• Molecular & Cellular Toxicology
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• v.3 no.3
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• pp.208-213
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• 2007

With the help of a development and popularization of microarray technology that enable to us to simultaneously investigate the expression pattern of thousands of genes, the toxicogenomics experimenters can interpret the genome-scale interaction between genes exposed in toxicant or toxicant-related environment. The ultimate and primary goal of toxicogenomics identifies functional context among the group of genes that are differentially or similarly coexpressed under the specific toxic substance. On the other side, public reference databases with transcriptom, proteom, and biological pathway information are needed for the analysis of these complex omics data. However, due to the heterogeneous and independent nature of these databases, it is hard to individually analyze a large omics annotations and their pathway information. Fortunately, several web sites of the public database provide information linked to other. Nevertheless it involves not only approriate information but also unnecessary information to users. Therefore, the systematically integrated database that is suitable to a demand of experimenters is needed. For these reasons, we propose SOP (Search of Omics Pathway) database system which is constructed as the integrated biological database converting heterogeneous feature of public databases into combined feature. In addition, SOP offers user-friendly web interfaces which enable users to submit gene queries for biological interpretation of gene lists derived from omics experiments. Outputs of SOP web interface are supported as the omics annotation table and the visualized pathway maps of KEGG PATHWAY database. We believe that SOP will appear as a helpful tool to perform biological interpretation of genes or proteins traced to omics experiments, lead to new discoveries from their pathway analysis, and design new hypothesis for a next toxicogenomics experiments.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.56-58
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• 2000

Post-genomics may be defined in different ways depending on how one views the challenges after the genome. A popular view is to follow the concept of the central dogma in molecular biology, namely from genome to transcriptome to proteome. Projects are going on to analyze gene expression profiles both at the mRNA and protein levels and to catalog protein 3D structure families, which will no doubt help the understanding of information in the genome. However complete, such catalogs of genes, RNAs, and proteins only tell us about the building blocks of life. They do not tell us much about the wiring (interaction) of building blocks, which is essential for uncovering systemic functional behaviors of the cell or the organism. Thus, an alternative view of post-genomics is to go up from the molecular level to the cellular level, and to understand, what I call, the "interactome"or a complete picture of molecular interactions in the cell. KEGG (http://www.genome.ad.jp/kegg/) is our attempt to computerize current knowledge on various cellular processes as a collection of "generalized"protein-protein interaction networks, to develop new graph-based algorithms for predicting such networks from the genome information, and to actually reconstruct the interactomes for all the completely sequenced genomes and some partial genomes. During the reconstruction process, it becomes readily apparent that certain pathways and molecular complexes are present or absent in each organism, indicating modular structures of the interactome. In addition, the reconstruction uncovers missing components in an otherwise complete pathway or complex, which may result from misannotation of the genome or misrepresentation of the KEGG pathway. When combined with additional experimental data on protein-protein interactions, such as by yeast two-hybrid systems, the reconstruction possibly uncovers unknown partners for a particular pathway or complex. Thus, the reconstruction is tightly coupled with the annotation of individual genes, which is maintained in the GENES database in KEGG. We are also trying to expand our literature surrey to include in the GENES database most up-to-date information about gene functions.

• Journal of Microbiology and Biotechnology
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• v.31 no.4
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• pp.621-629
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• 2021

Shigella flexneri is a facultative intracellular pathogen that causes bacillary dysentery in humans. Infection with S. flexneri can result in more than a million deaths yearly and most of the victims are children in developing countries. Therefore, identifying novel and unique drug targets against this pathogen is instrumental to overcome the problem of drug resistance to the antibiotics given to patients as the current therapy. In this study, a comparative analysis of the metabolic pathways of the host and pathogen was performed to identify this pathogen's essential enzymes for the survival and propose potential drug targets. First, we extracted the metabolic pathways of the host, Homo sapiens, and pathogen, S. flexneri, from the KEGG database. Next, we manually compared the pathways to categorize those that were exclusive to the pathogen. Further, all enzymes for the 26 unique pathways were extracted and submitted to the Geptop tool to identify essential enzymes for further screening in determining the feasibility of the therapeutic targets that were predicted and analyzed using PPI network analysis, subcellular localization, druggability testing, gene ontology and epitope mapping. Using these various criteria, we narrowed it down to prioritize 5 novel drug targets against S. flexneri and one vaccine drug targets against all strains of Shigella. Hence, we suggest the identified enzymes as the best putative drug targets for the effective treatment of S. flexneri.

