• 한국데이터정보과학회:학술대회논문집
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• 2005.04a
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• pp.47-53
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• 2005

• Nutritional Sciences
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• v.8 no.1
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• pp.23-27
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• 2005

The major garlic component, Allicin [diallylthiosulfinate, or (R, S)-diallyldissulfid-S-oxide] is known for its medicinal effects, such as antihypertensive activity, microbicidal activity, and antitumor activity. Allicin and diallyldisulfide, which is a converted form of allicin, inhibited the cholesterol level in hepatocytes, in vivo and in vitro. The metabolism of allicin reportedly occurs in the microsomes of hepatocytes, predominantly with the contribution of cytochrome P-450. However, little is known about how allicin affects the genes involved in the activity of hepatocytes in vivo. In the present study, we used the short-term intravenous injection of allicin to examine the in vivo genetic profile of hepatocytes. Allicin up-regulate ten genes in the hepatocytes. For example, the interferon regulator 1 (IRF-I), the wingless-related MMTV (mouse mammary tumor virus) integration site 4 (wnt-4), and the fatty acid binding protein 1. However, allicin down-regulated three genes: namely, glutathione S-transferase mu6, a-2-HS glycoprotein, and the corticosteroid binding globulin of hepatocytes. The up-regulated wnt-4, IRF-1, and mannose binding lectin genes can enhance the growth factors, cytokines, transcription activators and repressors that are involved in the immune defense mechanism. These primary data, which were generated with the aid of the Atlas Plastic Mouse 5 K Microarray, help to explain the mechanism which enables allicin to act as a therapeutic agent, to enhance immunity, and to prevent cancer. The data suggest that these benefits of allicin are partly caused by the up-regulated or down-regulated gene profiles of hepatocytes. To evaluate the genetic profile in more detail, we need to use a more extensive mouse genome array.

• BMB Reports
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• v.40 no.2
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• pp.226-231
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• 2007

Housekeeping genes are widely used as internal controls in a variety of study types, including real time RT-PCR, microarrays, Northern analysis and RNase protection assays. However, even commonly used housekeeping genes may vary in stability depending on the cell type or disease being studied. Thus, it is necessary to identify additional housekeeping-type genes that show sample-independent stability. Here, we used statistical analysis to examine a large human microarray database, seeking genes that were stably expressed in various tissues, disease states and cell lines. We further selected genes that were expressed at different levels, because reference and target genes should be present in similar copy numbers to achieve reliable quantitative results. Real time RT-PCR amplification of three newly identified reference genes, CGI-119, CTBP1 and GOLGAl, alongside three well-known housekeeping genes, B2M, GAPD, and TUBB, confirmed that the newly identified genes were more stably expressed in individual samples with similar ranges. These results collectively suggest that statistical analysis of microarray data can be used to identify new candidate housekeeping genes showing consistent expression across tissues and diseases. Our analysis identified three novel candidate housekeeping genes (CGI-119, GOLGA1, and CTBP1) that could prove useful for normalization across a variety of RNA-based techniques.

• The Korean Journal of Applied Statistics
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• v.23 no.4
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• pp.705-713
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• 2010

Predicting survival from microarray data is not easy due to the problem of high dimensionality of data and the existence of censored observations. Also the limitation of individual gene analysis causes the shift of focus to the level of gene sets with functionally related genes. For developing a survival prediction model based on pathway information, the methods for selecting a supergene using principal component analysis and testing its significance for each pathway are discussed. Besides, the performance of gene filtering is compared.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.7
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• pp.1044-1051
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• 2016

The present study compared the differential functions of two groups of adjuvants, Montanide incomplete Seppic adjuvant (ISA) series and Quil A, cholesterol, dimethyl dioctadecyl ammonium bromide, and Carbopol (QCDC) formulations, in chicken by analyzing published microarray data associated with each type of vaccine adjuvants. In the biological function analysis for differentially expressed genes altered by two different adjuvant groups, ISA series and QCDC formulations showed differential effects when chickens were immunized with a recombinant immunogenic protein of Eimeria. Among the biological functions, six categories were modified in both adjuvant types. However, with respect to "Response to stimulus", no biological process was modified by the two adjuvant groups at the same time. The QCDC adjuvants showed effects on the biological processes (BPs) including the innate immune response and the immune response to the external stimulus such as toxin and bacterium, while the ISA adjuvants modified the BPs to regulate cell movement and the response to stress. In pathway analysis, ISA adjuvants altered the genes involved in the functions related with cell junctions and the elimination of exogenous and endogenous macromolecules. The analysis in the present study could contribute to the development of precise adjuvants based on molecular signatures related with their immunological functions.

• International Journal of Industrial Entomology and Biomaterials
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• v.23 no.1
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• pp.137-141
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• 2011

A previous data was provided information for tissuespecific expression genes by means of whole-genome oligonucleotide microarray in the silkworm. We analyzed the tissue-specific expression patterns in the hemocyte tissue on 5 days of 5th instar larvae during the development of $B.$ $mori$. Total 5 candidates pick out from the $Bombyx$ $mori$ Microarray Database (BmMDB; http://silkworm.swu.edu.cn/microarray). To verify the hemocyte-specific expression, we analyzed by semi-quantitative and real-time quantitative RT-PCR using the highly expressed endogenous $Actin$ RNA as an intrinsic reference. In this study, we confirmed that one gene-sw17255- out of 5 candidates expressed in the hemocyte tissue, which was consistent with the previous data. Circulating hemocytes in the body fluid of the $B.$ $mori$ are most powerful target organ for producing biomaterials. We need further studies to find hemocyte-specific promoter region from sw17255 gene. Finally, this result can be applied in creating transgenic silkworms as a biomedical insect.

