• Molecules and Cells
• /
• 제38권9호
• /
• pp.765-772
• /
• 2015

Saussurea lappa has been reported to possess anti-atopic properties. In this study, we have confirmed the S. lappa's anti-atopic properties in Nc/Nga mice and investigated the candidate gene related with its properties using microarray. We determined the target gene using real time PCR in in vitro experiment. S. lappa showed the significant reduction in atoptic dermatitis (AD) score and immunoglobulin E compared with the AD induced Nc/Nga mice. In the results of microarray using back skin obtained from animals, we found that S. lappa's properties are closely associated with cytokine-cytokine receptor interaction and the JAK-STAT signaling pathway. Consistent with the microarray data, real-time RT-PCR confirmed these modulation at the mRNA level in skin tissues from S. lappa-treated mice. Among these genes, PI3Kca and $IL20R{\beta}$ were significantly downregulated by S. lappa treatment in Nc/Nga mouse model. In in vitro experiment using HaCaT cells, we found that the S. lappa components, including alantolactone, caryophyllene, costic acid, costunolide and dehydrocostus lactone significantly decreased the expression of PI3Kca but not $IL20R{\beta}$ in vitro. Therefore, our study suggests that PI3Kca-related signaling is closely related with the protective effects of S. lappa against the development of atopic-dermatitis.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
• /
• pp.438-441
• /
• 2007

Recently, due to molecular biology and engineering technology, DNA microarray makes people watch thousands of genes and the state of variation from the tissue samples of living body. With DNA Microarray, it is possible to construct a genetic group that has similar expression patterns and grasp the progress and variation of gene. This paper practices Cluster Analysis which purposes the discovery of biological subgroup or class by using gene expression information. Hence, the purpose of this paper is to predict a new class which is unknown, open leukaemia data are used for the experiment, and MCL (Markov CLustering) algorithm is applied as an analysis method. The MCL algorithm is based on probability and graph flow theory. MCL simulates random walks on a graph using Markov matrices to determine the transition probabilities among nodes of the graph. If you look at closely to the method, first, MCL algorithm should be applied after getting the distance by using Euclidean distance, then inflation and diagonal factors which are tuning modulus should be tuned, and finally the threshold using the average of each column should be gotten to distinguish one class from another class. Our method has improved the accuracy through using the threshold, namely the average of each column. Our experimental result shows about 70% of accuracy in average compared to the class that is known before. Also, for the comparison evaluation to other algorithm, the proposed method compared to and analyzed SOM (Self-Organizing Map) clustering algorithm which is divided into neural network and hierarchical clustering. The method shows the better result when compared to hierarchical clustering. In further study, it should be studied whether there will be a similar result when the parameter of inflation gotten from our experiment is applied to other gene expression data. We are also trying to make a systematic method to improve the accuracy by regulating the factors mentioned above.

• Genomics & Informatics
• /
• 제2권1호
• /
• pp.30-35
• /
• 2004

To investigate the XIST gene expression and its effect in a Klinefelter's patient, we used Klinefelter's syndrome (XXY) patient with azoospermia and also used a normal male (XY) and a normal female (XX) as the control, We were performed cytogenetic analysis, Y chromosomal microdeletion assay (Yq), semi-quantitative RT-PCR, and the Northern blot for Klinefelter's syndrome (KS) patient, a female and a male control, We extracted total RNA from the KS patient, and from the normal cells of the female and male control subjects using the RNA prep kit (Qiagen), cDNA microarray contained 218 human X chromosome-specific genes was fabricated. Each total RNA was reverse transcribed to the first strand cDNA and was labeled with Cy-3 and Cy-5 fluorescein, The microarray was scanned by ScanArray 4000XL system. XIST transcripts were detected from the Klinefelters patient and the female by RT-PCR and Northern blot analysis, but not from the normal male, In the cDNA microarray experiment, we found 24 genes and 14 genes are highly expressed in KS more than the normal male and females, respectively. We concluded that highly expressed genes in KS may be a resulted of the abnormal X inactivation mechanism.

• 응용통계연구
• /
• 제26권2호
• /
• pp.307-319
• /
• 2013

마이크로어레이 실험의 특성상 표본의 수가 많지 않는 단점을 보완하고 분석 결과를 일반화하기 위하여 공개 저장소에 축적된 자료 중에 연구 목적이 동일한 여러 연구들을 통합하여 분석하려는 시도가 활발하다. 그러나 실험에서 사용한 플랫폼이 서로 다른 경우에는 유전자 관찰값의 분포가 달라지기 때문에 통합이 어렵고 최상의 통합 방법이 제시되어 있지 않다. 본 논문에서는 순위 기반 중위수, 분위수 이산화와 표준화를 각각 이용하여 변환한 자료값을 직접 합치거나 메타분석을 하여 연구 결과를 합치는 방법을 알아 보았다. 또한 GEO에서 다운받은 실제 자료들을 이용하여 네 가지 방법의 장단점과 효과를 비교하였고 서로 다른 연구 자료를 통합하는 것의 영향을 알아보았다.

• Biotechnology and Bioprocess Engineering:BBE
• /
• 제10권5호
• /
• pp.444-450
• /
• 2005

Epidermal growth factor (EGF) activates many intracellular effector molecules, which subsequently influence the expression levels of many genes involved in cell growth, apoptosis and signal transduction, etc. In this study, the early response of gene expressions due to EGF treatment was monitored using oligonucleotide DNA microarrays in rat schwannoma cell lines. An immunoblotting experiment showed the successful activation of EGF receptors and an effector protein, STAT5, due to EGF treatment. The microarray study showed that 35 genes were significantly induced and 2 were repressed within 60 min after the treatment. The list of induced genes included early growth response 1, suppressor of cytokine signaling 3, c-fos, interferon regulatory factor 1 and early growth response 2, etc. According to the microarray data, six of these were induced by more than 10-fold, and showed at least two different induction patterns, indicating complicated regulatory mechanisms in the EGF signal transduction.