• Journal of Microbiology and Biotechnology
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• v.16 no.5
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• pp.748-756
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• 2006

We propose a genome-scale clustering approach to identify whole genome relationships using the functional groups given by the Kyoto Encyclopedia of Genes and Genomes Orthology (KO) database. The metabolic capabilities of each organism were defined by the number of genes in each functional category. The archaeal, bacterial, and eukaryotic genomes were compared by simultaneously applying a two-step clustering method, comprised of a self-organizing tree algorithm followed by unsupervised hierarchical clustering. The clustering results were consistent with various phenotypic characteristics of the organisms analyzed and, additionally, showed a different aspect of the relationship between genomes that have previously been established through rRNA-based comparisons. The proposed approach to collect and cluster the metabolic functional capabilities of organisms should make it a useful tool in predicting relationships among organisms.

• Journal of KIISE:Databases
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• v.32 no.2
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• pp.120-128
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• 2005

Recently main Bioinformatics databases(DDBJ, ENSEMBL, KEGG, etc.) provide analysis tools and data using web services for the convenience of bioinformaticians. Thus, defining SOAP server objects and their methods are very important to provide services for web services. We define SOAP server objects for interaction databases such as BIND, MINT and DIP.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.5
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• pp.1781-1787
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• 2015

Background: miRNAs are relatively recently discovered cancer biomarkers which have important implications for cancer early diagnosis, treatment and estimation of prognosis. Here we focussed on expression of mir-196a-5p in gastric cancer tissues and cell lines so as to analyse its significance for clinicopathologic characteristics and generate enriched KEGG pathways clustered by target genes for exploring its potential roles as a biomarker in gastric cancer. Materials and Methods: The expression of mir-196a-5p in poorly, moderate and well differentiated gastric cancer cell lines compared with GES-1 was detected by RT-qPCR, and the expression of mir-196a-5p in gastric cancer tissues comparing with adjacent non cancer tissues of 58 cases were also assessed by RT-qPCR. Subsequently, an analysis of clinical significance of mir-196a-5p in gastric cancer and enriched KEGG pathways was executed based on the miRWalk prediction database combined with bioinformatics tools DAVID 6.7 and Mirfocus 3.0. Results: RT-qPCR showed that mir-196a-5p was up-regulated in 6 poorly and moderate differentiated gastric cancer cell lines SGC-7901, MKN-45, MKN-28, MGC-803, BGC-823, HGC-27 compared with GES-1, but down-regulated in the highly differentiated gastric cancer cell line AGS. Clinical data indicated mir-196a-5p to beup-regulated in gastric cancer tissues (47/58). Overexpression of mir-196a-5p was associated with more extensive degree of lymph node metastasis and clinical stage (P < 0.05; x2 test). Enriched KEGG pathway analyses of predicted and validated targets in miRWalk combined with DAVID 6.7 and Mirfocus 3.0 showed that the targeted genes regulated by mir-196a-5p were involved in malignancy associated biology. Conclusions: Overexpression of mir-196a-5p is associated with lymph node metastasis and clinical stage, and enriched KEGG pathway analyses showed that targeted genes regulated by mir-196a-5p may contribute to tumorgenesis, suggesting roles as an oncogenic miRNA biomarker in gastric cancer.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.36 no.1
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• pp.50-63
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• 2023

Objectives : This study used a network pharmacology approach to explore the active compounds and therapeutic mechanisms of Yijin-tang on Meniere's disease. Methods : The active compounds of Yijin-tang were screened via the TCMSP database and their target proteins were screened via the STITCH database. The GeneCard was used to establish the Meniere's disease-related genes. The intersection targets were obtained through Venny 2.1.0. The related protein interaction network was constructed with the STRING database, and topology analysis was performed through CytoNCA. GO biological function analysis and KEGG enrichment analysis for core targets were performed through the ClueGO. Results : Network analysis identified 126 compounds in five herbal medicines of Yijin-tang. Among them, 15 compounds(naringenin, beta-sitosterol, stigmasterol, baicalein, baicalin, calycosin, dihydrocapsaicin, formononetin, glabridin, isorhamnetin, kaempferol, mairin, quercetin, sitosterol, nobiletin) were the key chemicals. The target proteins were 119, and 7 proteins(TNF, CASP9, PARP1, CCL2, CFTR, NOS2, NOS1) were linked to Meniere's disease-related genes. Core genes in this network were TNF, CASP9, and NOS2. GO/KEGG pathway analysis results indicate that these targets are primarily involved in regulating biological processes, such as excitotoxicity, oxidative stress, and apoptosis. Conclusion : Pharmacological network analysis can help to explain the applicability of Yijin-tang on Meniere's disease.

