• The Journal of Korean Medicine
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• v.25 no.2
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• pp.127-137
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• 2004

Objectives : The purpose of this investigation was to evaluate the effects of Sohaphyang-won (SH) on the alteration in gene expression in a hypoxia model using cultured rat cortical cells. Methods : E18 rat cortical cells were grown in neurobasal medium containing B27 supplement. On 12 DIV, SH was added ($20\mu\textrm{g}/ml$) to the culture media for 24 hrs. On 14 DIV, cells were given a hypoxic insult (2% O2/5% CO2, $37^{\circ}C$, 3 hrs), returned to normoxia and cultured for another 24 hrs. Total RNA was prepared from SH-untreated (control) and -treated cultures and alteration in gene expression was analyzed by microarray using rat 5K-TwinChips. Results : Effects on some of the genes whose functions are implicated in neural viability are as follows: 1) For most of the genes altered in expression, the global M values were between -05 to +0.5, Among these, 1517 genes were increased in their expression by more than global M +0.1, while 1480 genes were decreased by more than global M -0.1. 2) The expression of apoptosis-related genes such as Bad (global M =0.35), tumor protein p53 (T53) (global M =0.28) were increased, while v-akt murine thymoma viral oncogene homolog 1 (Akt1) was decreased. 3) The expression of hemoglobin alpha 1 (probably neuroglobin) was increased by about 3.2-fold (global M =1.7). 4) The expression of antioxidation-related catalase gene was increased (global M =0.26). 5) The expression of PKCzeta (prkcz), an upstream kinase of MAPK, was increased (global M =0.29). 6) The expression of retinoic acid receptor alpha (RAR), which may regulate transcription in hypoxic stress, was increased (global M =10.27). Conclusions : In summary, the microarray data suggest that SH doesn't increase the expression of oxygen capture-, anti-oxidation- and 'response to stress' -related genes but decreases some anti-apoptosis genes which would help protect the hypoxic cells from apoptosis.

• Journal of Computing Science and Engineering
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• v.11 no.4
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• pp.121-129
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• 2017

Feature selection has been widely established as an efficient technique for microarray data analysis. Feature selection aims to search for the most important feature/gene subset of a given dataset according to its relevance to the current target. Unsupervised feature selection is considered to be challenging due to the lack of label information. In this paper, we propose a novel method for unsupervised feature selection, which incorporates embedded learning and $l_{2,1}-norm$ sparse regression into a framework to select genes in microarray data analysis. Local tangent space alignment is applied during embedded learning to preserve the local data structure. The $l_{2,1}-norm$ sparse regression acts as a constraint to aid in learning the gene weights correlatively, by which the proposed method optimizes for selecting the informative genes which better capture the interesting natural classes of samples. We provide an effective algorithm to solve the optimization problem in our method. Finally, to validate the efficacy of the proposed method, we evaluate the proposed method on real microarray gene expression datasets. The experimental results demonstrate that the proposed method obtains quite promising performance.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.13-32
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• 2006

Biologists are attempting to group genes based on the temporal pattern of gene expression levels. So far, a number of methods have been proposed for clustering microarray data. However, the results of clustering depends on the genes selection, therefore the gene selection with significant expression difference is also very important to cluster for microarray data. Thus, this paper present the results of broad comparative studies to time course microarray data by considering methods of gene selection, clustering and cluster validation.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.205-213
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• 2007

In this paper, the normal mixture model subjected to general linear restriction for component-means based on linear regression is proposed, and its fitting method by EM algorithm and Lagrange multiplier is provided. This model is applied to gene clustering of microarray expression data, which demonstrates it has very good performances for real data set. This model also allows to obtain the clusters that an analyst wants to find out in the fashion that the hypothesis for component-means is represented by the design matrices and the linear restriction matrices.

• Molecular & Cellular Toxicology
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• v.1 no.3
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• pp.164-171
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• 2005

Toxicological studies have an object of detecting adverse effects of a chemical on an organism based on observed toxicity marker (i.e., serum biochemical markers and chemical-specific gene expression) or phenotypic outcome. To date, most toxicogenomic studies concentrated on hepatic toxicity. cDNA microarray analysis enable discrimination of the responses in animals exposed to different classes of hepatotoxicants. In an effort to further characterize the mechanisms of 2, 3, 7, 8,-Tetrachlorodibenzo-p-dioxin (TCDD or dioxin)-mediated toxicity, comprehensive temporal-responsive microarray analyses were performed on hepatic tissue from Sprague-Dawley rats treated with TCDD. Hepatic gene expression profiles were monitored using custom DNA chip containing 490 cDNA clones related with toxicology. Gene expression analysis identified 26 features which exhibited a significant change. In this study, we observed that the genes related with oxidative stress in rats exposed to Dioxin, such as CYPIIA3 and glutathione S-transferase, were up-regulated at 24hr after exposure. In this study, we carried out to discover novel evidence for previously unknown gene expression patterns related to mechanism of hepatic toxicity in rats exposed to dioxin, and to elucidate the effects of dioxin on the gene expression after exposure to dioxin.