• Journal of Embryo Transfer
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• v.30 no.1
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• pp.17-21
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• 2015

Alteration in ion channel or transporter expression levels affects cell volume which is produced by movement of water and ion across the plasma membrane. In particular, aquaporin (AQP) channels among ion channels play a crucial role in movement of water across the cell membrane. This study was performed to identify whether AQP expression is changed in bovine follicular cystic follicles using microarray, RT-PCR and Western blotting analyses. In microarray data, AQP4 expression was decreased, whereas AQP7 was increased in cystic follicles. Additional experiments were focused on the AQP7 expression increased in cystic follicles. The microarray data was confirmed by semi-quantitative polymerase chain reaction (PCR) and Western blot analysis. AQP7 mRNA and protein expressions were significantly increased in the cystic follicles (p<0.05). Application of estrogen ($10{\mu}g/ml$) to bovine ovarian cells showed a trend of increase in AQP7 expression. From these results, we suggest that the increase in AQP7 expression in cystic follicles may play an important role in movement of water in bovine ovary. In addition, AQP7, a aquaglyceroporin permeating water and glycerol, could be a good target in development of methods for the cryopreservation of bovine ovary.

• Molecules and Cells
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• v.26 no.4
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• pp.338-343
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• 2008

An embryonic stem cell is a powerful tool for investigation of early development in vitro. The study of embryonic stem cell mediated neuronal differentiation allows for improved understanding of the mechanisms involved in embryonic neuronal development. We investigated expression profile changes using time course cDNA microarray to identify clues for the signaling network of neuronal differentiation. For the short time course microarray data, pattern analysis based on the quadratic regression method is an effective approach for identification and classification of a variety of expressed genes that have biological relevance. We studied the expression patterns, at each of 5 stages, after neuronal induction at the mRNA level of embryonic stem cells using the quadratic regression method for pattern analysis. As a result, a total of 316 genes (3.1%) including 166 (1.7%) informative genes in 8 possible expression patterns were identified by pattern analysis. Among the selected genes associated with neurological system, all three genes showing linearly increasing pattern over time, and one gene showing decreasing pattern over time, were verified by RT-PCR. Therefore, an increase in gene expression over time, in a linear pattern, may be associated with embryonic development. The genes: Tcfap2c, Ttr, Wnt3a, Btg2 and Foxk1 detected by pattern analysis, and verified by RT-PCR simultaneously, may be candidate markers associated with the development of the nervous system. Our study shows that pattern analysis, using the quadratic regression method, is very useful for investigation of time course cDNA microarray data. The pattern analysis used in this study has biological significance for the study of embryonic stem cells.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.1
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• pp.1-7
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• 2005

Cluster analysis of microarray data has been often used to find biologically relevant Broups of genes based on their expression levels. Since many functionally related genes tend to be co-expressed, by identifying groups of genes with similar expression profiles, the functionalities of unknown genes can be inferred from those of known genes in the same group. In this Paper we address a novel clustering approach, called seed clustering, and investigate its applicability for microarray data analysis. In the seed clustering method, seed genes are first extracted by computational analysis of their expression profiles and then clusters are generated by taking the seed genes as prototype vectors for target clusters. Since it has strong mathematical foundations, the seed clustering method produces the stable and consistent results in a systematic way. Also, our empirical results indicate that the automatically extracted seed genes are well representative of potential clusters hidden in the data, and that its performance is favorable compared to current approaches.

• Journal of Periodontal and Implant Science
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• v.51 no.1
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• pp.18-29
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• 2021

Purpose: The aim of this study was to compare the characteristic expression patterns of advanced periodontitis in 2 cohort data sets analyzed using different microarray platforms, and to identify differentially expressed genes (DEGs) through a meta-analysis of both data sets. Methods: Twenty-two patients for cohort 1 and 40 patients for cohort 2 were recruited with the same inclusion criteria. The 2 cohort groups were analyzed using different platforms: Illumina and Agilent. A meta-analysis was performed to increase reliability by removing statistical differences between platforms. An integrative meta-analysis based on an empirical Bayesian methodology (ComBat) was conducted. DEGs for the integrated data sets were identified using the limma package to adjust for age, sex, and platform and compared with the results for cohorts 1 and 2. Clustering and pathway analyses were also performed. Results: This study detected 557 and 246 DEGs in cohorts 1 and 2, respectively, with 146 and 42 significantly enriched gene ontology (GO) terms. Overlapping between cohorts 1 and 2 was present in 59 DEGs and 18 GO terms. However, only 6 genes from the top 30 enriched DEGs overlapped, and there were no overlapping GO terms in the top 30 enriched pathways. The integrative meta-analysis detected 34 DEGs, of which 10 overlapped in all the integrated data sets of cohorts 1 and 2. Conclusions: The characteristic expression pattern differed between periodontitis and the healthy periodontium, but the consistency between the data sets from different cohorts and metadata was too low to suggest specific biomarkers for identifying periodontitis.