• Journal of Korean Neurosurgical Society
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• v.65 no.5
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• pp.697-709
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• 2022

Objective : The present study aimed to identify the function of ischemic stroke (IS) patients' peripheral blood and its role in IS, explore the pathogenesis, and provide direction for clinical research progress by comprehensive bioinformatics analysis. Methods : Two datasets, including GSE58294 and GSE22255, were downloaded from Gene Expression Omnibus database. GEO2R was utilized to obtain differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed using the database annotation, visualization and integrated discovery database. The protein-protein interaction (PPI) network of DEGs was constructed by search tool of searching interactive gene and visualized by Cytoscape software, and then the Hub gene was identified by degree analysis. The microRNA (miRNA) and miRNA target genes closely related to the onset of stroke were obtained through the miRNA gene regulatory network. Results : In total, 36 DEGs, containing 27 up-regulated and nine down-regulated DEGs, were identified. GO functional analysis showed that these DEGs were involved in regulation of apoptotic process, cytoplasm, protein binding and other biological processes. KEGG enrichment analysis showed that these DEGs mediated signaling pathways, including human T-cell lymphotropic virus (HTLV)-I infection and microRNAs in cancer. The results of PPI network and cytohubba showed that there was a relationship between DEGs, and five hub genes related to stroke were obtained : SOCS3, KRAS, PTGS2, EGR1, and DUSP1. Combined with the visualization of DEG-miRNAs, hsa-mir-16-5p, hsa-mir-181a-5p and hsa-mir-124-3p were predicted to be the key miRNAs in stroke, and three miRNAs were related to hub gene. Conclusion : Thirty-six DEGs, five Hub genes, and three miRNA were obtained from bioinformatics analysis of IS microarray data, which might provide potential targets for diagnosis and treatment of IS.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.4
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• pp.138-147
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• 2007

Metabolic pathway is a series of chemical reactions occuning within a cell and can be used for drug development and understanding of life phenomenon. Many biologists are trying to extract metabolic pathway information from huge literatures for their metabolic-circuit regulation study. We propose a text-mining technique based on the keyword and pattern. Proposed technique utilizes a web robot to collect huge papers and stores them into a local database. We use gene ontology to increase compound recognition rate and NCBI Tokenizer library to recognize useful information without compound destruction. Furthermore, we obtain useful sentence patterns representing metabolic pathway from papers and KEGG database. We have extracted 66 patterns in 20,000 documents for Glycosphingolipid species from KEGG, a representative metabolic database. We verify our system for nineteen compounds in Glycosphingolipid species. The result shows that the recall is 95.1%, the precision 96.3%, and the processing time 15 seconds. Proposed text mining system is expected to be used for metabolic pathway reconstruction.

• Journal of Life Science
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• v.34 no.3
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• pp.179-190
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• 2024

Cannabis sativa is a plant widely cultivated worldwide and has been used as a material for food, medicine, building materials and cosmetics. In this study, we assessed the functional effects of C. sativa stem and root extracts using network pharmacology and confirmed their novel functions. The components in stem and root ethanol extracts were identified by gas chromatography-mass spectrometry analysis, and networks between the components and proteins were constructed using the STICHI database. Functional annotation of the proteins was performed using the KEGG pathway. The effects of the extracts were confirmed in lysophosphatidylcholine-induced THP-1 cells using real-time PCR. A total of 21 and 32 components were identified in stem and root extracts, respectively, and 147 and 184 proteins were linked to stem and root components, respectively. KEGG pathway analysis showed that 69 pathways, including the MAPK signaling pathway, were commonly affected by the extracts. Further investigation using pathway networks revealed that terpenoid backbone biosynthesis was likely affected by the extracts, and the expression of the MVK and MVD genes, key proteins in terpenoid backbone biosynthesis, was decreased in LPC-induced THP-1 cells. Therefore, this study determined the diverse function of C. sativa extracts, providing information for predicting and researching the effects of C. sativa.