• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.407-411
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• 2005

KUGI (Korean UniGene Information) database contains the annotation information of the cDNA sequences obtained from the disease samples prevalent in Korean. A total of about 157,000 5'-EST high throughput sequences collected from cDNA libraries of stomach, liver, and some cancer tissues or established cell lines from Korean patients were clustered to about 35,000 contigs. From each cluster a representative clone having the longest high quality sequence or the start codon was selected. We stored the sequences of the representative clones and the clustered contigs in the KUGI database together with their information analyzed by running Blast against RefSeq, human mRNA, and UniGene databases from NCBI. We provide a web-based search engine fur the KUGI database using two types of user interfaces: attribute-based search and similarity search of the sequences. For attribute-based search, we use DBMS technology while we use BLAST that supports various similarity search options. The search system allows not only multiple queries, but also various query types. The results are as follows: 1) information of clones and libraries, 2) accession keys, location on genome, gene ontology, and pathways to public databases, 3) links to external programs, and 4) sequence information of contig and 5'-end of clones. We believe that the KUGI database and search system may provide very useful information that can be used in the study for elucidating the causes of the disease that are prevalent in Korean.

• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.511-515
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• 2004

In the post-genomic era, researches on proteins as well as genes have been increasingly required. Particularly, work on protein-protein interaction and protein network construction have been recently establishing. Most biologists publish their research results through papers or other media. However, biologists do not use the information effectively, since the published research results are very large. As the growth of internet, it becomes easy to access very large research results. It is significantly important to extract information with a biological meaning from varisous media. Therefore, in this research, we efficiently extract protein-protein interaction information from many open papers or other media and construct the database of the extracted information. We build a protein network from the established database and then design and implement various pathway analysis algorithms which find biological meaning from the protein network.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.13
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• pp.5325-5330
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• 2014

Background: Chronic myelocytic leukemia is a disease that threatens both adults and children. Great progress has been achieved in treatment but protein-protein interaction networks underlining chronic myelocytic leukemia are less known. Objective: To develop a protein-protein interaction network for chronic myelocytic leukemia based on gene expression and to predict biological pathways underlying molecular complexes in the network. Materials and Methods: Genes involved in chronic myelocytic leukemia were selected from OMIM database. Literature mining was performed by Agilent Literature Search plugin and a protein-protein interaction network of chronic myelocytic leukemia was established by Cytoscape. The molecular complexes in the network were detected by Clusterviz plugin and pathway enrichment of molecular complexes were performed by DAVID online. Results and Discussion: There are seventy-nine chronic myelocytic leukemia genes in the Mendelian Inheritance In Man Database. The protein-protein interaction network of chronic myelocytic leukemia contained 638 nodes, 1830 edges and perhaps 5 molecular complexes. Among them, complex 1 is involved in pathways that are related to cytokine secretion, cytokine-receptor binding, cytokine receptor signaling, while complex 3 is related to biological behavior of tumors which can provide the bioinformatic foundation for further understanding the mechanisms of chronic myelocytic leukemia.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.24
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• pp.10847-10853
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• 2015

Background: Lapatinib, a dual tyrosine kinase inhibitor that interrupts the epidermal growth factor receptor (EGFR) and HER2/neu pathways, has been indicated to have significant efficacy in treating HER2-positive breast cancer. However, acquired drug resistance has become a very serious clinical problem that hampers the use of this agent. In this study, we aimed to screen small molecule drugs that might reverse lapatinib-resistance of breast cancer by exploring differentially expressed genes (DEGs) via a bioinformatics method. Materials and Methods: We downloaded the gene expression profile of BT474-J4 (acquired lapatinib-resistant) and BT474 (lapatinib-sensitive) cell lines from the Gene Expression Omnibus (GEO) database and selected differentially expressed genes (DEGs) using dChip software. Then, gene ontology and pathway enrichment analyses were performed with the DAVID database. Finally, a connectivity map was utilized for predicting potential chemicals that reverse lapatinib-resistance. Results: A total of 1, 657 DEGs were obtained. These DEGs were enriched in 10 pathways, including cell cycling, regulation of actin cytoskeleton and focal adhesion associate examples. In addition, several small molecules were screened as the potential therapeutic agents capable of overcoming lapatinib-resistance. Conclusions: The results of our analysis provided a novel strategy for investigating the mechanism of lapatinib-resistance and identifying potential small molecule drugs for breast cancer treatment.