• 한국생물정보학회:학술대회논문집
• /
• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
• /
• pp.161-164
• /
• 2005

Data mining techniques can be applied to identify patterns of interest in the gene expression data. One goal in mining gene expression data is to determine how the expression of any particular gene might affect the expression of other genes. To find relationships between different genes, association rules have been applied to gene expression data set [1]. A notable limitation of association rule mining method is that only the association in a single profile experiment can be detected. It cannot be used to find rules across different condition profiles or different time point profile experiments. However, with the appearance of time-series microarray data, it became possible to analyze the temporal relationship between genes. In this paper, we analyze the time-series microarray gene expression data to extract the sequential patterns which are similar to the association rules between genes among different time points in the yeast cell cycle. The sequential patterns found in our work can catch the associations between different genes which express or repress at diverse time points. We have applied sequential pattern mining method to time-series microarray gene expression data and discovered a number of sequential patterns from two groups of genes (test, control) and more sequential patterns have been discovered from test group (same CO term group) than from the control group (different GO term group). This result can be a support for the potential of sequential patterns which is capable of catching the biologically meaningful association between genes.

• 한국통계학회:학술대회논문집
• /
• 한국통계학회 2004년도 학술발표논문집
• /
• pp.195-198
• /
• 2004

One Important problem in a cancer microarray study is to identify a set of genes from which a molecular prognostic indicator can be developed. In parallel with this problem is to validate the chosen set of genes. We develop in this note a K-fold cross validation procedure by combining a 'pre-validation' technique and a bootstrap resampling procedure in the Cox regression . The pre-validation technique predicts the microarray predictor of a case without having seen the true class level of the case. It was suggested by Tibshirani and Efron (2002) to avoid the possible over-fitting in the regression in which a microarray based predictor is employed. The bootstrap resampling procedure for the Cox regression was proposed by Sauerbrei and Schumacher (1992) as a means of overcoming the instability of a stepwise selection procedure. We apply this K-fold cross validation to the microarray data of 92 gastric cancers of which the experiment was conducted at Cancer Metastasis Research Center, Yonsei University. We also share some of our experience on the 'false positive' result due to the information leak.

• Interdisciplinary Bio Central
• /
• 제2권2호
• /
• pp.4.1-4.6
• /
• 2010

The most common type of microarray experiment has a simple design using microarray data obtained from two different groups or conditions. A typical method to identify differentially expressed genes (DEGs) between two conditions is the conventional Student's t-test. The t-test is based on the simple estimation of the population variance for a gene using the sample variance of its expression levels. Although empirical Bayes approach improves on the t-statistic by not giving a high rank to genes only because they have a small sample variance, the basic assumption for this is same as the ordinary t-test which is the equality of variances across experimental groups. The t-test and empirical Bayes approach suffer from low statistical power because of the assumption of normal and unimodal distributions for the microarray data analysis. We propose a method to address these problems that is robust to outliers or skewed data, while maintaining the advantages of the classical t-test or modified t-statistics. The resulting data transformation to fit the normality assumption increases the statistical power for identifying DEGs using these statistics.

• Genomics & Informatics
• /
• 제2권2호
• /
• pp.92-98
• /
• 2004

A cDNA microarray experiment is one of the most useful high-throughput experiments in medical informatics for monitoring gene expression levels. Statistical analysis with a cDNA microarray medical data requires a normalization procedure to reduce the systematic errors that are impossible to control by the experimental conditions. Despite the variety of normalization methods, this. paper suggests a more general and synthetic normalization algorithm with a control gene set based on previous studies of normalization. Iterative normalization method was used to select and include a new control gene set among the whole genes iteratively at every step of the normalization calculation initiated with the housekeeping genes. The objective of this iterative normalization was to maintain the pattern of the original data and to keep the gene expression levels stable. Spatial plots, M&A (ratio and average values of the intensity) plots and box plots showed a convergence to zero of the mean across all genes graphically after applying our iterative normalization. The practicability of the algorithm was demonstrated by applying our method to the data for the human photo aging study.

• KSBB Journal
• /
• 제22권4호
• /
• pp.260-264
• /
• 2007

본 실험에서는 마이크로어레이 형태로 펩타이드와의 공유결합에 의한 고정화를 시키기 위해 유리 칩의 표면을 아민기에서 thiol기로 개질하였다. 펩타이드의 lysine기와 thiol기와의 공유결합반응에는 12시간 정도의 반응시간이 필요하였고 실온보다는 35$^{\circ}C$가 유리함을 확인하였다. Trypsin-FITC와의 반응을 통해 trypsin 결합부위를 가진 target 펩타이드가 control 펩타이드보다 더 높은 형광 신호를 나타냄을 확인하였고, 이를 통해 target 펩타이드를 마이크로어레이 상에서 식별할 수 있었다. 이 trypsin-FITC와의 결합 친화도 차이를 별도의 QCM 실험을 통해서도 확인하였다. 또한 작은 부피의 spot과 높은 농도의 펩타이드 용액이 더욱 높은 표면형광신호를 생성함을 확인하였다. 본 실험을 통해 펩타이드 마이크로어레이 칩 개발을 위한 기초 조건을 확립하였다.