• Molecules and Cells
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• v.26 no.2
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• pp.121-130
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• 2008

Methamphetamine, a commonly used addictive drug, is a powerful addictive stimulant that dramatically affects the CNS. Repeated METH administration leads to a rewarding effect in a state of addiction that includes sensitization, dependence, and other phenomena. It is well known that susceptibility to the development of addiction is influenced by sources of reinforcement, variable neuroadaptive mechanisms, and neurochemical changes that together lead to altered homeostasis of the brain reward system. These behavioral abnormalities reflect neuroadaptive changes in signal transduction function and cellular gene expression produced by repeated drug exposure. To provide a better understanding of addiction and the mechanism of the rewarding effect, it is important to identify related genes. In the present study, we performed gene expression profiling using microarray analysis in a reward effect animal model. We also investigated gene expression in four important regions of the brain, the nucleus accumbens, striatum, hippocampus, and cingulated cortex, and analyzed the data by two clustering methods. Genes related to signaling pathways including G-protein-coupled receptor-related pathways predominated among the identified genes. The genes identified in our study may contribute to the development of a gene modeling network for methamphetamine addiction.

• Proceedings of the Microbiological Society of Korea Conference
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• 2002.10a
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• pp.121-124
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• 2002

This work was initiated to develope a recombinant oral poliovaccine (OPV), which is highly advanced in safety (minimizing VAPP) by introducing Type 2,3 poliovirus epitopes into our RPS-Vax system. We have introduced several potential vaccine epitopes of poliovirus Type 2, and 3 into RPS-Vax system, resulting in production of recombinant polioviruses. Any of these chimeric viruses, however, were not detected for their foreign gene expression by serotype-specific mouse antiserum. We have designed several folding units to stabilize the introduced vaccine protein and attached short epitope-concatamer or epitope-multimer to them, followed by production of chimeric viruses. Only those who have an HIV-1 Tat-mediated folding unit were nicely detected for the introduced foreign proteins by anti-Tat antiserum and type-specific peptide-induced antisera. Nevertheless, introduced epitopes were not detected in Western blot experiment with each serotype-specific antiserum. None of the mice inoculated with these chimeric viruses showed preventative immunity when challenged with Lansing and Leon wildtype 2 and 3 poliovirus, and the antiserum did not show neutralizing capacity in vitro. Conformational epitope covering B/C loop region of type 2 and 3 were newly designed by computer modeling, and introduced into the RPS-Vax vector system, followed by production of chimeric viruses. Introduced epitope regions were nicely detected by anti-Tag23 mAb or peptide antibody, but still not detected by poliovirus antiserum. Nevertheless, neutralizing antibody was detected in the Tg-PVR mice even when inoculated once with these chimeric viruses. Also, the immunized mice showed perfect preventative immunity against the wild Type poliovirus Lancing or Leon. When boosted appropriately, those chimeric virus-inoculated Tg-PVR mice produced equivalent amounts of neutralizing antibody to those in Sabin 2/3-immunized mice. These data strongly suggest that our recombinant poliovirus (RPS-PV2 and RPS-PV3) can be used as a safe and effective rec-OPV instead of any preexisting poliovaccine.

• Biomolecules & Therapeutics
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• v.19 no.1
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• pp.70-76
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• 2011

Bone remodeling is a dynamic process involving a constant balance between osteoclast-induced bone resorption and osteoblast-induced bone formation. Osteoclasts play a crucial homeostatic role in skeletal modeling and remodeling, and destroy bone in many pathological conditions. Previously, we reported that the hexane soluble fraction of Ficus carica inhibited osteoclast differentiation. Poly unsaturated fatty acids, such as ethyl docosahexaenoate (E-DHA), docosahexaenoic acid (DHA), cis-11,14-eicosadienoic acid (EDA) and eicosapentaenoic acid (EPA), were identified from the hexane soluble fraction of Ficus carica. Among them, E-DHA most potently inhibited osteoclastogenesis in RAW264.7 cells. E-DHA reduced the activities of JNK and NF-$\kappa}B$. E-DHA suppressed the expression of c-Fos and nuclear factor of activated T cells c1 (NFATc1). Interestingly, DHA increased the activity of alkaline phosphatase and expression of bone morphogenetic protein 2 (BMP2) more than E-DHA in MC3T3-E1 cells, suggesting that DHA may induce osteoblast differentiation. The data suggests that a combination of E-DHA and DHA has potential use in the treatment of diseases involving abnormal bone lysis, such as osteoporosis, rheumatoid arthritis and periodontal bone erosion.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.21
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• pp.9367-9373
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• 2014

In diagnosis of lung cancer, rapid distinction between small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) tumors is very important. Serum markers, including lactate dehydrogenase (LDH), C-reactive protein (CRP), carcino-embryonic antigen (CEA), neurone specific enolase (NSE) and Cyfra21-1, are reported to reflect lung cancer characteristics. In this study classification of lung tumors was made based on biomarkers (measured in 120 NSCLC and 60 SCLC patients) by setting up optimal biomarker joint models with a powerful computerized tool - gene expression programming (GEP). GEP is a learning algorithm that combines the advantages of genetic programming (GP) and genetic algorithms (GA). It specifically focuses on relationships between variables in sets of data and then builds models to explain these relationships, and has been successfully used in formula finding and function mining. As a basis for defining a GEP environment for SCLC and NSCLC prediction, three explicit predictive models were constructed. CEA and NSE are requentlyused lung cancer markers in clinical trials, CRP, LDH and Cyfra21-1 have significant meaning in lung cancer, basis on CEA and NSE we set up three GEP models-GEP 1(CEA, NSE, Cyfra21-1), GEP2 (CEA, NSE, LDH), GEP3 (CEA, NSE, CRP). The best classification result of GEP gained when CEA, NSE and Cyfra21-1 were combined: 128 of 135 subjects in the training set and 40 of 45 subjects in the test set were classified correctly, the accuracy rate is 94.8% in training set; on collection of samples for testing, the accuracy rate is 88.9%. With GEP2, the accuracy was significantly decreased by 1.5% and 6.6% in training set and test set, in GEP3 was 0.82% and 4.45% respectively. Serum Cyfra21-1 is a useful and sensitive serum biomarker in discriminating between NSCLC and SCLC. GEP modeling is a promising and excellent tool in diagnosis of lung cancer.