• Herbal Formula Science
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• v.32 no.1
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• pp.39-49
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• 2024

Objectives : The purpose of this study was to investigate the molecular targets and pathways of anti-inflammatory effects of Jakyakgamchotang with corydalis tuber (JC) using network pharmacology. Methods : The compounds in constituent herbal medicines of JC were searched in TCM systems pharmacology (TCMSP). Target gene informations of the components were collected using chemical-target interactions database provided by Pubchem. Afterwards, network analysis between compounds and inflammation-related target genes was performed using cytoscape. Go enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed on inflammation-related targets using DAVID database. Results : 70 active compounds related to inflammation were identified, and 295 target genes related to the anti-inflammatory activity of the compound of JC were identified. In the Go biological process DB and KEGG pathway DB, "inflammatory response", "cellular response to lipopolysaccharide", "positive regulation of interleukin-6 production", and "positive regulation of protein kinase B. signaling", "positive regulation of ERK1 and ERK2 cascade", "positive regulation of I-kappaB kinase/NF-kappaB signaling", "negative regulation of apoptotic process", and "PI3K-Akt signaling pathway" were found to be mechanisms related to the anti-inflammatory effects related to the target genes of JC. The main compounds predicted to be involved in the anti-inflammatory effect of JC were quercetin, licochalcone B, (+)-catechin, kaempferol, and emodin. Conclusions : This study provides the molecular targets and potential pathways of JC on inflammation. It can be used as a basic data for using JC for various inflammatory disease in traditional korean medicine clinic.

• The Journal of Korean Medicine
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• v.45 no.1
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• pp.114-125
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• 2024

Objectives: Network pharmacology is an analysis method that explores drug-centered efficacy and mechanism by constructing a compound-target-disease network based on system biology, and is attracting attention as a methodology for studying herbal medicine that has the characteristics for multi-compound therapeutics. Thus, we investigated the potential functions and pathways of Myrrha on Allergic Rhinitis (AR) via network pharmacology analysis and molecular docking. Methods: Using public databases and PubChem database, compounds of Myrrha and their target genes were collected. The putative target genes of Myrrha and known target genes of AR were compared and found the correlation. Then, the network was constructed using STRING database, and functional enrichment analysis was conducted based on the Gene Ontology (GO) Biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathways. Binding-Docking stimulation was performed using CB-Dock. Results: The result showed that total 3 compounds and 55 related genes were gathered from Myrrha. 33 genes were interacted with AR gene set, suggesting that the effects of Myrrha are closely related to AR. Target genes of Myrrha are considerably associated with various pathways including 'Fc epsilon RI signaling pathway' and 'JAK-STAT signaling pathway'. As a result of blinding docking, AKT1, which is involved in both mechanisms, had high binding energies for abietic acid and dehydroabietic acid, which are components of Myrrha. Conclusion: Through a network pharmacological method, Myrrha was predicted to have high relevance with AR by regulating AKT1. This study could be used as a basis for studying therapeutic effects of Myrrha on AR.

• Journal of Physiology & Pathology in Korean Medicine
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• v.38 no.1
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• pp.22-26
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• 2024

The purpose of this study was to identify the applicability, main compounds, and target genes of Cnidii Fructus (CF) in the treatment of gastritis using network pharmacology. The compounds in CF were searched in Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and a database of medicinal materials and chemical compounds in Northeast Asian traditional medicine (TM-MC). The target gene information of the compounds was collected from pubchem and cross-compared with the gastritis-related target gene information collected from Genecard to derive the target genes. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed on the derived target genes. Afterwards, network analysis between compounds and disease target genes was performed using cytoscape. We identified 121 active compounds and 139 target genes associated with gastritis. Pathways derived from the GO biological process and KEGG pathway DB primarily focus on target genes related to inflammation (IL-6, IL-8, TNF production, NF-κB transcription factor activity, and NF-κB signaling pathway) and cell death (PI3K-Akt, FoxO). Major targets for CF treatment of gastritis include TP53, TNF, BCL2, EGFR, NFKB1, ABCB1, PPARG, PTGS2, IL6, IL1B, and SOD1, along with major compounds such as coumarin, osthol, hexadecanoic acid, oleic acid, linoleic acid, and stigmasterol. This study provided CF's applicability for gastritis, related compounds, and target information. Evaluating CF's effectiveness in a preclinical gastritis model suggests its potential use in clinical practice for digestive system diseases.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.135-135
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• 2017

