• IMMUNE NETWORK
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• v.14 no.3
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• pp.123-127
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• 2014

Interleukin-32 (IL-32) is a cytokine inducing crucial inflammatory cytokines such as tumor necrosis factor-${\alpha}(TNF{\alpha})$ and IL-6 and its expression is elevated in various inflammatory autoimmune diseases, certain cancers, as well as viral infections. IL-32 gene was first cloned from activated T cells, however IL-32 expression was also found in other immune cells and non-immune cells. IL-32 gene was identified in most mammals except rodents. It is transcribed as multiple-spliced variants in the absence of a specific activity of each isoform. IL-32 has been studied mostly in clinical fields such as infection, autoimmune, cancer, vascular disease, and pulmonary diseases. It is difficult to investigate the precise role of IL-32 in vivo due to the absence of IL-32 gene in mouse. The lack of mouse IL-32 gene restricts in vivo studies and restrains further development of IL-32 research in clinical applications although IL-32 new cytokine getting a spotlight as an immune regulatory molecule processing important roles in autoimmune, infection, and cancer. In this review, we discuss the regulation and function of IL-32 in inflammatory bowel diseases and rheumatoid arthritis.

• IMMUNE NETWORK
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• v.2 no.2
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• pp.79-85
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• 2002

Background: In contrast to evidences of Ig H chain receptor editing in transformed cell lines and transgenic mouse models, there has been no direct evidence that this phenomenon occurs in human developing B cells. Methods: $V_HDJ_H$ rearrangements were obtained from genomic DNA of individual $IgM^-$ B cells from liver and $IgM^+B$ cells from bone marrow of 18 wk of gestation human fetus by PCR amplification and direct sequencing. Results: We found three examples of H chain receptor editing from $IgM^+$ and $IgM^-human$ fetal B cells. Two types of $V_H$ replacements were identified. The first involved $V_H$ hybrid formation, in which part of a $V_H$ gene from the initial VDJ rearrangement is replaced by part of an upstream $V_H$ gene at the site of cryptic RSS. The second involved a gene conversion like replacement of CDR2, in which another $V_H$ gene donated a portion of its CDR2 sequence to the initial VDJ rearrangement. Conclusion: These data provide evidence of receptor editing at the H chain loci in developing human B cells, and also the first evidence of a gene conversion event in human Ig genes.

• Biomolecules & Therapeutics
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• v.26 no.3
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• pp.290-297
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• 2018

We aimed to understand the molecular changes in host cells that accompany infection by the seasonal influenza A H1N1 virus because the initial response rapidly changes owing to the fact that the virus has a robust initial propagation phase. Human epithelial alveolar A549 cells were infected and total RNA was extracted at 30 min, 1 h, 2 h, 4 h, 8 h, 24 h, and 48 h post infection (h.p.i.). The differentially expressed host genes were clustered into two distinct sets of genes as the infection progressed over time. The patterns of expression were significantly different at the early stages of infection. One of the responses showed roles similar to those associated with the enrichment gene sets to known 'gp120 pathway in HIV.' This gene set contains genes known to play roles in preventing the progress of apoptosis, which infected cells undergo as a response to viral infection. The other gene set showed enrichment of 'Drug Metabolism Enzymes (DMEs).' The identification of two distinct gene sets indicates that the virus regulates the cell's mechanisms to create a favorable environment for its stable replication and protection of gene metabolites within 8 h.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.427-430
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• 2005

Studies on cellular pathways and networks are now one of the most actively researched topics in all fields of biomedicine ranging from developmental biology to etiology. Many databases have been developed and quantitative simulation models have been proposed. One of the eventual goals of pathway/network studies is to integrate different types of pathway/network models and databases to simulate overall cellular responses. A bottleneck to this goal is modeling gene expression since the mechanism of this process is not yet fully unveiled. We are developing a small scale computer program called CiRMU (Cis-Regulatory Machinery Unit model) for describing, viewing, analyzing, and modeling the process of gene expression. A prototype system is being designed and implemented for analyzing functions of nuclear receptors.

• BMB Reports
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• v.37 no.1
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• pp.28-34
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• 2004

The discovery of biochemical and cellular functions of unannotated gene products begins with a database search of proteins with structure/sequence homologues based on known genes. Very recently, a number of frontier groups in structural biology proposed a new paradigm to predict biological functions of an unknown protein on the basis of its three-dimensional structure on a genomic scale. Structural proteomics (genomics), a research area for structure-based functional discovery, aims to complete the protein-folding universe of all gene products in a cell. It would lead us to a complete understanding of a living organism from protein structure. Two major complementary experimental techniques, X-ray crystallography and NMR spectroscopy, combined with recently developed high throughput methods have played a central role in structural proteomics research; however, an integration of these methodologies together with comparative modeling and electron microscopy would speed up the goal for completing a full dictionary of protein folding space in the near future.

