healthy controls. We observed that the hierarchical clustering of DNA methylation, but not gene expression, clearly segregates the obese individuals from the controls, suggesting that the metabolic disturbance that occurs as a result of obesity at a young age may affect the DNA methylation of peripheral blood cells without accompanying transcriptional changes. To examine the genome-wide differences in the DNA methylation profiles of young obese and control individuals, we identified differentially methylated CpG sites and investigated their genomic and epigenomic contexts. The aberrant DNA methylation patterns in obese individuals can be summarized as relative gains and losses of DNA methylation in gene promoters and gene bodies, respectively. We also observed that the CpG islands of obese individuals are more susceptible to DNA methylation compared to controls. Our pilot study suggests that the genome-wide aberrant DNA methylation patterns of obese individuals may advance not only our understanding of the epigenomic pathogenesis but also early screening of obesity in youth.

• Genomics & Informatics
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• v.4 no.1
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• pp.16-22
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• 2006

The KFDA (Korea Food & Drug Administration) has performed a collaborative toxicogenomics project since 2003. Its aim is to construct a toxicology database of 12 compounds administered to mice at initial phase. We chose 6-MP (6-mercaptopurine) which has been used in the treatment of childhood leukemia. It was administered at low (0.224 mg/kg) and at high (2.24 mg/kg) dose (5 mice per group) intraperitonealy to the postnatal 6 weeks mice, then the serum and liver were collected at the indicated time (6, 24 and 72 h) after scarification. Serum biochemical markers for liver toxicity were measured and histopathologic studies also were carried out. The gene expression profiling was carried out by using Applied Biosystems 1700 Full Genome Expression Mouse. By self-organization maps (SOM), we identified groups with unique gene expression patterns, some of them are supposed to be related to 6-MP induced toxicity, including lipid metabolism abnormality, inflammatory response, oxidative stress, ATP depletion and cell death. The potential toxic effects appearing as gene expression changes are dependent of the time of 6-MP but independent of the dosage of it. This study would contribute to establishment of international database as well as national one about hepatotoxicity.

• Proceedings of the Zoological Society Korea Conference
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• 2003.08a
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• pp.56-56
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• 2003

No Abstract, See Full Text

• The Korea Genome Conference
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• 2005.09a
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• pp.68-68
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• 2005

• The Korea Genome Conference
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• 2005.09a
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• pp.93-93
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• 2005

• The Korea Genome Conference
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• 2005.09a
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• pp.65-65
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• 2005

• BMB Reports
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• v.49 no.4
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• pp.199-200
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• 2016

Gene regulation in the brain is essential for long-term plasticity and memory formation. Despite this established notion, the quantitative translational map in the brain during memory formation has not been reported. To systematically probe the changes in protein synthesis during memory formation, our recent study exploited ribosome profiling using the mouse hippocampal tissues at multiple time points after a learning event. Analysis of the resulting database revealed novel types of gene regulation after learning. First, the translation of a group of genes was rapidly suppressed without change in mRNA levels. At later time points, the expression of another group of genes was downregulated through reduction in mRNA levels. This reduction was predicted to be downstream of inhibition of ESR1 (Estrogen Receptor 1) signaling. Overexpressing Nrsn1, one of the genes whose translation was suppressed, or activating ESR1 by injecting an agonist interfered with memory formation, suggesting the functional importance of these findings. Moreover, the translation of genes encoding the translational machineries was found to be suppressed, among other genes in the mouse hippocampus. Together, this unbiased approach has revealed previously unidentified characteristics of gene regulation in the brain and highlighted the importance of repressive controls.

• 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.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.3027-3033
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• 2022

Background: In this study, various types of deep-learning models for predicting in vitro radiosensitivity from gene-expression profiling were compared. Methods: The clonogenic surviving fractions at 2 Gy from previous publications and microarray gene-expression data from the National Cancer Institute-60 cell lines were used to measure the radiosensitivity. Seven different prediction models including three distinct multi-layered perceptrons (MLP), four different convolutional neural networks (CNN) were compared. Folded cross-validation was applied to train and evaluate model performance. The criteria for correct prediction were absolute error < 0.02 or relative error < 10%. The models were compared in terms of prediction accuracy, training time per epoch, training fluctuations, and required calculation resources. Results: The strength of MLP-based models was their fast initial convergence and short training time per epoch. They represented significantly different prediction accuracy depending on the model configuration. The CNN-based models showed relatively high prediction accuracy, low training fluctuations, and a relatively small increase in the memory requirement as the model deepens. Conclusion: Our findings suggest that a CNN-based model with moderate depth would be appropriate when the prediction accuracy is important, and a shallow MLP-based model can be recommended when either the training resources or time are limited.