• Asian-Australasian Journal of Animal Sciences
• /
• v.23 no.8
• /
• pp.987-992
• /
• 2010

This study was to detect the expression of heart fatty acid-binding protein (H-FABP) and adipocyte fatty acid-binding protein (A-FABP) gene mRNA in different tissues of Rugao and Luyuan chickens at 56 d and 120 d by real-time fluorescence quantitative reverse transcription polymerase-chain reaction (FQ-RT-PCR). The primers were designed according to the sequences of HFABP, A-FABP and GAPDH genes in Gallus gallus, which were used as target genes and internal reference gene, respectively. The levels of H-FABP and A-FABP gene expression were detected by SYBR Green I FQ-RT-PCR. The relative H-FABP and A-FABP gene mRNA expression level was calculated with 2-$^{{\Delta}Ct}$. Melting curve analysis showed a single peak of three genes. Intramuscular fat (IMF) content in breast muscle and leg muscle of the two chicken breeds at 120 d was higher than at 56 d. IMF content in breast muscle and leg muscle at 56 d and 120 d in Luyuan was significantly higher than in Rugao, however, abdominal fat of Luyuan was significantly lower than that of Rugao. The relative H-FABP gene mRNA expression level in cardiac muscle was the highest in both chicken breeds. The relative H-FABP and A-FABP gene expression of different tissues in Luyuan was higher than in Rugao. H-FABP gene mRNA expression had a negative effect on IMF of leg and breast muscles, and was significantly negatively correlated with IMF content. The relative A-FABP gene mRNA level in abdominal fat was higher than in liver. The A-FABP gene mRNA was not expressed in leg, breast and cardiac muscles. A-FABP gene mRNA expression level was significantly positively correlated with abdominal fat and had a significant effect on abdominal fat but not IMF content.

• International Journal of Advanced Culture Technology
• /
• v.6 no.4
• /
• pp.275-283
• /
• 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.

• Toxicological Research
• /
• v.22 no.4
• /
• pp.323-328
• /
• 2006

Global gene expression profile was analyzed by microarray analysis of rat liver RNA after acute acetaminophen (APAP) administration. A single dose of 1g/kg body weight of APAP was given orally, and the liver samples were obtained after 24, 48 h, and 2 weeks. Histopathologic and biochemical studies enabled the classification of the APAP effect into injury (24 and 48 h) and regeneration (2 weeks) stages. The expression levels of 4900 clones on a custom rat gene microarray were analyzed and 484 clones were differentially expressed with more than a 1.625-fold difference(which equals 0.7 in log2 scale) at one or more time points. Two hundred ninety seven clones were classified as injury-specific clones, while 149 clones as regeneration-specific ones. Characteristic gene expression profiles could be associated with APAP-induced gene expression changes in lipid metabolism, stress response, and protein metabolism. We established a global gene expression profile utilizing microarray analysis in rat liver upon acute APAP administration with a full chronological profile that not only covers injury stage but also later point of regeneration stage.

• Journal of Periodontal and Implant Science
• /
• v.41 no.4
• /
• pp.167-175
• /
• 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.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.5
• /
• pp.707-711
• /
• 2007

Microarray gene expression data is believed to show the functions of living organism through the gene expression values. We have studied a method to get the informative genes from the microarray gene expression data. There are several ways for this. In recent researches to get more sophisticated and detailed results, it has used the intelligence information theory like fuzzy theory. Some methods are to add fudge factors to the significance test for more refined results. In this paper, we suggest a method to get informative genes from microarray gene expression data. We combined the difference of means between two groups and the fuzzy membership degree which reflects the variance of the gene expression data. We have called our significance test the Fuzzy Information method for Gene Expression data(FIGER). The FIGER calculates FIGER variation ratio and FIGER membership degree to show how strongly each object belongs to the each group and then it results in the significance degree of each gene. The FIGER is focused on the variation and distribution of the data set to adjust the significance level. Out simulation shows that the FIGER-test is an effective and useful significance test.