• Genomics & Informatics
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• v.6 no.4
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• pp.202-209
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• 2008

Biological pathways are known as collections of knowledge of certain biological processes. Although knowledge about a pathway is quite significant to further analysis, it covers only tiny portion of genes that exists. In this paper, we suggest a model to extend each individual pathway using a microarray expression data based on the known knowledge about the pathway. We take the Rosetta compendium dataset to extend pathways of Saccharomyces cerevisiae obtained from KEGG (Kyoto Encyclopedia of genes and genomes) database. Before applying our model, we verify the underlying assumption that microarray data reflect the interactive knowledge from pathway, and we evaluate our scoring system by introducing performance function. In the last step, we validate proposed candidates with the help of another type of biological information. We introduced a pathway extending model using its intrinsic structure and microarray expression data. The model provides the suitable candidate genes for each single biological pathway to extend it.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.1005-1021
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• 2007

Traditional procedures for DNA microarray data analysis are to preprocess and normalize the gene expression data, and then to analyze the normalized data using statistical tests. Drawbacks of the traditional methods are: genuine biological signal may be unwillingly eliminated together with artifacts, the limited number of arrays per gene make statistical tests difficult to use the normality assumption or nonparametric method, and genes are tested independently without consideration of interrelationships among genes. A novel method using average rank in each array is proposed to eliminate such drawbacks. This average rank method monitors differentially regulated genes among genetically different groups and the selected genes are somewhat different from those selected by traditional P-value method. Addition of genes selected by the average rank method to the traditional method will provide better understanding of genetic differences of groups.

• Biomedical Science Letters
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• v.15 no.3
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• pp.261-263
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• 2009

Methylprednisolone is a synthetic glucocorticoid which is usually taken intravenously for many neurosurgical diseases which cause edema including brain tumor, and trauma including spinal cord injury. Methylprednisolone reduces swelling and decreases the body's immune response. It is also used to treat many immune and allergic disorders, such as arthritis, lupus, psoriasis, asthma, ulcerative colitis, and Crohn's disease. To identify genes expressed during methylprednisolone treatment against neurons of rats (PC12 cells), DNA microarray method was used. We have isolated 2 gene groups (up- or down-regulated genes) which are methylprednisolone differentially expressed in neurons. Lipocalin 3 is the gene most significantly increased among 772 up-regulated genes (more than 2 fold over-expression) and Aristaless 3 is the gene most dramatically decreased among 959 down-regulated genes (more than 2 fold down-expression). The gene increased expression of Fgb, Thbd, Cfi, F3, Kngl, Serpinel, C3, Tnfrsf4 and Il8rb are involved stress-response gene, and Nfkbia, Casp7, Pik3rl, I11b, Unc5a, Tgfb2, Kitl and Fgf15 are strongly associated with development. Cell cycle associated genes (Mcm6, Ccnb2, Plk1, Ccnd1, E2f1, Cdc2a, Tgfa, Dusp6, Id3) and cell proliferation associated genes (Ccl2, Tnfsf13, Csf2, Kit, Pim1, Nr3c1, Chrm4, Fosl1, Spp1) are down-regulated more than 2 times by methylprednisolone treatment. Among the genes described above, 4 up-regulated genes are confirmed those expression by RT-PCR. We found that methylprednisolone is related to expression of many genes associated with stress response, development, cell cycle, and cell proliferation by DNA microarray analysis. However, We think further experimental molecular studies will be needed to figure out the exact biological function of various genes described above and the physiological change of neuronal cells by methylprednisolone. The resulting data will give the one of the good clues for understanding of methylprednisolone under molecular level in the neurons.

• Molecular & Cellular Toxicology
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• v.5 no.1
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• pp.51-57
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• 2009

Quantum Dot (QD) nanoparticles are used in various industrial applications, such as diagnostic, drug delivery, and imaging agents of biomedicine. Although QDs are extensively used in many medical science, several studies have been demonstrated the potential toxicity of nanoparticles. The first objective of this study was to investigate the nanotoxicity of QDs in the HaCaT human keratinocyte cell line by focusing on gene expression pattern. In order to evaluate the effect of QDs on gene expression profile in HaCaT cells, we analyzed the differential genes which related to oxidative stress and antioxidant defense mechanisms by using human cDNA microarray and PCR array. A human cDNA microarray was clone set, which was sorted for a list of genes correlated with cell mechanisms. We tried to confirm results of cDNA microarray by using PCR array, which is pathway-focused gene expression profiling technology using Real-Time PCR. Although we could not find the exactly same genes in both methods, we have screened the effects of QDs on global gene expression profiles in human skin cells. In addition, our results show that QD treatment somehow regulates cellular pathways of oxidative stress and antioxidant defense mechanisms. Therefore, we suggest that this study can enlarge our knowledge of the transcriptional profile and identify new candidate biomarker genes to evaluate the toxicity of nanotoxicology.

• The Korean Journal of Physiology and Pharmacology
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• v.15 no.1
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• pp.23-29
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• 2011

Melatonin, which is the main product of the pineal gland, has well documented antioxidant and immune-modulatory effects. Macrophages produce molecules that are known to play roles in inflammatory responses. We conducted microarray analysis to evaluate the global gene expression profiles in response to treatment with melatonin in lipopolysaccharide (LPS) activated RAW 264.7 macrophage cells. In addition, eight genes were subjected to real-time reverse transcription polymerase chain reaction (RT-PCR) to confirm the results of the microarray. The cells were treated with LPS or melatonin plus LPS for 24 hr. LPS induced the up-regulation of 1073 genes and the down-regulation of 1144 genes when compared to the control group. Melatonin pretreatment of LPS-stimulated RAW 264.7 cells resulted in the down regulation of 241 genes and up regulation of 164 genes. Interestingly, among genes related to macrophage-mediated immunity, LPS increased the expression of seven genes (Adora2b, Fcgr2b, Cish, Cxcl10, Clec4n, Il1a, and Il1b) and decreased the expression of one gene (Clec4a3). These changes in expression were attenuated by melatonin. Furthermore, the results of real-time PCR were similar to those of the microarray. Taken together, these results suggest that melatonin may have a suppressive effect on LPS-induced expression of genes involved in the regulation of immunity and defense in RAW 264.7 macrophage cells. Moreover, these results may explain beneficial effects of melatonin in the treatment of various inflammatory conditions.