• IMMUNE NETWORK
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• v.12 no.2
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• pp.58-65
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• 2012

Background: A cell line with transfected Wilms' tumor protein 1 (WT1) is has been used for the preclinical evaluation of novel treatment strategies of WT1 immunotherapy for leukemia due to the lack of appropriate murine leukemia cell line with endogenous WT1. However, silencing of the transgene occurs. Regarding the effects of hypomethylating agents (HMAs) on reactivation of silenced genes, HMAs are considered to be immune enhancers. Methods: We treated murine WT1- transfected C1498 (mWT1-C1498) with increasing doses of decitabine (DAC) and azacitidine (AZA) to analyze their effects on transgene reactivation. Results: DAC and AZA decreased the number of viable cells in a dose- or time-dependent manner. Quantification of WT1 mRNA level was analyzed by real-time polymerase chain reaction after mWT1-C1498 treated with increasing dose of HMA. DAC treatment for 48 h induced 1.4-, 14.6-, and 15.5-fold increment of WT1 mRNA level, compared to untreated sample, at 0.1, 1, and $10{\mu}M$, respectively. Further increment of WT1 expression in the presence of 1 and $10{\mu}M$ DAC was evident at 72 h. AZA treatment also induced up-regulation of mRNA, but not to the same degree as with DAC treatment. The correlation between the incremental increases in WT1 mRNA by DAC was confirmed by Western blot and concomitant down-regulation of WT1 promoter methylation was revealed. Conclusion: The in vitro data show that HMA can induce reactivation of WT1 transgene and that DAC is more effective, at least in mWT1-C1498 cells, which suggests that the combination of DAC and mWT1-C1498 can be used for the development of the experimental model of HMA-combined WT1 immunotherapy targeting leukemia.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1031-1031
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• 2001

The mammary gland is made up of remarkably sensitive tissue, which has the capability of producing a large volume of secretion, milk, under normal or healthy conditions. When bacteria enter the gland and establish an infection (mastitis), inflammation is initiated accompanied by an influx of white cells from the blood stream, by altered secretory function, and changes in the volume and composition of secretion. Cell numbers in milk are closely associated with inflammation and udder health. These somatic cell counts (SCC) are accepted as the international standard measurement of milk quality in dairy and for mastitis diagnosis. NIR Spectra of unhomogenized composite milk samples from 14 cows (healthy and mastitic), 7days after parturition and during the next 30 days of lactation were measured. Different multivariate analysis techniques were used to diagnose the disease at very early stage and determine how the spectral properties of milk vary with its composition and animal health. PLS model for prediction of somatic cell count (SCC) based on NIR milk spectra was made. The best accuracy of determination for the 1100-2500nm range was found using smoothed absorbance data and 10 PLS factors. The standard error of prediction for independent validation set of samples was 0.382, correlation coefficient 0.854 and the variation coefficient 7.63%. It has been found that SCC determination by NIR milk spectra was indirect and based on the related changes in milk composition. From the spectral changes, we learned that when mastitis occurred, the most significant factors that simultaneously influenced milk spectra were alteration of milk proteins and changes in ionic concentration of milk. It was consistent with the results we obtained further when applied 2DCOS. Two-dimensional correlation analysis of NIR milk spectra was done to assess the changes in milk composition, which occur when somatic cell count (SCC) levels vary. The synchronous correlation map revealed that when SCC increases, protein levels increase while water and lactose levels decrease. Results from the analysis of the asynchronous plot indicated that changes in water and fat absorptions occur before other milk components. In addition, the technique was used to assess the changes in milk during a period when SCC levels do not vary appreciably. Results indicated that milk components are in equilibrium and no appreciable change in a given component was seen with respect to another. This was found in both healthy and mastitic animals. However, milk components were found to vary with SCC content regardless of the range considered. This important finding demonstrates that 2-D correlation analysis may be used to track even subtle changes in milk composition in individual cows. To find out the right threshold for SCC when used for mastitis diagnosis at cow level, classification of milk samples was performed using soft independent modeling of class analogy (SIMCA) and different spectral data pretreatment. Two levels of SCC - 200 000 cells/$m\ell$ and 300 000 cells/$m\ell$, respectively, were set up and compared as thresholds to discriminate between healthy and mastitic cows. The best detection accuracy was found with 200 000 cells/$m\ell$ as threshold for mastitis and smoothed absorbance data: - 98% of the milk samples in the calibration set and 87% of the samples in the independent test set were correctly classified. When the spectral information was studied it was found that the successful mastitis diagnosis was based on reviling the spectral changes related to the corresponding changes in milk composition. NIRS combined with different ways of spectral data ruining can provide faster and nondestructive alternative to current methods for mastitis diagnosis and a new inside into disease understanding at molecular level.