• Archives of Pharmacal Research
• /
• v.23 no.3
• /
• pp.257-260
• /
• 2000

The tetracycline-controlled transactivator (tTA)-mediated gene activation system was examined in virus infected cells to determine its role in the control of gene expression. In the presence of tTA, the gene expression from the tetO-modified minimal promoter was efficiently activated in the uninfected cells, whereas essentially no activation was observed from the only minimal promoter without the seven direct repeats of 42 bp tetO sequences. However, essentially no activation was observed when only the minimal promoter was used, without the seven direct repetitions of the 42 bp tetO sequences. On the other hand, in the infected cells, a substantial background of $\beta$-glucuronidase expression was detected in the absence of tTA, even though tTA stimulated the gene expression by ~7-fold. This background expression indicates that the sequences within or nearby tetO are involved in the background stimulation of the gene expression by HCMV and HSV-1 . These results suggest that the application of the tTA-mediated gene activation system may not be extremely useful for studying the biological roles of HCMV and HSV genes In the viral replicative cycles, because of the basal activity of the gene expression.

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2003.10a
• /
• pp.170-177
• /
• 2003

Gene expression data are the quantitative measurements of expression levels and ratios of numberous genes in different situations based on microarray image analysis results. The process to draw meaningful information related to genomic diseases and various biological activities from gene expression data is known as gene expression data analysis. In this paper, we present a hierarchical clustering method of gene expression data based on self organizing map which can analyze the clustering result of gene expression data more efficiently. Using our proposed method, we could eliminate the uncertainty of cluster boundary which is the inherited disadvantage of self organizing map and use the visualization function of hierarchical clustering. And, we could process massive data using fast processing speed of self organizing map and interpret the clustering result of self organizing map more efficiently and user-friendly. To verify the efficiency of our proposed algorithm, we performed tests with following 3 data sets, animal feature data set, yeast gene expression data and leukemia gene expression data set. The result demonstrated the feasibility and utility of the proposed clustering algorithm.

• BMB Reports
• /
• v.31 no.5
• /
• pp.503-507
• /
• 1998

To investigate the effects of the Vitreoscilla hemoglobin (VHb) gene on the production of a heterologous protein, a comparative expression system for VHb and ferritin was constructed. First, the VHb gene was inserted into the downstream and upstream regions of the ferritin gene to construct pHF2 and pHF3, respectively. Next, the two plasmids pACHB1 and pVUTFH10, having the VHb gene and the ferritin gene respectively, were constructed in order to express the two genes in different plasmids by using a coplasmid expression system. It was observed that the cell growth was improved in all strains containing the VHb gene. Furthermore, in our coplasmid expression system, the presence of the VHb gene increased production of the ferritin by 1.8 times, as much as that in a strain not having the VHb gene.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.18
• /
• pp.8197-8201
• /
• 2016

Background: To investigate the expression of the fragile histidine triad (FHIT) gene in acute lymphoblastic leukemia and its clinical significance. Materials and Methods: The level of expressed FHIT mRNA in peripheral blood from 50 patients with acute lymphoblastic leukemia (ALL) and in 50 peripheral blood samples from healthy volunteers was measured via RT-PCR. Correlation analyses between FHIT gene expression and clinical characteristics (gender, age, white blood count, immunophenotype of acute lymphoblastic leukemia and percentage of blast cells) of the patients were performed. Results: The FHIT gene was expressed at $2.49{\pm}7.37$ of ALL patients against $14.4{\pm}17.9$ in the healthy volunteers. The difference in the expression levels between ALL patients and healthy volunteers was statistically significant. The rate of gene expression did not significantly vary with immunophenotype subtypes. Gene expression was also found to be correlated with increase of total leukocyte and decrease in platelets, but not with age, gender, immunophenotyping or percentage of blast cells. Conclusions: FHIT gene expression is low in acute lymphoblastic leukemia and could be a useful marker to monitor minimal residual disease. This gene is also a candidate target for the immunotherapy of acute lymphoblastic leukemia.

• Proceedings of the Korean Society of Plant Biotechnology Conference
• /
• 2005.11a
• /
• pp.158-158
• /
• 2005