• Nuclear Engineering and Technology
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• v.48 no.6
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• pp.1315-1320
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• 2016

The present study aims to predict the heat transfer characteristics around a square cylinder with different corner radii using multivariate adaptive regression splines (MARS). Further, the MARS-generated objective function is optimized by particle swarm optimization. The data for the prediction are taken from the recently published article by the present authors [P. Dey, A. Sarkar, A.K. Das, Development of GEP and ANN model to predict the unsteady forced convection over a cylinder, Neural Comput. Appl. (2015) 1-13]. Further, the MARS model is compared with artificial neural network and gene expression programming. It has been found that the MARS model is very efficient in predicting the heat transfer characteristics. It has also been found that MARS is more efficient than artificial neural network and gene expression programming in predicting the forced convection data, and also particle swarm optimization can efficiently optimize the heat transfer rate.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.4
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• pp.284-288
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• 2009

With explosively growing PPI databases, the computational approach for a prediction and configuration of PPI network has been a big stream in the bioinformatics area. Recent researches gradually consider physicochemical properties of proteins and support high resolution results with integration of experimental results. With regard to current research trend, it is very close future to complete a PPI network configuration of each organism. However, direct applying the PPI network to real field is complicated problem because PPI network is only a set of co-expressive proteins or gene products, and its network link means simple physical binding rather than in-depth knowledge of biological process. In this paper, we suggest a protein functional flow model which is a directed network based on a protein functions' relation of signaling transduction pathway. The vertex of the suggested model is a molecular function annotated by gene ontology, and the relations among the vertex are considered as edges. Thus, it is easy to trace a specific function's transition, and it can be a constraint to extract a meaningful sub-path from whole PPI network. To evaluate the model, 11 functional flow models of Homo sapiens were built from KEGG, and Cronbach's alpha values were measured (alpha=0.67). Among 1023 functional flows, 765 functional flows showed 0.6 or higher alpha values.

• The Korea Journal of Herbology
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• v.38 no.6
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• pp.73-91
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• 2023

Objectives : This study used a network pharmacology approach to elucidate the efficacy and molecular mechanisms of Daehwangmokdanpitang (DHMDPT) on Psoriasis. Methods : Using OASIS databases and PubChem database, compounds of DHMDPT and their target genes were collected. The putative target genes of DHMDPT and known target genes of psoriasis were compared and found the correlation. Then, the network was constructed using Cytoscape 3.10.1. The key target genes were screened by Analyzer network and their functional enrichment analysis was conducted based on the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathways to predict the mechanisms. Results : The result showed that total 30 compounds and 439 related genes were gathered from DHMDPT. 264 genes were interacted with psoriasis gene set, suggesting that the effects of DHMDPT are closely related to psoriasis. Based on GO enrichment analysis and KEGG pathways, 'Binding', 'Cytokine Activity', 'Receptor Ligand Activity' 'HIF-1 signaling pathway', 'IL-17 signaling pathway', 'Toll-like receptor signaling pathway', and 'TNF signaling pathway' were predicted as functional pathways of 16 key target genes of DHMDPT on psoriasis. Among the target genes, IL6, IL1B, TNF, AKT1 showed high correlation with the results of KEGG pathways. Additionally, Emodin, Acetovanillone, Gallic acid, and Ferulic acid showed a high relevance with key genes and their mechanisms. Conclusion : Through a network pharmacological method, DHMDPT was predicted to have high relevance with psoriasis. This study could be used as a basis for studying therapeutic effects of DHMDPT on psoriasis.

• Herbal Formula Science
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• v.32 no.1
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• pp.11-28
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• 2024

Objectives : GunRyeong-Tang(GRT) is a traditional herbal prescription that combines Oryeongsan and Sagunja-tang. This study employed network analysis methods on the components of GRT and target genes related to diabetes complications to predict the improvement effects of GRT on diabetes complications. Methods : The collection of active compounds of GRT and related target genes involved the utilization of public databases and the PubChem database. We selected diabetes complication-related genes using GeneCards and confirmed their correlation through comparative analysis with the target genes of GRT. We constructed a network using Cytoscape 3.9.1 and conducted topological analysis. To predict the mechanism, we performed functional enrichment analysis based on Gene Ontology (GO) biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Results : Through network analysis, 234 active compounds and 1361 related genes were collected from GRT. A total of 9,136 genes related to diabetes complications were collected, and 1,039 target genes overlapping with the components of GRT were identified. The core genes of this network were TP53, INS, AKT1, ALB, and EGFR. In addition, GRT significantly reduced the H9c2 cell size and the expression of myocardial hypertrophy biomarkers (ANP, BNP), which were increased by high glucose (HG). Conclusions : Through this study, we were able to predict the activity and mechanism of action of GRT on diabetes and diabetic complications, and confirmed the potential of GRT as a treatment for diabetes complications through the effect of GRT on improving myocardial hypertrophy for diabetic cardiomyopathy.

• Molecules and Cells
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• v.46 no.1
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• pp.65-67
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• 2023

SMILES (simplified molecular-input line-entry system) information of small molecules parsed by one-hot array is passed to a convolutional neural network called black box. Outputs data representing a gene signature is then matched to the genetic signature of a disease to predict the appropriate small molecule. Efficacy of the predicted small molecules is examined by in vivo animal models. GSEA, gene set enrichment analysis.