• Animal cells and systems
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• 제9권3호
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• pp.107-111
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• 2005

C. elegans DLK-1 has been reported to play an important role in synaptogenesis by shaping the structure of presynaptic terminal. In this study, we investigated the expression pattern and regulation of the dlk-1 gene in C. elegans. To determine the expression pattern, we made a dlk-1::gfp fusion construct, named pPDdg1, which consisted of -2.2 kb 5' upstream region, the first exon, the first intron, and a part of the second exon of the dlk-1 gene. By microinjecting this construct into the worm, we observed that the DLK-1::GFP was expressed mainly in neurons. We next examined the regulatory elements of gene expression by deletion analysis of pPDdg1. Removal of a large portion of the 5' upstream region (${\Delta}-361$ to -2246) of the gene had little effect on the expression pattern, whereas deletion of the first intron led to elimination of the DLK-1::GFP expression in most of the neurons. Our results suggest that the first intron of the C. elegans dlk-1 gene contains the regulatory element critical for gene expression.

• 제어로봇시스템학회논문지
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• 제11권10호
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• pp.830-836
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• 2005

Application of microarray technologies which monitor simultaneously the expression pattern of thousands of individual genes in different biological systems results in a tremendous increase of the amount of available gene expression data and have provided new insights into gene expression during drug development, within disease processes, and across species. There is a great need of data mining methods allowing straightforward interpretation, visualization and analysis of the relevant information contained in gene expression profiles. Specially, classifying biological samples into known classes or phenotypes is an important practical application for microarray gene expression profiles. Gene expression profiles obtained from tissue samples of patients thus allowcancer classification. In this research, molecular classification of microarray gene expression data is applied for multi-class cancer using computational biology such gene selection, principal component analysis and fuzzy clustering. The proposed method was applied to microarray data from leukemia patients; specifically, it was used to interpret the gene expression pattern and analyze the leukemia subtype whose expression profiles correlated with four cases of acute leukemia gene expression. A basic understanding of the microarray data analysis is also introduced.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.623-626
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• 2006

Group of genes controls the functioning of a cell by complex interactions. These interacting gene groups are called Gene Regulatory Networks (GRNs). Two previous data mining approaches, clustering and classification have been used to analyze gene expression data. While these mining tools are useful for determining membership of genes by homology, they don't identify the regulatory relationships among genes found in the same class of molecular actions. Furthermore, we need to understand the mechanism of how genes relate and how they regulate one another. In order to detect regulatory relationships among genes from time-series Microarray data, we propose a novel approach using frequent pattern mining and chain rule. In this approach, we propose a method for transforming gene expression data to make suitable for frequent pattern mining, and detect gene expression patterns applying FP-growth algorithm. And then, we construct gene regulatory network from frequent gene patterns using chain rule. Finally, we validated our proposed method by showing that our experimental results are consistent with published results.

• International Journal of Advanced Culture Technology
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• 제6권4호
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• pp.275-283
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• 2018

Cancer show distinct pattern of gene expression when it is compared to normal. This difference results malignant characteristic of cancer. Many cancer drugs are targeting this difference so that it can selectively kill cancer cells. One of the recent demand for personalized treating cancer is retrieving normal tissue from a patient so that the gene expression difference between cancer and normal be assessed. However, in most clinical situation it is hard to retrieve normal tissue from a patient. This is because biopsy of normal tissues may cause damage to the organ function or a risk of infection or side effect what a patient to take. Thus, there is a challenge to estimate normal cell's gene expression where cancers are originated from without taking additional biopsy. In this paper, we propose in-silico based prediction of normal cell's gene expression from gene expression data of a tumor sample. We call this challenge as reverting the cancer into normal. We divided this challenge into two parts. The first part is making a generator that is able to fool a pretrained discriminator. Pretrained discriminator is from the training of public data (9,601 cancers, 7,240 normals) which shows 0.997 of accuracy to discriminate if a given gene expression pattern is cancer or normal. Deceiving this pretrained discriminator means our method is capable of generating very normal-like gene expression data. The second part of the challenge is to address whether generated normal is similar to true reverse form of the input cancer data. We used, cycle-consistent adversarial networks to approach our challenges, since this network is capable of translating one domain to the other while maintaining original domain's feature and at the same time adding the new domain's feature. We evaluated that, if we put cancer data into a cycle-consistent adversarial network, it could retain most of the information from the input (cancer) and at the same time change the data into normal. We also evaluated if this generated gene expression of normal tissue would be the biological reverse form of the gene expression of cancer used as an input.

• Journal of Periodontal and Implant Science
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• 제41권4호
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• pp.167-175
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• 2011