The Gramene database (http://www.gramene.org) is a powerful online resource for agricultural researchers, plant breeders and educators that provides easy access to reference data, visualizations and analytical tools for conducting cross-species comparisons. Learn the benefits of using Gramene to enrich your lectures, accelerate your research goals, and respond to your organismal community needs. Gramene's genomes portal hosts browsers for 44 complete reference genomes, including crops and model organisms, each displaying functional annotations, gene-trees with orthologous and paralogous gene classification, and whole-genome alignments. SNP and structural diversity data, available for 11 species, are displayed in the context of gene annotation, protein domains and functional consequences on transcript structure (e.g., missense variant). Browsers from multiple species can be viewed simultaneously with links to community-driven organismal databases. Thus, while hosting the underlying data for comparative studies, the portal also provides unified access to diverse plant community resources, and the ability for communities to upload and display private data sets in multiple standard formats. Our BioMart data mining interface enable complex queries and bulk download of sequence, annotation, homology and variation data. Gramene's pathway portal, the Plant Reactome, hosts over 240 pathways curated in rice and inferred in 66 additional plant species by orthology projection. Users may compare pathways across species, query and visualize curated expression data from EMBL-EBI's Expression Atlas in the context of pathways, analyze genome-scale expression data, and conduct pathway enrichment analysis. Our integrated search database and modern user interface leverage these diverse annotations to facilitate finding genes through selecting auto-suggested filters with interactive views of the results.

• Journal of Life Science
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• v.29 no.1
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• pp.123-128
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• 2019

The use of 16S rDNA is commonplace in the determination of prokaryotic species. However, it has limitations, and there are few studies at the genus level. We investigated conserved genes and metabolic pathways at the genus level in 28 strains of 13 genera of prokaryotes using the COG database (conserved genes) and MetaCyc database (metabolic pathways). Conserved genes compared to total genes (core genome) at the genus level ranged from 27.62%(Nostoc genus) to 71.76%(Spiribacter genus), with an average of 46.72%. The lower ratio of core genome meant the higher ratio of peculiar genes of a prokaryote, namely specific biological activities or the habitat may be varied. The ratio of common metabolic pathways at the genus level was higher than the ratio of core genomes, from 58.79% (Clostridium genus) to 96.31%(Mycoplasma genus), with an average of 75.86%. When compared among other genera, members of the same genus were positioned in the closest nodes to each other. Interestingly, Bacillus and Clostridium genera were positioned in closer nodes than those of the other genera. Archaebacterial genera were grouped together in the ortholog and metabolic pathway nodes in a phylogenetic tree. The genera Granulicella, Nostoc, and Bradyrhizobium of the Acidobacteria, Cyanobacteria, and Proteobacteria phyla, respectively, were grouped in an ortholog content tree. The results of this study can be used for (i) the identification of common genes and metabolic pathways at each phylogenetic level and (ii) the improvement of strains through horizontal gene transfer or site-directed mutagenesis.

• Journal of Life Science
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• v.32 no.3
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• pp.249-255
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• 2022

Metabolism is essential for survival and reproduction, and there is a metabolic pathways entry in the clusters of orthologous groups of proteins (COGs) database, updated in 2020. In this study, the metabolic pathways of 1309 prokaryotes were analyzed using COGs. There were 822 COGs associated with 63 metabolic pathways, and the mean for each taxon was between 200.50 (mollicutes) and 527.07 (cyanobacteria) COGs. The metabolic pathway composition ratio (MPCR) was defined as the number of COGs present in one genome in relation to the total number of COGs constituting each metabolic pathway, and the number of pathways with 100% MPCR ranged from 0 to 26 in each prokaryote. Among 1309 species, the 100% MPCR pathways included murein biosynthesis associated with cell wall synthesis (922 species); glycine cleavage (918); and ribosomal 30S subunit synthesis (903). The metabolic pathways with 0% MPCR were those involving photosystem I (1263 species); archaea/vacuolar-type ATP synthase (1028); and Na⁺-translocation NADH dehydrogenase (976). Depending on the prokaryote, three to 49 metabolic pathways could not be performed at all. The sequence of most highly conserved metabolic pathways was ribosome 30S subunit synthesis (96.1% of 1309 species); murein biosynthesis (86.8%); arginine biosynthesis (80.4%); serine biosynthesis (80.3%); and aminoacyl-tRNA synthesis (82.2%). Protein and cell wall synthesis have been shown to be important metabolic pathways in prokaryotes, and the results of this study of COGs related to such pathways can be utilized in, for example, the development of antibiotics and artificial cells.