Purpose: Periodontal ligament (PDL) cell differentiation into osteoblasts is important in bone formation. Bone formation is a complex biological process and involves several tightly regulated gene expression patterns of bone-related proteins. The expression patterns of bone related proteins are regulated in a temporal manner both in vivo and in vitro. The aim of this study was to observe the gene expression profile in PDL cell proliferation, differentiation, and mineralization in vitro. Methods: PDL cells were grown until confluence, which were then designated as day 0, and nodule formation was induced by the addition of 50 ${\mu}g$/mL ascorbic acid, 10 mM ${\beta}$-glycerophosphate, and 100 nM dexamethasone to the medium. The dishes were stained with Alizarin Red S on days 1, 7, 14, and 21. Real-time polymerase chain reaction was performed for the detection of various genes on days 0, 1, 7, 14, and 21. Results: On day 0 with a confluent monolayer, in the active proliferative stage, c-myc gene expression was observed at its maximal level. On day 7 with a multilayer, alkaline phosphatase, bone morphogenetic protein (BMP)-2, and BMP-4 gene expression had increased and this was followed by maximal expression of osteocalcin on day 14 with the initiation of nodule mineralization. In relationship to apoptosis, c-fos gene expression peaked on day 21 and was characterized by the post-mineralization stage. Here, various genes were regulated in a temporal manner during PDL fibroblast proliferation, extracellular matrix maturation, and mineralization. The gene expression pattern was similar. Conclusions: We can speculate that the gene expression pattern occurs during PDL cell proliferation, differentiation, and mineralization. On the basis of these results, it might be possible to understand the various factors that influence PDL cell proliferation, extracellular matrix maturation, and mineralization with regard to gene expression patterns.

• Genomics & Informatics
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• 제1권1호
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• pp.32-39
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• 2003

We present a latent variable model-based approach to the analysis of gene expression patterns, coupled with topographic clustering. Aspect model, a latent variable model for dyadic data, is applied to extract latent patterns underlying complex variations of gene expression levels. Then a topographic clustering is performed to find coherent groups of genes, based on the extracted latent patterns as well as individual gene expression behaviors. Applied to cell cycle­regulated genes of the yeast Saccharomyces cerevisiae, the proposed method could discover biologically meaningful patterns related with characteristic expression behavior in particular cell cycle phases. In addition, the display of the variation in the composition of these latent patterns on the cluster map provided more facilitated interpretation of the resulting cluster structure. From this, we argue that latent variable models, coupled with topographic clustering, are a promising tool for explorative analysis of gene expression data.

• BMB Reports
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• 제30권3호
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• pp.200-204
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• 1997

We eliminated introns from replication independent chicken H3.3 histone gene using a H3.3 cDNA clone and a genomic H3.3 clone. After introduction into Rat 3 cells, we observed its pattern of expression by analyzing mRNA from different phases of the cell cycle. Even without introns, the H3.3 gene was expressed constitutively at a low level throughout the cell cycle. This indicates that the introns in the H3.3 gene are not responsible for the cell cycle-independent expression of the gene. This result contradicts previous reports that suggested their importance in cell cycle regulated expression. We believe that other regions of the gene, promoter, coding region, and/or 3'-end of the gene, are involved in its expression pattern.

• 응용통계연구
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• 제18권2호
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• pp.371-379
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• 2005

본 논문에서는 피어슨 상관계수를 이용한 시간경로 유전자 발현자료의 군집분석에서 군집의 대표적인 패턴에서 벗어나는 이질적인 패턴을 보이는 시계열을 탐지하기 위한 패턴일치지수를 제안하고, 이를 마이크로어레이 실험으로부터 얻어진 혈청 시간경로 유전자 발현자료에 적용하여 유용성을 검토해 본다.

• 대한의생명과학회지
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• 제17권3호
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• pp.185-190
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• 2011

Adenovirus type 5 (Ad5) vectors have been used for gene transfer to a wide variety of cell types in vivo and in vitro. The advantages of adenovirus vectors include the high titer of virus readily obtained in large scale preparations, their ability to transduce dividing and non dividing cells, and the high level of transgene expression. Since adenovirus vectors do not integrate in host cell DNA, there is a lack of insertional mutagenesis. However, many human tumor cells lack expression of the adenovirus 5 receptors and contain areas of hypoxia. In order to identify the pattern of replication and gene expression of oncolytic adenovirus in hypoxic condition, multiple different fiber modified Ads (Ad5F/S11, Ad5F/S35, Ad5F/K7, Ad5F/K21, and Ad5F/RGD) was compared. The replication of all fiber modified adenovirus was inhibited in hypoxic condition in HEK 293 cells, but gene expression has variety on different tumor cell lines and the level of coxackievirus and adenovirus receptor (CAR) expression. These data suggest that CAR expression pattern and hypoxic condition of tumor are considered for optimal oncolytic adenovirus application.

• BMB Reports
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• 제32권4호
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• pp.345-349
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• 1999

We fused the promoter region of an H3.2 chicken histone gene, whose expression is dependent on the cell cycle, to the 5' coding region of an H3.3 chicken histone gene, which is expressed constitutively at a low level throughout the cell cycle. This fusion gene showed a cell cycle-regulated pattern of expression, but in a different manner. The mRNA level of the fusion gene increase during the S phase of the cell cycle by about 3.7-fold at 6 h and 2.7-fold at 12 h after the serum stimulation. The mRNA level of the intact H3.2 gene, however, increased by an average of 3.6-fold at 6 h and 8.7-fold at 12 h. This different expression pattern might be due to the differences in their 3' end region that is responsible for mRNA stability. The 3' end of the H3.2 mRNA contains a stem-loop structure, instead of a poly(A) tail present in the H3.3 mRNA. We also constructed a similar fusion gene using a H3.3 histone gene whose introns had been eliminated to rule out the possibility of involvement of the introns in cell cycle-regulated expression. The expression of this fusion gene was almost identical to the fusion gene made previously. These results indicate that the promoter region of the H3.2 gene is only partially responsible for its expression during the S phase of the cell cycle.